Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.Published versio

A genetic network that suppresses genome rearrangements in Saccharomyces cerevisiae and contains defects in cancers.

Gross chromosomal rearrangements (GCRs) play an important role in human diseases, including cancer. The identity of all Genome Instability Suppressing (GIS) genes is not currently known. Here multiple Saccharomyces cerevisiae GCR assays and query mutations were crossed into arrays of mutants to identify progeny with increased GCR rates. One hundred eighty two GIS genes were identified that suppressed GCR formation. Another 438 cooperatively acting GIS genes were identified that were not GIS genes, but suppressed the increased genome instability caused by individual query mutations. Analysis of TCGA data using the human genes predicted to act in GIS pathways revealed that a minimum of 93% of ovarian and 66% of colorectal cancer cases had defects affecting one or more predicted GIS gene. These defects included loss-of-function mutations, copy-number changes associated with reduced expression, and silencing. In contrast, acute myeloid leukaemia cases did not appear to have defects affecting the predicted GIS genes

Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates

System-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism’s genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene’s fitness contribution to an organism “here and now” and the same gene’s historical importance as evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call “function-loss cost”, which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.This work was supported by the National Science Foundation, grant CCF-1219007 to YX; the Natural Sciences and Engineering Research Council of Canada, grant RGPIN-2014-03892 to YX; the National Institute of Health, grants 5R01GM089978 and 5R01GM103502 to DS; the Army Research Office - Multidisciplinary University Research Initiative, grant W911NF-12-1-0390 to DS; the US Department of Energy, grant DE-SC0012627 to DS; and by the Canada Research Chairs Program (YX). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (CCF-1219007 - National Science Foundation; RGPIN-2014-03892 - Natural Sciences and Engineering Research Council of Canada; 5R01GM089978 - National Institute of Health; 5R01GM103502 - National Institute of Health; W911NF-12-1-0390 - Army Research Office - Multidisciplinary University Research Initiative; DE-SC0012627 - US Department of Energy; Canada Research Chairs Program)Published versio

Metabolic and Chaperone Gene Loss Marks the Origin of Animals: Evidence for Hsp104 and Hsp78 Sharing Mitochondrial Clients

The evolution of animals involved acquisition of an emergent gene repertoire for gastrulation. Whether loss of genes also co-evolved with this developmental reprogramming has not yet been addressed. Here, we identify twenty-four genetic functions that are retained in fungi and choanoflagellates but undetectable in animals. These lost genes encode: (i) sixteen distinct biosynthetic functions; (ii) the two ancestral eukaryotic ClpB disaggregases, Hsp78 and Hsp104, which function in the mitochondria and cytosol, respectively; and (iii) six other assorted functions. We present computational and experimental data that are consistent with a joint function for the differentially localized ClpB disaggregases, and with the possibility of a shared client/chaperone relationship between the mitochondrial Fe/S homoaconitase encoded by the lost LYS4 gene and the two ClpBs. Our analyses lead to the hypothesis that the evolution of gastrulation-based multicellularity in animals led to efficient extraction of nutrients from dietary sources, loss of natural selection for maintenance of energetically expensive biosynthetic pathways, and subsequent loss of their attendant ClpB chaperones.Comment: This is a reformatted version from the recent official publication in PLoS ONE (2015). This version differs substantially from first three arXiV versions. This version uses a fixed-width font for DNA sequences as was done in the earlier arXiv versions but which is missing in the official PLoS ONE publication. The title has also been shortened slightly from the official publicatio

PTOMSM: A modified version of Topological Overlap Measure used for predicting Protein-Protein Interaction Network

A variety of methods are developed to integrating diverse biological data to predict novel interaction relationship between proteins. However, traditional integration can only generate protein interaction pairs within existing relationships. Therefore, we propose a modified version of Topological Overlap Measure to identify not only extant direct PPIs links, but also novel protein interactions that can be indirectly inferred from various relationships between proteins. Our method is more powerful than a naïve Bayesian-network-based integration in PPI prediction, and could generate more reliable candidate PPIs. Furthermore, we examined the influence of the sizes of training and test datasets on prediction, and further demonstrated the effectiveness of PTOMSM in predicting PPI. More importantly, this method can be extended naturally to predict other types of biological networks, and may be combined with Bayesian method to further improve the prediction

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research