Evolution of the multidomain protein wheat germ agglutinin

Summary We compared the homologous amino acid sequences of hevein and each of the four domains (A, B, C, and D) of wheat germ agglutinin and used them to construct a pseudophylogenetic tree relating these sequences to a hypothetical common ancestor sequence. In the crystal structure of the wheat germ agglutinin dimer, six pseudo-twofold rotational symmetry axes have previously been located in addition to the true twofold axis. Four of these relate two nonidentical domains to each other in each of the four possible pairs constituting the sugar-binding sites (A₁D₂, A₂D₁, B₁C₂, and B₂C₁). The remaining two relate contiguous unique pairs of sugar-binding sites to each other (A₁D₂ to B₁C₂, and A₂D₁ to B₂C₁). These latter two sets of pairs are related to each other by the true twofold axis. Side chains that mediate sugar binding in the interfaces of each of the four pairs were found to be largely conserved. The sequence homology, taken together with these pseudo-symmetry elements in the dimer structure, suggests a pathway for the evolution of the four-domain molecule from a single-domain dimer that can be correlated with simultaneous development of the saccharide-binding sites.     

The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes

Background

Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls.

Results

This consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%.

Conclusions

In our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1333-7) contains supplementary material, which is available to authorized users.     

Crowdsourcing as a Screening Tool to Detect Clinical Features of Glaucomatous Optic Neuropathy from Digital Photography

AimCrowdsourcing is the process of simplifying and outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing in the classification of normal and glaucomatous discs from optic disc images.MethodsOptic disc images (N = 127) with pre-determined disease status were selected by consensus agreement from grading experts from a large cohort study. After reading brief illustrative instructions, we requested that knowledge workers (KWs) from a crowdsourcing platform (Amazon MTurk) classified each image as normal or abnormal. Each image was classified 20 times by different KWs. Two study designs were examined to assess the effect of varying KW experience and both study designs were conducted twice for consistency. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC).ResultsOverall, 2,540 classifications were received in under 24 hours at minimal cost. The sensitivity ranged between 83–88% across both trials and study designs, however the specificity was poor, ranging between 35–43%. In trial 1, the highest AUC (95%CI) was 0.64(0.62–0.66) and in trial 2 it was 0.63(0.61–0.65). There were no significant differences between study design or trials conducted.ConclusionsCrowdsourcing represents a cost-effective method of image analysis which demonstrates good repeatability and a high sensitivity. Optimisation of variables such as reward schemes, mode of image presentation, expanded response options and incorporation of training modules should be examined to determine their effect on the accuracy and reliability of this technique in retinal image analysis.     

Segmental motion of forefoot and hindfoot as a diagnostic tool

Segmental motions derived from non-invasive motion analysis are being used to investigate the intrinsic functional behavior of the foot and ankle in health and disease. The goal of this research was to examine the ability of a generic segmented model of the foot to capture and differentiate changes in internal skeletal kinematics due to neuromuscular disease and/or trauma. A robotic apparatus that reproduces the kinematics and kinetics of gait in cadaver lower extremities was employed to produce motion under normal and aberrant neuromuscular activation patterns of tibialis posterior and/or tibialis anterior. Stance phase simulations were conducted on 10 donor limbs while recording three-dimensional kinematic trajectories of (1) skin-mounted markers used clinically to construct segmented foot models, and (2) bone-mounted marker clusters to capture actual internal bone motion as the gold standard for comparison. The models constructed from external marker data were able to differentiate the kinematic behaviors elicited by different neuromuscular conditions in a manner similar to that using the bone-derived data. Measurable differences between internal and externally measured kinematics were small, variable and random across the three axes of rotation and neuromuscular conditions, with a tendency toward more differences noted during early and late stance. Albeit slightly different, three-dimensional motion profiles of the hindfoot and forefoot segments correlated well with internal skeletal motion under all neuromuscular conditions, thereby confirming the utility of measuring segmental motions as a valid means of clinical assessment.     

Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements

SARS-CoV-2 is a betacoronavirus with a linear single-stranded, positive-sense RNA genome, whose outbreak caused the ongoing COVID-19 pandemic. The ability of coronaviruses to rapidly evolve, adapt, and cross species barriers makes the development of effective and durable therapeutic strategies a challenging and urgent need. As for other RNA viruses, genomic RNA structures are expected to play crucial roles in several steps of the coronavirus replication cycle. Despite this, only a handful of functionally-conserved coronavirus structural RNA elements have been identified to date. Here, we performed RNA structure probing to obtain single-base resolution secondary structure maps of the full SARS-CoV-2 coronavirus genome both in vitro and in living infected cells. Probing data recapitulate the previously described coronavirus RNA elements (5′ UTR and s2m), and reveal new structures. Of these, ∼10.2% show significant covariation among SARS-CoV-2 and other coronaviruses, hinting at their functionally-conserved role. Secondary structure-restrained 3D modeling of these segments further allowed for the identification of putative druggable pockets. In addition, we identify a set of single-stranded segments in vivo, showing high sequence conservation, suitable for the development of antisense oligonucleotide therapeutics. Collectively, our work lays the foundation for the development of innovative RNA-targeted therapeutic strategies to fight SARS-related infections.     

Numerical evaluation of transcatheter aortic valve performance during heart beating and its post-deployment fluid–structure interaction analysis

Biomechanics and Modeling in Mechanobiology - Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure that provides an effective alternative to open-heart surgical valve...