Multiorgan transcriptomics in mice identifies immunoglobulin heavy constant mu (Ighm) as a tissue-level aging biomarker

Abstract

Identifying aging-associated biomarkers applicable for multiple tissues is challenging but crucial for assessing tissue aging. Here, we obtained and analyzed 456 transcriptomes on 17 organs from 30 C57BL/6 J mice with different ages, revealing the consistently upregulated mRNAs of Ighm, C4b, and Ccl8 in most aged organs. This finding received support from independent transcriptomic and proteomic datasets and was further validated through western blot, enzyme-linked immunosorbent assay (ELISA), and immunofluorescence, arguing for both Ighm mRNA and protein as tissue-level aging biomarkers, at least in mice. Its sensitivity to antiaging interventions further emphasizes the significance of Ighm in assessing tissue aging in mice.

Keywords: Ighm; aging biomarker; tissue aging.

Fig. 1.

Identification of tissue-level aging biomarkers across 17 organs. (A) Study design and sample information (Created with BioRender.com). (B) Frailty index across 5 ages of the mouse lifespan. (C) Bar plot showing the number of age-related transcripts for each organ. (D) Expression trajectories of upregulated and downregulated transcripts across different organs. The solid red line represents the fitted mean value using the “loess” method in R. (E) Scatter plot displaying the FDR of each transcript, combining P-values from 17 organs using Fisher’s method. The top 100 genes with an FDR of <10⁻¹⁵ were colored by red. Transcripts with top 30 FDR values were labeled by their names. Transcripts are sorted by their chromosomal location. (F) Bar plot showing the number of organs with significant age-related upregulation or downregulation for the target transcripts. Fill color indicates different organs. (G) Heatmap of the top age-related transcripts. Numbers in the heatmap represent the coefficient of age for gene expression changes. *P < 0.05. (H) Volcano plot of age-related transcripts calculated using DESeq2, with sex and tissue as covariates. (I) Age-associated expression patterns of aging biomarkers across 17 organs. Top: The number of organs exhibiting significant upregulation (red) or downregulation (blue) for the aging biomarkers. Bottom: Box plot illustrates the distribution of age-related P-values for aging biomarkers across 17 organs.

Fig. 2.

Validation of tissue-level aging biomarkers. (A) Distribution of age-related P-values for aging biomarkers across 17 organs, based on transcriptome data of 27 tissues from 28 GEO RNA-seq datasets. (B) Changes in protein abundance of target genes with aging. Red dots represent significant upregulation (P < 0.05) of the corresponding protein with aging in specific tissues and published data, while gray dots represent nonsignificant changes. (C) Age-related changes in IGHM and CDKN2A protein abundance across six organs, quantified by western blot. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as a loading control. Within each organ and for each sex, relative protein abundance was calculated by dividing the IGHM/GAPDH or CDKN2A/GAPDH ratio by the mean ratio of the 1-mo group. n = 8 per age group (4 males and 4 females). (D), Quantitative analysis of IGHM immunofluorescence staining in the mouse spleen, liver, small intestine (SI), and heart from five different age groups (n = 4 to 5 mice per age group). Mean ± SEM. (E) Age-related changes in IgM protein concentration in serum, quantified by ELISA. n = 5 in each age group with 2 to 3 males/females. (F) Correlation of serum IgM concentration and FI. The solid line indicates the linear regression fit. (G) Expression changes of the Ighm mRNA in response to five antiaging interventions, assessed by RNA-seq, including calorie restriction (CR), methionine restriction (MR), acarbose supplementation (ACA), growth hormone receptor knockout (GHR), and Snell dwarf (SNELL) mice. *P < 0.05, **P < 0.01, ***P <0.001, and ****P < 0.0001.