{"paper_id":"2b32f9f5-51b2-4b46-a686-399d8092d3b2","body_text":"1 \nSepsis induces long-term reprogramming of human HSPCs and drives 1 \nmyeloid dysregulation in sepsis survivors 2 \nRunning title: Sepsis induces long-term reprogramming of HSPCs 3 \n 4 \nMarco De Zuani 1,#, Petra Lázni č ková1,2,#, Marcela Hortová Kohoutková 1,2,*, Veronika 5 \nBosáková1,2, Ivana Andrejč inová1,2,3, Natália Vadovič ová1,3, Veronika Tomášková4, Alexandra 6 \nMýtniková1,4, Julie Štíchová 5, Tomáš Tomáš 6, Ji ř í Hrdý 7, Kristýna Boráková 8, Stjepan 7 \nUldrijan1,3, Marcela Vlková 5, Vladimír Šrámek 4, Martin Helán 1,4, Kamila Bendí č ková1,2, Jan 8 \nFrič 1,2,9,* 9 \n 10 \n1 International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic. 11 \n2 International Clinical Research Center, Faculty of Medicine, Masaryk University, Brno, Czech Republic. 12 \n3 Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic. 13 \n4 Department of Anaesthesiology and Intensive Care, St. Anne’s University Hospital and Faculty of Medicine, 14 \nMasaryk University, Brno, Czech Republic. 15 \n5 Institute of Clinical Immunology and Allergology, St. Anne’s University Hospital and Faculty of Medicine, 16 \nMasaryk University, Brno, Czech Republic. 17 \n6 First Department of Orthopaedic Surgery, St. Anne’s University Hospital and Faculty of Medicine, Masaryk 18 \nUniversity, Brno, Czech Republic. 19 \n7 Institute of Clinical Immunology and Allergology, First Faculty of Medicine, Charles University and General 20 \nUniversity Hospital in Prague, Prague, Czech Republic. 21 \n8 Neonatology Department, Institute for the Care of Mother and Child, Prague, Czech Republic 22 \n9 Department of Modern Immunotherapy, Institute of Hematology and Blood Transfusion, Prague, Czech 23 \nRepublic. 24 \n 25 \n# These authors contributed equally to this work and share first authorship. 26 \n*jan.fric@fnusa.cz 27 \n 28 \nCorrespondence: Jan Fri č , International Clinical Research Center, St. Anne’s University 29 \nHospital Brno, Pekarska 53, Brno 60200, Czech Republic; e-mail address jan.fric@fnusa.cz , 30 \nTel: +420 511 158 279. 31 \n 32 \n 33 \nMain text word count: 4135 34 \nAbstract word count: 231 35 \nNumber of figures: 4 in the main text, 3 in Appendix 1 - Supplementary Methods and Figures 36 \nNumber of tables: 3 in Appendix 2 - Supplementary Tables 37 \nReference number: 52 38 \nSupplementary files count: 2  39 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n2 \nABSTRACT 40 \nSepsis is a life-threatening condition char acterised by an overwhelming immune response 41 \nand high fatality. While most research has focused on its acute phase, many sepsis 42 \nsurvivors remain immunologically weakened leaving them susceptible to serious 43 \ncomplications from even mild infections. The mechanisms underlying this prolonged immune 44 \ndysregulation remain unclear, limiting effect ive interventions. Here, we analysed whether 45 \nsepsis induced long-term “training” in hem atopoietic stem and progenitor cells (HSPCs), 46 \nimprinting changes that persist in their my eloid progeny. Peripheral blood analysis of 8 47 \nsepsis survivors, 12 patients with septic shock, and 10 healthy donors revealed a significant 48 \nexpansion of CD38+ progenitors in survivors, with increases in megakaryocyte-erythroid and 49 \ngranulocyte-monocyte progenitors, and reduced mature neutrophil counts. This shift 50 \nsuggests impaired granulopoiesis, favouring im mature, immunosuppressive granulocytes. 51 \nDifferentiated macrophages from survivors’ HSPCs exhibited impaired metabolic pathways 52 \nafter lipopolysaccharide stimulation, with dow nregulation of tricarboxylic acid cycle and 53 \nglycolysis genes, indicating altered immune metabolism. Pathway analysis revealed 54 \nenhanced type-I interferon (IFN) and JAK-STAT signalling in survivors’ macrophages, 55 \nreflective of potentially tolerance-prone reprogramming. Finally, exposing healthy donor 56 \nHSPCs to IFN β  during macrophage differentiation reduced HSPC proliferation, increased 57 \napoptosis, and induced a metabolic shift towards gl ycolysis over mitochondrial respiration. 58 \nTogether, these findings suggest that sepsis  induces lasting reprogramming in HSPCs 59 \nleading to myeloid progeny with altered i mmune memory that might drive immune 60 \ndysregulation in survivors. These data open av enues to explore potential targets to better 61 \nmanage long-term immune alterations in sepsis survivors. 62 \n 63 \nKEY POINTS 64 \n●  Sepsis induces long-term alterations in HSPCs, leading to the expansion of immature 65 \nprogenitors and metabolic dysregulation of their progeny. 66 \n●  Type-I IFN signalling reprograms macrophage differentiation, affecting their 67 \nmetabolic function and reducing cell proliferation. 68 \n  69 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n3 \nINTRODUCTION 70 \nSepsis is a life-threatening condition charac terised by a dysregulated host response to 71 \ninfection, leading to organ dysfunction and often high mortality. 1–3 In 2017, 48.9 million 72 \npeople were affected by this syndrome, with 19.7% succumbing to it. 4 Survivors face long-73 \nterm consequences, including increased susceptibility to secondary infections, persistent 74 \nimmune-cell alterations, low-grade inflammation, and release of damage-associated 75 \nmolecular pattern molecules. 5 Mechanistically, monocytes from sepsis survivors undergo 76 \ninflammatory reprogramming, which drives chronic inflammation and persistent immune 77 \nactivation. This process is sustained by elevated cytokines and nucleotide oligomerization 78 \ndomain-like receptor protein 3 (NLRP3) components that can persist for years after sepsis.6 79 \n 80 \nHematopoietic stem cells (HSCs), which are produced in the bone marrow (BM), give rise to 81 \nall blood-cell lineages. 7 In normal aging, HSCs frequency in the BM increases, with a 82 \ntendency towards a myeloid bias. 8 Interestingly, sepsis can similarly promote myelopoiesis 83 \nand trigger emergency haematopoiesis, which eventually leads to accelerated 84 \nimmunosenescence.5 Prolonged myelopoiesis, evidenced by an expansion of myeloid-85 \nderived suppressor cells (MDSCs) and subsequent  long-term changes in myeloid functions, 86 \nis observed in sepsis survivor s months after the acute phase. 9 Work conducted in murine 87 \nmodel of sepsis showed that after acute seps is, HSCs are less responsive to granulocyte-88 \ncolony stimulating factor and thus fail to in duce granulopoiesis, suggesting HSCs exhaustion 89 \nafter severe infection.10 However, the mechanisms by which these changes impact long-term 90 \nimmune function in sepsis survivors remain unclear. 91 \nTrained immunity – or innate immune memory – is a phenomenon whereby cells of the 92 \ninnate immune system, such as monocytes and macrophages, undergo epigenetic 93 \nreprogramming after exposure to certain pathogens, enhancing their re sponse to future 94 \ninfection.11 In a process referred to as ‘central trained immunity’, HSCs can also acquire 95 \n“memory”, generating myeloid progeny that retain this trained state. 12,13 For example, 96 \nBacillus Calmette-Guérin (BCG) vaccination instructs murine HSCs to produce trained 97 \nmyeloid progeny, which subs equently protects against M. tuberculosis  infection. 14 In the 98 \ncontext of sepsis, hematopoietic stem and progenitor cells (HSPCs) and BM-derived 99 \nmacrophages in surviving mice un dergo epigenetic reprogramming, 11 resulting in impaired 100 \ncytokine production.15 Interestingly, metabolic changes, such as a shift toward glycolysis, are 101 \nhighly linked to the induction of these epigenetic modifications. 11 For example, secondary 102 \nlipopolysaccharide (LPS) challenges in mice treated with β -glucan (a trained immunity 103 \ninducer in myeloid cells) leads to increases  in myelopoiesis, glycolysis, and cholesterol 104 \nbiosynthesis.16 105 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n4 \n 106 \nAlthough the impaired function of myeloid cells in sepsis survivors has been already 107 \nreported, the signalling pathways and metabo lic mechanisms underlying these long-term 108 \nalterations remain unexplored. As such, we lack the tools to identify or modulate immune 109 \ndysfunction in affected patients. Moreover, the long-term consequences of HSPC training 110 \nduring the acute phase of sepsis on immune resi lience in survivors are unclear. To address 111 \nthese knowledge gaps, we compared the profil e of circulating HSPCs and peripheral blood 112 \nsubpopulations in patients with acute septic s hock, long-term survivors (Surv; average 13.5 113 \nmonths post-sepsis), and age-matched healthy donors (HD). We then assessed the 114 \ntranscriptional and functional profiles of HSC-derived macrophages from Surv and HD to 115 \npinpoint potential contributors to macrophage functional and metabolic impairments. 116 \n 117 \nMETHODS 118 \nStudy participants 119 \nTwelve adult patients admitted to the intensive care unit (ICU) at St. Anne's University 120 \nHospital in Brno (Czech Republic) with early se ptic shock were prospectively enrolled into 121 \nthe study cohort. Two patients died before reac hing the second collection time point, while 122 \nthe first time point collection for another patient failed. Patients with chronic 123 \nimmunosuppression, ongoing active oncological disease, or who had received antibiotic 124 \ntherapy for more than 2 days were excluded. Addi tionally, eight sepsis survivors (Surv, 8 to 125 \n26 months - 13.5 months on average after the initial ICU admission) were retrospectively 126 \nenrolled in the “sepsis survivor cohort”. Fi nally, 10 healthy age- and comorbidity- matched 127 \nindividuals were recruited into the “age-matc hed healthy donors” (HD) cohort at the First 128 \nDepartment of Orthopaedic Surgery, St. Anne’s University Hospital in Brno. Patients with an 129 \nacute infection within the last 28 days, ongoi ng active oncological disease, or chronic 130 \nimmunosuppression were not included. Cohort details are summarised in Supplementary 131 \nTables 1 and 2. For in vitro  studies on the effect of IFN β  on HSPCs differentiation, buffy 132 \ncoats from adult blood donors were obtained from the Department of Transfusion & Tissue 133 \nMedicine of Brno University Hospital. Cord blood was obtained from women after childbirth 134 \nat the Institute for the Care of Mother and Child in Prague. Written informed consent was 135 \nobtained from all enrolled patients. All procedures were approved by the institutional Ethical 136 \nCommittee of St. Anne's University Hospital Brno (4G/2018; 10G/2021), Ethical Committee 137 \nof the Faculty of Medicine of Masaryk University (18/2023), and Ethical Committee of the 138 \nInstitute for the Care of Mother and Child (31/03/2014). All procedures complied with the 139 \nHelsinki Declaration of 1975, as revised in 2013.  140 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n5 \nFlow cytometry 141 \nFlow cytometry analyses followed the guidelines by Cossarizza et al. 17 A total of 200 µL and 142 \n500 µL of heparinized blood were used to label mature immune cells and circulating HSPCs, 143 \nrespectively. Whole blood was lysed in 1x RBC lysis buffer for 10 minutes at room 144 \ntemperature (RT). Where indicated, dead cells were labelled with Live/Dead fixable dyes 145 \n(Thermo Fisher Scientific) at a concentration of 1:800 in PBS. Cells were labelled in FACS 146 \nbuffer for 30 minutes on ice with the antibodies listed in Supplementary Table 3. Where 147 \nindicated, propidium iodide was added immediately before sample acquisition to discriminate 148 \ndead cells. Then, 10 µl of Precision Count Beads (BioLegend) were added to each sample 149 \nto obtain absolute counts of HSPCs and other immune subsets. All samples were acquired 150 \non a Sony SA3800 spectral analyser (Sony Biotechnologies). 151 \nHSPC-derived macrophage (HSDM) differentiation 152 \nHSDM differentiation was performed as described, with minor modifications. 18 CD34+ cells 153 \nwere isolated directly from PBMCs or fr om enriched HSPCs (RosetteSep Hematopoietic 154 \nProgenitor Enrichment Cocktail Kit, Stemcell Technologies) via immunomagnetic isolation 155 \n(Miltenyi Biotec). Extended experimental details are reported in the supplementary methods.  156 \nHSDM stimulation and RNA-sequencing 157 \nMature HSDMs (1 x 10 5) from HD and Surv were seeded in 96-well plates in Media C and 158 \nstimulated with 100 ng/mL LPS-EB (Invivogen) for 3 hours at 37ºC. Total RNA was extracted 159 \nusing the RNeasy Plus Micro Kit (Qiagen), ac cording to manufacturer’s recommendations. 160 \nRNA quality was assessed with Bioanalyzer2100 RNA Nano 6000 chips (Agilent 161 \nTechnologies), and samples with an RNA Integrity Number (RIN) > 8 were used for 162 \nsequencing. An Illumina sequencing library was prepared using the NEBNext Ultra II 163 \nDirectional RNA Library Prep Kit (New England Biolabs) following the manufacturer's 164 \nprotocols. Total RNA was used for poly-A enrichment, then fragmented, and reverse 165 \ntranscribed into cDNA. After universal adapter ligation, samples were barcoded using NEB 166 \ndual indexing primers and pooled equimolarly after quantitation with PicoGreen. The sample 167 \npool was sequenced using a Nextseq 550 sequencer (Illumina) with a 75-cycle high-output 168 \ncartridge. 169 \nRNA-seq analysis 170 \nRaw reads were quality checked, pre-processed, and mapped to the reference genome 171 \n(Ensembl GRCh38) with gene annotation (Ensembl v94). Mapped reads were counted and 172 \nsummarised by gene. After removing genes with < 10 counts, differentially expressed genes 173 \n(DEGs) were calculated using DESeq2. 19 Gene ontology (GO) and Gene Set Enrichment 174 \nAnalysis (GSEA) were performed using the clusterProfiler package. 20 GSEA was performed 175 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n6 \nafter adaptive shrinkage of the log2 fold-change (LFC) values. 21 To infer signalling pathway 176 \nactivity, the DecoupleR package 22 was used, fitting a Multivariate Linear Model using as 177 \ninput the Wald statistic results from DES eq2 and the top 500 responsive genes ranked by p-178 \nvalue in the PROGENy collection 23. Transcription factor activity was inferred by fitting a 179 \nUnivariate Linear Model (available with the DecoupleR package) using as input the Wald 180 \nstatistic results calculated by DESeq2 and the human regulons in the DoRothEA gene 181 \nregulatory network with confidence levels “curated/high (A)”, “likely (B)”, and “medium (C)”.24 182 \nStatistical analyses 183 \nStatistical analyses were performed with R v4.0.2 . Specific statistical tests are reported in 184 \neach figure legend. The Shapiro-Wilk test and vi sual inspection of QQ-plots were used to 185 \ndetermine the normality of distributions, guiding the selection of parametric or non-186 \nparametric tests. 187 \n 188 \nSample collection and preparation, extended HSPC-derived macrophage (HSDM) 189 \ndifferentiation, Immunofluorescence staining, Cell cycle profiling, Apoptosis assay, and 190 \nMetabolic profiling of HSDMs are descr ibed in Supplementary methods and figures 191 \n(Appendix 1). 192 \n 193 \nRESULTS 194 \nCirculating HSPCs are expanded in sepsis survivors 195 \nWe first aimed to investigate the effect of sepsis on HSPCs and their progeny, in order to 196 \nunderstand whether the long-term immunosuppres sion observed in sepsis survivors could 197 \nbe due to the reprogramming of HSPCs during sepsis. To do so, we enrolled 12 patients 198 \nwith septic shock at two time points (within 24 hours (T1) or 3-5 days (T2) from ICU 199 \nadmission), eight Surv (average 13.5 months since ICU discharge; Supp. Table 1), and 10 200 \nHD (Supp. Table 2). We began by analysing HSPCs in the peripheral blood of all 201 \nparticipants by flow cytometry (see Supp. Figure 1 for gating strategy) and found differences 202 \nin their absolute numbers across groups (Figure 1A, Supp. Figure 2A).  203 \n 204 \nWhen comparing HD with Surv, the latter showed a significant expansion of CD38+ 205 \nprogenitors (Figure 1A, p=0.034). Among these committed progenitors, megakaryocyte-206 \nerythroid progenitors (MEPs) were significantly increased in survivors, while common 207 \nmyeloid progenitors (CMPs) and granulocyt e-monocyte progenitors (GMPs) showed a 208 \nsimilar trend (Figure 1A, p=0.068 and p=0.101, respectively). When patients with septic 209 \nshock were included in the comparison, we found a significant increase in the absolute GMP 210 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n7 \ncounts in survivors compared to septic shock patients at both time points (p=0.0276 for TP1 211 \nand p=0.0288 for TP2; Supp. Figure 2A). MEP counts showed a similar trend when 212 \ncompared to TP1 (p=0.0528). Taken together, these results suggest that septic shock 213 \naffects the differentiation of HSCs into committed progenitors long after infection resolution, 214 \nfavouring the accumulation of MEPs and GMPs. 215 \n 216 \nTo determine whether the changes observed in the HSPC compartment were reflected in the 217 \nterminal differentiation of blood cells, we analysed the peripheral blood of Surv. Compared to 218 \nHD, Surv showed a decrease in absolute count of mature neutrophils (p=0.057, Figure 1B). 219 \nConsistent with the expansion of polymorphonuclear (PMN) cells and early MDSCs in the 220 \nperipheral blood of Surv, 9 these findings suggest that  sepsis induces long-term 221 \nreprogramming of the hematopoietic compartment, promoting a skew towards the 222 \ngranulocytic lineage and the development of immature and immunosuppressive 223 \ngranulocytes. The release of MEP from BM in peripheral blood has been described in a 224 \nmouse model of sepsis as a result of higher concentration of SCF in peripheral blood 225 \ncompared to BM. 26 Here we report expansion of MEP in Surv long after septic shock, 226 \nsuggesting that a similar mechanism might be still taking place long after recovering from 227 \nsepsis. 228 \nMacrophages derived from sepsis survivor HSPCs show metabolic impairments 229 \nHaving shown expanded CD38+ progenitors, increasing trends in CMP and GMP and 230 \ndecrease in absolute counts in neutrophils in Surv compared to HD, we hypothesized that 231 \nHSPCs in Surv could give rise to a myeloid pr ogeny with altered functionality. To test this, 232 \nwe adapted a protocol18 to differentiate macrophages in vitro from circulating CD34+ HSPCs 233 \n(HSPC-derived macrophages, HSDMs). We first used flow cytometry to characterise mature 234 \nHSDMs obtained from adult circulating HSPCs isol ated from adult blood donors. These cells 235 \nexpressed CD68, CD11b, CD33, CD14, CD16, CD206, CD86, HLA-DR, CCR5, TLR2 and 236 \nTLR4 (Supp. Figure 2B and C), indicative of  successfully differentiated macrophages. 237 \nMoreover, these cells demonstrated the ability to phagocytose and sequester 238 \nStaphylococcus aureus within their lysosomes, as shown by the uptake of pHrodo S. aureus 239 \nparticles (Supp. Figure 2D), confirming that their core immune functionality is retained. 240 \n 241 \nNext, we assessed whether the transcriptional pr ofile of HSDMs differentiated from Surv and 242 \nHD differed as a result of sepsis. Here, HSDMs from Surv and HD were stimulated with LPS 243 \nand subjected to bulk RNA-seq. We found that the stimulated HSDMs from Surv exhibited 244 \nupregulation of genes involved in the “respons e to lipopolysaccharide” and “regulation of 245 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n8 \ninnate immune response” pathways when compared to non-treated cells from the same 246 \nindividuals (Figure 2A and B). 247 \n 248 \nWhen comparing LPS-stimulated HSDMs from Surv and HD, we found that the former 249 \ndownregulated many genes involved in key metabolic pathways, including the TCA cycle, 250 \nglycolysis/gluconeogenesis, and pyruvate metabolism, as indicated by the negative gene-set 251 \nenrichment scores for these pathways (Figure 2C and D). These findings align with other 252 \nstudies indicating that trained immunity relie s on the metabolic reprogramming of myeloid 253 \ncells, and with studies showing a decrease in all major metabolic pathways as a hallmark of 254 \ntolerant monocytes.11,27 255 \n 256 \nAs initial exposure to bacterial molecules (LPS  in particular) can render myeloid cells more 257 \ntolerant to subsequent restimulation, 27,28 we explored whether a similar mechanism might 258 \naffect HSDMs derived from Surv. Using a pub licly available dataset of genes that are 259 \n“tolerizable” and “non-tolerizable” to TLR4-dependent LPS exposure in human 260 \nmacrophages,28 we performed gene set enrichm ent analysis (G SEA) and found no 261 \nenrichment in either category (adjusted p va lue=0.484 and 0.604, respectively; Figure 2E). 262 \nThis finding suggests that the mechanisms i nducing a tolerant phenotype in macrophages 263 \nafter LPS exposure differs from the one acting on HSPCs during sepsis. It is, thus, possible 264 \nthat the mechanisms governing endotox in tolerance and sepsis-mediated 265 \nimmunosuppression rely on different processes, the latter likely involving a metabolic and 266 \nepigenetic rewiring of HSPCs.  267 \nType-I IFN signalling characterises HSDMs in sepsis survivors 268 \nTo explore signalling pathways differences t hat might underlie our observations regarding 269 \nthe metabolic and transcriptional reprogrammi ng of HSDMs from Surv, we inferred the 270 \nactivity of signalling pathways and transcription factor (TF) regulons across conditions using 271 \nour RNA-seq data. In HSDMs from Surv, LPS stimulation led to induction of the NF- κ B 272 \npathway (Figure 3A). This was accompanied by the activation of TFs involved in the TLR4-273 \nmediated response to LPS, including NFKB1, RELA and RELB (Figure 3B), which are 274 \ncentral to NF- κ B signalling. 29,30 Additionally, we observed JAK-STAT pathway activation in 275 \nresponse to LPS stimulation. While NF- κ B is rapidly activated through TLR4 upon LPS 276 \nexposure, the JAK-STAT pathway can be activated in an autocrine manner following the 277 \ninduction of IFNβ  expression, which is also triggered by TLR4 activation via LPS.29,30 278 \n 279 \nWhen directly comparing LPS-stimulated HSDMs derived from Surv with those from HD, we 280 \nfound distinct signalling patterns. Specifically, JAK-STAT pathway activity was higher in 281 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n9 \nHSDMs from Surv, whereas the phosphatidylinositol 3-kinase (PI3K) pathway was more 282 \nactivated in HSDMs from HD (Figure 3C). Acco rdingly, we noticed the preferential activation 283 \nof STAT1 and IRF9 in HSDMs from Surv, compared to those from HD (Figure 3D). These 284 \nTFs are activated downstream of IFNAR1 and IFNAR2 receptors in response to type-I 285 \ninterferons (IFN) such as IFNα  or IFNβ , and regulate the expression of IFN-stimulated genes 286 \n(ISGs). Taken together, our results suggest a hyperactivation of type-I IFN signalling in 287 \nmacrophages differentiated from Surv HSPCs,  which could reflect a mechanism to 288 \novercome long-term sepsis-related immunosuppression and tolerance.31 289 \nIFNβ  affects the development of macrophages from HSPCs 290 \nGiven that HSDMs from Surv showed increased activation of the JAK-STAT pathway and 291 \nelevated STAT1 and IRF9 TFs activity, we hypothesised that type-I IFNs could be 292 \nresponsible for the dysregulation observed in the HSPC compartment of Surv. To determine 293 \nthe role of type-I IFNs during myeloid differentiation, we differentiated HSDMs from 294 \ncirculating HSPCs isolated from adult healthy blood donors in the presence or absence (not 295 \ntreated, NT) of IFN β , which is a primary mediator of ty pe-I IFN responses that influences 296 \nimmune cell differentiation and inflammatory signalling. 297 \nWe observed a reduction in total cell numbers after 7 and 14 days of differentiating IFN β -298 \nstimulated HSPCs, compared to untreated cells  (Figure 4A and B). This reduction was 299 \nsimilarly observed when using cord-blood HSPCs (Supp. Figure 3A). Nevertheless, IFN β -300 \ntreated cells from adult blood donors showed an increased proportion of CD14+ cells at 14 301 \ndays of differentiation (Supp. Figure 3B), suggesting that IFN β  stimulation might support 302 \nmonocytic differentiation over other lineages in vitro. 303 \n 304 \nTo assess whether the reduced cell numbers in IFN β -stimulated HSPCs from adult healthy 305 \nblood donors were due to decreased proliferation, we analysed a set of genes involved in 306 \ncell proliferation after 7 days of differentiation. Only two genes ( B2M and ANAPC2) were 307 \nsignificantly upregulated in IFN β -stimulated cells compared to untreated cells, indicating that 308 \ncell proliferation rates likely remained unchanged (Figure 4C). We then tested whether 309 \nincreased cell death during differentiation could account for the lower cell counts. Annexin-V 310 \nstaining revealed a significantly higher frequency of early apoptotic cells (Annexin-V+, PI-) in 311 \nIFNβ -treated cells (Figure 4D). Interestingly,  early and late apoptotic cells showed reduced 312 \nexpression of CD38 compared to live cells (Figure 4E and F), suggesting that these cells 313 \nmight represent less committed progenitors . We speculate that the increase in 314 \nCD34+CD38+ HSPCs and GMP in Surv compared to HD and the concomitant decrease in 315 \nmature neutrophils in peripheral blood in  Surv could be caused by the IFN β -induced 316 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n10 \napoptosis of CD38+ progenitors (Figure 1, Supp. Figure 2A). This would also be supported 317 \nby the higher activity in the p53 pathway in HSDMs from Surv compared to HD (Figure 3C). 318 \n 319 \nFinally, we examined whether IFN β  stimulation during HSDM differentiation induced any 320 \nmetabolic reprogramming. IFN β -stimulated HSDMs showed a significantly lower oxygen 321 \nconsumption rate-to-extracellular acidification rate (OCR-to-ECAR) ratio at basal levels 322 \n(Figure 4G, H). This finding suggests that, in the resting state, IFN β -stimulated HSDMs 323 \ndepend less on the TCA cycle and more on glycolysis, compared to untreated cells, which is 324 \nconsistent with a metabolic shift following classical macrophage activation. 32 Nevertheless, 325 \nin Surv the transcriptional regulation of the glycolytic pathway in LPS-stimulated HSDMs is 326 \nalso suppressed (Figure 2D), likely due to the immunosuppression induced by the cytokine 327 \nstorm and their consequential switch toward s alternatively-activated macrophages, as 328 \nevidenced elsewhere, 1 despite the activated hypoxia pathway (Figure 3C) that in general 329 \nfavours glycolysis in pro-inflammatory macrophages.33 330 \n 331 \nDISCUSSION 332 \nSepsis research has traditionally focused on  immune-cell responses during the acute phase 333 \ndue to its high fatality rate in this period. However, the persistent health complications 334 \nobserved in many sepsis survivors months after their initial recovery, highlights a significant 335 \ngap in our understanding of the long-term impacts of sepsis on the immune system. This 336 \nstudy aimed to explore these long-term effects specifically on HSPCs, investigating whether 337 \nsepsis induces “training” in these cells during septic shock that carries over to the myeloid 338 \nprogeny and alters their metabolism and signalling responses. 339 \n 340 \nPrevious studies, including our own,9,34 have identified alterations in immune cell phenotypes 341 \nin sepsis patients during the acute phase of septic shock, 35–37 as well as notable shifts in 342 \nMDSCs and PMN-MDSCs that are detectable 6-26 months after recovery. 9 Nevertheless, a 343 \nthorough analysis of the immunophenotypic changes that are apparent in sepsis survivors 344 \nhas not yet been performed, thus limiting our understanding of the adverse effects and 345 \nrecurrence of sepsis in these patients. We now provide evidence of a likely biologically 346 \nrelevant decrease in absolute neutrophil numbers in the peripheral blood of long-term sepsis 347 \nsurvivors (Surv). Furthermore, we observed an expansion of GMPs in Surv compared to 348 \npatients in the acute phase of septic shock, indicative of defective granulopoiesis in this 349 \ngroup. Together with an increase of immature PMN-MDSCs, these data suggest that 350 \nimpaired granulopoiesis gives rise to suppresso r cells rather than mature neutrophils in 351 \nsepsis survivors.  352 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n11 \n 353 \nWe also observed a long-term shift in the hematopoietic compartment of Surv, characterised 354 \nby an increased number of committed CD38+ progenitors and MEPs compared to HD. 355 \nAlthough thrombocytopenia commonly occurs in septic patients, 38 thromboembolism events 356 \nare also common. 39 Accordingly, in murine models of severe septic shock it has been 357 \nreported that MEP counts increase 26 and that platelets can exacerbate inflammation 40. The 358 \nMEP expansion observed in Surv might explain th e increased risks of myocardial infarction 359 \nand stroke observed in these patients. 41 The increase of CD38+ progenitors suggests that 360 \nHSCs might undergo “training” during septic shock, which influences their differentiation and 361 \nthe characteristics of their myeloid progeny. I ndeed, these findings resonate with an earlier 362 \nfinding that BCG vaccination trains monocyte and macrophage progeny. 14 We confirmed 363 \nwhether there exists a memory of sepsis-relat ed training in HSPC progeny by differentiating 364 \nmacrophages (HSDMs) in vitro  from Surv HSPCs and re-challenging them with LPS. Bulk 365 \nRNA sequencing revealed downregulation of gene s involved in glycolysis/gluconeogenesis, 366 \nthe TCA cycle, and pyruvate metabolism in these LPS-challenged Surv HSDMs compared to 367 \nHD. These findings align with previously described defects in glycolysis and oxidative 368 \nphosphorylation in monocytes from septic patients. 27 Despite published data suggesting 369 \nnormalisation of these metabolic determinants within days of recovery (> 7 days after 370 \nsepsis),27 we show that such defects persist in HSDMs differentiated from Surv. These 371 \ndiscrepancies might be caused by the different time frame after which the measurements 372 \nwere taken in these studies - quite early after the recovery vs months after the resolution of 373 \nsepsis. We speculate, therefore, that sepsis primes HSPCs in a way that persists over time, 374 \npotentially driving a myeloid bias in immune cell production. 375 \n 376 \nLPS-induced tolerant macrophages can protect against septic-shock-induced cell death and 377 \nimprove survival in mice. 42 Reprogramming into hyporesponsive, immunosuppressive 378 \nphenotypes depend on the p21-mediated DNA binding of NF- κ B p50 homodimers, which 379 \ninhibit mRNA transcription and reduce IFN β  production in mice.43 Here, we demonstrate that 380 \nreprogramming during septic shock differentially affects the response to LPS of HSDMs 381 \nderived from Surv compared to HD HSDMs. Pathway analysis provided further insights into 382 \nthe altered immune responses in Surv as a resu lt of this reprogramming: LPS stimulation 383 \ninduced NF- κ B and JAK-STAT pathway activation in HSDMs from Surv, the JAK-STAT 384 \nresponse notably stronger than observed in HD. This effect was coupled with elevated type-I 385 \nIFN activity, including STAT1 and IRF9 activation that is indicative of sustained type-I IFN 386 \nsignalling. JAK-STAT activation after LPS treatment and IFNAR1  via autocrine IFN β  387 \nproduction are consistent with findings from murine BM-derived macrophages. 44 The role of 388 \nIfnar1 in immune “training” has also been described in murine alveolar macrophages, 45 as 389 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n12 \nwell as in a mouse model of autoimmune systemic lupus erythematosus, where a type-I IFN 390 \nsignature promoted myelopoiesis. 46 Additionally, type-I IFN signalling impairs macrophage 391 \nanti-Mycobacterium tuberculosis  immunity in mice. 47 While IFNAR1 downregulation 392 \ncontributes to HSCs maintenance, 48 heightened IFN β  signalling through JAK-STAT in Surv 393 \ncontrasts with the hyporesponsive phenoty pe seen in murine macrophages, where IFN β  394 \nproduction is suppressed.43 Together, these findings suggest t hat immune training in Surv is 395 \ncharacterized by a long-lasting reprogrammi ng of myeloid cells characterised by an 396 \nimmunosuppressive metabolic state and heightened type-I IFN responses upon LPS 397 \nchallenge. These results point towards a potential model where “primed” HSPCs differentiate 398 \ninto myeloid cells more prone to mounting antiv iral over antibacterial  responses, possibly 399 \nunderlying the susceptibility to bacterial infect ions of sepsis survivors and the potential 400 \nsepsis recurrence.49 401 \n 402 \nCompared to HSDMs derived from Surv, we found that HSDMs from HD predominantly 403 \nactivate the PI3K pathway upon LPS treatment. PI3K signalling is associated with 404 \nmodulating inflammation and enhanced cell surviv al, and so might constitute a protective 405 \nmechanism to counterbalance excessive pro-in flammatory immune activation and cellular 406 \napoptosis.50 Interestingly, a dependency on PI3K has been shown in IFN β -driven regulation 407 \nof glucose metabolism. 51 Our findings suggest that IFN β  exposure during HSPC 408 \ndifferentiation from adult blood donors leads to increased early apoptosis (after 7 days of 409 \ndifferentiation in vitro ) and a bias towards CD14+ cell development (after 14 days of 410 \ndifferentiation in vitro). Finally, we found that HSDMs differentiated in the presence of IFN β  411 \ndepend on glycolysis. This finding is consist ent with our RNAseq data, which revealed 412 \nreduced TCA cycle activity in HSDMs derived from Surv but contradicts the reduced 413 \nglycolysis observed in HSDMs from Surv. This is  likely due to the different activation state of 414 \nthe cells and the length of the stimulation. A study evidenced that a 5-day “chronic” exposure 415 \nof human monocyte-derived macrophages to IFN β  suppresses both basal oxygen 416 \nconsumption rate and glycolysis.52 417 \n 418 \nDespite the limited number of samples analyzed and the simplified model of type-I IFN 419 \nactivity in sepsis modelled by prolonged IFNβ  stimulation of HSPCs from adult blood donors, 420 \nour study provides evidence of long-term r eprogramming in Surv compared to HD. It is 421 \nimportant to note that the cytokine storm and signalling dynamics during septic shock in vivo 422 \nare greatly more complex than the in vitro system tested in our study. Moreover, different 423 \ninfectious agents and sepsis severity might be driving different responses, and thus 424 \nreprogramming, in HSPCs. This could not be tested in our study due to the limited size of 425 \nour cohort. Finally, our results highlight  the need to evaluate the metabolic and 426 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n13 \ntranscriptional status of terminally differentia ted immune cells during and after sepsis. These 427 \nanalyses could support the development of ther apeutic targets to overcome the recurrence 428 \nand adverse effects of septic shock.  429 \nTaken together, we show that sepsis induces a long-term skew in HSPC differentiation and 430 \nmyeloid cell functionality in Surv. Compared to HD, Surv show increased numbers of 431 \ncommitted progenitors and decreased neutrophil counts. Importantly, the immune training of 432 \nHSPCs during the acute phase of sepsis pred isposes them to differential signalling upon 433 \nsubsequent LPS re-challenge in vitro. The evidence of metabolically impaired macrophages 434 \nshould be elaborated further and potentially used as a model to improve immune cell 435 \nresponses in sepsis survivors who suffer fr om opportunistic infections and face potential 436 \nsepsis recurrence. Potential therapeutic strategi es targeting immune cell-specific metabolic 437 \nmanipulations using nutrition supplements, or affecting the cytokine availability, such as 438 \nIFNβ , could help re-establish the homeostatic state of hematopoietic progenitors and 439 \nimmune cells after the septic shock episode. Overall, our results open potential therapeutic 440 \nopportunities to manage long-term immune-cell reprogramming in septic shock survivors.  441 \n 442 \nACKNOWLEDGEMENTS 443 \nThe research was supported by the Ministry of Health of the Czech Republic, grant nr. 444 \nNV21J-05-00056), all rights reserved and DRO (Institute of Hematology and Blood 445 \nTransfusion – UHKT, 00023736). The research  was also supported by project nr. 446 \nLX22NPO5107 (MEYS): Financed by European Union – Next Generation EU and the 447 \nEuropean Union's Horizon Europe research  and innovation programme under grant 448 \nagreement No. 101137484.  449 \nWe would like to thank the technical support team of the Center for Translational Medicine 450 \nfor technical support. Core Facility Genomics of CEITEC Masaryk University is gratefully 451 \nacknowledged for the obtaining of the scientific data presented in this paper. We would also 452 \nlike to thank Dr. Jessica Tamanini from Insight Editing London for critical review of the 453 \nmanuscript. 454 \n 455 \nAUTHORSHIP 456 \nContribution: MDZ and JF designed the study; MH, VS, MV, and JH supervised the cohort 457 \nrecruitment; VT, AM, JS, TT, KrB, and MH recruited the study participants; MDZ, PL, MHK, 458 \nKaB, VB, IA, NV, and SU performed the experiments and analyzed the data; MDZ, KaB, 459 \nMHK, and JF secured funding; MDZ, PL, KaB, and JF wrote and reviewed the manuscript. 460 \n 461 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n14 \nConflicts-of-interest disclosure: MDZ is an employee and owns stock in Ensocell 462 \nTherapeutics. Other authors declare no conflict of interest. 463 \n 464 \nAPPENDIXES 465 \nAppendix 1: Supplementary Methods and Figures. 466 \nAppendix 2: Supplementary Tables. 467 \n  468 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n15 \nREFERENCES 469 \n1. Cao M, Wang G, Xie J. Immune dysregulat ion in sepsis: experiences, lessons and 470 \nperspectives. Cell Death Discov. 2023;9(1):465. doi:10.1038/s41420-023-01766-7 471 \n2. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus 472 \nDefinitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810. 473 \ndoi:10.1001/jama.2016.0287 474 \n3. Jarczak D, Kluge S, Nierhaus A. 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It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n19 \n     FIGURE LEGENDS 644 \nFigure 1. Flow cytometry phenotyping of HSPCs and mature immune cells in 645 \nperipheral blood of Surv and HD. 646 \nA. Box plots representing the absolute number of  circulating HSPC subsets in age-matched 647 \nhealthy donors (HD) and long-term sepsis survivors (Surv).  648 \nB. Box plots representing the absolute number of circulating immune cells in HD and Surv. 649 \nThe differences between the two groups were tested with a Wilcoxon rank-sum test. * = p-650 \nvalue ≤  0.05. 651 \n 652 \nFigure 2. Transcriptional profiling of HSDMs from Surv and HD. 653 \nA. Volcano plot comparing LPS-treated HSDMs and untreated HSDMs from sepsis survivors 654 \n(Surv). Coloured dots indicate significant upregul ation in LPS-treated (purple, lfc > 1.5 and 655 \nadjusted p-value ≤  0.05) or untreated (orange, lfc < 1.5 and adjusted p-value ≤  0.05) cells. 656 \nB. Top 15 upregulated “biological process” pathways in Surv HSDMs stimulated with LPS 657 \ncompared to untreated cells from the same pati ents. The colour of each dot represents the 658 \nBenjamini-Hochberg adjusted p-value, while the size of the dot represents the number of 659 \ngenes enriched in each pathway. 660 \nC. Volcano plot comparing HSDMs derived from Surv compared to HD after LPS stimulation. 661 \nColoured dots indicate significant upregulation in Surv (purple, lfc > 1.5 and adjusted p-value 662 \n≤  0.05) or HD (green, lfc < 1.5 and adjusted p-value ≤  0.05).  663 \nD. Top 15 enriched terms from GSEA on the KEG G database, using differentially expressed 664 \ngenes between LPS-stimulated HSDMs derived from Surv and HD. Dot size indicates the 665 \nadjusted p-value, and dot colour indicates t he normalised enrichment score (NES). Green 666 \ndots indicate terms significantly enriched in HSDMs from HD (i. e. NES < 0), while purple 667 \ndots indicate terms significantly enriched in HSDMs derived from Surv. 668 \nE. GSEA plot illustrating the enrichment of “toler izable” (orange line) and “non-tolerizable” 669 \n(green line) genes28 in LPS-stimulated HSDMs derived from Surv compared to HD. The 670 \nbottom portion of the plot shows the ranked po sitions of genes by differential expression, 671 \nwith upregulated genes concentrated on the left (positive rank) and downregulated genes on 672 \nthe right (negative rank). 673 \n 674 \nFigure 3. Signalling pathways and transcription factor activity in HSDMs from Surv 675 \nand HD. 676 \nA. Enriched signalling pathways in Surv HSDMs stimulated with LPS compared to untreated 677 \ncells from the same donors. Dot size indicates the adjusted p-value, while dot colour 678 \nindicates the enrichment score (purple, ES  > 0 - enriched in LPS-treated HSDMs; orange, 679 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n20 \nES < 0 - enriched in untreated HSDMs). A black border around each dot indicates 680 \nstatistically significant enrichments (adjusted p-value ≤  0.05). 681 \nB. Transcription factor activity in Surv HSDMs stimulated with LPS compared to untreated 682 \ncells from the same donors. Each dot repr esents a transcription factor, with dot size 683 \nindicating the adjusted p-value, and dot colour indicating the activity score. Positive scores 684 \n(purple) indicate increased transcription factor activity in LPS-treated HSDMs, while negative 685 \nscores (orange) indicate increased transcription factor activity in untreated cells. A black 686 \nborder around each dot indicates statistically significant enrichments (adjusted p-value ≤  687 \n0.05). 688 \nC. Enriched signalling pathways in LPS-stimulat ed HSDMs derived from Surv versus HD. 689 \nDot size indicates the adjusted p-value, while dot colour indicates the enrichment score 690 \n(purple, ES > 0 - enriched in Surv HSDMs; green, ES < 0 - enriched in HD HSDMs). A black 691 \nborder around each dot indicates statistically significant enrichments (adjusted p-value ≤  692 \n0.05). 693 \nD. Transcription factor activity in LPS-stimulated HSDMs derived from Surv versus HD. Each 694 \ndot represents a transcription fa ctor, with dot size indicating the adjusted p-value and dot 695 \ncolour indicating the activity score. Positive scores (purple) indica te increased transcription 696 \nfactor activity in Surv HSDMs, while negat ive scores (orange) indicate increased 697 \ntranscription factor activity in healthy dono rs’ HSDMs. A black border around each dot 698 \nindicates statistically significant enrichments (adjusted p-value ≤  0.05). 699 \n 700 \nFigure 4. The effect of IFN β  on differentiation and function of HSDMs from adult blood 701 \ndonors. 702 \nA. Comparison of cell counts after 7 days of differentiation of HSPCs from adult blood 703 \ndonors in the presence (red) or absence of IFN β  (blue). Left: total cell count, tested with a 704 \npairwise t-test. Right: fold-change compared to not treated (NT) cells from each donor, 705 \ntested with a pairwise t-test. * = p-value ≤  0.05, ** = p-value ≤  0.01. 706 \nB. Comparison of cell counts after 14 days of differentiation of HSPCs from adult blood 707 \ndonors in the presence (red) or absence of IFN β  (blue). Left: total cell count, tested with a 708 \npairwise t-test. Right: fold-change compared to non-treated cells from each donor, tested 709 \nwith a pairwise t-test. * = p-value ≤  0.05, ** = p-value ≤  0.01. 710 \nC. Volcano plot comparing IFN β -treated (red) and NT cells (blue). Dotted lines indicate 711 \nstatistical significance thresholds (|FC| ≥  1.5, p-value ≤  0.05). 712 \nD. Percentage of early apoptotic, late apoptotic, live, and necrotic cells in NT (blue) and 713 \nIFNβ -treated cells (red) after 7 days of different iation. Differences between the two groups 714 \nwere tested with a pairwise t-test. * = p-value ≤  0.05. 715 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n21 \nE. Top: geometric mean fluorescence intensity (GMFI) of CD38 in live, early-apoptotic and 716 \nlate-apoptotic cells in NT sa mples. Differences between all groups was tested with a Tukey 717 \npost-hoc test. * = p-value ≤  0.05. Bottom: representative histogram showing CD38 levels in 718 \nearly-apoptotic and late-apoptotic cells in NT samples. 719 \nF. Top: GMFI of CD38 in live, early-apoptotic and late-apoptotic cells in IFN β -treated 720 \nsamples. Differences between all groups was tested with a Tukey post-hoc test. * = p-value 721 \n≤  0.05. Bottom: representative histogram showing CD38 levels in early-apoptotic and late-722 \napoptotic cells in IFNβ -treated samples. 723 \nG. Dot plot comparing the OCR-to-ECAR ratio in resting (left) and stressed (right) cells 724 \ndifferentiated in the presence (red)  or absence (NT, blue) of IFN β . Differences between the 725 \ntwo groups were tested with a pairwise t-test. * = p-value ≤  0.05. 726 \nH. Graph depicting the oxygen consumption rate (OCR) in HSDM differentiated in the 727 \npresence (red) or absence (NT, blue) of IFN β  at basal level, and after addition of oligomycin, 728 \nFCCP, and a combination of rotenone and antimycin A (Rot/AA). 729 \n 730 \nDATA AND CODE AVAILABILITY 731 \nThe code used to analyse the bulk RNA-seq data and generate the figures is available at 732 \nhttps://github.com/Deusu/HSDM-sepsis. The RNA-seq data generated in this study is 733 \navailable at Zenodo (10.5281/zenodo.14295543). 734 \n 735 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 17, 2024. ; https://doi.org/10.1101/2024.12.14.628447doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}