MSc project: Looking for novel Sieve Element specific genes using bioimaging at Københavns Universitet

Expires in 10 days

Background:
Understanding how plants allocate resources is crucial for developing resilient crops that can feed the growing population from croplands disturbed by climate change. The vascular system is the highroad for long-distance transport of resources in plants. The vasculature comprises the dead water conducting xylem and the live phloem. The phloem transports sugar form leaves to sink tissue such as roots and seeds and transmits systemic signals. At the heart of the phloem is a unique type of cells: the sieve elements.
Sieve elements (SEs) are highly specialized cells that loose the majority of organelles early in development, including the nucleus, plastids and all protein synthesis machinery. The cells remain alive while serving as a tube for sugar sap flow from source leaves to sinks. A few genes have been identified which accumulate specifically in mature SEs. Apart from a few well-described examples, it is largely unknown what genes make up the specific protein machinery in SEs and how they contribute to SE function.
We have compiled a list of putative SE specific genes using co-expression analysis on known SE markers. The majority of genes in the list remain uncharacterized and are thus exciting candidates for novel SE proteins. Some well-described hits in the list are SE specific, which warrants confidence in the approach.

Aim:

  • Confirm SE specificity of selected candidate genes
  • Investigate if selected candidates show any growth phenotype

To address this, you will:

  • Select lead candidates from the list of putative SE specific genes.
  • Create fluorescent reporter lines of these candidates and use microscopy to determine the localization of the proteins.
  • Use automated image analysis to characterize growth in knock out mutants of the genes.

Methods used: Confocal and epifluorescence microscopy, automated image processing, molecular cloning, Arabidopsis transformation, programming in R for statistics and FIJI (Python) for image analysis

Keywords: Bioimaging, Microscopy, Molecular biology, Cell biology, Plant biology

Project home page:https://dynamo.ku.dk/

Supervisor(s): Main supervisor: Alexander Schulz (als@plen.ku.dk), Co-supervisor: PhD student Niels Christian Sanden (ncsanden@plen.ku.dk)

Department of Plant and Environmental Sciences, University of Copenhagen

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