Biology subjects

Rutz, A.

Publications and source records attributed to Rutz, A..

8 recordsLinked to original sources

bioRxiv2026

ChEBI-N - Enrichment Analysis with the ChEBI Ontology

Introduction: Biological and chemical ontologies are structured representations that can be used to map entities such as metabolites onto broader chemical and biological categories. Enrichment analysis applied to such ontologies identifies classes whose representation within a set deviates from an expected background distribution, helping to interpret complex metabolomics measurements. Objectives: This study aimed to develop a tool for ChEBI (Chemical Entities of Biological Interest) ontology-based enrichment analysis, named ChEBI-N (ChEBI eNrichment). It is a successor of the previous tool BiNChE, with new features that broaden its applicability. Methods: We reimplemented BiNChE and extended it to accept SMILES or InChI strings as well as ChEBI IDs. These broaden applicability to datasets with entities that are not yet present in ChEBI. We also constructed organism-specific background populations. Results: We demonstrate the tool on three published metabolomics datasets covering wine, cheese, and human plasma samples. ChEBI-N correctly highlighted relevant enriched classes in all three datasets, such as flavonoids in the wine dataset and fatty acid classes in the cheese dataset. Comparing organism-specific and full-ChEBI backgrounds shows that restricted backgrounds reduce the inflated significance of generic classes, although the most enriched classes remain largely consistent. Conclusion: ChEBI-N is an extended successor to BiNChE for ontology-based enrichment analysis of metabolomics data, offering a hypothesis-generating complement to existing metabolomics analysis pipelines. Availability: ChEBI-N is freely available at chebin.hastingslab.org or as a Python package (pypi.org/project/chebin), with source code at github.com/ontology-tools/chebin, archived at Zenodo (doi:10.5281/zenodo.23264219) and Software Heritage (swh:1:rev:133a41792704eb7f1db746f36a5ae97e9247b2b7).

bioinformatics↗

bioRxiv2026

Direct Binding of Cysteine-367 Thiolate to the Active Site of the -Hydrogenase from Clostridium beijerinckii in the O2-stable State

[FeFe]-hydrogenases are very active biocatalysts for H2 conversion. However, their active site is vulnerable to irreversible degradation initiated by O2 binding at the catalytic iron ion (Fed) of the active center. CbA5H, the [FeFe]-hydrogenases from Clostridium beijerinckii exhibits stability towards oxygen (O2) due to its ability to reversibly enter an inactive state termed Hinact upon contact with O2. We previously proposed that the close distance of approximately 3.1 [A] between the thiol of a nearby cysteine (C367) and the Fed, based on a 2.9 [A] crystal structure of CbA5H in the Hinact state, enables their binding to each other. This binding therefore was suggested to shield the Fed from O2 damage. However, there is currently a lack of evidence to support this hypothesis. Furthermore, density functional theory (DFT) calculations based on a homologous model favored hydroxide as the binding ligand of the Fed over the thiol of C367. In this study, we present the crystal structure of CbA5H in the Hinact state at an improved resolution of 2.15 [A]. The structure reveals a direct binding between the thiol of C367 and the Fed with a distance of approximated 2.77 [A] which is well supported by our DFT calculations based on the new crystallographic data. It is noteworthy that the 2.77 [A] bond distance is strikingly long when compared with other iron-sulfur bonds. This finding may provide a crucial foundation for understanding the rapid reversibility of the Hinact state.

biochemistry↗

bioRxiv2025

Extracellular polymeric substance degradation shapes microbial community diversity

Competition for and exchange of nutrients play crucial roles in shaping microbial community function and dynamics. Although cross-feeding of small metabolites is known to drive carbon exchange among species, the importance of self-produced extracellular polymeric substances (EPS)-- which include proteins, polysaccharides, DNA, and humic-like substances-- remains less understood. Utilizing chitin-degrading microbial isolates and natural seawater communities, we found that 4-7% of the carbon from chitin degradation is converted into EPS, accounting for nearly a quarter of the exuded carbon. Different sources of EPS were found to select for distinct microbial communities. Through enzyme assays and untargeted metabolomics, we demonstrated that secreted enzymes degrade EPS in multiple steps that influence community diversity: larger oligomers are initially utilized by specialized degraders, while the subsequent breakdown into smaller oligomers, monosaccharides, and amino acids supports non-specialized consumers. These findings highlight the role of EPS as a significant carbon source exchanged between microbes, fueling metabolically diverse populations and enriching our understanding of carbon-mediated microbial interactions.

microbiology↗

bioRxiv2025

SAPID: a Strategy to Analyze Plant Extracts Taste In Depth. Application to the complex tasteof Swertia chirayita (Roxb.) H.Karst.

Bitterness is challenging to analyze due to the diversity of bitter compounds, variability in sensory perception, and its interplay with other tastes. To address this, we developed an untargeted approach to deconvolute the taste and molecular composition of complex plant extracts. We applied our methodology to an ethanolic extract of Swertia chirayita (Roxb.) H.Karst., a plant known for its unique bitterness. Chemical characterization was performed through nuclear magnetic resonance spectroscopy experiments together with untargeted liquid chromatography-high resolution tandem mass spectrometry analysis coupled to a charged aerosol detector. After clustering the fractions based on chemical similarity, we performed free sensory analysis and classical descriptive analysis on each cluster. Our results confirmed the attribution of bitterness to iridoids and highlighted the role of other important compounds in the overall taste. This method offers a systematic approach to analyzing and enhancing the taste profiles of plant-based beverages. HighlightsO_LIAn untargeted method to analyze in depth plant extracts taste and chemical composition has been developed. C_LIO_LIThe attribution of the bitterness of an ethanolic extract of Swertia chirayita (Roxb.) H.Karst. to well-known bitter major and minor iridoids was confirmed using untargeted methods. C_LIO_LIChemically informed tasting allowed to highlight other less pronounced tastes within the extract, contributing to its overall complexity. C_LIO_LIChemically informed tasting led to interesting insights into the sub-threshold impact on taste and taste modulating properties. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/628830v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1b0c4a3org.highwire.dtl.DTLVardef@3db05aorg.highwire.dtl.DTLVardef@50ddeorg.highwire.dtl.DTLVardef@1d16adb_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

Inventa: a computational tool to discover chemical novelty in natural extracts libraries

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of workflow that combines multiple-source information to facilitate the data interpretation. Results from different analysis techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa, a computational tool that highlights the chemical novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their chemical potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro-{beta}-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-{beta}-agarofuran base scaffold.

bioinformatics↗

bioRxiv2022

The Bioregistry: Unifying the Identification of Biomedical Entities through an Integrative, Open, Community-driven Metaregistry

The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry.

bioinformatics↗

bioRxiv2021

Open Natural Products Research: Curation and Dissemination of Biological Occurrences of Chemical Structures through Wikidata

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/433265v3_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@184136borg.highwire.dtl.DTLVardef@16e0d6org.highwire.dtl.DTLVardef@30ae1org.highwire.dtl.DTLVardef@1bf4825_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Taxonomically informed scoring enhances confidence in natural products annotation

Mass spectrometry (MS) hyphenated to liquid chromatography (LC)-MS offers unrivalled sensitivity for metabolite profiling of complex biological matrices encountered in natural products (NP) research. With advanced platforms LC, MS/MS spectra are acquired in an untargeted manner on most detected features. This generates massive and complex sets of spectral data that provide valuable structural information on most analytes. To interpret such datasets, computational methods are mandatory. To this extent, computerized annotation of metabolites links spectral data to candidate structures. When profiling complex extracts spectra are often organized in clusters by similarity via Molecular Networking (MN). A spectral matching score is usually established between the acquired data and experimental or theoretical spectral databases (DB). The process leads to various candidate structures for each MS features. At this stage, obtaining high annotation confidence level remains a challenge notably due to the high chemodiversity of specialized metabolomes.\n\nThe integration of additional information in a meta-score is a way to capture complementary experimental attributes and improve the annotation process. Here we show that integrating unambiguous taxonomic position of analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and weight the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5 to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata (particularly biological sources) is timely and critical for the NP research community.

bioinformatics↗