Centro de Biotecnologia e Química Fina (CBQF)
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Browsing Centro de Biotecnologia e Química Fina (CBQF) by Sustainable Development Goals (SDG) "15:Proteger a Vida Terrestre"
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- Exploring the biodegradation of PET in mangrove soil and its intermediates by enriched bacterial consortiaPublication . Saidu, Muhammad Bashir; Moreira, Irina S.; Amorim, Catarina L.; Wu, Rongben; Ho, Yuen-Wa; Fang, James Kar-Hei; Castro, Paula M. L.; Gonçalves, DavidThe biodegradation of Polyethylene terephthalate (PET) is important due to the environmental impact of plastic waste. This study investigates the degradation of PET films in soil microcosms, with and without mangrove plants, and with mangrove plants bioaugmented with a bacterial consortium (Bacillus sp.- GPB12 and Enterococcus sp.- WTP31B-5) while following the evolution of soil microcosm microbiome. The ability of bacterial consortia retrieved from soil microcosms of each tested condition to degrade PET intermediates - bis(2-hydroxyethyl) terephthalate (BHET), terephthalic acid (TPA), and monoethylene glycol (MEG) was also assessed. In the microcosms’ assays with mangrove plants, variations in functional groups and surface morphology detected by FTIR and SEM analysis indicated PET degradation. Soil microcosms microbiome evolved differently according to the conditions imposed, with dominance of phylum Proteobacteria in all final microcosms. After 270 days, bacterial consortia retrieved from all soil microcosms revealed to be able to completely degrade TPA within three days. MEG degradation reached ca. 84% using the consortium retrieved from the microcosm with bioaugmented mangrove plants. BHETdegradation was ca. 96% with the consortium obtained from the microcosm with non-bioaugmented mangrove plants. These intermediates are key molecules in PET degradation pathways; thus, their degradation is an indicator of biodegradation potential. To the best of authors’ knowledge, this is the first report on biodegradation of PET, BHET, TPA, and MEG by microbial community from mangrove soil, providing insights into key taxa involved in PET degradation. These findings can pave a way to develop bioremediation strategies and more efficient waste management solutions.
- Occurrence, distribution and bioremediation of endocrine disrupting chemicals in coastal sediments from Macao SAR, ChinaPublication . Lebel, Alexandre; Jun, Liu; Moreira, Irina; Peng, Xianzhi; Castro, Paula; Gonçalves, DavidIntroduction: The presence of endocrine disrupting chemicals (EDCs) in municipal and industrial effluents is a major issue for marine and coastal environments near big cities and the situation of the Pearl River Delta (PRD), China, is particularly interesting as it is one of the most densely populated area of the world. The occurrence of EDCs in the PRD region is well documented but specific data related to Macau is still unavailable. The levels of bisphenol-A (BPA), estrone (E1), 17α-estradiol (αE2), 17β-estradiol (βE2), estriol (E3), and 17α-ethynylestradiol (EE2) were measured from sediment samples collected along the coastline and intracoastal waterway of Macau. Methods: Simultaneous analysis of six EDC’s in sediments was performed by using a combination of ultra-sonicated and solid phase extraction, followed by silica gel fractionation, derivatization with dansyl chloride, and determination by LC/MS. Additionally, the sediment samples were used for biodegradation assays in order to evaluate the capacity of the ecosystem to degrade BPA and BPS which were measured by HPLC. Results: BPA was found in all 46 collected samples; Lower values were associated with the presence of mangrove trees; Bacterial diversity is higher in the presence of BPS than of BPA; After 10 days, BPS was still present in most samples, although full degradation of BPA was achieved in a few days in all samples. Final remarks: Mangrove trees seem to facilitate the degradation of BPA. However, slower degradation of BPS may cause accumulation in sediments. Ongoing characterization of the microorganisms present in the collected sediments may contribute to the development of new bioremediation strategies.
- Unraveling the microbiome–environmental change nexus to contribute to a more sustainable world: a comprehensive review of artificial intelligence approachesPublication . Barbosa, Maria Inês; Silva, Gabriel; Ribeiro, Pedro; Vieira, Eduarda; Perrotta, André; Moreira, Patrícia; Rodrigues, Pedro MiguelThis review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability.