Transcriptional networks implicated in human nonalcoholic fatty liver disease

Altered splicing factor and alternative splicing events in a mouse model of diet- and polychlorinated biphenyl-induced liver disease

Non-alcoholic fatty liver disease (NAFLD) is associated with human environmental exposure to polychlorinated biphenyls (PCBs). Alternative splicing (AS) is dysregulated in steatotic liver disease and is regulated by splicing factors (SFs) and N-6 methyladenosine (m6A) modification. Here integrated analysis of hepatic mRNA-sequencing data was used to identify differentially expressed SFs and differential AS events (ASEs) in the livers of high fat diet-fed C57BL/6 J male mice exposed to Aroclor1260, PCB126, Aroclor1260 + PCB126, or vehicle control. Aroclor1260 + PCB126 co-exposure altered 100 SFs and replicate multivariate analysis of transcript splicing (rMATS) identified 449 ASEs in 366 genes associated with NAFLD pathways. These ASEs were similar to those resulting from experimental perturbations in m6A writers, readers, and erasers. These results demonstrate specific hepatic SF and AS regulatory mechanisms are disrupted by HFD and PCB exposures, contributing to the expression of altered isoforms that may play a role in NAFLD progression to NASH.

Alternative RNA Splicing in Fatty Liver Disease

Alternative RNA splicing is a process by which introns are removed and exons are assembled to construct different RNA transcript isoforms from a single pre-mRNA. Previous studies have demonstrated an association between dysregulation of RNA splicing and a number of clinical syndromes, but the generality to common disease has not been established. Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease affecting one-third of adults worldwide, increasing the risk of cirrhosis and hepatocellular carcinoma (HCC). In this review we focus on the change in alternative RNA splicing in fatty liver disease and the role for splicing regulation in disease progression.

Dysregulation of the Splicing Machinery Is Associated to the Development of Nonalcoholic Fatty Liver Disease

CONTEXT

Nonalcoholic fatty liver disease (NAFLD) is a common obesity-associated pathology characterized by hepatic fat accumulation, which can progress to fibrosis, cirrhosis, and hepatocellular carcinoma. Obesity is associated with profound changes in gene-expression patterns of the liver, which could contribute to the onset of comorbidities.

OBJECTIVE

As these alterations might be linked to a dysregulation of the splicing process, we aimed to determine whether the dysregulation in the expression of splicing machinery components could be associated with NAFLD.

PARTICIPANTS

We collected 41 liver biopsies from nonalcoholic individuals with obesity, with or without hepatic steatosis, who underwent bariatric surgery.

INTERVENTIONS

The expression pattern of splicing machinery components was determined using a microfluidic quantitative PCR-based array. An in vitro approximation to determine lipid accumulation using HepG2 cells was also implemented.

RESULTS

The liver of patients with obesity and steatosis exhibited a severe dysregulation of certain splicing machinery components compared with patients with obesity without steatosis. Nonsupervised clustering analysis allowed the identification of three molecular phenotypes of NAFLD with a unique fingerprint of alterations in splicing machinery components, which also presented distinctive hepatic and clinical-metabolic alterations and a differential response to bariatric surgery after 1 year. In addition, in vitro silencing of certain splicing machinery components (i.e., PTBP1, RBM45, SND1) reduced fat accumulation and modulated the expression of key de novo lipogenesis enzymes, whereas conversely, fat accumulation did not alter spliceosome components expression.

CONCLUSION

There is a close relationship between splicing machinery dysregulation and NAFLD development, which should be further investigated to identify alternative therapeutic targets.

Revisiting Connectivity Map from a gene co-expression network analysis

The Connectivity Map (CMap) is a tool that has been extensively utilized to study drug repositioning and side-effect prediction. However, most of these analyses rely on signature genes, ignoring the pathways by which those genes are regulated, as well as the functional overlap of redundant genes. The present study utilized a systems biology approach referred to as Weighted Gene Co-expression Network Analysis (WGCNA) to dissect the transcriptional profiles of CMap and reveal these hidden factors. Seven common modules associated with protein binding, extracellular matrix organization and translation were identified. Furthermore, drugs were clustered based on module expression to infer their mechanism of action (MoA) based on common activity profiles. As an extension of this, an example of disease-based module projection to identify novel drugs was provided. The analysis developed in the present study may provide a novel framework for drug repositioning or discovering MoAs.

Transcription Factor Co-expression Networks of Adipose RNA-Seq Data Reveal Regulatory Mechanisms of Obesity

BACKGROUND

Transcription Factors (TFs) control actuation of genes in the genome and are key mediators of complex processes such as obesity. Master Regulators (MRs) are the genes at the top of a regulation hierarchy which regulate other genes.

OBJECTIVE

To elucidate clusters of highly co-expressed TFs (modules), involved pathways, highly inter-connected TFs (hub-TFs) and MRs leading to obesity and leanness, using porcine model for human obesity.

METHODS

We identified 817 expressed TFs in RNA-Sequencing dataset representing extreme degrees of obesity (DO; lean, obese). We built a single Weighted Transcription Factor Co-expression Network (WTFCN) and TF sub-networks (based on the DO). Hub-TFs and MRs (using iRegulon) were identi-fied in biologically relevant WTFCNs modules.

RESULTS

Single WTFCN detected the Red module significantly associated with DO (P < 0.03). This module was enriched for regulation processes in the immune system, e.g.: Immune system process (Padj = 2.50E-06) and metabolic lifestyle disorders, e.g. Circadian rhythm - mammal pathway (Padj = 2.33E-11). Detected MR, hub-TF SPI1 was involved in obesity, immunity and osteoporosis. Within the obese sub-network, the Red module suggested possible associations with immunity, e.g. TGF-beta signaling pathway (Padj = 1.73E-02) and osteoporosis, e.g. Osteoclast differentiation (Padj = 1.94E-02). Within the lean sub-network, the Magenta module displayed associations with type 2 diabetes, obesity and os-teoporosis e.g. Notch signaling pathway (Padj = 2.40E-03), osteoporosis e.g. hub-TF VDR (a prime candidate gene for osteoporosis).

CONCLUSION

Our results provide insights into the regulatory network of TFs and biologically relevant hub TFs in obesity.

Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer's disease

Although the genetic causes for several rare, familial forms of Alzheimer’s disease (AD) have been identified, the etiology of the sporadic form of AD remains unclear. Here, we report a systems-level study of disease-associated proteome changes in human frontal cortex of sporadic AD patients using an integrated approach that combines mass spectrometry-based quantitative proteomics, differential expression analysis, and co-expression network analysis. Our analyses of 16 human brain tissues from AD patients and age-matched controls showed organization of the cortical proteome into a network of 24 biologically meaningful modules of co-expressed proteins. Of these, 5 modules are positively correlated to AD phenotypes with hub proteins that are up-regulated in AD, and 6 modules are negatively correlated to AD phenotypes with hub proteins that are down-regulated in AD. Our study generated a molecular blueprint of altered protein networks in AD brain and uncovered the dysregulation of multiple pathways and processes in AD brain, including altered proteostasis, RNA homeostasis, immune response, neuroinflammation, synaptic transmission, vesicular transport, cell signaling, cellular metabolism, lipid homeostasis, mitochondrial dynamics and function, cytoskeleton organization, and myelin-axon interactions. Our findings provide new insights into AD pathogenesis and suggest novel candidates for future diagnostic and therapeutic development.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics

Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

Upregulation of miR-181a impairs hepatic glucose and lipid homeostasis

The contributions of altered post-transcriptional gene silencing to the development of metabolic disorders remain poorly understood thus far. The objective of this study was to evaluate the roles of miR-181a in the regulation of hepatic glucose and lipid metabolism. MiR-181a is abundantly expressed in the liver, and we found that blood and hepatic miR-181a levels were significantly increased in patients and dairy cows with non-alcoholic fatty liver disease, as well as in high-fat diet and ob/ob mice. We determined that sirtuin1 is a target of miR-181a. Moreover, we found that hepatic sirtuin1 and peroxisome proliferator-activated receptor-γ coactivator-1α expression levels are downregulated, and acetylated peroxisome proliferator-activated receptor-γ coactivator-1α expression levels are upregulated in patients and dairy cows with non-alcoholic fatty liver disease, as well as in high-fat diet and ob/ob mice. MiR-181a overexpression inhibits the sirtuin1-peroxisome proliferator-activated receptor-γ coactivator-1α pathway, reduces insulin sensitivity, and increases gluconeogenesis and lipid synthesis in dairy cow hepatocytes and HepG2 cells. Conversely, silencing of miR-181a over-activates the sirtuin1-peroxisome proliferator-activated receptor-γ coactivator-1α pathway, increases insulin sensitivity and glycogen content, and decreases gluconeogenesis and lipid synthesis in hepatocytes, even under non-esterified fatty acids treatment conditions. Furthermore, miR-181a overexpression or sirtuin1 knockdown in mice increases lipid accumulation and decreases insulin sensitivity and glycogen content in the liver. Taken together, these findings indicate that increased hepatic miR-181a impairs glucose and lipid homeostasis by silencing sirtuin1 in non-alcoholic fatty liver disease.

Alternative RNA Splicing in the Pathogenesis of Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is becoming increasingly prevalent due to the worldwide obesity epidemic and currently affects one-third of adults or about one billion people worldwide. NAFLD is predicted to affect over 50% of the world's population by the end of the next decade. It is the most common form of liver disease and is associated with increased risk for progression to a more severe form non-alcoholic steatohepatitis, as well as insulin resistance, type 2 diabetes mellitus, cirrhosis, and eventually hepatocellular carcinoma. This review article will focus on the role of alternative splicing in normal liver physiology and dysregulation in liver disease.

MicroRNAs in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) has become the most common liver disorder. Strongly linked to obesity and diabetes, NAFLD has the characteristics of complex diseases with substantial heterogeneity. Accordingly, our ability to predict the risk of advanced NAFLD and provide efficient treatment may improve by a better understanding of the relationship between genotype and phenotype. MicroRNAs (miRNAs) play a major role in the fine-tuning of gene expression and they have recently emerged as novel biomarkers and therapeutic tools in the management of NAFLD. These short non-coding RNA sequences act by partial repression or degradation of targeted mRNAs. Deregulation of miRNAs has been associated with different stages of NAFLD, while their biological role in the pathogenesis remains to be fully understood. Systems biology analyses based on predicted target genes have associated hepatic miRNAs with molecular pathways involved in NAFLD progression such as cholesterol and lipid metabolism, insulin signaling, oxidative stress, inflammation, and pathways of cell survival and proliferation. Moreover, circulating miRNAs have been identified as promising noninvasive biomarkers of NAFLD and linked to disease severity. This rapidly growing field is likely to result in major advances in the pathomechanism, prognostication, and treatment of NAFLD.

A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication

BACKGROUND

Bats are able to employ an astonishingly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vocal bat species, Phyllostomus discolor.

RESULTS

Gene network analysis typically needs large sample sizes for correct clustering, this can be prohibitive where samples are limited, such as in this study. To overcome this, we developed a novel bioinformatics methodology for identifying robust co-expression gene networks using few samples (N=6). Using this approach, we identified tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalisation. The most highly connected network identified represented a cluster of genes involved in glutamatergic synaptic transmission. Glutamatergic receptors play a significant role in vocalisation from the PAG, suggesting that this gene network may be mechanistically important for vocal-motor control in mammals.

CONCLUSION

We have developed an innovative approach to cluster co-expressing gene networks and show that it is highly effective in detecting robust functional gene networks with limited sample sizes. Moreover, this work represents the first gene network analysis performed in a bat brain and establishes bats as a novel, tractable model system for understanding the genetics of vocal mammalian communication.