Biology subjects

Chapkin, R.

Publications and source records attributed to Chapkin, R..

3 recordsLinked to original sources

bioRxiv2024

Beyond Differential Expression: Embracing Cell-to-Cell Variability in Single-Cell Gene Expression Data Analysis

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular variability by capturing gene expression profiles of individual cells. The importance of cell-to-cell variability in determining and shaping cell function has been widely appreciated. Nevertheless, differential expression (DE) analysis remains a cornerstone method in analytic practice, by focusing exclusively on mean expressional differences, current computational analyses overlook the rich information encoded by variability within the single-cell gene expression data. Thus, there is a need for more relevant approaches to assess data variability rather than the mean in order to offer a deeper understanding of cellular systems. Here we present spline-DV, a novel statistical framework for differential variability (DV) analysis using scRNA-seq data. The spline-DV method identifies genes exhibiting significantly increased or decreased expression variability among cells derived from two experimental conditions. Case studies show that DV genes identified using spline-DV are representative, and functionally relevant in the tested cellular conditions including obesity, fibrosis, and cancer.

bioinformatics↗

bioRxiv2019

Cross-talk between SIM2s and NFkB regulates cyclooxygenase 2 expression in breast cancer

BackgroundBreast cancer is a leading cause of cancer-related death for women in the United States. Thus, there a need to investigate novel prognostic markers and therapeutic strategies. Inflammation raises challenges to both treating and preventing the spread of breast cancer. Specifically, the nuclear factor kappa b (NFkB) pathway contributes to cancer progression by stimulating proliferation and preventing apoptosis. One target gene of this pathway is PTGS2, the gene that encodes for cyclooxygenase 2 (COX-2), which is upregulated in 40% of human breast carcinomas. COX-2 is an enzyme involved in inflammation. Here we investigate the effect of Singleminded 2s, a transcriptional tumor suppressor that is implicated in inhibition of tumor growth and metastasis, in regulating NFkB and COX-2.\n\nMethodsWe utilized in vitro reporter assays, immunoblot analyses, qPCR and immunohistochemical analysis to dissect the relationship between NF{kappa}B, SIM2s, and COX-2. Furthermore, we utilized COX-2 targeting strategies to determine tumor suppressive activities.\n\nResultsOur results reveal that SIM2s attenuates the activation of a NF{kappa}B via luciferase reporter assays. Furthermore, immunostaining of lysates from breast cancer cells over expressing SIM2s showed reduction in various NF{kappa}B signaling proteins, whereas knockdown of SIM2 revealed increases in the same NF{kappa}B signaling proteins. Additionally, by increasing NF{kappa}B translocation to the nucleus in DCIS.COM cells, we show that NF{kappa}B signaling can act in a reciprocal manner to decrease expression of SIM2s. Likewise, suppressing NF{kappa}B translocation in DCIS.COM cells increases SIM2s expression. We also found that NF{kappa}B/p65 represses SIM2 in via dose-dependent manner and when NF{kappa}B is suppressed the effect on the SIM2 is negated. Additionally, our CHIP analysis confirms that NF{kappa}B/p65 binds directly to SIM2 promoter site and that the NF{kappa}B sites in the SIM2 promoter are required for NFkB-mediated suppression of SIM2s. Finally, over expression of SIM2s decreases PTGS2 in vitro and COX-2 staining in vivo while decreasing PTGS2 and/or Cox-2 activity results in re-expression of SIM2. Our findings identify a novel role for SIM2s in NF{kappa}B signaling and COX-2 expression.\n\nConclusionsThese findings provide evidence for a mechanism where SIM2s may represses COX-2 expression to provide an overall better prognosis for breast cancer patients.

cancer biology↗

bioRxiv2018

Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

We propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.\n\nAuthor summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

bioinformatics↗