
==== Front
Skin Res Technol
Skin Res Technol
10.1111/(ISSN)1600-0846
SRT
Skin Research and Technology
0909-752X
1600-0846
John Wiley and Sons Inc. Hoboken

10.1111/srt.70035
SRT70035
Original Article
Original Article
Skin microbiome and causal relationships in three dermatological diseases: Evidence from Mendelian randomization and Bayesian weighting
LI et al.
Li Xiaojian https://orcid.org/0000-0002-9131-4659
1
Chen Shiyu 1
Chen Shupeng 1
Cheng Shiping 1 2
Lan Hongrong 1
Wu Yunbo 1 2
Qiu Guirong https://orcid.org/0000-0003-3136-6673
1 2
Zhang Lingjin 3 511916134@qq.com

1 Clinical Medical College Jiangxi University of Chinese Medicine Nanchang China
2 Dermatology Department Affiliated Hospital of Jiangxi University of Chinese Medicine Nanchang China
3 Dermatology Department Shenzhen Luohu Hospital of Traditional Chinese Medicine Shenzhen China
* Correspondence
Lingjin Zhang, Dermatology Department,Shenzhen Luohu Hospital of Traditional Chinese Medicine, Shenzhen, China.
Email: 511916134@qq.com

01 9 2024
9 2024
30 9 10.1111/srt.v30.9 e7003511 7 2024
17 8 2024
© 2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Background

Atopic dermatitis (AD), psoriasis (PSO), rosacea, and other related immune skin diseases are affected by multiple complex factors such as genetic and microbial components. This research investigates the causal relationships between specific skin microbiota and these diseases by using Mendelian randomization (MR), and Bayesian weighted Mendelian randomization (BWMR).

Methods

We utilized genome‐wide association study (GWAS) data to analyze the associations between various skin bacteria and three dermatological diseases. Single nucleotide polymorphisms (SNPs) served as instrumental variables (IVs) in MR methods, including inverse variance weighted (IVW), and MR Egger. BWMR was employed to validate results and address pleiotropy.

Results

The IVW analysis identified significant associations between specific skin microbiota and dermatological diseases. ASV006_Dry, ASV076_Dry, and Haemophilus_Dry were significantly positively associated with AD, whereas Kocuria_Dry was negatively associated. In PSO, ASV005_Dry was negatively associated, whereas ASV004_Dry, Rothia_Dry, and Streptococcus_Moist showed positive associations. For rosacea, ASV023_Dry was significantly positively associated, while ASV016_Moist, Finegoldia_Dry, and Rhodobacteraceae_Moist were significantly negatively associated. These results were corroborated by BWMR analysis.

Conclusion

Bacterial species such as Finegoldia, Rothia, and Streptococcus play crucial roles in the pathogenesis of AD, PSO, and rosacea. Understanding these microbial interactions can aid in developing targeted treatments and preventive strategies, enhancing patient outcomes and quality of life.

atopic dermatitis
finegoldia
mendelian randomization
psoriasis
rosacea
skin microbiome
National Natural Science Foundation of China Youth Science Fund Projec82205038 Technology Innovation Team of Jiangxi University of Chinese MedicineCXTD 220090 Luohu District Soft Science Research ProgramLX202402017 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:01.09.2024
Li X , Chen S , Chen S , et al. Skin microbiome and causal relationships in three dermatological diseases: Evidence from Mendelian randomization and Bayesian weighting. Skin Res Technol. 2024;30 :e70035. 10.1111/srt.70035
==== Body
pmc1 INTRODUCTION

The skin, as the body's largest immune organ, serves as a crucial protective barrier against external threats. 1 Various skin diseases are intricately linked with systemic homeostasis, encompassing inflammatory responses, immune status, metabolic levels, and the balance of the skin microbiome. 2 , 3 These conditions impose a substantial public health burden, affecting millions globally, diminishing physical and mental well‐being, lowering quality of life, and escalating healthcare costs. 4 Atopic dermatitis (AD), psoriasis (PSO), and rosacea are particularly prominent due to their high prevalence and extensive impact. Understanding the underlying mechanisms of these conditions is essential for developing effective treatment and prevention strategies. 5 –‐ 9

Recent advancements in omics technologies and statistical methods have opened new avenues for exploring the etiology of these diseases. Specifically, the analysis of the skin microbiome offers new approaches to elucidate disease mechanism and identify potential therapeutic targets. 10 The skin microbiome provides critical insights through various biological pathways, aiding researchers and clinicians in comprehending the complexity of skin diseases. 11 Due to its accessibility and ease of analysis, studying the skin microbiome under different environmental conditions (dry and moist) is an ideal tool for understanding the onset and progression of skin diseases. These analyses reflect local and systemic responses as well as immune status, unveiling the underlying mechanisms and systemic impacts of skin diseases. 12 , 13 Additionally, monitoring these microbiome changes dynamically can help assess disease progression and treatment efficacy, thereby supporting personalized medicine. 14

The goal of this study is to explore the connections between particular skin microbiota and three skin diseases—AD, PSO, and rosacea—using Mendelian randomization (MR) and Bayesian weighting analyses. In this approach, genetic variations are used as instrumental variables (IVs) to assess causal relationships, effectively minimizing confounding factors and reverse causation. 15 Bayesian weighting integrates data from diverse sources, providing robust analytical outcomes. 16 By combining these advanced methods, we aim to uncover causal relationships between specific skin microbiota and skin diseases, offering new insights and scientific evidence for the diagnosis, prevention, and personalized treatment of skin diseases, ultimately informing clinical practice.

2 MATERIALS AND METHODS

2.1 Study design

In MR analysis, single nucleotide polymorphisms (SNPs) serve as IVs. These IVs need to meet three essential assumptions depicted in Figure 1: (1) a strong correlation between the SNPs and the exposure; (2) no correlation between the SNPs and any confounders that might affect the exposure‐outcome relationship; (3) the SNPs affects the outcome exclusively through the exposure. 17

FIGURE 1 Plot of key assumptions for MR analysis. MR, Mendelian randomization.

2.2 Data sources

Data on the skin microbiome were obtained from 1 656 skin samples from participants of two cross‐sectional, 18 , 19 population‐based German cohorts: KORA FF4 (n = 324) and PopGen (n = 273). The skin samples were collected from dry environments (dorsal and volar forearm for PopGen), moist environments (antecubital fossa for both KORA FF4 and PopGen), and sebaceous environments (retroauricular fold for KORA FF4 and forehead for PopGen). Summary statistics for three skin diseases were obtained from the FinnGen genome‐wide association study (GWAS) (https://r8.finngen.fi/). The numbers of cases and controls for each phenotype are as follows: PSO (6 995 cases, 299 128 controls), AD (8 281 cases, 278 635 controls), rosacea (1 877 cases, 297 544 controls), as detailed in the Tables S1, S2.

2.3 Instrumental variable selection

SNPs robustly associated with the exposures were selected using a genome‐wide significance threshold of < 5.0×10−6. To eliminate linkage disequilibrium, an r 2 threshold of 0.001 and a clump window size of 10 000 kb were applied. 20 The strength of the IVs was assessed using the F‐statistic, calculated as F = β2/SE2, where β is the effect size of the allele and SE is the standard error. 21 An F‐statistic greater than 10 indicates the absence of weak IV bias, 22 as detailed in Table S3.

2.4 Statistical analysis

Using the selected IVs, two‐sample MR analyses were performed on the three skin diseases utilizing the TwoSampleMR and MRPRESSO packages in R (version 4.4.0). MR analysis was conducted using five methods: inverse‐variance weighted (IVW) as the primary method, supplemented by MR Egger, weighted median, simple mode, and weighted mode. The IVW method, which uses a meta‐analysis approach to combine the causal effect estimates from various SNPs, served as the basis for our analysis. 23 MR Egger accounts for nonzero mean pleiotropy but has reduced statistical power. 24 The Weighted median provides robust estimates if at least 50% of the weight comes from valid IVs. 25 Simple mode is a model‐based approach that offers robustness to pleiotropy, 26 while weighted mode is sensitive to heterogeneity. 27

To further address “weak instrument and weak level pleiotropy,” Bayesian weighted Mendelian randomization (BWMR) was employed. The BWMR model considers the uncertainty due to polygenicity and addresses violations of IV assumptions due to pleiotropy using Bayesian‐weighted outlier detection. 16

3 RESULTS

3.1 Skin microbiome and AD

IVW results indicate that ASV006_Dry, ASV021_Moist, ASV076_Dry, Haemophilus_Dry, Kocuria_Dry, and Streptococcaceae_Moist are significantly associated with AD (p < 0.05) (Figures 2 and 3). a‐F: ASV006_Dry, ASV021_Moist, ASV076_Dry, Haemophilus_Dry, Kocuria_Dry, and Strep‐tococcaceae.

FIGURE 2 MR results of skin microbiome and AD. AD, atopic dermatitis; MR, Mendelian randomization.

FIGURE 3 Scatterplot of skin microbiome and AD. AD, atopic dermatitis.

3.2 Skin microbiome and PSO

IVW results suggest that ASV004_Dry, ASV005_Dry, ASV008_Moist, Lactobacillales_Moist, Rothia_Dry, and Streptococcus_Moist are significantly associated with PSO (p < 0.05) (Figures 4 and 5). a‐F: ASV004_Dry, ASV005_Dry, ASV008_Moist, Lactobacillales_Moist, Rothia_Dry, and Str‐eptococcus_Moist.

FIGURE 4 MR results of skin microbiome and PSO. MR, Mendelian randomization; PSO, psoriasis.

FIGURE 5 Scatterplot of skin microbiome and PSO. PSO, psoriasis.

3.3 Skin microbiome and rosacea

IVW results show that ASV016_Moist, ASV023_Dry, ASV076_Dry, Finegoldia_Dry, Lactobacillales_Moist, and Rhodobacteraceae_Moist are significantly associated with rosacea (p < 0.05) (Figures 6 and 7). a‐f: ASV016_Moist, ASV023_Dry, ASV076_Dry, Finegoldia_Dry, Lactobacillales_Moist, Rhodobacteraceae_Moist.

FIGURE 6 MR results of skin microbiome and rosacea. MR, Mendelian randomization.

FIGURE 7 Scatterplot of skin microbiome and PSO. PSO, psoriasis.

3.4 Sensitivity analysis

The specific SNP spectra of the three groups, as well as the results for pleiotropy and heterogeneity are presented in Table S4. No evidence of pleiotropy was observed (p > 0.05). All groups had F‐statistics greater than 10, indicating no weak IV bias. Table S4 shows the MR analysis involving the skin microbiome and the three skin diseases. Notably, the heterogeneity test suggests the presence of heterogeneity within some groups (p < 0.05); therefore, Bayesian weighting analysis was performed.

3.5 Bayesian weighted Mendelian randomization analysis

After Bayesian weighting analysis, the following causal relationships remained significant: ASV006_Dry, ASV076_Dry, Haemophilus_Dry, and Kocuria_Dry with AD; ASV004_Dry, ASV005_Dry, Rothia_Dry, and Streptococcus_Moist with PSO; ASV016_Moist, ASV023_Dry, Finegoldia_Dry, and Rhodobacteraceae_Moist with rosacea (as detailed in Figure 8 and Table S5).

FIGURE 8 Results of BWMR analysis. BWMR, Bayesian weighted Mendelian randomization.

4 DISCUSSION

This study used MR and Bayesian weighted analysis to explore the connection between the skin microbiome and three dermatological conditions: AD, PSO, and rosacea. The results offer new understanding of the pathogenic mechanisms underlying these skin diseases.

Our research identified significant positive correlations between SV006_Dry, ASV076_Dry, and Haemophilus_Dry with AD, whereas Kocuria_Dry showed a significant negative correlation with AD. ASV006_Dry and ASV076_Dry are uncharacterized univariate microbial features derived from the dorsal forearm (dry skin). The Haemophilus genus comprises Gram‐negative bacteria typically present in the upper respiratory tract, capable of causing various infections, including otitis media, sinusitis, and severe infections such as meningitis. 28 Certain species of Haemophilus have been found to trigger host immune responses, resulting in the production of inflammatory mediators such as TNF‐α and IL‐6, which could potentially worsen skin inflammation. 29

Conversely, Kocuria_Dry's significant negative correlation with AD suggests a protective role. Staphylococcus aureus present on the skin of AD patients can worsen itchiness and skin lesions by releasing large amounts of exotoxins, proteases, and superantigens. 30 Evidence indicates that Kocuria bacteria, which are prominent in the normal skin microbiome, may reduce skin inflammation by inhibiting the growth of pathogenic bacteria. 31 Our findings revealed a marked reduction in bacterial diversity in severe AD skin compared to healthy skin. In vitro antibacterial and cellular experiments demonstrated that Kocuria culture supernatant significantly inhibited Staphylococcus aureus growth, biofilm formation, and the induction of interleukin (IL)−1α and IL‐6 secretion in HaCaT cells. These results are consistent with our findings, indicating that Kocuria may have therapeutic potential for AD. 32

Additionally, we found a significant negative correlation between ASV005_Dry and PSO, while ASV004_Dry, Rothia_Dry, and Streptococcus_Moist exhibited significant positive correlations with PSO. These findings suggest that these bacteria may play important roles in the pathogenesis of PSO.

Rothia is a genus of Gram‐positive bacteria primarily found in the human oral cavity and respiratory tract. They are part of the normal microbiota and are usually harmless, but can cause infections in immunocompromised individuals. Rothia mucilaginosa, the most common species of the genus, is known to cause infections such as pneumonia and endocarditis under certain conditions. 33 However, its role in skin pathology has also garnered attention. Studies indicate that Rothia may activate host immune responses through its metabolic products and cell wall components, leading to the release of inflammatory mediators such as tumor necrosis factor‐α (TNF‐α) and (IL‐17), which are crucial in the inflammatory response of PSO. 34 Rothia's cell wall contains polysaccharides and lipoteichoic acid (LTA), which can activate host immune responses. LTA can bind to pattern recognition receptors (PRRs) like Toll‐like receptors (TLRs) on host cells, inducing the release of pro‐inflammatory cytokines. 35 , 36 Rothia can also secrete extracellular enzymes which degrade host tissues and extracellular matrix, releasing pro‐inflammatory mediators. For example, the secretion of proteases and lipases can disrupt the host tissue barrier, promoting the infiltration of inflammatory cells. 37

Moreover, the increased abundance of Streptococcus in moist environments is also significantly associated with PSO. Streptococcus is a genus of Gram‐positive cocci widely present in the human respiratory tract, gastrointestinal tract, and urogenital system. 4 Group A Streptococcus (GAS), in particular, has been implicated in triggering PSO flare‐ups, especially guttate PSO. Approximately 80% of guttate PSO cases are associated with recent GAS infections. Streptococcal pharyngitis (strep throat) is often considered as a major trigger for guttate PSO flare‐ups, with symptoms typically appearing 2–3 weeks post‐infection. 5 GAS can secrete various exotoxins, such as streptolysins and enzymes, which activate the host immune system and lead to a robust inflammatory response. These exotoxins can induce the production of large amounts of pro‐inflammatory cytokines such as TNF‐α and ‐1β (IL‐1β), which further promote aberrant activation of skin T cells and rapid proliferation of epidermal cells, resulting in the characteristic scaly lesions of PSO. 38 Additionally, genetic predisposition is closely linked to GAS‐induced PSO, with individuals carrying the HLA‐Cw6 genotype being more susceptible to developing PSO following GAS infection. This finding aligns with our results. 39 Furthermore, streptococcal infections may trigger autoimmune responses through molecular mimicry, causing the immune system to attack healthy skin cells. 40

Finally, we found that ASV016_Moist, Finegoldia_Dry, and Rhodobacteraceae_Moist were significantly negatively associated with rosacea, while ASV023_Dry was significantly positively associated with rosacea. Finegoldia species are common Gram‐positive anaerobic bacteria typically present on the skin and mucous membranes. One study found that Finegoldia can induce inflammatory responses by activating neutrophils. The proteins FAF and L from these bacteria can stimulate neutrophils to release reactive oxygen species (ROS), indicating a neutrophil oxidative burst. 41 Additionally, co‐incubation of Finegoldia with neutrophils leads to an increased surface expression of CD66b, which is another marker of neutrophil activation. 42 During infection and inflammation, neutrophils can release structures known as neutrophil extracellular traps (NETs). NETs are composed of DNA, histones, and granule proteins (such as elastase and myeloperoxidase). 43 NETs, with their mesh‐like structure, can effectively capture and immobilize pathogens, preventing their spread and invasion into other tissues, thereby enhancing local antimicrobial defenses. 44

Rhodobacteraceae species are a family of Gram‐negative bacteria predominantly found in aquatic environments, including moist skin environments. Rhodobacteraceae may regulate the skin microenvironment through the secretion of metabolic products. For instance, short‐chain fatty acids (SCFAs) produced by some bacteria have anti‐inflammatory properties. Research suggests that SCFAs can promote the proliferation of thymus‐derived regulatory T cells (tTregs) through interaction with cell surface receptors. 45 Specifically, butyrate can bind to GPR41 on thymic epithelial cells, stimulating the expression of autoimmune regulator (AIRE), thereby inducing the production of tTregs. Additionally, SCFAs can modulate T lymphocyte activities intracellularly, affecting their differentiation (Th17/Treg), apoptosis mechanisms, and the release of cytokines such as IL‐6 and IL‐10. Upon entering cells, SCFAs inhibit histone deacetylases (HDACs), influencing gene expression. 46 SCFAs are also considered HDAC inhibitors, promoting the acetylation of histone H3 at the Foxp3 locus in the nucleus, inducing the expression of Foxp3 in naive CD4+ T cells, and driving their differentiation into peripheral regulatory T cells (pTregs). 47 Under certain conditions, SCFAs can reduce the production of pro‐inflammatory cytokines such as IL‐6 and IL‐23, which activate the expression of the RAR‐related orphan receptor γt (RORγt) gene, promoting Th17 cell differentiation. By inhibiting these cytokines, SCFAs can decrease the number of Th17 cells. 48

4.1 Strengths and limitations

This study employs advanced methods such as MR and Bayesian weighting analysis, covering a variety of skin diseases to provide a comprehensive perspective on the associations between different conditions. Our findings offer new insights for further studies into these skin diseases, helping to deepen our understanding of the pathogenic mechanisms of these diseases, thereby improving prevention and treatment strategies. The use of large‐scale GWAS data and advanced statistical methods like MR and Bayesian weighting enhances the robustness of our results by minimizing confounding factors and reverse causation.

However, the study has limitations, including the reliance on cross‐sectional data, which may limit temporal inferences, and potential residual confounding due to unmeasured variables. The focus on German cohorts may affect the generalizability of the results to other populations, and while we identified significant associations, the study provides limited mechanistic insights into how specific bacteria influence disease processes. Additionally, the reliability of findings may be compromised for weakly associated SNPs, and the assumptions and parameter choices in Bayesian weighting could influence the outcomes.

4.2 Clinical significance

This study significantly enhances our understanding of the relationships between the skin microbiome and skin diseases such as AD, PSO, and rosacea. By using advanced methods like MR and Bayesian weighting analysis, we have provided robust evidence of the causal links between specific skin microbiota and these conditions. Clinically, these findings suggest potential for developing targeted therapeutic strategies by modulating specific bacteria associated with skin diseases. Additionally, dynamic monitoring of the skin microbiome offers a promising tool for assessing disease progression and treatment efficacy, supporting personalized medicines. Understanding the impact of environmental factors on the skin microbiome can lead to better preventive measures, and the insights gained may drive the development of new drugs and therapies. Overall, this study lays the groundwork for innovative clinical applications that could transform the management and treatment of these common and impactful skin diseases, improving patient outcomes and advancing dermatological care.

5 CONCLUSION

In conclusion, this thorough study provides new insights into the causal relationships between specific skin microbiota and three major skin diseases: AD, PSO, and rosacea. Through the use of advanced analytical methods such as MR and Bayesian Weighting Analysis, the findings offer a strong understanding of the roles of various bacterial species in the pathogenesis of these conditions.

The research underscores the considerable impact of bacteria such as Rothia, Finegoldia, and Streptococcus on skin health. Understanding these bacterial interactions can help in developing more effective, personalized treatments and preventive strategies. This can ultimately lead to improved patient outcomes and quality of life.

Future research should focus on elucidating the specific biological mechanisms involved in these associations and exploring potential therapeutic interventions targeting these bacterial influences. The results of this study establish a solid basis for future investigations and underscore the significance of considering the skin microbiome into account in the context of dermatological diseases.

CONFLICT OF INTEREST STATEMENT

The authors declare no commercial or financial ties that could be perceived as a conflict of interest.

ETHICS STATEMENT

This study did not require ethical approval because all data were sourced from publicly available databases. Written consent from participants was not needed as this complies with national legislation and legal policies.

Supporting information

Supporting Information

ACKNOWLEDGMENTS

This research was supported by funding from the National Natural Science Foundation of China Youth Science Fund Projec (82205038) and Technology Innovation Team of Jiangxi University of Chinese Medicine (CXTD 220090) and Luohu District Soft Science Research Program (LX202402017).

DATA AVAILABILITY STATEMENT

The GWAS summary statistics on the skin microbiome generated in this study can be accessed in the GWAS catalog under the accession numbers GCST90133164‐GCST90133313. The phenotypic data for PopGen individuals can be obtained through the material data access form from the PopGen biobank in Schleswig‐Holstein, Germany. For more information on the material data access form and the application process, please visit http://www.uksh.de/p2n/Information+for+Researchers.html. The KORA data can be provided according to the requirements of the project agreement, detailed at https://www.helmholtz‐munich.de/en/kora/for‐scientists/cooperation‐with‐kora/index.html. The outcome data can be accessed from the biobank engine at https://biobankengine.stanford.edu/and the FinnGen repository at https://r8.finngen.fi/.
==== Refs
REFERENCES

1 Nørskov‐Lauritsen N . Classification, identification, and clinical significance of Haemophilus and Aggregatibacter species with host specificity for humans. Clin Microbiol Rev. 2014;27 (2 ):214–240.24696434
2 Eisenstein M . The skin microbiome. Nature. 2020;588 (7838 ):S209.33328671
3 Byrd A. L , Belkaid Y , Segre, J. A . The human skin microbiome. Nat Rev Microbiol. 2018;16 (3 ):143–155.29332945
4 Zbinden A , Bostanci N , Belibasakis GN . The novel species Streptococcus tigurinus and its association with oral infection. Virulence. 2015;6 (3 ):177–182.25483862
5 Weisenseel P , Laumbacher B , Besgen P , et al. Streptococcal infection distinguishes different types of psoriasis. J Med Genet. 2002;39 (10 ):767–768.12362037
6 Two AM , Wu W , Gallo RL , et al. Rosacea: part I. introduction, categorization, histology, pathogenesis, and risk factors. J Am Acad Dermatol. 2015;72 (5 ):749–758; quiz 759–60.25890455
7 Sroka‐Tomaszewska J , Trzeciak M . Molecular mechanisms of atopic dermatitis pathogenesis. Int J Mol Sci. 2021;22 (8 ):4130.33923629
8 Polak K , Bergler‐Czop B , Szczepanek M , et al. Psoriasis and gut microbiome‐current state of art. Int J Mol Sci. 2021;22 (9 ):4529.33926088
9 Li Z , Zhao C , Chen R , et al. Gut microbiota, skin microbiota, and alopecia areata: a Mendelian randomization study. Skin Res Technol. 2024;30 (7 ):e13845.39031933
10 Tseng J. C , Chang Y. C , Huang C. M , Hsu L. C . Therapeutic development based on the immunopathogenic mechanisms of psoriasis. Pharmaceutics. 2021;13 (7 ):1064.34371756
11 Cao Q , Guo J , Chang S , et al. Gut microbiota and acne: a Mendelian randomization study. Skin Res Technol. 2023;29 (9 ):e13473.37753688
12 Oh J , Byrd AL , Deming C , Conlan S , Kong HH , Segre JA ; NISC Comparative Sequencing Program ; Kong HH , Segre JA . Biogeography and individuality shape function in the human skin metagenome. Nature. 2014;514 (7520 ):59–64.25279917
13 Guo J , Luo Q , Li C , et al. Evidence for the gut‐skin axis: common genetic structures in inflammatory bowel disease and psoriasis. Skin Res Technol. 2024;30 (2 ):e13611.38348734
14 Fernandez‐Feo M , Wei G , Blumenkranz G , et al. The cultivable human oral gluten‐degrading microbiome and its potential implications in coeliac disease and gluten sensitivity. Clin Microbiol Infect. 2013;19 (9 ):E386–E394.23714165
15 Davey Smith G, Hemani G . Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23 (R1 ):R89–R98.25064373
16 Zhao J , Ming J , Hu X , et al. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics. 2020;36 (5 ):1501–1508.31593215
17 Hartwig FP , Davies NM , Hemani G , et al. Two‐sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016;45 (6 ):1717–1726.28338968
18 Holle R , Happich M , Löwel H , Wichmann HE ; MONICA/KORA Study Group . KORA–a research platform for population based health research. Gesundheitswesen. 2005;67 (suppl 1 ):S19–S25.16032513
19 Nöthlings U , Krawczak M . PopGen. Eine populationsbasierte Biobank mit Langzeitverfolgung der Kontrollkohorte [PopGen. A population‐based biobank with prospective follow‐up of a control group]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2012;55 (6‐7 ):831–885. German.22736164
20 Sanna S , van Zuydam NR , Mahajan A , et al. Causal relationships among the gut microbiome, short‐chain fatty acids and metabolic diseases. Nat Genet. 2019;51 (4 ):600–605.30778224
21 Bowden J , Del Greco MF , Minelli C , et al. Assessing the suitability of summary data for two‐sample Mendelian randomization analyses using MR‐Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45 (6 ):1961–1974.27616674
22 Burgess S , Thompson SG . Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40 (3 ):755–764.21414999
23 Lee YH . Causal association between smoking behavior and the decreased risk of osteoarthritis: a Mendelian randomization. Z Rheumatol. 2019;78 (5 ):461–466.29974223
24 Zheng C , He MH , Huang JR , et al.Causal relationships between social isolation and osteoarthritis: a Mendelian randomization study in European population. Int J Gen Med. 2021;14 :6777–6786.34703283
25 Bowden J , Davey Smith G , Haycock PC , Burgess S . Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40 (4 ):304–314.27061298
26 Li C , Niu M , Guo Z , et al. A mild causal relationship between tea consumption and obesity in general population: a two‐sample Mendelian randomization study. Front Genet. 2022;13 :795049.35281810
27 Hartwig FP , Davey Smith G , Bowden J . Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46 (6 ):1985–1998.29040600
28 Nørskov‐Lauritsen N . Classification, identification, and clinical significance of Haemophilus and Aggregatibacter species with host specificity for humans. Clin Microbiol Rev. 2014;27 (2 ):214–240.24696434
29 Wang Z , Locantore N , Haldar K , Ramsheh MY , et al. Inflammatory endotype‐associated airway microbiome in chronic obstructive pulmonary disease clinical stability and exacerbations: a multicohort longitudinal analysis. Am J Respir Crit Care Med. 2021;203 (12 ):1488–1502.33332995
30 Ananieva MM , Faustova MO , Basarab IO , Loban' GA . Kocuria rosea, Kocuria kristinae, Leuconostoc mesenteroides as caries‐causing representatives of oral microflora. Wiad Lek. 2017;70 (2 pt 2 ):296–298.29059646
31 Nurxat N , LI M , LIU Q . Progress in the correlation between atopic dermatitis and colonization of Staphylococcus. J Shanghai Jiaotong Univ (Med Sci). 2022;42 (11 ):1605–1611.
32 Song L , Wang Q , Zheng Y , et al. Cheek microbial communities vary in young children with atopic dermatitis in China. Dermatology. 2020;236 (2 ):160–169.31553991
33 Maraki S , Papadakis IS . Rothia mucilaginosa pneumonia: a literature review. Infect Dis (Lond). 2015;47 (3 ):125–129.25664502
34 Polak K , Bergler‐Czop B , Szczepanek M , Wojciechowska K , Frątczak A , Kiss N . Psoriasis and gut microbiome‐current state of art. Int J Mol Sci. 2021;22 (9 ):4529.33926088
35 Fatahi‐Bafghi M . Characterization of the Rothia spp. and their role in human clinical infections. Infect Genet Evol. 2021;93 :104877.33905886
36 Tseng JC , Chang YC , Huang CM , Hsu LC , Chuang TH . Therapeutic development based on the immunopathogenic mechanisms of psoriasis. Pharmaceutics. 2021;13 (7 ):1064.34371756
37 Fernandez‐Feo M , Wei G , Blumenkranz G , et al. The cultivable human oral gluten‐degrading microbiome and its potential implications in coeliac disease and gluten sensitivity. Clin Microbiol Infect. 2013;19 (9 ):E386–E394.23714165
38 Brogan TV , Nizet V , Waldhausen JH . Streptococcal skin infections. N Engl J Med. 1996;334 (22 ):1478.
39 Ovigne JM , Baker BS , Davison SC , Powles AV , Fry L . Epidermal CD8+ T cells reactive with group A streptococcal antigens in chronic plaque psoriasis. Exp Dermatol. 2002;11 (4 ):357–564.12190945
40 Ohashi A , Murayama MA , Miyabe Y , Yudoh K , Miyabe C . Streptococcal infection and autoimmune diseases. Front Immunol. 2024;15 :1361123.38464518
41 Bonnet É , Galinier JL , Fontenel B , Dongay B , Soula P . Endocardite à Finegoldia magna [Finegoldia magna endocarditis]. Med Mal Infect. 2017;47 (1 ):65–67. French.27692827
42 Neumann A , Björck L , Frick IM . Finegoldia magna, an anaerobic gram‐positive bacterium of the normal human microbiota, induces inflammation by activating neutrophils. Front Microbiol. 2020;11 :65.32117109
43 Tan C , Aziz M , Wang P . The vitals of NETs. J Leukoc Biol. 2021;110 (4 ):797–808.33378572
44 Kaplan MJ , Radic M . Neutrophil extracellular traps: double‐edged swords of innate immunity. J Immunol. 2012;189 (6 ):2689–2695.22956760
45 Nakajima, A , Kaga, N , Nakanishi, Y , et al. Maternal high fiber diet during pregnancy and lactation influences regulatory T cell differentiation in offspring in mice. J.Immunol. 2017;199 :3516–3524.29021375
46 Kim CH , Park J , Kim M . Gut microbiota‐derived short‐chain fatty acids, T cells, and inflammation. Im‐mune Netw. 2014;14 (6 ):277–288.
47 Martin‐Gallausiaux C , Béguet‐Crespel F , Marinelli L , et al. Butyrate produced by gut commensal bacteria activates TGF‐beta1 expression through the transcription factor SP1 in human intestinal epithelial cells. Sci Rep. 2018;8 (1 ):9742.29950699
48 Asarat M , Apostolopoulos V , Vasiljevic T , et al. Short‐chain fatty acids regulate cytokines and Th17/Treg cells in human peripheral blood mononuclear cells in vitro. Immunol Invest. 2016;45 (3 ):205–222.27018846
