Rapid Reactivation of Extralymphoid CD4 T Cells during Secondary Infection

After infection, extralymphoid tissues are enriched with effector and memory T cells of a highly activated phenotype. The capacity for rapid effector cytokine response from extralymphoid tissue-memory T cells suggests these cells may perform a ‘sentinel’ function in the tissue. While it has been demonstrated that extralymphoid CD4+ T cells can directly respond to secondary infection, little is known about how rapidly this response is initiated, and how early activation of T cells in the tissue may affect the innate response to infection. Here we use a mouse model of secondary heterosubtypic influenza infection to show that CD4+ T cells in the lung airways are reactivated within 24 hours of secondary challenge. Airway CD4+ T cells initiate an inflammatory cytokine and chemokine program that both alters the composition of the early innate response and contributes to the reduction of viral titers in the lung. These results show that, unlike a primary infection, extralymphoid tissue-memory CD4+ T cells respond alongside the innate response during secondary infection, thereby shaping the overall immune profile in the airways. These data provide new insights into the role of extralymphoid CD4+ T cells during secondary immune responses

A Synthetic Chloride Channel Restores Chloride Conductance in Human Cystic Fibrosis Epithelial Cells

Mutations in the gene-encoding cystic fibrosis transmembrane conductance regulator (CFTR) cause defective transepithelial transport of chloride (Cl−) ions and fluid, thereby becoming responsible for the onset of cystic fibrosis (CF). One strategy to reduce the pathophysiology associated with CF is to increase Cl− transport through alternative pathways. In this paper, we demonstrate that a small synthetic molecule which forms Cl− channels to mediate Cl− transport across lipid bilayer membranes is capable of restoring Cl− permeability in human CF epithelial cells; as a result, it has the potential to become a lead compound for the treatment of human diseases associated with Cl− channel dysfunction

Increased CSF levels of aromatic amino acids in hip fracture patients with delirium suggests higher monoaminergic activity

textabstractBackground: To examine whether delirium in hip fracture patients was associated with changes in the levels of amino acids and/or monoamine metabolites in cerebrospinal fluid (CSF) and serum. Methods: In this prospective cohort study, 77 patients admitted with an acute hip fracture to Oslo University Hospital, Norway, were studied. The concentrations of amino acids in CSF and serum were determined by high performance liquid chromatography. The patients were assessed daily for delirium by the Confusion Assessment Method (pre-operatively and post-operative day 1-5 (all) or until discharge (delirious patients)). Pre-fracture dementia status was decided by an expert panel. Serum was collected pre-operatively and CSF immediately before spinal anesthesia. Results: Fifty-three (71 %) hip fracture patients developed delirium. In hip fracture patients without dementia (n = 39), those with delirium had significantly higher CSF levels of tryptophan (40 % higher), tyrosine (60 % higher), phenylalanine (59 % higher) and the monoamine metabolite 5-hydroxyindoleacetate (23 % higher) compared to those without delirium. The same amino acids were also higher in CSF in delirious patients with dementia (n = 38). The correlations between serum and CSF amino acid levels were poor. Conclusion: Higher CSF levels of monoamine precursors in hip fracture patients with delirium suggest a higher monoaminergic activity in the central nervous system during delirium in this patient group

Large-scale integration of cancer microarray data identifies a robust common cancer signature

<p>Abstract</p> <p>Background</p> <p>There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.</p> <p>Results</p> <p>In this study, we collect and integrate ~ 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the <it>T</it>op-<it>S</it>coring <it>P</it>air of <it>G</it>roups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups; those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data.</p> <p>Conclusion</p> <p>By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.</p

Analysis of differential gene expression in human melanocytic tumour lesions by custom made oligonucleotide arrays

Melanoma is one of the most aggressive types of cancer and resection of the tumour prior to dissemination of tumour cells is still the most effective treatment. Therefore, early diagnosis of melanocytic lesions is important and identification of novel (molecular) markers would be helpful to improve diagnosis. Moreover, better understanding of molecular targets involved in melanocytic tumorigenesis could possibly lead to development of novel interventions. In this study, we used a custom made oligonucleotide array containing 298 genes that were previously found to be differentially expressed in human melanoma cell lines 1F6 (rarely metastasising) and Mel57 (frequently metastasising). We determined differential gene expression in human common nevocellular nevus and melanoma metastasis lesions. By performing nine dye-swap array experiments, using individual as well as pooled melanocytic lesions, a constant differential expression could be detected for 25 genes in eight out of nine or nine out of nine array analyses. For at least nine of these genes, namely THBD, FABP7, H2AFJ, RRAGD, MYADM, HR, CKS2, NCK2 and GDF15, the differential expression found by array analyses could be verified by semiquantitative and/or real-time quantitative RT–PCR. The genes that we identified to be differentially expressed during melanoma progression could be potent targets for diagnostic, prognostic and/or therapeutic interventions

Resting-State Functional Connectivity between Fronto-Parietal and Default Mode Networks in Obsessive-Compulsive Disorder

Background: Obsessive-compulsive disorder (OCD) is characterized by an excessive focus on upsetting or disturbing thoughts, feelings, and images that are internally-generated. Internally-focused thought processes are subserved by the ‘‘default mode network’ ’ (DMN), which has been found to be hyperactive in OCD during cognitive tasks. In healthy individuals, disengagement from internally-focused thought processes may rely on interactions between DMN and a frontoparietal network (FPN) associated with external attention and task execution. Altered connectivity between FPN and DMN may contribute to the dysfunctional behavior and brain activity found in OCD. Methods: The current study examined interactions between FPN and DMN during rest in 30 patients with OCD (17 unmedicated) and 32 control subjects (17 unmedicated). Timecourses from seven fronto-parietal seeds were correlated across the whole brain and compared between groups. Results: OCD patients exhibited altered connectivity between FPN seeds (primarily anterior insula) and several regions of DMN including posterior cingulate cortex, medial frontal cortex, posterior inferior parietal lobule, and parahippocampus. These differences were driven largely by a reduction of negative correlations among patients compared to controls. Patients also showed greater positive connectivity between FPN and regions outside DMN, including thalamus, lateral frontal cortex, and somatosensory/motor regions

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene–trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies