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

Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing

Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships(1,2). Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome(3), transcriptome(4) and proteome(5) data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes

Boosted b¯b decays with the ATLAS experiment at the LHC

A measurement and a search, both involving high transverse momentum bosons decaying to b-quarks, are performed using a dataset of proton-proton collisions at √ s = 8 TeV, collected in 2012 with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 19.5 fb−1. The production cross section of Z → b¯b is measured, where the Z boson has high transverse momentum. The measured value of the fiducial cross section is found to be in good agreement with next-to-leading-order Standard Model predictions. A search is made for TeV-scale resonances decaying via a pair of Higgs bosons to the b¯bb¯b final state. The graviton excitation, G∗, in the bulk Randall-Sundrum model is used as a baseline signal model. No evidence of a resonance is found. Upper limits are set on σ(pp→G∗) × BR(G∗→HH→b¯bb¯b)

Boosted hh→bbˉbbˉhh \rightarrow b\bar{b}b\bar{b}: a new topology in searches for TeV-scale resonances at the LHC

It is widely believed that fully hadronic final states are not competitive in searches for new physics at the Large Hadron Collider due to the overwhelming QCD backgrounds. In this letter, we present a particle-level study of the topology arising when a TeV-scale resonance decays to two Higgs bosons and these subsequently decay to $b\bar{b}$, leading to two back-to-back boosted dijet systems. We show that selecting events with this topology dramatically reduces all backgrounds, thus enabling very competitive searches for new physics in a variety of models. For a resonance with mass 1 TeV and width around 60 GeV, we find that ATLAS or CMS could have a sensitivity to a $\sigma \times BR$ as small as a few fb with the LHC data collected in 2012. These conclusions are also relevant to the boosted $Zh\rightarrow b\bar{b}b\bar{b}$ and $ZZ\rightarrow b\bar{b}b\bar{b}$ final states, which would further increase the potential sensitivity to new physics as well as to Standard Model processes like longitudinal vector boson scattering.It is widely believed that fully hadronic final states are not competitive in searches for new physics at the Large Hadron Collider due to the overwhelming QCD backgrounds. In this paper, we present a particle-level study of the topology arising when a TeV-scale resonance decays to two Higgs bosons and these subsequently decay to bb¯, leading to two back-to-back boosted dijet systems. We show that selecting events with this topology dramatically reduces all backgrounds, thus enabling very competitive searches for new physics in a variety of models. For a resonance with mass 1 TeV and width around 60 GeV, we find that ATLAS or CMS could have a sensitivity to a σ×BR as small as a few fb with the LHC data collected in 2012. These conclusions are also relevant to the boosted Zh→bb¯bb¯ and ZZ→bb¯bb¯ final states, which would further increase the potential sensitivity to new physics as well as to standard model processes like longitudinal vector boson scattering

Data from: 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