Cited by 2Evolution and Genetic DynamicsInsect Resistance and GeneticsNeurobiology and Insect Physiology Research

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Intersecting experimental evolution and CRISPR screens to identify novel toxin resistance loci

Open access

Published version of a preprint

Abstract

Understanding toxin resistance in insects is key to appreciating niche adaptations but remains challenging due to its often-polygenic basis. A well-known example is the specialized association of Drosophila sechellia with noni fruit ( Morinda citrifolia ), which is toxic to other insects, including Drosophila simulans and Drosophila melanogaster . The main noni toxin is octanoic acid (OA), but the mechanisms that determine sensitivity or resistance to OA in different species remain unclear. Here, we experimentally evolved D. simulans with increased OA resistance, identifying multiple loci under selection. Cross-referencing these with a genome-wide, OA resistance CRISPR screen in a D. melanogaster cell line highlighted two proteins: Kraken, a putative detoxification enzyme expressed in digestive and renal tissues, and Alkbh7, a mitochondrial protein linked to fatty acid metabolism. Both genes show elevated expression in D. sechellia and OA-resistant D. simulans . In D. melanogaster , kraken mutants are more OA-sensitive, while Alkbh7 overexpression increased OA resistance. Mutation of these genes in D. sechellia reduced OA tolerance. Our identification of genes contributing to OA resistance in laboratory and natural contexts demonstrates how complementary selection approaches can provide insights into complex mechanisms of toxin susceptibility and adaptation. Such methods could have practical applications in the characterization of natural and artificial insecticides.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.7554/elife.111773.3

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 141

  1. Abascal, 2010, TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations, Nucleic Acids Research, 38, W7, 10.1093/nar/gkq291 · DOI
  2. Abe, 2022, Shortened lifespan induced by a high-glucose diet is associated with intestinal immune dysfunction in Drosophila sechellia, The Journal of Experimental Biology, 225, 10.1242/jeb.244423 · DOI
  3. Altenhoff, 2021, OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more, Nucleic Acids Research, 49, D373, 10.1093/nar/gkaa1007 · DOI · Paper page
  4. Álvarez-Ocaña, 2023, Odor-regulated oviposition behavior in an ecological specialist, Nature Communications, 14, 10.1038/s41467-023-38722-z · DOI
  5. Amlou, 1997, Genetic analysis by interspecific crosses of the tolerance of Drosophila sechellia to major aliphatic acids of its host plant, Genetics Selection Evolution, 29, 511, 10.1186/1297-9686-29-4-511 · DOI
  6. Amlou, 1998, Larval tolerance in the Drosophila melanogaster species complex toward the two toxic acids of the D. sechellia host plant, Hereditas, 129, 7, 10.1111/j.1601-5223.1998.00007.x · DOI
  7. Ando, 2019, Nanopore formation in the cuticle of an insect olfactory sensillum, Current Biology, 29, 1512, 10.1016/j.cub.2019.03.043 · DOI
  8. Andrade López, 2017, Genetic basis of octanoic acid resistance in Drosophila sechellia: functional analysis of a fine-mapped region, Molecular Ecology, 26, 1148, 10.1111/mec.14001 · DOI
  9. Auer, 2020, Olfactory receptor and circuit evolution promote host specialization, Nature, 579, 402, 10.1038/s41586-020-2073-7 · DOI
  10. Auer, 2021, Drosophila sechellia: a genetic model for behavioral evolution and neuroecology, Annual Review of Genetics, 55, 527, 10.1146/annurev-genet-071719-020719 · DOI
  11. Balabanidou, 2018, Insect cuticle: a critical determinant of insecticide resistance, Current Opinion in Insect Science, 27, 68, 10.1016/j.cois.2018.03.001 · DOI
  12. Balabanidou, 2019, Mosquitoes cloak their legs to resist insecticides, Proceedings. Biological Sciences, 286, 10.1098/rspb.2019.1091 · DOI
  13. Barata, 2023, Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments, Journal of Evolutionary Biology, 36, 29, 10.1111/jeb.14134 · DOI
  14. Barghi, 2019, Genetic redundancy fuels polygenic adaptation in Drosophila, PLOS Biology, 17, 10.1371/journal.pbio.3000128 · DOI
  15. Barrett, 2005, Haploview: analysis and visualization of LD and haplotype maps, Bioinformatics, 21, 263, 10.1093/bioinformatics/bth457 · DOI
  16. Bischof, 2007, An optimized transgenesis system for Drosophila using germ-line-specific phiC31 integrases, PNAS, 104, 3312, 10.1073/pnas.0611511104 · DOI
  17. Bolger, 2014, Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics, 30, 2114, 10.1093/bioinformatics/btu170 · DOI · Paper page
  18. Bontonou, 2024, Evolution of chemosensory tissues and cells across ecologically diverse Drosophilids, Nature Communications, 15, 10.1038/s41467-023-44558-4 · DOI
  19. Borrull, 2015, New insights into the toxicity mechanism of octanoic and decanoic acids on Saccharomyces cerevisiae, Yeast, 32, 451, 10.1002/yea.3071 · DOI
  20. Busvine, 1951, Mechanism of resistance to insecticide in houseflies, Nature, 168, 193, 10.1038/168193a0 · DOI
  21. Camacho, 2009, BLAST+: architecture and applications, BMC Bioinformatics, 10, 10.1186/1471-2105-10-421 · DOI · Paper page
  22. Catteruccia, 2020, Malaria-carrying mosquitoes get a leg up on insecticides, Nature, 577, 319, 10.1038/d41586-019-03728-5 · DOI
  23. Chen, 2015, Proteomic mapping in live Drosophila tissues using an engineered ascorbate peroxidase, PNAS, 112, 12093, 10.1073/pnas.1515623112 · DOI
  24. Clarkson, 2018, The genomics of insecticide resistance: insights from recent studies in African malaria vectors, Current Opinion in Insect Science, 27, 111, 10.1016/j.cois.2018.05.017 · DOI
  25. Cochran, 1954, Some methods for strengthening the common χ 2 tests, Biometrics, 10, 10.2307/3001616 · DOI · Paper page
  26. Colson, 2004, Drosophila simulans’ response to laboratory selection for tolerance to a toxic food source used by its sister species D. sechellia, Evolutionary Ecology, 18, 15, 10.1023/B:EVEC.0000017669.56353.cb · DOI
  27. Danecek, 2011, The variant call format and VCFtools, Bioinformatics, 27, 2156, 10.1093/bioinformatics/btr330 · DOI
  28. Danecek, 2021, Twelve years of SAMtools and BCFtools, GigaScience, 10, 10.1093/gigascience/giab008 · DOI
  29. Dekker, 2006, Olfactory shifts parallel superspecialism for toxic fruit in Drosophila melanogaster sibling, D. sechellia, Current Biology, 16, 101, 10.1016/j.cub.2005.11.075 · DOI · Paper page
  30. Di Prisco, 2013, Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees, PNAS, 110, 18466, 10.1073/pnas.1314923110 · DOI
  31. Dong, 2007, Insect sodium channels and insecticide resistance, Invertebrate Neuroscience, 7, 17, 10.1007/s10158-006-0036-9 · DOI
  32. Dong, 2021, LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files, Briefings in Bioinformatics, 22, 10.1093/bib/bbaa227 · DOI
  33. Dow, 2022, Drosophila melanogaster: a simple genetic model of kidney structure, function and disease, Nature Reviews. Nephrology, 18, 417, 10.1038/s41581-022-00561-4 · DOI
  34. Edwin Chan, 1998, Identification and characterization of kraken, a gene encoding a putative hydrolytic enzyme in Drosophila melanogaster, Gene, 222, 195, 10.1016/s0378-1119(98)00497-1 · DOI
  35. Emelyanov, 2014, Drosophila TAP/p32 is a core histone chaperone that cooperates with NAP-1, NLP, and nucleophosmin in sperm chromatin remodeling during fertilization, Genes & Development, 28, 2027, 10.1101/gad.248583.114 · DOI
  36. Erb, 2019, Molecular interactions between plants and insect herbivores, Annual Review of Plant Biology, 70, 527, 10.1146/annurev-arplant-050718-095910 · DOI
  37. Fukasawa, 2015, MitoFates: improved prediction of mitochondrial targeting sequences and their cleavage sites, Molecular & Cellular Proteomics, 14, 1113, 10.1074/mcp.M114.043083 · DOI
  38. Galindo, 2009, The Bax/Bak ortholog in Drosophila, Debcl, exerts limited control over programmed cell death, Development, 136, 275, 10.1242/dev.019042 · DOI
  39. Gandara, 2024, Pervasive sublethal effects of agrochemicals on insects at environmentally relevant concentrations, Science, 386, 446, 10.1126/science.ado0251 · DOI
  40. Gloss, 2014, Evolution in an ancient detoxification pathway is coupled with a transition to herbivory in the drosophilidae, Molecular Biology and Evolution, 31, 2441, 10.1093/molbev/msu201 · DOI
  41. Gu, 2014, circlize implements and enhances circular visualization in R, Bioinformatics, 30, 2811, 10.1093/bioinformatics/btu393 · DOI
  42. Gutiérrez-Guerrero, 2020, Genomic consequences of dietary diversification and parallel evolution due to nectarivory in leaf-nosed bats, GigaScience, 9, 10.1093/gigascience/giaa059 · DOI
  43. Hellemans, 2007, qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data, Genome Biology, 8, 10.1186/gb-2007-8-2-r19 · DOI · Paper page
  44. Hu, 2017, The Drosophila gene expression tool (DGET) for expression analyses, BMC Bioinformatics, 18, 10.1186/s12859-017-1509-z · DOI
  45. Hu, 2023, PANGEA: a new gene set enrichment tool for Drosophila and common research organisms, Nucleic Acids Research, 51, W419, 10.1093/nar/gkad331 · DOI
  46. Huang, 2009, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nature Protocols, 4, 44, 10.1038/nprot.2008.211 · DOI · Paper page
  47. Huang, 2015, The genetics of resistance to morinda fruit toxin during the postembryonic stages in Drosophila sechellia, G3: Genes, Genomes, Genetics, 5, 1973, 10.1534/g3.114.015073 · DOI
  48. Hungate, 2013, A locus in Drosophila sechellia affecting tolerance of a host plant toxin, Genetics, 195, 1063, 10.1534/genetics.113.154773 · DOI
  49. Jones, 1998, The genetic basis of Drosophila sechellia’s resistance to a host plant toxin, Genetics, 149, 1899, 10.1093/genetics/149.4.1899 · DOI
  50. Jones, 2005, The genetics of adaptation in Drosophila sechellia, Genetica, 123, 137, 10.1007/s10709-004-2728-6 · DOI
  51. Jones, 2022, The dual function of elicitors and effectors from insects: reviewing the “arms race” against plant defenses, Plant Molecular Biology, 109, 427, 10.1007/s11103-021-01203-2 · DOI
  52. Kaczmarek, 2022, Octanoic acid-an insecticidal metabolite of Conidiobolus coronatus (Entomopthorales) that affects two majors antifungal protection systems in Galleria mellonella (Lepidoptera): cuticular lipids and hemocytes, International Journal of Molecular Sciences, 23, 10.3390/ijms23095204 · DOI
  53. Kaczmarek, 2024, Octanoic acid kills Lucilia sericata (Diptera: Calliphoridae) by affecting two major defence systems: cuticular free fatty acids and immunocompetent cells, Journal of Invertebrate Pathology, 206, 10.1016/j.jip.2024.108165 · DOI
  54. Kalra, 2024, cis- and trans-regulatory contributions to a hierarchy of factors influencing gene expression variation, Communications Biology, 7, 10.1038/s42003-024-07255-6 · DOI
  55. Keightley, 2014, Estimation of the spontaneous mutation rate per nucleotide site in a Drosophila melanogaster full-sib family, Genetics, 196, 313, 10.1534/genetics.113.158758 · DOI
  56. Kelly, 2019, Pervasive linked selection and intermediate-frequency alleles are implicated in an evolve-and-resequencing experiment of Drosophila simulans, Genetics, 211, 943, 10.1534/genetics.118.301824 · DOI
  57. Kojima, 2020, Estimation of population genetic parameters using an EM algorithm and sequence data from experimental evolution populations, Bioinformatics, 36, 221, 10.1093/bioinformatics/btz498 · DOI
  58. Kondo, 2013, Highly improved gene targeting by germline-specific Cas9 expression in Drosophila, Genetics, 195, 715, 10.1534/genetics.113.156737 · DOI
  59. Kosakovsky Pond, 2020, HyPhy 2.5—a customizable platform for evolutionary hypothesis testing using phylogenies, Molecular Biology and Evolution, 37, 295, 10.1093/molbev/msz197 · DOI
  60. Krause, 2022, FlyAtlas 2 in 2022: enhancements to the Drosophila melanogaster expression atlas, Nucleic Acids Research, 50, D1010, 10.1093/nar/gkab971 · DOI
  61. Kumar, 2021, A superfamily-wide activity atlas of serine hydrolases in Drosophila melanogaster, Biochemistry, 60, 1312, 10.1021/acs.biochem.1c00171 · DOI
  62. Lanno, 2019, Investigating the role of Osiris genes in Drosophila sechellia larval resistance to a host plant toxin, Ecology and Evolution, 9, 1922, 10.1002/ece3.4885 · DOI
  63. Legal, 1994, Molecular basis of Morinda citrifolia (L.): toxicity on Drosophila, Journal of Chemical Ecology, 20, 1931, 10.1007/BF02066234 · DOI
  64. Legal, 1999, The relation between structures and toxicity of oxygenated aliphatic compounds homologous to the insecticide octanoic acid and the chemotaxis of two species of Drosophila, Pesticide Biochemistry and Physiology, 65, 90, 10.1006/pest.1999.2430 · DOI
  65. Leinonen, 2010, Improvements to services at the European Nucleotide Archive, Nucleic Acids Research, 38, D39, 10.1093/nar/gkp998 · DOI
  66. Lemaitre, 2013, The digestive tract of Drosophila melanogaster, Annual Review of Genetics, 47, 377, 10.1146/annurev-genet-111212-133343 · DOI
  67. Lenče, 2021, PhD Thesis: The Role of m6A Modification on mRNA Processing in Drosophila melanogaster
  68. Li H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. https://arxiv.org/abs/1303.3997. · arXiv
  69. Li, 2014, MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens, Genome Biology, 15, 10.1186/s13059-014-0554-4 · DOI · Paper page
  70. Li, 2022, Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly, Science, 375, 10.1126/science.abk2432 · DOI
  71. Liu, 2013, Membrane stress caused by octanoic acid in Saccharomyces cerevisiae, Applied Microbiology and Biotechnology, 97, 3239, 10.1007/s00253-013-4773-5 · DOI
  72. Long, 2015, Elucidating the molecular architecture of adaptation via evolve and resequence experiments, Nature Reviews. Genetics, 16, 567, 10.1038/nrg3937 · DOI
  73. Lü, 2018, Selection of reference genes for the normalization of RT-qPCR data in gene expression studies in insects: a systematic review, Frontiers in Physiology, 9, 10.3389/fphys.2018.01560 · DOI
  74. Lucas, 2023, Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii, Nature Communications, 14, 10.1038/s41467-023-40693-0 · DOI
  75. Ma, 2018, Comparative transcriptomics across 14 Drosophila species reveals signatures of longevity, Aging Cell, 17, 10.1111/acel.12740 · DOI
  76. Madeira, 2024, The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024, Nucleic Acids Research, 52, W521, 10.1093/nar/gkae241 · DOI
  77. Mantel, 1959, Statistical aspects of the analysis of data from retrospective studies of disease, Journal of the National Cancer Institute, 22, 719
  78. Marconcini, 2026, Genome-wide association studies identify new candidate genes and tissues underlying resistance to a natural toxin in drosophilids, G3: Genes, Genomes, Genetics, 16, 10.1093/g3journal/jkag032 · DOI
  79. Markow, 2009, Egg size, embryonic development time and ovoviviparity in Drosophila species, Journal of Evolutionary Biology, 22, 430, 10.1111/j.1420-9101.2008.01649.x · DOI
  80. Markow, 2019, Host use and host shifts in Drosophila, Current Opinion in Insect Science, 31, 139, 10.1016/j.cois.2019.01.006 · DOI
  81. Martelli, 2020, Low doses of the neonicotinoid insecticide imidacloprid induce ROS triggering neurological and metabolic impairments in Drosophila, PNAS, 117, 25840, 10.1073/pnas.2011828117 · DOI
  82. Martelli, 2022, Low doses of the organic insecticide spinosad trigger lysosomal defects, elevated ROS, lipid dysregulation, and neurodegeneration in flies, eLife, 11, 10.7554/eLife.73812 · DOI
  83. Matsuda, 2020, Neonicotinoid insecticides: molecular targets, resistance, and toxicity, Annual Review of Pharmacology and Toxicology, 60, 241, 10.1146/annurev-pharmtox-010818-021747 · DOI
  84. McDonald, 1991, Adaptive protein evolution at the Adh locus in Drosophila, Nature, 351, 652, 10.1038/351652a0 · DOI
  85. Mota, 2024, Shared and more specific genetic determinants and pathways underlying yeast tolerance to acetic, butyric, and octanoic acids, Microbial Cell Factories, 23, 10.1186/s12934-024-02309-0 · DOI
  86. Murrell, 2013, FUBAR: a fast, unconstrained bayesian approximation for inferring selection, Molecular Biology and Evolution, 30, 1196, 10.1093/molbev/mst030 · DOI
  87. Orozco-terWengel, 2012, Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles, Molecular Ecology, 21, 4931, 10.1111/j.1365-294X.2012.05673.x · DOI
  88. Öztürk-Çolak, 2024, FlyBase: updates to the Drosophila genes and genomes database, Genetics, 227, 10.1093/genetics/iyad211 · DOI
  89. Pino, 2009, Volatile and non‐volatile acids of noni (Morinda citrifolia L.) fruit, Journal of the Science of Food and Agriculture, 89, 1247, 10.1002/jsfa.3560 · DOI
  90. Port, 2020, A large-scale resource for tissue-specific CRISPR mutagenesis in Drosophila, eLife, 9, 10.7554/eLife.53865 · DOI
  91. Purcell, 2007, PLINK: a tool set for whole-genome association and population-based linkage analyses, American Journal of Human Genetics, 81, 559, 10.1086/519795 · DOI · Paper page
  92. Quinlan, 2010, BEDTools: a flexible suite of utilities for comparing genomic features, Bioinformatics, 26, 841, 10.1093/bioinformatics/btq033 · DOI · Paper page
  93. Raisch, 2023, The modes of action of ion-channel-targeting neurotoxic insecticides: lessons from structural biology, Nature Structural & Molecular Biology, 30, 1411, 10.1038/s41594-023-01113-5 · DOI
  94. R’Kha, 1991, Host-plant specialization in the Drosophila melanogaster species complex: a physiological, behavioral, and genetical analysis, PNAS, 88, 1835, 10.1073/pnas.88.5.1835 · DOI
  95. R’ kha, 1997, Evolution of a lesser fitness trait: egg production in the specialist Drosophila sechellia, Genetical Research, 69, 17, 10.1017/s0016672396002546 · DOI
  96. Robert, 2014, Deciphering key features in protein structures with the new ENDscript server, Nucleic Acids Research, 42, W320, 10.1093/nar/gku316 · DOI · Paper page
  97. Rogers, 2014, Landscape of standing variation for tandem duplications in Drosophila yakuba and Drosophila simulans, Molecular Biology and Evolution, 31, 1750, 10.1093/molbev/msu124 · DOI
  98. Rozas, 2017, DnaSP 6: DNA sequence polymorphism analysis of large data sets, Molecular Biology and Evolution, 34, 3299, 10.1093/molbev/msx248 · DOI
  99. Sadia, 2024, The impact of agrochemical pollutant mixtures on the selection of insecticide resistance in the malaria vector Anopheles gambiae: insights from experimental evolution and transcriptomics, Malaria Journal, 23, 10.1186/s12936-023-04791-0 · DOI
  100. Sayers, 2025, Database resources of the National Center for Biotechnology Information in 2025, Nucleic Acids Research, 53, D20, 10.1093/nar/gkae979 · DOI
  101. Schlötterer, 2016, Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation, Heredity, 116, 10.1038/hdy.2015.85 · DOI
  102. Schrider, 2018, Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia, PLOS Genetics, 14, 10.1371/journal.pgen.1007341 · DOI
  103. Sen, 2022, Loss of Drosophila Clueless differentially affects the mitochondrial proteome compared to loss of Sod2 and Pink1, Frontiers in Physiology, 13, 10.3389/fphys.2022.1004099 · DOI
  104. Shahandeh, 2024, Circadian plasticity evolves through regulatory changes in a neuropeptide gene, Nature, 635, 951, 10.1038/s41586-024-08056-x · DOI
  105. Shahandeh, 2026, Laboratory and wild Drosophila sechellia have conserved niche specialization phenotypes, Royal Society Open Science, 13, 10.1098/rsos.260680 · DOI
  106. Shahrestani, 2021, The molecular architecture of Drosophila melanogaster defense against Beauveria bassiana explored through evolve and resequence and quantitative trait locus mapping, G3: Genes, Genomes, Genetics, 11, 10.1093/g3journal/jkab324 · DOI
  107. Sherman, 2022, DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update), Nucleic Acids Research, 50, W216, 10.1093/nar/gkac194 · DOI
  108. Signor, 2018, A large panel of drosophila simulans reveals an abundance of common variants, Genome Biology and Evolution, 10, 189, 10.1093/gbe/evx262 · DOI
  109. Smith, 2013, The insecticidal potential of venom peptides, Cellular and Molecular Life Sciences, 70, 3665, 10.1007/s00018-013-1315-3 · DOI
  110. Soderlund, 1989, Neurotoxic actions of pyrethroid insecticides, Annual Review of Entomology, 34, 77, 10.1146/annurev.en.34.010189.000453 · DOI
  111. Solberg, 2013, Deletion of mouse Alkbh7 leads to obesity, Journal of Molecular Cell Biology, 5, 194, 10.1093/jmcb/mjt012 · DOI
  112. Sparks, 2020, Insecticides, biologics and nematicides: Updates to IRAC’s mode of action classification - a tool for resistance management, Pesticide Biochemistry and Physiology, 167, 10.1016/j.pestbp.2020.104587 · DOI
  113. Stern, 2014, Identification of loci that cause phenotypic variation in diverse species with the reciprocal hemizygosity test, Trends in Genetics, 30, 547, 10.1016/j.tig.2014.09.006 · DOI
  114. Sun, 2024, Osiris gene family defines the cuticle nanopatterns of Drosophila, Genetics, 227, 10.1093/genetics/iyae065 · DOI
  115. Taus, 2017, Quantifying selection with pool-seq time series data, Molecular Biology and Evolution, 34, 3023, 10.1093/molbev/msx225 · DOI
  116. Tautz, 2026, Beyond Mendel: a call to revisit the genotype-phenotype map through new experimental paradigms, Genetics, 232, 10.1093/genetics/iyag024 · DOI
  117. Teotónio, 2009, Experimental evolution reveals natural selection on standing genetic variation, Nature Genetics, 41, 251, 10.1038/ng.289 · DOI
  118. Teufel, 2022, SignalP 6.0 predicts all five types of signal peptides using protein language models, Nature Biotechnology, 40, 1023, 10.1038/s41587-021-01156-3 · DOI
  119. Turner, 2012, Investigating natural variation in Drosophila courtship song by the evolve and resequence approach, Genetics, 191, 633, 10.1534/genetics.112.139337 · DOI
  120. Umetsu, 2020, Development of novel pesticides in the 21st century, Journal of Pesticide Science, 45, 54, 10.1584/jpestics.D20-201 · DOI
  121. Vandesompele, 2002, Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes, Genome Biology, 3, 10.1186/gb-2002-3-7-research0034 · DOI · Paper page
  122. Varadi, 2022, AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, Nucleic Acids Research, 50, D439, 10.1093/nar/gkab1061 · DOI · Paper page
  123. Viswanatha, 2018, Pooled genome-wide CRISPR screening for basal and context-specific fitness gene essentiality in Drosophila cells, eLife, 7, 10.7554/eLife.36333 · DOI · Paper page
  124. Viswanatha, 2019, Pooled CRISPR screens in Drosophila cells, Current Protocols in Molecular Biology, 129, 10.1002/cpmb.111 · DOI
  125. Viswanatha, 2021, Bioinformatic and cell-based tools for pooled CRISPR knockout screening in mosquitos, Nature Communications, 12, 10.1038/s41467-021-27129-3 · DOI
  126. Viswanatha, 2025, Higher resolution pooled genome-wide CRISPR knockout screening in Drosophila cells using integration and anti-CRISPR (IntAC), Nature Communications, 16, 10.1038/s41467-025-61692-3 · DOI
  127. Vlachos, 2018, MimicrEE2: Genome-wide forward simulations of Evolve and Resequencing studies, PLOS Computational Biology, 14, 10.1371/journal.pcbi.1006413 · DOI
  128. Vlachos, 2019, Benchmarking software tools for detecting and quantifying selection in evolve and resequencing studies, Genome Biology, 20, 10.1186/s13059-019-1770-8 · DOI
  129. Walker, 2014, Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement, PLOS ONE, 9, 10.1371/journal.pone.0112963 · DOI
  130. Watada, 2020, Divergence of Drosophila species: Longevity and reproduction under different nutrient balances, Genes to Cells, 25, 626, 10.1111/gtc.12798 · DOI
  131. Watanabe, 2019, Interspecies comparative analyses reveal distinct carbohydrate-responsive systems among Drosophila species, Cell Reports, 28, 2594, 10.1016/j.celrep.2019.08.030 · DOI
  132. Weetman, 2018, Copy number variation (CNV) and insecticide resistance in mosquitoes: evolving knowledge or an evolving problem?, Current Opinion in Insect Science, 27, 82, 10.1016/j.cois.2018.04.005 · DOI
  133. Wood, 2011, Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, Journal of the Royal Statistical Society Series B, 73, 3, 10.1111/j.1467-9868.2010.00749.x · DOI
  134. Xia, 2023, Pooled genome-wide CRISPR activation screening for rapamycin resistance genes in Drosophila cells, eLife, 12, 10.7554/eLife.85542 · DOI
  135. Xu, 2021, Multi-substrate selectivity based on key loops and non-homologous domains: new insight into ALKBH family, Cellular and Molecular Life Sciences, 78, 129, 10.1007/s00018-020-03594-9 · DOI
  136. Xu, 2022, CRISPR screens in Drosophila cells identify Vsg as a Tc toxin receptor, Nature, 610, 349, 10.1038/s41586-022-05250-7 · DOI
  137. Yang, 2007, A Drosophila systems approach to xenobiotic metabolism, Physiological Genomics, 30, 223, 10.1152/physiolgenomics.00018.2007 · DOI
  138. Zhang, 2019, PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files, Bioinformatics, 35, 1786, 10.1093/bioinformatics/bty875 · DOI
  139. Zhang, 2021, ALKBH7-mediated demethylation regulates mitochondrial polycistronic RNA processing, Nature Cell Biology, 23, 684, 10.1038/s41556-021-00709-7 · DOI
  140. Zimmer, 2018, Neofunctionalization of duplicated P450 genes drives the evolution of insecticide resistance in the brown planthopper, Current Biology, 28, 268, 10.1016/j.cub.2017.11.060 · DOI
  141. Zoh, 2021, Experimental evolution supports the potential of neonicotinoid-pyrethroid combination for managing insecticide resistance in malaria vectors, Scientific Reports, 11, 10.1038/s41598-021-99061-x · DOI

Metadata

Authors
Marconcini, M., Cruchet, S., Goswami, S., Viswanatha, R., Butnaru, M., De, J., Roselli, C., Hadjieconomou, D., Perrimon, N., Mohr, S. E., & Benton, R.
Affiliations
  • University of Lausanne (CH)
  • Harvard University (US)
  • Howard Hughes Medical Institute (US)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Benton, Richard
Venue
eLife
Published
2026-09-11
Volume
15
ISSN
2050-084X (ISSN-L)
OpenAlex
W7212264776
Cited by
2
References
2
Citations by year
2026: 2
Type
posted content
Language
en
Open access
Locations
  • eLife (publishedVersion, cc-by)
  • eLife (acceptedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • IRIS (submittedVersion)
  • PubMed Central (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Funders
  • European Research Council (10.3030/101117267, 10.3030/833548)
  • Howard Hughes Medical Institute
  • Schweizerische Nationalfonds zur Förderung der wissenschaftlichen Forschung (310030_219185, 3200-0-239882)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Insect Resistance and Genetics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurobiology and Insect Physiology Research
Keywords
toxin resistance, octanoic acid, Drosophila sechellia, Drosophila simulans, Drosophila melanogaster, experimental evolution, CRISPR screen, Kraken, noni fruit, insecticide resistance, polygenic adaptation, detoxification, fatty acid metabolism, niche adaptation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review