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Clean water is fundamental to human health and ecosystem integrity. However, water quality deteriorates due to novel anthropogenic pollutants present at microgram per liter concentrations in urban water cycles (termed micropollutants). Wastewater treatment plants (WWTP) have been identified as major point sources for aquatic (micro-)pollutants. Chemical and ecotoxicological analyses have shown that conventional biological WWTPs do not fully remove micropollutants and associated toxicities, which is often because of mobile, polar and/or recalcitrant compounds and transformation products (TPs). To minimize possible environmental risks, advanced wastewater treatment (AWWT) technologies could be a promising mitigation measure. Multiple processes are therefore being developed and evaluated such as ozonation and ozonation followed by granulated activated carbon (GAC) or biological filtration. Assessing the performance of these combined AWWTs was the focus the TransRisk project. Within this project, this thesis accomplished four major goals.
Firstly, the preparation of (waste)water samples was optimised for in vitro bioassays. Acidification, filtration and solid phase extraction (SPE) were tested for their impact on environmentally relevant in vitro endocrine activities, mutagenicity, genotoxicity and cytotoxicity. Significantly different outcomes of these assays were detected comparing neutral and acidified samples. Sample filtration had a lesser impact, but in some cases retention of particle-bound compounds could have caused significant toxicity losses. Out of three SPE sorbents the Telos C18/ENV at sample pH 2.5 extracted highest toxicity, some undetected in aqueous samples. These results indicate that sample preparation needs to be optimised for specific sample matrices and bioassays to avoid false-positive or -negative detects in effect-based analyses.
Secondly, the above listed in vitro toxicities were monitored in a protected region for drinking water production in South-West Germany (2012-2015). Out of 30 sampling sites surface water and groundwater were the least polluted. Nonetheless, a few groundwater samples induced high anti-estrogenic activity that prompted further monitoring. The latter included a waterworks in which no toxicity was detected. Hospital wastewater also had elevated in vitro toxicities and hospitals are, thus, relevant intervention points for source control. The biological WWTPs were effective in removing most of the detected toxicity, and the selected bioassays proved to be pertinent tools for water quality assessment and prioritisation of pollution hotspots.
Thirdly, the in vivo bioassay ISO10872 based on Caenorhabditis elegans (C. elegans) was adapted for this thesis. Using this model, a median effect concentration (EC50) for reproductive toxicity of the polycyclic aromatic hydrocarbon β-naphthoflavone (β- NF) of 114 µg/L was computed which is slightly lower than reported in the scientific literature. β-NF induced cyp-35A3::GFP (a biomarker in transgenic animals) in a time and concentration dependent manner (≤ 21.3–24 fold above controls). β-NF spiked wastewater samples supported earlier hypotheses on particle-bound pollutants. Reproductive toxicity (96 h) and cyp-35A3 induction (24 h) of biologically treated and/or ozonated wastewater extracts and growth promoting effects of GAC/biologically filtered ozonated wastewater extracts were observed. This suggested the presence of residual bioactive/toxic chemicals not included in the targeted chemical analysis. It also highlighted the importance of integrating multiple (apical and molecular) endpoints in wastewater assessments.
Fourthly, five in vitro and the adapted C. elegans bioassay were integrated into a wastewater quality evaluation (developed within TransRisk). Out of the five AWWT options, ozonation (at 1 g O3,applied/g DOC, HRT ~ 18 min) combined with nonaerated GAC filtration was rated most effective for toxicity removal. All five AWWTs largely removed estrogenic and (anti-)androgenic activities, but not anti-estrogenic activity and mutagenicity, which even increased during ozonation. This has been observed in related studies and points towards toxic TPs. These results also emphasized the need for implementing an effective post-treatment for ozonation. The results from a parallel in vivo study with Lumbriculus variegatus and Potamopyrgus antipodarum conducted on site at the WWTP (using flow through systems) were in accordance with the C. elegans results. In this context, it is suggested to further implement C. elegans as sensitive, feasible and ecologically relevant model.
In conclusion, this thesis shows how optimised sample preparation, long-term (in vitro) environmental monitoring, sensitive and ecologically relevant (in vivo) bioassays as well as innovative evaluation concepts, are pivotal in improving the removal of micropollutants and their toxicities with AWWTs. Future research should further develop and evaluate measures at sewer systems, conventional biological, tertiary and other advanced treatment technologies, as well as sociopolitical strategies (e.g., source control or natural conservation) and restoration projects. The effect-based tools optimised in this thesis will support assessing their success.
Bacterial biosynthetic assembly lines, such as non-ribosomal peptide synthetases (NRPS) and polyketide synthases, are often subject of synthetic biology – because they produce a variety of natural products invaluable for modern pharmacotherapy. Acquiring the ability to engineer these biosynthetic assembly lines allows the production of artificial non-ribosomal peptides (NRP), polyketides, and hybrids thereof with new or improved properties. However, traditional bioengineering approaches have suffered for decades from their very limited applicability and, unlike combinatorial chemistry, are stigmatized as inefficient because they cannot be linked to the high-throughput screening platforms of the pharmaceutical industry. Although combinatorial chemistry can generate new molecules cheaper, faster, and in greater numbers than traditional natural product discovery and bioengineering approaches, it does not meet current medical needs because it covers only a limited biologically relevant chemical space. Hence, methods for high-throughput generation of new natural product-like compound libraries could provide a new avenue towards the identification of new lead compounds. To this end, prior to this work, we introduced an artificial synthetic NRPS type, referred to as type S NRPS, to provide a first-of-its-kind bicombinatorial approach to parallelized high-throughput NRP library generation. However, a bottleneck of these first two generations of type S NRPS was a significant drop in production yields. To address this issue, we applied an iterative optimization process that enabled titer increases of up to 55-fold compared to the non-optimized equivalents, restoring them to wild-type levels and beyond.
Rationale: The AMP-activated protein kinase (AMPK) is stimulated by hypoxia, and although the AMPKα1 catalytic subunit has been implicated in angiogenesis, little is known about the role played by the AMPKα2 subunit in vascular repair.
Objective: To determine the role of the AMPKα2 subunit in vascular repair.
Methods and Results: Recovery of blood flow after femoral artery ligation was impaired (>80%) in AMPKα2-/- versus wild-type mice, a phenotype reproduced in mice lacking AMPKα2 in myeloid cells (AMPKα2ΔMC). Three days after ligation, neutrophil infiltration into ischemic limbs of AMPKα2ΔMC mice was lower than that in wild-type mice despite being higher after 24 hours. Neutrophil survival in ischemic tissue is required to attract monocytes that contribute to the angiogenic response. Indeed, apoptosis was increased in hypoxic neutrophils from AMPKα2ΔMC mice, fewer monocytes were recruited, and gene array analysis revealed attenuated expression of proangiogenic proteins in ischemic AMPKα2ΔMC hindlimbs. Many angiogenic growth factors are regulated by hypoxia-inducible factor, and hypoxia-inducible factor-1α induction was attenuated in AMPKα2-deficient cells and accompanied by its enhanced hydroxylation. Also, fewer proteins were regulated by hypoxia in neutrophils from AMPKα2ΔMC mice. Mechanistically, isocitrate dehydrogenase expression and the production of α-ketoglutarate, which negatively regulate hypoxia-inducible factor-1α stability, were attenuated in neutrophils from wild-type mice but remained elevated in cells from AMPKα2ΔMC mice.
Conclusions: AMPKα2 regulates α-ketoglutarate generation, hypoxia-inducible factor-1α stability, and neutrophil survival, which in turn determine further myeloid cell recruitment and repair potential. The activation of AMPKα2 in neutrophils is a decisive event in the initiation of vascular repair after ischemia.
In the published article, there was an error regarding the affiliation for Diana Abondano Almeida. As well as having affiliation 2, they should also have Department of Wildlife-/Zoo-Animal-Biology and Systematics, Faculty of Biological Sciences, Goethe Universität, Frankfurt, Germany.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions, using signals from nanopore direct RNA sequencing. CHEUI processes observed and expected signals with convolutional neural networks to achieve high single-molecule accuracy and outperform other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.