The increase in availability of whole genome sequences makes it possible to search for evidence of adaptation at an unprecedented scale. Despite recent progress, our understanding of the adaptive process is still very limited due to the difficulties in linking adaptive mutations to their phenotypic effects. In this study, we integrated different levels of biological information to pinpoint the ecologically relevant fitness effects and the underlying molecular and biochemical mechanisms of a putatively adaptive TE insertion in Drosophila melanogaster: the pogo transposon FBti0019627. We showed that other than being incorporated into Kmn1 transcript, FBti0019627 insertion also affects the polyadenylation signal choice of CG11699 gene. Consequently, only the short 3′UTR transcript of CG11699 gene is produced and the expression level of this gene is higher in flies with the insertion. Our results indicated that increased CG11699 expression leads to xenobiotic stress resistance through increased ALDH-III activity: flies with FBti0019627 insertion showed increased survival rate in response to benzaldehyde, a natural xenobiotic, and to carbofuran, a synthetic insecticide. Although differences in survival rate between flies with and without the insertion were not always significant, when they were, they were consistent with FBti0019627 mediating resistance to xenobiotics. Taken together, our results provide a plausible explanation for the increase in frequency of FBti0019627 in natural populations of D. melanogaster and add to the limited number of examples in which a natural genetic mutation has been linked to its ecologically relevant phenotype. Furthermore, the widespread distribution of TEs across the tree of life and conservation of stress response pathways across organisms make our results relevant not only for Drosophila, but for other organisms as well.

Summary The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

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The standard of care for advanced colorectal cancer (CRC) includes treatment with chemotherapeutic drugs that target the cell proliferation machinery 1 . In CRC patients with overt metastases, chemotherapy initially halts tumor growth but, almost inevitably, disease progresses after some cycles of treatment. Adjuvant chemotherapy is also administered to eliminate minimal residual disease, yet it only diminishes the risk of relapse by 10-25% 2 . Previous studies have shown that patient-derived organoids predict responses to chemotherapy 3-6 . Therefore, we used them as models to investigate the mechanisms behind the limited benefit of these treatments. Whereas CRC organoids expand from highly proliferative Lgr5+ tumor cells, we discovered that lack of optimal stem cell growth conditions specifies a latent Lgr5+ cell population. These cells expressed the gene Mex3a, were largely insensitive to chemotherapy and regenerated the organoid culture after treatment. In mouse models of metastatic latency, Mex3a+ cells contributed marginally to metastatic outgrowth. However, after chemotherapy treatment, Mex3a+ cells produced large cell clones that regenerated metastatic disease. Using lineage-tracing analysis combined with single cell profiling, we showed that drug-tolerant persister Mex3a+ cells downregulate the WNT/Lgr5+ stem cell program immediately after chemotherapy and adopt a transient regenerative state

Signatures of spatially varying selection have been investigated both at the genomic and transcriptomic level in several organisms. In Drosophila melanogaster, the majority of these studies have analyzed North American and Australian populations, leading to the identification of several loci and traits under selection. However, several studies based mainly in North American populations showed evidence of admixture that likely contributed to the observed population differentiation patterns. Thus, disentangling demography from selection might be challenging when analyzing these populations. European populations could help identify loci under spatially varying selection provided that no recent admixture from African populations would have occurred. In this work, we individually sequence the genome of 42 European strains collected in populations from contrasting environments: Stockholm (Sweden) and Castellana Grotte (Southern Italy). We found low levels of population structure and no evidence of recent African admixture in these two populations. We thus look for patterns of spatially varying selection affecting individual genes and gene sets. Besides single nucleotide polymorphisms, we also investigated the role of transposable elements in local adaptation. We concluded that European populations are a good dataset to identify candidate loci under spatially varying selection. The analysis of the two populations sequenced in this work in the context of all the available D. melanogaster data allowed us to pinpoint genes and biological processes likely to be relevant for local adaptation. Identifying and analyzing populations with low levels of population structure and admixture should help to disentangle selective from non-selective forces underlying patterns of population differentiation in other species as well.

The centromere is a chromatin region that is required for accurate inheritance of eukaryotic chromosomes during cell divisions. Among the different centromere-associated proteins (CENP) identified, CENP-B has been independently domesticated from a pogo-like transposase twice: Once in mammals and once in fission yeast. Recently, a third independent domestication restricted to holocentric lepidoptera has been described. In this work, we take advantage of the high-quality genome sequence and the wealth of functional information available for Drosophila melanogaster to further investigate the possibility of additional independent domestications of pogo-like transposases into host CENP-B related proteins. Our results showed that CENP-B related genes are not restricted to holocentric insects. Furthermore, we showed that at least three independent domestications of pogo-like transposases have occurred in metazoans. Our results highlight the importance of transposable elements as raw material for the recurrent evolution of important cellular functions.

SUMMARYThe Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

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