Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

Lee, I.; Tumanov, Sergey; Wong, Jason; Stocker, Roland; Ho, Joshua W. K.

doi:10.1016/j.isci.2023.106881

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…These three pathways may be the key metabolic pathways leading to lipid differences in different parts of Duhua pigs ( 25 ). After sorting the data, it can be concluded that compared with LDM and BM, the TG level in GL metabolism is significantly higher than CL, DG, and MG; compared with LDM and LM, the PE level in GL metabolism is significantly higher than CL and PC; LDM and FM compared with BM, the PC level in GL metabolism is close to the PE level; compared with LDM and PM, the PC level is slightly higher than PE; compared with BM and LM, the GP metabolism and GL metabolism levels are consistent; compared with BM and FM, the PC level in GL metabolism is slightly higher than that of PE.…”

Section: Resultsmentioning

confidence: 99%

Integrative analysis of proteomics and lipidomic profiles reveal the fat deposition and meat quality in Duroc × Guangdong small spotted pig

Wu,

Wang,

Wang

et al. 2024

Front. Vet. Sci.

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IntroductionThis study aims to explore the important factors affecting the characteristics of different parts of pork.MethodsLipidomics and proteomics methods were used to analyze DAL (differential lipids) and DAPs (differential proteins) in five different parts (longissimus dorsi, belly meat, loin, forelegs and buttocks) of Duhua pig (Duroc × Guangdong small spotted pig), to identify potential pathways affecting meat quality, investigating fat deposition in pork and its lipid-protein interactions.ResultsThe results show that TG (triglyceride) is the lipid subclass with the highest proportion in muscle, and the pathway with the most significantly enriched lipids is GP. DAP clustered on several GO terms closely related to lipid metabolism and lipogenesis (lipid binding, lipid metabolism, lipid transport, and lipid regulation). In KEGG analysis, there are two main DAP aggregation pathways related to lipid metabolism, namely Fatty acid degradation and oxidative phosphorylation. In PPI analysis, we screened out 31 core proteins, among which NDUFA6, NDUFA9 and ACO2 are the most critical.DiscussionPC (phosphatidylcholine) is regulated by SNX5, THBS1, ANXA7, TPP1, CAVIN2, and VDAC2 in the phospholipid binding pathway. TG is regulated by AUH/HADH/ACADM/ACADL/HADHA in the lipid oxidation and lipid modification pathways. Potential biomarkers are rich in SFA, MUFA and PUFA respectively, the amounts of SFA, MUFA and PUFA in the lipid measurement results are consistent with the up- and down-regulation of potential biomarker lipids. This study clarified the differences in protein and lipid compositions in different parts of Duhua pigs and provided data support for revealing the interactions between pork lipids and proteins. These findings provide contributions to the study of intramuscular fat deposition in pork from a genetic and nutritional perspective.

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Section: Resultsmentioning

confidence: 99%

Integrative analysis of proteomics and lipidomic profiles reveal the fat deposition and meat quality in Duroc × Guangdong small spotted pig

Wu,

Wang,

Wang

et al. 2024

Front. Vet. Sci.

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Research on Lipidomic Profiling and Biomarker Identification for Osteonecrosis of the Femoral Head

Yan,

Wang,

Wang

et al. 2024

Biomedicines

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Objectives: Abnormal lipid metabolism is increasingly recognized as a contributing factor to the development of osteonecrosis of the femoral head (ONFH). This study aimed to explore the lipidomic profiles of ONFH patients, focusing on distinguishing between traumatic ONFH (TONFH) and non-traumatic ONFH (NONFH) subtypes and identifying potential biomarkers for diagnosis and understanding pathogenesis. Methods: Plasma samples were collected from 92 ONFH patients (divided into TONFH and NONFH subtypes) and 33 healthy normal control (NC) participants. Lipidomic profiling was performed using ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS). Data analysis incorporated a machine learning-based feature selection method, least absolute shrinkage and selection operator (LASSO) regression, to identify significant lipid biomarkers. Results: Distinct lipidomic signatures were observed in both TONFH and NONFH groups compared to the NC group. LASSO regression identified 11 common lipid biomarkers that signify shared metabolic disruptions in both ONFH subtypes, several of which exhibited strong diagnostic performance with areas under the curve (AUCs) > 0.7. Additionally, subtype-specific lipid markers unique to TONFH and NONFH were identified, providing insights into the differential pathophysiological mechanisms underlying these subtypes. Conclusions: This study highlights the importance of lipidomic profiling in understanding ONFH-associated metabolic disorders and demonstrates the utility of machine learning approaches, such as LASSO regression, in high-dimensional data analysis. These findings not only improve disease characterization but also facilitate the discovery of diagnostic and mechanistic biomarkers, paving the way for more personalized therapeutic strategies in ONFH.

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Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

Cited by 2 publications

References 32 publications

Integrative analysis of proteomics and lipidomic profiles reveal the fat deposition and meat quality in Duroc × Guangdong small spotted pig

Integrative analysis of proteomics and lipidomic profiles reveal the fat deposition and meat quality in Duroc × Guangdong small spotted pig

Research on Lipidomic Profiling and Biomarker Identification for Osteonecrosis of the Femoral Head

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