Mol Plant , IF:12.084 , 2019 Jun , V12 (6) : P804-821 doi: 10.1016/j.molp.2019.05.006
Systems Biology of Plant-Microbiome Interactions.
Institute of Network Biology (INET), Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Munich, Germany.; Institute of Network Biology (INET), Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Munich, Germany; Institute of Environmental Medicine (IEM), UNIKA-T, Technical University of Munich, Augsburg, Germany.; Plant Genetics, TUM School of Life Science Weihenstephan, Technical University of Munich (TUM), Freising, Germany.; Institute of Network Biology (INET), Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Munich, Germany; Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universitat (LMU) Munchen, Munich, Germany. Electronic address: pascal.falter-braun@helmholtz-muenchen.de.
In natural environments, plants are exposed to diverse microbiota that they interact with in complex ways. While plant-pathogen interactions have been intensely studied to understand defense mechanisms in plants, many microbes and microbial communities can have substantial beneficial effects on their plant host. Such beneficial effects include improved acquisition of nutrients, accelerated growth, resilience against pathogens, and improved resistance against abiotic stress conditions such as heat, drought, and salinity. However, the beneficial effects of bacterial strains or consortia on their host are often cultivar and species specific, posing an obstacle to their general application. Remarkably, many of the signals that trigger plant immune responses are molecularly highly similar and often identical in pathogenic and beneficial microbes. Thus, it is unclear what determines the outcome of a particular microbe-host interaction and which factors enable plants to distinguish beneficials from pathogens. To unravel the complex network of genetic, microbial, and metabolic interactions, including the signaling events mediating microbe-host interactions, comprehensive quantitative systems biology approaches will be needed.
PMID: 31128275
Mol Plant , IF:12.084 , 2019 Jun , V12 (6) : P863-878 doi: 10.1016/j.molp.2019.05.003
Systems Biology Approach Pinpoints Minimum Requirements for Auxin Distribution during Fruit Opening.
Crop Genetics, John Innes Centre, Norwich NR4 7UH, UK.; Computational and Systems Biology, John Innes Centre, Norwich NR4 7UH, UK; Centre of Excellence in Computational and Experimental Developmental Biology, Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland.; Cell and Developmental Biology, John Innes Centre, Norwich NR4 7UH, UK.; Computational and Systems Biology, John Innes Centre, Norwich NR4 7UH, UK; School of Biosciences, Cardiff University, Cardiff CF10 3AX, Wales, UK.; Crop Genetics, John Innes Centre, Norwich NR4 7UH, UK. Electronic address: lars.ostergaard@jic.ac.uk.; Computational and Systems Biology, John Innes Centre, Norwich NR4 7UH, UK; School of Biosciences, Cardiff University, Cardiff CF10 3AX, Wales, UK. Electronic address: marees@cardiff.ac.uk.
The phytohormone auxin is implied in steering various developmental decisions during plant morphogenesis in a concentration-dependent manner. Auxin maxima have been shown to maintain meristematic activity, for example, of the root apical meristem, and position new sites of outgrowth, such as during lateral root initiation and phyllotaxis. More recently, it has been demonstrated that sites of auxin minima also provide positional information. In the developing Arabidopsis fruit, auxin minima are required for correct differentiation of the valve margin. It remains unclear, however, how this auxin minimum is generated and maintained. Here, we employ a systems biology approach to model auxin transport based on experimental observations. This allows us to determine the minimal requirements for its establishment. Our simulations reveal that two alternative processes-which we coin "flux-barrier" and "flux-passage"-are both able to generate an auxin minimum, but under different parameter settings. Both models are in principle able to yield similar auxin profiles but present qualitatively distinct patterns of auxin flux. The models were tested by tissue-specific inducible ablation, revealing that the auxin minimum in the fruit is most likely generated by a flux-passage process. Model predictions were further supported through 3D PIN localization imaging and implementing experimentally observed transporter localization. Through such an experimental-modeling cycle, we predict how the auxin minimum gradually matures during fruit development to ensure timely fruit opening and seed dispersal.
PMID: 31128274
Mol Plant , IF:12.084 , 2019 Jun , V12 (6) : P784-803 doi: 10.1016/j.molp.2019.03.015
The Systems Biology of Lateral Root Formation: Connecting the Dots.
Computational Developmental Biology Group, Department of Biology, Utrecht University, Utrecht, the Netherlands.; Computational Developmental Biology Group, Department of Biology, Utrecht University, Utrecht, the Netherlands. Electronic address: k.h.w.j.tentusscher@uu.nl.
The root system is a major determinant of a plant's access to water and nutrients. The architecture of the root system to a large extent depends on the repeated formation of new lateral roots. In this review, we discuss lateral root development from a systems biology perspective. We focus on studies combining experiments with computational modeling that have advanced our understanding of how the auxin-centered regulatory modules involved in different stages of lateral root development exert their specific functions. Moreover, we discuss how these regulatory networks may enable robust transitions from one developmental stage to the next, a subject that thus far has received limited attention. In addition, we analyze how environmental factors impinge on these modules, and the different manners in which these environmental signals are being integrated to enable coordinated developmental decision making. Finally, we provide some suggestions for extending current models of lateral root development to incorporate multiple processes and stages. Only through more comprehensive models we can fully elucidate the cooperative effects of multiple processes on later root formation, and how one stage drives the transition to the next.
PMID: 30953788
mSystems , IF:6.633 , 2019 Jun , V4 (4) doi: 10.1128/mSystems.00161-19
A Novel Cys2His2 Zinc Finger Homolog of AZF1 Modulates Holocellulase Expression in Trichoderma reesei.
FMRP-University of Sao Paulo, Ribeirao Preto, SP, Brazil.; FFCLRP-University of Sao Paulo, Ribeirao Preto, SP, Brazil.; FCFRP-University of Sao Paulo, Ribeirao Preto, SP, Brazil.; Institute for Advanced Study, Technical University of Munich, Garching, Germany.; FMRP-University of Sao Paulo, Ribeirao Preto, SP, Brazil silvarochar@gmail.com.
Filamentous fungi are remarkable producers of enzymes dedicated to the degradation of sugar polymers found in the plant cell wall. Here, we integrated transcriptomic data to identify novel transcription factors (TFs) related to the control of gene expression of lignocellulosic hydrolases in Trichoderma reesei and Aspergillus nidulans Using various sets of differentially expressed genes, we identified some putative cis-regulatory elements that were related to known binding sites for Saccharomyces cerevisiae TFs. Comparative genomics allowed the identification of six transcriptional factors in filamentous fungi that have corresponding S. cerevisiae homologs. Additionally, a knockout strain of T. reesei lacking one of these TFs (S. cerevisiae AZF1 homolog) displayed strong reductions in the levels of expression of several cellulase-encoding genes in response to both Avicel and sugarcane bagasse, revealing a new player in the complex regulatory network operating in filamentous fungi during plant biomass degradation. Finally, RNA sequencing (RNA-seq) analysis showed the scope of the AZF1 homologue in regulating a number of processes in T. reesei, and chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) provided evidence for the direct interaction of this TF in the promoter regions of cel7a, cel45a, and swo Therefore, we identified here a novel TF which plays a positive effect in the expression of cellulase-encoding genes in T. reesei IMPORTANCE In this work, we used a systems biology approach to map new regulatory interactions in Trichoderma reesei controlling the expression of genes encoding cellulase and hemicellulase. By integrating transcriptomics related to complex biomass degradation, we were able to identify a novel transcriptional regulator which is able to activate the expression of these genes in response to two different cellulose sources. In vivo experimental validation confirmed the role of this new regulator in several other processes related to carbon source utilization and nutrient transport. Therefore, this work revealed novel forms of regulatory interaction in this model system for plant biomass deconstruction and also represented a new approach that could be easy applied to other organisms.
PMID: 31213522
Neurobiol Dis , IF:5.332 , 2019 Jun , V126 : P23-35 doi: 10.1016/j.nbd.2018.08.003
Differences in structural and functional networks between young adult and aged rat brains before and after stroke lesion simulations.
Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands. Electronic address: M.Straathof-2@umcutrecht.nl.; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.
Neural network changes during aging may contribute to vulnerability and resilience to brain lesions in age-related neurological disorders, such as stroke. However, the relationship between age-related neural network features and stroke outcome is unknown. Therefore, we assessed structural and functional network status in young adult and aged rat brain, and measured the effects of simulated stroke lesions. Eleven rats underwent diffusion-weighted MRI and resting-state functional MRI at young adult age (post-natal day 88) and old age (between post-natal day 760 and 880). Structural and functional brain network features were calculated from graph-based network analysis. We performed three lesion simulations based on the brain injury pattern in frequently applied rodent stroke models, i.e. a small cortical lesion, a subcortical lesion, or a large cortical plus subcortical lesion, for which we computationally removed the involved network regions. Global network characteristics, i.e. integration and segregation, were not significantly different between the two age groups. However, we detected local differences in structural and functional networks between young adult and old rats, mainly reflected by shifts of hub regions. Stroke lesion simulations induced significant global and local network changes, characterized by lower efficiency and shifts of hub regions in structural and functional networks, which was most evident after a large cortical plus subcortical lesion. Functional and structural hub region shifts after lesion simulations differed between young adult and aged rats. Our lesion simulation study demonstrates that age-dependent brain network status affects structural and functional network reorganization after stroke, particularly involving hub shifts, which may influence functional outcome. Computational lesion studies offer a cheap and simple alternative to empirical studies and can complement or guide more complicated experimental studies in animal models and patients.
PMID: 30086387
Int J Mol Sci , IF:4.556 , 2019 Jun , V20 (12) doi: 10.3390/ijms20123071
A Gene Regulatory Network Controlled by BpERF2 and BpMYB102 in Birch under Drought Conditions.
CAS Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Urumqi 830011, China. wxj-329@163.com.; Turpan Eremophytes Botanical Garden, Chinese Academy of Sciences, Turpan 838008, China. wxj-329@163.com.; University of Chinese Academy of Sciences, Beijing 100049, China. wxj-329@163.com.; State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China. wjx206091@163.com.; CAS Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Urumqi 830011, China. Zhangdy@ms.xjb.ac.cn.; Turpan Eremophytes Botanical Garden, Chinese Academy of Sciences, Turpan 838008, China. Zhangdy@ms.xjb.ac.cn.; CAS Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Urumqi 830011, China. wangyucheng@ms.xjb.ac.cn.; Turpan Eremophytes Botanical Garden, Chinese Academy of Sciences, Turpan 838008, China. wangyucheng@ms.xjb.ac.cn.
Gene expression profiles are powerful tools for investigating mechanisms of plant stress tolerance. Betula platyphylla (birch) is a widely distributed tree, but its drought-tolerance mechanism has been little studied. Using RNA-Seq, we identified 2917 birch genes involved in its response to drought stress. These drought-responsive genes include the late embryogenesis abundant (LEA) family, heat shock protein (HSP) family, water shortage-related and ROS-scavenging proteins, and many transcription factors (TFs). Among the drought-induced TFs, the ethylene responsive factor (ERF) and myeloblastosis oncogene (MYB) families were the most abundant. BpERF2 and BpMYB102, which were strongly induced by drought and had high transcription levels, were selected to study their regulatory networks. BpERF2 and BpMYB102 both played roles in enhancing drought tolerance in birch. Chromatin immunoprecipitation combined with qRT-PCR indicated that BpERF2 regulated genes such as those in the LEA and HSP families, while BpMYB102 regulated genes such as Pathogenesis-related Protein 1 (PRP1) and 4-Coumarate:Coenzyme A Ligase 10 (4CL10). Multiple genes were regulated by both BpERF2 and BpMYB102. We further characterized the function of some of these genes, and the genes that encode Root Primordium Defective 1 (RPD1), PRP1, 4CL10, LEA1, SOD5, and HSPs were found to be involved in drought tolerance. Therefore, our results suggest that BpERF2 and BpMYB102 serve as transcription factors that regulate a series of drought-tolerance genes in B. platyphylla to improve drought tolerance.
PMID: 31234595
Hum Brain Mapp , IF:4.421 , 2019 Jun , V40 (8) : P2546-2555 doi: 10.1002/hbm.24543
Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.
Translational and Molecular Imaging Institute, Icahn School of Medicine, Mount Sinai Medical Center, New York, New York.; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.; Sagol Brain Institute Tel-Aviv Medical Center, Tel-Aviv, Israel.; Movement Disorders Unit, Neurological Institute, Tel-Aviv Medical Center, Tel-Aviv, Israel.; Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.; Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.; Department of Neurology and Parkinson Centre, Radboud University Medical Centre, Nijmegen, The Netherlands.; Tel-Aviv Medical Center, Genetic Institute, Tel-Aviv, Israel.; Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel-Aviv Medical Center, Tel-Aviv, Israel.
Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD) and have demonstrated task related fMRI changes. However, resting-state networks have received less research focus, thus this study aimed to assess the integrity of the motor, default mode (DMN), salience (SAL), and dorsal attention (DAN) networks among this unique population by using two different connectivity measures: interregional functional connectivity analysis and Dependency network analysis (DEP NA). Machine learning classification methods were used to distinguish connectivity between the two groups of participants. Forty-four NMC and 41 non-manifesting non-carriers (NMNC) participated in this study; while no behavioral differences on standard questionnaires could be detected, NMC demonstrated lower connectivity measures in the DMN, SAL, and DAN compared to NMNC but not in the motor network. Significant correlations between NMC connectivity measures in the SAL and attention were identified. Machine learning classification separated NMC from NMNC with an accuracy rate above 0.8. Reduced integrity of non-motor networks was detected among NMC of the G2019S mutation in the LRRK2 gene prior to identifiable changes in connectivity of the motor network, indicating significant non-motor cerebral changes among populations "at risk" for future development of PD.
PMID: 30793410
Ecol Appl , IF:4.248 , 2019 Jun , V29 (4) : Pe01890 doi: 10.1002/eap.1890
An approach to incorporating inferred connectivity of adult movement into marine protected area design with limited data.
School of Environmental Studies, University of Victoria, Victoria, British Columbia, V8W 2Y2, Canada.; Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Province of British Columbia, Victoria, British Columbia, V8W 9N1, Canada.; Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, British Columbia, V8L 4B2, Canada.; Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.; Department of Political Science, University of Central Florida, Orlando, Florida, 32816, USA.; Sustainable Coastal Systems Cluster, National Center for Integrated Coastal Research, University of Central Florida, Orlando, Florida, 32816, USA.
Marine protected areas (MPAs) are important conservation tools that can support the resilience of marine ecosystems. Many countries, including Canada, have committed to protecting at least 10% of their marine areas under the Convention on Biological Diversity's Aichi Target 11, which includes connectivity as a key aspect. Connectivity, the movement of individuals among habitats, can enhance population stability and resilience within and among MPAs. However, little is known about regional spatial patterns of marine ecological connectivity, particularly adult movement. We developed a method to assess and design MPA networks that maximize inferred connectivity within habitat types for adult movement when ecological data are limited. We used the Northern Shelf Bioregion in British Columbia, Canada, to explore two different approaches: (1) evaluating sites important for inferred regional connectivity (termed hotspots) and (2) assessing MPA network configurations based on their overlap with connectivity hotspots and interconnectedness between MPAs. To assess inferred connectivity via adult movement, we used two different threshold distances (15 and 50 km) to capture moderate home ranges, which are most appropriate to consider in MPA design. We applied graph theory to assess inferred connectivity within 16 habitat and depth categories (proxies for distinct ecological communities), and used novel multiplex network methodologies to perform an aggregated assessment of inferred connectivity. We evaluated inferred regional connectivity hotspots based on betweenness and eigenvector centrality metrics, finding that the existing MPA network overlapped a moderate proportion of these regional hotspots and identified key areas to be considered as candidate MPAs. Network density among existing MPAs was low within the individual habitat networks, as well as the multiplex. This work informs an ongoing MPA planning process, and approaches for incorporating connectivity into MPA design when data are limited, with lessons for other contexts.
PMID: 30929286
Genes (Basel) , IF:3.759 , 2019 Jun , V10 (6) doi: 10.3390/genes10060479
Electromagnetic Fields, Genomic Instability and Cancer: A Systems Biological View.
Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, FI-70210, Finland. jonne.naarala@uef.fi.; Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, FI-70210, Finland. mikko.kolehmainen@uef.fi.
This review discusses the use of systems biology in understanding the biological effectsof electromagnetic fields, with particular focus on induction of genomic instability and cancer. Weintroduce basic concepts of the dynamical systems theory such as the state space and attractors andthe use of these concepts in understanding the behavior of complex biological systems. We thendiscuss genomic instability in the framework of the dynamical systems theory, and describe thehypothesis that environmentally induced genomic instability corresponds to abnormal attractorstates; large enough environmental perturbations can force the biological system to leave normalevolutionarily optimized attractors (corresponding to normal cell phenotypes) and migrate to lessstable variant attractors. We discuss experimental approaches that can be coupled with theoreticalsystems biology such as testable predictions, derived from the theory and experimental methods,that can be used for measuring the state of the complex biological system. We also reviewpotentially informative studies and make recommendations for further studies.
PMID: 31242701
BMC Genomics , IF:3.594 , 2019 Jun , V20 (1) : P534 doi: 10.1186/s12864-019-5879-7
Construction and analysis of degradome-dependent microRNA regulatory networks in soybean.
State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.; State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China. huangji@njau.edu.cn.
BACKGROUND: Usually the microRNA (miRNA)-mediated gene regulatory network (GRN) is constructed from the investigation of miRNA expression profiling and target predictions. However, the higher/lower expression level of miRNAs does not always indicate the higher/lower level of cleavages and such analysis, thus, sometimes ignores the crucial cleavage events. In the present work, the degradome sequencing data were employed to construct the complete miRNA-mediated gene regulatory network in soybean, unlike the traditional approach starting with small RNA sequencing data. RESULTS: We constructed the root-, cotyledon-, leaf- and seed-specific miRNA regulatory networks with the degradome sequencing data and the forthcoming verification of miRNA profiling analysis. As a result, we identified 205 conserved miRNA-target interactions (MTIs) involved with 6 conserved gma-miRNA families and 365 tissue-specific MTIs containing 24 root-specific, 45 leaf-specific, 63 cotyledon-specific and 225 seed-specific MTIs. We found a total of 156 miRNAs in tissue-specific MTIs including 18 tissue-specific miRNAs, however, only 3 miRNAs have consistent tissue-specific expression. Our study showed the degradome-dependent miRNA regulatory networks (DDNs) in four soybean tissues and explored their conservations and specificities. CONCLUSIONS: The construction of DDNs may provide the complete miRNA-Target interactions in certain plant tissues, leading to the identification of the conserved and tissue-specific MTIs and sub-networks. Our work provides a basis for further investigation of the roles and mechanisms of miRNA-mediated regulation of tissue-specific growth and development in soybean.
PMID: 31253085
Insect Sci , IF:2.791 , 2019 Jun , V26 (3) : P569-586 doi: 10.1111/1744-7917.12555
Did maize domestication and early spread mediate the population genetics of corn leafhopper?
Department of Entomology, Texas A&M University, College Station, Texas, USA.; Department of Plant and Soil Sciences, University of Vermont, Burlington, Vermont, USA.
Investigating how crop domestication and early farming mediated crop attributes, distributions, and interactions with antagonists may shed light on today's agricultural pest problems. Crop domestication generally involved artificial selection for traits desirable to early farmers, for example, increased productivity or yield, and enhanced qualities, though invariably it altered the interactions between crops and insects, and expanded the geographical ranges of crops. Thus, some studies suggest that with crop domestication and spread, insect populations on wild crop ancestors gave rise to pestiferous insect populations on crops. Here, we addressed whether the emergence of corn leafhopper (Dalbulus maidis) as an agricultural pest may be associated with domestication and early spread of maize (Zea mays mays). We used AFLP markers and mitochondrial COI sequences to assess population genetic structuring and haplotype relationships among corn leafhopper samples from maize and its wild relative Zea diploperennis from multiple locations in Mexico and Argentina. We uncovered seven corn leafhopper haplotypes contained within two haplogroups, one haplogroup containing haplotypes associated with maize and the other containing haplotypes associated with Z. diploperennis in a mountainous habitat. Within the first haplogroup, one haplotype was predominant across Mexican locations, and another across Argentinean locations; both were considered pestiferous. We suggested that the divergence times of the maize-associated haplogroup and of the "pestiferous" haplotypes are correlated with the chronology of maize spread following its domestication. Overall, our results support a hypothesis positing that maize domestication favored corn leafhopper genotypes preadapted for exploiting maize so that they became pestiferous, and that with the geographical expansion of maize farming, corn leafhopper colonized Z. diploperennis, a host exclusive to secluded habitats that serves as a refuge for archaic corn leafhopper genotypic diversity. Broadly, our results help explain the extents to which crop domestication and early spread may have mediated the emergence of today's agricultural pests.
PMID: 29105309
Multivariate Behav Res , IF:2.75 , 2019 Jun : P1-15 doi: 10.1080/00273171.2019.1614898
Bridge Centrality: A Network Approach to Understanding Comorbidity.
a Department of Psychology , Harvard University.; b Department of Psychology , University of Waterloo.
Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.
PMID: 31179765
Math Biosci , IF:1.649 , 2019 Jun , V312 : P67-76 doi: 10.1016/j.mbs.2019.04.006
Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: ismalia@mail.dlut.edu.cn.; School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: kangqiang@mail.dlut.edu.cn.; School of Life Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: luanyush@dlut.edu.cn.; School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: mengjun@dlut.edu.cn.
It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological traits, and this interaction is more common. Many studies focus mainly on the analysis of microRNA(miRNA) and message RNA (mRNA) interactions. In this study, we investigated miRNA and long non-coding RNA (lncRNA) interactions using support vector regression (SVR) for prediction of new target genes in Arabidopsis thaliana and identify some regulatory roles in stress response. The networks of miRNA-mRNA, miRNA-lncRNA and miRNA-mRNA-lncRNA were constructed. They were further analyzed and interpreted in R. We showed that miRNA with low sequence number, targeted lncRNA with high sequence number and miRNA with high sequence number targeted lncRNA with low sequence number. The experimental results showed that there is a regulatory relationship between miRNA-lncRNA. New RNA targets were predicted using SVR with new gene expression mechanism and the stress related functions were annotated.
PMID: 31034845