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Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver , which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles. [ABSTRACT FROM AUTHOR]

Most current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. Our method treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency trajectory of a selected or neutral allele. We perform extensive simulations to evaluate the method and show that it uniformly improves power to detect selection compared to current popular methods such as nSL and SDS, and can provide reliable inferences of allele frequency trajectories under many conditions. We also explore the potential of our method to detect extremely recent changes in the strength of selection. We use the method to infer the past allele frequency trajectory for a lactase persistence SNP (MCM6) in Europeans. We also infer the trajectory of a SNP (EDAR) in Han Chinese, finding evidence that this allele’s age is much older than previously claimed. We also study a set of 11 pigmentation-associated variants. Several genes show evidence of strong selection particularly within the last 5,000 years, including ASIP, KITLG, and TYR. However, selection on OCA2/HERC2 seems to be much older and, in contrast to previous claims, we find no evidence of selection on TYRP1. [ABSTRACT FROM AUTHOR]

Bone mineral density (BMD) is a strong predictor of osteoporotic fracture. It is also one of the most heritable disease-associated quantitative traits. As a result, there has been considerable effort focused on dissecting its genetic basis. Here, we performed a genome-wide association study (GWAS) in a panel of inbred strains to identify associations influencing BMD. This analysis identified a significant (P = 3.1 x 10−12) BMD locus on Chromosome 3@52.5 Mbp that replicated in two separate inbred strain panels and overlapped a BMD quantitative trait locus (QTL) previously identified in a F2 intercross. The association mapped to a 300 Kbp region containing four genes; Gm2447, Gm20750, Cog6, and Lhfp. Further analysis found that Lipoma HMGIC Fusion Partner (Lhfp) was highly expressed in bone and osteoblasts. Furthermore, its expression was regulated by a local expression QTL (eQTL), which overlapped the BMD association. A co-expression network analysis revealed that Lhfp was strongly connected to genes involved in osteoblast differentiation. To directly evaluate its role in bone, Lhfp deficient mice (Lhfp-/-) were created using CRISPR/Cas9. Consistent with genetic and network predictions, bone marrow stromal cells (BMSCs) from Lhfp-/- mice displayed increased osteogenic differentiation. Lhfp-/- mice also had elevated BMD due to increased cortical bone mass. Lastly, we identified SNPs in human LHFP that were associated (P = 1.2 x 10−5) with heel BMD. In conclusion, we used GWAS and systems genetics to identify Lhfp as a regulator of osteoblast activity and bone mass. [ABSTRACT FROM AUTHOR]

The article discusses the authors' perspective on the study "Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness" by B. Hu and colleagues. They discuss the results of the study which suggested that single nucleotide polymorphism (SNP) is associated with rated facial attractiveness.

While the domestication history of Asian rice has been extensively studied, details of the evolution of African rice remain elusive. The inner Niger delta has been suggested as the center of origin but molecular data to support this hypothesis is lacking. Here, we present a comprehensive analysis of the evolutionary and domestication history of African rice. By analyzing whole genome re-sequencing data from 282 individuals of domesticated African rice Oryza glaberrima and its progenitor O. barthii, we hypothesize a non-centric (i.e. multiregional) domestication origin for African rice. Our analyses showed genetic structure within O. glaberrima that has a geographical association. Furthermore, we have evidence that the previously hypothesized O. barthii progenitor populations in West Africa have evolutionary signatures similar to domesticated rice and carried causal domestication mutations, suggesting those progenitors were either mislabeled or may actually represent feral wild-domesticated hybrids. Phylogeographic analysis of genes involved in the core domestication process suggests that the origins of causal domestication mutations could be traced to wild progenitors in multiple different locations in West and Central Africa. In addition, measurements of panicle threshability, a key early domestication trait for seed shattering, were consistent with the gene phylogeographic results. We suggest seed non-shattering was selected from multiple genotypes, possibly arising from different geographical regions. Based on our evidence, O. glaberrima was not domesticated from a single centric location but was a result of diffuse process where multiple regions contributed key alleles for different domestication traits. [ABSTRACT FROM AUTHOR]

Allopolyploidy is generally perceived as a major source of evolutionary novelties and as an instantaneous way to create isolation barriers. However, we do not have a clear understanding of how two subgenomes evolve and interact once they have fused in an allopolyploid species nor how isolated they are from their relatives. Here, we address these questions by analyzing genomic and transcriptomic data of allotetraploid Capsella bursa-pastoris in three differentiated populations, Asia, Europe, and the Middle East. We phased the two subgenomes, one descended from the outcrossing and highly diverse Capsella grandiflora (CbpCg) and the other one from the selfing and genetically depauperate Capsella orientalis (CbpCo). For each subgenome, we assessed its relationship with the diploid relatives, temporal changes of effective population size (Ne), signatures of positive and negative selection, and gene expression patterns. In all three regions, Ne of the two subgenomes decreased gradually over time and the CbpCo subgenome accumulated more deleterious changes than CbpCg. There were signs of widespread admixture between C. bursa-pastoris and its diploid relatives. The two subgenomes were impacted differentially depending on geographic region suggesting either strong interploidy gene flow or multiple origins of C. bursa-pastoris. Selective sweeps were more common on the CbpCg subgenome in Europe and the Middle East, and on the CbpCo subgenome in Asia. In contrast, differences in expression were limited with the CbpCg subgenome slightly more expressed than CbpCo in Europe and the Middle-East. In summary, after more than 100,000 generations of co-existence, the two subgenomes of C. bursa-pastoris still retained a strong signature of parental legacy but their evolutionary trajectory strongly varied across geographic regions. [ABSTRACT FROM AUTHOR]

Marine ecosystems are changing rapidly as the oceans warm and become more acidic. The physical factors and the changes to ocean chemistry that they drive can all be measured with great precision. Changes in the biological composition of communities in different ocean regions are far more challenging to measure because most biological monitoring methods focus on a limited taxonomic or size range. Environmental DNA (eDNA) analysis has the potential to solve this problem in biological oceanography, as it is capable of identifying a huge phylogenetic range of organisms to species level. Here we develop and apply a novel multi-gene molecular toolkit to eDNA isolated from bulk plankton samples collected over a five-year period from a single site. This temporal scale and level of detail is unprecedented in eDNA studies. We identified consistent seasonal assemblages of zooplankton species, which demonstrates the ability of our toolkit to audit community composition. We were also able to detect clear departures from the regular seasonal patterns that occurred during an extreme marine heatwave. The integration of eDNA analyses with existing biotic and abiotic surveys delivers a powerful new long-term approach to monitoring the health of our world’s oceans in the context of a rapidly changing climate. [ABSTRACT FROM AUTHOR]

More than any other genome components, Transposable Elements (TEs) have the capacity to move across species barriers through Horizontal Transfer (HT), with substantial evolutionary consequences. Previous large-scale surveys, based on full-genomes comparisons, have revealed the transposition mode as an important predictor of HT rates variation across TE superfamilies. However, host biology could represent another major explanatory factor, one that needs to be investigated through extensive taxonomic sampling. Here we test this hypothesis using a field collection of 460 Arthropod species from Tahiti and surrounding islands. Through targeted massive parallel sequencing, we uncover patterns of HT in three widely-distributed TE superfamilies with contrasted modes of transposition. In line with earlier findings, the DNA transposons under study (TC1-Mariner) were found to transfer horizontally at the highest frequency, closely followed by the LTR superfamily (Copia), in contrast with the non-LTR superfamily (Jockey), that mostly diversifies through vertical inheritance and persists longer within genomes. Strikingly, across all superfamilies, we observe a marked excess of HTs in Lepidoptera, an insect order that also commonly hosts baculoviruses, known for their ability to transport host TEs. These results turn the spotlight on baculoviruses as major potential vectors of TEs in Arthropods, and further emphasize the importance of non-vertical TE inheritance in genome evolution. [ABSTRACT FROM AUTHOR]

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors. [ABSTRACT FROM AUTHOR]

The earliest stage of animal development is controlled by maternally deposited mRNA transcripts and proteins. Once the zygote is able to transcribe its own genome, maternal transcripts are degraded, in a tightly regulated process known as the maternal to zygotic transition (MZT). While this process has been well-studied within model species, we have little knowledge of how the pools of maternal and zygotic transcripts evolve. To characterize the evolutionary dynamics and functional constraints on early embryonic expression, we created a transcriptomic dataset for 14 Drosophila species spanning over 50 million years of evolution, at developmental stages before and after the MZT, and compared our results with a previously published Aedes aegypti developmental time course. We found deep conservation over 250 million years of a core set of genes transcribed only by the zygote. This select group is highly enriched in transcription factors that play critical roles in early development. However, we also identify a surprisingly high level of change in the transcripts represented at both stages over the phylogeny. While mRNA levels of genes with maternally deposited transcripts are more highly conserved than zygotic genes, those maternal transcripts that are completely degraded at the MZT vary dramatically between species. We also show that hundreds of genes have different isoform usage between the maternal and zygotic genomes. Our work suggests that maternal transcript deposition and early zygotic transcription are remarkably dynamic over evolutionary time, despite the widespread conservation of early developmental processes. [ABSTRACT FROM AUTHOR]

Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient—experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa—along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at . [ABSTRACT FROM AUTHOR]

Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently—thanks for the improvement of sequencing techniques and the reduction of sequencing cost—there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME). SAME accounts for somatic mutation calling uncertainty using a likelihood based approach. It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations. Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty. Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data. SAME recovered some interesting findings that were missed by the generalized linear model. In addition, we demonstrated that mutation-level and gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively. [ABSTRACT FROM AUTHOR]

Y chromosomes are widely believed to evolve from a normal autosome through a process of massive gene loss (with preservation of some male genes), shaped by sex-antagonistic selection and complemented by occasional gains of male-related genes. The net result of these processes is a male-specialized chromosome. This might be expected to be an irreversible process, but it was found in 2005 that the Drosophila pseudoobscura Y chromosome was incorporated into an autosome. Y chromosome incorporations have important consequences: a formerly male-restricted chromosome reverts to autosomal inheritance, and the species may shift from an XY/XX to X0/XX sex-chromosome system. In order to assess the frequency and causes of this phenomenon we searched for Y chromosome incorporations in 400 species from Drosophila and related genera. We found one additional large scale event of Y chromosome incorporation, affecting the whole montium subgroup (40 species in our sample); overall 13% of the sampled species (52/400) have Y incorporations. While previous data indicated that after the Y incorporation the ancestral Y disappeared as a free chromosome, the much larger data set analyzed here indicates that a copy of the Y survived as a free chromosome both in montium and pseudoobscura species, and that the current Y of the pseudoobscura lineage results from a fusion between this free Y and the neoY. The 400 species sample also showed that the previously suggested causal connection between X-autosome fusions and Y incorporations is, at best, weak: the new case of Y incorporation (montium) does not have X-autosome fusion, whereas nine independent cases of X-autosome fusions were not followed by Y incorporations. Y incorporation is an underappreciated mechanism affecting Y chromosome evolution; our results show that at least in Drosophila it plays a relevant role and highlight the need of similar studies in other groups. [ABSTRACT FROM AUTHOR]

While population studies have resulted in detailed maps of genetic variation in humans, to date there are few robust maps of epigenetic variation. We identified sites containing clusters of CpGs with high inter-individual epigenetic variation, termed Variably Methylated Regions (VMRs) in five purified cell types. We observed that VMRs occur preferentially at enhancers and 3’ UTRs. While the majority of VMRs have high heritability, a subset of VMRs within the genome show highly correlated variation in trans, forming co-regulated networks that have low heritability, differ between cell types and are enriched for specific transcription factor binding sites and biological pathways of functional relevance to each tissue. For example, in T cells we defined a network of 95 co-regulated VMRs enriched for genes with roles in T-cell activation; in fibroblasts a network of 34 co-regulated VMRs comprising all four HOX gene clusters enriched for control of tissue growth; and in neurons a network of 18 VMRs enriched for roles in synaptic signaling. By culturing genetically-identical fibroblasts under varying environmental conditions, we experimentally demonstrated that some VMR networks are responsive to the environment, with methylation levels at these loci changing in a coordinated fashion in trans dependent on cellular growth. Intriguingly these environmentally-responsive VMRs showed a strong enrichment for imprinted loci (p<10−80), suggesting that these are particularly sensitive to environmental conditions. Our study provides a detailed map of common epigenetic variation in the human genome, showing that both genetic and environmental causes underlie this variation. [ABSTRACT FROM AUTHOR]

Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is crucial to develop the statistical machinery required to uncover which genomic regions have recently acquired haplotypes via introgression from a sister population. We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. FILET works by combining information from a number of population genetic summary statistics, including several new statistics that we introduce, that capture patterns of variation across two populations. We show that FILET is able to identify loci that have experienced gene flow between related species with high accuracy, and in most situations can correctly infer which population was the donor and which was the recipient. Here we describe a data set of outbred diploid Drosophila sechellia genomes, and combine them with data from D. simulans to examine recent introgression between these species using FILET. Although we find that these populations may have split more recently than previously appreciated, FILET confirms that there has indeed been appreciable recent introgression (some of which might have been adaptive) between these species, and reveals that this gene flow is primarily in the direction of D. simulans to D. sechellia. [ABSTRACT FROM AUTHOR]

Unlike most other fungi, molds of the genus Trichoderma (Hypocreales, Ascomycota) are aggressive parasites of other fungi and efficient decomposers of plant biomass. Although nutritional shifts are common among hypocrealean fungi, there are no examples of such broad substrate versatility as that observed in Trichoderma. A phylogenomic analysis of 23 hypocrealean fungi (including nine Trichoderma spp. and the related Escovopsis weberi) revealed that the genus Trichoderma has evolved from an ancestor with limited cellulolytic capability that fed on either fungi or arthropods. The evolutionary analysis of Trichoderma genes encoding plant cell wall-degrading carbohydrate-active enzymes and auxiliary proteins (pcwdCAZome, 122 gene families) based on a gene tree / species tree reconciliation demonstrated that the formation of the genus was accompanied by an unprecedented extent of lateral gene transfer (LGT). Nearly one-half of the genes in Trichoderma pcwdCAZome (47%) were obtained via LGT from plant-associated filamentous fungi belonging to different classes of Ascomycota, while no LGT was observed from other potential donors. In addition to the ability to feed on unrelated fungi (such as Basidiomycota), we also showed that Trichoderma is capable of endoparasitism on a broad range of Ascomycota, including extant LGT donors. This phenomenon was not observed in E. weberi and rarely in other mycoparasitic hypocrealean fungi. Thus, our study suggests that LGT is linked to the ability of Trichoderma to parasitize taxonomically related fungi (up to adelphoparasitism in strict sense). This may have allowed primarily mycotrophic Trichoderma fungi to evolve into decomposers of plant biomass. [ABSTRACT FROM AUTHOR]

Cyanobacteria are a monophyletic phylogenetic group of global importance and have received considerable attention as potential host organisms for the renewable synthesis of chemical bulk products from atmospheric CO2. The cyanobacterial phylum exhibits enormous metabolic diversity with respect to morphology, lifestyle and habitat. As yet, however, research has mostly focused on few model strains and cyanobacterial diversity is insufficiently understood. In this respect, the increasing availability of fully sequenced bacterial genomes opens new and unprecedented opportunities to investigate the genetic inventory of organisms in the context of their pan-genome. Here, we seek understand cyanobacterial diversity using a comparative genome analysis of 77 fully sequenced and assembled cyanobacterial genomes. We use phylogenetic profiling to analyze the co-occurrence of clusters of likely ortholog genes (CLOGs) and reveal novel functional associations between CLOGs that are not captured by co-localization of genes. Going beyond pair-wise co-occurrences, we propose a network approach that allows us to identify modules of co-occurring CLOGs. The extracted modules exhibit a high degree of functional coherence and reveal known as well as previously unknown functional associations. We argue that the high functional coherence observed for the modules is a consequence of the similar-yet-diverse nature of cyanobacteria. Our approach highlights the importance of a multi-strain analysis to understand gene functions and environmental adaptations, with implications beyond the cyanobacterial phylum. The analysis is augmented with a simple toolbox that facilitates further analysis to investigate the co-occurrence neighborhood of specific CLOGs of interest. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) infection is a common problem in the world, especially in China. More than 60–80% of hepatocellular carcinoma (HCC) cases can be attributed to HBV infection in high HBV prevalent regions. Although traditional Sanger sequencing has been extensively used to investigate HBV sequences, NGS is becoming more commonly used. Further, it is unknown whether word pattern frequencies of HBV reads by Next Generation Sequencing (NGS) can be used to investigate HBV genotypes and predict HCC status. In this study, we used NGS to sequence the pre-S region of the HBV sequence of 94 HCC patients and 45 chronic HBV (CHB) infected individuals. Word pattern frequencies among the sequence data of all individuals were calculated and compared using the Manhattan distance. The individuals were grouped using principal coordinate analysis (PCoA) and hierarchical clustering. Word pattern frequencies were also used to build prediction models for HCC status using both K-nearest neighbors (KNN) and support vector machine (SVM). We showed the extremely high power of analyzing HBV sequences using word patterns. Our key findings include that the first principal coordinate of the PCoA analysis was highly associated with the fraction of genotype B (or C) sequences and the second principal coordinate was significantly associated with the probability of having HCC. Hierarchical clustering first groups the individuals according to their major genotypes followed by their HCC status. Using cross-validation, high area under the receiver operational characteristic curve (AUC) of around 0.88 for KNN and 0.92 for SVM were obtained. In the independent data set of 46 HCC patients and 31 CHB individuals, a good AUC score of 0.77 was obtained using SVM. It was further shown that 3000 reads for each individual can yield stable prediction results for SVM. Thus, another key finding is that word patterns can be used to predict HCC status with high accuracy. Therefore, our study shows clearly that word pattern frequencies of HBV sequences contain much information about the composition of different HBV genotypes and the HCC status of an individual. [ABSTRACT FROM AUTHOR]

The ability to integrate experiential information and recall it in the form of memory is observed in a wide range of taxa, and is a hallmark of highly derived nervous systems. Storage of past experiences is critical for adaptive behaviors that anticipate both adverse and positive environmental factors. The process of memory formation and consolidation involve many synchronized biological events including gene transcription, protein modification, and intracellular trafficking: However, many of these molecular mechanisms remain illusive. With Drosophila as a model system we use a nonassociative memory paradigm and a systems level approach to uncover novel transcriptional patterns. RNA sequencing of Drosophila heads during and after memory formation identified a number of novel memory genes. Tracking the dynamic expression of these genes over time revealed complex gene networks involved in long term memory. In particular, this study focuses on two functional gene clusters of signal peptides and proteases. Bioinformatics network analysis and prediction in combination with high-throughput RNA sequencing identified previously unknown memory genes, which when genetically knocked down resulted in behaviorally validated memory defects. [ABSTRACT FROM AUTHOR]

The article focuses on the initiative "Up For A Challenge (U4C)—Stimulating Innovation in Breast Cancer Genetic Epidemiology" by the American organization National Cancer Institute (NCI). Topics discussed include understanding the role of disease risk through genome-wide association studies (GWAS) of the genetic variants of breast cancer, review by National Institutes of Health (NIH) of entries, and chart on U4C entries received.

Cotton bacterial blight (CBB), an important disease of (Gossypium hirsutum) in the early 20th century, had been controlled by resistant germplasm for over half a century. Recently, CBB re-emerged as an agronomic problem in the United States. Here, we report analysis of cotton variety planting statistics that indicate a steady increase in the percentage of susceptible cotton varieties grown each year since 2009. Phylogenetic analysis revealed that strains from the current outbreak cluster with race 18 Xanthomonas citri pv. malvacearum (Xcm) strains. Illumina based draft genomes were generated for thirteen Xcm isolates and analyzed along with 4 previously published Xcm genomes. These genomes encode 24 conserved and nine variable type three effectors. Strains in the race 18 clade contain 3 to 5 more effectors than other Xcm strains. SMRT sequencing of two geographically and temporally diverse strains of Xcm yielded circular chromosomes and accompanying plasmids. These genomes encode eight and thirteen distinct transcription activator-like effector genes. RNA-sequencing revealed 52 genes induced within two cotton cultivars by both tested Xcm strains. This gene list includes a homeologous pair of genes, with homology to the known susceptibility gene, MLO. In contrast, the two strains of Xcm induce different clade III SWEET sugar transporters. Subsequent genome wide analysis revealed patterns in the overall expression of homeologous gene pairs in cotton after inoculation by Xcm. These data reveal important insights into the Xcm-G. hirsutum disease complex and strategies for future development of resistant cultivars. [ABSTRACT FROM AUTHOR]

Base composition is highly variable among and within plant genomes, especially at third codon positions, ranging from GC-poor and homogeneous species to GC-rich and highly heterogeneous ones (particularly Monocots). Consequently, synonymous codon usage is biased in most species, even when base composition is relatively homogeneous. The causes of these variations are still under debate, with three main forces being possibly involved: mutational bias, selection and GC-biased gene conversion (gBGC). So far, both selection and gBGC have been detected in some species but how their relative strength varies among and within species remains unclear. Population genetics approaches allow to jointly estimating the intensity of selection, gBGC and mutational bias. We extended a recently developed method and applied it to a large population genomic dataset based on transcriptome sequencing of 11 angiosperm species spread across the phylogeny. We found that at synonymous positions, base composition is far from mutation-drift equilibrium in most genomes and that gBGC is a widespread and stronger process than selection. gBGC could strongly contribute to base composition variation among plant species, implying that it should be taken into account in plant genome analyses, especially for GC-rich ones. [ABSTRACT FROM AUTHOR]

Plant immunity protects plants from numerous potentially pathogenic microbes. The biological network that controls plant inducible immunity must function effectively even when network components are targeted and disabled by pathogen effectors. Network buffering could confer this resilience by allowing different parts of the network to compensate for loss of one another’s functions. Networks rich in buffering rely on interactions within the network, but these mechanisms are difficult to study by simple genetic means. Through a network reconstitution strategy, in which we disassemble and stepwise reassemble the plant immune network that mediates Pattern-Triggered-Immunity, we have resolved systems-level regulatory mechanisms underlying the Arabidopsis transcriptome response to the immune stimulant flagellin-22 (flg22). These mechanisms show widespread evidence of interactions among major sub-networks—we call these sectors—in the flg22-responsive transcriptome. Many of these interactions result in network buffering. Resolved regulatory mechanisms show unexpected patterns for how the jasmonate (JA), ethylene (ET), phytoalexin-deficient 4 (PAD4), and salicylate (SA) signaling sectors control the transcriptional response to flg22. We demonstrate that many of the regulatory mechanisms we resolved are not detectable by the traditional genetic approach of single-gene null-mutant analysis. Similar to potential pathogenic perturbations, null-mutant effects on immune signaling can be buffered by the network. [ABSTRACT FROM AUTHOR]

The article offers information on the impact of networks of signal transduction in plants for mediating and integrating induction of defense responses to pathogens. Topics include involvement of phosphorylation cascades and chemical networks and plant hormones such as jasmonic acid, ethylene, and abscisic acid in networks; gene expression changes underlying signal flow through the network, and assessing signaling information flow through the network.

A proportion of homologous recombination (HR) events in mammalian cells resolve by “long tract” gene conversion, reflecting copying of several kilobases from the donor sister chromatid prior to termination. Cells lacking the major hereditary breast/ovarian cancer predisposition genes, BRCA1 or BRCA2, or certain other HR-defective cells, reveal a bias in favor of long tract gene conversion, suggesting that this aberrant HR outcome might be connected with genomic instability. If termination of gene conversion occurs in regions lacking homology with the second end of the break, the normal mechanism of HR termination by annealing (i.e., homologous pairing) is not available and termination must occur by as yet poorly defined non-canonical mechanisms. Here we use a previously described HR reporter to analyze mechanisms of non-canonical termination of long tract gene conversion in mammalian cells. We find that non-canonical HR termination can occur in the absence of the classical non-homologous end joining gene XRCC4. We observe obligatory use of microhomology (MH)-mediated end joining and/or nucleotide addition during rejoining with the second end of the break. Notably, non-canonical HR termination is associated with complex breakpoints. We identify roles for homology-mediated template switching and, potentially, MH-mediated template switching/microhomology-mediated break-induced replication, in the formation of complex breakpoints at sites of non-canonical HR termination. This work identifies non-canonical HR termination as a potential contributor to genomic instability and to the formation of complex breakpoints in cancer. [ABSTRACT FROM AUTHOR]

The Rif1 protein is a negative regulator of DNA replication initiation in eukaryotes. Here we show that budding yeast Rif1 inhibits DNA replication initiation at the rDNA locus. Absence of Rif1, or disruption of its interaction with PP1/Glc7 phosphatase, leads to more intensive rDNA replication. The effect of Rif1-Glc7 on rDNA replication is similar to that of the Sir2 deacetylase, and the two would appear to act in the same pathway, since the rif1Δ sir2Δ double mutant shows no further increase in rDNA replication. Loss of Rif1-Glc7 activity is also accompanied by an increase in rDNA repeat instability that again is not additive with the effect of sir2Δ. We find, in addition, that the viability of rif1Δ cells is severely compromised in combination with disruption of the MRX or Ctf4-Mms22 complexes, both of which are implicated in stabilization of stalled replication forks. Significantly, we show that removal of the rDNA replication fork barrier (RFB) protein Fob1, alleviation of replisome pausing by deletion of the Tof1/Csm3 complex, or a large deletion of the rDNA repeat array all rescue this synthetic growth defect of rif1Δ cells lacking in addition either MRX or Ctf4-Mms22 activity. These data suggest that the repression of origin activation by Rif1-Glc7 is important to avoid the deleterious accumulation of stalled replication forks at the rDNA RFB, which become lethal when fork stability is compromised. Finally, we show that Rif1-Glc7, unlike Sir2, has an important effect on origin firing outside of the rDNA locus that serves to prevent activation of the DNA replication checkpoint. Our results thus provide insights into a mechanism of replication control within a large repetitive chromosomal domain and its importance for the maintenance of genome stability. These findings may have important implications for metazoans, where large blocks of repetitive sequences are much more common. [ABSTRACT FROM AUTHOR]

Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies of the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100bp) or in the gene body. Effects of deficiencies genome-wide are in genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds. [ABSTRACT FROM AUTHOR]

Human breast cancer susceptibility gene, BRCA2, encodes a 3418-amino acid protein that is essential for maintaining genomic integrity. Among the proteins that physically interact with BRCA2, Partner and Localizer of BRCA2 (PALB2), which binds to the N-terminal region of BRCA2, is vital for its function by facilitating its subnuclear localization. A functional redundancy has been reported between this N-terminal PALB2-binding domain and the C-terminal DNA-binding domain of BRCA2, which undermines the relevance of the interaction between these two proteins. Here, we describe a genetic approach to examine the functional significance of the interaction between BRCA2 and PALB2 by generating a knock-in mouse model of Brca2 carrying a single amino acid change (Gly25Arg, Brca2G25R) that disrupts this interaction. In addition, we have combined Brca2G25R homozygosity as well as hemizygosity with Palb2 and Trp53 heterozygosity to generate an array of genotypically and phenotypically distinct mouse models. Our findings reveal defects in body size, fertility, meiotic progression, and genome stability, as well as increased tumor susceptibility in these mice. The severity of the phenotype increased with a decrease in the interaction between BRCA2 and PALB2, highlighting the significance of this interaction. In addition, our findings also demonstrate that hypomorphic mutations such as Brca2G25R have the potential to be more detrimental than the functionally null alleles by increasing genomic instability to a level that induces tumorigenesis, rather than apoptosis. [ABSTRACT FROM AUTHOR]

XRN2 is a 5’-3’ exoribonuclease implicated in transcription termination. Here we demonstrate an unexpected role for XRN2 in the DNA damage response involving resolution of R-loop structures and prevention of DNA double-strand breaks (DSBs). We show that XRN2 undergoes DNA damage-inducible nuclear re-localization, co-localizing with 53BP1 and R loops, in a transcription and R-loop-dependent process. XRN2 loss leads to increased R loops, genomic instability, replication stress, DSBs and hypersensitivity of cells to various DNA damaging agents. We demonstrate that the DSBs that arise with XRN2 loss occur at transcriptional pause sites. XRN2-deficient cells also exhibited an R-loop- and transcription-dependent delay in DSB repair after ionizing radiation, suggesting a novel role for XRN2 in R-loop resolution, suppression of replication stress, and maintenance of genomic stability. Our study highlights the importance of regulating transcription-related activities as a critical component in maintaining genetic stability. [ABSTRACT FROM AUTHOR]

Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression. [ABSTRACT FROM AUTHOR]

Lager-style beers constitute the vast majority of the beer market, and yet, the genetic origin of the yeast strains that brew them has been shrouded in mystery and controversy. Unlike ale-style beers, which are generally brewed with Saccharomyces cerevisiae, lagers are brewed at colder temperatures with allopolyploid hybrids of Saccharomyces eubayanus x S. cerevisiae. Since the discovery of S. eubayanus in 2011, additional strains have been isolated from South America, North America, Australasia, and Asia, but only interspecies hybrids have been isolated in Europe. Here, using genome sequence data, we examine the relationships of these wild S. eubayanus strains to each other and to domesticated lager strains. Our results support the existence of a relatively low-diversity (π = 0.00197) lineage of S. eubayanus whose distribution stretches across the Holarctic ecozone and includes wild isolates from Tibet, new wild isolates from North America, and the S. eubayanus parents of lager yeasts. This Holarctic lineage is closely related to a population with higher diversity (π = 0.00275) that has been found primarily in South America but includes some widely distributed isolates. A second diverse South American population (π = 0.00354) and two early-diverging Asian subspecies are more distantly related. We further show that no single wild strain from the Holarctic lineage is the sole closest relative of lager yeasts. Instead, different parts of the genome portray different phylogenetic signals and ancestry, likely due to outcrossing and incomplete lineage sorting. Indeed, standing genetic variation within this wild Holarctic lineage of S. eubayanus is responsible for genetic variation still segregating among modern lager-brewing hybrids. We conclude that the relationships among wild strains of S. eubayanus and their domesticated hybrids reflect complex biogeographical and genetic processes. [ABSTRACT FROM AUTHOR]

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. [ABSTRACT FROM AUTHOR]

Bacteria have evolved various inducible genetic programs to face many types of stress that challenge their growth and survival. Competence is one such program. It enables genetic transformation, a major horizontal gene transfer process. Competence development in liquid cultures of Streptococcus pneumoniae is synchronized within the whole cell population. This collective behavior is known to depend on an exported signaling Competence Stimulating Peptide (CSP), whose action generates a positive feedback loop. However, it is unclear how this CSP-dependent population switch is coordinated. By monitoring spontaneous competence development in real time during growth of four distinct pneumococcal lineages, we have found that competence shift in the population relies on a self-activated cell fraction that arises via a growth time-dependent mechanism. We demonstrate that CSP remains bound to cells during this event, and conclude that the rate of competence development corresponds to the propagation of competence by contact between activated and quiescent cells. We validated this two-step cell-contact sensing mechanism by measuring competence development during co-cultivation of strains with altered capacity to produce or respond to CSP. Finally, we found that the membrane protein ComD retains the CSP, limiting its free diffusion in the medium. We propose that competence initiator cells originate stochastically in response to stress, to form a distinct subpopulation that then transmits the CSP by cell-cell contact. [ABSTRACT FROM AUTHOR]

Using robust, integrated analysis of multiple genomic datasets, we show that genes depleted for non-synonymous de novo mutations form a subnetwork of 72 members under strong selective constraint. We further show this subnetwork is preferentially expressed in the early development of the human hippocampus and is enriched for genes mutated in neurological Mendelian disorders. We thus conclude that carefully orchestrated developmental processes are under strong constraint in early brain development, and perturbations caused by mutation have adverse outcomes subject to strong purifying selection. Our findings demonstrate that selective forces can act on groups of genes involved in the same process, supporting the notion that purifying selection can act coordinately on multiple genes. Our approach provides a statistically robust, interpretable way to identify the tissues and developmental times where groups of disease genes are active. [ABSTRACT FROM AUTHOR]

The multispecies coalescent (MSC) is a statistical framework that models how gene genealogies grow within the branches of a species tree. The field of computational phylogenetics has witnessed an explosion in the development of methods for species tree inference under MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary processes such as hybridization or horizontal gene transfer. We report on a novel method for Bayesian inference of genome and species phylogenies under the multispecies network coalescent (MSNC). This framework models gene evolution within the branches of a phylogenetic network, thus incorporating reticulate evolutionary processes, such as hybridization, in addition to incomplete lineage sorting. As phylogenetic networks with different numbers of reticulation events correspond to points of different dimensions in the space of models, we devise a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under MSNC. We implemented the methods in the publicly available, open-source software package PhyloNet and studied their performance on simulated and biological data. The work extends the reach of Bayesian inference to phylogenetic networks and enables new evolutionary analyses that account for reticulation. [ABSTRACT FROM AUTHOR]

Replications forks are routinely hindered by different endogenous stresses. Because homologous recombination plays a pivotal role in the reactivation of arrested replication forks, defects in homologous recombination reveal the initial endogenous stress(es). Homologous recombination-defective cells consistently exhibit a spontaneously reduced replication speed, leading to mitotic extra centrosomes. Here, we identify oxidative stress as a major endogenous source of replication speed deceleration in homologous recombination-defective cells. The treatment of homologous recombination-defective cells with the antioxidant N-acetyl-cysteine or the maintenance of the cells at low O2 levels (3%) rescues both the replication fork speed, as monitored by single-molecule analysis (molecular combing), and the associated mitotic extra centrosome frequency. Reciprocally, the exposure of wild-type cells to H2O2 reduces the replication fork speed and generates mitotic extra centrosomes. Supplying deoxynucleotide precursors to H2O2-exposed cells rescued the replication speed. Remarkably, treatment with N-acetyl-cysteine strongly expanded the nucleotide pool, accounting for the replication speed rescue. Remarkably, homologous recombination-defective cells exhibit a high level of endogenous reactive oxygen species. Consistently, homologous recombination-defective cells accumulate spontaneous γH2AX or XRCC1 foci that are abolished by treatment with N-acetyl-cysteine or maintenance at 3% O2. Finally, oxidative stress stimulated homologous recombination, which is suppressed by supplying deoxynucleotide precursors. Therefore, the cellular redox status strongly impacts genome duplication and transmission. Oxidative stress should generate replication stress through different mechanisms, including DNA damage and nucleotide pool imbalance. These data highlight the intricacy of endogenous replication and oxidative stresses, which are both evoked during tumorigenesis and senescence initiation, and emphasize the importance of homologous recombination as a barrier against spontaneous genetic instability triggered by the endogenous oxidative/replication stress axis. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) has a complex genetic etiology, and recent studies suggest that high penetrance de novo mutations may account for only a small fraction of disease. In a multi-institutional cohort surveyed by exome sequencing, combining analysis of 987 individuals (discovery cohort of 59 affected trios and 59 control trios, and a replication cohort of 100 affected singletons and 533 unaffected singletons) we observe variation at novel and known loci related to a specific cardiac malformation the atrioventricular septal defect (AVSD). In a primary analysis, by combining developmental coexpression networks with inheritance modeling, we identify a de novo mutation in the DNA binding domain of NR1D2 (p.R175W). We show that p.R175W changes the transcriptional activity of Nr1d2 using an in vitro transactivation model in HUVEC cells. Finally, we demonstrate previously unrecognized cardiovascular malformations in the Nr1d2tm1-Dgen knockout mouse. In secondary analyses we map genetic variation to protein-interaction networks suggesting a role for two collagen genes in AVSD, which we corroborate by burden testing in a second replication cohort of 100 AVSDs and 533 controls (p = 8.37e-08). Finally, we apply a rare-disease inheritance model to identify variation in genes previously associated with CHD (ZFPM2, NSD1, NOTCH1, VCAN, and MYH6), cardiac malformations in mouse models (ADAM17, CHRD, IFT140, PTPRJ, RYR1 and ATE1), and hypomorphic alleles of genes causing syndromic CHD (EHMT1, SRCAP, BBS2, NOTCH2, and KMT2D) in 14 of 59 trios, greatly exceeding variation in control trios without CHD (p = 9.60e-06). In total, 32% of trios carried at least one putatively disease-associated variant across 19 loci,suggesting that inherited and de novo variation across a heterogeneous group of loci may contribute to disease risk. [ABSTRACT FROM AUTHOR]

Yra1 is an essential nuclear factor of the evolutionarily conserved family of hnRNP-like export factors that when overexpressed impairs mRNA export and cell growth. To investigate further the relevance of proper Yra1 stoichiometry in the cell, we overexpressed Yra1 by transforming yeast cells with YRA1 intron-less constructs and analyzed its effect on gene expression and genome integrity. We found that YRA1 overexpression induces DNA damage and leads to a transcription-associated hyperrecombination phenotype that is mediated by RNA:DNA hybrids. In addition, it confers a genome-wide replication retardation as seen by reduced BrdU incorporation and accumulation of the Rrm3 helicase. In addition, YRA1 overexpression causes a cell senescence-like phenotype and telomere shortening. ChIP-chip analysis shows that overexpressed Yra1 is loaded to transcribed chromatin along the genome and to Y’ telomeric regions, where Rrm3 is also accumulated, suggesting an impairment of telomere replication. Our work not only demonstrates that a proper stoichiometry of the Yra1 mRNA binding and export factor is required to maintain genome integrity and telomere homeostasis, but suggests that the cellular imbalance between transcribed RNA and specific RNA-binding factors may become a major cause of genome instability mediated by co-transcriptional replication impairment. [ABSTRACT FROM AUTHOR]

Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest in determining the importance of selection from standing variation in adaptation of natural populations, yet very few methods for inferring this model of adaptation at the genome scale have been introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. Moreover, we show that S/HIC is uniquely robust among its competitors to model misspecification. Thus, even if the true demographic model of a population differs catastrophically from that specified by the user, S/HIC still retains impressive discriminatory power. Finally, we apply S/HIC to the case of resequencing data from human chromosome 18 in a European population sample, and demonstrate that we can reliably recover selective sweeps that have been identified earlier using less specific and sensitive methods. [ABSTRACT FROM AUTHOR]

Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development. The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of the full likelihood which can be intractable with many species. Moreover, estimation at the quartet-level has the added computational benefit that it is easily parallelizable. Simulation studies comparing our method to a full likelihood approach show that our pseudolikelihood approach is much faster without compromising accuracy. We applied our method to reconstruct the evolutionary relationships among swordtails and platyfishes (Xiphophorus: Poeciliidae), which is characterized by widespread hybridizations. [ABSTRACT FROM AUTHOR]

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles. [ABSTRACT FROM AUTHOR]

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabidopsis-Botrytis pathosystem to explore the quantitative genetic architecture underlying host innate immune system in a population of Arabidopsis thaliana. By infecting a diverse panel of Arabidopsis accessions with four phenotypically and genotypically distinct isolates of the fungal necrotroph B. cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence of pathogen genetic variation in analyzing host quantitative resistance. While known resistance genes, such as receptor-like kinases (RLKs) and nucleotide-binding site leucine-rich repeat proteins (NLRs), were found to be enriched among associated genes, they only account for a small fraction of the total genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance, including defense hormone signaling and ROS production, as well as novel processes, such as leaf development. Validation of single gene T-DNA knockouts in a Col-0 background demonstrate a high success rate (60%) when accounting for differences in environmental and Botrytis genetic variation. This study shows that the genetic architecture underlying host innate immune system is extremely complex and is likely able to sense and respond to differential virulence among pathogen genotypes. [ABSTRACT FROM AUTHOR]

The extent and strength of epistasis is commonly unresolved in genetic studies, and observed epistasis is often difficult to interpret in terms of biological consequences or overall genetic architecture. We investigated the prevalence and consequences of epistasis by analyzing four body composition phenotypes—body weight, body fat percentage, femoral density, and femoral circumference—in a large F2 intercross of B6-lit/lit and C3.B6-lit/lit mice. We used Combined Analysis of Pleiotropy and Epistasis (CAPE) to examine interactions for the four phenotypes simultaneously, which revealed an extensive directed network of genetic loci interacting with each other, circulating IGF1, and sex to influence these phenotypes. The majority of epistatic interactions had small effects relative to additive effects of individual loci, and tended to stabilize phenotypes towards the mean of the population rather than extremes. Interactive effects of two alleles inherited from one parental strain commonly resulted in phenotypes closer to the population mean than the additive effects from the two loci, and often much closer to the mean than either single-locus model. Alternatively, combinations of alleles inherited from different parent strains contribute to more extreme phenotypes not observed in either parental strain. This class of phenotype-stabilizing interactions has effects that are close to additive and are thus difficult to detect except in very large intercrosses. Nevertheless, we found these interactions to be useful in generating hypotheses for functional relationships between genetic loci. Our findings suggest that while epistasis is often weak and unlikely to account for a large proportion of heritable variance, even small-effect genetic interactions can facilitate hypotheses of underlying biology in well-powered studies. [ABSTRACT FROM AUTHOR]

Modern enzymes are highly optimized biocatalysts that process their substrates with extreme efficiency. Many enzymes catalyze more than one reaction; however, the persistence of such ambiguities, their consequences and evolutionary causes are largely unknown. As a paradigmatic case, we study the history of bi-functionality for a time span of approximately two billion years for the sugar isomerase HisA from histidine biosynthesis. To look back in time, we computationally reconstructed and experimentally characterized three HisA predecessors. We show that these ancient enzymes catalyze not only the HisA reaction but also the isomerization of a similar substrate, which is commonly processed by the isomerase TrpF in tryptophan biosynthesis. Moreover, we found that three modern-day HisA enzymes from Proteobacteria and Thermotogae also possess low TrpF activity. We conclude that this bi-functionality was conserved for at least two billion years, most likely without any evolutionary pressure. Although not actively selected for, this trait can become advantageous in the case of a gene loss. Such exaptation is exemplified by the Actinobacteria that have lost the trpF gene but possess the bi-functional HisA homolog PriA, which adopts the roles of both HisA and TrpF. Our findings demonstrate that bi-functionality can perpetuate in the absence of selection for very long time-spans. [ABSTRACT FROM AUTHOR]

Receptor Tyrosine Kinase (RTK) signaling pathways induce multiple biological responses, often by regulating the expression of downstream genes. The HMG-box protein Capicua (Cic) is a transcriptional repressor that is downregulated in response to RTK signaling, thereby enabling RTK-dependent induction of Cic targets. In both Drosophila and mammals, Cic is expressed as two isoforms, long (Cic-L) and short (Cic-S), whose functional significance and mechanism of action are not well understood. Here we show that Drosophila Cic relies on the Groucho (Gro) corepressor during its function in the early embryo, but not during other stages of development. This Gro-dependent mechanism requires a short peptide motif, unique to Cic-S and designated N2, which is distinct from other previously defined Gro-interacting motifs and functions as an autonomous, transferable repressor element. Unexpectedly, our data indicate that the N2 motif is an evolutionary innovation that originated within dipteran insects, as the Cic-S isoform evolved from an ancestral Cic-L-type form. Accordingly, the Cic-L isoform lacking the N2 motif is completely inactive in early Drosophila embryos, indicating that the N2 motif endowed Cic-S with a novel Gro-dependent activity that is obligatory at this stage. We suggest that Cic-S and Gro coregulatory functions have facilitated the evolution of the complex transcriptional network regulated by Torso RTK signaling in modern fly embryos. Notably, our results also imply that mammalian Cic proteins are unlikely to act via Gro and that their Cic-S isoform must have evolved independently of fly Cic-S. Thus, Cic proteins employ distinct repressor mechanisms that are associated with discrete structural changes in the evolutionary history of this protein family. [ABSTRACT FROM AUTHOR]

We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy – leveraging macrophage epigenomic measurements – to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5′ regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5′ regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that β-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in macrophage emigration from the plaque during lipid lowering-induced regression, and they illustrate the discovery potential of an epigenome-guided, systems approach to understanding atherosclerosis regression. [ABSTRACT FROM AUTHOR]

Crossovers (COs) play a critical role in ensuring proper alignment and segregation of homologous chromosomes during meiosis. How the cell balances recombination between CO vs. noncrossover (NCO) outcomes is not completely understood. Further lacking is what constrains the extent of DNA repair such that multiple events do not arise from a single double-strand break (DSB). Here, by interpreting signatures that result from recombination genome-wide, we find that synaptonemal complex proteins promote crossing over in distinct ways. Our results suggest that Zip3 (RNF212) promotes biased cutting of the double Holliday-junction (dHJ) intermediate whereas surprisingly Msh4 does not. Moreover, detailed examination of conversion tracts in sgs1 and mms4-md mutants reveal distinct aberrant recombination events involving multiple chromatid invasions. In sgs1 mutants, these multiple invasions are generally multichromatid involving 3–4 chromatids; in mms4-md mutants the multiple invasions preferentially resolve into one or two chromatids. Our analysis suggests that Mus81/Mms4 (Eme1), rather than just being a minor resolvase for COs is crucial for both COs and NCOs in preventing chromosome entanglements by removing 3′- flaps to promote second-end capture. Together our results force a reevaluation of how key recombination enzymes collaborate to specify the outcome of meiotic DNA repair. [ABSTRACT FROM AUTHOR]

The regulatory logic underlying global transcriptional programs controlling development of visceral organs like the pancreas remains undiscovered. Here, we profiled gene expression in 12 purified populations of fetal and adult pancreatic epithelial cells representing crucial progenitor cell subsets, and their endocrine or exocrine progeny. Using probabilistic models to decode the general programs organizing gene expression, we identified co-expressed gene sets in cell subsets that revealed patterns and processes governing progenitor cell development, lineage specification, and endocrine cell maturation. Purification of Neurog3 mutant cells and module network analysis linked established regulators such as Neurog3 to unrecognized gene targets and roles in pancreas development. Iterative module network analysis nominated and prioritized transcriptional regulators, including diabetes risk genes. Functional validation of a subset of candidate regulators with corresponding mutant mice revealed that the transcription factors Etv1, Prdm16, Runx1t1 and Bcl11a are essential for pancreas development. Our integrated approach provides a unique framework for identifying regulatory genes and functional gene sets underlying pancreas development and associated diseases such as diabetes mellitus. [ABSTRACT FROM AUTHOR]

The genetic architecture of many phenotypic traits is such that genes often contribute to multiple traits, and mutations in these genes can therefore affect multiple phenotypes. These pleiotropic interactions often manifest as tradeoffs between traits where improvement in one property entails a cost in another. The life cycles of many pathogens include periods of growth within a host punctuated with transmission events, such as passage through a digestive tract or a passive stage of exposure in the environment. Populations exposed to such fluctuating selective pressures are expected to acquire mutations showing tradeoffs between reproduction within and survival outside of a host. We selected for individual mutations under fluctuating selective pressures for a ssDNA microvirid bacteriophage by alternating selection for increased growth rate with selection on biophysical properties of the phage capsid in high-temperature or low-pH conditions. Surprisingly, none of the seven unique mutations identified showed a pleiotropic cost; they all improved both growth rate and pH or temperature stability, suggesting that single mutations even in a simple genetic system can simultaneously improve two distinct traits. Selection on growth rate alone revealed tradeoffs, but some mutations still benefited both traits. Tradeoffs were therefore prevalent when selection acted on a single trait, but payoffs resulted when multiple traits were selected for simultaneously. We employed a molecular-dynamics simulation method to determine the mechanisms underlying beneficial effects for three heat-shock mutations. All three mutations significantly enhanced the affinities of protein-protein interfacial bindings, thereby improving capsid stability. The ancestral residues at the mutation sites did not contribute to protein-protein interfacial binding, indicating that these sites acquired a new function. Computational models, such as those used here, may be used in future work not only as predictive tools for mutational effects on protein stability but, ultimately, for evolution. [ABSTRACT FROM AUTHOR]

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways. [ABSTRACT FROM AUTHOR]