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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

The genetic architecture of Parkinson disease in Spain: characterizing population-specific risk, differential haplotype structures, and providing etiologic insight

BackgroundThe Iberian Peninsula stands out as having variable levels of population admixture and isolation, making Spain an interesting setting for studying the genetic architecture of neurodegenerative diseases.\n\nObjectivesTo perform the largest Parkinson disease (PD) genome-wide association study (GWAS) restricted to a single country.\n\nMethodsWe performed a GWAS for both risk of PD and age-at-onset (AAO) in 7,849 Spanish individuals. Further analyses included population-specific risk haplotype assessments, polygenic risk scoring through machine learning, Mendelian randomization of expression and methylation data to gain insight into disease-associated loci, heritability estimates, genetic correlations and burden analyses.\n\nResultsWe identified a novel population-specific GWAS signal at PARK2 associated with AAO. We replicated four genome-wide independent signals associated with PD risk, including SNCA, LRRK2, KANSL1/MAPT and HLA-DQB1. A significant trend for smaller risk haplotypes at known loci was found compared to similar studies of non-Spanish origin. Seventeen PD-related genes showed functional consequence via two-sample Mendelian randomization in expression and methylation datasets. Long runs of homozygosity at 28 known genes/loci were found to be enriched in cases versus controls.\n\nConclusionsOur data demonstrate the utility of the Spanish risk haplotype substructure for future fine-mapping efforts, showing how leveraging unique and diverse population histories can benefit genetic studies of complex diseases. The present study points to PARK2 as a major hallmark of PD etiology in Spain.

neuroscience↗
bioRxiv2019

Cell type-specific genetic regulation of expression in the granule cell layer of the human dentate gyrus

Laser capture microdissection followed by RNA-seq (LCM-seq) was used to profile the transcriptional landscape of the granule cell layer of the dentate gyrus (DG-GCL) in human hippocampus, and contrasted to homogenate tissue. We identified widespread cell type-specific aging and genetic effects in the DG-GCL that were either missing or directionally discordant in corresponding bulk hippocampus RNA-seq data from largely the same subjects. Of the [~]9 million eQTLs in the DG-GCL, 15% were not in bulk hippocampus, including 15 schizophrenia genome-wide association study (GWAS) risk variants. We then created custom transcriptome-wide association study (TWAS) genetic weights from the DG-GCL which identified many novel schizophrenia-associated genetic signals not found in TWAS from bulk hippocampus, including GRM3 and CACNA1C. These results highlight the biological resolution of cell type-specific expression profiling using targeted sampling strategies like LCM, and complement homogenate and single nuclei approaches in human brain.

neuroscience↗
bioRxiv2019

Genetic divergence outpaces phenotypic divergence among threespine stickleback populations in old freshwater habitats

Species such as threespine stickleback fish that are distributed across landscapes with divergent selective environments and that have diversified on different time scales can be valuable for understanding evolutionary processes. Here we synthesize high resolution genotypic and phenotypic data to explore a largely unstudied distribution of threespine stickleback populations living in marine and freshwater habitats along coastal and inland regions of northwestern Oregon. Because many inland aquatic habitats of Oregon were not glaciated during the last ice age, we hypothesized that some extant Oregon lake and river stickleback are descended from freshwater populations that were established long before the well-studied, postglacial freshwater populations of Alaska. Here we characterize the major phenotypic and genetic axes of differentiation in Oregon stickleback, and compare these patterns to their Alaska counterparts currently inhabiting regions that were covered by ice during the last glacial maximum. Phenotypic variation in Oregon stickleback is predictably partitioned between oceanic and freshwater habitats. However, we also found that genetic divergence in Oregon ecotypes is much greater than divergence among studied stickleback populations in Alaska. Additionally, we report a surprising phenotypic and genetic affinity between oceanic stickleback with freshwater populations that live far inland in two Oregon river basins.

evolutionary biology↗
bioRxiv2019

Geographic variation in the genetic basis of resistance to leaf rust in locally adapted ecotypes of the biofuel crop switchgrass (Panicum virgatum)

O_LILocal adaptation is an important process in plant evolution, which can be impacted by differential pathogen pressures along environmental gradients. However, the degree to which pathogen resistance loci vary in effect across space and time is incompletely described. C_LIO_LITo understand how the genetic architecture of resistance varies across time and geographic space, we quantified rust (Puccinia spp.) severity in switchgrass (Panicum virgatum) plantings at eight locations across the central United States for three years and conducted quantitative trait locus (QTL) mapping for rust progression. C_LIO_LIWe mapped several variable QTLs, but two large-effect QTLs which we have named Prr1 and Prr2 were consistently associated with rust severity in multiple sites and years, particularly in northern sites. In contrast, there were numerous small-effect QTLs at southern sites, indicating a genotype-by-environment interaction in rust resistance loci. Interestingly, Prr1 and Prr2 had a strong epistatic interaction, which also varied in strength and direction of effect across space. C_LIO_LIOur results suggest that abiotic factors covarying with latitude interact with the genetic loci underlying plant resistance to control rust infection severity. Further, our results indicate that segregating genetic variation in epistatically interacting loci may play a key role in determining response to infection across geographic space. C_LI

genomics↗
bioRxiv2019

Recombining your way out of trouble: The genetic architecture of hybrid fitness under environmental stress

Hybridization between species is a fundamental evolutionary force that can both promote and delay adaptation. There is a deficit in our understanding of the genetic basis of hybrid fitness, especially in non-domesticated organisms. We also know little about how hybrid fitness changes as a function of environmental stress. Here, we made genetically variable F2 hybrid populations from two divergent Saccharomyces yeast species, exposed populations to ten toxins, and sequenced the most resilient hybrids on low coverage using ddRADseq. We expected to find strong negative epistasis and heterozygote advantage in the hybrid genomes. We investigated three aspects of hybridness: 1) hybridity, 2) interspecific heterozygosity, and 3) epistasis (positive or negative associations between non-homologous chromosomes). Linear mixed effect models revealed strong genotype-by-environment interactions with many chromosomes and chromosomal interactions showing species-biased content depending on the environment. Against our predictions, we found extensive selection against heterozygosity such that homozygous allelic combinations from the same species were strongly overrepresented in an otherwise hybrid genomic background. We also observed multiple cases of positive epistasis between chromosomes from opposite species, confirmed by epistasis- and selection-free simulations, which is surprising given the large divergence of the parental species (~15% genome-wide). Together, these results suggest that stress-resilient hybrid genomes can be assembled from the best features of both parents, without paying high costs of negative epistasis across large evolutionary distances. Our findings illustrate the importance of measuring genetic trait architecture in an environmental context when determining the evolutionary potential of hybrid populations.

evolutionary biology↗
bioRxiv2019

New Insights into the Genetic Diversity of the Bacterial Plant Pathogen ‘Candidatus Liberibacter solanacearum’ as Revealed by a New Multilocus Sequence Analysis Scheme

Candidatus Liberibacter solanacearum (Lso) has emerged as a serious threat on solanaceous and apiaceous crops worldwide. Five Lso haplotypes (LsoA, LsoB, LsoC, LsoD and LsoE) have been identified so far. To decipher genetic relationships between Lso strains, a MLSA study of seven housekeeping genes (acnA, atpD, ftsZ, glnA, glyA, gnd and groEL) was performed on a representative bacterial collection of 49 Lso strains. In all, 5415 bp spanning the seven loci were obtained from each of the 49 strains of our bacterial collection. Analysis of sequence data was consistent with a clonal population structure with no evidence of recombination. Phylogenies reconstructed from individual genes, and with concatenated data, were globally congruent with each other. In addition to the five highly supported and distinct genetic clusters, which correspond to the five established haplotypes, our phylogenetic data revealed the presence of a sixth haplotype, designated LsoG. This new haplotype is currently represented by two strains from France which had distinct sequences in four out of the seven tested housekeeping genes. Altogether, the data presented here provide new information regarding the genetic structure of Lso and the evolutionary history of the haplotypes defined within this bacterial species.

microbiology↗
bioRxiv2019

Extensive genetic diversity of bat-borne polyomaviruses reveals inter-family host-switching events

Polyomaviruses (PyVs) are small, double-stranded DNA tumor viruses carried by diverse vertebrates. PyVs have previously been considered highly host restricted in mammalian hosts, with host-switching events thought rare or nonexistent. Prior investigations have revealed short-range host-switching events of PyVs in two different African bat species within the horseshoe bat genus Rhinolophus. Herein, we have conducted a systematic investigation of PyVs in 1,083 archived bat samples collected from five provinces across China, and identified 192 PyVs from 186 bats from 15 host species within 6 families (Rhinolophidae, Vespertilionidae, Hipposideridae, Emballonuridae, Miniopteridae and Pteropodidae) representing 28 newly-described PyVs, indicative of extensive genetic diversity of bat PyVs. Surprisingly, two PyVs were identified in multiple bat species from different families, and another PyV clustered phylogenetically with PyVs carried by bats from a different host family, indicative of three inter-family PyV host-switching events. The time to most recent common ancestor (tMRCA) of the three events was estimated at 0.02-11.6 million years ago (MYA), which is inconsistent with the estimated tMRCA of their respective bat hosts (36.3-66.7 MYA), and is most parsimoniously explained by host-switching events. PyVs identified from geographically separated Chinese horseshoe bat species in the present study showed close genetic identities, and clustered with each other and with PyVs from African horseshoe bats, allowing assessment of the effects of positive selection in VP1 within the horseshoe bat family Rhinolophidae. Correlation analysis indicated that co-evolution with their hosts contributed much more to evolutionary divergence of PyV than geographic distance. In conclusion, our findings provide the first evidence of inter-family host-switching events of PyV in mammals and challenge the prevailing evolutionary paradigm for strict host restriction of mammalian PyVs.\n\nAuthor summarySince the discovery of murine polyomavirus in the 1950s, polyomaviruses (PyVs) have been considered both genetically stable and highly host-restricted in their mammalian hosts. In this study, we have identified multiple cases of host-switching events of PyVs by large scale surveillance in diverse bat species collected in China. These host-switching events occurred between bat families living in the same colony, indicating that a large population with frequent contacts between different bat species may represent an ecological niche facilitating PyV host-switching. The cases studied involved members of bats from several families, including horseshoe bats, which were previously found to harbor a number of highly virulent viruses to both humans and domestic animals. Our findings have provided evidence that even highly host-specific DNA viruses can transmit between bats of different species and indicate an increased propensity for spillover events involving horseshoe bats. We propose an evolutionary scheme for bat-borne PyVs in which intra-host divergence and host-switching has generated the diverse PyVs in present day bats. This scheme provides a useful model to study the evolution of PyVs in other hosts and, potentially, the modeling of bat zoonoses and the transmission of other DNA viruses in other mammals, including humans.

microbiology↗
bioRxiv2019

Machine learning-based identification and cellular validation of Tropomyosin 1 as a genetic inhibitor of hematopoiesis.

Identifying causal variants and genes from human genetics studies of hematopoietic traits are important to enumerate basic regulatory mechanisms underlying these traits, and could ultimately augment translational efforts to generate platelets and/or red blood cells in vitro. To identify putative causal genes from these data, we performed computational modelling using available genome-wide association data sets for platelet traits. Our model identified a joint collection of genomic features enriched at established platelet trait associations and plausible candidate variants. Additional studies associating variation at these loci with change in gene expression highlighted the Tropomyosin 1 (TPM1) among our top-ranked candidate genes. CRISPR/Cas9-mediated TPM1 knockout in human induced pluripotent stem cells (iPSCs) enhanced hematopoietic progenitor development, increasing total megakaryocyte as well as erythroid cell yields. Our findings may help explain human genetics associations and identify a novel genetic strategy to enhance in vitro hematopoiesis.

genomics↗
bioRxiv2019

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data.\n\nOne Sentence SummaryNew statistical framework for modelling allelic expression characterizes genetic regulatory variation in populations and informs diagnosis in rare disease patients

genomics↗
bioRxiv2019

Integrating proteomics and genomics into systems genetics provides novel insights into the mechanisms of drought tolerance in maize

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms make the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype-phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein co-expression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified fifteen loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein co-expression network, and a reprogramming of the genetic control of the abundance of many proteins, notably those involved in stress response. Co-localizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the co-expression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

systems biology↗
bioRxiv2019

Bayesian multivariate reanalysis of large genetic studies identifies many new associations

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.\n\n1 Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time ( univariate); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously ( multivariate). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

genomics↗
bioRxiv2019

Tomato root transformation followed by inoculation with Ralstonia solanacearum for straightforward genetic analysis of bacterial wilt disease

Ralstonia solanacearum is a devastating soil borne vascular pathogen that is able to infect a large range of plant species, causing an important threat to agriculture. However, the Ralstonia model is considerably under-explored in comparison to other models involving bacterial plant pathogens, such as Pseudomonas syringae in Arabidopsis. Research targeted to understanding the interaction between Ralstonia and crop plants is essential to develop sustainable solutions to fight against bacterial wilt disease, but is currently hindered by the lack of straightforward experimental assays to characterize the different components of the interaction in native host plants. In this scenario, we have developed an easy method to perform genetic analysis of Ralstonia infection of tomato, a natural host of Ralstonia. This method is based on Agrobacterium rhizogenes-mediated transformation of tomato roots, followed by Ralstonia soil-drenching inoculation of the resulting plants, containing transformed roots expressing the construct of interest. The versatility of the root transformation assay allows performing either gene overexpression or gene silencing mediated by RNAi. As a proof of concept, we used this method to show that RNAi-mediated silencing of SlCESA6 of tomato roots conferred resistance to Ralstonia. Here, we describe this method in detail, enabling genetic approaches to understand bacterial wilt disease in a relative short time and with small requirements of equipment and plant growth space.\n\nSUMMARYA versatile method for tomato root transformation followed by inoculation with Ralstonia solanacearum to perform straightforward genetic analysis for the study of bacterial wilt disease.

plant biology↗
bioRxiv2019

An evolutionary-based approach to quantify the genetic barrier to drug resistance in fast-evolving viruses: an application to HIV-1 subtypes and integrase inhibitors

Viral pathogens causing global disease burdens are often characterised by high rates of evolutionary changes, facilitating escape from therapeutic or immune selective pressure. Extensive viral diversity at baseline can shorten the time to resistance emergence and alter mutational pathways, but the impact of genotypic background on the genetic barrier can be difficult to capture, in particular for antivirals in experimental stages, recently approved or expanded into new settings. We developed an evolutionary-based counting method to quantify the population genetic potential to resistance and assess differences between populations. We demonstrate its applicability to HIV-1 integrase inhibitors, as their increasing use globally contrasts with limited availability of non-B subtype resistant sequences and corresponding knowledge gap on drug resistance. A large sequence dataset encompassing most prevailing subtypes and resistance mutations of first- and second-generation inhibitors were investigated. A varying genetic potential for resistance across HIV-1 subtypes was detected for 15 mutations at 12 positions, with notably 140S in subtype B, while 140C was discarded to vary across subtypes. An additional analysis for HIV-1 reverse transcriptase inhibitors identified a higher potential for 65R in subtype C, on the basis of a differential codon usage not reported before. The evolutionary interpretation of genomic differences for antiviral treatment remains challenging. Our framework advances existing counting methods with an increased sensitivity that identified novel subtype dependencies as well as rejected previous statements. Future applications include novel HIV-1 drug classes as well as other viral pathogens.

microbiology↗
bioRxiv2019

Multi-ethnic genome-wide association study of decomposed cardioelectric phenotypes illustrates strategies to identify and characterize evidence of shared genetic effects for complex traits

BackgroundPublished genome-wide association studies (GWAS) are mainly European-centric, examine a narrow view of phenotypic variation, and infrequently interrogate genetic effects shared across traits. We therefore examined the extent to which a multi-ethnic, combined trait GWAS of phenotypes that map to well-defined biology can enable detection and characterization of complex trait loci.\n\nMethodsWith 1000 Genomes Phase 3 imputed data in 34,668 participants (15% African American; 3% Chinese American; 51% European American; 30% Hispanic/Latino), we performed covariate-adjusted univariate GWAS of six contiguous electrocardiogram (ECG) traits that decomposed an average heartbeat and two commonly reported composite ECG traits that summed contiguous traits. Combined phenotype testing was performed using the adaptive sum of powered scores test (aSPU).\n\nResultsWe identified six novel and 87 known ECG trait loci (aSPU p-value < 5E-9). Lead SNP rs3211938 at novel locus CD36 was common in African Americans (minor allele frequency=10%) and near-monomorphic in European Americans, with effect sizes for the composite trait, QT interval, among the largest reported. Only one novel locus was detected for the composite traits, due to opposite directions of effects across contiguous traits that summed to near-zero. Combined phenotype testing did not detect novel loci unapparent by univariate testing. However, this approach aided locus characterization, particularly when loci harbored multiple independent signals that differed by trait.\n\nConclusionsDespite including one-third as few participants as the largest published GWAS of ECG traits, our study identifies multiple novel ECG genetic loci, emphasizing the importance of ancestral diversity and phenotype measurement in this era of ever-growing GWAS.\n\nAUTHOR SUMMARYWe leveraged a multiethnic cohort with precise measures of cardioelectric function to identify novel genetic loci affecting this complex, multifaceted phenotype. The success of our approach stresses the importance of phenotypic precision and participant diversity for future locus discovery and characterization efforts, and cautions against compromises made in genome-wide association studies to pursue ever-growing sample sizes.

epidemiology↗
bioRxiv2019

The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells

The inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its quantification is confounded by genetic heterogeneity. Here we show that NGH can be accurately measured when informed by the molecular signatures of the normal cells of origin. We surveyed the transcriptomes of [~] 4000 normal fallopian tube epithelial (FTE) cells, the cells of origin of serous ovarian cancer (SOC), and identified six FTE subtypes. We used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH that was previously unrecognized. Importantly, NGH-based stratification of [~]1700 tumors robustly predicted survival. Our findings lay the foundation for accurate prognostic and therapeutic stratification of SOC.\n\nHighlightsO_LIThe projection of FTE subtypes refines the molecular classification of serous OC\nC_LIO_LIComprehensive single-cell profiling of FTE cells identifies 6 molecular subtypes\nC_LIO_LISubstantial non-genetic heterogeneity of HGSOC identified in 1700 tumors\nC_LIO_LIA mesenchymal-high HGSOC subtype is robustly correlated with poor prognosis\nC_LI

cancer biology↗
bioRxiv2019

The multiple population genetic and demographic routes to islands of genomic divergence

1. The way that organisms diverge into reproductively isolated species is a major question in biology. The recent accumulation of genomic data provides promising opportunities to understand the genomic landscape of divergence, which describes the distribution of differences across genomes. Genomic areas of unusually high differentiation have been called genomic islands of divergence. Their formation has been attributed to a variety of mechanisms, but a prominent hypothesis is that they result from divergent selection over a small portion of the genome, with surrounding areas homogenised by gene flow. Such islands have often been interpreted as being associated with divergence with gene flow. However other mechanisms related to genetic architecture and population history can also contribute to the formation of genomic islands of divergence.\n\n2. We currently lack a quantitative framework to examine the dynamics of genomic landscapes under the complex and nuanced conditions that are found in natural systems. Here, we develop an individual-based simulation to explore the dynamics of diverging genomes under various scenarios of gene flow, selection and genotype-phenotype maps.\n\n3. Our modelling results are consistent with empirical observations demonstrating the formation of genomic islands under genetic isolation. Importantly, we have quantified the range of conditions that produce genomic islands. We demonstrate that the initial level of genetic diversity, drift, time since divergence, linkage disequilibrium, strength of selection and gene flow are all important factors that can influence the formation of genomic islands. Because the accumulation of genomic differentiation over time tends to erode the signal of genomic islands, genomic islands are more likely to be observed in recently divergent taxa, although not all recently diverged taxa will necessarily exhibit islands of genomic divergence. Gene flow primarily slows the swamping of islands of divergence with time.\n\n4. By using this framework, further studies may explore the relative influence of particular suites of events that contribute to the emergence of genomic islands under sympatric, parapatric and allopatric conditions. This approach represents a novel tool to explore quantitative expectations of the speciation process, and should prove useful in elucidating past and projecting future genomic evolution of any taxa.

genomics↗
bioRxiv2019

Investigating unexplained genetic variation and its expression in the arbuscular mycorrhizal fungus Rhizophagus irregularis

Arbuscular mycorrhizal fungi (AMF) are important symbionts of plants. Recently, studies of the AMF Rhizophagus irregularis recorded within-isolate genetic variation that does not completely match the proposed homokaryon or heterokaryon state (where heterokaryons comprise a population of two distinct nucleus genotypes). We re-analysed published data showing that bi-allelic sites (and their frequencies), detected in proposed homo- and heterokaryote R. irregularis isolates, were similar across independent studies using different techniques. This indicated that observed within-fungus genetic variation was not an artefact of sequencing and that such within-fungus genetic variation possibly exists. We looked to see if bi-allelic transcripts from three R. irregularis isolates matched those observed in the genome as this would give a strong indication of whether bi-allelic sites recorded in the genome were reliable variants. In putative homokaryon isolates, very few bi-allelic transcripts matched those in the genome. In a putative heterokaryon, a large number of bi-allelic transcripts matched those in the genome. Bi-allelic transcripts also occurred in the same frequency in the putative heterokaryon as predicted from allele frequency in the genome. Our results indicate that while within-fungus genome variation in putative homokaryon and heterokaryon AMF was highly similar in 2 independent studies, there was little support that this variation is transcribed in homokaryons. In contrast, within-fungus variation thought to be segregated among two nucleus genotypes in a heterokaryon isolate was indeed transcribed in a way that is proportional to that seen in the genome.

microbiology↗
bioRxiv2019

Genetic and functional diversification of chemosensory pathway receptors in filarial nematode parasites

Lymphatic filariasis (LF) afflicts over 60 million people worldwide and leads to severe pathological outcomes in chronic cases. The nematode parasites (Nematoda: Filarioidea) that cause LF require both arthropod (mosquito) intermediate hosts and mammalian definitive hosts for their propagation. The invasion and migration of filarial worms through host tissues are complex and critical to survival, yet little is known about the receptors and signaling pathways that mediate directed migration in these medically important species. In order to better understand the role of chemosensory signaling in filarial worm taxis, we employ comparative genomics, transcriptomics, reverse genetics, and chemical approaches to identify putative chemosensory receptor proteins and perturb chemotaxis phenotypes in filarial worms. We find that chemoreceptor family size is correlated with the presence of environmental (extra-host) stages in nematode life cycles, and that filarial worms contain a compact and highly-diverged chemoreceptor complement and lineage-specific ion channels that are predicted to operate downstream of chemoreceptor activation. In Brugia malayi, an etiological agent of LF, chemoreceptor expression patterns correspond to distinct parasite migration events across the life cycle. To interrogate the role of chemosensation in the migration of larval worms, arthropod infectious stage (microfilariae) and mammalian infectious stage (L3) Brugia parasites were incubated in nicotinamide, an agonist of the nematode transient receptor potential (TRP) channel OSM-9. Exposure of microfilariae to nicotinamide alters intra-mosquito migration while exposure of L3s reduces chemotaxis towards host-associated cues in vitro. Nicotinamide also potently modulates thermosensory responses in L3s, suggesting a polymodal sensory role for Brugia osm-9. Reverse genetic studies implicate both Brugia osm-9 and the cyclic nucleotide-gated (CNG) channel subunit tax-4 in larval chemotaxis towards host serum, and these ion channel subunits rescue sensory defects in C. elegans osm-9 and tax-4 knock-out strains. Together, these data reveal genetic and functional diversification of chemosensory signaling proteins in filarial worms, and encourage a more thorough investigation of clade and parasite-specific facets of nematode sensory receptor biology.

microbiology↗