Dataset Information

Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.

ABSTRACT: Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple 'fingerprinting'; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ?.

SUBMITTER: Alves G

PROVIDER: S-EPMC4723618 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.

Alves Gelio G Wang Guanghui G Ogurtsov Aleksey Y AY Drake Steven K SK Gucek Marjan M Suffredini Anthony F AF Sacks David B DB Yu Yi-Kuo YK

Journal of the American Society for Mass Spectrometry 20151028 2

Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidat ...[more]

PMID: 26510657

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Project description:Ion mobility-mass spectrometry (IM-MS) has become a powerful tool for glycan structural characterization due to its ability to separate isomers and provide collision cross section (CCS) values that facilitate structural assignment. However, IM-based isomer analysis may be complicated by the presence of multiple gas-phase conformations of a single structure that not only increases difficulty in isomer separation but can also introduce the possibility for misinterpretation of conformers as isomers. Here, the ion mobility behavior of several sets of isomeric glycans, analyzed as their permethylated derivatives, in both nonreduced and reduced forms, was investigated by gated-trapped ion mobility spectrometry (G-TIMS). Notably, reducing-end reduction, commonly performed to remove anomerism-induced chromatographic peak splitting, did not eliminate the conformational heterogeneity of permethylated glycans in the gas phase. At a mobility resolving power of ∼100, 14 out of 22 structures showed more than one conformation. These results highlight the need to use IMS devices with high mobility resolving power for better separation of isomers and to acquire additional structural information that can differentiate isomers from conformers. Online electronic excitation dissociation (EED) MS/MS analysis of isomeric glycan mixtures following G-TIMS separation showed that EED can generate isomer-specific fragments while producing nearly identical tandem mass spectra for conformers, thus allowing confident identification of isomers with minimal evidence of any ambiguity resulting from the presence of conformers. G-TIMS EED MS/MS analysis of N-linked glycans released from ovalbumin revealed that several mobility features previously thought to arise from isomeric structures were conformers of a single structure. Finally, analysis of ovalbumin N-glycans from different sources showed that the G-TIMS EED MS/MS approach can accurately determine the batch-to-batch variations in glycosylation profiles at the isomer level, with confident assignment of each isomeric structure.

Project description:BackgroundLipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology.ResultsWe introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode.ConclusionsLipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

Dataset Information

Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.

Publications

Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.

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