JMassBalance: mass-balanced randomization and analysis of metabolic networks

Summary: Analysis of biological networks requires assessing the statistical significance of network-based predictions by using a realistic null model. However, the existing network null model, switch randomization, is unsuitable for metabolic networks, as it does not include physical constraints and generates unrealistic reactions. We present JMassBalance, a tool for mass-balanced randomization and analysis of metabolic networks. The tool allows efficient generation of large sets of randomized networks under the physical constraint of mass balance. In addition, various structural properties of the original and randomized networks can be calculated, facilitating the identification of the salient properties of metabolic networks with a biologically meaningful null model

Metabolite profiling of postharvest senescence in different strawberry cultivars

The cultivated strawberry (Fragaria x ananassa) is the berry most consumed worldwide, being well appreciated for its flavour and nutritional characteristics. However, strawberries possess a very short postharvest shelf-life due to their high respiration rate and their susceptibility to water loss, mechanical damage and fungi deterioration (Feliziani and Romanazzi, 2016). Extension of fruit shelf-life is a major economic goal, and measures are commercially taken to delay senescence, including the use of low temperature storage alone or in combination with controlled atmosphere (Pedreschi and Lurie, 2015). To improve our understanding of the molecular and biochemical mechanisms underlying the deterioration of fruit quality attributes during senescence, we realized a metabolite profiling of five commercial strawberry cultivars under different postharvest treatments. Ripe fruits were harvested and kept at 4ºC during three, six and ten days in ambient, CO2-enriched and O3-enriched atmospheres. We used a combination of gas chromatography-mass spectrometry (GC-TOF-MS), ultra-performance liquid chromatography-Orbitrap mass/mass spectrometry (UPLC-Orbitrap-MS/MS) and headspace solid phase micro extraction (HS-SPME) coupled with GC-MS to identify and semi-quantify 49 primary metabolites (sugars, amino and organic acids), 132 polar secondary metabolites (mainly polyphenols) and 70 volatile compounds. Multivariate statistical approaches were used to characterize the variation in metabolite content during the strawberry fruit postharvest life and to identify the biochemical pathways which are most affected in the senescence processes. Preliminary analysis pointed out that changes in primary metabolism were possibly related to responses to abiotic stress.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds

Introduction: To date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism. Objective: This study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues. Methods: The analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses. Results: Changes in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism. Conclusions: Overall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.Fil: Nunes Nesi, Adriano. Max Planck Institute Of Molecular Plant Physiology; Alemania. Universidade Federal de Viçosa.; BrasilFil: Alseekh, Saleh. Center Of Plant Systems Biology And Biotechnology; Bulgaria. Max Planck Institute Of Molecular Plant Physiology; AlemaniaFil: de Oliveira Silva, Franklin Magnum. Universidade Federal de Viçosa.; BrasilFil: Omranian, Nooshin. Max Planck Institute Of Molecular Plant Physiology; Alemania. Center Of Plant Systems Biology And Biotechnology; BulgariaFil: Lichtenstein, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Mirnezhad, Mohammad. Leiden University; Países BajosFil: Romero González, Roman R.. Leiden University; Países BajosFil: Sabio y Garcia, Julia Veronica. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias Castelar. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Conte, Mariana. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias Castelar. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; ArgentinaFil: Leiss, Kirsten A.. Leiden University; Países BajosFil: Klinkhamer, Peter G. L.. Leiden University; Países BajosFil: Nikoloski, Zoran. University of Potsdam; Alemania. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Carrari, Fernando Oscar. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias Castelar. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Fernie, Alisdair R.. Max Planck Institute of Molecular Plant Physiology; Alemania. Center of Plant System Biology and Biotechnology; Bulgari

EFSA Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids (CEF); Scientific Opinion on Flavouring Group Evaluation 96 (FGE.96): Consideration of 88 flavouring substances considered by EFSA for which EU production volumes / anticipated production volumes have been submitted on request by DG SANCO. Addendum to FGE. 51, 52, 53, 54, 56, 58, 61, 62, 63, 64, 68, 69, 70, 71, 73, 76, 77, 79, 80, 83, 84, 85 and 87

Overgrowth disorders are a heterogeneous group of conditions characterized by increased growth parameters and other variable clinical features such as intellectual disability and facial dysmorphism1. To identify new causes of human overgrowth, we performed exome sequencing in ten proband-parent trios and detected two de novo DNMT3A mutations. We identified 11 additional de novo mutations by sequencing DNMT3A in a further 142 individuals with overgrowth. The mutations alter residues in functional DNMT3A domains, and protein modeling suggests that they interfere with domain-domain interactions and histone binding. Similar mutations were not present in 1,000 UK population controls (13/152 cases versus 0/1,000 controls; P < 0.0001). Mutation carriers had a distinctive facial appearance, intellectual disability and greater height. DNMT3A encodes a DNA methyltransferase essential for establishing methylation during embryogenesis and is commonly somatically mutated in acute myeloid leukemia2, 3, 4. Thus, DNMT3A joins an emerging group of epigenetic DNA- and histone-modifying genes associated with both developmental growth disorders and hematological malignancie

Combined mutations of ASXL1, CBL, FLT3, IDH1, IDH2, JAK2, KRAS, NPM1, NRAS, RUNX1, TET2 and WT1 genes in myelodysplastic syndromes and acute myeloid leukemias

<p>Abstract</p> <p>Background</p> <p>Gene mutation is an important mechanism of myeloid leukemogenesis. However, the number and combination of gene mutated in myeloid malignancies is still a matter of investigation.</p> <p>Methods</p> <p>We searched for mutations in the <it>ASXL1, CBL, FLT3, IDH1, IDH2, JAK2, KRAS, NPM1, NRAS, RUNX1, TET2 </it>and <it>WT1 </it>genes in 65 myelodysplastic syndromes (MDSs) and 64 acute myeloid leukemias (AMLs) without balanced translocation or complex karyotype.</p> <p>Results</p> <p>Mutations in <it>ASXL1 </it>and <it>CBL </it>were frequent in refractory anemia with excess of blasts. Mutations in <it>TET2 </it>occurred with similar frequency in MDSs and AMLs and associated equally with either <it>ASXL1 </it>or <it>NPM1 </it>mutations. Mutations of <it>RUNX1 </it>were mutually exclusive with <it>TET2 </it>and combined with <it>ASXL1 </it>but not with <it>NPM1</it>. Mutations in <it>FLT3 (</it>mutation and internal tandem duplication), <it>IDH1</it>, <it>IDH2</it>, <it>NPM1 </it>and <it>WT1 </it>occurred primarily in AMLs.</p> <p>Conclusion</p> <p>Only 14% MDSs but half AMLs had at least two mutations in the genes studied. Based on the observed combinations and exclusions we classified the 12 genes into four classes and propose a highly speculative model that at least a mutation in one of each class is necessary for developing AML with simple or normal karyotype.</p

Mutations in ASXL1 are associated with poor prognosis across the spectrum of malignant myeloid diseases

The ASXL1 gene is one of the most frequently mutated genes in malignant myeloid diseases. The ASXL1 protein belongs to protein complexes involved in the epigenetic regulation of gene expression. ASXL1 mutations are found in myeloproliferative neoplasms (MPN), myelodysplastic syndromes (MDS), chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML). They are generally associated with signs of aggressiveness and poor clinical outcome. Because of this, a systematic determination of ASXL1 mutational status in myeloid malignancies should help in prognosis assessment

EZH2 Depletion Blocks the Proliferation of Colon Cancer Cells

The Enhancer of Zeste 2 (EZH2) protein has been reported to stimulate cell growth in some cancers and is therefore considered to represent an interesting new target for therapeutic intervention. Here, we investigated a possible role of EZH2 for the growth control of colon cancer cells. RNA interference (RNAi)-mediated intracellular EZH2 depletion led to cell cycle arrest of colon carcinoma cells at the G1/S transition. This was associated with a reduction of cell numbers upon transient transfection of synthetic EZH2-targeting siRNAs and with inhibition of their colony formation capacity upon stable expression of vector-borne siRNAs. We furthermore tested whether EZH2 may repress the growth-inhibitory p27 gene, as reported for pancreatic cancer. However, expression analyses of colon cancer cell lines and colon cancer biopsies did not reveal a consistent correlation between EZH2 and p27 levels. Moreover, EZH2 depletion did not re-induce p27 expression in colon cancer cells, indicating that p27 repression by EZH2 may be cell- or tissue-specific. Whole genome transcriptome analyses identified cellular genes affected by EZH2 depletion in colon cancer cell lines. They included several cancer-associated genes linked to cellular proliferation or invasion, such as Dag1, MageD1, SDC1, Timp2, and Tob1. In conclusion, our results demonstrate that EZH2 depletion blocks the growth of colon cancer cells. These findings might provide benefits for the treatment of colon cancer

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

Peer reviewedPublisher PD

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

Background: Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network. Methodology/Principal Findings: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. Conclusions/Significance: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches