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. 2024 Oct 16;47(6):2183–2199. doi: 10.1007/s13402-024-00996-w

Integrating bulk and single-cell transcriptomics to elucidate the role and potential mechanisms of autophagy in aging tissue

Zhenhua Zhu 1, Linsen Li 1, Youqiong Ye 2,3,✉, Qing Zhong 1,✉
PMCID: PMC12973991  PMID: 39414741

Abstract

Purpose

Autophagy is frequently observed in tissues during the aging process, yet the tissues most strongly correlated with autophagy during aging and the underlying regulatory mechanisms remain inadequately understood. The purpose of this study is to identify the tissues with the highest correlation between autophagy and aging, and to explore the functions and mechanisms of autophagy in the aging tissue microenvironment.

Methods

Integrated bulk RNA-seq from over 7000 normal tissue samples, single-cell sequencing data from blood samples of different ages, more than 2000 acute myeloid leukemia (AML) bulk RNA-seq, and multiple sets of AML single-cell data. The datasets were analysed using various bioinformatic approaches.

Results

Blood tissue exhibited the highest positive correlation between autophagy and aging among healthy tissues. Single-cell resolution analysis revealed that in aged blood, classical monocytes (C. monocytes) are most closely associated with elevated autophagy levels. Increased autophagy in these monocytes correlated with a higher proportion of C. monocytes, with hypoxia identified as a crucial contributing factor. In AML, a representative myeloid blood disease, enhanced autophagy was accompanied by an increased proportionof C. monocytes. High autophagy levels in monocytes are associated with pro-inflammatory gene upregulation and Reactive Oxygen Species (ROS) accumulation, contributing to tissue aging.

Conclusion

This study revealed that autophagy is most strongly correlated with aging in blood tissue. Enhanced autophagy levels in C. monocytes demonstrate a positive correlation with increased secretion of pro-inflammatory factors and elevated production of ROS, which may contribute to a more rapid aging process. This discovery underscores the critical role of autophagy in blood aging and suggests potential therapeutic targets to mitigate aging-related health issues.

Supplementary information

The online version contains supplementary material available at 10.1007/s13402-024-00996-w.

Keywords: Aging, Autophagy, Monocytes, Reactive oxygen species, Hypoxia

Introduction

Aging represents the gradual decline of organismal functions over time, ultimately culminating in death [1]. Concurrently, aging serves as a primary contributor to the high incidence of chronic diseases, which intensify during the elderly stages [2]. Cellular senescence, marked by an irreversible cessation of cell division following various stressors [3], plays a critical role in the aging process. Researchers have acknowledged the potential of anti-aging drugs to delay or mitigate numerous age-related diseases [4, 5]. Consequently, current research focuses on enhancing the quality of life for the elderly by decelerating the aging process, thereby reducing the occurrence and progression of chronic diseases [6]. It is noteworthy that while anti-aging drugs show promise, the complete elimination of all senescent cells remains a challenge [7]. Studies suggest that even the removal of 30% of senescent cells can substantially improve age-related conditions [8]. Thus, interventions aimed at reducing senescent cells may alleviate age-related diseases and long-term health issues [9]. A comprehensive understanding of the molecular mechanisms underlying aging is crucial for developing new therapies for age-related diseases and potentially extending human lifespan.

Aging is often accompanied by autophagy, a process responsible for degrading and recycling cellular components, ubiquitous among eukaryotic organisms. In mammalian cells, autophagy exists in three primary forms: microautophagy, macroautophagy, and chaperone-mediated autophagy (CMA) [10, 11]. As a self-protective mechanism, autophagy plays an important role in maintaining cellular balance and components, becoming active in response to various physiological stresses [12]. Its main functions include the elimination or renewal of long-lived or misfolded proteins, protein clusters, and damaged cellular components. Substantial evidence indicates that autophagy is closely linked to the occurrence of age-related diseases [13]. The critical role of autophagy in maintaining homeostasis and preventing disease is vital for the longevity and health of organisms. Although recent studies suggest a genetic connection between autophagy and aging, the positive or negative impact of autophagy on aging remains controversial [14]. Some research indicates increased autophagy levels during aging [15–18]. For instance, major autophagy pathways, macroautophagy (MA), and chaperone-mediated autophagy (CMA) are upregulated in senescent cells [15]. Conversely, other studies show that autophagy levels decline with age [18–20], including the anti-aging effects of myokine-induced autophagy [20]. The role of autophagy in various systemic or organ-specific diseases, such as metabolic dysfunction, cancer, and neurodegenerative diseases, remains unclear [21]. Therefore, it is imperative to clarify the role of autophagy in aging and age-related diseases.

Hypoxia-induced cellular and developmental responses are primarily mediated by HIF-1A, which plays a crucial role in promoting hypoxia-induced autophagy in cancer cells [22, 23]. A key target gene of HIF-1A is BCL-2/adenovirus E1B 19kDa interacting protein 3 (BNIP3), which is involved in autophagy coordination [24]. Additionally, HIF-1A can coordinate autophagy independently of BNIP3 and BNIP3L. In triple-negative breast cancer cells, HIF-1A increases the expression of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), promoting cell invasion and proliferation by activating autophagy [25]. Therefore, hypoxia plays an important role in inducing autophagy. Given the significance of hypoxia in autophagy, autophagy plays a crucial role in aging tissues; however, the coordination among hypoxia, autophagy, and aging during the organismal aging process remains unexplored.

Our study combines general and single-cell transcriptomics to reveal a significant positive correlation between autophagy and blood aging. Through functional enrichment, we discovered that the hypoxia pathway is significantly implicated in the elevation of autophagy levels in aging blood. Furthermore, we identified that C. monocytes in aging blood are the most significantly impacted subtype affecting autophagy in aging tissues. Hypoxia primarily enhances overall autophagy levels in aging blood tissues by activating autophagy in C. monocytes. Additionally, increased autophagy levels in C. monocytes is associated with higher levels of ROS, pro-inflammatory factors, and chemokine expression, which may be mechanisms through which autophagy promotes aging in age-related diseases.

Results

Identification of aging gene sets and correlation analysis with autophagy in different tissues

To elucidate our research framework, we devised a comprehensive workflow. Initially, we collected and integrated over 7000 healthy samples from 33 different tissues in the GTEx database [26].

For identifying the aging-promoting gene set, we utilized the Aging database (https://ngdc.cncb.ac.cn/aging/index) comprising 503 aging-related genes, supplemented with literature to further identify and validate genes that promote aging by filtering out genes with unknown functions. To define the autophagy gene set, we adopted a previously reported method [27]. Briefly, common genes were obtained by intersecting multiple autophagy gene sets, and the autophagy status was validated using multiple independent autophagy-inducing cohorts. Subsequently, we analyzed the activity states of autophagy and aging gene sets in over 7000 samples to calculate corresponding autophagy and aging scores, assessing the correlation between autophagy and aging activities across different tissues to identify the tissue with the highest correlation. Finally, we divided autophagy scores in aging samples into high and low groups based on median values, identifying common modular pathways of autophagy regulating aging tissues (Fig. 1A).

Fig. 1.

Fig. 1

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Identification of aging gene sets and correlation analysis with autophagy in different tissues. A Flowchart for identification of autophagy and aging genes and correlation between autophagy and aging. B Aging scores of 4 datasets based on 46-gene set signature in the aging score-high group (red) and aging score-low group (blue). Two sided student’s t test was used to assess the difference; p-value < 0.05. C Barplot of the correlation between autophagy and aging scores in different tissues (left panel) and tissue counts (right panel) (|Rs| > 0.4, FDR < 0.05). D Analysis of the correlation coefficients (Rs) between autophagy and aging score in whole blood

The screening criteria for aging-related genes initially involved excluding genes without a clear function, i.e., genes that do not explicitly promote or inhibit aging. Further screening selected genes explicitly reported to promote aging, ultimately retaining 44 genes (see Methods Sect. 5.4). To further validate the effectiveness of the aging gene set composed of these 44 genes, we identified cohorts with clearly defined aging states from 4 public databases (GSE126750, GSE155903, GSE206677, GSE210020). Utilizing the GSVA algorithm, we calculated aging scores for different samples and found that samples with high levels of aging had significantly higher aging scores than those with low levels of aging (Fig. 1B). In summary, we identified 503 genes associated with aging from the Aging database. Through a review of the literature, we identified a subset of 44 genes that promote aging. Subsequently, we validated the aging status to confirm the robustness of our selected gene set.

Combining the autophagy genes identified in our previous studies [27], we conducted a comprehensive analysis of 33 tissues to determine the association between autophagy and aging (calculation of aging and autophagy score; see Methods Sect. 5.4). To identify tissues with strong correlations between autophagy and aging, we analyzed autophagy and aging scores across different tissues, categorizing correlations above 0.8 as extremely strong and those between 0.6 and 0.8 as strong. The analysis showed that blood tissue had the highest correlation, suggesting a significant link between autophagy and aging in whole blood (Rs = 0.8, p < 0.0001). Furthermore, significant correlations were observed in various tissues, such as the stomach (Rs = 0.78, p < 0.0001), muscle skeletal (Rs = 0.75, p < 0.0001), colon (Rs = 0.7, p < 0.0001), and liver (Rs = 0.69, p < 0.0001) (Fig. 1C). To mitigate correlation bias arising from sample size variations, a correlation analysis was performed between the correlation coefficient of autophagy and aging in different tissues and the sample size. Results indicated no significant correlation between sample size and the correlation coefficient (Rs = 0.033; p = 0.86; Fig. S1).

Given the highest correlation between autophagy and aging in blood tissue, we then focused on investigating the genetic connection between autophagy and aging in blood cells. The aging and autophagy scores exhibited a robust correlation (Fig. 1D). In summary, these findings underscore a significant correlation between aging and autophagy across multiple tissues, and a high positive correlation in blood tissue.

C. monocytes are most affected by autophagy level in aging blood

Given the robust correlation between autophagy and aging observed in blood, and the regulation of myeloid cells by autophagy across various tissues, we sought to further elucidate the association between autophagy and cell subpopulations by analyzing single-cell blood samples from individuals of different ages. We utilized publicly available single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) across a spectrum of age groups. The cohorts consisted of young individuals (Young group, average age 30.7 years) and healthy older individuals (Old group, average age 85.8 years). These include 3 Young and 6 Old PBMCs samples (Fig. S2A).

Initial analyses revealed a significant increase in the autophagy score in the Old group compared to the Young group (Fig. 2A). The integration and classification of various cell subtypes resulted in 15 distinct categories, including memory CD4+ and CD8+ T cells, Naïve T cells, B cells, antigen-presenting B (APC B) cells, natural killer (NK) cells, and myeloid cells classified into classical (C. monocytes), intermediate (I. monocytes), and non-classical monocytes (NC. monocytes). Additionally, myeloid dendritic cells (mDCs), plasma cell-like DCs (pDC), platelets, and granulocytes were identified (Fig. 2B). Specific characteristic genes for each subpopulation were determined using cell surface protein assays, revealing high expression of CD79A, CD74, and MS4A1 in B cells and APC B cells, and elevated levels of NKG7, GNLY, KLRB1, and GZMB in NK cells (Fig. 2C).

Fig. 2.

Fig. 2

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Changes of autophagy levels in blood cell subtypes of different age groups were analyzed by single cell analysis. A The boxplot of autophagy scores in different age groups. B UMAP plot clustering analysis classifying PBMCs into 15 clusters. Distinct cell types are depicted with different colors. C Bubble plots showing marker genes for 15 distinct cell types. D Proportion of 15 major cell types in bar plots by different age groups. E Feature plot of autophagy scores in different age groups. F Bar plot of the difference in autophagy scores between the Old group and the Young group across various subtypes. G Boxplot for different age groups based on the VIPER algorithm to quantify single-cell autophagy protein activity. H C. monocytes GO enrichment analysis in the Old group and the Young group, with each group displaying the first five pathways with a p-value < 0.05

Comparative analysis between the Old and Young groups demonstrated a decline in Naïve, memory CD4+, and CD8+ T cells with age, while C. monocytes and NK cells increased (Fig. 2D). Notably, a significant difference in autophagy scores between the Old and Young groups was observed, particularly in C. monocytes (Fig. 2E). Quantification of this difference, achieved by subtracting the autophagy score of the Old group from that of the Young group, confirmed that C. monocytes are the most influential subtypes affecting aging and autophagy (Fig. 2F). These results align with our previous findings, suggesting that increased autophagy activates pathways related to leukocytes and myeloid cells in various tissues.

To complement transcriptional level analyses, Viper analysis on single cells was employed to predict protein levels based on our single-cell data (see Methods Sect. 5.8). Remarkably, autophagy scores in the Old groups remained elevated compared to the Young group at protein level (Fig. 2G). Further functional verification of C. monocytes in both aging and young groups revealed that the top five pathways in the Old group predominantly involve macroautophagy and inflammatory response pathways (Fig. 2H). Further validation of significant macroautophagy pathway activation was confirmed through GSEA enrichment analysis (Fig. S2B and C).

To further validate our findings, single-cell blood samples from individuals of varying ages were collected. Integration of three single-cell datasets (GSE116256, GSE154109, SC2018) and subsequent clustering identified 9 cell subtypes based on cell surface proteins (Fig. S3A and B). Individuals over 60 years were categorized into the Old group, while others were classified as the Young group. The analysis revealed higher autophagy levels in the Old group compared to the Young group (Fig. S3C), with C. monocytes exhibiting the highest autophagy levels (Fig. S3D). Furthermore, the autophagy level of C. monocytes in the aging group was significantly elevated compared to the Young group (Fig. S3E), and the proportion of C. monocytes in the Old group also increased (Fig. S3F). Furthermore, we calculated the differences in autophagy among various cell types in different age groups. and the difference in autophagy levels between the Old and Young groups is the most prominent (Fig. S3G).These findings collectively indicate that C. monocytes are the cell subtype most distinct in autophagy levels between aging and young blood.

Hypoxia activates autophagy levels of C. monocytes under aging conditions

To investigate the pathways influencing aging and autophagy in C. monocytes, we performed a differential analysis on groups with high and low autophagy scores in C. monocytes, as well as on Old and Young groups (|log2FC| > 0.25, p < 0.05). Venn diagram analysis revealed 377 genes common to both sets of differential analysis results (Fig. 3A). To further elucidate the expression trends of these genes, we generated a volcano plot, which revealed 202 upregulated and 175 downregulated genes between the high-autophagy and low-autophagy groups (Fig. S4A and B). A similar analysis in the Old and Young cohorts showed 200 upregulated and 177 downregulated genes in the Old group (Fig. S4A and B). Notably, 99% of the commonly upregulated genes and 98.9% of the downregulated genes were consistent across these groups (Fig. S4C and D). After excluding a few genes, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis highlighted significant enrichment in the HIF1 signaling pathway. Other significantly impacted pathways included those related to lysosomes, ribosomes, and immune-related functions, such as antigen presentation and differentiation of Th17, Th1, and Th2 cells (Fig. 3B). Further examination of HIF1 signaling pathway genes in high vs. low autophagy levels and aging vs. young C. monocytes revealed significant upregulation of HMOX1 and TIMP1 in aging C. monocytes, while HMOX1 and IL6 were significantly upregulated in C. monocytes with high autophagy scores (Fig. S4E and F). These findings suggest that hypoxia influences autophagy and aging in C. monocytes.

Fig. 3.

Fig. 3

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Hypoxia increases autophagy level with the aging of C. monocytes. A Venn diagram illustrating the comparison between differentially expressed genes associated with C. monocytes aging compared to young monocytes, and the comparison between the autophagy score-high group and the autophagy score-low group (|log2FC| > 0.25, p-value < 0.05). B KEGG enrichment analysis was conducted for the intersecting genes in (a), and the top 20 pathways with a p-value < 0.05 were selected. C Violin plot of HIF1A expression levels in the hypoxia score-high and score-low group. D, E HIF1A expression in different subtypes and age groups. F Boxplot of hypoxia scores in different age groups. G Boxplot for different age groups based on the VIPER algorithm to quantify single-cell hypoxia protein activity. H HIF1A expression in autophagy score high and low groups. I Autophagy levels in different subtypes stratified by high and low hypoxia scores. J Bar plot of the difference in autophagy scores between the high hypoxia group and the low hypoxia group across various subtypes. K GSEA of macrophagy, aging, protein autophosphorylation, and the MAPK cascade between hypoxia high and low groups. Genes were ranked by fold change in the expression between these two conditions. NES, normalized enrichment score

Despite demonstrating the influence of hypoxia on monocyte autophagy and aging, the connection between aging and hypoxia remains unclear. We hypothesize that the ability to perceive oxygen diminishes with aging. Previous studies have identified HIF1A as a crucial gene for sensing oxygen [28, 29]. To verify this hypothesis, we selected HIF1A as an indicator of oxygen perception. Utilizing a set of 15 hypoxia-promoting genes identified in our previous research, we calculated the hypoxia score (see Methods Sect. 5.5). Hypoxia levels were classified into high and low groups, and results revealed elevated HIF1A expression in the high hypoxia group (Fig. 3C), we analyzed 15 hypoxia-promoting genes. The results indicate that, in the high-hypoxia group, these hypoxia-regulated genes exhibit consistently elevated expression levels (Fig. S5A), confirming our hypothesis and establishing HIF1A as a reliable indicator of oxygen perception in aging. Additionally, HIF1A expression was highest among different subtypes (Fig. 3D), and both transcription and protein levels of HIF1A, along with the transcription levels of 15 hypoxia-regulated transcription factors, increased with aging (Fig. 3E and G, Fig. S5B)). Hypoxia scores were also higher in the Old group (Fig. 3F), and HIF1A expression levels were higher in the high autophagy score group (Fig. 3I). These results suggest that the ability of cells to perceive oxygen diminishes with aging, and that C. monocytes have the strongest oxygen perception capability.

Although it has been shown that hypoxia can activate autophagy, it is unclear which specific cell subtype exhibits the highest level of autophagy activation under hypoxic conditions. To identify these cell subtypes, samples within different subtypes were stratified into hypoxia-high and hypoxia-low groups based on median hypoxia scores for each subtype. Our findings revealed markedly elevated autophagy scores in the high hypoxia group across multiple subtypes (Fig. 3I). To identify the most influential subtypes wherein hypoxia strongly impacts autophagy levels, we calculated the difference in autophagy scores between the high and low hypoxia groups. The most significant difference was observed in C. monocytes (Fig. 3J). Hypoxia-induced pathways specifically affecting C. monocytes were identified through Gene Set Enrichment Analysis (GSEA) conducted by comparing the high and low hypoxia score groups within the C. monocyte subtypes. The results demonstrated significant changes in various pathways, including the activation of autophagy-related pathways such as macroautophagy (NES = 1.32, p = 0.0098), aging-related pathways (NES = 1.78, p = 4.9 × 10−6) (Fig. 3K), and the HIF1 signaling pathway (NES = 1.62, p = 5.9 × 10−4) (Fig. S4G). These findings indicate that hypoxia significantly enhances autophagy levels in C. monocytes.

To further elucidate the impact of heightened hypoxia levels on C. monocytes, a differential analysis was conducted between the hypoxia-high and hypoxia-low groups of C. monocytes. GO functional enrichment analysis revealed the activation of pathways related to immune responses (Fig. S4H and I). In the C. monocytes hypoxia-high group, elevated expression of genes associated with immune responses, including TNFB1, TNFSF13B, HLA-DRB5, IL6R, CXCL8, and HLA-DMA, was observed (Fig. S4J). In summary, our results demonstrate that hypoxia significantly activates the autophagy level of C. monocytes. We hypothesize that as aging progresses, C. monocytes will experience hypoxia, and the occurrence of hypoxia will involve the activation of inflammatory factors, while autophagy is significantly activated to mitigate the hypoxic state.

Autophagy in C. monocytes exhibits a correlation with the release of inflammatory factors and the accumulation of reactive oxygen species

Emerging evidence suggests that AML is a disease intricately associated with aging and stemming from the myeloid lineage [30–33]. To investigate the impact of autophagy on AML subtypes, we analyzed single-cell data from GEO databases (GSE154109 and GSE116256). In the GSE116256 dataset, we found that AML malignant cells exhibited higher levels of autophagy and hypoxia compared to Healthy donor and non-malignant cells within AML (AML normal) (Fig. 4A and B). Furthermore, in the GSE154109 dataset, we observed that autophagy and hypoxia levels were higher in the AML group compared to Healthy donor (Fig. 4C; Fig. S6E and F). The two datasets were independently clustered, uncovering 14 and 12 distinct clusters defined by the expression of marker genes associated with cell surface proteins. These clusters include CD4 T cells, CD8 T cells, Proliferating T cells, NK cells, B cells, Plasma cells, classical monocytes (C. monocytes), non-classical monocytes (NC. monocytes), classical dendritic cells (cDC), plasmacytoid dendritic cells (pDC), Granulocyte-Monocyte Progenitor (GMP), Progenitor (Prog), hematopoietic stem cells (HSC), and erythroid progenitors (Erypro) (Fig. 4C; Fig. S6A–D).

Fig. 4.

Fig. 4

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Autophagy in C. monocytes links to inflammation, ROS, and aging acceleration. A, B Differences in hypoxia and autophagy scores between Healthy donors, AML normal, and AML malignant cells in the GSE116256 dataset. C UMAP plot clustering analysis classified GSE116256 into different clusters. Distinct cell types are depicted with different colors. D Proportion of 14 major cell types among Healthy donors, AML normal, and AML malignant cells. E Violin plot of autophagy scores at different cell types. F Feature plot of autophagy scores in Healthy donor, AML normal and AML malignant. G Violin plot of autophagy scores in Healthy donor, AML normal and AML malignant C. monocytes. H Proportion of 14 major cell types showed in bar plots between Old and Young AML malignant. I Venn diagram illustrating the comparison between differentially expressed genes associated with C. monocytes aging compared to young monocytes, and the comparison between the autophagy score-high group and the autophagy score-low group (|log2FC| > 0.25, p-value < 0.05). J GO enrichment analysis was conducted for the intersecting genes in (i), and the top 20 pathways with a p-value < 0.05 were selected. K Violin plot of ROS scores in aging autophagy score-high and score-low AMLs. L Bar plot showing the fold change between the Old and Young in AML C. monocytes. M Bar plot showing the fold change between autophagy score-high and score-low groups in AML C. monocytes

In AML malignant, there was a significant increase in the proportion of C. monocytes (Fig. 4D; Fig. S6G), accompanied by higher levels of autophagy within these C. monocytes (Fig. 4E; Fig. S6H). Additionally, autophagy levels in AML C. monocytes were higher than AML normal and Healthy donor in C. monocytes (Fig. 4F and G; Fig. S6I).

Given that AML is an age-related disease, we subsequently stratified AML malignant cells into Old and Young groups. The Old group exhibited a significant increase in the proportion of C. monocytes (Fig. 4H), emphasizing their critical role in aging AML. To explore the role of autophagy levels in aging AML C. monocytes, we categorized the autophagy levels in AML C. monocytes into high and low groups to identify differentially expressed genes (|log2FC| > 0.25, p < 0.05). Analyzing the intersection of differentially expressed genes between the Old and Young groups revealed 88 common genes (Fig. 4I). Functional enrichment analysis of these genes uncovered significant activation of responses to reactive oxygen species (ROS), inflammatory response, and interferon-gamma (Fig. 4J). Elevated ROS levels in aging C. monocytes with high autophagy further supported this finding (Fig. 4K) [34, 35].

Building on our prior results demonstrating the influence of hypoxia on monocyte aging and autophagy levels, we established that high hypoxia in C. monocytes can promote autophagy (Fig. S6J) and activate the inflammatory response and ROS (Fig. S6K and L). Additionally, we discovered that autophagy could activate pro-inflammatory and chemokine genes in aging monocytes (Fig. 4L and M). To further investigate whether autophagy could also promote the activation of pro-inflammatory factors and release ROS into the blood during normal aging, our results confirmed significant activation of pro-inflammatory and ROS-related pathways (Fig. S7A). The enriched inflammatory genes in these pathways were also more highly expressed in the high autophagy group, which could then activate ROS activity (Fig. S7B and C). Ultimately, our findings suggest that the upregulation of pro-inflammatory and chemotactic genes in AML monocytes, along with the accumulation of ROS levels, collectively accelerates the aging process.

Association between autophagy and hallmarks of cancer and immune features across multiple AML datasets

While the effects of autophagy on aging mononuclear cells have been observed at the single-cell level, further validation with a larger sample size is required. Accordingly, we analyzed samples from over 2000 instances of AML and normal blood, revealing that the autophagy score in AML was consistently higher than that in normal blood across four cohorts (Fig. 5A–D). Additionally, we examined the correlation between autophagy scores and the enrichment scores of 50 cancer marker gene signatures, consistently finding positive associations between autophagy scores and immune inflammation pathways, including the interferon-gamma response pathway, the interferon alpha response, the inflammatory response, IL6-JAK-STAT signaling, and IL2-STAT5 signaling, across four independent AML cohorts (Fig. 5E).

Fig. 5.

Fig. 5

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Association between autophagy and hallmarks of cancer and immune features across 7 independent AML datasets. A–D Boxplot of the autophagy score differences between AML and healthy donors in 4 independent datasets, including GSE2191, GSE97485, GSE92778, and TCGA. E Heatmaps showing the Spearman’s rank correlation between autophagy scores and the GSVA enrichment scores of 50 cancer hallmarks pathways. F Spearman’s correlation of autophagy scores and the abundance of 22 immune cell types from transcriptome data based on the CIBERSORT algorithm. Black circle indicates p < 0.05. G Boxplot of the autophagy scores difference between Old and Young AML in GSE6891. H Scatter plots showing the correlation between the autophagy scores and ROS scores in GSE6891. I Scatter plots showing the correlation between the autophagy scores and ROS scores in GSE6891

To elucidate the relationship between autophagy and the abundance of immune cells in AML, we employed the CIBERSORT deconvolution algorithm. Correlation analysis highlighted the strongest correlation between monocyte abundance and autophagy scores in four AML cohorts (Fig. 5F). These findings demonstrate that autophagy is most strongly correlated with mononuclear cells in over 2000 cases of AML patients.

Further validation of the association between age and autophagy in AML patients was conducted using the largest cohort from GSE6891, which was divided into Old and Young groups based on age. Autophagy levels were higher in the Old group of AML patients. Genes promoting inflammation were identified from the immunology database, while ROS-related genes were sourced from the KEGG database. Our findings reveal a significant positive correlation between autophagy scores and both ROS activity and pro-inflammatory pathway scores in aging AML patients (Fig. 5G–I). These results suggest that high levels of autophagy may be associated with the immune response in AML patients, primarily acting through monocytes, and that autophagy negatively impacts the release of pro-inflammatory factors and accumulation of ROS levels in Old AML patients.

Discussion

The aging tissue microenvironment encompasses diverse cell types, including T cells, B cells, monocytes, macrophages, and other immune cells. Autophagy plays a pivotal role in maintaining homeostasis within this aging microenvironment [36, 37]. However, the precise relationship between aging and autophagy across different tissues remains unclear. This study integrates extensive RNA-seq and single-cell data to elucidate the correlation between autophagy and tissue aging and to uncover the autophagic mechanisms at play within distinct cell subsets in the aging microenvironment.

Numerous studies have implicated autophagy in aging-related diseases. In liver cancer, for instance, tumor cells enhance their survival in hypoxic and nutrient-deprived environments through autophagy, thereby promoting disease progression. Autophagy also facilitates tumor cell invasion by activating epithelial-mesenchymal transition [38]. Beyond cancer, inflammatory bowel disease (IBD) induces autophagy in intestinal macrophages through adrenal corticosteroid-releasing hormones, driving disease progression [39]. Our analysis reveals that autophagy significantly impacts monocytes in the myeloid lineage across multiple aging tissues, with recent studies indicating an age-associated increase in monocytes [40]. Importantly, the upregulation of autophagy with aging suggests a potentially detrimental role in the aging process, particularly through its effects on classical monocytes (C. monocytes).

In this context, our study also identified a significant association between hypoxia, autophagy, and aging in AML samples. The existing literature robustly supports hypoxia as an upstream regulator of autophagy. For instance, Li et al. demonstrated that hypoxia induces autophagy through ULK1 methylation [41], while He et al. found that HIF1α enhances autophagy to alleviate cellular stress [42]. Gao et al. further reported that hypoxia and HIF1α modulate cellular senescence via key senescence markers, and Erin J. Ciampa et al. underscored the role of HIF1 in promoting both placental and premature aging [43]. These studies collectively suggest that hypoxia may influence the aging process by modulating autophagy. However, it is important to acknowledge that our analysis identifies an association rather than establishing causality. Future research should endeavor to clarify the causal relationships between hypoxia, autophagy, and aging.

Monocytes, classified into classical, non-classical, and intermediate types based on surface proteins, constitute 85 to 90% of monocytes and exhibit phagocytic and antimicrobial activities. Under normal physiological conditions, these monocytes maintain these functions. However, in inflammatory and disease states, monocytes undergo alterations, leading to increased inflammatory factors and decreased cellular functions, ultimately contributing to aging-related diseases [44, 45]. In monocytes, peroxidase activity generates ROS, damaging intracellular macromolecules. Over time, the accumulation of ROS exacerbates DNA damage linked to aging, accelerating cellular aging processes [46]. Recent studies have shown that IL-17, secreted by immune cells, accelerates skin aging, indicating that targeting IL-17 might help delay this process [47]. Additionally, CD8+ T cells produce IFN-γ, which stimulates microglial cells in the brain, furthering brain aging [48]. Our research also demonstrates that aging monocytes secrete more pro-inflammatory factors. Moreover, increased autophagy levels in aging C. monocytes enhance ROS secretion and upregulate pro-inflammatory genes, suggesting that autophagy may influence aging-related diseases. The mechanisms by which autophagy promotes ROS and pro-inflammatory factors remain unclear. It has been reported that metabolic stress-induced autophagy helps maintain ROS homeostasis by eliminating damaged mitochondria, which can attract OPTN and NEMO, subsequently stimulating both autophagy and NF-κB-mediated inflammation [49]. Thus, autophagy’s role in promoting ROS likely involves the repair and removal of damaged mitochondria.

There has been significant interest in aging research to determine aging statuses. However, the vast literature on aging and autophagy genes is often obscure or contradictory. A prime example is the mTOR gene, where evidence on its function in aging is contradictory. Recognizing these challenges, we aimed to analyze core gene sets pivotal to autophagy and aging, employing a methodological approach consistent with recent precedents in the field [50, 51]. Based on this, we conducted additional analyses to identify 44 core aging genes that truly reflect the aging state. Subsequent research focused on a detailed analysis of the most significant differences observed in blood tissues. Functional enrichment analysis identified the hypoxia pathway as crucial in elevating autophagy levels in aging blood. We discovered that classical monocytes (C. monocytes) in aging blood are the most significant subtype affecting autophagy in aging tissues. Specifically, hypoxia predominantly increases overall autophagy levels in aging blood tissues by activating autophagy in C. monocytes. Elevated autophagy levels in C. monocytes result in increased ROS levels and the upregulation of pro-inflammatory factors and chemokines, thereby promoting aging in age-related diseases. As our analysis was confined to blood tissue, further research is necessary to determine whether these findings apply to other organs and tissues.

In conclusion, our research, utilizing extensive RNA-seq and single-cell resolution data, establishes a positive correlation between autophagy levels in aging tissues and the activation of monocytes. High autophagy levels in these cells lead to the upregulation of pro-inflammatory genes and the accumulation of ROS, thereby promoting aging. Consequently, inhibiting autophagy in monocytes could potentially counter aging and age-related diseases, ultimately contributing to the delay and prevention of age-related health complications.

Limitations of the study

Our study has some limitations. Firstly, the identification of aging-related genes from existing literature is somewhat limited due to a lack of multi-database integration. Nonetheless, our study identified 44 aging genes with elevated aging levels in the aging group, indicating the reliability of our identification. Secondly, since autophagy and aging processes are interconnected and mutually influential, we considered the overlap between autophagy and aging genes. To address this issue, we checked the core gene sets of autophagy and aging and found no overlapping genes, thus distinguishing between the two and preventing confounding. Finally, although we found that the release of pro-inflammatory factors and the accumulation of ROS at the single-cell level can accelerate aging, our conclusions still need further research to be fully validated.

In conclusion, this study found that autophagy levels increase in over 7000 aging organs during the aging process, which may be caused by the accumulation of pro-inflammatory factors and ROS levels. Our results reveal the relationship between autophagy and aging at the transcriptional level, providing a new insight into and perspective on slowing down the aging process.

Methods

Bulk RNA-seq datasets

We utilized healthy tissues RNA-seq data covering 33 tissues available at the GTEx portal (release V8) (https://www.gtexportal.org/) [26]. The collection of mRNA expression data for Acute Myeloid Leukemia (AML) samples from The Cancer Genome Atlas (TCGA) was downloaded from the TCGA data portal (https://portal.gdc.cancer.gov/) [52, 53]. Additionally, 4 samples with induced aging and without treatment from Gene-Expression Omnibus (GEO) and 6 Acute Myeloid Leukemia samples without treatment from GEO (Table S1) were included for further analysis (detailed information can be found in the Table S1). The data was analyzed using R (version 3.6.0) and R Bioconductor packages.

Single cell RNA-seq analysis of AML and aging blood datasets

For the analysis of aging blood subtypes, 3 single-cell transcriptomics datasets with metadata were obtained. We also obtained healthy blood datasets for old and young people from SC2018 (https://humandbs.biosciencedbc.jp/en/hum0229-v1). To analyze the subtypes of AML and normal blood, we obtained AML datasets from GSE154109 and GSE116256 (Table S1).

Gene set variation analysis (GSVA) and pathways enrichment analysis

In order to study the differences of autophagy and hypoxia statuses in aging tissues and AML hallmarks pathways, we utilized the “GSVA” R package [54] to conduct GSVA enrichment analysis. The gene set h.all.v6.1.symbols, c2.cp.kegg.v6.2 were retrieved from the MSigDB database (http://software.broadinstitute.org/gsea/msigdb/index.jsp) [55]. Pathway Enrichment analysis was performed using the fgsea [56] package and [57] the clusterProfiler Package [57].

Aging score and autophagy score calculation

We identified aging-related genes by referencing The Aging Atlas database [58] and incorporating insights from multiple studies. Next, based on 503 aging-related genes, we retrieved relevant literature and filtered accordingly. The main filtering criteria include excluding genes with unclear initial functions, for example, genes that only describe a connection with aging without specifying whether the gene promotes or inhibits aging, and further retaining genes that promote aging. This allowed us to pinpoint 44 genes (Table S2) that actively contribute to the aging process [15, 59–90]. For instance, KAT7 was identified as a driver of cellular senescence through CRISPR-based genome-wide screening [58], Sirt1 knockout demonstrated enhanced maintenance of aging HSCs’ quiescence [60], and knockdown of IRS2 extended lifespan and inhibited senescence [60]. Finally, 44 aging gene sets were identified. The status of these 44 aging gene sets was validated using four common datasets that induced aging, and multiple verifications were conducted using functional genomics. The autophagy score for each tumor samples was calculated using gene set variation analysis based on 37 mRNA-based autophagy signatures (Table S2) as previously described [27]. Aging and autophagy score for each sample was calculated using gene set variation analysis based on mRNA-based signatures. Autophagy scores of single cells were calculated as previously described (https://www.github.com/cssmillie/ulcerative_colitis) [91]. In brief, the gene signature score for each cell was computed by the mean scaled expression across all genes in the signatures.

Analysis of autophagy and hypoxia-associated immune features

The hypoxia score for each sample was calculated using gene set variation analysis based on 15 mRNA-based hypoxia signatures as previously described [92]. The richness of myeloid-related cell and pro-inflammation signatures were obtained from Thorsson et al. (https://gdc.cancer.gov/about-data/publications/panimmune) [93]. We used Spearman’s correlation to assess the relationship between autophagy scores and immune features, including the abundance of immune cell populations, ROS scores, and pro-inflammation scores. We considered FDR < 0.05 as a significant correlation.

Analysis of immune cell abundence

We used CIBERSORT with the LM22 matrix from Newman et al. (https://cibersort.stanford.edu/) [94] to quantify the relative abundance of 22 types of immune cells in AML and healthy donors. We performed 100 permutation tests. RNA-seq data did not undergo quantile normalization, while microarray data did.

Dimension reduction and clustering analysis

For the single-cell dataset Seurat, we scaled the data with features calculated by the FindVariableFeatures() function. To remove batch effects of different samples, we used the RunHarmony [95] method in the R package harmony. For clustering and visualization, we applied FindCluster() in Seurat to obtain cell clusters at various resolutions, and the dimensionality of the data was reduced using Uniform Manifold Approximation and Projection (UMAP) implemented in the RunUMAP function with the setting: reduction = ‘harmony’, dims = 1:10.

Protein activity inference

Protein activity was inferred by running the metaVIPER [96] algorithm with ARACNe networks across all patients on the SCTransform-scaled and Anchor-Integrated gene expression signature of single cells from each patient. Because the SCTransform-scaled gene expression signature is already normalized, the VIPER normalization parameter was set to “none”. The resulting patient-by-patient VIPER matrices were combined by sub-setting to the VIPER proteins to the activity that was inferred in each patient sample, resulting in 1589 proteins with successfully inferred activity across all patient samples. VIPER-Inferred Protein Activity matrices were loaded into a Seurat Object with CreateSeuratObject, then projected into their first 50 principal components using the RunPCA function in Seurat. They were further reduced into a 2-dimensional visualization space using the RunUMAP function with umap-learn and Pearson correlation as the distance metric between cells. Differential Gene Expression between clusters identified by resolution-optimized Louvain was computed using the bootstrapped t-test and ran with 100 bootstraps. The top proteins for each cluster were ranked by the score. Subsequently, we selected autophagy and hypoxia-related proteins to bind to GSVA to calculate the corresponding hypoxia and autophagy activities at the protein level.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (919.9KB, docx)
Supplementary Material 2 (15.1KB, xlsx)

Acknowledgements

The work was supported in part by grants from MOST (2023YFA0914900, 2019YFA0508602) to Q. Z., NSFC (91754205, 91957204, 31771523 and M-1040) to Q. Z., and Shanghai Municipal Science and Technology Project (20JC1411100, 19XD1402200) to Q. Z. It was also supported by the Shanghai Frontiers Science Center of Cellular Homeostasis and Human Diseases and the innovative research team of high-level local universities in Shanghai. The authors declare no competing financial interests. Some elements of the schematics were created with (https://biorender.com/).

Author contributions

Q.Z. and Y.Y. conceived and supervised the project. Z.Z., Y.Y., and Q.Z. designed and performed the research. Y.Y., L.L. and Z.Z. performed data analysis. Z.Z., Y.Y., and Q.Z. interpreted the results. Z.Z., Y.Y., and Q.Z. wrote the manuscript. All authors reviewed the manuscript.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Youqiong Ye, Email: youqiong.ye@shsmu.edu.cn.

Qing Zhong, Email: qingzhong@shsmu.edu.cn.

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Associated Data

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Supplementary Materials

Supplementary Material 1 (919.9KB, docx)
Supplementary Material 2 (15.1KB, xlsx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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