期刊论文详细信息
BMC Genomics
Comparative transcriptome profiling approach to glean virulence and immunomodulation-related genes of Fasciola hepatica
Mahmut Şamil Sağıroğlu2  Bayram Yüksel1  Pınar Kavak2  Mete Akgün2  Orçun Haçarız1 
[1] TÜBİTAK Marmara Research Center, Genetic Engineering and Biotechnology Institute, Gebze, 41470, Kocaeli, Turkey;TÜBİTAK Marmara Research Center, Information Technologies Institute, Gebze, Kocaeli, Turkey
关键词: Evasion;    Immunomodulation;    Virulence;    RNA-Seq;    Trematode;    Whole transcriptome;    Fasciola hepatica;   
Others  :  1204018
DOI  :  10.1186/s12864-015-1539-8
 received in 2015-03-10, accepted in 2015-04-15,  发布年份 2015
PDF
【 摘 要 】

Background

Fasciola hepatica causes chronic liver disease, fasciolosis, leading to significant losses in the livestock economy and concerns for human health in many countries. The identification of F. hepatica genes involved in the parasite’s virulence through modulation of host immune system is utmost important to comprehend evasion mechanisms of the parasite and develop more effective strategies against fasciolosis. In this study, to identify the parasite’s putative virulence genes which are associated with host immunomodulation, we explored whole transcriptome of an adult F. hepatica using current transcriptome profiling approaches integrated with detailed in silico analyses. In brief, the comparison of the parasite transcripts with the specialised public databases containing sequence data of non-parasitic organisms (Dugesiidae species and Caenorhabditis elegans) or of numerous pathogens and investigation of the sequences in terms of nucleotide evolution (directional selection) and cytokine signaling relation were conducted.

Results

NGS of the whole transcriptome resulted in 19,534,766 sequence reads, yielding a total of 40,260 transcripts (N50 = 522 bp). A number of the parasite transcripts (n = 1,671) were predicted to be virulence-related on the basis of the exclusive homology with the pathogen-associated data, positive selection or relationship with cytokine signaling. Of these, a group of the virulence-related genes (n = 62), not previously described, were found likely to be associated with immunomodulation based on in silico functional categorisation, showing significant sequence similarities with various immune receptors (i.e. MHC I class, TGF-β receptor, toll/interleukin-1 receptor, T-cell receptor, TNF receptor, and IL-18 receptor accessory protein), cytokines (i.e. TGF-β, interleukin-4/interleukin-13 and TNF-α), cluster of differentiations (e.g. CD48 and CD147) or molecules associated with other immunomodulatory mechanisms (such as regulation of macrophage activation). Some of the genes (n = 5) appeared to be under positive selection (Ka/Ks > 1), imitating proteins associated with cytokine signaling (through sequence homologies with thrombospondin type 1, toll/interleukin-1 receptor, TGF-β receptor and CD147).

Conclusions

With a comparative transcriptome profiling approach, we have identified a number of potential immunomodulator genes of F. hepatica (n = 62), which are firstly described here, could be employed for the development of better strategies (including RNAi) in the battle against both zoonotically and economically important disease, fasciolosis.

【 授权许可】

   
2015 Haçarız et al.; licensee BioMed Central.

【 预 览 】
附件列表
Files Size Format View
20150523040949491.pdf 1309KB PDF download
Figure 6. 36KB Image download
Figure 5. 13KB Image download
Figure 4. 35KB Image download
Figure 3. 39KB Image download
Figure 2. 6KB Image download
Figure 1. 58KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

【 参考文献 】
  • [1]Gonzales Santana B, Dalton JP, Vasquez Camargo F, Parkinson M, Ndao M. The diagnosis of human fascioliasis by enzyme-linked immunosorbent assay (ELISA) using recombinant cathepsin L protease. PLoS Negl Trop Dis. 2013; 7: Article ID e2414
  • [2]Carnevale S, Cabrera MG, Cucher MA, di Risio CA, Malandrini JB, Kamenetzky L et al.. Direct, immunological and molecular techniques for a fasciolosis survey in a rural area of San Luis, Argentina. J Parasit Dis. 2013; 37:251-9.
  • [3]Yılmaz B, Köklü S, Gedikoğlu G. Hepatic mass caused by Fasciola hepatica: a tricky differential diagnosis. Am J Trop Med Hyg. 2013; 89:1212-3.
  • [4]Mas-Coma S, Agramunt VH, Valero MA. Neurological and ocular fascioliasis in humans. Adv Parasitol. 2014; 84:27-149.
  • [5]Brockwell YM, Elliott TP, Anderson GR, Stanton R, Spithill TW, Sangster NC. Confirmation of Fasciola hepatica resistant to triclabendazole in naturally infected Australian beef and dairy cattle. Int J Parasitol Drugs Drug Resist. 2013; 4:48-54.
  • [6]Young ND, Hall RS, Jex AR, Cantacessi C, Gasser RB. Elucidating the transcriptome of Fasciola hepatica - a key to fundamental and biotechnological discoveries for a neglected parasite. Biotechnol Adv. 2010; 28:222-31.
  • [7]Lyons RE, Johnson AM. Gene sequence and transcription differences in 70 kDa heat shock protein correlate with murine virulence of Toxoplasma gondii. Int J Parasitol. 1998; 28:1041-51.
  • [8]Yu Y, Kim HS, Chua HH, Lin CH, Sim SH, Lin D et al.. Genomic patterns of pathogen evolution revealed by comparison of Burkholderia pseudomallei, the causative agent of melioidosis, to avirulent Burkholderia thailandensis. BMC Microbiol. 2006; 6:46.
  • [9]Załuga J, Stragier P, Baeyen S, Haegeman A, Van Vaerenbergh J, Maes M et al.. Comparative genome analysis of pathogenic and non-pathogenic Clavibacter strains reveals adaptations to their lifestyle. BMC Genomics. 2014; 15:392.
  • [10]Bello-Orti B, Aragon V, Pina-Pedrero S, Bensaid A. Genome comparison of three serovar 5 pathogenic strains of Haemophilus parasuis: insights into an evolving swine pathogen. Microbiology. 2014; 160(Pt 9):1974-84.
  • [11]Garg G, Ranganathan S. In silico secretome analysis approach for next generation sequencing transcriptomic data. BMC Genomics. 2011; 12 Suppl 3:S14.
  • [12]Garg G, Ranganathan S. Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs). BMC Genomics. 2012; 13 Suppl 7:S8.
  • [13]Donnelly S, O’Neill SM, Sekiya M, Mulcahy G, Dalton JP. Thioredoxin peroxidase secreted by Fasciola hepatica induces the alternative activation of macrophages. Infect Immun. 2005; 73:166-73.
  • [14]Flynn RJ, Mannion C, Golden O, Hacariz O, Mulcahy G. Experimental Fasciola hepatica infection alters responses to tests used for diagnosis of bovine tuberculosis. Infect Immun. 2007; 75:1373-81.
  • [15]He Y, Racz R, Sayers S, Lin Y, Todd T, Hur J et al.. Updates on the web-based VIOLIN vaccine database and analysis system. Nucleic Acids Res. 2014; 42(Database issue):D1124-32.
  • [16]Blaxter M, Koutsovoulos G. The evolution of parasitism in Nematoda. Parasitology. 2014; 25:1-14.
  • [17]Jackson AP. Genome evolution in trypanosomatid parasites. Parasitology. 2014; 28:1-17.
  • [18]Hurst LD. The Ka/Ks ratio: diagnosing the form of sequence evolution. Trends Genet. 2002; 18:486.
  • [19]Sutherland TE, Logan N, Rückerl D, Humbles AA, Allan SM, Papayannopoulos V et al.. Chitinase-like proteins promote IL-17-mediated neutrophilia in a tradeoff between nematode killing and host damage. Nat Immunol. 2014; 15:1116-25.
  • [20]Maizels RM, Nussey DH. Into the wild: digging at immunology’s evolutionary roots. Nat Immunol. 2013; 14:879-83.
  • [21]Zarowiecki M, Berriman M. What helminth genomes have taught us about parasite evolution. Parasitology. 2014; 8:1-13.
  • [22]Frech C, Chen N. Genome comparison of human and non-human malaria parasites reveals species subset-specific genes potentially linked to human disease. PLoS Comput Biol. 2011; 7: Article ID e1002320
  • [23]Hayashida K, Hara Y, Abe T, Yamasaki C, Toyoda A, Kosuge T et al.. Comparative genome analysis of three eukaryotic parasites with differing abilities to transform leukocytes reveals key mediators of Theileria-induced leukocyte transformation. MBio. 2012; 3:e00204-12.
  • [24]Haçarız O, Sayers G, Baykal AT. A proteomic approach to investigate the distribution and abundance of surface and internal Fasciola hepatica proteins during the chronic stage of natural liver fluke infection in cattle. J Proteome Res. 2012; 11:3592-604.
  • [25]Haçarız O, Baykal AT, Akgün M, Kavak P, Sağıroğlu MŞ, Sayers GP. Generating a detailed protein profile of Fasciola hepatica during the chronic stage of infection in cattle. Proteomics. 2014; 14:1519-30.
  • [26]Haçarız O, Sayers G, Mulcahy G. A preliminary study to understand the effect of Fasciola hepatica tegument on naïve macrophages and humoral responses in an ovine model. Vet Immunol Immunopathol. 2011; 139:245-9.
  • [27]Robinson MW, Menon R, Donnelly SM, Dalton JP, Ranganathan S. An integrated transcriptomics and proteomics analysis of the secretome of the helminth pathogen Fasciola hepatica: proteins associated with invasion and infection of the mammalian host. Mol Cell Proteomics. 2009; 8:1891-907.
  • [28]Smith RE, Spithill TW, Pike RN, Meeusen EN, Piedrafita D. Fasciola hepatica and Fasciola gigantica: cloning and characterisation of 70 kDa heat-shock proteins reveals variation in HSP70 gene expression between parasite species recovered from sheep. Exp Parasitol. 2008; 118:536-42.
  • [29]Chambers E, Ryan LA, Hoey EM, Trudgett A, McFerran NV, Fairweather I et al.. Liver fluke β-tubulin isotype 2 binds albendazole and is thus a probable target of this drug. Parasitol Res. 2010; 107:1257-64.
  • [30]Carlton JM, Adams JH, Silva JC, Bidwell SL, Lorenzi H, Caler E et al.. Comparative genomics of the neglected human malaria parasite Plasmodium vivax. Nature. 2008; 455:757-63.
  • [31]Hall N, Carlton J. Comparative genomics of malaria parasites. Curr Opin Genet Dev. 2005; 15:609-13.
  • [32]Carlton J, Silva J, Hall N. The genome of model malaria parasites, and comparative genomics. Curr Issues Mol Biol. 2005; 7:23-37.
  • [33]Pain A, Renauld H, Berriman M, Murphy L, Yeats CA, Weir W et al.. Genome of the host-cell transforming parasite Theileria annulata compared with T. parva. Science. 2005; 309:131-3.
  • [34]Dalton JP, Neill SO, Stack C, Collins P, Walshe A, Sekiya M et al.. Fasciola hepatica cathepsin L-like proteases: biology, function, and potential in the development of first generation liver fluke vaccines. Int J Parasitol. 2003; 33:1173-81.
  • [35]Stack CM, Caffrey CR, Donnelly SM, Seshaadri A, Lowther J, Tort JF et al.. Structural and functional relationships in the virulence-associated cathepsin L proteases of the parasitic liver fluke, Fasciola hepatica. J Biol Chem. 2008; 283:9896-908.
  • [36]Beckham SA, Law RH, Smooker PM, Quinsey NS, Caffrey CR, McKerrow JH et al.. Production and processing of a recombinant Fasciola hepatica cathepsin B-like enzyme (FhcatB1) reveals potential processing mechanisms in the parasite. Biol Chem. 2006; 387:1053-61.
  • [37]Chemale G, Morphew R, Moxon JV, Morassuti AL, Lacourse EJ, Barrett J et al.. Proteomic analysis of glutathione transferases from the liver fluke parasite, Fasciola hepatica. Proteomics. 2006; 6:6263-73.
  • [38]LaCourse EJ, Perally S, Morphew RM, Moxon JV, Prescott M, Dowling DJ et al.. The Sigma class glutathione transferase from the liver fluke Fasciola hepatica. PLoS Negl Trop Dis. 2012; 6: Article ID e1666
  • [39]Morphew RM, Eccleston N, Wilkinson TJ, McGarry J, Perally S, Prescott M et al.. Proteomics and in silico approaches to extend understanding of the glutathione transferasesuperfamily of the tropical liver fluke Fasciola gigantica. J Proteome Res. 2012; 11:5876-89.
  • [40]Salazar-Calderón M, Martín-Alonso JM, Castro AM, Parra F. Cloning, heterologous expression in Escherichia coli and characterization of a protein disulfide isomerase from Fasciola hepatica. Mol Biochem Parasitol. 2003; 126:15-23.
  • [41]Hernández-González A, Valero ML, del Pino MS, Oleaga A, Siles-Lucas M. Proteomic analysis of in vitro newly excysted juveniles from Fasciola hepatica. Mol Biochem Parasitol. 2010; 172:121-8.
  • [42]Shi Y, Toet H, Rathinasamy V, Young ND, Gasser RB, Beddoe T et al.. First insight into CD59-like molecules of adult Fasciola hepatica. Exp Parasitol. 2014; 144:57-64.
  • [43]Janeway CA, Travers P, Walport M, Shlomchik MJ. Immunobiology. 6th Edition. Garland Science Publishing; 2005.
  • [44]Akhmetshina A, Palumbo K, Dees C, Bergmann C, Venalis P, Zerr P et al.. Activation of canonical Wnt signaling is required for TGF-β-mediated fibrosis. Nat Commun. 2012; 3:735.
  • [45]Crawford SE, Stellmach V, Murphy-Ullrich JE, Ribeiro SM, Lawler J, Hynes RO et al.. Thrombospondin-1 is a major activator of TGF-beta1 in vivo. Cell. 1998; 93:1159-70.
  • [46]Hinck AP, Huang T. TGF-β antagonists: same knot, but different hold. Structure. 2013; 21:1269-70.
  • [47]O’Neill L. The Toll/interleukin-1 receptor domain: a molecular switch for inflammation and host defence. Biochem Soc Trans. 2000; 28:557-63.
  • [48]Locksley RM, Killeen N, Lenardo MJ. The TNF and TNF receptor superfamilies: integrating mammalian biology. Cell. 2001; 104:487-501.
  • [49]Cheung H, Chen NJ, Cao Z, Ono N, Ohashi PS, Yeh WC. Accessory protein-like is essential for IL-18-mediated signaling. J Immunol. 2005; 174:5351-7.
  • [50]Bouchery T, Kyle R, Ronchese F, Le Gros G. The differentiation of CD4(+) T-helper cell subsets in the context of helminth parasite infection. Front Immunol. 2014; 5:487.
  • [51]Tliba O, Moire N, Le Vern Y, Boulard C, Chauvin A, Sibille P. Early hepatic immune response in rats infected with Fasciola hepatica. Vet Res. 2002; 33:261-70.
  • [52]Haçarız O, Sayers G, McCullough M, Garrett M, O’Donovan J, Mulcahy G. The effect of Quil A adjuvant on the course of experimental Fasciola hepatica infection in sheep. Vaccine. 2009; 27:45-50.
  • [53]Pleasance J, Wiedosari E, Raadsma HW, Meeusen E, Piedrafita D. Resistance to liver fluke infection in the natural sheep host is correlated with a type-1 cytokine response. Parasite Immunol. 2011; 33:495-505.
  • [54]Cantacessi C, Young ND, Nejsum P, Jex AR, Campbell BE, Hall RS et al.. The transcriptome of Trichuris suis–first molecular insights into a parasite with curative properties for key immune diseases of humans. PLoS One. 2011; 6: Article ID e23590
  • [55]O’Garra A, Barrat FJ, Castro AG, Vicari A, Hawrylowicz C. Strategies for use of IL-10 or its antagonists in human disease. Immunol Rev. 2008; 223:114-31.
  • [56]Staffler G, Szekeres A, Schütz GJ, Säemann MD, Prager E, Zeyda M et al.. Selective inhibition of T cell activation via CD147 through novel modulation of lipid rafts. J Immunol. 2003; 171:1707-14.
  • [57]Landskron J, Taskén K. CD147 in regulatory T cells. Cell Immunol. 2013; 282:17-20.
  • [58]Elishmereni M, Levi-Schaffer F. CD48: A co-stimulatory receptor of immunity. Int J Biochem Cell Biol. 2011; 43:25-8.
  • [59]Liu A, Fang H, Dirsch O, Jin H, Dahmen U. Early release of macrophage migration inhibitory factor after liver ischemia and reperfusion injury in rats. Cytokine. 2012; 57:150-7.
  • [60]Liu B, Yang Y, Qiu Z, Staron M, Hong F, Li Y et al.. Folding of Toll-like receptors by the HSP90 paralogue gp96 requires a substrate-specific cochaperone. Nat Commun. 2010; 1:79.
  • [61]Heinze M, Kofler M, Freund C. Investigating the functional role of CD2BP2 in T cells. Int Immunol. 2007; 19:1313-8.
  • [62]Kofler MM, Freund C. The GYF domain. FEBS J. 2006; 273:245-56.
  • [63]Gordon S. Alternative activation of macrophages. Nat Rev Immunol. 2003; 3:23-35.
  • [64]Haçarız O, Sayers G, Flynn RJ, Lejeune A, Mulcahy G. IL-10 and TGF-beta1 are associated with variations in fluke burdens following experimental fasciolosis in sheep. Parasite Immunol. 2009; 31:613-22.
  • [65]Golbar HM, Izawa T, Juniantito V, Ichikawa C, Tanaka M, Kuwamura M et al.. Immunohistochemical characterization of macrophages and myofibroblasts in fibrotic liver lesions due to Fasciola infection in cattle. J Vet Med Sci. 2013; 75:857-65.
  • [66]Lomax KJ, Leto TL, Nunoi H, Gallin JI, Malech HL. Recombinant 47-kilodalton cytosol factor restores NADPH oxidase in chronic granulomatous disease. Science. 1989; 245:409-12.
  • [67]Larsen L, Röpke C. Suppressors of cytokine signalling: SOCS. APMIS. 2002; 110:833-44.
  • [68]Nakatsu Y, Matsuoka M, Chang TH, Otsuki N, Noda M, Kimura H et al.. Functionally distinct effects of the C-terminal regions of IKKε and TBK1 on type I IFN production. PLoS One. 2014; 9: Article ID e94999
  • [69]Fiscella M, Perry JW, Teng B, Bloom M, Zhang C, Leung K et al.. TIP, a T-cell factor identified using high-throughput screening increases survival in a graft-versus-host disease model. Nat Biotechnol. 2003; 21:302-7.
  • [70]Wolff MJ, Broadhurst MJ, Loke P. Helminthic therapy: improving mucosal barrier function. Trends Parasitol. 2012; 28:187-94.
  • [71]Robinson MW, Donnelly S, Dalton JP. Helminth defence molecules-immunomodulators designed by parasites! Front Microbiol. 2013; 4:296.
  • [72]Wammes LJ, Mpairwe H, Elliott AM, Yazdanbakhsh M. Helminth therapy or elimination: epidemiological, immunological, and clinical considerations. Lancet Infect Dis. 2014; 14:1150-62.
  • [73]Tanasescu R, Constantinescu CS. Helminth therapy for MS. Curr Top Behav Neurosci. 2014. in press.
  • [74]Haçarız O, Baykal AT. Investigation of the abundance of proteins secreted by Fasciola hepatica, which is exposed to environmental change in experimental studies, with an advanced proteomic approach. Turkiye Parazitol Derg. 2014; 38:106-10.
  • [75]Haçarız O, Sayers G. Fasciola hepatica - where is 28S ribosomal RNA? Exp Parasitol. 2013; 135:426-9.
  • [76]Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008; 18:821-9.
  • [77]Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012; 28:1086-92.
  • [78]Logan-Klumpler FJ, De Silva N, Boehme U, Rogers MB, Velarde G, McQuillan JA et al.. GeneDB–an annotation database for pathogens. Nucleic Acids Res. 2012; 40(Database issue):D98-108.
  • [79]Zerlotini A, Aguiar ER, Yu F, Xu H, Li Y, Young ND et al.. SchistoDB: an updated genome resource for the three key schistosomes of humans. Nucleic Acids Res. 2013; 41(Database issue):D728-31.
  • [80]McWilliam H, Li W, Uludag M, Squizzato S, Park YM, Buso N et al.. Analysis tool web services from the EMBL-EBI. Nucleic Acids Res. 2013; 41(Web Server issue):W597-600.
  • [81]Yandell M, Ence D. A beginner’s guide to eukaryotic genome annotation. Nat Rev Genet. 2012; 13:329-42.
  • [82]Zimin AV, Delcher AL, Florea L, Kelley DR, Schatz MC, Puiu D et al.. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 2009; 10:R42.
  • [83]Yang B, Sayers S, Xiang Z, He Y. Protegen: a web-based protective antigen database and analysis system. Nucleic Acids Res. 2011; 39(Database issue):D1073-8.
  • [84]He Y, Xiang Z. Bioinformatics analysis of bacterial protective antigens in manually curated Protegen database. Procedia Vaccinol. 2012; 6:3-9.
  • [85]Racz R, Chung M, Xiang Z, He Y. Systematic annotation and analysis of “virmugens”-virulence factors whose mutants can be used as live attenuated vaccines. Vaccine. 2013; 31:797-805.
  • [86]Racz R, Li X, Patel M, Xiang Z, He Y. DNAVaxDB: the first web-based DNA vaccine database and its data analysis. BMC Bioinformatics. 2014; 15:S2.
  • [87]Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A et al.. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 2012; 40(Database issue):D306-12.
  • [88]Marchler-Bauer A, Zheng C, Chitsaz F, Derbyshire MK, Geer LY, Geer RC et al.. CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res. 2013; 41(Database issue):D348-52.
  • [89]Rice P, Longden I, Bleasby A. EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000; 16:276-7.
  • [90]Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H et al.. Clustal W and Clustal X version 2.0. Bioinformatics. 2007; 23:2947-8.
  • [91]Zhang Z, Xiao J, Wu J, Zhang H, Liu G, Wang X et al.. ParaAT: a parallel tool for constructing multiple protein-coding DNA alignments. Biochem Biophys Res Commun. 2012; 419:779-81.
  • [92]Zhang Z, Li J, Zhao XQ, Wang J, Wong GK, Yu J. KaKs_Calculator: calculating Ka and Ks through model selection and model averaging. Genomics Proteomics Bioinformatics. 2006; 4:259-63.
  • [93]Yang Z, Nielsen R. Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol. 2000; 17:32-43.
  • [94]Zhang Z, Li J, Yu J. Computing Ka and Ks with a consideration of unequal transitional substitutions. BMC Evol Biol. 2006; 6:44.
  • [95]Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C et al.. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014; 30:1236-40.
  • [96]Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005; 21:3674-6.
  • [97]Bru C, Courcelle E, Carrère S, Beausse Y, Dalmar S, Kahn D. The ProDom database of protein domain families: more emphasis on 3D. Nucleic Acids Res. 2005; 33(Database issue):D212-5.
  • [98]Scordis P, Flower DR, Attwood TK. FingerPRINTScan: intelligent searching of the PRINTS motif database. Bioinformatics. 1999; 15:799-806.
  • [99]Wu CH, Yeh LS, Huang H, Arminski L, Castro-Alvear J, Chen Y et al.. The protein information resource. Nucleic Acids Res. 2003; 31:345-7.
  • [100]Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C et al.. The Pfam protein families database. Nucleic Acids Res. 2012; 40(Database issue):D290-301.
  • [101]Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR et al.. Pfam: the protein families database. Nucleic Acids Res. 2014; 42(Database issue):D222-30.
  • [102]Letunic I, Doerks T, Bork P. SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res. 2012; 40(Database issue):D302-5.
  • [103]Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 2013; 41(Database issue):D387-95.
  • [104]Sigrist CJ, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A et al.. New and continuing developments at PROSITE. Nucleic Acids Res. 2013; 41(Database issue):D344-7.
  • [105]Fuchs R. Predicting protein function: a versatile tool for the Apple Macintosh. Comput Appl Biosci. 1994; 10:171-8.
  • [106]Pedruzzi I, Rivoire C, Auchincloss AH, Coudert E, Keller G, de Castro E et al.. HAMAP in 2013, new developments in the protein family classification and annotation system. Nucleic Acids Res. 2013; 41(Database issue):D584-9.
  • [107]Gough J, Karplus K, Hughey R, Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol. 2001; 313:903-19.
  • [108]Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011; 8:785-6.
  • [109]Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001; 305:567-80.
  • [110]Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R et al.. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003; 13:2129-41.
  • [111]Mi H, Lazareva-Ulitsky B, Loo R, Kejariwal A, Vandergriff J, Rabkin S et al.. The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 2005; 33(Database issue):D284-8.
  • [112]Mi H, Muruganujan A, Thomas PD. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 2013; 41(Database issue):D377-86.
  • [113]Lees JG, Lee D, Studer RA, Dawson NL, Sillitoe I, Das S et al.. Gene3D: Multi-domain annotations for protein sequence and comparative genome analysis. Nucleic Acids Res. 2014; 42(Database issue):D240-5.
  • [114]Käll L, Krogh A, Sonnhammer EL. A combined transmembrane topology and signal peptide prediction method. J Mol Biol. 2004; 338:1027-36.
  • [115]Käll L, Krogh A, Sonnhammer EL. Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server. Nucleic Acids Res. 2007; 35(Web Server issue):W429-32.
  • [116]Lupas A, Van Dyke M, Stock J. Predicting coiled coils from protein sequences. Science. 1991; 252:1162-4.
  • [117]Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ et al.. WoLF PSORT: protein localization predictor. Nucleic Acids Res. 2007; 35(Web Server issue):W585-7.
  • [118]Micallef L, Rodgers P. eulerAPE: drawing area-proportional 3-Venn diagrams using ellipses. PLoS One. 2014; 9: Article ID e101717
  文献评价指标  
  下载次数:35次 浏览次数:13次