Zhang Lab

Yao Z*, Sun X*, Wu X, Zhu F, Huang J, Zhang W#, Ma W#, Hua H, Lin Y. Functional and evolutionary analysis of key enzymes triacylglycerol lipase, glycogen hydrolases in the glycerol and glucose biosynthesis pathway and cellular chaperones for freeze-tolerance of the Rice stem borer, Chilo suppressalis. Int. J. Biol. Macromol. 2024 Oct doi: doi.org/10.1016/j.ijbiomac.2024.136861.
倪嘉欣，张蔚#。蝶翅花纹的演化发育生物学研究进展。遗传 2024 doi: 10.16288/j.yczz.24-126.(Invited review)
Huang G*, Zhang Y*, Zhang W, Wei F#. Genetic mechanisms of animal camouflage: an interdisciplinary perspective. Trends Genet. 2024 Apr 20:S0168-9525(24)00073-8.
Zhang LJ#, Shi ZA, Chen ZY, von Rintelen T, Zhang W, Lou ZJ. Rediscovery and systematics of the enigmatic genus Helicostoa reveals a new species of sessile freshwater snail with remarkable sexual dimorphism. Proc. R. Soc. Lond. B Biol. Sci. Jan 10;291(2014):20231557.
Yang W, Cui J, Chen Y, Wang C, Yin Y, Zhang W, Liu S, Sun C, Li H, Duan Y, Song F, Cai W, Hines HM, Tian L#. Genetic modification of a Hox locus drives mimetic color pattern variation in a highly polymorphic bumble bee. Mol. Biol. Evol. 2023 Dec 1:msad261.
Zhang W#, Lohman DJ. Uncovering the functional basis of mantids that resemble plants. Sci. China Life Sci. Nov 6. doi: 10.1007/s11427-023-2450-5.
Teng D, Zhang W#. The diversification of butterfly wing patterns: Progress and prospects. Curr. Opin. Insect Sci. 2023 Nov 3:61:101137. (Invited review)
Wang Z*, Sun J*, Gao Y, Xue Y, Zhang Y, Li K, Zhang W, Zhang C, Zu J#, Zhang L#. Fusang: A framework for phylogenetic tree inference via deep learning. Nucleic Acids Res. 2023 Oct 11: gkad805.
Wang S, Girardello M, Zhang W#. Potential and progress of studying mountain biodiversity by means of butterfly genetics and genomics. J. Genet. Genomics 2023 Jun 10:j.jgg.2023.06.001 (Invited review)
Zhang Y*, Zhu Q*, Shao Y, Jiang Y, Ouyang Y, Zhang L#, Zhang W#. Inferring historical introgression with deep learning. Syst. Biol. 2023 May 31:syad033.（Cover Story）
Zeng H#*, Zhao D*, Zhang Z, Gao H, Zhang W#. Imperfect ant mimicry contributes to local adaptation in a jumping spider. iScience 2023 May 17:106747. (Reported by Cell Magazine, Science Magazine, NY Times, Phys and ScienceDaily)
Zhang W#, Zhang L, Zhang W. Editorial: The origination of genetic novelties: New genes, new regulations, and new cell types. Front. Genet. 2023 Jan 13:1118926.
Ge D*, Wen Z*, Feijo A*, Lissovsky A*, Zhang W*, Chen J, Yan C, She H, Zhang D, Cheng Y, Lu L, Wu X, Mu D, Zhang Y, Xia L, Qu Y#, Vogler AP#, Yang Q#. Genomic consequences and demographic response to pervasive hybridization over time in climate-sensitive pikas. Mol. Biol. Evol. 2022 Dec 23:msac274.
Wu N*, Evans E, van Schooten B, Meléndez-Rosa J, Ortiz Y, Planas Soto-Navarro SM, Van Belleghem SM, Counterman BA, Papa R#, Zhang W#. Widespread gene expression divergence in butterfly sensory tissues plays a fundamental role during reproductive isolation and speciation. Mol. Biol. Evol. 2022 Nov 3;39(11):msac225.（Cover Story）
Wang S*, Teng D*, Li X*, Yang P, Da W, Zhang Y, Zhang Y, Liu G, Zhang X, Wan W, Dong Z, Wang D, Huang S, Jiang Z, Wang Q, Lohman DJ, Wu Y, Zhang L, Jia F, Westerman E, Zhang L, Wang W, Zhang W#. The evolution and diversification of oakleaf butterflies. Cell 2022 Aug 18;185:1-15.（Cover Story）
王姝婷*，滕德群*，张蔚#。以枯叶蛱蝶属为例揭示山地生物多样性演化和遗传机制。遗传 doi: 10.16288/j.yczz.22-264。
Wang Y*, Fan Y*, Fan D*, Zhang Y, Zhou X, Zhang R, Wang Y, Sun Y, Zhang W, He Y, Deng XW#, Zhu D#. The Arabidopsis DREAM complex antagonizes WDR5A to modulate histone H3K4me2/3 deposition for a subset of genome repression. Proc. Natl. Acad. Sci. USA 2022 Jul 5;119(27):e2206075119. doi: 10.1073/pnas.2206075119.
He J*, Zhang R*, Yang J*, Chang Z*, Zhu L*, Lu S*, Xie F, Mao J, Dong Z, Liu G, Hu P, Dong Y, Wan W, Zhao R, Xiong T, León-Cortés JL, Mao C, Zhang W, Zhan S, Li J, Chen L#, Wang W#, Li X#. High-quality reference genomes of swallowtail butterflies provide insights into their coloration evolution. Zool. Res. 2022 May 18;43(3):367–379. doi: 10.24272/j.issn.2095-8137.2021.303.
Sun T, Song Y, Teng D, Chen Y, Dai J, Ma M, Zhang W, Pastor-Pareja JC#. Atypical laminin spots and pull-generated microtubule-actin projections mediate Drosophila wing adhesion. Cell Rep. 2021 Sep 7;36(10):109667. doi: 10.1016/j.celrep.2021.109667.
Zhang Y*, Teng D*, Lu W*, Liu M*, Zeng H, Cao L, Southcott L, Potdar S, Westerman E, Zhu AJ#, Zhang W#. A widely diverged locus involved in locomotor adaptation in Heliconius butterflies. Sci. Adv. 2021 Aug 4;7(32):eabh2340. doi: 10.1126/sciadv.abh2340.（Cover Story）
Teng D, Li F, Zhang W#. Using comprehensive machine-learning models to classify complex morphological characters. Ecol. Evol. 2021 Jun 27;11(15):10421-10431. doi: 10.1002/ece3.7845.
Feng Y*, Xu H*, Liu J*, Xie N, Gao L, He Y, Yao Y, Lv F, Zhang Y, Lu J, Zhang W, Li C, Hu X#, Yang Z#, Xiao R. Functional and adaptive significance of promoter mutations that affect divergent myocardial expressions of TRIM72 in primates. Mol. Biol. Evol. 2021 Mar 21;msab083. doi: 10.1093/molbev/msab083.
1. Yang J*, Wan W*, Xie M*, Mao J*, Dong Z*, Lu S, He J, Xie F, Liu G, Dai X, Chang Z, Zhao R, Zhang R, Wang S, Zhang Y, Zhang W#, Wang W#, Li X#. Chromosome-level reference genome assembly and gene editing of the dead-leaf butterfly. Mol. Ecol. Resour. 2020 Jul;20(4):1080-1092. doi: 10.1111/1755-0998.13185.（Cover Story）
1. Mullen SP#, VanKuren NW, Zhang W, Nallu S, Kristiansen EB, Wuyun Q, Liu K, Hill RI, Briscoe AD, Kronforst MR. Disentangling population history and character evolution among hybridizing lineages. Mol. Biol. Evol. 2020 Jan 13. pii: msaa004. doi: 10.1093/molbev/msaa004.
1. Zhang W#, Leon-Ricardo BX, van Schooten B, Van Belleghem SM, Counterman BA, McMillan WO, Kronforst MR, Papa R. Comparative transcriptomics provides insights into reticulate and adaptive evolution of a butterfly radiation. Genome Biol. Evol. 2019 Sep 13. 11(10):2963-2975. doi: 10.1093/gbe/evz202.
1. Chen J*#, Huang Y*, Brachi B*, Yun Q*, Zhang W, Lu W, Li H, Li W, Sun X, Wang G, He J, Zhou Z, Chen K, Ji Y, Shi M, Sun W, Yang Y#, Zhang R#, Abbott RJ#, Sun H#. Genome-wide analysis of Cushion willow provides insights into alpine plant divergence in a biodiversity hotspot. Nat. Commun. 2019 Nov 19;10(1):5230. doi: 10.1038/s41467-019-13128-y.
1. Westerman E*, VanKuren N*, Massardo D, Tanger-Trolander A, Zhang W, Hill RI, Perry M, Bayala E, Barr K, Chamberlain N, Douglas TE, Buerkle N, Palmer S, Kronfost MR#. Aristaless controls butterfly wing color variation used in mimicry and mate choice. Curr. Biol. 2018 Nov 5;28(21):3469-3474.e4. doi: 10.1016/j.cub.2018.08.051.
1. Nallu S, Hill JA, Don K, Sahagun C, Zhang W, Meslin C, Snell-Rood E, Clark NL， Morehouse NI, Bergelson J, Wheat CW, Kronforst MR#. The molecular genetic basis of herbivory between butterflies and their host-plants. Nat. Ecol. Evol. 2018 Sep;2(9):1418-1427. doi: 10.1038/s41559-018-0629-9. (Reported by Nature Plants)
1. Zhang W, Westerman E, Nitzany E, Palmer S, Kronforst MR#. Tracing the origin and evolution of supergene mimicry in butterflies. Nat. Commun. 2017 Nov 7;8(1):1269. (Reported by University of Chicago, PBS, and ScienceDaily)
1. Zhang W#, Dasmahapatra KK, Mallet J, Moreira GR, Kronforst MR#. Genome-wide introgression among distantly related Heliconius butterfly species. Genome Biol. 2016 Feb 27;17(1):25. (Research highlighted by Genome Biology)
1. Li X*, Fan D*, Zhang W*, Liu G*, Zhang L*, Zhao L, Fang X, Chen L, Dong Y, Chen Y, Ding Y, Zhao R, Feng M, Zhu Y, Feng Y, Jiang X, Zhu D, Xiang H, Feng X, Li S, Wang J, Zhang G, Kronforst MR#, Wang W#. Outbred genome sequencing and CRISPR/Cas9 gene editing in butterflies. Nat. Commun. 2015 Sep 10;6:8212.
1. Zhan S#, Zhang W, Niitepold K, Hsu J, Fernández Haeger J, Zalucki M, Altizer S, De Roode J, Reppert S, Kronforst MR#. The genetics of monarch butterfly migration and warning colouration. Nature. 2014 Oct 16;514(7522):317-21. (Reported by Nature News & Views, Science Magazine, NY Times, National Geographic, Washington Post and NBC News)
1. Kunte K*#, Zhang W*, Tenger-Trolander A, Palmer D, Martin A, Reed RD, Mullen SP, Kronforst MR#. doublesex is a mimicry supergene. Nature. 2014 Mar 13;507(7491):229-32. (Recommended by Faculty of 1000, reported by Nature News & Views, Nature Magazine, Science Magazine, NY Times, LA Times, Chicago Tribune and University of Chicago and included in An Introduction to Molecular Evolution and Phylogenetics, 2016 Edition and Genetics: From Genes to Genomes (Hartwell), 2017 Edition)
1. Kronforst MR#, Hansen MEB, Crawford NG, Gallant JR, Zhang W, Kulathinal RJ, Kapan DD, Mullen SP#. Hybridization reveals the evolving genomic architecture of speciation. Cell Rep. 2013 Nov 14;5(3):666-77. (Selected as Cell Report Best of 2013 and reported by Science Magazine, The Economist, LA Times and University of Chicago)
1. Zhang W, Kunte K, Kronforst MR#. Genome-wide characterization of adaptation and speciation in tiger swallowtail butterflies using de novo transcriptome assemblies. Genome Biol. Evol. 2013;5(6):1233-45. doi: 10.1093/gbe/evt090. (Cover Story)
Yang C, Zhang W, Ren M, Song L, Li T#, Zhao J#. Whole-genome sequence of Microcystis aeruginosa TAIHU98, a nontoxic bloom-forming strain isolated from Taihu Lake, China. Genome Announ. 2013 Jun 1(3). pii: e00333-13.
Hu Y, Zhang X, Shi Y, Zhou Y, Zhang W, Su XD, Xia B, Zhao J#, Jin C#. Structures of Anabaena calcium-binding Protein CcbP: insights into Ca2+ signaling during heterocyst differentiation. J Biol. Chem. 2011 Apr 286(14): 12381-12388.
Shi Y*, Zhao W*, Zhang W, Ye Z, Zhao J#. Regulation of intracellular free calcium concentration during heterocyst differentiation by HetR and NtcA in Anabaena sp. PCC 7120. Proc. Natl. Acad. Sci. USA 2006 Jul 103(30): 11334-11339.

Method to extract animal genomic dna

With the development of DNA sequencing technology, genome research on arthropods (such as insects and arachnids) has become more extensive and in-depth. Obtaining high-quality genomic DNA is an important basis for subsequent research. Arthropods are rich in chitin, which is a barrier for a large number of phenol-chloroform-based DNA extraction methods that are suitable for normal samples. Besides, DNA extraction kits based on silica film are low efficient and expensive. Therefore, we analyzed the defects of traditional DNA extraction methods that restrict the increase in yield and purity, combining with the characteristics of target samples, to improve the DNA extraction method. Finally, we invented a method that is powerful for DNA extraction from chitin-rich samples of animals, such as arthropods.

Artificial indoor breeding of nymphalidae

After many years of research, we have established a set of methods for indoor breeding and propagation of Nymphalidae under completely artificial conditions, which overcomes the key technical problem of how to effectively reproduce when Nymphalidae is cultivated indoors. Specifically, in the first aspect, the present invention provides a method for rearing and breeding Nymphalidae which means that breeding and breeding are carried out indoors. We use airflow to assist the mating of Nymphalidae. "Indoor" refers to the open space, open-air cultivation or semi-open-air environment of the butterfly's natural growth environment. It means that the butterfly is in a fully enclosed space under completely artificial conditions and is not required at any stage of a complete life history. Is placed in a natural environment. Both rearing and breeding are carried out indoors, which means that the breeding of the butterfly's complete life history is completed under fully artificial or fully artificial conditions, and the normal reproduction close to the natural state can be realized.

A deep learning-based approach for inferring complex evolutionary histories

Resolving the relationships among taxa is one of the fundamental tasks of evolutionary biology. With the development of sequencing technologies and algorithms, more hybridization events between species have been found, which suggests the strict bifurcation phylogenetic trees can not represent the full evolutionary history of species.
Recently, deep learning algorithms have shown remarkable potential for solving population genetics problems. Therefore, we provide a deep learning-based approach using comparative or population genomic data to infer topology structures among species. Simulated sequence data are used to train and test the convolutional neural network; and real genomic data are analyzed based on the trained neural network, combing with other population genetic analyses to determine the evolutionary relationships and introgression loci between taxa.

CRISPR/Cas9 gene editing in spiders

The CRISPR/Cas9 system has been widely used to edit genome sequences, which is useful for understanding the functions of target genes in many species, including diverse arthropods. However, there is no application of CRISPR/Cas9 system in the spiders, a basal arthropod group. Here, we show a CRISPR/Cas9 mediated knockout mutations of Hox genes in the common house spider Parasteatoda tepidariorum. Our results establish CRISPR in spiders that make a contribution to understand the genetic basis of appendages and study the origin and evolution of these features.