Natural diversity facilitates the discovery of conserved chemotherapeutic response mechanisms

Abstract

Organismal fitness depends on adaptation to complex niches where chemical compounds and pathogens are omnipresent. These stresses can lead to the fixation of alleles in both xenobiotic responses and proliferative signaling pathways that promote survival in these niches. However, both xenobiotic responses and proliferative pathways vary within and among species. For example, genetic differences can accumulate within populations because xenobiotic exposures are not constant and selection is variable. Additionally, neutral genetic variation can accumulate in conserved proliferative pathway genes because these systems are robust to genetic perturbations given their essential roles in normal cell-fate specification. For these reasons, sensitizing mutations or chemical perturbations can disrupt pathways and reveal cryptic variation. With this fundamental view of how organisms respond to cytotoxic compounds and cryptic variation in conserved signaling pathways, it is not surprising that human patients have highly variable responses to chemotherapeutic compounds and to the activities of proliferative pathways. These different patient responses result in the low FDA-approval rates for chemotherapeutics and underscore the need for new approaches to understand these diseases and therapeutic interventions. Model organisms, especially the classic invertebrate systems of Caenorhabditis elegans and Drosophila melanogaster, can be used to combine studies of natural variation across populations with responses to both xenobiotic compounds and chemotherapeutics targeted to conserved proliferative signaling pathways.

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

“…We quantified arsenic trioxide sensitivity in C. elegans using a high-throughput fitness assay that utilizes the COPAS BIOSORT (Andersen et al, 2015; Zdraljevic and Andersen, 2017). In this assay, three L4 larvae from each strain were sorted into arsenic trioxide or control conditions.…”

“…However, these studies were all performed in the genetic background of the standard C. elegans laboratory strain (N2). To date, 152 C. elegans strains have been isolated from various locations around the world (Andersen et al, 2012; Cook et al, 2016; Cook et al, 2017), which contain a largely unexplored pool of genetic diversity much of which could underlie adaptive responses to environmental perturbations (Zdraljevic and Andersen, 2017).…”

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

“…BIOSORT parameter, including five distribution quantiles and measures of dispersion (Andersen 579 et al 2015). Prior to principal component analysis (PCA), HTA phenotypes were scaled to have 580 a mean of zero and a standard deviation of one using the scale function in R. PCA was 581 performed using the prcomp function in R (Team 2017 Fine mapping 586 We previously described the identification of QTL confidence intervals and fine-mapping 587 (Zdraljevic et al 2017). In short, we used the process_correlations() and the 588 variant_correlation() functions in the cegwas package.…”

Common genomic regions underlie natural variation in diverse toxin responses

“…D. Singh et al 2016;Schmid et al 2015;Balla et al 2015;Zdraljevic et al 2017;Zamanian et al 2018a;Andersen et al 2014;Viñuela et al 2010;Doroszuk et al 2009;Snoek et al 2014;Rodriguez et al 2012;Kammenga et al 2007;Li et al 2006;Gutteling, Doroszuk, et al 2007;Glater, Rockman, and Bargmann 2014;Reddy et al 2009;Rockman, Skrovanek, and Kruglyak 2010;Seidel et al 2011;Seidel, Rockman, and Kruglyak 2008). Additionally, a high-throughput phenotyping platform to rapidly and accurately measure animal fitness could provide the replication and precision required to detect small-effect additive loci and to determine the relative contributions of additive and/ or epistatic loci to trait variation Zdraljevic et al 2017). Notably, the combination of this panel and phenotyping platform have facilitated linkage mappings of multiple distinct fitness parameters, resulting in the detection of a single QTL, in fact a single quantitative trait gene (QTG), that underlies several fitness-related traits Zdraljevic et al 2017).…”

“…Additionally, a high-throughput phenotyping platform to rapidly and accurately measure animal fitness could provide the replication and precision required to detect small-effect additive loci and to determine the relative contributions of additive and/ or epistatic loci to trait variation Zdraljevic et al 2017). Notably, the combination of this panel and phenotyping platform have facilitated linkage mappings of multiple distinct fitness parameters, resulting in the detection of a single QTL, in fact a single quantitative trait gene (QTG), that underlies several fitness-related traits Zdraljevic et al 2017).…”