Additional file 1 of A genomic perspective of the aging human and mouse lung with a focus on immune response and cellular senescence
Creators: name: He, Meng
name: Borlak, Jürgen
Publisher: name: figshare
Year: 2023
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Supplementary Figure S1. The age distribution of 89 individual mouse data sets retrieved from 15 studies. Supplementary Figure S2. Computation a linear regression model considering all the data sets (supplementary Table S1) but separated them by sex. We only considered DEGs that fulfilled the criteria: FDR adjusted p-value of 0.05 and a coefficient of determination R2&gt;0.5. Supplementary Figure S3. Linear Regression Model for mouse data (89 samples). Supplementary Figure S4. Overlapped genes between the linear regression model with significantly regulated genes as defined by the DESeq2 method in human test set. We retrieved RNA-Seq data of 107 histologically proven normal lung tissue samples from the TCGA repository, i.e. resection material from lung cancer patients (supplementary Table S2).The cohort consisted of individuals aged 42-86 years, and based on normalized counts the linear regression model fitted 237 significantly regulated genes (27 up- and 210 down-). We also analyzed the RNAseq data with the DESeq2 package and compared individuals aged 42-50 (N=5) to 71-86 (N=40) old ones. This defined 1430 genes (716 up-, 714 down-) with a FC≥1,5 and an FDR adjusted p-value &lt;0.05. Note, 56% of genes coming from the linear regression model overlap with significantly regulated genes as defined by the DESeq2 method. Supplementary Figure S5. Consensus among different gene ontology tools. Supplementary Figure S6. Continuous gene expression changes of the aging mouse lung by computing a linear regression model with 89 mouse samples. Supplementary Figure S7. Validation of marker genes by single cell RNA sequencing of pulmonary cells. We retrieved marker genes from various databases and considered their age-dependent regulation in pulmonary cells by single cell RNA sequencing. We computed enrichment scores based on a large set of genes (range 44-434 genes for the mouse, supplementary Table S16). By interrogating single cell RNAseq data of 7 different pulmonary cells, we confirmed the results in Figure 10B&amp;C by an independent method. We computed statistical significance with the “Wilcoxon rank-sum” test. ns: not significant, *p&lt;0.05, **p&lt;0.01, ***p&lt;0.001. Supplementary Figure S8. Comparative human pulmonary genomics of smokers and non-smokers. In order to examine the influence of tobacco product use on age-related gene expression changes, we compared the genomic data of morphologically unaltered lung tissue of 45 smokers to 7 non-smokers. We show that tobacco product use per se did not influence the expression of age-regulated genes reported in the present study; however, tobacco smoke exposure caused marked induction of xenobiotic defense genes. ns: not significant, Wilcoxon rank sum test, *p&lt;0.05. Supplementary Figure S9. Computation of DEGs for the mouse lung by two methods, i.e. Linear Models for Microarray data (LIMMA) and the hypergeometric test.