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.

A cryptic plasmid is among the most numerous genetic elements in the human gut.

Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.

Host DNA depletion on frozen human respiratory samples enables successful metagenomic sequencing for microbiome studies.

Background: Most respiratory microbiome studies have focused on amplicon rather than metagenomics sequencing due to high host DNA content. We evaluated efficacy of five host DNA depletion methods on previously frozen human bronchoalveolar lavage (BAL), nasal swabs, and sputum prior to metagenomic sequencing. Results: Median sequencing depth was 76.4 million reads per sample. Untreated nasal, sputum and BAL samples had 94.1%, 99.2%, and 99.7% host-reads. The effect of host depletion differed by sample type. Most treatment methods increased microbial reads, species richness and predicted functional richness; the increase in species and predicted functional richness was mediated by higher effective sequencing depth. For BAL and nasal samples, most methods did not change Morisita-Horn dissimilarity suggesting limited bias introduced by host depletion. Conclusions: Metagenomics sequencing without host depletion will underestimate microbial diversity of most respiratory samples due to shallow effective sequencing depth and is not recommended. Optimal host depletion methods vary by sample type.

Analyzing microbial evolution through gene and genome phylogenies.

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies.

happi: a hierarchical approach to pangenomics inference.

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation.

Analyzing microbial evolution through gene and genome phylogenies.

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing and interacting with a collection of bacterial gene phylogenies. dimension reduction, microbiome, non-Euclidean, statistical genetics, visualization.

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

Differences in gut metagenomes between dairy workers and community controls: a cross-sectional study.

Background: As a nexus of routine antibiotic use and zoonotic pathogen presence, the livestock farming environment is a potential hotspot for the emergence of zoonotic diseases and antibiotic resistant bacteria. Livestock can further facilitate disease transmission by serving as intermediary hosts for pathogens as they undergo evolution prior to a spillover event. In light of this, we are interested in characterizing the microbiome and resistome of dairy workers, whose exposure to the livestock farming environment places them at risk for facilitating community transmission of antibiotic resistant genes and emerging zoonotic diseases. Results: Using shotgun sequencing, we investigated differences in the taxonomy, diversity and gene presence of the human gut microbiome of 10 dairy farm workers and 6 community controls, supplementing these samples with additional publicly available gut metagenomes. We observed greater abundance of tetracycline resistance genes and prevalence of cephamycin resistance genes in dairy workers' metagenomes, and lower average gene diversity. We also found evidence of commensal organism association with plasmid-mediated tetracycline resistance genes in both dairy workers and community controls (including Faecalibacterium prausnitzii, Ligilactobacillus animalis, and Simiaoa sunii). However, we did not find significant differences in the prevalence of resistance genes or virulence factors overall, nor differences in the taxonomic composition of dairy worker and community control metagenomes. Conclusions: This study presents the first metagenomics analysis of United States dairy workers, providing insights into potential risks of exposure to antibiotics and pathogens in animal farming environments. Previous metagenomic studies of livestock workers in China and Europe have reported increased abundance and carriage of antibiotic resistance genes in livestock workers. While our investigation found no strong evidence for differences in the abundance or carriage of antibiotic resistance genes and virulence factors between dairy worker and community control gut metagenomes, we did observe patterns in the abundance of tetracycline resistance genes and the prevalence of cephamycin resistance genes that is consistent with previous work.

A highly conserved and globally prevalent cryptic plasmid is among the most numerous mobile genetic elements in the human gut.

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry 'cryptic' plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

Structure-informed microbial population genetics elucidate selective pressures that shape protein evolution.

Comprehensive sampling of natural genetic diversity with metagenomics enables highly resolved insights into the interplay between ecology and evolution. However, resolving adaptive, neutral, or purifying processes of evolution from intrapopulation genomic variation remains a challenge, partly due to the sole reliance on gene sequences to interpret variants. Here, we describe an approach to analyze genetic variation in the context of predicted protein structures and apply it to a marine microbial population within the SAR11 subclade 1a.3.V, which dominates low-latitude surface oceans. Our analyses reveal a tight association between genetic variation and protein structure. In a central gene in nitrogen metabolism, we observe decreased occurrence of nonsynonymous variants from ligand-binding sites as a function of nitrate concentrations, revealing genetic targets of distinct evolutionary pressures maintained by nutrient availability. Our work yields insights into the governing principles of evolution and enables structure-aware investigations of microbial population genetics.

A multiview model for relative and absolute microbial abundances.

The absolute abundance of bacterial taxa in human host-associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances.

Evaluating replicability in microbiome data.

High-throughput sequencing is widely used to study microbial communities. However, choice of laboratory protocol is known to affect the resulting microbiome data, which has an unquantified impact on many comparisons between communities of scientific interest. We propose a novel approach to evaluating replicability in high-dimensional data and apply it to assess the cross-laboratory replicability of signals in microbiome data using the Microbiome Quality Control Project data set. We learn distinctions between samples as measured by a single laboratory and evaluate whether the same distinctions hold in data produced by other laboratories. While most sequencing laboratories can consistently distinguish between samples (median correct classification 87% on genus-level proportion data), these distinctions frequently fail to hold in data from other laboratories (median correct classification 55% across laboratory on genus-level proportion data). As identical samples processed by different laboratories generate substantively different quantitative results, we conclude that 16S sequencing does not reliably resolve differences in human microbiome samples. However, because we observe greater replicability under certain data transformations, our results inform the analysis of microbiome data.

Segment number threshold determines juvenile onset of germline cluster expansion in Platynereis dumerilii.

Development of sexual characters and generation of gametes are tightly coupled with growth. Platynereis dumerilii is a marine annelid that has been used to study germline development and gametogenesis. P. dumerilii has germ cell clusters found across the body in the juvenile worms, and the clusters eventually form the gametes. Like other segmented worms, P. dumerilii grows by adding new segments at its posterior end. The number of segments reflect the growth state of the worms and therefore is a useful and measurable growth state metric to study the growth-reproduction crosstalk. To understand how growth correlates with progression of gametogenesis, we investigated germline development across several developmental stages. We discovered a distinct transition period when worms increase the number of germline clusters at a particular segment number threshold. Additionally, we found that keeping worms short in segment number, by manipulating environmental conditions or via amputations, supported a segment number threshold requirement for germline development. Finally, we asked if these clusters in P. dumerilii play a role in regeneration (as similar free-roaming cells are observed in Hydra and planarian regeneration) and found that the clusters were not required for regeneration in P. dumerilii, suggesting a strictly germline nature. Overall, these molecular analyses suggest a previously unidentified developmental transition dependent on the growth state of juvenile P. dumerilii leading to substantially increased germline expansion.

Estimating diversity in networked ecological communities.

Comparing ecological communities across environmental gradients can be challenging, especially when the number of different taxonomic groups in the communities is large. In this setting, community-level summaries called diversity indices are widely used to detect changes in the community ecology. However, estimation of diversity indices has received relatively little attention from the statistical community. The most common estimates of diversity are the maximum likelihood estimates of the parameters of a multinomial model, even though the multinomial model implies strict assumptions about the sampling mechanism. In particular, the multinomial model prohibits ecological networks, where taxa positively and negatively co-occur. In this article, we leverage models from the compositional data literature that explicitly account for co-occurrence networks and use them to estimate diversity. Instead of proposing new diversity indices, we estimate popular diversity indices under these models. While the methodology is general, we illustrate the approach for the estimation of the Shannon, Simpson, Bray-Curtis, and Euclidean diversity indices. We contrast our method to multinomial, low-rank, and nonparametric methods for estimating diversity indices. Under simulation, we find that the greatest gains of the method are in strongly networked communities with many taxa. Therefore, to illustrate the method, we analyze the microbiome of seafloor basalts based on a 16S amplicon sequencing dataset with 1425 taxa and 12 communities.

Genomic and functional characterization of a mucosal symbiont involved in early-stage colorectal cancer.

Colorectal cancer is a major health concern worldwide. Growing evidence for the role of the gut microbiota in the initiation of CRC has sparked interest in approaches that target these microorganisms. However, little is known about the composition and role of the microbiota associated with precancerous polyps. Here, we found distinct microbial signatures between patients with and without polyps and between polyp subtypes using sequencing and culturing techniques. We found a correlation between Bacteroides fragilis recovered and the level of inflammatory cytokines in the mucosa adjacent to the polyp. Additional analysis revealed that B. fragilis from patients with polyps are bft-negative, activate NF-κB through Toll-like receptor 4, induce a pro-inflammatory response, and are enriched in genes associated with LPS biosynthesis. This study provides fundamental insight into the microbial microenvironment of the pre-neoplastic polyp by highlighting strain-specific genomic and proteomic differences, as well as more broad compositional differences in the microbiome.

Lifetime mobility of an Arctic woolly mammoth.

Little is known about woolly mammoth (Mammuthus primigenius) mobility and range. Here we use high temporal resolution sequential analyses of strontium isotope ratios along an entire 1.7-meter-long tusk to reconstruct the movements of an Arctic woolly mammoth that lived 17,100 years ago, during the last ice age. We use an isotope-guided random walk approach to compare the tusk's strontium and oxygen isotope profiles to isotopic maps. Our modeling reveals patterns of movement across a geographically extensive range during the animal's ~28-year life span that varied with life stages. Maintenance of this level of mobility by megafaunal species such as mammoth would have been increasingly difficult as the ice age ended and the environment changed at high latitudes.

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.

Tuning parameter selection for a penalized estimator of species richness.

Our goal is to estimate the true number of classes in a population, called the species richness. We consider the case where multiple frequency count tables have been collected from a homogeneous population, and investigate a penalized maximum likelihood estimator under a negative binomial model. Because high probabilities of unobserved classes increase the variance of species richness estimates, our method penalizes the probability of a class being unobserved. Tuning the penalization parameter is challenging because the true species richness is never known, and so we propose and validate four novel methods for tuning the penalization parameter. We illustrate and contrast the performance of the proposed methods by estimating the strain-level microbial diversity of Lake Champlain over 3 consecutive years, and global human host-associated species-level microbial richness.

ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS.

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.