56
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Transcriptomic and genomic evidence for Streptococcus agalactiae adaptation to the bovine environment

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Streptococcus agalactiae is a major cause of bovine mastitis, which is the dominant health disorder affecting milk production within the dairy industry and is responsible for substantial financial losses to the industry worldwide. However, there is considerable evidence for host adaptation (ecotypes) within S. agalactiae, with both bovine and human sourced isolates showing a high degree of distinctiveness, suggesting differing ability to cause mastitis. Here, we (i) generate RNAseq data from three S. agalactiae isolates (two putative bovine adapted and one human) and (ii) compare publicly available whole genome shotgun sequence data from an additional 202 isolates, obtained from six host species, to elucidate possible genetic factors/adaptations likely important for S. agalactiae growth and survival in the bovine mammary gland.

          Results

          Tests for differential expression showed distinct expression profiles for the three isolates when grown in bovine milk. A key finding for the two putatively bovine adapted isolates was the up regulation of a lactose metabolism operon (Lac.2) that was strongly correlated with the bovine environment (all 36 bovine sourced isolates on GenBank possessed the operon, in contrast to only 8/151 human sourced isolates). Multi locus sequence typing of all genome sequences and phylogenetic analysis using conserved operon genes from 44  S. agalactiae isolates and 16 additional Streptococcus species provided strong evidence for acquisition of the operon via multiple lateral gene transfer events, with all Streptococcus species known to be major causes of mastitis, identified as possible donors. Furthermore, lactose fermentation tests were only positive for isolates possessing Lac.2. Combined, these findings suggest that lactose metabolism is likely an important adaptation to the bovine environment. Additional up regulation in the bovine adapted isolates included genes involved in copper homeostasis, metabolism of purine, pyrimidine, glycerol and glucose, and possibly aminoglycoside antibiotic resistance.

          Conclusion

          We detected several genetic factors likely important in S. agalactiae’s adaptation to the bovine environment, in particular lactose metabolism. Of concern is the up regulation of a putative antibiotic resistance gene (GCN5-related N-acetyltransferase) that might reflect an adaptation to the use of aminoglycoside antibiotics within this environment.

          Related collections

          Most cited references61

          • Record: found
          • Abstract: found
          • Article: not found

          Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome".

          The development of efficient and inexpensive genome sequencing methods has revolutionized the study of human bacterial pathogens and improved vaccine design. Unfortunately, the sequence of a single genome does not reflect how genetic variability drives pathogenesis within a bacterial species and also limits genome-wide screens for vaccine candidates or for antimicrobial targets. We have generated the genomic sequence of six strains representing the five major disease-causing serotypes of Streptococcus agalactiae, the main cause of neonatal infection in humans. Analysis of these genomes and those available in databases showed that the S. agalactiae species can be described by a pan-genome consisting of a core genome shared by all isolates, accounting for approximately 80% of any single genome, plus a dispensable genome consisting of partially shared and strain-specific genes. Mathematical extrapolation of the data suggests that the gene reservoir available for inclusion in the S. agalactiae pan-genome is vast and that unique genes will continue to be identified even after sequencing hundreds of genomes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Comparative analysis of the genome sequences of Bordetella pertussis, Bordetella parapertussis and Bordetella bronchiseptica.

            Bordetella pertussis, Bordetella parapertussis and Bordetella bronchiseptica are closely related Gram-negative beta-proteobacteria that colonize the respiratory tracts of mammals. B. pertussis is a strict human pathogen of recent evolutionary origin and is the primary etiologic agent of whooping cough. B. parapertussis can also cause whooping cough, and B. bronchiseptica causes chronic respiratory infections in a wide range of animals. We sequenced the genomes of B. bronchiseptica RB50 (5,338,400 bp; 5,007 predicted genes), B. parapertussis 12822 (4,773,551 bp; 4,404 genes) and B. pertussis Tohama I (4,086,186 bp; 3,816 genes). Our analysis indicates that B. parapertussis and B. pertussis are independent derivatives of B. bronchiseptica-like ancestors. During the evolution of these two host-restricted species there was large-scale gene loss and inactivation; host adaptation seems to be a consequence of loss, not gain, of function, and differences in virulence may be related to loss of regulatory or control functions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Evaluation of clustering algorithms for protein-protein interaction networks

              Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.
                Bookmark

                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2013
                27 December 2013
                : 14
                : 920
                Affiliations
                [1 ]Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
                [2 ]Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA
                [3 ]Current address: Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
                [4 ]Current address: Merck Animal Health, Global Ruminants Business Unit, Summit, NJ 07901, USA
                Article
                1471-2164-14-920
                10.1186/1471-2164-14-920
                3890567
                24369756
                fece029b-0188-4292-9d38-65a378721e62
                Copyright © 2013 Richards et al.; licensee 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.

                History
                : 1 July 2013
                : 21 December 2013
                Categories
                Research Article

                Genetics
                rnaseq,lactose operon,mastitis,differential gene expression,streptococcus agalactiae,bovine adapted,lateral gene transfer

                Comments

                Comment on this article