scholarly journals Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential progenitors

2021 ◽  
Author(s):  
Joanna Elzbieta Handzlik ◽  
Manu Manu
Keyword(s):  
Regulatory Networks ◽  
Data Driven ◽  
High Temporal Resolution ◽  
Mouse Bone Marrow ◽  
Modeling Framework ◽  
Dynamical Modeling ◽  
Gene Regulatory ◽  
Data Driven Modeling ◽  
Neutrophil Differentiation

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. We utilized high temporal-resolution gene-expression data from in vitro erythrocyte-neutrophil differentiation and a predictive data-driven dynamical modeling framework to learn the architecture and dynamics of gene regulation in a comprehensive twelve-gene GRN. The inferred genetic architecture is densely interconnected rather than hierarchical. The analysis of model dynamics revealed that neutrophil differentiation is driven by C/EBPα and Gfi1 rather than PU.1. This prediction is supported by the sequence of gene upregulation in an independent mouse bone marrow scRNA-Seq dataset. These results imply that neutrophil differentiation is not driven by the Gata1-PU.1 switch but by neutrophil-specific genes instead. More generally, this work shows that data-driven dynamical modeling can uncover the causality of developmental events that might otherwise be obscured.

scholarly journals Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors

2022 ◽  
Vol 18 (1) ◽  
pp. e1009779
Author(s):  
Joanna E. Handzlik ◽  
Manu
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Data Driven ◽  
Cytokine Receptors ◽  
Series Data ◽  
Cytokine Signaling ◽  
High Temporal Resolution ◽  
Mouse Bone Marrow ◽  
Bistable Switch ◽  
Gene Regulatory

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.

scholarly journals In vitro gene regulatory networks predict in vivo function of liver

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
Youping Deng ◽  
David R Johnson ◽  
Xin Guan ◽  
Choo Y Ang ◽  
Junmei Ai ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Xiang Chen ◽  
Min Li ◽  
Ruiqing Zheng ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Network ◽  
Construction Method ◽  
Data Driven ◽  
Regression Problem ◽  
Network Construction ◽  
Benchmark Datasets ◽  
Gene Regulatory ◽  
New Perspective

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

scholarly journals Mapping gene regulatory networks of primary CD4+T cells using single-cell genomics and genome engineering

2019 ◽  
Author(s):  
Rachel E. Gate ◽  
Min Cheol Kim ◽  
Andrew Lu ◽  
David Lee ◽  
Eric Shifrut ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Genome Engineering ◽  
Regulatory Elements ◽  
Mapping Gene ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractGene regulatory programs controlling the activation and polarization of CD4+T cells are incompletely mapped and the interindividual variability in these programs remain unknown. We sequenced the transcriptomes of ~160k CD4+T cells from 9 donors following pooled CRISPR perturbation targeting 140 regulators. We identified 134 regulators that affect T cell functionalization, includingIRF2as a positive regulator of Th2polarization. Leveraging correlation patterns between cells, we mapped 194 pairs of interacting regulators, including known (e.g.BATFandJUN) and novel interactions (e.g.ETS1andSTAT6). Finally, we identified 80 natural genetic variants with effects on gene expression, 48 of which are modified by a perturbation. In CD4+T cells, CRISPR perturbations can influencein vitropolarization and modify the effects oftransandcisregulatory elements on gene expression.

scholarly journals The emerging landscape of in vitro and in vivo epigenetic allelic effects

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2108 ◽  
Author(s):  
Christopher Gregg
Keyword(s):  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Disease Susceptibility ◽  
Monoallelic Expression ◽  
Functional Studies ◽  
Cell Type Specific ◽  
Gene Regulatory

Epigenetic mechanisms that cause maternally and paternally inherited alleles to be expressed differently in offspring have the potential to radically change our understanding of the mechanisms that shape disease susceptibility, phenotypic variation, cell fate, and gene expression. However, the nature and prevalence of these effects in vivo have been unclear and are debated. Here, I consider major new studies of epigenetic allelic effects in cell lines and primary cells and in vivo. The emerging picture is that these effects take on diverse forms, and this review attempts to clarify the nature of the different forms that have been uncovered for genomic imprinting and random monoallelic expression (RME). I also discuss apparent discrepancies between in vitro and in vivo studies. Importantly, multiple studies suggest that allelic effects are prevalent and can be developmental stage- and cell type-specific. I propose some possible functions and consider roles for allelic effects within the broader context of gene regulatory networks, cellular diversity, and plasticity. Overall, the field is ripe for discovery and is in need of mechanistic and functional studies.

scholarly journals Compensatory mutation can drive gene regulatory network evolution

2019 ◽  
Author(s):  
Yifei Wang ◽  
Marios Richards ◽  
Steve Dorus ◽  
Nicholas K. Priest ◽  
Joanna J. Bryson
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Evolution ◽  
Regulatory Function ◽  
Compensatory Mutation ◽  
Relaxed Selection ◽  
Mutation Site ◽  
Regulatory Pathways ◽  
Gene Regulatory

AbstractGene regulatory networks underlie every aspect of life; better understanding their assembly would better our understanding of evolution more generally. For example, evolutionary theory typically assumed that low-fitness intermediary pathways are not a significant factor in evolution, yet there is substantial empirical evidence of compensatory mutation. Here we revise theoretical assumptions to explore the possibility that compensatory mutation may drive rapid evolutionary recovery. Using a well-established in silico model of gene regulatory networks, we show that assuming only that deleterious mutations are not fatal, compensatory mutation is surprisingly frequent. Further, we find that it entails biases that drive the evolution of regulatory pathways. In our simulations, we find compensatory mutation to be common during periods of relaxed selection, with 8-15% of degraded networks having regulatory function restored by a single randomly-generated additional mutation. Though this process reduces average robustness, proportionally higher robustness is found in networks where compensatory mutations occur close to the deleterious mutation site, or where the compensatory mutation results in a large regulatory effect size. This location- and size-specific robustness systematically biases which networks are purged by selection for network stability, producing emergent changes to the population of regulatory networks. We show that over time, large-effect and co-located mutations accumulate, assuming only that episodes of relaxed selection occur, even very rarely. This accumulation results in an increase in regulatory complexity. Our findings help explain a process by which large-effect mutations structure complex regulatory networks, and may account for the speed and pervasiveness of observed occurrence of compensatory mutation, for example in the context of antibiotic resistance, which we discuss. If sustained by in vitro experiments, these results promise a significant breakthrough in the understanding of evolutionary and regulatory processes.

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

2021 ◽  
Vol 30 (04) ◽  
pp. 2150022
Author(s):  
Sergio Peignier ◽  
Pauline Schmitt ◽  
Federica Calevro
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Data Driven ◽  
Classification Algorithms ◽  
New Family ◽  
Gene Regulatory ◽  
High Throughput Gene Expression ◽  
Inference Methods

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

Sign in / Sign up

Export Citation Format

Share Document