Evolution of genes and repeats in the Nimrod superfamily

The recently identified Nimrod superfamily is characterized by the presence of a special type of EGF repeat, the NIM repeat, located right after a typical CCXGY/W amino acid motif. On the basis of structural features, nimrod genes can be divided into three types. The proteins encoded by Draper-type genes have an EMI domain at the N-terminal part and only one copy of the NIM motif, followed by a variable number of EGF-like repeats. The products of Nimrod B-type and Nimrod C-type genes (including the eater gene) have different kinds of N-terminal domains, and lack EGF-like repeats but contain a variable number of NIM repeats. Draper and Nimrod C-type (but not Nimrod B-type) proteins carry a transmembrane domain. Several members of the superfamily were claimed to function as receptors in phagocytosis and/or binding of bacteria, which indicates an important role in the cellular immunity and the elimination of apoptotic cells. In this paper, the evolution of the Nimrod superfamily is studied with various methods on the level of genes and repeats. A hypothesis is presented in which the NIM repeat, along with the EMI domain, emerged by structural reorganizations at the end of an EGF-like repeat chain, suggesting a mechanism for the formation of novel types of repeats. The analyses revealed diverse evolutionary patterns in the sequences containing multiple NIM repeats. Although in the Nimrod B and Nimrod C proteins show characteristics of independent evolution, many internal NIM repeats in Eater sequences seem to have undergone concerted evolution. An analysis of the nimrod genes has been performed using phylogenetic and other methods and an evolutionary scenario of the origin and diversification of the Nimrod superfamily is proposed. Our study presents an intriguing example how the evolution of multigene families may contribute to the complexity of the innate immune response

PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination

<p>Abstract</p> <p>Background</p> <p>We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.</p> <p>Results</p> <p>Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model.</p> <p>Conclusions</p> <p>Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED.</p> <p>Availability</p> <p>An implementation of the PICS-Ord algorithm is available from <url>http://scit.us/projects/ngila/wiki/PICS-Ord</url>. It requires both the statistical software, R <url>http://www.r-project.org</url> and the alignment software Ngila <url>http://scit.us/projects/ngila</url>.</p

Merkel cell carcinoma: a population-based study on mortality and the association with other cancers

Few population-based epidemiological data are available on Merkel cell carcinoma (MCC), a rare lethal non-melanoma skin cancer. We analysed multiple-cause-of-death records to describe MCC mortality and trends and the association with other primary cancers. We reviewed all 6,713,059 death certificates in Italy (1995-2006) to identify those mentioning MCC. We evaluated the association with other primary cancers by calculating the ratio of observed to expected deaths, using a standardized mortality ratio (SMR)-like analysis. We also evaluated the geographic distribution of deaths. We identified 351 death certificates with the mention of MCC. The age-adjusted mortality was 0.031/100,000, with a significant trend of increase and a slight north-south gradient. There was a significant deficit for solid cancers (SMR = 0.15) and a non-significant excess for lymphohematopoietic malignancies (SMR = 1.62). There were significant excesses for chronic lymphocytic leukemia (SMR = 4.07) and Waldenstrom's macroglobulinemia (SMR = 27.2) and a non-significant excess for chronic myeloid leukemia (SMR = 5.12). The increase in MCC mortality reflects the incidence trend in the literature. The association with chronic lymphocytic leukemia confirms the importance of immunologic factors in MCC. Regarding Waldenstrom's macroglobulinemia, an association with MCC has never been reported

TEAM-UP for quality: a cluster randomized controlled trial protocol focused on preventing pressure ulcers through repositioning frequency and precipitating factors

Background: Pressure ulcers/injuries (PrUs), a critical concern for nursing homes (NH), are responsible for chronic wounds, amputations, septic infections, and premature deaths. PrUs occur most commonly in older adults and NH residence is a risk factor for their development, with at least one of every nine U.S. NH residents experiencing a PrU and many NHs having high incidence and prevalence rates, in some instances well over 20%. PrU direct treatment costs are greater than prevention costs, making prevention-focused protocols critical. Current PrU prevention protocols recommend repositioning residents at moderate, high, and severe risk every 2 h. The advent of visco- elastic (VE) high-density foam support-surfaces over the past decade may now make it possible to extend the repositioning interval to every 3 or 4 h without increasing PrU development. The TEAM-UP (Turn Everyone And Move for Ulcer Prevention) study aims to determine: 1) whether repositioning interval can be extended for NH residents without compromising PrU incidence and 2) how changes in medical severity interact with changes in risk level and repositioning schedule to predict PrU development. Methods: In this proposed cluster randomized study, 9 NHs will be randomly assigned to one of three repositioning intervals (2, 3, or 4 h) for a 4-week period. Each enrolled site will use a single NH-wide repositioning interval as the standard of care for residents at low, moderate, and high risk of PrU development (N = 951) meeting the following criteria: minimum 3-day stay, without PrUs, no adhesive allergy, and using VE support surfaces (mattresses). An FDA-cleared patient monitoring system that records position/movement of these residents via individual wireless sensors will be used to visually cue staff when residents need repositioning and document compliance with repositioning protocols. Discussion: This study will advance knowledge about repositioning frequency and clinically assessed PrU risk level in relation to PrU incidence and medical severity. Outcomes of this research will contribute to future guidelines for more precise preventive nursing practices and refinement of PrU prevention guidelines. Trial registration: Clinical Trial Registration: NCT02996331

How reliably can we predict the reliability of protein structure predictions?

Background: Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences. Results: Stochastic sequence alignment methods define a posterior distribution of possible multiple alignments. They can highlight the most likely alignment, and above that, they can give posterior probabilities for each alignment column. We made a comprehensive study on the HOMSTRAD database of structural alignments, predicting secondary structures in four different ways. We showed that alignment posterior probabilities correlate with the reliability of secondary structure predictions, though the strength of the correlation is different for different protocols. The correspondence between the reliability of secondary structure predictions and alignment posterior probabilities is the closest to the identity function when the secondary structure posterior probabilities are calculated from the posterior distribution of multiple alignments. The largest deviation from the identity function has been obtained in the case of predicting secondary structures from a single optimal pairwise alignment. We also showed that alignment posterior probabilities correlate with the 3D distances between C α amino acids in superimposed tertiary structures. Conclusion: Alignment posterior probabilities can be used to a priori detect errors in comparative models on the sequence alignment level. </p

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

Background A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment. Results In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased. Conclusions The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign webcite

The epidemiology of Plasmodium vivax among adults in the Democratic Republic of the Congo

Reports of P. vivax infections among Duffy-negative hosts have accumulated throughout sub-Saharan Africa. Despite this growing body of evidence, no nationally representative epidemiological surveys of P. vivax in sub-Saharan Africa have been performed. To overcome this gap in knowledge, we screened over 17,000 adults in the Democratic Republic of the Congo (DRC) for P. vivax using samples from the 2013-2014 Demographic Health Survey. Overall, we found a 2.97% (95% CI: 2.28%, 3.65%) prevalence of P. vivax infections across the DRC. Infections were associated with few risk-factors and demonstrated a relatively flat distribution of prevalence across space with focal regions of relatively higher prevalence in the north and northeast. Mitochondrial genomes suggested that DRC P. vivax were distinct from circulating non-human ape strains and an ancestral European P. vivax strain, and instead may be part of a separate contemporary clade. Our findings suggest P. vivax is diffusely spread across the DRC at a low prevalence, which may be associated with long-term carriage of low parasitemia, frequent relapses, or a general pool of infections with limited forward propagation

Accounting For Alignment Uncertainty in Phylogenomics

Uncertainty in multiple sequence alignments has a large impact on phylogenetic analyses. Little has been done to evaluate the quality of individual positions in protein sequence alignments, which directly impact the accuracy of phylogenetic trees. Here we describe ZORRO, a probabilistic masking program that accounts for alignment uncertainty by assigning confidence scores to each alignment position. Using the BALIBASE database and in simulation studies, we demonstrate that masking by ZORRO significantly reduces the alignment uncertainty and improves the tree accuracy

Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases

Microorganisms that cause infectious diseases present critical issues of national security, public health, and economic welfare.  For example, in recent years, highly pathogenic strains of avian influenza have emerged in Asia, spread through Eastern Europe and threaten to become pandemic. As demonstrated by the coordinated response to Severe Acute Respiratory Syndrome (SARS) and influenza, agents of infectious disease are being addressed via large-scale genomic sequencing.  The goal of genomic sequencing projects are to rapidly put large amounts of data in the public domain to accelerate research on disease surveillance, treatment, and prevention. However, our ability to derive information from large comparative genomic datasets lags far behind acquisition.  Here we review the computational challenges of comparative genomic analyses, specifically sequence alignment and reconstruction of phylogenetic trees.  We present novel analytical results on from two important infectious diseases, Severe Acute Respiratory Syndrome (SARS) and influenza.SARS and influenza have similarities and important differences both as biological and comparative genomic analysis problems.  Influenza viruses (Orthymxyoviridae) are RNA based.  Current evidence indicates that influenza viruses originate in aquatic birds from wild populations. Influenza has been studied for decades via well-coordinated international efforts.  These efforts center on surveillance via antibody characterization of the hemagglutinin (HA) and neuraminidase (N) proteins of the circulating strains to inform vaccine design. However we still do not have a clear understanding of: 1) various transmission pathways such as the role of intermediate hosts such as swine and domestic birds and 2) the key mutation and genomic recombination events that underlie periodic pandemics of influenza.  In the past 30 years, sequence data from HA and N loci has become an important data type. In the past year, full genomic data has become prominent.  These data present exciting opportunities to address unanswered questions in influenza pandemics.SARS is caused by a previously unrecognized lineage of coronavirus, SARS-CoV, which like influenza has an RNA based genome.  Although SARS-CoV is widely believed to have originated in animals there remains disagreement over the candidate animal source that lead to the original outbreak of SARS.  In contrast to the long history of the study of influenza, SARS was only recognized in late 2002 and the virus that causes SARS has been documented primarily by genomic sequencing.In the past, most studies of influenza were performed on a limited number of isolates and genes suited to a particular problem.  Major goals in science today are to understand emerging diseases in broad geographic, environmental, societal, biological, and genomic contexts. Synthesizing diverse information brought together by various researchers is important to find out what can be done to prevent future outbreaks {JON03}.  Thus comprehensive means to organize and analyze large amounts of diverse information are critical.  For example, the relationships of isolates and patterns of genomic change observed in large datasets might not be consistent with hypotheses formed on partial data.  Moreover when researchers rely on partial datasets, they restrict the range of possible discoveries.Phylogenetics is well suited to the complex task of understanding emerging infectious disease. Phylogenetic analyses can test many hypotheses by comparing diverse isolates collected from various hosts, environments, and points in time and organizing these data into various evolutionary scenarios.  The products of a phylogenetic analysis are a graphical tree of ancestor-descendent relationships and an inferred summary of mutations, recombination events, host shifts, geographic, and temporal spread of the viruses.  However, this synthesis comes at a price.  The cost of computation of phylogenetic analysis expands combinatorially as the number of isolates considered increases. Thus, large datasets like those currently produced are commonly considered intractable.  We address this problem with synergistic development of heuristics tree search strategies and parallel computing.Fil: Janies, D.. Ohio State University; Estados UnidosFil: Pol, Diego. Ohio State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin