Permanent Genetic Resources added to Molecular Ecology Resources Database 1 August 2012 – 30 September 2012

This article documents the addition of 83 microsatellite marker loci and 96 pairs of single‐nucleotide polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database. Loci were developed for the following species: Bembidion lampros, Inimicus japonicus, Lymnaea stagnalis, Panopea abbreviata, Pentadesma butyracea, Sycoscapter hirticola and Thanatephorus cucumeris (anamorph: Rhizoctonia solani). These loci were cross‐tested on the following species: Pentadesma grandifolia and Pentadesma reyndersii. This article also documents the addition of 96 sequencing primer pairs and 88 allele‐specific primers or probes for Plutella xylostella.     

Pediatric Invasive Pneumococcal Disease Caused by Vaccine Serotypes following the Introduction of Conjugate Vaccination in Denmark

A seven-valent pneumococcal conjugate vaccine (PCV7) was introduced in the Danish childhood immunization program (2+1 schedule) in October 2007, followed by PCV13 starting from April 2010. The nationwide incidence of IPD among children younger than 5 years nearly halved after the introduction of PCV7 in the program, mainly due to a decline in IPD caused by PCV7-serotypes. We report the results from a nationwide population-based cohort study of laboratory confirmed IPD cases in children younger than 5 years during October 1, 2007 to December 31, 2010 and describe the characteristics of children suspected to present with a vaccine failure. The period between April 19 and December 31, 2010 was considered a PCV7/PCV13 transitional period, where both vaccines were offered. We identified 45 episodes of IPD caused by a PCV7 serotype (23% of the total number) and 105 (55%) caused by one of the 6 additional serotypes in PCV13. Ten children had received at least one PCV7 dose before the onset of IPD caused by a PCV7 serotype. Seven children were considered to be incompletely vaccinated before IPD, but only three cases fulfilled the criteria of vaccine failure (caused by serotypes 14, 19F and 23F). One case of vaccine failure was observed in a severely immunosuppressed child following three PCV7 doses, and two cases were observed in immunocompetent children following two infant doses before they were eligible for their booster. None of the IPD cases caused by the additional PCV13 serotypes had been vaccinated by PCV13 and there were therefore no PCV13-vaccine failures in the first 8-months after PCV13 introduction in Denmark.     

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.     

Core-satellite populations and seasonality of water meter biofilms in a metropolitan drinking water distribution system

Drinking water distribution systems (DWDSs) harbor the microorganisms in biofilms and suspended communities, yet the diversity and spatiotemporal distribution have been studied mainly in the suspended communities. This study examined the diversity of biofilms in an urban DWDS, its relationship with suspended communities and its dynamics. The studied DWDS in Urbana, Illinois received conventionally treated and disinfected water sourced from the groundwater. Over a 2-year span, biomass were sampled from household water meters (n=213) and tap water (n=20) to represent biofilm and suspended communities, respectively. A positive correlation between operational taxonomic unit (OTU) abundance and occupancy was observed. Examined under a ‘core-satellite'' model, the biofilm community comprised 31 core populations that encompassed 76.7% of total 16 S rRNA gene pyrosequences. The biofilm communities shared with the suspended community highly abundant and prevalent OTUs, which related to methano-/methylotrophs (i.e., Methylophilaceae and Methylococcaceae) and aerobic heterotrophs (Sphingomonadaceae and Comamonadaceae), yet differed by specific core populations and lower diversity and evenness. Multivariate tests indicated seasonality as the main contributor to community structure variation. This pattern was resilient to annual change and correlated to the cyclic fluctuations of core populations. The findings of a distinctive biofilm community assemblage and methano-/methyltrophic primary production provide critical insights for developing more targeted water quality monitoring programs and treatment strategies for groundwater-sourced drinking water systems.     

Seed viability and germination success of Acacia tortilis along land‐use and aridity gradients in the Eastern Sahara

Our study focuses on the keystone species Acacia tortilis and is the first to investigate the effect of domestic ungulates and aridity on seed viability and germination over an extensive part of the Eastern Sahara. Bruchids infest its seeds and reduce their viability and germination, but ingestion by ruminant herbivores diminishes infestation levels and enhances/promotes seed viability and germination. The degree of these effects seems to be correlated with animal body mass. Significantly reduced numbers of wild ruminant ungulates have increased the potential importance of domestic animals and pastoral nomadism for the functionality of arid North African and Middle Eastern ecosystems. We sampled seeds (16,543) from A. tortilis in eight areas in three regions with different aridity and land use. We tested the effect of geography and sampling context on seed infestation using random effects logistic regressions. We did a randomized and balanced germination experiment including 1193 seeds, treated with different manure. Germination time and rates across geography, sampling context, and infestation status were analyzed using time‐to‐event analyses, Kaplan–Meier curves and proportional hazards Cox regressions. Bruchid infestation is very high (80%), and the effects of context are significant. Neither partial infestation nor adding manure had a positive effect on germination. There is a strong indication that intact, uningested seeds from acacia populations in the extremely arid Western Desert germinate more slowly and have a higher fraction of hard seeds than in the Eastern Desert and the Red Sea Hills. For ingested seeds in the pastoralist areas we find that intact seeds from goat dung germinate significantly better than those from camel dung. This is contrary to the expected body‐mass effect. There is no effect of site or variation in tribal management.     

Associations of Physical Activity,Sports Participation and Active Commuting on Mathematic Performance and Inhibitory Control in Adolescents

Objectives

To examine objectively measured physical activity level, organized sports participation and active commuting to school in relation to mathematic performance and inhibitory control in adolescents.

Methods

The design was cross-sectional. A convenient sample of 869 sixth and seventh grade students (12–14 years) was invited to participate in the study. A total of 568 students fulfilled the inclusion criteria and comprised the final sample for this study. Mathematic performance was assessed by a customized test and inhibitory control was assessed by a modified Eriksen flanker task. Physical activity was assessed with GT3X and GT3X+ accelerometers presented in sex-specific quartiles of mean counts per minute and mean minutes per day in moderate-to-vigorous physical activity. Active commuting and sports participation was self-reported. Mixed model regression was applied. Total physical activity level was stratified by bicycling status in order to bypass measurement error subject to the accelerometer.

Results

Non-cyclists in the 2^nd quartile of counts per minute displayed a higher mathematic score, so did cyclists in the 2^nd and 3^rd quartile of moderate-to-vigorous physical activity relative to the least active quartile. Non-cyclists in the 3^rd quartile of counts per minute had an improved reaction time and cyclists in the 2^nd quartile of counts per minute and moderate-to-vigorous physical activity displayed an improved accuracy, whereas non-cyclists in the 2^nd quartile of counts per minute showed an inferior accuracy relative to the least active quartile. Bicycling to school and organized sports participation were positively associated with mathematic performance.

Conclusions

Sports participation and bicycling were positively associated with mathematic performance. Results regarding objectively measured physical activity were mixed. Although, no linear nor dose-response relationship was observed there was no indication of a higher activity level impairing the scholastic or cognitive performance.