Identification of the ESKAPE pathogens by mass spectrometric analysis of microbial membrane glycolipids.
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ABSTRACT:
SUBMITTER: Leung LM
PROVIDER: S-EPMC5526941 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In an effort to stem the rising tide of multi-resistant bacteria, researchers have turned to niche environments in the hope of discovering new varieties of antibiotics. We investigated an ethnopharmacological (cure) from an alkaline/radon soil in the area of Boho, in the Fermanagh Scarplands (N. Ireland) for the presence of Streptomyces, a well-known producer of antibiotics. From this soil we isolated a novel (closest relative 57% of genome relatedness) Streptomyces sp. capable of growth at high alkaline pH (10.5) and tolerant of gamma radiation to 4 kGy. Genomic sequencing identified many alkaline tolerance (antiporter/multi-resistance) genes compared to S. coelicolor M145 (at 3:1), hence we designated the strain Streptomyces sp. myrophorea, isolate McG1, from the Greek, myro (fragrance) and phorea (porter/carrier). In vitro tests demonstrated the ability of the Streptomyces sp. myrophorea, isolate McG1 to inhibit the growth of many strains of ESKAPE pathogens; most notably carbapenem-resistant Acinetobacter baumannii (a critical pathogen on the WHO priority list of antibiotic-resistant bacteria), vancomycin-resistant Enterococcus faecium, and methicillin-resistant Staphylococcus aureus (both listed as high priority pathogens). Further in silico prediction of antimicrobial potential of Streptomyces sp. myrophorea, isolate McG1 by anti-SMASH and RAST software identified many secondary metabolite and toxicity resistance gene clusters (45 and 27, respectively) as well as many antibiotic resistance genes potentially related to antibiotic production. Follow-up in vitro tests show that the Streptomyces sp. myrophorea, isolate McG1 was resistant to 28 out of 36 clinical antibiotics. Although not a comprehensive analysis, we think that some of the Boho soils' reputed curative properties may be linked to the ability of Streptomyces sp. myrophorea, isolate McG1 to inhibit ESKAPE pathogens. More importantly, further analysis may elucidate other key components that could alleviate the tide of multi-resistant nosocomial infections.
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Project description:By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.
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Project description:BACKGROUND: The public availability of over 180,000 bacterial 16S ribosomal RNA (rRNA) sequences has facilitated microbial identification and classification using hybridization and other molecular approaches. In their usual format, such assays are based on the presence of unique subsequences in the target RNA and require a prior knowledge of what organisms are likely to be in a sample. They are thus limited in generality when analyzing an unknown sample.Herein, we demonstrate the utility of catalogs of masses to characterize the bacterial 16S rRNA(s) in any sample. Sample nucleic acids are digested with a nuclease of known specificity and the products characterized using mass spectrometry. The resulting catalogs of masses can subsequently be compared to the masses known to occur in previously-sequenced 16S rRNAs allowing organism identification. Alternatively, if the organism is not in the existing database, it will still be possible to determine its genetic affinity relative to the known organisms. RESULTS: Ribonuclease T1 and ribonuclease A digestion patterns were calculated for 1,921 complete 16S rRNAs. Oligoribonucleotides generated by RNase T1 of length 9 and longer produce sufficient diversity of masses to be informative. In addition, individual fragments or combinations thereof can be used to recognize the presence of specific organisms in a complex sample. In this regard, 140 strains out of 1,921 organisms (7.3%) could be identified by the presence of a unique RNase T1-generated oligoribonucleotide mass. Combinations of just two and three oligoribonucleotide masses allowed 54% and 72% of the specific strains to be identified, respectively. An initial algorithm for recovering likely organisms present in complex samples is also described. CONCLUSION: The use of catalogs of compositions (masses) of characteristic oligoribonucleotides for microbial identification appears extremely promising. RNase T1 is more useful than ribonuclease A in generating characteristic masses, though RNase A produces oligomers which are more readily distinguished due to the large mass difference between A and G. Identification of multiple species in mixtures is also feasible. Practical applicability of the method depends on high performance mass spectrometric determination, and/or use of methods that increase the one dalton (Da) mass difference between uracil and cytosine.
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