Characterisation of RNA fragments obtained by mild nuclease digestion of 30-S ribosomal subunits from Escherichia coli.

When Escherichia coli 30-S ribosomal subunits are hydrolysed under mild conditions, two ribonucleoprotein fragments of unequal size are produced. Knowledge of the RNA sequences contained in these hydrolysis products was required for the experiments described in the preceding paper, and the RNA sub-fragments have therefore been examined by oligonucleotide analysis. Two well-defined small fragments of free RNA, produced concomitantly with the ribonucleoprotein fragments, were also analysed. The larger ribonucleoprotein fragment, containing predominantly proteins S4, S5, S8, S15, S16 (17) and S20, contains a complex mixture of RNA sub-fragments varying from about 100 to 800 nucleotides in length. All these fragments arose from the 5'-terminal 900 nucleotides of 16-S RNA, corresponding to the well-known 12-S fragment. No long-range interactions could be detected within this RNA region in these experiments. The RNA from the smaller ribonucleoprotein fragment (containing proteins S7, S9 S10, S14 and S19) has been described in detail previously, and consists of about 450 nucleotides near the 3' end of the 16-S RNA, but lacking the 3'-terminal 150 nucleotides. The two small free RNA fragments (above) partly account for these missing 150 nucleotides; both fragments arose from section A of the 16-S RNA, but section J (the 3'-terminal 50 nucleotides) was not found. This result suggests that the 3' region of 16-S RNA is not involved in stable interactions with protein.     

Localization of a base-paired interaction between small nuclear RNAs U4 and U6 in intact U4/U6 ribonucleoprotein particles by psoralen cross-linking

The small nuclear RNAs U4 and U6 display extensive sequence complementarity and co-exist in a single ribonucleoprotein particle. We have investigated intermolecular base-pairing between both RNAs by psoralen cross-linking, with emphasis on the native U4/U6 ribonucleoprotein complex. A mixture of small nuclear ribonucleoproteins U1 to U6 from HeLa cells, purified under non-denaturing conditions by immune affinity chromatography with antibodies specific for the trimethylguanosine cap of the small nuclear RNAs was treated with aminomethyltrioxsalen. A psoralen cross-linked U4/U6 RNA complex could be detected in denaturing polyacrylamide gels. Following digestion of the cross-linked U4/U6 RNA complex with ribonuclease T1, two-dimensional diagonal electrophoresis in denaturing polyacrylamide gels was used to isolate cross-linked fragments. These fragments were analysed by chemical sequencing methods and their positions identified within RNAs U4 and U6. Two overlapping fragments of U4 RNA, spanning positions 52 to 65, were cross-linked to one fragment of U6 RNA (positions 51 to 59). These fragments show complementarity over a contiguous stretch of eight nucleotides. From these results, we conclude that in the native U4/U6 ribonucleoprotein particle, both RNAs are base-paired via these complementary regions. The small nuclear RNAs U4 and U6 became cross-linked in the deproteinized U4/U6 RNA complex also, provided that small nuclear ribonucleoproteins were phenolized at 0 degree C. When the phenolization was performed at 65 degrees C, no cross-linking could be detected upon reincubation of the dissociated RNAs at lower temperature. These results indicate that proteins are not required to stabilize the mutual interactions between both RNAs, once they exist. They further suggest, however, that proteins may well be needed for exposing the complementary RNA regions for proper intermolecular base-pairing in the course of the assembly of the U4/U6 RNP complex from isolated RNAs. Our results are discussed also in terms of the different secondary structures that the small nuclear RNAs U4 and U6 may adopt in the U4/U6 ribonucleoprotein particle as opposed to the isolated RNAs.     

A Mycobacterium leprae-specific gene encoding an immunologically recognized 45 kDa protein

By screening a Mycobacterium leprae lambda gt11 expression library with a serum from an Ethiopian lepromatous leprosy (LL) patient a clone was isolated (LL4) belonging to hybridization group III of a panel of previously isolated M. leprae clones. Members of this hybridization group encode a serologically recognized 45 kDa protein. The complete DNA sequences of the partially overlapping clones LL4 and L1 (hybridization group III) are presented and these revealed the presence of an open reading frame (ORF) predicting a protein with a molecular size of 42 448 Da. Southern hybridizations on total genomic DNA of M. Ieprae, Mycobacterium tuberculosis and eight atypical mycobacteria showed that the LL4 DNA fragment is specific for M. Ieprae DNA even under low-stringency conditions. The M. Ieprae specificity of LL4 DNA was further confirmed by the polymerase chain reaction using four different sets of primers. Western blotting analyses showed that the M. Ieprae 45 kDa protein is frequently recognized by antibodies from leprosy patients and that this recognition is specific since no antibodies could be detected in sera of tuberculosis patients. T-cell proliferation assays also demonstrated T-cell recognition by leprosy patients and healthy contacts of the M. Ieprae 45 kDa protein. The specificity of the LL4 DNA region and the 45 kDa antigen that is encoded by hybridization group III could provide unique tools for the development of M. Ieprae-specific immunological and DNA reagents.     

The use of azidoarylimidoesters in RNA-protein cross-linking studies with Escherichia coli ribosomes

A series of related hetero-bifunctional RNA-protein cross-linking reagents has been prepared, carrying an imidoester or N-hydroxysuccinimide ester function at one end of the molecule, and a phenylazido function at the other. These compounds have been applied to RNA-protein cross-linking studies with ribosomal subunits, and one of them, p-azido-phenylacetic imidoester, has proved to be a particularly useful reagent for this purpose. The reagent first reacts specifically with protein amino groups, and subsequent photolysis of the azide group leads to cross-linking to the RNA in yields of up to 8% of the total protein. The whole reaction takes place under very mild conditions in aqueous solution.The individual proteins concerned in the cross-links have been identified by two-dimensional gel electrophoresis, and the existence of a covalent cross-link was confirmed by the isolation by two different methods of protein-oligonucleotide complexes carrying a ³²P label. Although most of the ribosomal proteins could be cross-linked to their corresponding ribosomal RNA within the individual subunits, RNA-protein cross-links at the ribosomal subunit interface were only detectable in vanishingly small amounts.The advantages of this type of genuine hetero-bifunctional reagent in RNA-protein cross-linking studies are discussed.     

Macrophage Specific Caspase-1/11 Deficiency Protects against Cholesterol Crystallization and Hepatic Inflammation in Hyperlipidemic Mice

Background & Aims

While non-alcoholic steatohepatitis (NASH) is characterized by hepatic steatosis combined with inflammation, the mechanisms triggering hepatic inflammation are unknown. In Ldlr^-/- mice, we have previously shown that lysosomal cholesterol accumulation in Kupffer cells (KCs) correlates with hepatic inflammation and cholesterol crystallization. Previously, cholesterol crystals have been shown to induce the activation of inflammasomes. Inflammasomes are protein complexes that induce the processing and release of pro-inflammatory cytokines IL-1b and IL-18 via caspase-1 activation. Whereas caspase-1 activation is independent of caspase-11 in the canonical pathway of inflammasome activation, caspase-11 was found to trigger caspase-1-dependent IL-1b and IL-18 in response to non-canonical inflammasome activators. So far, it has not been investigated whether inflammasome activation stimulates the formation of cholesterol crystals. We hypothesized that inflammasome activation in KCs stimulates cholesterol crystallization, thereby leading to hepatic inflammation.

Methods

Ldlr ^-/- mice were transplanted (tp) with wild-type (Wt) or caspase-1/11^-/- (dKO) bone marrow and fed either regular chow or a high-fat, high-cholesterol (HFC) diet for 12 weeks. In vitro, bone marrow derived macrophages (BMDM) from wt or caspase-1/11^-/- mice were incubated with oxLDL for 24h and autophagy was assessed.

Results

In line with our hypothesis, caspase-1/11^-/--tp mice had less severe hepatic inflammation than Wt-tp animals, as evident from liver histology and gene expression analysis in isolated KCs. Mechanistically, KCs from caspase-1/11^-/--tp mice showed less cholesterol crystals, enhanced cholesterol efflux and increased autophagy. In wt BMDM, oxLDL incubation led to disturbed autophagy activity whereas BMDM from caspase-1/11^-/- mice had normal autophagy activity.

Conclusion

Altogether, these data suggest a vicious cycle whereby disturbed autophagy and decreased cholesterol efflux leads to newly formed cholesterol crystals and thereby maintain hepatic inflammation during NASH by further activating the inflammasome.     

Caspase-1 deficiency in mice reduces intestinal triglyceride absorption and hepatic triglyceride secretion

Caspase-1 is known to activate the proinflammatory cytokines IL-1β and IL-18. Additionally, it can cleave other substrates, including proteins involved in metabolism. Recently, we showed that caspase-1 deficiency in mice strongly reduces high-fat diet-induced weight gain, at least partly caused by an increased energy production. Increased feces secretion by caspase-1-deficient mice suggests that lipid malabsorption possibly further reduces adipose tissue mass. In this study we investigated whether caspase-1 plays a role in triglyceride-(TG)-rich lipoprotein metabolism using caspase-1-deficient and wild-type mice. Caspase-1 deficiency reduced the postprandial TG response to an oral lipid load, whereas TG-derived fatty acid (FA) uptake by peripheral tissues was not affected, demonstrated by unaltered kinetics of [³H]TG-labeled very low-density lipoprotein (VLDL)-like emulsion particles. An oral gavage of [³H]TG-containing olive oil revealed that caspase-1 deficiency reduced TG absorption and subsequent uptake of TG-derived FA in liver, muscle, and adipose tissue. Similarly, despite an elevated hepatic TG content, caspase-1 deficiency reduced hepatic VLDL-TG production. Intestinal and hepatic gene expression analysis revealed that caspase-1 deficiency did not affect FA oxidation or FA uptake but rather reduced intracellular FA transport, thereby limiting lipid availability for the assembly and secretion of TG-rich lipoproteins. The current study reveals a novel function for caspase-1, or caspase-1-cleaved substrates, in controlling intestinal TG absorption and hepatic TG secretion.     

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.     

Clinical Evaluation of an Affordable Qualitative Viral Failure Assay for HIV Using Dried Blood Spots in Uganda

Background

WHO recommends regular viral load (VL) monitoring of patients on antiretroviral therapy (ART) for timely detection of virological failure, prevention of acquired HIV drug resistance (HIVDR) and avoiding unnecessary switching to second-line ART. However, the cost and complexity of routine VL testing remains prohibitive in most resource limited settings (RLS). We evaluated a simple, low–cost, qualitative viral–failure assay (VFA) on dried blood spots (DBS) in three clinical settings in Uganda.

Methods

We conducted a cross–sectional diagnostic accuracy study in three HIV/AIDS treatment centres at the Joint Clinical Research Centre in Uganda. The VFA employs semi-quantitative detection of HIV–1 RNA amplified from the LTR gene. We used paired dry blood spot (DBS) and plasma with the COBASAmpliPrep/COBASTaqMan, Roche version 2 (VL_ref) as the reference assay. We used the VFA at two thresholds of viral load, (>5,000 or >1,000 copies/ml).

Results

496 paired VFA and VL_ref results were available for comparative analysis. Overall, VFA demonstrated 78.4% sensitivity, (95% CI: 69.7%–87.1%), 93% specificity (95% CI: 89.7%–96.4%), 89.3% accuracy (95% CI: 85%–92%) and an agreement kappa = 0.72 as compared to the VL_ref. The predictive values of positivity and negativity among patients on ART for >12 months were 72.7% and 99.3%, respectively.

Conclusions

VFA allowed 89% of correct classification of VF. Only 11% of the patients were misclassified with the potential of unnecessary or late switch to second–line ART. Our findings present an opportunity to roll out simple and affordable VL monitoring for HIV–1 treatment in RLS.