Shah, S. J. | bioRxiv Science

bioRxiv2018

Host factors influence the sex of nematodes parasitizing roots of Arabidopsis thaliana

Plant-parasitic cyst nematodes induce hypermetabolic syncytial nurse cells in the roots of their host plants. Syncytia are their only food source. Cyst nematodes are sexually dimorphic, with their differentiation into male or female strongly influenced by host environmental conditions. Under favorable conditions with plenty of nutrients, more females develop, whereas mainly male nematodes develop under adverse conditions such as in resistant plants. Here, we developed and validated a method to predict the sex of beet cyst nematode (Heterodera schachtii) during the early stages of its parasitism in the host plant Arabidopsis thaliana. We collected root segments containing male-associated syncytia (MAS) or female-associated syncytia (FAS), isolated syncytial cells by laser microdissection, and performed a comparative transcriptome analysis. Genes belonging to categories of defense, nutrient deficiency, and nutrient starvation were overrepresented in MAS as compared to FAS. Conversely, gene categories related to metabolism, modification, and biosynthesis of cell walls were overrepresented in FAS. We used {beta}-glucuronidase (GUS) analysis, qRT-PCR, and loss-of-function mutants to characterize FAS- and MAS-specific candidate genes. Our results demonstrated that various plant-based factors, including immune response, nutrient availability, and structural modifications, influence the sexual fate sex determination of cyst the nematodes.

plant biology↗

bioRxiv2017

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

BackgroundCardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups.\n\nMethodsWe used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters.\n\nResultsFour SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5-33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1-52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters.\n\nConclusionsMachine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.

genomics↗

bioRxiv2017

Parent of Origin Effects on Quantitative Phenotypes in a Founder Population

The impact of the parental origin of associated alleles in GWAS has been largely ignored. Yet sequence variants could affect traits differently depending on whether they are inherited from the mother or the father. To explore this possibility, we studied 21 quantitative phenotypes in a large Hutterite pedigree. We first identified variants with significant single parent (maternal-only or paternal-only) effects, and then used a novel statistical model to identify variants with opposite parental effects. Overall, we identified parent of origin effects (POEs) on 11 phenotypes, most of which are risk factors for cardiovascular disease. Many of the loci with POEs have features of imprinted regions and many of the variants with POE are associated with the expression of nearby genes. Overall, our results indicate that POEs, which are often opposite in direction, are relatively common in humans, have potentially important clinical effects, and will be missed in traditional GWAS.

genetics↗