Computational integration of genomic traits into 16S rDNA microbiota sequencing studies

Inter- and intraspecific phytochemical variation correlate with epiphytic flower and leaf bacterial communities

Microbes associated with flowers and leaves affect plant health and fitness and modify the chemical phenotypes of plants with consequences for interactions of plants with their environment. However, the drivers of bacterial communities colonizing above-ground parts of grassland plants in the field remain largely unknown. We therefore examined the relationships between phytochemistry and the epiphytic bacterial community composition of flowers and leaves of Ranunculus acris and Trifolium pratense. On 252 plant individuals, we characterized primary and specialized metabolites, that is, surface sugars, volatile organic compounds (VOCs), and metabolic fingerprints, as well as epiphytic flower and leaf bacterial communities. The genomic potential of bacterial colonizers concerning metabolic capacities was assessed using bacterial reference genomes. Phytochemical composition displayed pronounced variation within and between plant species and organs, which explained part of the variation in bacterial community composition. Correlation network analysis suggests strain-specific correlations with metabolites. Analysis of bacterial reference genomes revealed taxon-specific metabolic capabilities that corresponded with genes involved in glycolysis and adaptation to osmotic stress. Our results show relationships between phytochemistry and the flower and leaf bacterial microbiomes suggesting that plants provide chemical niches for distinct bacterial communities. In turn, bacteria may induce alterations in the plants' chemical phenotype. Thus, our study may stimulate further research on the mechanisms of trait-based community assembly in epiphytic bacteria.

A review on the application of bioinformatics tools in food microbiome studies

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.

Venus flytrap microbiotas withstand harsh conditions during prey digestion

The carnivorous Venus flytrap (Dionaea muscipula) overcomes environmental nutrient limitation by capturing small animals. Such prey is digested with an acidic enzyme-containing mucilage that is secreted into the closed trap. However, surprisingly little is known about associations with microorganisms. Therefore, we assessed microbiotas of traps and petioles for the Venus flytrap by 16S amplicon meta-barcoding. We also performed time-series assessments of dynamics during digestion in traps and experimental acidification of petioles. We found that the traps hosted distinct microbiotas that differed from adjacent petioles. Further, they showed a significant taxonomic turnover during digestion. Following successful catches, prey-associated bacteria had strong effects on overall composition. With proceeding digestion, however, microbiotas were restored to compositions resembling pre-digestion stages. A comparable, yet less extensive shift was found when stimulating digestion with coronatine. Artificial acidification of petioles did not induce changes towards trap-like communities. Our results show that trap microbiota were maintained during digestion despite harsh conditions and recovered after short-term disturbances through prey microbiota. This indicates trap-specific and resilient associations. By mapping to known genomes, we predicted putative adaptations and functional implications for the system, yet direct mechanisms and quantification of host benefits, like the involvement in digestion, remain to be addressed.

Screening and identification of Lactic acid bacteria from Ya'an pickle water to effectively remove Pb2

Heavy metal lead, which enters the human body through food intake, endangers human health. Microbe has the ability of adsorbing heavy metal, among which lactic acid bacteria are promising microbes to adsorb and remove Pb2+. The purpose of this study was to screen lactic acid bacteria from Ya'an pickle water to effectively remove Pb2+. The 7 strains having strong ability to effectively remove Pb2+ were detected. These strains were identified by microscopic examination and 16S rDNA sequencing, 4 strains of Lactobacillus plantarum and 3 strains of Lactobacillus brevis were obtained. Then the bacteria had a blind adsorption effect on Pb2+. After microwave digestion, the Pb2+ concentration was measured by flame atomic absorption spectrometry. The highest removal reached 82.25%. The adsorption mechanism of lactic acid bacteria was mainly divided into biosorption and bioaccumulation. The 7 strains of lactic acid bacteria could provide potential for detoxification of contaminated foods and reduction of the Pb2+ accumulation in the human diet and animal feed. At the same time, this study was helpful to further understand the mechanism of Pb2+ being adsorbed by lactic acid bacteria.

A bioinformatics pipeline integrating predictive metagenomics profiling for the analysis of 16S rDNA/rRNA sequencing data originated from foods

The recent advances in molecular biology, such as the advent of next-generation sequencing (NGS) platforms, have paved the way to new exciting tools which rapidly transform food microbiology. Nowadays, NGS methods such as 16S rDNA/rRNA metagenomics or amplicon sequencing are used for the taxonomic profiling of the food microbial communities. Although 16S rDNA/rRNA NGS-based microbial data are not suited for the investigation of the functional potential of the identified operational taxonomic units as compared to shotgun metagenomics, advances in the bioinformatics discipline allow now the performance of such studies. In this paper, a bioinformatics workflow is described integrating predictive metagenomics profiling with specific application to food microbiology data. Bioinformatics tools pertinent to each sub-module of the pipeline are suggested as well. The published 16S rDNA/rRNA amplicon data originated from an Italian Grana-type cheese, using an NGS platform, was employed to demonstrate the predictive metagenomics profiling approach. The pipeline identified the microbial community and the changes that occurred in the microbial profile during manufacture of the food product studied (taxonomic profiling). The workflow also indicated significant changes in the functional profiling of the community. The tool may help to investigate the functional potential, alterations, and interactions of a microbial community. The proposed workflow may also find an application in the investigation of the ecology of foodborne pathogens encountered in various food products.

Exploring the human microbiome from multiple perspectives: factors altering its composition and function

Our microbiota presents peculiarities and characteristics that may be altered by multiple factors. The degree and consequences of these alterations depend on the nature, strength and duration of the perturbations as well as the structure and stability of each microbiota. The aim of this review is to sketch a very broad picture of the factors commonly influencing different body sites, and which have been associated with alterations in the human microbiota in terms of composition and function. To do so, first, a graphical representation of bacterial, fungal and archaeal genera reveals possible associations among genera affected by different factors. Then, the revision of sequence-based predictions provides associations with functions that become part of the active metabolism. Finally, examination of microbial metabolite contents and fluxes reveals whether metabolic alterations are a reflection of the differences observed at the level of population structure, and in the last step, link microorganisms to functions under perturbations that differ in nature and aetiology. The utilisation of complementary technologies and methods, with a special focus on metabolomics research, is thoroughly discussed to obtain a global picture of microbiota composition and microbiome function and to convey the urgent need for the standardisation of protocols.

Antibiotic use and microbiome function

Our microbiome should be understood as one of the most complex components of the human body. The use of β-lactam antibiotics is one of the microbiome covariates that influence its composition. The extent to which our microbiota changes after an antibiotic intervention depends not only on the chemical nature of the antibiotic or cocktail of antibiotics used to treat specific infections, but also on the type of administration, duration and dose, as well as the level of resistance that each microbiota develops. We have begun to appreciate that not all bacteria within our microbiota are vulnerable or reactive to different antibiotic interventions, and that their influence on both microbial composition and metabolism may differ. Antibiotics are being used worldwide on a huge scale and the prescription of antibiotics is continuing to rise; however, their effects on our microbiota have been reported for only a limited number of them. This article presents a critical review of the antibiotics or antibiotic cocktails whose use in humans has been linked to changes in the composition of our microbial communities, with a particular focus on the gut, oral, respiratory, skin and vaginal microbiota, and on their molecular agents (genes, proteins and metabolites). We review the state of the art as of June 2016, and cover a total of circa 68 different antibiotics. The data herein are the first to compile information about the bacteria, fungi, archaea and viruses most influenced by the main antibiotic treatments prescribed nowadays.

Laboratory rearing of solitary bees and wasps

Ecological experiments often require standardized methods that exclude natural variation and allow manipulation of a single parameter. It has been shown that domesticated honey bee larvae are raisable in a controlled environment. Here we demonstrate that this approach is also transferable to wild solitary bees and wasps without inducing negative effects on their development. Wells may also be supplemented with the antibiotic substance oxytetracycline to control the presence of bacteria. The method thus provides a useful tool to investigate offspring recruitment and larval development in solitary bees and wasps, plus their responses to manipulation of factors as for example diets, toxins and microbiota.

bcgTree: automatized phylogenetic tree building from bacterial core genomes

The need for multi-gene analyses in scientific fields such as phylogenetics and DNA barcoding has increased in recent years. In particular, these approaches are increasingly important for differentiating bacterial species, where reliance on the standard 16S rDNA marker can result in poor resolution. Additionally, the assembly of bacterial genomes has become a standard task due to advances in next-generation sequencing technologies. We created a bioinformatic pipeline, bcgTree, which uses assembled bacterial genomes either from databases or own sequencing results from the user to reconstruct their phylogenetic history. The pipeline automatically extracts 107 essential single-copy core genes, found in a majority of bacteria, using hidden Markov models and performs a partitioned maximum-likelihood analysis. Here, we describe the workflow of bcgTree and, as a proof-of-concept, its usefulness in resolving the phylogeny of 293 publically available bacterial strains of the genus Lactobacillus. We also evaluate its performance in both low- and high-level taxonomy test sets. The tool is freely available at github ( https://github.com/iimog/bcgTree ) and our institutional homepage ( http://www.dna-analytics.biozentrum.uni-wuerzburg.de ).

Functional Redundancy-Induced Stability of Gut Microbiota Subjected to Disturbance

The microbiota should be considered as just another component of the human epigenetic landscape. Thus, health is also a reflection of the diversity and composition of gut microbiota and its metabolic status. In defining host health, it remains unclear whether diversity is paramount, or whether greater weight is held by gut microbiota composition or mono- or multiple-functional capacity of the different taxa and the mechanisms involved. A network-biology approach may shed light on the key gut players acting to protect against, or promote, disorders or diseases. This could be achieved by integrating data on total and active species, proteins and molecules, and their association with host response. In this review, we discuss the utilization of top-down and bottom-up approaches, following a functional hierarchy perspective.

High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved

The systems biology of microbial communities, organismal communities inhabiting all ecological niches on earth, has in recent years been strongly facilitated by the rapid development of experimental, sequencing and data analysis methods. Novel experimental approaches and binning methods in metagenomics render the semi-automatic reconstructions of near-complete genomes of uncultivable bacteria possible, while advances in high-resolution amplicon analysis allow for efficient and less biased taxonomic community characterization. This will also facilitate predictive modeling approaches, hitherto limited by the low resolution of metagenomic data. In this review, we pinpoint the most promising current developments in metagenomics. They facilitate microbial systems biology towards a systemic understanding of mechanisms in microbial communities with scopes of application in many areas of our daily life.

Network construction and structure detection with metagenomic count data

Background

The human microbiome plays a critical role in human health. Massive amounts of metagenomic data have been generated with advances in next-generation sequencing technologies that characterize microbial communities via direct isolation and sequencing. How to extract, analyze, and transform these vast amounts of data into useful knowledge is a great challenge to bioinformaticians. Microbial biodiversity research has focused primarily on taxa composition and abundance and less on the co-occurrences among different taxa. However, taxa co-occurrences and their relationships to environmental and clinical conditions are important because network structure may help to understand how microbial taxa function together.

Results

We propose a systematic robust approach for bacteria network construction and structure detection using metagenomic count data. Pairwise similarity/distance measures between taxa are proposed by adapting distance measures for samples in ecology. We also extend the sparse inverse covariance approach to a sparse inverse of a similarity matrix from count data for network construction. Our approach is efficient for large metagenomic count data with thousands of bacterial taxa. We evaluate our method with real and simulated data. Our method identifies true and biologically significant network structures efficiently.

Conclusions

Network analysis is crucial for detecting subnetwork structures with metagenomic count data. We developed a software tool in MATLAB for network construction and biologically significant module detection. Software MetaNet can be downloaded from http://biostatistics.csmc.edu/MetaNet/.

Biotechnological applications of functional metagenomics in the food and pharmaceutical industries

Microorganisms are found throughout nature, thriving in a vast range of environmental conditions. The majority of them are unculturable or difficult to culture by traditional methods. Metagenomics enables the study of all microorganisms, regardless of whether they can be cultured or not, through the analysis of genomic data obtained directly from an environmental sample, providing knowledge of the species present, and allowing the extraction of information regarding the functionality of microbial communities in their natural habitat. Function-based screenings, following the cloning and expression of metagenomic DNA in a heterologous host, can be applied to the discovery of novel proteins of industrial interest encoded by the genes of previously inaccessible microorganisms. Functional metagenomics has considerable potential in the food and pharmaceutical industries, where it can, for instance, aid (i) the identification of enzymes with desirable technological properties, capable of catalyzing novel reactions or replacing existing chemically synthesized catalysts which may be difficult or expensive to produce, and able to work under a wide range of environmental conditions encountered in food and pharmaceutical processing cycles including extreme conditions of temperature, pH, osmolarity, etc; (ii) the discovery of novel bioactives including antimicrobials active against microorganisms of concern both in food and medical settings; (iii) the investigation of industrial and societal issues such as antibiotic resistance development. This review article summarizes the state-of-the-art functional metagenomic methods available and discusses the potential of functional metagenomic approaches to mine as yet unexplored environments to discover novel genes with biotechnological application in the food and pharmaceutical industries.

From next-generation sequencing to systematic modeling of the gut microbiome

Changes in the human gut microbiome are associated with altered human metabolism and health, yet the mechanisms of interactions between microbial species and human metabolism have not been clearly elucidated. Next-generation sequencing has revolutionized the human gut microbiome research, but most current applications concentrate on studying the microbial diversity of communities and have at best provided associations between specific gut bacteria and human health. However, little is known about the inner metabolic mechanisms in the gut ecosystem. Here we review recent progress in modeling the metabolic interactions of gut microbiome, with special focus on the utilization of metabolic modeling to infer host–microbe interactions and microbial species interactions. The systematic modeling of metabolic interactions could provide a predictive understanding of gut microbiome, and pave the way to synthetic microbiota design and personalized-microbiome medicine and healthcare. Finally, we discuss the integration of metabolic modeling and gut microbiome engineering, which offer a new way to explore metabolic interactions across members of the gut microbiota.