Publications by authors named "Lisa Van den Broeck"

11 Publications

  • Page 1 of 1

Analysis of the transcriptomic, metabolomic, and gene regulatory responses to Puccinia sorghi in maize.

Mol Plant Pathol 2021 Apr 28;22(4):465-479. Epub 2021 Feb 28.

Department of Entomology and Plant Pathology, NC State University, Raleigh, North Carolina, USA.

Common rust, caused by Puccinia sorghi, is a widespread and destructive disease of maize. The Rp1-D gene confers resistance to the P. sorghi IN2 isolate, mediating a hypersensitive cell death response (HR). To identify differentially expressed genes (DEGs) and metabolites associated with the compatible (susceptible) interaction and with Rp1-D-mediated resistance in maize, we performed transcriptomics and targeted metabolome analyses of P. sorghi IN2-infected leaves from the near-isogenic lines H95 and H95:Rp1-D, which differed for the presence of Rp1-D. We observed up-regulation of genes involved in the defence response and secondary metabolism, including the phenylpropanoid, flavonoid, and terpenoid pathways. Metabolome analyses confirmed that intermediates from several transcriptionally up-regulated pathways accumulated during the defence response. We identified a common response in H95:Rp1-D and H95 with an additional H95:Rp1-D-specific resistance response observed at early time points at both transcriptional and metabolic levels. To better understand the mechanisms underlying Rp1-D-mediated resistance, we inferred gene regulatory networks occurring in response to P. sorghi infection. A number of transcription factors including WRKY53, BHLH124, NKD1, BZIP84, and MYB100 were identified as potentially important signalling hubs in the resistance-specific response. Overall, this study provides a novel and multifaceted understanding of the maize susceptible and resistance-specific responses to P. sorghi.
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http://dx.doi.org/10.1111/mpp.13040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938627PMC
April 2021

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling.

Front Genet 2020 25;11:457. Epub 2020 May 25.

Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States.

Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.
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http://dx.doi.org/10.3389/fgene.2020.00457DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270862PMC
May 2020

Protein complex stoichiometry and expression dynamics of transcription factors modulate stem cell division.

Proc Natl Acad Sci U S A 2020 06 15;117(26):15332-15342. Epub 2020 Jun 15.

Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC 27695;

Stem cells divide and differentiate to form all of the specialized cell types in a multicellular organism. In the root, stem cells are maintained in an undifferentiated state by a less mitotically active population of cells called the quiescent center (QC). Determining how the QC regulates the surrounding stem cell initials, or what makes the QC fundamentally different from the actively dividing initials, is important for understanding how stem cell divisions are maintained. Here we gained insight into the differences between the QC and the cortex endodermis initials (CEI) by studying the mobile transcription factor SHORTROOT (SHR) and its binding partner SCARECROW (SCR). We constructed an ordinary differential equation model of SHR and SCR in the QC and CEI which incorporated the stoichiometry of the SHR-SCR complex as well as upstream transcriptional regulation of SHR and SCR. Our model prediction, coupled with experimental validation, showed that high levels of the SHR-SCR complex are associated with more CEI division but less QC division. Furthermore, our model prediction allowed us to propose the putative upstream SHR regulators SEUSS and WUSCHEL-RELATED HOMEOBOX 5 and to experimentally validate their roles in QC and CEI division. In addition, our model established the timing of QC and CEI division and suggests that SHR repression of QC division depends on formation of the SHR homodimer. Thus, our results support that SHR-SCR protein complex stoichiometry and regulation of SHR transcription modulate the division timing of two different specialized cell types in the root stem cell niche.
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http://dx.doi.org/10.1073/pnas.2002166117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334585PMC
June 2020

Novel Imaging Modalities Shedding Light on Plant Biology: Start Small and Grow Big.

Annu Rev Plant Biol 2020 04 2;71:789-816. Epub 2020 Mar 2.

Department of Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina 27695, USA; email:

The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.
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http://dx.doi.org/10.1146/annurev-arplant-050718-100038DOI Listing
April 2020

tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.

Plant J 2020 02 27;101(3):716-730. Epub 2019 Nov 27.

Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, 27695, USA.

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.
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http://dx.doi.org/10.1111/tpj.14558DOI Listing
February 2020

Early mannitol-triggered changes in the Arabidopsis leaf (phospho)proteome reveal growth regulators.

J Exp Bot 2018 08;69(19):4591-4607

Ghent University, Department of Plant Biotechnology and Bioinformatics, Belgium.

Leaf growth is a complex, quantitative trait, controlled by a plethora of regulatory mechanisms. Diverse environmental stimuli inhibit leaf growth to cope with the perceived stress. In plant research, mannitol is often used to impose osmotic stress and study the underlying growth-repressing mechanisms. In growing leaf tissue of plants briefly exposed to mannitol-induced stress, a highly interconnected gene regulatory network is induced. However, early signalling and associated protein phosphorylation events that probably precede part of these transcriptional changes and that potentially act at the onset of mannitol-induced leaf size reduction are largely unknown. Here, we performed a proteome and phosphoproteome analysis on growing leaf tissue of Arabidopsis thaliana plants exposed to mild mannitol-induced stress and captured the fast (within the first half hour) events associated with this stress. Based on this in-depth data analysis, 167 and 172 differentially regulated proteins and phosphorylated sites were found. We provide these data sets as a community resource and we flag differentially phosphorylated proteins with described growth-regulatory functions, but we also illustrate potential novel regulators of shoot growth.
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http://dx.doi.org/10.1093/jxb/ery261DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117580PMC
August 2018

The Pivotal Role of Ethylene in Plant Growth.

Trends Plant Sci 2018 04 7;23(4):311-323. Epub 2018 Feb 7.

Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium. Electronic address:

Being continuously exposed to variable environmental conditions, plants produce phytohormones to react quickly and specifically to these changes. The phytohormone ethylene is produced in response to multiple stresses. While the role of ethylene in defense responses to pathogens is widely recognized, recent studies in arabidopsis and crop species highlight an emerging key role for ethylene in the regulation of organ growth and yield under abiotic stress. Molecular connections between ethylene and growth-regulatory pathways have been uncovered, and altering the expression of ethylene response factors (ERFs) provides a new strategy for targeted ethylene-response engineering. Crops with optimized ethylene responses show improved growth in the field, opening new windows for future crop improvement. This review focuses on how ethylene regulates shoot growth, with an emphasis on leaves.
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http://dx.doi.org/10.1016/j.tplants.2018.01.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890734PMC
April 2018

From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress.

Mol Syst Biol 2017 12 21;13(12):961. Epub 2017 Dec 21.

Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

Plants have established different mechanisms to cope with environmental fluctuations and accordingly fine-tune their growth and development through the regulation of complex molecular networks. It is largely unknown how the network architectures change and what the key regulators in stress responses and plant growth are. Here, we investigated a complex, highly interconnected network of 20 Arabidopsis transcription factors (TFs) at the basis of leaf growth inhibition upon mild osmotic stress. We tracked the dynamic behavior of the stress-responsive TFs over time, showing the rapid induction following stress treatment, specifically in growing leaves. The connections between the TFs were uncovered using inducible overexpression lines and were validated with transient expression assays. This study resulted in the identification of a core network, composed of ERF6, ERF8, ERF9, ERF59, and ERF98, which is responsible for most transcriptional connections. The analyses highlight the biological function of this core network in environmental adaptation and its redundancy. Finally, a phenotypic analysis of loss-of-function and gain-of-function lines of the transcription factors established multiple connections between the stress-responsive network and leaf growth.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5740496PMC
http://dx.doi.org/10.15252/msb.20177840DOI Listing
December 2017

RALFL34 regulates formative cell divisions in Arabidopsis pericycle during lateral root initiation.

J Exp Bot 2016 08 18;67(16):4863-75. Epub 2016 Jul 18.

Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Loughborough LE12 5RD, UK Department of Plant Systems Biology, VIB, B-9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, B-9052 Ghent, Belgium Centre for Plant Integrative Biology, University of Nottingham, Loughborough LE12 5RD, UK

In plants, many signalling molecules, such as phytohormones, miRNAs, transcription factors, and small signalling peptides, drive growth and development. However, very few small signalling peptides have been shown to be necessary for lateral root development. Here, we describe the role of the peptide RALFL34 during early events in lateral root development, and demonstrate its specific importance in orchestrating formative cell divisions in the pericycle. Our results further suggest that this small signalling peptide acts on the transcriptional cascade leading to a new lateral root upstream of GATA23, an important player in lateral root formation. In addition, we describe a role for ETHYLENE RESPONSE FACTORs (ERFs) in regulating RALFL34 expression. Taken together, we put forward RALFL34 as a new, important player in lateral root initiation.
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http://dx.doi.org/10.1093/jxb/erw281DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983113PMC
August 2016

Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought.

Plant Cell Environ 2017 02 7;40(2):180-189. Epub 2016 Oct 7.

Department of Plant Systems Biology, VIB, B-9052, Ghent, Belgium.

Drought stress is a major problem for agriculture worldwide, causing significant yield losses. Plants have developed highly flexible mechanisms to deal with drought, including organ- and developmental stage-specific responses. In young leaves, growth is repressed as an active mechanism to save water and energy, increasing the chances of survival but decreasing yield. Despite its importance, the molecular basis for this growth inhibition is largely unknown. Here, we present a novel approach to explore early molecular mechanisms controlling Arabidopsis leaf growth inhibition following mild drought. We found that growth and transcriptome responses to drought are highly dynamic. Growth was only repressed by drought during the day, and our evidence suggests that this may be due to gating by the circadian clock. Similarly, time of day strongly affected the extent, specificity, and in certain cases even direction of drought-induced changes in gene expression. These findings underscore the importance of taking into account diurnal patterns to understand stress responses, as only a small core of drought-responsive genes are affected by drought at all times of the day. Finally, we leveraged our high-resolution data to demonstrate that phenotypic and transcriptome responses can be matched to identify putative novel regulators of growth under mild drought.
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http://dx.doi.org/10.1111/pce.12809DOI Listing
February 2017

The ETHYLENE RESPONSE FACTORs ERF6 and ERF11 Antagonistically Regulate Mannitol-Induced Growth Inhibition in Arabidopsis.

Plant Physiol 2015 Sep 20;169(1):166-79. Epub 2015 May 20.

Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (M.D., L.V.d.B., H.C., K.V.V., D.I.);Department of Plant Systems Biology, Vlaams Instituut voor Biotechnologie, 9052 Ghent, Belgium (M.D., L.V.d.B., H.C., K.V.V., D.I.); andRIKEN Center for Sustainable Resource Science, Kanagawa 230-0045, Japan (M.M.)

Leaf growth is a tightly regulated and complex process, which responds in a dynamic manner to changing environmental conditions, but the mechanisms that reduce growth under adverse conditions are rather poorly understood. We previously identified a growth inhibitory pathway regulating leaf growth upon exposure to a low concentration of mannitol and characterized the ETHYLENE RESPONSE FACTOR (ERF)/APETALA2 transcription factor ERF6 as a central activator of both leaf growth inhibition and induction of stress tolerance genes. Here, we describe the role of the transcriptional repressor ERF11 in relation to the ERF6-mediated stress response in Arabidopsis (Arabidopsis thaliana). Using inducible overexpression lines, we show that ERF6 induces the expression of ERF11. ERF11 in turn molecularly counteracts the action of ERF6 and represses at least some of the ERF6-induced genes by directly competing for the target gene promoters. As a phenotypical consequence of the ERF6-ERF11 antagonism, the extreme dwarfism caused by ERF6 overexpression is suppressed by overexpression of ERF11. Together, our data demonstrate that dynamic mechanisms exist to fine-tune the stress response and that ERF11 counteracts ERF6 to maintain a balance between plant growth and stress defense.
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http://dx.doi.org/10.1104/pp.15.00335DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577380PMC
September 2015