A combined ex vivo and in vivo RNAi screen for notch regulators in Drosophila reveals an extensive notch interaction network.

Notch signaling plays a fundamental role in cellular differentiation and has been linked to human diseases, including cancer. We report the use of comprehensive RNAi analyses to dissect Notch regulation and its connections to cellular pathways. A cell-based RNAi screen identified 900 candidate Notch regulators on a genome-wide scale. The subsequent use of a library of transgenic Drosophila expressing RNAi constructs enabled large-scale in vivo validation and confirmed 333 of 501 tested genes as Notch regulators. Mapping the phenotypic attributes of our data on an interaction network identified another 68 relevant genes and revealed several modules of unexpected Notch regulatory activity. In particular, we note an intriguing relationship to pyruvate metabolism, which may be relevant to cancer. Our study reveals a hitherto unappreciated diversity of tissue-specific modulators impinging on Notch and opens new avenues for studying Notch regulation and function in development and disease.

Anchoring gaze when categorizing faces' sex: evidence from eye-tracking data.

Previous research has shown that during recognition of frontal views of faces, the preferred landing positions of eye fixations are either on the nose or the eye region. Can these findings generalize to other facial views and a simpler perceptual task? An eye-tracking experiment investigated categorization of the sex of faces seen in four views. The results revealed a strategy, preferred in all views, which consisted of focusing gaze within an 'infraorbital region' of the face. This region was fixated more in the first than in subsequent fixations. Males anchored gaze lower and more centrally than females.

Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data.

With the advent of ultra high-throughput sequencing technologies, increasingly researchers are turning to deep sequencing for gene expression studies. Here we present a set of rigorous methods for normalization, quantification of noise, and co-expression analysis of deep sequencing data. Using these methods on 122 cap analysis of gene expression (CAGE) samples of transcription start sites, we construct genome-wide 'promoteromes' in human and mouse consisting of a three-tiered hierarchy of transcription start sites, transcription start clusters, and transcription start regions.

Transgenic mice expressing constitutive active MAPKAPK5 display gender-dependent differences in exploration and activity.

BACKGROUND: The mitogen-activated protein kinases, MAPKs for short, constitute cascades of signalling pathways involved in the regulation of several cellular processes that include cell proliferation, differentiation and motility. They also intervene in neurological processes like fear conditioning and memory. Since little remains known about the MAPK-Activated Protein Kinase, MAPKAPK5, we constructed the first MAPKAPK knockin mouse model, using a constitutive active variant of MAPKAPK5 and analyzed the resulting mice for changes in anxiety-related behaviour. METHODS: We performed primary SHIRPA observations during background breeding into the C57BL/6 background and assessed the behaviour of the background-bred animals on the elevated plus maze and in the light-dark test. Our results were analyzed using Chi-square tests and homo- and heteroscedatic T-tests. RESULTS: Female transgenic mice displayed increased amounts of head dips and open arm time on the maze, compared to littermate controls. In addition, they also explored further into the open arm on the elevated plus maze and were less active in the closed arm compared to littermate controls. Male transgenic mice displayed no differences in anxiety, but their locomotor activity increased compared to non-transgenic littermates. CONCLUSION: Our results revealed anxiety-related traits and locomotor differences between transgenic mice expressing constitutive active MAPKAPK5 and control littermates.

Intensity dependent confidence intervals on microarray measurements of differentially expressed genes: a case study of the effect of MK5, FKRP and TAF4 on the transcriptome.

To perform a quantitative analysis with gene-arrays, one must take into account inaccuracies (experimental variations, biological variations and other measurement errors) which are seldom known. In this paper we investigated amplification and noise propagation related errors by measuring intensity dependent variations. Based on a set of control samples, we create confidence intervals for up and down regulations. We validated our method through a qPCR experiment and compared it to standard analysis methods (including loess normalization and filtering methods based on genetic variability). The results reveal that amplification related errors are a major concern.

Adaptive contrast enhancement of two-dimensional electrophoretic protein gel images facilitates visualization, orientation and alignment.

2-DE is a powerful technique to discriminate post-translationally modified protein isoforms. However, all steps of 2-DE preparation and gel-staining may introduce unwanted artefacts, including inconsistent variation of background intensity over the entire 2-DE gel image. Background intensity variations limit the accuracy of gel orientation, overlay alignment and spot detection methods. We present a compact and efficient denoising algorithm that adaptively enhances the image contrast and then, through thresholding and median filtering, removes the gray-scale range covering the background. Applicability of the algorithm is demonstrated on immunoblots, isotope-labeled gels, and protein-stained gels. Validation is performed in contexts of (i) automatic gel orientation based on Hough transformation, (ii) overlay alignment based on cross correlation and (iii) spot detection. In gel stains with low background variability, e.g. Sypro Ruby, denoising will lower the spot detection sensitivity. In gel regions with high background levels denoising enhances spot detection. We propose that the denoising algorithm prepares images with high background for further automatic analysis, without requiring manual input on a gel-to-gel basis.

Proteomics of p53 in diagnostics and therapy of acute myeloid leukemia.

The anti-oncogene TP53 is frequently mutated in human cancer, but in hematological malignancies this is a rare feature. In acute myeloid leukemia (AML) more than 90% of the patients comprise wild type TP53 in their cancer cells, but if TP53 is mutated or deleted the disease is often found to be chemoresistant. In this review we define proteomics of the oncogene product p53 as the study of proteins in the p53 regulating signaling networks, as well as the protein study of members of the p53 family itself. Various messenger RNA splice forms as well as a multitude of post-translational modifications give a high number of protein isoforms in the p53 family. Some of the proteomic techniques allow detection of various isoforms, such as two-dimensional gel electrophoresis in combination with tandem mass spectrometry (MS/MS) and this methodology may therefore increasingly be used as a diagnostic tool in human disease. We introduce the p53 protein as an illustration of the complexity of post-translational modifications that may affect one highly connected protein and discuss the possible impact in AML diagnostics if the p53 profile is reflecting cell stress and status of signal transduction systems of the malignancy.

Correlation analysis of two-dimensional gel electrophoretic protein patterns and biological variables.

BACKGROUND: Two-dimensional gel electrophoresis (2DE) is a powerful technique to examine post-translational modifications of complexly modulated proteins. Currently, spot detection is a necessary step to assess relations between spots and biological variables. This often proves time consuming and difficult when working with non-perfect gels. We developed an analysis technique to measure correlation between 2DE images and biological variables on a pixel by pixel basis. After image alignment and normalization, the biological parameters and pixel values are replaced by their specific rank. These rank adjusted images and parameters are then put into a standard linear Pearson correlation and further tested for significance and variance. RESULTS: We validated this technique on a set of simulated 2DE images, which revealed also correct working under the presence of normalization factors. This was followed by an analysis of p53 2DE immunoblots from cancer cells, known to have unique signaling networks. Since p53 is altered through these signaling networks, we expected to find correlations between the cancer type (acute lymphoblastic leukemia and acute myeloid leukemia) and the p53 profiles. A second correlation analysis revealed a more complex relation between the differentiation stage in acute myeloid leukemia and p53 protein isoforms. CONCLUSION: The presented analysis method measures relations between 2DE images and external variables without requiring spot detection, thereby enabling the exploration of biosignatures of complex signaling networks in biological systems.