Certain changes in the aquatic environment affect the DNA structures of unicellular algae, particularly diatoms. Scientists are now planning to use them as bioindicators to monitor water parameter. However, it is still very difficult to identify in river samples at microscopic levels.
To get out from this issue, a team of biologists from University of Geneva (UNIGE), Switzerland have established a water quality index based on the DNA sequences of Diatoms. In the journal of Molecular Ecology Resources researchers explained, the index presents a revolutionary tool that allows scientists to process a large number of samples without identifying each species visually.
Human activities are resulting water pollution in the river, and the degree of pollution is reflecting the ecological status of a river. Several organisms have different ecological preferences and pollution tolerance that can be used as the bioindicators. The European Union and Switzerland defined diatoms as the perfect bioindicator for rivers and lakes.
Professor of Genetics and Evolution from the UNIGE Faculty of Science, Jan Pawlowski said,“The morphological identification of the different species present in each sample. However, no longer meets the needs of rapid and reliable bioassessment measures introduced to protect aquatic environments”. his team has prepared a Swiss diatom index (DI-CH) to determine the ecological status of rivers.
As per the report by Phys, researchers have collected more than 90 samples from the different rivers of Switzerland and analyzed the ecological status by using DI-CH. With the collaboration of Geneva Water Ecology Service (SECOE) and the PhycoEco, environmental office researchers created a molecular index from the DNA sequences characteristic of all the diatom species.
Researchers got the success for more than 80 percent of the samples by using this Swiss diatom index. Pawlowski also explained that increase the number diversity of samples will allow them to calibrate their method for the future routine that could be effective for large-scale analyses.
