Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Broeck, Lisa Van den; Gordon, Max; Inzé, Dirk; Williams, Cranos M.; Sozzani, Rosangela

doi:10.3389/fgene.2020.00457

Cited by 40 publications

(26 citation statements)

References 99 publications

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“…We have not used the specificity measure here because the GRN models are missing an unknown number of undiscovered gene interactions, and the solidity of the data underlying the connections the GRN model is variable. Additionally, there are other complexities inherent in developmental gene regulation that present limitations, including transient TF binding, multiple upstream regulators, and indirect interactions dependent on signaling [ 24 ]. The second measure we used was the set of new predictions , which designated new gene interactions predicted by the algorithm that were not yet known in the ground truth GRN.…”

Section: Resultsmentioning

confidence: 99%

Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

et al. 2021

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Gene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development–representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes–is one of the most challenging arenas for GRN prediction. In this work, we show that successful GRN predictions for a developmental network from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic Net. We test our GRN prediction methodology using two gene expression datasets for the purple sea urchin, Stronglyocentrotus purpuratus, and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results find a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 81.58%). We also generate novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. Published ChIPseq data and spatial co-expression analysis further support a subset of the top novel predictions. We conclude that GRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

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Section: Resultsmentioning

confidence: 99%

Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

et al. 2021

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“…Since in general plant responses to environmental signals are tightly controlled by a GRN consisting of multiple TFs (Song et al, 2016a;Van den Broeck et al, 2020), the regulatory network revealed in this study likely fine tunes root hair growth in response to fluctuating environmental conditions. Different letters indicate significant differences One-way ANOVA with post-hoc Tukey HSD test, p < 0.01).…”

Section: A Grn Enables Precise Regulation Of Genes Implicated In Root Hair Growthmentioning

confidence: 91%

GTL1 is required for a robust root hair growth response to avoid nutrient overloading

Shibata

et al. 2021

Preprint

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Root hair growth is tuned in response to the environment surrounding plants. While most of previous studies focused on the enhancement of root hair growth during nutrient starvation, few studies investigated the root hair response in the presence of excess nutrients. We report that the post-embryonic growth of wild-type Arabidopsis plants is strongly suppressed with increasing nutrient availability, particularly in the case of root hair growth. We further used gene expression profiling to analyze how excess nutrient availability affects root hair growth, and found that RHD6 subfamily genes, which are positive regulators of root hair growth, are down-regulated in this condition. On the other hand, defects in GTL1 and DF1, which are negative regulators of root hair growth, cause frail and swollen root hairs to form when excess nutrients are supplied. Additionally, we observed that the RHD6 subfamily genes are mis-expressed in gtl1-1 df1-1. Furthermore, overexpression of RSL4, an RHD6 subfamily gene, induces swollen root hairs in the face of a nutrient overload, while mutation of RSL4 in gtl1-1 df1-1 restore root hair swelling phenotype. In conclusion, our data suggest that GTL1 and DF1 prevent unnecessary root hair formation by repressing RSL4 under excess nutrient conditions.

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“…To identify key regulatory proteins among these 46 genes, we predicted causal relations between the TFs and downstream genes with high accuracy and constructed a gene regulatory network. We inferred the causal relations by leveraging our transcriptome data with a regression tree algorithm RTP-STAR (Figure 2b) (Huynh-u et al, 2010;Spurney et al, 2020;Van den Broeck et al, 2020). e inferred network contained 20 nodes, of which four are TFs (Figure 2b).…”

Section: Network Inference and Node Importance Analysis To Identify Functional Candidatesmentioning

confidence: 99%

A hybrid model connecting regulatory interactions with stem cell divisions in the root

et al. 2021

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Stem cells give rise to the entirety of cells within an organ. Maintaining stem cell identity and coordinately regulating stem cell divisions is crucial for proper development. In plants, mobile proteins, such as WUSCHEL-RELATED HOMEOBOX 5 (WOX5) and SHORTROOT (SHR), regulate divisions in the root stem cell niche. However, how these proteins coordinately function to establish systemic behaviour is not well understood. We propose a non-cell autonomous role for WOX5 in the cortex endodermis initial (CEI) and identify a regulator, ANGUSTIFOLIA (AN3)/GRF-INTERACTING FACTOR 1, that coordinates CEI divisions. Here, we show with a multi-scale hybrid model integrating ordinary differential equations (ODEs) and agent-based modeling that quiescent center (QC) and CEI divisions have different dynamics. Specifically, by combining continuous models to describe regulatory networks and agent-based rules, we model systemic behaviour, which led us to predict cell-type-specific expression dynamics of SHR, SCARECROW, WOX5, AN3 and CYCLIND6;1, and experimentally validate CEI cell divisions. Conclusively, our results show an interdependency between CEI and QC divisions.

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Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Cited by 40 publications

References 99 publications

Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

Developmental gene regulatory network connections predicted by machine learning from gene expression data alone

GTL1 is required for a robust root hair growth response to avoid nutrient overloading

A hybrid model connecting regulatory interactions with stem cell divisions in the root

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