New Phytol , IF:8.512 , 2020 Sep doi: 10.1111/nph.16923
Automated and accurate segmentation of leaf venation networks via deep learning.
Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Oxford, OX3 7DQ, UK.; Environmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.; Department of Environmental Science, Policy, and Management, University of California, 120 Mulford Hall, Berkeley, California, 94720, USA.; Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.
Leaf vein network geometry can predict levels of resource transport, defence, and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi-scale statistics from subsequent network graph representations. Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually-defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of 6 independently trained CNNs were used to segment networks from larger leaf regions (~100 mm(2)). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% +/- 6%, outperforming other current network extraction methods, and accurately described the widths, angles, and connectivity of veins. Multi-scale statistics then enabled identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multi-scale quantification of leaf vein networks, facilitating comparison across species and exploration of the functional significance of different leaf vein architectures.
PMID: 32964424
Genomics , IF:6.205 , 2020 Sep doi: 10.1016/j.ygeno.2020.08.028
Temperature differentially modulates the transcriptome response in Oryza sativa to Xanthomonas oryzae pv. oryzae infection.
Dept. of Molecular Biology and Biotechnology, Tezpur University, Napaam, Sonitpur, Assam, -784028, INDIA.; Dept. of Molecular Biology and Biotechnology, Tezpur University, Napaam, Sonitpur, Assam, -784028, INDIA. Electronic address: barah@tezu.ernet.in.
Bacterial blight is caused by the pathogen Xanthomonas oryzae pv. oryzae (Xoo). Genome scale integrative analysis on the interaction of high and low temperatures on the molecular response signature in rice during the Xoo infection has not been conducted yet. We analysed a unique RNA-Seq dataset generated on the susceptible rice variety IR24 under combined exposure of Xoo with low 29/21 degrees C (day/night) and high 35/31 degrees C (day/night) temperatures. Differentially regulated key genes and pathways in rice plants during both the stress conditions were identified. Differential dynamics of the regulatory network topology showed that WRKY and ERF families of transcription factors play a crucial role during signal crosstalk events in rice plants while responding to combined exposure of Xoo with low temperature vs. Xoo with high temperatures. Our study suggests that upon onset of high temperature, rice plants tend to switch its focus from defence response towards growth and reproduction.
PMID: 32896629
Mol Plant Pathol , IF:4.326 , 2020 Sep doi: 10.1111/mpp.12994
Impact of a resistance gene against a fungal pathogen on the plant host residue microbiome: The case of the Leptosphaeria maculans-Brassica napus pathosystem.
Universite Paris-Saclay, INRAE, UMR BIOGER, Thiverval-Grignon, France.; INRAE, Universite d'Angers, UMR IRHS, Beaucouze, France.
Oilseed rape residues are a crucial determinant of stem canker epidemiology as they support the sexual reproduction of the fungal pathogen Leptosphaeria maculans. The aim of this study was to characterize the impact of a resistance gene against L. maculans infection on residue microbial communities and to identify microorganisms interacting with this pathogen during residue degradation. We used near-isogenic lines to obtain healthy and infected host plants. The microbiome associated with the two types of plant residues was characterized by metabarcoding. A combination of linear discriminant analysis and ecological network analysis was used to compare the microbial communities and to identify microorganisms interacting with L. maculans. Fungal community structure differed between the two lines at harvest, but not subsequently, suggesting that the presence/absence of the resistance gene influences the microbiome at the base of the stem whilst the plant is alive, but that this does not necessarily lead to differential colonization of the residues by fungi. Direct interactions with other members of the community involved many fungal and bacterial amplicon sequence variants (ASVs). L. maculans appeared to play a minor role in networks, whereas one ASV affiliated to Plenodomus biglobosus (synonym Leptosphaeria biglobosa) from the Leptosphaeria species complex may be considered a keystone taxon in the networks at harvest. This approach could be used to identify and promote microorganisms with beneficial effects against residue-borne pathogens and, more broadly, to decipher the complex interactions between multispecies pathosystems and other microbial components in crop residues.
PMID: 32975002
Ann Bot , IF:4.005 , 2020 Sep , V126 (4) : P501-509 doi: 10.1093/aob/mcaa143
Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology.
INRAE UR4 URP3F, BP6, Lusignan, France.; Anhui Agricultural University, School of Agronomy, Hefei, Anhui Province, PR China.
BACKGROUND: Functional-structural plant models (FSPMs) explore and integrate relationships between a plant's structure and processes that underlie its growth and development. In the last 20 years, scientists interested in functional-structural plant modelling have expanded greatly the range of topics covered and now handle dynamical models of growth and development occurring from the microscopic scale, and involving cell division in plant meristems, to the macroscopic scales of whole plants and plant communities. SCOPE: The FSPM approach occupies a central position in plant science; it is at the crossroads of fundamental questions in systems biology and predictive ecology. This special issue of Annals of Botany features selected papers on critical areas covered by FSPMs and examples of comprehensive models that are used to solve theoretical and applied questions, ranging from developmental biology to plant phenotyping and management of plants for agronomic purposes. Altogether, they offer an opportunity to assess the progress, gaps and bottlenecks along the research path originally foreseen for FSPMs two decades ago. This review also allows discussion of current challenges of FSPMs regarding (1) integration of multidisciplinary knowledge, (2) methods for handling complex models, (3) standards to achieve interoperability and greater genericity and (4) understanding of plant functioning across scales. CONCLUSIONS: This approach has demonstrated considerable progress, but has yet to reach its full potential in terms of integration and heuristic knowledge production. The research agenda of functional-structural plant modellers in the coming years should place a greater emphasis on explaining robust emergent patterns, and on the causes of possible deviation from it. Modelling such patterns could indeed fuel both generic integration across scales and transdisciplinary transfer. In particular, it could be beneficial to emergent fields of research such as model-assisted phenotyping and predictive ecology in managed ecosystems.
PMID: 32725187
FEMS Microbiol Ecol , IF:3.675 , 2020 Oct , V96 (10) doi: 10.1093/femsec/fiaa165
Early ecological succession patterns of bacterial, fungal and plant communities along a chronosequence in a recently deglaciated area of the Italian Alps.
Department of Earth and Environmental Sciences (DISAT) - University of Milano-Bicocca, Milano, Italy.; Department of Environmental Science and Policy, University of Milano, Milano, Italy.; Department of Earth Science "Ardito Desio", University of Milano, Milano, Italy.; Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy.
In this study, the early ecological succession patterns of Forni Glacier (Ortles-Cevedale group, Italian Alps) forefield along an 18-year long chronosequence (with a temporal resolution of 1 year) has been reported. Bacterial and fungal community structures were inferred by high-throughput sequencing of 16S rRNA gene and ITS, respectively. In addition, the occurrence of both herbaceous and arboreous plants was also recorded at each plot. A significant decrease of alpha-diversity in more recently deglaciated areas was observed for both bacteria and plants. Time since deglaciation and pH affected the structure of both fungal and bacterial communities. Pioneer plants could be a major source of colonization for both bacterial and fungal communities. Consistently, some of the most abundant bacterial taxa and some of those significantly varying with pH along the chronosequence (Polaromonas, Granulicella, Thiobacillus, Acidiferrobacter) are known to be actively involved in rock-weathering processes due to their chemolithotrophic metabolism, thus suggesting that the early phase of the chronosequence could be mainly shaped by the biologically controlled bioavailability of metals and inorganic compounds. Fungal communities were dominated by ascomycetous filamentous fungi and basidiomycetous yeasts. Their role as cold-adapted organic matter decomposers, due to their heterotrophic metabolism, was suggested.
PMID: 32815995
BMC Genomics , IF:3.594 , 2020 Sep , V21 (1) : P609 doi: 10.1186/s12864-020-07044-5
Genome-wide analysis of lncRNA and mRNA expression and endogenous hormone regulation during tension wood formation in Catalpa bungei.
State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, PR China.; Luoyang Academy of Agriculture and Forestry Science, Luoyang, 471002, Henan Province, China.; State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, PR China. mwjlx.163@163.com.
BACKGROUND: Phytohormones are the key factors regulating vascular development in plants, and they are also involved in tension wood (TW) formation. Although the theory of hormone distribution in TW formation is widely supported, the effects of endogenous hormones on TW formation have not yet been assessed. In this study, TW formation was induced in Catalpa bungei by artificial bending. The phytohormone content of TW, opposite wood (OW) and normal wood (NW) was determined using liquid chromatography-mass spectrometry (LC-MS), and transcriptome sequencing was performed. The hormone content and related gene expression data were comprehensively analyzed. RESULTS: The results of analyses of the plant hormone contents indicated significantly higher levels of cis-zeatin (cZ), indoleacetic acid (IAA) and abscisic acid (ABA) in TW than in OW. Genes involved in the IAA and ABA synthesis pathways, such as ALDH (evm. MODEL: group5.1511) and UGT (evm. MODEL: scaffold36.20), were significantly upregulated in TW. and the expression levels of ARF (evm. MODEL: group5.1332), A-ARR (evm. MODEL: group0.1600), and TCH4 (evm. MODEL: group2.745), which participate in IAA, cZ and Brassinolide (BR) signal transduction, were significantly increased in TW. In particular, ARF expression may be regulated by long noncoding RNAs (lncRNAs) and the HD-ZIP transcription factor ATHB-15. CONCLUSIONS: We constructed a multiple hormone-mediated network of C. bungei TW formation based on hormone levels and transcriptional expression profiles were identified during TW formation.
PMID: 32891118
Biosystems , IF:1.808 , 2020 Oct , V196 : P104175 doi: 10.1016/j.biosystems.2020.104175
A computational approach to validate novel drug targets of gentianine from Swertiya chirayita in Plasmodium falciparum.
School of Biotechnology, KIIT Deemed to Be University, Bhubaneswar, 751024, Odisha, India. Electronic address: rmahapatra@kiitbiotech.ac.in.; School of Biotechnology, KIIT Deemed to Be University, Bhubaneswar, 751024, Odisha, India.
Gentianine is one of the compounds found in the plant Swertiya chirayita that is known for its antimalarial activity. However, its exact molecular mechanism of action is yet to be understood. In our present study, we applied several computational approaches to filter out and determine possible targets of gentianine in Plasmodium falciparum 3D7. Protein-protein networks formed the basis of one of our strategies along with orthologous protein analysis to establish essentiality. Out of 6 essential proteins from unique pathways, haloacid dehalogenase like-hydrolase (PfHAD1), phosphoenolpyruvate carboxykinase (PfPEPCK) and fumarate hydratase (PfFH) were screened as drug targets through this approach. Through our other strategy we established the predicted IC50 (PIC50) value of gentianine with a set of molecular descriptors from 123 Pathogen Box anti-malarial compounds. Afterwards through 2D structural similarity, L-lactate dehydrogenase (PfLDH) was established as another possible target. In our work, we performed in silico docking and analysed the binding of gentianine to the proteins. All of the proteins were reported with favourable binding results and were considered for complex molecular dynamics simulation approach. Our research clears up the molecular mechanism of antimalarial activity of gentianine to some extent paving way for experimental validation of the same in future.
PMID: 32593550