Ass. Prof. Dr. Lucie Bittner
From omic dark matter to traits: Exploring the (meta)omic bases of traits within microbial eukaryotes
The advent of high-throughput sequencing approaches has unveiled the extent of Earth biodiversity and revealed our ignorance with respect to the role of this diversity in ecosystems’ functioning. The fundamental molecular mechanisms associated to the ecological traits of organisms are poorly known, and often restricted to model organisms. For instance, symbiotic relationships are widespread and are critically important for the functioning of ecosystems but the genomic bases of the establishment and the maintenance of these associations remain largely unknown, and this especially between unicellular eukaryotes. Besides, the study of omic datasets from holobiont(s) involving non-model lineages represents bioinformatic challenges such as the production of chimeric sequences or deciphering the taxonomic origin of the sequences. Finally, the vast majority of these molecular sequences remain functionally unknown, limiting the analyses to a subpart of the genomic data newly produced. My research focuses on developing strategies to circumvent these pitfalls. I will present approaches developed in my team, that we applied on marine holobionts involving non model unicellular eukaryotic partners. First, using k-mer based similarity methods and independent assemblies, I will show how a significant diminution of de novo assembled chimeras compared to classical assembly methods can be obtained. Second, using sequence similarity network analyses, I will illustrate how one can investigate the (meta)genomic basis of organismal traits, while including the functionally unknown sequences. Following this strategy, we identified candidate protein domains associated to traits, and notably here to symbiosis. These genomic markers constitute working hypotheses, to be further confirmed by targeted molecular studies. This exercice represents one of the very few studies available to date to expand our knowledge about traits of non-model organisms, while exploring (meta-)omic datasets, and offers perspectives to study the ecology and evolution of microorganisms, this time concretely and truly, at a massive scale.
Prof. Dr. Marcel Deponte
University of Kaiserslautern
The oxidative stress hypothesis in malaria research: facts and fiction
The redox metabolism of the malaria parasite Plasmodium falciparum and its human host has been suggested to play a central role for parasite survival and clearance. For example, excessive hemoglobin degradation within the erythrocyte as well as rapid parasite growth and DNA synthesis are thought to cause intrinsic metabolically derived oxidative stress. Oxidative stress in malaria parasites was also suggested to be caused by extrinsic factors, including the immune system as well as genetic traits that are selected in malaria-endemic areas and that result in (partial) protection of the human host. Furthermore, oxidative stress might be involved in the mode of action of several antimalarial drugs. I will present and discuss supporting as well as conflicting data regarding the oxidative stress hypothesis in malaria research and will highlight current conceptual limitations and their general implications for redox research.
Prof. Dr. Jan Pawlowski
University of Geneva, Geneva, Switzerland
Polish Academy of Sciences, Poland
ID-Gene ecodiagnostics, Ltd, Switzerland
Protist metabarcoding and next generation biomonitoring
Biological monitoring is being revolutionized by the application of DNA barcoding and metabarcoding for species identification, biodiversity inventorying, and assessment of environmental impacts. Compared to the traditional biomonitoring that is based on visual observation and morphological identification of a few well-known, easily identifiable taxa, the DNA-based biomonitoring offers the possibility to expand the range of bioindicators and to take advantage of highly sensitive but often inconspicuous and difficult to identify groups of organisms. Some groups of protists have already been successfully tested as potential candidates to become new generation of bioindicators. However, wider application of these groups is often impeded by limited knowledge of their ecology, gaps in reference database for taxonomic assignment and biases related to quantitative interpretation of metabarcoding data. To overcome some of these limitations, taxonomy-free approaches to analyse metabarcoding data have been proposed recently. These approaches are based either on indicator values assigned directly to metabarcodes or on biotic indices predicted using machine learning analysis of training datasets of metabarcodes. The machine-learning has been shown to be as efficient as traditional biomonitoring for environmental impact assessment of some industrial activities. Further development of taxonomy-free approaches opens unlimited opportunities to use protists for fast, sensitive and cost-effective bioindication. However, to fully integrate them into regulatory compliant routine practice more research is needed to better understand the response of protist community to environmental pressures and to better manage the limitations and challenges of the new technology.
Dr. Bettina Sonntag
Lost world: Return of ciliates into planktonic food web analyses
In an international research project, we follow an interdisciplinary approach studying freshwater planktonic protists with a focus on ciliates. Our goal was to integrate and match morphological, molecular and ecological datasets to elucidate the autecology of ciliate species in aquatic food webs. In detail, we studied natural plankton assemblages in Lake Mondsee (Austria) and Lake Zurich (Switzerland) over a one-year cycle in biweekly intervals along vertical depths gradients. Apart from measuring abiotic parameters, we investigated almost all heterotrophic, autotrophic and mixotrophic protists as well as zooplankton and viruses. All organism-based analyses were carried out in parallel from a morphological quantitative assessment via microscopy, from single-cell sequencing of ciliates and from high throughput sequencing of raw water samples. Based on these morphological and molecular datasets, co-occurrence networks were constructed and key ciliate players in the two lakes identified. The networks in turn provided the basis for species-specific functional and numerical response experiments revealing predator-prey relationships, e.g., between an alga and a ciliate. Moreover, we involve citizens and pupils to understand that aquatic food webs form the basis for lake ecosystem functioning and why basic research is important. Overall, this challenging D-A-CH approach includes many networking scientists, students and co-workers who put their heart and soul into this interesting project finally gaining insight into a ciliates’ point of view living in an aquatic microbial food web.