Biology subjects

Tantisira, K.

Publications and source records attributed to Tantisira, K..

6 recordsLinked to original sources

bioRxiv2025

Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma

BackgroundAsthma is driven by complex interactions amongst structural airway cells, cells of the immune system, and the environmental. While sputum cell characterization has been instrumental in studying asthma pathogenesis and refining treatment strategies, the nuances of cellular transcriptomes and intercellular communication in asthmatic sputum remain poorly understood. MethodsWe employed single-cell RNA sequencing to analyze cells isolated form the sputum from 16 asthma patients and 8 non-asthmatic controls. Cell identities were established using curated marker genes and SingleR annotation. We compared cell-specific gene expression and communication networks between asthmatic and control groups, correlating findings with distinct pathways that were dysregulated in asthma. Findings37,565 cellular transcriptomes were captured and analyzed. 15 distinct cell populations were identified, including various macrophages, monocytes, dendritic cells, and lymphocytes, along with rare cell types such as mast cells, innate lymphoid cells, bronchial epithelial cells, and eosinophils. Intercellular communication analysis indicated heightened signaling activity in asthma compared to controls, particularly in CD4+ T cells and dendritic cells which exhibited the most significant increases in RNA expression of outgoing signaling molecules. Notably, the ADAM12-SDC4 and CCL22-CCR4 ligand-receptor pathways demonstrated the strongest shifts between asthma and control subjects, particularly between dendritic cells and CD4 lymphocytes. InterpretationSC RNA seq profiling the asthma cellular transcriptome analysis of sputum highlights both innate and adaptive immune mechanisms that are significantly amplified in asthma. The elevated expression of ADAM12-SCD4 and CCL22-CC4 point to their critical role in asthma pathogenesis, suggesting potential avenues for targeted therapies and improved management of this chronic condition. Research in contextO_ST_ABSEvidence Before This StudyC_ST_ABSAsthma is a chronic inflammatory disease of the airways driven by intricate interactions between airway structural and immune cells. Previous transcriptomic studies have focused on bulk RNA samples from the airway, leaving significant gaps in our understanding of the cellular dynamics that characterize the disease. Added Value of This StudyThis study pioneers the use of single-cell RNA sequencing on sputum samples from patients with asthma, revealing a detailed landscape of cell phenotypes and dynamic communication patterns that distinguish asthmatic individuals from those without the disease. Notably, heightened intercellular communication was observed in asthma, particularly between CD4+ T cells and dendritic cells, confirming that there is a robust network of interactions between immune and structural cells. The notable increase of ADAM12-CCR4 communication from dendritic cells to other cell populations further emphasizes the dysregulation present in asthma. Implications of All Available EvidenceOur transcriptomic profiling illuminates distinct and amplified communication pathways involving CD4+ T cells and dendritic cells, aligning with established paradigms of both adaptive and innate immune responses in asthma pathogenesis. The identification of ADAM12 and CCR4 pathway dysregulation adds a critical layer to our understanding of the molecular mechanisms underpinning asthma, paving the way for potential therapeutic targets and personalized treatment strategies. Single cell profiling of the sputum has the capacity to characterize the breadth of cellular phenotypes, their functional status, and the communication in the airway at a level not previously attainable.

genomics↗

bioRxiv2021

ETV2 regulates PARP-1 binding protein to induce ER stress-mediated cell death in tuberin-deficient cells

Lymphangioleiomyomatosis (LAM) is a rare progressive disease, characterized by mutations in the tuberous sclerosis complex genes (Tsc1 or Tsc2), and hyperactivation of mechanistic target of rapamycin complex 1 (mTORC1). The effectiveness of mTORC1 inhibitors is limited by their lack of cytotoxic effects. Here, we report that E26 transformation specific (ETS) Variant Transcription Factor 2 (ETV2) is a critical regulator of Tsc2-deficient cell survival. Nuclear localization of ETV2 in Tsc2-deficient cells is mTORC1-independent and is enhanced by spleen tyrosine kinase (Syk) inhibition. In the nucleus, ETV2 transcriptionally regulates poly(ADP-ribose) polymerase 1 binding protein (PARPBP), a coregulator of transcription, mRNA and protein expression. Silencing of ETV2 or PARPBP in Tsc2-deficient cells induced ER-stress and increased cell death in vitro and in vivo. We also found ETV2 expression in human cells with loss of heterozygosity for TSC2 lending support to the translational relevance of our findings. In conclusion, we report a novel signaling axis unique to Syk-inhibition is mTORC1-independent and promotes a cytocidal response in Tsc2-deficient cells, and therefore, maybe a potential alternative therapeutic target in LAM.

cell biology↗

bioRxiv2020

SARS-CoV-2 Spike Protein Interacts with Multiple Innate Immune Receptors

The spike (S) glycoprotein in the envelope of SARS-CoV-2 is densely glycosylated but the functions of its glycosylation are unknown. Here we demonstrate that S is recognized in a glycan-dependent manner by multiple innate immune receptors including the mannose receptor MR/CD206, DC-SIGN/CD209, L-SIGN/CD209L, and MGL/CLEC10A/CD301. Single-cell RNA sequencing analyses indicate that such receptors are highly expressed in innate immune cells in tissues susceptible to SARS-CoV-2 infection. Binding of the above receptors to S is characterized by affinities in the picomolar range and consistent with S glycosylation analysis demonstrating a variety of N- and O-glycans as receptor ligands. These results indicate multiple routes for SARS-CoV-2 to interact with human cells and suggest alternative strategies for therapeutic intervention.

biochemistry↗

bioRxiv2018

New Statistical Methods for Constructing Robust Differential Correlation Networks

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fishers Z-transformation test is usually used. However, the Fishers Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fishers Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.

bioinformatics↗

bioRxiv2018

Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes

More than 1,100 genetic loci have been correlated with drug response outcomes but disproportionately few have been translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing data from the 1000 Genomes Project. We identified 69,319 genetic variations within 160 pharmacogenomic genes, of which 8,207 variants are in strong LD (r2 > 0.8) with known pharmacogenomic variants. Of the latter, 8 are coding or structural variants predicted to have high-impact, with 19 additional missense variants that are predicted to have moderate-impact. In conclusion, we identified putatively functional variants within known pharmacogenomics loci that could account for the association signals and represent the missing causative variants underlying drug response phenotypes.

bioinformatics↗

bioRxiv2018

Detecting Differential Variable microRNAs via Model-Based Clustering

Identifying genomic probes (e.g., DNA methylation marks) is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in Statistics. For high-throughput genomic data, the probe-wise F test has been successfully used to detect biologically relevant DNA methylation marks that have different variances between two groups of subjects (e.g., cases vs. controls). In addition to DNA methylation, microRNA is another mechanism of epigenetics. However, to the best of our knowledge, no studies have identified differentially variable (DV) microRNAs. In this article, we proposed a novel model-based clustering to improve the power of the probe-wise F test to detect DV microRNAs. We imposed special structures on covariance matrices for each cluster of microRNAs based on the prior information about the relationship between variance in cases and variance in controls and about the independence among cases and controls. To the best of our knowledge, the proposed method is the first clustering algorithm that aims to detect DV genomic probes. Simulation studies showed that the proposed method outperformed the probe-wise F test and had certain robustness to the violation of the normality assumption. Based on two real datasets about human hepatocellular carcinoma (HCC), we identified 7 DV-only microRNAs (hsa-miR-1826, hsa-miR-191, hsa-miR-194-star, hsa-miR-222, hsa-miR-502-3p, hsa-miR-93, and hsa-miR-99b) using the proposed method, one (hsa-miR-1826) of which has not yet been reported to relate to HCC in the literature.

bioinformatics↗