物种分化因素影响下的巴尔通体差异转录组分析

doi:10.16288/j.yczz.24-201

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Hereditas(Beijing) ›› 2025, Vol. 47 ›› Issue (3): 366-381.doi: 10.16288/j.yczz.24-201

• Research Article • Previous Articles Next Articles

Differential transcriptome profiling of Bartonella spp. influenced by the species divergence factors

Min Chen(), Na Han, Yu Miao, Yujun Qiang, Wen Zhang, Pengbo Liu, Qiyong Liu, Dongmei Li()

National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China

Received:2024-07-01 Revised:2024-09-18 Online:2025-03-20 Published:2024-09-25
Contact: Dongmei Li E-mail:cm593691225@163.com;lidongmei@icdc.cn
Supported by:
National Natural Science Foundation of China(31970005);Operation of Public Health Emergency Response Mechanisms-Infectious Disease Prevention and Control(102393220020020000029)

Abstract

Abstract:

To reveal the differences in transcript levels of Bartonella spp. from different species and hosts and their impacts on phylogenetic relationships, we focus on 27 strains from four Bartonella species (B. henselae, B. koehlerae, B. clarridgeiae and B. quintana) and three hosts (Felis catus, Homo sapiens and Macaca mulatta) to conduct the transcriptome sequencing using Illumina high-throughput sequencing technology. Gene expression differences between strains from different species and hosts are analyzed, and the results of phylogenetic analysis at the transcriptome and genome levels are compared. The results show significant differences in gene transcription between strains from different species and hosts. Twelve genes are screened, including virB10, bepC and virB4, which may facilitate host-specific recognition. Furthermore, phylogenetic analysis based on SNPs within the core genes of the transcriptome demonstrate species-specific clustering patterns among strains. Further analysis indicate that host factors influence the genetic divergence of strains, while geographic factors exert a small impact on this process. These findings are congruent with the phylogenetic analysis of SNPs in the core genes of the genome. Our study uses differential transcriptome analysis to reveal the genetic divergence and phylogenetic relationships of Bartonella species. And the observed regular differences between strains from different species and hosts are found to correspond with the results of traditional genome analysis. Thus, our results indicate the utility of transcriptome data in efficiently investigating the genetic divergence between species.

Key words: Bartonella, transcriptome, species divergence, genome, phylogeny

Min Chen, Na Han, Yu Miao, Yujun Qiang, Wen Zhang, Pengbo Liu, Qiyong Liu, Dongmei Li. Differential transcriptome profiling of Bartonella spp. influenced by the species divergence factors[J]. Hereditas(Beijing), 2025, 47(3): 366-381.

Figures/Tables 11

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References 73

[1]	Wagner A, Dehio C. Role of distinct type-IV-secretion systems and secreted effector sets in host adaptation by pathogenic Bartonella species. Cell Microbiol, 2019, 21(3): e13004.
[2]	Chomel BB, Boulouis HJ, Breitschwerdt EB, Kasten RW, Vayssier-Taussat M, Birtles RJ, Koehler JE, Dehio C. Ecological fitness and strategies of adaptation of Bartonella species to their hosts and vectors. Vet Res, 2009, 40(2): 29.
[3]	Dehio C. Bartonella-host-cell interactions and vascular tumour formation. Nat Rev Microbiol, 2005, 3(8): 621-631. pmid: 16064054
[4]	Kosoy M, McKee C, Albayrak L, Fofanov Y. Genotyping of Bartonella bacteria and their animal hosts: current status and perspectives. Parasitology, 2018, 145(5): 543-562. doi: 10.1017/S0031182017001263 pmid: 28764816
[5]	O'Rourke F, Schmidgen T, Kaiser PO, Linke D, Kempf VAJ. Adhesins of Bartonella spp. Adv Exp Med Biol, 2011, 715: 51-70. doi: 10.1007/978-94-007-0940-9_4 pmid: 21557057
[6]	Tahmasebi Ashtiani Z, Ahmadinezhad M, Bagheri Amiri F, Esmaeili S. Geographical distribution of Bartonella spp in the countries of the WHO Eastern Mediterranean Region (WHO-EMRO). J Infect Public Health, 2024, 17(4): 612-618.
[7]	Sricharern W, Kaewchot S, Saengsawang P, Kaewmongkol S, Inpankaew T. Molecular detection of Bartonella quintana among long-tailed macaques (Macaca fascicularis) in Thailand. Pathogens, 2021, 10(5): 629.
[8]	Vayssier-Taussat M, Le Rhun D, Bonnet S, Cotté V. Insights in Bartonella host specificity. Ann N Y Acad Sci, 2009, 1166: 127-132.
[9]	Mosepele M, Mazo D, Cohn J. Bartonella infection in immunocompromised hosts: immunology of vascular infection and vasoproliferation. Clin Dev Immunol, 2012, 2012: 612809.
[10]	Regier Y, O Rourke F, Kempf VAJ. Bartonella spp. - a chance to establish One Health concepts in veterinary and human medicine. Parasit Vectors, 2016, 9(1): 261.
[11]	Yap SM, Saeed M, Logan P, Healy DG. Bartonella neuroretinitis (cat-scratch disease). Pract Neurol, 2020, 20(6): 505-506.
[12]	Su ZH, Sasaki A, Kusumi J, Chou PA, Tzeng HY, Li HQ, Yu H. Pollinator sharing, copollination, and speciation by host shifting among six closely related dioecious fig species. Commun Biol, 2022, 5(1): 284.
[13]	Maier PA, Vandergast AG, Bohonak AJ. Yosemite toad (Anaxyrus canorus) transcriptome reveals interplay between speciation genes and adaptive introgression. Mol Ecol, 2024, 33(8): e17317.
[14]	McCulloch GA, Foster BJ, Dutoit L, Harrop TWR, Guhlin J, Dearden PK, Waters JM. Genomics reveals widespread ecological speciation in flightless insects. Syst Biol, 2021, 70(5): 863-876. doi: 10.1093/sysbio/syaa094 pmid: 33346837
[15]	Wang YJ, Qiao ZL, Mao LY, Li F, Liang XL, An X, Zhang SZ, Liu X, Kuang ZR, Wan N, Nevo E, Li KX. Sympatric speciation of the spiny mouse from Evolution Canyon in Israel substantiated genomically and methylomically. Proc Natl Acad Sci USA, 2022, 119(13): e2121822119.
[16]	Liu X, Zhang SZ, Cai ZY, Kuang ZR, Wan N, Wang YJ, Mao LY, An X, Li F, Feng T, Liang XL, Qiao ZL, Nevo E, Li KX. Genomic insights into zokors' phylogeny and speciation in China. Proc Natl Acad Sci USA, 2022, 119(19): e2121819119.
[17]	Zhou CW, Xiao SJ, Liu YC, Mou ZB, Zhou JS, Pan YZ, Zhang C, Wang J, Deng XX, Zou M, Liu HP. Comprehensive transcriptome data for endemic Schizothoracinae fish in the Tibetan Plateau. Sci Data, 2020, 7(1): 28. doi: 10.1038/s41597-020-0361-6 pmid: 31964888
[18]	Wang XW, Zhao QY, Luan JB, Wang YJ, Yan GH, Liu SS. Analysis of a native whitefly transcriptome and its sequence divergence with two invasive whitefly species. BMC Genomics, 2012, 13: 529.
[19]	Abromaitis S, Nelson CS, Previte D, Yoon KS, Clark JM, DeRisi JL, Koehler JE. Bartonella quintana deploys host and vector temperature-specific transcriptomes. PLoS One, 2013, 8(3): e58773.
[20]	Tu N, Carroll RK, Weiss A, Shaw LN, Nicolas G, Thomas S, Lima A, Okaro U, Anderson B. A family of genus-specific RNAs in tandem with DNA-binding proteins control expression of the badA major virulence factor gene in Bartonella henselae. Microbiologyopen, 2017, 6(2): e00420.
[21]	Shen W, Le S, Li Y, Hu FQ. SeqKit: a cross-platform and ultrafast toolkit for fasta/q file manipulation. PLoS One, 2016, 11(10): e0163962.
[22]	Guo JY, Sun YL, Luo XY, Li MX, He P, He L, Zhao JL. De novo transcriptome sequencing and comparative analysis of Haemaphysalis flava Neumann, 1897 at larvae and nymph stages. Infect Genet Evol, 2019, 75: 104008.
[23]	Fu LM, Niu BF, Zhu ZW, Wu ST, Li WZ. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23): 3150-3152. doi: 10.1093/bioinformatics/bts565 pmid: 23060610
[24]	Seppey M, Manni M, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness. Methods Mol Biol, 2019, 1962: 227-245. doi: 10.1007/978-1-4939-9173-0_14 pmid: 31020564
[25]	Roegner ME, Watson RD. De novo transcriptome assembly and functional annotation for y-organs of the blue crab (Callinectes sapidus), and analysis of differentially expressed genes during pre-molt. Gen Comp Endocrinol, 2020, 298: 113567.
[26]	Fobe TL, Kazakov A, Riccardi D. Cys.sqlite: a structured-information approach to the comprehensive analysis of cysteine disulfide bonds in the protein databank. J Chem Inf Model, 2019, 59(2): 931-943. doi: 10.1021/acs.jcim.8b00950 pmid: 30694665
[27]	Zhao SR, Ye Z, Stanton R. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA, 2020, 26(8): 903-909. doi: 10.1261/rna.074922.120 pmid: 32284352
[28]	Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014, 15(12): 550.
[29]	Sarma RJ, Subbarayan S, Zohmingthanga J, Chenkual S, Zomuana T, Lalruatfela ST, Pautu JL, Maitra A, Kumar NS. Transcriptome analysis reveals SALL4 as a prognostic key gene in gastric adenocarcinoma. J Egypt Natl Canc Inst, 2022, 34(1): 11.
[30]	Yu GC, Wang LG, Han YY, He QY. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5): 284-287. doi: 10.1089/omi.2011.0118 pmid: 22455463
[31]	Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol, 2015, 33(3): 290-295. doi: 10.1038/nbt.3122 pmid: 25690850
[32]	Pertea G, Pertea M. Gff utilities: GffRead and GffCompare. F1000Res, 2020, 9: 304. doi: 10.12688/f1000research.23297.1 pmid: 32489650
[33]	Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics, 2014, 30(14): 2068-2069. doi: 10.1093/bioinformatics/btu153 pmid: 24642063
[34]	Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, Fookes M, Falush D, Keane JA, Parkhill J. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics, 2015, 31(22): 3691-3693. doi: 10.1093/bioinformatics/btv421 pmid: 26198102
[35]	Page AJ, Taylor B, Delaney AJ, Soares J, Seemann T, Keane JA, Harris SR. SNP-sites: rapid efficient extraction of SNPs from multi-fasta alignments. Microb Genom, 2016, 2(4): e000056.
[36]	Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol, 2015, 32(1): 268-274.
[37]	Huelsenbeck JP, Ronquist F. MrBayes: bayesian inference of phylogenetic trees. Bioinformatics, 2001, 17(8): 754-755. doi: 10.1093/bioinformatics/17.8.754 pmid: 11524383
[38]	Li YY, Lin SJ, Lu YH, Hu HL, Wang ZH. Research on diversity and phylogenetics of Trametes in Fujian province. Guangdong AG Sci, 2014, 41(10): 146-151, 159.
	李央央, 林顺吉, 芦宇航, 胡红莉, 王宗华. 福建栓菌多样性及分子系统学研究. 广东农业科学, 2014, 41(10): 146-151, 159.
[39]	Zheng ZY, Li Y, Li MJ, Li GT, Du X, Hongyin H, Yin M, Lu ZQ, Zhang X, Shrestha N, Liu JQ, Yang YZ. Whole-genome diversification analysis of the Hornbeam species reveals speciation and adaptation among closely related species. Front Plant Sci, 2021, 12: 581704.
[40]	Morales P, Gajardo F, Valdivieso C, Valladares MA, Di Genova A, Orellana A, Gutiérrez RA, González M, Montecino M, Maass A, Méndez MA, Allende ML. Genomes of the Orestias pupfish from the Andean Altiplano shed light on their evolutionary history and phylogenetic relationships within Cyprinodontiformes. BMC Genomics, 2024, 25(1): 614. doi: 10.1186/s12864-024-10416-w pmid: 38890559
[41]	Fu PC, Twyford AD, Hao YT, Zhang Y, Chen SL, Sun SS. Hybridization and divergent climatic preferences drive divergence of two allopatric Gentiana species on the Qinghai-Tibet Plateau. Ann Bot, 2023, 132(7): 1271-1288.
[42]	Dool SE, Picker MD, Eberhard MJB. Limited dispersal and local adaptation promote allopatric speciation in a biodiversity hotspot. Mol Ecol, 2022, 31(1): 279-295.
[43]	Liu R, Ma LY, Wang HM, Liu D, Lu XL, Huang XP, Huang SS, Liu XD. Comparative genomics reveals intraspecific divergence of Acidithiobacillus ferrooxidans: insights from evolutionary adaptation. Microb Genom, 2023, 9(6): mgen001038.
[44]	Cridland JM, Contino CE, Begun DJ. Selection and geography shape male reproductive tract transcriptomes in Drosophila melanogaster. Genetics, 2023, 224(1): iyad034.
[45]	Chen N, Zhang H, Zang E, Liu ZX, Lan YF, Hao WL, He S, Fan X, Sun GL, Wang YL. Adaptation insights from comparative transcriptome analysis of two Opisthopappus species in the Taihang mountains. BMC Genomics, 2022, 23(1): 466.
[46]	Jin WT, Gernandt DS, Wehenkel C, Xia XM, Wei XX, Wang XQ. Phylogenomic and ecological analysis reveal the spatiotemporal evolution of global pines. Proc Natl Acad Sci USA, 2021, 118(20): e2022302118.
[47]	Zhao YJ, Cao Y, Wang J, Xiong Z. Transcriptome sequencing of Pinus kesiya var. langbianensis and comparative analysis in the Pinus phylogeny. BMC Genomics, 2018, 19(1): 725.
[48]	McKee CD, Hayman DTS, Kosoy MY, Webb CT. Phylogenetic and geographic patterns of Bartonella host shifts among bat species. Infect Genet Evol, 2016, 44: 382-394.
[49]	McKee CD, Bai Y, Webb CT, Kosoy MY. Bats are key hosts in the radiation of mammal-associated Bartonella bacteria. Infect Genet Evol, 2021, 89: 104719.
[50]	Liu HW, Li CX, Li Fen, Lu CJ, Wu SY, Qin WQ. Transcriptome analysis and gene function annotation of Tetrastichus brontispae. Chin J Biol control, 2021, 37(3): 412-419.
	刘华伟, 李朝绪, 李芬, 吕朝军, 吴少英, 覃伟权. 椰心叶甲啮小蜂转录组分析及基因功能注释. 中国生物防治学报, 2021, 37(3): 412-419. doi: 10.16409/j.cnki.2095-039x.2021.03.016
[51]	Xin Jing, Li BIN, Ye Peng, Liu Cheng, Tang JR, Zhang GL, Xin PY. Transcriptome sequence analysis and functional annotation of Camellia fascicularis H. T. Chang. Non-wood Forest Res, 2020, 38(3): 85-94.
	辛静, 李斌, 叶鹏, 刘成, 唐军荣, 张贵良, 辛培尧. 云南金花茶转录组序列分析及功能注释. 经济林研究, 2020, 38(3): 85-94.
[52]	Liu D, Zeng QM, Liu B, Li Y, Chen SP. Transcriptome analysis and gene functional annotation in Phoebe bournei. BBR, 2020, 40(4): 613-622.
	刘丹, 曾钦朦, 刘斌, 李煜, 陈世品. 闽楠转录组分析及基因功能注释. 植物研究, 2020, 40(4): 613-622. doi: 10.7525/j.issn.1673-5102.2020.04.016
[53]	Faure E, Kwong K, Nguyen D. Pseudomonas aeruginosa in chronic lung infections: how to adapt within the host? Front Immunol, 2018, 9: 2416.
[54]	Buffet JP, Pisanu B, Brisse S, Roussel S, Félix B, Halos L, Chapuis JL, Vayssier-Taussat M. Deciphering Bartonella diversity, recombination, and host specificity in a rodent community. PLoS One, 2013, 8(7): e68956.
[55]	Fromm K, Dehio C. The impact of Bartonella VirB/VirD4 type IV secretion system effectors on eukaryotic host cells. Front Microbiol, 2021, 12: 762582.
[56]	Schröder G, Dehio C. Virulence-associated type IV secretion systems of Bartonella. Trends Microbiol, 2005, 13(7): 336-342. pmid: 15935675
[57]	Wang CY, Zhang HR, Fu JQ, Wang M, Cai YH, Ding TY, Jiang JZ, Koehler JE, Liu XY, Yuan CL. Bartonella type IV secretion effector BepC induces stress fiber formation through activation of GEF-H1. PLoS Pathog, 2021, 17(1): e1009065.
[58]	Villamil Giraldo AM, Mary C, Sivanesan D, Baron C. VirB6 and VirB10 from the Brucella type IV secretion system interact via the N-terminal periplasmic domain of VirB6. FEBS Lett, 2015, 589(15): 1883-1889. doi: 10.1016/j.febslet.2015.05.051 pmid: 26071378
[59]	Atmakuri K, Cascales E, Christie PJ. Energetic components VirD4, VirB11 and VirB4 mediate early DNA transfer reactions required for bacterial type IV secretion. Mol Microbiol, 2004, 54(5): 1199-1211. pmid: 15554962
[60]	Marlaire S, Dehio C. Bartonella effector protein C mediates actin stress fiber formation via recruitment of GEF-H1 to the plasma membrane. PLoS Pathog, 2021, 17(1): e1008548.
[61]	Truttmann MC, Rhomberg TA, Dehio C. Combined action of the type IV secretion effector proteins BepC and BepF promotes invasome formation of Bartonella henselae on endothelial and epithelial cells. Cell Microbiol, 2011, 13(2): 284-299. doi: 10.1111/j.1462-5822.2010.01535.x pmid: 20964799
[62]	Hook C, Eremina N, Zaytsev P, Varlamova D, Stoynova N. The Escherichia coli amino acid uptake protein CycA: regulation of its synthesis and practical application in l-Isoleucine production. Microorganisms, 2022, 10(3): 647.
[63]	Shi HM, Zhang L, Gu J, Li JY, Liu ZX, Deng JY. CycA-dependent glycine assimilation is connected to novobiocin susceptibility in Escherichia coli. Microbiol Spectr, 2022, 10(6): e0250122.
[64]	Phillips GJ, Silhavy TJ. The E. coli ffh gene is necessary for viability and efficient protein export. Nature, 1992, 359(6397): 744-746.
[65]	Patel S, Austen BM. Substitution of fifty four homologue (Ffh) in Escherichia coli with the mammalian 54-kDa protein of signal-recognition particle. Eur J Biochem, 1996, 238(3): 760-768. pmid: 8706678
[66]	Vörös A, Simm R, Slamti L, McKay MJ, Hegna IK, Nielsen-LeRoux C, Hassan KA, Paulsen IT, Lereclus D, Økstad OA, Molloy MP, Kolstø AB. SecDF as part of the sec-translocase facilitates efficient secretion of Bacillus cereus toxins and cell wall-associated proteins. PLoS One, 2014, 9(8): e103326.
[67]	Quiblier C, Zinkernagel AS, Schuepbach RA, Berger- Bächi B, Senn MM. Contribution of SecDF to Staphylococcus aureus resistance and expression of virulence factors. BMC Microbiol, 2011, 11: 72. doi: 10.1186/1471-2180-11-72 pmid: 21486434
[68]	Guo LN, Huang LX, Su YQ, Qin YX, Zhao LM, Yan QP. secA, secD, secF, yajC, and yidC contribute to the adhesion regulation of Vibrio alginolyticus. Microbiologyopen, 2018, 7(2): e00551.
[69]	Jain K, Stanage TH, Wood EA, Cox MM. The Escherichia coli serS gene promoter region overlaps with the rarA gene. PLoS One, 2022, 17(4): e0260282.
[70]	Sun CW, Dong ZD, Zhao L, Ren Y, Zhang N, Chen F. The wheat 660K SNP array demonstrates great potential for marker-assisted selection in polyploid wheat. Plant Biotechnol J, 2020, 18(6): 1354-1360. doi: 10.1111/pbi.13361 pmid: 32065714
[71]	Foster JT, Beckstrom-Sternberg SM, Pearson T, Beckstrom-Sternberg JS, Chain PSG, Roberto FF, Hnath J, Brettin T, Keim P. Whole-genome-based phylogeny and divergence of the genus Brucella. J Bacteriol, 2009, 191(8): 2864-2870.
[72]	Choi S, Jin GD, Park J, You I, Kim EB. Pan-genomics of Lactobacillus plantarum revealed group-specific genomic profiles without habitat association. J Microbiol Biotechnol, 2018, 28(8): 1352-1359.
[73]	Hahn MW, Koll U, Jezberová J, Camacho A. Global phylogeography of pelagic polynucleobacter bacteria: restricted geographic distribution of subgroups, isolation by distance and influence of climate. Environ Microbiol, 2015, 17(3): 829-840. doi: 10.1111/1462-2920.12532 pmid: 24920455

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宿主	种	数量	地区	菌株
家猫	B. henselae	12	北京	M2BJCW、M6BJND、M1BJ、M13BJ
			山东	M68SHDLC、M45SHDLC、M7SHDLC、M20SHD
			河南	M61HENAN、M145HEN、M8HEN
			美国	Houston-1(ATCC 49882)
	B. koehlerae	1	美国	C-29(ATCC 700693)
	B. clarridgeiae	2	美国	Houston-2 cat(ATCC 51734)
	B. clarridgeiae	2	河南	M9HENGX
猕猴	B. quintana	12	四川	HOU35SC、HOU38SC、HOU40SC、HOU52SC、HOU56SC、HOU98SC、HOU11SC、HOU128SC、HOU6SC
猕猴	B. quintana	12	北京	S13、M22
人	B. quintana	1	美国	Toulose(ATCC VR-358)

宿主	种	菌株	原始读数	干净读数	干净碱基(Gb)	Q30 (%)	GC (%)
家猫	B. henselae	M1BJ	19,373,610	17,135,816	2.4	94.48	49.28
		M2BJCW	20,214,804	17,079,420	2.4	94.26	39.19
		M6BJND	25,446,512	23,753,754	3.4	93.93	42.50
		M8HEN	22,528,882	20,764,324	3.0	91.79	41.87
		M13BJ	26,843,230	17,893,966	2.6	94.04	40.79
		M20SHDLC	24,957,434	16,708,256	2.4	93.39	39.83
		M61HENAN	25,530,012	23,739,144	3.4	94.73	49.26
		M68SHDLC	19,484,726	17,703,444	2.6	93.71	41.57
		M45SHDLC	28,372,858	21,239,212	3.0	91.98	40.21
		M145HEN	26,954,782	24,001,022	3.4	92.68	43.92
		M7SHDLC	22,564,912	17,713,422	2.6	93.10	41.14
		Houston-1	25,882,948	15,769,192	2.2	93.47	40.71
	B. koehlerae	C-29	31,326,614	11,339,002	1.6	94.68	44.30
	B. clarridgeiae	Houston-2 cat	24,183,352	17,036,452	2.4	93.89	37.92
	B. clarridgeiae	M9HENGX	24,928,200	15,652,306	2.2	93.51	36.36
猕猴	B. quintana	HOU98SC	53,833,058	37,058,642	5.4	92.18	45.11
		HOU6SC	28,717,110	17,282,722	2.4	93.75	43.31
		M22	25,862,670	14,615,018	2.0	94.69	41.72
		S13	19,242,020	17,893,966	2.6	95.50	43.90
		HOU52SC	23,663,466	20,378,338	3.0	91.45	45.73
		HOU35SC	31,005,158	25,245,900	3.6	93.42	49.65
		HOU38SC	18,593,934	13,888,642	2.6	91.44	43.41
		HOU40SC	24,852,202	23,369,370	3.4	95.04	48.24
		HOU56SC	24,512,310	18,701,146	2.6	93.88	45.53
		HOU128SC	23,994,176	19,540,850	2.8	94.27	45.11
		HOU11SC	23,544,806	17,121,884	2.4	94.26	45.57
人	B. quintana	Toulose	31,136,226	12,707,986	1.9	94.68	46.34

数据库	unigenes数量	占比
Pfam	17,405	52.04% (17,405/33,444)
Swiss-prot	30,210	90.33% (30,210/33,444)
eggNOG	17,087	51.09% (17,087/33,444)
GO	26,359	78.82% (26,359/33,444)
KEGG	22,657	67.75% (22,657/334,44)

编号	途径	编号	途径
ko03016	转运核糖核酸的生物生成	ko00785	硫辛酸代谢
ko03009	核糖体生物发生	ko00564	甘油磷脂代谢
ko03010	核糖体	ko00061	脂肪酸的生物合成
ko00240	嘧啶代谢	ko03400	DNA修复和重组蛋白
ko00230	嘌呤代谢	ko00300	赖氨酸的生物合成
ko03060	蛋白质输出	ko00020	柠檬酸循环(TCA循环)
ko02048	原核生物防御系统	ko03036	染色体和相关蛋白
ko00860	卟啉代谢	ko03110	伴侣蛋白和折叠催化剂
ko03011	核糖体	ko03410	碱基切除修复
ko01007	氨基酸相关酶	ko03070	细菌分泌系统
ko01011	肽聚糖生物合成和降解蛋白	ko00220	精氨酸的生物合成
ko00190	氧化磷酸化	ko00970	氨基酰-tRNA的生物合成

宿主	种	菌株	CDS数量
宿主	种	菌株	转录组	基因组
家猫	B. henselae	M1BJ	922	1,347
		M2BJCW	1,332	1,348
		M6BJND	1,277	1,342
		M8HEN	1,295	1,347
		M13BJ	1,326	1,326
		M20SHDLC	1,326	1,326
		M61HENAN	945	1,343
		M68SHDLC	1,315	1,341
		M45SHDLC	1,327	1,327
		M145HEN	1,220	1,330
		M7SHDLC	1,316	1,329
		Houston-1	1,348	1,348
	B. koehlerae	C-29	1,201	1,202
	B. clarridgeiae	Houston-2 cat	1,132	1,132
	B. clarridgeiae	M9HENGX	1,132	1,131
猕猴	B. quintana	HOU98SC	1,107	1,107
		HOU6SC	1,107	1,107
		M22	1,107	1,107
		S13	1,097	1,107
		HOU52SC	995	1,107
		HOU35SC	813	1,109
		HOU38SC	600	1,107
		HOU40SC	923	1,107
		HOU56SC	1,107	1,107
		HOU128SC	1,107	1,107
		HOU11SC	1,107	1,107
人	B. quintana	Toulose	1,104	1,105

Differential transcriptome profiling of Bartonella spp. influenced by the species divergence factors

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组别	基因组			转录组
组别	core genes数量	accessory genes数量	cloud genes数量	core genes数量	accessory genes数量	cloud genes数量
Bh	1325	24	5	686	666	4
Bc	1131	1	0	1,130	3	0
Bq	1105	2	2	451	657	2
All	84	1,900	1,288	56	1,918	1,303

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