Differences in body temperature drive sex-specific immune responses

Differences in body temperature drive sex-specific immune responses | medRxiv /* */ /* */ <!-- <!-- /*! * 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-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Differences in body temperature drive sex-specific immune responses View ORCID Profile Elizabeth Maloney , Jamie Sugrue , Marwan Sharawy , Maxime Rotival , Etienne Patin , Lluis Quintana-Murci , View ORCID Profile Darragh Duffy , Milieu Intérieur Consortium doi: https://doi.org/10.1101/2025.09.29.25336741 Elizabeth Maloney 1 Translational Immunology Unit, Institut Pasteur, Université Paris Cité , Paris 75015, France 2 Frontiers of Innovation in Research and Education PhD Program, LPI Doctoral School , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth Maloney For correspondence: elizabeth.maloney{at}pasteur.fr Jamie Sugrue 1 Translational Immunology Unit, Institut Pasteur, Université Paris Cité , Paris 75015, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marwan Sharawy 3 Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité , CNRS UMR2000, Paris 75015, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maxime Rotival 3 Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité , CNRS UMR2000, Paris 75015, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Etienne Patin 3 Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité , CNRS UMR2000, Paris 75015, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lluis Quintana-Murci 3 Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité , CNRS UMR2000, Paris 75015, France 4 Chair Human Genomics and Evolution , Collège de France, Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Darragh Duffy 1 Translational Immunology Unit, Institut Pasteur, Université Paris Cité , Paris 75015, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Darragh Duffy Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Human body temperature is sexually dimorphic, with females averaging 0.1-0.5°C warmer than males. Body temperature is also linked to immune responses through highly conserved fever and heat shock pathways. We hypothesized that subtle temperature differences observed between sexes could mediate sex differences in immune responses, with potential clinical implications. To test this, we analyzed 973 healthy adults from the Milieu Interieur cohort, first confirming significant sex and age effects on body temperature within the homeostatic range. Strikingly, within the narrow 36-38°C healthy range, we observed significant, sex-specific associations with both baseline and induced immune responses upon stimulation. In males, higher temperature was associated with decreased type I IFN pathway responses following bacterial and viral ligand stimulation, whereas in females, it correlated with decreased type II IFN responses after superantigen stimulation. Using precise ex vivo stimulation at fever temperatures and single-cell readouts, we confirmed that temperature-immune associations persist beyond homeostatic levels, highlighting their relevance in pathological conditions. This study provides new insights into how natural variation in body temperature across individuals and throughout life may contribute to the immune variation underlying sex disparities in disease. Introduction Sex disparities in disease represent a major challenge for health equity. Many autoimmune diseases show a 4:1 female predominance, while non-reproductive cancers are more common in males, with a 2:1 ratio 1 . In infectious disease, males are generally more susceptible to bacterial and viral infections, while females exhibit stronger antibody responses to vaccination and more adverse effects 2 , 3 . Although societal gender roles can partly explain this dimorphism 1 , 2 , population-immunology approaches have revealed the substantial effect of biological sex on immune responses 4 , with the underlying mechanisms still being elucidated. Emerging evidence has identified contributing factors, such as X-linked inactivation escape 5 , 6 and steroid hormone influences 7 – 9 ; however, a complete understanding is lacking. Body temperature is sexually dimorphic factor with known impact on immunity 10 , 11 . Temperature and immune responses are linked through highly conserved pathways such as heat shock response (HSR) and fever. Fever triggers HSR in both bacterial and human immune cells alike 12 – 14 , as hyperthermia activates this stress response to maintain protein folding under oxidative stress. Heat shock proteins (HSPs) induced during this process can exert proinflammatory effects by engaging toll-like receptors (TLRs), or anti-inflammatory effects by inducing interleukin (IL)-10 production in HSP-specific regulatory T cells 15 – 19 . Consistent with the proteins they generate, fever and hyperthermia can be either activating or suppressive, depending on cohort demographics, disease context, and immune cell population 20 – 25 . Specialized studies have failed to provide a unified systems-level understanding of how temperature modulates immune responses. This is further complicated by natural fluctuations in healthy body temperature, which varies both between and within individuals over daily, monthly, seasonal, and age-related cycles 10 . We hypothesized that subtle fluctuations in body temperature affect immune responses and may contribute to sex-specific differences in immunity. To address these questions, we utilized immunological datasets characterizing cellular, transcriptomic, and proteomic responses in 949 healthy donors from the Milieu Interieur (MI) cohort. We found sex- and pathogen-specific effects of healthy body temperature variation on immune responses that mirrored sex disparities seen in disease. Furthermore, we performed ex vivo whole blood stimulations in 24 healthy donors at both homeostatic and febrile temperatures, profiling cytokine and transcriptomic responses, which revealed diminished type II interferon signaling at febrile temperatures. Single-cell transcriptomic analyses confirmed these findings and delineated the affected specific cell types. Collectively, these studies provide new insights into how natural variation in body temperature may contribute to sex-specific differences in immune function. Results Interindividual body temperature variation explained by age, sex, and lifestyle To assess interindividual variability in body temperature within the MI cohort, we applied linear models to ear temperature measures taken over 3 visits within a 2-month period: visit 0 (V0), visit 1 (V1), and visit 2 (V2). We first analyzed the V1 temperature measures, when the immunological samples were taken, and identified associations between sex, age, and body temperature by applying a generalized linear model with gamma distribution (glmGamma) ( Figure 1A ). Female donors exhibited a higher average body temperature than males (FDR q = 1.8e-15, β = −0.46), and body temperature decreased with age in both sexes (FDR q = 4.1e-20, β = −0.0082) ( Figure 1A ). The age decline was more pronounced in females, as evidenced by a significant age×sex interaction (FDR q = 3.8e-5, β = 0.0051), with this sex difference eliminated when restricting analysis menopausal females and age-matched males (n = 192, FDR q = 0.678, β = 0.0042). For the age range with both pre-menopausal and menopausal donors, menopause was associated with cooler body temperature (44 – 55 years, n=63, FDR q = 0.044, β = −0.0050) ( Figure 1B ), and significant sex difference in temperature post-menopause. A subset of 500 donors had 3 consecutive temperature measurements in V0, V1, and V2 over a span of 2 - 8 weeks (median 4 weeks) ( Figure 1C ). In this subcohort, the same significant results were observed after modelling the mean by age (FDR q = 1.04e-23, β = - 0.00026), sex (FDR q = 4.4e-19, β = - 0.015), and their interaction (FDR q = 8.7e-07, β = 0.00018). Dispersion formula results revealed female donors showed overall more variability in body temperature than males (FDR q = 0.0032, β = 0.25), likely due to fluctuations associated with the menstrual cycle 10 . This was further supported by pre-menopausal female donors having more variability in body temperature than their post-menopausal counterparts (FDR q = 0.0036, β = 0.61) ( Figure 1D ). Download figure Open in new tab Figure 1 | Body temperature variation in the Milieu Intérieur cohort Figure 2 Scatter plots show MI V1 body temperature by age and applied regression models with the covariates of A age, sex and their interaction and B age and menopause in females 44-55 years old (with pre-menopausal females colored by oral contraception use for further illustration). C MI body temperature of 500 male and female donors over 3 visits spanning 8 weeks with applied mixed model and dispersion model. D MI body temperature of 250 female donors over 3 visits spanning 8 weeks with applied mixed model and dispersion model. Heatmap of significant associations between body temperature and 153 eCRF and steroid hormone variables identified by likelihood ratio tests of E all donors controlling for sex, age, and their interaction or controlling for age in G female donors and I male donors separately applying FDR correction. Values were plotted for the most significant novel association for each sub-cohort analysis: F heart rate in all donors (n=949), H oral contraceptive pill usage for female donors (no, yes, n=472), and J fried food consumption for male donors (n=477), * FDR q value <0.05, ** <0.01, *** <0.001. To investigate which factors, in addition to age and sex, may predict body temperature, we included 136 factors collected by questionnaire and plasma levels of 17 steroid hormones measured by mass spectrometry. We then tested whether each factor significantly improved the model using likelihood ratio testing (LRT). To maximize statistical power, we first analyzed all donors together while controlling for age, sex, and their interaction. Of the 153 variables tested, we identified heart rate (FDR q = 8.9e-09, β = 0.0065) and frequency of fried foods eaten per month (FDR q = 0.00067, β = 0.0085) as most significantly associated with body temperature, along with systolic blood pressure (FDR q = 0.043, β = 0.0022), number of fast-food meals per month (FDR q = 0.043, β = 0.0059), and estradiol levels (FDR q = 0.049, β = - 0.029) ( Figure 1E ). Heart rate was positively correlated with body temperature, consistent with previous reports 26 , 27 ( Figure 1F ). Since the regression analysis of all donors revealed estradiol, a sex hormone, as associated with body temperature, we assessed whether these factors correlated with body temperature differently depending on sex. We therefore repeated the analysis separately for female and male donors, still controlling for age, with 14 additional variables in the female analysis related to contraception, hormonal treatment, and family history of reproductive cancer. When analyzing the sexes separately, oral contraception (FDR q = 0.00012, β = 0.18) and heart rate (FDR q = 0.002, β = 0.0070) were significantly associated with female body temperature ( Figure 1G ). Oral contraception was positively correlated with body temperature, consistent with other studies ( Figure 1H ) 10 . Male body temperature was most significantly associated with heart rate (FDR q = 2.9e-05, β = 0.0062) and frequency of fried food consumption (FDR q = 0.0001, β = 0.0096), as well as systolic blood pressure (FDR q = 0.015, β = 0.0032), levels of corticosterone (FDR q = 0.021, β = −0.042), and the frequency of eating dessert (FDR q = 0.021, β = 0.0034), and fast food (FDR q = 0.037, β = 0.0059) ( Figure 1I ). Heart rate was the only factor associated with body temperature in both sexes, displaying a positive correlation and emphasizing the inextricable link between these two homeostatic measures ( Figure 1F ) 27 . The associations between body temperature and dietary habits were consistent across all donors and male donors alone ( Figure 1E,I ), with fattier diets correlating with higher body temperature ( Figure 1J ). Steroid hormones also emerged as sex-specific correlates of body temperature-estradiol in the pooled analysis and corticosterone in males, highlighting the importance of stratified analyses. The impact of homeostatic body temperature variation on baseline immune variability We next sought to determine how body temperature influences immune parameters, by applying linear mixed models to V1 flow cytometry data 28 . To describe natural variation of immune cell counts, ratios, and mean fluorescent intensities (MFIs) of relevant markers, ten eight-color immunophenotyping flow-cytometry panels were used on unstimulated blood to report a total of 166 distinct immunophenotypes 28 . We applied linear mixed models to these parameters with body temperature as a covariate while controlling for sex, age, age×sex interaction, smoking, BMI, hour of sampling, and day of sampling as a random effect, as well as cytomegalovirus (CMV) status when modeling cell counts. Similar models were applied to all donor data, with additional covariates for body temperature and the age×sex interaction term, as well as a spline fitted to hour of sampling to account for potential non-linear effects. In the innate cell compartment, eosinophil counts (FDR q = 0.03) were negatively correlated with body temperature along with cluster of differentiation (CD)203c expression in basophils (FDR q = 0.045) ( Figure 2A ). Additionally, the activation marker CD69 on CD69 + CD56 + natural killer (NK) cells (FDR q = 0.045) was positively correlated with temperature ( Figure 2A ), a relationship observed previously in T cells 21 . In the adaptive compartment, the counts of CD3 + T cells (FDR q = 0.03) and three T cell subsets, CD8b + T cells (FDR q = 0.012), central memory (CM) CD8b + T cells (FDR q = 0.012), and CD8b - CD4 - T cells (FDR q = 0.03), were all negatively correlated with body temperature ( Figure 2A ). From the calculated effect sizes, we estimated that a 1.5°C increase in body temperature correlated with a 0.5 log 2 decrease in the amount of circulating CD8b + T cells and CM CD8b + T cells ( Figure 2B ). As previously described 4 , 29 , 30 , blood from V1 was stimulated ex vivo in TruCulture tubes for 22 hours at 37°C with a range of stimuli including bacterial ( E. coli ; TLR 2, 4 agonist), viral (Poly I:C, TLR3 agonist), superantigen (SEB, T cell agonist), and a nonstimulated control. At 22 hours, cell transcriptomes from these stimulations were characterized by a Nanostring panel of 560 immune genes and cytokine secretion was characterized by a Luminex panel of 13 cytokines 31 . Because body temperature and cell counts are correlated across innate and adaptive subsets, we included proportions of 16 major cell types into subsequent models to control for their effects on immune responses neutrophils, basophils, eosinophils, monocytes, B cells, NK cells, dendritic cells (DCs), and 9 T cell subsets that includes different naïve, effector and memory cells. We first analyzed the nonstimulated control, modelling the log 2 gene expression with body temperature, while controlling for LRT-selected covariates of age, immune cell proportions, circular hour and day of sampling, smoking status, and CMV serostatus. Given the previously observed sex and age interaction in body temperature, we examined the effect of body temperature on gene expression in all donors (including sex and age×sex interaction as covariates), as well as in female and male donors separately. In the nonstimulated condition, significant associations (FDR q < 0.05) revealed six genes associated with body temperature in all donors, three of the same genes when analyzing female body temperature, and two genes unique to males ( Figure 2C ). In all donors, the expression of SH2D1A (FDR q < 0.013), GZMK (FDR q < 0.035), LAG3 (FDR q < 0.035), SLAMF1 (FDR q < 0.035), and CD1C0 (FDR q < 0.047) were negatively correlated with body temperature, whereas TLR2 expression (FDR q < 0.044) was positively correlated ( Figure 2D ). In females, the same negative correlations were observed for LAG3 (FDR q < 0.041), SH2D1A (FDR q < 0.041), and SLAMF1 (FDR q < 0.041), with larger effect sizes and confidence intervals ( Figure 2E ). In males, body temperature was uniquely and positively correlated with MX1 (FDR q < 0.0027) and STAT2 (FDR q < 0.0034) expression ( Figure 2F ). Given these sex-specific associations, and the established influence of sex on MI immune responses 4 , 31 , we kept a sex-stratified approach in subsequent analyses of stimulated conditions. Download figure Open in new tab Figure 2 | Body temperature effects on nonstimulated immune cells in the Milieu Intérieur cohort A Significant effects (FDR q < 0.05) of body temperature on circulating adaptive or innate immune cells and their cell surface markers in all donors. Estimates derived from applied linear mixed models with log-transformed immunophenotype as the response, controlling for factors previously found to affect these parameters, then transformed to the original scale. Adaptive immune parameters in purple, innate immune parameters in green. B Partial effect plots of the most significant associations by body temperature. C Barcode plot of genes whose expression was significantly associated with body temperature from multiple regression models applied to Nanostring gene expression data from the nonstimulated condition in all, female, or male donors. Models controlled for age, 16 major cell subtype proportions, hour of sampling, and day of sampling, with the addition of sex and an age×sex interaction for all donors. FDR q <0.05 in orange, q <0.01 in red. Quantification of the effect of body temperature on gene expression estimated from these models, then transformed to the original scale in D all, E female, and F male donors. (n=949 (all donors), n=477 (males), n=472 (females)) The sex-specific impact of homeostatic body temperature variation on immune responses To characterize the sex-specific effects of body temperature on ex vivo immune responses, we applied multiple regression models to the induced genes and cytokines (log 2 FC > 1 over the nonstimulated condition) for each condition, analyzing females and males separately. Multiple testing correction was performed within each sub-cohort using FDR (FDR q < 0.05 considered significant). Using LRT for covariate selection, we controlled for age, immune cell proportions, circular hour and day of sampling, and smoking, with the addition of CMV serostatus for the SEB condition. In female donors, body temperature was uniquely associated with immune responses to SEB stimulation ( Figure 3 , Supp Fig 1B, C ). Of the 305 genes induced after SEB stimulation, the transcriptional response of LAIR1 (FDR q = 0.0037), LTA (FDR q = 0.0037), IFNG (FDR q = 0.013, and IL27 (FDR q = 0.037) were associated with body temperature ( Figure 3A ). A 1°C increase in body temperature decreased IFNG response by 37% on average and increased LAIR1 response by 20% ( Figure 3B, C ). At the proteomic level, more than one third of the SEB induced cytokines in females were negatively correlated with body temperature: IFNγ (FDR q = 0.00023), TNFα (FDR q = 0.022), GM-CSF (FDR q = 0.014), and IL-10 (FDR q = 0.028) ( Figure 3D ). A 1°C increase in body temperature decreased IFNγ by 36% on average, confirming the pattern observed at the transcriptomic level ( Figure 3E ). Download figure Open in new tab Figure 3 | Body temperature effects on adaptive immune responses in female donors A Barcode plot of genes induced by SEB stimulation (log 2 FC over nonstimulated >1) with those whose expression is associated with female body temperature highlighted in color (FDR q < 0.05 in orange, q < 0.01 in red) B Quantification of the effect of body temperature on gene expression (log 2 FC over the nonstimulated control condition) post SEB stimulation in female donors. These were estimated in a multiple regression model controlling for age, 16 major cell subtype proportions, time and day of sampling on a circular scale, smoking status, and CMV serostatus then transformed to the original scale. Only genes with significant effect sizes (FDR corrected q -values < 0.05) were plotted. C Body temperature partial effect plots on the expression of the most significantly associated genes. D Spider plot showing cytokine levels in nonstimulated (grey) and SEB (purple) condition for females; cytokines were considered induced if the levels in the stimulated condition were doubled from the nonstimulated control. E Quantification of the effect of body temperature on log2-transformed induced cytokine levels in the SEB stimulation condition for females, effect sizes with FDR corrected q -values < 0.05 are in black, nonsignificant effects in grey (n=472) In contrast to female donors, male body temperature was specifically associated with the transcriptional responses upon stimulation with Poly I:C and E. coli ( Figure 4 , Supp Fig 1 A ). In the Poly I:C condition, the response of 24% of the induced genes (36/150) were associated with body temperature. The genes most significantly impacted by body temperature were MX1 (FDR q = 0.00083), IFIT2 (FDR q = 0.00083), STAT2 (FDR q = 0.0009), TNFSF10 (FDR q = 0.00083), and TNFSF13B (FDR q = 0.00083) ( Figure 4A ). The estimated effect of a 1°C change in body temperature ranged from 9% to 45% changes in transcriptional response ( Figure 4B ). Notably, 33 out of 36 temperature-associated genes were up-regulated with Poly I:C stimulation and exhibited weaker induction at high temperatures, while the remaining three were down-regulated with stimulation and displayed weaker repression at high temperatures, suggesting a general weakening of immune responses at high temperatures ( Figure 4C and Supp Table 1 ). A similar pattern was observed in the cytokine data: two of the highest-induced cytokines, IL-6 (FDR q = 0.048) and IL-12p70 (FDR q = 0.014) were negatively correlated with body temperature ( Figure 4D-F ). On average, a 1°C increase in body temperature decreased IL-6 levels by 27% and IL-12p70 levels by 35% ( Figure 4E ). Download figure Open in new tab Figure 4 | Body temperature and steroid hormones have independent immune effects To evaluate whether steroid hormones mediate the sex-specific body temperature effects on ex vivo immune responses, scaled data for body temperature and 17 steroid hormones were incorporated into the models from Figures 3 - 4 . Model improvement was assessed using likelihood ratio tests (LRT) with an FDR-corrected q -value threshold of ≤ 0.05. Percent variance explained was plotted as stacked bar graphs for significant models in the following order: the base model (without steroid hormones), an extended model with a single significant steroid hormone, and if applicable, a combined extended model with all significant steroid hormones. A Female responses to SEB stimulation, B Male responses to Poly I:C stimulation, and C Male responses to E. coli stimulation are shown. The results highlight independent and interactive contributions of body temperature and steroid hormones, with the addition of steroid hormones improving model fits in specific immune response contexts. (n=472 (females), n=477 (males)) In the E. coli condition, the response of 6% of the induced genes (13/220) were associated with body temperature, including seven which overlapped with the significant Poly I:C findings: CLEC4E (FDR q = 0.025), CYBB (FDR q = 0.018), GBP1 (FDR q = 0.045), IRF5 (FDR q = 0.038), SERPING1 (FDR q = 0.034), STAT2 (FDR q = 0.038), and TNFSF13B (FDR q = 0.013) ( Figure 4G ). For 12 of these genes, transcriptional response decreased by 12% to 36% per 1°C increase in body temperature and only one gene ( FCER1A ) had increased response with increased temperature ( Figure 4H ). As with Poly I:C, the gene positively correlated with body temperature ( FCER1A ) was down-regulated after E. coli stimulation, while the genes that were negatively correlated (e.g. IRF5 and TLR2 ) were up-regulated ( Figure 4I and Supp Table 1 ). The levels of the highest-induced cytokine, IL-6 (FDR q = 0.0056), along with TNFα (FDR q = 0.0056), were positively correlated with body temperature ( Figure 4J-L ). On average, a 1°C increase in body temperature increased IL-6 levels by 57% and TNFα levels by 50% after stimulation ( Figure 4K ). Given established links between steroid hormones and body temperature 10 , confirmed in our cohort ( Figure 1 ), we investigated whether steroid hormones mediate the sex-specific temperature effects on ex vivo immune responses ( Figures 3 - 4 ). To test this, we incorporated steroid hormones into the models where temperature was a significant predictor of ex vivo stimulated responses. We used LRT with FDR correction to test whether adding steroid hormones as predictors significantly impacted the temperature effect on immune responses. For genes and cytokines with significantly improved models, we calculated the percent variance explained by the covariates both with and without steroid hormones. In female immune responses following SEB stimulation, cortisol and corticosterone were negatively correlated with the transcriptional responses of IFNG and LTA as well as with the levels of IFNγ, consistent with the known anti-inflammatory effects of glucocorticoids ( Figure 5A ). However, their inclusion in the model only nominally shifted the body temperature effect, which remained significantly negative for all three immune measures. In male immune responses following Poly I:C and E. coli stimulation, cortisol and corticosterone were negatively correlated with CLEC4E response, but their inclusion in the model did not alter the body temperature effect ( Figure 5B, C ). Unique to E. coli response, cortisol was positively correlated with FCER1A response, as was the unaltered body temperature effect ( Figure 5C ). Unique to Poly I:C response, dihydrotestosterone (DHT) was positively correlated with IL-12p70 and IL-6 cytokine levels, which nominally diminished the negative body temperature effect by 1%, remaining significant ( Figure 5B ). Download figure Open in new tab Figure 5 | Body temperature effects on the immune response to Poly I:C in male donors A Poly I:C, G E. coli Barcode plot of genes induced by stimulation (log 2 FC over nonstimulated >1) with those whose expression is associated with male body temperature highlighted in color (FDR q < 0.05 in orange, q < 0.01 in red, q < 0.001 in purple). B Poly I:C, H E. coli Quantification of the effect of body temperature on gene expression (log 2 FC over the nonstimulated control condition) post stimulation in male donors. These were estimated in a multiple regression model controlling for age, 16 major cell subtype proportions, time and day of sampling, on a circular scale, and smoking status then transformed to the original scale. Only genes with significant effect sizes (FDR corrected q -values < 0.05) were plotted. C Poly I:C , I E. coli Body temperature partial effect plots on the expression of the most significantly associated genes. D Poly I:C (green), J E. coli (orange) Spider plot showing cytokine levels in nonstimulated (grey) and stimulated (color) conditions in males; cytokines were considered induced if the levels in the stimulated condition were doubled from the nonstimulated control. E Poly I:C , K E. coli Quantification of the effect of body temperature on log2-transformed induced cytokine levels in the stimulation condition for males, effect sizes with FDR corrected q -values < 0.05 are in black, nonsignificant effects in grey. F Poly I:C , L E. coli Body temperature partial effect plots on the levels of significantly associated cytokines. (n=477) Across all analyses, incorporating steroid hormones as predictors did not substantially affect the temperature effect by more than 3.5% (difference between body temperature effect in base model and model including steroid hormone), indicating that these immune effects are largely independent. Cortisol and corticosterone showed effects on immune responses concordant with those of body temperature, whereas DHT exhibited opposing effects. Overall, body temperature had a larger estimated effect on transcriptional response compared to the steroid hormones (average effect difference of 14%), while the steroid hormones had equal or slightly greater effects on cytokine levels (average effect difference of 1%). In fact, the only covariate that consistently explained a greater proportion of immune response variance across these models was immune cell proportions. Febrile temperature e=ects on healthy immune responses ex vivo Beyond homeostatic variation, changes in body temperature are observed with fever, a hallmark symptom of disease. After characterizing immune responses within the healthy temperature range, we investigated how these responses may change under elevated, febrile temperatures that coincide with immune activation. To explore temperature effects on immunity in the context of fever, we performed TruCulture healthy donor whole blood stimulations for 22 hours at homeostatic (36.8°C) and febrile (39°C) temperatures to determine the impact of this hallmark symptom on immune function (n=20 donors, 10 female, 10 male) ( Figure 6A ). Stimuli included the innate TLR stimuli lipopolysaccharide (LPS, TLR4 agonist) and R848 (TLR7/8 agonist), a T cell activating agonist (CytoStim), and a nonstimulated control. Cytokine production was assayed with a 14-cytokine LEGENDplex panel, and induced cytokines were determined for each condition as those with levels doubled over the nonstimulated control condition ((log 2 FC > 1). Linear mixed models were applied to log 2 -transformed cytokine levels including the covariates of stimuli, temperature, and their interaction, while controlling for donor and assay batch as random effects, to assess stimulation-dependent effects of temperature. Download figure Open in new tab Figure 6 | Febrile temperature reduces cytokine responses in ex vivo whole blood stimulations A Protocol used B, F Spider plots showing cytokine levels in the supernatant of healthy whole blood stimulations (n = 20) after 22 hours of incubation in the nonstimulated (grey) or stimulation (color) condition; cytokines were considered induced if the levels in the stimulation condition were doubled from the nonstimulated control. C, G Quantification of the interaction effect of stimulation and incubation temperature (36.8°C or 39°C), on the abundance of induced cytokines estimated in a linear mixed model with log 2 -transformed concentration as the response, controlled for donor and batch effect, then transformed to the original scale. Effect sizes with FDR corrected q -values < 0.05 are in black, nonsignificant effects in grey. D, H Models that showed a significant interaction between temperature and stimulation were visualized with line graphs of batch-adjusted cytokine concentrations. E, i Differentially expressed RNA pathways significantly downregulated in cells from a subset of the R848 or LPS stimulated healthy whole blood samples (n=11) at 39°C vs 36.8°C. Of the 12 cytokines induced by R848 stimulation ( Figure 6B ), incubation temperature influenced the expression of IFNγ (FDR q = 0.00064) and granulocyte colony stimulating factor (G-CSF) (FDR q = 0.03), as evidenced by a significant stimulation×temperature interaction ( Figure 6C ). Estimated effects revealed that febrile temperatures correlated with a 60% decrease in IFNγ levels and a 42% decrease in G-CSF levels ( Figure 6C ). These diminished cytokine levels are particularly evident when plotting batch-corrected log2 concentrations ( Figure 6D ). Of the 13 cytokines induced by LPS stimulation ( Figure 6F ), a significant stimulation×temperature interaction term indicated that incubation temperature influenced the levels of IFNγ (FDR q = 0.0073), G-CSF (FDR q = 0.0073), and chemokine ligand 10 (CXCL10) (FDR q = 0.017) ( Figure 6G ). Febrile temperature again correlated with lower proteomic levels for all three, with an estimated effect of up to 39% for IFNγ ( Figure 6G ). Residual plots illustrate this relationship between increased incubation temperature and diminished cytokine levels ( Figure 6H ). No temperature interaction was significant after FDR correction in the CytoStim condition, or temperature alone in the nonstimulated control condition ( Supp. Fig. 2A ). To ensure these temperature-induced decreases in cytokine levels in the R848 and LPS conditions were not due to cell death or protein instability, we isolated cells at 22 hours and performed flow cytometry with a fixable viability dye and bulk RNA sequencing (RNAseq). Flow cytometry confirmed comparable cell viability across all stimuli in both temperature conditions with averages of 89.8% at 36.8°C and 89.9% at 39°C ( Supp. Fig. 2B). RNAseq analysis involved gene set enrichment of differentially expressed genes, both across all donors and stratified by sex (n=11 donors, 6 female, 5 male). After R848 stimulation, we found febrile temperatures significantly attenuated oxidative phosphorylation and Myc target pathways for all donors and males alone, while significantly diminishing heme metabolism pathways in females only. After LPS stimulation, febrile temperatures significantly attenuated induced type I and II interferon signaling, TNFα signaling via NF-kB, and inflammatory response pathways across all cohorts, demonstrating pleotropic regulatory effects on bacterial immunity ( Figure 6G ). Cell-specific temperature e=ects To identify which specific immune cells underlie the observed sex-specific temperature effects, we performed additional ex vivo whole blood stimulations coupled with cellular indexing of transcriptome and epitope sequencing (CITE-seq). Blood from four donors (female n=2, male =2) was incubated at homeostatic (36.8°C – 37°C) and febrile temperatures (39°C) in nonstimulated control, CytoStim, Poly I:C, and E. coli TruCulture tubes for 4 hours. Post-incubation, we isolated PBMCs, labelled them with a pool of 20 antibodies, and captured over 640,000 high-quality single-cell transcriptomes ( Figure 7A ). Using transcriptome-based harmonized clusters ( Supp Fig 3A ), CITE-seq marker expression (Supp Fig 3C) , and a cell-assignment model trained on 1.5 million stimulated PBMCs, we identified 42 cell types across myeloid, B, T, and NK lineages ( Figure 7B and Supp Fig 3B ). Of these, 29 cell types displayed transcriptional changes with temperature in at least one stimulation condition ( Figure 7C and Supp Fig 3D ). The most temperature-responsive were CD14 + classical monocytes, CD16 + non-classical monocytes, naive CD8 + T cells, naive CD4 + T cells, and non-class-switched memory B cells. Across these, heat shock proteins and immune-specific response genes were up-regulated at 39°C. For example, in CD14 + classical monocytes, CXCL11 , IGHG2 , and CPM were all up-regulated at 39°C in the non-stimulated control condition, while at 39°C in the Poly I:C condition, CPM , MYCL were upregulated and IRF4 was down-regulated ( Supp Fig 3E) . Download figure Open in new tab Figure 7 | Single-cell resolution of temperature-induced immune responses reveals sex-specific immune adaptations A Experimental design: Whole blood from healthy donors (n=4) were cultured in TruCulture tubes at physiological (36.8°C) or febrile (39°C) temperatures for 4 hours under four conditions: non-stimulated control, Cytostim (T cell activation), Poly I:C (viral mimic), E. coli (bacterial), followed by PBMC isolation and CITE-seq analysis. B Stimulation-harmonized UMAP visualization of 42 immune cell types identified across all conditions. C Barplot quantifying temperature-responsive genes (39°C vs 36.8-37°C) across cell types and stimulation conditions (grey = negative control, purple = CytoStim, green = Poly I:C, orange = E. coli ). Hallmark pathway enrichment analysis showing temperature-responsive pathways across cell types for each stimulation condition: D non-stimulated control, E CytoStim, F Poly I:C, and G E. coli . Dot size indicates - log10(FDR) and color represents direction of regulation (red = upregulated, blue = downregulated at 39°C vs 36.8°C). H Cell type proportions stratified by biological sex and temperature, showing sex-specific responses to febrile conditions. I Sex differences in hallmark pathway temperature responses across stimulation conditions, with ratio of female vs male temperature sensitivity. J All cell types with significant sex differences in IFN alpha pathway responses to temperature (39°C vs 36.8°C) in E. coli stimulation, * FDR q value <0.05, ** <0.01, *** <0.001. Gene set analysis revealed stimulation-specific temperature effects ( Figure 7D-G ). In the nonstimulated control, febrile temperature most significantly affected E2F targets, G2-M checkpoint signaling, and mitotic spindle pathways, indicating altered proliferation ( Figure 7D ). These pathways were upregulated in CD8 + central memory T cells expressing CCR4 (CD8 + CM CCR4 + T) and CD8 + effector memory T cells (CD8 + EM T), but downregulated in CD4 + T helper 17 cells (Th17), Effector gamma delta (γδ) T cells, and non-class-switched memory B cells ( Figure 7D ). Mucosal-associated invariant T (MAIT) cells showed significant down-regulation of TNFα signaling via NF-kB. However, the same pathway was upregulated at 39°C after Cytostim in MAIT cells and 12 other cell types including CD8 + CM CCR4 + T cells ( Figure 7E ). In the Poly I:C condition, higher temperatures attenuated inflammatory responses, type I and II interferon responses, and TNFα signaling via NF-kB across much of the T cell compartment ( Figure 7F ), consistent with our earlier findings in the homeostatic range ( Figure 4 ). However, unlike 22h R848 responses ( Figure 6 ), innate immune responses to Myc targets and fatty acid metabolism were up-regulated in AXL+ SIGLEC6+ dendritic cells (ASDCs), monocytes, NK cells, and plasmacytoid dendritic cells (pDCs), potentially due to the different viral response mechanisms or the 4h versus 22h timepoint ( Figure 7F ). E. coli stimulation showed similar patterns, as higher temperatures attenuated type I and II interferon responses across 27 and 10 cell types respectively, as well as IL-6/Jak/Stat3 signaling in conventional dendritic cells (cDCs) ( Figure 7G ). These effects are consistent with the E. coli trends we saw at homeostatic temperatures ( Figure 4 ) and mirror the bulk LPS experiments ( Figure 6 ). Given our sample size, sex-stratified analysis was exploratory. Cell types with the greatest sex bias were CD56dim NK cells, which were more than doubled in males compared to females (males 19.2% vs females 7.5%), while MAIT and naive CD8+ T cells were more than doubled in females (f 7.8% vs m 2.7% and f 6.5%.vs m 2% respectively)( Figure 7H ), consistent with MI data 28 . CD14 + classical monocyte proportions were similar at homeostatic temperatures (f 11.8%, m 11.1%), but females had a 34.2% relative increase at 39°C (15.8% vs 11.8%), while males showed no change (11.0% vs 11.1%), indicating potential sex-specific temperature effects on CD14 expression or cell survival. Sex-stratified gene set analysis revealed that temperature had many sex-specific effects on immune pathways ( Figure 7I ). Across stimuli, there was female bias in temperature effects for lymphocytes (CD4+ and CD8+ T cells B cells), while male bias was more evident in innate immune cells (NK cells, monocytes) and Tregs, consistent with previous results from the MI cohort 28 . In E. coli responses, males had significant attenuation of type I and II interferon responses across 15 and 3 cell types respectively, including CD56dim NK cells, of which they have 2.5x as many ( Figure 7J ). These findings collectively suggest that sex differences in cell type abundance and temperature response may jointly drive immune response differences between females and males. Discussion Sexual reproduction generates evolutionarily advantageous diversity, driving homeostatic variation across human populations and life stages 32 – 34 . However, the extent of healthy variability in a key homeostatic parameter—body temperature—remains poorly defined. Furthermore, how temperature variation influences immune responses is largely unknown, with a notable lack of population-level studies. Addressing these questions is critical for understanding the biological mechanisms underlying sex differences in immune function and disease prevalence. Here, we conducted a multi-dimensional analysis of the impact of body temperature on immune responses in 973 healthy donors, revealing sex-specific effects: male responses were most affected for bacterial and viral stimuli, whereas female responses were most influenced for T cell stimuli. To our knowledge, this is the first in-depth large cohort study examining how healthy body temperature variation affects human immunity. First, we observed that females were on average 0.5°C warmer and showed greater temperature variation than males, consistent with previous studies 11 , 35 . Premenopausal females not using oral contraceptives exhibited the greatest variation, likely reflecting luteal-phase temperature increases. Additionally, oral contraceptives artificially sustained higher body temperatures, in line with prior findings 36 , 37 . Among males, dietary habits, such as dessert, fast food, and fried food consumption, correlated with warmer body temperatures, potentially reflecting interactions with androgens that influence muscle anabolism, glucose uptake, and adiposity 38 . This highlights how energy intake and hormonal fluctuations interact to modulate thermal homeostasis in a sex-specific manner. We also confirmed that homeostatic body temperature influences circulating innate and adaptive immune cell proportions. Notably, multiple T cell subtypes, including CD8 + and double-negative T cells, negatively correlate with temperature. Conversely, we observed upregulation of proliferation pathways in CD8 + T cell subsets after 4h incubation at 39°C, consistent with previous evidence that adaptive immunity can be enhanced at febrile temperatures 21 , 24 , 25 . In addition to previously described 28 sex biases in NK cell proportions for males, and MAIT and naïve CD8 + T cells for females, there was a 34.2% relative increase in female CD14 + classical monocytes in our experimental fever model, highlighting that temperature context is crucial for understanding sex differences in immunity. Temperature responses were also sex-specific across both homeostatic and febrile ranges. Among male donors only, seven of the same proinflammatory genes were negatively correlated with body temperature in both the viral (Poly I:C) and bacterial ( E.coli) conditions. These genes have distinct roles in pathogen recognition, type I interferon induction, and oxidative stress responses like HSR, with such connections to pyrogenic pathways demonstrating feedback inhibition with rising temperature 25 . Further evidencing this trend, body temperature was negatively correlated with pyrogenic IL-6 cytokine levels after viral stimulation for males only. Febrile temperatures attenuated type I and II interferon signaling to a greater extent in males after 4 hours of viral and bacterial stimulation, with significant pathway downregulation in nearly 3 times as many cell types. After 22h bacterial (LPS) and viral (R848) stimulation, this attenuation effect was mirrored by reduced levels of pro-inflammatory cytokines including IFNγ, G-CSF and CXCL10. RNAseq analysis confirmed this modulation at a transcriptomic level, suggesting an alternate way that hyperthermia modulates immune response, potentially through metabolic or steroidal intermediates 25 . Among female donors, body temperature was significantly associated with superantigen-induced T cell responses and T cell proliferation. IFNγ was negatively correlated with body temperature at both transcriptomic and proteomic levels, along with other proinflammatory genes and the pyrogenic cytokine TNFα. Our findings suggest that elevated temperature, one of the four classical hallmarks of inflammation, may in turn act as an anti-inflammatory signal in a negative feedback mechanism by dampening type II interferon responses, as has been recently characterized for NLRP3 inflammasomes 39 . Interestingly, after 4h Cytostim stimulation at 39°C, TNFα signaling was augmented in male innate cells such as CD16+ non-classical monocytes and NK cells, while febrile temperatures attenuated T and B cell cycling to a greater extent in females than males. These patterns persisted across all 4h ex vivo fever experiments, where exploratory sex-stratified CITE-seq analysis showed greater temperature response in the female adaptive immune compartment and the male innate immune compartment, concordant with our healthy temperature findings from MI. These sex-specific temperature effects mirror sex disparity in disease, with males more susceptible to pathogen-associated damage in infectious disease, while females are more likely to develop autoimmune disorders with pathogenic T cell activity 3 . The life history theory of evolution hypothesizes such differences are due to which immune compartment evolved to be more critical for each sex (innate for males, adaptive for females) due to reproductive necessity and resource allocation 40 – 43 . We hypothesize further that temperature can act as a fine-tuning mechanism on these adaptively significant immune compartments, and may contribute to sex disparities in disease or even be exploited for therapeutic effects. Surprisingly, steroid hormones did not mediate the observed sex-specific temperature effects. Consistent with the known anti-inflammatory role of glucocorticoids, cortisol and its inactive form corticosterone were negatively associated with immune responses, mirroring the correlations observed with body temperature. These results are consistent with the hypothesis that rising body temperature may exert an anti-inflammatory effect via negative feedback mechanisms. Seemingly contradictory with studies focusing on adaptive immune response 21 , 24 , 44 – 46 , these results could point to differential temporal effects of hyperthermia. There is growing evidence that hyperthermia can influence immune resolution and a return to homeostasis by repressing macrophage activity through heat shock factor (HSF)-1 binding to TNFα gene promoters and influencing NF-κB and MAPK pathways 25 , 47 – 49 . Further kinetic experiments at a single-cell level could help elucidate these cell subtype-specific mechanisms. Our study has some limitations. The MI body temperature measurements have a floor of 36°C, but considering most of the expected temperature distribution is observed above this level and our trends match those of larger cohorts, we feel justified in using these data for our analysis. Additionally, all MI ex vivo stimulations were performed at 37°C, a uniform temperature warmer than many donors. However, the presence of significant body temperature associations with induced responses despite this limitation indicates the lasting effect of this fundamental homeostatic parameter. Furthermore, a strength of MI was limiting genetic and cultural diversity to remove confounding factors in immune variability, but it necessitates the recapitulation of our temperature-immune interaction research in diverse cohorts to determine whether our findings are universal or vary with genetic and cultural factors. A limitation of our febrile investigation is a relatively small sample size, especially for the single-cell experiments. An increased sample size at single-cell resolution could more confidently identify sex differences in the immune response at both homeostatic and febrile temperatures. Finally, our study focuses on increased heat, while cold can also have unique, marked effects on immune responses such as those seen in cryopyrin-associated periodic syndromes and accidental hypothermia 50 , 51 , which also warrant systems biology investigations. In conclusion, natural variability in body temperature attenuates immune responses across homeostatic and febrile ranges and has sex-specific consequences. Understanding the complex mechanisms underlying homeostatic interactions between body temperature, metabolism, and immune responses will provide insight into the sources of population-level immune variation and pave the way for guided personalized interventions in the face of escalating extreme heat events 52 , 53 . Harnessing temperature effects on immunity could lead to new therapies, as already illustrated by hyperthermia as an adjuvant for cancer treatments, an anti-inflammatory treatment for ankylosing spondylitis, and a microwave-induced solution for refractory human papillomavirus skin lesions 20 , 21 , 54 – 56 . Materials and Methods Milieu Interieur Clinical Protocol Human samples were collected as part of the Milieu Interieur cohort, which was approved by the Comité de Protection des Personnes—Ouest C and the Agence Nationale de Sécurité du Médicament in June 2012 and sponsored by the Institut Pasteur (ID-RCB no. 2012-A00238-35). The study protocol was designed and conducted in accordance with the Declaration of Helsinki and good clinical practice as outlined in the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) Guidelines for Good Clinical Practice. The original protocol was registered under ClinicalTrials.gov ( NCT01699893 ). All donors gave written informed consent. The cohort consists of 949 healthy donors selected based on inclusion and exclusion criteria that were previously described 57 . Briefly, donors had no history or evidence of pathological conditions, substance abuse, or recent vaccination. Donors are equally distributed amongst males and females across 5 decades of life (20 – 69 years old). To decrease potential confounding variability, all donors self-declared to have 3 generations metropolitan French ancestry, and women could not be pregnant or perimenopausal. Sampling took place in the morning at a single site (Biotrial, Rennes 35000) over a continuous 11 month period from September 2011 to August 2012. Datasets Case Report Form variables Lifestyle, dietary, and medical history factors were collected in an electronic case report form (CRF)-based questionnaire as previously described 57 . Clinical parameters such as temperature and blood pressure were measured on the day of blood sampling by Biotrial staff and appended to the CRF. To select factors for analysis, we selected variables that had data for at least 5% of individuals in at least two categorical levels. Steroid Hormones To obtain free and non-covalently bonded steroid hormone measures, plasma samples from fasting EDTA-treated blood was frozen immediately upon collection at −80°C until aliquoting. Upon thawing, aliquoted samples were completely randomized prior to processing for mass spectrometry analysis. 17 steroid hormones were measured using the AbsoluteIDQ Stero17 kit (Biocrates, Austria), which included glucocorticoids (cortisol, cortisone, and 11-deoxycortisol), mineralocorticoids (aldosterone, corticosterone, and deoxycorticosterone), progestogens (17α-hydroxyprogesterone and progesterone), estrogens (estrone and estradiol), and androgens (androstenedione, androsterone, dehydroepiandrosterone, dehydroepiandrosterone sufate, dihydrotestosterone, etiocholanolone, and testosterone). Nanomolar concentrations were obtained from Biocrates in an Excel sheet along with a corresponding data report. Given that the measurements were taken across multiple plates, Biocrates calculated a project-based limit of detection (LOD), corresponding with the highest LOD in any plate for each steroid hormone. For measurements below LOD, Biocrates imputed missing values using the R package logspline. The logspline imputation method generates values between LOD and LOD/2, and applies the variance between values above LOD. Therefore, if there is high variance in the dataset, the imputed values may also exceed these limits. In this study, the steroid hormones were analyzed together with the CRF factors for their influence on body temperature. Peripheral immune cell parameters Ten eight-color immunophenotyping flow-cytometry panels were used on unstimulated blood to report a total of 166 distinct immunophenotypes, including counts, ratios, and MFIs of relevant markers as previously reported 28 . Milieu Interieur Ex-vivo stimulations and subsequent transcriptomic and proteomic assays TruCulture stimulations were performed as previously described 29 . Briefly, TruCulture tubes were prepared in batch for each stimulus, resuspended in a volume of 2 mL buffered media, and stored at −20°C until time of use. On day of sampling, 1 mL of whole blood was distributed into 37°C prewarmed TruCulture tubes for each stimulus within 15 minutes then incubated for 22 hours at 37°C in a dry block. Post-incubation, tubes were opened and a valve was inserted to separate the cells from the supernatant and stop the stimulation. Supernatants were aliquoted and frozen at −80°C until assayed and RNA samples were processed as previously described 30 . Transcriptomic profiling of the immune cells was completed as previously described using the Nanostring Human Immunology v2 Gene Expression CodeSet 4 . Proteomic profiling of the supernatants was completed by Rules Based Medicine (Austin, Texas, USA) using Luminex xMAP technology 31 . Data generation and analysis Unless otherwise stated, all displayed MI results were performed on the 949 individuals of the cohort who gave consent to share their data publicly. All regression model effect size plots are on a linear scale and display the extracted estimate and 95% confidence interval. Effects with significant FDR-corrected q values (<0.05) are in black, and those that did not reach significance are in grey. Temperature Variation analysis Multiple regression models were built to estimate the effects of intrinsic and behavioral factors on body temperature. In all donors, the model included the covariates of age, sex, and their interaction. In pre-menopausal donors, the covariates of age and oral contraceptive use were included, and for females between 44-55 years, age and menopause were included. A multiple regression model with dispersion formula was built to estimate the effects of age, sex, their interaction, time of day, day in the year on the variance in body temperature over 3 visits spanning 2-8 weeks and applied using the R glmmTMB package. eCRF analysis To test for associations between demographic variables and steroid hormone levels we used the R package stats to perform likelihood ratio tests comparing a base model controlling for age, sex and an age×sex interaction [lm(temperature ∼ age×sex)], with a model controlling for age, sex and an age sex interaction plus a predictor demographic variable [lm(temperature ∼ age×sex + demographic variable)]. We performed this analysis on 137 demographic variables in all donors, with an additional 14 variables in female only donors. An FDR correction was performed to account for multiple testing across all demographic features for each hormone. Heatmaps to display the q value from the LRT were generated using the R package pheatmap. Peripheral immune cell parameters analysis To model temperature effects on peripheral immune cells, linear mixed models were applied to 166 parameters with the covariates of body temperature, sex, age, age-sex interaction, smoking, BMI, hour of sampling as a spline, and day of sampling as a random effect, with the addition of CMV status when modelling counts. These covariates were selected as previously reported, with the addition of the age-sex interaction, body temperature, and a spline for hour of sampling 28 . Models were applied using the MIIC R package 28 . Ex vivo immune response analysis To model temperature effects on ex-vivo stimulation responses, linear models were applied to induced cytokine and gene expression data (log2FC over the nonstimulated control). Covariate selection: all models included age, circular hour and day of sampling and 16 immune cell proportions. Smoking and CMV were added as necessary whether they contributed to the models. For E. coli and Poly I:C, smoking was significant for some cytokines by drop1(model, test = “Chi”), but not CMV. Both were sometimes significant for SEB cytokines, so they were included in the models for SEB. Likehood ratio testing was applied to all significant genes and cytokines for each of the 17 steroid hormones measured by mass spectrometry and results were controlled for multiple testing with FDR correction. Those models with FDR q -value <0.05 were further interrogated by calculating the percent variance explained in the base model (without hormone) and the model with hormone(s) added. All continuous variables including body temperature, age, immune cell proportions, hormone levels, and temporal variables were standardized (mean-centered and scaled to unit variance) prior to analysis to allow effect size comparability. We quantified the relative importance of each predictor using the Lindeman-Merenda-Gold (LMG) method implemented in the ‘relaimpo’ R package. This approach calculates each predictor’s contribution to the model’s R², accounting for correlations between predictors by averaging over all possible orderings of predictors in sequential R² decompositions. The resulting values represent the percentage of variance in gene expression explained by each predictor. Healthy Donor Blood and Stimulation Healthy donor blood was collected by the ICAREB platform at Institut Pasteur under project M22 and COSIPOP 2023-30 ( NCT03925272 ) or through the Établissement français du sang (EFS). Blood was collected in heparin tubes and delivered within 6 hours. In round-bottom 96-well plates, 100 μL of blood was added to 200 μL of pre-warmed TruCulture media for each condition in duplicate or triplicate. Plates were placed in cell incubators set to 0% CO 2 , 0.02% O 2 , and 36.8°C or 39°C respectively for 22 hours resulting in 8 experimental conditions per donor (2 temperatures × 4 stimulations). Post-incubation, plates were centrifuged at 300g for 5 minutes. The supernatant was collected and stored at −20°C until time of use. Cells were either assayed by flow cytometry to confirm viability using a Fixable Viability Dye eBioscience eFluor 506, or diluted 2:1 with Trizol, shaken for 2 minutes at 2000 rpm, rested for 10 minutes at room temperature, then frozen at −80°C until time of use. For the single-cell experiments, 1 mL of blood was added to prewarmed TruCulture tubes for each stimulus and incubated at 36.8°C-37°C or 39°C respectively for 4 hours in dry blocks. Post-incubation, tubes were opened and a valve was inserted to separate the cells from 1.5 mL of supernatant and stop the stimulation. Supernatants were aliquoted and frozen at −80°C until assayed and the remainder of the 1.5 mL volume was layered onto 2 mL Ficoll in 5 mL tubes and spun for 20 minutes at 800g without braking to separate the PBMCs. We proceeded to wash the PBMCs twice in 15 mL conical tubes with 10 mL of PBS and spins of 5 minutes at 400g and resuspended the pellet in 1 mL PBS + 0.04% BSA for cell counting. The cells from each pool were counted using the Cell Countess 3 automated cell counter (Thermo Fisher Scientific) and the cell density was adjusted to 1,000 viable cells per µl of PBS + 0.04% BSA and we proceeded with 10X demonstrated protocols described below. LEGENDplex Cytokine assay and analysis On day of assaying, supernatants were thawed and centrifuged at 300g for 5 minutes. The concentration of 14 different analytes were measured using the LEGENDplex Human Covid-19 Cytokine Storm Panel 1(14-plex) kits and the manufacturer protocol was followed (BioLegend, San Diego, California, USA). Plates were read on a BD Fortessa II 5-color flow cytometer (Franklin Lakes, New Jersey, USA), and results were analyzed on the BioLegend portal ( https://legendplex.qognit.com/ ). Concentrations were log 2 -transformed and controlled for donor and LEGENDplex experiment batch effects via linear modelling with the package lme4. Median controlled-concentrations were found for each analyte per stimulation condition, and the log 2 FC of the stimulation over the nonstimulated control was calculated. Analytes with a log 2 FC of >1 were considered induced and were further analyzed for temperature effects. To achieve this, linear mixed models were applied to un-controlled log 2 -transformed concentrations using the lme4 package with the covariates of stimulation, temperature, a stimulation-temperature interaction, and the random effects of donor and Legendplex experiment. Covariate p-values were FDR-corrected by stimulation. RNA sequencing and analysis On day of extraction, cells in Trizol were thawed, and 2 100-μL aliquots per condition per donor were combined for extraction. RNA extraction was performed using the PureLink RNA Mini Kit (ThermoFisher Scientific, Waltham, Massachusetts, USA) per the manufacturer protocol with the optional DNase step and quantified with the Qubit High Sensitivity RNA assay (ThermoFisher Scientific, Waltham, Massachusetts, USA) and qualified with the Agilent High Sensitivity RNA ScreenTape (Agilent Technologies, Santa Clara, California, USA). RNA was sequenced via the BRBseq protocol by Alithea Genomics (Épalinges, Switzerland). The analysis was modelled after published methods using the edgeR , limma , glimma , and camera packages 58 . Using the edgeR package, initial filtering and normalization of the UMI-corrected counts per gene was completed and the log 2 fold change of the LPS condition over the nonstimulated control condition was calculated. Using the R limma package, differentially expressed genes between the 36.8°C and 39°C condition were calculated using empirical Bayes moderated t-statistics. We then applied the camera method to the significant genes for gene set testing with the Broad Institute’s MSigDB hallmark gene sets 59 . Single-cell RNA and cell surface marker sequencing Isolated PBMCs were labelled for CITE-seq according to the manufacturer’s instructions demonstrated protocol CG000149 (10x Genomics). Washed PBMCs were resuspended in chilled 1% BSA in phosphate-buffered saline and incubated with human TruStain FcX blocking solution (BioLegend) for 10 min at 4 °C. Cells were then stained with a cocktail of TotalSeq-B antibodies (BioLegend) previously centrifuged at 14,000 g for 10 min (Supplementary Table 2). The cells were incubated for 30 min at 4 °C in the dark and then washed three times. Cell concentration was again determined using Cell Countess 3 automated cell counter (Thermo Fisher Scientific) and density was then adjusted to 1,000 viable cells per µl in 1% BSA in phosphate-buffered saline. We generated scRNA-seq libraries and cell protein libraries with the Chromium Single Cell 3′ Reagent Kit (v.4), using the Feature Barcoding technology for Cell Surface Proteins (10x Genomics). Single-cell RNA sequencing data processing and quality control Single-cell RNA sequencing data from peripheral blood mononuclear cells (PBMCs) were processed using Scanpy (v1.9.3) in Python 3.11. Raw count matrices were loaded and merged across all samples, retaining cells with unique molecular identifier (UMI) counts between 200 and 30,000, and genes expressed in at least 3 cells. Doublets were identified and removed via Scrublet. Initial filtering removed cells with mitochondrial gene content exceeding 15%, hemoglobin content exceeding 10%, and less than 300 genes. After clustering and cell type annotation, a second round of filtering removed cells with mitochondrial gene content exceeding the 95 th percentile (5.3%) and less than the 5 th percentile of number of genes (712). After quality filtering, the dataset comprised of 640,871 cells and 3,396 genes from 4 healthy donors. Cell type annotation Cell types were assigned using a reference-based approach with pre-trained models from Cell Typist ( https://www.celltypist.org/ ) 60 . The final model applied was trained on 1.5 million stimulated PBMCs. Initial annotations were refined by examining canonical marker gene expression patterns including CD3 (T cells), CD20 (B cells), CD14/CD16 (monocytes), CD56 (NK cells), and lineage-specific markers. Rare cell populations including innate lymphoid cells (ILCs), plasmacytoid dendritic cells (pDCs), and AXL+ SIGLEC6+ dendritic cells (ASDCs) were identified based on specific marker combinations. Cell type assignments were validated through UMAP visualization, as well as marker gene and cell surface protein expression analysis. Pseudobulk differential gene expression and gene set analysis To leverage the statistical power of bulk RNA-seq methods while accounting for biological replication, we performed pseudobulk analysis by aggregating gene counts within each cell type, donor, and experimental condition combination (stimulation and temperature). Only cell type-condition combinations with at least 5 cells were retained for analysis. Gene expression was normalized using DESeq2’s median of ratios method, and genes with mean expression <1 TPM across conditions were filtered out. Differential expression analysis was performed using DESeq2 (v1.38.0) with the design formula: ∼ donor + condition. Comparisons were made between temperatures within each stimulation condition. Genes were considered significantly differentially expressed at FDR-adjusted p-value 0.2. We then applied the camera method to the significant genes for gene set testing with the Broad Institute’s MSigDB hallmark gene sets 59 . Analysis was completed for all donors and on a sex-stratified basis. Data Availability All data produced in the present study are available upon reasonable request to the authors https://dataset.owey.io/ https://redcap.pasteur.fr/surveys/?s=ND8TP8MDD3 Data and materials availability All published Milieu Intérieur pseudonymized datasets can be accessed on https://dataset.owey.io/ by submitting a data access request at https://redcap.pasteur.fr/surveys/?s=ND8TP8MDD3 . The request will be reviewed by the Milieu Intérieur data access committee (DAC, milieuinterieurdac{at}pasteur.fr ). The DAC informs the research participants of the data access request and grants data access if the request is consistent with the informed consent signed by the participants. Research on Milieu Intérieur datasets is restricted to research on the genetic and environmental determinants of human variation in immune responses. Data access is typically granted two months after request submission. Experimental data sets will be made available upon publication. Contributions E.M. conceived the study, performed analysis and experiments, wrote and edited the manuscript. M.S. supported single-cell analysis with consultations, code, methods and visualization advice. J.S. performed genetic analysis which was not included in the final paper. M.R. provided advice on linear regression and mixed modelling. E.P., provided advice on hormone mediation analysis. L.Q.M. obtained funding and coordinated the clinical samples. D.D conceived and supervised the study, edited the manuscript, obtained funding and coordinated the clinical samples. The Milieu Intérieur Consortium¶ is composed of the following team leaders: Laurent Abel (Hôpital Necker), Andres Alcover, Hugues Aschard, Philippe Bousso, Nollaig Bourke (Trinity College Dublin), Petter Brodin (Karolinska Institutet), Pierre Bruhns, Nadine Cerf-Bensussan (INSERM UMR 1163 – Institut Imagine), Ana Cumano, Christophe D’Enfert, Caroline Demangel, Ludovic Deriano, Marie-Agnès Dillies, James Di Santo, Gérard Eberl, Jost Enninga, Jacques Fellay (EPFL, Lausanne), Ivo Gomperts-Boneca, Milena Hasan, Gunilla Karlsson Hedestam (Karolinska Institutet), Serge Hercberg (Université Paris 13), Molly A Ingersoll (Institut Cochin and Institut Pasteur), Olivier Lantz (Institut Curie), Rose Anne Kenny (Trinity College Dublin), Mickaël Ménager (INSERM UMR 1163 – Institut Imagine), Frédérique Michel, Hugo Mouquet, Cliona O’Farrelly (Trinity College Dublin), Etienne Patin, Antonio Rausell (INSERM UMR 1163 – Institut Imagine), Frédéric Rieux-Laucat (INSERM UMR 1163 –Institut Imagine), Lars Rogge, Magnus Fontes (Institut Roche), Anavaj Sakuntabhai, Olivier Schwartz, Benno Schwikowski, Spencer Shorte, Frédéric Tangy, Antoine Toubert (Hôpital Saint-Louis), Mathilde Touvier (Université Paris 13), Marie-Noëlle Ungeheuer, Christophe Zimmer, Matthew L. Albert (Octant Biosciences), Darragh Duffy§, Lluis Quintana-Murci§, ¶ unless otherwise indicated, partners are located at Institut Pasteur, Paris § co-coordinators of the Milieu Intérieur Consortium Additional information can be found at: http://www.milieuinterieur.fr Declaration of Interests The authors declare no competing interests. Acknowledgements Elizabeth Maloney was supported by a stipend from the Pasteur - Paris University (PPU) International PhD program and by the LabEx Milieu Intérieur (French government’s Invest in the Future programme; reference ANR-10-LABX-69-01) and is a student from the FIRE Ph.D. program funded by the Bettencourt Schueller Foundation and the EURIP Graduate Program (ANR-17-EURE-0012), and received doctoral support from INCEPTION. We thank the ICAREB platform at Institut Pasteur for provision of fresh healthy donor blood and the Single cell biomarker platform and sequencing platform for access to technologies used in this study. We also thank Aurélie Bisiaux for CITE-seq protocol sharing and Jean-Marc Doisne for Fortessa sharing. We thank Emeline Perthame, Vincent Rouilly, Jacob Bergstedt, and Rafael De Andrade Moral for modelling advice, and Auxence Desrentes, Yann Aquino and Gaston Rijo de Leon for code sharing. Thanks to Anthony Bertrand for R and RNAseq analysis support, and Etienne Villain for additional R and batch correction support. Thanks also to Louise Dupuis and Herve Isambert for causal analysis discussions. Special thanks to Molly Ingersoll and her lab for their enthusiastic support. Another special thank you to Vincent Bondet for lab equipment management and our entire lab for support and advice throughout the project. List of Acronyms ASDC AXL + SIGLEC6 + dendritic cell BMI body mass index CD cluster of differentiation cDC conventional dendritic cell CITE-seq cellular indexing of transcriptome and epitope sequencing CM central memory CMV cytomegalovirus CXCL chemokine ligand DC dendritic cell DHT dihydrotestosterone E. coli Escherichia E2 coli estradiol eCRF electronic case report form FC fold change FDR false discovery rate G-CSF granulocyte colony stimulating factor GM-CSF granulocyte-macrophage colony-stimulating factor HSF heat shock factor HSP heat shock proteins HSR heat shock response IFN interferon IL interleukin IRF interferon regulatory factors LC-MS/MS liquid chromatography with tandem mass spectrometry LH luteinizing hormone LPS lipopolysaccharides LRT ikelihood ratio testing MAIT mucosal-associated invariant T cell MAPK mitogen activated protein kinase MFI mean fluorescence intensity MI Milieu Intérieur NF-κB nuclear factor kappa light chain enhancer of activated B cells NFAT nuclear factor of activated T cell NK cell natural killer cell NLRP3 NOD like receptor protein 3 PBMC peripheral blood mononuclear cell PCA principal component analysis pDC plasmacytoid dendritic cel Poly I:C polyinosinic:polycytidylic acid R848 resiquimod ROS reactive oxygen species SEB staphylococcal enterotoxin B STAT signal transducer and activator of transcription TCR T cell receptor Th17 T helper 17 TLR cell toll like receptor TNF tumor necrosis factor Treg regulatory T cell References 1. ↵ Mauvais-Jarvis , F. et al. 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Share Differences in body temperature drive sex-specific immune responses Elizabeth Maloney , Jamie Sugrue , Marwan Sharawy , Maxime Rotival , Etienne Patin , Lluis Quintana-Murci , Darragh Duffy , Milieu Intérieur Consortium medRxiv 2025.09.29.25336741; doi: https://doi.org/10.1101/2025.09.29.25336741 Share This Article: Copy Citation Tools Differences in body temperature drive sex-specific immune responses Elizabeth Maloney , Jamie Sugrue , Marwan Sharawy , Maxime Rotival , Etienne Patin , Lluis Quintana-Murci , Darragh Duffy , Milieu Intérieur Consortium medRxiv 2025.09.29.25336741; doi: https://doi.org/10.1101/2025.09.29.25336741 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Allergy and Immunology Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1510) Epidemiology (15230) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6609) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3337) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (664) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9237) Radiology and Imaging (2202) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a01976dd9bac300f',t:'MTc3OTc2Mzc0Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();