Host genetic factors related to innate immunity, environmental sensing and cellular functions are associated with human skin microbiota

Unveiling the interplay among skin microbiota, cytokines, and T2DM: an insightful Mendelian randomization study

BACKGROUND

Previous observational studies have indicated a correlation between the skin microbiome and Type 2 diabetes (T2DM). It is hypothesized that this causal relationship may be influenced by inflammatory responses. However, these factors as determinants of T2DM remain largely unexplored.

METHOD

This study incorporated data from the GWAS database on the skin microbiome, 91 types of inflammatory cytokines, and T2DM. We employed two-sample MR and multivariable MR methods to assess the correlation between the skin microbiome and T2DM, and to investigate whether this correlation is affected by inflammatory cytokines.

RESULTS

The results of the two-sample MR analysis indicate that within the skin microbiome, genetically predicted genus: Acinetobacter, class: Alphaproteobacteria, genus: Bacteroides, ASV005[Propionibacterium granulosum], and ASV072[Rothia mucilaginosa] are associated with an increased risk of T2DM, while phylum: Proteobacteria, genus: Enhydrobacter, family: Clostridiales, ASV006[Staphylococcus hominis] serve as protective factors against T2DM. Among the inflammatory cytokines, levels of Macrophage colony-stimulating factor 1, Tumor necrosis factor receptor superfamily member 9, Urokinase-type plasminogen activator, and C-C motif chemokine 28 are associated with an increased risk of T2DM. Multivariable MR analysis further revealed that Macrophage colony-stimulating factor 1 levels act as a mediating factor between ASV072[Rothia mucilaginosa] and T2DM.

CONCLUSION

In this study, we found a connection between the skin microbiome and T2DM, with inflammatory cytokines playing a key role in this relationship. This research helps us better understand this complex link and shows that addressing inflammation is important for preventing and treating diabetes. This could greatly benefit public health by reducing the impact of diabetes and its complications. Our results suggest that future studies should explore the specific biological interactions between the skin microbiome and diabetes to develop more effective risk management and treatment strategies from a microbial perspective.

Effect of the gut microbiome, skin microbiome, plasma metabolome, white blood cells subtype, immune cells, inflammatory proteins, and inflammatory cytokines on asthma: a two-sample Mendelian randomized study and mediation analysis

Background

Asthma is a chronic inflammatory disorder arising from incompletely understood heterogenic gene-environment interactions. This study aims to investigate causal relationships among gut microbiota, skin microbiota, plasma metabolomics, white blood cells subtype, immune cells, inflammatory proteins, inflammatory cytokines, and asthma.

Methods

First, two-sample Mendelian randomization analysis was used to identify causal relationships. The summary statistics of 412 gut microbiota traits (N = 7 738), 150 skin microbiota traits (N = 579), 1 400 plasma metabolite traits (N = 8 299), white blood cells subtype counts (N = 746 667), 731 immune cell traits (N = 3 669), 91 circulating inflammatory proteins (N = 14 744), 41 inflammatory cytokine traits (N = 8 293), and asthma traits (N = 244 562) were obtained from publicly available genome-wide association studies. Inverse-variance weighted regression was used as the primary Mendelian randomization method. A series of sensitivity analyses was performed to test the robustness of causal estimates. Subsequently, mediation analysis was performed to identify the pathway from gut or skin microbiota to asthma mediated by plasma metabolites, immune cells, and inflammatory proteins.

Results

Mendelian randomization revealed the causal effects of 31 gut bacterial features (abundances of 19 bacterial pathways and 12 microbiota), 10 skin bacterial features, 108 plasma metabolites (81 metabolites and 27 ratios), 81 immune cells, five circulating inflammatory proteins, and three inflammatory cytokines and asthma. Moreover, the mediation analysis results supported the mediating effects of one plasma metabolite, five immunophenotypes, and one inflammatory protein on the gut or skin microbiota in asthma pathogenesis.

Conclusion

The findings of this study support a causal relationship among gut microbiota, skin microbiota, plasma metabolites, immune cells, inflammatory proteins, inflammatory cytokines, and asthma. Mediating pathways through which the above factors may affect asthma were proposed. The biomarkers and mediation pathways identified in this work provide new insights into the mechanism of asthma and contribute to its prevention and treatment.

Bidirectional causal relationships between the skin microbiome and psoriasis: Insights from Mendelian randomization analysis

Psoriasis is a chronic inflammatory skin disease affecting 2% of the global population. Recent research suggests the skin microbiome plays a critical role in psoriasis. Skin microbiome data were obtained from the KORA FF4 study in Germany, and psoriasis data from FinnGen genome-wide association study summary statistics. Forward and reverse 2-sample Mendelian randomization (MR) analyses were conducted to assess causal relationships. Forward MR analysis identified several microbial features as risk factors for psoriasis, including the family Neisseriaceae in sebaceous skin (OR = 1.036, 95% CI: 1.010-1.062, P = .0054), ASV011 in dry skin (OR = 1.024, 95% CI: 1.000-1.048, P = .0490), and the order Clostridiales in moist skin (OR = 1.016, 95% CI: 1.000-1.032, P = .0449). Protective features included ASV016 (OR = 0.972, 95% CI: 0.949-0.994, P = .0136) and ASV053 (OR = 0.973, 95% CI: 0.954-0.992, P = .0054) in dry skin. Reverse MR analysis confirmed psoriasis as a significant risk factor for changes in the skin microbiome, with notable associations in the dry skin region for asv002 (OR = 1.266, 95% CI: 1.061-1.510, P = .027) and genus: Haemophilus (OR = 1.364, 95% CI: 1.065-1.746, P = .013). This study reveals bidirectional causal relationships between the skin microbiome and psoriasis, highlighting specific microbial features such as Neisseriaceae and Clostridiales as potential risk factors. Further research is needed to develop treatments that modulate the skin microbiome to improve psoriasis outcomes.

Investigating the causal relationship between skin microbiota and hypertrophic scar using bidirectional mendelian randomization

BACKGROUND

Hypertrophic scar (HS) is acknowledged as a pathological fibro-proliferative disease of the dermis, resulting from excessive connective tissue growth. HS significantly impacts patient quality of life due to both social and functional issues. Despite various treatments, therapeutic effectiveness remains limited, necessitating further exploration of underlying factors and mechanisms.

OBJECTIVE

The current study was designed to determine the causal relationship between skin microbiota and HS employing a bidirectional Mendelian randomization (MR) approach.

METHODS

We utilized genome-wide association study (GWAS) data from the PopGen cohort and the FinnGen database. Independent single nucleotide polymorphisms (SNPs) linked to the skin microbiota were identified as instrumental variables (IVs) chosen for the two-sample MR analysis. Key analytical approaches included inverse variance weighting (IVW), MR-Egger, simple median, simple mode, and weighted mode, with MR-Egger intercept test and Cochrane's Q test used to detect potential horizontal pleiotropy and heterogeneity.

RESULTS

The two-sample MR analysis identified significant causal relationships between specific skin microbiota features and HS. Notably, Enhydrobacter, Micrococcus, and Acinetobacter on moist skin exhibited protective effects against HS, whereas Finegoldia and Lactobacillales on dry skin were linked to an increased risk of HS. Sensitivity analyses verified the strength of these results, revealing no notable horizontal pleiotropy or heterogeneity.

CONCLUSION

Our research reveals a unidirectional causal relationship between certain skin microbiota and HS, suggesting that modulation of skin microbiota could be a novel therapeutic approach for HS management. These results emphasize the significance of considering skin microbiota in the pathogenesis and treatment of HS.

Roles of skin microbiota in hidradenitis suppurativa: insights from a two-sample mendelian randomization analysis

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease associated with unique lesional dysbiotic features. However, the role of the microbiology in the pathogenesis of HS remains in dispute. We aimed to conduct a two-sample Mendelian randomization study to investigate the relationship between skin microbiota and HS. A two-sample Mendelian randomization study was performed using the summary statistics of skin microbiota from summary GWAS data of the European descent from two cross-sectional, population-based German cohorts, KORA FF4 (n = 324) and PopGen (n = 273). The summary statistics of hidradenitis suppurativa were obtained from the FinnGen DF10 (1,070 cases and 394,105 controls). Inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode, weighted mode, and MRPRESSO were used to examine the causal association between skin microbiota and hidradenitis suppurativa. Cochran's Q statistics were used to quantify the heterogeneity of instrumental variables. Our study suggested that genus Corynebacterium in dry skin is significantly associated with HS after false discovery rate (FDR) correction (odds ratio (OR) = 1.04, 95% confidence interval (CI): 1.02-1.06, P = 0.0002, FDR adjusted P = 0.035). Additionally, we found genus Micrococcus in moist skin (OR = 1.10, 95% CI: 1.03-1.18, P = 0.0060, FDR adjusted P = 0.360), species Streptococcus salivarius in dry skin (OR = 1.03, 95% CI: 1.01-1.05, P = 0.0070, FDR adjusted P = 0.360), and species Propionibacterium granulosum (OR = 1.02, 95% CI: 1.00-1.04, P = 0.0460, FDR adjusted P = 0.970) are potentially associated with HS before FDR adjustment. No evidence of the effect in the reverse direction for HS on skin microbial features. This two-sample Mendelian randomization study found that genus Corynebacterium was causally associated with HS. Further studies are needed to clarify the protective effect of prebiotics, probiotics or microbiome transplants on HS.

Causal Association Between Skin Microbiota and Malignant Melanoma: Genetic Insights From Mendelian Randomization

Background

Malignant melanoma (MM) is an extremely aggressive type of skin cancer that represents a major risk to human health. Earlier observational research has indicated that skin microbiota could play a role in the development and advancement of MM. Nevertheless, the causal link between skin microbiota and MM is still unclear.

Methods

Utilizing data from genome-wide association studies (GWAS) conducted on a European cohort, we applied Mendelian randomization (MR) to evaluate the causal link between skin microbiota and MM. The analysis involved various MR methodologies, including inverse variance weighting (IVW), MR-Egger regression, weighted median, weighted mode and simple mode. Furthermore, we performed sensitivity analysis employing the intercept test of MR-Egger, the Cochran's Q test, the MR-PRESSO approach, and a leave-one-out method.

Results

By conducting MR analysis on the KORA FF4 cohort, we identified several skin microbiotas (ASV003 [Staphylococcus (unc).], ASV016 [Enhydrobacter (unc).], and ASV021 [Micrococcus (unc).]) related with an elevated risk of MM. Conversely, genus: Finegoldia and class: Alphaproteobacteria were shown to inhibit the occurrence of MM. Additionally, MR analysis of the PopGen cohort revealed that ASV021 [Micrococcus (unc).] and family: Moraxellaceae were identified as possible risk factors for MM.

Conclusion

Our research offers new insights into the connection between skin microbiota and MM, indicating that skin microbiota might affect the onset and advancement of MM. Therefore, focusing on skin microbiota could be a valuable strategy for the prevention, identification, and management of MM.

Bidirectional Mendelian Randomization Analysis to Study the Relationship Between Human Skin Microbiota and Radiation-Induced Skin Toxicity

Radiation-induced skin toxicity, resulting from ionizing or nonionizing radiation, is a common skin disorder. However, the underlying relationship between skin microbiota and radiation-induced skin toxicity remains largely unexplored. Herein, we uncover the microbiota-skin interaction based on a genome-wide association study (GWAS) featuring 150 skin microbiota and three types of skin microenvironment. Summary datasets of human skin microbiota were extracted from the GWAS catalog database, and summary datasets of radiation-induced skin toxicity from the FinnGen biobank. Mendelian Randomization (MR) analysis was leveraged to sort out the causal link between skin microbiota and radiation-induced skin toxicity. We identified 33 causal connections between human skin microbiota and radiation-induced skin toxicity, including 19 positive and 14 negative causative directions. Among these potential associations, the genus Staphylococcus could serve as a common risk factor for radiation-induced skin toxicity, especially for radiodermatitis. And Streptococcus salivarius was identified as a potential protective factor against radiation-induced skin toxicity. Additional analysis indicated no pleiotropy, heterogeneity, or reverse causal relationship in the results. We comprehensively assessed potential associations of skin microbiota with radiation-induced skin toxicity and identified several suggestive links. Our results provide promising targets for the prevention and treatment of radiation-induced skin toxicity.

Functionally enriched human polymorphisms associate to species in the chronic wound microbiome

Chronic wounds are a burden to millions of patients and healthcare providers worldwide. With rising incidence and prevalence, there is an urgent need to address non-healing wounds with novel approaches. Impaired wound healing has been shown to be associated with wound microbiota, and multiple bacterial species are known to contribute to delays in closure. Recent evidence suggests human genetics may shape differences in composition of wound microbiomes, and unraveling this relationship has important implications for understanding wound bioburden and tailoring treatment. Here, a two-stage microbiome genome wide association study (mbGWAS; n=509) was used to test effects of human genetics on the relative abundances of bacterial species detected in chronic wounds using bacterial 16S rRNA gene sequencing. Sixteen species were significantly associated to 193 genetic loci distributed across 25 non-overlapping genomic regions. No locus was associated with more than one species, with heritability estimates per species ranging up to 20%. Functional analyses on genomic regions and species resulted in overrepresentation pertaining to pathways relevant to microbial infection and wound healing, suggesting that genetic and species interactions jointly influence the wound microenvironment. Species associated to host genetics in turn exhibited significant co-occurrence relationships with common wound pathogens including Staphylococcus aureus and Finegoldia magna . Moreover, the overall genetic distance among patients was significantly related to differences in their overall wound microbiome composition. Identification of such genetic biomarkers reveals new mechanistic insight into patient-microbiome interactions and provides an avenue to identify predictive risk factors for diagnosis and management of chronic wounds.

The role of skin microbiota in lichen planus from a Mendelian randomization perspective

Lichen planus is a chronic skin lesion characterized by pruritic violaceous papules, which has a high risk of morbidity. Skin microbiota plays an important role in the maintenance of cutaneous mucosal barrier and human health and immune homeostasis. Studies have shown that skin microbiota may play a role in the pathogenesis of lichen planus, but it is not yet clear. MR studies have been performed to determine causal associations. Lichen planus samples from Finn database were extracted from published GWAS data, including 6,411 cases and 405,770 controls; skin microbiota samples were gathered from a meta-analysis of German population-based GWAS, which included 1,656 skin samples from two cohorts: KORA FF4 (n = 353) and PopGen (n = 294), comprising 4,685,714 SNPs. The IVW method was used as the main statistical method, supplemented by three methods: MR-Egger, weighted median and weighted mode. FDR correction and MR Steiger test were used to reduce false positives. IVW method revealed a negative correlation between Burkholderia in the moist anterior elbow and lichen planus (OR: 0.934, 95% CI: 0.910-0.986, P = 0.017). These associations remained stable following false discovery rate correction (P < 1e-5). Our study highlights a possible causal role of Burkholderia in the development of lichen planus and suggests that Burkholderia may reduce the occurrence of lichen planus by affecting macrophages.

Causal relationship between skin microbiota and Hidradenitis suppurativa: a two-sample Mendelian randomization study

BACKGROUND

Data from observational and clinical studies indicate an association between skin microbiota and hidradenitis suppurativa (HS). However, the causal relationship between skin microbiota and HS remains to be elucidated.

METHODS

We obtained data on skin microbiota and HS from summary statistics of genome-wide association studies and applied Mendelian randomization (MR) statistical methods to assess causality. Specifically, we employed both full MR and inverse MR methods, utilizing five statistical approaches, including inverse variance weighting, to evaluate causality. Furthermore, sensitivity analyses were conducted to validate the MR findings.

RESULTS

Inverse variance weighted analysis revealed that the order Burkholderiales (OR = 0.922, 95% CI 0.855-0.994, P = 0.033), the genus Enhydrobacter (OR = 0.897, 95% CI 0.815-0.986, P = 0.025), ASV037 (OR = 0.926, 95% CI 0.863-0.995, P = 0.036), and ASV016 (OR = 0.932, 95% CI 0.870-0.998, P = 0.043) exhibited a protective effect against HS. Conversely, ASV022 (OR = 1.098, 95% CI 1.027-1.175, P = 0.007) was identified as a risk factor.

CONCLUSION

Our analysis suggests a possible causal relationship between specific skin microbiota and HS. These findings shed light on the potential pathogenesis and therapeutic interventions.

Genetic association between human skin microbiota with vaginitis: a two-sample Mendelian randomization study

The human skin microbiome is closely associated with various diseases. We aimed to find the causal association of the human skin microbiome with vaginitis. A two-way two-sample Mendelian randomization study used summary statistics of the human skin microbiota from the largest genome-wide association study meta-analysis. Pooled statistics for vaginitis disease were obtained from the FinnGen consortium R10 release. Inverse variance weighted, MR-Egger, weighted median, weighted model, MR-PRESSO, and STEIGER methods were used to test for causal associations between different parts of the human skin microbiota and vaginitis disease in either dry or moist environments. IVW estimates suggest that phylococcus hominischaracterizes a protective effect against vaginitis lesions at the Antecubitalfossa wet site. Staphylococcus in the dry state at the Dorsalforearm site also demonstrated a protective effect against vaginitis lesions. Micrococcus showed a trend of up-regulation of vaginitis risk under IVW method estimation. In contrast, Anaerococcus and Veillonella were associated with a low risk of vaginitis as estimated by the IVW method: ASV061 and ASV065. In the Class, IVW estimation showed that Bacilli at Antecubitalfossa site had an increased risk effect on vaginitis in the moist skin condition. Similarly in Genus, Staphylococcus at Antecubitalfossa sites had an increased risk effect against vaginitis in the moist skin condition. Based on the results of the inverse MR analysis, no significant causal effect of vaginitis on the human skin microbiota was found. This two-sample bidirectional Mendelian randomization study found a causal relationship between seven human skin microbiota and vaginitis, further enriching our understanding of the value of skin flora in reproductive diseases such as vaginitis.

Unraveling the impact of host genetics and factors on the urinary microbiome in a young population

The significance of the urinary microbiome in maintaining health and contributing to disease development is increasingly recognized. However, a comprehensive understanding of this microbiome and its influencing factors remains elusive. Utilizing whole metagenomic and whole-genome sequencing, along with detailed metadata, we characterized the urinary microbiome and its influencing factors in a cohort of 1,579 Chinese individuals. Our findings unveil the distinctiveness of the urinary microbiome from other four body sites, delineating five unique urotypes dominated by Gardnerella vaginalis, Sphingobium fluviale, Lactobacillus iners, Variovorax sp. PDC80, and Acinetobacter junii, respectively. We identified 108 host factors significantly influencing the urinary microbiome, collectively explaining 12.92% of the variance in microbial composition. Notably, gender-related factors, including sex hormones, emerged as key determinants in defining urotype groups, microbial composition and pathways, with the urinary microbiome exhibiting strong predictive ability for gender (area under the curve [AUC] = 0.843). Furthermore, we discovered 43 genome-wide significant associations between host genetic loci and specific urinary bacteria, Acinetobacter in particular, linked to eight host loci (P < 5 × 10-8). These associations were also modulated by gender and sex hormone levels. In summary, our study provides novel insights into the impact of host genetics and other factors on the urinary microbiome, shedding light on its implications for host health and disease.

IMPORTANCE

The urinary microbiome, essential to human health, reveals its unique qualities in our study of 1,579 Chinese individuals. We identified distinctive microbial profiles, or "urotypes," and uncovered strong gender-related influences, particularly from sex hormones, on these microbial communities. Our research highlights significant genetic associations affecting specific urinary bacteria, indicating a deep interaction between our genetics and our microbiome. These insights not only enhance our understanding of the urinary microbiome's role in health and disease but also open new pathways for personalized medical strategies, making our findings crucial for future diagnostic and therapeutic innovations. This work underscores the intricate relationship between our body's biological processes and the microorganisms within, providing valuable knowledge for both scientific and medical communities.

Unveiling the hidden causal links: skin flora and cutaneous melanoma

Objective

The presence of skin flora (SF) has been identified as a significant factor in the onset and progression of cutaneous melanoma (CM). However, the vast diversity and abundance of SF present challenges to fully understanding the causal relationship between SF and CM.

Methods

A Two Sample Mendelian Randomization (TSMR) analysis was conducted to investigating the causal relationship between SF and CM. The Inverse-Variance Weighted (IVW) method was utilized as the primary approach to assess the causal relationship under investigation. Furthermore, an independent external cohort was employed to validate the initial findings, followed by a meta-analysis of the consolidated results. To address potential confounding factors related to the influence of SF on CM, a Multivariate Mendelian Randomization (MVMR) analysis was also conducted. Finally, a Reverse Mendelian Randomization (RMR) was conducted to further validate the causal association.

Results

TSMR results showed that 9 SF have a causal relationship with CM in the training cohort. Although these 9 SF weren't confirmed in the testing cohort, 4 SF remained significant in the meta-analysis after integrating results from both cohorts. MVMR analysis indicated that 3 SF were still significantly associated with CM after accounting for the interactions between different SF in the training cohort. No reverse causal relationship was identified in RMR analysis.

Conclusion

A total of 9 SF were identified as having a potential causal relationship with CM; however, a large randomized controlled trial is needed to verify these results.

The Causal Effect Between Human Microbiota and Scabies: A Study from the Genetic Perspective

Background

Previous studies have indicated that human flora may affect the development of scabies, however, no studies have proven a causal relationship between human flora and scabies, which would be detrimental to future in-depth studies on human flora and scabies.

Methods

Mendelian randomization (MR) was used to analyze the causal effect between human microbiota and scabies, with data on intestinal flora and skin flora from two large published studies and data on scabies from the FinnGen database. Five MR analysis methods were used to increase the reliability of the results, and sensitivity analyses were conducted to increase the robustness of the results.

Results

Our results suggest that 13 intestinal flora as well as 7 skin flora can have a causal effect on scabies.

Conclusion

Overall, our results demonstrate a causal relationship between intestinal and skin flora and scabies and are consistent with previous observational findings. This will contribute to the future development of probiotic agents for the prevention or treatment of scabies.

The host genotype actively shapes its microbiome across generations in laboratory mice

BACKGROUND

The microbiome greatly affects health and wellbeing. Evolutionarily, it is doubtful that a host would rely on chance alone to pass on microbial colonization to its offspring. However, the literature currently offers only limited evidence regarding two alternative hypotheses: active microbial shaping by host genetic factors or transmission of a microbial maternal legacy.

RESULTS

To further dissect the influence of host genetics and maternal inheritance, we collected two-cell stage embryos from two representative wild types, C57BL6/J and BALB/c, and transferred a mixture of both genotype embryos into hybrid recipient mice to be inoculated by an identical microbiome at birth.

CONCLUSIONS

Observing the offspring for six generations unequivocally emphasizes the impact of host genetic factors over maternal legacy in constant environments, akin to murine laboratory experiments. Interestingly, maternal legacy solely controlled the microbiome in the first offspring generation. However, current evidence supporting maternal legacy has not extended beyond this initial generation, resolving the aforementioned debate. Video Abstract.

Microbial interactions shaping host attractiveness: insights into dynamic behavioral relationships

Insects discern the presence of hosts (host plants) by integrating chemosensory, gustatory, and visual cues, with olfaction playing a pivotal role in this process. Among these factors, volatile signals produced by host-associated microbial communities significantly affect insect attraction. Microorganisms are widely and abundantly found on the surfaces of humans, plants, and insects. Notably, these microorganisms can metabolize compounds from the host surface and regulate the production of characteristic volatiles, which may guide the use of host microorganisms to modulate insect behavior. Essentially, the attraction of hosts to insects is intricately linked to the presence of their symbiotic microorganisms. This review underscores the critical role of microorganisms in shaping the dynamics of attractiveness between insects and their hosts.

The Causal Relationship Between Skin Microbiota and Facial Aging: A Mendelian Randomization Study

BACKGROUND

Facial aging is a complex process influenced by environmental factors, genetics, and lifestyle. The contribution of the skin microbiota to this process remains poorly understood.

METHODS

This two-sample Mendelian randomization (MR) study was performed using genome-wide genotype data from the UK Biobank and previously published studies on skin microbiota. The primary approach for MR analyses included inverse-variance weighting (IVW), MR-Egger regression, simple mode, weighted median, and weighted mode methods. Sensitivity analyses were performed to assess heterogeneity and pleiotropy, and reverse-direction MR analyses were performed to evaluate potential reverse causation.

RESULTS

The MR analysis identified ten skin microbiotas with potential causal relationships with facial aging. Protective skin microbiotas included Genus Finegoldia, ASV011 [Staphylococcus (unc.)], ASV008 [Staphylococcus (unc.)], phylum Firmicutes, Family Rhodobacteraceae, and ASV021 [Micrococcus (unc.)], which were negatively associated with facial aging. Conversely, Order Pseudomonadales, Family Moraxellaceae, ASV039 [Acinetobacter (unc.)], and phylum Bacteroidetes were positively associated with facial aging, indicating a risk factor for accelerated aging. Sensitivity analyses confirmed the robustness of these findings, and reverse-direction MR analyses did not suggest any reverse causation.

CONCLUSION

This study identified specific skin microbial that may influence facial aging and offered new insights into the rejuvenation strategies.

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Skin Microbiota, Immune Cell, and Skin Fibrosis: A Comprehensive Mendelian Randomization Study

BACKGROUND

Microbiota dysbiosis has been reported to lead to leaky epithelia and trigger numerous dermatological conditions. However, potential causal associations between skin microbiota and skin fibrosis and whether immune cells act as mediators remain unclear.

METHODS

Summary statistics of skin microbiota, immune cells, and skin fibrosis were identified from large-scale genome-wide association studies summary data. Bidirectional Mendelian randomization was performed to ascertain unidirectional causal effects between skin microbiota, immune cells, and skin fibrosis. We performed a mediation analysis to identify the role of immune cells in the pathway from skin microbiota to skin fibrosis.

RESULTS

Three specific skin microbiotas were positively associated with skin fibrosis, while the other three were negative. A total of 15 immune cell traits were associated with increased skin fibrosis risk, while 27 were associated with a decreased risk. Moreover, two immune cell traits were identified as mediating factors.

CONCLUSIONS

Causal associations were identified between skin microbiota, immune cells, and skin fibrosis. There is evidence that immune cells exert mediating effects on skin microbiota in skin fibrosis. In addition, some strains exhibit different effects on skin fibrosis in distinct environments.

Exploring the association between skin microbiota and inflammatory skin diseases: a two-sample Mendelian randomization analysis

Dysbiosis in the skin microbiome is closely associated with various inflammatory skin diseases. However, current research on the causal relationship between the skin microbiome and inflammatory skin diseases lacks comprehensive and detailed investigation. We used a two-sample Mendelian randomization (MR) approach to explore associations between the skin microbiome and seven inflammatory skin diseases, including acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria. The GWAS summary data for the skin microbiome was derived from 647 participants in two German population-based cohorts, and for the inflammatory skin diseases, they were sourced from the FinnGen consortium. Our primary MR analysis method was the inverse variance weighted (IVW) method, complemented by alternatives like MR-Egger regression, weighted median estimation, and constrained maximum likelihood. Sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and MR-PRESSO outlier detection, were conducted to validate and stabilize our findings. We identified significant causal relationships between the skin microbiome and seven inflammatory skin diseases: acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria, with 7, 6, 9, 1, 7, 4, and 7 respective causal relationships for each disease. These relationships comprise 20 protective and 14 risk causal relationships. We applied the false discovery rate correction to these results. Sensitivity analysis revealed no significant pleiotropy or heterogeneity. Our study revealed both beneficial and detrimental causal relationships between diverse skin microbiota and inflammatory skin diseases. Additionally, the ecological niche of the skin microbiome was crucial to its functional impact. This research provided new insights into how skin microbiota impacted skin diseases and the development of therapeutic strategies.

Decoding skin mysteries: Unveiling the link between microbiota and keloid scars through a Mendelian randomization study

The cause of keloids remains unclear, but studies suggest a link between skin microbiota and keloid formation. However, the causal relationship has not been confirmed. This study utilized Genome-Wide Association Studies (GWAS) data from 2 population-based German cohorts, comprising a total of 1656 skin samples. To bolster the reliability of our results, we incorporated GWAS data from 3 keloid cohorts, encompassing 2555 patients and 870,556 controls (GWAS ID: keloid1, ebi-a-GCST90018874; keloid2, bbj-a-131; keloid3, ebi-a-GCST90018654). Subsequently, we employed bidirectional 2-sample Mendelian randomization (MR) analysis to probe the causal relationship between the variables. The primary method employed was the inverse-variance weighted (IVW) method, supported by heterogeneity analysis, horizontal pleiotropy testing, outlier detection, and "leave-one-out" sensitivity analysis. By synthesizing the results from 3 groups of MR analyses, we discovered a negative causal association between a.ASV063 [Finegoldia (unc.)] located on the volar forearm and keloid disease (IVW (keloid1) odds ratio (OR): 0.939, 95% confidence interval (CI): 0.886-0.994, P = .032; IVW (keloid2) OR: 0.897, 95% CI: 0.813-0.990, P = .031; IVW (keloid3) OR: 0.900, 95% CI: 0.825-0.981, P = .017). Similarly, a negative causal relationship may also exist between the genus: Bacteroides from the antecubital fossa and keloid disease (IVW (keloid1) OR: 0.928, 95% CI: 0.884-0.973, P = .002; IVW (keloid2) OR: 0.891, 95% CI: 0.820-0.968, P = .007; IVW (keloid3) OR: 0.918, 95% CI: 0.849-0.992, P = .030). Additionally, no reverse causation was found, with all analyses showing no signs of horizontal pleiotropy or heterogeneity. This study offers new insights for the prevention and treatment of keloids.

Causal Relationship of Skin Microbiota on Psoriasis: A Mendelian Randomization Study

Objective

Epidemiological investigations have indicated an association between skin microbiota imbalance and psoriasis, however, the causal relationship has not been confirmed through Mendelian randomization (MR). MR employed genetic instrumental variables (IVs) to evaluate the causal relationship between skin microbiota and psoriasis, providing new insights for potential treatments.

Methods

Summary statistics for psoriasis and related traits were available from FinnGen R10 and United Kingdom Biobank (UKB) consortium. The genome-wide association studies (GWAS) on skin microbiota in three skin microenvironments came from two population-based German cohorts. Several selection processes were used to determine the optimal instrumental variables. Five MR methods were performed and different sensitivity analyses approaches yield robustness evidence under different assumptions.

Results

449 SNPs were employed as IVs for 53 bacterial genera, with F-statistics between 20.18 and 42.44, indicating no evidence of weak instrument bias. Bacteroides was associated with psoriasis from UKB in IVW (OR, 95% CI: 0.914, 0.869-0.961; P < 0.001, PB-H = 0.007). The taxon was also associated with psoriasis vulgaris (IVW: OR, 95% CI, 0.918, 0.872-0.967; P = 0.001, P B-H = 0.054) and psoriasis and related disorders (IVW: OR, 95% CI, 0.915, 0.875-0.957; P < 0.001, P B-H = 0.008). Consistent causal estimates were identified in terms of both magnitude and direction, indicating a protective effect of Bacteroides.

Conclusion

The MR study found that Bacteroides in the antecubital fossa may protect against psoriasis, offering genetic proof that skin microbiota helps prevent the condition.

Causal roles of skin and gut microbiota in skin appendage disorders suggested by genetic study

OBJECTIVES

There is evidence from observational studies that human microbiota is linked to skin appendage Disorders (SADs). Nevertheless, the causal association between microbiota and SADs is yet to be fully clarified.

METHODS

A comprehensive two-sample Mendelian randomization (MR) was first performed to determine the causal effect of skin and gut microbiota on SADs. A total of 294 skin taxa and 211 gut taxa based on phylum, class, order, family, genus, and ASV level information were identified. Summary data of SADs and eight subtypes (acne vulgaris, hidradenitis suppurativa, alopecia areata, rogenic alopecia, rosacea, rhinophyma, seborrhoeic dermatitis, and pilonidal cyst) were obtained from the FinnGen consortium. We performed bidirectional MR to determine whether the skin and gut microbiota are causally associated with multiple SADs. Furthermore, sensitivity analysis was conducted to examine horizontal pleiotropy and heterogeneity.

RESULTS

A total of 65 and 161 causal relationships between genetic liability in the skin and gut microbiota with SADs were identified, respectively. Among these, we separately found 5 and 11 strong causal associations that passed Bonferroni correction in the skin and gut microbiota with SADs. Several skin bacteria, such as Staphylococcus, Streptococcus, and Propionibacterium, were considered associated with multiple SADs. As gut probiotics, Bifidobacteria and Lactobacilli were associated with a protective effect on SAD risk. There was no significant heterogeneity in instrumental variables or horizontal pleiotropy.

CONCLUSIONS

Our MR analysis unveiled bidirectional causal relationships between SADs and the gut and skin microbiota, and had the potential to offer novel perspectives on the mechanistic of microbiota-facilitated dermatosis.

The Human Skin Microbiome in Health: CME Part 1

Human skin is home to a myriad of microorganisms, including bacteria, viruses, fungi, and mites, many of which are considered commensal microbes that aid in maintaining the overall homeostasis or steady-state condition of the skin and contribute to skin health. Our understanding of the complexities of the skin's interaction with its microorganisms is evolving. This knowledge is based primarily on in vitro and animal studies, and more work is needed to understand how this knowledge relates to humans. Here, we introduce the concept of the skin microbiome and discuss skin microbial ecology, some intrinsic factors with potential influence on the human skin microbiome, and possible microbiome-host interactions. The second article of this two-part CME series describes how microbiome alterations may be associated with skin disease, how medications can affect the microbiome, and what microbiome-based therapies are under investigation.

Causal effects of skin microbiota on intervertebral disk degeneration, low back pain and sciatica: a two-sample Mendelian randomization study

OBJECTIVE

The purpose of this study is to use two-sample Mendelian randomization (MR) to investigate the causal relationship between skin microbiota, especially Propionibacterium acnes, and intervertebral disc degeneration (IVDD), low back pain (LBP) and sciatica.

METHODS

We conducted a two-sample MR using the aggregated data from the whole genome-wide association studies (GWAS). 150 skin microbiota were derived from the GWAS catalog and IVDD, LBP and sciatica were obtained from the IEU Open GWAS project. Inverse-variance weighted (IVW) was the primary research method, with MR-Egger and Weighted median as supplementary methods. Perform sensitivity analysis and reverse MR analysis on all MR results and use multivariate MR to adjust for confounding factors.

RESULTS

MR revealed five skin microbiota associated with IVDD, four associated with LBP, and two with sciatica. Specifically, P.acnes in sebaceous skin environments were associated with reduced risk of IVDD; IVDD was found to increase the abundance of P.acnes in moist skin. Furthermore, ASV010 [Staphylococcus (unc.)] from dry skin was a risk factor for LBP and sciatica; ASV045 [Acinetobacter (unc.)] from dry skin and Genus Rothia from dry skin exhibited potential protective effects against LBP; ASV065 [Finegoldia (unc.)] from dry skin was a protective factor for IVDD and LBP. ASV054 [Enhydrobacter (unc.)] from moist skin, Genus Bacteroides from dry skin and Genus Kocuria from dry skin were identified as being associated with an increased risk of IVDD. Genus Streptococcus from moist skin was considered to be associated with an increased risk of sciatica.

CONCLUSIONS

This study identified a potential causal relationship between skin microbiota and IVDD, LBP, and sciatica. No evidence suggests skin-derived P.acnes is a risk factor for IVDD, LBP and sciatica. At the same time, IVDD can potentially cause an increase in P.acnes abundance, which supports the contamination theory.

Causal roles of skin microbiota in skin cancers suggested by genetic study

BACKGROUND

There is evidence from observational studies that skin microbiota is linked to skin cancers. Nevertheless, the causal association between skin microbiota and skin cancers is yet to be fully clarified.

METHODS

A bidirectional two-sample Mendelian randomization (MR) was performed to determine the causal relationship between skin microbiota and skin cancers. A total of 294 skin microbial taxa were identified from the first genome-wide association study across three skin microenvironments of two German population cohorts. Summary data of three skin cancers (malignant melanoma, squamous cell carcinoma, and basal cell carcinoma) were obtained from the FinnGen consortium. Moreover, sensitivity analysis examined horizontal pleiotropy and heterogeneity, and microenvironment-based meta-analysis confirmed the reliability of the results.

RESULTS

We identified 65 nominal causalities and 5 strong causal associations between skin microbiota and skin cancers. Among them, the class Bacilli revealed a bidirectional positive relationship with malignant melanoma. The class Betaproteobacteria and class Gammaproteobacteria demonstrated a causal association with an elevated risk of malignant melanoma and basal cell carcinoma, respectively. In the reverse MR analysis, malignant melanoma was associated with a lower abundance of phylum Bacteroidetes. There were no indications of significant heterogeneity in instrumental variables or evidence of horizontal pleiotropy.

CONCLUSION

Our MR analysis indicated bidirectional causal associations between skin microbiota and skin cancers, and had the potential to offer novel perspectives on the mechanistic of microbiota-facilitated carcinogenesis.

A Mendelian randomization analysis reveals the multifaceted role of the skin microbiota in liver cancer

Background

Hepatocellular carcinoma (HCC, or hepatic cancer, HC) and cholangiocarcinoma (CCA, or hepatic bile duct cancer, HBDC) are two major types of primary liver cancer (PLC). Previous studies have suggested that microbiota can either act as risk factors or preventive factors in PLC. However, no study has reported the relationship between skin microbiota and PLC. Therefore, we conducted a two-sample Mendelian randomization (MR) study to assess the causality between skin microbiota and PLC.

Methods

Data from the genome-wide association study (GWAS) on skin microbiota were collected. The GWAS summary data of GCST90018803 (HBDC) and GCST90018858 (HC) were utilized in the discovery and verification phases, respectively. The inverse variance weighted (IVW) method was utilized as the principal method in our MR study. The MR-Egger intercept test, Cochran's Q-test, MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and leave-one-out analysis were conducted to identify the heterogeneity and pleiotropy.

Results

The results showed that Veillonella (unc.) plays a protective role in HBDC, while the family Neisseriaceae has a positive association with HBDC risk. The class Betaproteobacteria, Veillonella (unc.), and the phylum Bacillota (Firmicutes) play a protective role in HC. Staphylococcus epidermidis, Corynebacterium (unc.), the family Neisseriaceae, and Pasteurellaceae sp. were associated with an increased risk of HC.

Conclusion

This study provided new evidence regarding the association between skin microbiota and PLC, suggesting that skin microbiota plays a role in PLC progression. Skin microbiota could be a novel and effective way for PLC diagnosis and treatment.

Genome-wide association study identifies host genetic variants influencing oral microbiota diversity and metabolic health

The microbial communities of the oral cavity are important elements of oral and systemic health. With emerging evidence highlighting the heritability of oral bacterial microbiota, this study aimed to identify host genome variants that influence oral microbial traits. Using data from 16S rRNA gene amplicon sequencing, we performed genome-wide association studies with univariate and multivariate traits of the salivary microbiota from 610 unrelated adults from the Danish ADDITION-PRO cohort. We identified six single nucleotide polymorphisms (SNPs) in human genomes that showed associations with abundance of bacterial taxa at different taxonomical tiers (P < 5 × 10-8). Notably, SNP rs17793860 surpassed our study-wide significance threshold (P < 1.19 × 10-9). Additionally, rs4530093 was linked to bacterial beta diversity (P < 5 × 10-8). Out of these seven SNPs identified, six exerted effects on metabolic traits, including glycated hemoglobin A1c, triglyceride and high-density lipoprotein cholesterol levels, the risk of type 2 diabetes and stroke. Our findings highlight the impact of specific host SNPs on the composition and diversity of the oral bacterial community. Importantly, our results indicate an intricate interplay between host genetics, the oral microbiota, and metabolic health. We emphasize the need for integrative approaches considering genetic, microbial, and metabolic factors.

Genetic and Immunological Pathogenesis of Atopic Dermatitis

Type 2 immune-mediated diseases give a clear answer to the issue of nature (genetics) versus nurture (environment). Both genetics and environment play vital complementary roles in the development of atopic dermatitis (AD). As a key component of the atopic march, AD demonstrates the interactive nature of genetic and environmental contributions to atopy. From sequence variants in the epithelial barrier gene encoding FLG to the hygiene hypothesis, AD combines a broad array of contributions into a single syndrome. This review will focus on the genetic contribution to AD and where genetics facilitates the elicitation or enhancement of AD pathogenesis.

A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome

The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Genetically predicted levels of circulating cytokines and the risk of six immune skin diseases: a two-sample Mendelian randomization study

Background

Circulating cytokines play a crucial role in the onset and progression of immune skin diseases. However, the causal relationships and the direction of causal effects require further investigation.

Methods

Two-sample Mendelian randomization (MR) analyses were conducted to assess the causal relationships between 41 circulating cytokines and six immune skin diseases including alopecia areata, chloasma, hidradenitis suppurativa (HS), lichen planus (LP), seborrheic dermatitis, and urticaria, using summary statistics from genome-wide association studies. Reverse MR analyses was performed to test for the reverse causation. Pleiotropy and heterogeneity tests were conducted to assess the robustness of the findings.

Results

Twelve unique cytokines showed a suggestive causal relationship with the risk of six immune skin diseases. Among them, the causal effects between 9 unique cytokines and immune skin diseases have strong statistical power. Additionally, the concentrations of six cytokines might be influenced by LP and urticaria. After Bonferroni correction, the following associations remained significant: the causal effect of beta-nerve growth factor on HS (odds ratio [OR] = 1.634, 95% confidence interval [CI] = 1.226-2.177, p = 7.97e-04), interleukin (IL)-6 on LP (OR = 0.615, 95% CI = 0.481-0.786, p = 1.04e-04), IL-4 on LP (OR = 1.099. 95% CI = 1.020-1.184, p = 1.26e-02), and IL-2 on urticaria (OR = 0.712, 95% CI = 0.531-0.955, p = 2.33e-02).

Conclusion

This study provides novel perspectives on the relationship between circulating cytokines and immune skin diseases, potentially providing valuable insights into their etiology, diagnostic approaches, and treatment.

Dupilumab but not cyclosporine treatment shifts the microbiome toward a healthy skin flora in patients with moderate-to-severe atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) patients display an altered skin microbiome which may not only be an indicator but also a driver of inflammation. We aimed to investigate associations among AD patients' skin microbiome, clinical data, and response to systemic therapy in patients of the TREATgermany registry.

METHODS

Skin swabs of 157 patients were profiled with 16S rRNA gene amplicon sequencing before and after 3 months of treatment with dupilumab or cyclosporine. For comparison, 16s microbiome data from 258 population-based healthy controls were used. Disease severity was assessed using established instruments such as the Eczema Area and Severity Index (EASI).

RESULTS

We confirmed the previously shown correlation of Staphylococcus aureus abundance and bacterial alpha diversity with AD severity as measured by EASI. Therapy with Dupilumab shifted the bacterial community toward the pattern seen in healthy controls. The relative abundance of Staphylococci and in particular S. aureus significantly decreased on both lesional and non-lesional skin, whereas the abundance of Staphylococcus hominis increased. These changes were largely independent from the degree of clinical improvement and were not observed for cyclosporine.

CONCLUSIONS

Systemic treatment with dupilumab but not cyclosporine tends to restore a healthy skin microbiome largely independent of the clinical response indicating potential effects of IL-4RA blockade on the microbiome.

Disease modification in inflammatory skin disorders: opportunities and challenges

Progress in understanding of the mechanisms underlying chronic inflammatory skin disorders, such as atopic dermatitis and psoriasis vulgaris, has led to new treatment options with the primary goal of alleviating symptoms. In addition, this knowledge has the potential to inform on new strategies aimed at inducing deep and therapy-free remission, that is, disease modification, potentially impacting on associated comorbidities. However, to reach this goal, key areas require further exploration, including the definitions of disease modification and disease activity index, further understanding of disease mechanisms and systemic spillover effects, potential windows of opportunity, biomarkers for patient stratification and successful intervention, as well as appropriate study design. This Perspective article assesses the opportunities and challenges in the discovery and development of disease-modifying therapies for chronic inflammatory skin disorders.

How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome

A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies.