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.

Social behaviour mediates the microbiome response to antibiotic treatment in a wild mammal.

High levels of social connectivity among group-living animals have been hypothesized to benefit individuals by creating opportunities to rapidly reseed the microbiome and maintain stability against disruption. We tested this hypothesis by perturbing the microbiome of a wild population of Grant's gazelles with an antibiotic and asking whether microbiome recovery differs between individuals with high versus low levels of social connectivity. We found that after treatment, individuals with high social connectivity experienced a faster increase in microbiome richness than less socially connected individuals. Unexpectedly, the rapid increase in microbiome richness of highly connected individuals that received treatment led to their microbiomes becoming more distinct relative to the background population. Our results suggest that the microbiome of individuals with high social connectivity can be rapidly recolonized after a perturbation event, but this leads to a microbiome that is more distinct from, rather than more similar to the unperturbed state. This work provides new insight into the role of social interactions in shaping the microbiome.

The impact of breastfeeding on the preterm infant's microbiome and metabolome: a pilot study.

BACKGROUND: Human milk is unquestionably beneficial for preterm infants. We investigated how the transition from tube to oral/breastfeeding impacts the preterm infants' oral and gut microbiome and metabolome. METHODS: We analyzed stool, saliva, and milk samples collected from a cohort of preterm infants enrolled in the MAP Study, a prospective observational trial. The microbiome and metabolome of the samples were analyzed from 4 longitudinal sample time points, 2 during tube feeds only and 2 after the initiation of oral/breastfeeding. RESULTS: We enrolled 11 mother-infant dyads (gestational age = 27.9 (23.4-32.2)) and analyzed a total of 39 stool, 44 saliva, and 43 milk samples over 4 timepoints. In saliva samples, there was a shift towards increased Streptococcus and decreased Staphylococcus after oral feeding/breastfeeding initiation (p < 0.05). Milk sample metabolites were strongly influenced by the route of feeding and milk type (p < 0.05) and represented the pathways of Vitamin E metabolism, Vitamin B12 metabolism, and Tryptophan metabolism. CONCLUSION: Our analysis demonstrated that the milk and preterm infant's saliva microbiome and metabolome changed over the course of the first four to 5 months of life, coinciding with the initiation of oral/breastfeeds. IMPACT: The microbiome and metabolome is altered in the infant's saliva but not their stool, and in mother's milk when feeds are transitioned from tube to oral/breastfeeding. We assessed the relationship between the gut and oral microbiome/metabolome with the milk microbiome/metabolome over a longitudinal period of time in preterm babies. Metabolites that changed in the infants saliva after the initiation of oral feeds have the potential to be used as biomarkers for disease risk.

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.

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.

Clearing the plate: a strategic approach to mitigate well-to-well contamination in large-scale microbiome studies.

Large-scale studies are essential to answer questions about complex microbial communities that can be extremely dynamic across hosts, environments, and time points. However, managing acquisition, processing, and analysis of large numbers of samples poses many challenges, with cross-contamination being the biggest obstacle. Contamination complicates analysis and results in sample loss, leading to higher costs and constraints on mixed sample type study designs. While many researchers opt for 96-well plates for their workflows, these plates present a significant issue: the shared seal and weak separation between wells leads to well-to-well contamination. To address this concern, we propose an innovative high-throughput approach, termed as the Matrix method, which employs barcoded Matrix Tubes for sample acquisition. This method is complemented by a paired nucleic acid and metabolite extraction, utilizing 95% (vol/vol) ethanol to stabilize microbial communities and as a solvent for extracting metabolites. Comparative analysis between conventional 96-well plate extractions and the Matrix method, measuring 16S rRNA gene levels via quantitative polymerase chain reaction, demonstrates a notable decrease in well-to-well contamination with the Matrix method. Metagenomics, 16S rRNA gene amplicon sequencing (16S), and untargeted metabolomics analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS) confirmed that the Matrix method recovers reproducible microbial and metabolite compositions that can distinguish between subjects. This advancement is critical for large-scale study design as it minimizes well-to-well contamination and technical variation, shortens processing times, and integrates with automated infrastructure for enhancing sample randomization and metadata generation. IMPORTANCE: Understanding dynamic microbial communities typically requires large-scale studies. However, handling large numbers of samples introduces many challenges, with cross-contamination being a major issue. It not only complicates analysis but also leads to sample loss and increased costs and restricts diverse study designs. The prevalent use of 96-well plates for nucleic acid and metabolite extractions exacerbates this problem due to their wells having little separation and being connected by a single plate seal. To address this, we propose a new strategy using barcoded Matrix Tubes, showing a significant reduction in cross-contamination compared to conventional plate-based approaches. Additionally, this method facilitates the extraction of both nucleic acids and metabolites from a single tubed sample, eliminating the need to collect separate aliquots for each extraction. This innovation improves large-scale study design by shortening processing times, simplifying analysis, facilitating metadata curation, and producing more reliable results.

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.

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.