Estimating drivers of cell state transitions using gene regulatory network models

Abstract

BackgroundSpecific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks.ResultsHere we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state.ConclusionsWe demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

“…The improved availability of African ancestry whole-genome sequence imputation reference panels available through initiatives such as the NHLBI-supported TOPMed program 52 should also provide high quality imputation of low and rare frequency variation in African ancestry populations, which will empower future studies. Lastly, we note better availability of other -omics datasets representing diverse ethnicities, such as transcriptomic data in tissue types relevant to asthma, will be needed to enable discoveries by utilizing the next generation of analysis tools 53–55 .…”

Estimating Sample-Specific Regulatory Networks

“…In contrast, the comparative study of networks (inter-network relationships) is still a relatively young field. However, a number of recent studies have used linear approaches to analyze and cluster sets of networks (Marbach et al., 2012, Schlauch et al., 2017, Mucha et al., 2010, Onnela et al., 2012).…”

Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer

“…The resistant cells are characterized by the stabilization of the high expression of the marker genes, which were transiently high in the drug-naive pre-resistant cells (Figure 5A) (Shaffer et al, 2017). Studies using network inference of gene expression data have suggested that the genetic networks undergo considerable rearrangements upon cellular transitions or reprogramming (Moignard et al, 2015;Schlauch et al, 2017). We wondered if the transcriptional bursting model can explain how the transient high expression in drug-naive cells might become permanent upon treatment with anti-cancer drugs.…”