A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche.

Fusobacterium nucleatum (Fn), a bacterium present in the human oral cavity and rarely found in the lower gastrointestinal tract of healthy individuals1, is enriched in human colorectal cancer (CRC) tumours2-5. High intratumoural Fn loads are associated with recurrence, metastases and poorer patient prognosis5-8. Here, to delineate Fn genetic factors facilitating tumour colonization, we generated closed genomes for 135 Fn strains; 80 oral strains from individuals without cancer and 55 unique cancer strains cultured from tumours from 51 patients with CRC. Pangenomic analyses identified 483 CRC-enriched genetic factors. Tumour-isolated strains predominantly belong to Fn subspecies animalis (Fna). However, genomic analyses reveal that Fna, considered a single subspecies, is instead composed of two distinct clades (Fna C1 and Fna C2). Of these, only Fna C2 dominates the CRC tumour niche. Inter-Fna analyses identified 195 Fna C2-associated genetic factors consistent with increased metabolic potential and colonization of the gastrointestinal tract. In support of this, Fna C2-treated mice had an increased number of intestinal adenomas and altered metabolites. Microbiome analysis of human tumour tissue from 116 patients with CRC demonstrated Fna C2 enrichment. Comparison of 62 paired specimens showed that only Fna C2 is tumour enriched compared to normal adjacent tissue. This was further supported by metagenomic analysis of stool samples from 627 patients with CRC and 619 healthy individuals. Collectively, our results identify the Fna clade bifurcation, show that specifically Fna C2 drives the reported Fn enrichment in human CRC and reveal the genetic underpinnings of pathoadaptation of Fna C2 to the CRC niche.

Scalable single-cell profiling of chromatin modifications with sciCUT&Tag.

Cleavage under targets and tagmentation (CUT&Tag) is an antibody-directed in situ chromatin profiling strategy that is rapidly replacing immune precipitation-based methods, such as chromatin immunoprecipitation-sequencing. The efficiency of the method enables chromatin profiling in single cells but is limited by the numbers of cells that can be profiled. Here, we describe a combinatorial barcoding strategy for CUT&Tag that harnesses a nanowell dispenser for simple, high-resolution, high-throughput, single-cell chromatin profiling. In this single-cell combinatorial indexing CUT&Tag (sciCUT&Tag) protocol, lightly cross-linked nuclei are bound to magnetic beads and incubated with primary and secondary antibodies in bulk and then arrayed in a 96-well plate for a first round of cellular indexing by antibody-directed Tn5 tagmentation. The sample is then repooled, mixed and arrayed across 5,184 nanowells at a density of 12-24 nuclei per well for a second round of cellular indexing during PCR amplification of the sequencing-ready library. This protocol can be completed in 1.5 days by a research technician, and we illustrate the optimized protocol by profiling histone modifications associated with developmental gene repression (H3K27me3) as well as transcriptional activation (H3K4me1-2-3) in human peripheral blood mononuclear cells and use single-nucleotide polymorphisms to facilitate collision removal. We have also used sciCUT&Tag for simultaneous profiling of multiple chromatin epitopes in single cells. The reduced cost, improved resolution and scalability of sciCUT&Tag make it an attractive platform to profile chromatin features in single cells.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies.

For studies using microbiome data, the ability to robustly combine data from technically and biologically distinct microbiome studies is a crucial means of supporting more robust and clinically relevant inferences. Formidable technical challenges arise when attempting to combine data from technically diverse 16S rRNA gene variable region amplicon sequencing (16S) studies. Closed operational taxonomic units and taxonomy are criticized as being heavily dependent upon reference sets and with limited precision relative to the underlying biology. Phylogenetic placement has been demonstrated to be a promising taxonomy-free manner of harmonizing microbiome data, but it has lacked a validated count-based feature suitable for use in machine learning and association studies. Here we introduce a phylogenetic-placement-based, taxonomy-independent, compositional feature of microbiota: phylotypes. Phylotypes were predictive of clinical outcomes such as obesity or pre-term birth on technically diverse independent validation sets harmonized post hoc. Thus, phylotypes enable the rigorous cross-validation of 16S-based clinical prognostic models and associative microbiome studies.

INVADEseq to identify cell-adherent or invasive bacteria and the associated host transcriptome at single-cell-level resolution.

Single-cell RNA sequencing (scRNAseq) technologies have been beneficial in revealing and describing cellular heterogeneity within mammalian tissues, including solid tumors. However, many of these techniques apply poly(A) selection of RNA, and thus have primarily focused on determining the gene signatures of eukaryotic cellular components of the tumor microenvironment. Microbiome analysis has revealed the presence of microbial ecosystems, including bacteria and fungi, within human tumor tissues from major cancer types. Imaging data have revealed that intratumoral bacteria may be located within epithelial and immune cell types. However, as bacterial RNA typically lacks a poly(A) tail, standard scRNAseq approaches have limited ability to capture this microbial component of the tumor microenvironment. To overcome this, we describe the invasion-adhesion-directed expression sequencing (INVADEseq) approach, whereby we adapt 10x Genomics 5' scRNAseq protocol by introducing a primer that targets a conserved region of the bacterial 16S ribosomal RNA gene in addition to the standard primer for eukaryotic poly(A) RNA selection. This 'add-on' approach enables the generation of eukaryotic and bacterial DNA libraries at eukaryotic single-cell level resolution, utilizing the 10x barcode to identify single cells with intracellular bacteria. The INVADEseq method takes 30 h to complete, including tissue processing, sequencing and computational analysis. As an output, INVADEseq has shown to be a reliable tool in human cancer cell lines and patient tumor specimens by detecting the proportion of human cells that harbor bacteria and the identities of human cells and intracellular bacteria, along with identifying host transcriptional programs that are modulated on the basis of associated bacteria.

phippery: a software suite for PhIP-Seq data analysis.

SUMMARY: We present the phippery software suite for analyzing data from phage display methods that use immunoprecipitation and deep sequencing to capture antibody binding to peptides, often referred to as PhIP-Seq. It has three main components that can be used separately or in conjunction: (i) a Nextflow pipeline, phip-flow, to process raw sequencing data into a compact, multidimensional dataset format and allows for end-to-end automation of reproducible workflows. (ii) a Python API, phippery, which provides interfaces for tasks such as count normalization, enrichment calculation, multidimensional scaling, and more, and (iii) a Streamlit application, phip-viz, as an interactive interface for visualizing the data as a heatmap in a flexible manner. AVAILABILITY AND IMPLEMENTATION: All software packages are publicly available under the MIT License. The phip-flow pipeline: https://github.com/matsengrp/phip-flow. The phippery library: https://github.com/matsengrp/phippery. The phip-viz Streamlit application: https://github.com/matsengrp/phip-viz.

Fusobacterium sphaericum sp. nov. , isolated from a human colon tumor, is prevalent in various human body sites and induces IL-8 secretion from colorectal cancer cells.

Cancerous tissue is a largely unexplored microbial niche that provides a unique environment for the colonization and growth of specific bacterial communities, and with it, the opportunity to identify novel bacterial species. Here, we report distinct features of a novel Fusobacterium species, F. sphaericum sp. nov. ( Fs ), isolated from primary colon adenocarcinoma tissue. We acquire the complete, closed genome of this organism and phylogenetically confirm its classification into the Fusobacterium genus. Phenotypic and genomic analysis of Fs reveal that this novel organism is of coccoid shape, rare for Fusobacterium members, and has species-distinct gene content. Fs displays a metabolic profile and antibiotic resistance repertoire consistent with other Fusobacterium species. In vitro, Fs has adherent and immunomodulatory capabilities, as it intimately associates with human colon cancer epithelial cells and promotes IL-8 secretion. Analysis of the prevalence and abundance of Fs in ∼1,750 human metagenomic samples shows that it is a moderately prevalent member of the human oral cavity and stool. Intriguingly, analysis of ∼1,270 specimens from patients with colorectal cancer demonstrate that Fs is significantly enriched in colonic and tumor tissue as compared to mucosa or feces. Our study sheds light on a novel bacterial species that is prevalent within the human intestinal microbiota and whose role in human health and disease requires further investigation.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome.

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

Specific host metabolite and gut microbiome alterations are associated with bone loss during spaceflight.

Understanding the axis of the human microbiome and physiological homeostasis is an essential task in managing deep-space-travel-associated health risks. The NASA-led Rodent Research 5 mission enabled an ancillary investigation of the gut microbiome, varying exposure to microgravity (flight) relative to ground controls in the context of previously shown bone mineral density (BMD) loss that was observed in these flight groups. We demonstrate elevated abundance of Lactobacillus murinus and Dorea sp. during microgravity exposure relative to ground control through whole-genome sequencing and 16S rRNA analyses. Specific functionally assigned gene clusters of L. murinus and Dorea sp. capable of producing metabolites, lactic acid, leucine/isoleucine, and glutathione are enriched. These metabolites are elevated in the microgravity-exposed host serum as shown by liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomic analysis. Along with BMD loss, ELISA reveals increases in osteocalcin and reductions in tartrate-resistant acid phosphatase 5b signifying additional loss of bone homeostasis in flight.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

VMAP: Vaginal Microbiome Atlas During Pregnancy.

The vaginal microbiome has been shown to be associated with pregnancy outcomes including preterm birth (PTB) risk. Here we present VMAP: Vaginal Microbiome Atlas during Pregnancy (http://vmapapp.org), an application to visualize features of 3,909 vaginal microbiome samples of 1,416 pregnant individuals from 11 studies, aggregated from raw public and newly generated sequences via an open-source tool, MaLiAmPi. Our visualization tool (http://vmapapp.org) includes microbial features such as various measures of diversity, VALENCIA community state types (CST), and composition (via phylotypes and taxonomy). This work serves as a resource for the research community to further analyze and visualize vaginal microbiome data in order to better understand both healthy term pregnancies and those associated with adverse outcomes.

Strain-level characterization of health-associated bacterial consortia that colonize the human gut during infancy.

Background: The human gut microbiome develops rapidly during infancy, a key window of development coinciding with maturation of the adaptive immune system. However, little is known of the microbiome growth dynamics over the first few months of life and whether there are any generalizable patterns across human populations. We performed metagenomic sequencing on stool samples (n=94) from a cohort of infants (n=15) at monthly intervals in the first six months of life, augmenting our dataset with seven published studies for a total of 4,441 metagenomes from 1,162 infants. Results: Strain-level de novo analysis was used to identify 592 of the most abundant organisms in the infant gut microbiome. Previously unrecognized consortia were identified which exhibited highly correlated abundances across samples and were composed of diverse species spanning multiple genera. Analysis of a cohort of infants with cystic fibrosis identified one such novel consortium of diverse Enterobacterales which was positively correlated with weight gain. While all studies showed an increased community stability during the first year of life, microbial dynamics varied widely in the first few months of life, both by study and by individual. Conclusion: By augmenting published metagenomic datasets with data from a newly established cohort we were able to identify novel groups of organisms that are correlated with measures of robust human development. We hypothesize that the presence of these groups may impact human health in aggregate in ways that individual species may not in isolation.

The cancer chemotherapeutic 5-fluorouracil is a potent Fusobacterium nucleatum inhibitor and its activity is modified by intratumoral microbiota.

Fusobacterium nucleatum (Fn) is a dominant bacterial species in colorectal cancer (CRC) tissue that is associated with cancer progression and poorer patient prognosis. Following a small-molecule inhibitor screen of 1,846 bioactive compounds against a Fn CRC isolate, we find that 15% of inhibitors are antineoplastic agents including fluoropyrimidines. Validation of these findings reveals that 5-fluorouracil (5-FU), a first-line CRC chemotherapeutic, is a potent inhibitor of Fn CRC isolates. We also identify members of the intratumoral microbiota, including Escherichia coli, that are resistant to 5-FU. Further, CRC E. coli isolates can modify 5-FU and relieve 5-FU toxicity toward otherwise-sensitive Fn and human CRC epithelial cells. Lastly, we demonstrate that ex vivo patient CRC tumor microbiota undergo community disruption after 5-FU exposure and have the potential to deplete 5-FU levels, reducing local drug efficacy. Together, these observations argue for further investigation into the role of the CRC intratumoral microbiota in patient response to chemotherapy.

Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer.

The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types1,2. Intratumoral host-microbiota studies have so far largely relied on bulk tissue analysis1-3, which obscures the spatial distribution and localized effect of the microbiota within tumours. Here, by applying in situ spatial-profiling technologies4 and single-cell RNA sequencing5 to oral squamous cell carcinoma and colorectal cancer, we reveal spatial, cellular and molecular host-microbe interactions. We adapted 10x Visium spatial transcriptomics to determine the identity and in situ location of intratumoral microbial communities within patient tissues. Using GeoMx digital spatial profiling6, we show that bacterial communities populate microniches that are less vascularized, highly immuno­suppressive and associated with malignant cells with lower levels of Ki-67 as compared to bacteria-negative tumour regions. We developed a single-cell RNA-sequencing method that we name INVADEseq (invasion-adhesion-directed expression sequencing) and, by applying this to patient tumours, identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair. Through functional studies, we show that cancer cells that are infected with bacteria invade their surrounding environment as single cells and recruit myeloid cells to bacterial regions. Collectively, our data reveal that the distribution of the microbiota within a tumour is not random; instead, it is highly organized in microniches with immune and epithelial cell functions that promote cancer progression.

Colonization of the live biotherapeutic product VE303 and modulation of the microbiota and metabolites in healthy volunteers.

Manipulation of the gut microbiota via fecal microbiota transplantation (FMT) has shown clinical promise in diseases such as recurrent Clostridioides difficile infection (rCDI). However, the variable nature of this approach makes it challenging to describe the relationship between fecal strain colonization, corresponding microbiota changes, and clinical efficacy. Live biotherapeutic products (LBPs) consisting of defined consortia of clonal bacterial isolates have been proposed as an alternative therapeutic class because of their promising preclinical results and safety profile. We describe VE303, an LBP comprising 8 commensal Clostridia strains under development for rCDI, and its early clinical development in healthy volunteers (HVs). In a phase 1a/b study in HVs, VE303 is determined to be safe and well-tolerated at all doses tested. VE303 strains optimally colonize HVs if dosed over multiple days after vancomycin pretreatment. VE303 promotes the establishment of a microbiota community known to provide colonization resistance.

geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response.

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe geneshot, a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data. By applying geneshot to two independent previously published cohorts, we identify microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth.

Human Tumor-Infiltrating MAIT Cells Display Hallmarks of Bacterial Antigen Recognition in Colorectal Cancer.

Growing evidence indicates a role for the gut microbiota in modulating anti-tumor treatment efficacy in human cancer. Here we study mucosa-associated invariant T (MAIT) cells to look for evidence of bacterial antigen recognition in human colon, lung, and kidney carcinomas. Using mass cytometry and single-cell mRNA sequencing, we identify a tumor-infiltrating MAIT cell subset expressing CD4 and Foxp3 and observe high expression of CD39 on MAIT cells from colorectal cancer (CRC) only, which we show in vitro to be expressed specifically after TCR stimulation. We further reveal that these cells are phenotypically and functionally exhausted. Sequencing data show high bacterial infiltration in CRC tumors and highlight an enriched species, Fusobacteria nucleatum, with capability to activate MAIT cells in a TCR-dependent way. Our results provide evidence of a MAIT cell response to microbial antigens in CRC and could pave the way for manipulating MAIT cells or the microbiome for cancer therapy.

Metagenomic Analysis Reveals Clinical SARS-CoV-2 Infection and Bacterial or Viral Superinfection and Colonization.

BACKGROUND: More than 2 months separated the initial description of SARS-CoV-2 and discovery of its widespread dissemination in the United States. Despite this lengthy interval, implementation of specific quantitative reverse transcription (qRT)-PCR-based SARS-CoV-2 tests in the US has been slow, and testing is still not widely available. Metagenomic sequencing offers the promise of unbiased detection of emerging pathogens, without requiring prior knowledge of the identity of the responsible agent or its genomic sequence. METHODS: To evaluate metagenomic approaches in the context of the current SARS-CoV-2 epidemic, laboratory-confirmed positive and negative samples from Seattle, WA were evaluated by metagenomic sequencing, with comparison to a 2019 reference genomic database created before the emergence of SARS-CoV-2. RESULTS: Within 36 h our results showed clear identification of a novel human Betacoronavirus, closely related to known Betacoronaviruses of bats, in laboratory-proven cases of SARS-CoV-2. A subset of samples also showed superinfection or colonization with human parainfluenza virus 3 or Moraxella species, highlighting the need to test directly for SARS-CoV-2 as opposed to ruling out an infection using a viral respiratory panel. Samples negative for SARS-CoV-2 by RT-PCR were also negative by metagenomic analysis, and positive for Rhinovirus A and C. Unlike targeted SARS-CoV-2 qRT-PCR testing, metagenomic analysis of these SARS-CoV-2 negative samples identified candidate etiological agents for the patients' respiratory symptoms. CONCLUSION: Taken together, these results demonstrate the value of metagenomic analysis in the monitoring and response to this and future viral pandemics.

Strategies to Facilitate Translational Advances from Microbiome Surveys.

Manipulating the microbiome has enormous potential to treat important human diseases. Microbiome surveys are often used to identify potential therapeutic targets by finding associations between microbial elements and disease status. We argue that many reported associations between the microbiome and disease are incompatible with translational research because they are insufficiently specific. We encourage the clear specification of manipulable microbial elements that can be tested in follow-up randomized experiments, and we provide multiple examples of specific and nonspecific microbial elements.