Biology subjects

Cobanoglu, M. C.

Publications and source records attributed to Cobanoglu, M. C..

6 recordsLinked to original sources

bioRxiv2021

Adrenergic signaling controls early transcriptional programs during CD8+ T cell responses to viral infection

Viral infections drive the expansion and differentiation of responding CD8+ T cells into variegated populations of cytolytic effector and memory cells. While pro-inflammatory cytokines and cell surface immune receptors play a key role in guiding T cell responses to infection, T cells are also markedly influenced by neurotransmitters. Norepinephrine is a key sympathetic neurotransmitter, which acts to suppress CD8 + T cell cytokine secretion and lytic activity by signaling through the {beta}2-adrenergic receptor (ADRB2). Although ADRB2 signaling is considered generally immunosuppressive, its role in regulating differentiation of effector T cells in response to infection has not been investigated. Using an adoptive transfer approach, we compared the expansion and differentiation of wild type (WT) to Adrb2-/- CD8 + T cells throughout the primary response to vesicular stomatitis virus (VSV) infection in vivo. We measured the dynamic changes in transcriptome profiles of antigen-specific CD8 + T cells as they responded to VSV. Within the first 7 days of infection, WT cells out-paced the expansion of Adrb2-/- cells, which correlated with reduced expression of IL-2 and the IL-2R in the absence of ADRB2. RNASeq analysis identified over 300 differentially expressed genes that were both temporally regulated following infection and selectively regulated in WT vs Adrb2-/- cells. These genes contributed to major transcriptional pathways including cytokine receptor activation, signaling in cancer, immune deficiency, and neurotransmitter pathways. By parsing genes within groups that were either induced or repressed over time in response to infection, we identified three main branches of genes that were differentially regulated by the ADRB2. These gene sets were predicted to be regulated by specific transcription factors involved in effector T cell development, such as Tbx21 and Eomes. Collectively, these data demonstrate a significant role for ADRB2 signaling in regulating key transcriptional pathways during CD8 + T cells responses to infection that may dramatically impact their functional capabilities and downstream memory cell development.

immunology↗

bioRxiv2021

TReNCo: Topologically associating domain (TAD) aware regulatory network construction

There has long been a desire to understand, describe, and model gene regulatory networks controlling numerous biologically meaningful processes like differentiation. Despite many notable improvements to models over the years, many models do not accurately capture subtle biological and chemical characteristics of the cell such as high-order chromatin domains of the chromosomes. Topologically Associated Domains (TAD) are one of these genomic regions that are enriched for contacts within themselves. Here we present TAD-aware Regulatory Network Construction or TReNCo, a memory-lean method utilizing epigenetic marks of enhancer and promoter activity, and gene expression to create context-specific transcription factor-gene regulatory networks. TReNCo utilizes common assays, ChIP-seq, RNA-seq, and TAD boundaries as a hard cutoff, instead of distance based, to efficiently create context-specific TF-gene regulatory networks. We used TReNCo to define the enhancer landscape and identify transcription factors (TFs) that drive the cardiac development of the mouse. Our results show that we are able to build specialized adjacency regulatory network graphs containing biologically relevant connections and time dependent dynamics.

bioinformatics↗

bioRxiv2021

Cancer drug discovery as a low rank tensor completion problem

The heterogeneity of cancer necessitates developing a multitude of targeted therapies. We propose the view that cancer drug discovery is a low rank tensor completion problem. We implement this vision by using heterogeneous public data to construct a tensor of drug-target-disease associations. We show the validity of this approach computationally by simulations, and experimentally by testing drug candidates. Specifically, we show that a novel drug candidate, SU11652, controls melanoma tumor growth, including BRAFWT melanoma. Independently, we show that another molecule, TC-E 5008, controls tumor proliferation on ex vivo ER+ human breast cancer. Most importantly, we identify these chemicals with only a few computationally selected experiments as opposed to brute-force screens. The efficiency of our approach enables use of ex vivo human tumor assays as a primary screening tool. We provide a web server, the Cancer Vulnerability Explorer (accessible at https://cavu.biohpc.swmed.edu), to facilitate the use of our methodology.

cancer biology↗

bioRxiv2020

Fast and interpretable scRNA-seq data analysis

One of the key challenges in single-cell data analysis is the annotation of cells with their cell types. This task is divided into two different sub-tasks: identifying known cell types and identifying novel cell types. In the former case, we can benefit from being able to transfer annotations from bulk RNA-seq because there are many more types profiled with that more established technology. In the latter case, we would benefit from interpretable models that can describe the reasons for grouping a number of cells together. We propose that both of these problems can be solved by generative Bayesian Dirichlet-multinomial models. In the supervised learning context, we propose a generative Bayesian Dirichlet-multinomial classifier. We show that such a classifier can effectively transfer cell labels from bulk to single-cell RNA-sequencing data. We also show that alternative well-established machine learning models have difficulty with this transition, even if they are effective within the same regime (i.e. single cell to single cell). In the unsupervised learning context, we propose a Bayesian Dirichlet-multinomial mixture model. We show that the proposed model learns meaningful clusters where the automatically learned relationships between cell types and genes overlap with ground truth associations. Furthermore, there are no density or connectivity based clustering assumptions in this model, which differs with almost every approach in this field. Consequently the clustering results from the generative method can effectively represent nuanced differences among cells. Contactmurat.cobanoglu@utsouthwestern.edu

bioinformatics↗

bioRxiv2018

Robust high content assessment of heterogeneous drug effects in 3D microenvironments

BackgroundEvery biological experiment requires a choice of throughput balanced against physiological relevance. Most primary drugs screens neglect critical parameters such as microenvironmental conditions, cell-cell heterogeneity, and specific readouts of cell fate for the sake of throughput. MethodsHere we describe a methodology to quantify proliferation and viability of single cells in 3D culture conditions by leveraging automated microscopy and image analysis to facilitate reliable and high-throughput measurements. We detail experimental conditions that can be adjusted to increase either throughput or robustness of the assay, and we provide a stand alone image analysis program for users who wish to implement this 3D drug screening assay in high throughput. ResultsWe demonstrate this approach by evaluating a combination of RAF and MEK inhibitors on melanoma cells, showing that cells cultured in 3D collagen-based matrices are more sensitive than cells grown in 2D culture, and that cell proliferation is much more sensitive than cell viability. We also find that cells grown in 3D cultured spheroids exhibit equivalent sensitivity to single cells grown in 3D collagen, suggesting that for the case of melanoma, a 3D single cell model may be equally effective for drug identification as 3D spheroids models. The single cell resolution of this approach enables stratification of heterogeneous populations of cells into differentially responsive subtypes upon drug treatment, which we demonstrate by determining the effect of RAK/MEK inhibition on melanoma cells co-cultured with fibroblasts. Furthermore, we show that spheroids grown from single cells exhibit dramatic heterogeneity to drug response, suggesting that heritable drug resistance can arise stochastically in single cells but be retained by subsequent generations. ConclusionIn summary, image-based analysis renders cell fate detection robust, sensitive, and high-throughput, enabling cell fate evaluation of single cells in more complex microenvironmental conditions.

cancer biology↗

bioRxiv2018

Accurate differential analysis of transcription factor activity from gene expression

We present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)

bioinformatics↗