Evolution of MHC class I genes in the endangered loggerhead sea turtle (Caretta caretta) revealed by 454 amplicon sequencing

Background: In evolutionary and conservation biology, parasitism is often highlighted as a major selective pressure. To fight against parasites and pathogens, genetic diversity of the immune genes of the major histocompatibility complex (MHC) are particularly important. However, the extensive degree of polymorphism observed in these genes makes it difficult to conduct thorough population screenings. Methods: We utilized a genotyping protocol that uses 454 amplicon sequencing to characterize the MHC class I in the endangered loggerhead sea turtle (Caretta caretta) and to investigate their evolution at multiple relevant levels of organization. Results: MHC class I genes revealed signatures of trans-species polymorphism across several reptile species. In the studied loggerhead turtle individuals, it results in the maintenance of two ancient allelic lineages. We also found that individuals carrying an intermediate number of MHC class I alleles are larger than those with either a low or high number of alleles. Conclusions: Multiple modes of evolution seem to maintain MHC diversity in the loggerhead turtles, with relatively high polymorphism for an endangered species

The effects of parameter choice on defining molecular operational taxonomic units and resulting ecological analyses of metabarcoding data

Copyright remains with the author(s) or their institution(s). Permission for reuse (free in most cases) can be obtained from RightsLink.This work was supported by a NSERC CREATE grant to M.E.C. and an Institutional Links grant 172726351 to E.L.C. under the Newton-Ungku Omar Fund, through the British Council in the UK and the Malaysian Industry-Government Group for High Technology in Malaysia. The Newton Fund is Overseas Development Assistance administered through the UK Department for Business Innovation and Skills (BIS). For further information, please visitwww.newtonfund.ac.uk

Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps

We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.Comment: 12 pages, 6 figure

A Linear Epitope in the N-Terminal Domain of CCR5 and Its Interaction with Antibody.

The CCR5 receptor plays a role in several key physiological and pathological processes and is an important therapeutic target. Inhibition of the CCR5 axis by passive or active immunisation offers one very selective strategy for intervention. In this study we define a new linear epitope within the extracellular domain of CCR5 recognised by two independently produced monoclonal antibodies. A short peptide encoding the linear epitope can induce antibodies which recognise the intact receptor when administered colinear with a tetanus toxoid helper T cell epitope. The monoclonal antibody RoAb 13 is shown to bind to both cells and peptide with moderate to high affinity (6x10^8 and 1.2x107 M-1 respectively), and binding to the peptide is enhanced by sulfation of tyrosines at positions 10 and 14. RoAb13, which has previously been shown to block HIV infection, also blocks migration of monocytes in response to CCR5 binding chemokines and to inflammatory macrophage conditioned medium. A Fab fragment of RoAb13 has been crystallised and a structure of the antibody is reported to 2.1 angstrom resolution

Long-lived quantum coherence in photosynthetic complexes at physiological temperature

Photosynthetic antenna complexes capture and concentrate solar radiation by transferring the excitation to the reaction center which stores energy from the photon in chemical bonds. This process occurs with near-perfect quantum efficiency. Recent experiments at cryogenic temperatures have revealed that coherent energy transfer - a wavelike transfer mechanism - occurs in many photosynthetic pigment-protein complexes (1-4). Using the Fenna-Matthews-Olson antenna complex (FMO) as a model system, theoretical studies incorporating both incoherent and coherent transfer as well as thermal dephasing predict that environmentally assisted quantum transfer efficiency peaks near physiological temperature; these studies further show that this process is equivalent to a quantum random walk algorithm (5-8). This theory requires long-lived quantum coherence at room temperature, which never has been observed in FMO. Here we present the first evidence that quantum coherence survives in FMO at physiological temperature for at least 300 fs, long enough to perform a rudimentary quantum computational operation. This data proves that the wave-like energy transfer process discovered at 77 K is directly relevant to biological function. Microscopically, we attribute this long coherence lifetime to correlated motions within the protein matrix encapsulating the chromophores, and we find that the degree of protection afforded by the protein appears constant between 77 K and 277 K. The protein shapes the energy landscape and mediates an efficient energy transfer despite thermal fluctuations. The persistence of quantum coherence in a dynamic, disordered system under these conditions suggests a new biomimetic strategy for designing dedicated quantum computational devices that can operate at high temperature.Comment: PDF files, 15 pages, 3 figures (included in the PDF file

Learning from Excellence: the 'Yaytix' programme

Background and aims: Learning from error can have a negative impact on the staff involved in the error ('second victim phenomenon'1). We created a project, based on the principles of the Learning from Excellence project,2 to learn from excellence and correct the imbalance of negative to positive feedback in the context of hospital practice. Methods and results: Using a questionnaire, we surveyed staff on existing feedback mechanisms and morale. We then introduced a system where staff recorded and commented on examples of excellence in practice. Recipients and their supervisors received copies of these reports and the feedback was analysed and discussed with senior staff (consultant, senior charge nurse, managers). We re-audited the staff two months after starting this project and noted improvements in staff morale and in positive reporting. Conclusions: This project has improved the process of giving and learning from positive feedback and had a significant impact on staff morale. We can also demonstrate an example of improved clinical practice (from feedback received) and will now attempt to measure clinical outcomes with a new prospective study. Finally, we hope to set up a regional programme of reporting excellence in South-East Scotland

Optimization and performance testing of a sequence processing pipeline applied to detection of nonindigenous species

Genetic taxonomic assignment can be more sensitive than morphological taxonomic assignment, particularly for small, cryptic or rare species. Sequence processing is essential to taxonomic assignment, but can also produce errors because optimal parameters are not known a priori. Here, we explored how sequence processing parameters influence taxonomic assignment of 18S sequences from bulk zooplankton samples produced by 454 pyrosequencing. We optimized a sequence processing pipeline for two common research goals, estimation of species richness and early detection of aquatic invasive species (AIS), and then tested most optimal models’ performances through simulations. We tested 1,050 parameter sets on 18S sequences from 20 AIS to determine optimal parameters for each research goal. We tested optimized pipelines’ performances (detectability and sensitivity) by computationally inoculating sequences of 20 AIS into ten bulk zooplankton samples from ports across Canada. We found that optimal parameter selection generally depends on the research goal. However, regardless of research goal, we found that metazoan 18S sequences produced by 454 pyrosequencing should be trimmed to 375–400 bp and sequence quality filtering should be relaxed (1.5 ≤ maximum expected error ≤ 3.0, Phred score = 10). Clustering and denoising were only viable for estimating species richness, because these processing steps made some species undetectable at low sequence abundances which would not be useful for early detection of AIS. With parameter sets optimized for early detection of AIS, 90% of AIS were detected with fewer than 11 target sequences, regardless of whether clustering or denoising was used. Despite developments in next-generation sequencing, sequence processing remains an important issue owing to difficulties in balancing false-positive and false-negative errors in metabarcoding data

De Novo Identification of Regulatory Regions in Intergenic Spaces of Prokaryotic Genomes

This project was begun to implement, test, and experimentally validate the results of a novel algorithm for genome-wide identification of candidate transcription-factor binding sites in prokaryotes. Most techniques used to identify regulatory regions rely on conservation between different genomes or have a predetermined sequence motif(s) to perform a genome-wide search. Therefore, such techniques cannot be used with new genome sequences, where information regarding such motifs has not yet been discovered. This project aimed to apply a de novo search algorithm to identify candidate binding-site motifs in intergenic regions of prokaryotic organisms, initially testing the available genomes of the Yersinia genus. We retrofitted existing nucleotide pattern-matching algorithms, analyzed the candidate sites identified by these algorithms as well as their target genes to screen for meaningful patterns. Using properly annotated prokaryotic genomes, this project aimed to develop a set of procedures to identify candidate intergenic sites important for gene regulation. We planned to demonstrate this in Yersinia pestis, a model biodefense, Category A Select Agent pathogen, and then follow up with experimental evidence that these regions are indeed involved in regulation. The ability to quickly characterize transcription-factor binding sites will help lead to a better understanding of how known virulence pathways are modulated in biodefense-related organisms, and will help our understanding and exploration of regulons--gene regulatory networks--and novel pathways for metabolic processes in environmental microbes

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

MOTIVATION: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. RESULTS: The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching  : >90% in some cases. SUMMARY: The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. AVAILABILITY AND IMPLEMENTATION: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 The Decombinator package is available at github.com/innate2adaptive/Decombinator The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online