Linking the association between circRNAs and Alzheimer's disease progression by multi-tissue circular RNA characterization.

Alzheimer's disease (AD) has devastating consequences for patients during its slow, progressive course. It is important to understand the pathology of AD onset. Recently, circular RNAs (circRNAs) have been found to participate in many human diseases including cancers and neurodegenerative conditions. In this study, we mined the published dataset on the AMP-AD Knowledge Portal from the Mount Sinai Brain Bank (MSBB) to describe the circRNA profiles at different AD stages in brain samples from four brain regions: anterior prefrontal cortex, superior temporal lobe, parahippocampal gyrus and inferior frontal gyrus. In total, we found 147 circRNAs to be differentially expressed (DE) for different AD severity levels in the four regions. We also characterized the mRNA-circRNA co-expression network and annotated the potential function of circRNAs based on the co-expressed modules. Based on our results, we found that the most circRNA-regulated region in AD patients with severe symptoms was the parahippocampal gyrus. The strongest negatively AD severity-correlated module in the parahippocampal gyrus was enriched in cognitive disability and pathological-associated pathways such as synapse organization and regulation of membrane potential. Finally, a regression model based on the expression pattern of DE circRNAs in the module could help to distinguish the disease severity of patients, further supporting a role for circRNAs in AD pathology. In conclusion, our findings indicate that circRNAs in parahippocampal gyrus are possible biomarkers and regulators of AD as well as potential therapeutic targets.

Construction and manipulation of functional three-dimensional droplet networks.

Previously, we reported the manual assembly of lipid-coated aqueous droplets in oil to form two-dimensional (2D) networks in which the droplets are connected through single lipid bilayers. Here we assemble lipid-coated droplets in robust, freestanding 3D geometries: for example, a 14-droplet pyramidal assembly. The networks are designed, and each droplet is placed in a designated position. When protein pores are inserted in the bilayers between specific constituent droplets, electrical and chemical communication pathways are generated. We further describe an improved means to construct 3D droplet networks with defined organizations by the manipulation of aqueous droplets containing encapsulated magnetic beads. The droplets are maneuvered in a magnetic field to form simple construction modules, which are then used to form larger 2D and 3D structures including a 10-droplet pyramid. A methodology to construct freestanding, functional 3D droplet networks is an important step toward the programmed and automated manufacture of synthetic minimal tissues.

Memoir: template-based structure prediction for membrane proteins.

Membrane proteins are estimated to be the targets of 50% of drugs that are currently in development, yet we have few membrane protein crystal structures. As a result, for a membrane protein of interest, the much-needed structural information usually comes from a homology model. Current homology modelling software is optimized for globular proteins, and ignores the constraints that the membrane is known to place on protein structure. Our Memoir server produces homology models using alignment and coordinate generation software that has been designed specifically for transmembrane proteins. Memoir is easy to use, with the only inputs being a structural template and the sequence that is to be modelled. We provide a video tutorial and a guide to assessing model quality. Supporting data aid manual refinement of the models. These data include a set of alternative conformations for each modelled loop, and a multiple sequence alignment that incorporates the query and template. Memoir works with both α-helical and ß-barrel types of membrane proteins and is freely available at http://opig.stats.ox.ac.uk/webapps/memoir.

MP-T: improving membrane protein alignment for structure prediction.

MOTIVATION: Membrane proteins are clinically relevant, yet their crystal structures are rare. Models of membrane proteins are typically built from template structures with low sequence identity to the target sequence, using a sequence-structure alignment as a blueprint. This alignment is usually made with programs designed for use on soluble proteins. Biological membranes have layers of varying hydrophobicity, and membrane proteins have different amino-acid substitution preferences from their soluble counterparts. Here we include these factors into an alignment method to improve alignments and consequently improve membrane protein models. RESULTS: We developed Membrane Protein Threader (MP-T), a sequence-structure alignment tool for membrane proteins based on multiple sequence alignment. Alignment accuracy is tested against seven other alignment methods over 165 non-redundant alignments of membrane proteins. MP-T produces more accurate alignments than all other methods tested (δF(M) from +0.9 to +5.5%). Alignments generated by MP-T also lead to significantly better models than those of the best alternative alignment tool (one-fourth of models see an increase in GDT_TS of ≥4%). AVAILABILITY: All source code, alignments and models are available at http://www.stats.ox.ac.uk/proteins/resources

Environment specific substitution tables improve membrane protein alignment.

MOTIVATION: Membrane proteins are both abundant and important in cells, but the small number of solved structures restricts our understanding of them. Here we consider whether membrane proteins undergo different substitutions from their soluble counterparts and whether these can be used to improve membrane protein alignments, and therefore improve prediction of their structure. RESULTS: We construct substitution tables for different environments within membrane proteins. As data is scarce, we develop a general metric to assess the quality of these asymmetric tables. Membrane proteins show markedly different substitution preferences from soluble proteins. For example, substitution preferences in lipid tail-contacting parts of membrane proteins are found to be distinct from all environments in soluble proteins, including buried residues. A principal component analysis of the tables identifies the greatest variation in substitution preferences to be due to changes in hydrophobicity; the second largest variation relates to secondary structure. We demonstrate the use of our tables in pairwise sequence-to-structure alignments (also known as 'threading') of membrane proteins using the FUGUE alignment program. On average, in the 10-25% sequence identity range, alignments are improved by 28 correctly aligned residues compared with alignments made using FUGUE's default substitution tables. Our alignments also lead to improved structural models. AVAILABILITY: Substitution tables are available at: http://www.stats.ox.ac.uk/proteins/resources.