Abstract
Gene expression analysis is crucial for uncovering components underlying important biological processes for a focal organism. Large-scale gene co-expression networks generally exhibit small-world, scale-free properties; and the degree distributions of these networks follow power-law forms. Topology properties are often informative for determining the key components of the biological systems and their genetic mechanisms. Some basic topology properties display dissimilarity in different tissues, which helps to elucidate the different genetic mechanisms underlying important biological processes among tissues from the top to bottom of trees. In this study, the topology properties of gene co-expression networks were compared in leaf, shoot, wood, and root tissues of poplar. The comparison results demonstrated that the differences of topology properties exist among tissues and the root tissue displays larger average degree and network density, indicating that genes in root tissue are more highly co-expressed than those in the other three tissues. The nodes with a large degree, also known as hub genes, were annotated by the NetAffx Analysis Center and the agriGO tool, with annotation results that these highly interconnected genes are involved in the key biological processes in each tissue. This study also revealed the topology properties’ differences between gene co-expression networks and random network, suggesting the existence of hierarchically organized module and small-world organizations in networks of poplar. The approach described in this research offers an effective strategy for identifying key genes involved in the important biological processes in poplar.
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References
Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815
Birnbaum KD (2016) How many ways are there to make a root? Curr Opin Plant Biol 34:61–67
Borgatti SP, Everett MG, Freeman LC (2002) Ucinet for windows: software for social network analysis. Anal Technol. Harvard
Bumgarner RE, Yeung KY (2009) Methods for the inference of biological pathways and networks. Methods Mol Biol 541:225–245
Bustamante M, Matus JT, Riechmann JL (2016) Genome-wide analyses for dissecting gene regulatory networks in the shoot apical meristem. J Exp Bot 67(6):1639–1648
Cai B, Li CH, Huang J (2014) Systematic identification of cell-wall related genes in populus based on analysis of functional modules in co-expression network. PLoS One 9(4):e95176
Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S (2009) AmiGO: online access to ontology and annotation data. Bioinformatics 25(2):288–289
Carter SL, Brechbühler CM, Griffin M, Bond AT (2004) Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20(14):2242–2250
Couto CMV, Comin CH, Costa LDF (2017) Effects of threshold on the topology of gene co-expression networks. Mol BioSyst 13(10):2024–2035
D’haeseleer P, Liang S, Somogyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8):707–726
Dai X, Hu Q, Cai Q, Feng K, Ye N, Tuskan GA, Milne R, Chen Y, Wan Z, Wang Z, Luo W, Wang K, Wan D, Wang M, Wang J, Liu J, Yin T (2014) The willow genome and divergent evolution from poplar after the common genome duplication. Cell Res 24(10):1274–1277
Dalal V, Dagan S, Friedlander G, Aviv E, Bock R, Charuvi D, Reich Z, Adam Z (2018) Transcriptome analysis highlights nuclear control of chloroplast development in the shoot apex. Sci Rep 8(1):8881
Davis S, Meltzer PS (2007) GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics 23(14):1846–1847
De Lucas M, Brady SM (2013) Gene regulatory networks in the Arabidopsis root. Curr Opin Plant Biol 16(1):50–55
Déjardin A, Laurans F, Arnaud D, Breton C, Pilate G, Leplé JC (2010) Wood formation in angiosperms. C R Biol 333(4):325–334
Du Y, Scheres B (2018) Lateral root formation and the multiple roles of auxin. J Exp Bot 69(2):155–167
Du Z, Zhou X, Ling Y, Zhang ZH, Su Z (2007) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38(Web Server issue):W64–W70
Duan L, Sebastian J, Dinneny JR (2015) Salt-stress regulation of root system growth and architecture in Arabidopsis seedlings. Methods Mol Biol 1242:105–122
Dudoit S, Gentleman RC, Quackenbush J (2003) Open source software for the analysis of microarray data. Biotechniques. Suppl:45–51
Fukushima A (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 518(1):209–214
Gene Ontology Consortium (2015) Gene ontology consortium: going forward. Nucleic Acids Res 43(Database issue):D1049–D1056
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80
Grönlund A, Bhalerao RP, Karlsson J (2009) Modular gene expression in poplar: a multilayer network approach. New Phytol 181(2):315–322
Gu J, Li Z, Mao Y, Struik PC, Zhang H, Liu L, Wang Z, Yang J (2018) Roles of nitrogen and cytokinin signals in root and shoot communications in maximizing of plant productivity and their agronomic applications. Plant Sci 274:320–331
Ha CM, Jun JH, Fletcher JC (2010) Shoot apical meristem form and function. Curr Top Dev Biol 91:103–140
Hill JL Jr, Hollender CA (2018) Branching out: new insights into the genetic regulation of shoot architecture in trees. Curr Opin Plant Biol 47:73–80
Huber W, Carey VJ, Long L, Falcon S, Gentleman R (2007) Graphs in molecular biology. BMC Bioinformatics 8:S8
Iorio F, Bernardo-Faura M, Gobbi A, Cokelaer T, Jurman G, Saez-Rodriguez J (2016) Efficient randomization of biological networks while preserving functional characterization of individual nodes. BMC Bioinformatics 17(1):542
Janiak A, Kwaśniewski M, Szarejko I (2016) Gene expression regulation in roots under drought. J Exp Bot 67(4):1003–1014
Jansson S, Douglas CJ (2007) Populus: a model system for plant biology. Annu Rev Plant Biol 58:435–458
Jeena GS, Fatima S, Tripathi P, Upadhyay S, Shukla RK (2017) Comparative transcriptome analysis of shoot and root tissue of Bacopa monnieri identifies potential genes related to triterpenoid saponin biosynthesis. BMC Genomics 18(1):490
Kauffman J, Kittas A, Bennett L, Tsoka S (2014) DyCoNet: a Gephi plugin for community detection in dynamic complex networks. PLoS One 9(7):e101357
Kiljan S, Meijer KA, Steenwijk MD, Pouwels PJW, Schoonheim MM, Schenk GJ, Geurts JJG, Douw L (2019) Structural network topology relates to tissue properties in multiple sclerosis. J Neurol 266(1):212–222
Kong X, Liu G, Liu J, Ding Z (2018) The root transition zone: a hot spot for signal crosstalk. Trends Plant Sci 23(5):403–409
Kuntal BK, Dutta A, Mande SS (2016) CompNet: a GUI based tool for comparison of multiple biological interaction networks. BMC Bioinformatics 17(1):185
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559
Liang Z, Xu M, Teng M, Niu L (2006) NetAlign: a web-based tool for comparison of protein interaction networks. Bioinformatics 22(17):2175–2177
Lin L, Fu Z, Jin C, Tian M, Wu S (2018) Small-world indices via network efficiency for brain networks from diffusion MRI. Exp Brain Res 236(10):2677–2689
Mahboubi A, Niittylä T (2018) Sucrose transport and carbon fluxes during wood formation. Physiol Plant 164(1):67–81
Malamy JE, Ryan KS (2001) Environmental regulation of lateral root initiation in Arabidopsis. Plant Physiol 127(3):899–909
Mason O, Verwoerd M (2007) Graph theory and networks in biology. ET Syst Biol 1(2):89–119
Mizrachi E, Myburg AA (2016) Systems genetics of wood formation. Curr Opin Plant Biol 30:94–100
Myburg AA, Grattapaglia D, Tuskan GA, Hellsten U, Hayes RD, Grimwood J, Jenkins J, Lindquist E, Tice H, Bauer D, Goodstein DM, Dubchak I, Poliakov A, Mizrachi E, Kullan AR, Hussey SG, Pinard D, van der Merwe K, Singh P, van Jaarsveld I, Silva-Junior OB, Togawa RC, Pappas MR, Faria DA, Sansaloni CP, Petroli CD, Yang X, Ranjan P, Tschaplinski TJ, Ye CY, Li T, Sterck L, Vanneste K, Murat F, Soler M, Clemente HS, Saidi N, Cassan-Wang H, Dunand C, Hefer CA, Bornberg-Bauer E, Kersting AR, Vining K, Amarasinghe V, Ranik M, Naithani S, Elser J, Boyd AE, Liston A, Spatafora JW, Dharmwardhana P, Raja R, Sullivan C, Romanel E, Alves-FerreiraM KC, FoleyW CV, Paiva J, Kudrna D, Brommonschenkel SH, Pasquali G, Byrne M, Rigault P, Tibbits J, Spokevicius A, Jones RC, Steane DA, Vaillancourt RE, Potts BM, Joubert F, Barry K, Pappas GJ, Strauss SH, Jaiswal P, Grima-Pettenati J, Salse J, Van de Peer Y, Rokhsar DS, Schmutz J (2014) The genome of Eucalyptus grandis. Nature 510(7505):356–362
Notaguchi M, Okamoto S (2015) Dynamics of long-distance signaling via plant vascular tissues. Front Plant Sci 6:161
Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin YC, Scofield DG, Vezzi F, Delhomme N, Giacomello S, Alexeyenko A, Vicedomini R, Sahlin K, Sherwood E, Elfstrand M, Gramzow L, Holmberg K, Hällman J, Keech O, Klasson L, Koriabine M, Kucukoglu M, Käller M, Luthman J, Lysholm F, Niittylä T, Olson A, Rilakovic N, Ritland C, Rosselló JA, Sena J, Svensson T, Talavera-López C, Theißen G, Tuominen H, Vanneste K, Wu ZQ, Zhang B, Zerbe P, Arvestad L, Bhalerao R, Bohlmann J, Bousquet J, Garcia Gil R, Hvidsten TR, de Jong P, MacKay J, Morgante M, Ritland K, Sundberg B, Thompson SL, Van de Peer Y, Andersson B, Nilsson O, Ingvarsson PK, Lundeberg J, Jansson S (2013) The Norway spruce genome sequence and conifer genome evolution. Nature 497(7451):579–584
Puig J, Pauluzzi G, Guiderdoni E, Gantet P (2012) Regulation of shoot and root development through mutual signaling. Mol Plant 5(5):974–583
Reimers M, Carey VJ (2006) Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol 411:119–134
Ruan J, Dean AK, Zhang W (2010) A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol 4:8
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504
Sjodin A, Street NR, Sandberg G, Gustafsson P, Jansson S (2009) The Populus genome integrative explorer (PopGenIE): a new resource for exploring the Populus genome. New Phytol 182:1013–1025
Song Y, Chen Q, Ci D, Shao X, Zhang D (2014) Effects of high temperature on photosynthesis and related gene expression in poplar. BMC Plant Biol 14:111
Steuer R, Kurths J, Daub CO, Weise J, Selbig J (2002) The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18(Suppl 2):S231–S240
Street NR, Skogström O, Sjödin A, Tucker J, Rodríguez-Acosta M, Nilsson P, Jansson S, Taylor G (2006) The genetics and genomics of the drought response in Populus. Plant J 48(3):321–341
Street NR, Sjödin A, Bylesjö M, Gustafsson P, Trygg J, Jansson S (2008) A cross-species transcriptomics approach to identify genes involved in leaf development. BMC Genomics 9:589
Street NR, Jansson S, Hvidsten TR (2011) A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation. BMC Plant Biol 11:13
Taylor R, Singhal M (2009) Biological network inference and analysis using SEBINI and CABIN. Methods Mol Biol 541:551–576
Tian Y, McEachin RC, Santos C, States DJ (2007) Patel JM.SAGA: a subgraph matching tool for biological graphs. Bioinformatics 23(2):232–239
Tian T, Liu Y, Yan H, You Q, Yi X, Du Z, Xu W, Su Z (2017) agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res 45(W1):W122–W129
Traas J (2018) Organogenesis at the shoot apical meristem. Plants (Basel) 8(1):E6
Tran LS, Nakashima K, Shinozaki K, Yamaguchi-Shinozaki K (2007) Plant gene networks in osmotic stress response: from genes to regulatory networks. Methods Enzymol 428:109–128
Tuskan GA, Difazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, Putnam N, Ralph S, Rombauts S, Salamov A, Schein J, Sterck L, Aerts A, Bhalerao RR, Bhalerao RP, Blaudez D, Boerjan W, Brun A, Brunner A, Busov V, CampbellM CJ, ChalotM CJ, Chen GL, Cooper D, Coutinho PM, Couturier J, Covert S, Cronk Q, Cunningham R, Davis J, Degroeve S, Déjardin A, Depamphilis C, Detter J, Dirks B, Dubchak I, Duplessis S, Ehlting J, Ellis B, Gendler K, Goodstein D, Gribskov M, Grimwood J, Groover A, Gunter L, Hamberger B, Heinze B, Helariutta Y, Henrissat B, Holligan D, Holt R, Huang W, Islam-Faridi N, Jones S, Jones-Rhoades M, Jorgensen R, Joshi C, Kangasjärvi J, Karlsson J, Kelleher C, Kirkpatrick R, KirstM KA, Kalluri U, Larimer F, Leebens-Mack J, Leplé JC, Locascio P, Lou Y, Lucas S, Martin F, Montanini B, Napoli C, Nelson DR, Nelson C, Nieminen K, Nilsson O, Pereda V, Peter G, Philippe R, Pilate G, Poliakov A, Razumovskaya J, Richardson P, Rinaldi C, Ritland K, Rouzé P, Ryaboy D, Schmutz J, Schrader J, Segerman B, Shin H, Siddiqui A, Sterky F, Terry A, Tsai CJ, Uberbacher E, Unneberg P, Vahala J, Wall K, Wessler S, Yang G, Yin T, Douglas C, Marra M, Sandberg G, Van de Peer Y, Rokhsar D (2006) The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313(5793):1596–1604
Wang XF, Chen GR (2003) Complex networks: small-world, scale-free and beyond. Circ Syst Mag 3(1):6–20
Xulvi-Brunet R, Li H (2010) Co-expression networks: graph properties and topological comparisons. Bioinformatics 26(2):205–214
Yang Y, Guo Y (2018) Unraveling salt stress signaling in plants. J Integr Plant Biol 60(9):796–804
Yang L, Wang SY, Zhou M, Chen XW, Zuo YC, Sun DJ, Lv YL (2016) Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties. Mol Gen Genomics 291(3):1227–1241
Ye ZH, Zhong RQ (2015) Molecular control of wood formation in trees. J Exp Bot 66(14):4119–4131
Yong Ed (2012) Tree’s leaves genetically different from its roots. Nat News. 2012-08-14
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:Article17
Zhang J, Nieminen K, Serra JA, Helariutta Y (2014) The formation of wood and its control. Curr Opin Plant Biol 17:56–63
Zhang J, Li Y, Chen H, Ding J, Yuan Z (2016) An investigation of the differences and similarities between generated small-world networks for right- and left-hand motor imageries. Sci Rep 6:36562
Data archiving statement
The detailed information of 231CEL files and some annotation results in this study are provided in the Supplementary files of Tables S1–S5. Microarray data of 231 CEL files with the accession number of GPL4359 in leaf, shoot, wood, and root tissues are available for download at Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL4359
Funding
This work was supported by the National Key Research and Development Plan of China (2016YFD0600101), and the National Natural Science Foundation of China (31570662 and 31500533). The funders guided the design of the study and participated in the manuscript revision.
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Table S1
Data series of Populus gene chips based on GPL4359 (XLSX 18 kb)
Table S2
AffyID of top lists of large degree nodes in four tissues (XLSX 17 kb)
Table S3
Annotation of large degree nodes in leaf, wood and shoot tissues (XLSX 19 kb)
Table S4
Annotated number in query list in shoot tissue by aigriGO (XLSX 12 kb)
Table S5
Significant GO terms in shoot tissue annotated by aigriGO (XLSX 16 kb)
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Zhang, H., Yin, T. Analysis of topology properties in different tissues of poplar based on gene co-expression networks. Tree Genetics & Genomes 16, 6 (2020). https://doi.org/10.1007/s11295-019-1400-3
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DOI: https://doi.org/10.1007/s11295-019-1400-3