Species-level functional profiling of metagenomes and metatranscriptomes.

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types

Impact of ocean acidification on the intestinal microbiota of the marine sea bream (Sparus aurata L.)

Within a scenario of increasing atmospheric CO2 and ocean acidification (OA), it is highly relevant to investigate its impacts not only on fish performance but also on fish intestinal microbiome and how that reflects on host performance and health. The main objective of this study was to establish if the intestinal microbiota of the sea bream (Sparus aurata) was affected by high level of CO2 in line with the predictions for this century. The bacterial communities of the intestinal fluid were characterized in animals kept at the present-day level of CO2 (400 μatm) and in animals switched to high CO2 (1200 μatm) for 1 month. Bacterial taxa identification was based on molecular methods, using the DNA coding for the 16S ribosomal RNA and primers targeting the regions V1-V3. Amplicons obtained from DNA samples of animals in the same tank were combined, cloned to obtain a bacterial DNA library, and the clones were sequenced. No significant differences were found between the two treatments for alpha diversity. However, beta diversity analysis revealed distinct dysbiosis in response to hypercapnia, with phylum Firmicutes absent from the bacterial communities of fish exposed to 1200 μatm CO2, whereas Proteobacteria relative abundance was increased at elevated CO2, due to the presence of Gammaproteobacteria (Vibrionaceae and Alteromonadaceae), a class not present in the control samples. This study provides a first glimpse at the impact of OA in fish intestinal microbiota and highlights potential downstream effects to the general condition of fishes under hypercapnia.Funding Agency Portuguese Foundation for Science and Technology PTDC/MAR-BIO/3034/2014 Portuguese Foundation for Science and Technology UID/Multi/04326/2019 Ministry of Science and Higher Education, Polandinfo:eu-repo/semantics/publishedVersio

Amniotic fluid from healthy term pregnancies does not harbor a detectable microbial community

Abstract Recent studies have conflicting data regarding the presence of intra-amniotic microbiota. Viral communities are increasingly recognized as important although overlooked components of the human microbiota. It is unknown if the developing fetus is exposed to a community of viruses (virome). Given the debate over the existence of an intra-amniotic microbial community and the importance of understanding how the infant gut is populated, we characterized the virome and bacterial microbiota of amniotic fluid from 24 uncomplicated term pregnancies using next-generation sequencing methods. Contrary to expectations, the bacterial microbiota of amniotic fluid was indistinguishable from contamination controls. Viral reads were sparse in the amniotic fluid, and we found no evidence of a core viral community across samples

A simple statistical test of taxonomic or functional homogeneity using replicated microbiome sequencing samples

One important question in microbiome analysis is how to assess the homogeneity of the microbial composition in a given environment, with respect to a given analysis method. Do different microbial samples taken from the same environment follow the same taxonomic distribution of organisms, or the same distribution of functions? Here we provide a non-parametric statistical “triangulation test” to address this type of question. The test requires that multiple replicates are available for each of the biological samples, and it is based on three-way computational comparisons of samples. To illustrate the application of the test, we collected three biological samples taken from different locations in one piece of human stool, each represented by three replicates, and analyzed them using MEGAN. (Despite its name, the triangulation test does not require that the number of biological samples or replicates be three.) The triangulation test rejects the null hypothesis that the three biological samples exhibit the same distribution of taxa or function (error probability ≤0.05), indicating that the microbial composition of the investigated human stool is not homogenous on a macroscopic scale, suggesting that pooling material from multiple locations is a reasonable practice. We provide an implementation of the test in our open source program MEGAN Community Edition

PIA : more accurate taxonomic assignment of metagenomic data demonstrated on sedaDNA from the North sea

Assigning metagenomic reads to taxa presents significant challenges. Existing approaches address some issues, but are mostly limited to metabarcoding or optimized for microbial data. We present PIA (Phylogenetic Intersection Analysis): a taxonomic binner that works from standard BLAST output while mitigating key effects of incomplete databases. Benchmarking against MEGAN using sedaDNA suggests that, while PIA is less sensitive, it can be more accurate. We use known sequences to estimate the accuracy of PIA at up to 96% when the real organism is not represented in the database. For ancient DNA, where taxa of interest are frequently over-represented domesticates or absent, poorly-known organisms, more accurate assignment is critical, even at the expense of sensitivity. PIA offers an approach to objectively filter out false positive hits without the need to manually remove taxa and so make presuppositions about past environments and their palaeoecologies

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Host‐derived population genomics data provides insights into bacterial and diatom composition of the killer whale skin

Recent exploration into the interactions and relationship between hosts and their microbiota has revealed a connection between many aspects of the host's biology, health and associated micro-organisms. Whereas amplicon sequencing has traditionally been used to characterize the microbiome, the increasing number of published population genomics data sets offers an underexploited opportunity to study microbial profiles from the host shotgun sequencing data. Here, we use sequence data originally generated from killer whale Orcinus orca skin biopsies for population genomics, to characterize the skin microbiome and investigate how host social and geographical factors influence the microbial community composition. Having identified 845 microbial taxa from 2.4 million reads that did not map to the killer whale reference genome, we found that both ecotypic and geographical factors influence community composition of killer whale skin microbiomes. Furthermore, we uncovered key taxa that drive the microbiome community composition and showed that they are embedded in unique networks, one of which is tentatively linked to diatom presence and poor skin condition. Community composition differed between Antarctic killer whales with and without diatom coverage, suggesting that the previously reported episodic migrations of Antarctic killer whales to warmer waters associated with skin turnover may control the effects of potentially pathogenic bacteria such as Tenacibaculum dicentrarchi. Our work demonstrates the feasibility of microbiome studies from host shotgun sequencing data and highlights the importance of metagenomics in understanding the relationship between host and microbial ecology

Rio de Janeiro water crisis: a metagenomic approach

In Rio de Janeiro’s metropolitan area comprise, over 8 million inhabitants receive drinking water from Company responsible for water and sewage, capted by the Guandu river’s water treatment station. Such water started to present unusual odour, taste and color in January 2020, as stated by the population itself. Results obtained through raw water microbiome metagenomic analysis inferred cyanobacteria presence, with Planktothricoides gender and Planktothricoides sp. SR001 being the most abundant gender and organism. Such do pose a risk to public health, as they are able to produce cyanotoxins that affect human health

A case study of salivary microbiome in smokers and non-smokers in Hungary: analysis by shotgun metagenome sequencing

Objective To investigate the role of cigarette smoking in disease-development through altering the composition of the oral microbial community. Periodontitis and oral cancer are highly prevalent in Hungary; therefore, the salivary microbiome of smoker and non-smoker Hungarian adults was characterized. Methods Shotgun metagenome sequencing of salivary DNA samples from 22 individuals (11 non-smokers and 11 current smokers) was performed using the Ion Torrent PGMTM platform. Quality-filtered reads were analysed by both alignment-based sequence similarity searches and genome-centric binning. Results Prevotella, Veillonella and Streptococcus were the predominant genera in the saliva of both groups. Although the overall composition and diversity of the microbiota were similar, Prevotella was significantly more abundant in salivary samples of current smokers compared to non-smokers. Members of the genus Prevotella were implicated in the development of inflammatory diseases and oral cancer. The abundance of the genus Megasphaera also increased in current smokers, whereas the genera Neisseria, Oribacterium, Capnocytophaga and Porphyromonas were significantly reduced. The data generated by read-based taxonomic classification and genome-centric binning mutually validated the two distinct metagenomic approaches. Conclusion Smoking-associated dysbiosis of the salivary microbiome in current cigarette smokers, especially increased abundance of Prevotella and Megasphaera genera, may facilitate disease development