When False-Positives Arise: Troubleshooting a SARS-Coronavirus-2 (SARS-CoV-2) Detection Assay on a Semi-Automated Platform.

BACKGROUND: During the COVID-19 pandemic, many molecular diagnostic laboratories performed high-throughput SARS-CoV-2 testing often with implementation of automated workflows. In parallel, vaccination campaigns resulted increasingly in specimens from fully vaccinated patients, with resultant clinical inquiries regarding positive results in this patient population. This prompted a quality improvement initiative to investigate the semi-automated testing workflow for false-positive results. The troubleshooting workflow is described and procedural improvements are outlined that serve as a resource for other molecular diagnostic laboratories that need to overcome testing anomalies in a semi-automated environment. METHODS: This workflow utilized the MagMax-96 Viral RNA kit and the CDC 2019-nCoV RT-qPCR Panel on the Agilent Bravo Liquid-Handler (Bravo). Screening of the environment, personnel, and the mechanical performance of instrumentation using low Ct checkerboard challenges was executed to identify sources of cross-contamination. Evaluation of the assay and reporting design was conducted. RESULTS: Specimen contamination was observed during the viral extraction process on the Bravo. Changes to the program reduced plate contamination by 50% and importantly revealed consistent hallmarks of contaminated samples. We adjusted the reporting algorithm using these indicators of false positives. False positives that were identified made up 0.11% of the 45 000+ tests conducted over the following 8 months. CONCLUSIONS: These adjustments provided confident and quality results while maintaining turnaround time for patients and pandemic-related public health initiatives. This corrected false-positive rate is concordant with previously published studies from diagnostic laboratories utilizing automated systems and may be considered a laboratory performance standard for this type of testing.

Functional assessment of somatic STK11 variants identified in primary human non-small cell lung cancers.

Serine/Threonine Kinase 11 (STK11) encodes an important tumor suppressor that is frequently mutated in lung adenocarcinoma. Clinical studies have shown that mutations in STK11 resulting in loss of function correlate with resistance to anti-PD-1 monoclonal antibody therapy in KRAS-driven non-small cell lung cancer (NSCLC), but the molecular mechanisms responsible remain unclear. Despite this uncertainty, STK11 functional status is emerging as a reliable biomarker for predicting non-response to anti-PD-1 therapy in NSCLC patients. The clinical utility of this biomarker ultimately depends upon accurate classification of STK11 variants. For nonsense variants occurring early in the STK11 coding region, this assessment is straightforward. However, rigorously demonstrating the functional impact of missense variants remains an unmet challenge. Here we present data characterizing four STK11 splice-site variants by analyzing tumor mRNA, and 28 STK11 missense variants using an in vitro kinase assay combined with a cell-based p53-dependent luciferase reporter assay. The variants we report were identified in primary human NSCLC biopsies in collaboration with the University of Vermont Genomic Medicine group. Additionally, we compare our experimental results with data from 22 in silico predictive algorithms. Our work highlights the power, utility and necessity of functional variant assessment and will aid STK11 variant curation, provide a platform to assess novel STK11 variants and help guide anti-PD-1 therapy utilization in KRAS-driven NSCLCs.

Across-Experiment Transcriptomics of Sheep Rumen Identifies Expression of Lipid/Oxo-Acid Metabolism and Muscle Cell Junction Genes Associated With Variation in Methane-Related Phenotypes.

Ruminants are significant contributors to the livestock generated component of the greenhouse gas, methane (CH4). The CH4 is primarily produced by the rumen microbes. Although the composition of the diet and animal intake amount have the largest effect on CH4 production and yield (CH4 production/dry matter intake, DMI), the host also influences CH4 yield. Shorter rumen feed mean retention time (MRT) is associated with higher dry matter intake and lower CH4 yield, but the molecular mechanism(s) by which the host affects CH4 production remain unclear. We integrated rumen wall transcriptome data and CH4 phenotypes from two independent experiments conducted with sheep in Australia (AUS, n = 62) and New Zealand (NZ, n = 24). The inclusion of the AUS data validated the previously identified clusters and gene sets representing rumen epithelial, metabolic and muscular functions. In addition, the expression of the cell cycle genes as a group was consistently positively correlated with acetate and butyrate concentrations (p < 0.05, based on AUS and NZ data together). The expression of a group of metabolic genes showed positive correlations in both AUS and NZ datasets with CH4 production (p < 0.05) and yield (p < 0.01). These genes encode key enzymes in the ketone body synthesis pathway and included members of the poorly characterized aldo-keto reductase 1C (AKR1C) family. Several AKR1C family genes appear to have ruminant specific evolution patterns, supporting their specialized roles in the ruminants. Combining differential gene expression in the rumen wall muscle of the shortest and longest MRT AUS animals (no data available for the NZ animals) with correlation and network analysis, we identified a set of rumen muscle genes involved in cell junctions as potential regulators of MRT, presumably by influencing contraction rates of the smooth muscle component of the rumen wall. Higher rumen expression of these genes, including SYNPO (synaptopodin, p < 0.01) and NEXN (nexilin, p < 0.05), was associated with lower CH4 yield in both AUS and NZ datasets. Unlike the metabolic genes, the variations in the expression of which may reflect the availability of rumen metabolites, the muscle genes are currently our best candidates for causal genes that influence CH4 yield.