Genomic islands, defined as large clusters of genes mobilized through horizontal gene transfer, have a profound impact on evolution of prokaryotes. Recently, we developed a new program, IslandCafe, ...for identifying such large localized structures in bacterial genomes. A unique attribute of IslandCafe is its ability to decipher mosaic structures within genomic islands. Mosaic genomic islands have generated immense interest due to novel traits that have been attributed to such islands. To provide the
Pseudomonas
research community a catalogue of mosaic islands in
Pseudomonas
spp., we applied IslandCafe to decipher genomic islands in 224 completely sequenced genomes of
Pseudomonas
spp. We also performed comparative genomic analysis using BLAST to infer potential sources of distinct segments within genomic islands. Of the total 4271 genomic islands identified in
Pseudomonas
spp., 1036 were found to be mosaic. We also identified drug-resistant and pathogenic genomic islands and their potential donors. Our analysis provides a useful resource for
Pseudomonas
research community to further examine and interrogate mosaic islands in the genomes of interest and understand their role in the emergence and evolution of novel traits.
One of the evolutionary forces driving bacterial genome evolution is the acquisition of clusters of genes through horizontal gene transfer (HGT). These genomic islands may confer adaptive advantages ...to the recipient bacteria, such as, the ability to thwart antibiotics, become virulent or hypervirulent, or acquire novel metabolic traits. Methods for detecting genomic islands either search for markers or features typical of islands or examine anomaly in oligonucleotide composition against the genome background. The former tends to underestimate, missing islands that have the markers either lost or degraded, while the latter tends to overestimate, due to their inability to discriminate compositional atypicality arising because of HGT from those that are a consequence of other biological factors. We propose here a framework that exploits the strengths of both these approaches while bypassing the pitfalls of either. Genomic islands lacking markers are identified by their association with genomic islands with markers. This was made possible by performing marker enrichment and phyletic pattern analyses within an integrated framework of recursive segmentation and clustering. The proposed method, IslandCafe, compared favorably with frequently used methods for genomic island detection on synthetic test datasets and on a test-set of known islands from 15 well-characterized bacterial species. Furthermore, IslandCafe identified novel islands with imprints of likely horizontal acquisition.
Pseudomonas aeruginosa is an opportunistic pathogen implicated in a myriad of infections and a leading pathogen responsible for mortality in patients with cystic fibrosis (CF). Horizontal transfers ...of genes among the microorganisms living within CF patients have led to highly virulent and multi-drug resistant strains such as the Liverpool epidemic strain of P. aeruginosa, namely the LESB58 strain that has the propensity to acquire virulence and antibiotic resistance genes. Often these genes are acquired in large clusters, referred to as "genomic islands (GIs)." To decipher GIs and understand their contributions to the evolution of virulence and antibiotic resistance in P. aeruginosa LESB58, we utilized a recursive segmentation and clustering procedure, presented here as a genome-mining tool, "GEMINI." GEMINI was validated on experimentally verified islands in the LESB58 strain before examining its potential to decipher novel islands. Of the 6062 genes in P. aeruginosa LESB58, 596 genes were identified to be resident on 20 GIs of which 12 have not been previously reported. Comparative genomics provided evidence in support of our novel predictions. Furthermore, GEMINI unraveled the mosaic structure of islands that are composed of segments of likely different evolutionary origins, and demonstrated its ability to identify potential strain biomarkers. These newly found islands likely have contributed to the hyper-virulence and multidrug resistance of the Liverpool epidemic strain of P. aeruginosa.
Staphylococcus aureus is a versatile pathogen that is capable of causing infections in both humans and animals. It can cause furuncles, septicaemia, pneumonia and endocarditis. Adaptation of S. ...aureus to the modern hospital environment has been facilitated, in part, by the horizontal acquisition of drug resistance genes, such as mecA gene that imparts resistance to methicillin. Horizontal acquisitions of islands of genes harbouring virulence and antibiotic resistance genes have made S. aureus resistant to commonly used antibiotics. To decipher genomic islands (GIs) in 22 hospital- and 9 community-associated methicillin-resistant S. aureus strains and classify a subset of GIs carrying virulence and resistance genes as pathogenicity and resistance islands respectively, we applied a host of methods for localizing genomic islands in prokaryotic genomes. Surprisingly, none of the frequently used GI prediction methods could perform well in delineating the resistance islands in the S. aureus genomes. Rather, a gene clustering procedure exploiting biases in codon usage for identifying horizontally transferred genes outperformed the current methods for GI detection, in particular in identifying the known islands in S. aureus including the SCCmec island that harbours the mecA resistance gene. The gene clustering approach also identified novel, as yet unreported islands, with many of these found to harbour virulence and/or resistance genes. These as yet unexplored islands may provide valuable information on the evolution of drug resistance in S. aureus.
Mobilization of clusters of genes called genomic islands (GIs) across bacterial lineages facilitates dissemination of traits, such as, resistance against antibiotics, virulence or hypervirulence, and ...versatile metabolic capabilities. Robust delineation of GIs is critical to understanding bacterial evolution that has a vast impact on different life forms. Methods for identification of GIs exploit different evolutionary features or signals encoded within the genomes of bacteria, however, the current state-of-the-art in GI detection still leaves much to be desired. Here, we have taken a combinatorial approach that accounted for GI specific features such as compositional bias, aberrant phyletic pattern, and marker gene enrichment within an integrative framework to delineate GIs in bacterial genomes. Our GI prediction tool, DICEP, was assessed on simulated genomes and well-characterized bacterial genomes. DICEP compared favorably with current GI detection tools on real and synthetic datasets.
•DICEP takes a combinatorial approach combining discriminative features to delineate genomic islands in bacterial genomes.•DICEP outperformed frequently used methods for genomic island detection on synthetic datasets and on a set of known islands.•Our study highlights complementary strengths of different approaches that can be used to improve genomic island detection.
antibiotic resistance is widespread and increasing worldwide. Routine detection of
mutations that invoke antimicrobial resistance may be a useful approach to guide antimicrobial therapy and possibly ...avert treatment failure. In this study, formalin-fixed, paraffin-embedded (FFPE) gastric biopsy specimens from a cohort of individuals from northern Ohio in the United States were examined using a next-generation sequencing (NGS) assay to detect
mutations that are known to confer resistance to clarithromycin, levofloxacin, and tetracycline. From January 2016 to January 2017, 133
-infected gastric biopsy specimens were identified histologically and subsequently analyzed by NGS to detect mutations in
, 23S rRNA, and 16S rRNA genes. The method successfully detected
in 126 of 133 cases (95% sensitivity). Mutations conferring resistance were present in 92 cases (73%), including 63 cases with one mutation (50%) and 29 cases with mutations in multiple genes (23%). Treatment outcomes were available in 58 cases. Sixteen of the 58 cases failed therapy (28%). Therapy failure correlated with the number of mutated genes: no failure in cases with no mutations (0/15), 19% (5/27) failure in cases with one gene mutation, and 69% (11/16) failure in cases with more than one mutated gene. Common 23S rRNA mutations (A2142G or A2413G) were present in 88% (14/16) of failed cases as opposed to in only 10% (4/42) of eradicated cases (
< 0.001). This NGS assay can be used on remnant specimens collected during standard-of-care testing to detect mutations that correlate with increased risk of treatment failure. A prospective study is needed to determine if the risk of treatment failure can be decreased by using this assay to guide antibiotic therapy.
Furcation perforation can have a negative impact on the prognosis of the affected tooth by compromising the attached apparatus. Hence these perforations require immediate repair. A variety of ...materials have been suggested for repair, of that MTA is the most promising material. The purpose of this study was to compare the ability of Gray and White MTA to seal furcation perforations using a dye extraction method under spectrophotometer.
A total of 60 permanent mandibular molars were randomly divided into four experimental groups of 15 samples each as follows: Group A: Perforation repaired with White MTA. Group B: Perforation repaired with Gray MTA. Group C: Perforation left unsealed (positive). Group D: without perforation (negative). Dye extraction was performed using full concentration nitric acid. Dye absorbance was measured at 550 nm using spectrophotometer. The data analyzed using one-way-Anova Ratio and Unpaired t-test showing statistically significance difference among the groups.
It was seen that Group D samples without perforation showed least absorbance followed by Group A (perforation repaired with White MTA) and Group B (perforation repaired with Gray MTA). Group C (perforation left unsealed) showed highest absorbance.
The White and Gray Mineral Trioxide Aggregate performed similarly as a furcation perforation repair material. There was no significant difference between the Gray MTA and White MTA.
Abstract
Objectives
Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant strains can be associated with increased transmissibility, more severe disease, and reduced ...effectiveness of treatments. To improve the availability of regional variant surveillance, we describe a variant genotyping system that is rapid, accurate, adaptable, and able to detect new low-level variants built with existing hospital infrastructure.
Methods
We used a tiered high-throughput SARS-CoV-2 screening program to characterize variants in a supraregional health system over 76 days. Combining targeted reverse transcription–polymerase chain reaction (RT-PCR) and selective sequencing, we screened SARS-CoV-2 reactive samples from all hospitals within our health care system for genotyping dominant and emerging variants.
Results
The median turnaround for genotyping was 2 days using the high-throughput RT-PCR–based screen, allowing us to rapidly characterize the emerging Delta variant. In our population, the Delta variant is associated with a lower cycle threshold value, lower age at infection, and increased vaccine-breakthrough cases. Detection of low-level and potentially emerging variants highlights the utility of a tiered approach.
Conclusions
These findings underscore the need for fast, low-cost, high-throughput monitoring of regional viral sequences as the pandemic unfolds and the emergence of SARS-CoV-2 variants increases. Combining RT-PCR–based screening with selective sequencing allows for rapid genotyping of variants and dynamic system improvement.
Abstract
Background
Urine culture images collected using bacteriology automation are currently interpreted by technologists during routine standard-of-care workflows. Machine learning may be able to ...improve the harmonization of and assist with these interpretations.
Methods
A deep learning model, BacterioSight, was developed, trained, and tested on standard BD-Kiestra images of routine blood agar urine cultures from 2 different medical centers.
Results
BacterioSight displayed performance on par with standard-of-care–trained technologist interpretations. BacterioSight accuracy ranged from 97% when compared to standard-of-care (single technologist) and reached 100% when compared to a consensus reached by a group of technologists (gold standard in this study). Variability in image interpretation by trained technologists was identified and annotation “fuzziness” was quantified and found to correlate with reduced confidence in BacterioSight interpretation. Intra-testing (training and testing performed within the same institution) performed well giving Area Under the Curve (AUC) ≥0.98 for negative and positive plates, whereas, cross-testing on images (trained on one institution’s images and tested on images from another institution) showed decreased performance with AUC ≥0.90 for negative and positive plates.
Conclusions
Our study provides a roadmap on how BacterioSight or similar deep learning prototypes may be implemented to screen for microbial growth, flag difficult cases for multi-personnel review, or auto-verify a subset of cultures with high confidence. In addition, our results highlight image interpretation variability by trained technologist within an institution and globally across institutions. We propose a model in which deep learning can enhance patient care by identifying inherent sample annotation variability and improving personnel training.
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