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Unravelling multiple removal pathways of oseltamivir in wastewater by microalgae through experimentation and computation.

ABSTRACT: Increased worldwide consumption of antiviral drugs (AVDs) amid COVID-19 has induced enormous burdens to the existing wastewater treatment systems. Microalgae-based bioremediation is a competitive alternative technology due to its simultaneous nutrient recovery and sustainable biomass production. However, knowledge about the fate, distribution, and interaction of AVDs with microalgae is yet to be determined. In this study, a concentration-determined influence of AVD oseltamivir (OT) was observed on the biochemical pathway of Chlorella sorkiniana (C.S-N1) in synthetic municipal wastewater. The results showed that high OT concentration inhibited biomass growth through increased oxidative stress and restrained photosynthesis. Nevertheless, complete OT removal was achieved at its optimized concentration of 10 mg/L by various biotic (82%) and abiotic processes (18.0%). The chemical alterations in three subtypes of extracellular polymeric substances (EPS) were primarily investigated by electrostatic (OT +8.22 mV vs. C.S-N1 -18.31 mV) and hydrophobic interactions between EPS-OT complexes supported by secondary structure protein analysis. Besides, six biodegradation-catalyzed transformation products were identified by quadrupole-time-of-flight mass spectrometer and by density functional theory. Moreover, all the TPs exhibited log Kow ≤ 5 and bioconcentration factor values of < 5000 L/kg, meeting the practical demands of environmental sustainability. This study broadens our understanding of microalgal bioadsorption and biodegradation, promoting microalgae bioremediation for nutrient recovery and AVDs removal.

SUBMITTER: Zeeshan QM

PROVIDER: S-EPMC8713958 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEvery year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment.ResultsThe current work describes the implementation of two approaches for robustly identifying reassortment events. The algorithms rest on the idea of significance of difference between phylogenetic trees or phylogenetic tree sets, and subtree pruning and regrafting operations, which mimic the effect of reassortment on tree topologies. The first method is based on a maximum likelihood (ML) framework (MLreassort) and the second implements a Bayesian approach (Breassort) for reassortment detection. We focus on reassortment events that are found by both methods. We test both methods on a simulated dataset and on a small collection of real viral data isolated in Hong Kong in 1999.ConclusionsThe nature of segmented viral genomes present many challenges with respect to disease. The algorithms developed here can effectively identify reassortment events in small viral datasets and can be applied not only to influenza but also to other segmented viruses. Owing to computational demands of comparing tree topologies, further development in this area is necessary to allow their application to larger datasets.

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Unravelling multiple removal pathways of oseltamivir in wastewater by microalgae through experimentation and computation.

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