Aging-related Genes Research Articles

Sex dimorphism and tissue specificity of gene expression changes in aging mice

BackgroundAging is a complex process that involves all tissues in an organism and shows sex dimorphism. While transcriptional changes in aging have been well characterized, the majority of studies have focused on a single sex and sex differences in gene expression in aging are poorly understood. In this study, we explore sex dimorphism in gene expression in aging mice across three tissues.MethodsWe collected gastrocnemius muscle, liver and white adipose tissue from young (6 months, n = 14) and old (24 months, n = 14) female and male C57BL/6NIA mice and performed RNA-seq. To investigate sex dimorphism in aging, we considered two levels of comparisons: (a) differentially expressed genes between females and males in the old age group and (b) comparisons between females and males across the aging process. We utilized differential expression analysis and gene feature selection to investigate candidate genes. Gene set enrichment analysis was performed to identify candidate molecular pathways. Furthermore, we performed a co-expression network analysis and chose the gene module(s) associated with aging independent of sex or tissue-type.ResultsWe identified both tissue-specific and tissue-independent genes associated with sex dimorphism in aged mice. Unique differentially expressed genes between old males and females across tissues were mainly enriched for pathways related to specific tissue function. We found similar results when exploring sex differences in the aging process, with the exception that in the liver genes enriched for lipid metabolism and digestive system were identified in both females and males. Combining enriched pathways across analyses, we identified amino acid metabolism, digestive system, and lipid metabolism as the core mechanisms of sex dimorphism in aging. Although the vast majority of age-related genes were sex and tissue specific, we identified 127 hub genes contributing to aging independent of sex and tissue that were enriched for the immune system and signal transduction.ConclusionsThere are clear sex differences in gene expression in aging across liver, muscle and white adipose. Core pathways, including amino acid metabolism, digestive system and lipid metabolism, contribute to sex differences in aging.

Is castration leading to biological aging in dogs? Assessment of lipid peroxidation, inflammation, telomere length, mitochondrial DNA copy number, and expression of telomerase and age-related genes

BackgroundBiological aging is a complex process influenced by various factors, including reproductive status and castration. This study aimed to evaluate the impact of castration on biological aging in dogs.MethodFifteen male crossbred dogs were randomly divided into a sham-operation control group (n = 5) and a castrated group (n = 10). Blood samples were collected at weeks 0, 4, 8, 12, 16, and 18 post-surgery. Malondialdehyde (MDA as indicator of Lipid peroxidation), C-reactive protein (as an indicator of inflammation), telomere length, mitochondrial DNA (mtDNA) copy number, and the expression of age-related (P16, P21, TBX2) and telomerase-related (TERT) genes were assessed in blood samples.ResultsPlasma MDA levels were higher in the control group at weeks 16 and 18, while CRP levels were higher only at week 18. Telomere length and mtDNA copy number were lower in the control group at week 18. Gene expression analysis showed that P16 was lower in the control group at weeks 8 and 12, P21 and TERT were lower at weeks 16 and 18, and TBX2 was lower at weeks 16 and 18. The TBX2/P16 ratio was lower in the control group at weeks 16 and 18 but higher at week 12, while the TBX2/P21 ratio did not differ between groups.ConclusionCastration appears to have a protective effect against biological aging in dogs, as evidenced by lower lipid peroxidation, inflammation, and age-related changes in telomere length, mtDNA copy number, and gene expression.

Association of aging related genes and immune microenvironment with major depressive disorder

ObjectiveTo study the relationship between aging related genes (ARGs) and Major Depressive Disorder (MDD). MethodsThe datasets GSE98793, GSE52790 and GSE39653 for MDD were obtained from the GEO database, and ARGs were obtained from the Human Aging Genome Resources database. Differential expression genes (DEGs) screening and GO, KEGG enrichment analysis were performed to uncover the underlying mechanisms. To identify key ARGs associated with MDD (key ARG-DEGs), we employed machine learning methods such as LASSO, SVM, and Random Forest, as well as the plug-ins CytoHubba-MCC and MCODE methods. SsGSEA was used to analyze the immune infiltration of MDD and healthy controls. Furthermore, we created risk prediction nomograms model and ROC curves to assess not only the ability of key ARG-DEGs to diagnose MDD, but also predicted miRNAs and transcription factors (TFs) that might interact. Finally, a two-sample Mendelian randomization (MR) study was performed to confirm the association of identified key ARG-DEGs with depression. ResultsDEGs of ARGs between MDD and healthy controls led to the identification of eight ARG-DEGs. GO and KEGG analysis revealed that the pathways associated with these eight ARG-DEGs were primarily concentrated in Foxo pathway, JAK-STAT pathway, Pl3K-AKT pathway, and metabolic diseases. A comprehensive analysis further narrowed down the 8 ARG-DEGs to 4 key ARG-DEGs: MMP9, IL7R, S100B, and EGF. Immune infiltration analysis indicated significant differences in CD8(+) T cells, macrophages, neutrophils, Th2 cells, and TIL cells between MDD and control groups, correlating with these four key ARG-DEGs. Based on these four key ARG-DEGs, a risk prediction model for MDD was developed. The miRNA-TF-mRNA interaction network of the key ARG-DEGs highlights the complexity of the regulatory process, providing valuable insights for future related research. The MR study suggested a potential causal relationship between MMP9 and the risk of depression. ConclusionThe process of aging, immune dysregulation, and MDD are closely interconnected. MMP9, IL7R, S100B, and EGF may be used as novel diagnostic biomarkers and potential therapeutic targets for MDD, especially MMP9.

Epigenetic Age Estimation by Detecting DNA Methylation Status in Buccal Swabs.

The analysis of DNA methylation (DNAm) levels at specific CpG sites represents one of the most promising molecular techniques for estimating an individual's age. To date, a considerable number of studies have reported the development of age prediction models on the basis of DNAm in body fluids, with only a few utilizing buccal swabs. The objective of this study was to identify age-dependent methylation CpG sites in three different genes (HOXC4, TRIM59, and ELOVL2) in buccal swab samples from the Chinese Han population. A total of 461 buccal swabs, with an age range of 0.4-80.8 years, were divided into a training set (n=325) and a validation set (n=136). Samples were analyzed by pyrosequencing in order to identify age-related genes with correlation coefficient. A random forest regression model was ultimately proposed, including eight CpGs in three genes, with a mean absolute error (MAE) of 2.119 years. The model performs independent validation set with an MAE of 4.391 years. Our findings illustrate that buccal swabs present a suitable alternative to biological traces for age prediction based on DNAm pattern using pyrosequencing and random forest regression, offering the additional advantage of being collected noninvasively.

Comprehensive analysis of aging-related gene expression patterns and identification of potential intervention targets.

This study aims to understand the molecular mechanisms underlying the aging process and identify potential interventions to mitigate age-related decline and diseases. This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis. Machine learning and Mendelian randomization analyses were employed to identify core aging-associated genes and potential drug targets. Molecular docking simulations and mediation analysis were also performed to explore potential compounds and mediators involved in the aging process. The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revealed 21 modules, including both positively and negatively correlated modules between older age and younger age groups. Further exploration identified 509 aging-related genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, including DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal relationship between genes and aging-related indicators. This comprehensive study provides valuable insights into the molecular mechanisms of aging and offers important implications for the development of anti-aging therapeutics. Key Messages What is already known on this topic - Prior research outlines aging's complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and therapeutic compounds, advancing our understanding of aging mechanisms. How this study might affect research, practice, or policy - Findings may inform targeted therapies for age-related conditions, influencing future research and clinical practices.

Machine learning-based identification and validation of aging-related genes in cardiomyocytes from patients with atrial fibrillation.

Aging is a key risk factor for atrial fibrillation (AF), a prevalent cardiac disorder among the elderly. This study aims to elucidate the genetic underpinnings of AF in the context of aging. We analyzed 12,403 genes from the GSE2240 database and 279 age-related genes from the CellAge database. Machine learning algorithms, including support vector machines and random forests, were employed to identify genes significantly associated with AF. Among the genes studied, 76 were found to be potential candidates in the development of AF. Notably, four genes - PTTG1, AR, RAD21, and YAP1 - stood out with a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.9, signifying high predictive power. Logistic regression, validated through 10-fold cross-validation and Bootstrap resampling, was determined as the most suitable model for internal validation. The discovery of these four genes could improve diagnostic accuracy for AF in the aged population. Additionally, our drug prediction model indicates that bisphenol A and cisplatin, among other substances, could be promising in treating age-associated AF, offering potential pathways for clinical intervention.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes—EP300, MTOR, NFE2L1, TXN—exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

Genome-Wide Identification and Analysis of the WRKY Transcription Factor Family Associated with Leaf Senescence in Alfalfa

Leaves are the most significant parts of forage crops such as alfalfa. Senescence is the terminal stage of leaf development and is controlled by an integrated myriad of endogenous signals and environmental stimuli. WRKY transcription factors (TFs) play essential roles in regulating leaf senescence; however, only a few studies on the analysis and identification of the WRKY TF family in Medicago Sativa have been reported. In this study, we identified 198 WRKY family members from the alfalfa (M. sativa L.) cultivar ’XinjiangDaye’ using phylogenetic analysis and categorized them into three subfamilies, Groups I, II, and III, based on their structural characteristics. Group II members were further divided into five subclasses. In addition, several hormone- and stress-related cis-acting elements were identified in the promoter regions of MsWRKYs. Furthermore, 14 aging-related MsWRKYs genes from a previous transcriptome in our laboratory were selected for RT-qPCR validation of their expression patterns, and subsequently cloned for overexpression examination. Finally, MsWRKY5, MsWRKY66, MsWRKY92, and MsWRKY141 were confirmed to cause leaf yellowing in Nicotiana benthaminana using a transient expression system. Our findings lay a groundwork for further studies on the mechanism of M. sativa leaf aging and for the creation of new germplasm resources.

Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis

BackgroundProstate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.MethodsThis study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.ResultsAn ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.ConclusionsThis study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.

The role of cellular senescence in the pathogenesis of Rheumatoid Arthritis: Focus on IL-6 as a target gene

BackgroundRheumatoid arthritis is a chronic autoimmune disease. However, the specific role of senescence in rheumatoid arthritis (RA) is unknown. This study aimed to identify potential aging-related genes that have diagnostic and therapeutic value for RA. MethodsThe GSE89408 dataset was downloaded from the Gene Expression Omnibus (GEO). Aging-related genes were downloaded from the HAGR database. Differentially expressed genes (DEGs) were subsequently identified with the “edgeR” tool. Next, hub genes were identified with a PPI network and CytoHubba analysis. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of these hub genes. Immune infiltration analysis was performed with the CIBERSORT algorithm. Additionally, molecular docking was performed with CB-Dock2. Finally, correlation experiments were performed to validate the bioinformatics and molecular docking results. ResultsA total of 22 ADEGs were identified. Combined PPI network and CytoHubba analyses identified a total of 7 hub genes, including IL-6, IL7R, IL2RG, CDK1, PTGS2, and LEP, which are associated mainly with inflammation and immune responses. ROC analysis revealed that the hub genes were highly predictive of RA. Analysis of immune infiltration revealed that the 6 hub genes were positively associated with M1 macrophages. Validation experiments revealed that the inhibition of IL-6 significantly decreased the degree of synovial fibroblast (FLS) senescence. Furthermore, molecular docking and validation experiments revealed that IL-6 is a potential target for drug therapy. ConclusionThis study demonstrated that RA-FLS senescence may promote the development of RA via inflammatory and immune mechanisms. Seven hub genes were identified, of which IL-6 is a reliable biomarker for the diagnosis and treatment of RA.

DRADTiP: Drug repurposing for aging disease through drug-target interaction prediction

MotivationThe greatest risk factor for many non-communicable diseases is aging. Studies on model organisms have demonstrated that genetic and chemical perturbation alterations can lengthen longevity and overall health. However, finding longevity-enhancing medications and their related targets is difficult. MethodIn this work, we designed a novel drug repurposing model by identifying the interaction between aging-related genes or targets and drugs similar to aging disease. Each disease is associated with certain specific genetic factors for the occurrence of that disease. The factors include gene expression, pathway, miRNA, and degree of genes in the protein-protein interaction network. In this paper, we aim to find the drugs that prolong the life span of humans with their aging-related targets using the above-mentioned factors. In addition, the contribution or importance of each factor may vary among drugs and targets. Therefore, we designed a novel multi-layer random walk-based network representation learning model including node and edge weight to learn the features of drugs and targets respectively. ResultThe performance of the proposed model is demonstrated using k-fold cross-validation (k = 5). This model achieved better performance with scores of 0.93 and 0.91 for precision and recall respectively. The drugs identified by the system are evaluated to be potential candidates for aging since the degree of interaction between the potential drugs and their gene sets are high. In addition, the genes that are interacting with drugs produce the same biological functions. Hence the life span of the human will be increased or prolonged.

Prognostic aging gene-based score for colorectal cancer: unveiling links to drug resistance, mutation burden, and personalized treatment strategies

ObjectiveColorectal cancer (CRC) is characterized by high incidence and mortality rates worldwide. In this study, we present a novel aging-related gene-based risk scoring system (Aging score) as a predictive tool for CRC prognosis. Method: We identified prognostic aging-related genes using univariate Cox regression analysis, revealing key biological processes in CRC progression. We then constructed a robust prognostic model using LASSO and multivariate Cox regression analyses, including four critical genes: CAV1, FOXM1, MAD2L1, and WT1. Result: The Aging score demonstrated high prognostic performance across the training, testing, and entire TCGA-CRC datasets, proving its reliability. High-risk patients identified by the Aging score had significantly shorter overall survival times than low-risk patients, indicating its potential for patient stratification and personalized treatment. The Aging score remained an independent prognostic factor compared to age, gender, and tumor stage. Additionally, the score was linked to tumor mutation burden and microsatellite instability, indicators of immune checkpoint inhibitor response. High-risk patients also showed higher estimated IC50 values for common chemotherapeutic drugs, suggesting possible treatment resistance. Conclusion: Our findings highlight the Aging score's potential to enhance clinical decision-making and pave the way for personalized CRC management.

The opposite aging effect to single cell transcriptome profile among cell subsets.

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes.

Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.

Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with aging in PD. In this study, we downloaded aging-related genes from the Human Ageing Gene Database. To screen and verify biomarkers for PD, we used whole-blood RNA-Seq data from 11 PD patients and 13 healthy control (HC) subjects as a training dataset and three datasets retrieved from the Gene Expression Omnibus (GEO) database as validation datasets. Using the limma package in R, 1435 differentially expressed genes (DEGs) were found in the training dataset. Of these genes, 29 genes were found to occur in both DEGs and 307 aging-related genes. By using machine learning algorithms (LASSO, RF, SVM, and RR), Venn diagrams, and LASSO regression, four of these genes were determined to be potential PD biomarkers; these were further validated in external validation datasets and by qRT-PCR in the peripheral blood mononuclear cells (PBMCs) of 10 PD patients and 10 HC subjects. Based on the biomarkers, a diagnostic model was developed that had reliable predictive ability for PD. Two of the identified biomarkers demonstrated a meaningful correlation with immune cell infiltration status in the PD patients and HC subjects. In conclusion, four aging-related genes were identified as robust diagnostic biomarkers and may serve as potential targets for PD therapeutics.

Identification of age-related genes in rotator cuff tendon.

Rotator cuff tear (RCT) is the leading cause of shoulder pain, primarily associated with age-related tendon degeneration. This study aimed to elucidate the potential differential gene expressions in tendons across different age groups, and to investigate their roles in tendon degeneration. Linear regression and differential expression (DE) analyses were performed on two transcriptome profiling datasets of torn supraspinatus tendons to identify age-related genes. Subsequent functional analyses were conducted on these candidate genes to explore their potential roles in tendon ageing. Additionally, a secondary DE analysis was performed on candidate genes by comparing their expressions between lesioned and normal tendons to explore their correlations with RCTs. We identified 49 genes in torn supraspinatus tendons associated with advancing age. Among them, five age-related genes showed DE in lesioned tendons compared to normal tendons. Functional analyses and previous studies have highlighted their specific enrichments in biological functions, such as muscle development (e.g. myosin heavy chain 3 (MYH3)), transcription regulation (e.g. CCAAT enhancer binding brotein delta (CEBPD)), and metal ion homeostasis (e.g. metallothionein 1X (MT1X)). This study uncovered molecular aspects of tendon ageing and their potential links to RCT development, offering insights for targeted interventions. These findings enhance our understanding of the mechanisms of tendon degeneration, allowing potential strategies to be made for reducing the incidence of RCT.

Gene Age Gap Estimate (GAGE) for major depressive disorder: a penalized biological age model using gene expression.

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Statistical and machine learning regression models of biological age have been trained on various sources of high dimensional data to predict chronological age. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose a machine learning approach that simplifies gene selection by using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We construct the LASSO-GAGE (L-GAGE) model in an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC and then test for accelerated biological aging in MDD. When testing L-GAGE association with MDD, we account for factors such as sex and chronological age to mitigate regression to the mean effects. The L-GAGE shows higher biological aging in MDD subjects than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age is significant in L-GAGE model. This effect remains statistically significant even after adjusting for chronological age and sex. We find cytomegalovirus (CMV) serostatus is associated with elevated L-GAGE. We also investigate feature selection methods Random Forest and nearest neighbor projected distance regression (NPDR) to characterize age related genes, and we find functional enrichment of infectious disease and SARS-COV pathways.

Integrin-linked kinase control dental pulp stem cell senescence via the mTOR signaling pathway.

Human dental pulp stem cells (HDPSCs) showed an age-dependent decline in proliferation and differentiation capacity. Decline in proliferation and differentiation capacity affects the dental stromal tissue homeostasis and impairs the regenerative capability of HDPSCs. However, which age-correlated proteins regulate the senescence of HDPSCs remain unknown. Our study investigated the proteomic characteristics of HDPSCs isolated from subjects of different ages and explored the molecular mechanism of age-related changes in HDPSCs. Our study showed that the proliferation and osteogenic differentiation of HDPSCs were decreased, while the expression of aging-related genes (p21, p53) and proportion of senescence-associated β-galactosidase (SA-β-gal)-positive cells were increased with aging. The bioinformatic analysis identified that significant proteins positively correlated with age were enriched in response to the mammalian target of rapamycin (mTOR) signaling pathway (ILK, MAPK3, mTOR, STAT1, and STAT3). We demonstrated that OSU-T315, an inhibitor of integrin-linked kinase (ILK), rejuvenated aged HDPSCs, similar to rapamycin (an inhibitor of mTOR). Treatment with OSU-T315 decreased the expression of aging-related genes (p21, p53) and proportion of SA-β-gal-positive cells in HDPSCs isolated from old (O-HDPSCs). Additionally, OSU-T315 promoted the osteoblastic differentiation capacity of O-HDPSCs in vitro and bone regeneration of O-HDPSCs in rat calvarial bone defects model. Our study indicated that the proliferation and osteoblastic differentiation of HDPSCs were impaired with aging. Notably, the ILK/AKT/mTOR/STAT1 signaling pathway may be a major factor in the regulation of HDPSC senescence, which help to provide interventions for HDPSC senescence.

Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics

BackgroundGlioblastoma (GBM) is a very common primary malignant tumor of the central nervous system (CNS). Aging, macrophage, autophagy, and methylation related genes are hypothesized to be crucial to its pathogenesis. In this study, we aimed to explore the role of these genes in the prognosis of GBM. MethodsThe RNA sequence (RNA-seq) and clinical information were downloaded from The Cancer Genome Atlas database (TCGA) and the Chinese Glioma Genome Atlas database (CGGA). We performed univariate and least absolute shrinkage and selection operator (LASSO) multivariate Cox regression analysis to identify risk signatures related to overall survival (OS). We further developed a nomogram to predict individual outcomes. In addition, the immune microenvironment was analyzed by CIBERSORT. Results256 differentially expressed genes (DEGs) were obtained based on aging, macrophage, autophagy, and methylation related genes between GBM samples and normal tissues in TCGA-GBM cohort. We identified five optimal risk signatures with prognostic values in TCGA-GBM cohort and established a prognostic risk score model. The validity of the model was verified in the CGGA cohort and Huanhu cohort. Finally, we constructed a nomogram for clinical application by combining age, O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and risk score. Activated NK cells and resting mast cells were highly expressed and memory B cells, plasma cells, resting NK cells, M1 macrophages, and neutrophils exhibited low expression in the high-risk score group. GBM patients with a low-risk score had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score. The risk score of hot tumors was higher than that of the cold tumors. Additionally, 29 genes involved in glucose and lipid metabolism were highly expressed with a high-risk score. 31 metabolism-related pathways were significantly different between high-risk and low-risk groups. ConclusionsWe constructed and validated a novel prognostic model for GBM. Aging, macrophage, autophagy, and methylation related genes may serve as prognostic and therapeutic biomarkers. The model developed may assist in guiding treatment for GBM patients. Our research had great significance in accurately predicting the prognosis of GBM and may offer reference for immunotherapy decision for GBM patients.

Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes

Acquired immunodeficiency syndrome (AIDS) has transitioned from a progressive, fatal disease to a chronic, manageable disease thanks to better defining of antiretroviral therapy, contributing to increased life expectancy. In parallel, a growing number of subjects without clinical signs of disease but living with chronic HIV infection (also indicated as PLWHs, i.e., People Living With HIV) are experiencing early cardiovascular disease, and the risk increases with age. However, a progressive increase in the prevalence of multiple comorbidity diseases has been reported as these patients age, including cardiovascular disease (CVD). Cardiovascular mortality can be related to viral infection, a progressive reduction in response to antiretroviral therapy, chronic inflammation, and lifestyle. Cardiovascular ageing represents a relevant issue in the management of HIV-infected patients. Although the exact pathophysiological mechanism that leads PLWHs to develop cardiovascular disease is not entirely understood, there is substantial evidence that they accumulate age-related conditions earlier than the general population. Furthermore, since the proportion of PLWHs growing older than 50 years has progressively increased, this results in a complex interaction between disease-related pathophysiology and the exposition of a growing burden of cardiovascular risk factors. We performed a study to relate the effect ageing gas on genes associated with HIV and cardiovascular diseases. We performed a systematic review of the genes most frequently associated with ageing in HIV-infected subjects, followed by a bioinformatic analysis to explore the biological impact of the ageing-related genes.

Ethanol Extract of Propolis Attenuates Liver Lipid Metabolism Disorder in High-Fat Diet-Fed SAMP8 Mice.

The prevalence of high-fat diet (HFD) consumption is increasing among middle-aged and older adults, which accelerates the aging process of this population and is more likely to induce lipid metabolism disorders. But the alleviation of ethanolic extract of propolis (EEP) on lipid metabolism disorders during aging remains unclear. This study assesseed the impact of EEP intervention (200 mgkg-1bw) on aging and lipid metabolism disorders in HFD-fed senescence accelerate mouse prone 8 (SAMP8) mice. Findings indicate that EEP ameliorates hair luster degradation and weight gain, reduces systemic inflammation and metabolism levels, enhances hepatic antioxidant enzyme activities, and improves the hepatic expression of senescence-associated secretory phenotype and aging-related genes in HFD-fed SAMP8 mice. Histological staining demonstrates that EEP improves hepatic lipid deposition and inflammatory cell infiltration. Transcriptomic and lipidomic analysis reveal that EEP promotes fatty acid β-oxidation by activating PPAR pathway, resulting in reduced hepatic lipid deposition, and attenuates bile acid (BA) accumulation by improving BA metabolism, which were ensured through qPCR validation of key genes and immunoblot validation of key proteins. CONCLUSIONS: EEP can regulate lipid metabolic dysregulation during aging accompanied by an HFD, potentially delaying the onset and progression of age-related diseases. This provides new approach for supporting healthy aging.