Virulence factor activity relationships: challenges and development approaches

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

Virulence factor activity relationships (VFARs): a bioinformatics perspective

Virulence factor activity relationships (VFARs) - a concept loosely based on quantitative structure-activity relationships (QSARs) for chemicals was proposed as a predictive tool for ranking risks due to microorganisms relevant to water safety. A rapid increase in sequencing capabilities and bioinformatics tools has significantly increased the potential for VFAR-based analyses. This review summarizes more than 20 bioinformatics databases and tools, developed over the last decade, along with their virulence and antimicrobial resistance prediction capabilities. With the number of bacterial whole genome sequences exceeding 241 000 and metagenomic analysis projects exceeding 13 000 and the ability to add additional genome sequences for few hundred dollars, it is evident that further development of VFARs is not limited by the availability of information at least at the genomic level. However, additional information related to co-occurrence, treatment response, modulation of virulence due to environmental and other factors, and economic impact must be gathered and incorporated in a manner that also addresses the associated uncertainties. Of the bioinformatics tools, a majority are either designed exclusively for virulence/resistance determination or equipped with a dedicated module. The remaining have the potential to be employed for evaluating virulence. This review focusing broadly on omics technologies and tools supports the notion that these tools are now sufficiently developed to allow the application of VFAR approaches combined with additional engineering and economic analyses to rank and prioritize organisms important to a given niche. Knowledge gaps do exist but can be filled with focused experimental and theoretical analyses that were unimaginable a decade ago. Further developments should consider the integration of the measurement of activity, risk, and uncertainty to improve the current capabilities.

Molecular quantification of virulence gene-containing Aeromonas in water samples collected from different drinking water treatment processes

Pathogenic species of Aeromonas produce a range of virulence factors, including aerolysin, cytotonic enterotoxins, and serine protease, to cause acute gastroenteritis and wound infections in humans and animals. Recognizing that not all Aeromonas strains are pathogenic, in this study, we proposed to evaluate Aeromonas removal effectiveness based on the presence of virulence gene-containing Aeromonas as a proper means to assess microbial risk of Aeromonas. We developed and applied real-time PCR assays to quantify serine protease (ser) gene- and heat-labile cytotonic enterotoxin (alt) gene-containing Aeromonas in water samples. Among 18 Aeromonas isolates from the source water, only three isolates possessed all three genes (aer, ser, and alt). A higher percent of isolates has either ser gene (89%) or alt gene (72%) compared to the percent of isolates containing aer gene (44%). Results of this study suggested that several different conventional and unconventional drinking water treatment processes could effectively remove Aeromonas from source water. As the comprehensive knowledge of the distribution of virulence factors in different Aeromonas species is currently not available, using real-time PCR to quantify various virulence factor genes in water samples and/or isolates can be a practical means for better assessment of microbial risks in water.

In situ-synthesized virulence and marker gene biochip for detection of bacterial pathogens in water

Pathogen detection tools with high reliability are needed for various applications, including food and water safety and clinical diagnostics. In this study, we designed and validated an in situ-synthesized biochip for detection of 12 microbial pathogens, including a suite of pathogens relevant to water safety. To enhance the reliability of presence/absence calls, probes were designed for multiple virulence and marker genes (VMGs) of each pathogen, and each VMG was targeted by an average of 17 probes. Hybridization of the biochip with amplicon mixtures demonstrated that 95% of the initially designed probes behaved as predicted in terms of positive/negative signals. The probes were further validated using DNA obtained from three different types of water samples and spiked with pathogen genomic DNA at decreasing relative abundance. Excellent specificity for making presence/absence calls was observed by using a cutoff of 0.5 for the positive fraction (i.e., the fraction of probes yielding a positive signal for a given VMG). A split multiplex PCR design for simultaneous amplification of the VMGs resulted in a detection limit of between 0.1 and 0.01% relative abundance, depending on the type of pathogen and the VMG. Thermodynamic analysis of the hybridization patterns obtained with DNA from the different water samples demonstrated that probes with a hybridization Gibbs free energy of approximately -19.3 kcal/mol provided the best trade-off between sensitivity and specificity. The developed biochip may be used to detect the described bacterial pathogens in water samples when parallel and specific detection is required.