Assembly of the eastern North American herpetofauna: new evidence from lizards and frogs

Darwin first recognized the importance of episodic intercontinental dispersal in the establishment of worldwide biotic diversity. Faunal exchange across the Bering Land Bridge is a major example of such dispersal. Here, we demonstrate with mitochondrial DNA evidence that three independent dispersal events from Asia to North America are the source for almost all lizard taxa found in continental eastern North America. Two other dispersal events across Beringia account for observed diversity among North American ranid frogs, one of the most species-rich groups of frogs in eastern North America. The contribution of faunal elements from Asia via dispersal across Beringia is a dominant theme in the historical assembly of the eastern North American herpetofauna.     

Cyanobacterial NDH-1 complexes: multiplicity in function and subunit composition

In cyanobacteria, the NAD(P)H:quinone oxidoreductase (NDH-1) is involved in a variety of functions like respiration, cyclic electron flow around PSI and CO₂ uptake. Several types of NDH-1 complexes, which differ in structure and are responsible for these functions, exist in cyanobacterial membranes. This minireview is based on data obtained by reverse genetics and proteomics studies and focuses on the structural and functional differences of the two types of cyanobacterial NDH-1 complexes: NDH-1L, important for respiration and PSI cyclic electron flow, and NDH-1MS, the low-CO₂ inducible complex participating in CO₂ uptake. The NDH-1 complexes in cyanobacteria share a common NDH-1M 'core' complex and differ in the composition of the distal membrane domain composed of specific NdhD and NdhF proteins, which in complexes involved in CO₂ uptake is further associated with the hydrophilic carbon uptake (CUP) domain. At present, however, very important questions concerning the nature of catalytically active subunits that constitute the electron input device (like NADH dehydrogenase module of the eubacterial 'model' NDH-1 analogs), the substrate specificity and reaction mechanisms of cyanobacterial complexes remain unanswered and are shortly discussed here.     

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.     

microRNAs and the evolution of complex multicellularity: identification of a large,diverse complement of microRNAs in the brown alga Ectocarpus

There is currently convincing evidence that microRNAs have evolved independently in at least six different eukaryotic lineages: animals, land plants, chlorophyte green algae, demosponges, slime molds and brown algae. MicroRNAs from different lineages are not homologous but some structural features are strongly conserved across the eukaryotic tree allowing the application of stringent criteria to identify novel microRNA loci. A large set of 63 microRNA families was identified in the brown alga Ectocarpus based on mapping of RNA-seq data and nine microRNAs were confirmed by northern blotting. The Ectocarpus microRNAs are highly diverse at the sequence level with few multi-gene families, and do not tend to occur in clusters but exhibit some highly conserved structural features such as the presence of a uracil at the first residue. No homologues of Ectocarpus microRNAs were found in other stramenopile genomes indicating that they emerged late in stramenopile evolution and are perhaps specific to the brown algae. The large number of microRNA loci in Ectocarpus is consistent with the developmental complexity of many brown algal species and supports a proposed link between the emergence and expansion of microRNA regulatory systems and the evolution of complex multicellularity.     

Clustering and Protein Dynamics of Drosophila melanogaster Telomeres

Telomeres are obligatory chromosomal landmarks that demarcate the ends of linear chromosomes to distinguish them from broken ends and can also serve to organize the genome. In both budding and fission yeast, they cluster at the periphery of the nucleus, potentially to establish a compartment of silent chromatin. To gain insight into telomere organization in higher organisms, we investigated their distribution in interphase nuclei of Drosophila melanogaster. We focused on the syncytial blastoderm, an excellent developmental stage for live imaging due to the synchronous division of the nuclei at this time. We followed the EGFP-labeled telomeric protein HOAP in vivo and found that the 16 telomeres yield four to six foci per nucleus, indicative of clustering. Furthermore, we confirmed clustering in other somatic tissues. Importantly, we observed that HOAP signal intensity in the clusters increases in interphase, potentially due to loading of HOAP to newly replicated telomeres. To determine the rules governing clustering, we used in vivo imaging and fluorescence in situ hybridization to test several predictions. First, we inspected mutant embryos that develop as haploids and found that clustering is not mediated by associations between homologs. Second, we probed specifically for a telomere of novel sequence and found strong evidence against DNA sequence identity and homology as critical factors. Third, we ruled out predominance of intrachromosomal interactions by marking both ends of a chromosome. Based on these results, we propose that clustering is independent of sequence and is likely maintained by an as yet undetermined factor.     

Leishmania amazonensis exhibits phosphatidylserine-dependent procoagulant activity,a process that is counteracted by sandfly saliva

Leishmania parasites expose phosphatidylserine (PS) on theirsurface, a process that has been associated with regulation of host''s immuneresponses. In this study we demonstrate that PS exposure by metacyclicpromastigotes of Leishmania amazonensis favours bloodcoagulation. L. amazonensis accelerates in vitro coagulation ofhuman plasma. In addition, L. amazonensis supports the assemblyof the prothrombinase complex, thus promoting thrombin formation. This processwas reversed by annexin V which blocks PS binding sites. During blood meal,Lutzomyia longipalpis sandfly inject saliva in the bitesite, which has a series of pharmacologically active compounds that inhibitblood coagulation. Since saliva and parasites are co-injected in the host duringnatural transmission, we evaluated the anticoagulant properties of sandflysaliva in counteracting the procoagulant activity of L.amazonensis . Lu. longipalpis saliva reversesplasma clotting promoted by promastigotes. It also inhibits thrombin formationby the prothrombinase complex assembled either in phosphatidylcholine (PC)/PSvesicles or in L. amazonensis . Sandfly saliva inhibits factorX activation by the intrinsic tenase complex assembled on PC/PS vesicles andblocks factor Xa catalytic activity. Altogether our results show that metacyclicpromastigotes of L. amazonensis are procoagulant due to PSexposure. Notably, this effect is efficiently counteracted by sandflysaliva.     

A Combined CXCL10, CXCL8 and H-FABP Panel for the Staging of Human African Trypanosomiasis Patients

Background

Human African trypanosomiasis (HAT), also known as sleeping sickness, is a parasitic tropical disease. It progresses from the first, haemolymphatic stage to a neurological second stage due to invasion of parasites into the central nervous system (CNS). As treatment depends on the stage of disease, there is a critical need for tools that efficiently discriminate the two stages of HAT. We hypothesized that markers of brain damage discovered by proteomic strategies and inflammation-related proteins could individually or in combination indicate the CNS invasion by the parasite.

Methods

Cerebrospinal fluid (CSF) originated from parasitologically confirmed Trypanosoma brucei gambiense patients. Patients were staged on the basis of CSF white blood cell (WBC) count and presence of parasites in CSF. One hundred samples were analysed: 21 from stage 1 (no trypanosomes in CSF and ≤5 WBC/µL) and 79 from stage 2 (trypanosomes in CSF and/or >5 WBC/µL) patients. The concentration of H-FABP, GSTP-1 and S100β in CSF was measured by ELISA. The levels of thirteen inflammation-related proteins (IL-1ra, IL-1β, IL-6, IL-9, IL-10, G-CSF, VEGF, IFN-γ, TNF-α, CCL2, CCL4, CXCL8 and CXCL10) were determined by bead suspension arrays.

Results

CXCL10 most accurately distinguished stage 1 and stage 2 patients, with a sensitivity of 84% and specificity of 100%. Rule Induction Like (RIL) analysis defined a panel characterized by CXCL10, CXCL8 and H-FABP that improved the detection of stage 2 patients to 97% sensitivity and 100% specificity.

Conclusion

This study highlights the value of CXCL10 as a single biomarker for staging T. b. gambiense-infected HAT patients. Further combination of CXCL10 with H-FABP and CXCL8 results in a panel that efficiently rules in stage 2 HAT patients. As these molecules could potentially be markers of other CNS infections and disorders, these results should be validated in a larger multi-centric cohort including other inflammatory diseases such as cerebral malaria and active tuberculosis.     

Evidence for recent horizontal transfer of gypsy-homologous LTR-retrotransposon gtwin into Drosophila erecta followed by its amplification with multiple aberrations

Long terminal repeat (LTR) retrotransposon gtwin was initially discovered in silico, and then it was isolated as gypsy-homologous sequence from Drosophila melanogaster strain, G32. The presence of ORF3 suggests, that gtwin, like gypsy, may be an endogenous retrovirus, which can leave the cell and infect another one. Therefore, in this study we decided to investigate the distribution of gtwin in different species of the melanogaster subgroup in order to find out whether gtwin can be transferred horizontally as well as vertically. Gtwin was found in all 9 species of this subgroup, hence it seems to have inhabited the host genomes for a long time. In addition, we have shown that in the Drosophila erecta genome two gtwin families are present. The first one has 93% of identity to D. melanogaster element and is likely to be a descendant of gtwin that existed in Drosophila before the divergence of the melanogaster subgroup species. The other one has >99% of identity to D. melanogaster gtwin. The most reasonable explanation is that this element has been recently horizontally transferred between D. melanogaster and D. erecta. The number and variety of gtwin copies from the "infectious" family suggest that after the horizontal transfer into D. erecta genome, gtwin underwent amplification and aberrations, leading to the rise of its diverse variants.