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Summary: The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83–0.93 in two independent datasets.

Competing Interests: Conflict of Interest SGK is an advisory board member/consultant for AbbVie, Celldex Therapeutics, Galderma, Incyte Corporation, Pfizer, Regeneron Pharmaceuticals, and Kiniksa Pharmaceuticals and has served as an investigator for Galderma, Kiniksa Pharmaceuticals, Pfizer, and Sanofi. NKA has received financial support from Pfizer. The remaining authors state no conflict of interest.

Patients with prurigo nodularis (PN) suffer from intractable itch and dramatic reduction in QOL. Although there is significant clinical heterogeneity in the presentation of PN, disease endotypes remain unknown. We assayed circulating plasma cytokine concentrations in patients with PN (n = 20) along with matched healthy controls and utilized an unsupervised machine learning algorithm to identify disease endotypes. We found two distinct clusters of patients with PN with noninflammatory (cluster 1) and inflammatory (cluster 2) plasma profiles. Cluster 2 had more African Americans (82%, n = 9 vs. 33%, n = 3; P = 0.028), higher Worst Itch Numeric Rating Scale scores (9.5 ± 0.9 vs. 8.3 ± 1.2; P = 0.036), and lower QOL as reflected by higher Dermatology Life Quality Index scores (21.9 ± 6.4 vs. 13.0 ± 4.1; P = 0.015). In addition, cluster 1 had a higher rate of myelopathy (67%, n = 6 vs. 18%, n = 2; P = 0.028). Compared with cluster 1, cluster 2 had higher levels of IL-1α, IL-4, IL-5, IL-6, IL-10, IL-17A, IL-22, IL-25, and IFN-α. With population-level analysis, African American patients with PN had higher erythrocyte sedimentation rate, C-reactive protein, ferritin, and eosinophils and lower transferrin than Caucasian patients with PN. These findings indicate discrete clusters of patients with PN with plasma biomarker profiles corresponding to distinct demographic and clinical characteristics, potentially allowing for precision medicine approaches to treat PN., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: There are limited data characterizing the association between renal disease and prurigo nodularis (PN)., Objective: To characterize the association and describe mechanistic disease linkages between PN and renal comorbidities., Methods: We conducted a cross-sectional analysis of renal comorbidities in PN using TriNetX, a global health research network providing access to medical records. Epidemiological findings provided the basis for translational studies on plasma and skin biopsy samples from PN patients stratified by race., Results: PN was associated with stages 1-5 of chronic kidney disease (CKD), end-stage renal disease, nephritic syndrome, nephrotic syndrome, glomerular disease, and tubulointerstitial disease, but the associations were significantly stronger in black patients. Compared to controls, CKD progression was faster (HR 2.88, 95% CI: 1.01-8.26) only in black PN (10-year survival: 63.5% black vs. 85.5% white). Circulating plasma angiotensinogen levels were dysregulated (P < 0.001) only in black PN patients. Cutaneous transcriptomic analysis of genes related to the renin-angiotensin-aldosterone system (RAAS) revealed considerable dysregulation in PN lesions with greater dysregulation in black patients., Conclusions: Significant dysregulation of the cutaneous and systemic RAAS in black PN patients may explain the increased incidence and severity of renal disease., Competing Interests: Conflicts of interest Dr. Kwatra is an advisory board member/consultant for Abbvie, Celldex Therapeutics, Galderma, Incyte Corporation, Pfizer, Regeneron Pharmaceuticals, and Kiniksa Pharmaceuticals and has served as an investigator for Galderma, Kiniksa Pharmaceuticals, Pfizer Inc., and Sanofi. Dr. Semenov is a consultant for Castle Biosciences and Incyte Corporation. All other authors report no conflicts of interest., (Copyright © 2022 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.)

Actinic keratoses (AKs) represent a premalignant skin condition due to chronic sun damage that dramatically increases in prevalence in the aging population. Currently, animal models of AKs utilize photocarcinogenesis, chemical carcinogens, or targeted gene modulation, and each method possesses unique strengths and weaknesses. Models using photodamage most comprehensively describe methods for preferentially selecting AK lesions, while replicating the pathogenesis of AKs with greater fidelity than models utilizing other carcinogenic methods. The following review of current murine models of AKs will aid in the selection of mouse models appropriate for future in vivo studies to test the efficacy of novel therapeutic agents for the treatment of AKs., (Copyright © 2021 Elsevier Inc. All rights reserved.)

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Introduction: This study aimed to characterize the tumor-infiltrating immune cells population in Kras/tumor protein 53 (Trp53)-driven lung tumors and to evaluate the combinatorial antitumor effect with MEK inhibitor (MEKi), trametinib, and immunomodulatory monoclonal antibodies (mAbs) targeting either programmed death -1 (PD-1) or programmed cell death ligand 1 (PD-L1) in vivo., Methods: Trp53 FloxFlox ;Kras G12D/+ ;Rosa26 LSL-Luciferase/LSL-Luciferase (PKL) genetically engineered mice were used to develop autochthonous lung tumors with intratracheal delivery of adenoviral Cre recombinase. Using these tumor-bearing lungs, tumor-infiltrating immune cells were characterized by both mass cytometry and flow cytometry. PKL-mediated immunocompetent syngeneic and transgenic lung cancer mouse models were treated with MEKi alone as well as in combination with either anti-PD-1 or anti-PD-L1 mAbs. Tumor growth and survival outcome were assessed. Finally, immune cell populations within spleens and tumors were evaluated by flow cytometry and immunohistochemistry., Results: Myeloid-derived suppressor cells (MDSCs) were significantly augmented in PKL-driven lung tumors compared to normal lungs of tumor-free mice. PD-L1 expression appeared to be highly positive in both lung tumor cells and, particularly MDSCs. The combinatory administration of MEKi with either anti-PD-1 or anti-PD-L1 mAbs synergistically increased antitumor response and survival outcome compared with single-agent therapy in both the PKL-mediated syngeneic and transgenic lung cancer models. Theses combinational treatments resulted in significant increases of tumor-infiltrating CD8 + and CD4 + T cells, whereas attenuation of CD11b + /Gr-1 high MDSCs, in particular, Ly6G high polymorphonuclear-MDSCs in the syngeneic model., Conclusions: These findings suggest a potential therapeutic approach for untargetable Kras/p53-driven lung cancers with synergy between targeted therapy using MEKi and immunotherapies., (Copyright © 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)