Trends Plant Sci , IF:14.416 , 2019 Dec , V24 (12) : P1075-1082 doi: 10.1016/j.tplants.2019.07.004
Challenges of Translating Gene Regulatory Information into Agronomic Improvements.
Department of Plant and Microbial Biology, University of Minnesota, St Paul, MN 55108, USA. Electronic address: springer@umn.edu.; Department of Agronomy, University of Wisconsin, Madison, WI 56706, USA.; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA. Electronic address: grotewol@msu.edu.
Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.
PMID: 31377174
Elife , IF:7.08 , 2019 Dec , V8 doi: 10.7554/eLife.49305
Evolution of C4 photosynthesis predicted by constraint-based modelling.
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Tubingen, Germany.; Computational Biology, Faculty of Biology, Bielefeld University, Universitatsstrasse, Bielefeld, Germany.
Constraint-based modelling (CBM) is a powerful tool for the analysis of evolutionary trajectories. Evolution, especially evolution in the distant past, is not easily accessible to laboratory experimentation. Modelling can provide a window into evolutionary processes by allowing the examination of selective pressures which lead to particular optimal solutions in the model. To study the evolution of C4 photosynthesis from a ground state of C3 photosynthesis, we initially construct a C3 model. After duplication into two cells to reflect typical C4 leaf architecture, we allow the model to predict the optimal metabolic solution under various conditions. The model thus identifies resource limitation in conjunction with high photorespiratory flux as a selective pressure relevant to the evolution of C4. It also predicts that light availability and distribution play a role in guiding the evolutionary choice of possible decarboxylation enzymes. The data shows evolutionary CBM in eukaryotes predicts molecular evolution with precision.
PMID: 31799932
Plant J , IF:6.141 , 2019 Dec , V100 (5) : P954-968 doi: 10.1111/tpj.14487
Inference of the gene regulatory network acting downstream of CROWN ROOTLESS 1 in rice reveals a regulatory cascade linking genes involved in auxin signaling, crown root initiation, and root meristem specification and maintenance.
UMR DIADE, Universite de Montpellier, IRD, 911 Avenue Agropolis, 34394, Montpellier Cedex 5, France.; Centre de Recherches de Chappes, Biogemma, Route d'Ennezat, 63720, Chappes, France.
Crown roots (CRs) are essential components of the rice root system. Several genes involved in CR initiation or development have been identified but our knowledge about how they organize to form a gene regulatory network (GRN) is still limited. To characterize the regulatory cascades acting during CR formation, we used a systems biology approach to infer the GRN controlling CR formation downstream of CROWN ROOTLESS 1 (CRL1), coding for an ASL (asymmetric leaves-2-like)/LBD (LOB domain) transcription factor necessary for CR initiation. A time-series transcriptomic dataset was generated after synchronized induction of CR formation by dexamethasone-mediated expression of CRL1 expression in a crl1 mutant background. This time series revealed three different genome expression phases during the early steps of CR formation and was further exploited to infer a GRN using a dedicated algorithm. The predicted GRN was confronted with experimental data and 72% of the inferred links were validated. Interestingly, this network revealed a regulatory cascade linking CRL1 to other genes involved in CR initiation, root meristem specification and maintenance, such as QUIESCENT-CENTER-SPECIFIC HOMEOBOX, and in auxin signalling. This predicted regulatory cascade was validated in vivo using transient activation assays. Thus, the CRL1-dependant GRN reflects major gene regulation events at play during CR formation and constitutes a valuable source of discovery to better understand this developmental process.
PMID: 31369175
Int J Mol Sci , IF:4.556 , 2019 Dec , V20 (24) doi: 10.3390/ijms20246166
A Regulatory Network for miR156-SPL Module in Arabidopsis thaliana.
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
Vegetative phase changes in plants describes the transition between juvenile and adult phases of vegetative growth before flowering. It is one of the most fundamental mechanisms for plants to sense developmental signals, presenting a complex process involving many still-unknown determinants. Several studies in annual and perennial plants have identified the conservative roles of miR156 and its targets, SBP/SPL genes, in guiding the switch of plant growth from juvenile to adult phases. Here, we review recent progress in understanding the regulation of miR156 expression and how miR156-SPLs mediated plant age affect other processes in Arabidopsis. Powerful high-throughput sequencing techniques have provided rich data to systematically study the regulatory mechanisms of miR156 regulation network. From this data, we draw an expanded miR156-regulated network that links plant developmental transition and other fundamental biological processes, gaining novel and broad insight into the molecular mechanisms of plant-age-related processes in Arabidopsis.
PMID: 31817723
BMC Genomics , IF:3.594 , 2019 Dec , V20 (1) : P925 doi: 10.1186/s12864-019-6161-8
Weighted gene co-expression network analysis unveils gene networks associated with the Fusarium head blight resistance in tetraploid wheat.
Aquatic and Crop Resource Development Centre, National Research Council Canada, Saskatoon, SK, Canada. ehsan.sari@usask.ca.; Aquatic and Crop Resource Development Centre, National Research Council Canada, Saskatoon, SK, Canada.; Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, SK, Canada.
BACKGROUND: Fusarium head blight (FHB) resistance in the durum wheat breeding gene pool is rarely reported. Triticum turgidum ssp. carthlicum line Blackbird is a tetraploid relative of durum wheat that offers partial FHB resistance. Resistance QTL were identified for the durum wheat cv. Strongfield x Blackbird population on chromosomes 1A, 2A, 2B, 3A, 6A, 6B and 7B in a previous study. The objective of this study was to identify the defense mechanisms underlying the resistance of Blackbird and report candidate regulator defense genes and single nucleotide polymorphism (SNP) markers within these genes for high-resolution mapping of resistance QTL reported for the durum wheat cv. Strongfield/Blackbird population. RESULTS: Gene network analysis identified five networks significantly (P < 0.05) associated with the resistance to FHB spread (Type II FHB resistance) one of which showed significant correlation with both plant height and relative maturity traits. Two gene networks showed subtle differences between Fusarium graminearum-inoculated and mock-inoculated plants, supporting their involvement in constitutive defense. The candidate regulator genes have been implicated in various layers of plant defense including pathogen recognition (mainly Nucleotide-binding Leucine-rich Repeat proteins), signaling pathways including the abscisic acid and mitogen activated protein (MAP) kinase, and downstream defense genes activation including transcription factors (mostly with dual roles in defense and development), and cell death regulator and cell wall reinforcement genes. The expression of five candidate genes measured by quantitative real-time PCR was correlated with that of RNA-seq, corroborating the technical and analytical accuracy of RNA-sequencing. CONCLUSIONS: Gene network analysis allowed identification of candidate regulator genes and genes associated with constitutive resistance, those that will not be detected using traditional differential expression analysis. This study also shed light on the association of developmental traits with FHB resistance and partially explained the co-localization of FHB resistance with plant height and maturity QTL reported in several previous studies. It also allowed the identification of candidate hub genes within the interval of three previously reported FHB resistance QTL for the Strongfield/Blackbird population and associated SNPs for future high resolution mapping studies.
PMID: 31795948
Plant Sci , IF:3.591 , 2019 Dec , V289 : P110280 doi: 10.1016/j.plantsci.2019.110280
Negative feedback loop between BpAP1 and BpPI/BpDEF heterodimer in Betula platyphylla x B. pendula.
State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: wangshuo19910909@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: haijiao_sea@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: han15046684579@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: 15245010786@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: 18745740869@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: liuguifeng@nefu.edu.cn.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: chensunefu@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China. Electronic address: jiangjing@nefu.edu.cn.
MADS-box genes encode transcription factors involved in the control of many important developmental processes, especially the flower development of angiosperms. Analysis on gene regulatory relationship between MADS-box genes is useful for understanding the molecular mechanism of flower development. In this study, we focused on the regulatory relationship between MADS-box transcription factors APETALA1 (AP1) and PISTILLATA(PI)/DEFICIENS (DEF) in birch. We found that BpPI was an authentic target gene of BpAP1, and BpAP1 activated the expression of BpPI via directly binding to the CArG box motif. Functional analysis of BpPI showed that overexpression of BpPI may delay flowering via restricting flowering activators, in which BpAP1 was significantly down-regulated. We further investigated the regulatory of BpAP1 by BpPI, and found that BpPI could directly bind to the promoter of BpAP1 to restrict BpAP1 expression. In addition, we also found that BpPI could interact with its hypothetical partner BpDEF to co-regulate BpAP1 in birch. Our results suggested that overexpression of BpPI may delay flowering via restricting flowering activators, and there is a negative feedback loop between BpAP1 and BpPI/BpDEF heterodimer in birch. Our results will bring new evidences for further analysis of the molecular mechanism of flower formation in plants that produced unisexual flowers.
PMID: 31623773
Appl Microbiol Biotechnol , IF:3.53 , 2019 Dec , V103 (23-24) : P9263-9275 doi: 10.1007/s00253-019-10175-9
Fermentation of plant-based milk alternatives for improved flavour and nutritional value.
Institute of Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrucken, Germany.; Institute of Material Sciences, Department of Biology, Nestle Research, Lausanne, Switzerland.; Institute of Systems Biotechnology, Saarland University, Campus A1.5, 66123, Saarbrucken, Germany. christoph.wittmann@uni-saarland.de.
Non-dairy milk alternatives (or milk analogues) are water extracts of plants and have become increasingly popular for human nutrition. Over the years, the global market for these products has become a multi-billion dollar business and will reach a value of approximately 26 billion USD within the next 5 years. Moreover, many consumers demand plant-based milk alternatives for sustainability, health-related, lifestyle and dietary reasons, resulting in an abundance of products based on nuts, seeds or beans. Unfortunately, plant-based milk alternatives are often nutritionally unbalanced, and their flavour profiles limit their acceptance. With the goal of producing more valuable and tasty products, fermentation can help to the improve sensory profiles, nutritional properties, texture and microbial safety of plant-based milk alternatives so that the amendment with additional ingredients, often perceived as artificial, can be avoided. To date, plant-based milk fermentation mainly uses mono-cultures of microbes, such as lactic acid bacteria, bacilli and yeasts, for this purpose. More recently, new concepts have proposed mixed-culture fermentations with two or more microbial species. These approaches promise synergistic effects to enhance the fermentation process and improve the quality of the final products. Here, we review the plant-based milk market, including nutritional, sensory and manufacturing aspects. In addition, we provide an overview of the state-of-the-art fermentation of plant materials using mono- and mixed-cultures. Due to the rapid progress in this field, we can expect well-balanced and naturally fermented plant-based milk alternatives in the coming years.
PMID: 31686143
Oecologia , IF:2.654 , 2019 Dec , V191 (4) : P873-886 doi: 10.1007/s00442-019-04543-5
Landscape context differentially drives diet breadth for two key pollinator species.
W.K. Kellogg Biological Station, Michigan State University, 3700 East Gull Lake Dr, Hickory Corners, MI, 49060, USA. sarah.cusser@gmail.com.; Central Texas Melittological Institute, 7307 Running Rope, Austin, TX, 78731, USA.; Department of Integrative Biology, Section of Integrative Biology, University of Texas at Austin, 205 W 24th Street, 401 Biological Laboratories, Austin, TX, 78712, USA.
An animal's diet contributes to its survival and reproduction. Variation in diet can alter the structure of community-level consumer-resource networks, with implications for ecological function. However, much remains unknown about the underlying drivers of diet breadth. Here we use a network approach to understand how consumer diet changes in response to local and landscape context and how these patterns compare between closely-related consumer species. We conducted field surveys to build 36 quantitative plant-pollinator networks using observation-based and pollen-based records of visitation across the gulf-coast cotton growing region of Texas, US. We focused on two key cotton pollinator species in the region: the social European honey bee, Apis mellifera, and the solitary native long-horned bee, Melissodes tepaneca. We demonstrate that diet breadth is highly context-dependent. Specifically, local factors better explain patterns of diet than regional factors for both species, but A. mellifera and M. tepaneca respond to local factors with contrasting patterns. Despite being collected directly from cotton blooms, both species exhibit significant preferences for non-cotton pollen, indicating a propensity to spend substantial effort foraging on remnant vegetation despite the rarity of these patches in the intensely managed cotton agroecosystem. Overall, our results demonstrate that diet is highly context- and species-dependent and thus an understanding of both factors is key for evaluating the conservation of important cotton pollinators.
PMID: 31676969
J Comput Biol , IF:1.054 , 2019 Dec , V26 (12) : P1349-1366 doi: 10.1089/cmb.2019.0221
A Genetic Algorithm to Optimize Weighted Gene Co-Expression Network Analysis.
Department of Plant Sciences, University of California, Davis, Davis, California.; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel.; Department of Fruit Tree Sciences, ARO, The Volcani Center, Rishon LeZion, Israel.
Weighted gene co-expression network analysis (WGCNA) is a widely used software tool that is used to establish relationships between phenotypic traits and gene expression data. It generates gene modules and then correlates their first principal component to phenotypic traits, proposing a functional relationship expressed by the correlation coefficient. However, gene modules often contain thousands of genes of different functional backgrounds. Here, we developed a stochastic optimization algorithm, known as genetic algorithm (GA), optimizing the trait to gene module relationship by gradually increasing the correlation between the trait and a subset of genes of the gene module. We exemplified the GA on a Japanese plum hormone profile and an RNA-seq dataset. The correlation between the subset of module genes and the trait increased, whereas the number of correlated genes became sufficiently small, allowing for their individual assessment. Gene ontology (GO) term enrichment analysis of the gene sets identified by the GA showed an increase in specificity of the GO terms associated with fruit hormone balance as compared with the GO enrichment analysis of the gene modules generated by WGCNA and other methods.
PMID: 31356119
Dev Sci , 2019 Dec : Pe12934 doi: 10.1111/desc.12934
Network structure reveals clusters of associations between childhood adversities and development outcomes.
Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, NC, USA.; Department of Health Sciences, Northeastern University, Boston, MA, USA.; Department of Psychology, Harvard University, Cambridge, MA, USA.
Exposure to childhood adversity is common and associated with a host of negative developmental outcomes. The most common approach used to examine the consequences of adversity exposure is a cumulative risk model. Recently, we have proposed a novel approach, the dimensional model of adversity and psychopathology (DMAP), where different dimensions of adversity are hypothesized to impact health and well-being through different pathways. We expect deprivation to primarily disrupt cognitive processing, whereas we expect threat to primarily alter emotional reactivity and automatic regulation. Recent hypothesis-driven approaches provide support for these differential associations of deprivation and threat on developmental outcomes. However, it is not clear whether these patterns would emerge using data-driven approaches. Here we use a network analytic approach to identify clusters of related adversity exposures and outcomes in an initial study (Study 1: N = 277 adolescents aged 16-17 years; 55.1% female) and a replication (Study 2: N = 262 children aged 8-16 years; 45.4% female). We statistically compare our observed clusters with our hypothesized DMAP model and a clustering we hypothesize would be the result of a cumulative stress model. In both samples we observed a network structure consistent with the DMAP model and statistically different than the hypothesized cumulative stress model. Future work seeking to identify in the pathways through which adversity impacts development should consider multiple dimensions of adversity.
PMID: 31869484
Heliyon , 2019 Dec , V5 (12) : Pe02968 doi: 10.1016/j.heliyon.2019.e02968
Optimization of protein extraction and proteomic studies in Cenchrus polystachion (L.) Schult.
Amity Institute of Biotechnology, Amity University, Mumbai Bhatan Road, Panvel, 410206, Mumbai, Maharastra, India.
Apomicts have been studied at their genetic levels, but there are no any direct evidence of its mechanism. In order to understand the mechanism involved, a close relative of Pennisetum, Cenchrus polystachion, an apomictic species was explored for more insights into protein expression in reproductive structures. Optimization of protein extraction was studied with the leaf tissue and optimized protocol was extrapolated to other five tissues. The phenol-based protein extraction emerged as the best method for plant leaf tissue providing a better protein yield, separation of bands, removal of non-protein components like polyphenolic compounds and nucleic acids. The proteome analysis of leaf, stigma, immature ovary, seed, anther sac and pollen tissues of Cenchrus polystachion were carried out identifying a total of 135407 proteins against the Poaceae database from UNIPROT/TrEMBL. The target candidate proteins found in all the tissues were identified and mainly comprised of Actin Protein, PIP, Starch Synthase, ATP Synthase, Glutathione S Transferase, Dehydroascorbate reductase, Ascorbate peroxidase and heat shock proteins. Visualization and descriptive statistics conveyed all the necessary information to understand the differential expression of proteins in Cenchrus polystachion. This study forms a base to understand the role of tissue specific expressed proteins in an apomictic plant.
PMID: 31853511