Ips nitidus is a well-known conifer pest that has contributed significantly to spruce forest disturbance in the Qinghai-Tibet Plateau and seriously threatens the ecological balance of these areas. We report a chromosome-level genome of I. nitidus determined by PacBio and Hi-C technology. Phylogenetic inference showed that it diverged from the common ancestor of I. typographus ∼2.27 mya. Gene family expansion in I. nitidus was characterized by DNA damage repair and energy metabolism, which may facilitate adaptation to high-altitude hypoxia. Interestingly, differential gene expression analysis revealed upregulated genes associated with high-altitude hypoxia adaptation and downregulated genes associated with detoxification after feeding and tunneling in fungal symbiont Ophiostoma bicolor-colonized substrates. Our findings provide evidence of the potential adaptability of I. nitidus to conifer host, high-altitude hypoxia and insight into how fungal symbiont assist in this process. This study enhances our understanding of insect adaptation, symbiosis, and pest management.

Faculty of Forestry and Wood Sciences EXTEMIT K and EVA 4 0 Unit Czech University of Life Sciences Kamýcká 1176 Prague 6 165 00 Suchdol Czech Republic

Key Laboratory of Forest Protection of National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry Beijing 100091 China

Maxiu Forest Farm Huangnan 811300 China

Shandong Research Center for Forestry Harmful Biological Control Engineering and Technology College of Plant Protection Shandong Agricultural University Tai'an 271018 China

Zobrazit více v PubMed

Deng T., Wu F., Zhou Z., Su T. Tibetan Plateau: An evolutionary junction for the history of modern biodiversity. Sci. China Earth Sci. 2020;63:172–187. doi: 10.1007/s11430-019-9507-5. DOI

Herzschuh U., Birks H.J.B., Ni J., Zhao Y., Liu H., Liu X., Grosse G. Holocene land-cover changes on the Tibetan Plateau. Holocene. 2010;20:91–104. doi: 10.1177/0959683609348882. DOI

Simonson T.S., Yang Y., Huff C.D., Yun H., Qin G., Witherspoon D.J., Bai Z., Lorenzo F.R., Xing J., Jorde L.B., et al. Genetic evidence for high-altitude adaptation in Tibet. Science. 2010;329:72–75. doi: 10.1126/science.1189406. PubMed DOI

Ge R.L., Cai Q., Shen Y.Y., San A., Ma L., Zhang Y., Yi X., Chen Y., Yang L., Huang Y., et al. Draft genome sequence of the Tibetan antelope. Nat. Commun. 2013;4:1858–1867. doi: 10.1038/ncomms2860. PubMed DOI PMC

Li M., Tian S., Jin L., Zhou G., Li Y., Zhang Y., Wang T., Yeung C.K.L., Chen L., Ma J., et al. Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars. Nat. Genet. 2013;45:1431–1438. doi: 10.1038/ng.2811. PubMed DOI

Li C., Wu Y., Chen B., Cai Y., Guo J., Leonard A.S., Kalds P., Zhou S., Zhang J., Zhou P., et al. Markhor-derived introgression of a genomic region encompassing PAPSS2 confers high-altitude adaptability in Tibetan goats. Mol. Biol. Evol. 2022;39:msac253. doi: 10.1093/molbev/msac253. PubMed DOI PMC

Qiu Q., Zhang G., Ma T., Qian W., Wang J., Ye Z., Cao C., Hu Q., Kim J., Larkin D.M., et al. The yak genome and adaptation to life at high altitude. Nat. Genet. 2012;44:946–949. doi: 10.1038/ng.2343. PubMed DOI

Zhang W., Fan Z., Han E., Hou R., Zhang L., Galaverni M., Huang J., Liu H., Silva P., Li P., et al. Hypoxia adaptations in the grey wolf (Canis lupus chanco) from Qinghai-Tibet Plateau. PLoS Genet. 2014;10 doi: 10.1371/journal.pgen.1004466. PubMed DOI PMC

Qu Y., Zhao H., Han N., Zhou G., Song G., Gao B., Tian S., Zhang J., Zhang R., Meng X., et al. Ground tit genome reveals avian adaptation to living at high altitudes in the Tibetan plateau. Nat. Commun. 2013;4:2071. doi: 10.1038/ncomms3071. PubMed DOI

Li J.T., Gao Y.D., Xie L., Deng C., Shi P., Guan M.L., Huang S., Ren J.L., Wu D.D., Ding L., et al. Comparative genomic investigation of high-elevation adaptation in ectothermic snakes. Proc. Natl. Acad. Sci. USA. 2018;115:8406–8411. doi: 10.1073/pnas.1805348115. PubMed DOI PMC

Wang G.D., Zhang B.L., Zhou W.W., Li Y.X., Jin J.Q., Shao Y., Yang H.C., Liu Y.H., Yan F., Chen H.M., et al. Selection and environmental adaptation along a path to speciation in the Tibetan frog Nanorana parkeri. Proc. Natl. Acad. Sci. USA. 2018;115:E5056–E5065. doi: 10.1073/pnas.1716257115. PubMed DOI PMC

Xiao S., Mou Z., Fan D., Zhou H., Zou M., Zou Y., Zhou C., Yang R., Liu J., Zhu S., et al. Genome of tetraploid fish Schizothorax o'connori provides insights into early re-diploidization and high-altitude adaptation. iScience. 2020;23 doi: 10.1016/j.isci.2020.101497. PubMed DOI PMC

Ding D., Liu G., Hou L., Gui W., Chen B., Kang L. Genetic variation in PTPN1 contributes to metabolic adaptation to high-altitude hypoxia in Tibetan migratory locusts. Nat. Commun. 2018;9:4991. doi: 10.1038/s41467-018-07529-8. PubMed DOI PMC

Misof B., Liu S., Meusemann K., Peters R.S., Donath A., Mayer C., Frandsen P.B., Ware J., Flouri T., Beutel R.G., et al. Phylogenomics resolves the timing and pattern of insect evolution. Science. 2014;346:763–767. doi: 10.1126/science.1257570. PubMed DOI

Biedermann P.H.W., Müller J., Grégoire J.C., Gruppe A., Hagge J., Hammerbacher A., Hofstetter R.W., Kandasamy D., Kolarik M., Kostovcik M., et al. Bark beetle population dynamics in the Anthropocene: challenges and solutions. Trends Ecol. Evol. 2019;34:914–924. doi: 10.1016/j.tree.2019.06.002. PubMed DOI

Jakoby O., Lischke H., Wermelinger B. Climate change alters elevational phenology patterns of the European spruce bark beetle (Ips typographus) Global Change Biol. 2019;25:4048–4063. doi: 10.1111/gcb.14766. PubMed DOI

Kurz W.A., Dymond C.C., Stinson G., Rampley G.J., Neilson E.T., Carroll A.L., Ebata T., Safranyik L. Mountain pine beetle and forest carbon feedback to climate change. Nature. 2008;452:987–990. doi: 10.1038/nature06777. PubMed DOI

Logan J.A., Régnière J., Powell J.A. Assessing the impacts of global warming on forest pest dynamics. Front. Ecol. Environ. 2003;1:130–137. doi: 10.1890/1540-9295(2003)001[0130:ATIOGW]2.0.CO;2. DOI

Pettit J.M., Voelker S.L., DeRose R.J., Burton J.I. Spruce beetle outbreak was not driven by drought stress: Evidence from a tree-ring iso-demographic approach indicates temperatures were more important. Global Change Biol. 2020;26:5829–5843. doi: 10.1111/gcb.15274. PubMed DOI

Dale V.H., Joyce L.A., McNulty S., Neilson R.P., Ayres M.P., Flannigan M.D., Hanson P.j., Irland L.C., Lugo A.E., Peterson C.J., et al. Climate change and forest disturbances. Bioscience. 2001;51:723–734. doi: 10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2. DOI

Grégoire J.C., Raffa K.F., Lindgren B.S. In: Bark Beetles. Vega F.E., Hofstetter R.W., editors. Academic Press; 2015. Chapter 15 - Economics and politics of bark beetles; pp. 585–613. DOI

KärvEmo S., Schroeder L.M. A comparison of outbreak dynamics of the spruce bark beetle in Sweden and the mountain pine beetle in Canada (Curculionidae: Scolytinae) Entomol. Tidskr. 2010;131:215–224.

Schelhaas M.J., Nabuurs G.J., Schuck A. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biol. 2003;9:1620–1633. doi: 10.1046/j.1365-2486.2003.00684.x. DOI

Seidl R., Schelhaas M.J., Lexer M.J. Unraveling the drivers of intensifying forest disturbance regimes in Europe. Global Change Biol. 2011;17:2842–2852. doi: 10.1111/j.1365-2486.2011.02452.x. DOI

Cognato A.I. In: Bark Beetles. Vega F.E., Hofstetter R.W., editors. Academic Press; 2015. Chapter 9. Biology, systematics, and evolution of Ips; pp. 351–370. DOI

Raffa K.F., Andersson M.N., Schlyter F. Host selection by bark beetles: playing the odds in a high-stakes game. Adv. Insect Physiol. 2016;50:1–74. doi: 10.1016/bs.aiip.2016.02.001. DOI

Zhang Q.H., Cao J., Hu Y.z., Huang Y.h., Lü S.y., Wei G.z., Zhao Y.f. Olfactory recognition and behavioural avoidance of angiosperm nonhost volatiles by conifer-inhabiting bark beetles. Agric. For. Entomol. 2004;20:1–6. doi: 10.1111/j.1461-9555.2004.00202.x. DOI

Birgersson G., Schlyter F., Löfqvist J., Bergström G. Quantitative variation of pheromone components in the spruce bark beetle Ips typographus from different attack phases. J. Chem. Ecol. 1984;10:1029–1055. doi: 10.1007/BF00987511. PubMed DOI

Schlyter F., Byers J.A., Löfqvist J. Attraction to pheromone sources of different quantity, quality, and spacing: Density-regulation mechanisms in bark beetle Ips typographus. J. Chem. Ecol. 1987;13:1503–1523. doi: 10.1007/BF01012294. PubMed DOI

Schlyter F., Birgersson G. In: Pheromones in Non-Lepidopteran Insects Associated with Agricultural Plants. Hardie J., Minks A.K., editors. Oxford; 1999. Forest Beetles; pp. 113–148.

Hlásny T., König L., Krokene P., Lindner M., Montagné-Huck C., Müller J., Qin H., Raffa K.F., Schelhaas M.J., Svoboda M., et al. Bark beetle outbreaks in Europe: state of knowledge and ways forward for management. Curr. For. Rep. 2021;7:138–165. doi: 10.1007/s40725-021-00142-x. DOI

Wang Z.B., Chen Y.Q., Wang F.Y., Wang H.H., Xu H.Z., Li X.P., Song L.W. Research on the application technology of aggregation pheromone of Ips typographus L. J Jilin For Sci Tech. 2013;42:24–26. doi: 10.3969/j.issn.1005-7129.2013.03.008. DOI

Yue C.Y., Hou Y.H., Alimu M., Sun G.Q. Control and division of Ips typographus Linnaeus in natural forest of Aertai mountain in Xinjiang region. Hubei Agric. Sci. 2014;53:4059–4062.

Furuta K. A comparison of endemic and epidemic populations of the spruce beetle (Ips typographus japonicus Niijima) in Hokkaido. J. Appl. Entomol. 1989;107:289–295. doi: 10.1111/j.1439-0418.1989.tb00258.x. DOI

Powell D., Groβe-Wilde E., Krokene P., Roy A., Chakraborty A., Löfstedt C., Vogel H., Andersson M.N., Schlyter F. A highly-contiguous genome assembly of the Eurasian spruce bark beetle, Ips typographus, provides insight into a major forest pest. Commun. Biol. 2021;4:1–9. doi: 10.1038/s42003-021-02602-3. PubMed DOI PMC

Wang Z., Ma X., Zhou Q., Zheng G., Xia W., Zhang Y., Wang C., Jin P., Lu Q., Zhang X. Identification of Ips species (Coleoptera: Scolytinae) in China. Sci. Silvae Sin. 2021;57:79–91.

Liu L., Wu J., Luo Y.Q., Li Z.Y., Wang G.C., Han F.Z. Morphological and biological investigation of two pioneer Ips bark beetles in natural spruce forests in Qinghai Province, northwest China. For. Stud. China. 2008;36:19–25. doi: 10.1007/s11632-008-0005-y. DOI

Jakuš R., Kalinová B., Hoskovec M., Knížek M., Schlyter F., Sun J.H., Zhang Q.H. 10th IUFRO Workshop of WP. Vol. 7. 2011. Outbreak of Ips nitidus and Ips shangrila in northeastern margin of the Tibetan Plateau; pp. 20–23.

Yin H.F., Huang F.S., Li Z.L. Science Press; 1984. Economic Insect Fauna of China (Fasc. 29) Coleoptera: Scolytidae; pp. 126–136.

Liu X., Serova L., Kvetnanský R., Sabban E.L., Qin D., Ren J. Response and dendroclimatic implications of δ13C in tree rings to increasing drought on the northeastern Tibetan Plateau. Ann. N. Y. Acad. Sci. 2008;1148:1–28. doi: 10.1029/2007JG000610. PubMed DOI

Netherer S., Kandasamy D., Jirosová A., Kalinová B., Schebeck M., Schlyter F. Interactions among Norway spruce, the bark beetle Ips typographus and its fungal symbionts in times of drought. J. Pest. Sci. 2021;1–24 doi: 10.1007/s10340-021-01341-y. PubMed DOI PMC

Fu H.E. The life habits of Ips nitidus and its control. J Beijing For Coll. 1983;4:30–38. doi: 10.13332/j.1000-1522.1983.04.004. DOI

Zhang Q.H., Ma J.H., Zhao F.Y., Song L.W., Sun J.H. Aggregation pheromone of the Qinghai spruce bark beetle, Ips nitidus Eggers. J. Chem. Ecol. 2009;35:610–617. doi: 10.1007/s10886-009-9634-4. PubMed DOI

Zhou J.X., Li M.L., Li H.H., Miller M.C. A Study on Luring Some Beetles of China by Using Pheromones of North American Beetles. J Northwest For Coll. 1995;10:74–80.

Fettig C.J., Hilszczański J. In: Bark Beetles. Vega F.E., Hofstetter R.W., editors. Academic Press; 2015. Chapter 14 - Management strategies for bark beetles in conifer forests; pp. 555–584. DOI

Kenrick P., Crane P.R. The origin and early evolution of plants on land. Nature. 1997;389:33–39. doi: 10.1038/37918. DOI

Farrell B.D. “Inordinate fondness” explained: Why are there so many beetles? Science. 1998;281:555–559. doi: 10.1126/science.281.5376.555. PubMed DOI

McKenna D.D., Shin S., Ahrens D., Balke M., Beza-Beza C., Clarke D.J., Donath A., Escalona H.E., Friedrich F., Letsch H., et al. The evolution and genomic basis of beetle diversity. Proc. Natl. Acad. Sci. USA. 2019;116:24729–24737. doi: 10.1073/pnas.1909655116. PubMed DOI PMC

Després L., David J.-P., Gallet C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 2007;22:298–307. doi: 10.1016/j.tree.2007.02.010. PubMed DOI

Seppey M., Ioannidis P., Emerson B.C., Pitteloud C., Robinson-Rechavi M., Roux J., Escalona H.E., McKenna D.D., Misof B., Shin S., et al. Genomic signatures accompanying the dietary shift to phytophagy in polyphagan beetles. Genome Biol. 2019;20:98. doi: 10.1186/s13059-019-1704-5. PubMed DOI PMC

Seybold S.J., Bohlmann J., Raffa K.F. Biosynthesis of coniferophagous bark beetle pheromones and conifer isoprenoids: evolutionary perspective and synthesis. Can. Entomol. 2000;132:697–753. doi: 10.4039/Ent132697-6. DOI

Celedon J.M., Bohlmann J. Oleoresin defenses in conifers: chemical diversity, terpene synthases and limitations of oleoresin defence under climate change. New Phytol. 2019;224:1444–1463. doi: 10.1111/nph.15984. PubMed DOI

Franceschi V.R., Krokene P., Christiansen E., Krekling T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. New Phytol. 2005;167:353–375. doi: 10.1111/j.1469-8137.2005.01436.x. PubMed DOI

Keeling C.I., Bohlmann J. Genes, enzymes and chemicals of terpenoid diversity in the constitutive and induced defence of conifers against insects and pathogens. New Phytol. 2006;170:657–675. doi: 10.1111/j.1469-8137.2006.01716.x. PubMed DOI

Phillips M.A., Croteau R.B. Resin-based defenses in conifers. Trends Plant Sci. 1999;4:184–190. doi: 10.1016/S1360-1385(99)01401-6. PubMed DOI

Trapp S., Croteau R. Defensive resin biosynthesis in conifers. Annu. Rev. Plant Biol. 2001;52:689–724. doi: 10.1146/annurev.arplant.52.1.689. PubMed DOI

Keeling C.I., Tittiger C., MacLean M., Blomquist G.J. In: Insect Pheromone Biochemistry and Molecular Biology. Blomquist G.J., Vogt R.G., editors. Academic Press; 2020. Pheromone production in bark beetles; pp. 123–162. DOI

Andersson M.N., Grosse-Wilde E., Keeling C.I., Bengtsson J.M., Yuen M.M.S., Li M., Hillbur Y., Bohlmann J., Hansson B.S., Schlyter F. Antennal transcriptome analysis of the chemosensory gene families in the tree killing bark beetles, Ips typographus and Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae) BMC Genom. 2013;14:198–216. doi: 10.1186/1471-2164-14-198. PubMed DOI PMC

Hou X.Q., Yuvaraj J.K., Roberts R.E., Zhang D.D., Unelius C.R., Löfstedt C., Andersson M.N. Functional evolution of a bark beetle odorant receptor clade detecting monoterpenoids of different ecological origins. Mol. Biol. Evol. 2021;38:4934–4947. doi: 10.1093/molbev/msab218. PubMed DOI PMC

Ramakrishnan R., Hradecký J., Roy A., Kalinová B., Mendezes R.C., Synek J., Bláha J., Svatoš A., Jirošová A. Metabolomics and transcriptomics of pheromone biosynthesis in an aggressive forest pest Ips typographus. Insect Biochem. Mol. Biol. 2022;140 doi: 10.1016/j.ibmb.2021.103680. PubMed DOI

Joga M.R., Mogilicherla K., Smagghe G., Roy A. RNA Interference-Based Forest Protection Products (FPPs) Against Wood-Boring Coleopterans: Hope or Hype? Front. Plant Sci. 2021;12:733608. doi: 10.3389/fpls.2021.733608. PubMed DOI PMC

Chakraborty A., Ashraf M.Z., Modlinger R., Synek J., Schlyter F., Roy A. Unravelling the gut bacteriome of Ips (Coleoptera: Curculionidae: Scolytinae): Identifying core bacterial assemblage and their ecological relevance. Sci. Rep. 2020;10:18572–18617. doi: 10.1038/s41598-020-75203-5. PubMed DOI PMC

Chakraborty A., Roy A. In: Plant-Pest Interactions: From Molecular Mechanisms to Chemical Ecology. Singh A., Singh I.K., editors. Springer; 2021. Microbial Influence on Plant–Insect Interaction; pp. 337–363. DOI

Liu F., Ye F., Cheng C., Kang Z., Kou H., Sun J. Symbiotic microbes aid host adaptation by metabolizing a deterrent host pine carbohydrate d-pinitol in a beetle-fungus invasive complex. Sci. Adv. 2022;8:eadd5051. doi: 10.1126/sciadv.add5051. PubMed DOI PMC

Salem H., Kaltenpoth M. Beetle–bacterial symbioses: endless forms most functional. Annu. Rev. Entomol. 2022;67:201–219. doi: 10.1146/annurev-ento-061421-063433. PubMed DOI

Schmidt K., Engel P. Mechanisms underlying gut microbiota–host interactions in insects. J. Exp. Bot. 2021;224 doi: 10.1242/jeb.207696. PubMed DOI

Joseph R., Keyhani N.O. Fungal mutualisms and pathosystems: life and death in the ambrosia beetle mycangia. Appl. Microbiol. Biotechnol. 2021;105:3393–3410. doi: 10.1007/s00253-021-11268-0. PubMed DOI

Nagendran S., Agrawal S.S., Abraham S. In: Symbiotic Soil Microorganisms. Shrivastava N., Varma A., editors. Springer; 2021. Symbiotic Harmony Between Insects and Fungi: A Mutualistic Approach; pp. 269–288. DOI

Hofstetter R.W., Dinkins-Bookwalter J., Davis T.S., Klepzig K.D. In: Bark Beetles. Vega F.E., Hofstetter R.W., editors. Academic Press; 2015. Symbiotic associations of bark beetles; pp. 209–245. DOI

Kandasamy D., Zaman R., Nakamura Y., Zhao T., Hartmann H., Andersson M.N., Hammerbacher A., Gershenzon J. Conifer-killing bark beetles locate fungal symbionts by detecting volatile fungal metabolites of host tree resin monoterpenes. PLoS Biol. 2023;21 doi: 10.1371/journal.pbio.3001887. PubMed DOI PMC

Zaman R., May C., Ullah A., Erbilgin N. Bark Beetles Utilize Ophiostomatoid Fungi to Circumvent Host Tree Defenses. Metabolites. 2023;13:239. doi: 10.3390/metabo13020239. PubMed DOI PMC

Chakraborty A., Modlinger R., Ashraf M.Z., Synek J., Schlyter F., Roy A. Core mycobiome and their ecological relevance in the gut of five Ips bark beetles (Coleoptera: Curculionidae: Scolytinae) Front. Microbiol. 2020;11:568853. doi: 10.3389/fmicb.2020.568853. PubMed DOI PMC

de Beer Z.W., Wingfield M.J. In: The Ophiostomatoid fungi: expanding frontiers. Seifert K.A., De Beer Z.W., Wingfield M.J., editors. CBS-KNAW Fungal Biodiversity Centre; 2013. Emerging lineages in the Ophiostomatales; pp. 21–46.

de Beer Z.W., Duong T.A., Barnes I., Wingfield B.D., Wingfield M.J. Redefining Ceratocystis and allied genera. Stud. Mycol. 2014;79:187–219. doi: 10.1016/j.simyco.2014.10.001. PubMed DOI PMC

Six D.L., Wingfield M.J. The role of phytopathogenicity in bark beetle–fungus symbioses: a challenge to the classic paradigm. Annu. Rev. Entomol. 2011;56:255–272. doi: 10.1146/annurev-ento-120709-144839. PubMed DOI

Bracewell R.R., Six D.L. Experimental evidence of bark beetle adaptation to a fungal symbiont. Ecol. Evol. 2015;5:5109–5119. doi: 10.1002/ece3.1772. PubMed DOI PMC

Davis T.S., Stewart J.E., Mann A., Bradley C., Hofstetter R.W. Evidence for multiple ecological roles of Leptographium abietinum, a symbiotic fungus associated with the North American spruce beetle. Fungal Ecol. 2019;38:62–70. doi: 10.1016/j.funeco.2018.04.008. DOI

Guevara-Rozo S., Hussain A., Cale J.A., Klutsch J.G., Rajabzadeh R., Erbilgin N. Nitrogen and Ergosterol Concentrations Varied in Live Jack Pine Phloem Following Inoculations with Fungal Associates of Mountain Pine Beetle. Front. Microbiol. 2020;11:1703. doi: 10.3389/fmicb.2020.01703. PubMed DOI PMC

Six D.L. The bark beetle holobiont: why microbes matter. J. Chem. Ecol. 2013;39:989–1002. doi: 10.1007/s10886-013-0318-8. PubMed DOI

DiGuistini S., Wang Y., Liao N.Y., Taylor G., Tanguay P., Feau N., Henrissat B., Chan S.K., Hesse-Orce U., Alamouti S.M., et al. Genome and transcriptome analyses of the mountain pine beetle-fungal symbiont Grosmannia clavigera, a lodgepole pine pathogen. Proc. Natl. Acad. Sci. USA. 2011;108:2504–2509. doi: 10.1073/pnas.1011289108. PubMed DOI PMC

Wadke N., Kandasamy D., Vogel H., Lah L., Wingfield B.D., Paetz C., Wright L.P., Gershenzon J., Hammerbacher A. The bark-beetle-associated fungus, Endoconidiophora polonica, utilizes the phenolic defense compounds of its host as a carbon source. Plant Physiol. 2016;171:914–931. doi: 10.1104/pp.15.01916. PubMed DOI PMC

Cale J.A., Ding R., Wang F., Rajabzadeh R., Erbilgin N. Ophiostomatoid fungi can emit the bark beetle pheromone verbenone and other semiochemicals in media amended with various pine chemicals and beetle-released compounds. Fungal Ecol. 2019;39:285–295. doi: 10.1016/j.funeco.2019.01.003. DOI

Kandasamy D., Gershenzon J., Andersson M.N., Hammerbacher A. Volatile organic compounds influence the interaction of the Eurasian spruce bark beetle (Ips typographus) with its fungal symbionts. ISME J. 2019;13:1788–1800. doi: 10.1038/s41396-019-0390-3. PubMed DOI PMC

Zhao T., Axelsson K., Krokene P., Borg-Karlson A.K. Fungal symbionts of the spruce bark beetle synthesize the beetle aggregation pheromone 2-methyl-3-buten-2-ol. J. Chem. Ecol. 2015;41:848–852. doi: 10.1007/s10886-015-0617-3. PubMed DOI

Zhao T., Ganji S., Schiebe C., Bohman B., Weinstein P., Krokene P., Borg-Karlson A.K., Unelius C.R. Convergent evolution of semiochemicals across Kingdoms: bark beetles and their fungal symbionts. ISME J. 2019;13:1535–1545. doi: 10.1038/s41396-019-0370-7. PubMed DOI PMC

Davis T.S., Hofstetter R.W., Foster J.T., Foote N.E., Keim P. Interactions between the yeast Ogataea pini and filamentous fungi associated with the western pine beetle. Microb. Ecol. 2011;61:626–634. doi: 10.1007/s00248-010-9773-8. PubMed DOI

Skelton J., Jusino M.A., Carlson P.S., Smith K., Banik M.T., Lindner D.L., Palmer J.M., Hulcr J. Relationships among wood-boring beetles, fungi, and the decomposition of forest biomass. Mol. Ecol. 2019;28:4971–4986. doi: 10.1111/mec.15263. PubMed DOI

Sahara K., Marec F., Traut W. TTAGG telomeric repeats in chromosomes of some insects and other arthropods. Chromosome Res. 1999;7:449–460. PubMed

Smith S.G. Chromosome numbers of Coleoptera. II. Heredity. 1953;7:31–48.

Wang C., Dai J., Zhao X., Li Y., Graham S.A., He D., Ran B., Meng J. Outward-growth of the Tibetan Plateau during the Cenozoic: A review. Tectonophysics. 2014;621:1–43. doi: 10.1016/j.tecto.2014.01.036. DOI

Chimpanzee Sequencing and Analysis Consortium. Hillier L., Eichler E., Zody M., Jaffe D., Yang S.P., Enard W., Hellmann I., Lindblad-Toh K., Altheide T.K., et al. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature. 2005;437:69–87. doi: 10.1038/nature04072. PubMed DOI

Sudmant P.H., Kitzman J.O., Antonacci F., Alkan C., Malig M., Tsalenko A., Sampas N., Bruhn L., Shendure J., 1000 Genomes Project. Eichler E.E. Diversity of human copy number variation and multicopy genes. Science. 2010;330:641–646. doi: 10.1126/science.1197005. PubMed DOI PMC

Wu N., Zhang S., Li X., Cao Y., Liu X., Wang Q., Liu Q., Liu H., Hu X., Zhou X.J., et al. Fall webworm genomes yield insights into rapid adaptation of invasive species. Nat. Ecol. Evol. 2019;3:105–115. doi: 10.1038/s41559-018-0746-5. PubMed DOI

Zhang T., Qiao Q., Novikova P.Y., Wang Q., Yue J., Guan Y., Ming S., Liu T., De J., Liu Y., et al. Genome of Crucihimalaya himalaica, a close relative of Arabidopsis, shows ecological adaptation to high altitude. Proc. Natl. Acad. Sci. USA. 2019;116:7137–7146. doi: 10.1073/pnas.1817580116. PubMed DOI PMC

Graham A.M., Lavretsky P., Muñoz-Fuentes V., Green A.J., Wilson R.E., McCracken K.G. Migration-selection balance drives genetic differentiation in genes associated with high-altitude function in the speckled teal (Anas flavirostris) in the Andes. Genome Biol. Evol. 2018;10:14–32. doi: 10.1093/gbe/evx253. PubMed DOI PMC

Palomera-Sanchez Z., Zurita M. Open, repair and close again: chromatin dynamics and the response to UV-induced DNA damage. DNA Repair. 2011;10:119–125. doi: 10.1016/j.dnarep.2010.10.010. PubMed DOI

Yu L., Wang G.D., Ruan J., Chen Y.B., Yang C.P., Cao X., Wu H., Liu Y.H., Du Z.L., Wang X.P., et al. Genomic analysis of snub-nosed monkeys (Rhinopithecus) identifies genes and processes related to high-altitude adaptation. Nat. Genet. 2016;48:947–952. doi: 10.1038/ng.3615. PubMed DOI

Guo W., Xin M., Wang Z., Yao Y., Hu Z., Song W., Yu K., Chen Y., Wang X., Guan P., et al. Origin and adaptation to high altitude of Tibetan semi-wild wheat. Nat. Commun. 2020;11:5085. doi: 10.1038/s41467-020-18738-5. PubMed DOI PMC

Xiong Y., Lei F. High-altitude adaptation in birds: From phenomics to genomics. Sci. Sin. -Vitae. 2019;49:361–370. doi: 10.1360/N052018-00203. DOI

Yang W., Qi Y., Fu J. Exploring the genetic basis of adaptation to high elevations in reptiles: a comparative transcriptome analysis of two toad-headed agamas (genus Phrynocephalus) PLoS One. 2014;9 doi: 10.1371/journal.pone.0112218. PubMed DOI PMC

Zhang Q.L., Zhang L., Yang X.Z., Wang X.T., Li X.P., Wang J., Chen J.Y., Yuan M.L. Comparative transcriptomic analysis of Tibetan Gynaephora to explore the genetic basis of insect adaptation to divergent altitude environments. Sci. Rep. 2017;7 doi: 10.1038/s41598-017-17051-4. PubMed DOI PMC

Zhang Q.L., Li H.W., Dong Z.X., Yang X.J., Lin L.B., Chen J.Y., Yuan M.L. Comparative transcriptomic analysis of fireflies (Coleoptera: Lampyridae) to explore the molecular adaptations to fresh water. Mol. Ecol. 2020;29:2676–2691. doi: 10.1111/mec.15504. PubMed DOI

Stork N.E. How many species of insects and other terrestrial arthropods are there on Earth? Annu. Rev. Entomol. 2018;63:31–45. doi: 10.1146/annurev-ento-020117-043348. PubMed DOI

Harrison M.C., Jongepier E., Robertson H.M., Arning N., Bitard-Feildel T., Chao H., Childers C.P., Dinh H., Doddapaneni H., Dugan S., et al. Hemimetabolous genomes reveal molecular basis of termite eusociality. Nat. Ecol. Evol. 2018;2:557–566. doi: 10.1038/s41559-017-0459-1. PubMed DOI PMC

Poulsen M., Hu H., Li C., Chen Z., Xu L., Otani S., Nygaard S., Nobre T., Klaubauf S., Schindler P.M., et al. Complementary symbiont contributions to plant decomposition in a fungus-farming termite. Proc. Natl. Acad. Sci. USA. 2014;111:14500–14505. doi: 10.1073/pnas.1319718111. PubMed DOI PMC

Terrapon N., Li C., Robertson H.M., Ji L., Meng X., Booth W., Chen Z., Childers C.P., Glastad K.M., Gokhale K., et al. Molecular traces of alternative social organization in a termite genome. Nat. Commun. 2014;5:3636. doi: 10.1038/ncomms4636. PubMed DOI

Watanabe H., Noda H., Tokuda G., Lo N. A cellulase gene of termite origin. Nature. 1998;394:330–331. doi: 10.1038/28527. PubMed DOI

Pedezzi R., Fonseca F.P.P., Santos Júnior C.D., Kishi L.T., Terra W.R., Henrique-Silva F. A novel β-fructofuranosidase in Coleoptera: Characterization of a β-fructofuranosidase from the sugarcane weevil, Sphenophorus levis. Insect Biochem. Mol. Biol. 2014;55:31–38. doi: 10.1016/j.ibmb.2014.10.005. PubMed DOI

Nagare M., Ayachit M., Agnihotri A., Schwab W., Joshi R. Glycosyltransferases: the multifaceted enzymatic regulator in insects. Insect Mol. Biol. 2021;30:123–137. doi: 10.1111/imb.12686. PubMed DOI

Daimon T., Hirayama C., Kanai M., Ruike Y., Meng Y., Kosegawa E., Nakamura M., Tsujimoto G., Katsuma S., Shimada T. The silkworm Green b locus encodes a quercetin 5-O-glucosyltransferase that produces green cocoons with UV-shielding properties. Proc. Natl. Acad. Sci. USA. 2010;107:11471–11476. doi: 10.1073/pnas.1000479107. PubMed DOI PMC

de Vries R.P., Visser J. Aspergillus enzymes involved in degradation of plant cell wall polysaccharides. Microbiol. Mol. Biol. Rev. 2001;65:497–522. doi: 10.1128/mmbr.65.4.497-522.2001. PubMed DOI PMC

Biedermann P.H.W., Vega F.E. Ecology and Evolution of Insect-Fungus Mutualisms. Annu. Rev. Entomol. 2020;65:431–455. doi: 10.1146/annurev-ento-011019-024910. PubMed DOI

Jagadeeswaran G., Veale L., Mort A.J. Do lytic polysaccharide monooxygenases aid in plant pathogenesis and herbivory? Trends Plant Sci. 2021;26:142–155. doi: 10.1016/j.tplants.2020.09.013. PubMed DOI

Sabbadin F., Hemsworth G.R., Ciano L., Henrissat B., Dupree P., Tryfona T., Marques R.D.S., Sweeney S.T., Besser K., Elias L., et al. An ancient family of lytic polysaccharide monooxygenases with roles in arthropod development and biomass digestion. Nat. Commun. 2018;9:756. doi: 10.1038/s41467-018-03142-x. PubMed DOI PMC

Keeling C.I., Yuen M.M.S., Liao N.Y., Docking T.R., Chan S.K., Taylor G.A., Palmquist D.L., Jackman S.D., Nguyen A., Li M., et al. Draft genome of the mountain pine beetle, Dendroctonus ponderosae Hopkins, a major forest pest. Genome Biol. 2013;14:R27. doi: 10.1186/gb-2013-14-3-r27. PubMed DOI PMC

Zhao T., Kandasamy D., Krokene P., Chen J., Gershenzon J., Hammerbacher A. Fungal associates of the tree-killing bark beetle, Ips typographus, vary in virulence, ability to degrade conifer phenolics and influence bark beetle tunneling behavior. Fungal Ecol. 2019;38:71–79.

Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–2120. doi: 10.1093/bioinformatics/btu170. PubMed DOI PMC

Marçais G., Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics. 2011;27:764–770. doi: 10.1093/bioinformatics/btr011. PubMed DOI PMC

Ranallo-Benavidez T.R., Jaron K.S., Schatz M.C. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat. Commun. 2020;11:1432–1510. doi: 10.1038/s41467-020-14998-3. PubMed DOI PMC

Koren S., Walenz B.P., Berlin K., Miller J.R., Bergman N.H., Phillippy A.M. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27:722–736. doi: 10.1101/gr.215087.116. PubMed DOI PMC

Guan D., McCarthy S.A., Wood J., Howe K., Wang Y., Durbin R. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics. 2020;36:2896–2898. doi: 10.1093/bioinformatics/btaa025. PubMed DOI PMC

Walker B.J., Abeel T., Shea T., Priest M., Abouelliel A., Sakthikumar S., Cuomo C.A., Zeng Q., Wortman J., Young S.K., Earl A.M. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9 doi: 10.1371/journal.pone.0112963. PubMed DOI PMC

Durand N.C., Shamim M.S., Machol I., Rao S.S.P., Huntley M.H., Lander E.S., Aiden E.L. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 2016;3:95–98. doi: 10.1016/j.cels.2016.07.002. PubMed DOI PMC

Dudchenko O., Batra S.S., Omer A.D., Nyquist S.K., Hoeger M., Durand N.C., Shamim M.S., Machol I., Lander E.S., Aiden A.P., Aiden E.L. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science. 2017;356:92–95. doi: 10.1126/science.aaI3327. PubMed DOI PMC

Durand N.C., Robinson J.T., Shamim M.S., Machol I., Mesirov J.P., Lander E.S., Aiden E.L. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 2016;3:99–101. doi: 10.1016/j.cels.2015.07.012. PubMed DOI PMC

Simão F.A., Waterhouse R.M., Ioannidis P., Kriventseva E.V., Zdobnov E.M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–3212. doi: 10.1093/bioinformatics/btv351. PubMed DOI

Bedell J.A., Korf I., Gish W. MaskerAid: A performance enhancement to RepeatMasker. Bioinformatics. 2000;16:1040–1041. doi: 10.1093/bioinformatics/16.11.1040. PubMed DOI

Tarailo-Graovac M., Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics. 2009;25:4. doi: 10.1002/0471250953.bi0410s25. PubMed DOI

Slater G.S.C., Birney E. Automated generation of heuristics for biological sequence comparison. BMC Bioinf. 2005;6:31. doi: 10.1186/1471-2105-6-31. PubMed DOI PMC

Ter-Hovhannisyan V., Lomsadze A., Chernoff Y.O., Borodovsky M. Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. Genome Res. 2008;18:1979–1990. doi: 10.1101/gr.081612.108. PubMed DOI PMC

Stanke M., Steinkamp R., Waack S., Morgenstern B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 2004;32:W309–W312. doi: 10.1093/nar/gkh379. PubMed DOI PMC

Haas B.J., Salzberg S.L., Zhu W., Pertea M., Allen J.E., Orvis J., White O., Buell C.R., Wortman J.R. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 2008;9:R7–R22. doi: 10.1186/gb-2008-9-1-r7. PubMed DOI PMC

Haas B.J., Delcher A.L., Mount S.M., Wortman J.R., Smith R.K., Hannick L.I., Maiti R., Ronning C.M., Rusch D.B., Town C.D., et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 2003;31:5654–5666. doi: 10.1093/nar/gkg770. PubMed DOI PMC

Nawrocki E.P. Annotating functional RNAs in genomes using Infernal. Methods Mol. Biol. 2014;1097:163–197. doi: 10.1007/978-1-62703-709-9_9. PubMed DOI

Lowe T.M., Eddy S.R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–964. doi: 10.1093/nar/25.5.955. PubMed DOI PMC

Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402. doi: 10.1093/nar/25.17.3389. PubMed DOI PMC

Meng X., Ji Y. Modern computational techniques for the HMMER sequence analysis. ISRN Bioinform. 2013;2013 doi: 10.1155/2013/252183. PubMed DOI PMC

Aramaki T., Blanc-Mathieu R., Endo H., Ohkubo K., Kanehisa M., Goto S., Ogata H. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2252. doi: 10.1093/bioinformatics/btz859. PubMed DOI PMC

Emms D.M., Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238. doi: 10.1186/s13059-019-1832-y. PubMed DOI PMC

Katoh K., Misawa K., Kuma K.i., Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–3066. doi: 10.1093/nar/gkf436. PubMed DOI PMC

Talavera G., Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 2007;56:564–577. doi: 10.1080/10635150701472164. PubMed DOI

Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–1313. doi: 10.1093/bioinformatics/btu033. PubMed DOI PMC

Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 2007;24:1586–1591. doi: 10.1093/molbev/msm088. PubMed DOI

De Bie T., Cristianini N., Demuth J.P., Hahn M.W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics. 2006;22:1269–1271. doi: 10.1093/bioinformatics/btl097. PubMed DOI

Alexa A., Rahnenführer J., Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22:1600–1607. doi: 10.1093/bioinformatics/btl140. PubMed DOI

Xie C., Mao X., Huang J., Ding Y., Wu J., Dong S., Kong L., Gao G., Li C.Y., Wei L. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39:W316–W322. doi: 10.1093/nar/gkr483. PubMed DOI PMC

Edgar R.C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–1797. doi: 10.1093/nar/gkh340. PubMed DOI PMC

Suyama M., Torrents D., Bork P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006;34:W609–W612. doi: 10.1093/nar/gkl315. PubMed DOI PMC

Grabherr M.G., Haas B.J., Yassour M., Levin J.Z., Thompson D.A., Amit I., Adiconis X., Fan L., Raychowdhury R., Zeng Q., et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011;29:644–652. doi: 10.1038/nbt.1883. PubMed DOI PMC

Kim D., Paggi J.M., Park C., Bennett C., Salzberg S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019;37:907–915. doi: 10.1038/s41587-019-0201-4. PubMed DOI PMC

Liao Y., Smyth G.K., Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–930. PubMed

Calderón-Cortés N., Quesada M., Cano-Camacho H., Zavala-Páramo G. A simple and rapid method for DNA isolation from xylophagous insects. Int. J. Mol. Sci. 2010;11:5056–5064. doi: 10.3390/ijms11125056. PubMed DOI PMC

Belaghzal H., Dekker J., Gibcus J.H. Hi-C 2.0: An optimized Hi-C procedure for high-resolution genome-wide mapping of chromosome conformation. Methods. 2017;123:56–65. doi: 10.1016/j.ymeth.2017.04.004. PubMed DOI PMC

Bao W., Kojima K.K., Kohany O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA. 2015;6:11–16. doi: 10.1186/s13100-015-0041-9. PubMed DOI PMC

Kalvari I., Nawrocki E.P., Ontiveros-Palacios N., Argasinska J., Lamkiewicz K., Marz M., Griffiths-Jones S., Toffano-Nioche C., Gautheret D., Weinberg Z., et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 2021;49:D192–D200. doi: 10.1093/nar/gkaa1047. PubMed DOI PMC

Huerta-Cepas J., Szklarczyk D., Heller D., Hernández-Plaza A., Forslund S.K., Cook H., Mende D.R., Letunic I., Rattei T., Jensen L.J., et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314. doi: 10.1093/nar/gky1085. PubMed DOI PMC

Zhang S., Shen S., Peng J., Zhou X., Kong X., Ren P., Liu F., Han L., Zhan S., Huang Y., et al. Chromosome-level genome assembly of an important pine defoliator, Dendrolimus punctatus (Lepidoptera; Lasiocampidae) Mol. Ecol. Resour. 2020;20:1023–1037. doi: 10.1111/1755-0998.13169. PubMed DOI

Zhang H., Yohe T., Huang L., Entwistle S., Wu P., Yang Z., Busk P.K., Xu Y., Yin Y. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101. doi: 10.1093/nar/gky418. PubMed DOI PMC

Darriba D., Taboada G.L., Doallo R., Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 2011;27:1164–1165. doi: 10.1093/bioinformatics/btr088. PubMed DOI PMC

Parkhomchuk D., Borodina T., Amstislavskiy V., Banaru M., Hallen L., Krobitsch S., Lehrach H., Soldatov A. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009;37:e123. doi: 10.1093/nar/gkp596. PubMed DOI PMC

Robinson M.D., McCarthy D.J., Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–140. PubMed PMC

Complex Genomic Landscape of Inversion Polymorphism in Europe's Most Destructive Forest Pest

Duplications and Losses of the Detoxification Enzyme Glycosyltransferase 1 Are Related to Insect Adaptations to Plant Feeding