Abstract
Insulin receptor (Insr) protein can be found at higher levels in pancreatic β-cells than in most other tissues, but the consequences of β-cell insulin resistance remain enigmatic. Ins1cre allele was used to delete Insr specifically in β-cells of both female and male mice which were compared to Ins1cre-containing littermate controls at multiple ages and on multiple diets. RNA-seq of purified recombined β-cells revealed transcriptomic consequences of Insr loss, which differed between female and male mice. Action potential and calcium oscillation frequencies were increased in Insr knockout β-cells from female, but not male mice, wherease only male βInsrKO mice had reduced ATP-coupled oxygen consumption rate and reduced expression of genes involved in ATP synthesis. Female βInsrKO and βInsrHET mice exhibited elevated insulin release in perifusion experiments, during hyperglycemic clamps, and following i.p. glucose challenge. Deletion of Insr did not reduce β-cell area up to 9 months of age, nor did it impair hyperglycemia-induced proliferation. Based on our data, we adapted a mathematical model to include β-cell insulin resistance, which predicted that β-cell Insr knockout would improve glucose tolerance depending on the degree of whole-body insulin resistance. Indeed, glucose tolerance was significantly improved in female βInsrKO and βInsrHET mice when compared to controls at 9, 21 and 39 weeks, and also in insulin-sensitive 4-week old males. We did not observe improved glucose tolerance in older male mice or in high fat diet-fed mice, corroborating the prediction that global insulin resistance obscures the effects of β-cell specific insulin resistance. The propensity for hyperinsulinemia was associated with mildly reduced fasting glucose and increased body weight. We further validated our main in vivo findings using the Ins1-CreERT transgenic line and found that male mice had improved glucose tolerance 4 weeks after tamoxifen-mediated Insr deletion. Collectively, our data show that loss of β-cell Insr alone is sufficient to drive glucose-induced hyperinsulinemia, thereby improving glucose homeostasis in otherwise insulin sensitive dietary and age contexts.
Introduction
Type 2 diabetes is a multifactorial disease. Several cell types, most prominently pancreatic β-cells, are dysfunctional prior to and after diagnosis (Prentki and Nolan, 2006). Hyperinsulinemia, insulin resistance, impaired fasting glucose, and impaired glucose tolerance can all be observed prior to the onset of frank diabetes (Tabak et al., 2012), but the causal relationships between these factors remain incompletely understood (Page and Johnson, 2018). Impaired insulin receptor (Insr) signaling is associated with obesity and often precedes the onset of overt type 2 diabetes, but it has been studied primarily in skeletal muscle, fat, and liver where it manifests differently (Boucher et al., 2014). Recent work in mice has established that β-cell specific insulin resistance can be observed early in the progression towards type 2 diabetes, when hyperinsulinemia is prominent, and independently of insulin resistance in other tissues (Paschen et al., 2019). The physiological consequences of reduced Insr in β-cells remain controversial.
It remains unresolved whether physiological insulin action on β-cells manifests as positive feedback to stimulate further insulin secretion, or negative feedback to inhibit its own release (Leibiger et al., 2008). Human studies provide evidence for both possibilities. In vivo hyperinsulinemic-euglycemic clamps can reduce circulating C-peptide, a marker of endogenous insulin secretion (Cavallo-Perin et al., 1993; Elahi et al., 1982). In some studies, this inhibition was impaired in the obese state suggesting that systemic insulin resistance also extends to the β-cells (Cavallo-Perin et al., 1993). Bouche and colleagues replicated this result at basal glucose, but also found evidence that insulin can potentiate glucose-stimulated insulin secretion under specific conditions (Bouche et al., 2010). Others have shown that insulin can either stimulate or inhibit its own secretion depending on the metabolic context (Mari et al., 2011). Administration of insulin to single β-cells in vitro increases intracellular Ca2+ (Johnson and Misler, 2002b) and, in some studies, stimulates exocytosis (Aspinwall et al., 1999). However, Ca2+ release from intracellular stores is not always sufficient to evoke insulin exocytosis. We previously did not detect robust exocytosis or C-peptide release from human β-cells in response to exogenous insulin despite observed changes in Ca2+ release (Luciani and Johnson, 2005).
Whether chronic changes in autocrine insulin signaling affect β-cell development, survival and adaptation conditions is also controversial. Mice with chronically reduced insulin production have impaired β-cell expansion in the context of a high fat diet (Mehran et al., 2012). In vitro, physiologically relevant concentrations of insulin support the survival of both human and mouse β-cells (Johnson et al., 2006a). We also reported insulin is sufficient to increase proliferation of cultured primary mouse β-cells and that blocking insulin secretion with somatostatin blunts proliferation induced by hyperglycemia (Beith et al., 2008) and that the majority of glucose-dependent changes in gene expression in MIN6 cells are Insr-dependent (Ohsugi et al., 2005). However, hyperglycemia-induced β-cell proliferation has been proposed to bypass Insr (Jhala et al., 2003; Stamateris et al., 2016). Thus, whether these signals from insulin and/or glucose are transmitted through Insr, Igf1r, or both receptors, remains unresolved.
To address the short- and long-term consequences of eliminating Insr signaling in vivo, Kulkarni and colleagues crossed mice with floxed Insr alleles and an Ins2 promoter driven Cre transgene (Kulkarni et al., 1999). Using this and related models, they reported that mice lacking β-cell Insr have profound glucose intolerance and frank diabetes in some cases, due to impaired glucose-stimulated insulin secretion, Glut2 loss, and insufficient β-cell mass (Kulkarni et al., 1999; Okada et al., 2007; Otani et al., 2004). These Insr deficient mice failed to exhibit the compensatory increase in β-cell mass that accompanies a high fat diet (Okada et al., 2007). Doubt was cast on these results when these Cre lines were subsequently shown to have off-target tissue effects owing to endogenous Ins2 expression in the brain and thymus (Fan et al., 2009; Johnson, 2014; Mehran et al., 2012; Wicksteed et al., 2010a). More recently, Wang and colleagues studied the roles of β-cell Insr in utero and in adult mice using an inducible Ins1-CreER transgenic mouse model (Oakie et al., 2020; Wang et al., 2018), but these studies are confounded by the presence of the human growth hormone (hGH) minigene (Oropeza et al., 2015), which necessitates the use of Cre-containing controls exclusively.
In the present study, we primarily used the constitutive Ins1Cre knock-in strain with robust and specific recombination in β-cells (Thorens et al., 2015) to precisely reduce Insr signaling and define its consequences on glucose homeostasis. We validated our findings on glucose homeostasis using an additional β-cell specific model. Using this approach, we find clear evidence that Insr signaling plays a suppressive role on insulin secretion by modulating β-cell electrical excitability and that this effect is absent in conditions of global insulin resistance.
Results
Insr abundance in human islets and β-cells
We initiated our studies by conducting an unbiased analysis of insulin receptor abundance across tissues using publicly accessible data. Pancreatic islets had the 2nd highest protein abundance of both isoforms of the insulin receptor across a panel of 24 human tissues, as quantified by mass-spectrometry (Fig. 1A). These results, which are not complicated by the limitations associated with anti-insr antibodies, show that islets have more INSR protein than ‘classical’ insulin target tissues, including the liver and adipose. This also supports our previous observations suggesting that insulin receptors are more abundant in β-cells relative to other cells in the pancreas (Boothe et al., 2016). Compilation of open source public single-cell RNA sequencing data from human islets demonstrated insulin receptor mRNA in 62.4% β-cells, alongside other islet cell types (Fig. 1A,B).
β-cell specific Insr deletion with Ins1Cremice
We next sought to examine the function of the Insr, and the consequences of β-cell-specific insulin resistance/sensitivity, using an in vivo β-cell specific knockout mouse model. To limit recombination of the floxed Insr allele to pancreatic β-cells, we used a Cre allele knocked into the endogenous Ins1 locus which, unlike Ins2 promoters, drives specific expression in β-cells (Thorens et al., 2015). Experimental Insrf/f;Ins1cre/wt;nTnG (βInsrKO) and Insrf/wt;Ins1cre/wt;nTnG (βInsrHET) mice and littermate control Insrwt/wt;Ins1cre/wt;nTnG mice were generated using a breeding scheme to ensure consistency of the Cre donor parent (Fig. 1C). Cre-recombinase efficiency was assessed using the nuclear TdTomato-to-nuclear EGFP (nTnG) lineage trace reporter (Prigge et al., 2013) and found to be robust on the Insrf/f;Ins1cre/wt;nTnG genetic background (Fig 1D). We confirmed by Western blotting that Insr protein was almost completely absent from βInsrKO islets and partially reduced from βInsrHET (Fig. 1E). qPCR showed that Insr mRNA was not decreased in any of the 18 tissues examined, including the whole brain or hypothalamus specifically, hence we did not perform Western blots for other tissues (Fig. 1F). Together with other published data on Ins1cre mice, these findings strongly suggest that Insr deletion with Ins1cre is robust and sufficiently specific to pancreatic β-cells.
Loss of β-cell Insr alters gene expression in purified β-cells
To establish a baseline gene expression profile of our β-cell specific Insr knockout model, we performed an unbiased analysis of gene expression in ∼100 FACS purified GFP-positive β-cells labelled with the nTnG reporter isolated from both female and male mice (Fig. 2A). We found that Insr mRNA expression is similar female and male β-cells (Fig. 2B). We confirmed a similar reduction in Insr in female and male βInsrKO β-cells, with an intermediate phenotype in found in the βInsrHET β-cells (Fig. 2B). We did not observe a compensatory change in Igf1r mRNA expression (Fig. 2C). After excluding samples with insufficient β-cell purity and analyzing across both sexes, RNA sequencing revealed significant differences in the expression of 12 genes between βInsrKO β-cells and wildtype β-cells (Fig. 2C,D, S1). However, when we analyzed female and male βInsrKO β-cells separately, we identified sex-specific gene expression changes (Fig. 2D-F). In female-only analysis, 5 genes (including 2 pseudo-genes) were differentially expressed, while in male-only analysis, 64 genes were differentially expressed with an adjusted p value of > 0.05. At this cut-off, there was no overlap between the sex-specific gene expression patterns. Although our study was not designed or powered for direct comparisons between sexes, these highlight the importance of considering both sexes separately.
Loss of β-cell Insr increases β-cell excitability
Insulin administration has been reported to open β-cell KATP channels, mediating negative feedback on insulin secretion (Khan et al., 2001). Lack of this endogenous insulin action through Insr would therefore be expected to lead to β-cell hyper-excitability in our mouse model in the context of high glucose. Indeed, electrophysiological analysis of single β-cells from female βInsrKO mice confirmed a significant increase in action potential firing frequency during glucose stimulation, when compared to control β-cells, with no differences in resting potential, firing threshold, or action potential height (Fig. 3A,B). The reversal potential was right-shifted in βInsrKO β-cells, further suggesting reduced K+ conductance (Fig. 3C). Hyper-excitability was not observed in Insr knockout β-cells from male mice, at the age we studied (Fig. 3D). We did not observe a statistically significant difference in depolarization induced exocytosis in single cells from either sex (Fig. 3E,F), suggesting that the late stages of insulin granule exocytosis are not altered under these conditions.
Next, we analyzed Ca2+ responses to 15 mM glucose in thousands of Fura-2-loaded dispersed islet cells and analyzed the data with an adaptation of our TraceCluster algorithm (Wills et al., 2016). In agreement with the electrophysiological data, Insr knockout β-cells from female mice exhibited a significantly greater number of oscillation peaks within the glucose stimulation period compared to control cells (Fig. 3G,H, S2A). A similar increase in excitability was observed in βInsrHET β-cells. This was not associated with significant differences in the intensity or gross localization of Glut2 protein using immunofluorescence imaging (Fig. S2B).
We examined mitochondrial function using the Seahorse bioanalyzer in the context of 10 mM glucose. In dispersed islet cells isolated from female mice, there were no significant differences between genotypes in oxygen consumption rate, with the exception of a reduced proton leak (Fig. 3I). In contrast, dispersed islet cells from both βInsrKO and βInsrHET males had a significant reduction in glucose-stimulated oxygen consumption rate compared to controls. Oligomycin injection revealed that this included a decrease in ATP-linked respiration, suggesting reduced ATP production in islet cells from male mice lacking Insr (Fig. 3J). Notably, RNA sequencing showed that male, but not female, βInsrKO had significantly decreased expression of many key mitochondria-related genes (e.g. Bckdk, Miga1, Park7, Me3), including 4 components of the NADH:ubiquinone oxidoreductase complex (complex 1 of the electron transport chain; Ndufb7, Ndufs6, Ndufb6, Ndufa2), 2 components of ubiquinol-cytochrome c reductase complex (electron transport chain complex 3; Uqcr11, Uqcrb), and 2 components of ATP synthase (Atp5g1, Atp5md) (Fig. 2D,F). Male-specific transcriptomic consequences of Insr loss could account for the sex difference in mitochondrial function that would be expected to impact ATP-dependent membrane potential and Ca2+ oscillations. Collectively, these experiments demonstrate that β-cells lacking Insr have increased electrical activity, so long as their mitochondrial ATP production is not impaired. Overall, our data support the concept that insulin normally has a negative feedback influence on excitability in the context of 10 mM glucose.
Loss of β-cell Insr causes insulin hypersecretion in the context of stimulatory glucose
Insulin secretion is driven by electrical excitability, so we next carefully examined the effects of partial and full β-cell Insr deletion on secretory function employing multiple orthogonal in vitro and in vivo assays. We used islet perifusion to examine the dynamics of insulin secretion ex vivo at rest (3 mM glucose) and in response to 20mM glucose or 10mM glucose, as well as direct depolarization with 30mM KCl. Islets from female 16 week-old βInsrKO and βInsrHET mice secreted more insulin in response to 20 mM glucose and 30 mM KCl compared to islets from control mice (Fig. 4A). No significant differences were observed at low glucose (Fig. 4A). Consistent with our electrophysiology data, we did not observe differences in islets from males of the same age and on the same diet (Fig. 4B).
This potentiation of high glucose-stimulated insulin secretion ex vivo in the complete and partial Insr knockout β-cells, led us to examine how insulin levels were affected by glucose stimulation in vivo using the hyperglycemic clamp technique in awake mice. For this cohort of mice, there were no significant differences in body mass (control 20.5 +/- 0.5g n=8 vs βInsrKO 20.8 +/- 0.4g n=10), lean mass (control 17.5 +/- 0.4g vs βInsrKO 17.3 +/- 0.2g), or fat mass (control 1.8 +/- 0.3g vs βInsrKO 2.2 +/- 0.2g). Glucose infusion rates were adjusted in order to reach hyperglycemic levels (∼19 mM) in βInsrKO and wild type control mice. Interestingly, slightly higher glucose infusion rates were necessary in female βInsrKO mice in comparison to control mice in order to reach similar hyperglycemic levels (Fig. 4C). In accordance with our ex vivo insulin secretion data, glucose-stimulated insulin secretion was higher in female, but not in male βInsrKO mice compared with control mice (Fig. 4C-H). We further tested whether in vivo insulin secretion would be potentiated after a single bolus of glucose in mice with reduced β-cell Insr. At 11 weeks of age, plasma insulin response, relative to baseline, was significantly elevated 30 min after i.p. injection of 2g glucose/kg body mass in female, but not in male, βInsrKO mice compared to controls (Fig. 4I, J). In accordance with the electrophysiology, Ca2+ oscillation, and islet perifusion data, we detected no statistical difference between female genotypes in fasting plasma insulin in vivo at multiple ages (Fig. S3). Together, these experiments suggest that β-cell Insr can play suppressive role in glucose-stimulated insulin secretion without much impact on basal insulin secretion.
Effects of β-cell Insr loss on insulin production, storage, processing and clearance
Insulin and insulin signaling can modulate protein synthesis in many cell types and we have previously provided evidence that soluble cellular insulin protein transiently increases in human islet cell cultures treated with exogenous insulin (Luciani and Johnson, 2005). To assess the quantitative contribution of Insr signaling to insulin production and mRNA translation rates we measured total islet insulin content after acid-ethanol extraction and examined total protein synthesis rate using S35-methoioine/cysteine pulse labelling in isolated islets. Total insulin content and protein synthesis in isolated islets were unaffected by Insr deletion under these basal glucose conditions (Fig. 4K-N).
To investigate the role of Insr signaling on β-cell stress and insulin clearance, we conducted analysis of plasma proinsulin to C-peptide ratios, and C-peptide to insulin ratios in the fasting state (4h) across multiple ages in both male and female mice, and on multiple diets (Fig. S4). While many of these parameters changed with age, no statistical differences between genotypes were seen in any of these parameters of mice fed either a low-fat diet (LFD) or a high fat diet (HFD). A trend towards lower insulin clearance was observed in LFD-fed female βInsrKO mice in comparison to wild type control mice at 7 weeks. Collectively, these experiments show that β-cell insulin receptor signaling has only a small, if any, effect on insulin processing and clearance at baseline glucose conditions.
Beta-cell area and hyperglycemia-induced proliferation in mice lacking β-cell Insr
Our RNA sequencing data revealed pathways downstream of Insr signaling that may affect β-cell proliferation capacity (e.g. Raf1)(Beith et al., 2008), specifically in male mice (Fig. 2F). Thus, we examined baseline β-cell area and proliferation reserve capacity. Islet architecture and β-cell-to-α-cell ratio were not obviously perturbed (Fig. 5A). We did not detect significant differences associated with genotype in β-cell area in either female or male mice, at 13, 42 weeks (LFD) or 54 weeks (HFD) of age (Fig. 5B-E). In comparison to control mice, HFD fed female βInsrKO mice had a tendency toward a smaller β-cell area at 54 weeks of age that were consistent with tendencies towards lower plasma insulin (Fig. 5D, S3C), proinsulin and C-peptide levels (Fig. S4C). These data suggest that Insr may help support age-dependent β-cell expansion under some conditions.
Prolonged hyperglycemia can stimulate β-cell proliferation in adult mouse β-cells (Sharma et al., 2015), but whether this requires intact insulin receptor signaling remains controversial. To examine the role of Insr-mediated signaling on hyperglycemia-induced β-cell proliferation, we performed 4-day hyperglycemic infusions in βInsrKO and wild type control mice. In female mice, hyperglycemia (>10 mM; Fig. 5F) resulted in mildly elevated insulin secretion in βInsrKO relative to control mice for the initial 48 h, which was not sustained for the duration of the experiment (Fig. 5G,H), while glucagon levels declined similarly in both genotypes (Fig. 5I,J). There was no effect of Insr deletion on hyperglycemia-induced proliferation of either β-cells or α-cells in females (Fig. 5K,L). In male mice, 96 h of hyperglycemia resulted in sustained hyperinsulinemia in βInsrKO mice (Fig. 5M-O), with no differences in circulating glucagon (Fig. 5P,Q). In male mice lacking β-cell Insr, this manipulation was associated with significantly more β-cell proliferation (Fig. 5R,S). The fact that we did not observe a suppression of glucose-induced proliferation of β-cells lacking Insr prompted us to determine the degree to which the, broadly defined, insulin signaling pathway was inhibited in our model. Indeed, glucose-induced Akt phosphorylation, shown by western blot of whole islet lysate, was statistically unaffected, and glucose-induced Erk phosphorylation was only reduced ∼50% in Insr knockout β-cells (Fig. 5T). It is likely that the Igf1r or another receptor tyrosine kinase or a receptor tyrosine-kinase-independent mechanism initiates parts of intracellular post-receptor ‘insulin signaling’ in the absence of Insr. Testing this hypothesis in the future will require truly β-cell specific double deletion of Insr and Igf1r, or additional receptors.
Modelling contributions of peripheral and β-cell specific insulin sensitivity to glucose homeostasis
The continuum between insulin sensitivity and resistance impacts multiple tissues, including pancreatic β-cells. We observed that β-cell-specific insulin resistance resulted in insulin hypersecretion in the context of unchanged β-cell mass. We next used mathematical modelling to generate quantitative predictions of the dependence of glucose tolerance on both β-cell and whole-body insulin resistance, both independently and synchronously. As described in the methods section, we modified the Topp model (Topp et al., 2000), adding insulin receptor-mediated negative feedback on insulin secretion, as indicated by our experimental data, with Sβ serving as the β-cell Insr-specific insulin sensitivity parameter (see equations in Fig. 6A,S5; for βInsrKO mice, Sβ = 0). Peripheral insulin sensitivity is represented by SP, (SI in the original Topp model). We used our hyperglycemic clamp data (Figs. 4C-E, S5A,B) to estimate Sβ in both female and male control mice and found Sβ,female to be significantly different from zero (Sβ,female = 3.4 +/- 1.5 nM-1, p = 10−25) and significantly different from Sβ,male (p = 10−24). Sβ,male was not significantly different from zero (Sβ,male = -0.05 +/- 1.0 nM-1, p = 0.7). In silico glucose tolerance tests found that decreased β-cell insulin-sensitivity (Sβ) (similar to βInsrKO mice) corresponded with elevated peak and plateau insulin secretion (Fig. 6B). As expected, the computations predicted more rapid clearance of blood glucose (Fig 6C). Analysis of areas under the curve for glucose and insulin resulting from in silico glucose tolerance tests while varying SP and Sβ indicated that β-cell insulin resistance should have a marked effect on insulin secretion and glucose tolerance, most dramatically in conditions of low peripheral insulin sensitivity. Next, we compared the predictions of this model with experimental results. We used the in silico AUCGlucose values as a function of both SP and Sβ combined with averaged experimental AUGC values to estimate SP as a function of age for the low-fat diet conditions (Fig. S5C,D). We found that male values of SP for wildtype and mutant were indistinguishable from each other while females showed significant differences from each other and from the male values at all ages. As expected, HFD led to reduced peripheral insulin sensitivity (Fig. S6). Collectively, these simulations show how β-cell insulin sensitivity and peripheral insulin sensitivity may combine to the regulation of glucose tolerance.
Condition-dependent improved glucose tolerance with reduced β-cell Insr signaling
To test our theoretical model experimentally, we examined glucose tolerance in female and male βInsrKO, βInsrHET, and control littermates at multiple ages between 4 and 52 weeks in the context of two diets. Significant improvements in glucose tolerance were observed in female mice with reduced Insr signaling at multiple ages, and in young males (Fig. 6F). Consistent with our mathematical modelling that suggested a diminished contribution of β-cell insulin resistance to glucose homeostasis in the context of greater whole-body insulin resistance, we did not observe significant effects of genotype in older male mice, or mice of either sex fed an insulin-resistance-inducing HFD, in contrast to models with both β-cell and brain Insr knockout (Kulkarni et al., 1999; Okada et al., 2007; Otani et al., 2004). Thus, Insr deletion specifically in β cells had little consequence on glucose tolerance in mice with already impaired pan-tissue insulin resistance, which we and others have shown increases with age and is more pronounced in male mice (Fig. S6).
The Ins1cre allele results in pre-natal gene deletion (Thorens et al., 2015). To assess the effects of Insr deletion in adult β-cells, and to determine the role of Insr on a different genetic background and under different housing conditions, we also phenotyped multiple cohorts of male mice in which the Insrf/f allele was recombined by the Ins1 promoter-driven CreERT transgenic allele (commonly known as MIP-Cre) after injection with tamoxifen. In agreement with our observations in mice with constitutive loss of β-cell Insr, we found that glucose tolerance was significantly improved 4 weeks after β-cell-specific Insr deletion in male mice (Fig. 6G). These differences were not maintained as the mice aged and became more insulin resistant. In these mice, there were no significant differences observed in fasting glucose (control 4.8 +/- 0.3mM n=7 vs βInsrKO 4.4 +/- 0.2mM n=10), β-cell mass (control 1.4 +/- 0.3% n=3 vs βInsrKO 2.1 +/- 0.2% n=5), or body mass (control 26.1 +/- 1.1g n=7 vs βInsrKO 23.1 +/- 0.7g n=17. Collectively, these observations using an independent model and independent housing conditions lend support to our conclusion that the initial consequence of β-cell specific Insr deletion is improved glucose tolerance. This experiment also demonstrates that the role of Insr in β-cell function is not sex specific.
Peripheral effects of β-cell specific Insr loss
We and others have shown that even modest differences in hyperinsulinemia can have profound consequences for insulin sensitivity, adiposity, fatty liver, longevity and cancer (Mehran et al., 2012; Templeman et al., 2017; Zhang et al., 2019). Thus, we asked how the context-dependent glucose-stimulated insulin hyper-secretion induced by targeted β-cell specific insulin resistance may affect insulin sensitivity, adiposity, and body mass over time. Insulin sensitivity was assessed at multiple ages in the same mice. Interestingly, insulin sensitivity was significantly improved in 10-week-old female βInsrHET mice compared to littermate controls without Insr deletion (Fig. S6A). On a high fat diet, male βInsrKO and βInsrHET mice had significantly improved insulin sensitivity compared to controls at 22 weeks of age (Fig. S6D). Longitudinal tracking of 4-h fasting glucose identified relative hypoglycemia in young LFD female βInsrKO and βInsrHET mice, older LDF male βInsrKO and βInsrHET mice, and across the tested ages in HDF female mice (Fig. 7A-D). Longitudinal tracking of body weight revealed that female mice with reduced β-cell Insr consistently weighed more than controls when fed a HFD (Fig. 7E-H), consistent with the known role of hyperinsulinemia in diet-induced obesity (Mehran et al., 2012; Templeman et al., 2015). We also examined the mass of several tissues at 13 weeks of age. Interestingly, liver mass was lower in both female and male mice lacking β-cell Insr (Fig. 7I,J). Pilot experiments suggested that liver Insr protein abundance may have been reduced in mice with partially or completely reduced β-cell Insr, in the context of the LFD but not the HFD (Fig. 7K). These data are consistent with the concept that modest hyperinsulinemia can drive down Insr levels and the concept that insulin signaling is a trophic signal for liver. Together, these data demonstrate that specifically preventing autocrine insulin feedback can have systemic effects on insulin sensitivity, body mass, and the size of some tissues. These changes may affect the eventual susceptibility to type 2 diabetes.
Discussion
The goal of the present study was to establish the role of β-cell Insr on glucose homeostasis using specific genetic loss-of-function tools. We found that in vivo Insr gene deletion potentiated glucose-stimulated insulin secretion by increasing action potential firing and Ca2+ oscillation frequency, leading to improved glucose tolerance in insulin sensitive animals. Our data therefore suggest a model in which insulin inhibits its own secretion in a context-dependent manner and that this local negative feedback loop has physiological consequences for glucose tolerance.
Autocrine signaling in endocrine cells is generally a negative feedback (Norman and Henry, 2014), with a few exceptions in specific conditions (Ma et al., 2005). Given the abundance of Insr protein in islets and the physiological modulation of both insulin and Insr signaling in health and disease, autocrine and paracrine insulin signaling have been topics of interest and controversy for decades (Braun et al., 2012; Leibiger et al., 2008). While some have questioned whether local insulin levels are sufficient for signaling within the islet, mathematical modelling of insulin hexamer dissolution estimated that monomeric insulin within islets is in the picomolar range (Wang et al., 2013), similar to the dose that maximally activates β-cell insulin signaling pathways (Alejandro et al., 2010; Beith et al., 2008). Consistent with a narrow range of responsiveness, our results also show that the contribution of autocrine insulin feedback to glucose homeostasis depends on whether mice are on a diet or at an age where insulin resistance is high, and potentially saturated in β-cells. Background genetics, diet, housing conditions, microbiome, or glucose concentrations could contribute to differences in observed phenotypes between our β-cell specific Insr knockout models and the frank diabetes reported for transgenic models that use fragments of the Ins2 or Pdx1 promoters to drive Cre-mediated β-cell Insr deletion (Kulkarni et al., 1999; Okada et al., 2007; Otani et al., 2004). However, we believe that the discrepancy is more likely due to depletion of Insr in key neuronal populations since both of Ins2 and Pdx1 are expressed in brain regions that influence glucose homeostasis (Vogt and Bruning, 2013; Wicksteed et al., 2010a). For example, Insr deletion in the brain with Nestin Cre causes insulin resistance (Plum et al., 2005) and impairs the sympathoadrenal response to hypoglycemia (Fisher et al., 2005), while Insr knockout using AgRP Cre results in abnormal suppression of hepatic glucose production (Konner et al., 2007). Our βInsrKO mouse line is the most tissue specific model used to date for the study of autocrine insulin signaling and β-cell insulin resistance.
Using genetic and genomic tools, our work complements previous studies in humans and animal models. Ex vivo studies of perfused canine pancreata found an inhibitory autocrine effect of insulin (Iversen and Miles, 1971). Similarly, exogenous insulin perfusion of canine pancreas in situ was shown to lead to reduced endogenous insulin production (Rappaport et al., 1972). In vivo insulin infusion rapidly suppressed C-peptide levels in healthy men, but not those with obesity and presumably global insulin resistance (Cavallo-Perin et al., 1993). In isolated human islets, perifusion studies showed that treatment with physiological doses of insulin had no effect on C-peptide release (Johnson and Misler, 2002a), while static incubation experiments found only moderate potentiation of glucose-stimulated insulin secretion with super-physiological levels of insulin (Braun et al., 2012). Our conclusions are in line with the recent work of Paschen et al reporting that a high fat, high sucrose diet induced tissue-selective insulin resistance in β-cells, as well as profound hyperinsulinemia and β-cell hyper-excitability (Paschen et al., 2019). Our mechanistic finding that Insr knockout β-cells have increased action potential firing frequency is consistent with previous observations that insulin directly increased KATP currents via PI3-kinase signaling (Khan et al., 2001) and that PI3-kinase inhibition with wortmannin potentiates glucose stimulated insulin secretion in normal, but not T2D, human islets (Kolic et al., 2016). In the islets from 16-week-old male Insr knockout mice, it seems likely that the significant reduction in ATP-production and differences in gene expression undercut this effect. Collectively, our studies further illustrate the molecular mechanisms of negative autocrine feedback in β-cells during high glucose stimulation.
Beyond the potentiation of glucose-stimulated insulin secretion, transcriptomic analysis of fully and partially Insr-deficient β-cells revealed a re-wiring of signaling pathways that could change how these cells respond to stresses (Fig. S1). For example, increased expression of the dual-specificity phosphatase Dusp26 βInsrKO cells is expected to modulate Erk signaling downstream of Insr. Dusp26 has been implicated as a negative regulator of β-cell identity and survival (Gomez-Banoy et al., 2019). Increased Ankrd28, a regulatory subunit of protein phosphatase 6 (Stefansson et al., 2008), is expected to modulate inflammatory signaling. Increased ceramide synthase 4 (Cers4) would affect sphingolipid signaling and has shown to mediate glucolipotoxicity in INS-1 β-cells (Veret et al., 2011). Increased mitochondrial glutaminase (Gls) would affect local glutamate synthesis, potentially affecting insulin secretion (Li et al., 2003). Increased Wls would support Wnt protein secretion. On the other hand, decreased branched-chain alpha-ketoacid dehydrogenase (Bckdk) would be expected to lower cellular levels of branched-chain amino acids. The consequences of decreased Crybb3 are not clear in this context.
Our transcriptomic analysis pointed to robust sex dependent consequences of β-cell specific Insr deletion. Specifically in female βInsrKO cells, we found increased expression of Ror1, a receptor tyrosine kinase implicated as a scaffold protein for caveolae-dependent endocytosis (Yamaguchi et al., 2019), which mediates Insr internalization and survival in β cells (Boothe et al., 2016), an inhibitory scaffold of Rho GTPases (Rtkn), and the calcium-dependent adhesion protein (Pcdha2). The transcriptomic changes in male βInsrKO cells were broad-based and included down-regulation of key genes controlling mitochondrial metabolism. There was also decreased Hsbp1, which links insulin signaling to longevity in C elegans (Chiang et al., 2012). The transferrin receptor (Tfrc) has been linked to insulin sensitivity and type 2 diabetes (Fernandez-Real et al., 2010; Jiang et al., 2014; Moreno-Navarrete et al., 2014). Male βInsrKO cells also had increased Rasa1, which is a suppressor of Ras. Male βInsrKO cells exhibited increased expression of signalling genes with known roles in β-cell function. Using MIN6 cells and primary β-cells from male mice, we previously showed that Raf1 mediates many of insulin’s effects on β-cell survival, proliferation and β-cell function (Alejandro and Johnson, 2008; Alejandro et al., 2010; Alejandro et al., 2011; Beith et al., 2008). Protein kinase C alpha (Prkca) is a direct upstream regulator of Raf1 and may also stimulate the β-cell expression of incretin receptors such as Gipr (Xu et al., 2007). Robo2 plays important roles in β-cell survival (Yang et al., 2013) and islet architecture (Adams et al., 2018). Male βInsrKO cells also had up-regulation of pancreatic polypeptide (Ppy), but whether this reflects a change in β-cell differentiation status or the purity of sorted cells remains is unclear. Additional studies, beyond the scope of this investigation, will be required to assess the contribution of these differentially expressed genes towards the phenotype of female and male βInsrKO mice.
The conditions under which these gene expression changes could manifest in long-term effects on β-cell proliferation or increased β-cell survival remain incompletely explored. Otani et al reported modestly reduced β-cell mass in non-diabetic Ins2-Cre transgenic Insr knockouts, which was exacerbated by diabetes, but they did not employ Cre controls (Otani et al., 2004). Okada et al reported impaired compensatory β-cell proliferation in the context of high fat diet or liver Insr knockout in the same experimental groups (Okada et al., 2007). These previous studies comparing β-cell knockout mice of Insr versus Igf1r, suggested a more important role for the former in β-cell proliferation and survival (Okada et al., 2007). Insr over-expression experiments also support the idea that β-cells are key insulin targets (Okamoto et al., 2004). In the present study, we were unable to identify conditions which would result in statistically significant differences in β-cell mass in mice with Insr deficiency, but variability was high and relatively few animals were studied at older ages. We have previously shown that the increase in β-cell mass resulting from high fat diet requires local insulin production and is independent of hyperglycemia (Mehran et al., 2012), consistent with the known direct anti-apoptotic and Raf1-dependent mitogenic effects of insulin in vitro (Beith et al., 2008; Johnson et al., 2006a; Muller et al., 2006). These findings can be reconciled by proposing that high insulin concentrations within the islet are sufficient to activate remaining Igf1 receptors linked to Raf1-biased post-receptor signaling. We observed that sustained hyperglycemia and hyperinsulinemia over 4 days was associated with increased proliferation, but whether these are effects are mediated through Igf1r function compensation will require double receptor knockout experiments. The recent identification of a negative modulator of both Insr/Igf1r action in β-cells with profound effects on glucose homeostasis, supports the concept that double Insr/Igf1r inhibition inhibits β-cell proliferation (Ansarullah et al., 2021). It is also possible that hyperglycemia itself is a major driver of β-cell proliferation under these conditions, through Irs2-Creb signaling that may bypass Insr/Igf1r (Jhala et al., 2003). It has also been demonstrated that ∼80% of the gene expression changes attributed to glucose in MIN6 cells require full insulin receptor expression (Ohsugi et al., 2005). We have previously found that glucose cannot stimulate primary mouse β-cell proliferation when autocrine insulin signaling is blocked by somatostatin and that ‘glucose-induced’ Erk phosphorylation requires full insulin secretion (Alejandro et al., 2010; Beith et al., 2008). On the other hand, inhibiting Insr in mouse islets with S961 or shRNA did not block glucose-induced proliferation of cultured β-cells (Stamateris et al., 2016). Additional future studies will be required to resolve this controversy.
Early hyperinsulinemia is a feature of β-cell Insr knockout models on multiple genetic backgrounds (Kulkarni et al., 1999; Okada et al., 2007; Otani et al., 2004), including the present study. Loss of Irs1 or Akt function results in basal hyperinsulinemia and, in some cases, increased β-cell mass (Bernal-Mizrachi et al., 2004; Kido et al., 2000), mimicking the early stages in human diabetes. Our experiments begin to shed light on the systemic consequences of the hyperinsulinemia caused by β-cell-specific insulin resistance, which may be an early event in the pathogenesis of type 2 diabetes. Human data suggests that β-cell insulin resistance can be found in the obese state prior to hyperglycemia (Cavallo-Perin et al., 1993). We and others have shown that hyperinsulinemia contributes to insulin resistance and obesity (Mehran et al., 2012; Templeman et al., 2015), through multiple mechanisms including the down-regulation of insulin receptors (Knutson et al., 1983). We observed propensity for excessive diet-induced body mass gain in mice with β-cell specific insulin resistance, as well as Insr protein down-regulation in the liver. Thus, β-cell defects such as impaired autocrine feedback through Insr may contribute to insulin hypersecretion and accelerate the early stages of type 2 diabetes (Esser et al., 2020). In the later stages, the lack of pro-survival insulin signaling, perhaps in combination with other molecular defects, may contribute to failures in β-cell compensation and survival, thereby further accelerating the course of the disease (Gunton et al., 2005; Kulkarni et al., 1999; Ohsugi et al., 2005; Okada et al., 2007; Otani et al., 2003).
Our studies illustrate the power of using both females and males to study integrated physiology, although it was not designed to test specifically for sex differences. Indeed, β-cell Insr loss led to increased β-cell action potentials, calcium oscillations, and glucose-stimulated insulin secretion in 16-week-old female mice, but not in age-matched males. No intrinsic sex differences of Insr mRNA levels were in a transcriptomic analysis of sorted β-cells (Stancill et al, 2019) and we confirmed this in our analysis of sorted β-cells (Fig. 2B). Given that we found identical Insr reduction in both sexes of βInsrKO mice, the sex differences in glucose-stimulated action potential firing rate, Ca oscillations, and insulin secretion must come from the sex-specific downstream transcriptomic consequences we identified (Fig. 2D-F). Of particular note, we identified a group of genes critical for mitochondrial ATP generation that were only significantly reduced in male β-cells, consistent with reduced ATP-coupled oxygen consumption at 10 mM glucose we measured in male, but not female, islets (Fig. 3K). This is likely to account for a least some of the observed sex difference. It is also possible that control male, but not female, β-cells were already maximally insulin resistant when studied. Given the abundance of data showing more pronounced insulin resistance in males (Parks et al., 2015; Yki-Jarvinen, 1984), this could be another reason for female-specific response to β-cell Insr knockout. To this point, glucose tolerance is improved in 4-week-old βInsrKO males, an age at which control males remain insulin-sensitive, and in male mice with acute loss of Insr. More work will be needed to confirm this possibility, and to determine factors in addition to sex hormones and sex chromosomes that impact these sex differences in insulin sensitivity and glucose homeostasis. The sex-specific nature of phenotypes arising from our genetic manipulation of Insr in β-cells highlights the importance of including both sexes to accurately interpret data and to draw conclusions that will apply to both sexes.
While our study is comprehensive and employs the best genetic tools available today, this work has limitations. Ins1Cre is the most β cell-specific Cre deletion strain available today (Thorens et al., 2015), but this fact does not preclude off-tissue effects that have yet to be discovered. Cre recombinase itself is not totally benign. These facts and our detailed comparison of Ins1wt/wt mice with Ins1Cre/wt mice (Figs. S9-12), highlight the importance of the Cre control group we employed throughout our studies. Recently, it was reported that some colonies of Ins1cre mice exhibited some silencing via DNA hypermethylation at the Ins1 locus and this was suggested as an explanation for discordance between the phenotypes compared to gene deletions using Pdx1-Cre and Ins2-Cre transgenic lines (Mosleh et al., 2020). In our study, we observed virtually complete recombination in β-cells and no evidence for off-tissue Insr deletion. We believe a major source of discrepancy with previously reported phenotypes stems from the propensity of previous promoter transgenic strains to recombine in the brain and robust expression of Insr throughout the brain. Another caveat of our experiments using βInsrKO mice is that Insr is expected to be deleted from β-cells starting in late fetal development (Thorens et al., 2015). In our hands, tamoxifen-inducible Ins1CreERT mice have insufficient β-cell specific recombination for in vivo physiological studies. Our validation experiments using the tamoxifen-inducible Ins1-CreERT address this limitation and confirm that β-cell insulin resistance improves glucose tolerance, at least under the initial insulin sensitive conditions. It should also be noted that, because we show that long-term deletion of Insr results in profound re-wiring of the β-cell transcriptome, the physiological changes can be the result of either direct or indirect action of Insr signaling. It should also be emphasized that, while we have deleted Insr, insulin can signal through Igf1r and Igf2r, especially at the higher concentrations predicted in the pancreas. A further caveat is that the molecular mechanisms involved in insulin secretion may be different in mouse and human β-cells (Rorsman and Ashcroft, 2018), although we note that direction of effect we surmise agrees with the majority of human and canine studies, indicating general agreement across species (Cavallo-Perin et al., 1993; Elahi et al., 1982; Iversen and Miles, 1971; Rappaport et al., 1972).
In conclusion, our work demonstrates a key modulatory role for autocrine insulin negative feedback and the lack thereof (i.e. β-cell resistance) in insulin secretion, glucose homeostasis and body mass, which depend on the physiological context studied. We hope our studies resolved longstanding and controversial questions about the local effect of insulin on β-cells, and lead to experimental and theoretical studies that incorporate Insr-mediated signaling in other islet cell types.
Materials and Methods
Bioinformatics
Human tissue-level proteome and transcriptome were downloaded from the ProteomicsDB resource (https://www.proteomicsdb.org)(Schmidt et al., 2018) in 2019 and sorted by relative protein abundance in Microsoft Excel. Publicly available human islet scRNAseq data were acquired from the panc8.SeuratData package and the SCTransform pipeline was followed to integrate the studies (Hafemeister and Satija, 2019). Expression data were normalized using the Seurat::NormalizeData function with default parameters and visualized using the Seurat::RidgePlot and Seurat::UMAPPlot functions, all from the Seurat package in R (Stuart et al., 2019).
Mouse model and husbandry
All animal protocols were approved by the University of British Columbia Animal Care Committee (Protocol A16-0022), the Institutional Animal Care and Use Committee of the University of Massachusetts Medical School (A-1991-17) and the University of Michigan Institutional Animal Care and use Committee, in accordance with national and international guidelines. Mice were housed at room temperature on a 12/12 light dark cycle at the UBC Modified Barrier Facility, unless otherwise indicated. Whenever possible, we used both female and male mice for experiments, and at multiple ages.
However, we could not always conduct the studies on both sexes side-by-side under the same conditions, and in some cases the group sizes are un-even between sexes and age-groups. Thus, although we can make confident statistically-backed conclusions about the role of Insr in both female and male mice, at the specific ages studied, our study was not designed to explicitly compare sex or age as biological variables.
Ins1cre mice were gifted to us by Jorge Ferrer (Thorens et al., 2015) (now commercially available, Jax #026801). The Insrf/f allele (#006955) and the nuclear TdTomato-to-nuclear EGFP (nTnG) lineage tracing allele (Prigge et al., 2013) were obtained from Jax (#023035) (Bar Harbor, ME). We generated two parental strains to avoid Cre effects during pregnancy; Ins1cre/wt;Insrf/wt male mice and Ins1cre/wt;nTnG/+ female mice. These two parental strains were crossed in order to generate full littermate insulin receptor knockout Insrf/f;Ins1cre/wt;nTnG/- (βInsrKO) mice, partial insulin receptor knockout Insrf/wt;Ins1cre/wt;nTnG/- mice mice (βInsrHET), and their control groups Insrwt/wt;Ins1cre/wt;nTnG/- mice (3 alleles of insulin) and Insrf/f;Ins1wt/wt;nTnG/- mice (4 alleles of insulin). Insrf, Ins1cre, and nTnG genotyping were done in accordance with Jax’s recommendations using a ProFlex PCR system (Thermo Fisher Scientific, Canada). NNT genotyping was done as described previously (Nicholson et al., 2010). Master mix for genotyping included 0.5μM primers (Integrated DNA technologies), 2mM dNTPs (New England Biolabs, #N0447S), 0.5U DreamTaq DNA polymerase (Fisher Scientific, #FEREP0702). Agarose gels varied from 1-2.5% (FroggaBio, #A87-500G).
In our studies, mice were fed 1 of 3 diets: either a chow diet (PicoLab Mouse Diet 20-5058); a low-fat diet (LFD; Research Diets D12450B) containing 20% of kcal protein, 10% of kcal fat, and 70% of kcal carbohydrate including 35% sucrose, or; a high-fat diet (HFD; Research Diets D12492) containing 20% of kcal protein, 60% of kcal fat, and 20% of kcal carbohydrate including 10% sucrose.
MIPcre mice were generously obtained from Dr Dempsey (Wicksteed et al., 2010b). The CAG-YFP reporter transgenic animals were from Jax (#011107). All animals were males that were intraperitoneally injected at 8 weeks of age for 5 consecutive days with tamoxifen (Sigma, T5648) freshly dissolved in corn oil (Sigma, C8267) with 3 injections at 200 mg/kg over a 1-week period.
Comparison of control genotypes
Before conducting our main study, we did a pilot experiment to determine whether the Ins1cre knock-in mice had any phenotype on their own under both low fat and high fat diets, and we tracked both ‘control’ genotypes for the majority of our studies. Although Insrwt/wt;Ins1cre/wt;nTnG and Insrf/f;Ins1wt/wt:nTnG control mice exhibited generally similar phenotypes, we observed key differences that reinforced the rationale for using controls containing Cre and lacking 1 allele of Ins1, matching the experimental genotypes. For example, male HFD-fed Insrf/f;Ins1wt/wt;nTnG mice showed significantly higher levels of plasma proinsulin in comparison to Insrwt/wt;Ins1cre/wt;nTnG mice at 16 and 28 weeks of age (Fig. S7). At several ages, both LFD and HFD fed female Insrf/f;Ins1wt/wt;nTnG/- mice exhibited trends toward slightly improved glucose tolerance (Fig. S8), most likely due to one extra allele of insulin, in comparison to Insrwt/wt;Ins1cre/wt;nTnG mice. Insulin sensitivity was generally similar, although not identical (Fig. S9). Longitudinal tracking of body weight revealed a consistent tendency for mice with a full complement of insulin gene alleles to be heavier than mice in which 1 allele of Ins1 had been replaced with Cre. With the statistical power we had available, female HFD-fed Insrf/f;Ins1wt/wt;nTnG mice had significantly increased body mass at 11 and 16 weeks of age in comparison to Insrwt/wt;Ins1cre/wt;nTnG mice (Fig. S10). Once we had established the effects of the Ins1cre allele on its own, we used a breeding strategy ensuring that all pups were born with 3 insulin alleles to control for any effects of reduced insulin gene dosage (See Fig. 1C). This strategy gave us cohorts of: Insrwt/wt;Ins1cre/wt;nTnG/- (βInsrKO) control mice, Insrf/wt;Ins1cre/wt;nTnG/- (βInsrKO) β-cell specific Insr heterozygous knockout mice, and Insrf/f;Ins1cre/wt;nTnG/- (βInsrKO) β-cell specific Insr complete knockout mice. In some studies, the nTnG allele was not present (see Figure legends).
Islet isolation and dispersion
Mouse islet isolations were conducted by ductal inflation and incubation with collagenase, followed by filtration and hand-picking as in our previous studies and following a protocol adapted from Salvalaggio et al. (Beith et al., 2008; Johnson et al., 2006a; Luciani and Johnson, 2005; Salvalaggio et al., 2002). 24h post islets isolations, islets were washed (x4) (Ca/Mg-Free Minimal Essential Medium for suspension cultures, Cellgro #15-015-CV), followed by gentle trypsinization (0.01%), and resuspended in RPMI 1640 (Thermo Fisher Scientific #11875-093), 10%FBS, 1%PS). Cells were seeded either on glass cover slips or in 96-well plates according to the experimental procedure (see below).
Immunoblotting
50 islets per sample were washed in PBS twice and then lysed and sonicated in RIPA buffer (10mM HEPES, 50mM β-glycerol phosphate, 1% Triton X-100) supplemented with complete mini protease inhibitor cocktail (Roche, Laval, QC) and phosphatase inhibitors (2mM EGTA, 70mM NaCl, 347 1mM Na3VO4, and 1mM NaF). Protein concentration was measured using Micro BCA Protein Assay Kit (ThermoFischer Scientific). 10 μg of total protein for each sample was separated by SDS-PAGE and transferred to Immun-Blot PVDF membrane (Bio-Rad Laboratories). Subsequently, membranes were blocked in I-Block (ThermoFischer Scientific) and probed with primary antibodies targeting insr-β subunit (1:1000, CST #3020S), ERK1/2 (1:1000, CST #4695), p-ERK1/2 (Thr202/Tyr204) (1:1000, CST #4370), AKT (1:1000, CST #9272), p-AKT (Thr308) (1:1000, CST #9275), ACTB (Novus Biologicals, NB600-501). Protein detection was performed by the use of the HRP-conjugated secondary antibodies: anti-rabbit (CST #7074) or anti-mouse (CST #7076) and Immobilon Forte Western HRP substrate (Millipore Sigma). Protein band intensity on exposed film was measured with Adobe Photoshop software.
Targeted gene expression analysis
Tissue samples were kept frozen during grinding using mortals and pestles. cDNA was synthesized using qScript™ cDNA synthesis kits (QuantaBio, #95047-500) following RNA was isolated from 50-100mg of sample using RNeasy mini kits (Qiagen, #74106) according to manufacturer’s recommendations. qPCR was performed in 15ul reaction volumes using a CFX384 Touch Real-Time PCR Detection System (BioRad). Primer sequences: Insr forward 5’-376 TTTGTCATGGATGGAGGCTA-3’ and Insr reverse 5’-CCTCATCTTGGGGTTGAACT-3’. Hprt forward 5’-TCAGTCAACGGGGGACATAAA-3’ and hprt reverse 5’-GGGGCTGT 379 ACTGCTTAACCAG-3’. Insr expression data were analyzed using the 2-ΔΔCT method using Hprt as a housekeeping gene. ΔCq=Cq(Insr)-Cq(Hprt) followed by normalization of the ΔCq(exp) to the mean of the Hprt expression in liver.
RNA sequencing
To generate transcriptomic data from β-cells lacking Insr and littermate controls, groups of 50 islets were dispersed using mild trypsin digest according to our standard protocol (Alejandro et al., 2010; Luciani and Johnson, 2005), and FACS purified based on the GFP-positivity of the Ins1Cre-induced nTnG allele. RNA isolation and library preparation were conducted in accordance with the SMART seq 2 protocol (Picelli et al., 2014). Sequencing of 100 GFP-positive β-cells was performed at the UBC Sequencing and Bioinformatics Consortium using Illumina NextSeq 500 with paired-end 75 bp reads. The number of read pairs per sample ranged from 4 million to 42 million, with a median of 18 million. Samples high glucagon reads (>10,000 CPM) were omitted, as presumed β-cell purification failures. Raw counts of gene reads were quantified by Kallisto (Bray et al., 2016) and filtered for low count by only keeping genes with at least 5 counts in more than 10 samples. Using DESeq2 package (Love et al., 2014), gene counts were then normalized by variance stabilizing transformation and analyzed for differential expression using Benjamini–Hochberg adjusted p value < 0.05 as the cut-off. Similar results were found when multiple analysis pipelines were applied and their results combined (Wang et al., 2019).
Islet metabolism and oxygen consumption analysis
The Seahorse XF Cell Mito Stress Test kit (cat#103015-100) was used to measure oxygen consumption rate (OCR) in dispersed mouse islets using an Agilent Seahorse XF96 Analyzer (Seahorse Bioscience, North Billerica, MA). Dispersed islet cells were seeded at a density of 40,000 cells/well in XF culture microplates. After seeding (48h), the Seahorse XF Sensor Cartridge was hydrated in 180μL of Seahorse XF Calibrant Solution (cat#100840-000) added to each well of the XF Utility Plate (cat#102416-100). The hydrated cartridge was kept in a non-CO2 incubator at 37C° for 24h to remove CO2 from media. To pre-equilibrate the cells, 180 μL of Seahorse XF base medium (minimal DMEM) containing 10 mM glucose, 4 mM L-glutamine, and 2 mM sodium pyruvate was added to each well of the culture plate 1 h prior to the run and was also present during extracellular flux measurements. Mitochondrial respiration was analyzed by sequential injections of modulators including oligomycin (2 µM) used to block ATP synthase, carbonyl-cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP 3 µM) to activate uncoupling of inner mitochondrial membrane allowing maximum electron flux through the electron transport chain, and finally a mix of rotenone (1 µM) and antimycin A (1 µM) to inhibit complexes I and III, respectively. Mitochondrial proton leak, ATP-linked oxygen consumption and non-mitochondrial respiration were calculated based on the resulting respiratory profile, as described (Brand and Nicholls, 2011).
Patch-clamp electrophysiology
Islets from 16-week-old chow-fed male and female mice were isolated at UBC. 100-300 islets from each mouse shipped in a blinded manner overnight to the University of Alberta in RPMI (Invitrogen, 11875) with 10% FBS (Invitrogen #12483020), and 1% penicillin-streptomycin (Thermo Fisher, #15070063). Islets were dissociated into single cells using StemPro accutase (Thermo Fisher Scientific, Cat# A11105-01) one day after receiving the islets. Dispersed cells were cultured in RPMI-1640 containing 11.1 mM glucose with 10% FBS and 100 U/ml penicillin/streptomycin for up to 2 days.
Membrane potential and current measurements were collected using a HEKA EPC10 amplifier and PatchMaster Software (HEKA Instruments Inc, Lambrecht/Pfalz, Germany) in either the current-or voltage-clamp mode in the perforated patch-clamp configuration. All the measures were done in a heated chamber (32–35°C). Membrane potential measurement was performed with patch pipettes pulled from thick-walled borosilicate glass tubes (Sutter Instrument), with resistances of 8–10 MΩ when filled with 76 mM K2SO4, 10 mM KCl, 10 mM NaCl, 1 mM MgCl2 and 5 mM Hepes (pH 7.25 with KOH), and back-filled with 0.24 mg/ml amphotericin B (Sigma, cat# a9528). The extracellular solution consisted of 140 mM NaCl, 3.6 mM KCl, 1.5 mM CaCl2, 0.5 mM MgSO4, 10 mM Hepes, 0.5 mM NaH2PO4, 5 mM NaHCO3, 5 mM glucose (pH 7.3 with NaOH). Membrane potential was measured with 5 mM G starting from the beginning, for 5 min, then changed to 1 mM G for 4-5 min, then changed to 10 mM G for 8-10 min, finally changed back to 5 mM G. KATP currents and reversal potential were recorded during and after membrane potential measurement on each cell. β-cells were distinguished by characteristic differences in the voltage-dependent inactivation of Na+ channel currents (Gopel et al., 2000).
Measurement of voltage-dependent exocytosis was performed in the whole-cell configuration. Before the start of whole-cell patch clamping, media were changed to bath solution containing (in mM): 118 NaCl, 20 Tetraethylammonium-Cl, 5.6 KCl, 1.2 MgCl2, 2.6 CaCl2, 5 HEPES, and 5 glucose (pH 7.4 with NaOH). For whole-cell patch-clamping, fire polished thin-walled borosilicate pipettes coated with Sylgard (3-5 MOhm), contained an intracellular solution with (in mM): 125 Cs-glutamate, 10 CsCl, 10 NaCl, 1 MgCl2, 0.05 EGTA, 5 HEPES, 0.1 cAMP, and 3 MgATP (pH 7.15 with CsOH). Quality control was assessed by the stability of seal (>10 GOhm) and access resistance (<15 MOhm) over the course of the experiment. Data were analysed using FitMaster (HEKA Instruments Inc) and Prism 6.0h (GraphPad Software Inc., San Diego, CA).
Calcium imaging and analysis
Two days following cell seeding on glass coverslips, adherent islet cells were loaded with 5μM of the acetoxymethyl (AM) ester form of the calcium indicator Fura-2 (Thermo Fisher Scientific #F1221) for 30min. Islet cells were perifused at 1mL/min for 45min prior to experimental procedure to ensure washout of excess FURA2. During experiments, cells were mounted on a temperature-controlled stage and held at 37°C on a Zeiss Axiovert 200M inverted microscope equipped with a FLUAR 20 objective (Carl Zeiss, Thornwood, NY), while perifused with Krebs-Ringer (KRB) solution (144mM NaCl, 5.5mM KCL, 1mM MgCl2, 2mM CaCl2, 20mM HEPES) of various glucose concentrations as indicated in figures.
Ca2+ traces were analyzed automatically, as follows. Taking a similar approach to that described previously (Wills et al., 2016), 8 features were extracted from the Traces (Fig S2). Peaks during each phase were identified as local maxima reaching a value with a percent difference above the median baseline level greater than 20%. P-values for calcium analysis were generated using ANOVA with correction for multiple comparisons performed using Tukey’s method. Figures were generated using the ggplot2 package in R (Wickham2016). Code used to analyze data and generate figures are available upon request.
Analysis of total protein synthesis rate
For the purpose of pulse labeling of newly translated proteins, 50 isolated islets were incubated in complete RPMI media without cysteine and methionine (MP Biomedicals, #SKU 091646454) for 1hr. Subsequently media was supplemented with 250 μCi of [35S]-cysteine/methionine mixture (PerkinElmer, NEG772002MC) and islets were incubated under normal conditions for 30 min. Islets were then lysed and proteins separated by SDS-gel electrophoresis as described above. Gels were fixed for 30 min in 50% (v/v) ethanol in water with 10% (v/v) acetic acid, dried in gel dryer (Bio-Rad model 583) and then exposed to storage phosphor screen (GE Healthcare) overnight. Screens were imaged and digitised using Typhoon FLA 9000 biomolecular imager (GE Healthcare). Protein bands intensity was quantified with Adobe Photoshop software.
Dynamic insulin secretion perifusion analysis
For perifusion experiments, islets from 16week old chow-fed male and female mice were isolated using collagenase, filtration and hand-picking as previously described (Johnson et al., 2006b). Our standard approach compared the insulin response to both 20mM and 10mM glucose stimulation as well as direct depolarization with 30 mM KCl. More specifically, 150 hand-picked islets per column were perifused (0.4 mL/min) with 3 mM glucose KRB solution containing (in mM) 129 NaCl, 4.8KCL, 1.2 MgSO4•7H2O, 1.2 KH2PO4, 2.5 CaCl2, NaHCO3, 10 HEPES, as well as 0.5% BSA (Sigma # A7030) for 60 min to equilibrate the islets to the KRB and flow rate, and then treated as indicated. Samples were analyzed using a rat insulin radioimmunoassay that has 100% cross-reactivity for mouse insulin (Millipore-Sigma #ri-13k). Insulin content was measured after acid-ethanol extraction using an insulin ELISA (Stellux Rodent Insulin ELISA, Alpco #80-INSMR-CH10).
Hyperglycemic clamps
In vivo hyperglycemic clamp experiments were performed at the National Mouse Metabolic Phenotyping Center (MMPC) at UMass Medical School. Body composition analysis was conducted by noninvasively measuring whole body fat mass and lean mass using 1H-MRS (Echo Medical Systems, Houston, TX). A survival surgery was performed at 5–6 days before hyperglycemic clamp experiments to establish an indwelling catheter in the jugular vein. On the day of experiment, mice were fasted overnight (∼15h), and a 2-h hyperglycemic clamp was conducted in awake mice by intravenously infusing 20% dextrose to rapidly raise and maintain plasma glucose levels at ∼19 mM (Zhang et al., 2012).
Intravenous 4-day glucose infusion
Mice were bred and genotyped at University of British Columbia and shipped at 5 weeks of age to the Division of Diabetes, Department of Medicine, University of Massachusetts Medical School, USA. In a blinded manner, glucose infusions were performed as described (Alonso et al., 2007). Jugular vein catheters were placed in 9-12-week-old male and female mice with blinded genotypes. From postoperative recovery through euthanasia mice were unrestrained and were maintained on a 12-h light/dark cycle, with access to 2.2 g diet (to ensure isocaloric intake across all mice) and water. After 2 days of recovery, mice received continuous 4-day intravenous infusions of 50% dextrose (Baxter) containing 500ug/mL BrdU. Tail blood was sampled for plasma insulin, glucagon and blood glucose at Day 0, 1, 2 and 4. Blood glucose was measured using ReliOn glucometer (Walmart), glucagon was measured using mouse Glucagon ELISA (Mercodia 10-1281-01), and plasma insulin was measured using mouse Insulin ELISA kit (Mercodia 10-1247-01). Mice were euthanized at the end of the experiment and pancreas and duodenum were harvested for histology. Tissues were fixed for 5 h in 10% formalin and then stored in 1X PBS until processing, paraffin embedding and sectioning. Images were acquired using a NIKON fully motorized for Phase and Fluorescence Ti-E microscope. Images were taken of at least 10 randomly selected islets, all four channels at the same time. To generate RGB images, channels were inserted to show Insulin-BrdU-DAPI, Glucagon-BrdU-DAPI or Insulin-Glucagon-DAPI. To generate yellow-magenta-white images to accommodate colorblind viewers, new files were generated in Adobe Photoshop in which original channel data were displayed in multiple channels using the merge function (e.g. to change green to yellow, green channel data were added to both green and red channels; for more detailed information please contact LCA). Cells were counted using Cell profiler automated counting software from Broad Institute (Cambridge, MA); all counts were manually checked.
β-Cell Mass and immunohistochemistry
Pancreata were perfused, then fixed for 24h with 4% paraformaldehyde, and then washed twice with 70% ethanol prior to paraffin embedding and sectioning (5 μm) to obtain 5 different regions of the pancreas (100 μm apart) by WAXit Inc. (Vancouver, Canada). Paraffin was removed by 5 min xylene incubation steps. Sections were rehydrated in decreasing concentrations of ethanol and rinsed with water and PBS. Epitope retrieval was done either by immersing samples 10 mM citrate buffer, pH 6.0 for 15min at 95°C, or by transferring sections to prewarmed 1N HCl for 25 min at 37°C. Samples were washed with PBS twice and were either blocked for 10 min at room temperature (Dako protein block #X0909), or with goat block (GB) with Triton X-100 (10% BSA + 5% Goat Serum with 0.5% Triton X-100) for 1-4 h at room temperature. Samples were incubated overnight at 4°C in primary antibodies targeting anti-insulin (1:200 Abcam #Ab7872), anti-glucagon (1:100 Cell Signaling Technologies, #2760S), anti-BrdU (1:250, Abcam ab6326), anti-GLUT2 (1:1000, Milipore, #07-1402). Following 3 PBS washes (5 min each), samples were incubated for 30min or 1h at room temperature in secondary antibodies in a light-deprived humid chamber. Secondary antibodies applied were anti-rabbit Alexa Fluor-488 (1:200, Invitrogen, # A-11008), anti-rabbit Alexa-488 (1:200, Invitrogen, #A11034), anti-rat Alexa-594 (1:200, Invitrogen, #A11007), anti-guinea pig Alexa-647 (1:200, Invitrogen, #A21450), anti-guinea pig Alexa-594 (1:200, Invitrogen #A-11076). Samples were mounted with either VECTASHIELD Hard Set Mounting Medium (Vector labs, # H-1500) or Fluoroshield both containing DAPI (Sigma-Aldrich, #F6182-20ML) following an additional three washes in PBS (10 min each). For β-cell area quantification, whole pancreas sections were imaged using an ImageXpressMICRO using a 10x (NA 0.3) objective and analyzed using the MetaXpress software (Molecular Devices, San Jose, CA, USA). Beta cell area was calculated as insulin positive area normalized to the entire pancreas of each section. The mean of five sections from 5 regions of the pancreas were quantified. For other immunofluorescence analysis of fixed tissue, we used a Zeiss 200M microscope using 20x air objective (NA 0.75), NIKON fully motorized for Phase and Fluorescence Ti-E microscope. For live cell imaging for recombination validation, islets from Ins1cre/wt:nTnG mice were incubated with CellMask™ Deep Red Plasma membrane stain (Invitrogen/Thermo Fisher - Catalog number: C10046) using a Leica confocal microscope.
For β-cell mass analysis of Insr knockouts using MIPCre, β-cell mass determination was performed by intensity thresholding using the Fiji 2.1.0-/1.53c image analysis package, after fluorescence immunostaining for insulin on 5 independent sections per animal, selected at random throughout the pancreas as previously described. The proportion of insulin positive staining to total area was them multiplied by the pancreatic weight to derive the β-cell mass. The data was subsequently analyzed using a non-parametric statistical test (Mann-Whiney) in Prism 8.4.3 (GraphPad, San Diego, CA).
Blood collection and in vivo analysis of glucose homeostasis and insulin secretion
Tail blood was collected for blood glucose measurements using a glucometer (OneTouch Ultra 2 meter, Lifescan, Canada) for single time points as well as during glucose and insulin tolerance tests. Mice were fasted for 4h or 16h during single timepoints and for 6h during glucose and insulin tolerance tests, as well as glucose stimulated insulin secretion tests. The i.p. glucose dose was 2g/kg unless otherwise specified. The i.p. Humalog (Eli Lilly and Co) insulin dose was 0.75U unless otherwise indicated. 1-2 days prior to femoral blood collection the experimental mice were tube handled and shaved. Femoral blood was collected for single timepoints, as well as for measurements of in vivo glucose-stimulated insulin secretion after i.p. injection of 2g/kg glucose. Blood samples were kept on ice during collection, centrifuged at 2000rpm for 10min at 4°C and stored as plasma at -20°C. Plasma samples were analysed for insulin (Stellux Chemi Rodent Insulin ELISA, Alpco #80-INSMR-CH10), proinsulin (Alpco #80-PINMS-E01), C-peptide (Alpco #80-CPTMS-E01), glucagon (Mercodia, #10-1281-01). Measurements were performed on a Spark plate reader (TECAN), and analysed using (GraphPad Software Inc., San Diego, CA).
Mathematical modelling
We used modified versions of the Topp model (Topp et al., 2000) to simulate glucose, insulin, and, in certain instances, β-cell dynamics (see equations in Fig. 6A and detailed process in Fig. S11). To capture the proposed insulin-receptor mediated negative feedback on insulin secretion, we introduced an inhibition factor, 1/(1 + Sβ I), multiplying the insulin secretion term, parametrized by Sβ, the β-cell Insr-specific insulin sensitivity. The other structural modification to the Topp model that we made was the removal of the β-cell mass equation when modelling glucose tolerance tests, justified by the slow nature of changes to β-cell mass compared to the fast changes in glucose and insulin levels during the test.
In glucose clamp conditions, a simple equation, derived in the Supplemental Material, relates Sβ to steady state insulin levels in wildtype and mutant mice: Sβ = (Im * - Iwt *) / (Im *)2, where Iwt * is the steady state insulin level for wild type control mice, and Im * is the equivalent for the βInsrKO mice. We assumed that, at each time point after the first one in the glucose-clamp insulin measurements, the blood insulin levels were hovering around steady state and all other parameters and variables were constant (Fig. S5A). We used those insulin steady-state data points together with the simple equation to estimate Sβ for the male and female wild-type mice. The equation for Sβ is parameter free, independent of the β-cell mass steady state, and depends only on the data so no parameter nor β-cell mass estimates were required.
For in silico glucose tolerance tests, we varied the values of Sβ and SP to see their influence on glucose homeostasis. To set the parameter values in our model for this exploration, we started with the Topp model, originally parametrized for humans and gradually varied them with two simultaneous objectives: (1) ensure that a simulated glucose tolerance test roughly matched a typical wildtype female low-fat diet glucose tolerance test time series and (2) the steady state β-cell mass of our full GIβ model roughly matched a typical observed β-cell mass in these mice. The resulting parameter values are given in Table S1 in the Supplemental Material. For in silico glucose tolerance tests, we then inserted the negative-feedback factor in the insulin secretion term (referred to here as our full GIβ model or our fast GI model for the constant-β version) and used the parameters values estimated as described above. We held the β-cell mass constant at the steady state β value of our full GIβ model, calculated using the clamp-data estimated Sβ value for wildtype female LFD mice and the SP value from the rough parameter estimation described above. For any individual tolerance test, we set Sβ and SP, calculated the steady state of our fast GI model and used the steady state insulin value as the initial condition for insulin and the steady state glucose value plus 20 mM as the initial condition for glucose. Fig. 6A shows sample in silico glucose tolerance tests for various values of Sβ, with SP fixed at 70. The area under the glucose curve (AUGC) for the glucose tolerance test was then calculated for a range of Sβ and SP values, generating a numerical map from Sβ and SP to AUGC. From the glucose tolerance tests in wildtype and mutant male mice and wildtype and mutant female mice, we calculated the experimental AUGC values at 4, 9, 21 and 39 weeks. Using the computed AUGC map and the 11-week Sβ values calculated from the glucose-clamp data, we estimated the SP value that would give the experimental AUGC (Fig. S5C).
Statistics
Data are presented as mean +/- SEM, with individual data points, unless otherwise indicated. T-tests were performed when only 2 groups were compared at a single timepoint. One-way ANOVA was applied when three groups were compared at a single timepoint. Mixed effects model statistics were used for statistical analysis of GTT, ITT, GSIS experiments. Specifically, a fitted mixed model (Prism 8, Graph Pad), which allows for missing measurements and un-even group sizes, was applied when 2 or more groups were compared at multiple time points (e.g. 4h fasted blood glucose, insulin, proinsulin, c-peptide and body weight).
Data availability
Raw RNAseq data are deposited in GEO (GSE159527)
Author contributions
S.S. led project design/management, conducted experiments, analyzed data, and wrote the manuscript.
E. P. conducted experiments, analyzed data, and edited the manuscript (protein synthesis, Westerns).
J. K. conducted experiments, analyzed data, and edited the manuscript (ex vivo insulin secretion).
H. H. C. analyzed data, and edited the manuscript (RNAseq analysis)
D. A. D. conducted experiments and analyzed data (in vivo glucose homeostasis).
X-Q. D. conducted experiments, analyzed data, and edited the manuscript (electrophysiology).
R. B. S. conducted experiments, analyzed data, and edited the manuscript (long-term hyperglycemia, proliferation).
L. E. conducted experiments, analyzed data, and edited the manuscript (MIPCre mice)
C. E. analyzed data, and edited the manuscript (Bioinformatics and calcium data analysis)
K. F. analyzed data, and edited the manuscript (mathematical modelling)
S. A. M. M. conducted experiments and analyzed data (beta-cell area)
N. N. conducted experiments (imaging)
P. O. conducted experiments, analyzed data, and edited the manuscript (Seahorse)
D. H. conducted experiments (mRNA expression qPCR)
X. H. conducted experiments (in vivo physiology)
H. L. conducted experiments (in vivo physiology)
H. M. conducted experiments, analyzed data, and edited the manuscript (FACS, RNAseq prep)
J. W. conducted experiments (beta cell INSR western blots)
J. D. B. conducted experiments (liver INSR western blots)
H. L. N. conducted experiments (hyperglycemic clamps)
S. S. conducted experiments (hyperglycemic clamps)
A.B. conducted experiments (electrophysiology).
R.K. conducted experiments (electrophysiology).
B. G. conducted experiments (long-term hyperglycemia, proliferation).
C. C-M. conducted experiments, and edited the manuscript (MIPCre mice)
S. F. analyzed data (RNAseq analysis)
S. S. conducted experiments (RNAseq)
D. S. L. conducted experiments, analyzed data, and edited the manuscript (Seahorse, nTnG validation)
C. N. supervised experiments, and edited the manuscript (RNAseq)
E.J.R supervised experiments, and edited the manuscript
E.N.C. supervised experiments, and edited the manuscript (mathematical modeling)
J. K. supervised studies, and edited the manuscript (Hyperglycemic clamps)
E. B-M. supervised studies, and edited the manuscript (MIPCre mice)
L. A. supervised studies, and edited the manuscript (long-term hyperglycemia, proliferation)
P. E. M. supervised studies, and edited the manuscript (electrophysiology)
J. D. J. conceived the project, oversaw its execution, edited the manuscript, and guarantees the work.
Funding
SS was supported by an Alfred Benzon Post-Doctoral fellowship. Work was primarily supported by a CIHR grant (133692) to JDJ. Work in Edmonton was supported by CIHR grant (148451) to PEM. Work in the Michigan Diabetes Research Center was supported by NIH (P30 DK020572). Work at U Mass was supported by an NIH grants (5U2C-DK093000) to JKK and (R01DK114686) to LCA. CN is supported as a Tier 1 CRC.
Conflict of Interest Statement
The authors report no conflicts of interest related to this work.
Figure Legends
Acknowledgements
We thank Johnson lab members for valuable feedback and those who provided insight when data was presented at conferences. We thank Sarah Hulme, Charles Nieh, and our animal care staff for supporting our animal husbandry, Bernard Thorens and Jorge Ferrer for sharing the Ins1cre knock-in mice, Eric Jan for allowing us to use his facilities to conduct S35 labeling. We thank Quin Wills for helpful discussion of the smartseq2 protocol, and Austin Taylor for discussion of genotyping.
Footnotes
This version has revised and expanded RNA sequencing analysis.