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.

Combinatorial GxGxE CRISPR screen identifies SLC25A39 in mitochondrial glutathione transport linking iron homeostasis to OXPHOS

Abstract: Abstract The SLC25 carrier family consists of 53 transporters that shuttle nutrients and co-factors across mitochondrial membranes. The family is highly redundant and their transport activities coupled to metabolic state. Here, we use a pooled, dual CRISPR screening strategy that knocks out pairs of transporters in four metabolic states — glucose, galactose, OXPHOS inhibition, and absence of pyruvate — designed to unmask the inter-dependence of these genes. In total, we screen 63 genes in four metabolic states, corresponding to 2016 single and pair-wise genetic perturbations. We recover 19 gene-by-environment (GxE) interactions and 9 gene-by-gene (GxG) interactions. One GxE interaction hit illustrates that the fitness defect in the mitochondrial folate carrier (SLC25A32) KO cells is genetically buffered in galactose due to a lack of substrate in de novo purine biosynthesis. GxG analysis highlights a buffering interaction between the iron transporter SLC25A37 (A37) and the poorly characterized SLC25A39 (A39). Mitochondrial metabolite profiling, organelle transport assays, and structure-guided mutagenesis identify A39 as critical for mitochondrial glutathione (GSH) import. Functional studies reveal that A39-mediated glutathione homeostasis and A37-mediated mitochondrial iron uptake operate jointly to support mitochondrial OXPHOS. Our work underscores the value of studying family-wide genetic interactions across different metabolic environments.

Multiplexed biomarkers of insulin resistance

Abstract: The invention, in some aspects, relates to methods for characterizing glucose-related metabolic disorders. In some aspects, the invention relates to methods and kits useful for diagnosing, classifying, profiling, and treating glucose-related metabolic disorders. In some aspects, the invention relates to methods useful for diagnosing, classifying, profiling, and treating diabetes.

Statistical concerns about the GSEA procedure - Reply

Abstract: To the editor: Mootha et al.1 propose a statistical method (Gene Set Enrichment Analysis; GSEA) to discern changes in expression levels of sets of genes selected a priori in transcriptional profiling experiments. Although consideration of groups of genes is an interesting strategy, the proposed test statistic may not necessarily determine “...if the members of a given gene set are enriched among the most differentially expressed genes between two classes”1. Situations will probably arise when using GSEA in which genes with the highest values of the difference metric will be ignored solely due to the size of the selected gene sets, unrelated to any biological context of the genes comprising the set. By way of illustration, consider the following hypothetical example. Assume that a given data set consists of three potentially interesting sets of genes S1, S2 and S3, of respective sizes n, 5n and 4n genes, where n is any integer. Assume also that all of the genes in S1 are ranked higher (i.e., they have greater differences in expression) than the genes in S2, which in turn are ranked higher than the genes in S3. The GSEA procedure yields enrichment scores (ES)1 of 3n, 4n and 0 for S1, S2 and S3, respectively. The maximum ES1 is 4n and is attributed to S2. S2 will therefore be singled out as the candidate for further investigation over S1, even though S1 comprises the highest ranked genes. This does not seem reasonable, because S2 has been chosen only by virtue of containing a larger number of genes. In other words, GSEA can be at odds with the picture suggested by the gene ranking. A second observation, using the same illustrative example as above, gives another counterintuitive result. In the absence of a defined third gene set (S3), the ES for S2 = 0 and the ES for S1 remains positive. Therefore, S1, and not S2, is chosen by GSEA, a result opposite to that of the previous scenario. An unusual situation has arisen in which a choice or preference between sets of high ranking is affected simply by the presence or absence of a lower ranking set. The behavior of GSEA can not be dismissed as one of the usual power issues encountered due to noise in data, small sample size or lack of robustness to model assumptions. The simple example outlined here indicates that the power of the test statistic is sensitive to the a priori definition of the hypotheses of interest. These limitations should be clearly understood in applying and interpreting the results of the approach.

Tumor cavitation among lung cancer patients receiving first-line chemotherapy at a tertiary care centre in India: association with histology and overall survival.

Abstract: Clinical significance of tumor cavitation (TC) prior to and following first-line chemotherapy of lung cancer is unclear. An evaluation of the incidence and prognostic role of TC among treatment naive lung cancer patients undergoing chemotherapy at a tertiary care institute in North India was undertaken. Retrospective data analysis and radiological review of newly diagnosed lung cancer patients initiated on chemotherapy over a 2-year period were carried out. Demographic characteristics and overall survival (OS) were compared between patients with and without TC at baseline. Patients who received 3 or more cycles of chemotherapy were included in analysis for response rates and new onset TC. Overall, 27 (7.8 %) of 347 patients had baseline TC. Among 271 non-small cell lung cancer (NSCLC) patients with (n = 26) and without (n = 245) baseline TC, histology was the only demographic characteristic that differed significantly [squamous 76.9 vs. 46.9 %; p = 0.004]. Majority (82.7 %) of NSCLC patients had advanced (stage IIIB/IV) disease. NSCLC patients with and without baseline TC alive at 6 months, 1 and 2 years were 34.6 versus 53.9 %, 11.5 versus 25.7 % and 3.8 versus 7.8 %, respectively. NSCLC patients with baseline TC had shorter median OS than those without (174 days [95 % confidence interval (CI) 106-242 days] vs. 235 days [95 % CI 207-263 days]). On multivariate Cox proportional hazard analysis, age [hazard ratio (HR) = 1.02, 95 % CI 1.01-1.04] and baseline TC [HR = 1.66, 95 % CI 1.03-2.69] were found significant. Response rates were similar between the two groups. Patients with TC after chemotherapy differed from those without in frequency of squamous histology (77.8 vs. 38.9 %; p < 0.001) and presence of metastatic disease (19.4 vs. 40.9 %; p = 0.016). Squamous histology has a significant association with presence of baseline TC and of new onset TC after chemotherapy. Presence of baseline TC has an independent association with shorter OS among NSCLC patients undergoing first-line chemotherapy.

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。