Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

B- and T-Lymphocyte Attenuator in Systemic Lupus Erythematosus Disease Pathogenesis.

B- and T-lymphocyte attenuator (BTLA; CD272) is an immunoglobulin superfamily member and part of a family of checkpoint inhibitory receptors that negatively regulate immune cell activation. The natural ligand for BTLA is herpes virus entry mediator (HVEM; TNFRSF14), and binding of HVEM to BTLA leads to attenuation of lymphocyte activation. In this study, we evaluated the role of BTLA and HVEM expression in the pathogenesis of systemic lupus erythematosus (SLE), a multisystem autoimmune disease. Peripheral blood mononuclear cells from healthy volunteers (N = 7) were evaluated by mass cytometry by time-of-flight to establish baseline expression of BTLA and HVEM on human lymphocytes compared with patients with SLE during a self-reported flare (N = 5). High levels of BTLA protein were observed on B cells, CD4+, and CD8+ T cells, and plasmacytoid dendritic cells in healthy participants. HVEM protein levels were lower in patients with SLE compared with healthy participants, while BTLA levels were similar between SLE and healthy groups. Correlations of BTLA-HVEM hub genes' expression with patient and disease characteristics were also analyzed using whole blood gene expression data from patients with SLE (N = 1,760) and compared with healthy participants (N = 60). HVEM, being one of the SLE-associated genes, showed an exceptionally strong negative association with disease activity. Several other genes in the BTLA-HVEM signaling network were strongly (negative or positive) correlated, while BTLA had a low association with disease activity. Collectively, these data provide a clinical rationale for targeting BTLA with an agonist in SLE patients with low HVEM expression.

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.