Abstract
Using high-throughput sequencing on metagenome to analyze marine microbial community, it is one of current main issues in the field of environmental microbe research. In this paper, we conducted the functional analysis on seven samples of metagenomic data from different depth seawater in Hawaii. The results of gene prediction and function annotation indicate that there are large amounts of potential novel genes of which functions remain unknown at present. Based on the gene annotation, codon usage bias is studied on ribosomal protein-related genes and shows an evident influence by the marine extreme environment. Furthermore, focusing on the marine environmental differences such as light intensity, dissolved oxygen, temperature and pressure among various depths, comparative analysis is carried out on related genes and metabolic pathways. Thus, the understanding as well as new insights into the correlation between marine environment and microbes are proposed at molecular level. Therefore, the studies herein afford a clue to reveal the special living strategies of microbial community from sea surface to deep sea.
摘要
对宏基因组高通量测序数据的分析, 是当前研究海洋微生物的重要方法。作者对夏威夷海域不同深度海水的微生物群落宏基因组进行功能基因组学分析。通过基因功能注释, 发现了大量潜在的新基因, 结果也表明极端环境加强了微生物密码子使用偏好。进一步根据光强、氧含量和低温高压等环境因素, 进行代谢通路和基因序列分析, 从分子水平上对海洋微生物与环境的关联和适应性提出若干新的认识, 探讨不同深度海洋环境中微生物特有的生存机制。
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References
Sogin ML, Morrison HG, Huber JA et al (2006) Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA 103:12115–12120
Kennedy J, Flemer B, Jackson SA et al (2010) Marine metagenomics: new tools for the study and exploitation of marine microbial metabolism. Mar Drugs 8:608–628
Wang FP, Lu SL, Orcutt BN et al (2013) Discovering the roles of subsurface microorganisms: progress and future of deep biosphere investigation. Chin Sci Bull 58:456–467
Hugenholtz P (2002) Exploring prokaryotic diversity in the genomic era. Genome Biol 3:REVIEWS0003
Giovannoni SJ, Rappe MS, Vergin KL et al (1996) 16S rRNA genes reveal stratified open ocean bacterioplankton populations related to the Green Non-Sulfur bacteria. Proc Natl Acad Sci USA 93:7979–7984
Martin-Cuadrado AB, Lopez-Garcia P, Alba JC et al (2007) Metagenomics of the deep Mediterranean, a warm bathypelagic habitat. PLoS One 2:e914
Biddle JF, Fitz-Gibbon S, Schuster SC et al (2008) Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. Proc Natl Acad Sci USA 105:10583–10588
Delong EF, Preston CM, Mincer T et al (2006) Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311:496–503
Kunin V, Copeland A, Lapidus A et al (2008) A bioinformatician’s guide to metagenomics. Microbiol Mol Biol Rev 72:557–578
Liu YC, Guo JT, Hu GQ et al (2013) Gene prediction in metagenomic fragments based on the SVM algorithm. BMC Bioinform 14:S12
Hu GQ, Guo JT, Liu YC et al (2009) MetaTISA: Metagenomic translation initiation site annotator for improving gene start prediction. Bioinformatics 25:1843–1845
Lai BB, Ding RG, Li Y et al (2012) A de novo metagenomic assembly program for shotgun DNA reads. Bioinformatics 28:1455–1462
Markowitz VM, Chen IMA, Chu K et al (2012) IMG/M: the integrated metagenome data management and comparative analysis system. Nucleic Acids Res 40:D123–D129
Tatusov RL, Fedorova ND, Jackson JD et al (2003) The COG database: an updated version includes eukaryotes. BMC Bioinform 4:41
Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30
Sharp PM, Li WH (1987) The Codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281–1295
Sun ZX, Sang LJ, Ju LN et al (2008) A new method for splice site prediction based on the sequence patterns of splicing signals and regulatory elements. Chin Sci Bull 53:3331–3340
Zhu H, Hu GQ, Yang YF et al (2007) MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes. BMC Bioinform 8:97
Sau K, Deb A (2009) Temperature influences synonymous codon and amino acid usage biases in the phages infecting extremely thermophilic prokaryotes. In Silico Biol 9:1–9
Noguchi H, Taniguchi T, Itoh T (2008) MetaGeneAnnotator: detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes. DNA Res 15:387–396
Pedros-Alio C (2006) Marine microbial diversity: Can it be determined? Trends Microbiol 14:257–263
Vieira-Silva S, Rocha EPC (2010) The systemic imprint of growth and its uses in ecological (meta) genomics. PLoS Genet 6:e1000808
Gouy M, Gautier C (1982) Codon usage in bacteria—correlation with gene expressivity. Nucleic Acids Res 10:7055–7074
Sharp PM, Bailes E, Grocock RJ et al (2005) Variation in the strength of selected codon usage bias among bacteria. Nucl Acids Res 33:1141–1153
Benson AA (2002) Following the path of carbon in photosynthesis: a personal story. Photosynth Res 73:31–49
Bustos SA, Schaefer MR, Golden SS (1990) Different and rapid responses of four cyanobacterial psbA transcripts to changes in light intensity. J Bacteriol 172:1998–2004
Tsinoremas NF, Schaefer MR, Golden SS (1994) Blue and red light reversibly control psbA expression in the cyanobacterium Synechococcus sp. strain PCC 7942. J Biol Chem 269:16143–16147
Kulkarni RD, Golden SS (1994) Adaptation to high light intensity in Synechococcus sp. strain PCC 7942: regulation of three psbA genes and two forms of the D1 protein. J Bacteriol 176:959–965
Kos PB, Deak Z, Cheregi O et al (2008) Differential regulation of psbA and psbD gene expression, and the role of the different D1 protein copies in the cyanobacterium Thermosynechococcus elongatus BP-1. Biochim Biophys Acta 1777:74–83
Tamura K, Peterson D, Peterson N et al (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739
Herter SM, Schiltz E, Drews G (1997) Protein and gene structure of the NADH-binding fragment of Rhodobacter capsulatus NADH:ubiquinone oxidoreductase. Eur J Biochem FEBS 246:800–808
Unden G, Bongaerts J (1997) Alternative respiratory pathways of Escherichia coli: energetics and transcriptional regulation in response to electron acceptors. BBA-Bioenerg 1320:217–234
Wackwitz B, Bongaerts J, Goodman SD et al (1999) Growth phase-dependent regulation of nuoA-N expression in Escherichia coli K-12 by the Fis protein: upstream binding sites and bioenergetic significance. Mol Gen Genet 262:876–883
Kelly DP (1990) Organic sulfur-compounds in the environment—biogeochemistry, microbiology, and ecological aspects. Adv Microb Ecol 11:345–385
Winter R, Jeworrek C (2009) Effect of pressure on membranes. Soft Matter 5:3157–3173
Delong EF, Yayanos AA (1985) Adaptation of the membrane-lipids of a deep-sea bacterium to changes in hydrostatic-pressure. Science 228:1101–1102
Black PN, Dirusso CC, Metzger AK et al (1992) Cloning, sequencing, and expression of the Fadd gene of Escherichia coli encoding acyl coenzyme—a synthetase. J Biol Chem 267:25513–25520
Fang JS, Kato C, Sato T et al (2004) Biosynthesis and dietary uptake of polyunsaturated fatty acids by piezophilic bacteria. Comp Biochem Phys B 137:455–461
Acknowledgements
This work was supported by the National “Twelfth Five-Year” Plan for Science and Technology of China (2012BAI06B02), the National Natural Science Foundation of China (91231119, 30970667 and 11021463), and the National Basic Research Program of China (2011CB707500). We thank Prof. Zuhong Lu of Peking University for his interest to the project and valuable discussions. We also thank Dr. Yongchu Liu, Dr. Xiaobin Zheng, Longshu Yang, Feifei He, Qiuyue Wang, Fumeng Wang, and Peng Zhai for their helpful discussions.
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The authors declare that they have no conflict of interest.
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Wang, X., Wang, Q., Guo, X. et al. Functional genomic analysis of Hawaii marine metagenomes. Sci. Bull. 60, 348–355 (2015). https://doi.org/10.1007/s11434-014-0658-y
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DOI: https://doi.org/10.1007/s11434-014-0658-y