Inferring the Genetic Basis of Sex Determination from the Genome of a Dioecious Nightshade.

Dissecting the genetic mechanisms underlying dioecy (i.e., separate female and male individuals) is critical for understanding the evolution of this pervasive reproductive strategy. Nonetheless, the genetic basis of sex determination remains unclear in many cases, especially in systems where dioecy has arisen recently. Within the economically important plant genus Solanum (∼2,000 species), dioecy is thought to have evolved independently at least 4 times across roughly 20 species. Here, we generate the first genome sequence of a dioecious Solanum and use it to ascertain the genetic basis of sex determination in this species. We de novo assembled and annotated the genome of Solanum appendiculatum (assembly size: ∼750 Mb scaffold N50: 0.92 Mb; ∼35,000 genes), identified sex-specific sequences and their locations in the genome, and inferred that males in this species are the heterogametic sex. We also analyzed gene expression patterns in floral tissues of males and females, finding approximately 100 genes that are differentially expressed between the sexes. These analyses, together with observed patterns of gene-family evolution specific to S. appendiculatum, consistently implicate a suite of genes from the regulatory network controlling pectin degradation and modification in the expression of sex. Furthermore, the genome of a species with a relatively young sex-determination system provides the foundational resources for future studies on the independent evolution of dioecy in this clade.

The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction.

MOTIVATION: The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The 'ortholog conjecture' proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. RESULTS: We use experimental annotations from over 40 000 proteins, drawn from over 80 000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of information that would be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Maximizing the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/predragradivojac/oc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantifying the risk of hemiplasy in phylogenetic inference.

Convergent evolution-the appearance of the same character state in apparently unrelated organisms-is often inferred when a trait is incongruent with the species tree. However, trait incongruence can also arise from changes that occur on discordant gene trees, a process referred to as hemiplasy. Hemiplasy is rarely taken into account in studies of convergent evolution, despite the fact that phylogenomic studies have revealed rampant discordance. Here, we study the relative probabilities of homoplasy (including convergence and reversal) and hemiplasy for an incongruent trait. We derive expressions for the probabilities of the two events, showing that they depend on many of the same parameters. We find that hemiplasy is as likely-or more likely-than homoplasy for a wide range of conditions, even when levels of discordance are low. We also present a method to calculate the ratio of these two probabilities (the "hemiplasy risk factor") along the branches of a phylogeny of arbitrary length. Such calculations can be applied to any tree to identify when and where incongruent traits may be due to hemiplasy.

Pervasive antagonistic interactions among hybrid incompatibility loci.

Species barriers, expressed as hybrid inviability and sterility, are often due to epistatic interactions between divergent loci from two lineages. Theoretical models indicate that the strength, direction, and complexity of these genetic interactions can strongly affect the expression of interspecific reproductive isolation and the rates at which new species evolve. Nonetheless, empirical analyses have not quantified the frequency with which loci are involved in interactions affecting hybrid fitness, and whether these loci predominantly interact synergistically or antagonistically, or preferentially involve loci that have strong individual effects on hybrid fitness. We systematically examined the prevalence of interactions between pairs of short chromosomal regions from one species (Solanum habrochaites) co-introgressed into a heterospecific genetic background (Solanum lycopersicum), using lines containing pairwise combinations of 15 chromosomal segments from S. habrochaites in the background of S. lycopersicum (i.e., 95 double introgression lines). We compared the strength of hybrid incompatibility (either pollen sterility or seed sterility) expressed in each double introgression line to the expected additive effect of its two component single introgressions. We found that epistasis was common among co-introgressed regions. Interactions for hybrid dysfunction were substantially more prevalent in pollen fertility compared to seed fertility phenotypes, and were overwhelmingly antagonistic (i.e., double hybrids were less unfit than expected from additive single introgression effects). This pervasive antagonism is expected to attenuate the rate at which hybrid infertility accumulates among lineages over time (i.e., giving diminishing returns as more reproductive isolation loci accumulate), as well as decouple patterns of accumulation of sterility loci and hybrid incompatibility phenotypes. This decoupling effect might explain observed differences between pollen and seed fertility in their fit to theoretical predictions of the accumulation of isolation loci, including the 'snowball' effect.

CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Complex selection on a regulator of social cognition: Evidence of balancing selection, regulatory interactions and population differentiation in the prairie vole Avpr1a locus.

Adaptive variation in social behaviour depends upon standing genetic variation, but we know little about how evolutionary forces shape genetic diversity relevant to brain and behaviour. In prairie voles (Microtus ochrogaster), variants at the Avpr1a locus predict expression of the vasopressin 1a receptor in the retrosplenial cortex (RSC), a brain region that mediates spatial and contextual memory; cortical V1aR abundance in turn predicts diversity in space use and sexual fidelity in the field. To examine the potential contributions of adaptive and neutral forces to variation at the Avpr1a locus, we explore sequence diversity at the Avpr1a locus and throughout the genome in two populations of wild prairie voles. First, we refine results demonstrating balancing selection at the locus by comparing the frequency spectrum of variants at the locus to a random sample of the genome. Next, we find that the four single nucleotide polymorphisms that predict high V1aR expression in the RSC are in stronger linkage disequilibrium than expected by chance despite high recombination among intervening variants, suggesting that epistatic selection maintains their association. Analysis of population structure and a haplotype network for two populations revealed that this excessive LD was unlikely to be due to admixture alone. Furthermore, the two populations differed considerably in the region shown to be a regulator of V1aR expression despite the extremely low levels of genomewide genetic differentiation. Together, our data suggest that complex selection on Avpr1a locus favours specific combinations of regulatory polymorphisms, maintains the resulting alleles at population-specific frequencies, and may contribute to unique patterns of spatial cognition and sexual fidelity among populations.

Transcriptomic analysis links gene expression to unilateral pollen-pistil reproductive barriers.

BACKGROUND: Unilateral incompatibility (UI) is an asymmetric reproductive barrier that unidirectionally prevents gene flow between species and/or populations. UI is characterized by a compatible interaction between partners in one direction, but in the reciprocal cross fertilization fails, generally due to pollen tube rejection by the pistil. Although UI has long been observed in crosses between different species, the underlying molecular mechanisms are only beginning to be characterized. The wild tomato relative Solanum habrochaites provides a unique study system to investigate the molecular basis of this reproductive barrier, as populations within the species exhibit both interspecific and interpopulation UI. Here we utilized a transcriptomic approach to identify genes in both pollen and pistil tissues that may be key players in UI. RESULTS: We confirmed UI at the pollen-pistil level between a self-incompatible population and a self-compatible population of S. habrochaites. A comparison of gene expression between pollinated styles exhibiting the incompatibility response and unpollinated controls revealed only a small number of differentially expressed transcripts. Many more differences in transcript profiles were identified between UI-competent versus UI-compromised reproductive tissues. A number of intriguing candidate genes were highly differentially expressed, including a putative pollen arabinogalactan protein, a stylar Kunitz family protease inhibitor, and a stylar peptide hormone Rapid ALkalinization Factor. Our data also provide transcriptomic evidence that fundamental processes including reactive oxygen species (ROS) signaling are likely key in UI pollen-pistil interactions between both populations and species. CONCLUSIONS: Gene expression analysis of reproductive tissues allowed us to better understand the molecular basis of interpopulation incompatibility at the level of pollen-pistil interactions. Our transcriptomic analysis highlighted specific genes, including those in ROS signaling pathways that warrant further study in investigations of UI. To our knowledge, this is the first report to identify candidate genes involved in unilateral barriers between populations within a species.

Topological features of a gene co-expression network predict patterns of natural diversity in environmental response.

Molecular interactions affect the evolution of complex traits. For instance, adaptation may be constrained by pleiotropic or epistatic effects, both of which can be reflected in the structure of molecular interaction networks. To date, empirical studies investigating the role of molecular interactions in phenotypic evolution have been idiosyncratic, offering no clear patterns. Here, we investigated the network topology of genes putatively involved in local adaptation to two abiotic stressors-drought and cold-in Arabidopsis thaliana Our findings suggest that the gene-interaction topologies for both cold and drought stress response are non-random, with genes that show genetic variation in drought expression response (eGxE) being significantly more peripheral and cold response genes being significantly more central than genes which do not show GxE. We suggest that the observed topologies reflect different constraints on the genetic pathways involved in environmental response. The approach presented here may inform predictive models linking genetic variation in molecular signalling networks with phenotypic variation, specifically traits involved in environmental response.

Speciation as a sieve for ancestral polymorphism.

Because they are considered rare, balanced polymorphisms are often discounted as crucial constituents of genome-wide variation in sequence diversity. Despite its perceived rarity, however, long-term balancing selection can elevate genetic diversity and significantly affect observed divergence between species. Here, we discuss how ancestral balanced polymorphisms can be "sieved" by the speciation process, which sorts them unequally across descendant lineages. After speciation, ancestral balancing selection is revealed by genomic regions of high divergence between species. This signature, which resembles that of other evolutionary processes, can potentially confound genomic studies of population divergence and inferences of "islands of speciation."

Molecular mechanisms of postmating prezygotic reproductive isolation uncovered by transcriptome analysis.

Little is known about the physiological responses and genetic mutations associated with reproductive isolation between species, especially for postmating prezygotic isolating barriers. Here, we examine changes in gene expression that accompany the expression of 'unilateral incompatibility' (UI)-a postmating prezygotic barrier in which fertilization is prevented by gamete rejection in the reproductive tract [in this case of pollen tubes (male gametophytes)] in one direction of a species cross, but is successful in the reciprocal crossing direction. We use whole-transcriptome sequencing of multiple developmental stages of male and female tissues in two Solanum species that exhibit UI to: (i) identify transcript differences between UI-competent and UI noncompetent tissues; (ii) characterize transcriptional changes specifically associated with the phenotypic expression of UI; and (iii) using these comparisons, evaluate the behaviour of a priori candidate loci for UI and identify new candidates for future manipulative work. In addition to describing transcriptome-wide changes in gene expression that accompany this isolating barrier, we identify at least five strong candidates for involvement in postmating prezygotic incompatibility between species. These include three novel candidates and two candidates that are strongly supported by prior developmental, functional, and quantitative trait locus mapping studies. These latter genes are known molecular players in the intraspecific expression of mate choice via genetic self-incompatibility, and our study supports prior evidence that these inter- and intraspecific postmating prezygotic reproductive behaviours share specific genetic and molecular mechanisms.

About PAR: the distinct evolutionary dynamics of the pseudoautosomal region.

Sex chromosomes differ from other chromosomes in the striking divergence they often show in size, structure, and gene content. Not only do they possess genes controlling sex determination that are restricted to either the X or Y (or Z or W) chromosomes, but in many taxa they also include recombining regions. In these 'pseudoautosomal regions' (PARs), sequence homology is maintained by meiotic pairing and exchange in the heterogametic sex. PARs are unique genomic regions, exhibiting some features of autosomes, but they are also influenced by their partial sex linkage. Here we review the distribution and structure of PARs among animals and plants, the theoretical predictions concerning their evolutionary dynamics, the reasons for their persistence, and the diversity and content of genes that reside within them. It is now clear that the evolution of the PAR differs in important ways from that of genes in either the non-recombining regions of sex chromosomes or the autosomes.

Matrix inversions for chromosomal inversions: a method to construct summary statistics in complex coalescent models.

Chromosomal inversions allow genetic divergence of locally adapted populations by reducing recombination between chromosomes with different arrangements. While patterns of genetic variation within inverted regions are increasingly documented, inferential methods are largely missing to analyze such data. Previous work has provided expectations for coalescence patterns of neutral sites linked to an inversion polymorphism in two locally adapted populations. Here, we define a method to construct summary statistics in such complex population structure models. Under a scenario of selection on the inversion breakpoints, we first construct estimators of the migration rate between the two habitats, and of the recombination rate of a nucleotide site between the two inversion backgrounds. Next, we analyze the disequilibrium between two sites within an inversion and provide an estimator of the distinct recombination rate between these two sites in homokaryotypes and heterokaryotypes. These estimators should be suitable summary statistics for simulation-based methods that can handle the complex dependences in the data.

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications.

The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 ß-lactamase alleles and 7 ß-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas.

Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance.

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications.

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 ß-lactamase alleles and seven ß-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance.

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Genetic Associations with Dynamic Placental Proteins Identify Causal Biomarkers for Hypertension in Pregnancy.

Preeclampsia (PE), a gestational hypertensive disorder, ranks as the second leading cause of maternal mortality worldwide. While PE is considered a multifactorial disease, placental insufficiency is believed to drive its progression. To noninvasively study placental physiology related to adverse pregnancy outcomes (APOs) and predict these outcomes before symptom onset, we measured nine placental protein levels in first- and second-trimester serum samples from 2,352 nulliparous pregnant women in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers- to-Be (nuMoM2b) study. The proteins analyzed include VEGF, PlGF, ENG, sFlt-1, ADAM-12, PAPP-A, fßHCG, INHA, and AFP. Currently, little is known about the genetic variants contributing to the heritability of these proteins during pregnancy, and no studies have explored the causal relationships between early pregnancy proteins and gestational hypertensive disorders. Our study has three objectives. First, we conducted genome-wide association study (GWAS) of nine placental proteins in maternal serum during the first and second trimesters and the difference between time points to understand how genetics may influence placental proteins in early pregnancy. Second, we examined whether early pregnancy placental proteins are causal factors for PE and gestational hypertension (gHTN). Lastly, we investigated the causal relationship between PE/gHTN and long-term HTN. In conclusion, our study discovered significant genetic associations with placental proteins ADAM-12, VEGF, and sFlt-1, offering insights into their regulation during pregnancy. Mendelian randomization (MR) analyses demonstrated evidence of causal relationships between placental proteins, particularly ADAM-12, and gHTN, potentially informing prevention and treatment strategies. Our findings suggest that placental proteins like ADAM-12 could serve as biomarkers for postpartum HTN risk.

Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b.

Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWAS) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display the extensive results, we developed a web-based tool GnuMoM2b ( https://gnumom2b.cumcobgyn.org/ ) for searching, visualizing, and sharing results from GWAS of 479 pregnancy traits as well as phenome-wide association studies (PheWAS) of more than 17 million single nucleotide polymorphisms (SNPs). The genetic results from three ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.

Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b.

Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWASs) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display extensive results, we developed a web-based tool GnuMoM2b (https://gnumom2b.cumcobgyn.org/) for searching, visualizing, and sharing results from a GWAS of 479 pregnancy traits as well as phenome-wide association studies of more than 17 million single nucleotide polymorphisms. The genetic results from 3 ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.