Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches

A novel duplex qPCR-HRMA technique for simultaneous detection of Listeria monocytogenes and Salmonella typhimurium in meat products

Developing rapid, accurate, and sensitive methods to detect bacterial pathogens such as Listeria monocytogenes and Salmonella typhimurium is very important, given the global rise in foodborne outbreaks. To address this, we developed a duplex real-time PCR assay with high-resolution melting analysis (qPCR-HRMA) to detect these pathogens in meat products. The assay was standardized and validated according to ISO 22118:2011. The assay was optimized for basic PCR parameters and melting rate for HRM analysis. The reaction sensitivity was determined to be 2 pg of DNA, equivalent to 124 copies for Listeria monocytogenes and 100 copies for Salmonella typhimurium. The method sensitivity was found to be 150 CFU/mL for both pathogens in spiked meat samples. The assay was validated with proficiency test samples and was finally used to test real-world samples, where 4 samples were detected positive for the pathogens. This assay holds significant potential for regulatory food testing and clinical investigations.

Fast detection of unique genomic regions

Unique genomic regions are of particular interest in two scenarios: When extracted from a single mammalian target genome, they are highly enriched for developmental genes. When extracted from target genomes compared to closely related neighbor genomes, they are highly enriched for diagnostic markers. Despite their biological importance and potential economic value, unique regions remain difficult to detect from whole genome sequences. In this review we survey three efficient programs for the detection of unique regions at scale, genmap, macle, and fur. We explain these programs and demonstrate their application by analyzing simulated and real data. Example scripts for searching for unique regions are available from the Github repository evolbioinf/sure as part of a detailed tutorial.

Performance and functional assessment of the Kimera P-IV point-of-care plasmonic qPCR prototype for ultra rapid pathogen detection of chlamydia trachomatis

Current standard microbiological techniques are generally very time consuming, usually requiring 24-72 h to establish a diagnosis. Consequentially, contemporary clinical practices implement broad-spectrum antibiotic administration prior to pathogen detection, prompting the emergence of extremely dangerous antibiotic-resistant bacteria. Additionally, lengthy test-to-result turnover times can greatly exacerbate the rate of disease spread. Rapid point-of-care (POC) diagnostics has quickly gained importance since the SARS-CoV-2 pandemic; accordingly, we have developed a rapid four-channel POC plasmonic quantitative polymerase chain reaction (qPCR) machine (Kimera P-IV) to respond to the deficiencies in infection control. Utilizing gold nanorods (GNRs) as nano-heaters and integrating vertical cavity surface emitting lasers (VCSEL) to replace traditional Peltier blocks, the Kimera P-IV has also incorporated quantitative real-time fluorescent monitoring. Using Chlamydia trachomatis genetic material to evaluate the rapid thermocycling performance of the platform, we have generated positive amplicons in less than 13 min; however, to achieve these results, several biological reagent considerations needed to be taken into account, specifically primer design. The device can achieve a limit of detection (LoD) of <101 DNA copies, a PCR efficiency of 88.3%, and can differentiate positive from negative results with 100% accuracy. Moreover, it can also analyze C. trachomatis DNA spiked urine samples via a simple dilution, suggesting that a separate nucleic acid step may not be needed for diagnosing infections. In conclusion, the operation of the Kimera P-IV prototype places it in a unique position of POC devices to revolutionize infectious disease diagnosis.

Marker discovery in the large

Motivation

Markers for diagnostic polymerase chain reactions are routinely constructed by taking regions common to the genomes of a target organism and subtracting the regions found in the targets' closest relatives, their neighbors. This approach is implemented in the published package Fur, which originally required memory proportional to the number of nucleotides in the neighborhood. This does not scale well.

Results

Here, we describe a new version of Fur that only requires memory proportional to the longest neighbor. In spite of its greater memory efficiency, the new Fur remains fast and is accurate. We demonstrate this by applying it to simulated sequences and comparing it to an efficient alternative. Then we use the new Fur to extract markers from 120 reference bacteria. To make this feasible, we also introduce software for automatically finding target and neighbor genomes and for assessing markers. We pick the best primers from the 10 most sequenced reference bacteria and show their excellent in silico sensitivity and specificity.

Availability and implementation

Fur is available from github.com/evolbioinf/fur, in the Docker image hub.docker.com/r/beatrizvm/mapro, and in the Code Ocean capsule 10.24433/CO.7955947.v1.

Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogens

Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

Cross-reactivity and inclusivity analysis of CRISPR-based diagnostic assays of coronavirus SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; initially named as 2019-nCoV) is the cause of the novel coronavirus disease 2019 (COVID-19) pandemic. Its diagnosis relies on the molecular detection of the viral RNA by polymerase chain reaction (PCR) while newer rapid CRISPR-based diagnostic tools are being developed. As molecular diagnostic assays rely on the detection of unique sequences of viral nucleic acid, the target regions must be common to all coronavirus SARS-CoV-2 circulating strains, yet unique to SARS-CoV-2 with no cross-reactivity with the genome of the host and other normal or pathogenic organisms potentially present in the patient samples. This stage 1 protocol proposes in silico cross-reactivity and inclusivity analysis of the recently developed CRISPR-based diagnostic assays. Cross-reactivity will be analyzed through comparison of target regions with the genome sequence of the human, seven coronaviruses and 21 other organisms. Inclusivity analysis will be performed through the verification of the sequence variability within the target regions using publicly available SARS-CoV-2 sequences from around the world. The absence of cross-reactivity and any mutations in target regions of the assay used would provide a higher degree of confidence in the CRISPR-based diagnostic tests being developed while the presence could help guide the assay development efforts. We believe that this study would provide potentially important information for clinicians, researchers, and decision-makers.