Characterization and biofouling potential analysis of two cyanobacterial strains isolated from Cape Verde and Morocco.
Maria João Leal RomeuJoão MoraisLuciana C GomesRaquel SilvaVítor VasconcelosFilipe J M MergulhãoPublished in: FEMS microbiology ecology (2023)
Cyanobacteria are new sources of value-added compounds but also ubiquitous and harmful microfoulers on marine biofouling. In this work, the isolation and identification of two cyanobacterial strains isolated from Cape Verde and Morocco, as well as their biofilm-forming ability on glass and Perspex under controlled hydrodynamic conditions were performed. Phylogenetic analysis revealed that cyanobacterial strains isolated belong to Leptothoe and Jaaginema clade (Leptothoe sp. LEGE 181153 and Jaaginema sp. LEGE 191154). From quantitative and qualitative data of wet weight, chlorophyll a content and biofilm thickness obtained by Optical Coherence Tomography (OCT), no significant differences were found in biofilms developed by the same cyanobacterial strain on different surfaces (glass and Perspex). However, the biofilm-forming potential of Leptothoe sp. LEGE 181153 proved to be higher when compared to Jaaginema sp. LEGE 191154, particularly at the maturation stage of biofilm development. Three-dimensional biofilm images obtained from Confocal Laser Scanning Microscopy (CLSM) showed different patterns between both cyanobacterial strains and also among the two surfaces. Since standard methodologies to evaluate cyanobacterial biofilms formation, as well as two different optical imaging techniques were used, this work also highlights the possibility to integrate different techniques to evaluate a complex phenomenon like cyanobacterial biofilm development.
Keyphrases
- candida albicans
- optical coherence tomography
- pseudomonas aeruginosa
- biofilm formation
- staphylococcus aureus
- high resolution
- escherichia coli
- south africa
- diabetic retinopathy
- body mass index
- systematic review
- drinking water
- optic nerve
- single molecule
- high throughput
- electronic health record
- convolutional neural network
- climate change
- mass spectrometry
- electron microscopy