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Bobadilla LK, Tranel PJ. Predicting the unpredictable: the regulatory nature and promiscuity of herbicide cross resistance. PEST MANAGEMENT SCIENCE 2024; 80:235-244. [PMID: 37595061 DOI: 10.1002/ps.7728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 08/20/2023]
Abstract
The emergence of herbicide-resistant weeds is a significant threat to modern agriculture. Cross resistance, a phenomenon where resistance to one herbicide confers resistance to another, is a particular concern owing to its unpredictability. Nontarget-site (NTS) cross resistance is especially challenging to predict, as it arises from genes that encode enzymes that do not directly involve the herbicide target site and can affect multiple herbicides. Recent advancements in genomic and structural biology techniques could provide new venues for predicting NTS resistance in weed species. In this review, we present an overview of the latest approaches that could be used. We discuss the use of genomic and epigenomics techniques such as ATAC-seq and DAP-seq to identify transcription factors and cis-regulatory elements associated with resistance traits. Enzyme/protein structure prediction and docking analysis are discussed as an initial step for predicting herbicide binding affinities with key enzymes to identify candidates for subsequent in vitro validation. We also provide example analyses that can be deployed toward elucidating cross resistance and its regulatory patterns. Ultimately, our review provides important insights into the latest scientific advancements and potential directions for predicting and managing herbicide cross resistance in weeds. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Lucas K Bobadilla
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - Patrick J Tranel
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
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Fang Y, Wang D, Xiao L, Quan M, Qi W, Song F, Zhou J, Liu X, Qin S, Du Q, Liu Q, El-Kassaby YA, Zhang D. Allelic variation in transcription factor PtoWRKY68 contributes to drought tolerance in Populus. PLANT PHYSIOLOGY 2023; 193:736-755. [PMID: 37247391 PMCID: PMC10469405 DOI: 10.1093/plphys/kiad315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/21/2023] [Accepted: 04/30/2023] [Indexed: 05/31/2023]
Abstract
Drought stress limits woody species productivity and influences tree distribution. However, dissecting the molecular mechanisms that underpin drought responses in forest trees can be challenging due to trait complexity. Here, using a panel of 300 Chinese white poplar (Populus tomentosa) accessions collected from different geographical climatic regions in China, we performed a genome-wide association study (GWAS) on seven drought-related traits and identified PtoWRKY68 as a candidate gene involved in the response to drought stress. A 12-bp insertion and/or deletion and three nonsynonymous variants in the PtoWRKY68 coding sequence categorized natural populations of P. tomentosa into two haplotype groups, PtoWRKY68hap1 and PtoWRKY68hap2. The allelic variation in these two PtoWRKY68 haplotypes conferred differential transcriptional regulatory activities and binding to the promoters of downstream abscisic acid (ABA) efflux and signaling genes. Overexpression of PtoWRKY68hap1 and PtoWRKY68hap2 in Arabidopsis (Arabidopsis thaliana) ameliorated the drought tolerance of two transgenic lines and increased ABA content by 42.7% and 14.3% compared to wild-type plants, respectively. Notably, PtoWRKY68hap1 (associated with drought tolerance) is ubiquitous in accessions in water-deficient environments, whereas the drought-sensitive allele PtoWRKY68hap2 is widely distributed in well-watered regions, consistent with the trends in local precipitation, suggesting that these alleles correspond to geographical adaptation in Populus. Moreover, quantitative trait loci analysis and an electrophoretic mobility shift assay showed that SHORT VEGETATIVE PHASE (PtoSVP.3) positively regulates the expression of PtoWRKY68 under drought stress. We propose a drought tolerance regulatory module in which PtoWRKY68 modulates ABA signaling and accumulation, providing insight into the genetic basis of drought tolerance in trees. Our findings will facilitate molecular breeding to improve the drought tolerance of forest trees.
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Affiliation(s)
- Yuanyuan Fang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Dan Wang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Liang Xiao
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Mingyang Quan
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Weina Qi
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Fangyuan Song
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Jiaxuan Zhou
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Xin Liu
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, People’s Republic of China
| | - Shitong Qin
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Qingzhang Du
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Qing Liu
- The Institute of Agriculture and Food Research, CSIRO Agriculture and Food, Black Mountain, Canberra ACT 2601, Australia
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, Forest Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Deqiang Zhang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
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Hale B, Ratnayake S, Flory A, Wijeratne R, Schmidt C, Robertson AE, Wijeratne AJ. Gene regulatory network inference in soybean upon infection by Phytophthora sojae. PLoS One 2023; 18:e0287590. [PMID: 37418376 PMCID: PMC10328377 DOI: 10.1371/journal.pone.0287590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/07/2023] [Indexed: 07/09/2023] Open
Abstract
Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.
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Affiliation(s)
- Brett Hale
- Molecular Biosciences Graduate Program, Arkansas State University, State University, AR, United States of America
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
- College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
| | - Sandaruwan Ratnayake
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
- College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
| | - Ashley Flory
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
| | | | - Clarice Schmidt
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America
| | - Alison E. Robertson
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America
| | - Asela J. Wijeratne
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
- College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
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Alali M, Imani M. Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks. PROCEEDINGS OF THE ... AMERICAN CONTROL CONFERENCE. AMERICAN CONTROL CONFERENCE 2023; 2023:3957-3964. [PMID: 37521901 PMCID: PMC10382224 DOI: 10.23919/acc55779.2023.10155867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.
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Affiliation(s)
- Mohammad Alali
- Department of Electrical and Computer Engineering at Northeastern University
| | - Mahdi Imani
- Department of Electrical and Computer Engineering at Northeastern University
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Zhu W, Miao X, Qian J, Chen S, Jin Q, Li M, Han L, Zhong W, Xie D, Shang X, Li L. A translatome-transcriptome multi-omics gene regulatory network reveals the complicated functional landscape of maize. Genome Biol 2023; 24:60. [PMID: 36991439 PMCID: PMC10053466 DOI: 10.1186/s13059-023-02890-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 03/04/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Maize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome. RESULTS We collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31. CONCLUSIONS Our findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.
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Affiliation(s)
- Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Xinxin Miao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Jia Qian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Sijia Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qixiao Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Mingzhu Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Wanshun Zhong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Dan Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- HuBei HongShan Laboratory, Wuhan, 430070, China.
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Chau T, Timilsena P, Li S. Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants. Methods Mol Biol 2023; 2698:259-275. [PMID: 37682480 DOI: 10.1007/978-1-0716-3354-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Single-cell multi-omics technology can be applied to plant cells to characterize gene expression and open chromatin regions in individual cells. In this chapter, we describe a computational pipeline for the analysis of single-cell data to construct gene regulatory networks. The major steps of this pipeline include the following: (1) normalize and integrate scRNA-seq and scATAC-seq data (2) identify cluster maker genes (3) perform motif finding for selected marker genes, and (4) identify regulatory networks with machine learning. The pipeline has been tested using data from the model species Arabidopsis and is generally applicable to other plant and animal species to characterize regulatory networks using single-cell multi-omics data.
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Affiliation(s)
- Tran Chau
- Graduate Program in Genetics, Bioinformatics and Computational Biology (GBCB), Blacksburg, VA, USA
| | - Prakash Timilsena
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Song Li
- Graduate Program in Genetics, Bioinformatics and Computational Biology (GBCB), Blacksburg, VA, USA.
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA.
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Jia Z, Zhang X. Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes. Front Genet 2022; 13:923339. [PMID: 36568360 PMCID: PMC9768335 DOI: 10.3389/fgene.2022.923339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022] Open
Abstract
Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.
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Affiliation(s)
- Zhigang Jia
- School of Mathematics and Statistics, Xinyang Normal University, Xinyang, China,Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China,*Correspondence: Xiujun Zhang,
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Wang X, Liu H, Zhang D, Zou D, Wang J, Zheng H, Jia Y, Qu Z, Sun B, Zhao H. Photosynthetic Carbon Fixation and Sucrose Metabolism Supplemented by Weighted Gene Co-expression Network Analysis in Response to Water Stress in Rice With Overlapping Growth Stages. FRONTIERS IN PLANT SCIENCE 2022; 13:864605. [PMID: 35528941 PMCID: PMC9069116 DOI: 10.3389/fpls.2022.864605] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/14/2022] [Indexed: 05/26/2023]
Abstract
Drought stress at jointing and booting phases of plant development directly affects plant growth and productivity in rice. Limited by natural factors, the jointing and booting stages in rice varieties are known to overlap in high-latitude areas that are more sensitive to water deficit. However, the regulation of photosynthetic carbon fixation and sucrose metabolism in rice leaves under different degrees of drought stress remains unclear. In this study, rice plants were subjected to three degrees of drought stress (-10, -25, -and 40 kPa) for 15 days during the jointing-booting stage, we investigated photosynthetic carbon sequestration and sucrose metabolism pathways in rice leaves and analyzed key genes and regulatory networks using transcriptome sequencing in 2016. And we investigated the effects of drought stress on the growth periods of rice with overlapping growth periods in 2016 and 2017. The results showed that short-term drought stress promoted photosynthetic carbon fixation. However, ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) activity significantly decreased, resulting in a significant decrease in photosynthetic rate. Drought stress increased the maximum activity of fructose-1,6-bisphosphate aldolase (FBA). FBA maintains the necessary photosynthetic rate during drought stress and provides a material base after the resumption of irrigation in the form of controlling the content of its reaction product triose phosphate. Drought stress significantly affected the activities of sucrose synthase (SuSase) and sucrose phosphate synthase (SPS). Vacuoles invertase (VIN) activity increased significantly, and the more severe the drought, the higher the VIN activity. Severe drought stress at the jointing-booting stage severely restricted the growth process of rice with overlapping growth stages and significantly delayed heading and anthesis stages. Transcriptome analysis showed that the number of differentially expressed genes was highest at 6-9 days after drought stress. Two invertase and four β-amylase genes with time-specific expression were involved in sucrose-starch metabolism in rice under drought stress. Combined with weighted gene co-expression network analysis, VIN and β-amylase genes up-regulated throughout drought stress were regulated by OsbZIP04 and OsWRKY62 transcription factors under drought stress. This study showed that any water deficit at the jointing-booting stage would have a serious effect on sucrose metabolism in leaves of rice with overlapping growth stages.
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Tian Y, Zhang C, Ma W, Huang A, Tian M, Zhao J, Dang Q, Sun Y. A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix. Aging (Albany NY) 2022; 14:3155-3174. [PMID: 35398839 PMCID: PMC9037255 DOI: 10.18632/aging.204004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/28/2022] [Indexed: 11/25/2022]
Abstract
The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).
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Affiliation(s)
- Yuan Tian
- Somatic Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong Provincial ENT Hospital, Jinan, Shandong 250023, PR China
| | - Caiqing Zhang
- Department of Respiratory and Critical Care Medicine, Shandong Second Provincial General Hospital, Shandong Provincial ENT Hospital, Shandong University, Jinan, Shandong 250023, PR China
| | - Wanru Ma
- Department of Blood Transfusion, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Alan Huang
- Department of Oncology, Jinan Central Hospital, The Hospital Affiliated with Shandong First Medical University, Jinan, Shandong 250013, PR China
| | - Mei Tian
- Respiratory Department, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, PR China
| | - Junyan Zhao
- Nursing Department, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong 250014, PR China
| | - Qi Dang
- Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250012, PR China
| | - Yuping Sun
- Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250012, PR China
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Paganos P, Voronov D, Musser JM, Arendt D, Arnone MI. Single-cell RNA sequencing of the Strongylocentrotus purpuratus larva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome. eLife 2021; 10:70416. [PMID: 34821556 PMCID: PMC8683087 DOI: 10.7554/elife.70416] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/24/2021] [Indexed: 12/15/2022] Open
Abstract
Identifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single-cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.
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Affiliation(s)
- Periklis Paganos
- Stazione Zoologica Anton Dohrn, Department of Biology and Evolution of Marine Organisms, Naples, Italy
| | - Danila Voronov
- Stazione Zoologica Anton Dohrn, Department of Biology and Evolution of Marine Organisms, Naples, Italy
| | - Jacob M Musser
- European Molecular Biology Laboratory, Developmental Biology Unit, Heidelberg, Germany
| | - Detlev Arendt
- European Molecular Biology Laboratory, Developmental Biology Unit, Heidelberg, Germany
| | - Maria Ina Arnone
- Stazione Zoologica Anton Dohrn, Department of Biology and Evolution of Marine Organisms, Naples, Italy
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DeMers LC, Raboy V, Li S, Saghai Maroof MA. Network Inference of Transcriptional Regulation in Germinating Low Phytic Acid Soybean Seeds. FRONTIERS IN PLANT SCIENCE 2021; 12:708286. [PMID: 34531883 PMCID: PMC8438133 DOI: 10.3389/fpls.2021.708286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/23/2021] [Indexed: 05/14/2023]
Abstract
The low phytic acid (lpa) trait in soybeans can be conferred by loss-of-function mutations in genes encoding myo-inositol phosphate synthase and two epistatically interacting genes encoding multidrug-resistance protein ATP-binding cassette (ABC) transporters. However, perturbations in phytic acid biosynthesis are associated with poor seed vigor. Since the benefits of the lpa trait, in terms of end-use quality and sustainability, far outweigh the negatives associated with poor seed performance, a fuller understanding of the molecular basis behind the negatives will assist crop breeders and engineers in producing variates with lpa and better germination rate. The gene regulatory network (GRN) for developing low and normal phytic acid soybean seeds was previously constructed, with genes modulating a variety of processes pertinent to phytic acid metabolism and seed viability being identified. In this study, a comparative time series analysis of low and normal phytic acid soybeans was carried out to investigate the transcriptional regulatory elements governing the transitional dynamics from dry seed to germinated seed. GRNs were reverse engineered from time series transcriptomic data of three distinct genotypic subsets composed of lpa soybean lines and their normal phytic acid sibling lines. Using a robust unsupervised network inference scheme, putative regulatory interactions were inferred for each subset of genotypes. These interactions were further validated by published regulatory interactions found in Arabidopsis thaliana and motif sequence analysis. Results indicate that lpa seeds have increased sensitivity to stress, which could be due to changes in phytic acid levels, disrupted inositol phosphate signaling, disrupted phosphate ion (Pi) homeostasis, and altered myo-inositol metabolism. Putative regulatory interactions were identified for the latter two processes. Changes in abscisic acid (ABA) signaling candidate transcription factors (TFs) putatively regulating genes in this process were identified as well. Analysis of the GRNs reveal altered regulation in processes that may be affecting the germination of lpa soybean seeds. Therefore, this work contributes to the ongoing effort to elucidate molecular mechanisms underlying altered seed viability, germination and field emergence of lpa crops, understanding of which is necessary in order to mitigate these problems.
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Affiliation(s)
- Lindsay C. DeMers
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Victor Raboy
- National Small Grains Germplasm Research Center, Agricultural Research Service (USDA), Aberdeen, ID, United States
| | - Song Li
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - M. A. Saghai Maroof
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
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12
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Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification. Methods Mol Biol 2021. [PMID: 34251619 DOI: 10.1007/978-1-0716-1534-8_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Gene expression data analysis and the prediction of causal relationships within gene regulatory networks (GRNs) have guided the identification of key regulatory factors and unraveled the dynamic properties of biological systems. However, drawing accurate and unbiased conclusions requires a comprehensive understanding of relevant tools, computational methods, and their workflows. The topics covered in this chapter encompass the entire workflow for GRN inference including: (1) experimental design; (2) RNA sequencing data processing; (3) differentially expressed gene (DEG) selection; (4) clustering prior to inference; (5) network inference techniques; and (6) network visualization and analysis. Moreover, this chapter aims to present a workflow feasible and accessible for plant biologists without a bioinformatics or computer science background. To address this need, TuxNet, a user-friendly graphical user interface that integrates RNA sequencing data analysis with GRN inference, is chosen for the purpose of providing a detailed tutorial.
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13
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Miculan M, Nelissen H, Ben Hassen M, Marroni F, Inzé D, Pè ME, Dell’Acqua M. A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 107:1056-1071. [PMID: 34087008 PMCID: PMC8519057 DOI: 10.1111/tpj.15364] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/31/2021] [Indexed: 05/13/2023]
Abstract
The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript-trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript-trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.
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Affiliation(s)
- Mara Miculan
- Institute of Life SciencesScuola Superiore Sant’AnnaPisa56127Italy
| | - Hilde Nelissen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Manel Ben Hassen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Fabio Marroni
- IGA Technology ServicesUdine33100Italy
- Department of Agricultural, FoodAT, Environmental and Animal Sciences (DI4A)University of UdineUdine33100Italy
| | - Dirk Inzé
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Mario Enrico Pè
- Institute of Life SciencesScuola Superiore Sant’AnnaPisa56127Italy
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14
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Zhang P, Guo Z, Ullah S, Melagraki G, Afantitis A, Lynch I. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. NATURE PLANTS 2021; 7:864-876. [PMID: 34168318 DOI: 10.1038/s41477-021-00946-6] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.
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Affiliation(s)
- Peng Zhang
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK.
| | - Zhiling Guo
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Sami Ullah
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | | | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
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15
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Alvarez JM, Brooks MD, Swift J, Coruzzi GM. Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:105-131. [PMID: 33667112 PMCID: PMC9312366 DOI: 10.1146/annurev-arplant-081320-090914] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.
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Affiliation(s)
- Jose M Alvarez
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
- ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Matthew D Brooks
- Global Change and Photosynthesis Research Unit, US Department of Agriculture Agricultural Research Service, Urbana, Illinois 61801, USA
| | - Joseph Swift
- Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA;
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16
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Gaillochet C, Develtere W, Jacobs TB. CRISPR screens in plants: approaches, guidelines, and future prospects. THE PLANT CELL 2021; 33:794-813. [PMID: 33823021 PMCID: PMC8226290 DOI: 10.1093/plcell/koab099] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 04/02/2021] [Indexed: 05/20/2023]
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR)-associated systems have revolutionized genome engineering by facilitating a wide range of targeted DNA perturbations. These systems have resulted in the development of powerful new screens to test gene functions at the genomic scale. While there is tremendous potential to map and interrogate gene regulatory networks at unprecedented speed and scale using CRISPR screens, their implementation in plants remains in its infancy. Here we discuss the general concepts, tools, and workflows for establishing CRISPR screens in plants and analyze the handful of recent reports describing the use of this strategy to generate mutant knockout collections or to diversify DNA sequences. In addition, we provide insight into how to design CRISPR knockout screens in plants given the current challenges and limitations and examine multiple design options. Finally, we discuss the unique multiplexing capabilities of CRISPR screens to investigate redundant gene functions in highly duplicated plant genomes. Combinatorial mutant screens have the potential to routinely generate higher-order mutant collections and facilitate the characterization of gene networks. By integrating this approach with the numerous genomic profiles that have been generated over the past two decades, the implementation of CRISPR screens offers new opportunities to analyze plant genomes at deeper resolution and will lead to great advances in functional and synthetic biology.
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Affiliation(s)
- Christophe Gaillochet
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Center for Plant Systems Biology, Ghent 9052, Belgium
| | - Ward Develtere
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Center for Plant Systems Biology, Ghent 9052, Belgium
| | - Thomas B Jacobs
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Center for Plant Systems Biology, Ghent 9052, Belgium
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17
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Vidal EA, Alvarez JM, Araus V, Riveras E, Brooks MD, Krouk G, Ruffel S, Lejay L, Crawford NM, Coruzzi GM, Gutiérrez RA. Nitrate in 2020: Thirty Years from Transport to Signaling Networks. THE PLANT CELL 2020; 32:2094-2119. [PMID: 32169959 PMCID: PMC7346567 DOI: 10.1105/tpc.19.00748] [Citation(s) in RCA: 173] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 02/05/2020] [Accepted: 03/10/2020] [Indexed: 05/18/2023]
Abstract
Nitrogen (N) is an essential macronutrient for plants and a major limiting factor for plant growth and crop production. Nitrate is the main source of N available to plants in agricultural soils and in many natural environments. Sustaining agricultural productivity is of paramount importance in the current scenario of increasing world population, diversification of crop uses, and climate change. Plant productivity for major crops around the world, however, is still supported by excess application of N-rich fertilizers with detrimental economic and environmental impacts. Thus, understanding how plants regulate nitrate uptake and metabolism is key for developing new crops with enhanced N use efficiency and to cope with future world food demands. The study of plant responses to nitrate has gained considerable interest over the last 30 years. This review provides an overview of key findings in nitrate research, spanning biochemistry, molecular genetics, genomics, and systems biology. We discuss how we have reached our current view of nitrate transport, local and systemic nitrate sensing/signaling, and the regulatory networks underlying nitrate-controlled outputs in plants. We hope this summary will serve not only as a timeline and information repository but also as a baseline to define outstanding questions for future research.
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Affiliation(s)
- Elena A Vidal
- Millennium Institute for Integrative Biology, Santiago, Chile, 7500565
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile, 8580745
- Escuela de Biotecnología, Facultad de Ciencias, Universidad Mayor, Santiago, Chile, 8580745
| | - José M Alvarez
- Millennium Institute for Integrative Biology, Santiago, Chile, 7500565
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile, 8580745
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - Viviana Araus
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - Eleodoro Riveras
- Millennium Institute for Integrative Biology, Santiago, Chile, 7500565
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile, 8331150
- FONDAP Center for Genome Regulation, Santiago, Chile, 8370415
| | - Matthew D Brooks
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - Gabriel Krouk
- Biochemistry and Plant Molecular Physiology, CNRS, INRA, Montpellier SupAgro, Universite Montpellier, Montpellier, France, 34060
| | - Sandrine Ruffel
- Biochemistry and Plant Molecular Physiology, CNRS, INRA, Montpellier SupAgro, Universite Montpellier, Montpellier, France, 34060
| | - Laurence Lejay
- Biochemistry and Plant Molecular Physiology, CNRS, INRA, Montpellier SupAgro, Universite Montpellier, Montpellier, France, 34060
| | - Nigel M Crawford
- Section of Cell and Developmental Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, California, 92093
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - Rodrigo A Gutiérrez
- Millennium Institute for Integrative Biology, Santiago, Chile, 7500565
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile, 8331150
- FONDAP Center for Genome Regulation, Santiago, Chile, 8370415
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18
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Nutrient dose-responsive transcriptome changes driven by Michaelis-Menten kinetics underlie plant growth rates. Proc Natl Acad Sci U S A 2020; 117:12531-12540. [PMID: 32414922 PMCID: PMC7293603 DOI: 10.1073/pnas.1918619117] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
An increase in nutrient dose leads to proportional increases in crop biomass and agricultural yield. However, the molecular underpinnings of this nutrient dose-response are largely unknown. To investigate, we assayed changes in the Arabidopsis root transcriptome to different doses of nitrogen (N)-a key plant nutrient-as a function of time. By these means, we found that rate changes of genome-wide transcript levels in response to N-dose could be explained by a simple kinetic principle: the Michaelis-Menten (MM) model. Fitting the MM model allowed us to estimate the maximum rate of transcript change (V max), as well as the N-dose at which one-half of V max was achieved (K m) for 1,153 N-dose-responsive genes. Since transcription factors (TFs) can act in part as the catalytic agents that determine the rates of transcript change, we investigated their role in regulating N-dose-responsive MM-modeled genes. We found that altering the abundance of TGA1, an early N-responsive TF, perturbed the maximum rates of N-dose transcriptomic responses (V max), K m, as well as the rate of N-dose-responsive plant growth. We experimentally validated that MM-modeled N-dose-responsive genes included both direct and indirect TGA1 targets, using a root cell TF assay to detect TF binding and/or TF regulation genome-wide. Taken together, our results support a molecular mechanism of transcriptional control that allows an increase in N-dose to lead to a proportional change in the rate of genome-wide expression and plant growth.
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19
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Zhou P, Li Z, Magnusson E, Gomez Cano F, Crisp PA, Noshay JM, Grotewold E, Hirsch CN, Briggs SP, Springer NM. Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions. THE PLANT CELL 2020; 32:1377-1396. [PMID: 32184350 PMCID: PMC7203921 DOI: 10.1105/tpc.20.00080] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/06/2020] [Accepted: 03/16/2020] [Indexed: 05/22/2023]
Abstract
The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.
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Affiliation(s)
- Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Erika Magnusson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Fabio Gomez Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Peter A Crisp
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Steven P Briggs
- Division of Biological Sciences, University of California, San Diego, La Jolla, California 92093
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
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20
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Jiang J, Xing F, Wang C, Zeng X, Zou Q. Investigation and development of maize fused network analysis with multi-omics. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2019; 141:380-387. [PMID: 31220804 DOI: 10.1016/j.plaphy.2019.06.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 06/12/2019] [Accepted: 06/12/2019] [Indexed: 05/19/2023]
Abstract
Maize is a critically important staple crop in the whole world, which has contributed to both economic security and food in planting areas. The main target for researchers and breeding is the improvement of maize quality and yield. The use of computational biology methods combined with multi-omics for selecting biomolecules of interest for maize breeding has been receiving more attention. Moreover, the rapid growth of high-throughput sequencing data provides the opportunity to explore biomolecules of interest at the molecular level in maize. Furthermore, we constructed weighted networks for each of the omics and then integrated them into a final fused weighted network based on a nonlinear combination method. We also analyzed the final fused network and mined the orphan nodes, some of which were shown to be transcription factors that played a key role in maize development. This study could help to improve maize production via insights at the multi-omics level and provide a new perspective for maize researchers. All related data have been released at http://lab.malab.cn/∼jj/maize.htm.
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Affiliation(s)
- Jing Jiang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361001, China
| | - Fei Xing
- School of Aerospace Engineering, Xiamen University, Xiamen, 361001, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, 410082, Changsha, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610000, China.
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21
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Coruzzi G, Varala K, Marshall‐Colon A, Brooks M, Ruffel S, Alvarez J, Pasquino A, Cirrone J, Shasha D. The 4th Dimension of Transcriptional Networks: TIME. FASEB J 2019. [DOI: 10.1096/fasebj.2019.33.1_supplement.343.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Kranthi Varala
- New York UniveristyNew YorkNY
- Purdue UniversityWest LafayetteIN
| | | | | | - Sandrine Ruffel
- Biochemistry and Plant Molecular Physiology Research UnitCNRS/INRA/MontpellierMontpellierFrance
| | | | | | - Jacopo Cirrone
- New York UniveristyNew YorkNY
- NYU Courant Institute of Mathematical SciencesNew YorkNY
| | - Dennis Shasha
- NYU Courant Institute of Mathematical SciencesNew YorkNY
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22
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Gupta C, Pereira A. Recent advances in gene function prediction using context-specific coexpression networks in plants. F1000Res 2019; 8:F1000 Faculty Rev-153. [PMID: 30800290 PMCID: PMC6364378 DOI: 10.12688/f1000research.17207.1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/30/2019] [Indexed: 12/11/2022] Open
Abstract
Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.
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Affiliation(s)
- Chirag Gupta
- Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Andy Pereira
- Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
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23
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Haque S, Ahmad JS, Clark NM, Williams CM, Sozzani R. Computational prediction of gene regulatory networks in plant growth and development. CURRENT OPINION IN PLANT BIOLOGY 2019; 47:96-105. [PMID: 30445315 DOI: 10.1016/j.pbi.2018.10.005] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/05/2018] [Accepted: 10/18/2018] [Indexed: 05/22/2023]
Abstract
Plants integrate a wide range of cellular, developmental, and environmental signals to regulate complex patterns of gene expression. Recent advances in genomic technologies enable differential gene expression analysis at a systems level, allowing for improved inference of the network of regulatory interactions between genes. These gene regulatory networks, or GRNs, are used to visualize the causal regulatory relationships between regulators and their downstream target genes. Accordingly, these GRNs can represent spatial, temporal, and/or environmental regulations and can identify functional genes. This review summarizes recent computational approaches applied to different types of gene expression data to infer GRNs in the context of plant growth and development. Three stages of GRN inference are described: first, data collection and analysis based on the dataset type; second, network inference application based on data availability and proposed hypotheses; and third, validation based on in silico, in vivo, and in planta methods. In addition, this review relates data collection strategies to biological questions, organizes inference algorithms based on statistical methods and data types, discusses experimental design considerations, and provides guidelines for GRN inference with an emphasis on the benefits of integrative approaches, especially when a priori information is limited. Finally, this review concludes that computational frameworks integrating large-scale heterogeneous datasets are needed for a more accurate (e.g. fewer false interactions), detailed (e.g. discrimination between direct versus indirect interactions), and comprehensive (e.g. genetic regulation under various conditions and spatial locations) inference of GRNs.
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Affiliation(s)
- Samiul Haque
- Electrical and Computer Engineering, North Carolina State University, Raleigh, USA
| | - Jabeen S Ahmad
- Plant and Microbial Biology, North Carolina State University, Raleigh, USA
| | - Natalie M Clark
- Plant and Microbial Biology, North Carolina State University, Raleigh, USA
| | - Cranos M Williams
- Electrical and Computer Engineering, North Carolina State University, Raleigh, USA.
| | - Rosangela Sozzani
- Plant and Microbial Biology, North Carolina State University, Raleigh, USA.
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24
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Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks. Methods Mol Biol 2018. [PMID: 30547398 DOI: 10.1007/978-1-4939-8882-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Biological networks are a very convenient modeling and visualization tool to discover knowledge from modern high-throughput genomics and post-genomics data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.
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25
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Lavarenne J, Guyomarc'h S, Sallaud C, Gantet P, Lucas M. The Spring of Systems Biology-Driven Breeding. TRENDS IN PLANT SCIENCE 2018; 23:706-720. [PMID: 29764727 DOI: 10.1016/j.tplants.2018.04.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 04/12/2018] [Accepted: 04/16/2018] [Indexed: 05/08/2023]
Abstract
Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies.
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Affiliation(s)
- Jérémy Lavarenne
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France; Biogemma, Centre de Recherches de Chappes, Route d'Ennezat, 63720 Chappes, France
| | - Soazig Guyomarc'h
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France
| | - Christophe Sallaud
- Biogemma, Centre de Recherches de Chappes, Route d'Ennezat, 63720 Chappes, France
| | - Pascal Gantet
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France.
| | - Mikaël Lucas
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France
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26
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Zamanighomi M, Zamanian M, Kimber M, Wang Z. Gene Regulatory Network Inference from Perturbed Time-Series Expression Data via Ordered Dynamical Expansion of Non-Steady State Actors. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1093-1106. [PMID: 26701893 DOI: 10.1109/tcbb.2015.2509992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.
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27
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Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. Proc Natl Acad Sci U S A 2018; 115:6494-6499. [PMID: 29769331 PMCID: PMC6016767 DOI: 10.1073/pnas.1721487115] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Our study exploits time—the relatively unexplored fourth dimension of gene regulatory networks (GRNs)—to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. We introduce several conceptual innovations to the analysis of time-series data in the area of predictive GRNs. Our resulting network now provides the “transcriptional logic” for transcription factor perturbations aimed at improving N-use efficiency, an important issue for global food production in marginal soils and for sustainable agriculture. More broadly, the combination of the time-based approaches we develop and deploy can be applied to uncover the temporal “transcriptional logic” for any response system in biology, agriculture, or medicine. This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO3− uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.
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Stevens RG, Baldet P, Bouchet JP, Causse M, Deborde C, Deschodt C, Faurobert M, Garchery C, Garcia V, Gautier H, Gouble B, Maucourt M, Moing A, Page D, Petit J, Poëssel JL, Truffault V, Rothan C. A Systems Biology Study in Tomato Fruit Reveals Correlations between the Ascorbate Pool and Genes Involved in Ribosome Biogenesis, Translation, and the Heat-Shock Response. FRONTIERS IN PLANT SCIENCE 2018; 9:137. [PMID: 29491875 PMCID: PMC5817626 DOI: 10.3389/fpls.2018.00137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 01/24/2018] [Indexed: 05/03/2023]
Abstract
Changing the balance between ascorbate, monodehydroascorbate, and dehydroascorbate in plant cells by manipulating the activity of enzymes involved in ascorbate synthesis or recycling of oxidized and reduced forms leads to multiple phenotypes. A systems biology approach including network analysis of the transcriptome, proteome and metabolites of RNAi lines for ascorbate oxidase, monodehydroascorbate reductase and galactonolactone dehydrogenase has been carried out in orange fruit pericarp of tomato (Solanum lycopersicum). The transcriptome of the RNAi ascorbate oxidase lines is inversed compared to the monodehydroascorbate reductase and galactonolactone dehydrogenase lines. Differentially expressed genes are involved in ribosome biogenesis and translation. This transcriptome inversion is also seen in response to different stresses in Arabidopsis. The transcriptome response is not well correlated with the proteome which, with the metabolites, are correlated to the activity of the ascorbate redox enzymes-ascorbate oxidase and monodehydroascorbate reductase. Differentially accumulated proteins include metacaspase, protein disulphide isomerase, chaperone DnaK and carbonic anhydrase and the metabolites chlorogenic acid, dehydroascorbate and alanine. The hub genes identified from the network analysis are involved in signaling, the heat-shock response and ribosome biogenesis. The results from this study therefore reveal one or several putative signals from the ascorbate pool which modify the transcriptional response and elements downstream.
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Affiliation(s)
- Rebecca G. Stevens
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Pierre Baldet
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
| | - Jean-Paul Bouchet
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Mathilde Causse
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Catherine Deborde
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
- Plateforme Métabolome du Centre de Génomique Fonctionnelle Bordeaux, Centre Institut National de la Recherche Agronomique de Bordeaux, Villenave d'Ornon, France
| | - Claire Deschodt
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Mireille Faurobert
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Cécile Garchery
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Virginie Garcia
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
| | - Hélène Gautier
- Institut National de la Recherche Agronomique, UR1115, Plantes et Systèmes de culture Horticoles, Avignon, France
| | - Barbara Gouble
- Institut National de la Recherche Agronomique, Université d'Avignon et des Pays du Vaucluse, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, Avignon, France
| | - Mickaël Maucourt
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
- Plateforme Métabolome du Centre de Génomique Fonctionnelle Bordeaux, Centre Institut National de la Recherche Agronomique de Bordeaux, Villenave d'Ornon, France
| | - Annick Moing
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
- Plateforme Métabolome du Centre de Génomique Fonctionnelle Bordeaux, Centre Institut National de la Recherche Agronomique de Bordeaux, Villenave d'Ornon, France
| | - David Page
- Institut National de la Recherche Agronomique, Université d'Avignon et des Pays du Vaucluse, UMR408 Sécurité et Qualité des Produits d'Origine Végétale, Avignon, France
| | - Johann Petit
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
| | - Jean-Luc Poëssel
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Vincent Truffault
- Institut National de la Recherche Agronomique, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
| | - Christophe Rothan
- Institut National de la Recherche Agronomique, Université de Bordeaux, UMR1332, Biologie du Fruit et Pathologie, Villenave d'Ornon, France
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Vandereyken K, Van Leene J, De Coninck B, Cammue BPA. Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs. FRONTIERS IN PLANT SCIENCE 2018; 9:694. [PMID: 29922309 PMCID: PMC5996676 DOI: 10.3389/fpls.2018.00694] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/07/2018] [Indexed: 05/20/2023]
Abstract
Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.
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Affiliation(s)
- Katy Vandereyken
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Jelle Van Leene
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Barbara De Coninck
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Division of Crop Biotechnics, KU Leuven, Heverlee, Belgium
| | - Bruno P. A. Cammue
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- *Correspondence: Bruno P. A. Cammue
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30
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Walker L, Boddington C, Jenkins D, Wang Y, Grønlund JT, Hulsmans J, Kumar S, Patel D, Moore JD, Carter A, Samavedam S, Bonomo G, Hersh DS, Coruzzi GM, Burroughs NJ, Gifford ML. Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture. THE PLANT CELL 2017; 29:2393-2412. [PMID: 28893852 PMCID: PMC5774560 DOI: 10.1105/tpc.16.00961] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 08/16/2017] [Accepted: 09/07/2017] [Indexed: 05/02/2023]
Abstract
Shaping of root architecture is a quintessential developmental response that involves the concerted action of many different cell types, is highly dynamic, and underpins root plasticity. To determine to what extent the environmental regulation of lateral root development is a product of cell-type preferential activities, we tracked transcriptomic responses to two different treatments that both change root development in Arabidopsis thaliana at an unprecedented level of temporal detail. We found that individual transcripts are expressed with a very high degree of temporal and spatial specificity, yet biological processes are commonly regulated, in a mechanism we term response nonredundancy. Using causative gene network inference to compare the genes regulated in different cell types and during responses to nitrogen and a biotic interaction, we found that common transcriptional modules often regulate the same gene families but control different individual members of these families, specific to response and cell type. This reinforces that the activity of a gene cannot be defined simply as molecular function; rather, it is a consequence of spatial location, expression timing, and environmental responsiveness.
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Affiliation(s)
- Liam Walker
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Clare Boddington
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
| | - Dafyd Jenkins
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
| | - Ying Wang
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
| | - Jesper T Grønlund
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Jo Hulsmans
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
| | - Sanjeev Kumar
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Dhaval Patel
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Jonathan D Moore
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
| | - Anthony Carter
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
| | - Siva Samavedam
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Giovanni Bonomo
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - David S Hersh
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003
| | - Nigel J Burroughs
- Warwick Systems Biology Centre, University of Warwick, Senate House, Coventry CV4 7AL, United Kingdom
- Warwick Mathematics Institute, University of Warwick, Zeeman Building, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Miriam L Gifford
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
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31
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Ezer D, Shepherd SJK, Brestovitsky A, Dickinson P, Cortijo S, Charoensawan V, Box MS, Biswas S, Jaeger KE, Wigge PA. The G-Box Transcriptional Regulatory Code in Arabidopsis. PLANT PHYSIOLOGY 2017; 175:628-640. [PMID: 28864470 PMCID: PMC5619884 DOI: 10.1104/pp.17.01086] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 08/30/2017] [Indexed: 05/19/2023]
Abstract
Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.
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Affiliation(s)
- Daphne Ezer
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Samuel J K Shepherd
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Anna Brestovitsky
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Patrick Dickinson
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Sandra Cortijo
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Varodom Charoensawan
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
- Department of Biochemistry, Faculty of Science, and Integrative Computational BioScience Center, Mahidol University, Bangkok 10400, Thailand
| | - Mathew S Box
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Surojit Biswas
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Katja E Jaeger
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
| | - Philip A Wigge
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
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32
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Pal S, Kisko M, Dubos C, Lacombe B, Berthomieu P, Krouk G, Rouached H. TransDetect Identifies a New Regulatory Module Controlling Phosphate Accumulation. PLANT PHYSIOLOGY 2017; 175:916-926. [PMID: 28827455 PMCID: PMC5619893 DOI: 10.1104/pp.17.00568] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 08/16/2017] [Indexed: 05/18/2023]
Abstract
Identifying transcription factor (TFs) cooperation controlling target gene expression is still an arduous challenge. The accuracy of current methods at genome scale significantly drops with the increase in number of genes, which limits their applicability to more complex genomes, like animals and plants. Here, we developed an algorithm, TransDetect, able to predict TF combinations controlling the expression level of a given gene. TransDetect was used to identify novel TF modules regulating the expression of Arabidopsis (Arabidopsis thaliana) phosphate transporter PHO1;H3 comprising MYB15, MYB84, bHLH35, and ICE1. These TFs were confirmed to interact between themselves and with the PHO1;H3 promoter. Phenotypic and genetic analyses of TF mutants enable the organization of these four TFs and PHO1;H3 in a new gene regulatory network controlling phosphate accumulation in zinc-dependent manner. This demonstrates the potential of TransDetect to extract directionality in nondynamic transcriptomes and to provide a blueprint to identify gene regulatory network involved in a given biological process.
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Affiliation(s)
- Sikander Pal
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
| | - Mushtak Kisko
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
| | - Christian Dubos
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
| | - Benoit Lacombe
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
| | - Pierre Berthomieu
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
| | - Gabriel Krouk
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
| | - Hatem Rouached
- Laboratoire de Biochimie and Physiologie Moléculaire des Plantes, UMR CNRS/INRA/Montpellier Supagro/UM, Institut de Biologie Intégrative des Plantes 'Claude Grignon', 34060 Montpellier, France
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33
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Huang J, Vendramin S, Shi L, McGinnis KM. Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data. PLANT PHYSIOLOGY 2017; 175:568-583. [PMID: 28768814 PMCID: PMC5580776 DOI: 10.1104/pp.17.00825] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 07/31/2017] [Indexed: 05/22/2023]
Abstract
With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.
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Affiliation(s)
- Ji Huang
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
| | - Stefania Vendramin
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
| | - Lizhen Shi
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306
| | - Karen M McGinnis
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
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34
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Greenham K, Guadagno CR, Gehan MA, Mockler TC, Weinig C, Ewers BE, McClung CR. Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa. eLife 2017; 6:e29655. [PMID: 28826479 PMCID: PMC5628015 DOI: 10.7554/elife.29655] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 08/11/2017] [Indexed: 11/13/2022] Open
Abstract
The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought.
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Affiliation(s)
- Kathleen Greenham
- Department of Biological SciencesDartmouth CollegeHanoverUnited States
| | | | - Malia A Gehan
- Donald Danforth Plant Science CenterSt. LouisUnited States
| | - Todd C Mockler
- Donald Danforth Plant Science CenterSt. LouisUnited States
| | - Cynthia Weinig
- Department of BotanyUniversity of WyomingLaramieUnited States
- Department of Molecular BiologyUniversity of WyomingLaramieUnited States
- Program in EcologyUniversity of WyomingLaramieUnited States
| | - Brent E Ewers
- Department of BotanyUniversity of WyomingLaramieUnited States
- Program in EcologyUniversity of WyomingLaramieUnited States
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35
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Banf M, Rhee SY. Enhancing gene regulatory network inference through data integration with markov random fields. Sci Rep 2017; 7:41174. [PMID: 28145456 PMCID: PMC5286517 DOI: 10.1038/srep41174] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 12/16/2016] [Indexed: 02/06/2023] Open
Abstract
A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.
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Affiliation(s)
- Michael Banf
- Department of Plant Biology, Carnegie Institution for Science, 93405 Stanford, USA
| | - Seung Y Rhee
- Department of Plant Biology, Carnegie Institution for Science, 93405 Stanford, USA
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Abstract
Plants, like other eukaryotes, have evolved complex mechanisms to coordinate gene expression during development, environmental response, and cellular homeostasis. Transcription factors (TFs), accompanied by basic cofactors and posttranscriptional regulators, are key players in gene-regulatory networks (GRNs). The coordinated control of gene activity is achieved by the interplay of these factors and by physical interactions between TFs and DNA. Here, we will briefly outline recent technological progress made to elucidate GRNs in plants. We will focus on techniques that allow us to characterize physical interactions in GRNs in plants and to analyze their regulatory consequences. Targeted manipulation allows us to test the relevance of specific gene-regulatory interactions. The combination of genome-wide experimental approaches with mathematical modeling allows us to get deeper insights into key-regulatory interactions and combinatorial control of important processes in plants.
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Affiliation(s)
- Kerstin Kaufmann
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.
| | - Dijun Chen
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.,Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
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Koda S, Onda Y, Matsui H, Takahagi K, Uehara-Yamaguchi Y, Shimizu M, Inoue K, Yoshida T, Sakurai T, Honda H, Eguchi S, Nishii R, Mochida K. Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon. FRONTIERS IN PLANT SCIENCE 2017; 8:2055. [PMID: 29234348 PMCID: PMC5712366 DOI: 10.3389/fpls.2017.02055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 11/16/2017] [Indexed: 05/08/2023]
Abstract
We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.
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Affiliation(s)
- Satoru Koda
- Graduate School of Mathematics, Kyushu University, Fukuoka, Japan
| | - Yoshihiko Onda
- Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
| | | | - Kotaro Takahagi
- Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
- Kihara Institute for Biological Research, Yokohama City University, Kanagawa, Japan
| | - Yukiko Uehara-Yamaguchi
- Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
| | - Minami Shimizu
- Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
| | - Komaki Inoue
- Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
| | - Takuhiro Yoshida
- Integrated Genome Informatics Research Unit, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
| | - Tetsuya Sakurai
- Integrated Genome Informatics Research Unit, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
- Research and Education Faculty, Multidisciplinary Science Cluster, Interdisciplinary Science Unit, Kochi University, Kochi, Japan
| | - Hiroshi Honda
- Graduate School of Mathematics, Kyushu University, Fukuoka, Japan
| | - Shinto Eguchi
- The Institute of Statistical Mathematics, Tokyo, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
| | - Keiichi Mochida
- Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Kanagawa, Japan
- Kihara Institute for Biological Research, Yokohama City University, Kanagawa, Japan
- Institute of Plant Science and Resources, Okayama University, Okayama, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
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Banf M, Rhee SY. Computational inference of gene regulatory networks: Approaches, limitations and opportunities. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2016; 1860:41-52. [PMID: 27641093 DOI: 10.1016/j.bbagrm.2016.09.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 09/08/2016] [Accepted: 09/08/2016] [Indexed: 10/21/2022]
Abstract
Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.
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Affiliation(s)
- Michael Banf
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
| | - Seung Y Rhee
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
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Walley JW, Sartor RC, Shen Z, Schmitz RJ, Wu KJ, Urich MA, Nery JR, Smith LG, Schnable JC, Ecker JR, Briggs SP. Integration of omic networks in a developmental atlas of maize. Science 2016; 353:814-8. [PMID: 27540173 DOI: 10.1126/science.aag1125] [Citation(s) in RCA: 296] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 07/25/2016] [Indexed: 12/23/2022]
Abstract
Coexpression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting functional roles of individual genes at a system-wide scale. To enable network reconstructions, we built a large-scale gene expression atlas composed of 62,547 messenger RNAs (mRNAs), 17,862 nonmodified proteins, and 6227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. Networks in which nodes are genes connected on the basis of highly correlated expression patterns of mRNAs were very different from networks that were based on coexpression of proteins. Roughly 85% of highly interconnected hubs were not conserved in expression between RNA and protein networks. However, networks from either data type were enriched in similar ontological categories and were effective in predicting known regulatory relationships. Integration of mRNA, protein, and phosphoprotein data sets greatly improved the predictive power of GRNs.
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Affiliation(s)
- Justin W Walley
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA. Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA
| | - Ryan C Sartor
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Zhouxin Shen
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Robert J Schmitz
- Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Kevin J Wu
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Mark A Urich
- Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Laurie G Smith
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Joseph R Ecker
- Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Steven P Briggs
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
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Swift J, Coruzzi GM. A matter of time - How transient transcription factor interactions create dynamic gene regulatory networks. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2016; 1860:75-83. [PMID: 27546191 DOI: 10.1016/j.bbagrm.2016.08.007] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 08/06/2016] [Accepted: 08/10/2016] [Indexed: 12/16/2022]
Abstract
Dynamic reprogramming of transcriptional networks enables cells to adapt to a changing environment. Thus, it is crucial not only to understand what gene targets are regulated by a transcription factor (TF) but also when. This review explores the way TFs function with respect to time, paying particular attention to discoveries made in plants - where coordinated, genome-wide responses to environmental change is crucial to the survival of these sessile organisms. We investigate the molecular mechanisms that mediate transient TF-DNA binding, and assess how these rapid and dynamic interactions translate to long-term temporal regulation of genomes. We also discuss how current molecular techniques can catch, and sometimes miss, transient TF-target interactions that underlie dynamic cellular responses. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.
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Affiliation(s)
- Joseph Swift
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York 10003, USA.
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York 10003, USA
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41
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Paul JW, Qi Y. CRISPR/Cas9 for plant genome editing: accomplishments, problems and prospects. PLANT CELL REPORTS 2016; 35:1417-27. [PMID: 27114166 DOI: 10.1007/s00299-016-1985-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 04/12/2016] [Indexed: 05/20/2023]
Abstract
The increasing burden of the world population on agriculture requires the development of more robust crops. Dissecting the basic biology that underlies plant development and stress responses will inform the design of better crops. One powerful tool for studying plants at the molecular level is the RNA-programmed genome editing system composed of a clustered regularly interspaced short palindromic repeats (CRISPR)-encoded guide RNA and the nuclease Cas9. Here, some of the recent advances in CRISPR/Cas9 technology that have profound implications for improving the study of plant biology are described. These tools are also paving the way towards new horizons for biotechnologies and crop development.
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Affiliation(s)
- Joseph W Paul
- Department of Biology, Thomas Harriot College of Arts and Sciences, East Carolina University, Greenville, NC, 27858, USA
| | - Yiping Qi
- Department of Biology, Thomas Harriot College of Arts and Sciences, East Carolina University, Greenville, NC, 27858, USA.
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Gallagher JP, Grover CE, Hu G, Wendel JF. Insights into the Ecology and Evolution of Polyploid Plants through Network Analysis. Mol Ecol 2016; 25:2644-60. [PMID: 27027619 DOI: 10.1111/mec.13626] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 03/09/2016] [Accepted: 03/22/2016] [Indexed: 12/18/2022]
Abstract
Polyploidy is a widespread phenomenon throughout eukaryotes, with important ecological and evolutionary consequences. Although genes operate as components of complex pathways and networks, polyploid changes in genes and gene expression have typically been evaluated as either individual genes or as a part of broad-scale analyses. Network analysis has been fruitful in associating genomic and other 'omic'-based changes with phenotype for many systems. In polyploid species, network analysis has the potential not only to facilitate a better understanding of the complex 'omic' underpinnings of phenotypic and ecological traits common to polyploidy, but also to provide novel insight into the interaction among duplicated genes and genomes. This adds perspective to the global patterns of expression (and other 'omic') change that accompany polyploidy and to the patterns of recruitment and/or loss of genes following polyploidization. While network analysis in polyploid species faces challenges common to other analyses of duplicated genomes, present technologies combined with thoughtful experimental design provide a powerful system to explore polyploid evolution. Here, we demonstrate the utility and potential of network analysis to questions pertaining to polyploidy with an example involving evolution of the transgressively superior cotton fibres found in polyploid Gossypium hirsutum. By combining network analysis with prior knowledge, we provide further insights into the role of profilins in fibre domestication and exemplify the potential for network analysis in polyploid species.
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Affiliation(s)
- Joseph P Gallagher
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Corrinne E Grover
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Guanjing Hu
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Jonathan F Wendel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
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Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics. Methods Mol Biol 2016; 1415:533-47. [PMID: 27115651 DOI: 10.1007/978-1-4939-3572-7_27] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.
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Petrovskiy ED, Saik OV, Tiys ES, Lavrik IN, Kolchanov NA, Ivanisenko VA. Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain. BMC Genomics 2015; 16 Suppl 13:S3. [PMID: 26693857 PMCID: PMC4686796 DOI: 10.1186/1471-2164-16-s13-s3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND An important issue in the target identification for the drug design is the tissue-specific effect of inhibition of target genes. The task of assessing the tissue-specific effect in suppressing gene activity is especially relevant in the studies of the brain, because a significant variability in gene expression levels among different areas of the brain was well documented. RESULTS A method is proposed for constructing statistical models to predict the potential effect of the knockout of target genes on the expression of genes involved in the regulation of apoptosis in various brain regions. The model connects the expression of the objective group of genes with expression of the target gene by means of machine learning models trained on available expression data. Information about the interactions between target and objective genes is determined by reconstruction of target-centric gene network. STRING and ANDSystem databases are used for the reconstruction of gene networks. The developed models have been used to analyse gene knockout effects of more than 7,500 target genes on the expression of 1,900 objective genes associated with the Gene Ontology category "apoptotic process". The tissue-specific effect was calculated for 12 main anatomical structures of the human brain. Initial values of gene expression in these anatomical structures were taken from the Allen Brain Atlas database. The results of the predictions of the effect of suppressing the activity of target genes on apoptosis, calculated on average for all brain structures, were in good agreement with experimental data on siRNA-inhibition. CONCLUSIONS This theoretical paper presents an approach that can be used to assess tissue-specific gene knockout effect on gene expression of the studied biological process in various structures of the brain. Genes that, according to the predictions of the model, have the highest values of tissue-specific effects on the apoptosis network can be considered as potential pharmacological targets for the development of drugs that would potentially have strong effect on the specific area of the brain and a much weaker effect on other brain structures. Further experiments should be provided in order to confirm the potential findings of the method.
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Affiliation(s)
- Evgeny D Petrovskiy
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- International Tomography Center, The Siberian Branch of the Russian Academy of Sciences, Institutskaya 3A, Novosibirsk, 630090, Russia
| | - Olga V Saik
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
| | - Evgeny S Tiys
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
| | - Inna N Lavrik
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- Otto von Guericke University Magdeburg, Medical Faculty, Department Translational Inflammation Research, Institute of Experimental Internal Medicine, Pfälzer Platz, Building 28, Magdeburg, 39106, Germany
| | - Nikolay A Kolchanov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
| | - Vladimir A Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
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Isewon I, Oyelade J, Brors B, Adebiyi E. In Silico Gene Regulatory Network of the Maurer's Cleft Pathway in Plasmodium falciparum. Evol Bioinform Online 2015; 11:231-8. [PMID: 26526876 PMCID: PMC4620995 DOI: 10.4137/ebo.s25585] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 07/28/2015] [Accepted: 08/03/2015] [Indexed: 11/15/2022] Open
Abstract
The Maurer's clefts (MCs) are very important for the survival of Plasmodium falciparum within an infected cell as they are induced by the parasite itself in the erythrocyte for protein trafficking. The MCs form an interesting part of the parasite's biology as they shed more light on how the parasite remodels the erythrocyte leading to host pathogenesis and death. Here, we predicted and analyzed the genetic regulatory network of genes identified to belong to the MCs using regularized graphical Gaussian model. Our network shows four major activators, their corresponding target genes, and predicted binding sites. One of these master activators is the serine repeat antigen 5 (SERA5), predominantly expressed among the SERA multigene family of P. falciparum, which is one of the blood-stage malaria vaccine candidates. Our results provide more details about functional interactions and the regulation of the genes in the MCs’ pathway of P. falciparum.
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Affiliation(s)
- Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Jelili Oyelade
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Benedikt Brors
- Department of Applied Bioinformatics, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
- Department of Applied Bioinformatics, German Cancer Research Centre (DKFZ), Heidelberg, Germany
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46
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Xie W, Huang J, Liu Y, Rao J, Luo D, He M. Exploring potential new floral organ morphogenesis genes of Arabidopsis thaliana using systems biology approach. FRONTIERS IN PLANT SCIENCE 2015; 6:829. [PMID: 26528302 PMCID: PMC4602108 DOI: 10.3389/fpls.2015.00829] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 09/22/2015] [Indexed: 05/24/2023]
Abstract
Flowering is one of the important defining features of angiosperms. The initiation of flower development and the formation of different floral organs are the results of the interplays among numerous genes. But until now, just fewer genes have been found linked with flower development. And the functions of lots of genes of Arabidopsis thaliana are still unknown. Although, the quartet model successfully simplified the ABCDE model to elaborate the molecular mechanism by introducing protein-protein interactions (PPIs). We still don't know much about several important aspects of flower development. So we need to discriminate even more genes involving in the flower development. In this study, we identified seven differentially modules through integrating the weighted gene co-expression network analysis (WGCNA) and Support Vector Machine (SVM) method to analyze co-expression network and PPIs using the public floral and non-floral expression profiles data of Arabidopsis thaliana. Gene set enrichment analysis was used for the functional annotation of the related genes, and some of the hub genes were identified in each module. The potential floral organ morphogenesis genes of two significant modules were integrated with PPI information in order to detail the inherent regulation mechanisms. Finally, the functions of the floral patterning genes were elucidated by combining the PPI and evolutionary information. It was indicated that the sub-networks or complexes, rather than the genes, were the regulation unit of flower development. We found that the most possible potential new genes underlining the floral pattern formation in A. thaliana were FY, CBL2, ZFN3, and AT1G77370; among them, FY, CBL2 acted as an upstream regulator of AP2; ZFN3 activated the flower primordial determining gene AP1 and AP2 by HY5/HYH gene via photo induction possibly. And AT1G77370 exhibited similar function in floral morphogenesis, same as ELF3. It possibly formed a complex between RFC3 and RPS15 in cytoplasm, which regulated TSO1 and CPSF160 in the nucleus, to control the floral organ morphogenesis. This process might also be fine tuning by AT5G53360 in the nucleus.
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Affiliation(s)
| | | | | | | | - Da Luo
- *Correspondence: Da Luo and Miao He, School of Life Sciences, Sun Yat-sen University, No. 135 West Xingang RD, Guangzhou 510275, Guangdong, China ;
| | - Miao He
- *Correspondence: Da Luo and Miao He, School of Life Sciences, Sun Yat-sen University, No. 135 West Xingang RD, Guangzhou 510275, Guangdong, China ;
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Yang X, Cushman JC, Borland AM, Edwards EJ, Wullschleger SD, Tuskan GA, Owen NA, Griffiths H, Smith JAC, De Paoli HC, Weston DJ, Cottingham R, Hartwell J, Davis SC, Silvera K, Ming R, Schlauch K, Abraham P, Stewart JR, Guo HB, Albion R, Ha J, Lim SD, Wone BWM, Yim WC, Garcia T, Mayer JA, Petereit J, Nair SS, Casey E, Hettich RL, Ceusters J, Ranjan P, Palla KJ, Yin H, Reyes-García C, Andrade JL, Freschi L, Beltrán JD, Dever LV, Boxall SF, Waller J, Davies J, Bupphada P, Kadu N, Winter K, Sage RF, Aguilar CN, Schmutz J, Jenkins J, Holtum JAM. A roadmap for research on crassulacean acid metabolism (CAM) to enhance sustainable food and bioenergy production in a hotter, drier world. THE NEW PHYTOLOGIST 2015; 207:491-504. [PMID: 26153373 DOI: 10.1111/nph.13393] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Crassulacean acid metabolism (CAM) is a specialized mode of photosynthesis that features nocturnal CO2 uptake, facilitates increased water-use efficiency (WUE), and enables CAM plants to inhabit water-limited environments such as semi-arid deserts or seasonally dry forests. Human population growth and global climate change now present challenges for agricultural production systems to increase food, feed, forage, fiber, and fuel production. One approach to meet these challenges is to increase reliance on CAM crops, such as Agave and Opuntia, for biomass production on semi-arid, abandoned, marginal, or degraded agricultural lands. Major research efforts are now underway to assess the productivity of CAM crop species and to harness the WUE of CAM by engineering this pathway into existing food, feed, and bioenergy crops. An improved understanding of CAM has potential for high returns on research investment. To exploit the potential of CAM crops and CAM bioengineering, it will be necessary to elucidate the evolution, genomic features, and regulatory mechanisms of CAM. Field trials and predictive models will be required to assess the productivity of CAM crops, while new synthetic biology approaches need to be developed for CAM engineering. Infrastructure will be needed for CAM model systems, field trials, mutant collections, and data management.
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Affiliation(s)
- Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - John C Cushman
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Anne M Borland
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
- School of Biology, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Erika J Edwards
- Department of Ecology and Evolutionary Biology, Brown University, Box G-W, Providence, RI, 02912, USA
| | - Stan D Wullschleger
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6301, USA
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - Nick A Owen
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK
| | - Howard Griffiths
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK
| | - J Andrew C Smith
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Henrique C De Paoli
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - David J Weston
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - Robert Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - James Hartwell
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Sarah C Davis
- Voinovich School of Leadership and Public Affairs and Department of Environmental and Plant Biology, Ohio University, Athens, OH, 45701, USA
| | - Katia Silvera
- Smithsonian Tropical Research Institute, PO Box 0843-03092, Balboa, Ancon, Republic of Panama
| | - Ray Ming
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Karen Schlauch
- Nevada Center for Bioinformatics, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Paul Abraham
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - J Ryan Stewart
- Department of Plant and Wildlife Sciences, Brigham Young University, 4105 Life Sciences Building, Provo, UT, 84602, USA
| | - Hao-Bo Guo
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Rebecca Albion
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Jungmin Ha
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Sung Don Lim
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Bernard W M Wone
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Won Cheol Yim
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Travis Garcia
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Jesse A Mayer
- Department of Biochemistry and Molecular Biology, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Juli Petereit
- Nevada Center for Bioinformatics, University of Nevada, MS330, Reno, NV, 89557-0330, USA
| | - Sujithkumar S Nair
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6301, USA
| | - Erin Casey
- School of Biology, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Robert L Hettich
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Johan Ceusters
- Department of M²S, Faculty of Engineering Technology, TC Bioengineering Technology, KU Leuven, Campus Geel, Kleinhoefstraat 4, B-2440, Geel, Belgium
| | - Priya Ranjan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - Kaitlin J Palla
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
| | - Hengfu Yin
- Key Laboratory of Forest Genetics and Breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, 311400, China
| | - Casandra Reyes-García
- Centro de Investigación Científica de Yucatán, Calle 43 No. 130, Colonia Chuburná de Hidalgo, CP 97200, Mérida, México
| | - José Luis Andrade
- Centro de Investigación Científica de Yucatán, Calle 43 No. 130, Colonia Chuburná de Hidalgo, CP 97200, Mérida, México
| | - Luciano Freschi
- Department of Botany, University of São Paulo, São Paulo, 05508-090, Brazil
| | - Juan D Beltrán
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Louisa V Dever
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Susanna F Boxall
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Jade Waller
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Jack Davies
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Phaitun Bupphada
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Nirja Kadu
- Department of Plant Sciences, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Klaus Winter
- Smithsonian Tropical Research Institute, PO Box 0843-03092, Balboa, Ancon, Republic of Panama
| | - Rowan F Sage
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, M5S3B2, Canada
| | - Cristobal N Aguilar
- Department of Food Research, School of Chemistry, Universidad Autónoma de Coahuila, Saltillo, México
| | - Jeremy Schmutz
- HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL, 35801, USA
- US Department of Energy Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA, 94598, USA
| | - Jerry Jenkins
- HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL, 35801, USA
| | - Joseph A M Holtum
- College of Marine and Environmental Sciences, James Cook University, Townsville, 4811, QLD, Australia
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Li Y, Varala K, Coruzzi GM. From milliseconds to lifetimes: tracking the dynamic behavior of transcription factors in gene networks. Trends Genet 2015; 31:509-15. [PMID: 26072453 DOI: 10.1016/j.tig.2015.05.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 05/08/2015] [Accepted: 05/12/2015] [Indexed: 12/17/2022]
Abstract
Modeling dynamic gene regulatory networks (GRNs) is a new frontier in systems biology. It has special implications for plants, whose survival requires rapid deployment of GRNs in response to environmental changes. However, capturing and dissecting transient interactions of transcription factors (TFs) and their targets in GRNs remains a considerable experimental challenge. Here we review recent progress in understanding GRNs as a function of time and discuss the relevance of these findings in plants to studies in other eukaryotes. We cover progress in profiling and modeling time-course transcriptome changes across plant species and the insights they have provided into the regulatory mechanisms underlying these temporal transcriptional responses, with a focus on the dynamic behavior of TFs. Lastly, we review state-of-the-art techniques to monitor the single-molecule dynamics of TFs in vivo. Together, these advances have helped develop new models for dynamic transcriptional control with relevance across eukaryotes.
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Affiliation(s)
- Ying Li
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Kranthi Varala
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA.
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Barah P, Bones AM. Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology. JOURNAL OF EXPERIMENTAL BOTANY 2015; 66:479-93. [PMID: 25538257 DOI: 10.1093/jxb/eru489] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The biggest challenge for modern biology is to integrate multidisciplinary approaches towards understanding the organizational and functional complexity of biological systems at different hierarchies, starting from the subcellular molecular mechanisms (microscopic) to the functional interactions of ecological communities (macroscopic). The plant-insect interaction is a good model for this purpose with the availability of an enormous amount of information at the molecular and the ecosystem levels. Changing global climatic conditions are abruptly resetting plant-insect interactions. Integration of discretely located heterogeneous information from the ecosystem to genes and pathways will be an advantage to understand the complexity of plant-insect interactions. This review will present the recent developments in omics-based high-throughput experimental approaches, with particular emphasis on studying plant defence responses against insect attack. The review highlights the importance of using integrative systems approaches to study plant-insect interactions from the macroscopic to the microscopic level. We analyse the current efforts in generating, integrating and modelling multiomics data to understand plant-insect interaction at a systems level. As a future prospect, we highlight the growing interest in utilizing the synthetic biology platform for engineering insect-resistant plants.
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Affiliation(s)
- Pankaj Barah
- Cell Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and Technology (NTNU), N 7491 Trondheim, Norway
| | - Atle M Bones
- Cell Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and Technology (NTNU), N 7491 Trondheim, Norway
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50
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Sulpice R, McKeown PC. Moving toward a comprehensive map of central plant metabolism. ANNUAL REVIEW OF PLANT BIOLOGY 2015; 66:187-210. [PMID: 25621519 DOI: 10.1146/annurev-arplant-043014-114720] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.
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