Novel pancreatic endocrine maturation pathways identified by genomic profiling and causal reasoning

STAT3 Regulates Mitochondrial Gene Expression in Pancreatic β-Cells and Its Deficiency Induces Glucose Intolerance in Obesity

Most obese and insulin-resistant individuals do not develop diabetes. This is the result of the capacity of β-cells to adapt and produce enough insulin to cover the needs of the organism. The underlying mechanism of β-cell adaptation in obesity, however, remains unclear. Previous studies have suggested a role for STAT3 in mediating β-cell development and human glucose homeostasis, but little is known about STAT3 in β-cells in obesity. We observed enhanced cytoplasmic expression of STAT3 in severely obese subjects with diabetes. To address the functional role of STAT3 in adult β-cells, we generated mice with tamoxifen-inducible partial or full deletion of STAT3 in β-cells and fed them a high-fat diet before analysis. Interestingly, β-cell heterozygous and homozygous STAT3-deficient mice showed glucose intolerance when fed a high-fat diet. Gene expression analysis with RNA sequencing showed that reduced expression of mitochondrial genes in STAT3 knocked down human EndoC-β¹H cells, confirmed in FACS-purified β-cells from obese STAT3-deficient mice. Moreover, silencing of STAT3 impaired mitochondria activity in EndoC-β¹H cells and human islets, suggesting a mechanism for STAT3-modulated β-cell function. Our study postulates STAT3 as a novel regulator of β-cell function in obesity.

Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks

BACKGROUND

Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks.

RESULTS

In this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor.

CONCLUSIONS

In this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.

Interleukin-6 as a potential positive modulator of human beta-cell function: an exploratory analysis-the Verona Newly Diagnosed Type 2 Diabetes Study (VNDS) 6

AIMS

Recent studies in mouse models of T2D showed that interleukin-6 (IL-6), released from skeletal muscle, is associated with increased glucose-dependent insulin secretion. Few data currently exist exploring the relationship between IL-6 and beta-cell function in humans. We investigated whether IL-6 is positively associated with beta-cell function in newly diagnosed T2D. We extended the same analyses to IL-10, because it regulated similarly to IL-6 in skeletal muscle, and TNF-α and C-reactive protein (CRP), as general biomarkers of inflammation.

METHODS

In 330 VNDS participants, we assessed (1) basal plasma concentrations of IL-6, IL-10, TNF-α, and CRP; (2) beta-cell function, estimated by OGTT minimal modeling and expressed as derivative (DC) and proportional control (PC); (3) insulin sensitivity, by euglycemic insulin clamp.

RESULTS

IL-6 was positively associated with PC in both univariate analysis (p = 0.04) and after adjustment for age, sex, BMI, HbA1c, and M-clamp (p = 0.01). HbA1c was the major independent contributor to the overall variance of PC (16 %), followed by BMI and IL-6 (~2 % each). Similar results were obtained for IL-10 (p = 0.048, univariate; p = 0.04, fully adjusted). TNF-α and CRP were not significantly associated with any component of beta-cell function.

CONCLUSIONS

Our data are the first evidence in human subjects that an endocrine loop involving IL-6 may act as positive modulator of glucose-dependent insulin secretion. Further functional studies are needed to corroborate IL-6 system as a potential druggable target in diabetes.

CLINICAL TRIAL REGISTRATION NUMBER

NCT01526720 ( http://www.clinicaltrial.gov ).

Endothelial cells mediate islet-specific maturation of human embryonic stem cell-derived pancreatic progenitor cells

It is well recognized that in vitro differentiation of embryonic stem cells (ESC) can be best achieved by closely recapitulating the in vivo developmental niche. Thus, implementation of directed differentiation strategies has yielded encouraging results in the area of pancreatic islet differentiation. These strategies have concentrated on direct addition of chemical signals, however, other aspect of the developmental niche are yet to be explored. During development, pancreatic progenitor (PP) cells grow as an epithelial sheet, which aggregates with endothelial cells (ECs) during the final stages of maturation. Several findings suggest that the interactions with EC play a role in pancreatic development. In this study, we recapitulated this phenomenon in an in vitro environment by maturing the human ESC (hESC)-derived PP cells in close contact with ECs. We find that co-culture with different ECs (but not fibroblast) alone results in pancreatic islet-specific differentiation of hESC-derived PP cells even in the absence of additional chemical induction. The differentiated cells responded to exogenous glucose levels by enhanced C-peptide synthesis. The co-culture system aligned well with endocrine development as determined by comprehensive analysis of involved signaling pathways. By recapitulating cell-cell interaction aspects of the developmental niche we achieved a differentiation model that aligns closely with islet organogenesis.

Profiling of embryonic stem cell differentiation

Embryonic stem (ES) cells have been shown to recapitulate normal developmental stages. They are therefore a highly useful tool in the study of developmental biology. Profiling of ES cell-derived cells has yielded important information about the characteristics of differentiated cells, and allowed the identification of novel marker genes and pathways of differentiation. In this review, we focus on recent results from profiling studies of mouse embryos, human islets, and human ES cell-derived differentiated cells from several research groups. Global gene expression data from mouse embryos have been used to identify novel genes or pathways involved in the developmental process, and to search for transcription factors that regulate direct reprogramming. We introduce gene expression databases of human pancreas cells (Beta Cell Gene Atlas, EuroDia database), and summarize profiling studies of islet- or human ES cell-derived pancreatic cells, with a focus on gene expression, microRNAs, epigenetics, and protein expression. Then, we describe our gene expression profile analyses and our search for novel endoderm, or pancreatic, progenitor marker genes. We differentiated mouse ES cells into mesendoderm, definitive endoderm (DE), mesoderm, ectoderm, and Pdx1-expressing pancreatic lineages, and performed DNA microarray analyses. Genes specifically expressed in DE, and/or in Pdx1-expressing cells, were extracted and their expression patterns in normal embryonic development were studied by in situ hybridization. Out of 54 genes examined, 27 were expressed in the DE of E8.5 mouse embryos, and 15 genes were expressed in distinct domains in the pancreatic buds of E14.5 mouse embryos. Akr1c19, Aebp2, Pbxip1, and Creb3l1 were all novel, and none has been described as being expressed, either in the DE, or in the pancreas. By introducing the profiling results of ES cell-derived cells, the benefits of using ES cells to study early embryonic development will be discussed.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach

MOTIVATION

The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.

METHODS

We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile.

RESULTS

Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated.

AVAILABILITY AND IMPLEMENTATION

Source code for the method is available upon request.