CD4(+) T cell lineage integrity is controlled by the histone deacetylases HDAC1 and HDAC2.

Molecular mechanisms that maintain lineage integrity of helper T cells are largely unknown. Here we show histone deacetylases 1 and 2 (HDAC1 and HDAC2) as crucial regulators of this process. Loss of HDAC1 and HDAC2 during late T cell development led to the appearance of major histocompatibility complex (MHC) class II-selected CD4(+) helper T cells that expressed CD8-lineage genes such as Cd8a and Cd8b1. HDAC1 and HDAC2-deficient T helper type 0 (TH0) and TH1 cells further upregulated CD8-lineage genes and acquired a CD8(+) effector T cell program in a manner dependent on Runx-CBFß complexes, whereas TH2 cells repressed features of the CD8(+) lineage independently of HDAC1 and HDAC2. These results demonstrate that HDAC1 and HDAC2 maintain integrity of the CD4 lineage by repressing Runx-CBFß complexes that otherwise induce a CD8(+) effector T cell-like program in CD4(+) T cells.

Human contamination in bacterial genomes has created thousands of spurious proteins.

Contaminant sequences that appear in published genomes can cause numerous problems for downstream analyses, particularly for evolutionary studies and metagenomics projects. Our large-scale scan of complete and draft bacterial and archaeal genomes in the NCBI RefSeq database reveals that 2250 genomes are contaminated by human sequence. The contaminant sequences derive primarily from high-copy human repeat regions, which themselves are not adequately represented in the current human reference genome, GRCh38. The absence of the sequences from the human assembly offers a likely explanation for their presence in bacterial assemblies. In some cases, the contaminating contigs have been erroneously annotated as containing protein-coding sequences, which over time have propagated to create spurious protein "families" across multiple prokaryotic and eukaryotic genomes. As a result, 3437 spurious protein entries are currently present in the widely used nr and TrEMBL protein databases. We report here an extensive list of contaminant sequences in bacterial genome assemblies and the proteins associated with them. We found that nearly all contaminants occurred in small contigs in draft genomes, which suggests that filtering out small contigs from draft genome assemblies may mitigate the issue of contamination while still keeping nearly all of the genuine genomic sequences.

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

BACKGROUND: Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. RESULTS: We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. CONCLUSIONS: Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

A review of methods and databases for metagenomic classification and assembly.

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.

Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification.

SUMMARY: Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers-such as Kraken, Centrifuge and MethaPhlAn-using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. AVAILABILITY AND IMPLEMENTATION: Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian.

Metagenomic next-generation sequencing of rectal swabs for the surveillance of antimicrobial-resistant organisms on the Illumina Miseq and Oxford MinION platforms.

Antimicrobial resistance (AMR) is a public health threat where efficient surveillance is needed to prevent outbreaks. Existing methods for detection of gastrointestinal colonization of multidrug-resistant organisms (MDRO) are limited to specific organisms or resistance mechanisms. Metagenomic next-generation sequencing (mNGS) is a more rapid and agnostic diagnostic approach for microbiome and resistome investigations. We determined if mNGS can detect MDRO from rectal swabs in concordance with standard microbiology results. We performed and compared mNGS performance on short-read Illumina MiSeq (N = 10) and long-read Nanopore MinION (N = 5) platforms directly from rectal swabs to detect vancomycin-resistant enterococci (VRE) and carbapenem-resistant Gram-negative organisms (CRO). We detected Enterococcus faecium (N = 8) and Enterococcus faecalis (N = 2) with associated van genes (9/10) in concordance with VRE culture-based results. We studied the microbiome and identified CRO, Pseudomonas aeruginosa (N = 1), Enterobacter cloacae (N = 1), and KPC-producing Klebsiella pneumoniae (N = 1). Nanopore real-time analysis detected the blaKPC gene in 2.3 min and provided genetic context (blaKPC harbored on pKPC_Kp46 IncF plasmid). Illumina sequencing provided accurate allelic variant determination (i.e., blaKPC-2) and strain typing of the K. pneumoniae (ST-15). We demonstrated an agnostic approach for surveillance of MDRO, examining advantages of both short- and long-read mNGS methods for AMR detection.

Centrifuge: rapid and sensitive classification of metagenomic sequences.

Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space.

Development and Optimization of Metagenomic Next-Generation Sequencing Methods for Cerebrospinal Fluid Diagnostics.

The purpose of this study was to develop and optimize different processing, extraction, amplification, and sequencing methods for metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) specimens. We applied mNGS to 10 CSF samples with known standard-of-care testing (SoC) results (8 positive and 2 negative). Each sample was subjected to nine different methods by varying the sample processing protocols (supernatant, pellet, neat CSF), sample pretreatment (with or without bead beating), and the requirement of nucleic acid amplification steps using DNA sequencing (DNASeq) (with or without whole-genome amplification [WGA]) and RNA sequencing (RNASeq) methods. Negative extraction controls (NECs) were used for each method variation (4/CSF sample). Host depletion (HD) was performed on a subset of samples. We correctly determined the pathogen in 7 of 8 positive samples by mNGS compared to SoC. The two negative samples were correctly interpreted as negative. The processing protocol applied to neat CSF specimens was found to be the most successful technique for all pathogen types. While bead beating introduced bias, we found it increased the detection yield of certain organism groups. WGA prior to DNASeq was beneficial for defining pathogens at the positive threshold, and a combined DNA and RNA approach yielded results with a higher confidence when detected by both methods. HD was required for detection of a low-level-positive enterovirus sample. We demonstrate that NECs are required for interpretation of these complex results and that it is important to understand the common contaminants introduced during mNGS. Optimizing mNGS requires the use of a combination of techniques to achieve the most sensitive, agnostic approach that nonetheless may be less sensitive than SoC tools.

Presence of Human Hepegivirus-1 in a Cohort of People Who Inject Drugs.

BACKGROUND: Next-generation metagenomic sequencing (NGMS) has opened new frontiers in microbial discovery but has been clinically characterized in only a few settings. OBJECTIVE: To explore the plasma virome of persons who inject drugs and to characterize the sensitivity and accuracy of NGMS compared with quantitative clinical standards. DESIGN: Longitudinal and cross-sectional studies. SETTING: A clinical trial (ClinicalTrials.gov: NCT01285050) and a well-characterized cohort study of persons who have injected drugs. PARTICIPANTS: Persons co-infected with hepatitis C virus (HCV) and HIV. MEASUREMENTS: Viral nucleic acid in plasma by NGMS and quantitative polymerase chain reaction (PCR). RESULTS: Next-generation metagenomic sequencing generated a total of 600 million reads, which included the expected HIV and HCV RNA sequences. HIV and HCV reads were consistently identified only when samples contained more than 10 000 copies/mL or IU/mL, respectively, as determined by quantitative PCR. A novel RNA virus, human hepegivirus-1 (HHpgV-1), was also detected by NGMS in 4 samples from 2 persons in the clinical trial. Through use of a quantitative PCR assay for HHpgV-1, infection was also detected in 17 (10.9%) of 156 members of a cohort of persons who injected drugs. In these persons, HHpgV-1 viremia persisted for a median of at least 4538 days and was associated with detection of other bloodborne viruses, such as HCV RNA and SEN virus D. LIMITATION: The medical importance of HHpgV-1 infection is unknown. CONCLUSION: Although NGMS is insensitive for detection of viruses with relatively low plasma nucleic acid concentrations, it may have broad potential for discovery of new viral infections of possible medical importance, such as HHpgV-1. PRIMARY FUNDING SOURCE: National Institutes of Health.

Comprehensive comparative and semiquantitative proteome of a very low number of native and matched epstein-barr-virus-transformed B lymphocytes infiltrating human melanoma.

Melanoma, the deadliest form of skin cancer, is highly immunogenic and frequently infiltrated with immune cells including B cells. The role of tumor-infiltrating B cells (TIBCs) in melanoma is as yet unresolved, possibly due to technical challenges in obtaining TIBCs in sufficient quantity for extensive studies and due to the limited life span of B cells in vitro. A comprehensive workflow has thus been developed for successful isolation and proteomic analysis of a low number of TIBCs from fresh, human melanoma tissue. In addition, we generated in vitro-proliferating TIBC cultures using simultaneous stimulation with Epstein-Barr virus (EBV) and the TLR9 ligand CpG-oligodesoxynucleotide (CpG ODN). The FASP method and iTRAQ labeling were utilized to obtain a comparative, semiquantitative proteome to assess EBV-induced changes in TIBCs. By using as few as 100 000 B cells (∼5 µg protein)/sample for our proteomic study, a total number of 6507 proteins were identified. EBV-induced changes in TIBCs are similar to those already reported for peripheral B cells and largely involve changes in cell cycle proliferation, apoptosis, and interferon response, while most of the proteins were not significantly altered. This study provides an essential, further step toward detailed characterization of TIBCs including functional in vitro analysis.

Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML.

Occurrence of the BCR-ABL(T315I) gatekeeper mutation is among the most pressing challenges in the therapy of chronic myeloid leukemia (CML). Several BCR-ABL inhibitors have multiple targets and pleiotropic effects that could be exploited for their synergistic potential. Testing combinations of such kinase inhibitors identified a strong synergy between danusertib and bosutinib that exclusively affected CML cells harboring BCR-ABL(T315I). To elucidate the underlying mechanisms, we applied a systems-level approach comprising phosphoproteomics, transcriptomics and chemical proteomics. Data integration revealed that both compounds targeted Mapk pathways downstream of BCR-ABL, resulting in impaired activity of c-Myc. Using pharmacological validation, we assessed that the relative contributions of danusertib and bosutinib could be mimicked individually by Mapk inhibitors and collectively by downregulation of c-Myc through Brd4 inhibition. Thus, integration of genome- and proteome-wide technologies enabled the elucidation of the mechanism by which a new drug synergy targets the dependency of BCR-ABL(T315I) CML cells on c-Myc through nonobvious off targets.

Multiple and sequential data acquisition method: an improved method for fragmentation and detection of cross-linked peptides on a hybrid linear trap quadrupole Orbitrap Velos mass spectrometer.

The identification and validation of cross-linked peptides by mass spectrometry remains a daunting challenge for protein-protein cross-linking approaches when investigating protein interactions. This includes the fragmentation of cross-linked peptides in the mass spectrometer per se and following database searching, the matching of the molecular masses of the fragment ions to the correct cross-linked peptides. The hybrid linear trap quadrupole (LTQ) Orbitrap Velos combines the speed of the tandem mass spectrometry (MS/MS) duty circle with high mass accuracy, and these features were utilized in the current study to substantially improve the confidence in the identification of cross-linked peptides. An MS/MS method termed multiple and sequential data acquisition method (MSDAM) was developed. Preliminary optimization of the MS/MS settings was performed with a synthetic peptide (TP1) cross-linked with bis[sulfosuccinimidyl] suberate (BS(3)). On the basis of these results, MSDAM was created and assessed on the BS(3)-cross-linked bovine serum albumin (BSA) homodimer. MSDAM applies a series of multiple sequential fragmentation events with a range of different normalized collision energies (NCE) to the same precursor ion. The combination of a series of NCE enabled a considerable improvement in the quality of the fragmentation spectra for cross-linked peptides, and ultimately aided in the identification of the sequences of the cross-linked peptides. Concurrently, MSDAM provides confirmatory evidence from the formation of reporter ions fragments, which reduces the false positive rate of incorrectly assigned cross-linked peptides.

Quantitative proteomics of aqueous and vitreous fluid from patients with idiopathic epiretinal membranes.

The purpose of the study was to perform a detailed, quantitative protein analysis of the aqueous (AF) and vitreous fluids (VF) from human eyes with idiopathic epiretinal membranes (iERM). The complementary approaches of quantitative liquid chromatography mass spectrometry (LCMS) and multiplex protein assays were utilised to reveal the protein composition in ocular fluids of this retinal disorder. In a prospective clinical trial, AF and VF was collected during surgery from twenty four eyes corresponding to twenty four patients with iERM. VF and AF from eight patients were labelled with the 4-plex iTRAQ reagent and analysed by LCMS. Each iTRAQ 4-plex experiment consisted of the AH and VH from two patients. A total of 323 proteins were identified in the AF and VF from eyes with iERM. Grouping the proteins according to involvement in biological processes, showed that the majority were involved in the classical and alternative pathway of complement activation (n = 27), proteolysis (n = 26) and cell adhesion (n = 28). iTRAQ relative quantitation revealed minimal variation in the protein content between both ocular compartments with only 3.96% of the identified proteins significantly, differentially-expressed. Eight proteins were expressed at a higher level in the VF compared to the AF; and 4 proteins were expressed at a lower level in the VF compared to the AF. For the multiplex bead assays, 29 growth factors and cytokines were assessed in the AF and VF from 16 additional patients with iERM. The protein profile was shown to be similar between VF and AF for the majority of factors except PDGF-AA. This factor was expressed at a higher level in the VF compared to the AF. The data presented in this study revealed that the majority of the proteins identified by LCMS and multiplex bead assays were present in both ocular compartments in similar quantities. This study is a first step, 'discovery phase' towards revealing and quantitating the protein content in the aqueous and vitreous fluid in human eyes with iERM.

A comparative proteomic study of human skin suction blister fluid from healthy individuals using immunodepletion and iTRAQ labeling.

Aberrations in skin morphology and functionality can cause acute and chronic skin-related diseases that are the focus of dermatological research. Mechanically induced skin suction blister fluid may serve as a potential, alternative human body fluid for quantitative mass spectrometry (MS)-based proteomics in order to assist in the understanding of the mechanisms and causes underlying skin-related diseases. The combination of abundant-protein removal with iTRAQ technology and multidimensional fractionation techniques improved the number of identified protein groups. A relative comparison of a cohort of 8 healthy volunteers was thus recruited in order to assess the net variability encountered in a healthy scenario. The technology enabled the identification, to date, of the highest number of reported protein groups (739) with concomitant relative quantitative data for over 90% of all proteins with high reproducibility and accuracy. The use of iTRAQ 8-plex resulted in a 66% decrease in protein identifications but, despite this, provided valuable insight into interindividual differences of the healthy control samples. The geometric mean ratio was close to 1 with 95% of all ratios ranging between 0.45 and 2.05 and a calculated mean coefficient of variation of 15.8%, indicating a lower biological variance than that reported for plasma or urine. By applying a multistep sample processing, the obtained sensitivity and accuracy of quantitative MS analysis demonstrates the prospective value of the approach in future research into skin diseases.

Metagenome analysis using the Kraken software suite.

Metagenomic experiments expose the wide range of microscopic organisms in any microbial environment through high-throughput DNA sequencing. The computational analysis of the sequencing data is critical for the accurate and complete characterization of the microbial community. To facilitate efficient and reproducible metagenomic analysis, we introduce a step-by-step protocol for the Kraken suite, an end-to-end pipeline for the classification, quantification and visualization of metagenomic datasets. Our protocol describes the execution of the Kraken programs, via a sequence of easy-to-use scripts, in two scenarios: (1) quantification of the species in a given metagenomics sample; and (2) detection of a pathogenic agent from a clinical sample taken from a human patient. The protocol, which is executed within 1-2 h, is targeted to biologists and clinicians working in microbiome or metagenomics analysis who are familiar with the Unix command-line environment.

Deconvoluting virome-wide antibody epitope reactivity profiles.

BACKGROUND: Comprehensive characterization of exposures and immune responses to viral infections is critical to a basic understanding of human health and disease. We previously developed the VirScan system, a programmable phage-display technology for profiling antibody binding to a library of peptides designed to span the human virome. Previous VirScan analytical approaches did not carefully account for antibody cross-reactivity among sequences shared by related viruses or for the disproportionate representation of individual viruses in the library. METHODS: Here we present the AntiViral Antibody Response Deconvolution Algorithm (AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection with species-level resolution by considering sequence alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. We further assessed AVARDA's utility in associating viral infection with type 1 diabetes and lupus. FINDINGS: By comparing acute and convalescent sera, AVARDA successfully confirmed or detected encephalitis-associated responses to human herpesviruses 1, 3, 4, 5, and 6, improving the rate of diagnosing viral encephalitis in this cohort by 44%. AVARDA analyses of VirScan data from the type 1 diabetes and lupus cohorts implicated enterovirus and herpesvirus infections, respectively. INTERPRETATION: AVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases. FUNDING: This work was made possible by support from the National Institutes of Health (NIH), the US Army Research Office, the Singapore Infectious Diseases Initiative (SIDI), the Singapore Ministry of Health's National Medical Research Council (NMRC) and the Singapore National Research Foundation (NRF).

Using iTRAQ combined with tandem affinity purification to enhance low-abundance proteins associated with somatically mutated EGFR core complexes in lung cancer.

In this study, we report a novel use for the iTRAQ reagent combined with a peptide mass inclusion list to enhance the signal of low-abundance proteins during analysis by mass spectrometry. C-tagged-SH-EGFR was retrovirally transduced into two mutant lung cancer cell lines (HCC827 and PC9), and the core protein complexes were enriched by tandem affinity purification. Tryptically digested peptides were derivatized with iTRAQ and analyzed by higher-energy collision-induced dissociation mass spectrometry. The data revealed that UBS3B is a member of the EGFR core complex in the HCC827 cell line, which was not apparent by standard, unbiased one-dimensional shotgun analysis and collision-induced dissociation. The expression level of UBS3B, however, was 6-10 times lower than that observed in the PC9 cell line. Thus, using iTRAQ in this fashion allows the identification of low-abundance interactors when combined with samples where the same protein has a higher abundance. Ultimately, this approach may uncover proteins that were previously unknown or only suspected as members of core protein complexes.

General statistical modeling of data from protein relative expression isobaric tags.

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.

A computational approach to analyze the mechanism of action of the kinase inhibitor bafetinib.

Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis.