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Psoriasis is a chronic, immune-mediated inflammatory skin disease associated with a polygenic mode of inheritance. There are few studies that explore the association of a psoriasis Polygenic Risk Score (PRS) with patient clinical characteristics, and to our knowledge there are no studies examining psoriasis PRS associations across different ethnicities. In this study, we used a multi-racial psoriasis cohort to investigate PRS associations with clinical phenotypes including age of onset, psoriatic arthritis, other comorbidities, psoriasis body location, psoriasis subtype, environmental triggers, and response to therapies. We collected patient data and Affymetrix genome-wide SNP data from a cohort of 607 psoriasis patients and calculated an 88-loci PRS (PRS-ALL), also partitioned between genetic loci within the HLA region (PRS-HLA; 11 SNPS) and loci outside the HLA region (PRS-NoHLA; 77 SNPS). We used t-test and logistic regression to analyze the association of PRS with clinical phenotypes. We found that PRS-HLA and PRS-noHLA had differing effects on psoriasis age of onset, psoriatic arthritis, psoriasis located on the ears, genitals, nails, soles of feet, skin folds, and palms, skin injury as an environmental trigger, cardiovascular comorbidities, and response to phototherapy. In some cases these PRS associations were ethnicity specific. Overall, these results show that the genetic basis for clinical manifestations of psoriasis are driven by distinct HLA and non-HLA effects, and that these PRS associations can be dependent on ethnicity., (© 2024. The Author(s).)

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition characterized by painful nodules, abscesses, and scarring, predominantly affecting intertriginous regions and it is often underdiagnosed. This study aimed to utilize single cell RNA and cell-surface protein sequencing (CITE-Seq) to delineate the immune composition of circulating cells in Hidradenitis suppurativa (HS) peripheral blood compared to healthy controls. CITE-Seq was used to analyze the gene and protein expression profiles of peripheral blood mononuclear cells (PBMCs) from 9 HS and 29 healthy controls. The study identified significant differences cell composition between HS patients and healthy controls, including increased proportions of CD14+ and CD16+ monocytes, cDC2, plasmablasts, and proliferating CD4+ T cells in HS patients. Differential expression analysis revealed upregulation of inflammatory markers such as TNF, IL1B , and NF-κB in monocytes, as well as chemokines and cell adhesion molecules involved in immune cell recruitment and tissue infiltration. Pathway enrichment analysis highlighted the involvement of IL-17, IL-26 and TNF signaling pathways in HS pathogenesis. Machine learning identified key markers for diagnostics and therapeutic development. The findings also support the potential for machine learning models to aid in the diagnosis of HS based on immune cell markers. These insights may inform future therapeutic strategies targeting specific immune pathways in HS., Competing Interests: Conflict of Interest WL has received research grant funding from Abbvie, Amgen, Janssen, Leo, Novartis, Pfizer, Regeneron, and TRex Bio. CP has received honoraria and/or travel support from MSD, BMS, Pierre Fabre, MERCK, Sanofi, Almirall, AbbVie, Pelpharma, Amgen, DSD, Takeda, Pfizer, Novartis, Leo, Janssen, Astra Zeneca, and Boehringer Ingelheim. AW received honoraria and travel support from AbbVie, Amgen, Biogen, Janssen, Leo, Novartis, UCB and Sanofi. HN has received grant support from AbbVie; consulting fees from 23andme, Abbvie, Aristea Therapeutics, Nimbus Therapeutics, Medscape, Sonoma Biotherapeutics, DAVA Oncology, Boehringer Ingelheim, Union Chimique Belge’s (UCB) and Novartis; investigator fees from Pfizer; and holds shares in Radera, Inc. She is also an Associate Editor for JAMA Dermatology and Vice President of the Hidradenitis Suppurativa Foundation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Purpose of Review: Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery., Recent Findings: This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, psoriasis biomarker identification, precision medicine enhancement, and AI-driven education strategies. These advancements promise to improve patient outcomes, especially in regions lacking specialist care. However, the success of AI in dermatology hinges on dermatologists' oversight to ensure that ML's potential is fully realized in patient care, preserving the essential human element in medicine., Summary: This collaboration between AI and human expertise could define the future of dermatological treatments, making personalized care more accessible and precise., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

This review highlights advances made in psoriasis genetics, including findings from genome-wide association studies, exome-sequencing studies, and copy number variant studies. The impact of genetic variants on various comorbidities and therapeutic responses is discussed., Competing Interests: Disclosure W. Liao has received research grant funding from AbbVie, United States, Amgen, United States, Janssen, United States, LEO, Denmark, Novartis, Switzerland, Pfizer, United States, Regeneron, United States, and TRex Bio., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Psoriasis is an immune-mediated inflammatory skin disease typically characterized by erythematous and scaly plaques. It affects 3% of the Newfoundland population while only affecting 1.7% of the general Canadian population. Recent genome-wide association studies (GWAS) in psoriasis have identified more than 63 genetic susceptibility loci that individually have modest effects. Prior studies have shown that a genetic risk score (GRS) combining multiple loci can improve psoriasis disease prediction. However, these prior GRS studies have not fully explored the association of GRS with patient clinical characteristics. In this study, we calculated three types of GRS: one using all known GWAS SNPs (GRS-ALL), one using a subset of SNPs from the HLA region (GRS-HLA), and the last using non-HLA SNPs (GRS-noHLA). We examined the relationship between these GRS and a number of psoriasis features within a well characterized Newfoundland psoriasis cohort. We found that both GRS-ALL and GRS-HLA were significantly associated with early age of psoriasis onset, psoriasis severity, first presentation of psoriasis at the elbow or knee, and the total number of body locations affected, while only GRS-ALL was associated with a positive family history of psoriasis. GRS-noHLA was uniquely associated with genital psoriasis. These findings clarify the relationship of the HLA and non-HLA components of GRS with important clinical features of psoriasis., Competing Interests: WL has received research grant funding from Abbvie, Amgen, Janssen, Leo, Novartis, Pfizer, Regeneron, and TRex Bio. LT has received support from Galderma, Janssen, and Novartis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bui, Kumar, Liu, Orcales, Gulliver, Tsoi, Gulliver and Liao.)

Like many viruses, Hepatitis C Virus (HCV) has a high mutation rate, which helps the virus adapt quickly, but mutations come with fitness costs. Fitness costs can be studied by different approaches, such as experimental or frequency-based approaches. The frequency-based approach is particularly useful to estimate in vivo fitness costs, but this approach works best with deep sequencing data from many hosts are. In this study, we applied the frequency-based approach to a large dataset of 195 patients and estimated the fitness costs of mutations at 7957 sites along the HCV genome. We used beta regression and random forest models to better understand how different factors influenced fitness costs. Our results revealed that costs of nonsynonymous mutations were three times higher than those of synonymous mutations, and mutations at nucleotides A or T had higher costs than those at C or G. Genome location had a modest effect, with lower costs for mutations in HVR1 and higher costs for mutations in Core and NS5B. Resistance mutations were, on average, costlier than other mutations. Our results show that in vivo fitness costs of mutations can be site and virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes., Competing Interests: The authors have declared that no competing interests exist.