Vamsi K. Mootha

Bio: Vamsi K. Mootha is an academic researcher from Broad Institute. The author has contributed to research in topics: Mitochondrion & Mitochondrial DNA. The author has an hindex of 85, co-authored 227 publications receiving 73860 citations. Previous affiliations of Vamsi K. Mootha include Harvard University & Beth Israel Deaconess Medical Center.

Errα and Gabpa/b specify PGC-1α-dependent oxidative phosphorylation gene expression that is altered in diabetic muscle

Abstract: Recent studies have shown that genes involved in oxidative phosphorylation (OXPHOS) exhibit reduced expression in skeletal muscle of diabetic and prediabetic humans. Moreover, these changes may be mediated by the transcriptional coactivator peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α). By combining PGC-1α-induced genome-wide transcriptional profiles with a computational strategy to detect cis-regulatory motifs, we identified estrogen-related receptor α (Errα) and GA repeat-binding protein α as key transcription factors regulating the OXPHOS pathway. Interestingly, the genes encoding these two transcription factors are themselves PGC-1α-inducible and contain variants of both motifs near their promoters. Cellular assays confirmed that Errα and GA-binding protein a partner with PGC-1α in muscle to form a double-positive-feedback loop that drives the expression of many OXPHOS genes. By using a synthetic inhibitor of Errα, we demonstrated its key role in PGC-1α-mediated effects on gene regulation and cellular respiration. These results illustrate the dissection of gene regulatory networks in a complex mammalian system, elucidate the mechanism of PGC-1α action in the OXPHOS pathway, and suggest that Errα agonists may ameliorate insulin-resistance in individuals with type 2 diabetes mellitus.

Mitochondrial disorders as windows into an ancient organelle

Abstract: Much of our current knowledge about mitochondria has come from studying patients who have respiratory chain disorders. These disorders comprise a large collection of individually rare syndromes, each presenting in a unique and often devastating way. In recent years, there has been great progress in defining their genetic basis, but we still know little about the cascade of events that gives rise to such diverse pathology. Here, we review these disorders and explore them in the context of a contemporary understanding of mitochondrial evolution, biochemistry and genetics. Fully deciphering their pathogenesis is a challenging next step that will inspire the development of drug treatments for rare and common diseases.

Engineered ascorbate peroxidase as a genetically encoded reporter for electron microscopy

Abstract: Electron microscopy (EM) is the standard method for imaging cellular structures with nanometer resolution, but existing genetic tags are inactive in most cellular compartments or require light and can be difficult to use. Here we report the development of 'APEX', a genetically encodable EM tag that is active in all cellular compartments and does not require light. APEX is a monomeric 28-kDa peroxidase that withstands strong EM fixation to give excellent ultrastructural preservation. We demonstrate the utility of APEX for high-resolution EM imaging of a variety of mammalian organelles and specific proteins using a simple and robust labeling procedure. We also fused APEX to the N or C terminus of the mitochondrial calcium uniporter (MCU), a recently identified channel whose topology is disputed. These fusions give EM contrast exclusively in the mitochondrial matrix, suggesting that both the N and C termini of MCU face the matrix. Because APEX staining is not dependent on light activation, APEX should make EM imaging of any cellular protein straightforward, regardless of the size or thickness of the specimen.

Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics

Abstract: Identifying the genes responsible for human diseases requires combining information about gene position with clues about biological function. The recent availability of whole-genome data sets of RNA and protein expression provides powerful new sources of functional insight. Here we illustrate how such data sets can expedite disease-gene discovery, by using them to identify the gene causing Leigh syndrome, French-Canadian type (LSFC, Online Mendelian Inheritance in Man no. 220111), a human cytochrome c oxidase deficiency that maps to chromosome 2p16-21. Using four public RNA expression data sets, we assigned to all human genes a “score” reflecting their similarity in RNA-expression profiles to known mitochondrial genes. Using a large survey of organellar proteomics, we similarly classified human genes according to the likelihood of their protein product being associated with the mitochondrion. By intersecting this information with the relevant genomic region, we identified a single clear candidate gene, LRPPRC. Resequencing identified two mutations on two independent haplotypes, providing definitive genetic proof that LRPPRC indeed causes LSFC. LRPPRC encodes an mRNA-binding protein likely involved with mtDNA transcript processing, suggesting an additional mechanism of mitochondrial pathophysiology. Similar strategies to integrate diverse genomic information can be applied likewise to other disease pathways and will become increasingly powerful with the growing wealth of diverse, functional genomics data.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。