Energy-Starved Inflammation in Autism: Failure of Glycolytic Compensation Under IL-10–Driven Metabolic Tolerance

Energy-Starved Inflammation in Autism: Failure of Glycolytic Compensation Under IL-10–Driven Metabolic Tolerance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Energy-Starved Inflammation in Autism: Failure of Glycolytic Compensation Under IL-10–Driven Metabolic Tolerance Albion Dervishi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8390063/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Immune activation and metabolic reprogramming are hallmarks of inflammation, yet their coordination in autism spectrum disorder (ASD) remains poorly understood. Here, we introduce the τ-axis, a transcriptomic systems framework that quantifies immune-driven metabolic demand relative to cellular energy-producing capacity, and apply it to whole-blood cohorts from ASD and acute sepsis. Applying τ reveals that comparable inflammatory signaling can arise from fundamentally distinct metabolic states. In sepsis, escalating immune activation is matched by scalable glycolysis and cytosolic substrate-level phosphorylation, enabling effective energetic compensation. In contrast, an IL-10 dominant ASD endotype exhibits elevated inflammatory signaling without proportional metabolic upregulation, resulting in energy-starved inflammation. This uncoupling is reflected in constrained cytosolic energy compensation ratios despite preserved expression of oxidative pathways. Together, these findings establish τ as a generalizable systems metric of immunometabolic demand-capacity mismatch and recast ASD as a chronic immunometabolic syndrome characterized by tractable energetic deficits. Health sciences/Diseases Biological sciences/Immunology Autism spectrum disorder immunometabolism IL-10 glycolysis systems biology transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction 1.1 Autism Heterogeneity and the missing metabolic stratification Autism spectrum disorder (ASD) is biologically heterogeneous, encompassing diverse developmental trajectories and systemic comorbidities[ 1 ]. While genetic, synaptic, and neurodevelopmental mechanisms have been extensively investigated, systematic metabolic stratification of ASD remains limited. A clinically important subgroup is characterized by developmental regression, affecting approximately 20–30% of individuals, often following immune or inflammatory triggers. Although immune activation, mitochondrial dysfunction, and redox imbalance are frequently reported in ASD[ 2 ], these findings remain mechanistically fragmented across studies and lack integration into a unified systems framework capable of explaining why regression occurs in only a subset of affected children. 1.2 Immune-activated ASD: a described subtype without a metabolic mechanism Multiple studies have identified ASD subtypes with elevated pro- and anti-inflammatory cytokines, including IL-6, TNF-α, and IL-10, indicating persistent immune activation accompanied by immune tolerance signaling[ 3 ][ 4 ]. Although IL-10 is classically anti-inflammatory, it actively suppresses glycolysis and HIF-1α–dependent metabolic reprogramming while enforcing a rigid mitochondrial oxidative program, suggesting that IL-10–dominant immune states may impose a distinct metabolic vulnerability [ 5 ][ 6 ]. Emerging work on the gut-immune-brain axis further implicates chronic microbial products such as lipopolysaccharide (LPS) as upstream drivers of this immune-metabolic coupling[ 7 ]. Immune-activated ASD has been stratified using cytokine ratios (e.g., IL-1β/IL-10), revealing subgroup-specific mitochondrial respiratory dysfunction, while serum microRNAs further link immune states to metabolic regulation [ 3 ]. Despite these advances, a quantitative framework linking immune activation to metabolic failure in ASD is still lacking. 1.3 Glycolytic compensation as the core survival mechanism of inflammation Cellular ATP production relies on three coordinated systems: oxidative phosphorylation (OXPHOS), mitochondrial substrate-level phosphorylation (mSLP), and cytosolic substrate-level phosphorylation (cSLP) via glycolysis. Under stress, inflammation, or hypoxia, cells suppress pyruvate dehydrogenase (PDH), reduce tricarboxylic acid (TCA) cycle flux, dim OXPHOS, and induce HIF-1α–driven aerobic glycolysis (the Warburg effect) [ 8 ]. This shift constitutes the primary energetic rescue mechanism supporting immune activation. In acute inflammatory states such as sepsis, failure of glycolytic compensation predicts organ dysfunction and mortality, establishing that inflammation becomes catastrophic when metabolic compensation collapses [ 9 ][ 10 ]. Whether a comparable failure of metabolic compensation exists in immune-activated ASD has not previously been examined. 1.4 The τ-axis as a quantitative measure of immune-driven metabolic demand Here, we introduce the τ-axis, a transcriptomic systems metric that quantifies immune-driven metabolic demand across coordinated gene modules representing inflammation, glycolysis, fatty acid oxidation (FAO), the TCA cycle, and oxidative phosphorylation. Conceptually, τ reflects immune-imposed energetic demand, while complementary energy compensation ratios capture metabolic supply capacity. We hypothesize that IL-10 dominant ASD represents a state of energy-starved inflammation, in which sustained immune signaling persists despite insufficient glycolytic compensation, resulting in intracellular energy deficit and increased vulnerability to neurodevelopmental regression. 2. Methods 2.1 Human cohorts and transcriptomic preprocessing We analysed two independent whole-blood transcriptomic cohorts using a unified preprocessing and module-scoring workflow to enable direct cross-disease comparison. Autism spectrum disorder cohort (GSE18123) We reprocessed the published autism microarray dataset GSE18123, comprising 66 male children with autism spectrum disorder (ASD; age 5.9 ± 2.1 years) and 33 age- and sex-matched typically developing male controls (age 6.1 ± 2.0 years)[ 11 ]. Raw Affymetrix CEL files were normalized using Affymetrix Power Tools, and probes were mapped to Ensembl gene identifiers using hgu133plus2.db (v3.13). For genes represented by multiple probes, the probe with the highest mean expression across all samples was retained. Expression values were converted to counts, trimmed mean of M-values (TMM) normalized, and transformed to log₂ counts per million (log₂-CPM) using edgeR (v4.2). Immune–metabolic subtyping was performed exclusively within the ASD cohort using unbiased tertiles of the IL10 module score as the primary stratifier. Stratification by TNFα/NF-κB tertiles was used as a sensitivity analysis (Extended Data Fig. 4 ), yielding concordant subgrouping. Module definitions, gene composition, and scoring parameters are provided in Supplementary Table S1 . To maximize biological contrast, the middle tertile was excluded, retaining ASD Low IL-10 (n = 16) and ASD High IL-10 (n = 16) groups for downstream analysis. Together with 19 matched controls, the final analytic ASD cohort comprised 51 individuals. Children with known genetic syndromes were excluded from the ASD group. Control subjects were screened to exclude ADHD, other neurodevelopmental disorders, and systemic disease. Sepsis cohort (GSE185263) We analysed whole-blood RNA-seq data from GSE185263, a multicentre cohort including 348 adults with community-acquired sepsis and 44 age-stratified healthy volunteers. Sequencing was performed on the Illumina HiSeq 2500 platform (GPL16791) [ 12 ]. Patients were stratified according to the Sepsis-3 Sequential Organ Failure Assessment (SOFA) score measured at 24 h. SOFA categories were defined as: Low-SOFA (score 7, n = 51), together with 44 healthy controls; three patients were excluded due to incomplete data, resulting in 389 adults included in the final sepsis analysis. Module gene composition is provided in Supplementary Table S2 . Cross-cohort gene harmonization To enable cross-disease inference, we derived a harmonized 160-gene τ-Axis panel representing the intersection of genes robustly expressed (≥ 1 CPM in ≥ 50% of samples) in both cohorts after isoform collapsing. 2.2 τ-axis computational framework For each sample j , the τ axis value was defined as the sum of pathway-level module scores: where: \(\:M\) = denotes a predefined immunometabolic pathway module and \(\:{S}_{j}\left(M\right)\) represents the activity score of modules \(\:M\) in sample \(\:j\) . This formulation allows immune and metabolic programs to jointly define immunometabolic demand without external weighting. 2.3 Gene module scoring Let \(\:{E}_{i,j}\) be expression of gene i in sample j , and let M be a module with \(\:\left|M\right|\) genes. The module score is defined as: This approach maps each transcriptome into pathway-level features representing coordinated biological programs. 2.4 Pathway modules The τ-axis integrates predefined metabolic and immunoregulatory modules capturing inflammation, energy production, and stress adaptation. Modules included: Glycolysis, Cytosolic Substrate-Level Phosphorylation (cSLP), Mitochondrial Substrate-Level Phosphorylation (mSLP), Propionyl-Succinyl Anaplerosis (PSA), HIF-1α response, TNFα/NF-κB signaling, Acute inflammatory response, IFN-γ signaling, IL-6/STAT3 signaling, Pyruvate Dehydrogenase (PDH), Tricarboxylic acid (TCA) cycle, Oxidative phosphorylation (OXPHOS), Fatty Acid Oxidation (FAO), IL-4/Th2 signaling and IL-10 signaling. Gene composition for each module is provided in Supplementary Table S1 and S2. 2.4 τ-normalization across datasets Because raw τ-values depend on expression platform and cohort characteristics, τ was standardized within each dataset using Z-normalization: where \(\:{\mu\:}_{\tau\:}\) and \(\:{\sigma\:}_{\tau\:}\) represent the mean and standard deviation of τ within the dataset. Standardized τ* values were used for cross-cohort comparisons. 2.5 Statistical analyses All statistical analyses were performed using R (version 4.3.2). Differential expression and module score comparisons were conducted using the limma package (v3.58.1) with linear models adjusted for age. Effect sizes were computed using Cliff’s delta via effsize (v0.8.1). Multiple testing was controlled using the Benjamini–Hochberg false discovery rate (FDR). Discriminatory performance of τ was evaluated using ROC curves and AUC estimates (pROC v1.18.5). Overlap of dysregulated modules between cohorts was assessed using Fisher’s exact test. All statistical tests were two-sided, with significance defined as FDR-adjusted p < 0.05. All figures were generated using ggplot2 (v3.5.1) and ComplexHeatmap (v2.18.0). Full analysis code is publicly available. 3. Results 3.1 Identification of an IL-10–dominant inflammatory ASD endotype To determine whether chronic immune activation in autism spectrum disorder (ASD) is associated with a distinct metabolic adaptation state, we focused on an IL-10–high inflammatory subtype. Unbiased stratification by TNFα/NF-κB module activity revealed pronounced inflammatory heterogeneity within the ASD cohort. IL-10 expression closely tracked this axis, defining two stable and biologically distinct ASD endotypes. Direct comparison of ASD Low-IL-10 (n = 16) and ASD High-IL-10 (n = 16) groups yielded near-complete separation of inflammatory signaling, with TNFα/NF-κB showing maximal effect size (adjusted p = 0.004; Supplementary Table S1 ). Nineteen age- and sex-matched typically developing controls served as a reference. These findings define a robust IL-10–dominant inflammatory ASD endotype for downstream metabolic analysis. 3.2 Sepsis exhibits severity-dependent inflammation with scalable metabolic compensation In the sepsis cohort, inflammatory signaling increased progressively across Sequential Organ Failure Assessment (SOFA) categories. Core inflammatory modules—including TNFα/NF-κB, IL-6/STAT3, IFNγ, IL-10, and acute inflammatory responses - showed stepwise upregulation from healthy controls through Low-SOFA (n = 234), Medium-SOFA (n = 60), and Severe-SOFA (n = 51) groups (Table 1 .b). Three patients were excluded due to incomplete data, yielding 389 individuals for analysis. Concomitant with inflammatory escalation, sepsis displayed marked metabolic reprogramming (Table 1 .b). Glycolysis and cytosolic substrate-level phosphorylation increased with SOFA severity, whereas mitochondrial oxidative modules showed relative attenuation, consistent with a shift away from oxidative metabolism under severe inflammatory stress. 3.3 Individual gene contribution across comparisons (ASD immune-activated vs adult sepsis) Glycolysis In ASD (high IL-10 subtype), glycolytic gene regulation was modest and heterogeneous (Figs. 1 and 2 A). HK2 (a rate-limiting hexokinase isoform) showed the strongest induction (Δ = +0.79, p = 4.0 × 10⁻⁴), while SLC2A3 (GLUT3 transporter) was moderately increased (Δ = +0.44, p = 0.049). In contrast, key commitment and uptake nodes were not coherently activated: PFKP (Δ = −0.26, non-significant), SLC2A1 (GLUT1; Δ = −0.20, non-significant), and PFKM (Δ = −0.49, non-significant) were reduced or unchanged, and most downstream catalytic enzymes (ALDOA, GPI, PGAM1, GAPDH) showed no significant changes. In sepsis, glycolysis displayed a coordinated (Figs. 1 and 2 A), severity-dependent induction across the pathway (Fig. 2 ). Strong upregulation was observed for GAPDH, PGK1, PKM, and glucose transporters, with SLC2A3 showing the largest effect (progressive Δ up to + 2.2 in severe sepsis). This pattern was consistent across early, mid, and late glycolytic steps, indicating a globally activated glycolytic program. Cytosolic substrate-level phosphorylation (cSLP) ASD exhibited a mixed cSLP response (Figs. 1 and 2 B). Upregulation was observed for LDHB (Δ = +1.10, p = 0.0018) and LDHA (Δ = +0.40, p = 0.0066), while several core ATP-generating or rate-limiting enzymes were reduced, including PFKM (Δ = −0.49), ENO2 (Δ = −0.34), and GAPDHS (Δ = −0.74). Brain-relevant isoforms (ALDOC, PKLR) showed no significant induction. Overall, the cSLP response in ASD lacked uniform activation of the canonical ATP-yielding chain (ALDOA → GAPDH → PGK1 → ENO1 → PKM). In contrast, sepsis demonstrated robust and coordinated induction of cSLP genes (Figs. 1 and 2 B), with progressive increases across severity for ALDOA (Δ = +0.83), GAPDH (Δ = +1.58), PGK1 (Δ = +1.25), ENO1 (Δ = +0.93), and PKM (Δ = +1.11), consistent with maximal cytosolic ATP generation under inflammatory stress. Pyruvate Dehydrogenase (PDH) In ASD, PDH gene regulation was heterogeneous (Figs. 1 and 2 C). The mitochondrial activator PDP1 was increased (Δ = +0.87, p = 4.4×10⁻⁴), while PDP2 was reduced (Δ = −0.33). Core catalytic subunits PDHB (Δ = +0.51) and PDHA1 (Δ = +0.35) showed modest increases, and the brain-enriched inhibitor PDK3 was decreased (Δ = -0.31). Other inhibitory kinases (PDK1/2/4) showed minimal change. In contrast, sepsis displayed coordinated PDH suppression (Figs. 1 and 2 C), with downregulation of PDP1/2, DLAT, PDHA1, PDHB, and PDHX, alongside increased PDK3, indicating progressive inhibition of mitochondrial pyruvate flux. TCA Cycle and Mitochondrial Substrate-Level Phosphorylation (mSLP) In ASD with high IL-10, TCA gene regulation showed selective activation (Fig. 1 , 2 D and 2 E). Strong upregulation was observed for DLD (Δ = +1.24, p = 4.2×10⁻⁵), SDHD (Δ = +0.54, p < 0.01), SDHB (Δ = +0.58, p < 0.01), SUCLA2 (Δ = +0.71, p < 0.01), SUCLG2 (Δ = +0.43, p < 0.01), and GLUD1/2 (mSLP; Δ = +0.20 / +0.10), indicating engagement of the succinate–succinyl-CoA axis. In contrast, GOT2 (mSLP; Δ = −0.48) and GPT2 (mSLP; Δ = −0.07) were reduced, alongside modest decreases in CS and OGDH, consistent with incomplete coupling of TCA flux. Other TCA components showed small or inconsistent changes. In severe sepsis, TCA remodeling was extensive (Fig. 1 , 2 D and 2 E), with large bidirectional shifts across SDHB (Δ = +0.70), IDH1 (Δ = +0.96), OGDH (Δ = +0.46), SUCLG2 (Δ = −0.81), and DLD (Δ = +0.53), accompanied by marked suppression of GOT2 (Δ = −0.93) and GLUD1 (Δ = −0.28), reflecting global mitochondrial reprogramming. Oxidative Phosphorylation (OXPHOS) In ASD with high IL-10, OXPHOS gene regulation showed selective upregulation across multiple respiratory chain complexes (Figs. 1 and 2 F). Significant increases were observed for COX6C (Δ = +1.68, p = 2.4×10⁻⁴), ATP5PD (Δ = +1.23, p = 3.9×10⁻⁵), NDUFS1 (Δ = +0.46), UQCRC2 (Δ = +0.41), and COX4I1 (Δ = +0.33), while several core subunits (CYC1, NDUFV1) were reduced or unchanged. Overall changes were heterogeneous and incomplete across complexes I–V. In sepsis, OXPHOS remodeling was broad and coherent (Figs. 1 and 2 F), with strong upregulation across complexes III–V (UQCRFS1, COX6C, ATP5F1B, UQCRC1/2) and coordinated modulation of complex I subunits, consistent with global respiratory chain reprogramming. HIF-1α Signaling In ASD with high IL-10, HIF-1α pathway genes showed moderate but selective activation (Figs. 1 and 2 G). Significant increases were observed for EGLN1 (PHD2) (Δ = +0.76, p = 2.4×10⁻⁴), HIF1A (Δ = +0.55, p = 6.2×10⁻⁴), PFKFB3 (Δ = +0.99, p = 0.001), and EGLN3 (PHD3) (Δ = +0.45, p = 0.0088), while downstream hypoxia targets (BNIP3, VEGFA, EPAS1) showed limited or inconsistent changes. In severe sepsis, HIF-1α activation was strong and consistent (Figs. 1 and 2 G), with marked induction of PFKFB3 (Δ = +2.89), HIF1A (Δ =+0.81), EPAS1(Δ =+1.51), and VEGFA, alongside strong repression of BNIP3 and EGLN3, indicating a fully engaged hypoxic transcriptional program. Fatty Acid Oxidation (FAO) In ASD with high IL-10, FAO gene regulation showed a mixed but selective pattern (Figs. 1 and 2 H). Significant increases were observed for ETFA (Δ = +0.83, p = 2.4×10⁻⁴), ACADM (Δ = +0.87), SLC25A20 (Δ = +0.48), CPT1A (Δ = +0.58), ACAA2 (Δ = +0.31), and HADHA (Δ = +0.39), while key long-chain enzymes (ACAD9, CPT1B, ECHS1, ACADS) were reduced. Overall changes suggested incomplete FAO engagement across chain-length steps. In sepsis, FAO remodeling was robust (Figs. 1 and 2 H), with strong induction of HADHB, ACADVL, ETFA, SLC25A20, CPT2, ETFDH, and coordinated shifts across β-oxidation and carnitine-shuttle components, indicating globally activated lipid oxidation. Propionyl-Succinyl Anaplerosis (PSA) In ASD with high IL-10, PSA gene regulation was largely attenuated (Figs. 1 and 2 I), with only modest induction of MCEE (Δ = +0.43, p = 0.033), while core catalytic enzymes MMUT, PCCA, and PCCB showed no significant change. This pattern indicates limited engagement of the propionyl-CoA entry pathway into mitochondrial metabolism. In severe sepsis, PSA remodeling was pronounced (Figs. 1 and 2 I), with strong suppression of PCCA (Δ = −0.95, p = 4.9×10⁻¹²) and concurrent induction of MMUT (Δ = +0.32), indicating disrupted propionate handling under extreme metabolic stress. Interferon-γ (IFN-γ) Signaling In ASD with high IL-10, IFN-γ signaling showed partial receptor-proximal activation without a fully developed downstream chemokine program (Fig. 3 A, G). Significant upregulation was observed for IFNGR1 (Δ = +0.88, p = 1.4×10⁻⁵), IFNGR2 (Δ = +0.46), JAK2 (Δ = +0.92), GBP2 (Δ = +0.78), and HLA-DRA (Δ = +0.48), while canonical effector genes (CXCL9, CXCL10, CXCL11, GBP1, STAT1) showed modest, inconsistent, or non-significant changes. In severe sepsis, IFN-γ activation was robust and coherent (Fig. 3 A, G), with strong induction of IFNGR1/2, JAK2, IRF1, GBP2, HLA-B, and marked regulation of CXCR3-ligands, reflecting a fully engaged type-1 inflammatory program. TNF–NF-κB Signaling In ASD with high IL-10, TNF–NF-κB signaling showed limited and compartmentalized activation (Fig. 3 B, H). Moderate upregulation was observed for CHUK/IKKα (Δ = +0.62), TNF (Δ = +0.82), and TNFRSF1A (Δ = +0.12), while the transcriptionally active subunit RELA was reduced (Δ = −0.29) and key regulators (NFKB1, IKBKB, TNFAIP3, NFKBIA) showed minimal or non-significant changes, indicating restrained downstream signaling. In severe sepsis, TNF–NF-κB activation exhibited coordinated upregulation (Fig. 3 B, H), with strong induction of NFKBIA (Δ = +1.38), TNFRSF1A (Δ = +0.93), NFKB1, CHUK, RELA, and TNFAIP3, reflecting a fully engaged canonical inflammatory cascade. IL-6–STAT3 Signaling In ASD with high IL-10, IL-6–STAT3 signaling showed partial receptor-level engagement with restrained downstream transcriptional execution (Fig. 3 C, I). Moderate upregulation was observed for SOCS3 (Δ = +0.78, p = 0.005) and IL6R (Δ = +0.41), while core signaling components (STAT3, IL6ST, TYK2) and canonical STAT3 target genes (BCL2L1, MCL1, CCL20) showed weak or non-significant changes, indicating attenuated effector signaling under regulatory control. In severe sepsis, IL-6–STAT3 activation was robust and coherent (Fig. 3 C, I), with strong induction of SOCS3, STAT3, TYK2, IL6R, and anti-apoptotic targets (MCL1, BCL2L1), consistent with a fully engaged inflammatory survival program. Acute Inflammatory Signaling In ASD with high IL-10, acute inflammatory signaling showed limited and fragmented activation (Fig. 3 D, J). Modest upregulation was observed for IL1B (Δ = +0.89), S100A8/A9 (Δ = +0.88 / +0.52), PTGS2 (Δ = +0.68), and CXCL8 (Δ = +0.92), while key inflammasome and chemokine components (NLRP3, IL6, CXCL1/2, CCL2/3/4) remained weak or non-significant, indicating restrained effector deployment. In severe sepsis, acute inflammation was robust and coordinated (Fig. 3 D, J), with strong induction of S100A8/A9, IL1B, NLRP3, ICAM1, and neutrophil-recruiting chemokines, reflecting a fully engaged innate inflammatory program. IL-10 Signaling In ASD with high IL-10, anti-inflammatory signaling was selectively engaged at the receptor and transcriptional repression level (Fig. 3 E, K). Significant upregulation was observed for IL10RB (Δ = +0.49, p = 0.0039) and BCL3 (Δ = +0.46), consistent with stabilization of repressive NF-κB p50/p50 complexes, while IL10RA showed a modest reduction (Δ = −0.21) and IL10 transcript levels were unchanged. In severe sepsis, IL-10 signaling was strongly induced (Fig. 3 E, K), with marked upregulation of IL10, IL10RB, and BCL3, reflecting a robust compensatory anti-inflammatory response. L-4 / Th2 Signaling In ASD with high IL-10, the IL-4/Th2 axis showed reduced components (Fig. 3 F, L), including JAK3 (Δ = −0.56, p < 0.001), CCL17 (Δ = −0.44, p = 0.014), and IRF4 (Δ = −0.42, p = 0.024), with trends toward lower STAT6 and IL4 expression. IL4R showed only a modest, non-significant increase, indicating preserved receptor availability without downstream transcriptional engagement. In severe sepsis, IL-4/Th2 signaling is strongly and coherently activated (Fig. 3 F, L), with marked upregulation of IL4R, JAK3, STAT6, SOCS1, and dynamic remodeling of GATA3 and IRF4, reflecting a late-stage compensatory Th2/immune-resolution program following overwhelming inflammation. 3.4 Module-level architecture of immune–metabolic activation in ASD and adult sepsis Table 1 a. Module-level τ-axis differences across ASD immune states. Module scores reflect mean log₂-CPM values for Controls, ASD Low IL-10, and ASD High IL-10. p-values correspond to Wilcoxon rank-sum tests (Control vs. ASD Low IL-10; Control vs. ASD High IL-10), and indicate FDR-adjusted significance for the High IL-10 comparison. Module Mean logCPM (Ctrl) Mean logCPM (ASD Low IL-10) p-value Mean logCPM (ASD High IL-10) p-value FAO 4.581 4.623 0.477 4.704 0.009 Glycolysis 6.978 7.033 0.428 7.072 0.186 HIF1A 4.628 4.716 0.631 4.978 0.002 IFN𝛾 5.861 6.03 0.151 6.259 0.008 IL10 4.911 4.51 < 0.001 5.103 0.043 IL4 Th2 4.947 4.682 < 0.001 4.697 0.002 IL6 STAT3 5.766 5.76 0.756 5.945 0.055 OXPHOS 6.189 6.482 0.0457 6.457 0.009 PDH 4.527 4.71 0.177 4.834 0.009 TCA 5.218 5.381 0.061 5.449 0.006 TNFA NFKB 5.898 6.011 0.101 6.051 0.05 Acute Inflammatory 3.728 3.915 0.078 4.133 0.002 cSLP 4.016 4.053 0.555 4.024 0.908 mSLP 3.387 3.29 0.555 3.343 0.731 PSA 3.858 3.922 0.584 3.95 0.123 Table 1 b. Module-level τ-axis differences across sepsis SOFA score groups. Mean log₂-CPM values are shown for Healthy individuals and Low, Medium, and Severe SOFA score groups. p-values indicate FDR-adjusted significance for each SOFA comparison relative to Healthy. Module Mean logCPM (Healthy) Mean logCPM (Low SOFA) p-value Mean logCPM (Medium SOFA) p-value Mean logCPM (Severe SOFA) p-value Acute Inflammation 2.639 3.09 < 0.001 3.258 < 0.001 3.45 < 0.001 FAO 3.973 4.178 < 0.001 4.23 < 0.001 4.119 < 0.001 Glycolysis 6.546 7.287 < 0.001 7.382 < 0.001 7.589 < 0.001 HIF1A 4.544 4.692 0.031 4.872 < 0.001 5.215 < 0.001 IFNγ 5.526 6.165 < 0.001 6.167 < 0.001 5.879 0.065 IL10 3.121 3.768 < 0.001 3.85 < 0.001 4.114 < 0.001 IL4_Th2 3.54 3.728 < 0.001 3.724 < 0.001 3.817 < 0.001 IL6_STAT3 6.578 6.999 < 0.001 7.108 < 0.001 7.381 < 0.001 OXPHOS 5.068 5.59 < 0.001 5.557 < 0.001 5.378 < 0.001 PDH 4.323 4.054 < 0.001 4.115 < 0.001 4.041 < 0.001 TCA 5.128 5.296 < 0.001 5.29 < 0.001 5.197 0.015 TNFA_NFKB 5.947 6.253 < 0.001 6.373 < 0.001 6.555 < 0.001 cSLP 2.954 3.107 < 0.001 3.148 < 0.001 3.217 < 0.001 mSLP 4.11 3.942 < 0.001 3.973 0.09 3.746 < 0.001 PSA 2.935 2.797 0.001 2.837 0.02 2.703 < 0.001 The τ-axis module scores revealed a conserved pattern of immune-metabolic activation across ASD immune states and 24-hour sepsis severity groups (Fig. 4 ; Tables 1 a and 1 b), while exposing a marked divergence in how inflammatory demand is metabolically accommodated. In the ASD cohort, module behavior clearly separated Low- and High-IL-10 subgroups. Inflammatory activation intensified markedly in ASD High-IL-10, whereas metabolic compensation remained constrained. The IL-10 module increased stepwise from controls to ASD Low-IL-10 and peaked in ASD High-IL-10 (mean log₂-CPM: 4.9 → 4.5 → 5.1), consistent with dominance of IL-10–mediated tolerance signaling. Parallel increases were observed across inflammatory and stress-response modules, including HIF-1α, TNFα/NF-κB, IFN-γ, IL-6/STAT3, and acute inflammatory signaling (p = 0.055), defining a cytokine-activated, pseudohypoxic state specific to the IL-10–high ASD subtype. Despite this inflammatory escalation, cytosolic energy-producing modules failed to scale. Glycolysis and cSLP showed only marginal or absent increases and did not rise proportionally with inflammatory activation. Oxidative stability modules (OXPHOS, PDH, TCA, FAO) exhibited only modest upregulation, while mitochondrial substrate-level phosphorylation (mSLP) showed no compensatory increase and propionyl-succinyl anaplerosis (PSA) remained largely unchanged. Together, these patterns indicate metabolically constrained inflammation in ASD High-IL-10. In contrast, the sepsis cohort exhibited a severity-dependent hyperinflammatory response tightly coupled to scalable metabolic compensation. Inflammatory modules rose progressively with SOFA score, including TNFα/NF-κB, IL-6/STAT3, acute inflammatory signaling, HIF-1α, and IL-10 (all p < 0.001), with IFN-γ rising in Low/Medium SOFA but not in Severe SOFA. Glycolysis and cSLP increased strongly across severity strata, exceeding the magnitude observed in ASD and indicating robust cytosolic ATP compensation. Mitochondrial and oxidative modules in sepsis showed a biphasic pattern, increasing from Healthy to Low/Medium SOFA before declining in Severe SOFA, consistent with mitochondrial stress. PDH activity showed an overall progressive decline with severity, accompanied by parallel declines in mSLP and PSA, reflecting progressive mitochondrial suppression. This inverse relationship between escalating inflammation and shifting metabolic strategy underscores the diametric polarity of the τ-axis, distinguishing ASD High-IL-10 characterized by constrained metabolism despite inflammation from severe sepsis, marked by unrestrained Warburg-like metabolic reprogramming. 3.5 Cytosolic energy compensation ratio (CECR core) In the autism spectrum disorder (ASD) cohort, the Cytosolic Energy Compensation Ratio (CECR core) demonstrated a progressive reduction across IL‑10–defined subgroups (Table 2 ). Controls exhibited a CECR core of 0.690, which declined modestly in ASD Low IL‑10 (0.669) and further in ASD High IL‑10 (0.663). This pattern indicates that, despite preserved or mildly increased glycolytic activity, ASD—particularly the High IL‑10 subtype—fails to mount sufficient cytosolic ATP compensation relative to mitochondrial oxidative capacity. In contrast, the sepsis cohort showed a severity‑dependent increase in CECR core. Healthy controls displayed a CECR core of 0.654, which rose in Low SOFA sepsis (0.696), Medium SOFA (0.704), and peaked in Severe SOFA (0.739). This trajectory reflects robust activation of glycolysis and cytosolic substrate‑level phosphorylation as compensatory mechanisms in response to acute mitochondrial stress. CECR core = (Glycolysis + cSLP) / (PDH + TCA + OXPHOS) Table 2 Cytosolic Energy Compensation Ratio (CECR core) Across ASD and Sepsis Cohort Group Glycolysis cSLP PDH TCA OXPHOS CECR core ASD Control 6.978 4.016 4.527 5.218 6.189 0.69 ASD Low IL-10 7.033 4.053 4.71 5.381 6.482 0.669 ASD High IL-10 7.072 4.024 4.834 5.449 6.457 0.663 Sepsis Healthy 6.546 2.954 4.323 5.128 5.068 0.654 Sepsis Low SOFA 7.287 3.107 4.054 5.296 5.59 0.696 Sepsis Medium SOFA 7.382 3.148 4.115 5.29 5.557 0.704 Sepsis Severe SOFA 7.589 3.217 4.041 5.197 5.378 0.739 3.6 Mitochondrial energy compensation ratio (MECR core) In the ASD cohort, MECR core values were reduced relative to controls across IL‑10 defined subgroups (Table 3 ). Controls exhibited an MECR core of 0.455, which declined in ASD Low IL‑10 (0.435) and remained low in ASD High IL‑10 (0.436). In contrast, the sepsis cohort demonstrated a progressive decline in MECR core with increasing disease severity. Healthy individuals showed the highest MECR core (0.485), which decreased in Low SOFA (0.451), Medium SOFA (0.455), and Severe SOFA sepsis (0.441). MECR core = (mSLP + PSA) / (PDH + TCA + OXPHOS) Table 3: Mitochondrial Energy Compensation Ratio (MECR core) Across ASD and Sepsis Cohort Group mSLP PSA PDH TCA OXPHOS MECR core ASD Control 3.387 3.858 4.527 5.218 6.189 0.455 ASD Low IL-10 3.29 3.922 4.71 5.381 6.482 0.435 ASD High IL-10 3.343 3.95 4.834 5.449 6.457 0.436 Sepsis Healthy 4.11 2.935 4.323 5.128 5.068 0.485 Sepsis Low SOFA 3.942 2.797 4.054 5.296 5.59 0.451 Sepsis Medium SOFA 3.973 2.837 4.115 5.29 5.557 0.455 Sepsis Severe SOFA 3.746 2.703 4.041 5.197 5.378 0.441 3.7 τ-index resolves inflammatory–metabolic severity in ASD and sepsis Table 4 . Distribution and discriminatory performance of the τ-index across ASD and sepsis severity groups. Group Patients=n Mean Tau SD Tau Median IQR Discrimination power AUC 95% CI Control 19 -0.406 0.871 -0.563 0.974 ASD_Low_IL10 16 -0.38 0.756 -0.386 0.809 Ctrl vs ASD Low IL-10 0.526 0.327 - 0.726 ASD_High_IL10 16 0.976 0.923 0.92 0.913 Ctrl vs ASD High IL-10 0.865 0.736 - 0.994 Healthy 44 -1.305 0.545 -1.391 0.687 Low SOFA 234 0.04 0.982 0.104 1.126 Healthy vs Low SOFA 0.885 0.842 - 0.927 Medium SOFA 60 0.353 0.788 0.331 1.213 Healthy vs Medium SOFA 0.962 0.929 - 0.995 Severe SOFA 51 0.524 0.582 0.538 0.766 Healthy vs Severe SOFA 0.985 0.967 - 1 Footnote: AUC calculated via ROC; CI via DeLong's method. Across both cohorts, the τ-index delineated a coherent gradient of inflammatory-metabolic activation (Table 4 ). In the ASD dataset, τ values rose stepwise from Controls (mean − 0.41 ± 0.87, n = 19) to ASD Low IL-10 (− 0.38 ± 0.76, n = 16), with a marked elevation in the ASD High IL-10 subgroup (0.98 ± 0.92, n = 16). This ordering reflects a transition from baseline metabolic turnover to a high-demand state characteristic of IL-10-associated immune tolerance. Consistent with this pattern, τ yielded limited separation between Controls and ASD Low IL-10 (AUC = 0.526; 95% CI: 0.327–0.726), but robust discrimination of the IL-10-high subgroup (AUC = 0.865; 95% CI: 0.736–0.994). These results indicate that τ preferentially captures the metabolically stressed, inflammation-tolerant ASD subtype. A similar monotonic trajectory was observed in sepsis. Healthy individuals exhibited strongly negative τ values (mean − 1.31 ± 0.55, n = 44), whereas Low, Medium, and Severe SOFA groups showed progressively higher τ levels (0.04 ± 0.98, n = 234; 0.35 ± 0.79, n = 60; 0.52 ± 0.58, n = 51, respectively). This progression corresponded to high discriminatory accuracy: Healthy vs. Low SOFA (AUC = 0.885), Healthy vs. Medium SOFA (AUC = 0.962), and Healthy vs. Severe SOFA (AUC = 0.985). Together, these findings position τ as a disease-agnostic quantitative axis that resolves immunometabolic severity across both chronic neurodevelopmental inflammation and acute systemic illness. 4. Discussion This study introduces the τ-axis as a quantitative framework to assess immune-driven metabolic demand relative to cellular energy-producing capacity. Applying τ to two distinct inflammatory contexts-autism spectrum disorder (ASD) and acute sepsis—reveals that similar immune activation can arise from divergent metabolic configurations. In sepsis, escalating immune demand is matched by scalable glycolysis and cytosolic substrate-level phosphorylation, enabling effective energetic compensation. By contrast, ASD subgroups with altered IL-10 profiles (often showing dysregulated ratios relative to pro-inflammatory cytokines like IL-1β) exhibit a marked dissociation between immune activation and metabolic support, defining energy-starved inflammation where immune tolerance persists amid inadequate ATP availability[ 13 ]. Thus, τ not only gauges disease severity but also distinguishes inflammation supported by metabolic compensation from that constrained by energetic limitation. Previous studies have highlighted ATP supply-demand imbalances as central to energetic dysfunction in sepsis, where impaired cellular metabolism and mitochondrial stress undermine organ function, calling for integrated systems models to capture metabolic load and compensation under acute inflammation. Building on this, mathematical modeling of energy consumption in inflammatory responses offers parallels to chronic immune dysregulation, reinforcing the need for quantitative indices like τ to resolve demand-capacity mismatches across contexts[ 14 ][ 15 ][ 9 ]. 4.1 τ as an index of immunometabolic mismatch τ quantifies stress by merging inflammatory signal strength with metabolic burden. Healthy states yield negative τ ( ≈ − 1), signaling minimal inflammation and peak mitochondrial efficiency—an optimal baseline. Sepsis features modest positive τ (≈ 0–0.5), indicating good compensation: immune escalation syncs with boosted glycolysis and ATP synthesis for acute defense. In high-IL-10 ASD, high positive τ ( ≈ + 1) flags mismatched inflammation—strong signaling but weak execution, with glycolysis, TCA cycle, and ATP yields failing to ramp up. Similar τ hikes thus reveal divergent states based on compensation. In essence, τ tracks demand, ATP efficiency measures supply. This dual framework maps adaptive hypermetabolism (sepsis) versus chronic, unsupported inflammation (ASD subtype). 4.2 Capped cytosolic and mitochondrial energy compensation in IL-10–dominant ASD To assess energetic accommodation of immune demand, we computed the Cytosolic Energy Compensation Ratio (CECR core) and Mitochondrial Energy Compensation Ratio (MECR core). These metrics quantify substrate-level phosphorylation contributions relative to oxidative demand, gauging energy adaptability under inflammation. In ASD, CECR core decreased across IL-10 subgroups: from 0.690 in controls to 0.669 in low-IL-10 and 0.663 in high-IL-10 ASD (Table 2 ). Despite stable or elevated glycolytic expression, cytosolic ATP fails to scale with mitochondrial needs, marking capped compensation where signaling outpaces execution. MECR core mirrored this, dropping from 0.455 in controls to ~ 0.436 in ASD subgroups (Table 3 ), with limited mitochondrial substrate-level phosphorylation and propionyl–succinyl Anaplerosis. This dual constraint yields inefficient inflammation. Conversely, sepsis showed rising CECR core with severity (0.654 in healthy to 0.739 in severe SOFA), via amplified glycolysis and cytosolic ATP, while MECR core fell (0.485 to 0.441), shifting flexibly to cytosolic support. Thus, IL-10–dominant ASD features rigid, inadequate compensation, unlike sepsis's dynamic reconfiguration, highlighting energy caps as a hallmark of chronic tolerance. 4.3 IL-10 as a metabolic gatekeeper enforcing energetic rigidity Though classically anti-inflammatory, IL-10 acts here as a gatekeeper restricting metabolic adaptability in chronic activation. In the IL-10–dominant ASD subtype, immune signaling remains elevated while both cytosolic and mitochondrial energy compensation are capped, indicating that IL-10 does not resolve inflammation but instead stabilizes a low-energy, immune-tolerant state[ 13 ][ 16 ]. Mechanistically, IL-10 induces SOCS3, BCL3, and inhibitory NF-κB complexes, curbing inflammation while repressing HIF-1α–driven glycolysis[ 5 ]. This severs signaling from metabolic backing, hindering glycolysis and substrate-level phosphorylation amid ongoing demand[ 17 ]. This gatekeeping effect is reflected in the constrained CECR core and MECR core observed in IL-10–high ASD. Even in the presence of HIF-1α pathway activation and modest upregulation of oxidative modules, ATP production fails to scale effectively. As a result, immune tolerance persists not because inflammation is resolved, but because the system is locked into an energetically restricted equilibrium that limits both immune escalation and metabolic rescue. In contrast, acute sepsis - despite marked IL-10 induction at later stages - retains the capacity for dynamic metabolic reconfiguration. There, IL-10 emerges within a context of high glycolytic flux and cytosolic ATP availability, functioning as a modulatory brake rather than a rigid metabolic lock. This distinction underscores that IL-10’s biological role is context-dependent: permissive and adaptive when energy supply is abundant, but restrictive and pathological when metabolic capacity is chronically constrained. Together, these findings reposition IL-10 from a passive anti-inflammatory marker to an active regulator of immunometabolic state. Repositioning IL-10 as immunometabolic regulator, it imposes rigidity in ASD, perpetuating energy-starved inflammation and reconciling tolerance with severity sans hyperinflammation. 4.4 Dysbiosis-driven HIF–PHD pseudo-hypoxia locks the immunometabolic system Our data support a model where chronic gut dysbiosis fosters persistent pseudo-hypoxia, stabilizing immunometabolic rigidity via HIF–prolyl hydroxylase (PHD) dysregulation. Immune and metabolic inputs converge to sustain HIF-1α signaling sans true hypoxia, thwarting energy compensation[ 18 ][ 19 ]. Low-grade microbial products like LPS drive TLR4–NF-κB/STAT3, inducing HIF1A[ 20 ][ 21 ][ 22 ]. In high-IL-10 ASD, this aligns with upregulated HIF1A, EGLN1, and EGLN3, signaling chronic oxygen-sensing activation over acute response. This reflects PHD upregulation amid HIF-1α stabilization[ 22 ][ 23 ]. At the metabolic level, dysbiosis-associated propionate exposure and altered amino acid flux promote succinate accumulation and rewiring of mitochondrial metabolism toward glutamate-linked anaplerotic buffering [ 24 ][ 25 ]. While α-ketoglutarate is a required co-substrate for PHD activity[ 26 ], succinate acts as a competitive inhibitor[ 27 ], creating a biochemical configuration in which PHD transcription is induced but enzymatic activity remains functionally suppressed. This metabolite-driven inhibition explains the paradoxical coexistence of elevated HIF1A, EGLN1, and EGLN3 expression with sustained HIF-1α activity. IL-10 reinforces this by curbing HIF-1α–driven glycolysis, capping cytosolic ATP despite signals[ 5 ]. These forces form a loop: dysbiosis induces HIF-1α; succinate blocks PHD; IL-10 restrains metabolism; compensation fails. The outcome is a locked attractor of inflammatory signaling, pseudo-hypoxic activation, and energy deficit[ 28 ]. This model provides a unifying mechanistic explanation for how immune tolerance, mitochondrial rewiring, and failed glycolytic compensation coexist in IL-10–dominant ASD. Rather than reflecting unresolved hypoxia or transient inflammation, pseudo-hypoxia emerges here as a dysbiosis-driven, metabolite-stabilized state that locks the system into energy-starved inflammation. 4.5 Propionyl–Succinate–α-Ketoglutarate Flux Imbalance Defines an Energetically Constrained ASD Metabolic State In ASD subsets characterized by altered IL-10–dominant immune profiles, metabolic adaptation appears to rely on partial anaplerotic support without full oxidative coupling. Propionyl-derived succinate entry can replenish TCA intermediates; however, constrained flux across the α-ketoglutarate–succinate segment limits downstream throughput[ 25 ]. This bottleneck favors α-ketoglutarate accumulation and redox-biased glutamate formation rather than efficient oxidative ATP generation. Concurrently, reduced mitochondrial aminotransferase activity and restricted malate–aspartate shuttle flux impairs nitrogen and redox exchange, promoting intracellular accumulation of aspartate and glutamate. Collectively, these findings indicate that mitochondria operate in a low-throughput, protective “safe-mode” configuration that limits oxidative flux to prevent propionate-driven metabolic instability. Chronic propionate burden therefore appears to impose an enforced constraint on mitochondrial throughput, sustaining survival at the cost of energetic efficiency and metabolic flexibility. 4.6 Clinical and translational implications These findings carry key implications for ASD research and care. First, they offer a quantitative framework for stratifying ASD into immunometabolic endotypes, transcending symptom-based diagnostics. Second, they pinpoint energy-starved inflammation as a mechanistic driver potentially underpinning regression vulnerability. Third, this state may be detectable via routine labs: altered serum IL-10 in subgroups; high urinary succinate and α-ketoglutarate in organic acids profiles; and eventually increased serum or urinary aspartic acid and glutamate (with variable glutamine)[ 4 ][ 29 ][ 30 ]. Fourth, τ metrics provide a cross-disease tool for juxtaposing chronic neuroinflammation with acute illness. Critically, the framework warns against inflammation-suppressing therapies alone, which could prove inadequate or harmful without restoring metabolic capacity. Instead, targeting bottlenecks - e.g., via glycolytic enhancers or microbiota interventions - may unlock IL-10–driven tolerance. 4.7 Limitations and future directions Key limitations include the indirect nature of transcriptomics for inferring metabolic flux, necessitating validation via metabolomics or fluxomics. Whole-blood profiling may overlook brain-specific dynamics, and cross-sectional data preclude causal inferences on dysbiosis–tolerance–metabolism links. Future studies should employ longitudinal designs, integrate metabolite assays, and test interventions (e.g., microbiota modulation or glycolytic boosters) to assess τ shifts and clinical impacts. Conclusion In summary, we define IL-10–dominant ASD as a state of uncompensated immune activation, marked by high immunometabolic demand amid inadequate energy supply. In contrast to sepsis - where inflammation is supported by scalable glycolytic and cytosolic substrate-level phosphorylation - ASD exhibits insufficient cytosolic energy compensation, resulting in persistent demand - capacity mismatch. The τ-axis captures this fundamental dichotomy, providing a cross-disease metric of immunometabolic imbalance. Ultimately, these insights recast ASD as a chronic immunometabolic syndrome characterized by constrained energetic adaptability and tractable cellular energy deficits. Declarations Ethics Approval This study is a secondary analysis of publicly available, de-identified human transcriptomic datasets obtained from the Gene Expression Omnibus (GEO) repository. The original data collection was conducted by the respective investigators in accordance with applicable ethical standards and with approval from their local ethics committees. As the present analysis involved no direct interaction with human participants and no access to identifiable private information, additional ethical approval from an Institutional Review Board (IRB) or ethics committee was not required. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent to Participate Informed consent was obtained from all participants in the original studies contributing data to the GEO repository. No additional consent was required for this secondary analysis of de-identified public data. Human Ethics and Consent to Participate Declarations Not applicable. This study involved only secondary analysis of publicly available, de-identified data. Clinical Trial Registration Clinical trial number: not applicable. Author Contributions A.D. conceived the study, designed the analytical framework, performed data analysis, interpreted the results, and wrote the manuscript. Funding This research received no external funding. Conflict of Interest The author declares no conflict of interest. Supplementary Information All analysis code used in this study is publicly available at: https://github.com/albiondervishi/Energy-Starved-Inflammation-in-Autism References S. J. 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16:13:04","extension":"xml","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135585,"visible":true,"origin":"","legend":"","description":"","filename":"15783e4b404d40aa81ed67223f2bf2fe1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/4be983135bed949863bdb9f1.xml"},{"id":99310403,"identity":"ce0b383c-2947-45a2-bb1c-6c31b4504db3","added_by":"auto","created_at":"2025-12-31 16:12:45","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147962,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/6101d720761bf728c80fc8a0.html"},{"id":99311154,"identity":"120c70fb-6214-4798-ba07-c71548feac3b","added_by":"auto","created_at":"2025-12-31 16:13:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":371510,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated immunometabolic network defining τ-axis regulation. Schematic of glycolysis and cytosolic substrate-level phosphorylation (cSLP), pyruvate dehydrogenase control, TCA cycle, mitochondrial substrate-level phosphorylation (mSLP), oxidative phosphorylation (OXPHOS), propionyl succinyl anaplerosis (PSA) and fatty acid oxidation (FAO).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/47deae3da1a362b961489872.png"},{"id":98934944,"identity":"724756f5-d89d-48cc-94fb-46851fef541a","added_by":"auto","created_at":"2025-12-24 09:50:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":290378,"visible":true,"origin":"","legend":"\u003cp\u003eGene-level metabolic remodeling across ASD and sepsis. Heatmaps depict mean expression differences (Δ) for metabolic and hypoxia-related genes in ASD Low-IL-10, ASD High-IL-10, and sepsis severity groups versus controls. ASD shows constrained and heterogeneous pathway activation, whereas sepsis exhibits coordinated, scalable metabolic reprogramming, distinguishing uncompensated from compensated inflammatory states.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/28c2d31d7b85404172266213.png"},{"id":98934942,"identity":"47eb1126-271f-4176-aada-fb4dc64d479f","added_by":"auto","created_at":"2025-12-24 09:50:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":282070,"visible":true,"origin":"","legend":"\u003cp\u003eDivergent inflammatory signaling architectures in ASD and sepsis. Schematic overview and gene-level heatmaps of core immune pathways (IFN-γ, TNF–NF-κB, IL-6–STAT3, acute inflammation, IL-10, and IL-4/Th2). Mean expression differences (Δ) are shown for ASD Low-IL-10, ASD High-IL-10, and sepsis severity groups versus controls.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/17a51e3d5a5a1d7d8faf121e.png"},{"id":99310319,"identity":"ca0acec9-15a8-490b-94f3-dee4f76bb593","added_by":"auto","created_at":"2025-12-31 16:12:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":343547,"visible":true,"origin":"","legend":"\u003cp\u003eτ-axis module patterns in ASD immune states and sepsis severity. Boxplots show τ-axis module scores across ASD groups (Control, Low IL-10, High IL-10) and sepsis severity (Healthy, Low, Medium, Severe SOFA). Brackets indicate significant pairwise differences (Wilcoxon test; P \u0026lt; 0.05 to P \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/4187f7b5444585ac18e43263.png"},{"id":102038882,"identity":"5c0431cd-9296-4970-a60c-6cb14d3926e2","added_by":"auto","created_at":"2026-02-06 12:27:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2965916,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/23b8fd3a-10a7-45bc-8675-011bf279b106.pdf"},{"id":98934946,"identity":"4df6d374-2d62-4ea7-b388-f8e38a15c73f","added_by":"auto","created_at":"2025-12-24 09:50:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":92199,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/bf394d45cbbeee465953c65c.xlsx"},{"id":99310156,"identity":"f521b84a-d017-45e0-8e9f-23808f995122","added_by":"auto","created_at":"2025-12-31 16:12:05","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":208706,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/54be2d827d927285deb9fc39.xlsx"},{"id":99310293,"identity":"a8f64b53-9001-4d60-acd7-dfda659604ed","added_by":"auto","created_at":"2025-12-31 16:12:27","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":248893,"visible":true,"origin":"","legend":"","description":"","filename":"AbstractFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8390063/v1/a3149003e9babe402b81bad4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Energy-Starved Inflammation in Autism: Failure of Glycolytic Compensation Under IL-10–Driven Metabolic Tolerance","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Autism Heterogeneity and the missing metabolic stratification\u003c/h2\u003e \u003cp\u003eAutism spectrum disorder (ASD) is biologically heterogeneous, encompassing diverse developmental trajectories and systemic comorbidities[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While genetic, synaptic, and neurodevelopmental mechanisms have been extensively investigated, systematic metabolic stratification of ASD remains limited. A clinically important subgroup is characterized by developmental regression, affecting approximately 20\u0026ndash;30% of individuals, often following immune or inflammatory triggers. Although immune activation, mitochondrial dysfunction, and redox imbalance are frequently reported in ASD[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], these findings remain mechanistically fragmented across studies and lack integration into a unified systems framework capable of explaining why regression occurs in only a subset of affected children.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Immune-activated ASD: a described subtype without a metabolic mechanism\u003c/h2\u003e \u003cp\u003eMultiple studies have identified ASD subtypes with elevated pro- and anti-inflammatory cytokines, including IL-6, TNF-α, and IL-10, indicating persistent immune activation accompanied by immune tolerance signaling[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although IL-10 is classically anti-inflammatory, it actively suppresses glycolysis and HIF-1α\u0026ndash;dependent metabolic reprogramming while enforcing a rigid mitochondrial oxidative program, suggesting that IL-10\u0026ndash;dominant immune states may impose a distinct metabolic vulnerability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Emerging work on the gut-immune-brain axis further implicates chronic microbial products such as lipopolysaccharide (LPS) as upstream drivers of this immune-metabolic coupling[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Immune-activated ASD has been stratified using cytokine ratios (e.g., IL-1β/IL-10), revealing subgroup-specific mitochondrial respiratory dysfunction, while serum microRNAs further link immune states to metabolic regulation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite these advances, a quantitative framework linking immune activation to metabolic failure in ASD is still lacking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Glycolytic compensation as the core survival mechanism of inflammation\u003c/h2\u003e \u003cp\u003eCellular ATP production relies on three coordinated systems: oxidative phosphorylation (OXPHOS), mitochondrial substrate-level phosphorylation (mSLP), and cytosolic substrate-level phosphorylation (cSLP) via glycolysis. Under stress, inflammation, or hypoxia, cells suppress pyruvate dehydrogenase (PDH), reduce tricarboxylic acid (TCA) cycle flux, dim OXPHOS, and induce HIF-1α\u0026ndash;driven aerobic glycolysis (the Warburg effect) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This shift constitutes the primary energetic rescue mechanism supporting immune activation. In acute inflammatory states such as sepsis, failure of glycolytic compensation predicts organ dysfunction and mortality, establishing that inflammation becomes catastrophic when metabolic compensation collapses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Whether a comparable failure of metabolic compensation exists in immune-activated ASD has not previously been examined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 The τ-axis as a quantitative measure of immune-driven metabolic demand\u003c/h2\u003e \u003cp\u003eHere, we introduce the τ-axis, a transcriptomic systems metric that quantifies immune-driven metabolic demand across coordinated gene modules representing inflammation, glycolysis, fatty acid oxidation (FAO), the TCA cycle, and oxidative phosphorylation. Conceptually, τ reflects immune-imposed energetic demand, while complementary energy compensation ratios capture metabolic supply capacity.\u003c/p\u003e \u003cp\u003eWe hypothesize that IL-10 dominant ASD represents a state of energy-starved inflammation, in which sustained immune signaling persists despite insufficient glycolytic compensation, resulting in intracellular energy deficit and increased vulnerability to neurodevelopmental regression.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Human cohorts and transcriptomic preprocessing\u003c/h2\u003e\n \u003cp\u003eWe analysed two independent whole-blood transcriptomic cohorts using a unified preprocessing and module-scoring workflow to enable direct cross-disease comparison.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAutism spectrum disorder cohort (GSE18123)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe reprocessed the published autism microarray dataset GSE18123, comprising 66 male children with autism spectrum disorder (ASD; age 5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 years) and 33 age- and sex-matched typically developing male controls (age 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 years)[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Raw Affymetrix CEL files were normalized using Affymetrix Power Tools, and probes were mapped to Ensembl gene identifiers using hgu133plus2.db (v3.13). For genes represented by multiple probes, the probe with the highest mean expression across all samples was retained. Expression values were converted to counts, trimmed mean of M-values (TMM) normalized, and transformed to log₂ counts per million (log₂-CPM) using edgeR (v4.2).\u003c/p\u003e\n \u003cp\u003eImmune\u0026ndash;metabolic subtyping was performed exclusively within the ASD cohort using unbiased tertiles of the IL10 module score as the primary stratifier. Stratification by TNF\u0026alpha;/NF-\u0026kappa;B tertiles was used as a sensitivity analysis (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), yielding concordant subgrouping. Module definitions, gene composition, and scoring parameters are provided in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. To maximize biological contrast, the middle tertile was excluded, retaining ASD Low IL-10 (n\u0026thinsp;=\u0026thinsp;16) and ASD High IL-10 (n\u0026thinsp;=\u0026thinsp;16) groups for downstream analysis. Together with 19 matched controls, the final analytic ASD cohort comprised 51 individuals. Children with known genetic syndromes were excluded from the ASD group. Control subjects were screened to exclude ADHD, other neurodevelopmental disorders, and systemic disease.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSepsis cohort (GSE185263)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe analysed whole-blood RNA-seq data from GSE185263, a multicentre cohort including 348 adults with community-acquired sepsis and 44 age-stratified healthy volunteers. Sequencing was performed on the Illumina HiSeq 2500 platform (GPL16791) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003ePatients were stratified according to the Sepsis-3 Sequential Organ Failure Assessment (SOFA) score measured at 24 h. SOFA categories were defined as: Low-SOFA (score\u0026thinsp;\u0026lt;\u0026thinsp;4, n\u0026thinsp;=\u0026thinsp;234), Medium-SOFA (score 4\u0026ndash;7, n\u0026thinsp;=\u0026thinsp;60), and Severe-SOFA (score\u0026thinsp;\u0026gt;\u0026thinsp;7, n\u0026thinsp;=\u0026thinsp;51), together with 44 healthy controls; three patients were excluded due to incomplete data, resulting in 389 adults included in the final sepsis analysis. Module gene composition is provided in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCross-cohort gene harmonization\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo enable cross-disease inference, we derived a harmonized 160-gene \u0026tau;-Axis panel representing the intersection of genes robustly expressed (\u0026ge;\u0026thinsp;1 CPM in \u0026ge;\u0026thinsp;50% of samples) in both cohorts after isoform collapsing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 \u0026tau;-axis computational framework\u003c/h2\u003e\n \u003cp\u003eFor each sample \u003cem\u003ej\u003c/em\u003e, the \u0026tau; axis value was defined as the sum of pathway-level module scores:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e = denotes a predefined immunometabolic pathway module and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{j}\\left(M\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the activity score of modules \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u003c/span\u003e in sample \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e. This formulation allows immune and metabolic programs to jointly define immunometabolic demand without external weighting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Gene module scoring\u003c/h2\u003e\n \u003cp\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e be expression of gene \u003cem\u003ei\u003c/em\u003e in sample \u003cem\u003ej\u003c/em\u003e, and let \u003cem\u003eM\u003c/em\u003e be a module with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left|M\\right|\\)\u003c/span\u003e\u003c/span\u003e genes.\u003c/p\u003e\n \u003cp\u003eThe module score is defined as:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eThis approach maps each transcriptome into pathway-level features representing coordinated biological programs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Pathway modules\u003c/h2\u003e\n \u003cp\u003eThe \u0026tau;-axis integrates predefined metabolic and immunoregulatory modules capturing inflammation, energy production, and stress adaptation. Modules included: Glycolysis, Cytosolic Substrate-Level Phosphorylation (cSLP),\u003c/p\u003e\n \u003cp\u003eMitochondrial Substrate-Level Phosphorylation (mSLP), Propionyl-Succinyl Anaplerosis (PSA), HIF-1\u0026alpha; response, TNF\u0026alpha;/NF-\u0026kappa;B signaling, Acute inflammatory response, IFN-\u0026gamma; signaling, IL-6/STAT3 signaling, Pyruvate Dehydrogenase (PDH), Tricarboxylic acid (TCA) cycle, Oxidative phosphorylation (OXPHOS), Fatty Acid Oxidation (FAO), IL-4/Th2 signaling and IL-10 signaling.\u003c/p\u003e\n \u003cp\u003eGene composition for each module is provided in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 \u0026tau;-normalization across datasets\u003c/h2\u003e\n \u003cp\u003eBecause raw \u0026tau;-values depend on expression platform and cohort characteristics, \u0026tau; was standardized within each dataset using Z-normalization:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{\\tau\\:}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{\\tau\\:}\\)\u003c/span\u003e\u003c/span\u003e represent the mean and standard deviation of \u0026tau; within the dataset.\u003c/p\u003e\n \u003cp\u003eStandardized \u0026tau;* values were used for cross-cohort comparisons.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed using R (version 4.3.2). Differential expression and module score comparisons were conducted using the limma package (v3.58.1) with linear models adjusted for age. Effect sizes were computed using Cliff\u0026rsquo;s delta via effsize (v0.8.1). Multiple testing was controlled using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR). Discriminatory performance of \u0026tau; was evaluated using ROC curves and AUC estimates (pROC v1.18.5). Overlap of dysregulated modules between cohorts was assessed using Fisher\u0026rsquo;s exact test. All statistical tests were two-sided, with significance defined as FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All figures were generated using ggplot2 (v3.5.1) and ComplexHeatmap (v2.18.0). Full analysis code is publicly available.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Identification of an IL-10\u0026ndash;dominant inflammatory ASD endotype\u003c/h2\u003e\n \u003cp\u003eTo determine whether chronic immune activation in autism spectrum disorder (ASD) is associated with a distinct metabolic adaptation state, we focused on an IL-10\u0026ndash;high inflammatory subtype. Unbiased stratification by TNF\u0026alpha;/NF-\u0026kappa;B module activity revealed pronounced inflammatory heterogeneity within the ASD cohort. IL-10 expression closely tracked this axis, defining two stable and biologically distinct ASD endotypes. Direct comparison of ASD Low-IL-10 (n\u0026thinsp;=\u0026thinsp;16) and ASD High-IL-10 (n\u0026thinsp;=\u0026thinsp;16) groups yielded near-complete separation of inflammatory signaling, with TNF\u0026alpha;/NF-\u0026kappa;B showing maximal effect size (adjusted p\u0026thinsp;=\u0026thinsp;0.004; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Nineteen age- and sex-matched typically developing controls served as a reference. These findings define a robust IL-10\u0026ndash;dominant inflammatory ASD endotype for downstream metabolic analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Sepsis exhibits severity-dependent inflammation with scalable metabolic compensation\u003c/h2\u003e\n \u003cp\u003eIn the sepsis cohort, inflammatory signaling increased progressively across Sequential Organ Failure Assessment (SOFA) categories. Core inflammatory modules\u0026mdash;including TNF\u0026alpha;/NF-\u0026kappa;B, IL-6/STAT3, IFN\u0026gamma;, IL-10, and acute inflammatory responses - showed stepwise upregulation from healthy controls through Low-SOFA (n\u0026thinsp;=\u0026thinsp;234), Medium-SOFA (n\u0026thinsp;=\u0026thinsp;60), and Severe-SOFA (n\u0026thinsp;=\u0026thinsp;51) groups (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.b). Three patients were excluded due to incomplete data, yielding 389 individuals for analysis.\u003c/p\u003e\n \u003cp\u003eConcomitant with inflammatory escalation, sepsis displayed marked metabolic reprogramming (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.b). Glycolysis and cytosolic substrate-level phosphorylation increased with SOFA severity, whereas mitochondrial oxidative modules showed relative attenuation, consistent with a shift away from oxidative metabolism under severe inflammatory stress.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Individual gene contribution across comparisons (ASD immune-activated vs adult sepsis)\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eGlycolysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD (high IL-10 subtype), glycolytic gene regulation was modest and heterogeneous (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). HK2 (a rate-limiting hexokinase isoform) showed the strongest induction (\u0026Delta; = +0.79, p\u0026thinsp;=\u0026thinsp;4.0 \u0026times; 10⁻⁴), while SLC2A3 (GLUT3 transporter) was moderately increased (\u0026Delta; = +0.44, p\u0026thinsp;=\u0026thinsp;0.049). In contrast, key commitment and uptake nodes were not coherently activated: PFKP (\u0026Delta; = \u0026minus;0.26, non-significant), SLC2A1 (GLUT1; \u0026Delta; = \u0026minus;0.20, non-significant), and PFKM (\u0026Delta; = \u0026minus;0.49, non-significant) were reduced or unchanged, and most downstream catalytic enzymes (ALDOA, GPI, PGAM1, GAPDH) showed no significant changes.\u003c/p\u003e\n \u003cp\u003eIn sepsis, glycolysis displayed a coordinated (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), severity-dependent induction across the pathway (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Strong upregulation was observed for GAPDH, PGK1, PKM, and glucose transporters, with SLC2A3 showing the largest effect (progressive \u0026Delta; up to +\u0026thinsp;2.2 in severe sepsis). This pattern was consistent across early, mid, and late glycolytic steps, indicating a globally activated glycolytic program.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCytosolic substrate-level phosphorylation (cSLP)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eASD exhibited a mixed cSLP response (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Upregulation was observed for LDHB (\u0026Delta; = +1.10, p\u0026thinsp;=\u0026thinsp;0.0018) and LDHA (\u0026Delta; = +0.40, p\u0026thinsp;=\u0026thinsp;0.0066), while several core ATP-generating or rate-limiting enzymes were reduced, including PFKM (\u0026Delta; = \u0026minus;0.49), ENO2 (\u0026Delta; = \u0026minus;0.34), and GAPDHS (\u0026Delta; = \u0026minus;0.74). Brain-relevant isoforms (ALDOC, PKLR) showed no significant induction. Overall, the cSLP response in ASD lacked uniform activation of the canonical ATP-yielding chain (ALDOA \u0026rarr; GAPDH \u0026rarr; PGK1 \u0026rarr; ENO1 \u0026rarr; PKM).\u003c/p\u003e\n \u003cp\u003eIn contrast, sepsis demonstrated robust and coordinated induction of cSLP genes (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), with progressive increases across severity for ALDOA (\u0026Delta; = +0.83), GAPDH (\u0026Delta; = +1.58), PGK1 (\u0026Delta; = +1.25), ENO1 (\u0026Delta; = +0.93), and PKM (\u0026Delta; = +1.11), consistent with maximal cytosolic ATP generation under inflammatory stress.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePyruvate Dehydrogenase (PDH)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD, PDH gene regulation was heterogeneous (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The mitochondrial activator PDP1 was increased (\u0026Delta; = +0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.4\u0026times;10⁻⁴), while PDP2 was reduced (\u0026Delta; = \u0026minus;0.33). Core catalytic subunits PDHB (\u0026Delta; = +0.51) and PDHA1 (\u0026Delta; = +0.35) showed modest increases, and the brain-enriched inhibitor PDK3 was decreased (\u0026Delta; = -0.31). Other inhibitory kinases (PDK1/2/4) showed minimal change.\u003c/p\u003e\n \u003cp\u003eIn contrast, sepsis displayed coordinated PDH suppression (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC), with downregulation of PDP1/2, DLAT, PDHA1, PDHB, and PDHX, alongside increased PDK3, indicating progressive inhibition of mitochondrial pyruvate flux.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTCA Cycle and Mitochondrial Substrate-Level Phosphorylation (mSLP)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, TCA gene regulation showed selective activation (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Strong upregulation was observed for DLD (\u0026Delta; = +1.24, p\u0026thinsp;=\u0026thinsp;4.2\u0026times;10⁻⁵), SDHD (\u0026Delta; = +0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), SDHB (\u0026Delta; = +0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), SUCLA2 (\u0026Delta; = +0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), SUCLG2 (\u0026Delta; = +0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and GLUD1/2 (mSLP; \u0026Delta; = +0.20 / +0.10), indicating engagement of the succinate\u0026ndash;succinyl-CoA axis. In contrast, GOT2 (mSLP; \u0026Delta; = \u0026minus;0.48) and GPT2 (mSLP; \u0026Delta; = \u0026minus;0.07) were reduced, alongside modest decreases in CS and OGDH, consistent with incomplete coupling of TCA flux. Other TCA components showed small or inconsistent changes.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, TCA remodeling was extensive (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE), with large bidirectional shifts across SDHB (\u0026Delta; = +0.70), IDH1 (\u0026Delta; = +0.96), OGDH (\u0026Delta; = +0.46), SUCLG2 (\u0026Delta; = \u0026minus;0.81), and DLD (\u0026Delta; = +0.53), accompanied by marked suppression of GOT2 (\u0026Delta; = \u0026minus;0.93) and GLUD1 (\u0026Delta; = \u0026minus;0.28), reflecting global mitochondrial reprogramming.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOxidative Phosphorylation (OXPHOS)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, OXPHOS gene regulation showed selective upregulation across multiple respiratory chain complexes (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). Significant increases were observed for COX6C (\u0026Delta; = +1.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4\u0026times;10⁻⁴), ATP5PD (\u0026Delta; = +1.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.9\u0026times;10⁻⁵), NDUFS1 (\u0026Delta; = +0.46), UQCRC2 (\u0026Delta; = +0.41), and COX4I1 (\u0026Delta; = +0.33), while several core subunits (CYC1, NDUFV1) were reduced or unchanged. Overall changes were heterogeneous and incomplete across complexes I\u0026ndash;V.\u003c/p\u003e\n \u003cp\u003eIn sepsis, OXPHOS remodeling was broad and coherent (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF), with strong upregulation across complexes III\u0026ndash;V (UQCRFS1, COX6C, ATP5F1B, UQCRC1/2) and coordinated modulation of complex I subunits, consistent with global respiratory chain reprogramming.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHIF-1\u0026alpha; Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, HIF-1\u0026alpha; pathway genes showed moderate but selective activation (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). Significant increases were observed for EGLN1 (PHD2) (\u0026Delta; = +0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4\u0026times;10⁻⁴), HIF1A (\u0026Delta; = +0.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.2\u0026times;10⁻⁴), PFKFB3 (\u0026Delta; = +0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and EGLN3 (PHD3) (\u0026Delta; = +0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0088), while downstream hypoxia targets (BNIP3, VEGFA, EPAS1) showed limited or inconsistent changes.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, HIF-1\u0026alpha; activation was strong and consistent (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG), with marked induction of PFKFB3 (\u0026Delta; = +2.89), HIF1A (\u0026Delta; =+0.81), EPAS1(\u0026Delta; =+1.51), and VEGFA, alongside strong repression of BNIP3 and EGLN3, indicating a fully engaged hypoxic transcriptional program.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFatty Acid Oxidation (FAO)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, FAO gene regulation showed a mixed but selective pattern (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH). Significant increases were observed for ETFA (\u0026Delta; = +0.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4\u0026times;10⁻⁴), ACADM (\u0026Delta; = +0.87), SLC25A20 (\u0026Delta; = +0.48), CPT1A (\u0026Delta; = +0.58), ACAA2 (\u0026Delta; = +0.31), and HADHA (\u0026Delta; = +0.39), while key long-chain enzymes (ACAD9, CPT1B, ECHS1, ACADS) were reduced. Overall changes suggested incomplete FAO engagement across chain-length steps.\u003c/p\u003e\n \u003cp\u003eIn sepsis, FAO remodeling was robust (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH), with strong induction of HADHB, ACADVL, ETFA, SLC25A20, CPT2, ETFDH, and coordinated shifts across \u0026beta;-oxidation and carnitine-shuttle components, indicating globally activated lipid oxidation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePropionyl-Succinyl Anaplerosis (PSA)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, PSA gene regulation was largely attenuated (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eI), with only modest induction of MCEE (\u0026Delta; = +0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), while core catalytic enzymes MMUT, PCCA, and PCCB showed no significant change. This pattern indicates limited engagement of the propionyl-CoA entry pathway into mitochondrial metabolism.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, PSA remodeling was pronounced (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eI), with strong suppression of PCCA (\u0026Delta; = \u0026minus;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.9\u0026times;10⁻\u0026sup1;\u0026sup2;) and concurrent induction of MMUT (\u0026Delta; = +0.32), indicating disrupted propionate handling under extreme metabolic stress.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInterferon-\u0026gamma; (IFN-\u0026gamma;) Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, IFN-\u0026gamma; signaling showed partial receptor-proximal activation without a fully developed downstream chemokine program (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, G). Significant upregulation was observed for IFNGR1 (\u0026Delta; = +0.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4\u0026times;10⁻⁵), IFNGR2 (\u0026Delta; = +0.46), JAK2 (\u0026Delta; = +0.92), GBP2 (\u0026Delta; = +0.78), and HLA-DRA (\u0026Delta; = +0.48), while canonical effector genes (CXCL9, CXCL10, CXCL11, GBP1, STAT1) showed modest, inconsistent, or non-significant changes.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, IFN-\u0026gamma; activation was robust and coherent (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, G), with strong induction of IFNGR1/2, JAK2, IRF1, GBP2, HLA-B, and marked regulation of CXCR3-ligands, reflecting a fully engaged type-1 inflammatory program.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTNF\u0026ndash;NF-\u0026kappa;B Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, TNF\u0026ndash;NF-\u0026kappa;B signaling showed limited and compartmentalized activation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, H). Moderate upregulation was observed for CHUK/IKK\u0026alpha; (\u0026Delta; = +0.62), TNF (\u0026Delta; = +0.82), and TNFRSF1A (\u0026Delta; = +0.12), while the transcriptionally active subunit RELA was reduced (\u0026Delta; = \u0026minus;0.29) and key regulators (NFKB1, IKBKB, TNFAIP3, NFKBIA) showed minimal or non-significant changes, indicating restrained downstream signaling.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, TNF\u0026ndash;NF-\u0026kappa;B activation exhibited coordinated upregulation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, H), with strong induction of NFKBIA (\u0026Delta; = +1.38), TNFRSF1A (\u0026Delta; = +0.93), NFKB1, CHUK, RELA, and TNFAIP3, reflecting a fully engaged canonical inflammatory cascade.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6\u0026ndash;STAT3 Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, IL-6\u0026ndash;STAT3 signaling showed partial receptor-level engagement with restrained downstream transcriptional execution (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, I). Moderate upregulation was observed for SOCS3 (\u0026Delta; = +0.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and IL6R (\u0026Delta; = +0.41), while core signaling components (STAT3, IL6ST, TYK2) and canonical STAT3 target genes (BCL2L1, MCL1, CCL20) showed weak or non-significant changes, indicating attenuated effector signaling under regulatory control.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, IL-6\u0026ndash;STAT3 activation was robust and coherent (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, I), with strong induction of SOCS3, STAT3, TYK2, IL6R, and anti-apoptotic targets (MCL1, BCL2L1), consistent with a fully engaged inflammatory survival program.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcute Inflammatory Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, acute inflammatory signaling showed limited and fragmented activation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, J). Modest upregulation was observed for IL1B (\u0026Delta; = +0.89), S100A8/A9 (\u0026Delta; = +0.88 / +0.52), PTGS2 (\u0026Delta; = +0.68), and CXCL8 (\u0026Delta; = +0.92), while key inflammasome and chemokine components (NLRP3, IL6, CXCL1/2, CCL2/3/4) remained weak or non-significant, indicating restrained effector deployment.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, acute inflammation was robust and coordinated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, J), with strong induction of S100A8/A9, IL1B, NLRP3, ICAM1, and neutrophil-recruiting chemokines, reflecting a fully engaged innate inflammatory program.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIL-10 Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, anti-inflammatory signaling was selectively engaged at the receptor and transcriptional repression level (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE, K). Significant upregulation was observed for IL10RB (\u0026Delta; = +0.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0039) and BCL3 (\u0026Delta; = +0.46), consistent with stabilization of repressive NF-\u0026kappa;B p50/p50 complexes, while IL10RA showed a modest reduction (\u0026Delta; = \u0026minus;0.21) and IL10 transcript levels were unchanged.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, IL-10 signaling was strongly induced (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE, K), with marked upregulation of IL10, IL10RB, and BCL3, reflecting a robust compensatory anti-inflammatory response.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eL-4 / Th2 Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn ASD with high IL-10, the IL-4/Th2 axis showed reduced components (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF, L), including JAK3 (\u0026Delta; = \u0026minus;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CCL17 (\u0026Delta; = \u0026minus;0.44, p\u0026thinsp;=\u0026thinsp;0.014), and IRF4 (\u0026Delta; = \u0026minus;0.42, p\u0026thinsp;=\u0026thinsp;0.024), with trends toward lower STAT6 and IL4 expression. IL4R showed only a modest, non-significant increase, indicating preserved receptor availability without downstream transcriptional engagement.\u003c/p\u003e\n \u003cp\u003eIn severe sepsis, IL-4/Th2 signaling is strongly and coherently activated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF, L), with marked upregulation of IL4R, JAK3, STAT6, SOCS1, and dynamic remodeling of GATA3 and IRF4, reflecting a late-stage compensatory Th2/immune-resolution program following overwhelming inflammation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Module-level architecture of immune\u0026ndash;metabolic activation in ASD and adult sepsis\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ea. Module-level \u0026tau;-axis differences across ASD immune states. Module scores reflect mean log₂-CPM values for Controls, ASD Low IL-10, and ASD High IL-10. p-values correspond to Wilcoxon rank-sum tests (Control vs. ASD Low IL-10; Control vs. ASD High IL-10), and indicate FDR-adjusted significance for the High IL-10 comparison.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModule\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM\u003c/p\u003e\n \u003cp\u003e(Ctrl)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM\u003c/p\u003e\n \u003cp\u003e(ASD Low IL-10)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM\u003c/p\u003e\n \u003cp\u003e(ASD High IL-10)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFN𝛾\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL4 Th2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL6 STAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOXPHOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0457\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNFA NFKB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute Inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecSLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emSLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eb. Module-level \u0026tau;-axis differences across sepsis SOFA score groups. Mean log₂-CPM values are shown for Healthy individuals and Low, Medium, and Severe SOFA score groups. p-values indicate FDR-adjusted significance for each SOFA comparison relative to Healthy.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModule\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM (Healthy)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM\u003c/p\u003e\n \u003cp\u003e(Low SOFA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM\u003c/p\u003e\n \u003cp\u003e(Medium SOFA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean logCPM\u003c/p\u003e\n \u003cp\u003e(Severe SOFA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute Inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFN\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL4_Th2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL6_STAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOXPHOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNFA_NFKB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecSLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emSLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe \u0026tau;-axis module scores revealed a conserved pattern of immune-metabolic activation across ASD immune states and 24-hour sepsis severity groups (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb), while exposing a marked divergence in how inflammatory demand is metabolically accommodated.\u003c/p\u003e\n \u003cp\u003eIn the ASD cohort, module behavior clearly separated Low- and High-IL-10 subgroups. Inflammatory activation intensified markedly in ASD High-IL-10, whereas metabolic compensation remained constrained. The IL-10 module increased stepwise from controls to ASD Low-IL-10 and peaked in ASD High-IL-10 (mean log₂-CPM: 4.9 \u0026rarr; 4.5 \u0026rarr; 5.1), consistent with dominance of IL-10\u0026ndash;mediated tolerance signaling. Parallel increases were observed across inflammatory and stress-response modules, including HIF-1\u0026alpha;, TNF\u0026alpha;/NF-\u0026kappa;B, IFN-\u0026gamma;, IL-6/STAT3, and acute inflammatory signaling (p\u0026thinsp;=\u0026thinsp;0.055), defining a cytokine-activated, pseudohypoxic state specific to the IL-10\u0026ndash;high ASD subtype.\u003c/p\u003e\n \u003cp\u003eDespite this inflammatory escalation, cytosolic energy-producing modules failed to scale. Glycolysis and cSLP showed only marginal or absent increases and did not rise proportionally with inflammatory activation. Oxidative stability modules (OXPHOS, PDH, TCA, FAO) exhibited only modest upregulation, while mitochondrial substrate-level phosphorylation (mSLP) showed no compensatory increase and propionyl-succinyl anaplerosis (PSA) remained largely unchanged. Together, these patterns indicate metabolically constrained inflammation in ASD High-IL-10.\u003c/p\u003e\n \u003cp\u003eIn contrast, the sepsis cohort exhibited a severity-dependent hyperinflammatory response tightly coupled to scalable metabolic compensation. Inflammatory modules rose progressively with SOFA score, including TNF\u0026alpha;/NF-\u0026kappa;B, IL-6/STAT3, acute inflammatory signaling, HIF-1\u0026alpha;, and IL-10 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with IFN-\u0026gamma; rising in Low/Medium SOFA but not in Severe SOFA. Glycolysis and cSLP increased strongly across severity strata, exceeding the magnitude observed in ASD and indicating robust cytosolic ATP compensation.\u003c/p\u003e\n \u003cp\u003eMitochondrial and oxidative modules in sepsis showed a biphasic pattern, increasing from Healthy to Low/Medium SOFA before declining in Severe SOFA, consistent with mitochondrial stress. PDH activity showed an overall progressive decline with severity, accompanied by parallel declines in mSLP and PSA, reflecting progressive mitochondrial suppression. This inverse relationship between escalating inflammation and shifting metabolic strategy underscores the diametric polarity of the \u0026tau;-axis, distinguishing ASD High-IL-10 characterized by constrained metabolism despite inflammation from severe sepsis, marked by unrestrained Warburg-like metabolic reprogramming.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Cytosolic energy compensation ratio (CECR core)\u003c/h2\u003e\n \u003cp\u003eIn the autism spectrum disorder (ASD) cohort, the Cytosolic Energy Compensation Ratio (CECR core) demonstrated a progressive reduction across IL‑10\u0026ndash;defined subgroups (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Controls exhibited a CECR core of 0.690, which declined modestly in ASD Low IL‑10 (0.669) and further in ASD High IL‑10 (0.663). This pattern indicates that, despite preserved or mildly increased glycolytic activity, ASD\u0026mdash;particularly the High IL‑10 subtype\u0026mdash;fails to mount sufficient cytosolic ATP compensation relative to mitochondrial oxidative capacity.\u003c/p\u003e\n \u003cp\u003eIn contrast, the sepsis cohort showed a severity‑dependent increase in CECR core. Healthy controls displayed a CECR core of 0.654, which rose in Low SOFA sepsis (0.696), Medium SOFA (0.704), and peaked in Severe SOFA (0.739). This trajectory reflects robust activation of glycolysis and cytosolic substrate‑level phosphorylation as compensatory mechanisms in response to acute mitochondrial stress.\u003c/p\u003e\n \u003cp\u003eCECR core = (Glycolysis\u0026thinsp;+\u0026thinsp;cSLP) / (PDH\u0026thinsp;+\u0026thinsp;TCA\u0026thinsp;+\u0026thinsp;OXPHOS)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCytosolic Energy Compensation Ratio (CECR core) Across ASD and Sepsis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlycolysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecSLP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOXPHOS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCECR core\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.663\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Mitochondrial energy compensation ratio (MECR core)\u003c/h2\u003e\n \u003cp\u003eIn the ASD cohort, MECR core values were reduced relative to controls across IL‑10 defined subgroups (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Controls exhibited an MECR core of 0.455, which declined in ASD Low IL‑10 (0.435) and remained low in ASD High IL‑10 (0.436).\u003c/p\u003e\n \u003cp\u003eIn contrast, the sepsis cohort demonstrated a progressive decline in MECR core with increasing disease severity. Healthy individuals showed the highest MECR core (0.485), which decreased in Low SOFA (0.451), Medium SOFA (0.455), and Severe SOFA sepsis (0.441).\u003c/p\u003e\n \u003cp\u003eMECR core = (mSLP\u0026thinsp;+\u0026thinsp;PSA) / (PDH\u0026thinsp;+\u0026thinsp;TCA\u0026thinsp;+\u0026thinsp;OXPHOS)\u003c/p\u003e\n \u003cp\u003eTable 3: Mitochondrial Energy Compensation Ratio (MECR core) Across ASD and Sepsis\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003emSLP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePDH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOXPHOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMECR core\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.435\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.436\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedium SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cstrong\u003e3.7 \u0026tau;-index resolves inflammatory\u0026ndash;metabolic severity in ASD and sepsis\u003c/strong\u003e\u003c/div\u003e\n \u003cp\u003eTable 4\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eDistribution and discriminatory performance of the \u0026tau;-index across ASD and sepsis severity groups.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients=n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Tau\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD Tau\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrimination power\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eASD_Low_IL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCtrl vs ASD Low IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.327 - 0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eASD_High_IL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCtrl vs ASD High IL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.736 - 0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLow SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy vs Low SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.842 - 0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMedium SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy vs Medium SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.929 - 0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSevere SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHealthy vs Severe SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.967 - 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003eFootnote: AUC calculated via ROC; CI via DeLong\u0026apos;s method.\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAcross both cohorts, the \u0026tau;-index delineated a coherent gradient of inflammatory-metabolic activation (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the ASD dataset, \u0026tau; values rose stepwise from Controls (mean \u0026minus;\u0026thinsp;0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87, n\u0026thinsp;=\u0026thinsp;19) to ASD Low IL-10 (\u0026minus;\u0026thinsp;0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76, n\u0026thinsp;=\u0026thinsp;16), with a marked elevation in the ASD High IL-10 subgroup (0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92, n\u0026thinsp;=\u0026thinsp;16). This ordering reflects a transition from baseline metabolic turnover to a high-demand state characteristic of IL-10-associated immune tolerance. Consistent with this pattern, \u0026tau; yielded limited separation between Controls and ASD Low IL-10 (AUC\u0026thinsp;=\u0026thinsp;0.526; 95% CI: 0.327\u0026ndash;0.726), but robust discrimination of the IL-10-high subgroup (AUC\u0026thinsp;=\u0026thinsp;0.865; 95% CI: 0.736\u0026ndash;0.994). These results indicate that \u0026tau; preferentially captures the metabolically stressed, inflammation-tolerant ASD subtype.\u003c/p\u003e\n \u003cp\u003eA similar monotonic trajectory was observed in sepsis. Healthy individuals exhibited strongly negative \u0026tau; values (mean \u0026minus;\u0026thinsp;1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55, n\u0026thinsp;=\u0026thinsp;44), whereas Low, Medium, and Severe SOFA groups showed progressively higher \u0026tau; levels (0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98, n\u0026thinsp;=\u0026thinsp;234; 0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79, n\u0026thinsp;=\u0026thinsp;60; 0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58, n\u0026thinsp;=\u0026thinsp;51, respectively). This progression corresponded to high discriminatory accuracy: Healthy vs. Low SOFA (AUC\u0026thinsp;=\u0026thinsp;0.885), Healthy vs. Medium SOFA (AUC\u0026thinsp;=\u0026thinsp;0.962), and Healthy vs. Severe SOFA (AUC\u0026thinsp;=\u0026thinsp;0.985).\u003c/p\u003e\n \u003cp\u003eTogether, these findings position \u0026tau; as a disease-agnostic quantitative axis that resolves immunometabolic severity across both chronic neurodevelopmental inflammation and acute systemic illness.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study introduces the τ-axis as a quantitative framework to assess immune-driven metabolic demand relative to cellular energy-producing capacity. Applying τ to two distinct inflammatory contexts-autism spectrum disorder (ASD) and acute sepsis\u0026mdash;reveals that similar immune activation can arise from divergent metabolic configurations. In sepsis, escalating immune demand is matched by scalable glycolysis and cytosolic substrate-level phosphorylation, enabling effective energetic compensation. By contrast, ASD subgroups with altered IL-10 profiles (often showing dysregulated ratios relative to pro-inflammatory cytokines like IL-1β) exhibit a marked dissociation between immune activation and metabolic support, defining energy-starved inflammation where immune tolerance persists amid inadequate ATP availability[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, τ not only gauges disease severity but also distinguishes inflammation supported by metabolic compensation from that constrained by energetic limitation.\u003c/p\u003e \u003cp\u003ePrevious studies have highlighted ATP supply-demand imbalances as central to energetic dysfunction in sepsis, where impaired cellular metabolism and mitochondrial stress undermine organ function, calling for integrated systems models to capture metabolic load and compensation under acute inflammation. Building on this, mathematical modeling of energy consumption in inflammatory responses offers parallels to chronic immune dysregulation, reinforcing the need for quantitative indices like τ to resolve demand-capacity mismatches across contexts[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 τ as an index of immunometabolic mismatch\u003c/h2\u003e \u003cp\u003eτ quantifies stress by merging inflammatory signal strength with metabolic burden. Healthy states yield negative τ (\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;1), signaling minimal inflammation and peak mitochondrial efficiency\u0026mdash;an optimal baseline. Sepsis features modest positive τ (\u0026asymp;\u0026thinsp;0\u0026ndash;0.5), indicating good compensation: immune escalation syncs with boosted glycolysis and ATP synthesis for acute defense.\u003c/p\u003e \u003cp\u003eIn high-IL-10 ASD, high positive τ (\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;1) flags mismatched inflammation\u0026mdash;strong signaling but weak execution, with glycolysis, TCA cycle, and ATP yields failing to ramp up. Similar τ hikes thus reveal divergent states based on compensation.\u003c/p\u003e \u003cp\u003eIn essence, τ tracks demand, ATP efficiency measures supply. This dual framework maps adaptive hypermetabolism (sepsis) versus chronic, unsupported inflammation (ASD subtype).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Capped cytosolic and mitochondrial energy compensation in IL-10\u0026ndash;dominant ASD\u003c/h2\u003e \u003cp\u003eTo assess energetic accommodation of immune demand, we computed the Cytosolic Energy Compensation Ratio (CECR core) and Mitochondrial Energy Compensation Ratio (MECR core). These metrics quantify substrate-level phosphorylation contributions relative to oxidative demand, gauging energy adaptability under inflammation.\u003c/p\u003e \u003cp\u003eIn ASD, CECR core decreased across IL-10 subgroups: from 0.690 in controls to 0.669 in low-IL-10 and 0.663 in high-IL-10 ASD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Despite stable or elevated glycolytic expression, cytosolic ATP fails to scale with mitochondrial needs, marking capped compensation where signaling outpaces execution.\u003c/p\u003e \u003cp\u003eMECR core mirrored this, dropping from 0.455 in controls to ~\u0026thinsp;0.436 in ASD subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with limited mitochondrial substrate-level phosphorylation and propionyl\u0026ndash;succinyl Anaplerosis. This dual constraint yields inefficient inflammation.\u003c/p\u003e \u003cp\u003eConversely, sepsis showed rising CECR core with severity (0.654 in healthy to 0.739 in severe SOFA), via amplified glycolysis and cytosolic ATP, while MECR core fell (0.485 to 0.441), shifting flexibly to cytosolic support. Thus, IL-10\u0026ndash;dominant ASD features rigid, inadequate compensation, unlike sepsis's dynamic reconfiguration, highlighting energy caps as a hallmark of chronic tolerance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 IL-10 as a metabolic gatekeeper enforcing energetic rigidity\u003c/h2\u003e \u003cp\u003eThough classically anti-inflammatory, IL-10 acts here as a gatekeeper restricting metabolic adaptability in chronic activation. In the IL-10\u0026ndash;dominant ASD subtype, immune signaling remains elevated while both cytosolic and mitochondrial energy compensation are capped, indicating that IL-10 does not resolve inflammation but instead stabilizes a low-energy, immune-tolerant state[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Mechanistically, IL-10 induces SOCS3, BCL3, and inhibitory NF-κB complexes, curbing inflammation while repressing HIF-1α\u0026ndash;driven glycolysis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This severs signaling from metabolic backing, hindering glycolysis and substrate-level phosphorylation amid ongoing demand[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis gatekeeping effect is reflected in the constrained CECR core and MECR core observed in IL-10\u0026ndash;high ASD. Even in the presence of HIF-1α pathway activation and modest upregulation of oxidative modules, ATP production fails to scale effectively. As a result, immune tolerance persists not because inflammation is resolved, but because the system is locked into an energetically restricted equilibrium that limits both immune escalation and metabolic rescue.\u003c/p\u003e \u003cp\u003eIn contrast, acute sepsis - despite marked IL-10 induction at later stages - retains the capacity for dynamic metabolic reconfiguration. There, IL-10 emerges within a context of high glycolytic flux and cytosolic ATP availability, functioning as a modulatory brake rather than a rigid metabolic lock. This distinction underscores that IL-10\u0026rsquo;s biological role is context-dependent: permissive and adaptive when energy supply is abundant, but restrictive and pathological when metabolic capacity is chronically constrained. Together, these findings reposition IL-10 from a passive anti-inflammatory marker to an active regulator of immunometabolic state.\u003c/p\u003e \u003cp\u003eRepositioning IL-10 as immunometabolic regulator, it imposes rigidity in ASD, perpetuating energy-starved inflammation and reconciling tolerance with severity sans hyperinflammation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Dysbiosis-driven HIF\u0026ndash;PHD pseudo-hypoxia locks the immunometabolic system\u003c/h2\u003e \u003cp\u003eOur data support a model where chronic gut dysbiosis fosters persistent pseudo-hypoxia, stabilizing immunometabolic rigidity via HIF\u0026ndash;prolyl hydroxylase (PHD) dysregulation. Immune and metabolic inputs converge to sustain HIF-1α signaling sans true hypoxia, thwarting energy compensation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLow-grade microbial products like LPS drive TLR4\u0026ndash;NF-κB/STAT3, inducing HIF1A[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn high-IL-10 ASD, this aligns with upregulated HIF1A, EGLN1, and EGLN3, signaling chronic oxygen-sensing activation over acute response. This reflects PHD upregulation amid HIF-1α stabilization[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the metabolic level, dysbiosis-associated propionate exposure and altered amino acid flux promote succinate accumulation and rewiring of mitochondrial metabolism toward glutamate-linked anaplerotic buffering [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While α-ketoglutarate is a required co-substrate for PHD activity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], succinate acts as a competitive inhibitor[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], creating a biochemical configuration in which PHD transcription is induced but enzymatic activity remains functionally suppressed. This metabolite-driven inhibition explains the paradoxical coexistence of elevated HIF1A, EGLN1, and EGLN3 expression with sustained HIF-1α activity.\u003c/p\u003e \u003cp\u003eIL-10 reinforces this by curbing HIF-1α\u0026ndash;driven glycolysis, capping cytosolic ATP despite signals[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese forces form a loop: dysbiosis induces HIF-1α; succinate blocks PHD; IL-10 restrains metabolism; compensation fails. The outcome is a locked attractor of inflammatory signaling, pseudo-hypoxic activation, and energy deficit[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis model provides a unifying mechanistic explanation for how immune tolerance, mitochondrial rewiring, and failed glycolytic compensation coexist in IL-10\u0026ndash;dominant ASD. Rather than reflecting unresolved hypoxia or transient inflammation, pseudo-hypoxia emerges here as a dysbiosis-driven, metabolite-stabilized state that locks the system into energy-starved inflammation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Propionyl\u0026ndash;Succinate\u0026ndash;α-Ketoglutarate Flux Imbalance Defines an Energetically Constrained ASD Metabolic State\u003c/h2\u003e \u003cp\u003eIn ASD subsets characterized by altered IL-10\u0026ndash;dominant immune profiles, metabolic adaptation appears to rely on partial anaplerotic support without full oxidative coupling. Propionyl-derived succinate entry can replenish TCA intermediates; however, constrained flux across the α-ketoglutarate\u0026ndash;succinate segment limits downstream throughput[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This bottleneck favors α-ketoglutarate accumulation and redox-biased glutamate formation rather than efficient oxidative ATP generation. Concurrently, reduced mitochondrial aminotransferase activity and restricted malate\u0026ndash;aspartate shuttle flux impairs nitrogen and redox exchange, promoting intracellular accumulation of aspartate and glutamate.\u003c/p\u003e \u003cp\u003eCollectively, these findings indicate that mitochondria operate in a low-throughput, protective \u0026ldquo;safe-mode\u0026rdquo; configuration that limits oxidative flux to prevent propionate-driven metabolic instability. Chronic propionate burden therefore appears to impose an enforced constraint on mitochondrial throughput, sustaining survival at the cost of energetic efficiency and metabolic flexibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Clinical and translational implications\u003c/h2\u003e \u003cp\u003eThese findings carry key implications for ASD research and care.\u003c/p\u003e \u003cp\u003eFirst, they offer a quantitative framework for stratifying ASD into immunometabolic endotypes, transcending symptom-based diagnostics.\u003c/p\u003e \u003cp\u003eSecond, they pinpoint energy-starved inflammation as a mechanistic driver potentially underpinning regression vulnerability.\u003c/p\u003e \u003cp\u003eThird, this state may be detectable via routine labs: altered serum IL-10 in subgroups; high urinary succinate and α-ketoglutarate in organic acids profiles; and eventually increased serum or urinary aspartic acid and glutamate (with variable glutamine)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, τ metrics provide a cross-disease tool for juxtaposing chronic neuroinflammation with acute illness.\u003c/p\u003e \u003cp\u003eCritically, the framework warns against inflammation-suppressing therapies alone, which could prove inadequate or harmful without restoring metabolic capacity. Instead, targeting bottlenecks - e.g., via glycolytic enhancers or microbiota interventions - may unlock IL-10\u0026ndash;driven tolerance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Limitations and future directions\u003c/h2\u003e \u003cp\u003eKey limitations include the indirect nature of transcriptomics for inferring metabolic flux, necessitating validation via metabolomics or fluxomics. Whole-blood profiling may overlook brain-specific dynamics, and cross-sectional data preclude causal inferences on dysbiosis\u0026ndash;tolerance\u0026ndash;metabolism links.\u003c/p\u003e \u003cp\u003eFuture studies should employ longitudinal designs, integrate metabolite assays, and test interventions (e.g., microbiota modulation or glycolytic boosters) to assess τ shifts and clinical impacts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we define IL-10\u0026ndash;dominant ASD as a state of uncompensated immune activation, marked by high immunometabolic demand amid inadequate energy supply. In contrast to sepsis - where inflammation is supported by scalable glycolytic and cytosolic substrate-level phosphorylation - ASD exhibits insufficient cytosolic energy compensation, resulting in persistent demand - capacity mismatch. The τ-axis captures this fundamental dichotomy, providing a cross-disease metric of immunometabolic imbalance. Ultimately, these insights recast ASD as a chronic immunometabolic syndrome characterized by constrained energetic adaptability and tractable cellular energy deficits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of publicly available, de-identified human transcriptomic datasets obtained from the Gene Expression Omnibus (GEO) repository. The original data collection was conducted by the respective investigators in accordance with applicable ethical standards and with approval from their local ethics committees. As the present analysis involved no direct interaction with human participants and no access to identifiable private information, additional ethical approval from an Institutional Review Board (IRB) or ethics committee was not required. The study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants in the original studies contributing data to the GEO repository. No additional consent was required for this secondary analysis of de-identified public data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate Declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study involved only secondary analysis of publicly available, de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.D. conceived the study, designed the analytical framework, performed data analysis, interpreted the results, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analysis code used in this study is publicly available at:\u003c/p\u003e\n\u003cp\u003ehttps://github.com/albiondervishi/Energy-Starved-Inflammation-in-Autism\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eS. 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Life Sci.\u003c/em\u003e, b. 966, 2014.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autism spectrum disorder, immunometabolism, IL-10, glycolysis, systems biology, transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-8390063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8390063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmune activation and metabolic reprogramming are hallmarks of inflammation, yet their coordination in autism spectrum disorder (ASD) remains poorly understood. Here, we introduce the τ-axis, a transcriptomic systems framework that quantifies immune-driven metabolic demand relative to cellular energy-producing capacity, and apply it to whole-blood cohorts from ASD and acute sepsis.\u003c/p\u003e \u003cp\u003eApplying τ reveals that comparable inflammatory signaling can arise from fundamentally distinct metabolic states. In sepsis, escalating immune activation is matched by scalable glycolysis and cytosolic substrate-level phosphorylation, enabling effective energetic compensation. In contrast, an IL-10 dominant ASD endotype exhibits elevated inflammatory signaling without proportional metabolic upregulation, resulting in energy-starved inflammation. This uncoupling is reflected in constrained cytosolic energy compensation ratios despite preserved expression of oxidative pathways.\u003c/p\u003e \u003cp\u003eTogether, these findings establish τ as a generalizable systems metric of immunometabolic demand-capacity mismatch and recast ASD as a chronic immunometabolic syndrome characterized by tractable energetic deficits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Energy-Starved Inflammation in Autism: Failure of Glycolytic Compensation Under IL-10–Driven Metabolic Tolerance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-24 09:50:37","doi":"10.21203/rs.3.rs-8390063/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9f903ea-ff4c-4bb7-b13e-1525336e85d2","owner":[],"postedDate":"December 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59990669,"name":"Health sciences/Diseases"},{"id":59990670,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-02-06T12:26:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-24 09:50:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8390063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8390063","identity":"rs-8390063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}