Genetics最新文献

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.

Human ApolipoproteinB (ApoB) exists in two isoforms that are packaged into low density lipoprotein particles and are major contributors to atherosclerosis. Alternatively, Drosophila Apolipoprotein Lipophorin (ApoLpp) also exists in two isoforms packaged into lipoprotein particles that cross the blood-brain barrier (BBB) in second instar larvae where they deliver lipids to neuroblasts. To extend our understanding of ApoLpp function to adult brains and suggest new hypotheses for human ApoB, we document evolutionary conservation between the two N-terminal isoforms human ApoB48 and fly ApoLppII. Then our tissue-specific analyses including rescue of apolpp lethality and apolpp RNAi showed that apolpp expression in the fat body is both necessary and sufficient for survival to adulthood. Our imaging studies of ApoLpp in the adult brain employed endogenous isoform-specific tagged proteins generated by the Fourth Chromosome Resource Project. Images revealed that both ApoLpp isoforms are present in the adult brain with ApoLppII accumulation prominent near glia. Nanobody morphotrap experiments that blocked tagged ApoLpp at the BBB demonstrated that ApoLpp detected inside the adult brain is exogenous. An N- and C-terminal tagged ApoLpp transgene expressed solely in the fat body facilitated tracking of each isoform from fat body secretion to the BBB and then inside the adult brain. Overall, our data suggest that the known role of ApoLpp in lipid delivery to larval brains likely continues in adults. Strong conservation between ApoLppII and ApoB48 supports the hypothesis that ApoB48 may have a role in the brain outside the circulatory system.

The ability to direct tissue-specific overexpression of transgenic proteins in genetically tractable organisms like Drosophila melanogaster has facilitated innumerable biological discoveries. However, transgenic proteins can themselves impact cellular and physiological processes in ways that are often ignored or poorly defined. Here we discovered that the yolk-GAL4 transgene, which directs strong expression of the yeast GAL4 transcription factor in the Drosophila fat body, induces significant physiological defects in adult female flies. We found that yolk-GAL4 disrupts adipose tissue integrity and reduces fat body lipid stores, egg production, and resistance to systemic bacterial infections. Knocking down GAL4 expression in yolk-GAL4 heterozygotes using RNAi fully suppressed each of these defects, thus confirming that the GAL4 transgene product induces these phenotypes. Comparing a panel of additional fat body driver lines, we found that GAL4 expression levels directly correlate with infection susceptibility, but not with fat levels or egg production. To determine whether other transgenic proteins can impair fat body function, we constructed new fly lines in which the yolk enhancer directs expression of either cytoplasmic or nuclear-localized mCherry, or an alternative transactivator, LexA. We found that only nuclear-localized mCherry and LexA increased infection susceptibility similarly to GAL4, suggesting that intranuclear transgenic proteins in general can curtail the fat body's induced immune response in a manner highly sensitive to transgene expression strength. Additionally, these new lines can be valuable tools for future studies. More broadly, our findings highlight the potential for transgenes to substantially impact organismal biology and emphasize the importance of rigorously characterizing genetic tools to optimally leverage model systems like Drosophila.

Sequence-specific transcription factors (TFs) are key regulators of many biological processes, controlling the expression of their target genes (TGs) through binding to the cis-regulatory regions such as promoters and enhancers. Each TF has unique DNA binding site motifs, and large-scale experiments have been conducted to characterize TF-DNA binding preferences. However, no comprehensive resource currently integrates these datasets for Drosophila. To address this need, we developed TF2TG ("transcription factor" to "target gene"), a comprehensive resource that combines both in vitro and in vivo datasets to link TFs to their TGs based on TF-DNA binding preferences along with the protein-protein interaction data, tissue-specific transcriptomic data, and chromatin accessibility data. Although the genome offers numerous potential binding sites for each TF, only a subset is actually bound in vivo, and of these, only a fraction is functionally relevant. For instance, some TFs bind to their specific sites due to synergistic interactions with other factors nearby. This integration provides users with a comprehensive list of potential candidates as well as aids users in ranking candidate genes and determining condition-specific TF binding for studying transcriptional regulation in Drosophila.

Protein translation regulation is critical for cellular responses and development, yet how elongation stage disruptions shape these processes remains incompletely understood. Here, we identify a single amino acid substitution (P55Q) in the ribosomal protein RPL-36A of Caenorhabditis elegans that confers complete resistance to the elongation inhibitor cycloheximide (CHX). Heterozygous animals carrying both wild-type RPL-36A and RPL-36A(P55Q) develop normally but show intermediate CHX resistance, indicating a partial dominant effect. Leveraging RPL-36A(P55Q) as a single-copy positive selection marker for CRISPR-based genome editing, we introduced targeted modifications into multiple ribosomal protein genes, confirming its broad utility for altering essential loci. In L4-stage heterozygotes, where CHX-sensitive and CHX-resistant ribosomes coexist, ribosome profiling revealed increased start-codon occupancy, reduced disome formation, and no codon-specific pausing. Surprisingly, chronic CHX treatment did not activate canonical stress pathways (ribosome quality control, integrated stress response, and ribotoxic stress response), as indicated by the absence of RPS-10 ubiquitination, eIF2α or PMK-1 phosphorylation, or ATF-4 induction. Instead, RNA-normalized ribosome footprints revealed selective changes in translation efficiency (TE), with reduced nucleolar/P-granule components and increased oocyte development genes. Consistently, premature oocyte development was observed in L4 animals. These findings suggest that partial inhibition of translation elongation disrupts developmental timing across tissues, likely by altering TE.

Understanding how genetic variants drive phenotypic differences is a major challenge in molecular biology. Single nucleotide polymorphisms form the vast majority of genetic variation and play critical roles in complex, polygenic phenotypes, yet their functional impact is poorly understood from traditional gene-level analyses. In-depth knowledge about the impact of single nucleotide polymorphisms has broad applications in health and disease, population genomic, and evolution studies. The wealth of genomic data and available functional genetic tools make Drosophila melanogaster an ideal model species for studies at single nucleotide resolution. However, to leverage these resources for genotype-phenotype research and potentially combine it with the power of functional genetics, it is essential to develop techniques to predict functional impact and causality of single nucleotide variants. Here, we present FlyCADD, a functional impact prediction tool for single nucleotide variants in D. melanogaster. FlyCADD, based on the Combined Annotation-Dependent Depletion (CADD) framework, integrates over 650 genomic features-including conservation scores, GC content, and DNA secondary structure-into a single metric reflecting a variant's predicted impact on evolutionary fitness. FlyCADD provides impact prediction scores for any single nucleotide variant on the D. melanogaster genome. We demonstrate the power of FlyCADD for typical applications, such as the ranking of phenotype-associated variants to prioritize variants for follow-up studies, evaluation of naturally occurring polymorphisms, and refining of CRISPR-Cas9 experimental design. FlyCADD provides a powerful framework for interpreting the functional impact of any single nucleotide variant in D. melanogaster, thereby improving our understanding of genotype-phenotype connections.

Codon usage bias refers to the nonequal usage of synonymous codons. This phenomenon is fundamentally important in biology as it is jointly shaped by mutation, genetic drift, and natural selection, and influences translation rate, decoding accuracy, and mRNA stability. However, popular tools for codon usage bias analysis are not flexible nor efficient enough and fail to incorporate recent advancements in this field. To address these issues, we developed the Codon Usage Bias Analysis in R (cubar) package. Cubar is highly modular and can calculate common codon usage indexes in a user-friendly manner. In addition, it can perform sliding-window analyses of codon usage, assess differential usage between gene sets, and optimize user-provided genes based on the codon usage of a target organism. Furthermore, cubar is highly efficient and can analyze millions of coding sequences within a few minutes on a laptop.