SABRes: in silico detection of drug resistance conferring mutations in subpopulations of SARS-CoV-2 genomes.

The emergence of resistance to antiviral drugs increasingly used to treat SARS-CoV-2 infections has been recognised as a significant threat to COVID-19 control. In addition, some SARS-CoV-2 variants of concern appear to be intrinsically resistant to several classes of these antiviral agents. Therefore, there is a critical need for rapid recognition of clinically relevant polymorphisms in SARS-CoV-2 genomes associated with significant reduction of drug activity in virus neutralisation experiments. Here we present SABRes, a bioinformatic tool, which leverages on expanding public datasets of SARS-CoV-2 genomes and allows detection of drug resistance mutations in consensus genomes as well as in viral subpopulations. We have applied SABRes to detect resistance-conferring mutations in 25,197 genomes generated over the course of the SARS-CoV-2 pandemic in Australia and identified 299 genomes containing resistance conferring mutations to the five antiviral therapeutics that retain effectiveness against currently circulating strains of SARS-CoV-2 - Sotrovimab, Bebtelovimab, Remdesivir, Nirmatrelvir and Molnupiravir. These genomes accounted for a 1.18% prevalence of resistant isolates discovered by SABRes, including 80 genomes with resistance conferring mutations found in viral subpopulations. Timely recognition of these mutations within subpopulations is critical as these mutations can provide an advantage under selective pressure and presents an important step forward in our ability to monitor SARS-CoV-2 drug resistance.

Clinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics-Current State and Whole Genome Sequencing Implementation Perspectives.

Antimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions of deaths annually. The rapid spread of AMR across continents warrants systematic changes in healthcare routines and protocols. One of the fundamental issues with AMR spread is the lack of rapid diagnostic tools for pathogen identification and AMR detection. Resistance profile identification often depends on pathogen culturing and thus may last up to several days. This contributes to the misuse of antibiotics for viral infection, the use of inappropriate antibiotics, the overuse of broad-spectrum antibiotics, or delayed infection treatment. Current DNA sequencing technologies offer the potential to develop rapid infection and AMR diagnostic tools that can provide information in a few hours rather than days. However, these techniques commonly require advanced bioinformatics knowledge and, at present, are not suited for routine lab use. In this review, we give an overview of the AMR burden on healthcare, describe current pathogen identification and AMR screening methods, and provide perspectives on how DNA sequencing may be used for rapid diagnostics. Additionally, we discuss the common steps used for DNA data analysis, currently available pipelines, and tools for analysis. Direct, culture-independent sequencing has the potential to complement current culture-based methods in routine clinical settings. However, there is a need for a minimum set of standards in terms of evaluating the results generated. Additionally, we discuss the use of machine learning algorithms regarding pathogen phenotype detection (resistance/susceptibility to an antibiotic).

Distinct codon usage bias evolutionary patterns between weakly and strongly virulent respiratory viruses.

Human respiratory viruses are of vastly different virulence, giving rise to symptoms ranging from common cold to severe pneumonia or even death. Although this most likely impacts molecular evolution of the corresponding viruses, the specific differences in their evolutionary patterns remain largely unknown. By comparing structural and nonstructural genes within respiratory viruses, greater similarities in codon usage bias (CUB) between nonstructural genes and humans were observed in weakly virulent viruses, whereas in strongly virulent viruses, it was structural genes whose CUBs were more similar to that of humans. Further comparisons between genomes of weakly and strongly virulent coronaviruses revealed greater similarities in CUBs between strongly virulent viruses and humans. Finally, using phylogenetic independent contrasts, dissimilation of viral CUB from that of humans was observed in SARS-CoV-2. Our work revealed distinct CUB evolutionary patterns between weakly and strongly virulent viruses, a previously unrecognized interaction between CUB and virulence in respiratory viruses.

A one-step, one-tube real-time RT-PCR based assay with an automated analysis for detection of SARS-CoV-2.

Early diagnosis of SARS-CoV-2 infected patients is essential to control the dynamics of the COVID-19 pandemic. We develop a rapid and accurate one-step multiplex TaqMan probe-based real-time RT-PCR assay, along with a computational tool to systematically analyse the data. Our assay could detect to a limit of 15 copies of SARS-CoV-2 transcripts-based on experiments performed by spiking total human RNA with in vitro synthesized viral transcripts. The assay was evaluated by performing 184 validations for the SARS-CoV-2 Nucleocapsid gene and human RNase P as an internal control reference gene with dilutions ranging from 1-100 ng for human RNA on a cohort of 26 clinical samples. 5 of 26 patients were confirmed to be infected with SARS-CoV-2, while 21 tested negative, consistent with the standards. The accuracy of the assay was found to be 100% sensitive and 100% specific based on the 26 clinical samples that need to be further verified using a large number of clinical samples. In summary, we present a rapid, easy to implement real-time PCR based assay with automated analysis using a novel COVID qPCR Analyzer tool with graphical user interface (GUI) to analyze the raw qRT-PCR data in an unbiased manner at a cost of under $3 per reaction and turnaround time of less than 2h, to enable in-house SARS-CoV-2 testing across laboratories.