Multi-omics Analysis Using Next-Generation Sequencer and Mass Spectrometer in Longevity Research

Importance of the Topic

Global population aging raises urgent challenges for healthy life expectancy, increasing social costs and healthcare burdens. Model organisms such as Drosophila melanogaster offer a rapid, manipulable system to explore molecular mechanisms of longevity. Integrating genomic, transcriptomic, proteomic and metabolomic data (multi-omics) provides a comprehensive view of aging pathways and potential intervention targets.

Study Objectives and Overview

This study compared wild-type and long-lived Drosophila strains across four omics layers. Using Oxford Nanopore sequencing, liquid chromatography-mass spectrometry and gas chromatography-tandem mass spectrometry, investigators aimed to identify genetic variants, expression changes, protein profiles and metabolic shifts that underlie extended lifespan.

Methodology and Instrumentation Used

Samples: Two biological replicates of wild-type and long-lived flies, 5–10 individuals per sample.

• Genomics: GridION (Oxford Nanopore) with Ligation Sequencing Kit V14 (R10.4.1 flow cells, SUP accuracy). QIAamp DNA Mini Kit extraction.
• Transcriptomics: PCR-cDNA Barcoding Kit on GridION (R9.4.1 flow cells, HAC accuracy). RNeasy Micro Kit extraction.
• Proteomics: Nexera Mikros LC coupled to LCMS-9050 Q-TOF. S-Trap digestion, trypsin, iodoacetamide alkylation. PEAKS Studio XPro for DDA data analysis.
• Metabolomics: GCMS-TQ8040 NX with Smart Metabolites Database™ Ver. 2. Wide-target MRM screening of ~500 metabolites; ~300 detected per sample.
• Data Integration: Shimadzu Multi-omics Analysis Package for pathway mapping, PCA, Volcano plots and correlation grids.

Key Results and Discussion

Genomics revealed ~830,000 SNVs/indels and ~40,000 structural variants. Transcriptomics identified 5,920 loci, 185 with p≤0.05. Proteomics detected 944 proteins: 162 wild-type specific, 126 long-lived specific and 656 common; ANOVA filtering (significance>10, fold change>1.3) highlighted 28 proteins. Notable proteins included eIF4A (CG9075) enriched in wild type and triosephosphate isomerase (CG2171) in long-lived flies. Metabolomics showed accumulation of homocysteine in long-lived flies and reduction of 2-aminoadipic acid.

Integrated pathway analysis mapped these changes onto amino acid and energy metabolism. The tryptophan–kynurenine pathway was suppressed in long-lived flies due to an insertion in the Vermillion (TDO2) locus, confirmed by DNA and RNA analyses. Correlation grids linked the stress-responsive protein CG31508 with sugar metabolites, suggesting altered ribose metabolism.

By filtering variables at each omics level (53 DNA, 94 RNA, 316 proteins, ~300 metabolites), the combined analysis reduced theoretical NGS-only analysis time from 12 days to 0.6 days while uncovering mechanistic links between genes, proteins and metabolites.

Benefits and Practical Applications

• Streamlines high-dimensional data into actionable targets.
• Reduces analysis time and complexity through targeted downstream measurements.
• Identifies molecular pathways and biomarkers for aging research.

Future Trends and Potential Applications

• Adoption of DIA proteomics for broader protein coverage.
• Pathway enrichment and machine-learning models to predict lifespan interventions.
• High-throughput metabolite screening for translational aging biomarkers.

Conclusion

An integrated multi-omics workflow, combining NGS and MS data with pathway-centric analysis, effectively dissects longevity mechanisms in Drosophila. This approach accelerates discovery of genetic mutations, expression changes, protein alterations and metabolic shifts, offering a scalable strategy for aging and therapeutic research.

Reference

1. United Nations, World Population Prospects 2022.
2. Ministry of Health, Labour and Welfare (Japan), Annual Vital Statistics Report 2022.
3. Nikkei Newspaper, “Is 120 the Upper Limit of Longevity?”, accessed Apr 2024.
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5. UniProt, Proteome of Drosophila melanogaster.
6. UniProt, RSSA_DROME (40S ribosomal protein SA).
7. Diamond Online, “High Homocysteine and Alzheimer’s Disease.”
8. David R Sell, “2-Aminoadipic Acid as a Marker of Protein Carbonyl Oxidation.”