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Biomolecular NMR: a chaperone to drug discovery.

Biomolecular NMR now contributes routinely to every step in the development of new chemical entities ahead of clinical trials. The versatility of NMR--from detection of ligand binding over a wide range of affinities and a wide range of drug targets with its wealth of molecular information, to metabolomic profiling, both ex vivo and in vivo--has paved the way for broadly distributed applications in academia and the pharmaceutical industry. Proteomics and initial target selection both benefit from NMR: screenings by NMR identify lead compounds capable of inhibiting protein-protein interactions, still one of the most difficult development tasks in drug discovery. NMR hardware improvements have given access to the microgram domain of phytochemistry, which should lead to the discovery of novel bioactive natural compounds. Steering medicinal chemists through the lead optimisation process by providing detailed information about protein-ligand interactions has led to impressive success in the development of novel drugs. The study of biofluid composition--metabonomics--provides information about pharmacokinetics and helps toxicological safety assessment in animal model systems. In vivo, magnetic resonance spectroscopy interrogates metabolite distributions in living cells and tissues with increasing precision, which significantly impacts the development of anticancer or neurological disorder therapeutics. An overview of different steps in recent drug discovery is presented to illuminate the links with the most recent advances in NMR methodology.

Drug Design↗

[Therapeutic gene clusters as drug action mechanisms].

Pharmacogenomics is defined to identify the therapeutic gene clusters which are involved in determining the responsiveness and to distinguish responders and non-responders to a given drug. Genome sequencing, transcriptome, proteome and metabolome analysis are of particular significance in pharmacogenomics. Sequencing is used to locate polymorphisms, and monitoring of functional gene expression can provide clue about the genomic response to disease and treatment. The transcriptome analysis can be done by methods of random cDNA sequencing, mRNA display(fluorescent differential display et al.) and differential hybridization(cDNA microarray, oligomicrochip et al.). We used transcriptome/proteome/metabolome analysis to identify therapeutic target genes by studying change of gene expression in animal models and human model cells of various diseases and found novel drug target candidates through this pharmacogenomic strategy. The present study describes combined transcriptome and metabolome analysis for therapeutic target validation in hypoxia-induced vascular remodeling. The pharmacogenomic analysis and pharmainformatics has the potential for strategy to define novel therapeutic gene clusters in various diseases and holds the promise that drugs might be tailor-maid for individuals and adapted to each person's own genetic makeup. These techniques provided an excellent strategy for screening and validation of targets.

Animals↗

Multi-omic underpinnings of heterogeneous aging across multiple organ systems.

Aging is the main determinant of chronic diseases and mortality, yet organ-specific aging trajectories vary, and the molecular basis underlying this heterogeneity remains unclear. To elucidate this, we integrated genomic, epigenomic, transcriptomic, proteomic, and metabolomic data, employing post-genome-wide association study methodologies to systematically investigate the molecular mechanisms of nine organ-specific aging clocks and four blood-based epigenetic clocks. We uncovered genetic correlations and specific phenotypic clusters among these aging-related traits, identified prioritized genetic drug targets for heterogeneous aging, and elucidated downstream proteomic and metabolomic effects mediated by heterogeneous aging. We constructed a cross-layer molecular interaction network of heterogeneous aging across multiple organ systems and characterized detectable biomarkers of this heterogeneity. Integrating these findings, we developed an R/Shiny-based framework that provides a comprehensive multi-omic molecular landscape of heterogeneous aging, thereby advancing the understanding of aging heterogeneity and informing precision medicine strategies to delay organ-specific aging and prevent or treat its associated chronic diseases.

Aging↗

Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops.

There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome "fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.

Alkaloids↗

Integrating metabolomics and transcriptomics for probing SE anticancer mechanisms.

Transcriptomics provides the tool for deciphering gene expression networks, and proteomics links these networks to protein products. The third crucial partner is metabolomics, which defines the metabolic network(s) linked to gene expression. NMR and mass spectrometry enable the broad screen analysis of the metabolome and its transformation pathways, transcending classical targeted metabolic studies. These tools were combined to investigate the anticancer mechanisms of different selenium forms in human lung cancer cells. Using 2-D NMR and tandem-MS, we mapped perturbations of 13C labeling patterns in numerous metabolites induced by selenite and selenomethionine. This information was used to interpret selenite-induced changes in gene expression networks. Linking metabolic dysfunctions to altered gene expression profiles provided new insights into the regulatory network underlying the metabolic dysfunctions, enabled the assembly of discrete gene expression events into functional pathways, and revealed protein targets for proteomic analysis.

Animals↗

Exploring Actinobacteria for new insecticides and their delivery in crop protection.

Crop protection is essential for agricultural production systems, safeguarding yields and product quality. Chemical controls are a mainstay of protection; however, regulatory and consumer demands, environmental concerns and a general overreliance resulting in resistance development in pest populations have led to increased interest in biopesticides and environmentally friendly alternatives. Biopesticides targeting insects include micro-organisms and their derivatives, such as peptides and specialized metabolites. Their target specificity, structural complexity, modes of action and environmental safety are key differentiators to chemical controls, and when used in integrated pest management programmes, biopesticides can reduce reliance on chemical pesticides and promote sustainable agriculture. As the demand for bioinsecticides grows, so too has the research and application of micro-organisms, alongside their taxonomic diversity and isolation sources. Of key interest are Actinobacteria as both promising and well-tested alternatives for managing insect pests in various agricultural settings, with several products commercialized for use across a variety of crops and target pests. Recent advances and investigations in metabolomics and genomics highlight the untapped and significant biochemical potential and value of Actinobacteria for natural product discovery. This review covers a broad spectrum of published literature that has reported on insecticidal biological activity data associated with Actinobacteria or their natural products. We also report on Actinobacteria-derived nematicides and acaricides that are significant for crop protection. The origin of these natural products, their structural diversity and notable substructures are discussed, along with new areas for discovery and avenues for enhancing screening methods and metabolo-genomics approaches.

Insecticides↗

Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation.

Nowadays techniques for non-targeted metabolite profiling allow for the generation of huge amounts of relevant data essential for the construction of dynamic metabolomic networks. Thus, metabolomics, besides transcriptomics or proteomics, provides a major tool for the characterization of postgenomic processes. In this work, we introduce comparative correlation analysis as a complementary approach to characterize the physiological states of various organs of diverse plant species with focus on specific participation of metabolites in different reaction networks. The correlations observed are induced by diminutive fluctuations in environmental conditions, which propagate through the system and induce specific patterns depending on the genomic background. In order to examine this hypothesis, numeric examples of such fluctuations are computed and compared with experimentally obtained metabolite data.

Algorithms↗

Multi-omics approaches in idiopathic pulmonary fibrosis: from molecular mechanisms to therapeutic targets and precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options and marked molecular heterogeneity. Despite available antifibrotic therapies, disease progression remains poorly predictable, highlighting the need for improved mechanistic understanding and therapeutic targeting. This review summarizes recent advances in multi-omics research to elucidate the molecular mechanisms underlying IPF and to identify potential biomarkers and pharmacological targets. Multi-omics studies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and single-cell sequencing, have revealed key pathogenic mechanisms in IPF. Genetic susceptibility factors such as MUC5B promoter variants and telomere-related genes contribute to disease risk. Epigenetic regulation, including DNA methylation, histone modifications, and non-coding RNAs, plays a central role in fibrotic remodeling. Transcriptomic and proteomic analyses have identified dysregulated signaling pathways, including TGF-β, mTOR, cellular senescence, and extracellular matrix remodeling. Metabolomic alterations indicate disrupted lipid and amino acid metabolism. Importantly, integration of multi-omics datasets enables the identification of molecular endotypes, candidate biomarkers, and potential therapeutic targets. However, challenges including data integration, tissue heterogeneity, limited cohort size, and the need for functional validation remain important barriers to clinical translation. Continued development of multi-omics approaches may facilitate more accurate disease classification and support the development of personalized therapeutic strategies for IPF.

biomarkers↗

Comparative Analysis of Primary Sarcopenia and End-Stage Renal Disease-Related Muscle Wasting Using Multi-Omics Approaches.

BACKGROUND: Age-related primary sarcopenia and end-stage renal disease (ESRD)-related muscle wasting are discrete entities; however, both manifest as a decline in skeletal muscle mass and strength. The etiological pathways differ, with aging factors implicated in sarcopenia and a combination of uremic factors, including haemodialysis, contributing to ESRD-related muscle wasting. Understanding these molecular nuances is imperative for targeted interventions, and the integration of proteomic and metabolomic data elucidate these intricate processes. METHODS: We generated detailed clinical data and multi-omics data (plasma proteomics and metabolomics) for 78 participants to characterise sarcopenia (n = 28; mean age, 72.6 ± 7.0 years) or ESRD (n = 22; 61.6 ± 5.5 years) compared with controls (n = 28; 69.3 ± 5.7 years). Muscle mass was measured using bioelectrical impedance analysis and handgrip strength. Five-times sit-to-stand test performance was measured for all participants. Sarcopenia was diagnosed in accordance with the 2019 Consensus Guidelines from the Asian Working Group for Sarcopenia. An abundance of 234 metabolites and 722 protein groups was quantified in all plasma samples using liquid chromatography with tandem mass spectrometry. RESULTS: Muscle mass, handgrip strength and lower limb muscle function significantly lower in the sarcopenia group and the ESRD group compared with those in the control group. Metabolomics revealed altered metabolites, highlighting exclusive differences in ESRD-related muscle wasting. Metabolite set enrichment analysis revealed the involvement of numerous metabolic intermediates associated with urea cycle, amino acid metabolism and nucleic acid metabolism. Catecholamines, including epinephrine, dopamine and serotonin, are significantly elevated in the plasma of patients within the ESRD group. Proteomics data exhibited a clearer distinction among the three groups compared with the metabolomics data, particularly in distinguishing the control group from the sarcopenia group. The ciliary neurotrophic factor receptor was top-ranked in terms of the variable importance of projection scores. Plasma AHNAK protein levels was higher in the sarcopenia group but was lower in the ESRD group. Proteomic set enrichment analysis revealed enrichment of several pathways related to sarcopenia, such as hemopexin, defence response and cell differentiation, in sarcopenia group. Multi-omic integration analysis revealed associations between relevant metabolites, including catecholamines, and a group of annotated proteins in extracellular exosomes. CONCLUSIONS: We identified distinct multi-omic signatures in individuals with ESRD or sarcopenia, providing new insights into the mechanisms underlying ESRD-related muscle wasting, which differ from primary sarcopenia. These findings may support interventions for context-dependent muscle loss and contribute to the development of targeted treatments and preventive strategies for muscle wasting.

Humans↗

Towards replacing closed with open target selection strategies.

Increasingly, microbial production processes are being improved by targeted approaches. In directed strain improvement, the selection of the relevant targets is the limiting step in metabolic engineering. Currently, the identification of leads is still a random process relying largely on expert knowledge. Recently, this approach has been complemented by metabolic flux and control analysis approaches. However, both are closed approaches, and biological processes or interactions that are not currently known to exist, or to be important for bioproduct formation, are not taken into account. By contrast, the recently introduced functional genomics technologies enable an open approach towards target selection. In the near future, we might see that metabolomics, and its integration with transcriptomics and/or proteomics into a systems biology approach, in combination with multivariate data analysis tools, will become of increasing importance for the unbiased selection and ranking of targets, not only for strain improvement but also for bioprocess improvement.

Bacteria↗

Stepping out of the dark: how metabolomics shed light on fungal biology.

Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.

Metabolomics↗

Ethnopharmacology and systems biology: a perfect holistic match.

Traditional medical doctors often apply a holistic approach in prescribing medicines to the patient. Each individual patient gets his own optimalized medicine, usually a mixture of different ingredients. The present day paradigm of drug development of , is based on a super reductionist approach which involves mostly tests of compounds at the molecular level in, e.g., receptor binding assays. This approach is not the best for studies on traditional medicines. A more holistic approach using systems biology seems much more suited to proof efficacy and to obtain information that might lead to understanding the mode of action. Synergy, prodrugs, novel targets, all these might be detected by a systems biology approach whereas the reductioinist approach only will recognize activity on already known targets, and will not detect synergism and prodrugs. Metabolomics will be a major tool in recognizing compounds connected with activity in the traditional medicines, and will also be very useful in recognizing the effect on the test organism, which can be the patient in case of clinical trials with well established traditional medicines.

Ethnopharmacology↗

Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis.

We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.

Algorithms↗

Pathogenicity of Cadophora luteo-olivacea on Quercus robur and multi-omics characterization of antagonism by Trichoderma atroviride.

Pedunculate oak (Quercus robur L.) is a foundation tree species in European forests and reforestation programs, but nursery propagated seedlings can harbor cryptic trunk diseases pathogens. Cadophora luteo-olivacea, known from grapevine trunk diseases, has been detected in oak nurseries, yet its pathogenicity on oak and interactions with antagonistic fungi remain unclear. We fulfilled Koch's postulates for C. luteo-olivacea isolate CZ_395 on Q. robur seedlings under experimental inoculation conditions and quantified growth reduction of C. luteo-olivacea by Trichoderma atroviride isolate CZ_180 in dual culture. Proteomic and metabolomic profiling of the contact zone was performed at two post contact sampling points, 4 and 8&#xa0;dpi, to identify candidate molecular signatures associated with the interaction. Inoculated seedlings developed extensive stem lesions (mean 11.9 cm), whereas controls showed minimal wound response (mean 0.9&#xa0;cm; p&#x2009;<&#x2009;0.001). In dual culture, T. atroviride reduced the visible colony development and radial growth of C. luteo-olivacea under the tested in vitro conditions. Contact zone proteomics revealed 257 differentially abundant proteins at 8&#xa0;days, including cell wall targeting hydrolases, secreted proteases, oxidoreductases (44 upregulated), and transporters. Metabolomics detected contact specific changes in amino acids, central carbon intermediates, and lipid-associated features, including reduced ergosterol. This study demonstrates that C. luteo-olivacea can induce necrotic lesions in Q. robur under experimental inoculation conditions and identifies proteomic and metabolomic signatures associated with the interaction between T. atroviride and C. luteo-olivacea, providing a basis for nursery risk assessment and future evaluation of biocontrol potential.

Quercus↗

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles.

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.

Automation↗

Comparison of rapid liquid chromatography-electrospray ionization-tandem mass spectrometry methods for determination of glycoalkaloids in transgenic field-grown potatoes.

Two rapid methods for highly selective detection and quantification of the two major glycoalkaloids in potatoes, alpha-chaconine and alpha-solanine, were compared for robustness in high-throughput operations for over 1000 analytical runs using potato tuber samples from field trials. Glycoalkaloids were analyzed using liquid chromatography coupled to tandem mass spectrometry in multiple reaction monitoring mode. An electrospray interface was used in the detection of glycoalkaloids in positive ion mode. Classical reversed phase (RP) and hydrophilic interaction (HILIC) columns were investigated for chromatographic separation, ruggedness, recovery, precision, and accuracy. During the validation procedure both methods proved to be precise and accurate enough in relation to the high degree of endogenous biological variability found for field-grown potato tubers. However, the RP method was found to be more precise, more accurate, and, more importantly, more rugged than the HILIC method for maintaining the analytes' peak shape symmetry in high-throughput operation. When applied to the comparison of six classically bred potato cultivars to six genetically modified (GM) lines engineered to synthesize health beneficial inulins, the glycoalkaloid content in potato peels of all GM lines was found within the range of the six cultivars. We suggest complementing current unbiased metabolomic strategies by validating quantitative analytical methods for important target analytes such as the toxic glycoalkaloids in potato plants.

Agriculture↗

Lipid metabolome-wide effects of the PPARgamma agonist rosiglitazone.

Successful therapy for chronic diseases must normalize a targeted aspect of metabolism without disrupting the regulation of other metabolic pathways essential for maintaining health. Use of a limited number of single molecule surrogates for disease, or biomarkers, to monitor the efficacy of a therapy may fail to predict undesirable side effects. In this study, a comprehensive metabolomic assessment of lipid metabolites was employed to determine the specific effects of the peroxisome proliferator-activated receptor gamma (PPARgamma) agonist rosiglitazone on structural lipid metabolism in a new mouse model of Type 2 diabetes. Dietary supplementation with rosiglitazone (200 mg/kg diet) suppressed Type 2 diabetes in obese (NZO x NON)F1 male mice, but chronic treatment markedly exacerbated hepatic steatosis. The metabolomic data revealed that rosiglitazone i) induced hypolipidemia (by dysregulating liver-plasma lipid exchange), ii) induced de novo fatty acid synthesis, iii) decreased the biosynthesis of lipids within the peroxisome, iv) substantially altered free fatty acid and cardiolipin metabolism in heart, and v) elicited an unusual accumulation of polyunsaturated fatty acids within adipose tissue. These observations suggest that the phenotypes induced by rosiglitazone are mediated by multiple tissue-specific metabolic variables. Because many of the effects of rosiglitazone on tissue metabolism were reflected in the plasma lipid metabolome, metabolomics has excellent potential for developing clinical assessments of metabolic response to drug therapy.

Adipose Tissue↗