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

doi:10.21203/rs.3.rs-4714686/v1

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

https://doi.org/10.21203/rs.3.rs-4714686/v1

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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

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.

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].

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).

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.

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.

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

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.

Supplementary data

Supplementary data is available at BMC microbiology Journal on line.

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

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Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Study design

Instrumental variables selection

Two-sample MR

Sensitivity analysis

Results

Selection of instrumental variables

Exploration of the causal effect of skin microbiota on PTWI

Exploration of the causal effect of PTWI on skin microbiota

Sensitivity analyses

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1