Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data.

Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs - two hosts in which infection was transmitted from one to the other - is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.

Comparative genomics and phylogenomics of Campylobacter unveil potential novel species and provide insights into niche segregation.

Campylobacter is a bacterial genus associated with community outbreaks and gastrointestinal symptoms. Studies on Campylobacter generally focus on specific pathogenic species such as C. coli and C. jejuni. Currently, there are thousands of publicly available Campylobacter genomes, allowing a more complete assessment of the genus diversity. In this work, we report a network-based analysis of all available Campylobacter genomes to explore the genus structure and diversity, revealing potentially new species and elucidating genus features. We also hypothesize that the previously established Clade III of C. coli is in fact a novel species (referred here as Campylobacter spp12). Finally, we found a negative correlation between pangenome fluidity and saturation coefficient, with potential implications to the lifestyles of distinct Campylobacter species. Since pangenome analysis depends on the number of available genomes, this correlation could help estimate pangenome metrics of Campylobacter species with less sequenced genomes, helping understand their lifestyle and niche adaptation. Together, our results indicate that the Campylobacter genus should be re-evaluated, with particular attention to the interplay between genome structure and niche segregation.

Probabilistic survival modeling in health research: an assessment using cohort data from hospitalized patients with COVID-19 in a Latin American city.

Probabilistic survival methods have been used in health research to analyze risk factors and adverse health outcomes associated with COVID-19. The aim of this study was to employ a probabilistic model selected among three distributions (exponential, Weibull, and lognormal) to investigate the time from hospitalization to death and determine the mortality risks among hospitalized patients with COVID-19. A retrospective cohort study was conducted for patients hospitalized due to COVID-19 within 30 days in Londrina, Brazil, between January 2021 and February 2022, registered in the database for severe acute respiratory infections (SIVEP-Gripe). Graphical and Akaike Information Criterion (AIC) methods were used to compare the efficiency of the three probabilistic models. The results from the final model were presented as hazard and event time ratios. Our study comprised of 7,684 individuals, with an overall case fatality rate of 32.78%. Data suggested that older age, male sex, severe comorbidity score, intensive care unit admission, and invasive ventilation significantly increased risks for in-hospital mortality. Our study highlights the conditions that confer higher risks for adverse clinical outcomes attributed to COVID-19. The step-by-step process for selecting appropriate probabilistic models may be extended to other investigations in health research to provide more reliable evidence on this topic.

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil.

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

Network analysis of ten thousand genomes shed light on Pseudomonas diversity and classification.

The growth of sequenced bacterial genomes has revolutionized the assessment of microbial diversity. Pseudomonas is a widely diverse genus, containing more than 254 species. Although type strains have been employed to estimate Pseudomonas diversity, they represent a small fraction of the genomic diversity at a genus level. We used 10,035 available Pseudomonas genomes, including 210 type strains, to build a genomic distance network to estimate the number of species through community identification. We identified taxonomic inconsistencies with several type strains and found that 25.65 % of the Pseudomonas genomes deposited on Genbank are misclassified. The phylogenetic tree using single-copy genes from representative genomes in each species cluster in the distance network revealed at least 14 Pseudomonas groups, including the P. alcaligenes group proposed here. We show that Pseudomonas is likely an admixture of different genera and should be further divided. This study provides an overview of Pseudomonas diversity from a network and phylogenomic perspective that may help reduce the propagation of mislabeled Pseudomonas genomes.

Genome sequencing of the vermicompost strain Stenotrophomonas maltophilia UENF-4GII and population structure analysis of the S. maltophilia Sm3 genogroup.

The Stenotrophomonas maltophilia complex (Smc) is a cosmopolitan bacterial group that has been proposed an emergent multidrug-resistant pathogen. Taxonomic studies support the genomic heterogeneity of Smc, which comprises genogroups exhibiting a range of phenotypically distinct strains from different sources. Here, we report the genome sequencing and in-depth analysis of S. maltophilia UENF-4GII, isolated from vermicompost. This genome harbors a unique region encoding a penicillin-binding protein (pbpX) that was carried by a transposon, as well as horizontally-transferred genomic islands involved in anti-phage defense via DNA modification, and pili glycosylation. We also analyzed all available Smc genomes to investigate genes associated with resistance and virulence, niche occupation, and population structure. S. maltophilia UENF-4GII belongs to genogroup 3 (Sm3), which comprises three phylogenetic clusters (PC). Pan-GWAS analysis uncovered 471 environment-associated and 791 PC-associated genes, including antimicrobial resistance (e.g. blaL1 and blaR1) and virulence determinants (e.g. treS and katG) that provide insights on the resistance and virulence potential of Sm3 strains. Together, the results presented here provide the grounds for more detailed clinical and ecological investigations of S. maltophilia.

Phylogenetic analysis and population structure of Pseudomonas alloputida.

The Pseudomonas putida group comprises strains with biotechnological and clinical relevance. P. alloputida was proposed as a new species and highlighted the misclassification of P. putida. Nevertheless, the population structure of P. alloputida remained unexplored. We retrieved 11,025 Pseudomonas genomes and used P. alloputida Kh7T to delineate the species. The P. alloputida population structure comprises at least 7 clonal complexes (CCs). Clinical isolates are mainly found in CC4 and acquired resistance genes are present at low frequency in plasmids. Virulence profiles support the potential of CC7 members to outcompete other plant or human pathogens through a type VI secretion system. Finally, we found that horizontal gene transfer had an important role in shaping the ability of P. alloputida to bioremediate aromatic compounds such as toluene. Our results provide the grounds to understand P. alloputida genetic diversity and its potential for biotechnological applications.

Genomic analysis of Neisseria elongata isolate from a patient with infective endocarditis.

Neisseria elongata is part of the commensal microbiota of the oropharynx. Although it is not considered pathogenic to humans, N. elongata has been implicated in several cases of infective endocarditis (IE). Here, we report a case of IE caused by N. elongata subsp. nitroreducens (Nel_M001) and compare its genome with 17 N. elongata genomes available in GenBank. We also evaluated resistance and virulence profiles with Comprehensive Antibiotic Resistance and Virulence Finder databases. The results showed a wide diversity among N. elongata isolates. Based on the pangenome cumulative curve, we demonstrate that N. elongata has an open pangenome. We found several different resistance genes, mainly associated with antibiotic efflux pumps. A wide range of virulence genes was observed, predominantly pilus formation genes. Nel_M001 was the only isolate to present two copies of some pilus genes and not present nspA gene. Together, our results provide insights into how this commensal microorganism can cause IE and may assist further biological investigations on nonpathogenic Neisseria spp. Case reporting and pangenome analyses are critical for enhancing our understanding of IE pathogenesis, as well as for alerting physicians and microbiologists to enable rapid identification and treatment to avoid unfavorable outcomes.

First Genome Sequences of Two Multidrug-Resistant Candida haemulonii var. vulnera Isolates From Pediatric Patients With Candidemia.

Candida haemulonii is a complex formed by C. haemulonii sensu stricto, C. haemulonii var. vulnera, and C. duobushaemulonii. Members of this complex are opportunistic pathogens closely related to C. pseudohaemulonii, C. lusitaniae, and C. auris, all members of a multidrug-resistant clade. Complete genome sequences for all members of this group are available in the GenBank database, except for C. haemulonii var. vulnera. Here, we report the first draft genomes of two C. haemulonii var. vulnera (isolates K1 and K2) and comparative genome analysis of closely related fungal species. The isolates were biofilm producers and non-susceptible to amphotericin B and fluconazole. The draft genomes comprised 350 and 387 contigs and total genome sizes of 13.21 and 13.26 Mb, with 5,479 and 5,507 protein-coding genes, respectively, allowing the identification of virulence and resistance genes. Comparative analyses of orthologous genes within the multidrug-resistant clade showed a total of 4,015 core clusters, supporting the conservation of 24,654 proteins and 3,849 single-copy gene clusters. Candida haemulonii var. vulnera shared a larger number of clusters with C. haemulonii and C. auris; however, more singletons were identified in C. lusitaniae and C. auris. Additionally, a multiple sequence alignment of Erg11p proteins revealed variants likely involved in reduced susceptibility to azole and polyene antifungal agents. The data presented in this work will, therefore, be of utmost importance for researchers studying the biology of the C. haemulonii complex and related species.

Population structure and pangenome analysis of Enterobacter bugandensis uncover the presence of blaCTX-M-55, blaNDM-5 and blaIMI-1, along with sophisticated iron acquisition strategies.

Enterobacter bugandensis is a recently described species that has been largely associated with nosocomial infections. We report the genome of a non-clinical E. bugandensis strain, which was integrated with publicly available genomes to study the pangenome and general population structure of E. bugandensis. Core- and whole-genome multilocus sequence typing allowed the detection of five E. bugandensis phylogroups (PG-A to E), which contain important antimicrobial resistance and virulence determinants. We uncovered several extended-spectrum ß-lactamases, including blaCTX-M-55 and blaNDM-5, present in an IncX replicon type plasmid, described here for the first time in E. bugandensis. Genetic context analysis of blaNDM-5 revealed the resemblance of this plasmid with other IncX plasmids from other bacteria from the same country. Three distinctive siderophore producing operons were found in E. bugandensis: enterobactin (ent), aerobactin (iuc/iut), and salmochelin (iro). Our findings provide novel insights on the lifestyle, physiology, antimicrobial, and virulence profiles of E. bugandensis.

Molecular Epidemiology of Multidrug-Resistant Klebsiella pneumoniae Isolates in a Brazilian Tertiary Hospital.

Multidrug-resistant (MDR) Klebsiella pneumoniae (Kp) is a major bacterial pathogen responsible for hospital outbreaks worldwide, mainly via the spread of high-risk clones and epidemic resistance plasmids. In this study, we evaluated the molecular epidemiology and ß-lactam resistance mechanisms of MDR-Kp strains isolated in a Brazilian academic care hospital. We used whole-genome sequencing to study drug resistance mechanisms and their relationships with a K. pneumoniae carbapenemase-producing (KPC) Kp outbreak. Forty-three Kp strains were collected between 2003 and 2012. Antimicrobial susceptibility testing was performed for 15 antimicrobial agents, and polymerase chain reaction (PCR) was used to detect 32 resistance genes. Mutations in ompk35, ompk36, and ompk37 were evaluated by PCR and DNA sequencing. Pulsed field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) were carried out to differentiate the strains. Based on distinct epidemiological periods, six Kp strains were subjected to whole-genome sequencing. ß-lactamase coding genes were widely distributed among isolates. Almost all isolates had mutations in porin genes, particularly ompk35. The presence of bla KPC promoted a very high increase in carbapenem minimum inhibitory concentration only when ompk35 and ompk36 were interrupted by insertion sequences. A major cluster was identified by PFGE analysis and all isolates from this cluster belonged to clonal group (CG) 258. We have also identified a large repertoire of resistance genes in the sequenced isolates. A bla KPC-2-bearing plasmid (pUFPRA2) was also identified, which was very similar to a plasmid previously described in the first Brazilian KPC-Kp (2005). We found high-risk clones (CG258) and an epidemic resistance plasmid throughout the duration of the study (2003 to 2012), emphasizing a persistent presence of MDR-Kp strains in the hospital setting. Finally, we found that horizontal transfer of resistance genes between clones may have played a key role in the evolution of the outbreak.

Genomic analysis unveils important aspects of population structure, virulence, and antimicrobial resistance in Klebsiella aerogenes.

Klebsiella aerogenes is an important pathogen in healthcare-associated infections. Nevertheless, in comparison to other clinically important pathogens, K. aerogenes population structure, genetic diversity, and pathogenicity remain poorly understood. Here, we elucidate K. aerogenes clonal complexes (CCs) and genomic features associated with resistance and virulence. We present a detailed description of the population structure of K. aerogenes based on 97 publicly available genomes by using both multilocus sequence typing and single-nucleotide polymorphisms extracted from the core genome. We also assessed virulence and resistance profiles using Virulence Finder Database and Comprehensive Antibiotic Resistance Database, respectively. We show that K. aerogenes has an open pangenome and a large effective population size, which account for its high genomic diversity and support that negative selection prevents fixation of most deleterious alleles. The population is structured in at least 10 CCs, including two novel ones identified here, CC9 and CC10. The repertoires of resistance genes comprise a high number of antibiotic efflux proteins as well as narrow- and extended-spectrum ß-lactamases. Regarding the population structure, we identified two clusters based on virulence profiles because of the presence of the toxin-encoding clb operon and the siderophore production genes, irp and ybt. Notably, CC3 comprises the majority of K. aerogenes isolates associated with hospital outbreaks, emphasizing the importance of constant monitoring of this pathogen. Collectively, our results may provide a foundation for the development of new therapeutic and surveillance strategies worldwide.

Molecular epidemiology of 16S rRNA methyltransferase in Brazil: RmtG in Klebsiella aerogenes ST93 (CC4).

Aminoglycosides are a class of antibiotics that play a key role in antimicrobial treatment of Multidrug resistant (MDR) Gram-negative bacilli, typically in combination with ß-lactams. Ribosomal 16S RNA modification by methyltransferases (e.g. RmtG) is an aminoglycoside resistance mechanism that, along with the occurrence carbapenem-resistant Enterobacteriaceae (CRE), has become a clinical concern. In Brazil, rmtG genes were initially reported in Klebsiella pneumoniae, and monitoring isolates from other species carrying this gene is critical for epidemiological studies and to prevent dissemination. Here we report the presence of rmtG in Klebisella aerogenes D3 and characterize its genetic context in comparison to isolates from other species. Further, we performed a phylogenetic reconstruction of 900 16S rRNA methyltransferases (16S-RMTases) and methyltransferase-related proteins. We show that, in K. aerogenes D3, rmtG co-occurs with sul2, near a transposon with an IS91-like insertion sequence. Resistome analysis revealed the co-production of RmtG and CTX-M-59. Ongoing surveillance of 16S-RMTases is crucial to delay the dissemination of such multiresistant isolates. Our results also highlight the reduction in treatment options for CRE infections, as well as the need of expanding prevention measures of these pathogens worldwide.

Genome sequencing and assessment of plant growth-promoting properties of a Serratia marcescens strain isolated from vermicompost.

BACKGROUND: Plant-bacteria associations have been extensively studied for their potential in increasing crop productivity in a sustainable manner. Serratia marcescens is a species of Enterobacteriaceae found in a wide range of environments, including soil. RESULTS: Here we describe the genome sequencing and assessment of plant growth-promoting abilities of S. marcescens UENF-22GI, a strain isolated from mature cattle manure vermicompost. In vitro, S. marcescens UENF-22GI is able to solubilize P and Zn, to produce indole compounds (likely IAA), to colonize hyphae and counter the growth of two phytopathogenic fungi. Inoculation of maize with this strain remarkably increased seedling growth and biomass under greenhouse conditions. The S. marcescens UENF-22GI genome has 5 Mb, assembled in 17 scaffolds comprising 4662 genes (4528 are protein-coding). No plasmids were identified. S. marcescens UENF-22GI is phylogenetically placed within a clade comprised almost exclusively of non-clinical strains. We identified genes and operons that are likely responsible for the interesting plant-growth promoting features that were experimentally described. The S. marcescens UENF-22GI genome harbors a horizontally-transferred genomic island involved in antibiotic production, antibiotic resistance, and anti-phage defense via a novel ADP-ribosyltransferase-like protein and possible modification of DNA by a deazapurine base, which likely contributes to its competitiveness against other bacteria. CONCLUSIONS: Collectively, our results suggest that S. marcescens UENF-22GI is a strong candidate to be used in the enrichment of substrates for plant growth promotion or as part of bioinoculants for agriculture.