Pollen epigenetics

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Published on: October 11, 2012

Biology learned in school and as a first year undergraduate is easily forgotten if it is not relevant to your current research. Today’s highlighted article required me to refresh my memory of plant germ line development, so I included my basic research here.

Highlighted article: Joseph P. Calarco, Filipe Borges, Mark T.A. Donoghue, Frédéric Van Ex, Pauline E. Jullien, Telma Lopes, Rui Gardner, Frédéric Berger, José A. Feijó, Jörg D. Becker and Robert A. Martienssen (2012) Reprogramming of DNA Methylation in Pollen Guides Epigenetic Inheritance via Small RNA. Cell 151:194-205.

Germline biosynthesis: A pollen mother cell undergoes meiosis to make haploid microspores, which unevenly split into a larger vegetative cell and a small generative cell. The generative cell splits symmetrically into two – these are the plant ‘sperm’ cells. Each pollen grain contains two sperm cells, which are surrounded by a vegetative cell. The vegetative nucleus contains completely decondensed heterochromatin, but DNA in generative nuclei is tightly condensed.

The female gametophyte develops from a megaspore mother cell. Both the megaspore mother cell and pollen mother cell are specified from somatic cells in developing flowers.

GFP staining in the two sperm nuclei and vegetative nucleus in the vegetative cell.

Bisulphite sequencing is a DNA sequencing method which determines methylation pattern by treating DNA with sodium bisulphite before sequencing it using a conventional DNA sequencing method. Bisulphite induces the conversion of unmethylated cytosines to uracil, but this is not a perfect technique so unmethylated DNA may be recorded as methylated. Additionally, bisulphite treatment can cause DNA degradation. Sequencing the DNA of interest multiple times, in the case of Calarco et al., anywhere from 7 to 17 times, improves reliability of the method. There is a brief overview of DNA methylation in this post. (more…)

Crop Plant Trait Ontology Workshop, Oregon State University

Categories: guest blogger, Workshops
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Published on: October 9, 2012

For the last few months, Ruth has been involved in organising a Crop Plant Trait Ontology Workshop which happened at Oregon State University on 13-15 September. Laura Cooper and the Crop Ontology Workshop Planning Committee put together a news piece about the event.

Plant breeders, biologists and bioinformatics specialists from ten countries and two plant agribusinesses gathered in Corvallis, Oregon for the Crop Plant Trait Ontology Workshop. The workshop was hosted by the Plant Ontology and the Trait Ontology consortiums, and co-organised by TransPlant, European Bioinformatics Institute, GARNet, Generation Challenge Program, Sol Genomics Network, and SoyBase.

The goal of the workshop was to engage researchers associated with major cultivated crops worldwide, in order to widen their awareness about ontologies and showcase the latest developments in ontologies for plants. In addition to hearing presentations, participants learned to use the ontology editor OBO-Edit and worked in small groups to classify plant trait terms which had been submitted in advance.

The main conclusion of the workshop was that there is a need for a broad, co-ordinated effort to create a semantic framework for meaningful cross-species queries using a Common Reference Ontology for Plants. This Reference Ontology will encompass all green plants and will facilitate queries for related gene expression and phenotype data from plant genomics, genetics experiments from the various species- and clade-specific databases, and describe accessions in the various international crop germplasm collections.

The hope is that in creating a Common Reference Trait Ontology for Plants, we will facilitate plant genetic and phenotypic data discovery and exchange.

For further information including a list of participants and sponsors, and links to presentations, visit the workshop wiki page at http://tinyurl.com/Trait-Ontology.

Other links:

Plant Ontology http://www.plantontology.org

Trait Ontology: http://www.gramene.org/plant_ontology/ontology_browse.html#to

TransPlant: http://transplantdb.eu/

European Bioinformatics Institute: http://www.ebi.ac.uk

GARNet: http://www.garnetcommunity.org.uk

Generation Challenge Program: http://www.generationcp.org

Sol Genomics Network: http://solgenomics.net/

SoyBase: http://soybase.org/

OBO-Edit; http://oboedit.org/

Friday Film: Automatic cell counting with ImageJ

Categories: methods, resource
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Published on: October 5, 2012

This is a video tutorial on quantifying cells using ImageJ. ImageJ is a free tool for image processing and analysis in Java. I chose to highlight this tutorial as it is clearly explained and may be of use to many plant scientists, but YouTube is a goldmine of other less professional tutorials on using ImageJ for any number of applications. I was particularly interested in this one on quantifying stained liver tissue, as I used to work on secondary cell walls and it would have been a handy tool for qualitative analysis of my many images of phloroglucinol stained tobacco stem cross-sections.

Created by Keene State College’s Center for Engagement, Learning, and Teaching.

NASC

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Published on: October 4, 2012

Many European Arabidopsis researchers use transgenic Arabidopsis lines from NASC, which has a large catalogue of germplasm kept up-to-date by donations from plant scientists and industry from all over the world. The catalogue is searchable by keyword, collection, or ontology and there is a help page containing a number of useful tips on searching, ordering and referencing the collection. Also available are photos of each line and tips on working with Arabidopsis.

NASC run an Affymetrix Service. The current lowest all inclusive price is £250 for RNA onto any plate based chip. NASC are happy to process any private consortium chip or a chip from another species, as well as any commercially available Affymetrix Chip.

Microarray data is available from NASC free of charge, and users can donate their own microarray data. Again, there is a help page for this service containing tips on data analysis, and elsewhere there is advice for new users of microarrays including advice on sample preparation and NASC’s requirements.

You can follow NASC on Twitter on @NASCArabidopsis for updates on new array experiments and germlines.

The cost of glucosinolate biosynthesis

Highighted article: Michaël Bekaert, Patrick P. Edger, Corey M. Hudson, J.Chris Pires, Gavin C. Conant (2012) Metabolic and evolutionary costs of herbivory defense: systems biology of glucosinolate synthesis. New Phytologist 196:596–605.

Research published in a current New Phytologist paper uses a systems biology approach to demonstrate the metabolic and evolutionary costs of producing glucosinolates for defence.  Bekart et al. used AraGEM (Oliveira Dal’Molin et al., 2010) as a starting point. They collected data on Arabidopsis glucosinolate genes by scouring published papers and downloading their expression patterns from AtGenExpress. This information was integrated into the basic dataset from AraGEM. The complete list of genes involved in glucosinolate reactions, including references, is in Supplementary Table S1 of the paper.

The team performed flux balance analysis on the integrated database to estimate metabolic and energy flux through reactions in the system both with glucosinolate biosynthesis activity and with none. They found that glucosinolate biosynthesis affected flux incidentally through 241 reactions in addition to the 196 reactions which are only active when glucosinolates are being produced.

The main finding of the research is the heavy cost of glucosinolate biosynthesis. Sulphur import dramatically increased when glucosinolates were being synthesised, and demand for water, carbon dioxide, ammonia, and photons increased too. Despite the increase in substrate import, biomass synthesis fell by around 15% during glucosinolate production. This cost is reflected in other studies demonstrating that the evolutionary competitive edge glucosinolates give to plants is a disadvantage when there are no predators around (Mauricio, 1997), and reduces the number of seeds and flowers produced per plant compared to non-producers (Stowe and Marquis, 2011). (more…)

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