Fatty liver and fibrosis in glycine N-methyltransferase knockout mice is prevented by nicotinamide

Deletion of glycine N-methyltransferase (GNMT) in mice, the main gene involved in liver S-adenosylmethionine (SAMe) catabolism, leads to the hepatic accumulation of this molecule and the development of fatty liver and fibrosis. To demonstrate that the excess of hepatic SAMe is the main agent contributing to liver disease in GNMT-KO mice, we treated 1.5-month old GNMT-KO mice for 6 weeks with nicotinamide (NAM), a substrate of the enzyme NAM N-methyltransferase. NAM administration markedly reduced hepatic SAMe content, prevented DNA-hypermethylation and normalized the expression of critical genes involved in fatty acid metabolism, oxidative stress, inflammation, cell proliferation, and apoptosis. More important, NAM treatment prevented the development of fatty liver and fibrosis in GNMT-KO mice. Because GNMT expression is down-regulated in patients with cirrhosis and there are subjects with GNMT mutations who have spontaneous liver disease, the clinical implication of the present findings is obvious at least with respect to these latter individuals. Especially since NAM has been used for many years to treat a broad spectrum of diseases including pellagra and diabetes without significant side effects, it should be considered in subjects with GNMT mutations.ConclusionsThese results indicate that the anomalous accumulation of SAMe in GNMT-KO mice can be corrected by NAM treatment leading to the normalization of the expression of many genes involved in fatty acid metabolism, oxidative stress, inflammation, cell proliferation and apoptosis, and to the reversion of the appearance of the pathologic phenotype

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment

The tumor’s physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations

A novel workflow correlating RNA-seq data to Phythophthora infestans resistance levels in wild Solanum species and potato clones

Comparative transcriptomics between species can provide valuable understanding of plant-pathogen interactions. Here, we focus on wild Solanum species and potato clones with varying degree of resistance against Phytophthora infestans, which causes the devastating late blight disease in potato. The transcriptomes of three wild Solanum species native to Southern Sweden, S. dulcamara, S. nigrum and S. physalifolium were compared to three potato clones, Desiree (cv.), SW93-1015 and Sarpo Mira. Desiree and S. physalifolium are susceptible to P. infestans whereas the other four have different degrees of resistance. By building transcript families based on de novo assembled RNA-seq across species and clones and correlating these to resistance phenotypes, we created a novel workflow to identify families with expanded or depleted number of transcripts in relation to the P. infestans resistance level. Analysis was facilitated by inferring functional annotations based on the family structure and semantic clustering. More transcript families were expanded in the resistant clones and species and the enriched functions of these were associated to expected gene ontology (GO) terms for resistance mechanisms such as hypersensitive response, host programmed cell death and endopeptidase activity. However, a number of unexpected functions and transcripts were also identified, for example transmembrane transport and protein acylation expanded in the susceptible group and a cluster of Zinc knuckle family proteins expanded in the resistant group. Over 400 expressed putative resistance (R-)gene were identified and resistant clones Sarpo Mira and SW93-1015 had ca 25% more expressed R-genes than susceptible cultivar Desiree. However, no differences in numbers of susceptibility (S-)gene homologs were seen between species and clones. In addition, we identified P. infestans transcripts including effectors in the early stages of P. infestans-Solanum interactions

Integrative genomic signatures of hepatocellular carcinoma derived from nonalcoholic Fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is a risk factor for Hepatocellular carcinoma (HCC), but he transition from NAFLD to HCC is poorly understood. Feature selection algorithms in human and genetically modified mice NAFLD and HCC microarray data were applied to generate signatures of NAFLD progression and HCC differential survival. These signatures were used to study the pathogenesis of NAFLD derived HCC and explore which subtypes of cancers that can be investigated using mouse models. Our findings show that: (I) HNF4 is a common potential transcription factor mediating the transcription of NAFLD progression genes (II) mice HCC derived from NAFLD co-cluster with a less aggressive human HCC subtype of differential prognosis and mixed etiology (III) the HCC survival signature is able to correctly classify 95% of the samples and gives Fgf20 and Tgfb1i1 as the most robust genes for prediction (IV) the expression values of genes composing the signature in an independent human HCC dataset revealed different HCC subtypes showing differences in survival time by a Logrank test. In summary, we present marker signatures for NAFLD derived HCC molecular pathogenesis both at the gene and pathway level

The MORPH-R web server and software tool for predicting missing genes in biological pathways

A biological pathway is the set of molecular entities involved in a given biological process and the interrelations among them. Even though biological pathways have been studied extensively, discovering missing genes in pathways remains a fundamental challenge. Here, we present an easy-to-use tool that allows users to run MORPH (MOdule-guided Ranking of candidate PatHway genes), an algorithm for revealing missing genes in biological pathways, and demonstrate its capabilities. MORPH supports the analysis in tomato, Arabidopsis and the two new species: rice and the newly sequenced potato genome. The new tool, called MORPH-R, is available both as a web server (at) and as standalone software that can be used locally. In the standalone version, the user can apply the tool to new organisms using any proprietary and public data sources

Kaplan-Meier plots showing the survival probability over time (days) of the 3 main clusters representing HCC subtypes of differential survival found in the independent HCC dataset when performing clustering analysis over the expression values of the genes composing the survival signature common for human and mouse.

<p>Kaplan-Meier plots showing the survival probability over time (days) of the 3 main clusters representing HCC subtypes of differential survival found in the independent HCC dataset when performing clustering analysis over the expression values of the genes composing the survival signature common for human and mouse.</p

Ensemble unique gene survival signature common for human and mouse resulting from the frequency based aggregation of the signatures produced by the 5 feature selection methods.

<p>The frequency of appearance of the selected genes among the 5 feature selection methods is recorded as a measure of stability.</p><p>Ensemble unique gene survival signature common for human and mouse resulting from the frequency based aggregation of the signatures produced by the 5 feature selection methods.</p

Data partition and aggregation procedures.

<p>A random partition of the data into mutually exclusive sets P₁, P₂, P₃, P₄ and P₅ is done. Feature selection is performed in each partition. It results in a feature subset for each partition. We perform frequency based aggregation by individually adding the most frequent features from the subsets and stop adding features when the performance of a mining algorithm starts to decrease. It results in a unique ensemble subset.</p