Characterization of a Novel Porcine Parvovirus Tentatively Designated PPV5

A new porcine parvovirus (PPV), provisionally designated as PPV5, was identified in U.S. pigs. Cloning and sequencing from a circular or head-to-tail concatemeric array revealed that the PPV5 possesses the typical genomic organization of parvoviruses with two major predicted open reading frames (ORF1 and ORF2), and is most closely related to PPV4 with overall genomic identities of 64.1–67.3%. The amino acid identities between PPV5 and PPV4 were 84.6%–85.1% for ORF1 and 54.0%–54.3% for ORF2. Unlike PPV4, but similar to bovine parvovirus 2 (BPV2), PPV5 lacks the additional ORF3 and has a much longer ORF2. Moreover, the amino acid sequences of ORF1 and ORF2 of BPV2 showed higher homologies to PPV5 than to PPV4. The conserved motifs of the Ca²⁺ binding loop (YXGXG) and the catalytic center (HDXXY) of phospholipase A₂ (PLA₂) were identified in VP1 (ORF2) of PPV5, as well as in BPV2, but were not present in PPV4. Phylogenetic analyses revealed that PPV5, PPV4 and BPV2 form a separate clade different from the genera Parvovirus and Bocavirus. Further epidemiologic investigations of PPV4 and PPV5 in U.S. pigs of different ages indicated a slightly higher prevalence for PPV5 (6.6%; 32/483) compared to PPV4 (4.1%; 20/483), with detection of concurrent PPV4 and PPV5 in 15.6% (7/45) of lungs of infected pigs. Evidence for potential vertical transmission or association with reproductive failure was minimal for both PPV4 and PPV5. The high similarity to PPV4 and the lack of ORF3 may suggest PPV5 is an intermediate of PPV4 during the evolution of parvoviruses in pigs.     

Altered Visual and Feet Proprioceptive Feedbacks during Quiet Standing Increase Postural Sway in Patients with Severe Knee Osteoarthritis

Objective

The objective was to investigate how postural control in knee osteoarthritis (KOA) patients, with different structural severities and pain levels, is reorganized under different sensory conditions.

Methods

Forty-two obese patients (BMI range from 30.1 to 48.7 kg*m⁻², age range from 50 to 74 years) with KOA were evaluated. One minute of quiet standing was assessed on a force platform during 4 different sensory conditions, applied 3 times at random: Eyes open (EO) and eyes closed (EC) standing on firm and soft (foam) surfaces (EO-soft and EC-soft). Centre of pressure (Cop) standard deviation, speed, range and Cop mean position in both directions (anterior-posterior and medial-lateral) were extracted from the force platform data. Structural disease severity was assessed from semiflexed standing radiographs and graded by the Kellgren and Lawrence (KL) score. Pain intensity immediately before the measurements was assessed by numeric rating scale (range: 0–10).

Results

The patients were divided into “less severe” (KL 1 and 2, n = 24) and “severe” (KL>2, n = 18) group. The CoP range in the medial-lateral direction was larger in the severe group when compared with the less severe group during EC-soft condition (P<0.01). Positive correlation between pain intensity and postural sway (range in medial-lateral direction) was found during EC condition, indicating that the higher the pain intensity, the less effective is the postural control applied to restore an equilibrium position while standing without visual information.

Conclusion

The results support that: (i) the postural reorganization under manipulation of the different sensory information is worse in obese KOA patients with severe degeneration and/or high pain intensity when compared with less impaired patients, and (ii) higher pain intensity is related to worse body balance in obese KOA patients.     

Phylogeny and physiology of candidate phylum ‘Atribacteria' (OP9/JS1) inferred from cultivation-independent genomics

The ‘Atribacteria'' is a candidate phylum in the Bacteria recently proposed to include members of the OP9 and JS1 lineages. OP9 and JS1 are globally distributed, and in some cases abundant, in anaerobic marine sediments, geothermal environments, anaerobic digesters and reactors and petroleum reservoirs. However, the monophyly of OP9 and JS1 has been questioned and their physiology and ecology remain largely enigmatic due to a lack of cultivated representatives. Here cultivation-independent genomic approaches were used to provide a first comprehensive view of the phylogeny, conserved genomic features and metabolic potential of members of this ubiquitous candidate phylum. Previously available and heretofore unpublished OP9 and JS1 single-cell genomic data sets were used as recruitment platforms for the reconstruction of atribacterial metagenome bins from a terephthalate-degrading reactor biofilm and from the monimolimnion of meromictic Sakinaw Lake. The single-cell genomes and metagenome bins together comprise six species- to genus-level groups that represent most major lineages within OP9 and JS1. Phylogenomic analyses of these combined data sets confirmed the monophyly of the ‘Atribacteria'' inclusive of OP9 and JS1. Additional conserved features within the ‘Atribacteria'' were identified, including a gene cluster encoding putative bacterial microcompartments that may be involved in aldehyde and sugar metabolism, energy conservation and carbon storage. Comparative analysis of the metabolic potential inferred from these data sets revealed that members of the ‘Atribacteria'' are likely to be heterotrophic anaerobes that lack respiratory capacity, with some lineages predicted to specialize in either primary fermentation of carbohydrates or secondary fermentation of organic acids, such as propionate.     

ProDeGe: a computational protocol for fully automated decontamination of genomes

Single amplified genomes and genomes assembled from metagenomes have enabled the exploration of uncultured microorganisms at an unprecedented scale. However, both these types of products are plagued by contamination. Since these genomes are now being generated in a high-throughput manner and sequences from them are propagating into public databases to drive novel scientific discoveries, rigorous quality controls and decontamination protocols are urgently needed. Here, we present ProDeGe (Protocol for fully automated Decontamination of Genomes), the first computational protocol for fully automated decontamination of draft genomes. ProDeGe classifies sequences into two classes—clean and contaminant—using a combination of homology and feature-based methodologies. On average, 84% of sequence from the non-target organism is removed from the data set (specificity) and 84% of the sequence from the target organism is retained (sensitivity). The procedure operates successfully at a rate of ~0.30 CPU core hours per megabase of sequence and can be applied to any type of genome sequence.Recent technological advancements have enabled the large-scale sampling of genomes from uncultured microbial taxa, through the high-throughput sequencing of single amplified genomes (SAGs; Rinke et al., 2013; Swan et al., 2013) and assembly and binning of genomes from metagenomes (GMGs; Cuvelier et al., 2010; Sharon and Banfield, 2013). The importance of these products in assessing community structure and function has been established beyond doubt (Kalisky and Quake, 2011). Multiple Displacement Amplification (MDA) and sequencing of single cells has been immensely successful in capturing rare and novel phyla, generating valuable references for phylogenetic anchoring. However, efforts to conduct MDA and sequencing in a high-throughput manner have been heavily impaired by contamination from DNA introduced by the environmental sample, as well as introduced during the MDA or sequencing process (Woyke et al., 2011; Engel et al., 2014; Field et al., 2014). Similarly, metagenome binning and assembly often carries various errors and artifacts depending on the methods used (Nielsen et al., 2014). Even cultured isolate genomes have been shown to lack immunity to contamination with other species (Parks et al., 2014; Mukherjee et al., 2015). As sequencing of these genome product types rapidly increases, contaminant sequences are finding their way into public databases as reference sequences. It is therefore extremely important to define standardized and automated protocols for quality control and decontamination, which would go a long way towards establishing quality standards for all microbial genome product types.Current procedures for decontamination and quality control of genome sequences in single cells and metagenome bins are heavily manual and can consume hours/megabase when performed by expert biologists. Supervised decontamination typically involves homology-based inspection of ribosomal RNA sequences and protein coding genes, as well as visual analysis of k-mer frequency plots and guanine–cytosine content (Clingenpeel, 2015). Manual decontamination is also possible through the software SmashCell (Harrington et al., 2010), which contains a tool for visual identification of contaminants from a self-organizing map and corresponding U-matrix. Another existing software tool, DeconSeq (Schmieder and Edwards, 2011), automatically removes contaminant sequences, however, the contaminant databases are required input. The former lacks automation, whereas the latter requires prior knowledge of contaminants, rendering both applications impractical for high-throughput decontamination.Here, we introduce ProDeGe, the first fully automated computational protocol for decontamination of genomes. ProDeGe uses a combination of homology-based and sequence composition-based approaches to separate contaminant sequences from the target genome draft. It has been pre-calibrated to discard at least 84% of the contaminant sequence, which results in retention of a median 84% of the target sequence. The standalone software is freely available at http://prodege.jgi-psf.org//downloads/src and can be run on any system that has Perl, R (R Core Team, 2014), Prodigal (Hyatt et al., 2010) and NCBI Blast (Camacho et al., 2009) installed. A graphical viewer allowing further exploration of data sets and exporting of contigs accompanies the web application for ProDeGe at http://prodege.jgi-psf.org, which is open to the wider scientific community as a decontamination service (Supplementary Figure S1).The assembly and corresponding NCBI taxonomy of the data set to be decontaminated are required inputs to ProDeGe (Figure 1a). Contigs are annotated with genes following which, eukaryotic contamination is removed based on homology of genes at the nucleotide level using the eukaryotic subset of NCBI''s Nucleotide database as the reference. For detecting prokaryotic contamination, a curated database of reference contigs from the set of high-quality genomes within the Integrated Microbial Genomes (IMG; Markowitz et al., 2014) system is used as the reference. This ensures that errors in public reference databases due to poor quality of sequencing, assembly and annotation do not negatively impact the decontamination process. Contigs determined as belonging to the target organism based on nucleotide level homology to sequences in the above database are defined as ‘Clean'', whereas those aligned to other organisms are defined as ‘Contaminant''. Contigs whose origin cannot be determined based on alignment are classified as ‘Undecided''. Classified clean and contaminated contigs are used to calibrate the separation in the subsequent 5-mer based binning module, which classifies undecided contigs as ‘Clean'' or ‘Contaminant'' using principal components analysis (PCA) of 5-mer frequencies. This parameter can also be specified by the user. When data sets do not have taxonomy deeper than phylum level, or a single confident taxonomic bin cannot be detected using sequence alignment, solely 9-mer based binning is used due to more accurate overall classification. In the absence of a user-defined cutoff, a pre-calibrated cutoff for 80% or more specificity separates the clean contigs from contaminated sequences in the resulting PCA of the 9-mer frequency matrix. Details on ProDeGe''s custom database, evaluation of the performance of the system and exploration of the parameter space to calibrate ProDeGe for a high accurate classification rate are provided in the Supplementary Material.Open in a separate windowFigure 1(a) Schematic overview of the ProDeGe engine. (b) Features of data sets used to validate ProDeGe: SAGs from the Arabidopsis endophyte sequencing project, MDM project, public data sets found in IMG but not sequenced at the JGI, as well as genomes from metagenomes. All the data and results can be found in Supplementary Table S3.The performance of ProDeGe was evaluated using 182 manually screened SAGs (Figure 1b,Supplementary Table S1) from two studies whose data sets are publicly available within the IMG system: genomes of 107 SAGs from an Arabidopsis endophyte sequencing project and 75 SAGs from the Microbial Dark Matter (MDM) project* (only 75/201 SAGs from the MDM project had 1:1 mapping between contigs in the unscreened and the manually screened versions, hence these were used; Rinke et al., 2013). Manual curation of these SAGs demonstrated that the use of ProDeGe prevented 5311 potentially contaminated contigs in these data sets from entering public databases. Figure 2a demonstrates the sensitivity vs specificity plot of ProDeGe results for the above data sets. Most of the data points in Figure 2a cluster in the top right of the box reflecting a median retention of 89% of the clean sequence (sensitivity) and a median rejection of 100% of the sequence of contaminant origin (specificity). In addition, on average, 84% of the bases of a data set are accurately classified. ProDeGe performs best when the target organism has sequenced homologs at the class level or deeper in its high-quality prokaryotic nucleotide reference database. If the target organism''s taxonomy is unknown or not deeper than domain level, or there are few contigs with taxonomic assignments, a target bin cannot be assessed and thus ProDeGe removes contaminant contigs using sequence composition only. The few samples in Figure 2a that demonstrate a higher rate of false positives (lower specificity) and/or reduced sensitivity typically occur when the data set contains few contaminant contigs or ProDeGe incorrectly assumes that the largest bin is the target bin. Some data sets contain a higher proportion of contamination than target sequence and ProDeGe''s performance can suffer under this condition. However, under all other conditions, ProDeGe demonstrates high speed, specificity and sensitivity (Figure 2). In addition, ProDeGe demonstrates better performance in overall classification when nucleotides are considered than when contigs are considered, illustrating that longer contigs are more accurately classified (Supplementary Table S1).Open in a separate windowFigure 2ProDeGe accuracy and performance scatterplots of 182 manually curated single amplified genomes (SAGs), where each symbol represents one SAG data set. (a) Accuracy shown by sensitivity (proportion of bases confirmed ‘Clean'') vs specificity (proportion of bases confirmed ‘Contaminant'') from the Endophyte and Microbial Dark Matter (MDM) data sets. Symbol size reflects input data set size in megabases. Most points cluster in the top right of the plot, showing ProDeGe''s high accuracy. Median and average overall results are shown in Supplementary Table S1. (b) ProDeGe completion time in central processing unit (CPU) core hours for the 182 SAGs. ProDeGe operates successfully at an average rate of 0.30 CPU core hours per megabase of sequence. Principal components analysis (PCA) of a 9-mer frequency matrix costs more computationally than PCA of a 5-mer frequency matrix used with blast-binning. The lack of known taxonomy for the MDM data sets prevents blast-binning, thus showing longer finishing times than the endophyte data sets, which have known taxonomy for use in blast-binning.All SAGs used in the evaluation of ProDeGe were assembled using SPAdes (Bankevich et al., 2012). In-house testing has shown that reads assembled with SPAdes from different strains or even slightly divergent species of the same genera may be combined into the same contig (Personal communications, KT and Robert Bowers). Ideally, the DNA in a well that gets sequenced belongs to a single cell. In the best case, contaminant sequences need to be at least from a different species to be recognized as such by the homology-based screening stage. In the absence of closely related sequenced organisms, contaminant sequences need to be at least from a different genus to be recognized as such by the composition-based screening stage (Supplementary Material). Thus, there is little risk of ProDeGe separating sequences from clonal populations or strains. We have found species- and genus-level contamination in MDA samples to be rare.To evaluate the quality of publicly available uncultured genomes, ProDeGe was used to screen 185 SAGs and 14 GMGs (Figure 1b). Compared with CheckM (Parks et al., 2014), a tool which calculates an estimate of genome sequence contamination using marker genes, ProDeGe generally marks a higher proportion of sequence as ‘Contaminant'' (Supplementary Table S2). This is because ProDeGe has been calibrated to perform at high specificity levels. The command line version of ProDeGe allows users to conduct their own calibration and specify a user-defined distance cutoff. Further, CheckM only outputs the proportion of contamination, but ProDeGe actually labels each contig as ‘Clean'' or ‘Contaminant'' during the process of automated removal.The web application for ProDeGe allows users to export clean and contaminant contigs, examine contig gene calls with their corresponding taxonomies, and discover contig clusters in the first three components of their k-dimensional space. Non-linear approaches for dimensionality reduction of k-mer vectors are gaining popularity (van der Maaten and Hinton, 2008), but we observed no systematic advantage of using t-Distributed Stochastic Neighbor Embedding over PCA (Supplementary Figure S2).ProDeGe is the first step towards establishing a standard for quality control of genomes from both cultured and uncultured microorganisms. It is valuable for preventing the dissemination of contaminated sequence data into public databases, avoiding resulting misleading analyses. The fully automated nature of the pipeline relieves scientists of hours of manual screening, producing reliably clean data sets and enabling the high-throughput screening of data sets for the first time. ProDeGe, therefore, represents a critical component in our toolkit during an era of next-generation DNA sequencing and cultivation-independent microbial genomics.     

Phytochemical Study of Juglans regia L. Pericarps from Greece with a Chemotaxonomic Approach

Phytochemical research of different polarity extracts from green Juglans regia L. pericarps from Greece afforded 32 compounds: four pentacyclic triterpenes (1 – 4), three sesquiterpenes (5 – 7), four tetralones (8 – 11), two naphthoquinones (12 and 13), seven phenolic acids (14 – 20), one diarylheptanoid (21), one neo‐lignan (22), seven flavonoids (23 – 29), two phenylethanoids (30 and 31) and one hydrolysed tannin (32). Compounds 4 and 29 are isolated for the first time from the species, while compounds 3, 7, 20, 22, 23, 24, 25, 26, 28, 30 are reported for the first time in Juglandaceae. Chemotaxonomic significance of isolated compounds into Junglandaceae family is thoroughly discussed.     

Aerobic Training in Patients with Congenital Myopathy

Introduction

Congenital myopathies (CM) often affect contractile proteins of the sarcomere, which could render patients susceptible to exercise-induced muscle damage. We investigated if exercise is safe and beneficial in patients with CM.

Methods

Patients exercised on a stationary bike for 30 minutes, three times weekly, for 10 weeks at 70% of their maximal oxygen uptake (VO_2max). Creatine kinase (CK) was monitored as a marker of muscle damage. VO_2max, functional tests, and questionnaires evaluated efficacy.

Results

Sixteen patients with CM were included in a controlled study. VO_2max increased by 14% (range, 6–25%; 95% CI 7–20; p < 0.001) in the seven patients who completed training, and tended to decrease in a non-intervention group (n = 7; change -3.5%; range, -11–3%, p = 0.083). CK levels were normal and remained stable during training. Baseline Fatigue Severity Scale scores were high, 4.9 (SE 1.9), and tended to decrease (to 4.4 (SE 1.7); p = 0.08) with training. Nine patients dropped out of the training program. Fatigue was the major single reason.

Conclusions

Ten weeks of endurance training is safe and improves fitness in patients with congenital myopathies. The training did not cause sarcomeric injury, even though sarcomeric function is affected by the genetic abnormalities in most patients with CM. Severe fatigue, which characterizes patients with CM, is a limiting factor for initiating training in CM, but tends to improve in those who train.

Trial Registration

The Regional Committee on Health Research Ethics of the Capital Region of Denmark H-2-2013-066 and ClinicalTrials.gov H2-2013-066     

Assessment of Overlap of Phylogenetic Transmission Clusters and Communities in Simple Sexual Contact Networks: Applications to HIV-1

Background

Transmission patterns of sexually-transmitted infections (STIs) could relate to the structure of the underlying sexual contact network, whose features are therefore of interest to clinicians. Conventionally, we represent sexual contacts in a population with a graph, that can reveal the existence of communities. Phylogenetic methods help infer the history of an epidemic and incidentally, may help detecting communities. In particular, phylogenetic analyses of HIV-1 epidemics among men who have sex with men (MSM) have revealed the existence of large transmission clusters, possibly resulting from within-community transmissions. Past studies have explored the association between contact networks and phylogenies, including transmission clusters, producing conflicting conclusions about whether network features significantly affect observed transmission history. As far as we know however, none of them thoroughly investigated the role of communities, defined with respect to the network graph, in the observation of clusters.

Methods

The present study investigates, through simulations, community detection from phylogenies. We simulate a large number of epidemics over both unweighted and weighted, undirected random interconnected-islands networks, with islands corresponding to communities. We use weighting to modulate distance between islands. We translate each epidemic into a phylogeny, that lets us partition our samples of infected subjects into transmission clusters, based on several common definitions from the literature. We measure similarity between subjects’ island membership indices and transmission cluster membership indices with the adjusted Rand index.

Results and Conclusion

Analyses reveal modest mean correspondence between communities in graphs and phylogenetic transmission clusters. We conclude that common methods often have limited success in detecting contact network communities from phylogenies. The rarely-fulfilled requirement that network communities correspond to clades in the phylogeny is their main drawback. Understanding the link between transmission clusters and communities in sexual contact networks could help inform policymaking to curb HIV incidence in MSMs.     

Characterization of the Variability of Epstein-Barr Virus Genes in Nasopharyngeal Biopsies: Potential Predictors for Carcinoma Progression

Epstein-Barr virus (EBV) infection is a significant factor in the pathogenesis of nasopharyngeal carcinoma, especially in the undifferentiated carcinoma of nasopharyngeal type (UCNT, World Health Organization type III), which is the dominant histopathological type in high-risk areas. The major EBV oncogene is latent membrane protein 1 (LMP1). LMP1 gene shows variability with different tumorigenic and immunogenic potentials. EBV nuclear antigen 1 (EBNA1) regulates progression of EBV-related tumors; however, the influence of EBNA1 sequence variability on tumor pathogenesis is controversial. The aims of this study were to characterize polymorphisms of EBV genes in non-endemic nasopharyngeal carcinoma biopsies and to investigate potential sequence patterns that correlate with the clinical presentation of nasopharyngeal carcinoma. In total, 116 tumor biopsies of undifferentiated carcinoma of nasopharyngeal type (UCNT), collected from 2008 to 2014, were evaluated in this study. The genes EBNA2, LMP1, and EBNA1 were amplified using nested-PCR. EBNA2 genotyping was performed by visualization of PCR products using gel electrophoresis. Investigation of LMP1 and EBNA1 included sequence, phylogenetic, and statistical analyses. The presence of EBV DNA was significantly distributed between TNM stages. LMP1 variability showed six variants, with the detection of the first China1 and North Carolina variants in European nasopharyngeal carcinoma biopsies. Newly discovered variants Srb1 and Srb2 were UCNT-specific LMP1 polymorphisms. The B95-8 and North Carolina variants are possible predictors for favorable TNM stages. In contrast, deletions in LMP1 are possible risk factors for the most disfavorable TNM stage, independent of EBNA2 or EBNA1 variability. A newly discovered EBNA1 subvariant, P-thr-sv-5, could be a potential diagnostic marker, as it represented a UCNT-specific EBNA1 subvariant. A particular combination of EBNA2, LMP1, and EBNA1 polymorphisms, type 1/Med/P-thr was identified as a possible risk factor for TNM stage IVB or progression to the N3 stage.     

Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer

Propensity scoring (PS) is an established tool to account for measured confounding in non-randomized studies. These methods are sensitive to missing values, which are a common problem in observational data. The combination of multiple imputation of missing values and different propensity scoring techniques is addressed in this work. For a sample of lymph node-positive vulvar cancer patients, we re-analyze associations between the application of radiotherapy and disease-related and non-related survival. Inverse-probability-of-treatment-weighting (IPTW) and PS stratification are applied after multiple imputation by chained equation (MICE). Methodological issues are described in detail. Interpretation of the results and methodological limitations are discussed.