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

Importance of the Topic

The aging population and rising healthcare challenges have increased the need to understand biological mechanisms underlying longevity. Multi-omics integration combining genomic, transcriptomic, proteomic, and metabolomic data provides a comprehensive view of molecular changes associated with lifespan extension.

Study Objectives and Overview

This study compares wild-type Drosophila melanogaster with a long-lived mutant line across multiple omics layers. The goals include identification of genomic variants, differential gene expression, proteomic profiles, and metabolite alterations to elucidate pathways linked to longevity.

Used Instrumentation

• Next-Generation Sequencer GridION (Oxford Nanopore Technologies) with Ligation Sequencing Kit V14 on R10.4.1 flow cell and Guppy basecaller
• Liquid Chromatograph–Mass Spectrometer Nexera Mikros with LCMS-9050 Q-TOF for proteomics and wide-target metabolomics
• Gas Chromatograph–Mass Spectrometer GCMS-TQ8040 NX with Smart Metabolites Database Ver.2 for targeted metabolite analysis
• Software tools: Shimadzu Multi-omics Analysis Package, PEAKS Studio XPro, Smart Metabolites Database

Methodology and Experimental Workflow

Genomic DNA was extracted using QIAamp DNA Mini Kit and sequenced to detect approximately 830 000 SNVs/Indels and 40 000 structural variants. Total RNA was isolated with RNeasy Micro Kit and sequenced using PCR-cDNA barcoding, identifying 5920 loci including 185 significant at p≤0.05. Proteins were prepared via bead disruption, S-Trap digestion, and analyzed by LC-MS/MS in data-dependent acquisition mode, yielding 944 identified proteins at 1% FDR. Metabolites were profiled by GC-MS in MRM mode, measuring 488 targets with around 300 detected per sample. A multi-stage filtering and statistical pipeline was applied at each omics level and results were projected onto metabolic pathway diagrams.

Key Results and Discussion

Integration of 654 filtered variables uncovered homocysteine accumulation and reduced 2-aminoadipic acid in long-lived flies, indicating altered methionine and lysine degradation. Proteomic profiling identified 162 wild-type–specific and 126 mutant-specific proteins, with 28 showing significant fold changes. Notable hits included ribosomal protein CG14792 and triosephosphate isomerase CG2171 in long-lived flies. Multi-omics mapping revealed suppression of the tryptophan catabolism pathway via TDO2 intronic insertion and correlated metabolite ratios with proteins such as CG31508.

Benefits and Practical Applications of the Method

The integrated workflow reduces analysis time from approximately 12 days for NGS-only to 0.6 days by narrowing variables at each omics layer and focusing on pathway-centric queries. This approach accelerates biomarker discovery and hypothesis generation in aging research and is adaptable to other model organisms and clinical studies.

Future Trends and Applications

Anticipated developments include adoption of data-independent acquisition proteomics, expanded targeted metabolite panels, machine learning–driven multi-omics integration, and application to human cohort studies. Enhanced databases and analytical software will further streamline pathway-based interpretation.

Conclusion

This work demonstrates a robust multi-omics platform combining Nanopore sequencing and mass spectrometry to dissect longevity mechanisms in Drosophila. The efficient filtering strategy and metabolic pathway mapping enabled rapid identification of key molecular alterations, establishing a foundation for future aging interventions.

References

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