Ernest Y. Lee

Chemokine (C-X-C motif) ligand 4 (CXCL4), an antimicrobial chemokine with multiple effector functions, is a known biomarker in systemic sclerosis (SSc), but it also plays important roles in inflammatory bowel diseases, atopic dermatitis, psoriasis, systemic lupus erythematosus and other chronic inflammatory diseases. In SSc, the presence of CXCL4 correlates with severe disease progression: CXCL4 contributes to the interferon-(IFN)-I signature by forming pro-inflammatory liquid crystalline complexes with self-DNA, which have the capacity to activate TLR9 in a multivalent manner and potently induce IFN-α in plasmacytoid dendritic cells. Here, we show that in order to chaperone DNA to amplify TLR9 responses effectively, CXCL4 needs to retain its ability to assemble into tetramers, in addition to forming the optimal liquid crystalline structure. By comparing various mutated CXCL4 derived peptides with altered cationic charge and with ablated capacity to from stable tetramers, we find the surprising result that the physiological pre-assembly of CXCL4 into tetramers, before DNA interaction, is important for strong amplified IFN-I secretion. Most importantly, small molecules that destabilize CXCL4 tetramerization block the plasmacytoid dendritic cells IFN-α response, suggesting new pharmacological interventions in SSc, and possibly in other autoimmune conditions characterized by the presence of high CXCL4 and CXCL4-DNA complexes expression.

Hidradenitis suppurativa (HS) is an underdiagnosed chronic, immune-mediated inflammatory skin disease that causes severe pain, drainage, and scarring, leading to significant physical and psychosocial burdens. HS is characterized by heterogenous molecular changes that are poorly understood, posing a significant challenge for drug development. Therapeutic options remain limited, and many patients experience disease relapse despite treatment. Therefore, precision medicine approaches are urgently needed to identify new therapies for HS. Here, we combine integrative transcriptomics, large-scale drug perturbational datasets, and translational immunology to identify sirolimus, pioglitazone, and fulvestrant as novel therapies for HS that can directly target and reverse the HS disease gene signature in immune cell types relevant to HS pathogenesis. Using a novel ex vivo HS skin model, sirolimus, pioglitazone, and fulvestrant inhibited T cell proliferation and activation, and suppressed the production of pro-inflammatory cytokines from HS skin. These results show that unbiased data-driven precision medicine approaches can identify novel therapies for HS and can serve more generally as a model approach for therapeutic discovery in other chronic inflammatory diseases. One Sentence Summary: Data-driven precision medicine approach identifies sirolimus, pioglitazone, and fulvestrant as novel therapies for hidradenitis suppurativa

BACKGROUND: Reversal reactions (RRs) in leprosy are acute immune episodes marked by inflammation and bacterial clearance, offering a model to study the dynamics of host responses to Mycobacterium leprae . These episodes are often severe and difficult to treat, frequently progressing to permanent disabilities. We aimed to characterize the immune mechanisms and identify antimicrobial effectors during RRs. METHODS: We performed RNA-Seq on paired skin biopsy specimens collected from 9 patients with leprosy before and at RR diagnosis, followed by differential gene expression and functional analysis. A machine-learning classifier was applied to predict membrane-permeabilizing proteins. Antimicrobial activity was assessed in M . leprae –infected macrophages and axenic cultures. RESULTS: In the paired pre-RR and RR biopsy specimens, a 64-gene antimicrobial response signature was upregulated during RR and correlated with reduced M . leprae burden. Predicted upstream regulators included IL-1β, TNF, IFN-γ, and IL-17, indicating activation of both the Th1 and Th17 pathways. A machine-learning classifier identified 28 genes with predicted membrane-permeabilizing antimicrobial activity, including S100A8. Four proteins (S100A7, S100A8, CCL17, and CCL19) demonstrated antimicrobial activity against M . leprae in vitro. Scanning electron microscopy revealed membrane damage in bacteria exposed to these proteins. CONCLUSION: RR is associated with a robust antimicrobial gene program regulated by Th1 and Th17 cytokines. We identified potentially novel host antimicrobial effectors that showed activity against M . leprae , suggesting potential strategies to bolster Th1 and Th17 responses for combating intracellular mycobacterial infections. FUNDING: NIH grants R01 AI022553, R01 AR040312, R01 AR073252, R01 AI166313, R01 AI169526, P50 AR080594, and 4R37 AI052453-21 and National Science Foundation (NSF) grant DMR2325840.

Classically, chemokines coordinate leukocyte trafficking; however, many chemokines also have direct antibacterial activity. The bacterial killing mechanism of chemokines and the biochemical properties that define which members of the chemokine superfamily are antimicrobial remain poorly understood. We report that the antimicrobial activity of chemokines is defined by their ability to bind phosphatidylglycerol and cardiolipin, two anionic phospholipids commonly found in the bacterial plasma membrane. We show that only chemokines able to bind these two phospholipids kill bacteria and that they exert rapid bacteriostatic and bactericidal effects with a higher potency than the antimicrobial peptide β-defensin 3. Both biochemical and genetic interference with the chemokine-cardiolipin interaction impaired microbial growth arrest, bacterial killing, and membrane disruption by chemokines. Moreover, unlike conventional antibiotics, Escherichia coli failed to develop resistance when placed under increasing antimicrobial chemokine pressure in vitro. Thus, we have identified cardiolipin and phosphatidylglycerol as binding partners for chemokines responsible for chemokine antimicrobial action.

Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.

Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.

Systemic lupus erythematosus (SLE) presents with diverse and heterogenous cutaneous manifestations. However, the molecular and immunologic pathways driving specific cutaneous manifestations of SLE are poorly understood. Here, we leverage transcriptomics from a large well-phenotyped longitudinal cohort of SLE patients to map molecular pathways linked to ten distinct SLE-related rashes. Through whole blood and immune cell-sorted bulk RNA sequencing, we identified immune signatures specific to cutaneous subtypes of SLE. Subacute cutaneous lupus (SCLE) exhibited broad upregulation of type I interferon, TNF-α, and IL6-JAK-STAT3, pathways suggesting potential unique therapeutic responses to JAK and type I interferon inhibition. While interferon signaling is prominent in SCLE, discoid lupus, and acute lupus, it is unexpectedly absent in patients with skin and mucosal ulcers. Pathway and cell-type enrichment analysis revealed unexpected roles for CD14+ monocytes in photosensitivity of SLE and NK cells in alopecia, mucosal ulceration, and livedo reticularis. These findings illuminate the immune heterogeneity of rashes in SLE, highlighting subtype-specific mechanistic targets, and presenting opportunities to identify precision therapies for SLE-associated skin phenotypes.

Most recent studies on the coronavirus disease 2019 (COVID‐19) pandemic and cutaneous melanoma (CM) focused more on delayed diagnosis or advanced presentation. We aimed to ascertain mortality trends of CM between 2012 and 2022, focusing on the effects of the COVID‐19 pandemic. In this serial population‐based study, the National Vital Statistics System dataset was queried for mortality data. Excess CM‐related mortality rates were estimated by calculating the difference between observed and projected mortality rates during the pandemic. Totally there were 108,853 CM‐associated deaths in 2012–2022. CM‐associated mortality saw a declining trend from 2012 to 2019 overall. However, it increased sharply in 2020 (ASMR 3.73 per 100,000 persons, 5.95% excess mortality), and remained high in 2021 and 2022, with the ASMRs of 3.82 and 3.81, corresponding to 11.17% and 13.20% excess mortality, respectively. The nonmetro areas had the most pronounced rise in mortality with 12.20% excess death in 2020, 15.33% in 2021 and 20.52% in 2022, corresponding to a 4–6 times excess mortality risk compared to large metro areas during the pandemic. The elderly had the most pronounced rise in mortality, but the mortality in the younger population was reduced.