Gertraud Burger | research and collaborations


General Research Interests

  1. Evolution of organelles and the eukaryotic cell as a whole.

  2. The make-up of the nuclear genome from primitive eukaryotes.

  3. Experimental and in silico comparative genomics.

  4. Post-transcriptional processes in mitochondria: machineries, mechanisms and evolution.

  5. Integrated biological databases.

  6. Development of bioinformatics approaches and software for comparative genomics research.

Current Projects

  1. Novel molecular mechanisms in Mitochondria of Early Diverging Eukaryotes
  2. We set out to discover new molecular mechanisms by studying the extraordinary biological diversity outside the 'main-stream' eukaryotic phyla. The system we study is the mitochondrion, an integral part of the eukaryotic cell. Our work also contributes to a better understanding of mitochondrial genome evolution and diversification.

    My laboratory is investigating mtDNAs of Euglenozoa, a large group of unicellular eukaryotes that are believed to have emerged very early in eukaryotic history. This monophyletic lineage includes most diverse phyla such as euglenids, kinetoplastids, and diplonemids. The latter two are sistergroups that split off after the divergence of euglenids.

    The main focus of our work is on diplonemids (see list of publications). In Diplonema papillatum, we discovered an unprecedented mitochondrial genome architecture and gene structure. This mtDNA consists of >100 different circular chromosomes, each of which carries a small gene module. Gene modules are transcribed individually and then joined together to a contiguous mRNA by trans-splicing. Further, we detected a few events RNA editing, proceeding by insertion of multiple Us at a given site (Marande & Burger, 2007; Kiethega et al., 2012).
    Our working hypothesis is that trans-splicing and RNA editing in diplonemids is mediated by trans-factors. To test this hypothesis, we use genomics, transcriptomics, proteomics and bioinformatics approaches. Experimental data help to formulate specific search strategies and vice versa, bioinformatics hypotheses are being tested experimentally.

    Finally, we have initiated exploring mtDNAs of euglenids (Roy et al, 2007a, b), basal kinetoplastids and yet undescribed diplonemids, in order to trace back the evolution and dispersal of RNA editing in Euglenozoa.

  3. Nuclear Genome Exploration
  4. To get insight into the trans-splicing and RNA editing machinery in diplonemid mitochondria (see project described above), we are sequencing the nuclear genome of Diplonema papillatum, in collaboration with Julius Lukes (University of South Bohemia), and Cestmir Vlcek (Institute of Molecular Genetics, Prag), where most of the sequencing takes place.

    The second genome project is the Unicorn initiative, a collaboration of seven research groups from Canada, UK, and USA, and endorsed by the National Human Genome Research Institute (NHGRI). This project aims at understanding how multicellularity first evolved. Genomic data are being generated from unicellular relatives of animals and fungi, i.e., choanflagellates, Ichthyosporea, Nuclearidae, chytrids, zygomycetes and apusozoa (outgroup) (see e.g., Ruiz-Trillo I et al, 2007).

  5. -Omics for Phytoremediation
  6. The GenoRem project aims at studing the inter-relationships between plants, fungi, and bacteria in the detoxification of soils that are contaminated with organic or inorganic compounds. The experimental approaches include genomics, transcriptomics, proteomics, and metabolomics. Bioinformatics methodologies assure thorough data management and analysis. This project is conducted by ~30 collaborating researchers of the Universite de Montreal and McGill University, and is financed by Genome Canada and Genome Quebec. Long-term goal is to select optimal combinations of plants and microorganisms for particular soil contaminations.


Past Projects

  1. Organelle Genome Database (GOBASE)
  2. This comparative database ties together and unifies the various data on mitochondrial and chloroplast genomes and the organism which contain them, by making the information network-accessible to the scientific community. Data validation and addition of missing information is at the center of this project (O'Brien E et al 2007).

  3. EST Surveys
  4. The Protist EST Program ((PEP), a collaboration involving eight Canadian research groups, aims to determine the expressed portions of genomes from a taxonomically broad collection of mostly unicellular eukaryotes. The organismal group studied by myself and my collegues BF. Lang and MW. Gray are jakobid flagellates, which are believed to be amongst the most primitive extant eukaryotes. The goal of this project is to better understand early eukaryotic cell evolution (Keeling PJ et al, 2005). See also TBestDB below.

  5. Bioinformatics Development
  6. TBestDB organizes c-DNA and EST data from poorly-investigated protistan eukaryotes, generated by the pan-Canadian Protist EST program. Cross-referencing will be possible between the various PEP projects and with data from model organisms, including yeast, flatworm, but also cyanobacteria and alpha-proteobacteria, the ancestors of eukaryotic organelles. Interoparability with single-organism databases will be possible via the gene ontology framework (developped by the GO consortium). Released data are deposited into TBestDB for network access and download by the scientific community (O'Brien E et al, 2007).
    AutoFact is an automated pipeline to annotate EST sequences in a comprehensive and informative way. It is currently used to annotate PEP data, and available as open source.
    AnaBench, a web-based, integrated analysis environment, provides biologists access to diverse bioinformatics tools. The current prototype, which is accessible to the public, includes translation, Blast searches, multiple alignment, tRNA search, and more.
    Prediction of protein function and cellular localization. In typical genome projects, only ~50% of the protein-coding genes can be assigned to a function, and even less to a particular cellular location. This highlights the need of sensitive and efficient prediction methods. The main objective of this project is to apply machine learning methods (predictive data mining), to detect hidden signatures and patterns in integrated biological data, and to employ this new knowledge for deciphering genomic data at a large scale (Shen & Burger, 2008; Kannan & Burger, 2008a, b).

  7. Organelle Genome Megasequencing Program (OGMP)
  8. The OGMP is a collaborative project aiming at complete sequencing of mitochondrial and chloroplast genomes of a phylogenetically broad collection of mostly unicellular eukaryotes (Protista) (see Gray et al 2004, Annu Rev Genet).
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