Microbiome analyses of blood and tissues suggest cancer diagnostic approach.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1-10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.

Tomato salt-responsive pseudo-response regulator 1, SlSRP1, negatively regulates the high-salt and dehydration stress responses.

Under severe environmental stress conditions, plants inhibit their growth and development and initiate various defense mechanisms to survive. The pseudo-response regulator (PRRs) genes have been known to be involved in fruit ripening and plant immunity in various plant species, but their role in responses to environmental stresses, especially high salinity and dehydration, remains unclear. Here, we focused on PRRs in tomato plants and identified two PRR2-like genes, SlSRP1 and SlSRP1H, from the leaves of salt-treated tomato plants. After exposure to dehydration and high-salt stresses, expression of SISRP1, but not SlSRP1H, was significantly induced in tomato leaves. Subcellular localization analysis showed that SlSRP1 was predominantly located in the nucleus, while SlSRP1H was equally distributed in the nucleus and cytoplasm. To further investigate the potential role of SlSRP1 in the osmotic stress response, we generated SISRP1-silenced tomato plants. Compared to control plants, SISRP1-silenced tomato plants exhibited enhanced tolerance to high salinity, as evidenced by a high accumulation of proline and reduced chlorosis, ion leakage, and lipid peroxidation. Moreover, SISRP1-silenced tomato plants showed dehydration-tolerant phenotypes with enhanced abscisic acid sensitivity and increased expression of stress-related genes, including SlRD29, SlAREB, and SlDREB2. Overall, our findings suggest that SlSRP1 negatively regulates the osmotic stress response.

Learning representations of microbe-metabolite interactions.

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Major shifts in gut microbiota during development and its relationship to growth in ostriches.

The development of gut microbiota during ontogeny is emerging as an important process influencing physiology, immunity and fitness in vertebrates. However, knowledge of how bacteria colonize the juvenile gut, how this is influenced by changes in the diversity of gut bacteria and to what extent this influences host fitness, particularly in nonmodel organisms, is lacking. Here we used 16S rRNA gene sequencing to describe the successional development of the faecal microbiome in ostriches (Struthio camelus, n = 66, repeatedly sampled) over the first 3 months of life and its relationship to growth. We found a gradual increase in microbial diversity with age that involved multiple colonization and extinction events and a major taxonomic shift in bacteria that coincided with the cessation of yolk absorption. Comparisons with the microbiota of adults (n = 5) revealed that the chicks became more similar in their microbial diversity and composition to adults as they aged. There was a five-fold difference in juvenile growth during development, and growth during the first week of age was strongly positively correlated with the abundance of the genus Bacteroides and negatively correlated with Akkermansia. After the first week, the abundances of six phylogenetically diverse families (Peptococcaceae, S24-7, Verrucomicrobiae, Anaeroplasmataceae, Streptococcaceae, Methanobacteriaceae) were associated with subsequent reductions in chick growth in an age-specific and transient manner. These results have broad implications for our understanding of the development of gut microbiota and its associations with animal growth.

The human microbiome in evolution.

The trillions of microbes living in the gut-the gut microbiota-play an important role in human biology and disease. While much has been done to explore its diversity, a full understanding of our microbiomes demands an evolutionary perspective. In this review, we compare microbiomes from human populations, placing them in the context of microbes from humanity's near and distant animal relatives. We discuss potential mechanisms to generate host-specific microbiome configurations and the consequences of disrupting those configurations. Finally, we propose that this broader phylogenetic perspective is useful for understanding the mechanisms underlying human-microbiome interactions.

Stress response, gut microbial diversity and sexual signals correlate with social interactions.

Theory predicts that social interactions are dynamically linked to phenotype. Yet because social interactions are difficult to quantify, little is known about the precise details on how interactivity is linked to phenotype. Here, we deployed proximity loggers on North American barn swallows (Hirundo rustica erythrogaster) to examine intercorrelations among social interactions, morphology and features of the phenotype that are sensitive to the social context: stress-induced corticosterone (CORT) and gut microbial diversity. We analysed relationships at two spatial scales of interaction: (i) body contact and (ii) social interactions occurring between 0.1 and 5 m. Network analysis revealed that relationships between social interactions, morphology, CORT and gut microbial diversity varied depending on the sexes of the individuals interacting and the spatial scale of interaction proximity. We found evidence that body contact interactions were related to diversity of socially transmitted microbes and that looser social interactions were related to signalling traits and CORT.

Convergence of gut microbiomes in myrmecophagous mammals.

Mammals have diversified into many dietary niches. Specialized myrmecophagous (ant- and termite-eating) placental mammals represent a textbook example of evolutionary convergence driven by extreme diet specialization. Armadillos, anteaters, aardvarks, pangolins and aardwolves thus provide a model system for understanding the potential role of gut microbiota in the convergent adaptation to myrmecophagy. Here, we expand upon previous mammalian gut microbiome studies by using high-throughput barcoded Illumina sequencing of the 16S rRNA gene to characterize the composition of gut microbiota in 15 species representing all placental myrmecophagous lineages and their close relatives from zoo- and field-collected samples. We confirm that both diet and phylogeny drive the evolution of mammalian gut microbiota, with cases of convergence in global composition, but also examples of phylogenetic inertia. Our results reveal specialized placental myrmecophages as a spectacular case of large-scale convergence in gut microbiome composition. Indeed, neighbour-net networks and beta-diversity plots based on UniFrac distances show significant clustering of myrmecophagous species (anteaters, aardvarks and aardwolves), even though they belong to phylogenetically distant lineages representing different orders. The aardwolf, which diverged from carnivorous hyenas only in the last 10 million years, experienced a convergent shift in the composition of its gut microbiome to become more similar to other myrmecophages. These results confirm diet adaptation to be a major driving factor of convergence in gut microbiome composition over evolutionary timescales. This study sets the scene for future metagenomic studies aiming at evaluating potential convergence in functional gene content in the microbiomes of specialized mammalian myrmecophages.

Effects of variation in sample storage conditions and swab order on 16S vaginal microbiome analyses.

IMPORTANCE: The composition of the human vaginal microbiome has been linked to a variety of medical conditions including yeast infection, bacterial vaginosis, and sexually transmitted infection. The vaginal microbiome is becoming increasingly acknowledged as a key factor in personal health, and it is essential to establish methods to collect and process accurate samples with self-collection techniques to allow large, population-based studies. In this study, we investigate if using AssayAssure Genelock, a nucleic acid preservative, introduces microbial biases in self-collected vaginal samples. To our knowledge, we also contribute some of the first evidence regarding the impacts of multiple swabs taken at one time point. Vaginal samples have relatively low biomass, so the ability to collect multiple swabs from a unique participant at a single time would greatly improve the replicability and data available for future studies. This will hopefully lay the groundwork to gain a more complete and accurate understanding of the vaginal microbiome.

Robustness of cancer microbiome signals over a broad range of methodological variation.

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Feasibility of Home Collection for Urogenital Microbiome Samples.

IMPORTANCE: Feasibility of home urogenital microbiome specimen collection is unknown. OBJECTIVES: This study aimed to evaluate successful sample collection rates from home and clinical research centers. STUDY DESIGN: Adult women participants enrolled in a multicentered cohort study were recruited to an in-person research center evaluation, including self-collected urogenital samples. A nested feasibility substudy evaluated home biospecimen collection prior to the scheduled in-person evaluation using a home collection kit with written instructions, sample collection supplies, and a Peezy™ urine collection device. Participants self-collected samples at home and shipped them to a central laboratory 1 day prior to and the day of the in-person evaluation. We defined successful collection as receipt of at least one urine specimen that was visibly viable for sequencing. RESULTS: Of 156 participants invited to the feasibility substudy, 134 were enrolled and sent collection kits with 89% (119/134) returning at least 1 home urine specimen; the laboratory determined that 79% (106/134) of these urine samples were visually viable for analysis. The laboratory received self-collected urine from the research center visit in 97% (115/119); 76% (91/119) were visually viable for sequencing. Among 401 women who did not participate in the feasibility home collection substudy, 98% (394/401) self-collected urine at the research center with 80% (321/401) returned and visibly viable for sequencing. CONCLUSIONS: Home collection of urogenital microbiome samples for research is feasible, with comparable success to clinical research center collection. Sample size adjustment should plan for technical and logistical difficulties, regardless of specimen collection site.

Effects of Variation in Urine Sample Storage Conditions on 16S Urogenital Microbiome Analyses.

Replicability is a well-established challenge in microbiome research with a variety of contributing factors at all stages, from sample collection to code execution. Here, we focus on voided urine sample storage conditions for urogenital microbiome analysis. Using urine samples collected from 10 adult females, we investigated the microbiome preservation efficacy of AssayAssure Genelock (Genelock), compared with no preservative, under different temperature conditions. We varied temperature over 48 h in order to examine the impact of conditions samples may experience with home voided urine collection and shipping to a central biorepository. The following common lab and shipping conditions were investigated: -20°C, ambient temperature, 4°C, freeze-thaw cycle, and heat cycle. At 48 h, all samples were stored at -80°C until processing. After generating 16S rRNA gene amplicon sequencing data using the highly sensitive KatharoSeq protocol, we observed individual variation in both alpha and beta diversity metrics below interhuman differences, corroborating reports of individual microbiome variability in other specimen types. While there was no significant difference in beta diversity when comparing Genelock versus no preservative, we did observe a higher concordance with Genelock samples shipped at colder temperatures (-20°C and 4°C) when compared with the samples shipped at -20°C without preservative. Our results indicate that Genelock does not introduce a significant amount of microbial bias when used on a range of temperatures and is most effective at colder temperatures. IMPORTANCE The urogenital microbiome is an understudied yet important human microbiome niche. Research has been stimulated by the relatively recent discovery that urine is not sterile; urinary tract microbes have been linked to health problems, including urinary infections, incontinence, and cancer. The quality of life and economic impact of UTIs and urgency incontinence alone are enormous, with $3.5 billion and $82.6 billion, respectively, spent in the United States. annually. Given the low biomass of urine, novelty of the field, and limited reproducibility evidence, it is critical to study urine sample storage conditions to optimize scientific rigor. Efficient and reliable preservation methods inform methods for home self-sample collection and shipping, increasing the potential use in larger-scale studies. Here, we examined both buffer and temperature variation effects on 16S rRNA gene amplicon sequencing results from urogenital samples, providing data on the consequences of common storage methods on urogenital microbiome results.

Global branches and local states of the human gut microbiome define associations with environmental and intrinsic factors.

The gut microbiome is important for human health, yet modulation requires more insight into inter-individual variation. Here, we explored latent structures of the human gut microbiome across the human lifespan, applying partitioning, pseudotime, and ordination approaches to >35,000 samples. Specifically, three major gut microbiome branches were identified, within which multiple partitions were observed in adulthood, with differential abundances of species along branches. Different compositions and metabolic functions characterized the branches' tips, reflecting ecological differences. An unsupervised network analysis from longitudinal data from 745 individuals showed that partitions exhibited connected gut microbiome states rather than over-partitioning. Stability in the Bacteroides-enriched branch was associated with specific ratios of Faecalibacterium:Bacteroides. We also showed that associations with factors (intrinsic and extrinsic) could be generic, branch- or partition-specific. Our ecological framework for cross-sectional and longitudinal data allows a better understanding of overall variation in the human gut microbiome and disentangles factors associated with specific configurations.

A multi-study analysis enables identification of potential microbial features associated with skin aging signs.

Introduction: During adulthood, the skin microbiota can be relatively stable if environmental conditions are also stable, yet physiological changes of the skin with age may affect the skin microbiome and its function. The microbiome is an important factor to consider in aging since it constitutes most of the genes that are expressed on the human body. However, severity of specific aging signs (one of the parameters used to measure "apparent" age) and skin surface quality (e.g., texture, hydration, pH, sebum, etc.) may not be indicative of chronological age. For example, older individuals can have young looking skin (young apparent age) and young individuals can be of older apparent age. Methods: Here we aim to identify microbial taxa of interest associated to skin quality/aging signs using a multi-study analysis of 13 microbiome datasets consisting of 16S rRNA amplicon sequence data and paired skin clinical data from the face. Results: We show that there is a negative relationship between microbiome diversity and transepidermal water loss, and a positive association between microbiome diversity and age. Aligned with a tight link between age and wrinkles, we report a global positive association between microbiome diversity and Crow's feet wrinkles, but with this relationship varying significantly by sub-study. Finally, we identify taxa potentially associated with wrinkles, TEWL and corneometer measures. Discussion: These findings represent a key step towards understanding the implication of the skin microbiota in skin aging signs.

Greengenes2 unifies microbial data in a single reference tree.

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

The gastrointestinal and microbiome impact of a resistant starch blend from potato, banana, and apple fibers: A randomized clinical trial using smart caps.

The gastrointestinal (GI) impact of fibers including resistant starch (RS) consumption depends on various types and amounts of fibers, the initial microbiome states, and accurate intake measurements. A randomized clinical trial evaluated the GI impact of varying doses of a novel resistant starch blend (RSB) with smart cap monitoring. RSB contained at least 50% RS and was a proprietary mixture of a potato starch, green banana flour, and apple fiber powder (a source of apple pectin, not resistant starch). The study design randomized participants to one of four arms: 10 g/day of potato starch (0 RSB), 10 g/day of RSB, 10 to 20 to 20 g/day of RSB or 10 to 20 to 30 g/day RSB for two-week intervals over 6 weeks. Results confirmed that while resistant starch of approximately 5 g per day improves GI symptoms at 2, 4, and 6 weeks, it did not demonstrate a detectable effect on short chain fatty acids. Increasing doses of the blend (RSB) led to a decrease in the diarrhea score. Using an estimate of total consumption of RSB based on smart cap recordings of container openings and protocol-specified doses of RSB, a reduction in the sleep disturbance score was associated with higher RSB dose. The exploratory microbiome evaluation demonstrated that among the 16S rRNA gene sequences most associated with the consumption of the novel blend RSB, two belong to taxa of notable interest to human health: Faecalibacterium and Akkermansia.

Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype.

Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data. IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.

A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project.

BACKGROUND: Individual diet components and specific dietary regimens have been shown to impact the gut microbiome. OBJECTIVES: Here, we explored the contribution of long-term diet by searching for dietary patterns that would best associate with the gut microbiome in a population-based cohort. METHODS: Using a priori and a posteriori approaches, we constructed dietary patterns from an FFQ completed by 1800 adults in the American Gut Project. Dietary patterns were defined as groups of participants or combinations of food variables (factors) driven by criteria ranging from individual nutrients to overall diet. We associated these patterns with 16S ribosomal RNA-based gut microbiome data for a subset of 744 participants. RESULTS: Compared to individual features (e.g., fiber and protein), or to factors representing a reduced number of dietary features, 5 a posteriori dietary patterns based on food groups were best associated with gut microbiome beta diversity (P ≤ 0.0002). Two patterns followed Prudent-like diets-Plant-Based and Flexitarian-and exhibited the highest Healthy Eating Index 2010 (HEI-2010) scores. Two other patterns presented Western-like diets with a gradient in HEI-2010 scores. A fifth pattern consisted mostly of participants following an Exclusion diet (e.g., low carbohydrate). Notably, gut microbiome alpha diversity was significantly lower in the most Western pattern compared to the Flexitarian pattern (P ≤ 0.009), and the Exclusion diet pattern was associated with low relative abundance of Bifidobacterium (P ≤ 1.2 × 10-7), which was better explained by diet than health status. CONCLUSIONS: We demonstrated that global-diet a posteriori patterns were more associated with gut microbiome variations than individual dietary features among adults in the United States. These results confirm that evaluating diet as a whole is important when studying the gut microbiome. It will also facilitate the design of more personalized dietary strategies in general populations.