Search results for: Jean-Daniel Zucker

Authors: Tzu-Hao Li, Shie-Liang Hsieh, Han-Chieh Lin, Ying-Ying Yang

Abstract:

TNF superfamily-stimulated pathogenic cascades and macrophage (M1)/kupffer cells (KC) polarization are important in the pathogenesis of ischemia-reperfusion (IR) liver injury in animals with hepatic steatosis (HS). Decoy receptor 3 (DcR3) is a common upstream inhibitor of the above-mentioned pathogenic cascades. The study evaluated whether modulation of these DcR3-related cascades was able to protect steatotic liver from IR injury. Serum and hepatic DcR3 levels were lower in patients and animals with HS. Accordingly, the effects of pharmacologic and genetic DcR3 replacement on the IR-related pathogenic changes were measured. Significantly, DcR3 replacement protected IR-Zucker(HS) rats and IR-DcR3-Tg(HS) mice from IR liver injury. The beneficial effects of DcR3 replacement were accompanied by decreased serum/hepatic TNF, soluble TNF-like cytokine 1A (TL1A), Fas ligand (Fas-L) and LIGHT, T-helper-cell-1 cytokine (INF) levels, neutrophil infiltration, M1 polarization, neutrophil-macrophage/KC-T-cell interaction, hepatocyte apoptosis and improved hepatic microcirculatory failure among animals with IR-injured steatotic livers. Additionally, TL1A, Fas-L, LIGHT and TLR4/NFB signals were found to mediate the DcR3-related protective effects of steatotic livers from IR injury. Using multimodal in vivo and in vitro approaches, we found that DcR3 was a potential agent to protect steatotic livers from IR injury by simultaneous blocking the multiple IR injury-related pathogenic changes.

Keywords: Decoy 3 receptor, ischemia-reperfusion injury, M1 polarization, TNF superfamily

Procedia PDF Downloads 177

Authors: Leixuri Aguirre, Iñaki Milton-Laskibar, Elizabeth Hijona, Luis Bujanda, Agnes M. Rimando, Maria P. Portillo

Abstract:

Introduction: In recent years great attention has been paid by scientific community to phenolic compounds as active biomolecules naturally present in foodstuffs due to their beneficial effects on health. Pterostilbene is a resveratrol dimethylether derivative which shows higher biodisponibility. Objective. To analyze the effects of two doses of pterostilbene on several markers of thermogenic capacity in a model of genetic obesity, which shows reduced thermogenesis. Methods: The experiment was conducted with thirty Zucker (fa/fa) rats that were distributed in 3 experimental groups, the control group and two groups orally administered with pterostilbene at 15 and 30 mg/kg body weight/day for 6 weeks. Gene expression of Ucp1, Pgc-1α, Cpt1b, Pparα, Nfr1, Tfam and Cox-2 were assessed by RT-PCR, protein expression of UCP1 and GLUT4 by western blot and enzyme activity of carnitine palmitoyl transferase 1b and citrate synthase by spectrophotometry in interscapular brown adipose tissue (iBAT). Statistical analysis was performed by using one way ANOVA and Newman-Keuls as post-hoc test. Results: Pterostilbene did not change gene expression of Pgc-1α. However, significant increases were found in the expression of Ucp1, Pparα, Nfr-1 and Cox-2. Protein expression of UCP1 and GLUT4 was increased in animals treated with pterostilbene, as well as the activities of CPT-1b and CS. These effects were observed with both doses of pterostilbene, without differences between them. Conclusions: These results show that pterostilbene increases thermogenic and oxidative capacity of brown adipose tissue in obese rats. Whether these effects effectively contribute to the anti-obesity properties of these compound needs further research. Acknowledgments: MINECO-FEDER (AGL2015-65719-R), Basque Government (IT-572-13), University of the Basque Country (ELDUNANOTEK UFI11/32), Institut of Health Carlos III (CIBERobn). Iñaki Milton is a fellowship from the Basque Government.

Keywords: brown adipose tissue, pterostilbene, thermogenesis, uncoupling protein 1

Procedia PDF Downloads 264

Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

Abstract:

Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

Procedia PDF Downloads 96