Biology subjects

Zhang, J. G.

Publications and source records attributed to Zhang, J. G..

9 recordsLinked to original sources

bioRxiv2023

Evidence for the role of selection for reproductively advantageous alleles in human aging

The antagonistic pleiotropy hypothesis posits that natural selection for pleiotropic mutations that confer earlier or more reproduction but impair the post-reproductive life causes aging. This hypothesis of the evolutionary origin of aging is supported by case studies but lacks unambiguous genomic evidence. Here we genomically test this hypothesis using the genotypes, reproductive phenotypes, and death registry of 276,406 UK Biobank participants. We observe a strong, negative genetic correlation between reproductive traits and lifespan. Individuals with higher polygenetic scores for reproduction (PGSR) have lower survivorships to age 76 (SV76), and PGSR increased over birth cohorts from 1940 to 1969. Similar trends are found from individual genetic variants examined. PGSR and SV76 remain negatively correlated upon the control of the offspring number, revealing horizontal pleiotropy between reproduction and lifespan. Intriguingly, regardless of PGSR, having two children maximizes SV76. These and other findings strongly support the antagonistic pleiotropy hypothesis of aging in humans.

evolutionary biology↗

bioRxiv2023

Contraception ends the genetic maintenance of human same-sex sexual behavior

Because human same-sex sexual behavior (SSB) is heritable and leads to fewer offspring, it is puzzling why SSB-associated alleles have not been selectively purged. Current evidence supports the antagonistic pleiotropy hypothesis that SSB-associated alleles benefit individuals exclusively performing opposite-sex sexual behavior by increasing their number of sexual partners and consequently their number of offspring. However, here we show that having more sexual partners no longer predicts more offspring since the availability of oral contraceptives in the 1960s and that SSB is now negatively genetically correlated with the number of offspring, indicating a loss of SSBs genetic maintenance in modern societies.

genetics↗

bioRxiv2023

Transcriptomic analysis of diverse organisms reveals the rareness of genetic assimilation in environmental adaptations

Genetic assimilation is the evolutionary process by which an environmentally induced phenotype becomes genetically encoded and constitutive. Genetic assimilation has been proposed as the concluding step in the plasticity-first model of environmental adaptation and has been observed in multiple species, but its prevalence has not been systematically investigated. By analyzing transcriptomic data collected upon reciprocal transplant, we address this question in the experimental evolution, domestication, or natural evolution of a bacterial, a fungal, a plant, and four animal species. We find that genetic assimilation of environment-induced gene expression is the exception rather than the rule and that substantially more genes retain than lose their expression plasticity upon organismal adaptations to new environments. The probability of genetic assimilation varies among genes and decreases with the number of transcription factors controlling the gene and the expression level of the gene, supporting the hypothesis that genetic assimilation results primarily from passive losses of gene regulations that are not mutationally robust. Therefore, at the level of gene expression, our findings argue against the purported importance of genetic assimilation to environmental adaptation.

evolutionary biology↗

bioRxiv2021

The genetic code is not optimized for resource conservation

Shenhav and Zeevi conclude in a recent article (Science 370:683-687) that the universal genetic code (UGC) is optimized for resource conservation because mutations are less likely to increase proteomic nitrogen and carbon uses under the UGC than under random genetic codes (RGCs). Their finding results from miscalculating mutational effects and benchmarking against biased RGCs. Our reanalysis refutes their conclusion.

evolutionary biology↗

bioRxiv2020

Fly wing evolution explained by a neutral model with mutational pleiotropy

To what extent the speed of mutational production of phenotypic variation determines the rate of long-term phenotypic evolution is a central question in evolutionary biology. In a recent study, Houle et al. addressed this question by studying the mutational variation, microevolution, and macroevolution of locations of vein intersections on fly wings, reporting very slow phenotypic evolution relative to the rates of mutational input, high phylogenetic signals of these traits, and a strong, linear correlation between the mutational variance of a trait and its rate of evolution. Houle et al. examined multiple models of phenotypic evolution but found none consistent with all these observations. Here we demonstrate that the purported linear correlation between mutational variance and evolutionary divergence is an artifact. More importantly, patterns of fly wing evolution are explainable by a simple model in which the wing traits are neutral or neutral within a range of phenotypic values but their evolutionary rates are reduced because most mutations affecting these traits are purged owing to their pleiotropic effects on other traits that are under stabilizing selection. We conclude that the evolutionary patterns of fly wing morphologies are explainable under the existing theoretical framework of phenotypic evolution.

evolutionary biology↗

bioRxiv2020

No support for the adaptive hypothesis of lagging-strand encoding in bacterial genomes

Genes are preferentially encoded on the leading instead of the lagging strand of DNA replication in most bacterial genomes1. This bias likely results from selection against lagging-strand encoding, which can cause head-on collisions between DNA polymerases and RNA polymerases that induce transcriptional abortion, replication delay, and possibly mutagenesis1. But there are still genes encoded on the lagging strand, an observation that has been explained by a balance between deleterious mutations bringing genes from the leading to the lagging strand and purifying selection purging such mutations2. This mutation-selection balance hypothesis predicts that the probability that a gene is encoded on the lagging strand decreases with the detriment of its lagging-strand encoding relative to leading-strand encoding, explaining why highly expressed genes and essential genes are underrepresented on the lagging strand3,4. In a recent study, Merrikh and Merrikh proposed that the observed lagging-strand encoding is adaptive instead of detrimental, due to beneficial mutations brought by the potentially increased mutagenesis resulting from head-on collisions5. They reported empirical observations from comparative genomics that were purported to support their hypothesis5. Here we point out methodological flaws and errors in their analyses and logical problems of their interpretation. Our reanalysis of their data finds no evidence for the adaptive hypothesis.

evolutionary biology↗

bioRxiv2019

Deep residual neural networks resolve quartet molecular phylogenies

Phylogenetic inference is of fundamental importance to evolutionary as well as other fields of biology, and molecular sequences have emerged as the primary data for this task. Although many phylogenetic methods have been developed to explicitly take into account substitution models of sequence evolution, such methods could fail due to model misspecification and insufficiency, especially in the face of heterogeneities in substitution processes across sites and among lineages. In this study, we propose to infer topologies of four-taxon trees using deep residual neural networks, a machine learning approach needing no explicit modeling of the subject system and having a record of success in solving complex non-linear inference problems. We train residual networks on simulated protein sequence data with extensive amino acid substitution heterogeneities. We show that the well-trained residual network predictors can outperform existing state-of-the-art inference methods such as the maximum likelihood method on diverse simulated test data, especially under extensive substitution heterogeneities. Reassuringly, residual network predictors generally agree with existing methods in the trees inferred from real phylogenetic data with known or widely believed topologies. We conclude that deep learning represents a powerful new approach to phylogenetic reconstruction, especially when sequences evolve via heterogeneous substitution processes. We present our best trained predictor in a freely available program named Phylogenetics by Deep Learning (PhyDL, https://gitlab.com/ztzou/phydl).

evolutionary biology↗

bioRxiv2019

Missense mutations in the MLKL brace region lead to lethal neonatal inflammation in mice and are present in high frequency in humans.

We have isolated a mouse strain with a single missense mutation in the gene encoding MLKL, the essential effector of necroptotic cell death. The resulting substitution lies within the two-helix brace and confers constitutive, RIPK3 independent, killing activity to MLKL. Mice homozygous for MlklD139V develop lethal inflammation within days of birth, implicating the salivary glands and pericardium as hotspots for necroptosis and inflammatory infiltration. The normal development of MlklD139V homozygotes until birth, and the absence of any overt phenotype in heterozygotes provides important in vivo precedent for the capacity of cells to clear activated MLKL. These observations offer an important insight into the potential disease-modulating roles of three common human MLKL polymorphisms that encode amino acid substitutions within or adjacent to the brace region. Compound heterozygosity of these variants is found at up to 12-fold the expected frequency in patients that suffer from a pediatric autoinflammatory disease, CRMO.

cell biology↗

bioRxiv2019

Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells

Gene expression is subject to stochastic noise, but to what extent and by which means such stochastic variations are coordinated among different genes are unclear. We hypothesize that neighboring genes on the same chromosome co-fluctuate in expression because of their common chromatin dynamics, and verify it at the genomic scale using allele-specific single-cell RNA-sequencing data of mouse cells. Unexpectedly, the co-fluctuation extends to genes that are over 60 million bases apart. We provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity, which is much closer intra-chromosomally than inter-chromosomally. We further show that genes encoding components of the same protein complex tend to be chromosomally linked, likely resulting from natural selection for intracellular among-component dosage balance. These findings have implications for both the evolution of genome organization and optimal design of synthetic genomes in the face of gene expression noise.

genomics↗