Phenotypic carbapenemase algorithm in Pseudomonas aeruginosa
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB82838 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Aminoglycosides resistance mechanisms inference algorithm
| PRJEB29576 | ENA
Project description:VIPR: A probabilistic algorithm for analysis of microbial detection microarrays
| PRJNA126409 | ENA
Project description:The development of rapid and sensitive assays capable of detecting a wide range of infectious agents is critical for the effective diagnosis of diseases that have multiple etiologies. In recent years, many microarray-based diagnostics have been developed to identify viruses present in clinical specimens in a highly parallel fashion. Unfortunately, the rate of development of algorithms to interpret data generated from such platforms has not been commensurate. In particular, none of the existing interpretive algorithms is capable of utilizing empirical training data in a Bayesian framework. We have developed an interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), to capitalize on our ability to generate positive control data for analysis of microbial diagnostic arrays. To illustrate this approach, we have focused on the analysis of viruses that cause hemorrhagic fever (HF). To assess the efficacy of VIPR, we hybridized 33 viruses to 100 microarrays and applied our algorithm to this dataset. A microarray composed of nearly 15,000 oligonucleotides was designed using a custom viral taxonomy-based strategy. The performance of VIPR was assessed by performing a leave-one-out cross validation. VIPR was able to identity the infecting virus with an accuracy of 94%. VIPR outperformed previously described algorithms for the set of HF viruses tested. Bayesian interpretative algorithms such as VIPR should be considered for diagnostic microarray applications.
2011-04-19 | GSE21407 | GEO
Project description:Pseudomonas aeruginosa is a major opportunistic pathogen causing a wide range of infections and one of the most problematic bacteria regarding antibiotic resistance, with an increasing incidence of multidrug and extensively-drug resistant strains, including resistance to last resource antibiotics such as carbapenems. Resistances are often due to complex interplays of naturally and acquired resistance mechanisms which are enhanced by its remarkably large regulatory network. Thus, the use of non-targeted shotgun methodologies such as mass spectrometry-based proteomics is crucial to understand these interplays and to reveal possible strain and species-specific novel mechanisms of antibiotic resistance. The aim of this study was to determine the proteomic response of two carbapenem-resistant and extensively-drug-resistant P. aeruginosa strains to subminimal inhibitory concentrations (sub-MICs) of meropenem. The strains belonged to high-risk clones ST235 and ST395, one carrying a class 1 integron-encoded VIM-4 metallo-β-lactamase and one carrying no acquired antibiotic resistance genes. Each strain was cultivated with three different sub-MICs of meropenem, and a quantitative shotgun proteomic approach was applied, using tandem mass tag (TMT) isobaric labeling followed by nano-liquid chromatography tandem-mass spectrometry, to determine significantly up- or down-regulated proteins and enriched groups of proteins and pathways. Cultivation of both strains with ½ and ¼ of the MIC, resulted in hundreds of differentially regulated proteins, including several β-lactamases, transport-related proteins (including multiple porins and efflux pumps), proteins associated with peptidoglycan metabolism and cell wall organization and dozens of regulatory proteins. Remarkably, all components of the H1 type VI secretion system were up-regulated in one of the strains. Enrichment analyses revealed that multiple metabolic pathways were affected. Additionally, numerous proteins of unknown function were also differentially-regulated in each strain. In conclusion, high subminimal-inhibitory concentrations of meropenem cause massive changes in the proteomes of carbapenem-resistant P. aeruginosa strains, involving a wide range of common and strain-specific mechanisms and proteins, many still uncharacterized which might potentially play a role in the susceptibility of P. aeruginosa to meropenem.
2023-01-05 | PXD034987 | Pride
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.
2015-04-29 | GSE57162 | GEO
Project description:The development of rapid and sensitive assays capable of detecting a wide range of infectious agents is critical for the effective diagnosis of diseases that have multiple etiologies. In recent years, many microarray-based diagnostics have been developed to identify viruses present in clinical specimens in a highly parallel fashion. Unfortunately, the rate of development of algorithms to interpret data generated from such platforms has not been commensurate. In particular, none of the existing interpretive algorithms is capable of utilizing empirical training data in a Bayesian framework. We have developed an interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), to capitalize on our ability to generate positive control data for analysis of microbial diagnostic arrays. To illustrate this approach, we have focused on the analysis of viruses that cause hemorrhagic fever (HF). To assess the efficacy of VIPR, we hybridized 33 viruses to 100 microarrays and applied our algorithm to this dataset. A microarray composed of nearly 15,000 oligonucleotides was designed using a custom viral taxonomy-based strategy. The performance of VIPR was assessed by performing a leave-one-out cross validation. VIPR was able to identity the infecting virus with an accuracy of 94%. VIPR outperformed previously described algorithms for the set of HF viruses tested. Bayesian interpretative algorithms such as VIPR should be considered for diagnostic microarray applications. In this study, 33 viruses including virtually every known hemorrhagic fever virus and a selection of their close relatives were grown in culture and hybridized to 102 microarrays. In addition, 8 uninfected samples were hybridized (110 total hybridizations). These hybridizations were used to test a novel algorithm for diagnosing the infecting virus from a hybridization pattern.
2011-04-19 | E-GEOD-21407 | biostudies-arrayexpress
Project description:The spread of carbapenemase-producing Enterobacterales (CPE) is emerging as a significant clinical concern in tertiary hospitals and in particular, long-term care facilities with deficiencies in infection control. This study aims to evaluate an advanced matrix-assisted laser desorption/ionization mass spectrometry (A-MALDI) method for the identification of carbapenemases and further discrimination of their subtypes in clinical isolates. The A-MALDI method was employed to detect CPE target proteins. Enhancements were made to improve detectability and mass accuracy through the optimization of MALDI-TOF settings and internal mass calibration. A total of 581 clinical isolates were analyzed, including 469 CPE isolates (388 KPC, 51 NDM, 40 OXA, and 2 GES) and 112 carbapenemase-negative isolates. Clinical evaluation of the A-MALDI demonstrated 100% accuracy and precision in identifying all the collected CPE isolates. Additionally, A-MALDI successfully discriminated individual carbapenemase subtypes (KPC-2 or KPC-3/4; OXA-48 or OXA-181 or OXA-232; GES-5 or GES-24) and also differentiated co-producing carbapenemase strains (KPC & NDM; KPC & OXA; KPC & GES; NDM & OXA), attributed to its high mass accuracy and simultaneous detection capability. A-MALDI is considered a valuable diagnostic tool for accurately identifying CPE and carbapenemase’s subtypes in clinical isolates. It may also aid in selecting appropriate antibiotics for each carbapenemase subtype. Ultimately, we expect that the A-MALDI method will contribute to preventing the spread of antibiotic resistance and improving human public health.
2024-11-25 | PXD046179 | Pride
Project description:The spread of carbapenemase-producing Enterobacterales (CPE) is emerging as a significant clinical concern in tertiary hospitals and in particular, long-term care facilities with deficiencies in infection control. This study aims to evaluate an advanced matrix-assisted laser desorption/ionization mass spectrometry (A-MALDI) method for the identification of carbapenemases and further discrimination of their subtypes in clinical isolates. The A-MALDI method was employed to detect CPE target proteins. Enhancements were made to improve detectability and mass accuracy through the optimization of MALDI-TOF settings and internal mass calibration. A total of 581 clinical isolates were analyzed, including 469 CPE isolates (388 KPC, 51 NDM, 40 OXA, and 2 GES) and 112 carbapenemase-negative isolates. Clinical evaluation of the A-MALDI demonstrated 100% accuracy and precision in identifying all the collected CPE isolates. Additionally, A-MALDI successfully discriminated individual carbapenemase subtypes (KPC-2 or KPC-3/4; OXA-48 or OXA-181 or OXA-232; GES-5 or GES-24) and also differentiated co-producing carbapenemase strains (KPC & NDM; KPC & OXA; KPC & GES; NDM & OXA), attributed to its high mass accuracy and simultaneous detection capability. A-MALDI is considered a valuable diagnostic tool for accurately identifying CPE and carbapenemase’s subtypes in clinical isolates. It may also aid in selecting appropriate antibiotics for each carbapenemase subtype. Ultimately, we expect that the A-MALDI method will contribute to preventing the spread of antibiotic resistance and improving human public health.
2024-11-26 | PXD058284 | Pride
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.
2015-04-29 | E-GEOD-57162 | biostudies-arrayexpress