Cross talk between skin microbiota and post-traumatic wound infection: a bidirectional mendelian randomization analysis

Cross talk between skin microbiota and post-traumatic wound infection: a bidirectional mendelian randomization analysis | 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 Cross talk between skin microbiota and post-traumatic wound infection: a bidirectional mendelian randomization analysis Qingsong Chen, Yuke Zhang, Guangbin Huang, Bohao Zhang, Yuan Cheng, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4714686/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Post-traumatic wound infection (PTWI) is a major challenge in trauma, burns, and surgeries. The skin microbiota is crucial for defense and may influence PTWI occurrence, though the relationship is unclear. This study explores the causal link between the skin microbiome and PTWI using bidirectional two-sample Mendelian randomization (MR) analysis. Methods A two-sample MR analysis was conducted using genome wide association studies (GWAS) data of 147 skin microbiota taxa and PTWI. The inverse-variance weighted (IVW) method was the primary analysis technique, while the MR-Egger and weighted median were used as supplementary analysis methods. Cochran’s Q test was used to perform heterogeneity analysis. The MR-Egger intercept test and MR-PRESSO were employed to assess potential horizontal pleiotropy. The leave-one-out method was utilized to evaluate the impact of individual SNPs on the overall causal effect. Results The two-sample MR analysis identified significant causal relationships between 12 skin microbiota species and PTWI. Five species were potentially beneficial: asv045 [Acinetobacter (unc.)] (OR = 0.971, P = 0.044), asv092 [C. kroppenstedtii] (OR = 0.966, P = 6.88e − 03), asv093 [Staphylococcus (unc.)] (OR = 0.911, P = 0.044), genus Finegoldia (OR = 0.965, P = 0.043), and genus Kocuria (OR = 0.95, P = 0.025). Seven species were potentially harmful: asv001 [P. acnes] (OR = 1.187, P = 0.041), asv005 [P. granulosum] (OR = 1.259, P = 6.06e − 03), family Micrococcaceae (OR = 1.24, P = 0.014), family Neisseriaceae (OR = 1.161, P = 0.038), genus Enhydrobacter (OR = 1.039, P = 0.013; OR = 1.202, P = 0.017), and order Bacteroidales (OR = 1.202, P = 0.012). PTWI may also induce skin microenvironment changes, disrupting homeostasis and increasing the likelihood of pathogenic microbiota, such as class Betaproteobacteria, genus Chryseobacterium, asv007 [Anaerococcus (unc.)], and family Flavobacteriaceae. Conversely, PTWI might promote beneficial microbiota, like asv005 [P. granulosum]. Conclusions This study provides strong evidence of a causal link between the skin microbiome and PTWI, emphasizing their complex interactions. These findings offer new insights for preventing and treating PTWI. Further research on the underlying mechanisms and similar studies in different populations are essential. Skin microbiota Post-traumatic wound infection Causal association Two-sample Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Background Post-traumatic wound infection (PTWI) is a major concern, involving microbial colonization in wounds from trauma [ 1 ], burns [ 2 ], or surgeries [ 3 ]. These infections hinder healing, increase healthcare costs, and pose serious health risks. Recent studies shed light on the epidemiology, classification, and management of combat-related wounds, emphasizing their polymicrobial nature and treatment complexities [ 4 ]. Managing PTWI requires decisions about antibiotic use, wound cleaning, and possibly surgical intervention. Recent research highlights the polymicrobial nature of these infections and the challenge of antibiotic-resistant pathogens, necessitating new treatment and prevention strategies [ 5 , 6 ]. This area of study is vital for improving trauma care outcomes. Musculoskeletal trauma, causing fractures and soft tissue damage, also poses significant risks. Advances in orthopedic trauma surgery and medical devices allow for prompt repair and healing of severe injuries, but trauma-related infections remain a complication [ 7 ]. The skin is the largest organ of the human body, serving as the first barrier against injury and pathogen invasion. It works in coordination with immune cells and microorganisms to maintain the skin's physical and immune barrier functions under healthy homeostasis, while also playing a crucial role during various stresses such as trauma and infection [ 8 , 9 ]. The skin microbiota, together with the skin, constitutes the body's first line of defense, significantly influencing the occurrence, development, and progression of diseases [ 10 ]. The human skin hosts a large number of microorganisms, including bacteria, fungi, and viruses [ 11 ]. The diversity of skin microbiota among different populations is influenced by environmental factors, the local microenvironment of the skin, and genetic diversity [ 12 – 14 ]. Human skin can be categorized into four types based on physiological and histological characteristics, microenvironment, and microbiota: dry skin (e.g., palms, forearms, anterior legs), characterized by dryness and low moisture content, with common microbiota including Propionibacterium, Staphylococcus, Corynebacterium, and Streptococcus, along with a large number of transient microorganisms [ 15 , 16 ]; moist skin (e.g., axillae, elbow folds, groin area, popliteal fossae), which is moist due to high moisture content and commonly harbors Staphylococcus and Corynebacterium; sebaceous skin, or oily skin (e.g., face, chest, back), characterized by rich sebum secretion, with common resident bacteria that are lipophilic, such as Cutibacterium acnes; and foot skin, which has unique resident microbiota, primarily fungi such as Aspergillus, Cryptococcus, and Malassezia[ 8 , 17 , 18 ]. To date, there are no reports on the skin microbiota, especially skin bacteria, and their association with wound infection post-trauma. Thus, understanding the interaction between skin microbiota and wound infection is crucial. Mendelian randomization (MR) has emerged as a valuable analytical method for inferring causal relationships between exposures and outcomes. This approach is particularly useful in situations where randomized controlled trials are lacking or not feasible. By utilizing single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), MR design allows for the estimation of causal effects by proxying the phenotypes of interest [ 19 ]. One of the key advantages of MR is its ability to minimize confounding biases. The random allocation of genetic variants during fertilization mimics the randomization process in an RCT, thereby reducing the likelihood of confounding factors such as sex and age influencing the causal inference [ 20 ]. MR analysis is increasingly utilized to establish causal relationships between potentially modifiable risk factors and various outcomes [ 21 ]. Overall, MR provides a robust alternative strategy to investigate the causal relationship between exposures and disease risks, especially when RCTs are impractical or unavailable. Currently, there isn't specific widespread research on SNPs directly related to post-traumatic wound infections. Research in this area typically focuses on the broader context of wound healing, infection susceptibility, and response to treatment, where genetic factors, including SNPs, might play a role. For more detailed and specific studies, a thorough review of scientific literature or databases specializing in genetics and medical research would be necessary. Therefore, the exploration of skin microbiota associated with the development of PTWI is not only valuable for early screening and prevention but also essential for gaining insights into the biological mechanisms underlying PTWI for treatment. However, the causality between skin microbiota and PTWI remains unclear. In this study, a two-sample MR analysis was applied to explore the causal relationship between the latest genome-wide association studies (GWAS) data of 147 types of skin microbiota [ 22 ] and PTWI. The ultimate goal of this MR analysis was to clarify the direction and magnitude of the causal relationship between skin microbiota and PTWI. Methods Study design We employed two-sample MR to investigate the causal effects of 147 types of skin microbiota on the risk of PTWI. Our MR design adhered to three essential assumptions: (1) the genetic instruments were strongly associated with the exposures of interest; (2) the genetic instruments were not linked to any confounding variables; and (3) the genetic instruments influenced the outcome solely through the exposures of interest [ 23 ]. The second and third assumptions are collectively known as the independence of horizontal pleiotropy. We used various statistical methods to test the validity of these assumptions [ 24 ]. The GWAS data for PTWI was sourced from [ 25 ]. FinnGen release v7, which includes 443 cases and 308,355 controls of European ancestry. The skin microbiota data was obtained from a previously published study [ 26 ]. The authors conducted a GWAS meta-analysis of skin microbiota data based on two European cohorts: KORA FF4 (n = 324) and PopGen (n = 273). We obtained the GWAS summary data from the GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). The data included information from both the KORA FF4 and PopGen datasets [ 22 , 34 ]. We performed the GWAS meta-analysis using METAL software [ 27 ]. Finally, we obtained GWAS data for 147 skin microbiota species. Figure 1 provides an overview of the study design. Table S1 showed the data sources used in our study. Instrumental variables selection In our study, we used multiple steps to screen for instrumental variables. For PTWI and skin microbiome, we used the same filtering methods. First, SNPs with genome-wide significance (p < 1 × 10 − 5 ) were retained. Next, SNPs with a minor allele frequency (MAF) less than 0.01 were excluded. Then, based on data from European samples of the 1000 Genomes Project, we removed SNPs in linkage disequilibrium (r² = 0.001, clumping window = 10,000 kb). Subsequently, we used LDtrait [ 28 ] ( https://ldlink.nih.gov/?tab=ldtrait ) to check if the SNPs were associated with confounding factors. Using a threshold of p < 5 × 10 − 8 , SNPs associated with diabetes, smoking, alcohol consumption, and HIV were excluded. Next, we assessed the strength of the SNPs using the F statistic, calculated as F = R² × (N − 2) / (1 − R²) [ 29 ]. R² represents the proportion of phenotypic variance explained by each instrument. N represents the sample size of the GWAS data. R² is calculated using the formula R² = (2 × EAF × (1 − EAF) × beta²) / [(2 × EAF × (1 − EAF) × beta²) + (2 × EAF × (1 − EAF) × N × se²)]. EAF is the effect allele frequency, beta is the estimated genetic effect on the exposures, se is the standard error of the genetic effect, and N represents the sample size of the GWAS data. SNPs with an F value greater than 10 were retained. Finally, palindromic SNPs with a MAF greater than 0.42 were excluded. The remaining SNPs after these filters were used for subsequent MR analysis. Two-sample MR In our research, we primarily employed the inverse-variance weighted (IVW) method for causal estimation. MR-Egger and weighted median methods were used as supplementary approaches to ensure the robustness of our results. To assess the reliability of our analyses, we utilized Cochran’s Q test to evaluate heterogeneity within our analysis. The MR-Egger intercept test [ 30 ] and MR-pleiotropy residual sum and outlier (MR-PRESSO) [ 31 ] were used to assess horizontal pleiotropy. Additionally, we used MR-PRESSO to detect outliers in the MR analysis. After removing outliers, we repeated the MR analysis until no further outliers were detected. Leave-one-out analysis was employed to assess the influence of individual SNPs on the overall causal effect. Sensitivity analysis In our study, we conducted analyses using R 4.3.2 software, available at http://www.Rproject.org . Sensitivity analysis played a critical role in our MR study by addressing issues such as horizontal pleiotropy and heterogeneity, which can potentially bias MR estimates. Horizontal pleiotropy occurs when IVs influence the outcome through pathways unrelated to the exposure under study. To ensure the robustness of our MR estimates, we employed three key methods: (1) Cochran’s Q test: This test detects heterogeneity in results, with a significance level set at p < 0.05 indicating significant heterogeneity in our analysis. (2) MR-Egger intercept test: This method identifies and adjusts for horizontal pleiotropy by testing whether the intercept differs significantly from zero [ 30 ]. (3) MR-PRESSO: This technique detects and corrects for pleiotropy by identifying outlier SNPs that may bias MR estimates and assessing the impact of their removal [ 31 ]. By implementing these methods, we aimed to ensure the reliability of our MR estimates and minimize biases due to horizontal pleiotropy or heterogeneity in IVs [ 32 ]. Results Selection of instrumental variables As described in the Materials and Methods section, we employed multiple steps to screen IVs. SNPs excluded due to associations with potential confounding factors are presented in Table S2 . The IVs representing exposures are shown in Tables S3-4. In the MR analysis, we further filtered these IVs. Palindromic IVs with a MAF greater than 0.42 were excluded. After each MR analysis, we used MR-PRESSO to identify outliers and re-conducted the MR analysis after removing them until no outliers were detected by MR-PRESSO. The excluded outliers are presented in Table S5. Exploration of the causal effect of skin microbiota on PTWI The results of the two-sample MR analyses indicated causal effects of 12 types of skin microbiota on PTWI. Among these, five microbiotas demonstrated protective effects: asv045 [Acinetobacter (unc.)] (OR = 0.971, 95% CI = 0.943 to 0.999, P = 0.044), asv092 [C. kroppenstedtii] (OR = 0.966, 95% CI = 0.864 to 1.079, P = 6.88 × 10 − 3 ), asv093 [Staphylococcus (unc.)] (OR = 0.911, 95% CI = 0.832 to 0.997, P = 0.044), genus: Finegoldia (OR = 0.965, 95% CI = 0.933 to 0.999, P = 0.043), and genus: Kocuria (OR = 0.95, 95% CI = 0.908 to 0.994, P = 0.025) (Fig. 2 and Table S6). Notably, for asv093 [Staphylococcus (unc.)] and genus: Kocuria, the results of the IVW and MR-Egger analyses were inconsistent in direction, indicating that these findings were not robust. Seven microbiotas showed harmful effects: asv001 [P. acnes] (OR = 1.187, 95% CI = 1.007 to 1.400, P = 0.041), asv005 [P. granulosum] (OR = 1.259, 95% CI = 1.068 to 1.483, P = 6.06 × 10 − 3 ), family: Micrococcaceae (OR = 1.24, 95% CI = 1.045 to 1.473, P = 0.014), family: Neisseriaceae (OR = 1.161, 95% CI = 1.009 to 1.337, P = 0.038), genus: Enhydrobacter (OR = 1.039, 95% CI = 1.008 to 1.072, P = 0.013), genus: Enhydrobacter (OR = 1.202, 95% CI = 1.033 to 1.398, P = 0.017), and order: Bacteroidales (OR = 1.202, 95% CI = 1.020 to 1.175, P = 0.012) (Fig. 2 and Table S6). Exploration of the causal effect of PTWI on skin microbiota Using PTWI as the exposure and 147 skin microbiotas as the outcomes, we identified five types of skin microbiotas closely associated with PTWI. Among these, PTWI was associated with an increased abundance of class Betaproteobacteria (Beta = 0.263, 95% CI = 0.032 to 0.493, P = 0.026), genus Chryseobacterium (Beta = 0.395, 95% CI = 0.114 to 0.675, P = 5.85 × 10 − 3 ), asv007 [Anaerococcus (unc.)] (Beta = 0.438, 95% CI = 0.089 to 0.788, P = 0.014), and family Flavobacteriaceae (Beta = 0.372, 95% CI = 0.016 to 0.728, P = 0.04) based on IVW analysis. In contrast, PTWI was negatively associated with the abundance of asv005 [P. granulosum] (Beta = − 0.358, 95% CI = − 0.619 to − 0.097, P = 7.2 × 10 − 3 ). Sensitivity analyses To evaluate heterogeneity among instrumental variables, we used Cochran's Q statistic, finding low heterogeneity, which supports the statistical consistency of SNP effects. We assessed potential pleiotropy using MR-Egger regression, with an insignificant intercept suggesting minimal pleiotropic bias. The consistent MR-Egger slope with IVW method results further indicated that undetected pleiotropy is unlikely to affect our findings (Table S8 and S9). A scatter plot showed that most SNP effect sizes are near zero, indicating minor impacts and low heterogeneity, validating their use as instrumental variables (Figs. S1 and S4). Leave-one-out analysis revealed that no single SNP significantly influenced the overall MR estimate, demonstrating the robustness of our analysis (Figs. S2 and S5). The symmetric distribution around the funnel plot's center line suggested no significant publication or selection bias, confirming the reliability of our MR analysis (Figs. S3 and S6). Discussion To our best knowledge, this study is among the first to systematically evaluate the causal relationships between the skin microbiota and PTWI from a genetic perspective. Our two-sample MR study provided strong evidence that genetically predicted abundance of specific skin microbes plays significant roles in the occurrence and progression of PTWI. Additionally, the MR confirmed and strengthened the role of PTWI on the skin microbiota. By leveraging molecular genetic markers as instrumental variables, our MR approaches largely avoided the confounding factors (e.g., diabetes mellitus, smoking, alcohol consumption) and reversed causality that often compromise observational studies. This study's investigation into the role of specific skin microbiota in PTWI underscores the complexity of host-pathogen interactions. As discussed in the introduction, the theory of skin microbiota and wound communication suggests a possible influence of skin microbiota on PTWI. Approximately 1,000 species of bacteria and about 100 billion microbiomes are detectable on human skin [ 33 ]. These bacteria are divided into four phyla: Actinobacteria (51.8%), Firmicutes (24.4%), Proteobacteria (16.5%), and Bacteroidetes (6.3%), including the genera Corynebacterium, Propionibacterium, and Staphylococcus [ 34 ]. During healing, cell interaction with the wound microbiome is hypothesized to regulate the innate immune response beneficially [ 35 ]. Conversely, pathogenic microbiota negatively affects wound healing [ 36 , 37 ]. Statistics show that wounds contain diverse microbiota, primarily Staphylococcus, Pseudomonas, Corynebacterium, Streptococcus, Anaerococcus, and Enterococcus [ 38 ]. Future research should explore the genetic underpinnings of immune responses to better understand the mechanisms driving disease susceptibility and progression. This could lead to more personalized approaches to treatment and prevention, leveraging insights from GWAS and MR studies to identify individuals at higher risk for specific infections or adverse outcomes following PTWI. Our study identified seven bacterial taxa as potential risk factors for PTWI: asv001 [P. acnes], asv005 [P. granulosum], family: Micrococcaceae, family: Neisseriaceae, genus: Enhydrobacter, and order: Bacteroidales. Asv001 [P. acnes] is a Gram-positive bacterium colonizing the skin and the oral and genital tracts, associated with various infections and clinical conditions linked to specific lineages [ 39 ]. Asv005 [P. granulosum] is an anaerobic Gram-positive bacterium involved in maintaining the skin microenvironment and associated with inflammatory responses, such as postoperative and device-related infections [ 40 ]. The family Micrococcaceae includes various Gram-positive bacteria, with notable species like Staphylococcus haemolyticus, a common pathogen in wound and surgical site infections [ 41 , 42 ], and Staphylococcus epidermidis, a frequent nosocomial pathogen capable of biofilm formation, leading to severe infections [ 43 ]. The family Neisseriaceae, though not commonly part of the skin microbiota, can occasionally cause challenging skin and wound infections due to antibiotic resistance [ 44 ]. The genus Enhydrobacter, a Gram-negative bacterium typically found in aquatic environments, was identified on both dry and moist skin in our study, and has been detected in public transportation systems [ 45 ]. The order Bacteroidales, primarily part of the gut microbiota, was found in the dry skin environment and negatively associated with PTWI, and has been detected in damaged and infected skin wounds [ 46 ] Burn wounds and traumatic wounds differ significantly in terms of damage mechanisms, pathological changes, wound characteristics, infection risks, and prognosis [ 47 ]. Studies show that the most common pathogens in acute burn wounds are Staphylococcus aureus, followed by Escherichia coli, Pseudomonas aeruginosa, and coagulase-negative staphylococci [ 48 , 49 ]. Given that E. coli and P. aeruginosa belong to the same phylum as Neisseriaceae and Enhydrobacter, it can be inferred that specific skin microbiota may be closely related to post-burn wound infections. Common bacteria in traumatic wounds are similar to those in burn wounds, suggesting common pathogenic mechanisms, development processes, and prognosis, though further exploration is required. Our study also identified five bacterial taxa that might be beneficial for PTWI: asv045 [Acinetobacter (unc.)], asv092 [C. kroppenstedtii], asv093 [Staphylococcus (unc.)], genus: Finegoldia, and genus: Kocuria. Although insufficient experimental evidence directly proves their beneficial effects on wound infections, the diversity of skin microbiota plays a dual role in wound infection and healing [ 50 ]. Skin-resident bacteria may prevent and resolve wound infections by altering the microenvironment of the skin and wounds [ 51 ], promoting wound healing [ 35 ]. Research indicates that early inflammatory and immune responses triggered by certain resident bacteria post-injury can prevent infection and promote wound healing. For instance, epidermal staphylococci limit inflammation via the TLR-2 signaling pathway, improving epithelialization and granulation tissue formation, thus accelerating wound healing [ 52 ]. However, resident bacteria can also produce proteases and reactive oxygen species that adversely affect wound healing [ 53 ], potentially causing severe infections, depending on the quantity of resident bacteria, local microenvironment, and skin condition. For example, Staphylococcus aureus, though rare on the skin, can severely infect wounds and cause chronic infections through the formation of pore-forming toxins and biofilms [ 54 ]. Therefore, the beneficial and harmful skin microbiota identified in our study have significant implications for PTWI research. They facilitate targeted studies on specific skin microbiota based on traditional taxonomic classifications, avoiding the waste of time and resources associated with broad-spectrum approaches. Beneficial microbiota can be investigated for their potential use as probiotics in topical and systemic treatments, an area where significant progress has been made [ 55 , 56 ]. This evidence partially explains why adjusting specific skin microbiota may alter the relationship between skin microbiota and PTWI. Although many studies have been reported, the underlying mechanisms have not been fully elucidated. Therefore, specialized mechanistic studies are needed to understand the unique roles of skin microbiota in different skin types, as most current research remains at the biological level of the overall skin microbiota. Our study complements and expands on existing research, further indicating that PTWI may disrupt the microenvironment of the skin microbiota, leading to homeostasis disturbance. The results show that the homeostasis of four taxa—class: Betaproteobacteria, genus: Chryseobacterium, asv007 [Anaerococcus (unc.)], and family: Flavobacteriaceae—was disrupted. Class: Betaproteobacteria, a group within the phylum Proteobacteria, includes bacteria with various metabolic pathways. Our findings support the notion that PTWI may increase the prevalence of class Betaproteobacteria in the skin microbiota. Burkholderia, a genus within this class, is known to heavily colonize burn and general wound sites [ 57 , 58 ]. The genus Chryseobacterium consists of Gram-negative, aerobic, non-motile, oxidase-positive, catalase-positive, and indole-positive bacteria, often causing severe infections such as bacteremia and wound sepsis [ 59 ]. Asv007 [Anaerococcus (unc.)] is a Gram-positive, anaerobic coccus that is part of the normal human microbiota, especially on the skin and in the oral cavity. It is an opportunistic pathogen found in various types of wounds, often associated with persistent and difficult-to-heal infections [ 60 ]. The family Flavobacteriaceae, comprising mostly aerobic Gram-negative bacteria, is part of the mixed skin microbiota [ 34 ]. Members of this family are both opportunistic pathogens and resident bacteria [ 61 ]. Elizabethkingia, a notable bacterium within this family, can cause neonatal meningitis and has been detected in traumatic wounds [ 62 , 63 ]. Our MR results confirm the potential of PTWI to increase the pathogenicity of these four taxa. The MR design, aside from randomized controlled trials (RCTs), allows for more reliable results and represents the highest level of evidence [ 64 ]. However, while our findings provide strong causal evidence for the involvement of these taxa, they do not address specific bacterial species. It is also important to note that the effect of wound infection on bacteria is likely dual-sided. Our study identified only one taxon, asv005 [P. granulosum], a common resident bacterium of the skin microbiota, which may play a beneficial role in the progression of wound infection post-trauma. Thus, a comprehensive understanding of the roles and mechanisms of skin microbiota in PTWI is a subject for further research, which could provide strong evidence and reference for effectively preventing and treating PTWI. This study has several strengths. First, the MR analysis results are less likely to be influenced by confounding factors. Second, by separating the samples in our data on skin microbiota and PTWI, we ensured that the two-sample MR analysis avoided the impact of weak instrumental variables. Third, we combined GWAS data from two independent large-scale cohorts to perform the two-sample MR analysis, ensuring the reliability and generalizability of the causal relationships. Fourth, we conducted heterogeneity analysis and horizontal pleiotropy tests, thoroughly validating the feasibility of the instrumental variable assumptions. However, this study also has limitations. First, the number of instrumental variables for each bacterial taxon varied between 7 and 213, which may result in inaccurate estimates for taxa with fewer instrumental variables. Nonetheless, each instrumental variable passed the heterogeneity and horizontal pleiotropy tests. Second, this study primarily involved individuals of European ancestry, which may limit the generalizability of the findings. Further research involving other populations may require the collection of new samples. Conclusions To our knowledge, this is the first study to investigate the causal relationship between skin microbiota and PTWI using data from a European population. Our research delineates the bidirectional effects, illustrating how skin microbiota influence PTWI and how PTWI affects skin microbiota. Additionally, it elaborates on the causal relationships between skin microbiota and PTWI across different skin types. These findings provide new strategies and references for the prevention and treatment of PTWI. Abbreviations PTWI Post-traumatic wound infection MR Mendelian randomization GWAS Genome wide association studies IVW Inverse-variance weighted SNP Single nucleotide polymorphism IV Instrumental variables LD Linkage disequilibrium ASV Amplicon sequence variants CI Confidence interval Declarations Ethics approval and consent to participate Not applicable. Availability of data and materials The data sets used and/or analysed during this study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no conflicts of interest. Funding This study was funded by the Major Research Project of the National Trauma Regional Medical Center (jointly sponsored by the Chongqing Municipal Government and Health Commission (jjzx2021-gjcsqyylzx01), the open topics of the Key Laboratory of Emergency Medicine in Chongqing (2022KFKI07), and the Chongqing Elite Research Project (cstc2022ycjh-bgzxm0245). Authors’ contributions QSC and YKZ: conceptualization, data curation, formal analysis, investigation, methodology, software and writing-original draft. GBH, BHZ, YC, LS and: data curation, validation and visualization. JXL, HL, QZ and PH: formal analysis and writing-review & editing. YML: conceptualization, project administration, resources, software and writing-review & editing. DYD: conceptualization, funding acquisition, project administration, resources and writing-review & editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. 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Neutralizing Staphylococcus aureus Virulence with AZD6389, a Three mAb Combination, Accelerates Closure of a Diabetic Polymicrobial Wound. mSphere. 2022;7(3):e0013022; doi: 10.1128/msphere.00130-22. Habeebuddin M, Karnati RK, Shiroorkar PN, Nagaraja S, Asdaq SMB, Khalid Anwer M, et al. Topical Probiotics: More Than a Skin Deep. Pharmaceutics. 2022;14(3); doi: 10.3390/pharmaceutics14030557. Peral MC, Rachid MM, Gobbato NM, Huaman Martinez MA, Valdez JC. Interleukin-8 production by polymorphonuclear leukocytes from patients with chronic infected leg ulcers treated with Lactobacillus plantarum. Clin Microbiol Infect. 2010;16(3):281-6; doi: 10.1111/j.1469-0691.2009.02793.x. Church D, Elsayed S, Reid O, Winston B, Lindsay R. Burn wound infections. Clin Microbiol Rev. 2006;19(2):403-34; doi: 10.1128/cmr.19.2.403-434.2006. Ku JWK, Marsh ST, Nai MH, Robinson KS, Teo DET, Zhong FL, et al. Skin models for cutaneous melioidosis reveal Burkholderia infection dynamics at wound's edge with inflammasome activation, keratinocyte extrusion and epidermal detachment. Emerg Microbes Infect. 2021;10(1):2326-39; doi: 10.1080/22221751.2021.2011621. Hsueh PR, Hsiue TR, Wu JJ, Teng LJ, Ho SW, Hsieh WC, et al. Flavobacterium indologenes bacteremia: clinical and microbiological characteristics. Clin Infect Dis. 1996;23(3):550-5; doi: 10.1093/clinids/23.3.550. Dunyach-Remy C, Salipante F, Lavigne JP, Brunaud M, Demattei C, Yahiaoui-Martinez A, et al. Pressure ulcers microbiota dynamics and wound evolution. Sci Rep. 2021;11(1):18506; doi: 10.1038/s41598-021-98073-x. Bernardet JF, Nakagawa Y, Holmes B, Subcommittee On The Taxonomy Of F, Cytophaga-Like Bacteria Of The International Committee On Systematics Of P. Proposed minimal standards for describing new taxa of the family Flavobacteriaceae and emended description of the family. Int J Syst Evol Microbiol. 2002;52(Pt 3):1049-70; doi: 10.1099/00207713-52-3-1049. Kumar C, Sastry AS, Plakkal N. Neonatal Sepsis and Meningitis Caused by Elizabethkingia. Indian J Pediatr. 2021;88(6):598; doi: 10.1007/s12098-021-03737-1. Quick J, Constantinidou C, Pallen MJ, Oppenheim B, Loman NJ. Draft Genome Sequence of Elizabethkingia meningoseptica Isolated from a Traumatic Wound. Genome Announc. 2014;2(3); doi: 10.1128/genomeA.00355-14. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj. 2018;362:k601; doi: 10.1136/bmj.k601. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfile2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 16 Jul, 2024 Submission checks completed at journal 16 Jul, 2024 First submitted to journal 09 Jul, 2024 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-4714686","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327724632,"identity":"7fac5ce4-7b41-4477-a1fb-edf1e0f583a0","order_by":0,"name":"Qingsong Chen","email":"","orcid":"","institution":"School of Microelectronics and Communication Engineering of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, 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Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDCCAwwGByAs5gMka2FLIF4LlMVjgFchHPDdSN54uODX4cT+2T0fb7xhsJPTbSCgRfJGWsHhmX2HE2fcObvZcg5DsrHZAQJaDG7kGBzm7Tmc2HAjd5s0D8OBxG1Ea5l/I+cZCVp4fhxO3HAjh404LZJnnhUc5m1IN954I83Yco4BEX7hO568+TPPH2vZeTeSH954U2EnR1ALGDC2NYNpCWKjBgj+1EG1EK1jFIyCUTAKRhIAAAmvTUihWQ29AAAAAElFTkSuQmCC","orcid":"","institution":"Department of Traumatology, National Regional Trauma Center, Traumatic Research Center of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center)","correspondingAuthor":true,"prefix":"","firstName":"Dingyuan","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-07-10 00:30:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4714686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4714686/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62653710,"identity":"13524688-9940-4859-b62d-1fa2c0607bb4","added_by":"auto","created_at":"2024-08-17 01:17:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":696670,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the study design and two-sample MR analysis. \u003cstrong\u003ea\u003c/strong\u003e The causal diagram of the relationship between skin microbiota and PTWI investigated in the two-sample MR analysis. \u003cstrong\u003eb\u003c/strong\u003e Overall study design and workflow step by step. MR, Mendelian randomization; GWAS, genome wide association studies; PTWI, post-traumatic wound infection; SNPs, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4714686/v1/9252a56e14594fe78489f04d.png"},{"id":62653715,"identity":"0fe93ca4-813f-4177-b8da-0352e4228581","added_by":"auto","created_at":"2024-08-17 01:17:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1424988,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant MR results of causal links between skin microbiome and PTWI. \u003cstrong\u003ea\u003c/strong\u003e The forest plot shows the significant causal associations with P \u0026lt; 0.05 and the estimated OR with 95% CI. \u003cstrong\u003eb\u003c/strong\u003e Heatmap plot shows the causal associations between skin microbiota and PTWI with IVW method. MR, Mendelian randomization; PTWI, post-traumatic wound infection; OR, odds ratio; CI, confidence interval; IVW, inverse-variance weighted.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4714686/v1/6ce64a5b9a811d4799eef16a.png"},{"id":62653712,"identity":"3cf6935e-5267-49c6-a608-ef674f51889b","added_by":"auto","created_at":"2024-08-17 01:17:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1969133,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant MR results of causal links between PTWI and skin microbiome. \u003cstrong\u003ea\u003c/strong\u003e The forest plot shows the significant causal associations with P \u0026lt; 0.05 and the estimated OR with 95% CI. \u003cstrong\u003eb\u003c/strong\u003eHeatmap plot shows the causal associations between PTWI and skin microbiome with IVW method. MR, Mendelian randomization; PTWI, post-traumatic wound infection; OR, odds ratio; CI, confidence interval; IVW, inverse-variance weighted.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4714686/v1/775ac41a9f23d48c1f5cb4ba.png"},{"id":62655652,"identity":"124a7bd2-c65f-47de-afa0-d55a868ad254","added_by":"auto","created_at":"2024-08-17 01:41:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4099221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4714686/v1/cd19ba61-4c54-4e50-8fee-5cf19c16d304.pdf"},{"id":62655301,"identity":"11f447f6-7528-47ed-ae77-e77d741d2f43","added_by":"auto","created_at":"2024-08-17 01:33:51","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1939513,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4714686/v1/b509f78d381c77473bedbf4f.xlsx"},{"id":62654527,"identity":"c7478f2f-2388-4c52-8617-c158eda13093","added_by":"auto","created_at":"2024-08-17 01:25:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1986452,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4714686/v1/5cefbf8f2de2054e76d4bd91.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cross talk between skin microbiota and post-traumatic wound infection: a bidirectional mendelian randomization analysis","fulltext":[{"header":"Background","content":"\u003cp\u003ePost-traumatic wound infection (PTWI) is a major concern, involving microbial colonization in wounds from trauma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], burns [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], or surgeries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These infections hinder healing, increase healthcare costs, and pose serious health risks. Recent studies shed light on the epidemiology, classification, and management of combat-related wounds, emphasizing their polymicrobial nature and treatment complexities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Managing PTWI requires decisions about antibiotic use, wound cleaning, and possibly surgical intervention. Recent research highlights the polymicrobial nature of these infections and the challenge of antibiotic-resistant pathogens, necessitating new treatment and prevention strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This area of study is vital for improving trauma care outcomes.\u003c/p\u003e \u003cp\u003eMusculoskeletal trauma, causing fractures and soft tissue damage, also poses significant risks. Advances in orthopedic trauma surgery and medical devices allow for prompt repair and healing of severe injuries, but trauma-related infections remain a complication [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe skin is the largest organ of the human body, serving as the first barrier against injury and pathogen invasion. It works in coordination with immune cells and microorganisms to maintain the skin's physical and immune barrier functions under healthy homeostasis, while also playing a crucial role during various stresses such as trauma and infection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The skin microbiota, together with the skin, constitutes the body's first line of defense, significantly influencing the occurrence, development, and progression of diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The human skin hosts a large number of microorganisms, including bacteria, fungi, and viruses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The diversity of skin microbiota among different populations is influenced by environmental factors, the local microenvironment of the skin, and genetic diversity [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Human skin can be categorized into four types based on physiological and histological characteristics, microenvironment, and microbiota: dry skin (e.g., palms, forearms, anterior legs), characterized by dryness and low moisture content, with common microbiota including Propionibacterium, Staphylococcus, Corynebacterium, and Streptococcus, along with a large number of transient microorganisms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; moist skin (e.g., axillae, elbow folds, groin area, popliteal fossae), which is moist due to high moisture content and commonly harbors Staphylococcus and Corynebacterium; sebaceous skin, or oily skin (e.g., face, chest, back), characterized by rich sebum secretion, with common resident bacteria that are lipophilic, such as Cutibacterium acnes; and foot skin, which has unique resident microbiota, primarily fungi such as Aspergillus, Cryptococcus, and Malassezia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To date, there are no reports on the skin microbiota, especially skin bacteria, and their association with wound infection post-trauma. Thus, understanding the interaction between skin microbiota and wound infection is crucial.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) has emerged as a valuable analytical method for inferring causal relationships between exposures and outcomes. This approach is particularly useful in situations where randomized controlled trials are lacking or not feasible. By utilizing single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), MR design allows for the estimation of causal effects by proxying the phenotypes of interest [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. One of the key advantages of MR is its ability to minimize confounding biases. The random allocation of genetic variants during fertilization mimics the randomization process in an RCT, thereby reducing the likelihood of confounding factors such as sex and age influencing the causal inference [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. MR analysis is increasingly utilized to establish causal relationships between potentially modifiable risk factors and various outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Overall, MR provides a robust alternative strategy to investigate the causal relationship between exposures and disease risks, especially when RCTs are impractical or unavailable.\u003c/p\u003e \u003cp\u003eCurrently, there isn't specific widespread research on SNPs directly related to post-traumatic wound infections. Research in this area typically focuses on the broader context of wound healing, infection susceptibility, and response to treatment, where genetic factors, including SNPs, might play a role. For more detailed and specific studies, a thorough review of scientific literature or databases specializing in genetics and medical research would be necessary. Therefore, the exploration of skin microbiota associated with the development of PTWI is not only valuable for early screening and prevention but also essential for gaining insights into the biological mechanisms underlying PTWI for treatment. However, the causality between skin microbiota and PTWI remains unclear. In this study, a two-sample MR analysis was applied to explore the causal relationship between the latest genome-wide association studies (GWAS) data of 147 types of skin microbiota [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and PTWI. The ultimate goal of this MR analysis was to clarify the direction and magnitude of the causal relationship between skin microbiota and PTWI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe employed two-sample MR to investigate the causal effects of 147 types of skin microbiota on the risk of PTWI. Our MR design adhered to three essential assumptions: (1) the genetic instruments were strongly associated with the exposures of interest; (2) the genetic instruments were not linked to any confounding variables; and (3) the genetic instruments influenced the outcome solely through the exposures of interest [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The second and third assumptions are collectively known as the independence of horizontal pleiotropy. We used various statistical methods to test the validity of these assumptions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The GWAS data for PTWI was sourced from [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. FinnGen release v7, which includes 443 cases and 308,355 controls of European ancestry. The skin microbiota data was obtained from a previously published study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The authors conducted a GWAS meta-analysis of skin microbiota data based on two European cohorts: KORA FF4 (n\u0026thinsp;=\u0026thinsp;324) and PopGen (n\u0026thinsp;=\u0026thinsp;273). We obtained the GWAS summary data from the GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data included information from both the KORA FF4 and PopGen datasets [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We performed the GWAS meta-analysis using METAL software [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Finally, we obtained GWAS data for 147 skin microbiota species. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the study design. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e showed the data sources used in our study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variables selection\u003c/h2\u003e \u003cp\u003eIn our study, we used multiple steps to screen for instrumental variables. For PTWI and skin microbiome, we used the same filtering methods. First, SNPs with genome-wide significance (p\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) were retained. Next, SNPs with a minor allele frequency (MAF) less than 0.01 were excluded. Then, based on data from European samples of the 1000 Genomes Project, we removed SNPs in linkage disequilibrium (r\u0026sup2; = 0.001, clumping window\u0026thinsp;=\u0026thinsp;10,000 kb). Subsequently, we used LDtrait [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ldlink.nih.gov/?tab=ldtrait\u003c/span\u003e\u003cspan address=\"https://ldlink.nih.gov/?tab=ldtrait\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to check if the SNPs were associated with confounding factors. Using a threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, SNPs associated with diabetes, smoking, alcohol consumption, and HIV were excluded. Next, we assessed the strength of the SNPs using the F statistic, calculated as F\u0026thinsp;=\u0026thinsp;R\u0026sup2; \u0026times; (N\u0026thinsp;\u0026minus;\u0026thinsp;2) / (1\u0026thinsp;\u0026minus;\u0026thinsp;R\u0026sup2;) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. R\u0026sup2; represents the proportion of phenotypic variance explained by each instrument. N represents the sample size of the GWAS data. R\u0026sup2; is calculated using the formula R\u0026sup2; = (2 \u0026times; EAF \u0026times; (1\u0026thinsp;\u0026minus;\u0026thinsp;EAF) \u0026times; beta\u0026sup2;) / [(2 \u0026times; EAF \u0026times; (1\u0026thinsp;\u0026minus;\u0026thinsp;EAF) \u0026times; beta\u0026sup2;) + (2 \u0026times; EAF \u0026times; (1\u0026thinsp;\u0026minus;\u0026thinsp;EAF) \u0026times; N \u0026times; se\u0026sup2;)]. EAF is the effect allele frequency, beta is the estimated genetic effect on the exposures, se is the standard error of the genetic effect, and N represents the sample size of the GWAS data. SNPs with an F value greater than 10 were retained. Finally, palindromic SNPs with a MAF greater than 0.42 were excluded. The remaining SNPs after these filters were used for subsequent MR analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTwo-sample MR\u003c/h2\u003e \u003cp\u003eIn our research, we primarily employed the inverse-variance weighted (IVW) method for causal estimation. MR-Egger and weighted median methods were used as supplementary approaches to ensure the robustness of our results. To assess the reliability of our analyses, we utilized Cochran\u0026rsquo;s Q test to evaluate heterogeneity within our analysis. The MR-Egger intercept test [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and MR-pleiotropy residual sum and outlier (MR-PRESSO) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] were used to assess horizontal pleiotropy. Additionally, we used MR-PRESSO to detect outliers in the MR analysis. After removing outliers, we repeated the MR analysis until no further outliers were detected. Leave-one-out analysis was employed to assess the influence of individual SNPs on the overall causal effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eIn our study, we conducted analyses using R 4.3.2 software, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Sensitivity analysis played a critical role in our MR study by addressing issues such as horizontal pleiotropy and heterogeneity, which can potentially bias MR estimates. Horizontal pleiotropy occurs when IVs influence the outcome through pathways unrelated to the exposure under study. To ensure the robustness of our MR estimates, we employed three key methods: (1) Cochran\u0026rsquo;s Q test: This test detects heterogeneity in results, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant heterogeneity in our analysis. (2) MR-Egger intercept test: This method identifies and adjusts for horizontal pleiotropy by testing whether the intercept differs significantly from zero [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. (3) MR-PRESSO: This technique detects and corrects for pleiotropy by identifying outlier SNPs that may bias MR estimates and assessing the impact of their removal [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By implementing these methods, we aimed to ensure the reliability of our MR estimates and minimize biases due to horizontal pleiotropy or heterogeneity in IVs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelection of instrumental variables\u003c/h2\u003e \u003cp\u003eAs described in the Materials and \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section, we employed multiple steps to screen IVs. SNPs excluded due to associations with potential confounding factors are presented in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The IVs representing exposures are shown in Tables S3-4. In the MR analysis, we further filtered these IVs. Palindromic IVs with a MAF greater than 0.42 were excluded. After each MR analysis, we used MR-PRESSO to identify outliers and re-conducted the MR analysis after removing them until no outliers were detected by MR-PRESSO. The excluded outliers are presented in Table S5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eExploration of the causal effect of skin microbiota on PTWI\u003c/h2\u003e \u003cp\u003eThe results of the two-sample MR analyses indicated causal effects of 12 types of skin microbiota on PTWI. Among these, five microbiotas demonstrated protective effects: asv045 [Acinetobacter (unc.)] (OR\u0026thinsp;=\u0026thinsp;0.971, 95% CI\u0026thinsp;=\u0026thinsp;0.943 to 0.999, P\u0026thinsp;=\u0026thinsp;0.044), asv092 [C. kroppenstedtii] (OR\u0026thinsp;=\u0026thinsp;0.966, 95% CI\u0026thinsp;=\u0026thinsp;0.864 to 1.079, P\u0026thinsp;=\u0026thinsp;6.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), asv093 [Staphylococcus (unc.)] (OR\u0026thinsp;=\u0026thinsp;0.911, 95% CI\u0026thinsp;=\u0026thinsp;0.832 to 0.997, P\u0026thinsp;=\u0026thinsp;0.044), genus: Finegoldia (OR\u0026thinsp;=\u0026thinsp;0.965, 95% CI\u0026thinsp;=\u0026thinsp;0.933 to 0.999, P\u0026thinsp;=\u0026thinsp;0.043), and genus: Kocuria (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI\u0026thinsp;=\u0026thinsp;0.908 to 0.994, P\u0026thinsp;=\u0026thinsp;0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S6). Notably, for asv093 [Staphylococcus (unc.)] and genus: Kocuria, the results of the IVW and MR-Egger analyses were inconsistent in direction, indicating that these findings were not robust. Seven microbiotas showed harmful effects: asv001 [P. acnes] (OR\u0026thinsp;=\u0026thinsp;1.187, 95% CI\u0026thinsp;=\u0026thinsp;1.007 to 1.400, P\u0026thinsp;=\u0026thinsp;0.041), asv005 [P. granulosum] (OR\u0026thinsp;=\u0026thinsp;1.259, 95% CI\u0026thinsp;=\u0026thinsp;1.068 to 1.483, P\u0026thinsp;=\u0026thinsp;6.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), family: Micrococcaceae (OR\u0026thinsp;=\u0026thinsp;1.24, 95% CI\u0026thinsp;=\u0026thinsp;1.045 to 1.473, P\u0026thinsp;=\u0026thinsp;0.014), family: Neisseriaceae (OR\u0026thinsp;=\u0026thinsp;1.161, 95% CI\u0026thinsp;=\u0026thinsp;1.009 to 1.337, P\u0026thinsp;=\u0026thinsp;0.038), genus: Enhydrobacter (OR\u0026thinsp;=\u0026thinsp;1.039, 95% CI\u0026thinsp;=\u0026thinsp;1.008 to 1.072, P\u0026thinsp;=\u0026thinsp;0.013), genus: Enhydrobacter (OR\u0026thinsp;=\u0026thinsp;1.202, 95% CI\u0026thinsp;=\u0026thinsp;1.033 to 1.398, P\u0026thinsp;=\u0026thinsp;0.017), and order: Bacteroidales (OR\u0026thinsp;=\u0026thinsp;1.202, 95% CI\u0026thinsp;=\u0026thinsp;1.020 to 1.175, P\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eExploration of the causal effect of PTWI on skin microbiota\u003c/h2\u003e \u003cp\u003eUsing PTWI as the exposure and 147 skin microbiotas as the outcomes, we identified five types of skin microbiotas closely associated with PTWI. Among these, PTWI was associated with an increased abundance of class Betaproteobacteria (Beta\u0026thinsp;=\u0026thinsp;0.263, 95% CI\u0026thinsp;=\u0026thinsp;0.032 to 0.493, P\u0026thinsp;=\u0026thinsp;0.026), genus Chryseobacterium (Beta\u0026thinsp;=\u0026thinsp;0.395, 95% CI\u0026thinsp;=\u0026thinsp;0.114 to 0.675, P\u0026thinsp;=\u0026thinsp;5.85 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), asv007 [Anaerococcus (unc.)] (Beta\u0026thinsp;=\u0026thinsp;0.438, 95% CI\u0026thinsp;=\u0026thinsp;0.089 to 0.788, P\u0026thinsp;=\u0026thinsp;0.014), and family Flavobacteriaceae (Beta\u0026thinsp;=\u0026thinsp;0.372, 95% CI\u0026thinsp;=\u0026thinsp;0.016 to 0.728, P\u0026thinsp;=\u0026thinsp;0.04) based on IVW analysis. In contrast, PTWI was negatively associated with the abundance of asv005 [P. granulosum] (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.358, 95% CI\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.619 to \u0026minus;\u0026thinsp;0.097, P\u0026thinsp;=\u0026thinsp;7.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eTo evaluate heterogeneity among instrumental variables, we used Cochran's Q statistic, finding low heterogeneity, which supports the statistical consistency of SNP effects. We assessed potential pleiotropy using MR-Egger regression, with an insignificant intercept suggesting minimal pleiotropic bias. The consistent MR-Egger slope with IVW method results further indicated that undetected pleiotropy is unlikely to affect our findings (Table S8 and S9). A scatter plot showed that most SNP effect sizes are near zero, indicating minor impacts and low heterogeneity, validating their use as instrumental variables (Figs. S1 and S4). Leave-one-out analysis revealed that no single SNP significantly influenced the overall MR estimate, demonstrating the robustness of our analysis (Figs. S2 and S5). The symmetric distribution around the funnel plot's center line suggested no significant publication or selection bias, confirming the reliability of our MR analysis (Figs. S3 and S6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our best knowledge, this study is among the first to systematically evaluate the causal relationships between the skin microbiota and PTWI from a genetic perspective. Our two-sample MR study provided strong evidence that genetically predicted abundance of specific skin microbes plays significant roles in the occurrence and progression of PTWI. Additionally, the MR confirmed and strengthened the role of PTWI on the skin microbiota. By leveraging molecular genetic markers as instrumental variables, our MR approaches largely avoided the confounding factors (e.g., diabetes mellitus, smoking, alcohol consumption) and reversed causality that often compromise observational studies. This study's investigation into the role of specific skin microbiota in PTWI underscores the complexity of host-pathogen interactions.\u003c/p\u003e \u003cp\u003eAs discussed in the introduction, the theory of skin microbiota and wound communication suggests a possible influence of skin microbiota on PTWI. Approximately 1,000 species of bacteria and about 100\u0026nbsp;billion microbiomes are detectable on human skin [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These bacteria are divided into four phyla: Actinobacteria (51.8%), Firmicutes (24.4%), Proteobacteria (16.5%), and Bacteroidetes (6.3%), including the genera Corynebacterium, Propionibacterium, and Staphylococcus [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. During healing, cell interaction with the wound microbiome is hypothesized to regulate the innate immune response beneficially [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Conversely, pathogenic microbiota negatively affects wound healing [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Statistics show that wounds contain diverse microbiota, primarily Staphylococcus, Pseudomonas, Corynebacterium, Streptococcus, Anaerococcus, and Enterococcus [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Future research should explore the genetic underpinnings of immune responses to better understand the mechanisms driving disease susceptibility and progression. This could lead to more personalized approaches to treatment and prevention, leveraging insights from GWAS and MR studies to identify individuals at higher risk for specific infections or adverse outcomes following PTWI. Our study identified seven bacterial taxa as potential risk factors for PTWI: asv001 [P. acnes], asv005 [P. granulosum], family: Micrococcaceae, family: Neisseriaceae, genus: Enhydrobacter, and order: Bacteroidales. Asv001 [P. acnes] is a Gram-positive bacterium colonizing the skin and the oral and genital tracts, associated with various infections and clinical conditions linked to specific lineages [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Asv005 [P. granulosum] is an anaerobic Gram-positive bacterium involved in maintaining the skin microenvironment and associated with inflammatory responses, such as postoperative and device-related infections [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The family Micrococcaceae includes various Gram-positive bacteria, with notable species like Staphylococcus haemolyticus, a common pathogen in wound and surgical site infections [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and Staphylococcus epidermidis, a frequent nosocomial pathogen capable of biofilm formation, leading to severe infections [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The family Neisseriaceae, though not commonly part of the skin microbiota, can occasionally cause challenging skin and wound infections due to antibiotic resistance [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The genus Enhydrobacter, a Gram-negative bacterium typically found in aquatic environments, was identified on both dry and moist skin in our study, and has been detected in public transportation systems [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The order Bacteroidales, primarily part of the gut microbiota, was found in the dry skin environment and negatively associated with PTWI, and has been detected in damaged and infected skin wounds [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Burn wounds and traumatic wounds differ significantly in terms of damage mechanisms, pathological changes, wound characteristics, infection risks, and prognosis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Studies show that the most common pathogens in acute burn wounds are Staphylococcus aureus, followed by Escherichia coli, Pseudomonas aeruginosa, and coagulase-negative staphylococci [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Given that E. coli and P. aeruginosa belong to the same phylum as Neisseriaceae and Enhydrobacter, it can be inferred that specific skin microbiota may be closely related to post-burn wound infections. Common bacteria in traumatic wounds are similar to those in burn wounds, suggesting common pathogenic mechanisms, development processes, and prognosis, though further exploration is required. Our study also identified five bacterial taxa that might be beneficial for PTWI: asv045 [Acinetobacter (unc.)], asv092 [C. kroppenstedtii], asv093 [Staphylococcus (unc.)], genus: Finegoldia, and genus: Kocuria. Although insufficient experimental evidence directly proves their beneficial effects on wound infections, the diversity of skin microbiota plays a dual role in wound infection and healing [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Skin-resident bacteria may prevent and resolve wound infections by altering the microenvironment of the skin and wounds [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], promoting wound healing [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Research indicates that early inflammatory and immune responses triggered by certain resident bacteria post-injury can prevent infection and promote wound healing. For instance, epidermal staphylococci limit inflammation via the TLR-2 signaling pathway, improving epithelialization and granulation tissue formation, thus accelerating wound healing [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, resident bacteria can also produce proteases and reactive oxygen species that adversely affect wound healing [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], potentially causing severe infections, depending on the quantity of resident bacteria, local microenvironment, and skin condition. For example, Staphylococcus aureus, though rare on the skin, can severely infect wounds and cause chronic infections through the formation of pore-forming toxins and biofilms [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Therefore, the beneficial and harmful skin microbiota identified in our study have significant implications for PTWI research. They facilitate targeted studies on specific skin microbiota based on traditional taxonomic classifications, avoiding the waste of time and resources associated with broad-spectrum approaches. Beneficial microbiota can be investigated for their potential use as probiotics in topical and systemic treatments, an area where significant progress has been made [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This evidence partially explains why adjusting specific skin microbiota may alter the relationship between skin microbiota and PTWI. Although many studies have been reported, the underlying mechanisms have not been fully elucidated. Therefore, specialized mechanistic studies are needed to understand the unique roles of skin microbiota in different skin types, as most current research remains at the biological level of the overall skin microbiota.\u003c/p\u003e \u003cp\u003eOur study complements and expands on existing research, further indicating that PTWI may disrupt the microenvironment of the skin microbiota, leading to homeostasis disturbance. The results show that the homeostasis of four taxa\u0026mdash;class: Betaproteobacteria, genus: Chryseobacterium, asv007 [Anaerococcus (unc.)], and family: Flavobacteriaceae\u0026mdash;was disrupted. Class: Betaproteobacteria, a group within the phylum Proteobacteria, includes bacteria with various metabolic pathways. Our findings support the notion that PTWI may increase the prevalence of class Betaproteobacteria in the skin microbiota. Burkholderia, a genus within this class, is known to heavily colonize burn and general wound sites [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The genus Chryseobacterium consists of Gram-negative, aerobic, non-motile, oxidase-positive, catalase-positive, and indole-positive bacteria, often causing severe infections such as bacteremia and wound sepsis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Asv007 [Anaerococcus (unc.)] is a Gram-positive, anaerobic coccus that is part of the normal human microbiota, especially on the skin and in the oral cavity. It is an opportunistic pathogen found in various types of wounds, often associated with persistent and difficult-to-heal infections [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The family Flavobacteriaceae, comprising mostly aerobic Gram-negative bacteria, is part of the mixed skin microbiota [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Members of this family are both opportunistic pathogens and resident bacteria [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Elizabethkingia, a notable bacterium within this family, can cause neonatal meningitis and has been detected in traumatic wounds [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Our MR results confirm the potential of PTWI to increase the pathogenicity of these four taxa. The MR design, aside from randomized controlled trials (RCTs), allows for more reliable results and represents the highest level of evidence [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, while our findings provide strong causal evidence for the involvement of these taxa, they do not address specific bacterial species. It is also important to note that the effect of wound infection on bacteria is likely dual-sided. Our study identified only one taxon, asv005 [P. granulosum], a common resident bacterium of the skin microbiota, which may play a beneficial role in the progression of wound infection post-trauma. Thus, a comprehensive understanding of the roles and mechanisms of skin microbiota in PTWI is a subject for further research, which could provide strong evidence and reference for effectively preventing and treating PTWI.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, the MR analysis results are less likely to be influenced by confounding factors. Second, by separating the samples in our data on skin microbiota and PTWI, we ensured that the two-sample MR analysis avoided the impact of weak instrumental variables. Third, we combined GWAS data from two independent large-scale cohorts to perform the two-sample MR analysis, ensuring the reliability and generalizability of the causal relationships. Fourth, we conducted heterogeneity analysis and horizontal pleiotropy tests, thoroughly validating the feasibility of the instrumental variable assumptions.\u003c/p\u003e \u003cp\u003eHowever, this study also has limitations. First, the number of instrumental variables for each bacterial taxon varied between 7 and 213, which may result in inaccurate estimates for taxa with fewer instrumental variables. Nonetheless, each instrumental variable passed the heterogeneity and horizontal pleiotropy tests. Second, this study primarily involved individuals of European ancestry, which may limit the generalizability of the findings. Further research involving other populations may require the collection of new samples.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo our knowledge, this is the first study to investigate the causal relationship between skin microbiota and PTWI using data from a European population. Our research delineates the bidirectional effects, illustrating how skin microbiota influence PTWI and how PTWI affects skin microbiota. Additionally, it elaborates on the causal relationships between skin microbiota and PTWI across different skin types. These findings provide new strategies and references for the prevention and treatment of PTWI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePTWI Post-traumatic wound infection \u003c/p\u003e\n\u003cp\u003eMR Mendelian randomization \u003c/p\u003e\n\u003cp\u003eGWAS Genome wide association studies \u003c/p\u003e\n\u003cp\u003eIVW Inverse-variance weighted \u003c/p\u003e\n\u003cp\u003eSNP Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eIV Instrumental variables \u003c/p\u003e\n\u003cp\u003eLD Linkage disequilibrium \u003c/p\u003e\n\u003cp\u003eASV Amplicon sequence variants \u003c/p\u003e\n\u003cp\u003eCI Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and/or analysed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Major Research Project of the National Trauma Regional Medical Center (jointly sponsored by the Chongqing Municipal Government and Health Commission (jjzx2021-gjcsqyylzx01), the open topics of the Key Laboratory of Emergency Medicine in Chongqing (2022KFKI07), and the Chongqing Elite Research Project (cstc2022ycjh-bgzxm0245).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQSC and YKZ: conceptualization, data curation, formal analysis, investigation, methodology, software and writing-original draft. GBH, BHZ, YC, LS and: data curation, validation and visualization. JXL, HL, QZ and PH: formal analysis and writing-review \u0026amp; editing. YML: conceptualization, project administration, resources, software and writing-review \u0026amp; editing. DYD: conceptualization, funding acquisition, project administration, resources and writing-review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data is available at BMC microbiology Journal on line.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKomori A, Iriyama H, Kainoh T, Aoki M, Naito T, Abe T. The impact of infection complications after trauma differs according to trauma severity. 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Bmj. 2018;362:k601; doi: 10.1136/bmj.k601.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Skin microbiota, Post-traumatic wound infection, Causal association, Two-sample Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4714686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4714686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePost-traumatic wound infection (PTWI) is a major challenge in trauma, burns, and surgeries. The skin microbiota is crucial for defense and may influence PTWI occurrence, though the relationship is unclear. This study explores the causal link between the skin microbiome and PTWI using bidirectional two-sample Mendelian randomization (MR) analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA two-sample MR analysis was conducted using genome wide association studies (GWAS) data of 147 skin microbiota taxa and PTWI. The inverse-variance weighted (IVW) method was the primary analysis technique, while the MR-Egger and weighted median were used as supplementary analysis methods. Cochran\u0026rsquo;s Q test was used to perform heterogeneity analysis. The MR-Egger intercept test and MR-PRESSO were employed to assess potential horizontal pleiotropy. The leave-one-out method was utilized to evaluate the impact of individual SNPs on the overall causal effect.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe two-sample MR analysis identified significant causal relationships between 12 skin microbiota species and PTWI. Five species were potentially beneficial: asv045 [Acinetobacter (unc.)] (OR\u0026thinsp;=\u0026thinsp;0.971, P\u0026thinsp;=\u0026thinsp;0.044), asv092 [C. kroppenstedtii] (OR\u0026thinsp;=\u0026thinsp;0.966, P\u0026thinsp;=\u0026thinsp;6.88e\u0026thinsp;\u0026minus;\u0026thinsp;03), asv093 [Staphylococcus (unc.)] (OR\u0026thinsp;=\u0026thinsp;0.911, P\u0026thinsp;=\u0026thinsp;0.044), genus Finegoldia (OR\u0026thinsp;=\u0026thinsp;0.965, P\u0026thinsp;=\u0026thinsp;0.043), and genus Kocuria (OR\u0026thinsp;=\u0026thinsp;0.95, P\u0026thinsp;=\u0026thinsp;0.025). Seven species were potentially harmful: asv001 [P. acnes] (OR\u0026thinsp;=\u0026thinsp;1.187, P\u0026thinsp;=\u0026thinsp;0.041), asv005 [P. granulosum] (OR\u0026thinsp;=\u0026thinsp;1.259, P\u0026thinsp;=\u0026thinsp;6.06e\u0026thinsp;\u0026minus;\u0026thinsp;03), family Micrococcaceae (OR\u0026thinsp;=\u0026thinsp;1.24, P\u0026thinsp;=\u0026thinsp;0.014), family Neisseriaceae (OR\u0026thinsp;=\u0026thinsp;1.161, P\u0026thinsp;=\u0026thinsp;0.038), genus Enhydrobacter (OR\u0026thinsp;=\u0026thinsp;1.039, P\u0026thinsp;=\u0026thinsp;0.013; OR\u0026thinsp;=\u0026thinsp;1.202, P\u0026thinsp;=\u0026thinsp;0.017), and order Bacteroidales (OR\u0026thinsp;=\u0026thinsp;1.202, P\u0026thinsp;=\u0026thinsp;0.012). PTWI may also induce skin microenvironment changes, disrupting homeostasis and increasing the likelihood of pathogenic microbiota, such as class Betaproteobacteria, genus Chryseobacterium, asv007 [Anaerococcus (unc.)], and family Flavobacteriaceae. Conversely, PTWI might promote beneficial microbiota, like asv005 [P. granulosum].\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides strong evidence of a causal link between the skin microbiome and PTWI, emphasizing their complex interactions. These findings offer new insights for preventing and treating PTWI. Further research on the underlying mechanisms and similar studies in different populations are essential.\u003c/p\u003e","manuscriptTitle":"Cross talk between skin microbiota and post-traumatic wound infection: a bidirectional mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:17:46","doi":"10.21203/rs.3.rs-4714686/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-16T13:46:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-16T13:46:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-07-10T00:28:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cf73547-878f-4a24-91d5-d7e43da04ab6","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-14T07:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 01:17:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4714686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4714686","identity":"rs-4714686","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}