Restriction of Intravenous Fluid in ICU Patients with Septic Shock.

BACKGROUND: Intravenous fluids are recommended for the treatment of patients who are in septic shock, but higher fluid volumes have been associated with harm in patients who are in the intensive care unit (ICU). METHODS: In this international, randomized trial, we assigned patients with septic shock in the ICU who had received at least 1 liter of intravenous fluid to receive restricted intravenous fluid or standard intravenous fluid therapy; patients were included if the onset of shock had been within 12 hours before screening. The primary outcome was death from any cause within 90 days after randomization. RESULTS: We enrolled 1554 patients; 770 were assigned to the restrictive-fluid group and 784 to the standard-fluid group. Primary outcome data were available for 1545 patients (99.4%). In the ICU, the restrictive-fluid group received a median of 1798 ml of intravenous fluid (interquartile range, 500 to 4366); the standard-fluid group received a median of 3811 ml (interquartile range, 1861 to 6762). At 90 days, death had occurred in 323 of 764 patients (42.3%) in the restrictive-fluid group, as compared with 329 of 781 patients (42.1%) in the standard-fluid group (adjusted absolute difference, 0.1 percentage points; 95% confidence interval [CI], -4.7 to 4.9; P = 0.96). In the ICU, serious adverse events occurred at least once in 221 of 751 patients (29.4%) in the restrictive-fluid group and in 238 of 772 patients (30.8%) in the standard-fluid group (adjusted absolute difference, -1.7 percentage points; 99% CI, -7.7 to 4.3). At 90 days after randomization, the numbers of days alive without life support and days alive and out of the hospital were similar in the two groups. CONCLUSIONS: Among adult patients with septic shock in the ICU, intravenous fluid restriction did not result in fewer deaths at 90 days than standard intravenous fluid therapy. (Funded by the Novo Nordisk Foundation and others; CLASSIC ClinicalTrials.gov number, NCT03668236.).

Effects of magnesium, phosphate, or zinc supplementation in intensive care unit patients-A systematic review and meta-analysis.

BACKGROUND: Low-serum levels of magnesium, phosphate, and zinc are observed in many intensive care unit (ICU) patients, but clinical equipoise exists regarding supplementation strategies. We aimed to assess the desirable and undesirable effects of supplementation with magnesium, phosphate, or zinc in adult ICU patients. METHODS: We conducted a systematic review with meta-analysis of randomised clinical trials assessing the effects of supplementation with magnesium, phosphate, or zinc in adult ICU patients. Primary outcomes were mortality and duration of mechanical ventilation. We registered the protocol, followed the Preferred Reporting Items for Systematic Review and Meta-Analysis statement, used the Cochrane risk of bias 2 tool, and the grading of recommendations, assessment, development and evaluation (GRADE) approach for assessing the certainty of the evidence. RESULTS: We identified no low risk of bias trials. For magnesium supplementation, we included three trials (n = 235); the relative risk (RR) for mortality was 0.54, 95% confidence interval (CI) 0.30-0.96 compared to no supplementation (very low certainty of evidence). For zinc supplementation, two trials were included (n = 168); the RR for mortality was 0.73, 95% CI 0.41-1.28 compared to control. No trials assessed the effects of phosphate supplementation on mortality. For outcomes other than mortality, only zero or one trial was available. CONCLUSIONS: In adult ICU patients, the certainty of evidence for the effects of supplementation with magnesium, phosphate, or zinc was very low. High-quality trials are needed to assess the value of supplementation strategies in these patients.

A scored human protein-protein interaction network to catalyze genomic interpretation.

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

A genome-wide association study of men with symptoms of testicular dysgenesis syndrome and its network biology interpretation.

BACKGROUND: Testicular dysgenesis syndrome (TDS) is a common disease that links testicular germ cell cancer, cryptorchidism and some cases of hypospadias and male infertility with impaired development of the testis. The incidence of these disorders has increased over the last few decades, and testicular cancer now affects 1% of the Danish and Norwegian male population. METHODS: To identify genetic variants that span the four TDS phenotypes, the authors performed a genome-wide association study (GWAS) using Affymetrix Human SNP Array 6.0 to screen 488 patients with symptoms of TDS and 439 selected controls with excellent reproductive health. Furthermore, they developed a novel integrative method that combines GWAS data with other TDS-relevant data types and identified additional TDS markers. The most significant findings were replicated in an independent cohort of 671 Nordic men. RESULTS: Markers located in the region of TGFBR3 and BMP7 showed association with all TDS phenotypes in both the discovery and replication cohorts. An immunohistochemistry investigation confirmed the presence of transforming growth factor ß receptor type III (TGFBR3) in peritubular and Leydig cells, in both fetal and adult testis. Single-nucleotide polymorphisms in the KITLG gene showed significant associations, but only with testicular cancer. CONCLUSIONS: The association of single-nucleotide polymorphisms in the TGFBR3 and BMP7 genes, which belong to the transforming growth factor ß signalling pathway, suggests a role for this pathway in the pathogenesis of TDS. Integrating data from multiple layers can highlight findings in GWAS that are biologically relevant despite having border significance at currently accepted statistical levels.

Cyclebase.org: version 2.0, an updated comprehensive, multi-species repository of cell cycle experiments and derived analysis results.

Cell division involves a complex series of events orchestrated by thousands of molecules. To study this process, researchers have employed mRNA expression profiling of synchronously growing cell cultures progressing through the cell cycle. These experiments, which have been carried out in several organisms, are not easy to access, combine and evaluate. Complicating factors include variation in interdivision time between experiments and differences in relative duration of each cell-cycle phase across organisms. To address these problems, we created Cyclebase, an online resource of cell-cycle-related experiments. This database provides an easy-to-use web interface that facilitates visualization and download of genome-wide cell-cycle data and analysis results. Data from different experiments are normalized to a common timescale and are complimented with key cell-cycle information and derived analysis results. In Cyclebase version 2.0, we have updated the entire database to reflect changes to genome annotations, included information on cyclin-dependent kinase (CDK) substrates, predicted degradation signals and loss-of-function phenotypes from genome-wide screens. The web interface has been improved and provides a single, gene-centric graph summarizing the available cell-cycle experiments. Finally, key information and links to orthologous and paralogous genes are now included to further facilitate comparison of cell-cycle regulation across species. Cyclebase version 2.0 is available at http://www.cyclebase.org.

Protein feature based identification of cell cycle regulated proteins in yeast.

DNA microarrays have been used extensively to identify cell cycle regulated genes in yeast; however, the overlap in the genes identified is surprisingly small. We show that certain protein features can be used to distinguish cell cycle regulated genes from other genes with high confidence (features include protein phosphorylation, glycosylation, subcellular location and instability/degradation). We demonstrate that co-expressed, periodic genes encode proteins which share combinations of features, and provide an overview of the proteome dynamics during the cycle. A large set of novel putative cell cycle regulated proteins were identified, many of which have no known function.

Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

OBJECTIVE: Candidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes. RESEARCH DESIGN AND METHODS: By integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS). RESULTS: 273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations. CONCLUSIONS: Using a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.

Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis.

Eukaryotic cells replicate by a complex series of evolutionarily conserved events that are tightly regulated at defined stages of the cell division cycle. Progression through this cycle involves a large number of dedicated protein complexes and signaling pathways, and deregulation of this process is implicated in tumorigenesis. We applied high-resolution mass spectrometry-based proteomics to investigate the proteome and phosphoproteome of the human cell cycle on a global scale and quantified 6027 proteins and 20,443 unique phosphorylation sites and their dynamics. Co-regulated proteins and phosphorylation sites were grouped according to their cell cycle kinetics and compared to publicly available messenger RNA microarray data. Most detected phosphorylation sites and more than 20% of all quantified proteins showed substantial regulation, mainly in mitotic cells. Kinase-motif analysis revealed global activation during S phase of the DNA damage response network, which was mediated by phosphorylation by ATM or ATR or DNA-dependent protein kinases. We determined site-specific stoichiometry of more than 5000 sites and found that most of the up-regulated sites phosphorylated by cyclin-dependent kinase 1 (CDK1) or CDK2 were almost fully phosphorylated in mitotic cells. In particular, nuclear proteins and proteins involved in regulating metabolic processes have high phosphorylation site occupancy in mitosis. This suggests that these proteins may be inactivated by phosphorylation in mitotic cells.

The more the merrier: comparative analysis of microarray studies on cell cycle-regulated genes in fission yeast.

The last two years have seen the publication of three genome-wide gene expression studies of the fission yeast cell cycle. While these microarray papers largely agree on the main patterns of cell cycle-regulated transcription and its control, there are discrepancies with regard to the identity and numbers of periodically expressed genes. We present benchmark and reproducibility analyses showing that the main discrepancies do not reflect differences in the data themselves (microarray or synchronization methods seem to lead only to minor biases) but rather in the interpretation of the data. Our reanalysis of the three datasets reveals that combining all independent information leads to an improved identification of periodically expressed genes. These evaluations suggest that the available microarray data do not allow reliable identification of more than about 500 cell cycle-regulated genes. The temporal expression pattern of the top 500 periodically expressed genes is generally consistent across experiments and the three studies, together with our integrated analysis, provide a coherent and rich source of information on cell cycle-regulated gene expression in Schizosaccharomyces pombe. The reanalysed datasets and other supplementary information are available from an accompanying website: http://www.cbs.dtu.dk/cellcycle/. We hope that this paper will resolve the apparent discrepancies between the previous studies and be useful both for wet-lab biologists and for theoretical scientists who wish to take advantage of the data for follow-up work.

Comparison of computational methods for the identification of cell cycle-regulated genes.

MOTIVATION: DNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes. RESULTS: Here, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods.