Proteomics focuses on the identification, quantification, and modification analysis of proteins in cells and tissues, making proteins the subject of study. Metabolomics involves the identification and quantitative analysis of metabolites in specific biological samples under defined conditions. In the post-genomic era, where proteins serve as carriers for gene function, proteomics is a primary focus for studying gene function. Metabolites, positioned downstream in protein regulatory networks, have a direct regulatory relationship with proteomics. Moreover, metabolomics provides terminal biological information that is closest to phenotypic changes. Joint analysis of proteomics and metabolomics allows for the simultaneous exploration of biological regulatory mechanisms at both the protein and metabolic levels. Compared to individual omics analyses, this approach systematically and comprehensively reveals the processes of life activities. It holds significant guiding value for understanding gene function, assessing phenotypic impacts, establishing molecular mechanism models, and clinical applications in the later stages.

There exist a variety of methodologies that enable the combined analysis of proteomics and metabolomics, which can be generally bifurcated into three main correlation strategies: firstly, the approach of association analysis grounded on KEGG pathway investigations; a point-biserial correlation analysis based on standard Pearson calculation; and lastly, the correlation analysis focused on O2PLS modelling.

Integrative Analysis Based on Regulatory Mechanisms

The Acute Impact of Metabolic Cofactor Supplementation: A Potential Therapeutic Strategy Against Non-Alcoholic Fatty Liver Disease

Journal: Molecular Systems Biology
Impact Factor: 9.9
Published: March 10, 2020

Abstract: Implementing a network analysis approach, the researchers deciphered probable molecular mechanisms that correspond to the evolution of non-alcoholic fatty liver disease (NAFLD), leading them to earmark potential metabolic cofactors that might attenuate hepatic fat accumulation in human subjects. These findings were calibrated through pharmacotoxicological studies conducted on rats, following which the safe dosage for human application was ascertained. Subsequently, human volunteers were recruited for clinical trials. Blood samples were systematically taken from both the test group and the control group at numerous time intervals throughout the course of a day. The study aimed to discern differences in the metabolic cofactor supplements, metabolites related to NAFLD, other selective metabolites, and plasma proteins associated with inflammation in the collected samples before and post drug administration, culminating in an integrated data interpretation.

Conclusion: Based on the integration of metabolomic and proteomic results, the authors observed a significant impact of the supplement on lipid, amino acid, and antioxidant metabolism in humans. They developed a system of ordinary differential equations to predict the daily blood concentrations of these compounds during prolonged supplementation. Additionally, a pharmacokinetic model was established to predict serine concentrations in the liver. These findings will inform the adjustment of individual metabolic cofactor dosages for future clinical trials in humans.

Recommended technical services: Untargeted metabolomics, proteomics

Integrating Metabolic Mechanism Models Based on UnTargeted Metabolism and Inflammatory Protein Detection

Integrative Analyses Based on Correlation

Metabolomics analysis of post-traumatic stress disorder symptoms in World Trade Center responders

Journal: Translational Psychiatry
Impact Factor: 6.8
Published: April 28, 2022

Experimental Design: Un-targeted metabolomics and complex lipid profiling were conducted on plasma samples from 124 World Trade Center first responders (56 with post-traumatic stress disorder [PTSD] and 68 controls). Machine learning and network regulatory mechanisms were employed to identify relevant proteins and metabolites.

Conclusion: Hexosylceramide HCER(26:1) was associated with PTSD, exhibiting an area under the curve (AUC) of 0.839 for the multi-metabolite composite score compared to controls. Independent component analysis (ICA) identified three metabolomic modules significantly correlated with PTSD, enriched in bile acid metabolism, fatty acid metabolism, and pregnenolone ketosteroids, contributing to innate immunity, inflammatory processes, and neuronal excitability, respectively.

Correlation analysis of metabolomics and proteomics datasets revealed seven protein modules significantly correlated with metabolomic modules associated with PTSD.

Recommended technical services: Untargeted metabolomics, proteomics, Untargeted lipidomics

Evaluation of Multimetabolite Models and Pathway Analysis of Three Metabolite Modules Associated with PTSD

Significantly Correlated Metabolite Modules Associated with PTSD and Pearson Correlation Coefficients with Related Protein Modules

Integrative Analyses Based on O2PLS

Linking Gene Expression and Membrane Lipid Composition of Arabidopsis

Journal: The Plant Cell
Impact Factor: 11.6
Published: March 26, 2014

Background: In documenting the intricate responses of plant glycerolipid metabolism to fluctuations in light intensity and ambient temperature, the researchers unveiled consequent changes in membrane lipid composition, which act as a mechanism to guarantee the biochemical and physical performance of plants in changed circumstances. Numerous accounts have pointed the spotlight on post-transcriptional regulatory maneuvers implicated in this procedure, yet, the involvement of transcriptional regulation in these adaptive lipidomic shifts continues to elude conclusive identification.

Experimental Design: In the process of conducting our research, we collected rosette leaves from the Arabidopsis plant, subjecting them to extensive transcriptomic and lipidomic analyses. A thorough investigation was then embarked upon using Orthogonal Projections to Latent Structures (O2PLS) multivariate regression methodologies. Our primary objective was to elucidate the reciprocal regulation between gene expression and variations in glycerolipid levels as the Arabidopsis plant responds to alterations in light intensity and thermal conditions.

Conclusion: The study indicates that gene expression responses are tightly coordinated at the biochemical pathway level, occurring in parallel with changes in specific glycerolipid pools. Five intriguing candidate genes were selected for further analysis from a larger pool of candidates based on their close associations with various glycerolipid classes. Lipidomic analysis of mutant lines with these five genes knocked out revealed a significant relationship between the coordinated transcriptome changes in response to changing environments and glycerolipid levels. The impact of single-gene perturbations also exhibited a significant correlation.

Recommended technical services: Transcriptomics, Untargeted metabolomics

Overview on the O2PLS Model Structures Obtained for Integration of the Transcript and Lipid Data.

Locations of the Selected Gene Candidates on the O2PLS-DA Correlation Loading Space. The correlation loadings of lipids are denoted as triangles, and those of transcripts are denoted as squares.

References

1. Zhang C, Bjornson E, Arif M, Tebani A, Lovric A, Benfeitas R, Ozcan M, Juszczak K, Kim W, Kim JT, Bidkhori G, Ståhlman M, Bergh PO, Adiels M, Turkez H, Taskinen MR, Bosley J, Marschall HU, Nielsen J, Uhlén M, Borén J, Mardinoglu A. The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease. Mol Syst Biol. 2020 Apr;16(4):e9495. doi: 10.15252/msb.209495. PMID: 32337855; PMCID: PMC7184219.
2. Kuan PF, Yang X, Kotov R, Clouston S, Bromet E, Luft BJ. Metabolomics analysis of post-traumatic stress disorder symptoms in World Trade Center responders. Transl Psychiatry. 2022 Apr 28;12(1):174. doi: 10.1038/s41398-022-01940-y. PMID: 35484105; PMCID: PMC9050707.
3. Szymanski J, Brotman Y, Willmitzer L, Cuadros-Inostroza Á. Linking gene expression and membrane lipid composition of Arabidopsis. Plant Cell. 2014 Mar;26(3):915-28. doi: 10.1105/tpc.113.118919. Epub 2014 Mar 18. PMID: 24642935; PMCID: PMC4001401.