Exome Capture Reveals ZNF423 and CEP164 Mutations, Linking Renal Ciliopathies to DNA Damage Response Signaling

Nephronophthisis-related ciliopathies (NPHP-RC) are degenerative recessive diseases that affect kidney, retina, and brain. Genetic defects in NPHP gene products that localize to cilia and centrosomes defined them as "ciliopathies." However, disease mechanisms remain poorly understood. Here, we identify by whole-exome resequencing, mutations of MRE11, ZNF423, and CEP164 as causing NPHP-RC. All three genes function within the DNA damage response (DDR) pathway. We demonstrate that, upon induced DNA damage, the NPHP-RC proteins ZNF423, CEP164, and NPHP10 colocalize to nuclear foci positive for TIP60, known to activate ATM at sites?of DNA damage. We show that knockdown of CEP164 or ZNF423 causes sensitivity to DNA damaging agents and that cep164 knockdown in zebrafish results in dysregulated DDR and an NPHP-RC phenotype. Our findings link degenerative diseases of the kidney and retina, disorders of increasing prevalence, to mechanisms of DDR. PAPERFLICK:     

MIPortal: a high capacity server for molecular imaging research

The introduction of novel molecular tools in research and clinical medicine has created a need for more refined information management systems. This article describes the design and implementation of such a new information platform: the Molecular Imaging Portal (MIPortal). The platform was created to organize, archive, and rapidly retrieve large datasets using Web-based browsers as access points. The system has been implemented in a heterogeneous, academic research environment serving Macintosh, Unix, and Microsoft Windows clients and has been shown to be extraordinarily robust and versatile. In addition, it has served as a useful tool for clinical trials and collaborative multi-institutional small-animal imaging research.     

The role of electrostatic energy in prediction of obligate protein-protein interactions

Background

Prediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types.

Results

We propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction.

Conclusions

Our results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

     

Rac1 regulates pancreatic islet morphogenesis

Background  

Pancreatic islets of Langerhans originate from endocrine progenitors within the pancreatic ductal epithelium. Concomitant with differentiation of these progenitors into hormone-producing cells such cells delaminate, aggregate and migrate away from the ductal epithelium. The cellular and molecular mechanisms regulating islet cell delamination and cell migration are poorly understood. Extensive biochemical and cell biological studies using cultured cells demonstrated that Rac1, a member of the Rho family of small GTPases, acts as a key regulator of cell migration.     

Blind identification of the aortic pressure waveform from multiple peripheral artery pressure waveforms

We have developed a new technique to estimate the clinically relevant aortic pressure waveform from multiple, less invasively measured peripheral artery pressure waveforms. The technique is based on multichannel blind system identification in which two or more measured outputs (peripheral artery pressure waveforms) of a single-input, multi-output system (arterial tree) are mathematically analyzed so as to reconstruct the common unobserved input (aortic pressure waveform) to within an arbitrary scale factor. The technique then invokes Poiseuille's law to calibrate the reconstructed waveform to absolute pressure. Consequently, in contrast to previous related efforts, the technique does not utilize a generalized transfer function or any training data and is therefore entirely patient and time specific. To demonstrate proof of concept, we have evaluated the technique with respect to four swine in which peripheral artery pressure waveforms from the femoral and radial arteries and a reference aortic pressure waveform from the descending thoracic aorta were simultaneously measured during diverse hemodynamic interventions. We report that the technique reliably estimated the entire aortic pressure waveform with an overall root mean squared error (RMSE) of 4.6 mmHg. For comparison, the average overall RMSE between the peripheral artery pressure and reference aortic pressure waveforms was 8.6 mmHg. Thus the technique reduced the RMSE by 47%. As a result, the technique also provided similar improvements in the estimation of systolic pressure, pulse pressure, and the ejection interval. With further successful testing, the technique may ultimately be employed for more precise monitoring and titration of therapy in, for example, critically ill and hypertension patients.     

Estimation of the total peripheral resistance baroreflex impulse response from spontaneous hemodynamic variability

We previously developed a mathematical analysis technique for estimating the static gain values of the arterial total peripheral resistance (TPR) baroreflex (G(A)) and the cardiopulmonary TPR baroreflex (G(C)) from small, spontaneous beat-to-beat fluctuations in arterial blood pressure, cardiac output, and stroke volume. Here, we extended the mathematical analysis so as to also estimate the entire arterial TPR baroreflex impulse response [h(A)(t)] as well as the lumped arterial compliance (AC). The extended technique may therefore provide a linear dynamic characterization of TPR baroreflex systems during normal physiological conditions from potentially noninvasive measurements. We theoretically evaluated the technique with respect to realistic spontaneous hemodynamic variability generated by a cardiovascular simulator with known system properties. Our results showed that the technique reliably estimated h(A)(t) [error = 30.2 +/- 2.6% for the square root of energy (E(A)), 19.7 +/- 1.6% for absolute peak amplitude (P(A)), 37.3 +/- 2.5% for G(A), and 33.1 +/- 4.9% for the overall time constant] and AC (error = 17.6 +/- 4.2%) under various simulator parameter values and reliably tracked changes in G(C). We also experimentally evaluated the technique with respect to spontaneous hemodynamic variability measured from seven conscious dogs before and after chronic arterial baroreceptor denervation. Our results showed that the technique correctly predicted the abolishment of h(A)(t) [E(A) = 1.0 +/- 0.2 to 0.3 +/- 0.1, P(A) = 0.3 +/- 0.1 to 0.1 +/- 0.0 s(-1), and G(A) = -2.1 +/- 0.6 to 0.3 +/- 0.2 (P < 0.05)] and the enhancement of G(C) [-0.7 +/- 0.44 to -1.8 +/- 0.2 (P < 0.05)] following the chronic intervention. Moreover, the technique yielded estimates whose values were consistent with those reported with more invasive and/or experimentally difficult methods.     

Interactions between GIPC-APPL and GIPC-TRP1 regulate melanosomal protein trafficking and melanogenesis in human melanocytes

By virtue of the presence of multiple protein–protein interaction and signaling domains, PDZ proteins play important roles in assembling protein complexes that participate in diverse cell biological processes. GIPC is a versatile PDZ protein that binds a variety of target proteins in different cell types. In previous studies we showed that, in epidermal melanocytes, GIPC interacts with newly synthesized melanosomal protein TRP1 in the Golgi region and proposed that this interaction may facilitate intracellular trafficking of TRP1. However, since GIPC contains a single PDZ domain and no other known protein interaction motifs, it is not known how GIPC–TRP1 interaction affects melanosome biogenesis and/or melanin pigmentation. Here, we show that in human primary melanocytes GIPC interacts with AKT-binding protein APPL (adaptor protein containing pleckstrin homology, leucine zipper and phosphotyrosine binding domains), which readily co-precipitates with newly synthesized TRP1. Knockdown of either GIPC or APPL inhibits melanogenesis by decreasing tyrosinase protein levels and enzyme activity. In melanocytes, APPL exists in a complex with GIPC and phospho-AKT. Inhibition of AKT phosphorylation using a PI3-kinase inhibitor abolishes this interaction and results in retardation TRP1 in the Golgi. These data suggest that interactions between TRP1–GIPC and GIPC–APPL–AKT provide a potential link between melanogenesis and PI3 kinase signaling.