Decoupling gene functions from knockout effects by evolutionary analyses.

Li Liu,Xionglei He,Shanjun Deng,Mengdi Liu,Piaopiao Chen,Jing Yang,Yunhan Xie,Di Zhang

doi:10.1093/nsr/nwaa079

Abstract

Genic functions have long been confounded by pleiotropic mutational effects. To understand such genetic effects, we examine HAP4, a well-studied transcription factor in Saccharomyces cerevisiae that functions by forming a tetramer with HAP2, HAP3 and HAP5. Deletion of HAP4 results in highly pleiotropic gene expression responses, some of which are clustered in related cellular processes (clustered effects) while most are distributed randomly across diverse cellular processes (distributed effects). Strikingly, the distributed effects that account for much of HAP4 pleiotropy tend to be non-heritable in a population, suggesting they have few evolutionary consequences. Indeed, these effects are poorly conserved in closely related yeasts. We further show substantial overlaps of clustered effects, but not distributed effects, among the four genes encoding the HAP2/3/4/5 tetramer. This pattern holds for other biochemically characterized yeast protein complexes or metabolic pathways. Examination of a set of cell morphological traits of the deletion lines yields consistent results. Hence, only some deletion effects of a gene support related biochemical understandings with the rest being often pleiotropic and evolutionarily decoupled from the gene's normal functions. This study suggests a new framework for reverse genetic analysis.

Highlights

We started with a known yeast Saccharomyces cerevisiae gene HAP4(17)
It is a non-essential transcription factor that has been subjected to extensive studies since its discovery 30 years ago[18]
Deletion effects and 130 distributed deletion effects are supported by similar P-values and fold changes (P = 0.20 and 0.46, respectively, Mann-Whitney U-test; Fig. 1B)

Summary

Introduction

Mutation analysis has long been used to understand the functions of a gene[1].It appears clear that a gene can often affect various seemingly unrelated traits[2], a phenomenon termed pleiotropy[3].For instance, a large-scale gene knockdown assay in the nematode worm Caenorhabditis elegans finds on average a gene affects ~10%of 44 assessed traits[4].are mainly from mechanistic perspectives[5, 6], by considering the focal gene’s multiple molecular functions or multiple cellular processes associated with a single molecular function[7].our understandings in how a gene functions.Attempts to understand such pleiotropic mutational effectsThe resulting pictures are, often complex, confusingSince biological systems are all evolutionary products with history, mechanistic perspectives alone may bias the efforts for delineating a biological phenomenon[8, 9].This is exemplified by the debates on the ENCODE project in which up to 80% of the human genome was claimed to be functional despite that only 10% appears to be under selection[10,11,12].there is a transcription factor (TF) that recognizes a DNA motif, say, ATCGATC. Mutation analysis has long been used to understand the functions of a gene[1]. It appears clear that a gene can often affect various seemingly unrelated traits[2], a phenomenon termed pleiotropy[3]. Since biological systems are all evolutionary products with history, mechanistic perspectives alone may bias the efforts for delineating a biological phenomenon[8, 9]. This is exemplified by the debates on the ENCODE project in which up to 80% of the human genome was claimed to be functional despite that only 10% appears to be under selection[10,11,12].

Methods

Results

Conclusion