A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery

The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.

Comparative metabolomic study of fungal foliar endophytes and their long-lived host Astrocaryum sciophilum: a model for exploring the chemodiversity of host-microbe interactions

Introduction

In contrast to the dynamics observed in plant/pathogen interactions, endophytic fungi have the capacity to establish enduring associations within their hosts, leading to the development of a mutually beneficial relationship that relies on specialized chemical interactions. Research indicates that the presence of endophytic fungi has the ability to significantly modify the chemical makeup of the host organism. Our hypothesis proposes the existence of a reciprocal exchange of chemical signals between plants and fungi, facilitated by specialized chemical processes that could potentially manifest within the tissues of the host. This research aimed to precisely quantify the portion of the cumulative fungal endophytic community's metabolome detectable within host leaves, and tentatively evaluate its relevance to the host-endophyte interplay. The understory palm Astrocaryum sciophilum (Miq.) Pulle was used as a interesting host plant because of its notable resilience and prolonged life cycle, in a tropical ecosystem.

Method

Using advanced metabolome characterization, including UHPLC-HRMS/MS and molecular networking, the study explored enriched metabolomes of both host leaves and 15 endophytic fungi. The intention was to capture a metabolomic "snapshot" of both host and endophytic community, to achieve a thorough and detailed analysis.

Results and discussion

This approach yielded an extended MS-based molecular network, integrating diverse metadata for identifying host- and endophyte-derived metabolites. The exploration of such data (>24000 features in positive ionization mode) enabled effective metabolome comparison, yielding insights into cultivable endophyte chemodiversity and occurrence of common metabolites between the holobiont and its fungal communities. Surprisingly, a minor subset of features overlapped between host leaf and fungal samples despite significant plant metabolome enrichment. This indicated that fungal metabolic signatures produced in vitro remain sparingly detectable in the leaf. Several classes of primary metabolites were possibly shared. Specific fungal metabolites and/or compounds of their chemical classes were only occasionally discernible in the leaf, highlighting endophytes partial contribution to the overall holobiont metabolome. To our knowledge, the metabolomic study of a plant host and its microbiome has rarely been performed in such a comprehensive manner. The general analytical strategy proposed in this paper seems well-adapted for any study in the field of microbial- or microbiome-related MS and can be applied to most host-microbe interactions.

Open and reusable annotated mass spectrometry dataset of a chemodiverse collection of 1,600 plant extracts

As privileged structures, natural products often display potent biological activities. However, the discovery of novel bioactive scaffolds is often hampered by the chemical complexity of the biological matrices they are found in. Large natural extract collections are thus extremely valuable for their chemical novelty potential but also complicated to exploit in the frame of drug-discovery projects. In the end, it is the pure chemical substances that are desired for structural determination purposes and bioactivity evaluation. Researchers interested in the exploration of large and chemodiverse extract collections should thus establish strategies aiming to efficiently tackle such chemical complexity and access these structures. Establishing carefully crafted digital layers documenting the spectral and chemical complexity as well as bioactivity results of natural extracts collections can help prioritize time-consuming but mandatory isolation efforts. In this note, we report the results of our initial exploration of a collection of 1,600 plant extracts in the frame of a drug-discovery effort. After describing the taxonomic coverage of this collection, we present the results of its liquid chromatography high-resolution mass spectrometric profiling and the exploitation of these profiles using computational solutions. The resulting annotated mass spectral dataset and associated chemical and taxonomic metadata are made available to the community, and data reuse cases are proposed. We are currently continuing our exploration of this plant extract collection for drug-discovery purposes (notably looking for novel antitrypanosomatids, anti-infective and prometabolic compounds) and ecometabolomics insights. We believe that such a dataset can be exploited and reused by researchers interested in computational natural products exploration.

Metabolomics in Ecology and Bioactive Natural Products Discovery: Challenges and Prospects for a Comprehensive Study of the Specialised Metabolome

Metabolomics is playing an increasingly prominent role in chemical ecology and in the discovery of bioactive natural products (NPs). The identification of metabolites is a common/central objective in both research fields. NPs have significant biological properties and play roles in multiple chemical-ecological interactions. Classically, in pharmacognosy, their chemical structure is determined after a complex process of isolating and interpreting spectroscopic data. With the advent of powerful analytical techniques such as liquid chromatography-mass spectrometry (LC-MS) the annotation process of the specialised metabolome of plants and microorganisms has improved considerably. In this article, we summarise the possibilities opened by these advances and illustrate how we harnessed them in our own research to automate annotations of NPs and target the isolation of key compounds. In addition, we are also discussing the analytical and computational challenges associated with these emerging approaches and their perspective.

Inventa: A computational tool to discover structural 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 a workflow that combines multiple-source information to facilitate the data interpretation. Results from different analytical 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 structural 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 structural 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-β-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-β-agarofuran base scaffold.

The LOTUS initiative for open knowledge management in natural products research

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.

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 since the experimental variation—changes in chromatographical or mass spectrometric conditions - 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 considering 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.

A Mass Spectrometry Based Metabolite Profiling Workflow for Selecting Abundant Specific Markers and Their Structurally Related Multi-Component Signatures in Traditional Chinese Medicine Multi-Herb Formulae

In Traditional Chinese Medicine (TCM), herbal preparations often consist of a mixture of herbs. Their quality control is challenging because every single herb contains hundreds of components (secondary metabolites). A typical 10 herb TCM formula was selected to develop an innovative strategy for its comprehensive chemical characterization and to study the specific contribution of each herb to the formula in an exploratory manner. Metabolite profiling of the TCM formula and the extract of each single herb were acquired with liquid chromatography coupled to high-resolution mass spectrometry for qualitative analyses, and to evaporative light scattering detection (ELSD) for semi-quantitative evaluation. The acquired data were organized as a feature-based molecular network (FBMN) which provided a comprehensive view of all types of secondary metabolites and their occurrence in the formula and all single herbs. These features were annotated by combining MS/MS-based in silico spectral match, manual evaluation of the structural consistency in the FBMN clusters, and taxonomy information. ELSD detection was used as a filter to select the most abundant features. At least one marker per herb was highlighted based on its specificity and abundance. A single large-scale fractionation from the enriched formula enabled the isolation and formal identification of most of them. The obtained markers allowed an improved annotation of associated features by manually propagating this information through the FBMN. These data were incorporated in the high-resolution metabolite profiling of the formula, which highlighted specific series of related components to each individual herb markers. These series of components, named multi-component signatures, may serve to improve the traceability of each herb in the formula. Altogether, the strategy provided highly informative compositional data of the TCM formula and detailed visualizations of the contribution of each herb by FBMN, filtered feature maps, and reconstituted chromatogram traces of all components linked to each specific marker. This comprehensive MS-based analytical workflow allowed a generic and unbiased selection of specific and abundant markers and the identification of multiple related sub-markers. This exploratory approach could serve as a starting point to develop more simple and targeted quality control methods with adapted marker specificity selection criteria to given TCM formula.

129I/127I ratio measurements in bovine thyroids from the North Cotentin area (France)

Iodine-129 is routinely released in the gaseous and liquid low-level radioactive effluents of nuclear spent fuel reprocessing plants. Environmental impact assessment of these discharges are performed based on monitoring samples of different types of natural indicators. Thyroid is considered as a relevant organ to monitor radioactive iodine isotopes. In this study, bovine thyroids were collected in herds located in the Cotentin area under the influence of authorized low level gaseous effluents of the La Hague nuclear fuel-reprocessing plant. For low level 129I/127I ratios, characteristic of environmental samples (10(-12) to 10(-7)), RNAA or SMA that includes important radiochemical preparation steps are used. This paper demonstrates the interest of direct gamma-X spectrometry to measure 129I activities, associated to INAA to measure 127I to attain higher ratios levels (10(-6) to 10(-4)). This study shows the interest of monitoring bovine thyroids considered as sentinel organs to characterize the dispersion in space and time of 129I discharged in low level radioactive gaseous effluents by the La Hague reprocessing plant.

Reliability and accuracy of environmental analytical data on moss samples: an interlaboratory comparison

An inter-comparison exercise was performed between two laboratories on mineralised moss samples. Ten samples, together with three certified reference materials (CRM 61, CRM 62 and CRM 482) and five blanks were mineralised independently in the two laboratories and then analysed by both with different techniques (AAS, ICP-MS, and INAA). The elements analysed were: Cd, Cr, Cu, Fe, Ni, and Pb. Accuracy tests showed that, for AAS, the relative standard deviations for five repetitions of the same sample were between 0.2% for Cd and 2.9 % for Cr and that the concentration values found reference materials were withn one standard deviation from the certified values, with the exception of Cr and Cd for CRM 482. The statistical analysis was performed first with a simple linear regression and a Student t-test taking into account the possible influence of the mineralisation procedure and of the analytical technique, in order to evaluate their reliability. The results showed that more than 78% of the regressions had a correlation coefficient higher than 0.750, and that 83% of the slopes were not different from one with a level of significance of 0.01. Afterwards, the precision of the measurements were evaluated using maximum likelihood; the normalised errors showed that ICP-MS had a better performance on most of the elements analysed (with the exception of Fe) and that both the mineralisation procedures gave satisfactory results. The two laboratories were then found to produce reliable results.

Urinary selenium excretion in patients with cervical uterine cancer

In this work, we report on a relationship between urinary selenium and the development of cervical uterine cancer. A simple chemical method was developed to concentrate trace amounts of selenium from relatively large urine samples by use of small activated carbon filters. When these filters are irradiated with thermal neutrons, selenium can be determined either by 77mSe (t1/2 = 17.5 s) or 75Se (t1/2 = 120 d). In this article, we report the results for 82 urine samples from women with cervical uterine cancer in several stages of development and from healthy controls. These results show a statistically significant increase of selenium excretion in cancer patients as compared to controls. Urinary selenium excretion is highest for patients in the intermediate stages of the disease.