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A systems biology pipeline identifies regulatory networks for stem cell engineering.

ABSTRACT: A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering.

SUBMITTER: Kinney MA

PROVIDER: S-EPMC7235931 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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A systems biology pipeline identifies regulatory networks for stem cell engineering.

Kinney Melissa A MA Vo Linda T LT Frame Jenna M JM Barragan Jessica J Conway Ashlee J AJ Li Shuai S Wong Kwok-Kin KK Collins James J JJ Cahan Patrick P North Trista E TE Lauffenburger Douglas A DA Daley George Q GQ

Nature biotechnology 20190701 7

A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, ...[more]

PMID: 31267104

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Project description:ObjectiveThe transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process.MethodologyA systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM) was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation.ResultsClustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for Pparg which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving Klf4, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving Prdm1, Sox11, Pax5, miR-200a, and miR-30a) progressively decreases repression of Xbp1 gene transcription, in concert with decreased translational repression by miR-214. The transcription factor Xbp1 mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with Atf6. Transfection studies show that Xbp1 activates the Mist1 promoter [corrected]. In addition, Xbp1 and Mist1 each activate the parotid secretory protein (Psp) gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation.ConclusionThis study identifies novel expression patterns of Pparg, Klf4, and Sox11 during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a genetic switch involving transcription factors and microRNAs, and transition to an Xbp1 driven differentiation network. This proposed network suggests key regulatory interactions in parotid gland terminal differentiation.

Project description: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 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 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 reverse transcriptase polymerase chain reaction (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., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor 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.

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A systems biology pipeline identifies regulatory networks for stem cell engineering.

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A systems biology pipeline identifies regulatory networks for stem cell engineering.

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