Consolidating and Exploring Antibiotic Resistance Gene Data Resources.
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ABSTRACT:
SUBMITTER: Xavier BB
PROVIDER: S-EPMC4809949 | biostudies-literature | 2016 Apr
REPOSITORIES: biostudies-literature
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Project description:The rise of antibiotic resistance (AR) in clinical settings is of great concern. Therefore, the understanding of AR mechanisms, evolution, and global distribution is a priority for patient survival. Despite all efforts in the elucidation of AR mechanisms in clinical strains, little is known about its prevalence and evolution in environmental microorganisms. We used 293 metagenomic samples from the TARA Oceans project to detect and quantify environmental antibiotic resistance genes (ARGs) using machine learning tools. After manual curation of ARGs, their abundance and distribution in the global ocean are presented. Additionally, the potential of horizontal ARG transfer by plasmids and their correlation with environmental and geographical parameters is shown. A total of 99,205 environmental open reading frames (ORFs) were classified as 1 of 560 different ARGs conferring resistance to 26 antibiotic classes. We found 24,567 ORFs in putative plasmid sequences, suggesting the importance of mobile genetic elements in the dynamics of environmental ARG transmission. Moreover, 4,804 contigs with >=2 putative ARGs were found, including 2 plasmid-like contigs with 5 different ARGs, highlighting the potential presence of multi-resistant microorganisms in the natural ocean environment. Finally, we identified ARGs conferring resistance to some of the most relevant clinical antibiotics, revealing the presence of 15 ARGs similar to mobilized colistin resistance genes (mcr) with high abundance on polar biomes. Of these, 5 are assigned to Psychrobacter, a genus including opportunistic human pathogens. This study uncovers the diversity and abundance of ARGs in the global ocean metagenome. Our results are available on Zenodo in MySQL database dump format, and all the code used for the analyses, including a Jupyter notebook js avaliable on Github. We also developed a dashboard web application (http://www.resistomedb.com) for data visualization.
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Project description:Abstract This article is presented in two parts: in the first part we discuss the use of R and R‐related tools when implementing a data science curriculum in the classroom and direct readers to helpful R resources in education, and in the second part, we demonstrate the use of R in exploring COVID‐19 data. In particular, we explore ethnic/racial distributions and COVID‐19 death rates. Supplementary R markdown files are also included allowing all graphics to be easily reproduced. It is advised that this article be discussed with sensitivity and mindfulness to potentially vulnerable students, especially as there may be students who have lost relatives due to the COVID‐19 virus.
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Project description:Leuconostoc spp. are environmental microorganisms commonly associated with fermented foods. Absence of antibiotic resistance (AR) in bacteria is a critical issue for global food safety. Herein, we updated the occurrence of AR genes in the Leuconostoc genus through in silico analyses of the genomes of 17 type strains. A total of 131 putative AR traits associated with the main clinically relevant antibiotics were detected. We found, for the first time, the lsaA gene in L. fallax ATCC 700006T and L. pseudomesenteroides NCDO 768T. Their amino acid sequences displayed high similarities (59.07% and 52.21%) with LsaA of Enterococcusfaecalis V583, involved in clindamycin (CLI) and quinupristin-dalfopristin (QUD) resistance. This trait has different distribution patterns in Leuconostoc nontype strains-i.e., L. pseudomesenteroides, L. lactis and L. falkenbergense isolates from fermented vegetables, cheeses, and starters. To better explore the role of lsaA, MIC for CLI and QUD were assessed in ATCC 700006T and NCDO 768T; both strains were resistant towards CLI, potentially linking lsaA to their resistant phenotype. Contrarily, NCDO 768T was sensitive towards QUD; however, expression of lsaA increased in presence of this antibiotic, indicating an active involvement of this trait and thus suggesting a revision of the QUD thresholds for this species.
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Project description:Evidence based information sources for physicians are needed for informed antibiotic prescribing practices. The aim of this study was to explore physicians' preferred sources of information and evaluate physicians' awareness of available information and initiatives on prudent antibiotic prescribing in Jordan. A cross-sectional study was conducted utilizing an online questionnaire and included physicians (n = 409) from all sectors and specialties in Jordan. Published guidelines (31.8%), the workplace (25.7%), colleagues or peers (20.0%), group or conference training (18.3%), and the medical professional body (18.1%) were the main sources of information about avoiding unnecessary antibiotic prescribing, with the influence of these sources on changing prescribers' views being 34.7%, 17.1%, 11%, 13.4%, and 7.6%, respectively. One-third of physicians (33.7%) reported no knowledge of any initiatives on antibiotic awareness and resistance. Regarding awareness of national action plans on antimicrobial resistance, 10.5%, 34%, and 55.5% of physicians were aware, unaware, and unsure of the presence of any national action plans, respectively. Physicians showed interest in receiving more information on resistance to antibiotics (58.9%), how to use antibiotics (42.2%), medical conditions for which antibiotics are used (41.3%), prescribing of antibiotics (35.2%), and links between the health of humans, animals, and the environment (19.8%). The findings can inform interventions needed to design effective antimicrobial stewardship, enabling physicians to prescribe antibiotics appropriately.
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Project description:Riemerella anatipestifer is an important pathogen in waterfowl, and is generally multidrug resistant. This study assessed the current status of Riemerella anatipestifer antibiotic resistance and antibiotic-resistance genes (ARGs), compared the results of different detection methods, and evaluated a new method of studying the association between antibiotic resistance and ARGs in Riemerella anatipestifer. In this study, 51 strains of Riemerella anatipestifer were isolated from ducks on several farms, their resistance to 28 antibiotics was assessed, and the isolates were subjected to whole-genome sequencing. The number of ARGs carried by Riemerella anatipestifer was predicted, compared, and analyzed, and the consistency between ARGs and antibiotic-resistance phenotypes was assessed. The potential for loss of resistance genes during the sequencing and assembly of genome-wide framework map was assessed, and a new ARG detection method was pilot tested. The 51 strains of Riemerella anatipestifer were multidrug resistant (MDR) and had high level of resistance to aminoglycosides, trimethoprim, lincosamides, polypeptides, and macrolides. Based on the genome-wide framework map of the 51 strains, 3 local databases of ABRicate software and 1 online database of CARD website were used to detect ARGs, and a mean of 4 to 5 ARGs were identified per isolate. Although the detection results differed according to the database used, the general performance was consistent. The online website detected more types of ARGs than the ABRicate software. The association between ARGs and antibiotic-resistance phenotypes was assessed, and the ermF gene was identified as a possible key ARGs regulating macrolide resistance of Riemerella anatipestifer. The method used to investigate and detect Riemerella anatipestifer ARGs was convenient and rapid, and had strong accuracy and pertinence. The ARGs detection method reported here combined the advantages of PCR and genome detection, and could greatly reduce workload and detect ARGs more precisely.
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Project description:Plasmids operate as independent genetic elements in microorganism communities. Through horizontal gene transfer (HGT), they can provide their host microorganisms with important functions such as antibiotic resistance and heavy metal resistance. In this study, six metagenomic libraries were constructed with plasmid DNA extracted from influent, activated sludge (AS) and digested sludge (DS) of two wastewater treatment plants (WWTPs). Compared with the metagenomes of the total DNA extracted from the same sectors of the wastewater treatment plant, the plasmid metagenomes had significantly higher annotation rates, indicating that the functional genes on plasmids are commonly shared by those studied microorganisms. Meanwhile, the plasmid metagenomes also encoded many more genes related to defense mechanisms, including ARGs. Searching against an antibiotic resistance genes (ARGs) database and a metal resistance genes (MRGs) database revealed a broad-spectrum of antibiotic (323 out of a total 618 subtypes) and MRGs (23 out of a total 23 types) on these plasmid metagenomes. The influent plasmid metagenomes contained many more resistance genes (both ARGs and MRGs) than the AS and the DS metagenomes. Sixteen novel plasmids with a complete circular structure that carried these resistance genes were assembled from the plasmid metagenomes. The results of this study demonstrated that the plasmids in WWTPs could be important reservoirs for resistance genes, and may play a significant role in the horizontal transfer of these genes.
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Project description:Gene set analysis plays a critical role in the functional interpretation of omics data. Although this is typically done for one omics experiment at a time, there is an increasing need to combine gene set analysis results from multiple experiments performed on the same or different omics platforms, such as in multi-omics studies. Integrating results from multiple experiments is challenging, and annotation redundancy between gene sets further obscures clear conclusions. We propose to use a weighted set cover algorithm to reduce redundancy of gene sets identified in a single experiment. Next, we use affinity propagation to consolidate similar gene sets identified from multiple experiments into clusters and to automatically determine the most representative gene set for each cluster. Using three examples from over representation analysis and gene set enrichment analysis, we showed that weighted set cover outperformed a previously published set cover method and reduced the number of gene sets by 52-77%. Focusing on overlapping genes between the list of input genes and the enriched gene sets in over-representation analysis and leading-edge genes in gene set enrichment analysis further reduced the number of gene sets. A use case combining enrichment analysis results from RNA-Seq and proteomics data comparing basal and luminal A breast cancer samples highlighted the known difference in proliferation and DNA damage response. Finally, we used these algorithms for a pan-cancer survival analysis. Our analysis clearly revealed prognosis-related pathways common to multiple cancer types or specific to individual cancer types, as well as pathways associated with prognosis in different directions in different cancer types. We implemented these two algorithms in an R package, Sumer, which generates tables and static and interactive plots for exploration and publication. Sumer is publicly available at https://github.com/bzhanglab/sumer.
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Project description:BACKGROUND: Affymetrix GeneChip typically contains multiple probe sets per gene, defined as sibling probe sets in this study. These probe sets may or may not behave similar across treatments. The most appropriate way of consolidating sibling probe sets suitable for analysis is an open problem. We propose the Analysis of Variance (ANOVA) framework to decide which sibling probe sets can be consolidated. RESULTS: The ANOVA model allows us to separate the sibling probe sets into two types: those behave similarly across treatments and those behave differently across treatments. We found that consolidation of sibling probe sets of the former type results in large increase in the number of differentially expressed genes under various statistical criteria. The approach to selecting sibling probe sets suitable for consolidating is implemented in R language and freely available from http://research.stowers-institute.org/hul/affy/. CONCLUSION: Our ANOVA analysis of sibling probe sets provides a statistical framework for selecting sibling probe sets for consolidation. Consolidating sibling probe sets by pooling data from each greatly improves the estimates of a gene expression level and results in identification of more biologically relevant genes. Sibling probe sets that do not qualify for consolidation may represent annotation errors or other artifacts, or may correspond to differentially processed transcripts of the same gene that require further analysis.
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Project description:BackgroundAnalysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.ResultsHere we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings.ConclusionsEfficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at http://metmask.sourceforge.net, that implements our ideas.
| S-EPMC2879285 | biostudies-literature
Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.
| S-EPMC7673880 | biostudies-literature