A novel series of compositionally biased substitution matrices for comparing Plasmodium proteins

<p>Abstract</p> <p>Background</p> <p>The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent a fully accurate substitution model for proteins characterized by a biased amino acid composition. This problem has been addressed recently by adjusting existing matrices, however, to date, no empirical approach has been taken to build matrices which offer a substitution model for comparing proteins sharing an amino acid compositional bias. Here, we present a novel procedure to construct series of symmetrical substitution matrices to align proteins from similarly biased <it>Plasmodium </it>proteomes.</p> <p>Results</p> <p>We generated substitution matrices by selecting from the BLOCKS database those multiple alignments with a compositional bias similar to that of <it>P. falciparum </it>and <it>P. yoelii </it>proteins. A novel 'fuzzy' clustering method was adopted to group sequences within these alignments, showing that this method retains more complete information on the amino acid substitutions when compared to hierarchical clustering. We assessed the performance against the BLOSUM62 series and showed that the usage of our matrices results in an improvement in the performance of BLAST database searches, greatly reducing the number of false positive hits. We then demonstrated applications of the use of novel matrices to improve the annotation of homologs between the two <it>Plasmodium </it>species and to classify members of the <it>P. falciparum </it>RIFIN/STEVOR family.</p> <p>Conclusion</p> <p>We confirmed that in the case of compositionally biased proteins, standard BLOSUM matrices are not suited for optimal alignments, and specific substitution matrices are required. In addition, we showed that the usage of these matrices leads to a reduction of false positive hits, facilitating the automatic annotation process.</p

InVERT molding for scalable control of tissue microarchitecture

Complex tissues contain multiple cell types that are hierarchically organized within morphologically and functionally distinct compartments. Construction of engineered tissues with optimized tissue architecture has been limited by tissue fabrication techniques, which do not enable versatile microscale organization of multiple cell types in tissues of size adequate for physiological studies and tissue therapies. Here we present an ‘Intaglio-Void/Embed-Relief Topographic molding’ method for microscale organization of many cell types, including induced pluripotent stem cell-derived progeny, within a variety of synthetic and natural extracellular matrices and across tissues of sizes appropriate for in vitro, pre-clinical, and clinical studies. We demonstrate that compartmental placement of non-parenchymal cells relative to primary or induced pluripotent stem cell-derived hepatocytes, compartment microstructure, and cellular composition modulate hepatic functions. Configurations found to sustain physiological function in vitro also result in survival and function in mice for at least 4 weeks, demonstrating the importance of architectural optimization before implantation.National Institutes of Health (U.S.) (EB008396)National Institutes of Health (U.S.) (DK56966)National Cancer Institute (U.S.) (Cancer Center Support Core Grant P30-CA14051)National Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (1F32DK091007)National Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (1F32DK095529)National Science Foundation (U.S.). Graduate Research Fellowship Program (1122374

A Domain Decomposition Strategy for Alignment of Multiple Biological Sequences on Multiprocessor Platforms

Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing number of sequences. On the other hand, with the advent of new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with the increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantage in terms of execution time and memory requirements. The proposed strategy allows to decrease the time complexity of any known heuristic of O(N)^x complexity by a factor of O(1/p)^x, where N is the number of sequences, x depends on the underlying heuristic approach, and p is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.Comment: 36 pages, 17 figures, Accepted manuscript in Journal of Parallel and Distributed Computing(JPDC

Determination of the Proteolytic Cleavage Sites of the Amyloid Precursor-Like Protein 2 by the Proteases ADAM10, BACE1 and γ-Secretase

Regulated intramembrane proteolysis of the amyloid precursor protein (APP) by the protease activities α-, β- and γ-secretase controls the generation of the neurotoxic amyloid β peptide. APLP2, the amyloid precursor-like protein 2, is a homolog of APP, which shows functional overlap with APP, but lacks an amyloid β domain. Compared to APP, less is known about the proteolytic processing of APLP2, in particular in neurons, and the cleavage sites have not yet been determined. APLP2 is cleaved by the β-secretase BACE1 and additionally by an α-secretase activity. The two metalloproteases ADAM10 and ADAM17 have been suggested as candidate APLP2 α-secretases in cell lines. Here, we used RNA interference and found that ADAM10, but not ADAM17, is required for the constitutive α-secretase cleavage of APLP2 in HEK293 and SH-SY5Y cells. Likewise, in primary murine neurons knock-down of ADAM10 suppressed APLP2 α-secretase cleavage. Using mass spectrometry we determined the proteolytic cleavage sites in the APLP2 sequence. ADAM10 was found to cleave APLP2 after arginine 670, whereas BACE1 cleaves after leucine 659. Both cleavage sites are located in close proximity to the membrane. γ-secretase cleavage was found to occur at different peptide bonds between alanine 694 and valine 700, which is close to the N-terminus of the predicted APLP2 transmembrane domain. Determination of the APLP2 cleavage sites enables functional studies of the different APLP2 ectodomain fragments and the production of cleavage-site specific antibodies for APLP2, which may be used for biomarker development

Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate “top 5” list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions

Estimating the potential impact of Attractive Targeted Sugar Baits (ATSBs) as a new vector control tool for Plasmodium falciparum malaria

Background Attractive targeted sugar baits (ATSBs) are a promising new tool for malaria control as they can target outdoor-feeding mosquito populations, in contrast to current vector control tools which predominantly target indoor-feeding mosquitoes. Methods It was sought to estimate the potential impact of these new tools on Plasmodium falciparum malaria prevalence in African settings by combining data from a recent entomological field trial of ATSBs undertaken in Mali with mathematical models of malaria transmission. The key parameter determining impact on the mosquito population is the excess mortality due to ATSBs, which is estimated from the observed reduction in mosquito catch numbers. A mathematical model capturing the life cycle of P. falciparum malaria in mosquitoes and humans and incorporating the excess mortality was used to estimate the potential epidemiological effect of ATSBs. Results The entomological study showed a significant reduction of ~ 57% (95% CI 33–72%) in mosquito catch numbers, and a larger reduction of ~ 89% (95% CI 75–100%) in the entomological inoculation rate due to the fact that, in the presence of ATSBs, most mosquitoes do not live long enough to transmit malaria. The excess mortality due to ATSBs was estimated to be lower (mean 0.09 per mosquito per day, seasonal range 0.07–0.11 per day) than the bait feeding rate obtained from one-day staining tests (mean 0.34 per mosquito per day, seasonal range 0.28–0.38 per day). Conclusions From epidemiological modelling, it was predicted that ATSBs could result in large reductions (> 30% annually) in prevalence and clinical incidence of malaria, even in regions with an existing high malaria burden. These results suggest that this new tool could provide a promising addition to existing vector control tools and result in significant reductions in malaria burden across a range of malaria-endemic settings

Antiepileptic drugs’ tolerability and safety – a systematic review and meta-analysis of adverse effects in dogs

<p>Various anti-epileptic drugs (AEDs) are used for the management of idiopathic epilepsy (IE) in dogs. Their safety profile is an important consideration for regulatory bodies, owners and prescribing clinicians. However, information on their adverse effects still remains limited with most of it derived from non-blinded non-randomized uncontrolled trials and case reports.</p><p><span>This poster won third place, which was presented at the Veterinary Evidence Today conference, Edinburgh November 1-3, 2016. </span></p><br /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/oa-icon.jpg" alt="Open Access" /

The effects of low-impact mutations in digital organisms

<p>Abstract</p> <p>Background</p> <p>Avida is a computer program that performs evolution experiments with digital organisms. Previous work has used the program to study the evolutionary origin of complex features, namely logic operations, but has consistently used extremely large mutational fitness effects. The present study uses Avida to better understand the role of low-impact mutations in evolution.</p> <p>Results</p> <p>When mutational fitness effects were approximately 0.075 or less, no new logic operations evolved, and those that had previously evolved were lost. When fitness effects were approximately 0.2, only half of the operations evolved, reflecting a threshold for selection breakdown. In contrast, when Avida's default fitness effects were used, all operations routinely evolved to high frequencies and fitness increased by an average of 20 million in only 10,000 generations.</p> <p>Conclusions</p> <p>Avidian organisms evolve new logic operations only when mutations producing them are assigned high-impact fitness effects. Furthermore, purifying selection cannot protect operations with low-impact benefits from mutational deterioration. These results suggest that selection breaks down for low-impact mutations below a certain fitness effect, the <it>selection threshold</it>. Experiments using biologically relevant parameter settings show the tendency for increasing genetic load to lead to loss of biological functionality. An understanding of such genetic deterioration is relevant to human disease, and may be applicable to the control of pathogens by use of lethal mutagenesis.</p

Cellular Radiosensitivity: How much better do we understand it?

Purpose: Ionizing radiation exposure gives rise to a variety of lesions in DNA that result in genetic instability and potentially tumorigenesis or cell death. Radiation extends its effects on DNA by direct interaction or by radiolysis of H2O that generates free radicals or aqueous electrons capable of interacting with and causing indirect damage to DNA. While the various lesions arising in DNA after radiation exposure can contribute to the mutagenising effects of this agent, the potentially most damaging lesion is the DNA double strand break (DSB) that contributes to genome instability and/or cell death. Thus in many cases failure to recognise and/or repair this lesion determines the radiosensitivity status of the cell. DNA repair mechanisms including homologous recombination (HR) and non-homologous end-joining (NHEJ) have evolved to protect cells against DNA DSB. Mutations in proteins that constitute these repair pathways are characterised by radiosensitivity and genome instability. Defects in a number of these proteins also give rise to genetic disorders that feature not only genetic instability but also immunodeficiency, cancer predisposition, neurodegeneration and other pathologies. Conclusions: In the past fifty years our understanding of the cellular response to radiation damage has advanced enormously with insight being gained from a wide range of approaches extending from more basic early studies to the sophisticated approaches used today. In this review we discuss our current understanding of the impact of radiation on the cell and the organism gained from the array of past and present studies and attempt to provide an explanation for what it is that determines the response to radiation