A comparison of computational methods for identifying virulence factors.

Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, Hubei, China.
PLoS ONE (Impact Factor: 3.53). 01/2012; 7(8):e42517. DOI: 10.1371/journal.pone.0042517
Source: PubMed

ABSTRACT Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.

1 Bookmark
  • [Show abstract] [Hide abstract]
    ABSTRACT: Bacterial ADP-ribosyltransferase toxins (bARTTs) transfer ADP-ribose to eukaryotic proteins to promote bacterial pathogenesis. In this Review, we use prototype bARTTs, such as diphtheria toxin and pertussis toxin, as references for the characterization of several new bARTTs from human, insect and plant pathogens, which were recently identified by bioinformatic analyses. Several of these toxins, including cholix toxin (ChxA) from Vibrio cholerae, SpyA from Streptococcus pyogenes, HopU1 from Pseudomonas syringae and the Tcc toxins from Photorhabdus luminescens, ADP-ribosylate novel substrates and have unique organizations, which distinguish them from the reference toxins. The characterization of these toxins increases our appreciation of the range of structural and functional properties that are possessed by bARTTs and their roles in bacterial pathogenesis.
    Nature Reviews Microbiology 07/2014; · 23.32 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The Gram-negative saprophyte Burkholderia pseudomallei is the causative agent of melioidosis, an infectious disease which is endemic in Southeast Asia and northern Australia. This bacterium possesses many virulence factors which are thought to contribute to its survival and pathogenicity. Using a virulent clinical isolate of B. pseudomallei and an attenuated strain of the same B. pseudomallei isolate, 6 genes BPSL2033, BP1026B_I2784, BP1026B_I2780, BURPS1106A_A0094, BURPS1106A_1131, and BURPS1710A_1419 were identified earlier by PCR-based subtractive hybridization. These genes were extensively characterized at the molecular level, together with an additional gene BPSL3147 that had been identified by other investigators. Through a reverse genetic approach, single-gene knockout mutants were successfully constructed by using site-specific insertion mutagenesis and were confirmed by PCR. BPSL2033::Km and BURPS1710A_1419::Km mutants showed reduced rates of survival inside macrophage RAW 264.7 cells and also low levels of virulence in the nematode infection model. BPSL2033::Km demonstrated weak statistical significance (P = 0.049) at 8 hours after infection in macrophage infection study but this was not seen in BURPS1710A_1419::Km. Nevertheless, complemented strains of both genes were able to partially restore the gene defects in both in vitro and in vivo studies, thus suggesting that they individually play a minor role in the virulence of B. pseudomallei.
    TheScientificWorldJournal. 01/2014; 2014:590803.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Latent tuberculosis is a clinical syndrome that occurs after an individual has been exposed to the Mycobacterium tuberculosis (Mtb) Bacillus, the infection has been established and an immune response has been generated to control the pathogen and force it into a quiescent state. Mtb can exit this quiescent state where it is unresponsive to treatment and elusive to the immune response, and enter a rapid replicating state, hence causing infection reactivation. It remains a gray area to understand how the pathogen causes a persistent infection and it is unclear whether the organism will be in a slow replicating state or a dormant non-replicating state. The ability of the pathogen to adapt to changing host immune response mechanisms, in which it is exposed to hypoxia, low pH, nitric oxide (NO), nutrient starvation, and several other anti-microbial effectors, is associated with a high metabolic plasticity that enables it to metabolize under these different conditions. Adaptive gene regulatory mechanisms are thought to coordinate how the pathogen changes their metabolic pathways through mechanisms that sense changes in oxygen tension and other stress factors, hence stimulating the pathogen to make necessary adjustments to ensure survival. Here, we review studies that give insights into latency/dormancy regulatory mechanisms that enable infection persistence and pathogen adaptation to different stress conditions. We highlight what mathematical and computational models can do and what they should do to enhance our current understanding of TB latency.
    Frontiers in Bioengineering and Biotechnology 08/2013;

Full-text (2 Sources)

Available from
May 20, 2014