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Nature Communications Jun 2023Mycobacterium tuberculosis is one of the global leading causes of death due to a single infectious agent. Pretomanid and delamanid are new antitubercular agents that...
Mycobacterium tuberculosis is one of the global leading causes of death due to a single infectious agent. Pretomanid and delamanid are new antitubercular agents that have progressed through the drug discovery pipeline. These compounds are bicyclic nitroimidazoles that act as pro-drugs, requiring activation by a mycobacterial enzyme; however, the precise mechanisms of action of the active metabolite(s) are unclear. Here, we identify a molecular target of activated pretomanid and delamanid: the DprE2 subunit of decaprenylphosphoribose-2'-epimerase, an enzyme required for the synthesis of cell wall arabinogalactan. We also provide evidence for an NAD-adduct as the active metabolite of pretomanid. Our results highlight DprE2 as a potential antimycobacterial target and provide a foundation for future exploration into the active metabolites and clinical development of pretomanid and delamanid.
Topics: Antitubercular Agents; Molecular Targeted Therapy; Mycobacterium tuberculosis; Alcohol Oxidoreductases; Nitroimidazoles; Cell Wall; Drug Resistance; Prodrugs; Spectrophotometry; NAD; Kinetics
PubMed: 37380634
DOI: 10.1038/s41467-023-39300-z -
Annual Review of Microbiology Sep 2023The metabolism of a bacterial cell stretches beyond its boundaries, often connecting with the metabolism of other cells to form extended metabolic networks that stretch... (Review)
Review
The metabolism of a bacterial cell stretches beyond its boundaries, often connecting with the metabolism of other cells to form extended metabolic networks that stretch across communities, and even the globe. Among the least intuitive metabolic connections are those involving cross-feeding of canonically intracellular metabolites. How and why are these intracellular metabolites externalized? Are bacteria simply leaky? Here I consider what it means for a bacterium to be leaky, and I review mechanisms of metabolite externalization from the context of cross-feeding. Despite common claims, diffusion of most intracellular metabolites across a membrane is unlikely. Instead, passive and active transporters are likely involved, possibly purging excess metabolites as part of homeostasis. Re-acquisition of metabolites by a producer limits the opportunities for cross-feeding. However, a competitive recipient can stimulate metabolite externalization and initiate a positive-feedback loop of reciprocal cross-feeding.
Topics: Bacteria; Homeostasis
PubMed: 37285553
DOI: 10.1146/annurev-micro-032521-023815 -
Frontiers in Psychiatry 2023Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis...
BACKGROUND
Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis and treatment of disease. Previous works have mainly focused on the global topological information of metabolite and disease similarity networks. However, the local tiny structure of metabolites and diseases may have been ignored, leading to insufficiency and inaccuracy in the latent metabolite-disease interaction mining.
METHODS
To solve the aforementioned problem, we propose a novel metabolite-disease interaction prediction method with logical matrix factorization and local nearest neighbor constraints (LMFLNC). First, the algorithm constructs metabolite-metabolite and disease-disease similarity networks by integrating multi-source heterogeneous microbiome data. Then, the local spectral matrices based on these two networks are established and used as the input of the model, together with the known metabolite-disease interaction network. Finally, the probability of metabolite-disease interaction is calculated according to the learned latent representations of metabolites and diseases.
RESULTS
Extensive experiments on the metabolite-disease interaction data were conducted. The results show that the proposed LMFLNC method outperformed the second-best algorithm by 5.28 and 5.61% in the AUPR and F1, respectively. The LMFLNC method also exhibited several potential metabolite-disease interactions, such as "Cortisol" (HMDB0000063), relating to "21-Hydroxylase deficiency," and "3-Hydroxybutyric acid" (HMDB0000011) and "Acetoacetic acid" (HMDB0000060), both relating to "3-Hydroxy-3-methylglutaryl-CoA lyase deficiency."
CONCLUSION
The proposed LMFLNC method can well preserve the geometrical structure of original data and can thus effectively predict the underlying associations between metabolites and diseases. The experimental results show its effectiveness in metabolite-disease interaction prediction.
PubMed: 37342171
DOI: 10.3389/fpsyt.2023.1149947 -
Metabolites Mar 2016The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the... (Review)
Review
The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world's largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.
PubMed: 26950159
DOI: 10.3390/metabo6010010 -
Advanced Science (Weinheim,... May 2023Phase separation (PS) is a fundamental principle in diverse life processes including immunosurveillance. Despite numerous studies on PS, little is known about its...
Phase separation (PS) is a fundamental principle in diverse life processes including immunosurveillance. Despite numerous studies on PS, little is known about its dissolution. Here, it is shown that oleic acid (OA) dissolves the cyclic GMP-AMP synthase (cGAS)-deoxyribonucleic acid (DNA) PS and inhibits immune surveillance of DNA. As solvent components control PS and metabolites are abundant cellular components, it is speculated that some metabolite(s) may dissolve PS. Metabolite-screening reveals that the cGAS-DNA condensates formed via PS are markedly dissolved by long-chain fatty acids, including OA. OA revokes intracellular cGAS-PS and DNA-induced activation. OA attenuates cGAS-mediated antiviral and anticancer immunosurveillance. These results link metabolism and immunity by dissolving PS, which may be targeted for therapeutic interventions.
Topics: DNA; Nucleotidyltransferases; Oleic Acid
PubMed: 36950761
DOI: 10.1002/advs.202206820 -
Frontiers in Toxicology 2022Scientists' ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large...
Scientists' ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large amounts of raw experimental data. In fact, the quantity of data produced has become a challenge due to effort required to convert raw data into useful insights. Various cheminformatics tools have been developed to address these metabolite identification challenges. This article describes the current state of these tools. They can be split into two categories: Pre-experimental metabolite generation and post-experimental data analysis. The former can be subdivided into rule-based, machine learning-based, and docking-based approaches. Post-experimental tools help scientists automatically perform chromatographic deconvolution of LC/MS data and identify metabolites. They can use pre-experimental predictions to improve metabolite identification, but they are not limited to these predictions: unexpected metabolites can also be discovered through fractional mass filtering. In addition to a review of available software tools, we present a description of pre-experimental and post-experimental metabolite structure generation using MetaSense. These software tools improve upon manual techniques, increasing scientist productivity and enabling efficient handling of large datasets. However, the trend of increasingly large datasets and highly data-driven workflows requires a more sophisticated informatics transition in metabolite identification labs. Experimental work has traditionally been separated from the information technology tools that handle our data. We argue that these IT tools can help scientists draw connections data visualizations and preserve and share results searchable centralized databases. In addition, data marshalling and homogenization techniques enable future data mining and machine learning.
PubMed: 35800176
DOI: 10.3389/ftox.2022.932445 -
Frontiers in Oncology 2022Dysregulated metabolism in cancers is, by now, well established. Although metabolic adaptations provide cancers with the ability to synthesize the precursors required... (Review)
Review
Dysregulated metabolism in cancers is, by now, well established. Although metabolic adaptations provide cancers with the ability to synthesize the precursors required for rapid biosynthesis, some metabolites have direct functional, or bioactive, effects in human cells. Here we summarize recently identified metabolites that have bioactive roles either as post-translational modifications (PTMs) on proteins or in, yet unknown ways. We propose that these metabolites could play a bioactive role in promoting or inhibiting cancer cell phenotypes in a manner that is mostly unexplored. To study these potentially important bioactive roles, we discuss several novel metabolomic and proteomic approaches aimed at defining novel PTMs and metabolite-protein interactions. Understanding metabolite PTMs and protein interactors of bioactive metabolites may provide entirely new therapeutic targets for cancer.
PubMed: 36249070
DOI: 10.3389/fonc.2022.1014748 -
Expert Opinion on Drug Metabolism &... Sep 2010Due to growing concerns over toxic or active metabolites, significant efforts have been focused on qualitative identification of potential in vivo metabolites from in... (Review)
Review
IMPORTANCE OF THE FIELD
Due to growing concerns over toxic or active metabolites, significant efforts have been focused on qualitative identification of potential in vivo metabolites from in vitro data. However, limited tools are available to quantitatively predict their human exposures.
AREAS COVERED IN THIS REVIEW
Theory of clearance predictions and metabolite kinetics is reviewed together with supporting experimental data. In vitro and in vivo data of known circulating metabolites and their parent drugs were collected and the predictions of in vivo exposures of the metabolites were evaluated.
WHAT THE READER WILL GAIN
The theory and data reviewed will be useful in early identification of human metabolites that will circulate at significant levels in vivo and help in designing in vivo studies that focus on characterization of metabolites. It will also assist in rationalization of metabolite-to-parent ratios used as markers of specific enzyme activity.
TAKE HOME MESSAGE
The relative importance of a metabolite in comparison to the parent compound as well as other metabolites in vivo can only be predicted using the metabolite's in vitro formation and elimination clearances, and the in vivo disposition of a metabolite can only be rationalized when the elimination pathways of that metabolite are known.
Topics: Drug Evaluation; Enzyme Activation; Humans; Microsomes, Liver; Pharmaceutical Preparations; Pharmacokinetics
PubMed: 20557268
DOI: 10.1517/17425255.2010.497487 -
Metabolites Aug 2017Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites... (Review)
Review
Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than the extraction and analysis of intracellular metabolites as there is no need for cell rupture. Many analytical methods are already available and have been used for the analysis of extracellular metabolites from microorganisms over the last two decades. Here, we review the applications and benefits of extracellular metabolite analysis. We also discuss different sample preparation protocols available in the literature for both types (e.g., metabolites in solution and in gas) of extracellular microbial metabolites. Lastly, we evaluate the authenticity of using extracellular metabolomics data in the metabolic modelling of different industrially important microorganisms.
PubMed: 28829385
DOI: 10.3390/metabo7030043 -
Journal of Pharmaceutical and... Oct 2023The present study focuses on the development and validation of an HPLC-DAD methodology for the detection of a potent chemotherapeutic agent, Maytansinoid Ravtansine... (Review)
Review
The present study focuses on the development and validation of an HPLC-DAD methodology for the detection of a potent chemotherapeutic agent, Maytansinoid Ravtansine (DM4), and its metabolite, S-methyl-DM4 (S-Me-DM4), in plasma samples. Methodologically, after a simple protein precipitation with acetonitrile and after drying 1 mL of supernatant, the sample (suspended with N,N-Dimethylacetamide, DMA) was directly analyzed by HPLC under isocratic elution using a mobile phase comprising milliQ water and methanol (25:75, v:v), both acidified with 0.1 % v:v formic acid. Employing a flow rate of 1.0 mL/min and a reversed-phase GraceSmart RP18 column thermostated at 40 °C, we achieved complete resolution and separation of DM4 and S-Me-DM4 within 13 min. The optimized injection volume of 20 μL and the wavelength set at 254 nm were utilized for quantitative analyses. Rigorous validation has not only ensured its reliability and reproducibility but has also addressed potential limitations associated with methodological inconsistency. The limit of detection and quantification of the method were 0.025 and 0.06 μg/mL for both the analytes, respectively. The calibration curve showed a good linearity in the range 0.06-20 μg/mL. For both analytes, the intraday precision and trueness were 2.3-8.2 % and -1.1 to 3.1 %, respectively, while the interday values were 0.7-10.1 % and -10.4 to 7.5 %, respectively. The developed methodology enables the concurrent determination and quantification of free DM4 and its metabolite, free S-Me-DM4, making it a valuable tool for assessing the pharmacokinetics and pharmacodynamics of DM4-based therapies. In addition, the procedure was successfully applied to analyse the presence of free DM4 or its metabolite, free S-Me-DM4, in human plasma samples spiked with the 1959-sss/DM4 antibody-drug conjugate (ADC). The utilization of the herein validated methodology allowed to confirm the presence of these analytes, thereby providing insights into their potential release from the ADC structure.
Topics: Humans; Reproducibility of Results; Chromatography, High Pressure Liquid; Maytansine; Pharmaceutical Preparations
PubMed: 37586307
DOI: 10.1016/j.jpba.2023.115642