Annu Rev Plant Biol , IF:26.379 , 2021 Jun , V72 : P105-131 doi: 10.1146/annurev-arplant-081320-090914
Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.
Centro de Genomica y Bioinformatica, Facultad de Ciencias, Universidad Mayor, Santiago, Chile.; ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile.; Global Change and Photosynthesis Research Unit, US Department of Agriculture Agricultural Research Service, Urbana, Illinois 61801, USA.; Salk Institute for Biological Studies, La Jolla, California 92037, USA.; Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA; email: gc2@nyu.edu.
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.
PMID: 33667112
Curr Opin Plant Biol , IF:7.834 , 2021 Jun , V62 : P102057 doi: 10.1016/j.pbi.2021.102057
Network biology to uncover functional and structural properties of the plant immune system.
Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA.; Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA. Electronic address: smukhtar@uab.edu.
In the last two decades, advances in network science have facilitated the discovery of important systems' entities in diverse biological networks. This graph-based technique has revealed numerous emergent properties of a system that enable us to understand several complex biological processes including plant immune systems. With the accumulation of multiomics data sets, the comprehensive understanding of plant-pathogen interactions can be achieved through the analyses and efficacious integration of multidimensional qualitative and quantitative relationships among the components of hosts and their microbes. This review highlights comparative network topology analyses in plant-pathogen co-expression networks and interactomes, outlines dynamic network modeling for cell-specific immune regulatory networks, and discusses the new frontiers of single-cell sequencing as well as multiomics data integration that are necessary for unraveling the intricacies of plant immune systems.
PMID: 34102601
Plant Cell Environ , IF:7.228 , 2021 Jul , V44 (7) : P2006-2017 doi: 10.1111/pce.14012
Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience.
Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Global warming poses major challenges for plant survival and agricultural productivity. Thus, efforts to enhance stress resilience in plants are key strategies for protecting food security. Gene regulatory networks (GRNs) are a critical mechanism conferring stress resilience. Until recently, predicting GRNs of the individual cells that make up plants and other multicellular organisms was impeded by aggregate population scale measurements of transcriptome and other genome-scale features. With the advancement of high-throughput single cell RNA-seq and other single cell assays, learning GRNs for individual cells is now possible, in principle. In this article, we report on recent advances in experimental and analytical methodologies for single cell sequencing assays especially as they have been applied to the study of plants. We highlight recent advances and ongoing challenges for scGRN prediction, and finally, we highlight the opportunity to use scGRN discovery for studying and ultimately enhancing abiotic stress resilience in plants.
PMID: 33522607
Plant Sci , IF:4.729 , 2021 Jul , V308 : P110926 doi: 10.1016/j.plantsci.2021.110926
Transcriptome, degradome and physiological analysis provide new insights into the mechanism of inhibition of litchi fruit senescence by melatonin.
College of Food Science and Engineering, Hainan University, Haikou, 570228, China.; Horticultural Sciences Department, PO Box 110690, IFAS, University of Florida, Gainesville, FL, 32611-0690, USA.; Key Laboratory of Plant Resource Conservation and Sustainable Utilization, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China; Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou, 510650, China.; Key Laboratory of Plant Resource Conservation and Sustainable Utilization, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China.; Key Laboratory of Plant Resource Conservation and Sustainable Utilization, Key Laboratory of Post-Harvest Handling of Fruits, Ministry of Agriculture, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China; Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou, 510650, China. Electronic address: taotaoli@scbg.ac.cn.
Litchi fruit has high commercial value on the international market, but senesces rapidly after harvest. We used weighted gene co-expression network analysis (WGCNA) and degradome technology to investigate the molecular mechanisms of melatonin-mediated delay of litchi fruit senescence through application of exogenous melatonin and p-chlorophenylalanine (p-CPA, an inhibitor of melatonin biosynthesis) treatments. Results demonstrated that exogenous melatonin treatment delayed litchi fruit senescence while p-CPA accelerated senescence. Coupled analyses of transcriptome and physiological parameters of litchi fruit provided the correlation of network modules with dynamic changes in browning index during storage. Additionally, we found that microRNAs (miR858 and miR160a) and their targets were actively involved in melatonin-mediated delay of litchi fruit senescence. Melatonin treatment decreased abscisic acid (ABA) content but increased PP2C and F-box expression levels, suggesting the involvement of ABA signaling in melatonin-mediated antisenescence. The transcriptions of ZAT, NAC and DREB1 were activated by melatonin treatment. Moreover, the major functional genes involved in histone methylation, gamma-aminobutyric acid (GABA) metabolism, energy production, reactive oxygen species (ROS) accumulation and cell death were identified in the melatonin-inhibited litchi pericarp browning. Taken together, we first constructed the global map of the important regulators and pathways to delay litchi senescence and pericarp browning mediated by melatonin.
PMID: 34034874
Ecol Appl , IF:4.657 , 2021 Jun : Pe2387 doi: 10.1002/eap.2387
Modeling an aspirational connected network of protected areas across North America.
The Wilderness Society, Bozeman, 59701.
Connecting protected areas remains an important global conservation strategy in the face of ongoing and future threats to biodiversity. Amid our growing understanding of how species' distributions will respond to climate change, conservation scientists need to plan for connectivity conservation across entire continents. We modeled multi-scale connectivity priorities based on the least human-modified lands between large protected areas of North America using least-cost and circuit theory approaches. We first identified priority corridors between large protected areas, then characterized the network's structure to unveil priority linkages most important for maintaining network and regional-level connectivity. Agreement between least-cost corridors and current flow varied throughout North America, reflecting permeable landscape conditions and 'pinch points' where potential ecological flows may concentrate between protected areas. Priority network-level linkages derived from each approach were similar throughout the continental network (e.g., Rocky Mountains and Canadian boreal), but critical linkages that bridged regional protected area networks varied. We emphasize the importance of planning for connectivity at continental scales and demonstrate the utility of multiple methods when mapping connectivity priorities across large spatial extents with wide gradients in landscape conditions.
PMID: 34137106
Plant Physiol Biochem , IF:4.27 , 2021 Jun , V166 : P361-375 doi: 10.1016/j.plaphy.2021.05.050
Identification and functional characterization of Gh_D01G0514 (GhNAC072) transcription factor in response to drought stress tolerance in cotton.
State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Science (ICR, CAAS), Anyang, Henan, 455000, China; Ethiopian Institute of Agricultural Research, Mekhoni Agricultural Research Center, P.O Box 47, Mekhoni, Tigray, Ethiopia.; State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Science (ICR, CAAS), Anyang, Henan, 455000, China.; State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Science (ICR, CAAS), Anyang, Henan, 455000, China; School of Biological and Physical Sciences (SBPS), Main Campus, Jaramogi Oginga Odinga University of Science and Technology (JOOUST), Main Campus, P.O. Box 210-40601, Bondo, Kenya.; State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Science (ICR, CAAS), Anyang, Henan, 455000, China. Electronic address: caixy@cricaas.com.cn.; State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Science (ICR, CAAS), Anyang, Henan, 455000, China. Electronic address: zhouzl@cricaas.com.cn.; State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Science (ICR, CAAS), Anyang, Henan, 455000, China; School of Agricultural Sciences, Zhengzhou University, Zhengzhou, Henan, 450001, PR China. Electronic address: liufcri@163.com.
Cotton encounters long-term drought stress problems resulting in major yield losses. Transcription factors (TFs) plays an important role in response to biotic and abiotic stresses. The coexpression patterns of gene networks associated with drought stress tolerance were investigated using transcriptome profiles. Applying a weighted gene coexpression network analysis, we discovered a salmon module with 144 genes strongly linked to drought stress tolerance. Based on coexpression and RT-qPCR analysis GH_D01G0514 was selected as the candidate gene, as it was also identified as a hub gene in both roots and leaves with a consistent expression in response to drought stress in both tissues. For validation of GH_D01G0514, Virus Induced Gene Silencing was performed and VIGS plants showed significantly higher excised leaf water loss and ion leakage, while lower relative water and chlorophyll contents as compared to WT (Wild type) and positive control plants. Furthermore, the WT and positive control seedlings showed higher CAT and SOD activities, and lower activities of hydrogen peroxide and MDA enzymes as compared to the VIGS plants. Gh_D01G0514 (GhNAC072) was localized in the nucleus and cytoplasm. Y2H assay demonstrates that Gh_D01G0514 has a potential of auto activation. It was observed that the Gh_D01G0514 was highly upregulated in both tissues based on RNA Seq and RT-qPCR analysis. Thus, we inferred that, this candidate gene might be responsible for drought stress tolerance in cotton. This finding adds significantly to the existing knowledge of drought stress tolerance in cotton and deep molecular analysis are required to understand the molecular mechanisms underlying drought stress tolerance in cotton.
PMID: 34153881
BMC Genomics , IF:3.969 , 2021 Jun , V22 (1) : P458 doi: 10.1186/s12864-021-07793-x
Online database for brain cancer-implicated genes: exploring the subtype-specific mechanisms of brain cancer.
School of Science, Technology and Engineering, University of the Sunshine Coast, Maroochydore DC, Sippy Downs, Queensland, 4558, Australia.; The School of Public Health, Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou, China.; School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China.; School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China. qudc@bit.edu.cn.; Information Center, China Association for Science and Technology, Beijing, 100863, China. qudc@bit.edu.cn.; Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, 100871, P.R. China. quh@mail.cbi.pku.edu.cn.
BACKGROUND: Brain cancer is one of the eight most common cancers occurring in people aged 40+ and is the fifth-leading cause of cancer-related deaths for males aged 40-59. Accurate subtype identification is crucial for precise therapeutic treatment, which largely depends on understanding the biological pathways and regulatory mechanisms associated with different brain cancer subtypes. Unfortunately, the subtype-implicated genes that have been identified are scattered in thousands of published studies. So, systematic literature curation and cross-validation could provide a solid base for comparative genetic studies about major subtypes. RESULTS: Here, we constructed a literature-based brain cancer gene database (BCGene). In the current release, we have a collection of 1421 unique human genes gathered through an extensive manual examination of over 6000 PubMed abstracts. We comprehensively annotated those curated genes to facilitate biological pathway identification, cancer genomic comparison, and differential expression analysis in various anatomical brain regions. By curating cancer subtypes from the literature, our database provides a basis for exploring the common and unique genetic mechanisms among 40 brain cancer subtypes. By further prioritizing the relative importance of those curated genes in the development of brain cancer, we identified 33 top-ranked genes with evidence mentioned only once in the literature, which were significantly associated with survival rates in a combined dataset of 2997 brain cancer cases. CONCLUSION: BCGene provides a useful tool for exploring the genetic mechanisms of and gene priorities in brain cancer. BCGene is freely available to academic users at http://soft.bioinfo-minzhao.org/bcgene/ .
PMID: 34144671
BMC Genomics , IF:3.969 , 2021 Jun , V22 (1) : P468 doi: 10.1186/s12864-021-07797-7
Transcriptome profiling of developing leaf and shoot apices to reveal the molecular mechanism and co-expression genes responsible for the wheat heading date.
The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, 100081, Beijing, China.; Hebei sub-center of National Maize Improvement Center of China, Key Laboratory of Crop Germplasm Resources of Northern China (Ministry of Education), College of Agronomy, Hebei Agricultural University, 071001, Baoding, China.; The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, 100081, Beijing, China. kongxiuying@caas.cn.
BACKGROUND: Wheat is one of the most widely planted crops worldwide. The heading date is important for wheat environmental adaptability, as it not only controls flowering time but also determines the yield component in terms of grain number per spike. RESULTS: In this research, homozygous genotypes with early and late heading dates derived from backcrossed progeny were selected to conduct RNA-Seq analysis at the double ridge stage (W2.0) and androgynous primordium differentiation stage (W3.5) of the leaf and apical meristem, respectively. In total, 18,352 differentially expressed genes (DEGs) were identified, many of which are strongly associated with wheat heading date genes. Gene Ontology (GO) enrichment analysis revealed that carbohydrate metabolism, trehalose metabolic process, photosynthesis, and light reaction are closely related to the flowering time regulation pathway. Based on MapMan metabolic analysis, the DEGs are mainly involved in the light reaction, hormone signaling, lipid metabolism, secondary metabolism, and nucleotide synthesis. In addition, 1,225 DEGs were annotated to 45 transcription factor gene families, including LFY, SBP, and MADS-box transcription factors closely related to flowering time. Weighted gene co-expression network analysis (WGCNA) showed that 16, 336, 446, and 124 DEGs have biological connections with Vrn1-5 A, Vrn3-7B, Ppd-1D, and WSOC1, respectively. Furthermore, TraesCS2D02G181400 encodes a MADS-MIKC transcription factor and is co-expressed with Vrn1-5 A, which indicates that this gene may be related to flowering time. CONCLUSIONS: RNA-Seq analysis provided transcriptome data for the wheat heading date at key flower development stages of double ridge (W2.0) and androgynous primordium differentiation (W3.5). Based on the DEGs identified, co-expression networks of key flowering time genes in Vrn1-5 A, Vrn3-7B, WSOC1, and Ppd-1D were established. Moreover, we discovered a potential candidate flowering time gene, TraesCS2D02G181400. Taken together, these results serve as a foundation for further study on the regulatory mechanism of the wheat heading date.
PMID: 34162321
BMC Genomics , IF:3.969 , 2021 Jun , V22 (1) : P465 doi: 10.1186/s12864-021-07778-w
Genome-wide expression and network analyses of mutants in key brassinosteroid signaling genes.
Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.; Department of Information Technology, IDLab, imec, Ghent University, Ghent, Belgium.; Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.; Ministry of Education Key Laboratory of Cell Activities and Stress Adaptations, School of Life Sciences, Lanzhou University, Lanzhou, 730000, China.; Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium. kathleen.marchal@ugent.be.; Department of Information Technology, IDLab, imec, Ghent University, Ghent, Belgium. kathleen.marchal@ugent.be.; Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa. kathleen.marchal@ugent.be.; Ministry of Education Key Laboratory of Cell Activities and Stress Adaptations, School of Life Sciences, Lanzhou University, Lanzhou, 730000, China. lijia@lzu.edu.cn.
BACKGROUND: Brassinosteroid (BR) signaling regulates plant growth and development in concert with other signaling pathways. Although many genes have been identified that play a role in BR signaling, the biological and functional consequences of disrupting those key BR genes still require detailed investigation. RESULTS: Here we performed phenotypic and transcriptomic comparisons of A. thaliana lines carrying a loss-of-function mutation in BRI1 gene, bri1-5, that exhibits a dwarf phenotype and its three activation-tag suppressor lines that were able to partially revert the bri1-5 mutant phenotype to a WS2 phenotype, namely bri1-5/bri1-1D, bri1-5/brs1-1D, and bri1-5/bak1-1D. From the three investigated bri1-5 suppressors, bri1-5/bak1-1D was the most effective suppressor at the transcriptional level. All three bri1-5 suppressors showed altered expression of the genes in the abscisic acid (ABA signaling) pathway, indicating that ABA likely contributes to the partial recovery of the wild-type phenotype in these bri1-5 suppressors. Network analysis revealed crosstalk between BR and other phytohormone signaling pathways, suggesting that interference with one hormone signaling pathway affects other hormone signaling pathways. In addition, differential expression analysis suggested the existence of a strong negative feedback from BR signaling on BR biosynthesis and also predicted that BRS1, rather than being directly involved in signaling, might be responsible for providing an optimal environment for the interaction between BRI1 and its ligand. CONCLUSIONS: Our study provides insights into the molecular mechanisms and functions of key brassinosteroid (BR) signaling genes, especially BRS1.
PMID: 34157989
Clin Neurophysiol , IF:3.708 , 2021 Jul , V132 (7) : P1663-1676 doi: 10.1016/j.clinph.2021.04.008
Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy.
Department of Neurology, Oslo University Hospital, Oslo, Norway. Electronic address: chr.hh@pm.me.; Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcon, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcon, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain. Electronic address: ricardo.bruna@ctb.upm.es.; Department of Neurology, Drammen Hospital, Vestre Viken Health Care Trust, Drammen, Norway. Electronic address: marsyv@vestreviken.no.; Department of Neurology, Oslo University Hospital, Oslo, Norway; Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway. Electronic address: akseri@ous-hf.no.; Department of Neurology, Oslo University Hospital, Oslo, Norway. Electronic address: vebjoran@uio.no.; Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy. Electronic address: fabrizio.vecchio@sanraffaele.it.; Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy. Electronic address: fra.miraglia@gmail.com.; Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy. Electronic address: paolomaria.rossini@sanraffaele.it.; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki and Aalto University School of Science, Helsinki, Finland. Electronic address: hanna.renvall@aalto.fi.; Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. Electronic address: erik.tauboll@medisin.uio.no.; Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcon, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcon, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain. Electronic address: fernando.maestu@ctb.upm.es.; Department of Neurology, Oslo University Hospital, Oslo, Norway. Electronic address: i.h.haraldsen@medisin.uio.no.
OBJECTIVE: The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life. METHODS: We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands. RESULTS: We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation. CONCLUSIONS: Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy. SIGNIFICANCE: Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.
PMID: 34044189