Genetics最新文献

Ancient whole-genome duplications (WGDs) are believed to facilitate novelty and adaptation by providing the raw fuel for new genes. However, it is unclear how recent WGDs may contribute to evolvability within recent polyploids. Hybridization accompanying some WGDs may combine divergent gene content among diploid species. Some theory and evidence suggest that polyploids have a greater accumulation and tolerance of gene presence-absence and genomic structural variation, but it is unclear to what extent either is true. To test how recent polyploidy may influence pangenomic variation, we sequenced, assembled, and annotated twelve complete, chromosome-scale genomes of Camelina sativa, an allohexaploid biofuel crop with three distinct subgenomes. Using pangenomic comparative analyses, we characterized gene presence-absence and genomic structural variation both within and between the subgenomes. We found over 75% of ortholog gene clusters are core in Camelina sativa and <10% of sequence space was affected by genomic structural rearrangements. In contrast, 19% of gene clusters were unique to one subgenome, and the majority of these were Camelina-specific (no ortholog in Arabidopsis). We identified an inversion that may contribute to vernalization requirements in winter-type Camelina, and an enrichment of Camelina-specific genes with enzymatic processes related to seed oil quality and Camelina's unique glucosinolate profile. Genes related to these traits exhibited little presence-absence variation. Our results reveal minimal pangenomic variation in this species, and instead show how hybridization accompanied by WGD may benefit polyploids by merging diverged gene content of different species.

Gene co-expression networks typically comprise modules and their associated hub genes, which are regulating numerous downstream interactions within the network. Methods for hub screening, as well as data-driven estimation of hub co-expression networks using graphical models, can serve as useful tools for identifying these hubs. Graphical model-based penalization methods typically have one or multiple regularization terms, each of which encourages some favorable characteristics (e.g., sparsity, hubs, power-law) to the estimated complex gene network. It is common practice to find a single optimal graphical model corresponding to a specific value of the regularization parameter(s). However, instead of doing this, one could aggregate information across several graphical models, all of which depend on the same data set, along the solution path in the hub gene detection process. We propose a novel method for detecting hub genes that utilizes the information available in the solution path. Our procedure is related to stability selection, but we replace resampling with a simple statistic. This procedure amalgamates information from each node of the data-driven graphical models into a single influence statistic, similar to Cook's distance. We call this statistic the Mean Degree Squared Distance (MDSD). Our simulation and empirical studies demonstrate that the MDSD statistic maintains a good balance between false positive and true positive hubs. An R package MDSD is publicly available on GitHub under the General Public License https://github.com/markkukuismin/MDSD.

Budding yeast (Saccharomyces cerevisiae) is the most extensively characterized eukaryotic model organism and has long been used to gain insight into the fundamentals of genetics, cellular biology, and the functions of specific genes and proteins. The Saccharomyces Genome Database (SGD) is a scientific resource that provides information about the genome and biology of S. cerevisiae. For more than 30 years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation for budding yeast along with search and analysis tools to explore these data. Here we describe recent updates at SGD, including the two most recent reference genome annotation updates, expanded biochemical pathways representation, changes to SGD search and data files, and other enhancements to the SGD website and user interface. These activities are part of our continuing effort to promote insights gained from yeast to enable the discovery of functional relationships between sequence and gene products in fungi and higher eukaryotes.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change. Predicting allele frequency changes under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Metabolic mechanisms conferring pyrethroid resistance in malaria vectors are jeopardizing the effectiveness of insecticide-based interventions, and identification of their markers is a key requirement for robust resistance management. Here, using a field-lab-field approach, we demonstrated that a single mutation G454A in the P450 CYP9K1 is driving pyrethroid resistance in the major malaria vector Anopheles funestus in East and Central Africa. Drastic reduction in CYP9K1 diversity was observed in Ugandan samples collected in 2014, with selection of a predominant haplotype (G454A mutation at 90%), which was completely absent in the other African regions. However, six years later (2020) the Ugandan 454A-CYP9K1 haplotype was found predominant in Cameroon (84.6%), but absent in Malawi (Southern Africa) and Ghana (West Africa). Comparative in vitro heterologous expression and metabolism assays revealed that the mutant 454A-CYP9K1 (R) allele significantly metabolises more type II pyrethroid (deltamethrin) compared with the wild G454-CYP9K1 (S) allele. Transgenic Drosophila melanogaster flies expressing 454A-CYP9K1 (R) allele exhibited significantly higher type I and II pyrethroids resistance compared to flies expressing the wild G454-CYP9K1 (S) allele. Furthermore, laboratory testing and field experimental hut trials in Cameroon demonstrated that mosquitoes harbouring the resistant 454A-CYP9K1 allele significantly survived to pyrethroids exposure (Odds ratio = 567, p < 0.0001). This study highlights the rapid spread of pyrethroid resistant CYP9K1 allele, under directional selection in East and Central Africa, contributing to reduced bed net efficacy. The newly designed DNA-based assay here will add to the toolbox of resistance monitoring and improving its management strategies.

Patterson's f-statistics are among the most heavily utilized tools for analyzing genome-wide allele frequency data for demographic inference. Beyond studying admixture, f3- and f4-statistics are also used for clustering populations to identify groups with similar histories. However, previous studies have noted an unexpected behavior of f-statistics: multiple populations from a certain region systematically show higher genetic affinity to a more distant population than to their neighbors, a pattern that is mismatched with alternative measures of genetic similarity. We call this counter-intuitive pattern "sister repulsion". We first present a novel instance of sister repulsion, where genomes from Bronze Age East Anatolian sites show higher affinity toward Bronze Age Greece rather than each other. This is observed both using f3- and f4-statistics, contrasts with archaeological/historical expectation, and also contradicts genetic affinity patterns captured using principal components analysis or multidimensional scaling on genetic distances. We then propose a simple demographic model to explain this pattern, where sister populations receive gene flow from a genetically distant source. We calculate f3- and f4-statistics using simulated genetic data with varying population genetic parameters, confirming that low-level gene flow from an external source into populations from 1 region can create sister repulsion in f-statistics. Unidirectional gene flow between the studied regions (without an external source) can likewise create repulsion. Meanwhile, similar to our empirical observations, multidimensional scaling analyses of genetic distances still cluster sister populations together. Overall, our results highlight the impact of low-level admixture events when inferring demographic history using f-statistics.

We consider how the genetic architecture underlying locally adaptive traits determines the strength of a barrier to gene flow in a mainland-island model. Assuming a general life cycle, we derive an expression for the effective migration rate when local adaptation is due to genetic variation at many loci under directional selection on the island, allowing for arbitrary fitness and dominance effects across loci. We show how the effective migration rate can be combined with classical single-locus diffusion theory to accurately predict multilocus differentiation between the mainland and island at migration-selection-drift equilibrium and determine the migration rate beyond which local adaptation collapses, while accounting for genetic drift and weak linkage. Using our efficient numerical tools, we then present a detailed study of the effects of dominance on barriers to gene flow, showing that when total selection is sufficiently strong, more recessive local adaptation generates stronger barriers to gene flow. We then study how heterogeneous genetic architectures of local adaptation affect barriers to gene flow, characterizing adaptive differentiation at migration-selection balance for different distributions of fitness effects. We find that a more heterogeneous genetic architecture generally yields a stronger genome-wide barrier to gene flow and that the detailed genetic architecture underlying locally adaptive traits can have an important effect on observable differentiation when divergence is not too large. Lastly, we study the limits of our approach as loci become more tightly linked, showing that our predictions remain accurate over a large biologically relevant domain.

Raf protein kinases act as Ras-GTP sensing components of the ERK signal transduction pathway in animal cells, influencing cell proliferation, differentiation, and survival. In humans, somatic and germline mutations in the genes BRAF and RAF1 are associated with malignancies and developmental disorders. Recent studies shed light on the structure of activated Raf, a heterotetramer consisting of Raf and 14-3-3 dimers, and raised the possibility that a Raf C-terminal distal tail segment (DTS) regulates activation. We investigated the role of the DTS using the Caenorhabditis elegans Raf ortholog lin-45. Truncations removing the DTS strongly enhanced lin-45(S312A), a weak gain-of-function allele equivalent to RAF1 mutations found in patients with Noonan Syndrome. We genetically defined three elements of the LIN-45 DTS, which we termed the active site binding sequence (ASBS), the KTP motif, and the aromatic cluster. In the context of lin-45(S312A), the mutation of each of these elements enhanced activity. We used AlphaFold to predict DTS protein interactions for LIN-45, fly Raf, and human BRAF within the activated heterotetramer complex. We propose the following distinct functions for the LIN-45 DTS elements: (1) the ASBS binds the kinase active site as an inhibitor; (2) phosphorylation of the KTP motif modulates the DTS-kinase domain interaction; and (3) the aromatic cluster anchors the DTS in an inhibitory conformation. Human RASopathy-associated variants in BRAF affect residues of the DTS, consistent with these predictions. This work establishes that the Raf/LIN-45 DTS negatively regulates signaling in C. elegans and provides a model for its function in other Raf proteins.

During challenge of Caenorhabditis elegans with human bacterial pathogens such as Pseudomonas aeruginosa and Enterococcus faecalis, the elicited host response can be damaging if not properly controlled. The activation of Nrf (nuclear factor erythroid-related factor)/CNC (Cap-n-collar) transcriptional regulators modulates the response by upregulating genes that neutralize damaging molecules and promote repair processes. Activation of the C. elegans Nrf ortholog, SKN-1, is tightly controlled by a myriad of regulatory mechanisms, but a central feature is an activating phosphorylation accomplished by the p38 mitogen-activated kinase (MAPK) cascade. In this work, loss of CDC-48, an AAA+ ATPase, was observed to severely compromise SKN-1 activation on pathogen and we sought to understand the mechanism. CDC-48 is part of the endoplasmic reticulum (ER)-associated degradation (ERAD) complex where it functions as a remodeling chaperone enabling the translocation of proteins from the ER to the cytoplasm for degradation by the proteosome. Interestingly, one of the proteins retrotranslocated by ERAD, a process necessary for its activation, is SKN-1A, the ER isoform of SKN-1. However, we discovered that SKN-1A is not activated by pathogen exposure in marked contrast to the cytoplasmic-associated isoform SKN-1C. Rather, loss of CDC-48 blocks the antioxidant response normally orchestrated by SKN-1C by strongly inducing the unfolded protein response (UPRER). The data are consistent with the model of these 2 pathways being mutually inhibitory and support the emerging paradigm in the field of coordinated cooperation between different stress responses.

The cytochrome P450s enzyme family metabolizes ∼80% of small molecule drugs. Variants in cytochrome P450s can substantially alter drug metabolism, leading to improper dosing and severe adverse drug reactions. Due to low sequence conservation, predicting variant effects across cytochrome P450s is challenging. Even closely related cytochrome P450s like CYP2C9 and CYP2C19, which share 92% amino acid sequence identity, display distinct phenotypic properties. Using variant abundance by massively parallel sequencing, we measured the steady-state protein abundance of 7,660 single amino acid variants in CYP2C19 expressed in cultured human cells. Our findings confirmed critical positions and structural features essential for cytochrome P450 function, and revealed how variants at conserved positions influence abundance. We jointly analyzed 4,670 variants whose abundance was measured in both CYP2C19 and CYP2C9, finding that the homologs have different variant abundances in substrate recognition sites within the hydrophobic core. We also measured the abundance of all single and some multiple wild type amino acid exchanges between CYP2C19 and CYP2C9. While most exchanges had no effect, substitutions in substrate recognition site 4 reduced abundance in CYP2C19. Double and triple mutants showed distinct interactions, highlighting a region that points to differing thermodynamic properties between the 2 homologs. These positions are known contributors to substrate specificity, suggesting an evolutionary tradeoff between stability and enzymatic function. Finally, we analyzed 368 previously unannotated human variants, finding that 43% had decreased abundance. By comparing variant effects between these homologs, we uncovered regions underlying their functional differences, advancing our understanding of this versatile family of enzymes.