Genetics最新文献

Dihydropyrimidine dehydrogenase (DPD), encoded by DPYD, is a key enzyme in pyrimidine catabolism, and its deficiency leads to severe toxicity in patients treated with 5-fluorouracil (5-FU). While pathogenic DPYD variants account for many cases of DPD deficiency, they do not fully explain all instances of 5-FU sensitivity, suggesting additional genetic factors are involved. Recent studies have implicated variants in CIAO1, a gene encoding a subunit of the cytosolic iron-sulfur (Fe-S) cluster assembly targeting complex, in reducing DPD stability and function. In this study, we established a C. elegans model to assess DPD deficiency and 5-FU sensitivity. Using a dpyd-1 knockout and CRISPR-generated ciao-1 variants that mirror patient-derived variants (p.Trp184Cys, p.His193Tyr, and p.Arg65Trp), we provide the first in vivo evidence that pathogenic variants in CIA complex components can lead to DPD deficiency and, consequently, heightened 5-FU toxicity. Our findings highlight the critical role of CIAO1 in DPD function and 5-FU tolerance, expanding the genetic landscape of DPD deficiency and offering a robust platform for functional evaluation of pathogenic variants.

Skin coloration is crucial for the survival of animals and ranges from spectacular colorful displays used to attract a mate to cryptic camouflage used to avoid predators. Among the 3 main types of chromatophores, melanophores are the most widespread in vertebrates and can set the skin tone by the amount of melanin they produce and store in dedicated vesicles, the melanosomes. Mutations associated with melanophore differentiation and maturation result in hypomelanistic and amelanistic phenotypes, both extensively studied in mammals but less so in snakes and lizards. Here, we characterize at the genomic, transcriptomic, and histological level, the Hypomelanistic corn snake morph and 3 hypomelanistic leopard gecko morphs. To minimize bias in studying leopard gecko color morphs, we first assembled a chromosome-level genome from a wild-type individual in terms of coloration. We propose that candidate mutations in 3 melanogenesis factors generate these phenotypes: (i) tyrosinase (TYR), an essential enzyme for melanin synthesis, (ii) NCKX5 (SLC24A5), an ion exchanger involved in melanosome maturation, and (iii) the P protein (OCA2), a transmembrane transporter for tyrosine. Our extended bulk RNA sequencing analyses show that additional pigmentation-related genes, affecting melanin production, melanosome motility, and melanophore migration, are dysregulated in the embryonic skin of the mutated animals. This observation highlights the likely associations among the corresponding pathways and is in line with our electron microscopy imaging results. Indeed, the subcellular structure of melanophores is uniquely altered at each of the 4 morphs and likely reflects a multigenic effect. These findings demonstrate that conserved pigmentation genes can produce species-specific effects, underscoring the modular nature of skin coloration in vertebrates. Our work establishes reptiles as comparative models for studying pigment cell biology and reveals evolutionary flexibility in the genetic regulation of melanogenesis.

Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet, predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics and related fields such as phylodynamics and phylogeography largely lack theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework based on Markov decision processes, we model how sampling interacts with a population's demographic history to shape the ancestral or genealogical relationships of sampled individuals. By probabilistically considering these ancestral relationships, we can use Markov decision processes to predict the expected value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies even when the informational value of sampling depends on past or future sampling events. To illustrate our framework, we develop Markov decision processes for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the Markov decision process allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing the associated costs of sampling.

Many traits of interest in biology evolved long ago and are fixed in a particular species, distinguishing it from other sister taxa. Elucidating the mechanisms underlying such divergences across reproductive barriers has been a key challenge for evolutionary biologists. The yeast Saccharomyces cerevisiae is unique among its relatives for its ability to thrive at high temperature. The genetic determinants of the trait remain incompletely understood, and we sought to understand the role in its architecture of species variation in mitochondrial DNA. We used mitochondrial transgenesis to show that S. cerevisiae mitotypes were sufficient for a partial boost to thermotolerance and respiration in the Saccharomyces paradoxus background. These mitochondrial alleles worked best when the background also harbored a pro-thermotolerance nuclear genotype, attesting to positive epistasis between the two genomes. The benefits of S. cerevisiae alleles in terms of respiration and growth at high temperature came at the cost of worse performance in cooler conditions. Together, our results establish this system as a case in which mitoalleles drive fitness benefits in a manner compatible with, and fostered by, the nuclear genome.

Traumatic brain injuries (TBIs) are a global source of hospitalization, long-term disability, and death. However, age, sex, and other demographics differ widely among patients and the variable characteristics of TBIs further obscure injury outcomes. Here, we used Drosophila melanogaster to assess the effects of mild, repeated TBI (multi-day, MD) compared to single, severe TBI (single-day, SD). In all genotypes tested, flies given an SD injury exhibited higher acute mortality, but in some genotypes, the surviving flies had a longer lifespan and better long-term locomotor ability than flies given an MD injury. We hypothesized that different immune responses to MD vs SD injury may mediate differences in short- and long-term outcomes. We measured antimicrobial peptide gene expression and found that it increased after each strike of the MD injury and was eventually equivalent or greater than in flies given SD injury. Additionally, increased expression of some immune genes persisted for up to four weeks, predominantly in flies given MD injuries. We measured TBI outcomes of mutant flies for each arm of the innate immune system (Imd and Toll) and found that Imd null mutants had worse short- and long-term survival across both injury conditions, indicating that Imd signaling is protective against both injury types. Interestingly, a partial loss of function mutant for Toll signaling led to higher acute mortality following SD injury, but lower acute mortality and longer lifespan after MD injury, suggesting that Toll signaling is detrimental following MD injury. However, weak and strong ubiquitous Gal4-driven RNAi knockdowns of Toll and Imd varied in their effects on acute mortality and lifespan, suggesting that the degree of immune signaling also contributes to TBI outcomes. Understanding differences in innate immune response to different types of TBI could enable development of targeted therapeutics.

Cellular diversification in processes from development to cancer progression and affinity maturation is often linked to the appearance of new mutations, generating genetic heterogeneity. Describing the underlying coupled genetic and growth processes that result in the observed diversity in cell populations is informative about the timing, drivers and outcomes of cell fates. Current approaches based on phylogenetic methods do not cover the entire range of evolutionary rates, often making artificial assumptions about the timing of events. We introduce CBA, a probabilistic method that infers the division, degradation and mutation rates from the observed genetic diversity in a population of cells. It uses a summarized backbone tree, intermediary between the true cell tree and the allelic tree representing the ancestral relationships between types, called a monogram, which allows for efficient sampling of possible phylogenies consistent with the observed mutational signatures. We demonstrate the accuracy of our method on simulated data and compare its performance to standard phylogenetic approaches.

Recombination is a fundamental evolutionary process essential for generating genetic diversity, facilitating adaptation, and driving speciation. However, direct measurement of recombination rate remains challenging, as standard methods-such as chiasma counts or genetic linkage maps-are labor intensive and often infeasible for nonmodel species. In this study, we identify chromosome number and mean chromosome size as practical proxies for genome-wide recombination rate by analyzing genetic map data from 73 insect species and supplementary analyses of 157 monocentric flowering plants. We confirm the long-standing hypothesis that monocentric species have nearly twice as many crossovers per chromosome as holocentric species, reflecting structural constraints imposed by diffuse centromeres. Using both ordinary and phylogenetically informed Bayesian regression models, we show that recombination rate increases with chromosome number and decreases with mean chromosome size. Crucially, mean chromosome size is a significantly better predictor, particularly in holocentric species. This insight enables recombination rate estimation in thousands of species with known chromosome sizes, thereby allowing hypothesis testing at scales previously unattainable. Building on these results, we present predictive models applicable to poorly studied holocentric plants. Overall, our study highlights the pivotal role of chromosome architecture in recombination evolution and provides an accessible framework for evolutionary genomic research across diverse lineages.

Unhealthy diets, obesity, and low fertility are associated in Drosophila and humans. We previously showed that a high sugar diet, but not obesity, reduces Drosophila female fertility owing to increased death of newly formed germline cysts and vitellogenic follicles. Drosophila strains carrying mutations in the yellow (y) and white (w) pigmentation genes are routinely used for investigating the effects of high sugar diets, but it has remained unclear how this genetic background interacts with high sugar. Here, we show that the loss of y function is responsible for the high sugar diet-induced death of early germline cysts and vitellogenic follicles previously observed in y w mutant females. Dopamine supplementation prevents follicle death in y mutants on a high sugar diet. Conversely, severe dopamine imbalance or lack of dopamine production in the central nervous system causes follicle death regardless of diet or genetic background, while early germline cyst survival does not depend on dopamine. Our findings are broadly relevant to our understanding of how the effects of unhealthy diets might differ depending on genetic factors and highlight a key connection between dopamine metabolism in the central nervous system and ovarian follicle survival.

Genetic relatedness is a central concept in genetics, underpinning studies of population and quantitative genetics in human, animal, and plant settings. It is typically stored as a genetic relatedness matrix, whose elements are pairwise relatedness values between individuals. This relatedness has been defined in various contexts based on pedigree, genotype, phylogeny, coalescent times, and, recently, ancestral recombination graph. For some downstream applications, including association studies, using ancestral recombination graph-based genetic relatedness matrices has led to better performance relative to the genotype genetic relatedness matrix. However, they present computational challenges due to their inherent quadratic time and space complexity. Here, we first discuss the different definitions of relatedness in a unifying context, making use of the additive model of a quantitative trait to provide a definition of "branch relatedness" and the corresponding "branch genetic relatedness matrix". We explore the relationship between branch relatedness and pedigree relatedness (i.e. kinship) through a case study of French-Canadian individuals that have a known pedigree. Through the tree sequence encoding of an ancestral recombination graph, we then derive an efficient algorithm for computing products between the branch genetic relatedness matrix and a general vector, without explicitly forming the branch genetic relatedness matrix. This algorithm leverages the sparse encoding of genomes with the tree sequence and hence enables large-scale computations with the branch genetic relatedness matrix. We demonstrate the power of this algorithm by developing a randomized principal components algorithm for tree sequences that easily scales to millions of genomes. All algorithms are implemented in the open source tskit Python package. Taken together, this work consolidates the different notions of relatedness as branch relatedness and, by leveraging the tree sequence encoding of an ancestral recombination graph, provides efficient algorithms that enable computations with the branch genetic relatedness matrix that scale to mega-scale genomic datasets.