Accuracy of saliva for SARS-CoV-2 detection in outpatients and their household contacts during the circulation of the Omicron variant of concern.

BACKGROUND: While nasopharyngeal (NP) swabs are considered the gold standard for severe acute respiratory coronavirus 2 (SARS-CoV-2) real-time reverse transcriptase-polymerase chain reaction (RT-PCR) detection, several studies have shown that saliva is an alternative specimen for COVID-19 diagnosis and screening. METHODS: To analyze the utility of saliva for the diagnosis of COVID-19 during the circulation of the Omicron variant, participants were enrolled in an ongoing cohort designed to assess the natural history of SARS-CoV-2 infection in adults and children. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen's kappa coefficient were calculated to assess diagnostic performance. RESULTS: Overall, 818 samples were collected from 365 outpatients from January 3 to February 2, 2022. The median age was 32.8 years (range: 3-94 years). RT-PCR for SARS-CoV-2 was confirmed in 97/121 symptomatic patients (80.2%) and 62/244 (25.4%) asymptomatic patients. Substantial agreement between saliva and combined nasopharyngeal/oropharyngeal samples was observed with a Cohen's kappa value of 0.74 [95% confidence interval (CI): 0.67-0.81]. Sensitivity was 77% (95% CI: 70.9-82.2), specificity 95% (95% CI: 91.9-97), PPV 89.8% (95% CI: 83.1-94.4), NPV 87.9% (95% CI: 83.6-91.5), and accuracy 88.5% (95% CI: 85.0-91.4). Sensitivity was higher among samples collected from symptomatic children aged three years and older and adolescents [84% (95% CI: 70.5-92)] with a Cohen's kappa value of 0.63 (95% CI: 0.35-0.91). CONCLUSIONS: Saliva is a reliable fluid for detecting SARS-CoV-2, especially in symptomatic children and adolescents during the circulation of the Omicron variant.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851.

BACKGROUND: Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES: In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS: We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS: Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS: Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa.

BACKGROUND: Healthcare-associated infections caused by bacteria such as Pseudomonas aeruginosa are a major public health problem worldwide. Gene regulatory networks (GRN) computationally represent interactions among regulatory genes and their targets. They are an important approach to help understand bacterial behaviour and to provide novel ways of overcoming scientific challenges, including the identification of potential therapeutic targets and the development of new drugs. OBJECTIVES: The goal of this study was to reconstruct the multidrug-resistant (MDR) P. aeruginosa GRN and to analyse its topological properties. METHODS: The methodology used in this study was based on gene orthology inference using the reciprocal best hit method. We used the genome of P. aeruginosa CCBH4851 as the basis of the reconstruction process. This MDR strain is representative of the sequence type 277, which was involved in an endemic outbreak in Brazil. FINDINGS: We obtained a network with a larger number of regulatory genes, target genes and interactions as compared to the previously reported network. Topological analysis results are in accordance with the complex network representation of biological processes. MAIN CONCLUSIONS: The properties of the network were consistent with the biological features of P. aeruginosa. To the best of our knowledge, the P. aeruginosa GRN presented here is the most complete version available to date.

A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models.

Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

Characterization of a SPM-1 metallo-beta-lactamase-producing Pseudomonas aeruginosa by comparative genomics and phenotypic analysis.

Pseudomonas aeruginosa is one of the most common pathogens related to healthcare-associated infections. The Brazilian isolate, named CCBH4851, is a multidrug-resistant clone belonging to the sequence type 277. The antimicrobial resistance mechanisms of the CCBH4851 strain are associated with the presence of the bla[Formula: see text] gene, encoding a metallo-beta-lactamase, in combination with other exogenously acquired genes. Whole-genome sequencing studies focusing on emerging pathogens are essential to identify key features of their physiology that may lead to the identification of new targets for therapy. Using both Illumina and PacBio sequencing data, we obtained a single contig representing the CCBH4851 genome with annotated features that were consistent with data reported for the species. However, comparative analysis with other Pseudomonas aeruginosa strains revealed genomic differences regarding virulence factors and regulatory proteins. In addition, we performed phenotypic assays that revealed CCBH4851 is impaired in bacterial motilities and biofilm formation. On the other hand, CCBH4851 genome contained acquired genomic islands that carry transcriptional factors, virulence and antimicrobial resistance-related genes. Presence of single nucleotide polymorphisms in the core genome, mainly those located in resistance-associated genes, suggests that these mutations may also influence the multidrug-resistant behavior of CCBH4851. Overall, characterization of Pseudomonas aeruginosa CCBH4851 complete genome revealed the presence of features that strongly relates to the virulence and antibiotic resistance profile of this important infectious agent.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

BACKGROUND Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

BACKGROUND Healthcare-associated infections caused by bacteria such as Pseudomonas aeruginosa are a major public health problem worldwide. Gene regulatory networks (GRN) computationally represent interactions among regulatory genes and their targets. They are an important approach to help understand bacterial behaviour and to provide novel ways of overcoming scientific challenges, including the identification of potential therapeutic targets and the development of new drugs. OBJECTIVES The goal of this study was to reconstruct the multidrug-resistant (MDR) P. aeruginosa GRN and to analyse its topological properties. METHODS The methodology used in this study was based on gene orthology inference using the reciprocal best hit method. We used the genome of P. aeruginosa CCBH4851 as the basis of the reconstruction process. This MDR strain is representative of the sequence type 277, which was involved in an endemic outbreak in Brazil. FINDINGS We obtained a network with a larger number of regulatory genes, target genes and interactions as compared to the previously reported network. Topological analysis results are in accordance with the complex network representation of biological processes. MAIN CONCLUSIONS The properties of the network were consistent with the biological features of P. aeruginosa. To the best of our knowledge, the P. aeruginosa GRN presented here is the most complete version available to date.