Mispositioned Neurokinin-1 Receptor-Expressing Neurons Underlie Heat Hyperalgesia in Disabled-1 Mutant Mice.

Reelin (Reln) and Disabled-1 (Dab1) participate in the Reln-signaling pathway and when either is deleted, mutant mice have the same spinally mediated behavioral abnormalities, increased sensitivity to noxious heat and a profound loss in mechanical sensitivity. Both Reln and Dab1 are highly expressed in dorsal horn areas that receive and convey nociceptive information, Laminae I-II, lateral Lamina V, and the lateral spinal nucleus (LSN). Lamina I contains both projection neurons and interneurons that express Neurokinin-1 receptors (NK1Rs) and they transmit information about noxious heat both within the dorsal horn and to the brain. Here, we ask whether the increased heat nociception in Reln and dab1 mutants is due to incorrectly positioned dorsal horn neurons that express NK1Rs. We found more NK1R-expressing neurons in Reln-/- and dab1-/- Laminae I-II than in their respective wild-type mice, and some NK1R neurons co-expressed Dab1 and the transcription factor Lmx1b, confirming their excitatory phenotype. Importantly, heat stimulation in dab1-/- mice induced Fos in incorrectly positioned NK1R neurons in Laminae I-II. Next, we asked whether these ectopically placed and noxious-heat responsive NK1R neurons participated in pain behavior. Ablation of the superficial NK1Rs with an intrathecal injection of a substance P analog conjugated to the toxin saporin (SSP-SAP) eliminated the thermal hypersensitivity of dab1-/- mice, without altering their mechanical insensitivity. These results suggest that ectopically positioned NK1R-expressing neurons underlie the heat hyperalgesia of Reelin-signaling pathway mutants, but do not contribute to their profound mechanical insensitivity

Immune-Mediated Inflammation May Contribute to the Pathogenesis of Cardiovascular Disease in Mucopolysaccharidosis Type I.

BackgroundCardiovascular disease, a progressive manifestation of α-L-iduronidase deficiency or mucopolysaccharidosis type I, continues in patients both untreated and treated with hematopoietic stem cell transplantation or intravenous enzyme replacement. Few studies have examined the effects of α-L-iduronidase deficiency and subsequent glycosaminoglycan storage upon arterial gene expression to understand the pathogenesis of cardiovascular disease.MethodsGene expression in carotid artery, ascending, and descending aortas from four non-tolerized, non-enzyme treated 19 month-old mucopolysaccharidosis type I dogs was compared with expression in corresponding vascular segments from three normal, age-matched dogs. Data were analyzed using R and whole genome network correlation analysis, a bias-free method of categorizing expression level and significance into discrete modules. Genes were further categorized based on module-trait relationships. Expression of clusterin, a protein implicated in other etiologies of cardiovascular disease, was assessed in canine and murine mucopolysaccharidosis type I aortas via Western blot and in situ immunohistochemistry.ResultsGene families with more than two-fold, significant increased expression involved lysosomal function, proteasome function, and immune regulation. Significantly downregulated genes were related to cellular adhesion, cytoskeletal elements, and calcium regulation. Clusterin gene overexpression (9-fold) and protein overexpression (1.3 to 1.62-fold) was confirmed and located specifically in arterial plaques of mucopolysaccharidosis-affected dogs and mice.ConclusionsOverexpression of lysosomal and proteasomal-related genes are expected responses to cellular stress induced by lysosomal storage in mucopolysaccharidosis type I. Upregulation of immunity-related genes implicates the potential involvement of glycosaminoglycan-induced inflammation in the pathogenesis of mucopolysaccharidosis-related arterial disease, for which clusterin represents a potential biomarker

(-)-Epicatechin and NADPH oxidase inhibitors prevent bile acid-induced Caco-2 monolayer permeabilization through ERK1/2 modulation

Secondary bile acids promote gastrointestinal (GI) tract permeabilization both in vivo and in vitro. Consumption of high fat diet s increases bile acid levels in the GI tract which can contribute to intestinal permeabilization and consequent local and systemic inflammation. This work investigated the mechanisms involved in bile acid (deoxycholic acid (DCA))-induced intestinal epithelial cell monolayer permeabilization and the preventive capacity of (-)-epicatechin (EC). While EC prevented high fat diet-induced intestinal permeabilization in mice, it did not mitigate the associated increase in fecal/cecal total and individual bile acids. In vitro, using differentiated Caco-2 cells as a model of epithelial barrier, EC and other NADPH oxidase inhibitors (VAS-2870 and apocynin) mitigated DCA-induced Caco-2 monolayer permeabilization. While EC inhibited DCA-mediated increase in cell oxidants, it did not prevent DCA-induced mitochondrial oxidant production. Prevention of DCA-induced ERK1/2 activation with EC, VAS-2870, apocynin and the MEK inhibitor U0126, also prevented monolayer permeabilization, stressing the key involvement of ERK1/2 in this process and its redox regulation. Downstream, DCA promoted myosin light chain (MLC) phosphorylation which was related to MLC phosphatase (MLCP) inhibition by ERK1/2. DCA also decreased the levels of the tight junction proteins ZO-1 and occludin, which can be related to MMP-2 activation and consequent ZO-1 and occludin degradation. Both events were prevented by EC, NADPH oxidase and ERK1/2 inhibitors. Thus, DCA-induced Caco-2 monolayer permeabilization occurs mainly secondary to a redox-regulated ERK1/2 activation and downstream disruption of TJ structure and dynamic. EC's capacity to mitigate in vivo the gastrointestinal permeabilization caused by consumption of high-fat diets can be in part related to its capacity to inhibit bile-induced NADPH oxidase and ERK1/2 activation

MEPicides: Potent antimalarial prodrugs targeting isoprenoid biosynthesis

AbstractThe emergence of Plasmodium falciparum resistant to frontline therapeutics has prompted efforts to identify and validate agents with novel mechanisms of action. MEPicides represent a new class of antimalarials that inhibit enzymes of the methylerythritol phosphate (MEP) pathway of isoprenoid biosynthesis, including the clinically validated target, deoxyxylulose phosphate reductoisomerase (Dxr). Here we describe RCB-185, a lipophilic prodrug with nanomolar activity against asexual parasites. Growth of P. falciparum treated with RCB-185 was rescued by isoprenoid precursor supplementation, and treatment substantially reduced metabolite levels downstream of the Dxr enzyme. In addition, parasites that produced higher levels of the Dxr substrate were resistant to RCB-185. Notably, environmental isolates resistant to current therapies remained sensitive to RCB-185, the compound effectively treated sexually-committed parasites, and was both safe and efficacious in malaria-infected mice. Collectively, our data demonstrate that RCB-185 potently and selectively inhibits Dxr in P. falciparum, and represents a promising lead compound for further drug development.</jats:p

Mapping functional transcription factor networks from gene expression data

A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein–DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1

Progesterone Inhibits Epithelial-to-Mesenchymal Transition in Endometrial Cancer

Background: Every year approximately 74,000 women die of endometrial cancer, mainly due to recurrent or metastatic disease. The presence of tumor infiltrating lymphocytes (TILs) as well as progesterone receptor (PR) positivity has been correlated with improved prognosis. This study describes two mechanisms by which progesterone inhibits metastatic spread of endometrial cancer: by stimulating T-cell infiltration and by inhibiting epithelial-to-mesenchymal cell transition (EMT). Methodology and Principal Findings: Paraffin sections from patients with (n = 9) or without (n = 9) progressive endometrial cancer (recurrent or metastatic disease) were assessed for the presence of CD4+ (helper), CD8+ (cytotoxic) and Foxp3+ (regulatory) T-lymphocytes and PR expression. Progressive disease was observed to be associated with significant loss of TILs and loss of PR expression. Frozen tumor samples, used for genome-wide expression analysis, showed significant regulation of pathways Conclusion: Intact progesterone signaling in non-progressive endometrial cancer seems to be an important factor stimulating immunosurveilance and inhibiting transition from an epithelial to a more mesenchymal, more invasive phenotype

Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease

Malonyl-CoA Mediates Leptin Hypothalamic Control of Feeding Independent of Inhibition of CPT-1a

Hypothalamic fatty acid metabolism is involved in central nervous system controls of feeding and energy balance. Malonyl-CoA, an intermediate of fatty acid biosynthesis, is emerging as a significant player in these processes. Notably, hypothalamic malonyl-CoA has been implicated in leptin's feeding effect. Leptin treatment increases malonyl-CoA level in the hypothalamic arcuate nucleus (Arc), and this increase is required for leptin-induced decrease in food intake. However, the intracellular downstream mediators of malonyl-CoA's feeding effect have not been identified. A primary biochemical action of malonyl-CoA is the inhibition of the acyltransferase activity of carnitine palmitoyltransferase-1 (CPT-1). In the hypothalamus, the predominant isoform of CPT-1 that possesses the acyltransferase activity is CPT-1 liver type (CPT-1a). To address the role of CPT-1a in malonyl-CoA's anorectic action, we used a recombinant adenovirus expressing a mutant CPT-1a that is insensitive to malonyl-CoA inhibition. We show that Arc overexpression of the mutant CPT-1a blocked the malonyl-CoA-mediated inhibition of CPT-1 activity. However, the overexpression of this mutant did not affect the anorectic actions of leptin or central cerulenin for which an increase in Arc malonyl-CoA level is also required. Thus, CPT-1a does not appear to be involved in the malonyl-CoA's anorectic actions induced by leptin. Furthermore, long-chain fatty acyl-CoAs, substrates of CPT-1a, dissociate from malonyl-CoA's actions in the Arc under different feeding states. Together, our results suggest that Arc intracellular mechanisms of malonyl-CoA's anorectic actions induced by leptin are independent of CPT-1a. The data suggest that target(s), rather than CPT-1a, mediates malonyl-CoA action on feeding