Biology subjects

Metzger, B. P. H.

Publications and source records attributed to Metzger, B. P. H..

7 recordsLinked to original sources

bioRxiv2024

Response to "Protein sequence landscapes are not so simple: on reference-free versus reference-based inference"

We recently reanalyzed 20 combinatorial mutagenesis datasets using a novel reference-free analysis (RFA) method and showed that high-order epistasis contributes negligibly to protein sequence-function relationships in every case. Dupic, Phillips, and Desai (DPD) commented on a preprint of our work. In our published paper, we addressed all the major issues they raised, but we respond directly to them here. 1) DPDs claim that RFA is equivalent to estimating reference-based analysis (RBA) models by regression neglects fundamental differences in how the two formalisms dissect the causal relationship between sequence and function. It also misinterprets the observation that using regression to estimate any truncated model of genetic architecture will always yield the same predicted phenotypes and variance partition; the resulting estimates correspond to those of the RFA formalism but are inaccurate representations of the true RBA model. 2) DPDs claim that high-order epistasis is widespread and significant while somehow explaining little phenotypic variance is an artifact of two strong biases in the use of regression to estimate RBA models: this procedure underestimates the phenotypic variance explained by RBA epistatic terms while at the same time inflating the magnitude of individual terms. 3) DPD erroneously claim that RFA is "exactly equivalent" to Fourier analysis (FA) and background-averaged analysis (BA). This error arises because DPD used an incorrect mathematical definition of RFA and were misled by a simple numerical relationship among the models that only holds only for the simplest kinds of datasets. 4) DPD argue that using a nonlinear transformation to account for global nonlinearities in sequence-function relationships is often unnecessary and may artifactually absorb specific epistatic interactions. We show that nonspecific epistasis caused by a limited dynamic range affects datasets of all types, even when the phenotype is represented on a free-energy scale. Moreover, using a nonlinear transformation in a joint fitting procedure does not underestimate specific epistasis under realistic conditions, even if the data are not affected by nonspecific epistasis. The conclusions of our work therefore hold: the genetic architecture of all 20 protein datasets we analyzed can be efficiently and accurately described in an RFA framework by first-order amino acid effects and pairwise interactions with a simple model of global nonlinearity. We are grateful for DPDs commentary, which helped us improve our paper.

genetics↗

bioRxiv2023

The simplicity of protein sequence-function relationships

How complicated is the genetic architecture of proteins - the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a proteins function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence - causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions - or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a proteins entire genotype-phenotype map. This method yields a maximally efficient explanation of a proteins genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.

genetics↗

bioRxiv2021

Mutational sources of trans-regulatory variation affecting gene expression in Saccharomyces cerevisiae

Heritable variation in a genes expression arises from mutations impacting cis- and trans-acting components of its regulatory network, with expression variation often derived from trans-regulatory mutations within species. Here, we investigate how trans-regulatory mutations are distributed within the genome and within a gene regulatory network by identifying and characterizing 69 mutations with trans-regulatory effects on expression of the same focal gene in Saccharomyces cerevisiae. Relative to 1766 mutations without effects on expression of this focal gene, we found that these trans-regulatory mutations were enriched in coding sequences of transcription factors previously predicted to regulate expression of the focal gene. However, over 90% of the trans-regulatory mutations identified mapped to other types of genes involved in diverse biological processes including chromatin state, metabolism and signal transduction. Finally, we find that the genomic distribution of these new regulatory mutations significantly overlaps with the genomic distribution of trans-regulatory variants segregating within S. cerevisiae.

genetics↗

bioRxiv2020

Chance, contingency, and necessity in the experimental evolution of ancestral proteins

To understand why evolution produced the biological systems that exist today, we must know how important chance, contingency, and necessity were during history. Previous observations suggest that each of these modes of causality affects evolution in various settings, but their relative roles and interactions are not well characterized because they have never been systematically assessed in a single system or on a timescale relevant to evolutionary history. To this end, we reconstructed ancestral B-cell-lymphoma-2-family proteins and developed a continuous evolution method to select for defined protein-protein interaction specificities. By repeatedly evolving a series of ancestral proteins to acquire specificities that occurred during history, we show that contingency steadily overwhelms chance and erases necessity as the primary cause of sequence variation in proteins over long phylogenetic timescales. As a result, evolutionary trajectories launched from distant starting points are essentially unpredictable, even under strong and identical selection pressures. Genetic dissection of the outcomes shows that chance arises because numerous sets of mutations can alter specificity at any point in time, while contingency arises because historical substitutions change these sets. Patterns of variation in extant protein sequences are therefore largely the idiosyncratic product of a particular course of unpredictable historical events.

evolutionary biology↗

bioRxiv2019

Complex polymorphic genetic architecture underlies trans-regulatory variation among strains of Saccharomyces cerevisiae

Heritable variation in gene expression is common within species. Much of this variation is due to genetic changes at loci other than the affected gene and is thus trans-acting. This trans-regulatory variation is often polygenic, with individual variants typically having small effects, making the genetic architecture of trans-regulatory variation challenging to study. Consequently, key questions about trans-regulatory variation remain, including how selection affects this variation and how trans-regulatory variants are distributed throughout the genome and within species. Here, we show that trans-regulatory variation affecting TDH3 promoter activity is common among strains of Saccharomyces cerevisiae. Comparing this variation to neutral models of trans-regulatory evolution based on empirical measures of mutational effects revealed that stabilizing selection has constrained this variation. Using a powerful quantitative trait locus (QTL) mapping method, we identified [~]100 loci altering expression between a reference strain and each of three genetically distinct strains. In all three cases, the non-reference strain alleles increased and decreased TDH3 promoter activity with similar frequencies, suggesting that stabilizing selection maintained many trans-acting variants with opposing effects. Loci altering expression were located throughout the genome, with many loci being strain specific and others being shared among multiple strains. These findings are consistent with theory showing stabilizing selection for quantitative traits can maintain many alleles with opposing effects, and the wide-spread distribution of QTL throughout the genome is consistent with the omnigenic model of complex trait variation. Furthermore, the prevalence of alleles with opposing effects might provide raw material for compensatory evolution and developmental systems drift.\n\nSignificance statementGene expression varies among individuals in a population due to genetic differences in regulatory components. To determine how this variation is distributed within genomes and species, we used a powerful genetic mapping approach to examine multiple strains of Saccharomyces cerevisiae. Despite evidence of stabilizing selection maintaining gene expression levels among strains, we find hundreds of loci that affect expression of a single gene. These loci vary among strains and include similar frequencies of alleles that increase and decrease expression. As a result, each strain contains a unique set of compensatory alleles that lead to similar levels of gene expression among strains. This regulatory variation might form the basis for large scale regulatory rewiring observed between distantly related species.

evolutionary biology↗

bioRxiv2018

Fitness effects of altering gene expression noise in Saccharomyces cerevisiae

Gene expression noise is an evolvable property of biological systems that describes differences in gene expression among genetically identical cells in the same environment. Prior work has shown that expression noise is heritable and can be shaped by natural selection, but the impact of variation in expression noise on organismal fitness has proven difficult to measure. Here, we quantify the fitness effects of altering expression noise for the TDH3 gene in Saccharomyces cerevisiae. We show that increases in expression noise can be deleterious or beneficial depending on the difference between the average expression level of a genotype and the expression level maximizing fitness. We also show that a simple model relating single-cell expression levels to population growth produces patterns that are consistent with our empirical data. We use this model to explore a broad range of average expression levels and expression noise, providing additional insight into the fitness effects of variation in expression noise.

evolutionary biology↗

bioRxiv2017

Effects of mutation and selection on plasticity of TDH3 promoter activity in Saccharomyces cerevisiae

Phenotypic plasticity is an evolvable property of biological systems that can arise from environment-specific regulation of gene expression. To better understand the evolutionary and molecular mechanisms that give rise to plasticity in gene expression, we quantified the effects of 235 single nucleotide mutations in the Saccharomyces cerevisiae TDH3 promoter (PTDH3) on the activity of this promoter in media containing glucose, galactose, or glycerol as a carbon source. We found that the distributions of mutational effects differed among environments because many mutations altered the plastic response exhibited by the wild type allele. Comparing the effects of these mutations to the effects of 30 PTDH3 polymorphisms on expression plasticity in the same environments provided evidence of natural selection acting to prevent the plastic response in PTDH3 activity between glucose and galactose from becoming larger. The largest changes in expression plasticity were observed between fermentable (glucose or galactose) and nonfermentable (glycerol) carbon sources and were caused by mutations located in the RAP1 and GCR1 transcription factor binding sites. Mutations altered expression plasticity most frequently between the two fermentable environments, however, with mutations causing significant changes in plasticity between glucose and galactose distributed throughout the promoter, suggesting they might affect chromatin structure. Taken together, these results provide insight into the molecular mechanisms underlying gene-by-environment interactions affecting gene expression as well as the evolutionary dynamics affecting natural variation in plasticity of gene expression.\n\nSignificance StatementFrom seasonal variation in the color of butterfly wings to trees bending toward the light, organisms often change in response to their environment. These changes, known as phenotypic plasticity, can result from differences in how genes are expressed among environments. Mutations causing environment-specific changes in gene expression provide raw material for phenotypic plasticity, but their frequency, effect size and direction of effects among environments are not well understood. This study shows that mutations in the promoter of a yeast metabolic gene often display environment-dependent effects on gene expression and that these environment-dependent effects have been shaped by selection in natural populations.

genetics↗