Notes

DisCoTune: versatile reliable plasmids to the production of disulphide-containing proteins and peptides inside the E. coli T7 technique.
Copyright © 2020 American Society for Microbiology.Background Clinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients.Methods We enrolled 1764 subjects, including clinically diagnosed PTB patients, microbiologically-confirmed PTB cases, non-TB disease controls and healthy controls, in three cohorts (Screening, Selection and Validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and qRT-PCR in the Screening Cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the Selection Cohort. These models were evaluated by AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort.Results Three differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHRs (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in differentiating clinically diagnosed PTB from non-TB disease controls of the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. The nomogram also had a discriminative power (AUC 0.90, sensitivity 0.85, specificity 0.81) in identifying microbiologically-confirmed PTB patients.Conclusions LncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence. Copyright © 2020 Hu et al.Mycobacterium abscessus complex (MABC) are multidrug resistant nontuberculous mycobacteria and cause opportunistic pulmonary infections in individuals with cystic fibrosis (CF). In this study, genomic analysis of MABC was performed to gain greater insights into the epidemiology of circulating strains in Ireland.Whole genome sequencing (WGS) was performed on 70 MABC isolates that had been referred to the Irish Mycobacteria Reference Laboratory between 2006 and 2017 across nine Irish healthcare centres. MABC comprised of 52 isolates from 27 CF patients and 18 isolates from 10 non-CF patients.WGS identified 57 (81.4%) as M. abscessus subsp. abscessus (MAB), 10 (14.3%) as M. abscessus subsp. massiliense (MMAS) and 3 (4.3%) as M. abscessus subsp. bolletii (MBOL). Forty-nine isolates (94%) from 25 CF patients were identified as MAB whereas 3 (6%) isolates from 2 CF patients were identified as MMAS. Among non-CF patients, 44% (8/18) were identified as MAB, 39% (7/18) as MMAS and 17% (3/18) as MBOL. WGS detected two clusters of closely related MAB that included isolates from different CF centres.There was greater genomic diversity of MABC among non-CF compared to CF patients. Although WGS failed to show direct evidence of patient to patient transmission among CF patients, there was a predominance of two different strains of MAB. Furthermore, some MABC were closely related to global strains suggesting their international spread. Future prospective real-time epidemiological and clinical data along with contemporary MABC sequence analysis may elucidate sources and routes of transmission among patients infected with MABC. Copyright © 2020 American Society for Microbiology.Mycoplasma bovis causes pneumonia, pharyngitis, otitis, arthritis, mastitis and reproductive disorders in cattle and bison. Two multilocus sequence typing (MLST) schemes have been developed for M. bovis, with one serving as the PubMLST reference method, but no comparison of the schemes has been undertaken. Although the PubMLST scheme has proven to be highly discriminatory and informative, the recent discovery of isolates missing one of the typing loci, adh-1, raises concern about its suitability for continued use. The goal of our study was to compare the performance of the two MLST schemes and identify a new reference scheme capable of fully typing all isolates. We evaluated 448 isolates from diverse geographic and anatomic sites that collectively represent cattle, bison, deer and a goat. The discrimination index (DI) for the PubMLST and alternative scheme is 0.909 (91 STs) and 0.842 (77 STs), respectively. Although the PubMLST scheme outperformed the alternative scheme, the adh-1 locus must be retired from the PubMLST scheme if it is to be retained as a reference method. The DI obtained using the six remaining PubMLST loci (0.897, 79 STs) fails to reach the benchmark recommended for a reference method (0.900), mandating the addition of a seventh locus. Comparative analysis of genome sequences from the isolates used here identified the dnaA locus from the alternative scheme as the optimal replacement for adh-1 This revised scheme, which will be implemented as the new PubMLST reference method, has a DI of 0.914 and distinguishes 88 STs from the 448 isolates evaluated. Copyright © 2020 American Society for Microbiology.Whole genome sequencing (WGS) is now routinely performed in clinical microbiology laboratories to assess isolate relatedness. With appropriately developed analytics, the same data can be used for prediction of antimicrobial susceptibility. We assessed WGS data for identification using open source tools and antibiotic susceptibility testing (AST) prediction using ARESdb compared to matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF) identification and broth microdilution phenotypic susceptibility testing on clinical isolates from a multicenter clinical trial of the FDA-cleared Unyvero LRT Application (Curetis). For the trial, more than 2,000 patient samples were collected from ICUs across nine hospitals and tested for lower respiratory tract infection (LRTI). The isolate subset used in this study included 620 clinical isolates originating from 455 LRTI culture-positive patient samples. Isolates were sequenced using the Illumina Nextera XT protocol and FASTQ-files with raw reads uploaded to the ARESdb cloud platform (ares-genetics.cloud, released for research use in 2020). The platform combines Ares Genetics' proprietary database ARESdb, with state-of-the-art bioinformatics tools and curated public data. MitoPQ For identification, WGS showed 99 and 93% concordance with MALDI-TOF at the genus and species levels, respectively. WGS-predicted susceptibility showed 89% categorical agreement with phenotypic susceptibility across a total of 129 species-compound pairs analyzed, with categorical agreement exceeding 90% in 78 and reaching 100% in 32 species-compound pairs. Results of this study add to the growing body of literature showing that, with improvement of analytics, WGS data could be used to predict antimicrobial susceptibility. Copyright © 2020 Ferreira et al.Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. Mathison's study used computer vision AI (https//doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology. Copyright © 2020 American Society for Microbiology.Intestinal protozoa are responsible for relatively few infections in the developed world but the testing volume is disproportionately high. Manual light microscopy of stool remains the gold standard but can be insensitive, time consuming, and difficult to maintain competency. Artificial intelligence and digital slide scanning show promise for revolutionizing the clinical parasitology laboratory by augmenting detection of parasites and slide interpretation using a convolutional neural network (CNN) model. The goal of this study was to develop a sensitive model that could screen out negative trichrome slides, while flagging potential parasites for manual confirmation. Conventional protozoa were trained as "classes" in a deep CNN. Between 1394 and 23566 exemplars per class were used for training, based on specimen availability, from a minimum of 10 unique slides per class.. Scanning was performed using a 40X dry objective automated slide scanner. Data labeling was performed using a proprietary web interface. Clinical validation of the model was performed using 10 unique positive slides per class and 125 negative slides. Accuracy was calculated as slide-level agreement (e.g. parasite present or absent) with microscopy. Positive agreement was 98.88% [95% CI 93.76% to 99.98%] and negative agreement was 98.11% [95% CI 93.35% to 99.77%]. The model showed excellent reproducibility using slides containing multiple classes, a single class, or no parasites. The limit of detection of the model and scanner using serially diluted stool was 5-fold more sensitive than manual examinations by multiple parasitologists using 4 unique slide sets. Digital slide scanning and a CNN model are robust tools for augmenting the conventional detection of intestinal protozoa. Copyright © 2020 American Society for Microbiology.Applying dPCR technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from qPCR that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false negative reactions and amplicon HRM to distinguish true positives from false positives. Incorporation of internal amplification controls in dPCR is challenging because of the limited fluorescence channels available on most machines, and the application of HRM is hindered by the separation of heating and imaging functions on most dPCR systems. We use a custom digital HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and false negatives in dPCR. We show that detection of an exogenous internal control using dHRM reduces the inclusion of false negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM enables classification of partitions that would otherwise be considered ambiguous "rain", which accounts for up to ∼3% and ∼10% of partitions in intercalating dye and hydrolysis probe dPCR respectively. We focused on developing an internal control method that would be compatible with broad-based microbial detection in dPCR+dHRM. Our approach can be applied to a number of DNA detection methods including microbial profiling, and may advance the utility of dPCR in clinical applications where accurate quantification is imperative. Copyright © 2020 American Society for Microbiology.
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