Red-Wine Gene Networks Linked to Exceptional Longevity in Humans
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design and Gene List Generation
2.2. Gene Review and Validation
2.3. Artificial Intelligence (AI) Utilization for Literature Summarization
2.4. Data Analysis
2.5. Definition of Wine Consumption and Mid/Late Life
3. Results
3.1. Gene List Generation
3.2. Network Topology Analysis and Over-Representation Analysis
3.3. Gene Set Enrichment Analysis
3.3.1. Biological Pathways
3.3.2. Tissue Distribution
3.3.3. Disease Association
4. Discussion
4.1. Biological Pathways and Tissue Distributions
4.2. Disease Associations
4.3. Methodological Considerations and Limitations
4.4. Considerations of Alcohol on Cancer
4.4.1. Debate About Alcohol Consumption and Hormesis
4.4.2. Debate About Wine Consumption
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
CHD | coronary heart disease |
GSEA | Gene set enrichment analysis |
ORA | Over-representation analysis |
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Term Description | Gene Hits | Control Gene Pool | FDR | Matching Proteins in Network |
---|---|---|---|---|
Heart disease and stroke | ||||
Atherosclerosis | 8 | 21 | 3.95 × 10−12 | CCL2, APOB, APOA1, APOE, NOS3, IL6, ADIPOQ, TNF |
Vascular disease | 13 | 249 | 6.12 × 10−12 | SERPINE1, CCL2, APOB, APOA1, APOE, ALB, NOS3, CASP3, MMP9, IL6, ADIPOQ, TNF, AKT1 |
Artery disease | 10 | 130 | 1.85 × 10−10 | CCL2, APOB, APOA1, APOE, ALB, NOS3, MMP9, IL6, ADIPOQ, TNF |
Coronary artery disease | 7 | 36 | 1.63 × 10−9 | APOB, APOA1, APOE, ALB, NOS3, IL6, TNF |
Ischemia | 6 | 30 | 3.90 × 10−8 | ALB, NOS3, CASP3, IL6, TNF, AKT1 |
Cerebrovascular disease | 6 | 46 | 3.09 × 10−8 | APOE, ALB, NOS3, CASP3, IL6, TNF |
Diabetes and gastrointestinal disease | ||||
Type 2 diabetes mellitus | 7 | 25 | 2.17 × 10−10 | APOE, ALB, IGF1, IL6, ADIPOQ, TNF, AKT1 |
Intestinal disease | 11 | 216 | 5.83 × 10−10 | CCL2, TP53, ALB, TERT, KLK3, MMP9, CDKN1A, IL6, TNF, AKT1, SNCA |
Pancreas disease | 8 | 70 | 1.82 × 10−9 | TP53, TERT, CASP9, IGF1, IL6, ADIPOQ, TNF, AKT1 |
Gastrointestinal system disease | 13 | 576 | 4.60 × 10−8 | CCL2, TP53, ALB, TERT, CASP3, KLK3, MMP9, CDKN1A, IL6, TNF, AKT1, LDLR, SNCA |
Hepatobiliary disease | 8 | 168 | 6.79 × 10−7 | TP53, ALB, TERT, CASP3, IL6, TNF, AKT1, LDLR |
Liver disease | 6 | 97 | 9.24 × 10−6 | TP53, ALB, CASP3, IL6, TNF, AKT1 |
Metabolic disease | ||||
Inherited metabolic disorder | 14 | 949 | 1.06 × 10−6 | APOB, APOA1, APOE, APP, ALB, IGF1, IL6, ADIPOQ, TNF, PRNP, AKT1, LDLR, ELANE, SNCA |
Hyperglycemia | 4 | 14 | 6.76 × 10−6 | ALB, IL6, ADIPOQ, AKT1 |
Immune disease | ||||
Immune system disease | 17 | 675 | 1.55 × 10−11 | CCL2, APOE, CYBA, IL1A, TP53, IL18, NOS3, CASP3, KLK3, ITGAL, CASP8, FASLG, BCL2L1, IL6, TNF, AKT1, FAS |
Lymphatic system disease | 8 | 174 | 8.18 × 10−7 | TP53, CASP3, KLK3, CASP8, BCL2L1, TNF, AKT1, FAS |
Hematopoietic system disease | 10 | 473 | 6.71 × 10−6 | GSR, TP53, CASP3, ITGAL, CASP8, BCL2L1, IL6, TNF, ELANE, FAS |
Disease by infectious agent | 9 | 368 | 9.24 × 10−6 | APOE, TP53, APP, ALB, IL6, TNF, PRNP, LDLR, SNCA |
Cancer/Neoplasm | ||||
Organ system cancer | 13 | 757 | 8.18 × 10−7 | APOE, TP53, ALB, TERT, CASP3, KLK3, CASP8, BCL2L1, CDKN1A, IL6, TNF, AKT1, FAS |
Disease of cellular proliferation | 15 | 1101 | 8.18 × 10−7 | APOE, TP53, ALB, TERT, CASP3, KLK3, SIRT6, CASP8, BCL2L1, IGF1, CDKN1A, IL6, TNF, AKT1, FAS |
Cancer | 14 | 978 | 1.40 × 10−6 | APOE, TP53, ALB, TERT, CASP3, KLK3, SIRT6, CASP8, BCL2L1, CDKN1A, IL6, TNF, AKT1, FAS |
Lymphatic system cancer | 7 | 120 | 1.40 × 10−6 | TP53, CASP3, CASP8, BCL2L1, TNF, AKT1, FAS |
Tissue and other disease | ||||
Lung disease | 9 | 178 | 4.95 × 10−8 | CCL2, CYBA, TP53, ALB, TERT, IL6, TNF, AKT1, ELANE |
Skin disease | 12 | 518 | 1.78 × 10−7 | CCL2, APOA1, APOE, CYBA, IL1A, TP53, ALB, NOS3, TERT, CASP8, IGF1, TNF |
Brain disease | 13 | 806 | 1.40 × 10−6 | APOE, TP53, SOD1, APP, ALB, NOS3, TERT, CASP3, MMP9, IL6, TNF, PRNP, SNCA |
Toxic encephalopathy | 4 | 9 | 1.79 × 10−6 | APP, CASP3, IL6, SNCA |
Disease of anatomical entity | 34 | 4798 | 1.58 × 10−11 | GSR, SERPINE1, CCL2, APOB, APOA1, APOE, CYBA, IL1A, TP53, SOD1, IL18, APP, ALB, NOS3, TERT, CASP3, KLK3, CASP9, ITGAL, CASP8, FASLG, MMP9, BCL2L1, IGF1, CDKN1A, IL6, ADIPOQ, TNF, PRNP, AKT1, LDLR, ELANE, FAS, SNCA |
Connective tissue disease | 12 | 774 | 6.76 × 10−6 | CCL2, CYBA, IL1A, TP53, IL18, ALB, NOS3, TERT, MMP9, IL6, TNF, FAS |
Endocrine gland cancer | 6 | 93 | 7.48 × 10−6 | APOE, TP53, ALB, TERT, CASP3, AKT1 |
Prion disease | 4 | 16 | 9.37 × 10−6 | APOE, APP, PRNP, SNCA |
Categories | GSEA | ORA | Topology Network-GO Terms |
---|---|---|---|
Cell proliferation | Regulation of smooth muscle cells in the gastrointestinal, cardiovascular, renal, and respiratory systems) | Muscle cell proliferation | None |
Cell death/apoptosis, inflammation and immune systems | Regulation of neuronal apoptosis/death (neural death and leukocyte apoptosis); Regulation of neuroinflammatory responses; Response to lipopolysaccharide | Neuron apoptotic process, regulation of apoptotic signaling pathway, and leukocyte apoptotic process; regulation of inflammatory response, response to molecule of bacterial origin (overlap with stress response) | Cell death, programmed cell death, apoptotic process, neuron apoptotic process, positive regulation of programmed cell death, apoptotic signaling pathway, regulation of neuron apoptotic process |
Stress response | Response to ultraviolet (UV) radiation (DNA damage response) | Response to oxidative stress, response to molecule of bacterial origin, cellular response to chemical stress, response to radiation | Response to stress, response to external stimulus, cellular response to chemical stimulus |
Metabolism | Regulation of small-molecule metabolism (lipoprotein metabolism) | Regulation of small molecule metabolic process (lipoprotein metabolism) | None |
Method | Description | Advantages | Limitations |
---|---|---|---|
Over-Representation Analysis (ORA) | Assesses the proportion of genes within a pathway that are present among genes with differential expression. | Straightforward to interpret. Effective for pinpointing pathways that exhibit significant over-representations. | Highly sensitive to the user-specified cutoff for differentially expressed genes, which may overlook subtle changes. Also disregards the interactions among genes as well as quantitative data on gene expression. |
Gene Set Enrichment Analysis (GSEA) | Evaluates if specific gene sets are concentrated at the extremes of a ranked gene list. | More sensitive than ORA, particularly in identifying subtle but coordinated alterations in pathways. It does not depend on random cutoffs. | May miss weaker associations among the clusters as well as possible cross-interaction between pathways. |
Topology Network Analysis | Incorporates the framework of pathways into the evaluation, taking into account gene interactions. | Provides the most biological context and has the ability to reveal significant pathways that may overlook by taking gene-gene interactions into account. | Can be more complex than with ORA or GSEA and it relies on the accuracy and completeness of the reference network. |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Lacayo, P.; Martignoni, A.; Park, K.; Castro, C.; Murakami, S. Red-Wine Gene Networks Linked to Exceptional Longevity in Humans. Biomolecules 2025, 15, 1414. https://doi.org/10.3390/biom15101414
Lacayo P, Martignoni A, Park K, Castro C, Murakami S. Red-Wine Gene Networks Linked to Exceptional Longevity in Humans. Biomolecules. 2025; 15(10):1414. https://doi.org/10.3390/biom15101414
Chicago/Turabian StyleLacayo, Patricia, Alexandria Martignoni, Kenneth Park, Christianne Castro, and Shin Murakami. 2025. "Red-Wine Gene Networks Linked to Exceptional Longevity in Humans" Biomolecules 15, no. 10: 1414. https://doi.org/10.3390/biom15101414
APA StyleLacayo, P., Martignoni, A., Park, K., Castro, C., & Murakami, S. (2025). Red-Wine Gene Networks Linked to Exceptional Longevity in Humans. Biomolecules, 15(10), 1414. https://doi.org/10.3390/biom15101414