DOCSLIB.ORG

Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy Resource Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy Graphical Abstract Authors Michael H. Kramer, Jean-Claude Farre´ , Koyel Mitra, ..., J. Michael Cherry, Suresh Subramani, Trey Ideker Correspondence [email protected] (S.S.), [email protected] (T.I.) In Brief Kramer et al. present a general procedure that guides molecular interaction mapping to assemble a hierarchical model of any biological system. Application to autophagy reveals the hierarchical organization of this process, including many new biological components and functions. This work provides an archetype for future studies in systems biology. Highlights d Diverse -omics data integrated to assemble a unified hierarchical model of autophagy d Post hoc analysis prioritizes the most informative future data types d Consequently, 156,364 genetic interactions measured in autophagy-activating conditions d Multiple new functions involve Gyp1, Atg24, Atg26, Ssd1, Did4, Stp22, and others Kramer et al., 2017, Molecular Cell 65, 761–774 February 16, 2017 ª 2017 Elsevier Inc. http://dx.doi.org/10.1016/j.molcel.2016.12.024 Molecular Cell Resource Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy Michael H. Kramer,1,4 Jean-Claude Farre´ ,2,4 Koyel Mitra,1 Michael Ku Yu,1 Keiichiro Ono,1 Barry Demchak,1 Katherine Licon,1 Mitchell Flagg,1 Rama Balakrishnan,3 J. Michael Cherry,3 Suresh Subramani,2,* and Trey Ideker1,5,* 1Department of Medicine 2Section of Molecular Biology, Division of Biological Sciences University of California, San Diego, La Jolla, CA 92093, USA 3Department of Genetics, Stanford University, Stanford, CA 94304, USA 4Co-first author 5Lead Contact *Correspondence: [email protected] (S.S.), [email protected] (T.I.) http://dx.doi.org/10.1016/j.molcel.2016.12.024 SUMMARY 2004; Malleshaiah et al., 2010). Model predictions are system- atically compared to laboratory measurements, with discrep- We have developed a general progressive proced- ancies used to improve the model and design further ure, Active Interaction Mapping, to guide assembly experiments. of the hierarchy of functions encoding any biolo- Initially, formulating a model can be laborious, requires that gical system. Using this process, we assemble an much is already known about the system, and may be biased to- ontology of functions comprising autophagy, a cen- ward well-studied components. Alternatively, many studies have tral recycling process implicated in numerous dis- sought to construct models of biological systems directly from systematic datasets (Ergun€ et al., 2007; Ho et al., 2002). eases. A first-generation model, built from existing Saccharomyces Genome-scale data, including profiles of mRNAs, proteins or gene networks in , captures most metabolites, and gene and protein interactions, are analyzed to known autophagy components in broad relation to infer a network of genes describing the system (Behrends vesicle transport, cell cycle, and stress response. et al., 2010; Havugimana et al., 2012; Kim et al., 2011; Lefebvre Systematic analysis identifies synthetic-lethal inter- et al., 2012; Picotti et al., 2013). Such network models have been actions as most informative for further experiments; increasingly used to describe key biological systems, including consequently, we saturate the model with 156,364 tissue specificity (Greene et al., 2015), cell differentiation (Cahan such measurements across autophagy-activating et al., 2014), and diseases such as cancer (Carro et al., 2010; conditions. These targeted interactions provide Creixell et al., 2015; Leiserson et al., 2015). more information about autophagy than all previous At this juncture, we see two challenges. First, biological sys- datasets, producing a second-generation ontology tems do not merely consist of flat networks of genes; they are exquisitely hierarchical in nature. Models should seek to cap- of 220 functions. Approximately half are previously ture this hierarchy, which extends over multiple scales from unknown; we confirm roles for Gyp1 at the phago- nucleotides (<1 nm scale) to proteins (1–10 nm), protein com- phore-assembly site, Atg24 in cargo engulfment, plexes (10–100 nm), cellular processes (100 nm), organelles Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, (1 mm), cells (1–10 mm), tissues (100 mm–100 mm), and com- Did4, and others in selective and non-selective auto- plex organisms (>1 m). Second, while the field has taken a phagy. The procedure and autophagy hierarchy are foundational step by enabling the construction of models at http://atgo.ucsd.edu/. directly from data rather than only literature, a next step will be to determine how to iteratively improve these models with the most informative new data. The ability to rationally INTRODUCTION motivate and design new -omics datasets is sorely needed in genomics; far too many large-scale experiments are per- A key promise of systems biology is to advance our understand- formed simply because one can, without justification or guid- ing of biological systems by combining genome-scale data with ance for why those experiments might lead to an improved model building in a virtuous cycle (Ideker et al., 2001a; Kitano, biological understanding. 2002). New genomics data improve the biological model and, Toward the first challenge, a hierarchical biological model in turn, analysis of the model informs collection of new datasets. already in widespread use is the Gene Ontology (GO) (Ashburner Systems biology typically surveys the literature to assemble et al., 2000). Through extensive literature curation efforts, GO knowledge of specific molecules, interactions, and reactions aims to factor the cell into a complete hierarchy of cellular com- involved in a biological system, then uses this knowledge to ponents and processes, each represented by a GO ‘‘term.’’ initialize a computational model (Covert et al., 2004; Davidson Recently, we showed that an ontology of the cell very much et al., 2002; Ideker et al., 2001b; Karr et al., 2012; King et al., like GO could be automatically constructed from diverse Molecular Cell 65, 761–774, February 16, 2017 ª 2017 Elsevier Inc. 761 A Integrate data to best capture curated knowledge ii Protein-protein interactions Modeling i Genetic interactions Initialize Convert network to with public iii hierarchical model data Co-expression interactions 191 transcription factors, unknown relationship Regulators of casein kinase 2 bud neck proteins AtgO:220 autophagy membrane trafficking catalytic subunits and related processes promotion of condensin recruitment transcriptional membrane protein Rho1-dependent to replication fork 8 response to iron blockage site localization and polarized cell growth insertion / stress depletion salt tolerance kinases Golgi-localized, response ubiquitin related CAMK/CMGC kinases GET complex actin-regulating γ-adaptin ear palmitoylated AAA ATPases and process and kinase family spore morphogenesis vacuolar membrane cytoskeletal functions homology, Arf-binding specificity factors AtgO:185 vacuolar trafficking regulation proteins ankyrin proteins genes, unknown nucleosome staurosporine transport of repeat-containing, casein kinase I-like CDK9-related acetyltransferase of Atg19-receptor subgroup resistance pathway palmitoyl transferase MAP kinase and a proteins cyclin-dependent ARFs and ARF H4 components and cargos proteins MAPK inactivator (no plasma EGO complex kinases binding proteins actin related proteins Mapping membrane-associated subunits + Pib2, Pep7 ADP ribosylation piccolo NuA4 histone published casein kinase I-like EGO complex factors actin and nucleation acetyltransferase membrane trafficking, relationship) proteins subunits and Pib2 guanine nucleotide factor complex subunits cell growth and stress negative regulation of ADP ribosylation exchange factors for Sec7-Arf1 golgi responses I Hog1 signaling Gtr1-Gtr2 GTPase factors involved in ARFs vesicle transport complex Rho1 GTPase and Golgi vesicular regulation complex effector trafficking eIF2B subunits and chromosomal BET5 TRS23 regulator passenger complex regulation of TRS33 membrane trafficking, subunits polyamine uptake TRS130 TRS20 TRAPP Complex cell growth and stress eIF2B subunits BET3 responses II TRS31 TRS85 AtgO:138 late secretory GEA1 pathway (vesicle GYP1 SYS1 Ubp3-Bre5 ubiquitin GARP complex vacuolar H+-ATPase trafficking, docking ATG32 protease complex subunits GET3 GYP7 and endosome to and fusion) ATG22 CLG1 BRE5 Ypt31/Ypt32 family golgi traffic SEC18 VPS1 RAV1 UBP3 VPS51 VPS52 TLG2 PEP7 Rab GTPases SEC17 RIC1 VPS45 YPT32 membrane fusion conserved oligomeric 232 V-ATPase, V0 golgi (COG) complex PEP8 HOPS/CORVET AtgO:140 domain subunits complexes and COG4 VMA3 localization YPT53 YPT31 Vma12/Vma22 VMA11 VPS8 assembly complex YPT52 COG2 COG1 COG5 retromer complex VPS9 Align with GO, VMA22 VPH2 VPS3 COG8 cargo selection V-ATPase nucleotide COG3 COG6 COG7 subunits binding V1 subunits VTC1 vacuolar transporter HSV2 UTH1 VPS29 VMA2 VMA1 HOPS complex VTC4 chaperone complex VTI1 late secretory Q-snares VPS21 VPS33 VTC3 vacuolar transporter pathway / vacuolar VPS41 chaperone complex, membrane dynamics, PEP3 PKC1 REG1 SSK2 VTC2 VAM3 vacuolar docking and VAM3 cytosolic facing cell growth and stress PEP12 VAM7 VPS16 PCL1 responses fusion PEP5 CDC7 CDC28 Other ‘omics datasets componentsHierarchical model ATG32 GTR2 PDK1 homolog YPT7 BCK1 GCN3 Observe new VAM6 ATG20 ATG4 PKH1 Mon1-Ccz1 complex ATG18 KIN4 ATG29 kinases GEA1 HSL1 Intraluminal vesicle PTC6 KES1 PTC6 PKH2 ATG17 YAK1 ATG2 neck formation CCZ1 MON1
Load more

Recommended publications
  • Genome-Wide Approach to Identify Risk Factors for Therapy-Related Myeloid Leukemia
    Leukemia (2006) 20, 239–246 & 2006 Nature Publishing Group All rights reserved 0887-6924/06 $30.00 www.nature.com/leu ORIGINAL ARTICLE Genome-wide approach to identify risk factors for therapy-related myeloid leukemia A Bogni1, C Cheng2, W Liu2, W Yang1, J Pfeffer1, S Mukatira3, D French1, JR Downing4, C-H Pui4,5,6 and MV Relling1,6 1Department of Pharmaceutical Sciences, The University of Tennessee, Memphis, TN, USA; 2Department of Biostatistics, The University of Tennessee, Memphis, TN, USA; 3Hartwell Center, The University of Tennessee, Memphis, TN, USA; 4Department of Pathology, The University of Tennessee, Memphis, TN, USA; 5Department of Hematology/Oncology St Jude Children’s Research Hospital, The University of Tennessee, Memphis, TN, USA; and 6Colleges of Medicine and Pharmacy, The University of Tennessee, Memphis, TN, USA Using a target gene approach, only a few host genetic risk therapy increases, the importance of identifying host factors for factors for treatment-related myeloid leukemia (t-ML) have been secondary neoplasms increases. defined. Gene expression microarrays allow for a more 4 genome-wide approach to assess possible genetic risk factors Because DNA microarrays interrogate multiple ( 10 000) for t-ML. We assessed gene expression profiles (n ¼ 12 625 genes in one experiment, they allow for a ‘genome-wide’ probe sets) in diagnostic acute lymphoblastic leukemic cells assessment of genes that may predispose to leukemogenesis. from 228 children treated on protocols that included leukemo- DNA microarray analysis of gene expression has been used to genic agents such as etoposide, 13 of whom developed t-ML. identify distinct expression profiles that are characteristic of Expression of 68 probes, corresponding to 63 genes, was different leukemia subtypes.13,14 Studies using this method have significantly related to risk of t-ML.
    [Show full text]
  • A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
    Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context.
    [Show full text]
  • High Resolution Physical Map of Porcine Chromosome 7 QTL Region and Comparative Mapping of This Region Among Vertebrate Genomes
    High resolution physical map of porcine chromosome 7 QTL region and comparative mapping of this region among vertebrate genomes Julie Demars, Juliette Riquet, Katia Feve, Mathieu Gautier, Mireille Morisson, Olivier Demeure, Christine Renard, Patrick Chardon, Denis Milan To cite this version: Julie Demars, Juliette Riquet, Katia Feve, Mathieu Gautier, Mireille Morisson, et al.. High resolution physical map of porcine chromosome 7 QTL region and comparative mapping of this region among vertebrate genomes. BMC Genomics, BioMed Central, 2006, 7, pp.13. 10.1186/1471-2164-7-13. hal-02661326 HAL Id: hal-02661326 https://hal.inrae.fr/hal-02661326 Submitted on 30 May 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. BMC Genomics BioMed Central Research article Open Access High resolution physical map of porcine chromosome 7 QTL region and comparative mapping of this region among vertebrate genomes Julie Demars1, Juliette Riquet1, Katia Feve1, Mathieu Gautier2, Mireille Morisson1, Olivier Demeure3, Christine Renard4, Patrick Chardon4 and Denis Milan*1 Address:
    [Show full text]
  • Downloaded from the Saccharomyces Genome Database (SGD) Using the Tophat Genes 2021, 12, 428 4 of 21
    G C A T T A C G G C A T genes Article Comparing Transcriptome Profiles of Saccharomyces Cerevisiae Cells Exposed to Cadmium Selenide/Zinc Sulfide and Indium Phosphide/Zinc Sulfide Cullen Horstmann 1,2 and Kyoungtae Kim 1,* 1 Department of Biology, Missouri State University, 901 S National, Springfield, MO 65897, USA; [email protected] 2 Jordan Valley Innovation Center, Missouri State University, 542 N Boonville, Springfield, MO 65806, USA * Correspondence: [email protected] Abstract: The primary focus of our research was to obtain global gene expression data in baker’s yeast exposed to sub-lethal doses of quantum dots (QDs), such as green-emitting CdSe/ZnS and InP/ZnS, to reveal novel insights on their unique mechanisms of toxicity. Despite their promising applications, their toxicity and long-lasting effects on the environment are not well understood. To assess toxicity, we conducted cell viability assays, ROS detection assays, and assessed their effects on the trafficking of Vps10-GFP toward the trans-Golgi network with confocal microscopy. Most notably, we used RNA-sequencing (RNA-seq) to obtain gene expression profiles and gene identities of differ- entially expressed genes (DEGs) in QD-treated yeast. We found CdSe/ZnS QDs significantly altered genes implicated in carboxylic acid, amino acid, nitrogen compounds, protein metabolic processes, transmembrane transport, cellular homeostasis, cell wall organization, translation, and ribosomal biogenesis. Additionally, we found InP/ZnS QDs to alter genes associated with oxidation-reduction, transmembrane transport, metal ion homeostasis, cellular component organization, translation, and Citation: Horstmann, C.; Kim, K. protein and nitrogen compound metabolic processes. Interestingly, we observed an increase in Comparing Transcriptome Profiles of Saccharomyces Cerevisiae Cells reactive oxygen species (ROS) in CdSe/ZnS-treated cells and a decrease in ROS levels in InP/ZnS- Exposed to Cadmium Selenide/Zinc treated cells.
    [Show full text]
  • VPS52 Antibody / SACM2L (F54565)
    VPS52 Antibody / SACM2L (F54565) Catalog No. Formulation Size F54565-0.4ML In 1X PBS, pH 7.4, with 0.09% sodium azide 0.4 ml F54565-0.08ML In 1X PBS, pH 7.4, with 0.09% sodium azide 0.08 ml Bulk quote request Availability 1-3 business days Species Reactivity Human Format Purified Clonality Polyclonal (rabbit origin) Isotype Rabbit Ig Purity Antigen affinity purified UniProt Q8N1B4 Localization Cytoplasmic Applications Immunofluorescence : 1:25 Flow cytometry : 1:25 (1x10e6 cells) Immunohistochemistry (FFPE) : 1:25 Western blot : 1:500-1:2000 Limitations This VPS52 antibody is available for research use only. Western blot testing of human MDA-MB-435 cell lysate with VPS52 antibody. Predicted molecular weight ~82 kDa (isoform 1) and 68 kDa (isoform 2). IHC testing of FFPE human colon carcinoma tissue with VPS52 antibody. HIER: steam section in pH6 citrate buffer for 20 min and allow to cool prior to staining. Immunofluorescent staining of human MCF7 cells with VPS52 antibody (green) and DAPI nuclear stain (blue). Flow cytometry testing of human MDA-MB-435 cells with VPS52 antibody; Blue=isotype control, Green= VPS52 antibody. Description This gene encodes a protein that is similar to the yeast suppressor of actin mutations 2 gene. The yeast protein forms a subunit of the tetrameric Golgi-associated retrograde protein complex that is involved in vesicle trafficking from from both early and late endosomes, back to the trans-Golgi network. This gene is located on chromosome 6 in a head-to-head orientation with the gene encoding ribosomal protein S18. Application Notes The stated application concentrations are suggested starting points.
    [Show full text]
  • Genome-Wide Analysis of the TORC1 and Osmotic Stress Signaling Network in Saccharomyces Cerevisiae
    INVESTIGATION Genome-Wide Analysis of the TORC1 and Osmotic Stress Signaling Network in Saccharomyces cerevisiae Jeremy Worley,*,1 Arron Sullivan,*,1 Xiangxia Luo,* Matthew E. Kaplan,† and Andrew P. Capaldi*,†,2 *Department of Molecular and Cellular Biology, and †Functional Genomics Core Facility, University of Arizona, Tucson, Arizona, 85721-0206 ABSTRACT The Target of Rapamycin kinase Complex I (TORC1) is a master regulator of cell growth and KEYWORDS metabolism in eukaryotes. Studies in yeast and human cells have shown that nitrogen/amino acid starvation TORC1 signals act through Npr2/Npr3 and the small GTPases Gtr1/Gtr2 (Rags in humans) to inhibit TORC1. osmotic stress However, it is unclear how other stress and starvation stimuli inhibit TORC1, and/or act in parallel with yeast knock-out the TORC1 pathway, to control cell growth. To help answer these questions, we developed a novel collection automated pipeline and used it to measure the expression of a TORC1-dependent ribosome biogenesis high-throughput gene (NSR1) during osmotic stress in 4700 Saccharomyces cerevisiae strains from the yeast knock-out screen collection. This led to the identification of 440 strains with significant and reproducible defects in NSR1 repression. The cell growth control and stress response proteins deleted in these strains form a highly connected network, including 56 proteins involved in vesicle trafficking and vacuolar function; 53 proteins that act downstream of TORC1 according to a rapamycin assay—including components of the HDAC Rpd3L, Elongator, and the INO80, CAF-1 and SWI/SNF chromatin remodeling complexes; over 100 pro- teins involved in signaling and metabolism; and 17 proteins that directly interact with TORC1.
    [Show full text]
  • A Major Quantitative Trait Locus Affecting Resistance to Tilapia Lake
    www.nature.com/hdy ARTICLE OPEN A major quantitative trait locus affecting resistance to Tilapia lake virus in farmed Nile tilapia (Oreochromis niloticus) 1 ọ ố ị 2 2 1 1 1 Agustin Barría ,Tr ng Qu cTrnh , Mahirah Mahmuddin , Carolina✉ Peñaloza , Athina Papadopoulou , Ophelie Gervais , V. Mohan Chadag 2, John A. H. Benzie2 and Ross D. Houston 1 © The Author(s) 2021, corrected publication 2021 Enhancing host resistance to infectious disease has received increasing attention in recent years as a major goal of farm animal breeding programs. Combining field data with genomic tools can provide opportunities to understand the genetic architecture of disease resistance, leading to new opportunities for disease control. In the current study, a genome-wide association study was performed to assess resistance to the Tilapia lake virus (TiLV), one of the biggest threats affecting Nile tilapia (Oreochromis niloticus); a key aquaculture species globally. A pond outbreak of TiLV in a pedigreed population of the GIFT strain was observed, with 950 fish classified as either survivor or mortality, and genotyped using a 65 K SNP array. A significant QTL of large effect was identified on chromosome Oni22. The average mortality rate of tilapia homozygous for the resistance allele at the most significant SNP (P value = 4.51E−10) was 11%, compared to 43% for tilapia homozygous for the susceptibility allele. Several candidate genes related to host response to viral infection were identified within this QTL, including lgals17, vps52, and trim29. These results provide a rare example of a major QTL affecting a trait of major importance to a farmed animal.
    [Show full text]
  • Supporting Information
    Supporting Information Poulogiannis et al. 10.1073/pnas.1009941107 SI Materials and Methods Loss of Heterozygosity (LOH) Analysis of PARK2. Seven microsatellite Bioinformatic Analysis of Genome and Transcriptome Data. The markers (D6S1550, D6S253, D6S305, D6S955, D6S980, D6S1599, aCGH package in R was used to identify significant DNA copy and D6S396) were amplified for LOH analysis within the PARK2 number (DCN) changes in our collection of 100 sporadic CRCs locus using primers that were previously described (8). (1) (Gene Expression Omnibus, accession no. GSE12520). The MSP of the PARK2 Promoter. CpG sites within the PARK2 promoter aCGH analysis of cell lines and liver metastases was derived region were detected using the Methprimer software (http://www. from published data (2, 3). Chromosome 6 tiling-path array- urogene.org/methprimer/index.html). Methylation-specificand CGH was used to identify the smallest and most frequently al- control primers were designed using the Primo MSP software tered regions of DNA copy number change on chromosome 6. (http://www.changbioscience.com/primo/primom.html); bisulfite An integrative approach was used to correlate expression pro- modification of genomic DNA was performed as described pre- files with genomic copy number data from a SNP array from the viously (9). All tumor DNA samples from primary CRC tumors same tumors (n = 48) (4) (GSE16125), using Pearson’s corre- (n = 100) and CRC lines (n = 5), as well as those from the leukemia lation coefficient analysis to identify the relationships between cell lines KG-1a (acute myeloid leukemia, AML), U937 (acute DNA copy number changes and gene expression of those genes lymphoblastic leukemia, ALL), and Raji (Burkitt lymphoma, BL) SssI located within the small frequently altered regions of DCN were screened as part of this analysis.
    [Show full text]
  • Title: a Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic
    bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 1 Title Page: 2 3 Title: A yeast phenomic model for the influence of Warburg metabolism on genetic 4 buffering of doxorubicin 5 6 Authors: Sean M. Santos1 and John L. Hartman IV1 7 1. University of Alabama at Birmingham, Department of Genetics, Birmingham, AL 8 Email: [email protected], [email protected] 9 Corresponding author: [email protected] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 1 bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 26 Abstract: 27 Background: 28 Saccharomyces cerevisiae represses respiration in the presence of adequate glucose, 29 mimicking the Warburg effect, termed aerobic glycolysis. We conducted yeast phenomic 30 experiments to characterize differential doxorubicin-gene interaction, in the context of 31 respiration vs. glycolysis. The resulting systems level biology about doxorubicin 32 cytotoxicity, including the influence of the Warburg effect, was integrated with cancer 33 pharmacogenomics data to identify potentially causal correlations between differential 34 gene expression and anti-cancer efficacy.
    [Show full text]
  • VPS52 Antibody (F54565)
    VPS52 Antibody / SACM2L (F54565) Catalog No. Formulation Size F54565-0.4ML In 1X PBS, pH 7.4, with 0.09% sodium azide 0.4 ml F54565-0.08ML In 1X PBS, pH 7.4, with 0.09% sodium azide 0.08 ml Bulk quote request Availability 1-3 business days Species Reactivity Human Format Purified Clonality Polyclonal (rabbit origin) Isotype Rabbit Ig Purity Antigen affinity purified UniProt Q8N1B4 Localization Cytoplasmic Applications Immunofluorescence : 1:25 Flow cytometry : 1:25 (1x10e6 cells) Immunohistochemistry (FFPE) : 1:25 Western blot : 1:500-1:2000 Limitations This VPS52 antibody is available for research use only. Western blot testing of human MDA-MB-435 cell lysate with VPS52 antibody. Predicted molecular weight ~82 kDa (isoform 1) and 68 kDa (isoform 2). IHC testing of FFPE human colon carcinoma tissue with VPS52 antibody. HIER: steam section in pH6 citrate buffer for 20 min and allow to cool prior to staining. Immunofluorescent staining of human MCF7 cells with VPS52 antibody (green) and DAPI nuclear stain (blue). Flow cytometry testing of human MDA-MB-435 cells with VPS52 antibody; Blue=isotype control, Green= VPS52 antibody. Description This gene encodes a protein that is similar to the yeast suppressor of actin mutations 2 gene. The yeast protein forms a subunit of the tetrameric Golgi-associated retrograde protein complex that is involved in vesicle trafficking from from both early and late endosomes, back to the trans-Golgi network. This gene is located on chromosome 6 in a head-to-head orientation with the gene encoding ribosomal protein S18. Application Notes The stated application concentrations are suggested starting points.
    [Show full text]
  • The Vacuolar Protein Sorting Genes in Insects: a Comparative Genome View
    Insect Biochemistry and Molecular Biology 62 (2015) 211e225 Contents lists available at ScienceDirect Insect Biochemistry and Molecular Biology journal homepage: www.elsevier.com/locate/ibmb The vacuolar protein sorting genes in insects: A comparative genome view * Zhaofei Li a, , Gary Blissard b a State Key Laboratory of Crop Stress Biology for Arid Areas, Key Laboratory of Northwest Loess Plateau Crop Pest Management of Ministry of Agriculture, College of Plant Protection, Northwest A&F University, Taicheng Road, Yangling, Shaanxi 712100, China b Boyce Thompson Institute, Cornell University, Ithaca, NY 14853, USA article info abstract Article history: In eukaryotic cells, regulated vesicular trafficking is critical for directing protein transport and for Received 3 September 2014 recycling and degradation of membrane lipids and proteins. Through carefully regulated transport Received in revised form vesicles, the endomembrane system performs a large and important array of dynamic cellular functions 6 November 2014 while maintaining the integrity of the cellular membrane system. Genetic studies in yeast Saccharomyces Accepted 21 November 2014 cerevisiae have identified approximately 50 vacuolar protein sorting (VPS) genes involved in vesicle Available online 5 December 2014 trafficking, and most of these genes are also characterized in mammals. The VPS proteins form distinct functional complexes, which include complexes known as ESCRT, retromer, CORVET, HOPS, GARP, and Keywords: VPS PI3K-III. Little is known about the orthologs of VPS proteins in insects. Here, with the newly annotated ESCRT Manduca sexta genome, we carried out genomic comparative analysis of VPS proteins in yeast, humans, Retromer and 13 sequenced insect genomes representing the Orders Hymenoptera, Diptera, Hemiptera, Phthir- VPS-C aptera, Lepidoptera, and Coleoptera.
    [Show full text]
  • Proteomic Analysis Identifies Distinct Glomerular Extracellular Matrix In
    CLINICAL RESEARCH www.jasn.org Proteomic Analysis Identifies Distinct Glomerular Extracellular Matrix in Collapsing Focal Segmental Glomerulosclerosis Michael L. Merchant,1 Michelle T. Barati,1 Dawn J. Caster ,1 Jessica L. Hata,2 Liliane Hobeika,3 Susan Coventry,2 Michael E. Brier,1 Daniel W. Wilkey,1 Ming Li,1 Ilse M. Rood,4 Jeroen K. Deegens,4 Jack F. Wetzels,4 Christopher P. Larsen,5 Jonathan P. Troost,6 Jeffrey B. Hodgin,7 Laura H. Mariani,8 Matthias Kretzler ,8 Jon B. Klein,1,9 and Kenneth R. McLeish1 Due to the number of contributing authors, the affiliations are listed at the end of this article. ABSTRACT Background The mechanisms leading to extracellular matrix (ECM) replacement of areas of glomerular capillaries in histologic variants of FSGS are unknown. This study used proteomics to test the hypothesis that glomerular ECM composition in collapsing FSGS (cFSGS) differs from that of other variants. Methods ECM proteins in glomeruli from biopsy specimens of patients with FSGS not otherwise specified (FSGS-NOS) or cFSGS and from normal controls were distinguished and quantified using mass spectrom- etry, verified and localized using immunohistochemistry (IHC) and confocal microscopy, and assessed for gene expression. The analysis also quantified urinary excretion of ECM proteins and peptides. Results Of 58 ECM proteins that differed in abundance between cFSGS and FSGS-NOS, 41 were more abundant in cFSGS and 17 in FSGS-NOS. IHC showed that glomerular tuft staining for cathepsin B, ca- thepsin C, and annexin A3 in cFSGS was significantly greater than in other FSGS variants, in minimal change disease, or in membranous nephropathy.
    [Show full text]