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.

A communal catalogue reveals Earth's multiscale microbial diversity.

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.

Origin of microbial biomineralization and magnetotaxis during the Archean.

Microbes that synthesize minerals, a process known as microbial biomineralization, contributed substantially to the evolution of current planetary environments through numerous important geochemical processes. Despite its geological significance, the origin and evolution of microbial biomineralization remain poorly understood. Through combined metagenomic and phylogenetic analyses of deep-branching magnetotactic bacteria from the Nitrospirae phylum, and using a Bayesian molecular clock-dating method, we show here that the gene cluster responsible for biomineralization of magnetosomes, and the arrangement of magnetosome chain(s) within cells, both originated before or near the Archean divergence between the Nitrospirae and Proteobacteria This phylogenetic divergence occurred well before the Great Oxygenation Event. Magnetotaxis likely evolved due to environmental pressures conferring an evolutionary advantage to navigation via the geomagnetic field. Earth's dynamo must therefore have been sufficiently strong to sustain microbial magnetotaxis in the Archean, suggesting that magnetotaxis coevolved with the geodynamo over geological time.

Microbiome and lipidomic analysis reveal the interplay between skin bacteria and lipids in a cohort study.

Human skin acts as a protective barrier between the body and the external environment. Skin microbiome and intercellular lipids in the stratum corneum (SC) are essential for maintaining skin barrier function. However, the interplay between skin bacteria and the lipids is not fully understood. In this study, we characterized the skin microbiome and SC lipid profiles from the forearm and face in a cohort of 57 healthy participants. 16S rRNA gene sequencing showed the skin microbial composition is significantly different between body locations and genders. Female forearm samples have the highest microbial diversity. The relative abundance of Staphylococcus hominis, Micrococcus luteus, Corynebacterium tuberculostearicum, Finegoldia magna, and Moraxellaceae sp. are significantly higher in the forearm than the face. The predictive functional analysis of 16S rRNA gene sequencing by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) and ANCOM-BC showed different bacterial metabolic pathway profiles between body locations or genders, and identified 271 differential pathways, including arginine and polyamine biosynthesis, chorismate biosynthesis pathways, which are more abundant in the female forearm, and sulfur oxidation pathway, which is more abundant in the male face. The SC lipid profiles differ between the body locations as well. Total free fatty acids (FFA), cholesterol sulfate and sphingosine are more abundant in the face. Dihydro-/6-hydroxy/phyto-ceramides are more abundant in the forearm. The correlation analysis of 16S rRNA gene sequencing and lipids revealed novel interplay between the bacteria and skin lipids. Shannon entropy and S. hominis negatively correlated with FFA, cholesterol sulfate and sphingosine; while positively correlated with dihydro-/6-hydroxy/phyto-ceramides. The correlation of predictive pathway profiles and lipids identified pathways involved in amino acids metabolism, carbohydrates degradation, aromatic compounds metabolism and fatty acid degradation metabolism are positively correlated with dihydro-/6-hydroxy/phyto-ceramides and negatively correlated with FFA, cholesterol sulfate and sphingosine. This study provides insights on the potential correlation between skin microbiome and lipids.

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.

SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data.

MOTIVATION: The application of next-generation sequencing (NGS) technologies to RNAs directly extracted from a community of organisms yields a mixture of fragments characterizing both coding and non-coding types of RNAs. The task to distinguish among these and to further categorize the families of messenger RNAs and ribosomal RNAs (rRNAs) is an important step for examining gene expression patterns of an interactive environment and the phylogenetic classification of the constituting species. RESULTS: We present SortMeRNA, a new software designed to rapidly filter rRNA fragments from metatranscriptomic data. It is capable of handling large sets of reads and sorting out all fragments matching to the rRNA database with high sensitivity and low running time.

Multi-omic approach to decipher the impact of skincare products with pre/postbiotics on skin microbiome and metabolome.

Introduction: Although pre/pro/postbiotics have become more prevalent in dermatologic and cosmetic fields, the mode of action when topically applied is largely unknown. A multi-omic approach was applied to decipher the impact of the skincare products with pre/postbiotics on skin microbiome and metabolome. Methods: Subjects with dry skin applied a body wash and body lotion with or without pre/postbiotics for 6 weeks. Skin hydration was measured at baseline, 3 and 6 weeks. Skin swabs were collected for 16S rRNA gene sequencing, metagenomics and metabolomics analysis. Results: Skin hydration significantly increased in both groups. The prebiotic group significantly reduced opportunistic pathogens, e.g., Pseudomonas stutzeri and Sphingomonas anadarae, and increased the commensals, e.g., Staphylococcus equorum, Streptococcus mitis, Halomonas desiderata. Bacterial sugar degradation pathways were enriched in the prebiotic group, while fatty acid biosynthesis pathways were reduced in control. The changes on skin metabolome profiles by the products were more prominent. The prebiotic group performed greater modulation on many clinically-relevant metabolites compared to control. Correlation analysis showed H. desiderata and S. mitis positively correlated with skin hydration, P. stutzeri and S. anadarae negatively correlated with the metabolites that are positively associated with skin hydration improvement. Conclusion: This holistic study supported a hypothesis that the pre/postbiotics increased skin hydration through the modulation of skin microbiome, metabolic pathways and metabolome.

B. infantis EVC001 Is Well-Tolerated and Improves Human Milk Oligosaccharide Utilization in Preterm Infants in the Neonatal Intensive Care Unit.

Not all infants carry specialized gut microbes, meaning they cannot digest human milk oligosaccharides and therefore do not receive complete benefits from human milk. B. infantis EVC001 is equipped to convert the full array of complex oligosaccharides into compounds usable by the infant, making it an ideal candidate to stabilize gut function and improve nutrition in preterm infants. A prospective, open-label study design was used to evaluate the tolerability of B. infantis EVC001 and its effects on the fecal microbiota in preterm infants in a Neonatal Intensive Care Unit. Thirty preterm infants <1,500 g and/or <33 weeks gestation at birth were divided into two matched groups, and control infants were enrolled and discharged prior to enrolling EVC001 infants to prevent cross-colonization of B. infantis: (1) fifteen control infants received no EVC001, and (2) fifteen infants received once-daily feedings of B. infantis EVC001 (8.0 x 109 CFU) in MCT oil. Clinical information regarding medications, growth, nutrition, gastrointestinal events, diagnoses, and procedures was collected throughout admission. Infant stool samples were collected at baseline, Study Days 14 and 28, and 34-, 36-, and 38-weeks of gestation. Taxonomic composition of the fecal microbiota, functional microbiota analysis, B. infantis, and human milk oligosaccharides (HMOs) in the stool were determined or quantified using 16S rRNA gene sequencing, metagenomic sequencing, qPCR, and mass spectrometry, respectively. No adverse events or tolerability issues related to EVC001 were reported. Control infants had no detectable levels of B. infantis. EVC001 infants achieved high levels of B. infantis (mean = 9.7 Log10 CFU/µg fecal DNA) by Study Day 14, correlating with less fecal HMOs (ρ = -0.83, P < 0.0001), indicating better HMO utilization in the gut. In this study, B. infantis EVC001 was shown to be safe, well-tolerated, and efficient in colonizing the preterm infant gut and able to increase the abundance of bifidobacteria capable of metabolizing HMOs, resulting in significantly improved utilization of human milk. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT03939546, identifier: NCT03939546.

Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0.

Microbial genomes are available at an ever-increasing pace, as cultivation and sequencing become cheaper and obtaining metagenome-assembled genomes (MAGs) becomes more effective. Phylogenetic placement methods to contextualize hundreds of thousands of genomes must thus be efficiently scalable and sensitive from closely related strains to divergent phyla. We present PhyloPhlAn 3.0, an accurate, rapid, and easy-to-use method for large-scale microbial genome characterization and phylogenetic analysis at multiple levels of resolution. PhyloPhlAn 3.0 can assign genomes from isolate sequencing or MAGs to species-level genome bins built from >230,000 publically available sequences. For individual clades of interest, it reconstructs strain-level phylogenies from among the closest species using clade-specific maximally informative markers. At the other extreme of resolution, it scales to large phylogenies comprising >17,000 microbial species. Examples including Staphylococcus aureus isolates, gut metagenomes, and meta-analyses demonstrate the ability of PhyloPhlAn 3.0 to support genomic and metagenomic analyses.

Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea.

Rapid growth of genome data provides opportunities for updating microbial evolutionary relationships, but this is challenged by the discordant evolution of individual genes. Here we build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on a comprehensive set of 381 markers, using multiple strategies. Our trees indicate remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that were limited to fewer "core" genes, such as the ribosomal proteins. The robustness of the results was tested with respect to several variables, including taxon and site sampling, amino acid substitution heterogeneity and saturation, non-vertical evolution, and the impact of exclusion of candidate phyla radiation (CPR) taxa. Our results provide an updated view of domain-level relationships.

Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns.

High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur's ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur. IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.

Open-Source Sequence Clustering Methods Improve the State Of the Art.

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

Deciphering metatranscriptomic data.

Metatranscriptomic data contributes another piece of the puzzle to understanding the phylogenetic structure and function of a community of organisms. High-quality total RNA is a bountiful mixture of ribosomal, transfer, messenger and other noncoding RNAs, where each family of RNA is vital to answering questions concerning the hidden microbial world. Software tools designed for deciphering metatranscriptomic data fall under two main categories: the first is to reassemble millions of short nucleotide fragments produced by high-throughput sequencing technologies into the original full-length transcriptomes for all organisms within a sample, and the second is to taxonomically classify the organisms and determine their individual functional roles within a community. Species identification is mainly established using the ribosomal RNA genes, whereas the behavior and functionality of a community is revealed by the messenger RNA of the expressed genes. Numerous chemical and computational methods exist to separate families of RNA prior to conducting further downstream analyses, primarily suitable for isolating mRNA or rRNA from a total RNA sample. In this chapter, we demonstrate a computational technique for filtering rRNA from total RNA using the software SortMeRNA. Additionally, we propose a post-processing pipeline using the latest software tools to conduct further studies on the filtered data, including the reconstruction of mRNA transcripts for functional analyses and phylogenetic classification of a community using the ribosomal RNA.

Erratum to: Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity.

[This corrects the article DOI: 10.1186/s40168-015-0081-x.].

Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity.

BACKGROUND: The operational taxonomic unit (OTU) is widely used in microbial ecology. Reproducibility in microbial ecology research depends on the reliability of OTU-based 16S ribosomal subunit RNA (rRNA) analyses. RESULTS: Here, we report that many hierarchical and greedy clustering methods produce unstable OTUs, with membership that depends on the number of sequences clustered. If OTUs are regenerated with additional sequences or samples, sequences originally assigned to a given OTU can be split into different OTUs. Alternatively, sequences assigned to different OTUs can be merged into a single OTU. This OTU instability affects alpha-diversity analyses such as rarefaction curves, beta-diversity analyses such as distance-based ordination (for example, Principal Coordinate Analysis (PCoA)), and the identification of differentially represented OTUs. Our results show that the proportion of unstable OTUs varies for different clustering methods. We found that the closed-reference method is the only one that produces completely stable OTUs, with the caveat that sequences that do not match a pre-existing reference sequence collection are discarded. CONCLUSIONS: As a compromise to the factors listed above, we propose using an open-reference method to enhance OTU stability. This type of method clusters sequences against a database and includes unmatched sequences by clustering them via a relatively stable de novo clustering method. OTU stability is an important consideration when analyzing microbial diversity and is a feature that should be taken into account during the development of novel OTU clustering methods.