Biology subjects

de Magalhaes, J. P.

Publications and source records attributed to de Magalhaes, J. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Spike-in-normalised single-cell RNA-seq reveals cell-type-specific transcriptional repression during ageing

Transcriptome analyses are widely used for biomarker discovery and to gain insights into normal processes and diseases. Age-related changes in gene expression inferred from RNA-seq are typically reported relative to the transcriptome composition using library-size normalisation. As such, absolute changes in transcript abundance with age remain poorly characterised. Here, using external spike-in normalisation in the Tabula Muris Senis dataset, we quantify age-related variation in total mRNA content and gene expression across mouse cell types. We observe widespread changes in total mRNA abundance, with decreases predominantly in non-immune cell types and increases predominantly in immune cell types. In parallel, the number of genes expressed declines across most cell types, including immune populations. Differential expression analysis based on spike-in-normalised counts identifies genes consistently downregulated across cell types, enriched for functions in RNA metabolism and protein processing. Furthermore, genes downregulated during ageing and during proliferation arrest show partial overlap, suggesting that these transcriptional changes may share regulatory processes. Together, these results are consistent with a general repression of transcriptional and metabolic activity with age, modulated by immune-specific responses. HighlightsO_LITotal mRNA content decreases with age in several non-immune cell types and increases in most immune cell types. C_LIO_LIThe number of detected genes decreases with age in most cell types. C_LIO_LIGenes downregulated based on absolute normalisation are associated with RNA and protein metabolism. C_LIO_LIAgeing-associated transcriptional repression overlaps with gene expression programs linked to proliferation arrest. C_LI

genomics↗

bioRxiv2026

Translational toolkit for reproducible, cross-study profiling of human ageing hallmarks in human blood and tissue

BackgroundAgeing is a complex, multi-dimensional process, underpinned by interacting biological hallmarks that collectively contribute to functional decline and increased susceptibility to disease. While considerable progress has been made in delineating individual ageing pathways, translation into human studies has been hindered by methodological heterogeneity and a lack of standardised, multi-system approaches. Here, we describe a validated, high-resolution toolkit for the simultaneous quantification of multiple ageing hallmarks in clinically accessible human samples, encompassing cellular senescence, immune ageing, inflammation, mitochondrial function, mTOR signalling, autophagy, genomic instability, and stem cell exhaustion. MethodsBlood (25ml) was obtained from young and aged donors (26-81y). Deep immunophenotyping was performed using a novel 30-colour spectral flow cytometry panel. T-cell mTOR activation and autophagic flux were assessed by flow cytometry. Metabolic flux was measured by Seahorse. From whole blood (4 ml), muscle, and adipose tissue (AT) (obtained during elective hip arthroplasty) RNA, DNA, AT stem cells, and myoblasts were isolated. DNA copy number and senescent cell burden were assessed by q-PCR and SA-{beta}-gal staining, respectively. FindingsUtilising this toolkit, we identified pronounced age-related immune remodelling, increased senescent T-cell burden, diminished mitochondrial capacity and altered mTOR-autophagy signalling between healthy young and aged donors. Furthermore, metabolism was significantly affected by anti-coagulant and freezing sample before analysis. InterpretationThis integrated platform provides a foundation for reproducible, cross-study analyses and facilitates translational investigation of interventions targeting health-span extension. FundingWellcome Leap Dynamic Resilience program (co-funded by Temasek Trust).

immunology↗

bioRxiv2025

Reporting quality, effect sizes, and biases for aging interventions: a methodological appraisal of the DrugAge database

Though interest has grown significantly over the past decades in interventions that may slow the aging process, most evidence for these interventions still comes from experiments in non-human animals. These studies may suffer from design, quality and reporting issues. The quality and reporting of preclinical studies have not yet been studied systematically in anti-aging research. Here we analyzed the DrugAge database, assessing reporting study quality, bias and effect sizes across 667 anti-aging preclinical studies. We found significant shortcomings in reporting of crucial design features such as randomization and blinding, as well as large variation in reporting quality and effects across species. Non-mammal findings typically did not translate to mammals. Although anti-aging interventions may have different effects depending on when they are started, most studies began giving the intervention under investigation very early in the organisms lifespan. Our findings suggest there is substantial room for improvement in preclinical anti-aging research.

physiology↗

bioRxiv2024

Sex-specific insights into drug-induced lifespan extension and weight loss in mice

The DrugAge database serves as a comprehensive resource for the study of compounds that increase lifespan in model organisms. In the latest version of DrugAge, we have implemented multiple updates, predominantly focusing on mouse (Mus musculus) studies to enhance data accuracy and consistency. Key improvements include the re-recording of mouse data from original sources, standardization of drug dosages to parts per million (ppm), and detailed recording of administration routes, treatment initiation ages, and durations. The user interface has also been upgraded. Additionally, weight change data were included to address the potential impact of caloric restriction induced by drug administration on lifespan. Our analysis revealed significant correlations between weight loss and lifespan extension in male mice, particularly in studies conducted by the Interventions Testing Program (ITP), highlighting the importance of considering weight change in lifespan studies. We also observed notable sex-related differences in lifespan and weight change responses, underscoring the need for gender-specific analyses in aging research.

pharmacology and toxicology↗

bioRxiv2024

Predicting Mouse Lifespan-Extending Chemical Compounds with Machine Learning

Pharmacological interventions targeting the biological processes of ageing hold significant potential to extend healthspan and promote longevity. This, to our knowledge, is the first study that uses Machine Learning models trained specifically on mouse lifespan data (from DrugAge) to predict lifespan-extending compounds. The use of mammalian data significantly elevates translational relevance compared to previously available models trained predominately on C. elegans data. Our most successful Random Forest classifiers were trained on direct drug-target annotations, including Gene Ontology, UniProt Keywords, pathways (KEGG, Reactome, Wiki) and protein domains (InterPro), whereas models trained on gene expression (LINCS) and chemical substructures (PubChem) underperformed. Models trained on male datasets performed better than those trained on mixed-sex and female datasets, with the latter suffering from severe class imbalance due to much fewer positive-class instances. Notably, features related to G-protein coupled receptors, especially receptors for neurotransmitters, metabolic hormones and sex hormones, were identified as strong predictors of lifespan extension. We used ensemble classifiers comprised of top models to screen compounds from DrugBank, highlighting novel candidates for longevity studies. Major clusters of compounds with the highest predicted longevity-promoting effects target IGF1 and insulin receptors, beta adrenergic receptors, carbonic anhydrases, dopamine and serotonin receptors, voltage-gated potassium and calcium channels, sodium-dependent dopamine, serotonin and noradrenalin transporters, muscarinic acetylcholine receptors and adenosine receptors. We tested 22 predicted compounds in C. elegans and found that 6 of them significantly extended median lifespan: dihydroergotamine, mianserin, bromocriptine, voxtalisib, bms-754807 and solifenacine. We have also created a public web server with our top performing classifier ensembles: https://www.cs.kent.ac.uk/projects/lodprime/ Our study not only provides an important contribution to the longevity pharmacology field but also informs research on the fundamental mechanisms of ageing.

bioinformatics↗

bioRxiv2024

Mosaic Regulation of Stress Pathways Underlies Senescent Cell Heterogeneity

Cellular senescence (CS) and quiescence (CQ) are stress responses characterised by persistent and reversible cell cycle arrest, respectively. These phenotypes are heterogeneous, dependent on the cell type arrested and the insult inciting arrest. Because a universal biomarker for CS has yet to be identified, combinations of senescence-associated biomarkers linked to various biological stress responses including lysosomal activity ({beta}-galactosidase staining), inflammation (senescence-associated secretory phenotypes, SASPs), and apoptosis (senescent cell anti-apoptotic pathways) are used to identify senescent cells. Using in vitro human bulk RNA-seq datasets, we find that senescent states enrich for various stress responses in a cell-type, temporal, and insult-dependent manner. We further demonstrate that various gene signatures used to identify senescent cells in the literature also enrich for stress responses, and are inadequate for universally and exclusively identifying senescent samples. Genes regulating stress responses - including transcription factors and genes controlling chromatin accessibility - are contextually differentially expressed, along with key enzymes involved in metabolism across arrest phenotypes. Additionally, significant numbers of SASP proteins can be predicted from senescent cell transcriptomes and also heterogeneously enrich for various stress responses in a context-dependent manner. We propose that senescence cannot be meaningfully defined due to the lack of underlying preserved biology across senescent states, and CS is instead a mosaic of stress-induced phenotypes regulated by various factors, including metabolism, TFs, and chromatin accessibility. We introduce the concept of Stress Response Modules, clusters of genes modulating stress responses, and present a new model of CS and CQ induction conceptualised as the differential activation of these clusters.

bioinformatics↗

bioRxiv2024

Pleiotropy and Disease Interactors: The Dual Nature of Genes Linking Ageing and Ageing-related Diseases

Ageing-related diseases (ARDs) exhibit a broad spectrum of phenotypes yet consistently increase in incidence with advancing age. This suggests that, despite their diversity, ARDs could potentially share common biological processes deeply rooted in the mechanisms of ageing, presenting opportunities for unified therapeutic strategies. Using a network approach, we analysed gene proximity to 52 ARDs from the UK Biobank, integrating with protein-protein interaction (PPI), gene coexpression, KEGG pathways, and ageing-related genes. Interestingly, while most ageing-related genes did not associate with ARDs, they were closer to multiple ARDs than random genes. This was mainly due to indirect connections to diverse Communities of ARDs (ARCs), what we call iARC-Interactors, implying indirect association to multiple ARDs through interaction with ARD-related genes, primarily via PPI and KEGG. Genes that are associated with multiple ARCs, i.e., Pleiotropic genes, were predominantly related to immunological disorders. We found a polarizing effect. When compared to multiple ageing- and disease-related genes, high Pleiotropic genes showed the highest tissue specificity and lowest coexpression with themselves and other diseases. In contrast, high iARC-interactive genes (as those of ageing) significantly displayed the exact opposite effects, suggesting two mechanisms for genes to affect multiple ARDs, one operating through modulatory genes that simultaneously affect numerous tissues and processes; and another rather specialised, affecting single tissues that are widespread across the body, as potentially occurring in autoimmune diseases. Lastly, we used Machine Learning (ML) to predict potentially novel ageing-related genes based on each networks iARC-Interactions and genes proximity to ARDs. PPI and KEGG showed the best performance with their top candidate genes enriched for regulation of protein metabolic process, protein stabilization, positive regulation of developmental process, and cellular response to chemical stimulus. This work paints a deeper picture of the multiple types of interactions between ageing-related processes and ARDs.

bioinformatics↗

bioRxiv2024

Unveiling Ageing-Associated and Caloric Restriction-Associated Changes in miRNA Expression in Rat Skeletal Muscle and the Mechanisms Mediating their Effects on Muscle Cell Function

The mechanisms underlying skeletal muscle ageing, whilst poorly understood, are thought to involve dysregulated micro (mi)RNA expression. Using young and aged rat skeletal muscle tissue, we applied high-throughput RNA sequencing to comprehensively study alterations in miRNA expression occurring with age, as well as the impact of caloric restriction (CR) on these changes. Furthermore, the function of the proteins targeted by these age- and CR-associated miRNAs was ascertained. Numerous known and novel age-associated miRNAs were identified of which CR normalised 45.5% to youthful levels. Our results suggested miRNAs upregulated with age to downregulated proteins involved in muscle tissue development and metabolism, as well as longevity pathways, such as AMPK and autophagy. Furthermore, our results found miRNAs downregulated with age to upregulate pro-inflammatory proteins, particularly those involved in innate immunity and the complement and coagulation cascades. Interestingly, CR was particularly effective at normalising miRNAs upregulated with age, rescuing their associated protein coding genes but was less effective at rescuing anti-inflammatory miRNAs downregulated with age. Lastly, the effects of a specific miRNA, miR-96-5p, identified by our analysis to be upregulated with age, were studied in culture C2C12 myoblasts. We demonstrated miR-96-5p to decrease cell viability and markers of mitochondrial biogenesis, myogenic differentiation and autophagy. Overall, our results provide useful information regarding how miRNA expression changes in skeletal muscle, as well as the consequences of these changes and how they are ameliorated by CR.

molecular biology↗

bioRxiv2024

Unravelling the Transcriptomic Symphony of Sarcopenia: Key Pathways and Hub Genes Altered by Muscle Ageing and Caloric Restriction Revealed by RNA Sequencing

Sarcopenia is a disease involving extensive loss of muscle mass and strength with age and is a major cause of disability and accidents in the elderly. Mechanisms purported to be involved in muscle ageing and sarcopenia are numerous but poorly understood, necessitating deeper study. Hence, we employed high-throughput RNA sequencing to explicate the global changes in protein-coding gene expression occurring in skeletal muscle with age. Caloric restriction (CR) is a proven prophylactic intervention against sarcopenia. Therefore, total RNA was isolated from the muscle tissue of both rats fed ad libitum and CR rats. Collected data were subjected to Gene Ontology, pathway, co-expression, and interaction network analyses. This revealed the functional pathways most activated by both ageing and CR, as well as the key "hub" proteins involved in their activation. RNA-seq revealed 442 protein-coding genes to be upregulated and 377 to be downregulated in aged muscle, compared to young muscle. Upregulated genes were commonly involved in protein folding and the immune response; meanwhile, downregulated genes were often related to developmental biology. CR was found to suppress 69.7% and rescue 57.8% of the genes found to be upregulated and downregulated in aged muscle, respectively. In addition, CR uniquely upregulated 291 and downregulated 304 protein-coding genes. Hub genes implicated in both ageing and CR included Gc, Plg, Irf7, Ifit3, Usp18, Rsad2, Blm and RT1-A2, whilst those exclusively implicated in CR responses included Alb, Apoa1, Ambp, F2, Apoh, Orm1, Mx1, Oasl2 and Rtp4. Hub genes involved in ageing but unaffected by CR included Fgg, Fga, Fgb and Serpinc1. In conclusion, this comprehensive RNA sequencing study highlighted gene expression patterns, hub genes and signalling pathways most affected by ageing in skeletal muscle. This data may provide the initial evidence for several targets for therapeutic interventions against sarcopenia.

genomics↗

bioRxiv2023

Human Ageing Genomic Resources: updates on key databases in ageing research

Ageing is a complex and multifactorial process. For two decades, the Human Ageing Genomic Resources (HAGR) have aided researchers in the study of various aspects of ageing and its manipulation. Here we present the key features and recent enhancements of these resources, focusing on its six main databases. One database, GenAge, focuses on genes related to ageing, featuring 307 genes linked to human ageing and 2205 genes associated with longevity and ageing in model organisms. AnAge focuses on ageing, longevity, and life-history across animal species, containing data on 4645 species. DrugAge includes information about 1097 longevity drugs and compounds in model organisms such as mice, rats, flies, worms, and yeast. GenDR provides a list of 214 genes associated with the life-extending benefits of dietary restriction in model organisms. CellAge contains a catalogue of 866 genes associated with cellular senescence. The LongevityMap serves as a repository for genetic variants associated with human longevity, encompassing 3144 variants pertaining to 884 genes. Additionally, HAGR provides various tools as well as gene expression signatures of ageing, dietary restriction, and replicative senescence based on meta-analyses. Our databases are integrated, regularly updated, and manually curated by experts. HAGR is freely available online (https://genomics.senescence.info/).

bioinformatics↗

bioRxiv2023

Epigenetic fidelity in complex biological systems and implications for ageing

The study of age is plagued by a lack of delineation between the causes and effects within the ageing phenotype. This has made it difficult to fully explain the biological ageing process from first principles with a single definition. Lacking a clear description of the underlying root cause of biological age confounds clarity in this critical field. In this paper, we demonstrate that the epigenetic system has a built-in, unavoidable fidelity limitation and consequently demonstrate that there is a distinct class of DNA methylation loci that increases in variance in a manner tightly correlated with chronological age. We demonstrate the existence of epigenetic activation functions and that topological features beyond these activation functions represent deregulation. We show that the measurement of epigenetic fidelity is an accurate predictor of cross-species age and present a deep-learning model that predicts chronological age exclusively from knowledge of variance. We find that the classes of epigenetic loci in which variation correlates with chronological age control genes that regulate transcription and suggest that the inevitable consequence of this is a feedback cycle of system-wide deregulation causing a progressive collapse into the phenotype of age. This paper represents a novel theory of biological systemic ageing with arguments as to why, how and when epigenetic ageing is inevitable.

genomics↗

bioRxiv2023

Evidence of a pan-tissue decline in stemness during human aging

Despite their biological importance, the role of stem cells in human aging remains to be elucidated. In this work, we applied a machine learning methodology to GTEx transcriptome data and assigned stemness scores to 17,382 healthy samples from 30 human tissues aged between 20 and 79 years. We found that [~]60% of the studied tissues present a significant negative correlation between the subjects age and stemness score. The only significant exception to this pattern was the uterus, where we observed an increased stemness with age. Moreover, we observed a global trend of positive correlations between cell proliferation and stemness. When analyzing the tissues individually, we found that [~]50% of human tissues present a positive correlation between stemness and proliferation and 20% a negative correlation. Furthermore, all our analyses show negative correlations between stemness and cellular senescence, with significant results in [~]80% of the tissues analyzed. Finally, we also observed a trend that hematopoietic stem cells derived from old patients might have more stemness. In short, we assigned stemness scores to human samples and show evidence of a pan-tissue loss of stemness during human aging, which adds weight to the idea that stem cell deterioration contributes to human ageing.

genomics↗

bioRxiv2022

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. We found a loss of cell identity during ageing in around 40% of tissues. In cancer, we observed a pattern of downregulation of genes specific to the tissue of origin of the tumor for most cancer types. Moreover, we observed an activation of genes not usually expressed in a tissue as well as an upregulation of genes specific to other tissues in cancer. These patterns in cancer were associated with patient survival. The age of the patient, however, does not influence these patterns. Thus, we identified a moderate but statistically significant pattern of loss of cell identity in ageing, and a clear pattern in cancer, where during tumorigenesis, cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

bioinformatics↗

bioRxiv2022

Predicting lifespan-extending chemical compounds with machine learning and biologically interpretable features

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as on the use of machine learning for analysing ageing-related data. In this work we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBases Phenotype Ontology. To analyse these datasets we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. The two best models were learned using GO terms and protein interactors as features, with predictive accuracies of about 82% and 80%, respectively. In addition, we interpreted the most important features in those two best models in light of the biology of ageing, and we also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds.

bioinformatics↗

bioRxiv2021

Rilmenidine mimics caloric restriction via the nischarin I1-imidazoline receptor to extend lifespan in C. elegans

Caloric restriction increases lifespan across species and has health benefits in humans. Because complying with a low-calorie diet is challenging, here we investigated pharmacological interventions mimicking the benefits of caloric restriction. Searching for compounds that elicit a similar gene expression signature to caloric restriction, we identified rilmenidine, an I1-imidazoline receptor agonist and prescription medication for the treatment of hypertension. We then show that treating C. elegans with rilmenidine at young and older ages increases lifespan. We also demonstrate that the stress-resilience, healthspan, and lifespan benefits upon rilmenidine treatment in worms are mediated by the I1-imidazoline receptor nish-1, implicating this receptor as a potential longevity target. Furthermore, we show that rilmenidine treatment increased ERK phosphorylation via NISH-1. Consistent with the shared caloric-restriction-mimicking gene signature, supplementing rilmenidine to caloric restricted C. elegans, genetic reduction of TORC1 function, or rapamycin treatment did not further increase lifespan. The rilmenidine-induced longevity required the transcription factors FOXO/DAF-16 and NRF1,2,3/SKN-1, both important for caloric restriction-mediated longevity. Furthermore, we find that autophagy, but not AMPK signaling, was needed for rilmenidine-induced longevity. Lastly, we find that treating mice with rilmenidine showed transcriptional changes in liver and kidney similar to caloric restriction. Overall, our findings reveal rilmenidine as a caloric restriction mimetic and as a novel geroprotective compound.

pharmacology and toxicology↗

bioRxiv2021

scAgeCom: a murine atlas of age-related changes in intercellular communication inferred with the package scDiffCom

Dysregulation of intercellular communication is a well-established hallmark of aging. To better understand how this process contributes to the aging phenotype, we built scAgeCom, a comprehensive atlas presenting how cell-type to cell-type interactions vary with age in 23 mouse tissues. We first created an R package, scDiffCom, designed to perform differential intercellular communication analysis between two conditions of interest in any mouse or human single-cell RNA-seq dataset. The package relies on its own list of curated ligand-receptor interactions compiled from seven established studies. We applied this tool to single-cell transcriptomics data from the Tabula Muris Senis consortium and the Calico murine aging cell atlas. All the results can be accessed online, using a user-friendly, interactive web application (https://scagecom.org). The most widespread changes we observed include upregulation of immune system processes, inflammation and lipid metabolism, and downregulation of extracellular matrix organization, growth, development and angiogenesis. More specific interpretations are also provided.

bioinformatics↗

bioRxiv2021

Machine-learning-based predictions of caloricrestriction associations across ageing-related genes

Caloric restriction (CR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel CR-related genes and CR-related genetic features. This work used a Machine Learning (ML) approach to classify ageing-related genes as CR-related or NotCR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype-Tissue Expression (GTEx) expression features, Gene-Friends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking CR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict CR-relatedness among ageing-related genes currently lacking CR-related annotations in the data, resulting in a set of promising candidate CR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted CR-relatedness remain to be validated in future wet-lab experiments.

bioinformatics↗

bioRxiv2021

Positive selection and enhancer evolution shaped lifespan and body mass in great apes

Within primates, the great apes are outliers both in terms of body size and lifespan, since they include the largest and longest-lived species in the order. Yet, the molecular bases underlying such features are poorly understood. Here, we leveraged an integrated approach to investigate multiple sources of molecular variation across primates, focusing on ~1,550 genes previously described as tumor suppressors, oncogenes, ageing genes in addition to a novel Build of the CellAge database of cell-senescence genes (version 2), herein presented for the first time. Specifically, we analyzed dN/dS rates, positive selection, gene expression (RNA-seq) and gene regulation (ChIP-seq). By analyzing the correlation between dN/dS, maximum lifespan and body mass we identified 67 genes that in primates co-evolved with those traits. Further, we identified 6 genes, important for immunity, neurodevelopment and telomere maintenance (including TERF2), under positive selection in the great ape ancestor. RNA-seq data, generated from the liver of six species representing all the primate lineages, revealed that ~8% of the longevity genes are differentially expressed in apes relative to other primates. Importantly, by integrating RNA-seq with ChIP-seq for H3K27ac (which marks active enhancers), we show that the differentially expressed longevity genes are significantly more likely than expected to be located near a novel "ape-specific" enhancer. Moreover, these particular ape-specific enhancers are enriched for young transposable elements, and specifically SINE-Vntr-Alus (SVAs). In summary, we demonstrate that multiple evolutionary forces have contributed to the evolution of lifespan and body size in primates.

genomics↗