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Integrative PTM and targeted metabolomics analysis uncovers molecular mechanisms, enhances biomarker discovery, and accelerates precision research.

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Integrative Analysis Services of PTMs and Metabolomics

What is Integrative Analysis of PTMs and Metabolomics?

Post-translational modifications (PTMs) are chemical changes to proteins that alter their activity, stability, or localisation. These modifications include phosphorylation, acetylation, ubiquitination, glycosylation, and others. They act as molecular switches that control how proteins function in cells. Metabolites, the small molecules produced by cellular metabolism, reflect the functional state of biochemical pathways.

Proteins and metabolites are tightly interconnected. For example, the activity of a metabolic enzyme can be switched on or off by phosphorylation, which directly affects the production of metabolites. Conversely, specific metabolites can influence PTMs as substrates, cofactors, or regulators for modifying enzymes. Studying these layers separately often provides incomplete information.  

Why is Integrative Analysis Necessary?

Studying PTMs or metabolites in isolation provides only a partial view of cellular regulation. PTMs act as molecular switches, controlling proteins' activity, stability, and localisation, including key enzymes in metabolic pathways. Metabolites, on the other hand, reflect the functional output of these enzymes and provide a snapshot of the cell's biochemical state.

Integrating PTMs with metabolite analysis is essential because it links cause and effect at the molecular level. For instance, a protein may be phosphorylated, but the biological consequence remains unclear without knowing how this modification changes metabolite levels. Similarly, observing a shift in metabolite concentration does not reveal which protein modifications drive the change. This integrative approach uncovers the direct connections between protein regulation and metabolism, enabling researchers to:

Post-translational modifications in metabolic diseases.

Figure 1. PTMs in metabolic diseases (Wu X, et al., 2023).

Common PTMs in metabolic regulation

PTM Type Target Proteins / Enzymes Biological Role in Metabolic Regulation Mechanistic Notes
Phosphorylation Kinases, metabolic enzymes, transcription factors. Rapid modulation of enzyme activity, signall transduction, nutrient sensing. Reversible addition of phosphate groups to Ser, Thr, Tyr residues; triggers conformational changes, activation/inhibition.
Acetylation Histones, metabolic enzymes (e.g., glycolytic and TCA cycle enzymes). Links energy status to transcriptional and metabolic control; modulates enzyme activity and protein-protein interactions. Acetylation via acetyl-CoA; can activate or inhibit enzymes depending on site and context.
Ubiquitination Metabolic enzymes, transcription factors, signalling proteins. Controls protein stability, turnover, and degradation; regulates flux through metabolic pathways. Add ubiquitin to lysine residues; can target protein for proteasomal or lysosomal degradation.
Glycosylation Secreted proteins, membrane transporters, enzymes. Modulates enzyme activity, stability, and localisation; regulates secretion and signalling. Addition of glycans to Asn, Ser, or Thr residues; affects folding and trafficking.
Methylation Histones, transcription factors, metabolic enzymes. Epigenetic and transcriptional regulation of metabolic genes; modulates protein activity. Lysine or arginine methylation; can activate or repress transcription, alter enzyme function.
Disulfide bond formation Enzymes, redox-sensitive proteins. Stabilizes protein structure; regulates redox-sensitive metabolic enzymes. Formation of covalent S–S bonds between cysteine residues; can switch enzyme activity in response to oxidative stress.

Advantages of Our Integrative Analysis Services of PTMs and Metabolomics

Workflow for PTMs and Metabolomics Integration Analysis

Workflow for PTM and metabolomics integration analysis

Deliverables and Reporting Standards

Raw Data and Instrument Files

Processed Quantitative Data

Visualizations for Interpretation

Applications of PTM and Metabolomics Integration Analysis

Simple Requirements

Sample Type Minimum Quantity Required Storage Conditions Special Considerations
Cell Pellets ≥ 5 × 10⁶ cells Snap-freeze in liquid nitrogen; store at –80°C. Avoid repeated freeze-thaw cycles; wash with PBS before storage.
Tissue Samples ≥ 50 mg Snap-freeze immediately; store at –80°C. Homogenize quickly to prevent PTM degradation.
Serum / Plasma ≥ 200 µL Store at –80°C; avoid hemolysis. Use EDTA or heparin tubes; minimize exposure to room temperature.
Urine Samples ≥ 500 µL Store at –80°C after centrifugation. Collect midstream; avoid preservatives unless specified.
Protein Extracts ≥ 100 µg total protein Store at –80°C in aliquots. Quantify accurately; provide buffer composition for reproducibility.
Metabolite Extracts ≥ 200 µL Store at –80°C under inert gas if possible. Avoid contamination; record solvent details.
labellled Samples (optional) ≥ 100 µg Store at –80°C. Provide labelling strategy and isotopic information for integration.

Why Choose Creative Proteomics

FAQ

Q1: What sample types are compatible with integrative PTM-metabolomics?

A1: Cells, tissues, plasma, serum, cerebrospinal fluid, urine, and extracts from plants or microbes are compatible. Choose matrixes based on the biological question. Preserve samples at -80°C.

Q2: How to choose targeted metabolomics or non-targeted?

A2: Targeted assays are ideal for testing specific hypotheses and achieving precise, absolute quantification of molecules. They are particularly advantageous when high sensitivity, reproducibility, and measurement accuracy are critical. Targeted panels allow focused analysis of selected metabolites or proteins, minimizing background noise and enabling reliable comparisons across samples or experimental conditions.

Q3: How are low-abundance PTMs or metabolites handled?

A3: Enrichment strategies, highly sensitive mass spectrometry, and targeted metabolomics (MRM/PRM) are essential. Combining fractionation with high-resolution LC-MS/MS maximizes detection coverage.

Q4: Can PTMs-metabolomics be applied to microbiome studies?

A4: Yes. Host-microbe interactions can be dissected by integrating microbial PTMs with metabolite fluxes, revealing metabolic crosstalk and signalling dynamics.

Q5: What validation strategies are advised?

A5: Validation may involve targeted MS (SRM/PRM), isotope labelling, enzymatic assays, or genetic perturbation (knockdown/overexpression of the modified protein).

Demo

Demo: Effects of moderate intensity exercise on liver metabolism in mice based on multi-omics analysis

Proteomics and lactylation modification analyses

Figure 2. Proteomics and lactylation modification analyses (Wang F, et al., 2024).

Case Study

Case: Integrated phosphoproteomic and metabolomic profiling reveals perturbed pathways in the hippocampus of gut microbiota dysbiosis mice

Abstract

The study explores how imbalances in gut microbes affect brain function and behaviour through the microbiota-gut-brain axis. While previous research links gut microbiome changes to psychiatric disorders, the mechanisms remain unclear. To investigate this, the authors combine hippocampal phosphoproteomics with metabolomics from serum and hippocampal tissues to uncover molecular pathways connecting gut health to depression-like behaviours.

Methods

  • Animal models: Germ-free (GF) versus specific pathogen-free (SPF) mice; fecal-microbiota-transplant (FMT) recipients colonized with "depression microbiota" or "healthy microbiota."
  • Tissue/sample collection: Hippocampus tissue for phosphoproteomics; serum and hippocampus for metabolomics.
  • Proteomic workflow: Mass-spectrometry-based phosphoproteomic profiling to identify and quantify phosphorylation sites.
  • Metabolomic workflow: Unbiased metabolomics of serum and hippocampus to profile lipid and amino-acid metabolites.
  • Data integration: For validation, joint pathway and enrichment analyses to link differential phosphosites with metabolite changes; cross-comparison to other phosphoproteomic datasets (stress models and human MDD postmortem tissue).

Results

  • Phosphorylation changes: Significant dysregulation of phosphorylation was observed. The authors report hundreds of differential phosphosites.
  • Metabolic disturbances: The integrated analysis showed consistent lipid and amino acid metabolism disruptions in both serum and hippocampus, indicating changes in metabolite profiles and lipid composition that may affect cell membranes and signalling.
  • signalling pathways: CAMKII–CREB signalling emerged as a commonly perturbed neuronal pathway responding to metabolic and phosphorylation changes. Glutamatergic neurotransmission components showed altered phosphorylation.
  • Novel findings: Dysbiosis affected spliceosome activity, a previously overlooked neuronal function. NCBP1 phosphorylation was identified as a potential pathogenic target. Cross-species analysis revealed shared phosphoproteins between animal models and human MDD tissues.
Stepwise experimental flowchart depicting the analysis of this study.

Figure 3. Stepwise experimental flowchart depicting the analysis of this study.

Bioinformatics analysis for phosphoproteins.

Figure 4. Bioinformatics analysis for the microbial-specific phosphoproteins.

Integrated phosphoproteomic analysis of depressive rodents and patients.

Figure 4. Integrated phosphoproteomic analysis of depressive rodents and patients with major depression disorders.

Conclusion

The authors conclude that an imbalance in gut microbiota can trigger depression-like behaviours by simultaneously altering protein phosphorylation and metabolism. Their integrated phosphoproteomic and metabolomic analysis revealed connections between lipid and amino acid metabolism changes and neuronal signalling, particularly the CAMKII–CREB and glutamate pathways. The study highlights potential molecular targets, such as NCBP1 phosphorylation, for further investigation. Limitations include the exclusive use of male mice, focus on the hippocampus, and the need for additional validation across broader samples.

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