Combining stage - specificity and metabolomic profiling to advance drug discovery for malaria
Ontology highlight
ABSTRACT:
ORGANISM(S): Plasmodium Falciparum
TISSUE(S): Cultured Cells
DISEASE(S): Malaria
PROVIDER: ST001232 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| mwtab | Other |
Items per page: 5 1 - 1 of 1 |
Similar Datasets
Project description:We report detailed susceptibility profiling of asexual blood stages of the malaria parasite Plasmodium falciparum to clinical and experimental antimalarials, combined with metabolomic fingerprinting. Results revealed a variety of stage-specific and metabolic profiles that differentiated the modes of action of clinical antimalarials including chloroquine, piperaquine, lumefantrine, and mefloquine, and identified late trophozoite-specific peak activity and stage-specific biphasic dose-responses for the mitochondrial inhibitors DSM265 and atovaquone. We also identified experimental antimalarials hitting previously unexplored druggable pathways as reflected by their unique stage specificity and/or metabolic profiles. These included several ring-active compounds, ones affecting hemoglobin catabolism through distinct pathways, and mitochondrial inhibitors with lower propensities for resistance than either DSM265 or atovaquone. This approach, also applicable to other microbes that undergo multiple differentiation steps, provides an effective tool to prioritize compounds for further development within the context of combination therapies.
| S-EPMC7031696 | biostudies-literature
Project description:Background and aims: Chronic drug-induced liver injury (DILI) is a rare but under-researched adverse drug reaction-related disease, which is highly likely to progress into liver fibrosis and even cirrhosis. In this study, metabolomics was used to screen out characteristic metabolites related to the histological progression of fibrosis in chronic DILI and analyze the metabolic changes during the development of fibrosis to explain the underlying mechanism. Methods: Chronic DILI patients who underwent liver biopsy were divided into different fibrosis grades. Serum was analyzed by untargeted metabolomics to find serological characteristic metabolite fingerprints. The screened fingerprints were validated by the validation group patients, and the identification ability of fingerprints was compared using FibroScan. Results: A total of 31 metabolites associated with fibrosis and 11 metabolites associated with advanced fibrosis were identified. The validation group confirmed the accuracy of the two metabolite fingerprints [area under the curve (AUC) value 0.753 and 0.944]. In addition, the fingerprints showed the ability to distinguish the grades of fibrosis by comparing using FibroScan. The metabolite fingerprint pathway showed that bile acid synthesis is disturbed while lipid metabolism is extremely active, resulting in an overload of lipid metabolites in the occurrence and development of chronic DILI-associated fibrosis. Conclusions: Our metabolomic analysis reveals the unique metabolomic fingerprints associated with chronic DILI fibrosis, which have potential clinical diagnostic and prognostic significances. The metabolomic fingerprints suggest the disturbance of the lipid metabolites as the most important factor in the development of DILI fibrosis.
| S-EPMC9163384 | biostudies-literature
Project description:The Malaria Drug Accelerator (MalDA) is a consortium of 15 leading scientific laboratories. The aim of MalDA is to improve and accelerate the early antimalarial drug discovery process by identifying new, essential, druggable targets. In addition, it seeks to produce early lead inhibitors that may be advanced into drug candidates suitable for preclinical development and subsequent clinical testing in humans. By sharing resources, including expertise, knowledge, materials, and reagents, the consortium strives to eliminate the structural barriers often encountered in the drug discovery process. Here we discuss the mission of the consortium and its scientific achievements, including the identification of new chemically and biologically validated targets, as well as future scientific directions.
| S-EPMC8261838 | biostudies-literature
Project description:There is no specific test for diagnosing neuromyelitis optica spectrum disorder (NMOSD), a disabling autoimmune disease of the central nervous system. Instead, diagnosis relies on ruling out other related disorders with overlapping clinical symptoms. An urgency for NMOSD biomarker discovery is underscored by adverse responses to treatment following misdiagnosis and poor prognosis following the delayed onset of treatment. Pathogenic autoantibiotics that target the water channel aquaporin-4 (AQP4) and myelin oligodendrocyte glycoprotein (MOG) contribute to NMOSD pathology. The importance of early diagnosis between AQP4-Ab+ NMOSD, MOG-Ab+ NMOSD, AQP4-Ab- MOG-Ab- NMOSD, and related disorders cannot be overemphasized. Here, we provide a comprehensive data collection and analysis of the currently known metabolomic perturbations and related proteomic outcomes of NMOSD. We highlight short chain fatty acids, lipoproteins, amino acids, and lactate as candidate diagnostic biomarkers. Although the application of metabolomic profiling to individual NMOSD patient care shows promise, more research is needed.
| S-EPMC7570337 | biostudies-literature
Project description:The threat of widespread drug resistance to frontline antimalarials has renewed the urgency for identifying inexpensive chemotherapeutic compounds that are effective against Plasmodium falciparum, the parasite species responsible for the greatest number of malaria-related deaths worldwide. To aid in the fight against malaria, a recent extensive screening campaign has generated thousands of lead compounds with low micromolar activity against blood stage parasites. A subset of these leads has been compiled by the Medicines for Malaria Venture (MMV) into a collection of structurally diverse compounds known as the MMV Malaria Box. Currently, little is known regarding the activity of these Malaria Box compounds on parasite metabolism during intraerythrocytic development, and a majority of the targets for these drugs have yet to be defined. Here we interrogated the in vitro metabolic effects of 189 drugs (including 169 of the drug-like compounds from the Malaria Box) using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). The resulting metabolic fingerprints provide information on the parasite biochemical pathways affected by pharmacologic intervention and offer a critical blueprint for selecting and advancing lead compounds as next-generation antimalarial drugs. Our results reveal several major classes of metabolic disruption, which allow us to predict the mode of action (MoA) for many of the Malaria Box compounds. We anticipate that future combination therapies will be greatly informed by these results, allowing for the selection of appropriate drug combinations that simultaneously target multiple metabolic pathways, with the aim of eliminating malaria and forestalling the expansion of drug-resistant parasites in the field.
| S-EPMC5075069 | biostudies-literature
Project description:A mathematical model which predicts the intraerythrocytic stages of Plasmodium falciparum infection was developed using data from malaria-infected mice. Variables selected accounted for levels of healthy red blood cells, merozoite (Plasmodium asexual phase) infected red blood cells, gametocyte (Plasmodium sexual phase) infected red blood cells and a phenomenological variable which accounts for the mean activity of the immune system of the host. The model built was able to reproduce the behavior of three different scenarios of malaria. It predicts the later dynamics of malaria-infected humans well after the first peak of parasitemia, the qualitative response of malaria-infected monkeys to vaccination and the changes observed in malaria-infected mice when they are treated with antimalarial drugs. The mathematical model was used to identify new targets to be focused on drug design. Optimization methodologies were applied to identify five targets for minimizing the parasite load; four of the targets thus identified have never before been taken into account in drug design. The potential targets include: 1) increasing the death rate of the gametocytes, 2) decreasing the invasion rate of the red blood cells by the merozoites, 3) increasing the transformation of merozoites into gametocytes, 4) decreasing the activation of the immune system by the gametocytes, and finally 5) a combination of the previous target with decreasing the recycling rate of the red blood cells. The first target is already used in current therapies, whereas the remainders are proposals for potential new targets. Furthermore, the combined target (the simultaneous decrease of the activation of IS by gRBC and the decrease of the influence of IS on the recycling of hRBC) is interesting, since this combination does not affect the parasite directly. Thus, it is not expected to generate selective pressure on the parasites, which means that it would not produce resistance in Plasmodium.
| S-EPMC3610898 | biostudies-literature
Project description:Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.
| S-EPMC4277370 | biostudies-literature
Project description:Malaria remains a major global health problem, with more than half of the world population at risk of contracting the disease and nearly a million deaths each year. Here, we report the discovery of inhibitors that target multiple stages of malaria parasite growth. To identify these inhibitors, we took advantage of the Tres Cantos Antimalarial Compound Set (TCAMS) small-molecule library, which is comprised of diverse and potent chemical scaffolds with activities against the blood stage of the malaria parasite, and investigated their effects against the elusive liver stage of the malaria parasite using a forward chemical screen. From a screen of nearly 14,000 compounds, we identified and confirmed 103 compounds as dual-stage malaria inhibitors. Interestingly, these compounds show preferential inhibition of parasite growth in liver- versus blood-stage malaria parasite assays, highlighting the drug susceptibility of this parasite form. Mode-of-action studies were completed using genetically modified and drug-resistant Plasmodium parasite strains. While we identified some compound targets as classical antimalarial pathways, such as the mitochondrial electron transport chain through cytochrome bc1 complex inhibition or the folate biosynthesis pathway, most compounds induced parasite death through as yet unknown mechanisms of action. Importantly, the identification of new chemotypes with different modes of action in killing Plasmodium parasites represents a promising opportunity for probing essential and novel molecular processes that remain to be discovered. The chemical scaffolds identified with activity against drug-resistant Plasmodium parasites represent starting points for dual-stage antimalarial development to surmount the threat of malaria parasite drug resistance.
| S-EPMC4775961 | biostudies-literature
Project description:Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.
| S-EPMC7048302 | biostudies-literature
Project description:Feline hepatic lipidosis (FHL) is a common liver dysfunction caused by metabolic disorders. The objective was to evaluate the metabolic alteration in the cats of FHL and to identify biomarkers that can serve as biomarker for FHL. Differential metabolites in the serum of spontaneous FHL cats (FS, n = 12) and healthy cats (CS group, n = 12) were analyzed using GC/MS metabolomics. Differential metabolites with diagnostic significance were identified through receiver operating characteristic (ROC) curves. The expression level of the differential metabolite 2-hydroxybutyric acid (2-HB) was detected in the serum of the FS and CS groups, and biomarker were established. The biomarker efficacy of 2-HB for FHL was verified using serum samples from cats with FHL caused by different etiologies (F, n = 10) and healthy cats (C, n = 50). There were 13 significantly different metabolites between the CS and FS groups (VIP > 1, P < 0.05) with the area under the ROC curve (AUC) greater than 0.70. The AUC for serum 2-HB was 0.90 (95% confidence interval 0.767-1.000, P < 0.001), with an optimal critical value of 564.8 ng/L. By randomly detecting serum 2-HB in groups F and C (the optimal cut-off value is 564.8 ng/L), the detection rate for FHL diagnosis was 100% and the false positive rate was 0%. In cats with FHL, metabolic changes occur in amino acids, nucleotide sugars, glycerophospholipids, phenylalanine, galactose, alpha-linolenic acid, and glycerides. A serum 2-HB level greater than 564.8 ng/L serves as a biomarker for FHL.
| S-EPMC11885465 | biostudies-literature