CEREBROVASCULAR CARBON DIOXIDE REACTIVITY DURING EXPOSURE TO EQUIPOTENT ISOFLURANE AND ISOFLURANE IN NITROUS OXIDE ANAESTHESIA

We have studied the effects of hypocapnia on cerebrovascular changes in two MAC-equivalent anaesthetic regimens, using the transcranial Doppler technique as an index of cerebral blood flow (CBF) in 24healthy ASA I patients undergoing spinal surgery. Eight of the patients were subjected to carbon dioxide reactivity challenges in the awake state. Before surgery, the other 16 patients received, in random order, either 1.15% isoflurane in oxygen or 0.5% isoflurane with 70% nitrous oxide. Carbon dioxide reactivity was calculated for each group as the increase in flow velocity per kPa change in PÉCO2 (cm s−1 kPa−1). It was significantly greater for the isoflurane group (14.09 (SD 2.44) cm s−1 kPa−1) and significantly less for the isoflurane—nitrous oxide group (7.95 (1.32) cm s-−1 kPa−1) compared with the awake group (11.24 (0.95) cm s−1 kPa−1). We conclude that cerebrovascular responsiveness to changes in arterial carbon dioxide concentration is influenced markedly by the anaesthetic procedure. Hyperventilation is more likely to affect CBF during isoflurane anaesthesia than during an MAC-equivalent isoflurane—nitrous oxide anaesthesi

Impact of loss of high-molecular-weight von Willebrand factor multimers on blood loss after aortic valve replacement

Background Severe aortic stenosis is associated with loss of the largest von Willebrand factor (vWF) multimers, which could affect primary haemostasis. We hypothesized that the altered multimer structure with the loss of the largest multimers increases postoperative bleeding in patients undergoing aortic valve replacement. Methods We prospectively included 60 subjects with severe aortic stenosis. Before and after aortic valve replacement, vWF antigen, activity, and multimer structure were determined and platelet function was measured by impedance aggregometry. Blood loss from mediastinal drainage and the use of blood and haemostatic products were evaluated perioperatively. Results Before operation, the altered multimer structure was present in 48 subjects (80%). Baseline characteristics and laboratory data were similar in all subjects. The median blood loss after 6 h was 250 (105-400) and 145 (85-240) ml in the groups with the altered and normal multimer structures, respectively (P=0.182). After 24 h, the cumulative loss was 495 (270-650) and 375 (310-600) ml in the groups with the altered and normal multimer structures, respectively (P=0.713). Multivariable analysis revealed no significant influence of multimer structure and platelet function on bleeding volumes after 6 and 24 h. After 24 h, there was no obvious difference in vWF antigen, activity, and multimer structure in subjects with and without the altered multimer structure before operation or in subjects with and without perioperative plasma transfusion. Conclusions The altered vWF multimer structure before operation was not associated with increased bleeding after aortic valve replacement. Our findings might be explained by perioperative release of vWF and rapid recovery of the largest vWF multimer

Effect of promoter architecture on the cell-to-cell variability in gene expression

According to recent experimental evidence, the architecture of a promoter, defined as the number, strength and regulatory role of the operators that control the promoter, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect noise in gene expression in a systematic rather than case-by-case fashion. In this article, we make such a systematic investigation, based on a simple microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters (cooperative, independent, simultaneous) and how each of these affects the level of variability in transcription product from cell-to-cell. We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can discriminate between different kinetic models of gene regulation.Comment: 35 pages, 6 figures, Submitte

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

A complete set of nascent transcription rates for yeast genes

The amount of mRNA in a cell is the result of two opposite reactions: transcription and mRNA degradation. These reactions are governed by kinetics laws, and the most regulated step for many genes is the transcription rate. The transcription rate, which is assumed to be exercised mainly at the RNA polymerase recruitment level, can be calculated using the RNA polymerase densities determined either by run-on or immunoprecipitation using specific antibodies. The yeast Saccharomyces cerevisiae is the ideal model organism to generate a complete set of nascent transcription rates that will prove useful for many gene regulation studies. By combining genomic data from both the GRO (Genomic Run-on) and the RNA pol ChIP-on-chip methods we generated a new, more accurate nascent transcription rate dataset. By comparing this dataset with the indirect ones obtained from the mRNA stabilities and mRNA amount datasets, we are able to obtain biological information about posttranscriptional regulation processes and a genomic snapshot of the location of the active transcriptional machinery. We have obtained nascent transcription rates for 4,670 yeast genes. The median RNA polymerase II density in the genes is 0.078 molecules/kb, which corresponds to an average of 0.096 molecules/gene. Most genes have transcription rates of between 2 and 30 mRNAs/hour and less than 1% of yeast genes have >1 RNA polymerase molecule/gene. Histone and ribosomal protein genes are the highest transcribed groups of genes and other than these exceptions the transcription of genes is an infrequent phenomenon in a yeast cell

The CCR4-NOT Complex Physically and Functionally Interacts with TRAMP and the Nuclear Exosome

BACKGROUND: Ccr4-Not is a highly conserved multi-protein complex consisting in yeast of 9 subunits, including Not5 and the major yeast deadenylase Ccr4. It has been connected functionally in the nucleus to transcription by RNA polymerase II and in the cytoplasm to mRNA degradation. However, there has been no evidence so far that this complex is important for RNA degradation in the nucleus. METHODOLOGY/PRINCIPAL FINDINGS: In this work we point to a new role for the Ccr4-Not complex in nuclear RNA metabolism. We determine the importance of the Ccr4-Not complex for the levels of non-coding nuclear RNAs, such as mis-processed and polyadenylated snoRNAs, whose turnover depends upon the nuclear exosome and TRAMP. Consistently, mutation of both the Ccr4-Not complex and the nuclear exosome results in synthetic slow growth phenotypes. We demonstrate physical interactions between the Ccr4-Not complex and the exosome. First, Not5 co-purifies with the exosome. Second, several exosome subunits co-purify with the Ccr4-Not complex. Third, the Ccr4-Not complex is important for the integrity of large exosome-containing complexes. Finally, we reveal a connection between the Ccr4-Not complex and TRAMP through the association of the Mtr4 helicase with the Ccr4-Not complex and the importance of specific subunits of Ccr4-Not for the association of Mtr4 with the nuclear exosome subunit Rrp6. CONCLUSIONS/SIGNIFICANCE: We propose a model in which the Ccr4-Not complex may provide a platform contributing to dynamic interactions between the nuclear exosome and its co-factor TRAMP. Our findings connect for the first time the different players involved in nuclear and cytoplasmic RNA degradation

Transcriptome-Wide Binding Sites for Components of the Saccharomyces cerevisiae Non-Poly(A) Termination Pathway: Nrd1, Nab3, and Sen1

RNA polymerase II synthesizes a diverse set of transcripts including both protein-coding and non-coding RNAs. One major difference between these two classes of transcripts is the mechanism of termination. Messenger RNA transcripts terminate downstream of the coding region in a process that is coupled to cleavage and polyadenylation reactions. Non-coding transcripts like Saccharomyces cerevisiae snoRNAs terminate in a process that requires the RNA–binding proteins Nrd1, Nab3, and Sen1. We report here the transcriptome-wide distribution of these termination factors. These data sets derived from in vivo protein–RNA cross-linking provide high-resolution definition of non-poly(A) terminators, identify novel genes regulated by attenuation of nascent transcripts close to the promoter, and demonstrate the widespread occurrence of Nrd1-bound 3′ antisense transcripts on genes that are poorly expressed. In addition, we show that Sen1 does not cross-link efficiently to many expected non-coding RNAs but does cross-link to the 3′ end of most pre–mRNA transcripts, suggesting an extensive role in mRNA 3′ end formation and/or termination

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings

High resolution imaging reveals heterogeneity in chromatin states between cells that is not inherited through cell division

BACKGROUND: Genomes of eukaryotes exist as chromatin, and it is known that different chromatin states can influence gene regulation. Chromatin is not a static structure, but is known to be dynamic and vary between cells. In order to monitor the organisation of chromatin in live cells we have engineered fluorescent fusion proteins which recognize specific operator sequences to tag pairs of syntenic gene loci. The separation of these loci was then tracked in three dimensions over time using fluorescence microscopy. RESULTS: We established a work flow for measuring the distance between two fluorescently tagged, syntenic gene loci with a mean measurement error of 63 nm. In general, physical separation was observed to increase with increasing genomic separations. However, the extent to which chromatin is compressed varies for different genomic regions. No correlation was observed between compaction and the distribution of chromatin markers from genomic datasets or with contacts identified using capture based approaches. Variation in spatial separation was also observed within cells over time and between cells. Differences in the conformation of individual loci can persist for minutes in individual cells. Separation of reporter loci was found to be similar in related and unrelated daughter cell pairs. CONCLUSIONS: The directly observed physical separation of reporter loci in live cells is highly dynamic both over time and from cell to cell. However, consistent differences in separation are observed over some chromosomal regions that do not correlate with factors known to influence chromatin states. We conclude that as yet unidentified parameters influence chromatin configuration. We also find that while heterogeneity in chromatin states can be maintained for minutes between cells, it is not inherited through cell division. This may contribute to cell-to-cell transcriptional heterogeneity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12860-016-0111-y) contains supplementary material, which is available to authorized users