Three-dimensional studies of pathogenic peptides from the c-terminal of Trypanosoma cruzi ribosomal P proteins and their interaction with a monoclonal antibody structural model

The acidic C-terminal peptides from Trypanosoma cruzi ribosomal P proteins are the major target of the antibody response in patients suffering Chagas chronic heart disease. It has been proposed that the disease is triggered by the cross-reaction of these antibodies with the second extra cellular loop of the β1-adrenoreceptor, brought about by the molecular mimicry between the acidic C-terminal peptides and the receptor's loop. To improve the understanding of the structural basis of the autoimmune response against heart receptors, the 3-dimensional structure of the C-terminal peptides of Trypanosoma cruzi ribosomal proteins P0 (EDDDDDFGMGALF) and P2β (EEEDDDMGFGLFD) were solved using the Electrostaticaly Driven MonteCarlo method. Their structures were compared with the second extra-cellular loop of our homology model of human rhodopsin and the existing experimental NMR structures of the C-terminal peptides from human P0 (EESDDDMGFGLFD) and from Leishmania braziliensis P0 (EEADDDMGFGLFD). Docking of Trypanosoma cruzi peptides P0, P2β and human rhodopsin loop into our anti-P2β monoclonal antibody homology model allowed to explore their interactions

The exchangeability of shape

<p>Abstract</p> <p>Background</p> <p>Landmark based geometric morphometrics (GM) allows the quantitative comparison of organismal shapes. When applied to systematics, it is able to score shape changes which often are undetectable by traditional morphological studies and even by classical morphometric approaches. It has thus become a fast and low cost candidate to identify cryptic species. Due to inherent mathematical properties, shape variables derived from one set of coordinates cannot be compared with shape variables derived from another set. Raw coordinates which produce these shape variables could be used for data exchange, however they contain measurement error. The latter may represent a significant obstacle when the objective is to distinguish very similar species.</p> <p>Results</p> <p>We show here that a single user derived dataset produces much less classification error than a multiple one. The question then becomes how to circumvent the lack of exchangeability of shape variables while preserving a single user dataset. A solution to this question could lead to the creation of a relatively fast and inexpensive systematic tool adapted for the recognition of cryptic species.</p> <p>Conclusions</p> <p>To preserve both exchangeability of shape and a single user derived dataset, our suggestion is to create a free access bank of reference images from which one can produce raw coordinates and use them for comparison with external specimens. Thus, we propose an alternative geometric descriptive system that separates 2-D data gathering and analyzes.</p

Longitudinal In Vivo Imaging of Retinal Ganglion Cells and Retinal Thickness Changes Following Optic Nerve Injury in Mice

Retinal ganglion cells (RGCs) die in sight-threatening eye diseases. Imaging RGCs in humans is not currently possible and proof of principle in experimental models is fundamental for future development. Our objective was to quantify RGC density and retinal thickness following optic nerve transection in transgenic mice expressing cyan fluorescent protein (CFP) under control of the Thy1 promoter, expressed by RGCs and other neurons.A modified confocal scanning laser ophthalmoscopy (CSLO)/spectral-domain optical coherence tomography (SD-OCT) camera was used to image and quantify CFP+ cells in mice from the B6.Cg-Tg(Thy1-CFP)23Jrs/J line. SD-OCT circle (1 B-scan), raster (37 B-scans) and radial (24 B-scans) scans of the retina were also obtained. CSLO was performed at baseline (n = 11) and 3 (n = 11), 5 (n = 4), 7 (n = 10), 10 (n = 6), 14 (n = 7) and 21 (n = 5) days post-transection, while SD-OCT was performed at baseline and 7, 14 and 35 days (n = 9) post-transection. Longitudinal change in CFP+ cell density and retinal thickness were computed. Compared to baseline, the mean (SD) percentage CFP+ cells remaining at 3, 5, 7, 10, 14 and 21 days post-transection was 86 (9)%, 63 (11)%, 45 (11)%, 31 (9)%, 20 (9)% and 8 (4)%, respectively. Compared to baseline, the mean (SD) retinal thickness at 7 days post-transection was 97 (3)%, 98 (2)% and 97 (4)% for the circle, raster and radial scans, respectively. The corresponding figures at 14 and 35 days post-transection were 96 (3)%, 97 (2)% and 95 (3)%; and 93 (3)%, 94 (3)% and 92 (3)%.Longitudinal imaging showed an exponential decline in CFP+ cell density and a small (≤8%) reduction in SD-OCT measured retinal thickness post-transection. SD-OCT is a promising tool for detecting structural changes in experimental optic neuropathy. These results represent an important step towards translation for clinical use

Broadening the infection prevention and control network globally; 2017 Geneva IPC-think tank (part 3).

Background: Healthcare-associated infection (HAI) is a major challenge for patient safety worldwide, and is further complicated by antimicrobial resistance (AMR) due to excessive antimicrobial use in both humans and animals. Existing infection prevention and control (IPC) networks must be strengthened and adapted to better address the global challenges presented by emerging AMR. Methods: In June 2017, 42 international experts convened in Geneva, Switzerland, to discuss two key areas for strengthening the global IPC network: 1) broadening collaboration in IPC; and 2) how to bring the fields IPC and AMR control together. Results: The US Centers for Disease Prevention and Control, the European Centre for Disease Prevention and Control, and the World Health Organization (WHO) convened together with international experts to discuss collaboration and networks, demonstrating the participating organizations' commitment to close collaboration in IPC. The challenge of emerging AMR can only be addressed by strengthening this collaboration across international organisations and between public health and academia. The WHO SAVE LIVES: Clean Your Hands initiative is an example of a successful collaboration between multiple global stakeholders including academia and international public health organisations; it can be used as a model. IPC-strategies are included within the four pillars to combat AMR: surveillance, IPC, antimicrobial and diagnostic stewardship, research and development. The prevention of transmission of multidrug-resistant microorganisms is a patient safety issue, and must be strengthened in the fight against AMR. Conclusions: The working group determined that international organisations should take the lead in creating new networks, which will in turn attract academia and other stakeholders to join. At the same time, they should invest in bringing existing IPC and AMR networks under one umbrella. Transmission of multidrug-resistant microorganisms in hospitals and in the community threatens the success of antimicrobial stewardship programmes, and thus, research and development in IPC should be addressed as an enhanced global priority

Convergence of marine megafauna movement patterns in coastal and open oceans

The extent of increasing anthropogenic impacts on large marine vertebrates partly depends on the animals’ movement patterns. Effective conservation requires identification of the key drivers of movement including intrinsic properties and extrinsic constraints associated with the dynamic nature of the environments the animals inhabit. However, the relative importance of intrinsic versus extrinsic factors remains elusive. We analyze a global dataset of ∼2.8 million locations from >2,600 tracked individuals across 50 marine vertebrates evolutionarily separated by millions of years and using different locomotion modes (fly, swim, walk/paddle). Strikingly, movement patterns show a remarkable convergence, being strongly conserved across species and independent of body length and mass, despite these traits ranging over 10 orders of magnitude among the species studied. This represents a fundamental difference between marine and terrestrial vertebrates not previously identified, likely linked to the reduced costs of locomotion in water. Movement patterns were primarily explained by the interaction between species-specific traits and the habitat(s) they move through, resulting in complex movement patterns when moving close to coasts compared with more predictable patterns when moving in open oceans. This distinct difference may be associated with greater complexity within coastal microhabitats, highlighting a critical role of preferred habitat in shaping marine vertebrate global movements. Efforts to develop understanding of the characteristics of vertebrate movement should consider the habitat(s) through which they move to identify how movement patterns will alter with forecasted severe ocean changes, such as reduced Arctic sea ice cover, sea level rise, and declining oxygen content

Autoinhibition of TBCB regulates EB1-mediated microtubule dynamics

Tubulin cofactors (TBCs) participate in the folding, dimerization, and dissociation pathways of the tubulin dimer. Among them, TBCB and TBCE are two CAP-Gly domain-containing proteins that interact and dissociate the tubulin dimer. Here we show how TBCB localizes at spindle and midzone microtubules during mitosis. Furthermore, the motif DEI/M-COO– present in TBCB, which is similar to the EEY/F-COO– element characteristic of EB proteins, CLIP-170, and α-tubulin, is required for TBCE–TBCB heterodimer formation and thus for tubulin dimer dissociation. This motif is responsible for TBCB autoinhibition, and our analysis suggests that TBCB is a monomer in solution. Mutants of TBCB lacking this motif are derepressed and induce microtubule depolymerization through an interaction with EB1 associated to microtubule tips. TBCB is also able to bind to the chaperonin complex CCT containing α-tubulin, suggesting that it could escort tubulin to facilitate its folding and dimerization, recycling or degradation

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p