The Identification of Novel Proteins of Interest in Pancreatic Islets and the Characterization of NIPAL1 As a Regulator of Secretion

The Identification of Novel Proteins of Interest in Pancreatic Islets and the Characterization of NIPAL1 as a Regulator of Secretion

Yousef Manialawy

A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto

THE IDENTIFICATION OF NOVEL PROTEINS OF INTEREST IN PANCREATIC ISLETS AND THE CHARACTERIZATION OF NIPAL1 AS A REGULATOR OF SECRETION Yousef Manialawy, M.Sc. 2019 Institute of Medical Science, University of Toronto

Abstract

Single-cell RNA-sequencing (scRNA-seq) has offered novel insight into individual islet cells.

I hypothesize that investigation of scRNA-seq data will reveal previously uncharacterized genes related to islet function and secretion. Several recent datasets were explored in tandem with established databases. 915 candidates were investigated in silico; 28 of these progressed to transcriptional validation via qPCR; 4 progressed to translational validation via immunofluorescence studies. Through this process, magnesium influx transporter NIPAL1 was novelly identified in α-cells, Min6-K8 and α-TC6 cell lines. NIPAL1 shows partial Golgi co- localization and magnesium responsiveness. Neither overexpression nor knockdown affected

Min6 insulin secretion under standard culture conditions. However, knockdown under hypo- and hypermagnesemic conditions disrupted basal secretion and decreased total insulin content.

Overexpression increased total insulin content. These patterns were attributed to a magnesium- dependent increase in Min6 insulin secretion, which is regulated by NIPAL1. By extension,

NIPAL1 may contribute to magnesium homeostasis important to α-cell secretion in islets.

Acknowledgements

I owe a sincere debt of gratitude to my supervisor, Dr. Michael B. Wheeler, for the all of the time and patience that he has put into guiding my project and pushing me to grow not only as a scientist but as an individual. The pursuit of scientific knowledge is no doubt an arduous and humbling experience, and under his mentorship I have come to appreciate the standards by which we must hold ourselves to in unraveling the unknown.

I am also grateful to Dr. Alpana Bhatacharjee, Dr. Ying Liu and Dr. Feihan Dai for all of their support in teaching me how to effectively conduct diabetes research. I must also thank each of my colleagues, past and present, for their teachings and advice throughout my project – Dr. Saifur

Khan, Dr. Battsettseg (Tseegii) Batchuluun, Dr. Haneesha Mohan, Dr. Ashley Untereiner, Dr. Mia

Lai, Dana Al Rijjal, Himaben Gohil, Jay Xu, and Anne Wu.

I would also like to thank my committee members – Dr. Brian Cox, Dr. Adria Giacca, Dr.

Maria Rozakis and Dr. Andrew Paterson – for lending me their valuable time in offering their feedback and shaping my project.

A special thanks goes out to Dr. Nadeeja Wijesekara for providing me with the space, materials and insight to carry out my pancreas staining experiments. I must also thank Dr. Sergio

Grinstein and two members of his lab – Dr. Ziv Roth and Dr. Johannes Westman – for providing me with the antibodies for co-localization studies.

I am also grateful to my parents, Amal and Mohsen, as well as my brother Moustafa for the support that they have shown throughout my many ups and downs over the past two years.

Lastly, I must thank those donors who gave their bodies to science, without whom I could not have completed this project.

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Statement of Contributions

All data presented in this thesis are exclusively the work of the author.

Table of Contents Abstract ...... ii Acknowledgements ...... iii Statement of Contributions ...... iv List of Abbreviations ...... vii List of Figures ...... ix List of Tables ...... x Introduction ...... 1 Type 2 Diabetes ...... 1 Overview of stimulus-secretion coupling in islets ...... 2 Insulin ...... 2 Glucagon ...... 3 Overview of insulin biosynthesis ...... 7 The importance of ionic flux in secretion and the emerging role of Mg2+...... 8 The value of islet cell lines: Min6 and α-TC ...... 11 The advent of single-cell RNA-sequencing ...... 13 Introduction to NIPAL1 ...... 15 Rationale and Hypothesis ...... 19 Materials and Methods ...... 20 Tissue sample acquisition and preparation ...... 20 Pancreatic islets and exocrine pancreas ...... 21 Liver and kidney ...... 22 Cell line culture and transfection ...... 23 Standard culture and special Mg2+ culture conditions ...... 23 Transfection of plasmids for overexpression and siRNAs for knockdown ...... 23 Standard and real-time quantitative polymerase chain reaction (PCR and qPCR) ...... 24 Immunofluorescence studies ...... 25 Dissociated islets and cell lines ...... 25 Mouse pancreas section ...... 26 Co-localization studies and quantification of relative NIPAL1 intensity ...... 27 Min6-K8 glucose-stimulated secretion assay (GSIS) and Homogeneous Time-Resolved Fluorescence (HTRF) ...... 27 Statistical analyses ...... 28 Results ...... 29 Overview of candidate selection process ...... 29

Shortlisted candidate profiles ...... 34 Transcriptional validation ...... 36 Primary tissue analysis ...... 36 Cell line analysis ...... 41 Translational validation ...... 42 Immunofluorescence studies in dissociated islets ...... 42 Immunofluorescence studies of NIPAL1 in mouse pancreas sections ...... 47 Immunofluorescence studies of NIPAL1 in cell lines ...... 48 NIPAL1 expression is upregulated in the presence of Mg2+ ...... 51 Functional studies of NIPAL1 in cell lines ...... 54 Transfection and overexpression confirmation in alpha-TC, Min6 and CHO ...... 54 NIPAL1 overexpression does not impact Min6 insulin secretion under standard culture conditions .... 56 NIPAL1 knockdown does not impact Min6 insulin secretion under standard culture conditions ...... 58 NIPAL1 knockdown interferes with a Mg2+-dependent increase in Min6 insulin secretion ...... 58 NIPAL1 regulates total insulin content in Min6 cells ...... 64 General Discussion ...... 67 Summary of findings ...... 67 Candidate selection process and its limitations ...... 68 The relationship between magnesium and NIPAL1 ...... 71 The relationship between magnesium and secretion ...... 72 The potential functional relevance of NIPAL1 in islets ...... 74 Conclusions ...... 76 Future Directions ...... 77 Investigate mechanism for observed increase in Min6 secretion with higher magnesium ...... 77 Assess effect of NIPAL1 manipulation on glucagon secretion and in islets ...... 77 Investigate ionic flux and insulin granule maturation under NIPAL1 manipulation ...... 78 Explore potential relevance of NIPAL4 ...... 78 Generate islet-specific NIPAL1 knockout model ...... 79 Supplementary Materials ...... 80 References ...... 84

List of Abbreviations

α-TC6 Alpha-TC1 clone 6 cells ADP Adenosine diphosphate AMPK Adenosine monophosphate activated protein kinase AMP-PCP Adenylylmethylenediphosphonate disodium salt ANOVA Analysis of variance ATAC-seq Assay for transposase-accessible chromatin sequencing ATP Adenosine triphosphate BSA Bovine serum albumin cDNA Complementary DNA CHO Chinese hamster ovary cells CRISPR Clustered regularly interspaced short palindromic repeats cRNA Complementary RNA DAPI 4′,6-diamidino-2-phenylindole DMEM Dulbecco’s Modified Eagle Medium eQTL Expressive quantitative trait loci EV Empty vector FACS Fluorescence-activated cell sorting FBS Fetal bovine serum Gcgr Glucagon receptor GFP Green fluorescent protein GLP-1 Glucagon-like peptide 1 GLP1R Glucagon-like peptide 1 receptor GLUT2 Glucose transporter 2 GM-130 Golgi marker 130 GSIS Glucose-stimulated insulin secretion GTEx Genotype-Tissue Expression database HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HFD High fat diet HTRF Homogeneous time-resolved fluorescence IAPP Islet amyloid peptide IF Immunofluorescence studies

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IP3 Inositol triphosphate

KATP ATP-sensitive potassium channel KCNJ11 Potassium voltage-gated channel subfamily J, member 11 KRB Krebs Ringer Buffer

KV Voltage-gated potassium channel MDCK Madin-Darby canine kidney cells MeSH Medical Subject Headings Min6-K8 Mouse insulinoma 6, subclone K8

Nav Voltage-gated sodium channel NIPA1 Non-imprinted in Prader-Willi/Angelman Syndrome 1 NIPA2 Non-imprinted in Prader-Willi/Angelman Syndrome 2 OAT3 Organic anion transporter 3 OSCC Oral squamous cell carcinoma P/S Penicillin/streptomycin PBS Phosphate buffered saline PCC Pearson’s correlation coefficient PCR Polymerase chain reaction p-CREB Phospho-cAMP-responsive element binding protein PEPCK Phosphoenol pyruvate carboxykinase PSCs Pancreatic stellate cells qPCR Quantitative real-time PCR RNA-seq RNA sequencing ROS Reactive oxygen species SCG5 Secretogranin 5 scRNA-seq Single-cell RNA sequencing SEM Standard error mean siRNA Small interfering RNA T2D Type 2 diabetes TSS Transcription start site TRPM7 Transient receptor potential cation channel subfamily M member 7 UV Ultraviolet VGCC Voltage-gated calcium channels

*** Full names of all abbreviated candidate genes in this study are available in Table 1

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List of Figures Figure 1: An overview of the characteristics and complications of Type 2 diabetes...... 1

Figure 2: An overview of insulin secretion under low vs. high glucose ...... 4

Figure 3: An overview of glucagon secretion under low vs. high glucose...... 6

Figure 4: The main roles of Mg2+ in the body and its known relations to diabetes and metabolism .. 8

Figure 5: The application of single-cell RNA sequencing in islets has provided a wealth of novel insights ...... 14

Figure 6: NIPAL1 (a.k.a NIPA3) is part of two closely related families of influx Mg2+ transporters – NIPA and NIPAL...... 17

Figure 7: A brief overview of the process to sequentially identify and eliminate candidates of interest in pancreatic islets...... 30

Figure 8: Overview of candidate selection process to find novel candidates of interests in α- and β- cells...... 32

Figure 9: Shortlisted candidate expression in (A) mouse tissues and (B) human tissues measured via qPCR ...... 39

Figure 10: Expression of top candidates NIPAL1, SYT13, VAT1L and TSPAN13 in α-TC6 and Min6-K8 cell lines as measured by qPCR ...... 41

Figure 11: Representative images of immunofluorescent staining of TSPAN13 in dissociated mouse and human islets ...... 43

Figure 12: Representative images of immunofluorescent staining of VAT1L in dissociated mouse and human islets...... 44

Figure 13: Representative images of immunofluorescent staining of SYT13 in dissociated mouse and human islets...... 45

Figure 14: Representative images of immunofluorescent staining of NIPAL1 in dissociated mouse and human islets...... 46

Figure 15: Representative images of immunofluorescent staining of NIPAL1 in CD1 mouse pancreas section...... 48

Figure 16: Representative images of immunofluorescent staining of NIPAL1 in α-TC6 and Min6- K8 cell lines ...... 50

Figure 17: Investigation of effect of magnesium on NIPAL1 transcription...... 52

Figure 18: Immunofluorescence studies of NIPAL1 expression in Min6-K8 and α-TC6 cells following culture under varying magnesium conditions ...... 53

Figure 19: Representative images of efficient transfection in α-TC6, Min6-K8 and CHO cell lines with GFP...... 55

Figure 20: Immunofluorescence studies of cell lines transfected with either empty vector or mouse NIPAL1-FLAG cDNA overexpression vector...... 56

Figure 21: Overexpression of NIPAL1 in Min6-K8 cells did not affect GSIS...... 57

Figure 22: Knockdown of NIPAL1 in Min6-K8 cells did not affect GSIS ...... 59

Figure 23: Representative brightfield images (x20) of Min6-K8 cells cultured under varying magnesium concentrations and transfection conditions ...... 60

Figure 24: Magnesium concentration correlates with insulin secretion in Min6-K8 cells, and basal secretion is reduced under NIPAL1 knockdown ...... 63

Figure 25: Knockdown of NIPAL in Min6 reduces total insulin under low magnesium, while overexpression increases total insulin ...... 65

Figure S1: The proportion of candidates shared between the groups of datasets used for generating the initial pool of candidates in the candidate selection process ...... 83

List of Tables Table 1: A summary of the key characteristics of shortlisted candidates...... 34

Table 2: Primer sequences for qPCR and PCR: ...... 80

Introduction

Type 2 Diabetes

Type 2 diabetes (T2D) is a chronic metabolic disease characterized by pancreatic β-cell

dysfunction and peripheral insulin resistance1. It has rapidly evolved as a public health concern

across the globe, with an estimated 425 million adults living with the condition as of 2017, and

another 352 million at risk of developing it2. The inability of a diabetic individual to effectively

secrete or respond to insulin results in the dysregulation of carbohydrate, lipid and protein

metabolism. This dysregulation greatly enhances the risks of downstream microvascular

complications (i.e. retinopathy, nephropathy, neuropathy) and macrovascular complications such

as stroke and cardiovascular disease1 (Fig. 1). Thus, T2D represents a highly complex,

A B

Figure 1: An overview of the characteristics and complications of Type 2 diabetes. (A) In prediabetes, insulin production typically increases to compensate for growing peripheral insulin resistance in an effort to maintain normoglycemic fasting plasma glucose levels; control becomes more difficult following meals. In the transition to Type 2 diabetes proper, β-cells are no longer able to keep up with metabolic demand. This is typically characterized by loss of β-cell mass and insufficient insulin production. As a result, T2D patients are at greater risk of developing significant micro- and macro-vascular complications (B) stemming from dysregulation of plasma glucose levels. [Fig. 1A adapted from King, A. J. The use of animal models in diabetes research. Br. J. Pharmacol. 166, 877–894 (2012)].

multifaceted disease that requires significant levels of patient self-management to maintain a healthy lifestyle in the absence of effective insulin action. Identifying biological targets that influence insulin secretion and islet function therefore offers the potential to improve the lives of diabetic patients and further our understanding of T2D.

While there has been significant progress in characterizing human pancreatic islets in recent years, it is believed that many key proteins important to islet function remain to be elucidated, particularly within the context of diabetes. This stems from the sheer complexity of the regulatory network dictating islet stimulus-secretion coupling of insulin. The β-cell responds to a wide variety of stimuli; in addition to basic nutrients such as glucose, fatty acids and amino acids, secretion is also prompted in response to hormones from across the body (e.g. glucagon, GLP-1, estrogen, leptin, etc.)3,4, neuronal input from the brain5, and to feedback from sudden shifts in whole-body metabolic states such as exercise (i.e. pyruvate, lactate)3. There are also many points of regulation within the β-cell. Mitochondrial products such as citrate, glutamate and reactive oxygen species

(ROS) and cytosolic intermediates such as AMPK and IP3 all greatly influence cellular responses and downstream secretory dynamics. Thus, novel proteins that influence any of these targets could potentially have a downstream impact on secretion.

Overview of stimulus-secretion coupling in islets

Insulin

β-cells within the islet directly respond to the nutritional state of the organism and secrete corresponding amounts of insulin under appropriate conditions (Fig. 2). While they respond most robustly to increases in plasma glucose concentration via constitutively expressed GLUT1 and

GLUT2 transporters, amino acids and fatty acids may also evoke substantial secretion6. Glucose is taken up by the cell and subsequently phosphorylated by glucokinase, kickstarting glycolysis and downstream mitochondrial respiration processes to ultimately generate ATP7. The surge in

ATP production rapidly increases the intracellular ATP:ADP ratio, causing membrane-bound KATP

+ channels (i.e. sensitive to ATP) to close. The closure of KATP channels halts the leak of K ions from the cell, rapidly depolarizing the membrane and stimulating the opening of voltage-gated

Ca2+ channels (VGCC). This depolarization is additionally amplified by the opening of voltage-

+ + 8 gated Na and K (i.e. Nav and Kv) channels that promote an influx of their respective ions . The resultant influx of Ca2+ ions triggers the opening of intracellular calcium stores, in turn promoting the release of immature proinsulin granules from the rough endoplasmic reticulum and shuttling of granules to the Golgi apparatus for development into mature insulin granules. Calcium subsequently facilitates the vesicular trafficking needed for eventual exocytosis of mature insulin into the bloodstream. This ultimately results in the uptake of nutrients into insulin-sensitive tissue and halts hepatic glucose production and release.

Glucagon

In contrast to insulin, glucagon is secreted from pancreatic α-cells under the ‘fasted’ state in order to promote an increase in hepatic glucose production and release9. Despite its role as an endocrinal counterweight to insulin, glucagon is known to share a substantial proportion of cellular machinery needed for secretion (Fig. 3). This is unsurprising considering that all islet cell types are derived from the same pancreatic progenitors10, further supported by the observation that α- cells possess the ability to transdifferentiate into fully functioning β-cells11. While glucagon secretion is not as well-understood as insulin secretion, the differences in the deployment of their

A Insulin Secretion

Figure 2: An overview of insulin secretion under low vs. high glucose. Under lower glucose conditions in β-cells (A), mitochondrial output of ATP is low. This generates a relatively low ATP:ADP ratio, keeping ATP-sensitive K+ + channels (KATP) open and permitting K ion leakage that maintains the membrane in a polarized state. Voltage-gated 2+ + + Ca channels (VGCC) and voltage-gated Na and K channels (Nav, Kv) remain closed as a result, preventing effective insulin secretion. In the presence of high glucose (B), glucose undergoes mitochondrial respiration to generate ATP. + This causes the ATP:ADP ratio to increase, causing closure of KATP channels. The halt of K leakage causes the membrane to rapidly depolarize, triggering the opening of VGCC and Nav and Kv channels that in turn induce more VGCC to open. This is followed by an influx of Ca2+ through VGCC that triggers the release of intracellular Ca2+ stores. The flood of Ca2+ in the cytosol triggers insulin granule maturation and release from the endoplasmic reticulum (ER), followed by vesicular trafficking to the membrane and eventual exocytosis into the bloodstream.

shared exocytotic pathways largely lies in the degree of neuronal input and sensitivity to changes in the ATP:ADP ratio and membrane depolarization12. Under low glucose (i.e. fasted) conditions that prevent efficient ATP production in β-cells, α-cells retain a relatively elevated ATP:ADP ratio that results in the closure of most KATP channels. As in β-cells, the resulting membrane depolarization triggers the opening of VGCC and accompanying Nav and Kv channels, leading to the exocytosis of glucagon vesicles (largely mediated by the same machinery used in insulin secretion)13. However, under high glucose (i.e. fed) conditions, it is believed that further closure of KATP channels intensifies depolarization above a certain threshold, at which VGCC and Nav are deactivated in a depolarization-dependent manner14,15. As a result, glucagon secretion is inhibited.

While dysfunction related to insulin production and sensitivity remains the principal determinant of diabetes, recent evidence has implicated glucagon dysregulation in diabetes in a manner that is wholly separate of insulin-dependent suppression of glucagon secretion. Studies in glucagon receptor-null mice (i.e. Gcgr-/- mice backcrossed onto C57/BLJ6 background) that are insensitive to glucagon have been shown to be immune to developing diabetic abnormalities associated with β-cell destruction and resultant insulin deficiency following injection with streptozotocin16. Whereas control mice had to be euthanized after 6 weeks due to ketoacidosis and extreme hyperglycemia, Gcgr-/- mice did not become hyperketonemic or hyperglycemic.

Furthermore, hepatic glucagon action was also absent as determined by unchanged levels of the intermediary p-CREB17 and gluconeogenic PEPCK/PCK1 proteins. However, whether these findings translate to human patients remains to be seen.

A Glucagon Secretion

Figure 3: An overview of glucagon secretion under low vs. high glucose. Under low glucose conditions in α-cells (A), mitochondrial output of ATP is low, but the cells retain a relatively elevated ATP:ADP ratio compared to β-cells. + + This is enough to induce closure of a moderate percentage of ATP-sensitive K channels (KATP) to stymie K ion leakage. This causes the membrane to depolarize, triggering the opening of VGCC and Nav and Kv channels that in turn induce more VGCC to open. This is followed by an influx of Ca2+ through VGCC that triggers the release of intracellular Ca2+ stores. The flood of Ca2+ into the cytosol contributes to glucagon granule maturation, release from the endoplasmic reticulum (ER) and eventual exocytosis into the bloodstream. With the availability of high levels of glucose (B), mitochondrial output of ATP is substantially increased. This causes the ATP:ADP ratio to greatly increase and induce the closure of significantly more KATP channels. As a result, depolarization is intensified above a certain threshold, at which VGCC, Nav and Kv are deactivated in a depolarization-dependent manner. As a result, glucagon secretion is inhibited.

Overview of insulin biosynthesis

As with insulin secretion, the effective regulation of insulin biosynthesis is of paramount importance to ensure that mature insulin granules are readily available for exocytosis. To do so, insulin stocks must be rapidly replenished via biosynthesis to prime β-cells for variable secretory responses that are in direct proportion to the magnitude of induction by glucose. Synthesized preproinsulin (the nascent precursor of insulin) is fed directly into the rough endoplasmic reticulum

(rER) lumen, where it is cleaved to yield proinsulin18. Proinsulin is subsequently folded into its tertiary structure by a host of ER chaperone proteins19. Interestingly, the generation of proinsulin is stimulated under 2-4 mM of glucose, which is notably lower than the 4-6 mM needed for insulin secretion; this ensures that proinsulin production takes place even in the absence of active secretion, in order to maintain ample secretory stores20. Unlike vesicular trafficking however, proinsulin biosynthesis is Ca2+-independent, relying instead on the output of mitochondrial metabolites in response to nutrient metabolism21.

Once in the appropriate conformation, folded proinsulin is trafficked from the rER to specific regions within the trans-Golgi network, where it is packaged to yield immature secretory vesicles22. While still in the Golgi, proinsulin is processed by various peptidases to generate granules containing mature insulin and cleaved C-peptide fragments, in addition to secondary products such as amylin23. Both the trafficking of immature granules and cleaving of proinsulin to generate mature insulin are both highly dependent on the influx of Ca2+ stemming from the opening of VGCC and intracellular calcium stores24. Maturation is also dependent on the influx of Zn2+ via zinc transporters such as ZNT825 and ZNT1026, which promote the formation of hexameric crystals of insulin at a ratio of six insulin molecules per pair of Zn2+ ions26. These mature granules are ultimately trafficked to the membrane in a Ca2+-dependent manner upon stimulation of secretion.

The importance of ionic flux in secretion and the emerging role of Mg2+

As explored in previous sections, modulation of ionic flux is imperative to ensure effective and glycemically appropriate secretion of insulin and glucagon in islets, in addition to insulin biosynthesis. Na+ and K+ regulate the membrane depolarization state, Ca2+ regulates granule maturation and exocytosis, and Zn2+ has more recently been shown to regulate insulin biosynthesis and secretion25. However, the relevance of Mg2+ to islet secretion and diabetes remains relatively underexplored. This is due in part to the wide variety of roles played by magnesium: it has been implicated in membrane stability, cell growth, energy metabolism, and enzyme activity due to its role as an ATP-carrying cofactor27–29 (Fig. 4). At the cellular level, transport of free Mg2+ across the plasma membrane typically occurs via either passive transport (Mg2+ accumulation) or exchanger mechanisms (Mg2+ extrusion). Although not as well-characterized as most other major cations, a host of studies have shown that substantial fluxes of Mg2+ occur across the plasma membrane under appropriate hormonal or metabolic stimuli30,31. Experiments in cardiomyocytes32 and liver cells33 have demonstrated extrusion of 10-20% of total cellular magnesium within 10

Figure 4: The main roles of Mg2+ in the body and its known relations to diabetes and metabolism.

minutes of administering adrenergic stimuli. Despite these large changes in total magnesium content, relatively small changes are typically observed in intracellular free Mg2+ levels, suggesting that these cells can rapidly buffer the loss or accumulation of free Mg2+ ions34.

Almost all known magnesium channels and transporters are found at the plasma membrane, as is the case for most cations35. Within the cytosol, levels of free Mg2+ are in the order of 0.5 mM, which closely resembles the levels of free Mg2+ found in both plasma and interstitial fluid36; however, this value is not uniform across the cell37. Some organelles/subcellular compartments have been found to retain higher concentrations. For example, concentrations of 0.8-1.2 mM free

Mg2+ have been measured in the mitochondrial matrix of cardiac and hepatic cells38,39. While Mg2+ levels in other organelles have yet to be accurately measured due to technological limitations, the presence of magnesium transporters in the sarcoplasmic reticulum40 and Golgi apparatus37 would suggest local concentrations that differ from the cytosol.

However, it is difficult to ascertain exactly why these organelles would need to regulate their magnesium content independently of the rest of the cell. In the case of the Golgi (and the closely related rER), one Japanese study conducted on the parathyroid glands of golden hamsters would suggest an acute sensitivity to magnesium. Following injection with magnesium, collected parathyroid glands showed cells with significant decreases in the area of both the Golgi and the cisternae of the rER compared to control animals41. This change was found to last up to one hour after injection. Additionally, the hamsters were found to have lower serum calcium levels. These changes were tied to a reduction in the biosynthesis of parathyroid hormone as a result of the decreased metabolic output of the Golgi in particular. However, it remains unclear why exactly the Golgi would respond to an acute influx of magnesium in this manner. Given that the vesicular trafficking and granule maturation pathways that take place within the Golgi are largely Ca2+-

dependent (as mentioned in the previous section regarding insulin biosynthesis), the effect of Mg2+ on the Golgi may be explained in part by disruption of Ca2+ binding and Ca2+ channel activity by free Mg2+ ions42,43. This is supported by the aforementioned decrease in serum calcium levels in treated hamsters. Thus, the reduction in Golgi area may represent a protective response against the disruptive influence of a sudden influx of Mg2+. However, an alternative explanation might be that the Golgi is simply shrinking in response to the sudden shift in cytosolic osmolarity, to which it would be especially sensitive to on account of its local magnesium transporters. While it is worth noting that an increased abundance of free Mg2+ could potentially increase the formation of Mg-

ATP complexes critical for fuelling vesicular trafficking, any positive effects would likely be outweighed by the loss of calcium signalling (as evidenced by decreased output of parathyroid hormone biosynthesis).

While our understanding of the regulation of cellular magnesium has grown rapidly in recent decades, the importance of free Mg2+ in islets is still unclear. This is significant considering the substantial evidence linking Mg2+ to the regulation of dietary metabolism and T2D. An estimated

30% of patients with T2D are believed to suffer from hypomagnesemia (i.e. < 0.7 mmol/L Mg2+ in plasma)44–47; this strong association makes hypomagnesemia a potential risk factor for the development and progression of T2D48–50. This may be due in part to the importance of Mg2+ in regulating insulin receptor tyrosine kinase activity46,51–53, and it has been posited that Mg2+ levels are directly linked to insulin resistance and dyslipidemia44. Two independent meta-analyses have suggested that correcting for hypomagnesemia with long-term oral Mg2+ supplementation improves both insulin sensitivity and fasting plasma glucose in T2D patients54,55. However, the apparent benefits of added Mg2+ may not translate well to islets. In vitro studies in islets have demonstrated that pancreatic Mg2+ perfusion inhibits both insulin and glucagon secretion, due to

magnesium’s aforementioned disruption of critical Ca2+ binding and Ca2+ channel activity; this in turn alters the sensitivity of these cells to glucose56–59. The elusiveness of magnesium’s role becomes even more complicated when examining in vivo studies. One study demonstrated that magnesium-deficient rats develop significantly elevated blood-glucose values versus pair-fed controls60; however, another study showed that low dietary Mg2+ intake was beneficial in ameliorating high fat diet (HFD) induced obesity in mice, marked by decreased fasting serum glucose levels and improved insulin sensitivity61. The difference in results suggests that, in vivo,

Mg2+ is beneficial for glucose control and insulin sensitivity, but detrimental to combatting metabolic stress incurred by lipid-based obesity stemming from HFD. While further studies of magnesium’s role in whole-body metabolism are undoubtedly warranted, elucidating pathways governing magnesium flux specifically within islets may provide further insight as to its role in islet secretion and metabolism, particularly within the context of T2D.

The value of islet cell lines: Min6 and α-TC

In vitro studies conducted directly in primary islets undoubtedly offer the closest model of in vivo conditions. However, the relative scarcity and fragility of islets can make it difficult to study them under physiologically abnormal conditions for extended periods of time. To overcome this limitation, the use of murine β-cell and α-cell lines such as Min6 and α-TC (respectively) offers more robust models that share many of the functional characteristics of their primary counterparts.

Min6 cells are immortalized pancreatic β-cells originating from a transgenic C57BL/6 mouse expressing the SV40 large T antigen oncogene under the control of the insulin promoter62. They share many morphological characteristics with primary β-cells, and critically express GLUT2

63 transporters, KATP channels and glucokinase . For this reason, the cells retain glucose sensitivity

and readily exhibit glucose-inducible insulin secretion. As an insulinoma, the secretory response is expectedly far more robust than in primary cells, although the cells display significant levels of basal secretion even in the absence of glucose; however, continued passaging can cause the cells to abruptly lose their glucose sensitivity. Overall, Min6 cells thus offer a remarkably useful model for examining the effects of treatments on insulin secretion. However, as with other islet cell lines, there is a loss of intra-islet crosstalk when studying Min6 secretion in isolation64. Interestingly, while insulin is by far the main product of these cells, they also secrete notable levels of glucagon, somatostatin and ghrelin65. It has been suggested then that Min6 should not be considered a pure

β-cell line, and that it could be useful as a model for the development, differentiation and function of pancreatic islets. In this particular study, a subclone of Min6 cells known as Min6-K8 (derived from IT6 mice) was used for investigation of candidate genes. Min6-K8 cells show an enhanced secretory response to incretins such as GLP-1 and GIP, and demonstrate cell clustering behaviour that is more reminiscent of primary islets66.

α-TC cells (specifically the commonly used α-TC 1 clone 6) are immortalized pancreatic α- cells derived from an adenoma generated in transgenic mice. These cells express the SV40 large

T antigen oncogene under the control of the preproglucagon promoter67. As would be expected, α-

TC cells are characterized by exceptionally high levels of glucagon expression, in addition to GLP-

1 (a well-known by-product of preproglucagon). Like primary α-cells, α-TC cells express insulin receptors that make them sensitive to insulin-dependent antagonism of glucagon secretion68. Other

69 similarities include expression of KATP channels , and glucagon secretion in response to acute stimulation with free fatty acids70. However, unlike in islets, α-TC cells demonstrate elevated rather than decreased proglucagon mRNA levels and basal glucagon secretion in the presence of high glucose71. This difference can be problematic when using the α-TC model to try and evaluate

the effects of treatments on glucagon secretion, especially since the secretory response is not particularly robust (when compared to glucose induction of insulin secretion in Min6 cells or islets). This weak response is largely attributed to the importance of regulatory input from the nervous system that is absent in in vitro studies72.

To overcome this limitation, some studies in α-TC cells have resorted to inducing secretion via the addition of arginine71,73. However, the lack of any standardized stimulated glucagon assay in α-TC cells, coupled with the inherent limitations of an α-cell model of secretion in vitro, ultimately casts doubt on any interpretations arising from this method. For this reason, the investigation of genes commonly expressed between α-TC and Min6 cell lines would be better off focusing on Min6 if impacts on secretion and hormonal biosynthesis are of interest; the previously described similarities in the cellular mechanisms governing glucagon and insulin secretion largely increase the likelihood that the relevance of a gene in one secretion pathway would make it applicable to the other.

The advent of single-cell RNA-sequencing

Gene expression profiles for tissues have traditionally been generated through the input of millions of cells into large-scale approaches ranging from microarrays74 to high-throughput RNA sequencing (RNA-seq)75. While beneficial for examining the average transcriptional landscape across a large population of cells, these technologies carry certain limitations. A demanding quantity of input cells makes it difficult to effectively characterize rare cell types, such as ε-cells in pancreatic islets76. The averaging nature of these technologies also prevents the identification of unique subtypes of cells within a perceived homogeneous population. The advent of single-cell

RNA-sequencing (scRNA-seq) in recent years has offered a way to address these limitations, as made possible through several advancements in the efficiency of sample acquisition and data storage77, namely: (1) The development of effective methods to isolate single cells, particularly from highly heterogeneous tissues, (2) the preparation of cDNA libraries with miniscule inputs of

RNA, and (3) the tailoring of computational methods for single-cell analysis.

As pancreatic islets are both highly heterogeneous and relatively rare (making up under 2% of pancreatic mass78), they are exceptionally well-suited to benefitting from scRNA-seq technology. The ability to profile individual islet cells under scRNA-seq has offered novel insight into cell-specific signals that may have been drowned out under traditional whole-islet sequencing

(Fig. 5). While fluorescence-activated cell sorting (FACS)-enriched transcriptomic data for islets

Figure 5: The application of single-cell RNA sequencing in islets has provided a wealth of novel insights. do exist79–81, these have typically been limited to the major pancreatic cell types (i.e. α, β and acinar) and have lacked the sensitivity to control for inter-individual differences stemming from needing multiple donors for significant data output. Through its ability to provide large-scale, cell- specific expression profiles, scRNA-seq has already yielded several exciting insights. The technology has delivered deeper characterization of rare cell types such as such as δ, ε and γ/PP cells within islets82–86 and uncommon exocrine pancreatic stellate cells (PSCs)82. Cell-specific

differences in gene expression between healthy patients and type 2 diabetics have also emerged82,83,86,87. The technology has even been used to identify four distinct subtypes of β-cells within human islets88, whereas it had previously been assumed to be a fairly homogeneous population. Thus, the rapid addition of islet scRNA-seq data to the literature is expected to lead to many more bona fide discoveries that will ultimately redefine our understanding of islets.

That being said, it is important to address the limitations of scRNA-seq. Compared to bulk

RNA-seq, the data generated from scRNA-seq tends to be considerably noisier and more variable.

It also suffers from lower capture efficiency and a higher rate of dropouts (i.e. where the transcript is not detected due to failed capture/amplification)89. Furthermore, scRNA-seq reads are subject to temporal fluctuation resulting from inconsistent expression at the single-cell level (which is not an issue for averaging RNA-seq). Thus, it is important not to discount the value of traditional

RNA-seq. Combining the strengths of the technologies may very well serve to shore up their independent limitations.

Introduction to NIPAL1

Through the selection process conducted in this study, the NIPA-like domain containing 1

(NIPAL1) gene was identified as an entirely novel α-cell gene with relatively strong enrichment in both murine and human islets. NIPAL1 is also known as Non-imprinted in Prader-Willi/Angelman syndrome 3 (NIPA3) on account of its structural similarity to paralogs NIPA1 and NIPA2, which have both been implicated in the neurodevelopmental disorder Prader-Willi/Angelman syndrome90,91. However, despite the shared name, NIPAL1/NIPA3 has not been implicated in the disease; hence the ‘NIPA-like’ nomenclature. NIPAL1/NIPA3 is part of the four-member NIPA

family of magnesium transporters with varying degrees of exclusivity to Mg2+ transport. Alongside the aforementioned NIPA1 and NIPA2, NIPA4 (a.k.a NIPAL4) is the final member of this family, although mutation of this gene has been implicated in autosomal recessive congenital ichthyosis

(i.e. dry skin from improper lipid bilayer formation)92–97 rather than any neurological condition.

Human NIPAL1/NIPA3 has a roughly 66% amino acid similarity with NIPA2 and NIPA4, but only a roughly 36% similarity with NIPA198. Human NIPAL1 is additionally quite well-conserved between species, with 87.1% and 88.5% similarity with mouse and rat homologs, respectively.

Although proteomic analysis has yet to be performed on any member of the NIPA family, one study used in-silico analysis of secondary structure to predict the presence of 9 transmembrane domains for both NIPA1 and NIPA299. By extension, the high amino acid similarity with these paralogs would suggest that NIPAL1/NIPA3 follows suit. In addition to the NIPA family, NIPAL1 and NIPAL4 are also members of a separate NIPAL family with NIPAL2 and NIPAL3, but neither of these genes have been studied in appreciable detail. The familial organization of NIPAL1 is depicted in Figure 6 for clarity.

The human Genotype-Tissue Expression (GTEx) database indicates that NIPAL1 has significant levels of expression in the skin, colon, bladder and vagina, with more moderate expression in the small intestine and liver; while islet data is unavailable on the platform, whole pancreas expression is notably absent100. Interestingly, the GeneAtlas U133A mouse dataset on the BioGPS database indicates that Nipal1 has substantial expression in Min6 (i.e. mouse insulinoma) cells101. As of this writing, there are only a handful of publications available that directly reference NIPAL1, none which have studied it in the context of islets or diabetes. Curiously however, a mutation in the NIPA1 paralog has been associated with increased T2D risk in African-

American and Hispanic-American women102. One study aiming to functionally characterize the

NIPA family induced NIPAL1 expression in oocytes via cRNA injection, and determined that the

transporter was inward rectifying via voltage clamp experiments in the presence of Mg2+ 98.

Notably, Mg2+ uptake was not disrupted by the addition of the similarly divalent Ca2+, indicating

a substantial degree of specificity. Further studies identified a nearly three-fold increase in

expression in kidney tissue harvested from mice on a magnesium-restricted diet; as NIPAL1 is

inward rectifying; this suggests that its expression is upregulated to promote Mg2+ uptake into

cells.

Figure 6: NIPAL1 (a.k.a NIPA3) is part of two closely related families of influx Mg2+ transporters – NIPA and NIPAL. While there have been studies characterizing their roles in various pathologies, most members remain relatively unexplored.

While NIPAL1 has not been directly linked to any magnesium-related disease, a NIPAL1

mutation in a male Japanese cohort has been linked to gout103, which in a later study was identified

as promoting renal underexcretion of urate in the nephron distal tubule despite not directly

transporting urate104. NIPAL1 has also been implicated as a potential tumor-promoting factor in

oral squamous cell carcinoma (OSCC), with functional analysis suggesting that it regulates growth and adhesion of OSCC tumor and endothelial cells105. However, NIPAL1 has yet to be linked to diabetes or any similarly metabolic condition. Despite this, its observed expression in α-cells suggested a possible role in islet function, and thus warranted further investigation in the context of islets and secretion.

Rationale and Hypothesis

Single-cell RNA-sequencing (scRNA-seq) has provided researchers with the ability to characterize patterns of gene expression within individual cells. This has offered novel insight into the minutiae of transcriptional landscapes across various tissues, including pancreatic islets. The application of scRNA-seq data to the islet has generated specific profiles for its wide variety of cells, highlighting expression of genes that may have been drowned out in traditional whole-islet

RNA-sequencing. These data therefore offer the potential to identify novel targets of interest that may be related to islet function. It is thus hypothesized that effective implementation of multiple scRNA-seq datasets to identify novel candidates in α- and β-cells will reveal previously uncharacterized genes that regulate islet function and hormonal secretion.

To take advantage of this data, a sequential investigation process was designed to select for and eliminate candidates of interest. Candidates were first selected from datasets in silico through the application of defined parameters to generate an initial pool of candidates. Selection criteria were applied to generate a shortlist of candidates, which were then subjected to in vitro validation of islet enrichment via qPCR and immunofluorescence studies. Through this process, the magnesium transporter gene NIPAL1 emerged as a strong α-cell candidate in both murine and human islets. This is interesting as emerging evidence has implicated magnesium in diabetes and metabolic regulation. Furthermore, magnesium’s well-studied ability to interfere with calcium- based signalling may be of relevance to the ionic flux that underpins secretory processes in pancreatic islets. Thus, it is further hypothesized that NIPAL1 affects secretion in a magnesium- dependent manner.

Materials and Methods

Dataset selection and basic pre-processing of data for candidate selection

Several scRNA-seq and traditional RNA-seq studies provided the datasets used for candidate

selection in this study (an unpublished scRNA-seq dataset from the Wheeler lab was additionally

included). All data used were acquired in supplementary datasets with results pre-processed or

filtered to varying degrees. Specific details can be found in the table below:

Publication Application, Islet Species, Figures/Tables used Pre-processing by Pre-processing in this study Dataset type author Benner et al., BMC scRNA-seq Additional file 12 Mean RPKM values of Sorted candidates by decreasing Genomics (2014) Human and mouse genes in mouse, human mean RPKM and took top 200 α/β Most enriched genes α/β cells genes for both species Segerstolpe et al., scRNA-seq - Tables S1, S2 All tables had Top 200 α/β genes used from Cell Metab. (2016) Human (Enriched genes) mean/median enriched tables; α/β tabs of Table Most enriched genes + - Table S6 (Differential log2RPKM ≥ 1, FDR adj. S6 used as provided Differentially expr. under T2D expr. under T2D) p < 0.01 Xin et al., Cell scRNA-seq Table S4 Mean RPKM values of Sorted candidates by decreasing Metab. (2016) Human and mouse genes in mouse, human mean RPKM and took top 200 α/β Most enriched genes α/β cells genes for both species

Li et al.,EMBO scRNA-seq Supp. Dataset EV6 Mean RPKM values of Sorted candidates by decreasing Rep. (2016) Human genes for islet and mean RPKM and took top 200 α/β Most enriched genes exocrine cells genes DiGruccio et al., scRNA-seq File (.txt) in Gene Mean log2RPKM values Cell clusters labelled (matched Mol. Metab. Mouse Expression Omnibus for FACS-sorted cell based on paper details) and sorted (2016) Most enriched genes (Acc. # GSE80673) clusters provided in decreasing order of mean expr. for α/β (top 200 taken from each group) Lawlor et al., scRNA-seq Supp. Table S6, Mean log2CPM values Sorted candidates by decreasing Genome Res. Human ‘NonT2D.Beta.vs.Alpha’ of genes for α/β cells mean log2CPM and took top 200 (2017) Most enriched genes tab α/β genes Ottosson-Laakso Whole-islet RNA-seq Supp. Tables S4, S5, S8 FDR-corrected p-value Only included initial candidates et al., Diabetes Human [q] < 0.05, mean with log2-transformed fold change (2017) Differentially expr. under log2CPM provided ≥ 0.5 or ≤ -0.5 glucose exposure Varshney et al., Whole-islet RNA-seq Interactive platform: Only islet eQTL variants Top decile of islet specificity, PNAS (2017) Human (islets + 14 other http://theparkerlab.org (3314); classified into default parameters for strong tissues) /tools/isleteqtl/ deciles of islet transcription, active TSS, ATAC-seq Islet-specific eQTL specificity + epigenetic ‘footprint’ state

All initially selected candidates were filtered into a final shortlist if they met the following specific criteria:

(1) Strong α/β-cell specificity, where mean expression of a candidate was at least double that of other islet (or exocrine) cell types in at least one of the scRNA-seq datasets in which it was identified (where applicable); candidates identified in a whole-islet RNA-seq dataset were cross- referenced in scRNA-seq datasets (where possible).

(2) Strong islet specificity, assessed using the GeneAtlas U133A (gcrma) human tissue dataset

(79 tissues total) on the BioGPS database (http://biogps.org/dataset/GSE1133/geneatlas-u133a- gcrma/). The dataset easily distinguishes candidates with mean expression in human islets that is at least triple the median of all tissues, and this was selected as the cut-off point for this criterion.

If unavailable on BioGPS, the GTeX and ProteinAtlas databases were used to ensure that candidates were not ubiquitously expressed.

(3) Novelty in islets, assessed via a thorough PubMed search employing a broad range of

MeSH terms, e.g. ‘NIPAL1 AND (islet OR diabetes OR pancreas OR pancreatic OR insulin OR glucagon OR secretion). Candidates with any detailed/focused studies in an islet/diabetes context were deemed not to be novel and excluded. Candidates with no publications were still considered.

Tissue sample acquisition and preparation

Pancreatic islets and exocrine pancreas

All animal research protocols were approved by the Animal Care Committee at the University of Toronto and were handled in accordance with Canadian Council of Animal Care guidelines.

Pancreatic islets were isolated from healthy male CD1 strain mice ranging from 8-12 weeks of age. A total of 37 mice were used for qPCR and immunofluorescence experiments involving

mouse tissues. All mice were acquired from Charles River (Wilington, MA, USA) and were fed a standard chow diet. A 0.08% collagenase type V (Millipore-Sigma, Etobicoke, ON, Canada) mixture was prepared in RPMI-1640 (Millipore-Sigma, Etobicoke, ON, Canada) supplemented with 2% BSA and 100 U/ml penicillin/streptomycin (P/S). The ampulla of Vater (where the common bile duct and pancreatic duct merge to connect to the duodenum) was clamped with forceps and the collagenase mixture was injected into the common bile duct to feed it into the pancreatic duct. Perfused pancreata were excised and digested in the mixture for 9 min at 37°C.

Digestion was halted with the addition of complete RPMI (5% P/S and 10% FBS). Freed islets were then handpicked and incubated in complete media under 37°C and 5% CO2 conditions.

Exocrine pancreas samples were collected from the same dishes. All samples not used immediately were washed with D-PBS (Millipore-Sigma, Etobicoke, ON, Canada) and stored at -80C.

Human islets from 6 non-diabetic donors (3 male and 3 female, average age 49.8±16.4 years, average BMI 29.8±4.5) were received from either the IsletCore laboratory at the Alberta Diabetes

Institute (University of Alberta) or through the Islet Program organized by the Banting and Best

Diabetes Centre (University of Toronto). Islets were procured from cadaveric donors following approval by either the University of Alberta’s Human Research Ethics Board or Research Ethics

Boards of the University Health Network and University of Toronto. Pancreatic tissue was digested and underwent quality control prior to arrival in the laboratory. Both islets and exocrine tissue were handpicked. Samples were cultured or stored in the same manner as mouse samples.

Liver and kidney

Liver and kidney samples were collected from CD1 mice by excision of sizable portions of the respective tissue. Samples were flash frozen using liquid nitrogen and ground into fine powders via mortar and pestle. Human liver samples were received from the Islet Program and underwent

quality control prior to arrival in the laboratory. All samples were stored at -80°C until use. All protocols were given ethical approval as described in the islets section above.

Cell line culture and transfection

Standard culture and special Mg2+ culture conditions

Min6-K8 cells were cultured in high glucose DMEM (Millipore-Sigma, Etobicoke, ON,

Canada) supplemented with 10% FBS, 100U/ml P/S and 1.75ul of β-mercaptoethanol. α-TC6 cells were cultured in low glucose DMEM (Millipore-Sigma, Etobicoke, ON, Canada) supplemented with 10% FBS, 100U/ml P/S, 0.02% BSA and 15 mM HEPES. Both cell lines were maintained at

37°C and 5% CO2. For experiments requiring specialized magnesium conditions, cells were first seeded in standard media for 24h, washed thrice with PBS (to remove trace magnesium) and then media was replaced with custom magnesium-free high glucose DMEM (Wisent, St. Bruno, QC,

Canada) supplemented with 2% FBS (to minimize magnesium content), 100U/ml P/S and MgSO4 corresponding to the desired concentration. Replacement with special magnesium media was done concurrently with transfection of plasmids/siRNAs. Phase-contrast images of live cells were taken at x10 magnification using a Leica DMi8 Inverted Microscope equipped with a Leica DFC9000

Microscope Camera (Leica Biosystems, Concord, ON, Canada).

Transfection of plasmids for overexpression and siRNAs for knockdown

GFP, empty vector and mouse pCMV6-Entry-Nipal1-Myc-DDK cDNA plasmids (Origene,

Rockville, MD, USA) were transformed into XL-10 Gold® Ultracompetent E. coli cells and bacteria were grown on LB agar plates supplemented with vector-corresponding antibiotics.

Resulting colonies were used to generate cultures by inoculation of LB broth at 37°C with shaking overnight. Plasmids were isolated using a Plasmid Midi Kit (Qiagen, Toronto, ON, Canada)

according to the manufacturer’s instructions. Plasmids were transfected into cell lines using

Lipofectamine 2000 (Invitrogen, ThermoFisher, Burlington, ON, Canada) following optimization in accordance with the manufacturer’s instructions. GFP was used to confirm efficient transfection, while immunofluorescence studies of FLAG expression were used to confirm overexpression. siGENOME Non-Targeting siRNA Pool #1 and siGENOME Mouse Nipal1 (70701) siRNA

SMARTpool (Dharmocon, Inc., Chicago, IL, USA) were also transfected into cell lines using

Lipofectamine 2000 following optimization in accordance with the manufacturer’s instructions. qPCR analysis was used to confirm effective knockdown of Nipal1 expression.

Standard and real-time quantitative polymerase chain reaction (PCR and qPCR)

Tissue and cell line samples were briefly lysed and homogenized prior to total RNA extraction using an RNeasy Plus Mini Kit (Qiagen, Toronto, ON, Canada) in accordance with the instructions of the manufacturer. RNA sample concentration was determined via Nanodrop (ThermoFisher,

Burlington, ON, Canada) and underwent quality control using a BioAnalyzer 2100 machine

(Agilent, Mississauga, ON, Canada) hosted by the Princess Margaret Genomics Centre (University

Health Network). RNA samples were converted to cDNA using a reverse transcription kit

(Millipore-Sigma, Etobicoke, ON, Canada) according to the manufacturer’s instructions.

Primers were designed using the NCBI Primer-BLAST tool hosted by the National Institutes of Health (Bethesda, MD, USA). Primers were specifically designed to target exon-exon junctions in provided gene transcripts (available in the NCBI Nucleotide database) to increase specificity towards generated cDNA (i.e. lacking introns). For qPCR primers, minimum and maximum PCR product sizes of 50 and 200 were used, respectively. Otherwise, default settings were used. For genes possessing multiple transcript variants, the isoform labelled as having a consensus CDS on the NCBI database (i.e. typically the longer isoform) was used.

For PCR, cDNA samples were mixed with RedTaq PCR Reaction Mix (Millipore-Sigma,

Etobicoke, ON, Canada) then run in 1.5% agarose gels mixed with RedSafe DNA dye (Froggabio,

Toronto, ON, Canada) and imaged under UV light exposure. For qPCR, samples were mixed with

Power SYBR green PCR master mix in accordance with the manufacturer’s protocol (Applied

Biosystems, ThermoFisher, Burlington, ON, Canada). The software used for analysis was

QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems, ThermoFisher, Burlington, ON,

Canada).

Immunofluorescence studies

Dissociated islets and cell lines

Mouse and human islets were washed in saline and dissociated using 0.05% trypsin TrypLE

Express (Gibco, ThermoFisher, Burlington, ON, Canada) at 37°C. Islet cells were then strained through a 40-um cell strainer, spun down and resuspended in complete RPMI. Cells were spun down onto glass slides coated in poly-L-lysine using a CytospinTM 4 Cytocentrifuge

(ThermoFisher, Burlington, ON, Canada). For cell lines, cells were seeded in 8-well chambered cell culture slides (Falcon, Fisher Scientific, Ottawa, ON, Canada) overnight prior to any transfection or staining. For both islets and cell lines, the following steps were performed with 3 washes with PBS++ in between steps: Cells were fixed with 4% paraformaldehyde for 15 min.

The fixed cells were then permeabilized with 0.2% Triton X-100 for 10 min and blocked overnight with 5% BSA at 4°C. All antibodies were diluted in prepared dilution buffer (1% BSA + 0.1%

Triton X-100 in PBS++). Slides were incubated in corresponding primary antibody mixtures overnight at 4°C: Mouse anti-glucagon (1:200, Abcam, Toronto, ON, Canada), guinea-pig anti- insulin (1:100, Dako, Agilent, Mississauga, ON, Canada), rabbit anti-NIPAL1 (1:200, targets human NIPAL1 aa 388-410, Abcam, Toronto, ON, Canada), rabbit anti-SYT13 (1:100, targets

human SYT13 aa 290-370, Boster Bio, Pleasanton, CA, USA), rabbit anti-TSPAN13 (1:100,

GeneTex, Irvine, CA, USA), rabbit anti-VAT1L (1:50, Bioss, Woburn, MA, USA); mouse anti-

FLAG (1:1000,Origene, Rockville, MD, USA) was used in confirmation of overexpression. The next day, slides were incubated in corresponding secondary antibody mixtures at 1:500 dilution for 2h at room temperature: Goat anti-mouse Alexa Fluor®555 and goat anti-rabbit Cy5 (Abcam,

Toronto, ON, Canada), donkey anti-guinea pig Alexa Fluor®488 (Jackson ImmunoResearch,

West Grove, PA, USA). Slides were counterstained with DAPI nuclear solution at 1:500 in PBS++ for 5 min at room temperature. Images were acquired on a Zeiss LSM 880 Elyra confocal microscope at x20, x63 and x100 magnifications after false signal was subtracted using a background slide stained only with secondary antibodies and DAPI. Images were processed in

ZEN 2.6 blue edition software.

Mouse pancreas section

CD1 mouse pancreas cross-sections (approx. 10 um thickness) embedded in paraffin were used for staining. Paraffin was dissolved in xylene and gradually rehydrated with decreasing concentrations of ethanol. After rinsing in water for 5 min, slides were placed in Tris-EDTA buffer

(10 mM Tris Base, 1 mM EDTA, 0.05% Tween 20, pH 9.0) and microwaved for 10 min for antigen retrieval. Slides were rinsed for 1 min, dried and blocked in 3% donkey serum-PBS for 1 hr at room temperature. All primary antibodies were prepared in 1% donkey serum-PBS (dilutions and manufacturers in previous section) and added to slides overnight at 4°C. The next day, slides were washed with PBS with shaking three times for 10 min. Secondary antibodies were prepared in PBS

(dilutions and manufacturers in previous section) and added to slides for 1 hr at room temperature.

Slides were washed with PBS and shaking for 10 min, counterstained with DAPI (1:500), washed

with PBS twice more, rinsed for 5 min and mounted. Image acquisition was performed as in the previous section.

Co-localization studies and quantification of relative NIPAL1 intensity

Min6 and α-TC cells were seeded in 8-well chambered culture slides and stained with primary anti-glucagon/anti-insulin and anti-NIPAL1 (as described in the opening section for IF), in addition to mouse anti-GM-130 for cis-Golgi staining (courtesy of Dr. Sergio Grinstein and Dr.

Ziv Roth – BD Biosciences, San Jose, CA, USA). Secondary antibody staining, DAPI staining and imaging was performed as described previously. Pearson’s correlation coefficient (PCC) was calculated to determine co-localization of two stains using the ‘Coloc2’ feature of Fiji ImageJ106 software with 100 Costes randomisations. PCC has a range of +1 (perfect correlation) to -1 (perfect negative correlation), with 0 denoting the absence of a relationship107. In studies examining the relative NIPAL1 protein expression under different magnesium conditions, staining and images were done as described previously, ensuring that consistent laser intensities and detection thresholds were used between images. Relative NIPAL1 intensity was determined by first automatically thresholding images to create regions of interest confined to stained cells (using Fiji

ImageJ software), then measuring mean pixel intensity of NIPAL1 staining in cells across multiple images (30-50 cells per treatment group).

Min6-K8 glucose-stimulated secretion assay (GSIS) and Homogeneous Time-

Resolved Fluorescence (HTRF)

GSIS assays were performed to conduct the effect of treatments on insulin secretion from

Min6 cells. Cells were first seeded in 24-well plates under standard culture conditions and then treated overnight. For GSIS, cells were pre-incubated for 1 hr at 37°C in KRB media (128.8 mM

NaCl, 4.8 mM KCl, 1.2 mM KH2PO4, 1.2 mM MgSO4, 2.5 mM CaCl2, 5 mM NaHCO3, 10 mM

HEPES, 0.1% fatty acid free-BSA, pH 7.4). Media was then replaced with KRB supplemented with the following glucose concentrations in successive 20-minute intervals under incubation at

37°C: 0 mM (basal), 16.7 mM, 16.7 mM + 25 mM KCl. Samples containing secreted insulin were collected at the end of each interval and frozen at -20°C until HTRF analysis. DNA was extracted via freeze-thaw cycles to lyse cells in ultrapure H2O and measure via Nanodrop. Total insulin was collected via lysis of cells in acid ethanol (150 EtOH: 47 H2O; 3 HCl) followed by overnight storage at 4°C and concentration via a SpeedVac® Vacuum Concentrator (ThermoFisher,

Burlington, ON, Canada), before being reconstituted in ultrapure H2O. Secreted and total insulin samples were diluted, and insulin concentration was measured via an HTRF assay (Cisbio,

Bedford, MA, USA) in accordance with the instructions of the manufacturer on a PHERAstar plate reader (BMG Labtech, Ortenberg, Germany). Insulin levels were normalized to DNA content for each treatment.

Statistical analyses

For qPCR results, one-way ANOVA and post-hoc Tukey’s honest significant difference

(HSD) tests were performed to determine statistical significance between islet expression and expression in other tissues. Student’s t-test was employed for all other statistical analyses. *p<0.05 was considered statistically significant.

Results

Overview of candidate selection process

An in silico analysis was performed to identify novel candidates of interest in pancreatic α- and β-cells that may be related to the secretion of glucagon and insulin, respectively. This represents the first phase of the overarching strategy to sequentially select for and eliminate candidates, as depicted in Figure 7. A wide variety of published datasets were used for this purpose, beginning with seven independent scRNA-seq datasets of pancreatic islets. Six of these datasets were extracted from the literature80,82–84,86,108, while the seventh is taken from unpublished data in the Wheeler laboratory. Four of the studies, including our data, were exclusive to human islets82,86,108. One study was exclusive to mouse islets84, while the remaining two investigated expression in both mouse and human islets80,83. All seven of these datasets provided the most enriched cell-specific gene candidates of unique islet cell types; a handful of studies additionally provided data on exocrine pancreatic cell types. In addition to enriched candidates, one study identified the most differentially expressed candidates between healthy human and T2D donors82.

In the interest of broadening the scope of potential candidates, two human whole-islet RNA-seq studies were additionally included on account of the unique datasets that they offered. One study examined differentially expressed candidates under acute and prolonged glucose exposure109 to identify genes that are glucose-responsive and whose expression may theoretically be implicated in T2D under sustained hyperglycemic conditions. The second study identified islet-specific expressive quantitative trait loci (eQTL) by investigating expression in human islets versus 14 other tissue types110, in combination with Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) to identify genes that were normally transcriptionally active. Results were organized into an interactive database (available at http://theparkerlab.org/tools/isleteqtl/).

Figure 7: A brief overview of the process to sequentially identify and eliminate candidates of interest in pancreatic islets. The process is divided into in silico and in vitro phases. In the in silico phase, candidates are selected and investigated from several scRNA-seq studies (in addition to a unique pair of whole-islet RNA-seq studies) and evaluated using measured expression, established gene databases and the literature. Shortlisted candidates move onto the in vitro phase for transcriptional validation; top candidates are selected for translational validation, and the primary candidate is selected for functional characterization.

The selected datasets were subjected to a manual investigation process to identify novel

candidates of interest in islets, as portrayed in Figure 8. The top 200 most enriched α- and β-cell

candidates of each scRNA-seq dataset were pooled for comparison. Within this pool, candidates

that did not appear in at least two independent datasets were excluded in order to limit the initial

search to candidates that were unlikely to have been detected under chance conditions. This

narrowed down the pool to an initial total of 672 candidates. In the dataset of differentially

expressed genes under T2D, a combined total of 117 α- and β-cell candidates met the study’s

significance threshold (i.e. mean and median log2 RPKM >= 1, adjusted p-value for FDR < 0.01)

and were all included in the initial pool. In the whole-islet dataset of differentially expressed genes

under varying glucose exposures, candidates were provided in a pre-filtered list displaying only

genes with an FDR-corrected P value [q] that was < 0.05. Of this list, 118 whole-islet candidates

were included in the pool as they demonstrated an average log2-transformed fold change in expression that was ≥ 0.5 or ≤ -0.5. The interactive platform provided in the islet eQTL study provided a more unique set of criteria by which to select initial candidates, beginning with a total of 3,314 islet eQTL variants. Genes were organized into deciles of islet specificity, and candidates were first excluded to those in the top decile. This initial group was further narrowed down by selecting for candidates measured as having strong transcription (i.e. > 0.653 eQTL log2 fold enrichment), an active transcription start site (i.e. > 1.021 eQTL log2 fold enrichment) and a detectable ‘footprint’ indicating an open chromatin state with active transcription factors (as determined via ATAC-seq); thresholds used for each criterion represent the respective default averages provided by the platform. Employing these criteria, 46 candidates were ultimately added to the initial pool. The initial candidates from all groups were combined and 38 candidates were found to be shared (a Venn diagram is available in the Supplementary Figure S1). After subtracting the shared genes, a grand total of 915 unique candidates were selected to progress to manual investigation.

All 915 candidates were independently investigated and shortlisted if they managed to meet three specific criteria where applicable: (1) Relatively strong α/β-cell specificity, (2) relative islet specificity, and (3) novelty in islets. For candidates identified in scRNA-seq datasets, the measured average expression across donors was used to determine relative α-cell and/or β-cell specificity within islets (and within the pancreas if exocrine cells were included in the dataset). A candidate was deemed sufficiently specific if its α and/or β-cell expression was at least double that of all other cell types assayed. As whole-islet studies could not provide cell-specific data, candidates identified in these studies were cross-referenced in one of the scRNA-seq studies and similarly checked for α/β-cell specificity (if available).

Figure 8: Overview of candidate selection process to find novel candidates of interest in islets. Candidates were taken from 7 datasets highlighting the most enriched candidates, one highlighting differentially expressed candidates under T2D, one showing differential expression under glucose induction, and one highlighting islet-specific eQTL. All significance thresholds for log2-transformed expression and FDR were selected by the studies and were provided in the form of pre-filtered lists. A combined total of 915 unique candidates met the initial criteria across all sources. This initial pool was manually investigated for relative α/β-cell specificity (where applicable), islet specificity (based on established gene expression databases) and novelty in islets (assessed via thorough PubMed search). Through this process, the pool of 915 candidates was narrowed down to a final shortlist of 28 candidates, all of which progressed to the next phase of transcriptional validation via qPCR.

To determine relative islet specificity, a trio of established expression databases were employed – BioGPS101, GTEx100 and ProteinAtlas111. The GeneAtlas U133A (gcrma) dataset on

BioGPS was used as the primary determinant of relative islet specificity on account of being the only database to provide specific mRNA expression measurement in human islets alongside whole pancreas expression (whereas other databases typically only include pancreas expression).

Candidates were deemed relatively islet-specific if measured islet expression was > 3 times the median value of the 79 unique human tissues profiled in the dataset. Candidates that met this criterion were additionally explored on GTEx, whose data were acquired via a modernized RNA- seq approach (as opposed to the microarray approach of BioGPS); however, as this database lacks independent islet expression data, it was only used as a secondary measure by which to ensure that candidates were not obviously ubiquitously expressed. ProteinAtlas was additionally used as a secondary measure to determine whether there was any immunohistochemical evidence of candidate expression in islets (as well as whether there were any patterns of interest such as endocrine or neurological tissue specificity). However, ProteinAtlas was ultimately supplementary to decision-making rather than a source of any definitive criterion.

The final criterion of novelty in islets was intended to maximize the potential of contribution to the literature by selecting for candidates that have not previously been linked to insulin secretion or diabetes. Novelty was assessed via a thorough PubMed search combining the candidate name with a broad range of MeSH terms related to insulin, glucagon, islets, pancreas, secretion, and diabetes. While the idea of establishing a threshold for novelty based on publication count was initially considered, this was deemed insufficient as this approach could not effectively account for candidates whose relation to insulin/diabetes had only recently been established. Thus, an independent exploration of available publications was employed instead. The nonspecific, open-

ended nature of this search proved sufficient for the vast majority of candidates; candidates without

any established link to islets/diabetes typically had fewer than 15 publications whose

titles/abstracts could be quickly perused, while linked candidates typically had upwards of 50

publications and were generally excluded from further investigation. Ultimately, of the 915

candidates that were initially subjected to the shortlist criteria, a final shortlist of 28 candidates

was generated: 5 enriched α/β-cell candidates, 9 T2D differentially expressed candidates, 10

glucose-induced candidates, and 4 islet-specific eQTL. A summary of the key characteristics of

shortlisted candidates is displayed in Table 1.

Shortlisted candidate profiles

Table 1: A summary of the key characteristics of shortlisted candidates.

Gene Full Name Expr. Predicted Biological Known Molecular Notable features for selection in Subcellular Location Process Functions Islet APOH apolipoprotein H α Extracellular/ Lipid Lipoprotein Upregulated in T2D α; strong expression islets, but Secreted metabolism mainly produced by liver BEST3 bestrophin-3 N/A Cell Membrane Channel Calcium-sensitive Downregulated in T2D islets; Ca2+-sensitive chloride chloride channel channel, permeable to bicarbonate subunit C10ORF10 decidual protein induced Likely Mitochondria Mitochondrial Modulator of Downregulated in T2D α; highly expressed in β /DEPP by progesterone α & β metabolism autophagy via ROS (ROS) CNTN1 contactin 1 β Cell Membrane Cell adhesion Mediates cell Upregulated in T2D β; Highly expressed in islets surface interactions CPNE3 copine-3 α Cell Membrane, Phospholipid Kinase Upregulated in T2D α; Ca2+ dependent phospholipid Nucleus binding binding CYSTM1 cysteine rich α Cell Membrane Cellular stress Unknown Highly expressed in α, interacts with GLP-1R112 transmembrane module response containing 1 DOCK1 dedicator of cytokinesis N/A Cytoskeleton/ Cell Phagocytosis Cytoskeletal Downregulated in T2D islets; associated with ELMO1, protein 1 Membrane and motility rearrangement, GEF which is also downregulated in T2D islets ELAVL4 embryonic lethal, α Nucleus RNA binding Neuron-specific Enriched in islets and other endocrine tissues abnormal vision like RNA protein RNA processing binding protein 4 ELMO1 engulfment and cell N/A Cytoskeleton/ Cell Phagocytosis Cytoskeletal Downregulated in T2D islets; associated with DOCK1, motility protein 1 Membrane and motility rearrangement which is also downregulated in T2D islets FAM159B family with sequence Whol Cell Membrane Unknown Unknown Confirmed islet protein presence, but role unknown similarity 159 member B e Islet FITM2 fat storage-inducing N/A ER Lipid Facilitates lipid Downregulated in T2D islets; role in lipid droplet transmembrane protein metabolism droplet accumulation, regulation of cell morphology and 2 accumulation cytoskeletal organization

Gene Full Name Expr. Predicted Biological Known Molecular Notable features for selection in Subcellular Location Process Functions Islet FNBP1L formin-binding protein 1- α Cell Membrane, Endocytosis Coordinates Upregulated in T2D α; strong expression islets like Cytoskeleton membrane tubulation IGSF11 immunoglobulin N/A Cell Membrane Cell adhesion, Cell adhesion Downregulated in T2D islets; expression correlates superfamily member 11 growth molecule with insulin secretion KCNA6 potassium voltage-gated N/A Cell Membrane Ion transport Voltage-gated Islet eQTL with high islet specificity, active TSS/open channel subfamily A potassium channel chromatin; voltage-gated potassium channel member 6 subunit KCNAB2 voltage-gated potassium N/A Cell Membrane Ion transport Voltage-gated Downregulated in T2D islets; voltage-gated potassium channel subunit beta-2 potassium channel channel subunit, involved in nerve signalling subunit KCTD12 potassium channel α Cell Membrane Ion transport Potassium channel Upregulated in T2D α; auxiliary subunit of GABA-B tetramerization domain subunit receptor containing 12 NIPAL1 magnesium transporter α and Cell Membrane Ion transport Magnesium Islet eQTL with high islet specificity, active TSS/open NIPA3 β transporter chromatin; Mg2+ transporter NIPAL4 magnesium transporter β Cell Membrane Ion transport Magnesium Islet eQTL with high islet specificity, active TSS/open NIPA4 transporter chromatin; Mg2+ transporter NPTX2 neuronal pentraxin 2 β Extracellular/ Excitatory Unknown Enriched in islets and other endocrine tissues Secreted, Cell synapse membrane formation PM20D1 N-fatty-acyl-amino acid N/A Mitochondria/ Mitochondrial Synthase Islet eQTL with high islet specificity, active TSS/open synthase/hydrolase Secreted and amino acid /Hydrolase chromatin; zinc-binding chemical uncoupler of metabolism mitochondrial regulation REEP5 receptor expression- β ER (Suspected) Unknown Upregulated in T2D β; Highly expressed in islets; enhancing protein 5 Olfactory REEP1 and REEP2 linked to insulin exocytosis113; Sister receptor gene REEP6 MAY be involved in transport of receptors expression from ER to cell surface SYT13 synaptogamin-13 β Unknown Vesicular Ca2+ and Downregulated in T2D islets; likely involved in trafficking phospholipid- transport vesicle docking to the plasma membrane; binding calcium-dependent phospholipid binding SYT4 synaptogamin-4 β Unknown Vesicular Ca2+ and Upregulated in islets under high glucose in trafficking phospholipid- hyperglycemic donors, but not regulated by glucose binding in normoglycemic donors; likely important in Ca2+- dependent exocytosis of secretory vesicles in neurons TM4SF4 transmembrane 4 L six α Cell Membrane Epithelial Unknown Confirmed α-cell protein presence, but role unknown family member 4 proliferation TMBIM6 transmembrane BAX β ER Cell survival, Protein chaperone, Upregulated in T2D β; Among a number of roles, inhibitor motif ion transport calcium leak modulates ER calcium homeostasis by acting as a containing 6 / Bax channel calcium-leak channel inhibitor 1 TMEM64 transmembrane protein N/A ER Cell Membrane-based Downregulated in T2D, not regulated by acute 64 differentiation, regulator of Ca2+- hyperglycemia; Association between TMEM64 and ROS mediated signalling SERCA2 in the ER leads to cytosolic Ca (2+) spiking for ROS production and differentiation in osteoclasts TSPAN13 tetraspanin-13 β Cell Membrane Unknown Unknown Upregulated in T2D β; calcium channel regulator (Nucleus?) activity; interacts with GLP1R112 VAT1L vesicle amine transport- Likely Unknown (Suspected) Oxidoreductase Downregulated in T2D islets; strongly expressed in like 1/ synapatic vesicle α & β Vesicular islets; zinc-binding oxidoreductase, involved in membrane protein VAT- trafficking vesicular transport 1 homolog-like

Transcriptional validation

Primary tissue analysis

The 28 candidates that made the final shortlist were subjected to transcriptional validation.

While undeniably powerful, the large-scale approaches employed by scRNA-seq and RNA-seq are vulnerable to significant variability and cannot feasibly verify the reliability of all measurements. Thus, the aim of this stage was to identify whether shortlisted candidates could be

(1) validated as being significantly expressed in islets and (2) whether that expression is significantly elevated in islets compared to select tissues as a basic measure of relative specificity.

To this end, quantitative real-time polymerase chain reaction (qPCR) was employed to measure mRNA expression levels of the given candidates in both mouse and human samples. Mouse candidate expression was measured in isolated islets, in addition to liver, kidney and mixed pancreas samples (i.e. while efforts were made to handpick only exocrine tissue, samples were not purified/sorted and thus would contain negligible amounts of islet cells). Liver and kidney were deemed to be suitable tissues for comparison to islets, although for different reasons. The liver and pancreas share a common endodermal lineage114,115 that results in a greater percentage of shared genes with islets than tissues without these qualities; this similarity is additionally bolstered by the liver’s secondary endocrine qualities116. While the kidney also possesses secondary endocrine qualities117, its mesodermal origins distinguish it from the pancreas evolutionarily118. Despite this, it retains strong functional interrelations with the pancreas, and disorders in the two organs are often linked119. Shared expression with kidney may therefore help to eliminate genes whose expression is not specifically important to islet function, but rather involved in the kidney-pancreas dynamic. With these considerations in mind, candidates with substantial expression in islets that do not demonstrate similar normalized expression in liver/kidney would thus be expected to have

a greater chance of having evolved to serve a more islet-specific purpose. In a similar vein, comparing pure picked islets to mixed/exocrine pancreas serves to distinguish islet-enriched candidates from those with pancreas-wide expression that would be less likely to hold an islet- specific purpose.

Distinction between islets and exocrine pancreas was deemed to be the strongest indicator of islet specificity, and thus results are displayed as fold over pancreas following normalization to housekeeping gene beta-actin (Fig. 9A). Hepatic enzyme gene Pepck/Pck1120, renal organic ion transporter gene Oat3/Slc22a8121, and potassium channel subunit gene Kcnj11122 were used as positive/reference controls for liver, kidney and islet, respectively. One-way ANOVA and the post- hoc Tukey’s HSD test were employed to determine whether the difference between candidate expression in islets vs. comparison tissues was statistically significant (i.e. p < 0.05*). Displaying fold expression was chosen over directly measured expression as simply comparing enrichment between candidates fails to account for differences in expression stemming from the class of protein tied to the gene. For example, biosynthesis of islet proteins that are abundantly secreted

(such as islet amyloid polypeptide, IAPP)123 or prohormone enzyme chaperones (such as secretogranin-5, SCG5/7B2)124 have substantial expression levels at 34% and 54% of human beta- actin101, respectively. These values dwarf the expression levels of membrane transporters (such as potassium voltage-gated channel subunit J11, KCNJ11)122 and receptors (such as glucagon like peptide 1 receptor, GLP1R)112 at <4% and <1% expression101, respectively. Despite these low values however, KCNJ11 has an established role within insulin stimulus-secretion coupling125 and

GLP1R is imperative to the secretion-amplifying incretin effect126. Thus, raw enrichment values cannot serve as a reliable proxy for importance to islets in the case of most genes, and fold expression is a better measure of specificity when comparing tissues.

Within the mouse candidate expression results, Nipal1 showed the greatest fold expression in islets by far at just over 90. The next highest candidate, synaptotagmin-13 (Syt13) showed roughly half the level of fold expression over pancreas with a value of 47. The remaining candidates all demonstrated fold expression values under 6. Candidates that demonstrated statistically significant higher expression in islets vs. pancreas, liver and kidney include Nipal1, Syt13, Fam159b, Vat1l,

Syt4, Tm4sf4 and Tspan13. Nipal4 failed to meet the threshold for significance vs. pancreas (p =

0.17) but was significant vs. liver and kidney.

Candidate expression was similarly assessed in human islet, liver and pancreas samples, and normalized results were also portrayed as fold over pancreas (Fig. 9B). In comparison to mouse results, the range of values in human islets was significantly tighter, with an upper bound of 5.48 for FAM159B and lower bound of 0.49 for C10ORF10. Candidates that demonstrated statistically significant higher expression in islets vs. both pancreas and liver include NIPAL4, IGSF11,

NIPAL1, CYSTM1 and BEST3. While SYT4 and SYT13 were both statistically significant vs. liver, they fell short of significance vs. pancreas at p = 0.093 and p = 0.084, respectively.

Ultimately, four candidates – NIPAL1, SYT13, TSPAN13 and VAT1L – were selected to progress to translational validation studies. NIPAL1 was deemed to be the primary candidate of interest among the 28 that were assessed, as it was the only candidate to consistently demonstrate statistical significance vs. all other tissues in both mouse and human results. As discussed previously, its role as a magnesium transporter and the emerging evidence implicating magnesium in diabetes provided another point of interest to pursue the candidate further. Also noteworthy is

NIPAL4 which, while falling short of significance vs. pancreas in mouse, was consistently significant in human islets, thereby lending further credence to the interest in NIPAL1 as its paralog.

B A

Figure 9: Shortlisted candidate expression in (A) mouse tissues and (B) human tissues measured via qPCR. In mouse, gene expression was measured in islets, liver, kidney and exocrine pancreas. In human, gene expression was measured in islets, liver and exocrine pancreas. Raw expression values were normalized to beta-actin, then displayed as fold expression over pancreas. Results were then organized in decreasing order of fold islet expression over pancreas, after results for controls PEPCK (liver), OAT3 (kidney) and KCNJ11 (islet). NIPAL1 is highlighted in each dataset by a red box. Statistical significance between islets and comparison tissues was evaluated via one-way ANOVA and Tukey’s HSD (not shown here) (n=3, values displayed as mean ± SEM).

SYT13 was also deemed to be quite interesting on account of its strong performance in mouse islets and its membership within the synaptotagmin family. Synaptotagmins are a family of calcium-dependent membrane-trafficking proteins whose members have been implicated in a wide range of secretory processes, including in both neurons and islets127. Synaptotagmins 1, 7 and 9

(SYT1, SYT7, SYT9) are all established mediators of glucose-stimulated insulin secretion128–130.

Furthermore, while SYT4 had not previously been directly associated with secretion at the time of candidate selection, a very recent publication also linked the gene to GSIS in a calcium- dependent manner131, negating its novelty. Thus, only SYT13 was selected to move on to the next stage. It is also noteworthy that expression of SYT13 was downregulated in β-cells of human T2D donors82, and that palmitate (known to induce insulin secretion) has been found to upregulate Syt13 expression in the islets of Wistar rats132.

Tetraspanin-13 (TSPAN13) and vesicle amine transport 1 like (VAT1L) were the final two candidates selected to progress to translational validation. They were among the candidates demonstrating the strongest significance in mouse islets vs. other tissues (all p < 0.005***), although their significance was limited to islets vs. liver in human samples. However, their novelty combined with their expected/observed functional characteristics warranted further exploration.

TSPAN13 was observed to be upregulated in β-cells of human T2D donors82 and has been shown

133 to be an interactive partner of N-type Cav2.2 calcium channels , which could prove of potential relevance to calcium signalling in islets. Furthermore, a previous Wheeler lab publication identified it as a moderate interactor with GLP1R112. VAT1L was observed to be downregulated in human T2D islets and downregulated in response to acute glucose treatment109. Its structural similarity to the better-studied vesicle amine transport 1 (VAT1) gene in neurons134 suggests that

VAT1L functions as a zinc-binding oxidoreductase involved in vesicular trafficking. This was

interesting given our lab’s previous work highlighting the regulatory role of zinc homeostasis in insulin secretion135. VAT1L is additionally almost 99% conserved between humans and rodents

(Homologene).

Cell line analysis

The expression of the four selected candidates was also investigated via qPCR in two cell lines - αTC6 and Min6-K8 (mouse α and β-cell lines, respectively) (Fig. 10). As with primary tissue expression, results were normalized to beta-actin. Secretogranin-5 (Scg5) was used as a positive control due to its properties as a general secretory/endocrine tissue marker, while kidney- specific organic ion transporter 3 (Oat3/Slc22a8) was used as an indicator of low/negligible expression. Nipal1, Syt13 and Tspan13 all demonstrated substantial expression in both cell lines.

However, Vat1l expression in both cell lines was observed to be comparable to Oat3 values at

<2% and was therefore deemed to be absent/negligible in both cell lines. Of the four candidates assayed, only Syt13 had significantly higher expression in Min6-K8.

Figure 10: Expression of top candidates NIPAL1, SYT13, VAT1L and TSPAN13 in α-TC6 and Min6-K8 cell lines as measured by qPCR. Results were normalized to beta-actin expression. SCG5 and OAT3 were used as positive and negative controls for expression, respectively. NIPAL1, SYT13 and TSPAN13 all demonstrated substantial expression in both cell lines relative to the negative control, but VAT1L expression showed fairly minimal expression. SYT13 was observed to be significantly more highly expressed in Min6 cells than α-TC cells (p<0.05) (n = 3, values displayed as mean ± SEM).

Translational validation

Immunofluorescence studies in dissociated islets

The four candidates identified in the transcriptional validation phase – TSPAN13, VAT1L,

SYT13 and NIPAL1 – were subjected to immunofluorescence (IF) studies in dissociated mouse and human islets as part of translational validation. Dissociated islets were co-stained with DAPI (i.e. nuclear marker), glucagon, insulin, and candidate antibodies in order to localize the candidates to specific cell types. Glucagon and insulin served as distinct markers of α- and β-cells, respectively, and cells without a robust signal of either hormone were interpreted as being of a tertiary islet cell type. Similar to the original candidate selection process, candidates were assessed on whether their expression was specifically confined to α-cells or β-cells; however, candidates demonstrating expression in both remained open to consideration.

As exhibited in Figure 11, TSPAN13 was observed to be expressed in both CD1 mouse and human islets. In mouse, TSPAN13 has clear expression in both α- and β-cells, and does not appear to be more strongly expressed in one cell type over the other. It also appears to be well-distributed throughout the entirety of the cell. However, its expression looks to be especially concentrated in the nucleus, which is most easily observed in the merged images. This distinction in the nucleus is lost in human islets, where expression intensity appears more uniform throughout the cell. These observations were surprising, as one study in which TSPAN13 constructs were transfected into

CHO cells would suggest that TSPAN13 would be found at the plasma membrane133. In both species, images suggest that TSPAN13 expression is absent from non-α/β cells, as highlighted by arrows indicating the presence of nuclei without any detectable glucagon/insulin staining. Thus, it

can be concluded that TSPAN13 is limited to α- and β-cells within islets with widespread distribution across the cell.

Figure 11: Representative images of immunofluorescent staining of TSPAN13 in dissociated mouse and human islets. TSPAN13 protein expression was investigated in primary islets co-stained with glucagon, insulin and DAPI (nucleus). Staining appears limited to α (i.e. glucagon-positive) and β (i.e. insulin-positive) cells, as denoted by arrows highlighting nuclei without visible glucagon, insulin or TSPAN13 expression. TSPAN13 shows robust nuclear staining, with milder cytosolic expression (n=3).

VAT1L is similarly expressed in both α- and β-cells in mouse islets and human islets (Fig.

12). While a few cells appear to show VAT1L staining within the nucleus of x100 merged mouse islet images, this is most likely artefactual. Most cells in the image are free of nuclear VAT1L staining, which is more clearly observed in x20 mouse images. Furthermore, human islets portray a distinct cytosolic presence for VAT1L, with a punctuated and likely vesicular distribution. A non-α/β cell observed in the x100 mouse islet images (indicated by arrows) shows faint VAT1L expression, suggesting that VAT1L has a presence in other islet cell types (and may potentially be expressed across the islet).

Figure 12: Representative images of immunofluorescent staining of VAT1L in dissociated mouse and human islets. VAT1L protein expression was investigated in primary islets co-stained with glucagon, insulin and DAPI (nucleus). VAT1L shows robust expression in both α (i.e. glucagon-positive) and β (i.e. insulin-positive) cells in both species, but also appears to be faintly expressed in non-α/β cells lacking glucagon/insulin staining as denoted by arrows. Staining of VAT1L indicates exclusive cytosolic expression (n=3).

SYT13 demonstrates consistent cytosolic expression across both α- and β-cells in both species of islets (Fig. 13). However, staining appears more intense in β-cells when observing merged images with insulin. Its appearance is quite punctuated, suggesting that it is localized to vesicular bodies. This would be unsurprising considering that synaptotagmins are functionally involved in vesicular trafficking as previously mentioned. The observation of non-α/β cells (highlighted by arrows in the x20 mouse images) without detectable SYT13 expression indicates that SYT13 is limited to α- and β-cells.

Figure 13: Representative images of immunofluorescent staining of SYT13 in dissociated mouse and human islets. SYT13 protein expression was investigated in primary islets co-stained with glucagon, insulin and DAPI (nucleus). SYT13 staining is observed in both α (i.e. glucagon-positive) and β (i.e. insulin-positive) cells but looks to be stronger in β-cells. Expression is absent from non-α/β cells as denoted by arrows highlighting nuclei devoid of detectable SYT13, glucagon or insulin expression. SYT13 staining is well-distributed throughout the cytosol and appears punctuated, suggesting a vesicular presence (n=3).

The fourth and final candidate, NIPAL1, demonstrated the most interesting staining pattern with α-cell specific expression in both mouse and human islets, overlapping quite well with glucagon staining (Fig. 14A). Like SYT13, NIPAL1 appears to be cytosolic and punctuated, which would align well with IF studies of paralogs NIPA1 and NIPA2 showing that the transporters are carried on early endosomes98,136. To ensure that the perceived α-cell specificity was not due to any bleedthrough effect, single staining of NIPAL1 was also performed in mouse islets (Fig. 14B).

Only a small percentage of the cells are labelled with the antibody as would be expected, given that α-cells comprise 15-20% of mouse islets137. This specificity was quite surprising as scRNA- seq data indicated substantial NIPAL1 expression in both α- and β-cells, with higher expression in

B C

Figure 14: Representative images of immunofluorescent staining of NIPAL1 in dissociated mouse and human islets. (A) NIPAL1 protein expression was investigated in primary islets co-stained with glucagon, insulin and DAPI (nucleus) (n=3). NIPAL1 staining was observed in α-cells (i.e. glucagon-positive) as denoted by arrows, but was absent from other cell types. The staining pattern indicates that NIPAL1C is confined to the cytosol, but it appears to be mainly concentrated directly adjacent to the nucleus. (B) To confirm that the observed α-cell specific expression was not due to bleedthrough, single-staining of NIPAL1 was done (with DAPI) in dissociated mouse islets. Results confirmed the findings in (A), with an expected minority of cells showing robust NIPAL1 staining. (C) Pearson’s correlation coefficient (PCC) analysis of NIPAL1 staining co-localization with glucagon, insulin and nuclear stains. PCC = +1 (perfect correlation); PCC = 0 (absence of relationship); PCC = -1 (perfect negative correlation). A PCC value of 0.8+ is typically considered indicative of significant co-localizationC (n=20-30 cells across 3 independent studies).

β-cells in multiple studies80,82. This is further compounded by observed expression of Nipal1 in

Min6 rather than just α-TC. These results suggest either different post-transcriptional processing in NIPAL1 expression between α- and β-cells, or post-translational modifications that result in a disconnect between measured gene and protein expression. Given this observed specificity and

NIPAL1’s novelty, it was selected as the primary candidate for further studies.

In light of the apparent overlap of NIPAL1 and glucagon staining, Pearson’s correlation coefficient (PCC) analysis was performed to determine whether NIPAL1 co-localizes with glucagon granules (Fig. 14C, where PCC of -1 = perfect anti-correlation, 0 = no correlation, and 1

= perfect correlation). In mouse islets, NIPAL1 and glucagon had an average PCC of 0.60±0.09, which was significantly higher than 0.20±0.05 measured against insulin and an expected absence from nuclei with a value of -0.23±0.04. A similar trend was observed in human islets, with NIPAL1

PCC values of 0.48±0.08 against glucagon, -0.05±0.01 against insulin and -0.18±0.04 against nuclei. Typically a PCC value of at least 0.80 is indicative of strong co-localization112, and thus it does not appear that NIPAL1 co-localizes with glucagon granules, but rather just co-occupies the cytosol.

Immunofluorescence studies of NIPAL1 in mouse pancreas sections

To ensure that NIPAL1 expression was limited to islets, CD1 mouse pancreas sections with embedded intact islets were stained in similar fashion to dissociated islets (Fig. 15). At low magnification (10x), NIPAL1 staining could only be observed in areas of the section with prominent insulin and glucagon staining, indicating that NIPAL1 is absent from exocrine pancreas.

High magnification (100x) showed that, as with dissociated islets, NIPAL1 expression was strong in α-cells that are typically arranged close to the border of mouse islets137.

Figure 15: Representative images of immunofluorescent staining of NIPAL1 in CD1 mouse pancreas section. Intact pancreas sections were additionally co-stained with glucagon, insulin and DAPI (nucleus). Wide images at x10 magnification were taken, indicating that NIPAL1 expression is absent from exocrine pancreas as it is only detectable within islets stained by glucagon and insulin. Close-up images of islets at x100 magnification demonstrate that NIPAL1 expression is confined to glucagon-stained α-cells (n=3).

Immunofluorescence studies of NIPAL1 in cell lines

With translational validation of NIPAL1 in pancreatic α-cells of both mouse and human islets, and confirmation of its absence in exocrine pancreas cells, the next step was to determine whether it was also expressed in α-TC and Min6 at the protein level. IF studies were carried out in both cell lines in the same manner as in previous experiments (Fig. 16A). However, as α-TC and Min6 are respectively glucagonoma and insulinoma cell lines, only the relevant glucagon/insulin antibody was used. Abundant NIPAL1 expression was observed in both cell lines, with a punctuated cytosolic distribution similar to its appearance in islets. It is interesting that NIPAL1 would be strongly observed in Min6 despite being apparently absent from primary β-cells; this may be due to the aforementioned fact that Min6 is a somewhat ‘dedifferentiated’ cell line that secretes

detectable levels of glucagon despite primarily secreting insulin65. This means that Min6 likely retains certain aspects of α-cells.

As in islets, PCC analysis indicated that NIPAL1 does not co-localize with glucagon granules in

α-TC (0.59±0.06) nor insulin granules in Min6 (0.38±0.04) (Fig. 16B). However, unlike in islets,

NIPAL1 appears to be concentrated in an area directly adjacent to the nucleus in all cells. In Min6, this area overlaps fairly well with a concentrated region of insulin granules. As Min6 is an insulinoma, this defined region represents an overabundance of insulin granules packed into the

Golgi apparatus, which is generally found close to the nucleus138.

To determine whether NIPAL1 was indeed concentrated in the Golgi, IF co-localization studies were performed using an antibody against Golgi matrix protein 130 (GM-130), a marker of the cis-Golgi network (Fig. 16B). The strong overlap between the concentrated region of

NIPAL1 and GM-130 in both cell lines made it apparent that NIPAL1 was indeed most abundant in the Golgi. However, there remains a clear cytosolic presence outside of the Golgi, as evidenced by the relatively low PCC values of NIPAL1 against GM-130 in α-TC (0.45±0.06) and Min6

(0.44±0.04) (Fig. 16C). It is suspected that this cytosolic presence can be attributed to early endosomes based on evidence offered by co-localization studies of NIPA1136 and NIPA298, although this was not tested in the present study.

Figure 16: Representative images of immunofluorescent C staining of NIPAL1 in α-TC6 and Min6-K8 cell lines. (A) α-TC cells were co-stained with glucagon, Min6 was co- stained with insulin, and both were co-stained with DAPI (nucleus). NIPAL1 staining is observed to have a punctuated (likely vesicular) presence throughout much of the cytosol in both cell lines, but is most concentrated in regions adjacent to the nuclei (n=3). (B) Subcellular localization studies with GM-130 (Golgi) staining in both cell lines identified that this concentrated region was confined to the Golgi apparatus. However, NIPAL1 still shows distribution outside of the Golgi (n=3). (C) Pearson’s correlation coefficient (PCC) analysis was performed to quantify co-localization between NIPAL1 and other markers. PCC = +1 (perfect correlation); PCC = 0 (absence of relationship); PCC = -1 (perfect negative correlation). A PCC value of 0.8+ is typically considered indicative of significant co-localization (n=30-45 cells across 3 independent studies, values displayed as mean ± SEM).

NIPAL1 expression is upregulated in the presence of Mg2+

As NIPAL1 is a magnesium transporter, cells were cultured in media with differing Mg2+ concentrations in order to determine whether NIPAL1 expression is directly responsive.

Customized Mg-free media was acquired and supplemented with corresponding amounts of

2+ MgSO4. Min6 cells were cultured under three different Mg conditions: magnesium-free (0.0 mM), standard magnesium (0.8 mM), and high magnesium (5.0 mM). These conditions were selected to emulate the concentrations used in the study of the paralog NIPA1 by Goytain et al136.

The 0.8 mM concentration was used as the standard reference point based on the same concentration of Mg2+ in the formula for standard media; this concentration is also within the range of healthy circulating Mg2+ levels of 0.7-1.1 mM57. However, as 10% FBS conditions contain a significant concentration of Mg2+ (~0.3 mM)139, cells were instead cultured under 2% FBS conditions following confirmation that overnight culture did not noticeably affect healthy cell morphology and formation of clusters. After 24h in standard media, Min6 cells were cultured under special Mg2+ conditions overnight and Nipal1 expression was measured using qPCR analysis (Fig. 17A). No significant fold change was observed between standard (0.8 mM) and high

Mg2+ (5.0 mM) conditions, although culture under nominally Mg-free (0.0 mM) conditions roughly halved Nipal1 expression. α-TC cells were similarly cultured, although a low (0.1 mM) concentration was included to explore whether there may be a graduated response between Mg- free and standard conditions. Due to unforeseen difficulties in qPCR analysis of α-TC cells cultured under these conditions, polymerase chain reaction (PCR) analysis was employed instead

(Fig. 17B). While not as directly quantitative as qPCR, there appears to be a gradient in expression,

with Nipal1 bands absent under 0.0 and 0.1 mM Mg2+ conditions, faint under 0.8 mM Mg2+ and

strong under 5.0 mM Mg2+. However, as the β-actin band is relatively faint for the 0.8 mM sample

compared to 5.0 mM, quantification of NIPAL1 band area over the respective β-actin band was

performed using ImageJ software. The 0.8 mM sample yielded a value of 0.73, while the 5.0 mM

sample yielded 1.14. This would suggest a miniscule difference, but is still open to question;

regardless, there remains a clear difference in detectable expression between the two higher and

two lower concentrations. Coupled with Min6 results, these findings initially suggest that Nipal1

expression is induced by Mg2+ in a concentration-dependent manner.

To determine whether this trend translated well at the protein level, immunofluorescence

studies were additionally conducted in both Min6 and α-TC cells cultured under the same

conditions (Fig. 18A). Imaging conditions were carefully controlled for consistency in order to

ensure that any perceived differences were derived from treatment conditions. Qualitatively, while

A B

Figure 17: Investigation of effect of magnesium on NIPAL1 transcription. (A) Min6-K8 cells were cultured in media with varying Mg2+ concentrations (0.0, 0.8, 5.0 mM), and NIPAL1 expression was measured via qPCR. Expression was halved in Mg- free conditions (0.0 mM) relative to standard conditions (0.8 mM), with no change under high conditions (5.0 mM) (n=3, values displayed as mean ± SEM, *p<0.05). (B) Representative image of NIPAL1 expression in α-TC measured via PCR, following the same culture conditions as in (A). NIPAL1 bands are absent under 0.0 and 0.1 mM Mg2+ conditions, but present under 0.8 mM and 5.0 mM conditions (n=3).

there was no obvious difference in staining between Mg-free (0.0 mM) and standard Mg2+ (0.8

mM) conditions in either cell line, there was a visible increase in staining intensity under high

Mg2+ (5.0 mM) within a small area inside each cell. Previous co-localization experiments would

indicate that this observed intensity is almost certainly confined to the Golgi. To ascertain whether

this perceived increase was indeed legitimate, relative NIPAL1 pixel intensity was measured

across multiple images for each condition and quantified via ImageJ analysis (Fig 18B). As

expected, results showed a statistically significant (*p < 0.05) increase under high Mg2+ in α-TC

A B

Figure 18: Immunofluorescence studies of NIPAL1 expression in Min6-K8 and α-TC6 cells following culture under varying magnesium conditions. (A) Min6 and α-TC cells were stained with both NIPAL1 and DAPI (nucleus). Laser intensities and detection thresholds were kept consistent to ensure that perceived differences were due to biological rather than technical reasons. The intensity of NIPAL1 appeared notably stronger under high Mg2+ conditions (5.0 mM), particularly in the region adjacent to the nucleus (identified as the Golgi). (B) Average pixel intensity analysis of defined cell regions was conducted, and NIPAL1 intensity was found to be significantly higher under high Mg2+ conditions in α-TC. High Mg2+ trended towards significance in Min6 (p<0.10). (n=30-45 cells across 3 independent studies, values displayed as mean ± SEM, *p<0.05).

cells. However, significance was not reached under high Mg2+ in Min6 cells (p = 0.078). It should be mentioned that while Western blotting remains superior to IF studies for quantitative analysis of protein, several studies140–143 have successfully demonstrated that quantification of pixel intensity is suitable if imaging conditions are kept consistent (and images are not oversaturated) as in this study.

Functional studies of NIPAL1 in cell lines

Transfection and overexpression confirmation in alpha-TC, Min6 and CHO

In order to determine whether NIPAL1 plays a role in secretion, overexpression studies were conducted in α-TC and Min6 cells via transfection of NIPAL1 cDNA. Transfection conditions were first optimized in both cell lines using GFP as a fluorescent marker to measure transfection efficiency (Fig. 19). Transfection efficiency was also optimized in Chinese hamster ovary (CHO) cells to use as a negative control for later confirmation of overexpression in IF studies. CHO was selected as NIPAL1 expression is not detected in ovary samples of any of the three databases used in candidate selection. α-TC, Min6 and CHO cells all demonstrated robust transfection efficiencies across independent experiments. As CHO cells are notoriously easy to transfect144, transfection efficiency consistently appeared highest in these cells. Transfected cells did not show any loss of healthy morphology or attachment to culture plates.

With transfection conditions optimized, IF studies were conducted to confirm successful overexpression of Nipal1 in α-TC, Min6 and CHO cells (Fig. 19). Cells were transfected with either non-specific empty vector (EV) or the Nipal1 cDNA vector. The Nipal1 vector used

Figure 19: Representative images of efficient transfection in α-TC6, Min6-K8 and CHO cell lines with GFP. GFP transfection was used as a visual marker to optimize transfection conditions for the different cell lines. All cell lines demonstrated robust transfection efficiency across all experiments, with qualitative assessment suggesting the highest transfection efficiency in CHO cells. Cells did not demonstrate obvious signs of toxicity under the conditions used.

(pCMV6-Entry, Origene) includes a FLAG tag for independent verification of overexpression. In the images taken of α-TC (Fig. 20A) and Min6 (Fig. 20B) transfected with EV, only endogenous

NIPAL1 expression was observed, with characteristic concentration in the Golgi and sparser cytosolic distribution. No FLAG expression was detectable. In contrast, cells transfected with the

Nipal1 vector demonstrated a distinct increase in signal intensity under the same imaging conditions (as highlighted by arrows). A proportion of cells were also observed to demonstrate strong FLAG expression, and this expression co-localized well with regions of high NIPAL1 intensity. The fact that the increase in intensity is not uniform throughout the cell suggests that overexpression is mainly in the Golgi. In the case of CHO cells (Fig. 20C), endogenous NIPAL1 expression was entirely absent under transfection with empty vector. Detectable FLAG expression was also absent (only artefactual spots). In comparison, cells transfected with the Nipal1 vector were observed to have NIPAL1 expression only in cells with strong FLAG expression.

Figure 20: Immunofluorescence studies of cell lines transfected with either empty vector or mouse NIPAL1- FLAG cDNA overexpression vector. Cells were co-stained with NIPAL1 and FLAG-tag antibodies, as well as DAPI (nucleus). Representative images are shown of (A) α-TC6 and (B) Min6-K8 cells displaying overexpression of NIPAL1. Under transfection of empty vector, only endogenous expression is observed. Transfection of NIPAL1 vector shows a distinct increase in staining intensity that overlaps with FLAG staining, as denoted by arrows. (C) Overexpression was also conducted in CHO cells, which lack endogenous NIPAL1 expression as observed under empty vector transfection. Under NIPAL1 vector transfection, NIPAL1 staining is only visible where FLAG expression is observed (n = 3).

NIPAL1 overexpression does not impact Min6 insulin secretion under standard culture conditions

In order to determine whether Nipal1 overexpression has an effect on insulin secretion, glucose-stimulated insulin secretion (GSIS) assays were conducted in Min6 cells (Fig. 21).

Following overnight culture of cells transfected with GFP, EV or Nipal1 cDNA, samples were collected in the absence of glucose (0 mM), under high glucose (16.7 mM) and with the addition of KCl (16.7 mM glucose + 25 mM KCl). Following GSIS, cells were lysed for protein content.

Collected samples had their insulin content measured via homogenous time-resolved fluorescence

Figure 21: Overexpression of NIPAL1 in Min6-K8 cells did not affect GSIS. Min6-K8 cells were transfected with either GFP/empty vector controls or a mouse NIPAL1 overexpression vector. GSIS assays were performed on cells. Following 1hr incubation in glucose-free KRB media, media was replaced with glucose-free (0 mM, basal), high glucose (16.7 mM), and high glucose + KCl (25 mM) KRB in 20-minute intervals under incubation. Media was collected for insulin and measured via HTRF. No significant difference was observed between treatment groups under any glucose condition (n=3, values displayed as mean normalized to protein content ± SEM).

(HTRF) analysis, and results were normalized to total protein content. Relative to both GFP and

EV, Nipal1-transfected cells showed no significant differences in overall insulin secretion. As

Nipal1 already has substantial expression in Min6 cells, it is possible that increasing the abundance of NIPAL1 may have a redundant impact in the context of secretion.

NIPAL1 knockdown does not impact Min6 insulin secretion under standard culture conditions

In the absence of a detectable difference in insulin secretion under Nipal1 overexpression, experiments were conducted to determine whether knockdown of NIPAL1 might instead have an impact. Min6 cells were treated with either non-targeting siRNA (i.e. scramble) or Nipal1 siRNA.

Effective knockdown was first confirmed by qPCR analysis, with consistent knockdown above

70% of expression (or < 0.3-fold expression) relative to scramble (Fig. 22A). With knockdown confirmed, Min6 cells were treated with siRNA overnight and subjected to GSIS and HTRF in similar fashion to previous overexpression studies (Fig. 22B). However, no significant difference in insulin secretion was observed under any of the glucose treatment conditions tested.

NIPAL1 knockdown interferes with a Mg2+-dependent increase in Min6 insulin secretion

No significant difference was observed in insulin secretion following knockdown under standard culture conditions. However, as NIPAL1 is a magnesium transporter, it was suspected that an impact might emerge under magnesium-induced stress. To test this, Min6 cells were first cultured under controlled magnesium levels (in similar fashion to previous experiments relating

Mg2+ to Nipal1 expression). After seeding the cells in standard media conditions (containing

A B

Figure 22: Knockdown of NIPAL1 in Min6-K8 cells did not affect GSIS. Min6-K8 cells were transfected with either non- targeting scramble siRNA control or mouse NIPAL1 siRNA. (A) NIPAL1 expression was measured via qPCR and normalized to beta-actin expression. Scramble expression was set to 1 and NIPAL1 siRNA fold expression showed ~72% decreased expression, confirming efficient knockdown (n=3, values displayed as mean ± SEM). (B) GSIS assays were performed on transfected cells. Following 1hr incubation in glucose-free KRB media, media was replaced with glucose-free (0 mM, basal), high glucose (16.7 mM), and high glucose + KCl (25 mM) KRB in 20-minute intervals under incubation. Media was collected for insulin and measured via HTRF. No significant difference was observed between treatment groups under any glucose condition (n=3, values displayed as mean ± SEM).

0.8mM Mg2+) for 24h, media was replaced with either fresh standard media or specialized media

(24h culture) with 2% FBS and supplemented MgSO4 to yield final concentrations of 0.0 mM, 0.4

mM, 0.8 mM, 5.0 mM or 10.0 mM Mg2+. Phase-contrast light microscope images of the cells were

then taken to observe the impact of hypo- and hypermagnesemic conditions on cell growth and

morphology (Fig. 23A). Under standard culture conditions, Min6 cells grow as angular/diamond-

like cells that form fairly dense clustering patterns with fibrous projections sent out to contact

neighbouring cells. This clustering behaviour is characteristic of both primary β-cells145 and

Min6146,147 cells in order to enhance insulin secretion via intercellular communication, which

appears to be particularly important in murine islets compared to human islets148. Under

hypomagnesemic conditions of 0.0 and 0.4 mM Mg2+, an increased proportion of cells lose their

B A

Figure 23: Representative brightfield images (x20) of Min6-K8 cells cultured under varying magnesium concentrations and transfection conditions. Following seeding in standard media and recovery overnight, media was replaced with specialized media with controlled magnesium content for 24h and imaged. (A) Untransfected cells become rounded and lose clustering under hypermagnesemic conditions (5.0 and 10.0 mM Mg2+) relative to standard 0.8 mM Mg2+ (n=3). (B) This trend is consistent in cells transfected with non-targeting scramble siRNA, most notably under 10.0 mM Mg2+. However, NIPAL1 knockdown at the same Mg2+ concentration consistently appears to show significant recovery of cell morphology and clustering, suggesting a protective effect against magnesium-induced toxicity (n=3).

morphology and exhibit rounding indicative of subpar health, and clustering is somewhat reduced.

Isomagnesemic 0.8 mM Mg2+ treatment expectedly shows healthier growth that is comparable to standard culture conditions as they both contain similar magnesium content. Under hypermagnesemic conditions at 5.0 mM Mg2+, a large proportion of cells appear rounded and clustering noticeably decreases; this is greatly exacerbated under 10.0 mM conditions, in which the majority of cells appear misshapen and clustering behaviour is greatly diminished.

In order to investigate whether magnesium content affected insulin secretion in Min6 cells, cells cultured under these special conditions were subjected to GSIS and HTRF analysis (Fig.

24A). Cells cultured under standard conditions were included for comparison. In addition to measuring basal secretion under 0.0 mM glucose, normoglycemic (5.5 mM), hyperglycemic (16.7 mM), and 16.7 mM + KCl conditions were included in GSIS. However, unlike under standard media culture conditions, specialized magnesium conditions appeared to have an inhibitory effect on the glucose responsiveness of the cells that was only alleviated with the addition of KCl. Thus, only basal and 16.7 mM + KCl results are shown. Interestingly, it quickly become apparent that there was a positive correlation between Mg2+ concentration and insulin secretion under both basal conditions and KCl-induced secretion. Under virtually magnesium free conditions (0.0 mM), basal insulin secretion (2.53±0.76) was nearly halved compared to standard conditions (4.53±0.86).

KCl-induced secretion under magnesium-free conditions (5.34±2.25) mimics this loss against standard conditions (11.24±1.06). However, by increasing the magnesium concentration, a gradual recovery in secretion is observed. At the two highest concentrations of 5.0 and 10.0 mM Mg2+, basal secretion (3.53±0.13 and 3.50±0.14, respectively) approached standard conditions

(4.53±0.86), while KCl-induced secretion (9.85±2.12 and 10.77±1.00, respectively) nearly equals standard conditions (11.24±1.06). Additionally, both 5.0mM and 10mM Mg2+ conditions were

significantly elevated vs. 0.4mM and 0.8mM Mg2+ for basal secretion; under KCl-induced secretion, this trend was only retained for 10.0 mM Mg2+. These results thus suggest that magnesium plays a role in bolstering secretion from Min6 cells. However, it is important to emphasize that these results were collected under low FBS conditions intended to minimize unadded magnesium content and place cells under more precisely controlled levels of magnesium- induced stress. This is best reflected in the differences in secretion vs. standard (i.e. 10% FBS) and

0.8 mM Mg2+ conditions despite containing similar concentrations of magnesium (Fig. 24A).

Thus, it cannot be ruled out that this trend would not emerge under 10% FBS conditions. However, it is highly likely that the magnesium-secretion correlation is legitimate considering the observed recovery under hypermagnesemic conditions (i.e. 5.0 and 10.0 mM Mg2+) that cells would not normally be exposed to; KCl-induced secretion in particular recovers to near-standard levels, underscoring the importance of magnesium’s effect.

To examine how Nipal1 knockdown might affect the secretory response under magnesium- induced stress, cells were transfected with either scramble siRNA or Nipal1 siRNA and similarly cultured under varying magnesium concentrations. Brightfield images under scramble transfection showed similar effects on cell growth as the same concentrations under untransfected conditions, most notably with lost shape and clustering under 10 mM Mg2+ (Fig. 23B). Curiously however, healthy clustering was consistently observed to recover with Nipal1 knockdown, suggesting a protective effect against magnesium-induced cytotoxicity. Cells cultured under these conditions were subsequently subjected to GSIS and HTRF. Once again, only basal and KCl results are shown

(Figs. 24B and C). Quite interestingly, Nipal1 knockdown showed significantly decreased basal insulin secretion under all conditions except 5.0 mM Mg2+. Scramble siRNA values ranged from

Figure 24: Magnesium concentration correlates with insulin secretion in Min6-K8 cells, and basal secretion is reduced under NIPAL1 knockdown. (A) Min6 cells were cultured in either standard media (~0.8 mM Mg2+ with 10% FBS) or special media under varying magnesium concentrations (with 2% FBS to minimize additional Mg2+) for 24h following overnight recovery in standard media. Cells were incubated in KRB media with glucose concentrations of 0.0 mM (basal), 5.5 mM, 16.7 mM and 16.7mM + KCl (25 mM); however, special media conditions appeared to interfere with glucose responsivness, and thus only basal and KCl conditions are shown. A clear trend was observed between increasing Mg2+ concentration and insulin secretion, with significant increases in basal secretion for 5.0 mM and 10.0 mM Mg2+, and KCl-induced secretion under 10.0 mM Mg2+. (B) Cells cultured under the same conditions were transfected with non-targeting scramble siRNA or NIPAL1 siRNA. NIPAL1 knockdown was found to significantly decrease basal secretion under all Mg2+ concentrations except 5.0 mM. (C) Secretion under NIPAL1 knockdown appeared to be rescued with KCl induction (n=3, values displayed as mean ± SEM).

3.68±0.44 to 5.33±0.61 (ng/ml)/ug DNA, whereas Nipal1 siRNA values were measured at a much lower 2.30±0.58 to 3.20±1.21 (ng/ml)/ug DNA. Furthermore, the trend of rising secretion with higher Mg2+ concentration under scramble transfection appeared to be stifled with Nipal1 knockdown as well. This difference between scramble and Nipal1 knockdown was abolished under

KCl-induced secretion (Fig. 24C), with scramble (ranging from 4.13±0.46 to 7.11±0.98) and

NIPAL1 knockdown (ranging from 3.52±0.66 to 9.37±1.21) showing no significant differences under any Mg2+ concentration. Furthermore, there was a recovery of the magnesium-secretion correlation lost in basal secretion under Nipal1 knockdown.

NIPAL1 regulates total insulin content in Min6 cells

Additional tests were performed to determine whether Nipal1 knockdown and overexpression had any impact on total insulin content, with the results yielding some interesting trends. Under

Nipal1 knockdown (Fig. 25A), culture under 0.1, 0.4 and 0.8 mM Mg2+ conditions all appeared to drastically reduce total insulin content, with apparent recovery under 5.0 and 10.0 mM Mg2+. The opposite trend was observed under overexpression of Nipal1 (Fig. 25B), where total insulin content was significantly increased under 0.4, 0.8, 5.0 and 10.0 mM Mg2+ conditions. Thus, it appears that the NIPAL1 protein contributes in some way to total insulin content in Min6 cells, and that higher

Mg2+ levels can compensate for loss of insulin production in the absence of NIPAL1.

Altogether, results suggest that the loss of NIPAL1 attenuates the ability of Min6 cells to effectively respond to changes in extracellular magnesium levels, which in turn reduces effective secretion; this loss of secretion is recovered under stimulation with KCl. This may be related to a potential reduction in total insulin content observed under Nipal1 knockdown, with an opposite

Figure 25: Knockdown of NIPAL in Min6 reduces total insulin under low magnesium, while overexpression increases total insulin. (A) Min6 cells cultured under varying magnesium conditions were transfected with either scramble siRNA control or NIPAL1 siRNA and measured for total insulin content. Total insulin was observed to be significantly decreased with NIPAL1 knockdown under 0.1 mM, 0.4 mM and 0.8 mM Mg2+ conditions, but recovered under hypermagnesemic 5.0 mM and 10.0 mM Mg2+ conditions. (B) Cells cultured under the same conditions were transfected with either empty vector control or NIPAL1 cDNA and measured for total insulin content. Total insulin was found to be significantly increased under 0.4 mM, 0.8 mM, 5.0 mM and 10.0 mM Mg2+ conditions (n=3, values displayed as mean ± SEM, *p<0.05).

effect observed under Nipal1 overexpression. However, it is important to emphasize that these trends were only observed under conditions with magnesium-induced stress, as no significant effects on secretion were observed under functional manipulation in standard culture conditions as previously mentioned (Figs. 20 and 21). This indicates that NIPAL1 would not normally play a major role in influencing secretion, but that it may be important in enabling the cell to adapt to changing magnesium conditions and maintain ionic homeostasis.

General Discussion

Summary of findings

Insulin secretion is governed by a complex web of interactions with many points of regulation at which novel proteins of interest might be identified. The advent of scRNA-seq has offered novel insight into uncharacterized genes in islets by enabling analysis at the level of the individual cell; this is a highly advantageous step forward as it overcomes the limitations of previous whole-islet

RNA-seq analyses of the highly heterogeneous islet. In this study, a sequential process was developed to identify novel candidates of interest. To accomplish this, several available scRNA- seq datasets were used in tandem with whole-islet studies, established gene expression databases, and available literature to generate a candidate selection process. This process was used to narrow down an initial pool of 915 candidates to a final shortlist of 28. These 28 candidates were subjected to transcriptional validation to help identify islet-enriched candidates, and 4 of these candidates

(TSPAN13, VAT1L, SYT13, and NIPAL1) progressed to translational validation. Magnesium transporter NIPAL1 was selected for further studies on account of its observed α-cell specificity, novelty, and relevance to accumulating evidence regarding magnesium’s importance in diabetes and metabolism. NIPAL1 was identified as having partial co-localization with the Golgi, and its expression was observed to correlate with magnesium concentration in α-TC and Min6 cells.

Neither overexpression nor knockdown of Nipal1 in Min6 was found to impact insulin secretion under standard culture conditions. However, manipulating the magnesium content of media revealed that magnesium concentration correlates with Min6 secretion. Knockdown under hypo- and hypermagnesemic conditions was found to reduce basal insulin secretion and abolish the magnesium-secretion correlation. Induction under 16.7 mM glucose + KCl recovered both secretion levels and the magnesium-secretion correlation under Nipal1 knockdown, although this

is effect is attributed solely to the addition of KCl due to the poor glucose response observed under

5.5 mM and 16.7 mM glucose conditions. Studies on total insulin content in Min6 additionally revealed that Nipal1 knockdown decreases total insulin, while Nipal1 overexpression does the opposite. Overall, NIPAL1 appears to important to promoting efficient insulin production and stabilizing the insulin secretory response in the face of fluctuating magnesium levels.

Candidate selection process and its limitations

A selection process was designed to identify novel genes of interest in α- and β-cells from available scRNA-seq data and supporting databases/literature, and then sequentially eliminate them through transcriptional and translational validation in islets. Combining multiple independent sources was done to try and minimize selection of candidates that might have been detected under chance conditions in datasets with high variability. Furthermore, investigating datasets exploring gene expression under a variety of different conditions (i.e. simple enrichment, T2D, glucose response, islet eQTL) offered a broad scope for discovery. Cross-checking against established databases provided another method by which to filter out candidates that might have a substantial presence in islets, but which are ubiquitously expressed, reducing the chance of selecting for candidates irrelevant to islet secretion.

While manual selection and investigation of candidates certainly provided a good measure of control over the process, this same quality undoubtedly made aspects of the process arbitrary in nature. Within the scRNA-seq datasets, selection of only the top 200 candidates from each study for comparison was done on the grounds of feasibility for the given timeframe. An alternative option would have been to first correct for batch effects causing variability between datasets149

and then calculating a significance threshold at which candidates were deemed ‘sufficiently enriched’ to find an appropriate cut-off point. When selecting candidates based on their perceived potential to secretion and islet function, investigating genetic interaction datasets with tools such as Cytoscape150 and protein-protein interaction networks with tools such as STRING151 would have additionally been beneficial. Linking novel candidates to established players in islet secretion pathways could have provided enhanced insight into whether a candidate is likely to be relevant based on association.

Independent transcriptional validation of candidates across both mouse and human islets was a powerful measure to determine whether candidates identified as enriched or differentially expressed were consistent between species. Candidates such as NIPAL1 with significant fold enrichment in both are more likely to be evolutionarily conserved and therefore more likely to play an important function than non-conserved genes152. However, the limitation with this approach is that it may very well overlook candidates that are relevant to human islets but not mouse islets, precisely because of some relevant evolutionary difference. While mice have undoubtedly been a highly productive and useful model to study islet function, it is well-known that there are drastic differences in the cytoarchitecture of murine and human islets78, as well in the pancreas as a whole153. Mouse islets are typically organized with a central core of β-cells and surrounding mantle of α, δ and PP cells in apparent subdivisions that line up with microcirculation154. In contrast, most human β-cells are associated with non-β cells, and endocrine cells appear randomly aligned with blood vessels78. Human islets are additionally reported to contain fewer blood vessels overall155.

These differences result in functional discrepancies, such as in paracrine communication156, islet response to injury157, and susceptibility to developing diabetes158. Thus, there are benefits to focusing on human-specific candidates. It is important to note, however, that the human islets used

in this study may not be well-representative of the general population. While all donors were non- diabetic, the relatively high average BMI (29.8±4.5 kg/m2, compared to a healthier global average between 24.0-24.6159) may have influenced the expression of investigated gene candidates.

Furthermore, significant variability in islet preparation, preservation and quality control are all factors that may affect the functional properties of human islet samples160.

Comparison to liver, kidney and exocrine pancreas added another layer to transcriptional validation by giving some insight into the relative specificity of candidates in islets vs. tissues with shared developmental lineages. However, this approach is undoubtedly quite limited when considering the dozens of other tissues available for comparison. Transcriptional validation might have been enhanced through the inclusion of other primary endocrine tissues such as the thyroid or adrenal glands, whose shared secretory capacity with islets might have given insight into whether an islet-enriched candidate would be expected to be related to secretion if also enriched in these tissues. Comparison with neurons would have also been useful, as neural and pancreatic endocrine cells share a genetic toolbox despite not deriving from a common tissue or embryonic germ layer161; this means that islets likely co-opted various neuronal transcription programs involved in ionic flux, vesicular trafficking, and other shared secretory pathways. Novel candidates shared between neurons and islets may therefore be of relevance to secretion.

The final phase of translational validation offered relative simplicity: candidates should have expression restricted to α- and β-cells, but preferably be confined to one or the other. This would favour selection of candidates that are distinctly expressed in only one islet cell type for a specific functional purpose. Based on this approach, NIPAL1 stood out for its α-cell specificity, and confirmation of its absence from exocrine pancreas meant that it was not a general pancreatic gene.

However, it is important to note that the observed α- and β-cell specificity of TSPAN13 and SYT13

does not necessarily discount them from further studies, as shared secretory pathways of glucagon and insulin would explicably mean dual expression. Overall, translational validation might have been improved with staining of liver and kidney sections for consistency with transcriptional validation, and staining in human pancreas sections would have undoubtedly been insightful due to aforementioned differences between murine and human pancreata.

The relationship between magnesium and NIPAL1

Nipal1 expression was observed to increase with magnesium concentration in both α-TC and

Min6 cells. Cells cultured under conditions with increasing magnesium concentrations demonstrated this difference at the transcriptional level via qPCR and PCR analysis, and translational level via IF studies. While it does not come as a surprise that Nipal1 shows sensitivity to magnesium in these cell lines, the observation of NIPAL1 expression increasing rather than decreasing with magnesium was certainly unexpected for a number of reasons. As mentioned previously, a study that examined Nipa/Nipal family expression in mice on a magnesium-restricted diet found that expression was upregulated in harvested kidney tissue98. Additionally, paralogs

NIPA1 and NIPA2 have both been observed to have enhanced gene expression under reduced magnesium conditions98,136. This is supported by IF co-localization studies in COS7 cells (i.e. monkey kidney cell line, for endogenous NIPA1) and transfected MDCK cells (i.e. dog kidney cell line, for NIPA2) in which the respective protein showed a significantly enhanced membrane presence under low magnesium conditions. Both of these transporters promote Mg2+ influx into cells, and thus contribute to intracellular magnesium homeostasis when extracellular magnesium is scarce. Like NIPA1 and NIPA2, NIPAL1 has been measured to have inward currents indicating that it is an influx transporter; its opposite sensitivity to magnesium thus puts it out of step with its

paralogs. However, it is important to note that while it shares some structural features of NIPA1 and NIPA2, NIPAL1 is specifically characterized as “NIPA-like domain containing 1”. It is possible then that NIPAL1 retained magnesium transport domains when evolutionarily diverging from its paralogs, but that domains regulating its sensitivity to magnesium were essentially

“flipped” to instead respond to high magnesium. This could be determined through phylogenetic profiling of NIPA family members. In the context of Min6 cells, there may be some advantage to deploying more NIPAL1 transporters to increase magnesium influx and, from observed trends, insulin secretion. However, whether this would be the case for islets remains to be seen.

The relationship between magnesium and secretion

After it was found that Nipal1 overexpression and knockdown did not significantly impact insulin secretion from Min6 cells under standard culture conditions, conditions were adjusted to enable hypo- and hypermagnesemic conditions. In untransfected cells, while hypomagnesemic conditions (< 0.8 mM Mg2+) appeared to mildly impact cell health and clustering capacity, these negative impacts were quite apparent under hypermagnesemic conditions (> 0.8 mM Mg2+). It is interesting then that the highest levels of basal and KCl-induced insulin secretion were measured under hypermagnesemic conditions, in line with a magnesium-secretion correlation. This is unlikely to be due to loss of insulin leaked into the media from compromised cells, as cells are washed prior to a brief 20-minute incubation for sample collection. Thus, this observation does appear to be genuine. However, it remains at odds with what was expected based on similar experiments in primary tissue. Independent experiments in which magnesium was perfused in canine pancreas58,162 and rat pancreas59,163 consistently demonstrate that magnesium has an inhibitory effect on both insulin and glucagon secretion. A more recent study that knocked down

Trpm7 (i.e. a ubiquitous, bifunctional kinase-ion channel important for intracellular Mg2+ homeostasis164) in INS-1 cells (i.e. rat insulinoma) reported a 3-fold increase in insulin secretion under stimulatory glucose conditions165. However, the same study reported that halving Mg2+ concentration from 1.0 mM to 0.5 mM had no impact on GSIS in mouse islets or INS-1.

The observed inhibitory effect of Mg2+ on secretion is largely attributed to Mg2+ competing for voltage-dependent Ca2+ and Na+ channels56,58 involved in stimulus-secretion coupling; this disruption has been characterized in other tissues such as smooth muscle, where magnesium is observed to inhibit calcium-induced cell death, making it anti-apoptotic57. It is possible then that this unexpected trend of Mg2+ benefitting Min6 may be somehow linked to its characteristics as an insulinoma. Increased magnesium may be beneficial to secretory efficiency in a manner that outweighs the disruptive impact on channels; however, the disruptive effects may outweigh any gains in primary secretory tissues such as islets. While magnesium has various roles in the cell, its most notable contribution to secretion is its capacity to act as an ATP-binding cofactor (Mg-ATP) facilitating phosphate transfer reactions28,29 that are critical for fueling secretory granule movements166,167 and both glycolysis and the citric acid cycle168,169. In an exceptionally demanding secretory cell line such as a Min6 insulinoma growing under high glucose conditions (thereby maximizing ATP production), it is possible then that higher Mg2+ increases Mg-ATP complex formation that subsequently increases mitochondrial output, insulin granule maturation and exocytosis. Increased Mg-ATP formation may also influence the ATP:ADP ratio regulating the

2+ closure of KATP channels, while extracellular free Mg has also been proposed to regulate KATP channels170. Fluctuation in extracellular Mg2+ levels may also have a substantial effect on membrane potential and by extension the likelihood of triggering secretion.

The potential functional relevance of NIPAL1 in islets

NIPAL1 contributes to magnesium influx in cells in which it is expressed136. Experiments conducted in this study have shown that increased magnesium enhances basal insulin secretion from Min6 cells (observed under both untransfected and scramble-transfected conditions) and that knockdown of Nipal1 disrupts this trend. The loss of this trend is rescued by induction of secretion by KCl. Increased magnesium also appears to increase total insulin content in Min6, which is disrupted by Nipal1 knockdown under lower Mg2+ concentrations and enhanced under Nipal1 overexpression. Knockdown of Nipal1 may also provide some level of protection against magnesium-induced cytotoxicity under strongly hypermagnesemic conditions. However, as this assessment was only made through qualitative evaluation of cell morphology following treatment, it would have to be confirmed via some form of viability assay such as an MTT or XTT assay171.

With the evidence at hand, it stands to reason that knockdown of Nipal1 is most likely reducing the intake of Mg2+ that is somehow beneficial to secretion (at least under acute 24h culture in

Min6). The rescue observed with the addition of KCl is less clear. KCl (added in combination with high glucose) induces sustained depolarization and a resultant surge in intracellular Ca2+ meant to flush out much of the remaining insulin vesicles in the cell’s reserve pool172,173. This may very well compensate for the lack of magnesium diminishing basal secretion under Nipal1 knockdown, but is still insufficient to explain why higher overall secretion is observed under hypermagnesemic

5.0 mM and 10.0 mM Mg2+ conditions. As suggested previously, Mg2+ may potentially influence the sensitivity of KATP channels and augment depolarization, causing greater release of insulin.

However, a more likely explanation is that – in light of the increase in total insulin content observed with Nipal1 overexpression and decrease with knockdown – more magnesium increases insulin granule production/availability, which in turn translates proportionally to greater secretion. Thus,

it may be less so that higher magnesium influences the secretory machinery than increases the size of the cell’s reserve pool of insulin granules. As for the increase in total insulin itself, this may potentially be explained by the aforementioned role of magnesium as an ATP-binding cofactor.

Greater intracellular Mg2+ availability could translate into more efficient execution of highly ATP- dependent insulin granule transport and maturation within the Golgi. The concentration of NIPAL1 within the Golgi would suggest that it can influence local Mg2+ concentrations within the cell, thereby influencing any magnesium-dependent activity. However, a potential caveat to these results is the effect of using 2% FBS over standard 10% FBS in these experiments. In future studies, it would be beneficial to devise a more sophisticated method to chelate out free Mg2+ so that other components of 10% FBS are kept consistent (although this was beyond the scope of the current study).

While these results are not doubt interesting, it remains to be seen how well such findings would apply to islets. Most notably, NIPAL1 expression was observed in α-cells rather than β- cells, meaning that observations of the effects on insulin secretion would not necessarily translate to glucagon secretion. However, considering that the secretion pathways of both hormones are largely similar – particularly with regards to ionic flux and secretory machinery – it is believed that NIPAL1 would similarly influence glucagon secretion in a magnesium-dependent manner.

With the aforementioned relations of magnesium to secretion, metabolism and diabetes risk,

NIPAL1 may potentially be important in maintaining magnesium homeostasis in α-cells to preserve functional pathways and prevent dysregulation of glucagon secretion. Further investigation of NIPAL1 in islets and specifically in relation to glucagon secretion is thus warranted.

Conclusions

This study used a sequential candidate selection process to identify novel candidates of interest in islets using single-cell RNA-sequencing data in tandem with additional resources. This process resulted in the identification of magnesium transporter NIPAL1 as an entirely novel α-cell protein. Experiments demonstrated that NIPAL1 expression correlates with magnesium, and that it is functionally important in permitting magnesium-dependent increases in insulin production and secretion from Min6 cells under stressed conditions. The prominence of NIPAL1 in α-cells in islets and the common secretory pathways shared between insulin and glucagon secretion suggests a potential regulatory role for NIPAL1 in the indirect regulation of glucagon via modulation of magnesium homeostasis, but further experiments are needed to determine whether this is the case.

Future Directions

Investigate mechanism for observed increase in Min6 secretion with higher magnesium

Two potential explanations were proposed for the observed increase of insulin secretion with magnesium: greater Mg-ATP complex formation and Mg-dependent regulation of KATP channels.

To investigate whether complex formation is involved, a non-hydrolysable ATP analogue such as

AMP-PCP167,174 could be added to cells alongside increasing magnesium concentrations to determine if the effect on secretion is indeed Mg-ATP-dependent. To explore a potential role in

KATP regulation, voltage clamp experiments could be conducted to examine KATP currents in the presence of escalating magnesium concentrations, as well as gauge whether the channel is more sensitive to ATP as a result.

Assess effect of NIPAL1 manipulation on glucagon secretion and in islets

While the observed effects of Nipal1 manipulation in Min6 are no doubt interesting, the next logical step would be to determine whether results are consistent in the context of glucagon secretion. While α-TC cells readily secrete glucagon, they are not ideal models given that they are not nearly as glucose-responsive as Min6 cells, and also since glucagon secretion in primary islets involves substantial neuronal input from the brain5. However, glucagon secretion has been demonstrated to be reliably stimulated by both L- and D-arginine in islets73,163, which may offer an effective way to measure the effects of functional manipulation. Preliminary studies excluded from this work suggest that the effect of arginine is consistent in α-TC cells, and this may therefore be an effective way to measure the effect of NIPAL1 on glucagon secretion before moving to primary islets.

While the link between Nipal1 and magnesium-dependent insulin secretion in Min6 is interesting, the absence of strong protein staining in β-cells makes it unlikely that manipulation of

NIPAL1 would have any direct effect on insulin secretion in islets. However, it may very well be linked to glucagon secretion, which would make it an indirect regulator of insulin secretion given that glucagon is a β-cell agonist. Despite the results observed in Min6, an increase in secretion in islets under high magnesium seems unlikely given previous studies on this topic showing magnesium-based disruption. Instead, it is possible that Nipal1 knockdown may affect insulin secretion similarly to how Trpm7 knockdown was found to increase secretion in INS-1 cells165 by restricting Mg2+ influx. If this is shown to be the case, it may highlight an important distinction between primary islet cells and islet cell lines with regards to ionic flux.

Investigate ionic flux and insulin granule maturation under NIPAL1 manipulation

Considering the dynamic interplay between ions in the context of secretion – particularly between Ca2+ and Mg2+ – imaging experiments with calcium and magnesium could be conducted in both cell lines and primary islets to explore how functional manipulation of NIPAL1 affects the movement of these ions. As Ca2+ is important to both insulin biosynthesis and granule maturation25, it could be determined whether Mg2+ somehow interferes with or otherwise affects these processes, both independently of and in the context of NIPAL1 functional manipulation.

Explore potential relevance of NIPAL4

NIPAL1 was originally selected for further study following observations of its significant fold expression in both mouse and human islets. While not nearly as prominent as Nipal1 in mouse results, Nipal4 still showed significant fold expression vs. comparison tissues, and demonstrated similar fold expressions as NIPAL1 in human islets. As NIPAL4 is similarly an influx magnesium

transporter, it is possible then that both of these genes are involved in islet magnesium homeostasis.

Functional experiments involving both NIPAL1 and NIPAL4 manipulation in tandem may be worth pursuing to see if any enhanced effects are observed on secretion, cell viability, etc.

Generate islet-specific NIPAL1 knockout model

If NIPAL1 is successfully identified as being important to the regulation of hormonal secretion and biosynthesis in islets, it may be worthwhile to develop an islet-specific knockout mouse model of Nipal1 for valuable insight into in vivo function. An islet-specific knockout would be preferable to a whole-body knockout, as potential disruption of magnesium homeostasis in other tissues would make it difficult to ascertain whether the resulting complications can be attributed to islet dysfunction. It would be interesting to explore how this knockout may impact glucose homeostasis and insulin secretion/sensitivity when the mice are placed on magnesium-free or high-magnesium diets. In vitro studies of islets taken from these mice (i.e. GSIS, ion flux imaging, electron microscopy of granule maturation) would also complement in vivo studies well.

Supplementary Materials

Table 2: Primer sequences for qPCR and PCR:

Gene Name (Mouse) Application Sequence Forward: 5’- TGCCATGTTGCTATTGCAGGA - 3’ APOH qPCR Reverse: 5’- GGCTTGCAGGAGTAGACAATCT - 3’ Forward: 5’- GGCTGTATTCCCCTCCATCG - 3’ ACTB (Beta-actin) qPCR Reverse: 5’- CCAGTTGGTAACAATGCCATGT - 3’ Forward: 5’- GACCACCTCAAATCGCCTCAT - 3’ BEST3 qPCR Reverse: 5’- CTTCATTCCGGGCTTTGGTTG - 3’ Forward: 5’- CACCTCCCAGATTGCTCTAATG - 3’ C10ORF10 qPCR Reverse: 5’- TCCTTTGCCCTTGGTTTCTC - 3’ Forward: 5’- TTGTCTAGGAGACTTTACCTGGC - 3’ CNTN1 qPCR Reverse: 5’- AAATGGTATTGATTGGCTGCTCT - 3’ Forward: 5’- TCCCGATGAACCCAGAGAA - 3’ CYSTM1 qPCR Reverse: 5’- GTTGTGGATAAGGTGGGTATGG - 3’ Forward: 5’- CTGCTTCCGAAGTTGATCCTG - 3’ CYYR1 qPCR Reverse: 5’- CGTGCCCGAGAGAATGTTCC - 3’ Forward: 5’- CAGGAAGCATAAATACCTCGCC - 3’ DOCK1 qPCR Reverse: 5’- CAGCTCATCCGATTGTCTTTGT - 3’ Forward: 5’- GCCTCAGGTGTCAAATGGACC - 3’ ELAVL4 qPCR Reverse: 5’- ACCCTAAACTCTGTCCTGTGAT - 3’ Forward: 5’- GAGAACAGCAGCCGAGAAGAT - 3’ ELMO1 qPCR Reverse: 5’- GTTGCAGGTCTCACTAGGCAG - 3’ Forward: 5’- TCGGGCTACTACAGCCTCAA - 3’ FAM159B qPCR Reverse: 5’- CGATTCCCAATCCAACCAGAG - 3’ Forward: 5’- TCGGTCGTCAAGGAGCTGT - 3’ FITM2 qPCR Reverse: 5’- GAGGACGTTGCGCTTGTTG - 3’ Forward: 5’- GGAGAGTGGCAAGATGGATTC - 3’ FNBP1L qPCR Reverse: 5’- CCTCTGCTCTGGTGGCAAAT - 3’ Forward: 5’- CTGCTCGGTGTCGCAACAT - 3’ IGSF11 qPCR Reverse: 5’- GACATTGAGGTTCAGGAGGGC - 3’ Forward: 5’- GAGTCCGTTTCTTTGACCCCT - 3’ KCNA6 qPCR Reverse: 5’- GGCGACCTCCAGATTGATAGTA - 3’ Forward: 5’- GGCCAGATCACGGATGAGATG - 3’ KCNAB2 qPCR Reverse: 5’- CCGTATCGAACAGGTTGATGC - 3’ Forward: 5’- AAGGGCATTATCCCTGAGGAA - 3’ KCNJ11 qPCR Reverse: 5’- TTGCCTTTCTTGGACACGAAG - 3’ Forward: 5’- GACAGCACCCGAGGATTACC - 3’ KCTD12 qPCR Reverse: 5’- TCCCACGTTCAGCTCTACGA - 3’ Forward: 5’- TTATCCACGCCCCACAAGAG - 3’ NIPAL1 qPCR Reverse: 5’- AGGAAACGAACCCTGGATCTC - 3’ Forward: 5’- TGCGAGAACGGCTCCATAG - 3’ NIPAL4 qPCR Reverse: 5’- GCTCTTCAGGACTGATGCCTC - 3’ Forward: 5’- CCTGCTTCATAATGAGACCTCG - 3’ NPTX2 qPCR Reverse: 5’- CTGGTGACTTGAATGCACTGT - 3’ Forward: 5’- GCTCGAGTGGGAAGTATGATAG - 3’ OAT3 (SLC22A8) qPCR Reverse: 5’- CAGTAGAGTCATGGTCCCAAAG - 3’ Forward: 5’- CTGCATAACGGTCTGGACTTC - 3’ PEPCK (PCK1) qPCR Reverse: 5’- CAGCAACTGCCCGTACTCC - 3’ Forward: 5’- CTTCTCTTTTTCGCTACGGTCT - 3’ PM20D1 qPCR Reverse: 5’- CACCTTTCAGCGCCTCTTTTAT - 3’ Forward: 5’- GGTTCCTGCACGAGAAGAACT - 3’ REEP5 qPCR Reverse: 5’- GAGAGAGGCTCCATAACCGAA - 3’ Forward: 5’- CTGTTTGACGTGGTCAGTAAGAT - 3’ RRAGD qPCR Reverse: 5’- GTTGAGTCCTTGTCATACGGG - 3’

Forward: 5’- AGGGAAGCACTACGGTGTGTA - 3’ RXRG qPCR Reverse: 5’- TCCCGACAGGTGTAGATGAGA - 3’ Forward: 5’- TGCTTTTGGCCTCGTCTTCA - 3’ SYT4 qPCR Reverse: 5’- GCGGTTTTACCCTTCACTTCAC - 3’ Forward: 5’- CGGCCCAACAGTTCAACATTA - 3’ SYT13 qPCR Reverse: 5’- GCCGGGGACCATAGATGTC - 3’ Forward: 5’- AAGCCACCTTTCGGATGAGG - 3’ TM4SF4 qPCR Reverse: 5’- CGCAGCAGTCGTTGTTCTG - 3’ Forward: 5’- TCCCACATAACTCCCTCGACA - 3’ TMBIM6 qPCR Reverse: 5’- GTGTGTGACCACATGGACATAG - 3’ Forward: 5’- GTGGCCGAGGTGAGAAACTG - 3’ TMEM64 qPCR Reverse: 5’- TTAAGCACGATGTAGCCCCAA - 3’ Forward: 5’- GGAGCTATAACCCAAATGACACC - 3’ TSPAN13 qPCR Reverse: 5’- AGCATACTCTCCGATTATGGGG - 3’ Forward: 5’- AAGGATATGAGATTGGAGACCGT - 3’ VAT1L qPCR Reverse: 5’- CATCCGGGATCTTGTAGACAAAC - 3’ Forward: 5’- GGTCGGTGTGAACGGATTT - 3’ GAPDH PCR Reverse: 5’- GTGGATGCAGGGATGATGTT - 3’ Forward: 5’- GTGGCCGAGGTGAGAAACTG - 3’ NIPAL1 (Set 1) PCR Reverse: 5’- TTAAGCACGATGTAGCCCCAA - 3’ Forward: 5’- GGAGCTATAACCCAAATGACACC - 3’ NIPAL1 (Set 2) PCR Reverse: 5’- AGCATACTCTCCGATTATGGGG - 3’

Gene Name (Human) Application Sequence Forward: 5’- ATACAATTACCTGCACGACACAT - 3’ APOH qPCR Reverse: 5’- GGCCATCCAGAGAATATCCATCA - 3’ Forward: 5’- CACCAACTGGGACGACAT - 3’ ACTB (Beta-actin) qPCR Reverse: 5’- ACAGCCTGGATAGCAACG - 3’ Forward: 5’- AGGTAGCAGAGCAGCTTATCA - 3’ BEST3 qPCR Reverse: 5’- TGCATTTCGTCCACAGCTAAAA - 3’ Forward: 5’- CTGACCTAAGAAGCTGGACTTT - 3’ C10ORF10 qPCR Reverse: 5’- GGTGCGAGTAGAGTGTTCTG - 3’ Forward: 5’- GCATGATGGCAAGCTGTATTC - 3’ CNTN1 qPCR Reverse: 5’- CGAACCTCCACAACGTATTCT - 3’ Forward: 5’- ATTTGGGGTTTATGACATCGACA - 3’ CPNE3 qPCR Reverse: 5’- GCTGAAATCGTAATGCTCCCTT - 3’ Forward: 5’- TGAACCAAGAGAACCCTCCA - 3’ CYSTM1 qPCR Reverse: 5’- CCCATTGGTTGTGGTGGATAAG - 3’ Forward: 5’- CTCAGGACGACTCACATCAAC - 3’ CYYR1 qPCR Reverse: 5’- CACAGTATTCCATCTCGTGGTC - 3’ Forward: 5’- ACCGAGGTTACACGTTACGAA - 3’ DOCK1 qPCR Reverse: 5’- TCGGAGTGTCGTGGTGACTT - 3’ Forward: 5’- AACCTCTATGTTAGCGGCCTT - 3’ ELAVL4 qPCR Reverse: 5’- TGGACACTCCTGTGACTTGAT - 3’ Forward: 5’- GGAGCAGGTTATGAGAGCACT - 3’ ELMO1 qPCR Reverse: 5’- GGGCGGGACTGGAAATCTTC - 3’ Forward: 5’- CCCTACAAGCACAGCTACAT - 3’ FAM159B qPCR Reverse: 5’- CACTGATGACAAAGGCAAGTAAG - 3’ Forward: 5’- GTCTGTGCTGCATGAGGTGAA - 3’ FITM2 qPCR Reverse: 5’- CCAGATGAAAGTCAGAATGCCC - 3’ Forward: 5’- GGATCAGTTCGACAGCTTAGAC - 3’ FNBP1L qPCR Reverse: 5’- AGGCTACACACGAGGTAAACC - 3’ Forward: 5’- GTCATTTGGATGGTCACTCCTC - 3’ IGSF11 qPCR Reverse: 5’- AATCCTACCCTACCGTGGAAC - 3’

Forward: 5’- GATGACGATTCGCTTTTTCCCA - 3’ KCNA6 qPCR Reverse: 5’- ATGTCCCCGTAACCTACCGT - 3’ Forward: 5’- TCAACCTCTTCGATACAGCAGA - 3’ KCNAB2 qPCR Reverse: 5’- AAACACCACATCCACGTACTC - 3’ Forward: 5’- GAGGAATACGTGCTGACACGCC - 3’ KCNJ11 qPCR Reverse: 5’- GGTGGGCTTGGGCAGTCTTCA - 3’ Forward: 5’- CGCTACACCTCGCGCTATTA - 3’ KCTD12 qPCR Reverse: 5’- CGTACTCGGTGTAGCTGGTC - 3’ Forward: 5’- TCCATGCCCCACAAGAAGAG- 3’ NIPAL1 qPCR Reverse: 5’- ACAGCAAAGGAAATAAACCCTGG - 3’ Forward: 5’- AATTGCCCAAGATGCCGGG - 3’ NIPAL4 qPCR Reverse: 5’- AGCAGCCATGGTGAGAAATCC - 3’ Forward: 5’- ACGGGCAAGGACACTATGG - 3’ NPTX2 qPCR Reverse: 5’- ATTGGACACGTTTGCTCTGAG - 3’ Forward: 5’- AGCACCGTCATCTTGAATGTG - 3’ OAT3 (SLC22A8) qPCR Reverse: 5’- AGGTGTAGCAGTACCCGAGTG - 3’ Forward: 5’- GATGACATTGCCTGGATGAA - 3’ PEPCK (PCK1) qPCR Reverse: 5’- GATTGTGTTCTTCTGGATGGT - 3’ Forward: 5’- AGAAGTCCAATACTACAGCCCT - 3’ PM20D1 qPCR Reverse: 5’- CACCACATCAAAGTGAGCCAT - 3’ Forward: 5’- AAGAACTGCATGACTGACCTTC - 3’ REEP5 qPCR Reverse: 5’- GAGGCTCCATAACCGAACACC - 3’ Forward: 5’- ATGGGATACCCTACACCCAAAT- 3’ SYT4 qPCR Reverse: 5’- TCCCGAGAGAGGAATTAGAACTT - 3’ Forward: 5’- TGACGCTGACCTTGAGGAC - 3’ SYT13 qPCR Reverse: 5’- GGCTCCTTCGCTGAAGTCTT- 3’ Forward: 5’- CAAGGGTCCTAAATGCCTCAT - 3’ TM4SF4 qPCR Reverse: 5’- CTCTCGGCACTTGTTCCATAA - 3’ Forward: 5’- CATATAACCCCGTCAACGCAG - 3’ TMBIM6 qPCR Reverse: 5’- GCAGCCGCCACAAACATAC - 3’ Forward: 5’- TGGGTGGAGAGCCTTGACT - 3’ TMEM64 qPCR Reverse: 5’- GAAGGTGCCGATGAGGACG - 3’ Forward: 5’- GATTTCCAGTCTCCGAGTGGT - 3’ TSPAN13 qPCR Reverse: 5’- GGCTAAACAAGCGCAAGATACAG - 3’ Forward: 5’- AAGGATATGAGATTGGAGACCGT - 3’ VAT1L qPCR Reverse: 5’- CATCCGGGATCTTGTAGACAAAC - 3’ Forward: 5’- CCCTGGTGACTCCCATTTATT - 3’ NIPAL1 PCR Reverse: 5’- GATGTCGTCATCATACCCAGAG - 3’

Figure S1: The proportion of candidates shared between the groups of datasets used for generating the initial pool of candidates in the candidate selection process. Four groups are displayed, with the number of candidates that they contributed to the initial pool shown below. The group of 7 datasets highlighting enriched candidates in α- and β-cells shared 20 and 11 candidates with datasets of differentially expressed candidates under T2D and glucose stimulation, respectively; the latter two datasets also shared 2 candidates between them. One candidate involved in ER stress and folding of proinsulin – ERO1LB – was shared between the 3 groups. The final group highlighting islet- specific eQTL shared 3 genes with the enriched group, but none with the other groups.

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