Reduced protein-coding transcript diversity in severe dengue emphasises the role of alternative splicing.

Dengue fever, a neglected tropical arboviral disease, has emerged as a global health concern in the past decade. Necessitating a nuanced comprehension of the intricate dynamics of host-virus interactions influencing disease severity, we analysed transcriptomic patterns using bulk RNA-seq from 112 age- and gender-matched NS1 antigen-confirmed hospital-admitted dengue patients with varying severity. Severe cases exhibited reduced platelet count, increased lymphocytosis, and neutropenia, indicating a dysregulated immune response. Using bulk RNA-seq, our analysis revealed a minimal overlap between the differentially expressed gene and transcript isoform, with a distinct expression pattern across the disease severity. Severe patients showed enrichment in retained intron and nonsense-mediated decay transcript biotypes, suggesting altered splicing efficiency. Furthermore, an up-regulated programmed cell death, a haemolytic response, and an impaired interferon and antiviral response at the transcript level were observed. We also identified the potential involvement of the RBM39 gene among others in the innate immune response during dengue viral pathogenesis, warranting further investigation. These findings provide valuable insights into potential therapeutic targets, underscoring the importance of exploring transcriptomic landscapes between different disease sub-phenotypes in infectious diseases.

Dynamics of Whole Transcriptome Analysis (WTA) and Surface markers expression (AbSeq) in Immune Cells of COVID-19 Patients and Recovered captured through Single Cell Genomics.

Introduction: Single-cell multi-omics studies, such as multidimensional transcriptomics (whole transcriptomic analysis, WTA), and surface marker analysis (antibody sequencing, AbSeq), have turned out to be valuable techniques that offer inaccessible possibilities for single-cell profiling of mRNA, lncRNA, and proteins. Methods: We used this technique to understand the dynamics of mRNA and protein-level differences in healthy, COVID-19-infected and recovered individuals using peripheral blood mononuclear cells (PBMCs). Our results demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. Results: We demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. We observed high levels of cell identity and regulatory markers, CD3E, CD4, CD8A, CD5, CD7, GITR, and KLRB1 in healthy individuals, whereas markers related to cell activation, CD38, CD28, CD69, CD62L, CD14, and CD16 elevated in the SARS-CoV-2 infected patients at both WTA and AbSeq levels. Curiously, in recovered individuals, there was a high expression of cytokine and chemokine receptors (CCR5, CCR7, CCR4, CXCR3, and PTGRD2). We also observed variations in the expression of markers within cell populations under different states. Discussion: Furthermore, our study emphasizes the significance of employing an oligo-based method (AbSeq) that can help in diagnosis, prognosis, and protection from disease/s by identifying cell surface markers that are unique to different cell types or states. It also allows simultaneous study of a vast array of markers, surpassing the constraints of techniques like FACS to query the vast repertoire of proteins.

Suppressed transcript diversity and immune response in COVID-19 ICU patients: a longitudinal study.

Understanding the dynamic changes in gene expression during Acute Respiratory Distress Syndrome (ARDS) progression in post-acute infection patients is crucial for unraveling the underlying mechanisms. Study investigates the longitudinal changes in gene/transcript expression patterns in hospital-admitted severe COVID-19 patients with ARDS post-acute SARS-CoV-2 infection. Blood samples were collected at three time points and patients were stratified into severe and mild ARDS, based on their oxygenation saturation (SpO2/FiO2) kinetics over 7 d. Decline in transcript diversity was observed over time, particularly in patients with higher severity, indicating dysregulated transcriptional landscape. Comparing gene/transcript-level analyses highlighted a rather limited overlap. With disease progression, a transition towards an inflammatory state was evident. Strong association was found between antibody response and disease severity, characterized by decreased antibody response and activated B cell population in severe cases. Bayesian network analysis identified various factors associated with disease progression and severity, viz. humoral response, TLR signaling, inflammatory response, interferon response, and effector T cell abundance. The findings highlight dynamic gene/transcript expression changes during ARDS progression, impact on tissue oxygenation and elucidate disease pathogenesis.