11
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dynamic sporulation gene co-expression networks for Bacillus subtilis 168 and the food-borne isolate Bacillus amyloliquefaciens: a transcriptomic model

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      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

          Sporulation is a survival strategy, adapted by bacterial cells in response to harsh environmental adversities. The adaptation potential differs between strains and the variations may arise from differences in gene regulation. Gene networks are a valuable way of studying such regulation processes and establishing associations between genes. We reconstructed and compared sporulation gene co-expression networks (GCNs) of the model laboratory strain Bacillus subtilis 168 and the food-borne industrial isolate Bacillus amyloliquefaciens. Transcriptome data obtained from samples of six stages during the sporulation process were used for network inference. Subsequently, a gene set enrichment analysis was performed to compare the reconstructed GCNs of B. subtilis 168 and B. amyloliquefaciens with respect to biological functions, which showed the enriched modules with coherent functional groups associated with sporulation. On basis of the GCNs and time-evolution of differentially expressed genes, we could identify novel candidate genes strongly associated with sporulation in B. subtilis 168 and B. amyloliquefaciens. The GCNs offer a framework for exploring transcription factors, their targets, and co-expressed genes during sporulation. Furthermore, the methodology described here can conveniently be applied to other species or biological processes.

          Related collections

          Most cited references61

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Error and attack tolerance of complex networks

            Many complex systems, such as communication networks, display a surprising degree of robustness: while key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. In this paper we demonstrate that error tolerance is not shared by all redundant systems, but it is displayed only by a class of inhomogeneously wired networks, called scale-free networks. We find that scale-free networks, describing a number of systems, such as the World Wide Web, Internet, social networks or a cell, display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected by even unrealistically high failure rates. However, error tolerance comes at a high price: these networks are extremely vulnerable to attacks, i.e. to the selection and removal of a few nodes that play the most important role in assuring the network's connectivity.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

              Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
                Bookmark

                Author and article information

                Journal
                Microb Genom
                Microb Genom
                MGen
                Microbial Genomics
                Microbiology Society
                2057-5858
                February 2018
                9 February 2018
                9 February 2018
                : 4
                : 2
                : e000157
                Affiliations
                [ 1]Laboratory of Molecular Genetics, University of Groningen , 9747 AG Groningen, The Netherlands
                [ 2]Top Institute Food and Nutrition (TIFN) , Nieuwe Kanaal 9A, 6709 PA Wageningen, The Netherlands
                [ 3]NIZO Food Research , B.V., P.O. Box 20, Ede 6710 BA, Ede, The Netherlands
                Author notes
                *Correspondence: Oscar P. Kuipers, o.p.kuipers@ 123456rug.nl
                Article
                mgen000157
                10.1099/mgen.0.000157
                5857382
                29424683
                4e8c44eb-0631-43ef-8a98-aa7e0b16df98
                © 2018 The Authors

                This is an open access article under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 June 2017
                : 19 January 2018
                Funding
                Funded by: TI Food and Nutrition, Wageningen, The Netherlands. Funding for open access charge: TI Food and Nutrition
                Categories
                Research Article
                Systems Microbiology: Transcriptomics, Proteomics, Networks
                Custom metadata
                0

                sporulation,stages of sporulation,gene co-expression network,rna-seq,bacillus subtilis,bacillus amyloliquefaciens

                Comments

                Comment on this article