18Quantitative genetics provides the tools for linking polymorphic loci (QTLs) to trait 19 variation. Linkage analysis of gene expression is an established and widely applied 20 method, leading to the identification of expression quantitative trait loci (eQTLs). (e)QTL 21 detection facilitates the identification and understanding of the underlying molecular 22 components and pathways, yet (e)QTL data access and mining often is a bottleneck. Here 23 we present WormQTL2 (www.bioinformatics.nl/WormQTL2/), a database and platform 24 for comparative investigations and meta-analyses of published (e)QTL datasets in the 25 model nematode worm C. elegans. WormQTL2 integrates six eQTL studies spanning 11 26 conditions as-well-as over 1000 traits from 32 studies and allows experimental results to 27 be compared, reused, and extended upon to guide further experiments and conduct 28 systems-genetic analyses. For example, one can easily screen a locus for specific cis-eQTLs 29 that could be linked to variation in other traits, detect gene-by-environment interactions 30 by comparing eQTLs under different conditions, or find correlations between QTL 31 profiles of classical traits and gene expression.32 100 (2,3,13,23,30,34,41,47,49,(53)(54)(55)(56)(57)(58)(59)61,76,(80)(81)(82)(83)(84)(85)(86)(87)(88)(89)(90) (Table 2). In this paper, we present WormQTL2 101 and showcase its use by presenting short research scenarios. 102 Results 103 104 eQTL studies in WormQTL2 105WormQTL2 is a browser-based interactive platform and database for investigating expression 106 and other Quantitative Trait Locus (QTL) studies conducted in C. elegans (Figure 1). It enables 107 access to the mapping data of six previously published eQTL studies (Table 1) (28,50,(76)(77)(78)(79). 108Together, these studies cover over 700 samples, including expression measurements of 109 approximately 20,000 different genes across different life stages and environmental conditions. 110The effect of genetic variation on gene expression is presented in 11 genome-wide sets of 111 eQTLs from three different RIL populations. The three populations consist of two different 112 CB4856 x N2 populations, recombinant inbred lines (RILs) (28) and recombinant inbred 113 advanced intercross lines (RIAILs) (91,92) and a mutation introgressed RIL population 114 resulting from a cross between a let-60 gain-of-function mutant in an N2 background, MT2124, 115 with CB4856 (11,50). For the Li et al. 2006, Viñuela & Snoek et al. 2010 and Li et al. 2010 116 Figure 1: WormQTL2 Homepage. On the top of the page the navigation bar can be found. This includes the WormQTL2 logo, which functions as a home button. It also includes a fast link to the Correlation and Locus overviews as well as links for help, data download, and visual examples. The search box is located in the centre, in which genes, phenotypes and GO terms can be entered. Shown in the blue middle square are the buttons for the investigations of single traits, correlating QTL profiles, QTLs at a specific locus, all eQTLs of an expe...

No abstract

No abstract

Plants deposit photosynthetically-fixed carbon in the rhizosphere, the thin soil layer directly around the root, thereby creating a hospitable environment for microbes. To manage the inhabitants of this nutrient-rich environment, plant roots exude and dynamically adjust microbe-attracting and -repelling compounds to stimulate specific members of the microbiome. Previously, we demonstrated that foliar infection of Arabidopsis thaliana by the biotrophic downy mildew pathogen Hyaloperonospora arabidopsidis (Hpa) leads to a disease-induced modification of the rhizosphere microbiome. Soil conditioned with Hpa-infected plants provided enhanced protection against foliar downy mildew infection in a subsequent population of plants, a phenomenon dubbed the soil-borne legacy (SBL). Here, we show that for the creation of the SBL, plant-produced coumarins play a prominent role as coumarin-deficient myb72 and f6’h1 mutants were defective in creating a Hpa-induced SBL. Root exudation profiles changed significantly in Col-0 upon foliar Hpa infection, and this was accompanied by a compositional shift in the root microbiome that was significantly different from microbial shifts occurring on roots of Hpa-infected coumarin-deficient mutants. Our data further show that the Hpa-induced SBL primes Col-0 plants growing in SBL-conditioned soil for salicylic acid (SA)-dependent defenses. The SA-signaling mutants sid2 and npr1 were unresponsive to the Hpa-induced SBL, suggesting that the protective effect of the Hpa-induced shift in the root microbiome results from an induced systemic resistance that requires SA-signaling in the plant.

Metabolic homeostasis is sustained by complex biological networks responding to nutrient availability. Disruption of this equilibrium involving intricate interactions between genetic and environmental factors can lead to metabolic disorders, including obesity and type 2 diabetes.To identify the genetic factors controlling metabolism, we applied a quantitative genetic strategy using a Caenorhabditis elegans population consisting of 199 recombinant inbred lines (RILs) originally derived from crossing parental strains Bristol N2 and Hawaii CB4856.We focused on the genetic factors that control metabolite levels and measured fatty acid (FA) and amino acid (AA) composition in the 199 RILs using targeted metabolomics. For both FA and AA profiles, we observed large variation in metabolite levels with 32-82% heritability between the RILs. We performed metabolite-metabolite correlation analysis and detected strongly co-correlated metabolite clusters. To identify natural genetic variants responsible for the observed metabolite variations, we performed QTL mapping and detected 36 significant metabolite QTL (mQTL). We focused on the mQTL that displayed high significant linkage and heritability, including an mQTL for the FA C14:1 on chromosome I, and another mQTL for the FA C18:2 on chromosome IV. Using introgression lines (ILs) we were able to narrow down both mQTL to a 1.4 Mbp and a 3.6 Mbp region, respectively. Overall, this systems approach provides us with a powerful platform to study the genetic basis of C. elegans metabolism. It also allows us to investigate additional interventions, such as nutrients and stresses that maintain or disturb the regulatory network controlling metabolic homeostasis, and identify gene-by-environment interactions.