SNPLims: a data management system for genome wide association studies

<p>Abstract</p> <p>Background</p> <p>Recent progresses in genotyping technologies allow the generation high-density genetic maps using hundreds of thousands of genetic markers for each DNA sample. The availability of this large amount of genotypic data facilitates the whole genome search for genetic basis of diseases.</p> <p>We need a suitable information management system to efficiently manage the data flow produced by whole genome genotyping and to make it available for further analyses.</p> <p>Results</p> <p>We have developed an information system mainly devoted to the storage and management of SNP genotype data produced by the Illumina platform from the raw outputs of genotyping into a relational database.</p> <p>The relational database can be accessed in order to import any existing data and export user-defined formats compatible with many different genetic analysis programs.</p> <p>After calculating family-based or case-control association study data, the results can be imported in SNPLims. One of the main features is to allow the user to rapidly identify and annotate statistically relevant polymorphisms from the large volume of data analyzed. Results can be easily visualized either graphically or creating ASCII comma separated format output files, which can be used as input to further analyses.</p> <p>Conclusions</p> <p>The proposed infrastructure allows to manage a relatively large amount of genotypes for each sample and an arbitrary number of samples and phenotypes. Moreover, it enables the users to control the quality of the data and to perform the most common screening analyses and identify genes that become “candidate” for the disease under consideration.</p

Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data

<p>Abstract</p> <p>Background</p> <p>Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis.</p> <p>Methods</p> <p>We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis.</p> <p>Results</p> <p>Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters.</p> <p>Conclusions</p> <p>We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis.</p

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

Comparison of exhaled breath condensate pH using two commercially available devices in healthy controls, asthma and COPD patients

<p>Abstract</p> <p>Background</p> <p>Analysis of exhaled breath condensate (EBC) is a non-invasive method for studying the acidity (pH) of airway secretions in patients with inflammatory lung diseases.</p> <p>Aim</p> <p>To assess the reproducibility of EBC pH for two commercially available devices (portable RTube and non-portable ECoScreen) in healthy controls, patients with asthma or COPD, and subjects suffering from an acute cold with lower-airway symptoms. In addition, we assessed the repeatability in healthy controls.</p> <p>Methods</p> <p>EBC was collected from 40 subjects (n = 10 in each of the above groups) using RTube and ECoScreen. EBC was collected from controls on two separate occasions within 5 days. pH in EBC was assessed after degasification with argon for 20 min.</p> <p>Results</p> <p>In controls, pH-measurements in EBC collected by RTube or ECoScreen showed no significant difference between devices (p = 0.754) or between days (repeatability coefficient RTube: 0.47; ECoScreen: 0.42) of collection. A comparison between EBC pH collected by the two devices in asthma, COPD and cold patients also showed good reproducibility. No differences in pH values were observed between controls (mean pH 8.27; RTube) and patients with COPD (pH 7.97) or asthma (pH 8.20), but lower values were found using both devices in patients with a cold (pH 7.56; RTube, p < 0.01; ECoScreen, p < 0.05).</p> <p>Conclusion</p> <p>We conclude that pH measurements in EBC collected by RTube and ECoScreen are repeatable and reproducible in healthy controls, and are reproducible and comparable in healthy controls, COPD and asthma patients, and subjects with a common cold.</p

Motivational interviewing for low mood and adjustment early after stroke: a feasibility randomised trial

Background Management of psychological adjustment and low mood after stroke can result in positive health outcomes. We have adapted a talk-based therapy, motivational interviewing (MI), and shown it to be potentially effective for managing low mood and supporting psychological adjustment post-stroke in a single-centre trial. In the current study, we aimed to explore the feasibility of delivering MI using clinical stroke team members, and using an attention control (AC), to inform the protocol for a future definitive trial. Methods This parallel two-arm feasibility trial took place in north-west England. Recruitment occurred between December 2012 and November 2013. Participants were stroke patients aged 18 years or over, who were medically stable, had no severe communication problems, and were residents of the hospital catchment. Randomisation was to MI or AC, and was conducted by a researcher not involved in recruitment using opaque sealed envelopes. The main outcome measures were descriptions of study feasibility (recruitment/retention rates, MI delivery by clinical staff, use of AC) and acceptability (through qualitative interviews and completion of study measures), and fidelity to MI and AC (through review of session audio-recordings). Information was also collected on participants’ mood, quality of life, adjustment, and resource-use. Results Over 12 months, 461 patients were screened, 124 were screened eligible, and 49 were randomised: 23 to MI, 26 to AC. At 3 months, 13 MI and 18 AC participants completed the follow-up assessment (63% retention). This was less than expected based on our original trial. An AC was successfully implemented. Alternative approaches would be required to ensure the feasibility of clinical staff delivering MI. The study measures, MI, and AC interventions were considered acceptable, and there was good fidelity to the interventions. There were no adverse events related to study participation. Conclusions It was possible to recruit and retain participants, train clinical staff to deliver MI, and implement an appropriate AC. Changes would be necessary to conduct a future multi-centre trial, including: assuming a recruitment rate lower than that in the current study; implementing more strategies to increase participant retention; and considering alternative clinical staff groups to undertake the delivery of MI and AC

RNAseq Analyses Identify Tumor Necrosis Factor-Mediated Inflammation as a Major Abnormality in ALS Spinal Cord

ALS is a rapidly progressive, devastating neurodegenerative illness of adults that produces disabling weakness and spasticity arising from death of lower and upper motor neurons. No meaningful therapies exist to slow ALS progression, and molecular insights into pathogenesis and progression are sorely needed. In that context, we used high-depth, next generation RNA sequencing (RNAseq, Illumina) to define gene network abnormalities in RNA samples depleted of rRNA and isolated from cervical spinal cord sections of 7 ALS and 8 CTL samples. We aligned \u3e50 million 2X150 bp paired-end sequences/sample to the hg19 human genome and applied three different algorithms (Cuffdiff2, DEseq2, EdgeR) for identification of differentially expressed genes (DEG’s). Ingenuity Pathways Analysis (IPA) and Weighted Gene Co-expression Network Analysis (WGCNA) identified inflammatory processes as significantly elevated in our ALS samples, with tumor necrosis factor (TNF) found to be a major pathway regulator (IPA) and TNFα-induced protein 2 (TNFAIP2) as a major network “hub” gene (WGCNA). Using the oPOSSUM algorithm, we analyzed transcription factors (TF) controlling expression of the nine DEG/hub genes in the ALS samples and identified TF’s involved in inflammation (NFkB, REL, NFkB1) and macrophage function (NR1H2::RXRA heterodimer). Transient expression in human iPSC-derived motor neurons of TNFAIP2 (also a DEG identified by all three algorithms) reduced cell viability and induced caspase 3/7 activation. Using high-density RNAseq, multiple algorithms for DEG identification, and an unsupervised gene co-expression network approach, we identified significant elevation of inflammatory processes in ALS spinal cord with TNF as a major regulatory molecule. Overexpression of the DEG TNFAIP2 in human motor neurons, the population most vulnerable to die in ALS, increased cell death and caspase 3/7 activation. We propose that therapies targeted to reduce inflammatory TNFα signaling may be helpful in ALS patients

Incorporating gene co-expression network in identification of cancer prognosis markers

<p>Abstract</p> <p>Background</p> <p>Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them.</p> <p>Results</p> <p>We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives.</p> <p>Conclusions</p> <p>The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.</p

Thin, fine and with sensitivity: a metamethodology of intuitions

Do philosophers use intuitions? Should philosophers use intuitions? Can philosophical methods (where intuitions are concerned) be improved upon? In order to answer these questions we need to have some idea of how we should go about answering them. I defend a way of going about methodology of intuitions: a metamethodology. I claim the following: (i) we should approach methodological questions about intuitions with a thin conception of intuitions in mind; (ii) we should carve intuitions finely; and, (iii) we should carve to a grain to which we are sensitive in our everyday philosophising. The reason is that, unless we do so, we don’t get what we want from philosophical methodology. I argue that what we want is information that will aid us in formulating practical advice concerning how to do philosophy responsibly/well/better

Neuromyopathy with congenital cataracts and glaucoma: A distinct syndrome caused by POLG variants

We identified three non-related patients manifesting a childhood-onset progressive neuromyopathy with congenital cataracts, delayed walking, distal weakness and wasting, glaucoma and swallowing difficulties. Electrophysiology and nerve biopsies showed a mixed axonal and demyelinating neuropathy, while muscle biopsy disclosed both neurogenic and myopathic changes with ragged red fibers, and muscle MRI showed consistent features across patients, with a peculiar concentric disto-proximal gradient of fatty replacement. We used targeted next generation sequencing and candidate gene approach to study these families. Compound biallelic heterozygous variants, p.[(Pro648Arg)]; [(His932Tyr)] and p.[(Thr251Ile),(Pro587Leu)]; [(Arg943Cys)], were found in the three patients causing this homogeneous phenotype. Our report on a subset of unrelated patients, that showed a distinct autosomal recessive childhood-onset neuromyopathy with congenital cataracts and glaucoma, expands the clinical spectrum of POLG-related disorders. It also confirms the association between cataracts and neuropathy with variants in POLG. Early onset cataract is otherwise rare in POLG-related disorders and so far reported only in a few patients with the clinical pattern of distal myopathy or neuromyopathy

Microglial Morphology and Dynamic Behavior Is Regulated by Ionotropic Glutamatergic and GABAergic Neurotransmission

PURPOSE: Microglia represent the primary resident immune cells in the CNS, and have been implicated in the pathology of neurodegenerative diseases. Under basal or "resting" conditions, microglia possess ramified morphologies and exhibit dynamic surveying movements in their processes. Despite the prominence of this phenomenon, the function and regulation of microglial morphology and dynamic behavior are incompletely understood. We investigate here whether and how neurotransmission regulates "resting" microglial morphology and behavior. METHODS: We employed an ex vivo mouse retinal explant system in which endogenous neurotransmission and dynamic microglial behavior are present. We utilized live-cell time-lapse confocal imaging to study the morphology and behavior of GFP-labeled retinal microglia in response to neurotransmitter agonists and antagonists. Patch clamp electrophysiology and immunohistochemical localization of glutamate receptors were also used to investigate direct-versus-indirect effects of neurotransmission by microglia. RESULTS: Retinal microglial morphology and dynamic behavior were not cell-autonomously regulated but are instead modulated by endogenous neurotransmission. Morphological parameters and process motility were differentially regulated by different modes of neurotransmission and were increased by ionotropic glutamatergic neurotransmission and decreased by ionotropic GABAergic neurotransmission. These neurotransmitter influences on retinal microglia were however unlikely to be directly mediated; local applications of neurotransmitters were unable to elicit electrical responses on microglia patch-clamp recordings and ionotropic glutamatergic receptors were not located on microglial cell bodies or processes by immunofluorescent labeling. Instead, these influences were mediated indirectly via extracellular ATP, released in response to glutamatergic neurotransmission through probenecid-sensitive pannexin hemichannels. CONCLUSIONS: Our results demonstrate that neurotransmission plays an endogenous role in regulating the morphology and behavior of "resting" microglia in the retina. These findings illustrate a mode of constitutive signaling between the neural and immune compartments of the CNS through which immune cells may be regulated in concert with levels of neural activity