Using dynamic gene module map analysis to identify targets that modulate free fatty acid induced cytotoxicity

Global regulation of alternative RNA splicing by the SR-rich protein RBM39

BACKGROUND

RBM39 is a serine/arginine-rich RNA-binding protein that is highly homologous to the splicing factor U2AF65. However, the role of RBM39 in alternative splicing is poorly understood.

METHODS

In this study, RBM39-mediated global alternative splicing was investigated using RNA-Seq and genome-wide RBM39-RNA interactions were mapped via cross-linking and immunoprecipitation coupled with deep sequencing (CLIP-Seq) in wild-type and RBM39-knockdown MCF-7 cells.

RESULTS

RBM39 was involved in the up- or down-regulation of the transcript levels of various genes. Hundreds of alternative splicing events regulated by endogenous RBM39 were identified. The majority of these events were cassette exons. Genes containing RBM39-regulated alternative exons were found to be linked to G2/M transition, cellular response to DNA damage, adherens junctions and endocytosis. CLIP-Seq analysis showed that the binding site of RBM39 was mainly in proximity to 5' and 3' splicing sites. Considerable RBM39 binding to mRNAs encoding proteins involved in translation was observed. Of particular importance, ~20% of the alternative splicing events that were significantly regulated by RBM39 were similarly regulated by U2AF65.

CONCLUSIONS

RBM39 is extensively involved in alternative splicing of RNA and helps regulate transcript levels. RBM39 may modulate alternative splicing similarly to U2AF65 by either directly binding to RNA or recruiting other splicing factors, such as U2AF65.

GENERAL SIGNIFICANCE

The current study offers a genome-wide view of RBM39's regulatory function in alternative splicing. RBM39 may play important roles in multiple cellular processes by regulating both alternative splicing of RNA molecules and transcript levels.

Quantitative assessment of gene expression network module-validation methods

Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.

Spatiotemporal positioning of multipotent modules in diverse biological networks

A biological network exhibits a modular organization. The modular structure dependent on functional module is of great significance in understanding the organization and dynamics of network functions. A huge variety of module identification methods as well as approaches to analyze modularity and dynamics of the inter- and intra-module interactions have emerged recently, but they are facing unexpected challenges in further practical applications. Here, we discuss recent progress in understanding how such a modular network can be deconstructed spatiotemporally. We focus particularly on elucidating how various deciphering mechanisms operate to ensure precise module identification and assembly. In this case, a system-level understanding of the entire mechanism of module construction is within reach, with important implications for reasonable perspectives in both constructing a modular analysis framework and deconstructing different modular hierarchical structures.

Synergy analysis reveals association between insulin signaling and desmoplakin expression in palmitate treated HepG2 cells

The regulation of complex cellular activities in palmitate treated HepG2 cells, and the ensuing cytotoxic phenotype, involves cooperative interactions between genes. While previous approaches have largely focused on identifying individual target genes, elucidating interacting genes has thus far remained elusive. We applied the concept of information synergy to reconstruct a “gene-cooperativity” network for palmititate-induced cytotoxicity in liver cells. Our approach integrated gene expression data with metabolic profiles to select a subset of genes for network reconstruction. Subsequent analysis of the network revealed insulin signaling as the most significantly enriched pathway, and desmoplakin (DSP) as its top neighbor. We determined that palmitate significantly reduces DSP expression, and treatment with insulin restores the lost expression of DSP. Insulin resistance is a common pathological feature of fatty liver and related ailments, whereas loss of DSP has been noted in liver carcinoma. Reduced DSP expression can lead to loss of cell-cell adhesion via desmosomes, and disrupt the keratin intermediate filament network. Our findings suggest that DSP expression may be perturbed by palmitate and, along with insulin resistance, may play a role in palmitate induced cytotoxicity, and serve as potential targets for further studies on non-alcoholic fatty liver disease (NAFLD).

Hyperglycemia magnifies Schwann cell dysfunction and cell death triggered by PA-induced lipotoxicity

Lipid overload resulting in lipotoxicity is prominent in a number of chronic diseases and has been associated with cellular dysfunction and cell death. This study characterizes palmitic acid-induced lipotoxicity (PA-LTx) in Schwann cell cultures grown in normal and high glucose concentrations. The study shows for the first time that Schwann cell (SC) cultures exposed to elevated levels of PA exhibit a dose- and time-dependent loss in cell viability. Hoescht and Annexin V/7AAD staining confirmed cell death through apoptosis and the lipotoxic effect was more dramatic in SC cultures grown under high glucose conditions. The first indication of cellular dysfunction in treated SC cultures was a decrease in Ca(++) levels in the endoplasmic reticulum (ER, [Ca(++)](ER)) observed five minutes following the initial challenge with PA. This decrease in [Ca(++) ](ER) was followed by a significant increase in the expression of ER stress signature genes CHOP, Xbp1 and GRP78. The early ER stress response induced by PA-LTx was followed by a strong mitochondrial membrane depolarization. Flow cytometry using 2', 7'-dichlorodihydrofluorescein diacetate (H(2)DCFDA) showed an increase in oxidative stress within three to six hours after PA treatment. Treatment of cultures undergoing PA-LTx with the calcium chelator BAPTA-AM and the anti-oxidant MCI-186 significantly reversed the lipotoxic effect by decreasing the generation of ROS and significantly increasing cell viability. We conclude that lipotoxicity in Schwann cells results in cellular dysfunction and cell death that involves a robust ER stress response, mitochondrial dysfunction and an augmented state of cellular oxidative stress (ASCOS).

Reconstruct modular phenotype-specific gene networks by knowledge-driven matrix factorization

MOTIVATION

Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks.

RESULTS

Considering the reconstruction of gene network as a matrix factorization problem, we first use the gene expression data to estimate a correlation matrix, and then factorize the correlation matrix to recover the gene modules and the interactions between them. Prior knowledge from Gene Ontology is integrated into the matrix factorization. We applied this KMF algorithm to hepatocellular carcinoma (HepG2) cells treated with free fatty acids (FFAs). By comparing the module networks for the different conditions, we identified the specific modules that are involved in conferring the cytotoxic phenotype induced by palmitate. Further analysis of the gene modules of the different conditions suggested individual genes that play important roles in palmitate-induced cytotoxicity. In summary, KMF can efficiently integrate gene expression data with prior knowledge, thereby providing a powerful method of reconstructing phenotype-specific gene networks and valuable insights into the mechanisms that govern the phenotype.

Integrative analysis of cancer pathway progression and coherence

We analyzed the cancer pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The database provides a collective of signaling pathway members involved in cancer progression. However, the KEGG cancer pathways, unlike signaling pathways, have not been analyzed extensively with gene expression and mutation data. We transformed the colorectal cancer pathway into discrete X and Y scales and analyzed the relative expression levels of adenoma and carcinoma samples as well as the distribution of mutation targets. The X scale corresponds to the downstream location in a pathway, whereas the Y scale corresponds to the stage of the tumor. The gene expression values of the early stage pathway members are significantly higher than of the rest of the pathway members in colorectal adenoma tissues. The colorectal cancer pathway shows some degree of coherence in the carcinoma samples. The correlated gene pairs responsible for the coherence of the colorectal cancer pathway in the carcinoma samples are supported, in part, by the literature and may suggest novel regulatory associations. Finally, there are more mutation targets in the nucleus as well as the late tumor stages of the KEGG colorectal cancer pathway.

Systems biology for identifying liver toxicity pathways

Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined. The objective of this paper is to describe systems biology methods for identifying pathways involved in liver toxicity induced by free fatty acids (FFA) and tumor necrosis factor (TNF)-α in human hepatoblastoma cells (HepG2/C3A). Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity. Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

Gene module level analysis: identification to networks and dynamics

Nature exhibits modular design in biological systems. Gene module level analysis is based on this module concept, aiming to understand biological network design and systems behavior in disease and development by emphasizing on modules of genes rather than individual genes. Module level analysis has been extensively applied in genome wide level analysis, exploring the organization of biological systems from identifying modules to reconstructing module networks and analyzing module dynamics. Such module level perspective provides a high level representation of the regulatory scenario and design of biological systems, promising to revolutionize our view of systems biology, genetic engineering as well as disease mechanisms and molecular medicine.

Short time-series microarray analysis: methods and challenges

The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this data.