Computational identification and experimental characterization of substrate binding determinants of nucleotide pyrophosphatase/phosphodiesterase 7

<p>Abstract</p> <p>Background</p> <p>Nucleotide pyrophosphatase/phosphodiesterase 7 (NPP7) is the only member of the mammalian NPP enzyme family that has been confirmed to act as a sphingomyelinase, hydrolyzing sphingomyelin (SM) to form phosphocholine and ceramide. NPP7 additionally hydrolyzes lysophosphatidylcholine (LPC), a substrate preference shared with the NPP2/autotaxin(ATX) and NPP6 mammalian family members. This study utilizes a synergistic combination of molecular modeling validated by experimental site-directed mutagenesis to explore the molecular basis for the unique ability of NPP7 to hydrolyze SM.</p> <p>Results</p> <p>The catalytic function of NPP7 against SM, LPC, platelet activating factor (PAF) and para-nitrophenylphosphorylcholine (pNPPC) is impaired in the F275A mutant relative to wild type NPP7, but different impacts are noted for mutations at other sites. These results are consistent with a previously described role of F275 to interact with the choline headgroup, where all substrates share a common functionality. The L107F mutation showed enhanced hydrolysis of LPC, PAF and pNPPC but reduced hydrolysis of SM. Modeling suggests this difference can be explained by the gain of cation-pi interactions with the choline headgroups of all four substrates, opposed by increased steric crowding against the sphingoid tail of SM. Modeling also revealed that the long and flexible hydrophobic tails of substrates exhibit considerable dynamic flexibility in the binding pocket, reducing the entropic penalty that might otherwise be incurred upon substrate binding.</p> <p>Conclusions</p> <p>Substrate recognition by NPP7 includes several important contributions, ranging from cation-pi interactions between F275 and the choline headgroup of all substrates, to tail-group binding pockets that accommodate the inherent flexibility of the lipid hydrophobic tails. Two contributions to the unique ability of NPP7 to hydrolyze SM were identified. First, the second hydrophobic tail of SM occupies a second hydrophobic binding pocket. Second, the leucine residue present at position 107 contrasts with a conserved phenylalanine in NPP enzymes that do not utilize SM as a substrate, consistent with the observed reduction in SM hydrolysis by the NPP7-L107F mutant.</p

Social housing exit points, outcomes and future pathways: an administrative data analysis

The research provides new national evidence on social housing pathways using longitudinal and linked national data. · The ‘success’ of a social housing pathway should be judged on relative, rather than definitive terms. · Most Australian social housing pathways are stable or involve entry into social housing with subsequent stability. · Some pathways are considered transitory, involving multiple moves and changes between tenures. · Transitory pathways are associated with more time in receipt of income support and more residential instability. Correspondingly, people with stable social housing pathways spent less time in receipt of income assistance (and were more residentially stable).Emma Baker, Chris Leishman, Rebecca Bentley, Ngoc Thien Anh Pham, Lyrian Danie

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images

Nanoprodrugs of NSAIDs: Preparation and Characterization of Flufenamic Acid Nanoprodrugs

We demonstrated that hydrophobic derivatives of the nonsteroidal anti-inflammatory drug (NSAID)flufenamic acid (FA), can be formed into stable nanometer-sized prodrugs (nanoprodrugs) that inhibit the growth of glioma cells, suggesting their potential application as anticancer agent. We synthesized highly hydrophobic monomeric and dimeric prodrugs of FA via esterification and prepared nanoprodrugs using spontaneous emulsification mechanism. The nanoprodrugs were in the size range of 120 to 140 nm and physicochemically stable upon long-term storage as aqueous suspension, which is attributed to the strong hydrophobic interaction between prodrug molecules. Importantly, despite the highly hydrophobic nature and water insolubility, nanoprodrugs could be readily activated into the parent drug by porcine liver esterase, presenting a potential new strategy for novel NSAID prodrug design. The nanoprodrug inhibited the growth of U87-MG glioma cells with IC50 of 20 μM, whereas FA showed IC50 of 100 μM, suggesting that more efficient drug delivery was achieved with nanoprodrugs

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Sensing remote nuclear spins

Sensing single nuclear spins is a central challenge in magnetic resonance based imaging techniques. Although different methods and especially diamond defect based sensing and imaging techniques in principle have shown sufficient sensitivity, signals from single nuclear spins are usually too weak to be distinguished from background noise. Here, we present the detection and identification of remote single C-13 nuclear spins embedded in nuclear spin baths surrounding a single electron spins of a nitrogen-vacancy centre in diamond. With dynamical decoupling control of the centre electron spin, the weak magnetic field ~10 nT from a single nuclear spin located ~3 nm from the centre with hyperfine coupling as weak as ~500 Hz is amplified and detected. The quantum nature of the coupling is confirmed and precise position and the vector components of the nuclear field are determined. Given the distance over which nuclear magnetic fields can be detected the technique marks a firm step towards imaging, detecting and controlling nuclear spin species external to the diamond sensor