Expanding role of gut microbiota in lipid metabolism

This review highlights recent advances in the emerging role that gut microbiota play in modulating metabolic phenotypes, with a particular focus on lipid metabolism

Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales

Comprehensive Profiling of Metaplastic Breast Carcinoma Reveals Frequent Over-Expression of PD-L1

Background: Metaplastic breast carcinoma (MBC) is a rare subtype of breast carcinoma less responsive to conventional chemotherapy relative to usual breast carcinomas such as ductal and lobular subtype. In molecular terms, MBC usually clusters with triple negative breast cancers (TNBC), but MBCs portray a worse prognosis in comparison with TNBC. Published studies investigating MBCs for specific biomarkers of therapy response are rare and limited by the methodological approaches. Methods: 297 samples [MBC (n=75), triple-negative breast cancer of no-special-type (TNBC-NOS, n=106), HER2-positive breast cancers (n=32) and luminal breast cancers (n=84)] were profiled using direct sequencing analysis [Illumina MiSeq Next Generation Sequencing (NGS)]. Immunohistochemistry for PD-L1 (SP142, Spring Bioscience) and PD-1 (NAT105, Ventana) was performed using automated procedures. Results: 89% MBCs exhibited triple-negative immunophenotype (ER-/PR-/HER2-). The most common mutations in MBCs included TP53 (67%) and PIK3CA mutations (23%). Other mutations were rare including HRAS mutations (7%), STK11 (5%), FBXW7, PTEN, c-MET and JAK3 (4%, respectively). PD-L1 expression on cancer cells was detected in significantly higher proportion of MBCs (46%) than in other molecular subtypes (6% in luminal and HER2+ breast cancers, respectively and 9% in TNBC-NOS, p\u3c0.001). PD-1 positive tumors infiltrating lymphocytes (TILs) varied greatly in MBCs (0 to \u3e50/mm2). Conclusion: Comprehensive profiling of a large cohort of this rare carcinoma highlighted predominance of TP53 mutations, wild type EGFR gene expression, a distinct increase in proportion of PD-L1 expression in carcinoma cells, and PD-1 expression in TILs. The latter propert

Genomic analysis of metabolic pathway gene expression in mice

BACKGROUND: A segregating population of (C57BL/6J × DBA/2J)F2 intercross mice was studied for obesity-related traits and for global gene expression in liver. Quantitative trait locus analyses were applied to the subcutaneous fat-mass trait and all gene-expression data. These data were then used to identify gene sets that are differentially perturbed in lean and obese mice. RESULTS: We integrated global gene-expression data with phenotypic and genetic segregation analyses to evaluate metabolic pathways associated with obesity. Using two approaches we identified 13 metabolic pathways whose genes are coordinately regulated in association with obesity. Four genomic regions on chromosomes 3, 6, 16, and 19 were found to control the coordinated expression of these pathways. Using criteria that included trait correlation, differential gene expression, and linkage to genomic regions, we identified novel genes potentially associated with the identified pathways. CONCLUSION: This study demonstrates that genetic and gene-expression data can be integrated to identify pathways associated with clinical traits and their underlying genetic determinants

Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest

Multiplatform molecular profiling identifies potentially targetable biomarkers in malignant phyllodes tumors of the breast.

Malignant phyllodes tumor is a rare breast malignancy with sarcomatous overgrowth and with limited effective treatment options for recurrent and metastatic cases. Recent clinical trials indicated a potential for anti-angiogenic, anti-EGFR and immunotherapeutic approaches for patients with sarcomas, which led us to investigate these and other targetable pathways in malignant phyllodes tumor of the breast. Thirty-six malignant phyllodes tumors (including 8 metastatic tumors with two cases having matched primary and metastatic tumors) were profiled using gene sequencing, gene copy number analysis, whole genome expression, and protein expression. Whole genome expression analysis demonstrated consistent over-expression of genes involved in angiogenesis including VEGFA, Angiopoietin-2, VCAM1, PDGFRA, and PTTG1. EGFR protein overexpression was observed in 26/27 (96%) of cases with amplification of the EGFR gene in 8/24 (33%) cases. Two EGFR mutations were identified including EGFRvIII and a presumed pathogenic V774M mutation, respectively. The most common pathogenic mutations included TP53 (50%) and PIK3CA (15%). Cases with matched primary and metastatic tumors harbored identical mutations in both sites (PIK3CA/KRAS and RB1 gene mutations, respectively). Tumor expression of PD-L1 immunoregulatory protein was observed in 3/22 (14%) of cases. Overexpression of molecular biomarkers of increased angiogenesis, EGFR and immune checkpoints provides novel targeted therapy options in malignant phyllodes tumors of the breast

Gene networks associated with conditional fear in mice identified using a systems genetics approach

<p>Abstract</p> <p>Background</p> <p>Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution.</p> <p>Results</p> <p>A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included <it>Psmd6</it>, <it>Ube2a </it>and <it>Usp33</it>, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. <it>Rps15a, Kif3a, Stard7, 6330503K22RIK</it>, and <it>Plvap </it>were among the individual genes whose transcript abundance were strongly associated with fear phenotypes.</p> <p>Conclusion</p> <p>Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.</p

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm