Biology subjects

Allard, P.-M.

Publications and source records attributed to Allard, P.-M..

11 recordsLinked to original sources

bioRxiv2026

An Integrated Miniaturized Metabologenomics Workflow for High-Throughput Actinomycetes Prioritization

Antibiotic resistance is a growing global health threat, while the discovery of new antimicrobial agents remains constrained by labor-intensive cultivation, extraction, and screening workflows. We developed a miniaturized, high-throughput metabologenomics workflow integrating Duetz system-based microfermentation, extraction-free UHPLC-HRMS/MS profiling, antibacterial screening, and genome mining. A collection of 80 soil-derived actinomycetes was screened against clinically relevant pathogens, identifying seven isolates active against methicillin-resistant Staphylococcus aureus (MRSA), Enterococcus faecium, or both. All isolates were cultivated in Duetz system-based microfermentation plates, and cell-free culture filtrates were analyzed directly by UHPLC-HRMS/MS without solvent extraction. Compared with a conventional workflow, the approach reduced culture-medium consumption by approximately 99%, decreased recurring consumable costs by approximately 96%, and reduced the time required to obtain LC-MS-ready samples from typically 5 days to approximately 20 min. Integration of antibacterial, metabolomic, and genomic datasets supported the annotation of diverse metabolite families, including actinomycins, polyether ionophores, ansamycins, angucyclines, hydroxamate siderophores, ectoines, tambromycin-associated metabolites, and putative lydicamycin metabolites. Selected annotations were validated using commercial standards. Overall, this study demonstrates that integrating miniaturized Duetz system-based cultivation, extraction-free metabolomics, antibacterial screening, and genome mining provides a scalable, robust, and cost-effective platform for high-throughput strain prioritization and natural product discovery.

microbiology↗

bioRxiv2025

METRIN-KG: A knowledge graph integrating plant metabolites, traits and biotic interactions

BackgroundIn recent years, biodiversity data management has emerged as a critical pillar in global conservation efforts. Today, the ability to efficiently collect, structure, and analyze biodiversity data is central to breakthroughs in conservation, drug development, disease monitoring, ecological forecasting, and agri-tech innovation. However, due to the vastness and heterogeneity of biodiversity data, it is often confined to databases for specific research areas in isolated formats and disconnected from other relevant resources. Crucial components of such data in kingdom Plantae comprise of metabolomes - the vast array of compounds produced by plants; traits - measurable characteristics of plants that influence their growth, survival, and reproduction, and that affect ecosystem processes; and biotic interactions - relationships of plants with other living organisms, affecting the ecosystem functions. ResultsIn this work, we present METRIN-KG (MEtabolomes, TRaits, and INteractions-Knowledge Graph) a powerful data resource simplifying the integration of diverse and heterogeneous data resources such as plant metabolomes, traits, and biotic interactions. ConclusionsThe proposed knowledge graph provides an interface to interactively search for data relating plant metabolomes, traits, and interactions. This, in turn, will facilitate development of research questions in life-sciences. In this context, we provide representative case studies on how to frame queries that can be used to search for relevant data in the knowledge graph.

bioinformatics↗

bioRxiv2024

Sanctioning of bacterial cheaters by the host plant in nitrogen-fixing symbiosis between Medicago truncatula and Sinorhizobium meliloti

In plant-microbe interactions, the host plant invests considerable amounts of resources in the microbial partner until the symbiotic machinery is established. If the microbial partner does not reciprocate with a comparable symbiotic benefit, the interaction represents a parasitic relationship. This is thought to elicit a plants response to prevent the selective disadvantage of being parasitized by such microbial cheaters. Indeed, negative feedback against bad mutualists, known as sanctioning, has been observed in interactions such as the arbuscular mycorrhizal and legume-rhizobium symbioses. Here, to study sanctioning by the plant host, we manipulate the exchange of resources between the model legume Medicago truncatula and its bacterial partner Sinorhizobium meliloti by three ways: mutating the bacterial nitrogenase enzyme, replacing nitrogen in the atmosphere with argon gas, and supplying high nitrate to the host. Then, we follow the consequences for the interaction by examining the metabolome, proteome, and phosphoproteome of nodules. We find that such cheating conditions result in sanctioning of the bacterial partner, and observe characteristic shifts including induced defense markers, repressed symbiotic markers, and changes in central metabolism that may be relevant for microbial fitness and that could therefore contribute to sanctioning.

plant biology↗

bioRxiv2024

plantMASST - Community-driven chemotaxonomic digitization of plants

Understanding the distribution of hundreds of thousands of plant metabolites across the plant kingdom presents a challenge. To address this, we curated publicly available LC-MS/MS data from 19,075 plant extracts and developed the plantMASST reference database encompassing 246 botanical families, 1,469 genera, and 2,793 species. This taxonomically focused database facilitates the exploration of plant-derived molecules using tandem mass spectrometry (MS/MS) spectra. This tool will aid in drug discovery, biosynthesis, (chemo)taxonomy, and the evolutionary ecology of herbivore interactions.

plant biology↗

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

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

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↗