Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics (preprint)

The study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases like 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 also seem to impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study sheds light on the crucial role of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could potentially improve patient outcomes in diseases characterized by altered platelet function.

Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics (preprint)

IntroductionThe transcriptional heterogeneity of activated platelets, play a significant role in contributing to negative outcomes in sepsis, COVID-19, and autoimmune diseases such as systemic lupus erythematosus (SLE). Despite this, our understanding of these heterogeneous platelet responses remains limited. In this study, we aim to investigate the diverse transcriptional profiles of activated platelets in these diseases, with the goal of deciphering this platelet heterogeneity for new therapeutic strategies to target abnormal and pathogenic platelet subtypes. Materials and methodsWe obtained the single cell transcriptional profiles of blood platelets from patients with COVID-19, sepsis, and SLE. Utilizing machine learning algorithms, Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB), we discerned the distinct transcriptomic signatures indicative of fatal versus survival clinical outcomes. Our methodological framework incorporated source data annotations and platelet markers and used SingleR and Seurat for detailed profiling. Additionally, we implemented Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization, aiding in the detection of various platelet subtypes and their correlation with disease status and patient outcomes. ResultsOur study identified distinct platelet subpopulations that are associated with disease severity. We demonstrated that alterations in platelet transcription patterns can exacerbate endotheliopathy, potentially heightening the risk of coagulation in fatal patients. Moreover, these changes can also influence lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. ConclusionsEnhanced transcriptional heterogeneity in activated platelets is linked to adverse outcomes in conditions such as sepsis, COVID-19, and autoimmune diseases. The discovery of these unique platelet subpopulations paves the way for innovative therapeutic strategies targeting platelet activation, which could potentially improve patient outcomes. Summary sentenceSingle-Cell RNA Sequencing Analysis of Platelets from COVID-19, Sepsis, and SLE Reveals Disease Signatures and Treatment Options to Prevent Patient Mortality. Graphical AbstractsO_LIThe platelet to T cell ratio proportion in PBMC was identified as the most potent predictor for distinguishing survivors from fatal patients, underscores the potential of this ratio as a prognostic biomarker. C_LIO_LIThe discovery of different platelet subgroups, especially active coagulation, hypoxic, and quiescent clusters, in fatal COVID-19 patients, indicates potential targeted treatment strategies. C_LIO_LIIn patients with severe and fatal conditions, we observed three key phenomena: the aggregation of platelets with monocytes, the amplification of endothelial dysfunction by platelets, and a decrease in lymphocyte activation and differentiation due to platelets. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/572680v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@141ca55org.highwire.dtl.DTLVardef@4ad1aborg.highwire.dtl.DTLVardef@b400forg.highwire.dtl.DTLVardef@9ad44e_HPS_FORMAT_FIGEXP M_FIG C_FIG

ViralVar: A Web Tool for Multilevel Visualization of SARS-CoV-2 Genomes (preprint)

The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. A majority of the currently available tools either require computational expertise to deploy or limit user input to pre-selected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: 1) visualization of spatiotemporal dynamics of SARS-CoV-2 lineages, and 2) structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in the pediatric, adult, and elderly population (n > 1.7 million genomes). While the spatiotemporal dynamics of variants did not differ between these age groups, several USA-specific sublineages arose relative to the rest of the world. Our development and utilization of ViralVar to provide insights on the evolution of SARS-CoV-2 in the USA demonstrates the importance of developing accessible tools to facilitate and accelerate large-scale surveillance of circulating pathogens. The ViralVar webserver is freely available at http://viralvar.org/.

Monitoring for SARS-CoV-2 drug resistance mutations in broad viral populations (preprint)

The search for drugs against COVID-19 and other diseases caused by coronaviruses focuses on the most conserved and essential proteins, mainly the main (Mpro) and the papain-like (PLpro) proteases and the RNA-dependent RNA polymerase (RdRp). Nirmatrelvir, an inhibitor for Mpro, was recently approved by FDA as a part of a two-drug combination, Paxlovid, and many more drugs are in various stages of development. Multiple candidates for the PLpro inhibitors are being studied, but none have yet progressed to clinical trials. Several repurposed inhibitors of RdRp are already in use. We can expect that once anti-COVID-19 drugs become widely used, resistant variants of SARS-CoV-2 will emerge, and we already see that for the drugs targeting SARS-CoV-2 RdRp. We hypothesize that emergence of such variants can be anticipated by identifying possible escape mutations already present in the existing populations of viruses. Our group previously developed the coronavirus3D server (https://coronavirus3d.org), tracking the evolution of SARS- CoV-2 in the context of the three-dimensional structures of its proteins. Here we introduce dedicated pages tracking the emergence of potential drug resistant mutations to Mpro and PLpro, showing that such mutations are already circulating in the SARS-CoV-2 viral population. With regular updates, the drug resistance tracker provides an easy way to monitor and potentially predict the emergence of drug resistance-conferring mutations in the SARS-CoV-2 virus.

Increased frequency of recurrent in-frame deletions in new expanding lineages of SARS CoV-2 reflects immune selective pressure (preprint)

Most of the attention in the surveillance of evolution of SARS-CoV-2 has been centered on single nucleotide substitutions in the spike glycoprotein. We show that in-frame deletions (IFDs) also play a significant role in the evolution of viral genome. The percentage of genomes and lineages with IFDs is growing rapidly and they co-occur independently in multiple lineages, including emerging variants of concerns. IFDs distribution is correlated with spike mutations associated with immune escape and concentrated in proteins involved in interactions with the host immune system. Structural analysis suggests that IFDs remodel viral proteins surfaces at common epitopes and interaction interfaces, affecting the virus interactions with the immune system. We hypothesize that the increased frequency of IFDs is an adaptive response to elevated global population immunity.

The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins (preprint)

Fast evolution of the SARS-CoV-2 virus provides us with unique information about the patterns of genetic changes in a single pathogen in the timescale of months. This data is used extensively to track the phylodynamic of the pandemics spread and its split into distinct clades. Here we show that the patterns of SARS-CoV-2 virus mutations along its genome are closely correlated with the structural features of the coded proteins. We show that the foldability of proteins 3D structures and conservation of their functions are the universal factors driving evolutionary selection in protein-coding genes. Insights from the analysis of mutation distribution in the context of the SARS-CoV-2 proteins structures and functions have practical implications including evaluating potential antigen epitopes or selection of primers for PCR-based COVID-19 tests.

The crystal structure of nsp10-nsp16 heterodimer from SARS CoV-2in complex with S-adenosylmethionine (preprint)

SARS-CoV-2 is a member of the coronaviridae family and is the etiological agent of the respiratory Coronavirus Disease 2019. The virus has spread rapidly around the world resulting in over two million cases and nearly 150,000 deaths as of April 17, 2020. Since no treatments or vaccines are available to treat COVID-19 and SARS-CoV-2, respiratory complications derived from the infections have overwhelmed healthcare systems around the world. This virus is related to SARS-CoV-1, the virus that caused the 2002-2004 outbreak of Severe Acute Respiratory Syndrome. In January 2020, the Center for Structural Genomics of Infectious Diseases implemented a structural genomics pipeline to solve the structures of proteins essential for coronavirus replication-transcription. Here we show the first structure of the SARS-CoV-2 nsp10-nsp16 2-O-methyltransferase complex with S-adenosylmethionine at a resolution of 1.80 [A]. This heterodimer complex is essential for capping viral mRNA transcripts for efficient translation and to evade immune surveillance.