The beta-cell is responsible for producing and secreting multiple secretory granule proteins including insulin, chromogranin-A (ChgA), and islet amyloid polypeptide (IAPP). The endoplasmic reticulum (ER) is the site of protein production and relies heavily on Ca²⁺ concentrations to maintain the environment needed for proper protein synthesis and folding (Figure 2A) (10). Insulin secretory demand makes the beta-cell particularly vulnerable to exceeding ER protein folding capacity, which leads to the accumulation of misfolded proteins and a disruption of ER homeostasis (Figure 2F) (11). This physiological state is termed ER stress (12). Prolonged efforts by the cell to correct misfolded proteins can lead to unregulated changes in enzyme activity, reduced beta-cell function, and induction of apoptosis (13–16). To meet the metabolic demands of glucose-stimulated insulin secretion (GSIS), the beta-cell requires a tightly-coupled process with cellular metabolism to properly maintain euglycemia (17). In brief, glucose is transported into the beta-cell via the glucose transporter 2 (Glut2) in rodents (GLUT1 and 3 in humans), converted to pyruvate, and shuttled into the mitochondria where it is used for ATP production (18). Changes in the ATP to ADP ratio lead to beta-cell depolarization, Ca²⁺ influx, and insulin release (19–21). Insulin mRNA is translated at the ER following nutrient stimulation, which in rodents can signal up to a 10-fold increase in insulin synthesis at a rate of 1 million molecules per minute (22, 23).

To meet these high demands, beta-cells have an extensive ER with multiple chaperones to aid in protein folding, packaging, and secretion. However, high protein synthesis puts a significant amount of stress on the ER. The unfolded protein response (UPR) is triggered when an excessive amount of misfolded proteins accumulate in the ER, which can be caused by overnutrition, increased reactive oxygen species (ROS), or proinflammatory cytokines (24, 25). Three major sensors of the UPR are protein kinase RNA-like endoplasmic reticulum kinase (PERK), inositol-requiring enzyme 1 alpha (IRE1α) and activating transcription factor 6 (ATF6) (13, 26). In an unstressed state, these sensors are bound to the ER chaperone binding immunoglobulin protein (BiP) (Figure 2A). Accumulation of misfolded proteins leads to the dissociation of BiP from the three UPR sensors (Figure 2F) (27, 28). Together, the UPR sensors alleviate ER stress by attenuating global protein synthesis to reduce the load of unfolded proteins, increasing chaperone synthesis to guide protein degradation or refolding misfolded proteins, and synthesizing lipids to increase ER volume (29).

In addition to protein synthesis, the ER is also responsible for storage of intracellular Ca²⁺ and therefore, regulates calcium-dependent signaling within the cell, such as protein folding and enzymatic function (13). ER stress disrupts intracellular Ca²⁺ balance influencing multiple processes, including activation of cytosolic post-translational modification (PTM) enzymes by facilitating their translocation into subcellular compartments. This imparts downstream changes in gene expression, protein conformation, and enzyme activity (30–33). Dysregulation of PTM enzymes has been linked to the development of rheumatoid arthritis, celiac disease, and T1D (34–38). In T1D, this includes citrullinating peptidyl arginine deiminase (PAD) enzymes and tissue transglutaminase 2 (tTG2) deaminating enzyme (13, 39). PAD, tTG2, and similar enzymes can alter the binding affinity of peptide epitopes, such as insulin, to major histocompatibility complex (MHC) class II, resulting in increased CD4 T cell activation (36, 40). Inhibition of systemic PAD enzymes in NOD mice can protect against diabetes progression, suggesting a role in T1D initiation (41).

Stress-induced PTMs can also result in the creation of neo-antigens in peripheral tissues for which the thymus has not established tolerance. Many T1D neo-antigens generated from PTMs have been identified (42). Some PTMs can lead to non-functional protein products resulting from alternately spliced RNA called defective ribosomal products (DRiPs) (43). Increased expression of DRiPs from insulin have been measured in beta-cells in response to ER stress and can be recognized by T cells from patients with T1D (44, 45). Hybrid insulin peptides (HIPs) are another group of neo-antigens generated from transpeptidation, a PTM where insulin peptides are covalently linked to other beta-cell granule peptides including insulin C-peptide, IAPP, and ChgA (Figure 2G) (46, 47). HIPs are not only recognized by autoreactive CD4 T cells in mouse models of T1D, but CD4⁺ T cells from patients with T1D recognized HIPs as well, signifying their potential role in disease initiation and progression (48–50). Our understanding of how neo-antigens are generated and contribute to the development of autoreactivity in T1D is currently unknown. Future studies are warranted to further define how ER stress and subsequent downstream disruptions induced by the secretory demands of the beta-cell can influence autoreactive T cell responses and beta-cell vulnerability in T1D (Figure 1).

Oxidative stress occurs when there is an imbalance between ROS generation and antioxidant activity (51). Superoxide is primarily a byproduct of normal cellular metabolism that is generated in the mitochondria and cytoplasm (Figure 2B) and is an initiating free radical that can result in the formation of other reactive species such as hydrogen peroxide (H₂O₂), hydroxyl radical, and peroxynitrite (52). Free radicals are highly reactive and can induce cellular damage, but antioxidants including superoxide dismutase (SOD), catalase, glutathione peroxidase (GPx), peroxiredoxins, thioredoxin, and glutathione protect the cell by detoxifying these reactive species (53, 54). SOD dismutates superoxide to molecular oxygen and H₂O₂, a less destructive oxidant and signaling molecule. H₂O₂ regulates insulin secretion by activating the second messenger c-Jun N-terminal Kinase (JNK) (55). This leads to decreased insulin production through the translocation of the transcription factor pancreatic and duodenal homeobox 1 (Pdx1) from the nucleus to the cytoplasm, resulting in decreased Insulin transcription (56) (Figure 2K). H₂O₂ is further converted to oxygen and water by catalase, GPx, and peroxiredoxin. Increased levels of H₂O₂ can form extremely reactive hydroxyl radicals through Fenton reactions with free iron present in the cytoplasm, which has downstream negative effects on intracellular calcium levels, protein synthesis, glycosylation, and redox status (53). Beta-cells, however, have decreased antioxidant levels and therefore, are highly susceptible to free radical-mediated damage (Figure 2E). Rodent and human beta-cells have reduced transcriptional and protein levels of cytosolic copper/zinc (Cu/Zn) SOD1, mitochondrial manganese (Mn) SOD2, catalase, and GPx, which can result in exacerbated levels of superoxide, H₂O₂, hydroxyl radical, and peroxynitrite that are implicated in beta-cell death in T1D (57–61). The inability to properly restore cellular homeostasis due to the negative effects of oxidative and ER stress can induce apoptosis in insulin-secreting beta-cells. Increased beta-cell apoptosis has been measured in patients with T1D and NOD mice when compared to healthy controls (14, 62–66). In addition to oxidative stress, the beta-cell is also impacted by the islet microenvironment in which it is closely associated.

Importantly for T1D pathology, islets are highly vascularized. This provides an interface by which immune cells, even from distant sites, can gain local access to pancreatic islets (67, 68). Islets contain a glomerular-like network of fenestrated capillaries that comprise about 8-10% of islet volume. Islet capillary density is estimated to be 10 times higher than that of the exocrine pancreas and is driven by high local production of VEGF-A (69, 70). This rich vascularization and high islet blood flow is autonomously regulated through complex interactions between hormones, metabolites, and the nervous system. While islet blood flow is innately required for and coupled to insulin sensing and release, extensive vasculature also makes the beta-cell uniquely poised for interactions with the immune system.

The dense islet vasculature network facilitates activated immune cell trafficking across the vascular endothelium into the islet (Figure 1). This causes a local inflammatory microenvironment which in turn, further increases permeability, facilitating access even for naïve T cells (71). Interestingly, this “open” environment remains, even after reversal of diabetes with anti-CD3 treatment. In addition to naïve T cell infiltration, activated immune cells that are primed locally in the pancreatic lymph nodes (pLNs) can also cross the vascular endothelium. pLNs may contribute to T1D pathogenesis as drainage from the pancreas and local gut regions provides a crossroad for the immune cells traveling between these compartments (67, 72). The interplay between the microbiome, the immune system, and a “leaky gut” has been implicated as a key factor in T1D pathogenesis (73, 74). Toll-like receptors (TLRs) are a family of innate pattern recognition receptors important for microbial clearance by the immune system. Many TLRs signal through the MyD88 adapter protein. NOD mice deficient in MyD88 exhibit microbiota-dependent protection from autoimmunity development (75). Specific manipulation of TLR expression and microbiota composition can further regulate disease progression or prevention (76). Sex-specific autoimmune risk can also be influenced by microbiome manipulation (77–79). With new data unveiling the importance of the microbiome for the gut immune environment and shaping peripheral tolerance, the relationship between pLNs, islet vasculature, and immune cell trafficking is increasingly relevant to beta-cell vulnerability and T1D pathogenesis (80, 81). In addition to facilitating interactions between islet cells and immune cells, the islet vasculature also sensitizes the beta-cell to the damaging effects of circulating proinflammatory cytokines.

Beta-cells are sensitive to cytokine-mediated damage. Cytokines can alter crucial beta-cell characteristics including insulin secretion, mitochondrial function, and intracellular calcium stores (82, 83). Inflammatory cytokines including tumor necrosis factor alpha (TNFα), interferon gamma (IFNγ), and interleukin-1 beta (IL-1β), cause beta-cell dysfunction by impairing ATP production, inducing DNA damage, and promoting apoptosis (Figure 3) (84, 85). Proinflammatory cytokine exposure inhibits GSIS due to the limited availability of ATP in both rodent and human islets, as well as in beta-cell lines (86–89). Islet exposure to cytokines triggers nuclear factor κB (NFκB) induction of inducible nitric oxide synthase (iNOS), which increases NO formation in the beta-cell (87). NO has temporal effects on beta-cell responses, as early and transient levels of NO (less than 24 hours) facilitate the repair of cytokine-induced DNA damage by inhibiting the activation of the DNA damage response and preventing the induction of apoptosis (90). However, prolonged exposure to NO can induce beta-cell death due to DNA damage, UPR activation, and decreased mitochondrial oxidation (i.e., ATP production) (87, 91). In cultured islets, pre-exposure treatment with NO inhibitors, such as aminoguanidine, attenuates cytokine-mediated beta-cell death (92). Cytokine-mediated beta-cell death becomes exacerbated in an inflammatory microenvironment in the pancreas, creating a positive feedback loop resulting in more inflammation, stress, vulnerability, and eventually cell death (Figure 3). Unfortunately, clinical trials with anti-cytokine therapies such as Anakinra, an IL-1 receptor antagonist, were not efficacious in delaying T1D, suggesting a more complex interaction between cytokines and beta-cells in vivo (92). Nonetheless, not every beta-cell is lost in T1D, nor do they all respond negatively to ER and/or oxidative stress, indicating an intrinsic beta-cell heterogeneity in response to disease promoting factors.

Evidence for functional beta-cell heterogeneity in calcium flux, metabolism, ion channel conductance, and insulin secretion has been appreciated for almost 30 years (96, 97). More recently, this functional diversity has been specifically defined by many research groups into beta-cell subpopulations (Table 1). Using novel cell surface markers (ST8SIA1 and CD9) identified by immunizing mice with human islets, four human beta-cell subtypes with unique basal and GSIS responses were defined as β1-4 (93). All subtypes contain insulin granules but exhibit variable functionality and abundance; β1 is the most abundant and glucose responsive, while β4 is the rarest and least responsive, with highest basal insulin secretion. Interestingly there are no correlations found between subtype ratios and sex, age, or obesity, but subtype abundance is altered and much more variable in Type 2 diabetes (T2D). For example, β3 and β4 subsets are unusually overrepresented in T2D islets, but abundance of these two subsets varied much more than in control non-diabetic tissue. It is unknown if β1-4 cells exist in unique patterns before or after T1D diagnosis and disease progression.

In 2016, Johnston et al. discovered specialized beta-cells they termed “hub” cells that exert disproportionate control over blood glucose (94). Hub cells, comprising 1-10% of islet beta-cell mass, are metabolically active, exhibit evidence of transcriptional immaturity (low or absent Pdx1 and Nkx6.1 transcription factor levels), and are hypothesized to act as a pacemaker within the islet. Supporting this model, calcium tracing showed that surrounding cells, termed “followers”, respond to glucose stimulation slightly after the hub cell. Using optogenetic and pharmacological techniques, silencing hubs caused desynchrony in the calcium-induced response of beta-cells. The Huising group reported on a beta-cell subtype located in the islet periphery that is both transcriptionally and functionally immature, called the “virgin” beta-cell (95). Virgin cells lack the maturity marker urocortin 3 (UCN3) and are incapable of sensing glucose or proper calcium influx. Using lineage tracing, they found that these cells are transdifferentiation intermediates between alpha- and beta-cells. This work defined a “neogenic niche” of new beta-cells originating from an alpha-cell lineage, establishing a plasticity between these cell types that had also been suggested by models of extreme beta-cell loss (100). The hub versus virgin subpopulations can be compared to flattop⁺ and flattop^– populations, where this novel marker differentiates between a mature, functional beta-cell population (flattop⁺) and an immature and highly proliferative one (flattop^-) (101).

In 2020, the Benninger lab defined yet another subpopulation that is functionally distinct from those previously described (102). These cells are termed “first-responder” beta-cells, as defined by calcium dynamics. They found that first-phase response time of beta-cells is spatially organized and dependent on the physical distance from the first-responder cell. How these sub-populations of cells as defined by these specific markers may be unique or overlapping remains to be determined.

The implementation of single-cell transcriptomics has allowed exploration of beta-cell heterogeneity on a scale that was not previously possible. The Kubicek lab published the first application of single-cell transcriptomics to human islet cells in 2016 (103); while the number of beta-cells identified was extremely low, they showed transcriptome heterogeneity for genes such as DLK1, a T1D associated gene by GWAS (Genome-Wide Association Study) which will be discussed later in more detail. That same year the Kaestner lab published single-cell mass cytometric analysis of human islets and found heterogeneity in markers including Ki67, identifying four distinct beta-cell subpopulations (104). Several groups have since published large-scale single-cell transcriptome analyses of human and mouse beta-cells and identified unique subpopulations. These populations display differential levels of various beta-cell characteristics including maturity (UCN3), aging (IGF1R), and ER stress response (SRXN1, SQSTM1) (105, 106). Genes involved in ER/oxidative stress have been some of the strongest distinguishers of subpopulation clusters in various studies, with ER stress markers also correlating to proliferation and reduced beta-cell function (107–109). As discussed earlier, beta-cell ER stress is extremely relevant to an understanding of T1D contribution and response. Perhaps certain populations of proliferative-ER stressed beta-cells are the first to be lost during T1D pathogenesis.

For years, technical hurdles to interrogating mRNA in islets due to the digestive enzymes present in the surrounding exocrine pancreas limited our understanding of transcriptional dynamics and heterogeneity in the beta-cell. Dr. Shalev Itzkovitz at the Weitzmann Institute developed methods to visualize the dynamics of beta-cell mRNAs. His group designed an optimized single-molecule FISH (fluorescence in situ hybridization) protocol that allowed for assessment of transcriptional heterogeneity within the beta-cell population (110). This method revealed a subpopulation of “extreme” beta-cells that contain high levels of insulin and secretory factor mRNAs (IAPP, ChgA), but interestingly, low levels of insulin protein. The investigators suggested this may impart a specialization for basal insulin secretion. Additionally, beta-cell mRNAs displayed a uniquely polarized pattern, with elevated Insulin mRNA concentration in apical ER enriched compartments of the cell. The ratio of extreme beta-cells was increased in db/db diabetic mice, potentially facilitating increased requirements for basal insulin. This work gives rise to another unappreciated aspect of beta-cell heterogeneity: transcriptional heterogeneity. Future studies are needed to explore the proportion of extreme beta-cells in T1D and how this type of transcriptional variability may affect vulnerability to autoimmune recognition and attack.

The relationship between these different beta-cell subpopulations is still being defined, as the markers discussed above may represent unique or overlapping populations. For example, each of these functional and transcriptional subpopulations represents a unique niche that may have specific susceptibilities or contributions to T1D. Is a hub cell the same as a β1 or flattop⁺ cell? What are the differences and are they physiologically relevant? What is the relationship between these cell types and how do those relationships change with age and nutrition state? Understanding the relationship between functionality, heterogeneity, and vulnerability will provide a deeper understanding of T1D etiology, potentially setting the stage for more effective therapeutic strategies.

Chemokines are a family of small molecules involved in lymphoid physiology, pathology, and hemopoietic cell migration (111, 112). Chemokines can be broadly separated into two categories: constitutive and inducible (113). Constitutive chemokines perform homeostatic functions involving non-inflammatory leukocyte trafficking, while inducible chemokines are produced in response to inflammation to recruit activated leukocytes to the sites of damage or stress (114). Multiple chemokines, detailed below, are secreted by the beta-cell and contribute to immune infiltration into the islet (Figure 2I). These secreted factors make the beta-cell a target for immune invasion and destruction.

Of the infiltrating cells causing insulitis in T1D, T cells are the major destroyer of beta-cells, with both CD4 and CD8 T cells being required to effectively transfer disease (146–148). CD4 and CD8 T cells have different roles in disease development (149). When activated, CD4 T cells or T “helper” cells influence the activation of surrounding immune cells through the production of pro- or anti-inflammatory cytokines (149). Human CD4 T cells conventionally recognize peptides presented on HLA-II molecules expressed by antigen-presenting cells (APCs), but the expression of HLA-II has also been detected on beta-cells from patients with T1D (150). CD8 T cells recognize peptides presented on MHC-I on mouse cells and HLA-I on human cells (Figure 2D). Islets biopsied from patients with T1D displayed HLA-I hyperexpression, which warrants their susceptibility to CD8 T cell-mediated destruction (151). The activation of CD8 T cells leads to the differentiation of CD8 T cells to become cytolytic T lymphocytes (CTLs) resulting in the directed release of cytotoxic cytokines, cytolytic granules, and Fas ligand (FasL)-mediated death of the target cell. Beta-cells from diabetic patients not only express HLA-I/II molecules, but also the Fas receptor (CD95/Apo-1) (152–154) (Figure 2J). Fas/FasL signaling is suggested to play a role in T1D pathology as NOD mice deficient in Fas do not develop inflammation or diabetes (155). Fas-deficient mice are also protected against adoptive transfer of splenocytes from diabetic NOD mice. In rodent and human islets, the expression of Fas receptor in beta-cells is induced by proinflammatory cytokines IL-1α, IL-1β, IFNγ, and the upregulation of iNOS, as sequestering NO in the beta-cell decreases Fas expression (156, 157). Fas/FasL signaling in the beta-cell leads to apoptosis via the activation of caspase 8 and the mitochondrial pathway of apoptosis (158). Beta-cell-derived proinflammatory chemokines, HLA-I/II (or MHC-I/II) molecules, and Fas/FasL receptors can perpetuate T1D disease progression by promoting immune cell recruitment, T cell activation, and subsequent beta-cell destruction. Since autoantibodies can be detected in circulation for years prior to disease onset (159) and patients from the Medalist study (discussed below) retain a portion of insulin-secreting beta-cells, these observations provide evidence that at least some beta-cell populations may possess mechanisms to evade the immune response.

As mentioned above, after the HLA locus, the 5’ upstream region of the INS locus is the genomic region with the strongest association with T1D risk (167). Specifically, it is the INS-VNTR (variable number tandem repeat) locus that confers susceptibility differences. The VNTR alleles are defined by two classes: class I (26-63 repeats) and class III (140-200 repeats). The shorter class I VNTRs confer a 2-5-fold increase in T1D risk while the longer class III allele is protective against T1D, this is thought to be due to effects on proinsulin expression in the thymus (169). Class III VNTRs are associated with increased INSULIN transcription in the thymus during induction of central immune tolerance. The authors proposed that these increased thymic insulin levels may promote negative selection of insulin-specific T cells, ultimately leading to a protective effect on T1D susceptibility. Class I VNTRs result in decreased INSULIN transcription in the thymus and potentially allow insulin-specific autoreactive T cells to escape from the thymus due to defects in central tolerance and negative selection. This class I versus III allele-specific mechanism illustrates the complexity of T1D risk, and while this susceptibility locus can absolutely be seen as a beta-cell associated SNP, the proposed mechanisms that have been defined thus far are still largely immune system focused. So, despite the 100 years since the discovery of insulin, large gaps in our understanding of how it is involved in the response of the beta-cell itself in T1D remain.

Due to the scarcity of studies of beta-cell contributions to T1D, many associations have been made between beta-cell death and failure in T1D and T2D. Remarkably, GWAS indicates there are very few susceptibility loci associated with both maladies. Variations of the Kruppel-like zinc finger transcription factor GLIS3 are one of the few that have been strongly associated with both T1 and T2D (170). Also a known MODY (Maturity Onset Diabetes of the Young) gene, GLIS3 is expressed predominantly in the pancreas, thyroid, and kidney. While there have been some discrepancies in the literature regarding the exact timing of expression, in GLIS3-EGFP knock-in mice, GLIS3 mainly co-expresses with Sox9 in bipotent pancreatic islet progenitor cells and is absent from the acinar progenitors at embryonic day (E)13.5. This pattern correlates with its importance in development of the pancreatic endocrine lineage and seeming negligibility for the exocrine portion of the pancreas. The association between GLIS3 and T1D was first identified in European populations but has more recently been recapitulated in a Pakistani cohort (171, 172). Interestingly, a GLIS3 variant (A908V) is associated with T1D resistance in Japanese patients (173). Considering the thymic expression of GLIS3, the authors propose that perhaps this variant induces central or peripheral immune tolerance more efficiently than the wild-type variant, but more studies are needed to understand this mechanism. Regarding the molecular mechanisms that underlie the associations of GLIS3 and diabetes, little was known until multiple groups independently generated both global and beta-cell-specific GLIS3 knockout models (174–176). Not only do GLIS3^-/- mice die within the first few days of life, but their islet area is approximately 15% that of littermate controls (177). Insulin production was reduced by 80%, making it difficult to assess GSIS in these mice. These groups also found that the endocrine progenitor gene, Ngn3, is a GLIS3 target, and that GLIS3 physically and functionally interacts with the beta-cell transcription factor Pdx1 to regulate insulin transcription. Interestingly, GLIS3 overexpression leads to an upregulation of Ngn3 mRNA in ductal cells, further supporting the role of GLIS3 in pancreatic islet progenitor specification. Pancreatic progenitors, as well as the adult acinar compartment, seem to be unperturbed in GLIS3 knockouts showing an islet progenitor specificity to its role during development. These mechanisms do not tie directly to T1D association but understanding the role of GLIS3 in beta-cell identity may help unveil the mechanisms involved in both T1 and T2D disease susceptibility.

C-type lectin domain family 16, member A (CLEC16a) is a gene locus associated with T1D, multiple sclerosis, and adrenal dysfunction (98, 178, 179). Though genetic associations have been long established, until the work of Soleimanpour et al., a molecular basis for how CLEC16A might increase T1D was unknown. Interestingly, these investigators found that mouse Clec16a interacts with Nrdp1 (an E3 ubiquitin ligase) and has roles in normal GSIS in the beta-cell (180). Pancreatic Clec16a deletion causes reduced ATP levels and mitochondrial oxygen consumption, establishing the factor as a novel regulator of beta-cell mitophagy. Additionally, patients with the T1D-associated SNP in the CLEC16A gene exhibit reductions in CLEC16A expression and perturbed insulin secretion.

These observations of impaired insulin and glucose homeostasis, along with ER-stress in their mouse model, are some of the few providing insight into the non-immune related mechanisms of T1D (180). ER-stress and perturbations in first-phase insulin release are among the earliest signs of T1D, predating immune infiltration and insulitis (181, 182). The role of Clec16a in these processes not only highlights its crucial role in beta-cell function, but also establishes it as a potential player in the first steps of beta-cell vulnerability in T1D.

Delta-like 1 (DLK1), also known as DLL1 or Pref-1 (preadipocyte factor 1), is a transmembrane protein that belongs to the Delta-Notch signaling family. Both mouse and human Dlk1 are known to be subject to genomic imprinting, and Dlk1 is paternally inherited, with the maternal gene being silenced during development. This becomes potentially interesting considering the sexual discordance in inheritance risk in T1DM, as risk of transmission to offspring is 1.7 fold higher from diabetic fathers than mothers (183). However, Wurst et al. found that, in the case of gestational diabetes mellitus (GDM), serum Dlk1 levels were not significantly different between diabetic and control patients (184). Mouse Dlk1 is expressed highly and ubiquitously during development in the embryo and placenta, starting around E11.5, but becomes downregulated in most adult tissues. Adult Dlk1 expression becomes restricted to the beta-cell, bone marrow, pituitary, and adrenal glands. Some evidence suggests that Dlk1 may help undifferentiated cells maintain their pluripotent state, working as a growth factor to maintain proliferation. In preadipocytes, Dlk1 must be downregulated for differentiation to occur (185). Rodent models have remained somewhat controversial, as Dlk1 null mice have partially penetrant neonatal lethality and complex adult and developmental phenotypes, yet conditional loss of function models in various tissues using floxed mice failed to recapitulate null phenotypes (186, 187). Dlk1 beta-cell knockout mice were found to be fully viable with normal islet architecture up to six weeks of age, though glycemic control was not assessed. More thorough analyses of Dlk1 in the beta-cell, including insulin secretion and glucose tolerance, are required to fully understand if and how it may contribute to both function and potentially pathogenesis of T1D.

The genetic basis of T1D pathogenesis is complicated and still poorly understood. A majority of these studies have been conducted using data exclusively from Caucasian patients, and inclusion of multi-ethnic populations is required for a more complete and accurate understanding of genetic variants. The few studies using African-ancestry participants have already yielded unique haplotypes and signatures (188, 189). Additionally, as mentioned above, non-coding DNA regulatory regions make up a majority of T1D associated SNPs, which suggests that genetic variation may be impacting regulatory functions rather than gene-coding abilities (165, 166). Gene expression can be controlled via long-range interactions, with regulatory elements impacting genes that are hundreds of kilobases away. Understanding these potential interactions requires employment of techniques such as chromatin conformation capture, building a more complete picture of how these SNPs may regulate distant genes through physical contact with non-adjacent promoters. Recently, the use of chromatin-accessibility quantitative trait loci (caQTL) and fine mapping analysis expanded the genetic variants and loci associated with T1D and provided novel molecular targets to investigate (190). Whether it be immune or beta-cell related, understanding these “true” gene targets is a vital steppingstone in leveraging this genetic information to develop diagnostic and therapeutic solutions to T1D.

Development of compounds targeting ER-stress pathways are being explored to prevent beta-cell death in early onset T1D. The three UPR sensors PERK, ATF6, and IRE1α regulate apoptosis and thus make a promising target for reducing ER stress and subsequent death in the beta-cell (191). Tauroursdoxycholic acid (TUDCA), a naturally occurring bile acid, can reduce ER-stress by inhibiting the dissociation of BiP from PERK, preventing cell death (192). In a multiple low-dose STZ C57BL/6 mouse model of beta-cell death, TUDCA improved glucose tolerance, increased beta-cell mass, and improved glycemia compared to control diabetic mice (193). The benefits of TUDCA and other UPR chaperones continue to be investigated for their ability to prevent ER-stress induced apoptosis in T1D (194).

An ongoing clinical trial using imatinib mesylate (brand name Gleevec), a tyrosine kinase inhibitor, shows promising results in targeting beta-cell ER stress (195). The efficacy of imatinib for the treatment of various immune-mediated diseases is currently being tested. Initially found to abrogate type 2 diabetes in db/db mice, imatinib treatment in the NOD mouse was able to reverse autoimmune diabetes (196, 197). By blunting IRE1α RNase hyperactivity, imatinib reduces beta-cell apoptosis and preserves physiological function. In humans, a clinical trial found imatinib preserved beta-cell function at 12 months in adults with recent-onset T1D (195). Ongoing studies will investigate dose and duration of therapy as well as safety and efficacy for use in children.

Considering the major role of oxidative stress in T1D pathogenesis, therapies designed to improve antioxidant defenses in beta-cells are another promising avenue for clinical use. Thioredoxin interacting protein (TXNIP), a thioredoxin (TRX) inhibitor of the peroxiredoxin/thioredoxin detoxification pathway has demonstrated clinical potential in both animal models and initial clinical trials (198). The binding of TXNIP to TRX promotes oxidative stress by preventing peroxide clearance. TXNIP is elevated in patients with T1D and T2D (199). In vivo overexpression of TXNIP in mouse beta-cells induces apoptosis, while inhibition is protective against STZ-induced diabetes (200). Anti-diabetic agents including insulin and metformin, were found to augment TXNIP degradation through activation of adenosine monophosphate activated protein kinase (AMPK), supporting the idea that TXNIP may be a viable clinical target (201). Verapamil, which blocks voltage-gated calcium channels, decreased TXNIP and enhanced beta-cell survival in both human and rodent islets (202). A clinical trial in which Verapamil was administered (along with insulin therapy) promoted patient beta-cell function and lowered exogenous insulin requirements (203). TXNIP is expressed in multiple cell types throughout the body and additional clinical trials (NCT04545151, NCT04233034) are ongoing to explore the efficacy of TXNIP inhibition in protecting beta-cells and affecting autoimmune responses in patients with T1D.

For both imatinib, an FDA approved anti-leukemia drug, and verapamil, a widely used anti-hypertensive, the ability to repurpose drugs already on the market and with established safety profiles for T1D is an attractive way to expedite the often lengthy and costly process of bringing drugs to market.

The development of human stem cells for clinical use may provide a long-term solution to T1D without the challenge of organ shortage and HLA mismatch. While for years now, stem cell-derived insulin-producing cells can be generated and studied in the lab, improvements in cell viability, identity, and reproducibility may be needed before they can be applied as a safe and affordable therapy (204). Beta-cell differentiation from multiple cell sources have been attempted, the most promising of which seem to be induced pluripotent stem cell (iPSC)-derived beta-cells (205). Clinical trials are in progress investigating the efficacy of treating T1D with one version of iPSC derived cells designed by Viacyte, Inc. and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) Therapeutics (NCT03163511).

Combining iPSC-derived cells with CRISPR gene editing technology allows the potential to correct monoallelic mutations in genes, such as those that cause MODY diabetes. Optimal for T1D treatment, some therapies are also focused on creating “stealthy” beta-cells and islets, that can be specifically engineered to evade immune recognition. One gene of interest is renalase (Rnls), which encodes for an FAD-dependent amine oxidase enzyme that was identified in NIT-1 cells. Rnls deletion elicits beta-cell protection against autoimmune attack. NIT-1 cells carrying the Rnls mutation improved graft survival when transplanted into diabetic NOD mice (206). Using CRISPR, human iPSCs were generated lacking RNLS. These RNLS^-/- iPSCs could be successfully differentiated and exhibited normal insulin secretion in vitro. Thus far, the in vivo function of these cells has not been reported. Similarly, some groups have recently generated iPSC lines that are “hypoimmune” by inactivating MHC class I and II genes and overexpressing protective marker CD47 (207, 208). These stem cells evade immune rejection in fully competent recipients, while maintaining their pluripotency. Hypoimmune stem cells have been used to treat pulmonary and cardiovascular disease and have major implications for universal transplantation (209). The widespread availability of techniques such as single-cell RNA sequencing can bolster the design of iPSC derived beta-cells by identifying the gene expression repertoire needed to obtain the appropriate distribution of cell types within the islet (210).

An exciting new technology that may aid in our understanding of the T1D beta-cell is Patch-Seq, a powerful method that can link single-cell transcriptomes with electrophysiology measurements (211). Groups using this technique in the beta-cell may be able to uncover various levels of beta-cell heterogeneity and link it to functionality in both healthy and T1D contexts. The Yoshihara group is also working on combining concepts with their work reproducing disease with 3D organoids engineered to model immune invasion (212). Overexpression of PD-L1 in human islet organoids was able to protect xenografts from immune invasion and restore glucose homeostasis for 50 days in immune competent mice. Similarly, the Melero-Martin group has used a combinatorial approach to incorporate the importance of islet vasculature in their studies. Their “vascular organoids” include microvessels that become perfused during transplantation and even reduce the islet requirement for transplantation, highlighting the importance of vascular cell types for ideal glucose regulation (213). More combinatorial therapies should be explored as we continue to uncover the nuances of the beta-cell and immunological interface of T1D pathology.