Coping with alpine habitats: genomic insights into the adaptation strategies of Triplostegia glandulifera (Caprifoliaceae)

Jian Zhang , Kai-Lin Dong , Miao-Zhen Ren , Zhi-Wen Wang , Jian-Hua Li , Wen-Jing Sun , Xiang Zhao , Xin-Xing Fu , Jian-Fei Ye , Bing Liu , Da-Ming Zhang , Mo-Zhu Wang , Gang Zeng , Yan-Ting Niu , Li-Min Lu , Jun-Xia Su , Zhong-Jian Liu , Pamela S. Soltis , Douglas E. Soltis , Zhi-Duan Chen

Horticulture Research ›› 2024, Vol. 11 ›› Issue (5) : 077

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Horticulture Research ›› 2024, Vol. 11 ›› Issue (5) :077 DOI: 10.1093/hr/uhae077
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Coping with alpine habitats: genomic insights into the adaptation strategies of Triplostegia glandulifera (Caprifoliaceae)
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Abstract

How plants find a way to thrive in alpine habitats remains largely unknown. Here we present a chromosome-level genome assembly for an alpine medicinal herb, Triplostegia glandulifera (Caprifoliaceae), and 13 transcriptomes from other species of Dipsacales. We detected a whole-genome duplication event in T. glandulifera that occurred prior to the diversification of Dipsacales. Preferential gene retention after whole-genome duplication was found to contribute to increasing cold-related genes in T. glandulifera. A series of genes putatively associated with alpine adaptation (e.g. CBF s, ERF-VII s, and RAD51C ) exhibited higher expression levels in T. glandulifera than in its low-elevation relative, Lonicera japonica. Comparative genomic analysis among five pairs of high- vs low-elevation species, including a comparison of T. glandulifera and L. japonica, indicated that the gene families related to disease resistance experienced a significantly convergent contraction in alpine plants compared with their lowland relatives. The reduction in gene repertory size was largely concentrated in clades of genes for pathogen recognition (e.g. CNL s, prRLP s, and XII RLK s), while the clades for signal transduction and development remained nearly unchanged. This finding reflects an energy-saving strategy for survival in hostile alpine areas, where there is a tradeoff with less challenge from pathogens and limited resources for growth. We also identified candidate genes for alpine adaptation (e.g. RAD1, DMC1, and MSH3 ) that were under convergent positive selection or that exhibited a convergent acceleration in evolutionary rate in the investigated alpine plants. Overall, our study provides novel insights into the high-elevation adaptation strategies of this and other alpine plants.

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Jian Zhang, Kai-Lin Dong, Miao-Zhen Ren, Zhi-Wen Wang, Jian-Hua Li, Wen-Jing Sun, Xiang Zhao, Xin-Xing Fu, Jian-Fei Ye, Bing Liu, Da-Ming Zhang, Mo-Zhu Wang, Gang Zeng, Yan-Ting Niu, Li-Min Lu, Jun-Xia Su, Zhong-Jian Liu, Pamela S. Soltis, Douglas E. Soltis, Zhi-Duan Chen. Coping with alpine habitats: genomic insights into the adaptation strategies of Triplostegia glandulifera (Caprifoliaceae). Horticulture Research, 2024, 11 (5) : 077 DOI:10.1093/hr/uhae077

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Acknowledgements

This work was supported by grants from the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 32221001), the National Natural Science Foundation of China (General Program) (No. 32070233), the National Key Research Development Program of China (No. 2023YFF0805800), and the Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University (No. 72202200205). We thank Rong-Hua Liang and Jin-Dan Zhang from the Plant Science Facility of the Institute of Botany, Chinese Academy of Sciences, for their excellent technical assistance. We thank Li-Xin Zhou for providing plant photo.

Author contributions

Z.D.C. and D.E.S. led and managed the project; Z.D.C. and J.Z. conceived the study; J.Z., K.L.D., and M.Z.R. wrote the manuscript; Z.D.C., J.F.Y., and B.L. collected and identified the plant materials, and provided the photos of the investigated plants; X.X.F., G.Z., J.Z., K.L.D., M.Z.R., and Y.T.N. cultured and prepared materials; X.X.F. and J.Z. performed the flow cytometry estimate; X.X.F., J.Z., and D.M.Z. performed the chromosome number estimate; Z.W.W. and X.Z. assembled and annotated the genomes and transcriptomes; Z.W.W., X.Z., and J.Z. performed phylogenomic and divergence time analyses; K.L.D., M.Z.R., and J.Z. analyzed gene families; Z.W.W., X.Z., and J.Z. conducted WGD analysis; Z.W.W., X.Z., W.J.S., and M.Z.W. performed gene expression and functional enrichment analyses; Z.W.W., X.Z., J.Z., and K.L.D. detected the FEGs and PSGs. Z.D.C., D.E.S., P.S.S., Z.J.L., J.H.L., L.M.L., and J.X.S. evaluated the results and contributed substantially to revisions. All authors read and approved the manuscript.

Data availability

The raw sequencing data of the T. glandulifera genome and transcriptome have been deposited in the National Genomics Data Center (https://ngdc.cncb.ac.cn/, CNCB) under the BioProject accession number PRJCA019252. The T. glandulifera genome assembly and annotation files have been submitted to FigShare (http://doi.org/10.6084/m9.figshare.25018103). The RNA-seq reads of 13 Dipsacales species are available from CNCB under the BioProject accession number PRJCA019358. Public transcriptomes used in this study are available from NCBI under the accession numbers SRX7804715 and DRX054302. All data are available from the corresponding author upon reasonable request.

Conflict of interest statement

The authors declare no competing interests.

Supplementary data

Supplementary data are available at Horticulture Research online.

References

[1]

Antonelli A, Kissling WD, Flantua SGA. et al. Geological and climatic influences on mountain biodiversity. Nat Geosci. 2018; 11: 718-25

[2]

Pepin N, Bradley R, Diaz H. et al. Elevation-dependent warming in mountain regions of the world. Nat Clim Chang. 2015; 5:424-30

[3]

Körner C. Alpine Plant Life. Berlin: Springer; 2003:

[4]

Sun H, Niu Y, Chen YS. et al. Survival and reproduction of plant species in the Qinghai-Tibet plateau. J Syst Evol. 2014; 52:378-96

[5]

Zhang X, Kuang T, Dong W. et al. Genomic convergence underlying high-altitude adaptation in alpine plants. J Integr Plant Biol. 2023; 65:1620-35

[6]

Wang X, Liu S, Zuo H. et al. Genomic basis of high-altitude adaptation in Tibetan Prunus fruit trees. Curr Biol. 2021; 31:3848-3860.e8

[7]

Liu XW, Wang YH, Shen SK. Transcriptomic and metabolomic analyses reveal the altitude adaptability and evolution of different-colored flowers in alpine Rhododendron species. Tree Physiol. 2022; 42:1100-13

[8]

Ma L, Sun XD, Kong XX. et al. Physiological, biochemical and proteomics analysis reveals the adaptation strategies of the alpine plant Potentilla saundersiana at altitude gradient of the northwestern Tibetan plateau. J Proteomics. 2015; 112:63-82

[9]

Mao KS, Wang Y, Liu JQ. Evolutionary origin of species diversity on the Qinghai-Tibet plateau. J Syst Evol. 2021; 59:1142-58.

[10]

Dullinger S, Gattringer A, Thuiller W. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat Clim Chang. 2012; 2:619-22

[11]

Nomoto HA, Alexander JM. Drivers of local extinction risk in alpine plants under warming climate. Ecol Lett. 2021; 24:1157-66

[12]

Niu YT, Ye JF, Zhang JL. et al. Long-distance dispersal or postglacial contraction? Insights into disjunction between Himalaya-Hengduan Mountains and Taiwan in a cold-adapted herbaceous genus. Ecol Evol. 2018; 8:1131-46

[13]

Hong D, Ma L, Barrie FR. Dipsacaceae. In: Wu Z, Hong D, Raven PH, Science Press eds. Flora of China. & Missouri Botanical Garden Press: Beijing & St Louis, 2011,654-60

[14]

Angiosperm Phylogeny Group. An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG IV. Bot J Linn Soc. 2016; 181:1-20

[15]

Kadereit JW, Bittrich V. (eds). Flowering Plants. Eudicots: Aquifoliales, Boraginales, Bruniales, Dipsacales, Escalloniales, Garryales, Paracryphiales, Solanales (except Convolvulaceae), Icacinaceae, Metteniusaceae, Vahliaceae. Cham: Springer; 2016:

[16]

Pu X, Li Z, Tian Y. et al. The honeysuckle genome provides insight into the molecular mechanism of carotenoid metabolism underlying dynamic flower coloration. New Phytol. 2020; 227:930-43

[17]

Simão FA, Waterhouse RM, Ioannidis P. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015; 31:3210-2

[18]

Linnert C, Robinson SA, Lees JA. et al. Evidence for global cooling in the late cretaceous. Nat Commun. 2014; 5:4194

[19]

Scotese CR, Song H, Mills BJW. et al. Phanerozoic paleotemperatures: the earth’s changing climate during the last 540 million years. Earth Sci Rev. 2021; 215:103503

[20]

Jiao Y, Leebens-Mack J, Ayyampalayam S. et al. A genome triplication associated with early diversification of the core eudicots. Genome Biol. 2012; 13:R3

[21]

Jaillon O, Aury JM, Noel B. et al. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature. 2007; 449:463-7

[22]

Qiao X, Li Q, Yin H. et al. Gene duplication and evolution in recurring polyploidization-diploidization cycles in plants. Genome Biol. 2019; 20:38

[23]

Gusain S, Joshi S, Joshi R. Sensing, signalling, and regulatory mechanism of cold-stress tolerance in plants. Plant Physiol Biochem. 2023; 197:107646

[24]

Shi Y, Ding Y, Yang S. Molecular regulation of CBF signaling in cold acclimation. Trends Plant Sci. 2018; 23:623-37

[25]

Satyakam ZG, Singh RK. et al. Cold adaptation strategies in plants—an emerging role of epigenetics and antifreeze proteins to engineer cold resilient plants. Front Genet. 2022; 13:909007

[26]

Loreti E, Perata P. The many facets of hypoxia in plants. Plants (Basel). 2020; 9:745

[27]

Aoyagi Blue Y, Kusumi J, Satake A. Copy number analyses of DNA repair genes reveal the role of poly(ADP-ribose) polymerase (PARP) in tree longevity. iScience. 2021; 24:102779

[28]

Szurman-Zubrzycka M, Jędrzejek P, Szarejko I. How do plants cope with DNA damage? A concise review on the DDR pathway in plants. Int J Mol Sci. 2023; 24:2404

[29]

De Bie T, Cristianini N, Demuth JP. et al. CAFÉ: a computational tool for the study of gene family evolution. Bioinformatics. 2006; 22:1269-71

[30]

Liu Y, Zhang YM, Tang Y. et al. The evolution of plant NLR immune receptors and downstream signal components. Curr Opin Plant Biol. 2023; 73:102363

[31]

Wang Y, Bouwmeester K. L-type lectin receptor kinases: new forces in plant immunity. PLoS Pathog. 2017; 13:e1006433

[32]

Zhang Y, Tian H, Chen D. et al. Cysteine-rich receptor-like protein kinases: emerging regulators of plant stress responses. Trends Plant Sci. 2023; 28:776-94

[33]

Jones JDG, Dangl JL. The plant immune system. Nature. 2006; 444:323-9

[34]

Ngou BPM, Ding P, Jones JDG. Thirty years of resistance: zig-zag through the plant immune system. Plant Cell. 2022; 34:1447-78

[35]

Shao ZQ, Xue JY, Wu P. et al. Large-scale analyses of angiosperm nucleotide-binding site-leucine-rich repeat genes reveal three anciently diverged classes with distinct evolutionary patterns. Plant Physiol. 2016; 170:2095-109

[36]

Bentham AR, Zdrzałek R, De la Concepcion JC. et al. Uncoiling CNLs: structure/function approaches to understanding CC domain function in plant NLRs. Plant Cell Physiol. 2018; 59:2398-408

[37]

Kang WH, Yeom SI. Genome-wide identification, classification, and expression analysis of the receptor-like protein family in tomato. Plant Pathol J. 2018; 34:435-44

[38]

Steidele CE, Stam R. Multi-omics approach highlights differences between RLP classes in Arabidopsis thaliana. BMC Genomics. 2021; 22:557

[39]

Dufayard JF, Bettembourg M, Fischer I. et al. New insights on leucine-rich repeats receptor-like kinase orthologous relationships in angiosperms. Front. Plant Sci. 2017; 8:381

[40]

Tenaillon O, Rodríguez-Verdugo A, Gaut RL. The molecular diversity of adaptive convergence. Science. 2012; 335:457-61

[41]

Moreno-Estrada A, Tang K, Sikora M. et al. Interrogating 11 fast evolving genes for signatures of recent positive selection in worldwide human populations. Mol Biol Evol. 2009; 26:2285-97

[42]

Wu RH, Li SB, He S. et al. CFL1, a WW domain protein, regulates cuticle development by modulating the function of HDG1, a class IV homeodomain transcription factor, in rice and Arabidopsis. Plant Cell. 2011; 23:3392-411

[43]

Deeks MJ, Kaloriti D, Davies B. et al. Arabidopsis NAP1 is essential for Arp2/3-dependent trichome morphogenesis. Curr Biol. 2004; 14:1410-4

[44]

Rojo E, Gillmor CS, Kovaleva V. et al. VACUOLELESS1 is an essential gene required for vacuole formation and morphogenesis in Arabidopsis. Dev Cell. 2001; 1:303-10

[45]

Preston JC, Sandve SR. Adaptation to seasonality and the winter freeze. Front Plant Sci. 2013; 4:167

[46]

Wu S, Han B, Jiao Y. Genetic contribution of paleopolyploidy to adaptive evolution in angiosperms. Mol Plant. 2020; 13:59-71

[47]

Song XM, Wang JP, Sun PC. et al. Preferential gene retention increases the robustness of cold regulation in Brassicaceae and other plants after polyploidization. Hortic Res. 2020; 7:20

[48]

Van de Peer Y, Ashman TL, Soltis PS. et al. Polyploidy: an evolutionary and ecological force in stressful times. Plant Cell. 2021; 33:11-26

[49]

Nottingham AT, Fierer N, Turner BL. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Geol Soc Am Bull. 2019; 100:e01452

[50]

Shen C, Shi Y, Fan K. et al. Soil pH dominates elevational diversity pattern for bacteria in high elevation alkaline soils on the Tibetan plateau. FEMS Microbiol Ecol. 2019; 95

[51]

Ngou BPM, Heal R, Wyler M. et al. Concerted expansion and contraction of immune receptor gene repertoires in plant genomes. Nat Plants. 2022; 8:1146-52

[52]

Qu Y, Zhao HG, Han NJ. et al. Ground tit genome reveals avian adaptation to living at high altitudes in the Tibetan plateau. Nat Commun. 2013; 4:2071

[53]

Zhou C, James JG, Xu Y. et al. Genome-wide analysis sheds light on the high-altitude adaptation of the buff-throated partridge ( Tetraophasis szechenyii ). Mol Gen Genomics. 2020; 295:31-46

[54]

Takken FLW, Tameling WIL. To nibble at plant resistance proteins. Science. 2009; 324:744-6

[55]

Denancé N, Sánchez-Vallet A, Goffner D. et al. Disease resistance or growth: the role of plant hormones in balancing immune responses and fitness costs. Front. Plant Sci. 2013; 4:155

[56]

Tian D, Traw MB, Chen JQ. et al. Fitness costs of R-gene-mediated resistance in Arabidopsis thaliana. Nature. 2003; 423:74-7

[57]

Fukao T, Bailey-Serres J. Plant responses to hypoxia—is survival a balancing act? Trends Plant Sci. 2004; 9:449-56

[58]

Kerbler SM, Taylor NL, Millar AH. Cold sensitivity of mitochondrial ATP synthase restricts oxidative phosphorylation in Arabidopsis thaliana. New Phytol. 2019; 221:1776-88

[59]

Karasov TL, Chae E, Herman JJ. et al. Mechanisms to mitigate the trade-off between growth and defense. Plant Cell. 2017; 29:666-80

[60]

Chae E, Tran DTN, Weigel D. Cooperation and conflict in the plant immune system. PLoS Pathog. 2016; 12:e1005452

[61]

Feng L, Lin H, Kang M. et al. A chromosome-level genome assembly of an alpine plant Crucihimalaya lasiocarpa provides insights into high-altitude adaptation. DNA Res. 2022; 29:dsac004

[62]

Shang L, He W, Wang T. et al. A complete assembly of the rice Nipponbare reference genome. Mol Plant. 2023; 16:1232-6

[63]

Chin CS, Peluso P, Sedlazeck FJ. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat Methods. 2016; 13:1050-4

[64]

Chin CS, Alexander DH, Marks P. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods. 2013; 10:563-9

[65]

Walker BJ, Abeel T, Shea T. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014; 9:e112963

[66]

Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013. Preprint at arXiv:1303.3997

[67]

Adey A, Kitzman JO, Burton JN. et al. In vitro, long-range sequence information for de novo genome assembly via transposase contiguity. Genome Res. 2014; 24:2041-9

[68]

Burton JN, Adey A, Patwardhan RP. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat Biotechnol. 2013; 31:1119-25

[69]

Grabherr MG, Haas BJ, Yassour M. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011; 29:644-52

[70]

Dhiman N, Kumar A, Kumar D. et al. De novo transcriptome analysis of the critically endangered alpine Himalayan herb Nardostachys jatamansi reveals the biosynthesis pathway genes of tissue-specific secondary metabolites. Sci Rep. 2020; 10:17186

[71]

Zhao KK, Wang HF, Sakaguchi S. et al. Development and characterization of EST-SSR markers in an east Asian temperate plant genus Diabelia (Caprifoliaceae). Plant Species Biology. 2017; 32:247-51

[72]

Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999; 27:573-80

[73]

Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009; 25:4.10.1-14.

[74]

Jurka J, Kapitonov VV, Pavlicek A. et al. Repbase update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005; 110:462-7

[75]

Stanke M, Steinkamp R, Waack S. et al. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 2004; 32:W309-12

[76]

Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004; 20:2878-9

[77]

Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004; 5:59

[78]

Alioto T, Blanco E, Parra G. et al. Using geneid to identify genes. Curr Protoc Bioinformatics. 2018; 64:e56

[79]

Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J Mol Biol. 1997; 268:78-94

[80]

Trapnell C, Roberts A, Goff L. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012; 7:562-78

[81]

Haas BJ, Delcher AL, Mount SM. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 2003; 31:5654-66

[82]

Haas BJ, Salzberg SL, Zhu W. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008; 9:R7

[83]

Kalvari I, Argasinska J, Quinones-Olvera N. et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. 2018; 46:D335-42

[84]

Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013; 29:2933-5

[85]

Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997; 25:955-64

[86]

Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015; 16:157

[87]

Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019; 20:1160-6

[88]

Suyama M, Torrents D, Bork P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006; 34:W609-12

[89]

Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014; 30:1312-3

[90]

Mirarab S, Warnow T. ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics. 2015; 31:i44-52

[91]

Yang Z. PAML 4: Phylogenetic Analysis by Maximum Likelihood. Mol Biol Evol. 2007; 24:1586-91

[92]

Magallón S, Gómez-Acevedo S, Sánchez-Reyes LL. et al. A meta calibrated time-tree documents the early rise of flowering plant phylogenetic diversity. New Phytol. 2015; 207:437-53

[93]

Lee AK, Gilman IS, Srivastav M. et al. Reconstructing Dipsacales phylogeny using Angiosperms353: issues and insights. Am J Bot. 2021; 108:1122-42

[94]

Moore BR, Donoghue MJ. Correlates of diversification in the plant clade Dipsacales: geographic movement and evolutionary innovations. Am Nat. 2007; 170:S28-55

[95]

Zwaenepoel A, Van de Peer Y. wgd—simple command line tools for the analysis of ancient whole-genome duplications. Bioinformatics. 2019; 35:2153-5

[96]

Proost S, Fostier J, De Witte D. et al. I-ADHoRe 3.0-fast and sensitive detection of genomic homology in extremely large data sets. Nucleic Acids Res. 2012; 40:e11

[97]

Li Z, Baniaga AE, Sessa EB. et al.Early genome duplications in conifers and other seed plants. Sci Adv. 2015; 1:e1501084

[98]

Tang H, Bowers JE, Wang X. et al. Synteny and collinearity in plant genomes. Science. 2008; 320:486-8

[99]

Patro R, Duggal G, Love MI. et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017; 14:417-9

[100]

Zhang M, Li MX, Fu HW. et al. Transcriptomic analysis unravels the molecular response of Lonicera japonica leaves to chilling stress. Front. Plant Sci. 2022; 13:1092857

[101]

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26:139-40

[102]

Yu G, Wang LG, Han Y. et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16:284-7

[103]

Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011; 7:e1002195

[104]

Artur MAS, Zhao T, Ligterink W. et al. Dissecting the genomic diversification of late embryogenesis abundant (LEA) protein gene families in plants. Genome Biol Evol. 2019; 11:459-71

[105]

Hundertmark M, Hincha DK. LEA (late embryogenesis abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics. 2008; 9:118

[106]

Jones JDG, Vance RE, Dangl JL. Intracellular innate immune surveillance devices in plants and animals. Science. 2016; 354:aaf6395

[107]

Dubey N, Singh K. Role of NBS-LRR proteins in plant defense. In: Singh A, Singh IK,eds. Molecular Aspects of Plant-Pathogen Interaction. Springer: Singapore, 2018,115-38

[108]

Lupas A, Van Dyke M, Stock J. Predicting coiled coils from protein sequences. Science. 1991; 252:1162-4

[109]

Seo E, Kim S, Yeom SI. et al. Genome-wide comparative analyses reveal the dynamic evolution of nucleotide-binding leucine-rich repeat gene family among Solanaceae plants. Front. Plant Sci. 2016; 7:1205

[110]

Sun J, Li L, Wang P. et al. Genome-wide characterization, evolution, and expression analysis of the leucine-rich repeat receptor-like protein kinase (LRR-RLK) gene family in Rosaceae genomes. BMC Genomics. 2017; 18:763

[111]

Liu PL, Du L, Huang Y. et al. Origin and diversification of leucine-rich repeat receptor-like protein kinase (LRR-RLK) genes in plants. BMC Evol Biol. 2017; 17:47

[112]

Krogh A, Larsson B, von Heijne G. et al. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001; 305:567-80

[113]

Zipfel C. Plant pattern-recognition receptors. Trends Immunol. 2014; 35:345-51

[114]

Wang G, Ellendorff U, Kemp B. et al. A genome-wide functional investigation into the roles of receptor-like proteins in Arabidopsis. Plant Physiol. 2008; 147:503-17

[115]

Minh BQ, Schmidt HA, Chernomor O. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020; 37:1530-4

[116]

Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000; 17:540-52

[117]

Storey JD, Tibshirani R. Statistical significance for genome wide studies. Proc Natl Acad Sci USA. 2003; 100:9440-5

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