CD34/QBEND10 immunostaining in bone marrow biopsies: an additional parameter for the diagnosis and classification of myelodysplastic syndromes.

CD34/QBEND10 immunostaining has been assessed in 150 bone marrow biopsies (BMB) including 91 myelodysplastic syndromes (MDS), 16 MDS-related AML, 25 reactive BMB, and 18 cases where RA could neither be established nor ruled out. All cases were reviewed and classified according to the clinical and morphological FAB criteria. The percentage of CD34-positive (CD34 +) hematopoietic cells and the number of clusters of CD34+ cells in 10 HPF were determined. In most cases the CD34+ cell count was similar to the blast percentage determined morphologically. In RA, however, not only typical blasts but also less immature hemopoietic cells lying morphologically between blasts and promyelocytes were stained with CD34. The CD34+ cell count and cluster values were significantly higher in RA than in BMB with reactive changes (p<0.0001 for both), in RAEB than in RA (p=0.0006 and p=0.0189, respectively), in RAEBt than in RAEB (p=0.0001 and p=0.0038), and in MDS-AML than in RAEBt (p<0.0001 and p=0.0007). Presence of CD34+ cell clusters in RA correlated with increased risk of progression of the disease. We conclude that CD34 immunostaining in BMB is a useful tool for distinguishing RA from other anemias, assessing blast percentage in MDS cases, classifying them according to FAB, and following their evolution.

An Adeno-Associated Virus-Based Intracellular Sensor of Pathological Nuclear Factor-κB Activation for Disease-Inducible Gene Transfer.

Stimulation of resident cells by NF-κB activating cytokines is a central element of inflammatory and degenerative disorders of the central nervous system (CNS). This disease-mediated NF-κB activation could be used to drive transgene expression selectively in affected cells, using adeno-associated virus (AAV)-mediated gene transfer. We have constructed a series of AAV vectors expressing GFP under the control of different promoters including NF-κB -responsive elements. As an initial screen, the vectors were tested in vitro in HEK-293T cells treated with TNF-α. The best profile of GFP induction was obtained with a promoter containing two blocks of four NF-κB -responsive sequences from the human JCV neurotropic polyoma virus promoter, fused to a new tight minimal CMV promoter, optimally distant from each other. A therapeutical gene, glial cell line-derived neurotrophic factor (GDNF) cDNA under the control of serotype 1-encapsidated NF-κB -responsive AAV vector (AAV-NF) was protective in senescent cultures of mouse cortical neurons. AAV-NF was then evaluated in vivo in the kainic acid (KA)-induced status epilepticus rat model for temporal lobe epilepsy, a major neurological disorder with a central pathophysiological role for NF-κB activation. We demonstrate that AAV-NF, injected in the hippocampus, responded to disease induction by mediating GFP expression, preferentially in CA1 and CA3 neurons and astrocytes, specifically in regions where inflammatory markers were also induced. Altogether, these data demonstrate the feasibility to use disease-activated transcription factor-responsive elements in order to drive transgene expression specifically in affected cells in inflammatory CNS disorders using AAV-mediated gene transfer.

Peroxisome proliferator-activated receptor beta/delta (PPARbeta/delta) but not PPARalpha serves as a plasma free fatty acid sensor in liver.

Peroxisome proliferator-activated receptor alpha (PPARalpha) is an important transcription factor in liver that can be activated physiologically by fasting or pharmacologically by using high-affinity synthetic agonists. Here we initially set out to elucidate the similarities in gene induction between Wy14643 and fasting. Numerous genes were commonly regulated in liver between the two treatments, including many classical PPARalpha target genes, such as Aldh3a2 and Cpt2. Remarkably, several genes induced by Wy14643 were upregulated by fasting independently of PPARalpha, including Lpin2 and St3gal5, suggesting involvement of another transcription factor. Using chromatin immunoprecipitation, Lpin2 and St3gal5 were shown to be direct targets of PPARbeta/delta during fasting, whereas Aldh3a2 and Cpt2 were exclusive targets of PPARalpha. Binding of PPARbeta/delta to the Lpin2 and St3gal5 genes followed the plasma free fatty acid (FFA) concentration, consistent with activation of PPARbeta/delta by plasma FFAs. Subsequent experiments using transgenic and knockout mice for Angptl4, a potent stimulant of adipose tissue lipolysis, confirmed the stimulatory effect of plasma FFAs on Lpin2 and St3gal5 expression levels via PPARbeta/delta. In contrast, the data did not support activation of PPARalpha by plasma FFAs. The results identify Lpin2 and St3gal5 as novel PPARbeta/delta target genes and show that upregulation of gene expression by PPARbeta/delta is sensitive to plasma FFA levels. In contrast, this is not the case for PPARalpha, revealing a novel mechanism for functional differentiation between PPARs.

A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature.

BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

Regulatory roles of the GacS/GacA two-component system in plant-associated and other gram-negative bacteria.

The sensor kinase GacS and the response regulator GacA are members of a two-component system that is present in a wide variety of gram-negative bacteria and has been studied mainly in enteric bacteria and fluorescent pseudomonads. The GacS/GacA system controls the production of secondary metabolites and extracellular enzymes involved in pathogenicity to plants and animals, biocontrol of soilborne plant diseases, ecological fitness, or tolerance to stress. A current model proposes that GacS senses a still-unknown signal and activates, via a phosphorelay mechanism, the GacA transcription regulator, which in turn triggers the expression of target genes. The GacS protein belongs to the unorthodox sensor kinases, characterized by an autophosphorylation, a receiver, and an output domain. The periplasmic loop domain of GacS is poorly conserved in diverse bacteria. Thus, a common signal interacting with this domain would be unexpected. Based on a comparison with the transcriptional regulator NarL, a secondary structure can be predicted for the GacA sensor kinases. Certain genes whose expression is regulated by the GacS/GacA system are regulated in parallel by the small RNA binding protein RsmA (CsrA) at a posttranscriptional level. It is suggested that the GacS/GacA system operates a switch between primary and secondary metabolism, with a major involvement of posttranscriptional control mechanisms.

The unfolded protein response: integrating stress signals through the stress sensor IRE1α.

Stress induced by accumulation of unfolded proteins at the endoplasmic reticulum (ER) is a classic feature of secretory cells and is observed in many tissues in human diseases including cancer, diabetes, obesity, and neurodegeneration. Cellular adaptation to ER stress is achieved by the activation of the unfolded protein response (UPR), an integrated signal transduction pathway that transmits information about the protein folding status at the ER to the nucleus and cytosol to restore ER homeostasis. Inositol-requiring transmembrane kinase/endonuclease-1 (IRE1α), the most conserved UPR stress sensor, functions as an endoribonuclease that processes the mRNA of the transcription factor X-box binding protein-1 (XBP1). IRE1α signaling is a highly regulated process, controlled by the formation of a dynamic scaffold onto which many regulatory components assemble, here referred to as the UPRosome. Here we provide an overview of the signaling and regulatory mechanisms underlying IRE1α function and discuss the emerging role of the UPR in adaptation to protein folding stress in specialized secretory cells and in pathological conditions associated with alterations in ER homeostasis.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Electrochemical As(III) whole-cell based biochip sensor.

The development of a whole-cell based sensor for arsenite detection coupling biological engineering and electrochemical techniques is presented. This strategy takes advantage of the natural Escherichia coli resistance mechanism against toxic arsenic species, such as arsenite, which consists of the selective intracellular recognition of arsenite and its pumping out from the cell. A whole-cell based biosensor can be produced by coupling the intracellular recognition of arsenite to the generation of an electrochemical signal. Hereto, E. coli was equipped with a genetic circuit in which synthesis of beta-galactosidase is under control of the arsenite-derepressable arsR-promoter. The E. coli reporter strain was filled in a microchip containing 16 independent electrochemical cells (i.e. two-electrode cell), which was then employed for analysis of tap and groundwater samples. The developed arsenic-sensitive electrochemical biochip is easy to use and outperforms state-of-the-art bacterial bioreporters assays specifically in its simplicity and response time, while keeping a very good limit of detection in tap water, i.e. 0.8ppb. Additionally, a very good linear response in the ranges of concentration tested (0.94ppb to 3.75ppb, R(2)=0.9975 and 3.75 ppb to 30ppb, R(2)=0.9991) was obtained, complying perfectly with the acceptable arsenic concentration limits defined by the World Health Organization for drinking water samples (i.e. 10ppb). Therefore, the proposed assay provides a very good alternative for the portable quantification of As (III) in water as corroborated by the analysis of natural groundwater samples from Swiss mountains, which showed a very good agreement with the results obtained by atomic absorption spectroscopy.

Risk evaluation for spoofing against a sensor supplied with liveness detection

The aim of this paper is to evaluate the risks associated with the use of fake fingerprints on a livescan supplied with a method of liveness detection. The method is based on optical properties of the skin. The sensor uses several polarizations and illuminations to capture the information of the different layers of the human skin. These experiments also allow for the determination under which conditions the system is deceived and if there is an influence respectively of the nature of the fake, the mould used for the production or the individuals involved in the attack. These experiments showed that current multispectral sensors can be deceived by the use of fake fingerprints created with or without the cooperation of the subject. Fakes created from direct casts perform better than those produced by fakes created from indirect casts. The results showed that the success of the attack is influenced by two main factors. The first is the quality of the fakes, and by extension the quality of the original fingerprint. The second is the combination of the general patterns involved in the attacks since an appropriate combination can strongly increase the rates of successful attacks.

The endoplasmic reticulum: a sensor of cellular stress that modulates immune responses.

Many inflammatory and infectious diseases are characterized by the activation of signaling pathways steaming from the endoplasmic reticulum (ER). These pathways, primarily associated with loss of ER homeostasis, are emerging as key regulators of inflammation and infection. Recent advances shed light on the mechanisms linking ER-stress and immune responses.