Phenotypic and genotypic characterization of antibiotic resistance of Salmonella Heidelberg in the south of Brazil

Salmonella is the main human pathogen present in the poultry chain. Salmonella Heidelberg is one of the most important serovars for public health since it has been frequently isolated in broiler chickens from different countries and may present multidrug resistance (MDR). This study was carried out with 130 S. Heidelberg isolates collected from pre-slaughter broiler farms in 2019 and 2020 in 18 cities from three Brazilian states to study relevant aspects regarding their genotypic and phenotypic resistance. The isolates were tested and identified using somatic and flagellar antiserum (0:4, H:2, and H:r), and an antimicrobial susceptibility test (AST) was performed against 11 antibiotics for veterinary use. The strains were typed by Enterobacterial Repetitive Intergenic Consensus (ERIC)-PCR, and representatives of the main clusters of the identified profiles were sequenced by Whole Genome Sequencing (WGS). AST results showed that all isolates were resistant to sulfonamide, 54 % (70/130) were resistant to amoxicillin, and only one was sensitive to tetracycline. Twelve isolates (15.4 %) were MDR. The dendrogram obtained from the ERIC-PCR showed that the strains were grouped into 27 clusters with similarity above 90 %, with some isolates showing 100 % similarity but with different phenotypic profiles of antimicrobial resistance. Identical strains collected on the same farm on other dates were identified, indicating that they were residents. WGS identified 66 antibiotic-resistance genes. The sul2 (present in all sequenced samples) and tet(A) genes were highlighted and validated in the experimental analysis. The fosA7 gene was also identified in all sequenced samples, but resistance was not observed in the phenotypic test, possibly due to the heteroresistance of the S. Heidelberg strains evaluated. Considering that chicken meat is one of the most consumed meats in the world, the data obtained in the present study can corroborate the mapping of the origin and trends of antimicrobial resistance.

Genomic Surveillance of SARS-CoV-2 in Healthcare Workers: A Critical Sentinel Group for Monitoring the SARS-CoV-2 Variant Shift

SARS-CoV-2 genome surveillance is important for monitoring risk groups and health workers as well as data on new cases and mortality rate due to COVID-19. We characterized the circulation of SARS-CoV-2 variants from May 2021 to April 2022 in the state of Santa Catarina, southern Brazil, and evaluated the similarity between variants present in the population and healthcare workers (HCW). A total of 5291 sequenced genomes demonstrated the circulation of 55 strains and four variants of concern (Alpha, Delta, Gamma and Omicron-sublineages BA.1 and BA.2). The number of cases was relatively low in May 2021, but the number of deaths was higher with the Gamma variant. There was a significant increase in both numbers between December 2021 and February 2022, peaking in mid-January 2022, when the Omicron variant dominated. After May 2021, two distinct variant groups (Delta and Omicron) were observed, equally distributed among the five Santa Catarina mesoregions. Moreover, from November 2021 to February 2022, similar variant profiles between HCW and the general population were observed, and a quicker shift from Delta to Omicron in HCW than in the general population. This demonstrates the importance of HCW as a sentinel group for monitoring disease trends in the general population.

AnnotaPipeline: An integrated tool to annotate eukaryotic proteins using multi-omics data

Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.

FastProtein-an automated software for in silico proteomic analysis

Although various tools provide proteomic information, each tool has limitations related to execution platforms, libraries, versions, and data output format. Integrating data generated from different software is a laborious process that can prolong analysis time. Here, we present FastProtein, a protein analysis pipeline that is user-friendly, easily installable, and outputs important information about subcellular location, transmembrane domains, signal peptide, molecular weight, isoelectric point, hydropathy, aromaticity, gene ontology, endoplasmic reticulum retention domains, and N-glycosylation domains. It also helps determine the presence of glycosylphosphatidylinositol and obtain functional information from InterProScan, PANTHER, Pfam, and alignment-based annotation searches. FastProtein provides the scientific community with an easy-to-use computational tool for proteomic data analysis. It is applicable to both small datasets and proteome-wide studies. It can be used through the command line interface mode or a web interface installed on a local server. FastProtein significantly enhances proteomics analysis workflows by producing multiple results in a single-step process, thereby streamlining and accelerating the overall analysis. The software is open-source and freely available. Installation and execution instructions, as well as the source code and test files generated for tool validation, are available at https://github.com/bioinformatics-ufsc/FastProtein.