Sylvie Abouna

Differential regulation by ambient pH of putative virulence factor secretion by the phytopathogenic fungus Botrytis cinerea

Abstract The fungal pathogen Botrytis cinerea is capable of developing on a wide variety of host plants that differ greatly in their pH values and biochemical defences. To evaluate whether the pH of the host tissue can regulate the production of pathogenicity factors by this fungus, we examined the ability of two isolates of B. cinerea that originated from different plant species to secrete putative virulence elements on synthetic media buffered at pH 2.0 to pH 7.0. Even though differing in the intensity of their responses, both isolates reacted similarly to their ambient pH. The production of extracellular polysaccharides and oxalic acid was detectable above pH 4.0 and pH 5.0 respectively. Conversely, the production of aspartic acid proteases could only be seen between pH 3.0 and 4.0. Finally, the secretion of polygalacturonase and laccase activity was found to exhibit two maxima, one around pH 3.1 and one around pH 6.0. Thus, pathogenicity factor production was found to be minimal between pH 4.5 and 5.5 and a different set of factors was produced at pH 3.1 and 6.0, two values that were found to correspond respectively to the average host fruit and leaf pH. These results demonstrate that ambient pH differentially regulates the synthesis of pathogenicity factors by Botrytis and may act as a novel regulatory element to assist this fungus in tuning its virulence machinery to the composition of its host tissue.

Brief inactivation of c-Myc is not sufficient for sustained regression of c-Myc-induced tumours of pancreatic islets and skin epidermis

BackgroundTumour regression observed in many conditional mouse models following oncogene inactivation provides the impetus to develop, and a platform to preclinically evaluate, novel therapeutics to inactivate specific oncogenes. Inactivating single oncogenes, such as c-Myc, can reverse even advanced tumours. Intriguingly, transient c-Myc inactivation proved sufficient for sustained osteosarcoma regression; the resulting osteocyte differentiation potentially explaining loss of c-Mycs oncogenic properties. But would this apply to other tumours?ResultsWe show that brief inactivation of c-Myc does not sustain tumour regression in two distinct tissue types; tumour cells in pancreatic islets and skin epidermis continue to avoid apoptosis after c-Myc reactivation, by virtue of Bcl-xL over-expression or a favourable microenvironment, respectively. Moreover, tumours progress despite reacquiring a differentiated phenotype and partial loss of vasculature during c-Myc inactivation. Interestingly, reactivating c-Myc in β-cell tumours appears to result not only in further growth of the tumour, but also re-expansion of the accompanying angiogenesis and more pronounced β-cell invasion (adenocarcinoma).ConclusionsGiven that transient c-Myc inactivation could under some circumstances produce sustained tumour regression, the possible application of this potentially less toxic strategy in treating other tumours has been suggested. We show that brief inactivation of c-Myc fails to sustain tumour regression in two distinct models of tumourigenesis: pancreatic islets and skin epidermis. These findings challenge the potential for cancer therapies aimed at transient oncogene inactivation, at least under those circumstances where tumour cell differentiation and alteration of epigenetic context fail to reinstate apoptosis. Together, these results suggest that treatment schedules will need to be informed by knowledge of the molecular basis and environmental context of any given cancer.

WHIDE--a web tool for visual data mining colocation patterns in multivariate bioimages.

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDEs applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de

Non-β-cell progenitors of β-cells in pregnant mice

Pregnancy is a normal physiological condition in which the maternal β-cell mass increases rapidly about two-fold to adapt to new metabolic challenges. We have used a lineage tracing of β-cells to analyse the origin of new β-cells during this rapid expansion in pregnancy. Double transgenic mice bearing a tamoxifen-dependent Cre-recombinase construct under the control of a rat insulin promoter, together with a reporter Z/AP gene, were generated. Then, in response to a pulse of tamoxifen before pregnancy, β-cells in these animals were marked irreversibly and heritably with the human placental alkaline phosphatase (HPAP). First, we conclude that the lineage tracing system was highly specific for β-cells. Secondly, we scored the proportion of the β-cells marked with HPAP during a subsequent chase period in pregnant and non-pregnant females. We observed a dilution in this labelling index in pregnant animal pancreata, compared to non-pregnant controls, during a single pregnancy in the chase period. To extend these observations we also analysed the labelling index in pancreata of animals during the second of two pregnancies in the chase period. The combined data revealed statistically-significant dilution during pregnancy, indicating a contribution to new beta cells from a non-β-cell source. Thus for the first time in a normal physiological condition, we have demonstrated not only β-cell duplication, but also the activation of a non-β-cell progenitor population. Further, there was no transdifferentiation of β-cells to other cell types in a two and half month period following labelling, including the period of pregnancy.

RAMTaB: Robust Alignment of Multi-Tag Bioimages

Background In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images. Methodology/Principal Findings We employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies. Conclusions For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.

c-Myc directly induces both impaired insulin secretion and loss of β-cell mass, independently of hyperglycemia in vivo

c-Myc (Myc) is a mediator of glucotoxicity but could also independently compromise β-cell survival and function. We have shown that after Myc activation in adult β-cells in vivo, apoptosis is preceded by hyperglycemia, suggesting glucotoxicity might contribute to Myc-induced apoptosis. To address this question conditional Myc was activated in β-cells of adult pIns-c-MycERTAM mice in vivo in the presence or absence of various glucose-lowering treatments, including exogenous insulin and prior to transplantation with wild-type islets. Changes in blood glucose levels were subsequently correlated with changes in β-cell mass and markers of function/differentiation. Activation of c-Myc resulted in reduced insulin secretion, hyperglycemia and loss of β-cell differentiation, followed by reduction in mass. Glucose-lowering interventions did not prevent loss of β-cells. Therefore, Myc can cause diabetes by direct effects on β-cell apoptosis even in the absence of potentially confounding secondary hyperglycemia. Moreover, as loss of β-cell differentiation/function and hyperglycemia are not prevented by preventing β-cell apoptosis, we conclude that Myc might contribute to the pathogenesis of diabetes by directly coupling cell cycle entry and β-cell failure through 2 distinct pathways.

Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue

The challenging problem of computational bioimage analysis receives growing attention from life sciences. Fluorescence microscopy is capable of simultaneously visualizing multiple molecules by staining with different fluorescent dyes. In the analysis of the result multichannel images, segmentation of ROIs resembles only a first step which must be followed by a second step towards the analysis of the ROIs signals in the different channels. In this paper we present a system that combines image segmentation and information visualization principles for an integrated analysis of fluorescence micrographs of tissue samples. The analysis aims at the detection and annotation of cells of the Islets of Langerhans and the whole pancreas, which is of great importance in diabetes studies and in the search for new anti-diabetes treatments. The system operates with two modules. The automatic annotation module applies supervised machine learning for cell detection and segmentation. The second information visualization module can be used for an interactive classification and visualization of cell types following the link-and-brush principle for filtering. We can compare the results obtained with our system with results obtained manually by an expert, who evaluated a set of example images three times to account for his intra-observer variance. The comparison shows that using our system the images can be evaluated with high accuracy which allows a considerable speed up of the time-consuming evaluation process.

Re-Expression of IGF-II Is Important for Beta Cell Regeneration in Adult Mice

Background The key factors which support re-expansion of beta cell numbers after injury are largely unknown. Insulin-like growth factor II (IGF-II) plays a critical role in supporting cell division and differentiation during ontogeny but its role in the adult is not known. In this study we investigated the effect of IGF-II on beta cell regeneration. Methodology/Principal Findings We employed an in vivo model of ‘switchable’ c-Myc-induced beta cell ablation, pIns-c-MycERTAM, in which 90% of beta cells are lost following 11 days of c-Myc (Myc) activation in vivo. Importantly, such ablation is normally followed by beta cell regeneration once Myc is deactivated, enabling functional studies of beta cell regeneration in vivo. IGF-II was shown to be re-expressed in the adult pancreas of pIns-c-MycERTAM/IGF-II+/+ (MIG) mice, following beta cell injury. As expected in the presence of IGF-II beta cell mass and numbers recover rapidly after ablation. In contrast, in pIns-c-MycERTAM/IGF-II+/− (MIGKO) mice, which express no IGF-II, recovery of beta cell mass and numbers were delayed and impaired. Despite failure of beta cell number increase, MIGKO mice recovered from hyperglycaemia, although this was delayed. Conclusions/Significance Our results demonstrate that beta cell regeneration in adult mice depends on re-expression of IGF-II, and supports the utility of using such ablation-recovery models for identifying other potential factors critical for underpinning successful beta cell regeneration in vivo. The potential therapeutic benefits of manipulating the IGF-II signaling systems merit further exploration.

First Description of Sulphur-Oxidizing Bacterial Symbiosis in a Cnidarian (Medusozoa) Living in Sulphidic Shallow-Water Environments

Background Since the discovery of thioautotrophic bacterial symbiosis in the giant tubeworm Riftia pachyptila, there has been great impetus to investigate such partnerships in other invertebrates. In this study, we present the occurrence of a sulphur-oxidizing symbiosis in a metazoan belonging to the phylum Cnidaria in which this event has never been described previously. Methodology/Principal Findings Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM) observations and Energy-dispersive X-ray spectroscopy (EDXs) analysis, were employed to unveil the presence of prokaryotes population bearing elemental sulphur granules, growing on the body surface of the metazoan. Phylogenetic assessments were also undertaken to identify this invertebrate and microorganisms in thiotrophic symbiosis. Our results showed the occurrence of a thiotrophic symbiosis in a cnidarian identified as Cladonema sp. Conclusions/Significance This is the first report describing the occurrence of a sulphur-oxidizing symbiosis in a cnidarian. Furthermore, of the two adult morphologies, the polyp and medusa, this mutualistic association was found restricted to the polyp form of Cladonema sp.

A machine learning based system for multichannel fluorescence analysis in pancreatic tissue bioimages

Fluorescence microscopy has regained much attention in the last years especially in the field of systems biology. It has been recognized as a rich source of information extending the existing sources since it allows simultaneous collection of spatial and temporal protein information. In order to enable a high-throughput and high-content image analysis, sophisticated image processing routines become essential. We present a machine learning based approach for semantic image annotation i.e. identifying biologically meaningful objects. A semantic annotation becomes necessary, if image variables have to be associated to single biological objects, for example cells. We apply our method to pancreatic tissue sample images to detect and annotate cells of the Islets of Langerhans and whole pancreas. Based on the annotation, aligned multichannel fluorescence images are evaluated for cell type classification allowing accurate and rapid determination of the cell number and mass. This high-throughput analytical technique, requiring only few parameters, should be of great value in diabetes studies and for screening of new anti-diabetes treatments.