FADS2 and ALDOC as potential adipose tissue biomarkers in obesity: responses to low-calorie diet-feeding

Obesity is a global epidemic and is the cause of a profound health crisis worldwide. A low-calorie diet (LCD) can alleviate obesity, but the precise mechanisms involved have yet to be thoroughly elucidated. Leveraging publicly available transcriptomic data from the adipose tissue of individuals with obesity collected before and after the LCD intervention, we conducted a differential expression analysis, employed machine-learning techniques to identify candidate genes, subsequently using SHapley Additive exPlanations (SHAP) values to interpret feature contributions and weighted gene coexpression network analysis (WGCNA) to identify hub genes. We then used the gene expression profile obtained to screen for potential small molecule drug candidates for obesity. Initially, we identified 296 differentially expressed genes (DEGs) within the GSE95640 dataset, an adipose tissue transcriptomic dataset obtained from individuals with obesity who had undergone an LCD intervention. We next identified eight genes with important properties using two machine-learning algorithms. The SHAP analysis revealed that FADS2 was the most important feature gene, with CCDC162P and ALDOC ranking as the second and third most significant contributors, respectively. Further WGCNA analysis identified FADS2and ALDOC as two mediators of the obesity-ameliorating effects of the LCD intervention. Moreover, cross-validation using a separate dataset (GSE24432), integrated DEG profiling, and machine-learning algorithms confirmed that ALDOC and FADS2 were prospective markers of obesity. L1000CDS2 analysis identified 50 small molecule candidates, 42 of which were unique, which replicated the effects of the LCD on gene expression in obese adipose tissue. Among these, T542500 (hycamptamine hydrochloride monohydrate) ranked top with respect to targeting both ALDOC and FADS2. Moreover, molecular dynamics (MD) analysis revealed that the T542500–ALDOC and T542500–FADS2 complexes exhibited transient stability, remaining bound for 375ns and 385ns, respectively, before they dissociated. ALDOC and FADS2 represent biomarkers in obese adipose tissue that are affected by LCD consumption, and T542500 represents a potential small molecule candidate for the treatment of obesity, because it targets both ALDOC and FADS2 and mimics the beneficial effects of an LCD. Owing to the relatively short duration of stable binding, T542500 requires further structural optimization to meet the pharmacodynamic requirements for a therapeutic substance. Nevertheless, given the inherent constraints of computational models, wet-lab experimental validation of the anticipated findings is necessary.

Authors

Institutions

Publication Details

Journal
Journal of Translational Medicine
Published
2025-12-24
DOI
https://doi.org/10.1186/s12967-025-07424-z
Primary Topic
Adipokines, Inflammation, and Metabolic Diseases
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

FADS2 and ALDOC as potential adipose tissue biomarkers in obesity: responses to low-calorie diet-feeding

Shaojun Chen, Lihua Zhang
Journal of Translational Medicine
Adipokines, Inflammation, and Metabolic Diseases
article

FADS2 and ALDOC as potential adipose tissue biomarkers in obesity: responses to low-calorie diet-feeding

Shaojun Chen, Lihua Zhang
article en

Abstract

Obesity is a global epidemic and is the cause of a profound health crisis worldwide. A low-calorie diet (LCD) can alleviate obesity, but the precise mechanisms involved have yet to be thoroughly elucidated. Leveraging publicly available transcriptomic data from the adipose tissue of individuals with obesity collected before and after the LCD intervention, we conducted a differential expression analysis, employed machine-learning techniques to identify candidate genes, subsequently using SHapley Additive exPlanations (SHAP) values to interpret feature contributions and weighted gene coexpression network analysis (WGCNA) to identify hub genes. We then used the gene expression profile obtained to screen for potential small molecule drug candidates for obesity. Initially, we identified 296 differentially expressed genes (DEGs) within the GSE95640 dataset, an adipose tissue transcriptomic dataset obtained from individuals with obesity who had undergone an LCD intervention. We next identified eight genes with important properties using two machine-learning algorithms. The SHAP analysis revealed that FADS2 was the most important feature gene, with CCDC162P and ALDOC ranking as the second and third most significant contributors, respectively. Further WGCNA analysis identified FADS2and ALDOC as two mediators of the obesity-ameliorating effects of the LCD intervention. Moreover, cross-validation using a separate dataset (GSE24432), integrated DEG profiling, and machine-learning algorithms confirmed that ALDOC and FADS2 were prospective markers of obesity. L1000CDS2 analysis identified 50 small molecule candidates, 42 of which were unique, which replicated the effects of the LCD on gene expression in obese adipose tissue. Among these, T542500 (hycamptamine hydrochloride monohydrate) ranked top with respect to targeting both ALDOC and FADS2. Moreover, molecular dynamics (MD) analysis revealed that the T542500–ALDOC and T542500–FADS2 complexes exhibited transient stability, remaining bound for 375ns and 385ns, respectively, before they dissociated. ALDOC and FADS2 represent biomarkers in obese adipose tissue that are affected by LCD consumption, and T542500 represents a potential small molecule candidate for the treatment of obesity, because it targets both ALDOC and FADS2 and mimics the beneficial effects of an LCD. Owing to the relatively short duration of stable binding, T542500 requires further structural optimization to meet the pharmacodynamic requirements for a therapeutic substance. Nevertheless, given the inherent constraints of computational models, wet-lab experimental validation of the anticipated findings is necessary.

Journal of Translational MedicineVol. 23(1)
Ningbo University (CN), Zhejiang Pharmaceutical College (CN)
Natural Science Foundation of Ningbo
Good health and well-being
Openalex Percentile: Top 30%
Adipokines, Inflammation, and Metabolic Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.