Additional file 2 of Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions
Creators: name: Kar, Asha
name: Alvarez, Marcus
name: Garske, Kristina M.
name: Huang, Huiling
name: Lee, Seung Hyuk T.
name: Deal, Milena
name: Das, Sankha Subhra
name: Koka, Amogha
name: Jamal, Zoeb
name: Mohlke, Karen L.
name: Laakso, Markku
name: Heinonen, Sini
name: Pietiläinen, Kirsi H.
name: Pajukanta, Päivi
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 2: Table S1. Summary of the estimated proportions of the 5 major subcutaneous adipose tissue cell-types in FTC (n=100). Table S2. Wilcoxon tests comparing proportions of the 5 major subcutaneous adipose tissue cell-types between the unrelated individuals below (n=28) and above 40 years old (n=22) in FTC. Table S3. Summary of the estimated proportions of the 20 major subcutaneous adipose tissue subcell-types/cell-types in METSIM (n=335). Table S4. Down sampling the numbers of individuals to match sample sizes between the younger and older groups preserves the observed SAT ASPC proportion differences in FTC and METSIM (see Table 1). Table S5. ASPC marker genes from SAT snRNA-seq data of 6 individuals in FTC (see the excel file Additional file 3: Table S5.xlsx). Table S6. Known motifs from HOMER enriched in promoter regions of the 151 unique SAT ASPC marker genes. Table S7. Motifs determined by de novo motif discovery in HOMER to be significantly enriched (p&lt;1x10-10) in the promoter regions of the 151 unique SAT ASPC marker genes. Table S8. Seventy-six SAT ASPC genes (i.e. 72 ASPC marker genes and four transcription factor genes regulating ASPC marker genes) are significantly differentially expressed (DE) by age (FDR&lt;0.05) using the ASPC data from the SAT snRNA-seq cohort (n=15) (see the excel file Additional file 3: Table S8.xlsx). Table S9. Our additional analysis in the independent METSIM cohort shows that 41% of the 76 age-DE SAT ASPC genes exhibit consistent DE (FDR&lt;0.1 and the same direction as in the discovery snRNA-seq cohort) in the ASPCs from the subcutaneous adipose snRNA-seq data of the youngest and oldest age quartiles of the METSIM cohort. Table S10. Our additional analysis in the independent METSIM cohort shows that 49% of the 76 age-DE SAT ASPC genes exhibit consistent DE (FDR&lt;0.1 and the same direction of effect as in the discovery cohort) in the ASPCs from the subcutaneous adipose snRNA-seq data of nonobese (BMI&lt;30) individuals from the METSIM cohort (n=38).Table S11. The pathway tool WebGestalt identifies significantly (FDR&lt;0.05) overrepresented developmental and differentiation-related Gene Ontology biological processes within the 76 age-DE SAT ASPC genes (see the excel file Additional file 3: Table S11.xlsx). Table S12. The pathway tool WebGestalt identifies significantly (FDR&lt;0.05) overrepresented Gene Ontology biological processes within the 79 non-age-DE SAT ASPC genes (see the excel file Additional file 3: Table S12.xlsx). Table S13. Significant correlations (FDR&lt;0.05) between the expression of the 76 age-DE SAT ASPC genes and cardiometabolic phenotypes using subcutaneous adipose bulk RNA-seq data from METSIM (n=335) (see the excel file Additional file 3: Table S13.xlsx). Table S14. Significant correlations (FDR&lt;0.05) between the expression of the 79 non-age-DE SAT ASPC marker genes and cardiometabolic phenotypes using subcutaneous adipose bulk RNA-seq data from METSIM (n=335) (see the excel file Additional file 3: Table S14.xlsx). Table S15. Nearly half (47.4%) of the 76 age-DE SAT ASPC genes show significant DE by age (FDR&lt;0.05 in the same direction as in the SAT snRNA-seq cohort) in VAT ASPCs from a publicly available VAT snRNA-seq data of 10 individuals (see the excel file Additional file 3: Table S15.xlsx). Table S16. The pathway tool WebGestalt identifies significantly (FDR&lt;0.05) overrepresented Gene Ontology (GO) biological processes within the 36 age-DE ASPC marker genes with consistent DE across ASPCs from the SAT and VAT fat depots (see the excel file Additional file 3: Table S16.xlsx). Table S17. Seventy-five of the 76 age-DE SAT ASPC genes are DE across the 6 measured timepoints during a 14-day SAT ASPC differentiation (i.e. adipogenesis) experiment (see the excel file Additional file 3: Table S17.xlsx). Table S18. Clustering of the age-DE SAT ASPC genes by their expression profiles during a 14-day SAT ASPC differentiation (i.e. adipogenesis) experiment (see the excel file Additional file 3: Table S18.xlsx). Table S19. Gene set enrichment analysis using EnrichR and the human subset of the ChIP Enrichment Analysis (CHEA) 2022 database identifies 5 subgroups significantly enriched for common transcription factor targets. Table S20. Intercellular interactions involving the 76 age-DE SAT ASPC genes are significantly stronger (p&lt;0.05) in the older individuals of the adipose snRNA-seq cohort compared to the younger individuals. Table S21. Interactions involving the gene RORAshow the largest differences in strength between the younger and older individuals (see the excel file Additional file 3: Table S21.xlsx). Table S22. Variance in body mass index (BMI) explained by the genome-wide and regional polygenic risk scores (PRSs). Table S23. Estimated effect of the interaction between the age-DE regional BMI PRS and age on BMI in the individuals with normal BMI (BMI&lt;25) and in the obese individuals (BMI&gt;=30) in the UK Biobank. Table S24. Local variants within the age-DE SAT ASPC genes landing in ASPC open chromatin regions interact with age on BMI in obese individuals of the UK Biobank (n=45,203).