ARMADA: Using motif activity dynamics to infer gene regulatory networks from gene expression data

A systematic comparison of computational methods for expression forecasting

Due to the abundance of single cell RNA-seq data, a number of methods for predicting expression after perturbation have recently been published. Expression prediction methods are enticing because they promise to answer pressing questions in fields ranging from developmental genetics to cell fate engineering and because they are faster, cheaper, and higher-throughput than their experimental counterparts. However, the absolute and relative accuracy of these methods is poorly characterized, limiting their informed use, their improvement, and the interpretation of their predictions. To address these issues, we created a benchmarking platform that combines a panel of large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to current methods. We used our platform to systematically assess methods, parameters, and sources of auxiliary data. We found that uninformed baseline predictions, which were not always included in prior evaluations, yielded the same or better mean absolute error than benchmarked methods in all test cases. These results cast doubt on the ability of current expression forecasting methods to provide mechanistic insights or to rank hypotheses for experimental follow-up. However, given the rapid pace of innovation in the field, new approaches may yield more accurate expression predictions. Our platform will serve as a neutral benchmark to improve methods and to identify contexts in which expression prediction can succeed.

Late phase endothelial cell inflammation is characterized by interferon response genes and driven by JAK/STAT, not NFκB

Chronic vascular inflammation underlies many diseases, including atherosclerosis, autoimmune vasculitides and transplant rejection. The resolution of inflammation is critical for proper healing and restoration of homeostasis, but the timing and signaling mechanisms involved in the return to a non-inflamed state are not well understood. Pro-adhesive gene expression, phenotype and secretome of human endothelial cells was measured in primary human aortic endothelium under chronic TNFα stimulation, and after short-term TNFα priming followed by withdrawal. The effects of NFκB, MAPK and JAK1/2 inhibitors on TNFα-induced gene expression were tested. The majority of inducible TNFα effectors, such as E-selectin, VCAM-1 and most chemokines, required continuous exposure for reinforcement of the altered phenotype, and were NFκB dependent. However, 3 h priming with TNFα induced late phase STAT activation and interferon response genes after 18 h, as well as enhanced ICAM-1, BST2 and CXCR3 ligand expression. Chronic activation was autonomous of continuous TNFα, and could be blocked by the JAK1/2 inhibitor ruxolitinib. The results demonstrate that NFκB is not a significant driver of the later phase of endothelial cell activation by TNFα, but that sustained inflammation is JAK1/2-dependent and characterized by adaptive chemokines.

Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach

How to mine the gene regulatory relationship and construct gene regulatory network (GRN) is of utmost interest within the whole biological community, however, which has been consistently a challenging problem since the tremendous complexity in cellular systems. In present work, we construct gene regulatory network using an improved three-phase dependency analysis algorithm (TPDA) Bayesian network learning method, which includes the steps of Drafting, Thickening, and Thinning. In order to solve the problem of learning result is not reliable due to the high order conditional independence test, we use the entropy estimation approach of Gaussian kernel probability density estimator to calculate the (conditional) mutual information between genes. The experiment on the public benchmark data sets show the improved method outperforms the other nine kinds of Bayesian network learning methods when to process the data with large sample size, with small number of discrete values, and the frequency of different discrete values is about same. In addition, the improved TPDA method was further applied on a real large gene expression data set on RNA-seq from a global collection with 368 elite maize inbred lines. Experiment results show it performs better than the original TPDA method and the other nine kinds of Bayesian network learning algorithms significantly.