Systems-based approaches to unravel multi-species microbial community functioning

Symbioses between fungi and bacteria: from mechanisms to impacts on biodiversity

Symbiotic interactions between fungi and bacteria range from positive to negative. They are ubiquitous in free-living as well as host-associated microbial communities worldwide. Yet, the impact of fungal-bacterial symbioses on the organization and dynamics of microbial communities is uncertain. There are two reasons for this uncertainty: (1) knowledge gaps in the understanding of the genetic mechanisms underpinning fungal-bacterial symbioses and (2) prevailing interpretations of ecological theory that favor antagonistic interactions as drivers stabilizing biological communities despite the existence of models emphasizing contributions of positive interactions. This review synthesizes information on fungal-bacterial symbioses common in the free-living microbial communities of the soil as well as in host-associated polymicrobial biofilms. The interdomain partnerships are considered in the context of the relevant community ecology models, which are discussed critically.

Unveiling the regulatory mechanism of poly-γ-glutamic acid on soil characteristics under drought stress through integrated metagenomics and metabolomics analysis

It is of utmost importance to understand the characteristics and regulatory mechanisms of soil in order to optimize soil management and enhance crop yield. Poly-γ-glutamic acid (γ-PGA), a stress-resistant amino acid polymer, plays a crucial role in plant drought stress resistance. However, little is known about the effects of γ-PGA on soil characteristics during drought treatments. In this study, the effects of different forms of γ-PGA on soil texture and basic physical and chemical properties under short-term drought conditions were investigated. Furthermore, the impact of γ-PGA on the microbial community and metabolic function of maize was analyzed. Under drought conditions, the introduction of γ-PGA into the soil resulted in notable improvements in the mechanical composition ratio and infiltration capacity of the soil. Concurrently, this led to a reduction in soil bulk density and improved soil organic matter content and fertility. Additionally, metagenomic analysis revealed that under drought conditions, the incorporation of γ-PGA into the soil enhanced the soil microbiota structure. This shift led to the predominance of bacteria that are crucial for carbon, nitrogen, and phosphorus cycles in the soil. Metabolomics analysis revealed that under drought treatment, γ-PGA affected soil metabolic patterns, with a particular focus on alterations in amino acid and vitamin metabolism pathways. Correlation analysis between the soil metagenome and metabolites showed that microorganisms played a significant role in metabolite accumulation. These results demonstrated that γ-PGA could improve soil characteristics under drought conditions and play an important role in soil microorganisms and microbial metabolism, providing further insights into the changes in soil characteristics under drought conditions.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

The microbial food revolution

Our current food system relies on unsustainable practices, which often fail to provide healthy diets to a growing population. Therefore, there is an urgent demand for new sustainable nutrition sources and processes. Microorganisms have gained attention as a new food source solution, due to their low carbon footprint, low reliance on land, water and seasonal variations coupled with a favourable nutritional profile. Furthermore, with the emergence and use of new tools, specifically in synthetic biology, the uses of microorganisms have expanded showing great potential to fulfil many of our dietary needs. In this review, we look at the different applications of microorganisms in food, and examine the history, state-of-the-art and potential to disrupt current foods systems. We cover both the use of microbes to produce whole foods out of their biomass and as cell factories to make highly functional and nutritional ingredients. The technical, economical, and societal limitations are also discussed together with the current and future perspectives.

Impact of different oral treatments on the composition of the supragingival plaque microbiome

Background

Dental plaque consists of a diverse microbial community embedded in a complex structure of exopolysaccharides. Dental biofilms form a natural barrier against pathogens but lead to oral diseases in a dysbiotic state.

Objective

Using a metaproteome approach combined with a standard plaque-regrowth study, this pilot study examined the impact of different concentrations of lactoperoxidase (LPO) on early plaque formation, and active biological processes.

Design

Sixteen orally healthy subjects received four local treatments as a randomized single-blind study based on a cross-over design. Two lozenges containing components of the LPO-system in different concentrations were compared to a placebo and Listerine®. The newly formed dental plaque was analyzed by mass spectrometry (nLC-MS/MS). 

Results

On average 1,916 metaproteins per sample were identified, which could be assigned to 116 genera and 1,316 protein functions. Listerine® reduced the number of metaproteins and their relative abundance, confirming the plaque inhibiting effect. The LPO-lozenges triggered mainly higher metaprotein abundances of early and secondary colonizers as well as bacteria associated with dental health but also periodontitis. Functional information indicated plaque biofilm growth.

Conclusion

In conclusion, the mechanisms on plaque biofilm formation of Listerine® and the LPO-system containing lozenges are different. In contrast to Listerine®, the lozenges led to a higher bacterial diversity.

The Promises, Challenges, and Opportunities of Omics for Studying the Plant Holobiont

Microorganisms are critical drivers of biological processes that contribute significantly to plant sustainability and productivity. In recent years, emerging research on plant holobiont theory and microbial invasion ecology has radically transformed how we study plant–microbe interactions. Over the last few years, we have witnessed an accelerating pace of advancements and breadth of questions answered using omic technologies. Herein, we discuss how current state-of-the-art genomics, transcriptomics, proteomics, and metabolomics techniques reliably transcend the task of studying plant–microbe interactions while acknowledging existing limitations impeding our understanding of plant holobionts.

Metagenomics: A Tool for Exploring Key Microbiome With the Potentials for Improving Sustainable Agriculture

Microorganisms are immense in nature and exist in every imaginable ecological niche, performing a wide range of metabolic processes. Unfortunately, using traditional microbiological methods, most microorganisms remain unculturable. The emergence of metagenomics has resolved the challenge of capturing the entire microbial community in an environmental sample by enabling the analysis of whole genomes without requiring culturing. Metagenomics as a non-culture approach encompasses a greater amount of genetic information than traditional approaches. The plant root-associated microbial community is essential for plant growth and development, hence the interactions between microorganisms, soil, and plants is essential to understand and improve crop yields in rural and urban agriculture. Although some of these microorganisms are currently unculturable in the laboratory, metagenomic techniques may nevertheless be used to identify the microorganisms and their functional traits. A detailed understanding of these organisms and their interactions should facilitate an improvement of plant growth and sustainable crop production in soil and soilless agriculture. Therefore, the objective of this review is to provide insights into metagenomic techniques to study plant root-associated microbiota and microbial ecology. In addition, the different DNA-based techniques and their role in elaborating plant microbiomes are discussed. As an understanding of these microorganisms and their biotechnological potentials are unlocked through metagenomics, they can be used to develop new, useful and unique bio-fertilizers and bio-pesticides that are not harmful to the environment.

Harnessing rhizobacteria to fulfil inter-linked nutrient dependency on soil and alleviate stresses in plants

Plant rhizo-microbiome comprises of complex microbial communities that colonizes at the interphase of plant roots and soil. Plant-growth-promoting rhizobacteria (PGPR) in the rhizosphere provides important ecosystem services ranging from release of essential nutrients for enhancing soil quality and improving plant health to imparting protection to plants against rising biotic and abiotic stresses. Hence, PGPR serve as restoring agents to rejuvenate soil health and mediate plant fitness in the facet of changing climate. Though, it is evident that nutrients availability in soil are managed through inter-linked mechanisms, how PGPR expediate these processes remain less recognized. Promising results of PGPR inoculation on plant growth are continually reported in controlled environmental conditions, however, their field application often fails due to competition with native microbiota and low colonization efficiency in roots. The development of highly efficient and smart bacterial synthetic communities by integrating bacterial ecological and genetic features provides better opportunities for successful inoculant formulations. This review provides an overview of the inter-play between nutrient availability and disease suppression governed by rhizobacteria in soil followed by the role of synthetic bacterial communities in developing efficient microbial inoculants. Moreover, an outlook on the beneficial activities of rhizobacteria in modifying soil characteristics to sustainably boost agroecosystem functioning is also provided.

Metabolomics insight into the influence of environmental factors in responses of freshwater biofilms to the model herbicide diuron

Freshwater biofilms have been increasingly used during the last decade in ecotoxicology due to their ecological relevance to assess the effect(s) of environmental stress at the community level. Despite growing knowledge about the effect of various stressors on the structure and the function of these microbial communities, a strong research effort is still required to better understand their response to chemical stress and the influence of environmental stressors in this response. To tackle this challenge, untargeted metabolomics is an approach of choice because of its capacity to give an integrative picture of the exposure to multiple stress and associated effect as well as identifying the molecular pathways involved in these responses. In this context, the present study aimed to explore the use of an untargeted metabolomics approach to unravel at the molecular/biochemical level the response of the whole biofilm to chemical stress and the influence of various environmental factors in this response. To this end, archived high-resolution mass spectrometry data from previous experiments at our laboratory on the effect of the model photosynthesis inhibitor diuron on freshwater biofilm were investigated by using innovative solutions for OMICs data (e.g., DRomics) and more usual chemometric approaches (multivariate and univariate statistical analyses). The results showed a faster (1 min) and more sensitive response of the metabolome to diuron than usual functional descriptors, including photosynthesis. Also, the metabolomics response to diuron resulted from metabolites following various trends (increasing, decreasing, U/bell shape) along increasing concentration and time. This metabolomics response was influenced by the temperature, photoperiod, and flow. A focus on a plant-specific omega-3 (eicosapentaenoic acid) playing a key role in the trophic chain highlighted the potential relevance of metabolomics approach to establish the link between molecular alteration and ecosystem structure/functioning impairment but also how complex is the response and the influence of all the tested factors on this response at the metabolomics level. Altogether, our results underline that more fundamental researches are needed to decipher the metabolomics response of freshwater biofilm to chemical stress and its link with physiological, structural, and functional responses toward the unraveling of adverse outcome pathways (AOP) for key ecosystem functions (e.g., primary production).

Biofilm through the Looking Glass: A Microbial Food Safety Perspective

Food-processing facilities harbor a wide diversity of microorganisms that persist and interact in multispecies biofilms, which could provide an ecological niche for pathogens to better colonize and gain tolerance against sanitization. Biofilm formation by foodborne pathogens is a serious threat to food safety and public health. Biofilms are formed in an environment through synergistic interactions within the microbial community through mutual adaptive response to their long-term coexistence. Mixed-species biofilms are more tolerant to sanitizers than single-species biofilms or their planktonic equivalents. Hence, there is a need to explore how multispecies biofilms help in protecting the foodborne pathogen from common sanitizers and disseminate biofilm cells from hotspots and contaminate food products. This knowledge will help in designing microbial interventions to mitigate foodborne pathogens in the processing environment. As the global need for safe, high-quality, and nutritious food increases, it is vital to study foodborne pathogen behavior and engineer new interventions that safeguard food from contamination with pathogens. This review focuses on the potential food safety issues associated with biofilms in the food-processing environment.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Bottom-Up Community Proteome Analysis of Saliva Samples and Tongue Swabs by Data-Dependent Acquisition Nano LC-MS/MS Mass Spectrometry

Analysis using mass spectrometry enables the characterization of metaproteomes in their native environments and overcomes the limitation of proteomics of pure cultures. Metaproteomics is a promising approach to link functions of currently actively expressed genes to the phylogenetic composition of the microbiome in their habitat. In this chapter, we describe the preparation of saliva samples and tongue swabs for nLC-MS/MS measurements and their bioinformatic analysis based on the Trans-Proteomic Pipeline and Prophane to study the oral microbiome .

Cyanobacterial blooms in wastewater treatment facilities: Significance and emerging monitoring strategies

Municipal wastewater treatment facilities (WWTFs) are prone to the proliferation of cyanobacterial species which thrive in stable, nutrient-rich environments. Dense cyanobacterial blooms frequently disrupt treatment processes and the supply of recycled water due to their production of extracellular polymeric substances, which hinder microfiltration, and toxins, which pose a health risk to end-users. A variety of methods are employed by water utilities for the identification and monitoring of cyanobacteria and their toxins in WWTFs, including microscopy, flow cytometry, ELISA, chemoanalytical methods, and more recently, molecular methods. Here we review the literature on the occurrence and significance of cyanobacterial blooms in WWTFs and discuss the pros and cons of the various strategies for monitoring these potentially hazardous events. Particular focus is directed towards next-generation metagenomic sequencing technologies for the development of site-specific cyanobacterial bloom management strategies. Long-term multi-omic observations will enable the identification of indicator species and the development of site-specific bloom dynamics models for the mitigation and management of cyanobacterial blooms in WWTFs. While emerging metagenomic tools could potentially provide deep insight into the diversity and flux of problematic cyanobacterial species in these systems, they should be considered a complement to, rather than a replacement of, quantitative chemoanalytical approaches.

Experimental and computational approaches to unravel microbial community assembly

Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.

Microbial model communities: To understand complexity, harness the power of simplicity

Natural microbial communities are complex ecosystems with myriads of interactions. To deal with this complexity, we can apply lessons learned from the study of model organisms and try to find simpler systems that can shed light on the same questions. Here, microbial model communities are essential, as they can allow us to learn about the metabolic interactions, genetic mechanisms and ecological principles governing and structuring communities. A variety of microbial model communities of varying complexity have already been developed, representing different purposes, environments and phenomena. However, choosing a suitable model community for one's research question is no easy task. This review aims to be a guide in the selection process, which can help the researcher to select a sufficiently well-studied model community that also fulfills other relevant criteria. For example, a good model community should consist of species that are easy to grow, have been evaluated for community behaviors, provide simple readouts and - in some cases - be of relevance for natural ecosystems. Finally, there is a need to standardize growth conditions for microbial model communities and agree on definitions of community-specific phenomena and frameworks for community interactions. Such developments would be the key to harnessing the power of simplicity to start disentangling complex community interactions.

Metabolomics as an Emerging Tool in the Search for Astrobiologically Relevant Biomarkers

It is now routinely possible to sequence and recover microbial genomes from environmental samples. To the degree it is feasible to assign transcriptional and translational functions to these genomes, it should be possible, in principle, to largely understand the complete molecular inputs and outputs of a microbial community. However, gene-based tools alone are presently insufficient to describe the full suite of chemical reactions and small molecules that compose a living cell. Metabolomic tools have developed quickly and now enable rapid detection and identification of small molecules within biological and environmental samples. The convergence of these technologies will soon facilitate the detection of novel enzymatic activities, novel organisms, and potentially extraterrestrial life-forms on solar system bodies. This review explores the methodological problems and scientific opportunities facing researchers who hope to apply metabolomic methods in astrobiology-related fields, and how present challenges might be overcome.

Modulating the Intestinal Microbiota: Therapeutic Opportunities in Liver Disease

Gut microbiota has been demonstrated to have a significant impact on the initiation, progression and development of complications associated with multiple liver diseases. Notably, nonalcoholic fatty liver diseases, including nonalcoholic steatohepatitis and cirrhosis, severe alcoholic hepatitis, primary sclerosing cholangitis and hepatic encephalopathy, have strong links to dysbiosis - or a pathobiological change in the microbiota. In this review, we provide clear and concise discussions on the human gut microbiota, methods of identifying gut microbiota and its functionality, liver diseases that are affected by the gut microbiota, including novel associations under research, and provide current evidence on the modulation of gut microbiota and its effects on specific liver disease conditions.

Modeling population heterogeneity from microbial communities to immune response in cells

Heterogeneity is universally observed in all natural systems and across multiple scales. Understanding population heterogeneity is an intriguing and attractive topic of research in different disciplines, including microbiology and immunology. Microbes and mammalian immune cells present obviously rather different system-specific biological features. Nevertheless, as typically occurs in science, similar methods can be used to study both types of cells. This is particularly true for mathematical modeling, in which key features of a system are translated into algorithms to challenge our mechanistic understanding of the underlying biology. In this review, we first present a broad overview of the experimental developments that allowed observing heterogeneity at the single cell level. We then highlight how this "data revolution" requires the parallel advancement of algorithms and computing infrastructure for data processing and analysis, and finally present representative examples of computational models of population heterogeneity, from microbial communities to immune response in cells.

Current challenges and best-practice protocols for microbiome analysis

Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

A robust, cost-effective method for DNA, RNA and protein co-extraction from soil, other complex microbiomes and pure cultures

The soil microbiome is inherently complex with high biological diversity, and spatial heterogeneity typically occurring on the submillimetre scale. To study the microbial ecology of soils, and other microbiomes, biomolecules, that is, nucleic acids and proteins, must be efficiently and reliably co-recovered from the same biological samples. Commercial kits are currently available for the co-extraction of DNA, RNA and proteins but none has been developed for soil samples. We present a new protocol drawing on existing phenol-chloroform-based methods for nucleic acids co-extraction but incorporating targeted precipitation of proteins from the phenol phase. The protocol is cost-effective and robust, and easily implemented using reagents commonly available in laboratories. The method is estimated to be eight times cheaper than using disparate commercial kits for the isolation of DNA and/or RNA, and proteins, from soil. The method is effective, providing good quality biomolecules from a diverse range of soil types, with clay contents varying from 9.5% to 35.1%, which we successfully used for downstream, high-throughput gene sequencing and metaproteomics. Additionally, we demonstrate that the protocol can also be easily implemented for biomolecule co-extraction from other complex microbiome samples, including cattle slurry and microbial communities recovered from anaerobic bioreactors, as well as from Gram-positive and Gram-negative pure cultures.

Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS

The suppression of soilborne crop pathogens such as Rhizoctonia solani AG8 may offer a sustainable and enduring method for disease control, though soils with these properties are difficult to identify. In this study, we analysed the soil metabolic profiles of suppressive and non-suppressive soils over 2 years of cereal production. We collected bulk and rhizosphere soil at different cropping stages and subjected soil extracts to liquid chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance spectroscopy (¹H NMR) analyses. Community analyses of suppressive and non-suppressive soils using principal component analyses and predictive modelling of LC-MS and NMR datasets respectively, revealed distinct biochemical profiles for the two soil types with clustering based on suppressiveness and cropping stage. NMR spectra revealed the suppressive soils to be more abundant in sugar molecules than non-suppressive soils, which were more abundant in lipids and terpenes. LC-MS features that were significantly more abundant in the suppressive soil were identified and assessed as potential biomarkers for disease suppression. The structures of a potential class of LC-MS biomarkers were elucidated using accurate mass data and MS fragmentation spectrum information. The most abundant compound found in association with suppressive soils was confirmed to be a macrocarpal, which is an antimicrobial secondary metabolite. Our study has demonstrated the utility of environmental metabolomics for the study of disease suppressive soils, resulting in the discovery of a macrocarpal biomarker for R. solani AG8 suppressive soil which can be further studied functionally in association with suppression pot trials and microbial isolation studies.

Functional metagenomics: a tool to gain knowledge for agronomic and veterinary sciences

The increased global demand for food production has motivated agroindustries to increase their own levels of production. Scientific efforts have contributed to improving these production systems, aiding to solve problems and establishing novel conceptual views and sustainable alternatives to cope with the increasing demand. Although microorganisms are key players in biological systems and may drive certain desired responses toward food production, little is known about the microbial communities that constitute the microbiomes associated with agricultural and veterinary activities. Understanding the diversity, structure and in situ interactions of microbes, together with how these interactions occur within microbial communities and with respect to their environments (including hosts), constitutes a major challenge with an enormous relevance for agriculture and biotechnology. The emergence of high-throughput sequencing technologies, together with novel and more accessible bioinformatics tools, has allowed researchers to learn more about the functional potential and functional activity of these microbial communities. These tools constitute a relevant approach for understanding the metabolic processes that can occur or are currently occurring in a given system and for implementing novel strategies focused on solving production problems or improving sustainability. Several 'omics' sciences and their applications in agriculture are discussed in this review, and the usage of functional metagenomics is proposed to achieve substantial advances for food agroindustries and veterinary sciences.

Short-Term Transcriptional Response of Microbial Communities to Nitrogen Fertilization in a Pine Forest Soil

Numerous studies have examined the long-term effect of experimental nitrogen (N) deposition in terrestrial ecosystems; however, N-specific mechanistic markers are difficult to disentangle from responses to other environmental changes. The strongest picture of N-responsive mechanistic markers is likely to arise from measurements over a short (hours to days) time scale immediately after inorganic N deposition. Therefore, we assessed the short-term (3-day) transcriptional response of microbial communities in two soil strata from a pine forest to a high dose of N fertilization (ca. 1 mg/g of soil material) in laboratory microcosms. We hypothesized that N fertilization would repress the expression of fungal and bacterial genes linked to N mining from plant litter. However, despite N suppression of microbial respiration, the most pronounced differences in functional gene expression were between strata rather than in response to the N addition. Overall, ∼4% of metabolic genes changed in expression with N addition, while three times as many (∼12%) were significantly different across the different soil strata in the microcosms. In particular, we found little evidence of N changing expression levels of metabolic genes associated with complex carbohydrate degradation (CAZymes) or inorganic N utilization. This suggests that direct N repression of microbial functional gene expression is not the principle mechanism for reduced soil respiration immediately after N deposition. Instead, changes in expression with N addition occurred primarily in general cell maintenance areas, for example, in ribosome-related transcripts. Transcriptional changes in functional gene abundance in response to N addition observed in longer-term field studies likely result from changes in microbial composition.IMPORTANCE Ecosystems are receiving increased nitrogen (N) from anthropogenic sources, including fertilizers and emissions from factories and automobiles. High levels of N change ecosystem functioning. For example, high inorganic N decreases the microbial decomposition of plant litter, potentially reducing nutrient recycling for plant growth. Understanding how N regulates microbial decomposition can improve the prediction of ecosystem functioning over extended time scales. We found little support for the conventional view that high N supply represses the expression of genes involved in decomposition or alters the expression of bacterial genes for inorganic N cycling. Instead, our study of pine forest soil 3 days after N addition showed changes in microbial gene expression related to cell maintenance and stress response. This highlights the challenge of establishing predictive links between microbial gene expression levels and measures of ecosystem function.

Linking Microbial Community Structure and Function During the Acidified Anaerobic Digestion of Grass

Harvesting valuable bioproducts from various renewable feedstocks is necessary for the critical development of a sustainable bioeconomy. Anaerobic digestion is a well-established technology for the conversion of wastewater and solid feedstocks to energy with the additional potential for production of process intermediates of high market values (e.g., carboxylates). In recent years, first-generation biofuels typically derived from food crops have been widely utilized as a renewable source of energy. The environmental and socioeconomic limitations of such strategy, however, have led to the development of second-generation biofuels utilizing, amongst other feedstocks, lignocellulosic biomass. In this context, the anaerobic digestion of perennial grass holds great promise for the conversion of sustainable renewable feedstock to energy and other process intermediates. The advancement of this technology however, and its implementation for industrial applications, relies on a greater understanding of the microbiome underpinning the process. To this end, microbial communities recovered from replicated anaerobic bioreactors digesting grass were analyzed. The bioreactors leachates were not buffered and acidic pH (between 5.5 and 6.3) prevailed at the time of sampling as a result of microbial activities. Community composition and transcriptionally active taxa were examined using 16S rRNA sequencing and microbial functions were investigated using metaproteomics. Bioreactor fraction, i.e., grass or leachate, was found to be the main discriminator of community analysis across the three molecular level of investigation (DNA, RNA, and proteins). Six taxa, namely Bacteroidia, Betaproteobacteria, Clostridia, Gammaproteobacteria, Methanomicrobia, and Negativicutes accounted for the large majority of the three datasets. The initial stages of grass hydrolysis were carried out by Bacteroidia, Gammaproteobacteria, and Negativicutes in the grass biofilms, in addition to Clostridia in the bioreactor leachates. Numerous glycolytic enzymes and carbohydrate transporters were detected throughout the bioreactors in addition to proteins involved in butanol and lactate production. Finally, evidence of the prevalence of stressful conditions within the bioreactors and particularly impacting Clostridia was observed in the metaproteomes. Taken together, this study highlights the functional importance of Clostridia during the anaerobic digestion of grass and thus research avenues allowing members of this taxon to thrive should be explored.

Comparative analysis of Salivette® and paraffin gum preparations for establishment of a metaproteomics analysis pipeline for stimulated human saliva

The value of saliva as a diagnostic tool can be increased by taxonomic and functional analyses of the microbiota as recently demonstrated. In this proof-of-principle study, we compare two collection methods (Salivette® (SV) and paraffin gum (PG)) for stimulated saliva from five healthy participants and present a workflow including PG preparation which is suitable for metaproteomics.

Utilization of a Detergent-Based Method for Direct Microbial Cellular Lysis/Proteome Extraction from Soil Samples for Metaproteomics Studies

Soil metaproteomics is a rapidly developing and rather complex field aimed at understanding the functionalities of soil microbial communities. One of the main challenges of such an approach is the availability of a robust and efficient protocol to extract proteins from soil microbes inhabiting this complex matrix. The wide range of soil types and the innumerable variations in soil properties confound this experimental goal. Here we present a detergent based, heat-assisted cellular lysis method coupled with trichloroacetic acid (TCA) precipitation of soil microbial proteins that has been developed in our lab and found to be reasonably robust and unbiased in extracting microbial proteins from a broad range of soils for downstream mass spectrometric characterizations of microbial metabolic activities in natural ecosystems.

Sequencing genomes from mixed DNA samples - evaluating the metagenome skimming approach in lichenized fungi

The metagenome skimming approach, i.e. low coverage shotgun sequencing of multi-species assemblages and subsequent reconstruction of individual genomes, is increasingly used for in-depth genomic characterization of ecological communities. This approach is a promising tool for reconstructing genomes of facultative symbionts, such as lichen-forming fungi, from metagenomic reads. However, no study has so far tested accuracy and completeness of assemblies based on metagenomic sequences compared to assemblies based on pure culture strains of lichenized fungi. Here we assembled the genomes of Evernia prunastri and Pseudevernia furfuracea based on metagenomic sequences derived from whole lichen thalli. We extracted fungal contigs using two different taxonomic binning methods, and performed gene prediction on the fungal contig subsets. We then assessed quality and completeness of the metagenome-based assemblies using genome assemblies as reference which are based on pure culture strains of the two fungal species. Our comparison showed that we were able to reconstruct fungal genomes from uncultured lichen thalli, and also cover most of the gene space (86-90%). Metagenome skimming will facilitate genome mining, comparative (phylo)genomics, and population genetics of lichen-forming fungi by circumventing the time-consuming, sometimes unfeasible, step of aposymbiotic cultivation.

Transcriptomic response of Debaryomyces hansenii during mixed culture in a liquid model cheese medium with Yarrowia lipolytica

Yeasts play a crucial role in cheese ripening. They contribute to the curd deacidification, the establishment of acid-sensitive bacterial communities, and flavour compounds production via proteolysis and catabolism of amino acids (AA). Negative yeast-yeast interaction was observed between the yeast Yarrowia lipolytica 1E07 (YL1E07) and the yeast Debaryomyces hansenii 1L25 (DH1L25) in a model cheese but need elucidation. YL1E07 and DH1L25 were cultivated in mono and co-cultures in a liquid synthetic medium (SM) mimicking the cheese environment and the growth inhibition of DH1L25 in the presence of YL1E07 was reproduced. We carried out microbiological, biochemical (lactose, lactate, AA consumption and ammonia production) and transcriptomic analyses by microarray technology to highlight the interaction mechanisms. We showed that the DH1L25 growth inhibition in the presence of YL1E07 was neither due to the ammonia production nor to the nutritional competition for the medium carbon sources between the two yeasts. The transcriptomic study was the key toward the comprehension of yeast-yeast interaction, and revealed that the inhibition of DH1L25 in co-culture is due to a decrease of the mitochondrial respiratory chain functioning.

Insights into the microbial diversity and community dynamics of Chinese traditional fermented foods from using high-throughput sequencing approaches

Chinese traditional fermented foods have a very long history dating back thousands of years and have become an indispensable part of Chinese dietary culture. A plethora of research has been conducted to unravel the composition and dynamics of microbial consortia associated with Chinese traditional fermented foods using culture-dependent as well as culture-independent methods, like different high-throughput sequencing (HTS) techniques. These HTS techniques enable us to understand the relationship between a food product and its microbes to a greater extent than ever before. Considering the importance of Chinese traditional fermented products, the objective of this paper is to review the diversity and dynamics of microbiota in Chinese traditional fermented foods revealed by HTS approaches.

Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

Interest in the human microbiome is at an all time high. The number of human microbiome studies is growing exponentially, as are reported associations between microbial communities and disease. However, we have not been able to translate the ever-growing amount of microbiome sequence data into better health. To do this, we need a practical means of transforming a disease-associated microbiome into a health-associated microbiome. This will require a framework that can be used to generate predictions about community dynamics within the microbiome under different conditions, predictions that can be tested and validated. In this review, using the gut microbiome to illustrate, we describe two classes of model that are currently being used to generate predictions about microbial community dynamics: ecological models and metabolic models. We outline the strengths and weaknesses of each approach and discuss the insights into the gut microbiome that have emerged from modeling thus far. We then argue that the two approaches can be combined to yield a community metabolic model, which will supply the framework needed to move from high-throughput omics data to testable predictions about how prebiotic, probiotic, and nutritional interventions affect the microbiome. We are confident that with a suitable model, researchers and clinicians will be able to harness the stream of sequence data and begin designing strategies to make targeted alterations to the microbiome and improve health.

A Community Multi-Omics Approach towards the Assessment of Surface Water Quality in an Urban River System

A multi-omics approach was applied to an urban river system (the Brisbane River (BR), Queensland, Australia) in order to investigate surface water quality and characterize the bacterial population with respect to water contaminants. To do this, bacterial metagenomic amplicon-sequencing using Illumina next-generation sequencing (NGS) of the V5-V6 hypervariable regions of the 16S rRNA gene and untargeted community metabolomics using gas chromatography coupled with mass spectrometry (GC-MS) were utilized. The multi-omics data, in combination with fecal indicator bacteria (FIB) counts, trace metal concentrations (by inductively coupled plasma mass spectrometry (ICP-MS)) and in-situ water quality measurements collected from various locations along the BR were then used to assess the health of the river ecosystem. Sites sampled represented the transition from less affected (upstream) to polluted (downstream) environments along the BR. Chemometric analysis of the combined datasets indicated a clear separation between the sampled environments. Burkholderiales and Cyanobacteria were common key factors for differentiation of pristine waters. Increased sugar alcohol and short-chain fatty acid production was observed by Actinomycetales and Rhodospirillaceae that are known to form biofilms in urban polluted and brackish waters. Results from this study indicate that a multi-omics approach enables a deep understanding of the health of an aquatic ecosystem, providing insight into the bacterial diversity present and the metabolic output of the population when exposed to environmental contaminants.

Molecular perspectives and recent advances in microbial remediation of persistent organic pollutants

Nutrition and pollution stress stimulate genetic adaptation in microorganisms and assist in evolution of diverse metabolic pathways for their survival on several complex organic compounds. Persistent organic pollutants (POPs) are highly lipophilic in nature and cause adverse effects to the environment and human health by biomagnification through the food chain. Diverse microorganisms, harboring numerous plasmids and catabolic genes, acclimatize to these environmentally unfavorable conditions by gene duplication, mutational drift, hypermutation, and recombination. Genetic aspects of some major POP catabolic genes such as biphenyl dioxygenase (bph), DDT 2,3-dioxygenase, and angular dioxygenase assist in degradation of biphenyl, organochlorine pesticides, and dioxins/furans, respectively. Microbial metagenome constitutes the largest genetic reservoir with miscellaneous enzymatic activities implicated in degradation. To tap the metabolic potential of microorganisms, recent techniques like sequence and function-based screening and substrate-induced gene expression are proficient in tracing out novel catabolic genes from the entire metagenome for utilization in enhanced biodegradation. The major endeavor of today's scientific world is to characterize the exact genetic mechanisms of microbes for bioremediation of these toxic compounds by excavating into the uncultured plethora. This review entails the effect of POPs on the environment and involvement of microbial catabolic genes for their removal with the advanced techniques of bioremediation.

Studying the Human Microbiota

There are a range of methodologies available to study the human microbiota, ranging from traditional approaches such as culturing through to state-of-the-art developments in next generation DNA sequencing technologies. The advent of molecular techniques in particular has opened up tremendous new avenues for research, and has galvanised interest in the study of our microbial inhabitants. Given the dazzling array of available options, however, it is important to understand the inherent advantages and limitations of each technique so that the best approach can be employed to address the particular research objective. In this chapter we cover some of the most widely used current techniques in human microbiota research and highlight the particular strengths and caveats associated with each approach.

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes.

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

The bovine milk microbiota: insights and perspectives from -omics studies

Recent findings and future perspectives of -omics studies on the bovine milk microbiota, focusing on its impact on animal health.

A review: what is the spermosphere and how can it be studied?

The spermosphere is the zone surrounding seeds where interactions between the soil, microbial communities and germinating seeds take place. The concept of the spermosphere is usually only applied during germination sensu stricto. Despite the transient nature of this very small zone of soil around the germinating seed, the microbial activities which occur there may have long-lasting impacts on plants. The spermosphere is indirectly characterized by either (i) seed exudates, which could be inhibitors or stimulators of micro-organism growth or (ii) the composition of the microbiome on and around the germinating seeds. The microbial communities present in the spermosphere directly reflect that of the germination medium or are host-dependent and influenced quantitatively and qualitatively by host exudates. Despite its strong impact on the future development of plants, the spermosphere remains little studied. This can be explained by the technical difficulties related to characterizing this concept due to its short duration, small size and biomass, and the number and complexity of the interactions that take place. However, recent technical methods, such as metabolite profiling, combining phenotypic methods with DNA- and RNA-based methods, could be used to investigate seed exudates, microbial communities and their interactions with the soil environment.