# Age-related response to an acute innate immune challenge in mice: proteomics reveals a telomere maintenance-related cost.

François Criscuolo, Gabriele Sorci, Margaux Behaim-Delarbre, Sandrine Zahn, Bruno Faivre, Fabrice Bertile

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## ► To cite this version:

François Criscuolo, Gabriele Sorci, Margaux Behaim-Delarbre, Sandrine Zahn, Bruno Faivre, et al. Age-related response to an acute innate immune challenge in mice: proteomics reveals a telomere maintenance-related cost. Proceedings of the Royal Society B: Biological Sciences, 2018, 285 (1892), pp.20181877. 10.1098/rspb.2018.1877. hal-01973151

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1 Age-related response to an acute innate immune challenge in mice: proteomics reveals a

2 telomere maintenance-related cost.

3

4 François Criscuolo¹‡, Gabriele Sorci², Margaux Behaim-Delarbre¹, Sandrine Zahn¹, Bruno Faivre²

5 and Fabrice Bertile¹‡

6

7 ¹Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France.

8 ²Biogéosciences, CNRS UMR 6282, Université Bourgogne Franche-Comté, Dijon, France.

9

10

11

12

13 Running head : proteomics of immune response to LPS

14 Keywords: immune challenge, ageing, proteomics, telomere, LPS

15 ‡Corresponding authors ([francois.criscuolo@iphc.cnrs.fr](mailto:francois.criscuolo@iphc.cnrs.fr), [fbertile@unistra.fr](mailto:fbertile@unistra.fr))

16
17 Abstract

Ageing is characterized by the impairment of the acute innate immune response and the up-regulation
of low-grade inflammation, i.e. inflammaging. At the cellular level, telomeres are considered as a
marker of biological ageing as their length is progressively eroded in the absence of repair mechanisms.

However, the link between telomeres and inflammaging remains underexplored. We aimed to identify
proteins that are differentially expressed between age classes in response to an acute inflammatory
challenge. We challenged young (2 months) and old (12 months) C57BL/6 mice using bacterial
lipopolysaccharide (LPS) and measured telomere length and proteomic profiles in splenocytes. 233 out
of the 1966 proteins we quantified differed among experimental groups. A hierarchical clustering
analysis revealed that nine of those 233 proteins highly affected experimental groups differentiations.

Young mice responded to LPS by increasing the expression of proteins involved in the innate immune
response, and interestingly, in telomere length maintenance. However, this regulation was impaired
at older ages. These results are in accord with the assumption that the strength of selection declines
with age, potentially explaining the maintenance of costly, dysregulated, immune responses at old age.

We establish that the immune response is competing with telomere maintenance process, highlighting
how telomeres reflect quality of ageing even in a species where telomere length is not related to
lifespan.

34
35

**Introduction**

The immune system is usually depicted as a two edged sword, with confering benefits but also generating costs. While the benefits of immune protection are obvious [1], the immune response has been shown to incur energy-dependent and independent costs. Constitutive and induced immune responses draw host resources away from other physiological functions and this can generate trade-offs between immunity and life-history traits such as growth or reproduction [2, 3]. In addition to these resource-based trade-offs, immunity can incur costs if immune effectors target host tissues and organs [4]. These costs are referred to as immunopathology. Although immune-mediated diseases have been identified for quite a long time, the importance of immunopathology in the context of the ecology and evolution of host-parasite interactions has emerged as a new concept during the last two decades [5]. The risk of immune-induced damage is particularly strong during the process of acute and chronic inflammation. Broadly speaking, infection induces the activation of innate immunity effectors within minutes to hours [6]. Such rapid response and the associated local inflammation then favours mounting of the adaptive immune response (i.e. via lymphocyte recruitment, [7]) within days and weeks. Innate and adaptive responses not only differ in terms of rapidity, but also in terms of specificity. While adaptive response is highly specific, innate immunity is essentially based on the recognition of conserved pathogen molecular patterns such as structural components of the bacterial wall (lipopolysaccharide in gram-negative bacteria or peptidoglycan in gram-positive bacteria), by pattern recognition receptors (such as Toll-like receptors) and the induction of cytotoxic effectors (oxidative burst) [7, 8]. By their inherent function/nature, these effectors cannot discriminate invading pathogens from host cells and tissues and therefore can induce substantial damage if they get out from control [9].

Failure in the resolution of acute and chronic inflammation can induce life-threatening diseases [10, 11]. Indeed, inflammation and activation of oxidative attacks on intruders may have many collateral negative impacts on the host itself, including ultimate costs on life history traits [12]. Ageing has been shown to be associated with the up-regulation of low grade inflammation, i.e. inflammaging
[13, 14], which makes elderly more susceptible to many degenerative age-associated pathologies such as cancer, diabetes, cardio-vascular diseases [15], and ultimately impairs lifespan of individuals. Old individuals have also been shown to pay disproportionately high costs of inflammatory activation when regulatory mechanisms are experimentally broken-down, suggesting that ageing individuals are more prone to immunopathology [16]. In a companion study, we notably reported that LPS-induced oxidative stress in the liver induces immunopathology costs in old mice [17]. Accumulation of senescent cells in old organisms may be one of the mechanisms leading to chronic inflammation [18].

Telomeres, which are repeated DNA sequences that protect chromosome extremity, are one of the structures sensitive to the mechanisms involved in the inflammatory process. Telomeres. The length of these repeated motifs are supposed to reflect biological age, since cellular replication and stress erodes them (although mechanisms that restore telomere caps exist) [19]. Telomere length is thus used as a marker of cell senescence [20] as well as the whole organism's survival [21-24]. Telomeres have been previously linked with the immune cell response to pathogens [25]. They are particularly susceptible to erosion during infection and the associated inflammatory response. This was shown for instance in a wild passerine, where a mild malaria infection over life induces an accelerated erosion of blood cell telomere, resulting in a subsequent significant reduction in lifetime reproduction output and adult lifespan [26]. It is therefore possible that up-regulated chronic inflammation at old age (inflammaging) and the lower ability of old individuals to cope with acute inflammatory responses are accelerating factors promoting telomere attrition [27], leading to a vicious circle between telomere attrition, emergence of senescent cells and inflammaging.

The aim of this study was twofold. First, we wished to explore if an acute inflammatory challenge induces age-dependent variation in protein expression in an immune organ, the spleen, using a quantitative proteomic approach. Second, we investigated whether telomere length differed with age and whether the proteomic profile activated by the immune challenge could reveal mechanisms involved in telomere attrition. In doing so, we wanted to test if the cost of an immune challenge is mirrored in telomere malfunction even in a species where telomerase is active at adulthood.
87

## 88 Materials and methods

### 89 Ethical statement

The present experiment has been conducted under the permit n° AL/05/34/12/12 delivered by the 'Comité Régional d'Ethique en Matière d'Expérimentation Animale de Strasbourg'. Animals were maintained in captivity within the IPHC-DEPE animal husbandry (French Department of Veterinary Services n° E67-482-18) and under standardized rearing conditions previously indicated in [17]. F. Criscuolo is the holder of an animal experimentation licence granted by French authorities (no. 67-78).

### 96 Experimental design

The experiment was conducted on 26 C57BL/6J male mice from our own colony and originated from Elevage-Janvier laboratory [12 mice were 2 months old (hereafter young), and 14 mice were 12 months old (hereafter old)], reared in age-separated cages of 6-7 animals in our laboratory under constant temperature (24 ± 2 °C), humidity (50-55%) and photoperiod (13:11 L:D cycle) and fed *ad libitum* on a standard diet (SAFE A03). Mice in each age class were randomly allocated to the two experimental groups: 6 young and 8 old mice received an intraperitoneal injection of 25 µg/kg body mass of lipopolysaccharide [LPS; *Escherichia coli*, Sigma Aldrich, a non-lethal dose based on a previous study [17]], and the other mice were kept as controls and injected with sterile phosphate buffered solution (PBS). LPS is a TLR4 ligand that stimulates neutrophils and macrophages, induces the release of pro-inflammatory cytokines (IL-6, IFN-γ, TNF-α, IL-1β), cytotoxic reactive oxygen and reactive nitrogen species (ROS, RNS) (e.g. [28]). As previously observed, the LPS-injected mice stopped eating and remained motionless for up-to 24h after injection, after what they started gaining body mass [17]. One old mouse died in the LPS group, reducing the sample size of this group to seven.

Twenty-four hours post-challenge, all mice were euthanized by cervical dislocation and two aliquots of the spleen were immediately snap-frozen in liquid nitrogen before storage at -80°C, until telomere length measurements by qPCR, proteomic and Western blot analyses (Methodological
details are available in the electronic supplementary material, ESM 1). Body mass (± 0.1 g) was also measured just before the intraperitoneal injection and 24h post-challenge (ESM3). We chose this short-term measurements (and age classes) based on previous LPS-injection experiments conducted on the same mouse line [17] showing that body mass loss and oxidative costs was maximal at that time (i.e. the putative cost being easier to measure at the telomere level) and will not be compensated by the triggering of telomerase activity during recovery.

## Statistical analyses

We selected a three-step analysis for the proteomic and molecular data obtained on spleen tissues. As we did in previous studies [17, 29], we first sorted the significant effects of age (young and old) and treatment (PBS and LPS) as fixed factors using ANOVA for each of the 1966 quantified proteins (Electronic Supplementary Material, Table S2). We thus selected the 233 proteins for which i) ANOVA p-values were below 0.05, and ii) the post-hoc Tukey test p-value (with Bonferroni correction) was below 0.05 and the absolute fold change was > 1.5 fold for at least one pairwise comparison. In a second step, we used this subsample to run a hierarchical clustering on principal component analysis (PCA) to obtain a dendrogram that assembles proteins based on similarities of variation according to experimental groups [30]. This second step was done to further reduce the number of variables to avoid multicollinearity among a too large number of variables. We chose to set-up the hierarchical clustering analysis with four clusters, based on inertia gain choice (4 clusters explained a total of 82% of the total variance while 3 clusters explained 74%), and because two of these clusters (3 and 4) explained a large fraction of the total variance in both axes (81.1% and 6.1%, respectively figure 1). These clusters (3 and 4) encompassed 9 proteins. The final step of analysis consisted of merging the proteomic variables so far selected (9 proteins) and the physiological and molecular data (body mass and telomere length) to run a Principal Component Analysis (PCA) with varimax rotation (determinant of 0.001). PCA resulted in three axes (PCA1, PCA2 and PCA3) explaining 36.2%, 24.4% and 13.9% of the variance, respectively (total 74.5%). Kaiser-Meyer-Olkin (KMO) value was of 0.66 and Bartlett's test of sphericity confirmed an adequate correlation among variables for PCA ($\chi^2$ (55) = 140.9, P < 0.001). Given
the number of variables, only components of the axis with an eigenvalue over 0.4 were retained (Kaiser's criterion). PCA scores of each individual were then used as the dependent variable in a Generalized Linear Model (GLM) with age (young vs. old), immune challenge (LPS vs. PBS) and their interaction. To confirm the significance of the relationship between individual telomere length and the protein signals, we chose to run an additional analysis on the raw values (not on the PCA values). To do so, we finally investigated the association between telomere length and the 9 proteins (among which some were previously known to be involved in telomere dynamics, e.g. chitinase-like protein 3) using a model selection after testing for absence of multicollinearity using variance inflation factor (minimum Tolerance index > 0.19). For western-blot analysis of shelterin proteins, we performed one-way ANOVA with Tukey posthoc tests. Adjustment of p values was done according to Bonferroni-Holm (p < 0.05), after checking for normality distribution (Shapiro-Wilk test) and homoscedasticity (Bartlett test). All statistics were run on the program R using RStudio (version 1.1.414). Hierarchical clustering and PCA were conducted using FactoMineR R package [31]. We used the dredge function of the MuMln package (version 1.15.1) to run model selection. Normality of the residuals was checked using QQ plots and Kolmogorov-Smirnov tests. Significance threshold used is $P \le 0.05$.

## Results

The proteomics analysis yielded the robust identification of 2788 proteins, of which 1966 fulfilled the criteria for quantification (ESM 2, table S2). A total of 285 proteins were differentially expressed as they exhibited ANOVA p-values below 0.05, and this number dropped to 233 when considering Tukey p-values below 0.05 and absolute fold changes above 1.5 for at least one pairwise comparison. Hierarchical clustering pointed to nine of these proteins as major contributors belonging to two different clusters (3 and 4) that explained most of the variation on both axes of the dendrogram (figure 1A) and which are well characterizing the young PBS and young LPS groups (figure 1B). To test how these proteomic data, once added to biological variables, vary with age and treatment, we used
these nine proteins, changes in body mass during the 24h post-challenge and telomere length to run
a principal component analysis. The contribution of the different variables to the three principal
components (PCA1, 2 and 3) are reported in table 1. PCA1 (36.2% of variance explained) positively
loaded with protein S100-A9, lysozyme C-2, and annexin A1; PCA2 (24.4% of variance explained) had
positive loads with coronin 1A and RAS-C3, and negative loads with endoplasmin HSP90b1, GRP-78,
carbonic-anhydrase 1, and changes in body mass; finally PCA3 (13.9% of variance explained) positively
loaded with endoplasmin HSP90b1, GRP-78 and chitinase-like protein 3, and negatively with telomere
length. Based on Gene Ontology annotations and literature analysis, the three principal component
axes may be attributed to the following biological functions: Immune response and inflammation
(PCA1), response to stress and ageing (PCA2) and stress and telomere function/length regulation
(PCA3).

We then used the individual PCA values on each of the three axes as the dependent variables
in a general linear model to investigate how these values varied as a function of age and immune
treatment. The results showed that PCA1 was significantly affected by the interaction age x immune
treatment. In particular, young LPS-treated mice had higher PCA1 values compared to the other
experimental groups, suggesting an up-regulated expression of protein S100, annexin A1 and lysozyme
(table 2). Old mice had similar PCA1 values, whatever their immune treatment (LPS vs. PBS; estimates
0.267 ±0.317, NS.).

The PCA2 axis was only affected by age (table 2), with old mice having lower values than young
mice. This suggests an age-dependent up-regulation of carbonic-anhydrase 1, GRP 70 kDa and Hsp 90,
and a down-regulation of Coronin 1A and RAS-C3.

As for PCA1, the PCA3 axis was also affected by the interaction between age and the immune
treatment (table 2): while young mice significantly increased their PCA3 values in response to LPS, old
mice presented unchanged PCA3 values after the immune treatment (and had mean overall higher
values throughout the study). This suggests an up-regulated expression of GRP 70 kDa, Hsp 90 and
chitinase-like protein 3, and shorter telomeres in old mice compared to young mice.
Given that chitinase-like protein 3 and Hsp 90 (over-expressed in old mice), and the protein S100-A9 (over-expressed in young mice in the LPS group) have been shown to have a function in the regulation of telomere length, we further investigate the relationship between telomere length and the proteomic profile at the individual level. Model selection indicates that chitinase-like protein 3 and Hsp 90 proteins were the best predictors of telomere length independently of the treatment or age (table 3). In addition, we found that chitinase-like protein 3 was negatively correlated with telomere length, both when the regression was conducted using the entire dataset or separated by treatments (PBS and LPS, figure 2). The regression was not significant when the dataset is separated by age (young mice only, r = 0.373, r² = 0.139, F₁, 11 = 1.619, P = 0.232 and old mice only r = 0.530, r² = 0.281, F₁, 11 = 3.903, P = 0.076).

Finally, of the different shelterin proteins implicated in telomere length regulation, western blot analysis highlighted that only TRF1-interacting nuclear factor 2 (TIN2) expression levels exhibited significant differences among mice groups: a non-significant increase following LPS injection was observed especially in young mice, and there was a significantly higher level of expression for LPS-injected younger mice compared to old ones (figure 3, see ESM4 for statistics and ESM5 for additional representative blots).

## Discussion

Our results show that the induction of an acute inflammatory response produced age-dependent changes in the proteomic profile of mouse splenocytes. LPS challenge induced an up-regulated expression of protein S100-A9, lysozyme C-2 and annexin A1 in young mice compared to the other groups, while in old mice, this proteomic profile of did not change in relation to LPS.. Similarly, over the whole experiment old mice presented a significantly higher level of expression of proteins associated with immune response and oxidative stress, as well as shorter telomeres compared to young individuals.
Analysis of the PCA1 axis which contained protein S100-A9, lysozyme and annexin A1 showed higher values in LPS treated young mice compared to control mice. These proteins play key roles as anti-bacterial effectors and in the regulation of the inflammatory response. For instance, lysozyme degrades the membrane of gram positive bacteria [32], and protein S100-A9, released by apoptotic neutrophils, favors the resolution of the inflammatory response by promoting their elimination by macrophages, and down-regulate pro-inflammatory signals [33]. Similarly, annexin A1, a protein expressed in innate immune cells, has an anti-inflammatory effect [34] and mice that do not express annexin A1 (KO) suffer from fatal septic shock following a LPS injection [35]. Therefore, young mice challenged with LPS had a proteomic signature in splenocytes indicating both the up-regulation of anti-bacterial effectors, and proteins involved in the fine-tuned control of the acute inflammatory response.

In a companion study, we showed that the liver proteome of C57BL/6 old mice, exposed to a 24h-LPS challenge, was characterized by the up-regulation of proteins involved in the oxidative balance compared to young mice; in the same study, the expression of proteins with specific anti-bacterial functions was not affected by the immune challenge [17]. Using spleen tissue, we found a comparable pattern of variation for PCA2, old mice being similar in their protein profiles before and after injection, but being different from young mice in that they expressed more HSP90, HSP70 (GRP-78 kDa) and carbonic anhydrase 1. Carbonic anhydrase 1 has been previously reported to be over-expressed in old mice [36]. Of particular interest is the up-regulated expression of HSP90 and HSP70 in old mice, because these proteins have pleiotropic functions, with both protective [37] and potentially damaging properties [38-40]. Compared to young mice, old mice also had a lower expression of RAS3 and coronin 1A. These proteins are involved in the regulation of the stress response [41] and immunodeficiency [42].

## Telomere maintenance as a cost of innate immunity

Mice have highly active telomerase [43, 44], and as a consequence, it is generally assumed that telomere length does not decrease with age in laboratory mice [45]. However, this does not mean that mice are not exposed to telomere erosion when facing stressful events, i.e. when telomerase activity
is potentially inhibited for instance by increased corticosterone levels [46]. Since marginal telomere shortening with age in some mice tissue has been reported (see [43]), we do know that telomere shortening over generations in telomerase knock-out mice does negatively affect organ functioning and lifespan [47]. This particularly affects highly proliferative tissues which are lined by the hematopoietic cells [48]. In agreement with this, our finding that old mice had shorter telomeres compared to young mice (see ESM, Table S3) could be explained by a transient inhibition of telomerase activity due to the trade-off between telomere maintenance and the immune response. This is in accordance with the recent idea that telomere maintenance *per se* could be energy costly [49]. Telomere shortening in old mice was concomitant with over-expression of HSP90, HSP70, and chitinase-like protein 3. Interestingly, HSP90 binds to the catalytic subunit of the telomerase, allowing the assembly of the whole enzyme and maintenance of its activity [50]. Immune cells are quite diverse in function, from hematopoietic stem cells to peripheral blood cells, all which have different proliferation capacities and telomerase activity [51]. The dynamics of telomere length in white blood cells during infection and particularly after mitogen and LPS injections was previously studied [52]. Subsequent results provided evidence for an increased telomerase activity in response to infection, mostly restricted to lymphocyte cell lines. However, mice lacking the telomerase catalytic subunit TERC-/- also showed higher inflammation responses [53] and we detected the chitinase-like protein 3 in mice spleen, which is highly expressed in activated macrophages [54]. This suggests that (i) macrophages may indeed be largely present in our sampled cell population, and (ii) in old mice, HSP90 expression may be linked to an (unsuccessful) response to save telomeres *via* telomerase activity. Chitinase-like protein 3 has a dual key role for the immune system (i) by inducing macrophage activation *via* the degradation of bacterial chitin, which triggers pro-inflammatory cytokine secretion, and (ii) by insuring a negative feedback loop on the inflammation process necessary to the remodeling of injured tissues [55]. The fact that chitinase-like protein 3 is more highly expressed in old mice as well as in young mice after LPS injection is in accordance with a lower capacity to control inflammation at old ages. In addition, chitinase-like protein 3 expression and telomere ends and telomerase
dysfunction have been previously linked [56, 57], suggesting that chitinase-like protein 3 is a robust
ageing biomarker. Again, our results are in accordance with a direct or indirect implication of chitinase-
like protein 3 in telomere maintenance, since we highlighted that chitinase-like protein 3 expression
levels are negatively linked with telomere lengths irrespectively of age but in relation to LPS treatment
(figure 3). As a consequence, chitinase-like protein 3 may be an element explaining the immune trade-
off faced by young mice injected with LPS, independently of previously suggested pathways (e.g.
collateral direct oxidative stress). Chitinase-like protein 3 may be one of the links between
inflammatory reaction and telomere shortening. Although the nature of such a mechanism remains to
be established, macrophages functioning is likely to be implicated. Another pathway that may be
implicated concerns the shelterin TIN2 expression, which clearly tended to be up-regulated in young-
LPS mice. TIN2 is regulating the access of telomerase to telomere ends and is necessary for telomere
maintenance and telomere end adequate functioning [58, 59]. In addition, TIN2 has been shown to
insure cell viability independently of any telomere length regulation [60], underlying that its expression
may also participate to the mitigation of negative side-effects of inflammation.

Previous studies suggested that telomere length is involved in the capacity of individuals to respond
to infection [61, 62]. Based on our data, we hypothesize here, using a proteomic exploratory approach,
that the age-related costs of the innate response to LPS implies at least partly a telomere and/or
telomerase-related pathway. In mice, even if telomere length is not related to lifespan [63], transient
telomere shortening may be the outcome of life-history trade-offs via the direct competition for
energy of telomere maintenance pathways or due to the indirect effect of stress hormones.

287

288 Acknowledgements

We thank Dr Hélène Gachot-Neveu, Aurélie Kranitsky and Marie-Laure Rizzi for their work in the animal
husbandry.

291 Competing interests

We declare no competing interests.
293 **Author contributions**

294 F.C. conceived the study and carried out the experimental work, F.B. and M. B.-D. conducted proteomic
295 and Western Blot analyses while S.Z. conducted telomere length measurements, F.C. ran the statistical
296 analyses and F.C., G.S., F.B. and B.F. drafted the manuscript. All authors gave final approval for
297 publication.

298 **Fundings**

299 This work was financially supported by the CNRS and Strasbourg University (H2E project; IdEx Unistra),
300 the French Proteomic Infrastructure (ProFI; ANR-10-INSB-08-03), and a CNRS ‘Projet Exploratoire
301 Premier Soutien’ (PEPS INEE, 2012). During the tenure of this study, MBD was the recipient of a grant
302 from the Strasbourg University (IdEx Unistra).

303 **Data accessibility**

304 Raw data are given in the supplementary material files (table S3).
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475
476

477 **Figure legends**

478 **Figure 1**

479 **A.** Hierarchical dendogram built on the 233 proteins detected in mice spleen (n=24). The number of
480 protein clusters selected (k = 4, indicated by the arrow in dots) was chosen because it increased the
481 percentage of total variance explained by the classification by 7.4% and then thereafter only by 4%.
482 Among the four clusters thereby selected, two were explaining more the total variation of axes 1 and
483 2 (cluster 3 and 4 indicated in shadow), which gathered 9 proteins (cluster 3, ID 2156, 1548, 277, 2242,
484 656; cluster 4, ID 928, 471, 132,546). Numbers refer to identification N° in Table S2. Height represents
485 the numbers of mice present in each cluster.

486 **B.** Hierarchical dendrogram built using mice spleen proteomic profiles (based on the 233 proteins
487 quantified), showing mice' distribution (n=24) among clusters (k = 3, separating old mice, and young
488 PBS (dark shadow) and young LPS (grey shadow) groups. Setting the number of clusters to 4 or more
489 did not show any sharp increase in the total variance explained (9% for 4 clusters, and 8% for 5
490 clusters...). Clusters of mice correspond to the protein clusters represented in figure 1A. Height
491 represents the numbers of proteins present in each cluster.

492

493 **Figure 2**

494 Regression checking for linear relationship between individual telomere length and proteomic levels
495 of expression of the chitinase-like 3 protein. Lines represent 95% confidence interval. Log10 (T/S ratio)
496 = 0.10 - 7.95 x 10⁻¹⁰ (chitinase-like 3 protein), r = 0.636, r² = 0.404, F₁,₂₃ = 14.934, P = 0.001. Dispersion
497 of the experimental groups is shown by different colors, filled circles indicate young-PBS mice, open
498 circles indicate young-LPS mice, dark-grey circles indicate old-PBS mice and light-grey circles indicate
499 old-LPS mice. Regressions tested by treatment are also both significant (PBS: r = 0.635, r² = 0.403, F₁,₁₁
500 = 6.763, P = 0.026, LPS: r = 0.623, r² = 0.389, F₁,₁₁ = 6.357, P = 0.030).

501
502 *Figure 3*

503 TIN2 protein levels in mice spleen.

504 Representative western blots for protein levels of TERF1-interacting nuclear factor 2 (TIN2) in the
spleen of mice are shown in the upper-panel. Fold changes in young mice injected with LPS and old
505 mice injected with PBS or LPS relative to the mean value in young mice injected with PBS (which was
506 arbitrarily set to 1) are shown in the lower-panel. Values that do not share the same superscript letter
507 are significantly different (see ESM4 and ESM5).

509
<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /><figcaption>Figure 1B</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 2</figcaption></figure>

519

520

521

522
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
528

529 Tables

530 Table 1

<table><thead><tr><th></th><th>ID N°</th><th>PCA1</th><th>PCA2</th><th>PCA 3</th></tr></thead><tbody><tr><td colspan="5"><strong>Proteomic signals</strong></td></tr><tr><td>Protein S100-A9</td><td>2156</td><td>0.922</td><td></td><td></td></tr><tr><td>Lysozyme C-2</td><td>1548</td><td>0.819</td><td></td><td></td></tr><tr><td>Annexin A1</td><td>277</td><td>0.882</td><td></td><td></td></tr><tr><td>Endoplasmin Hsp90b1</td><td>928</td><td></td><td>-0.441</td><td>0.742</td></tr><tr><td>GRP-78 kDa</td><td>132</td><td></td><td>-0.463</td><td>0.588</td></tr><tr><td>Carbonic-anhydrase 1</td><td>471</td><td></td><td>-0.853</td><td></td></tr><tr><td>Coronin 1A</td><td>656</td><td></td><td>0.865</td><td></td></tr><tr><td>RAS-C3</td><td>2242</td><td></td><td>0.817</td><td></td></tr><tr><td>Chitinase-like protein 3</td><td>546</td><td></td><td></td><td>0.818</td></tr><tr><td colspan="5"><strong>Physiological signals</strong></td></tr><tr><td>Body mass loss</td><td></td><td></td><td>-0.535</td><td></td></tr><tr><td>Telomere length</td><td></td><td></td><td></td><td>-0.892</td></tr><tr><td><strong>% variance explained</strong></td><td></td><td><strong>36.2</strong></td><td><strong>24.4</strong></td><td><strong>13.9</strong></td></tr></tbody></table>

531 Nature of the variables forming the main axes (PCA1, PCA2 and PCA3) of the Principal Components

532 Analysis conducted on proteomic and physiological variables describing spleen cellular responses to

533 an immune challenge (LPS) among old and young mice. Only eigenvalues over 0.4 were taken into

534 account and are indicated (see text for details). Total variance explained by the model: 74.5%. ID N°

535 refers to "Identification N°" of proteins that are listed in Table S2.

536
<table><thead><tr><th>Variables: physiological and proteomic signals</th><th>Estimates</th><th>D.F</th><th>F</th><th>P</th></tr></thead><tbody><tr><td colspan="5"><strong>(a) PCA1</strong></td></tr><tr><td>Age (Old vs. young)</td><td>0.319 ± 0.317</td><td>1, 23</td><td>7.208</td><td>0.007</td></tr><tr><td>Immune Treatment (LPS vs. PBS)</td><td>2.110 ± 0.317</td><td>1, 23</td><td>28.047</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Age x Immune Treatment</td><td></td><td>1, 23</td><td>16.867</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Young LPS vs. Old LPS</td><td>1.524 ± 0.317</td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>vs. Old PBS</td><td>1.791 ± 0.408</td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>vs. Young PBS</td><td>2.110 ± 0.419</td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td colspan="5"><strong>(b) PCA2</strong></td></tr><tr><td>Age (Old vs. young)</td><td>-1.172 ± 0.466</td><td>1, 23</td><td>10.846</td><td><strong>0.001</strong></td></tr><tr><td>Immune Treatment (LPS vs. PBS)</td><td>-0.276 ± 0.386</td><td>1, 23</td><td>0.332</td><td>0.565</td></tr><tr><td>Age x Immune Treatment</td><td></td><td>1, 23</td><td>0.068</td><td>0.794</td></tr><tr><td colspan="5"><strong>(c) PCA3</strong></td></tr><tr><td>Age (Old vs. young)</td><td>1.900 ± 0.368</td><td>1, 23</td><td>27.656</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Immune Treatment (LPS vs. PBS)</td><td>0.780 ± 0.517</td><td>1, 23</td><td>0.907</td><td>0.341</td></tr><tr><td>Age x Immune Treatment</td><td></td><td>1, 23</td><td>4.217</td><td><strong>0.040</strong></td></tr><tr><td>Young LPS vs. Old PBS</td><td>-1.119 ± 0.368</td><td></td><td></td><td><strong>0.014</strong></td></tr><tr><td>vs. Young PBS</td><td>0.781 ± 0.370</td><td></td><td></td><td><strong>0.034</strong></td></tr><tr><td>Young PBS vs. Old PBS</td><td>-1.614 ± 0.408</td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>vs. Old LPS</td><td>-1.190 ± 0.419</td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr></tbody></table>

Generalized linear models explaining the variability in the components axes (PC1, PC2, PC3) of proteomic and physiological signals in old and young mice challenged with PBS or LPS (n=6 in each group). Generalized linear model analysis separated by treatment (a) was used to assess Interaction Age x Immune treatment differences among groups (indicated in bold). Estimates and mean values are given ± SE and only significant differences among Age x Treatment interactions are indicated (using Bonferroni correction). Residuals of each models followed a normal distribution (checked using Kolmogorov-Smirnov test and QQ plot).
<table><caption>Table 3</caption><thead><tr><th>Fixed effect</th><th>Estimates</th><th>Standard error</th><th>z-value</th><th>P-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.105</td><td>0.089</td><td>1.130</td><td>0.259</td></tr><tr><td><strong>Chitinase-like protein 3</strong></td><td><strong>-7.926<sup>-10</sup></strong></td><td><strong>2.535<sup>-10</sup></strong></td><td><strong>2.967</strong></td><td><strong>0.003</strong></td></tr><tr><td><strong>Annexin A1</strong></td><td><strong>7.587<sup>-10</sup></strong></td><td><strong>5.000<sup>-10</sup></strong></td><td><strong>1.439</strong></td><td><strong>0.150</strong></td></tr><tr><td>Lysozyme C-2</td><td>-1.238<sup>-9</sup></td><td>8.560<sup>-10</sup></td><td>1.380</td><td>0.168</td></tr><tr><td>Endoplasmin Hsp90b1</td><td>-1.087<sup>-9</sup></td><td>8.788<sup>-10</sup></td><td>1.194</td><td>0.232</td></tr><tr><td>Protein S100-A9</td><td>1.051<sup>-9</sup></td><td>9.059<sup>-10</sup></td><td>1.160</td><td>0.250</td></tr><tr><td>Carbonic-anhydrase 1</td><td>3.030<sup>-10</sup></td><td>3.610<sup>-10</sup></td><td>0.915</td><td>0.360</td></tr><tr><td>RAS-C3</td><td>-3.124<sup>-10</sup></td><td>7.753<sup>-10</sup></td><td>0.403</td><td>0.687</td></tr><tr><td>GRP-78 kDa</td><td>3.365<sup>-10</sup></td><td>6.524<sup>-10</sup></td><td>0.490</td><td>0.624</td></tr><tr><td>Coronin 1A</td><td>-1.344<sup>-10</sup></td><td>6.473<sup>-10</sup></td><td>0.196</td><td>0.845</td></tr></tbody></table>

Model of the relationship between (i) the spleen proteomic expression levels of 9 proteins and (ii) the spleen telomere length (log-transformed). The estimates and *P*-values for the fixed effects are averaged over the 7 best general linear models for which the ΔAIC < 2 in comparison to the best model (AIC=-47.6, which used Annexin A1 and chitinase-like 3 as fixed factors (indicated in bold)). Fixed factors used in those 7 best models are indicated in italic.