Mitochondrial related genome-wide mendelian randomization identifies putatively causal genes in the pathogenesis of sepsis

doi:10.21203/rs.3.rs-4922996/v1

Download PDF

Research Article

Mitochondrial related genome-wide mendelian randomization identifies putatively causal genes in the pathogenesis of sepsis

https://doi.org/10.21203/rs.3.rs-4922996/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

The dysfunction of mitochondria has been associated with the development of sepsis, but the specific mitochondrial-related genes and their roles in sepsis have not been fully elucidated. We employed Mendelian randomization and colocalization analysis to investigate the association between mitochondrial-related genes and sepsis by integrating multi-omics data.

Methods

Summary-level data on mitochondrial gene methylation, expression, and protein abundance levels were obtained from corresponding studies on methylation, expression, and protein quantitative trait loci, respectively. Genetic associations with sepsis were obtained from the GWAS catalog database. We utilized the MitoCarta3.0 database, which contains an updated list of 1,136 human mitochondrial genes, to identify mitochondrial genes. To assess the associations between mitochondrial gene-related molecular features and sepsis, we conducted summary-data-based Mendelian randomization analysis. Additionally, we performed colocalization analysis to determine whether the identified signal pairs shared a causal genetic variant.

Findings

After integrating the multi-omics data between mQTL-eQTL and eQTL-pQTL, we identified FIS1 as having tier 1 evidence for its association with sepsis. Methylation of cg01299997 in FIS1 was found to be associated with lower expression of FIS1, an increased risk of sepsis, and a positive role in cg01299997 methylation. Furthermore, NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, and TFAM genes were associated with sepsis risk with tier 2 evidence. Both gene expression and protein abundance levels of NUDT2 were observed to be associated with an increased risk of sepsis. Additionally, ATP5MC1 and VWA8 genes were associated with sepsis risk with tier 3 evidence. Among these tertiary genes, ATP5MC1 gene expression level showed a negative correlation (PPH4=0.9242), while the gene expression level of VWA8 exhibited a positive correlation (PPH4=0.7270).

Interpretations

We found that the mitochondrial FIS1, NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, TFAM, ATP5MC1 and VWA8 gene was putatively associated with sepsis risk with evidence from multi-omics levels. This study identified mitochondrial genes in relation to sepsis, which may enhance the understanding of the pathogenic mechanisms of sepsis development.

Funding

This work was supported by the Wuxi Health Commission Scientific Research Project [grant number No. Z202102].

Sepsis

Mendelian randomization

Mitochondrial Genes

Methylation

Gene expression

Pharmaceutical targets

Evidence before this study

Although emerging research points towards a pivotal role of mitochondrial dysfunction in the pathogenesis of sepsis, the comprehensive genetic underpinnings of mitochondrial dysfunction remain to be fully elucidated.

Added value of this study

One mitochondrial gene, including FIS1, has demonstrated with tier 1 evidence of the association with sepsis. Furthermore, NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, and TFAM displayed evidence of associations with ulcerative colitis risk, backed by tier 2 evidence. ATP5MC1 and VWA8 displayed evidence of associations with ulcerative colitis risk, backed by tier 3 evidence.

Implications of all the available evidence

The roles of specific mitochondrial genes in sepsis pathogenesis were further illuminated, offering promising directions for future research and potential therapeutic targets.

Sepsis, a severe systemic infection, affects over 30 million people worldwide each year and is one of the leading causes of death among critically ill patients. Anyone can develop sepsis when infected, and the incidence rate in hospitalized patients can reach 1–2%. Without timely and effective interventions, the mortality rate rapidly exceeds 30–35% [1, 2]. Currently, the treatment of sepsis involves three main aspects: infection control, hemodynamic management, and modulation of host response [3]. Although these treatments have significantly improved the survival rate of sepsis patients, the mortality rate remains high due to the heterogeneity of sepsis patients. Therefore, further research is needed to investigate the pathogenesis of sepsis and explore new therapeutic drugs and targets to overcome the treatment challenges of sepsis [3, 4].

Mitochondria are cell organelles present in eukaryotic cells that play a crucial role in energy production and metabolism. Recent studies have highlighted the involvement of mitochondria in the occurrence, development, and potential therapeutic targets of sepsis [5, 6]. Some mitochondria-related genes have also been considered as potential biomarkers for sepsis diagnosis, treatment, and prognosis prediction [7, 8]. In sepsis patients, mitochondrial damage can lead to cellular metabolic disorders, inadequate energy production, and oxidative stress, ultimately causing organ dysfunction. Additionally, the apoptosis of critical cells can disrupt suppressor function, leading to multiple organ failure and even death [2].

Mitochondrial DNA damage, leading to diminished quality and number, and dysfunction, is a critical factor in post-sepsis acute kidney injury [9, 10]. However, the relationship between mitochondria-related genes and sepsis patients requires further clarification. Previous studies have identified various drugs (such as BAM15) that directly or indirectly target mitochondria and show potential in sepsis treatment [11–14]. These studies provide promising avenues for targeting mitochondria in sepsis treatment and suggest that mitochondrial therapy may hold potential as a new approach for managing sepsis. Although the critical role of mitochondria in sepsis pathogenesis has been recognized, specific mitochondria-associated genes and their precise roles in sepsis remain elusive. Furthermore, it is important to note that current studies investigating the pathogenesis of mitochondria and sepsis primarily rely on observational research, which presents limitations in establishing causal relationships [15]. Therefore, further research employing more effective methodologies is needed to explore the causal link between mitochondria and the onset of sepsis.

Mendelian Randomization (MR) is a widely utilized method for causal inference that leverages genetic variations as instrumental variables. By utilizing these genetic variations, MR helps eliminate reverse causation and reduce bias, thereby strengthening the validity of causal inferences in research. Notably, it is important to note that these genetic variations remain fixed at conception and are not influenced by subsequent outcomes or diseases. Furthermore, they are not affected by confounding factors such as sociodemographic characteristics, behavioral factors, or existing health conditions [16]. The availability of data from Genome-Wide Association Studies (GWAS) and molecular quantitative trait loci (QTL) has significantly contributed to the advancement of MR studies. GWAS enables the identification of genetic associations based on single nucleotide polymorphisms (SNPs). These associations, in turn, can be integrated with gene expression and DNA methylation data, allowing for the identification of expression or methylation quantitative trait loci (eQTL, pQTL, or mQTL) [17, 18].

This study innovatively employs MR analysis based on multi-omics data to investigate the association between mitochondrial genes and sepsis. This approach provides insights into the role of specific mitochondrial genes in the pathogenesis of sepsis. Furthermore, it offers promising directions for identifying potential therapeutic targets in sepsis research.

Figure 1 showed the overall design of the study. The current MR analysis was based on publicly available datasets including the GWAS catalog database [17], the UK Biobank study [19], and the eQTLGen alliance [20] (Table S1). In this study, instrumental variables for mitochondrial genes were extracted at the methylation, gene expression, and protein abundance levels. Subsequent MR analyses were conducted separately for sepsis at each biological level. To strengthen the causal inference, colocalization analyses were then applied. The GWAS catalog was the primary discovery dataset, with UK Biobank and eQTLGen Consortium datasets used for validation.

Specifically, these datasets were employed to investigate gene methylation, gene expression, and protein abundance levels, respectively. Through the integration of results obtained from MR analyses at these three distinct levels, we identified causal candidate genes. There was no overlap in samples between the exposure and outcome populations.

Figure 1 Study design. SMR, summary-based Mendelian randomization; QTL, quantitative trait loci; SNP, single nucleotide polymorphisms; PPH4, posterior probability of H4.

Data sources of methylation, expression, and protein quantitative trait loci

Integration of multi-omics data enabled us to illuminate the underlying molecular networks of mitochondrial dysfunction. QTLs could reveal the associations of single nucleotide polymorphisms (SNPs) with levels of DNA methylation, gene expression, and protein abundance. SNP-CpG associations in blood were obtained from methylation quantitative trait loci (mQTL) data by McRae et al. in 1980 European ancestry individuals [18]. Individual methylation probes were normalized using a generalized linear model using a logistic link function with corrections for the chip, sex, age, age², sex × age, and sex × age² [18]. The dataset of blood expression quantitative trait loci (eQTL) data was extracted from the eQTLGen consortium which included 31,684 individuals [20]. Summary statistics of genetic associations with circulating protein levels were extracted from a protein quantitative trait loci (pQTL) study by Ferkingstad et al. comprising 35,559 Icelanders [21]. Rank-inverse normal transformed levels were adjusted for age, sex, and sample age for each protein tested [21].

Genetic associations with sepsis were identified by extracting data from the GWAS catalog database, encompassing a total of 1573 sepsis cases and 454,775 control individuals. The GWAS catalog served as a valuable resource for researchers, compiling a comprehensive collection of genetic variants associated with various diseases. In the context of sepsis, these genetic associations shed light on the underlying genetic factors that contribute to the susceptibility or resilience to this severe medical condition. By analyzing the genetic profiles of a large cohort of individuals, researchers could uncover potential biomarkers, pathways, or therapeutic targets that may aid in the development of more effective diagnostic tools and targeted interventions for sepsis patients. The inclusion of a substantial number of control individuals in the study design ensured statistical robustness and allowed for meaningful comparisons between cases and controls, enhancing the reliability of the identified genetic associations.

Mitochondrial-related genes were identified by MitoCarta3.0 which contained an updated inventory of 1136 human mitochondrial genes [22]. The MitoCarta identified all protein components resident in the mitochondrion based on the Bayesian integration of seven experimental and sequence features. Each mitochondrial gene product was subjected to an independent literature-guided review in the updated MitoCarta3.0, thus providing an inventory of 1136 human mitochondrial genes [22]. Leveraging the inventory, we separately identified mitochondrial genes in the QTL datasets.

Summary-data-based MR analysis

Summary-data-based Mendelian randomization (SMR) was employed to estimate the association of mitochondrial gene methylation, expression, and protein abundance with the risk of sepsis [17]. Based on the top associated cis-QTL, the SMR could reach a much higher statistical power than conventional MR analysis when exposure and outcome were available from two independent samples with large sample sizes [17]. The top associated cis-QTL were selected by considering a window centered around the corresponding gene (± 1000 kb) and passing a P-value threshold of 5.0 × 10^− 8. The SNPs with allele frequency differences larger than the specified threshold (set as 0.2 in the current study) between any pairwise data sets, including the LD reference sample, the QTL summary data, and the outcome summary data, were excluded. Heterogeneity in the dependent instrument (HEIDI) test was applied to distinguish pleiotropy from linkage, where P-HEIDI < 0.01 were considered likely due to pleiotropy and thus discarded from the analysis. SMR and HEIDI tests were implemented using the SMR software tool (SMR v1.3.1). The P-values were adjusted to control the false discovery rate (FDR) at α = 0.05 using the Benjamini-Hochberg method. Associations with the FDR-corrected P-value < 0.05 and P-HEIDI > 0.01 were undertaken for colocalization analysis.

Colocalization analysis

We conducted colocalization analyses to detect shared causal variants between sepsis and identified mitochondrial-related mQTLs, eQTLs, or pQTLs with coloc R package [23]. In colocalization analysis, five different posterior probabilities are reported, which correspond to the five hypotheses: five exclusive hypotheses: 1) no causal variants for either of the two traits (H0); 2) a causal variant for gene expression only (H1); 3) a causal variant for disease risk only (H2); 4) distinct causal variants for two traits (H3); 5) and the same shared causal variant for both traits (H4). For colocalization of pQTL-GWAS [24], eQTL-GWAS [25] and mQTL-GWAS [26], the colocalization region windows were ± 1000 kb, ± 1000 kb and ± 500 kb respectively according to published articles. The prior probabilities that the causal variants were associated with only trait 1 (i.e., mQTL), only trait 2 (i.e., sepsis) and both were respectively set at 10^− 4, 10^− 4 and 10^− 5. The posterior probability of H4 (PPH4) > 0.70 was considered supporting evidence of colocalization with its cutoff corresponds to a false discovery rate of < 5%, which strengthened the evidence for a causal relationship [27].

Integrating results from multi-omics level of evidence

To obtain a full picture of associations between regulation of mitochondrial-related genes and sepsis at different levels, we integrated results from three different gene regulation tiers. Since proteins were the ultimate expression products of genes and establishing evidence of causation at the protein level was a fundamental requirement, all three tiers of causal candidate genes in our classification were required to have genes causally associated with sepsis at the protein level. Based on this principle, we divided the causal candidate genes into three tiers using the following criteria: 1) tier 1 genes were defined to have gene-sepsis associations at protein abundance level (FDR-corrected P-value < 0.05), PPH4 of colocalization > 0.7, and associations with sepsis at both methylation and expression levels (FDR-corrected P-value < 0.05); 2) tier 2 genes were defined to have gene-sepsis association at protein abundance level (FDR-corrected P-value < 0.05), PPH4 of colocalization > 0.7, and associations with sepsis at methylation or expression levels (FDR-corrected P-value < 0.05); 3) tier 3 genes were defined to have gene-sepsis associations at protein abundance level (FDR-corrected P-value < 0.05), PPH4 of colocalization of ≥ 0.5 and < 0.7, and associations with sepsis at both methylation and expression levels (original P-value < 0.05). To further explore the potential regulation among gene methylation, expression, and protein abundance, we conducted MR analysis of the causal associations between mitochondrial-related gene methylation and expression, gene expression, and protein abundance. We further performed colocalization analysis for identified associations to rule out the possibility that the association is caused by linkage disequilibrium.

Ethics

Included studies had been approved by corresponding ethical review committees and all participants signed the consent forms.

Role of funders

The funders had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the manuscript for publication.

Mitochondrial gene methylation and sepsis

Results for causal effects of mitochondrial gene methylation on sepsis were visualized in Fig. 2. After correction for multiple testing, we identified 72 CpG sites near 42 unique genes (Fig. 2). Of the 42 identified signals, 28 near 17 unique genes were found to have strong colocalization evidence support (PPH4 > 0.70) including ACOT11 (cg01445479, cg05102794, cg10453148), BCAT2 (cg21978618), CASP9 (cg14078231), CHCHD3 (g19918623), CLYBL (cg09676794), COX7A2 (cg06809298, cg12555646, cg19367436), CPT1C (cg21723184), CYB5R3 (cg24251862), FIS1 (cg01299997, cg17944572), IMMP2L (cg02729030), LONP1 (cg13624418), LYRM4 (cg09618197), MRPL28 (cg00101154), NIPSNAP3B (cg04239375, cg14392031), PC (cg11525980), PCCA (cg22432024), and SPATA19 (cg13461336, cg07778534, cg02601318, cg05853013, cg12457901, cg26467571). Gene ACOT11 CpG loci cg21448423, for example, the results showed cg21448423. Associated with sepsis between (OR = 1.2585, 95% CI: 1.0348–1.5304, P = 0.0213), and does not exist Pleiotropic (p_HEIDI = 0.1610), indicating that the SMR results of this study are sound (Table S2).

Mitochondrial gene expression and sepsis

Results for causal effects of mitochondrial gene expression on sepsis were presented in Fig. 3. In total, 35 associations were identified to be associated with sepsis at the nominally significant level (P < 0.05) (Table S3). After multiple testing correction and colocalization analysis, genetically predicted higher levels of expression of IMMP2L (OR 1.5332, 95%CI: 1.1607–2.0253, P = 0.0016), DGLUCY (OR 1.3177, 95%CI: 1.0185–1.7049, P = 0.0358), CHCHD3 (OR 2.7575, 95%CI: 1.3033–5.8341, P = 0.0080), and FIS1 (OR 0.7880, 95%CI: 0.6614–0.9389, P = 0.0077) were positively associated with sepsis risk. In the case of FIS1, the results showed that FIS1 associated with sepsis between (OR 1.3739, 95%CI: 1.0785–1.7503, P = 0.0077), with no pleiotropy observed (p_HEIDI = 0.2240), indicating that the SMR results of this study was sound.

Mitochondrial protein and sepsis

There were 6 mitochondrial proteins separately associated with sepsis risk at P < 0.05 level (Table S4). After adjustment for multiple testing, genetically predicted higher levels of FIS1 (OR 0.5009, 95%CI: 0.2999–0.8366, P = 0.0083), NUDT2 (OR 1.3567, 95%CI: 1.0177–1.8087, P = 0.0376), ACAA1 (OR 0.4365, 95%CI: 0.1945–0.9797, P = 0.0445), DCXR (OR 0.3897, 95%CI: 0.1776–0.8555, P = 0.0188), FIS1 (OR 0.5208, 95%CI: 0.3211–0.8445, P = 0.0082), and GRHPR (OR 1.3308, 95%CI: 1.0049–1.7625, P = 0.0462) were associated with a decreased risk of sepsis (Fig. 4). The colocalization evidence was observed between FIS1 (PPH4 = 0.2070), NUDT2 (PPH4 = 0.6220), ACAA1 (PPH4 = 0.4550), DCXR (PPH4 = 0.3610), FIS1 (PPH4 = 0.0640), and GRHPR (PPH4 = 0.1320) had high support evidence of colocalization in sepsis.

Integrating evidence from multi-omics levels

After integrating the multi-omics evidence, the results were shown in Fig. 5. We identified a gene with level-1 multi-omics evidence, FIS1 (Table S5), that expressed protein abundance at levels consistent with the causal association with sepsis and that were associated with a reduced risk of sepsis. Seven genes were identified as secondary genes in sepsis (Table S5), including NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, and TFAM). NUDT2 was associated with an increased risk of sepsis. Two genes were identified as tertiary genes in sepsis (Table S5), including ATP5MC1 and VWA8. Among the tertiary genes, ATP5MC1 gene expression level was negatively correlated and PPH4 = 0.9242 Gene expression levels were positively correlated and VWA8 PPH4 = 0.7270.

In this study, we performed MR and colocalization analyses to explore the associations of genetically predicted mitochondrial gene methylation, expression, and protein abundance levels with sepsis. We found that the mitochondrial FIS1, NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, TFAM, ATP5MC1 and VWA8 genes were putatively associated with sepsis risk with multi-omics evidence. For the FIS1 gene, we observed that methylation at its cg01299997 locus was associated with reduced FIS1 gene expression levels. This suggested that methylation may play an important role in the regulation of the FIS1 gene and that a low expression of the FIS1 gene was associated with an increased risk of sepsis. This finding provided clues for further study of the function and regulatory mechanism of FIS1 gene in sepsis [28,29].

In the analysis of mQTL, eQTL, and pQTL, NUDT2 was found to be associated with sepsis. NUDT2 gene was involved in the regulation and metabolism of nucleotide diphosphate compounds. Its abnormal expression may affect the regulation of the immune system and the balance of inflammatory response, thus related to the risk of sepsis [30] .The IMMP2L gene encoded an inner mitochondrial membrane peptidase-like protein. The role of this gene in sepsis was not fully understood, but studies have found that some variants of the IMMP2L gene may be associated with the risk of sepsis. This may involve its effects on mitochondrial function, protein handling, and metabolism [31]. The LYRM4 gene encoded a LYR domain protein. LYRM4 protein played an important role in the maintenance of mitochondrial structure and function. Studies have shown that some mutations in LYRM4 gene may be related to the sensitivity and prognosis of sepsis and may be involved in the pathogenesis of sepsis by regulating mitochondrial function and oxidative stress response [32]. MRPL10, MRPL17 and MTIF3 genes encodes protein components of mitochondria or were involved in the regulation of mitochondrial protein synthesis and translation. Studies have found that mutations or polymorphisms in the MRPL10, MRPL17 and MTIF3 genes may be associated with an increased risk of sepsis. Abnormal variations in these genes may affect mitochondrial protein synthesis and function, which in turn affect cellular energy metabolism and immune regulation [33–35]. The TFAM gene encodes mitochondrial transcription factor A, which was responsible for the regulation of mitochondrial DNA transcription and replication. Studies have found that some variants of the TFAM gene were associated with the susceptibility and prognosis of sepsis. The abnormal variation of TFAM gene may lead to mitochondrial dysfunction and mitochondrial DNA damage, thereby affecting the balance of cellular energy metabolism and inflammatory response [36].

An advantage of our investigation was that we utilized MR and colocalization, two methods that together utilized genetic variants to estimate the causal effects of mitochondrial gene methylation expression and protein abundance. Moreover, we integrated results from multi-omics levels evidence which strengthened the causal relationship between mitochondrial-related genes and sepsis risks. The MR design complementarily minimized bias from confounding and reversed causality, thereby improving causal inference, while the colocalization approaches have proven to be a powerful tool in eliminating potential bias caused by linkage disequilibrium. Besides, GWASs with large sample sizes helped increase the statistical power of our study. Furthermore, the consistency of our results across multiple datasets provided additional support for our findings.

Due to the limited number of mitochondrial-related proteins in the pQTL dataset, the current study did not fully explore the causal relationship between mitochondrial proteins and the risk of sepsis. A further constraint of the study was the interpretation of the posterior probabilities (PPH4) in colocalization should be approached with caution, as a low PPH4 may not necessarily indicate a lack of evidence for colocalization when PPH3 was also low due to insufficient statistical power. Given the limited number of mitochondrial-related proteins in the available pQTL dataset, future studies should aim to incorporate a more extensive range of mitochondrial proteins. By doing so, a more comprehensive understanding of the causal relationship between mitochondrial proteins and the risk of sepsis can be achieved. Although GWASs with large sample sizes have been advantageous in increasing the statistical power of studies, further efforts should be made to obtain even larger and more diverse datasets. This would provide additional strength to the findings and improve the reliability of causal inference in relation to mitochondrial gene methylation expression and protein abundance. While colocalization approaches have proven to be valuable in eliminating potential bias caused by linkage disequilibrium, the interpretation of posterior probabilities (PPH4) should be approached with caution. Future research should focus on refining and improving the colocalization methods to enhance their accuracy and reliability in assessing colocalization evidence, particularly in cases where statistical power might be insufficient.

The current MR study explored the potential causal relationships of mitochondrial-related gene methylation, expression, and protein abundance with sepsis and demonstrated the importance of several mitochondrial-related genes and their regulation in the pathogenesis of sepsis. These findings deepen the pathological understanding of sepsis and potentially revealed pharmacological targets. By addressing these future research directions, we can overcome the limitations of the current study and further advance our knowledge regarding the causal relationships between mitochondrial-related genes, their expression, and protein abundance in relation to sepsis risks.

Contributors

Jiaojiao Sun (Conceptualization: Equal; Methodology: Equal; Formal analysis: Equal; Data curation: Equal; and Writing - review & editing: Equal).

Yaxian Wu (Conceptualization: Equal; Methodology: Equal; Formal analysis: Equal; Data curation: Equal; and Writing—original draft: Equal).

Sihao Jin (Conceptualization: Equal; Methodology: Equal; Formal analysis: Equal; Data curation: Equal; and Writing—original draft: Equal).

Xiaolin Li (Conceptualization: Supporting; Writing–review & editing: Supporting).

Fan Chen (Conceptualization: Equal; Methodology: Equal; Writing–review & editing: Equal).

Jun Zhu (Conceptualization: Leading; Data curation: Equal; Writing–review & editing: Leading).

Chuanxin Liu (Conceptualization: Supporting; Writing–review & editing: Supporting).

Lingyun Man (Conceptualization: Equal; Methodology: Equal; Writing–review & editing: Equal).

Rixiang Huang (Conceptualization: Equal; Methodology: Equal).

Yuan Wong (Conceptualization: Equal; Methodology: Equal; Formal analysis: Equal; Data curation: Equal; and Writing—original draft: Equal).

Zhiqiang Wang (Conceptualization: Equal; Data curation: Equal; Funding acquisition: Leading; and Writing–review & editing: Leading).

All authors read and approved the final manuscript.

Data sharing statement

Data can be obtained upon a reasonable request to corresponding authors.

Declaration of interests

The authors declare that they have no competing interests.

Acknowledgements

We thank all participants and investigator involved in the McRae AF. et al. genome-wide association analysis on methylation, the eQTLGen consortium, the Genotype-Tissue Expression (GTEx) project, Ferkingstad E. et al. genome-wide association analysis on proteome, the Inflammatory Bowel Disease Genetics Consortium, the FinnGen study, and the UK Biobank study for sharing data.

Liu Y-C, Yao Y, Yu M-M, Gao Y-L, Qi A-L, Jiang T-Y, et al. Frequency and mortality of sepsis and septic shock in China: a systematic review and meta-analysis. BMC Infect Dis 2022; 22:564. https://doi.org/10.1186/s12879-022-07543-8.
Huang M, Cai S, Su J. The Pathogenesis of Sepsis and Potential Therapeutic Targets. Int J Mol Sci 2019; 20:5376. https://doi.org/10.3390/ijms20215376.
Vincent J-L. Current sepsis therapeutics. EBioMedicine 2022; 86:104318. https://doi.org/10.1016/j.ebiom.2022.104318.
Wang W, Liu C-F. Sepsis heterogeneity. World J Pediatr 2023; 19:919–27. https://doi.org/10.1007/s12519-023-00689-8.
Sun J, Zhang J, Tian J, Virzì GM, Digvijay K, Cueto L, et al. Mitochondria in Sepsis-Induced AKI. J Am Soc Nephrol 2019; 30:1151–61. https://doi.org/10.1681/ASN.2018111126.
Supinski GS, Schroder EA, Callahan LA. Mitochondria and Critical Illness. Chest 2020;157:310–22. https://doi.org/10.1016/j.chest.2019.08.2182.
Shu Q, She H, Chen X, Zhong L, Zhu J, Fang L. Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis. Front Immunol 2023; 14:1184126. https://doi.org/10.3389/fimmu.2023.1184126.
Shu Q, Du Y, She H, Mo J, Zhu Z, Zhong L, et al. Construction and validation of a mitochondria-associated genes prognostic signature and immune microenvironment characteristic of sepsis. Int Immunopharmacol 2024; 126:111275. https://doi.org/10.1016/j.intimp.2023.111275.
van der Slikke EC, Star BS, van Meurs M, Henning RH, Moser J, Bouma HR. Sepsis is associated with mitochondrial DNA damage and a reduced mitochondrial mass in the kidney of patients with sepsis-AKI. Crit Care 2021; 25:36. https://doi.org/10.1186/s13054-020-03424-1.
Tsuji N, Tsuji T, Yamashita T, Hayase N, Hu X, Yuen PS, et al. BAM15 treats mouse sepsis and kidney injury, linking mortality, mitochondrial DNA, tubule damage, and neutrophils. J Clin Invest 2023;133: e152401. https://doi.org/10.1172/JCI152401.
Itagaki K, Riça I, Konecna B, Kim HI, Park J, Kaczmarek E, et al. Role of Mitochondria-Derived Danger Signals Released After Injury in Systemic Inflammation and Sepsis. Antioxid Redox Signal 2021; 35:1273–90. https://doi.org/10.1089/ars.2021.0052.
Xu S, Li L, Wu J, An S, Fang H, Han Y, et al. Melatonin Attenuates Sepsis-Induced Small-Intestine Injury by Upregulating SIRT3-Mediated Oxidative-Stress Inhibition, Mitochondrial Protection, and Autophagy Induction. Front Immunol 2021; 12:625627. https://doi.org/10.3389/fimmu.2021.625627.
Lin Y, Xu Y, Zhang Z. Sepsis-Induced Myocardial Dysfunction (SIMD): the Pathophysiological Mechanisms and Therapeutic Strategies Targeting Mitochondria. Inflammation 2020; 43:1184–200. https://doi.org/10.1007/s10753-020-01233-w.
Patoli D, Mignotte F, Deckert V, Dusuel A, Dumont A, Rieu A, et al. Inhibition of mitophagy drives macrophage activation and antibacterial defense during sepsis. J Clin Invest 2020; 130:5858–74. https://doi.org/10.1172/JCI130996.
Prada-Ramallal G, Takkouche B, Figueiras A. Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review. BMC Med Res Methodol 2019; 19:53. https://doi.org/10.1186/s12874-019-0695-y.
Yao C, Zhang Y, Lu P, Xiao B, Sun P, Tao J, et al. Exploring the bidirectional relationship between pain and mental disorders: a comprehensive Mendelian randomization study. J Headache Pain 2023; 24:82. https://doi.org/10.1186/s10194-023-01612-2.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 2016; 48:481–7. https://doi.org/10.1038/ng.3538.
McRae AF, Marioni RE, Shah S, Yang J, Powell JE, Harris SE, et al. Identification of 55,000 Replicated DNA Methylation QTL. Sci Rep 2018; 8:17605. https://doi.org/10.1038/s41598-018-35871-w.
Zhou W, Zhao Z, Nielsen JB, Fritsche LG, LeFaive J, Gagliano Taliun SA, et al. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nat Genet 2020; 52:634–9. https://doi.org/10.1038/s41588-020-0621-6.
Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021; 53:1300–10. https://doi.org/10.1038/s41588-021-00913-z.
Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 2021; 53:1712–21. https://doi.org/10.1038/s41588-021-00978-w.
Rath S, Sharma R, Gupta R, Ast T, Chan C, Durham TJ, et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res 2021;49: D1541–7. https://doi.org/10.1093/nar/gkaa1011.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 2014;10: e1004383. https://doi.org/10.1371/journal.pgen.1004383.
Yoshiji S, Butler-Laporte G, Lu T, Willett JDS, Su C-Y, Nakanishi T, et al. Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity. Nat Metab 2023; 5:248–64. https://doi.org/10.1038/s42255-023-00742-w.
Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S, Wucher V, Gewirtz ADH, Cotter DJ, et al. The impact of sex on gene expression across human tissues. Science 2020;369: eaba3066. https://doi.org/10.1126/science.aba3066.
Morrow JD, Glass K, Cho MH, Hersh CP, Pinto-Plata V, Celli B, et al. Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci. Am J Respir Crit Care Med 2018; 197:1275–84. https://doi.org/10.1164/rccm.201707-1434OC.
Dashti HS, Daghlas I, Lane JM, Huang Y, Udler MS, Wang H, et al. Genetic determinants of daytime napping and effects on cardiometabolic health. Nat Commun 2021; 12:900. https://doi.org/10.1038/s41467-020-20585-3.
An S, Yao Y, Hu H, Wu J, Li J, Li L, et al. PDHA1 hyperacetylation-mediated lactate overproduction promotes sepsis-induced acute kidney injury via Fis1 lactylation. Cell Death Dis 2023; 14:457. https://doi.org/10.1038/s41419-023-05952-4.
Mukherjee R, Tompkins CA, Ostberg NP, Joshi AU, Massis LM, Vijayan V, et al. Drp1/Fis1-Dependent Pathologic Fission and Associated Damaged Extracellular Mitochondria Contribute to Macrophage Dysfunction in Endotoxin Tolerance. Crit Care Med 2022;50: e504–15. https://doi.org/10.1097/CCM.0000000000005437.
Husain RA, Jiao X, Hennings JC, Giesecke J, Palsule G, Beck-Wödl S, et al. Biallelic NUDT2 variants defective in mRNA decapping cause a neurodevelopmental disease. Brain 2024; 147:1197–205. https://doi.org/10.1093/brain/awad434.
L S, G P, H K, Jc M, A D, Rd H, et al. Genome-wide association study of sepsis in extremely premature infants. Archives of Disease in Childhood Fetal and Neonatal Edition 2017;102. https://doi.org/10.1136/archdischild-2016-311545.
Bar-Yaacov D, Hadjivasiliou Z, Levin L, Barshad G, Zarivach R, Bouskila A, et al. Mitochondrial Involvement in Vertebrate Speciation? The Case of Mito-nuclear Genetic Divergence in Chameleons. Genome Biol Evol 2015; 7:3322–36. https://doi.org/10.1093/gbe/evv226.
Lee S, Park D, Lim C, Kim J-I, Min K-T. mtIF3 is locally translated in axons and regulates mitochondrial translation for axonal growth. BMC Biol 2022; 20:12. https://doi.org/10.1186/s12915-021-01215-w.
Box JM, Kaur J, Stuart RA. MrpL35, a mitospecific component of mitoribosomes, plays a key role in cytochrome c oxidase assembly. Mol Biol Cell 2017; 28:3489–99. https://doi.org/10.1091/mbc.E17-04-0239.
Feng Y, Ma Y, Feng F, Chen X, Qi W, Ma Z, et al. Accumulation of 22 kDa α-zein-mediated nonzein protein in protein body of maize endosperm. New Phytol 2022; 233:265–81. https://doi.org/10.1111/nph.17796.
Zhao M, Wang Y, Li L, Liu S, Wang C, Yuan Y, et al. Mitochondrial ROS promote mitochondrial dysfunction and inflammation in ischemic acute kidney injury by disrupting TFAM-mediated mtDNA maintenance. Theranostics 2021; 11:1845–63. https://doi.org/10.7150/thno.50905.

No competing interests reported.

Download PDF

Editor assigned by journal
16 Aug, 2024
Submission checks completed at journal
16 Aug, 2024
First submitted to journal
16 Aug, 2024

You are reading this latest preprint version

Mitochondrial related genome-wide mendelian randomization identifies putatively causal genes in the pathogenesis of sepsis

Status:

Version 1

Abstract

Figures

Research in context

Introduction

Methods

Data sources of methylation, expression, and protein quantitative trait loci

Summary-data-based MR analysis

Colocalization analysis

Integrating results from multi-omics level of evidence

Ethics

Role of funders

Results

Mitochondrial gene methylation and sepsis

Mitochondrial gene expression and sepsis

Mitochondrial protein and sepsis

Integrating evidence from multi-omics levels

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1