Biology subjects

Wolfender, J.-L.

Publications and source records attributed to Wolfender, J.-L..

9 recordsLinked to original sources

bioRxiv2026

Human airway organoids as a high-throughput screening platform for antiviral natural products discovery

Antiviral drug discovery for respiratory viruses is hindered by the lack of scalable physiologically relevant systems. Here, we report the first high-throughput screen of 764 natural plant extracts against respiratory syncytial virus (RSV) using human primary airway organoids as a relevant model. A parallel screen conducted in A549 cells allowed the identification of 70 extracts with organoid-specific antiviral activity from which 45 active phytocompounds were purified. We identified early- and late-acting antiviral compounds and demonstrated a polarization-dependent activity for some of them. Collectively, our results establish the use of airway organoids as a scalable first-line platform for high-throughput antiviral discovery and exploit the plant-derived chemical space as an underexplored source of RSV inhibitors.

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↗

bioRxiv2024

Revisiting sour rot of grapevine through disease-associated microbiomes: a tripartite co-infection?

Sour rot in grapevines is thought to result from berry infection by yeasts, acetic acid bacteria and vinegar flies. To better characterize the role of each of the actors involved in sour rot expression, we conducted experiments involving the isolation of 1593 fungi and bacteria to understand the composition and dynamics of the microbiomes associated with healthy berries, diseased berries and insect vectors. As some grape clusters became symptomatic for sour rot in the absence of acetic acid bacteria, the latter might not necessarily be needed for disease expression. Similar to other yeast genera, the yeast genus Geotrichum is here also reported for the first time to be able to initiate sour rot in grapes, however, this finding has to be confirmed by further studies. By allowing or denying the access of insect vectors to intact or artificially wounded grapes we could emphasize that they accelerate the expression of the disease when berries are injured. Moreover, the microbial communities identified on native vinegar flies (Drosophila spp.) and the introduced Drosophila suzukii were similar and yeast dominated. This highlights the key role of these insect vectors in the transmission of microorganisms inflicting sour rot to wounded berries. Finally, our data also suggest that sour rot and grey mould can coexist in a vineyard at an advanced stage of grape decomposition, which runs counter to recent studies that emphasize the supremacy of sour rot over grey mould. IMPORTANCEThis study sheds new light on the complex interactions between microbiomes, insect vectors and physical factors favoring the development of sour rot. While previous studies suggested that acetic acid bacteria were mandatory for sour rot expression our results suggest that grape sour rot could also result solely from yeast infection. Moreover, native vinegar flies and the introduced D. suzukii host and vectorize a similar microbial community to injured berries in vineyards and their presence accelerates the infection process and consequently the expression of sour rot.

pathology↗

bioRxiv2023

Leaf metabolic traits reveal hidden dimensions of plant form and function

The plant metabolome encompasses the biochemical mechanisms through which evolutionary and ecological processes shape plant form and function1,2. However, while the metabolome should thus be an important component of plant life-history variation3, we know little about how it varies across the plant kingdom. Here, we use the plant functional trait concept4 - a powerful framework for describing plant form and function5-7 - to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five discriminant metabolic functional traits reveals that plants vary along two major axes of leaf metabolic specialization - a leaf chemical defense spectrum and an expression of leaf longevity. These axes are qualitatively consistent for tropical and temperate species, with many trait combinations being viable. However, axes of leaf metabolic specialization vary orthogonally to life-history strategies described by widely used functional traits5-7, while being at least equally important to them. Our findings question classical trait6 and plant defense8 theory that predicts relationships between the leaf chemical phenotype, plant productivity, and pace of life. Moreover, we show that metabolic functional traits describe unique dimensions of plant life-history variation that are complementary to, and independent from, those captured by existing plant functional traits.

ecology↗

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↗

bioRxiv2021

MEMO: Mass Spectrometry-based Sample Vectorization to Explore Chemodiverse Datasets

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g. via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods. Indeed, the experimental variation - changes in chromatographical or mass spectrometric conditions - often hinders the direct comparison of the profiled samples. Here we introduce MEMO - MS2 BasEd SaMple VectOrization - a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics taking into account fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/474089v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@c540c0org.highwire.dtl.DTLVardef@1e07e7org.highwire.dtl.DTLVardef@198e030org.highwire.dtl.DTLVardef@1a6a15e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecules calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/473622v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@730ecaorg.highwire.dtl.DTLVardef@1d012e1org.highwire.dtl.DTLVardef@187a1e5org.highwire.dtl.DTLVardef@195c53f_HPS_FORMAT_FIGEXP M_FIG C_FIG

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↗