scholarly journals Large-scale plasma-metabolome analysis identifies potential biomarkers of psoriasis and its clinical subtypes

2021 ◽  
Author(s):  
Toshihiro Kishikawa ◽  
Noriko Arase ◽  
Shigeyoshi Tsuji ◽  
Yuichi Maeda ◽  
Takuro Nii ◽  
...  
Keyword(s):  
Large Scale ◽  
Metabolome Analysis ◽  
Plasma Metabolome ◽  
Potential Biomarkers

scholarly journals Potential Biomarkers of Fatigue Identified by Plasma Metabolome Analysis in Rats

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0120106 ◽  
Author(s):  
Satoshi Kume ◽  
Masanori Yamato ◽  
Yasuhisa Tamura ◽  
Guanghua Jin ◽  
Masayuki Nakano ◽  
...  
Keyword(s):  
Metabolome Analysis ◽  
Plasma Metabolome ◽  
Potential Biomarkers

scholarly journals Plasma Metabolome Profiling Identifies Metabolic Subtypes of Pancreatic Ductal Adenocarcinoma

Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1821
Author(s):  
Ujjwal Mukund Mahajan ◽  
Ahmed Alnatsha ◽  
Qi Li ◽  
Bettina Oehrle ◽  
Frank-Ulrich Weiss ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Enrichment Analysis ◽  
Response Prediction ◽  
Ductal Adenocarcinoma ◽  
Pathway Enrichment Analysis ◽  
Metabolome Analysis ◽  
Dismal Prognosis ◽  
Chemotherapeutic Response ◽  
Plasma Metabolome ◽  
Metabolome Profiling

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most among subtypes. Weighted correlation network analysis (WGCNA) implied PDAC subtypes differed in their metabolic programs. Interestingly, a reduced expression among related pathway genes in tumor tissue was associated with the lowest survival rate. However, our metabolic PDAC subtypes did not show any correlation to the described molecular PDAC subtypes. Our findings pave the way for further studies investigating sphingolipids metabolisms in PDAC.

scholarly journals 2-Methyl-pentanoyl-carnitine (2-MPC): a urine biomarker for patent Ascaris lumbricoides infection

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ole Lagatie ◽  
Ann Verheyen ◽  
Stijn Van Asten ◽  
Maurice R. Odiere ◽  
Yenny Djuardi ◽  
...  
Keyword(s):  
Ascaris Lumbricoides ◽  
Large Scale ◽  
Infection Intensity ◽  
World Health ◽  
Infection Model ◽  
Metabolome Analysis ◽  
Control Programs ◽  
Urine Biomarker ◽  
Intestinal Worms ◽  
Health Organization

Abstract Infections with intestinal worms, such as Ascaris lumbricoides, affect hundreds of millions of people in all tropical and subtropical regions of the world. Through large-scale deworming programs, World Health Organization aims to reduce moderate-to-heavy intensity infections below 1%. Current diagnosis and monitoring of these control programs are solely based on the detection of worm eggs in stool. Here we describe how metabolome analysis was used to identify the A. lumbricoides-specific urine biomarker 2-methyl pentanoyl carnitine (2-MPC). This biomarker was found to be 85.7% accurate in determining infection and 90.5% accurate in determining a moderate-to-heavy infection. Our results also demonstrate that there is a correlation between 2-MPC levels in urine and A. lumbricoides DNA detected in stool. Furthermore, the levels of 2-MPC in urine were shown to rapidly and strongly decrease upon administration of a standard treatment (single oral dose of 400 mg albendazole). In an Ascaris suum infection model in pigs, it was found that, although 2-MPC levels were much lower compared to humans, there was a significant association between urinary 2-MPC levels and both worm counts (p = 0.023) and the number of eggs per gram (epg) counts (p < 0.001). This report demonstrates that urinary 2-MPC can be considered an A. lumbricoides-specific biomarker that can be used to monitor infection intensity.

scholarly journals Prediction of vascular aging based on smartphone acquired PPG signals

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lorenzo Dall’Olio ◽  
Nico Curti ◽  
Daniel Remondini ◽  
Yosef Safi Harb ◽  
Folkert W. Asselbergs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Screening Tool ◽  
Computational Cost ◽  
Penalized Regression ◽  
Prediction Performance ◽  
Second Derivative ◽  
Vascular Aging ◽  
Non Invasive ◽  
Potential Biomarkers

AbstractPhotoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae

2013 ◽  
Vol 9 (7) ◽  
pp. 1743 ◽  
Author(s):  
Tobias Maier ◽  
Josep Marcos ◽  
Judith A. H. Wodke ◽  
Bernhard Paetzold ◽  
Manuel Liebeke ◽  
...  
Keyword(s):  
Mycoplasma Pneumoniae ◽  
Large Scale ◽  
Proteomics Data ◽  
Metabolome Analysis ◽  
Genomics And Proteomics ◽  
Quantitative Integration

scholarly journals Large-scale lipidomic profiling identifies novel potential biomarkers for prion diseases and highlights lipid raft-related pathways

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Yong-Chan Kim ◽  
Junbeom Lee ◽  
Dae-Weon Lee ◽  
Byung-Hoon Jeong
Keyword(s):  
Prion Protein ◽  
Prion Disease ◽  
Large Scale ◽  
Right Hemisphere ◽  
Prion Diseases ◽  
Lipid Extraction ◽  
Transmissible Spongiform Encephalopathies ◽  
Pathway Enrichment Analysis ◽  
The Brain ◽  
Potential Biomarkers

AbstractPrion diseases are transmissible spongiform encephalopathies induced by the abnormally-folded prion protein (PrPSc), which is derived from the normal prion protein (PrPC). Previous studies have reported that lipid rafts play a pivotal role in the conversion of PrPC into PrPSc, and several therapeutic strategies targeting lipids have led to prolonged survival times in prion diseases. In addition, phosphatidylethanolamine, a glycerophospholipid member, accelerated prion disease progression. Although several studies have shown that prion diseases are significantly associated with lipids, lipidomic analyses of prion diseases have not been reported thus far. We intraperitoneally injected phosphate-buffered saline (PBS) or ME7 mouse prions into mice and sacrificed them at different time points (3 and 7 months) post-injection. To detect PrPSc in the mouse brain, we carried out western blotting analysis of the left hemisphere of the brain. To identify potential novel lipid biomarkers, we performed lipid extraction on the right hemisphere of the brain and liquid chromatography mass spectrometry (LC/MS) to analyze the lipidomic profiling between non-infected mice and prion-infected mice. Finally, we analyzed the altered lipid-related pathways by a lipid pathway enrichment analysis (LIPEA). We identified a total of 43 and 75 novel potential biomarkers at 3 and 7 months in prion-infected mice compared to non-infected mice, respectively. Among these novel potential biomarkers, approximately 75% of total lipids are glycerophospholipids. In addition, altered lipids between the non-infected and prion-infected mice were related to sphingolipid, glycerophospholipid and glycosylphosphatidylinositol (GPI)-anchor-related pathways. In the present study, we found novel potential biomarkers and therapeutic targets of prion disease. To the best of our knowledge, this study reports the first large-scale lipidomic profiling in prion diseases.

scholarly journals Plasma metabolome analysis during human zinc depletion and repletion

2013 ◽  
Vol 27 (S1) ◽  
Author(s):  
Yasmeen Nkrumah‐Elie ◽  
Jay Kirkwood ◽  
Jan Fred Stevens ◽  
Robert L Tanguay ◽  
Carolyn Chung ◽  
...  
Keyword(s):  
Metabolome Analysis ◽  
Plasma Metabolome ◽  
Zinc Depletion

scholarly journals PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

2018 ◽  
Author(s):  
Kristian Peters ◽  
James Bradbury ◽  
Sven Bergmann ◽  
Marco Capuccini ◽  
Marta Cascante ◽  
...  
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Large Scale ◽  
Open Data ◽  
Research Field ◽  
Metabolome Analysis ◽  
Data Repositories ◽  
Metabolomics Data ◽  
Web Interfaces ◽  
Data Formats

AbstractBackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism’s metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent – and sometimes incompatible – analysis methods that are difficult to connect into a useful and complete data analysis solution.FindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project’s continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.ConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.

Sign in / Sign up

Export Citation Format

Share Document