Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations

Purpose: We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers. Methods: The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables. Results: For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p <0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results. Conclusion: The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across center

Development of an invasively monitored porcine model of acetaminophen-induced acute liver failure

Background: The development of effective therapies for acute liver failure (ALF) is limited by our knowledge of the pathophysiology of this condition, and the lack of suitable large animal models of acetaminophen toxicity. Our aim was to develop a reproducible invasively-monitored porcine model of acetaminophen-induced ALF. Method: 35kg pigs were maintained under general anaesthesia and invasively monitored. Control pigs received a saline infusion, whereas ALF pigs received acetaminophen intravenously for 12 hours to maintain blood concentrations between 200-300 mg/l. Animals surviving 28 hours were euthanased. Results: Cytochrome p450 levels in phenobarbital pre-treated animals were significantly higher than non pre-treated animals (300 vs 100 pmol/mg protein). Control pigs (n=4) survived 28-hour anaesthesia without incident. Of nine pigs that received acetaminophen, four survived 20 hours and two survived 28 hours. Injured animals developed hypotension (mean arterial pressure; 40.8+/-5.9 vs 59+/-2.0 mmHg), increased cardiac output (7.26+/-1.86 vs 3.30+/-0.40 l/min) and decreased systemic vascular resistance (8.48+/-2.75 vs 16.2+/-1.76 mPa/s/m3). Dyspnoea developed as liver injury progressed and the increased pulmonary vascular resistance (636+/-95 vs 301+/-26.9 mPa/s/m3) observed may reflect the development of respiratory distress syndrome. Liver damage was confirmed by deterioration in pH (7.23+/-0.05 vs 7.45+/-0.02) and prothrombin time (36+/-2 vs 8.9+/-0.3 seconds) compared with controls. Factor V and VII levels were reduced to 9.3 and 15.5% of starting values in injured animals. A marked increase in serum AST (471.5+/-210 vs 42+/-8.14) coincided with a marked reduction in serum albumin (11.5+/-1.71 vs 25+/-1 g/dL) in injured animals. Animals displayed evidence of renal impairment; mean creatinine levels 280.2+/-36.5 vs 131.6+/-9.33 mumol/l. Liver histology revealed evidence of severe centrilobular necrosis with coagulative necrosis. Marked renal tubular necrosis was also seen. Methaemoglobin levels did not rise >5%. Intracranial hypertension was not seen (ICP monitoring), but there was biochemical evidence of encephalopathy by the reduction of Fischer's ratio from 5.6 +/- 1.1 to 0.45 +/- 0.06. Conclusion: We have developed a reproducible large animal model of acetaminophen-induced liver failure, which allows in-depth investigation of the pathophysiological basis of this condition. Furthermore, this represents an important large animal model for testing artificial liver support systems

Characterization of a Large Group of Individuals with Huntington Disease and Their Relatives Enrolled in the COHORT Study

Careful characterization of the phenotype and genotype of Huntington disease (HD) can foster better understanding of the condition.We conducted a cohort study in the United States, Canada, and Australia of members of families affected by HD. We collected demographic and clinical data, conducted the Unified Huntington's Disease Rating Scale and Mini-Mental State Examination, and determined Huntingtin trinucleotide CAG repeat length. We report primarily on cross-sectional baseline data from this recently completed prospective, longitudinal, observational study.As of December 31, 2009, 2,318 individuals enrolled; of these, 1,985 (85.6%) were classified into six analysis groups. Three groups had expanded CAG alleles (36 repeats or more): individuals with clinically diagnosed HD [n = 930], and clinically unaffected first-degree relatives who had previously pursued [n = 248] or not pursued [n = 112] predictive DNA testing. Three groups lacked expanded alleles: first-degree relatives who had previously pursued [n = 41] or not pursued [n = 224] genetic testing, and spouses and caregivers [n = 430]. Baseline mean performance differed across groups in all motor, behavioral, cognitive, and functional measures (p<0.001). Clinically unaffected individuals with expanded alleles weighed less (76.0 vs. 79.6 kg; p = 0.01) and had lower cognitive scores (28.5 vs. 29.1 on the Mini Mental State Examination; p = 0.008) than individuals without expanded alleles. The frequency of "high normal" repeat lengths (27 to 35) was 2.5% and repeat lengths associated with reduced penetrance (36 to 39) was 2.7%.Baseline analysis of COHORT study participants revealed differences that emerge prior to clinical diagnosis. Longitudinal investigation of this cohort will further characterize the natural history of HD and genetic and biological modifiers.Clinicaltrials.gov NCT00313495

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Shrub Invasion Decreases Diversity and Alters Community Stability in Northern Chihuahuan Desert Plant Communities

BACKGROUND:Global climate change is rapidly altering species range distributions and interactions within communities. As ranges expand, invading species change interactions in communities which may reduce stability, a mechanism known to affect biodiversity. In aridland ecosystems worldwide, the range of native shrubs is expanding as they invade and replace native grassland vegetation with significant consequences for biodiversity and ecosystem functioning. METHODOLOGY:We used two long-term data sets to determine the effects of shrub encroachment by Larrea tridentata on subdominant community composition and stability in formerly native perennial grassland dominated by Bouteloua eriopoda in New Mexico, USA. PRINCIPAL FINDINGS:Our results indicated that Larrea invasion decreased species richness during the last 100 years. We also found that over shorter temporal scales species-poor subdominant communities in areas invaded by Larrea were less stable (more variable in time) compared to species rich communities in grass-dominated vegetation. Compositional stability increased as cover of Bouteloua increased and decreased as cover of Larrea increased. SIGNIFICANCE:Changes in community stability due to altered interspecific interactions may be one mechanism by which biodiversity declines in grasslands following shrub invasion. As global warming increases, shrub encroachment into native grasslands worldwide will continue to alter species interactions and community stability both of which may lead to a decline in biodiversity

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases