Global metabolome profiling of COVID-19 respiratory specimen using high-resolution mass spectrometry (HRMS).
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ABSTRACT:
SUBMITTER: Sharma N
PROVIDER: S-EPMC8639449 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Retinoblastoma is an ocular neoplastic cancer caused primarily due to the mutation/deletion of RB1 gene. Due to the rarity of the disease very limited information is available on molecular changes in primary retinoblastoma. High throughput analysis of retinoblastoma transcriptome is available however the proteomic landscape of retinoblastoma remains unexplored. In the present study we used high resolution mass spectrometry-based quantitative proteomics to identify proteins associated with pathogenesis of retinoblastoma. METHODS:We used five pooled normal retina and five pooled retinoblastoma tissues to prepare tissue lysates. Equivalent amount of proteins from each group was trypsin digested and labeled with iTRAQ tags. The samples were analyzed on Orbitrap Velos mass spectrometer. We further validated few of the differentially expressed proteins by immunohistochemistry on primary tumors. RESULTS:We identified and quantified a total of 3587 proteins in retinoblastoma when compared with normal adult retina. In total, we identified 899 proteins that were differentially expressed in retinoblastoma with a fold change of ?2 of which 402 proteins were upregulated and 497 were down regulated. Insulin growth factor 2 mRNA binding protein 1 (IGF2BP1), chromogranin A, fetuin A (ASHG), Rac GTPase-activating protein 1 and midkine that were found to be overexpressed in retinoblastoma were further confirmed by immunohistochemistry by staining 15 independent retinoblastoma tissue sections. We further verified the effect of IGF2BP1 on cell proliferation and migration capability of a retinoblastoma cell line using knockdown studies. CONCLUSIONS:In the present study mass spectrometry-based quantitative proteomic approach was applied to identify proteins differentially expressed in retinoblastoma tumor. This study identified the mitochondrial dysfunction and lipid metabolism pathways as the major pathways to be deregulated in retinoblastoma. Further knockdown studies of IGF2BP1 in retinoblastoma cell lines revealed it as a prospective therapeutic target for retinoblastoma.
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Project description:Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.
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Project description:Introduction: Serratia marcescens, an opportunistic human pathogen, is reported as an important cause of nosocomial infection and outbreaks. Although the genome of S. marcescens (ATCC 13880) was completely sequenced by 2014, there are no studies on the proteomic profile of the organism. The objective of the present study is to analyze the protein profile of S. marcescens (ATCC 13880) using a high resolution mass spectrometry (MS). Methods: Serratia marcescens ATCC 13880 strain was grown in Luria-Bertani broth and the protein extracted was subjected to trypsin digestion, followed by basic reverse phase liquid chromatography fractionation. The peptide fractions were then analysed using Orbitrap Fusion Mass Spectrometry and the raw MS data were processed in Proteome Discoverer software. Results: The proteomic analysis identified 15?009 unique peptides mapping to 2541 unique protein groups, which corresponds to approximately 54% of the computationally predicted protein-coding genes. Bioinformatic analysis of these identified proteins showed their involvement in biological processes such as cell wall organization, chaperone-mediated protein folding and ATP binding. Pathway analysis revealed that some of these proteins are associated with bacterial chemotaxis and beta-lactam resistance pathway. Conclusion: To the best of our knowledge, this is the first high-throughput proteomics study of S. marcescens (ATCC 13880). These novel observations provide a crucial baseline molecular profile of the S. marcescens proteome which will prove to be helpful for the future research in understanding the host-pathogen interactions during infection, elucidating the mechanism of multidrug resistance, and developing novel diagnostic markers or vaccine for the disease.
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Project description:Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.
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Project description:The lipidome of royal jelly (RJ) consists of medium-chained (8-12 carbon atoms) free fatty acids. We present herein a liquid chromatography-high resolution mass spectrometry (HRMS) method that permits the determination of RJ fatty acids and at the same time the detection of suspect fatty acids. The method allows for the direct quantification of seven free fatty acids of RJ, avoiding any derivatization step. It was validated and applied in seven RJ samples, where the major RJ fatty acid trans-10-hydroxy-2-decenoic acid (10-HDA) was found to vary from 0.771 ± 0.08 to 0.928 ± 0.04 g/100 g fresh RJ. Four additional suspect fatty acids were simultaneously detected taking advantage of the HRMS detection.
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Project description:Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.
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Project description:Alternaria spp. occur as plant pathogens worldwide under field and storage conditions. They lead to food spoilage and also produce several classes of secondary metabolites that contaminate the food production chain. From a food safety perspective, the major challenge of assessing the risk of Alternaria contamination is the lack of a clear consensus on their species-level taxonomy. Furthermore, there are currently no reliable DNA sequencing methods to allow for differentiation of the toxigenic potential of these fungi. Our objective was to determine which species of Alternaria exist in Canada, and to describe the compounds they make. To address these issues, we performed metabolomic profiling using liquid chromatography high-resolution mass spectrometry (LC-HRMS) on 128 Canadian strains of Alternaria to determine their chemotaxonomy. The Alternaria strains were analyzed using principal component analysis (PCA) and unbiased k-means clustering to identify metabolites with significant differences (p < 0.001) between groups. Four populations or 'chemotypes' were identified within the strains studied, and several known secondary metabolites of Alternaria were identified as distinguishing metabolites, including tenuazonic acid, phomapyrones, and altenuene. Though species-level identifications could not be concluded for all groups through metabolomics alone, A. infectoria was able to be identified as a distinct population.
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Project description:Cyclooxygenase, lipoxygenase, and epoxygenase derived oxylipins, especially eicosanoids, play important roles in many physiological processes. Assessment of oxidized fatty acid levels is important for understanding their homeostatic and pathophysiological roles. Most reported methods examine these pathways in isolation. The work described here employed a solid phase extraction-liquid chromatography-electrospray ionization MS/MS (SPE-LC-ESI MS/MS) method to monitor these metabolites. In 21 min, 39 oxylipins were quantified along with eight corresponding internal standards. The limits of quantification were between 0.07 and 32 pg (20 pM-10 nM). Finally, the validated method was used to evaluate oxylipin profiles in lipopolysaccharide-exposed mice, an established septic inflammatory model. The method described here offers a useful tool for the evaluation of complex regulatory oxylipin responses in in vitro or in vivo studies.
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Project description:The rumen primary and secondary metabolite content is intimately related to its community of bacteria, protozoa, fungi, archaea and bacteriophages, ingested feed and the host. Despite the myriad of interactions and novel compounds to be discovered, few studies have explored the rumen metabolome. Here, we present the first study using ultra-high performance liquid chromatography tandem mass-spectrometry and Molecular Networking approach, and various extraction methods on the cell-free rumen fluid of a non-lactating Holstein cow. Putative molecules were annotated based on accurate fragmentation matching the Global Natural Products Social Molecular Networking library, public spectral libraries, or annotated manually. The combination of five extraction methods resulted on 1,882 molecular features observed. Liquid-liquid extraction resulted on the highest molecular features abundance, 1,166 (61.96% of total). Sixty-seven compounds were annotated using Global Natural Products Social Molecular Networking library and public libraries, such as hydrocinnamic and azelaic acid, and monensin. Only 3.56% of molecular features (67) observed had positive match with available libraries, which shows the potential of the rumen as reservoir of novel compounds. The use of untargeted metabolomics in this study provided a snapshot of the rumen fluid metabolome. The complexity of the rumen will remain long unknown, but the use of new tools should be encouraged to foster advances on the rumen metabolome.
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Project description:The application of metabolic phenotyping to epidemiological studies involving thousands of biofluid samples presents a challenge for the selection of analytical platforms that meet the requirements of high-throughput precision analysis and cost-effectiveness. Here direct infusion-nanoelectrospray (DI-nESI) was compared with an ultra-performance liquid chromatography (UPLC)-high-resolution mass spectrometry (HRMS) method for metabolic profiling of an exemplary set of 132 human urine samples from a large epidemiological cohort. Both methods were developed and optimized to allow the simultaneous collection of high-resolution urinary metabolic profiles and quantitative data for a selected panel of 35 metabolites. The total run time for measuring the sample set in both polarities by UPLC-HRMS was 5 days compared with 9 h by DI-nESI-HRMS. To compare the classification ability of the two MS methods, we performed exploratory analysis of the full-scan HRMS profiles to detect sex-related differences in biochemical composition. Although metabolite identification is less specific in DI-nESI-HRMS, the significant features responsible for discrimination between sexes were mostly the same in both MS-based platforms. Using the quantitative data, we showed that 10 metabolites have strong correlation (Pearson's r > 0.9 and Passing-Bablok regression slope of 0.8-1.3) and good agreement assessed by Bland-Altman plots between UPLC-HRMS and DI-nESI-HRMS and thus can be measured using a cheaper and less sample- and time-consuming method. A further twenty metabolites showed acceptable correlation between the two methods with only five metabolites showing weak correlation (Pearson's r < 0.4) and poor agreement due to the overestimation of the results by DI-nESI-HRMS.
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