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The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and specificity) and prevalence, and as a function of predictive values and prevalence. Mutual information calculated from a 2 × 2 prediction-realization table for a specified risk score threshold on an ROC curve is the same as the mutual information calculated at the same risk score threshold on a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold that maximizes mutual information is the same on both the ROC curve and the corresponding PROC curve. Phytopathologists and clinicians who have previously relied solely on ROC curve summary statistics when formulating risk thresholds for application in practical agricultural or clinical decision-making contexts are thus presented with a methodology that brings predictive values within the scope of that formulation.

Pathogens that infect plants and animals use a diverse arsenal of effector proteins to suppress the host immune system and promote infection. Identification of effectors in pathogen genomes is foundational to understanding mechanisms of pathogenesis, for monitoring field pathogen populations, and for breeding disease resistance. We identified candidate effectors from the lettuce downy mildew pathogen Bremia lactucae by searching the predicted proteome for the WY domain, a structural fold found in effectors that has been implicated in immune suppression as well as effector recognition by host resistance proteins. We predicted 55 WY domain containing proteins in the genome of B. lactucae and found substantial variation in both sequence and domain architecture. These candidate effectors exhibit several characteristics of pathogen effectors, including an N-terminal signal peptide, lineage specificity, and expression during infection. Unexpectedly, only a minority of B. lactucae WY effectors contain the canonical N-terminal RXLR motif, which is a conserved feature in the majority of cytoplasmic effectors reported in Phytophthora spp. Functional analysis of 21 effectors containing WY domains revealed 11 that elicited cell death on wild accessions and domesticated lettuce lines containing resistance genes, indicative of recognition of these effectors by the host immune system. Only two of the 11 recognized effectors contained the canonical RXLR motif, suggesting that there has been an evolutionary divergence in sequence motifs between genera; this has major consequences for robust effector prediction in oomycete pathogens.

Background: Current molecular diagnostics are limited in the number and type of detectable pathogens. Metagenomic next generation sequencing (mNGS) is an emerging, and increasingly feasible, pathogen-agnostic diagnostic approach. Translational barriers prohibit the widespread adoption of this technology in clinical laboratories. We validate an end-to-end mNGS assay for detection of respiratory viruses. Our assay is optimized to reduce turnaround time, lower cost-per-sample, increase throughput, and deploy secure and actionable bioinformatic results., Methods: We validated our assay using residual nasopharyngeal swab specimens from Vancouver General Hospital (n = 359), RT-PCR-positive, or negative for Influenza, SARS-CoV-2, and RSV. We quantified sample stability, assay precision, the effect of background nucleic acid levels, and analytical limits of detection. Diagnostic performance metrics were estimated., Results: We report that our mNGS assay is highly precise, semi-quantitative, with analytical limits of detection ranging from 103-104 copies/mL. Our assay is highly specific (100%) and sensitive (61.9% Overall: 86.8%; RT-PCR Ct < 30). Multiplexing capabilities enable processing of up to 55-specimens simultaneously on an Oxford Nanopore GridION device, with results reported within 12-hours., Conclusions: This study outlines the diagnostic performance and feasibility of mNGS for respiratory viral diagnostics, infection control, and public health surveillance. We addressed translational barriers to widespread mNGS adoption., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)