Performance assessment of genomic island prediction tools with an improved version of Design-Island

Genomic Islands (GIs) play an important role in the evolution and adaptation of prokaryotes. The origin and extent of ecological diversity of prokaryotes can be analyzed by comparing GIs across closely or distantly related prokaryotes. Understanding the importance of GI and to study the bacterial evolution, several GI prediction tools have been generated. An unsupervised method, Design-Island, was developed to identify GIs using Monte-Carlo statistical test on randomly selected segments of a chromosome. Here, in the present study Design-Island was modified with the incorporation of majority voting, multiple hypothesis testing correction. The performance of the modified version, Design-Island-II was tested and compared with the existing GI prediction tools. The performance assessment and benchmarking of the GI prediction tools require experimentally validated dataset, which is lacking. So, different datasets, generated or taken from literature were utilized to compare the sensitivity (SN), specificity (SP), precision (PPV) and accuracy (AC) of Design-Island-II. It showed substantial enhancement in term of SN, SP, PPV and AC, and significantly reduced the computation time of the algorithm. The performance of Design-Island-II has also been compared with several GI prediction tools using curated dataset of putative horizontally transferred genes. Design-Island-II showed the highest sensitivity and F1 score, comparable specificity, precision and accuracy in comparison to the other available methods. IslandViewer4 and Islander outperformed all the available methods in terms of AC and PPV respectively. Our study suggested Design-Island-II, IslandViewer4 and GIHunter among the top performing GI prediction tools considering both sensitivity and specificity of the methods.

Comparative Genomic Analysis of Statistically Significant Genomic Islands of Helicobacter pylori strains for better understanding the disease prognosis

Bacterial virulence factors are often located in their genomic islands (GIs). Helicobacter pylori, a highly diverse organism is reported to be associated with several gastrointestinal diseases like, gastritis, gastric cancer, peptic ulcer, duodenal ulcer etc. A novel similarity score-based comparative analysis with GIs of fifty H. pylori strains revealed clear idea of the various factors which promote disease progression. Two putative pathogenic GIs in some of the H. pylori strains were identified. One GI, having a putative labile enterotoxin and other dynamin-like proteins (DLPs), is predicted to increase the release of toxin by membrane vesicular formation. Another island contains a virulence-associated protein D (vapD) which is a component of a type-II toxin-antitoxin system (TAs), leads to enhance the severity of the H. pylori infection. Besides the well-known virulence factors like CagA, and VacA, several GIs have been identified which showed to have direct or indirect impact on H. pylori clinical outcomes. One such GI, containing lipopolysaccharide (LPS) biosynthesis genes was revealed to be directly connected with disease development by inhibiting the immune response. Another collagenase-containing GI worsens ulcers by slowing down the healing process. GI consisted of fliD operon was found to be connected to flagellar assembly and biofilm production. By residing in biofilms, bacteria can avoid antibiotic therapy, resulting in chronic infection. Along with well-studied CagA and VacA virulent genes, it is equally important to study these identified virulence factors for better understanding H. pylori induced disease prognosis.

Identification and analysis of genomic islands in Burkholderia cenocepacia AU 1054 with emphasis on pathogenicity islands

BACKGROUND

Genomic islands (GIs) are genomic regions that reveal evidence of horizontal DNA transfer. They can code for many functions and may augment a bacterium's adaptation to its host or environment. GIs have been identified in strain J2315 of Burkholderia cenocepacia, whereas in strain AU 1054 there has been no published works on such regions according to our text mining and keyword search in Medline.

RESULTS

In this study, we identified 21 GIs in AU 1054 by combining two computational tools. Feature analyses suggested that the predictions are highly reliable and hence illustrated the advantage of joint predictions by two independent methods. Based on putative virulence factors, four GIs were further identified as pathogenicity islands (PAIs). Through experiments of gene deletion mutants in live bacteria, two putative PAIs were confirmed, and the virulence factors involved were identified as lipA and copR. The importance of the genes lipA (from PAI 1) and copR (from PAI 2) for bacterial invasion and replication indicates that they are required for the invasive properties of B. cenocepacia and may function as virulence determinants for bacterial pathogenesis and host infection.

CONCLUSIONS

This approach of in silico prediction of GIs and subsequent identification of potential virulence factors in the putative island regions with final validation using wet experiments could be used as an effective strategy to rapidly discover novel virulence factors in other bacterial species and strains.

Global response of Acidithiobacillus ferrooxidans ATCC 53993 to high concentrations of copper: A quantitative proteomics approach

Acidithiobacillus ferrooxidans is used in industrial bioleaching of minerals to extract valuable metals. A. ferrooxidans strain ATCC 53993 is much more resistant to copper than other strains of this microorganism and it has been proposed that genes present in an exclusive genomic island (GI) of this strain would contribute to its extreme copper tolerance. ICPL (isotope-coded protein labeling) quantitative proteomics was used to study in detail the response of this bacterium to copper. A high overexpression of RND efflux systems and CusF copper chaperones, both present in the genome and the GI of strain ATCC 53993 was found. Also, changes in the levels of the respiratory system proteins such as AcoP and Rus copper binding proteins and several proteins with other predicted functions suggest that numerous metabolic changes are apparently involved in controlling the effects of the toxic metal on this acidophile.

SIGNIFICANCE

Using quantitative proteomics we overview the adaptation mechanisms that biomining acidophiles use to stand their harsh environment. The overexpression of several genes present in an exclusive genomic island strongly suggests the importance of the proteins coded in this DNA region in the high tolerance of A. ferrooxidans ATCC 53993 to metals.