Biomarker panels for characterizing microbial community biofilm formation as composite molecular process

Biofilm formation in xenobiotic-degrading microorganisms

The increased presence of xenobiotics affects living organisms and the environment at large on a global scale. Microbial degradation is effective for the removal of xenobiotics from the ecosystem. In natural habitats, biofilms are formed by single or multiple populations attached to biotic/abiotic surfaces and interfaces. The attachment of microbial cells to these surfaces is possible via the matrix of extracellular polymeric substances (EPSs). However, the molecular machinery underlying the development of biofilms differs depending on the microbial species. Biofilms act as biocatalysts and degrade xenobiotic compounds, thereby removing them from the environment. Quorum sensing (QS) helps with biofilm formation and is linked to the development of biofilms in natural contaminated sites. To date, scant information is available about the biofilm-mediated degradation of toxic chemicals from the environment. Therefore, we review novel insights into the impact of microbial biofilms in xenobiotic contamination remediation, the regulation of biofilms in contaminated sites, and the implications for large-scale xenobiotic compound treatment.

Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus

Biofilm forming Staphylococcus aureus is a major threat to the health-care industry. It is important to understand the differences between planktonic and biofilm growth forms in the pathogen since conventional treatments targeting the planktonic forms are not effective against biofilms. The current study conducts a meta-analysis of three public transcriptomic profiles to examine the differences in gene expression between the planktonic and biofilm states of S. aureus using random-effects modeling. Mean effect sizes were calculated for 2847 genes among which 726 differentially expressed genes were taken for further analysis. Major genes that are discriminatory between the two conditions were mined using supervised learning techniques and validated by high-accuracy classifiers. Ten different feature selection algorithms were applied and used to rank the most important genes in S. aureus biofilms. Finally, an optimal set of 36 genes are presented as candidate genes in biofilm formation or development while throwing light on the novel roles of an acyl-CoA thioesterase enzyme and 10 hypothetical proteins in biofilms. The relevance of the identified gene set was further validated by building five different classification models using SVM, RF, kNN, NB and DT algorithms that were compared with models built from other relevant gene sets and by reviewing the functional role of 25 previously known genes in biofilm development. The study combines meta-analysis of differential expression with supervised machine learning strategies and feature selection for the first time to identify and validate a discriminatory set of genes important in biofilms of S. aureus. The functional roles of the identified genes predicted to be important in biofilms are further scrutinized and can be considered as a signature target list to develop anti-biofilm therapeutics in S. aureus.

High-throughput DNA sequencing technologies for water and wastewater analysis

Conventional microbiological water monitoring uses culture-dependent techniques to screen indicator microbial species such as Escherichia coli and fecal coliforms. With high-throughput, second-generation sequencing technologies becoming less expensive, water quality monitoring programs can now leverage the massively parallel nature of second-generation sequencing technologies for batch sample processing to simultaneously obtain compositional and functional information of culturable and as yet uncultured microbial organisms. This review provides an introduction to the technical capabilities and considerations necessary for the use of second-generation sequencing technologies, specifically 16S rDNA amplicon and whole-metagenome sequencing, to investigate the composition and functional potential of microbiomes found in water and wastewater systems.