A Multidimensional Systems Biology Analysis of Cellular Senescence in Ageing and Disease

Avelar, Roberto A.; Ortega, Javier Gómez; Tăcutu, Robi; Tyler, Eleanor J.; Bennett, Dominic; Binetti, Paolo; Budovsky, Arie; Chatsirisupachai, Kasit; Johnson, Emily; Murray, Alex; Shields, Samuel; Tejada‐Martinez, Daniela; Thornton, Daniel; Fraifeld, Vadim E.; Bishop, Cleo L.; Magalhães, João Pedro de

doi:10.1101/743781

Cited by 9 publications

(6 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, an unweighted co-expression network of highly correlated genes, extracted from the GeneFriends co-expression map, was also used. This co-expression network is reported by Avelar et al (2019).…”

Section: Network Analysissupporting

confidence: 74%

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Palmer

Fabris

Doherty

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding the expression changes that come with age is an important step in understanding the ageing process as a whole. By combining such transcriptomic data with other sources of information, for instance protein-protein interaction (PPI) data, it is possible to make inferences about the functional changes that occur with age. To address this, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, to identify genes that are commonly differentially expressed with age in mammals. We also conducted analyses on subsets of these datasets, to produce transcriptomic signatures for brain, heart and muscle tissues, all of which are important tissues in the pathophysiology of ageing. This approach identified the transcriptomic signatures of the ageing system, as well as brain, heart and muscle tissues. We then applied enrichment analysis and machine learning to functionally describe those signatures. This revealed a typical ageing signature including the overexpression of immune and stress response genes and the underexpression of metabolic and developmental genes. Further analysis of the ageing expression signatures revealed that genes differentially expressed with age tend to be broadly expressed across tissues, rather than be tissue-specific, and that the ageing expression signatures (particularly the overexpressed signatures) of the whole system, brain and muscle tend to include genes that are central in PPI networks. We also show that genes underexpressed in the brain are highly central in a co-expression map, suggesting that underexpression of these genes may play a part in cognitive ageing. In sum, we show numerous functional similarities between the ageing

show abstract

Section: Network Analysissupporting

confidence: 74%

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Palmer

Fabris

Doherty

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…From the GEO database, 1 the schizophrenia data set GSE92538 was selected as the training group and GSE21935 as the test group (13). CellAge database 2 was used to retrieve 279 cellular senescence-related genes (14). Figure 1 shows the process flow and the complete information dataset is given in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction

Feng

Shen

et al. 2023

Front. Psychiatry

View full text Add to dashboard Cite

BackgroundSchizophrenia (SC) is one of the most common psychiatric diseases. Its potential pathogenic genes and effective treatment methods are still unclear. Cell senescence has been confirmed in mental diseases. A link exists between cellular senescence and immunity, and immune-related problems affect suicide rates in individuals suffering from schizophrenia. Therefore, the aims of this study were to identify candidate genes based on cell senescence that can affect the diagnosis and treatment of schizophrenia.MethodsTwo data sets of schizophrenia were provided by the Gene Expression Omnibus (GEO) database, one was taken as training and the other as a validation group. The genes related to cell senescence were obtained from the CellAge database. DEGs were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). The function enrichment analysis was conducted, followed by machine learning-based identification for least absolute shrinking and selection operators (LASSO) regression. Random Forest were used to identify candidate immune-related central genes and establish artificial neural networks for verification of the candidate genes. The receiver operating characteristic curve (ROC curve) was used for the diagnosis of schizophrenia. Immune cell infiltrates were constructed to study immune cell dysregulation in schizophrenia, and relevant drugs with candidate genes were collected from the DrugBank database.ResultsThirteen co-expression modules were screened for schizophrenia, of which 124 were the most relevant genes.There were 23 intersected genes of schizophrenia (including DEGs and the cellular senescence-related genes), and through machine learning six candidate genes were finally screened out. The diagnostic value was evaluated using the ROC curve data. Based on these results it was confirmed that these candidate genes have high diagnostic value.Two drugs related to candidate genes, Fostamatinib and Ritodine, were collected from the DrugBanks database.ConclusionSix potential candidate genes (SFN, KDM5B, MYLK, IRF3, IRF7, and ID1) had been identified, all of which had diagnostic significance. Fostamatinib might be a drug choice for patients with schizophrenia to develop immune thrombocytopenic purpura (ITP) after treatment, providing effective evidence for the pathogenesis and drug treatment of schizophrenia.

show abstract

“…Regarding the prediction of vascular events, we will specifically calculate the Khorana and related scores 17 for comparison, and report the difference in performance. Further, for all biomarkers we find, we will check their association with cellular senescence, by manual inspection, literature investigation, comparison to CellAge 93 and the SASP Atlas 94 or by formal enrichment analyses if the number of biomarkers is sufficiently large to do this in a meaningful way. Also, in a final step, we plan to identify and filter out the biomarkers that are volatile in the controls.…”

Section: Methodsmentioning

confidence: 99%

Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study

et al. 2020

View full text Add to dashboard Cite

IntroductionAgeing-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co)morbidity, including cancer, thromboembolism and stroke. Interfering with these processes may delay, stop or reverse morbidity. The aim of this study is to investigate the link between (co)morbidity and ageing by exploring biomarkers and molecular mechanisms of disease-triggered deterioration in patients with pancreatic ductal adenocarcinoma (PDAC) and (thromboembolic) ischaemic stroke (IS).Methods and analysisWe will recruit 50 patients with PDAC, 50 patients with (thromboembolic) IS and 50 controls at Rostock University Medical Center, Germany. We will gather routine blood data, clinical performance measurements and patient-reported outcomes at up to seven points in time, alongside in-depth transcriptomics and proteomics at two of the early time points. Aiming for clinically relevant biomarkers, the primary outcome is a composite of probable sarcopenia, clinical performance (described by ECOG Performance Status for patients with PDAC and the Modified Rankin Scale for patients with stroke) and quality of life. Further outcomes cover other aspects of morbidity such as cognitive decline and of comorbidity such as vascular or cancerous events. The data analysis is comprehensive in that it includes biostatistics and machine learning, both following standard role models and additional explorative approaches. Prognostic and predictive biomarkers for interventions addressing senescence may become available if the biomarkers that we find are specifically related to ageing/cellular senescence. Similarly, diagnostic biomarkers will be explored. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems biology modelling as well as insights from preclinical animal models. We anticipate that our detailed study protocol and data analysis plan may also guide other biomarker exploration trials.Ethics and disseminationThe study was approved by the local ethics committee (Ethikkommission an der Medizinischen Fakultät der Universität Rostock, A2019-0174), registered at the German Clinical Trials Register (DRKS00021184), and results will be published following standard guidelines.

show abstract

A Multidimensional Systems Biology Analysis of Cellular Senescence in Ageing and Disease

Cited by 9 publications

References 102 publications

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction

Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study

Contact Info

Product

Resources

About