Abstract: Production of struvite (MgNH4PO4·6H2O) from waste streams is increasingly implemented to recover phosphorus (P), which is listed as a critical raw material in the European Union (EU). To facilitate EU-wide trade of P-containing secondary raw materials such as struvite, the EU issued a revised fertilizer regulation in 2019. A comprehensive overview of the supply of struvite and its quality is presently missing. This study aimed: i) to determine the current EU struvite production volumes, ii) to evaluate all legislated physicochemical characteristics and pathogen content of European struvite against newly set regulatory limits, and iii) to compare not-regulated struvite characteristics. It is estimated that in 2020, between 990 and 1250 ton P are recovered as struvite in the EU. Struvite from 24 European production plants, accounting for 30% of the 80 struvite installations worldwide was sampled. Three samples failed the physicochemical legal limits; one had a P content of <7% and three exceeded the organic carbon content of 3% dry weight (DW). Mineralogical analysis revealed that six samples had a struvite content of 80–90% DW, and 13 samples a content of >90% DW. All samples showed a heavy metal content below the legal limits. Microbiological analyses indicated that struvite may exceed certain legal limits. Differences in morphology and particle size distribution were observed for struvite sourced from digestate (rod shaped; transparent; 82 mass% < 1 mm), dewatering liquor (spherical; opaque; 65 mass% 1–2 mm) and effluent from upflow anaerobic sludge blanket reactor processing potato wastewater (spherical; opaque; 51 mass% < 1 mm and 34 mass% > 2 mm). A uniform soil-plant P-availability pattern of 3.5–6.5 mg P/L soil/d over a 28 days sampling period was observed. No differences for plant biomass yield were observed. In conclusion, the results highlight the suitability of most struvite to enter the EU fertilizer market.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) and glioblastoma multiforme (GBM) are two of the most malignant solid tumor types with poor survival rates, which underscore the urgency of novel and efficacious treatment strategies. Within the last decade, immunotherapy has been established as a breakthrough in cancer therapy. This mainly has been driven by the clinical data and approval associated with several immune checkpoint inhibitors (e.g. anti-CTLA-4 and anti-PD-1/L1). Despite the clinical benefit in specific tumor types, these inhibitors have not yet fulfilled their promise in low immunogenic tumors such as PDAC and GBM. Oxidative stress in cancer cells due to elevated reactive oxygen species (ROS) and an inability to balance intracellular redox state has recently been highlighted as promising target for anticancer treatment strategies with possible immunogenic effects. In this PhD dissertation, I investigated novel oxidative stress-mediated treatment approaches to target PDAC and GBM and to enhance immunogenicity by inducing immunogenic cell death (ICD). In the first part of this thesis (chapter 2), I reviewed the mechanistic responses of cancer cells towards different oxidative stress-inducing treatment strategies and their immunomodulating effects. The resulting literature demonstrated that different exogenous and endogenous ROS-inducing therapies show direct and indirect immunomodulating effects, which can be either immunostimulatory or immunosuppressive. One of the indirect immunostimulatory effects of the ROS-mediating therapies is the capacity of inducing immunogenic cell death (ICD) in tumor cells, which can increase the immunogenicity and consequently can trigger an antitumoral immune response. In chapter 3, I investigated a novel exogenous ROS-inducing treatment method, namely cold atmospheric plasma, to determine the therapeutic and ICD-inducing effects in PDAC, in vitro. I revealed that plasma-treated PBS (pPBS) has the potential to induce ICD in pancreatic cancer cells (PCCs) and to reduce the immunosuppressive tumor microenvironment (TME) by attacking the tumor supportive pancreatic stellate cells (PSCs). Although the cell death induced in PSCs was non-immunogenic as seen by the lack of danger-associated molecular patterns (DAMPs) emission and DC activation, I showed that pPBS could disrupt the physical barrier and lower the immunosuppressive secretion profile (lower TGF-β) of PSCs. In contrast, DAMPs were released by PCCs after treatment with pPBS which resulted in activation and maturation of DCs and a more immunostimulatory secretion profile (higher TNF-α, IFN-γ). Hence, indirect plasma treatment via pPBS has the potential to enhance immunogenicity in PDAC by triggering ICD and by attacking the immunosuppressive PSCs. Tumor cells can evolve adaptation mechanisms to protect themselves against intrinsic oxidative stress by upregulation of pro-survival molecules and their antioxidant defense system to maintain the redox balance. As such, tumor cells can become resistant towards exogenous ROS-inducing therapies, like plasma. Dual targeting of the redox balance of tumor cells by increasing exogenous levels of ROS and inhibiting the antioxidant defense system can maximally exploit ROS-mediated cell death mechanisms as therapeutic anticancer strategy. In this regard, cold atmospheric plasma was combined with auranofin, a thioredoxin reductase inhibitor, in GBM (chapter 4). A synergistic effect was shown after this combination treatment in 2D and 3D, however, in 3D only high concentrations of auranofin synergized with plasma treatment. I confirmed a ROS-mediated response after combination treatment, which was able to induce distinct cell death mechanisms, specifically apoptosis and ferroptosis. Additionally, the auranofin and plasma combined treatment strategy induced cell death, which resulted in an increased release of DAMPs. Together with the observed DC maturation, these results indicates the potential increase in immunogenicity, though, the phagocytotic capacity of DCs was inhibited by auranofin. In chapter 5, I evaluated this promising oxidative stress combination therapy in GBM, in vivo. A decrease in tumor kinetics and an increased survival in GBM-bearing mice was observed when auranofin was sequentially combined with direct plasma treatment. No T cell infiltration was observed after auranofin monotherapy. However, further characterization of the TME after the combination therapy is necessary to provide more insight in the immunogenic effects in vivo. In conclusion, this PhD dissertation comprises novel and important therapeutic and immunogenic insights in cold atmospheric plasma and auranofin as promising oxidative stress-mediated treatment strategies for low immunogenic tumors, like PDAC and GBM. These preclinical results provide a solid basis for future research towards combinations with immunotherapeutic approaches.

Abstract: Quantitative PCR (qPCR) is broadly used as the gold standard to quantify microbial community fractions in environmental microbiology and biotechnology. Benchmarking efforts to ensure the comparability of qPCR data for environmental bioprocesses are still scarce. Also, for partial nitritation/anammox (PN/A) systems systematic investigations are still missing, rendering meta-analysis of reported trends and generic insights potentially precarious. We report a baseline investigation of the variability of qPCR-based analyses for microbial communities applied to PN/A systems. Round-robin testing was performed for three PN/A biomass samples in six laboratories, using the respective in-house DNA extraction and qPCR protocols. The concentration of extracted DNA was significantly different between labs, ranged between 2.7 and 328 ng mg−1 wet biomass. The variability among the qPCR abundance data of different labs was very high (1−7 log fold) but differed for different target microbial guilds. DNA extraction caused maximum variation (3–7 log fold), followed by the primers (1–3 log fold). These insights will guide environmental scientists and engineers as well as treatment plant operators in the interpretation of qPCR data.

Abstract: Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.