DataSheet_1_The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.docx

BackgroundObservational studies have shown that gut microbiota is closely associated with inflammatory dermatoses such as psoriasis, rosacea, and atopic dermatitis (AD). However, the causal relationship between gut microbiota and inflammatory dermatosis remains unclear.MethodsBased on Maximum Likelihood (ML), MR-Egger regression, Inverse Variance Weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median Estimator (WME) methods, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to explore the causal relationship between gut microbiota and inflammatory dermatosis. The genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of inflammatory dermatosis (including psoriasis, AD, rosacea, vitiligo, acne, and eczema) came from the FinnGen consortium and IEU Open GWAS project. Cochran’s IVW Q test tested the heterogeneity among instrumental variables (IVs). The horizontal pleiotropy was tested by MR-Egger regression intercept analysis and MR-PRESSO analysis.ResultsEventually, the results indicated that 5, 16, 17, 11, 15, and 12 gut microbiota had significant causal effects on psoriasis, rosacea, AD, vitiligo, acne, and eczema, respectively, including 42 protective and 34 risk causal relationships. Especially, Lactobacilli and Bifidobacteria at the Family and Genus Level, as common probiotics, were identified as protective factors for the corresponding inflammatory dermatoses. The results of reverse MR analysis suggested a bidirectional causal effect between AD and genus Eubacterium brachy group, vitiligo and genus Ruminococcaceae UCG004. The causal relationship between gut microbiota and psoriasis, rosacea, acne, and eczema is unidirectional. There was no significant heterogeneity among these IVs. In conclusion, this bidirectional two-sample MR study identified 76 causal relationships between the gut microbiome and six inflammatory dermatoses, which may be helpful for the clinical prevention and treatment of inflammatory dermatoses.</p

Table_1_The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.xlsx

BackgroundObservational studies have shown that gut microbiota is closely associated with inflammatory dermatoses such as psoriasis, rosacea, and atopic dermatitis (AD). However, the causal relationship between gut microbiota and inflammatory dermatosis remains unclear.MethodsBased on Maximum Likelihood (ML), MR-Egger regression, Inverse Variance Weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median Estimator (WME) methods, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to explore the causal relationship between gut microbiota and inflammatory dermatosis. The genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of inflammatory dermatosis (including psoriasis, AD, rosacea, vitiligo, acne, and eczema) came from the FinnGen consortium and IEU Open GWAS project. Cochran’s IVW Q test tested the heterogeneity among instrumental variables (IVs). The horizontal pleiotropy was tested by MR-Egger regression intercept analysis and MR-PRESSO analysis.ResultsEventually, the results indicated that 5, 16, 17, 11, 15, and 12 gut microbiota had significant causal effects on psoriasis, rosacea, AD, vitiligo, acne, and eczema, respectively, including 42 protective and 34 risk causal relationships. Especially, Lactobacilli and Bifidobacteria at the Family and Genus Level, as common probiotics, were identified as protective factors for the corresponding inflammatory dermatoses. The results of reverse MR analysis suggested a bidirectional causal effect between AD and genus Eubacterium brachy group, vitiligo and genus Ruminococcaceae UCG004. The causal relationship between gut microbiota and psoriasis, rosacea, acne, and eczema is unidirectional. There was no significant heterogeneity among these IVs. In conclusion, this bidirectional two-sample MR study identified 76 causal relationships between the gut microbiome and six inflammatory dermatoses, which may be helpful for the clinical prevention and treatment of inflammatory dermatoses.</p

The pseudo-code of PSOSVM.

<p>The details of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#pone.0104049.e134" target="_blank">Eq. 16</a> are illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#s2" target="_blank">Materials and Methods</a>.</p

Feature extraction approaches for calculating signal strength of splice sites and similarity of intron and the flanking exons.

<p>A. The sequence extraction approach for calculating signal strength of splice sites; B. The sequence extraction approach for calculating increment of diversity (ID).</p

Feature vectors of experimental dataset.

<p>Feature vectors of experimental dataset.</p

The ROC curves of random forest versus PSOSVM.

<p>The ROC curve of random forest is shown by the solid line and PSOSVM by the dashed line. The classification accuracy of these two methods is measured by AUC (the area under the ROC curve). Random forest gains significant advantages compared to PSOSVM (i.e., 0.900 versus 0.844).</p

Numbers of various RNA types annotated in TAIR10 gene annotation for <i>Arabidopsis</i>.

<p>Each horizontal bar (with the number) indicates the number for a given RNA type.</p

Comparative Analyses between Retained Introns and Constitutively Spliced Introns in <i>Arabidopsis thaliana</i> Using Random Forest and Support Vector Machine

<div><p>One of the important modes of pre-mRNA post-transcriptional modification is alternative splicing. Alternative splicing allows creation of many distinct mature mRNA transcripts from a single gene by utilizing different splice sites. In plants like <i>Arabidopsis thaliana</i>, the most common type of alternative splicing is intron retention. Many studies in the past focus on positional distribution of retained introns (RIs) among different genic regions and their expression regulations, while little systematic classification of RIs from constitutively spliced introns (CSIs) has been conducted using machine learning approaches. We used random forest and support vector machine (SVM) with radial basis kernel function (RBF) to differentiate these two types of introns in <i>Arabidopsis</i>. By comparing coordinates of introns of all annotated mRNAs from TAIR10, we obtained our high-quality experimental data. To distinguish RIs from CSIs, We investigated the unique characteristics of RIs in comparison with CSIs and finally extracted 37 quantitative features: local and global nucleotide sequence features of introns, frequent motifs, the signal strength of splice sites, and the similarity between sequences of introns and their flanking regions. We demonstrated that our proposed feature extraction approach was more accurate in effectively classifying RIs from CSIs in comparison with other four approaches. The optimal penalty parameter C and the RBF kernel parameter in SVM were set based on particle swarm optimization algorithm (PSOSVM). Our classification performance showed F-Measure of 80.8% (random forest) and 77.4% (PSOSVM). Not only the basic sequence features and positional distribution characteristics of RIs were obtained, but also putative regulatory motifs in intron splicing were predicted based on our feature extraction approach. Clearly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.</p></div

Performance of random forest and PSOSVM (F-Measure) in five different feature sets.

<p>Classification accuracy is assessed with F-Measure. Each solid round dot represents the accuracy of random forest and each triangle means the accuracy of PSOSVM for a given feature set. Compared with the other feature sets, our combined <b>A+B+C</b> feature set obtains the optimal classification performance by using both classifiers.</p

Optimal parameters and performances of random forest and PSOSVM using five different feature sets.

<p>Optimal parameters and performances of random forest and PSOSVM using five different feature sets.</p