Within-host transition to GES-55 during a GES-6-producing Serratia marcescens outbreak: emergence of ceftazidime-avibactam resistance and increased susceptibility to carbapenems.

OBJECTIVES: To describe the in vivo emergence of ceftazidime-avibactam resistance in GES-type carbapenemases and to characterize an unusual outbreak of GES-6-producing Serratia marcescens during the COVID-19 pandemic in Spain. METHODS: Retrospective study to describe a GES-CPSM outbreak based on whole genome sequencing and antimicrobial susceptibility testing (AST). Transferability of blaGES-carrying plasmid was assessed by conjugation experiments. RESULTS: In December 2020, we identified a cluster of S. marcescens harbouring blaGES-6 involving 9 patients. Whole-genome sequence analysis revealed a clonal relationship (≤3 SNPs) between the first isolates identified in each of the evolved patients and environmental samples with GES-CPSM detection. Plasmid analysis showed that the blaGES-6 gene was located in an IncQ3-type plasmid. Triparental mating experiments using a helper plasmid demonstrated mobilization of the blaGES-6-carrying plasmid. Our results also demonstrate within-host evolution in S. marcescens isolates, leading to a transition from blaGES-6 to the new blaGES-55, caused by the P162S mutation, in a subsequent infection in one of the affected patients. In blaGES-55 we identified emergence of ceftazidime-avibactam resistance along with an increase of carbapenems susceptibility. This patient had been treated with a 14-day course of ceftazidime-avibactam. AST of the transformants bearing blaGES-6 and blaGES-55 plasmids, confirmed susceptibility variation affecting ceftazidime-avibactam and carbapenems. CONCLUSIONS: We report an unusual outbreak of GES-6 whose incidence is becoming increasing. Transition from GES-6 to GES-55 may readily occur in vivo leading to ceftazidime-avibactam resistance, which brings to the fore the critical need for developing more accurate diagnosis tools for detection of GES ß-lactamases and optimise the use of antimicrobials.

Suboptimal bioinformatic predictions of antimicrobial resistance from whole-genome sequences in multidrug-resistant Corynebacterium isolates.

Herein, we combined different bioinformatics tools and databases (BV-BRC, ResFinder, RAST, and KmerResistance) to perform a prediction of antimicrobial resistance (AMR) in the genomic sequences of 107 Corynebacterium striatum isolates for which trustable antimicrobial susceptibility (AST) phenotypes could be retrieved. Then, the reliabilities of the AMR predictions were evaluated by different metrics: area under the ROC curve (AUC); Major Error Rates (MERs) and Very Major Error Rates (VMERs); Matthews Correlation Coefficient (MCC); F1-Score; and Accuracy. Out of 15 genes that were reliably detected in the C. striatum isolates, only tetW yielded predictive values for tetracycline resistance that were acceptable considering Food and Drug Administration (FDA)'s criteria for quality (MER < 3.0% and VMER with a 95% C.I. ≤1.5-≤7.5%); this was accompanied by a MCC score higher than 0.9 for this gene. Noteworthy, our results indicate that other commonly used metrics (AUC, F1-score, and Accuracy) may render overoptimistic evaluations of AMR-prediction reliabilities on imbalanced datasets. Accordingly, out of 10 genes tested by PCR on additional multidrug-resistant Corynebacterium spp. isolates (n = 18), the tetW gene rendered the best agreement values with AST profiles (94.11%). Overall, our results indicate that genome-based AMR prediction can still be challenging for MDR clinical isolates of emerging Corynebacterium spp.

Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy.

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.

Co-Existence of blaNDM-1, blaOXA-23, blaOXA-64, blaPER-7 and blaADC-57 in a Clinical Isolate of Acinetobacter baumannii from Alexandria, Egypt.

The increasing rates of antimicrobial resistance among carbapenem-resistant Acinetobacter baumannii in the Middle East and North Africa are one of the major concerns for healthcare settings. We characterised the first A. baumannii isolate harbouring five ß-lactamases identified in Egypt. The isolate Ale25 was obtained from an ICU patient of a hospital from Alexandria. The isolate was phenotypically and genotypically screened for carbapenemase genes. The isolate was resistant to carbapenems, aminoglycosides, fluoroquinolones and cefiderocol. Whole-Genome Sequencing identified five ß-lactamase genes, blaNDM-1, blaOXA-23, blaOXA-64, blaPER-7 and blaADC-57, together with other antibiotic resistance genes, conferring resistance to sulfonamides, macrolides, tetracyclines, rifamycin and chloramphenicol. Virulome analysis showed the presence of genes involved in adhesion and biofilm production, type II and VI secretion systems, exotoxins, etc. Multi-Locus Sequence Typing analysis identified the isolate as Sequence Types 113Pas and 2246Oxf, belonging to International Clone 7. Sequencing experiments revealed the presence of four plasmids of 2.7, 22.3, 70.4 and 240.8 Kb. All the ß-lactamase genes were located in the chromosome, except the blaPER-7, gene which was found within the plasmid of 240.8 Kb. This study highlights the threat of the emergence and dissemination of these types of isolates.

Single cell transcriptomic analysis of Graves' disease thyroid glands reveals the broad immunoregulatory potential of thyroid follicular and stromal cells and implies a major re-interpretation of the role of aberrant HLA class II expression in autoimmunity.

The study of the immune response in thyroid autoimmunity has been mostly focused on the autoantibodies and lymphocytes, but there are indications that intrinsic features of thyroid tissue cells may play a role in disrupting tolerance that needs further investigation. The overexpression of HLA and adhesion molecules by thyroid follicular cells (TFC) and our recent demonstration that PD-L1 is also moderately expressed by TFCs in autoimmune thyroid indicates that TFCs they may activate but also inhibit the autoimmune response. Intriguingly, we have recently found that in vitro cultured TFCs are able to suppress the proliferation of autologous lymphocyte T in a contact-dependent manner which is independent of the PD-1/PD-L1 signaling pathway. To get a more comprehensive picture of TFC activating and inhibitory molecules/pathways driving the autoimmune response in the thyroid glands, preparations of TFCs and stromal cells from five Graves' disease (GD) and four control thyroid glands were compared by scRNA-seq. The results confirmed the previously described interferon type I and type II signatures in GD TFCs and showed unequivocally that they express the full array of genes that intervene in the processing and presentation of endogenous and exogeneous antigens. GD TFCs lack however expression of costimulatory molecules CD80 and CD86 required for priming T cells. A moderate overexpression of CD40 by TFCs was confirmed. GD Fibroblasts showed widespread upregulation of cytokine genes. The results from this first single transcriptomic profiling of TFC and thyroid stromal cells provides a more granular view of the events occurring in GD. The new data point at an important contribution of stromal cells and prompt a major re-interpretation of the role of MHC over-expression by TFC, from deleterious to protective. Most importantly this re-interpretation could also apply to other tissues, like pancreatic beta cells, where MHC over-expression has been detected in diabetic pancreas.

Automatic classification of Candida species using Raman spectroscopy and machine learning.

One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.

Plasmid content of carbapenem resistant Acinetobacter baumannii isolates belonging to five International Clones collected from hospitals of Alexandria, Egypt.

Multidrug resistant Acinetobacter baumannii is one of the most important nosocomial pathogens worldwide. During the last decades it has become a major threat for healthcare settings due to the high antibiotic resistance rates among these isolates. Many resistance determinants are coded by conjugative or mobilizable plasmids, facilitating their dissemination. The majority of plasmids harbored by Acinetobacter species are less than 20 Kb, however, high molecular weight elements are the most clinically relevant since they usually contain antibiotic resistance genes. The aim of this work was to describe, classify and determine the genetic content of plasmids harbored by carbapem resistant A. baumannii isolates belonging to predominant clonal lineages circulating in hospitals from Alexandria, Egypt. The isolates were subjected to S1-Pulsed Field Gel Electrophoresis experiments to identify high molecular weight plasmids. To further analyze the plasmid content and the genetic localization of the antibiotic resistance genes, isolates were sequenced by Illumina Miseq and MinION Mk1C and a hybrid assembly was performed using Unicycler v0.5.0. Plasmids were detected with MOBsuite 3.0.3 and Copla.py v.1.0 and mapped using the online software Proksee.ca. Replicase genes were further analyzed through a BLAST against the Acinetobacter Plasmid Typing database. Eleven plasmids ranging in size from 4.9 to 205.6 Kb were characterized and mapped. All isolates contained plasmids, and, in many cases, more than two elements were identified. Antimicrobial resistance genes such as bla OXA-23, bla GES-like, aph(3')-VI and qacEΔ1 were found in likely conjugative large plasmids; while virulence determinants such as septicolysin or TonB-dependent receptors were identified in plasmids of small size. Some of these resistance determinants were, in turn, located within transposons and class 1 integrons. Among the identified plasmids, the majority encoded proteins belonging to the Rep_3 family, but replicases of the RepPriCT_1 (Aci6) family were also identified. Plasmids are of high interest as antibiotic resistance control tools, since they may be used as genetic markers for antibiotic resistance and virulence, and also as targets for the development of compounds that can inhibit transfer processes.

Prevalence and Population Diversity of Listeria monocytogenes Isolated from Dairy Cattle Farms in the Cantabria Region of Spain.

Listeria monocytogenes is an opportunistic pathogen that is widely distributed in the environment. Here we show the prevalence and transmission of L. monocytogenes in dairy farms in the Cantabria region, on the northern coast of Spain. A total of 424 samples was collected from 14 dairy farms (5 organic and 9 conventional) and 211 L. monocytogenes isolates were recovered following conventional microbiological methods. There were no statistically significant differences in antimicrobial resistance ratios between organic and conventional farms. A clonal relationship among the isolates was assessed by pulsed field gel electrophoresis (PFGE) analysis and 64 different pulsotypes were obtained. Most isolates (89%, n = 187) were classified as PCR serogroup IVb by using a multiplex PCR assay. In this case, 45 isolates of PCR serogroup IVb were whole genome-sequenced to perform a further analysis at genomic level. In silico MLST analysis showed the presence of 12 sequence types (ST), of which ST1, ST54 and ST666 were the most common. Our data indicate that the environment of cattle farms retains a high incidence of L. monocytogenes, including subtypes involved in human listeriosis reports and outbreaks. This pathogen is shed in the feces and could easily colonize dairy products, as a result of fecal contamination. Effective herd and manure management are needed in order to prevent possible outbreaks.

SARS-CoV-2 B.1.1.7 Decline Is Not Driven by the Introduction of a More Successful Variant.

The SARS-CoV-2 variant of concern (VOC) Delta (B.617.2 lineage) displaced the predominant VOC Alpha (B.1.1.7 lineage) in the United Kingdom. In Madrid, recent start of the decline of predominant VOC Alpha suggested an equivalent phenomenon. However, 11 different variants, none overrepresented in frequency, occupied progressively over a period of 7 weeks the niche previously dominated by VOC Alpha. Only after these 7 weeks, VOC Delta started to emerge. Viral competition due to the entry of VOC Delta is not the major force driving the start of VOC Alpha decline in Madrid. IMPORTANCE Our data indicate that the dynamics of SARS-CoV-2 VOCs turnover in our setting differ from those proposed for other countries. A systematic genomic analysis, updated on a weekly basis, of representative randomly selected samples of SARS-CoV-2 circulating variants allowed us to define a lapse of 7 weeks between the start of VOC Alpha decline and the final emergence of VOC Delta. During this period, VOC Alpha showed a sustained decline, while 11 VOCs, variants of interest (VOIs), and other identified variants, none overrepresented, occupied the niche left by VOC Alpha. Only after these 7 weeks, emergence of VOC Delta occurred, indicating that viral competition involving VOC Delta was not the exclusive direct driving force behind the starting of VOC Alpha decline.

Measuring Plasmid Conjugation Using Antibiotic Selection.

Plasmid conjugation is intimately linked to the transmission of antibiotic resistances, and many naturally occurring plasmids carry antibiotic resistance genes. Here we describe classical methods based on the transmission of antibiotic resistance determinants routinely used to quantify plasmid conjugation under laboratory conditions. Methods described here are suitable for most plasmid incompatibility groups from Proteobacteria and can be readily adapted to other bacterial species.