Systematic Analysis of Stability Patterns in Plant Primary Metabolism

Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM) is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models

Direct Repeat 6 from Human Herpesvirus-6B Encodes a Nuclear Protein that Forms a Complex with the Viral DNA Processivity Factor p41

The SalI-L fragment from human herpesvirus 6A (HHV-6A) encodes a protein DR7 that has been reported to produce fibrosarcomas when injected into nude mice, to transform NIH3T3 cells, and to interact with and inhibit the function of p53. The homologous gene in HHV-6B is dr6. Since p53 is deregulated in both HHV-6A and -6B, we characterized the expression of dr6 mRNA and the localization of the translated protein during HHV-6B infection of HCT116 cells. Expression of mRNA from dr6 was inhibited by cycloheximide and partly by phosphonoacetic acid, a known characteristic of herpesvirus early/late genes. DR6 could be detected as a nuclear protein at 24 hpi and accumulated to high levels at 48 and 72 hpi. DR6 located in dots resembling viral replication compartments. Furthermore, a novel interaction between DR6 and the viral DNA processivity factor, p41, could be detected by confocal microscopy and by co-immunoprecipitation analysis. In contrast, DR6 and p53 were found at distinct subcellular locations. Together, our data imply a novel function of DR6 during HHV-6B replication

The Formation and Evolution of the First Massive Black Holes

The first massive astrophysical black holes likely formed at high redshifts (z>10) at the centers of low mass (~10^6 Msun) dark matter concentrations. These black holes grow by mergers and gas accretion, evolve into the population of bright quasars observed at lower redshifts, and eventually leave the supermassive black hole remnants that are ubiquitous at the centers of galaxies in the nearby universe. The astrophysical processes responsible for the formation of the earliest seed black holes are poorly understood. The purpose of this review is threefold: (1) to describe theoretical expectations for the formation and growth of the earliest black holes within the general paradigm of hierarchical cold dark matter cosmologies, (2) to summarize several relevant recent observations that have implications for the formation of the earliest black holes, and (3) to look into the future and assess the power of forthcoming observations to probe the physics of the first active galactic nuclei.Comment: 39 pages, review for "Supermassive Black Holes in the Distant Universe", Ed. A. J. Barger, Kluwer Academic Publisher

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p

Dystrophin Is Required for the Normal Function of the Cardio-Protective KATP Channel in Cardiomyocytes

Duchenne and Becker muscular dystrophy patients often develop a cardiomyopathy for which the pathogenesis is still unknown. We have employed the murine animal model of Duchenne muscular dystrophy (mdx), which develops a cardiomyopathy that includes some characteristics of the human disease, to study the molecular basis of this pathology. Here we show that the mdx mouse heart has defects consistent with alteration in compounds that regulate energy homeostasis including a marked decrease in creatine-phosphate (PC). In addition, the mdx heart is more susceptible to anoxia than controls. Since the cardio-protective ATP sensitive potassium channel (KATP) complex and PC have been shown to interact we investigated whether deficits in PC levels correlate with other molecular events including KATP ion channel complex presence, its functionality and interaction with dystrophin. We found that this channel complex is present in the dystrophic cardiac cell membrane but its ability to sense a drop in the intracellular ATP concentration and consequently open is compromised by the absence of dystrophin. We further demonstrate that the creatine kinase muscle isoform (CKm) is displaced from the plasma membrane of the mdx cardiac cells. Considering that CKm is a determinant of KATP channel complex function we hypothesize that dystrophin acts as a scaffolding protein organizing the KATP channel complex and the enzymes necessary for its correct functioning. Therefore, the lack of proper functioning of the cardio-protective KATP system in the mdx cardiomyocytes may be part of the mechanism contributing to development of cardiac disease in dystrophic patients

Evaluation of qPCR-Based Assays for Leprosy Diagnosis Directly in Clinical Specimens

The increased reliability and efficiency of the quantitative polymerase chain reaction (qPCR) makes it a promising tool for performing large-scale screening for infectious disease among high-risk individuals. To date, no study has evaluated the specificity and sensitivity of different qPCR assays for leprosy diagnosis using a range of clinical samples that could bias molecular results such as difficult-to-diagnose cases. In this study, qPCR assays amplifying different M. leprae gene targets, sodA, 16S rRNA, RLEP and Ag 85B were compared for leprosy differential diagnosis. qPCR assays were performed on frozen skin biopsy samples from a total of 62 patients: 21 untreated multibacillary (MB), 26 untreated paucibacillary (PB) leprosy patients, as well as 10 patients suffering from other dermatological diseases and 5 healthy donors. To develop standardized protocols and to overcome the bias resulted from using chromosome count cutoffs arbitrarily defined for different assays, decision tree classifiers were used to estimate optimum cutoffs and to evaluate the assays. As a result, we found a decreasing sensitivity for Ag 85B (66.1%), 16S rRNA (62.9%), and sodA (59.7%) optimized assay classifiers, but with similar maximum specificity for leprosy diagnosis. Conversely, the RLEP assay showed to be the most sensitive (87.1%). Moreover, RLEP assay was positive for 3 samples of patients originally not diagnosed as having leprosy, but these patients developed leprosy 5–10 years after the collection of the biopsy. In addition, 4 other samples of patients clinically classified as non-leprosy presented detectable chromosome counts in their samples by the RLEP assay suggesting that those patients either had leprosy that was misdiagnosed or a subclinical state of leprosy. Overall, these results are encouraging and suggest that RLEP assay could be useful as a sensitive diagnostic test to detect M. leprae infection before major clinical manifestations

PPAR-α and glucocorticoid receptor synergize to promote erythroid progenitor self-renewal

Many acute and chronic anaemias, including haemolysis, sepsis and genetic bone marrow failure diseases such as Diamond–Blackfan anaemia, are not treatable with erythropoietin (Epo), because the colony-forming unit erythroid progenitors (CFU-Es) that respond to Epo are either too few in number or are not sensitive enough to Epo to maintain sufficient red blood cell production. Treatment of these anaemias requires a drug that acts at an earlier stage of red cell formation and enhances the formation of Epo-sensitive CFU-E progenitors. Recently, we showed that glucocorticoids specifically stimulate self-renewal of an early erythroid progenitor, burst-forming unit erythroid (BFU-E), and increase the production of terminally differentiated erythroid cells. Here we show that activation of the peroxisome proliferator-activated receptor α (PPAR-α) by the PPAR-α agonists GW7647 and fenofibrate synergizes with the glucocorticoid receptor (GR) to promote BFU-E self-renewal. Over time these agonists greatly increase production of mature red blood cells in cultures of both mouse fetal liver BFU-Es and mobilized human adult CD34+ peripheral blood progenitors, with a new and effective culture system being used for the human cells that generates normal enucleated reticulocytes. Although Ppara−/− mice show no haematological difference from wild-type mice in both normal and phenylhydrazine (PHZ)-induced stress erythropoiesis, PPAR-α agonists facilitate recovery of wild-type but not Ppara−/− mice from PHZ-induced acute haemolytic anaemia. We also show that PPAR-α alleviates anaemia in a mouse model of chronic anaemia. Finally, both in control and corticosteroid-treated BFU-E cells, PPAR-α co-occupies many chromatin sites with GR; when activated by PPAR-α agonists, additional PPAR-α is recruited to GR-adjacent sites and presumably facilitates GR-dependent BFU-E self-renewal. Our discovery of the role of PPAR-α agonists in stimulating self-renewal of early erythroid progenitor cells suggests that the clinically tested PPAR-α agonists we used may improve the efficacy of corticosteroids in treating Epo-resistant anaemias.United States. Defense Advanced Research Projects Agency (Grant HR0011-14-2-0005)United States. Army Medical Research and Materiel Command (Grant W81WH-12-1-0449)National Heart, Lung, and Blood Institute (Grant 2 P01 HL032262-25