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Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature.

ABSTRACT: Ageing is characterised at the molecular level by six transcriptional 'hallmarks of ageing', that are commonly described as progressively affected as time passes. By contrast, the 'Smurf' assay separates high-and-constant-mortality risk individuals from healthy, zero-mortality risk individuals, based on increased intestinal permeability. Performing whole body total RNA sequencing, we found that Smurfness distinguishes transcriptional changes associated with chronological age from those associated with biological age. We show that transcriptional heterogeneity increases with chronological age in non-Smurf individuals preceding the other five hallmarks of ageing that are specifically associated with the Smurf state. Using this approach, we also devise targeted pro-longevity genetic interventions delaying entry in the Smurf state. We anticipate that increased attention to the evolutionary conserved Smurf phenotype will bring about significant advances in our understanding of the mechanisms of ageing.

SUBMITTER: Zane F

PROVIDER: S-EPMC10652310 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature.

Zane Flaminia F Bouzid Hayet H Sosa Marmol Sofia S Brazane Mira M Besse Savandara S Molina Julia Lisa JL Cansell Céline C Aprahamian Fanny F Durand Sylvère S Ayache Jessica J Antoniewski Christophe C Todd Nicolas N Carré Clément C Rera Michael M

Aging cell 20231012 11

Ageing is characterised at the molecular level by six transcriptional 'hallmarks of ageing', that are commonly described as progressively affected as time passes. By contrast, the 'Smurf' assay separates high-and-constant-mortality risk individuals from healthy, zero-mortality risk individuals, based on increased intestinal permeability. Performing whole body total RNA sequencing, we found that Smurfness distinguishes transcriptional changes associated with chronological age from those associate ...[more]

PMID: 37822253

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Project description:BackgroundProgrammed cell death protein 1 (PD-1) is a key immune checkpoint that regulates peripheral tolerance and protects against autoimmunity. Programmed death ligand-2 (PD-L2) is a less studied ligand to PD-1 and has yet to be fully explored, especially in pancreatic ductal adenocarcinoma (PDAC).MethodsIn this study, we performed immunohistochemistry to detect the PD-L2, CD3, CD8, transforming growth factor-β2 (TGF-β2) and FOXP3 levels in paraffin sections from 305 patients with resected PDAC as a training set. Expression levels of intratumoral and stromal immune markers were compared in relation to survival using Kaplan-Meier curves, random survival forest model and survival tree analysis. A multivariable Cox proportional-hazards model of associated markers was used to calculate the risk scores.ResultsPD-L2 was expressed in 71.5% of PDAC samples and showed strong correlations with CD3+, CD8+ T cells and FOXP3+ regulatory T cell densities. High levels of intratumoral PD-L2 and FOXP3 were related to poor survival; only stromal FOXP3 overexpression was associated with worse prognosis. Four patterns generated from survival tree analysis demonstrated that PD-L2lowstromalFOXP3low patients had the longest survival, while PD-L2highintratumoralCD3low patients had the shortest survival (P < 0.001). The area under the curve was 0.631(95% confidence interval (CI): 0.447-0.826) for the immune marker-based signature and 0.549 (95% CI: 0.323-0.829; P < 0.001) for the clinical parameter-based signature, which was consistent with the results in the validation set including 150 patients (P < 0.001). A higher risk score indicated shorter survival and could serve as an independent prognostic factor. PD-L2 was also showed associated with TGF-β2 and other immune molecules based on bioinformatics analysis.ConclusionsOur work highlighted PD-L2 as a promising immunotherapeutic target with prognostic value combined with complex tumor infiltrating cells in PDAC.

Dataset Information

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature.

Publications

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature.

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