Query Large Scale Microarray Compendium Datasets Using a Model-Based Bayesian Approach with Variable Selection

In microarray gene expression data analysis, it is often of interest to identify genes that share similar expression profiles with a particular gene such as a key regulatory protein. Multiple studies have been conducted using various correlation measures to identify co-expressed genes. While working well for small datasets, the heterogeneity introduced from increased sample size inevitably reduces the sensitivity and specificity of these approaches. This is because most co-expression relationships do not extend to all experimental conditions. With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge. We develop a model-based gene expression query algorithm built under the Bayesian model selection framework. It is capable of detecting co-expression profiles under a subset of samples/experimental conditions. In addition, it allows linearly transformed expression patterns to be recognized and is robust against sporadic outliers in the data. Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets. Our simulation studies suggest that this method outperforms existing correlation coefficients or mutual information-based query tools. When we apply this new method to the Escherichia coli microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors

The Alpha Linolenic Acid Content of Flaxseed is Associated with an Induction of Adipose Leptin Expression

Dietary flaxseed has cardioprotective effects that may be achieved through its rich content of the omega-3 fatty acid, alpha linolenic acid (ALA). Because ALA can be stored in adipose tissue, it is possible that some of its beneficial actions may be due to effects it has on the adipose tissue. We investigated the effects of dietary flaxseed both with and without an atherogenic cholesterol-enriched diet to determine the effects of dietary flaxseed on the expression of the adipose cytokines leptin and adiponectin. Rabbits were fed one of four diets: a regular (RG) diet, or a regular diet with added 0.5% cholesterol (CH), or 10% ground flaxseed (FX), or both (CF) for 8 weeks. Levels of leptin and adiponectin expression were assessed by RT-PCR in visceral adipose tissue. Consumption of flaxseed significantly increased plasma and adipose levels of ALA. Leptin protein and mRNA expression were lower in CH animals and were elevated in CF animals. Changes in leptin expression were strongly and positively correlated with adipose ALA levels and inversely correlated with levels of en face atherosclerosis. Adiponectin expression was not significantly affected by any of the dietary interventions. Our data demonstrate that the type of fat in the diet as well as its caloric content can specifically influence leptin expression. The findings support the hypothesis that the beneficial cardiovascular effects associated with flaxseed consumption may be related to a change in leptin expression

A randomised clinical trial of methotrexate points to possible efficacy and adaptive immune dysfunction in psychosis

NMDA autoantibody encephalitis presenting as schizophrenia suggests the possible role of adaptive cell-mediated immunity in idiopathic schizophrenia. However, to our knowledge there have been no trials of the immune-suppressant methotrexate in schizophrenia. We tested if low-dose methotrexate as used in the treatment of systemic autoimmune disorders would be tolerable and effective in people with schizophrenia in a feasibility study. Ninety-two participants within 5 years of schizophrenia diagnosis were recruited from inpatient and outpatient facilities in Karachi, Pakistan. They were randomised to receive once weekly 10-mg oral methotrexate (n = 45) or matching placebo (n = 47) both with daily 5-mg folic acid, in addition to treatment as usual for 12 weeks. There were eight dropouts per group. Side effects were non-significantly more common in those on methotrexate and were not severe. One person developed leukopenia. Positive symptom scores improved more in those receiving methotrexate than placebo (β = −2.5; [95% CI −4.7 to −0.4]), whereas negative symptoms were unaffected by treatment (β = −0.39; [95% CI −2.01 to 1.23]). There were no immune biomarkers but methotrexate did not affect group mean leucocyte counts or C-reactive protein. We conclude that further studies are feasible but should be focussed on subgroups identified by advances in neuroimmune profiling. Methotrexate is thought to work in autoimmune disorders by resetting systemic regulatory T-cell control of immune signalling; we show that a similar action in the CNS would account for otherwise puzzling features of the immuno-pathogenesis of schizophrenia

Biocatalytic Synthesis of Polymers of Precisely Defined Structures

The fabrication of functional nanoscale devices requires the construction of complex architectures at length scales characteristic of atoms and molecules. Currently microlithography and micro-machining of macroscopic objects are the preferred methods for construction of small devices, but these methods are limited to the micron scale. An intriguing approach to nanoscale fabrication involves the association of individual molecular components into the desired architectures by supramolecular assembly. This process requires the precise specification of intermolecular interactions, which in turn requires precise control of molecular structure

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest

Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity

It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a “surprising” anomaly, possibly indicative of a hitherto hidden fragment of the underlying “ground-truth”. What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neuron's activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework

GATA6 Activates Wnt Signaling in Pancreatic Cancer by Negatively Regulating the Wnt Antagonist Dickkopf-1

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease characterized by late diagnosis and treatment resistance. Recurrent genetic alterations in defined genes in association with perturbations of developmental cell signaling pathways have been associated with PDAC development and progression. Here, we show that GATA6 contributes to pancreatic carcinogenesis during the temporal progression of pancreatic intraepithelial neoplasia by virtue of Wnt pathway activation. GATA6 is recurrently amplified by both quantitative-PCR and fluorescent in-situ hybridization in human pancreatic intraepithelial neoplasia and in PDAC tissues, and GATA6 copy number is significantly correlated with overall patient survival. Forced overexpression of GATA6 in cancer cell lines enhanced cell proliferation and colony formation in soft agar in vitro and growth in vivo, as well as increased Wnt signaling. By contrast siRNA mediated knockdown of GATA6 led to corresponding decreases in these same parameters. The effects of GATA6 were found to be due to its ability to bind DNA, as forced overexpression of a DNA-binding mutant of GATA6 had no effects on cell growth in vitro or in vivo, nor did they affect Wnt signaling levels in these same cells. A microarray analysis revealed the Wnt antagonist Dickopf-1 (DKK1) as a dysregulated gene in association with GATA6 knockdown, and direct binding of GATA6 to the DKK1 promoter was confirmed by chromatin immunoprecipitation and electrophoretic mobility shift assays. Transient transfection of GATA6, but not mutant GATA6, into cancer cell lines led to decreased DKK1 mRNA expression and secretion of DKK1 protein into culture media. Forced overexpression of DKK1 antagonized the effects of GATA6 on Wnt signaling in pancreatic cancer cells. These findings illustrate that one mechanism by which GATA6 promotes pancreatic carcinogenesis is by virtue of its activation of canonical Wnt signaling via regulation of DKK1

Association between Catechol-O-Methyltrasferase Val108/158Met Genotype and Prefrontal Hemodynamic Response in Schizophrenia

BACKGROUND:"Imaging genetics" studies have shown that brain function by neuroimaging is a sensitive intermediate phenotype that bridges the gap between genes and psychiatric conditions. Although the evidence of association between functional val108/158met polymorphism of the catechol-O-methyltransferase gene (COMT) and increasing risk for developing schizophrenia from genetic association studies remains to be elucidated, one of the most topical findings from imaging genetics studies is the association between COMT genotype and prefrontal function in schizophrenia. The next important step in the translational approach is to establish a useful neuroimaging tool in clinical settings that is sensitive to COMT variation, so that the clinician could use the index to predict clinical response such as improvement in cognitive dysfunction by medication. Here, we investigated spatiotemporal characteristics of the association between prefrontal hemodynamic activation and the COMT genotype using a noninvasive neuroimaging technique, near-infrared spectroscopy (NIRS). METHODOLOGY/PRINCIPAL FINDINGS:Study participants included 45 patients with schizophrenia and 60 healthy controls matched for age and gender. Signals that are assumed to reflect regional cerebral blood volume were monitored over prefrontal regions from 52-channel NIRS and compared between two COMT genotype subgroups (Met carriers and Val/Val individuals) matched for age, gender, premorbid IQ, and task performance. The [oxy-Hb] increase in the Met carriers during the verbal fluency task was significantly greater than that in the Val/Val individuals in the frontopolar prefrontal cortex of patients with schizophrenia, although neither medication nor clinical symptoms differed significantly between the two subgroups. These differences were not found to be significant in healthy controls. CONCLUSIONS/SIGNIFICANCE:These data suggest that the prefrontal NIRS signals can noninvasively detect the impact of COMT variation in patients with schizophrenia. NIRS may be a promising candidate translational approach in psychiatric neuroimaging

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients