Bryant Dang

Analysis of the TGFβ-induced program in primary airway epithelial cells shows essential role of NF-κB/RelA signaling network in type II epithelial mesenchymal transition

BackgroundThe airway epithelial cell plays a central role in coordinating the pulmonary response to injury and inflammation. Here, transforming growth factor-β (TGFβ) activates gene expression programs to induce stem cell-like properties, inhibit expression of differentiated epithelial adhesion proteins and express mesenchymal contractile proteins. This process is known as epithelial mesenchymal transition (EMT); although much is known about the role of EMT in cellular metastasis in an oncogene-transformed cell, less is known about Type II EMT, that occurring in normal epithelial cells. In this study, we applied next generation sequencing (RNA-Seq) in primary human airway epithelial cells to understand the gene program controlling Type II EMT and how cytokine-induced inflammation modifies it.ResultsGeneralized linear modeling was performed on a two-factor RNA-Seq experiment of 6 treatments of telomerase immortalized human small airway epithelial cells (3 replicates). Using a stringent cut-off, we identified 3,478 differentially expressed genes (DEGs) in response to EMT. Unbiased transcription factor enrichment analysis identified three clusters of EMT regulators, one including SMADs/TP63 and another NF-κB/RelA. Surprisingly, we also observed 527 of the EMT DEGs were also regulated by the TNF-NF-κB/RelA pathway. This Type II EMT program was compared to Type III EMT in TGFβ stimulated A549 alveolar lung cancer cells, revealing significant functional differences. Moreover, we observe that Type II EMT modifies the outcome of the TNF program, reducing IFN signaling and enhancing integrin signaling. We confirmed experimentally that TGFβ-induced the NF-κB/RelA pathway by observing a 2-fold change in NF-κB/RelA nuclear translocation. A small molecule IKK inhibitor blocked TGFβ-induced core transcription factor (SNAIL1, ZEB1 and Twist1) and mesenchymal gene (FN1 and VIM) expression.ConclusionsThese data indicate that NF-κB/RelA controls a SMAD-independent gene network whose regulation is required for initiation of Type II EMT. Type II EMT dramatically affects the induction and kinetics of TNF-dependent gene networks.

Unlocking proteomic heterogeneity in complex diseases through visual analytics

Despite years of preclinical development, biological interventions designed to treat complex diseases such as asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks has been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject‐protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject‐protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker‐based clinical trials, and accelerating the development of personalized approaches to medicine.

Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses

Abstract Background: Recent studies have shown that epigenetic differences can increase the risk of spontaneous preterm birth (PTB). However, little is known about heterogeneity underlying such epigenetic differences, which could lead to hypotheses for biological pathways in specific patient subgroups, and corresponding targeted interventions critical for precision medicine. Using bipartite network analysis of fetal DNA methylation data we demonstrate a novel method for classification of PTB. Methods: The data consisted of DNA methylation across the genome (HumanMethylation450 BeadChip) in cord blood from 50 African-American subjects consisting of 22 cases of early spontaneous PTB (24–34 weeks of gestation) and 28 controls (>39 weeks of gestation). These data were analyzed using a combination of (1) a supervised method to select the top 10 significant methylation sites, (2) unsupervised “subject-variable” bipartite networks to visualize and quantitatively analyze how those 10 methylation sites co-occurred across all the subjects, and across only the cases with the goal of analyzing subgroups and their underlying pathways, and (3) a simple linear regression to test whether there was an association between the total methylation in the cases, and gestational age. Results: The bipartite network analysis of all subjects and significant methylation sites revealed statistically significant clustering consisting of an inverse symmetrical relationship in the methylation profiles between a case-enriched subgroup and a control-enriched subgroup: the former was predominantly hypermethylated across seven methylation sites, and hypomethylated across three methylation sites, whereas the latter was predominantly hypomethylated across the above seven methylation sites and hypermethylated across the three methylation sites. Furthermore, the analysis of only cases revealed one subgroup that was predominantly hypomethylated across seven methylation sites, and another subgroup that was hypomethylated across all methylation sites suggesting the presence of heterogeneity in PTB pathophysiology. Finally, the analysis found a strong inverse linear relationship between total methylation and gestational age suggesting that methylation differences could be used as predictive markers for gestational length. Conclusions: The results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.