Hypoxia induces differential translation of enolase/MBP-1

<p>Abstract</p> <p>Background</p> <p>Hypoxic microenvironments in tumors contribute to transformation, which may alter metabolism, growth, and therapeutic responsiveness. The α-enolase gene encodes both a glycolytic enzyme (α-enolase) and a DNA-binding tumor suppressor protein, c-myc binding protein (MBP-1). These divergent α-enolase gene products play central roles in glucose metabolism and growth regulation and their differential regulation may be critical for tumor adaptation to hypoxia. We have previously shown that MBP-1 and its binding to the c-myc P₂promoter regulates the metabolic and cellular growth changes that occur in response to altered exogenous glucose concentrations.</p> <p>Results</p> <p>To examine the regulation of α-enolase and MBP-1 by a hypoxic microenvironment in breast cancer, MCF-7 cells were grown in low, physiologic, or high glucose under 1% oxygen. Our results demonstrate that adaptation to hypoxia involves attenuation of MBP-1 translation and loss of MBP-1-mediated regulation of c-myc transcription, evidenced by decreased MBP-1 binding to the c-myc P₂promoter. This allows for a robust increase in c-myc expression, "early c-myc response", which stimulates aerobic glycolysis resulting in tumor acclimation to oxidative stress. Increased α-enolase mRNA and preferential translation/post-translational modification may also allow for acclimatization to low oxygen, particularly under low glucose concentrations.</p> <p>Conclusions</p> <p>These results demonstrate that malignant cells adapt to hypoxia by modulating α-enolase/MBP-1 levels and suggest a mechanism for tumor cell induction of the hyperglycolytic state. This important "feedback" mechanism may help transformed cells to escape the apoptotic cascade, allowing for survival during limited glucose and oxygen availability.</p

Human brain harbors single nucleotide somatic variations in functionally relevant genes possibly mediated by oxidative stress

Somatic variation in DNA can cause cells to deviate from the preordained genomic path in both disease and healthy conditions. Here, using exome sequencing of paired tissue samples, we show that the normal human brain harbors somatic single base variations measuring up to 0.48% of the total variations. Interestingly, about 64% of these somatic variations in the brain are expected to lead to non-synonymous changes, and as much as 87% of these represent G:C>T:A transversion events. Further, the transversion events in the brain were mostly found in the frontal cortex, whereas the corpus callosum from the same individuals harbors the reference genotype. We found a significantly higher amount of 8-OHdG (oxidative stress marker) in the frontal cortex compared to the corpus callosum of the same subjects (p<0.01), correlating with the higher G:C>T:A transversions in the cortex. We found significant enrichment for axon guidance and related pathways for genes harbouring somatic variations. This could represent either a directed selection of genetic variations in these pathways or increased susceptibility of some loci towards oxidative stress. This study highlights that oxidative stress possibly influence single nucleotide somatic variations in normal human brain

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods

Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force

Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial

Background: The Whole Systems Demonstrator was a large, pragmatic, cluster randomised trial that compared telehealth with usual care among 3,230 patients with long-term conditions in three areas of England. Telehealth involved the regular transmission of physiological information such as blood glucose to health professionals working remotely. We examined whether telehealth led to changes in glycosylated haemoglobin (HbA1c) among the subset of patients with type 2 diabetes. Methods: The general practice electronic medical record was used as the source of information on HbA1c. Effects on HbA1c were assessed using a repeated measures model that included all HbA1c readings recorded during the 12-month trial period, and adjusted for differences in HbA1c readings recorded before recruitment. Secondary analysis averaged multiple HbA1c readings recorded for each individual during the trial period. Results: 513 of the 3,230 participants were identified as having type 2 diabetes and thus were included in the study. Telehealth was associated with lower HbA1c than usual care during the trial period (difference 0.21% or 2.3 mmol/mol, 95% CI, 0.04% to 0.38%, p = 0.013). Among the 457 patients in the secondary analysis, mean HbA1c showed little change for controls following recruitment, but fell for intervention patients from 8.38% to 8.15% (68 to 66 mmol/mol). A higher proportion of intervention patients than controls had HbA1c below the 7.5% (58 mmol/mol) threshold that was targeted by general practices (30.4% vs. 38.0%). This difference, however, did not quite reach statistical significance (adjusted odds ratio 1.63, 95% CI, 0.99 to 2.68, p = 0.053). Conclusions: Telehealth modestly improved glycaemic control in patients with type 2 diabetes over 12 months. The scale of the improvements is consistent with previous meta-analyses, but was relatively modest and seems unlikely to produce significant patient benefit

Rapid Etiological Classification of Meningitis by NMR Spectroscopy Based on Metabolite Profiles and Host Response

Bacterial meningitis is an acute disease with high mortality that is reduced by early treatment. Identification of the causative microorganism by culture is sensitive but slow. Large volumes of cerebrospinal fluid (CSF) are required to maximise sensitivity and establish a provisional diagnosis. We have utilised nuclear magnetic resonance (NMR) spectroscopy to rapidly characterise the biochemical profile of CSF from normal rats and animals with pneumococcal or cryptococcal meningitis. Use of a miniaturised capillary NMR system overcame limitations caused by small CSF volumes and low metabolite concentrations. The analysis of the complex NMR spectroscopic data by a supervised statistical classification strategy included major, minor and unidentified metabolites. Reproducible spectral profiles were generated within less than three minutes, and revealed differences in the relative amounts of glucose, lactate, citrate, amino acid residues, acetate and polyols in the three groups. Contributions from microbial metabolism and inflammatory cells were evident. The computerised statistical classification strategy is based on both major metabolites and minor, partially unidentified metabolites. This data analysis proved highly specific for diagnosis (100% specificity in the final validation set), provided those with visible blood contamination were excluded from analysis; 6-8% of samples were classified as indeterminate. This proof of principle study suggests that a rapid etiologic diagnosis of meningitis is possible without prior culture. The method can be fully automated and avoids delays due to processing and selective identification of specific pathogens that are inherent in DNA-based techniques

Impact on Disease Development, Genomic Location and Biological Function of Copy Number Alterations in Non-Small Cell Lung Cancer

Lung cancer, of which more than 80% is non-small cell, is the leading cause of cancer-related death in the United States. Copy number alterations (CNAs) in lung cancer have been shown to be positionally clustered in certain genomic regions. However, it remains unclear whether genes with copy number changes are functionally clustered. Using a dense single nucleotide polymorphism array, we performed genome-wide copy number analyses of a large collection of non-small cell lung tumors (n = 301). We proposed a formal statistical test for CNAs between different groups (e.g., non-involved lung vs. tumors, early vs. late stage tumors). We also customized the gene set enrichment analysis (GSEA) algorithm to investigate the overrepresentation of genes with CNAs in predefined biological pathways and gene sets (i.e., functional clustering). We found that CNAs events increase substantially from germline, early stage to late stage tumor. In addition to genomic position, CNAs tend to occur away from the gene locations, especially in germline, non-involved tissue and early stage tumors. Such tendency decreases from germline to early stage and then to late stage tumors, suggesting a relaxation of selection during tumor progression. Furthermore, genes with CNAs in non-small cell lung tumors were enriched in certain gene sets and biological pathways that play crucial roles in oncogenesis and cancer progression, demonstrating the functional aspect of CNAs in the context of biological pathways that were overlooked previously. We conclude that CNAs increase with disease progression and CNAs are both positionally and functionally clustered. The potential functional capabilities acquired via CNAs may be sufficient for normal cells to transform into malignant cells

Co-evolution, opportunity seeking and institutional change: Entrepreneurship and the Indian telecommunications industry 1923-2009

"This is an Author's Original Manuscript of an article submitted for consideration in Business History [copyright Taylor & Francis]; Business History is available online at http://www.tandfonline.com/." 10.1080/00076791.2012.687538In this paper, we demonstrate the importance for entrepreneurship of historical contexts and processes, and the co-evolution of institutions, practices, discourses and cultural norms. Drawing on discourse and institutional theories, we develop a model of the entrepreneurial field, and apply this in analysing the rise to global prominence of the Indian telecommunications industry. We draw on entrepreneurial life histories to show how various discourses and discursive processes ultimately worked to generate change and the creation of new business opportunities. We propose that entrepreneurship involves more than individual acts of business creation, but also implies collective endeavours to shape the future direction of the entrepreneurial field