A mechanobiological model of endothelial cell migration and proliferation

How angiogenesis is regulated by local environmental cues is still not fully understood despite its importance to many regenerative events. Although mechanics is known to influence angiogenesis, the specific cellular mechanisms influenced by mechanical loading are poorly understood. This study adopts a lattice-based modelling approach to simulate endothelial cell (EC) migration and proliferation in order to explore how mechanical stretch regulates their behaviour. The approach enables the explicit modelling of ECs and, in particular, their migration/proliferation (specifically, rate and directionality) in response to such mechanical cues. The model was first used to simulate previously reported experiments of EC migration and proliferation in an unloaded environment. Next, three potential effects (increased cell migration, increased cell proliferation and biased cellular migration) of mechanical stretch on EC behaviour were simulated using the model and the observed changes in cell population characteristics were compared to experimental findings. Combinations of these three potential drivers were also investigated. The model demonstrates that only by incorporating all three changes in cellular physiology (increased EC migration, increased EC proliferation and biased EC migration in the direction perpendicular to the applied strain) in response to dynamic loading, it is possible to successfully predict experimental findings. This provides support for the underlying model hypotheses for how mechanics regulates EC behaviour.     

Replica exchange molecular dynamics simulation of cross‐fibrillation of IAPP and PrP106‐126

Aggregation of proteins into amyloid is the central hallmark of a number of protein diseases. Most studies were carried out on the aggregation between proteins of similar species. However, it was observed that some patients with certain protein disease can easily acquire another unrelated protein disease. As such, it is also important to examine aggregation between proteins of different species. Usually aggregation between proteins of the same species can be attributed to the similarity between their respective amino acid sequences. In this article, we were motivated by an experimental study of aggregation between amylin (Islet Amyloid Polypeptide, IAPP) and prion_106‐126 (PrP106‐126) fragment (JACS, 2013, 135, 13582–9). It was found that the two non‐homologous peptides can aggregate quickly to form fibrils in the presence of negatively charged lipid bilayer. We attempted to elucidate the molecular mechanism of the early stage of dimerization of these two peptides through extensive replica exchange molecular dynamics simulations. Conformations consisting of various degrees of β‐sheets structures, both intra‐chain and inter‐chain, were found in the simulations. The conformations of the aggregated complex are very diverse, which suggests that the cross‐species fibrils formed between the two proteins are highly polymorphic. The driving forces are mainly hydrophobic interactions, including aromatic‐aliphatic interactions. The palindromic region of PrP106‐126 and SNNFGAIL region of IAPP were found to play important roles in the interaction. Our study sheds insight into the exciting research of protein cross‐fibrillation. Proteins 2016; 84:1134–1146. © 2016 Wiley Periodicals, Inc.     

The macrocyclizing protease butelase 1 remains autocatalytic and reveals the structural basis for ligase activity

Plant asparaginyl endopeptidases (AEPs) are expressed as inactive zymogens that perform maturation of seed storage protein upon cleavage‐dependent autoactivation in the low‐pH environment of storage vacuoles. The AEPs have attracted attention for their macrocyclization reactions, and have been classified as cleavage or ligation specialists. However, we have recently shown that the ability of AEPs to produce either cyclic or acyclic products can be altered by mutations to the active site region, and that several AEPs are capable of macrocyclization given favorable pH conditions. One AEP extracted from Clitoria ternatea seeds (butelase 1) is classified as a ligase rather than a protease, presenting an opportunity to test for loss of cleavage activity. Here, making recombinant butelase 1 and rescuing an Arabidopsis thaliana mutant lacking AEP, we show that butelase 1 retains cleavage functions in vitro and in vivo. The in vivo rescue was incomplete, consistent with some trade‐off for butelase 1 specialization toward macrocyclization. Its crystal structure showed an active site with only subtle differences from cleaving AEPs, suggesting the many differences in its peptide‐binding region are the source of its efficient macrocyclization. All considered, it seems that either butelase 1 has not fully specialized or a requirement for autocatalytic cleavage is an evolutionary constraint upon macrocyclizing AEPs.     

QHELIX: A Computational Tool for the Improved Measurement of Inter-Helical Angles in Proteins

Knowledge about the assembled structures of the secondary elements in proteins is essential to understanding protein folding and functionality. In particular, the analysis of helix geometry is required to study helix packing with the rest of the protein and formation of super secondary structures, such as, coiled coils and helix bundles, formed by packing of two or more helices. Here we present an improved computational method, QHELIX, for the calculation of the orientation angles between helices. Since a large number of helices are known to be in curved shapes, an appropriate definition of helical axes is a prerequisite for calculating the orientation angle between helices. The present method provides a quantitative measure on the irregularity of helical shape, resulting in discriminating irregular-shaped helices from helices with an ideal geometry in a large-scale analysis of helix geometry. It is also capable of straightforwardly assigning the direction of orientation angles in a consistent way. These improvements will find applications in finding a new insight on the assembly of protein secondary structure. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Identification and comparative analysis of novel alternatively spliced transcripts of RhoGEF domain encoding gene in C. elegans and C. briggsae

This study was aimed at identifying, in 203 patients with Alzheimer's disease followed during long-term treatment with Acetylcholinesterase inhibitors (ChEIs), the predictive factors of the clinical response among cognition (MMSE), functioning (BADL and IADL) measures and age and gender at the baseline (T0). The ANCOVA test showed a significant association between MMSE scores at time T0 and T3, and the variation T9 to T0, T15 to T0 and T21 to T0 of the MMSE scores, using also gender, age and drug as covariates. The significance was higher for the patients affected by mild dementia. Regarding functional activities, a significant relationship was detected, by the ANCOVA test, only between the scores at T3 and the variation T15 to T0 for BADL, and the variation T9 to T0, T15 to T0 for IADL, respectively. Our results confirm, in a real world setting, that ChEIs provide long-term cognitive benefit, which is correlated to, and predictable by, the short-term response (within the third month) as well as the cognitive status (evaluated by means of the MMSE) at the beginning of the treatment. These factors should be the basis of any cost/effectiveness algorithm in health economic decision models.     

Deep learning‐based color holographic microscopy

We report a framework based on a generative adversarial network that performs high‐fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network learns to eliminate missing‐phase‐related artifacts, and generates an accurate color transformation for the reconstructed image. Our framework is experimentally demonstrated using lung and prostate tissue sections that are labeled with different histological stains. This framework is envisaged to be applicable to point‐of‐care histopathology and presents a significant improvement in the throughput of coherent microscopy systems given that only a single hologram of the specimen is required for accurate color imaging.     

转录因子结合位点的计算预测方法研究进展

转录因子结合位点的计算预测是研究基因转录调控的重要环节,但现有算法的预测特异性偏低.在深入分析转录因子结合位点生物特征的基础上,对当前基于保守模体和基于比较基因组学的两类计算预测方法进行了综述,指出了方法各自的优点和不足,并探讨了可能的改进方向.     

Computational prediction of Caenorhabditis box H/ACA snoRNAs using genomic properties of their host genes

Identification of small nucleolar RNAs (snoRNAs) in genomic sequences has been challenging due to the relative paucity of sequence features. Many current prediction algorithms rely on detection of snoRNA motifs complementary to target sites in snRNAs and rRNAs. However, recent discovery of snoRNAs without apparent targets requires development of alternative prediction methods. We present an approach that combines rule-based filters and a Bayesian Classifier to identify a class of snoRNAs (H/ACA) without requiring target sequence information. It takes advantage of unique attributes of their genomic organization and improved species-specific motif characterization to predict snoRNAs that may otherwise be difficult to discover. Searches in the genomes of Caenorhabditis elegans and the closely related Caenorhabditis briggsae suggest that our method performs well compared to recent benchmark algorithms. Our results illustrate the benefits of training gene discovery engines on features restricted to particular phylogenetic groups and the utility of incorporating diverse data types in gene prediction.     

Computational neuroscience in China

The ultimate goal of Computational Neuroscience(CNS) is to use and develop mathematical models and approaches to elucidate brain functions.CNS is a young and highly multidisciplinary field.It heavily interacts with experimental neuroscience and such other research areas as artificial intelligence,robotics,computer vision,information science and machine learning.This paper reviews the history of CNS in China,its current status and the prospects for its future development.Examples of CNS research in China are...