Profiling tumor bacteria: Bacteria are well-known residents in human tumors, but whether their presence is advantageous to the tumors or to the bacteria themselves has been unclear. As an initial step toward addressing this question, Nejman et al. produced an exhaustive catalog of the bacteria present in more than 1500 human tumors representing seven different tumor types (see the Perspective by Atreya and Turnbaugh). They found that the bacteria within tumors were localized within both cancer cells and immune cells and that the bacterial composition varied according to tumor type. Certain biologically plausible associations were identified. For example, breast cancer subtypes characterized by increased oxidative stress were enriched in bacteria that produce mycothiol, which can detoxify reactive oxygen species. Science , this issue p. 973 ; see also p. 938

One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition of sampled communities. Here, we present PICRUSt2 ( https://github.com/picrust/picrust2 ), which expands the capabilities of the original PICRUSt method ¹ to predict the functional potential of a community based on marker gene sequencing profiles. This updated method and implementation includes several improvements over the previous algorithm: an expanded database of gene families and reference genomes, a new approach now compatible with any OTU-picking or denoising algorithm, and novel phenotype predictions. Upon evaluation, PICRUSt2 was more accurate than PICRUSt1 and other current approaches overall. PICRUSt2 is also now more flexible and allows the addition of custom reference databases. We highlight these improvements and also important caveats regarding the use of predicted metagenomes, which are related to the inherent challenges of analyzing metagenome data in general.

Crohn’s disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients’ ongoing treatments. Additionally, most analyses of CD patients’ microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples. We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients’ response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response. We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers’ microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)—CAF/08/01.

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.

We examined the differences between bacterial and eukaryotic soil communities associated with natural and managed habitats of wild blueberry, Vaccinium angustifolium. In total, 138 bacterial and 130 eukaryotic soil and rhizosphere communities across seven blueberry fields, all established at least 30 years ago and from two forest areas adjacent to some of these fields, were analyzed. We analyzed correlations between soil chemical factors and the structure of eukaryotic and bacterial communities, including differences in the microbiome between bulk and rhizosphere soils, and between rhizospheres of plants growing in natural and managed habitats. Characterization of a broad selection of fields across the province of Nova Scotia, Canada, allowed us to tentatively identify specific signatures from several distinct soil niches. Our data indicate that bacterial and eukaryotic communities differ in how they correlate with soil chemical properties. Also, while eukaryotic communities correlate stronger with soil fertility than bacterial communities, plant selection had a stronger effect on bacterial microbiomes than on eukaryote microbiomes. Additionally, we found that the composition of root-associated bacterial communities differs between managed and natural wild blueberry habitats, confirming previous reports that management can affect rhizosphere microbiomes.

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.