Revealing disease-associated pathways by network integration of untargeted metabolomics.

Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.

APOE4 is Associated with Differential Regional Vulnerability to Bioenergetic Deficits in Aged APOE Mice.

The ε4 allele of apolipoprotein E (APOE) is the dominant genetic risk factor for late-onset Alzheimer's disease (AD). However, the reason for the association between APOE4 and AD remains unclear. While much of the research has focused on the ability of the apoE4 protein to increase the aggregation and decrease the clearance of Aß, there is also an abundance of data showing that APOE4 negatively impacts many additional processes in the brain, including bioenergetics. In order to gain a more comprehensive understanding of APOE4's role in AD pathogenesis, we performed a transcriptomics analysis of APOE4 vs. APOE3 expression in the entorhinal cortex (EC) and primary visual cortex (PVC) of aged APOE mice. This study revealed EC-specific upregulation of genes related to oxidative phosphorylation (OxPhos). Follow-up analysis utilizing the Seahorse platform showed decreased mitochondrial respiration with age in the hippocampus and cortex of APOE4 vs. APOE3 mice, but not in the EC of these mice. Additional studies, as well as the original transcriptomics data, suggest that multiple bioenergetic pathways are differentially regulated by APOE4 expression in the EC of aged APOE mice in order to increase the mitochondrial coupling efficiency in this region. Given the importance of the EC as one of the first regions to be affected by AD pathology in humans, the observation that the EC is susceptible to differential bioenergetic regulation in response to a metabolic stressor such as APOE4 may point to a causative factor in the pathogenesis of AD.

Comparative proteome and transcriptome analyses of embryonic stem cells during embryoid body-based differentiation.

Gene expression analyses of embryonic stem cells (ESCs) will help to uncover or further define signaling pathways and molecular mechanisms involved in the maintenance of self-renewal and pluripotency. We employed a 2-DE-based proteomics approach to analyze human ESC line, Royan H5, in undifferentiated cells and different stages of spontaneous differentiation (days 3, 6, 12, and 20) by embryoid body formation. Out of 945 proteins reproducibly detected on gels, the expression of 96 spots changed during differentiation. Using MS, 87 ESC-associated proteins were identified including several proteins involved in cell proliferation, cell apoptosis, transcription, translation, mRNA processing, and protein folding. Transcriptional changes accompanying differentiation of Royan H5 were also analyzed using microarrays. We developed a comprehensive data set that shows the use of human ESC lines in vitro to mimic gastrulation and organogenesis. Our results showed that proteomics and transcriptomics data are complementary rather than duplicative. Although regulation of many genes during differentiation were observed only at transcript level, modulation of several proteins was revealed only by proteome analysis.

Evolutionary mechanisms underlying the functional divergence of duplicate genes involved in vertebrates' circadian rhythm pathway.

Circadian rhythms, that are governed physiologically and behaviorally by endogenous clock, have been described in many species. Living organisms use this endogenous circadian clock to anticipate environmental transitions, perform activities at biologically advantageous times during the day, and undergo characteristic seasonal responses. Gene duplication is one of the most important mechanisms in the evolution of gene diversity. After duplication, one or both of duplicates can accumulate amino acid changes, thereby promoting functional divergence through the action of natural selection. The circadian system, like many other multigene families, has undergone this genetic revolution, and so circadian genes that are found in single copies in insects are duplicated in vertebrates. We analyzed six groups of genes involved in vertebrates' circadian rhythm pathway to find signatures of molecular evolutionary processes such as gene duplication, natural selection, recombination, and functional divergence. The obtained results, then, were used to determine what evolutionary forces have influenced the fates of duplicated genes of each group. We showed in this research that recombination has not been widespread during the evolution of circadian genes and that purifying selection has been the prominent natural pressure operating on circadian genes. We also showed that the evolution of circadian genes has been depended on gene duplication and functional divergence. Finally, we put forward models best describing the evolutionary fates of circadian duplicates.

The performances of the chi-square test and complexity measures for signal recognition in biological sequences.

With large amounts of experimental data, modern molecular biology needs appropriate methods to deal with biological sequences. In this work, we apply a statistical method (Pearson's chi-square test) to recognize the signals appear in the whole genome of the Escherichia coli. To show the effectiveness of the method, we compare the Pearson's chi-square test with linguistic complexity on the complete genome of E. coli. The results suggest that Pearson's chi-square test is an efficient method for distinguishing genes (coding regions) form pseudogenes (noncoding regions). On the other hand, the performance of the linguistic complexity is much lower than the chi-square test method. We also use the Pearson's chi-square test method to determine which parts of the Open Reading Frame (ORF) have significant effect on discriminating genes form pseudogenes. Moreover, different complexity measures and Pearson's chi-square test applied on the genes with high value of Pearson's chi-square statistic. We also compute the measures on homologous of these genes. The results illustrate that there is a region near the start codon with high value of chi-square statistic and low complexity that is conserve between homologous genes.

Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements.

The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We apply this method to public data for a large cohort of Huntington's disease patients and controls, identifying and prioritizing phenotype-associated genes. We verify the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrate that inhibiting the enzyme, sphingosine-1-phosphate lyase 1 (SPL), has neuroprotective effects in Huntington's disease models. Finally, we show that one consequence of inhibiting SPL is intracellular inhibition of histone deacetylases, thus linking our observations in sphingolipid metabolism to a well-characterized Huntington's disease pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes.Identifying gene subsets affecting disease phenotypes from transcriptome data is challenge. Here, the authors develop a method that combines transcriptional data with disease ordinal clinical measurements to discover a sphingolipid metabolism regulator involving in Huntington's disease progression.

Inferring interaction type in gene regulatory networks using co-expression data.

BACKGROUND: Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. RESULTS: This paper describes a novel algorithm, "Signing of Regulatory Networks" (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. CONCLUSIONS: SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.

Erratum to: Inferring interaction type in gene regulatory networks using co-expression data.

[This corrects the article DOI: 10.1186/s13015-015-0054-4.].

Poly-glutamine expanded huntingtin dramatically alters the genome wide binding of HSF1.

In Huntington's disease (HD), polyglutamine expansions in the huntingtin (Htt) protein cause subtle changes in cellular functions that, over-time, lead to neurodegeneration and death. Studies have indicated that activation of the heat shock response can reduce many of the effects of mutant Htt in disease models, suggesting that the heat shock response is impaired in the disease. To understand the basis for this impairment, we have used genome-wide chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) to examine the effects of mutant Htt on the master regulator of the heat shock response, HSF1. We find that, under normal conditions, HSF1 function is highly similar in cells carrying either wild-type or mutant Htt. However, polyQ-expanded Htt severely blunts the HSF1-mediated stress response. Surprisingly, we find that the HSF1 targets most affected upon stress are not directly associated with proteostasis, but with cytoskeletal binding, focal adhesion and GTPase activity. Our data raise the intriguing hypothesis that the accumulated damage from life-long impairment in these stress responses may contribute significantly to the etiology of Huntington's disease.

Proteomic analysis of monkey embryonic stem cell during differentiation.

Proteome analyses of embryonic stem cells (ESCs) will help to uncover mechanisms underlying cellular differentiation, expansion, and self-renewal. We applied a 2-DE based proteomic approach coupled with mass spectrometry to identify genes controlling monkey ESCs proliferation and differentiation. We analyzed proteome of ESCs during proliferation and different stages of spontaneous differentiation (day 3, 6, 12, and 30) by embryoid body formation. Out of about 663 +/- 15 protein spots reproducible detected on gels, 127 proteins showed significant changes during differentiation. Mass spectrometry analysis of differentially expressed proteins resulted in identification of 95 proteins involved in cell cycle progression and proliferation, cell growth, transcription and chromatin remodeling, translation, metabolism, energy production and Ras signaling. In addition, we created protein interaction maps and distinctly different topology was observed in the protein interaction maps of the monkey ESC proteome clusters compared with maps created using randomly generated sets of proteins. Taken together, the results presented here revealed novel key proteins and pathways that are active during ESC differentiation.