Biology subjects

Puzovic, N.

Publications and source records attributed to Puzovic, N..

4 recordsLinked to original sources

bioRxiv2023

When is gene expression noise advantageous?

The variability of gene expression levels, also known as gene expression noise, is an evolvable trait subject to selection. While gene expression noise is detrimental in constant environments where the expression level is under stabilizing selection, it may be beneficial in changing environments when the phenotype is far from the optimum. However, expression noise propagates along the gene network, making the evolution of connected genes interdependent. Here, we explore how their position in the gene network constrains the evolution of genes under selection using an in silico evolution experiment. We simulate the evolution of populations of model gene regulatory networks under directional and fluctuating selection on the gene expression level while allowing the basal expression level and expression noise level to mutate. We find that expression noise is only transiently favoured under directional selection, but high levels of noise can be maintained in a fluctuating selection regime. Furthermore, target genes, regulated by other genes, were more likely to increase their gene-specific expression noise than regulator genes. These findings suggest that both the mean and variance of gene expression levels respond to selection due to changing environments - and do so in a network-dependent manner. They further point at gene expression noise as a putative mechanism for populations to escape extinction when facing environmental changes.

evolutionary biology↗

bioRxiv2022

Being noisy in a crowd: differential selective pressure on gene expression noise in model gene regulatory networks

Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the mean expression is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution.

evolutionary biology↗

bioRxiv2018

Inference of recombination maps from a single pair of genomes and its application to archaic samples

Understanding the causes and consequences of recombination rate evolution is a fundamental goal in genetics that requires recombination maps from across the tree of life. Since statistical inference of recombination maps typically depends on large samples, reaching out studies to non-model organisms requires alternative tools. Here we extend the sequentially Markovian coalescent model to jointly infer demography and the variation in recombination along a pair of genomes. Using extensive simulations and sequence data from humans, fruit-flies and a fungal pathogen, we demonstrate that iSMC accurately infers recombination maps under a wide range of scenarios - remarkably, even from a single pair of unphased genomes. We exploit this possibility and reconstruct the recombination maps of archaic hominids. We report that the evolution of the recombination landscape follows the established phylogeny of Neandertals, Denisovans and modern human populations, as expected if the genomic distribution of crossovers in hominids is largely neutral.

evolutionary biology↗

bioRxiv2017

Selection at the pathway level drives the evolution of gene-specific transcriptional noise

Biochemical reactions within individual cells result from the interactions of molecules, often in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generate noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, in particular owing to the advent of single cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. We took advantage of two recently published data sets of the single-cell transcriptome of the domestic mouse Mus musculus in order to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We showed that noise levels in the mRNA distributions (a.k.a. transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraint on the encoded protein and gene age. The position of the encoded protein in biological pathways, however, is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with mean expression level in functional and evolutionary studies of gene expression.

evolutionary biology↗