PLoS Genetics最新文献

Gamete killers are genetic loci that distort segregation in the progeny of hybrids because the killer allele promotes the elimination of the gametes that carry the sensitive allele. They are widely distributed in eukaryotes and are important for understanding genome evolution and speciation. We had previously identified a pollen killer in hybrids between two distant natural accessions of Arabidopsis thaliana. This pollen killer involves three genetically linked genes, and we previously reported the identification of the gene encoding the antidote that protects pollen grains from the killer activity. In this study, we identified the two other genes of the pollen killer by using CRISPR-Cas9 induced mutants. These two genes are necessary for the killer activity that we demonstrated to be specific to pollen. The cellular localization of the pollen killer encoded proteins suggests that the pollen killer activity involves the mitochondria. Sequence analyses reveal predicted domains from the same families in the killer proteins. In addition, the C-terminal half of one of the killer proteins is identical to the antidote, and one amino acid, crucial for the antidote activity, is also essential for the killer function. Investigating more than 700 worldwide accessions of A. thaliana, we confirmed that the locus is subject to important structural rearrangements and copy number variation. By exploiting available de novo genomic sequences, we propose a scenario for the emergence of this pollen killer in A. thaliana. Furthermore, we report the co-occurrence and behavior of killer and sensitive genotypes in several local populations, a prerequisite for studying gamete killer evolution in the wild. This highlights the potential of the Arabidopsis model not only for functional studies of gamete killers but also for investigating their evolutionary trajectories at complementary geographical scales.

During chromosome segregation, the spindle assembly checkpoint (SAC) detects errors in kinetochore-microtubule attachments. Timely activation and maintenance of the SAC until defects are corrected is essential for genome stability. Here, we show that shugoshin (Sgo1), a conserved tension-sensing protein, ensures the maintenance of SAC signals in response to unattached kinetochores during mitosis in a basidiomycete budding yeast Cryptococcus neoformans. Sgo1 maintains optimum levels of Aurora B kinase Ipl1 and protein phosphatase 1 (PP1) at kinetochores. The absence of Sgo1 results in the loss of Aurora BIpl1 with a concomitant increase in PP1 levels at kinetochores. This leads to a premature reduction in the kinetochore-bound Bub1 levels and early termination of the SAC signals. Intriguingly, the kinase function of Bub1 is dispensable for shugoshin's subcellular localization. Sgo1 is predominantly localized to spindle pole bodies (SPBs) and along the mitotic spindle with a minor pool at kinetochores. In the absence of proper kinetochore-microtubule attachments, Sgo1 reinforces the Aurora B kinaseIpl1-PP1 phosphatase balance, which is critical for prolonged maintenance of the SAC response.

Hox proteins, a sub-group of the homeodomain (HD) transcription factor family, provide positional information for axial patterning in development and evolution. Hox protein functional specificity is reached, at least in part, through interactions with Pbc (Extradenticle (Exd) in Drosophila) and Meis/Prep (Homothorax (Hth) in Drosophila) proteins. Most of our current knowledge of Hox protein specificity stems from the study of anterior and central Hox proteins, identifying the molecular and structural bases for Hox/Pbc/Meis-Prep cooperative action. Posterior Hox class proteins, Abdominal-B (Abd-B) in Drosophila and Hox9-13 in vertebrates, have been comparatively less studied. They strongly diverge from anterior and central class Hox proteins, with a low degree of HD sequence conservation and the absence of a core canonical Pbc interaction motif. Here we explore how Abd-B function interface with that of Exd/Hth using several developmental contexts, studying mutual expression control, functional dependency and intrinsic protein requirements. Results identify cross-regulatory interactions setting relative expression and activity levels required for proper development. They also reveal organ-specific requirement and a binary functional interplay with Exd and Hth, either antagonistic, as previously reported, or synergistic. This highlights context specific use of Exd/Hth, and a similar context specific use of Abd-B intrinsic protein requirements.

Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package.

Understanding the genetic regulatory mechanisms of gene expression is an ongoing challenge. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%-2% of 10,000 traits, among the 8 methods we consider ADELLE is clearly the most powerful overall, with either the highest power or power not significantly different from the highest for all settings in that range. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. We also apply ADELLE to trans-eQTL mapping in the eQTLGen data, and for 1,451 previously identified trans-eQTLs, we discover trans association with additional expression traits beyond those previously identified. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

The reversible glycosylation of nuclear and cytoplasmic proteins (O-GlcNAcylation) is catalyzed by a single enzyme, namely O-GlcNAc transferase (OGT). The mammalian Ogt gene is X-linked, and it is essential for embryonic development and for the viability of proliferating cells. We perturbed OGT's function in vivo by creating a murine allelic series of four single amino acid substitutions, reducing OGT's catalytic activity to a range of degrees. The severity of the embryonic lethality was proportional to the extent of impairment of OGT's catalysis, demonstrating that the O-GlcNAc modification itself is required for early development. We identified hypomorphic Ogt alleles that perturb O-GlcNAc homeostasis while being compatible with embryogenesis. The analysis of the transcriptomes of the mutant embryos at different developmental stages suggested a sexually-dimorphic developmental delay caused by the decrease in O-GlcNAc. Furthermore, a mild reduction of OGT's enzymatic activity was sufficient to loosen the silencing of endogenous retroviruses in vivo.

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per region. Using the UK Biobank (UKB) data to simulate genomic regions, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS, particularly when there are only a few causal variants in the fine-mapped region. Using data from 2,940 plasma proteins from the UKB study, we observed that on average FINEMAP identified 2.5 causal variants at an association signal and captured 36% more regional heritability than the variant with the lowest P-value. We estimate that in genomic regions with notable contribution to the total heritability, FINEMAP captures on average 13% and 40% more heritability than BOLT and HESS respectively. Our analysis shows how FINEMAP, BOLT and HESS relate to each other in cases where inference of a variant-level picture of the regional genetic architecture is possible.

Inference of evolutionary and demographic parameters from a sample of genome sequences often proceeds by first inferring identical-by-descent (IBD) genome segments. By exploiting efficient data encoding based on the ancestral recombination graph (ARG), we obtain three major advantages over current approaches: (i) no need to impose a length threshold on IBD segments, (ii) IBD can be defined without the hard-to-verify requirement of no recombination, and (iii) computation time can be reduced with little loss of statistical efficiency using only the IBD segments from a set of sequence pairs that scales linearly with sample size. We first demonstrate powerful inferences when true IBD information is available from simulated data. For IBD inferred from real data, we propose an approximate Bayesian computation inference algorithm and use it to show that even poorly-inferred short IBD segments can improve estimation. Our mutation-rate estimator achieves precision similar to a previously-published method despite a 4 000-fold reduction in data used for inference, and we identify significant differences between human populations. Computational cost limits model complexity in our approach, but we are able to incorporate unknown nuisance parameters and model misspecification, still finding improved parameter inference.

A key property of the circadian clock is that it is reset by light to remain synchronized with the day-night cycle. An attractive model to explore light input to the circadian clock in vertebrates is the zebrafish. Circadian clocks in zebrafish peripheral tissues and even zebrafish-derived cell lines are entrainable by direct light exposure thus providing unique insight into the function and evolution of light regulatory pathways. Our previous work has revealed that light-induced gene transcription is a key step in the entrainment of the circadian clock as well as enabling the more general adaptation of zebrafish cells to sunlight exposure. However, considerable evidence points to post-transcriptional regulatory mechanisms, notably microRNAs (miRNAs), playing an essential role in shaping dynamic changes in mRNA levels. Therefore, does light directly impact the function of miRNAs? Are there light-regulated miRNAs and if so, which classes of mRNA do they target? To address these questions, we performed a complete sequencing analysis of light-induced changes in the zebrafish transcriptome, encompassing small non-coding RNAs as well as mRNAs. Importantly, we identified sets of light-regulated miRNAs, with many regulatory targets representing light-inducible mRNAs including circadian clock genes and genes involved in redox homeostasis. We subsequently focused on the light-responsive miR-204-3-3p and miR-430a-3p which are predicted to regulate the expression of cryptochrome genes (cry1a and cry1b). Luciferase reporter assays validated the target binding of miR-204-3-3p and miR-430a-3p to the 3'UTRs of cry1a and cry1b, respectively. Furthermore, treatment with mimics and inhibitors of these two miRNAs significantly affected the dynamic expression of their target genes but also other core clock components (clock1a, bmal1b, per1b, per2, per3), as well as the rhythmic locomotor activity of zebrafish larvae. Thus, our identification of light-responsive miRNAs reveals new intricacy in the multi-level regulation of the circadian clockwork by light.

Polygenic prediction of complex trait phenotypes has become important in human genetics, especially in the context of precision medicine. Recently, mr.mash, a flexible and computationally efficient method that models multiple phenotypes jointly and leverages sharing of effects across such phenotypes to improve prediction accuracy, was introduced. However, a drawback of mr.mash is that it requires individual-level data, which are often not publicly available. In this work, we introduce mr.mash-rss, an extension of the mr.mash model that requires only summary statistics from Genome-Wide Association Studies (GWAS) and linkage disequilibrium (LD) estimates from a reference panel. By using summary data, we achieve the twin goal of increasing the applicability of the mr.mash model to data sets that are not publicly available and making it scalable to biobank-size data. Through simulations, we show that mr.mash-rss is competitive with, and often outperforms, current state-of-the-art methods for single- and multi-phenotype polygenic prediction in a variety of scenarios that differ in the pattern of effect sharing across phenotypes, the number of phenotypes, the number of causal variants, and the genomic heritability. We also present a real data analysis of 16 blood cell phenotypes in the UK Biobank, showing that mr.mash-rss achieves higher prediction accuracy than competing methods for the majority of traits, especially when the data set has smaller sample size.