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Vangala, P.

Publications and source records attributed to Vangala, P..

2 recordsLinked to original sources

The Tec kinase ITK differentially optimizes NFAT, NF-κB, and MAPK signaling during early T cell activation to regulate graded gene induction

The strength of peptide:MHC interactions with the T cell receptor (TCR) is correlated with the time to first cell division, the relative scale of the effector cell response, and the graded expression of activation-associated proteins like IRF4. To regulate T cell activation programming, the TCR and the TCR proximal kinase ITK simultaneously trigger many biochemically separate TCR signaling cascades. T cells lacking ITK exhibit selective impairments in effector T cell responses after activation, but under the strongest signaling conditions ITK activity is dispensable. To gain insight into whether TCR signal strength and ITK activity tune observed graded gene expression through unequal activation of disparate signaling pathways, we examined Erk1/2 activation and NFAT, NF-{kappa}B translocation in naive OT-I CD8+ cell nuclei. We observed consistent digital activation of NFAT1 and Erk-MAPK, but NF-{kappa}B displayed dynamic, graded activation in response to variation in TCR signal strength and was tunable by treatment with an ITK inhibitor. Inhibitor-treated cells showed dampened induction of AP-1 factors Fos and Fosb, NF-{kappa}B response gene transcripts, and survival factor Il2 transcripts. ATAC-seq analysis also revealed genomic regions most sensitive to ITK inhibition were enriched for NF-{kappa}B and AP-1 motifs. Specific inhibition of NF-{kappa}B during peptide stimulation tuned expression of early gene products like c-Fos. Together, these data indicate a key role for ITK in orchestrating optimal activation of separate TCR downstream pathways, specifically aiding NF-{kappa}B activation. More broadly, we revealed a mechanism by which variation in TCR signal strength can produce patterns of graded gene expression in activated T cells.

immunology

Single-cell analysis of upper airway cells reveals host-viral dynamics in influenza infected adults

Influenza virus infections are major causes of morbidity and mortality. Research using cultured cells, bulk tissue, and animal models cannot fully capture human disease dynamics. Many aspects of virus-host interactions in a natural setting remain unclear, including the specific cell types that are infected and how they and neighboring bystander cells contribute to the overall antiviral response. To address these questions, we performed single-cell RNA sequencing (scRNA-Seq) on cells from freshly collected nasal washes from healthy human donors and donors diagnosed with acute influenza during the 2017-18 season. We describe a previously uncharacterized goblet cell population, specific to infected individuals, with high expression of MHC class II genes. Furthermore, leveraging scRNA-Seq reads, we obtained deep viral genome coverage and developed a model to rigorously identify infected cells that detected influenza infection in all epithelial cell types and even some immune cells. Our data revealed that each donor was infected by a unique influenza variant and that each variant was separated by at least one unique non-synonymous difference. Our results demonstrate the power of massively-parallel scRNA-Seq to study viral variation, as well as host and viral transcriptional activity during human infection.

genomics