Abstract
Pseudomonas aeruginosa has developed antibiotic resistance, a major health concern worldwide, through different mechanisms including the formation of biofilms. Thus far, typing has been primarily assay based. However, the many sequences available from the US National Center for Biotechnology Information (NCBI) and the International Consortium of Pseudomonas Database (IPCD) offer alternative ways of characterizing the biofilm formation genes which would reveal novel therapeutic targets for intervention. The current study employed profile hidden Markov models (pHMMs) in the characterization of biofilm formation genes and identification of possible variations based on the different ecological niches occupied by strains of P. aeruginosa. Statistical analyses were performed in R (v. 3.1.3) to determine the significance of these variations. Validated pHMMs identified a total of 197 hits for the 13 different ecological niches, with the human metagenomes recording 144 hits (73%) while the non-human metagenomes recorded 53 hits (27%). Human metagenomes had a significantly higher density of hits, with the abscess metagenomes indicating the highest density of hits. The overall result indicated a significant variation in density of hits between the different sites within the human metagenomes. This study successfully highlighted the significant value of already sequenced metagenomes in the identification of potential targets for novel therapeutic compounds. The profile hidden Markov model successfully identified 197 unique biofilm gene clusters emphasizing its importance in analyzing different sequenced pathogenic strains. The study recommends that experimental assays could be carried out to shed further light on the different biofilm formation gene clusters. Key words: Profile hidden Markov model, metagenomics, biofilm formation, antibiotic resistance.
Highlights
Conventional investigation of Pseudomonas aeruginosa biofilm formation genes has primarily been based on biological assays such as the evolution assays
The current study explored the use of profile HMMs in revealing the variation and conservation patterns of biofilm formation genes in different strains of P. aeruginosa
We constructed individual models for the 13 biofilm formation genes that were retrieved by the custom python scripts. This made it possible to construct gene-specific profiles and evaluate how different strain sequences fitted into the specific profiles to provide an overview of how the genes are distributed in the pathogen strains occupying different ecological niches. These analyses provided insights into the relationship between the biofilm formation genes and pathogens occupying specific ecological niches
Summary
Conventional investigation of Pseudomonas aeruginosa biofilm formation genes has primarily been based on biological assays such as the evolution assays. These assay-based analyses are time consuming and labor. Intensive, and may give negative results if changes occur due to mutations in the biofilm formation genes (Gong et al, 2012). In the last few years P. aeruginosa sequences have become increasingly available, so that a database search and pair-wise comparisons are alternative ways of characterizing them
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