ABSTRACT
Population scale sweeps of viral pathogens, such as SARS-CoV-2, require high intensity testing for effective management. Here, we describe "Systematic Parallel Analysis of RNA coupled to Sequencing for Covid-19 screening" (C19-SPAR-Seq), a multiplexed, scalable, readily automated platform for SARS-CoV-2 detection that is capable of analyzing tens of thousands of patient samples in a single run. To address strict requirements for control of assay parameters and output demanded by clinical diagnostics, we employ a control-based Precision-Recall and Receiver Operator Characteristics (coPR) analysis to assign run-specific quality control metrics. C19-SPAR-Seq coupled to coPR on a trial cohort of several hundred patients performs with a specificity of 100% and sensitivity of 91% on samples with low viral loads, and a sensitivity of >95% on high viral loads associated with disease onset and peak transmissibility. This study establishes the feasibility of employing C19-SPAR-Seq for the large-scale monitoring of SARS-CoV-2 and other pathogens.
Subject(s)
High-Throughput Nucleotide Sequencing/methods , SARS-CoV-2/growth & development , SARS-CoV-2/pathogenicity , COVID-19/genetics , COVID-19/immunology , COVID-19/virology , Humans , Molecular Diagnostic Techniques , Nucleic Acid Amplification Techniques , RNA, Viral/genetics , Reverse Transcriptase Polymerase Chain Reaction , Viral LoadABSTRACT
Evolutionary trade-offs occur when selection on one trait has detrimental effects on other traits. In pathogenic microbes, it has been hypothesized that antibiotic resistance trades off with fitness in the absence of antibiotic. Although studies of single resistance mutations support this hypothesis, it is unclear whether trade-offs are maintained over time, due to compensatory evolution and broader effects of genetic background. Here, we leverage natural variation in 39 extraintestinal clinical isolates of Escherichia coli to assess trade-offs between growth rates and resistance to fluoroquinolone and cephalosporin antibiotics. Whole-genome sequencing identifies a broad range of clinically relevant resistance determinants in these strains. We find evidence for a negative correlation between growth rate and antibiotic resistance, consistent with a persistent trade-off between resistance and growth. However, this relationship is sometimes weak and depends on the environment in which growth rates are measured. Using in vitro selection experiments, we find that compensatory evolution in one environment does not guarantee compensation in other environments. Thus, even in the face of compensatory evolution and other genetic background effects, resistance may be broadly costly, supporting the use of drug restriction protocols to limit the spread of resistance. Furthermore, our study demonstrates the power of using natural variation to study evolutionary trade-offs in microbes.
