SEARCH · PubMed Health

Results for “Targeted metabolomics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources

PubMed2006

Engineering primary metabolic pathways of industrial micro-organisms.

Metabolic engineering is a powerful tool for the optimisation and the introduction of new cellular processes. This is mostly done by genetic engineering. Since the introduction of this multidisciplinary approach, the success stories keep accumulating. The primary metabolism of industrial micro-organisms has been studied for long time and most biochemical pathways and reaction networks have been elucidated. This large pool of biochemical information, together with data from proteomics, metabolomics and genomics underpins the strategies for design of experiments and choice of targets for manipulation by metabolic engineers. These targets are often located in the primary metabolic pathways, such as glycolysis, pentose phosphate pathway, the TCA cycle and amino acid biosynthesis and mostly at major branch points within these pathways. This paper describes approaches taken for metabolic engineering of these pathways in bacteria, yeast and filamentous fungi.

Bacteria↗

PubMed2002

Selecting nucleic acids for biosensor applications.

In vitro selection can be used to generate nucleic acid binding species (aptamers) and catalysts (ribozymes) that can recognize a variety of molecules. Because nucleic acid function is largely derived from readily tabulated secondary structures, it has proven possible to engineer aptamers and ribozymes to function as biosensors. Labeling nucleic acids with reporter molecules has yielded simple antibody substitutes, but by relying on ligand-dependent conformational changes it has also proven possible to generate biosensors that can recognize and specifically report the presence of ligands in homogenous solution. It may prove possible to generate signaling aptamers and allosteric ribozymes (aptazymes) that are responsive to a large fraction of an organismal proteome or metabolome using automated methods. Nucleic acid biosensor arrays for non-nucleic acid targets could likely be generated with the same facility as DNA chips.

Base Sequence↗

PubMed2005

What it takes to get a herbicide's mode of action. Physionomics, a classical approach in a new complexion.

Discovering new herbicides with novel modes of action is a priority assignment in plant protection research. However, for active compounds identified in greenhouse screens, the crucial point is to tread the most efficient path in determining a herbicide's target site, regarding chance of success, time and research costs. Today, in the literature, molecular (functional genomics, transcriptomics), biochemical (proteomics) and analytical (metabolomics) approaches are particularly discussed. So far, less attention has been focused on the comprehensive physiological profiling of the complex plant system as a procedure which enables new herbicides, with an unknown target site for their mode of action, to be screened rapidly. Here, the concept of an array of 'functional' bioassays is presented which has ultimately been developed from the classical tool of mode of action diagnosis by symptoms. These bioassays are designed to differentiate between the distinct responses of the multiple organization units (plant, tissue, meristematic cell, organelle), developmental stages, types of metabolism (phototrophic, heterotrophic) and physiological processes in the plant organism. The response pattern to a herbicide can be viewed as the end result of changes induced in the molecular and biochemical process chain and should be diagnostic of its physiological mode of action. The results can be interpreted directly or a fingerprint database for all known modes of action to be screened for analogy. The term 'physionomics' is proposed for this comprehensive physiological profiling of the plant system, following the parallel terminology of the molecular and biochemical 'omics' technologies. Physionomics procedures provide a first clue to the mode of action of a new herbicide that can direct more time-consuming and costly molecular, biochemical, histochemical or analytical studies to identify a target site more efficiently.

Biological Assay↗

PubMed2006

Functional analysis of type II thioesterase of Streptomyces lydicus AS 4.2501.

Constructing a mutant strain of single gene disruption is the basis for the study of gene function and metabolomics. Systematic and complete genome sequencing is the basis of genetic manipulation. In the case of a little knowledge about the Streptomyces lydicus genome and the speculation that polyketide synthases (type I) might be responsible for the polyketide side chain biosynthesis of streptolydigin, a 588-bp fragment was amplified by polymerase chain reaction (PCR) according to the homology existing in the same functional genes among Streptomyces. A mutant strain of this gene was constructed by single crossover homologous recombination. The results of sequence analysis as well as the metabolite analysis of the mutant and the original strain by liquid chromatography/mass spectroscopy indicated that this fragment was part of type II thioesterase (TE) gene, which was required for streptolydigin biosynthesis like other type II TEs function in related antibiotics biosynthesis. Furthermore, targeted gene manipulation based on PCR was a powerful tool for studying gene function and metabolomics, especially when little was known about the genomic sequence of streptomyces.

Amino Acid Sequence↗

PubMed2004

Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis.

The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (beta2-agonists). In addition to their primary pharmacological target, beta2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized beta2-agonist, gives rise to an almost identical expression pattern as the beta2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study.

Adrenergic beta-2 Receptor Agonists↗

PubMed2006

Tale of two metal reducers: comparative proteome analysis of Geobacter sulferreducens PCA and Shewanella oneidensis MR-1.

Geobacter sulfurreducens and S. oneidensis are the subjects of intense research efforts due to their potential applications to bioremediation. The characterization of their proteomes, being done in parallel with the analysis of their genome sequences, transcriptomes, and metabolomes, is providing valuable insights to both their similarities and their differences. A primary target of interest in the proteomes of both of these metal-reducing microbes is the characterization of their c-type cytochromes. The discovery of their full compliment of c-type cytochromes and the description of what growth conditions trigger their expression is central to harnessing their bioremediation potential. Proteome analyses thus far show that both G. sulfurreducens and S. oneidensis share the common location of a majority of their c-type cytochromes in their outer membranes. The c-type cytochromes of G. sulfurreducens, however, appear to be less soluble and therefore more difficult to isolate from the membranes than those expressed by S. oneidensis. The majority of the G. sulfurreducens c-type cytochromes also differ from those of S. oneidensis in that they have higher isoelectric points, most higher than pH 8.0. These characteristics of solubility and isoelectric point could be related and could indicate an underlying functional difference in the strategy for metal reduction between these two microbes. The global proteome results available for G. sulfurreducens and S. oneidensis at the time of this writing are primarily the result of 2DE analysis coupled to protein identification by LC-MS/MS of tryptic peptides from in-gel digests and represent the most abundant proteins detected by Coomassie blue or silver nitrate staining. Currently, several complimentary efforts utilising the 2D-LC-MS/MS approaches are in progress, promising a more complete protein inventory for these microbes in the near future. As these data are added to those already available, the intricate network of metabolic processes, regulation of protein synthesis and protein function, transport of nutrients, and signal transduction will be elucidated. The existing tools of proteomics will be complimented with newer methods such as protein chips and phage display to further characterize these microbial systems. The end result, in the not too distant future, will be predictive models of G. sulfurreducens and S. oneidensis behavior in their natural habitats under a variety of environmental conditions.

Bacterial Proteins↗

PubMed2026

Genomic and proteogenomic insights into Spontaneous Coronary Artery Dissection (SCAD): A systematic review of emerging multi-omic evidence.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is a major cause of myocardial infarction in young women without traditional cardiovascular risk factors (Hayes et al., 2018; Adlam et al., 2018 [1, 2]). Despite growing awareness, its biological underpinnings remain incompletely understood, and clinical management is largely based on observational evidence rather than mechanistic insight (Saw et al., 2014; Lettieri et al., 2015; Steg et al., 2024 [3-5]). OBJECTIVES: To systematically integrate genomic, epitranscriptomic, proteomic, and metabolomic data in order to characterize the multi-omic architecture of SCAD and identify potential biomarkers and therapeutic targets. METHODS: A systematic review was conducted in accordance with the PRISMA 2020 statement (Arbelo et al., 2023 [6]). PubMed/MEDLINE was searched for original studies investigating genomic and multi-omic features of SCAD. Data were extracted on study design, patient characteristics, identified variants, circulating biomarkers, and implicated biological pathways. Functional enrichment analysis was performed using the DAVID bioinformatics resource (Page et al., 2021 [7]). RESULTS: A total of 16 studies were included. Genome-wide association studies consistently identified susceptibility loci related to arterial structure and extracellular matrix integrity, including ADAMTSL4, PHACTR1/EDN1, LRP1, and FBN1 (Huang et al., 2009; Saw et al., 2020; Turley et al., 2020 [8-10]). Rare variant analyses further supported the role of genes involved in extracellular matrix remodeling and vascular smooth muscle cell function, including COL3A1, COL4A1/2, SMAD3, and TLN1 (Adlam et al., 2023; Turley et al., 2021, 2019; Carss et al., 2020; Zekavat et al., 2022; Wang et al., 2022 [11-16]), while ancestry-specific signals such as TSR1 variants were observed in distinct populations (Turley et al., 2023 [17]). Proteogenomic approaches linked genetic susceptibility loci to circulating proteins involved in matrix remodeling and inflammation, including cathepsin B and ECM1 (Maioli et al., 2010 [18]). Epitranscriptomic analyses identified differential microRNA expression profiles associated with vascular injury and repair pathways (Sun et al., 2019 [19]). CONCLUSIONS: SCAD is characterized by a complex, multi-layered biological architecture involving genetic susceptibility, extracellular matrix dysregulation, and vascular signaling pathways. Integration of multi-omic data provides novel insights into disease mechanisms and highlights potential biomarkers and targets for precision medicine approaches in SCAD.

Animals↗

PubMed2006

A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human.

Type 2 diabetes mellitus is the result of a combination of impaired insulin secretion with reduced insulin sensitivity of target tissues. There are an estimated 150 million affected individuals worldwide, of whom a large proportion remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. In this study, NMR-based metabolomic analysis in conjunction with multivariate statistics was applied to examine the urinary metabolic changes in two rodent models of type 2 diabetes mellitus as well as unmedicated human sufferers. The db/db mouse and obese Zucker (fa/fa) rat have autosomal recessive defects in the leptin receptor gene, causing type 2 diabetes. 1H-NMR spectra of urine were used in conjunction with uni- and multivariate statistics to identify disease-related metabolic changes in these two animal models and human sufferers. This study demonstrates metabolic similarities between the three species examined, including metabolic responses associated with general systemic stress, changes in the TCA cycle, and perturbations in nucleotide metabolism and in methylamine metabolism. All three species demonstrated profound changes in nucleotide metabolism, including that of N-methylnicotinamide and N-methyl-2-pyridone-5-carboxamide, which may provide unique biomarkers for following type 2 diabetes mellitus progression.

Animals↗

PubMed2003

Genomics and metabolomics as markers for the interaction of diet and health: lessons from lipids.

Foods are not purified compounds acting on single molecular targets, but complex mixtures of molecules that modulate many biochemical pathways simultaneously. Diet affects the probability of developing various diseases. Nevertheless, specific recommendations for individual diets are not simple. Recommending nutrient intakes above and beyond those needed to provide adequacy requires scientific knowledge and regulatory scrutiny to ensure the efficacy and safety even of essential nutrients. Designing a diet to improve metabolic health is a bold and ambitious goal. It is possible to design foods that will alter metabolism, but what change will make everyone who is otherwise healthy even healthier? Changing one aspect of metabolism to lower the risk of one disease does not improve overall health if it comes at the expense of disrupting another aspect of metabolism that increases the risk of another disease. This issue has: 1) frustrated nutritional recommendations that could provide benefits to the health of large subsets of the population, 2) caused the recall of drugs with many beneficial effects and 3) caused harm by implying that single nutrients/foods could be healthy for everyone. An individualized system for metabolic assessment would establish the efficacy and safety of nutrients such as amino acids or fatty acids when these are designed to be consumed at levels providing improved metabolic health. The need to document the lack of an adverse effect of a food or drug on physiology necessitates a global, i.e. metabolomic approach.

Animals↗

PubMed2026

Analysis of the dual role of amyloid-beta in Alzheimer's disease through multi-omics integration.

Accumulation of amyloid-beta is highly important in the development of Alzheimer's disease. Given the limitations of the amyloid cascade hypothesis and the repeated clinical failures of anti-amyloid-beta therapies, researchers are increasingly exploring the infection hypothesis. This review explores the dual behaviors of amyloid-beta in Alzheimer's disease, with a particular focus on its protective role against infection by microorganisms and its complicated connections with innate immune system. This new opinion holds that amyloid-beta can play an antimicrobial peptide role. During microbial invasion, its original role is to protect neural tissue, but prolonged accumulation leads to chronic deposition and involvement in pathological processes. Evidence from in vitro experiments, animal models, and clinical studies indicates that amyloid-beta may possess antiviral and antibacterial properties, particularly against infections such as herpes simplex virus, human immunodeficiency virus, and Porphyromonas gingivalis . However, excessive accumulation of amyloid beta triggers a neuroinflammatory cascade that impairs neuronal regeneration and cognitive function. Despite substantial research into Alzheimer's disease, current treatments have not yielded significant clinical benefits. Although monoclonal antibodies such as Aducanumab , Lecanemab , and Donanemab have been approved for marketing, their strict indications and high costs pose challenges for widespread promotion. The infection hypothesis of amyloid-beta has spurred clinical trials investigating vaccines targeting specific pathogens to assess their potential in preventing or treating Alzheimer's disease. This highlights the need for further exploring the multifaceted role of amyloid-beta in Alzheimer's disease. In addition, microbial infections can also trigger or regulate genetic and epigenetic factors, accelerating amyloid beta deposition. Among them, the apolipoprotein E epsilon 4 allele is the strongest genetic risk factor for Alzheimer's disease, as it exacerbates the accumulation of amyloid beta and promotes neuroinflammation. Strategies targeting epigenetic regulation may provide novel approaches to inhibit Alzheimer's disease pathology. This review also integrates various technologies such as genomics, proteomics, and metabolomics. This provides a broader system-level understanding of the risk gene loci, protein interaction networks, and metabolic changes associated with amyloid beta under the influence of microbial infections. Such techniques may lead to the identification of new molecular targets, the development of individualized treatment strategies, and the creation of early biomarkers for use in clinical research. In conclusion, this review suggests that amyloid-beta is not merely a pathological by-product but an environmentally responsive molecule with dual functions. A deeper understanding of the dynamic regulation of amyloid-beta, considering infection status and disease stage, can provide new directions for treatment strategies aimed at the prevention and treatment of Alzheimer's disease.

Herpesvirus 1↗

PubMed2026

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BACKGROUND: Cerebral infarction (CI) is a major cause of adult disability and mortality worldwide. Mounting evidence supports the critical role of the gut-brain axis in cerebrovascular disease progression. This study aimed to characterize the alterations in gut microbiota, serum metabolome, and serum proteome in patients with CI, and to identify multi-omics signatures associated with clinical symptoms. METHODS: A total of 20 CI patients and 20 healthy controls (HC) were enrolled. Fecal microbiota was profiled using 16 S rRNA gene high-throughput sequencing. Serum metabolomics and proteomics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and data-independent acquisition (DIA) proteomics, respectively. Spearman correlation and multi-omics integration were applied to explore the associations among microbiota, metabolites, proteins, and clinical indicators. RESULTS: CI patients displayed significant gut microbiota dysbiosis, with a markedly lower gut microbiota health index (GMHI) and higher microbiota disorder index (MDI) compared with HC (P < 0.001). The genera g_norank_o_RF39 and Oxalobacter were significantly enriched in CI patients, whereas Clostridium_sensu_stricto_1 and Agathobacter were enriched in HC. Metabolomic analysis identified 445 differential metabolites, mainly involved in glycerophospholipid metabolism, phenylalanine metabolism, and caffeine metabolism. Proteomic analysis revealed 140 differentially expressed proteins linked to inflammatory responses, calcium signaling, and NF-κB signaling. Multi-omics integration showed that signature gut microbiota was strongly correlated (P < 0.005) with key serum metabolites and proteins implicated in CI pathogenesis. CONCLUSIONS: This integrated multi-omics study revealed distinct gut microbiota, serum metabolomic, and proteomic alterations in CI patients. The microbiota-metabolite-protein regulatory axes provide novel insights into the gut-brain axis in CI and may serve as potential diagnostic biomarkers or therapeutic targets.

Humans↗

PubMed2026

A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.

Metabolic bioengineering has emerged as a transformative approach for reshaping plant defense by targeting intrinsic biosynthetic pathways to enhance immunity in modern agriculture. Moving beyond proof-of-concept metabolomics to broad-spectrum programmable pathway engineering addresses gaps in plant rational design and optimizes resilience in response to diverse environmental cues. This review aims to comprehensively highlight the transition of innovative approaches to phenolics, alkaloids, flavonoids, terpenoids, and benzoxazinoids, inferring adaptive reprogramming that mediates the growth-defense balance and functions as molecular sentinels in plants. Furthermore, decoding the volatile metabolome reveals a dynamic signaling interface that influences defense responses and stress-induced plant-microbe interactions, with the shikimate, jasmonate, and salicylate pathways functioning as central hubs for microbial deterrence and priming immune memory. Recent developments in multi-scalar genome-editing strategies, including CRISPR-driven combinatorial edits, enzyme orthogonalization, fluxomics, and spatially resolved multi-omics, reconfigure central and specialized metabolic fluxes toward improved defense function and regulation. Additionally, emerging tools, such as WUSCHEL2 and BABY BOOM transcriptional modules, and artificial engineering strategies integrating deep learning model-driven predictions facilitate rapid development of synthetic genetic circuits and support a predictive engineering of plants. Moreover, Mass spectrometry imaging (MSI) in spatial metabolomics enables to obtain structures and locations of unidentified endogenous metabolites within cells and tissues. Overall, this review emphasizes a diverse array of primary and secondary metabolites, spanning molecular concepts to recent advances in plant immune mechanisms. It also illustrates new frontiers in programmable metabolic engineering that accelerate the understanding of plant-microbe-metabolite cross-talks, offering strategies to improve plant resistance and advance sustainable agricultural solutions.

metabolic bioengineering↗

PubMed2026

Machine Learning and Metabolomics to Characterize Warburg-Like Metabolic Subtypes in Human Retinal Endothelial Cells Exposed to Risk Factors Associated With Proliferative Diabetic Retinopathy.

PURPOSE: High glucose (HG), hypoxia (Hyp), and their combination are major risk factors for proliferative diabetic retinopathy (PDR). Although these conditions induce features of the Warburg-like metabolic reprogramming in human retinal endothelial cells (HRECs), it remains unclear whether they produce distinct metabolic and angiogenic subtypes. This study aimed to characterize the Warburg-like-associated metabolic heterogeneity induced by these PDR-related risk factors and evaluate the ability of supervised machine-learning models to distinguish these subtypes. METHODS: HRECs were cultured under normoglycemic, HG, Hyp (2% O2), and combined HG-Hyp conditions. Untargeted LC-MS/MS metabolomics quantified metabolites spanning carbohydrates, amino acids, nucleotides, and lipids. Principal component analysis (PCA) assessed overall metabolic variation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified metabolic pathways associated with angiogenesis. In vitro angiogenesis assays measured endothelial tube formation and branching. Nine supervised classifiers (decision tree, logistic regression, naïve Bayes, random forest, K-Nearest Neighbors, neural network, gradient boosting, AdaBoost, and Support Vector Machine) were trained on the highest-ranked metabolites selected by the Information Gain Ratio feature-ranking approach. Model performance was evaluated using 10-fold cross-validation, leave-one-out cross-validation (LOOCV), permutation testing, and a classifier stability analysis under biologically meaningful distributional shift using an independent chemically induced hypoxia model (CoCl2). RESULTS: PCA revealed partial separation of metabolic profiles across conditions, indicating different Warburg-like metabolic subtypes. The combined HG-Hyp condition exhibited enhanced angiogenic potential relative to either HG or Hyp alone. KEGG pathway enrichment analysis identified fatty acid biosynthesis and elongation among the most significantly enriched pathways in HRECs under combined HG-Hyp conditions, alongside amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, the pentose phosphate pathway, and glycolysis/gluconeogenesis. Supervised machine-learning classifiers distinguished these metabolic subtypes, with AdaBoost and gradient Boosting showing the most balanced, reproducible performance across 10-fold cross-validation, LOOCV, and permutation testing, and remaining the most reliable classifiers under domain-shift testing (area under the curve = 0.88, P = 0.0061). CONCLUSIONS: In this exploratory analysis, HG, Hyp, and their combination drive metabolically and functionally distinct subtypes of Warburg-like metabolic reprogramming in HRECs, with HG-Hyp in combination producing a highly angiogenic phenotype. Boosting-based ensemble classifiers provide a promising framework for detecting these subtypes even under domain-shift conditions, warranting validation in larger independent datasets. TRANSLATIONAL RELEVANCE: Integrating metabolomics with machine-learning classification offers a strategy to identify Warburg-like metabolic subtypes in retinal endothelial cells, providing insights into angiogenic mechanisms and guiding the development of targeted diagnostics or therapeutics for PDR.

Humans↗

PubMed2026

Multimodal analysis of CD38 in T-cell Acute Lymphoblastic Leukemia Identifies Combinatorial Therapeutic Strategies.

Outcomes for pediatric patients with refractory or relapsed T-cell acute lymphoblastic leukemia (T-ALL) are poor, underscoring the need for improved therapeutic strategies. CD38, a type II transmembrane glycoprotein, is a promising target in T-ALL, with clinical trials evaluating CD38-targeting immunotherapies in frontline and relapsed settings. However, the biological role of CD38 in T-ALL has not been systematically defined. We interrogated CD38 biology through multimodal profiling of pediatric T-ALL samples. Bulk RNA sequencing of 1,335 primary tumors revealed that CD38 expression varies across genomic and immunophenotypic subtypes in T-ALL. Flow cytometry of 150 primary samples and CITE-sequencing of 40 cases demonstrated broad surface expression of CD38. A transcription factor CRISPR-screen identified RUNX1, RUNX3, and TP53 as candidate positive regulators of CD38. Metabolomic profiling of cell lines further revealed disruption of the polyamine pathway following CD38 perturbation. Supporting this finding, co-targeting CD38 with difluoromethylornithine (DFMO), a polyamine metabolism disruptor, improved survival in preclinical models. Across transcriptomic datasets, including primary tumors, cell lines, and patient-derived xenograft models, IL32 expression consistently decreased following CD38 loss or negativity, supporting an association between CD38 and inflammatory signaling pathways. Additionally, CD38 and LCK expression were positively correlated across majority of genomic subtypes, implicating SRC kinase signaling. Consistent with this, daratumumab in cell lines increased LCK phosphorylation, and combination therapy with dasatinib improved survival compared to monotherapy. Collectively, these findings define previously unrecognized interactions between CD38 and targetable pathways and genes in T-ALL and identify rational combinatorial strategies to enhance CD38-directed therapies and reduce relapse risk.

Journal Article↗

PubMed2026

Phytolacca acinosa Roxb. induces intestinal toxicity through the histamine-MLCK-tight junction axis: Integrated evidence from proteomics, metabolomics, intestinal organoids and epithelial barrier validation.

Phytolacca acinosa Roxb. (PR) is a saponin-rich medicinal plant associated with gastrointestinal toxicity, but the mechanisms underlying PR-induced intestinal barrier injury remain unclear. In this study, raw PR extract was analytically characterized by UPLC-ZenoTOF-MS/MS, confirming triterpenoid saponins as the predominant constituents. C57BL/6 J mice were orally exposed to characterized PR extract (1.20 or 12.0 g/kg for 5 h), and Caco-2 cells and mouse intestinal organoids were used to assess epithelial toxicity and barrier disruption. Histopathology, ELISA, FITC-dextran permeability assays, immunofluorescence, CCK-8, LDH release, western blotting, DIA-based proteomics and untargeted metabolomics were integrated to define toxicological mechanisms. PR induced dose-dependent intestinal inflammation and barrier dysfunction, with the ileum as the most sensitive target. PR increased serum DAO and D-lactate and intestinal TNF-α and IL-1β, disrupted organoid morphology, enhanced epithelial permeability, and reduced ZO-1 expression. Proteomics revealed changes in inflammatory, lipid-metabolic, cytoskeletal and tight-junction pathways, including upregulation of MLCK3 and phospholipase-related proteins and downregulation of ZO-1 and ZO-2. Metabolomics identified histidine metabolism disturbance and histamine accumulation. Integrated multi-omics and pharmacological validation indicated that histamine activated the PLC/IP₃/Ca²⁺/CaM/MLCK cascade, promoting MLC phosphorylation, tight-junction disassembly and epithelial leakiness. MLCK inhibition partially restored ZO-1/ZO-2 expression and attenuated PR-induced epithelial injury. These findings identify the histamine-MLCK-tight junction axis as a key mechanism of PR-induced intestinal toxicity and support hazard identification of saponin-rich PR exposure.

Animals↗

PubMed2004

Ecotoxicogenomics: the challenge of integrating genomics into aquatic and terrestrial ecotoxicology.

Rapid progress in the field of genomics (the study of how an individual's entire genetic make-up, the genome, translates into biological functions) is beginning to provide tools that may assist our understanding of how chemicals can impact on human and ecosystem health. In many ways, if scientific and regulatory efforts in the 20th century have sought to establish which chemicals cause damage to ecosystems, then the challenge in ecotoxicology for the 21st century is to understand the mechanisms of toxicity to different wildlife species. In the human context, 'toxicogenomics' is the study of expression of genes important in adaptive responses to toxic exposures and a reflection of the toxic processes per se. Given the parallel implications for ecological (environmental) risk assessment, we propose the term 'ecotoxicogenomics' to describe the integration of genomics (transcriptomics, proteomics and metabolomics) into ecotoxicology. Ecotoxicogenomics is defined as the study of gene and protein expression in non-target organisms that is important in responses to environmental toxicant exposures. The potential of ecotoxicogenomic tools in ecological risk assessment seems great. Many of the standardized methods used to assess potential impact of chemicals on aquatic organisms rely on measuring whole-organism responses (e.g. mortality, growth, reproduction) of generally sensitive indicator species at maintained concentrations, and deriving 'endpoints' based on these phenomena (e.g. median lethal concentrations, no observed effect concentrations, etc.). Whilst such phenomenological approaches are useful for identifying chemicals of potential concern they provide little understanding of the mechanism of chemical toxicity. Without this understanding, it will be difficult to address some of the key challenges that currently face aquatic ecotoxicology, e.g. predicting toxicant responses across the very broad diversity of the phylogenetic groups present in aquatic ecosystems; estimating how changes at one ecological level or organisation will affect other levels (e.g. predicting population-level effects); predicting the influence of time-varying exposure on toxicant responses. Ecotoxicogenomic tools may provide us with a better mechanistic understanding of aquatic ecotoxicology. For ecotoxicogenomics to fulfil its potential, collaborative efforts are necessary through the parallel use of model microorganisms (e.g. Saccharomyces cerevisiae) together with aquatic (e.g. Danio rerio, Daphnia magna, Lemna minor and Xenopus tropicalis) and terrestrial (e.g. Arabidopsis thailiana, Caenorhabdites elegans and Eisenia foetida) plants, animals and microorganisms.

Ecology↗

PubMed2004

Application of proteomic technologies in the drug development process.

Proteins are the principal targets of drug discovery. Most large pharmaceutical companies now have a proteomics-oriented biotech or academic partner or have started their own proteomics division. Common applications of proteomics in the drug industry include target identification and validation, identification of efficacy and toxicity biomarkers from readily accessible biological fluids, and investigations into mechanisms of drug action or toxicity. Target identification and validation involves identifying proteins whose expression levels or activities change in disease states. These proteins may serve as potential therapeutic targets or may be used to classify patients for clinical trials. Proteomics technologies may also help identify protein-protein interactions that influence either the disease state or the proposed therapy. Efficacy biomarkers are used to assess whether target modulation has occurred. They are used for the characterization of disease models and to assess the effects and mechanism of action of lead candidates in animal models. Toxicity (safety) biomarkers are used to screen compounds in pre-clinical studies for target organ toxicities as well as later on in development during clinical trials. Complementary approaches such as metabolomics and genomics can be used in conjunction with proteomics throughout the drug development process to create more of a unified, systems biology approach.

Animals↗

PubMed2003

Metabolic biomarker and kinase drug target discovery in cancer using stable isotope-based dynamic metabolic profiling (SIDMAP).

Tumor cells respond to growth signals by the activation of protein kinases, altered gene expression and significant modifications in substrate flow and re-distribution among biosynthetic pathways. This results in a proliferating phenotype with altered cellular function. These transformed cells exhibit unique anabolic characteristics, which includes increased and preferential utilization of glucose through the non-oxidative steps of the pentose cycle for nucleic acid synthesis but limited de novo fatty acid synthesis and TCA cycle glucose oxidation. This primarily non-oxidative anabolic profile reflects an undifferentiated highly proliferative aneuploid cell phenotype and serves as a reliable metabolic biomarker to determine cell proliferation rate and the level of cell transformation/differentiation in response to drug treatment. Novel drugs effective in particular cancers exert their anti-proliferative effects by inducing significant reversions of a few specific non-oxidative anabolic pathways. Here we present evidence that cell transformation of various mechanisms is sustained by a unique disproportional substrate distribution between the two branches of the pentose cycle for nucleic acid synthesis, glycolysis and the TCA cycle for fatty acid synthesis and glucose oxidation. This can be demonstrated by the broad labeling and unique specificity of [1,2-(13)C(2)]glucose to trace a large number of metabolites in the metabolome. Stable isotope-based dynamic metabolic profiles (SIDMAP) serve the drug discovery process by providing a powerful new tool that integrates the metabolome into a functional genomics approach to developing new drugs. It can be used in screening kinases and their metabolic targets, which can therefore be more efficiently characterized, speeding up and improving drug testing, approval and labeling processes by saving trial and error type study costs in drug testing.

Biomarkers, Tumor↗