ProteoVision: web server for advanced visualization of ribosomal proteins.

ProteoVision is a web server designed to explore protein structure and evolution through simultaneous visualization of multiple sequence alignments, topology diagrams and 3D structures. Starting with a multiple sequence alignment, ProteoVision computes conservation scores and a variety of physicochemical properties and simultaneously maps and visualizes alignments and other data on multiple levels of representation. The web server calculates and displays frequencies of amino acids. ProteoVision is optimized for ribosomal proteins but is applicable to analysis of any protein. ProteoVision handles internally generated and user uploaded alignments and connects them with a selected structure, found in the PDB or uploaded by the user. It can generate de novo topology diagrams from three-dimensional structures. All displayed data is interactive and can be saved in various formats as publication quality images or external datasets or PyMol Scripts. ProteoVision enables detailed study of protein fragments defined by Evolutionary Classification of protein Domains (ECOD) classification. ProteoVision is available at http://proteovision.chemistry.gatech.edu/.

Genomic Analysis of the Predominant Strains and Antimicrobial Resistance Determinants Within 1479 Neisseria gonorrhoeae Isolates From the US Gonococcal Isolate Surveillance Project in 2018.

BACKGROUND: The prevalence of Neisseria gonorrhoeae (GC) isolates with elevated minimum inhibitory concentrations to various antibiotics continues to rise in the United States and globally. Genomic analysis provides a powerful tool for surveillance of circulating strains, antimicrobial resistance determinants, and understanding of transmission through a population. METHODS: Neisseria gonorrhoeae isolates collected from the US Gonococcal Isolate Surveillance Project in 2018 (n = 1479) were sequenced and characterized. Whole-genome sequencing was used to identify sequence types, antimicrobial resistance profiles, and phylogenetic relationships across demographic and geographic populations. RESULTS: Genetic characterization identified that (1) 80% of the GC isolates were represented in 33 multilocus sequence types, (2) isolates clustered in 23 major phylogenetic clusters with select phenotypic and demographic prevalence, and (3) common antimicrobial resistance determinants associated with low-level or high-level decreased susceptibility or resistance to relevant antibiotics. CONCLUSIONS: Characterization of this 2018 Gonococcal Isolate Surveillance Project genomic data set, which is the largest US whole-genome sequence data set to date, sets the basis for future prospective studies, and establishes a genomic baseline of GC populations for local and national monitoring.

Exploring and Comparing the Structure of Sexual Networks Affected by Neisseria gonorrhoeae Using Sexual Partner Services Investigation and Genomic Data.

BACKGROUND: Sexual networks are difficult to construct because of incomplete sexual partner data. The proximity of people within a network may be inferred from genetically similar infections. We explored genomic data combined with partner services investigation (PSI) data to extend our understanding of sexual networks affected by Neisseria gonorrhoeae (NG). METHODS: We used 2017-2019 PSI and whole-genome sequencing (WGS) data from 8 jurisdictions participating in Centers for Disease Control and Prevention's Strengthening the US Response to Resistant Gonorrhea (SURRG) project. Clusters were identified from sexual contacts and through genetically similar NG isolates. Sexual mixing patterns were characterized by describing the clusters by the individual's gender and gender of their sex partners. RESULTS: Our study included 4627 diagnoses of NG infection (81% sequenced), 2455 people received a PSI, 393 people were negative contacts of cases, and 495 were contacts with an unknown NG status. We identified 823 distinct clusters using PSI data combined with WGS data. Of cases that were not linked to any other case using PSI data, 37% were linked when using WGS data. Overall, 40% of PSI cases were allocated to a larger cluster when PSI and WGS data were combined compared with PSI data alone. Mixed clusters containing women, men who report sex with women, and men who report sex with men were common when using the WGS data either alone or in combination with the PSI data. CONCLUSIONS: Combining PSI and WGS data improves our understanding of sexual network connectivity.

Genomic analysis of 1710 surveillance-based Neisseria gonorrhoeae isolates from the USA in 2019 identifies predominant strain types and chromosomal antimicrobial-resistance determinants.

This study characterized high-quality whole-genome sequences of a sentinel, surveillance-based collection of 1710 Neisseria gonorrhoeae (GC) isolates from 2019 collected in the USA as part of the Gonococcal Isolate Surveillance Project (GISP). It aims to provide a detailed report of strain diversity, phylogenetic relationships and resistance determinant profiles associated with reduced susceptibilities to antibiotics of concern. The 1710 isolates represented 164 multilocus sequence types and 21 predominant phylogenetic clades. Common genomic determinants defined most strains' phenotypic, reduced susceptibility to current and historic antibiotics (e.g. bla TEM plasmid for penicillin, tetM plasmid for tetracycline, gyrA for ciprofloxacin, 23S rRNA and/or mosaic mtr operon for azithromycin, and mosaic penA for cefixime and ceftriaxone). The most predominant phylogenetic clade accounted for 21 % of the isolates, included a majority of the isolates with low-level elevated MICs to azithromycin (2.0 µg ml-1), carried a mosaic mtr operon and variants in PorB, and showed expansion with respect to data previously reported from 2018. The second largest clade predominantly carried the GyrA S91F variant, was largely ciprofloxacin resistant (MIC ≥1.0 µg ml-1), and showed significant expansion with respect to 2018. Overall, a low proportion of isolates had medium- to high-level elevated MIC to azithromycin ((≥4.0 µg ml-1), based on C2611T or A2059G 23S rRNA variants). One isolate carried the penA 60.001 allele resulting in elevated MICs to cefixime and ceftriaxone of 1.0 µg ml-1. This high-resolution snapshot of genetic profiles of 1710 GC sequences, through a comparison with 2018 data (1479 GC sequences) within the sentinel system, highlights change in proportions and expansion of select GC strains and the associated genetic mechanisms of resistance. The knowledge gained through molecular surveillance may support rapid identification of outbreaks of concern. Continued monitoring may inform public health responses to limit the development and spread of antibiotic-resistant gonorrhoea.