bioRxivImmunologyPreprint
Bacterial exposure shapes the trajectory of immune ageing
Erik FeldmaniD, Michal Hrúzik, Holden Terry MaeckeriD, Alain RouletiD, Benjamin LelouvieriD, Neta MilmaniD, Martin LukačišiniD, Shai S. Shen-OrriD
Abstract
The immune system is uniquely capable of both regenerating and ageing the rest of the organism based on its own state1. Understanding the driving forces behind alterations in the immune system with age2 has thus potential to lead to interventions against ageing in general3. However, the factors driving immune ageing remain largely unknown4. Here we show that alterations in response to bacterial exposure are a key driving force of immune ageing. Studying a longitudinal cohort with their immune system characterised in high-dimension2, we devised a phenotype-driven gene co-regulation analysis through which we unravel the immune-ageing role for genes regulated by miRNAs previously reported to cause gut leakiness5. Hypothesizing increased bacterial exposure as a driver of immune ageing, we estimated the exposure of each individuals immune system to bacteria through analysing the residual bacterial DNA content in their immune cells6. We found that the total bacterial load is unrelated to the subjects chronological or immune age, but predicts the magnitude of change in immune age over the next year. Finally, we rationalise previously observed alterations in cellular composition of the immune system associated with ageing2 by finding that the immune cell subtypes most increasing in their frequency are highly enriched in expressing bacterial response genes. Our results thus earmark bacterial exposure as the candidate driver to be counteracted in anti-ageing interventions and the immune cell residual bacterial DNA content as a biomarker for the immune systems most at risk of ageing. Overall, these results offer a new paradigm for investigating gut health in the context of immune ageing7,8.
- organism: human
- assay: bulk RNA-seq
- research focus: host-pathogen & immune response
- approach: statistical model
- Citations
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