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.

/pdf/gene-set-enrichment-analysis-a-knowledge-based-approach-for-mbpti6qljw.pdf

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

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.

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/Nature%20Protocols%20DAVID.pdf

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

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.

/pdf/limma-powers-differential-expression-analyses-for-rna-4yl7ae3arh.pdf

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

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。

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

http://bartellab.wi.mit.edu/publication_reprints/Bartel_Cell09.pdf

MicroRNAs: Target Recognition and Regulatory Functions

A rare childhood disorder caused by mitochondrial dysfunction is treated by the drug rapamycin in a mouse model of the disease. [Also see Report by Johnson et al.] Leigh syndrome is a fatal, infantile neurodegenerative disease first described more than 60 years ago (1). Children with Leigh syndrome typically are born with normal prenatal development, but decline after intermittent episodes of encephalopathy and metabolic acidosis, leading to death within the first few years of life. The diagnosis is based on magnetic resonance imaging of the brain, which reveals bilaterally symmetric lesions in the brainstem and basal ganglia (see the figure) that correspond to regions of necrosis, gliosis, and hypervascularity, with relative sparing of neurons in the early stages of the disease. At present, no effective therapies are available for Leigh syndrome, and the mainstay of management is supportive care. On page 1524 of this issue, Johnson et al. (2) demonstrate that rapamycin, a compound that inhibits a protein kinase called mechanistic target of rapamycin (mTOR), delays the onset and progression of neurological symptoms in a mouse model of Leigh syndrome. mTOR lies at the hub of cellular signaling, sensing nutrient availability to regulate protein translation, autophagy, and metabolism. The new connection to mitochondrial disease widens our view of the signaling pathway, with potential therapeutic implications.

https://science.sciencemag.org/content/sci/342/6165/1453.full.pdf

A Common Pathway for a Rare Disease

The reduction of plasma low-density lipoprotein levels by HMG-CoA reductase inhibitors, or statins, has had a revolutionary impact in medicine, but muscle-related side effects remain a dose-limiting toxicity in many patients. We describe a chemical epistasis approach that can be useful in refining the mechanism of statin muscle toxicity, as well as in screening for agents that suppress muscle toxicity while preserving the ability of statins to increase the expression of the low-density lipoprotein receptor. Using this approach, we identified one compound that attenuates the muscle side effects in both cellular and animal models of statin toxicity, likely by influencing Rab prenylation. Our proof-of-concept screen lays the foundation for truly high-throughput screens that could help lead to the development of clinically useful adjuvants that can one day be co-administered with statins.

/pdf/a-small-molecule-screening-strategy-to-identify-suppressors-10206vd86x.pdf

A small-molecule screening strategy to identify suppressors of statin myopathy.

Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS.

Advances in analytical biochemistry have recently made it possible to obtain global snapshots of metabolism. A new study couples such technology with genome-wide genetic analysis to explore inherited variation in human metabolism.

Vamsi K. Mootha

Papers

A Common Pathway for a Rare Disease

A small-molecule screening strategy to identify suppressors of statin myopathy.

Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS.

Inborn variation in metabolism

The Sarcoid-Tuberculosis link: evidence from a high TB prevalence country.