---
title: "Mitochondria-associated genes and sepsis: source article content not available"
id: "frontiers-in-immunology-15-potential-mitochondria-associated-pathogenic-genes-in-sepsis-a-multi-omics"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-potential-mitochondria-associated-pathogenic-genes-in-sepsis-a-multi-omics"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1881375"
published_at: "2026-08-20T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Mitochondria-associated genes and sepsis: source article content not available
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-potential-mitochondria-associated-pathogenic-genes-in-sepsis-a-multi-omics
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1881375)
- **Published At:** 2026-08-20T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The referenced article title is "Potential mitochondria-associated pathogenic genes in sepsis: a multi-omics Mendelian randomization study" and the source is Frontiers in Immunology. The article page was identified but the provided source content contains only site navigation and journal boilerplate, not the article text or data. - The source text included no abstract, methods, results, gene names, statistical outcomes, or author conclusions. Key details such as cohorts, genetic instruments, multi-omics datasets, identified genes, effect sizes, and p values were not present in the supplied content. - Because the supplied source lacked study content, no study-specific clinical findings, actionable biomarkers, or validated pathogenic genes can be extracted or summarized from this input. - The article likely concerns **mitochondria**, **sepsis**, and the use of **Mendelian randomization** with multi-omics data, but the source did not report how those elements were implemented or what was discovered. - For clinicians and researchers seeking the study’s evidence, the key next steps are to access the full article on the journal site or request the authors’ data; the supplied source does not permit assessment of validity, reproducibility, or clinical applicability. - The source did not report conflicts of interest, funding, data availability statements, or limitations. Therefore no risk-of-bias appraisal or clinical recommendation can be drawn from the provided content.
## Clinical Analysis & Structured Key Points
Frontiers | Potential mitochondria-associated pathogenic genes in sepsis: a multi-omics Mendelian randomization study ORIGINAL RESEARCH article Front. Immunol. , 20 August 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1881375 Published in Frontiers in Immunology Inflammation 7 impact factor 11.3 citescore Editor & Reviewers Edited by L B Lisardo Bosca Reviewed by K M Katalin Maricza R S Rashi Sehgal Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article ORIGINAL RESEARCH article Front. Immunol. , 20 August 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1881375 Potential mitochondria-associated pathogenic genes in sepsis: a multi-omics Mendelian randomization study L W Lu Wang 1 † Z G Zhen Gao 2,3 † C W Chengjin Wang 2,3 † Y Z Yan Zhao 3 † Z D Zihui Deng 4 Y B Yang Bai 5 M Y Mengmeng Yang 3 Y Z Yuhang Zou 2,3 H K Hongjun Kang 3,6 * 1. Department of Critical Care Medicine, the Fifth Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China 2. Chinese People’s Liberation Army (PLA) Medical School, Beijing, China 3. Department of Critical Care Medicine, the First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China 4. Department of Basic Medicine, Graduate School, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China 5. Department of Emergency Medicine, the First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China 6. National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China See more Article metrics View details Abstract Background: Mitochondrial dysfunction has been implicated in the pathophysiology of sepsis. However, human genetic evidence linking mitochondria-related genes to sepsis susceptibility remains limited. This study aimed to identify mitochondria-related genes associated with sepsis risk using a multi-omics Mendelian randomization framework. Methods: Summary-data-based Mendelian randomization (SMR) was applied using sepsis genome-wide association study (GWAS) summary statistics from the UK Biobank and FinnGen databases. Expression, methylation, single-cell, and protein quantitative trait loci (QTLs) were used as genetic instruments. Colocalization analyses were conducted to evaluate whether SMR associations were driven by shared genetic variants. Expression of prioritized candidate genes was further examined in clinical septic samples, and correlations with disease severity (SOFA scores) were assessed. Results: SMR analysis prioritized 13 mitochondria-related genes associated with sepsis risk. Immune cell-specific eQTL analysis suggested that genetically predicted SURF1 expression in memory B cells and naïve T cells was associated with sepsis risk. Differential expression of 12 candidate genes was confirmed in septic patients by qPCR, and PPOX expression showed a negative correlation with SOFA scores. Integration of mQTL and eQTL data supported a regulatory relationship between methylation at cg06661924 and AK4 expression. Increased genetically predicted AK4 expression was associated with higher sepsis risk (OR = 1.21, 95% CI 1.02-1.42). Protein-level analysis identified DUT as a potential sepsis-associated candidate, with consistent evidence across streptococcal and pneumococcal septicemia subtypes. Subtype analyses also suggested heterogeneous genetic signals across different sepsis subtypes. Conclusion: This study prioritized several mitochondria-related genes associated with sepsis susceptibility based on human genetic evidence. These findings provide candidate targets for further mechanistic and translational investigation. Background Sepsis is a life-threatening syndrome arising from a dysregulated host response to infection and frequently leading to acute organ dysfunction. It represents a major global health burden. Epidemiological analyses estimate that more than 30 million people develop sepsis each year and that the condition contributes to nearly six million deaths worldwide ( 1 , 2 ). Clinically, sepsis is characterized by systemic inflammation, circulatory instability, and progressive organ failure when not recognized and treated promptly ( 2 ). Individuals of advanced age, patients with chronic illnesses such as diabetes or cardiovascular disease, those with impaired immunity, and individuals with repeated hospital exposure are particularly vulnerable to severe infection and subsequent sepsis ( 2 ). Early treatment with antimicrobial therapy, fluid resuscitation, and vasopressor support can improve outcomes, yet delayed diagnosis and intervention remain common and are associated with increased mortality ( 3 ). Despite substantial advances in intensive care medicine, sepsis continues to impose a heavy clinical burden. One major obstacle is the pronounced biological heterogeneity observed across patients, which complicates the identification of reliable molecular markers and broadly effective therapeutic targets. Mitochondria play a central role in cellular energy metabolism and have increasingly been implicated in the pathophysiology of sepsis. These double-membrane organelles generate adenosine triphosphate (ATP) through oxidative phosphorylation and regulate multiple aspects of cellular homeostasis ( 4 ). Impairment of mitochondrial function can reduce ATP production and promote excessive reactive oxygen species generation, thereby disrupting redox balance and damaging cellular structures ( 5 ). Consistent with this, mitochondrial dysfunction-associated ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has been shown to contribute to sepsis-induced cardiac injury ( 6 ). Accumulating evidence indicates that mitochondrial dysfunction is closely associated with the progression of sepsis ( 7 , 8 ). Experimental models have shown that systemic infection can impair mitochondrial respiration and bioenergetic capacity, suggesting that mitochondrial injury contributes to the development of organ dysfunction in septic states ( 8 ). In addition to disturbances in energy metabolism, mitochondrial signaling pathways have also been linked to inflammatory regulation, oxidative stress responses, and immune cell function during sepsis ( 7 ). These findings collectively highlight the potential importance of mitochondrial pathways in sepsis biology. Nevertheless, most available evidence originates from experimental systems or small clinical cohorts, and the contribution of mitochondrial genes to sepsis susceptibility at the population level remains incompletely understood. Human genetic approaches offer an opportunity to investigate such relationships in a less confounded framework. Mendelian randomization (MR) uses genetic variants as instrumental variables to explore potential causal relationships between biological traits and disease outcomes, thereby reducing confounding that commonly affects observational studies ( 9 ). Building on this principle, summary-data-based Mendelian randomization (SMR) integrates genome-wide association study (GWAS) signals with molecular quantitative trait loci (QTL) data to prioritize genes whose molecular regulation may influence complex traits ( 10 ). The HEIDI test further evaluates whether observed associations are likely driven by a shared causal variant rather than linkage disequilibrium between nearby loci ( 11 ). By integrating genetic association signals with molecular regulatory data, this framework provides a systematic strategy for connecting disease-associated loci with candidate functional genes. In the present study, we applied a multi-omics SMR framework to prioritize candidate mitochondria-related genes and regulatory loci that may causally contribute to sepsis susceptibility, thereby generating a shortlist of functional targets for future mechanistic investigation. Sepsis genetic association data were drawn from the UK Biobank, a population-based cohort of predominantly European ancestry ( 12 ), with independent replication using the Finnish FinnGen resource ( 13 ). Mitochondrial gene candidates were curated from established mitochondrial gene resources and interrogated using large-scale molecular QTL datasets spanning gene expression, DNA methylation and protein abundance, predominantly derived from populations of European ancestry, together with single-cell regulatory variation captured in the OneK1K cohort of 982 European-ancestry donors recruited in Australia ( 14 ). Signals supported by SMR and colocalization evidence were prioritized as candidate genes. To enhance biological relevance, genetically predicted effects were subsequently evaluated in clinical samples from septic patients, enabling comparison between predicted regulatory directions and observed gene expression patterns as well as their correlation with disease severity assessed by SOFA scores. Materials and methods Study design The overall analytical framework of this study is illustrated in Figure 1 . A multi-layer integrative analysis strategy based on summary-data-based Mendelian randomization (SMR) was applied to investigate mitochondria-related genes potentially associated with sepsis susceptibility. Genome-wide association study (GWAS) summary statistics for sepsis from the UK Biobank (UKB) were used as the primary discovery dataset. Independent cohorts derived from previously published GWAS studies and the FinnGen project were further incorporated to evaluate the consistency of the observed associations. Genetic variants influencing molecular traits related to mitochondrial biology were used as instrumental variables, enabling the integration of molecular quantitative trait loci (QTL) signals with disease GWAS signals. Associations were systematically assessed across several biological layers, including DNA methylation, gene expression, single-cell expression, and circulating protein levels. This design allowed the investigation of potential regulatory relationships from epigenetic variation to transcriptional and proteomic alterations. To strengthen the interpretation of genetically supported associations, Bayesian colocalization analyses were subsequently performed to determine whether the molecular QTL signals and sepsis GWAS signals within a locus were likely to share a causal variant. The exposure and outcome datasets used in the SMR analyses were derived from independent populations, thereby minimizing bias introduced by sample overlap. Clinical evaluation of the prioritized candidates was subsequently conducted in an independently recruited Chinese sepsis cohort, accessible through the authors’ clinical practice, to examine whether the direction of genetically predicted regulation was reflected in observed gene expression. All analyses and reporting procedures followed the recommendations of the STROBE-MR guideline for Mendelian randomization studies ( 9 ). Figure 1 Study design and analytical framework. Overview of the multi-omics summary-data-based Mendelian randomization (SMR) pipeline used to prioritize mitochondria-related genes associated with sepsis. Genome-wide association study (GWAS) summary statistics for sepsis were integrated with multiple molecular quantitative trait loci (QTL) datasets, including blood expression QTLs (eQTLs), methylation QTLs (mQTLs), protein QTLs (pQTLs), and single-cell eQTLs. The analytical framework incorporates the SMR test, the heterogeneity in dependent instruments (HEIDI) test, and Bayesian colocalization analysis. Downstream analyses include clinical validation using quantitative real-time PCR (qPCR), correlation with disease severity measured by Sequential Organ Failure Assessment (SOFA) scores, subtype heterogeneity analyses in FinnGen cohorts, and immune cell compartment–specific regulatory analyses. Clinical samples and ethical approval Peripheral blood samples were collected from patients diagnosed with sepsis in the intensive care unit, and from healthy control individuals at the First Medical Center of the Chinese PLA General Hospital between November 1, 2022 and December 31, 2023. Sepsis was diagnosed according to the Sepsis-3 clinical definition, which requires confirmed or suspected infection accompanied by organ dysfunction defined as an increase of at least two points in the Sequential Organ Failure Assessment (SOFA) score relative to baseline. Individuals meeting these criteria during the recruitment period were eligible for inclusion. Patients were excluded if they were pregnant; had an immunosuppressed status, including long-term corticosteroid, immunosuppressant, antifungal, or antiviral therapy, HIV infection, other immunodeficiency, or a prior organ transplant; had received immune-enhancing agents such as thymosin, interleukins, or intravenous immunoglobulin; or presented with active malignancy. Healthy control individuals were confirmed to be free of active infection, inflammatory disease, and autoimmune disease, and had no chronic underlying conditions affecting the heart, brain, lungs, or kidneys. A total of twenty patients with sepsis and twenty healthy controls were included in the experimental validation cohort. Baseline characteristics of the validation cohort, including age, sex, SOFA score, and primary site of infection, are summarized in Supplementary Table 1 . The study protocol was reviewed and approved by the Ethics Committee of the Chinese PLA General Hospital (approval No. S2022-735-01). Written informed consent was obtained from all participants or their legally authorized representatives prior to enrollment. GWAS datasets and molecular QTL resources Summary statistics for sepsis were obtained from publicly available genome-wide association studies. The primary discovery analysis used the UK Biobank cohort, which included 11,643 individuals with sepsis and 474,841 controls of European ancestry. Sepsis cases were identified from linked hospital episode records using ICD-10 codes A02, A39, A40, and A41 in the primary or secondary diagnostic position, with self-reported cases and those recorded only in primary care excluded; the remaining cohort served as controls, and genetic associations were estimated with adjustment for age, sex, genotyping chip, and the first ten genetic principal components ( 15 ). Validation analyses were conducted using GWAS summary statistics from the FinnGen R12 release, including the six sepsis-related phenotypes available in this release: “Other septicemia”, “Streptococcal septicemia”, “Puerperal sepsis”, “Bacterial sepsis of newborn”, “Pneumococcal septicemia”, and “Hemophilus septicemia”. Like the UK Biobank, FinnGen is a population-based biobank of predominantly European ancestry in which sepsis cases are ascertained through linkage to nationwide hospital and health registry records using standardized ICD-coded diagnoses, providing a case-ascertainment strategy broadly comparable to that of the discovery dataset. Among these, “Other septicemia” (ICD-10 A41) was designated as the primary validation phenotype. Together with “Streptococcal septicemia” (ICD-10 A40), it is one of two FinnGen phenotypes that correspond directly to a component of the discovery phenotype from UK Biobank. It was selected as primary because it spans multiple bacterial etiologies rather than a single pathogen, better reflecting the etiological breadth of the discovery cohort. However, it does not capture the Salmonella- or meningococcus-associated cases (ICD-10 A02, A39) included in the discovery phenotype, for which FinnGen provides no corresponding endpoint. “Streptococcal septicemia” and the remaining, more narrowly defined FinnGen R12 phenotypes were used for subtype-specific validation, testing whether the identified associations held across clinically distinct presentations of sepsis. Detailed information regarding all outcome datasets is provided in Supplementary Table 2 . A comprehensive list of mitochondria-related genes was curated from the MitoCarta 3.0 database ( Supplementary Table 3 ), which systematically catalogs human mitochondrial proteins and their associated genes based on experimental evidence and integrative annotations ( 16 ). In total, 1,136 mitochondrial genes were included in the present analysis. Molecular QTL data spanning four layers of gene regulation were incorporated into the analysis: DNA methylation, whole-blood and single-cell gene expression, and circulating protein abundance. These layers traced genetically mediated effects from an upstream epigenetic mark to the downstream protein product. Genetic regulation of gene expression in whole blood was obtained from the eQTLGen consortium, which analyzed expression quantitative trait loci across 31,684 individuals from 37 independent cohorts, with cohort-specific adjustment for age, sex, genotype principal components, and PEER factors ( 17 ). To investigate immune cell-specific regulatory effects, single-cell expression quantitative trait locus (sc-eQTL) data were obtained from the OneK1K cohort, comprising single-cell RNA sequencing profiles of 1.27 million peripheral blood mononuclear cells across 14 immune cell subsets from 982 donors, with adjustment for PEER factors, sex, and genotype principal components within each cell type ( 14 ). DNA methylation quantitative trait loci were obtained from a meta-analysis of whole-blood DNA methylation, profiled with the Illumina HumanMethylation450 array, combining the Brisbane Systems Genetics Study and the Lothian Birth Cohorts, adjusted for age, sex, and estimated blood cell-type proportions ( 18 ). Genetic associations with circulating protein abundance were obtained from a SomaScan-based plasma proteomic QTL study including 35,559 Icelanders, adjusted for age, sex, genotype principal components, and sample collection batch ( 19 ). Because all genetic association datasets used in this study were obtained from previously published resources with existing ethical approval, no additional ethical approval was required for the use of these summary statistics. Summary-data-based Mendelian randomization analysis The SMR method (SMR software, version 1.4.0) was applied to integrate GWAS summary statistics with molecular QTL data to test whether genetic variants associated with molecular traits also influence disease risk through the same regulatory pathway ( 10 ). In the primary analysis, molecular QTLs, comprising eQTLs, mQTLs, sc-eQTLs, and pQTLs, served as the exposure, and sepsis GWAS summary statistics served as the outcome. In this framework, a single top cis-acting QTL variant for each molecular trait was selected as the instrumental variable in the original SMR model. Cis-QTLs were defined as variants located within ±1,000 kilobases of the gene locus, with a minor allele frequency greater than 0.01, and reaching genome-wide significance (P < 5 × 10 -8 ) ( 20 ). Variants showing allele frequency discrepancies greater than 0.1 between datasets were excluded to minimize potential bias arising from allele mismatching across datasets, a filter that also indirectly controls for ambiguous palindromic SNPs. Allele harmonization between exposure and outcome datasets was performed using the SMR software’s built-in allele-matching procedure. All analyses were conducted on the GRCh37/hg19 genome build; pQTL summary statistics, originally reported in GRCh38, were lifted over to GRCh37 prior to analysis. Linkage disequilibrium among variants within each cis-regulatory region was estimated using the 1000 Genomes Project Phase 3 European reference panel ( 21 ), consistent with the predominantly European ancestry of the GWAS and QTL datasets used in this study. Instrument strength was assessed using the F-statistic, calculated as the squared ratio of the SNP-exposure e
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