The role of adaptive immunity in early cancer development is controversial. Here we show that chronic inflammation and fibrosis in humans and mice with non-alcoholic fatty liver disease is ...accompanied by accumulation of liver-resident immunoglobulin-A-producing (IgA
) cells. These cells also express programmed death ligand 1 (PD-L1) and interleukin-10, and directly suppress liver cytotoxic CD8
T lymphocytes, which prevent emergence of hepatocellular carcinoma and express a limited repertoire of T-cell receptors against tumour-associated antigens. Whereas CD8
T-cell ablation accelerates hepatocellular carcinoma, genetic or pharmacological interference with IgA
cell generation attenuates liver carcinogenesis and induces cytotoxic T-lymphocyte-mediated regression of established hepatocellular carcinoma. These findings establish the importance of inflammation-induced suppression of cytotoxic CD8
T-lymphocyte activation as a tumour-promoting mechanism.
High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation ...of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur's ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur.
Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.
Recent algorithmic advances in amplicon-based microbiome studies enable the inference of exact amplicon sequence fragments. These new methods enable the investigation of sub-operational taxonomic ...units (sOTU) by removing erroneous sequences. However, short (e.g., 150-nucleotide nt) DNA sequence fragments do not contain sufficient phylogenetic signal to reproduce a reasonable tree, introducing a barrier in the utilization of critical phylogenetically aware metrics such as Faith's PD or UniFrac. Although fragment insertion methods do exist, those methods have not been tested for sOTUs from high-throughput amplicon studies in insertions against a broad reference phylogeny. We benchmarked the SATé-enabled phylogenetic placement (SEPP) technique explicitly against 16S V4 sequence fragments and showed that it outperforms the conceptually problematic but often-used practice of reconstructing
phylogenies. In addition, we provide a BSD-licensed QIIME2 plugin (https://github.com/biocore/q2-fragment-insertion) for SEPP and integration into the microbial study management platform QIITA.
The move from OTU-based to sOTU-based analysis, while providing additional resolution, also introduces computational challenges. We demonstrate that one popular method of dealing with sOTUs (building a
tree from the short sequences) can provide incorrect results in human gut metagenomic studies and show that phylogenetic placement of the new sequences with SEPP resolves this problem while also yielding other benefits over existing methods.
We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to ...operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to "classic" open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, "classic" open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of "classic" open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by "classic" open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME's uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME's OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.
Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated ...into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.
The use of sterile swabs is a convenient and common way to collect microbiome samples, and many studies have shown that the effects of room-temperature storage are smaller than physiologically ...relevant differences between subjects. However, several bacterial taxa, notably members of the class
, grow at room temperature, sometimes confusing microbiome results, particularly when stability is assumed. Although comparative benchmarking has shown that several preservation methods, including the use of 95% ethanol, fecal occult blood test (FOBT) and FTA cards, and Omnigene-GUT kits, reduce changes in taxon abundance during room-temperature storage, these techniques all have drawbacks and cannot be applied retrospectively to samples that have already been collected. Here we performed a meta-analysis using several different microbiome sample storage condition studies, showing consistent trends in which specific bacteria grew (i.e., "bloomed") at room temperature, and introduce a procedure for removing the sequences that most distort analyses. In contrast to similarity-based clustering using operational taxonomic units (OTUs), we use a new technique called "Deblur" to identify the exact sequences corresponding to blooming taxa, greatly reducing false positives and also dramatically decreasing runtime. We show that applying this technique to samples collected for the American Gut Project (AGP), for which participants simply mail samples back without the use of ice packs or other preservatives, yields results consistent with published microbiome studies performed with frozen or otherwise preserved samples.
In many microbiome studies, the necessity to store samples at room temperature (i.e., remote fieldwork) and the ability to ship samples without hazardous materials that require special handling training, such as ethanol (i.e., citizen science efforts), is paramount. However, although room-temperature storage for a few days has been shown not to obscure physiologically relevant microbiome differences between comparison groups, there are still changes in specific bacterial taxa, notably, in members of the class
, that can make microbiome profiles difficult to interpret. Here we identify the most problematic taxa and show that removing sequences from just a few fast-growing taxa is sufficient to correct microbiome profiles.
Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary ...studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples.
By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss.
: An author video summary of this article is available.
Our bodies and natural environment contain complex microbial communities, colloquially termed microbiomes. We previously created a web-based application, EMPeror, for visualizing ordinations derived ...from comparisons of these microbiome communities. We have now improved EMPeror to create interactive animations that connect successive samples to highlight patterns over time.
Our bodies and natural environment contain complex microbial communities, colloquially termed microbiomes. We previously created a web-based application, EMPeror, for visualizing ordinations derived from comparisons of these microbiome communities. We have now improved EMPeror to create interactive animations that connect successive samples to highlight patterns over time.
Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of ...subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release.
Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).
The isolation of circulating tumour cells (CTCs) in colorectal cancer (CRC) mostly relies on the expression of epithelial markers such as EpCAM, and phenotypic characterisation is usually performed ...under fluorescence microscopy with only one or two additional markers. This limits the ability to detect different CTC subpopulations based on multiple markers. The aim of this work was to develop a novel protocol combining two platforms (IsoFlux
and ImageStream
) to improve CTC evaluation. Cancer cell lines and peripheral blood from healthy donors were used to evaluate the efficiency of each platform independently and in combination. Peripheral blood was extracted from 16 early CRC patients (before loco-regional surgery) to demonstrate the suitability of the protocol for CTC assessment. Additionally, peripheral blood was extracted from nine patients one month after surgery to validate the utility of our protocol for identifying CTC subpopulation changes over time. Results: Our protocol had a mean recovery efficiency of 69.5% and a limit of detection of at least four cells per millilitre. We developed an analysis method to reduce noise from magnetic beads used for CTC isolation. CTCs were isolated from CRC patients with a median of 37 CTCs (IQ 13.0-85.5) at baseline. CTCs from CRC patients were significantly (
< 0.0001) larger than cytokeratin (CK)-negative cells, and patients were stratified into two groups based on BRAF
and PD-L1 expression on CK-positive cells. The changes observed over time included not only the number of CTCs but also their distribution into four different subpopulations defined according to BRAF
and PD-L1 positivity. We developed a novel protocol for semi-automatic CTC isolation and phenotypic characterisation by combining two platforms. Assessment of CTCs from early CRC patients using our protocol allowed the identification of two clusters of patients with changing phenotypes over time.
PDF
