Medial prefrontal cortex serotonin 1A and 2A receptor binding interacts to predict threat-related amygdala reactivity

Background\ud The amygdala and medial prefrontal cortex (mPFC) comprise a key corticolimbic circuit that helps shape individual differences in sensitivity to threat and the related risk for psychopathology. Although serotonin (5-HT) is known to be a key modulator of this circuit, the specific receptors mediating this modulation are unclear. The colocalization of 5-HT1A and 5-HT2A receptors on mPFC glutamatergic neurons suggests that their functional interactions may mediate 5-HT effects on this circuit through top-down regulation of amygdala reactivity. Using a multimodal neuroimaging strategy in 39 healthy volunteers, we determined whether threat-related amygdala reactivity, assessed with blood oxygen level-dependent functional magnetic resonance imaging, was significantly predicted by the interaction between mPFC 5-HT1A and 5-HT2A receptor levels, assessed by positron emission tomography.\ud \ud Results\ud 5-HT1A binding in the mPFC significantly moderated an inverse correlation between mPFC 5-HT2A binding and threat-related amygdala reactivity. Specifically, mPFC 5-HT2A binding was significantly inversely correlated with amygdala reactivity only when mPFC 5-HT1A binding was relatively low.\ud \ud Conclusions\ud Our findings provide evidence that 5-HT1A and 5-HT2A receptors interact to shape serotonergic modulation of a functional circuit between the amygdala and mPFC. The effect of the interaction between mPFC 5-HT1A and 5-HT2A binding and amygdala reactivity is consistent with the colocalization of these receptors on glutamatergic neurons in the mPFC

The Role of the Yap5 Transcription Factor in Remodeling Gene Expression in Response to Fe Bioavailability

The budding yeast Saccharomyces cerevisiae has developed several mechanisms to avoid either the drastic consequences of iron deprivation or the toxic effects of iron excess. In this work, we analysed the global gene expression changes occurring in yeast cells undergoing iron overload. Several genes directly or indirectly involved in iron homeostasis showed altered expression and the relevance of these changes are discussed. Microarray analyses were also performed to identify new targets of the iron responsive factor Yap5. Besides the iron vacuolar transporter CCC1, Yap5 also controls the expression of glutaredoxin GRX4, previously known to be involved in the regulation of Aft1 nuclear localization. Consistently, we show that in the absence of Yap5 Aft1 nuclear exclusion is slightly impaired. These studies provide further evidence that cells control iron homeostasis by using multiple pathways

Case-based reported mortality associated with laboratory-confirmed influenza A(H1N1) 2009 virus infection in the Netherlands: the 2009-2010 pandemic season versus the 2010-2011 influenza season

<p>Abstract</p> <p>Background</p> <p>In contrast to seasonal influenza epidemics, where the majority of deaths occur amongst elderly, a considerable part of the 2009 pandemic influenza related deaths concerned relatively young people. In the Netherlands, all deaths associated with laboratory-confirmed influenza A(H1N1) 2009 virus infection had to be notified, both during the 2009-2010 pandemic season and the 2010-2011 influenza season. To assess whether and to what extent pandemic mortality patterns were reverting back to seasonal patterns, a retrospective analyses of all notified fatal cases associated with laboratory-confirmed influenza A(H1N1) 2009 virus infection was performed.</p> <p>Methods</p> <p>The notification database, including detailed information about the clinical characteristics of all notified deaths, was used to perform a comprehensive analysis of all deceased patients with a laboratory-confirmed influenza A(H1N1) 2009 virus infection. Characteristics of the fatalities with respect to age and underlying medical conditions were analysed, comparing the 2009-2010 pandemic and the 2010-2011 influenza season.</p> <p>Results</p> <p>A total of 65 fatalities with a laboratory-confirmed influenza A(H1N1) 2009 virus infection were notified in 2009-2010 and 38 in 2010-2011. During the pandemic season, the population mortality rates peaked in persons aged 0-15 and 55-64 years. In the 2010-2011 influenza season, peaks in mortality were seen in persons aged 0-15 and 75-84 years. During the 2010-2011 influenza season, the height of first peak was lower compared to that during the pandemic season. Underlying immunological disorders were more common in the pandemic season compared to the 2010-2011 season (p = 0.02), and cardiovascular disorders were more common in the 2010-2011 season (p = 0.005).</p> <p>Conclusions</p> <p>The mortality pattern in the 2010-2011 influenza season still resembled the 2009-2010 pandemic season with a peak in relatively young age groups, but concurrently a clear shift toward seasonal patterns was seen, with a peak in mortality in the elderly, i.e. ≥ 75 years of age.</p

Temporal resolution of protein–protein interactions in the live-cell plasma membrane

We have recently devised a method to quantify interactions between a membrane protein (“bait”) and a fluorophore-labeled protein (“prey”) directly in the live-cell plasma membrane (Schwarzenbacher et al. Nature Methods 5:1053–1060 2008). The idea is to seed cells on surfaces containing micro-patterned antibodies against the exoplasmic domain of the bait, and monitor the co-patterning of the fluorescent prey via fluorescence microscopy. Here, we characterized the time course of bait and prey micropattern formation upon seeding the cells onto the micro-biochip. Patterns were formed immediately after contact of the cells with the surface. Cells were able to migrate over the chip surface without affecting the micropattern contrast, which remained constant over hours. On single cells, bait contrast may be subject to fluctuations, indicating that the bait can be released from and recaptured on the micropatterns. We conclude that interaction studies can be performed at any time-point ranging from 5 min to several hours post seeding. Monitoring interactions with time opens up the possibility for new assays, which are briefly sketched in the discussion section

Transcription factor site dependencies in human, mouse and rat genomes

<p>Abstract</p> <p>Background</p> <p>It is known that transcription factors frequently act together to regulate gene expression in eukaryotes. In this paper we describe a computational analysis of transcription factor site dependencies in human, mouse and rat genomes.</p> <p>Results</p> <p>Our approach for quantifying tendencies of transcription factor binding sites to co-occur is based on a binding site scoring function which incorporates dependencies between positions, the use of information about the structural class of each transcription factor (major/minor groove binder), and also considered the possible implications of varying GC content of the sequences. Significant tendencies (dependencies) have been detected by non-parametric statistical methodology (permutation tests). Evaluation of obtained results has been performed in several ways: reports from literature (many of the significant dependencies between transcription factors have previously been confirmed experimentally); dependencies between transcription factors are not biased due to similarities in their DNA-binding sites; the number of dependent transcription factors that belong to the same functional and structural class is significantly higher than would be expected by chance; supporting evidence from GO clustering of targeting genes. Based on dependencies between two transcription factor binding sites (second-order dependencies), it is possible to construct higher-order dependencies (networks). Moreover results about transcription factor binding sites dependencies can be used for prediction of groups of dependent transcription factors on a given promoter sequence. Our results, as well as a scanning tool for predicting groups of dependent transcription factors binding sites are available on the Internet.</p> <p>Conclusion</p> <p>We show that the computational analysis of transcription factor site dependencies is a valuable complement to experimental approaches for discovering transcription regulatory interactions and networks. Scanning promoter sequences with dependent groups of transcription factor binding sites improve the quality of transcription factor predictions.</p

Targeted Deletion of p73 in Mice Reveals Its Role in T Cell Development and Lymphomagenesis

Transcriptional silencing of the p73 gene through methylation has been demonstrated in human leukemias and lymphomas. However, the role of p73 in the malignant process remains to be explored. We show here that p73 acts as a T cell-specific tumor suppressor in a genetically defined mouse model, and that concomitant ablation of p53 and p73 predisposes mice to an increased incidence of thymic lymphomas compared to the loss of p53 alone. Our results demonstrate a causal role for loss of p73 in progression of T cell lymphomas to the stage of aggressive, disseminated disease. We provide evidence that tumorigenesis in mice lacking p53 and p73 proceeds through mechanisms involving altered patterns of gene expression, defects in early T cell development, impaired apoptosis, and the ensuing accumulation of chromosomal aberrations. Collectively, our data imply that tumor suppressive properties of p73 are highly dependent on cellular context, wherein p73 plays a major role in T cell development and neoplasia

Collagen-Binding Peptidoglycans Inhibit MMP Mediated Collagen Degradation and Reduce Dermal Scarring

Scarring of the skin is a large unmet clinical problem that is of high patient concern and impact. Wound healing is complex and involves numerous pathways that are highly orchestrated, leaving the skin sealed, but with abnormal organization and composition of tissue components, namely collagen and proteoglycans, that are then remodeled over time. To improve healing and reduce or eliminate scarring, more rapid restoration of healthy tissue composition and organization offers a unique approach for development of new therapeutics. A synthetic collagen-binding peptidoglycan has been developed that inhibits matrix metalloproteinase-1 and 13 (MMP-1 and MMP-13) mediated collagen degradation. We investigated the synthetic peptidoglycan in a rat incisional model in which a single dose was delivered in a hyaluronic acid (HA) vehicle at the time of surgery prior to wound closure. The peptidoglycan treatment resulted in a significant reduction in scar tissue at 21 days as measured by histology and visual analysis. Improved collagen architecture of the treated wounds was demonstrated by increased tensile strength and transmission electron microscopy (TEM) analysis of collagen fibril diameters compared to untreated and HA controls. The peptidoglycan's mechanism of action includes masking existing collagen and inhibiting MMP-mediated collagen degradation while modulating collagen organization. The peptidoglycan can be synthesized at low cost with unique design control, and together with demonstrated preclinical efficacy in reducing scarring, warrants further investigation for dermal wound healing

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p