Natural variation in<i>C. elegans</i>arsenic toxicity is explained by differences in branched chain amino acid metabolism

Zdraljevic, Stefan; Fox, Bennett W.; Strand, Christine; Panda, Oishika; Tenjo, Francisco J.; Brady, Shannon C.; Crombie, Timothy A.; Doench, John G.; Schroeder, Frank C.; Andersen, Erik C.

doi:10.1101/373787

Cited by 3 publications

(3 citation statements)

References 75 publications

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“…We compiled the list of studies using two reviews listing older studies, mostly pre-2000 (1, 35), and performed a literature search for more recent studies from 2000-onwards. For many studies we could obtain the relevant data from the supplemental information (2,3,5,6,21,34,41,47,49,53,54,57–59,76,80,81,83–86,89–91). Where such data was not provided in journal supplements, approaching the authors was successful for most studies (6-8,11,13,55,82,87,88,91).…”

Section: Resultsmentioning

confidence: 99%

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Snoek

Sterken

Hartanto

et al. 2019

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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...

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Section: Resultsmentioning

confidence: 99%

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Snoek

Sterken

Hartanto

et al. 2019

Preprint

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“…Trait architectures underlying natural variation in C. elegans differ strongly over traits. For example, currently 25 quantitative trait nucleotides (QTNs) are known in C. elegans , capturing a majority of the heritable variation for particular traits (reviewed by (R ockman 2012) and (G aertner and P hillips 2010) and studies by (A ndersen et al 2014; N oble et al 2015; S chmid et al 2015; C ook et al 2016; G reene et al 2016a; G reene et al 2016b; L arge et al 2016; B en -D avid et al 2017; S terken et al 2017; Z draljevic et al 2017; H ahnel et al 2018; Z draljevic et al 2019). However, there are also examples of traits that are highly heritable but have only yielded complex or few QTL (let alone QTNs).…”

Section: Discussionmentioning

confidence: 99%

Dissecting the eQTL micro-architecture inCaenorhabditis elegans

Sterken

Bevers

Volkers

et al. 2019

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The study of expression quantitative trait loci (eQTL) using natural variation in inbred populations has yielded detailed information about the transcriptional regulation of complex traits. Studies on eQTL using recombinant inbred lines (RILs) led to insights on local and distant regulatory loci of transcript abundance. However, determining the underlying causal polymorphic genes or variants is difficult, but ultimately essential for the understanding of regulatory networks of complex traits. This requires insight into whether associated loci are single eQTL or a combination of closely linked eQTL, and how this QTL micro-architecture depends on the environment. We addressed these questions by mapping eQTL in N2 x CB4856 C. elegans RIL populations across three different environments (control, heat-stress, and recovery). To test for independent replication of the RIL eQTL, we used introgression lines (ILs). Both populations indicate that the overall heritability, number, and position of eQTL differed among environments. Across environments we were able to replicate 70% of the local- and 40% of the distant-eQTL using the ILs. Simulation models revealed that additive effects explain up to 60-93% of RIL/IL heritability across environments. Closely linked eQTL explained up to 40% of RIL/IL heritability in the control environment whereas only 7% in the heat-stress and recovery environments. In conclusion, we show that reproducibility of eQTL was higher for local vs. distant eQTL and that the environment affects the eQTL micro-architecture.

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“…Taken together, these results suggest that 627 natural variation in scb-1 function underlies the bleomycin-response difference between the N2 628 and CB4856 strains. Previous studies have leveraged both linkage mapping and GWA in C. elegans to identify 725 genetic variants that underlie drug-response differences (Zdraljevic et al , 2019. In each of 726 these studies, drug-response QTL overlap between linkage mapping and GWA, and variants in 727 common between both mapping strain sets have been shown to underlie drug-response QTL.…”

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A nematode-specific gene underlies bleomycin-response variation inCaenorhabditis elegans

Brady¹,

Zdraljevic²,

Bisaga³

et al. 2019

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Bleomycin is a powerful chemotherapeutic drug used to treat a variety of cancers. However, individual patients vary in their responses to bleomycin. The identification of genetic differences that underlie this response variation could improve treatment outcomes by tailoring bleomycin dosages to each patient. We used the model organism Caenorhabditis elegans to identify genetic determinants of bleomycin-response differences by performing linkage mapping on recombinants derived from a cross between the laboratory strain (N2) and a wild strain (CB4856). This approach identified a small genomic region on chromosome V that underlies bleomycin-response variation. Using near-isogenic lines and strains with CRISPR-Cas9 mediated deletions and allele replacements, we discovered that a novel nematode-specific gene (scb-1) is required for bleomycin resistance. Although the mechanism by which this gene causes variation in bleomycin responses is unknown, we suggest that a rare variant present in the CB4856 strain might cause differences in the potential stress-response function of scb-1 between the N2 and CB4856 strains, thereby leading to differences in bleomycin resistance.

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Natural variation inC. elegansarsenic toxicity is explained by differences in branched chain amino acid metabolism

Abstract: 39Organisms are often exposed to the environmentally ubiquitous toxic metalloid arsenic, and 40 genetic differences unique to individuals can cause differential susceptibility to arsenic.

Cited by 3 publications

References 75 publications

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Dissecting the eQTL micro-architecture inCaenorhabditis elegans

A nematode-specific gene underlies bleomycin-response variation inCaenorhabditis elegans

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