{"doi":"10.3389/fimmu.2025.1593342","title":"Value of animal sepsis research in navigating the translational labyrinth","abstract":"Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection (1), as manifested by early activation of both pro-and anti-inflammatory responses (2), along with major alterations in non-immunologic pathways such as cardiovascular, neuronal, autonomic, hormonal, bioenergetic, metabolic, and coagulation (3). It accounts for almost 20% of total deaths worldwide (4), and annually costs more than $60 billion in the U.S. alone. The onset of the disease and the intricate interplay of various immune cells, inflammatory mediators, signaling pathways, and organ systems makes studying sepsis in humans ethically and logistically challenging. This necessitates the use of animal models to systematically dissect its intricate pathophysiology and evaluate potential therapies in a controlled setting.Animal models allow researchers to manipulate key variables such as infection type and severity, intervention timing, and the genetic background (e.g., gene knockout or knock in strategy) of experimental animals (5)(6)(7)(8). This level of control enables researchers to isolate the effects of specific interventions and identify potential therapeutic targets, such as tumor necrosis factor (TNF) (9), high mobility group box 1 (HMGB1) (10), cold-inducible RNA-binding protein (CIRP) (11), sequestosome-1 (SQSTM1) (12), and procathepsin L (pCTS-L) (13). Moreover, these models allow for tracking the temporal progression of sepsis from initial insult to subsequent organ dysfunctions (14) and eventual outcomes (7,8), offering invaluable insight into the complex interplay of multiple pathophysiological processes (15,16), including hyperinflammation (17), immunocoagulation (18,19), pyroptosis-mediated immune cell death (20), and immunosuppression (21,22). Given the influence of comorbidities and other factors on disease progression and treatment response in human sepsis, it is essential to incorporate comorbidities (e.g., diabetes, hypertension, or coronary artery disease) and pre-existing injuries (e.g., smoke inhalation occurring in burn patients) into animal modeling, thereby improving the translatability of experimental findings into future clinical therapies (6)(7)(8)23).Despite advancements in understanding sepsis pathophysiology, translating preclinical findings into effective human therapies remains challenging, as exemplified by the failure of anti-TNF antibodies in clinical trials (3,24). However, attributing this translational gap solely to the limitations of animal models is an oversimplification (6), because the inherent complexity and heterogeneity of human sepsis, coupled with challenges in clinical trial design, also contribute to this difficulty.Animal models typically use a single, standardized insult in genetically homogeneous animals.However, this genetic and environmental homogeneity of laboratory animals contrasts sharply with the genetic and environmental diversity of human populations, as well as the variety of infections in clinical sepsis (8,25). The inherent heterogeneity in septic patients is further compounded by other factors such as age, sex, underlying health conditions (comorbidities), environmental exposure/history, and time to treatment initiation (2). Because patient variability often creates a broad spectrum of pathophysiological endotypes, it is important to develop animal models to recapitulate some human sepsis endotypes. While comprehensive immune profiling (cytokine/chemokine levels, immune cell function, gene expression) can potentially characterize \"endotypes\" in animal models, their accuracy in reflecting human sepsis endotypes (such as hyper-or hypo-inflammatory states) remains unclear (6)(7)(8), presenting challenges for translational research. Thus, the failure of identifying and recruiting homogenous patient subgroups in previous clinical trials might have diluted treatment effects due to potential outcome variations (8,23,26).In addition, potential differences in immune res","journal":"Frontiers in Immunology","year":2025,"id":516199,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9564,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":506696,"name":"Alfred Ayala","orcid":"0000-0002-5034-2995","position":1,"is_corresponding":false},{"id":247093,"name":"Monowar Aziz","orcid":"0000-0002-8195-4505","position":2,"is_corresponding":false},{"id":246951,"name":"Timothy R. Billiar","orcid":"0000-0002-2949-0811","position":3,"is_corresponding":false},{"id":45560,"name":"Clifford S. Deutschman","orcid":"0000-0001-9490-4024","position":4,"is_corresponding":false},{"id":451665,"name":"Samithamby Jeyaseelan","orcid":"0000-0002-1959-1328","position":5,"is_corresponding":false},{"id":103781,"name":"Daolin Tang","orcid":"0000-0002-1903-6180","position":6,"is_corresponding":false},{"id":247094,"name":"Ping Wang","orcid":"0000-0002-1557-0394","position":7,"is_corresponding":false},{"id":109941,"name":"Haichao Wang","orcid":"0000-0002-0211-9000","position":0,"is_corresponding":true}],"reference_count":67,"raw_metadata":null,"created_at":"2026-07-19T02:48:49.486328Z","pmid":"40303397","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}