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 algorithm13 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.

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 selfrenewal 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.

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 Aβ, 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. Possession of the ε4 allele of apolipoprotein E (APOE) is the major genetic risk factor for late-onset Alzheimer's disease (AD). In normal physiology, the apoE protein plays a vital role in the transport of cholesterol and other lipids through the bloodstream, as well as within the brain 1-3. Although the three common isoforms of apoE (E2, E3 and E4) differ from each other at only two amino acids-apoE2 (Cys112, Cys158), apoE3 (Cys112, Arg158), apoE4 (Arg112, Arg158)-this small change in amino acid sequence has a large effect on the protein structure, resulting in differential affinities towards apoE's lipid cargo, as well as its receptors [see reviews by 1,4 ].

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.

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.

BackgroundKnowledge 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.ResultsThis 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.ConclusionsSIREN 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0054-4) contains supplementary material, which is available to authorized users.

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.

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