scholarly journals Intracellular Metabolites in Marine Microorganisms during an Experiment Evaluating Microbial Mortality

Metabolites ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 105
Author(s):  
Krista Longnecker ◽  
Elizabeth B. Kujawinski
Keyword(s):  
Mass Spectrometry ◽  
Organic Compounds ◽  
Dominant Role ◽  
Compatible Solutes ◽  
Mass Spectrometry Data ◽  
Marine Microorganisms ◽  
Intracellular Metabolites ◽  
Protozoan Grazing ◽  
Metabolite Concentrations ◽  
The Impact

Metabolomics is a tool with immense potential for providing insight into the impact of biological processes on the environment. Here, we used metabolomics methods to characterize intracellular metabolites within marine microorganisms during a manipulation experiment that was designed to test the impact of two sources of microbial mortality, protozoan grazing and viral lysis. Intracellular metabolites were analyzed with targeted and untargeted mass spectrometry methods. The treatment with reduced viral mortality showed the largest changes in metabolite concentrations, although there were organic compounds that shifted when the impact of protozoan grazers was reduced. Intracellular concentrations of guanine, phenylalanine, glutamic acid, and ectoine presented significant responses to changes in the source of mortality. Unexpectedly, variability in metabolite concentrations were not accompanied by increases in microbial abundance which indicates that marine microorganisms altered their internal organic carbon stores without changes in biomass or microbial growth. We used Weighted Correlation Network Analysis (WGCNA) to identify correlations between the targeted and untargeted mass spectrometry data. This analysis revealed multiple unknown organic compounds were correlated with compatible solutes, also called osmolytes or chemical chaperones, which emphasizes the dominant role of compatible solutes in marine microorganisms.

scholarly journals VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography - Mass Spectrometry Data

2019 ◽  
Author(s):  
Yaser Alkhalifah ◽  
Iain Phillips ◽  
Andrea Soltoggio ◽  
Kareen Darnley ◽  
William H. Nailon ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Organic Compounds ◽  
Mass Spectra ◽  
Mass Spectrometry Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Feature Clustering ◽  
Software Packages ◽  
Volatile Organic ◽  
Clustering Approach

<div>Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC-MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC-MS breath with similar mass spectra and retention index profiles.</div>

scholarly journals VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography - Mass Spectrometry Data

2019 ◽  
Author(s):  
Yaser Alkhalifah ◽  
Iain Phillips ◽  
Andrea Soltoggio ◽  
Kareen Darnley ◽  
William H. Nailon ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Organic Compounds ◽  
Mass Spectra ◽  
Mass Spectrometry Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Feature Clustering ◽  
Software Packages ◽  
Volatile Organic ◽  
Clustering Approach

<div>Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC-MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC-MS breath with similar mass spectra and retention index profiles.</div>

An Analysis Model of Protein Mass Spectrometry Data and Its Application

2019 ◽  
Vol 14 ◽  
Author(s):  
Pingan He ◽  
Longao Hou ◽  
Hong Tao ◽  
Qi Dai ◽  
Yuhua Yao
Keyword(s):  
Mass Spectrometry ◽  
Ovarian Cancer ◽  
Theoretical Models ◽  
Mass Spectrometry Data ◽  
Support Vector ◽  
Analysis Model ◽  
Data Set ◽  
Protein Mass ◽  
Early Diagnosis Of Cancer ◽  
The Impact

Backgroud: The impact of cancer in the society has created the necessity of new and faster theoretical models for the early diagnosis of cancer. Methods: In the work, A mass spectrometry (MS) data analysis method based on star-like graph of protein and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the MS data set. Firstly, the MS data is reduced and transformed into the corresponding protein sequence. And then, the topological indexes of the star-like graph are calculated to describe each MS data of cancer sample. Finally, the SVM model is suggested to classify the MS data. Results: Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models. The average prediction accuracy, sensitivity, and specificity of the model were 96.45%, 96.88%, and 95.67%, respectively, for [0,1] normalization data. and the model were 94.43%, 96.25%, and 91.11%, respectively, for [-1,1] normalization data. Conclusion: The model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

scholarly journals On the impact of the pangenome and annotation discrepancies while building protein sequence databases for bacteria proteogenomics

2018 ◽  
Author(s):  
K.C.T. Machado ◽  
S. Fortuin ◽  
G.G. Tomazella ◽  
A.F. Fonseca ◽  
R. Warren ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mycobacterium Tuberculosis ◽  
Protein Sequence ◽  
Protein Identification ◽  
Gene Annotation ◽  
Relevant Information ◽  
Mass Spectrometry Data ◽  
Computational Time ◽  
Sequence Databases ◽  
The Impact

AbstractIn proteomics, peptide information within mass spectrometry data from a specific organism sample is routinely challenged against a protein sequence database that best represent such organism. However, if the species/strain in the sample is unknown or poorly genetically characterized, it becomes challenging to determine a database which can represent such sample. Building customized protein sequence databases merging multiple strains for a given species has become a strategy to overcome such restrictions. However, as more genetic information is publicly available and interesting genetic features such as the existence of pan- and core genes within a species are revealed, we questioned how efficient such merging strategies are to report relevant information. To test this assumption, we constructed databases containing conserved and unique sequences for ten different species. Features that are relevant for probabilistic-based protein identification by proteomics were then monitored. As expected, increase in database complexity correlates with pangenomic complexity. However, Mycobacterium tuberculosis and Bortedella pertusis generated very complex databases even having low pangenomic complexity or no pangenome at all. This suggests that discrepancies in gene annotation is higher than average between strains of those species. We further tested database performance by using mass spectrometry data from eight clinical strains from Mycobacterium tuberculosis, and from two published datasets from Staphylococcus aureus. We show that by using an approach where database size is controlled by removing repeated identical tryptic sequences across strains/species, computational time can be reduced drastically as database complexity increases.

154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

The impact of applying the Common Customs Tariff on the armenian export of Cut and Polished Gems to Russia

2019 ◽  
pp. 33-41
Author(s):  
V. L. Harutyunyan ◽  
S. V. Dokholyan ◽  
A. R. Makaryan
Keyword(s):  
Manufacturing Sector ◽  
Practical Implication ◽  
Dominant Role ◽  
New Technology ◽  
Manufacturing Companies ◽  
Medium Term ◽  
Diamond Cutting ◽  
Global Trends ◽  
The Common ◽  
The Impact

The presented study discusses the issues of applying the Common Customs Tariff (CCT) rates of the Eurasian Economic Union (EAEU) on rough diamonds and the impact thereof on the exports of stones cut and polished inArmeniaand then exported toRussia.Aim. The study aims to identify the possible strategies Armenian diamond cutting and polishing companies could adopt as a response to the application of the CCT rates on rough diamonds and how it would affect exports to various destinations, namely to Russia.Tasks. The authors analyze the current state of the gems and jewelry sector and substantiate the need to either integrate it into the jewelry manufacturing sector or to apply various strategies to facilitate exports to either Russia or other destinations in the medium term in response to the application of the CCT rates.Methods. This study uses general scientific methods of cognition, including analytical and methodological approaches and elements of forecasting. Possible strategies the Armenian diamond cutting and polishing companies could adopt in the medium term in response to the application of the EAEU CCT rates are determined using the analytical research method, forecasts in the context of the developments in the Armenian gem processing and jewelry market and global trends, statistical data on the imports and exports of cut and polished gems and jewelry for 2014–2018 published by the UN Comtrade Statistics.Results. Statistics on the exports of processed diamonds from 2014 to 2018 highlights the issue associated with the loss of competitiveness suffered by Armenian companies (mainly in comparison with Indian diamond cutters). The major global trends in the diamond cutting and polishing business indicate that it could be virtually impossible for Armenian cutters and polishers to compete with Indian companies in the medium term if they do not comes to investing in new technology to achieve operational efficiency. For these companies, it is important not to lose the Russian market due to an increase in the tariff rate and concentrate on the processing of gems that are larger than 1 carat. Another strategy to avoid an increase in the customs tariff rates would depend on the Armenian government’s ability to negotiate with Russia in respect of direct imports of diamond stones from Russian manufactures. Two other options for Armenian cutters involve focusing on cutting and polishing of rubies, sapphires, emeralds, etc. or integrating into the jewelry sector either by being the primary supplier or by considering this business as a channel to sell processed diamond stones by setting up their own jewelry manufacturing companies.Conclusions. With CCT going into effect in January 2021 and India’s dominant role in the diamond cutting and polishing business, Armenia needs to carefully consider all of the strategies the Armenian companies could adopt, as discussed above. As a member state of the EAEU, Armenia freely exports to Russia, however, further exports to Russia would depend on Armenia’s ability to ensure that cost-effective operations are in place, or to concentrate on the processing of precious gems rather than diamonds, or to switch to the manufacturing of jewelry items as a major export item.Practical Implication. The findings of this study could be of interest to the Ministry of Economy of the Republic of Armenia and Business Armenia that could be used in elaborating the strategy for the development of Armenian gems and jewelry sector of the economy.

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