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The thyroid gland, a vital component of the endocrine system, plays a pivotal role in regulating metabolic processes, growth, and development. To better characterize thyroid system disrupting chemicals (TSDC), we followed the next-generation risk assessment approach, which further considers the mechanistic profile of xenobiotics. We combined targeted in vitro testing with untargeted metabolomics. Four known TSDC, propyl-thiouracil (PTU), sodium perchlorate, triclosan, and 5-pregnen-3ß-ol-20-one-16αcarbonitrile (PCN) were investigated using rat in vitro models, including primary hepatocytes, PCCL3 cells, thyroid microsomes, and three-dimensional thyroid follicles. We confirmed each compound's mode of action, PTU inhibited thyroperoxidase activity and thyroid hormones secretion in thyroid cells model, sodium perchlorate induced a NIS-mediated iodide uptake decrease as triclosan to a lesser extent, and PCN activated expression and activity of hepatic enzymes (CYPs and UGTs) involved in thyroid hormones metabolism. In parallel, we characterized intracellular metabolites of interest. We identified disrupted basal metabolic pathways, but also metabolites directly linked to the compound's mode of action as tyrosine derivates for sodium perchlorate and triclosan, bile acids involved in beta-oxidation, and precursors of cytochrome P450 synthesis for PCN. This pilot study has provided metabolomic fingerprinting of dedicated TSDC exposures, which could be used to screen and differentiate specific modes of action.
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BACKGROUND: The dramatic clinical improvement offered by mechanical thrombectomy raised questions about the relevance of prior intravenous thrombolysis in large-vessel occlusion strokes. Hence, studying intravenous thrombolysis susceptibility and its dependence on thrombus composition is crucial. We used an observational proteomic study of whole thrombi retrieved by mechanical thrombectomy to identify factors associated with fibrin content and fibrinolytic activity (FA). METHODS: In 104 stroke patients, the thrombi proteome was established by mass spectrometry coupled to liquid chromatography. FA was estimated in clots both outside (FAout) by measuring D-dimer levels at the blood-thrombus interface and inside (FAin) by evaluating the ratio of fibrinogen α to its plasmin-cleaved forms using proteomics coupled with protein electrophoresis. The factors associated with fibrin content, FAin, and FAout were determined by intravenous thrombolysis-adjusted linear regression. RESULTS: FAout (P<0.0001) and FAin (P=0.0147) were driven by recombinant tissue-type plasminogen activator (r-tPA) administration (47/104) and thrombus composition. Indeed, FAout was greater with fibrin-rich than erythrocyte-rich thrombi, presumably because of more (r)tPA substrates. Thus, FAout was increased with cardioembolic thrombi (72/104), which are rich in fibrin (P=0.0300). Opposite results were found inside the thrombus, suggesting that (r)tPA penetrability was hampered by the density of the fibrinous cap. Moreover, blood cells had a strong impact on thrombus structure and susceptibility to (r)tPA. Indeed, fibrin content was negatively associated with erythrocyte-specific proteins in the thrombus, admission hematocrit (P=0.0139), and hemoglobin level (P=0.0080), which underlines the key role of erythrocytes in thrombus composition. Also, an increased number of neutrophils impaired FAout (P=0.0225), which suggests that their aggregation around the thrombus prevented the (r)tPA attack. Only FAout was significantly associated with reduced thrombus weight (P=0.0310), increased recanalization rate (P=0.0150), good clinical outcome (P=0.0480), and reduced mortality (P=0.0080). CONCLUSIONS: Proteomics can offer new insights into the close relationship between thrombus composition and susceptibility to fibrinolysis, paving the way for new adjuvant therapies.
Subject(s)
Fibrinolysis , Intracranial Thrombosis , Proteomics , Stroke , Humans , Male , Female , Fibrinolysis/drug effects , Aged , Middle Aged , Intracranial Thrombosis/metabolism , Intracranial Thrombosis/drug therapy , Stroke/metabolism , Stroke/drug therapy , Thrombectomy/methods , Tissue Plasminogen Activator , Fibrin/metabolism , Aged, 80 and over , Thrombolytic Therapy , Thrombosis/metabolismABSTRACT
BACKGROUND: Intake of potassium iodide (KI) reduces the accumulation of radioactive iodine in the thyroid gland in the event of possible contamination by radioactive iodine released from a nuclear facility. The WHO has stated the need for research for optimal timing, appropriate dosing regimen and safety for repetitive iodine thyroid blocking (ITB). The French PRIODAC project, addressed all these issues, involving prolonged or repeated releases of radioactive iodine. Preclinical studies established an effective dose through pharmacokinetic modeling, demonstrating the safety of repetitive KI treatment without toxicity. SUMMARY: Recent preclinical studies have determined an optimal effective dose for repetitive administration, associated with pharmacokinetic modelling. The results show the safety and absence of toxicity of repetitive treatment with KI. Good laboratory practice level preclinical studies corresponding to individuals > 12 years have shown a safety margin established between animal doses without toxic effect. After approval from the French health authorities, the market authorization of the 2 tablets of KI-65mg/day was defined with a new dosing scheme of a daily repetitive intake of the treatment up to 7 days unless otherwise instructed by the competent authorities for all categories of population except pregnant women, and children under the age of 12 years. CONCLUSIONS: This new marketed authorization resulting from scientific-based evidence obtained as part of the PRIODAC project may serve as an example to further harmonize the application of KI for repetitive ITB in situations of prolonged radioactive release at the European and International levels, under the umbrella of the WHO.
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The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.
Subject(s)
Brain Neoplasms , Glioma , Humans , Adult , Brain Neoplasms/diagnosis , Brain Neoplasms/genetics , Brain Neoplasms/chemistry , Formaldehyde , Paraffin , Paraffin Embedding/methods , Isocitrate Dehydrogenase/genetics , Glioma/diagnosis , Glioma/genetics , MutationABSTRACT
Purpose: Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design: Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results: PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion: Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.
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Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20-30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich®) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich® FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842-1), an AUC of 0.945 (0.833-1), and an F1 score of 0.947 (0.842-1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.
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Sonchus oleraceus (L.) L. (Asteraceae) is an edible wild plant, known for its uses in traditional medicine. The aim of this study is to explore the phytochemical composition of the aerial parts (AP) and roots (R) of aqueous extracts of Sonchus oleraceus L. growing in Tunisia, using liquid chromatography-tandem mass spectrometry(LC/MS/MS), and determine the content of polyphenols and antioxidant activities. Results showed that aqueous extracts of AP and R contained, respectively, 195.25±33â µg/g and 118.66±14â µg/g gallic acid equivalent (GAE), and 52.58±7â µg/g and 3.2±0.3µg/g quercetin equivalent. AP and R extracts also contained tannins, 581.78±33â µg/g and 948.44±19â µg/g GAE. The AP extract in the 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) scavenging activities, hydroxyl radical scavenging (OH-) and in cupric reducing antioxidant activity (CUPRAC) assays were respectively 0.325±0.036â mg/mL, 0.053±0.018â mg/mL, 0.696±0.031â mg/mL and 60.94±0.004â µMTE/g, while the R extract using the same assays showed, 0.209±0.052â mg/mL, 0.034±0.002â mg/mL, 0.444±0.014â mg/mL and 50.63±0.006â µM Trolox equivalent/g, respectively. A total of 68â compounds were tentatively identified by LC/MS/MS in both extracts in which quinic acid, pyrogallol, osthrutin, piperine, gentisic acid, fisetin, luteolin, caffeic acid, gingerol, were the most abundant in the LC/MS/MS spectrum. Many of these metabolites were found for the first time in Tunisian Sonchus oleraceus L. which may take account for the antioxidant activities exhibited by the plant.
Subject(s)
Antioxidants , Sonchus , Antioxidants/pharmacology , Antioxidants/chemistry , Tandem Mass Spectrometry/methods , Plant Extracts/chemistry , Polyphenols/chemistry , Gallic Acid , Flavonoids/chemistryABSTRACT
Telomere shortening is a hallmark of aging and is counteracted by telomerase. As in humans, the zebrafish gut is one of the organs with the fastest rate of telomere decline, triggering early tissue dysfunction during normal zebrafish aging and in prematurely aged telomerase mutants. However, whether telomere-dependent aging of an individual organ, the gut, causes systemic aging is unknown. Here we show that tissue-specific telomerase expression in the gut can prevent telomere shortening and rescues premature aging of tert-/-. Induction of telomerase rescues gut senescence and low cell proliferation, while restoring tissue integrity, inflammation and age-dependent microbiota dysbiosis. Averting gut aging causes systemic beneficial impacts, rescuing aging of distant organs such as reproductive and hematopoietic systems. Conclusively, we show that gut-specific telomerase expression extends the lifespan of tert-/- by 40%, while ameliorating natural aging. Our work demonstrates that gut-specific rescue of telomerase expression leading to telomere elongation is sufficient to systemically counteract aging in zebrafish.
Subject(s)
Aging, Premature , Telomerase , Humans , Animals , Aged , Zebrafish/genetics , Telomerase/genetics , Aging/genetics , Telomere Shortening/genetics , Aging, Premature/geneticsABSTRACT
PURPOSE: Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer. METHODS: This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I-II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry. RESULTS: A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples. CONCLUSIONS: This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.
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Objectives: The COVID-19 pandemic has been a serious worldwide public health crisis since 2020 and is still challenging healthcare systems. New tools for the prognosis and diagnosis of COVID-19 patients remain important issues. Design: Here, we studied the metabolome of plasma samples of COVID-19 patients for the identification of prognosis biomarkers. Patients: Plasma samples of eighty-six SARS-CoV-2-infected subjects and 24 healthy controls were collected during the first peak of the COVID-19 pandemic in France in 2020. Main results: Plasma metabolome fingerprinting allowed the successful discrimination of healthy controls, mild SARS-CoV-2 subjects, and moderate and severe COVID-19 patients at hospital admission. We found a strong effect of SARS-CoV-2 infection on the plasma metabolome in mild cases. Our results revealed that plasma lipids and alterations in their saturation level are important biomarkers for the detection of the infection. We also identified deoxy-fructosyl-amino acids as new putative plasma biomarkers for SARS-CoV-2 infection and COVID-19 severity. Finally, our results highlight a key role for plasma levels of tryptophan and kynurenine in the symptoms of COVID-19 patients. Conclusion: Our results showed that plasma metabolome profiling is an efficient tool for the diagnosis and prognosis of SARS-CoV-2 infection.
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BACKGROUND: Presently, there is a wide variety of classification methods and deep neural network approaches in bioinformatics. Deep neural networks have proven their effectiveness for classification tasks, and have outperformed classical methods, but they suffer from a lack of interpretability. Therefore, these innovative methods are not appropriate for decision support systems in healthcare. Indeed, to allow clinicians to make informed and well thought out decisions, the algorithm should provide the main pieces of information used to compute the predicted diagnosis and/or prognosis, as well as a confidence score for this prediction. METHODS: Herein, we used a new supervised autoencoder (SAE) approach for classification of clinical metabolomic data. This new method has the advantage of providing a confidence score for each prediction thanks to a softmax classifier and a meaningful latent space visualization and to include a new efficient feature selection method, with a structured constraint, which allows for biologically interpretable results. RESULTS: Experimental results on three metabolomics datasets of clinical samples illustrate the effectiveness of our SAE and its confidence score. The supervised autoencoder provides an accurate localization of the patients in the latent space, and an efficient confidence score. Experiments show that the SAE outperforms classical methods (PLS-DA, Random Forests, SVM, and neural networks (NN)). Furthermore, the metabolites selected by the SAE were found to be biologically relevant. CONCLUSION: In this paper, we describe a new efficient SAE method to support diagnostic or prognostic evaluation based on metabolomics analyses.
Subject(s)
Machine Learning , Neural Networks, Computer , Algorithms , Computational Biology , Humans , Metabolomics/methodsABSTRACT
BACKGROUND: Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH. RESULTS: PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate. CONCLUSION: PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.
Subject(s)
Metabolomics , Support Vector Machine , Discriminant Analysis , Humans , Least-Squares AnalysisABSTRACT
This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population.
Subject(s)
Cell Cycle/physiology , Image Processing, Computer-Assisted/methods , Mitotic Index/methods , Supervised Machine Learning , Algorithms , Cell Cycle/drug effects , HeLa Cells , Humans , Microscopy , Nocodazole/pharmacologyABSTRACT
Non-ideal behaviour of mixed ions is disclosed in skin absorption experiments of mixed halide anions in excised pig skin. Comparison of skin absorption of pure and mixed ions shows enhanced penetration of chaotropic ions from mixed solutions. An experimental design and statistical analysis using a Scheffé {3,2} simplex-lattice allows investigating the full ternary diagram of anion mixtures of fluoride, bromide and iodide. Synergism in mixed absorption is observed for chaotropic bromide and iodide anions. A refined analysis highlighting specific interactions is made by considering the ratio of the absorbed amount to the ion activity instead of the directly measured absorbed amount. Statistical analysis discards non-significant effects and discloses specific interactions. Such interactions between bromide and iodide cause an absorption enhancement of their partner by a factor of 2-3 with respect to the case of ideal mixing. It is proposed that enhanced absorption from mixed solution involves the formation of neutral complex species of mixed bromide and iodide with endogenous magnesium or calcium inside stratum corneum.
Subject(s)
Skin Absorption , Water , Animals , Anions/metabolism , Fluorides/metabolism , Skin/metabolism , Solutions , Swine , Water/metabolismABSTRACT
The sodium/iodide symporter (NIS) expresses at the basolateral plasma membrane of the thyroid follicular cell and mediates iodide accumulation required for normal thyroid hormonogenesis. Loss-of-function NIS variants cause congenital hypothyroidism due to impaired iodide accumulation in thyroid follicular cells underscoring the significance of NIS for thyroid physiology. Here we report novel findings derived from the thorough characterization of the nonsense NIS mutant p.R636* NIS-leading to a truncated protein missing the last eight amino acids-identified in twins with congenital hypothyroidism. R636* NIS is severely mislocalized into intracellular vesicular compartments due to the lack of a conserved carboxy-terminal type 1 PDZ-binding motif. As a result, R636* NIS is barely targeted to the plasma membrane and therefore iodide transport is reduced. Deletion of the PDZ-binding motif causes NIS accumulation into late endosomes and lysosomes. Using PDZ domain arrays, we revealed that the PDZ-domain containing protein SCRIB binds to the carboxy-terminus of NIS by a PDZ-PDZ interaction. Furthermore, in CRISPR/Cas9-based SCRIB deficient cells, NIS expression at the basolateral plasma membrane is compromised, leading to NIS localization into intracellular vesicular compartments. We conclude that the PDZ-binding motif is a plasma membrane retention signal that participates in the polarized expression of NIS by selectively interacting with the PDZ-domain containing protein SCRIB, thus retaining the transporter at the basolateral plasma membrane. Our data provide insights into the molecular mechanisms that regulate NIS expression at the plasma membrane, a topic of great interest in the thyroid cancer field considering the relevance of NIS-mediated radioactive iodide therapy for differentiated thyroid carcinoma.
Subject(s)
Membrane Proteins/metabolism , Symporters/metabolism , Tumor Suppressor Proteins/metabolism , Amino Acid Sequence , Animals , Cell Line , Cell Membrane/metabolism , Codon, Nonsense , Congenital Hypothyroidism/genetics , Congenital Hypothyroidism/metabolism , Conserved Sequence , Dogs , Endosomes/metabolism , HEK293 Cells , Humans , Lysosomes/metabolism , Madin Darby Canine Kidney Cells , Membrane Proteins/chemistry , Membrane Proteins/genetics , Models, Molecular , Mutagenesis, Site-Directed , PDZ Domains/genetics , Protein Structure, Secondary , Rats , Recombinant Fusion Proteins/chemistry , Recombinant Fusion Proteins/genetics , Recombinant Fusion Proteins/metabolism , Symporters/chemistry , Symporters/genetics , Thyroid Gland/metabolism , Tumor Suppressor Proteins/chemistry , Tumor Suppressor Proteins/geneticsABSTRACT
Background and Purpose: The diagnosis of cardioembolic stroke can be challenging for patient management in secondary stroke prevention, particularly in the case of covert paroxysmal atrial fibrillation. The molecular composition of a cerebral thrombus is related to its origin. Therefore, proteomic and metabolomic analyses of the retrieved thrombotic material should allow the identification of biomarkers or signatures to improve the etiological diagnosis of stroke. Methods: In this pilot study, the proteome and metabolome of cerebral thrombi from atherothrombotic and cardioembolic stroke patients were studied according to ASCOD phenotyping (A: atherosclerosis; S: small-vessel disease; C: cardiac pathology; O: other causes; D: dissection), with the highest causality grade, from the ThrombiOMIC cohort (consecutive patients with stroke recanalized by mechanical thrombectomy in an acute phase). Proteomic and metabolomic results were used separately or combined, and the obtained omic signatures were compared with classical cardioembolic stroke predictors using pairwise comparisons of the area under receiver operating characteristics. Results: Among 59 patients of the ThrombiOMIC cohort, 34 patients with stroke showed a cardioembolic phenotype and 7 had an atherothrombotic phenotype. Two thousand four hundred fifty-six proteins and 5019 molecular features of the cerebral thrombi were identified using untargeted proteomic and metabolomic approaches, respectively. Area under receiver operating characteristics to predict the cardioembolic origin of stroke were calculated using the proteomic results (0.945 [95% CI, 0.8711]), the metabolomic results (0.836 [95% CI, 0.7140.958]), and combined signatures (0.996 [95% CI, 0.9841]). The diagnostic performance of the combined signatures was significantly higher than that of classical predictors such as the plasmatic BNP (B-type natriuretic peptide) level (area under receiver operating characteristics, 0.803 [95% CI, 0.6290.976]). Conclusions: The combined proteomic and metabolomic analyses of retrieved cerebral thrombi is a very promising molecular approach to predict the cardioembolic cause of stroke and to improve secondary stroke prevention strategies.
Subject(s)
Brain Ischemia/etiology , Embolic Stroke/surgery , Intracranial Thrombosis/complications , Stroke/surgery , Thrombosis/pathology , Aged , Aged, 80 and over , Atrial Fibrillation/complications , Brain Ischemia/complications , Embolic Stroke/complications , Female , Humans , Intracranial Thrombosis/surgery , Male , Middle Aged , Pilot Projects , Proteomics , Stroke/complications , Stroke/diagnosis , Thrombosis/surgeryABSTRACT
We present here a label-free development based on preexisting Quantitative Phase Imaging (QPI) that allows non-invasive live monitoring of both individual cells and cell populations. Growth, death, effect of toxic compounds are quantified under visible light with a standard inverted microscope. We show that considering the global biomass of a cell population is a more robust and accurate method to assess its growth parameters in comparison to compiling individually segmented cells. This is especially true for confluent conditions. This method expands the use of light microscopy in answering biological questions concerning live cell populations even at high density. In contrast to labeling or lysis of cells this method does not alter the cells and could be useful in high-throughput screening and toxicity studies.
Subject(s)
Microscopy/methods , Biomass , Cell Line , HEK293 Cells , High-Throughput Screening Assays/methods , HumansABSTRACT
The role of ketone bodies in the cerebral energy homeostasis of neurological diseases has begun to attract recent attention particularly in acute neurological diseases. In ketogenic therapies, ketosis is achieved by either a ketogenic diet or by the administration of exogenous ketone bodies. The oral ingestion of the ketone ester (KE), (R)-3-hydroxybutyl (R)-3-hydroxybutyrate, is a new method to generate rapid and significant ketosis (i.e., above 6 mmol/L) in humans. KE is hydrolyzed into ß-hydroxybutyrate (ßHB) and its precursor 1,3-butanediol. Here, we investigate the effect of oral KE administration (3 mg KE/g of body weight) on brain metabolism of non-fasted mice using liquid chromatography in tandem with mass spectrometry. Ketosis (Cmax = 6.83 ± 0.19 mmol/L) was obtained at Tmax = 30 min after oral KE-gavage. We found that ßHB uptake into the brain strongly correlated with the plasma ßHB concentration and was preferentially distributed in the neocortex. We showed for the first time that oral KE led to an increase of acetyl-CoA and citric cycle intermediates in the brain of non-fasted mice. Furthermore, we found that the increased level of acetyl-CoA inhibited glycolysis by a feedback mechanism and thus competed with glucose under physiological conditions. The brain pharmacodynamics of this oral KE strongly suggest that this agent should be considered for acute neurological diseases.
Subject(s)
Acetyl Coenzyme A/metabolism , Brain/metabolism , Carbohydrate Metabolism/genetics , Ketones/metabolism , Animals , Diet, Ketogenic/adverse effects , Eating , Esters/metabolism , Glucose/metabolism , Glycolysis/genetics , Humans , Ketone Bodies/metabolism , Ketosis/metabolism , Ketosis/pathology , MiceABSTRACT
For decades, sodium/iodide symporter NIS-mediated iodide uptake has played a crucial role in the radioactive ablation of thyroid cancer cells. NIS-based gene therapy has also become a promising tool for the treatment of tumors of extrathyroidal origin. But its applicability has been hampered by reduced expression of NIS, resulting in a moderated capacity to accumulate 131I and in inefficient ablation. Despite numerous preclinical enhancement strategies, the understanding of NIS expression within tumors remains limited. This study aims at a better understanding of the functional behavior of exogenous NIS expression in the context of malignant solid tumors that are characterized by rapid growth with an insufficient vasculature, leading to hypoxia and quiescence. Using subcutaneous HT29NIS and K7M2NIS tumors, we show that NIS-mediated uptake and NIS expression at the plasma membrane of cancer cells are impaired in the intratumoral regions. For a better understanding of the underlying molecular mechanisms induced by hypoxia and quiescence (separately and in combination), we performed experiments on HT29NIS cancer cells. Hypoxia and quiescence were both found to impair NIS-mediated uptake through mechanisms including NIS mis-localization. Modifications in the expression of proteins and metabolites involved in plasma membrane localization and in energy metabolism were found using untargeted proteomics and metabolomics approaches. In conclusion, our results provide evidence that hypoxia and quiescence impair NIS expression at the plasma membrane, and iodide uptake. Our study also shows that the tumor microenvironment is an important parameter for successful NIS-based cancer treatment.