Rare genetic variants of NLRP12 in Admixed Latino-American Children with SARS-CoV-2-related Multisystem Inflammatory Syndrome.

Multisystem Inflammatory Syndrome in Children (MIS-C) is a rare, potentially fatal complication of SARS-CoV-2 infection. Genetic defects in inflammation-related pathways have been linked to MIS-C, but additional research is needed, especially in diverse ethnic groups. The present study aimed to identify genetic variants underlying MIS-C in Brazilian patients. Whole-exome sequencing was performed, focusing on genes involved in the host immune response to SARS-CoV-2. Functional assays assessed the impact of selected variants on NF-κB signaling. Nine rare, potentially deleterious variants were found in eight of 21 patients, located in IL17RC, IFNA10, or NLRP12 genes. Unlike the wild-type NLRP12 protein, which inhibits NF-κB activation in HEK 293T cells, the mutant NLRP12 proteins have significantly reduced inhibitory properties. In conclusion, our results indicate that rare autosomal variants in immune-related genes may underlie MIS-C, highlighting the potential role of NLRP12 in its predisposition. These findings provide new insights for the appropriate management of MIS-C.

Real-life evaluation of a rapid antigen test (DPP SARS-CoV-2 Antigen) for COVID-19 diagnosis of primary healthcare patients, in the context of the Omicron-dominant wave in Brazil.

OBJECTIVES: We aimed to investigate the real-life performance of the rapid antigen test in the context of a primary healthcare setting, including symptomatic and asymptomatic individuals that sought diagnosis during an Omicron infection wave. METHODS: We prospectively accessed the performance of the DPP SARS-CoV-2 Antigen test in the context of an Omicron-dominant real-life setting. We evaluated 347 unselected individuals (all-comers) from a public testing centre in Brazil, performing the rapid antigen test diagnosis at point-of-care with fresh samples. The combinatory result from two distinct real-time quantitative PCR (RT-qPCR) methods was employed as a reference and 13 samples with discordant PCR results were excluded. RESULTS: The assessment of the rapid test in 67 PCR-positive and 265 negative samples revealed an overall sensitivity of 80.5% (CI 95% = 69.1%-89.2%), specificity of 99.2% (CI 95% = 97.3%-99.1%) and positive/negative predictive values higher than 95%. However, we observed that the sensitivity was dependent on the viral load (sensitivity in Ct < 31 = 93.7%, CI = 82.8%-98.7%; Ct > 31 = 47.4%, CI = 24.4%-71.1%). The positive samples evaluated in the study were Omicron (BA.1/BA.1.1) by whole-genome sequencing (n = 40) and multiplex RT-qPCR (n = 17). CONCLUSIONS: Altogether, the data obtained from a real-life prospective cohort supports that the rapid antigen test sensitivity for Omicron remains high and underscores the reliability of the test for COVID-19 diagnosis in settings with high disease prevalence and limited PCR testing capability.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications.

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

Comparative Genomics of Acinetobacter baumannii Clinical Strains From Brazil Reveals Polyclonal Dissemination and Selective Exchange of Mobile Genetic Elements Associated With Resistance Genes.

Acinetobacter baumannii is an opportunistic bacterial pathogen infecting immunocompromised patients and has gained attention worldwide due to its increased antimicrobial resistance. Here, we report a comparative whole-genome sequencing and analysis coupled with an assessment of antibiotic resistance of 46 Acinetobacter strains (45 A. baumannii plus one Acinetobacter nosocomialis) originated from five hospitals from the city of Recife, Brazil, between 2010 and 2014. An average of 3,809 genes were identified per genome, although only 2,006 genes were single copy orthologs or core genes conserved across all sequenced strains, with an average of 42 new genes found per strain. We evaluated genetic distance through a phylogenetic analysis and MLST as well as the presence of antibiotic resistance genes, virulence markers and mobile genetic elements (MGE). The phylogenetic analysis recovered distinct monophyletic A. baumannii groups corresponding to five known (ST1, ST15, ST25, ST79, and ST113) and one novel ST (ST881, related to ST1). A large number of ST specific genes were found, with the ST79 strains having the largest number of genes in common that were missing from the other STs. Multiple genes associated with resistance to ß-lactams, aminoglycosides and other antibiotics were found. Some of those were clearly mapped to defined MGEs and an analysis of those revealed known elements as well as a novel Tn7-Tn3 transposon with a clear ST specific distribution. An association of selected resistance/virulence markers with specific STs was indeed observed, as well as the recent spread of the OXA-253 carbapenemase encoding gene. Virulence genes associated with the synthesis of the capsular antigens were noticeably more variable in the ST113 and ST79 strains. Indeed, several resistance and virulence genes were common to the ST79 and ST113 strains only, despite a greater genetic distance between them, suggesting common means of genetic exchange. Our comparative analysis reveals the spread of multiple STs and the genomic plasticity of A. baumannii from different hospitals in a single metropolitan area. It also highlights differences in the spread of resistance markers and other MGEs between the investigated STs, impacting on the monitoring and treatment of Acinetobacter in the ongoing and future outbreaks.