Scrt1, a transcriptional regulator of β-cell proliferation identified by differential chromatin accessibility during islet maturation

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Characterizing the SREB G protein-coupled receptor family in fish: Brain gene expression and genomic differences in upstream transcription factor binding sites

The SREB (Super-conserved Receptors Expressed in Brain) family of orphan G protein-coupled receptors is highly conserved in vertebrates and consists of three members: SREB1 (orphan designation GPR27), SREB2 (GPR85), and SREB3 (GPR173). SREBs are associated with processes ranging from neuronal plasticity to reproductive control. Relatively little is known about similarities across the entire family, or how mammalian gene expression patterns compare to non-mammalian vertebrates. In fish, this system may be particularly complex, as some species have gained a fourth member (SREB3B) while others have lost genes. To better understand the system, the present study aimed to: 1) use qPCR to characterize sreb and related gene expression patterns in the brains of three fish species with different systems, and 2) identify possible differences in transcriptional regulation among the receptors, using upstream transcription factor binding sites across 70 ray-finned fish genomes. Overall, regional patterns of sreb expression were abundant in forebrain-related areas. However, some species-specific patterns were detected, such as abundant expression of receptors in zebrafish (Danio rerio) hypothalamic-containing sections, and divergence between sreb3a and sreb3b in pufferfish (Dichotomyctere nigroviridis). In addition, a gene possibly related to the system (dkk3a) was spatially correlated with the receptors in all three species. Genomic regions upstream of sreb2 and sreb3b, but largely not sreb1 or sreb3a, contained many highly conserved transcription factor binding sites. These results provide novel information about expression differences and transcriptional regulation across fish that may inform future research to better understand these receptors.

Understanding cell fate acquisition in stem-cell-derived pancreatic islets using single-cell multiome-inferred regulomes

Pancreatic islet cells derived from human pluripotent stem cells hold great promise for modeling and treating diabetes. Differences between stem-cell-derived and primary islets remain, but molecular insights to inform improvements are limited. Here, we acquire single-cell transcriptomes and accessible chromatin profiles during in vitro islet differentiation and pancreas from childhood and adult donors for comparison. We delineate major cell types, define their regulomes, and describe spatiotemporal gene regulatory relationships between transcription factors. CDX2 emerged as a regulator of enterochromaffin-like cells, which we show resemble a transient, previously unrecognized, serotonin-producing pre-β cell population in fetal pancreas, arguing against a proposed non-pancreatic origin. Furthermore, we observe insufficient activation of signal-dependent transcriptional programs during in vitro β cell maturation and identify sex hormones as drivers of β cell proliferation in childhood. Altogether, our analysis provides a comprehensive understanding of cell fate acquisition in stem-cell-derived islets and a framework for manipulating cell identities and maturity.

Functional, metabolic and transcriptional maturation of human pancreatic islets derived from stem cells

Transplantation of pancreatic islet cells derived from human pluripotent stem cells is a promising treatment for diabetes. Despite progress in the generation of stem-cell-derived islets (SC-islets), no detailed characterization of their functional properties has been conducted. Here, we generated functionally mature SC-islets using an optimized protocol and benchmarked them comprehensively against primary adult islets. Biphasic glucose-stimulated insulin secretion developed during in vitro maturation, associated with cytoarchitectural reorganization and the increasing presence of alpha cells. Electrophysiology, signaling and exocytosis of SC-islets were similar to those of adult islets. Glucose-responsive insulin secretion was achieved despite differences in glycolytic and mitochondrial glucose metabolism. Single-cell transcriptomics of SC-islets in vitro and throughout 6 months of engraftment in mice revealed a continuous maturation trajectory culminating in a transcriptional landscape closely resembling that of primary islets. Our thorough evaluation of SC-islet maturation highlights their advanced degree of functionality and supports their use in further efforts to understand and combat diabetes.

Pancreatic islets derived from stem cells are benchmarked against primary cells.

Mitogen Synergy: An Emerging Route to Boosting Human Beta Cell Proliferation

Decreased number and function of beta cells are a key aspect of diabetes mellitus (diabetes), a disease that remains an onerous global health problem. Means of restoring beta cell mass are urgently being sought as a potential cure for diabetes. Several strategies, such as de novo beta cell derivation via pluripotent stem cell differentiation or mature somatic cell transdifferentiation, have yielded promising results. Beta cell expansion is another promising strategy, rendered challenging by the very low proliferative capacity of beta cells. Many effective mitogens have been identified in rodents, but the vast majority do not have similar mitogenic effects in human beta cells. Extensive research has led to the identification of several human beta cell mitogens, but their efficacy and specificity remain insufficient. An approach based on the simultaneous application of several mitogens has recently emerged and can yield human beta cell proliferation rates of up to 8%. Here, we discuss recent advances in restoration of the beta cell population, focusing on mitogen synergy, and the contribution of RNA-sequencing (RNA-seq) to accelerating the elucidation of signaling pathways in proliferating beta cells and the discovery of novel mitogens. Together, these approaches have taken beta cell research up a level, bringing us closer to a cure for diabetes.