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).

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.

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.

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.

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.

CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise.

We assembled the sequences from deep RNA sequencing experiments by the Genotype-Tissue Expression (GTEx) project, to create a new catalog of human genes and transcripts, called CHESS. The new database contains 42,611 genes, of which 20,352 are potentially protein-coding and 22,259 are noncoding, and a total of 323,258 transcripts. These include 224 novel protein-coding genes and 116,156 novel transcripts. We detected over 30 million additional transcripts at more than 650,000 genomic loci, nearly all of which are likely nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells. The CHESS database is available at http://ccb.jhu.edu/chess .

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.

Identifying Corneal Infections in Formalin-Fixed Specimens Using Next Generation Sequencing.

Purpose: We test the ability of next-generation sequencing, combined with computational analysis, to identify a range of organisms causing infectious keratitis. Methods: This retrospective study evaluated 16 cases of infectious keratitis and four control corneas in formalin-fixed tissues from the pathology laboratory. Infectious cases also were analyzed in the microbiology laboratory using culture, polymerase chain reaction, and direct staining. Classified sequence reads were analyzed with two different metagenomics classification engines, Kraken and Centrifuge, and visualized using the Pavian software tool. Results: Sequencing generated 20 to 46 million reads per sample. On average, 96% of the reads were classified as human, 0.3% corresponded to known vectors or contaminant sequences, 1.7% represented microbial sequences, and 2.4% could not be classified. The two computational strategies successfully identified the fungal, bacterial, and amoebal pathogens in most patients, including all four bacterial and mycobacterial cases, five of six fungal cases, three of three Acanthamoeba cases, and one of three herpetic keratitis cases. In several cases, additional potential pathogens also were identified. In one case with cytomegalovirus identified by Kraken and Centrifuge, the virus was confirmed by direct testing, while two where Staphylococcus aureus or cytomegalovirus were identified by Centrifuge but not Kraken could not be confirmed. Confirmation was not attempted for an additional three potential pathogens identified by Kraken and 11 identified by Centrifuge. Conclusions: Next generation sequencing combined with computational analysis can identify a wide range of pathogens in formalin-fixed corneal specimens, with potential applications in clinical diagnostics and research.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

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.

Intracellular HIV-1 RNA and CD4+ T-cell activation in patients starting antiretrovirals.

OBJECTIVE: To assess if the reduction in HIV-1 RNA in CD4 T cells is correlated with the persistence of immune activation following early antiretroviral therapy (ART). DESIGN: Clinical trial (NCT01285050). METHODS: Next-generation sequencing was used to study total RNA from activated CD4 T cells (CD38 and human leukocyte antigen - antigen D related (HLA-DR) expressing) collected from 19 treatment-naïve HIV-1/hepatitis C virus-infected patients before and early after ART initiation (≥12 weeks after plasma HIV-1 RNA <50 copies/ml). To validate comparisons, pre and post-ART measures were adjusted for input RNA and overall read number. RESULTS: As expected, ART use was associated with a median [interquartile range (IQR)] 4.3% (2.2-8.3) reduction in the proportion of activated CD4 T cells (P = 0.0008). Whereas in those activated CD4 T cells no consistent differences in overall gene expression were detected, interferon-stimulated gene expression declined (P < 2 × 10). Pre-ART, sorted activated CD4 T cells contained a median (IQR) of 959 (252-1614) HIV-1 reads/10 reads compared with 72 (55-152) HIV-1 reads/10 reads after at least 12 weeks of suppressive ART (P = 8 × 10). The decrease in HIV-1 reads in activated CD4 T cells was associated with the change in plasma HIV-1 RNA levels (r = 0.77, P = 2 × 10) and the change in the proportion of activated CD4 T cells (r = 0.70, P = 0.0016). CONCLUSION: Months of ART led to a marked decrease in cell-associated HIV-1 RNA and interferon-stimulated genes expression in activated CD4 T cells that were strongly associated with the reduction in the proportion of activated CD4 T cells.

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.

Next-generation sequencing in neuropathologic diagnosis of infections of the nervous system.

OBJECTIVE: To determine the feasibility of next-generation sequencing (NGS) microbiome approaches in the diagnosis of infectious disorders in brain or spinal cord biopsies in patients with suspected CNS infections. METHODS: In a prospective pilot study, we applied NGS in combination with a new computational analysis pipeline to detect the presence of pathogenic microbes in brain or spinal cord biopsies from 10 patients with neurologic problems indicating possible infection but for whom conventional clinical and microbiology studies yielded negative or inconclusive results. RESULTS: Direct DNA and RNA sequencing of brain tissue biopsies generated 8.3 million to 29.1 million sequence reads per sample, which successfully identified with high confidence the infectious agent in 3 patients for whom validation techniques confirmed the pathogens identified by NGS. Although NGS was unable to identify with precision infectious agents in the remaining cases, it contributed to the understanding of neuropathologic processes in 5 others, demonstrating the power of large-scale unbiased sequencing as a novel diagnostic tool. Clinical outcomes were consistent with the findings yielded by NGS on the presence or absence of an infectious pathogenic process in 8 of 10 cases, and were noncontributory in the remaining 2. CONCLUSIONS: NGS-guided metagenomic studies of brain, spinal cord, or meningeal biopsies offer the possibility for dramatic improvements in our ability to detect (or rule out) a wide range of CNS pathogens, with potential benefits in speed, sensitivity, and cost. NGS-based microbiome approaches present a major new opportunity to investigate the potential role of infectious pathogens in the pathogenesis of neuroinflammatory disorders.

A Comprehensive Analysis of the Dynamic Response to Aphidicolin-Mediated Replication Stress Uncovers Targets for ATM and ATMIN.

The cellular response to replication stress requires the DNA-damage-responsive kinase ATM and its cofactor ATMIN; however, the roles of this signaling pathway following replication stress are unclear. To identify the functions of ATM and ATMIN in response to replication stress, we utilized both transcriptomics and quantitative mass-spectrometry-based phosphoproteomics. We found that replication stress induced by aphidicolin triggered widespread changes in both gene expression and protein phosphorylation patterns. These changes gave rise to distinct early and late replication stress responses. Furthermore, our analysis revealed previously unknown targets of ATM and ATMIN downstream of replication stress. We demonstrate ATMIN-dependent phosphorylation of H2AX and of CRMP2, a protein previously implicated in Alzheimer's disease but not in the DNA damage response. Overall, our dataset provides a comprehensive resource for discovering the cellular responses to replication stress and, potentially, associated pathologies.

Re-analysis of metagenomic sequences from acute flaccid myelitis patients reveals alternatives to enterovirus D68 infection.

Metagenomic sequence data can be used to detect the presence of infectious viruses and bacteria, but normal microbial flora make this process challenging. We re-analyzed metagenomic RNA sequence data collected during a recent outbreak of acute flaccid myelitis (AFM), caused in some cases by infection with enterovirus D68. We found that among the patients whose symptoms were previously attributed to enterovirus D68, one patient had clear evidence of infection with Haemophilus influenzae, and a second patient had a severe Staphylococcus aureus infection caused by a methicillin-resistant strain. Neither of these bacteria were identified in the original study. These observations may have relevance in cases that present with flaccid paralysis because bacterial infections, co-infections or post-infection immune responses may trigger pathogenic processes that may present as poliomyelitis-like syndromes and may mimic AFM.  A separate finding was that large numbers of human sequences were present in each of the publicly released samples, although the original study reported that human sequences had been removed before deposition.

The Lipid-Modifying Enzyme SMPDL3B Negatively Regulates Innate Immunity.

Lipid metabolism and receptor-mediated signaling are highly intertwined processes that cooperate to fulfill cellular functions and safeguard cellular homeostasis. Activation of Toll-like receptors (TLRs) leads to a complex cellular response, orchestrating a diverse range of inflammatory events that need to be tightly controlled. Here, we identified the GPI-anchored Sphingomyelin Phosphodiesterase, Acid-Like 3B (SMPDL3B) in a mass spectrometry screening campaign for membrane proteins co-purifying with TLRs. Deficiency of Smpdl3b in macrophages enhanced responsiveness to TLR stimulation and profoundly changed the cellular lipid composition and membrane fluidity. Increased cellular responses could be reverted by re-introducing affected ceramides, functionally linking membrane lipid composition and innate immune signaling. Finally, Smpdl3b-deficient mice displayed an intensified inflammatory response in TLR-dependent peritonitis models, establishing its negative regulatory role in vivo. Taken together, our results identify the membrane-modulating enzyme SMPDL3B as a negative regulator of TLR signaling that functions at the interface of membrane biology and innate immunity.

Identification of kinase inhibitor targets in the lung cancer microenvironment by chemical and phosphoproteomics.

A growing number of gene mutations, which are recognized as cancer drivers, can be successfully targeted with drugs. The redundant and dynamic nature of oncogenic signaling networks and complex interactions between cancer cells and the microenvironment, however, can cause drug resistance. While these challenges can be addressed by developing drug combinations or polypharmacology drugs, this benefits greatly from a detailed understanding of the proteome-wide target profiles. Using mass spectrometry-based chemical proteomics, we report the comprehensive characterization of the drug-protein interaction networks for the multikinase inhibitors dasatinib and sunitinib in primary lung cancer tissue specimens derived from patients. We observed in excess of 100 protein kinase targets plus various protein complexes involving, for instance, AMPK, TBK1 (sunitinib), and ILK (dasatinib). Importantly, comparison with lung cancer cell lines and mouse xenografts thereof showed that most targets were shared between cell lines and tissues. Several targets, however, were only present in tumor tissues. In xenografts, most of these proteins were of mouse origin suggesting that they originate from the tumor microenvironment. Furthermore, intersection with subsequent global phosphoproteomic analysis identified several activated signaling pathways. These included MAPK, immune, and integrin signaling, which were affected by these drugs in both cancer cells and the microenvironment. Thus, the combination of chemical and phosphoproteomics can generate a systems view of proteins, complexes, and signaling pathways that are simultaneously engaged by multitargeted drugs in cancer cells and the tumor microenvironment. This may allow for the design of novel anticancer therapies that concurrently target multiple tumor compartments.