How clot composition influences fibrinolysis in the acute phase of stroke: a proteomic study of cerebral thrombi.

Background: The dramatic clinical improvement offered by mechanical thrombectomy (MT) raised questions about the relevance of prior intravenous thrombolysis (IVT) in large vessel occlusion strokes. Hence, studying IVT susceptibility and its dependence on thrombus composition is crucial. We used observational proteomic study of whole thrombi retrieved by MT 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 were 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 ⍺ to its plasmin-cleaved forms using proteomics coupled with protein electrophoresis. The factors associated with fibrin content, FAin and FAout were determined by IVT-adjusted linear regression. Results: FAout (p<0.0001) and FAin (p=0.0147) were driven by rt-PA administration (47/104) and thrombus composition. Indeed, FAout was greater with fibrin-rich than erythrocyte-rich thrombi, presumably because of more (r)t-PA substrates. Thus, FAout was increased with cardioembolic thrombi (72/104), which are rich in fibrin (p=0.0300). Opposite results were found inside thrombus, suggesting that (r)t-PA penetrability was hampered by the density of the fibrinous cap. Moreover, blood cells had a strong impact on thrombus structure and susceptibility to (r)t-PA. 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)t-PA 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 relation between thrombus composition and susceptibility to fibrinolysis, paving the way for new adjuvant therapies.

Review of the PRIODAC project on thyroid protection from radioactive iodine by repeated iodine intake in individuals aged 12.

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.

Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults.

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.

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.

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.

Pilot Study on the Use of Untargeted Metabolomic Fingerprinting of Liquid-Cytology Fluids as a Diagnostic Tool of Malignancy for Thyroid Nodules.

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.

Metabolomics Profiling of Tunisian Sonchus oleraceus L. Extracts and Their Antioxidant Activities.

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±33 µg/g and 118.66±14 µg/g gallic acid equivalent (GAE), and 52.58±7 µg/g and 3.2±0.3µg/g quercetin equivalent. AP and R extracts also contained tannins, 581.78±33 µg/g and 948.44±19 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±0.036 mg/mL, 0.053±0.018 mg/mL, 0.696±0.031 mg/mL and 60.94±0.004 µMTE/g, while the R extract using the same assays showed, 0.209±0.052 mg/mL, 0.034±0.002 mg/mL, 0.444±0.014 mg/mL and 50.63±0.006 µM Trolox equivalent/g, respectively. A total of 68 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.

Gut-specific telomerase expression counteracts systemic aging in telomerase-deficient zebrafish.

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.

Metabolomic Signatures of Scarff-Bloom-Richardson (SBR) Grade in Non-Metastatic Breast Cancer.

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.

Untargeted plasma metabolomic fingerprinting highlights several biomarkers for the diagnosis and prognosis of coronavirus disease 19.

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.

Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.

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.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies.

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.

Mitotic Index Determination on Live Cells From Label-Free Acquired Quantitative Phase Images Using a Supervised Autoencoder.

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.

Skin absorption of mixed halide anions from concentrated aqueous solutions.

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.

The PDZ protein SCRIB regulates sodium/iodide symporter (NIS) expression at the basolateral plasma membrane.

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.

Combined Omic Analyzes of Cerebral Thrombi: A New Molecular Approach to Identify Cardioembolic Stroke Origin.

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.

Quantitative phase microscopy for non-invasive live cell population monitoring.

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.

Ingested Ketone Ester Leads to a Rapid Rise of Acetyl-CoA and Competes with Glucose Metabolism in the Brain of Non-Fasted Mice.

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.

Tumor microenvironment affects exogenous sodium/iodide symporter expression.

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.

Metabolome of Cerebral Thrombi Reveals an Association between High Glycemia at Stroke Onset and Good Clinical Outcome.

Despite the fact that glucose is the main fuel of the brain, hyperglycemia at hospital admission is generally associated with a poor functional outcome in stroke patients. This paradox may be explained by the lack of information about the blood glucose level at stroke onset. Here, we analyzed the metabolome of blood cells entrapped in cerebral thrombi to gain insight into their metabolism at stroke onset. Fourty-one consecutive stroke patients completely recanalized by mechanical thrombectomy within 6 h were included. The metabolome of retrieved thrombi was analyzed by liquid chromatography tandem with mass spectrometry. Discriminant Analysis (sparse Partial Least Squares Discriminant Analysis (sPLS-DA)) was performed to identify classification models and significant associated features of favorable clinical outcome at 3 months (modified Rankin Scale (mRS) < 2). sPLS-DA of the metabolomes of cerebral thrombi discriminated between stroke patients with a favorable or poor clinical outcome (Area Under the Curve (AUC) = 0.992 (0.931-1)). In addition, our results revealed that high sorbitol and glucose levels in the thrombi positively correlated with favorable clinical outcomes. Sorbitol, a short-term glycemic index reflecting a high blood glucose level at stroke onset, was found to be an independent predictor of good outcome (AUC = 0.908 (0.807-0.995)). This study demonstrates that a high blood glucose level at stroke onset is beneficial to the clinical outcome of the patient.