Age-related Gene Research Articles

Toward comprehensive functional analysis of gene lists weighted by gene essentiality scores.

Gene functional enrichment analysis represents one of the most popular bioinformatics methods for annotating the pathways and function categories of a given gene list. Current algorithms for enrichment computation such as Fisher's exact test and hypergeometric test totally depend on the category count numbers of the gene list and one gene set. In this case, whatever the genes are, they were treated equally. However, actually genes show different scores in their essentiality in a gene list and in a gene set. It is thus hypothesized that the essentiality scores could be important and should be considered in gene functional analysis. For this purpose, here, we proposed weighted enrichment analysis tool (WEAT) (https://www.cuilab.cn/weat/), a weighted gene set enrichment algorithm and online tool by weighting genes using essentiality scores. We confirmed the usefulness of WEAT using three case studies, the functional analysis of one aging-related gene list, one gene list involved in Lung Squamous Cell Carcinoma and one cardiomyopathy gene list from Drosophila model. Finally, we believe that the WEAT method and tool could provide more possibilities for further exploring the functions of given gene lists. The datasets generated and analyzed during the current study are available on our website at https://www.cuilab.cn/weat/. Supplementary data are available at Bioinformatics online.

Pleiotrophin attenuates the senescence of dental pulp stem cells.

Pleiotrophin (PTN), a secreted extracellular matrix-associated protein, plays an important role in regulating the osteo/dentinogenic differentiation potential of dental pulp stem cells (DPSCs). Our previous study has demonstrated that PTN expression in young DPSCs was is 10-fold higher than that in aged DPSCs. However, the role of PTN on the in maintaining the stemness of senescent DPSCs remains unclear. The present study aimed to investigate the effect of PTN on senescent DPSCs in vitro. Dental pulp stem cells were isolated from human third molars. PTN was knocked down using short hairpin RNAs to study the role of PTN on the senescence of DPSCs. DPSCs with aging performance were obtained by a replicative senescence cell model was obtained by the long-term culture of DPSCs to the 15th passage in vitro (P15). We then investigated the effect of PTN on senescent DPSCs (P15 DPSCs). Real-time RT-PCR, western blotting, alizarin red staining, quantitative calcium analysis, SA-β-Gal staining, CFSE, and cell-counting kit-8 (CCK8) assays were used to study cellular senescence and function. The depletion of PTN increased the ratio of SA-β-gal-positive cells, upregulated the expression of p16, and down-regulated the expression of TERT and p-p38. Furthermore, 50pg/ml of PTN recombinant protein rescued these changes the altered ratio of SA-β-gal-positive cells, decreased the expression of p16, enhanced TERT and p-p38 expression, as well as telomere activity, caused by PTN depletion and long-term culture. The15th passage cells displayed typical aging characteristic, including high ratio of SA-β-gal-positive cells, increased aging-related gene expression, decreased proliferation rate, high level of Cyclin D expression, and impaired osteo/dentinogenic differentiation potential. However, 50pg/ml of PTN recombinant protein could partially reverse these alteration rescue these changes. The present study demonstrated that PTN could protect DPSCs from senescence by improving the proliferation and osteo/dentinogenic differentiation ability, probably through the p38 MAPK pathway.

Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma.

Simple SummaryFew studies have employed expression data of normal tissues to explore the survival of cancer patients. Hence, we identified aging-related genes and microRNAs in normal tissues from patients with various types of cancer and investigated their effects on survival. We built statistical models to predict the survival of cancer patients using the expression levels of aging-related genes and microRNAs. We found that five aging-related genes (DUSP22, MAPK14, MAPKAPK3, STAT1, and VCP) from kidney cancer patients were significantly related to patient survival. We experimentally showed that there is an increase in the five aging-related genes in the macrophages of aged mice and, among these five genes, the invasion of cancer cells is inhibited by downregulating DUSP22. Using the zebrafish model of tumor cell dissemination, it was also confirmed that in vivo treatment with a small molecule inhibitor of DUSP22 blocked the metastasis of cancer cells in the early stage.The relationship between expression of aging-related genes in normal tissues and cancer patient survival has not been assessed. We developed a genome-wide transcriptomic analysis approach for normal tissues adjacent to the tumor to identify aging-related transcripts associated with survival outcome, and applied it to 12 cancer types. As a result, five aging-related genes (DUSP22, MAPK14, MAPKAPK3, STAT1, and VCP) in normal tissues were found to be significantly associated with a worse survival outcome in patients with renal cell carcinoma (RCC). This computational approach was investigated using nontumorigenic immune cells purified from young and aged mice. Aged immune cells showed upregulated expression of all five aging-related genes and promoted RCC invasion compared to young immune cells. Further studies revealed DUSP22 as a regulator and druggable target of metastasis. DUSP22 gene knockdown reduced RCC invasion and the small molecule inhibitor BML-260 prevented RCC dissemination in a tumor/immune cell xenograft model. Overall, these results demonstrate that deciphering the relationship between aging-related gene expression in normal tissues and cancer patient survival can provide new prognostic markers, regulators of tumorigenesis and novel targets for drug development.

Age related gene DST represents an independent prognostic factor for MYCN non-amplified neuroblastoma

BackgroundMYCN amplification and age are two critical prognostic factors of pediatric neuroblastoma. Previously, we had revealed the prognosis of MYCN target genes. However, the prognostic effects of age related genes in neuroblastoma are unclear.MethodsThe prognostic significance of age and MYCN amplification was determined through multivariate cox regression and Kaplan-Meier survival analysis. Genes differentially expressed in MYCN non-amplified younger neuroblastoma patients were identified using Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) datasets. The prognostic effects of age related genes ALCAM, CACNA2D3, DST, EPB41L4A and KIF1B in pediatric neuroblastoma patients were determined by Kaplan-Meier survival.ResultsIn a pediatric pan-cancer analysis, age was associated with the overall survival of pediatric B-lineage acute lymphoblastic leukemia, neuroblastoma and wilms tumor in TARGET dataset. Moreover, the prognostic effects of age in neuroblastoma were validated using two independent neuroblastoma cohorts. Furthermore, age and MYCN amplification were independent prognostic factors in pediatric neuroblastoma. Compared with MYCN non-amplified older neuroblastoma patients, MYCN non-amplified younger neuroblastoma patients had better clinical outcomes. ALCAM, CACNA2D3, DST, EPB41L4A and KIF1B were highly expressed in MYCN non-amplified younger neuroblastoma patients. And the higher expression levels of ALCAM, CACNA2D3, DST, EPB41L4A or KIF1B were associated with better prognosis of MYCN non-amplified neuroblastoma patients. DST was an independent prognostic factor in MYCN non-amplified neuroblastoma patients and MYCN non-amplified neuroblastoma younger patients with higher DST expression levels had the best clinical overall survival.ConclusionsAge related gene DST was an independent prognostic factor in MYCN non-amplified neuroblastoma. MYCN non-amplified younger neuroblastoma patients with higher DST expression levels had the best clinical overall survival.

MADS-box transcription factors MADS11 and DAL1 interact to mediate the vegetative-to-reproductive transition in pine.

The reproductive transition is an important event that is crucial for plant survival and reproduction. Relative to the thorough understanding of the vegetative phase transition in angiosperms, a little is known about this process in perennial conifers. To gain insight into the molecular basis of the regulatory mechanism in conifers, we used temporal dynamic transcriptome analysis with samples from seven different ages of Pinus tabuliformis to identify a gene module substantially associated with aging. The results first demonstrated that the phase change in P. tabuliformis occurred as an unexpectedly rapid transition rather than a slow, gradual progression. The age-related gene module contains 33 transcription factors and was enriched in genes that belong to the MADS (MCMl, AGAMOUS, DEFICIENS, SRF)-box family, including six SOC1-like genes and DAL1 and DAL10. Expression analysis in P. tabuliformis and a late-cone-setting P. bungeana mutant showed a tight association between PtMADS11 and reproductive competence. We then confirmed that MADS11 and DAL1 coordinate the aging pathway through physical interaction. Overexpression of PtMADS11 and PtDAL1 partially rescued the flowering of 35S::miR156A and spl1,2,3,4,5,6 mutants in Arabidopsis (Arabidopsis thaliana), but only PtMADS11 could rescue the flowering of the ft-10 mutant, suggesting PtMADS11 and PtDAL1 play different roles in flowering regulatory networks in Arabidopsis. The PtMADS11 could not alter the flowering phenotype of soc1-1-2, indicating it may function differently from AtSOC1 in Arabidopsis. In this study, we identified the MADS11 gene in pine as a regulatory mediator of the juvenile-to-adult transition with functions differentiated from the angiosperm SOC1.

Aberrant Methylation of Aging-Related Genes in Asthma.

Background: Asthma is a complex pulmonary inflammatory disease which is common among older adults. Aging-related alterations have also been found in structural cells and immune cells of asthma patients. Nonetheless, the underlying mechanism by which differenced aging-related gene contributes to asthma pathology remains unclear. Of note, DNA methylation (DNAm) has been proven to play a critical mechanism for age-related gene expression changes. However, the methylation changes of aging-related genes in asthma patients are still obscure. Methods: First, changes in DNAm and gene expression were detected with multiple targeted bisulfite enrichment sequencing (MethTarget) and qPCR in peripheral blood of 51 healthy controls (HCs) and 55 asthmatic patients. Second, the correlation between the DNAm levels of specific altered CpG sites and the pulmonary function indicators of asthma patients was evaluated. Last, the receiver operator characteristic (ROC) curve and principal component analysis (PCA) were used to identify the feasibility of the candidate CpG sites as biomarkers for asthma. Results: Compared with HCs, there was a differential mRNA expression for nine aging-related genes in peripheral blood of asthma patients. Besides, the methylation levels of the nine aging-related genes were also altered in asthma patients, and a total of 68 CpG sites were associated with the severity of asthma. Notably, 9 of the 68 CpG sites were significantly associated with pulmonary function parameters. Moreover, ROC curve and PCA analysis showed that the candidate differential methylation sites (DMSs) can be used as potential biomarkers for asthma. Conclusions: In summary, this study confirmed the differentially expressed mRNA and aberrant DNAm level of aging-related genes in asthma patients. DMSs are associated with the clinical evaluation indicators of asthma, which indicate the involvement of aging-related genes in the pathogenesis of asthma and provide some new possible biomarkers for asthma.

Aging-related genes are potential prognostic biomarkers for patients with gliomas.

Aging has a significant role in the proliferation and development of cancers. This study explored the expression profiles, prognostic value, and potential roles of aging-related genes in gliomas. We designed risk score and cluster models based on aging-related genes and glioma cases using LASSO Cox regression analysis, consensus clustering analysis and univariate cox regression analyses. High risk score was related to malignant clinical features and poor prognosis based on 10 datasets, 2953 cases altogether. Genetic alterations analysis revealed that high risk scores were associated with genomic aberrations of aging-related oncogenes. GSVA analysis exhibited the potential function of the aging-related genes. More immune cell infiltration was found in high-risk group cases, and glioma patients in high-risk group may be more responsive to immunotherapy. Knock-down of CTSC, an aging-related gene, can inhibit cell cycle progression, colony formation, cell proliferation and increase cell senescence in glioma cell lines in vitro. Indeed, high expression of CTSC was associated with poor prognosis in glioma cases. In conclusion, this study revealed that aging-related genes have prognostic potential for glioma patients and further identified potential mechanisms for aging-related genes in tumorigenesis and progression in gliomas.

18P Age-related breast cancer gene expression signature with strong prognostic value

Breast cancer (BC) diagnosed at ages <40 years is known to present more aggressive tumor phenotypes and poorer clinical outcome. To gain a better understanding of the age-related expression of BC promoting genes, we aimed to identify transcriptional alterations supporting cancer progression in BC of young women.

Supervised Prediction of Aging-Related Genes From a Context-Specific Protein Interaction Subnetwork.

Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.

A signature of 24 aging\u2011related gene pairs predict overall survival in gastric cancer

BackgroundAging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients.MethodsThe mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log‐rank test and a Cox proportional hazards regression model.ResultsThe 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value − 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan–Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345–7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients.ConclusionOur study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.

Aging and pathological aging signatures of the brain: through the focusing lens of SIRT6.

Brain-specific SIRT6-KO mice present increased DNA damage, learning impairments, and neurodegenerative phenotypes, placing SIRT6 as a key protein in preventing neurodegeneration. In the aging brain, SIRT6 levels/activity decline, which is accentuated in Alzheimer’s patients. To understand SIRT6 roles in transcript pattern changes, we analyzed transcriptomes of young WT, old WT and young SIRT6-KO mice brains, and found changes in gene expression related to healthy and pathological aging. In addition, we traced these differences in human and mouse samples of Alzheimer’s and Parkinson’s diseases, healthy aging and calorie restriction (CR). Our results define four gene expression categories that change with age in a pathological or non-pathological manner, which are either reversed or not by CR. We found that each of these gene expression categories is associated with specific transcription factors, thus serving as potential candidates for their category-specific regulation. One of these candidates is YY1, which we found to act together with SIRT6 regulating specific processes. We thus argue that SIRT6 has a pivotal role in preventing age-related transcriptional changes in brains. Therefore, reduced SIRT6 activity may drive pathological age-related gene expression signatures in the brain.

The aging-related risk signature in colorectal cancer.

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. The opening of the TCGA and GEO databases has promoted the progress of CRC prognostic assessment, while the aging-related risk signature has never been mentioned.Methods: R software packages, GSEA software, Venn diagram, Metascape, STRING, Cytoscape, cBioPortal, TIMER and GeneMANIA website were used in this study.Results: Aging-related gene sets, GO_AGING, GO_CELL_AGING and GO_CELLULAR_SENESCENCE, were activated significantly in CRC tissues. We constructed an aging-related risk signature using LASSO COX regression in training group TCGA and validated in testing group GSE39582. The risk score was significantly associated with the overall survival of CRC patients, whose stability was clarified by stratified survival analysis and accuracy was demonstrated using the ROC curve. The risk score was significantly increased in the advanced stage, T3-4, N1-3 and M1 and positively correlated with the richness of immune cell infiltration in the tumor microenvironment. We further investigated the molecular characteristics of 15 hub genes at the DNA and protein levels and performed GSEA between high- and low-risk groups.Conclusions: The aging-related signature is a reliable prognostic analysis model and can predict the severity and immune cell infiltration of CRC patients.

Age-Related Gene Alteration in Naïve and Memory T cells Using Precise Age-Tracking Model.

In aged individuals, age-related changes in immune cells, especially T cell deficiency, are associated with an increased incidence of infection, tumor, and autoimmune disease, as well as an impaired response to vaccination. However, the features of gene expression levels in aged T cells are still unknown. Our previous study successfully tracked aged T cells generated from one wave of developing thymocytes of young age by a lineage-specific and inducible Cre-controlled reporter (TCRδCreERR26ZsGreen mouse strain). In this study, we utilized this model and genome-wide transcriptomic analysis to examine changes in gene expression in aged naïve and memory T cell populations during the aging process. We identified profound gene alterations in aged CD4 and CD8 T cells. Both aged CD4+ and CD8+ naïve T cells showed significantly decreased organelle function. Importantly, genes associated with lymphocyte activation and function demonstrated a significant increase in aged memory T cells, accompanied by upregulation of immunosuppressive markers and immune checkpoints, revealing an abnormal T cell function in aged cells. Furthermore, aging significantly affects T cell survival and death signaling. While aged CD4 memory T cells exhibited pro-apoptotic gene signatures, aged CD8 memory T cells expressed anti-apoptotic genes. Thus, the transcriptional analysis of gene expression and signaling pathways in aged T cell subsets shed light on our understanding of altered immune function with aging, which will have great potential for clinical interventions for older adults.

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.

Hints for Gentle Submacular Injection in Non-Human Primates Based on Intraoperative OCT Guidance

PurposeDelivery of Advanced Therapy Medicinal Products to the submacular space is increasingly evolving into a therapeutic modality. Cell replacement for age-related macular degeneration (AMD) and gene therapy for RPE65 are recent successful examples. Herein, a nonhuman primate (NHP) model was used to investigate surgical means to detach the macula.MethodsSixteen eyes of 13 healthy macaques underwent a 25-gauge vitrectomy and subretinal injection of balanced salt solution monitored by microscope-integrated intraoperative optical coherence tomography (miOCT). The animals were followed with OCT and histology.ResultsThe miOCT monitoring allowed a more precise definition of surgical trauma ranging from an initial full-thickness foveal tear, or induction of a cystoid macular edema (CME), until no foveal defect was discernible, as the technique improved. However, as the subretinal fluid wave detached the fovea, the aforementioned lesions formed, whereas persistent retinal adhesion reproducibly proved to remain in the distal parafoveal semi-annulus. Measures to reduce foveal trauma during submacular fluid injection included reducing intraocular pressure, injection volume, and velocity, as well as the retinal location for bleb initiation, use of a vitreous tamponade, and a dual-bore subretinal cannula.ConclusionsA stable very low intraocular pressure and careful subretinal injection may avoid tangential macular stretching or mechanical CME formation, while vitreous tamponade may facilitate a more lamellar subretinal flow, all thereby reducing foveal trauma during submacular injection in NHP.Translational RelevanceThese results can be relevant to any submacular surgery procedure used today, as they synergistically reduce the risk of compromising foveal integrity.

Analysis of Hub Genes Involved in Distinction Between Aged and Fetal Bone Marrow Mesenchymal Stem Cells by Robust Rank Aggregation and Multiple Functional Annotation Methods.

Stem cells from fetal tissue protect against aging and possess greater proliferative capacity than their adult counterparts. These cells can more readily expand in vitro and senesce later in culture. However, the underlying molecular mechanisms for these differences are still not fully understood. In this study, we used a robust rank aggregation (RRA) method to discover robust differentially expressed genes (DEGs) between fetal bone marrow mesenchymal stem cells (fMSCs) and aged adult bone marrow mesenchymal stem cells (aMSCs). Multiple methods, including gene set enrichment analysis (GSEA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for functional annotation of the robust DEGs, and the results were visualized using the R software. The hub genes and other genes with which they interacted directly were detected by protein–protein interaction (PPI) network analysis. Correlation of gene expression was measured by Pearson correlation coefficient. A total of 388 up-regulated and 289 down-regulated DEGs were identified between aMSCs and fMSCs. We found that the down-regulated genes were mainly involved in the cell cycle, telomerase activity, and stem cell proliferation. The up-regulated DEGs were associated with cell adhesion molecules, extracellular matrix (ECM)–receptor interactions, and the immune response. We screened out four hub genes, MYC, KIF20A, HLA-DRA, and HLA-DPA1, through PPI-network analysis. The MYC gene was negatively correlated with TXNIP, an age-related gene, and KIF20A was extensively involved in the cell cycle. The results suggested that MSCs derived from the bone marrow of an elderly donor present a pro-inflammatory phenotype compared with that of fMSCs, and the HLA-DRA and HLA-DPA1 genes are related to the immune response. These findings provide new insights into the differences between aMSCs and fMSCs and may suggest novel strategies for ex vivo expansion and application of adult MSCs.

Blood Cell DNA Methylation of Aging-Related Ubiquitination Gene DZIP3 Can Predict the Onset of Early Stage Colorectal Cancer.

There is a body of evidence that the aging immune system is linked to cancer. In this study, with aging- and immune-related DNA methylation data, we investigated the DNA methylation regulation changes in promoters with other regions of genes during aging and their association with the immune-cell proportion in the circulating whole blood of individuals. The analyses for aging- and CD4+ T cell proportion-derived differential genes showed that ubiquitination plays an important role in the aging immune system and tumorigenesis. Therefore, starting from a set of pre-annotated ubiquitination genes, we found that among the differentially ubiquitinated genes, DZIP3, an E3 ubiquitin ligase with no reports on its function in immune cells and tumorigenesis, was significantly associated with both aging (P-value = 3.86e-06) and CD4+ T cell proportion (P-value = 1.97e-05) in circulating blood. By collecting a cohort of 100 colon cancer patients and 50 healthy individuals, we validated that the 1st exon DNA methylation of DZIP3 could predict the onset of early stage (AUC = 0.833, OR = 8.82) and all pTNM stages of colorectal cancer (AUC = 0.782, OR = 5.70). Thus, the epigenetically regulated ubiquitination machine plays an important role in immune aging and tumorigenesis.

Age-related gene expression alterations by SARS-CoV-2 infection contribute to poor prognosis in elderly

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people worldwide and with notable heterogeneity in its clinical presentation. Probability of contracting this highly contagious infection is similar across age groups but disease severity and fatality among aged patients with or without comorbidities are reportedly higher. Previous studies suggest that age associated transcriptional changes in lung and immune system results in a proinflammatory state and increased susceptibility to infectious lung diseases. Similarly, SARS-CoV-2 infection could augment ageing-related gene expression alterations resulting in severe outcomes in elderly patients. To identify genes that can potentially increase covid-19 disease severity in ageing people, we compared age associated gene expression changes with disease-associated expression changes in lung/BALF and whole blood obtained from publicly available data. We observed (i) a significant overlap of gene expression profiles of patients’ BALF and blood with lung and blood of the healthy group, respectively; (ii) a more pronounced overlap in blood compared to lung; and (iii) a similar overlap between host genes interacting with SARS-CoV-2 and ageing blood transcriptome. Pathway enrichment analysis of overlapping gene sets suggest that infection alters expression of genes already dysregulated in the elderly, which together may lead to poor prognosis. eQTLs in these genes may also confer poor outcome in young patients worsening with age and comorbidities. Further, the pronounced overlap observed in blood may explain clinical symptoms including blood clots, strokes, heart attack, multi-organ failure etc. in severe cases. This model based on a limited patient dataset seems robust and holds promise for testing larger tissue specific datasets from patients with varied severity and across populations.Electronic supplementary materialThe online version of this article (10.1007/s12041-020-01233-7) contains supplementary material, which is available to authorized users.

Age-related gene expression and DNA methylation changes in rhesus macaque

Aging is a very complicated biological process that can change gene expressions. The Chinese rhesus macaque (Macaca mulatta lasiota; CR) is closely related to humans. We explored gene expression with increasing age and DNA methylation changes in young and old CRs. Results showed blood transcriptome and DNA methylome significantly changed from young to old CRs. The age-associated differentially expressed genes (DEGs) and differentially methylated regions (DMRs) were associated with age-related biological features, such as immunity, blood coagulation, and biosynthetic process. The measurements of coagulation indicators confirmed old CRs had shorter coagulation time than young CRs, and the activities of coagulation factor II (FII) and factor VIII (FVIII) were enhanced in old CRs. Humans and CRs exhibited the same enhanced blood coagulation with age phenotype. Our study found aging is a critical factor affecting gene expression in CRs, and also provided new insights into the blood coagulation changes in non-human primates.

RNAAgeCalc: A multi-tissue transcriptional age calculator.

Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Based on these genes, we develop new across-tissue and tissue-specific age predictors. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Our results also indicate that both racial and tissue differences are associated with transcriptional age. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#RNAAgeCalc, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.