A multi-omic analysis of an Enterococcus faecium mutant reveals specific genetic mutations and dramatic changes in mRNA and protein expression
© Chang et al.; licensee BioMed Central Ltd. 2013
Received: 7 October 2013
Accepted: 24 December 2013
Published: 28 December 2013
For a long time, Enterococcus faecium was considered a harmless commensal of the mammalian gastrointestinal (GI) tract and was used as a probiotic in fermented foods. In recent decades, E. faecium has been recognised as an opportunistic pathogen that causes diseases such as neonatal meningitis, urinary tract infections, bacteremia, bacterial endocarditis and diverticulitis. E. faecium could be taken into space with astronauts and exposed to the space environment. Thus, it is necessary to observe the phenotypic and molecular changes of E. faecium after spaceflight.
An E. faecium mutant with biochemical features that are different from those of the wild-type strain was obtained from subculture after flight on the SHENZHOU-8 spacecraft. To understand the underlying mechanism causing these changes, the whole genomes of both the mutant and the WT strains were sequenced using Illumina technology. The genomic comparison revealed that dprA, a recombination-mediator gene, and arpU, a gene associated with cell wall growth, were mutated. Comparative transcriptomic and proteomic analyses showed that differentially expressed genes or proteins were involved with replication, recombination, repair, cell wall biogenesis, glycometabolism, lipid metabolism, amino acid metabolism, predicted general function and energy production/conversion.
This study analysed the comprehensive genomic, transcriptomic and proteomic changes of an E. faecium mutant from subcultures that were loaded on the SHENZHOU-8 spacecraft. The implications of these gene mutations and expression changes and their underlying mechanisms should be investigated in the future. We hope that the current exploration of multiple “-omics” analyses of this E. faecium mutant will provide clues for future studies on this opportunistic pathogen.
KeywordsE. faecium Genome Transcriptome Proteome Multi-omics
In the past, E. faecium was considered to be a harmless commensal of the mammalian GI tract and was used as a probiotic in fermented foods [1, 2]. In recent decades, E. faecium has been recognised as an opportunistic pathogen that causes diseases such as neonatal meningitis, urinary tract infections, bacteremia, bacterial endocarditis and diverticulitis [3–7]. Therefore, E. faecium can penetrate and survive in many environments in the human body, which could potentially lead to unpredictable consequences.
Due to revolutionary advances in high-throughput DNA sequencing technologies  and computer-based genetic analyses, genome decoding and transcriptome sequencing (RNA-seq) [9, 10] analyses are rapid and available at low costs. Moreover, the development of mass spectrometry-based proteomic analysis provides a simple and convenient approach to identify and quantify thousands of proteins in a single experiment [11, 12]. By employing these high-throughput technologies, the mechanisms underlying the systematic changes of a mutant and wild-type microbe could be revealed. Here we employed multi-omic technologies, including genomic, transcriptomic and proteomic analysis of a mutant strain of E. faecium and the corresponding wild-type strain to understand the complex mechanisms behind the mutations resulting in altered biochemical metabolic features.
Acquisition of the mutant
Phenotypic characteristics of the mutant (LCT-EF258) and the control strain (LCT-EF90) used in this study
p-hydroxy- phenylacetic acid
DNA, RNA and protein preparation
Both the mutant and the control strains were grown in Luria-Bertani (LB) medium at 37°C; genomic DNA was prepared by conventional phenol-chloroform extraction methods; RNAs were exacted using TIANGEN RNAprep pure Kit (Beijing, China) according to the manufacturer’s instructions. Protein was extracted and quantified and was subsequently analysed by SDS-polyacrylamide gel electrophoretogram. After digestion with trypsin, the samples were labelled using the iTRAQ reagents (Applied Biosystems), which fractionates the proteins using strong cationic exchange (SCX) chromatography (Shimadzu). Each fraction was separated using a splitless nanoACQuity (Waters) system coupled to the Triple TOF 5600 System (AB SCIEX, Concord, ON).
Genome sequencing and annotation
Sequencing and filtering
Using genomic DNA from the two samples, we constructed short (500 bp) and large (6 kb) random sequencing libraries and selected 90-bp read lengths for both libraries. Raw data were generated from the Illumina Hiseq2000 next-generation sequencing (NGS) platform with Illumina 1.5 format encoding a Phred quality score from 2 to 62 using ASCII 66 to 126. The raw data were then filtered through four steps, including removing reads with 5 bp of Ns’ base numbers, removing reads with 20 bp of low quality (≤Q20) base numbers, removing adapter contamination, and removing duplication reads. Finally, a total of 55 million base pairs of reads were generated to reach a depth of ~190-fold of total genome coverage.
Repetitive sequences analysis
We searched the genome for tandem repeats using Tandem Repeats Finder  and Repbase  (composed of many transposable elements) to identify the interspersed repeats. Transposable elements in the genome assembly were identified both at the DNA and protein level. For identification of transposable elements at the DNA level, RepeatMasker  was applied using a custom library comprising a combination of Repbase. At the protein level, RepeatProteinMask, which is updated software in the RepeatMasker package, was used to perform RM-BlastX against the transposable elements protein database.
ncRNA sequences analysis
The tRNA genes were predicted by tRNAscan . Aligning the rRNA template sequences from animals using BlastN with an E-value of 1e-5 identified the rRNA fragments. The miRNA and snRNA genes were predicted by INFERNAL software  against the Rfam database .
Gene functional annotation
To ensure the biological meaning, we chose the highest quality alignment result to annotate the genes. We used BLAST to accomplish functional annotation in combination with different databases. We provided BLAST results in m8 format and produced the annotation results by alignment with selected databases.
Nucleotide sequence accession number
The whole-genome sequences of the wild-type and mutant E. faecium strains in this study have been deposited at DDBJ/EMBL/GenBank under the accession numbers ANAJ00000000 and ANAI00000000, respectively.
Comparative genomic analysis
Raw SNPs were identified using software MUMmer (Version 3.22)  and SOAPaligner (Version 2.21). In all, raw SNPs were filtered by the following criteria: SNPs with quality scores < 20, SNPs covered by < 10 paired-end reads, SNPs within 5 bp on the edge of reads, and SNPs within 5 bp of two or more existing mutations. Finally, SNPs in repetitive regions found using the “Repetitive sequences analysis” method were also filtered.
Small size InDel variants calling
First, InDels (insertions and deletions) with lengths of less than 10 bp were extracted from the gap extension alignment between the genome assembly and the reference using LASTZ (Version 1.01.50). Second, we removed the unreliable InDels containing N base within 50 bp upstream and downstream, and we removed InDels with more than two mismatches within a total of 20 bp upstream and downstream. Finally, the candidate InDels were verified by comparing sample reads to the surrounding region of the InDels (100 bp each side) with the reference sequence by using BWA (Version 0.5.8) .
The LCT-EF258 target sequences were ordered according to the reference sequence based on MUMmer. Then, the X and Y axes of the two-dimensional synteny graphs and the upper and following axes of linear syntenic graphs were constructed after the same proportion of size reduction in the length of both sequences. The protein set P1 of the target sequence was aligned with the protein set P2 of the reference sequence using BLASTP (e-value < = 1e-5, identity > = 85%, and the best hit of each protein was selected). Finally, the results with the best-hit value were reserved and the average of two consistent values was obtained.
Transcriptome sequencing and comparison
Sequencing and filtering
Total RNAs were purified using TRIzol (Invitrogen) and rRNA was removed. Then, cDNA synthesis was performed with random hexamers and Superscript II reverse transcriptase (Invitrogen). Meanwhile, double-stranded cDNAs were purified with a Qiaquick PCR purification kit (Qiagen) and sheared with a nebuliser (Invitrogen) to ~200 bp fragments. After end repair and poly (A) addition, the cDNAs were ligated to Illumina N-acetyl-D-galactosamine (pair end) adapter oligo mix and suitable fragments were selected as templates by gel purification. Next, the libraries were PCR amplified and were sequenced using the Illumina Hiseq 2000 platform and the paired-end sequencing module.
The filtration consisted of three steps: removing reads with 1 bp of Ns’ base numbers, removing reads with 40 bp of low quality (≤Q20) base numbers, and removing adapter contamination. Additionally, reads mapped to the reference (LCT-EF90) rRNA sequences were removed. All gene expression data generated in this study have been deposited under accession numbers SRR922447 and SRR922448 (https://trace.ddbj.nig.ac.jp/DRASearch/).
Gene expression value statistics
The gene coverage was evaluated by mapping clean reads to the reference genes using SOAPaligner software, and the gene expression value was calculated by the RPKM (Reads Per kb per Million reads) formula based on the method described in Ali et al. . The RPKM method was able to eliminate the influence of gene length and sequencing discrepancy on the gene expression calculation. Therefore, the calculated gene expression could be directly used for comparing the gene expression among difference samples.
Differential gene expression analysis
To control error rate and identify true differentially expressed genes (DEGs), the p-value was rectified using the FDR (False Discovery Rate) control method . Both the FDR value and the RPKM ratio in different samples were calculated. Finally, genes with an RPKM ratio ≥ 2 and a FDR ≤ 0.001 between different samples were defined as DEGs. Different DEGs were enriched and clustered according to the GO and KEGG functions.
Quantitative proteomics were performed using iTRAQ technology coupled with 2D-nanoLC-nano-ESI-MS/MS to examine the difference of protein profiles . After identification by the TripleTOF 5600 System, data acquisition was performed with a TripleTOF 5600 System (AB SCIEX, Concord, ON) fitted with a Nanospray III source (AB SCIEX, Concord, ON) with a pulled quartz tip as the emitter (New Objectives, Woburn, MA). Data analysis, including protein identification and relative quantification, were performed with the ProteinPilotTM software 4.0.8085 using the Paragon Algorithm version 22.214.171.124 as the search engine. Each MS/MS spectrum was searched against the genome annotation database (5263 protein sequences), and the search parameters allowed for Cys. The local FDR was set to 5%, and all identified proteins were grouped by the ProGroup algorithm (ABI) to minimise redundancy. Proteins were identified based on at least one peptide with a percent confidence above 95%. Some of the identified peptides were excluded according to the following conditions: (i) Peptides with low ID confidence (<15%) were excluded. (ii) Peptide peaks corresponding to the ITRAQ labels were not observed. (iii) Shared MS/MS spectra, due to either identical peptide sequences in more than one protein or when more than one peptide was fragmented simultaneously, were excluded. (iv) Any peptide ratio in which the S/N (signal-to-noise ratio) is too low was excluded. Several quantitative estimates provided for each protein by the Protein Pilot were utilised, including the fold change ratios of differential expression between labelled protein extracts and the P value, which represents the probability that the observed ratio is different to 1 by chance. All experiments were performed in three replicates, and the differentially expression proteins (DEPs) were selected if they appeared at least twice and the fold change was larger than 1.2 with a p-value less than 0.05. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD000326.
Gene ontology and GO enrichment analysis
where N was the number of all genes with GO annotation; n was the number of DEGs in N; M was the number of all genes that were annotated to certain GO terms; m was the number of DEGs in M. The calculated p-value required a corrected p-value ≤ 0.05 as a threshold by Bonferroni correction.
Pathway analysis and pathway enrichment analysis
Gene interactions play key roles in many biological functions. Pathway enrichment of DEGs was analysed by the KEGG pathway . This analysis identified significantly enriched metabolic pathways in DEGs when compared with the genome background. The same analysis utilized in the GO enrichment was used for the pathway enrichment analysis. Here, N was the number of all genes with KEGG annotation, n was the number of DEGs in N, M was the number of all genes annotated to specific pathways, and m was the number of DEGs in M.
COG function analysis
Cluster of Orthologous Groups of proteins (COG) is the database for gene/protein orthologous classification (http://www.ncbi.nlm.nih.gov/COG/). Every gene/protein in a COG is supposed to be derived from a single gene/protein ancestor. Orthologs are gene/proteins derived from different species of one vertical family and have the same functions as the ancestor. Paralogs are proteins derived from gene expression and may have new, related functions. We compared identified proteins with the COG database to predict the gene or proteins’ function.
Genomic sequencing, assembly and annotation
Genomic DNA from both samples was sequenced using a whole-genome shotgun sequencing (WGS) approach on the Illumina Hiseq2000 system. The short (500 bp) and large (6 kb) random sequencing libraries were constructed, and the mean read length was 90 bp for both libraries. A total of 55 million base pairs of reads were generated to reach a depth of ~190-fold genome coverage (see Methods for details). The genomes were assembled using SOAPdenovo (Version 1.05) , which resulted in the final high quality genomic assemblies.
Comparative genomic analysis
To detect more variations, we used the LASTZ (Version 1.01.50) tool to identify InDels less than or equal to 10 bp (see Methods for details). After a series of filtering conditions, we have found 8 InDels between LCT-EF90 and LCT-EF258 (Additional file 1: Table S3), including 7 InDels in intergenic regions and only one in a coding region. The coding region InDel was identified in LCT-EF90GL000008, which is annotated as an arpU family gene related to transcriptional regulators in the NR database (Additional file 1: Table S4) but not in VFDB (Virulence Factors Database). While small size InDels were found in sample LCT-EF258, we were also interested in large scale structural variations. We aligned the two samples with a reference at the nucleic acid level (see Methods for details) but did not identify any large scale SVs. The probable reason may be that the generation time was so short that the variations did not have enough time to accumulate.
Different DEGs were enriched and clustered according to GO, COG and KEGG analyses. For COG, the up-regulated and down-regulated genes were summed and were compared with unchanged genes. The most change was annotated into the translation, ribosomal structure and biogenesis function classes (Figure 3B). For gene ontology, the DEGs that showed statistical significance (P-value ≤0.05) were the component, function and process ontologies. For LCT-EF90 and LCT-EF258, seven categories, including 601 DEGs (identical DEGs may fall into different categories), were shown to be meaningful (Figure 3C). For the KEGG functional cluster, there were eleven categories, including 283 DEGs, between LCT-EF90 and LCT-EF258. Most of the genes were annotated into three categories: purine metabolism, pyrimidine metabolism and ribosome (Figure 3D).
Comparative proteomic analysis
Integration of transcriptomic and proteomic analysis
This study was the first to perform comprehensive genomic, transcriptomic and proteomic analysis of an E. faecium mutant, an opportunistic pathogen often present in the GI tract of space inhabitants. We identified dprA and arpU mutations, which affect genes and proteins with different expressions clustered into glycometabolism, lipid metabolism, amino acid metabolism, predicted general function, energy production, DNA recombination and cell wall biogenesis, etc. We hope that the current exploration of multiple “-omics” analyses of the E. faecium mutant could aid future studies of this opportunistic pathogen and determine the effects of the space environment on bacteria. However, the biochemical metabolism of bacteria is so complex that the biological meanings underlying the changes of E. faecium in this study is not fully understood. The implications of these gene mutations and expressions, and the mechanisms between the changes of biological features and the underlying molecular changes, should be investigated in the future. Moreover, the high cost of loading biological samples onto spacecraft and the difficult setting limits this type of exploration.
All authors proposed and designed the study. DC performed the approach and analyzed the results. All authors contributed to the writing of the manuscript. All authors read and approved the final manuscript.
This work was supported by National Basic Research Program of China (973 program, No.2014CB744400 ), the Key Pre-Research Foundation of Military Equipment of China (Grant No. 9140A26040312JB10078), the Key Program of Medical Research in the Military “the 12th 5-year Plan”, China (No. BWS12J046), the China Postdoctoral Science Foundation (Grant No. 201104776, No. 2012 M521873) and Beijing Novel Program ( No. Z131107000413105).
- Franz CM, Stiles ME, Schleifer KH, Holzapfel WH: Enterococci in foods–a conundrum for food safety. Int J Food Microbiol. 2003, 88 (2–3): 105-122.PubMedView ArticleGoogle Scholar
- Lund B, Edlund C: Probiotic Enterococcus faecium strain is a possible recipient of the vanA gene cluster. Clin Infect Dis. 2001, 32 (9): 1384-1385. 10.1086/319994.PubMedView ArticleGoogle Scholar
- Knoll BM, Hellmann M, Kotton CN: Vancomycin-resistant Enterococcus faecium meningitis in adults: case series and review of the literature. Scand J Infect Dis. 2013, 45 (2): 131-139. 10.3109/00365548.2012.717711.PubMedView ArticleGoogle Scholar
- Simjee S, White DG, McDermott PF, Wagner DD, Zervos MJ, Donabedian SM, English LL, Hayes JR, Walker RD: Characterization of Tn1546 in vancomycin-resistant Enterococcus faecium isolated from canine urinary tract infections: evidence of gene exchange between human and animal enterococci. J Clin Microbiol. 2002, 40 (12): 4659-4665. 10.1128/JCM.40.12.4659-4665.2002.PubMedPubMed CentralView ArticleGoogle Scholar
- Polidori M, Nuccorini A, Tascini C, Gemignani G, Iapoce R, Leonildi A, Tagliaferri E, Menichetti F: Vancomycin-resistant Enterococcus faecium (VRE) bacteremia in infective endocarditis successfully treated with combination daptomycin and tigecycline. J Chemother. 2011, 23 (4): 240-241.PubMedView ArticleGoogle Scholar
- Arias CA, Mendes RE, Stilwell MG, Jones RN, Murray BE: Unmet needs and prospects for oritavancin in the management of vancomycin-resistant enterococcal infections. Clin Infect Dis. 2012, 54 (Suppl 3): S233-S238. 10.1093/cid/cir924.PubMedView ArticleGoogle Scholar
- Olofsson MB, Pornull KJ, Karnell A, Telander B, Svenungsson B: Fecal carriage of vancomycin- and ampicillin-resistant Enterococci observed in Swedish adult patients with diarrhea but not among healthy subjects. Scand J Infect Dis. 2001, 33 (9): 659-662. 10.1080/00365540110027097.PubMedView ArticleGoogle Scholar
- Shendure J, Ji H: Next-generation DNA sequencing. Nat Biotechnol. 2008, 26 (10): 1135-1145. 10.1038/nbt1486.PubMedView ArticleGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10 (1): 57-63. 10.1038/nrg2484.PubMedPubMed CentralView ArticleGoogle Scholar
- Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M, Usadel B: RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res. 2012, 40 (Web Server issue): W622-W627.PubMedPubMed CentralView ArticleGoogle Scholar
- Nanjo Y, Skultety L, Uvackova L, Klubicova K, Hajduch M, Komatsu S: Mass spectrometry-based analysis of proteomic changes in the root tips of flooded soybean seedlings. J Proteome Res. 2012, 11 (1): 372-385. 10.1021/pr200701y.PubMedView ArticleGoogle Scholar
- Tomazella GG, Risberg K, Mylvaganam H, Lindemann PC, Thiede B, de Souza GA, Wiker HG: Proteomic analysis of a multi-resistant clinical Escherichia coli isolate of unknown genomic background. J Proteomics. 2012, 75 (6): 1830-1837. 10.1016/j.jprot.2011.12.024.PubMedView ArticleGoogle Scholar
- Benson G: Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999, 27 (2): 573-580. 10.1093/nar/27.2.573.PubMedPubMed CentralView ArticleGoogle Scholar
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J: Repbase update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005, 110 (1–4): 462-467.PubMedView ArticleGoogle Scholar
- Chen N: Current Protocols in Bioinformatics/Editoral Board, Andreas D Baxevanis [et al.] 2004, Chapter 4:Unit 4 10. Using RepeatMasker to Identify Repetitive Elements in Genomic Sequences. 2004View ArticleGoogle Scholar
- Lowe TM, Eddy SR: tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997, 25 (5): 955-964.PubMedPubMed CentralView ArticleGoogle Scholar
- Nawrocki EP, Eddy SR: Query-dependent banding (QDB) for faster RNA similarity searches. PLoS Comput Biol. 2007, 3 (3): e56-10.1371/journal.pcbi.0030056.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR: Rfam: an RNA family database. Nucleic Acids Res. 2003, 31 (1): 439-441. 10.1093/nar/gkg006.PubMedPubMed CentralView ArticleGoogle Scholar
- Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL: Versatile and open software for comparing large genomes. Genome Biol. 2004, 5 (2): R12-10.1186/gb-2004-5-2-r12.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Durbin R: Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009, 25 (14): 1754-1760. 10.1093/bioinformatics/btp324.PubMedPubMed CentralView ArticleGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5 (7): 621-628. 10.1038/nmeth.1226.PubMedView ArticleGoogle Scholar
- Audic S, Claverie JM: The significance of digital gene expression profiles. Genome Res. 1997, 7 (10): 986-995.PubMedGoogle Scholar
- Unwin RD, Griffiths JR, Whetton AD: Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat Protoc. 2010, 5 (9): 1574-1582. 10.1038/nprot.2010.123.PubMedView ArticleGoogle Scholar
- Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO:TermFinder–open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 2004, 20 (18): 3710-3715. 10.1093/bioinformatics/bth456.PubMedPubMed CentralView ArticleGoogle Scholar
- Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010, 38 (Database issue): D355-D360.PubMedPubMed CentralView ArticleGoogle Scholar
- Li R, Zhu H, Ruan J, Qian W, Fang X, Shi Z, Li Y, Li S, Shan G, Kristiansen K, et al: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010, 20 (2): 265-272. 10.1101/gr.097261.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Delcher AL, Bratke KA, Powers EC, Salzberg SL: Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. 2007, 23 (6): 673-679. 10.1093/bioinformatics/btm009.PubMedPubMed CentralView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
- Tatusov RL, Galperin MY, Natale DA, Koonin EV: The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000, 28 (1): 33-36. 10.1093/nar/28.1.33.PubMedPubMed CentralView ArticleGoogle Scholar
- Bairoch A, Apweiler R: The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1999. Nucleic Acids Res. 1999, 27 (1): 49-54. 10.1093/nar/27.1.49.PubMedPubMed CentralView ArticleGoogle Scholar
- Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O’Donovan C, Phan I, et al: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 2003, 31 (1): 365-370. 10.1093/nar/gkg095.PubMedPubMed CentralView ArticleGoogle Scholar
- 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 issue): D233-D238.PubMedPubMed CentralView ArticleGoogle Scholar
- Winnenburg R, Baldwin TK, Urban M, Rawlings C, Kohler J, Hammond-Kosack KE: PHI-base: a new database for pathogen host interactions. Nucleic Acids Res. 2006, 34 (Database issue): D459-D464.PubMedPubMed CentralView ArticleGoogle Scholar
- Chakraborty A, Ghosh S, Chowdhary G, Maulik U, Chakrabarti S: DBETH: a database of bacterial exotoxins for human. Nucleic Acids Res. 2012, 40 (Database issue): D615-D620.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, Jin Q: VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005, 33 (Database issue): D325-D328.PubMedPubMed CentralView ArticleGoogle Scholar
- Delcher AL, Salzberg SL, Phillippy AM: Current Protocols in Bioinformatics/Editoral Board, Andreas D Baxevanis [et al.] 2003, Chapter 10:Unit 10 13. Using MUMmer to Identify Similar Regions in Large Sequence Sets. 2003View ArticleGoogle Scholar
- Lemeer S, Hahne H, Pachl F, Kuster B: Software tools for MS-based quantitative proteomics: a brief overview. Methods Mol Biol. 2012, 893: 489-499. 10.1007/978-1-61779-885-6_29.PubMedView ArticleGoogle Scholar
- Greenbaum D, Jansen R, Gerstein M: Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics. 2002, 18 (4): 585-596. 10.1093/bioinformatics/18.4.585.PubMedView ArticleGoogle Scholar
- Zhang W, Culley DE, Scholten JC, Hogan M, Vitiritti L, Brockman FJ: Global transcriptomic analysis of Desulfovibrio vulgaris on different electron donors. Antonie Van Leeuwenhoek. 2006, 89 (2): 221-237. 10.1007/s10482-005-9024-z.PubMedView ArticleGoogle Scholar
- Nie L, Wu G, Culley DE, Scholten JC, Zhang W: Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications. Crit Rev Biotechnol. 2007, 27 (2): 63-75. 10.1080/07388550701334212.PubMedView ArticleGoogle Scholar
- Crick F: Central dogma of molecular biology. Nature. 1970, 227 (5258): 561-563. 10.1038/227561a0.PubMedView ArticleGoogle Scholar
- Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999, 19 (3): 1720-1730.PubMedPubMed CentralView ArticleGoogle Scholar
- Lleo MM, Fontana R, Solioz M: Identification of a gene (arpU) controlling muramidase-2 export in Enterococcus hirae. J Bacteriol. 1995, 177 (20): 5912-5917.PubMedPubMed CentralGoogle Scholar
- Stieglmeier M, Wirth R, Kminek G, Moissl-Eichinger C: Cultivation of anaerobic and facultatively anaerobic bacteria from spacecraft-associated clean rooms. Appl Environ Microbiol. 2009, 75 (11): 3484-3491. 10.1128/AEM.02565-08.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang XS, Blaser MJ: DprB facilitates inter- and intragenomic recombination in Helicobacter pylori. J Bacteriol. 2012, 194 (15): 3891-3903. 10.1128/JB.00346-12.PubMedPubMed CentralView ArticleGoogle Scholar
- Tadesse S, Graumann PL: DprA/Smf protein localizes at the DNA uptake machinery in competent Bacillus subtilis cells. BMC Microbiol. 2007, 7: 105-10.1186/1471-2180-7-105.PubMedPubMed CentralView ArticleGoogle Scholar
- Mortier-Barriere I, Velten M, Dupaigne P, Mirouze N, Pietrement O, McGovern S, Fichant G, Martin B, Noirot P, Le Cam E, et al: A key presynaptic role in transformation for a widespread bacterial protein: DprA conveys incoming ssDNA to RecA. Cell. 2007, 130 (5): 824-836. 10.1016/j.cell.2007.07.038.PubMedView ArticleGoogle Scholar
- Yadav T, Carrasco B, Myers AR, George NP, Keck JL, Alonso JC: Genetic recombination in Bacillus subtilis: a division of labor between two single-strand DNA-binding proteins. Nucleic Acids Res. 2012, 40 (12): 5546-5559. 10.1093/nar/gks173.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.