期刊论文详细信息
Biotechnology for Biofuels
Phylogeny-structured carbohydrate metabolism across microbiomes collected from different units in wastewater treatment process
Tong Zhang1  Yuanqing Chao3  Francis Y. L. Chin2  Yu Xia1 
[1]Environmental Biotechnology Laboratory, The University of Hong Kong, Hong Kong, SAR, China
[2]Department of Computing, Hang Seng Management College, Hong Kong, SAR, China
[3]School of Environmental Science and Engineering, Sun Yat-sen University, Guangdong, China
关键词: Salinity;    Dissolved oxygen;    Temperature;    Glycoside hydrolase;    Carbohydrate metabolism;    Metagenomic;   
Others  :  1229676
DOI  :  10.1186/s13068-015-0348-2
 received in 2015-08-06, accepted in 2015-09-25,  发布年份 2015
【 摘 要 】

Background

With respect to global priority for bioenergy production from plant biomass, understanding the fundamental genetic associations underlying carbohydrate metabolisms is crucial for the development of effective biorefinery process. Compared with gut microbiome of ruminal animals and wood-feed insects, knowledge on carbohydrate metabolisms of engineered biosystems is limited.

Results

In this study, comparative metagenomics coupled with metabolic network analysis was carried out to study the inter-species cooperation and competition among carbohydrate-active microbes in typical units of wastewater treatment process including activated sludge and anaerobic digestion. For the first time, sludge metagenomes demonstrated rather diverse pool of carbohydrate-active genes (CAGs) comparable to that of rumen microbiota. Overall, the CAG composition correlated strongly with the microbial phylogenetic structure across sludge types. Gene-centric clustering analysis showed the carbohydrate pathways of sludge systems were shaped by different environmental factors, including dissolved oxygen and salinity, and the latter showed more determinative influence of phylogenetic composition. Eventually, the highly clustered co-occurrence network of CAGs and saccharolytic phenotypes, revealed three metabolic modules in which the prevalent populations of Actinomycetales, Clostridiales and Thermotogales, respectively, play significant roles as interaction hubs, while broad negative co-exclusion correlations observed between anaerobic and aerobic microbes, probably implicated roles of niche separation by dissolved oxygen in determining the microbial assembly.

Conclusions

Sludge microbiomes encoding diverse pool of CAGs was another potential source for effective lignocellulosic biomass breakdown. But unlike gut microbiomes in which Clostridiales, Lactobacillales and Bacteroidales play a vital role, the carbohydrate metabolism of sludge systems is built on the inter-species cooperation and competition among Actinomycetales, Clostridiales and Thermotogales.

【 授权许可】

   
2015 Xia et al.

附件列表
Files Size Format View
Fig.3. 73KB Image download
Fig.2. 27KB Image download
Fig.1. 88KB Image download
Fig.3. 73KB Image download
Fig.2. 27KB Image download
Fig.1. 88KB Image download
【 图 表 】

Fig.1.

Fig.2.

Fig.3.

Fig.1.

Fig.2.

Fig.3.

【 参考文献 】
  • [1]Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI: Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol 2008, 6:776-788.
  • [2]Hess M, Sczyrba A, Egan R, Kim T-W, Chokhawala H, Schroth G, Luo S, Clark DS, Chen F, Pennacchio LA, Tringe SG, Visel A, Woyke T, Wang Z, Rubin EM: Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science 2011, 331:463-467.
  • [3]Pope PB, Denman SE, Jones M, Tringe SG, Barry K, Malfatti SA, McHardy AC, Cheng J-F, Hugenholtz P, McSweeney CS, Morrison M: Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores. Proc Natl Acad Sci 2010, 107:14793-14798.
  • [4]Warnecke F, Luginbuhl P, Ivanova N, Ghassemian M, Richardson TH, Stege JT, Cayouette M, McHardy AC, Djordjevic G, Aboushadi N, Sorek R, Tringe SG, Podar M, Martin HG, Kunin V, Dalevi D, Madejska J, Kirton E, Platt D, Szeto E, Salamov A, Barry K, Mikhailova N, Kyrpides NC, Matson EG, Ottesen EA, Zhang X, Hernandez M, Murillo C, Acosta LG, et al.: Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 2007, 450:560-565.
  • [5]Daims H, Taylor MW, Wagner M: Wastewater treatment: a model system for microbial ecology. Trends Biotechnol 2006, 24:483-489.
  • [6]Khalid A, Arshad M, Anjum M, Mahmood T, Dawson L: The anaerobic digestion of solid organic waste. Waste Manag 2011, 31:1737-1744.
  • [7]Mumme J, Linke B, Tölle R: Novel upflow anaerobic solid-state (UASS) reactor. Bioresour Technol 2010, 101:592-599.
  • [8]Park C, Lee C, Kim S, Chen Y, Chase HA: Upgrading of anaerobic digestion by incorporating two different hydrolysis processes. J Biosci Bioeng 2005, 100:164-167.
  • [9]Zhang T, Yang Y, Pruden A: Effect of temperature on removal of antibiotic resistance genes by anaerobic digestion of activated sludge revealed by metagenomic approach. Appl Microbiol Biotechnol 2015:1–9.
  • [10]Xia Y, Wang Y, Fang HHP, Jin T, Zhong H, Zhang T: Thermophilic microbial cellulose decomposition and methanogenesis pathways recharacterized by metatranscriptomic and metagenomic analysis. Sci Rep 2014, 4:6708.
  • [11]Ju F, Guo F, Ye L, Xia Y, Zhang T: Metagenomic analysis on seasonal microbial variations of activated sludge from a full-scale wastewater treatment plant over 4 years. Environ Microbiol Rep 2013, 5:80-89.
  • [12]Henrissat B, Bairoch A: New families in the classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J 1993, 293(Pt 3):781-788.
  • [13]Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS: Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev 2002, 66:506-577.
  • [14]Brulc JM, Antonopoulos DA, Berg Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, Edwards RE, Frank ED, Emerson JB, Wacklin P, et al.: Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci 1948, 2009:106.
  • [15]Newman ME: The structure and function of complex networks. SIAM Rev 2003, 45:167-256.
  • [16]Newman MEJ: Modularity and community structure in networks. Proc Natl Acad Sci 2006, 103:8577-8582.
  • [17]Watts DJ, Strogatz SH: Collective dynamics of “small-world” networks. Nature 1998, 393:440-442.
  • [18]Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E: Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008, 2008(10):P10008.
  • [19]Chassard C, Delmas E, Robert C, Bernalier-Donadille A: The cellulose-degrading microbial community of the human gut varies according to the presence or absence of methanogens. FEMS Microbiol Ecol 2010, 74:205-213.
  • [20]Fukusumi S, Kamizono A, Horinouchi S, Beppu T: Cloning and nucleotide sequence of a heat-stable amylase gene from an anaerobic thermophile, Dictyoglomus thermophilum. Eur J Biochem 1988, 174:15-21.
  • [21]Laderman KA, Asada K, Uemori T, Mukai H, Taguchi Y, Kato I, Anfinsen CB: Alpha-amylase from the hyperthermophilic archaebacterium Pyrococcus furiosus. Cloning and sequencing of the gene and expression in Escherichia coli. J Biol Chem 1993, 268:24402-24407.
  • [22]Jumas-Bilak E, Marchandin H: The Phylum Synergistetes. In The Prokaryotes. Edited by Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F. Springer, Berlin, Heidelberg; 2014:931-954.
  • [23]Kendall MM, Boone DR: The Order Methanosarcinales. In: Martin D, Falkow S, Rosenberg E, Schleifer K-H, Stackebrandt E, editors. The Prokaryotes. New York: Springer; 2006. p. 244–56.
  • [24]Zhaxybayeva O, Swithers KS, Lapierre P, Fournier GP, Bickhart DM, DeBoy RT, Nelson KE, Nesbø CL, Doolittle WF, Gogarten JP, Noll KM: On the chimeric nature, thermophilic origin, and phylogenetic placement of the Thermotogales. Proc Natl Acad Sci 2009, 106:5865-5870.
  • [25]Chaen K, Noguchi J, Omori T, Kakuta Y, Kimura M: Crystal structure of the rice branching enzyme I (BEI) in complex with maltopentaose. Biochem Biophys Res Commun 2012, 424:508-511.
  • [26]Sim L, Beeren SR, Findinier J, Dauvillée D, Ball SG, Henriksen A, Palcic MM: Crystal Structure of the Chlamydomonas starch debranching enzyme isoamylase ISA1 reveals insights into the mechanism of branch trimming and complex assembly. J Biol Chem 2014, 289:22991-23003.
  • [27]Fujimoto Z, Jackson A, Michikawa M, Maehara T, Momma M, Henrissat B, Gilbert HJ, Kaneko S: The structure of a Streptomyces avermitilis α-L-rhamnosidase reveals a novel carbohydrate-binding module CBM67 within the six-domain arrangement. J Biol Chem 2013, 288:12376-12385.
  • [28]Ezer A, Matalon E, Jindou S, Borovok I, Atamna N, Yu Z, Morrison M, Bayer EA, Lamed R: Cell surface enzyme attachment is mediated by family 37 carbohydrate-binding modules, unique to Ruminococcus albus. J Bacteriol 2008, 190:8220-8222.
  • [29]Xu Q, Morrison M, Nelson KE, Bayer EA, Atamna N, Lamed R: A novel family of carbohydrate-binding modules identified with Ruminococcus albus proteins. FEBS Lett 2004, 566:11-16.
  • [30]Huson DH, Mitra S, Ruscheweyh H-J, Weber N, Schuster SC: Integrative analysis of environmental sequences using MEGAN4. Genome Res 2011, 21:1552-1560.
  • [31]Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y: dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 2012, 40(Web Server issue):W445–451.
  • [32]Guo F, Zhang T: Biases during DNA extraction of activated sludge samples revealed by high throughput sequencing. Appl Microbiol Biotechnol 2013, 97:4607-4616.
  • [33]Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A: others: The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform 2008, 9:386. BioMed Central Full Text
  • [34]Namiki T, Hachiya T, Tanaka H, Sakakibara Y: MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res 2012, 40:e155.
  • [35]Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008, 18:821-829.
  • [36]Albertsen M, Hansen LBS, Saunders AM, Nielsen PH, Nielsen KL: A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J 2011, 6:1094-1106.
  • [37]Zhu W, Lomsadze A, Borodovsky M: Ab initio gene identification in metagenomic sequences. Nucleic Acids Res 2010, 38:e132.
  • [38]Eddy SR: Accelerated Profile HMM Searches. PLoS Comput Biol 2011, 7:e1002195.
  • [39]Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B: The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res 2009, 37(Database):D233-D238.
  • [40]Park BH, Karpinets TV, Syed MH, Leuze MR, Uberbacher EC: CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database. Glycobiology 2010, 20:1574-1584.
  • [41]Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL: BLAST+: architecture and applications. BMC Bioinform 2009, 10:421. BioMed Central Full Text
  • [42]Mackelprang R, Waldrop MP, DeAngelis KM, David MM, Chavarria KL, Blazewicz SJ, Rubin EM, Jansson JK: Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 2011, 480:368-371.
  • [43]Ye Y, Choi J-H, Tang H: RAPSearch: a fast protein similarity search tool for short reads. BMC Bioinform 2011, 12:159. BioMed Central Full Text
  • [44]Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009, 10:R25. BioMed Central Full Text
  • [45]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The sequence alignment/map format and SAMtools. Bioinformatics 2009, 25:2078-2079.
  • [46]Xia Y, Ju F, Fang HHP, Zhang T: Mining of Novel Thermo-Stable Cellulolytic Genes from a Thermophilic Cellulose-Degrading Consortium by Metagenomics. PLoS ONE 2013, 8:e53779.
  • [47]Forsberg KJ, Patel S, Gibson MK, Lauber CL, Knight R, Fierer N, Dantas G: Bacterial phylogeny structures soil resistomes across habitats. Nature 2014, 509:612-616.
  • [48]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 1995, 57:289-300.
  • [49]Junker BH, Schreiber F: Analysis of biological networks. Wiley, New Jersey; 2011.
  • [50]Bastian M, Heymann S, Jacomy M, et al.: Gephi: an open source software for exploring and manipulating networks. ICWSM 2009, 8:361-362.
  • [51]Erdös P, Rényi A: Additive properties of random sequences of positive integers. Acta Arith 1960, 1:83-110.
  • [52]Csardi G, Nepusz T: The igraph software package for complex network research. InterJ Complex Syst 2006, 1695:38.
  • [53]Barberán A, Bates ST, Casamayor EO, Fierer N: Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 2011, 6:343-351.
  文献评价指标  
  下载次数:58次 浏览次数:28次