In contemporary biological research, two fundamental branches of omics science have emerged as crucial investigative approaches. The systematic study of proteins, known as proteomics, investigates their structural elements, biological roles, molecular associations, and post-translational alterations across entire cellular systems. Metabolomics, in contrast, centers on analyzing the fluctuating patterns of low molecular weight metabolic compounds and their correlations to biological functions within living systems. When researchers integrate findings from both proteomic and metabolomic analyses, they gain deeper insights into biological systems. This comprehensive approach enhances our knowledge of intricate cellular pathways, pathological processes, and facilitates the identification of novel diagnostic indicators.

Why to Integrated Analyze Proteomics and Metabolomics

Despite significant advancements in single-omics research, the limitations of single-omics approaches are becoming increasingly apparent in the study of complex biological systems. To more comprehensively address biological phenomena, multi-omics integration methods have gradually emerged in recent years. The combined analysis of proteomics and metabolomics has found widespread application in fields such as cancer research, metabolic diseases, drug development, and precision medicine. Through cross-omics data integration, the deeper relationships between cellular functions and metabolic pathways can be revealed, driving progress in both scientific research and clinical diagnostics.

How to Integrated Analysis of Proteomics and Metabolomics

Pathway-based Integrative Analysis

Integrating analytical data from both proteomic and metabolomic studies enables researchers to elucidate biochemical mechanisms through systematic pathway investigation. Researchers frequently map their experimental findings onto established biochemical networks, utilizing resources like the KEGG database to determine which cellular pathways are implicated in particular physiological responses. When investigating disorders affecting metabolism, malignant conditions, and various other pathologies, this integrative methodology helps scientists pinpoint functionally linked proteins and small molecules that work in concert. Such comprehensive analysis proves particularly valuable for deciphering the underlying molecular basis of disease states and their progression.

Expression Level-based Integrative Analysis

Beyond network mapping approaches, quantitative integration of proteomic and metabolomic datasets provides crucial insights through abundance-based analytical methods. Statistical evaluation of relationships between metabolic compounds and their associated proteins reveals coordinated expression patterns and functional associations. Through sophisticated computational techniques, scientists can generate comprehensive interaction frameworks that illustrate the dynamic interplay of proteins with small molecules. These data-driven models enhance our understanding of how biomolecules function together within specific physiological contexts and regulatory systems.

Case of Integrated Analysis of Proteomics and Metabolomics

Proteomic-metabolomic combination analysis reveals novel biomarkers of meat quality that differ between young and older ducks

Journal: Poult Sci (IF=3.8)
Published: April 2024
Sample Type: Duck breast muscle
Research Method: Proteomics, Metabolomics

The study investigates the differences and underlying mechanisms between ducks of different ages (60 days, 300 days, and 900 days). The results showed that the breast muscle fibers in 900-day-old ducks were more compact, with a significant increase in redness. Proteomic and metabolomic analyses revealed significant differences between the 900-day-old ducks and the other groups, particularly in purine metabolism. These differences may contribute to the higher nutritional value and unique biochemical characteristics of older duck meat. These characteristics could serve as biomarkers to distinguish between young and older ducks.

Proteomics analysis

Label-free proteomics analyses were conducted to compare the D900 group with both the D60 and D300 groups, aiming to investigate how age influences protein variations in duck breast muscle. Through the use of volcano plots, Venn diagrams, and clustering maps of differentially expressed proteins (DEPs), a total of 616 DEPs were identified, with 295 proteins being upregulated and 321 downregulated across the three groups.

Figure 1 .Changes in protein expression during aging in ducks.

GO and KEGG enrichment analysis

To identify the characteristic DEPs of the D900 group, 61 proteins were selected from the 616 DEPs and subjected to heatmap analysis, comparing them with the D60 and D300 groups. GO and KEGG enrichment analyses were then performed. The results of the PPI network analysis of the characteristic DEPs in the D900 group indicated that these DEPs are primarily involved in purine and lipid metabolism processes.

Figure 2 .Selected differential proteins of the D900 group.

Metabolomics analysis

Metabolomics analysis of the breast muscle was conducted using HPLC-MS. The results revealed 31 differential metabolites, with 18 in positive ion mode and 13 in negative ion mode. KEGG pathway analysis indicated that these metabolic pathways play a crucial role in flavor regulation.

Figure 3 .Overlapping metabolites in a comparative analysis of the D900 vs. D60 and D900 vs. D300 groups.

Integrated Analysis

A combined analysis of metabolomics and proteomics revealed a shared signaling pathway, with the purine metabolism pathway involved in the regulation of flavor in older ducks. A total of four proteins and metabolites were found to be jointly involved.

Figure 4 .Integrated analysis of the unique differential proteins and metabolites.

An integrated proteomics and metabolomics analysis of methylglyoxal-induced neurotoxicity in a human neuroblastoma cell line

Journal: NPJ Sci Food (Impact Factor:6.3)
Published: October 25, 2024
Sample Type: SH-SY5Y human neuroblastoma cells
Research Method: Proteomics, Metabolomics (Label-free quantitative nanoLC-MS/MS proteomics, targeted LC-TQ-MS/MS-based metabolomics)

This study investigates the molecular and metabolic changes induced by methylglyoxal (MGO) exposure in SH-SY5Y human neuroblastoma cells and explores the underlying neurotoxic mechanisms. Through integrated proteomics and metabolomics analysis, it was found that MGO exposure significantly impacted key processes such as protein synthesis, mitochondrial function, oxidative stress responses, and amino acid metabolism, disrupting cellular structure and energy balance, leading to toxicity. Simultaneously, cells exhibited adaptive responses by activating the Nrf2 pathway and reprogramming metabolism. A set of key proteins and metabolites associated with MGO concentration were identified, offering potential as biomarkers. These findings provide new insights into the mechanisms of MGO-induced neurotoxicity and potential therapeutic targets.

Proteomics analysis

Quantitative proteomics was used to analyze the effects of different concentrations of methylglyoxal (MGO) exposure (500, 750, and 1000 μM) on the proteome of SH-SY5Y cells. Principal component analysis (PCA) revealed that MGO exposure significantly altered the protein expression profile. Differentially expressed proteins (DEPs) were identified, showing concentration-dependent changes. Additionally, a heatmap analysis of DEPs in all treatment groups indicated significant alterations in protein expression at higher MGO concentrations.

Figure 5 .Proteomic analysis of SH-SY5Y cells after 24 h exposure to MGO.

PPI analysis was performed on the differentially expressed proteins (DEPs), extracting the top five tightly connected modules and analyzing their specific protein compositions. Enrichment and PPI network analysis results indicated that MGO exposure led to significant alterations in several key cellular processes, including abnormal protein synthesis, compromised cellular structural integrity, metabolic dysfunction, and an enhanced response to oxidative stress.

Metabolomics analysis

A total of 75 out of 98 compounds were detected and quantified in the samples. Principal component analysis (PCA) revealed that MGO exposure, particularly at the highest concentration, significantly altered the metabolic profile of the cells. The analysis identified differential metabolites (DMs) across low, medium, and high MGO concentrations compared to the control group, showing a concentration-dependent shift. Furthermore, heatmap analysis of DMs across all treatment groups with high MGO concentrations revealed distinct metabolic changes.

Figure 6 .Metabolomic analysis of SH-SY5Y cells after 24 h exposure to MGO.

Integrated Analysis

KEGG enrichment analysis of the differential metabolites (DMs) identified in the high MGO treatment group revealed that high concentrations of MGO exposure induce metabolic dysfunction in cells. An intensive network of interactions between differentially expressed proteins (DEPs) and DMs was also observed. Combined pathway enrichment analysis indicated that arginine biosynthesis was the most significantly affected pathway.

Figure 7 .Metabolic pathway enrichment and integrated proteomics and metabolomics.

In conclusion, the integration of proteomics and metabolomics offers a comprehensive and powerful approach to investigating complex biological systems. By combining the analysis of proteins and metabolites, researchers can uncover deeper insights into cellular processes, metabolic pathways, and disease mechanisms that would be difficult to achieve through single-omics studies. The case studies presented here illustrate how this integrated approach can lead to the identification of novel biomarkers, enhance our understanding of disease pathology, and contribute to the development of therapeutic strategies. As the field continues to evolve, integrated omics analysis is poised to drive significant advancements in precision medicine, drug development, and the study of complex biological phenomena.

References

1. Gu, T., Duan, M., Chen, L., et.al. (2024). Proteomic-metabolomic combination analysis reveals novel biomarkers of meat quality that differ between young and older ducks. Poultry science, 103(4), 103530. https://doi.org/10.1016/j.psj.2024.103530
2. Wang, H., Boeren, S., Bakker, W., et.al. (2024). An integrated proteomics and metabolomics analysis of methylglyoxal-induced neurotoxicity in a human neuroblastoma cell line. NPJ science of food, 8(1), 84. https://doi.org/10.1038/s41538-024-00328-0