Development of quantitative PCR (QPCR) assays typically requires extensive screening within and across a given species to ensure specific detection and lucid identification among various pathogenic and nonpathogenic strains and to generate standard curves. To minimize screening requirements, multiple virulence and marker genes (VMGs) were targeted simultaneously to enhance reliability, and a predictive threshold cycle (C T ) equation was developed to calculate the number of starting copies based on an experimental C T . The empirical equation was developed with Sybr green detection in nanoliter-volume QPCR chambers (OpenArray) and tested with 220 previously unvalidated primer pairs targeting 200 VMGs from 30 pathogens. A high correlation (R 2 ؍ 0.816) was observed between the predicted and experimental C T s based on the organism's genome size, guanine and cytosine (GC) content, amplicon length, and stability of the primer's 3 end. The performance of the predictive C T equation was tested using 36 validation samples consisting of pathogenic organisms spiked into genomic DNA extracted from three environmental waters. In addition, the primer success rate was dependent on the GC content of the target organisms and primer sequences. Targeting multiple assays per organism and using the predictive C T equation are expected to reduce the extent of the validation necessary when developing QPCR arrays for a large number of pathogens or other targets.
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.
Despite substantial standardization, polymerase chain reaction (PCR) experiments frequently fail. Troubleshooting failed PCRs can be costly in both time and money. Using a crowdsourced data set spanning 290 real PCRs from six active research laboratories, we investigate the degree to which PCR success rates can be improved by machine learning. While human designed PCRs succeed at a rate of 55-64%, we find that a machine learning model can accurately predict reaction outcome 81% of the time. We validate this level of improvement by then using the model to guide the design and predict the outcome of 39 new PCR experiments. In addition to improving outcomes, the model identifies 15 features of PCRs that researchers did not optimize well compared to the learned model. These results suggest that PCR success rates can easily be improved by 17-26%, potentially saving millions of dollars and thousands of hours of researcher time each year across the scientific community. Other common laboratory methods may benefit from similar data-driven optimization effort.
Part I: Occurrence, Fate, and Transport (this literature review) summarizes research appearing in 2011 on the occurrence of emerging pollutants in wastewater and environmental waters, sources of emerging pollutants, the fate and transport of emerging pollutants in the environment, monitoring approaches, modeling, and regulatory discussions. Toxicity studies are included where relevant specifically to wastewater. Part II: Treatment (the companion to this review) includes discussion of water and wastewater treatment technologies on emerging pollutants. Overview The term emerging pollutants primarily refers to those for which no regulations currently require monitoring or public reporting of their presence in our water supply or wastewater discharges. Many constituents described as emerging pollutants are pharmaceuticals or personal care products (PCPs), including endocrine disrupting compounds (EDCs), that may enter the environment through excretion in human and animal urine and feces, through flushing of unused medications, household uses, or bathing, and result in nanogram-per-liter (ng/L) to microgram-per-liter (µg/L) concentrations in the environment.This review paper (Emerging Pollutants-Part I:Occurrence, Fate, and Transport) includes discussion of
Parallel detection approaches are of interest to many researchers interested in identifying multiple water and foodborne pathogens simultaneously. Availability and cost-effectiveness are two key factors determining the usefulness of such approaches for laboratories with limited resources. In this study, we developed and validated a high-density microarray for simultaneous screening of 14 bacterial pathogens using an approach that employs gold labeling with silver enhancement (GLS) protocol. In total, 8,887 probes (50-mer) were designed using an in-house database of virulence and marker genes (VMGs), and synthesized in quadruplicate on glass slides using an in-situ synthesis technology. Target VMG amplicons were obtained using multiplex polymerase chain reaction (PCR), labeled with biotin, and hybridized to the microarray. The signals generated after gold deposition and silver enhancement, were quantified using a flatbed scanner having 2-μm resolution. Data analysis indicated that reliable presence/absence calls could be made, if: i) over four probes were used per gene, ii) the signal-to-noise ratio (SNR) cutoff was greater than or equal to two, and iii) the positive fraction (PF), i.e., number of probes with SNR > 2 for a given VMG was greater than 0.75. Hybridization of the array with blind samples resulted in 100% correct calls, and no false positive. Because amplicons were obtained by multiplex PCR, sensitivity of this method is similar to PCR. This assay is an inexpensive and reliable technique for high throughput screening of multiple pathogens.
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