Bacterial exposure drives immune ageing

Bacterial exposure drives immune ageing | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Bacterial exposure drives immune ageing Erik Feldman , Michal Hrúzik , View ORCID Profile Martin Lukačišin , View ORCID Profile Shai S. Shen-Orr doi: https://doi.org/10.1101/2025.10.10.681722 Erik Feldman 1 Department of Immunology, Faculty of Medicine, Technion - Israel Institute of Technology , 1 Efron St, 35254, Haifa, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michal Hrúzik 2 Department of Genetics, Faculty of Natural Sciences, Comenius University , Ilkovičova 6, 84104, Bratislava, Slovakia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Lukačišin 1 Department of Immunology, Faculty of Medicine, Technion - Israel Institute of Technology , 1 Efron St, 35254, Haifa, Israel 2 Department of Genetics, Faculty of Natural Sciences, Comenius University , Ilkovičova 6, 84104, Bratislava, Slovakia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Lukačišin For correspondence: martin.lukacisin{at}uniba.sk shenorr{at}technion.ac.il Shai S. Shen-Orr 1 Department of Immunology, Faculty of Medicine, Technion - Israel Institute of Technology , 1 Efron St, 35254, Haifa, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shai S. Shen-Orr For correspondence: martin.lukacisin{at}uniba.sk shenorr{at}technion.ac.il Abstract Full Text Info/History Metrics Preview PDF Abstract The immune system is uniquely capable of both regenerating and ageing the rest of the organism based on its own state 1 . Understanding the driving forces behind alterations in the immune system with age has thus potential to lead to interventions against ageing in general 2 . However, the factors driving immune ageing remain largely unknown 3 . 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-dimension 4 , we devised a phenotype-driven gene co-regulation analysis through which we unravel the immune-ageing role for multiple cellular functions, including genes regulated by miRNAs previously reported to cause gut leakiness 5 . Hypothesizing increased bacterial exposure as a driver of immune ageing, we quantified the exposure of each individual’s immune system to bacteria through analysing the residual bacterial DNA content in their immune cells 6 . We found that the total bacterial load is unrelated to the subject’s chronological or immune age, but predicts the change in immune age over the next year. Finally, we rationalise previously observed alterations in cellular composition of the immune system associated with ageing 4 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 ageing 7 , 8 . Main Ageing of biological systems is commonly understood as a time-dependent decline in their essential physiological functions 9 . Ageing in humans has been recently characterised in a variety of molecular and organismal readouts 10 – 13 . Connection of molecular readouts with phenotypes such as morbidity or mortality allowed for creation of a number of phenotypic ageing metrics 4 , 14 – 19 - their joint characteristic is that they replace the chronological age of a subject with a biological age, akin to a probability that the phenotype in question, such as death, ensues. Through such phenotypic ageing metrics, it has been shown that different organs in the same organism can age differentially and thus a given individual can possess multiple biological ‘ages’ pertaining to different components of its own organism 20 – 23 . The immune system, however, plays a special role in the aging of the entire organism 24 . An aged immune system transplanted into a young animal ages its peripheral organs, just as the immune system from the young animal rejuvenates an aged individual 1 . The immune system thus warrants special attention in investigating biological ageing in general. We and others have recently devised immune ageing metrics and quantitatively characterised phenotypic changes associated with the ageing of the immune system 2 , 4 , 25 , 26 . While variation in how people age is prevalent, certain stereotyped patterns, such as changes in frequency of specific immune cell subtypes, can be identified 4 . These phenotypic descriptions are a necessary pre-condition for investigating the drivers of immune ageing; however, the progress in this regard has been piecemeal 27 . In this work we develop a statistical method to characterise gene regulatory changes during immune ageing and use this framework to generate the hypothesis that it is an increased exposure of the immune system to bacteria, potentially through increased gut permeability, that drives immune ageing. We then experimentally validate this hypothesis by showing that bacterial DNA present in the immune cells quantitatively predicts ageing of the immune system in one year following the original sampling. Finally, we conceptually link the previous phenotypic description of changes in immune cell frequencies during ageing to this driver through finding that cell subtypes previously found to increase during ageing are those expressing genes involved in responding to bacterial presence. Gene-regulatory rewiring during immune ageing In the search for drivers of systemic changes in the ageing immune system, we reasoned that it would be advantageous to quantify not only changes in gene expression levels during immune ageing 4 , but also changes in gene regulatory associations 28 . To disentangle such age-associated rewiring of gene regulation, we devised an approach that we term phenotype-driven gene correlation network analysis (pdGCNA). Briefly, the gene expression measurements for individual samples are first ordered according to a quantitative phenotype, such as immune age, followed by quantification of pairwise gene-gene correlations within a sliding window ( Fig. 1a ). The underlying principle is that since the regulatory relationships themselves might change and in that way bring about the change in phenotype, we divide the entire set into multiple subsets stratified by the phenotype in question ( Fig. 1a ). This stands in contrast to the commonly performed gene correlation network analysis 29 , where the gene-gene wiring is assumed to be constant and the phenotypes are associated with changes in the expression level of the clusters of co-regulated genes, rather than with changes in their wiring. Download figure Open in new tab Figure 1. Phenotype-driven gene correlation network analysis (pdGCNA) of immune ageing. a , Schematic of pdGCNA on microarray samples from Stanford Elison cohort. Samples are ordered according to the donor’s immune age as determined from immune cell proportions and pairwise gene expression correlations calculated within a sliding window of half the sample size. Gene-gene correlations are then correlated with IMM-AGE across all window positions. b , Heatmap of correlations between IMM-AGE and gene-gene correlations after hierarchical clustering. Colour bar shows cluster identity for genes according to the thresholding of the distance tree shown at the top. c , Functional enrichment of genes in two largest clusters from ( b .) using Reactome gene ontology. Top five enriched terms with p adj < 0.05 are shown for each cluster. d , Boxplots of pairwise gene-gene correlations for all sliding-window positions for genes in the two largest gene clusters. The sets of boxplots show gene-gene correlations within the gene sets (left and middle) or between the gene sets (right); black line - median, grey area - interquartile range. We performed pdGCNA on the gene expression microarray data from Stanford Elison cohort, a longitudinal cohort of healthy people previously characterised using multiple high-throughput modalities 4 , 30 , 31 . Specifically, we ordered the samples by their inferred IMM-AGE (ref. 4; Methods ), a metric of immune age determined by diffusion analysis of relative frequencies of immune cell subsets, shown to be predictive of all-cause mortality even after regressing out the most common mortality predictors 4 . Performing pdGCNA, we discovered two clusters of genes ( Fig. 1b ) that with increasing immune age are increasingly co-regulated within the clusters, but increasingly anti-coregulated between the two clusters ( Fig. 1c ). Functional enrichment of these clusters showed that they can be identified with interferon signalling pathway and translation, respectively ( Fig. 1d ). Interferon signalling genes have been previously shown to decrease translation in a number of ways 32 ; however, here we found that with increasing immune age, this functional link between interferon activation and decrease in translation becomes stronger ( Fig. 1c right ). These findings raised the question of what driving factors might underlie the strengthened interferon–translation coupling observed with immune ageing, prompting us to search for regulators of this rewiring. Rewiring implicates miRNAs causing gut leakiness To further explore the age-related strengthening of interferon signalling impact on suppression of translation, we next asked which genes have their expression correlated with this rewiring in our cohort. To do that, we employed the same sliding window principle as in our pdGCNA analysis. For each window, we quantified the correlation between interferon and translation clusters ( Fig. 1b and 1d ) and the average expression of each gene. Next, we correlated these two quantities across our entire cohort for each gene and ranked genes according to this metric ( Fig. 2a ). Applying gene set enrichment analysis 33 on the resulting ranked list, we identified biological processes most associated, positively or negatively, with the immune ageing-related rewiring ( Fig. 2b ). Notably, besides broad non-specific cellular processes of translation and mitochondrial respiration, we identified an enriched set of genes all targeted by miRNA in the lymphocytes ( Fig. 2b ). The expression of these genes was negatively associated with ageing. This could potentially mean that the ageing process could be driven by an overexpression of a particular miRNA, as miRNAs generally suppress their targets 34 . Download figure Open in new tab Figure 2. Age-dependent gene rewiring is associated with lowered expression of miRNA regulated genes. a , Schematic of the strategy used for finding candidate drivers of age-associated gene rewiring. Gene expression of individual genes is averaged within each position of the sliding window used in pdGCNA and is then correlated with translation-interferon signalling correlation ( Fig. 1d right ) across all window positions. The resulting list of genes ranked by these correlations is then analysed using Gene Set Enrichment Analysis (GSEA). b , Ridgeplot of correlations with age-dependent signalling rewiring for genes in enriched functional groups found by GSEA. Wikipathways (WP) gene ontology was used; terms with Benjamin-Hochberg corrected hypergeometric test p adj < 0.01 are shown. c , Enrichment analysis of miRNA target motifs in transcripts of genes enriched in ( b .) belonging to ‘miR targeted genes in lymphocytes’ WP gene set using enrichMiR . Top 3 most significant miRNA binding motifs are labelled with the names of the corresponding miRNAs that target the motif. To further explore the potential identity of the driver miRNA or the processes implicated, we performed a reverse search of miRNA target motifs on these genes 35 ( Fig. 2c ). Strikingly, the two motifs with lowest FDR-values were represented by MiR-1-3p and MiR-124-3p, respectively ( Fig. 2c ), which were previously reported to synergistically damage the intestinal barrier in ageing gut, leading to increased bacterial permeability - a so-called gut leakiness 36 . Since interferon signalling is known to be downstream of many pathogen sensing mechanisms 37 , including bacterial, these results together led us to hypothesise that the increased signalling through interferon pathway associated with immune ageing could be due to an increased bacterial exposure of the immune system through the leaky gut. Bacterial DNA from immune cells predicts immune ageing To further explore the hypothesis that increased bacterial exposure, potentially through a leaky gut, might drive immune ageing, we leveraged recent reports that immune cells of even healthy individuals contain measurable amounts of bacterial DNA debris 6 . We reasoned that if immune ageing is driven by increased bacterial exposure, the amount of bacterial debris in the immune cells should be predictive of immune ageing. We quantified bacterial DNA extracted from PBMCs of subsets of individuals from our cohort using the same method as in the original study 6 , by performing qPCR and sequencing with primers highly specific to the bacterial ribosomal 16S RNA ( Extended Data Fig. 1 and 2 ). We first related the total bacterial DNA load in the immune cells to the change in the immune age of the donors over the next year as quantified previously 4 in this longitudinal cohort using their immune cell profiles ( Fig. 3a , Methods ). We observed that the total bacterial DNA load was predictive of the change in immune age in a non-monotonic fashion ( Fig. 3b ). More specifically, the magnitude of excess immune ageing rate depended exponentially on the bacterial DNA load ( p = 1.6 · 10 -5 , Fig. 3c ). Intriguingly, for those ageing slower than average, bacterial DNA load was positively associated with ageing; conversely, for those ageing faster than average, bacterial DNA presence was negatively associated with ageing. No similar association was observed for the yearly IMM-AGE before the sampling nor for the subject’s chronological or immune age at the time of sampling ( Extended Data Fig. 3a -c ). Download figure Open in new tab Figure 3. Bacterial DNA present in immune cells predicts one-year rate of immune ageing. a , Schematic of the experimental design. DNA from the PBMCs sampled as part of the Stanford Elison cohort was extracted and the bacterial DNA quantified using qPCR with 16S specific primers. b , Yearly immune ageing of a subset of Stanford-Elison cohort in relation to the total bacterial DNA load found in their PBMCs. Bacterial DNA load was determined by qPCR with bacterial 16S rRNA-specific primers on DNA extracted from PBMCs. Immune age at the time of this sampling and one year later was determined using immune cell profiles at the respective timepoints. The red dashed line represents the average yearly rate of ageing in the entire cohort; blue lines correspond to a fitted line in ( c .). c , Exponential fit of the dependence between total bacterial DNA load from ( b .) and the magnitude of excess ageing. Excess ageing was determined as the yearly change in immune age compared to the average yearly change in the entire cohort. P value and R 2 are shown for the linear fit plotted in blue. d , Functional enrichment of differentially expressed genes between subjects with positive and negative excess yearly immune aging in Stanford Elison cohort. Samples with positive excess ageing over the next year were compared to those with negative excess ageing; 10 most significant GO Cellular Component terms are shown that are enriched among genes overexpressed in samples with positive excess ageing; no terms among under-expressed genes reached top 10 significance. To rationalise this unusual dependence, we postulated that the presence of bacterial DNA debris in the PBMCs might be a compound readout of the opposing effects of frequency of bacterial encounters and the rate of bacterial clearance. To further investigate this hypothesis, we performed a whole blood differential gene expression analysis between all samples in our cohort with higher-than-averaging immune ageing rate as judged from a paired sample one year later, and those with similarly determined lower-than-average ageing rate. In those ageing faster than average, we found an overexpression of genes involved in vesicle-mediated transport ( Fig. 3d ), including extracellular exosomes, potentially a part in a recently described antibacterial defense mechanism 40 . These results are broadly consistent with a scenario where a threshold level of bacterial exposure switches on a defence mechanism resulting in an increased rate of debris clearance, however, at the cost of accelerated ageing. More importantly, it suggests that the bacterial DNA content can quantitatively predict the rate of future ageing over a one year period if supplemented with a gene expression readout. We next wondered whether it could be an exposure to specific types of bacteria that is driving the immune ageing. We analysed the presence of specific bacterial taxons in the immune cells as quantified from 16S sequencing and their relation to immune age and its annual change. We considered both relative and absolute quantities of different bacterial phyla, orders, families and genera. After accounting for multiple hypothesis testing, only Ralstonia genus showed mildly significant correlation with the change in donor immune age over the next year ( p adj = 0.049, Extended Data Fig. 3d ). This suggests that it is likely the burden of the total bacterial exposure, rather than a specific bacterial taxon, that is driving the ageing of the immune system. Cell types increasing during immune ageing respond to bacteria Previously, we have characterised immune ageing through quantitative changes in immune profiles, i.e. the frequencies of individual immune cell subtypes in one’s immune system 4 . Therefore, we sought to find a plausible rationale of why particular immune cell subtypes should be increasing in their frequency due to increased bacterial exposure. To address this question in an unbiased way, we subjected the immune cell subtypes that we previously identified to be associated with immune ageing to a functional analysis of their gene expression profiles. First, to generate gene expression profiles of these highly specific cell subsets, we turned to a CITE-seq atlas of PBMCs 41 and generated pseudobulk gene expression profiles after in silico gating ( Fig. 4a , Extended Data Fig. 4 and 5 ). To rank each gene for its association with increase in cell types where it is expressed, we correlated its expression in each of the cell subtypes with the relative increase of that cell subtype with age ( Fig. 4b ). Through functional gene set enrichment analysis of this ranked list, we found that the immune cell types most increasing with age are those that most express the genes involved in response to bacterial lipopolysaccharide ( Fig. 4c ; p adj = 10 -5 ). This effect was not driven by a small number of cell types, but rather reflects a systemic tendency of cells expressing these bacterial-response genes to increase in frequency during immune ageing ( Fig. 4d ). Download figure Open in new tab Figure 4. Immune cell subtypes increasing during immune ageing express bacterial response genes. a , Schematic of the approach. Single-cell CITE-seq data from atlas of PBMCs were gated in silico to pseudobulk gene expression in immune cell subtypes previously found associated with immune ageing ( left, middle ). Gene expression of individual genes was then associated with changes in frequencies of expressing cells across immune cell types associated with ageing ( right ). b , Example of a gene, the expression of which ( right ) is associated with the ageing-dependent change in the frequencies of immune cell subtypes ( left ). c , GSEA of genes ranked by the Spearman correlation between their expression in respective cell types and the cell frequency change with immune ageing. GO gene ontology was used. d , Expression heatmap of genes from GO:0071222 term Cellular Response to Lipopolysaccharide . Gene-wise Z -score across cell types associated with immune ageing is shown. Cell types are ordered according to their relative change with age as in ( b ); genes are ordered according to the correlation of their expression in respective cell types with the age-dependent change in frequencies of these cell types; top half of the genes is shown. Discussion Age-associated changes in the immune system composition in terms of specific immune cell subsets have been connected to increased mortality 4 . In this study, we devised the phenotype-driven gene correlation network analysis, a way to analyse changes in gene regulatory networks concomitant with these age-dependent changes in cell frequencies. We found rewiring changes suggestive of increasing bacterial exposure of the immune system through leaky gut. We subsequently found that the presence of bacterial DNA in immune cells as determined by 16S-sequencing in the peripheral blood mononuclear cells (PBMCs) quantitatively predicts the ageing of the immune system over the next year. This finding paves the way for a simple diagnostic marker to screen for immune systems most at risk for ageing and calls for a new research direction into antibacterial interventions in seemingly healthy people primed for decline. It remains to be explored what processes in the human body lead to the presence of bacterial DNA in PBMCs, predictive of immune ageing. While some have suggested the presence of live bacteria in the blood of healthy people 42 , 43 , it is more likely that the DNA present in PBMCs is a sentinel of what is going on elsewhere in the body, as recently demonstrated for another peripheral blood component, the platelets 44 . One possibility is that the increased bacterial DNA content in the immune cells is a reflection of the failure to clear external bacterial pathogens in peripheral organs, which increases with age 45 . However, in the context of the findings of our study, it is fitting to consider that the provenance of the increased exposure of immune cells to bacteria might be one’s own gut microbiome. Indeed, it has been recently shown that the immune cells primed by their exposure to the gut microbiome can migrate out of the gut and even influence organs as distant as the brain 46 , 47 and that ageing in model organisms can be described with a two-phase model determined by gut permeability 38 , 39 . Our findings thus call for additional studies where the age-dependent changes of the gut and of the immune system are investigated in conjunction 48 . Our results additionally suggest that the changes of immune cell profiles with age are adaptive to increased bacterial threats - immune cell types that increase most are those that are involved in responding to the bacterial presence. Why should then this re-calibration of the immune system towards bacterial threats be connected with ageing and increased mortality? One possible explanation lies in the increasingly explored trade-offs that the immune system faces - evolutionary examples suggest that the immune system trades increased resistance to bacterial threats for susceptibility to other threats, such as viral ones 49 . Given that evolutionary plasticity often phenocopies the environmental plasticity 50 , 51 , the ageing immune system might be attuning itself to overburdening by bacterial exposure through changes in cellular proportions only to make itself more susceptible to viral or retroviral 52 threats. Holistic studies of the immune system burdens and their consequences in terms of the immune system long-term dynamics are thus acutely needed to quantify their relative contribution to the ageing of the immune system. Methods Phenotype-driven gene correlation analysis Source data Phenotype-driven gene correlation analysis was performed using whole blood microarray gene expression data from Alpert et al. (ref. 4). First, the data were deconvolved as in the original study to account for variations in selected cell-type proportions by removing their effect on gene expression values from each gene expression profile within each year separately. Next, in order to exclude batch effects that can exert profound effects in correlation analysis, only data from the years 2013-2015 that were processed in a single batch were considered ( n = 192). Pseudotime IMM-AGE data were calculated based on frequencies of specific immune cell subtypes as in the original study 4 ; however, we only used data from years 2012-2015 that contained a harmonised antibody panel. Briefly, we normalized abundance levels before trajectory assembly. Per cell subset, we first calculated the mean and s.d. of frequency values between the 10th and 90th percentiles. We used these values to normalize cellular frequencies by subtraction of the mean and division by s.d. We applied the diffusion maps algorithm destiny 53 (v.3.18.0) on the scaled cellular frequencies of the selected cell subsets that were measured in 2012-2015. The resulting diffusion pseudotime values were scaled to a range of 0-1. Correlation analysis All samples included in the analysis were ordered according to their increasing immune age. A sliding window of half the sample size (96) was used to calculate pairwise gene-gene Pearson correlation between all samples within the window. For reasons of computational power, only the 20% most-variable genes were considered ( n = 3,858). The IMM-AGE of samples within the window was averaged using arithmetic mean. The resulting vector of IMM-AGE across all sliding window positions was correlated to gene-gene correlations across the positions of the sliding window using Pearson correlation. Functional analysis of clusters of phenotype-associated rewiring The resulting matrix of phenotype-associated gene-gene rewiring was hierarchically clustered with single linkage and Canberra distance using the hclust function from WGCNA 29 (v.1.72-1). The clustering tree was arbitrarily cut after visual inspection to produce two major clusters. Functional enrichment in these clusters was determined using ReactomePA 54 (v.1.42.0) gene ontology through over-representation analysis by clusterProfiler 55 (v.4.6.0). Candidate driver analysis To find candidate drivers of rewiring, for each position of the sliding window, the average pairwise correlation between genes from the two different clusters was calculated. Then, for all genes in the microarray gene expression dataset ( n = 19,288), mean expression in each sliding was calculated and was correlated through Pearson correlation to the average pairwise correlation between clusters. Genes were ranked according to this correlation and the resulting list was subjected to gene set enrichment analysis using Wikipathways gene ontology through clusterProfiler (v.4.6.0). miRNA regulator inference The genes driving the enrichment of the functional term WP2004: miR targeted genes in lymphocytes were subjected to search of target motifs and potential upstream regulator using web-based enrichMiR 35 (v.0.99.32). Bacterial DNA determination in PBMCs PBMCs Peripheral blood mononuclear cells were obtained from archived samples from Stanford-Ellison longitudinal aging (SELA) cohort, a 9-year observational study previously described in detail 4 , 30 , 31 . Briefly, individuals were generally healthy at enrollment and were followed longitudinally with repeated blood draws, before and after influenza vaccination. For the present analysis, we used PBMC samples from before the yearly vaccination from a subset of 47 individuals profiled in years 2012-2014 (year 6 to year 8). Genomic DNA extraction DNA was extracted from the samples using an optimized tissue-specific technique. Total genomic DNA was collected in a final 100μl extraction volume. Total DNA concentrations were determined by UV spectroscopy (Nanodrop®, Thermo Scientific). All the samples from the same tissue type were processed within the same DNA extraction run by the same single experimenter and were spatially and temporally isolated from the DNA extraction runs of other tissue types. Specific measures to avoid cross-contamination have been taken such as cleaning and DNA decontamination of the safety hoods in-between each extraction run. qPCR quantitation The 16S rRNA gene copies present in the samples were measured by qPCR in triplicate and normalized using a plasmid-based standard scale. The number of bacterial 16S rRNA gene copies is assessed using the Universal 16S Real Time qPCR workflow established by Vaiomer (Vaiomer SAS, Labège, France). Real-time PCR amplification was performed using 16S universal primers targeting the V3-V4 region of the bacterial 16S ribosomal gene ( Vaiomer universal 16S primers ). The qPCR step is performed on a VIIA 7® PCR system (Life Technologies) using Sybr Green technology and the following amplification cycles: hold stage of 10 min at 95°C, then 40 cycles of 15 sec at 95°C, 1 min at 63°C and 1 min 72°C. The absolute number of copies of 16S rRNA gene was determined by comparison with a quantitative standard curve of 16S rRNA gene plasmids generated by serial dilution of plasmid standards ( Vaiomer Universal standard plasmids ). The total number of bacterial 16S rRNA gene copies present in the samples was measured by qPCR in triplicate and normalized using a plasmid-based standard range. The specificity of all qPCR products was assessed by systematic analysis of the post-PCR dissociation curve performed between 60°C to 95°C. 16S rDNA sequencing The microbial populations present in the samples have been determined using next generation high throughput sequencing of variable regions of the 16S rRNA bacterial gene 56 . The PCR amplification was performed using 16S universal primers targeting the V3-V4 regions of the bacterial 16S ribosomal gene (Vaiomer universal 16S primers). For each sample, a sequencing library was generated by addition of sequencing adapters. The detection of the sequencing fragments was performed with the MiSeq Illumina technology using the 2 × 300 paired-end MiSeq kit V3. 16S rDNA sequence analysis The targeted metagenomic sequences from microbiota were analyzed using a FROGS bioinformatics pipeline (v.3.2.3) established previously 57 . Briefly, after demultiplexing of the barcoded Illumina paired reads, single read sequences were cleaned and paired for each sample independently into longer fragments. The following specific filters have been applied: amplicons without the two PCR primers were removed (10% of mismatches are allowed); amplicons with at least one ambiguous nucleotides (N) were removed; the last 10 bases of reads R1 were removed; the last 50 bases of reads R2 were removed; amplicons with a length 500 nt were removed. Samples with less than 5000 sequences after FROGS processing were not included in the downstream analysis. Operational taxonomic units (OTUs) were produced via single-linkage clustering and taxonomic assignment was performed in order to determine community profiles. OTUs matching any of the following filtering criteria were removed: OTUs with abundance lower than 0.005% of the whole dataset abundance; OTUs identified as chimeras with vsearch 58 (v.2.17); OTUs with a strong similarity (coverage and identity ≥ 80%) with the phiX (library used as a control for Illumina sequencing runs). OTUs were clustered in two passes of the swarm 59 algorithm (v.3.0). The first pass was a clustering with an aggregation distance equal to 1, the second pass a clustering with an aggregation distance equal to 3.The taxonomic assignment was produced by Blast+ 60 (v2.10.1) with the databank Silva 61 (v.138.1 Parc). Rarefaction calculations were performed using an R package Vegan 62 (v.2.5-6), used in Python with rpy2 . Scikit-Bio (v.0.4.2) was used for calculating α-diversity. Quality controls and exclusions To assess the potential DNA contamination from the environment and the sampling material, four freezing media samples, four replicates of the pool of supernatants and four DNA extraction negative controls were added and carried over throughout the 16S rRNA gene sequencing pipeline and assessed for difference in bacterial DNA content to the PBMC samples. There was a clear separation between PBMC samples, the DNA extraction negative controls, and the supernatant pool, as judged by the quantity of bacterial DNA. For four PBMC samples (15-015-01-15, 15-019-01-15, 15-080-03-15 and 15-129-01-15), only reads belonging to Pseudomonas were detected. These samples were also the four having the highest amount of bacterial DNA in qPCR. This is consistent with either an ongoing bacterial infection present in the donor(s), or a contamination of the original samples with Pseudomonas DNA/bacteria. These samples were therefore excluded from further analysis. Seven samples (15-041-03-13, 15-048-01-14, 15-061-01-13, 15-062-01-13, 15-073-01-14, 15-125-01-15, 15-131-01-14) which amplified without issue in the qPCR, did not amplify correctly in PCR during the 16S sequencing library preparation. These samples which may contain more PCR inhibitors, were reprocessed in a new PCR step with different conditions. The seven samples were successfully amplified in the new PCR, but this introduced a contaminant ( Aeromonas genus) corresponding to 7 % to 18.8 % of the reads of these 7 samples while being virtually absent from all other samples. This taxon was subtracted during the bioinformatic analysis, removing the corresponding 7% to 18.8 % of the total reads in the seven samples, and between 0 % and 0.006 % of the total reads in the other samples. Finally, samples of PBMCs from after the vaccination were not considered in the analysis of bacterial DNA effect on immune age to exclude the effect of vaccination itself. Association of bacterial DNA to immune ageing The total bacterial load as normalised to the unit mass of DNA extracted from PBMCs was related to the IMM-AGE change over the next year by subtracting the mean yearly IMM-AGE change in the entire cohort from the IMM-AGE change data, taking an absolute value and logarithm and subsequently fitting a linear regression model to its relation to the bacterial load using stat_regline_equation function of ggpubr 63 (v.0.6.0). The presence of specific bacterial taxons in the immune cells as quantified from 16S sequencing was related to immune ageing in four different ways - the taxon relative frequency to current immune age, the taxon relative frequency to change in IMM-AGE over the next year., the taxon absolute abundance to current immune age and the taxon absolute abundance to change in IMM-AGE over the next year. Absolute taxon abundance was quantified by multiplying its relative abundance in the respective donor and the total 16S DNA count from the donor per unit mass DNA isolated from PBMCs. We considered bacterial taxons at the level of phylum, order, family and genus. Function cor . test from R package stats (v.4.3.1) was used to quantify Pearson correlation and the corresponding p value, that was subsequently Bonferroni-adjusted by multiplying with the number of taxa tested at the given level. Differential gene expression for 1-year immune ageing To analyse differentially expressed genes between people with positive and negative excess ageing, the same gene expression dataset was used as for the pdGCNA (see above), but subsetted for samples that had a paired sample from the same donor with IMM-AGE pseudotime from one year later ( n = 156). Excess immune ageing was then defined as the difference between the actual ageing in the next year for the given sample and the mean one-year ageing across this subset of samples. The sample set was then divided into two groups of positive and negative excess ageing and differentially expressed genes determined using limma 64 (v. 3.54.1). Top 5% most overexpressed and underexpressed genes according to the moderated t- statistic were subjected to functional over-representation analysis with Cellular Component Gene Ontology using ShinyGO 65 (v.0.85). Gene expression analysis of cell-types associated with ageing Dataset Used The dataset originates from a study on human peripheral blood mononuclear cells (PBMCs), sampled from eight donors (aged 20-49) participating in an HIV vaccine trial and sequenced using the CITE-seq protocol 41 . The uniqueness of this dataset lies in the breadth of antibody library used for this study – the total number of antibody markers sums up to 228; in total, this dataset consists of ∼211,000 cells that were sequenced by this experimental setup. The dataset was downloaded from Gene Expression Omnibus, acc. no. GSE164378, and converted into a Python-compatible AnnData structure. Only donors in the control group were considered for our study ( n = 5; ∼16,000 cells). Cell Subtype Identification Our immune cell subtypes definitions in terms of surface markers were tailored to closely follow those used by the original IMM-AGE study 4 . We identified several surface markers that were used to identify cell subtypes associated with immune ageing in the original study, but were not present in the CITE-seq dataset. These were CD33, CD94, CXCR3, PD1 and CCR7. For our analysis, we substituted CCR7 with CD27 and excluded the cell types that required one of the non-measured surface markers from further analysis. To determine the expression threshold for binarisation of marker expression used assisted non-linear least squares fitting to model the bimodal distribution of expression ( Extended Data Fig. 4 ). After visual inspection of the expression histograms, some markers were gated manually ( Extended Data Fig. 5 ). Applying a sequential combination of logical AND conditions, derived from the marker composition of a specific immune cell subtype, to the antibody-derived tag (ADT) matrix, produced a boolean mask that marked the cells belonging to the queried subtype. This mask was then used for the quantification of gene expression stratified by cell subtype by pseudobulking - single-cell records were aggregated by computing the mean expression value per cell subtype. Relating Gene Expression to Immune Ageing Dynamics We assessed the relationship between the pseudobulk gene expression patterns and the yearly mean frequency changes reported in the original immune ageing study 4 using Pearson correlation. Specifically, for each gene, we correlated its expression across cell types with the logarithm of mean yearly frequency change of the cell types as reported in Alpert et al., 2019. The resulting correlation values were used to form a ranking of genes associated with age-dependent cellular dynamics, which we employed for gene set enrichment analysis (GSEA). For the implementation of the gene set enrichment analysis, we chose the module prerank introduced in the Python library GSEApy 66 . This method follows the original algorithm introduced by Subramanian et al. in their GSEA study 33 . We used GO_Biological_Process_2023 as the gene ontology. Data Availability Data associated with this study will be made available at the time of publication. Code Availability The code associated with the analysis in this study will be made available on GitHub at the time of publication. Author Contributions Conceptualisation: E.F., M.L., S.S.S.O.; Study design: E.F., M.L., S.S.S.O.; Data analysis and investigation: E.F., M.H., M.L.; Figure design: M.H., M.L.; Writing - original draft: M.L.; Writing - review and editing: E.F., M.L., S.S.S.O.; Supervision: M.L., S.S.S.O.; Funding acquisition: M.L., S.S.S.O. Additional Information Statement Correspondence and requests for materials should be addressed to Martin Lukačišin, martin.lukacisin{at}uniba.sk , and Shai S. Shen-Orr, shenorr{at}technion.ac.il . Ethics Declaration S.S.S.O. holds equity and is a consultant of CytoReason and holds an unpaid position with the Human Immunome Project; M.L. holds equity and is a CEO of Macrobian. Extended Data Figures Download figure Open in new tab Extended Data Figure 1. Quality controls for bacterial DNA 16S qPCR and sequencing. a , Bacterial DNA content detected by 16S qPCR in PBMCs samples and negative controls; for samples containing cells it is stated relative to 10 6 PBMCs; for cell-free negative controls, it is stated by arbitrary choice relative to 1 ul of volume. Note that irrespective of this choice, the Freezing Media samples, Supernatant pool replicates and DNA extraction negative controls have a quantity of bacterial DNA measured in 16S qPCR several orders of magnitude lower than the levels found in PBMCs. b ,Rarefaction analysis was used to assess the exhaustivity of bacterial taxa derived from the sequencing results. Rarefaction curves were created by randomly re-sampling the pool of N sequences multiple times and plotting the average number of OTUs found in each sample. The saturating shape of the rarefaction curves suggests that the number of reads was sufficient to capture the bacterial diversity with a good precision. Download figure Open in new tab Extended Data Figure 2. Relative abundance of taxa for different taxonomic levels Graphical representations of the relative abundance of taxa for the taxonomic level of phylum ( a .) and genus ( b .) for all study samples. The most abundant taxa are identified by name in the plot, while the less abundant ones are merged into the “Other” category. The DNA extraction negative controls display variability between replicates, confirming the absence of strong contamination of the molecular biology pipeline. OTUs that are affiliated to two or more taxa are merged into a multi-affiliation (‘_Multi’) category. Download figure Open in new tab Extended Data Figure 3. Bacterial DNA present in PBMCs is not associated with age, immune age or change in immune age over the previous year, nor is specific bacterial taxon related to immune ageing. a , Exponential fit of the dependence between total bacterial DNA load and the magnitude of excess ageing over the previous year. Excess ageing was determined as the yearly change in immune age compared to the average yearly change in the entire cohort. P value and R 2 are shown for the linear fit plotted in blue. b , Linear fit for the dependence of total bacterial DNA load and donor chronological age at the time of sampling. c , Linear fit for the dependence of total bacterial DNA load and donor immune age at the time of sampling. d , Linear fit for the dependence between the absolute abundance of 16S DNA from Ralstonia genus and the donor’s immune age change over the next year. No other taxon showed correlation with immune age or 1-year change in immune age with p adj < 0.05. Bonferroni correction was applied to the analysis at each taxonomic level separately (phylum, order, family, genus). Download figure Open in new tab Extended Data Figure 4. Automated thresholding for immune cell subtyping Visualisation of thresholds produced by automatic approximation (see Methods ). Each of the 12 histograms represent the distribution of the respective marker expression (a.u.) across all cells. The dashed red line represents the first fitted Gaussian, the green line the second one. The blue dashed line represents the estimated threshold defined by the intersection of said Gaussians. Download figure Open in new tab Extended Data Figure 5. Manual thresholding for immune cell subtyping. Visualization of manually defined thresholds. Each of the 11 histograms represent the distribution of the respective marker expression (a.u.) across all cells. The blue dashed line represents the threshold defined by an educated choice. Acknowledgment We would like to thank Holden T. Maecker, Benjamin Lelouvier, Alain Roulet, Anne Deflisque and Neta Milman for technical and organisational assistance, Timothy J. Few-Cooper and all members of S.S.S.O. and M.L. laboratories for stimulating discussions, Marta Lukačišinová for fruitful comments and critical reading of the manuscript. This study was supported in part by NIH NIAID award PO1 No. A153559-01 (S.S.S.O.) and the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I01-03-V05-00005 (M.L.). Funder Information Declared NIH NIAID , PO1 No. A153559-01 EU NextGenerationEU , 09I01-03-V05-00005 Footnotes ↵ # These authors jointly supervised the work References 1. ↵ Yousefzadeh , M. J. et al. An aged immune system drives senescence and ageing of solid organs . Nature 594 , 100 – 105 ( 2021 ). OpenUrl CrossRef PubMed 2. ↵ Sayed , N. et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging . 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