Decisive role of intermediate filament typing of tumor cells in the differential diagnosis of difficult fine needle aspirates

Thirty-six diagnostically difficult fine needle aspirates from enlarged lymph nodes and malignant soft tissue tumors, containing tumor cells with scanty or no obvious light microscopic features indicative of their differentiation, were assessed by a panel of six cytopathologists. Their diagnoses were recorded and then compared with the definitive diagnosis established by combining the cytologic findings with the results of intermediate filament typing of tumor cells in the smears using monoclonal antibodies specific for each filament type. The results show that use of these antibodies can markedly improve the accuracy of the cytologic diagnosis of tumor type as well as revise or prevent erroneous cytologic diagnoses in difficult cases. This pertains especially to the differential diagnoses of carcinoma versus malignant lymphoma, carcinoma versus malignant melanoma, carcinoma versus sarcoma and squamous carcinoma versus carcinoma of simple epithelia. Intermediate filament typing of tumor cells in aspirates as an objective histogenetic criterium makes the differential diagnosis of the difficult aspirates much more reliable and reproducible, provided that appropriate questions are asked, monoclonal antibodies with well-defined specificities are used and the antigenicity of the intermediate filaments in smears is preserved.     

Genome sequence of the clover-nodulating Rhizobium leguminosarum bv. trifolii strain TA1

Rhizobium leguminosarum bv. trifolii strain TA1 is an aerobic, motile, Gram-negative, non-spore-forming rod that is an effective nitrogen fixing microsymbiont on the perennial clovers originating from Europe and the Mediterranean basin. TA1 however is ineffective with many annual and perennial clovers originating from Africa and America. Here we describe the features of R. leguminosarum bv. trifolii strain TA1, together with genome sequence information and annotation. The 8,618,824 bp high-quality-draft genome is arranged in a 6 scaffold of 32 contigs, contains 8,493 protein-coding genes and 83 RNA-only encoding genes, and is one of 20 rhizobial genomes sequenced as part of the DOE Joint Genome Institute 2010 Community Sequencing Program.     

Genome sequence of Ensifer meliloti strain WSM1022; a highly effective microsymbiont of the model legume Medicago truncatula A17

Ensifer meliloti WSM1022 is an aerobic, motile, Gram-negative, non-spore-forming rod that can exist as a soil saprophyte or as a legume microsymbiont of Medicago. WSM1022 was isolated in 1987 from a nodule recovered from the roots of the annual Medicago orbicularis growing on the Cyclades Island of Naxos in Greece. WSM1022 is highly effective at fixing nitrogen with M. truncatula and other annual species such as M. tornata and M. littoralis and is also highly effective with the perennial M. sativa (alfalfa or lucerne). In common with other characterized E. meliloti strains, WSM1022 will nodulate but fixes poorly with M. polymorpha and M. sphaerocarpos and does not nodulate M. murex. Here we describe the features of E. meliloti WSM1022, together with genome sequence information and its annotation. The 6,649,661 bp high-quality-draft genome is arranged into 121 scaffolds of 125 contigs containing 6,323 protein-coding genes and 75 RNA-only encoding genes, and is one of 100 rhizobial genomes sequenced as part of the DOE Joint Genome Institute 2010 Genomic Encyclopedia for Bacteria and Archaea-Root Nodule Bacteria (GEBA-RNB) project.     

Comparison of 26 Sphingomonad Genomes Reveals Diverse Environmental Adaptations and Biodegradative Capabilities

Sphingomonads comprise a physiologically versatile group within the Alphaproteobacteria that includes strains of interest for biotechnology, human health, and environmental nutrient cycling. In this study, we compared 26 sphingomonad genome sequences to gain insight into their ecology, metabolic versatility, and environmental adaptations. Our multilocus phylogenetic and average amino acid identity (AAI) analyses confirm that Sphingomonas, Sphingobium, Sphingopyxis, and Novosphingobium are well-resolved monophyletic groups with the exception of Sphingomonas sp. strain SKA58, which we propose belongs to the genus Sphingobium. Our pan-genomic analysis of sphingomonads reveals numerous species-specific open reading frames (ORFs) but few signatures of genus-specific cores. The organization and coding potential of the sphingomonad genomes appear to be highly variable, and plasmid-mediated gene transfer and chromosome-plasmid recombination, together with prophage- and transposon-mediated rearrangements, appear to play prominent roles in the genome evolution of this group. We find that many of the sphingomonad genomes encode numerous oxygenases and glycoside hydrolases, which are likely responsible for their ability to degrade various recalcitrant aromatic compounds and polysaccharides, respectively. Many of these enzymes are encoded on megaplasmids, suggesting that they may be readily transferred between species. We also identified enzymes putatively used for the catabolism of sulfonate and nitroaromatic compounds in many of the genomes, suggesting that plant-based compounds or chemical contaminants may be sources of nitrogen and sulfur. Many of these sphingomonads appear to be adapted to oligotrophic environments, but several contain genomic features indicative of host associations. Our work provides a basis for understanding the ecological strategies employed by sphingomonads and their role in environmental nutrient cycling.     

Multiple Single-Cell Genomes Provide Insight into Functions of Uncultured Deltaproteobacteria in the Human Oral Cavity

Despite a long history of investigation, many bacteria associated with the human oral cavity have yet to be cultured. Studies that correlate the presence or abundance of uncultured species with oral health or disease highlight the importance of these community members. Thus, we sequenced several single-cell genomic amplicons from Desulfobulbus and Desulfovibrio (class Deltaproteobacteria) to better understand their function within the human oral community and their association with periodontitis, as well as other systemic diseases. Genomic data from oral Desulfobulbus and Desulfovibrio species were compared to other available deltaproteobacterial genomes, including from a subset of host-associated species. While both groups share a large number of genes with other environmental Deltaproteobacteria genomes, they encode a wide array of unique genes that appear to function in survival in a host environment. Many of these genes are similar to virulence and host adaptation factors of known human pathogens, suggesting that the oral Deltaproteobacteria have the potential to play a role in the etiology of periodontal disease.     

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.     

High-quality draft genome sequence of Pseudomonas aeruginosa san ai,an environmental isolate resistant to heavy metals

Extremophiles - The strain Pseudomonas aeruginosa san ai, isolated from an extreme environment (industrial mineral cutting oil, pH 10), is able to survive and persist in the presence of a variety...     

The genome of Syntrophorhabdus aromaticivorans strain UI provides new insights for syntrophic aromatic compound metabolism and electron flow

How aromatic compounds are degraded in various anaerobic ecosystems (e.g. groundwater, sediments, soils and wastewater) is currently poorly understood. Under methanogenic conditions (i.e. groundwater and wastewater treatment), syntrophic metabolizers are known to play an important role. This study explored the draft genome of Syntrophorhabdus aromaticivorans strain UI and identified the first syntrophic phenol‐degrading phenylphosphate synthase (PpsAB) and phenylphosphate carboxylase (PpcABCD) and syntrophic terephthalate‐degrading decarboxylase complexes. The strain UI genome also encodes benzoate degradation through hydration of the dienoyl‐coenzyme A intermediate as observed in Geobacter metallireducens and Syntrophus aciditrophicus. Strain UI possesses electron transfer flavoproteins, hydrogenases and formate dehydrogenases essential for syntrophic metabolism. However, the biochemical mechanisms for electron transport between these H₂/formate‐generating proteins and syntrophic substrate degradation remain unknown for many syntrophic metabolizers, including strain UI. Analysis of the strain UI genome revealed that heterodisulfide reductases (HdrABC), which are poorly understood electron transfer genes, may contribute to syntrophic H₂ and formate generation. The genome analysis further identified a putative ion‐translocating ferredoxin : NADH oxidoreductase (IfoAB) that may interact with HdrABC and dissimilatory sulfite reductase gamma subunit (DsrC) to perform novel electron transfer mechanisms associated with syntrophic metabolism.