A severely affected MRL/Lpr mouse exhibits a divergent clinical–immune profile associated with a profound metabolic alteration

23 Systemic lupus erythematosus (SLE) displays marked clinical and biological heterogeneity that 24 is incompletely captured by group-based analyses. Although the MRL/Lpr mouse is a well -25 established lupus model, individual -level divergence within this genetically homogeneous 26 strain remains poorly characterized. 27 Here, we describe an MRL/Lpr mouse exhibiting an exceptionally severe s ystemic phenotype 28 and provide an integrative characterization combining clinical assessment, inflammatory 29 profiling, neuroaxonal injury, and targeted metabolomics. Despite pronounced clinical 30 deterioration, including severe proteinuria, reduced organ weigh ts, and marked neuroaxonal 31 damage, this mouse did not show a globally exacerbated cytokine profile relative to other 32 MRL/Lpr animals. In contrast, plasma neurofilament light chain levels were massively 33 elevated, indicating substantial neuroaxonal injury. Targeted metabolomic analysis revealed a 34 profoundly altered biochemical signature, with coordinated disruptions in nitrogen handling, 35 sulfur amino acid metabolism, and neurometabolic pathways, clearly separating this animal 36 from both control and lupus-prone peers. 37 These findings illustrate that extreme disease severity can emerge independently of overt 38 cytokine escalation and identify metabolic dysregulation as a major dimension of pathological 39 divergence at advanced disease stages. Although descriptive and based on a single individual, 40 this work highlights the value of extreme phenotypes for uncovering biological inflection points 41 that remain concealed in averaged analyses and supports the integration of metabolic readouts 42 alongside inflammatory markers in autoimmune disease research. 43

44 Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disorder marked by 45 fluctuating inflammatory activity, multisystem involvement, and substantial inter -individual 46 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 3 variability in disease trajectories [1–3]. The MRL/MpJ-Faslpr (MRL/Lpr) mouse strain is one 47 of the most established spontaneous models of systemic autoimmunity, recapitulating key 48 features such as lymphoproliferation, glomerulonephritis, neuro behavioral alterations, 49 dermatitis, and chronic inflammation [4, 5] . Despite its genetic homogeneity this model 50 nonetheless displays notable variability in disease onset and severity, thereby mirroring 51 essential aspects of human SLE heterogeneity [6, 7] . Such intra -strain divergence is rarely 52 explored at the individual level, although unusually severe presentations can reveal mechanisms 53 undetectable in group -averaged analyses [8]. This is particularly relevant in chronic 54 autoimmune disease, in which inflammatory burden, metabolic stress, and tissue injury may 55 diverge substantially between individuals [9]. 56 In this Short Communication, we describe an MRL/Lpr mouse exhibiting an exceptionally 57 severe systemic phenotype. By integrating clinical, inflammatory, and metabolic assessments, 58 we illustrate how disease severity can emerge independently of cytokine escalation. 59

& Methods 60 Animals and pathological assessment 61 Female MRL/Lpr and congenic MRL+/+ mice (The Jackson Laboratory , US ) were housed 62 under standard conditions with ad libitum access to food and water. At 17 -weeks of age , 63 corresponding to the peak of systemic autoimmunity, animals were anesthetized with isoflurane 64 and euthanized by decapitation. Blood was collected immediately and plasma was obtained by 65 centrifugation (2,000 g, 5 min). Kidneys, spleen, brain, and spinal cord were rapidly dissected 66 and weighed. Terminal proteinuria was assessed using Albustix® reagent strips (Siemens). One 67 MRL/Lpr mouse ( #15) displayed an unusually severe systemic phenotype and was therefore 68 examined descriptively as an extreme case. All procedures complied with EU Directive 69 2010/63/EU and were approved by the institutional ethics committee (APAFIS#35144). 70 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 4 Cytokine and NfL quantification 71 Plasma cytokines levels were quantified using a custom U-PLEX multiplex assay (Meso Scale 72 Discovery, MSD ®) and n eurofilament light chain (NfL) was measured using a U -PLEX 73 singleplex kit from the same manufacturer. EDTA plasma samples were processed according to 74 the manufacturer’s instructions, and electrochemiluminescence signals were acquired on a 75 MESO QuickPlex SQ 120. Concentrations were interpolated from standard curves and 76 expressed in pg/mL. 77 Targeted metabolomic profiling 78 Plasma amino acids and related metabolites were quant ified using the MassChrom® Amino 79 Acids kit (Chromsystems), a standardized clinical LC -MS/MS method , following the 80 manufacturer’s instructions. Final concentrations (µmol/L) were used for downstream analyses. 81 Statistical analysis 82 Normality was assessed using the Shapiro–Wilk test and variance homogeneity with Levene’s 83 test. Depending on these assumptions, comparisons were performed between MRL+/+ and 84 MRL/Lpr mice using an unpaired t -test, Welch’s t-test, or a Mann –Whitney test. PCA was 85 computed on scaled cytokine or metabolite data. The composite inflammatory score was 86 generated by z-normalizing each cytokine. Mouse #15 was included in the MRL/Lpr group for 87 all statistical analyses; its profile was then described qualitatively to il lustrate intra -group 88 variability. Analyses were performed in R (v4.3). 89

90 Clinical phenotype reveals a severely affected MRL/Lpr mouse. 91 At terminal assessment, MRL/Lpr mice showed the expected systemic alterations relative to 92 MRL+/+ controls. Body weight was broadly comparable within the MRL/Lpr group, although 93 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 5 mouse #15 exhibited the lowest weight of all animals (Figure 1a). Kidney, spleen, and spinal 94 cord weights were significantly increased in MRL/Lpr mice, along with elevated proteinuria 95 (Figure 1 b–c). Interestingly, mouse #15 displayed an atypical organ pattern : kidney weight 96 remained within the typical MRL/Lpr range, but spleen, brain, and spinal cord weights were 97 the lowest of the group, together with a maximal proteinuria score (5/5). This animal also 98 showed severe ulcerative dermatitis with complete loss of snout fur, associated with a visibly 99 deteriorated general condition (Figure 1d). 100 Inflammatory profiling shows limited cytokine dysregulation in mouse #15, but 101 pronounced neuro-axonal injury. 102 Principal component analysis (PCA) of plasma cytokines did not separate mouse #15 from the 103 remainder of the MRL/Lpr group (Figure 1e), indicating that its marked clinical deterioration 104 was not associated with an overtly exaggerated cytokine profile. Other animals, particularly 105 #20 and #22, exhibited more divergent inflammatory signatures. Nevertheless, the radar plot 106 revealed selective increases in TNF α and IL-1β in mouse #15, with values exceeding the 107 MRL/Lpr group mean and ranking among the highest in the cohort, notably, TNFα reached the 108 maximum recorded level (Figure 1 f–h). Consistently, t he composite inflammatory score 109 confirmed this pattern, placing mouse #15 in the upper range of group variability without 110 exceeding the expected dispersion (Figure 1i). 111 In contrast, plasma NfL levels showed a striking divergence: mouse #15 displayed a massive 112 elevation, far surpassing all other animals (Figure 1 j). This divergence indicates that neuro-113 axonal injury can escalate independently of systemic cytokine levels. 114 Metabolic profiling reveals a profoundly altered signature in mouse #15. 115 Targeted metabolomics showed a strongly altered biochemical profile in mouse #15. PCA 116 clearly isolated this animal from both MRL+/+ controls and the remaining MRL/Lpr mice, 117 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 6 indicating a global shift in amino -acid homeostasis (Figure 2a). This separation re flects a 118 coordinated shift across multiple metabolites rather than isolated abnormalities . Fold-change 119 analysis (e.g., |log₂FC| > 2 ) identified major elevations in citrulline, alanine, and 3 -120 methylhistidine, reflecting disruptions in nitrogen handling and muscle -derived metabolites, 121 together with marked increases in cystathionine and taurine, indicative of altered sulfur amino-122 acid metabolism (Figure 2b –c). Conversely, several metabolites were markedly decreased, 123 including a near-complete depletion of GABA. Pathway enrichment of metabolites meeting the 124 |log₂FC| > 2 threshold highlighted significant over-representation of the urea cycle, glutamate 125 metabolism, ammonia recycling, and related nitrogen-processing pathways (Figure 2d). 126 Together, these alterations delineate a profoundly dysregulated metabolic phenotype. This 127 pattern suggests that severe metabolic dysregulation may better capture late-stage deterioration 128 than inflammatory cytokine measures. An integrative schematic summarizing the constellation 129 of clinical, inflammatory, and metabolic abnormalities observed in mouse #15 is provided in 130 Figure 2e. 131

132 This study illustrates that, even within a highly penetrant autoimmune model, an extrem e 133 systemic phenotype can emerge without a corresponding escalation in inflammatory activity. 134 Although based on a single individual, documenting such an extreme presentation remains 135 informative, highlighting biological divergence within a genetically homogeneous strain. These 136 findings suggest that, at advanced disease stages, systemic inflammatory markers may reach a 137 plateau beyond which they no longer discriminate severity, thereby limiting their capacity to 138 capture divergent pathological trajectories [9, 10]. 139 In contrast, metabolic profiling revealed a distinctly altered biochemical landscape that clearly 140 set this mouse apart from all other. The magnitude and coherence of these metabolic 141 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 7 abnormalities, including coordinated disruptions in urea-cycle–related nitrogen handling, sulfur 142 amino-acid metabolism, and muscle -derived metabolites, indicate that downstream metabolic 143 dysfunction may serve as a more discriminating correlate of extreme systemic deterioration. 144 This aligns with observations in human lupus, where metabolic phenotypes often diverge 145 independently of inflammatory indices [11–13], supporting the notion that metabolic 146 dysregulation becomes a predominant dimension of disease heterogeneity associated with 147 extreme disease severity once inflammation stabilizes at chronically elevated levels. In this 148 context, metabolic readouts may offer greater sensitivity for detecting late -stage systemic 149 deterioration than cytokine measurements alone. 150 Overall, this work underlines the importance of integrating metabolic assessments alongside 151 inflammatory markers in autoimmune research. Extreme phenotypes can reveal mechanistic 152 inflection points that remain concealed in group -averaged analyses, thereby enhancing the 153 translational relevance of preclinical models. Although causality cannot be inferred from a 154 single observation, the coherence of the clinical and metabolic alterations strengthens its value 155 as a representative extreme phenotype . Such rare divergences highlight t he importance of 156 considering individual-level variation when interpreting autoimmune phenotypes. 157 158 159 160 161 Author Contributions 162 KM: Conceptualization, data curation, formal analysis, investigation, visualization, writing –163 original draft, writing – review & editing. CJ: Investigation, writing – review & editing. DB: 164 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 8 Investigation, writing – review & editing. AGMN: Investigation, writing – review & editing. 165 HJD: Conceptualization, investigation, writing –review & editing. 166 Figure legends 167 Figure 1. Systemic and behavioral profile of MRL/lpr mice at 17 weeks, illustratin g the 168 markedly aggravated condition of mouse #15. (a–c) Body and organs weights, and terminal 169 proteinuria in MRL +/+ and MRL/lpr mice. Brain and spinal cord weights are included to 170 document central nervous system tissue mass at endpoint. (d) Representative photographs 171 showing the external phenotype of an MRL+/+ mouse, a typical MRL/lpr mouse, and mouse 172 #15 at sacrifice. (e) Principal component analysis (PCA) integrating all measured inflammatory 173 cytokines. (f) Radar plot summarizing cytokine levels across groups, with the profile of mouse 174 #15 highlighted. (g-h) Circulating concentrations of TNF α (g) and IL -1β (h). (i) Composite 175 inflammatory score derived from normalized cytokine values. (j) Plasma neurofilament light 176 chain (NfL) levels as a marker of neuroaxonal injury. Data are presented as individual values 177 with mean ± SEM. Statistical procedures are detailed in the Met hods section. Across all 178 scatterplots, mouse #15 is represented as a triangle and all other individuals as circles. Data are 179 presented as individual values with mean ± SEM. Statistical procedures are detailed in the 180

section. 181 Figure 2. Metabolic profiling reveals a distinct biochemical signature in mouse #15. (a) 182 PCA of plasma metabolites in MRL+/+ and MRL/lpr mice, highlighting the isolated metabolic 183 position of mouse #15. (b) Log₂ fold -change distribution of all quantified metabolites 184 comparing mouse #15 with the remaining MRL/lpr mice, illustrating the amplitude and 185 direction of metabolic deviations. (c) Representative individual metabolite concentrations, 186 including markers of nitrogen handling (urea cycle), sulfur amino -acid metabolis m, muscle 187 catabolism, and neurochemical imbalance . Mouse #15 is displayed as a triangle across all 188 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 9 scatterplots. (d) Pathway enrichment analysis based on altered metabolites, highlighting over -189 represented metabolic pathways (FDR -adjusted). (e) Integrative schematic summarizing the 190 systemic phenotype of mouse #15, linking major clinical features with the corresponding 191 metabolic alterations. Data are shown as individual values with mean ± SEM. Statistical 192 procedures are described in the Methods. 193

194 [1] Sammaritano LR, Askanase A, Bermas BL, et al. 2025 American College of 195 Rheumatology ( ACR ) Guideline for the Treatment of Systemic Lupus Erythematosus. 196 Arthritis Care & Research 2025; acr.25690. 197 [2] Tsokos GC. Systemic Lupus Erythematosus. N Engl J Med 2011; 365; 2110–21. 198 [3] Dai X, Fan Y , Zhao X. Systemic lupus erythematosus: updated insights on the 199 pathogenesis, diagnosis, prevention and therapeutics. Sig Transduct Target Ther 2025; 10; 200 102. 201 [4] Jeltsch-David H, Muller S. Neuropsychiatric systemic lupus erythematosus and cognitive 202 dysfunction: The MRL -lpr mouse strain as a model. Autoimmunity Reviews 2014; 13; 203 963–73. 204 [5] Perry D, Sang A, Yin Y , et al. Murine models of systemic lupus erythematosus. J Biomed 205 Biotechnol 2011; 2011; 271694. 206 [6] Cabana-Puig X, Bond JM, Wang Z, et al. Phenotypic Drift in Lupus-Prone MRL/lpr Mice: 207 Potential Roles of MicroRNAs and Gut Microbiota. Immunohorizons 2022; 6; 36–46. 208 [7] Richard ML, Gilkeson G. Mouse models of lupus: what they tell us and what they don’t. 209 Lupus Sci Med 2018; 5; e000199. 210 [8] Banchereau R, Hong S, Cantarel B, et al. Personalized Immunomonitoring Uncovers 211 Molecular Networks that Stratify Lupus Patients. Cell 2016; 165; 1548–50. 212 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint 10 [9] Wang FQ, Dang X, Yang W. Transcriptomic studies unravel the molecular and cellular 213 complexity of systemic lupus erythematosus: A review. Clinical Immunology 2024; 268; 214 110367. 215 [10] Crow MK. Type I interferon in the pathogenesis of lupus. J Immunol 2014; 192; 5459–68. 216 [11] Zhang W, Zhao H, Du P, et al. Integration of metabolomics and lipidomics reveals serum 217 biomarkers for systemic lupus erythematosus with different organs involvement. Clinical 218 Immunology 2022; 241; 109057. 219 [12] Terrell M, Morel L. The Intersection of Cellular and Systemic Metabolism: Metabolic 220 Syndrome in Systemic Lupus Erythematosus. Endocrinology 2022; 163; bqac067. 221 [13] Zhang C, Wang H, Yin L, et al. Immunometabolism in the pathogenesis of systemic lupus 222 erythematosus. Journal of Translational Autoimmunity 2020; 3; 100046. 223 224 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint Figure 1 a b c e f h g i j d MRL/Lpr #15 MRL/Lpr MRL+/+ .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint Figure 2 a b d c e .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 9, 2026. ; https://doi.org/10.64898/2026.01.07.698139doi: bioRxiv preprint