Milica Ng | Search bioRxiv

bioRxiv2016

13C metabolic flux ratio analysis by direct measurement of free metabolic intermediates in L. mexicana using gas chromatography-mass spectrometry

Until recently, 13C-based flux analyses have almost exclusively relied on analysis of labelled amino acids in proteins. This approach is not directly applicable to Leishmania, as these parasites scavenge most of their amino acids from the media. Leishmania are also unusual in that they i) share little genomic similarity with other organisms ii) constitutively express their metabolic genes and iii) display minimal changes in the enzyme levels throughout their life cycle stages. The three factors have contributed to an early development of comprehensive and reproducible 13C-based metabolomics approaches in these parasites. The work presented here contributes to the creation of new 13C-based metabolic flux approaches based on the isotopologue analysis of free metabolite pools in Leishmania mexicana. Namely, a new approach is presented for simultaneous calculation of in vivo fractional fluxes (or flux ratios) into two or more metabolite nodes with carbon dioxide condensation, based on isotopologue analysis of free metabolite pools. This method is used to perform the first quantitative in vivo fractional flux calculation of central carbon metabolism in any human parasite.

Systems Biology↗

bioRxiv2016

Combining multiple tools outperforms individual methods in gene set enrichment analyses

Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular data set. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions.\n\nResults: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. redEGSEAs gene set database contains around 25,000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse data sets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes.\n\nAvailability and Implementation: EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/. The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEA/.

Bioinformatics↗