Biology subjects

Bueschl, C.

Publications and source records attributed to Bueschl, C..

10 recordsLinked to original sources

bioRxiv2026

Beyond efficacy: Persistence and off-target effects of three biological nitrification inhibitors in two contrasting agricultural soils

The use of nitrogen (N) fertilizers to meet global food demands is expected to continue rising. However, up to 70% of N applied to agricultural soils is lost through microbially mediated processes such as nitrification. Inhibiting nitrification is thus a key strategy to reduce N losses and improve fertilizer N use efficiency. Various plant-derived compounds, termed biological nitrification inhibitors (BNIs), have been shown to reduce accumulation of nitrification products, intermediates, and byproducts (nitrite, nitrate, nitric and nitrous oxides). However, the mechanisms by which BNIs affect nitrifiers, along with their specificity and persistence in soil are not well understood. Here, we evaluated the effects of three BNIs: methyl 3-(4-hydroxyphenyl) acrylate (MHPA), 6-methoxy-2(3H)-benzoxazolone (MBOA), and limonene, on ammonia-oxidizing, total microbial, and fungal communities in two soils with contrasting pH. Their persistence in each soil was also evaluated. Although ammonia-oxidizing archaea initially dominated nitrifier communities in both soils, their bacterial counterparts significantly increased after mineral N addition but also were more sensitive to BNI application. Limonene and the synthetic inhibitor DMPP stimulated ammonium immobilization, as total soil mineral N was significantly reduced. Limonene and MHPA had the strongest off-target effects, increasing the relative abundance of hydrocarbon-degrading bacteria and potential fungal pathogens, respectively. In contrast, MBOA inhibited nitrification with minimal off-target effects. Among the tested BNIs, MBOA was also the most persistent in the high-pH, high-nitrification-rate soil. Our results show that MBOA is a promising biological inhibitor and highlight the importance of understanding BNIs ecological effects to develop targeted and sustainable N management strategies.

microbiology↗

bioRxiv2026

Potential of exogenous biological nitrification inhibitor addition to improve soil nitrogen availability for crop growth.

Modern agriculture is characterized by substantial fertilizer nitrogen (N) losses from soils, resulting in low crop N-use efficiency. Biological nitrification inhibitors (BNIs) are studied as a strategy to improve N retention in soils by suppressing nitrification. However, the impacts of applying exogenous BNIs to crops with unknown intrinsic BNI capacity remain poorly understood. In this study, we evaluated the impacts of adding three BNIs (methyl 3-(4-hydroxyphenyl) acrylate [MHPA], 6-methoxy-2(3H)-benzoxazolone [MBOA], and limonene), their mixture, and the synthetic nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) on barley (Hordeum vulgare L.) growth, plant and soil N dynamics, and soil microbial communities. Using a rhizobox system with planted and bare-soil compartments, combined with 15N isotope tracing and molecular microbial community analyses, we assessed the spatio-temporal dynamics of N transformations, losses, plant N uptake, and microbial community responses in an alkaline agricultural soil. Independent of inhibitor application, the applied fertilizer N was lost primarily through NO- leaching (3-9% of the applied N). In contrast, N2O emissions represented only 0.001-0.028% of the applied N and varied with inhibitor type. MHPA increased dissolved inorganic N soil pools without affecting plant biomass or 15N uptake or strongly shifting microbial community composition. MBOA reduced NO3- concentrations in soil pore water without influencing plant growth or N uptake but shifted soil microbial community composition. In contrast, limonene reduced plant growth and 15N uptake and most significantly altered microbial community composition, without significantly changing N availability. Applying a BNI mixture, as well as limonene alone, was detrimental to plant growth and 15N uptake. DMPP showed only minor effects on N pools, plant growth, plant N uptake and microbial community composition. Overall, our results reveal both the potential and limitations of exogenous BNI application for improving N retention in crop systems.

microbiology↗

bioRxiv2025

Functional profiling reveals a non-enzymatic role of NUDT5 in repressing purine de novo synthesis

Folate metabolism is intricately linked to purine de novo synthesis through the incorporation of folate-derived one-carbon units into the purine scaffold. Here, we investigate the chemical and genetic dependencies caused by mutations in the folate enzyme MTHFD1 and discover a key role for Nudix hydrolase 5 (NUDT5) in regulating purine de novo synthesis. Through genetic knockout and development of a selective chemical NUDT5 degrader, we uncover an unprecedented scaffolding role rather than NUDT5 enzymatic activity is responsible for this phenotype. We find that NUDT5 interacts with the rate-limiting enzyme of purine de novo synthesis, PPAT, to repress the pathway in response to elevated purine levels. Our findings establish NUDT5 as an important regulator of purine de novo synthesis and elucidate its role in mediating sensitivities to 6-thioguanine in cancer treatment and to adenosine in MTHFD1 deficiency.

molecular biology↗

bioRxiv2024

Metabolic mapping of the human solute carrier superfamily

Solute carrier (SLC) transporters govern most of the chemical exchange across cellular membranes and are integral to metabolic regulation, which in turn is linked to cellular function and identity. Despite their key role, individual functions of the members of the SLC superfamily were not evaluated systematically. We determined the metabolic and transcriptional profiles upon SLC overexpression in knock-out or wild-type isogenic cell backgrounds. Targeted metabolomics provided a fingerprint of 189 intracellular metabolites, while transcriptomics offered insights into cellular programs modulated by SLC expression. Beyond the metabolic profiles of 102 SLCs directly related to their known substrates, we also identified putative substrates or metabolic pathway connections for 71 SLCs without previously annotated bona fide substrates, including SLC45A4 as a new polyamine transporter. By comparing the molecular profiles, we identified functionally related SLC groups, including some with distinct impacts on osmolyte balancing and glycosylation. The assessment of functionally related human genes presented here may serve as a blueprint for other systematic studies of human gene function and supports future investigations into the functional roles of SLCs.

systems biology↗

bioRxiv2023

Tailored mass spectral data exploration using the specXplore interactive dashboard

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two dimensional t-SNE embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of of state of the art ms2deepscore pairwise spectral similarities as a quantitative backbone, while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part in mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest. Technical TermsO_LIA network is a collection of connected features. In our case, a network consists of MS/MS spectral features connected provided their spectral similarity is high. Networks are represented using node-link-diagrams. C_LIO_LINode-link diagram -a term commonly used to refer to the graphical representation of a network via nodes and links (i.e. edges). In this paper, we use node-link diagram and network-view interchangeably. C_LIO_LIA node is a feature in a network that can be connected to other features via edges. An alternative term for node is vertex. C_LIO_LIAn edge is a connection between two nodes. Other terms for edges are links or vertices. C_LIO_LINetwork layout refers to the spatial arrangement of nodes and edges on an usually two dimensional plotting surface. Network layout is also sometimes referred to as embedding. This term is avoided in this paper to avoid confusion with embedding in the machine learning sense. C_LIO_LIGiven a network G(V, E), where V denotes its nodes and E its (weighted) edges, we define its topology as the relationships between individual (groups of) nodes and edges or the network as a whole, irrespective of the networks layout. C_LIO_LIMolecular Networking (MN) is an exploratory data analysis technique merging spectral similarity-based topological clustering and visualization as node-link diagrams. C_LIO_LIThe plain English words group/grouping are wherever appropriate to avoid jargon terms such as clustering (as in k-medoid or k-means clustering), embedding (as in projection of groups of features into a close-by lower dimensional space), or molecular families. The latter are groups of spectral data features clustered and visualized as network-views via traditional MN or feature based molecular networking (FBMN). Molecular families, usually represent smaller, disconnected networks that are part of a larger dataset. When we refer to this disconnected nature, we use the phrasing disjoint sub-network for emphasis. C_LI

bioinformatics↗

bioRxiv2022

Homologue Series Detection and Management in LC-MS data with homologueDiscoverer

SummaryUntargeted metabolomics data analysis is highly labor intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series are a particular case of features that can either represent large numbers of noise features, or alternatively represent features of interest with large peak redundancy. Here we present homologueDiscoverer, an R package which allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AvailabilityhomologueDiscoverer is freely available at github https://github.com/kevinmildau/homologueDiscoverer. Contactkevin.mildau@univie.ac.at, christoph.bueschl@boku.ac.at, juergen.zanghellini@univie.ac.at

bioinformatics↗

bioRxiv2022

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS is quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB, and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5% to 4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

systems biology↗

bioRxiv2022

Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii) contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

bioinformatics↗

bioRxiv2021

CPExtract, a Software for the Automated Tracer-Based Pathway Specific Screening of Secondary Metabolites in LC-HRMS Data

The use of stable isotopically labeled tracers is a long-proven way of specifically detecting and tracking derived metabolites through a metabolic network of interest. While recently developed stable isotope assisted methods and associated, supporting data analysis tools have greatly improved untargeted metabolomics approaches, no software tool is currently available that allows to automatically search LC-HRMS chromatograms for completely free user-definable isotopolog patterns expected for the metabolism of labeled tracer substances. Here we present Custom Pattern Extract (CPExtract), a versatile software tool that allows for the first time the high-through-put search for user-defined isotopolog patterns in LC-HRMS data. The patterns can be specified via a set of rules including the presence or absence of certain isotopologs, their relative intensity ratios as well as chromatographic co-elution. Each isotopolog pattern satisfying the respective rules is verified on a MS-scan level and also in the chromatographic domain. The CPExtract algorithm allows the use of both labeled tracer compounds in non-labeled biological samples as well as a reversed tracer approach, employing non-labeled tracer compounds along with globally labeled biological samples. In a proof of concept study we searched for metabolites specifically arising from the malonate pathway of the filamentous fungi Fusarium graminearum and Trichoderma reesei. 1,2,3-13C3-malonic acid diethyl ester and native malonic acid monomethyl ester were used as tracers. We were able to reliably detect expected fatty acids and known polyketides. In addition, up to 189 and 270 further, unknown metabolites presumably including novel polyketides were detected in the F. graminearum and T. reesei culture samples respectively, all of which exhibited the user-predicted isotopolog patterns originating from the malonate tracer incorporation. The software can be used for every conceivable tracer approach. Furthermore, the rule sets can be easily adapted or extended if necessary. CPExtract is available free of charge for non-commercial use at https://metabolomics-ifa.boku.ac.at/CPExtract.

bioinformatics↗

bioRxiv2021

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBots architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AvailabilityThe package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. Contactchristoph.bueschl@univie.ac.at Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗