In silico discovery of blood cell macromolecular associations.

BACKGROUND: Physical molecular interactions are the basis of intracellular signalling and gene regulatory networks, and comprehensive, accessible databases are needed for their discovery. Highly correlated transcripts may reflect important functional associations, but identification of such associations from primary data are cumbersome. We have constructed and adapted a user-friendly web application to discover and identify putative macromolecular associations in human peripheral blood based on significant correlations at the transcriptional level. METHODS: The blood transcriptome was characterized by quantification of 17,328 RNA species, including 341 mature microRNAs in 105 clinically well-characterized postmenopausal women. Intercorrelation of detected transcripts signal levels generated a matrix with > 150 million correlations recognizing the human blood RNA interactome. The correlations with calculated adjusted p-values were made easily accessible by a novel web application. RESULTS: We found that significant transcript correlations within the giant matrix reflect experimentally documented interactions involving select ubiquitous blood relevant transcription factors (CREB1, GATA1, and the glucocorticoid receptor (GR, NR3C1)). Their responsive genes recapitulated up to 91% of these as significant correlations, and were replicated in an independent cohort of 1204 individual blood samples from the Framingham Heart Study. Furthermore, experimentally documented mRNAs/miRNA associations were also reproduced in the matrix, and their predicted functional co-expression described. The blood transcript web application is available at http://app.uio.no/med/klinmed/correlation-browser/blood/index.php and works on all commonly used internet browsers. CONCLUSIONS: Using in silico analyses and a novel web application, we found that correlated blood transcripts across 105 postmenopausal women reflected experimentally proven molecular associations. Furthermore, the associations were reproduced in a much larger and more heterogeneous cohort and should therefore be generally representative. The web application lends itself to be a useful hypothesis generating tool for identification of regulatory mechanisms in complex biological data sets.

Nuclear Magnetic Resonance Spectroscopy to Identify Metabolite Biomarkers of Nonresponsiveness to Targeted Therapy in Glioblastoma Tumor Stem Cells.

Glioblastoma is the most common and malignant brain tumor, and current therapies confer only modest survival benefits. A major obstacle is our ability to monitor treatment effect on tumors. Current imaging modalities are ambiguous, and repeated biopsies are not encouraged. To scout for markers of treatment response, we used NMR spectroscopy to study the effects of a survivin inhibitor on the metabolome of primary glioblastoma cancer stem cells. Applying high resolution NMR spectroscopy (1H resonance frequency: 800.03 MHz) to just 3 million cells per sample, we achieved sensitive and high resolving determinations of, e.g., amino acids, nucleosides, and constituents of the citric acid cycle. For control samples that were cultured, prepared, and measured at varying dates, peak area relative standard deviations were 15-20%. Analyses of unfractionated lysates were performed for straightforward compound identification with COLMAR and HMDB databases. Principal component analysis revealed that citrate levels were clearly upregulated in nonresponsive cells, while lactate levels substantially decreased following treatment for both responsive and nonresponsive cells. Hence, lactate and citrate may be potential markers of successful drug uptake and poor response to survivin inhibitors, respectively. Our metabolomics approach provided alternative biomarker candidates compared to spectrometry-based proteomics, underlining benefits of complementary methodologies. These initial findings make a foundation for exploring in vivo MR spectroscopy (MRS) of brain tumors, as citrate and lactate are MRS-visible. In sum, NMR metabolomics is a tool for addressing glioblastoma.

The Role of Plasma and Urine Metabolomics in Identifying New Biomarkers in Severe Newborn Asphyxia: A Study of Asphyxiated Newborn Pigs following Cardiopulmonary Resuscitation.

BACKGROUND: Optimizing resuscitation is important to prevent morbidity and mortality from perinatal asphyxia. The metabolism of cells and tissues is severely disturbed during asphyxia and resuscitation, and metabolomic analyses provide a snapshot of many small molecular weight metabolites in body fluids or tissues. In this study metabolomics profiles were studied in newborn pigs that were asphyxiated and resuscitated using different protocols to identify biomarkers for subject characterization, intervention effects and possibly prognosis. METHODS: A total of 125 newborn Noroc pigs were anesthetized, mechanically ventilated and inflicted progressive asphyxia until asystole. Pigs were randomized to resuscitation with a FiO2 0.21 or 1.0, different duration of ventilation before initiation of chest compressions (CC), and different CC to ventilation ratios. Plasma and urine samples were obtained at baseline, and 2 h and 4 h after return of spontaneous circulation (ROSC, heart rate > = 100 bpm). Metabolomics profiles of the samples were analyzed by nuclear magnetic resonance spectroscopy. RESULTS: Plasma and urine showed severe metabolic alterations consistent with hypoxia and acidosis 2 h and 4 h after ROSC. Baseline plasma hypoxanthine and lipoprotein concentrations were inversely correlated to the duration of hypoxia sustained before asystole occurred, but there was no evidence for a differential metabolic response to the different resuscitation protocols or in terms of survival. CONCLUSIONS: Metabolic profiles of asphyxiated newborn pigs showed severe metabolic alterations. Consistent with previously published reports, we found no evidence of differences between established and alternative resuscitation protocols. Lactate and pyruvate may have a prognostic value, but have to be independently confirmed.

RNA-sequencing analysis of HepG2 cells treated with atorvastatin.

The cholesterol-lowering drug atorvastatin is among the most prescribed drug in the world. Alternative splicing in a number of genes has been reported to be associated with variable statin response. RNA-seq has proven to be a powerful technique for genome-wide splice variant analysis. In the present study, we sought to investigate atorvastatin responsive splice variants in HepG2 cells using RNA-seq analysis to identify novel candidate genes implicated in cholesterol homeostasis and in the statin response. HepG2 cells were treated with 10 µM atorvastatin for 24 hours. RNA-seq and exon array analyses were performed. The validation of selected genes was performed using Taqman gene expression assays. RNA-seq analysis identified 121 genes and 98 specific splice variants, of which four were minor splice variants to be differentially expressed, 11 were genes with potential changes in their splicing patterns (SYCP3, ZNF195, ZNF674, MYD88, WHSC1, KIF16B, ZNF92, AGER, FCHO1, SLC6A12 and AKAP9), and one was a gene (RAP1GAP) with differential promoter usage. The IL21R transcript was detected to be differentially expressed via RNA-seq and RT-qPCR, but not in the exon array. In conclusion, several novel candidate genes that are affected by atorvastatin treatment were identified in this study. Further studies are needed to determine the biological significance of the atorvastatin responsive splice variants that have been uniquely identified using RNA-seq.

Urinary metabolite profiles in premature infants show early postnatal metabolic adaptation and maturation.

OBJECTIVES: Early nutrition influences metabolic programming and long-term health. We explored the urinary metabolite profiles of 48 premature infants (birth weight < 1500 g) randomized to an enhanced or a standard diet during neonatal hospitalization. METHODS: Metabolomics using nuclear magnetic resonance spectroscopy (NMR) was conducted on urine samples obtained during the first week of life and thereafter fortnightly. RESULTS: The intervention group received significantly higher amounts of energy, protein, lipids, vitamin A, arachidonic acid and docosahexaenoic acid as compared to the control group. Enhanced nutrition did not appear to affect the urine profiles to an extent exceeding individual variation. However, in all infants the glucogenic amino acids glycine, threonine, hydroxyproline and tyrosine increased substantially during the early postnatal period, along with metabolites of the tricarboxylic acid cycle (succinate, oxoglutarate, fumarate and citrate). The metabolite changes correlated with postmenstrual age. Moreover, we observed elevated threonine and glycine levels in first-week urine samples of the small for gestational age (SGA; birth weight < 10th percentile for gestational age) as compared to the appropriate for gestational age infants. CONCLUSION: This first nutri-metabolomics study in premature infants demonstrates that the physiological adaptation during the fetal-postnatal transition as well as maturation influences metabolism during the breastfeeding period. Elevated glycine and threonine levels were found in the first week urine samples of the SGA infants and emerged as potential biomarkers of an altered metabolic phenotype.

Urine NMR metabolomics analysis of breastfeeding biomarkers during and after pregnancy in a large prospective cohort study.

BACKGROUND: Modern metabolomic profiling has not yet been applied to human breastfeeding research. A common reason for breastfeeding cessation is perceived insufficient milk production. We investigated broad biochemical profiles in maternal urine collected during and after pregnancy to identify biomarkers related to reduced reported breastfeeding. METHODS: Fasting urine was collected at three consultations (visit V1: gestational week 8-20; V2: week 28 ± 2; V3: 10-16 weeks postpartum) in the STORK Groruddalen program, a prospective, multiethnic cohort study of gestational diabetes involving healthy, pregnant women in Oslo, Norway, and analyzed using NMR spectroscopy. Breastfeeding at V3 was recorded in three categories: Exclusively breastfeeding (n = 326), partially breastfeeding (n = 156) and formula feeding (n = 67). RESULTS: Five metabolites were relevant to breastfeeding. Lactose was detected at V1 and increased to 0.1 mM/mM creatinine at V2. Postpartum excretion at V3 was significantly higher in exclusively breastfeeding women than partially or non-breastfeeding (median = 0.29, 0.23 and 0.04 mM/mM creatine, respectively; ANOVA p-value = 2e-70). Glycine excretion at V3 (0.12, 0.10 and 0.06, respectively; p = 2e-5) and at V2 were associated with breastfeeding (0.34, 0.33 and 0.26, respectively; p = 4e-5). Creatine and two unidentified substances also correlated with breastfeeding. NMR metabolomics found no other metabolites differing between categories during pregnancy (V1, V2), and did not predict individual breastfeeding postpartum (V3). CONCLUSION: Decreased glycine excretion at V2 may indicate difficulties meeting the metabolic demands of the growing fetus, but urine profiles contained otherwise little indication of early adaptations during pregnancy towards reduced biological potential to breastfeed.

Context-dependent competition in a model gut bacterial community.

Understanding the ecological processes that generate complex community structures may provide insight into the establishment and maintenance of a normal microbial community in the human gastrointestinal tract, yet very little is known about how biotic interactions influence community dynamics in this system. Here, we use natural strains of Escherichia coli and a simplified model microbiota to demonstrate that the colonization process on the strain level can be context dependent, in the sense that the outcome of intra-specific competition may be determined by the composition of the background community. These results are consistent with previous models for competition between organisms where one competitor has adapted to low resource environments whereas the other is optimized for rapid reproduction when resources are abundant. The genomic profiles of E. coli strains representing these differing ecological strategies provide clues for deciphering the genetic underpinnings of niche adaptation within a single species. Our findings extend the role of ecological theory in understanding microbial systems and the conceptual toolbox for describing microbial community dynamics. There are few, if any, concrete examples of context-dependent competition on a single trophic level. However, this phenomenon can have potentially dramatic effects on which bacteria will successfully establish and persist in the gastrointestinal system, and the principle should be equally applicable to other microbial ecosystems.

Identification of transcriptional macromolecular associations in human bone using browser based in silico analysis in a giant correlation matrix.

Intracellular signaling is critically dependent on gene regulatory networks comprising physical molecular interactions. Presently, there is a lack of comprehensive databases for most human tissue types to verify such macromolecular interactions. We present a user friendly browser which helps to identify functional macromolecular interactions in human bone as significant correlations at the transcriptional level. The molecular skeletal phenotype has been characterized by transcriptome analysis of iliac crest bone biopsies from 84 postmenopausal women through quantifications of ~23,000 mRNA species. When the signal levels were inter-correlated, an array containing >260 million correlations was generated, thus recognizing the human bone interactome at the RNA level. The matrix correlation and p values were made easily accessible by a freely available online browser. We show that significant correlations within the giant matrix are reproduced in a replica set of 13 male vertebral biopsies. The identified correlations differ somewhat from transcriptional interactions identified in cell culture experiments and transgenic mice, thus demonstrating that care should be taken in extrapolating such results to the in vivo situation in human bone. The current giant matrix and web browser are a valuable tool for easy access to the human bone transcriptome and molecular interactions represented as significant correlations at the RNA-level. The browser and matrix should be a valuable hypothesis generating tool for identification of regulatory mechanisms and serve as a library of transcript relationships in human bone, a relatively inaccessible tissue.

The Accu-Chek Mobile blood glucose monitoring system used under controlled conditions meets ISO 15197 standards in the hands of diabetes patients.

BACKGROUND: Self-monitoring of blood glucose is a cornerstone of diabetes management. The aim of this study was to evaluate the analytical quality and the ease of use of the Accu-Chek Mobile, a new glucose monitoring system designed for capillary blood testing by diabetic patients. MATERIALS AND METHODS: The performance of the Accu-Chek Mobile was evaluated both in the hands of a scientist and of diabetes patients. The designated comparative method was a hexokinase-based laboratory method (Architect ci8200). Diabetics (N = 88) with previous experience of self-testing were recruited for the study. Patient samples, containing glucose in concentrations mainly between ˜4 and ˜20 mmol/L, were analyzed in duplicates both on the Accu-Chek Mobile and with the comparative method. The patients answered a questionnaire about the ease of use of the meter. RESULTS: The meter yields reproducible readings, with an imprecision CV <5% as required by the American Diabetes Association (ADA). Of the glucose concentrations obtained by both the scientist and the patients, more than 95% of the individual results were within ± 20% of the comparative method, meeting the ISO 15197 accuracy goal, but not the stricter ± 10% ADA goal. CONCLUSION: Accu-Chek Mobile is a user-friendly glucometer that in a normo- and hyperglycemic range fulfils the ISO 15197 accuracy requirement, also in the hands of diabetes patients.

Metabolic changes in urine during and after pregnancy in a large, multiethnic population-based cohort study of gestational diabetes.

This study aims to identify novel markers for gestational diabetes (GDM) in the biochemical profile of maternal urine using NMR metabolomics. It also catalogs the general effects of pregnancy and delivery on the urine profile. Urine samples were collected at three time points (visit V1: gestational week 8-20; V2: week 28±2; V3 10-16 weeks post partum) from participants in the STORK Groruddalen program, a prospective, multiethnic cohort study of 823 healthy, pregnant women in Oslo, Norway, and analyzed using (1)H-NMR spectroscopy. Metabolites were identified and quantified where possible. PCA, PLS-DA and univariate statistics were applied and found substantial differences between the time points, dominated by a steady increase of urinary lactose concentrations, and an increase during pregnancy and subsequent dramatic reduction of several unidentified NMR signals between 0.5 and 1.1 ppm. Multivariate methods could not reliably identify GDM cases based on the WHO or graded criteria based on IADPSG definitions, indicating that the pattern of urinary metabolites above micromolar concentrations is not influenced strongly and consistently enough by the disease. However, univariate analysis suggests elevated mean citrate concentrations with increasing hyperglycemia. Multivariate classification with respect to ethnic background produced weak but statistically significant models. These results suggest that although NMR-based metabolomics can monitor changes in the urinary excretion profile of pregnant women, it may not be a prudent choice for the study of GDM.