Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Introduction

Population aging is a key social, economic, and medical challenge on a global scale, which has created a major, growing need to develop interventions that target the aging process. Because age-associated changes in homeostasis are the major risk factors for the most prevalent human diseases (such as neurological, metabolic, fibrotic, and inflammatory conditions), developing interventions that target aging would also impact multiple age-related diseases and result in unprecedented health benefits [1, 2]. Importantly, the stated goal of geroscience is to extend not only lifespan but also health life expectancy, or healthspan [2], enabling wellbeing in older age, the so-called “healthy aging”. Research into longevity pharmacology has exploded in recent years with hundreds of compounds now known to extend lifespan in model organisms [3–7]. Major challenges remain, however, in translating these findings into humans, and ultimately extending lifespan and healthspan by targeting aging mechanisms.

Although the underlying molecular mechanisms of aging remain the subject of debate, several key pathways and processes have been associated with aging processes. These have been conceptualized in the hallmarks of aging composed of: Altered intercellular communications, Cellular senescence, Deregulated nutrient signaling, Epigenetic shift, Genomic instability, Impaired proteostasis, Mitochondrial dysfunction, Stem cell exhaustion, Telomere attrition [8]. The hallmarks of aging have been widely used in the field as a starting point for studies, although they are not perfect, and arguably other mechanisms such as extracellular matrix stiffness [9], retrotranspositions [10] and inflammation [11] have also been reported to play an important role in aging. These heterogeneous processes have in turn been associated with age-related diseases. For example, cellular senescence has been associated with pathologies such as cancer, type 2 diabetes, and atherosclerosis [12], as well as pulmonary, neurological, renal, hepatic, infectious, musculoskeletal, and endocrine diseases [13]. At the genetic level, there is a substantial overlap between the genetics of aging and age-associated diseases (AADs) [14]. For example, some known aging-related targets, such as mTOR, AMPK, IGFR, NF-kB, S6K, TGF-β, AT1, Fgf21, FOXO3a, SIRT1, HIF-1, NRF2, and Klotho, may also impact multiple age-associated diseases [1, 8, 15]. Therefore, given that aging is associated with mechanisms that ultimately lead to age-related comorbidities, drugs that act on targets implicated in aging may potentially reduce the severity of gerolavic (from Greek, géros “old man” and epilavís, “harmful”) diseases and preventing multimorbidity [16].

A substantial percentage of the human clinical trials, including those evaluating investigational anti-aging drugs, fail in Phase II, a phase where efficacy of the drug is tested [17, 18]. This poor success is in part due to inadequate target choice and the inability to identify a group of patients who will most likely respond to specific agents. This challenge is further complicated by the differences in biological age of the patients, as importance of therapeutic targets varies between the age groups. Hence, identifying potential targets that are implicated in multiple age-associated diseases, and also play a role in the basic biology of aging, may have substantial benefits.

Given the large number of datasets being generated, data-driven approaches (such as artificial intelligence [AI] and machine learning) are becoming instrumental across various fields in biology, including biomarker discovery and target prediction in aging [19]. Indeed, a number of studies by our group and others have employed computational and machine learning analysis to identify new candidates in the context of aging and AADs. These approaches have led to the detection of disease-related genes, caloric restriction genes, and longevity drugs [20–24]. The application of AI in the pharmaceutical industry also aims to reduce the tremendous amount of cost and time conventionally needed to discover new therapeutic targets in various diseases. There are multiple philosophies for the formulation of disease hypothesis, prioritization of pathways implicated in a disease, and selection of promising therapeutic targets. Multiple data types can be used for target discovery including text, imaging, and omics. In recent years, machine learning, and especially deep learning technologies, are rapidly increasing in popularity for target discovery. Advanced signaling pathway modeling such as iPANDA [25] and deep neural networks were used to identify promising protein targets driving complex biological processes implicated in cancer and other diseases [26], drug repurposing [27], and geroprotector discovery [28, 29]. Many of these approaches were implemented in PandaOmics, an industrial target discovery engine. Recently, PandaOmics, has successfully identified and nominated novel targets for idiopathic pulmonary fibrosis (IPF) and kidney fibrosis [30–32].

This platform utilizes advanced deep learning models and AI approaches to predict the target genes associated with a given disease through a combination of Omics AI scores, Text-based AI scores, Finance scores, and Key opinion leader (KOL) scores (Figure 1), and is currently being employed in both academic and industry settings. The algorithm also allows the prioritization of protein targets for novelty, confidence, commercial tractability, druggability, safety, and other key properties that drive target selection decisions. The integrated omics database consists of a vast amount of published systems biology data, spanning over 1,500 diseases and 10,000 disease subtypes. The database includes approximately 1.9 trillion data points derived from over 10 million samples with microarrays, RNA sequencing, proteomes, and methylomes, among other data types. PandaOmics’ text database embeds information from over 40 million documents, including patents, grants (that amount to over $2 trillion in funding), publications, clinical trial results, and company reports, among other text-based sources.

Figure 1. Workflow of the present study. Thirty-three diseases were separated into either age-associated diseases (AADs) or non-age-associated diseases (NAADs) based on the impact of age on the risk of the disease’s onset. Their corresponding transcriptomic datasets were retrieved from public repositories and processed by PandaOmics. Age bias between case and control groups has been considered during dataset selection. With multiple levels of novelty settings, targets implicated in AADs and NAADs were identified by ‘PandaOmics - target identification’. PandaOmics prioritized targets for one disease and refined the targets based on several flexible druggability filters. The target-disease associations were ranked according to over 20 artificial intelligence and bioinformatics models ranging from Omics AI scores, Text-based AI scores, Finance scores to KOL scores. Target identification was performed independently for each disease. Top-ranked targets shared by both disease categories were regarded as common targets, while targets unique to AADs were defined as age-associated targets (AAD targets). All common targets and AAD targets were subjected to the hallmarks of aging assessment by searching the literature for their evidence in modulating longevity or longevity pathways. To propose potential targets with a dual role in anti-aging and disease treatment, hallmark-associated targets were further evaluated based on their expression profiles across AADs, mechanism of action, and safety. A total of 9 targets were selected, with three levels of novelty. Abbreviation: KOL: Key opinion leader.

In this study, we used PandaOmics to identify a list of potential aging-associated therapeutic targets across various AADs that may be used for drug discovery. We successfully established and validated our unique approach with the application of varied target identification and prioritization techniques offered by PandaOmics and downstream analyses, yielding a list of dual-purpose targets associated with both the aging process and AADs. First, a list of 145 aging-related targets was generated upon the hallmarks of aging assessment. Subsequently, we further narrowed down the number of potential candidates to a total of 9 highly promising therapeutic targets associated with the aging process and AADs based on multiple selection criteria.

Results

Discovery of targets implicated in multiple age-associated diseases

A combined list of 484 high confidence, 448 medium novel, and 381 highly novel targets were generated from the lists of top-100 targets prioritized by PandaOmics in each of the AADs (Supplementary Figure 1). The top-100 targets for three levels of novelty settings were selected by (1) the occurrence of the target genes in 14 AADs (Supplementary Figure 2) and (2) the average ranking of the target gene in its corresponding disease (Supplementary Table 1). The same approach was applied to non-age-associated diseases (NAADs). Diseases selected for this study and their corresponding disease classes were listed in Table 1. Only the top-100 genes of this combined list from AADs were subjected to the hallmarks of aging assessment by finding their corresponding evidence in modulating longevity or aging pathways in literature and expression analysis. Under high confidence settings, the top genes identified were CASP3, VEGFA, and MMP9, which were highly ranked in all of the 14 AADs (Figure 2 and Supplementary Figure 2A). LYN was the top gene identified under medium novelty settings, which was also highly implicated in all AADs (Supplementary Figures 2B and 3). For high novelty settings, PPP2CB, CDC34, FES, RHOF, and RAB24 were the top-ranked genes in 12 out of 14 AADs (Supplementary Figures 2C and 4). The top-ranked genes shared by both AADs and NAADs were regarded as common targets, while those genes unique to AADs were defined as age-associated targets, or AAD targets (Venn diagram, Figure 1). Intersecting the two lists of genes obtained from AADs and NAADs resulted in 42 AAD targets in high confidence setting (Supplementary Figure 5A). The remaining 58 genes were considered as common targets. For medium and high novelty settings, 37 and 29 AAD targets were identified, respectively (Supplementary Figure 5B–5C).

Table 1. List of diseases and datasets employed.

Disease	Disease class	Number of comparisons
Age-associated diseases (14 diseases, 87 comparisons)
Alzheimer's disease	Neurological	12
Amyotrophic lateral sclerosis	Neurological	10
Chronic kidney disease	Metabolic	7
Chronic obstructive pulmonary disease	Inflammatory	6
Cirrhosis of liver	Fibrotic	5
Idiopathic Pulmonary Fibrosis	Fibrotic	11
Obesity	Metabolic	10
Osteoarthritis	Inflammatory	5
Osteoporosis	Metabolic	2
Parkinson's disease	Neurological	4
Primary myelofibrosis	Fibrotic	2
Pulmonary arterial hypertension	Metabolic	5
Rheumatoid Arthritis	Inflammatory	4
Type II diabetes mellitus	Metabolic	4
Non-age-associated diseases (19 diseases, 126 comparisons)
Acromegaly	Metabolic	2
Asthma	Inflammatory	13
Bipolar disorder	Neurological	4
Celiac disease	Inflammatory	3
Crohn's disease	Inflammatory	8
Cystic fibrosis	Fibrotic	5
Hepatitis, alcoholic	Metabolic	3
Hepatitis C virus infection	Infectious	2
Huntington's disease	Neurological	5
Infectious meningitis	Infectious	3
Influenza	Infectious	5
Multiple sclerosis	Inflammatory	11
Psoriasis	Inflammatory	11
Pulmonary tuberculosis	Infectious	7
Schizophrenia	Neurological	4
Systemic lupus erythematosus	Inflammatory	9
Systemic scleroderma	Fibrotic	6
Type I diabetes mellitus	Metabolic	12
Ulcerative colitis	Inflammatory	13

Figure 2. Ranking of the top-100 gene set for AADs under high confidence settings. The ranking of the targets in AADs and NAADs are colored in blue-white and red-white thermal scales respectively. High color intensity stands for high ranking. The lowest ranking was capped at 100. Targets associated with the hallmark(s) of aging are labeled in green. Abbreviation: COPD: Chronic obstructive pulmonary disease.

Genes implicated in AADs are associated with the hallmarks of aging

In our analysis, 300 genes identified under three different novelty settings were subjected to a literature review (see Materials and Methods) for their association with the hallmarks of aging. Their corresponding roles in aging processes were summarized in Supplementary Table 2. In total, 145 genes (69 high confidence, 48 medium, and 28 highly novel targets) were linked to at least one aging hallmark (Figure 3, Supplementary Table 2). The most frequently associated aging hallmark was inflammation (n = 48), followed by genomic instability (n = 35), altered intercellular communications (n = 33), mitochondrial dysfunction (n = 32), impaired proteostasis (n = 31) and extracellular matrix stiffness (n = 30). Eighty-six genes (including several well-known aging-associated genes) were found to be associated with more than one hallmark. In particular, MTOR, SIRT1, IGF1, and AKT1 were associated with all hallmarks of aging, due to their wide range of interactions with aging-associated pathways. In addition, IGF1R was linked to deregulated nutrient signaling, genomic instability, inflammation, mitochondrial dysfunction, retrotranspositions, and stem cell exhaustion. Whereas HGF was associated with all hallmarks except epigenetic shift, retrotranspositions, and telomere attrition. Moreover, some novel targets were also identified to be associated with multiple hallmarks of aging. For example, MYSM1 was associated with cellular senescence, inflammation, and stem cell exhaustion; KAT6A was associated with cellular senescence, epigenetic shift, and stem cell exhaustion; UBE2E3 was linked to cellular senescence, impaired proteostasis, and stem cell exhaustion; RAB7B was linked to impaired proteostasis, inflammation, and mitochondrial dysfunction; whereas RAB8B and USP2 were related to altered intercellular communications, impaired proteostasis, and mitochondrial dysfunction. Furthermore, a recently proposed hallmark of aging, extracellular matrix stiffness, was associated with 30 target genes identified by PandaOmics in this study, consisting of 18 high confidence (AKT1, CHUK, CASP3, DNMT1, EGFR, FGF2, HGF, IGF1, ITGAV, LOX, MMP1, MMP2, MMP7, MMP9, MTOR, SIRT1, SPP1, TRAF6), 8 medium novel (FAM20C, GALNT1, ITGB5, MMP25, PLOD1, PLOD3, RAB14, TNIK), and 4 highly novel targets (ADAMTS14, ITGA11, RAP2C, RNF14). Among the 145 genes associated with hallmarks of aging, 55 genes are known cancer drivers (Figure 3) [33], pointing to the aging components underlying cancer pathogenesis.

Figure 3. Targets associated with hallmarks of aging. Age-associated targets and common targets (n = 145) were mapped to the corresponding hallmark(s) of aging based on the literature. For novel targets, their participating pathways were also used for the assessment of their association with the hallmark(s) of aging. The four targets connected to all hallmarks (AKT1, MTOR, SIRT1 and IGF1) are shown in the inner circle of the plot. The target names are labeled in blue for age-associated targets, and black for common targets. Targets annotated as cancer driver genes in the NCG7.0 database are underlined.

Genes consistently dysregulated in multiple AADs were implicated as potential dual-purpose targets

To study the dysregulation state of genes identified under three different novelty settings, their consistency of expression change in each AAD class was summarized in Figure 4. Genes that were consistently dysregulated in two or more disease classes in a unidirectional manner were selected for further analysis. For high confidence targets, 52 genes were selected, of which 10 (CASP3, CXCL10, CXCL12, CYBA, HGF, ITGAM, ITGAV, PLAU, SPP1, and TGFB1) were consistently upregulated while MAPK8 was the only gene that was downregulated in all disease classes; 24 genes were upregulated and 8 were downregulated in 3 disease classes. Forty-four medium novel targets were selected, with 4 genes (CLEC5A, FPR3, ITGB5, and RAB31) and PPM1A being upregulated and downregulated in all disease classes, respectively; 15 genes were upregulated and 10 were downregulated in 3 disease classes. For highly novel targets, 5 of the 45 genes (MX2, P2RX1, PRSS23, RAB7B, and RNASE6) were upregulated in all classes; 6 genes were upregulated while 21 were downregulated in 3 disease classes. As described above, upon the hallmarks of aging assessment, 145 genes were considered as potential aging-related targets. Here, these genes were further selected with reference to their expression patterns, and a subset of the candidates was considered as potential dual-purpose targets for subsequent analysis (Supplementary Figure 1).

Figure 4. Expression of target genes in 4 AAD classes. The consistency of gene dysregulation in each disease class is indicated by the thermal scale, with red standing for upregulation and blue for downregulation. The color intensity indicates the level of consistency. Target genes consistently dysregulated (≥60% of comparisons) in 4 AAD classes in a unidirectional manner are shown in the black boxes.

Validation by intersecting the AI-derived targets with well-known aging-associated genes

The significance of the mTOR, the insulin/IGF, and the sirtuin pathways in longevity has been extensively reported, delineating their critical roles in counteracting multiple hallmarks of aging to delay the aging process or to extend lifespan [8, 34, 35]. Remarkedly, Food and Drug Administration (FDA)-approved mTOR inhibitor, rapamycin, was demonstrated to slow down aging and AADs in both preclinical settings and clinical trials [36, 37]. mTOR regulates several hallmarks of aging including nutrient sensing, stem cell exhaustion, proteostasis, and cellular senescence [38]. Upon insulin/IGF receptor activation following the insulin/IGF1 binding, mTOR, a nutrient sensor, regulates cellular functions linked to proliferation, growth, and survival via Akt-mediated activation. Increased insulin sensitivity favored lifespan extension. For example, growth hormone receptor (GHR)-knockout mice showed higher sensitivity to insulin, decelerated senescence, and displayed more phenotypic features related to anti-aging [39]. In addition, activation of SIRT1 suppressed aging by ensuring telomere integrity [40, 41], antagonizing oxidative stress [42, 43], regulating nutrient signaling [8] and maintaining proteostasis [44, 45].

Our approach utilizing PandaOmics identified a set of well-known aging-associated genes that are part of the mTOR, insulin/IGF and sirtuin family signaling (including IGF1, IGF1R, AKT1, MTOR, and SIRT1), strongly supporting the validity of this promising method for the identification of aging-associated genes. The aging-associated genes listed above were identified as common targets, suggesting their relevance in both aging and other diseases as well as their involvement in multiple signaling networks. It is worth noting that aging genes such as FOXO that did not meet the criteria for druggability (see Materials and Methods) were filtered out. To further evaluate whether our approach could identify aging-related targets with potential clinical relevance, the 100 high confidence targets were compared with a pool of well-known aging-associated genes curated from http://ClinicalTrials.gov (focusing on the treatment of aging), publication, http://Geroprotectors.org [46] (Supplementary Tables 3–5) and aging gene database, GenAge [47]. Fourteen high confidence targets (ABL1, AR, ESR1, GHR, IGF1, IGF1R, KIT, MAPK14, MTOR, NR3C1, PDGFRB, SIRT1, SRC and VDR) were identified in the pool of 62 genes procured from the aging trials (expected [Exp] = 1.10, fold = 12.70, p = 1.79E-12, Supplementary Table 3). Twenty- four high confidence targets (AKT1, CASP3, CAT, CHUK, DNMT1, EGFR, HDAC9, IGF1, IGF1R, IL1B, IL6, JAK2, MAPK8, MMP1, MMP2, MMP9, MTOR, PPARA, PTEN, SIRT1, SOD2, TGFB1, TGFBR2 and TNF) were identified in the pool of 48 genes procured from publications (Exp = 0.85, fold = 28.13, p = 1.21E-30, Supplementary Table 4). While seven high confidence targets (CASP1, CASP3, CHUK, ESR1, HSPA5, IKBKB and MTOR) were further identified in the pool of 52 genes procured from geroprotectors (Exp = 0.92, fold = 7.57, p = 3.15E-5, Supplementary Table 5). This significant enrichment might indicate the potential clinical relevance of our AI-derived targets in treating aging-related processes and AADs. Moreover, significant overrepresentation was also observed in 38 high confidence targets that were overlapped with 149 aging-associated genes obtained from the benchmark aging gene database, GenAge (Exp = 2.65, fold = 14.35, p = 1.28E-35), further validating the approach used in this study.

Linking the AI-derived targets to aging-associated pathways by pathway enrichment analysis

Pathway enrichment analysis was performed on 145 AI-derived targets using Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY Database [48]. Genes were mapped to 225 KEGG pathways, of which 151 were significantly enriched (p < 0.05) (Supplementary Table 6) with 110 of the 145 potential targets. PI3K-AKT signaling pathway (hsa04151), MAPK signaling pathway (hsa04010) and FOXO signaling pathway (hsa04068) were in the top 10 enriched signaling axes known to be associated with aging. Notably, the AI-derived targets crosstalk with multiple key aging-associated pathways, such as those regulated by MAPK, PI3K-AKT and FOXO signaling networks (Figure 5), consequently contributing to modulating apoptosis, autophagy, cell proliferation, cell survival, DNA repair, epigenetic alteration, extracellular matrix organization, inflammation, mitochondrial maintenance, stemness and telomere maintenance (Supplementary Table 2).

Figure 5. AI-derived targets crosstalk to aging-associated signaling pathways. Pathway enrichment analysis was performed on our 145 AI-derived targets based on KEGG PATHWAY Database. (A) MAPK signaling pathway (hsa04010), (B) PI3K-AKT signaling pathway (hsa04151) and (C) FOXO signaling pathway (hsa04068) were among the top 10 enriched pathways that were known to be associated with aging. Forty-six AI-derived targets were involved. Target-target interactions were identified in the contexts of pathways and networks retrieved from KEGG PATHWAY Database and literature (Supplementary Table 8). Abbreviation: PAMPs: Pathogen-associated molecular patterns.

AI-derived targets demonstrate dual roles in aging and AADs

The dual-purpose candidates were selected under the considerations of hallmarks of aging assessment (Supplementary Table 2), expression analysis (Figure 4), ranking calculated by PandaOmics, safety assessment, clinical trial status and druggability, yielding a list of 9 potential candidates (Table 2). Selected promising high confidence targets and novel targets are discussed below.

Table 2. List of prioritized targets.

Target1	Protein family	Hallmarks of aging	Dysregulation in AAD classes	Role in aging	Drugs in clinical trials	Severe toxicity3	Reference
High confidence
CXCL12	Cytokine	Inflammation, Stem cell exhaustion	ALL Upregulated	CXCL12 is an aging-upregulated gene and a mediator of the crosstalk between vascular cells and many brain cell types (pro-aging; therapy approach: antagonist)	Tinzaparin (phase 4)	No evidence	[94]
SPP1	Chemokine	Extracellular matrix stiffness, Inflammation, Stem cell exhaustion	ALL Upregulated	Age-dependent increase in SPP1 levels inhibited skeletal muscle regeneration (pro-aging; therapy approach: antagonist)	ASK-8007 (phase ½)	No evidence	[95, 96] NCT00411424
Medium novel
ITGB5	Receptor	Altered intercellular communications, Extracellular matrix stiffness	ALL Upregulated	ITGB5 is a TGF-β activator. TGF-β signaling, being downstream of other signals, was shown to repress body size as well as lifespan in vivo (pro-aging; therapy approach: antagonist)	Cilengitide (phase 3)	No evidence	[97] NCT00689221
PPM1A	Esterase	Deregulated nutrient signaling, Inflammation	ALL Downregulated	PPM1A stimulated macrophages to produce TNF through TLR4 (anti-aging; therapy approach: agonist)	No	No evidence; absence in DEG	[98]
Highly novel
RAB7B	Hydrolase	Impaired proteostasis, Inflammation, Mitochondrial dysfunction	ALL Upregulated	RAB7B negatively regulated TLR4 signaling in macrophages and autophagic flux as well as prevented inflammation and autophagy upon damage (anti-aging2; therapy approach: agonist)	No	No evidence; absence in DEG	[99]
ADAMTS14	Peptidase	Extracellular matrix stiffness	Upregulated in neurological and fibrotic diseases	ADAMTS14 is responsible for the degradation of ECM collagen. During aging, fibroblast-ECM interactions become disrupted due to the fragmentation of collagen fibrils. Fibroblasts synthesized fewer ECM proteins and more matrix-degrading metalloproteinases (pro-aging; therapy approach: antagonist)	No	No evidence, absence in DEG	[100]
KDM7A	Oxidoreductase	Altered intercellular communications, Genome instability	Downregulated in neurological and fibrotic diseases	Age-related neural dedifferentiation might contribute to many cognitive abilities decline with age. KDM7A regulated neural differentiation through FGF4, and was associated with Wnt signaling (anti-aging; therapy approach: agonist)	No	No evidence	[101, 102]
MYSM1	Peptidase	Cellular senescence, Inflammation, Stem cell exhaustion	Downregulated in neurological, fibrotic and metabolic diseases	MYSM1 functionally reduced cellular senescence and the aging process. MYSM1 deficiency promoted the aging process and decreased lifespan while its overexpression inhibited the aging process and increased lifespan in vivo. (anti-aging; therapy approach: agonist)	No	No evidence	[103]
MTMR4	Esterase	Altered intercellular communications	Downregulated in neurological, fibrotic and metabolic diseases	Skeletal muscle atrophy accompanies many chronic disease states and normal aging (anti-aging; therapy approach: agonist)	No	No evidence	[104]
Note: 1Targets selected for comprehensive target review are in BOLD. 2Based on its mechanism of action i.e., protective role. 3Database of Essential Gene (DEG) is freely accessible from the website http://tubic.tju.edu.cn/deg.

CXCL12

Aging was associated with elevated levels of proinflammatory cytokines, consequently leading to a decrease in mesenchymal stem cell (MSC) ability to regenerate and differentiate in inflammatory conditions [49]. Despite rescuing oxidative stress-induced hematopoietic stem cell (HSC) damage at the mitochondrial level, C-X-C motif chemokine ligand 12 (CXCL12) acted as a proinflammatory cytokine [50]. CXCL12 was uniformly upregulated in more than 70% comparisons in all four AAD classes i.e., neurological, metabolic, inflammatory, and fibrotic diseases (Figure 6A). In general, the log-fold change (logFC) of CXCL12 in AADs was significantly higher than in NAADs (p < 0.001, Figure 6A). Accumulating evidence demonstrates that CXCL12 upregulation was implicated in AADs including IPF [51], rheumatoid arthritis (RA) [52], and amyotrophic lateral sclerosis (ALS) [53]. Consistent with our findings, upregulation of CXCL12 was suggested to promote migration and proliferation of human lung fibroblast in IPF as well as to enhance monocytes infiltration into the synovial tissue in RA [51, 52]. Treatment with an antagonist of CXCR4, the receptor for CXCL12, extended lifespan, improved motor function, and led to weight loss in ALS in vivo [54]. Aging-associated degenerative diseases such as osteoporosis were linked to dysfunctional stem cell differentiation and a decline in the regenerative capacity of musculoskeletal stem cells, resulting from the secretion of pro-inflammatory cytokines such as CXCL12 [49]. Tinzaparin, a CXCL12 inhibitor, is an FDA-approved drug for the treatment of deep vein thrombosis and pulmonary embolism, which are considered as AADs. Taken together, suppression of CXCL12 is one of the potential therapeutic approaches that may be considered towards slowing down aging-associated processes and preventing the onset of AADs.

Figure 6. Expression of target genes in different diseases. The logFC of gene expression were shown for (A) CXCL12, (B) SPP1, (C) ITGB5, or (D) ADAMTS14 in AADs and NAADs. For each gene, comparisons of the logFC value were conducted between NAAD and each of the AAD classes, with significant difference indicated by asterisks (two-tailed t-test, ^*p < 0.05, ^**p < 0.01, ^***p < 0.001).

SPP1

Secreted phosphoprotein 1 (SPP1) functions as Th1 cytokine and is a secreted matrix glycoprotein located in bone and produced by osteoblasts, osteocytes, other hematopoietic cells, or immune cells [55]. SPP1 was uniformly upregulated in more than 80% comparisons in all four AAD classes, with significantly higher logFC in AADs than in NAADs (p < 0.001, Figure 6B). It was suggested that SPP1 may aggravate neurodegenerative, auto-immune, and inflammatory conditions. For example, SPP1 expressed by fast fatigue-resistant or slow motor neurons contributed to the second-wave neurodegeneration in ALS in vivo [56]. In addition, elevated levels of SPP1 in cerebrospinal fluid of subjects with Parkinson’s disease were associated with more severe motor symptoms. Importantly, SPP1-null mice demonstrated reduced neurodegeneration [57]. Besides, SPP1 upregulated lysyl oxidase, an enzyme involved in cross-linking insoluble collagen in fibroblasts. An excess of SPP1 was associated with left-ventricular stiffness and systolic dysfunction in patients with chronic heart failure and hypertensive heart disease [58]. The levels of active TGF-beta and MMP-2, two essential fibrogenic signaling mediators, as well as type I collagen expression were significantly attenuated in SPP1-null mice treated with bleomycin, fibrosis inducer, compared to wild-type controls [59]. Taken together, these findings strongly suggest that suppression of SPP1 is a highly potential therapeutic approach for aging and AADs.

ITGB5

Integrin alpha V beta 5 (ITGB5) encodes a subunit of integrin that can interact with several alpha chains to form a variety of integrin heterodimers. ITGB5 was consistently upregulated in more than 60% comparisons in all four AAD classes, and the logFC of ITGB5 in AADs was significantly higher than NAADs (p < 0.05, Figure 6C). Consistently, ITGB5 was also found to be upregulated in chronic kidney disease and psoriatic arthritis [60, 61]. In particular, ITGB5 was significantly increased in the serum of patients with psoriatic arthritis, a distinct inflammatory arthritis occurring in 30% of psoriasis patients [60]. ITGB5 was suggested as a biomarker for both nonprogressive and progressive kidney diseases [61], and was also one of the genes strongly associated with ischemic heart disease [62]. Moreover, ITGB5 served as a ligand for Cyr61, a molecule stimulating the production of IL-6, which is considered an aging biomarker, via itgav/itgb5/Akt/NF-κB signaling pathway in RA [63, 64], further supporting its role in various mechanisms underlying multiple AADs.

ADAMTS14

A disintegrin and metalloproteinase with thrombospondin motifs 14 (ADAMTS14) cleaves the amino-propeptides of fibrillar collagens, enabling collagen fibril formation prior to assembly of collagen, a major extracellular matrix (ECM) protein. ADAMTS14 was uniformly upregulated in more than 65% comparisons in neurological and fibrotic diseases. The logFC of ADAMTS14 in AADs was also significantly higher than in NAADs (p < 0.01, Figure 6D). Significant upregulation of ADAMTS14 was observed in human osteoarthritis (OA) cartilage, suggesting its involvement in cartilage matrix anabolism [65]. ADAMTS14 was also linked to the susceptibility to aging-related Alzheimer's disease as well as the regulation of immune functions via TGF-beta signaling [66]. ADAMTS14-deficient mice remained healthy, fertile, and displayed normal amino-procollagen processing [67], suggesting that antagonizing ADAMTS14 is unlikely to result in severe toxicity. As such, pharmaceutical inhibition of ADAMTS14 may provide a promising therapeutic approach for aging and AADs.

Abbreviations

ADAMTS14: A disintegrin and metalloproteinase with thrombospondin motifs 14; AAD: Age-associated disease; AI: Artificial Intelligence; ALS: Amyotrophic lateral sclerosis; COPD: Chronic obstructive pulmonary disease; CXCR4: C-X-C Motif Chemokine Receptor 4; CXCL12: C-X-C Motif Chemokine Ligand 12; DEG: Database of essential genes; DNMT1: DNA (cytosine-5)-methyltransferase 1; ECM: Extracellular matrix; FDA: Food and Drug Administration; GHR: Growth Hormone Receptor; HSC: Hematopoietic stem cell; IPF: Idiopathic pulmonary fibrosis; ITGB5: Integrin subunit beta 5; KEGG: Kyoto Encyclopedia of Genes and Genomes; KOL: Key opinion leader; logFC: log-fold change; MMP: Matrix metalloproteinase; MSC: Mesenchymal stem cell; NAAD: Non-age-associated disease; RA: Rheumatoid arthritis; SIRT1: Sirtuin 1; SPP1: Secreted phosphoprotein 1; TLR4: Toll-like receptor 4; OA: Osteoarthritis.

Author Contributions

FP performed data analysis, participated in result interpretation and project administration, and drafted the manuscript. GL and HL analyzed data, performed visualization, participated in result interpretation, and drafted the manuscript. BL participated in result interpretation and drafted the manuscript. XL performed the statistical analysis and drafted the manuscript. IO prepared data curation, methodology, and software. JW and FR participated in the result interpretation. AA designed the study and provided resources. EI and AM reviewed the manuscript. JPM participated in result interpretation and drafted the manuscript. AZ provided conceptualization, reviewed the manuscript, provided resources, and supervised the projects. All authors read and approved the final manuscript.

Acknowledgments

We thank Ms. Elizaveta Ekimova and Mr. Mark Grigorenko for their technical assistance with figure design.

Conflicts of Interest

FP, GL, HL, BL, XL, IO, JW, FR, AA, and AZ are affiliated with Insilico Medicine, a commercial company developing AI solutions for aging research, drug discovery, and longevity medicine. JPM is a consultant to Insilico Medicine.

Funding

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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