Genetics最新文献

Ligand-gated acetylcholine receptors (AChRs) are pentameric transmembrane proteins that display extensive molecular diversity through combinatorial assembly of subunits. In the nematode Caenorhabditis elegans, this diversity is exceptionally expanded, with nearly 60 AChR subunit genes, yet the composition and function of many receptor subtypes remain poorly defined. At neuromuscular junctions (NMJs), L-AChRs are heteromeric AChRs sensitive to levamisole, a nematode-specific L-AChR agonist, that play a crucial role in locomotion. Forward genetic screens for mutants resistant to levamisole have identified the L-AChR canonical subunits (LEV-1, UNC-29, LEV-8, UNC-63 and UNC-38) along with key biosynthetic regulators. ACR-8 is an alternative L-AChR subunit that has been poorly characterized, largely because acr-8 mutants remain sensitive to levamisole. Using genetic approaches and in vivo imaging, we show that ACR-8-containing L-AChRs (ACR-8*) are present at the NMJ and, surprisingly, they are more abundant than the well-characterized LEV-8-containing L-AChRs (LEV-8*). Although both LEV-8* and ACR-8* are enriched at synapses and rely on the same clustering machinery, they segregate into distinct postsynaptic nanodomains. The two populations play a redundant function in worm locomotion, and in mutant conditions, one receptor population can compensate for the loss of the other. Interestingly, ACR-8* are already present at high levels at the NMJ from early developmental stages, whereas LEV-8* levels gradually increase throughout development. Our work sheds new light on the molecular landscape of AChRs of the C. elegans NMJ.

Heterochromatin is a repressive epigenetic state that suppresses transcription and safeguards genomic integrity. However, the full mechanism of its regulation remains elusive. Here, we focus on a previously described RNA polymerase II (Pol II) variant called m203 in Schizosaccharomyces pombe, which has a single substitution mutation within the Rpb2 subunit of Pol II (rpb2-N44Y) that reduces RNA interference (RNAi)-dependent heterochromatin at a pericentromeric reporter locus. Through CRISPR-Cas9 site-directed mutagenesis, we find that rpb2-N44Y is a gain-of-function mutation. Furthermore, the heterochromatin defects of the m203 variant require a subunit of the Elongator complex called Elongator Protein 1 (Elp1), a protein that canonically promotes mcm5s2U34 tRNA modifications. While the single knockout of Elp1 in the m203 strain majorly restored heterochromatin formation, single knockouts of the Elp3 or the Elp5 subunits of Elongator showed only modest effects. Furthermore, mcm5s2 U34 tRNA modifications are dispensable for Elp1-dependent heterochromatin. In contrast, the heterochromatin required core factors that are critical for heterochromatin formation, including protein mediators of the RNAi pathway and H3K9 methylation. Overall, our study reveals two distinct Rpb2-centric pathways, via RNAi or Elp1 that can positively or negatively regulate heterochromatin, respectively. Furthermore, our findings reveal a chromatin function for Elp1 that does not rely on Elongator-dependent mcm5s2U34 tRNA modifications. This work expands our understanding of how Elp1 can influence chromatin biology.

Forward-in-time population genetic simulations enable modelling of a wide array of complex evolutionary scenarios. Simulating small genomic regions, and rescaling by reducing population size by a scaling factor while maintaining population-scaled parameters, are common approaches for improving computational tractability. However, simulating whole chromosomes in large populations remains computationally prohibitive, and it is unclear what scaling factors produce consistent evolutionary dynamics between rescaled and unscaled populations. Here, we thoroughly test the effects of rescaling in populations experiencing direct and linked effects of selection for various scaling factors and chromosomal lengths in Drosophila melanogaster-like populations by comparing them with theoretical expectations. We find that while rescaling has minimal effects with most length and scaling factor combinations, simulating long regions with large scaling factors can introduce biases in summary statistics including neutral diversity, allele frequencies, linkage disequilibrium, and nonneutral divergence, even within expected diffusion limits. These deviations occur particularly when the crossover rate exceeds 0.44 per individual/generation because substantial multiple-crossover events are likely to occur within individuals, reducing the effect of recombination. In addition, when the genome-wide deleterious mutation rate is high (U≫1) in highly rescaled long regions, we observe increased Hill-Robertson interference effects and progeny skew, the extent of which was strongly dependent on the fitness effects of selected mutations. We find that hitchhiking effects near functional regions are relatively unaffected by rescaling the population, even with large scaling factors. Our findings expose potential pitfalls when simulating long regions with rescaling and highlight parameter spaces within which expected evolutionary dynamics are conserved.

In a sample of chromosomes from a recombining population, each pair of individuals will have different most recent common genetic ancestors at different loci. We consider the distribution of the time to the most recent of these most recent common ancestors-the most recent time at which any pair of individuals in the sample share a common genetic ancestor at any locus. We use simple heuristic arguments, formal calculations, and coalescent simulations to find that as long as the chromosomal map length R is sufficiently long and the sample size n is not too large, the distribution of this time is peaked around a characteristic value. This value has the unusual scaling ∝N/R/n, where N is the effective size of the population.

Mendelian genetics provides us with a framework for studying allelic dominance relationships at a locus at the phenotypic level. These dominance relationships result from different molecular factors, including the mapping of molecular activity onto fitness. For homomeric proteins, physical interactions between alleles provide a mechanism by which 1 allele can have a dominant effect on the activity of the other. Here, we refer to the effect of these interactions as molecular dominance and examine how they determine total protein activity and contribute to phenotypic dominance. The relative impact of such molecular dominance effects depends on the proportion of subunits that heteromerize relative to those that form homomers. In addition, we show how the effect of physical interactions on phenotypic dominance depends on the function linking protein activity to fitness. Our results show the complex relationships between molecular and phenotypic dominance and highlight the fundamental difference in dominance landscapes for monomeric and homomeric proteins.

Replicability analysis is a cornerstone for identifying genuine genetic associations in genome-wide association studies (GWAS), yet existing methods are constrained by their failure to account for linkage disequilibrium (LD) structure among single nucleotide polymorphisms (SNPs) or underuse of auxiliary information, limiting their reliability and statistical power. We develop CARLIS, a comprehensive covariate-assisted replicability analysis method to enhance both statistical rigor and biological interpretability while maintaining asymptotic false discovery rate control. CARLIS innovatively leverages a triplet hidden Markov model (HMM) to jointly characterize heterogeneous LD structures across two primary studies and an integrated auxiliary covariate (obtained via the Cauchy combination rule). The derived CARLIS statistic enables more efficient ranking of replicable SNPs by synthesizing cross-study and cross-SNP information through forward and backward probabilities. Computational scalability to genome-wide analyses is achieved through semiparametric estimation of composite null proportions and heterogeneous non-null density functions embedded in the HMM forward-backward algorithm. Extensive simulations demonstrate that CARLIS outperforms competing methods in statistical power while maintaining asymptotic false discovery rate control. Applications to replicability analysis of Parkinson's disease GWAS and pleiotropy analysis of bipolar disorder and schizophrenia GWAS show that CARLIS identifies more biologically relevant replicable variants, highlighting its potential to accelerate functional genomics discovery and advance precision medicine.

Homologous genetic recombination is required for the faithful repair of broken DNA to continue life and for genetic diversification to propel evolution. The bacterial RecBCD enzyme promotes these processes through coordinated DNA helicase and nuclease activities, which are regulated by a Chi recombination hotspot sequence (5'-GCTGGTGG-3' in enteric bacteria) and the loading of the RecA DNA strand-exchange protein onto the newly generated 3' single-stranded DNA end. Chi's control of RecBCD requires a complex interaction of all three subunits at widely dispersed points in the 330 kDa three-subunit protein. Here, we describe an additional point that is critical for Chi's site-specific stimulation of recombination in Escherichia coli. This point, on the surface of RecBCD, is where the middle of the 19-amino-acid tether connecting the RecB helicase and nuclease domains fits into a groove on the surface of RecC. Deleting or changing even a single amino acid in this crosspoint dramatically reduces Chi hotspot activity. Surprisingly, severing the tether at the RecB helicase junction leaves Chi and RecBCD fully active, but severing the tether at the RecB nuclease junction abolishes Chi activity and strongly reduces recombination proficiency. This difference is accounted for by the critical role of the tether-groove interaction described here. We discuss how Chi controls RecBCD via the coordinated interaction of the tether-groove crosspoint and 13 other widely spaced points throughout RecBCD.

Vps13 transporters are large, rod-shaped proteins that mediate the bulk transfer of lipids between intracellular membranes via a hydrophobic channel formed by multiple "repeating beta-groove" (RBG) domains. Gain-of-function mutants in yeast Vps13 bypass the need for phospholipid trafficking by the endoplasmic reticulum-mitochondrion contact site complex ERMES. This work shows these same mutants can partially compensate for defects in lipid transfer at a different membrane contact site, suggesting that these VPS13 alleles encode a protein that is more efficient than wild type Vps13 at lipid transfer. The gain-of-function mutations map to similar positions on different RBG repeats within the predicted Vps13 structure. Computational modeling of the structural dynamics of individual RBG repeats indicates that these mutations are biased to regions that act as hinges within the protein. We propose that lipid transport by Vps13 involves cycling between conformational states and that these mutants increase lipid transport by altering the dynamics of this conformational shift.

Heritability is a fundamental parameter of diseases and other traits, quantifying the contribution of genetics to that trait. Kinship matrices are required for heritability estimation with variance components models. However, the most common "standard" kinship estimator employed by Genome-wide Complex Trait Analysis and other approaches, can be severely biased in structured populations. In this study, we characterize two heritability estimation biases in Genome-wide Complex Trait Analysis due to kinship estimation biases under population structure. For the standard ratio-of-means kinship estimator, we derive a closed-form expression for first heritability bias given by the mean kinship and the true heritability. The standard mean-of-ratios estimator, which is the most widely used in practice, exhibits both the bias shared with ratio-of-means and an additional, more severe bias caused by the upweighting of low-frequency variants. Using simulations with admixture and family structures, as well as simulated traits from 1,000 Genomes genotypes, we find that only Popkin-the only unbiased population kinship estimator-produces unbiased heritability estimates in structured settings. Pedigree-only estimates have upward heritability biases when there is population structure. Finally, we analyze three structured datasets with real phenotypes-the San Antonio Family Study, the Hispanic Community Health Study / Study of Latinos, and a multiethnic Nephrotic Syndrome cohort. The standard mean-of-ratios estimator can produce both downward and upward heritability biases depending on population structure and variant frequency spectrum, compared with the other two estimators. Overall, common kinship estimators result in heritability estimation biases when applied to structured populations, a challenge that Popkin successfully overcomes.

First-line cancer treatment frequently fails due to initially rare therapeutic resistance. An important clinical question is then how to schedule subsequent treatments to maximize the probability of tumor eradication. Here, we provide a theoretical solution to this problem by using mathematical analysis and extensive stochastic simulations within the framework of evolutionary rescue theory to determine how best to exploit the vulnerability of small tumors to stochastic extinction. Whereas standard clinical practice is to wait for evidence of relapse, we confirm a recent hypothesis that the optimal time to switch to a second treatment is when the tumor is close to its minimum size before relapse, when it is likely undetectable. This optimum can lie slightly before or slightly after the nadir, depending on tumor parameters. Given that this exact time point may be difficult to determine in practice, we study windows of high extinction probability that lie around the optimal switching point, showing that switching after the relapse has begun is typically better than switching too early. We further reveal how treatment efficacy and tumor demographic and evolutionary parameters influence the predicted clinical outcome, and we determine how best to schedule drugs of unequal efficacy. Our work establishes a foundation for further experimental and clinical investigation of this evolutionarily-informed multi-strike treatment strategy.