Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes.

A long-standing question is to what degree genetic drift and selection drive the divergence in rare accessory gene content between closely related bacteria. Rare genes, including singletons, make up a large proportion of pangenomes (all genes in a set of genomes), but it remains unclear how many such genes are adaptive, deleterious or neutral to their host genome. Estimates of species' effective population sizes (Ne) are positively associated with pangenome size and fluidity, which has independently been interpreted as evidence for both neutral and adaptive pangenome models. We hypothesized that pseudogenes, used as a neutral reference, could be used to distinguish these models. We find that most functional categories are depleted for rare pseudogenes when a genome encodes only a single intact copy of a gene family. In contrast, transposons are enriched in pseudogenes, suggesting they are mostly neutral or deleterious to the host genome. Thus, even if individual rare accessory genes vary in their effects on host fitness, we can confidently reject a model of entirely neutral or deleterious rare genes. We also define the ratio of singleton intact genes to singleton pseudogenes (si/sp) within a pangenome, compare this measure across 668 prokaryotic species and detect a signal consistent with the adaptive value of many rare accessory genes. Taken together, our work demonstrates that comparing with pseudogenes can improve inferences of the evolutionary forces driving pangenome variation.

Longitudinal genomic surveillance of multidrug-resistant Escherichia coli carriage in critical care patients.

Colonization with multidrug-resistant Escherichia coli strains causes a substantial health burden in hospitalized patients. We performed a longitudinal genomics study to investigate the colonization of resistant E. coli strains in critically ill patients and to identify evolutionary changes and strain replacement events within patients. Patients were admitted to the intensive care unit and hematology wards at a major hospital in Lebanon. Perianal swabs were collected from participants on admission and during hospitalization, which were screened for extended-spectrum beta-lactamases and carbapenem-resistant Enterobacterales. We performed whole-genome sequencing and analysis on E. coli strains isolated from patients at multiple time points. The E. coli isolates were genetically diverse, with 11 sequence types (STs) identified among 22 isolates sequenced. Five patients were colonized by E. coli sequence type 131 (ST131)-encoding CTX-M-27, an emerging clone not previously observed in clinical samples from Lebanon. Among the eight patients whose resident E. coli strains were tracked over time, five harbored the same E. coli strain with relatively few mutations over the 5 to 10 days of hospitalization. The other three patients were colonized by different E. coli strains over time. Our study provides evidence of strain diversity within patients during their hospitalization. While strains varied in their antimicrobial resistance profiles, the number of resistance genes did not increase over time. We also show that ST131-encoding CTX-M-27, which appears to be emerging as a globally important multidrug-resistant E. coli strain, is also prevalent among critical care patients and deserves further monitoring.IMPORTANCEUnderstanding the evolution of bacteria over time in hospitalized patients is of utmost significance in the field of infectious diseases. While numerous studies have surveyed genetic diversity and resistance mechanisms in nosocomial infections, time series of within-patient dynamics are rare, and high-income countries are over-represented, leaving low- and middle-income countries understudied. Our study aims to bridge these research gaps by conducting a longitudinal survey of critically ill patients in Lebanon. This allowed us to track Escherichia coli evolution and strain replacements within individual patients over extended periods. Through whole-genome sequencing, we found extensive strain diversity, including the first evidence of the emerging E. coli sequence type 131 clone encoding the CTX-M-27 beta-lactamase in a clinical sample from Lebanon, as well as likely strain replacement events during hospitalization.

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv.

MOTIVATION: Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so. RESULTS: We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions. AVAILABILITY AND IMPLEMENTATION: FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating phylogenetic and functional data in microbiome studies.

MOTIVATION: Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions. RESULTS: The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. AVAILABILITY AND IMPLEMENTATION: These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Microbiome differential abundance methods produce different results across 38 datasets.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Genic Selection Within Prokaryotic Pangenomes.

Understanding the evolutionary forces shaping prokaryotic pangenome structure is a major goal of microbial evolution research. Recent work has highlighted that a substantial proportion of accessory genes appear to confer niche-specific adaptations. This work has primarily focused on selection acting at the level of individual cells. Herein, we discuss a lower level of selection that also contributes to pangenome variation: genic selection. This refers to cases where genetic elements, rather than individual cells, are the entities under selection. The clearest examples of this form of selection are selfish mobile genetic elements, which are those that have either a neutral or a deleterious effect on host fitness. We review the major classes of these and other mobile elements and discuss the characteristic features of such elements that could be under genic selection. We also discuss how genetic elements that are beneficial to hosts can also be under genic selection, a scenario that may be more prevalent but not widely appreciated, because disentangling the effects of selection at different levels (i.e., organisms vs. genes) is challenging. Nonetheless, an appreciation for the potential action and implications of genic selection is important to better understand the evolution of prokaryotic pangenomes.

Transcriptomic profiling of Brassica napus responses to Pseudomonas aeruginosa.

Pseudomonas aeruginosa is an opportunistic bacterial pathogen of plants. Unlike the well-characterized plant defense responses to highly adapted bacterial phytopathogens, little is known about plant response to P. aeruginosa infection. In this study, we examined the Brassica napus (canola) tissue-specific response to P. aeruginosa infection using RNA sequencing. Transcriptomic analysis of canola seedlings over a 5 day P. aeruginosa infection revealed that many molecular processes involved in plant innate immunity were up-regulated, whereas photosynthesis was down-regulated. Phytohormones control many vital biological processes within plants, including growth and development, senescence, seed setting, fruit ripening, and innate immunity. The three main phytohormones involved in plant innate immunity are salicylic acid (SA), jasmonic acid (JA), and ethylene (ET). Many bacterial pathogens have evolved multiple strategies to manipulate these hormone responses in order to infect plants successfully. Interestingly, gene expression within all three phytohormone (SA, JA, and ET) signaling pathways was up-regulated in response to P. aeruginosa infection. This study identified a unique plant hormone response to the opportunistic bacterial pathogen P. aeruginosa infection.

Re-evaluating the relationship between missing heritability and the microbiome.

Human genome-wide association studies (GWASs) have recurrently estimated lower heritability estimates than familial studies. Many explanations have been suggested to explain these lower estimates, including that a substantial proportion of genetic variation and gene-by-environment interactions are unmeasured in typical GWASs. The human microbiome is potentially related to both of these explanations, but it has been more commonly considered as a source of unmeasured genetic variation. In particular, it has recently been argued that the genetic variation within the human microbiome should be included when estimating trait heritability. We outline issues with this argument, which in its strictest form depends on the holobiont model of human-microbiome interactions. Instead, we argue that the microbiome could be leveraged to help control for environmental variation across a population, although that remains to be determined. We discuss potential approaches that could be explored to determine whether integrating microbiome sequencing data into GWASs is useful. Video abstract.

The human tumor microbiome is composed of tumor type-specific intracellular bacteria.

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.

Detection of Helicobacter pylori Microevolution and Multiple Infection from Gastric Biopsies by Housekeeping Gene Amplicon Sequencing.

Despite the great efforts devoted to research on Helicobacter pylori, the prevalence of single-strain infection or H. pylori mixed infection and its implications in the mode of transmission of this bacterium are still controversial. In this study, we explored the usefulness of housekeeping gene amplicon sequencing in the detection of H. pylori microevolution and multiple infections. DNA was extracted from five gastric biopsies from four patients infected with distinct histopathological diagnoses. PCR amplification of six H. pylori-specific housekeeping genes was then assessed on each sample. Optimal results were obtained for the cgt and luxS genes, which were selected for amplicon sequencing. A total of 11,833 cgt and 403 luxS amplicon sequences were obtained, 2042 and 112 of which were unique sequences, respectively. All cgt and luxS sequences were clustered at 97% to 9 and 13 operational taxonomic units (OTUs), respectively. For each sample from a different patient, a single OTU comprised the majority of sequences in both genes, but more than one OTU was detected in all samples. These results suggest that multiple infections with a predominant strain together with other minority strains are the main way by which H. pylori colonizes the human stomach.

Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes.

High-throughput shotgun metagenomics sequencing has enabled the profiling of myriad natural communities. These data are commonly used to identify gene families and pathways that were potentially gained or lost in an environment and which may be involved in microbial adaptation. Despite the widespread interest in these events, there are no established best practices for identifying gene gain and loss in metagenomics data. Horizontal gene transfer (HGT) represents several mechanisms of gene gain that are especially of interest in clinical microbiology due to the rapid spread of antibiotic resistance genes in natural communities. Several additional mechanisms of gene gain and loss, including gene duplication, gene loss-of-function events, and de novo gene birth are also important to consider in the context of metagenomes but have been less studied. This review is largely focused on detecting HGT in prokaryotic metagenomes, but methods for detecting these other mechanisms are first discussed. For this article to be self-contained, we provide a general background on HGT and the different possible signatures of this process. Lastly, we discuss how improved assembly of genomes from metagenomes would be the most straight-forward approach for improving the inference of gene gain and loss events. Several recent technological advances could help improve metagenome assemblies: long-read sequencing, determining the physical proximity of contigs, optical mapping of short sequences along chromosomes, and single-cell metagenomics. The benefits and limitations of these advances are discussed and open questions in this area are highlighted.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Metagenomic Functional Shifts to Plant Induced Environmental Changes.

The Vaccinium angustifolium (wild blueberry) agricultural system involves transformation of the environment surrounding the plant to intensify plant propagation and to improve fruit yield, and therefore is an advantageous model to study the interaction between soil microorganisms and plant-host interactions. We studied this system to address the question of a trade-off between microbial adaptation to a plant-influenced environment and its general metabolic capabilities. We found that many basic metabolic functions were similarly represented in bulk soil and rhizosphere microbiomes overall. However, we identified a niche-specific difference in functions potentially beneficial for microbial survival in the rhizosphere but that might also reduce the ability of microbes to withstand stresses in bulk soils. These functions could provide the microbiome with additional capabilities to respond to environmental fluctuations in the rhizosphere triggered by changes in the composition of root exudates. Based on our analysis we hypothesize that the rhizosphere-specific pathways involved in xenobiotics biodegradation could provide the microbiome with functional flexibility to respond to plant stress status.

Enhancement of the gut barrier integrity by a microbial metabolite through the Nrf2 pathway.

The importance of gut microbiota in human health and pathophysiology is undisputable. Despite the abundance of metagenomics data, the functional dynamics of gut microbiota in human health and disease remain elusive. Urolithin A (UroA), a major microbial metabolite derived from polyphenolics of berries and pomegranate fruits displays anti-inflammatory, anti-oxidative, and anti-ageing activities. Here, we show that UroA and its potent synthetic analogue (UAS03) significantly enhance gut barrier function and inhibit unwarranted inflammation. We demonstrate that UroA and UAS03 exert their barrier functions through activation of aryl hydrocarbon receptor (AhR)- nuclear factor erythroid 2-related factor 2 (Nrf2)-dependent pathways to upregulate epithelial tight junction proteins. Importantly, treatment with these compounds attenuated colitis in pre-clinical models by remedying barrier dysfunction in addition to anti-inflammatory activities. Cumulatively, the results highlight how microbial metabolites provide two-pronged beneficial activities at gut epithelium by enhancing barrier functions and reducing inflammation to protect from colonic diseases.

Processing a 16S rRNA Sequencing Dataset with the Microbiome Helper Workflow.

Sequencing microbiome samples has recently become a fast and cost-effective method to taxonomically profile communities. The growing interest in analyzing microbial sequencing data has attracted many new researchers to the field. Here, we present a straightforward bioinformatic pipeline that aims to streamline the processing of 16S rRNA sequencing data. This workflow is part of the larger project called Microbiome Helper (Comeau et al. mSyst 2:e00127-16, 2017), which includes other bioinformatic workflows, tutorials, and scripts available here: https://github.com/mlangill/microbiome_helper/wiki .

Predicting the Functional Potential of the Microbiome from Marker Genes Using PICRUSt.

Marker-gene sequencing is a cost-effective method of taxonomically profiling microbial communities. Unlike metagenomic approaches, marker-gene sequencing does not provide direct information about the functional genes that are present in the genomes of community members. However, by capitalizing on the rapid growth in the number of sequenced genomes, it is possible to infer which functions are likely associated with a marker gene based on its sequence similarity with a reference genome. The PICRUSt tool is based on this idea and can predict functional category abundances based on an input marker gene. In brief, this method requires a reference phylogeny with tips corresponding to taxa with reference genomes as well as taxa lacking sequenced genomes. A modified ancestral state reconstruction (ASR) method is then used to infer counts of functional categories for taxa without reference genomes. The predictions are written to pre-calculated files, which can be cross-referenced with other datasets to quickly generate predictions of functional potential for a community. This chapter will give an in-depth description of these methods and describe how PICRUSt should be used.