in

Using RNA-seq to characterize pollen–stigma interactions for pollination studies

  • 1.

    Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072. https://doi.org/10.1111/j.1461-0248.2011.01669.x (2011).

    Article 
    PubMed 

    Google Scholar 

  • 2.

    Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721 (2007).

    Article 
    PubMed 

    Google Scholar 

  • 3.

    Kearns, C. A. & Inouye, A. D. W. Techniques for Pollination Biologists (University Press Colorado, 1993).

    Google Scholar 

  • 4.

    Rader, R. et al. Alternative pollinator taxa are equally efficient but not as effective as the honeybee in a mass flowering crop. J. Appl. Ecol. 46, 1080–1087. https://doi.org/10.1111/j.1365-2664.2009.01700.x (2009).

    Article 

    Google Scholar 

  • 5.

    Ne’eman, G., Jurgens, A., Newstrom-Lloyd, L., Potts, S. G. & Dafni, A. A framework for comparing pollinator performance: Effectiveness and efficiency. Biol. Rev. Camb. Philos. Soc. 85, 435–451. https://doi.org/10.1111/j.1469-185X.2009.00108.x (2010).

    Article 
    PubMed 

    Google Scholar 

  • 6.

    King, C., Ballantyne, G., Willmer, P. G. & Freckleton, R. Why flower visitation is a poor proxy for pollination: Measuring single-visit pollen deposition, with implications for pollination networks and conservation. Methods Ecol. Evol. 4, 811–818. https://doi.org/10.1111/2041-210x.12074 (2013).

    Article 

    Google Scholar 

  • 7.

    Wang, H. et al. Evaluation of pollinator effectiveness based on pollen deposition and seed production in a gynodieocious alpine plant, Cyananthus delavayi. Ecol. Evol. 7, 8156–8160. https://doi.org/10.1002/ece3.3391 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 8.

    Ashman, T. L., Alonso, C., Parra-Tabla, V. & Arceo-Gomez, G. Pollen on stigmas as proxies of pollinator competition and facilitation: Complexities, caveats, and future directions. Ann. Bot. https://doi.org/10.1093/aob/mcaa012 (2020).

    Article 
    PubMed 

    Google Scholar 

  • 9.

    Wodehouse, R. P. Pollen grains in the identification and classification of plants 1. The Ambrosiaceae. Bull. Torrey Bot. Club 55, 20 (1928).

    Google Scholar 

  • 10.

    Currie, J., Noiton, D., Lawes, S. & Bailey, D. Preliminary results of differentiating apple sports by pollen ultrastructure. Euphytica 98, 155–161. https://doi.org/10.1023/a:1003174529263 (1997).

    Article 

    Google Scholar 

  • 11.

    Bock, J. H. & Norris, D. O. Additional Approaches in Forensic Plant Science. 129–147. https://doi.org/10.1016/b978-0-12-801475-2.00010-5 (2016).

  • 12.

    Depciuch, J., Kasprzyk, I., Drzymala, E. & Parlinska-Wojtan, M. Identification of birch pollen species using FTIR spectroscopy. Aerobiologia (Bologna) 34, 525–538. https://doi.org/10.1007/s10453-018-9528-4 (2018).

    Article 

    Google Scholar 

  • 13.

    Galimberti, A. et al. A DNA barcoding approach to characterize pollen collected by honeybees. PLoS One 9, e109363. https://doi.org/10.1371/journal.pone.0109363 (2014).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 14.

    Keller, A. et al. Evaluating multiplexed next-generation sequencing as a method in palynology for mixed pollen samples. Plant Biol. (Stuttg.) 17, 558–566. https://doi.org/10.1111/plb.12251 (2015).

    CAS 
    Article 

    Google Scholar 

  • 15.

    Sickel, W. et al. Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach. BMC Ecol. 15, 20. https://doi.org/10.1186/s12898-015-0051-y (2015).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 16.

    Bell, K. L. et al. Pollen DNA barcoding: Current applications and future prospects. Genome 59, 629–640. https://doi.org/10.1139/gen-2015-0200 (2016).

    Article 
    PubMed 

    Google Scholar 

  • 17.

    Galliot, J.-N. et al. Investigating a flower-insect forager network in a mountain grassland community using pollen DNA barcoding. J. Insect. Conserv. 21, 827–837. https://doi.org/10.1007/s10841-017-0022-z (2017).

    Article 

    Google Scholar 

  • 18.

    Bell, K. L. et al. Quantitative and qualitative assessment of pollen DNA metabarcoding using constructed species mixtures. Mol. Ecol. 28, 431–455. https://doi.org/10.1111/mec.14840 (2019).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 19.

    Broderick, R. et al. RNA-sequencing reveals early, dynamic transcriptome changes in the corollas of pollinated petunias. BMC Plant Biol. 14, 10 (2014).

    Article 

    Google Scholar 

  • 20.

    Gómez, E. M., Buti, M., Sargent, D. J., Dicenta, F. & Ortega, E. Transcriptomic analysis of pollen–pistil interactions in almond (Prunus dulcis) identifies candidate genes for components of gametophytic self-incompatibility. Tree Genet Genomes https://doi.org/10.1007/s11295-019-1360-7 (2019).

    Article 

    Google Scholar 

  • 21.

    Zhang, C. C. et al. Transcriptome analysis reveals self-incompatibility in the tea plant (Camellia sinensis) might be under gametophytic control. BMC Genom. 17, 359. https://doi.org/10.1186/s12864-016-2703-5 (2016).

    CAS 
    Article 

    Google Scholar 

  • 22.

    Zhang, T. et al. Time-course transcriptome analysis of compatible and incompatible pollen-stigma interactions in Brassica napus L.. Front Plant Sci. 8, 682. https://doi.org/10.3389/fpls.2017.00682 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 23.

    Li, K., Wang, Y. & Qu, H. RNA-Seq analysis of compatible and incompatible styles of Pyrus species at the beginning of pollination. Plant Mol. Biol. 102, 287–306. https://doi.org/10.1007/s11103-019-00948-1 (2020).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 24.

    Rutley, N. & Twell, D. A decade of pollen transcriptomics. Plant Reprod. 28, 73–89. https://doi.org/10.1007/s00497-015-0261-7 (2015).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 25.

    Conze, L. L., Berlin, S., Le Bail, A. & Kost, B. Transcriptome profiling of tobacco (Nicotiana tabacum) pollen and pollen tubes. BMC Genom. 18, 581. https://doi.org/10.1186/s12864-017-3972-3 (2017).

    CAS 
    Article 

    Google Scholar 

  • 26.

    He, Y. et al. Transcriptome analysis of self- and cross-pollinated pistils revealing candidate unigenes of self-incompatibility in Camellia oleifera. J. Hortic. Sci. Biotechnol. 95, 19–31. https://doi.org/10.1080/14620316.2019.1632749 (2019).

    CAS 
    Article 

    Google Scholar 

  • 27.

    Pérez-de-Castro, M. et al. Application of genomic tools in plant breeding. Curr. Genom. 13, 179–195 (2012).

    Article 

    Google Scholar 

  • 28.

    Leydon, A. R. et al. The molecular dialog between flowering plant reproductive partners defined by SNP-informed RNA-sequencing. Plant Cell 29, 984–1006. https://doi.org/10.1105/tpc.16.00816 (2017).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 29.

    Shi, D. et al. Transcriptome and phytohormone analysis reveals a comprehensive phytohormone and pathogen defence response in pear self-/cross-pollination. Plant Cell Rep. 36, 1785–1799. https://doi.org/10.1007/s00299-017-2194-0 (2017).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 30.

    Kron, P. & Husband, B. C. The effects of pollen diversity on plant reproduction: Insights from apple. Sex. Plant Reprod. 19, 125–131. https://doi.org/10.1007/s00497-006-0028-2 (2006).

    CAS 
    Article 

    Google Scholar 

  • 31.

    Matsumoto, S., Soejima, J. & Maejima, T. Influence of repeated pollination on seed number and fruit shape of ‘Fuji’ apples. Sci. Hortic. 137, 131–137. https://doi.org/10.1016/j.scienta.2012.01.033 (2012).

    Article 

    Google Scholar 

  • 32.

    Garratt, M. P. et al. Avoiding a bad apple: Insect pollination enhances fruit quality and economic value. Agric. Ecosyst. Environ. 184, 34–40. https://doi.org/10.1016/j.agee.2013.10.032 (2014).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 33.

    Stavert, J. R., Bailey, C., Kirkland, L. & Rader, R. Pollen tube growth from multiple pollinator visits more accurately quantifies pollinator performance and plant reproduction. Sci. Rep. 10, 16958. https://doi.org/10.1038/s41598-020-73637-5 (2020).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 34.

    Rader, R., Howlett, B. G., Cunningham, S. A., Westcott, D. A. & Edwards, W. Spatial and temporal variation in pollinator effectiveness: Do unmanaged insects provide consistent pollination services to mass flowering crops?. J. Appl. Ecol. 49, 126–134. https://doi.org/10.1111/j.1365-2664.2011.02066.x (2012).

    Article 

    Google Scholar 

  • 35.

    Sorin, Y. B., Mitchell, R. J., Trapnell, D. W. & Karron, J. D. Effects of pollination and postpollination processes on selfing rate in Mimulus ringens. Am. J. Bot. 103, 1524–1528. https://doi.org/10.3732/ajb.1600145 (2016).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 36.

    DeLong, C. N., Yoder, K. S., Combs, L., Veilleux, R. E. & Peck, G. M. Apple pollen tube growth rates are regulated by parentage and environment. J. Am. Soc. Hortic. Sci. 141, 548–554. https://doi.org/10.21273/jashs03824-16 (2016).

    Article 

    Google Scholar 

  • 37.

    Zhao, P., Wang, M. & Zhao, L. Dissecting stylar responses to self-pollination in wild tomato self-compatible and self-incompatible species using comparative proteomics. Plant Physiol. Biochem. 106, 177–186. https://doi.org/10.1016/j.plaphy.2016.05.001 (2016).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 38.

    Rao, P. et al. Dynamic transcriptomic analysis of the early response of female flowers of Populus alba x P. glandulosa to pollination. Sci. Rep. 7, 6048. https://doi.org/10.1038/s41598-017-06255-3 (2017).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 39.

    Tu, D. et al. Developmental, chemical and transcriptional characteristics of artificially pollinated and hormone-induced parthenocarpic fruits of Siraitia grosvenorii. RSC Adv. 7, 12419–12428. https://doi.org/10.1039/c6ra28341a (2017).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 40.

    Hiscock, S. J. & Allen, A. M. Diverse cell signalling pathways regulate pollen–stigma interactions: The search for consensus. New Phytol. 179, 286–317. https://doi.org/10.1111/j.1469-8137.2008.02457.x (2008).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 41.

    Xu, X. H., Wang, F., Chen, H., Sun, W. & Zhang, X. S. Transcript profile analyses of maize silks reveal effective activation of genes involved in microtubule-based movement, ubiquitin-dependent protein degradation, and transport in the pollination process. PLoS One 8, e53545. https://doi.org/10.1371/journal.pone.0053545 (2013).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 42.

    Habu, T. & Tao, R. Transcriptome analysis of self- and cross-pollinated pistils of Japanese Apricot (Prunus mume Sieb. et Zucc.). J. Jpn. Soc. Hortic. Sci. 83, 95–107. https://doi.org/10.2503/jjshs1.CH-086 (2014).

    CAS 
    Article 

    Google Scholar 

  • 43.

    Sun, Y. & Xiao, H. Identification of alternative splicing events by RNA sequencing in early growth tomato fruits. BMC Genom. 16, 948. https://doi.org/10.1186/s12864-015-2128-6 (2015).

    CAS 
    Article 

    Google Scholar 

  • 44.

    Zhao, Y., Li, D. & Liu, T. Pollination-induced transcriptome and phylogenetic analysis in Cymbidium tortisepalum (Orchidaceae). Russ. J. Plant Physiol. 66, 618–627. https://doi.org/10.1134/s1021443719040174 (2019).

    CAS 
    Article 

    Google Scholar 

  • 45.

    Nishida, S. et al. Pollen–pistil interactions in reproductive interference: Comparisons of heterospecific pollen tube growth from alien species between two native Taraxacum species. Funct. Ecol. 28, 450–457. https://doi.org/10.1111/1365-2435.12165 (2014).

    Article 

    Google Scholar 

  • 46.

    Briggs, H. M. et al. Heterospecific pollen deposition in Delphinium barbeyi: Linking stigmatic pollen loads to reproductive output in the field. Ann. Bot. 117, 341–347. https://doi.org/10.1093/aob/mcv175 (2016).

    Article 
    PubMed 

    Google Scholar 

  • 47.

    Richardson, R. T. et al. Quantitative multi-locus metabarcoding and waggle dance interpretation reveal honey bee spring foraging patterns in Midwest agroecosystems. Mol. Ecol. 28, 686–697. https://doi.org/10.1111/mec.14975 (2019).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 48.

    Peel, N. et al. Semi-quantitative characterisation of mixed pollen samples using MinION sequencing and Reverse Metagenomics (RevMet). Methods Ecol. Evol. 10, 1690–1701. https://doi.org/10.1111/2041-210x.13265 (2019).

    Article 

    Google Scholar 

  • 49.

    Baksay, S. et al. Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL. Sci. Rep. 10, 4202. https://doi.org/10.1038/s41598-020-61198-6 (2020).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 50.

    Washburn, J. D. et al. Genome-guided phylo-transcriptomic methods and the nuclear phylogentic tree of the paniceae grasses. Sci. Rep. 7, 13528. https://doi.org/10.1038/s41598-017-13236-z (2017).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 51.

    Piñeiro Fernández, L. et al. A Phylogenomic analysis of the floral transcriptomes of sexually deceptive and rewarding European Orchids, Ophrys and Gymnadenia. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01553 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 52.

    Pawelkowicz, M. et al. Comparative transcriptome analysis reveals new molecular pathways for cucumber genes related to sex determination. Plant Reprod. 32, 193–216. https://doi.org/10.1007/s00497-019-00362-z (2019).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 53.

    Li, X. et al. Comparative transcriptomic analysis provides insight into the domestication and improvement of pear (P. pyrifolia) fruit. Plant Physiol. 180, 435–452. https://doi.org/10.1104/pp.18.01322 (2019).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 54.

    Sassa, H., Kakui, H. & Minamikawa, M. Pollen-expressed F-box gene family and mechanism of S-RNase-based gametophytic self-incompatibility (GSI) in Rosaceae. Sex Plant Reprod. 23, 39–43. https://doi.org/10.1007/s00497-009-0111-6 (2010).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 55.

    Ramírez, F. & Davenport, T. L. Apple pollination: A review. Sci. Hortic. 162, 188–203. https://doi.org/10.1016/j.scienta.2013.08.007 (2013).

    Article 

    Google Scholar 

  • 56.

    Gu, C., Wang, L., Korban, S. S. & Han, Y. Identification and characterization of S-RNasegenes andS-genotypes in Prunus and Malus species. Can. J. Plant Sci. 95, 213–225. https://doi.org/10.4141/cjps-2014-254 (2015).

    CAS 
    Article 

    Google Scholar 

  • 57.

    Sassa, H. Molecular mechanism of the S-RNase-based gametophytic self-incompatibility in fruit trees of Rosaceae. Breed. Sci. 66, 116–121. https://doi.org/10.1270/jsbbs.66.116 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 58.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 59.

    Andrews, S. (Babraham, UK, 2010).

  • 60.

    Daccord, N. et al. High-quality de novo assembly of the apple genome and methylome dynamics of early fruit development. Nat. Genet. 49, 1099–1106. https://doi.org/10.1038/ng.3886 (2017).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 61.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360. https://doi.org/10.1038/nmeth.3317 (2015).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 62.

    Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667. https://doi.org/10.1038/nprot.2016.095 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 63.

    Williams, C. R., Baccarella, A., Parrish, J. Z. & Kim, C. C. Trimming of sequence reads alters RNA-Seq gene expression estimates. BMC Bioinform. 17, 103. https://doi.org/10.1186/s12859-016-0956-2 (2016).

    CAS 
    Article 

    Google Scholar 

  • 64.

    Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13. https://doi.org/10.1186/s13059-016-0881-8 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 65.

    Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295. https://doi.org/10.1038/nbt.3122 (2015).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 66.

    Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257. https://doi.org/10.1038/ncomms11257 (2016).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 67.

    Ballgown: Flexible, Isoform-Level Differential Expression Analysis v. 2.20.0. (Bioconductor, 2020).

  • 68.

    Tello, D. et al. NGSEP3: Accurate variant calling across species and sequencing protocols. Bioinformatics 35, 4716–4723. https://doi.org/10.1093/bioinformatics/btz275 (2019).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 69.

    Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267. https://doi.org/10.1093/molbev/msj030 (2006).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 70.

    Milne, I. et al. Flapjack–graphical genotype visualization. Bioinformatics 26, 3133–3134. https://doi.org/10.1093/bioinformatics/btq580 (2010).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 71.

    Duitama, J. et al. An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments. Nucleic Acids Res. 42, e44. https://doi.org/10.1093/nar/gkt1381 (2014).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 


  • Source: Ecology - nature.com

    Candidatus Eremiobacterota, a metabolically and phylogenetically diverse terrestrial phylum with acid-tolerant adaptations

    Study reveals plunge in lithium-ion battery costs