Fine mapping and subphenotyping implicates ADRA1B gene variants in psoriasis susceptibility in a Chinese population

Variants in PRKCE and KLC1, Potential Regulators of Type I Psoriasis

Purpose

Psoriasis is a multifactorial disease with a complex genetic predisposition. The pathophysiology of psoriasis is associated with genetic variants. To better characterize gene variants in psoriasis and identify the relationship between clinical characteristics and variant genes in its pathogenesis.

Patients and Methods

DNA was extracted and purified from eight pairs of monozygotic twins with psoriasis discordance and 282 type I psoriasis patients. Thirteen variable genes were amplified and sequenced using the Sanger method after whole genome sequencing.

Results

Thirteen genes were found to be variable in eight pairs of monozygotic twins with psoriasis discordance. Among the 13 genes, the variant frequencies of protein kinase C epsilon (PRKCE) (c.240T>C, 35.9% vs 47.7%, P < 0.05) and kinesin light chain 1 (KLC1) (c.216A>G, 2.9% vs 98.1%, P< 0.01) were significantly lower in psoriasis than in normal Asian individuals. Additionally, we found considerable differences in the relationship between variants in genes CADM2, JPH2, SPTLC3 and clinical characteristics stratified by medical history and family history. Moreover, the variants in MEGF6 (39.52% vs 22.50%, χ²=3.83, p < 0.05) showed a stronger association with the mild group (PASI ≤10) than the heavy group.

Conclusion

Our results provide a comprehensive correlation analysis of regulatory genes that are regulated in psoriasis. This integrated analysis offers novel insight into the pathogenic mechanisms involved in psoriasis.

Identifying Novel Psoriatic Disease Drug Targets Using a Genetics-Based Priority Index Pipeline

BACKGROUND

Despite numerous genome-wide association studies conducted in psoriasis and psoriatic arthritis, only a small fraction of the identified genes has been therapeutically targeted.

OBJECTIVE

We sought to identify and analyze potential therapeutic targets for psoriasis and psoriatic arthritis (PsA) using the priority index (Pi), a genetics-dependent drug target prioritization approach.

METHODS

Significant genetic variants from GWAS for psoriasis, PsA, and combined psoriatic disease were annotated and run through the Pi pipeline. Potential drug targets were identified based on genomic predictors, annotation predictors, pathway enrichment, and pathway crosstalk.

RESULTS

Several gene targets were identified for psoriasis and PsA that demonstrated biological associations to their respective diseases. Some are currently being explored as potential therapeutic targets (i.e. ICAM1, NF-kB, REV3L, ADRA1B for psoriasis; CCL11 for PsA); others have not yet been investigated (i.e. LNPEP, LCE3 for psoriasis; UBLCP1 for PsA). Additionally, many nodal points of potential intervention were identified as promising therapeutic targets. Of these, some are currently being studied such as TYK2 for psoriasis, and others have yet to be explored (i.e. PPP2CA, YAP1, PI3K, AKT, FOXO1, RELA, CSF2, IFNGR1, IFNGR2 for psoriasis; GNAQ, PLCB1, GNAI2 for PsA).

CONCLUSION

Through Pi, we identified data-driven candidate therapeutic gene targets and pathways for psoriasis and PsA. Given the sparse PsA specific genetic studies and PsA specific drug targets, this analysis could prove to be particularly valuable in the pipeline for novel psoriatic therapies.

Fertility Relevance Probability Analysis Shortlists Genetic Markers for Male Fertility Impairment

Impairment of male fertility is one of the major public health issues worldwide. Nevertheless, genetic causes of male sub- and infertility can often only be suspected due to the lack of reliable and easy-to-use routine tests. Yet, the development of a marker panel is complicated by the large quantity of potentially predictive markers. Actually, hundreds or even thousands of genes could have fertility relevance. Thus, a systematic method enabling a selection of the most predictive markers out of the many candidates is required. As a criterion for marker selection, we derived a gene-specific score, which we refer to as fertility relevance probability (FRP). For this purpose, we first categorized 2,753 testis-expressed genes as either candidate markers or non-candidates, according to phenotypes in male knockout mice. In a parallel approach, 2,502 genes were classified as candidate markers or non-candidates based on phenotypes in men. Subsequently, we conducted logistic regression analyses with evolutionary rates of genes (dN/dS), transcription levels in testis relative to other organs, and connectivity of the encoded proteins in a protein-protein interaction network as covariates. In confirmation of the procedure, FRP values showed the expected pattern, thus being overall higher in genes with known relevance for fertility than in their counterparts without corresponding evidence. In addition, higher FRP values corresponded with an increased dysregulation of protein abundance in spermatozoa of 37 men with normal and 38 men with impaired fertility. Present analyses resulted in a ranking of genes according to their probable predictive power as candidate markers for male fertility impairment. Thus, AKAP4, TNP1, DAZL, BRDT, DMRT1, SPO11, ZPBP, HORMAD1, and SMC1B are prime candidates toward a marker panel for male fertility impairment. Additional candidate markers are DDX4, SHCBP1L, CCDC155, ODF1, DMRTB1, ASZ1, BOLL, FKBP6, SLC25A31, PRSS21, and RNF17. FRP inference additionally provides clues for potential new markers, thereunder TEX37 and POU4F2. The results of our logistic regression analyses are freely available at the PreFer Genes website (https://prefer-genes.uni-mainz.de/).

Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

Background

There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa.

Methods

We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases.

Results

Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8⁺ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases.

Conclusion

PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.

A novel DNA methylation 10-CpG prognostic signature of disease-free survival reveal that MYBL2 is associated with high risk in prostate cancer

BACKGROUND

Prostate cancer (PC) is the most common non-cutaneous malignancy among men in the western world. However, heterogeneity remains a pressing clinical problem.

RESEARCH DESIGN AND METHODS

The least absolute shrinkage and selection operator (LASSO) was used to screen the prognostic signature. Weighted correlation network analysis (WGCNA) was used to identify the target genes associated with high-risk characteristics. Gene set enrichment analysis was used to suggest the molecular mechanism of MYBL2 in PC. In addition, in vitro experiments were carried out to validate the role of MYBL2 in PC.

RESULTS

Ten DNA methylation sites were selected as the prognostic signature. A high expression of MYBL2 was associated with a poor prognosis in PC patients. The effect of MYBL2 in PC was related to KRAS, AKT, IL21, and ATM. MYBL2 facilitates the proliferation, migration, invasion, and metastasis of PC cells.

CONCLUSIONS

We developed a DNA methylation 10-CpG prognostic signature to predict the prognosis of PC patients. And the high expression of MYBL2 in PC may be related to poor prognosis.

Investigation on the Mechanism of Qubi Formula in Treating Psoriasis Based on Network Pharmacology

Objective

To elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.

Methods

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets.

Results

The ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.

Conclusion

In this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a "multicompound, multitarget, and multipathway" manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

Variation at ACOT12 and CT62 locus represents susceptibility to psoriasis in Han population

Background

Psoriasis is a chronic inflammatory disorder of the skin, and genetic factors are reported to be involved in the disease pathogenesis. Many studies have named psoriasis candidate genes.

Objective

In this study, we determined the mutation frequency of 7 variable genes in 1,027 psoriatic patients and investigated its possible mechanism associated with psoriasis.

Method

A total of 7 variable genes from 1,027 psoriatic patients were amplified and sequenced using the Sanger method. The mutation frequency was compared to that of non‐psoriatic individuals in Asia using information from databases.

Results

Among the 7 investigated genes, the mutation frequency of ACOT12 (c.80A>G, 9.98% vs. 5.85%, p < .05) and CT62 (c.476C>T,15.8% vs. 9.93%, p < .05) was found to be significantly higher than among non‐psoriatic Asian individuals. The mutation frequencies of CASZ1(c.599T>G), SPRED1(c.155A>G), and ACOT12 (c.80A>G) differed significantly between the groups organized by medical history, PASI, and family history. SPRED1 gene variants (17.25% vs. 7.78%, p < .01) showed a stronger association with the family history group at the onset of psoriasis than with the no family history group.

Conclusions

Our results provide a comprehensive correlation analysis of susceptibility genes in psoriasis patients. Clinical characteristics of patients play important roles in the development of psoriatic skin.