Function-based discovery of significant transcriptional temporal patterns in insulin stimulated muscle cells

Human and mouse muscle transcriptomic analyses identify insulin receptor mRNA downregulation in hyperinsulinemia-associated insulin resistance

Hyperinsulinemia is commonly viewed as a compensatory response to insulin resistance, yet studies have demonstrated that chronically elevated insulin may also drive insulin resistance. The molecular mechanisms underpinning this potentially cyclic process remain poorly defined, especially on a transcriptome-wide level. Transcriptomic meta-analysis in >450 human samples demonstrated that fasting insulin reliably and negatively correlated with INSR mRNA in skeletal muscle. To establish causality and study the direct effects of prolonged exposure to excess insulin in muscle cells, we incubated C2C12 myotubes with elevated insulin for 16 h, followed by 6 h of serum starvation, and established that acute AKT and ERK signaling were attenuated in this model of in vitro hyperinsulinemia. Global RNA-sequencing of cells both before and after nutrient withdrawal highlighted genes in the insulin receptor (INSR) signaling, FOXO signaling, and glucose metabolism pathways indicative of 'hyperinsulinemia' and 'starvation' programs. Consistently, we observed that hyperinsulinemia led to a substantial reduction in Insr gene expression, and subsequently a reduced surface INSR and total INSR protein, both in vitro and in vivo. Bioinformatic modeling combined with RNAi identified SIN3A as a negative regulator of Insr mRNA (and JUND, MAX, and MXI as positive regulators of Irs2 mRNA). Together, our analysis identifies mechanisms which may explain the cyclic processes underlying hyperinsulinemia-induced insulin resistance in muscle, a process directly relevant to the etiology and disease progression of type 2 diabetes.

Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving β-Cell Maturation

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca²⁺ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

FunPat: function-based pattern analysis on RNA-seq time series data

BACKGROUND

Dynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis.

METHODS

Here we present FunPat, an R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile.

RESULTS

FunPat performance was assessed on both simulated and real data. With respect to a stand-alone selection step, the integration of the clustering step is able to improve the recall without altering the false discovery rate. FunPat also shows high precision and recall in detecting the correct temporal expression patterns; in particular, the recall is significantly higher than hierarchical, k-means and a model-based clustering approach specifically designed for RNA sequencing data. Moreover, when biological replicates are missing, FunPat is able to provide reproducible lists of significant genes. The application to real time series expression data shows the ability of FunPat to select differentially expressed genes with high reproducibility, indirectly confirming high precision and recall in gene selection. Moreover, the expression patterns obtained as output allow an easy interpretation of the results.

CONCLUSIONS

A novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of gene selection by integrating the statistical evidence of differential expression with the information on temporal profiles and the functional annotations. Significant genes are associated to both the most informative functional terms, avoiding redundancy of information, and the most representative temporal patterns, thus improving the readability of the results. FunPat package is provided in R/Bioconductor at link: http://sysbiobig.dei.unipd.it/?q=node/79.

A community computational challenge to predict the activity of pairs of compounds

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

A novel insulin receptor-binding protein from Momordica charantia enhances glucose uptake and glucose clearance in vitro and in vivo through triggering insulin receptor signaling pathway

Diabetes, a common metabolic disorder, is characterized by hyperglycemia. Insulin is the principal mediator of glucose homeostasis. In a previous study, we identified a trypsin inhibitor, named Momordica charantia insulin receptor (IR)-binding protein (mcIRBP) in this study, that might interact with IR. The physical and functional interactions between mcIRBP and IR were clearly analyzed in the present study. Photo-cross-linking coupled with mass spectrometry showed that three regions (17-21, 34-40, and 59-66 residues) located on mcIRBP physically interacted with leucine-rich repeat domain and cysteine-rich region of IR. IR-binding assay showed that the binding behavior of mcIRBP and insulin displayed a cooperative manner. After binding to IR, mcIRBP activated the kinase activity of IR by (5.87 ± 0.45)-fold, increased the amount of phospho-IR protein by (1.31 ± 0.03)-fold, affected phosphoinositide-3-kinase/Akt pathways, and consequently stimulated the uptake of glucose in 3T3-L1 cells by (1.36 ± 0.12)-fold. Intraperitoneal injection of 2.5 nmol/kg mcIRBP significantly decreased the blood glucose levels by 20.9 ± 3.2% and 10.8 ± 3.6% in normal and diabetic mice, respectively. Microarray analysis showed that mcIRBP affected genes involved in insulin signaling transduction pathway in mice. In conclusion, our findings suggest that mcIRBP is a novel IRBP that binds to sites different from the insulin-binding sites on IR and stimulates both the glucose uptake in cells and the glucose clearance in mice.

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells

BACKGROUND

Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events.

RESULTS

Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3β) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3β, with a significant increase at 100 μM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3β and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3β, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition.

CONCLUSIONS

These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3β phosphorylation and glycogen synthesis.

Insulin-dependent transcriptional control in L6 rat myotubes is associated with modulation of histone acetylation and accumulation of the histone variant H2A.Z in the proximity of the transcriptional start site

Besides its direct metabolic effects, insulin induces transcriptional alterations in its target tissues. However, whether such changes are accompanied by epigenetic changes on the chromatin template encompassing insulin responsive genes is unclear. Here, mRNA levels of insulin-responsive genes hexokinase 2 (Hk2), insulin receptor substrate (Irs2), and the PI3K subunit p85β (Pik3r2) were compared in control versus insulin-stimulated L6 myotubes. Chromatin immunoprecipitation (ChIP) was performed with antibodies directed to histone H2A, histone variant H2A.Z, acetylated histone H3 on lysines 9/14, and acetylated H2A.Z. Insulin induced a more than 2-fold Hk2 mRNA increase, while Irs2 and Pik3r2 were downregulated. ChIP to H2A and H2A.Z showed higher H2A.Z accumulation around the transcriptional start site (TSS) of these insulin-modulated genes, while H2A.Z accumulation was lower distally to the TSS in the Hk2 promoter. H2A.Z levels and H3K9/14 acetylation correlated on several loci along the Hk2 gene, and H3K9/14 as well as H2A.Z acetylation was enhanced by insulin treatment. On the contrary, reduced H3K9/14 acetylation was observed in insulin-repressed Irs2 and Pik3r2, and recovery of acetylation by treatment with the histone deacetylase inhibitor trichostatin A reverted insulin-induced Irs2 downregulation. The chromatin regions encompassing selected insulin-responsive genes are thus featured by accumulation of H2A.Z around the TSS. H2A.Z accumulation facilitates insulin-dependent modulation of pharmacologically treatable H3K9/14 and H2A.Z acetylations. Indeed, inhibition of histone deacetylases by TSA treatment reverted insulin induced Irs2 gene downregulation. Dysregulated histone acetylation may thus be potentially targeted with histone deacetylase inhibitors.

p90 ribosomal S6 kinases play a significant role in early gene regulation in the cardiomyocyte response to G(q)-protein-coupled receptor stimuli, endothelin-1 and α(1)-adrenergic receptor agonists

ERK1/2 (extracellular-signal-regulated kinase 1/2) and their substrates RSKs (p90 ribosomal S6 kinases) phosphorylate different transcription factors, contributing differentially to transcriptomic profiles. In cardiomyocytes ERK1/2 are required for >70% of the transcriptomic response to endothelin-1. In the present study we investigated the role of RSKs in the transcriptomic responses to the G_q-protein-coupled receptor agonists endothelin-1, phenylephrine (a generic α₁-adrenergic receptor agonist) and A61603 (α_1A-adrenergic receptor selective). Phospho-ERK1/2 and phospho-RSKs appeared in cardiomyocyte nuclei within 2–3 min of stimulation (endothelin-1>A61603≈phenylephrine). All agonists increased nuclear RSK2, but only endothelin-1 increased the nuclear RSK1 content. PD184352 (inhibits ERK1/2 activation) and BI-D1870 (inhibits RSKs) were used to dissect the contribution of RSKs to the endothelin-1-responsive transcriptome. Of the 213 RNAs up-regulated after 1 h, 51% required RSKs for their up-regulation, whereas 29% required ERK1/2 but not RSKs. The transcriptomic response to phenylephrine overlapped with, but was not identical with, endothelin-1. As with endothelin-1, PD184352 inhibited the up-regulation of most phenylephrine-responsive transcripts, but the greater variation in the effects of BI-D1870 suggests that differential RSK signalling influences global gene expression. A61603 induced similar changes in RNA expression in cardiomyocytes as phenylephrine, indicating that the signal was mediated largely through α_1A-adrenergic receptors. A61603 also increased expression of immediate early genes in perfused adult rat hearts and, as in cardiomyocytes, up-regulation of the majority of genes was inhibited by PD184352. PD184352 or BI-D1870 prevented the increased surface area induced by endothelin-1 in cardiomyocytes. Thus RSKs play a significant role in regulating cardiomyocyte gene expression and hypertrophy in response to G_q-protein-coupled receptor stimulation.

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.