H Dong / J He (@5.5) vs Z Kulambayeva / Y Ma (@1.12)
10-09-2019

Our Prediction:

Z Kulambayeva / Y Ma will win
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H Dong / J He – Z Kulambayeva / Y Ma Match Prediction | 10-09-2019 02:30

Protein three-dimensional (3D) structures are of great significance for protein function analysis. Structural models with medium accuracy up to 6 of Root-Mean-Square Deviation (RMSD) are often useful for understanding protein functions [2]. For example, high-accuracy models can be directly used in studying catalytic activity of enzymes and provide a basis for drug design. It is also a viable and efficient approach to study proteins [1]. Experimental methods for protein structure determination such as X-ray crystallography and nuclear magnetic resonance (NMR) are costly and time consuming. Computational prediction provides an economic way to bridge the increasing gap between the number of available protein primary sequences and 3D structures. Although efforts of decades, protein structure prediction from primary sequences is still a research challenge [3], [4].

The importance of Wnt/ -catenin signaling was further demonstrated by -catenin-deficient mouse embryos that exhibited widespread vascular defects in the CNS while peripheral vessel formation was unaffected [36]. Recent studies have identified several pathways believed to be critical to BBB induction and maintenance, including hedgehog (Hh) [6] and canonical Wnt signaling [34, 35]. The unique properties of BMECs are induced by the surrounding neuroectodermal environment during development, although the exact mechanisms responsible remain poorly understood [33]. Initial evidence from quail-chick chimera transplantation studies showed that non-CNS tissue grafted to the brain could develop BBB characteristics, while CNS-tissue grafted to non-CNS regions could not [33].

The major role of this protein is to release newly produced viral particles by cleaving sialic acid residues on host cells facilitating further infection. The genome of H5N1 virus is composed of eight segmented RNA fragments of negative sense [5]. The first three segments encode viral polymerase basic proteins PB2, PB1 and PA respectively. The seventh viral segment encodes two different proteins M1 and M2 by using alternative reading frames and the adamantane class of drugs target this proton channel in the virus surface. Mutations in the NA segment (eg: H275Y) are also associated with decreased antiviral susceptibility to the NA inhibitor drug class (oseltamivir, zanamivir). The sixth segment encodes another important surface-exposed glycoprotein, neuraminidase (NA). The final viral segment encodes two nonstructural proteins NS1 and NS2 due to alternatively splicing events. HA is responsible for attaching the virions to the host sialic acid receptors on respiratory epithelia and is a critical determinant of pathogenicity. Nucleoprotein (NP), encoded by the fifth segment, binds to and encapsidates viral RNA in the infected cell nucleus. The fourth viral segment encodes hemagglutinin (HA), which is an important surface glycoprotein and the major antigen of the virus.

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Astrocyte processes are terminated in end-feet that completely ensheath microvessels and capillaries in the brain [74]. This position as an intermediary allows astrocytes to coordinate key aspects of neurovascular coupling, including the regulation of blood flow to match local neuronal activity [29]. Astrocytes mediate signaling between neurons and BMECs. A single astrocyte contacts on average five different blood vessels and four different neuronal somata, supporting the function of roughly 2 million synapses [75, 76].

TEER is measured between electrodes located in each compartment. (b) A microfluidic version of the transwell model. TEER and permeability measurements for assessing barrier function. (a) The transwell model, with an EC monolayer on the apical side of the membrane, and supporting cell types in the contact and non-contact positions on the underside of the membrane and in the basolateral chamber. Permeability is measured by introducing a solute of interest into the apical chamber and measuring the time-dependent concentration in the basolateral chamber.

Interestingly, while most pericytes are believed to be of mesodermal origin, some studies have suggested that CNS pericytes derive from the neural crest [58,59,60,61], and thus may be functionally distinct from peripheral pericytes [8]. Additionally, the increased ratio of pericytesto ECs found in the brain (1:31:1, as compared to 1:100 in skeletal muscle) further support an important role for pericytes in BBB function, as increased pericyte coverage throughout the body has been correlated with increased vessel tightness [62]. An important question regarding BBB induction by pericytes is how this interaction is localized to the CNS, as pericytes are found throughout the body.

2017; Ding et al. From the medical point of view, however, rather than thinking what and how much can be measured on a large scale, one should also consider what is the source of information that is most useful for the particular prediction task. For systematic mapping of compound-target interactions, instead of generating more and more compound-target bioactivity data, a more effective approach might be to train machine learning models based on the existing data and then use these models to predict what parts of the massive compound-target universe one should experimentally explore in order to get most benefit from the expensive laboratory experiments (Azencott et al. 2018). We argue that such data-driven predictive approach will be more cost-effective, compared to the exhaustive approach of sequencing everything, which has been the dominating approach so far in many international efforts. Collection and integration of the already available data are by no means straightforward, requiring both infrastructure developments and common standards for integrating and sharing data from various experimental assays and laboratories. However, such community-based approach will likely provide not only a cost-effective but also a faster track to new biomedical discoveries, as it can also collect large-enough patient cohorts for single cancer types, hence avoiding the need for pan-cancer approaches that may miss important cancer-specific findings. For clinical translation, feature selection remains a critical part of precision oncology as large-scale profiling of each cancer patient is not likely to be possible within the coming years, rather the treatment selection will be based on targeted assays of most predictive markers for a given cancer type. The same approach should be useful also for drug response prediction task, where we already have large-scale data in cancer cell lines, and hopefully soon also in patient samples, to start making more comprehensive machine learning exercises to prioritize the next phases of experimentation.

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hiPSC-derived BMECs have been obtained through a co-differentiation of ECs/neural cells, followed by a purification based on selective adhesion [20, 101,102,103]. hiPSC-derived BMECs also exhibit physiological values of TEER [20, 101,102,103]. In some cases, especially with low intrinsic TEER values, co-culture with pericytes and neural progenitor cell-derived astrocytes and neurons may increase TEER [23]. hiPSC-derived BMECs possess localized AJs and TJs, express BBB nutrient transporters and demonstrate polarized efflux of rhodamine 123 [20, 101,102,103].

Genetic analysis of specific amino acid mutations in the viral genome has also provided insight into the evolution and variation associated with the host-shift, drug-resistance and virulence of the viruses. In particular, it was reported that this mutation emerged during the anti-viral treatment [75]. The first drug-resistant H5N1 virus whose NA protein had a histidine-to-tyrosine substitution at position 275 (N1 numbering) was isolated from a Vietnamese girl in 2005 [72] and this mutation has been reported to confer resistance to oseltamivir [73,74]. For instance, the first HPAI H5N1 isolate from human from Hong Kong in 1997 possessed the RERRRKK motif at the basic cleavage site of the HA protein, which is considered a sign of HPAI viruses [71]. The D627K amino acid substitution in the PB2 protein was reported to increase the virulence of H5N1 viruses in mice [78, 79] and it has been observed in some human strains from Thailand [56] and Egypt [63]. More recently, it was also reported that a single mutation at position 192 or a double mutation at positions 129 and 151 of the HA protein could have increased the human-type receptor specificity of HPAI viruses that newly emerged in birds in Egypt [80]. Similarly, viruses with the S31N mutation in the M2 protein, which is associated with amantadine resistance [77], have been isolated from humans from Hong Kong [65]. Viruses with the S294N mutation in the NA protein were isolated from humans from Egypt in late 2006 [76] and this amino acid substitution has been reported to reduce the susceptibility of the viruses to oseltamivir [72].

Another class of polarized transporters is the efflux pumps of the ATP-binding cassette (ABC) superfamily. Differential expression of these transporters on the luminal and abluminal membranes of BMECs regulate CNS nutrient uptake and waste removal. Notable efflux pumps include P-gp, BCRP, and Multidrug Resistance-associated Proteins (MRPs) [2]. Small lipophilic molecules, which would typically diffuse through non-brain ECs, are actively effluxed back to the blood by BMECs. Efflux pumps often work in tandem with metabolizing enzymes, together breaking down and pumping out potentially toxic substances, including many conventional therapeutics [27]. One class of polarized transporters is the solute carrier (SLC) family, which enables the passive transport of polar nutrients essential to CNS function, such as glucose (Glut-1) and amino acids (LAT-1, among others).