Mapping transcription mechanisms from multimodal genomic data

Background Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. Results We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. Conclusions The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.National Human Genome Research Institute (U.S.) (R01HG003354)National Institute of Allergy and Infectious Diseases (U.S.) (U19 AI067854-05)National Heart, Lung, and Blood Institute (grant T32 HL007427-28)National Institutes of Health (U.S.) (grant K99 LM009826

Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects

The heritability of chronic obstructive pulmonary disease (COPD) cannot be fully explained by recognized genetic risk factors identified as achieving genome-wide significance. In addition, the combined contribution of genetic variation to COPD risk has not been fully explored. We sought to determine: (1) whether studies of variants from previous studies of COPD or lung function in a larger sample could identify additional associated variants, particularly for severe COPD; and (2) the impact of genetic risk scores on COPD. We genotyped 3,346 single-nucleotide polymorphisms (SNPs) in 2,588 cases (1,803 severe COPD) and 1,782 control subjects from four cohorts, and performed association testing with COPD, combining these results with existing genotyping data from 6,633 cases (3,497 severe COPD) and 5,704 control subjects. In addition, we developed genetic risk scores from SNPs associated with lung function and COPD and tested their discriminatory power for COPD-related measures. We identified significant associations between SNPs near PPIC (P = 1.28 X 10-8) and PPP4R4/SERPINA1 (P = 1.0131028) and severe COPD; the latter association may be driven by recognized variants in SERPINA1. Genetic risk scores based on SNPs previously associated with COPD and lung function had a modest ability to discriminate COPD (area under the curve, ~0.6), and accounted for a mean 0.9–1.9% lower forced expiratory volume in 1 second percent predicted for each additional risk allele. In a large genetic association analysis, we identified associations with severe COPD near PPIC and SERPINA1. A risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function

Dystropathology increases energy expenditure and protein turnover in the mdx mouse model of Duchenne muscular dystrophy

The skeletal muscles in Duchenne muscular dystrophy and the mdx mouse model lack functional dystrophin and undergo repeated bouts of necrosis, regeneration, and growth. These processes have a high metabolic cost. However, the consequences for whole body energy and protein metabolism, and on the dietary requirements for these macronutrients at different stages of the disease, are not well-understood. This study used juvenile (4- to 5- wk-old) and adult (12- to 14-wk-old) male dystrophic C57BL/10ScSn-mdx/J and age-matched C57BL/10ScSn/J control male mice to measure total and resting energy expenditure, food intake, spontaneous activity, body composition, whole body protein turnover, and muscle protein synthesis rates. In juvenile mdx mice that have extensive muscle damage, energy expenditure, muscle protein synthesis, and whole body protein turnover rates were higher than in age-matched controls. Adaptations in food intake and decreased activity were insufficient to meet the increased energy and protein needs of juvenile mdx mice and resulted in stunted growth. In (non-growing) adult mdx mice with less severe dystropathology, energy expenditure, muscle protein synthesis, and whole body protein turnover rates were also higher than in age-matched controls. Food intake was sufficient to meet their protein and energy needs, but insufficient to result in fat deposition. These data show that dystropathology impacts the protein and energy needs of mdx mice and that tailored dietary interventions are necessary to redress this imbalance. If not met, the resultant imbalance blunts growth, and may limit the benefits of therapies designed to protect and repair dystrophic muscles

Disease Gene Interaction Pathways: A Potential Framework for How Disease Genes Associate by Disease-Risk Modules

BACKGROUND: Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as 'a form of biological systems', consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways. CONCLUSIONS/SIGNIFICANCE: Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases

Skeletal Muscle Phenotypically Converts and Selectively Inhibits Metastatic Cells in Mice

Skeletal muscle is rarely a site of malignant metastasis; the molecular and cellular basis for this rarity is not understood. We report that myogenic cells exert pronounced effects upon co-culture with metastatic melanoma (B16-F10) or carcinoma (LLC1) cells including conversion to the myogenic lineage in vitro and in vivo, as well as inhibition of melanin production in melanoma cells coupled with cytotoxic and cytostatic effects. No effect is seen with non-tumorigenic cells. Tumor suppression assays reveal that the muscle-mediated tumor suppressor effects do not generate resistant clones but function through the down-regulation of the transcription factor MiTF, a master regulator of melanocyte development and a melanoma oncogene. Our findings point to skeletal muscle as a source of therapeutic agents in the treatment of metastatic cancers