Psoriasis prediction from genome-wide SNP profiles

Intravenous As2O3 as a promising treatment for psoriasis - an experimental study in psoriasis-like mouse model

OBJECTIVE

To evaluate the efficacy and mechanistic bases of the intravenous injection of arsenic trioxide at clinical-relevant doses for treating an imiquimod-induced psoriasis-like mouse model.

METHODS

After inducing psoriasis-like skin lesions on the back of mice with imiquimod, mice in each group were injected with a clinical dose of arsenic trioxide through the tail vein. The changes in the gene expression, protein expression and distribution of relevant inflammatory factors were evaluated in the inflicted skin area, for mechanisms underlying the efficacy of intravenous As₂O₃ intervention. HaCaT cells were used to establish an in vitro psoriasis model and pcDNA3.1-NF-κB overexpression plasmid was transfected into cells to overexpress P65, which further confirmed the role of the NF-κB signaling pathway in the effectiveness of As₂O₃.

RESULTS

Clinical dose of As₂O₃ can significantly improve abnormal symptoms and pathological changes in psoriasis-like skin lesions induced by IMQ in mice. While IMQ induced abnormal expression and distribution of inflammatory factors in the RIG-I pathway and the microRNA-31 (miR-31) pathway in psoriatic skin tissues, intravenous As₂O₃ can effectively regulate and restore the normality. The leading role of NF-κB signaling was evidenced in vivo and validated in vitro using the NF-κB-overexpressed HaCaT cell model.

CONCLUSION

Clinical dosage of As₂O₃ may achieve effective treatment of IMQ-induced psoriatic skin lesions by modulating the NF-κB signaling pathway which regulates both the RIG-I and the miR-31 lines of action. Our data provided strong evidence supporting the claim that systemic As₂O₃ administration of clinical doses can be a promising treatment for psoriasis patients.

Diagnostic Test Accuracy of Genetic Tests in Diagnosing Psoriasis: A Systematic Review

The pathogenesis of psoriasis involves the interaction of several environmental and genetic factors. Predicting the disease risk cannot depend on individual genetic alleles. Consequently, some studies have evaluated the use of genetic risk scores that combine several psoriasis susceptibility loci to increase the accuracy of predicting/diagnosing the disease. This meta-analysis summarizes the evidence regarding using genetic risk scores (GRS) in the diagnosis or prediction of psoriasis. A search of MEDLINE/PubMed, the Latin American Caribbean Health Sciences Literature (LILACS) database, Cochrane Library, Scopus, Web of Science, and ProQuest was conducted in July 2022. The primary objective was to record the area under the curve (AUC) for GRS of psoriasis. Secondary objectives included characteristics of studies and patients. The risk of bias (ROB) was assessed using the PROBAST tool. Five studies fulfilled the eligibility criteria of this review. None of the studies described the clinical criteria (reference standard) that were employed to diagnose psoriasis. The AUCs of the 11 GRS models ranged from 0.6029-0.8583 (median: 0.75). Marked heterogeneity was detected (Cochran Q: 1250.051, p < 0.001, and I2 index: 99.2%). So, pooling of the results of the included studies was not performed. The ROB was high for all studies and clinical application was not described. Genetic risk scores are promising tools for the prediction of psoriasis with fair to good accuracy. However, further research is required to identify the most accurate combination of loci and to validate the scores in variable ethnicities.

Robust Reference Powered Association Test of Genome-Wide Association Studies

Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.

Nuclear Norm Clustering: a promising alternative method for clustering tasks

Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulated annealing techniques to choose an optimal label vector that minimizes nuclear norm of the pooled within cluster residual matrix. To evaluate the performance of the NNC algorithm, we compared the performance of both 15 public datasets and 2 genome-wide association studies (GWAS) on psoriasis, comparing our method with other classic methods. The results indicate that NNC method has a competitive performance in terms of F-score on 15 benchmarked public datasets and 2 psoriasis GWAS datasets. So NNC is a promising alternative method for clustering tasks.

Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances

Over 14 common single nucleotide polymorphisms (SNP) have been consistently identified from genome-wide association studies (GWAS) as associated with glioma risk in European background. The extent to which and how these genetic variants can improve the prediction of glioma risk has was not been investigated. In this study, we employed three independent case-control datasets in Chinese populations, tested GWAS signals in dataset1, validated association results in dataset2, developed prediction models in dataset2 for the consistently replicated SNPs, refined the consistently replicated SNPs in dataset3 and developed tailored models for Chinese populations. For model construction, we aggregated the contribution of multiple SNPs into genetic risk scores (count GRS and weighed GRS) or predicted risks from logistic regression analyses (PRFLR).

In dataset2, the area under receiver operating characteristic curves (AUC) of the 5 consistently replicated SNPs by PRFLR(SNPs) was 0.615, higher than those of all GRSs(ranging from 0.607 to 0.611, all P>0.05). The AUC of genetic profile significantly exceeded that of family history (fmc) alone (AUC=0.535, all P<0.001). The best model in our study comprised “PRURA +fmc” (AUC=0.646) in dataset3. Further model assessment analyses provided additional evidence.

This study indicates that genetic markers have potential value for risk prediction of glioma.

Random Bits Forest: a Strong Classifier/Regressor for Big Data

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

Phenotype prediction from genome-wide association studies: application to smoking behaviors

Background

A great success of the genome wide association study enabled us to give more attention on the personal genome and clinical application such as diagnosis and disease risk prediction. However, previous prediction studies using known disease associated loci have not been successful (Area Under Curve 0.55 ~ 0.68 for type 2 diabetes and coronary heart disease). There are several reasons for poor predictability such as small number of known disease-associated loci, simple analysis not considering complexity in phenotype, and a limited number of features used for prediction.

Methods

In this research, we investigated the effect of feature selection and prediction algorithm on the performance of prediction method thoroughly. In particular, we considered the following feature selection and prediction methods: regression analysis, regularized regression analysis, linear discriminant analysis, non-linear support vector machine, and random forest. For these methods, we studied the effects of feature selection and the number of features on prediction. Our investigation was based on the analysis of 8,842 Korean individuals genotyped by Affymetrix SNP array 5.0, for predicting smoking behaviors.

Results

To observe the effect of feature selection methods on prediction performance, selected features were used for prediction and area under the curve score was measured. For feature selection, the performances of support vector machine (SVM) and elastic-net (EN) showed better results than those of linear discriminant analysis (LDA), random forest (RF) and simple logistic regression (LR) methods. For prediction, SVM showed the best performance based on area under the curve score. With less than 100 SNPs, EN was the best prediction method while SVM was the best if over 400 SNPs were used for the prediction.

Conclusions

Based on combination of feature selection and prediction methods, SVM showed the best performance in feature selection and prediction.

Effects of Thai medicinal herb extracts with anti-psoriatic activity on the expression on NF-κB signaling biomarkers in HaCaT keratinocytes

Psoriasis is a chronic inflammatory skin disorder characterized by rapid proliferation of keratinocytes and incomplete keratinization. Discovery of safer and more effective anti-psoriatic drugs remains an area of active research at the present time. Using a HaCaT keratinocyte cell line as an in vitro model, we had previously found that ethanolic extracts from three Thai medicinal herbs, namely Alpinia galanga, Curcuma longa and Annona squamosa, possessed anti-psoriatic activity. In the current study, we aimed at investigating if these Thai medicinal herb extracts played a molecular role in suppressing psoriasis via regulation of NF-κB signaling biomarkers. Using semi-quantitative RT-PCR and report gene assays, we analyzed the effects of these potential herbal extracts on 10 different genes of the NF-κB signaling network in HaCaT cells. In accordance with our hypothesis, we found that the extract derived from Alpinia galanga significantly increased the expression of TNFAIP3 and significantly reduced the expression of CSF-1 and NF-κB2. Curcuma longa extract significantly decreased the expression of CSF-1, IL-8, NF-κB2, NF-κB1 and RelA, while Annona squamosa extract significantly lowered the expression of CD40 and NF-κB1. Therefore, this in vitro study suggested that these herbal extracts capable of functioning against psoriasis, might exert their activity by controlling the expression of NF-κB signaling biomarkers.