Is human blood a good surrogate for brain tissue in transcriptional studies?

Abstract Background Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). Results We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10-90); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. Conclusions Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable

Complications after Laparoscopic Appendectomy for Complicated Appendicitis

Background. Laparoscopic appendectomy is established method in the treatment of complicated appendicitis. Certain advantages of the technique do not fulfill the expectations for its superiority over the open appendectomy as when it is used for uncomplicated appendicitis. This is generally caused because of the high variety of postoperative complications reported in different series for complicated appendicitis. Material and methods. This prospective interventional clinical study analyzes 61 patients operated with laparoscopic and open appendectomy due to complicated appendicitis, with an end point of comparing the intra and postoperative complications in both groups. Results. Conversion in open appendectomy was forced in one patient (1.63%). The operative time was significantly shorter in the laparoscopic group (p = 0.048). Wound infection was significantly predominant in the open group (p = 0.045). Postoperative intraabdominal abscess occurred in one patient in the laparoscopic group (p = 0.52). The overall morbidity was 26.2% (7 patients in the laparoscopic, and 9 in the open group; p = 0.59). Length of stay was significantly shorter in the laparoscopic group (p = 0.00001). Conclusion. Certain significant advantages of the laparoscopic appendectomy as low incidence of wound infection, short hospitalization, less postoperative pain and faster socialization makes the laparoscopy up to date method in the treatment of complicated appendicitis

Protein expression based multimarker analysis of breast cancer samples

<p>Abstract</p> <p>Background</p> <p>Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.</p> <p>Methods</p> <p>We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.</p> <p>Results</p> <p>We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.</p> <p>Conclusions</p> <p>We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.</p

Pattern Formation in Interface Depinning and Other Models: Erratically Moving Spatial Structures

We study erratically moving spatial structures that are found in a driven interface in a random medium at the depinning threshold. We introduce a bond-disordered variant of the Sneppen model and study the effect of extremal dynamics on the morphology of the interface. We find evidence for the formation of a structure which moves along with the growth site. The time average of the structure, which is defined with respect to the active spot of growth, defines an activity-centered pattern. Extensive Monte Carlo simulations show that the pattern has a tail which decays slowly, as a power law. To understand this sort of pattern formation, we write down an approximate integral equation involving the local interface dynamics and long-ranged jumps of the growth spot. We clarify the nature of the approximation by considering a model for which the integral equation is exactly derivable from an extended master equation. Improvements to the equation are considered by adding a second coupled equation which provides a self-consistent description. The pattern, which defines a one-point correlation function, is shown to have a strong effect on ordinary space-fixed two-point correlation functions. Finally we present evidence that this sort of pattern formation is not confined to the interface problem, but is generic to situations in which the activity at succesive time steps is correlated, as for instance in several other extremal models. We present numerical results for activity-centered patterns in the Bak-Sneppen model of evolution and the Zaitsev model of low-temperature creep.Comment: RevTeX, 18 pages, 19 eps-figures, To appear in Phys. Rev.

Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice.

To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo

Gene network interconnectedness and the generalized topological overlap measure

BACKGROUND: Network methods are increasingly used to represent the interactions of genes and/or proteins. Genes or proteins that are directly linked may have a similar biological function or may be part of the same biological pathway. Since the information on the connection (adjacency) between 2 nodes may be noisy or incomplete, it can be desirable to consider alternative measures of pairwise interconnectedness. Here we study a class of measures that are proportional to the number of neighbors that a pair of nodes share in common. For example, the topological overlap measure by Ravasz et al. [1] can be interpreted as a measure of agreement between the m = 1 step neighborhoods of 2 nodes. Several studies have shown that two proteins having a higher topological overlap are more likely to belong to the same functional class than proteins having a lower topological overlap. Here we address the question whether a measure of topological overlap based on higher-order neighborhoods could give rise to a more robust and sensitive measure of interconnectedness. RESULTS: We generalize the topological overlap measure from m = 1 step neighborhoods to m ≥ 2 step neighborhoods. This allows us to define the m-th order generalized topological overlap measure (GTOM) by (i) counting the number of m-step neighbors that a pair of nodes share and (ii) normalizing it to take a value between 0 and 1. Using theoretical arguments, a yeast co-expression network application, and a fly protein network application, we illustrate the usefulness of the proposed measure for module detection and gene neighborhood analysis. CONCLUSION: Topological overlap can serve as an important filter to counter the effects of spurious or missing connections between network nodes. The m-th order topological overlap measure allows one to trade-off sensitivity versus specificity when it comes to defining pairwise interconnectedness and network modules

Chalk-steel Interface testing for marine energy foundations

The Energy Technology Partnership (ETP) and Lloyd’s Register EMEA are gratefully acknowledged for the funding of this project. The authors would also like to acknowledge the support of the European Regional Development Fund (ERDF) SMART Centre at the University of Dundee that allowed purchase of the equipment used during this study. The views expressed are those of the authors alone, and do not necessarily represent the views of their respective companies or employing organizations.Peer reviewedPostprin

Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems