Genetic background modifies phenotypic and transcriptional responses in a C. elegans model of α-synuclein toxicity

Wang, Yiru A.; Snoek, L. Basten; Sterken, Mark G.; Riksen, J.A.G.; Šťastná, Jana; Kammenga, J.E.; Harvey, Simon C.

doi:10.1186/s12864-019-5597-1

Cited by 14 publications

(35 citation statements)

References 58 publications

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“…We investigated the cause of difference in susceptibility in N2 and CB4856 nematodes 30h post infection. As earlier studies have shown that CB4856 nematodes feed at least as much as N2 nematodes [35,36], the initially lower susceptibility of CB4856 nematodes could not be explained by reduced food intake. Therefore, another explanation would be a different number of infected individuals or cells.…”

Section: Stress-related Phenotypes In the N2 And Cb4856 Strainmentioning

confidence: 63%

Balancing selection of the Intracellular Pathogen Response in natural Caenorhabditis elegans populations

Sluijs

Bosman

Pankok

et al. 2019

Preprint

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23 0031317482998 24 Mark.Sterken@wur.nl 25 Our work provides evidence that balancing genetic selection shapes the transcriptional defence against pathogens 48 in C. elegans. The transcriptional and genetic data in this study demonstrate the functional diversity that can 49 develop within antiviral transcriptional responses in natural host populations. 50 Page 3 of 28 Background 51 The continuous battle between host and virus drives host genetic variation to arise in antiviral mechanisms such 52 as transcriptional responses. Regulatory genetic variation affects the viral susceptibility after infection, making 53 some individuals within the population more resistant than others [1-4]. Yet, the universality and mode-of-action 54 of genetic diversity in shaping antiviral transcriptional responses within natural populations remains largely 55 unknown. 56 Caenorhabditis elegans and its natural pathogen Orsay virus (OrV) are used as a powerful genetic 57 model system to study host-virus interactions [5]. OrV is a positive-sense single-stranded RNA virus infecting C. 58 elegans intestinal cells where it causes local disruptions of the cellular structures [5, 6]. Two major groups of 59 antiviral genes respond to viral infection in C. elegans: genes related to the RNA interference (RNAi) pathway 60 [5, 7-12] and genes related to the Intracellular Pathogen Response (IPR) [13-16]. The RNAi pathway activity is 61 controlled by the gene sta-1 which in turn is activated by the viral sensor sid-3 that is hypothesized to directly 62 interact with the Orsay virus [7]. Subsequently, the antiviral RNAi components dcr-1, drh-1, and rde-1 degrade 63 the viral RNA [9], but the RNAi genes themselves remain equally expressed during infection [9, 16]. The IPR 64 counteracts infection by intracellular pathogens (including OrV) and increases the ability to handle proteotoxic 65 stress [13-15]. The gene pals-22 co-operates together with pals-25 to control the IPR pathway by functioning as 66 a molecular switch between growth and antiviral defence. Pals-22 promotes development and lifespan, whereas 67 pals-25 stimulates pathogen resistance. Together pals-22 and pals-25 regulate a set of 80 genes that are 68 upregulated upon intracellular infection including 25 genes in the pals-family and several members of the 69 ubiquitination response [14, 15].. Both pals-22 and pals-25 do not change gene expression following OrV 70 infection. In total, the pals-gene family contains 39 members mostly found in five genetic clusters on 71 chromosome I, III, and V.. Recently, a third antiviral defence was identified which degrades the viral genome 72 after uridylation by the gene cde-1 [17]. Together, these antiviral pathways are key in controlling OrV infection 73 in C. elegans. 74The transcriptional responses following infection have so far been studied in the C. elegans laboratory 75 strain N2, in RNAi deficient mutants in the N2 background such as rde-1 and dcr-1 and in the RNAi-deficient 76 wild isolate JU1580 [7, 9, 10, 13, 14, 16]. These studies indica...

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Section: Stress-related Phenotypes In the N2 And Cb4856 Strainmentioning

confidence: 63%

Balancing selection of the Intracellular Pathogen Response in natural Caenorhabditis elegans populations

Sluijs

Bosman

Pankok

et al. 2019

Preprint

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“…Furthermore, recent in-depth studies on the ecology of C. elegans yielded even more phenotypic and genotypic information on novel wild isolates (34,36–46). Inclusion of diverse genetic backgrounds in experiments is therefore a welcome and useful addition to the work on the canonical N2 strain as it can identify novel modifiers of well-studied pathways and thereby shed light on molecular mechanisms of genetic variation (46–52). A collection of data on wild isolates, including genome sequences, is curated and available via the C. elegans Natural Diversity Resource (CeNDR) (33).…”

Section: Introductionmentioning

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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“…In addition to the traditional mouse models, the genetic model systems such as worm, fly and yeast, in which natural variation can be readily combined with modeling the gain-of-function mutations by transgenesis, offer new opportunities to identify the cryptic modifier pathways for neurodegeneration and protein misfolding and aggregation (10,(111)(112)(113)(114)(115). Examples of this approach include a recent study in C. elegans that showed that the ability of α-synuclein to cause transcriptional and phenotypic changes is substantially modified by the genetic background (40), and a study using a Drosophila Genetic Reference Panel (116), that uncovered an unexpected role of heparin sulfate protein modifications in modifying the toxic effects of the misfolded mutant of human insulin, a cause of permanent neonatal diabetes (117). The important feature of the cryptic modifier pathways that can be identified by these approaches is that they harbor natural variants shaped by selection, and thus will pinpoint the naturally plastic potential genes and networks (14), amenable to pharmacological manipulation without negative effects on the organism.…”

Section: Discussionmentioning

confidence: 99%

“…The polyQ aggregation in these genetically variable animals showed transgressive segregation, indicating that multiple additive or interacting alleles in parental backgrounds were acting as modifiers (38). A recent study has shown that natural variation also modulates the phenotypes and transcriptional changes caused by expression of α-synuclein transgene in the body-wall muscle cells of C. elegans (40). Thus, natural genetic variation within C. elegans wild strains can be used to investigate the mechanisms and pathways controlling the toxic effects of protein misfolding and aggregation.…”

Section: Introductionmentioning

confidence: 99%

Natural variants in C. elegans atg-5 3’UTR uncover divergent effects of autophagy on polyglutamine aggregation in different tissues

Alexander-Floyd

Haroon²,

Ying

et al. 2019

Preprint

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Diseases caused by protein misfolding and aggregation, in addition to cell selectivity, often exhibit variation among individuals in the age of onset, progression, and severity of disease.Genetic variation has been shown to contribute to such clinical variation. We have previously found that protein aggregation-related phenotypes in a model organism, C. elegans, can be modified by destabilizing polymorphisms in the genetic background and by natural genetic variation. Here, we identified a large modifier locus in a Californian wild strain of C. elegans, DR1350, that alters the susceptibility of the head muscle cells to polyglutamine (polyQ) aggregation, and causes an increase in overall aggregation, without changing the basal activity of the muscle proteostasis pathways known to affect polyQ aggregation. We found that the two phenotypes were genetically separable, and identified regulatory variants in a gene encoding a conserved autophagy protein ATG-5 (ATG5 in humans) as being responsible for the overall increase in aggregation. The atg-5 gene conferred a dosage-dependent enhancement of polyQ aggregation, with DR1350-derived atg-5 allele behaving as a hypermorph. Examination of autophagy in animals bearing the modifier locus indicated enhanced response to an autophagyactivating treatment. Because autophagy is known to be required for the clearance of polyQ aggregates, this result was surprising. Thus, we tested whether directly activating autophagy, either pharmacologically or genetically, affected the polyQ aggregation in our model. Strikingly, we found that the effect of autophagy on polyQ aggregation was tissue-dependent, such that activation of autophagy decreased polyQ aggregation in the intestine, but increased it in the muscle cells. Our data show that cryptic genetic variants in genes encoding proteostasis components, although not causing visible phenotypes under normal conditions, can have profound effects on the behavior of aggregation-prone proteins, and suggest that activation of autophagy may have divergent effects on the clearance of such proteins in different cell types.

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Genetic background modifies phenotypic and transcriptional responses in a C. elegans model of α-synuclein toxicity

Cited by 14 publications

References 58 publications

Balancing selection of the Intracellular Pathogen Response in natural Caenorhabditis elegans populations

Balancing selection of the Intracellular Pathogen Response in natural Caenorhabditis elegans populations

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Natural variants in C. elegans atg-5 3’UTR uncover divergent effects of autophagy on polyglutamine aggregation in different tissues

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