An Alteration in ELMOD3, an Arl2 GTPase-Activating Protein,Is Associated with Hearing Impairment in Humans

Exome sequencing coupled with homozygosity mapping was used to identify a transition mutation (c.794T>C; p.Leu265Ser) in ELMOD3 at the DFNB88 locus that is associated with nonsyndromic deafness in a large Pakistani family, PKDF468. The affected individuals of this family exhibited pre-lingual, severe-to-profound degrees of mixed hearing loss. ELMOD3 belongs to the engulfment and cell motility (ELMO) family, which consists of six paralogs in mammals. Several members of the ELMO family have been shown to regulate a subset of GTPases within the Ras superfamily. However, ELMOD3 is a largely uncharacterized protein that has no previously known biochemical activities. We found that in rodents, within the sensory epithelia of the inner ear, ELMOD3 appears most pronounced in the stereocilia of cochlear hair cells. Fluorescently tagged ELMOD3 co-localized with the actin cytoskeleton in MDCK cells and actin-based microvilli of LLC-PK1-CL4 epithelial cells. The p.Leu265Ser mutation in the ELMO domain impaired each of these activities. Super-resolution imaging revealed instances of close association of ELMOD3 with actin at the plasma membrane of MDCK cells. Furthermore, recombinant human GST-ELMOD3 exhibited GTPase activating protein (GAP) activity against the Arl2 GTPase, which was completely abolished by the p.Leu265Ser mutation. Collectively, our data provide the first insights into the expression and biochemical properties of ELMOD3 and highlight its functional links to sound perception and actin cytoskeleton.     

The fluted giant clam (Tridacna squamosa) increases nitrate absorption and upregulates the expression of a homolog of SIALIN (H+:2NO3− cotransporter) in the ctenidium during light exposure

Coral Reefs - Giant clams flourish in nutrient-poor waters of tropical Indo-Pacific because they live in symbiosis with extracellular dinoflagellates (zooxanthellae) and receive photosynthates from...     

Silent,generic and plant kairomone sensitive odorant receptors from the Southern house mosquito

The Southern house mosquito Culex quinquefasciatus has the largest repertoire of odorant receptors (ORs) of all mosquitoes and dipteran species whose genomes have been sequenced to date. Previously, we have identified and de-orphanized two ORs expressed in female antennae, CquiOR2 and CquiOR10, which are sensitive to oviposition attractants. In view of a new nomenclature for the Culex genome (VectorBase) we renamed these ORs as CquiOR21 (formerly CquiOR10) and CquiOR121 (CquiOR2). In addition, we selected ORs from six different phylogenetic groups for deorphanization. We cloned four of them by using cDNA from female antennae as a template. Attempts to clone CquiOR87 and CquiOR110 were unsuccessful either because they are pseudogenes or are not expressed in adult female antennae, the main olfactory tissue. By contrast, CquiOR1, CquiOR44, CquiOR73, and CquiOR161 were highly expressed in female antennae. To de-orphanize these ORs, we employed the Xenopus oocyte recording system. CquiORx–CquiOrco-expressed oocytes were challenged with a panel of 90 compounds, including known oviposition attractants, human and vertebrate host odorants, plant kairomones, and naturally occurring repellents. While CquiOR161 did not respond to any test compound in two different laboratories, CquiOR1 showed the features of a generic OR, with strong responses to 1-octen-3-ol and other ligands. CquiOR44 and CquiOR73 showed preference to plant-derived terpenoids and phenolic compounds, respectively. While fenchone was the best ligand for the former, 3,5-dimethylphenol elicited the strongest responses in the latter. The newly de-orphanized ORs may be involved in reception of plant kairomones and/or natural repellents.     

Oxazolopyridines and thiazolopyridines as monoamine oxidase B inhibitors for the treatment of Parkinson’s disease

In Parkinson’s disease, the motor impairments are mainly caused by the death of dopaminergic neurons. Among the enzymes which are involved in the biosynthesis and catabolism of dopamine, monoamine oxidase B (MAO-B) has been a therapeutic target of Parkinson’s disease. However, due to the undesirable adverse effects, development of alternative MAO-B inhibitors with greater optimal therapeutic potential towards Parkinson’s disease is urgently required. In this study, we designed and synthesized the oxazolopyridine and thiazolopyridine derivatives, and biologically evaluated their inhibitory activities against MAO-B. Structure–activity relationship study revealed that the piperidino group was the best choice for the R¹ amino substituent to the oxazolopyridine core structure and the activities of the oxazolopyridines with various phenyl rings were between 267.1 and 889.5 nM in IC₅₀ values. Interestingly, by replacement of the core structure from oxazolopyrine to thiazolopyridine, the activities were significantly improved and the compound 1n with the thiazolopyridine core structure showed the most potent activity with the IC₅₀ value of 26.5 nM. Molecular docking study showed that van der Waals interaction in the human MAO-B active site could explain the enhanced inhibitory activities of thiazolopyridine derivatives.     

Discovery of aryl-biphenyl-2-ylmethylpiperazines as novel scaffolds for 5-HT7 ligands and role of the aromatic substituents in binding to the target receptor

It has been reported that 5-HT₇ receptors are promising targets of depression and neuropathic pain. 5-HT₇ receptor antagonists have exhibited antidepressant-like profiles, while agonists have represented potential therapeutics for pain. In the course of our ongoing efforts to discover novel 5-HT₇ modulators, we designed an arylpiperazine scaffold with a substituted biphenyl-2-ylmethyl group. A series of biphenyl-2-yl-arylpiperazinylmethanes were then prepared, which showed a broad spectrum of binding affinities to the 5-HT₇ receptor depending upon the substituents attached to the biphenyl and aryl functionalities. Among those synthesized compounds, the compounds 1–24 and 1–26 showed the best binding affinities to the 5-HT₇ receptor with K_i values of 43.0 and 46.0 nM, respectively. Structure–activity relationship study in conjunction with molecular docking study proposed that the 5-HT₇ receptor might have two distinctive hydrophobic binding sites, one specific for aromatic 2-OCH₃ substituents within the arylpiperazine and the other for biphenyl methoxy group.     

Application of higher order statistics for atrial arrhythmia classification

Atrial fibrillation (AF) and atrial flutter (AFL) are the two common atrial arrhythmia encountered in the clinical practice. In order to diagnose these abnormalities the electrocardiogram (ECG) is widely used. The conventional linear time and frequency domain methods cannot decipher the hidden complexity present in these signals. The ECG is inherently a non-linear, non-stationary and non-Gaussian signal. The non-linear models can provide improved results and capture minute variations present in the time series. Higher order spectra (HOS) is a non-linear dynamical method which is highly rugged to noise. In the present study, the performances of two methods are compared: (i) 3rd order HOS cumulants and (ii) HOS bispectrum. The 3rd order cumulant and bispectrum coefficients are subjected to dimensionality reduction using independent component analysis (ICA) and classified using classification and regression tree (CART), random forest (RF), artificial neural network (ANN) and k-nearest neighbor (KNN) classifiers to select the best classifier. The ICA components of cumulant coefficients have provided the average accuracy, sensitivity, specificity and positive predictive value of 99.50%, 100%, 99.22% and 99.72% respectively using KNN classifier. Similarly, the ICA components of HOS bispectrum coefficients have yielded the average accuracy, sensitivity, specificity and PPV of 97.65%, 98.16%, 98.75% and 99.53% respectively using KNN. So, the ICA performed on the 3rd order HOS cumulants coupled with KNN classifier performed better than the HOS bispectrum method. The proposed methodology is robust and can be used in mass screening of cardiac patients.     

ECG beat classification using PCA,LDA, ICA and Discrete Wavelet Transform

Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value (σ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme.     

Prediction of drugs having opposite effects on disease genes in a directed network

Background

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.

Methods

We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.

Results

We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.

Conclusions

Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

     

A Lightweight Radio Propagation Model for Vehicular Communication in Road Tunnels

Radio propagation models (RPMs) are generally employed in Vehicular Ad Hoc Networks (VANETs) to predict path loss in multiple operating environments (e.g. modern road infrastructure such as flyovers, underpasses and road tunnels). For example, different RPMs have been developed to predict propagation behaviour in road tunnels. However, most existing RPMs for road tunnels are computationally complex and are based on field measurements in frequency band not suitable for VANET deployment. Furthermore, in tunnel applications, consequences of moving radio obstacles, such as large buses and delivery trucks, are generally not considered in existing RPMs. This paper proposes a computationally inexpensive RPM with minimal set of parameters to predict path loss in an acceptable range for road tunnels. The proposed RPM utilizes geometric properties of the tunnel, such as height and width along with the distance between sender and receiver, to predict the path loss. The proposed RPM also considers the additional attenuation caused by the moving radio obstacles in road tunnels, while requiring a negligible overhead in terms of computational complexity. To demonstrate the utility of our proposed RPM, we conduct a comparative summary and evaluate its performance. Specifically, an extensive data gathering campaign is carried out in order to evaluate the proposed RPM. The field measurements use the 5 GHz frequency band, which is suitable for vehicular communication. The results demonstrate that a close match exists between the predicted values and measured values of path loss. In particular, an average accuracy of 94% is found with R² = 0.86.