Optimised Murine Model of Influenza-Induced Respiratory Sepsis for Studying Acute Kidney Injury

Optimised Murine Model of Influenza-Induced Respiratory Sepsis for Studying Acute Kidney Injury | 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 Research Article Optimised Murine Model of Influenza-Induced Respiratory Sepsis for Studying Acute Kidney Injury Yaqing Jiao, Will Lung Chan, Yuee Cai, Arthur Chuxi Liu, Yilin Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8559525/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 Background Viral respiratory sepsis, driven by influenza and COVID-19, is increasingly prominent clinically. However, there is a lack of preclinical models that reliably replicate its associated acute kidney injury (AKI), a major contributor to morbidity and mortality. Methods We refined an established model of influenza-induced respiratory sepsis by administering graded intranasal H1N1 A/PR/8/34 doses (3.7 × 10¹, 3.7 × 10³, and 3.7 × 10⁴ TCID₅₀) in male BALB/c mice. We defined humane endpoints to ≥ 30% body weight loss, monitored clinical severity, weight, and glycaemia daily for 14 days, and assessed multi-organ dysfunction via serum biochemistry, histopathology, renal qPCR, and longitudinal serum neutrophil gelatinase-associated lipocalin (NGAL) ELISA. Results The 3.7 × 10⁴ TCID₅₀ dose yielded 66.7% mortality by day 8, with a peak clinical score (MSS) of 10, > 30% weight loss, and hypoglycaemia (blood glucose < 70 mg/dL). Infected mice exhibited dose-dependent multi-organ dysfunction, with substantial elevations in serum creatinine (median: 204 [IQR: 142–600] µmol/L), bilirubin (40.62 [29.3–124.9] µmol/L), and creatine phosphokinase (CPK) (9822 [1272–11352] U/L). Renal NGAL expression increased 7-fold, aligning with rising serum creatinine levels and histopathological glomerular enlargement. Serum NGAL rose sharply by day 2 (4990 ng/mL) and sustained 5.7–7.6-fold elevation versus sham controls through days 4–8. Conclusion This optimised model simulates influenza-induced respiratory sepsis with AKI, highlighting serum NGAL as an early, dynamic biomarker. It provides a valuable preclinical tool for mechanistic and therapeutic studies in respiratory sepsis-associated AKI. AKI influenza murine model NGAL respiratory sepsis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Sepsis affects 49 million people annually and causes 11 million deaths worldwide ( 1 , 2 ). Acute kidney injury (AKI), characterised by abrupt kidney dysfunction, is one complication of sepsis, closely linked to high mortality ( 3 , 4 ). Viral respiratory infections, fuelled by escalating community transmission of seasonal influenza and COVID-19, drive an underexplored subset of sepsis-associated AKI. Conventional AKI diagnosis hinges on serum creatinine, a lagging and nonspecific indicator of renal impairment ( 5 ). Neutrophil gelatinase-associated lipocalin (NGAL) is gaining recognition as an early biomarker of tubular injury, rapidly upregulated and secreted from renal epithelium in response to insults ( 5 – 7 ). NGAL rises prior to creatinine alterations, enabling proactive AKI identification ( 8 , 9 ). Notably, patients with sepsis-associated AKI tend to exhibit higher plasma and serum NGAL levels than those with AKI from non-septic causes ( 6 ). Despite its promise, the effectiveness of using NGAL as an AKI marker remains controversial ( 9 – 11 ). Building on our prior established murine model of influenza-induced respiratory sepsis ( 12 ), we further optimised this platform to delineate AKI pathogenesis. Emphasis was placed on renal NGAL expression, inflammation cytokines, and longitudinal tracking of serum NGAL to clarify the role of NGAL as a sensitive, dynamic parameter for early AKI inception and escalation. Integrated with clinical scoring, histopathological examination, and systemic inflammatory analyses, this refined model establishes a dependable preclinical tool for biomarker refinement and therapeutic innovation in viral respiratory sepsis and its associated AKI. Materials and Methods Animal Model and Viral Inoculation Male BALB/c mice (eight weeks old) were used to establish a murine model of viral respiratory sepsis. Mice were intranasally inoculated with the H1N1 influenza A/PR/8/34 strain at predetermined doses of 3.7 × 10 1 , 3.7 × 10 3 , 3.7 × 10 4 median tissue culture infectious dose (TCID50) selected based on our prior work to elicit graded severities of respiratory sepsis (n = 3 per dose) (12). Control animals (sham group, n = 3) received phosphate-buffered saline (PBS) via the same route. This exploratory sample size was expected to attain statistically significant differences of p < 0.05 with an ~72 - 75% probability in generating kidney dysfunction as reflected by elevated levels of serum creatinine according to our previous study (12) (Lamorte’s Power Calculations). All procedures were conducted under approved institutional animal care protocols. The workflow is shown in Supplementary file 1. Clinical Monitoring and Endpoint Criteria Mice were monitored daily for 14 days following viral inoculation. Clinical severity was assessed using the Murine Sepsis Score (MSS). Humane endpoints (HEP) were defined as ≥30% body weight loss, a modification from the previously used 20% threshold. In earlier studies, the 20% weight loss criterion may have led to premature euthanasia, potentially precluding the full development of sepsis and associated organ dysfunction. Therefore, this study extended the threshold to 30% weight loss to allow sufficient time for the emergency of clinically relevant sepsis phenotypes. Physiological parameters, including body weight and blood glucose levels, were recorded daily to monitor disease progression. Tissue Collection and Histological Analysis Pathological assessment served as the gold standard for confirming organ injury. Animals were euthanised either upon reaching HEP or at the conclusion of the 14-day observation period. Organs including the heart, lungs, spleen, liver, and kidneys were harvested for histological evaluation, as previously described (12, 13). Tissue sections were stained with haematoxylin and eosin (H&E). For each tissue from each mouse, five representative microscopic images were captured at 20× magnification. These images were analysed semi-quantitatively, as detailed in Supplementary file 2. Normal histological features were referenced from educational resources such as Pathweb (National University of Singapore, https://medicine.nus.edu.sg/pathweb/). Assessment of Organ Function and Inflammatory Markers Blood samples were collected for biochemical analysis of systemic organ function using the Sysmex BX-3010 chemistry analyser (Sysmex, Norderstedt, Germany). Parameters measured included serum creatinine, creatinine phosphokinase (CPK), lactate dehydrogenase (LDH), and total bilirubin. Platelet counts were determined using the Sysmex XN-1000V haematology analyser. Kidney injury and inflammation were further evaluated by quantifying the expression of NGAL and inflammatory cytokines (IL-1β, IL-6, TNF, and IL-10) via quantitative polymerase chain reaction (qPCR) using the Roche LightCycler 480 system. The primer information was shown in Supplementary file 3. Serum NGAL levels were assessed from tail-vein blood samples collected at two-day intervals to track longitudinal changes and quantified using an enzyme-linked immunosorbent assay (ELISA) kit (Thermo Fisher Scientific, Cat# EMLCN2). Statistical Analysis All statistical analyses were performed using GraphPad Prism (v.10 GraphPad Software, USA). Data were expressed as median with 95% confidence intervals (CI) or mean ± standard deviation (SD), as appropriate. Group comparisons were conducted using the Man-Whitney U test, Student’s t-test, or Kruskal-Wallis Test. Correlation analyses were performed using Spearman’s rank correlation coefficient. A p-value < 0.05 was considered statistically significant. Results Temporal profiles of sepsis following H1N1 infection. Survival r ate: Mice challenged with the highest viral dose (3.7 × 10⁴ TCID₅₀) exhibited 66.7% mortality by day 8 (Figure 1A), whereas no fatalities occurred in the lower-dose groups, indicating a threshold for lethality at higher viral loads. Clinical severity (MSS): MSS increased in a dose-response manner, peaking on day 8 post-infection. An MSS of 10 was recorded in mice exposed to the highest viral dose (3.7 × 10⁴ TCID₅₀), characterised by extensive back piloerection and suppressed activity (Figure 1B), indicative of severe systemic distress. Glycaemia: Hypoglycaemia (blood glucose < 70 mg/dL) was evident from day 3, particularly in the higher dose groups, indicating metabolic dysregulation (Figure 1C). Body weight loss: Progressive weight loss was observed across all infected groups. In the 3.7 × 10⁴ TCID₅₀ group, two mice reached the humane endpoint threshold of 30% loss (Figure 1D). Correlation analysis revealed significant associations between MSS and body weight loss (Spearman r = -0.5469 [95% CI -0.6523 to -0.4208]), as well as MSS and glycaemia (r = -0.4687 [95% CI: -0.6263 to -0.2741]) (Figure 2), supporting the utility of these parameters in tracking viral respiratory sepsis severity. Biochemical assessment of organ dysfunction Kidney function: Serum creatinine levels remained low and stable in sham mice (median [IQR]: 38 [32-38] µmol/L). In infected mice, levels increased in a dose-dependent manner, with values of 54 [50 – 67], 148 [10 – 166], and 204 [142 – 600] µmol/L across ascending H1N1 doses (Figure 3A; Supplementary file 4). Based on the proposed murine-SOFA creatinine criteria (Supplementary file 5), individual mice in the 3.7 × 10 3 and 3.7 × 10 4 TCID50 groups scored from 1 to 4 for renal dysfunction, whereas all sham mice scored 0 (Supplementary file 6). Liver function: Serum bilirubin levels also rose progressively with viral doses. Sham mice exhibited a median of 8.15 [6.86 – 8.67] µmol/L. Infected mice showed elevations to 15.93 [11.62 – 18.18], 31.38 [19.68 – 35.84] and 40.62 [29.3 – 124.9] µmol/L for the 3.7 × 10¹, 3.7 × 10³, and 3.7 × 10⁴ TCID₅₀ groups, respectively (Figure 3B; Supplementary file 4). Four mice scored 1 and one scored 2 on the murine-SOFA bilirubin criterion, indicating liver dysfunction (Supplementary file 6). Coagulation: Platelet counts declined slightly only in the highest dose group (1554 [1477-2226] x10 3 /µL) compared with sham (2066 [1786 – 2520] x10 3 /µL) (Figure 3C; Supplementary file 4). All mice scored 0 on the murine-SOFA platelet criterion, suggesting no significant coagulopathy (Supplementary file 6). Cardiac and systemic injury: Serum CPK levels increased markedly in response to escalating viral doses (Figure 3D; Supplementary file 4), compared with low levels in sham mice (336.0 [130 – 465] U/L). In the 3.7 × 10⁴ TCID₅₀ group, hyperCPKemia (> 10,000 U/L) was observed, suggesting substantial cardiac muscle injury. LDH levels followed a similar trend (Figure 3E; Supplementary file 4). Overall, biochemistry parameters of creatinine, bilirubin, platelets, CPK, and LDH reflected the onset of multiple organ dysfunction after influenza H1N1 infection. Histopathological findings of tissue damage Lung: Lung tissues from sham mice exhibited classic thin alveolar septa (Figure 4), indicative of normal pulmonary architecture and efficient respiratory gas exchange. In contrast, mice infected with H1N1 at doses of 3.7 × 10³ TCID₅₀ and 3.7 × 10⁴ TCID₅₀ showed extensive inflammatory cellular infiltration, reflecting severe lung inflammation and injury following H1N1 invasion. Kidney: Sham mice displayed normal glomeruli with clear Bowman’s spaces, whereasinfected mice particularly those receiving the highest viral dose (3.7 × 10⁴ TCID₅₀) exhibited enlarged glomeruli, suggesting kidney injury and impaired renal function. Liver: Hepatic tissues from sham mice demonstrated abundant glycogen accumulation within hepatocytes. In mice infected with 3.7 × 10⁴ TCID₅₀, there is a notable depletion of glycogen within hepatocytes, accompanied by cytoplasmic vacuolisation, indicative of hepatocellular stress and metabolic disruption. Heart: Cardiac tissues from infected mice (3.7 × 10³ TCID₅₀ and 3.7 × 10⁴ TCID₅₀) revealed wavy cardiac muscle fibres, a hallmark of myocardial infarction which may be secondary to hypoxia resulting from severe lung inflammation. Spleen: In sham mice, spleen architecture was well-preserved, with clearly defined white pulps (WP) and surrounding red pulps (RP). In H1N1 infected mice, the white pulp was expanded by increased numbers of lymphocytes, and the boundary between WP and RP appeared blurred, likely reflecting an active immune response to viral infection. Kidney injury and inflammatory response NGAL expression: Kidney NGAL mRNA levels were significantly elevated in influenza-infected mice, with 2-fold and 7-fold increases observed in non-surviving mice at the highest dose (3.7 × 10⁴ TCID₅₀; P = 0.0483) (Figure 5A). This upregulation positively correlated with serum creatinine levels, supporting virus-induced kidney injury. IL-1β expression: Kidney IL-1β mRNA levels were also significantly increased in the highest dose group (Median [IQR]: 1.72 [1.64 – 1.95]; P = 0.0315) (Figure 5B). As IL-1β is a known driver of renal NGAL production (16), its elevation at day 8 post-infection supports ongoing injury and repair processes. Other cytokines: IL-6, TNF-α, and IL-10 were undetectable in kidney tissue across all groups, possibly due to tissue-specific immune responses or the timing of sample collection. The kidney, not being a primary target of influenza A virus, may exhibit minimal systemic cytokine expression in the absence of secondary inflammation. Temporal Changes in Serum NGAL Levels Serum NGAL levels, initially low at baseline (median 191 ng/mL; IQR 169 – 212), rose sharply to 4990 ng/mL by day 2 post-infection in the most severely affected mouse (peak MSS: 9; 33% body weight loss) (Figure 6). Levels then declined to 1438 ng/mL by day 4, yet remained markedly elevated (5.7 – 7.6-fold higher) compared to sham controls through days 4 - 8 (Figure 6). These dynamics highlight serum NGAL as a sensitive early and dynamic biomarker of AKI in influenza-induced sepsis. Discussion We have developed and optimised a murine model of influenza-induced respiratory sepsis that induces kidney injury. This model provides a valuable preclinical platform for studying respiratory sepsis-associated AKI, supporting biomarker discovery and therapeutic development. Using this model, we further demonstrate that serum NGAL serves as a sensitive and dynamic biomarker, exhibiting a marked elevation as early as day 2 post-infection, with sustained increases aligning with disease progression. The murine model effectively recapitulated key clinical features of severe respiratory sepsis. The overall mortality rate among patients with severe respiratory sepsis ranges from 30% to 70% ( 14 , 15 ). At the highest viral dose (3.7 × 10⁴ TCID₅₀), the high mortality rate of 66.7% in mice mirrors this clinical reality. Variability in disease severity observed at the same dose may be explained by individual genetic susceptibility to sepsis. Alongside significant weight loss, severe clinical scores and hypoglycaemia which provide useful complementary parameters for assessing disease progression and severity, biochemical analyses and histopathological observations confirmed organ injury across the lung, kidney, heart, liver and spleen, reflecting the systemic nature of influenza-associated sepsis. One refinement in humane endpoints, extending the weight loss criteria from 20% to 30%, allowed fuller manifestation of organ dysfunction and sepsis phenotypes before euthanasia, thereby enhancing model validity. This refinement also enabled detection of a dose-dependent effect, contrasting with our previous study using 20% weight loss as the HEP in which no dose-response relationships were observed between organ dysfunction and virus doses ( 12 ). This dose-dependent severity further highlights the model’s utility for studying various stages of systemic illness, including progressive multi-organ dysfunction. Histopathological and molecular analyses confirmed prominent AKI in severely infected mice, with glomerular enlargement, 7-fold upregulation of renal NGAL mRNA, and increased IL-1β expression. IL-1β is a known driver of renal NGAL production, and its elevation indicates ongoing local inflammation and tissue repair processes ( 16 ). Notably, other inflammatory cytokines (IL-6, TNF, and IL-10) were undetectable in kidney tissue at day 8 post-infection. This absence likely reflects the temporal dynamics that IL-6 and TNF typically peak within hours of stimulation and decline rapidly due to short half-lives and tight regulation ( 17 – 20 ). IL-1β can persist longer due to continuous processing and release via the inflammasome in response to ongoing cellular stress or damage, thus contributing to more sustained tissue responses in the kidney microenvironment ( 21 ). Additionally, influenza A virus primarily targets the respiratory tract, so renal cytokine expression may remain minimal without direct viral invasion. AKI in this model therefore probably arises indirectly via hypoxaemia from severe lung inflammation, cardiac compromise, oxidative stress, or systemic metabolic dysregulation ( 22 ). The observed multi-organ pathology including lung infiltration, myocardial wavy fibre, hepatic glycogen depletion, and splenic lymphoid hyperplasia, further supports interconnected mechanisms that amplify kidney injury. One key limitation of this study is the small blood volume obtainable via serial tail-vein sampling (~ 10 µL, yielding ~ 4 µL serum). This volume was sufficient for sensitive assays such as NGAL ELISA but insufficient for serum creatinine measurement, which typically requires > 100 µL serum and thus necessitates terminal bleeding. This constraint precluded longitudinal monitoring of serum creatinine in individual animals, preventing direct within-subject comparisons of creatinine and NGAL dynamics. Nevertheless, the early and marked elevations of serum NGAL, as early as day 2 post-infection, with sustained increases through day 8, supports its potential as a sensitive and dynamic biomarker of AKI in influenza-induced sepsis. Future work should explore methods that enable simultaneous serial measurement of NGAL and creatinine in this viral sepsis model. Such combined monitoring could provide a more nuanced picture of AKI progression ( 23 – 26 ). Other limitations of this study include the relatively small sample sizes per group (n = 3) and the focus on a single viral strain and host species, which may limit generalisability. As this was an exploratory dose-ranging study, the use of n = 3 per group, is consistent with the Reduction principle of the 3Rs and institutional minimum guidelines. Future studies could benefit from larger group sizes (e.g., n = 5 per group for ~ 90% power) to enhance detection of significant effects. Additionally, the absence of more detailed cytokine profiling and mechanistic studies on NGAL regulation during viral infection calls for further investigation. Future work should include validation of the NGAL in clinical cohorts of viral respiratory infections, exploration of serum NGAL measures, and the potential impact of therapeutic interventions on biomarker dynamics. Conclusions This work provides a preclinical tool for studying AKI the context of viral respiratory sepsis. Our data also demonstrate that serum NGAL is a promising early and sensitive biomarker, offering potential to improve timely diagnosis and management of sepsis-associated AKI in patients with influenza and other viral respiratory infections. Abbreviations AKI: Acute Kidney Injury BALB/c: Bagg Albino Laboratory-bred/c CI: Confidence Interval CPK: Creatine Phosphokinase H&E: Haematoxylin and Eosin HEP: Humane Endpoint IL-1β: Interleukin-1 Beta IL-6: Interleukin-6 IL-10: Interleukin-10 LDH: Lactate Dehydrogenase MSS: Murine Sepsis Score NGAL: Neutrophil Gelatinase-Associated Lipocalin PBS: Phosphate-Buffered Saline qPCR: Quantitative Polymerase Chain Reaction SD: Standard Deviation SOFA: Sequential Organ Failure Assessment TCID50: Median Tissue Culture Infectious Dose TNF: Tumour Necrosis Factor Declarations Ethics approval and consent to participate This study was conducted under the license of the Committee on the Use of Live Animals in Teaching and Research (approval number: 23-507). This study was carried out by persons holding valid Cap. 340 licenses issued by the Department of Health, and the principles of laboratory animal care were followed along the study. Consent for publication Not applicable. A vailability of data and materials All datasets, on which the conclusions of the manuscript rely on, are presented in the paper. Competing interests The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported. Funding This work was funded by HKU Seed Fund for PI Research - Basic Research from the University Research Committee (URC; project code: 2302101485) to THR. Authors’ contributions YJ and TR conceived the study and designed the study. YJ, LC, YC, CL and YZ performed the experiments. YJ, JN and TR analysed the data. YJ drafted the manuscript. TR revised the manuscript for critical content and approved the submitted version of the manuscript. Acknowledgements We acknowledge Prof. Hui-Ling Yen from the School of Public Health, The University of Hong Kong, for kindly providing the H1N1 influenza A/PR/8/34 strain used in this study. 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Biomed Res Int. 2015;2015:791926. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfilesSEPSISAKINGALJIAO20260109.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8559525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611567140,"identity":"4fa53b35-4fb9-4874-b6df-1506331e9214","order_by":0,"name":"Yaqing Jiao","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Yaqing","middleName":"","lastName":"Jiao","suffix":""},{"id":611567141,"identity":"d2e6e320-6a89-45e0-beab-a88b1616f5b4","order_by":1,"name":"Will Lung Chan","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Will","middleName":"Lung","lastName":"Chan","suffix":""},{"id":611567142,"identity":"e6446758-3445-4f3f-90ed-8cbf25df37d3","order_by":2,"name":"Yuee Cai","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Yuee","middleName":"","lastName":"Cai","suffix":""},{"id":611567143,"identity":"d75b16d2-793d-4aaa-9fb5-e43871c1431d","order_by":3,"name":"Arthur Chuxi Liu","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Arthur","middleName":"Chuxi","lastName":"Liu","suffix":""},{"id":611567144,"identity":"946d2a77-b932-4c78-abd9-625d698be3e2","order_by":4,"name":"Yilin Zhang","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Zhang","suffix":""},{"id":611567145,"identity":"3181890f-422b-4151-9131-a4e554ebed92","order_by":5,"name":"John M. 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Rainer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCSBOAGJ+CTYGBsaGBMI6eGBaJGeQpAUEDG4Qq8Veuvfhh4c7bPKNb7elSTDuSCPCFpnjxhKJZ9Ist905dkyC8UwOMQ5LY5BIbDtsYHYjvU2Csa2CKC3MPxLb/hsYzyBBCxvQlgMGBhJpQIe1EeOwG2lsFoltyQYSN9KSgQwivM8+I4355s82OwP+GWmGNz62JRPWggoSSNUwCkbBKBgFowA7AABHATSs4kxqFgAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":true,"prefix":"","firstName":"Timothy","middleName":"H.","lastName":"Rainer","suffix":""}],"badges":[],"createdAt":"2026-01-09 09:53:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8559525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8559525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105478624,"identity":"21cec607-5e14-4b74-8e07-dd434bef19a5","added_by":"auto","created_at":"2026-03-26 13:13:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysical conditions of 8-week-old male BALB/c mice after intranasally inoculated with influenza virus H1N1 strain A/PR/8/34\u003c/strong\u003e. Doses of 3.7 × 10\u003csup\u003e1\u003c/sup\u003e, 3.7 × 10\u003csup\u003e3\u003c/sup\u003e and 3.7 × 10\u003csup\u003e4\u003c/sup\u003e median tissue culture infectious dose (TCID50) were used. Survival curve (A), Murine Sepsis Score (MSS) (B), glycemia (C), and body weight loss (D) were evaluated over 14 days. Sham: PBS control. n = 3 per group. Data are presented as median with 95% confidence interval (CI).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/691da3f51dfcb8a98c4f2838.png"},{"id":105566459,"identity":"a0359e01-4e70-4103-9806-77c457472985","added_by":"auto","created_at":"2026-03-27 12:56:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between MSS, body weight loss, and glycaemia. \u003c/strong\u003eCorrelation analyses were performed using Spearman’s rank correlation coefficient. 95% CI: 95% confidence interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/74899f05a67ab9bf1c827880.png"},{"id":105478623,"identity":"02261dba-7ecb-4add-bafc-5c4a56bba433","added_by":"auto","created_at":"2026-03-26 13:13:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluenza virus-induced organ dysfunction assessed by serum biomarkers.\u003c/strong\u003e Mice were intranasally administrated influenza A virus H1N1 strain A/PR/8/34 at doses of 3.7 × 10\u003csup\u003e1\u003c/sup\u003e, 3.7 × 10\u003csup\u003e3\u003c/sup\u003e, or 3.7 × 10\u003csup\u003e4\u003c/sup\u003e median tissue culture infectious dose (TCID50), or sham-treated with PBS (control). Shown are serum levels of (A) creatinine (renal function), (B) bilirubin (liver function), (C) platelet counts (coagulopathy), (D) creatinine phosphokinase (CPK; cardiac muscle injury), and (E) lactate dehydrogenase (LDH; systemic tissue injury/inflammation). Data are presented as medians with 95% confidence intervals (CI); n = 3 per group. Statistical analysis: Kruskal-Wallis test with uncorrected Dunn’s multiple comparisons (asterisks indicate significant comparisons only: * P \u0026lt; 0.05; ** P \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/374842527d99899f4855b648.png"},{"id":105478628,"identity":"8d7ac383-3e32-4f9b-8a97-9dc7ebbd1e8b","added_by":"auto","created_at":"2026-03-26 13:13:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1827508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistopathological changes in multiple organs of sham (PBS control) and influenza A/PR/8/34 (H1N1) infected mice. \u003c/strong\u003eRepresentative hematoxylin and eosin (H\u0026amp;E)-stained sections of lung, liver, heart, spleen, and kidney are shown. Key features are indicated:\u003cstrong\u003e \u003c/strong\u003eICI, inflammatory cellular infiltration; white arrow, enlarged glomeruli; black arrow, hepatocytes containing abundant glycogen; black frame, wavy myocardial fibres; RP, red pulp; WP, white pulp. All images were acquired at 20× magnification. For the kidney, additional images at 60× magnification are provided to highlight glomerular enlargement. Histology scores for each organ were compared using the Kruskal-Wallis test followed by uncorrected Dunn’s post hoc test. Statistical significance is indicated only for comparisons reaching significance (* P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, **** P \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/766ea882cb10cc40399eb46c.png"},{"id":105566704,"identity":"8d341033-8f10-4a5f-95eb-4c41c17f0e7f","added_by":"auto","created_at":"2026-03-27 12:57:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative real-time PCR (qPCR) analysis of NGAL (A) and IL-1β (B) mRNA expression in mouse kidneys following influenza A/PR/8/34 (H1N1) infection.\u003c/strong\u003e Mice were intranasally inoculated with influenza A/PR/8/34 (H1N1) virus at doses of 3.7 × 10¹, 3.7 × 10³, and 3.7 × 10⁴ TCID₅₀. Kidney tissues were collected at the humane endpoint (HEP) or on day 14 post-infection. mRNA levels were normalised to GAPDH and relative expression was calculated using the ΔΔCt method. Data are shown as median with 95% confidence interval (CI; n = 3 per group). Statistical analysis: Kruskal-Wallis test with uncorrected Dunn’s multiple comparisons (asterisks indicate significant comparisons only: * P \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/282e21b600bfb4353cd89ee6.png"},{"id":105566888,"identity":"0b4e212e-7d80-489c-a691-d5e82db691db","added_by":"auto","created_at":"2026-03-27 12:57:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamic changes in serum NGAL levels following influenza A/PR/8/34 (H1N1) infection.\u003c/strong\u003eMice (n = 3) were intranasally inoculated with 3.7 × 10⁴ TCID₅₀ of influenza A/PR/8/34 (H1N1) virus. Infected mice exhibited variable disease severity, with peak Mouse Sepsis Score (MSS) of 9 (mouse 1; 33% body weight loss), 9 (mouse 2; 26% body weight loss), and 3 (mouse 3; 4% body weight loss). Serum NGAL levels were measured in duplicate by ELISA using tail vein blood samples collected serially at 2-day intervals post-infection. Data are presented as median with 95% confidence interval (CI).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/fd4b2673d82ca6695819fcf9.png"},{"id":105570271,"identity":"d76b6b12-45cd-49a3-ac90-27fb4896ba62","added_by":"auto","created_at":"2026-03-27 13:15:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3346714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/bc2068c4-876f-4bdb-a56e-92c36421be40.pdf"},{"id":105478626,"identity":"c3950f5a-ba21-4cc6-9b32-0b3a7fe4f282","added_by":"auto","created_at":"2026-03-26 13:13:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1787768,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfilesSEPSISAKINGALJIAO20260109.docx","url":"https://assets-eu.researchsquare.com/files/rs-8559525/v1/3d77181a2bd1804220c5ce5f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimised Murine Model of Influenza-Induced Respiratory Sepsis for Studying Acute Kidney Injury","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis affects 49\u0026nbsp;million people annually and causes 11\u0026nbsp;million deaths worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Acute kidney injury (AKI), characterised by abrupt kidney dysfunction, is one complication of sepsis, closely linked to high mortality (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Viral respiratory infections, fuelled by escalating community transmission of seasonal influenza and COVID-19, drive an underexplored subset of sepsis-associated AKI.\u003c/p\u003e \u003cp\u003eConventional AKI diagnosis hinges on serum creatinine, a lagging and nonspecific indicator of renal impairment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Neutrophil gelatinase-associated lipocalin (NGAL) is gaining recognition as an early biomarker of tubular injury, rapidly upregulated and secreted from renal epithelium in response to insults (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). NGAL rises prior to creatinine alterations, enabling proactive AKI identification (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Notably, patients with sepsis-associated AKI tend to exhibit higher plasma and serum NGAL levels than those with AKI from non-septic causes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Despite its promise, the effectiveness of using NGAL as an AKI marker remains controversial (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on our prior established murine model of influenza-induced respiratory sepsis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), we further optimised this platform to delineate AKI pathogenesis. Emphasis was placed on renal NGAL expression, inflammation cytokines, and longitudinal tracking of serum NGAL to clarify the role of NGAL as a sensitive, dynamic parameter for early AKI inception and escalation. Integrated with clinical scoring, histopathological examination, and systemic inflammatory analyses, this refined model establishes a dependable preclinical tool for biomarker refinement and therapeutic innovation in viral respiratory sepsis and its associated AKI.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal Model and Viral Inoculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale BALB/c mice (eight weeks old) were used to establish a murine model of viral respiratory sepsis. Mice were intranasally inoculated with the H1N1 influenza A/PR/8/34 strain at predetermined doses of 3.7 \u0026times; 10\u003csup\u003e1\u003c/sup\u003e, 3.7 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e, 3.7 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e median tissue culture infectious dose (TCID50) selected based on our prior work to elicit graded severities of respiratory sepsis (n = 3 per dose) (12). Control animals (sham group, n = 3) received phosphate-buffered saline (PBS) via the same route. This exploratory sample size was expected to attain statistically significant differences of p \u0026lt; 0.05 with an ~72 - 75% probability in generating kidney dysfunction as reflected by elevated levels of serum creatinine according to our previous study (12) (Lamorte\u0026rsquo;s Power Calculations). All procedures were conducted under approved institutional animal care protocols. The workflow is shown in Supplementary file 1.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eClinical Monitoring and Endpoint Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMice were monitored daily for 14 days following viral inoculation. Clinical severity was assessed using the Murine Sepsis Score (MSS). Humane endpoints (HEP) were defined as \u0026ge;30% body weight loss, a modification from the previously used 20% threshold. In earlier studies, the 20% weight loss criterion may have led to premature euthanasia, potentially precluding the full development of sepsis and associated organ dysfunction. Therefore, this study extended the threshold to 30% weight loss to allow sufficient time for the emergency of clinically relevant sepsis phenotypes. Physiological parameters, including body weight and blood glucose levels, were recorded daily to monitor disease progression. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTissue Collection and Histological Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathological assessment served as the gold standard for confirming organ injury. Animals were euthanised either upon reaching HEP or at the conclusion of the 14-day observation period. Organs including the heart, lungs, spleen, liver, and kidneys were harvested for histological evaluation, as previously described (12, 13). Tissue sections were stained with haematoxylin and eosin (H\u0026amp;E). For each tissue from each mouse, five representative microscopic images were captured at 20\u0026times; magnification. These images were analysed semi-quantitatively, as detailed in Supplementary file 2. Normal histological features were referenced from educational resources such as Pathweb (National University of Singapore, https://medicine.nus.edu.sg/pathweb/).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAssessment of Organ Function and Inflammatory Markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected for biochemical analysis of systemic organ function using the Sysmex BX-3010 chemistry analyser (Sysmex, Norderstedt, Germany). Parameters measured included serum creatinine, creatinine phosphokinase (CPK), lactate dehydrogenase (LDH), and total bilirubin. Platelet counts were determined using the Sysmex XN-1000V haematology analyser. Kidney injury and inflammation were further evaluated by quantifying the expression of NGAL and inflammatory cytokines (IL-1\u0026beta;, IL-6, TNF, and IL-10) via quantitative polymerase chain reaction (qPCR) using the Roche LightCycler 480 system. The primer information was shown in Supplementary file 3. Serum NGAL levels were assessed from tail-vein blood samples collected at two-day intervals to track longitudinal changes and quantified using an enzyme-linked immunosorbent assay (ELISA) kit (Thermo Fisher Scientific, Cat# EMLCN2). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using GraphPad Prism (v.10 GraphPad Software, USA). Data were expressed as median with 95% confidence intervals (CI) or mean \u0026plusmn; standard deviation (SD), as appropriate. Group comparisons were conducted using the Man-Whitney U test, Student\u0026rsquo;s t-test, or Kruskal-Wallis Test. Correlation analyses were performed using Spearman\u0026rsquo;s rank correlation coefficient. A p-value \u0026lt; 0.05 was considered statistically significant. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTemporal profiles of sepsis following H1N1 infection.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival \u003c/strong\u003e\u003cstrong\u003er\u003c/strong\u003e\u003cstrong\u003eate:\u003c/strong\u003e Mice challenged with the highest viral dose (3.7 \u0026times; 10⁴ TCID₅₀) exhibited 66.7% mortality by day 8 (Figure 1A), whereas no fatalities occurred in the lower-dose groups, indicating a threshold for lethality at higher viral loads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical severity (MSS):\u003c/strong\u003e MSS increased in a dose-response manner, peaking on day 8 post-infection. An MSS of 10 was recorded in mice exposed to the highest viral dose (3.7 \u0026times; 10⁴ TCID₅₀), characterised by extensive back piloerection and suppressed activity (Figure 1B), indicative of severe systemic distress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlycaemia:\u003c/strong\u003e Hypoglycaemia (blood glucose \u0026lt; 70 mg/dL) was evident from day 3, particularly in the higher dose groups, indicating metabolic dysregulation (Figure 1C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody weight loss:\u003c/strong\u003e Progressive weight loss was observed across all infected groups. In the 3.7 \u0026times; 10⁴ TCID₅₀ group, two mice reached the humane endpoint threshold of 30% loss (Figure 1D).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed significant associations between MSS and body weight loss (Spearman r = -0.5469 [95% CI -0.6523 to -0.4208]), as well as MSS and glycaemia (r = -0.4687 [95% CI: -0.6263 to -0.2741]) (Figure 2), supporting the utility of these parameters in tracking viral respiratory sepsis severity.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiochemical assessment of organ dysfunction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKidney function:\u003c/strong\u003e Serum creatinine levels remained low and stable in sham mice (median [IQR]: 38 [32-38] \u0026micro;mol/L). In infected mice, levels increased in a dose-dependent manner, with values of 54 [50 \u0026ndash; 67], 148 [10 \u0026ndash; 166], and 204 [142 \u0026ndash; 600] \u0026micro;mol/L across ascending H1N1 doses (Figure 3A; Supplementary file 4). Based on the proposed murine-SOFA creatinine criteria (Supplementary file 5), individual mice in the 3.7 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e and 3.7 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e TCID50 groups scored from 1 to 4 for renal dysfunction, whereas all sham mice scored 0 (Supplementary file 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiver function:\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eSerum bilirubin levels also rose progressively with viral doses. Sham mice exhibited a median of 8.15 [6.86 \u0026ndash; 8.67] \u0026micro;mol/L. Infected mice showed elevations to 15.93 [11.62 \u0026ndash; 18.18], 31.38 [19.68 \u0026ndash; 35.84] and 40.62 [29.3 \u0026ndash; 124.9] \u0026micro;mol/L for the 3.7 \u0026times; 10\u0026sup1;, 3.7 \u0026times; 10\u0026sup3;, and 3.7 \u0026times; 10⁴ TCID₅₀ groups, respectively (Figure 3B; Supplementary file 4). Four mice scored 1 and one scored 2 on the murine-SOFA bilirubin criterion, indicating liver dysfunction (Supplementary file 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoagulation:\u003c/strong\u003e Platelet counts declined slightly only in the highest dose group (1554 [1477-2226] x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L) compared with sham (2066 [1786 \u0026ndash; 2520] x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L) (Figure 3C; Supplementary file 4). All mice scored 0 on the murine-SOFA platelet criterion, suggesting no significant coagulopathy (Supplementary file 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiac and systemic injury:\u003c/strong\u003e Serum CPK levels increased markedly in response to escalating viral doses (Figure 3D; Supplementary file 4), compared with low levels in sham mice (336.0 [130 \u0026ndash; 465] U/L). In the 3.7 \u0026times; 10⁴ TCID₅₀ group, hyperCPKemia (\u0026gt; 10,000 U/L) was observed, suggesting substantial cardiac muscle injury. LDH levels followed a similar trend (Figure 3E; Supplementary file 4). \u003c/p\u003e\n\u003cp\u003eOverall, biochemistry parameters of creatinine, bilirubin, platelets, CPK, and LDH reflected the onset of multiple organ dysfunction after influenza H1N1 infection. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHistopathological findings of tissue damage\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLung:\u003c/strong\u003e Lung tissues from sham mice exhibited classic thin alveolar septa (Figure 4), indicative of normal pulmonary architecture and efficient respiratory gas exchange. In contrast, mice infected with H1N1 at doses of 3.7 \u0026times; 10\u0026sup3; TCID₅₀ and 3.7 \u0026times; 10⁴ TCID₅₀ showed extensive inflammatory cellular infiltration, reflecting severe lung inflammation and injury following H1N1 invasion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKidney: \u003c/strong\u003eSham mice displayed normal glomeruli with clear Bowman\u0026rsquo;s spaces, whereasinfected mice particularly those receiving the highest viral dose (3.7 \u0026times; 10⁴ TCID₅₀) exhibited enlarged glomeruli, suggesting kidney injury and impaired renal function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiver:\u003c/strong\u003e Hepatic tissues from sham mice demonstrated abundant glycogen accumulation within hepatocytes. In mice infected with 3.7 \u0026times; 10⁴ TCID₅₀, there is a notable depletion of glycogen within hepatocytes, accompanied by cytoplasmic vacuolisation, indicative of hepatocellular stress and metabolic disruption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeart:\u003c/strong\u003e Cardiac tissues from infected mice (3.7 \u0026times; 10\u0026sup3; TCID₅₀ and 3.7 \u0026times; 10⁴ TCID₅₀) revealed wavy cardiac muscle fibres, a hallmark of myocardial infarction which may be secondary to hypoxia resulting from severe lung inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpleen:\u003c/strong\u003e In sham mice, spleen architecture was well-preserved, with clearly defined white pulps (WP) and surrounding red pulps (RP). In H1N1 infected mice, the white pulp was expanded by increased numbers of lymphocytes, and the boundary between WP and RP appeared blurred, likely reflecting an active immune response to viral infection.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKidney injury and inflammatory response\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNGAL expression:\u003c/strong\u003e Kidney NGAL mRNA levels were significantly elevated in influenza-infected mice, with 2-fold and 7-fold increases observed in non-surviving mice at the highest dose (3.7 \u0026times; 10⁴ TCID₅₀; P = 0.0483) (Figure 5A). This upregulation positively correlated with serum creatinine levels, supporting virus-induced kidney injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-1\u0026beta; expression:\u003c/strong\u003e Kidney IL-1\u0026beta; mRNA levels were also significantly increased in the highest dose group (Median [IQR]: 1.72 [1.64 \u0026ndash; 1.95]; P = 0.0315) (Figure 5B). As IL-1\u0026beta; is a known driver of renal NGAL production (16), its elevation at day 8 post-infection supports ongoing injury and repair processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther cytokines:\u003c/strong\u003e IL-6, TNF-\u0026alpha;, and IL-10 were undetectable in kidney tissue across all groups, possibly due to tissue-specific immune responses or the timing of sample collection. The kidney, not being a primary target of influenza A virus, may exhibit minimal systemic cytokine expression in the absence of secondary inflammation.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTemporal Changes in Serum NGAL Levels\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum NGAL levels, initially low at baseline (median 191 ng/mL; IQR 169 \u0026ndash; 212), rose sharply to 4990 ng/mL by day 2 post-infection in the most severely affected mouse (peak MSS: 9; 33% body weight loss) (Figure 6). Levels then declined to 1438 ng/mL by day 4, yet remained markedly elevated (5.7 \u0026ndash; 7.6-fold higher) compared to sham controls through days 4 - 8 (Figure 6). These dynamics highlight serum NGAL as a sensitive early and dynamic biomarker of AKI in influenza-induced sepsis. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe have developed and optimised a murine model of influenza-induced respiratory sepsis that induces kidney injury. This model provides a valuable preclinical platform for studying respiratory sepsis-associated AKI, supporting biomarker discovery and therapeutic development. Using this model, we further demonstrate that serum NGAL serves as a sensitive and dynamic biomarker, exhibiting a marked elevation as early as day 2 post-infection, with sustained increases aligning with disease progression.\u003c/p\u003e \u003cp\u003eThe murine model effectively recapitulated key clinical features of severe respiratory sepsis. The overall mortality rate among patients with severe respiratory sepsis ranges from 30% to 70% (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e). At the highest viral dose (3.7 × 10⁴ TCID₅₀), the high mortality rate of 66.7% in mice mirrors this clinical reality. Variability in disease severity observed at the same dose may be explained by individual genetic susceptibility to sepsis. Alongside significant weight loss, severe clinical scores and hypoglycaemia which provide useful complementary parameters for assessing disease progression and severity, biochemical analyses and histopathological observations confirmed organ injury across the lung, kidney, heart, liver and spleen, reflecting the systemic nature of influenza-associated sepsis.\u003c/p\u003e \u003cp\u003eOne refinement in humane endpoints, extending the weight loss criteria from 20% to 30%, allowed fuller manifestation of organ dysfunction and sepsis phenotypes before euthanasia, thereby enhancing model validity. This refinement also enabled detection of a dose-dependent effect, contrasting with our previous study using 20% weight loss as the HEP in which no dose-response relationships were observed between organ dysfunction and virus doses (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). This dose-dependent severity further highlights the model’s utility for studying various stages of systemic illness, including progressive multi-organ dysfunction.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eHistopathological and molecular analyses confirmed prominent AKI in severely infected mice, with glomerular enlargement, 7-fold upregulation of renal NGAL mRNA, and increased IL-1β expression. IL-1β is a known driver of renal NGAL production, and its elevation indicates ongoing local inflammation and tissue repair processes (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). Notably, other inflammatory cytokines (IL-6, TNF, and IL-10) were undetectable in kidney tissue at day 8 post-infection. This absence likely reflects the temporal dynamics that IL-6 and TNF typically peak within hours of stimulation and decline rapidly due to short half-lives and tight regulation (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). IL-1β can persist longer due to continuous processing and release via the inflammasome in response to ongoing cellular stress or damage, thus contributing to more sustained tissue responses in the kidney microenvironment (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e). Additionally, influenza A virus primarily targets the respiratory tract, so renal cytokine expression may remain minimal without direct viral invasion. AKI in this model therefore probably arises indirectly via hypoxaemia from severe lung inflammation, cardiac compromise, oxidative stress, or systemic metabolic dysregulation (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). The observed multi-organ pathology including lung infiltration, myocardial wavy fibre, hepatic glycogen depletion, and splenic lymphoid hyperplasia, further supports interconnected mechanisms that amplify kidney injury.\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOne key limitation of this study is the small blood volume obtainable via serial tail-vein sampling (~ 10 µL, yielding ~ 4 µL serum). This volume was sufficient for sensitive assays such as NGAL ELISA but insufficient for serum creatinine measurement, which typically requires \u0026gt; 100 µL serum and thus necessitates terminal bleeding. This constraint precluded longitudinal monitoring of serum creatinine in individual animals, preventing direct within-subject comparisons of creatinine and NGAL dynamics. Nevertheless, the early and marked elevations of serum NGAL, as early as day 2 post-infection, with sustained increases through day 8, supports its potential as a sensitive and dynamic biomarker of AKI in influenza-induced sepsis. Future work should explore methods that enable simultaneous serial measurement of NGAL and creatinine in this viral sepsis model. Such combined monitoring could provide a more nuanced picture of AKI progression (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther limitations of this study include the relatively small sample sizes per group (n = 3) and the focus on a single viral strain and host species, which may limit generalisability. As this was an exploratory dose-ranging study, the use of n = 3 per group, is consistent with the Reduction principle of the 3Rs and institutional minimum guidelines. Future studies could benefit from larger group sizes (e.g., n = 5 per group for ~ 90% power) to enhance detection of significant effects. Additionally, the absence of more detailed cytokine profiling and mechanistic studies on NGAL regulation during viral infection calls for further investigation. Future work should include validation of the NGAL in clinical cohorts of viral respiratory infections, exploration of serum NGAL measures, and the potential impact of therapeutic interventions on biomarker dynamics.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis work provides a preclinical tool for studying AKI the context of viral respiratory sepsis. Our data also demonstrate that serum NGAL is a promising early and sensitive biomarker, offering potential to improve timely diagnosis and management of sepsis-associated AKI in patients with influenza and other viral respiratory infections.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAKI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAcute Kidney Injury\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBALB/c:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eBagg Albino Laboratory-bred/c\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI:\u003c/em\u003e\u003c/strong\u003e Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCPK:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eCreatine Phosphokinase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u0026amp;E:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHaematoxylin and Eosin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHEP:\u003c/em\u003e\u003c/strong\u003e Humane Endpoint\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIL-1\u0026beta;:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInterleukin-1 Beta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIL-6:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInterleukin-6\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIL-10:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInterleukin-10\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLDH:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eLactate Dehydrogenase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMSS:\u003c/em\u003e\u003c/strong\u003e\u0026nbsp; Murine Sepsis Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNGAL:\u003c/em\u003e\u003c/strong\u003e Neutrophil Gelatinase-Associated Lipocalin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePBS:\u003c/em\u003e\u003c/strong\u003e Phosphate-Buffered Saline\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eqPCR:\u003c/em\u003e\u003c/strong\u003e Quantitative Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD:\u003c/em\u003e\u003c/strong\u003e Standard Deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSOFA:\u003c/em\u003e\u003c/strong\u003e Sequential Organ Failure Assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTCID50:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eMedian Tissue Culture Infectious Dose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTNF:\u003c/em\u003e\u003c/strong\u003e Tumour Necrosis Factor\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the license of the Committee on the Use of Live Animals in Teaching and Research (approval number: 23-507). This study was carried out by persons holding valid Cap. 340 licenses issued by the Department of Health, and the principles of laboratory animal care were followed along the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003evailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets, on which the conclusions of the manuscript rely on, are presented in the paper.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by \u003cem\u003eHKU Seed Fund for PI\u003c/em\u003e \u003cem\u003eResearch - Basic Research\u003c/em\u003e from the University Research Committee (URC; project code: 2302101485) to THR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYJ and TR conceived the study and designed the study. YJ, LC, YC, CL and YZ performed the experiments. YJ, JN and TR analysed the data. YJ drafted the manuscript. TR revised the manuscript for critical content and approved the submitted version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Prof. Hui-Ling Yen from the School of Public Health, The University of Hong Kong, for kindly providing the H1N1 influenza A/PR/8/34 strain used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eRhee C, Dantes R, Epstein L, Murphy DJ, Seymour CW, Iwashyna TJ, et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014. JAMA. 2017;318(13):1241\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eDong L, Xie Y-L, Zhang R-T, Hu Q-Y. Models of sepsis-induced acute kidney injury. Life Sci. 2024;352:122873.\u003c/li\u003e\n\u003cli\u003eProwle JR, Bagshaw SM, Forni LG. Tackling sepsis-associated AKI: are there any chances of REVIVAL with new approaches? Intensive Care Med. 2024;50(1):131\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eOstermann M, Legrand M, Meersch M, Srisawat N, Zarbock A, Kellum JA. Biomarkers in acute kidney injury. Ann Intensive Care. 2024;14(1):145.\u003c/li\u003e\n\u003cli\u003eBagshaw SM, Bennett M, Haase M, Haase-Fielitz A, Egi M, Morimatsu H, et al. Plasma and urine neutrophil gelatinase-associated lipocalin in septic versus non-septic acute kidney injury in critical illness. Intensive Care Med. 2010;36(3):452\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003ePeerapornratana S, Manrique-Caballero CL, G\u0026oacute;mez H, Kellum JA. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 2019;96(5):1083\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eZhang A, Cai Y, Wang P-F, Qu J-N, Luo Z-C, Chen X-D, et al. Diagnosis and prognosis of neutrophil gelatinase-associated lipocalin for acute kidney injury with sepsis: a systematic review and meta-analysis. Crit Care. 2016;20(1):41.\u003c/li\u003e\n\u003cli\u003eKhawaja S, Jafri L, Siddiqui I, Hashmi M, Ghani F. The utility of neutrophil gelatinase-associated Lipocalin (NGAL) as a marker of acute kidney injury (AKI) in critically ill patients. Biomark Res. 2019;7(1):4.\u003c/li\u003e\n\u003cli\u003eT\u0026ouml;rnblom S, Nisula S, Pet\u0026auml;j\u0026auml; L, Vaara ST, Haapio M, Pesonen E, et al. Urine NGAL as a biomarker for septic AKI: a critical appraisal of clinical utility\u0026mdash;data from the observational FINNAKI study. Ann Intensive Care. 2020;10(1):51.\u003c/li\u003e\n\u003cli\u003eXie Y, Huang P, Zhang J, Tian R, Jin W, Xie H, et al. Biomarkers for the diagnosis of sepsis-associated acute kidney injury: systematic review and meta-analysis. Ann Palliat Med. 2021;10(4):4159\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eJiao Y, Cai Y, Zhang Y, Choy K-T, Cheng K-M, Nicholls JM, et al. Use of H1N1 strain A/PR/8/34 influenza to build a mouse model of viral respiratory sepsis. Lab Anim Res. 2025;41(1):16.\u003c/li\u003e\n\u003cli\u003eJiao Y, Tong CSW, Zhao L, Zhang Y, Nicholls JM, Rainer TH. Intraperitoneal versus intranasal administration of lipopolysaccharide in causing sepsis severity in a murine model: a preliminary comparison. Lab Anim Res. 2024;40(1):18.\u003c/li\u003e\n\u003cli\u003eMohamed AKS, Mehta AA, James P. Predictors of mortality of severe sepsis among adult patients in the medical Intensive Care Unit. Lung India. 2017;34(4):330\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eWang DH, Jia HM, Zheng X, Xi XM, Zheng Y, Li WX. Attributable mortality of ARDS among critically ill patients with sepsis: a multicenter, retrospective cohort study. BMC Pulm Med. 2024;24(1):110.\u003c/li\u003e\n\u003cli\u003eBonnemaison ML, Marks ES, Boesen EI. Interleukin-1\u0026beta; as a driver of renal NGAL production. Cytokine. 2017;91:38\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eKolb M, Margetts PJ, Anthony DC, Pitossi F, Gauldie J. Transient expression of IL-1\u0026beta; induces acute lung injury and chronic repair leading to pulmonary fibrosis. J Clinical Invest. 2001;107(12):1529\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eHeld F, Hoppe E, Cvijovic M, Jirstrand M, Gabrielsson J. Challenge model of TNF\u0026alpha; turnover at varying LPS and drug provocations. J Pharmacokinet Pharmacodyn. 2019;46(3):223\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eHirano T. IL-6 in inflammation, autoimmunity and cancer. Int Immunol. 2020;33(3):127\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eTavares LP, Teixeira MM, Garcia CC. The inflammatory response triggered by Influenza virus: a two edged sword. Inflamm Res. 2017;66(4):283\u0026ndash;302.\u003c/li\u003e\n\u003cli\u003eAnders HJ. Of inflammasomes and alarmins: IL-1\u0026beta; and IL-1\u0026alpha; in kidney disease. J Am Soc Nephrol. 2016;27(9):2564\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eAbd-Elshafy DN, Nasraa MH, Nadeem R, Bahgat MM. Impact of genetic background and gender on mouse susceptibility to H1N1-PR8: implication of the host immune responses. Turk J Immunol. 2025;13(1).\u003c/li\u003e\n\u003cli\u003ePan H-C, Yang S-Y, Chiou TT-Y, Shiao C-C, Wu C-H, Huang C-T, et al. Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis. Crit Care. 2022;26(1):349.\u003c/li\u003e\n\u003cli\u003eWagener G, Minhaz M, Mattis FA, Kim M, Emond JC, Lee HT. Urinary neutrophil gelatinase-associated lipocalin as a marker of acute kidney injury after orthotopic liver transplantation. Nephrol Dial Transplant. 2011;26(5):1717\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eHelmersson-Karlqvist J, \u0026Auml;rnl\u0026ouml;v J, Larsson A. Day-to-day variation of urinary NGAL and rational for creatinine correction. Clin Biochem. 2013;46(1):70\u0026ndash;2.\u003c/li\u003e\n\u003cli\u003eChoi JW, Fujii T, Fujii N. Significance of neutrophil gelatinase-associated lipocalin level-to-serum creatinine ratio for assessing severity of inflammation in patients with renal dysfunction. Biomed Res Int. 2015;2015:791926.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"AKI, influenza, murine model, NGAL, respiratory sepsis","lastPublishedDoi":"10.21203/rs.3.rs-8559525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8559525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eViral respiratory sepsis, driven by influenza and COVID-19, is increasingly prominent clinically. However, there is a lack of preclinical models that reliably replicate its associated acute kidney injury (AKI), a major contributor to morbidity and mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe refined an established model of influenza-induced respiratory sepsis by administering graded intranasal H1N1 A/PR/8/34 doses (3.7 \u0026times; 10\u0026sup1;, 3.7 \u0026times; 10\u0026sup3;, and 3.7 \u0026times; 10⁴ TCID₅₀) in male BALB/c mice. We defined humane endpoints to \u0026ge;\u0026thinsp;30% body weight loss, monitored clinical severity, weight, and glycaemia daily for 14 days, and assessed multi-organ dysfunction via serum biochemistry, histopathology, renal qPCR, and longitudinal serum neutrophil gelatinase-associated lipocalin (NGAL) ELISA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 3.7 \u0026times; 10⁴ TCID₅₀ dose yielded 66.7% mortality by day 8, with a peak clinical score (MSS) of 10, \u0026gt;\u0026thinsp;30% weight loss, and hypoglycaemia (blood glucose\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dL). Infected mice exhibited dose-dependent multi-organ dysfunction, with substantial elevations in serum creatinine (median: 204 [IQR: 142\u0026ndash;600] \u0026micro;mol/L), bilirubin (40.62 [29.3\u0026ndash;124.9] \u0026micro;mol/L), and creatine phosphokinase (CPK) (9822 [1272\u0026ndash;11352] U/L). Renal NGAL expression increased 7-fold, aligning with rising serum creatinine levels and histopathological glomerular enlargement. Serum NGAL rose sharply by day 2 (4990 ng/mL) and sustained 5.7\u0026ndash;7.6-fold elevation versus sham controls through days 4\u0026ndash;8.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis optimised model simulates influenza-induced respiratory sepsis with AKI, highlighting serum NGAL as an early, dynamic biomarker. It provides a valuable preclinical tool for mechanistic and therapeutic studies in respiratory sepsis-associated AKI.\u003c/p\u003e","manuscriptTitle":"Optimised Murine Model of Influenza-Induced Respiratory Sepsis for Studying Acute Kidney Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 13:13:19","doi":"10.21203/rs.3.rs-8559525/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":"e6103133-22ff-490d-ae4f-a45752a67aec","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T03:09:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 13:13:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8559525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8559525","identity":"rs-8559525","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}