Untargeted metabolomics analysis revealed changes in the composition of glycerolipids and phospholipids in Bacillus subtilis under 1-butanol stress

2015 ◽  
Vol 99 (14) ◽  
pp. 5971-5983 ◽  
Author(s):  
Nawaporn Vinayavekhin ◽  
Gumpanat Mahipant ◽  
Alisa S. Vangnai ◽  
Polkit Sangvanich
2021 ◽  
Author(s):  
Tiago F. Leao ◽  
Chase M. Clark ◽  
Anelize Bauermeister ◽  
Emmanuel O. Elijah ◽  
Emily C. Gentry ◽  
...  

2016 ◽  
Vol 15 (12) ◽  
pp. 4591-4600 ◽  
Author(s):  
Mie R. Rasmussen ◽  
Kirstine L. Nielsen ◽  
Mia R. Laursen ◽  
Camilla B. Nielsen ◽  
Pia Svendsen ◽  
...  

2020 ◽  
Author(s):  
Lingling Wan ◽  
Yutong He ◽  
Qingyi Liu ◽  
Di Liang ◽  
Yongdong Guo ◽  
...  

Abstract Background: Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis. Methods: In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients, and the early diagnostic value of these assays in lung cancer was analyzed. Results: 62.5% (30/48) of lung cancer patients had ≥ 1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC > 0.9). Conclusions: Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer. This possibility requires more in-depth large-sample research for verification.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
M. A. Fernández-Peralbo ◽  
E. Gómez-Gómez ◽  
M. Calderón-Santiago ◽  
J. Carrasco-Valiente ◽  
J. Ruiz-García ◽  
...  

2020 ◽  
Vol 1 (1) ◽  
pp. 31-50
Author(s):  
Yun Nian Tan ◽  
Jian Hua Zhang ◽  
Wei Ning Chen

GC-MS-based metabolomics were used to investigate metabolic changes in prawn shell waste during fermentation. Microbial strains Lactobacillus plantarum and Bacillus subtilis were co-fermented in a shake flask comprising of 5% (w/v) prawn shell waste and 20% (w/v) glucose as a carbon source. Analysis of the prawn shell waste fermentation showed a total of 376 metabolites detected in the culture supernatant, including 14 amino acids, 106 organic acids, and 90 antimicrobial molecules. Results show that the liquid fraction of the co-fermentation is promising for harvesting valuable metabolites for probiotics application.


Metabolomics ◽  
2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Traci L. Parry ◽  
Joseph W. Starnes ◽  
Sara K. O’Neal ◽  
James R. Bain ◽  
Michael J. Muehlbauer ◽  
...  

Metabolites ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 49 ◽  
Author(s):  
Abu Hanifah ◽  
Awang Maharijaya ◽  
Sastia P. Putri ◽  
Walter A. Laviña ◽  
Sobir

Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. To gain insight on the diversity of eggplant metabolites, twenty-one eggplant accessions were analyzed by untargeted metabolomics using GC-MS and LC-MS. The dataset of eggplant fruit morphologies, and metabolites specific to different eggplant fruit accessions were used for correlation analysis. Untargeted metabolomics analysis using LC-MS and GC-MS was able to detect 136 and 207 peaks, respectively. Fifty-one (51) metabolites from the LC-MS analysis and 207 metabolites from the GC-MS analysis were putatively identified, which included alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. Spearman correlation analysis revealed that 14 fruit morphologies were correlated with several metabolites. This information will be very useful for the development of strategies for eggplant breeding.


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