An In Vivo Screen for Genes Involved in Central Nervous System Control of Obesity Identifies Diacylglycerol Kinase as a Regulator of Insulin Secretion
by
Irene Trinh
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Molecular Genetics University of Toronto
© Copyright by Irene Trinh, 2017.
An In Vivo Screen for Genes Involved in Central Nervous System Control of Obesity Identifies Diacylglycerol Kinase as a Regulator of Insulin Secretion
Irene Trinh
Doctor of Philosophy
Department of Molecular Genetics University of Toronto
2017
Abstract
The increasing prevalence of obesity as well as its association with many chronic diseases has turned obesity into a major health concern worldwide. Obesity has many underlying environmental and genetic factors that disturb the balance between energy intake and energy expenditure, which is controlled by the central nervous system. The goal of my project is to help further our understanding of these CNS mechanisms by using the powerful tools available in
Drosophila melanogaster to identify neuronal genes involved in energy homeostasis. Using the neuronal fru-Gal4 driver we performed an RNAi screen with 1748 genes and assayed for obese or lean phenotypes defined as increases or decreases in triacylglycerol (TAG) levels. After 3 rounds of screening I identified 25 hits that were reproducible and confirmed these phenotypes with independent RNAi lines.
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One of these hits was Diacylglycerol kinase (Dgk) whose mammalian homologues have been implicated in genome-wide association studies for metabolic defects. Manipulation of neuronal
Dgk levels affects TAG and carbohydrate levels but these effects don’t seem to be mediated through Dgk’s regulation of DAG, PA levels or PKC activity. I hypothesized that Dgk acts in the insulin-producing cells (IPCs), a set of neurosecretory neurons that secrete Drosophila insulin-like peptides (dILPs) and are involved in the regulation of dILP secretion. Knockdown or overexpression of Dgk within the IPCs reproduces the same TAG, glucose and glycogen phenotypes seen with fru-Gal4. Moreover, overexpression of kinase-dead Dgk, but not wild- type, decreased circulating dILP2 and dILP5 levels resulting in lower insulin signalling activity.
Conversely, despite having higher circulating dILP levels, Dgk RNAi flies have decreased pathway activity suggesting that they are insulin-resistant.
Thus, after screening over 1700 genes in vivo I identified 25 genes as potential neuronal mediators of energy homeostasis. My results have also shown that one of these genes, Dgk, acts within the insulin-producing cells to regulate the secretion of dILPs and energy homeostasis in
Drosophila.
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Acknowledgments
This thesis has been a long time coming and wouldn’t have been possible without the people around me. First, I would like to thank Gabrielle for allowing me the opportunity to work in her lab. I believe that the environment here has helped shape me into a better scientist and critical thinker. I also want to thank her for the continuous guidance and support through the many ups and downs over the years that helped me to see this project through.
I am grateful to my committee members Sabine Cordes and Helen McNeill for their helpful feedback through this whole process and for their support to the end (even reassuring me just before my defense).
Thank you to all current and past members of the lab. I especially need to thank Oxana who, without her help, I would still not be done my thesis work and without her presence there, the lab would have been a less cheerful place. Thank you to Maeve, Tanya, Brenda, Dave and Mike who have offered help and advice even when my questions were less than intelligent. I also want to thank Alessandra and Sili who provided a sympathetic ear or a welcome distraction whenever I needed it.
Lastly, I want to thank my family for their unconditional support and encouragement throughout grad school (even though they still don’t really understand what I’ve been studying all these years).
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Table of Contents
Acknowledgments...... iv
Table of Contents ...... v
List of Tables ...... ix
List of Figures ...... x
List of Appendices ...... xii
List of Abbreviations ...... xiii
1 Introduction ...... 1
1.1 Global obesity epidemic ...... 2
1.2 Etiology of obesity and metabolic disorders ...... 2
1.2.1 Genetics of human obesity ...... 3
1.2.2 Approaches to identify obesity susceptibility genes ...... 3
1.3 Drosophila models of human diseases ...... 5
1.3.1 Drosophila models of metabolic disorders ...... 5
1.3.2 Utility of Drosophila in the study of energy homeostasis ...... 7
1.4 The central nervous system in energy homeostasis ...... 9
1.4.1 The CNS in mammalian energy homeostasis ...... 10
1.4.2 The CNS in Drosophila energy homeostasis ...... 12
1.5 Insulin signalling in energy homeostasis ...... 18
1.5.1 Insulin signalling pathway ...... 18
1.5.2 Insulin functions in mammalian energy homeostasis ...... 21
1.5.3 Drosophila ILP signalling functions in energy homeostasis...... 23
1.5.4 Regulation of insulin secretion ...... 25
1.6 Project rationale ...... 30
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2 RNAi Screen to Identify Genes Involved in Central Nervous System Control of Energy Homeostasis ...... 32
2.1 Introduction ...... 33
2.2 Materials and Methods ...... 33
2.2.1 Fly stocks and husbandry ...... 33
2.2.2 TAG assay ...... 34
2.2.3 Analysis of enriched annotation terms among screen hits ...... 34
2.2.4 Analysis of screen hits for homologues involved in human obesity ...... 34
2.3 Results ...... 34
2.3.1 Screen methodology and selection of a driver ...... 34
2.3.2 First round of screening identifies 510 genes that have statistically significant changes in TAG when knocked down with fru-Gal4 ...... 36
2.3.3 Analysis of hits after first round of screen ...... 38
2.3.4 Confirmation and validation of TAG phenotypes ...... 41
2.4 Discussion ...... 43
2.4.1 Use of fru-Gal4 as the driver for RNAi screen ...... 44
2.4.2 Phospholipid metabolism and protein kinases in energy balance ...... 44
2.4.3 Identification of known metabolic genes by RNAi screen ...... 45
2.4.4 Conclusions ...... 46
3 Elucidating the Role of Diacylglycerol Kinase in Drosophila Energy Homeostasis ...... 47
3.1 Introduction ...... 48
3.1.1 The DGK gene family ...... 49
3.1.2 DGK functions in metabolism ...... 51
3.1.3 Drosophila DGKs ...... 54
3.1.4 Rationale ...... 56
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3.2 Materials and Methods ...... 56
3.2.1 Fly stocks and husbandry ...... 56
3.2.2 Generation of UAS-Dgk transgenics ...... 57
3.2.3 DNA sample preparation and PCR ...... 58
3.2.4 TAG, glucose and glycogen assays ...... 58
3.2.5 Trehalose assay ...... 59
3.2.6 Feeding assay ...... 59
3.2.7 Hemolymph extraction and dILP ELISA ...... 60
3.2.8 Quantitative PCR ...... 60
3.2.9 Western blotting ...... 61
3.3 Results ...... 61
3.3.1 Generation of UAS-Dgk transgenic lines ...... 61
3.3.2 Metabolic profiling of flies with knockdown or overexpression of Dgk in Fru- Gal4-expressing neurons ...... 65
3.3.3 Are the metabolic effects mediated by changes in DAG and/or PA levels? ...... 67
3.3.4 Does altering insulin-like peptide (dILP) levels in Fru-Gal4-expressing neurons affect lipid and sugar levels? ...... 70
3.3.5 Dgk regulates secretion of insulin-like peptides ...... 71
3.4 Discussion ...... 74
4 Discussion and Future Directions ...... 77
4.1 Summary of Thesis Results ...... 78
4.1.1 RNAi screen ...... 78
4.1.2 Role of Dgk in energy homeostasis ...... 79
4.2 DGKs in insulin secretion: a kinase-dependent model ...... 81
4.2.1 Activation of Dgk ...... 81
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4.2.2 Dgk’s catalytic activity regulates downstream effectors ...... 82
4.3 Dgk function in dILP secretion: kinase-independent model...... 83
4.3.1 Overexpression of wild-type vs. kinase-dead Dgk ...... 84
4.3.2 Potential alternative mechanisms Dgk function ...... 85
4.4 Dgk and insulin resistance ...... 90
4.5 Conclusions ...... 91
5 Appendix ...... 93
References ...... 147
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List of Tables
Table 1.1 Functionally analogous metabolic tissues between mammals and Drosophila...... 6
Table 1.2 Effects of peripheral insulin signalling on carbohydrate, lipid and protein metabolism...... 21
Table 2.1 Top 10 enriched functional annotation terms from 1st round screen hits...... 39
Table 2.2 Screen hits with human homologues that came out of genome-wide association studies for obesity-related traits...... 40
Table 2.3 Hits with homologues from obesity-related GWAS or from enriched functional annotations undergoing second round screening...... 41
Table 2.4 Enriched functional annotation terms of hits after third round of screening...... 43
Table 3.1 Dgk transcript and protein sizes...... 62
Table 4.1 Summary of metabolic phenotypes seen with Fru-Gal4-driven expression of Dgk RNAi or overexpression of wild-type or kinase-dead Dgk...... 80
Table 4.2 Physical interactors of human type I DGKs...... 86
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List of Figures
Figure 1.1 Inter-organ communication in Drosophila energy homeostasis...... 13
Figure 1.2 Conservation of insulin/IGF signalling pathway between Drosophila and mammals...... 19
Figure 1.3 Signalling pathways regulating insulin secretion from pancreatic β-cells...... 26
Figure 2.1 Silencing or hyperactivation of Fru-Gal4-expressing neurons alters levels of stored fats...... 35
Figure 2.2 Flowchart of RNAi screening procedure...... 37
Figure 2.3 TAG levels of all 1748 genes tested in the first round of RNAi screen...... 38
Figure 2.4 TAG levels from 3 rounds of screening for final 25 hits...... 42
Figure 3.1 DGKs phosphorylate DAG to form PA which both regulate an array of signalling molecules...... 48
Figure 3.2 Protein domain structure of mammalian and Drosophila DGKs by subtype...... 50
Figure 3.3 Generation of UAS-Dgk transgenic lines………………………………………...63
Figure 3.4 Confirmation of UAS-Dgk construct insertion and Dgk.V5 protein expression in transgenic lines…………………………………………………………………...64
Figure 3.5 Metabolic profiling of Dgk RNAi and overexpression using fru-Gal4 driver...... 66
Figure 3.6 Manipulation of DAG and/or PA levels using fru-Gal4 does not correlate with TAG or glycogen levels………………………………………………………….68
Figure 3.7 Fru-Gal4-driven knockdown of PKCs produces phenotypes similar to Dgk knockdown……………………………………………………………………….69
Figure 3.8 Overexpression and knockdown of dIlps affects TAG, glucose and glycogen levels……………………………………………………………………………...71
Figure 3.9 Dgk regulates insulin-like peptide secretion and insulin signaling pathway activity……………………………………………………………………………72
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Figure 4.1 Kinase-dependent model of Dgk regulation of dILP secretion from the IPCs...... 83
Figure 4.2 Kinase-independent model of Dgk regulation of dILP secretion...... 89
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List of Appendices
Table A.1 Data from first round of RNAi screen...... 93
Table A.2 Data from second round of RNAi screen...... 132
Table A.3 Data from third round of RNAi screen...... 138
Figure A.1 V5 tag does not affect the TAG, glucose and glycogen phenotypes caused by Dgk.V5 overexpression...... 145
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List of Abbreviations
α-MSH α-melanocyte stimulating hormone
AgRP Agouti-related protein
AP area postrema apoLTP Apolipoprotein lipid transfer particle
ARC arcuate nucleus
βarr β-arrestin
BCA bicinchoninic acid assay, used to measure protein levels
BMI body-mass index, mass/height2 (kg/m2)
BSA bovine serum albumin
CNS central nervous system
CREB cAMP-responsive element binding protein
DAG diacylglycerol
DGK Diacylglycerol kinase dILP Drosophila insulin-like peptide
DTT dithiothreitol
ECL enhanced chemiluminescence
EDTA ethylenediaminetetraacetic acid
ELISA Enzyme-linked immunosorbent assay
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FBS fetal bovine serum
FFA free fatty acid, also called non-esterified fatty acids (NEFAs)
FOXO forkhead box, subgroup O
Fwd four-wheel drive
GAL4 galactose-responsive transcription factor 4
GI gastrointestinal
GPCR G-protein coupled receptor
GSIS glucose-stimulate insulin secretion
GSK3β glycogen synthase kinase 3β
GWAS genome-wide association study
HRP horseradish peroxidase
IGF insulin-like growth factor
IIS insulin/insulin-like growth factor signalling
InR Drosophila insulin-like receptor
IPCs insulin-producing cells
IR insulin receptor
IRS insulin receptor substrate
MARCKS myristoylated alanine-rich C kinase substrate
NDS normal donkey serum
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NGS normal goat serum
NPY Neuropeptide Y
NTS nucleus of the solitary tract
PBS phosphate-buffered saline
PCR polymerase chain reaction
PDK1 phosphoinositide-dependent kinase 1
PDZ post synaptic density protein/Drosophila disc large/zonula occludens-1
PH pleckstrin homology
PI pars intercerebralis
PI3K phosphoinositide 3-kinase
PIP3 phosphoinsitol (3,4,5)-trisphosphate
PKC protein kinase C
PLC phospholipase C
PLD phospholipase D
PNS peripheral nervous system
POMC pro-opiomelanocortin
PP2A protein phosphatase 2A
PTEN phosphatase and tensin homolog
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PVDF polyvinylidene fluoride
RNAi RNA interference
SAM sterile α motif
SDS sodium dodecyl sulphate
SH2 Src-homology 2 domain
SIK2 salt-inducible kinase 2
SNP single-nucleotide polymorphism
TAG triacylglycerol or triglyceride
TBST Tris-buffered saline + Tween-20
TMB 3,3’,5,5’-teramethylbenzidine, substrate for HRP used in ELISAs
TOR target of rapamycin
TORC1, TORC2 TOR complex 1, TOR complex 2
TRPC transient receptor potential channel
Tsc1, Tsc2 Tuberous sclerosis 1, Tuberous sclerosis 2
UAS Upstream Activating Sequence
V5 epitope tag derived from simian virus 5, sequence GKPIPNPLLGLDST
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1 Introduction
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1.1 Global obesity epidemic
Obesity is a metabolic disorder that is the pathological result of disturbances to energy balance. In the past few decades the prevalence of obesity has rapidly increased, affecting significant portions of the global population and prompting the World Health Organization (WHO) to recognize obesity as a worldwide epidemic. In 2014, the WHO estimated that 39% of the global adult population was overweight and 13% was obese (World Health Organization, 2016).
Obesity is often measured using body-mass index [BMI, weight/height2 (kg/m2)] where BMI 25- 30 is considered overweight and BMI > 30 is obese. High BMI is associated with increased mortality (Flegal et al., 2013; Global BMI Mortality Collaboration, 2016; Prospective Studies Collaboration et al., 2009) due to the increased risk of many chronic diseases including type 2 diabetes, cardiovascular disease, fatty liver disease and some forms of cancer (Anderson et al., 2015; Emerging Risk Factors Collaboration et al., 2011; Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects) et al., 2014; Kodama et al., 2014; Koppe, 2014; Lotta et al., 2015; Mongraw-Chaffin et al., 2015).
Moreover, not only is obesity a public health threat, it also presents an economic burden: in Canada, the direct cost of overweight, obesity and their associated co-morbidities was $6 billion in 2006 (Anis et al., 2010). Therefore, there is medical and economic incentive to understand the biology of energy balance and the pathological mechanisms that lead to the development of obesity.
1.2 Etiology of obesity and metabolic disorders
Obesity results from the accumulation of fat stores due to chronic perturbations to energy homeostasis (i.e. energy intake exceeds energy expenditure). The rapid rise in obesity rates points to our modern “obesogenic” environment (abundant, calorically-dense food and sedentary lifestyles) as a causative factor. However, there is also a genetic component and obesity is broadly recognized as resulting from the complex interactions between environmental factors and genetic predispositions that produce defective energy homeostatic mechanisms that are unable to cope with the relentless perturbations by our modern environment.
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1.2.1 Genetics of human obesity
Family and twin studies have estimated the heritability of obesity to be between 40-70% (Barsh et al., 2000; Hjelmborg et al., 2008; Stunkard et al., 1986). There are two major types of obesity: monogenic and polygenic. Monogenic cases are rare but present with severe, early- onset obese phenotypes (Farooqi and O’Rahilly, 2005). To date, there are only 11 genes involved in all identified monogenic forms (Pigeyre et al., 2016; Rankinen et al., 2006) and have provided some of the initial breakthroughs in identifying etiological mechanisms in obesity. For instance, many of these genes affect the leptin-melanocortin pathway in the hypothalamus that controls food intake and is a central regulator of energy homeostasis (Kim et al., 2014; Shimizu et al., 2007). This pathway will be discussed in more detail in Section 1.4.
The far more prevalent form, polygenic obesity, has been more difficult to study due to the underlying gene-gene and gene-environment interactions. Some of the methods for uncovering susceptibility genes are discussed below.
1.2.2 Approaches to identify obesity susceptibility genes
1.2.2.1 Early methods
Prior to 2007, the identification of obesity genes was mainly achieved using candidate gene or genome-wide linkage studies. Genome-wide linkage studies are unbiased surveys of the whole genome to identify highly polymorphic markers for association with the disease state. The candidate gene approach examines the association of genetic variations within a gene of interest with a trait or disease state. Both methods have been successful in contributing to the identification of genetic risk factors in obesity: the last Human Obesity Gene Map update reported 253 identified loci and 127 candidate genes in obesity-related traits (Rankinen et al., 2006).
However, both candidate gene and linkage studies have inherent disadvantages in their designs. Genome-wide linkage studies have coarse resolution and often require follow-up studies to determine the responsible gene(s) within the identified loci while the candidate gene approach relies on our current models of metabolism and etiological mechanisms in obesity making it difficult to uncover novel pathways. In addition, these are both broad epidemiological
4 approaches and require large sample sizes to allow for the detection of small effects of genetic variants in a polygenic disease or trait.
1.2.2.2 Genome-wide association studies
Genome-wide association studies (GWAS) are large-scale assays to systematically screen millions of common variants (usually single nucleotide polymorphism, SNPs) for an association with a trait or disease state. Several factors have recently come together to make GWAS more feasible: the sequencing of the human genome, completion of the Haplotype Map (HapMap) of common human genetic variations and the substantial decrease in genotyping costs. The first obesity-related GWAS were published in 2007 and they have now become a common tool and are rapidly advancing the rate of gene discovery. To date, GWAS have identified hundreds of variants associated with obesity and fat mass distribution traits (Pigeyre et al., 2016).
The most promising finding from these GWAS studies was the identification of the fat mass and obesity associated (FTO) gene which had not been previously linked to obesity. Many studies have confirmed that common SNPs within FTO are associated with obesity and these findings have been replicated in many European, Asian and African populations (Albuquerque et al., 2015; Cheung and Mao, 2012; Fall and Ingelsson, 2014). However, the effect of FTO risk variants on BMI is modest with individuals carrying two copies of the most severe risk allele weighing, on average, 3 kg more than those with the protective allele (Frayling et al., 2007; Scuteri et al., 2007).
Altogether, all the risk variants identified by GWAS can only account for a small proportion of the variance in BMI: ~3% (Bogardus, 2009; Locke et al., 2015) compared to the 40-70% heritability in BMI determined by family and twin studies. This “missing heritability” has been attributed, in part, to the current methodology’s inability to detect rare variants, variants with small effect sizes, epistatic interactions and epigenetic inheritance.
Thus, while studies in human populations have helped make significant progress in the generation of an obesity gene map and broadened our understanding of the genetic basis of common obesity, there is still a large portion of heritability and etiology that remains unknown.
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1.3 Drosophila models of human diseases
The fruit fly Drosophila melanogaster has been an invaluable genetic model for many biological processes and has greatly contributed to our understanding of the mechanisms governing development. There are many advantages of using Drosophila: they are relatively inexpensive to maintain and have a smaller genome and shorter generation time compared to rodent or other mammalian models. In addition, Drosophila are one of the most genetically tractable multicellular organisms with a wide array of available genetic tools that allow for spatial and temporal control of transgene expression, the ability to easily generate clones and mutations, and to perform large-scale forward genetic screens.
More recently, Drosophila have also become a powerful tool in the study of human diseases as approximately 75% of known human disease-related genes are conserved in flies (Bier, 2005; Reiter et al., 2001). Flies are currently utilized as models for many human diseases including cancer, neurodegenerative diseases, epilepsy, inflammatory disorders, and cardiovascular diseases (Marsh and Thompson, 2006; Pandey and Nichols, 2011; Parker et al., 2011; Rudrapatna et al., 2012; Wolf et al., 2006).
1.3.1 Drosophila models of metabolic disorders
In recent decades, Drosophila have been used as models for metabolic disorders since flies possess many of the same basic metabolic functions as mammals including the ability to maintain glucose homeostasis, storing and mobilizing energy stores, and modulating food intake in response to nutritional cues. In addition, the intermediary metabolism pathways are present in the fly including glucose, glycogen, fatty acid and triacylglyceride synthesis, lipo- and glycogenolysis, glycolysis and β-oxidation. Furthermore, many of the metabolic organs and tissues in mammals have functionally analogous counterparts in flies (Table 1.1): food is digested and absorbed by the crop and midgut (similar to the stomach and intestine); energy is stored as triacylglycerol (TAG) and glycogen in the fat body (analogous to white adipose tissue and liver) and hepatocyte-like cells called oenocytes are believed to be involved in lipid processing (Canavoso et al., 2001; Gutierrez et al., 2007). In addition, the pars intercerebralis- corpora cardiac system analogous to the hypothalamus-pituitary axis (Hartenstein, 2006) secretes the fly equivalents of insulin and glucagon (called insulin-like peptides and adipokinetic
6 hormone) that regulate many aspects of carbohydrate and lipid metabolism (Baker and Thummel, 2007; Haselton and Fridell, 2010; Teleman, 2010).
Table 1.1. Functionally analogous metabolic tissues between mammals and Drosophila. Metabolic function Mammals Drosophila Digestion and nutrient Gastrointestinal tract Digestive tract (crop, absorption (stomach, intestines) midgut) Lipid synthesis Liver Fat body, oenocytes Lipid storage Adipose tissue Fat body Glucose and glycogen Liver Fat body synthesis Insulin synthesis/secretion Pancreatic β-cells Insulin-producing cells in pars intercerebralis Glucagon synthesis/secretion Pancreatic α-cells Corpora cardiaca
Many of the molecular mechanisms that regulate these metabolic processes are largely conserved including the insulin, target of rapamycin and leptin pathways (Grewal, 2009; Rajan and Perrimon, 2012; Teleman, 2010). With the interest in fly models of metabolism, researchers have developed many assays that can measure lipid, sugar and metabolite levels, feeding behaviour, metabolic rate and locomotion (Smith et al., 2014; Tennessen et al., 2014).
However, it should be noted that a larger proportion of metabolic studies in flies has been performed in larvae rather than adults. This is an important distinction because these different life stages have different metabolic parameters: larvae consume large volumes of food for growth and in preparation for metamorphosis while adults exercise a homeostatic balance to their metabolic programs. Thus, while larval studies can uncover molecular mechanisms relevant in adults, the physiological effects may not be broadly applicable.
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1.3.2 Utility of Drosophila in the study of energy homeostasis
1.3.2.1 Forward genetic screens in Drosophila
It is clear that the current methods used in human genetic studies are insufficient to account for the variation in BMI and we need to find alternative strategies. Drosophila could prove to be useful in this aspect as large-scale in vivo screens are one of the major strengths of flies as genetic models. Researchers have access to a large number of resources including multiple online databases and libraries of publically available transgenic lines (Matthews et al., 2005; St Johnston, 2002). Many of these libraries are insertional mutation libraries generated by mobilizing and reinserting a transposable element into the genome. These transposons have been modified so that the insertion into the genome can disrupt, overexpress or epitope-tag genes near the insertion site (Ryder and Russell, 2003; Thibault et al., 2004). These libraries have already been used to screen for metabolic regulators in flies including factors involved in adiposity (Liu et al., 2014; Reis et al., 2010), glucose regulation (Ugrankar et al., 2015), and lifespan (Landis et al., 2003; Nakayama et al., 2014).
There are also several large RNAi libraries which cover most of the fly genome. Using one of these libraries, Pospisilik et al. screened over 11,500 transgenic RNAi lines corresponding to about 10,500 genes for changes in fat levels in adult flies (Pospisilik et al., 2010). Of the 500 hits, 60% have human orthologs including genes involved in insulin signalling, glucose and lipid homeostasis, nutrient transport and adipocyte development and function. In addition, this screen identified a large number of potential lipid storage regulators that were not previously associated with obesity. Although many of these candidate genes have not been further characterized, there is potential here to find novel susceptibility genes or to uncover molecular mechanisms of disease that could be relevant to human obesity. Interestingly, gene ontology analysis of the hits from this screen found hedgehog signalling to be the most highly enriched signalling pathway and the authors determined that this pathway acts within the fat body. It was subsequently shown that hedgehog signalling in mice regulates early adipogenic factors that function in white vs. brown adipocyte specification thus exemplifying how genetic work in flies can impact our knowledge of mammalian physiology (Pospisilik et al., 2010).
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The screen described above screened flies with ubiquitous knockdown but it is also possible to perform tissue-specific knockdown. This is another powerful tool in Drosophila and many drivers that can drive the expression of transgenes or RNAi constructs in metabolically relevant tissues have already been generated (Smith et al., 2014). For instance, using a fat body-specific driver, about half of all Drosophila genes were screened for effects on adiposity (Baumbach et al., 2014). They identified store-operated calcium entry components that communicate with the brain to affect feeding behaviour and expression of lipid metabolic enzymes.
Thus, the tools available in flies afford researchers the ability and ease to perform large-scale, targeted metabolic screens. Moreover, with the multiple libraries of transgenic lines, hits from these screens could be easily validated by confirming the phenotype with different mutations.
1.3.2.2 Understanding obesity as a complex disorder
As previously mentioned, the common forms of obesity are likely multigenic and involve gene- gene interactions. Thus, studying individual genes in isolation may not accurately reflect the disease state or etiological mechanisms and therefore highlights the importance of using multigenic models. This is more easily accomplished in the genetically tractable fruit fly which also possesses high fecundity, a short generation time and less genomic redundancy than higher organisms.
It is also important to be able to identify novel genetic interactions in obesity. This can be achieved with another widely used method in flies: modifier screens in which genetic interactors of a gene of interest are screened in a high-throughput manner. For example, this approach has been previously used to identify interactors of components of the insulin signalling pathway including the insulin receptor (Honegger et al., 2008; Wittwer et al., 2005) and FOXO (Zhang et al., 2011b). Overexpression of the insulin receptor in the eye causes a hyperplasia phenotype and genes were screened for the ability to suppress or exacerbate this phenotype. These studies identified suppressors of insulin signalling: a novel negative regulator called Susi (Wittwer et al., 2005) and another called imaginal morphogenesis protein-Late 2 (Imp-L2) that was the first identified functional homologue to the insulin growth factor-binding proteins (Honegger et al., 2008). Imp-L2 can bind insulin-like peptides to attenuate insulin signalling and overexpression
9 of Imp-L2 produces phenotypes associated with impaired insulin signalling including increased fat stores, extended lifespan, and reduced growth (Alic et al., 2011; Honegger et al., 2008).
The effects of environmental factors on metabolism in Drosophila have also been studied and mimic the phenotypes seen in mammalian models. Flies exposed to high sugar diets develop diabetic-like phenotypes (Musselman et al., 2011; Skorupa et al., 2008) while flies on high fat diets become obese and develop cardiac dysfunction (Birse et al., 2010). Thus, flies would be a useful model to study gene-environment interactions. For example, Pendse et al. screened fly homologues to human type 2 diabetes GWAS hits. Disruption of several of these genes resulted in lethality in response to high sugar diet demonstrating functional interaction of candidate genes identified in humans with an environmental risk factor (Pendse et al., 2013).
Altogether, many metabolic tissues, functions and mechanisms are conserved between mammals and flies. Combined with all the advantages of flies as genetic models, Drosophila are a suitable and attractive system in which to study energy homeostasis.
1.4 The central nervous system in energy homeostasis
Physiologically, energy homeostasis is maintained by the central nervous system (CNS). Hormone, nutrient and satiety signals generated by the peripheral metabolic tissues convey the body’s energy status to key brain areas. The information is processed to produce the appropriate autonomic, endocrine and behavioural outputs both for the long-term (body weight maintenance) and short-term (meal initiation and satiation). Consistent with its pivotal role, about 25% of all the genes in the last Human Obesity Gene Map and nearly all the genes implicated in monogenic obesity are known to be expressed in the brain (Rankinen et al., 2006). Furthermore, several genome-wide association studies in humans have also implicated single-nucleotide polymorphisms in several neuronal genes with predisposition to high BMI (Lee et al., 2011; Thorleifsson et al., 2009; Willer et al., 2009).
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1.4.1 The CNS in mammalian energy homeostasis
1.4.1.1 Hypothalamic control of body weight and food intake
The hypothalamus is the control center in the maintenance of many physiological parameters including body temperature, electrolyte levels, circadian rhythms and sleep-wake cycles. Various hypothalamic nuclei are known to affect energy homeostasis including the arcuate nucleus (ARC), ventromedial hypothalamus, paraventricular nucleus, dorsal hypothalamus and lateral hypothalamic area (Rui, 2013; Sánchez-Lasheras et al., 2010; Schneeberger et al., 2014). There are reciprocal synaptic connections between these nuclei as well as to extrahypothalamic areas including the hunger/satiety circuits in the hindbrain and the corticolimbic food reward system (Rui, 2013; Schneeberger et al., 2014).
A region of intense focus is the arcuate nucleus in the mediobasal hypothalamus which is situated in an area with a semi-permeable blood brain barrier allowing it to sense circulating hormones and nutrients. The hormones leptin and insulin are so-called adiposity factors which are secreted from adipose tissue and the pancreas respectively, in proportion to the levels of fat stores (Bagdade et al., 1967; Considine et al., 1996; Schwartz et al., 1996). There are at least two major neuronal populations in the arcuate nucleus regulating energy homeostasis that respond to leptin and insulin: a subset of neurons that express pro-opiomelanocortin (POMC) which is processed into the anorexigenic neuropeptide α-melanocyte stimulating hormone (α- MSH) and another population that expresses the orexigenic peptides Neuropeptide Y (NPY) and Agouti-related peptide (AgRP, a melanocortin receptor antagonist) (Könner et al., 2009; Pang and Han, 2012; Sánchez-Lasheras et al., 2010; Schneeberger et al., 2014). Thus, when there is an energy surplus, there is an increase in leptin and insulin levels released into the bloodstream where they circulate and bind to their receptors on ARC neurons to initiate signalling. Activation of leptin and insulin signalling increases POMC mRNA expression, decreases NPY and AgRP expression as well as altering the electrical activity of these two neuronal populations.
Mutations and single-nucleotide polymorphisms in many of the genes in this pathway have been implicated in human monogenic and multigenic obesity including leptin, leptin receptor, SH2B adaptor protein 1 (a key regulator of leptin signalling), POMC, pro-protein convertase
11 subtilisin/kexin type 1 (that cleaves POMC to α-MSH) and melanocortin 3 and 4 receptors (Pigeyre et al., 2016; Rankinen et al., 2006).
In addition to leptin and insulin, these hypothalamic circuits are also responsive to other signals from the periphery including nutrients (glucose, fatty acids, and some amino acids) and ghrelin, a hormone secreted by the stomach during fasting involved in meal initiation and long-term body weight maintenance (De Vriese and Delporte, 2007).
The POMC and AgRP/NPY neurons project to second-order neurons that express receptors for NPY and α-MSH which further relay the information to other brain areas and ultimately result in decreased food intake and energy expenditure to counteract the excess energy and therefore restore homeostasis (Gautron et al., 2015; Münzberg et al., 2016; Schneeberger et al., 2014). For example, second-order neurons within the paraventricular nucleus project to the hindbrain which is involved in the homeostatic regulation of satiety, to the autonomic nervous system that innervates the peripheral metabolic tissues and to the pituitary, the master endocrine gland.
1.4.1.2 Hindbrain circuits in the regulation of satiety
The hindbrain circuits involved in energy balance are responsible for the integration of inputs relaying the energy status of the animal including circulating hormones from peripheral metabolic tissues, viscerosensory information from the gastrointestinal tract, and neuronal inputs from the forebrain. A key site of integration is the dorsal vagal complex that encompasses the nucleus of the solitary tract (NTS), area postrema (AP) and dorsal motor nucleus of vagus (DMV).
The NTS receives descending projections from several hypothalamic nuclei including the ARC to regulate food intake and energy expenditure (Skibicka and Grill, 2009; Williams et al., 2000) and from the gastrointestinal tract via the vagus nerve which conveys information about nutritional content of a meal, luminal distension and GI peptides to regulate meal size and duration (Schwartz et al., 1999; Travagli et al., 2006). The NTS and AP area also has a leaky blood-brain barrier and is thus directly exposed to circulating nutrients and satiety hormones that can regulate NTS and AP neuron activity.
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Thus, the role of the mammalian CNS network in energy homeostasis is complex, with a multitude of inputs and cross-talk between different brain regions and neuronal populations. However, these circuits are crucial in the maintenance of energy balance and hence, continue to be an active area of research.
1.4.2 The CNS in Drosophila energy homeostasis
The fly central nervous system is comprised of more than 100 000 neurons that form distinct circuits that regulate many complex behaviours including feeding, learning and memory, courtship and mating, circadian rhythms, and flight. Similar to mammals, the CNS has also recently been shown to play a crucial role in regulating energy metabolism in Drosophila. In a genome-wide RNAi screen, one-third of the approximately 500 genes found to affect stored fat levels when ubiquitously knocked down, were also able to alter fat levels if their expression was specifically reduced in neurons (Pospisilik et al., 2010).
Another screen identified two distinct neuronal populations that are involved in the regulation of lipid stores (Al-Anzi et al., 2009). Altering the neuronal activity of these neuronal populations resulted in changes in food consumption, lipid metabolism and macromolecule metabolism that could be reversed by restoring normal neuronal activity. However, while these screens implicate the CNS in fly energy metabolism and despite the importance of the CNS in mammalian energy balance, the molecular mechanisms are only beginning to be uncovered and, like mammals, there is extensive crosstalk between the Drosophila CNS and peripheral metabolic tissues (Figure 1.1).
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Figure 1.1. Inter-organ communication in Drosophila energy homeostasis. Akh, adipokinetic hormone; dILPs, Drosophila insulin-like peptides. Dashed arrows represent presumed interactions, but require further study. See text for details.
1.4.2.1 Neuronal signalling network and outputs from the CNS
In flies, the neuronal circuitry mediating energy balance has yet to be fully characterized. Many of the known circuits involve neurosecretory neurons in the pars intercerebralis (PI) called the insulin-producing cells (IPCs) that produce three of the eight known Drosophila insulin-like peptides (dILPs) in adults. The pars intercerebralis is considered to be analogous to the
14 mammalian hypothalamus (Hartenstein, 2006) and the development of the IPCs involves pathways homologous to those involved in the development of the hypothalamus-pituitary axis (Wang et al., 2007). Similar to the hypothalamus, the IPCs within the PI receive and integrate many signals from the periphery (discussed below) and from other brain regions (see Section 1.5) that convey information about the body’s energy status. Although many of these studies demonstrate an effect on dILP expression or secretion, the IPCs also express other neuropeptides (Cao et al., 2014; Söderberg et al., 2012). For instance, IPCs express Drosulfakinin (DSK) which regulates feeding and food choice (Söderberg et al., 2012) and administration of DSK protein decreases contraction of the crop, a part of the alimentary canal used to store food before digestion (Palmer et al., 2007). Therefore, other IPC-expressed neuropeptides could be a functional target for these peripheral signals and neuronal circuits involved in energy homeostasis and require further investigation.
The IPCs send processes to several tissues including the heart, the ring gland (the fly endocrine gland), and parts of the digestive tract (Cao and Brown, 2001; Cognigni et al., 2011), suggesting that these are the target tissues for CNS outputs although this area requires further study. The fat body is one of the targets for the dILPs that are released into the circulation and activation of insulin receptor signalling results in effects on lipid and carbohydrate metabolism (Liu et al., 2009). Some of these processes will be discussed in more detail in Section 1.5.
Another functional output of the central nervous system in energy homeostasis is the regulation of feeding behaviours. Insulin-like peptide signalling is also involved in feeding specifically in foraging, modulating sensitivity to odors and response to noxious food (Erion and Sehgal, 2013). Accordingly, alterations to levels of a gene that regulates expression of the insulin receptor affects food intake (Ryuda et al., 2011).
Another component of the hypothalamic homeostatic circuit that is also conserved is Neuropeptide Y. In flies, there are two members of the Neuropeptide Y family called Neuropeptide F (NPF) and short neuropeptide F (sNPF) (Nässel and Wegener, 2011). NPF is involved in the regulation of feeding in larvae, especially foraging, and the motivation to eat (Wu et al., 2003, 2005a, 2005b). Short NPF is also involved in feeding as well as growth (Lee et al., 2004, 2008), response to starvation (Kahsai et al., 2010a), and locomotor behaviour (Kahsai et
15 al., 2010b). Thus, NPY functions are conserved in Drosophila NPF and sNPF in the stimulation of feeding, secretion of certain pituitary hormones, and inhibition of energy expenditure (Barb et al., 2006; Kalra and Kalra, 2004; Sohn, 2015; Zhang et al., 2011a).
Neuromedin U is another mammalian neuropeptide involved in energy homeostasis and feeding regulation (Budhiraja and Chugh, 2009; Martinez and O’Driscoll, 2015). A homologue called hugin was discovered in flies when the expression of hugin was found to be altered during starvation and in mutants with foraging and food intake defects (Melcher and Pankratz, 2005). Hugin-expressing neurons project to the insulin-producing cells, the ring gland, brain regions that innervate the muscles required for feeding and directly to the pharyngeal muscles (Bader et al., 2007; Melcher and Pankratz, 2005). While the functional significance of some of these projections is not known, the connections to the feeding machinery could be involved in the role of Hugin in the shift in motor programs during meal termination (e.g. suppression of the motor program for feeding and induction of the motor program for locomotion) (Schoofs et al., 2014).
Other neuronal factors have been shown to be involved in regulating feeding behaviour in flies including dopamine (Bjordal et al., 2014; Inagaki et al., 2012; Marella et al., 2012), leucokinin (Al-Anzi et al., 2010; Liu et al., 2015), corazonin (Kapan et al., 2012), Allatostatin A (Hergarden et al., 2012), and central circadian clock neurons (DiAngelo et al., 2011). Several distinct neuronal populations of neurons involved in regulating feeding downstream of sensory neurons have also been identified but the exact molecular mechanisms active within these neurons have yet to be elucidated (Flood et al., 2013; Sun et al., 2014).
1.4.2.2 Nutrient sensing
There have been many studies demonstrating the ability of flies to sense nutrient levels and adjust their feeding behaviour and metabolism accordingly. For instance, bitter tastes produce aversive behaviour and inhibits feeding (French et al., 2015) while lipid ingestion invokes an appetitive response and increased intake (Masek and Keene, 2013).
Non-sensory neurons in the Drosophila brain can also detect nutrients. Similar to mammals, alteration in the activity of the nutrient-sensing target of rapamycin (TOR) pathway in the brain can affect feeding behaviour, potentially in response to nutritional status (Ribeiro and Dickson,
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2010). The insulin-producing cells have been shown to respond to leucine which stimulates the secretion of dILP2 and dILP5 (Manière et al., 2016). A population of neurons with reciprocal connections with the IPCs can also detect dietary lipid composition via Apolipoprotein lipid transfer particle (apoLTP) to regulate the secretion of dILP2 (Brankatschk et al., 2014). In addition, certain gustatory receptors are expressed on some non-sensory neurons in brain regions implicated in metabolism and feeding behaviour suggesting a potential function of these receptors in nutrient sensing in these neurons (Miyamoto et al., 2012; Thorne and Amrein, 2008).
In addition, flies can determine the nutritional quality of different sugars regardless of palatability (Burke and Waddell, 2011; Qi et al., 2015; Stafford et al., 2012) and increase the intake of higher caloric sugars after starvation (Qi et al., 2015).
1.4.2.3 Humoral signals to the brain
Recently, many circulating factors secreted into the hemolymph have been identified. Endocrine corpora cardiaca cells sense dietary sugar and release adipokinetic hormone into the hemolymph which binds its receptor on the insulin-producing cells to regulate dILP3 secretion (Kim and Neufeld, 2015).
In mammals, adipose tissue produces many adipokines that signal to the brain to maintain energy homeostasis. For many years it was known that the fly fat body secretes humoral factors that signal to the brain in response to nutritional status (Colombani et al., 2003; Géminard et al., 2009). One of the responsible factors was eventually identified as Unpaired-2 (Upd-2), a functional analogue of leptin (Rajan and Perrimon, 2012). Similar to leptin, Upd-2 is a cytokine secreted by the fat body in the fed state to activate a Jak/STAT signalling cascade in the brain. One population of Upd-2-responsive neurons regulates the secretion of dILPs from the IPCs which mediate the growth and metabolic functions of Upd-2. Moreover, the metabolic phenotypes seen in upd-2 mutants could be rescued with injections of human leptin (Rajan and Perrimon, 2012).
Another mammalian adipokine called adiponectin does not have a known Drosophila homologue; however, a homologue for adiponection receptor-1 called dAdipoR has been
17 identified and found to be expressed in the IPCs (Kwak et al., 2013). IPC-specific knockdown of dAdipoR impairs dILP secretion resulting in elevated circulating sugar and whole-body fat levels. In addition, ex vivo cultures of Drosophila brains with human adiponectin protein stimulated dILP2 secretion.
Other factors produced by the fat body have also been found to alter dILP secretion from the insulin-producing cells to regulate growth in larvae including Eiger (Agrawal et al., 2016), Stunted (Delanoue et al., 2016), CCHamide-2 (Sano et al., 2015), and Growth-Blocking Peptides 1 and 2 (Koyama and Mirth, 2016). However, as previously mentioned, larvae and adults have different metabolic regimes. Therefore, whether these factors mediating larval metabolism are also involved in energy homeostasis in adults is not known.
1.4.2.4 Gut signals to the brain
In mammals, the gastrointestinal tract communicates with the brain through satiety factors (that circulate in the blood or activate vagal afferent nerve fibers) and neuronal inputs relaying luminal distension and nutritional content of ingested food. Stretch receptors in the foregut of blowflies have been identified that inhibit feeding (Gelperin, 1967) but whether Drosophila have these receptors as well is not known. The Drosophila intestine is extensively innervated by sensory and efferent fibers specifically where there are sphincters and valves suggesting that these fibers are involved in sensing and/or regulating the movement of food through the digestive tract (Cognigni et al., 2011).
In mammals, enteroendocrine cells produce incretin hormones in response to dietary sugar that stimulates the secretion of insulin from pancreatic β-cells. Drosophila endocrine cells associated with the gut produce a hormone called Limnostatin (Lst) that functions in opposition to incretins: Lst is secreted in response to sugar restriction and binds to its receptor on IPCs to inhibit dILP2 secretion (Alfa et al., 2015).
The endocrine cells in the midgut also produce Allatostatin A (AstA) and Diuretic hormone 31 (Dh31) (Veenstra et al., 2008). AstA has been shown to be upregulated after ingestion of sugars and its receptor is expressed on the IPCs (Hentze et al., 2015). Both AstA and Dh31 have receptors on a set of lateral brain neurons that directly project to the IPCs suggesting another
18 method to regulate IPC function in response to gut-derived signals (Johnson et al., 2005; Veenstra et al., 2008). Another peptide produced in the endocrine cells of the midgut is CCHamide-2 (Li et al., 2013). While the physiological function of this enteroendocrine-derived CCHamide-2 has yet to be determined, as mentioned above, CCHamide-2 produced by the fat body regulates insulin-like peptide secretion. It is therefore possible that the CCHamide-2 signal from the gut also contributes to this process.
Thus, Drosophila energy homeostatic mechanisms have many parallels to those of mammalian systems including the ability to sense nutrients and other circulating factors to modulate feeding and whole-body metabolism. Moreover, flies provide a simpler model that makes it possible to generate maps of neuronal circuits and molecules that govern these processes.
1.5 Insulin signalling in energy homeostasis
As discussed in Section 1.4, signals from peripheral metabolic tissues to the CNS are crucial in the maintenance of energy homeostasis by informing the CNS about the body’s energy status. These signals can come in the form of hormones such as insulin that is produced by the β-cells in the pancreas in response to high blood sugar levels. Insulin plays a critical role in many aspects of energy homeostasis; accordingly, insulin resistance is associated with obesity and type 2 diabetes.
The insulin/insulin-like growth factor (IGF) signalling (IIS) pathway is highly conserved across all metazoans (Claeys et al., 2002; Oldham and Hafen, 2003; Porte et al., 2005) and the physiological functions of the mammalian IIS pathway are conserved in flies including the regulation of metabolism, growth, reproduction and longevity (Das and Dobens, 2015; Kannan and Fridell, 2013; Nässel and Vanden Broeck, 2016; Teleman, 2010).
1.5.1 Insulin signalling pathway
The core components of the IIS pathway are conserved between mammals and Drosophila (Figure 1.2). There are eight Drosophila insulin-like peptides that bind to a single insulin-like
19 receptor (InR) (Ikeya et al., 2002) to mediate insulin and IGF-like functions in insects (Kannan and Fridell, 2013; Nässel and Vanden Broeck, 2016; Teleman, 2010).
Figure 1.2. Conservation of insulin/IGF signalling pathway between Drosophila and mammals. Arrows indicate activation while lines ending with a bar indicate inhibitory interactions. See text for details.
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Like mammalian insulin and IGF receptors, InR is a receptor tyrosine kinase that, upon ligand binding, autophosphorylates (Ruan et al., 1995) and recruits the insulin receptor substrates called Chico and Lnk in flies (Böhni et al., 1999; Werz et al., 2009). The phosphorylated receptor and substrate act as docking sites for proteins containing Src-homology 2 (SH2) domains such as the regulatory subunit of phosphoinositide 3-kinase (PI3K) (Oldham et al., 2002). Once at the cell membrane, PI3K is activated and catalyzes the conversion of phosphoinositides within the cell membrane into phosphoinositol (3,4,5)-trisphosphate (PIP3). The function of PI3K is opposed by Pten which converts PIP3 to PIP2, acting as a negative feedback mechanism to attenuate downstream signalling (Goberdhan et al., 1999; Huang et al., 1999; Scanga et al., 2000).
The accumulation of PIP3 recruits two kinases containing lipid-binding pleckstrin homology (PH) domains: phosphoinositide-dependent kinase 1 (PDK1) and Akt. PDK1 becomes activated and phosphorylates Akt which, in turn, regulates several key targets that mediate the metabolic functions of the insulin/IGF signalling pathway. There are other conserved regulatory inputs to Akt including activation of Akt by TOR complex 2 (TORC2) and protein phosphatase 2A (PP2A) that dephosphorylates and deactivates Akt (Hietakangas and Cohen, 2007; Vereshchagina et al., 2008).
One important Akt target is the transcription factor FOXO; phosphorylation of FOXO by Akt prevents its entry into the nucleus and inhibits its transcriptional activity (Puig et al., 2003; Van Der Heide et al., 2004). Many of the anabolic effects of the IIS pathway result from the attenuation of FOXO activity and the overexpression of FOXO can phenocopy starvation (Kramer et al., 2003). FOXO has many target genes that regulate protein synthesis, mitochondrial function, and carbohydrate, lipid and protein homeostasis (Bai et al., 2013; Gershman et al., 2007; Wang et al., 2011).
Other Akt targets include glycogen synthase kinase 3 β (GSK3β), Tsc2 (also known as gigas), Proline-rich Akt substrate 40 kDa (PRAS40), salt-inducible kinase 2 (SIK2) and enzymes involved in glycolysis and fatty acid synthesis (Franke, 2008; Schultze et al., 2011). These targets in turn, regulate other factors. For instance, SIK2 regulates a transcriptional co-activator for cAMP-responsive-element binding protein (CREB) that induces catabolic and gluconeogenic genes (Wang et al., 2008).
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Tsc2 and PRAS40 regulate TOR complex 1 (TORC1) (Kim and Lee, 2015; Pallares-Cartes et al., 2012), a central regulator of cellular metabolism (André and Cota, 2012). TORC1 functions through phosphorylation of more effectors such as S6 kinase and 4E-BP which regulate protein synthesis and SREBP, an important regulator of lipogenesis (André and Cota, 2012; Kunte et al., 2006; Rosenfeld and Osborne, 1998; Zhang et al., 2000).
1.5.2 Insulin functions in mammalian energy homeostasis
1.5.2.1 Peripheral functions
Insulin is widely known as a critical anabolic regulator in peripheral metabolic tissues: in response to an elevation of plasma glucose levels insulin is released into the bloodstream to induce glucose uptake in muscle and liver and some amino acids into muscle and adipose tissue (Dimitriadis et al., 2011; Saltiel, 2016). Insulin also stimulates storage of glucose and lipids as glycogen and triacylglycerol respectively while inhibiting the breakdown of these energy stores and the synthesis of glucose (see Table 1.2).
Table 1.2. Effects of peripheral insulin signalling on carbohydrate, lipid and protein metabolism.
Liver Adipose tissue Skeletal muscle
Carbohydrate ↑ glucose uptake ↑ glucose uptake metabolism ↓gluconeogenesis ↑ glycolysis ↑ glycolysis ↑ glycogen synthesis ↑ glycogen synthesis ↓ glycogen breakdown ↓ glycogen breakdown Lipid ↑ fatty acid synthesis ↑ triacylglycerol ↑ fatty acid uptake metabolism synthesis ↓ lipolysis Protein ↑ amino acid uptake ↑ amino acid uptake metabolism ↑ protein synthesis
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1.5.2.2 Central insulin functions
As briefly discussed in Section 1.4.1.1., insulin also functions in the CNS to regulate energy homeostasis. While the relative levels of circulating insulin fluctuate during the day in response to feeding, the overall amount is proportion to the levels of fat stores in the body (Bagdade et al., 1967) and insulin levels in the brain are proportional to serum levels (Margolis and Altszuler, 1967; Woods and Porte, 1977).
The insulin receptor (IR) is expressed in many areas of the brain with the highest concentrations in the hypothalamus, cerebral cortex, cerebellum, hippocampus and olfactory bulb (Havrankova et al., 1978; van Houten et al., 1979; Unger et al., 1991). Deletion of IR specifically in neurons increases food intake, weight, adiposity and plasma insulin levels in mice (Brüning et al., 2000).
As discussed in Section 1.4.1.1., one of the important insulin-responsive brain regions in energy homeostasis is the hypothalamus. Reduction of IR signalling in the hypothalamus induces hyperphagia, insulin resistance and increases hepatic glucose production (Obici et al., 2002a, 2002b).
In the arcuate nucleus, activation of insulin signalling in the POMC and AgRP/NPY neuronal populations results in increased Pomc and decreased Agrp gene expression (Kitamura et al., 2006). IR signalling affects the subcellular distribution of FOXO, excluding it from the nucleus where it functions as a transcriptional repressor of Pomc and activator of Agrp. These two populations are thought to mediate different aspects of central insulin function: IR signalling in POMC neurons may be involved in the regulation of locomotor activity and energy expenditure while AgRP neurons mediate insulin’s effects on hepatic glucose production (Lin et al., 2010).
Neurons in the ventromedial nucleus of the hypothalamus (VMH) are also responsive to insulin and infusion of insulin to the VMH causes hypophagia and reduced body weight in rats (McGowan et al., 1990). Specific loss of IR in the VMH of adult mice results in defective glucose metabolism and pancreatic α- and β-cell function (Klöckener et al., 2011).
Neuronal insulin signalling has also been shown to regulate the sensitivity of glucose-sensing neurons in the hypothalamus suggesting an involvement in the sympathetic/adrenal response to hypoglycemia (Diggs-Andrews et al., 2010; Fisher et al., 2005). In addition, insulin signalling in
23 the mediobasal hypothalamus regulates peripheral lipid metabolism by modulating sympathetic innervations to white adipose tissue (WAT) to regulate WAT mass, adipocyte size, lipogenesis and lipolysis (Koch et al., 2008; Scherer et al., 2011).
1.5.3 Drosophila ILP signalling functions in energy homeostasis
The eight Drosophila insulin-like peptides vary in their expression patterns and regulation suggesting that they may have distinct functions (Nässel et al., 2013; Teleman, 2010). The most well characterized dILPs are dILP2, 3 and 5 which are expressed in the insulin-producing cells of the brain (Brogiolo et al., 2001; Ikeya et al., 2002; Rulifson et al., 2002). These dILPs are regulated by nutritional cues (Colombani et al., 2003; Ikeya et al., 2002) and are thought to mediate most metabolic functions in the fly (Nässel et al., 2013; Teleman, 2010). Ablation of the IPCs that secrete these dILPs produces flies with elevated circulating sugars, glycogen and triglyceride levels in adults (Belgacem and Martin, 2006; Broughton et al., 2005; Haselton et al., 2010).
1.5.3.1 Carbohydrate and lipid metabolism
In addition to the dILPs, mutations to many of the other components of the insulin signalling pathway also show defects in carbohydrate and lipid metabolism including InR, chico, Lnk, Akt, Pten and FOXO (Böhni et al., 1999; Dionne et al., 2006; Murillo-Maldonado et al., 2011; Oldham et al., 2002; Werz et al., 2009) similar to the mammalian IIS pathway. However, there has been less focus on the molecular mechanisms acting downstream of the core IIS pathway in metabolic regulation.
One of the dILP target tissues is the fat body that performs functions analogous to those of adipose tissue and liver that regulate the body’s energy stores in the form of glycogen and triglycerides (TAG) (Baker and Thummel, 2007; Bharucha, 2009; Liu et al., 2009). Secretion of dILPs from the IPCs has been shown to negatively regulate the expression of an α-glucosidase called Tobi that catalyzes the breakdown of glycogen (Buch et al., 2008). Another regulator of glycogen metabolism is GSK3β which stimulates glycogenesis in mammals. While Drosophila GSK3β is phosphorylated in a PI3K-dependent manner in vivo (Papadopoulou et al., 2004), it is not known whether this interaction gives rise to any metabolic effects. In addition, phosphoenolpyruvate carboxykinase (PEPCK) which functions in gluconeogenesis and glycogen
24 synthesis (Okamura et al., 2007), has been shown to be a FOXO target gene in cell culture (Wang et al., 2011).
DILP signalling is believed to be involved in regulating lipid energy stores like mammalian insulin and is involved in lipolysis in Tsetse flies (Baumann et al., 2013). Both FOXO and Tor activity regulate expression of 4E-BP that induces lipid mobilization under starvation conditions (Teleman et al., 2005). Another mechanism may be through brummer (bmm), a lipase that breaks down stored TAG to release fatty acids (Grönke et al., 2005, 2007). Bmm has been shown to be transcriptionally regulated by FOXO in culture (Wang et al., 2011) but whether it occurs in vivo is not known.
IIS signalling is also involved in gluconeogenesis through Akt-mediated regulation of glucogenic enzymes. As mentioned above, FOXO, which is phosphorylated and inactivated by Akt, can regulate PEPCK expression. Another Akt target is salt-inducible kinase 2 that regulates a transcriptional co-activator for cAMP-responsive-element binding protein (CREB) to induce catabolic and gluconeogenic genes (Wang et al., 2008).
1.5.3.2 Feeding behaviour
Similar to mammals, the insulin/IGF signalling in the CNS of flies has been implicated in modulating feeding behaviour. Limited food availability or hunger arouses a motivated foraging response in animals to acquire food while satiation reduces foraging. In agreement with its role as a satiety signal, neuronal overexpression of dILP2 or dILP4 or constitutive activation of a downstream effector reduced foraging motivation (Oldham and Hafen, 2003; Wu et al., 2005a). This effect may be due, in part, to insulin signalling in odorant receptor neurons that process olfactory inputs. Increased sensitivity to olfactory cues during starvation aids in stimulating motivated foraging and depends on short neuropeptide F signalling (Root et al., 2011). In the fed state, IIS within these neurons reduces expression of the sNPF receptor and ectopic expression of a constitutively active InR in these neurons was able to dampen odor sensitivity and reduce foraging in starved flies.
DILPs are also involved in the increase in food intake after starvation. Inhibition or ablation of the insulin-producing cells abolishes this behavioural response (Broughton et al., 2010; Cognigni
25 et al., 2011). One of the targets for dILPs from the IPCs are neurons within the mushroom body (a region involved in learning and memory). Impairment of insulin signalling in these neurons attenuates the increase in feeding in response to food deprivation and reducing IIS in fed flies is sufficient to induce a mild hypophagia (Zhao and Campos, 2012).
IIS is also involved in the shifts in behaviour that are dependent on the characteristics of ingested food such as palatability and nutritive content. Flies will normally avoid noxious or bitter foods and larvae prefer liquid to solid foods but under starvation conditions they will consume these more aversive foods. However, downregulation of IIS in NPFR neurons can induce fed larvae to feed on these foods mimicking the starvation response (Wu et al., 2005b).
As mentioned in Section 1.4.2.2, flies can sense the caloric content of food independent of palatability. Initially, flies will have a preference for the more palatable food source but eventually this preference will shift towards the food with higher caloric content (Stafford et al., 2012). This shift occurs more rapidly when flies are starved and is dependent on InR signalling. Flies carrying mutations in dIlp2 or dIlp3 or pan-neuronal expression of a dominant negative InR all show an enhanced initial preference for the more caloric food source.
1.5.4 Regulation of insulin secretion
Metabolic disorders can occur when β-cell function and insulin secretion cannot compensate for increasing insulin resistance. In fact, impaired β-cell function begins to decline before decreases in β-cell mass and the development of type 2 diabetes (Rahier et al., 2008). Abnormal insulin secretion patterns are seen in type 2 diabetes patients and are a hallmark diagnostic feature for type 1 diabetes (Hunter et al., 1996; Lang et al., 1981; Lin et al., 2002; Meier et al., 2013). Hence, this impaired glucose tolerance and dysregulation of insulin secretion is important in the development of insulin resistance (Satin et al., 2015; Schofield and Sutherland, 2012).
There are several parallels between mammalian and fly insulin processing including expression of insulin/dILPs as pre-propeptides, trafficking through the secretory pathway, post-translational modification of the peptides and packaging into secretory granules ready for exocytosis upon stimulation (Alfa and Kim, 2016).
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1.5.4.1 Nutrient-stimulated secretion
In mammals, postprandial insulin secretion from β-cells is stimulated by nutrient, hormonal and neuronal inputs. The primary regulator of secretion is glucose whose uptake and subsequent oxidative metabolism triggers many mechanisms controlling secretion (Figure 1.3). One result is + the increase in the ATP:ADP ratio resulting in the closure of ATP-sensitive K (KATP) channels causing depolarization of the cell and inducing the influx of Ca2+ through voltage-gated Ca2+ channels. The increased intracellular Ca2+ levels stimulate the exocytosis of preloaded insulin- containing secretory granules.
Figure 1.3. Signalling pathways regulating insulin secretion from pancreatic β-cells. Glucose uptake and metabolism increases ATP/ADP ratio resulting in closure of ATP-sensitive + 2+ K (KATP) channels. The subsequent depolarization triggers the opening of voltage-gated Ca channels leading to influx of Ca2+ that stimulates insulin granule exocytosis. The binding of ligands such as ACh and fatty acids to their Gq-coupled receptors activates phospholipase C 2+ (PLC) that converts PIP2 to DAG and IP3. IP3 stimulates the release of Ca from intracellular
27 stores resulting in increased Ca2+ levels. Meanwhile, DAG activates PKC and Munc13 that potentiate insulin secretion. The binding of ligands such as catecholamines, glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP) to their Gs-coupled receptors activates adenyl cyclase (AC) that generates cAMP. Increased cAMP levels activates PKA and Epac which potentiate secretion.
Secretion of dILPs from the insulin-producing cells in the CNS is similarly thought to be stimulated by sugars. Adult flies given an oral glucose tolerance test show increase dILP secretion that is dependent on a membrane glucose transporter (Park et al., 2014). Exposure of 2+ IPCs to glucose resulted in KATP channel-dependent membrane depolarization and Ca influx (Fridell et al., 2009; Kréneisz et al., 2010) (Figure 1.3). However, these studies didn’t measure actual dILP secretion and the Ca2+ channel involved in the Ca2+ influx is not known.
Another potassium ion channel involved in secretion is the large-conductance Ca2+-regulated K+ (BK) channel. BK knockout mice have impaired glucose tolerance and deletion of BK channels from islets dampened glucose-stimulated insulin secretion (Düfer et al., 2011). Similarly, activity of a Drosophila BK channel called Slowpoke (Slo) in IPCs regulates dILP output, IIS pathway activity and levels of circulating sugars (Sheldon et al., 2011).
Metabolism of glucose through the Krebs cycle also increases the levels of diacylglycerol
(DAG). DAG accumulation is also stimulated by free fatty acids (FFAs) that bind Gq/11-protein coupled GPR40 receptors (Figure 1.3). The binding of ligands to these receptors leads to activation of phospholipase C that converts PIP2 to DAG and IP3. DAG activates several effectors, mainly protein kinase C (PKC) and Munc-13 that promote insulin secretion (Jones et al., 1991; Rhee et al., 2002; Wollheim and Regazzi, 1990). IP3 also induces secretion through mediating the release of Ca2+ from intracellular stores.
Elevated amino acid levels or particular combinations of amino acids can also enhance glucose- stimulated insulin secretion in β-cells (Newsholme and Krause, 2012). Fly IPCs can directly or indirectly sense amino acids and lipids which can impact dILP secretion [(Kim and Neufeld, 2015) and see Section 1.4.2.2] although the molecular mechanisms have not been completely elucidated.
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1.5.4.2 Neuronal inputs
There are also autonomic innervations to β-cells that regulate insulin secretion. Parasympathetic innervation releases acetylcholine that binds M3 muscarinic receptors which are Gq/11-protein coupled leading to increases in DAG and IP3 levels (Figure 1.3). Sympathetic activation of β- adrenergic receptors stimulates adenylyl cyclase producing an increase in cAMP levels. Elevated cAMP levels activate several effectors including protein kinase A (PKA) and Epac that potentiate insulin secretion (Kaihara et al., 2013, 2015; Ozaki et al., 2000; Seino et al., 2009) (Figure 1.3). For instance, PKA phosphorylates several proteins directly involved in insulin secretion (Brozzi et al., 2012; Song et al., 2011) while Epac interacts with sulfonylurea receptor
1 (SUR1), the regulatory subunit of KATP channels (Kang et al., 2006; Ozaki et al., 2000). While flies have a SUR1 homologue that is involved in dILP secretion (Fridell et al., 2009; Kim and Rulifson, 2004), its regulation is not known.
The insulin-producing cells are also regulated by neuronal factors including sNPF, serotonin, GABA, Corazonin, tackykinin, and Jeb (Birse et al., 2011; Enell et al., 2010; Kapan et al., 2012; Luo et al., 2012; Okamoto and Nishimura, 2015; Rajan and Perrimon, 2012). For instance, Upd- 2 is produced by the fat body in response to nutrients and regulates GABAergic neurons that directly project to the IPCs and regulates dILP secretion (Enell et al., 2010; Rajan and Perrimon, 2012).
Neuropeptide Y can inhibit glucose-stimulated insulin secretion from perfused pancreas or isolated islets (Alwmark and Ahrén, 1987; Moltz and McDonald, 1985; Wang et al., 1994). Receptors for the NPY homologue short NPF are present on the insulin-producing cells and, upon ligand binding, can activate ERK signalling that in turn regulates the expression of dILPs (Kapan et al., 2012; Lee et al., 2008).
1.5.4.3 Hormonal inputs
Glucagon-like peptide and gastric inhibitory peptide are incretin hormones produced by the GI tract called incretins that stimulate insulin secretion. These hormones activate G-protein coupled receptors leading to accumulation of cAMP and activation of PKA similar to sympathetic β- adrenergic receptors. The Drosophila CNS also receives signals from the digestive tract in response to feeding that could regulate dILP secretion (see Section 1.4.2.4) but the molecular
29 mechanisms remain largely unknown. Signalling from the receptor for the Drosophila glucagon analogue called Adipokinetic hormone stimulates secretion of dILP3 from IPCs in response to sugars (Kim and Neufeld, 2015). This is presumably through PKA activation (Huang et al., 2010; Patel et al., 2006) which can regulate intracellular Ca2+ levels in insects (Wicher, 2001).
The fat body is also involved in the regulation of dILP expression and/or secretion through detection of glucose and other nutrients and conveying this information through humoral factors including Upd-2, Eiger, Stunted, CCHamide-2, Growth-Blocking Peptides 1 and 2 and an unidentified AdipoR ligand (see Section 1.4.2). For instance, the fat body secretes Stunted in response to dietary amino acids which circulates and binds to its receptor Methuselah on the IPCs to promote secretion (Delanoue et al., 2016).
Pancreatic β-cells also express insulin receptors and thus receive autocrine feedback from secreted insulin. Insulin signalling within the β-cells regulates transcription, translation, proliferation, β-cell survival as well as insulin secretion (Braun et al., 2012; Leibiger et al., 2008). Drosophila insulin-producing cells also express InR and may be involved in the dILP- mediated regulation of the expression of other dILPs. For instance, dILP3 is required for IPC expression of dILP2 and dILP5 (Grönke et al., 2010) while knockdown of dILP2 increases dIlp3 and dIlp5 transcription which, at least for dIlp3, involves InR and FOXO within the IPCs (Broughton et al., 2008). However, these effects may not be due entirely to autocrine regulation but may also involve reciprocal interactions with the fat body. DILPs from the IPCs activate IIS signalling in the fat body. This regulates the transcriptional activity of FOXO which in turn regulates the expression of dILP6. DILP6 is secreted and feeds back to bind InR on the IPCs to mediate IIS signalling (Bai et al., 2012). Overexpression of dILP6 in the fat body reduces dIlp2 and dIlp5 expression and dILP2 secretion from the IPCs.
Thus, defects in mechanisms modulating insulin release causes dysregulation of glucose levels by reducing glucose uptake in insulin-responsive tissues and impaired inhibition of hepatic glucose production. In addition, lack of insulin signalling in adipocytes results in dysregulated lipolysis and increased levels of free fatty acids. FFAs, in addition to elevated glucose levels, can impair insulin action and islet function (Chang-Chen et al., 2008; Poitout and Robertson, 2008; Poitout et al., 2010). Central insulin signalling is also affected resulting in increased food
30 intake and body weight further exacerbating insulin functions. Altogether, these feed-forward mechanisms can culminate in insulin resistance, diabetes, and obesity. Therefore, furthering our knowledge of the mechanisms controlling β-cell function and insulin secretion will help in understanding the pathological progression of these metabolic disorders.
The physiological functions of insulin are conserved in the Drosophila insulin-like peptides and defects in the pathway result in similar metabolic phenotypes. Moreover, the insulin-producing cells share many parallels to pancreatic β-cells and thus provides a model for studying these processes in vivo.
1.6 Project rationale
The identification of a functional leptin analogue in Drosophila that shares little sequence homology demonstrates the potential for the discovery of homologues for other neuronal factors that play important roles in mammalian energy homeostasis but have no clear counterpart in flies. Given the conservation of other metabolic genes and pathways between flies and mammals, it is possible that additional neuronal factors and mechanisms are functionally conserved as well. However, uncovering these homologues will require functional assays rather than relying on sequence analysis.
Obesity is a perturbation of energy homeostasis; therefore, as the central modulator, insights into the role of the CNS would not only further our understanding of the homeostatic mechanisms and potential genetic susceptibilities but may also provide new therapeutic targets. The identification of leptin and leptin receptor in mammals made a dramatic impact on our understanding of energy homeostasis so the identification of novel factors should not be underestimated.
The goal of my project is to identify neuronal factors involved in energy homeostasis in Drosophila and to determine the mechanism through which the gene of interest functions in energy balance. To this end, I performed an RNAi screen in neurons and assayed for alterations to the levels of stored lipids (Chapter 2). After three rounds of screening to verify the reproducibility and specificity of the lipid storage phenotypes, 25 genes were identified as hits. One of these hits was Diacylglycerol kinase whose homologues have been associated with
31 obesity in human populations and can regulate insulin secretion in vitro. In Chapter 3 I performed the first functional study of Dgk in flies and showed that it is involved in lipid and carbohydrate metabolism. I also found that Dgk is likely functioning within the insulin- producing cells to regulate dILP secretion and impacting systemic insulin pathway activity.
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2 RNAi Screen to Identify Genes Involved in Central Nervous System Control of Energy Homeostasis
Statement of Contributions: Data from the RNAi screen was acquired with help from O. Glucencova who assisted with fly work and processing of samples. All assays and data analyses were performed by I. Trinh. Sources of reagents and transgenic lines can be found in the Materials and Methods section.
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2.1 Introduction
In the study of obesity and other metabolic disorders, the central nervous system is an area under intense focus since it plays a critical role in energy homeostasis. Various inputs from peripheral metabolic tissues are communicated to the CNS which must receive these signals, relay it to other parts of the brain for processing and integration before producing appropriate outputs to maintain energy balance. Thus, the process is complex but the functional dissection of the molecular mechanisms and neuronal signalling networks is important in our understanding of the pathological process as well as providing potential therapeutic targets.
Therefore, to identify novel neuronal genes involved in energy homeostasis I used a forward genetic screen approach in Drosophila melanogaster. Using the genetically tractable fruit fly allows the rapid screening of a large number of genes in an in vivo system which is required to study whole-organism physiology. Specifically, I screened an RNA interference (RNAi) library to see if knockdown of individual genes within the adult CNS of Drosophila would produce obese or lean phenotypes defined as increases or decreases in lipid stores, respectively.
2.2 Materials and Methods 2.2.1 Fly stocks and husbandry
The RNAi lines used in the first and second round of screening were generated by the Transgenic RNAi Project (TRiP) VALIUM10 library (Ni et al., 2009). The RNAi lines used in the third round came from the TRiP VALIUM1 and VALIUM20 libraries and the Vienna Drosophila RNAi Center GD and KK libraries. Other lines used include: fru-Gal4 from B. Dickson (Stockinger et al., 2005), UAS-shits1 from T. Kitamoto (Kitamoto, 2001) and UAS-NaChBac from B.H. White (Luan et al., 2006).
Crosses were maintained at 25°C with a 12-hour light-dark cycle at 65% humidity on molasses- based food.
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2.2.2 TAG assay
Ten 6-10 days old adult male flies were homogenized in 100 μL 0.5% Tween-20 using the Bullet Blender (Next Advance Inc.) for 3 min on Speed 8. The lysates were incubated at 70°C for 5 min then spun down twice at 5000 rpm for 1 min and the supernatant was transferred to a fresh tube. The resulting lysates were stored at -20°C.
To measure TAG levels, 2 μL of lysate was mixed with 40 μL Triglyceride Reagent and 160 μL Free Glycerol Reagent (Sigma Serum Triglyceride Determination Kit) and incubated at 37°C for 30 min. Absorbance at 540 nm was measured using a VersaMax 190 Microplate Reader (Molecular Devices). TAG levels were normalized to protein levels as measured by BCA Protein Assay (Pierce). Statistical significance was determined by Student’s t-test (Figure 2.1) or one-way ANOVA and Holm-Sidak post hoc test using Sigma Plot software (RNAi screen).
2.2.3 Analysis of enriched annotation terms among screen hits
Analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resource v6.7 (Huang et al., 2009). The FlyBase IDs of the screen hits were entered as input and the 1748 genes tested in the screen were used as the background list for analyses.
2.2.4 Analysis of screen hits for homologues involved in human obesity
The list of 510 hits after the first round of the screen was uploaded to the FlyMine Resource (Lyne et al., 2007) and used to generate a list of mammalian homologues. This list was analyzed using the MetabolicMine resource (Lyne et al., 2013) Gene --> GWAS hit template.
2.3 Results 2.3.1 Screen methodology and selection of a driver
I selected an RNAi approach to identify neuronal genes involved in energy homeostasis since there are several available libraries of transgenic flies harbouring RNAi constructs and this approach allows for rapid identification of the affected gene. Tissue-specific knockdown was
35 achieved through the use of the Gal4/upstream activating sequence (UAS) system in which the yeast transcriptional activator Gal4 binds cis-regulatory UAS sequences and activates transcription of downstream genes (Brand and Perrimon, 1993). Thus, a neuronally-expressed Gal4 was used to drive expression of a UAS-RNAi library and the resulting flies were assayed for changes in levels of stored fats (in the form of triacylglycerides or TAG).
To drive the expression of the UAS-RNAi constructs I used a fruitless (fru)-Gal4 line that expresses the Gal4 protein in a widespread neuronal population in the late pupal and adult central and peripheral nervous systems (Stockinger et al., 2005). Since this driver does not express during embryonic or larval stages, it will allow me to eliminate any hits that were due to developmental rather than metabolic effects. Moreover, I am interested in the homeostatic mechanisms in adults and, as mentioned in Chapter 1, larvae have different metabolic requirements than adults. Importantly, Fru-Gal4-expressing neurons have been previously shown to affect TAG levels when their neuronal activity is altered (Al-Anzi et al., 2009) which I have personally verified (Figure 2.1).
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Figure 2.1. Silencing or hyperactivation of Fru-Gal4-expressing neurons alters levels of stored fats. A) Flies expressing fru-Gal4 and the UAS-shits1 flies (red bar) that causes neuronal silencing have higher TAG levels than the Gal4 or UAS constructs alone (blue bars). B) Flies expressing fru-Gal4 and the UAS-NaChBac2 construct (red bar) that causes neuronal hyperactivation have lower TAG levels than the Gal4 or UAS constructs alone (blue bars). Data shown is the TAG levels normalized to protein levels ± SD. Asterisks denote p-values based on Student’s t-test: *p < 0.05, **p < 0.01, ***p < 0.001.
2.3.2 First round of screening identifies 510 genes that have statistically significant changes in TAG when knocked down with fru-Gal4
For the screen I selected the Transgenic RNAi Project (TRiP) UAS-RNAi lines to cross with my fru-Gal4 driver. The advantage of these lines over other RNAi libraries is that these lines were generated by targeted insertions of the UAS construct and therefore, the levels of expression of the hairpin RNAs should be the similar between all lines. In particular, I opted to use the TRiP VALIUM10 collection which was specifically generated against genes known to be expressed in the nervous system (Ni et al., 2009).
See Figure 2.2 for flowchart of RNAi screening procedure. I screened all 1748 genes targeted by the TRiP VALIUM10 collection and found 510 that had statistically significant changes in TAG levels compared to a fru-Gal4/+control (one-way ANOVA). See Appendix A for a table of first round screening data (Table A.1). See Figure 2.3 for graph of data from first round of screen.
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Figure 2.2. Flowchart of RNAi screening procedure. 1748 genes were tested for a statistically significant change in triglyceride levels compared to control producing 510 hits. Hits resulting in >25% ΔTAG, from enriched functional annotations (phospholipid metabolism, protein kinases) or have homologues identified from GWAS underwent a second round of screening to confirm the phenotype. Verified hits were then screened a third time by testing the TAG phenotype of other independent RNAi lines targeting the same gene. 25 genes were confirmed by at least one independent RNAi line.
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Figure 2.3. TAG levels of all 1748 genes tested in the first round of RNAi screen. TAG levels of fru > RNAi flies normalized to a fru-Gal4/+ control.
2.3.3 Analysis of hits after first round of screen
Analysis of the list of 510 hits was performed using the DAVID Bioinformatics Resource to find enriched annotation terms from several categories from databases for protein domains (SMART, InterPro), signalling pathways (KEGG, BioCarta) and gene ontology terms. The top 10 most enriched annotations are listed in Table 2.1. Many of these seem to fall under two general groups, protein kinases and phospholipid metabolism, which comprise 22 genes. In addition, the human homologues of the 510 hits were analyzed using the MetabolicMine resource to identify any hits from obesity-related genome-wide association studies. Single-nucleotide polymorphisms near homologues of 9 hits were associated with obesity-related phenotypes including BMI, weight, metabolic syndrome, fasting glucose levels and triglyceride levels (Table 2.2). These 9 hits, together with the 22 genes from the enriched functional annotations (see Table 2.3 for list of genes) and the genes that produced >25% increase in TAG, are the genes (220 in total) that underwent further testing.
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Table 2.1. Top 10 enriched functional annotation terms from first round screen hits. Analysis of 510 hits after first round of screening was performed with DAVID Bioinformatics Resource. GOTERM_BP, gene ontology: biological process; GOTERM_MF, gene ontology: molecular function.
Term Category Gene Count P-value Ser/Thr-type protein SMART 11 0.0028 kinases AGC-kinase, C- INTERPRO 11 0.0031 terminal protein transporter GOTERM_MF_FAT 12 0.0080 activity phosphoinositide GOTERM_BP_FAT 7 0.0104 metabolic process organophosphate GOTERM_BP_FAT 9 0.0166 metabolic process kinase SP_PIR_KEYWORDS 31 0.0207 lipid modification GOTERM_BP_FAT 5 0.0293 pore complex GOTERM_CC_FAT 9 0.0350 phospholipid metabolic GOTERM_BP_FAT 8 0.0358 process glycerophospholipid GOTERM_BP_FAT 7 0.0444 metabolic process
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Table 2.2. Screen hits with human homologues that came out of genome-wide association studies for obesity-related traits. Human Gene GWAS phenotype Reference(s) homologue(s) Metabolic syndrome, (Chen et al., 2008; Kraja Mdr65 ABCB11 fasting plasma glucose et al., 2011) (Barrett et al., 2009; Waist-hip ratio, Type 1 Cooper et al., 2008; cnc BACH2, NFE2L3 diabetes Grant et al., 2009; Heid et al., 2010) (Dupuis et al., 2010; Body mass index, weight, Kristiansson et al., 2012; Dgk DGKB, DGKG fasting glucose-related Manning et al., 2012; traits Thorleifsson et al., 2009) (Sladek et al., 2007; Timpson et al., 2009; Metabolic syndrome, Type Voight et al., 2010; pan LEF1, TCF7L2 2 diabetes Zabaneh and Balding, 2010; Zeggini et al., 2007) CG4328 LMX1B Body mass index (Speliotes et al., 2010) (Aulchenko et al., 2009; Hypertriglyceridemia, Johansen et al., 2010; Mio MLXIPL Triglycerides Kathiresan et al., 2008; Kooner et al., 2008) (Berndt et al., 2013; Body mass index, obesity, Speliotes et al., 2010; CG31646 NEGR1 overweight Thorleifsson et al., 2009; Willer et al., 2009) Tom40, TOMM40 Triglycerides (Aulchenko et al., 2009) tomboy40
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Table 2.3. Hits with homologues from obesity-related GWAS or from enriched functional annotations undergoing second round screening.
homologues phospholipid protein kinases from GWAS metabolism CG31646 aPKC CG42237 CG4328 CG12069 CG42271 cnc CG4839 CSN1b Dgk Gprk1 Dgk Mdr65 Pka-C3 fwd Mio Pkcδ Ipp pan Pkg21D Pi3K92E Tom40 Pkn Pten tomboy40 rok rdgA S6kII rdgB wts Tpi
2.3.4 Confirmation and validation of TAG phenotypes
The 220 genes underwent a second round of screening to verify their effect on TAG levels by retesting the same RNAi line to validate the phenotype. See Appendix A for table of second round screening data (Table A.2). 96 genes recapitulated the results from the first round and were screened a third time by testing the TAG levels of additional RNAi lines against the genes of interest to verify that the original RNAi line did not exhibit off-target effects. 25 hits had at least one independent line targeting a different part of the gene produce the same TAG phenotype thus confirming that the gene targeted by RNAi had a specific effect on TAG levels. See Appendix A for data from third round of screen (Table A.3). See Figure 2.4 for data from all three rounds of RNAi screen for 25 remaining hits. Table 2.4 shows the enriched functional annotation terms among these 25 hits which are similar to those seen after the first round of screening, mainly terms related to phospholipid metabolism and protein kinases.
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160% 1st round 2nd round 3rd round 140% 120% 100% 80% 60%
TAG (% (% control) TAG 40% 20% 0% CG1602 CG4404 CG18480 CG31809 CG32532 CG32809 CG42271 CG42594 Cpn 160% 140% 120% 100% 80% 60%
TAG (% (% control) TAG 40% 20% 0% CrebB debcl Dgk dnc dpr10 Drak E(z) fwd
160% 140% 120% 100% 80% 60%
TAG (% (% control) TAG 40% 20% 0% hep Hk n-syb Pen Ret rok shakB Slob
Figure 2.4. TAG levels from 3 rounds of screening for final 25 hits. Data shown as TAG levels of fru > RNAi flies normalized to a fru-Gal4/+ control ± SD. Data from the first and second round are from TRiP VALIUM10 RNAi line. The 3rd round data is from VDRC and other TRiP RNAi lines and only shows the lines with significantly different TAG levels compared to control (by one-way ANOVA).
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Table 2.4. Enriched functional annotation terms of hits after third round of screening.
Drosophila gene Human homologue(s)
Phospholipid metabolism Diacylglycerol kinase DGKG, DGKA, DGKB CG42271 INPP4A, INPP4B four wheel drive PI4KB Regulation of phosphorylation, regulation of phosphate metabolic process Diacylglycerol kinase DGKG, DGKA, DGKB Rho-kinase ROCK1, ROCK2 hemipterous MAP2K7 Protein kinase activity CG42271 INPP4A, INPP4B Enhancer of zeste EZH2, EZH1 Death-associated protein TTN kinase related Ret oncogene RET Rho-kinase ROCK1, ROCK2 Slowpoke binding protein PXK four wheel drive PI4KB hemipterous MAP2K7
2.4 Discussion
My objective was to identify neuronal genes involved in energy homeostasis in Drosophila which I accomplished through systematically screening 1748 genes for effects on stored TAG levels when knocked down using the neuron-specific fru-Gal4 driver. 510 genes were found to affect TAG levels. Among these hits, a subset of hits was selected for further testing. The hits selected either caused a >25% change in TAG levels, their associated gene ontology terms were enriched among the hits or they have homologues associated with metabolic phenotypes by genome-wide association studies. A second round of screening confirmed the TAG phenotypes for 96 genes. Independent RNAi lines for these 96 genes were tested to confirm that the original RNAi line did not display off-target effects on the TAG phenotypes. After these three rounds of screening, I identified 25 hits that were reproducible and validated by an independent RNAi line.
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2.4.1 Use of fru-Gal4 as the driver for RNAi screen
The fru-Gal4 driver was used to knockdown genes in the nervous system to look for an effect on stored lipids. This driver was selected for the screen since it is expressed in late pupal and adult stages thereby eliminating any confounding developmental or lethal effects. This proved to be true as none of the RNAi lines caused lethality when crossed to fru-Gal4. There was one line (RNAi against Eclosion hormone, Eh) that produced a wing defect where the flies were unable to unfold their wings after eclosion. Although it was not explicitly tested for, no other obvious developmental defects were apparent using the other RNAi lines.
In terms of TAG phenotypes, a large proportion of the hits from the screen resulted in increased levels: 440 of 510 (86.3%) statistically significant hits after the first round of screening (Figure 2.3) and 24 of 25 hits after the third round (Figure 2.5). This could be attributed to: 1) silencing of Fru-Gal4-expressing neurons results in increased TAG levels whereas hyperactivation reduces levels (Figure 2.1); and 2) it is likely that there would be a larger proportion of genes that would decrease neuronal function when knocked down rather than increasing it. Thus, the screen seems to be unintentionally biased towards genes that negatively regulate TAG (and therefore would result in increased TAG when knocked out) and missed genes that positively regulate lipid storage.
2.4.2 Phospholipid metabolism and protein kinases in energy balance
Functional annotation analyses of the hits after the first and third round of the screen found an enrichment for terms related to protein kinases and phospholipid metabolism which have been linked to metabolism. For instance, several genes involved in phospholipid signalling and metabolism were identified in a screen for increased lifespan in flies (Landis et al., 2003) which is tightly linked to metabolism (Fadini et al., 2011; Zhang and Liu, 2014). In addition, phospholipid homeostasis has been shown to regulate lipid metabolism and cardiac function in flies (Lim et al., 2011) and in particular, the activity of sterol regulatory element-binding protein (SREBP), an important lipogenic regulator, is affected by phosphatidylcholine in worms and mice (Walker et al., 2011) or phosphatidylethanolamine in flies (Dobrosotskaya et al., 2002). Phosphoinositides also play a role in energy homeostasis; phosphoinositol 3-kinase (PI3K)
45 activity is required for insulin signalling activity (Guo, 2014) and for the ability of hypothalamic leptin and insulin signalling to inhibit food intake and energy expenditure (Hill et al., 2008; Morrison et al., 2005; Xu et al., 2005; Yang et al., 2010).
Protein kinases regulate a diverse array of cellular functions including growth, proliferation and survival. In particular, all the protein kinases identified in the screen were from the AGC (protein kinase A, protein kinase G, protein kinase C) family of serine/threonine kinases. Several AGC kinases have been implicated in diabetes including ribosomal protein S6 kinase 1 (S6K1) and Rho-associated coiled-coil containing kinase 1 (ROCK1) which result in altered insulin sensitivity (Um et al., 2004) or resistance (Lee et al., 2009, 2014) respectively, when knocked out in mice. In addition, S6K has been shown to regulate food intake in flies (Ribeiro and Dickson, 2010), mice (Stevanovic et al., 2013), and rats (Blouet et al., 2008). Akt, another AGC kinase, is a crucial mediator of the physiological functions of the insulin signalling pathway including glucose uptake, gluconeogenesis, and glycogen synthesis (Guo, 2014; Hay, 2011; Schultze et al., 2011). Moreover, knockout of Akt2 results in insulin resistance (Cho et al., 2001; Dummler et al., 2006; Garofalo et al., 2003).
Altogether, these two molecular functions, phospholipid metabolism and AGC protein kinases, have conserved roles in energy homeostasis in mammals and flies. Thus, the identification of these categories by the screen proves the validity of the screen and its hits in their relevance to energy homeostasis.
2.4.3 Identification of known metabolic genes by RNAi screen
Among the 25 hits identified in the screen, several of the genes are known to function in energy homeostasis in flies including Cyclic-AMP response element binding protein B (CrebB) and hemipterous (hep), which are involved in carbohydrate and triglyceride metabolism (Hull- Thompson et al., 2009; Iijima et al., 2009). Slowpoke binding protein (Slob) has been implicated in the response to starvation (Shahidullah et al., 2009) and in the regulation of insulin-like peptide secretion (Sheldon et al., 2011). In addition, four wheel drive (fwd) and hep are involved in determination of lifespan (Landis et al., 2003; Wang et al., 2003).
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Homologues of CrebB have also been shown to function in many aspects of mammalian glucose and lipid metabolism (Altarejos and Montminy, 2011; Oh et al., 2013). The homologues of Rho- kinase (Rok) called the Rho-associated kinases (ROCKs) are involved in peripheral insulin signalling (Lee et al., 2009, 2014) and are critical for hypothalamic leptin signalling which is required to maintain energy balance (Huang et al., 2012).
2.4.4 Conclusions
Altogether, the RNAi screen identified and confirmed 25 genes that act in neurons to regulate lipid levels. Several of these genes or their homologues have been previously known to affect energy homeostasis thereby proving that the screen was able to identify relevant genes in flies and mammals.
One of the screen hits, Diacylglycerol kinase (Dgk), is involved in phospholipid metabolism (one of the enriched functional annotations among the screen hits) in addition to having mammalian homologues implicated in genome-wide association studies examining obesity-related measures. Hence, Dgk is a gene of interest and characterizing its role in energy homeostasis in flies is the focus of Chapter 3.
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3 Elucidating the Role of Diacylglycerol Kinase in Drosophila Energy Homeostasis
Statement of Contributions: All data acquired in this chapter was generated by I. Trinh. Sources of reagents and transgenic lines can be found in the Materials and Methods section.
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3.1 Introduction
One of the hits identified in the RNAi screen (Chapter 2) was Diacylglycerol kinase (Dgk). Diacylglycerol kinases (DGKs) are a family of intracellular lipid kinases that phosphorylate diacylglycerol (DAG) to form phosphatidic acid (PA) (Figure 3.1). These molecules are major intermediates in the synthesis of other lipids including many phospholipids and also function as important signalling mediators (Carrasco and Mérida, 2007; English, 1996; English et al., 1996). For instance, DAG is best known as an allosteric activator of protein kinase C (PKC) that regulates a broad array of cellular functions including growth, apoptosis and cytoskeletal dynamics (Antal and Newton, 2014; Toker, 1998). An abbreviated list of other target proteins of DAG and PA are listed in Figure 3.1. Hence, DGKs play a crucial role in the cell by maintaining the appropriate levels of DAG and PA.
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Figure 3.1. DGKs phosphorylate DAG to form PA which both regulate an array of signalling molecules. Proteins listed under DAG and PA are target proteins regulated by DAG or PA. Proteins highlighted in red are the interactions that have been shown to be dependent on DGK activity. cPKC, classical protein kinase C; mTOR, mechanistic target of rapamycin; nPKC, novel protein kinase C; PKD, protein kinase D; TRPC, transient receptor potential channel.
3.1.1 The DGK gene family
Diacylglycerol kinases are conserved among a diverse range of organisms including bacteria, yeast, worms, plants, flies and mammals (Mérida et al., 2008; Sakane et al., 2007). Multicellular organisms express several DGKs that have become specialized in more complex species. For example, E. coli and the slime mold Dictyostelium discoideum each only have a single DGK (Badola and Sanders, 1997; De La Roche et al., 2002). Meanwhile, the fly genome encodes five and mammals have ten (many of which also undergo alternative splicing) which have overlapping as well as distinct regulation, functions and localization as discussed below.
3.1.1.1 Mammalian DGKs
The mammalian diacylglycerol kinases are the best characterized DGKs. All ten isoforms have a catalytic kinase domain and at least two C1 domains that are similar to the DAG-binding motifs in PKC (Hurley et al., 1997). Outside of these domains are regulatory domains which are used to categorize the DGK isoforms into five subtypes (Figure 3.2). Type I DGKs (α, β and γ) contain Ca2+-binding EF hand motifs. The type II isoforms DGKδ, η and κ have a pleckstrin homology (PH) domain that binds phosphoinositides with DGKδ also possessing a sterile α motif (SAM). DGKε is the sole member of the type III subgroup and does not contain any discernable domains outside of the kinase and C1 domains. Type IV DGKs (ζ and ι) have a myristoylated alanine-rich C kinase substrate (MARCKS) homology domain that acts as a nuclear localization sequence as well as ankyrin repeats and a PDZ binding motif that may be involved in protein-protein interactions. The type V isoform DGKθ has a PH domain that contains a Ras-association domain.
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Figure 3.2. Protein domain structure of mammalian and Drosophila DGKs by subtype. All DGKs contain a catalytic domain (red) and at least two C1 domains (blue). The presence of other regulatory domains is used to categorize them into five subtypes. EPAP, Glu-Pro-Ala-Pro;
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MARCKS, myristoylated alanine-rich C kinase substrate homology; PH, pleckstrin homology; SAM, sterile α motif.
3.1.1.2 DGK subcellular localization and tissue distribution
Most DGKs are cytosolic in unstimulated cells and translocate to membranes (where their substrate, DAG is located) as a general activation method that is often mediated by their regulatory domains. For example, Ca2+ binding to the EF hand motifs in DGKα results in translocation to the plasma membrane as well as activation of its kinase activity (Flores et al., 1999; Sanjuán et al., 2001). DGKs have been detected in many cellular compartments and there are often multiple DGKs in the same subcellular region including the plasma membrane (van Baal et al., 2005; Imai et al., 2005; Sakane et al., 2002; Sanjuán et al., 2003; Santos et al., 2002), nucleus and perinuclear space (Ding et al., 1998; Flores et al., 1996; Tabellini et al., 2003; Topham et al., 1998; Wada et al., 1996), Golgi (Kobayashi et al., 2007), endosomes (Murakami et al., 2003), endoplasmic reticulum (Kobayashi et al., 2007; Nagaya et al., 2002) and associated with the cytoskeleton (Houssa et al., 1999; Kobayashi et al., 2007; Luo et al., 2004).
DGKs are expressed almost ubiquitously and are most abundant in nervous and hematopoetic tissues (Goto et al., 2007; Mérida et al., 2008; Sakane et al., 2007). Most tissues express several DGKs, often with at least one member of each subtype present suggesting that each subtype plays a distinct function. The functions of DGKs expressed in metabolic tissues are discussed below.
3.1.2 DGK functions in metabolism
The best characterized functions of DGKs include immune function, cell proliferation and cancer, vision and cardiac function. Their roles in metabolism have received less attention but a few studies have shown functions for several DGKs in different aspects of energy homeostasis.
3.1.2.1 Regulation of energy balance
One of the crucial regulators of energy homeostasis is the activation of the leptin signalling pathway in the hypothalamus. Mutations in leptin or its receptor result in profound obesity and hyperphagia in rodents and humans (Allison and Myers, 2014; Park and Ahima, 2015; Sáinz et al., 2015). To expand our knowledge of mediators of the leptin pathway, Liu et al. performed a
52 yeast two-hybrid screen to identify interactors of the leptin receptor and identified a fragment of DGKζ that includes its ankyrin repeats (Liu et al., 2001). DGKζ is expressed in hypothalamic nuclei known to be involved in energy homeostasis and its mRNA levels in the hypothalamus are reduced in animals with elevated circulating leptin levels. Hence, the authors proposed that DGKζ is negatively regulated by leptin signalling and that lower levels of DGKζ are associated with obesity.
None of the other DGKs were found to interact with the leptin receptor including DGKι which also contains ankyrin repeats suggesting that this function is specific for DGKζ. However, the mechanism through which leptin signalling regulates DGKζ levels and the physiological function of hypothalamic DGKζ remain unknown.
3.1.2.2 Insulin resistance
Insulin resistance occurs when insulin-sensitive tissues, particularly skeletal muscle, liver, and pancreas, lack the ability to respond to insulin. It is involved in the pathogenesis of many metabolic disorders including obesity and type 2 diabetes, lipodystrophy, and non-alcoholic fatty liver disease and is a key feature in metabolic syndrome.
There are several hypotheses concerning the development of insulin resistance currently being investigated including lipotoxicity – the accumulation of lipids leading to inappropriate signalling and impaired insulin response. For instance, hyperglycemia promotes the accumulation of DAG due to increased de novo synthesis (Kraegen et al., 2006). This excess DAG leads to activation of PKC isotypes which impair insulin pathway activity through the phosphorylation of the insulin receptor substrate 1 (IRS-1) (Cortright et al., 2000; Yu et al., 2002). Other DAG effectors may also be involved in the development of insulin resistance including RasGRPs, Munc13s, and chimaerins (Brose and Rosenmund, 2002). Thus, enzymes such as DGKs which attenuate DAG-mediated signalling are crucial players in this pathological process.
DGKδ in particular, has been shown to be involved the dysregulation of DAG levels associated with insulin resistance. Haploinsufficient DGKδ mice have increased DAG, glucose intolerance and are obese (Chibalin et al., 2008). In addition, skeletal muscles from type 2 diabetic patients
53 show reduced DGKδ expression and activity. This defect is also seen in mice and can be rescued by normalizing blood sugar levels. DGKα, ε and ζ are also expressed in skeletal muscle (Chibalin et al., 2008) but whether they are also involved in insulin resistance has not been investigated.
DGKθ has been associated with insulin resistance in the liver. In cultured mouse hepatocytes, overexpression of DGKθ and the accompanying increase in PA levels rather than effects on DAG levels was associated with impaired insulin signalling (Zhang et al., 2015).
3.1.2.3 Insulin secretion
Postprandial insulin secretion from β-cells is stimulated by the uptake and metabolism of glucose, subsequent depolarization of the cell and influx of Ca2+ triggering the exocytosis of insulin-containing secretory vesicles. Loss of β-cell mass or β-cell dysfunction is responsible for Type 1 diabetes and other metabolic disorders can occur when β-cell function and insulin secretion cannot compensate for increasing insulin resistance.
There are a few studies that implicate DAG, PA and DGKs in the regulation of insulin secretion: DAG activates PKCs (Jones et al., 1991; Uchida et al., 2007; Wollheim and Regazzi, 1990) and Munc13, a synaptic protein that regulates vesicle release (Rhee et al., 2002), while PA increases insulin granule trafficking and exocytosis (Hughes et al., 2004; McDonald et al., 2007).
DGKα and γ are highly expressed in mouse pancreatic islets and the MIN6 β-cell line (Kurohane Kaneko et al., 2013). Treatment of β-cell lines with type I DGK inhibitors or a DAG analogue inhibits both high K+- and glucose-stimulated accumulation of intracellular Ca2+ (Thomas and Pek, 1992) and attenuates insulin secretion (Kurohane Kaneko et al., 2013; Thomas and Pek, 1992). A similar dampening of stimulated insulin secretion was seen with double-knockdown of DGKα and γ suggesting that DGKs are positive regulator of insulin release. Ex vivo experiments with mouse and rat pancreatic islets treated with DGK inhibitor confirmed this effect. However, whether this also occurs in vivo and the physiological consequences of loss of DGK activity and dampening of insulin secretion is unknown. In addition, this would be difficult to study in vivo in mammals since there is redundancy in DGK function: single knockdown of either DGKα or DGKγ only produce a mild decrease in insulin secretion and thus would likely require a double
54 mutant to study. In this respect, these experiments could be simplified by using Drosophila since there is a single Type I DGK (discussed in Section 3.1.3.1 below).
3.1.3 Drosophila DGKs
There are five DGKs in Drosophila with each one representing a different subtype (Figure 3.2): Dgk (type I), CG34384 (type II), Diacylglycerol kinase ε (Dgkε, type III), retinal degeneration A (rdgA, type IV), and CG31140 (type V).
The only Drosophila DGK that has been studied in any detail is rdgA. As its name implies, it was originally identified for its severe retinal degeneration phenotype (Raghu et al., 2000). RdgA was found to be important for the production of PA that acts as a crucial mediator in several phototransduction pathways (Kwon and Montell, 2006). As a result, rdgA mutations prevent the termination of light-sensitive transient receptor potential (TRP) channel activity, uncontrolled Ca2+influx and eventual degeneration of photoreceptor cells (Raghu et al., 2000). RdgA is also involved in other sensory modalities as mutations in rdgA impair the perception of auditory (Senthilan et al., 2012) and olfactory stimuli (Kain et al., 2008).
The function of the other Drosophila DGKs remains largely uncharacterized. There are no published studies of CG34384 and CG31140. A single paper about Dgkε confirmed that it does possess DGK activity and outlined its fairly ubiquitous expression pattern with an enrichment in testes (Frolov et al., 2001). However, the authors failed to find a role for Dgkε in development.
3.1.3.1 Dgk and its homologues
Dgk is homologous to mammalian type I DGKs and therefore possesses Ca2+-responsive EF hand motifs in addition to the C1 and kinase domains (Figure 3.2). Dgk is expressed during embryonic, pupal and adult stages with low levels in larval stages (Harden et al., 1993). In late- stage embryos, Dgk transcripts are widely expressed with a distinct enrichment in the central nervous system and head particularly in the larval optic nerve and the ventral unpaired median neurons (Harden et al., 1993). In adults, Dgk transcripts are expressed predominantly in muscles and the head especially in the retina and brain cortex (Masai et al., 1992). However, there have been no studies to determine the function(s) of Dgk in Drosophila.
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Dgk is homologous to the mammalian type I DGKs: DGKα, β and γ. DGKα is abundantly expressed in the thymus and peripheral T-cells (Schaap et al., 1990) and is involved in many aspects of immunity including antigen-mediated T-cell activation, immune tolerance, T-cell proliferation and neutrophil function (Mérida et al., 2008). DGKα is also expressed in oligodendrocytes of the brain but not in neurons (Goto and Kondo, 1999; Goto et al., 1992). It has been suggested to be involved in myelin regeneration although a direct connection has not been proven (Chakraborty et al., 2003).
DGKβ is predominantly expressed in the brain with some expression in the small intestine and adrenal gland (Goto and Kondo, 1999). It has a distinct expression pattern within the brain: it has been detected in neurons in the caudate-putamen, nucleus accumbens, olfactory tubercle and bulb, hippocampal pyramidal cell layer and frontal cortex (Goto and Kondo, 1993, 1999). These regions are known to receive dopaminergic input suggesting that DGKβ plays a role in cognitive and emotional processes. In the hippocampus DGKβ’s enzymatic activity regulates dendrite outgrowth and neurite spine maturation in developing neurons and loss of DGKβ impairs spatial and long-term memory (Hozumi et al., 2009; Shirai et al., 2010).
DGKγ is expressed in many tissues (Goto et al., 1994); however, most tissues other than the brain and retina express an inactive splice-form of DGKγ with a 25-residue deletion in the catalytic domain (Kai et al., 1994). There have been few studies of DGKγ function in vivo but in vitro studies have shown that it can regulate PKCγ activity (Shirai et al., 2000), Rac1 activity (Tsushima et al., 2004; Yasuda et al., 2007) and cell cycle progression (Topham et al., 1998).
There have been few studies of the type I DGKs in the context of energy homeostasis aside from the previously mentioned studies that implicated them in regulation of insulin secretion (Section 3.1.2.3). Interestingly, single nucleotide polymorphisms (SNPs) near two of the Dgk homologues have been identified as risk variants by genome-wide association studies for metabolism: DGKG for BMI and weight (Thorleifsson et al., 2009) while DGKB is implicated in metabolic syndrome (Kristiansson et al., 2012) and fasting glucose-related traits (Dupuis et al., 2010; Manning et al., 2012). In addition, D-α-tocopherol which is used in the treatment of diabetic nephropathy exerts it function through the regulation of DGKα translocation and activation thereby preventing glomerular dysfunction (Fukunaga-Takenaka et al., 2005). Hence,
56 there is still much to be learned about Drosophila Dgk and its homologues in terms of energy balance that could have implications for metabolic disorders in humans.
3.1.4 Rationale
The RNAi screen (Chapter 2) identified Dgk as a neuronal factor that affects the levels of stored TAG. Dgk is a member of the DGK family which converts DAG to PA and is involved in a wide range of physiological functions. Several DGKs have been implicated in energy homeostatic processes including central control of energy balance and insulin resistance. In addition, Dgk’s homologues, the type I DGKs, have been shown to regulate insulin secretion from β-cells. Two of these homologues have been identified as hits from genome-wide association studies for obesity-related measures.
Thus, I hypothesize that Dgk affects energy homeostasis by regulating DAG and/or PA levels which are involved in insulin-like peptide secretion in Drosophila. To test this, I will:
i. assay for other metabolic defects in flies with knockdown or overexpression of Dgk, ii. compare the Dgk phenotypes with those seen from knockdown of other enzymes involved in DAG and/PA metabolism to determine whether the levels of DAG and/or PA correlate with metabolic dysfunction, iii. knockdown the dILPs and compare the phenotypes to Dgk knockdown, and iv. determine if Dgk affects dILP secretion and insulin pathway activity.
3.2 Materials and Methods 3.2.1 Fly stocks and husbandry
All RNAi lines used were generated by the Transgenic RNAi Project (TRiP) except for UAS- Lpin RNAi which was obtained from the Vienna Drosophila RNAi Centre (stock #36007). Other lines used include: fru-Gal4 from B. Dickson (Stockinger et al., 2005), dIlp2-Gal4 from E.
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Rulifson (Rulifson et al., 2002), and UAS-dIlp2 and UAS-dIlp5 lines from E. Hafen (Ikeya et al., 2002).
To generate the Gal4/+ controls for all experiments, the Gal4 line was crossed to y1v1;+;attP2 [for TRiP lines, provided by N. Perrimon (Ni et al., 2009)], w1118 (for VDRC line, stock #60000), y1 M{vas-int.Dm}ZH-2A w; M{3xP3-RFP.attP'}ZH-68E (for UAS-Dgk lines, Bloomington Stock Centre stock #24485), or yw1118 (for UAS-dIlp2 and UAS-dIlp5 lines).
Crosses were maintained at 25°C with a 12 hour light-dark cycle at 65% humidity on molasses- based food.
3.2.2 Generation of UAS-Dgk transgenics
Total RNA from w1118 flies was extracted using the High Pure RNA Isolation Kit (Roche). 5 μg of RNA was used to generate cDNA using Superscript First-Strand Synthesis System (Invitrogen). The Dgk-RF transcript was amplified using the primers: Dgk.For 5’-AGA AAC GGT CTT GAG TTC ATC AGT A-3’ and Dgk.Rev 5’-ATA CTC GTA CTT AGC CTA GGG CAT AAA A-3’.
The PCR product was run on a 0.8% agarose gel and the band was purified using the PureLink Quick Gel Extraction Kit (Invitrogen). Dgk-RF was then inserted into the pAc5.1/V5-His A vector (Invitrogen) in order to V5-tag the protein. EcoRI and XbaI restriction sites were added to flank the Dgk coding region by PCR using the primers: EcoRI-Dgk.For 5’-CCG GAA TTC CAC CAT GAA TAT TGG CAT CGC AGC-3’ (also adds Kozak sequence before ATG start codon) and Dgk-XbaI.Rev 5’-CCG GAA TTC CAC CAT GAA TAT TGG CAT CGC AGC-3’. The PCR product and vector were digested with EcoRI and XbaI (Thermo Scientific). The reactions were gel purified and ligated using T4 DNA ligase (Thermo Scientific). The resulting construct was designated pAc5.1-DgkRF.V5.
Next, the Dgk construct was inserted into the pUASTattB vector provided by K. Basler (Bischof et al., 2007) to add a UAS sequence that permits Gal4 control of expression of the transgene and an attB sequence that allows for PhiC31-mediated transgenesis. A stop codon and NotI restriction site was added to the 3’ end of the V5 epitope in pAc5.1-DgkRF.V5 using the primers EcoRI-Dgk.For and V5-NotI.Rev 5’- ATA GTT TAG CGG CCG CTT ACG TAG AAT CGA
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GAC CGA GGA-3’. The PCR product and pUASTattB vector were digested with EcoRI and NotI (Thermo Scientific), gel purified and ligated together. The resulting construct was designated pUASTattB-DgkRF.V5.
The G509D point mutation was generated using the QuikChange Lightning Mutagenesis Kit (Agilent Technologies) and the primers: DgkRF-G509D.For 5’- AGC CGA CGG TGT CGT CGC CGC CA-3’ and DgkRF-G509D.Rev 5’- TGG CGG CGA CGA CAC CGT CGG CT-3’. The resulting construct was designated pUASTattB-DgkRFG509D.V5.
Sequencing was performed at each step to confirm that I had the correct construct. Sequencing was completed by the Hospital for Sick Children’s Centre for Applied Genomics. Transgenesis of pUASTattB-DgkRF.V5 and pUASTattB-DgkRFG509D.V5 constructs into y1 M{vas- int.Dm}ZH-2A w; M{3xP3-RFP.attP'}ZH-68E (Bloomington Stock Centre stock #24485) was performed by Best Gene Inc.
3.2.3 DNA sample preparation and PCR
A single fly was squished in 50 μL of squishing buffer (10 mM Tris-HCl pH 8.2, 1 mM EDTA, 25 mM NaCl, 200 μg/mL proteinase K) and incubated for 37° for 30 min then 95°C for 10 min.
1 μL of reaction was used in 20 μL PCR reactions with the conditions: 1.5 mM MgCl2, 0.2 mM dNTPs, 0.5 μM primers.
Primers used were attL.For 5’-GGG CGT GCC CTT GAG TTC TCT C-3’, 68E1.Rev 5’-GCC GGA AGT GTT GCA ATA GAT GCC-3’, UAS-Dgk.68E1.For 5’-AGG TGA CCC ACA AGA ACC AG-3’, UAS-Dgk.68E1.Rev 5’-TGC CAC ATC ACA ATC GAC TTA-3’, Rp49.For 5’- AAT TCG GAT CGA TTC CTG TG-3’ and Rp49.Rev 5’-TTG AAG CTG GAA GGA CAC AA-3’.
3.2.4 TAG, glucose and glycogen assays
Ten 7-11 days old adult male flies were homogenized in 100 μL 0.5% Tween-20 using the Bullet Blender (Next Advance Inc.) for 3 min on Speed 8. The lysates were incubated at 70°C for 5 min then spun down twice at 5000 rpm for 1 min. The supernatant was transferred to a fresh tube and stored at -20°C.
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To measure TAG levels, 2 μL of lysate was mixed with 40 μL Triglyceride Reagent and 160 μL Free Glycerol Reagent (Sigma Serum Triglyceride Determination Kit) and incubated at 37°C for 30 min. Absorbance at 540 nm was measured using a VersaMax 190 Microplate Reader (Molecular Devices).
To measure glucose and glycogen levels, 5 μL of lysate was mixed with 10 μL of H2O (glucose assay) or 2.5 μL of lysate was mixed with 10 μL of Starch Assay Reagent (glycogen assay, Sigma). The reactions were incubated at 60°C for 15 min. The plate was cooled to room temperature before 200 μL of Glucose Assay Reagent (Sigma) was added. The reactions were incubated at room temperature for 15 min before measuring absorbance at 340 nm using a VersaMax 190 Microplate Reader (Molecular Devices).
Five biological replicates were tested for each genotype. All TAG, glucose, and glycogen levels were normalized to protein levels measured by BCA Protein Assay (Pierce).
3.2.5 Trehalose assay
Protocol was adapted from the method developed by (Tennessen et al., 2014). Ten 7-11 days old adult males were homogenized in 100 μL of trehalase buffer pH 5.7 (5 mM Tris pH 6.6, 137 mM NaCl, 2.7 mM KCl). The lysates were incubated at 70°C for 5 min. Two 45 μL aliquots of lysate were transferred into a 96-well assay plate. 45 μL of 3% porcine trehalase (Sigma) was added to one set of samples (+ trehalase) while 45 μL of trehalase buffer was added to the other set (- trehalase). The reactions were incubated at 37°C for 20 hours. 200 μL of Glucose Assay Reagent (Sigma) was added and the reactions were incubated at room temperature for 15 min before measuring absorbance at 340 nm. Trehalose levels were normalized to protein levels measured by BCA Protein Assay (Pierce).
3.2.6 Feeding assay
Protocol is adapted from the method developed by (Ja et al., 2007). Briefly, three adult males were transferred without anesthetization to a vial containing 1% agar. 30 replicates were tested for each genotype. A 5 μL microcapillary containing a solution of 5% sucrose, 5% yeast extract, 2% red food colouring was inserted into each vial. The flies are allowed to acclimatize for 24 hours after which the microcapillary is replaced with a new one containing exactly 5 μL of
60 solution. After 24 hours the amount of solution missing from the microcapillary was measured. Empty vials without flies were used as evaporation controls.
3.2.7 Hemolymph extraction and dILP ELISA
Twenty 7-11-day old males were decapitated and placed in a 0.2 mL PCR tube with a hole punctured in the bottom. The tube was placed into another collection tube and centrifuged at 5000 rpm for 3 min at 4°C. The extracted hemolymph was diluted with 100 μL cold PBS. 45 μL of the diluted hemolymph was coated on a MaxiSorp flat-bottom 96 well plate (Nunc) overnight at room temperature. The samples were discarded and the plate was incubated in block (0.02M NaPO4 buffer pH 7.4, 150 mM NaCl, 1.27 mM EDTA, 1% BSA) for 1 hour at room temperature. The plate was washed twice with 0.05% Tween-20 in PBS then incubated with either rat anti-dILP2 (1:1000) or rabbit anti-dILP5 (1:2000) for 2 hours at room temperature. After three washes the plate was incubated with goat anti-rat HRP (1:2500, Santa Cruz) or donkey anti-rabbit HRP (1:2500, Santa Cruz) secondary antibody for 1 hour at room temperature. The plate was washed three times then incubated with 1X TMB ELISA substrate solution (eBioscience) for 15 min. The reaction was stopped with 1M phosphoric acid and the absorbance at 450 nm was measured using a VersaMax 190 Microplate Reader (Molecular Devices). dILP2 and dILP5 levels were normalized to protein levels measured by BCA Protein Assay (Pierce). Anti-dILP2 and anti-dILP5 antibodies were provided by P. Leopold (Géminard et al., 2009).
3.2.8 Quantitative PCR
The RNA from the heads of fifty 7-11 day old males was extracted using the High Pure RNA Isolation Kit (Roche). 500 ng of RNA was used to generate cDNA using the SensiFAST cDNA Synthesis Kit (Froggabio). The cDNA was diluted 1:1 and 1 μL was used in 10 μL qPCR reactions with the SensiFAST Probe Lo-ROX Kit (Froggabio). The Taqman probes used were: dILP2 - Dm01822534_g1, dILP3 - Dm01801937_g1, dILP5 - Dm01798339_g1 and Rp49 - Dm02151827_g1 (Life Technologies). Reactions were performed using a ViiA7 Real-Time PCR System (Applied Biosystems). Reaction conditions: 95°C for 20s, 40 cycles - 95°C for 1s, 60°C for 20s.
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Data is calculated from 3 technical and 3 biological replicates normalized to Rp49 transcript levels. Relative quantification of mRNA levels was calculated using the ΔΔCT method.
3.2.9 Western blotting
Ten 7-11 days old adult males were homogenized in 100 μL of RIPA buffer (10 mM Tris-HCl pH 8, 1 mM EDTA, 1% Triton-X, 0.1% sodium deoxycholate, 0.1% SDS, 150 mM NaCl) plus protease and phosphatase inhibitors (Roche) and left on ice to lyse for 1 hour. The lysates were centrifuged at 14 000 rpm for 10 min at 4°C. 100 μL of 2x SDS buffer (100 mM Tris-HCl pH 6.8, 4% SDS, 0.2% bromophenol blue, 20% glycerol, 200 mM DTT) was added before the lysates were heated to 95°C for 5 min.
20 μL of each sample was run on a 10% polyacrylamide gel and transferred to PVDF membrane (Millipore). The membrane was incubated in block (5% BSA, 5% milk powder, 4% FBS, 2% NGS, 2% NDS in TBST) for 1 hour at room temperature before adding primary antibody and left at 4°C overnight. Primary antibodies used: mouse anti-V5 (1:2000, Invitrogen), rabbit anti-Akt (1:500, Cell Signalling Technology), rabbit anti-phosphorylated Ser505 Akt (1:1000, Cell Signalling Technology) and mouse anti-actin (1:4000, Abcam). The membrane was washed three times with 1x TBST before incubating with HRP secondary antibody (1:10 000, Santa Cruz) for 2 hours at room temperature. After three washes, Western Lightning Plus-ECL substrate (Perkin Elmer) was added to the membrane for 2 min before it was exposed to film and developed.
The quantification of Western blots was performed using ImageJ Software and represents three biological replicates for each genotype.
3.3 Results 3.3.1 Generation of UAS-Dgk transgenic lines
To elucidate the role of Dgk I generated UAS-Dgk transgenic lines to overexpress Dgk in a tissue-specific manner. The Dgk gene is predicted to produce 7 different transcripts and proteins of varying sizes (Table 3.1). However, when Dgk was initially identified in flies, Northern blot analysis using porcine λDGK1 cDNA only detected transcripts approximately 3.5kb and 6kb in
62 size (Masai et al., 1992). Based on sizes, this seems to correspond to the Dgk-RG and Dgk-RH transcripts. However, the predicted protein product from the Dgk-RG transcript lacks the N- terminal portion that contains the EF-hand motifs and C1 domains that are important in the regulation of DGK activity and localization (Flores et al., 1999; Sanjuán et al., 2001). Therefore, when cloning Dgk to generate my transgenic lines, I designed primers to target the Dgk-RH transcript. However, I was only able to successfully clone Dgk-RF and Dgk-RJ (confirmed by sequencing), becoming the first experimental detection of these transcripts. I selected the slightly longer Dgk-RF transcript to generate the UAS-Dgk lines.
Table 3.1. Dgk transcript and protein sizes.
Dgk transcript Transcript size Protein size Dgk-RH 6282 bp 1230 a.a. Dgk-RE 4450 bp 1211 a.a. Dgk-RF 4238 bp 1108 a.a. Dgk-RJ 4214 bp 1139 a.a. Dgk-RK 4214 bp 1100 a.a. Dgk-RG 3152 bp 791 a.a. Dgk-RI 2923 bp 747 a.a.
I generated transgenic lines overexpressing wild-type (Dgk.V5) or kinase-dead (DgkG509D.V5) proteins fused with a V5 tag. The G509D point mutation (Figure 3.3A) is a mutation in the ATP-binding motif in the kinase domain that has been shown to abolish kinase activity in several mammalian DGKs (Evangelisti et al., 2007; Sanjuán et al., 2001; Topham and Prescott, 2001; Tsushima et al., 2004). These lines were generated by PhiC31 integrase-mediated targeted insertion (Bischof et al., 2007) into the same landing site (cytogenetic site 68E1 on chromosome III, Figure 3.3B). I confirmed the insertions by two different PCRs: both reverse primers bind near the 68E1 genomic landing site with one forward primer that binds the attL site or another that binds in the Dgk coding region of the insertion (Figure 3.3B). Both PCRs produce a positive band at the expected size in both the UAS-Dgk.V5 and UAS-DgkG509D.V5 lines (Figure 3.4A).
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Figure 3.3. Generation of UAS-Dgk transgenic lines. A) Alignment of DGK kinase domains showing conservation of ATP-binding motif (blue box) and critical Gly residue (red box). The kinase domain of Dgk-PF is aligned with the domains of the mammalian DGKs that have been previously used to generate kinase-dead mutants. B) Schematic for PhiC31-mediated transgenesis to generate UAS-Dgk lines. The pUASTattB-DgkRF.V5 plasmid contains the white+ selectable marker, UAS-Dgk-RF.V5 construct and attB site. This vector was injected into transgenic embryos containing an attP landing site inserted at cytogenetic site 68E1 on chromosome III. The PhiC31 integrase catalyzes the recombination between the attB and attP
64 sites resulting in the insertion of the vector into the landing site. Binding sites of primers used to confirm insertion by PCR are shown (arrows).
In addition, I confirmed by Western blot that these lines are expressing V5-tagged Dgk protein (Figure 3.4B). Using either actin-Gal4 (a ubiquitous driver) or fru-Gal4, UAS- Dgk.V5 expresses V5-tagged Dgk protein at higher levels than UAS- DgkG509D.V5 (Figure 3.4C) which was unexpected since the lines were generated by insertion into the same landing site. This difference will need to be kept in mind when interpreting any results using these lines.
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Figure 3.4. Confirmation of UAS-Dgk construct insertion and Dgk.V5 protein expression in transgenic lines. A) PCRs using two different sets of primers confirm insertion of UAS-Dgk constructs. NTC, no template control. B) Anti-V5 Western blot shows that both wild-type and kinase-dead Dgk lines express V5-tagged Dgk protein (125 kDa) using either actin- or fru-Gal4. C) Quantification of anti-V5 Western blots in B). Data is represented as fold change compared to act > or fru > Dgk.V5.
3.3.2 Metabolic profiling of flies with knockdown or overexpression of Dgk in Fru-Gal4-expressing neurons
First, I performed a phenotypic analysis of flies with Dgk knockdown or overexpression using the fru-Gal4 driver. Knockdown of Dgk resulted in higher levels of TAG, whole body glucose and glycogen (Figure 3.5A). Conversely, overexpression of either wild-type or kinase-dead Dgk resulted in lower TAG levels (Figure 3.5B). In addition, overexpression of DgkG509D.V5 produced decreases in glucose and glycogen levels (Figure 3.5B). None of the manipulations of Dgk levels seemed to affect trehalose (the main circulating sugar in flies) levels (Figures 3.5A- B).
Since fru-Gal4 is expressed in the CNS I also assayed for any effects of Dgk on feeding behaviour. Measurement of food consumption over a 24 hour period found that only overexpression of kinase-dead Dgk but not wild-type or knockdown of Dgk (Figures 3.5C-D) had an effect.
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Figure 3.5. Metabolic profiling of Dgk knockdown and overexpression using fru-Gal4 driver. A) Knockdown of Dgk results in increased TAG, glucose, glycogen but not trehalose levels. B) Overexpression of either wild-type or kinase-dead Dgk results in lower TAG levels but do not alter trehalose levels. Kinase-dead Dgk but not wild-type Dgk also decreases glucose and glycogen levels. Data is represented as percent of a fru-Gal4/+ control ± SD. C-D) Knockdown or overexpression of wild-type Dgk does not affect food intake. Flies overexpressing kinase-dead Dgk are hyperphagic. Asterisks denote p-values based on Student’s t-test: *p < 0.05, **p < 0.01, ***p < 0.001.
3.3.3 Are the metabolic effects mediated by changes in DAG and/or PA levels?
The main function of many DGKs is through the regulation of the availability of DAG and/or PA which, in turn, regulate many effectors such as PKCs. Therefore, I was interested in whether Dgk plays a similar role in energy homeostasis. Using fru-Gal4, I tested RNAi lines targeting the enzymes involved in the biosynthesis of DAG and/or PA to see if they affected TAG and/or glycogen levels. See Figure 3.6A for biosynthesis pathways and enzymes tested. I also tested RNAi lines for all 4 Drosophila classical and novel PKCs.
If DAG or PA are involved, knockdowns that produce higher vs. lower levels should show opposing TAG and glycogen phenotypes. This doesn’t appear to be the case with either DAG or PA since there isn’t a clear correlation between DAG or PA levels and TAG levels (Figure 3.6B). The same is true with glycogen levels (Figure 3.6B).
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Figure 3.6. Manipulation of DAG and/or PA levels using fru-Gal4 does not correlate with TAG or glycogen levels. A) Biosynthesis pathways involved in DAG and/or PA synthesis. Drosophila homologues of biosynthesis enzymes tested in B) are highlighted in boxes. CDP- DAG, cytidine diphosphate diacylglycerol; CDS, CDP-DAG synthase; CEPT, choline/ ethanolamine phosphotransferase; DAG, diacylglycerol; LPA, lysophosphatidic acid; LPAAT, lysophosphatidic acid acyltransferase; PA, phosphatidic acid; PAP, phosphatidic acid phosphatase; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PLA, phospholipase A; PLC, phospholipase C; PLD, phospholipase D; PS, phosphatidylserine; SMS, sphingomyelin synthase. Adapted from (Shulga et al., 2011). B) TAG and glycogen levels of fru-Gal4-driven knockdown of DAG/PA biosynthetic enzymes. Data shown are the TAG or glycogen levels normalized to a fru-Gal4/+ control ± SD. Red bars are the results for fru > Dgk RNAi. Black bars denote p < 0.05 (Student’s t-test). For genes listed multiple times, each bar represents an independent RNAi line.
If PKCs are involved in Dgk-mediated energy balance then PKC RNAi should show opposite phenotypes to Dgk RNAi. This is not the result I obtained; in fact, knockdown of PKC or Dgk produce similar phenotypes – increased TAG and glycogen levels (Figure 3.7). Hence, Dgk may be affecting energy homeostasis through a mechanism independent of DAG, PA and PKCs. However, these initial results require follow-up to determine whether they are conclusive.
Figure 3.7. Fru-Gal4-driven knockdown of PKCs produces phenotypes similar to Dgk knockdown. TAG and glycogen levels of fru-Gal4-driven knockdown of PKCs. Data shown as the TAG or glycogen levels normalized to a fru-Gal4/+ control ± SD. Red bars denote p < 0.05 (Student’s t-test). For genes listed multiple times, each bar represents an independent RNAi line for that gene.
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3.3.4 Does altering insulin-like peptide (dILP) levels in Fru-Gal4-expressing neurons affect lipid and sugar levels?
I hypothesized that Dgk is involved in dILP secretion resulting in the effects on lipid and carbohydrate metabolism seen in Figure 3.5. Therefore, I tested whether knockdown or overexpression of dILPs using fru-Gal4 would also show a similar change in TAG, glucose and glycogen levels as did manipulations of Dgk levels. It is known that ablation of the insulin- producing cells (IPCs) in adults yields flies with elevated TAG, glycogen and trehalose levels (Belgacem and Martin, 2006; Haselton et al., 2010). However, these cells produce other neuropeptides and neurohormones so it’s difficult to determine if these phenotypes are purely due to loss of dILPs. Knockdown of dIlp2 in the IPCs in adults recapitulates the hypertrehalosemia but not the lipid or glycogen phenotypes (Broughton et al., 2008). Thus, the metabolic effects from dILP manipulations within the adult IPCs are not clear.
I crossed fru-Gal4 with RNAi lines for dIlp2, 3 and 5 (the three dILPs expressed in the adult IPCs). Knockdown of any of the dILPs resulted in elevated TAG, glucose and glycogen levels (Figure 3.8A). However, while overexpression of dILP2 did not have any phenotypes, dILP5 also increased glucose and glycogen levels (Figure 3.8B). It’s possible that dILP5 overexpression and knockdown yield the same phenotype because overexpression of dILP5 could be inducing insulin resistance and thus phenocopies the knockdown. Thus, it is difficult to determine if or how Dgk is regulating dILP secretion from correlating TAG and glycogen phenotypes and will require investigating the direct effects of Dgk on the dILPs expressed in the IPCs.
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Figure 3.8. Overexpression and knockdown of dIlps affects TAG, glucose and glycogen levels. A) Knockdown of dIlp2, dIlp3 or dIlp5 increases TAG, glucose and glycogen levels. B) Overexpression of dIlp5but not dIlp2increases glucose and glycogen levels. Data shown are the TAG, glucose or glycogen levels normalized to a fru-Gal4/+ control ± SD. Asterisks denote p- values based on Student’s t-test: *p < 0.05, **p < 0.01, ***p < 0.001.
3.3.5 Dgk regulates secretion of insulin-like peptides
If Dgk is involved in insulin-like peptide secretion, it should be present in the insulin-producing cells. Knockdown or overexpression of Dgk using dIlp2-Gal4 that expresses in the IPCs (Figures 3.9A-B) gives rise to similar TAG, glucose and glycogen phenotypes as seen using fru- Gal4 (Figure 3.5A). This result, combined with the fact that fru-Gal4 also expresses in the IPCs (Al-Anzi et al., 2009), suggests that the Dgk phenotypes seen using fru-Gal4 are due to Dgk’s function within the IPCs.
To directly measure if Dgk affects dILP secretion I extracted hemolymph and measured dILP2 and dILP5 by ELISAs. Fru-Gal4-mediated knockdown of Dgk increases both dILP2 and dILP5 levels (Figure 3.9C) while overexpression of kinase-dead Dgk produces lower levels but is not
72 statistically significant (Figure 3.9D). These effects are not due to changes in expression levels of the dILPs since quantitative PCR from head extracts did not show a difference in dIlp2 or dIlp5 transcript levels (Figures 3.9E-F).
To determine whether these changes in circulating dILP levels alters insulin signalling, I quantified the levels of phosphorylated Akt Ser505 (corresponding to Ser473 in mammalian Akt) which is a marker for pathway activity (Lizcano et al., 2003). Dgk RNAi flies do have decreased pathway activity (Figure 3.9G) despite having higher circulating dILP levels suggesting that they are insulin-resistant. Kinase-dead Dgk flies have lower levels of pathway activation (Figure 3.9G) consistent with their lower hemolymph dILP levels.
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Figure 3.9. Dgk regulates insulin-like peptide secretion and insulin signalling pathway activity. A) Knockdown of Dgk using dIlp2-Gal4 results in increased TAG, glucose, and glycogen levels. B) dIlp2-Gal4-driven overexpression of kinase-dead Dgk but not wild-type Dgk also decreases glucose and glycogen levels. These phenotypes in A-B) are similar to those seen using fru-Gal4. C) fru > Dgk RNAi increases dILP2 and dILP5 levels in the hemolymph. D) Overexpression of either Dgk.V5 or DgkG509D.V5 using fru-Gal4 does not affect hemolymph dILP2 or dILP5 levels. E-F) Knockdown or overexpression of Dgk with fru-Gal4 doesn’t affect dIlp2 or dIlp5 transcript levels. Overexpression of DgkG509D.V5 increases dIlp3 levels. G) fru-
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Gal4-driven Dgk RNAi or overexpression of DgkG509D.V5 result in lowered insulin pathway activity as measured by p-Ser505 Akt/total Akt levels. fru > Dgk.V5 did not affect pAkt/Akt levels. All data shown is normalized to a Gal4/+ control ± SD. Asterisks denote p-values based on Student’s t-test: *p < 0.05, **p < 0.01, ***p < 0.001.
3.4 Discussion
Dgk was identified in the screen described in Chapter 2 as a neuronal factor in the regulation of energy homeostasis in Drosophila. Dgk is homologous to mammalian Type I diacylglycerol kinases which were identified in human genome-wide association studies for various obesity- related measures and were implicated in the regulation of insulin secretion in vitro. Despite this, the role that Type I DGKs play in energy balance has not been studied in vivo.
I have shown that manipulating Dgk levels in neurons perturbs energy balance in flies (Figure 3.3). Knockdown of Dgk increased lipid and sugar stores while overexpression of DgkG509D.V5 decreased them. These phenotypes are not due to altered feeding behaviour since fru > Dgk RNAi flies consumed similar amounts of food compared to control. However, their food intake was only measured over a 24-hour period and it’s possible that they do have a feeding phenotype but it requires a longer assay period to detect a difference. On the other hand, fru > DgkG509D.V5 flies are hyperphagic despite having lower levels of TAG and glycogen. This suggests that these flies may have increased energy expenditure resulting in smaller energy stores and the hyperphagia is a compensatory mechanism. It will require measuring the metabolic rate and locomotor activity of these flies to determine if this is true.
Trehalose levels appear to be unaffected by any manipulations of Dgk (Figures 3.3A-B) but this may reflect a limitation of the assay method. Trehalose levels from whole-fly lysates were measured but the signal is overpowered by glucose levels. Therefore, the sensitivity of the assay would be enhanced if the trehalose levels in the hemolymph were measured. Moreover, the circulating levels are more relevant to gauge any defects in carbohydrate homeostasis.
Mechanistically, the cellular functions of DGKs are thought to mainly stem from the conversion of DAG to PA. Previous studies linking other DAG and/or PA converting enzymes with metabolism support the hypothesis that DGKs affect energy homeostasis through the regulation of DAG and/or PA levels in the cell. In flies, mutations in Lipin (Lpin) which catalyzes the
75 opposite reaction to the DGKs leads to reduced TAG and fat mass (Ugrankar et al., 2011) and increased circulating sugars (Schmitt et al., 2015) which may be due to insulin resistance. Mutations in the yeast homolog of Lpin called Pah1 shortens chronological lifespan which can be rescued by the loss of Dgk1 (Park et al., 2015). Another enzyme, CDP-DAG synthetase (CdsA) that converts PA to CDP-DAG which, in turn, feeds into the phosphoinositol synthesis pathway, is involved in the regulation of lipid metabolism and growth via the insulin pathway (Liu et al., 2014).
My results failed to find a correlation between DAG or PA levels in Fru-Gal4-expressing neurons and levels of TAG or glycogen (Figure 3.6B). Knockdown of the other Drosophila DGKs also exhibited phenotypes that are not entirely consistent with Dgk knockdown. While rdgA (a Type IV DGK) did show the same increased TAG and glycogen as Dgk, Dgkε (Type III) and CG31140 (Type V) showed lower levels of glycogen or TAG respectively (Figure 3.6B). It’s possible that since fru-Gal4 is expressed in many different brain regions, different DGKs may function in different neuronal populations resulting in varying effects on energy homeostasis. The same may be true with the other DAG and/or PA metabolizing enzymes so it would be more informative to use a more spatially restricted driver (e.g. dIlp2-Gal4) and to directly measure the levels of DAG and PA. In addition, since DGKs are often targeted to specific subcellular compartments, the signalling effects of DAG and PA may be localized. Hence, other enzymes expressed in different compartments may not affect the specific signalling pathways that Dgk regulates. There are also many different DAG and PA species depending on the source with different fatty-acyl groups that could confer distinct functions (Hodgkin et al., 1998; Wakelam, 1998).
I also tested knockdown of PKCs which are regulated by DAG levels did not appear to behave as if they were dependent on Dgk (Figure 3.7). However, all PKCs seemed to increase TAG and glycogen levels when knocked down with fru-Gal4 so they could have role in energy balance but may not involve Dgk.
Some studies in vitro have suggested a role for DGKs in regulating secretion of insulin from cultured pancreatic β-cells (Kurohane Kaneko et al., 2013; Miele et al., 2007; Thomas and Pek, 1992). I have shown that Dgk is acting within the insulin-producing cells in vivo to regulate the
76 secretion of dILP2 and dILP5 through several lines of evidence. First, the TAG, glucose and glycogen phenotypes seen with knockdown or overexpression of Dgk using fru-Gal4 can be recapitulated with dIlp2-Gal4 (Figure 3.9A-B). Furthermore, Dgk manipulations affect the levels of dILP2 and dILP5 in the hemolymph (Figure 3.9C); this regulation is at the level of secretion since dIlp2 and dIlp5 transcript levels are unchanged (Figure 3.9E-F). This disturbance in dILP secretion alters insulin signalling activity (Figure 3.9G) which is likely responsible for the changes in lipid and sugar energy stores.
Overexpression of DgkG509D.V5 decreased hemolymph dILP2 and dILP5 levels and pathway activity. Interestingly, while dIlp2 and dIlp5 transcript levels were unchanged, dIlp3 levels were increased. This is consistent with studies that have previously shown differential regulation of the expression of the different dILPs within the IPCs (Colombani et al., 2003; Ikeya et al., 2002; Wu et al., 2005). Unfortunately, an antibody to measure hemolymph dILP3 protein levels is not available. However, this increase in dIlp3 transcripts doesn’t seem sufficient to offset the reduced hemolymph dILP2 and dILP5 levels since all dILPs signal through a single insulin receptor (InR) and fru > DgkG509D.V5 flies have systemically attenuated pathway activity.
Altogether, I have shown that Dgk in insulin-producing cells negatively regulates dILP secretion that is required for proper insulin signalling to control energy homeostasis leading to obesity and insulin resistance.
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4 Discussion and Future Directions
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4.1 Summary of Thesis Results
The rapidly increasing prevalence of obesity in addition to its association with many chronic disorders has turned obesity into a global health concern. Obesity is the result of a disturbance to the balance between energy intake and expenditure due to genetic and environmental factors. However, our current list of genetic susceptibility factors has fallen short of the estimated heritability for obesity-related measures and we must therefore find alternate methods to identify these risk factors. To this end, the use of Drosophila as models for metabolic disorders allows researchers to take advantage of the powerful genetic tools available in flies to perform large- scale genetic screens in vivo and to study gene-gene and gene-environment interactions with relative ease. Previous work has already established that the metabolic tissues and signalling networks regulating energy homeostasis in flies show many similarities with those in mammals.
An area of particular interest is the central nervous system which is responsible for the maintenance of energy homeostasis at the physiological level in Drosophila and mammals. Peripheral metabolic tissues and the digestive tract send signals to the CNS conveying information about the nutrient content of ingested food and the body’s energy status. The CNS processes these inputs and modulates feeding behaviour and energy expenditure to maintain homeostasis. Therefore, the CNS is central to energy balance and further insights into its role could prove useful to understanding the pathology of obesity and related metabolic disorders.
4.1.1 RNAi screen
The goal of my thesis was to identify neuronal genes involved in energy homeostasis in Drosophila and elucidate the mechanisms through which they function in this process. I started by systematically screening 1748 genes for effects on stored TAG levels when knocked down using the neuron-specific fru-Gal4 driver. 510 genes were found to result in significantly altered TAG levels and a subset of hits were selected for further testing based on the severity of the phenotype and/or whether their associated gene ontology terms were enriched among the hits. After an additional two rounds of screening I identified 25 reproducible hits whose TAG phenotypes were confirmed with independent RNAi lines thus eliminating any off-target effects of the original RNAi line.
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Functional annotation analysis of the 510 hits after the first round of screening and the final 25 hits found the same enriched annotations: phospholipid metabolism and AGC Ser/Thr protein kinases. Genes associated with both of these categories have been shown to be involved in energy homeostasis in mammals and Drosophila.
Several hits from the final list of 25 are known to be involved in lipid and carbohydrate metabolism, response to starvation, dILP secretion and lifespan. In addition, some of the hits have homologues that are involved in mammalian energy homeostasis. Thus, the screen was able to identify factors relevant to energy balance in flies and mammals.
4.1.2 Role of Dgk in energy homeostasis
One of the hits identified in the RNAi screen (Chapter 2) was Diacylglycerol kinase. DGKs are lipid kinases that phosphorylate diacylglycerol to form phosphatidic acid thereby regulating the cellular levels of these two signalling molecules. DGKs are conserved across a diversity of species with more complex, multicellular organisms possessing several DGKs with differing protein domains, expression patterns and functions. Some of these functions include metabolically-relevant ones including central control of energy homeostasis, regulation of insulin secretion and insulin resistance.
There is evidence that Type I DGKs in particular, are involved in energy homeostasis: DGKα and DGKγ have been associated with insulin secretion from pancreatic β-cells in vitro and SNPs near DGKB and DGKG have been associated with obesity related-measures by genome-wide association studies.
Little is known about the sole Drosophila type I DGK called Dgk and this project constitutes the first functional study of Dgk. The RNAi screen had identified Dgk as a hit resulting in increased TAG levels when knocked down in neurons. Additionally, I tested other metabolic phenotypes in fru > Dgk RNAi flies as well as in flies overexpressing wild-type or kinase-dead (DgkG509D) Dgk. See Table 4.1 for summary of metabolic profiling of Dgk lines.
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Table 4.1. Summary of metabolic phenotypes seen with Fru-Gal4-driven expression of Dgk RNAi or overexpression of wild-type or kinase-dead Dgk.
fru > Dgk fru > phenotype fru > Dgk.V5 RNAi DgkG509D.V5 TAG ↑ ↓ ↓ Glycogen ↑ ― ↓ Whole-body glucose ↑ ― ↓ Whole-body ― ― ― trehalose Food intake ― ― ↑
To determine how Dgk regulates energy homeostasis I first examined whether this function is dependent on its role in DAG and PA metabolism. To this end, I manipulated DAG and PA levels by expressing RNAi for other enzymes involved in DAG and/or PA metabolism. However, there wasn’t a clear correlation between DAG or PA levels and TAG or glycogen levels. In addition, knockdown of PKCs which are DAG effectors did not display the expected TAG and glycogen phenotypes if their function was regulated by Dgk. Hence, these results suggest DAG, PA and PKCs are not involved in Dgk-mediated regulation of energy homeostasis.
Since Dgk’s homologues have been implicated in insulin secretion, I tested if this function is conserved in Drosophila. Initially, I tried to determine if manipulations of dILP levels produced similar TAG and glycogen phenotypes as knockdown or overexpression of Dgk. However, the results were inconclusive. I did show that Dgk’s metabolic effects were due to its function within the insulin-producing cells. Neuronal knockdown of Dgk increased dILP2 and dILP5 levels in the hemolymph while overexpression of kinase-dead Dgk produced lower levels, although the reductions were not statistically significant. These effects occur without changes in dIlp2 or dIlp5 transcript levels. Measurements of insulin signalling pathway activity showed that the reduced circulating dILP levels in fru > DgkG509D.V5 flies resulted in reduced InR signalling. However, despite elevated hemolymph dILP levels, fru > Dgk RNAi flies also had lower IIS activity suggesting that these flies are insulin resistant.
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Altogether, my screen for neuronal factors in energy homeostasis in Drosophila identified 25 reproducible and validated hits which include genes already known to be involved in Drosophila and mammalian energy balance. One of the hits, Dgk, is involved in the regulation of insulin- like peptides secretion from insulin-producing cells. A similar function has been suggested for its mammalian homologues in cultured pancreatic β-cells but my results constitute the first in vivo confirmation of this function as well as its physiological consequences. Thus, genetic screens are capable of identifying genes involved in energy homeostasis that can be applied to mammals and therefore expand our understanding of the etiological mechanisms underlying human metabolic disorders.
4.2 DGKs in insulin secretion: a kinase-dependent model
DGKα and DGKγ have been shown to potentiate insulin secretion in vitro and knockdown of both DGKs or exposure to a DGK inhibitor attenuates glucose- and high K+-stimulated secretion (Kurohane Kaneko et al., 2013). I have shown that Dgk is involved in the regulation of dILP secretion and studies in cultured mammalian β-cells and pancreatic islets have associated DAG, PA and DGKs with the regulation of insulin secretion. However, my results could not find a correlation between TAG and glycogen phenotypes with DAG, PA or PKC levels but the results are not conclusive and require further study (discussed in Chapter 3, Section 3.4). In addition, all the mammalian in vitro studies point to a positive effect on insulin secretion by DAG, PA and type I DGKs. However, these findings don’t correlate with the ability of Dgk to deplete DAG levels and increase PA levels (i.e. DAG should have opposing effects to DGK and PA). It has been proposed that it is the balance between DAG and PA levels that is required for normal secretion and the function of DGK is to maintain that balance (Kaneko and Ishikawa, 2015).
4.2.1 Activation of Dgk
Many DGKs are cytosolic until they are activated and recruited to cellular membranes where their substrate, DAG, is located. How DGK is activated or recruited in β-cells to regulate insulin secretion is not known. However, glucose, which is a potent stimulator of insulin secretion (called a secretagogue), is associated with rapid and transient increases in DGKα and DGKδ activity and translocation to the plasma membrane in skeletal muscle (Miele et al., 2007). Glucose uptake and metabolism results in depolarization and influx of Ca2+ which can bind to
82 the EF hand motifs in type I DGKs. This Ca2+ has been shown to regulate DGKα activity and localization in T lymphocytes (Flores et al., 1999; Sanjuán et al., 2001).
Targeting of DGKs to the membrane also requires the production of DAG. Exposure to glucose and other depolarizing agents also induces a spike in DAG concentration in the plasma membrane (Tengholm and Gylfe, 2009; Wuttke et al., 2013). For instance, several insulin secretagogues induce DAG accumulation in β-cells due to activation of Gαq/11-protein coupled receptors which stimulate phospholipase C (PLC) to convert PIP2 to DAG and IP3.
To establish a mechanism of Dgk activation, the subcellular localization of Dgk will have to be determined under stimulated and unstimulated conditions. This stimulation could be elicited by manipulations of extracellular glucose levels, overexpression or knockdown of K+ and Ca2+ channels or altering DAG levels through expression of other DAG-metabolizing enzymes or the addition of DAG analogues such as phorbol ester myristate or 1-oleoyl-2-acetyl-sn-glycerol.
4.2.2 Dgk’s catalytic activity regulates downstream effectors
Thus, DGK is activated and targeted to the plasma membrane where it can catalyze its substrate DAG. DAG recruits and activates effectors that potentiate insulin secretion including PKCs and Munc13, a synaptic protein that regulates vesicle release (Jones et al., 1991; Rhee et al., 2002; Uchida et al., 2007; Wollheim and Regazzi, 1990).
DAG is converted to phosphatidic acid by DGK but PA can also be generated by phospholipase D (PLD) which is also stimulated by glucose and other insulin secretagogues (Hughes et al., 2004). PA positively regulates insulin granule trafficking and exocytosis (Hughes et al., 2004; McDonald et al., 2007) resulting in insulin secretion.
Although the papers discussed above were based on mammalian models, there are similarities between Drosophila dILP and insulin secretion (see Chapter 1, Section 1.5.4) so the Dgk- mediated mechanism could be conserved as well. See Figure 4.1 for proposed model of Dgk regulation of insulin-like peptide secretion that is dependent on its catalytic conversion of DAG to PA. If the role of Dgk in regulating dILP secretion is dependent on its catalytic activity, feeding wild-type flies a pharmacological DGK inhibitor would phenocopy the metabolic defects seen in fru > Dgk RNAi flies.
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Figure 4.1. Kinase-dependent model of Dgk regulation of dILP secretion from the IPCs. 2+ Glucose-stimulated increase in Ca levels and Gq-coupled receptor mediated increase in DAG levels together activate and recruit Dgk to DAG at the plasma membrane where it converts DAG to PA. DAG regulates PKC and Munc13 that stimulate secretion while PA positively regulates granule trafficking and exocytosis. Dgk functions to maintain a balance between DAG and PA levels in the cell to ensure appropriate secretion of dILPs.
4.3 Dgk function in dILP secretion: kinase-independent model
The large majority of functions attributed to DGKs are dependent on their kinase function and its regulation of cellular DAG and PA levels (Mérida et al., 2008; Shulga et al., 2011; Topham and Prescott, 1999). However, while my results from RNAi-mediated manipulations of DAG and PA levels are not conclusive, a possible kinase-independent function of Dgk in energy homeostasis is supported by my data from overexpression of wild-type and kinase-dead Dgk.
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4.3.1 Overexpression of wild-type vs. kinase-dead Dgk
With the exception of low TAG levels, overexpression of wild-type Dgk.V5 did not exhibit any defects in the other measured phenotypes. Instead, overexpression of kinase-dead DgkG509D.V5 gave many phenotypes that might be expected with wild-type Dgk i.e. opposing phenotypes compared to Dgk RNAi. These include TAG, glycogen, whole-body glucose, and dILP levels in the hemolymph. Both overexpression lines were sequenced to confirm the correct insertion of the transgenic construct. I also tested tagged and untagged versions of the Dgk overexpression lines and found that the V5 tag does not affect either wild-type or kinase-dead Dgk function (see Appendix A, Figure A.1).
What then, could explain these unexpected results? It’s possible that the G509D point mutation didn’t abolish kinase activity. There are assays to measure DGK activity (Epand and Topham, 2007; Sato et al., 2013) so this could be tested. However, it seems unlikely that this is the case. The G509D mutation occurs in the ATP binding site of the kinase domain whose consensus sequence GXGXXG is also shared by protein kinases; similar point mutations have abolished kinase activity in other DGKs and protein kinases (Evangelisti et al., 2007; Hanks et al., 1988; Sanjuán et al., 2001; Topham and Prescott, 2001; Tsushima et al., 2004).
Western blot analysis proved that both lines are expressing V5-tagged protein and that wild-type Dgk is expressed at higher levels (Chapter 3, Figure 3.2). However, there is no antibody to measure the endogenous levels of Dgk in these lines so it may be informative to perform these experiments again in a Dgk null or hypomorphic background. It’s possible that the high levels of Dgk protein in fru > Dgk.V5 flies are triggering a negative feedback mechanism that post- translationally inactivates Dgk.
Additionally, only 3.5 kb and 6 kb Dgk transcripts have been detected in adult heads (Masai et al., 1992). These sizes do not correspond to the Dgk-RF (about 4.2 kb) I used to generate the UAS-Dgk lines. Therefore, neuronal expression of Dgk.V5 may be ectopically expressing the Dgk-RF transcript that is not endogenously produced by neurons. The 3.5 kb and 6 kb transcripts do show developmentally and spatially regulated expression patterns (Masai et al., 1992) suggesting that the proteins produced from the different Dgk transcripts have different
85 functions. Thus, it’s difficult to determine whether the phenotypes using the UAS-Dgk lines are due, in part, to this ectopic expression of a different Dgk isoform.
4.3.2 Potential alternative mechanisms Dgk function
An alternative explanation of the kinase-dead Dgk overexpression phenotypes is that the role of Dgk in energy homeostasis may be independent of its enzymatic activity. There have been a few instances where the kinase function of DGK is not important for its function. For instance, DGKδ contains a pleckstrin homology (PH) domain and sterile α-motif (SAM) in addition to C1 and catalytic domains. The catalytic activity of DGKδ was not required for its ability to regulate ER-to-Golgi trafficking and was instead dependent on its PH and SAM domains which target it to the ER and prevent the formation of ER export sites (Nagaya et al., 2002).
In neuroblastoma cells, overexpression of a kinase-dead DGKγ stimulates the formation of filopodia-like protrusions to a similar extent as a wild-type DGKγ (Tanino et al., 2013). Interestingly, protrusion formation seemed to require the localization of DGKγ to the plasma membrane and an intact catalytic domain was important for this function. In addition, the DAG- binding C1 domains that regulate the membrane localization in DGKα (Flores et al., 1999; Sanjuán et al., 2001) were dispensable for plasma membrane targeting of DGKγ in this context.
Additionally, DGKζ-deficient fibroblasts have reduced RhoA activation that can be rescued with either wild-type or kinase-dead DGKζ (Ard et al., 2012). In this context, DGKζ seems to function as a scaffold for a protein complex consisting of RhoA, its inhibitor RhoGDI and PKCα. This complex facilitates the phosphorylation of RhoGDI by PKCα relieving its inhibition of RhoA and thereby inducing activation of the RhoA pathway.
Hence, these contexts where DGK kinase activity is not required demonstrate that DGKs could act through protein-protein interactions to bind signalling molecules and effectors to affect their function or localization. Therefore, Dgk could be functioning to recruit or sequester other signalling proteins to regulate dILP secretion from the insulin-producing cells in Drosophila.
There are no known interactions, either physical or genetic, with Drosophila Dgk. However, physical interactors of human type I DGKs have been identified (see Table 4.1). Two of these interactors have been associated with insulin secretion from β-cells: β-arrestin 1 (encoded by
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ARRB1 gene) involved in regulating G-protein coupled receptor signalling and Smad2 that is an effector of the Activin signalling pathway.
Table 4.2. Physical interactors of human type I DGKs.
DGK Interactor Interaction detection method Reference
DGKA FUS affinity chromatography technology (Wang et al., 2015) IL21 imaging technique, proximity ligation (Chen et al., 2014) assay ORC5 biochemical (Havugimana et al., 2012) SMAD2 affinity chromatography technology (Brown et al., 2008) SRC coimmunoprecipitation, pull down (Baldanzi et al., 2008) DGKB KRT31 affinity chromatography technology, (Ewing et al., 2007) anti bait coimmunoprecipitation PCDHB5 affinity chromatography technology, (Ewing et al., 2007) anti bait coimmunoprecipitation DGKG ALB affinity chromatography technology (Zhou et al., 2004) ARRB1 affinity chromatography technology (Xiao et al., 2007)
4.3.2.1 G-protein coupled receptors and β-arrestin 1
G-protein coupled receptors (GPCRs) are 7-transmembrane domain proteins coupled with G proteins which are trimers consisting of Gα, Gβ and Gγ subunits. After agonist binding, GPCRs activate the coupled G protein which dissociates its subunits and releases them to regulate downstream effectors. The Gα subunit regulates the levels of different secondary messengers that is dependent on the type of Gα subunit: the Gαs and Gαi/o pathways regulate adenyl cyclase and cAMP levels; the Gαq/11pathway activates phospholipase C (PLC) that converts PIP2 to DAG and IP3; and the effectors of the Gα12/13 pathway are RhoGEFs that activate the small GTPase Rho.
Many activated GPCRs are phosphorylated by G-protein coupled receptor kinases (GRKs) which recruit β-arrestins (βarr) to the GPCR. Initially, β-arrestins were believed to be negative regulators of GPCR signalling through steric hinderance of recoupling of the GPCR with G proteins and through targeting the GPCRs for endocytosis. However, later studies showed that in
87 some contexts, β-arrestins can function as transducers of GPCR signalling by recruiting kinases and other effectors to GPCRs (Luttrell and Lefkowitz, 2002).
There are many GPCRs that are involved in signalling in β-cells to regulate insulin secretion and several of them have been targeted for therapeutics to treat diabetes (Ahrén, 2009). These receptors respond to many extrinsic factors including fatty acids, sympathetic and parasympathetic innervations, GI hormones and satiety factors (Ahrén, 2009).
4.3.2.2 Activin signalling and SMAD2
Activin is a member of the TGFβ superfamily and components of the activin signalling have been detected in pancreatic islets (Ogawa et al., 1993; Wada et al., 1996; Yamaoka et al., 1998). Activin binds its receptor leading to receptor phosphorylation and recruitment of Smad2 and Smad3. Smad2 and Smad3 are then phosphorylated by the receptor and released to bind with Smad4 to form a trimeric complex that can translocate to the nucleus to activate transcription of target genes.
Specifically, Activin A, activin type IIB receptor (ActRIIB) and Smad2 are involved in pancreatic development, proliferation and function. Exposure of wild-type islets to Activin A increased glucose-stimulated insulin secretion (GSIS) and was dependent on Smad2 but not Smad3 (Wu et al., 2014). Heterozygous Smad2 or compound heterozygous ActRIIB+/-Smad2+/- mutants have defective islet formation and reduced β-cell mass (Goto et al., 2007; Harmon et al., 2004). ActRIIB+/-Smad2+/- mice also show an impairment of glucose tolerance in a gene-dosage- dependent manner (Goto et al., 2007). β-cell-specific knockout of Smad2 or β-cell-specific expression of a constitutively active ActRIIB impairs GSIS and alters KATP channel activity (Nomura et al., 2014a, 2014b). Altogether these studies suggest that Activin A signals through ActRIIB and Smad2 to regulate glucose-stimulated insulin secretion and that an appropriate level of signalling through this pathway is required for proper GSIS.
4.3.2.3 Model of Dgk function to regulate dILP secretion
From my results, Dgk seems to be acting as a negative regulator of insulin-like peptide secretion from the insulin-producing cells and that it may not require its catalytic activity for this function. This model is not mutually exclusive with the model proposed in Figure 4.1 and it is possible
88 that Dgk has both kinase-dependent and kinase-independent functions in this process. In the kinase-independent model, Dgk regulates dILP secretion through its interaction with two factors: β-arrestin and Smox (the fly Smad2 orthologue).
β-arrestins can function as attenuators of G-protein coupled receptors or as GPCR transducers. In the regulation of insulin exocytosis by GPCRs, β-arrestins seem to be signal transducers that act as scaffolding proteins that aid in the recruitment of effectors to the activated GPCR to stimulate insulin secretion (Dalle et al., 2011; Kong et al., 2010; Mancini et al., 2015; Ning et al., 2015; Sonoda et al., 2008). All three mammalian type I DGKs can co-immunoprecipitate with β-arrestin 1 (Nelson et al., 2007) and DGKγ was identified in an IP-MS survey of β-arrestin 1 interactors (Xiao et al., 2007). Thus, Dgk could physically interact with β-arrestin to sequester it and/or prevent it from binding the GPCR effectors thereby attenuating dILP secretion (Figure 4.2).
Activin signalling in the IPCs results in the activation and phosphorylation of Smox which then binds Medea/Smad4 to form a functional transcriptional activator. Human DGKα has been shown to interact with Smad2 by affinity chromatography coupled to mass spectrometry (Brown et al., 2008). Thus, activated Dgk could bind Smox, tethering it to the plasma membrane and either prevent its binding to Medea or prevent the Smox/Medea complex from translocating to the nucleus to activate transcription of target genes. Activin signalling mediated by Smad2 positively regulates insulin secretion in mammals (Nomura et al., 2014a, 2014b); therefore, Dgk could reduce dILP secretion by abrogating Smox function in the IPCs (Figure 4.2).
Thus, Dgk is activated upon IPC stimulation to modulate dILP secretion through interactions with Actvin-β and GPRC pathways. Knockdown or overexpression of Dgk therefore results in reduced or enhanced inhibition of these pathways, respectively, and changes to levels of dILP secretion.
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Figure 4.2. Kinase-independent model of Dgk regulation of dILP secretion. Dgk prevents GPCR-mediated potentiation of dILP secretion by preventing the recruitment of β-arrestin or the ability of β-arrestin to act as a scaffold for GPCR effectors. Dgk attenuates Activin-β signaling by binding Smox, preventing its interaction with the Atr-I/babo receptor and/or Med or inhibiting the function of the Med-Smox complex and thereby reducing dILP secretion.
There is much work to be done to prove this model. First, experiments will have to be performed that determine whether the Activin-β/Smox pathway and β-arrestin are involved in energy homeostasis and whether they perform this function within the insulin-producing cells. Components of these pathways will be knocked down or overexpressed in the IPCs and their effects on dILP secretion, TAG, and glycogen levels can be measured.
The physical interaction between Dgk and Smox and/or β-arrestin will also have to be confirmed by co-IP and whether this interaction affects Activin-β/Smox and GPCR signalling and dILP secretion will have to be studied. DGKγ may interact through its cysteine-rich C1 domains with the C-terminus of β-arrestin (Nelson et al., 2007). In addition, the C-terminus of DGKγ (which
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includes its catalytic domain) may be involved in its translocation to the plasma membrane (Tsushima et al., 2004). Therefore, the use of various Dgk deletion constructs could be used to ask whether it is the binding of Dgk to β-arrestin or its trafficking to the plasma membrane is important for dILP secretion.
The domains that mediate the interaction between human DGKα and Smad2 are not known. Deletion constructs of Dgk and Smox will have to be generated and tested to determine which domains are required for their interaction. Mutant Dgk proteins that are unable to bind Smox could then be expressed in the IPCs in a Dgk null or hypomorphic background to determine if they fail to rescue Dgk function in regulating dILP secretion and TAG and glycogen levels.
4.4 Dgk and insulin resistance
Fru > DgkG509D.V5 flies also had reduced IIS pathway activity but this was in conjunction with slightly reduced hemolymph dILP levels and decreased TAG and glycogen levels. This result is consistent with the ability of insulin to stimulate TAG and glycogen synthesis while inhibiting their breakdown. However, an interesting result from the knockdown of Dgk was that despite high circulating dILP2 and dILP5 levels, the flies had reduced insulin signalling pathway activity suggesting that these flies are insulin resistant.
The development of insulin resistance is progressive. Therefore, the elevated hemolymph dILP levels caused by Dgk knockdown would initially increase insulin pathway activity leading to increased energy stores in the form of TAG and glycogen. Hyperinsulinemia and increased fat stores have been implicated in the development of insulin resistance in mammals (Schofield and Sutherland, 2012; Shanik et al., 2008; Ye, 2013). Thus, the high circulating dILP and TAG levels could lead to insulin-desensitization of tissues and therefore decrease IIS activity. This is supported by lipid accumulation in the fat body of flies when IIS activity is attenuated through expression of a constitutively active of FOXO (Hwangbo et al., 2004; Luong et al., 2006). Additional experiments will be required to prove this hypothesis; specifically, the development of resistance through showing the progressive attenuation of insulin pathway activity in conjunction with elevated hemolymph dILP levels. Simultaneously, TAG and glycogen levels
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will be measured to determine if the severity of these phenotypes increases with increasing insulin resistance. In addition, heterozygous insulin receptor mutants and flies exposed to high sugar diets also show insulin resistance (Morris et al., 2012; Musselman et al., 2011; Park et al., 2014; Tatar et al., 2001) so these interventions could be used to speed up the progression to insulin resistance to measure its effects on metabolism.
DGKs have been implicated in insulin resistance through effects on the insulin signalling pathway in mammals (Chibalin et al., 2008; Zhang et al., 2015). This function often involves DAG and PKCs and experiments in Drosophila cell culture have also shown antagonism of IIS activity by PKC (Mattila et al., 2008). IPCs also express InR and respond to autocrine and paracrine dILPs to regulate endogenous dILP expression and secretion (Bai et al., 2012; Broughton et al., 2008). Therefore, knockdown of Dgk could be resulting in insulin resistance in peripheral metabolic tissues and within the IPCs leading to elevated hemolymph dILP levels and further increasing resistance.
4.5 Conclusions
With the current global obesity epidemic leading to increased health and economic consequences it is imperative to understand the etiology and pathological progression to treat and prevent obesity. The central nervous system has been under intense scrutiny since studies in human and many model organisms have pointed towards a critical role for the CNS in energy homeostasis.
In recent decades, several models of metabolic disorders have been developed in many model organisms that could help increase the rate of disease-linked gene discovery and uncovering their mechanism of action. I used the genetically tractable fruit fly Drosophila melanogaster to perform an in vivo genetic screen for neuronal genes involved in energy homeostasis. After 3 rounds of screening I identified 25 hits that were reproducible and confirmed these phenotypes with independent RNAi lines. These hits included genes with homologues involved in mammalian energy homeostasis demonstrating that the screen was not only able to identify relevant factors in mammalian energy balance, but also reinforces the idea that these pathways and mechanisms are conserved in flies. Thus, further study of these genes could contribute to
92 our understanding of the neuronal processes governing energy homeostasis and pathological mechanisms leading to obesity.
One of the screen hits was Diacylglycerol kinase. Previously, little was known about Dgk and my project is the first function study of Dgk in flies. It is also the first study to show that a DGK is involved in insulin secretion in vivo and to show a physiological consequence of this interaction.
Altogether, I identified and characterized a novel regulator of insulin-like peptide secretion from the IPCs in Drosophila to modulate lipid and carbohydrate metabolism. Since Dgk’s mammalian homologues have been been shown to regulate insulin secretion in vitro and have been implicated in genome-wide association studies for metabolic defects, it suggests that this function may be conserved. Thus, further dissection of the role of Dgk in Drosophila could further our understanding of mammalian metabolism and its dysfunction in obesity.
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A Appendix
94
Table A.1. Data from first round of RNAi screen. Highlighted rows are hits that resulted in a change in TAG levels that is statistically significant by one-way ANOVA (unadjusted P value < critical level).
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0260793 42573 2mit 28553 1.656 8.212 <0.001 0.004 0.247 FBgn0004168 16720 5-HT1A 25834 0.974 0.427 0.672 0.025 0.097 FBgn0004572 15113 5-HT1B 25833 1.091 1.482 0.145 0.006 0.066 FBgn0087012 1056 5-HT2 31882 1.277 5.109 <0.001 0.005 0.079 FBgn0004573 12073 5-HT7 27273 1.263 4.578 <0.001 0.006 0.064 FBgn0259178 42283 5PtaseI 29620 1.005 0.099 0.922 0.050 0.020 FBgn0041707 1168 7B2 27989 1.241 3.636 <0.001 0.006 0.106 FBgn0004907 17870 14-3-3ζ 28327 1.076 1.549 0.128 0.013 0.113 FBgn0004364 8896 18w 30498 1.148 1.665 0.101 0.005 0.085 FBgn0052062 32062 A2bp1 27286 1.153 2.268 0.027 0.005 0.063 FBgn0028550 11405 A3-3 26741 1.214 2.748 0.008 0.013 0.163 FBgn0086443 10687 Aats-asn 28317 0.909 1.524 0.133 0.007 0.099 FBgn0023129 3705 aay 28577 0.968 0.287 FBgn0259750 4807 ab 29407 1.078 1.334 0.187 0.009 0.125 FBgn0000014 10325 abd-A 28739 1.172 2.469 0.016 0.004 0.121 FBgn0000015 11648 Abd-B 26746 1.205 3.038 0.003 0.004 0.089 FBgn0000017 4032 Abl 28325 1.099 1.689 0.097 0.006 0.084 FBgn0000022 3796 ac 29586 0.861 2.370 0.021 0.004 0.118 FBgn0023416 1506 Ac3 28626 1.098 1.421 0.161 0.005 0.096 FBgn0000024 17907 Ace 25958 1.243 4.986 <0.001 0.006 0.101 FBgn0033749 8819 achi 31903 1.093 2.043 0.046 0.009 0.085 FBgn0000028 9151 acj6 29335 1.088 0.993 0.324 0.007 0.033 FBgn0028484 14992 Ack 27652 1.039 0.633 0.530 0.017 0.144 FBgn0003034 17673 Acp70A 25998 1.271 5.546 <0.001 0.005 0.067 FBgn0010609 8732 Acsl 27729 1.008 0.163 0.871 0.050 0.047 FBgn0015008 8953 Actn3 26737 1.168 1.885 0.064 0.004 0.133 FBgn0024913 11062 Actβ 29597 1.122 1.852 0.069 0.006 0.125 FBgn0037661 11994 Ada 26015 1.182 2.558 0.013 0.005 0.107 FBgn0026086 12598 Adar 28311 1.052 0.729 0.475 0.025 0.105 FBgn0000054 15845 Adf1 28680 1.123 1.991 0.053 0.006 0.108 FBgn0000055 3481 Adh 28627 0.853 2.781 0.008 0.006 0.063 FBgn0039747 9753 AdoR 27536 1.176 1.976 0.052 0.003 0.101 FBgn0005694 5683 Aef1 31942 1.060 1.339 0.187 0.013 0.057 FBgn0027932 13388 Akap200 28532 1.113 1.785 0.080 0.013 0.082 FBgn0004552 1171 Akh 27031 1.163 2.242 0.029 0.005 0.215 FBgn0000061 3935 al 26747 0.892 1.845 0.070 0.005 0.060 FBgn0036789 13702 AlCR2 25940 1.109 1.719 0.091 0.017 0.224 FBgn0000064 6058 Ald 26301 0.968 0.448 0.656 0.009 0.096 FBgn0013746 9556 alien 28908 0.975 0.280 0.780 0.025 0.183 FBgn0040505 8250 Alk 27518 1.094 1.249 0.216 0.006 0.059 FBgn0034005 16827 αPS4 28535 1.089 1.819 0.075 0.010 0.059
95
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0028961 2872 AlstR 27280 0.972 0.408 0.685 0.017 0.089 FBgn0086782 11937 amn 25797 1.114 1.690 0.096 0.007 0.110 FBgn0023179 6438 amon 28583 0.769 2.313 0.029 0.006 0.049 FBgn0027356 8604 Amph 28048 0.942 0.656 0.514 0.009 0.197 FBgn0011746 8084 ana 27515 0.915 1.124 0.265 0.007 0.096 FBgn0085445 42734 Ank2 29438 1.321 4.185 <0.001 0.009 0.078 FBgn0260642 1028 Antp 27675 1.030 0.418 0.678 0.013 0.119 FBgn0000097 3166 aop 26759 0.954 0.819 0.417 0.025 0.056 FBgn0029512 12276 Aos1 28972 1.344 5.634 <0.001 0.005 0.119 FBgn0000099 8376 ap 26748 1.012 0.219 0.827 0.025 0.044 FBgn0030089 9113 AP-1γ 27533 0.999 0.010 0.992 0.050 0.106 FBgn0043012 6056 AP-2σ 27322 1.070 0.750 0.457 0.017 0.080 FBgn0024833 9388 AP-47 27534 0.943 0.639 0.525 0.010 0.104 FBgn0024832 7057 AP-50 28040 0.832 1.504 0.145 0.009 0.213 FBgn0015589 1451 Apc 28582 0.900 1.512 0.139 0.025 0.175 FBgn0026598 6193 Apc2 28585 1.013 0.180 0.857 0.017 0.133 FBgn0016123 1462 Aph-4 28740 1.148 2.037 0.046 0.006 0.058 FBgn0022131 42783 aPKC 25946 1.257 4.998 <0.001 0.005 0.129 FBgn0040281 1200 Aplip1 26024 1.014 0.190 0.850 0.025 0.081 FBgn0000108 7727 Appl 28043 0.977 0.313 0.755 0.010 0.084 FBgn0015903 5393 apt 26236 0.823 1.893 0.064 0.005 0.193 FBgn0039595 10001 AR-2 25935 1.135 1.523 0.133 0.006 0.143 FBgn0015904 10571 ara 27060 0.831 2.830 0.006 0.004 0.084 FBgn0033926 12505 Arc1 25954 1.221 3.272 0.004 0.013 0.049 FBgn0032859 10954 Arc-p34 28011 0.860 2.330 0.023 0.004 0.125 FBgn0013749 11027 Arf102F 27268 0.977 0.356 0.723 0.017 0.072 FBgn0013750 8156 Arf51F 27261 1.196 2.598 0.012 0.003 0.115 FBgn0000115 6025 Arf72A 27052 0.917 0.888 0.379 0.010 0.198 FBgn0010348 8385 Arf79F 29538 0.892 0.095 0.925 0.025 0.191 FBgn0004908 7435 Arf84F 29588 1.108 1.492 0.141 0.005 0.137 FBgn0000116 32031 Argk 31959 1.076 1.542 0.129 0.006 0.065 FBgn0004569 4531 argos 28383 1.029 0.083 FBgn0017418 5659 ari-1 29416 1.025 0.244 0.810 0.050 0.059 FBgn0011742 9901 Arp14D 27705 1.159 1.792 0.078 0.004 0.180 FBgn0011745 6174 Arp87C 32032 1.181 6.011 <0.001 0.004 0.041 FBgn0038369 4560 Arpc3A 27044 0.967 0.570 0.571 0.017 0.088 FBgn0065032 8936 Arpc3B 27528 0.854 1.642 0.106 0.005 0.126 FBgn0000137 3258 ase 31895 1.214 3.430 0.001 0.017 0.109 FBgn0000140 6875 asp 28741 1.170 2.361 0.021 0.005 0.080 FBgn0015591 13633 Ast 25866 1.154 2.436 0.018 0.007 0.074 FBgn0032336 14919 Ast-C 25868 1.072 1.170 0.248 0.009 0.143 FBgn0039946 17172 ATbp 26763 1.064 1.307 0.197 0.025 0.055 FBgn0050420 30420 Atf-2 26210 1.141 2.900 0.005 0.009 0.062 FBgn0033010 3136 Atf6 26211 0.969 0.451 0.654 0.010 0.075 FBgn0010715 10967 Atg1 26731 0.996 0.066 0.947 0.050 0.112
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0036255 10861 Atg12 27552 0.966 0.572 0.570 0.025 0.063 FBgn0035850 7986 Atg18 28061 0.953 0.628 0.533 0.010 0.119 FBgn0044452 1241 Atg2 27706 1.083 1.141 0.259 0.009 0.097 FBgn0031298 4428 Atg4 28367 0.916 1.427 0.159 0.007 0.086 FBgn0029943 1643 Atg5 27551 1.016 0.230 0.819 0.050 0.121 FBgn0010709 5429 Atg6 28060 0.812 2.011 0.050 0.005 0.196 FBgn0034366 5489 Atg7 27707 0.863 1.466 0.149 0.006 0.178 FBgn0052672 32672 Atg8a 28989 1.152 2.961 0.005 0.006 0.096 FBgn0038539 12334 Atg8b 27554 1.018 0.273 0.788 0.050 0.090 FBgn0034110 3615 Atg9 28055 0.995 0.087 0.931 0.050 0.069 FBgn0010433 7508 ato 26316 0.995 0.074 0.942 0.050 0.057 FBgn0019644 8189 ATPsyn-b 28062 0.881 1.582 0.119 0.004 0.158 FBgn0010217 11154 ATPsyn-β 28056 0.995 0.051 FBgn0020235 7610 ATPsyn-γ 28723 1.122 1.098 0.282 0.013 0.218 FBgn0002921 5670 Atpα 28073 0.755 2.611 0.012 0.004 0.141 FBgn0029907 4547 Atx-1 28536 1.065 0.097 FBgn0037218 1107 aux 28509 1.117 2.018 0.048 0.005 0.056 FBgn0013751 1072 Awh 31772 1.380 7.007 <0.001 0.004 0.053 FBgn0025185 1605 az2 26230 1.032 0.462 0.646 0.009 0.137 FBgn0000153 7811 b 27511 0.994 0.092 0.927 0.025 0.170 FBgn0011300 8224 babo 25933 1.046 0.609 0.545 0.013 0.123 FBgn0004862 7902 bap 27061 0.880 1.600 0.115 0.004 0.154 FBgn0010380 12532 Bap 28328 1.218 2.824 0.011 0.017 0.071 FBgn0042085 3274 Bap170 26308 1.046 0.675 0.503 0.006 0.087 FBgn0024251 1414 bbx 26215 1.204 2.809 0.007 0.005 0.119 FBgn0000166 1034 bcd 28586 1.021 0.288 0.774 0.017 0.184 FBgn0015602 10159 BEAF-32 29734 0.751 1.547 0.127 0.006 0.089 FBgn0038498 14334 beat-Iia 28072 1.084 1.325 0.191 0.025 0.087 FBgn0032629 15138 beat-IIIc 29607 1.215 3.500 0.001 0.005 0.045 FBgn0038092 31298 beat-Vb 28758 1.296 3.857 0.001 0.010 0.065 FBgn0000171 9748 bel 28049 0.963 0.415 0.679 0.013 0.141 FBgn0000173 18319 ben 28721 1.079 1.520 0.135 0.017 0.019 FBgn0010395 1762 βInt-ν 28601 1.137 1.988 0.052 0.004 0.088 FBgn0013753 7959 Bgb 26213 0.948 0.966 0.338 0.010 0.099 FBgn0027348 4501 bgm 28639 1.052 0.138 FBgn0000179 3578 bi 28341 0.942 0.987 0.328 0.013 0.088 FBgn0000180 4722 bib 27691 0.913 1.624 0.110 0.006 0.077 FBgn0000183 6605 BicD 28571 1.027 0.245 0.808 0.025 0.131 FBgn0014133 1822 bif 28372 1.338 0.115 FBgn0039509 3350 bigmax 29325 0.977 0.337 0.738 0.025 0.054 FBgn0011211 3612 blw 28059 0.969 0.521 0.604 0.025 0.044 FBgn0036449 5295 bmm 25926 1.041 0.105 FBgn0023097 5206 bon 27047 1.071 0.761 0.450 0.013 0.078 FBgn0004893 10021 bowl 27074 1.149 1.671 0.100 0.004 0.215 FBgn0000210 11491 br 27272 1.275 2.356 0.038 0.017 0.151
97
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0033155 1845 Br140 27081 0.839 2.761 0.010 0.013 0.096 FBgn0010300 10719 brat 28590 0.980 0.338 0.737 0.050 0.094 FBgn0086694 10542 Bre1 28019 1.010 0.140 0.889 0.050 0.054 FBgn0038499 31256 Brf 29321 0.839 3.298 0.002 0.007 0.091 FBgn0259246 42344 brp 25891 0.964 0.465 0.648 0.050 0.088 FBgn0004101 3411 bs 26755 0.895 1.802 0.077 0.006 0.081 FBgn0000529 10604 bsh 29336 1.044 0.727 0.470 0.017 0.046 FBgn0012049 11494 BtbVII 28912 1.101 1.502 0.138 0.010 0.084 FBgn0000233 12653 btd 29453 1.273 4.101 <0.001 0.006 0.048 FBgn0025679 2932 Bteb2 27075 0.967 0.483 0.631 0.007 0.124 FBgn0003502 8049 Btk29A 25791 0.857 1.895 0.063 0.004 0.146 FBgn0023096 15148 btv 28373 1.075 1.216 0.230 0.007 0.100 FBgn0045862 12878 btz 30482 1.045 0.643 0.524 0.050 0.124 FBgn0040491 8238 Buffy 29608 1.100 1.325 0.190 0.006 0.175 FBgn0259176 42281 bun 28322 0.954 0.891 0.377 0.013 0.085 FBgn0038901 13419 burs 26719 1.024 0.031 FBgn0038901 13969 bwa 29409 1.247 3.898 <0.001 0.005 0.071 FBgn0000242 6500 Bx 29454 1.179 2.483 0.016 0.004 0.195 FBgn0004863 7937 C15 27649 1.114 1.508 0.136 0.005 0.090 FBgn0005563 1522 cac 27244 0.970 0.435 0.666 0.013 0.088 FBgn0036715 6445 Cad74A 27485 0.770 3.010 0.004 0.004 0.106 FBgn0037839 42601 Cad86C 27045 1.068 0.890 0.385 0.013 0.175 FBgn0037963 6977 Cad87A 28716 1.250 3.473 <0.001 0.003 0.084 FBgn0038247 3389 Cad88C 29303 1.064 1.098 0.277 0.010 0.119 FBgn0038439 14900 Cad89D 26287 1.167 2.710 0.009 0.005 0.080 FBgn0022800 10244 Cad96Ca 27266 0.960 0.570 0.571 0.009 0.103 FBgn0039294 13664 Cad96Cb 25860 0.882 1.857 0.069 0.010 0.153 FBgn0039709 31009 Cad99C 27510 1.185 3.799 <0.001 0.006 0.029 FBgn0015609 7100 CadN 27503 1.206 2.863 0.006 0.004 0.142 FBgn0032655 7527 CadN2 27508 0.931 0.960 0.341 0.007 0.092 FBgn0030054 12109 Caf1-180 28918 1.098 1.482 0.145 0.025 0.107 FBgn0013759 6703 Caki 27556 0.867 1.328 0.196 0.010 0.072 FBgn0012051 7563 CalpA 29455 0.924 0.677 0.504 0.017 0.098 FBgn0025866 8107 CalpB 25963 0.999 0.010 0.992 0.050 0.020 FBgn0039928 11059 cals 25839 1.051 0.779 0.439 0.009 0.051 FBgn0013995 5685 Calx 28306 0.693 2.943 0.007 0.006 0.098 FBgn0016126 1495 CaMKI 26726 1.228 3.139 0.003 0.004 0.056 FBgn0004624 18069 CaMKII 29401 1.257 5.261 <0.001 0.005 0.028 FBgn0259234 42332 Camta 27062 0.974 0.501 0.619 0.025 0.032 FBgn0010015 1455 CanA1 25850 1.147 2.023 0.048 0.006 0.067 FBgn0010014 4209 CanB 27307 0.865 2.299 0.025 0.004 0.119 FBgn0015614 11217 CanB2 27270 1.297 4.490 <0.001 0.005 0.077 FBgn0033504 18408 CAP 30506 1.054 1.101 0.276 0.050 0.076 FBgn0004551 3725 Ca-P60A 25928 0.883 1.999 0.050 0.005 0.085 FBgn0039722 15520 capa 28345 1.014 0.176 0.861 0.050 0.108
98
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0037100 14575 capaR 27275 1.069 1.126 0.266 0.010 0.082 FBgn0023095 11282 caps 28020 1.275 3.537 <0.001 0.006 0.035 FBgn0053653 33653 Caps 31984 1.049 1.067 0.291 0.010 0.069 FBgn0022213 13281 Cas 28337 1.280 4.421 <0.001 0.005 0.026 FBgn0004878 2102 cas 26310 0.833 2.424 0.019 0.004 0.156 FBgn0000261 6871 Cat 31894 1.130 2.870 0.006 0.005 0.062 FBgn0029093 1548 cathD 28978 1.111 1.618 0.111 0.005 0.172 FBgn0024249 7760 cato 26317 1.064 1.085 0.282 0.006 0.085 FBgn0001991 4894 Ca-α1D 25830 0.942 0.618 0.539 0.025 0.105 FBgn0029846 15899 Ca-α1T 26251 1.059 0.953 0.346 0.013 0.060 FBgn0259822 42403 Ca-β 29575 1.052 0.625 0.539 0.025 0.020 FBgn0020224 7037 Cbl 27500 0.982 0.249 0.804 0.013 0.074 FBgn0004580 6702 Cbp53E 28302 0.898 1.414 0.162 0.006 0.088 FBgn0039007 4910 Ccap 29569 0.904 1.023 0.311 0.007 0.071 FBgn0050106 30106 CCHa1r 27669 1.008 0.115 0.909 0.025 0.142 FBgn0033058 14593 CCHa2r 25855 1.219 2.369 0.021 0.013 0.181 FBgn0259231 42301 CCKLR-17D1 27494 1.073 1.420 0.162 0.010 0.057 FBgn0030954 32540 CCKLR-17D3 28333 1.121 2.357 0.023 0.007 0.113 FBgn0004106 5363 cdc2 28368 1.077 0.093 FBgn0004107 10498 cdc2c 28952 0.887 0.338 0.737 0.013 0.048 FBgn0011573 12019 Cdc37 28756 1.434 7.550 <0.001 0.004 0.100 FBgn0010341 12530 Cdc42 28021 1.272 3.807 0.001 0.010 0.135 FBgn0004876 3027 cdi 28369 0.964 0.522 0.604 0.006 0.107 FBgn0016131 5072 Cdk4 27714 1.088 0.941 0.351 0.009 0.197 FBgn0013762 8203 Cdk5 27517 1.039 0.514 0.609 0.017 0.056 FBgn0027491 5387 Cdk5α 27048 0.888 1.199 0.236 0.006 0.208 FBgn0010350 7962 CdsA 28075 0.887 1.504 0.138 0.005 0.089 FBgn0032409 5336 Ced-12 28556 0.997 0.036 0.971 0.050 0.074 FBgn0086697 31258 Cenp-C 26311 1.188 3.664 <0.001 0.006 0.079 FBgn0034443 10460 cer 25875 0.814 2.671 0.010 0.004 0.094 FBgn0000289 8367 cg 29359 0.948 0.736 0.464 0.009 0.057 FBgn0037238 1090 CG1090 25806 1.100 1.376 0.174 0.007 0.115 FBgn0037279 1129 CG1129 28615 0.919 1.111 0.271 0.010 0.042 FBgn0035137 1233 CG1233 31950 1.128 2.354 0.022 0.010 0.109 FBgn0035501 1299 CG1299 26284 0.945 0.952 0.345 0.006 0.058 FBgn0032961 1416 CG1416 28762 1.001 0.018 0.986 0.050 0.122 FBgn0031100 1504 CG1504 28517 1.209 3.593 <0.001 0.004 0.067 FBgn0033186 1602 CG1602 31920 1.270 4.986 <0.001 0.005 0.085 FBgn0033185 1603 CG1603 27063 0.988 0.203 0.840 0.025 0.103 FBgn0033182 1621 CG1621 29350 1.018 0.050 0.960 0.050 0.041 FBgn0033449 1663 CG1663 27078 1.049 0.833 0.408 0.006 0.140 FBgn0027589 1688 CG1688 25809 0.975 0.426 0.672 0.013 0.096 FBgn0039915 1732 CG1732 29422 1.125 1.848 0.076 0.017 0.103 FBgn0030303 1756 CG1756 27033 0.684 5.096 <0.001 0.007 0.116 FBgn0039860 1792 CG1792 26709 0.947 0.855 0.400 0.025 0.157
99
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0032979 1832 CG1832 27080 0.737 4.497 <0.001 0.009 0.046 FBgn0039911 1909 CG1909 28024 1.100 1.379 0.178 0.013 0.076 FBgn0039909 1970 CG1970 28573 0.890 1.516 0.140 0.010 0.187 FBgn0250839 2016 CG2016 28771 0.971 0.490 0.626 0.009 0.056 FBgn0035271 2021 CG2021 28579 0.839 2.771 0.008 0.004 0.076 FBgn0039905 2052 CG2052 26711 1.034 0.577 0.566 0.007 0.144 FBgn0030003 2116 CG2116 26712 0.856 1.871 0.071 0.007 0.099 FBgn0035213 2199 CG2199 27082 1.013 0.167 0.869 0.025 0.101 FBgn0030240 2202 CG2202 26241 1.408 3.578 <0.001 0.005 0.129 FBgn0032957 2225 CG2225 29619 1.324 2.842 0.006 0.007 0.136 FBgn0037518 2641 CG2641 28753 1.200 2.756 0.010 0.006 0.121 FBgn0014931 2678 CG2678 28630 1.126 1.735 0.093 0.009 0.106 FBgn0037541 2747 CG2747 29322 0.987 0.151 0.881 0.050 0.119 FBgn0031266 2807 CG2807 28965 0.870 2.240 0.029 0.005 0.062 FBgn0029672 2875 CG2875 29333 0.983 0.286 0.776 0.017 0.093 FBgn0040030 2893 CG2893 25851 0.930 1.329 0.190 0.010 0.091 FBgn0030186 2962 CG2962 28319 0.841 3.015 0.004 0.005 0.089 FBgn0034834 3162 CG3162 29404 1.542 6.637 <0.001 0.004 0.287 FBgn0029896 3168 CG3168 29301 1.053 1.004 0.320 0.017 0.081 FBgn0034568 3216 CG3216 31877 1.499 11.731 <0.001 0.004 0.050 FBgn0034978 3257 CG3257 28611 0.906 1.772 0.082 0.007 0.023 FBgn0031628 3294 CG3294 27296 0.853 2.787 0.007 0.005 0.088 FBgn0038869 3353 CG3353 28609 0.917 1.565 0.124 0.009 0.109 FBgn0035153 3371 CG3371 28296 1.219 3.337 0.002 0.005 0.074 FBgn0037975 3397 CG3397 25818 1.108 0.948 0.348 0.017 0.080 FBgn0035008 3494 CG3494 27056 0.789 4.018 <0.001 0.005 0.051 FBgn0027571 3523 CG3523 28930 0.778 4.233 <0.001 0.004 0.072 FBgn0028497 3530 CG3530 25864 0.940 1.138 0.260 0.009 0.063 FBgn0037028 3618 CG3618 28303 0.769 4.412 <0.001 0.004 0.070 FBgn0040350 3690 CG3690 27688 0.979 0.409 0.684 0.017 0.078 FBgn0040348 3703 CG3703 29596 0.996 0.070 0.945 0.050 0.079 FBgn0025692 3814 CG3814 29451 1.088 1.677 0.099 0.007 0.142 FBgn0038837 3822 CG3822 25852 0.852 2.828 0.007 0.006 0.060 FBgn0032130 3838 CG3838 31922 1.090 1.978 0.053 0.006 0.043 FBgn0037788 3940 CG3940 31880 1.204 3.277 0.002 0.025 0.070 FBgn0035989 3967 CG3967 28777 0.851 2.834 0.006 0.005 0.049 FBgn0038472 3995 CG3995 27997 1.011 0.205 0.838 0.025 0.098 FBgn0031257 4133 CG4133 28370 0.960 0.754 0.454 0.010 0.084 FBgn0028888 4168 CG4168 28736 1.030 0.544 0.588 0.013 0.110 FBgn0034114 4282 CG4282 26313 1.284 4.186 <0.001 0.004 0.088 FBgn0038799 4288 CG4288 29305 0.955 0.664 0.510 0.010 0.096 FBgn0036274 4328 CG4328 27987 1.219 3.237 0.002 0.005 0.090 FBgn0030437 4395 CG4395 29623 1.102 1.502 0.139 0.006 0.113 FBgn0030432 4404 CG4404 31923 1.378 6.073 <0.001 0.004 0.134 FBgn0029920 4575 CG4575 31975 1.022 0.532 0.597 0.013 0.066
100
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0028863 4587 CG4587 25893 1.151 2.160 0.035 0.006 0.121 FBgn0037849 4596 CG4596 28617 0.981 0.280 0.781 0.017 0.063 FBgn0029936 4617 CG4617 25993 0.979 0.316 0.753 0.013 0.078 FBgn0031299 4629 CG4629 27310 1.014 0.213 0.832 0.050 0.099 FBgn0033814 4670 CG4670 29307 1.015 0.220 0.827 0.025 0.130 FBgn0069913 4681 CG4681 25873 1.086 1.263 0.213 0.006 0.206 FBgn0031220 4822 CG4822 25894 0.918 1.214 0.231 0.007 0.086 FBgn0032187 4839 CG4839 27046 1.217 4.051 <0.001 0.005 0.068 FBgn0030799 4872 CG4872 28738 1.158 2.942 0.005 0.007 0.044 FBgn0036587 4950 CG4950 30508 1.071 1.332 0.189 0.010 0.104 FBgn0036579 5027 CG5027 28305 1.106 1.974 0.054 0.009 0.112 FBgn0034300 5098 CG5098 27313 1.040 0.752 0.455 0.013 0.043 FBgn0031906 5160 CG5160 27660 1.034 0.487 0.628 0.025 0.107 FBgn0034355 5226 CG5226 28069 0.956 0.643 0.523 0.013 0.075 FBgn0036565 5235 CG5235 27694 0.894 1.537 0.130 0.006 0.090 FBgn0030603 5541 CG5541 30486 0.980 0.288 0.774 0.050 0.125 FBgn0030605 5548 CG5548 30511 1.079 1.080 0.285 0.006 0.088 FBgn0036760 5567 CG5567 28962 1.043 0.629 0.532 0.017 0.113 FBgn0034926 5591 CG5591 25994 0.824 2.712 0.009 0.005 0.085 FBgn0036975 5618 CG5618 27318 1.032 0.491 0.626 0.013 0.068 FBgn0038840 5621 CG5621 25822 0.918 1.259 0.214 0.006 0.076 FBgn0035948 5644 CG5644 28619 0.985 0.236 0.815 0.025 0.057 FBgn0037083 5656 CG5656 28564 0.948 0.754 0.454 0.009 0.152 FBgn0034717 5819 CG5819 31867 1.191 5.205 <0.001 0.007 0.068 FBgn0032167 5853 CG5853 27668 0.995 0.078 0.938 0.050 0.043 FBgn0039380 5890 CG5890 27321 1.090 1.395 0.169 0.006 0.115 FBgn0036202 6024 CG6024 28625 1.000 0.007 0.995 0.050 0.086 FBgn0034725 6044 CG6044 28610 1.180 3.169 0.003 0.004 0.147 FBgn0036533 6151 CG6151 27323 0.889 1.965 0.055 0.010 0.124 FBgn0039156 6178 CG6178 28917 1.142 2.509 0.015 0.007 0.139 FBgn0038321 6218 CG6218 28386 1.216 3.803 <0.001 0.004 0.079 FBgn0036126 6272 CG6272 29331 1.194 2.776 0.007 0.004 0.101 FBgn0038316 6276 CG6276 27064 1.101 1.687 0.097 0.013 0.044 FBgn0033872 6329 CG6329 28297 1.044 0.773 0.443 0.025 0.097 FBgn0039178 6356 CG6356 28745 0.814 3.097 0.003 0.005 0.066 FBgn0027550 6495 CG6495 29452 1.154 2.714 0.009 0.005 0.041 FBgn0036511 6498 CG6498 27698 1.146 2.567 0.013 0.006 0.075 FBgn0035675 6610 CG6610 31870 1.156 4.429 <0.001 0.005 0.088 FBgn0036403 6661 CG6661 30500 1.095 1.440 0.156 0.009 0.092 FBgn0035902 6683 CG6683 26233 1.152 2.289 0.026 0.006 0.133 FBgn0038918 6690 CG6690 28364 1.120 1.814 0.075 0.007 0.174 FBgn0027526 6697 CG6697 28751 1.175 2.637 0.011 0.006 0.074 FBgn0033889 6701 CG6701 27560 1.270 4.071 <0.001 0.005 0.103 FBgn0036058 6707 CG6707 28316 1.089 1.338 0.187 0.017 0.053 FBgn0037895 6723 CG6723 29425 1.287 4.096 <0.001 0.004 0.097
101
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0032400 6770 CG6770 27561 1.028 0.417 0.679 0.050 0.095 FBgn0037923 6813 CG6813 29369 1.117 1.766 0.083 0.013 0.060 FBgn0032633 6860 CG6860 31871 1.154 3.100 0.003 0.004 0.056 FBgn0031711 6907 CG6907 27716 1.303 4.576 <0.001 0.004 0.091 FBgn0029733 6927 CG6927 25895 1.117 1.770 0.083 0.010 0.100 FBgn0030088 7039 CG7039 27501 1.051 0.737 0.464 0.025 0.025 FBgn0038978 7045 CG7045 28656 1.093 1.418 0.162 0.009 0.117 FBgn0038979 7046 CG7046 28657 1.092 1.400 0.167 0.010 0.069 FBgn0037116 7158 CG7158 28533 1.075 1.148 0.256 0.017 0.075 FBgn0035866 7197 CG7197 29450 1.106 1.528 0.132 0.007 0.101 FBgn0031730 7236 CG7236 27505 1.196 2.990 0.004 0.004 0.105 FBgn0032282 7299 CG7299 28658 0.898 1.552 0.127 0.006 0.078 FBgn0037188 7369 CG7369 28063 1.175 2.678 0.010 0.004 0.143 FBgn0038542 7431 CG7431 25857 1.157 2.237 0.029 0.006 0.116 FBgn0032258 7456 CG7456 28634 1.231 3.078 0.003 0.004 0.125 FBgn0036727 7589 CG7589 27090 1.014 0.189 0.851 0.050 0.096 FBgn0038641 7708 CG7708 28613 1.158 2.112 0.040 0.006 0.058 FBgn0033616 7745 CG7745 26234 1.064 0.850 0.399 0.013 0.131 FBgn0036915 7757 CG7757 27717 1.156 1.967 0.055 0.006 0.156 FBgn0037552 7800 CG7800 28922 1.097 1.299 0.200 0.010 0.069 FBgn0036124 7839 CG7839 25992 0.983 0.221 0.826 0.025 0.166 FBgn0036502 7841 CG7841 28752 1.201 2.688 0.010 0.005 0.080 FBgn0039728 7896 CG7896 28565 1.139 1.850 0.070 0.007 0.128 FBgn0036417 7906 CG7906 30519 0.989 0.157 0.876 0.050 0.095 FBgn0036505 7945 CG7945 28779 0.907 0.108 FBgn0037600 8007 CG8007 25874 0.875 0.043 FBgn0027567 8108 CG8108 27562 0.991 0.147 FBgn0033358 8216 CG8216 27069 1.102 0.115 FBgn0033031 8245 CG8245 29426 0.945 0.097 FBgn0026573 8290 CG8290 27065 0.893 0.143 FBgn0037634 8359 CG8359 27066 0.909 0.111 FBgn0035785 8546 CG8546 25825 0.962 0.102 FBgn0033762 8632 CG8632 31933 1.158 3.205 0.002 0.005 0.063 FBgn0031684 8680 CG8680 28576 1.140 1.509 0.137 0.009 0.187 FBgn0033257 8713 CG8713 25853 1.136 1.464 0.149 0.010 0.127 FBgn0036900 8765 CG8765 29447 1.082 0.882 0.382 0.050 0.085 FBgn0038140 8784 CG8784 29624 1.253 2.718 0.009 0.005 0.087 FBgn0038139 8795 CG8795 28781 1.403 4.328 <0.001 0.004 0.165 FBgn0031548 8852 CG8852 29515 1.051 0.958 0.343 0.013 0.151 FBgn0037676 8861 CG8861 29427 1.250 2.685 0.010 0.005 0.081 FBgn0038466 8907 CG8907 28636 1.135 1.448 0.154 0.013 0.086 FBgn0030707 8916 CG8916 25854 1.113 1.714 0.093 0.006 0.053 FBgn0030710 8924 CG8924 29348 1.219 3.319 0.002 0.004 0.087 FBgn0030680 8944 CG8944 31925 0.904 2.107 0.040 0.007 0.116 FBgn0035202 9139 CG9139 29357 0.950 0.758 0.452 0.013 0.070
102
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0031771 9140 CG9140 29534 1.117 1.769 0.083 0.005 0.090 FBgn0035192 9194 CG9194 25921 0.914 1.309 0.196 0.010 0.068 FBgn0032897 9336 CG9336 31988 1.129 2.459 0.017 0.006 0.026 FBgn0034564 9344 CG9344 29532 0.877 1.610 0.114 0.006 0.060 FBgn0036890 9368 CG9368 28292 1.104 1.671 0.101 0.017 0.115 FBgn0030569 9411 CG9411 28320 1.208 3.332 0.002 0.007 0.121 FBgn0026582 9418 CG9418 26216 1.013 0.191 0.849 0.025 0.196 FBgn0034599 9437 CG9437 26754 1.252 4.036 <0.001 0.005 0.069 FBgn0037758 9467 CG9467 26724 1.073 1.038 0.304 0.013 0.071 FBgn0038166 9588 CG9588 28527 1.306 4.915 <0.001 0.005 0.117 FBgn0037556 9636 CG9636 28070 1.171 2.742 0.008 0.009 0.048 FBgn0029939 9650 CG9650 26713 1.147 2.359 0.022 0.010 0.099 FBgn0029950 9657 CG9657 28384 1.249 3.991 <0.001 0.006 0.108 FBgn0036661 9705 CG9705 31901 1.087 2.884 0.006 0.010 0.047 FBgn0038209 9722 CG9722 28052 1.164 2.276 0.027 0.013 0.173 FBgn0037445 9727 CG9727 26762 1.091 1.460 0.151 0.025 0.120 FBgn0030734 9911 CG9911 29606 0.842 2.524 0.014 0.005 0.037 FBgn0038201 9918 CG9918 27539 0.944 0.887 0.379 0.009 0.066 FBgn0032467 9934 CG9934 27540 1.052 0.826 0.412 0.013 0.108 FBgn0039916 9935 CG9935 28506 1.003 0.044 0.965 0.050 0.118 FBgn0035371 9977 CG9977 28523 0.832 2.686 0.009 0.004 0.050 FBgn0032798 10132 CG10132 31992 1.042 0.895 0.375 0.025 0.045 FBgn0032800 10137 CG10137 28730 1.216 1.939 0.063 0.007 0.189 FBgn0035702 10147 CG10147 31943 0.802 4.177 <0.001 0.005 0.041 FBgn0033960 10151 CG10151 28304 0.888 2.103 0.040 0.005 0.076 FBgn0039088 10164 CG10164 28377 0.845 0.331 0.742 0.017 0.168 FBgn0039083 10177 CG10177 25945 1.026 0.378 0.707 0.010 0.071 FBgn0033968 10200 CG10200 28759 1.101 1.446 0.153 0.006 0.063 FBgn0033970 10205 CG10205 28640 1.159 2.281 0.026 0.004 0.068 FBgn0037446 10267 CG10267 29360 0.882 1.697 0.095 0.005 0.074 FBgn0035690 10274 CG10274 26239 0.924 1.091 0.279 0.007 0.203 FBgn0034643 10321 CG10321 26764 0.978 0.315 0.754 0.017 0.053 FBgn0032707 10348 CG10348 27076 1.242 0.171 FBgn0032814 10366 CG10366 26765 1.085 1.215 0.229 0.006 0.238 FBgn0036277 10418 CG10418 30490 0.991 0.136 0.893 0.050 0.077 FBgn0034636 10440 CG10440 25846 1.016 0.224 0.823 0.025 0.060 FBgn0032815 10462 CG10462 31945 1.148 3.110 0.003 0.005 0.043 FBgn0033017 10465 CG10465 26002 1.243 3.495 <0.001 0.003 0.104 FBgn0034570 10543 CG10543 31964 1.103 2.160 0.036 0.009 0.035 FBgn0037050 10566 CG10566 28379 0.793 1.822 0.073 0.005 0.045 FBgn0032817 10631 CG10631 28001 1.018 0.278 0.782 0.025 0.116 FBgn0036294 10654 CG10654 27998 1.061 1.011 0.316 0.013 0.097 FBgn0039329 10669 CG10669 31946 0.984 0.346 0.731 0.050 0.108 FBgn0036368 10738 CG10738 28580 1.052 0.797 0.429 0.007 0.056 FBgn0029663 10804 CG10804 29599 1.131 2.195 0.032 0.005 0.087
103
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0033830 10814 CG10814 28557 1.109 1.825 0.073 0.005 0.133 FBgn0038839 10830 CG10830 25848 0.904 1.604 0.114 0.006 0.054 FBgn0035458 10858 CG10858 27239 0.891 1.822 0.073 0.006 0.080 FBgn0038621 10864 CG10864 25878 0.927 1.220 0.227 0.009 0.084 FBgn0032858 10949 CG10949 26231 0.761 3.986 <0.001 0.004 0.048 FBgn0030010 10959 CG10959 29361 1.027 1.192 0.238 0.010 0.167 FBgn0037379 10979 CG10979 29362 1.141 2.143 0.037 0.006 0.053 FBgn0037384 10981 CG10981 27267 1.226 3.443 0.001 0.004 0.128 FBgn0030521 10992 CG10992 28602 1.103 1.561 0.126 0.017 0.112 FBgn0030532 11071 CG11071 26766 1.235 3.377 0.001 0.005 0.094 FBgn0030281 11105 CG11105 26020 1.048 0.724 0.472 0.013 0.099 FBgn0030518 11134 CG11134 28637 1.050 0.758 0.451 0.010 0.093 FBgn0039936 11148 CG11148 28896 1.066 0.851 0.398 0.017 0.116 FBgn0039927 11155 CG11155 31991 1.045 1.203 0.235 0.010 0.047 FBgn0025693 11163 CG11163 28638 1.304 3.908 <0.001 0.005 0.154 FBgn0034528 11180 CG11180 28629 1.246 3.158 0.003 0.009 0.150 FBgn0031855 11221 CG11221 29603 1.284 3.652 <0.001 0.005 0.053 FBgn0037120 11247 CG11247 31947 0.968 0.844 0.403 0.013 0.047 FBgn0034889 11293 CG11293 31973 1.143 3.793 <0.001 0.005 0.028 FBgn0030058 11294 CG11294 28641 1.149 0.052 FBgn0039816 11317 CG11317 28065 1.055 0.703 0.485 0.050 0.079 FBgn0039840 11340 CG11340 26003 1.255 3.284 0.002 0.007 0.125 FBgn0031216 11376 CG11376 28005 0.956 0.374 0.715 0.050 0.081 FBgn0024985 11448 CG11448 28309 1.242 3.109 0.003 0.010 0.126 FBgn0037031 11456 CG11456 26240 0.924 0.648 0.530 0.025 0.047 FBgn0039733 11504 CG11504 31917 1.098 2.602 0.012 0.006 0.081 FBgn0046296 11534 CG11534 28312 1.239 3.556 <0.001 0.003 0.098 FBgn0039864 11550 CG11550 29609 1.336 4.316 <0.001 0.004 0.202 FBgn0036249 11560 CG11560 31939 1.054 1.420 0.162 0.025 0.070 FBgn0039882 11576 CG11576 29531 1.294 4.431 <0.001 0.007 0.094 FBgn0030314 11696 CG11696 27999 1.040 0.146 FBgn0037777 11722 CG11722 28567 1.207 0.201 FBgn0031391 11723 CG11723 29349 1.318 5.520 <0.001 0.006 0.062 FBgn0039309 11891 CG11891 26021 1.244 3.086 0.005 0.009 0.158 FBgn0028647 11902 CG11902 29363 1.076 1.041 0.308 0.017 0.173 FBgn0034425 11906 CG11906 26767 1.251 3.652 0.001 0.007 0.100 FBgn0039332 11910 CG11910 28624 1.167 2.433 0.023 0.013 0.063 FBgn0035454 12029 CG12029 31987 1.202 5.739 <0.001 0.004 0.039 FBgn0035179 12038 CG12038 28382 1.123 2.142 0.037 0.013 0.050 FBgn0032915 12050 CG12050 28592 1.218 3.786 <0.001 0.009 0.060 FBgn0040031 12061 CG12061 26248 0.954 0.586 0.563 0.025 0.005 FBgn0039796 12069 CG12069 27677 1.232 2.924 0.007 0.010 0.054 FBgn0039808 12071 CG12071 26768 1.436 7.575 <0.001 0.004 0.058 FBgn0035402 12082 CG12082 31886 1.147 3.120 0.003 0.006 0.052 FBgn0030112 12124 CG12124 25970 1.093 2.249 0.029 0.007 0.065
104
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0029957 12155 CG12155 26232 1.089 0.784 0.437 0.025 0.107 FBgn0043796 12219 CG12219 28000 0.996 0.076 0.939 0.050 0.103 FBgn0029822 12236 CG12236 31949 0.878 3.467 0.001 0.007 0.034 FBgn0029810 12239 CG12239 28727 1.088 1.531 0.132 0.017 0.097 FBgn0039419 12290 CG12290 31873 1.082 2.174 0.035 0.009 0.061 FBgn0036514 12301 CG12301 29412 1.014 0.483 0.631 0.009 0.050 FBgn0033558 12344 CG12344 26250 1.039 0.506 0.617 0.025 0.144 FBgn0033744 12370 CG12370 29610 1.145 1.903 0.069 0.013 0.129 FBgn0033581 12391 CG12391 31951 1.053 1.499 0.139 0.025 0.049 FBgn0030843 12432 CG12432 28754 1.021 0.185 0.854 0.050 0.134 FBgn0028859 12455 CG12455 25882 0.927 0.822 0.419 0.017 0.093 FBgn0037941 12594 CG12594 29611 1.228 2.812 0.009 0.009 0.148 FBgn0035481 12605 CG12605 29364 1.189 2.472 0.021 0.010 0.025 FBgn0030181 12645 CG12645 28318 1.018 0.239 0.813 0.050 0.106 FBgn0040929 12659 CG12659 31962 1.138 3.926 <0.001 0.006 0.071 FBgn0037206 12768 CG12768 31918 1.105 2.407 0.020 0.007 0.015 FBgn0033252 12769 CG12769 26769 1.138 2.068 0.044 0.010 0.163 FBgn0038448 12783 CG12783 27678 1.060 0.902 0.371 0.017 0.044 FBgn0029909 12796 CG12796 29612 1.216 3.242 0.002 0.009 0.067 FBgn0033958 12858 CG12858 29418 1.426 6.396 <0.001 0.004 0.136 FBgn0033569 12942 CG12942 27077 1.047 0.707 0.483 0.025 0.103 FBgn0032127 13114 CG13114 28632 1.379 5.691 <0.001 0.005 0.109 FBgn0032142 13120 CG13120 27240 1.041 0.620 0.538 0.050 0.071 FBgn0032145 13121 CG13121 25883 0.870 1.604 0.115 0.007 0.039 FBgn0032150 13123 CG13123 29545 1.257 3.867 <0.001 0.007 0.112 FBgn0033627 13204 CG13204 31919 1.171 2.752 0.008 0.005 0.055 FBgn0033579 13229 CG13229 29419 1.287 4.303 <0.001 0.005 0.097 FBgn0036984 13248 CG13248 28761 1.156 2.279 0.026 0.007 0.135 FBgn0037015 13253 CG13253 28642 1.117 1.750 0.086 0.013 0.142 FBgn0034788 13532 CG13532 28643 1.317 5.003 <0.001 0.004 0.063 FBgn0034965 13568 CG13568 27241 1.174 2.743 0.008 0.006 0.092 FBgn0034996 13575 CG13575 25827 1.134 2.117 0.039 0.009 0.072 FBgn0035010 13579 CG13579 28644 0.975 0.398 0.692 0.050 0.100 FBgn0039137 13604 CG13604 28597 1.216 3.402 0.001 0.006 0.034 FBgn0039209 13624 CG13624 25983 1.014 0.170 0.865 0.050 0.179 FBgn0040954 13779 CG13779 28057 1.156 1.915 0.061 0.006 0.089 FBgn0031897 13784 CG13784 28058 1.172 2.120 0.039 0.005 0.071 FBgn0035157 13894 CG13894 27243 1.112 1.380 0.174 0.009 0.182 FBgn0035160 13897 CG13897 29613 1.224 2.756 0.008 0.005 0.155 FBgn0035178 13908 CG13908 28645 0.951 0.598 0.553 0.017 0.104 FBgn0035246 13928 CG13928 28646 1.171 2.105 0.040 0.006 0.089 FBgn0031770 13995 CG13995 26733 1.019 0.230 0.819 0.025 0.108 FBgn0031718 14014 CG14014 29456 1.238 2.928 0.005 0.004 0.151 FBgn0040392 14050 CG14050 27073 1.049 0.608 0.546 0.013 0.138 FBgn0036322 14119 CG14119 27555 0.899 1.243 0.219 0.010 0.147
105
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0036935 14186 CG14186 29420 1.161 1.742 0.087 0.025 0.118 FBgn0039424 14239 CG14239 28012 1.406 4.391 <0.001 0.004 0.243 FBgn0032023 14274 CG14274 28763 1.748 8.097 <0.001 0.004 0.099 FBgn0038579 14313 CG14313 29614 1.331 3.581 <0.001 0.006 0.078 FBgn0029639 14419 CG14419 28647 1.349 3.779 <0.001 0.005 0.102 FBgn0039647 14509 CG14509 28764 1.240 2.599 0.012 0.009 0.040 FBgn0037244 14647 CG14647 27032 1.003 0.037 0.971 0.050 0.114 FBgn0037275 14655 CG14655 26770 1.255 2.759 0.008 0.007 0.032 FBgn0037317 14667 CG14667 29365 1.305 3.301 0.002 0.006 0.263 FBgn0037829 14691 CG14691 31986 1.275 4.444 <0.001 0.004 0.081 FBgn0037920 14710 CG14710 26771 1.175 1.899 0.063 0.017 0.082 FBgn0033250 14762 CG14762 29615 1.237 2.569 0.013 0.010 0.089 FBgn0035407 14962 CG14962 26772 1.196 2.457 0.017 0.005 0.031 FBgn0035544 15021 CG15021 28765 1.389 4.871 <0.001 0.004 0.191 FBgn0034376 15072 CG15072 28366 1.049 0.711 0.480 0.025 0.092 FBgn0034379 15073 CG15073 29366 1.083 1.040 0.303 0.017 0.064 FBgn0030331 15221 CG15221 31888 1.106 1.718 0.092 0.007 0.111 FBgn0033108 15236 CG15236 28649 1.160 2.006 0.050 0.006 0.080 FBgn0028879 15270 CG15270 28650 1.177 2.221 0.031 0.005 0.155 FBgn0030183 15309 CG15309 29530 1.065 0.767 0.446 0.050 0.043 FBgn0031580 15423 CG15423 28631 1.106 1.331 0.189 0.009 0.173 FBgn0039712 15514 CG15514 31940 0.985 0.234 0.816 0.050 0.064 FBgn0039723 15522 CG15522 28766 1.099 1.236 0.222 0.010 0.079 FBgn0039770 15537 CG15537 28767 1.197 2.471 0.017 0.004 0.212 FBgn0039817 15553 CG15553 28016 0.831 1.830 0.073 0.006 0.109 FBgn0039839 15555 CG15555 26006 1.124 1.557 0.125 0.007 0.188 FBgn0034567 15651 CG15651 29616 1.091 1.135 0.262 0.013 0.083 FBgn0034602 15658 CG15658 28976 1.067 0.841 0.404 0.025 0.072 FBgn0034120 15710 CG15710 26773 1.107 1.504 0.138 0.009 0.115 FBgn0030466 15744 CG15744 28516 1.061 0.905 0.369 0.013 0.104 FBgn0029814 15765 CG15765 29421 0.990 0.149 0.882 0.050 0.066 FBgn0028476 15817 CG15817 28356 0.952 0.635 0.528 0.007 0.142 FBgn0030576 15890 CG15890 28768 1.120 1.594 0.116 0.004 0.088 FBgn0003715 16778 CG16778 31915 1.226 5.280 <0.001 0.009 0.059 FBgn0028482 16857 CG16857 31974 1.356 8.305 <0.001 0.004 0.056 FBgn0034498 16868 CG16868 29617 0.966 0.456 0.650 0.010 0.098 FBgn0032307 17139 CG17139 26007 1.110 1.465 0.148 0.005 0.142 FBgn0039941 17167 CG17167 27245 0.991 0.118 0.906 0.017 0.113 FBgn0035144 17181 CG17181 26775 1.218 2.901 0.005 0.004 0.097 FBgn0038741 17186 CG17186 27079 1.269 3.574 <0.001 0.004 0.061 FBgn0031494 17219 CG17219 28769 0.902 1.306 0.197 0.005 0.131 FBgn0032719 17321 CG17321 29618 0.960 0.537 0.593 0.009 0.117 FBgn0028895 17328 CG17328 28898 1.057 0.718 0.476 0.006 0.033 FBgn0036396 17359 CG17359 26776 0.992 0.109 0.913 0.025 0.112 FBgn0033934 17385 CG17385 31954 1.113 2.120 0.039 0.009 0.103
106
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0033936 17386 CG17386 25956 0.975 0.364 0.717 0.017 0.129 FBgn0032999 17528 CG17528 26292 0.918 1.214 0.230 0.006 0.036 FBgn0032763 17568 CG17568 31955 1.006 0.111 0.912 0.025 0.117 FBgn0031544 17593 CG17593 28344 0.892 1.502 0.138 0.006 0.092 FBgn0031362 17646 CG17646 26008 1.032 0.479 0.634 0.013 0.115 FBgn0034883 17664 CG17664 25924 1.330 4.864 <0.001 0.003 0.100 FBgn0033710 17739 CG17739 28770 1.164 2.421 0.018 0.005 0.093 FBgn0032240 17768 CG17768 29537 0.947 0.781 0.438 0.007 0.089 FBgn0038547 17803 CG17803 29367 0.849 2.226 0.030 0.005 0.052 FBgn0038548 17806 CG17806 31956 1.421 7.874 <0.001 0.004 0.065 FBgn0038919 17843 CG17843 28023 0.790 3.099 0.003 0.004 0.028 FBgn0032600 17912 CG17912 27996 0.960 0.589 0.558 0.009 0.062 FBgn0034656 17922 CG17922 26009 0.966 0.500 0.619 0.010 0.036 FBgn0039677 18110 CG18110 25810 0.757 3.589 <0.001 0.004 0.084 FBgn0037553 18249 CG18249 28521 0.816 2.715 0.009 0.004 0.061 FBgn0036725 18265 CG18265 28560 0.985 0.194 0.847 0.050 0.143 FBgn0033836 18278 CG18278 28520 1.107 1.262 0.212 0.005 0.120 FBgn0037931 18476 CG18476 26710 1.108 1.473 0.146 0.004 0.065 FBgn0028518 18480 CG18480 28529 1.282 3.841 <0.001 0.003 0.179 FBgn0032202 18619 CG18619 29542 1.109 1.483 0.143 0.004 0.047 FBgn0050020 30020 CG30020 29368 0.831 2.475 0.016 0.006 0.099 FBgn0050046 30046 CG30046 28651 0.871 1.751 0.085 0.004 0.051 FBgn0260475 30059 CG30059 28607 0.997 0.041 0.968 0.050 0.100 FBgn0050060 30060 CG30060 28555 0.961 0.530 0.598 0.010 0.184 FBgn0050089 30089 CG30089 28321 0.958 0.566 0.574 0.009 0.081 FBgn0050172 30172 CG30172 29423 1.193 2.816 0.007 0.004 0.178 FBgn0050203 30203 CG30203 28772 1.023 0.301 0.765 0.017 0.103 FBgn0050340 30340 CG30340 28652 0.971 0.398 0.692 0.013 0.118 FBgn0050379 30379 CG30379 28988 0.936 0.818 0.417 0.006 0.080 FBgn0050382 30382 CG30382 27557 0.949 0.650 0.518 0.007 0.181 FBgn0050414 30414 CG30414 27687 0.946 0.738 0.463 0.006 0.099 FBgn0051065 31065 CG31065 27087 0.953 0.995 0.324 0.009 0.038 FBgn0051103 31103 CG31103 28359 1.005 0.090 0.929 0.025 0.112 FBgn0051105 31105 CG31105 28706 1.001 0.012 0.991 0.050 0.049 FBgn0051106 31106 CG31106 29582 0.923 1.622 0.110 0.006 0.110 FBgn0051183 31183 CG31183 28604 0.973 0.539 0.592 0.013 0.112 FBgn0051235 31235 CG31235 28773 0.869 2.751 0.008 0.005 0.047 FBgn0051272 31272 CG31272 28030 1.015 0.325 0.746 0.017 0.044 FBgn0051302 31302 CG31302 29312 1.052 1.088 0.281 0.007 0.078 FBgn0051324 31324 CG31324 28774 1.026 0.543 0.589 0.010 0.120 FBgn0051460 31460 CG31460 26312 1.262 5.600 <0.001 0.005 0.033 FBgn0051469 31469 CG31469 27550 0.901 2.092 0.041 0.005 0.066 FBgn0051646 31646 CG31646 28654 0.693 6.462 <0.001 0.004 0.078 FBgn0051665 31665 CG31665 29457 1.215 4.180 <0.001 0.005 0.086 FBgn0051716 31716 CG31716 28775 0.844 3.284 0.002 0.004 0.063
107
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0051720 31720 CG31720 28017 1.018 0.256 0.799 0.025 0.097 FBgn0051809 31809 CG31809 29424 1.312 4.641 <0.001 0.003 0.129 FBgn0051955 31955 CG31955 31967 1.110 2.226 0.030 0.005 0.086 FBgn0052000 32000 CG32000 30499 1.039 0.757 0.453 0.017 0.077 FBgn0052105 32105 CG32105 31905 1.105 2.123 0.039 0.005 0.081 FBgn0042177 32164 CG32164 28692 1.238 3.530 <0.001 0.004 0.085 FBgn0052204 32204 CG32204 28308 1.132 1.957 0.055 0.006 0.148 FBgn0052264 32264 CG32264 28340 1.151 2.240 0.029 0.006 0.103 FBgn0260480 32295 CG32295 28956 1.218 3.245 0.002 0.004 0.109 FBgn0052372 32372 CG32372 29527 1.237 3.323 0.002 0.013 0.086 FBgn0052432 32432 CG32432 29621 1.357 5.013 <0.001 0.005 0.035 FBgn0052506 32506 CG32506 28776 1.245 3.438 0.001 0.006 0.045 FBgn0052532 32532 CG32532 26750 1.254 3.381 0.001 0.009 0.093 FBgn0052547 32547 CG32547 28621 1.152 2.129 0.037 0.025 0.052 FBgn0052626 32626 CG32626 25962 1.232 3.254 0.002 0.017 0.140 FBgn0052702 32702 CG32702 28702 1.379 5.326 <0.001 0.005 0.069 FBgn0052778 32778 CG43284 26715 1.399 5.596 <0.001 0.004 0.131 FBgn0052792 32792 CG32792 25814 1.404 5.673 <0.001 0.004 0.126 FBgn0023531 32809 CG32809 28822 1.345 4.837 <0.001 0.006 0.119 FBgn0025394 32810 CG32810 25889 1.238 3.340 0.001 0.010 0.160 FBgn0052944 32944 CG32944 27290 1.136 1.818 0.074 0.050 0.100 FBgn0053017 33017 CG33017 31927 1.254 5.571 <0.001 0.004 0.048 FBgn0053143 33143 CG33143 28823 1.283 3.382 0.001 0.007 0.072 FBgn0053159 33159 CG33159 26306 1.472 6.630 <0.001 0.003 0.129 FBgn0053213 33213 CG33213 29458 1.057 0.842 0.403 0.025 0.061 FBgn0053231 33231 CG33231 28914 1.365 6.192 <0.001 0.004 0.042 FBgn0053289 33289 CG33289 25816 1.353 5.648 <0.001 0.004 0.115 FBgn0053470 33470 CG33470 28540 1.183 3.109 0.003 0.009 0.089 FBgn0053523 33523 CG33523 25838 1.295 5.004 <0.001 0.005 0.090 FBgn0053639 33639 CG33639 28614 1.426 7.226 <0.001 0.003 0.101 FBgn0053673 33673 CG33673 28051 1.339 5.754 <0.001 0.004 0.098 FBgn0053696 33696 CG33696 28380 1.117 1.987 0.051 0.013 0.049 FBgn0053958 33958 CG33958 30507 1.331 5.610 <0.001 0.005 0.087 FBgn0053960 33960 CG33960 28932 1.242 3.407 0.001 0.007 0.138 FBgn0053967 33967 CG33967 28683 1.291 4.942 <0.001 0.006 0.060 FBgn0085383 34354 CG34354 27301 1.085 1.444 0.154 0.017 0.072 FBgn0085386 34357 CG34357 28524 1.014 0.237 0.813 0.050 0.058 FBgn0085398 34369 CG34369 25892 1.132 2.241 0.029 0.010 0.031 FBgn0085399 34370 CG34370 28729 1.227 3.502 <0.001 0.006 0.171 FBgn0085405 34376 CG34376 29371 0.995 0.066 0.948 0.050 0.055 FBgn0085425 34396 CG34396 26011 1.187 3.171 0.002 0.007 0.164 FBgn0085431 34402 CG34402 26016 1.111 2.153 0.036 0.009 0.066 FBgn0085440 34411 CG34411 28655 1.220 2.754 0.008 0.013 0.090 FBgn0250862 42237 CG42237 25877 1.213 2.662 0.010 0.017 0.095 FBgn0259145 42260 CG42260 26723 1.401 5.017 <0.001 0.006 0.089
108
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0259166 42271 CG42271 29411 1.330 4.125 <0.001 0.009 0.115 FBgn0259221 42321 CG42321 28558 1.311 3.666 <0.001 0.010 0.076 FBgn0259242 42340 CG42340 27257 1.095 1.194 0.237 0.050 0.124 FBgn0259244 42342 CG42342 28648 1.520 6.510 <0.001 0.004 0.084 FBgn0259677 42346 CG42346 28958 1.335 4.196 <0.001 0.007 0.132 FBgn0259678 42347 CG42347 26735 1.422 4.975 <0.001 0.006 0.088 FBgn0259701 42355 CG42355 28937 1.210 2.632 0.011 0.025 0.139 FBgn0259712 42366 CG42366 27294 1.502 6.278 <0.001 0.005 0.102 FBgn0260795 42575 CG42575 29408 1.779 9.187 <0.001 0.004 0.183 FBgn0260971 42594 CG42594 25888 1.362 6.252 <0.001 0.005 0.138 FBgn0036312 42709 CG42709 28581 1.107 2.766 0.008 0.007 0.038 FBgn0261698 42732 CG42732 32034 0.936 1.671 0.101 0.010 0.043 FBgn0262617 43143 CG43143 31885 1.157 4.067 <0.001 0.005 0.090 FBgn0000303 12345 Cha 25856 1.206 3.713 <0.001 0.005 0.053 FBgn0000319 9012 Chc 27530 1.127 1.935 0.059 0.005 0.099 FBgn0067312 33320 CheB38a 28348 1.171 3.651 <0.001 0.007 0.129 FBgn0067311 33321 CheB38b 28014 1.071 1.512 0.136 0.050 0.048 FBgn0066293 33351 CheB42b 27255 1.159 3.400 0.001 0.009 0.057 FBgn0066292 33350 CheB42c 27089 1.088 1.876 0.066 0.017 0.076 FBgn0014141 3937 cher 26307 1.236 2.886 0.006 0.013 0.099 FBgn0029504 12690 CHES-1-like 26760 1.160 2.877 0.006 0.006 0.074 FBgn0024248 5686 chico 28329 1.189 2.926 0.005 0.005 0.091 FBgn0086758 31666 chinmo 26777 1.240 7.355 <0.001 0.004 0.060 FBgn0028387 5229 chm 27027 1.051 0.738 0.464 0.010 0.066 FBgn0036805 4108 Chmp1 28906 1.293 3.584 <0.001 0.006 0.102 FBgn0035589 4618 CHMP2B 28531 1.081 1.193 0.238 0.009 0.088 FBgn0015371 11798 chn 26779 0.964 0.512 0.611 0.017 0.056 FBgn0015372 3870 chrw 28033 1.280 3.429 0.001 0.007 0.121 FBgn0004859 2125 ci 28984 1.377 5.522 <0.001 0.004 0.218 FBgn0026084 4944 cib 28003 0.965 0.656 0.515 0.017 0.104 FBgn0028386 5067 cic 25995 1.327 6.099 <0.001 0.005 0.049 FBgn0000316 2945 cin 28949 1.036 0.534 0.596 0.050 0.076 FBgn0033313 8639 Cirl 27524 1.151 1.619 0.112 0.007 0.117 FBgn0044323 7392 Cka 28927 0.861 2.122 0.038 0.005 0.146 FBgn0015024 2028 CkIα 25786 1.185 2.704 0.009 0.006 0.078 FBgn0024814 6948 Clc 27496 1.148 2.259 0.028 0.005 0.077 FBgn0033755 8594 ClC-b 25826 0.850 0.055 FBgn0036566 5284 ClC-c 27034 1.198 2.874 0.006 0.005 0.160 FBgn0026255 8681 clumsy 28351 1.107 1.081 0.285 0.025 0.211 FBgn0000330 3035 cm 27282 1.166 2.432 0.018 0.009 0.056 FBgn0000338 17894 cnc 25984 1.293 5.997 <0.001 0.004 0.087 FBgn0014462 42701 Cng 26014 1.365 5.136 <0.001 0.004 0.153 FBgn0029090 9176 cngl 28684 0.999 0.011 0.991 0.050 0.021 FBgn0010105 17943 comm 28381 1.185 2.698 0.009 0.005 0.068 FBgn0005775 7503 Con 28967 1.108 1.444 0.155 0.009 0.111
109
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0037240 1084 Cont 28923 1.114 1.670 0.101 0.017 0.103 FBgn0261269 8561 conv 28578 0.909 0.034 FBgn0037574 9613 Coq2 27054 1.220 3.528 <0.001 0.006 0.071 FBgn0010434 11949 cora 28933 1.090 1.117 0.269 0.009 0.219 FBgn0019624 14724 CoVa 27548 1.187 3.365 0.001 0.006 0.083 FBgn0011570 17158 cpb 26298 1.182 2.655 0.010 0.005 0.112 FBgn0261714 4795 Cpn 31881 1.295 4.729 <0.001 0.005 0.047 FBgn0000363 31243 cpo 28360 1.179 2.611 0.012 0.006 0.097 FBgn0051044 31044 CR31044 28653 1.069 0.935 0.353 0.005 0.058 FBgn0259685 6383 crb 27697 0.941 1.049 0.299 0.017 0.081 FBgn0000370 8669 crc 25985 1.190 2.042 0.046 0.006 0.293 FBgn0004396 7450 CrebA 27648 0.979 0.286 0.776 0.017 0.182 FBgn0014467 6103 CrebB-17A 29332 1.374 2.837 0.007 0.009 0.074 FBgn0023023 1411 CRMP 31876 0.897 0.984 0.329 0.006 0.094 FBgn0000377 3193 crn 29535 1.324 3.968 <0.001 0.005 0.141 FBgn0014143 5069 croc 27071 1.215 4.011 <0.001 0.006 0.051 FBgn0001994 7664 crp 31896 1.059 1.230 0.224 0.017 0.094 FBgn0025680 3772 cry 25859 1.036 0.437 0.664 0.050 0.110 FBgn0013767 3302 Crz 25999 1.341 4.171 <0.001 0.005 0.150 FBgn0027057 3889 CSN1b 27303 1.251 3.067 0.003 0.010 0.107 FBgn0027053 14884 CSN5 28732 1.079 1.416 0.163 0.013 0.090 FBgn0004198 11387 ct 29625 0.874 2.011 0.049 0.006 0.073 FBgn0036478 6854 CTPsyn 31924 0.994 0.126 0.900 0.050 0.122 FBgn0032956 1512 Cul-2 30494 1.184 2.338 0.023 0.013 0.049 FBgn0261268 42616 Cul-3 28899 1.280 3.948 <0.001 0.004 0.114 FBgn0000392 11181 cup 26732 0.863 2.188 0.033 0.006 0.068 FBgn0259938 17100 cwo 26318 1.048 0.696 0.489 0.050 0.099 FBgn0023094 8727 cyc 29400 1.187 2.011 0.050 0.006 0.080 FBgn0000404 5940 CycA 29313 1.028 0.408 0.685 0.017 0.149 FBgn0010315 9096 CycD 27718 0.964 0.553 0.583 0.025 0.067 FBgn0010382 3938 CycE 29314 1.059 0.717 0.477 0.025 0.070 FBgn0039858 11525 CycG 29315 0.875 2.255 0.028 0.009 0.081 FBgn0030304 11715 Cyp4g15 28077 1.042 0.749 0.457 0.025 0.068 FBgn0000411 5893 D 26217 1.035 0.533 0.596 0.010 0.170 FBgn0086896 10595 d 27664 0.818 2.906 0.005 0.004 0.127 FBgn0000412 9745 D1 28616 1.023 0.342 0.734 0.050 0.099 FBgn0053517 33517 D2R 26001 1.111 2.366 0.021 0.013 0.054 FBgn0033015 2682 d4 28623 1.082 1.202 0.235 0.025 0.099 FBgn0000413 5102 da 29326 1.003 0.054 0.957 0.050 0.137 FBgn0005677 4952 dac 26758 0.817 3.224 0.002 0.006 0.056 FBgn0020493 5201 Dad 26235 0.940 0.816 0.418 0.009 0.175 FBgn0030093 7055 dalao 26218 1.048 0.730 0.469 0.050 0.165 FBgn0011577 4974 dally 28747 1.020 0.370 0.713 0.025 0.094 FBgn0039283 13651 danr 28378 0.947 0.956 0.343 0.017 0.091 FBgn0023388 1099 Dap160 25879 1.184 2.695 0.009 0.006 0.119
110
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0015582 12390 dare 28743 1.130 2.344 0.023 0.007 0.042 FBgn0019643 3318 Dat 26243 1.268 3.276 0.002 0.009 0.049 FBgn0261723 42234 Dbx 31904 0.857 3.715 <0.001 0.006 0.013 FBgn0002413 2048 dco 27719 1.283 4.145 <0.001 0.004 0.107 FBgn0010501 5370 Dcp-1 28909 0.785 3.120 0.003 0.004 0.041 FBgn0039016 4792 Dcr-1 28598 0.856 1.610 0.114 0.005 0.106 FBgn0034246 6493 Dcr-2 27486 0.881 2.094 0.041 0.009 0.061 FBgn0000422 10697 Ddc 27030 1.099 1.411 0.163 0.007 0.062 FBgn0015075 9054 Ddx1 27531 1.106 1.605 0.115 0.007 0.273 FBgn0029131 33134 debcl 27083 1.262 5.594 <0.001 0.005 0.066 FBgn0010385 1385 Def 29524 1.247 3.618 <0.001 0.005 0.105 FBgn0036038 18176 defl 27720 1.065 0.956 0.343 0.017 0.056 FBgn0008649 5441 dei 25973 0.691 4.489 <0.001 0.004 0.092 FBgn0026533 5935 Dek 28696 1.220 3.404 0.001 0.004 0.046 FBgn0000439 2189 Dfd 26751 1.088 1.352 0.182 0.013 0.110 FBgn0085390 34361 Dgk 29459 1.191 2.697 0.009 0.013 0.077 FBgn0012344 8348 Dh 25804 0.861 0.192 FBgn0052843 32843 Dh31-R1 25925 1.242 5.172 <0.001 0.006 0.087 FBgn0033932 8422 Dh44-R1 28780 0.967 0.147 FBgn0010349 7507 Dhc64C 28749 1.185 2.467 0.017 0.005 0.070 FBgn0011202 1768 dia 28541 1.160 2.341 0.023 0.010 0.112 FBgn0015933 2146 didum 28620 1.365 3.200 0.002 0.006 0.155 FBgn0011274 6794 Dif 30513 0.964 0.538 0.593 0.025 0.092 FBgn0030891 7098 dik 28905 1.106 1.618 0.112 0.006 0.067 FBgn0040466 9771 Dip2 27265 0.992 0.129 0.898 0.025 0.155 FBgn0040465 12767 Dip3 27067 1.092 1.662 0.102 0.010 0.120 FBgn0000459 9908 disco 28659 0.948 0.833 0.408 0.010 0.117 FBgn0029088 2019 disp 27247 1.223 3.265 0.002 0.005 0.055 FBgn0000463 3619 Dl 28032 1.143 1.755 0.085 0.017 0.055 FBgn0260632 6667 dl 27650 1.278 4.195 <0.001 0.005 0.130 FBgn0001624 1725 dlg1 25780 1.166 2.438 0.018 0.007 0.127 FBgn0000157 3629 Dll 29337 1.297 3.635 <0.001 0.006 0.144 FBgn0024510 32315 dlt 27288 1.144 6.337 <0.001 0.004 0.052 FBgn0000472 10798 dm 25784 1.074 1.064 0.292 0.010 0.069 FBgn0021825 8269 Dmn 28596 0.964 0.047 FBgn0038851 5737 dmrt93B 27657 0.906 1.092 0.280 0.007 0.152 FBgn0039683 15504 dmrt99B 31982 0.926 1.200 0.236 0.009 0.163 FBgn0011581 6440 Dms 26245 0.847 2.706 0.009 0.006 0.040 FBgn0035331 8985 DmsR-1 27529 1.048 0.721 0.474 0.017 0.076 FBgn0035329 13803 DmsR-2 25832 0.993 0.133 0.895 0.050 0.067 FBgn0000479 32498 dnc 27250 1.344 3.072 0.003 0.010 0.092 FBgn0038916 6560 dnd 27488 1.118 1.782 0.081 0.009 0.098 FBgn0028789 5133 Doc1 31931 1.042 0.940 0.351 0.025 0.034 FBgn0035954 5093 Doc3 31932 0.919 1.797 0.078 0.010 0.063 FBgn0010583 3727 dock 27728 0.835 2.396 0.020 0.005 0.091
111
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0043903 14226 dome 28983 1.226 4.060 <0.001 0.004 0.089 FBgn0035538 18314 DopEcR 31981 1.242 4.522 <0.001 0.005 0.061 FBgn0015129 18741 DopR2 26018 0.900 1.463 0.149 0.010 0.108 FBgn0027835 5170 Dp1 31966 0.720 6.240 <0.001 0.004 0.058 FBgn0010109 8704 dpn 26320 1.107 1.150 0.256 0.017 0.102 FBgn0000490 9885 dpp 25782 0.963 0.589 0.558 0.017 0.083 FBgn0052057 32057 dpr10 27991 1.297 5.133 <0.001 0.006 0.095 FBgn0085414 34385 dpr12 28782 1.275 3.872 <0.001 0.005 0.070 FBgn0029974 10946 dpr14 29626 0.916 1.340 0.186 0.007 0.129 FBgn0030723 14948 dpr18 29604 1.131 1.915 0.061 0.009 0.117 FBgn0035170 12191 dpr20 28293 1.208 3.749 <0.001 0.005 0.160 FBgn0037908 5308 dpr5 29627 0.908 1.008 0.318 0.007 0.046 FBgn0052600 32600 dpr8 28744 1.072 1.543 0.129 0.025 0.051 FBgn0034407 10794 DptB 28975 0.840 2.556 0.013 0.005 0.089 FBgn0000492 1897 Dr 26224 0.829 3.453 0.001 0.007 0.119 FBgn0052666 32666 Drak 29449 1.388 5.441 <0.001 0.004 0.128 FBgn0015664 5838 Dref 31941 1.256 5.708 <0.001 0.005 0.041 FBgn0028407 8364 Drep-3 28628 1.068 0.787 0.435 0.009 0.058 FBgn0004638 6033 drk 27563 1.075 1.285 0.204 0.007 0.048 FBgn0015380 17348 drl 29602 0.887 1.925 0.061 0.017 0.134 FBgn0033791 3915 Drl-2 25961 1.094 1.678 0.099 0.006 0.072 FBgn0026722 8730 drosha 27704 0.799 4.031 <0.001 0.004 0.077 FBgn0026479 3210 Drp1 27682 1.054 1.048 0.299 0.009 0.087 FBgn0000497 17941 ds 28008 1.057 0.962 0.342 0.050 0.038 FBgn0033159 17800 Dscam 29628 0.921 1.349 0.185 0.025 0.088 FBgn0015381 9019 dsf 29373 1.054 0.808 0.422 0.007 0.068 FBgn0000500 18090 Dsk 25869 1.097 1.397 0.167 0.006 0.099 FBgn0010269 15793 Dsor1 28685 1.122 2.076 0.044 0.013 0.124 FBgn0011764 12223 Dsp1 31960 1.070 1.854 0.070 0.017 0.038 FBgn0000504 11094 dsx 26716 1.231 3.487 0.001 0.007 0.095 FBgn0023090 31623 dtr 25812 0.926 1.063 0.291 0.009 0.138 FBgn0000996 8171 dup 29562 1.182 2.105 0.041 0.007 0.155 FBgn0020307 5799 dve 26225 1.278 4.593 <0.001 0.013 0.097 FBgn0000524 3929 dx 27041 0.870 2.309 0.025 0.006 0.078 FBgn0040228 10846 dyn-p25 30491 1.029 0.428 0.671 0.025 0.165 FBgn0039411 32474 dys 26321 1.117 1.686 0.096 0.005 0.063 FBgn0000527 3331 e 28612 1.221 4.262 <0.001 0.004 0.095 FBgn0000581 7776 E(Pc) 28686 1.208 1.826 0.074 0.013 0.062 FBgn0000591 8365 E(spl) 26322 1.245 2.823 0.007 0.005 0.085 FBgn0260243 11971 E(var)3-9 31948 0.916 1.913 0.062 0.013 0.029 FBgn0000629 6502 E(z) 27993 1.344 5.189 <0.001 0.006 0.102 FBgn0020445 3327 E23 26252 1.110 2.127 0.038 0.005 0.040 FBgn0011766 6376 E2f 27564 1.181 3.126 0.003 0.004 0.070 FBgn0024371 1071 E2f2 27995 1.223 4.492 <0.001 0.006 0.085 FBgn0026441 4913 ear 28068 1.284 4.976 <0.001 0.010 0.059
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0243514 6124 eater 25863 1.136 2.347 0.022 0.005 0.095 FBgn0027066 3265 Eb1 28605 1.016 0.303 0.763 0.017 0.009 FBgn0000546 1765 EcR 29374 1.164 3.305 0.002 0.009 0.020 FBgn0036735 6311 Edc3 28584 1.097 1.670 0.101 0.006 0.050 FBgn0032198 4912 eEF1δ 29605 1.150 2.649 0.011 0.006 0.084 FBgn0011217 7425 eff 31875 1.014 1.170 0.247 0.006 0.041 FBgn0000560 7383 eg 29629 1.242 2.798 0.007 0.006 0.178 FBgn0003731 10079 Egfr 25781 0.930 1.182 0.243 0.009 0.108 FBgn0001404 9659 egh 28687 1.050 0.746 0.459 0.009 0.158 FBgn0000562 4051 egl 28969 0.808 3.419 0.001 0.004 0.041 FBgn0000564 5400 Eh 26244 set up cross 2x and saw wing phenotype both times FBgn0026250 8053 eIF-1A 29316 1.046 0.532 0.597 0.017 0.161 FBgn0037249 9805 eIF3-S10 27565 0.814 2.781 0.007 0.004 0.079 FBgn0005640 10579 Eip63E 28901 0.914 1.448 0.154 0.007 0.152 FBgn0000567 32180 Eip74EF 29353 1.083 1.196 0.236 0.007 0.088 FBgn0000568 8127 Eip75B 29525 1.009 0.088 0.930 0.050 0.158 FBgn0004865 18023 Eip78C 26718 1.060 0.867 0.389 0.010 0.126 FBgn0260400 4262 elav 28371 0.975 0.412 0.682 0.013 0.117 FBgn0011589 5076 elk 25821 1.039 0.687 0.495 0.050 0.080 FBgn0000575 1007 emc 26738 1.240 4.840 <0.001 0.005 0.072 FBgn0000576 2988 ems 28726 1.140 2.472 0.017 0.006 0.064 FBgn0000577 9015 en 26752 1.019 0.282 0.779 0.025 0.058 FBgn0038659 14296 endoA 27679 1.029 0.364 0.717 0.025 0.155 FBgn0034433 9834 endoB 27537 0.863 2.292 0.026 0.005 0.074 FBgn0000579 17654 Eno 26300 1.136 2.303 0.027 0.010 0.103 FBgn0034975 11290 enok 29518 1.072 1.089 0.282 0.017 0.030 FBgn0085421 34392 Epac 29317 1.165 2.665 0.009 0.006 0.113 FBgn0025936 1511 Eph 28511 1.233 4.696 <0.001 0.006 0.060 FBgn0040324 1862 Ephrin 27039 1.023 0.456 0.650 0.025 0.069 FBgn0035060 16932 Eps-15 29578 1.201 3.418 0.001 0.006 0.063 FBgn0027496 9543 εCOP 28890 1.144 2.152 0.036 0.005 0.157 FBgn0031375 31670 erm 26778 0.793 4.346 <0.001 0.004 0.058 FBgn0035849 7404 ERR 27085 1.099 1.869 0.067 0.013 0.041 FBgn0001981 3758 esg 28514 1.131 2.332 0.024 0.005 0.091 FBgn0028738 18105 ETH 26242 1.051 0.949 0.347 0.010 0.090 FBgn0038874 5911 ETHR 28783 1.051 0.775 0.442 0.050 0.126 FBgn0039225 6892 Ets96B 31935 1.129 2.847 0.006 0.005 0.047 FBgn0004510 6338 Ets97D 25795 0.957 0.746 0.459 0.013 0.145 FBgn0005659 5583 Ets98B 28700 1.305 5.351 <0.001 0.006 0.116 FBgn0000606 2328 eve 28734 1.254 4.918 <0.001 0.006 0.100 FBgn0004583 4114 ex 28703 0.951 0.872 0.387 0.010 0.068 FBgn0250753 2922 exba 27248 1.092 1.778 0.081 0.006 0.039 FBgn0000611 8933 exd 29338 0.859 2.829 0.007 0.006 0.141 FBgn0035892 7127 exo70 28041 1.157 2.964 0.005 0.007 0.111 FBgn0260946 6095 exo84 28712 1.156 2.693 0.009 0.004 0.088
113
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0000615 8994 exu 28902 0.864 2.729 0.009 0.009 0.088 FBgn0005558 1464 ey 29339 1.265 5.349 <0.001 0.005 0.141 FBgn0000320 9554 eya 28733 1.064 0.955 0.343 0.006 0.102 FBgn0000625 10488 eyg 26226 1.045 0.760 0.451 0.013 0.068 FBgn0020440 10023 Fak56D 29323 1.200 3.372 0.001 0.004 0.100 FBgn0028379 7919 fan 28044 1.055 1.047 0.300 0.017 0.055 FBgn0000635 3665 Fas2 28990 1.073 1.296 0.201 0.007 0.143 FBgn0004896 3668 fd59A 31937 1.288 4.623 <0.001 0.005 0.095 FBgn0036134 11799 fd68A 27994 0.950 0.838 0.406 0.017 0.057 FBgn0037735 16899 fd85E 26774 0.918 0.948 0.347 0.006 0.087 FBgn0004898 11922 fd96Cb 26761 1.081 1.012 0.318 0.009 0.199 FBgn0045063 8824 fdl 28298 0.827 3.484 0.001 0.005 0.048 FBgn0030241 11207 feo 28926 0.927 1.095 0.279 0.013 0.098 FBgn0037475 33323 Fer1 27737 1.127 2.047 0.044 0.007 0.120 FBgn0038402 5952 Fer2 28697 1.339 5.942 <0.001 0.005 0.116 FBgn0037937 6913 Fer3 25974 1.070 1.208 0.232 0.009 0.045 FBgn0085397 34368 Fili 28568 1.372 5.989 <0.001 0.003 0.110 FBgn0035498 14991 Fit1 25966 1.090 1.526 0.135 0.013 0.068 FBgn0000658 10917 fj 28009 1.039 0.651 0.518 0.017 0.081 FBgn0029174 4535 FKBP59 28349 1.128 2.253 0.029 0.007 0.051 FBgn0000659 10002 fkh 27072 1.129 2.171 0.035 0.006 0.070 FBgn0024555 9351 flfl 31961 1.045 0.987 0.328 0.013 0.035 FBgn0000709 1484 fliI 27566 1.337 6.806 <0.001 0.004 0.049 FBgn0028734 6203 Fmr1 27484 1.076 1.310 0.195 0.006 0.098 FBgn0000715 2346 Fmrf 25870 1.133 2.571 0.013 0.010 0.074 FBgn0086675 4396 fne 28784 1.080 1.410 0.164 0.009 0.140 FBgn0011591 10580 fng 25947 0.867 2.238 0.030 0.005 0.060 FBgn0038197 3143 foxo 25997 1.009 0.182 0.857 0.050 0.064 FBgn0035385 2114 FR 25858 1.069 1.340 0.186 0.025 0.064 FBgn0030897 5744 Frq1 27696 1.213 3.724 <0.001 0.017 0.089 FBgn0083228 5907 Frq2 28711 1.302 5.290 <0.001 0.007 0.070 FBgn0000986 2637 Fs(2)Ket 27567 1.183 3.556 <0.001 0.007 0.023 FBgn0016650 7665 Fsh 27509 1.105 1.988 0.052 0.010 0.102 FBgn0043010 4643 Fsn 30515 0.927 0.731 0.468 0.010 0.120 FBgn0001075 3352 ft 29566 1.062 1.195 0.237 0.007 0.094 FBgn0001078 4059 ftz-f1 27659 1.041 0.720 0.475 0.017 0.127 FBgn0029173 9233 fu2 28554 0.877 1.834 0.072 0.006 0.098 FBgn0004509 10772 Fur1 25837 0.859 2.364 0.022 0.005 0.043 FBgn0004598 18734 Fur2 25959 1.102 1.470 0.146 0.006 0.092 FBgn0001083 1500 fw 28307 1.133 2.690 0.009 0.013 0.071 FBgn0004373 7004 fwd 29396 1.351 6.639 <0.001 0.005 0.137 FBgn0016797 9739 fz2 27568 0.836 2.453 0.017 0.005 0.108 FBgn0040372 2995 G9a 29541 0.892 1.970 0.054 0.006 0.132 FBgn0260446 15274 GABA-B-R1 28353 1.259 4.392 <0.001 0.006 0.053 FBgn0027575 6706 GABA-B-R2 27699 1.053 0.908 0.368 0.010 0.076
114
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0031275 3022 GABA-B-R3 26729 1.024 0.472 0.639 0.010 0.092 FBgn0031495 17257 GABPI 27028 1.183 3.104 0.003 0.006 0.047 FBgn0004516 14994 Gad1 28079 1.124 2.107 0.042 0.007 0.158 FBgn0028968 1528 γCop 28889 0.972 0.677 0.501 0.017 0.095 FBgn0028552 3988 γSnap 28707 1.047 0.832 0.409 0.013 0.041 FBgn0001092 8893 Gapdh2 26302 0.831 3.396 0.001 0.005 0.058 FBgn0030627 42739 gce 26323 1.073 1.183 0.241 0.009 0.070 FBgn0014179 12245 gcm 28913 0.875 1.559 0.127 0.006 0.171 FBgn0019809 3858 gcm2 28904 1.068 1.204 0.234 0.009 0.114 FBgn0004868 4422 Gdi 27309 1.231 4.065 <0.001 0.005 0.081 FBgn0021873 9491 Gef26 28928 1.009 0.134 0.894 0.050 0.140 FBgn0050011 30011 gem 26214 1.161 2.320 0.023 0.004 0.070 FBgn0086778 32538 gfA 27251 1.245 3.954 <0.001 0.003 0.115 FBgn0250823 6963 gish 28066 1.049 0.919 0.362 0.050 0.082 FBgn0001108 9206 Gl 27721 1.405 6.101 <0.001 0.005 0.184 FBgn0004618 7672 gl 26780 1.228 4.306 <0.001 0.006 0.044 FBgn0001987 3903 Gli 31869 0.956 0.234 0.816 0.050 0.088 FBgn0004619 8442 Glu-RI 27521 1.052 1.035 0.305 0.025 0.046 FBgn0028431 4481 Glu-RIB 27673 0.993 0.128 0.899 0.025 0.037 FBgn0004620 6992 GluRIIA 27497 1.055 1.046 0.301 0.025 0.070 FBgn0020429 7234 GluRIIB 28718 1.357 6.745 <0.001 0.004 0.105 FBgn0046113 4226 GluRIIC 25836 0.965 0.617 0.540 0.025 0.079 FBgn0051201 31201 GluRIIE 25942 1.208 2.998 0.004 0.003 0.049 FBgn0004919 2679 gol 28785 1.160 3.102 0.003 0.009 0.072 FBgn0001122 2204 G-oα47A 28010 1.086 1.660 0.103 0.017 0.054 FBgn0260798 40129 Gprk1 28354 1.235 3.781 <0.001 0.004 0.136 FBgn0041233 33151 Gr59e 25815 1.185 2.974 0.004 0.004 0.096 FBgn0001134 7446 Grd 29589 1.252 4.761 <0.001 0.005 0.077 FBgn0259211 42311 grh 28820 1.179 2.878 0.005 0.005 0.126 FBgn0025595 11325 GRHR 29577 1.061 0.764 0.449 0.013 0.104 FBgn0036278 10698 GRHRII 26017 0.939 1.034 0.306 0.010 0.069 FBgn0029830 14447 Grip 28334 1.103 1.277 0.209 0.007 0.057 FBgn0001138 9656 grn 27658 0.958 0.627 0.533 0.013 0.073 FBgn0011598 17161 grp 27277 1.169 2.870 0.007 0.007 0.061 FBgn0001148 3388 gsb 29600 0.990 0.184 0.854 0.025 0.111 FBgn0001147 2692 gsb-n 28078 1.009 0.166 0.869 0.050 0.098 FBgn0010226 8938 GstS1 28885 0.836 3.299 0.002 0.006 0.076 FBgn0001123 2835 G-sα60A 29576 1.315 6.116 <0.001 0.005 0.067 FBgn0001150 7952 gt 26742 1.109 2.052 0.045 0.009 0.047 FBgn0260654 42636 Gyc76C 28660 1.217 3.501 <0.001 0.004 0.072 FBgn0038295 4154 Gyc88E 28608 1.148 2.629 0.011 0.005 0.091 FBgn0038435 14885 Gyc-89Da 30502 1.045 0.711 0.481 0.025 0.066 FBgn0038436 14886 Gyc-89Db 29529 1.141 2.383 0.022 0.006 0.078 FBgn0013972 1912 Gycα99B 28748 1.122 2.369 0.022 0.013 0.041 FBgn0013973 1470 Gycβ100B 28786 1.381 7.690 <0.001 0.004 0.088
115
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0010223 12232 Gα73B 25930 0.809 2.376 0.023 0.006 0.088 FBgn0030011 10763 Gβ5 28310 0.989 0.178 0.860 0.050 0.086 FBgn0004623 8770 Gβ76C 28507 1.012 0.238 0.813 0.050 0.078 FBgn0004921 8261 Gγ1 25934 0.982 0.210 0.834 0.025 0.189 FBgn0028433 3694 Gγ30A 25932 0.971 0.511 0.612 0.050 0.039 FBgn0001169 5460 H 27315 1.338 5.912 <0.001 0.006 0.047 FBgn0001168 6494 h 27738 1.043 0.733 0.467 0.017 0.085 FBgn0031349 14351 haf 28528 1.405 4.382 <0.001 0.005 0.204 FBgn0045852 31753 ham 26728 1.020 0.292 0.771 0.017 0.080 FBgn0032209 18144 Hand 28977 1.014 0.204 0.839 0.050 0.103 FBgn0001180 9786 hb 29630 0.951 0.730 0.469 0.010 0.148 FBgn0008636 33152 hbn 31906 1.055 1.200 0.236 0.007 0.050 FBgn0041210 1770 HDAC4 28549 1.084 1.687 0.097 0.017 0.062 FBgn0005619 3454 Hdc 26000 1.210 4.058 <0.001 0.004 0.087 FBgn0010113 15532 hdc 30489 1.111 1.876 0.068 0.009 0.103 FBgn0011771 5837 Hem 29406 1.449 7.870 <0.001 0.004 0.087 FBgn0010303 4353 hep 28710 1.258 4.540 <0.001 0.004 0.139 FBgn0011224 31000 heph 27040 1.160 2.312 0.024 0.004 0.121 FBgn0030899 5927 Her 27654 1.286 5.011 <0.001 0.009 0.114 FBgn0027788 11194 Hey 31898 1.086 2.269 0.028 0.006 0.091 FBgn0001189 3095 hfw 28561 1.115 2.221 0.031 0.005 0.060 FBgn0040318 13475 HGTX 29382 1.038 0.442 0.661 0.017 0.170 FBgn0004644 4637 hh 25794 1.041 0.714 0.478 0.010 0.053 FBgn0010114 2040 hig 28376 1.266 5.169 <0.001 0.006 0.097 FBgn0001197 5499 His2Av 28966 1.056 0.647 0.521 0.013 0.077 FBgn0037950 14723 HisCl1 28013 1.104 1.759 0.086 0.010 0.034 FBgn0030600 32592 hiw 28031 0.944 0.904 0.369 0.010 0.088 FBgn0001203 32688 Hk 28330 1.288 4.634 <0.001 0.003 0.051 FBgn0261434 9768 hkb 31976 1.109 3.144 0.003 0.009 0.024 FBgn0001565 1666 Hlc 28991 1.019 0.376 0.709 0.050 0.029 FBgn0015234 8522 HLH106 25975 0.851 2.814 0.007 0.007 0.069 FBgn0011276 2655 HLH3B 26324 1.314 6.088 <0.001 0.005 0.132 FBgn0011277 3052 HLH4C 25976 0.984 0.303 0.763 0.013 0.079 FBgn0022740 5005 HLH54F 28698 1.088 1.545 0.129 0.025 0.107 FBgn0002609 8346 HLHm3 25977 1.234 2.706 0.009 0.006 0.196 FBgn0002631 6096 HLHm5 26201 1.042 0.720 0.475 0.025 0.098 FBgn0002633 8361 HLHm7 29327 1.296 3.421 0.001 0.005 0.111 FBgn0002733 14548 HLHmβ 26202 1.077 1.295 0.203 0.017 0.088 FBgn0002735 8333 HLHmγ 25978 1.360 4.160 <0.001 0.004 0.069 FBgn0002734 8328 HLHmδ 26203 1.229 4.899 <0.001 0.004 0.062 FBgn0010228 17921 HmgZ 26219 1.107 1.537 0.129 0.005 0.145 FBgn0001208 7399 Hn 29540 1.180 3.407 0.001 0.006 0.051 FBgn0004914 9310 Hnf4 29375 1.175 2.618 0.011 0.004 0.084 FBgn0031649 15624 hoe2 28661 0.998 0.034 0.973 0.050 0.153 FBgn0025777 11324 homer 27271 1.053 0.805 0.425 0.025 0.067
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0034453 11228 hpo 27661 1.095 1.431 0.159 0.010 0.138 FBgn0014859 1864 Hr38 29376 1.136 2.744 0.008 0.010 0.045 FBgn0010229 8676 Hr39 27086 0.936 1.291 0.202 0.013 0.100 FBgn0023546 16902 Hr4 31868 1.308 7.192 <0.001 0.006 0.052 FBgn0000448 33183 Hr46 27253 0.980 0.324 0.747 0.025 0.063 FBgn0015239 7199 Hr78 31990 1.105 2.183 0.034 0.009 0.087 FBgn0015240 11783 Hr96 27992 0.985 0.223 0.824 0.025 0.065 FBgn0015949 9854 hrg 28603 0.882 1.717 0.092 0.006 0.133 FBgn0031450 2903 Hrs 28026 1.416 8.064 <0.001 0.004 0.083 FBgn0053147 33147 Hs3st-A 28618 1.145 2.331 0.023 0.006 0.069 FBgn0001219 4264 Hsc70-4 28709 1.185 3.255 0.002 0.005 0.109 FBgn0001222 5748 Hsf 27070 1.125 1.884 0.066 0.009 0.084 FBgn0051354 5834 Hsp70Bbb 28787 1.367 6.428 <0.001 0.005 0.043 FBgn0001235 17117 hth 27655 1.166 2.819 0.007 0.009 0.130 FBgn0038233 8464 HtrA2 28544 1.058 1.164 0.250 0.017 0.071 FBgn0028374 6371 hug 28705 0.989 0.193 0.848 0.050 0.098 FBgn0024227 6620 ial 28691 1.117 2.014 0.049 0.005 0.101 FBgn0086693 4536 iav 25865 1.012 0.220 0.827 0.017 0.090 FBgn0001250 9623 if 27544 0.959 0.620 0.538 0.017 0.077 FBgn0053527 33527 IFa 29428 1.180 2.901 0.005 0.005 0.096 FBgn0013467 18285 igl 29598 1.161 2.324 0.023 0.004 0.108 FBgn0028428 8585 Ih 29574 0.933 1.343 0.185 0.010 0.059 FBgn0033835 18279 IM10 28518 1.048 0.699 0.487 0.013 0.191 FBgn0025583 18106 IM2 28788 1.184 2.649 0.010 0.003 0.119 FBgn0025776 11551 ind 28662 0.999 0.014 0.989 0.050 0.092 FBgn0027108 4590 inx2 29306 0.995 0.089 0.929 0.050 0.053 FBgn0028373 1448 inx3 30501 0.964 0.554 0.582 0.006 0.076 FBgn0030989 7537 inx5 28042 0.970 0.475 0.637 0.009 0.092 FBgn0027107 17063 inx6 31889 1.340 7.933 <0.001 0.004 0.063 FBgn0027106 2977 inx7 26297 1.212 5.218 <0.001 0.017 0.074 FBgn0016672 3028 Ipp 28028 1.273 6.705 <0.001 0.006 0.073 FBgn0039061 6747 Ir 25823 0.916 1.451 0.153 0.006 0.037 FBgn0031634 15627 Ir25a 29539 1.060 1.041 0.303 0.025 0.089 FBgn0052704 32704 Ir8a 25813 0.981 0.457 0.650 0.025 0.072 FBgn0011774 5247 Irbp 29594 1.040 0.703 0.485 0.013 0.073 FBgn0039081 4370 Irk2 25820 0.987 0.247 0.806 0.025 0.064 FBgn0032706 10369 Irk3 26720 1.209 4.451 <0.001 0.004 0.047 FBgn0035023 13586 itp 25799 0.813 4.200 <0.001 0.005 0.041 FBgn0010051 1063 Itp-r83A 25937 1.080 2.268 0.027 0.006 0.042 FBgn0011225 5695 jar 28064 1.133 2.694 0.01 0.006 0.042 FBgn0036004 3654 Jarid2 26184 1.202 5.193 <0.001 0.010 0.013 FBgn0039350 17383 jigr1 31921 1.047 0.879 0.383 0.013 0.111 FBgn0086655 9397 jing 27024 1.135 2.260 0.028 0.005 0.062 FBgn0028422 18039 KaiRIA 26010 0.855 2.305 0.026 0.007 0.126 FBgn0024889 8548 Kap-α1 27523 1.212 4.547 <0.001 0.004 0.072
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0027338 9423 Kap-α3 27535 1.093 1.561 0.125 0.010 0.157 FBgn0040208 10229 katanin-60 28375 0.999 0.011 0.991 0.050 0.021 FBgn0001297 33956 kay 27722 0.998 0.039 0.969 0.050 0.087 FBgn0033494 33135 KCNQ 27252 0.872 3.000 0.004 0.005 0.061 FBgn0015400 4977 kek2 31874 0.939 1.224 0.226 0.007 0.064 FBgn0001308 7765 Khc 25898 0.985 0.246 0.807 0.017 0.155 FBgn0001316 17046 klar 28313 1.111 1.911 0.062 0.007 0.114 FBgn0017590 6669 klg 28746 1.107 1.848 0.07 0.005 0.053 FBgn0004379 10923 Klp67A 27549 1.186 3.142 0.003 0.006 0.039 FBgn0004381 7293 Klp68D 29410 1.009 0.140 0.889 0.025 0.042 FBgn0013469 12296 klu 28731 1.154 3.742 <0.001 0.005 0.051 FBgn0001319 10197 kn 31916 1.299 6.294 <0.001 0.004 0.054 FBgn0001320 4717 kni 27259 1.130 2.262 0.028 0.006 0.063 FBgn0026063 17216 KP78b 31890 1.217 5.058 <0.001 0.010 0.055 FBgn0001325 3340 Kr 27666 1.220 5.407 <0.001 0.013 0.065 FBgn0040206 1487 krz 29523 0.963 0.891 0.377 0.013 0.040 FBgn0041627 18801 Ku80 27710 1.093 1.712 0.094 0.009 0.094 FBgn0038476 5175 kuk 28750 1.188 3.271 0.002 0.005 0.104 FBgn0002561 3839 l(1)sc 27058 1.210 5.406 <0.001 0.009 0.070 FBgn0250747 4713 l(2)gd1 27311 1.085 1.491 0.142 0.009 0.084 FBgn0002174 5504 l(2)tid 28594 1.086 1.754 0.086 0.010 0.097 FBgn0002283 4195 l(3)73Ah 28979 1.180 3.333 0.002 0.006 0.072 FBgn0002441 5954 l(3)mbt 28076 1.041 0.713 0.479 0.017 0.054 FBgn0002522 1264 lab 26753 1.344 8.387 <0.001 0.004 0.101 FBgn0010238 12369 Lac 28940 1.061 1.748 0.086 0.013 0.030 FBgn0002526 10236 LanA 28071 1.029 0.617 0.54 0.025 0.123 FBgn0086372 2520 lap 28358 1.242 5.957 <0.001 0.009 0.086 FBgn0033984 10255 Lap1 27036 1.033 0.706 0.483 0.017 0.031 FBgn0011640 8597 lark 27703 0.945 1.189 0.24 0.007 0.085 FBgn0063485 3849 Lasp 26305 1.284 7.310 <0.001 0.006 0.040 FBgn0005654 4088 lat 25876 1.302 5.588 <0.001 0.004 0.052 FBgn0011278 6545 lbe 28374 1.098 1.701 0.095 0.006 0.083 FBgn0034083 8434 lbk 28903 1.140 3.006 0.004 0.006 0.074 FBgn0016032 2374 lbm 27278 1.406 9.980 <0.001 0.004 0.081 FBgn0034657 17952 LBR 25982 1.067 1.155 0.253 0.017 0.063 FBgn0010240 17336 Lcch3 32019 1.294 6.851 <0.001 0.006 0.081 FBgn0002543 5481 lea 27317 1.133 2.315 0.025 0.006 0.088 FBgn0032230 13139 lft 28755 1.266 3.994 <0.001 0.006 0.116 FBgn0039354 31096 Lgr3 28789 1.061 1.129 0.265 0.013 0.132 FBgn0031759 9088 lid 28944 1.042 0.893 0.376 0.013 0.108 FBgn0041111 8817 lilli 26314 1.053 1.138 0.26 0.009 0.079 FBgn0026411 11354 Lim1 29341 1.132 2.220 0.031 0.007 0.096 FBgn0002023 10699 Lim3 26227 1.075 1.584 0.119 0.009 0.070 FBgn0041203 1848 LIMK1 28948 1.026 0.794 0.431 0.017 0.052 FBgn0015509 1877 lin19 29520 0.997 0.094 0.925 0.050 0.047
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0035626 17334 lin-28 29564 1.110 1.910 0.062 0.009 0.136 FBgn0032264 6113 Lip4 28925 1.107 1.853 0.069 0.004 0.090 FBgn0034720 11206 Liprin-γ 28301 1.264 3.394 0.001 0.006 0.085 FBgn0015754 8440 Lis-1 28663 1.144 3.091 0.003 0.005 0.102 FBgn0028418 13480 Lk 25798 0.925 1.670 0.101 0.017 0.053 FBgn0017581 17342 Lk6 28357 1.081 1.399 0.168 0.01 0.046 FBgn0035610 10626 Lkr 25936 1.161 3.412 0.001 0.005 0.082 FBgn0036672 42679 Lmpt 28790 0.975 0.574 0.569 0.050 0.026 FBgn0005630 12052 lola 26714 1.068 1.642 0.107 0.009 0.068 FBgn0032515 6866 loqs 28963 0.985 0.229 0.82 0.013 0.093 FBgn0066101 31094 LpR1 27249 1.218 4.008 <0.001 0.005 0.142 FBgn0028582 8532 lqf 27522 1.149 3.199 0.002 0.005 0.015 FBgn0261279 42250 lqfR 28987 0.928 1.466 0.150 0.013 0.074 FBgn0010398 6098 Lrr47 28959 0.926 1.118 0.269 0.009 0.048 FBgn0034540 11136 Lrt 28893 1.060 0.773 0.443 0.025 0.131 FBgn0261067 4279 LSm1 30495 1.093 1.440 0.156 0.005 0.047 FBgn0067622 3367 LSm-4 28953 1.128 3.148 0.003 0.025 0.052 FBgn0002563 4178 Lsp1β 27042 1.218 4.031 <0.001 0.006 0.024 FBgn0002567 8024 ltd 28002 1.029 0.460 0.648 0.010 0.045 FBgn0040765 33473 luna 27084 0.825 4.104 <0.001 0.005 0.066 FBgn0002576 1689 lz 27985 1.124 3.502 <0.001 0.005 0.039 FBgn0002629 6099 m4 29378 1.155 2.685 0.01 0.004 0.219 FBgn0000037 4356 mAcR-60C 27571 1.150 2.779 0.008 0.007 0.121 FBgn0034534 9954 maf-S 25986 0.996 0.060 0.953 0.025 0.042 FBgn0034590 30388 Magi 25792 0.853 2.695 0.010 0.006 0.073 FBgn0002736 9401 mago 28931 0.998 0.034 0.973 0.050 0.055 FBgn0002643 8118 mam 28046 0.964 0.579 0.565 0.007 0.061 FBgn0038965 13852 mats 29567 0.927 1.634 0.109 0.025 0.023 FBgn0017578 9648 Max 29328 0.879 2.035 0.047 0.006 0.140 FBgn0038016 10042 MBD-R2 27029 1.043 0.916 0.363 0.013 0.082 FBgn0026208 4143 mbf1 28550 1.226 4.186 <0.001 0.005 0.150 FBgn0053197 33197 mbl 29585 0.923 1.572 0.122 0.009 0.025 FBgn0025743 18582 mbt 29379 1.031 0.574 0.569 0.025 0.068 FBgn0004513 10181 Mdr65 28664 1.139 2.948 0.005 0.006 0.087 FBgn0004419 4916 me31B 28566 1.014 0.236 0.814 0.017 0.077 FBgn0011655 1775 Med 31928 1.011 0.205 0.838 0.050 0.087 FBgn0036761 5546 MED19 27559 1.112 2.276 0.027 0.009 0.047 FBgn0039923 1793 MED26 28572 1.051 1.527 0.132 0.010 0.097 FBgn0011656 1429 Mef2 28699 1.230 6.468 <0.001 0.004 0.095 FBgn0261260 42611 Megalin 29324 1.201 2.832 0.006 0.010 0.127 FBgn0024329 7717 Mekk1 28587 1.098 1.559 0.125 0.005 0.149 FBgn0086384 14228 Mer 28007 0.760 5.068 <0.001 0.004 0.052 FBgn0043070 15669 MESK2 29380 1.233 4.034 <0.001 0.004 0.092 FBgn0002723 1705 Met 26205 1.083 2.518 0.015 0.006 0.041 FBgn0004456 1771 mew 27543 1.077 2.310 0.025 0.007 0.034
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0035294 1017 Mfap1 29536 1.026 0.398 0.692 0.010 0.115 FBgn0019985 11144 mGluRA 25938 1.006 0.105 0.917 0.050 0.131 FBgn0086783 17927 Mhc 26299 1.111 1.915 0.061 0.006 0.112 FBgn0036558 5841 mib1 27320 1.073 1.477 0.146 0.013 0.070 FBgn0036333 11259 MICAL-like 28595 1.095 1.516 0.136 0.009 0.090 FBgn0022201 13777 milt 28385 0.861 3.124 0.003 0.007 0.066 FBgn0032940 18362 Mio 27059 1.238 4.372 <0.001 0.004 0.071 FBgn0036713 6456 Mip 26246 0.933 1.158 0.252 0.007 0.057 FBgn0027111 1221 miple 28067 0.982 0.505 0.616 0.025 0.055 FBgn0039140 5410 Miro 27695 1.121 1.995 0.051 0.007 0.064 FBgn0014343 10601 mirr 31907 1.053 1.115 0.270 0.017 0.081 FBgn0002772 5596 Mlc1 27547 1.035 0.702 0.486 0.025 0.066 FBgn0083077 34100 mld 31952 1.057 1.515 0.136 0.010 0.040 FBgn0259209 42309 Mlp60A 29381 0.965 0.826 0.413 0.017 0.065 FBgn0259168 42273 mnb 28888 0.960 0.806 0.425 0.025 0.074 FBgn0023215 13316 Mnt 29329 0.884 2.592 0.013 0.010 0.063 FBgn0002780 2050 mod 28314 1.090 2.715 0.009 0.005 0.020 FBgn0011661 10701 Moe 31872 1.071 1.688 0.096 0.007 0.062 FBgn0086711 4482 mol 29429 1.062 1.149 0.256 0.010 0.068 FBgn0036448 9311 mop 28522 1.175 2.940 0.005 0.004 0.061 FBgn0260660 42543 mp 28299 1.162 3.790 <0.001 0.006 0.086 FBgn0015765 5475 Mpk2 27316 1.171 2.989 0.004 0.005 0.103 FBgn0035107 1216 mri 25881 1.071 1.991 0.052 0.009 0.031 FBgn0052296 32296 Mrtf 31930 0.972 0.574 0.569 0.013 0.065 FBgn0026252 7935 msk 27572 1.095 1.513 0.137 0.006 0.058 FBgn0010909 16973 msn 28791 0.925 1.305 0.198 0.013 0.109 FBgn0020269 10145 mspo 29460 1.200 4.244 <0.001 0.005 0.058 FBgn0020272 1424 mst 29601 1.156 4.399 <0.001 0.004 0.063 FBgn0015770 1149 MstProx 28526 1.079 2.233 0.03 0.007 0.074 FBgn0011361 9160 mtacp1 29528 1.007 0.125 0.901 0.017 0.156 FBgn0027786 6851 Mtch 28294 0.992 0.123 0.902 0.050 0.099 FBgn0023000 6936 mth 27495 0.947 0.816 0.418 0.006 0.087 FBgn0014865 8175 Mtk 28546 0.997 0.039 0.969 0.025 0.134 FBgn0039532 5588 Mtl 28622 1.162 3.285 0.002 0.005 0.060 FBgn0004177 7109 mts 27723 0.879 1.885 0.065 0.004 0.046 FBgn0036197 42631 mtTFB1 28054 1.075 1.762 0.085 0.009 0.028 FBgn0037778 3910 mtTFB2 27055 1.357 9.190 <0.001 0.005 0.105 FBgn0002873 12047 mud 28074 1.121 2.935 0.005 0.006 0.088 FBgn0002914 9045 Myb 26237 0.963 0.793 0.431 0.017 0.036 FBgn0004657 1560 mys 27735 0.966 0.897 0.374 0.010 0.042 FBgn0004647 3936 N 28981 1.076 2.286 0.026 0.009 0.033 FBgn0002917 1517 na 25808 0.897 2.913 0.005 0.005 0.019 FBgn0024319 8178 Nach 27262 1.160 2.550 0.014 0.005 0.087 FBgn0085434 34405 NaCP60E 26012 0.897 2.416 0.020 0.007 0.107 FBgn0032151 4128 nAcRα-30D 25835 1.027 0.506 0.615 0.017 0.115
120
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0028875 32975 nAcRα-34E 25943 0.823 4.165 <0.001 0.004 0.067 FBgn0015519 2302 nAcRα-7E 27671 1.229 5.633 <0.001 0.010 0.071 FBgn0037212 12414 nAcRα-80B 31985 1.169 4.463 <0.001 0.004 0.089 FBgn0000036 5610 nAcRα-96Aa 28688 1.204 4.142 <0.001 0.005 0.053 FBgn0000039 6844 nAcRα-96Ab 27493 1.031 0.485 0.629 0.007 0.113 FBgn0031261 11822 nAcRβ-21C 25927 0.798 3.210 0.002 0.006 0.096 FBgn0000038 11348 nAcRβ-64B 31883 0.970 0.803 0.426 0.017 0.057 FBgn0004118 6798 nAcRβ-96A 28038 1.104 1.795 0.078 0.005 0.062 FBgn0010488 3845 NAT1 27302 1.212 5.137 <0.001 0.017 0.089 FBgn0002922 10250 nau 31899 1.093 1.955 0.056 0.010 0.065 FBgn0013303 7641 Nca 29461 0.709 4.362 <0.001 0.004 0.122 FBgn0036279 4357 Ncc69 28682 1.048 0.894 0.376 0.013 0.097 FBgn0028704 18660 Nckx30C 27246 1.103 1.640 0.108 0.010 0.083 FBgn0039234 7012 nct 27498 0.897 1.597 0.116 0.005 0.125 FBgn0017567 3944 ND23 30487 0.986 0.210 0.834 0.017 0.089 FBgn0019957 6343 ND42 28894 1.003 0.056 0.955 0.050 0.078 FBgn0017566 2286 ND75 27739 1.143 4.328 <0.001 0.004 0.058 FBgn0004374 10718 neb 28897 1.210 3.549 <0.001 0.005 0.127 FBgn0015624 15319 nej 27724 0.946 1.213 0.231 0.050 0.106 FBgn0029970 17256 Nek2 28600 0.981 0.385 0.702 0.050 0.030 FBgn0004842 5811 NepYr 25944 1.072 1.453 0.153 0.017 0.090 FBgn0028999 13906 nerfin-1 28324 0.839 3.602 <0.001 0.006 0.081 FBgn0041105 12809 nerfin-2 28551 0.899 2.454 0.017 0.006 0.047 FBgn0002931 11450 net 26204 0.969 0.522 0.604 0.025 0.068 FBgn0015774 10521 NetB 25861 1.063 1.337 0.187 0.010 0.083 FBgn0002932 11988 neur 26023 1.025 0.366 0.715 0.017 0.083 FBgn0031866 13772 neuroligin 28331 0.832 3.259 0.002 0.006 0.036 FBgn0015269 8318 Nf1 25845 1.089 1.412 0.164 0.006 0.056 FBgn0042696 2380 NfI 31929 1.169 4.203 <0.001 0.006 0.080 FBgn0035993 3891 Nf-YA 25991 1.236 6.084 <0.001 0.007 0.074 FBgn0026787 12178 Nhe1 28589 1.034 0.826 0.412 0.010 0.076 FBgn0028545 8942 nimC1 25787 1.047 1.009 0.317 0.010 0.075 FBgn0028939 18146 nimC2 25960 1.181 3.136 0.003 0.006 0.062 FBgn0002938 5125 ninaC 27693 0.994 0.107 0.915 0.050 0.113 FBgn0026401 17704 Nipped-B 28961 1.134 2.007 0.05 0.006 0.089 FBgn0024321 8524 NK7.1 31908 0.870 2.635 0.011 0.007 0.051 FBgn0013305 3798 Nmda1 28361 1.307 7.898 <0.001 0.005 0.058 FBgn0010399 2902 Nmdar1 25941 1.306 7.530 <0.001 0.005 0.049 FBgn0053513 33513 Nmdar2 26019 0.880 2.820 0.007 0.006 0.083 FBgn0039254 13645 Nmnat 29402 0.902 2.202 0.032 0.013 0.089 FBgn0011817 7892 nmo 25793 1.001 0.022 0.983 0.050 0.130 FBgn0005771 4491 noc 29370 1.213 3.709 <0.001 0.004 0.075 FBgn0030205 17255 nocte 28895 1.015 0.169 0.866 0.013 0.042 FBgn0016047 13207 nompA 27990 0.867 2.976 0.005 0.009 0.103 FBgn0016919 12548 nompB 28665 1.004 0.100 0.921 0.025 0.057
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0002962 5637 nos 28300 1.139 2.670 0.01 0.007 0.155 FBgn0011676 6713 Nos 28792 1.046 0.796 0.429 0.010 0.059 FBgn0013717 4166 not 28725 1.114 1.827 0.074 0.009 0.097 FBgn0085436 34407 Not1 28681 0.933 1.561 0.126 0.010 0.096 FBgn0027109 10342 npf 27237 1.172 2.461 0.017 0.005 0.104 FBgn0037408 1147 NPFR1 25939 1.236 3.371 0.001 0.004 0.145 FBgn0035092 3441 Nplp1 25872 1.195 5.027 <0.001 0.025 0.060 FBgn0040813 11051 Nplp2 29430 1.270 4.554 <0.001 0.004 0.101 FBgn0042201 13061 Nplp3 28760 0.975 0.605 0.548 0.013 0.107 FBgn0040717 15361 Nplp4 28793 1.059 1.019 0.313 0.05 0.059 FBgn0002968 1634 Nrg 28724 1.080 2.248 0.029 0.006 0.050 FBgn0004108 9704 Nrt 28742 1.037 0.625 0.535 0.013 0.065 FBgn0015777 9261 nrv2 28666 1.110 1.841 0.071 0.009 0.091 FBgn0032946 8663 nrv3 29431 1.028 0.590 0.557 0.025 0.084 FBgn0038975 7050 Nrx-1 27502 0.971 0.455 0.651 0.009 0.075 FBgn0013997 6827 Nrx-IV 28715 1.042 0.724 0.472 0.013 0.050 FBgn0038473 3983 ns1 29622 1.083 2.515 0.015 0.006 0.027 FBgn0013998 33101 Nsf2 27685 0.897 2.420 0.019 0.006 0.043 FBgn0013342 17248 n-syb 31983 1.283 6.606 <0.001 0.007 0.038 FBgn0031145 1740 Ntf-2 28633 1.096 2.899 0.005 0.004 0.053 FBgn0032680 10174 Ntf-2r 27553 1.136 2.887 0.006 0.006 0.046 FBgn0029147 6698 NtR 28037 1.195 3.365 0.001 0.005 0.114 FBgn0085424 34395 nub 28338 0.863 2.766 0.008 0.006 0.072 FBgn0013718 33991 nuf 27573 0.930 1.634 0.109 0.007 0.063 FBgn0061200 4453 Nup153 30504 1.000 0.003 0.998 0.050 0.061 FBgn0034310 5733 Nup75 28315 1.142 2.886 0.006 0.006 0.073 FBgn0039120 10198 Nup98 28562 1.239 5.082 <0.001 0.004 0.086 FBgn0005636 3385 nvy 29413 1.185 4.754 <0.001 0.050 0.031 FBgn0035966 4684 nwk 27713 1.010 0.180 0.857 0.025 0.098 FBgn0033939 42702 Oaz 25923 0.817 4.269 <0.001 0.005 0.043 FBgn0033268 2297 Obp44a 28794 1.371 9.118 <0.001 0.005 0.052 FBgn0004102 12154 oc 29342 1.162 3.929 <0.001 0.004 0.055 FBgn0002985 3851 odd 28295 1.295 7.597 <0.001 0.006 0.043 FBgn0026058 6352 OdsH 31909 1.160 3.541 <0.001 0.004 0.065 FBgn0033901 12366 O-fut1 26283 1.158 3.826 <0.001 0.005 0.059 FBgn0004646 3039 ogre 27283 1.060 1.477 0.146 0.050 0.037 FBgn0037153 12673 olf413 29547 0.996 0.107 0.915 0.017 0.046 FBgn0032651 5545 Oli 25979 1.366 6.528 <0.001 0.004 0.058 FBgn0028996 1922 onecut 29343 1.088 2.646 0.011 0.005 0.033 FBgn0025360 18455 Optix 31910 1.114 2.133 0.038 0.007 0.032 FBgn0003008 3029 or 28347 1.298 7.332 <0.001 0.006 0.077 FBgn0041626 18859 Or19a 31892 1.326 6.100 <0.001 0.005 0.058 FBgn0026395 9880 Or23a 28050 1.111 1.858 0.069 0.007 0.082 FBgn0023523 3206 Or2a 29302 1.250 6.136 <0.001 0.007 0.045 FBgn0026389 1854 Or43a 26295 1.033 1.010 0.317 0.013 0.029
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0026385 13206 Or47b 27274 1.001 0.032 0.975 0.050 0.043 FBgn0041625 32401 Or65a 27683 0.797 3.524 <0.001 0.005 0.026 FBgn0041624 32402 Or65b 27289 1.065 1.522 0.134 0.010 0.092 FBgn0041623 32403 Or65c 29584 0.949 1.199 0.236 0.013 0.079 FBgn0037399 15581 Or83c 27276 1.186 3.220 0.002 0.005 0.070 FBgn0038203 14360 Or88a 27038 0.805 4.366 <0.001 0.005 0.038 FBgn0004882 10868 orb 25843 1.191 3.228 0.002 0.005 0.090 FBgn0035938 5735 orb2 27050 0.979 0.418 0.678 0.050 0.039 FBgn0017561 1615 Ork1 25885 0.995 0.134 0.894 0.050 0.020 FBgn0003011 7411 ort 25824 0.972 0.452 0.653 0.013 0.054 FBgn0004956 5993 os 28722 0.972 0.483 0.631 0.025 0.069 FBgn0003015 10901 osk 25844 1.074 1.243 0.22 0.010 0.089 FBgn0004839 8967 otk 28916 1.142 3.037 0.004 0.006 0.055 FBgn0031437 9881 p16-ARC 28720 1.114 1.908 0.062 0.006 0.090 FBgn0024846 7393 p38b 29405 1.120 1.869 0.067 0.004 0.085 FBgn0039044 33336 p53 29351 0.952 1.131 0.263 0.017 0.037 FBgn0003031 5119 pAbp 28821 1.083 1.454 0.152 0.010 0.142 FBgn0038418 10309 pad 31944 1.087 1.835 0.073 0.013 0.093 FBgn0014001 10295 Pak 28945 1.122 2.583 0.012 0.007 0.111 FBgn0085432 34403 pan 26743 0.791 4.880 <0.001 0.005 0.039 FBgn0039861 1800 pasha 26293 0.995 0.149 0.882 0.025 0.098 FBgn0034950 3105 Pask 29581 0.858 2.681 0.010 0.006 0.050 FBgn0067864 12021 Patj 26282 1.073 1.233 0.224 0.013 0.134 FBgn0041789 31794 Pax 28695 0.998 0.032 0.974 0.050 0.055 FBgn0051481 31481 pb 29595 0.946 0.984 0.330 0.017 0.056 FBgn0003041 8114 pbl 28343 1.189 3.841 <0.001 0.004 0.066 FBgn0011280 1668 Pbprp2 29432 1.168 3.105 0.003 0.007 0.101 FBgn0003048 3443 pcx 28552 1.033 0.938 0.352 0.050 0.046 FBgn0259225 42325 Pde1c 28728 1.052 1.349 0.184 0.017 0.050 FBgn0038237 8279 Pde6 25828 1.018 0.374 0.710 0.025 0.054 FBgn0023178 6496 Pdf 25802 1.077 1.698 0.095 0.007 0.053 FBgn0014002 6988 Pdi 28039 1.007 0.136 0.892 0.050 0.089 FBgn0017558 8808 Pdk 28635 0.988 0.254 0.801 0.050 0.078 FBgn0004394 12287 pdm2 29543 0.951 1.289 0.203 0.050 0.039 FBgn0033288 42698 pdm3 26749 1.055 1.415 0.164 0.013 0.118 FBgn0016694 17888 Pdp1 26212 1.217 4.064 <0.001 0.006 0.078 FBgn0021967 8844 Pdsw 29592 1.114 2.508 0.015 0.006 0.087 FBgn0003053 12212 peb 28735 0.868 3.759 <0.001 0.006 0.049 FBgn0011823 4799 Pen 27692 1.267 4.281 <0.001 0.007 0.145 FBgn0053198 33198 pen-2 27298 1.057 1.256 0.215 0.006 0.045 FBgn0035405 15812 pfk 31914 0.957 0.698 0.488 0.013 0.095 FBgn0250906 3127 Pgk 28053 1.154 3.851 <0.001 0.006 0.026 FBgn0014869 1721 Pglym78 26303 1.205 3.774 <0.001 0.006 0.074 FBgn0035438 12013 PHGPx 25968 1.053 1.519 0.134 0.017 0.027 FBgn0003082 11205 phr 27676 0.998 0.052 0.959 0.025 0.009
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0013725 10108 phyl 29433 1.156 5.189 <0.001 0.005 0.036 FBgn0015279 4141 Pi3K92E 27690 1.191 3.059 0.004 0.050 0.060 FBgn0024315 8098 Picot 25920 1.134 2.722 0.009 0.006 0.064 FBgn0030670 9245 Pis 29383 1.039 0.845 0.402 0.017 0.049 FBgn0004462 7001 Pk17E 28757 1.178 3.691 <0.001 0.005 0.092 FBgn0020386 1210 Pk61C 27725 1.010 0.221 0.826 0.025 0.068 FBgn0000489 6117 Pka-C3 27569 1.148 3.268 0.002 0.004 0.084 FBgn0259243 42341 Pka-R1 27308 1.004 0.098 0.923 0.050 0.068 FBgn0022382 15862 Pka-R2 27680 1.017 0.278 0.782 0.025 0.090 FBgn0003091 6622 Pkc53E 27491 1.053 1.170 0.247 0.010 0.080 FBgn0003093 1954 Pkc98E 29311 1.062 1.143 0.258 0.025 0.049 FBgn0259680 42349 Pkcδ 28355 1.111 2.874 0.006 0.006 0.043 FBgn0038603 7125 PKD 28717 1.096 1.997 0.051 0.010 0.046 FBgn0000442 3324 Pkg21D 27686 1.287 4.601 <0.001 0.006 0.126 FBgn0020621 2049 Pkn 28335 1.180 4.486 <0.001 0.005 0.066 FBgn0005626 10118 ple 25796 1.075 2.493 0.016 0.013 0.057 FBgn0025741 11081 plexA 30483 1.013 0.307 0.760 0.013 0.049 FBgn0025740 17245 plexB 28911 1.212 4.939 <0.001 0.013 0.023 FBgn0063127 33938 pncr002:3R 28936 1.003 0.088 0.930 0.050 0.043 FBgn0047133 31696 pncr003:2L 28957 0.949 1.033 0.307 0.010 0.037 FBgn0047095 33939 pncr004:X 28547 1.058 1.466 0.149 0.013 0.089 FBgn0047000 33946 pncr008:3L 29526 1.210 5.592 <0.001 0.004 0.074 FBgn0083068 33947 pncr011:3L 28970 1.043 1.156 0.253 0.017 0.061 FBgn0041005 33941 pncr013:4 28591 1.099 2.632 0.011 0.006 0.047 FBgn0063083 33948 pncr015:3L 28941 1.027 0.725 0.472 0.025 0.046 FBgn0062961 33942 pncr016:2R 30509 1.116 3.097 0.003 0.006 0.072 FBgn0046756 33945 pncr017:3R 28593 1.084 2.108 0.040 0.009 0.016 FBgn0003117 3978 pnr 28935 0.728 4.364 <0.001 0.006 0.065 FBgn0003118 17077 pnt 31936 1.310 7.242 <0.001 0.005 0.045 FBgn0013726 8705 pnut 27712 1.138 2.793 0.007 0.005 0.028 FBgn0053526 33526 PNUTS 30485 1.173 3.790 <0.001 0.005 0.107 FBgn0036239 5684 Pop2 30492 1.095 0.302 0.764 0.017 0.096 FBgn0004363 6647 porin 29572 1.021 0.436 0.665 0.017 0.075 FBgn0069354 17137 Porin2 25886 1.324 7.566 <0.001 0.005 0.056 FBgn0003129 9610 Poxm 26757 1.067 2.219 0.031 0.017 0.066 FBgn0003130 8246 Poxn 26238 1.043 0.864 0.392 0.013 0.079 FBgn0260439 17291 Pp2A-29B 29384 0.863 3.208 0.002 0.017 0.129 FBgn0042693 7913 PP2A-B' 30512 1.136 2.655 0.011 0.006 0.080 FBgn0011826 9842 Pp2B-14D 25929 1.171 5.657 <0.001 0.005 0.026 FBgn0023177 32505 Pp4-19C 27726 0.996 0.084 0.934 0.050 0.034 FBgn0261285 5629 Ppcs 28778 0.899 2.256 0.028 0.007 0.084 FBgn0023489 2819 Pph13 31911 1.147 3.671 <0.001 0.009 0.073 FBgn0020258 3478 ppk 29571 1.259 4.150 <0.001 0.009 0.148 FBgn0065110 34042 ppk10 27256 1.144 3.849 <0.001 0.005 0.044 FBgn0065109 34058 ppk11 26253 1.092 2.438 0.018 0.007 0.048
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0034730 10972 ppk12 27092 0.890 2.309 0.025 0.006 0.089 FBgn0053508 33508 ppk13 25817 1.043 0.950 0.347 0.017 0.094 FBgn0031803 9501 ppk14 27091 1.027 0.881 0.383 0.025 0.030 FBgn0065108 34059 ppk16 25890 1.123 3.264 0.002 0.005 0.098 FBgn0039679 18287 ppk19 25887 1.025 0.475 0.637 0.017 0.107 FBgn0039676 7577 ppk20 25897 1.042 0.863 0.392 0.025 0.059 FBgn0039675 12048 ppk21 25849 1.191 5.446 <0.001 0.005 0.048 FBgn0030844 8527 ppk23 28350 1.188 3.813 <0.001 0.004 0.073 FBgn0053349 33349 ppk25 27088 0.928 1.586 0.119 0.005 0.070 FBgn0030795 4805 ppk28 31878 0.911 1.995 0.051 0.009 0.118 FBgn0034489 11209 ppk6 25880 1.098 2.606 0.012 0.006 0.065 FBgn0031802 9499 ppk7 25922 1.205 4.491 <0.001 0.004 0.093 FBgn0027945 7758 ppl 27700 1.075 1.550 0.127 0.013 0.090 FBgn0030208 2890 PPP4R2r 26296 1.091 2.278 0.027 0.013 0.046 FBgn0030057 12108 Ppt1 25952 1.068 1.937 0.058 0.010 0.045 FBgn0032358 4851 Ppt2 28362 1.214 1.874 0.067 0.010 0.158 FBgn0013955 3969 PR2 28694 1.219 3.519 <0.001 0.013 0.060 FBgn0029723 6986 Proc-R 29414 1.127 2.627 0.011 0.006 0.067 FBgn0045038 7105 Proct 29570 1.331 6.861 <0.001 0.004 0.059 FBgn0004595 17228 pros 26745 1.069 1.612 0.113 0.025 0.099 FBgn0040752 30483 Prosap 27284 0.972 0.559 0.579 0.017 0.074 FBgn0261552 42670 ps 27516 0.910 2.333 0.024 0.009 0.060 FBgn0019947 18803 Psn 27681 1.217 4.061 <0.001 0.006 0.095 FBgn0004399 2368 psq 28693 1.099 2.470 0.017 0.010 0.050 FBgn0003892 2411 ptc 28795 1.088 2.199 0.032 0.017 0.016 FBgn0026379 5671 Pten 25841 1.147 3.271 0.002 0.006 0.046 FBgn0014007 10975 Ptp69D 29462 1.132 2.782 0.008 0.006 0.082 FBgn0004369 11516 Ptp99A 25840 0.843 4.155 <0.001 0.005 0.037 FBgn0033068 11212 Ptr 28973 0.943 1.505 0.139 0.009 0.075 FBgn0028577 12085 pUf68 25951 0.882 3.129 0.003 0.006 0.074 FBgn0003165 9755 pum 26725 1.523 4.584 <0.001 0.004 0.157 FBgn0003169 7904 put 27514 1.289 6.004 <0.001 0.005 0.115 FBgn0038538 7660 pxt 28934 1.112 2.314 0.025 0.007 0.033 FBgn0003177 31349 pyd 28920 1.054 1.087 0.282 0.009 0.053 FBgn0028572 14039 qtc 28667 1.058 0.938 0.353 0.010 0.044 FBgn0004636 1956 R 29434 1.044 1.105 0.274 0.050 0.062 FBgn0016700 3320 Rab1 27299 0.837 3.085 0.003 0.004 0.078 FBgn0015789 17060 Rab10 26289 1.002 0.032 0.975 0.050 0.129 FBgn0015790 5771 Rab11 27730 1.076 1.434 0.157 0.006 0.046 FBgn0015791 4212 Rab14 28708 0.875 2.376 0.021 0.006 0.034 FBgn0014009 3269 Rab2 28701 0.945 1.030 0.308 0.013 0.051 FBgn0039966 17515 Rab21 29403 1.020 0.292 0.771 0.025 0.057 FBgn0037364 2108 Rab23 28025 0.972 0.481 0.632 0.010 0.041 FBgn0025382 14791 Rab27 31887 1.114 1.740 0.088 0.006 0.119 FBgn0031090 9575 Rab35 28342 0.989 0.198 0.844 0.050 0.056
125
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0029959 12156 Rab39 25953 1.103 2.939 0.005 0.009 0.072 FBgn0030613 5627 rab3-GEF 28954 0.924 1.429 0.159 0.007 0.071 FBgn0030391 1900 Rab40 29579 0.814 3.192 0.004 0.010 0.038 FBgn0014010 3664 Rab5 30518 1.227 3.449 0.001 0.004 0.108 FBgn0015797 6601 Rab6 27490 1.156 2.930 0.005 0.005 0.084 FBgn0015795 5915 Rab7 27051 1.065 1.223 0.227 0.009 0.105 FBgn0015796 8287 Rab8 27519 0.757 4.558 <0.001 0.004 0.057 FBgn0015794 3129 Rab-RP4 27665 1.029 0.541 0.591 0.050 0.116 FBgn0051118 31118 RabX4 28704 1.055 1.162 0.250 0.006 0.065 FBgn0035255 7980 RabX5 28045 1.033 0.620 0.538 0.017 0.024 FBgn0035155 12015 RabX6 26281 1.033 0.457 0.652 0.050 0.080 FBgn0010333 2248 Rac1 28985 0.960 0.478 0.636 0.017 0.041 FBgn0015286 2849 Rala 29580 1.151 3.763 <0.001 0.007 0.062 FBgn0053180 33180 Ranbp16 27263 1.065 1.417 0.162 0.006 0.031 FBgn0003346 9999 RanGap 29565 1.119 3.946 <0.001 0.006 0.034 FBgn0036497 7815 ran-like 27512 1.009 0.294 0.770 0.050 0.061 FBgn0025806 3204 Rap2l 29568 1.221 5.520 <0.001 0.005 0.097 FBgn0040080 5692 raps 29310 1.044 0.978 0.333 0.017 0.123 FBgn0003206 1167 Ras64B 29318 1.219 4.039 <0.001 0.006 0.060 FBgn0003205 9375 Ras85D 29319 1.170 3.723 <0.001 0.005 0.082 FBgn0024194 11495 rasp 28921 1.079 2.107 0.040 0.007 0.040 FBgn0039055 4656 Rassf 27663 0.977 0.465 0.644 0.025 0.040 FBgn0039352 5053 RASSF8 28323 0.664 6.273 <0.001 0.004 0.070 FBgn0003210 11427 rb 28668 1.184 4.893 <0.001 0.005 0.052 FBgn0010263 3151 Rbp9 28669 1.049 1.235 0.223 0.025 0.094 FBgn0085373 42667 rdgA 29435 1.189 4.911 <0.001 0.005 0.038 FBgn0003218 11111 rdgB 28796 1.298 7.910 <0.001 0.004 0.048 FBgn0014018 11992 Rel 28943 1.076 1.997 0.051 0.010 0.083 FBgn0026378 8432 Rep 28047 1.102 2.081 0.042 0.009 0.075 FBgn0011701 31240 repo 28339 1.087 1.753 0.086 0.006 0.141 FBgn0011829 14396 Ret 25948 1.267 4.304 <0.001 0.005 0.148 FBgn0004795 5403 retn 26309 1.188 4.200 <0.001 0.006 0.065 FBgn0087002 11064 Rfabg 28946 1.002 0.049 0.961 0.050 0.091 FBgn0020379 6312 Rfx 29355 0.979 0.461 0.647 0.013 0.077 FBgn0026376 8865 Rgl 28938 1.131 2.878 0.006 0.005 0.033 FBgn0033310 8643 rgr 31926 1.091 1.852 0.069 0.010 0.122 FBgn0260442 6831 rhea 28950 1.057 1.183 0.242 0.009 0.034 FBgn0004635 1004 rho 28690 1.161 2.977 0.004 0.009 0.064 FBgn0014020 8416 Rho1 27727 1.019 0.379 0.706 0.017 0.073 FBgn0017549 8418 Ric 27520 0.982 0.374 0.710 0.050 0.074 FBgn0028292 15797 ric8a 28910 1.117 1.886 0.065 0.006 0.071 FBgn0053547 33547 Rim 27300 1.051 1.117 0.269 0.009 0.060 FBgn0003255 8930 rk 31958 1.061 1.340 0.186 0.010 0.059 FBgn0020620 8085 RN-tre 28670 1.136 2.765 0.008 0.006 0.109 FBgn0003267 6348 ro 28671 1.086 1.910 0.061 0.006 0.046
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0024196 10751 robl 31977 1.119 2.162 0.035 0.007 0.060 FBgn0041097 5423 robo3 29398 1.237 5.292 <0.001 0.005 0.058 FBgn0026181 9774 rok 28797 0.901 3.276 0.002 0.007 0.058 FBgn0004574 15811 Rop 28929 1.035 0.560 0.578 0.017 0.014 FBgn0033998 8092 row 25971 1.045 1.096 0.278 0.050 0.028 FBgn0019661 32777 roX1 27734 1.033 0.724 0.472 0.025 0.118 FBgn0019660 32665 roX2 28986 1.048 1.059 0.294 0.013 0.086 FBgn0005649 5422 Rox8 28035 0.996 0.085 0.932 0.050 0.050 FBgn0030230 11556 Rph 25950 1.139 3.667 <0.001 0.005 0.016 FBgn0022981 1058 rpk 25847 1.095 1.755 0.085 0.013 0.103 FBgn0024733 17521 RpL10 29356 1.006 0.108 0.914 0.050 0.073 FBgn0261396 42641 Rpn3 30503 1.034 0.891 0.377 0.025 0.056 FBgn0028689 10149 Rpn6 29385 1.197 5.616 <0.001 0.004 0.046 FBgn0024941 9108 RSG7 28574 1.024 0.529 0.599 0.025 0.057 FBgn0003285 4125 rst 28672 1.231 3.709 <0.001 0.010 0.079 FBgn0086253 31152 rumi 26727 1.002 0.037 0.97 0.050 0.095 FBgn0003300 1849 run 28673 1.062 1.151 0.255 0.017 0.138 FBgn0003301 9533 rut 27035 1.117 3.667 <0.001 0.006 0.022 FBgn0020617 10052 Rx 28674 1.012 0.251 0.803 0.025 0.093 FBgn0011286 10844 Rya-r44F 29445 0.931 1.296 0.200 0.005 0.203 FBgn0011285 17596 S6kII 27731 0.846 3.093 0.003 0.005 0.073 FBgn0037672 12952 sage 25980 1.028 0.580 0.564 0.025 0.065 FBgn0000287 4881 salr 29549 1.130 2.707 0.009 0.005 0.088 FBgn0005278 2674 Sam-S 29415 1.278 4.453 <0.001 0.005 0.026 FBgn0025697 12789 santa-maria 29550 0.967 0.671 0.505 0.017 0.049 FBgn0013334 8884 Sap47 27527 1.149 4.538 <0.001 0.006 0.075 FBgn0053193 33193 sav 28006 1.139 2.787 0.007 0.006 0.088 FBgn0010575 5580 sbb 27049 1.086 1.940 0.058 0.010 0.115 FBgn0003321 1664 sbr 28924 1.375 3.285 0.002 0.006 0.233 FBgn0004170 3827 sc 26206 0.997 0.078 0.938 0.050 0.050 FBgn0003326 17579 sca 28675 0.879 2.573 0.013 0.006 0.055 FBgn0033033 11066 scarface 29386 1.009 0.164 0.870 0.050 0.046 FBgn0003328 8095 scb 27545 1.234 5.696 <0.001 0.006 0.043 FBgn0038042 5657 Scgβ 29551 1.120 2.988 0.004 0.005 0.043 FBgn0025391 14808 Scgδ 25964 0.977 0.510 0.612 0.013 0.053 FBgn0040918 3576 schlank 29340 1.200 5.156 <0.001 0.013 0.021 FBgn0260936 5505 scny 27558 0.988 0.254 0.800 0.017 0.042 FBgn0020908 15848 Scp1 31957 1.103 2.429 0.018 0.005 0.063 FBgn0003339 1030 Scr 28676 0.989 0.216 0.830 0.050 0.055 FBgn0261263 42614 scrib 29552 1.065 1.310 0.195 0.007 0.107 FBgn0028993 17594 scro 29387 1.158 3.364 0.001 0.004 0.093 FBgn0004880 1130 scrt 27025 1.034 0.702 0.485 0.013 0.040 FBgn0050044 30044 s-cup 27281 1.044 0.930 0.356 0.013 0.028 FBgn0003345 8544 sd 29352 1.083 2.029 0.048 0.013 0.072 FBgn0027103 6159 sec10 27483 1.107 2.183 0.033 0.005 0.064
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0038856 7034 sec15 27499 1.088 1.795 0.078 0.006 0.092 FBgn0031537 8843 sec5 27526 0.804 5.953 <0.001 0.005 0.049 FBgn0034367 5341 sec6 27314 1.098 2.042 0.046 0.006 0.073 FBgn0261270 8553 SelD 29553 0.956 1.349 0.184 0.013 0.016 FBgn0011259 18405 Sema-1a 29554 1.136 2.910 0.005 0.005 0.059 FBgn0016059 6446 Sema-1b 28588 1.102 2.560 0.013 0.005 0.071 FBgn0011260 4700 Sema-2a 29519 1.072 1.511 0.137 0.010 0.058 FBgn0250876 5661 Sema-5c 29436 1.028 0.702 0.486 0.010 0.056 FBgn0002573 32120 sens 27287 1.089 1.784 0.080 0.007 0.051 FBgn0051632 31632 sens-2 27285 0.936 1.283 0.205 0.013 0.119 FBgn0011710 1403 Sep1 27709 1.047 1.347 0.183 0.009 0.024 FBgn0014029 4173 Sep2 28004 1.024 0.484 0.630 0.010 0.106 FBgn0004197 6127 Ser 28713 0.960 1.003 0.320 0.009 0.067 FBgn0025571 5836 SF1 28036 1.099 2.469 0.017 0.006 0.081 FBgn0040284 6987 SF2 29522 1.195 3.999 <0.001 0.005 0.050 FBgn0032475 16975 Sfmbt 28677 1.147 3.201 0.002 0.005 0.050 FBgn0040475 6757 SH3PX1 27653 1.025 0.509 0.612 0.025 0.049 FBgn0003383 1066 Shab 25805 1.135 3.865 <0.001 0.006 0.057 FBgn0085387 34358 shakB 27292 1.355 7.104 <0.001 0.004 0.107 FBgn0005564 9262 Shal 31879 1.066 1.443 0.155 0.009 0.107 FBgn0015295 18247 shark 25788 1.065 1.383 0.172 0.010 0.078 FBgn0003386 2822 Shaw 28346 1.155 3.132 0.003 0.006 0.057 FBgn0085395 34366 Shawl 25819 1.046 0.654 0.516 0.025 0.131 FBgn0003391 3722 shg 27689 0.798 4.822 <0.001 0.004 0.073 FBgn0003392 18102 shi 28513 1.070 1.027 0.309 0.013 0.098 FBgn0013733 18076 shot 28336 1.025 0.543 0.589 0.025 0.075 FBgn0085447 34418 sif 25789 1.138 2.774 0.007 0.004 0.077 FBgn0038880 10823 SIFR 25831 0.869 3.285 0.002 0.004 0.050 FBgn0004666 7771 sim 26739 1.051 1.148 0.257 0.017 0.055 FBgn0015542 7951 sima 26207 1.411 10.004 <0.001 0.004 0.047 FBgn0003411 1641 sisA 29330 1.197 5.653 <0.001 0.005 0.060 FBgn0027364 3871 Six4 30510 0.533 11.133 <0.001 0.004 0.024 FBgn0029761 10706 SK 27238 1.072 1.344 0.185 0.004 0.089 FBgn0036786 13701 skl 28678 1.022 2.296 0.025 0.004 0.062 FBgn0025637 16983 skpA 28974 0.882 2.507 0.015 0.006 0.100 FBgn0016984 9985 sktl 27715 0.988 0.270 0.788 0.013 0.019 FBgn0005638 4354 slbo 27043 0.998 0.049 0.961 0.050 0.104 FBgn0026173 5186 slim 29437 1.079 1.650 0.105 0.009 0.045 FBgn0003429 10693 slo 26247 1.060 1.128 0.264 0.006 0.037 FBgn0024290 6772 Slob 27492 1.311 7.004 <0.001 0.004 0.112 FBgn0002941 6534 slou 29344 1.088 1.972 0.054 0.009 0.087 FBgn0003430 16738 slp1 29354 1.135 2.920 0.005 0.006 0.100 FBgn0025469 8717 slv 29388 1.139 3.483 <0.001 0.004 0.042 FBgn0010083 5352 SmB 28887 1.088 1.845 0.070 0.006 0.060 FBgn0023167 8427 SmD3 30534 1.062 1.502 0.139 0.025 0.087
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0000426 16792 SmF 26734 1.190 2.776 0.007 0.004 0.117 FBgn0036641 16725 Smn 26288 1.113 2.461 0.017 0.006 0.054 FBgn0003444 11561 smo 27037 1.054 1.150 0.256 0.010 0.055 FBgn0025800 2262 Smox 26756 1.059 1.113 0.270 0.010 0.107 FBgn0024308 4013 Smr 27068 0.955 1.232 0.223 0.010 0.032 FBgn0026170 4494 smt3 28034 1.053 1.462 0.149 0.009 0.068 FBgn0003448 3956 sna 28679 0.734 7.335 <0.001 0.004 0.039 FBgn0250791 6625 Snap 29587 0.942 1.174 0.245 0.007 0.072 FBgn0028401 9474 Snap24 28719 1.248 5.454 <0.001 0.005 0.063 FBgn0011288 40452 Snap25 27306 1.053 1.069 0.289 0.009 0.049 FBgn0031455 9958 snapin 27541 0.962 0.842 0.404 0.010 0.095 FBgn0023169 3051 SNF1A 25931 0.937 1.502 0.138 0.007 0.069 FBgn0025803 17299 SNF4Aγ 26291 1.039 0.838 0.405 0.017 0.109 FBgn0030026 10964 sni 31978 1.234 4.790 <0.001 0.004 0.072 FBgn0085450 34421 Snoo 31934 1.074 1.493 0.140 0.006 0.061 FBgn0032840 13968 sNPF 25867 1.073 0.907 0.370 0.010 0.103 FBgn0036934 7395 sNPF-R 27507 1.287 4.321 <0.001 0.006 0.090 FBgn0003460 11121 so 31912 1.020 0.504 0.616 0.017 0.058 FBgn0003462 11793 Sod 29389 1.190 4.019 <0.001 0.005 0.021 FBgn0010213 8905 Sod2 25969 0.983 0.526 0.602 0.025 0.072 FBgn0003464 1391 sol 29463 1.127 3.627 <0.001 0.006 0.033 FBgn0039938 11153 Sox102F 26220 1.014 0.260 0.796 0.017 0.062 FBgn0005612 3090 Sox14 26221 0.993 0.167 0.868 0.025 0.042 FBgn0036411 7345 Sox21a 31902 0.960 0.776 0.441 0.010 0.053 FBgn0029123 18024 SoxN 25996 0.855 3.100 0.003 0.004 0.062 FBgn0260470 14041 SP555 29555 0.986 0.303 0.763 0.025 0.088 FBgn0039141 5977 spas 27570 1.009 0.213 0.832 0.025 0.036 FBgn0005672 10334 spi 28387 0.987 0.286 0.776 0.013 0.037 FBgn0086676 8428 spin 27702 1.183 4.454 <0.001 0.009 0.094 FBgn0003475 10076 spir 30516 1.209 4.271 <0.001 0.005 0.082 FBgn0028985 9453 Spn4 31979 1.062 1.564 0.123 0.009 0.056 FBgn0033112 9454 Spn42Db 28737 1.399 6.394 <0.001 0.004 0.084 FBgn0052451 32451 SPoCk 28352 0.999 0.010 0.992 0.050 0.079 FBgn0014032 12117 Sptr 28968 1.196 4.139 <0.001 0.005 0.113 FBgn0003495 6134 spz 28538 0.956 0.893 0.376 0.010 0.068 FBgn0003499 7847 sr 27701 1.250 5.639 <0.001 0.005 0.063 FBgn0086370 6072 sra 27260 0.858 3.542 <0.001 0.005 0.096 FBgn0003501 7524 Src64B 30517 0.949 0.981 0.331 0.007 0.048 FBgn0003507 3992 srp 28606 0.886 3.151 0.003 0.005 0.069 FBgn0024285 4602 Srp54 30533 1.012 0.302 0.763 0.025 0.069 FBgn0003513 6993 ss 26208 0.966 0.702 0.486 0.017 0.099 FBgn0037202 11115 Ssl1 29464 1.029 0.541 0.591 0.010 0.082 FBgn0036248 17153 ssp 29358 1.343 4.558 <0.001 0.003 0.151 FBgn0010278 4817 Ssrp 26222 0.858 2.974 0.004 0.004 0.059 FBgn0027363 6521 Stam 27487 1.052 1.054 0.296 0.009 0.078
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0024836 11895 stan 26022 0.949 1.075 0.287 0.013 0.038 FBgn0036790 7285 star1 27506 0.972 0.546 0.588 0.013 0.052 FBgn0003525 1395 stg 29556 1.200 5.744 <0.001 0.004 0.035 FBgn0033870 12295 stj 25807 1.125 2.551 0.013 0.007 0.041 FBgn0046692 40293 Stlk 27305 1.070 1.409 0.164 0.006 0.053 FBgn0016975 12473 stnB 28510 1.153 3.127 0.003 0.006 0.156 FBgn0013988 18255 Strn-Mlck 26736 1.181 3.855 <0.001 0.004 0.052 FBgn0005355 6054 Su(fu) 28559 0.914 2.143 0.037 0.007 0.026 FBgn0004837 3497 Su(H) 28900 1.376 3.297 0.002 0.005 0.422 FBgn0003612 8068 Su(var)2-10 29448 1.283 6.886 <0.001 0.005 0.041 Su(var)2- FBgn0026427 12864 25972 0.961 0.789 0.433 0.010 0.108 HP2 FBgn0003545 12298 sub 28570 0.900 2.049 0.045 0.009 0.053 FBgn0033782 3850 sug 27026 0.936 1.535 0.130 0.006 0.039 FBgn0038504 5407 Sur-8 29557 1.028 0.544 0.589 0.050 0.085 FBgn0005561 11049 sv 27269 1.056 1.045 0.301 0.006 0.034 FBgn0003651 11502 svp 28689 1.014 0.303 0.763 0.050 0.063 FBgn0003654 18000 sw 30505 1.019 0.425 0.672 0.010 0.075 FBgn0016974 32747 swaPsi 30484 0.999 0.016 0.988 0.050 0.117 FBgn0034262 14485 swi2 28530 0.946 1.193 0.238 0.005 0.043 FBgn0002044 10084 swm 28548 1.012 0.258 0.798 0.017 0.081 FBgn0003656 2212 sws 31993 1.049 0.990 0.327 0.017 0.062 FBgn0004575 3985 Syn 27304 1.057 1.569 0.122 0.007 0.049 FBgn0037130 7152 Syn1 27504 0.987 0.246 0.807 0.017 0.098 FBgn0034135 4905 Syn2 28363 1.084 1.761 0.084 0.007 0.097 FBgn0053094 33094 Synd 27297 1.043 0.866 0.389 0.010 0.064 FBgn0034691 6562 synj 27489 1.024 0.489 0.627 0.050 0.132 FBgn0261085 10617 Syt12 28508 0.938 1.164 0.249 0.005 0.061 FBgn0261086 9778 Syt14 28365 1.075 1.550 0.128 0.007 0.030 FBgn0028400 10047 Syt4 26730 1.028 0.605 0.548 0.007 0.076 FBgn0039900 2381 Syt7 27279 1.018 0.357 0.722 0.050 0.076 FBgn0261089 5559 Sytα 29308 1.046 0.958 0.342 0.025 0.061 FBgn0259235 42333 Sytβ 27293 0.983 0.347 0.730 0.013 0.047 FBgn0036341 11278 Syx13 27984 1.114 2.097 0.041 0.009 0.060 FBgn0031106 1467 Syx16 25884 1.028 0.813 0.420 0.010 0.019 FBgn0035540 7452 Syx17 25896 0.877 2.371 0.021 0.005 0.059 FBgn0039212 13626 Syx18 26721 1.011 0.285 0.777 0.050 0.063 FBgn0013343 31136 Syx1A 25811 0.920 1.606 0.115 0.010 0.049 FBgn0011708 4214 Syx5 29397 1.058 1.603 0.115 0.006 0.061 FBgn0037084 7736 Syx6 28505 1.248 4.780 <0.001 0.004 0.090 FBgn0086377 5081 Syx7 29546 0.947 1.101 0.276 0.017 0.047 FBgn0036643 4109 Syx8 26013 0.889 3.071 0.003 0.006 0.029 FBgn0086358 7417 Tab2 29417 0.996 0.069 0.946 0.050 0.038 FBgn0041092 13109 tai 28971 1.098 2.323 0.023 0.006 0.061 FBgn0004841 6515 Takr86C 31884 0.939 1.365 0.179 0.013 0.035 FBgn0004622 7887 Takr99D 27513 1.211 5.132 <0.001 0.006 0.075
130
BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0015550 7659 tap 26209 1.041 0.791 0.432 0.009 0.112 FBgn0038165 9637 Task6 28015 1.007 0.155 0.877 0.050 0.051 FBgn0037690 9361 Task7 27264 1.275 6.031 <0.001 0.004 0.073 FBgn0051057 31057 tau 28891 0.962 0.715 0.478 0.025 0.050 FBgn0010329 1543 Tbh 27667 1.161 4.614 <0.001 0.005 0.067 FBgn0025790 10327 TBPH 29517 1.114 2.442 0.018 0.005 0.068 FBgn0050445 30445 Tdc1 25801 0.860 2.809 0.007 0.005 0.039 FBgn0050446 30446 Tdc2 25871 1.081 1.630 0.109 0.009 0.093 FBgn0026760 7121 Tehao 29533 0.956 0.934 0.355 0.025 0.027 FBgn0259240 42338 Ten-a 29439 1.093 1.871 0.066 0.005 0.074 FBgn0004449 5723 Ten-m 29390 1.176 4.376 <0.001 0.004 0.025 FBgn0261014 2331 TER94 31968 1.123 2.480 0.016 0.009 0.100 FBgn0038805 4217 TFAM 26744 1.023 0.623 0.536 0.025 0.052 FBgn0015014 11987 tgo 26740 1.044 0.927 0.358 0.025 0.074 FBgn0026869 1981 Thd1 30514 1.017 0.337 0.738 0.017 0.065 FBgn0031390 31671 tho2 28537 0.922 1.562 0.123 0.005 0.062 FBgn0014395 14620 tilB 29391 0.915 1.845 0.070 0.009 0.031 FBgn0014396 3234 tim 29583 1.038 0.778 0.440 0.007 0.049 FBgn0004110 7895 tin 28539 1.339 8.243 <0.001 0.005 0.045 FBgn0026080 6121 Tip60 28563 0.991 0.219 0.827 0.017 0.064 FBgn0003710 1232 tipE 26249 1.011 0.322 0.749 0.017 0.042 FBgn0000964 10034 tj 25987 0.983 0.372 0.712 0.010 0.027 FBgn0037976 14734 Tk 25800 1.020 0.429 0.669 0.017 0.078 FBgn0003720 1378 tll 27242 1.016 0.450 0.654 0.013 0.066 FBgn0026160 7958 tna 29372 1.196 4.765 <0.001 0.007 0.071 FBgn0036285 10704 toe 29345 0.990 0.187 0.852 0.025 0.089 FBgn0032095 18241 Toll-4 28543 0.884 2.474 0.016 0.007 0.083 FBgn0034476 8595 Toll-7 30488 0.947 0.282 0.779 0.025 0.081 FBgn0036978 5528 Toll-9 30535 1.163 4.102 <0.001 0.003 0.107 FBgn0029114 6890 Tollo 28519 0.904 1.968 0.054 0.005 0.096 FBgn0016041 12157 Tom40 26005 1.231 4.730 <0.001 0.004 0.070 FBgn0033074 8330 tomboy40 29573 1.134 3.264 0.002 0.010 0.051 FBgn0030412 17762 tomosyn 31980 0.992 0.170 0.865 0.025 0.060 FBgn0036746 6064 TORC 28886 0.892 2.688 0.010 0.006 0.054 FBgn0019650 11186 toy 29346 1.054 1.015 0.314 0.007 0.086 FBgn0086355 2171 Tpi 26304 1.157 3.207 0.002 0.004 0.072 FBgn0013348 2981 TpnC41C 27053 0.851 3.551 <0.001 0.004 0.090 FBgn0010423 9073 TpnC47D 26172 1.223 6.775 <0.001 0.004 0.062 FBgn0003741 16724 tra 28512 0.852 3.218 0.002 0.005 0.064 FBgn0003742 10128 tra2 28018 0.946 1.158 0.252 0.006 0.061 FBgn0041775 10686 tral 28542 0.890 2.051 0.045 0.004 0.065 FBgn0003744 8637 trc 28326 1.062 1.887 0.065 0.010 0.036 FBgn0046687 3171 Tre1 27672 0.857 3.406 0.001 0.005 0.030 FBgn0003749 6883 trh 27986 1.132 2.964 0.005 0.006 0.051 FBgn0035187 9122 Trh 25842 1.207 5.941 <0.001 0.005 0.062
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0024277 18214 trio 27732 1.013 0.286 0.776 0.050 0.072 FBgn0024921 7398 Trn 27546 0.946 1.034 0.306 0.006 0.027 FBgn0010452 11280 trn 28525 0.953 0.993 0.325 0.017 0.082 FBgn0031456 2848 Trn-SR 25988 1.151 3.045 0.004 0.006 0.095 FBgn0261451 33950 trol 29440 1.035 0.783 0.437 0.007 0.054 FBgn0005614 18345 trpl 26722 1.217 4.115 <0.001 0.003 0.151 FBgn0023518 3848 trr 29563 0.973 0.639 0.525 0.010 0.088 FBgn0003866 1374 tsh 28022 1.243 6.951 <0.001 0.004 0.071 FBgn0031850 11326 Tsp 29399 1.178 3.772 <0.001 0.006 0.127 FBgn0032943 8666 Tsp39D 25829 0.956 1.332 0.189 0.017 0.048 FBgn0033132 12143 Tsp42Ej 29392 1.179 3.449 0.001 0.006 0.037 FBgn0037848 4591 Tsp86D 28515 1.119 2.497 0.015 0.005 0.114 FBgn0033378 8781 tsu 28955 1.068 2.074 0.043 0.009 0.029 FBgn0003870 1856 ttk 26315 1.294 6.021 <0.001 0.004 0.063 FBgn0086356 13345 tum 28982 0.787 4.614 <0.001 0.004 0.077 FBgn0031957 14534 TwdlE 31963 1.095 2.385 0.020 0.006 0.025 FBgn0003900 2956 twi 25981 1.054 1.092 0.280 0.013 0.096 FBgn0004889 6235 tws 28714 1.180 3.675 <0.001 0.004 0.051 FBgn0004514 7485 TyrR 28332 1.062 1.191 0.239 0.006 0.071 FBgn0038541 16766 TyrRII 27670 1.149 3.237 0.002 0.004 0.073 FBgn0017457 3582 U2af38 29304 0.898 2.442 0.018 0.005 0.099 FBgn0005411 9998 U2af50 27542 0.868 2.833 0.007 0.004 0.037 FBgn0023143 1782 Uba1 25957 1.202 4.074 <0.001 0.005 0.082 FBgn0029113 7528 Uba2 28569 1.009 0.173 0.863 0.025 0.082 FBgn0061469 6190 Ube3a 31972 1.071 1.440 0.156 0.006 0.028 FBgn0003944 10388 Ubx 31913 1.008 0.176 0.861 0.050 0.092 FBgn0259936 32957 Uhg3 28545 1.151 3.033 0.003 0.003 0.046 FBgn0003950 1501 unc 31969 1.061 1.739 0.087 0.007 0.042 FBgn0034155 8566 unc-104 28951 1.017 0.455 0.651 0.050 0.048 FBgn0025726 2999 unc-13 29548 0.906 2.251 0.028 0.006 0.052 FBgn0035756 32381 unc-13-4A 32033 1.198 4.415 <0.001 0.004 0.058 FBgn0015561 1650 unpg 29393 1.284 5.726 <0.001 0.004 0.031 FBgn0035895 7015 Unr 29334 1.038 0.770 0.444 0.013 0.054 FBgn0053542 33542 upd3 28575 0.944 0.675 0.508 0.017 0.030 FBgn0003963 2762 ush 29516 1.022 0.452 0.653 0.013 0.051 FBgn0034913 11173 usnp 25862 0.980 0.380 0.706 0.013 0.051 FBgn0003964 4380 usp 27258 0.984 0.331 0.742 0.025 0.042 FBgn0004055 3533 uzip 29558 1.009 0.223 0.825 0.017 0.070 FBgn0015323 32848 VAChT 27684 1.064 1.276 0.208 0.017 0.093 FBgn0003969 9209 vap 29394 1.167 3.945 <0.001 0.003 0.075 FBgn0029687 5014 Vap-33-1 27312 0.998 0.045 0.964 0.050 0.093 FBgn0250785 9326 vari 28599 1.129 2.825 0.007 0.006 0.063 FBgn0003970 3506 vas 30496 1.109 2.225 0.030 0.005 0.072 FBgn0039269 7662 veli 29590 1.183 3.520 <0.001 0.005 0.159 FBgn0003975 3830 vg 31970 1.027 0.649 0.519 0.009 0.047
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BDSC TAG unadjusted critical standard FlyBase ID CG# gene t stock # (% control) P level deviation FBgn0031424 9887 VGlut 27538 1.008 0.174 0.863 0.025 0.073 FBgn0028671 1709 Vha100-1 26290 1.043 0.883 0.381 0.005 0.116 FBgn0032294 6737 Vha16-5 25803 0.812 2.691 0.009 0.005 0.095 FBgn0028664 11589 VhaM9.7-1 26004 1.131 2.780 0.008 0.007 0.061 FBgn0004397 3299 Vinc 25965 0.861 2.830 0.007 0.004 0.013 FBgn0033748 8821 vis 29544 0.904 2.927 0.005 0.006 0.043 FBgn0003986 6172 vnd 27733 1.099 2.240 0.030 0.006 0.024 FBgn0022027 14750 Vps25 26286 0.932 1.478 0.145 0.010 0.045 FBgn0016076 14029 vri 25989 1.092 1.990 0.051 0.007 0.086 FBgn0053980 33980 Vsx2 26223 1.162 3.242 0.002 0.005 0.074 FBgn0086680 10037 vvl 26228 0.949 1.089 0.281 0.006 0.040 FBgn0003996 2759 w 28980 0.973 0.538 0.593 0.025 0.043 FBgn0024273 1520 WASp 25955 0.996 0.119 0.906 0.050 0.057 FBgn0004002 42677 wb 29559 1.142 2.702 0.009 0.004 0.059 FBgn0027492 5643 wdb 28939 0.975 0.625 0.535 0.013 0.118 FBgn0034718 3413 wdp 28907 1.019 0.448 0.655 0.013 0.039 FBgn0024179 10776 wit 25949 0.947 0.986 0.328 0.009 0.047 FBgn0036896 8789 wnd 27525 1.085 2.568 0.013 0.007 0.056 FBgn0031903 4971 Wnt10 31989 0.943 1.193 0.238 0.013 0.076 FBgn0004360 1916 Wnt2 29441 1.247 4.986 <0.001 0.005 0.061 FBgn0010453 4698 Wnt4 29442 1.040 0.818 0.417 0.006 0.110 FBgn0010194 6407 Wnt5 28534 1.129 2.629 0.011 0.004 0.047 FBgn0031902 4969 Wnt6 30493 1.010 2.147 0.036 0.004 0.052 FBgn0038134 8458 wntD 28947 1.065 1.593 0.117 0.017 0.064 FBgn0010328 5965 woc 27057 1.109 2.391 0.021 0.006 0.109 FBgn0025878 10382 wrapper 29561 0.975 0.546 0.587 0.009 0.061 FBgn0011739 12072 wts 27662 1.244 5.169 <0.001 0.004 0.074 FBgn0004028 7178 wupA 31893 1.088 1.992 0.052 0.007 0.076 FBgn0026313 5675 X11L 29309 1.008 0.206 0.837 0.050 0.030 FBgn0021872 9415 Xbp1 25990 1.260 5.700 <0.001 0.005 0.066 FBgn0039338 4548 XNP 29444 0.975 0.511 0.611 0.009 0.080 FBgn0015565 2913 yin 31971 1.322 6.490 <0.001 0.004 0.080 FBgn0034970 4005 yki 31965 1.032 0.871 0.388 0.017 0.057 FBgn0032321 4621 YL-1 31938 1.144 3.001 0.004 0.004 0.053 FBgn0022959 5654 yps 30810 1.096 2.126 0.038 0.005 0.070 FBgn0004049 9764 yrt 31771 1.058 1.452 0.152 0.010 0.072 FBgn0004050 7803 z 29446 1.163 3.675 <0.001 0.005 0.048 FBgn0004053 1046 zen 26229 1.006 0.185 0.854 0.025 0.052 FBgn0040512 3948 zetaCOP 28960 1.034 0.924 0.360 0.013 0.046 FBgn0004606 1322 zfh1 29347 1.044 0.856 0.396 0.006 0.077 FBgn0024177 10125 zpg 27674 1.084 1.812 0.076 0.005 0.094 FBgn0011642 32018 Zyx102EF 29591 0.988 0.240 0.811 0.017 0.107
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Table A.2. Data from second round of RNAi screen. Highlighted rows are hits that recapitulated TAG phenotypes from the first round (the error bars of TAG levels from the two rounds overlap each other).
round 2 round 1 BDSC FlyBase ID CG# gene TAG standard TAG standard stock # (% control) deviation (% control) deviation FBgn0260793 42573 2mit 28553 1.360 0.086 1.656 0.247 FBgn0087012 1056 5-HT2 31882 1.048 0.061 1.277 0.079 FBgn0004573 12073 5-HT7 27273 1.046 0.023 1.263 0.064 FBgn0003034 17673 Acp70A 25998 1.224 0.063 1.271 0.067 FBgn0085445 42734 Ank2 29438 1.232 0.115 1.321 0.078 FBgn0029512 12276 Aos1 28972 1.061 0.074 1.344 0.119 FBgn0022131 42783 aPKC 25946 1.164 0.108 1.257 0.129 FBgn0013751 1072 Awh 31772 1.207 0.071 1.380 0.053 FBgn0038092 31298 beat-Vb 28758 0.912 0.106 1.296 0.065 FBgn0014133 1822 bif 28372 1.130 0.043 1.338 0.115 FBgn0000210 11491 br 27272 0.996 0.060 1.275 0.151 FBgn0000233 12653 btd 29453 1.024 0.068 1.273 0.048 FBgn0037963 6977 Cad87A 28716 0.949 0.104 1.250 0.084 FBgn0013995 5685 Calx 28306 0.974 0.046 0.693 0.098 FBgn0004624 18069 CaMKII 29401 1.346 0.098 1.257 0.028 FBgn0015614 11217 CanB2 27270 1.016 0.092 1.297 0.092 FBgn0023095 11282 caps 28020 1.031 0.073 1.275 0.035 FBgn0022213 13281 Cas 28337 1.198 0.071 1.280 0.026 FBgn0011573 12019 Cdc37 28756 1.023 0.119 1.434 0.100 FBgn0010341 12530 Cdc42 28021 0.975 0.054 1.272 0.135 FBgn0033186 1602 CG1602 31920 1.196 0.130 1.270 0.085 FBgn0030303 1756 CG1756 27033 1.006 0.042 0.684 0.116 FBgn0032979 1832 CG1832 27080 1.141 0.044 0.737 0.046 FBgn0030240 2202 CG2202 26241 0.871 0.076 1.408 0.129 FBgn0032957 2225 CG2225 29619 0.921 0.064 1.324 0.136 FBgn0034834 3162 CG3162 29404 1.007 0.101 1.542 0.287 FBgn0034568 3216 CG3216 31877 1.065 0.093 1.499 0.050 FBgn0034114 4282 CG4282 26313 1.227 0.086 1.284 0.088 FBgn0036274 4328 CG4328 27987 1.226 0.1061 1.219 0.090 FBgn0030432 4404 CG4404 31923 1.192 0.094 1.378 0.134 FBgn0032187 4839 CG4839 27046 1.100 0.0716 1.217 0.068 FBgn0033889 6701 CG6701 27560 1.015 0.071 1.270 0.103 FBgn0037895 6723 CG6723 29425 1.091 0.056 1.287 0.097 FBgn0031711 6907 CG6907 27716 1.124 0.079 1.303 0.091 FBgn0038140 8784 CG8784 29624 0.826 0.083 1.253 0.087 FBgn0038140 8784 CG8784 29624 1.149 0.104 1.253 0.087 FBgn0038139 8795 CG8795 28781 0.999 0.072 1.403 0.165 FBgn0034599 9437 CG9437 26754 1.175 0.061 1.252 0.069 FBgn0038166 9588 CG9588 28527 0.950 0.088 1.306 0.117 FBgn0025693 11163 CG11163 28638 1.024 0.093 1.304 0.154
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round 2 round 1 BDSC FlyBase ID CG# gene TAG standard TAG standard stock # (% control) deviation (% control) deviation FBgn0031855 11221 CG11221 29603 0.939 0.056 1.284 0.053 FBgn0039840 11340 CG11340 26003 1.047 0.049 1.255 0.125 FBgn0039864 11550 CG11550 29609 0.960 0.041 1.336 0.202 FBgn0039882 11576 CG11576 29531 1.134 0.047 1.294 0.094 FBgn0031391 11723 CG11723 29349 0.976 0.033 1.318 0.062 FBgn0034425 11906 CG11906 26767 1.174 0.095 1.251 0.100 FBgn0039796 12069 CG12069 27677 0.965 0.035 1.232 0.054 FBgn0039808 12071 CG12071 26768 1.165 0.133 1.436 0.058 FBgn0033958 12858 CG12858 29418 1.004 0.043 1.426 0.136 FBgn0032127 13114 CG13114 28632 0.982 0.082 1.379 0.109 FBgn0032150 13123 CG13123 29545 1.095 0.053 1.257 0.112 FBgn0033579 13229 CG13229 29419 1.104 0.094 1.287 0.097 FBgn0034788 13532 CG13532 28643 1.247 0.118 1.317 0.063 FBgn0039424 14239 CG14239 28012 1.208 0.080 1.406 0.243 FBgn0032023 14274 CG14274 28763 1.096 0.095 1.748 0.099 FBgn0038579 14313 CG14313 29614 1.081 0.030 1.331 0.078 FBgn0029639 14419 CG14419 28647 1.351 0.078 1.349 0.102 FBgn0037275 14655 CG14655 26770 1.148 0.091 1.255 0.032 FBgn0037317 14667 CG14667 29365 1.227 0.089 1.305 0.116 FBgn0037829 14691 CG14691 31986 1.391 0.071 1.275 0.081 FBgn0035544 15021 CG15021 28765 1.159 0.059 1.389 0.191 FBgn0028482 16857 CG16857 31974 1.121 0.066 1.356 0.056 FBgn0038741 17186 CG17186 27079 1.138 0.074 1.269 0.061 FBgn0034883 17664 CG17664 25924 1.306 0.107 1.330 0.100 FBgn0038548 17806 CG17806 31956 1.308 0.129 1.421 0.065 FBgn0028518 18480 CG18480 28529 1.239 0.042 1.282 0.179 FBgn0051460 31460 CG31460 26312 1.283 0.023 1.262 0.033 FBgn0051646 31646 CG31646 28654 0.882 0.063 0.693 0.078 FBgn0051646 31646 CG31646 28654 1.036 0.055 0.693 0.078 FBgn0051809 31809 CG31809 29424 1.277 0.122 1.312 0.129 FBgn0052432 32432 CG32432 29621 1.323 0.117 1.357 0.035 FBgn0052532 32532 CG32532 26750 1.199 0.119 1.254 0.093 FBgn0052702 32702 CG32702 28702 1.332 0.079 1.379 0.069 FBgn0052778 32778 CG43284 26715 1.339 0.063 1.399 0.131 FBgn0052792 32792 CG32792 25814 1.225 0.049 1.404 0.126 FBgn0023531 32809 CG32809 28822 1.327 0.060 1.345 0.119 FBgn0053017 33017 CG33017 31927 1.206 0.088 1.254 0.048 FBgn0053143 33143 CG33143 28823 1.275 0.148 1.283 0.072 FBgn0053159 33159 CG33159 26306 1.331 0.046 1.472 0.129 FBgn0053231 33231 CG33231 28914 1.391 0.052 1.365 0.042 FBgn0053289 33289 CG33289 25816 1.188 0.039 1.353 0.115 FBgn0053523 33523 CG33523 25838 1.178 0.117 1.295 0.090 FBgn0053639 33639 CG33639 28614 1.390 0.062 1.426 0.101 FBgn0053673 33673 CG33673 28051 1.299 0.097 1.339 0.098
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round 2 round 1 BDSC FlyBase ID CG# gene TAG standard TAG standard stock # (% control) deviation (% control) deviation FBgn0053958 33958 CG33958 30507 1.234 0.057 1.331 0.087 FBgn0053967 33967 CG33967 28683 1.124 0.082 1.291 0.060 FBgn0250862 42237 CG42237 25877 0.989 0.080 1.213 0.095 FBgn0259145 42260 CG42260 26723 1.204 0.061 1.401 0.089 FBgn0259166 42271 CG42271 29411 1.210 0.065 1.330 0.115 FBgn0259221 42321 CG42321 28558 1.227 0.079 1.311 0.076 FBgn0259244 42342 CG42342 28648 1.302 0.108 1.520 0.084 FBgn0259677 42346 CG42346 28958 1.214 0.072 1.335 0.132 FBgn0259678 42347 CG42347 26735 1.278 0.032 1.422 0.088 FBgn0259712 42366 CG42366 27294 1.387 0.028 1.502 0.102 FBgn0260795 42575 CG42575 29408 1.335 0.069 1.779 0.183 FBgn0260971 42594 CG42594 25888 1.209 0.069 1.362 0.138 FBgn0036805 4108 Chmp1 28906 1.148 0.057 1.293 0.102 FBgn0015372 3870 chrw 28033 0.949 0.062 1.280 0.121 FBgn0004859 2125 ci 28984 1.048 0.155 1.377 0.218 FBgn0028386 5067 cic 25995 1.145 0.070 1.327 0.049 FBgn0000338 17894 cnc 25984 1.029 0.055 1.293 0.087 FBgn0014462 42701 Cng 26014 1.252 0.118 1.365 0.153 FBgn0261714 4795 Cpn 31881 1.261 0.112 1.295 0.047 FBgn0014467 6103 CrebB-17A 29332 1.264 0.066 1.374 0.074 FBgn0000377 3193 crn 29535 0.931 0.081 1.324 0.141 FBgn0013767 3302 Crz 25999 1.024 0.084 1.341 0.150 FBgn0027057 3889 CSN1b 27303 1.046 0.085 1.251 0.107 FBgn0261268 42616 cul-3 28899 1.105 0.076 1.280 0.114 FBgn0019643 3318 Dat 26243 0.841 0.092 1.268 0.049 FBgn0002413 2048 dco 27719 1.149 0.112 1.283 0.107 FBgn0029131 33134 debcl 27083 1.297 0.032 1.262 0.066 FBgn0008649 5441 dei 25973 0.745 0.079 0.691 0.092 FBgn0085390 34361 Dgk 29459 1.210 0.156 1.191 0.077 FBgn0015933 2146 didum 28620 1.010 0.096 1.365 0.155 FBgn0260632 6667 dl 27650 0.964 0.069 1.278 0.130 FBgn0000157 3629 Dll 29337 0.852 0.017 1.297 0.144 FBgn0000479 32498 dnc 27250 1.324 0.030 1.344 0.092 FBgn0027835 5170 Dp1 31966 0.943 0.082 0.720 0.058 FBgn0052057 32057 dpr10 27991 1.221 0.153 1.297 0.095 FBgn0085414 34385 dpr12 28782 1.318 0.062 1.275 0.070 FBgn0052666 32666 Drak 29449 1.296 0.100 1.388 0.128 FBgn0015664 5838 Dref 31941 1.174 0.012 1.256 0.041 FBgn0020307 5799 dve 26225 0.999 0.042 1.278 0.097 FBgn0000629 6502 E(z) 27993 1.180 0.062 1.344 0.102 FBgn0026441 4913 ear 28068 1.206 0.115 1.284 0.059 FBgn0005659 5583 Ets98B 28700 1.097 0.085 1.305 0.263 FBgn0000606 2328 eve 28734 1.066 0.084 1.254 0.100 FBgn0005558 1464 ey 29339 1.178 0.097 1.265 0.141
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round 2 round 1 BDSC FlyBase ID CG# gene TAG standard TAG standard stock # (% control) deviation (% control) deviation FBgn0004896 3668 fd59A 31937 0.900 0.094 1.288 0.095 FBgn0038402 5952 Fer2 28697 1.098 0.069 1.339 0.116 FBgn0085397 34368 Fili 28568 0.944 0.092 1.372 0.110 FBgn0000709 1484 fliI 27566 1.069 0.081 1.337 0.049 FBgn0083228 5907 Frq2 28711 1.049 0.070 1.302 0.070 FBgn0004373 7004 fwd 29396 1.264 0.285 1.351 0.137 FBgn0004373 7004 fwd 29396 1.440 0.161 1.351 0.137 FBgn0260446 15274 GABA-B-R1 28353 1.121 0.076 1.259 0.053 FBgn0001108 9206 Gl 27721 0.991 0.071 1.405 0.184 FBgn0020429 7234 GluRIIB 28718 1.334 0.109 1.357 0.105 FBgn0260798 40129 Gprk1 28354 0.944 0.043 1.235 0.136 FBgn0001134 7446 Grd 29589 1.314 0.098 1.252 0.077 FBgn0001123 2835 G-sα60A 29576 0.905 0.067 1.315 0.067 FBgn0013973 1470 Gycβ100B 28786 1.116 0.065 1.381 0.088 FBgn0001169 5460 H 27315 1.258 0.117 1.338 0.047 FBgn0031349 14351 haf 28528 0.916 0.054 1.405 0.204 FBgn0011771 5837 Hem 29406 1.137 0.046 1.449 0.087 FBgn0010303 4353 hep 28710 1.100 0.066 1.258 0.139 FBgn0030899 5927 Her 27654 1.280 0.035 1.286 0.114 FBgn0010114 2040 hig 28376 1.038 0.143 1.266 0.097 FBgn0001203 32688 Hk 28330 1.306 0.073 1.288 0.051 FBgn0011276 2655 HLH3B 26324 0.811 0.229 1.314 0.132 FBgn0002633 8361 HLHm7 29327 0.864 0.082 1.296 0.111 FBgn0002735 8333 HLHmγ 25978 1.040 0.047 1.360 0.069 FBgn0023546 16902 Hr4 31868 1.162 0.075 1.308 0.052 FBgn0031450 2903 Hrs 28026 1.028 0.025 1.416 0.083 FBgn0051354 5834 Hsp70Bbb 28787 1.195 0.076 1.367 0.043 FBgn0027107 17063 inx6 31889 1.160 0.064 1.340 0.063 FBgn0016672 3028 Ipp 28028 1.054 0.065 1.273 0.073 FBgn0001319 10197 kn 31916 1.074 0.053 1.299 0.054 FBgn0002522 1264 lab 26753 1.321 0.073 1.344 0.101 FBgn0063485 3849 Lasp 26305 1.101 0.039 1.284 0.040 FBgn0005654 4088 lat 25876 1.120 0.080 1.302 0.052 FBgn0016032 2374 lbm 27278 0.876 0.184 1.406 0.081 FBgn0010240 17336 Lcch3 32019 1.168 0.031 1.294 0.081 FBgn0032230 13139 lft 28755 0.930 0.036 1.266 0.116 FBgn0034720 11206 Liprin-γ 28301 0.987 0.047 1.264 0.085 FBgn0004513 10181 Mdr65 28664 1.079 0.086 1.139 0.087 FBgn0032940 18362 Mio 27059 0.963 0.034 1.238 0.071 FBgn0037778 3910 mtTFB2 27055 1.053 0.062 1.357 0.105 FBgn0013303 7641 Nca 29461 1.075 0.050 0.709 0.122 FBgn0013305 3798 Nmda1 28361 0.950 0.059 1.307 0.058 FBgn0010399 2902 Nmdar1 25941 0.925 0.093 1.306 0.049 FBgn0040813 11051 Nplp2 29430 1.093 0.046 1.270 0.101
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round 2 round 1 BDSC FlyBase ID CG# gene TAG standard TAG standard stock # (% control) deviation (% control) deviation FBgn0013342 17248 n-syb 31983 1.241 0.129 1.283 0.038 FBgn0033268 2297 Obp44a 28794 0.966 0.104 1.371 0.052 FBgn0002985 3851 odd 28295 0.899 0.071 1.295 0.043 FBgn0032651 5545 Oli 25979 1.182 0.081 1.366 0.058 FBgn0003008 3029 or 28347 1.166 0.079 1.298 0.077 FBgn0041626 18859 Or19a 31892 1.196 0.125 1.326 0.058 FBgn0085432 34403 pan 26743 0.952 0.080 0.791 0.039 FBgn0011823 4799 Pen 27692 1.120 0.042 1.267 0.145 FBgn0015279 4141 Pi3K92E 27690 0.972 0.050 1.191 0.060 FBgn0000489 6117 Pka-C3 27569 0.974 0.039 1.148 0.084 FBgn0259680 42349 Pkcδ 28355 1.032 0.070 1.111 0.043 FBgn0000442 3324 Pkg21D 27686 0.817 0.062 1.287 0.126 FBgn0020621 2049 Pkn 28335 1.052 0.032 1.180 0.066 FBgn0003117 3978 pnr 28935 1.036 0.052 0.728 0.065 FBgn0003118 17077 pnt 31936 1.040 0.048 1.310 0.045 FBgn0069354 17137 Porin2 25886 1.117 0.053 1.324 0.056 FBgn0020258 3478 ppk 29571 1.031 0.064 1.259 0.148 FBgn0045038 7105 Proct 29570 1.374 0.183 1.331 0.059 FBgn0026379 5671 Pten 25841 0.988 0.073 1.147 0.046 FBgn0003165 9755 pum 26725 1.093 0.128 1.523 0.157 FBgn0003169 7904 put 27514 1.090 0.067 1.289 0.115 FBgn0039352 5053 RASSF8 28323 0.848 0.031 0.664 0.070 FBgn0085373 42667 rdgA 29435 1.314 0.079 1.189 0.038 FBgn0003218 11111 rdgB 28796 1.070 0.049 1.298 0.048 FBgn0011829 14396 Ret 25948 1.082 0.150 1.267 0.148 FBgn0026181 9774 rok 28797 0.968 0.117 0.901 0.058 FBgn0011285 17596 S6kII 27731 0.997 0.025 0.846 0.073 FBgn0005278 2674 Sam-S 29415 1.200 0.125 1.278 0.026 FBgn0003321 1664 sbr 28924 1.001 0.050 1.375 0.233 FBgn0085387 34358 shakB 27292 1.429 0.121 1.355 0.107 FBgn0015542 7951 sima 26207 1.240 0.076 1.411 0.047 FBgn0027364 3871 Six4 30510 0.955 0.064 0.533 0.024 FBgn0024290 6772 Slob 27492 1.262 0.064 1.311 0.112 FBgn0003448 3956 sna 28679 0.893 0.079 0.734 0.039 FBgn0036934 7395 sNPF-R 27507 1.269 0.100 1.287 0.090 FBgn0033112 9454 Spn42Db 28737 1.360 0.070 1.399 0.084 FBgn0003499 7847 sr 27701 1.268 0.082 1.250 0.063 FBgn0036248 17153 ssp 29358 0.978 0.051 1.343 0.151 FBgn0004837 3497 Su(H) 28900 0.970 0.039 1.376 0.422 FBgn0003612 8068 Su(var)2-10 29448 1.134 0.080 1.283 0.041 FBgn0037690 9361 Task7 27264 1.055 0.053 1.275 0.073 FBgn0004110 7895 tin 28539 1.084 0.040 1.339 0.045 FBgn0016041 12157 Tom40 26005 1.294 0.036 1.231 0.070 FBgn0033074 8330 tomboy40 29573 1.243 0.119 1.134 0.051
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round 2 round 1 BDSC FlyBase ID CG# gene TAG standard TAG standard stock # (% control) deviation (% control) deviation FBgn0086355 2171 Tpi 26304 1.199 0.114 1.157 0.072 FBgn0003870 1856 ttk 26315 1.157 0.084 1.294 0.063 FBgn0015561 1650 unpg 29393 1.204 0.152 1.284 0.031 FBgn0011739 12072 wts 27662 0.980 0.056 1.244 0.074 FBgn0021872 9415 Xbp1 25990 1.019 0.156 1.260 0.066 FBgn0015565 2913 yin 31971 1.100 0.032 1.322 0.080
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Table A.3. Data from third round of RNAi screen. Highlighted rows are hits that resulted in a change in TAG levels that is statistically significant by one-way ANOVA (unadjusted P value < critical level). TRiP, Transgenic RNAi Project; VDRC, Vienna Drosophila RNAi Centre.
TAG standard unadjusted critical FlyBase ID CG# gene library stock # (% control) deviation P level FBgn0260793 42573 2mit VDRC (GD) 11434 0.830 0.035 <0.001 0.005 FBgn0260793 42573 2mit VDRC (GD) 27157 1.147 0.080 0.005 0.010 FBgn0260793 42573 2mit VDRC (GD) 44841 1.017 0.054 0.850 0.025 FBgn0260793 42573 2mit VDRC (KK) 104758 1.055 0.090 0.262 0.013 FBgn0260793 42573 2mit VDRC (GD) 8384 1.126 0.042 0.075 0.005 FBgn0260793 42573 2mit VDRC (GD) 27159 0.877 0.063 0.007 0.009 FBgn0000479 17673 Acp70A VDRC (KK) 109175 1.020 0.057 FBgn0085445 42734 Ank2 TRiP (VAL20) 33414 0.649 0.082 <0.001 0.005 FBgn0085445 42734 Ank2 VDRC (GD) 46225 0.942 0.123 0.405 0.009 FBgn0085445 42734 Ank2 VDRC (KK) 101586 1.007 0.108 0.909 0.050 FBgn0085445 42734 Ank2 VDRC (KK) 104833 1.143 0.073 0.005 0.005 FBgn0085445 42734 Ank2 VDRC (KK) 107238 0.864 0.090 0.003 0.004 FBgn0085445 42734 Ank2 VDRC (KK) 107369 1.078 0.072 0.080 0.007 FBgn0085445 42734 Ank2 VDRC (GD) 26121 0.859 0.073 0.002 0.006 FBgn0085445 42734 Ank2 VDRC (GD) 26122 1.054 0.028 0.224 0.017 FBgn0022131 42783 aPKC TRiP (VAL20) 34332 0.830 0.029 0.005 0.010 FBgn0022131 42783 aPKC TRiP (VAL20) 35001 1.224 0.175 <0.001 0.009 FBgn0022131 42783 aPKC VDRC (KK) 105624 1.078 0.052 0.119 0.009 FBgn0004624 18069 CaMKII VDRC (GD) 47280 0.788 0.090 0.003 0.004 FBgn0004624 18069 CaMKII VDRC (KK) 100265 0.993 0.061 FBgn0022213 13281 Cas VDRC (GD) 12647 0.898 0.106 0.049 0.006 FBgn0022213 13281 Cas VDRC (KK) 110215 0.981 0.153 0.739 0.013 FBgn0022213 13281 Cas VDRC (GD) 12648 1.239 0.097 0.006 0.005 FBgn0033186 1602 CG1602 VDRC (GD) 32658 1.166 0.173 0.001 0.006 FBgn0033186 1602 CG1602 VDRC (KK) 107956 0.982 0.073 0.692 0.017 FBgn0033186 1602 CG1602 VDRC (GD) 32659 0.986 0.061 0.692 0.050 FBgn0036274 4328 CG4328 VDRC (GD) 30516 0.980 0.126 0.743 0.017 FBgn0030432 4404 CG4404 VDRC (GD) 26531 1.251 0.100 <0.001 0.007 FBgn0030432 4404 CG4404 VDRC (GD) 48183 1.082 0.080 0.144 0.007 FBgn0032187 4839 CG4839 VDRC (KK) 100999 0.962 0.067 0.496 0.007 FBgn0032187 4839 CG4839 VDRC (GD) 26641 1.043 0.112 0.248 0.010 FBgn0032187 4839 CG4839 VDRC (GD) 26642 1.024 0.015 0.529 0.025 FBgn0038140 8784 CG8784 VDRC (KK) 103822 0.890 0.066 0.024 0.005 FBgn0038140 8784 CG8784 VDRC (GD) 15989 0.970 0.147 0.591 0.025 FBgn0014462 9437 CG9437 VDRC (KK) 100101 1.069 0.051 0.268 0.009 FBgn0034425 11906 CG11906 VDRC (GD) 16364 0.898 0.072 0.049 0.006 FBgn0034425 11906 CG11906 VDRC (KK) 100083 1.076 0.091
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FBgn0039796 12069 CG12069 VDRC (GD) 23717 0.789 0.091 <0.001 0.006 FBgn0039796 12069 CG12069 VDRC (KK) 100886 1.150 0.088 0.009 0.004 FBgn0039796 12069 CG12069 VDRC (GD) 23719 1.027 0.050 0.465 0.013 FBgn0034599 13229 CG13229 VDRC (KK) 100433 1.038 0.163 0.542 0.013 FBgn0034788 13532 CG13532 VDRC (GD) 12848 0.950 0.069 0.354 0.010 FBgn0034788 13532 CG13532 VDRC (KK) 105696 0.935 0.030 0.186 0.010 FBgn0023531 14239 CG14239 VDRC (GD) 42523 1.145 0.063 <0.001 0.009 FBgn0023531 14239 CG14239 VDRC (KK) 109855 0.959 0.080 FBgn0029131 14419 CG14419 VDRC (KK) 107498 1.013 0.126 0.838 0.050 FBgn0037275 14655 CG14655 VDRC (KK) 104293 0.785 0.078 <0.001 0.004 FBgn0037275 14655 CG14655 VDRC (GD) 13136 0.897 0.087 0.215 0.009 FBgn0037317 14667 CG14667 VDRC (GD) 24903 1.236 0.062 <0.001 0.009 FBgn0037317 14667 CG14667 VDRC (KK) 107476 1.123 0.047 0.007 0.005 FBgn0037829 14691 CG14691 VDRC (GD) 46365 0.775 0.089 0.003 0.004 FBgn0037829 14691 CG14691 VDRC (KK) 102107 1.324 0.120 <0.001 0.004 FBgn0035544 15021 CG15021 VDRC (GD) 52038 0.823 0.072 <0.001 0.007 FBgn0035544 15021 CG15021 VDRC (KK) 106362 1.085 0.060 0.088 0.007 FBgn0013342 17186 CG17186 VDRC (GD) 45018 1.097 0.095 0.007 0.013 FBgn0013342 17186 CG17186 VDRC (KK) 102847 0.985 0.061 0.814 0.025 FBgn0034883 17664 CG17664 VDRC (GD) 49978 1.024 0.084 0.665 0.025 FBgn0034883 17664 CG17664 VDRC (GD) 49979 0.955 0.082 0.413 0.013 FBgn0034883 17664 CG17664 VDRC (KK) 101847 1.083 0.083 0.182 0.007 FBgn0038548 17806 CG17806 VDRC (KK) 101592 1.128 0.034 0.028 0.006 FBgn0028518 18480 CG18480 VDRC (KK) 106622 0.855 0.051 0.013 0.006 FBgn0028518 18480 CG18480 VDRC (GD) 1070 1.234 0.042 <0.001 0.005 FBgn0051460 31460 CG31460 VDRC (GD) 33323 0.987 0.053 0.817 0.025 FBgn0051460 31460 CG31460 VDRC (KK) 103626 0.882 0.072 0.016 0.004 FBgn0051646 31646 CG31646 VDRC (KK) 100781 1.246 0.090 <0.001 0.006 FBgn0051809 31809 CG31809 VDRC (GD) 32522 1.043 0.065 0.372 0.009 FBgn0051809 31809 CG31809 VDRC (GD) 50644 0.902 0.051 0.028 0.025 FBgn0085414 31809 CG31809 VDRC (GD) 50643 1.126 0.047 <0.001 0.007 FBgn0052432 32432 CG32432 VDRC (GD) 8540 0.988 0.039 0.764 0.017 FBgn0052432 32432 CG32432 VDRC (KK) 107878 0.974 0.061 0.550 0.013 FBgn0052432 32432 CG32432 VDRC (GD) 8539 0.971 0.106 0.726 0.025 FBgn0052532 32532 CG32532 VDRC (GD) 37047 1.038 0.035 0.433 0.010 FBgn0052532 32532 CG32532 VDRC (KK) 106534 0.983 0.061 0.737 0.050 FBgn0052532 32532 CG32532 VDRC (GD) 37046 1.133 0.053 <0.001 0.006 FBgn0052702 32702 CG32702 VDRC (GD) 14613 0.957 0.083 0.400 0.013 FBgn0052702 32702 CG32702 VDRC (GD) 14614 0.852 0.034 0.005 0.005 FBgn0023531 32809 CG32809 VDRC (GD) 45133 1.046 0.118 0.613 0.013 FBgn0023531 32809 CG32809 VDRC (KK) 104480 0.867 0.042 0.007 0.004 FBgn0023531 32809 CG32809 VDRC (GD) 26282 1.037 0.079 0.432 0.050 FBgn0023531 32809 CG32809 VDRC (GD) 41954 1.243 0.059 <0.001 0.004
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FBgn0053017 33017 CG33017 VDRC (GD) 40021 1.155 0.076 0.038 0.005 FBgn0053017 33017 CG33017 VDRC (GD) 40022 0.974 0.084 0.709 0.017 FBgn0053017 33017 CG33017 VDRC (KK) 103968 0.972 0.097 0.555 0.017 FBgn0053143 33143 CG33143 VDRC (KK) 101893 1.102 0.076 0.078 0.006 FBgn0053159 33159 CG33159 VDRC (GD) 51582 0.903 0.033 0.030 0.050 FBgn0053159 33159 CG33159 VDRC (GD) 51581 1.099 0.046 0.010 0.006 FBgn0053231 33231 CG33231 TRiP (VAL20) 34016 0.835 0.057 0.002 0.006 FBgn0053231 33231 CG33231 VDRC (GD) 34509 0.999 0.025 0.990 0.050 FBgn0053231 33231 CG33231 VDRC (GD) 48439 1.096 0.031 0.136 0.006 FBgn0053231 33231 CG33231 VDRC (KK) 108962 0.988 0.050 0.805 0.025 FBgn0053523 33523 CG33523 VDRC (GD) 37237 1.132 0.075 0.008 0.006 FBgn0053523 33523 CG33523 VDRC (GD) 37238 0.977 0.022 0.638 0.017 FBgn0053523 33523 CG33523 VDRC (KK) 110519 1.064 0.133 0.250 0.006 FBgn0053639 33639 CG33639 VDRC (GD) 29644 1.100 0.058 0.051 0.017 FBgn0053639 33639 CG33639 VDRC (KK) 108753 0.820 0.061 <0.001 0.004 FBgn0053673 33673 CG33673 VDRC (KK) 101780 0.970 0.060 0.595 0.017 FBgn0053958 33958 CG33958 VDRC (GD) 4974 1.014 0.083 0.739 0.013 FBgn0053958 33958 CG33958 VDRC (GD) 4975 1.005 0.067 0.912 0.050 FBgn0053958 33958 CG33958 VDRC (GD) 20107 0.806 0.085 <0.001 0.004 FBgn0053958 33958 CG33958 VDRC (KK) 106547 0.830 0.139 <0.001 0.004 FBgn0053958 33958 CG33958 VDRC (KK) 101861 1.019 0.058 0.763 0.017 FBgn0250862 42237 CG42237 VDRC (GD) 37389 1.084 0.106 0.197 0.006 FBgn0259166 42271 CG42271 VDRC (KK) 100176 1.109 0.071 FBgn0259166 42271 CG42271 VDRC (GD) 41672 1.089 0.045 0.012 0.017 FBgn0259221 42321 CG42321 VDRC (KK) 107000 0.868 0.056 0.004 0.005 FBgn0259221 42321 CG42321 VDRC (GD) 8136 0.923 0.074 0.206 0.009 FBgn0259221 42321 CG42321 VDRC (GD) 33668 1.207 0.028 <0.001 0.005 FBgn0259677 42346 CG42346 VDRC (GD) 36218 1.223 0.094 <0.001 0.005 FBgn0259677 42346 CG42346 VDRC (GD) 4812 1.034 0.174 0.625 0.017 FBgn0259677 42346 CG42346 VDRC (GD) 6704 0.898 0.089 0.096 0.006 FBgn0259712 42366 CG42366 TRiP (VAL20) 36112 0.765 0.051 <0.001 0.006 FBgn0259712 42366 CG42366 VDRC (GD) 39904 1.049 0.085 0.476 0.010 FBgn0259712 42366 CG42366 VDRC (KK) 108102 0.879 0.085 0.008 0.006 FBgn0259712 42366 CG42366 VDRC (GD) 25813 1.163 0.102 0.005 0.006 FBgn0259712 42366 CG42366 VDRC (GD) 25814 1.041 0.112 0.469 0.017 FBgn0260971 42594 CG42594 TRiP (VAL10) 29593 1.166 0.114 0.001 0.005 FBgn0260971 42594 CG42594 TRiP (VAL20) 35006 0.887 0.099 0.053 0.013 FBgn0260971 42594 CG42594 VDRC (GD) 22911 0.879 0.057 0.043 0.005 FBgn0260971 42594 CG42594 VDRC (GD) 46415 1.073 0.198 0.292 0.005 FBgn0260971 42594 CG42594 VDRC (GD) 46416 1.060 0.106 0.389 0.007 FBgn0260971 42594 CG42594 VDRC (GD) 7042 1.008 0.103 0.895 0.050 FBgn0263772 43689 CG43689 VDRC (GD) 15480 0.904 0.084 0.063 0.007 FBgn0263772 43689 CG43689 VDRC (KK) 103499 1.049 0.059 0.306 0.009
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FBgn0036805 4108 Chmp1 VDRC (GD) 21788 1.124 0.022 0.040 0.007 FBgn0004859 2125 ci TRiP (VAL1) 31236 0.965 0.100 0.482 0.025 FBgn0004859 2125 ci TRiP (VAL1) 31320 1.129 0.078 0.012 0.006 FBgn0004859 2125 ci TRiP (VAL1) 31321 1.025 0.102 0.611 0.050 FBgn0004859 2125 ci VDRC (KK) 105620 1.136 0.095 0.007 0.006 FBgn0004859 2125 ci VDRC (GD) 51479 1.024 0.057 0.528 0.017 FBgn0000338 17894 cnc TRiP (VAL20) 32863 1.138 0.042 0.067 0.010 FBgn0000338 17894 cnc VDRC (GD) 37674 1.072 0.114 0.239 0.007 FBgn0000338 17894 cnc VDRC (KK) 101235 1.230 0.151 <0.001 0.006 FBgn0000338 17894 cnc VDRC (KK) 108127 1.039 0.070 0.518 0.010 FBgn0014462 42701 Cng VDRC (KK) 102783 0.949 0.086 0.292 0.007 FBgn0014462 42701 Cng VDRC (GD) 28625 0.867 0.058 0.004 0.007 FBgn0014462 42701 Cng VDRC (GD) 29046 0.928 0.0633 0.106 0.013 FBgn0014462 42701 Cng VDRC (GD) 51844 1.046 0.029 0.221 0.009 FBgn0014462 42701 Cng VDRC (KK) 101745 1.185 0.070 0.004 0.006 FBgn0261714 4795 Cpn VDRC (GD) 13086 0.959 0.107 0.416 0.017 FBgn0261714 4795 Cpn VDRC (GD) 13422 0.912 0.050 0.108 0.009 FBgn0261714 4795 Cpn VDRC (KK) 101962 1.345 0.092 <0.001 0.004 CrebB- FBgn0014467 6103 VDRC (KK) 101512 1.298 0.046 <0.001 0.005 17A FBgn0027057 3889 CSN1b VDRC (KK) 110477 0.949 0.081 0.363 0.006 FBgn0027057 3889 CSN1b VDRC (GD) 34727 1.012 0.042 0.751 0.050 FBgn0261268 42616 Cul-3 TRiP (VAL20) 36684 0.697 0.080 <0.001 0.005 FBgn0261268 42616 Cul-3 VDRC (GD) 25875 1.282 0.050 <0.001 0.006 FBgn0261268 42616 Cul-3 VDRC (GD) 16331 1.052 0.054 0.352 0.010 FBgn0261268 42616 Cul-3 VDRC (KK) 109415 1.054 0.181 FBgn0002413 2048 dco TRiP (VAL1) 31763 0.910 0.044 0.072 0.010 FBgn0002413 2048 dco TRiP (VAL20) 35134 1.003 0.059 0.957 0.050 FBgn0002413 2048 dco VDRC (GD) 9241 0.809 0.128 0.024 0.006 FBgn0029131 33134 debcl VDRC (GD) 47515 1.241 0.057 <0.001 0.004 FBgn0029131 33134 debcl VDRC (GD) 47516 1.256 0.143 <0.001 0.004 FBgn0029131 33134 debcl VDRC (GD) 47518 1.140 0.090 0.014 0.005 FBgn0029131 33134 debcl VDRC (KK) 106669 1.207 0.082 0.003 0.005 FBgn0085390 34361 Dgk VDRC (GD) 38239 1.191 0.076 0.003 0.004 FBgn0085390 34361 Dgk VDRC (GD) 38241 1.022 0.027 0.661 0.050 FBgn0085390 34361 Dgk VDRC (KK) 105753 1.022 0.122 0.716 0.025 FBgn0000479 32498 dnc VDRC (GD) 14534 0.991 0.053 0.863 0.050 FBgn0000479 32498 dnc VDRC (GD) 16717 0.987 0.098 0.793 0.025 FBgn0000479 32498 dnc VDRC (GD) 24615 1.094 0.088 0.107 0.025 FBgn0000479 32498 dnc VDRC (GD) 24616 1.062 0.123 0.246 0.050 FBgn0000479 32498 dnc VDRC (GD) 26299 1.453 0.051 <0.001 0.004 FBgn0000479 32498 dnc VDRC (GD) 15444 1.093 0.124 0.263 0.013 FBgn0000479 32498 dnc VDRC (GD) 18140 1.096 0.057 0.091 0.009
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FBgn0000479 32498 dnc VDRC (KK) 107967 1.044 0.073 FBgn0052057 32057 dpr10 VDRC (GD) 18919 1.313 0.046 <0.001 0.005 FBgn0052057 32057 dpr10 VDRC (GD) 18920 1.260 0.064 <0.001 0.005 FBgn0052057 32057 dpr10 VDRC (KK) 103511 0.944 0.149 0.364 0.010 FBgn0085414 34385 dpr12 VDRC (GD) 44740 0.931 0.135 0.322 0.006 FBgn0085414 34385 dpr12 VDRC (GD) 44741 1.007 0.056 0.920 0.050 FBgn0085414 34385 dpr12 VDRC (GD) 48729 0.997 0.079 0.951 0.050 FBgn0085414 34385 dpr12 VDRC (KK) 106788 0.761 0.062 <0.001 0.004 FBgn0085414 34385 dpr12 VDRC (GD) 48730 1.072 0.043 0.116 0.025 FBgn0052666 32666 Drak VDRC (GD) 44374 0.924 0.052 0.339 0.006 FBgn0052666 32666 Drak VDRC (KK) 107263 0.924 0.097 0.089 0.009 FBgn0052666 32666 Drak VDRC (GD) 32960 1.162 0.048 <0.001 0.006 FBgn0000629 6502 E(z) TRiP (VAL1) 31617 0.939 0.075 0.216 0.017 FBgn0000629 6502 E(z) TRiP (VAL20) 33659 0.918 0.082 0.100 0.013 FBgn0000629 6502 E(z) VDRC (KK) 107072 1.002 0.070 0.960 0.050 FBgn0000629 6502 E(z) VDRC (GD) 27646 1.136 0.055 0.003 0.006 FBgn0026441 4913 ear TRiP (VAL20) 34798 1.239 0.099 <0.001 0.006 FBgn0026441 4913 ear VDRC (KK) 109659 0.859 0.051 0.002 0.004 FBgn0026441 4913 ear VDRC (GD) 15671 1.195 0.204 0.045 0.006 FBgn0026441 4913 ear VDRC (GD) 15672 1.095 0.177 0.253 0.010 FBgn0005659 5583 Ets98B VDRC (KK) 107292 1.082 0.078 0.068 0.006 FBgn0005659 5583 Ets98B VDRC (GD) 10932 1.002 0.143 0.981 0.050 FBgn0000606 2328 eve TRiP (VAL20) 34325 0.755 0.069 <0.001 0.007 FBgn0000606 2328 eve VDRC (GD) 9284 0.926 0.052 0.373 0.017 FBgn0000606 2328 eve VDRC (KK) 110769 0.985 0.138 FBgn0005558 1464 ey TRiP (VAL20) 32486 0.873 0.041 0.012 0.007 FBgn0005558 1464 ey VDRC (KK) 106628 0.830 0.062 0.001 0.005 FBgn0004373 7004 fwd TRiP (VAL1) 31187 1.277 0.087 <0.001 0.007 FBgn0004373 7004 fwd VDRC (GD) 27785 1.359 0.111 <0.001 0.005 FBgn0004373 7004 fwd VDRC (GD) 27786 1.243 0.080 <0.001 0.005 FBgn0004373 7004 fwd VDRC (KK) 110159 0.919 0.075 GABA-B- FBgn0260446 15274 VDRC (KK) 101440 1.049 0.119 0.392 0.013 R1 FBgn0020429 7234 GluRIIB VDRC (KK) 105581 0.976 0.092 0.631 0.025 FBgn0020429 7234 GluRIIB VDRC (GD) 7878 0.963 0.052 0.546 0.013 FBgn0001134 7446 Grd VDRC (GD) 5329 0.905 0.157 0.123 0.007 FBgn0001169 5460 H TRiP (VAL20) 34703 1.110 0.073 0.061 0.017 FBgn0001169 5460 H VDRC (KK) 110046 1.148 0.133 0.010 0.005 FBgn0001169 5460 H VDRC (GD) 24466 1.048 0.078 0.392 0.013 FBgn0010303 4353 hep VDRC (GD) 47507 1.028 0.059 0.613 0.017 FBgn0010303 4353 hep VDRC (GD) 47509 1.168 0.130 0.004 0.005 FBgn0010303 4353 hep VDRC (KK) 109277 0.909 0.059 FBgn0030899 5927 Her VDRC (KK) 102130 1.077 0.108 0.181 0.010
144
FBgn0030899 5927 Her VDRC (GD) 17853 1.212 0.074 <0.001 0.006 FBgn0039424 2040 hig VDRC (KK) 109863 0.948 0.088 FBgn0001203 43388 Hk VDRC (KK) 101402 0.915 0.108 0.138 0.009 FBgn0001203 43388 Hk VDRC (GD) 47805 1.125 0.035 <0.001 0.010 FBgn0016672 3028 Ipp VDRC (GD) 25615 1.054 0.077 0.151 0.006 FBgn0016672 3028 Ipp VDRC (KK) 110775 1.139 0.049 0.028 0.006 FBgn0002522 1264 lab VDRC (KK) 100311 1.019 0.049 0.735 0.025 FBgn0002522 1264 lab VDRC (GD) 2990 1.017 0.124 0.780 0.025 FBgn0002522 1264 lab VDRC (GD) 2991 1.043 0.093 0.506 0.010 FBgn0004513 10181 Mdr65 TRiP (VAL20) 35035 1.084 0.194 0.260 0.017 FBgn0032940 18362 Mio VDRC (GD) 52606 1.105 0.103 0.045 0.025 FBgn0032940 18362 Mio VDRC (GD) 52607 1.277 0.078 <0.001 0.013 FBgn0032940 18362 Mio VDRC (KK) 109821 0.853 0.081 0.018 0.007 FBgn0013342 17248 n-syb VDRC (GD) 49202 0.912 0.053 0.169 0.009 FBgn0013342 17248 n-syb VDRC (KK) 104531 0.908 0.024 0.058 0.006 FBgn0013342 17248 n-syb VDRC (GD) 44011 1.177 0.037 <0.001 0.005 FBgn0003008 3029 or VDRC (GD) 25014 1.341 0.070 <0.001 0.005 FBgn0003008 3029 or VDRC (GD) 25015 1.132 0.052 0.011 0.013 FBgn0003008 3029 or VDRC (KK) 104019 0.971 0.089 0.548 0.013 FBgn0041626 18859 Or19a VDRC (GD) 49231 0.874 0.044 0.035 0.006 FBgn0041626 18859 Or19a VDRC (GD) 49232 1.076 0.081 0.171 0.010 FBgn0041626 18859 Or19a VDRC (KK) 109388 0.954 0.060 0.299 0.010 FBgn0085432 34403 pan VDRC (GD) 3014 0.961 0.096 0.523 0.013 FBgn0085432 34403 pan VDRC (GD) 25940 1.082 0.217 0.205 0.006 FBgn0085432 34403 pan VDRC (KK) 108679 1.024 0.040 0.691 0.017 FBgn0011823 4799 Pen VDRC (GD) 34265 1.209 0.072 <0.001 0.005 FBgn0011823 4799 Pen VDRC (GD) 34266 1.093 0.047 0.059 0.007 FBgn0011823 4799 Pen VDRC (KK) 102627 0.924 0.084 0.114 0.006 FBgn0259680 42349 Pkcδ VDRC (GD) 22755 1.122 0.089 0.007 0.013 FBgn0259680 42349 Pkcδ VDRC (GD) 33838 0.861 0.049 0.003 0.010 FBgn0259680 42349 Pkcδ VDRC (KK) 101029 1.038 0.069 0.500 0.009 FBgn0259680 42349 Pkcδ VDRC (KK) 101421 1.004 0.024 0.943 0.050 FBgn0259680 42349 Pkcδ VDRC (GD) 33837 1.047 0.047 0.213 0.007 FBgn0000442 3324 Pkg21D VDRC (GD) 34594 0.728 0.076 <0.001 0.006 FBgn0000442 3324 Pkg21D VDRC (GD) 34595 0.881 0.064 0.009 0.017 FBgn0000442 3324 Pkg21D VDRC (KK) 103513 0.983 0.030 0.765 0.025 FBgn0020621 2049 Pkn VDRC (KK) 108870 1.017 0.041 0.754 0.017 FBgn0045038 7105 Proct VDRC (KK) 102488 1.014 0.078 0.764 0.025 FBgn0026379 5671 Pten TRiP (VAL10) 25967 1.179 0.094 0.001 0.005 FBgn0026379 5671 Pten TRiP (VAL20) 33643 1.039 0.116 0.596 0.050 FBgn0085373 42667 rdgA VDRC (GD) 3024 1.001 0.101 0.986 0.050 FBgn0085373 42667 rdgA VDRC (GD) 28557 1.058 0.060 0.341 0.009 FBgn0085373 42667 rdgA VDRC (KK) 102909 1.038 0.076 0.553 0.013
145
FBgn0085373 42667 rdgA VDRC (KK) 104660 0.919 0.068 0.179 0.009 FBgn0003218 11111 rdgB VDRC (GD) 6226 1.146 0.021 <0.001 0.005 FBgn0011829 14396 Ret VDRC (GD) 843 1.130 0.063 0.003 0.005 FBgn0011829 14396 Ret VDRC (GD) 12116 0.992 0.070 0.852 0.025 FBgn0011829 14396 Ret VDRC (GD) 30832 0.966 0.066 0.484 0.013 FBgn0011829 14396 Ret VDRC (GD) 842 1.098 0.043 0.111 0.006 FBgn0011829 14396 Ret VDRC (GD) 29907 0.963 0.029 0.398 0.025 FBgn0011829 14396 Ret VDRC (KK) 107648 1.018 0.022 FBgn0026181 9774 rok VDRC (KK) 104675 0.786 0.075 <0.001 0.004 FBgn0026181 9774 rok VDRC (GD) 3793 1.108 0.065 0.006 0.005 FBgn0011285 17596 S6kII VDRC (KK) 101451 1.029 0.097 0.605 0.010 FBgn0005278 2674 Sam-S VDRC (GD) 7167 1.105 0.091 0.014 0.006 FBgn0005278 2674 Sam-S VDRC (GD) 7168 1.023 0.072 0.581 0.010 FBgn0005278 2674 Sam-S VDRC (KK) 103143 0.898 0.071 0.035 0.005 FBgn0085387 34358 shakB TRiP (VAL10) 27291 0.865 0.019 0.021 0.009 FBgn0085387 34358 shakB VDRC (GD) 26802 1.288 0.089 <0.001 0.006 FBgn0024290 6772 Slob VDRC (KK) 100987 1.136 0.100 0.020 0.005 FBgn0024290 6772 Slob VDRC (GD) 30673 1.096 0.121 0.063 0.010 FBgn0024290 6772 Slob VDRC (GD) 30674 1.157 0.100 <0.001 0.005 FBgn0036934 7395 sNPF-R VDRC (GD) 9379 1.210 0.112 0.014 0.005 FBgn0033112 9454 Spn42Db VDRC (KK) 104263 1.047 0.077 0.330 0.010 FBgn0033112 9454 Spn42Db VDRC (GD) 24033 0.984 0.110 0.777 0.050 FBgn0003499 7847 sr VDRC (KK) 105282 0.948 0.073 0.292 0.017 FBgn0003499 7847 sr VDRC (GD) 9921 0.852 0.196 0.079 0.007 FBgn0004837 3497 Su(H) VDRC (KK) 103597 0.996 0.100 0.935 0.050 FBgn0016041 12157 Tom40 VDRC (GD) 13177 1.180 0.059 0.004 0.004 FBgn0016041 12157 Tom40 VDRC (GD) 13178 1.087 0.068 0.157 0.005 FBgn0033074 8330 tomboy40 VDRC (GD) 23763 1.134 0.076 0.031 0.004 FBgn0033074 8330 tomboy40 VDRC (GD) 23764 1.134 0.092 0.041 0.005 FBgn0033074 8330 tomboy40 VDRC (GD) 42439 1.124 0.052 0.021 0.017 FBgn0033074 8330 tomboy40 VDRC (KK) 105557 1.013 0.099 0.827 0.050 FBgn0086355 2171 Tpi VDRC (GD) 25643 0.942 0.051 0.345 0.010 FBgn0086355 2171 Tpi VDRC (GD) 25644 0.992 0.066 0.896 0.025 FBgn0003870 1856 ttk TRiP (VAL20) 36748 1.066 0.067 0.254 0.025 FBgn0003870 1856 ttk VDRC (KK) 101980 1.099 0.101 0.086 0.007 FBgn0015561 1650 unpg VDRC (GD) 12567 0.965 0.062 0.402 0.007 FBgn0015561 1650 unpg VDRC (GD) 12568 0.970 0.071 0.464 0.009 FBgn0015561 1650 unpg VDRC (KK) 100301 0.992 0.119 FBgn0011739 12072 wts TRiP (VAL20) 34064 1.154 0.060 0.004 0.007 FBgn0011739 12072 wts VDRC (GD) 9928 0.831 0.086 <0.001 0.009 FBgn0011739 12072 wts VDRC (KK) 106174 0.931 0.071 0.217 0.005
146
140%
120%
100% ** * * ** * fru/+ *** *** 80% fru > Dgk.V5 fru > Dgk
% % control 60% fru > Dgk[G509D].V5DgkG509D.V5 40% fru > Dgk[G509D]DgkG509D 20%
0% TAG glucose glycogen
Figure A.1. V5 tag does not affect the TAG, glucose and glycogen phenotypes caused by Dgk.V5 overexpression. Untagged or V5-tagged UAS-Dgk and UAS-DgkG509D lines crossed to fru-Gal4 show mostly similar TAG, glucose and glycogen phenotypes. Data is represented as percent of a fru-Gal4/+ control ± SD. Asterisks denote p-values based on Student’s t-test: *p < 0.05, **p < 0.01, ***p < 0.001.
147
References
Agrawal, N., Delanoue, R., Mauri, A., Basco, D., Pasco, M., Thorens, B., and Léopold, P. (2016). The Drosophila TNF Eiger Is an Adipokine that Acts on Insulin-Producing Cells to Mediate Nutrient Response. Cell Metab. 23, 675–684.
Ahrén, B. (2009). Islet G protein-coupled receptors as potential targets for treatment of type 2 diabetes. Nat. Rev. Drug Discov. 8, 369–385.
Al-Anzi, B., Sapin, V., Waters, C., Zinn, K., Wyman, R.J., and Benzer, S. (2009). Obesity- blocking neurons in Drosophila. Neuron 63, 329–341.
Al-Anzi, B., Armand, E., Nagamei, P., Olszewski, M., Sapin, V., Waters, C., Zinn, K., Wyman, R.J., and Benzer, S. (2010). The leucokinin pathway and its neurons regulate meal size in Drosophila. Curr. Biol. CB 20, 969–978.
Albuquerque, D., Stice, E., Rodríguez-López, R., Manco, L., and Nóbrega, C. (2015). Current review of genetics of human obesity: from molecular mechanisms to an evolutionary perspective. Mol. Genet. Genomics MGG 290, 1191–1221.
Alfa, R.W., and Kim, S.K. (2016). Using Drosophila to discover mechanisms underlying type 2 diabetes. Dis. Model. Mech. 9, 365–376.
Alfa, R.W., Park, S., Skelly, K.-R., Poffenberger, G., Jain, N., Gu, X., Kockel, L., Wang, J., Liu, Y., Powers, A.C., et al. (2015). Suppression of insulin production and secretion by a decretin hormone. Cell Metab. 21, 323–333.
Alic, N., Hoddinott, M.P., Vinti, G., and Partridge, L. (2011). Lifespan extension by increased expression of the Drosophila homologue of the IGFBP7 tumour suppressor. Aging Cell 10, 137– 147.
Allison, M.B., and Myers, M.G. (2014). 20 years of leptin: connecting leptin signaling to biological function. J. Endocrinol. 223, T25–T35.
Altarejos, J.Y., and Montminy, M. (2011). CREB and the CRTC co-activators: sensors for hormonal and metabolic signals. Nat. Rev. Mol. Cell Biol. 12, 141–151.
Alwmark, A., and Ahrén, B. (1987). Phentolamine reverses NPY-induced inhibition of insulin secretion in isolated rat islets. Eur. J. Pharmacol. 135, 307–311.
Anderson, A.S., Key, T.J., Norat, T., Scoccianti, C., Cecchini, M., Berrino, F., Boutron-Ruault, M.-C., Espina, C., Leitzmann, M., Powers, H., et al. (2015). European Code against Cancer 4th Edition: Obesity, body fatness and cancer. Cancer Epidemiol. 39 Suppl 1, S34–S45.
André, C., and Cota, D. (2012). Coupling nutrient sensing to metabolic homoeostasis: the role of the mammalian target of rapamycin complex 1 pathway. Proc. Nutr. Soc. 71, 502–510.
148
Anis, A.H., Zhang, W., Bansback, N., Guh, D.P., Amarsi, Z., and Birmingham, C.L. (2010). Obesity and overweight in Canada: an updated cost-of-illness study. Obes. Rev. Off. J. Int. Assoc. Study Obes. 11, 31–40.
Antal, C.E., and Newton, A.C. (2014). Tuning the signalling output of protein kinase C. Biochem. Soc. Trans. 42, 1477–1483.
Ard, R., Mulatz, K., Abramovici, H., Maillet, J.-C., Fottinger, A., Foley, T., Byham, M.-R., Iqbal, T.A., Yoneda, A., Couchman, J.R., et al. (2012). Diacylglycerol kinase ζ regulates RhoA activation via a kinase-independent scaffolding mechanism. Mol. Biol. Cell 23, 4008–4019.
Aulchenko, Y.S., Ripatti, S., Lindqvist, I., Boomsma, D., Heid, I.M., Pramstaller, P.P., Penninx, B.W.J.H., Janssens, A.C.J.W., Wilson, J.F., Spector, T., et al. (2009). Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet. 41, 47–55. van Baal, J., de Widt, J., Divecha, N., and van Blitterswijk, W.J. (2005). Translocation of diacylglycerol kinase theta from cytosol to plasma membrane in response to activation of G protein-coupled receptors and protein kinase C. J. Biol. Chem. 280, 9870–9878.
Bader, R., Colomb, J., Pankratz, B., Schröck, A., Stocker, R.F., and Pankratz, M.J. (2007). Genetic dissection of neural circuit anatomy underlying feeding behavior in Drosophila: distinct classes of hugin-expressing neurons. J. Comp. Neurol. 502, 848–856.
Badola, P., and Sanders, C.R. (1997). Escherichia coli diacylglycerol kinase is an evolutionarily optimized membrane enzyme and catalyzes direct phosphoryl transfer. J. Biol. Chem. 272, 24176–24182.
Bagdade, J.D., Bierman, E.L., and Porte, D. (1967). The significance of basal insulin levels in the evaluation of the insulin response to glucose in diabetic and nondiabetic subjects. J. Clin. Invest. 46, 1549–1557.
Bai, H., Kang, P., and Tatar, M. (2012). Drosophila insulin-like peptide-6 (dilp6) expression from fat body extends lifespan and represses secretion of Drosophila insulin-like peptide-2 from the brain. Aging Cell 11, 978–985.
Bai, H., Kang, P., Hernandez, A.M., and Tatar, M. (2013). Activin signaling targeted by insulin/dFOXO regulates aging and muscle proteostasis in Drosophila. PLoS Genet. 9, e1003941.
Baker, K.D., and Thummel, C.S. (2007). Diabetic larvae and obese flies-emerging studies of metabolism in Drosophila. Cell Metab. 6, 257–266.
Baldanzi, G., Cutrupi, S., Chianale, F., Gnocchi, V., Rainero, E., Porporato, P., Filigheddu, N., van Blitterswijk, W.J., Parolini, O., Bussolino, F., et al. (2008). Diacylglycerol kinase-alpha phosphorylation by Src on Y335 is required for activation, membrane recruitment and Hgf- induced cell motility. Oncogene 27, 942–956.
149
Barb, C.R., Kraeling, R.R., Rampacek, G.B., and Hausman, G.J. (2006). The role of neuropeptide Y and interaction with leptin in regulating feed intake and luteinizing hormone and growth hormone secretion in the pig. Reprod. Camb. Engl. 131, 1127–1135.
Barrett, J.C., Clayton, D.G., Concannon, P., Akolkar, B., Cooper, J.D., Erlich, H.A., Julier, C., Morahan, G., Nerup, J., Nierras, C., et al. (2009). Genome-wide association study and meta- analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703–707.
Barsh, G.S., Farooqi, I.S., and O’Rahilly, S. (2000). Genetics of body-weight regulation. Nature 404, 644–651.
Baumann, A.A., Benoit, J.B., Michalkova, V., Mireji, P.O., Attardo, G.M., Moulton, J.K., Wilson, T.G., and Aksoy, S. (2013). Juvenile hormone and insulin suppress lipolysis between periods of lactation during tsetse fly pregnancy. Mol. Cell. Endocrinol. 372, 30–41.
Baumbach, J., Hummel, P., Bickmeyer, I., Kowalczyk, K.M., Frank, M., Knorr, K., Hildebrandt, A., Riedel, D., Jäckle, H., and Kühnlein, R.P. (2014). A Drosophila in vivo screen identifies store-operated calcium entry as a key regulator of adiposity. Cell Metab. 19, 331–343.
Belgacem, Y.H., and Martin, J.-R. (2006). Disruption of insulin pathways alters trehalose level and abolishes sexual dimorphism in locomotor activity in Drosophila. J. Neurobiol. 66, 19–32.
Berndt, S.I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M.F., Justice, A.E., Monda, K.L., Croteau-Chonka, D.C., Day, F.R., et al. (2013). Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512.
Bharucha, K.N. (2009). The epicurean fly: using Drosophila melanogaster to study metabolism. Pediatr. Res. 65, 132–137.
Bier, E. (2005). Drosophila, the golden bug, emerges as a tool for human genetics. Nat. Rev. Genet. 6, 9–23.
Birse, R.T., Choi, J., Reardon, K., Rodriguez, J., Graham, S., Diop, S., Ocorr, K., Bodmer, R., and Oldham, S. (2010). High-fat-diet-induced obesity and heart dysfunction are regulated by the TOR pathway in Drosophila. Cell Metab. 12, 533–544.
Birse, R.T., Söderberg, J.A.E., Luo, J., Winther, A.M.E., and Nässel, D.R. (2011). Regulation of insulin-producing cells in the adult Drosophila brain via the tachykinin peptide receptor DTKR. J. Exp. Biol. 214, 4201–4208.
Bischof, J., Maeda, R.K., Hediger, M., Karch, F., and Basler, K. (2007). An optimized transgenesis system for Drosophila using germ-line-specific phiC31 integrases. Proc. Natl. Acad. Sci. U. S. A. 104, 3312–3317.
Bjordal, M., Arquier, N., Kniazeff, J., Pin, J.P., and Léopold, P. (2014). Sensing of amino acids in a dopaminergic circuitry promotes rejection of an incomplete diet in Drosophila. Cell 156, 510–521.
150
Blouet, C., Ono, H., and Schwartz, G.J. (2008). Mediobasal hypothalamic p70 S6 kinase 1 modulates the control of energy homeostasis. Cell Metab. 8, 459–467.
Bogardus, C. (2009). Missing heritability and GWAS utility. Obes. Silver Spring Md 17, 209– 210.
Böhni, R., Riesgo-Escovar, J., Oldham, S., Brogiolo, W., Stocker, H., Andruss, B.F., Beckingham, K., and Hafen, E. (1999). Autonomous control of cell and organ size by CHICO, a Drosophila homolog of vertebrate IRS1-4. Cell 97, 865–875.
Brand, A.H., and Perrimon, N. (1993). Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Dev. Camb. Engl. 118, 401–415.
Brankatschk, M., Dunst, S., Nemetschke, L., and Eaton, S. (2014). Delivery of circulating lipoproteins to specific neurons in the Drosophila brain regulates systemic insulin signaling. eLife 3.
Braun, M., Ramracheya, R., and Rorsman, P. (2012). Autocrine regulation of insulin secretion. Diabetes Obes. Metab. 14 Suppl 3, 143–151.
Brogiolo, W., Stocker, H., Ikeya, T., Rintelen, F., Fernandez, R., and Hafen, E. (2001). An evolutionarily conserved function of the Drosophila insulin receptor and insulin-like peptides in growth control. Curr. Biol. CB 11, 213–221.
Brose, N., and Rosenmund, C. (2002). Move over protein kinase C, you’ve got company: alternative cellular effectors of diacylglycerol and phorbol esters. J. Cell Sci. 115, 4399–4411.
Broughton, S., Alic, N., Slack, C., Bass, T., Ikeya, T., Vinti, G., Tommasi, A.M., Driege, Y., Hafen, E., and Partridge, L. (2008). Reduction of DILP2 in Drosophila triages a metabolic phenotype from lifespan revealing redundancy and compensation among DILPs. PloS One 3, e3721.
Broughton, S.J., Piper, M.D.W., Ikeya, T., Bass, T.M., Jacobson, J., Driege, Y., Martinez, P., Hafen, E., Withers, D.J., Leevers, S.J., et al. (2005). Longer lifespan, altered metabolism, and stress resistance in Drosophila from ablation of cells making insulin-like ligands. Proc. Natl. Acad. Sci. U. S. A. 102, 3105–3110.
Broughton, S.J., Slack, C., Alic, N., Metaxakis, A., Bass, T.M., Driege, Y., and Partridge, L. (2010). DILP-producing median neurosecretory cells in the Drosophila brain mediate the response of lifespan to nutrition. Aging Cell 9, 336–346.
Brown, K.A., Ham, A.-J.L., Clark, C.N., Meller, N., Law, B.K., Chytil, A., Cheng, N., Pietenpol, J.A., and Moses, H.L. (2008). Identification of novel Smad2 and Smad3 associated proteins in response to TGF-beta1. J. Cell. Biochem. 105, 596–611.
Brozzi, F., Lajus, S., Diraison, F., Rajatileka, S., Hayward, K., Regazzi, R., Molnár, E., and Váradi, A. (2012). MyRIP interaction with MyoVa on secretory granules is controlled by the cAMP-PKA pathway. Mol. Biol. Cell 23, 4444–4455.
151
Brüning, J.C., Gautam, D., Burks, D.J., Gillette, J., Schubert, M., Orban, P.C., Klein, R., Krone, W., Müller-Wieland, D., and Kahn, C.R. (2000). Role of brain insulin receptor in control of body weight and reproduction. Science 289, 2122–2125.
Buch, S., Melcher, C., Bauer, M., Katzenberger, J., and Pankratz, M.J. (2008). Opposing effects of dietary protein and sugar regulate a transcriptional target of Drosophila insulin-like peptide signaling. Cell Metab. 7, 321–332.
Budhiraja, S., and Chugh, A. (2009). Neuromedin U: physiology, pharmacology and therapeutic potential. Fundam. Clin. Pharmacol. 23, 149–157.
Burke, C.J., and Waddell, S. (2011). Remembering nutrient quality of sugar in Drosophila. Curr. Biol. CB 21, 746–750.
Canavoso, L.E., Jouni, Z.E., Karnas, K.J., Pennington, J.E., and Wells, M.A. (2001). Fat metabolism in insects. Annu. Rev. Nutr. 21, 23–46.
Cao, C., and Brown, M.R. (2001). Localization of an insulin-like peptide in brains of two flies. Cell Tissue Res. 304, 317–321.
Cao, J., Ni, J., Ma, W., Shiu, V., Milla, L.A., Park, S., Spletter, M.L., Tang, S., Zhang, J., Wei, X., et al. (2014). Insight into insulin secretion from transcriptome and genetic analysis of insulin- producing cells of Drosophila. Genetics 197, 175–192.
Carrasco, S., and Mérida, I. (2007). Diacylglycerol, when simplicity becomes complex. Trends Biochem. Sci. 32, 27–36.
Chakraborty, G., Reddy, R., Drivas, A., and Ledeen, R.W. (2003). Interleukin-2 receptors and interleukin-2-mediated signaling in myelin: activation of diacylglycerol kinase and phosphatidylinositol 3-kinase. Neuroscience 122, 967–973.
Chang-Chen, K.J., Mullur, R., and Bernal-Mizrachi, E. (2008). Beta-cell failure as a complication of diabetes. Rev. Endocr. Metab. Disord. 9, 329–343.
Chen, T.-C., Lin, K.-T., Chen, C.-H., Lee, S.-A., Lee, P.-Y., Liu, Y.-W., Kuo, Y.-L., Wang, F.- S., Lai, J.-M., and Huang, C.-Y.F. (2014). Using an in situ proximity ligation assay to systematically profile endogenous protein-protein interactions in a pathway network. J. Proteome Res. 13, 5339–5346.
Chen, W.-M., Erdos, M.R., Jackson, A.U., Saxena, R., Sanna, S., Silver, K.D., Timpson, N.J., Hansen, T., Orrù, M., Grazia Piras, M., et al. (2008). Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. J. Clin. Invest. 118, 2620–2628.
Cheung, W.W., and Mao, P. (2012). Recent advances in obesity: genetics and beyond. ISRN Endocrinol. 2012, 536905.
Chibalin, A.V., Leng, Y., Vieira, E., Krook, A., Björnholm, M., Long, Y.C., Kotova, O., Zhong, Z., Sakane, F., Steiler, T., et al. (2008). Downregulation of diacylglycerol kinase delta contributes to hyperglycemia-induced insulin resistance. Cell 132, 375–386.
152
Cho, H., Mu, J., Kim, J.K., Thorvaldsen, J.L., Chu, Q., Crenshaw, E.B., Kaestner, K.H., Bartolomei, M.S., Shulman, G.I., and Birnbaum, M.J. (2001). Insulin resistance and a diabetes mellitus-like syndrome in mice lacking the protein kinase Akt2 (PKB beta). Science 292, 1728– 1731.
Claeys, I., Simonet, G., Poels, J., Van Loy, T., Vercammen, L., De Loof, A., and Vanden Broeck, J. (2002). Insulin-related peptides and their conserved signal transduction pathway. Peptides 23, 807–816.
Cognigni, P., Bailey, A.P., and Miguel-Aliaga, I. (2011). Enteric neurons and systemic signals couple nutritional and reproductive status with intestinal homeostasis. Cell Metab. 13, 92–104.
Colombani, J., Raisin, S., Pantalacci, S., Radimerski, T., Montagne, J., and Léopold, P. (2003). A nutrient sensor mechanism controls Drosophila growth. Cell 114, 739–749.
Considine, R.V., Sinha, M.K., Heiman, M.L., Kriauciunas, A., Stephens, T.W., Nyce, M.R., Ohannesian, J.P., Marco, C.C., McKee, L.J., and Bauer, T.L. (1996). Serum immunoreactive- leptin concentrations in normal-weight and obese humans. N. Engl. J. Med. 334, 292–295.
Cooper, J.D., Smyth, D.J., Smiles, A.M., Plagnol, V., Walker, N.M., Allen, J.E., Downes, K., Barrett, J.C., Healy, B.C., Mychaleckyj, J.C., et al. (2008). Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat. Genet. 40, 1399–1401.
Cortright, R.N., Azevedo, J.L., Zhou, Q., Sinha, M., Pories, W.J., Itani, S.I., and Dohm, G.L. (2000). Protein kinase C modulates insulin action in human skeletal muscle. Am. J. Physiol. Endocrinol. Metab. 278, E553–E562.
Dalle, S., Ravier, M.A., and Bertrand, G. (2011). Emerging roles for β-arrestin-1 in the control of the pancreatic β-cell function and mass: new therapeutic strategies and consequences for drug screening. Cell. Signal. 23, 522–528.
Das, R., and Dobens, L.L. (2015). Conservation of gene and tissue networks regulating insulin signalling in flies and vertebrates. Biochem. Soc. Trans. 43, 1057–1062.
Delanoue, R., Meschi, E., Agrawal, N., Mauri, A., Tsatskis, Y., McNeill, H., and Léopold, P. (2016). Drosophila insulin release is triggered by adipose Stunted ligand to brain Methuselah receptor. Science 353, 1553–1556.
De La Roche, M.A., Smith, J.L., Rico, M., Carrasco, S., Merida, I., Licate, L., Côté, G.P., and Egelhoff, T.T. (2002). Dictyostelium discoideum has a single diacylglycerol kinase gene with similarity to mammalian theta isoforms. Biochem. J. 368, 809–815.
De Vriese, C., and Delporte, C. (2007). Influence of ghrelin on food intake and energy homeostasis. Curr. Opin. Clin. Nutr. Metab. Care 10, 615–619.
DiAngelo, J.R., Erion, R., Crocker, A., and Sehgal, A. (2011). The central clock neurons regulate lipid storage in Drosophila. PloS One 6, e19921.
153
Diggs-Andrews, K.A., Zhang, X., Song, Z., Daphna-Iken, D., Routh, V.H., and Fisher, S.J. (2010). Brain insulin action regulates hypothalamic glucose sensing and the counterregulatory response to hypoglycemia. Diabetes 59, 2271–2280.
Dimitriadis, G., Mitrou, P., Lambadiari, V., Maratou, E., and Raptis, S.A. (2011). Insulin effects in muscle and adipose tissue. Diabetes Res. Clin. Pract. 93 Suppl 1, S52–S59.
Ding, L., Traer, E., McIntyre, T.M., Zimmerman, G.A., and Prescott, S.M. (1998). The cloning and characterization of a novel human diacylglycerol kinase, DGKiota. J. Biol. Chem. 273, 32746–32752.
Dionne, M.S., Pham, L.N., Shirasu-Hiza, M., and Schneider, D.S. (2006). Akt and FOXO dysregulation contribute to infection-induced wasting in Drosophila. Curr. Biol. CB 16, 1977– 1985.
Dobrosotskaya, I.Y., Seegmiller, A.C., Brown, M.S., Goldstein, J.L., and Rawson, R.B. (2002). Regulation of SREBP processing and membrane lipid production by phospholipids in Drosophila. Science 296, 879–883.
Düfer, M., Neye, Y., Hörth, K., Krippeit-Drews, P., Hennige, A., Widmer, H., McClafferty, H., Shipston, M.J., Häring, H.-U., Ruth, P., et al. (2011). BK channels affect glucose homeostasis and cell viability of murine pancreatic beta cells. Diabetologia 54, 423–432.
Dummler, B., Tschopp, O., Hynx, D., Yang, Z.-Z., Dirnhofer, S., and Hemmings, B.A. (2006). Life with a single isoform of Akt: mice lacking Akt2 and Akt3 are viable but display impaired glucose homeostasis and growth deficiencies. Mol. Cell. Biol. 26, 8042–8051.
Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, A.U., Wheeler, E., Glazer, N.L., Bouatia-Naji, N., Gloyn, A.L., et al. (2010). New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116.
Emerging Risk Factors Collaboration, Wormser, D., Kaptoge, S., Di Angelantonio, E., Wood, A.M., Pennells, L., Thompson, A., Sarwar, N., Kizer, J.R., Lawlor, D.A., et al. (2011). Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet Lond. Engl. 377, 1085–1095.
Enell, L.E., Kapan, N., Söderberg, J.A.E., Kahsai, L., and Nässel, D.R. (2010). Insulin signaling, lifespan and stress resistance are modulated by metabotropic GABA receptors on insulin producing cells in the brain of Drosophila. PloS One 5, e15780.
English, D. (1996). Phosphatidic acid: a lipid messenger involved in intracellular and extracellular signalling. Cell. Signal. 8, 341–347.
English, D., Cui, Y., and Siddiqui, R.A. (1996). Messenger functions of phosphatidic acid. Chem. Phys. Lipids 80, 117–132.
Epand, R.M., and Topham, M.K. (2007). Measurement of mammalian diacylglycerol kinase activity in vitro and in cells. Methods Enzymol. 434, 293–304.
154
Erion, R., and Sehgal, A. (2013). Regulation of insect behavior via the insulin-signaling pathway. Front. Physiol. 4, 353.
Evangelisti, C., Tazzari, P.L., Riccio, M., Fiume, R., Hozumi, Y., Falà, F., Goto, K., Manzoli, L., Cocco, L., and Martelli, A.M. (2007). Nuclear diacylglycerol kinase-zeta is a negative regulator of cell cycle progression in C2C12 mouse myoblasts. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 21, 3297–3307.
Ewing, R.M., Chu, P., Elisma, F., Li, H., Taylor, P., Climie, S., McBroom-Cerajewski, L., Robinson, M.D., O’Connor, L., Li, M., et al. (2007). Large-scale mapping of human protein- protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 89.
Fadini, G.P., Ceolotto, G., Pagnin, E., de Kreutzenberg, S., and Avogaro, A. (2011). At the crossroads of longevity and metabolism: the metabolic syndrome and lifespan determinant pathways. Aging Cell 10, 10–17.
Fall, T., and Ingelsson, E. (2014). Genome-wide association studies of obesity and metabolic syndrome. Mol. Cell. Endocrinol. 382, 740–757.
Farooqi, I.S., and O’Rahilly, S. (2005). Monogenic obesity in humans. Annu. Rev. Med. 56, 443–458.
Fisher, S.J., Brüning, J.C., Lannon, S., and Kahn, C.R. (2005). Insulin signaling in the central nervous system is critical for the normal sympathoadrenal response to hypoglycemia. Diabetes 54, 1447–1451.
Flegal, K.M., Kit, B.K., Orpana, H., and Graubard, B.I. (2013). Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 309, 71–82.
Flood, T.F., Iguchi, S., Gorczyca, M., White, B., Ito, K., and Yoshihara, M. (2013). A single pair of interneurons commands the Drosophila feeding motor program. Nature 499, 83–87.
Flores, I., Casaseca, T., Martinez-A, C., Kanoh, H., and Merida, I. (1996). Phosphatidic acid generation through interleukin 2 (IL-2)-induced alpha-diacylglycerol kinase activation is an essential step in IL-2-mediated lymphocyte proliferation. J. Biol. Chem. 271, 10334–10340.
Flores, I., Jones, D.R., Ciprés, A., Díaz-Flores, E., Sanjuan, M.A., and Mérida, I. (1999). Diacylglycerol kinase inhibition prevents IL-2-induced G1 to S transition through a phosphatidylinositol-3 kinase-independent mechanism. J. Immunol. Baltim. Md 1950 163, 708– 714.
Franke, T.F. (2008). PI3K/Akt: getting it right matters. Oncogene 27, 6473–6488.
Frayling, T.M., Timpson, N.J., Weedon, M.N., Zeggini, E., Freathy, R.M., Lindgren, C.M., Perry, J.R.B., Elliott, K.S., Lango, H., Rayner, N.W., et al. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894.
155
French, A., Ali Agha, M., Mitra, A., Yanagawa, A., Sellier, M.-J., and Marion-Poll, F. (2015). Drosophila Bitter Taste(s). Front. Integr. Neurosci. 9, 58.
Fridell, Y.-W.C., Hoh, M., Kréneisz, O., Hosier, S., Chang, C., Scantling, D., Mulkey, D.K., and Helfand, S.L. (2009). Increased uncoupling protein (UCP) activity in Drosophila insulin- producing neurons attenuates insulin signaling and extends lifespan. Aging 1, 699–713.
Frolov, M.V., Benevolenskaya, E.V., and Birchler, J.A. (2001). Molecular analysis of a novel Drosophila diacylglycerol kinase, DGKepsilon. Biochim. Biophys. Acta 1538, 339–352.
Fukunaga-Takenaka, R., Shirai, Y., Yagi, K., Adachi, N., Sakai, N., Merino, E., Merida, I., and Saito, N. (2005). Importance of chroman ring and tyrosine phosphorylation in the subtype- specific translocation and activation of diacylglycerol kinase alpha by D-alpha-tocopherol. Genes Cells Devoted Mol. Cell. Mech. 10, 311–319.
Garofalo, R.S., Orena, S.J., Rafidi, K., Torchia, A.J., Stock, J.L., Hildebrandt, A.L., Coskran, T., Black, S.C., Brees, D.J., Wicks, J.R., et al. (2003). Severe diabetes, age-dependent loss of adipose tissue, and mild growth deficiency in mice lacking Akt2/PKB beta. J. Clin. Invest. 112, 197–208.
Gautron, L., Elmquist, J.K., and Williams, K.W. (2015). Neural control of energy balance: translating circuits to therapies. Cell 161, 133–145.
Gelperin, A. (1967). Stretch receptors in the foregut of the blowfly. Science 157, 208–210.
Géminard, C., Rulifson, E.J., and Léopold, P. (2009). Remote control of insulin secretion by fat cells in Drosophila. Cell Metab. 10, 199–207.
Gershman, B., Puig, O., Hang, L., Peitzsch, R.M., Tatar, M., and Garofalo, R.S. (2007). High- resolution dynamics of the transcriptional response to nutrition in Drosophila: a key role for dFOXO. Physiol. Genomics 29, 24–34.
Global BMI Mortality Collaboration (2016). Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet Lond. Engl.
Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects), Lu, Y., Hajifathalian, K., Ezzati, M., Woodward, M., Rimm, E.B., and Danaei, G. (2014). Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet Lond. Engl. 383, 970–983.
Goberdhan, D.C., Paricio, N., Goodman, E.C., Mlodzik, M., and Wilson, C. (1999). Drosophila tumor suppressor PTEN controls cell size and number by antagonizing the Chico/PI3-kinase signaling pathway. Genes Dev. 13, 3244–3258.
Goto, K., and Kondo, H. (1993). Molecular cloning and expression of a 90-kDa diacylglycerol kinase that predominantly localizes in neurons. Proc. Natl. Acad. Sci. U. S. A. 90, 7598–7602.
156
Goto, K., and Kondo, H. (1999). Diacylglycerol kinase in the central nervous system--molecular heterogeneity and gene expression. Chem. Phys. Lipids 98, 109–117.
Goto, K., Watanabe, M., Kondo, H., Yuasa, H., Sakane, F., and Kanoh, H. (1992). Gene cloning, sequence, expression and in situ localization of 80 kDa diacylglycerol kinase specific to oligodendrocyte of rat brain. Brain Res. Mol. Brain Res. 16, 75–87.
Goto, K., Funayama, M., and Kondo, H. (1994). Cloning and expression of a cytoskeleton- associated diacylglycerol kinase that is dominantly expressed in cerebellum. Proc. Natl. Acad. Sci. U. S. A. 91, 13042–13046.
Goto, K., Hozumi, Y., Nakano, T., Saino, S.S., and Kondo, H. (2007a). Cell biology and pathophysiology of the diacylglycerol kinase family: morphological aspects in tissues and organs. Int. Rev. Cytol. 264, 25–63.
Goto, Y., Nomura, M., Tanaka, K., Kondo, A., Morinaga, H., Okabe, T., Yanase, T., Nawata, H., Takayanagi, R., and Li, E. (2007b). Genetic interactions between activin type IIB receptor and Smad2 genes in asymmetrical patterning of the thoracic organs and the development of pancreas islets. Dev. Dyn. Off. Publ. Am. Assoc. Anat. 236, 2865–2874.
Grant, S.F.A., Qu, H.-Q., Bradfield, J.P., Marchand, L., Kim, C.E., Glessner, J.T., Grabs, R., Taback, S.P., Frackelton, E.C., Eckert, A.W., et al. (2009). Follow-up analysis of genome-wide association data identifies novel loci for type 1 diabetes. Diabetes 58, 290–295.
Grewal, S.S. (2009). Insulin/TOR signaling in growth and homeostasis: a view from the fly world. Int. J. Biochem. Cell Biol. 41, 1006–1010.
Grönke, S., Mildner, A., Fellert, S., Tennagels, N., Petry, S., Müller, G., Jäckle, H., and Kühnlein, R.P. (2005). Brummer lipase is an evolutionary conserved fat storage regulator in Drosophila. Cell Metab. 1, 323–330.
Grönke, S., Müller, G., Hirsch, J., Fellert, S., Andreou, A., Haase, T., Jäckle, H., and Kühnlein, R.P. (2007). Dual lipolytic control of body fat storage and mobilization in Drosophila. PLoS Biol. 5, e137.
Grönke, S., Clarke, D.-F., Broughton, S., Andrews, T.D., and Partridge, L. (2010). Molecular evolution and functional characterization of Drosophila insulin-like peptides. PLoS Genet. 6, e1000857.
Guo, S. (2014). Insulin signaling, resistance, and the metabolic syndrome: insights from mouse models into disease mechanisms. J. Endocrinol. 220, T1–T23.
Gutierrez, E., Wiggins, D., Fielding, B., and Gould, A.P. (2007). Specialized hepatocyte-like cells regulate Drosophila lipid metabolism. Nature 445, 275–280.
Hanks, S.K., Quinn, A.M., and Hunter, T. (1988). The protein kinase family: conserved features and deduced phylogeny of the catalytic domains. Science 241, 42–52.
157
Harden, N., Yap, S.F., Chiam, M.A., and Lim, L. (1993). A Drosophila gene encoding a protein with similarity to diacylglycerol kinase is expressed in specific neurons. Biochem. J. 289 ( Pt 2), 439–444.
Harmon, E.B., Apelqvist, A.A., Smart, N.G., Gu, X., Osborne, D.H., and Kim, S.K. (2004). GDF11 modulates NGN3+ islet progenitor cell number and promotes beta-cell differentiation in pancreas development. Dev. Camb. Engl. 131, 6163–6174.
Hartenstein, V. (2006). The neuroendocrine system of invertebrates: a developmental and evolutionary perspective. J. Endocrinol. 190, 555–570.
Haselton, A.T., and Fridell, Y.-W.C. (2010). Adult Drosophila melanogaster as a model for the study of glucose homeostasis. Aging 2, 523–526.
Haselton, A., Sharmin, E., Schrader, J., Sah, M., Poon, P., and Fridell, Y.-W.C. (2010). Partial ablation of adult Drosophila insulin-producing neurons modulates glucose homeostasis and extends life span without insulin resistance. Cell Cycle Georget. Tex 9, 3063–3071.
Havrankova, J., Roth, J., and Brownstein, M. (1978). Insulin receptors are widely distributed in the central nervous system of the rat. Nature 272, 827–829.
Havugimana, P.C., Hart, G.T., Nepusz, T., Yang, H., Turinsky, A.L., Li, Z., Wang, P.I., Boutz, D.R., Fong, V., Phanse, S., et al. (2012). A census of human soluble protein complexes. Cell 150, 1068–1081.
Hay, N. (2011). Akt isoforms and glucose homeostasis - the leptin connection. Trends Endocrinol. Metab. TEM 22, 66–73.
Heid, I.M., Jackson, A.U., Randall, J.C., Winkler, T.W., Qi, L., Steinthorsdottir, V., Thorleifsson, G., Zillikens, M.C., Speliotes, E.K., Mägi, R., et al. (2010). Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960.
Hentze, J.L., Carlsson, M.A., Kondo, S., Nässel, D.R., and Rewitz, K.F. (2015). The Neuropeptide Allatostatin A Regulates Metabolism and Feeding Decisions in Drosophila. Sci. Rep. 5, 11680.
Hergarden, A.C., Tayler, T.D., and Anderson, D.J. (2012). Allatostatin-A neurons inhibit feeding behavior in adult Drosophila. Proc. Natl. Acad. Sci. U. S. A. 109, 3967–3972.
Hietakangas, V., and Cohen, S.M. (2007). Re-evaluating AKT regulation: role of TOR complex 2 in tissue growth. Genes Dev. 21, 632–637.
Hill, J.W., Williams, K.W., Ye, C., Luo, J., Balthasar, N., Coppari, R., Cowley, M.A., Cantley, L.C., Lowell, B.B., and Elmquist, J.K. (2008). Acute effects of leptin require PI3K signaling in hypothalamic proopiomelanocortin neurons in mice. J. Clin. Invest. 118, 1796–1805.
158
Hjelmborg, J. v B., Fagnani, C., Silventoinen, K., McGue, M., Korkeila, M., Christensen, K., Rissanen, A., and Kaprio, J. (2008). Genetic influences on growth traits of BMI: a longitudinal study of adult twins. Obes. Silver Spring Md 16, 847–852.
Hodgkin, M.N., Pettitt, T.R., Martin, A., Michell, R.H., Pemberton, A.J., and Wakelam, M.J. (1998). Diacylglycerols and phosphatidates: which molecular species are intracellular messengers? Trends Biochem. Sci. 23, 200–204.
Honegger, B., Galic, M., Köhler, K., Wittwer, F., Brogiolo, W., Hafen, E., and Stocker, H. (2008). Imp-L2, a putative homolog of vertebrate IGF-binding protein 7, counteracts insulin signaling in Drosophila and is essential for starvation resistance. J. Biol. 7, 10.
Houssa, B., de Widt, J., Kranenburg, O., Moolenaar, W.H., and van Blitterswijk, W.J. (1999). Diacylglycerol kinase theta binds to and is negatively regulated by active RhoA. J. Biol. Chem. 274, 6820–6822. van Houten, M., Posner, B.I., Kopriwa, B.M., and Brawer, J.R. (1979). Insulin-binding sites in the rat brain: in vivo localization to the circumventricular organs by quantitative radioautography. Endocrinology 105, 666–673.
Hozumi, Y., Watanabe, M., Otani, K., and Goto, K. (2009). Diacylglycerol kinase beta promotes dendritic outgrowth and spine maturation in developing hippocampal neurons. BMC Neurosci. 10, 99.
Huang, D.W., Sherman, B.T., and Lempicki, R.A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57.
Huang, H., Potter, C.J., Tao, W., Li, D.M., Brogiolo, W., Hafen, E., Sun, H., and Xu, T. (1999). PTEN affects cell size, cell proliferation and apoptosis during Drosophila eye development. Dev. Camb. Engl. 126, 5365–5372.
Huang, H., He, X., Deng, X., Li, G., Ying, G., Sun, Y., Shi, L., Benovic, J.L., and Zhou, N. (2010). Bombyx adipokinetic hormone receptor activates extracellular signal-regulated kinase 1 and 2 via G protein-dependent PKA and PKC but β-arrestin-independent pathways. Biochemistry (Mosc.) 49, 10862–10872.
Huang, H., Kong, D., Byun, K.H., Ye, C., Koda, S., Lee, D.H., Oh, B.-C., Lee, S.W., Lee, B., Zabolotny, J.M., et al. (2012). Rho-kinase regulates energy balance by targeting hypothalamic leptin receptor signaling. Nat. Neurosci. 15, 1391–1398.
Hughes, W.E., Elgundi, Z., Huang, P., Frohman, M.A., and Biden, T.J. (2004). Phospholipase D1 regulates secretagogue-stimulated insulin release in pancreatic beta-cells. J. Biol. Chem. 279, 27534–27541.
Hull-Thompson, J., Muffat, J., Sanchez, D., Walker, D.W., Benzer, S., Ganfornina, M.D., and Jasper, H. (2009). Control of metabolic homeostasis by stress signaling is mediated by the lipocalin NLaz. PLoS Genet. 5, e1000460.
159
Hunter, S.J., Atkinson, A.B., Ennis, C.N., Sheridan, B., and Bell, P.M. (1996). Association between insulin secretory pulse frequency and peripheral insulin action in NIDDM and normal subjects. Diabetes 45, 683–686.
Hurley, J.H., Newton, A.C., Parker, P.J., Blumberg, P.M., and Nishizuka, Y. (1997). Taxonomy and function of C1 protein kinase C homology domains. Protein Sci. Publ. Protein Soc. 6, 477– 480.
Hwangbo, D.S., Gershman, B., Gersham, B., Tu, M.-P., Palmer, M., and Tatar, M. (2004). Drosophila dFOXO controls lifespan and regulates insulin signalling in brain and fat body. Nature 429, 562–566.
Iijima, K., Zhao, L., Shenton, C., and Iijima-Ando, K. (2009). Regulation of energy stores and feeding by neuronal and peripheral CREB activity in Drosophila. PloS One 4, e8498.
Ikeya, T., Galic, M., Belawat, P., Nairz, K., and Hafen, E. (2002). Nutrient-dependent expression of insulin-like peptides from neuroendocrine cells in the CNS contributes to growth regulation in Drosophila. Curr. Biol. CB 12, 1293–1300.
Imai, S., Kai, M., Yasuda, S., Kanoh, H., and Sakane, F. (2005). Identification and characterization of a novel human type II diacylglycerol kinase, DGK kappa. J. Biol. Chem. 280, 39870–39881.
Inagaki, H.K., Ben-Tabou de-Leon, S., Wong, A.M., Jagadish, S., Ishimoto, H., Barnea, G., Kitamoto, T., Axel, R., and Anderson, D.J. (2012). Visualizing neuromodulation in vivo: TANGO-mapping of dopamine signaling reveals appetite control of sugar sensing. Cell 148, 583–595.
Ja, W.W., Gil B. Carvalho, Mak, E.M., de la Rosa, N.N., Fang, A.Y., Liong, J.C., Brummel, T., and Benzer, S. (2007). Prandiology of Drosophila and the CAFE assay. Proc. Natl. Acad. Sci. U. S. A. 104, 8253–8256.
Johansen, C.T., Wang, J., Lanktree, M.B., Cao, H., McIntyre, A.D., Ban, M.R., Martins, R.A., Kennedy, B.A., Hassell, R.G., Visser, M.E., et al. (2010). Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat. Genet. 42, 684–687.
Johnson, E.C., Shafer, O.T., Trigg, J.S., Park, J., Schooley, D.A., Dow, J.A., and Taghert, P.H. (2005). A novel diuretic hormone receptor in Drosophila: evidence for conservation of CGRP signaling. J. Exp. Biol. 208, 1239–1246.
Jones, P.M., Persaud, S.J., and Howell, S.L. (1991). Protein kinase C and the regulation of insulin secretion from pancreatic B cells. J. Mol. Endocrinol. 6, 121–127.
Kahsai, L., Kapan, N., Dircksen, H., Winther, A.M.E., and Nässel, D.R. (2010a). Metabolic stress responses in Drosophila are modulated by brain neurosecretory cells that produce multiple neuropeptides. PloS One 5, e11480.
Kahsai, L., Martin, J.-R., and Winther, A.M.E. (2010b). Neuropeptides in the Drosophila central complex in modulation of locomotor behavior. J. Exp. Biol. 213, 2256–2265.
160
Kai, M., Sakane, F., Imai, S., Wada, I., and Kanoh, H. (1994). Molecular cloning of a diacylglycerol kinase isozyme predominantly expressed in human retina with a truncated and inactive enzyme expression in most other human cells. J. Biol. Chem. 269, 18492–18498.
Kaihara, K.A., Dickson, L.M., Jacobson, D.A., Tamarina, N., Roe, M.W., Philipson, L.H., and Wicksteed, B. (2013). β-Cell-specific protein kinase A activation enhances the efficiency of glucose control by increasing acute-phase insulin secretion. Diabetes 62, 1527–1536.
Kaihara, K.A., Dickson, L.M., Ellenbroek, J.H., Orr, C.M.D., Layden, B.T., and Wicksteed, B. (2015). PKA Enhances the Acute Insulin Response Leading to the Restoration of Glucose Control. Diabetes 64, 1688–1697.
Kain, P., Chakraborty, T.S., Sundaram, S., Siddiqi, O., Rodrigues, V., and Hasan, G. (2008). Reduced odor responses from antennal neurons of G(q)alpha, phospholipase Cbeta, and rdgA mutants in Drosophila support a role for a phospholipid intermediate in insect olfactory transduction. J. Neurosci. Off. J. Soc. Neurosci. 28, 4745–4755.
Kalra, S.P., and Kalra, P.S. (2004). NPY and cohorts in regulating appetite, obesity and metabolic syndrome: beneficial effects of gene therapy. Neuropeptides 38, 201–211.
Kaneko, Y.K., and Ishikawa, T. (2015). Diacylglycerol Signaling Pathway in Pancreatic β-Cells: An Essential Role of Diacylglycerol Kinase in the Regulation of Insulin Secretion. Biol. Pharm. Bull. 38, 669–673.
Kang, G., Chepurny, O.G., Malester, B., Rindler, M.J., Rehmann, H., Bos, J.L., Schwede, F., Coetzee, W.A., and Holz, G.G. (2006). cAMP sensor Epac as a determinant of ATP-sensitive potassium channel activity in human pancreatic beta cells and rat INS-1 cells. J. Physiol. 573, 595–609.
Kannan, K., and Fridell, Y.-W.C. (2013). Functional implications of Drosophila insulin-like peptides in metabolism, aging, and dietary restriction. Front. Physiol. 4, 288.
Kapan, N., Lushchak, O.V., Luo, J., and Nässel, D.R. (2012). Identified peptidergic neurons in the Drosophila brain regulate insulin-producing cells, stress responses and metabolism by coexpressed short neuropeptide F and corazonin. Cell. Mol. Life Sci. CMLS.
Kathiresan, S., Melander, O., Guiducci, C., Surti, A., Burtt, N.P., Rieder, M.J., Cooper, G.M., Roos, C., Voight, B.F., Havulinna, A.S., et al. (2008). Six new loci associated with blood low- density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 40, 189–197.
Kim, J., and Neufeld, T.P. (2015). Dietary sugar promotes systemic TOR activation in Drosophila through AKH-dependent selective secretion of Dilp3. Nat. Commun. 6, 6846.
Kim, M., and Lee, J.H. (2015). Identification of an AMPK phosphorylation site in Drosophila TSC2 (gigas) that regulate cell growth. Int. J. Mol. Sci. 16, 7015–7026.
Kim, S.K., and Rulifson, E.J. (2004). Conserved mechanisms of glucose sensing and regulation by Drosophila corpora cardiaca cells. Nature 431, 316–320.
161
Kim, J.D., Leyva, S., and Diano, S. (2014). Hormonal regulation of the hypothalamic melanocortin system. Front. Physiol. 5, 480.
Kitamoto, T. (2001). Conditional modification of behavior in Drosophila by targeted expression of a temperature-sensitive shibire allele in defined neurons. J. Neurobiol. 47, 81–92.
Kitamura, T., Feng, Y., Kitamura, Y.I., Chua, S.C., Xu, A.W., Barsh, G.S., Rossetti, L., and Accili, D. (2006). Forkhead protein FoxO1 mediates Agrp-dependent effects of leptin on food intake. Nat. Med. 12, 534–540.
Klöckener, T., Hess, S., Belgardt, B.F., Paeger, L., Verhagen, L.A.W., Husch, A., Sohn, J.-W., Hampel, B., Dhillon, H., Zigman, J.M., et al. (2011). High-fat feeding promotes obesity via insulin receptor/PI3K-dependent inhibition of SF-1 VMH neurons. Nat. Neurosci. 14, 911–918.
Kobayashi, N., Hozumi, Y., Ito, T., Hosoya, T., Kondo, H., and Goto, K. (2007). Differential subcellular targeting and activity-dependent subcellular localization of diacylglycerol kinase isozymes in transfected cells. Eur. J. Cell Biol. 86, 433–444.
Koch, L., Wunderlich, F.T., Seibler, J., Könner, A.C., Hampel, B., Irlenbusch, S., Brabant, G., Kahn, C.R., Schwenk, F., and Brüning, J.C. (2008). Central insulin action regulates peripheral glucose and fat metabolism in mice. J. Clin. Invest. 118, 2132–2147.
Kodama, S., Horikawa, C., Fujihara, K., Yoshizawa, S., Yachi, Y., Tanaka, S., Ohara, N., Matsunaga, S., Yamada, T., Hanyu, O., et al. (2014). Quantitative relationship between body weight gain in adulthood and incident type 2 diabetes: a meta-analysis. Obes. Rev. Off. J. Int. Assoc. Study Obes. 15, 202–214.
Kong, K.C., Butcher, A.J., McWilliams, P., Jones, D., Wess, J., Hamdan, F.F., Werry, T., Rosethorne, E.M., Charlton, S.J., Munson, S.E., et al. (2010). M3-muscarinic receptor promotes insulin release via receptor phosphorylation/arrestin-dependent activation of protein kinase D1. Proc. Natl. Acad. Sci. U. S. A. 107, 21181–21186.
Könner, A.C., Klöckener, T., and Brüning, J.C. (2009). Control of energy homeostasis by insulin and leptin: targeting the arcuate nucleus and beyond. Physiol. Behav. 97, 632–638.
Kooner, J.S., Chambers, J.C., Aguilar-Salinas, C.A., Hinds, D.A., Hyde, C.L., Warnes, G.R., Gómez Pérez, F.J., Frazer, K.A., Elliott, P., Scott, J., et al. (2008). Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat. Genet. 40, 149–151.
Koppe, S.W.P. (2014). Obesity and the liver: nonalcoholic fatty liver disease. Transl. Res. J. Lab. Clin. Med. 164, 312–322.
Koyama, T., and Mirth, C.K. (2016). Growth-Blocking Peptides As Nutrition-Sensitive Signals for Insulin Secretion and Body Size Regulation. PLoS Biol. 14, e1002392.
Kraegen, E.W., Saha, A.K., Preston, E., Wilks, D., Hoy, A.J., Cooney, G.J., and Ruderman, N.B. (2006). Increased malonyl-CoA and diacylglycerol content and reduced AMPK activity accompany insulin resistance induced by glucose infusion in muscle and liver of rats. Am. J. Physiol. Endocrinol. Metab. 290, E471–E479.
162
Kraja, A.T., Vaidya, D., Pankow, J.S., Goodarzi, M.O., Assimes, T.L., Kullo, I.J., Sovio, U., Mathias, R.A., Sun, Y.V., Franceschini, N., et al. (2011). A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium. Diabetes 60, 1329–1339.
Kramer, J.M., Davidge, J.T., Lockyer, J.M., and Staveley, B.E. (2003). Expression of Drosophila FOXO regulates growth and can phenocopy starvation. BMC Dev. Biol. 3, 5.
Kréneisz, O., Chen, X., Fridell, Y.-W.C., and Mulkey, D.K. (2010). Glucose increases activity and Ca2+ in insulin-producing cells of adult Drosophila. Neuroreport 21, 1116–1120.
Kristiansson, K., Perola, M., Tikkanen, E., Kettunen, J., Surakka, I., Havulinna, A.S., Stancáková, A., Barnes, C., Widen, E., Kajantie, E., et al. (2012). Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits. Circ. Cardiovasc. Genet. 5, 242–249.
Kunte, A.S., Matthews, K.A., and Rawson, R.B. (2006). Fatty acid auxotrophy in Drosophila larvae lacking SREBP. Cell Metab. 3, 439–448.
Kurohane Kaneko, Y., Kobayashi, Y., Motoki, K., Nakata, K., Miyagawa, S., Yamamoto, M., Hayashi, D., Shirai, Y., Sakane, F., and Ishikawa, T. (2013). Depression of type I diacylglycerol kinases in pancreatic β-cells from male mice results in impaired insulin secretion. Endocrinology 154, 4089–4098.
Kwak, S.-J., Hong, S.-H., Bajracharya, R., Yang, S.-Y., Lee, K.-S., and Yu, K. (2013). Drosophila adiponectin receptor in insulin producing cells regulates glucose and lipid metabolism by controlling insulin secretion. PloS One 8, e68641.
Kwon, Y., and Montell, C. (2006). Dependence on the Lazaro phosphatidic acid phosphatase for the maximum light response. Curr. Biol. CB 16, 723–729.
Landis, G.N., Bhole, D., and Tower, J. (2003). A search for doxycycline-dependent mutations that increase Drosophila melanogaster life span identifies the VhaSFD, Sugar baby, filamin, fwd and Cctl genes. Genome Biol. 4, R8.
Lang, D.A., Matthews, D.R., Burnett, M., and Turner, R.C. (1981). Brief, irregular oscillations of basal plasma insulin and glucose concentrations in diabetic man. Diabetes 30, 435–439.
Lee, D.H., Shi, J., Jeoung, N.H., Kim, M.S., Zabolotny, J.M., Lee, S.W., White, M.F., Wei, L., and Kim, Y.-B. (2009). Targeted disruption of ROCK1 causes insulin resistance in vivo. J. Biol. Chem. 284, 11776–11780.
Lee, K.-S., You, K.-H., Choo, J.-K., Han, Y.-M., and Yu, K. (2004). Drosophila short neuropeptide F regulates food intake and body size. J. Biol. Chem. 279, 50781–50789.
Lee, K.-S., Kwon, O.-Y., Lee, J.H., Kwon, K., Min, K.-J., Jung, S.-A., Kim, A.-K., You, K.-H., Tatar, M., and Yu, K. (2008). Drosophila short neuropeptide F signalling regulates growth by ERK-mediated insulin signalling. Nat. Cell Biol. 10, 468–475.
163
Lee, K.-T., Byun, M.-J., Kang, K.-S., Park, E.-W., Lee, S.-H., Cho, S., Kim, H., Kim, K.-W., Lee, T., Park, J.-E., et al. (2011). Neuronal genes for subcutaneous fat thickness in human and pig are identified by local genomic sequencing and combined SNP association study. PloS One 6, e16356.
Lee, S.-H., Huang, H., Choi, K., Lee, D.H., Shi, J., Liu, T., Chun, K.H., Seo, J.A., Lima, I.S., Zabolotny, J.M., et al. (2014). ROCK1 isoform-specific deletion reveals a role for diet-induced insulin resistance. Am. J. Physiol. Endocrinol. Metab. 306, E332–E343.
Leibiger, I.B., Leibiger, B., and Berggren, P.-O. (2008). Insulin signaling in the pancreatic beta- cell. Annu. Rev. Nutr. 28, 233–251.
Li, S., Torre-Muruzabal, T., Søgaard, K.C., Ren, G.R., Hauser, F., Engelsen, S.M., Pødenphanth, M.D., Desjardins, A., and Grimmelikhuijzen, C.J.P. (2013). Expression patterns of the Drosophila neuropeptide CCHamide-2 and its receptor may suggest hormonal signaling from the gut to the brain. PloS One 8, e76131.
Lim, H.-Y., Wang, W., Wessells, R.J., Ocorr, K., and Bodmer, R. (2011). Phospholipid homeostasis regulates lipid metabolism and cardiac function through SREBP signaling in Drosophila. Genes Dev. 25, 189–200.
Lin, H.V., Plum, L., Ono, H., Gutiérrez-Juárez, R., Shanabrough, M., Borok, E., Horvath, T.L., Rossetti, L., and Accili, D. (2010). Divergent regulation of energy expenditure and hepatic glucose production by insulin receptor in agouti-related protein and POMC neurons. Diabetes 59, 337–346.
Lin, J.-M., Fabregat, M.E., Gomis, R., and Bergsten, P. (2002). Pulsatile insulin release from islets isolated from three subjects with type 2 diabetes. Diabetes 51, 988–993.
Liu, Y., Liu, H., Liu, S., Wang, S., Jiang, R.-J., and Li, S. (2009). Hormonal and nutritional regulation of insect fat body development and function. Arch. Insect Biochem. Physiol. 71, 16– 30.
Liu, Y., Wang, W., Shui, G., and Huang, X. (2014). CDP-diacylglycerol synthetase coordinates cell growth and fat storage through phosphatidylinositol metabolism and the insulin pathway. PLoS Genet. 10, e1004172.
Liu, Y., Luo, J., Carlsson, M.A., and Nässel, D.R. (2015). Serotonin and insulin-like peptides modulate leucokinin-producing neurons that affect feeding and water homeostasis in Drosophila. J. Comp. Neurol. 523, 1840–1863.
Liu, Z., Chang, G.Q., and Leibowitz, S.F. (2001). Diacylglycerol kinase zeta in hypothalamus interacts with long form leptin receptor. Relation to dietary fat and body weight regulation. J. Biol. Chem. 276, 5900–5907.
Lizcano, J.M., Alrubaie, S., Kieloch, A., Deak, M., Leevers, S.J., and Alessi, D.R. (2003). Insulin-induced Drosophila S6 kinase activation requires phosphoinositide 3-kinase and protein kinase B. Biochem. J. 374, 297–306.
164
Locke, A.E., Kahali, B., Berndt, S.I., Justice, A.E., Pers, T.H., Day, F.R., Powell, C., Vedantam, S., Buchkovich, M.L., Yang, J., et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206.
Lotta, L.A., Abbasi, A., Sharp, S.J., Sahlqvist, A.-S., Waterworth, D., Brosnan, J.M., Scott, R.A., Langenberg, C., and Wareham, N.J. (2015). Definitions of Metabolic Health and Risk of Future Type 2 Diabetes in BMI Categories: A Systematic Review and Network Meta-analysis. Diabetes Care 38, 2177–2187.
Luan, H., Lemon, W.C., Peabody, N.C., Pohl, J.B., Zelensky, P.K., Wang, D., Nitabach, M.N., Holmes, T.C., and White, B.H. (2006). Functional dissection of a neuronal network required for cuticle tanning and wing expansion in Drosophila. J. Neurosci. Off. J. Soc. Neurosci. 26, 573– 584.
Luo, B., Prescott, S.M., and Topham, M.K. (2004). Diacylglycerol kinase zeta regulates phosphatidylinositol 4-phosphate 5-kinase Ialpha by a novel mechanism. Cell. Signal. 16, 891– 897.
Luo, J., Becnel, J., Nichols, C.D., and Nässel, D.R. (2012). Insulin-producing cells in the brain of adult Drosophila are regulated by the serotonin 5-HT1A receptor. Cell. Mol. Life Sci. CMLS 69, 471–484.
Luong, N., Davies, C.R., Wessells, R.J., Graham, S.M., King, M.T., Veech, R., Bodmer, R., and Oldham, S.M. (2006). Activated FOXO-mediated insulin resistance is blocked by reduction of TOR activity. Cell Metab. 4, 133–142.
Luttrell, L.M., and Lefkowitz, R.J. (2002). The role of beta-arrestins in the termination and transduction of G-protein-coupled receptor signals. J. Cell Sci. 115, 455–465.
Lyne, M., Smith, R.N., Lyne, R., Aleksic, J., Hu, F., Kalderimis, A., Stepan, R., and Micklem, G. (2013). metabolicMine: an integrated genomics, genetics and proteomics data warehouse for common metabolic disease research. Database J. Biol. Databases Curation 2013, bat060.
Lyne, R., Smith, R., Rutherford, K., Wakeling, M., Varley, A., Guillier, F., Janssens, H., Ji, W., Mclaren, P., North, P., et al. (2007). FlyMine: an integrated database for Drosophila and Anopheles genomics. Genome Biol. 8, R129.
Mancini, A.D., Bertrand, G., Vivot, K., Carpentier, É., Tremblay, C., Ghislain, J., Bouvier, M., and Poitout, V. (2015). β-Arrestin Recruitment and Biased Agonism at Free Fatty Acid Receptor 1. J. Biol. Chem. 290, 21131–21140.
Manière, G., Ziegler, A.B., Geillon, F., Featherstone, D.E., and Grosjean, Y. (2016). Direct Sensing of Nutrients via a LAT1-like Transporter in Drosophila Insulin-Producing Cells. Cell Rep. 17, 137–148.
Manning, A.K., Hivert, M.-F., Scott, R.A., Grimsby, J.L., Bouatia-Naji, N., Chen, H., Rybin, D., Liu, C.-T., Bielak, L.F., Prokopenko, I., et al. (2012). A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669.
165
Marella, S., Mann, K., and Scott, K. (2012). Dopaminergic modulation of sucrose acceptance behavior in Drosophila. Neuron 73, 941–950.
Margolis, R.U., and Altszuler, N. (1967). Insulin in the cerebrospinal fluid. Nature 215, 1375– 1376.
Marsh, J.L., and Thompson, L.M. (2006). Drosophila in the study of neurodegenerative disease. Neuron 52, 169–178.
Martinez, V.G., and O’Driscoll, L. (2015). Neuromedin U: a multifunctional neuropeptide with pleiotropic roles. Clin. Chem. 61, 471–482.
Masai, I., Hosoya, T., Kojima, S., and Hotta, Y. (1992). Molecular cloning of a Drosophila diacylglycerol kinase gene that is expressed in the nervous system and muscle. Proc. Natl. Acad. Sci. U. S. A. 89, 6030–6034.
Masek, P., and Keene, A.C. (2013). Drosophila fatty acid taste signals through the PLC pathway in sugar-sensing neurons. PLoS Genet. 9, e1003710.
Matthews, K.A., Kaufman, T.C., and Gelbart, W.M. (2005). Research resources for Drosophila: the expanding universe. Nat. Rev. Genet. 6, 179–193.
Mattila, J., Kallijärvi, J., and Puig, O. (2008). RNAi screening for kinases and phosphatases identifies FoxO regulators. Proc. Natl. Acad. Sci. U. S. A. 105, 14873–14878.
McDonald, P., Veluthakal, R., Kaur, H., and Kowluru, A. (2007). Biologically active lipids promote trafficking and membrane association of Rac1 in insulin-secreting INS 832/13 cells. Am. J. Physiol. Cell Physiol. 292, C1216–C1220.
McGowan, M.K., Andrews, K.M., Kelly, J., and Grossman, S.P. (1990). Effects of chronic intrahypothalamic infusion of insulin on food intake and diurnal meal patterning in the rat. Behav. Neurosci. 104, 373–385.
Meier, J.J., Pennartz, C., Schenker, N., Menge, B.A., Schmidt, W.E., Heise, T., Kapitza, C., and Veldhuis, J.D. (2013). Hyperglycaemia is associated with impaired pulsatile insulin secretion: effect of basal insulin therapy. Diabetes Obes. Metab. 15, 258–263.
Melcher, C., and Pankratz, M.J. (2005). Candidate gustatory interneurons modulating feeding behavior in the Drosophila brain. PLoS Biol. 3, e305.
Mérida, I., Avila-Flores, A., and Merino, E. (2008). Diacylglycerol kinases: at the hub of cell signalling. Biochem. J. 409, 1–18.
Miele, C., Paturzo, F., Teperino, R., Sakane, F., Fiory, F., Oriente, F., Ungaro, P., Valentino, R., Beguinot, F., and Formisano, P. (2007). Glucose regulates diacylglycerol intracellular levels and protein kinase C activity by modulating diacylglycerol kinase subcellular localization. J. Biol. Chem. 282, 31835–31843.
166
Miyamoto, T., Slone, J., Song, X., and Amrein, H. (2012). A fructose receptor functions as a nutrient sensor in the Drosophila brain. Cell 151, 1113–1125.
Moltz, J.H., and McDonald, J.K. (1985). Neuropeptide Y: direct and indirect action on insulin secretion in the rat. Peptides 6, 1155–1159.
Mongraw-Chaffin, M.L., Peters, S.A.E., Huxley, R.R., and Woodward, M. (2015). The sex- specific association between BMI and coronary heart disease: a systematic review and meta- analysis of 95 cohorts with 1·2 million participants. Lancet Diabetes Endocrinol. 3, 437–449.
Morris, S.N.S., Coogan, C., Chamseddin, K., Fernandez-Kim, S.O., Kolli, S., Keller, J.N., and Bauer, J.H. (2012). Development of diet-induced insulin resistance in adult Drosophila melanogaster. Biochim. Biophys. Acta 1822, 1230–1237.
Morrison, C.D., Morton, G.J., Niswender, K.D., Gelling, R.W., and Schwartz, M.W. (2005). Leptin inhibits hypothalamic Npy and Agrp gene expression via a mechanism that requires phosphatidylinositol 3-OH-kinase signaling. Am. J. Physiol. Endocrinol. Metab. 289, E1051– E1057.
Münzberg, H., Qualls-Creekmore, E., Berthoud, H.-R., Morrison, C.D., and Yu, S. (2016). Neural Control of Energy Expenditure. Handb. Exp. Pharmacol. 233, 173–194.
Murakami, T., Sakane, F., Imai, S., Houkin, K., and Kanoh, H. (2003). Identification and characterization of two splice variants of human diacylglycerol kinase eta. J. Biol. Chem. 278, 34364–34372.
Murillo-Maldonado, J.M., Sánchez-Chávez, G., Salgado, L.M., Salceda, R., and Riesgo-Escovar, J.R. (2011). Drosophila insulin pathway mutants affect visual physiology and brain function besides growth, lipid, and carbohydrate metabolism. Diabetes 60, 1632–1636.
Musselman, L.P., Fink, J.L., Narzinski, K., Ramachandran, P.V., Hathiramani, S.S., Cagan, R.L., and Baranski, T.J. (2011). A high-sugar diet produces obesity and insulin resistance in wild-type Drosophila. Dis. Model. Mech. 4, 842–849.
Nagaya, H., Wada, I., Jia, Y.-J., and Kanoh, H. (2002). Diacylglycerol kinase delta suppresses ER-to-Golgi traffic via its SAM and PH domains. Mol. Biol. Cell 13, 302–316.
Nakayama, M., Ishibashi, T., Ishikawa, H.O., Sato, H., Usui, T., Okuda, T., Yashiro, H., Ishikawa, H., Taikou, Y., Minami, A., et al. (2014). A gain-of-function screen to identify genes that reduce lifespan in the adult of Drosophila melanogaster. BMC Genet. 15, 46.
Nässel, D.R., and Vanden Broeck, J. (2016). Insulin/IGF signaling in Drosophila and other insects: factors that regulate production, release and post-release action of the insulin-like peptides. Cell. Mol. Life Sci. CMLS 73, 271–290.
Nässel, D.R., and Wegener, C. (2011). A comparative review of short and long neuropeptide F signaling in invertebrates: Any similarities to vertebrate neuropeptide Y signaling? Peptides 32, 1335–1355.
167
Nässel, D.R., Kubrak, O.I., Liu, Y., Luo, J., and Lushchak, O.V. (2013). Factors that regulate insulin producing cells and their output in Drosophila. Front. Physiol. 4, 252.
Nelson, C.D., Perry, S.J., Regier, D.S., Prescott, S.M., Topham, M.K., and Lefkowitz, R.J. (2007). Targeting of diacylglycerol degradation to M1 muscarinic receptors by beta-arrestins. Science 315, 663–666.
Newsholme, P., and Krause, M. (2012). Nutritional regulation of insulin secretion: implications for diabetes. Clin. Biochem. Rev. 33, 35–47.
Ni, J.-Q., Liu, L.-P., Binari, R., Hardy, R., Shim, H.-S., Cavallaro, A., Booker, M., Pfeiffer, B.D., Markstein, M., Wang, H., et al. (2009). A Drosophila resource of transgenic RNAi lines for neurogenetics. Genetics 182, 1089–1100.
Ning, S., Zheng, W., Su, J., Liang, N., Li, H., Zhang, D., Liu, C., Dong, J., Zhang, Z., Cui, M., et al. (2015). Different downstream signalling of CCK1 receptors regulates distinct functions of CCK in pancreatic beta cells. Br. J. Pharmacol. 172, 5050–5067.
Nomura, M., Zhu, H.-L., Wang, L., Morinaga, H., Takayanagi, R., and Teramoto, N. (2014a). SMAD2 disruption in mouse pancreatic beta cells leads to islet hyperplasia and impaired insulin secretion due to the attenuation of ATP-sensitive K+ channel activity. Diabetologia 57, 157–166.
Nomura, M., Morinaga, H., Zhu, H.-L., Wang, L., Hasuzawa, N., Takayanagi, R., and Teramoto, N. (2014b). Activation of activin type IB receptor signals in pancreatic β cells leads to defective insulin secretion through the attenuation of ATP-sensitive K+ channel activity. Biochem. Biophys. Res. Commun. 450, 440–446.
Obici, S., Feng, Z., Karkanias, G., Baskin, D.G., and Rossetti, L. (2002a). Decreasing hypothalamic insulin receptors causes hyperphagia and insulin resistance in rats. Nat. Neurosci. 5, 566–572.
Obici, S., Zhang, B.B., Karkanias, G., and Rossetti, L. (2002b). Hypothalamic insulin signaling is required for inhibition of glucose production. Nat. Med. 8, 1376–1382.
Ogawa, K., Abe, K., Kurosawa, N., Kurohmaru, M., Sugino, H., Takahashi, M., and Hayashi, Y. (1993). Expression of alpha, beta A and beta B subunits of inhibin or activin and follistatin in rat pancreatic islets. FEBS Lett. 319, 217–220.
Oh, K.-J., Han, H.-S., Kim, M.-J., and Koo, S.-H. (2013). CREB and FoxO1: two transcription factors for the regulation of hepatic gluconeogenesis. BMB Rep. 46, 567–574.
Okamoto, N., and Nishimura, T. (2015). Signaling from Glia and Cholinergic Neurons Controls Nutrient-Dependent Production of an Insulin-like Peptide for Drosophila Body Growth. Dev. Cell 35, 295–310.
Okamura, T., Shimizu, H., Nagao, T., Ueda, R., and Ishii, S. (2007). ATF-2 regulates fat metabolism in Drosophila. Mol. Biol. Cell 18, 1519–1529.
168
Oldham, S., and Hafen, E. (2003). Insulin/IGF and target of rapamycin signaling: a TOR de force in growth control. Trends Cell Biol. 13, 79–85.
Oldham, S., Stocker, H., Laffargue, M., Wittwer, F., Wymann, M., and Hafen, E. (2002). The Drosophila insulin/IGF receptor controls growth and size by modulating PtdInsP(3) levels. Dev. Camb. Engl. 129, 4103–4109.
Ozaki, N., Shibasaki, T., Kashima, Y., Miki, T., Takahashi, K., Ueno, H., Sunaga, Y., Yano, H., Matsuura, Y., Iwanaga, T., et al. (2000). cAMP-GEFII is a direct target of cAMP in regulated exocytosis. Nat. Cell Biol. 2, 805–811.
Pallares-Cartes, C., Cakan-Akdogan, G., and Teleman, A.A. (2012). Tissue-specific coupling between insulin/IGF and TORC1 signaling via PRAS40 in Drosophila. Dev. Cell 22, 172–182.
Palmer, G.C., Tran, T., Duttlinger, A., and Nichols, R. (2007). The drosulfakinin 0 (DSK 0) peptide encoded in the conserved Dsk gene affects adult Drosophila melanogaster crop contractions. J. Insect Physiol. 53, 1125–1133.
Pandey, U.B., and Nichols, C.D. (2011). Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery. Pharmacol. Rev. 63, 411–436.
Pang, Z.P., and Han, W. (2012). Regulation of synaptic functions in central nervous system by endocrine hormones and the maintenance of energy homoeostasis. Biosci. Rep. 32, 423–432.
Papadopoulou, D., Bianchi, M.W., and Bourouis, M. (2004). Functional studies of shaggy/glycogen synthase kinase 3 phosphorylation sites in Drosophila melanogaster. Mol. Cell. Biol. 24, 4909–4919.
Park, H.-K., and Ahima, R.S. (2015). Physiology of leptin: energy homeostasis, neuroendocrine function and metabolism. Metabolism. 64, 24–34.
Park, S., Alfa, R.W., Topper, S.M., Kim, G.E.S., Kockel, L., and Kim, S.K. (2014). A genetic strategy to measure circulating Drosophila insulin reveals genes regulating insulin production and secretion. PLoS Genet. 10, e1004555.
Park, Y., Han, G.-S., Mileykovskaya, E., Garrett, T.A., and Carman, G.M. (2015). Altered Lipid Synthesis by Lack of Yeast Pah1 Phosphatidate Phosphatase Reduces Chronological Life Span. J. Biol. Chem. 290, 25382–25394.
Parker, L., Howlett, I.C., Rusan, Z.M., and Tanouye, M.A. (2011). Seizure and epilepsy: studies of seizure disorders in Drosophila. Int. Rev. Neurobiol. 99, 1–21.
Patel, R.T., Soulages, J.L., and Arrese, E.L. (2006). Adipokinetic hormone-induced mobilization of fat body triglyceride stores in Manduca sexta: role of TG-lipase and lipid droplets. Arch. Insect Biochem. Physiol. 63, 73–81.
Pendse, J., Ramachandran, P.V., Na, J., Narisu, N., Fink, J.L., Cagan, R.L., Collins, F.S., and Baranski, T.J. (2013). A Drosophila functional evaluation of candidates from human genome-
169 wide association studies of type 2 diabetes and related metabolic traits identifies tissue-specific roles for dHHEX. BMC Genomics 14, 136.
Pigeyre, M., Yazdi, F.T., Kaur, Y., and Meyre, D. (2016). Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin. Sci. Lond. Engl. 1979 130, 943–986.
Poitout, V., and Robertson, R.P. (2008). Glucolipotoxicity: fuel excess and beta-cell dysfunction. Endocr. Rev. 29, 351–366.
Poitout, V., Amyot, J., Semache, M., Zarrouki, B., Hagman, D., and Fontés, G. (2010). Glucolipotoxicity of the pancreatic beta cell. Biochim. Biophys. Acta 1801, 289–298.
Porte, D., Baskin, D.G., and Schwartz, M.W. (2005). Insulin signaling in the central nervous system: a critical role in metabolic homeostasis and disease from C. elegans to humans. Diabetes 54, 1264–1276.
Pospisilik, J.A., Schramek, D., Schnidar, H., Cronin, S.J.F., Nehme, N.T., Zhang, X., Knauf, C., Cani, P.D., Aumayr, K., Todoric, J., et al. (2010). Drosophila genome-wide obesity screen reveals hedgehog as a determinant of brown versus white adipose cell fate. Cell 140, 148–160.
Prospective Studies Collaboration, Whitlock, G., Lewington, S., Sherliker, P., Clarke, R., Emberson, J., Halsey, J., Qizilbash, N., Collins, R., and Peto, R. (2009). Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet Lond. Engl. 373, 1083–1096.
Puig, O., Marr, M.T., Ruhf, M.L., and Tjian, R. (2003). Control of cell number by Drosophila FOXO: downstream and feedback regulation of the insulin receptor pathway. Genes Dev. 17, 2006–2020.
Qi, W., Yang, Z., Lin, Z., Park, J.-Y., Suh, G.S.B., and Wang, L. (2015). A quantitative feeding assay in adult Drosophila reveals rapid modulation of food ingestion by its nutritional value. Mol. Brain 8, 87.
Raghu, P., Usher, K., Jonas, S., Chyb, S., Polyanovsky, A., and Hardie, R.C. (2000). Constitutive activity of the light-sensitive channels TRP and TRPL in the Drosophila diacylglycerol kinase mutant, rdgA. Neuron 26, 169–179.
Rahier, J., Guiot, Y., Goebbels, R.M., Sempoux, C., and Henquin, J.C. (2008). Pancreatic beta- cell mass in European subjects with type 2 diabetes. Diabetes Obes. Metab. 10 Suppl 4, 32–42.
Rajan, A., and Perrimon, N. (2012). Drosophila cytokine unpaired 2 regulates physiological homeostasis by remotely controlling insulin secretion. Cell 151, 123–137.
Rankinen, T., Zuberi, A., Chagnon, Y.C., Weisnagel, S.J., Argyropoulos, G., Walts, B., Pérusse, L., and Bouchard, C. (2006). The human obesity gene map: the 2005 update. Obes. Silver Spring Md 14, 529–644.
170
Reis, T., Van Gilst, M.R., and Hariharan, I.K. (2010). A buoyancy-based screen of Drosophila larvae for fat-storage mutants reveals a role for Sir2 in coupling fat storage to nutrient availability. PLoS Genet. 6, e1001206.
Reiter, L.T., Potocki, L., Chien, S., Gribskov, M., and Bier, E. (2001). A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster. Genome Res. 11, 1114– 1125.
Rhee, J.S., Betz, A., Pyott, S., Reim, K., Varoqueaux, F., Augustin, I., Hesse, D., Südhof, T.C., Takahashi, M., Rosenmund, C., et al. (2002). Beta phorbol ester- and diacylglycerol-induced augmentation of transmitter release is mediated by Munc13s and not by PKCs. Cell 108, 121– 133.
Ribeiro, C., and Dickson, B.J. (2010). Sex peptide receptor and neuronal TOR/S6K signaling modulate nutrient balancing in Drosophila. Curr. Biol. CB 20, 1000–1005.
Root, C.M., Ko, K.I., Jafari, A., and Wang, J.W. (2011). Presynaptic facilitation by neuropeptide signaling mediates odor-driven food search. Cell 145, 133–144.
Rosenfeld, J.M., and Osborne, T.F. (1998). HLH106, a Drosophila sterol regulatory element- binding protein in a natural cholesterol auxotroph. J. Biol. Chem. 273, 16112–16121.
Ruan, Y., Chen, C., Cao, Y., and Garofalo, R.S. (1995). The Drosophila insulin receptor contains a novel carboxyl-terminal extension likely to play an important role in signal transduction. J. Biol. Chem. 270, 4236–4243.
Rudrapatna, V.A., Cagan, R.L., and Das, T.K. (2012). Drosophila cancer models. Dev. Dyn. Off. Publ. Am. Assoc. Anat. 241, 107–118.
Rui, L. (2013). Brain regulation of energy balance and body weight. Rev. Endocr. Metab. Disord. 14, 387–407.
Rulifson, E.J., Kim, S.K., and Nusse, R. (2002). Ablation of insulin-producing neurons in flies: growth and diabetic phenotypes. Science 296, 1118–1120.
Ryder, E., and Russell, S. (2003). Transposable elements as tools for genomics and genetics in Drosophila. Brief. Funct. Genomic. Proteomic. 2, 57–71.
Ryuda, M., Tsuzuki, S., Matsumoto, H., Oda, Y., Tanimura, T., and Hayakawa, Y. (2011). Identification of a novel gene, anorexia, regulating feeding activity via insulin signaling in Drosophila melanogaster. J. Biol. Chem. 286, 38417–38426.
Sáinz, N., González-Navarro, C.J., Martínez, J.A., and Moreno-Aliaga, M.J. (2015). Leptin signaling as a therapeutic target of obesity. Expert Opin. Ther. Targets 19, 893–909.
Sakane, F., Imai, S.-I., Yamada, K., Murakami, T., Tsushima, S., and Kanoh, H. (2002). Alternative splicing of the human diacylglycerol kinase delta gene generates two isoforms differing in their expression patterns and in regulatory functions. J. Biol. Chem. 277, 43519– 43526.
171
Sakane, F., Imai, S.-I., Kai, M., Yasuda, S., and Kanoh, H. (2007). Diacylglycerol kinases: why so many of them? Biochim. Biophys. Acta 1771, 793–806.
Saltiel, A.R. (2016). Insulin Signaling in the Control of Glucose and Lipid Homeostasis. Handb. Exp. Pharmacol. 233, 51–71.
Sánchez-Lasheras, C., Könner, A.C., and Brüning, J.C. (2010). Integrative neurobiology of energy homeostasis-neurocircuits, signals and mediators. Front. Neuroendocrinol. 31, 4–15.
Sanjuán, M.A., Jones, D.R., Izquierdo, M., and Mérida, I. (2001). Role of diacylglycerol kinase alpha in the attenuation of receptor signaling. J. Cell Biol. 153, 207–220.
Sanjuán, M.A., Pradet-Balade, B., Jones, D.R., Martínez-A, C., Stone, J.C., Garcia-Sanz, J.A., and Mérida, I. (2003). T cell activation in vivo targets diacylglycerol kinase alpha to the membrane: a novel mechanism for Ras attenuation. J. Immunol. Baltim. Md 1950 170, 2877– 2883.
Sano, H., Nakamura, A., Texada, M.J., Truman, J.W., Ishimoto, H., Kamikouchi, A., Nibu, Y., Kume, K., Ida, T., and Kojima, M. (2015). The Nutrient-Responsive Hormone CCHamide-2 Controls Growth by Regulating Insulin-like Peptides in the Brain of Drosophila melanogaster. PLoS Genet. 11, e1005209.
Santos, T., Carrasco, S., Jones, D.R., Mérida, I., and Eguinoa, A. (2002). Dynamics of diacylglycerol kinase zeta translocation in living T-cells. Study of the structural domain requirements for translocation and activity. J. Biol. Chem. 277, 30300–30309.
Satin, L.S., Butler, P.C., Ha, J., and Sherman, A.S. (2015). Pulsatile insulin secretion, impaired glucose tolerance and type 2 diabetes. Mol. Aspects Med. 42, 61–77.
Sato, M., Liu, K., Sasaki, S., Kunii, N., Sakai, H., Mizuno, H., Saga, H., and Sakane, F. (2013). Evaluations of the selectivities of the diacylglycerol kinase inhibitors R59022 and R59949 among diacylglycerol kinase isozymes using a new non-radioactive assay method. Pharmacology 92, 99–107.
Scanga, S.E., Ruel, L., Binari, R.C., Snow, B., Stambolic, V., Bouchard, D., Peters, M., Calvieri, B., Mak, T.W., Woodgett, J.R., et al. (2000). The conserved PI3’K/PTEN/Akt signaling pathway regulates both cell size and survival in Drosophila. Oncogene 19, 3971–3977.
Schaap, D., de Widt, J., van der Wal, J., Vandekerckhove, J., van Damme, J., Gussow, D., Ploegh, H.L., van Blitterswijk, W.J., and van der Bend, R.L. (1990). Purification, cDNA-cloning and expression of human diacylglycerol kinase. FEBS Lett. 275, 151–158.
Scherer, T., O’Hare, J., Diggs-Andrews, K., Schweiger, M., Cheng, B., Lindtner, C., Zielinski, E., Vempati, P., Su, K., Dighe, S., et al. (2011). Brain insulin controls adipose tissue lipolysis and lipogenesis. Cell Metab. 13, 183–194.
Schmitt, S., Ugrankar, R., Greene, S.E., Prajapati, M., and Lehmann, M. (2015). Drosophila Lipin interacts with insulin and TOR signaling pathways in the control of growth and lipid metabolism. J. Cell Sci. 128, 4395–4406.
172
Schneeberger, M., Gomis, R., and Claret, M. (2014). Hypothalamic and brainstem neuronal circuits controlling homeostatic energy balance. J. Endocrinol. 220, T25–T46.
Schofield, C.J., and Sutherland, C. (2012). Disordered insulin secretion in the development of insulin resistance and Type 2 diabetes. Diabet. Med. J. Br. Diabet. Assoc. 29, 972–979.
Schoofs, A., Hückesfeld, S., Schlegel, P., Miroschnikow, A., Peters, M., Zeymer, M., Spieß, R., Chiang, A.-S., and Pankratz, M.J. (2014). Selection of motor programs for suppressing food intake and inducing locomotion in the Drosophila brain. PLoS Biol. 12, e1001893.
Schultze, S.M., Jensen, J., Hemmings, B.A., Tschopp, O., and Niessen, M. (2011). Promiscuous affairs of PKB/AKT isoforms in metabolism. Arch. Physiol. Biochem. 117, 70–77.
Schwartz, G.J., Salorio, C.F., Skoglund, C., and Moran, T.H. (1999). Gut vagal afferent lesions increase meal size but do not block gastric preload-induced feeding suppression. Am. J. Physiol. 276, R1623–R1629.
Schwartz, M.W., Peskind, E., Raskind, M., Boyko, E.J., and Porte, D. (1996). Cerebrospinal fluid leptin levels: relationship to plasma levels and to adiposity in humans. Nat. Med. 2, 589– 593.
Scuteri, A., Sanna, S., Chen, W.-M., Uda, M., Albai, G., Strait, J., Najjar, S., Nagaraja, R., Orrú, M., Usala, G., et al. (2007). Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3, e115.
Seino, S., Takahashi, H., Fujimoto, W., and Shibasaki, T. (2009). Roles of cAMP signalling in insulin granule exocytosis. Diabetes Obes. Metab. 11 Suppl 4, 180–188.
Senthilan, P.R., Piepenbrock, D., Ovezmyradov, G., Nadrowski, B., Bechstedt, S., Pauls, S., Winkler, M., Möbius, W., Howard, J., and Göpfert, M.C. (2012). Drosophila auditory organ genes and genetic hearing defects. Cell 150, 1042–1054.
Shahidullah, M., Reddy, S., Fei, H., and Levitan, I.B. (2009). In vivo role of a potassium channel-binding protein in regulating neuronal excitability and behavior. J. Neurosci. Off. J. Soc. Neurosci. 29, 13328–13337.
Shanik, M.H., Xu, Y., Skrha, J., Dankner, R., Zick, Y., and Roth, J. (2008). Insulin resistance and hyperinsulinemia: is hyperinsulinemia the cart or the horse? Diabetes Care 31 Suppl 2, S262–S268.
Sheldon, A.L., Zhang, J., Fei, H., and Levitan, I.B. (2011). SLOB, a SLOWPOKE channel binding protein, regulates insulin pathway signaling and metabolism in Drosophila. PloS One 6, e23343.
Shimizu, H., Inoue, K., and Mori, M. (2007). The leptin-dependent and -independent melanocortin signaling system: regulation of feeding and energy expenditure. J. Endocrinol. 193, 1–9.
173
Shirai, Y., Segawa, S., Kuriyama, M., Goto, K., Sakai, N., and Saito, N. (2000). Subtype-specific translocation of diacylglycerol kinase alpha and gamma and its correlation with protein kinase C. J. Biol. Chem. 275, 24760–24766.
Shirai, Y., Kouzuki, T., Kakefuda, K., Moriguchi, S., Oyagi, A., Horie, K., Morita, S., Shimazawa, M., Fukunaga, K., Takeda, J., et al. (2010). Essential role of neuron-enriched diacylglycerol kinase (DGK), DGKbeta in neurite spine formation, contributing to cognitive function. PloS One 5, e11602.
Shulga, Y.V., Topham, M.K., and Epand, R.M. (2011). Regulation and functions of diacylglycerol kinases. Chem. Rev. 111, 6186–6208.
Skibicka, K.P., and Grill, H.J. (2009). Hypothalamic and hindbrain melanocortin receptors contribute to the feeding, thermogenic, and cardiovascular action of melanocortins. Endocrinology 150, 5351–5361.
Skorupa, D.A., Dervisefendic, A., Zwiener, J., and Pletcher, S.D. (2008). Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster. Aging Cell 7, 478–490.
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007). A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885.
Smith, W.W., Thomas, J., Liu, J., Li, T., and Moran, T.H. (2014). From fat fruit fly to human obesity. Physiol. Behav. 136, 15–21.
Söderberg, J.A.E., Carlsson, M.A., and Nässel, D.R. (2012). Insulin-Producing Cells in the Drosophila Brain also Express Satiety-Inducing Cholecystokinin-Like Peptide, Drosulfakinin. Front. Endocrinol. 3, 109.
Sohn, J.-W. (2015). Network of hypothalamic neurons that control appetite. BMB Rep. 48, 229– 233.
Song, W.-J., Seshadri, M., Ashraf, U., Mdluli, T., Mondal, P., Keil, M., Azevedo, M., Kirschner, L.S., Stratakis, C.A., and Hussain, M.A. (2011). Snapin mediates incretin action and augments glucose-dependent insulin secretion. Cell Metab. 13, 308–319.
Sonoda, N., Imamura, T., Yoshizaki, T., Babendure, J.L., Lu, J.-C., and Olefsky, J.M. (2008). Beta-Arrestin-1 mediates glucagon-like peptide-1 signaling to insulin secretion in cultured pancreatic beta cells. Proc. Natl. Acad. Sci. U. S. A. 105, 6614–6619.
Speliotes, E.K., Willer, C.J., Berndt, S.I., Monda, K.L., Thorleifsson, G., Jackson, A.U., Lango Allen, H., Lindgren, C.M., Luan, J. ’an, Mägi, R., et al. (2010). Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948.
Stafford, J.W., Lynd, K.M., Jung, A.Y., and Gordon, M.D. (2012). Integration of taste and calorie sensing in Drosophila. J. Neurosci. Off. J. Soc. Neurosci. 32, 14767–14774.
174
Stevanovic, D., Trajkovic, V., Müller-Lühlhoff, S., Brandt, E., Abplanalp, W., Bumke-Vogt, C., Liehl, B., Wiedmer, P., Janjetovic, K., Starcevic, V., et al. (2013). Ghrelin-induced food intake and adiposity depend on central mTORC1/S6K1 signaling. Mol. Cell. Endocrinol. 381, 280–290.
St Johnston, D. (2002). The art and design of genetic screens: Drosophila melanogaster. Nat. Rev. Genet. 3, 176–188.
Stockinger, P., Kvitsiani, D., Rotkopf, S., Tirián, L., and Dickson, B.J. (2005). Neural circuitry that governs Drosophila male courtship behavior. Cell 121, 795–807.
Stunkard, A.J., Foch, T.T., and Hrubec, Z. (1986). A twin study of human obesity. JAMA 256, 51–54.
Sun, F., Wang, Y., Zhou, Y., Van Swinderen, B., Gong, Z., and Liu, L. (2014). Identification of neurons responsible for feeding behavior in the Drosophila brain. Sci. China Life Sci. 57, 391– 402.
Tabellini, G., Bortul, R., Santi, S., Riccio, M., Baldini, G., Cappellini, A., Billi, A.M., Berezney, R., Ruggeri, A., Cocco, L., et al. (2003). Diacylglycerol kinase-theta is localized in the speckle domains of the nucleus. Exp. Cell Res. 287, 143–154.
Tanino, F., Maeda, Y., Sakai, H., and Sakane, F. (2013). Induction of filopodia-like protrusions in N1E-115 neuroblastoma cells by diacylglycerol kinase γ independent of its enzymatic activity: potential novel function of the C-terminal region containing the catalytic domain of diacylglycerol kinase γ. Mol. Cell. Biochem. 373, 85–93.
Tatar, M., Kopelman, A., Epstein, D., Tu, M.P., Yin, C.M., and Garofalo, R.S. (2001). A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science 292, 107–110.
Teleman, A.A. (2010). Molecular mechanisms of metabolic regulation by insulin in Drosophila. Biochem. J. 425, 13–26.
Teleman, A.A., Chen, Y.-W., and Cohen, S.M. (2005). 4E-BP functions as a metabolic brake used under stress conditions but not during normal growth. Genes Dev. 19, 1844–1848.
Tengholm, A., and Gylfe, E. (2009). Oscillatory control of insulin secretion. Mol. Cell. Endocrinol. 297, 58–72.
Tennessen, J.M., Barry, W.E., Cox, J., and Thummel, C.S. (2014). Methods for studying metabolism in Drosophila. Methods San Diego Calif 68, 105–115.
Thibault, S.T., Singer, M.A., Miyazaki, W.Y., Milash, B., Dompe, N.A., Singh, C.M., Buchholz, R., Demsky, M., Fawcett, R., Francis-Lang, H.L., et al. (2004). A complementary transposon tool kit for Drosophila melanogaster using P and piggyBac. Nat. Genet. 36, 283–287.
Thomas, T.P., and Pek, S.B. (1992). Diacylglycerol inhibits potassium-induced calcium influx and insulin release by a protein kinase-C-independent mechanism in HIT T-15 islet cells. Endocrinology 131, 1985–1992.
175
Thorleifsson, G., Walters, G.B., Gudbjartsson, D.F., Steinthorsdottir, V., Sulem, P., Helgadottir, A., Styrkarsdottir, U., Gretarsdottir, S., Thorlacius, S., Jonsdottir, I., et al. (2009). Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24.
Thorne, N., and Amrein, H. (2008). Atypical expression of Drosophila gustatory receptor genes in sensory and central neurons. J. Comp. Neurol. 506, 548–568.
Timpson, N.J., Lindgren, C.M., Weedon, M.N., Randall, J., Ouwehand, W.H., Strachan, D.P., Rayner, N.W., Walker, M., Hitman, G.A., Doney, A.S.F., et al. (2009). Adiposity-related heterogeneity in patterns of type 2 diabetes susceptibility observed in genome-wide association data. Diabetes 58, 505–510.
Toker, A. (1998). Signaling through protein kinase C. Front. Biosci. J. Virtual Libr. 3, D1134– D1147.
Topham, M.K., and Prescott, S.M. (1999). Mammalian diacylglycerol kinases, a family of lipid kinases with signaling functions. J. Biol. Chem. 274, 11447–11450.
Topham, M.K., and Prescott, S.M. (2001). Diacylglycerol Kinase ζ Regulates Ras Activation by a Novel Mechanism. J. Cell Biol. 152, 1135–1144.
Topham, M.K., Bunting, M., Zimmerman, G.A., McIntyre, T.M., Blackshear, P.J., and Prescott, S.M. (1998). Protein kinase C regulates the nuclear localization of diacylglycerol kinase-zeta. Nature 394, 697–700.
Travagli, R.A., Hermann, G.E., Browning, K.N., and Rogers, R.C. (2006). Brainstem circuits regulating gastric function. Annu. Rev. Physiol. 68, 279–305.
Tsushima, S., Kai, M., Yamada, K., Imai, S., Houkin, K., Kanoh, H., and Sakane, F. (2004). Diacylglycerol kinase gamma serves as an upstream suppressor of Rac1 and lamellipodium formation. J. Biol. Chem. 279, 28603–28613.
Uchida, T., Iwashita, N., Ohara-Imaizumi, M., Ogihara, T., Nagai, S., Choi, J.B., Tamura, Y., Tada, N., Kawamori, R., Nakayama, K.I., et al. (2007). Protein kinase Cdelta plays a non- redundant role in insulin secretion in pancreatic beta cells. J. Biol. Chem. 282, 2707–2716.
Ugrankar, R., Liu, Y., Provaznik, J., Schmitt, S., and Lehmann, M. (2011). Lipin is a central regulator of adipose tissue development and function in Drosophila melanogaster. Mol. Cell. Biol. 31, 1646–1656.
Ugrankar, R., Berglund, E., Akdemir, F., Tran, C., Kim, M.S., Noh, J., Schneider, R., Ebert, B., and Graff, J.M. (2015). Drosophila glucome screening identifies Ck1alpha as a regulator of mammalian glucose metabolism. Nat. Commun. 6, 7102.
Um, S.H., Frigerio, F., Watanabe, M., Picard, F., Joaquin, M., Sticker, M., Fumagalli, S., Allegrini, P.R., Kozma, S.C., Auwerx, J., et al. (2004). Absence of S6K1 protects against age- and diet-induced obesity while enhancing insulin sensitivity. Nature 431, 200–205.
176
Unger, J.W., Livingston, J.N., and Moss, A.M. (1991). Insulin receptors in the central nervous system: localization, signalling mechanisms and functional aspects. Prog. Neurobiol. 36, 343– 362.
Van Der Heide, L.P., Hoekman, M.F.M., and Smidt, M.P. (2004). The ins and outs of FoxO shuttling: mechanisms of FoxO translocation and transcriptional regulation. Biochem. J. 380, 297–309.
Veenstra, J.A., Agricola, H.-J., and Sellami, A. (2008). Regulatory peptides in fruit fly midgut. Cell Tissue Res. 334, 499–516.
Vereshchagina, N., Ramel, M.-C., Bitoun, E., and Wilson, C. (2008). The protein phosphatase PP2A-B’ subunit Widerborst is a negative regulator of cytoplasmic activated Akt and lipid metabolism in Drosophila. J. Cell Sci. 121, 3383–3392.
Voight, B.F., Scott, L.J., Steinthorsdottir, V., Morris, A.P., Dina, C., Welch, R.P., Zeggini, E., Huth, C., Aulchenko, Y.S., Thorleifsson, G., et al. (2010). Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589.
Wada, I., Kai, M., Imai, S., Sakane, F., and Kanoh, H. (1996a). Translocation of diacylglycerol kinase alpha to the nuclear matrix of rat thymocytes and peripheral T-lymphocytes. FEBS Lett. 393, 48–52.
Wada, M., Shintani, Y., Kosaka, M., Sano, T., Hizawa, K., and Saito, S. (1996b). Immunohistochemical localization of activin A and follistatin in human tissues. Endocr. J. 43, 375–385.
Wakelam, M.J. (1998). Diacylglycerol--when is it an intracellular messenger? Biochim. Biophys. Acta 1436, 117–126.
Walker, A.K., Jacobs, R.L., Watts, J.L., Rottiers, V., Jiang, K., Finnegan, D.M., Shioda, T., Hansen, M., Yang, F., Niebergall, L.J., et al. (2011). A conserved SREBP-1/phosphatidylcholine feedback circuit regulates lipogenesis in metazoans. Cell 147, 840–852.
Wang, B., Goode, J., Best, J., Meltzer, J., Schilman, P.E., Chen, J., Garza, D., Thomas, J.B., and Montminy, M. (2008). The insulin-regulated CREB coactivator TORC promotes stress resistance in Drosophila. Cell Metab. 7, 434–444.
Wang, B., Moya, N., Niessen, S., Hoover, H., Mihaylova, M.M., Shaw, R.J., Yates, J.R., Fischer, W.H., Thomas, J.B., and Montminy, M. (2011). A hormone-dependent module regulating energy balance. Cell 145, 596–606.
Wang, M.C., Bohmann, D., and Jasper, H. (2003). JNK signaling confers tolerance to oxidative stress and extends lifespan in Drosophila. Dev. Cell 5, 811–816.
Wang, S., Tulina, N., Carlin, D.L., and Rulifson, E.J. (2007). The origin of islet-like cells in Drosophila identifies parallels to the vertebrate endocrine axis. Proc. Natl. Acad. Sci. U. S. A. 104, 19873–19878.
177
Wang, T., Jiang, X., Chen, G., and Xu, J. (2015). Interaction of amyotrophic lateral sclerosis/frontotemporal lobar degeneration-associated fused-in-sarcoma with proteins involved in metabolic and protein degradation pathways. Neurobiol. Aging 36, 527–535.
Wang, Z.L., Bennet, W.M., Wang, R.M., Ghatei, M.A., and Bloom, S.R. (1994). Evidence of a paracrine role of neuropeptide-Y in the regulation of insulin release from pancreatic islets of normal and dexamethasone-treated rats. Endocrinology 135, 200–206.
Werz, C., Köhler, K., Hafen, E., and Stocker, H. (2009). The Drosophila SH2B family adaptor Lnk acts in parallel to chico in the insulin signaling pathway. PLoS Genet. 5, e1000596.
Wicher, D. (2001). Peptidergic modulation of insect voltage-gated Ca(2+) currents: role of resting Ca(2+) current and protein kinases A and C. J. Neurophysiol. 86, 2353–2362.
Willer, C.J., Speliotes, E.K., Loos, R.J.F., Li, S., Lindgren, C.M., Heid, I.M., Berndt, S.I., Elliott, A.L., Jackson, A.U., Lamina, C., et al. (2009). Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34.
Williams, D.L., Kaplan, J.M., and Grill, H.J. (2000). The role of the dorsal vagal complex and the vagus nerve in feeding effects of melanocortin-3/4 receptor stimulation. Endocrinology 141, 1332–1337.
Wittwer, F., Jaquenoud, M., Brogiolo, W., Zarske, M., Wüstemann, P., Fernandez, R., Stocker, H., Wymann, M.P., and Hafen, E. (2005). Susi, a negative regulator of Drosophila PI3-kinase. Dev. Cell 8, 817–827.
Wolf, M.J., Amrein, H., Izatt, J.A., Choma, M.A., Reedy, M.C., and Rockman, H.A. (2006). Drosophila as a model for the identification of genes causing adult human heart disease. Proc. Natl. Acad. Sci. U. S. A. 103, 1394–1399.
Wollheim, C.B., and Regazzi, R. (1990). Protein kinase C in insulin releasing cells. Putative role in stimulus secretion coupling. FEBS Lett. 268, 376–380.
Woods, S.C., and Porte, D. (1977). Relationship between plasma and cerebrospinal fluid insulin levels of dogs. Am. J. Physiol. 233, E331–E334.
World Health Organization (2016). WHO | Obesity and overweight.
Wu, H., Mezghenna, K., Marmol, P., Guo, T., Moliner, A., Yang, S.-N., Berggren, P.-O., and Ibáñez, C.F. (2014). Differential regulation of mouse pancreatic islet insulin secretion and Smad proteins by activin ligands. Diabetologia 57, 148–156.
Wu, Q., Wen, T., Lee, G., Park, J.H., Cai, H.N., and Shen, P. (2003). Developmental control of foraging and social behavior by the Drosophila neuropeptide Y-like system. Neuron 39, 147– 161.
Wu, Q., Zhang, Y., Xu, J., and Shen, P. (2005a). Regulation of hunger-driven behaviors by neural ribosomal S6 kinase in Drosophila. Proc. Natl. Acad. Sci. U. S. A. 102, 13289–13294.
178
Wu, Q., Zhao, Z., and Shen, P. (2005b). Regulation of aversion to noxious food by Drosophila neuropeptide Y- and insulin-like systems. Nat. Neurosci. 8, 1350–1355.
Wuttke, A., Idevall-Hagren, O., and Tengholm, A. (2013). P2Y₁ receptor-dependent diacylglycerol signaling microdomains in β cells promote insulin secretion. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 27, 1610–1620.
Xiao, K., McClatchy, D.B., Shukla, A.K., Zhao, Y., Chen, M., Shenoy, S.K., Yates, J.R., and Lefkowitz, R.J. (2007). Functional specialization of beta-arrestin interactions revealed by proteomic analysis. Proc. Natl. Acad. Sci. U. S. A. 104, 12011–12016.
Xu, A.W., Kaelin, C.B., Takeda, K., Akira, S., Schwartz, M.W., and Barsh, G.S. (2005). PI3K integrates the action of insulin and leptin on hypothalamic neurons. J. Clin. Invest. 115, 951–958.
Yamaoka, T., Idehara, C., Yano, M., Matsushita, T., Yamada, T., Ii, S., Moritani, M., Hata, J., Sugino, H., Noji, S., et al. (1998). Hypoplasia of pancreatic islets in transgenic mice expressing activin receptor mutants. J. Clin. Invest. 102, 294–301.
Yang, M.-J., Wang, F., Wang, J.-H., Wu, W.-N., Hu, Z.-L., Cheng, J., Yu, D.-F., Long, L.-H., Fu, H., Xie, N., et al. (2010). PI3K integrates the effects of insulin and leptin on large- conductance Ca2+-activated K+ channels in neuropeptide Y neurons of the hypothalamic arcuate nucleus. Am. J. Physiol. Endocrinol. Metab. 298, E193–E201.
Yasuda, S., Kai, M., Imai, S.-I., Kanoh, H., and Sakane, F. (2007). Diacylglycerol kinase gamma interacts with and activates beta2-chimaerin, a Rac-specific GAP, in response to epidermal growth factor. FEBS Lett. 581, 551–557.
Ye, J. (2013). Mechanisms of insulin resistance in obesity. Front. Med. 7, 14–24.
Yu, C., Chen, Y., Cline, G.W., Zhang, D., Zong, H., Wang, Y., Bergeron, R., Kim, J.K., Cushman, S.W., Cooney, G.J., et al. (2002). Mechanism by which fatty acids inhibit insulin activation of insulin receptor substrate-1 (IRS-1)-associated phosphatidylinositol 3-kinase activity in muscle. J. Biol. Chem. 277, 50230–50236.
Zabaneh, D., and Balding, D.J. (2010). A genome-wide association study of the metabolic syndrome in Indian Asian men. PloS One 5, e11961.
Zeggini, E., Weedon, M.N., Lindgren, C.M., Frayling, T.M., Elliott, K.S., Lango, H., Timpson, N.J., Perry, J.R.B., Rayner, N.W., Freathy, R.M., et al. (2007). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341.
Zhang, J., and Liu, F. (2014). Tissue-specific insulin signaling in the regulation of metabolism and aging. IUBMB Life 66, 485–495.
Zhang, C., Hwarng, G., Cooper, D.E., Grevengoed, T.J., Eaton, J.M., Natarajan, V., Harris, T.E., and Coleman, R.A. (2015). Inhibited insulin signaling in mouse hepatocytes is associated with increased phosphatidic acid but not diacylglycerol. J. Biol. Chem. 290, 3519–3528.
179
Zhang, H., Stallock, J.P., Ng, J.C., Reinhard, C., and Neufeld, T.P. (2000). Regulation of cellular growth by the Drosophila target of rapamycin dTOR. Genes Dev. 14, 2712–2724.
Zhang, L., Bijker, M.S., and Herzog, H. (2011a). The neuropeptide Y system: pathophysiological and therapeutic implications in obesity and cancer. Pharmacol. Ther. 131, 91–113.
Zhang, W., Thompson, B.J., Hietakangas, V., and Cohen, S.M. (2011b). MAPK/ERK signaling regulates insulin sensitivity to control glucose metabolism in Drosophila. PLoS Genet. 7, e1002429.
Zhao, X.L., and Campos, A.R. (2012). Insulin signalling in mushroom body neurons regulates feeding behaviour in Drosophila larvae. J. Exp. Biol. 215, 2696–2702.
Zhou, M., Lucas, D.A., Chan, K.C., Issaq, H.J., Petricoin, E.F., Liotta, L.A., Veenstra, T.D., and Conrads, T.P. (2004). An investigation into the human serum “interactome.” Electrophoresis 25, 1289–1298.