Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia.

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19.

Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications.

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition.

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

Diagnosis of Seronegative and Ultrashort Celiac Disease.

In its conventional form, celiac disease (CeD) is characterized by both positive serology and flat villi in the duodenum, and is well known by gastroenterologists and general practitioners. The aim of this review was to shed light on 2 neglected and not yet well-defined celiac phenotypes, that is, seronegative and ultrashort CeD. Seronegative CeD can be suspected in the presence of flat villi, positive HLA-DQ2 and/or HLA-DQ8, and the absence of CeD antibodies. After ruling out other seronegative enteropathies, the diagnosis can be confirmed by both clinical and histologic improvements after 1 year of a gluten-free diet. Ultrashort CeD is characterized by the finding of flat villi in the duodenal bulb in the absence of mucosal damage in the distal duodenum and with serologic positivity. Data on the prevalence, clinical manifestations, histologic lesions, genetic features, and outcome of seronegative and ultrashort CeD are inconclusive due to the few studies available and the small number of patients diagnosed. Some additional diagnostic tools have been developed recently, such as assessing intestinal transglutaminase 2 deposits, flow cytometry technique, microRNA detection, or proteomic analysis, and they seem to be useful in the identification of complex cases. Further cooperative studies are highly desirable to improve the knowledge of these 2 still-obscure variants of CeD.

Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.

MOTIVATION: Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS: In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power.

Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.

Molecular methods enhance the detection of pyoderma-related Streptococcus pyogenes and emm-type distribution in children.

BACKGROUND: Streptococcus pyogenes-related skin infections are increasingly implicated in the development of rheumatic heart disease (RHD) in lower-resourced settings, where they are often associated with scabies. The true prevalence of S. pyogenes-related pyoderma may be underestimated by bacterial culture. METHODS: A multiplex qPCR for S. pyogenes, Staphylococcus aureus and Sarcoptes scabiei was applied to 250 pyoderma swabs from a cross-sectional study of children <5 years in The Gambia. Direct PCR-based emm-typing was used to supplement previous whole genome sequencing (WGS) of cultured isolates. RESULTS: Pyoderma lesions with S. pyogenes increased from 51% (127/250) using culture to 80% (199/250) with qPCR. Compared to qPCR, the sensitivity of culture was 95.4% for S. pyogenes (95% CI 77.2-99.9) in samples with S. pyogenes alone (22/250, 9%), but 59.9% (95% CI 52.3-67.2) for samples with S. aureus co-infection (177/250, 71%). Direct PCR-based emm-typing was successful in 50% (46/92) of cases, identifying 27 emm-types, including six not identified by WGS (total 52 emm-types). CONCLUSIONS: Bacterial culture significantly underestimates the burden of S. pyogenes in pyoderma, particularly when co-infected with S. aureus. Molecular methods should be used to enhance the detection of S. pyogenes in surveillance studies and clinical trials of preventative measures in RHD-endemic settings.

A novel qPCR-based technique for identifying avian sex: An illustration within embryonic craniofacial bone.

Sex is a biological variable important to consider in all biomedical experiments. However, doing so in avian embryos can be challenging as sex can be morphologically indistinguishable. Unlike humans, female birds are the heterogametic sex with Z and W sex chromosomes. The female-specific W chromosome has previously been identified in chick using a species-specific polymerase chain reaction (PCR) technique. We developed a novel reverse transcription quantitative PCR (RT-qPCR) technique that amplifies the W chromosome gene histidine triad nucleotide-binding protein W (HINTW) in chick, quail, and duck. Accuracy of the HINTW RT-qPCR primer set was confirmed in all three species using species-specific PCR, including a novel quail-specific HINTW PCR primer set. Bone development-related gene expression was then analyzed by sex in embryonic lower jaws of duck and quail, as adult duck beak size is known to be sexually dimorphic while quail beak size is not. Trends toward sex differences were found in duck gene expression but not in quail, as expected. With these novel RT-qPCR and PCR embryo sexing methods, sex of chick, quail, and duck embryos can now be assessed by either/both RNA and DNA, which facilitates analysis of sex as a biological variable in studies using these model organisms.

CAraCAl: CAMML with the integration of chromatin accessibility.

BACKGROUND: A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. RESULTS: To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. CONCLUSIONS: We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.

A Community-Curated DokuWiki Resource on Diagnostics, Diversity, Pathogenicity, and Genetic Control of Xanthomonads.

Xanthomonads, including Xanthomonas and Xylella species, constitute a large and significant group of economically and ecologically important plant pathogens. Up-to-date knowledge of these pathogens and their hosts is essential for the development of suitable control measures. Traditional review articles or book chapters have inherent limitations, including static content and rapid obsolescence. To address these challenges, we have developed a Web-based knowledge platform dedicated to xanthomonads, inspired by the concept of living systematic reviews. This platform offers a dynamic resource that encompasses bacterial virulence factors, plant resistance genes, and tools for diagnostics and genetic diversity studies. Our goal is to facilitate access for newcomers to the field, provide continuing education opportunities for students, assist plant protection services with diagnostics, provide valuable information to breeders on sources of resistance and breeding targets, and offer comprehensive expert knowledge to other stakeholders interested in plant-pathogenic xanthomonads. This resource is available for queries and updates at https://euroxanth.ipn.pt. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Antimicrobial resistance and heterogeneity of Neisseria gonorrhoeae isolated from patients attending sexually transmitted infection clinics in Lusaka, Zambia.

BACKGROUND: Antimicrobial resistance (AMR) of Neisseria gonorrhoeae is a threat to public health as strains have developed resistance to antimicrobials available for the treatment of gonorrhea. Whole genome sequencing (WGS) can detect and predict antimicrobial resistance to enhance the control and prevention of gonorrhea. Data on the molecular epidemiology of N. gonorrhoeae is sparse in Zambia. This study aimed to determine the genetic diversity of N. gonorrhoeae isolated from patients attending sexually transmitted infection (STI) clinics in Lusaka, Zambia. METHODS: A cross-sectional study that sequenced 38 N. gonorrhoeae isolated from 122 patients with gonorrhea from 2019 to 2020 was conducted. The AMR profiles were determined by the E-test, and the DNA was extracted using the NucliSens easyMaG magnetic device. Whole genome sequencing was performed on the Illumina NextSeq550 platform. The Bacterial analysis pipeline (BAP) that is readily available at: https://cge.cbs.dtu.dk/services/CGEpipeline-1.1 was used for the identification of the species, assembling the genome, multi-locus sequence typing (MLST), detection of plasmids and AMR genes. Phylogeny by single nucleotide polymorphisms (SNPs) was determined with the CCphylo dataset. RESULTS: The most frequent STs with 18.4% of isolates each were ST7363, ST1921 and ST1582, followed by ST1583 (13%), novel ST17026 (7.9%), ST1588 (7.9%), ST1596 (5.3%), ST11181 (5.3%), ST11750 (2.6/%) and ST11241 (2.6%) among the 38 genotyped isolates. The blaTeM-1B and tetM (55%) was the most prevalent combination of AMR genes, followed by blaTeM-1B (18.4%), tetM (15.8%), and the combination of blaTeM-1B, ermT, and tetL was 2.6% of the isolates. The AMR phenotypes were predicted in ciprofloxacin, penicillin, tetracycline, azithromycin, and cefixime. The combination of mutations 23.7% was gryA (S91F), parC (E91G), ponA (L421) and rpsJ (V57M), followed by 18.4% in gyrA (S91F), ponA (L421P), rpsJ (V57M), and 18.4% in gyrA (D95G, S91F), ponA (L421P), and rpsJ (V57M). The combinations in gyrA (D95G, S91F) and rpsJ (V57M), and gyrA (D95G, S91F), parC (E91F), ponA (L421P) and rpsJ (V57M) were 13.2% each of the isolates. Plasmid TEM-1 (84.2%), tetM (15.8%), and gonococcal genetic island (GGI) was detected in all isolates. CONCLUSION: This study revealed remarkable heterogeneity of N. gonorrhoeae with blaTEM-1, tetM, ponA, gyrA, and parC genes associated with high resistance to penicillin, tetracycline, and ciprofloxacin demanding revision of the standard treatment guidelines and improved antimicrobial stewardship in Zambia.

Environmental Hot Spots and Resistance-Associated Application Practices for Azole-Resistant Aspergillus fumigatus, Denmark, 2020-2023.

Azole-resistant Aspergillus fumigatus (ARAf) fungi have been found inconsistently in the environment in Denmark since 2010. During 2018-2020, nationwide surveillance of clinical A. fumigatus fungi reported environmental TR34/L98H or TR46/Y121F/T289A resistance mutations in 3.6% of isolates, prompting environmental sampling for ARAf and azole fungicides and investigation of selected ARAf in field and microcosmos experiments. ARAf was ubiquitous (20% of 366 samples; 16% TR34/L98H- and 4% TR46/Y121F/T289A-related mechanisms), constituting 4.2% of 4,538 A. fumigatus isolates. The highest proportions were in flower- and compost-related samples but were not correlated with azole-fungicide application concentrations. Genotyping showed clustering of tandem repeat-related ARAf and overlaps with clinical isolates in Denmark. A. fumigatus fungi grew poorly in the field experiment with no postapplication change in ARAf proportions. However, in microcosmos experiments, a sustained complete (tebuconazole) or partial (prothioconazole) inhibition against wild-type A. fumigatus but not ARAf indicated that, under some conditions, azole fungicides may favor growth of ARAf in soil.

Newly Recognized Spotted Fever Group Rickettsia as Cause of Severe Rocky Mountain Spotted Fever-Like Illness, Northern California, USA.

The incidence of spotted fever group (SFG) rickettsioses in the United States has tripled since 2010. Rocky Mountain spotted fever, the most severe SFG rickettsiosis, is caused by Rickettsia rickettsii. The lack of species-specific confirmatory testing obfuscates the relative contribution of R. rickettsii and other SFG Rickettsia to this increase. We report a newly recognized rickettsial pathogen, Rickettsia sp. CA6269, as the cause of severe Rocky Mountain spotted fever-like illness in 2 case-patients residing in northern California. Multilocus sequence typing supported the recognition of this pathogen as a novel Rickettsia genotype most closely related to R. rickettsii. Cross-reactivity observed for an established molecular diagnostic test indicated that Rickettsia sp. CA6269 might be misidentified as R. rickettsii. We developed a Rickettsia sp. CA6269-specific real-time PCR to help resolve this diagnostic challenge and better characterize the spectrum of clinical disease and ecologic epidemiology of this pathogen.

Clinical performance of the novel full-genotyping OncoPredict HPV Quantitative Typing assay using the VALGENT framework.

Clinical validation of human papillomavirus (HPV) assays according to international criteria is prerequisite for their implementation in cervical cancer screening. OncoPredict HPV Quantitative Typing (QT) assay (Hiantis Srl, Milan, Italy) is a novel full-genotyping multiplex real-time PCR quantitative assay targeting E6/E7 genes, allowing individual viral load determination of 12 high-risk (HR) HPV types. Quality controls for sample adequacy, efficiency of nucleic acid extraction and PCR inhibition are included in the assay. Clinical performance of OncoPredict HPV QT test was assessed as part of the "Validation of HPV Genotyping Tests" (VALGENT-2) framework, consisting of 1300 cervical liquid-based cytology (LBC) samples of women aged between 20 and 60 years who had originally attended for routine cervical screening in Scotland. The clinical accuracy of the OncoPredict HPV QT (index test) for the detection of CIN2+ was assessed relative to the GP5+/6+ Enzyme ImmunoAssay (GP5+/6+ EIA) (comparator test), using noninferiority criteria. Intra- and interlaboratory reproducibility of the assay was assessed on a subpopulation, comprising 526 samples. The relative sensitivity and specificity for OncoPredict HPV QT vs GP5+/6+-PCR-EIA were 1.01 (95% CI: 0.99-1.03) and 1.03 (95% CI: 1.0-1.06) respectively. The P-values for noninferiority were ≤0.001. The intra- and inter-laboratory reproducibility demonstrated a high concordance (>98.7%) with kappas for individual types ranging from 0.66 to 1.00. OncoPredict HPV QT fulfills the international validation criteria for the use of HPV tests in cervical cancer screening.

Genomic analysis of inter-hospital transmission of vancomycin-resistant Enterococcus faecium sequence type 80 isolated during an outbreak in Hiroshima, Japan.

Outbreaks caused by vancomycin-resistant enterococci that transcend jurisdictional boundaries are occurring worldwide. This study focused on a vancomycin-resistant enterococcus outbreak that occurred between 2018 and 2021 across two cities in Hiroshima, Japan. The study involved genetic and phylogenetic analyses using whole-genome sequencing of 103 isolates of vancomycin-resistant enterococci to identify the source and transmission routes of the outbreak. Phylogenetic analysis was performed using core genome multilocus sequence typing and core single-nucleotide polymorphisms; infection routes between hospitals were inferred using BadTrIP. The outbreak was caused by Enterococcus faecium sequence type (ST) 80 carrying the vanA plasmid, which was derived from strain A10290 isolated in India. Of the 103 isolates, 93 were E. faecium ST80 transmitted across hospitals. The circular vanA plasmid of the Hiroshima isolates was similar to the vanA plasmid of strain A10290 and transferred from E. faecium ST80 to other STs of E. faecium and other Enterococcus species by conjugation. The inferred transmission routes across hospitals suggest the existence of a central hospital serving as a hub, propagating vancomycin-resistant enterococci to multiple hospitals. Our study highlights the importance of early intervention at the key central hospital to prevent the spread of the infection to small medical facilities, such as nursing homes, with limited medical resources and a high number of vulnerable individuals.

Development and implementation of a core genome multilocus sequence typing scheme for Yersinia enterocolitica: a tool for surveillance and outbreak detection.

Yersinia enterocolitica (Y. enterocolitica) is the most frequent etiological agent of yersiniosis and has been responsible for several national outbreaks in Norway and elsewhere. A standardized high-resolution method, such as core genome Multilocus Sequence Typing (cgMLST), is needed for pathogen traceability at the national and international levels. In this study, we developed and implemented a cgMLST scheme for Y. enterocolitica. We designed a cgMLST scheme in SeqSphere + using high-quality genomes from different Y. enterocolitica biotype sublineages. The scheme was validated if more than 95% of targets were found across all tested Y. enterocolitica: 563 Norwegian genomes collected between 2012 and 2022 and 327 genomes from public data sets. We applied the scheme to known outbreaks to establish a threshold for identifying major complex types (CTs) based on the number of allelic differences. The final cgMLST scheme included 2,582 genes with a median of 97.9% (interquartile range 97.6%-98.8%) targets found across all tested genomes. Analysis of outbreaks identified all outbreak strains using single linkage clustering at four allelic differences. This threshold identified 311 unique CTs in Norway, of which CT18, CT12, and CT5 were identified as the most frequently associated with outbreaks. The cgMLST scheme showed a very good performance in typing Y. enterocolitica using diverse data sources and was able to identify outbreak clusters. We recommend the implementation of this scheme nationally and internationally to facilitate Y. enterocolitica surveillance and improve outbreak response in national and cross-border outbreaks.

Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing.

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

First Canadian report of transmission of fluconazole-resistant Candida parapsilosis within two hospital networks confirmed by genomic analysis.

Candida parapsilosis is a common cause of non-albicans candidemia. It can be transmitted in healthcare settings resulting in serious healthcare-associated infections and can develop drug resistance to commonly used antifungal agents. Following a significant increase in the percentage of fluconazole (FLU)-nonsusceptible isolates from sterile site specimens of patients in two Ontario acute care hospital networks, we used whole genome sequence (WGS) analysis to retrospectively investigate the genetic relatedness of isolates and to assess potential in-hospital spread. Phylogenomic analysis was conducted on all 19 FLU-resistant and seven susceptible-dose dependent (SDD) isolates from the two hospital networks, as well as 13 FLU susceptible C. parapsilosis isolates from the same facilities and 20 isolates from patients not related to the investigation. Twenty-five of 26 FLU-nonsusceptible isolates (resistant or SDD) and two susceptible isolates from the two hospital networks formed a phylogenomic cluster that was highly similar genetically and distinct from other isolates. The results suggest the presence of a persistent strain of FLU-nonsusceptible C. parapsilosis causing infections over a 5.5-year period. Results from WGS were largely comparable to microsatellite typing. Twenty-seven of 28 cluster isolates had a K143R substitution in lanosterol 14-α-demethylase (ERG11) associated with azole resistance. As the first report of a healthcare-associated outbreak of FLU-nonsusceptible C. parapsilosis in Canada, this study underscores the importance of monitoring local antimicrobial resistance trends and demonstrates the value of WGS analysis to detect and characterize clusters and outbreaks. Timely access to genomic epidemiological information can inform targeted infection control measures.

Multi-locus sequencing typing reveals geographically related intraspecies variability of Sporothrix brasiliensis.

Sporotrichosis is a subcutaneous mycosis caused by pathogenic Sporothrix species. Among them, Sporothrix brasiliensis is the main species associated with endemic regions in South America, especially Brazil. It is highly virulent and can be spread through zoonotic transmission. Molecular epidemiological surveys are needed to determine the extent of genetic variation, to investigate outbreaks, and to identify genotypes associated with antifungal resistance and susceptibility. This study investigated the sequence variation of different constitutive genes and established a novel multilocus sequence typing (MLST) scheme for S. brasiliensis. Specific primers were designed for 16 genes using Primer-BLAST software based on the genome sequences of three S. brasiliensis strains (ATCC MYA-4823, A001 and A005). Ninety-one human, animal, and environmental S. brasiliensis isolates from different Brazilian geographic regions (South, Southeast, Midwest and Northeast) andtwo isolates from Paraguay were sequenced. The loci that presented the highest nucleotide diversity (π) were selected for the MLST scheme. Among the 16 studied genetic loci, four presented increased π value and were able to distinguish all S. brasiliensis isolates into seven distinct haplotypes. The PCR conditions were standardized for four loci. Some of the obtained haplotypes were associated with the geographic origin of the strains. This study presents an important advance in the understanding of this important agent of sporotrichosis in Brazil. It significantly increased the discriminatory power for genotyping of S. brasiliensis isolates, and enabled new contributions to the epidemiological studies of this human and animal pathogen in Brazil and in other countries.