Novel Insights into Embryonic Stem Cell Self‐Renewal Revealed Through Comparative Human and Mouse Systems Biology Networks

Abstract

Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1100 conditions) and 62 mouse studies (~2.4 million data points collected under 1085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and RT-PCR validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g. POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g. threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g. LIF-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated FGF in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation.

Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity

“…Live cell imaging and single cell RNA-seq methods have recently revealed unexpected heterogeneity of the TF network associated with pluripotency (Filipczyk et al, 2015, Kolodziejczyk et al, 2015), suggesting we still do not fully understand this TF network state. Consistent with this, several pluripotency TF network models recently built from detailed knowledge of the key TFs regulating pluripotency (Xu et al, 2014, Dunn et al, 2014, Dowell et al, 2014) were unable to fully predict cellular behavior.…”

“…To date, most network models are relatively simple, often consisting of simple diagrams of TF sub-networks annotated with nodes and edges. However, Boolean (Xu et al, 2014, Dunn et al, 2014, Moignard et al, 2015) and Bayesian (Dowell et al, 2014) modeling approaches have been built from such information to provide dynamic and executable network models. PetriNets, a mathematical modeling approach to graphically model networks, have also been successfully used to computationally encode TF networks (Bonzanni et al, 2013).…”

Adult Stem Cells and Diseases of Aging

“…Regulation of stem cell pluripotency and differentiation has been studied at the transcriptional and epigenetic level in ESCs, particularly mouse ESCs [216–223]. High-throughput sequencing methodologies are now used to characterize whole networks of regulation in ESCs [224,225] and analyze the roles of overlapping and interactive regulatory networks in determining stem cell fate, including the role of microRNAs [226] and epigenetic marks (reviewed in [227]).…”

Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories

“…Novel biological insights gained using our network analysis approach include: 1) differences in ESC aggregate spatiotemporal pattern kinetics can be explained by a combined paracrine signaling methodology, and 2) gastrulation in cichlid fishes can be partitioned into a set of discrete stages. In the case of ESCs, a large body of literature exists that suggests differentiation is heavily influenced by ESC aggregate size 26,27,41–43 ; soluble gp130 proteins have been identified as a paracrine mechanism which modulate of differentiation 44,45 The two paracrine process proposed here can explain these differences (one in a secreted factor is responsible for maintain pluripotency, and the other where more differentiated cells secrete a factor which induces differentiation), and mirrors the known properties of soluble LIF and FGF4 signaling respectively 24,46–48 . Surprisingly but interestingly, the lack of local neighbor-to-neighbor regulation of phenotypic state, as analyzed by our methodology, suggests that transmission of cell state information by intercellular cues, such as Notch, may impact later stages of differentiation than the time period examined here.…”