Using proteins to study how microbes contribute to soil ecosystem services: The current state and future perspectives of soil metaproteomics

Soil Metaproteomics for Microbial Community Profiling: Methodologies and Challenges

Soil represents a complex and dynamic ecosystem, hosting a myriad of microorganisms that coexist and play vital roles in nutrient cycling and organic matter transformation. Among these microorganisms, bacteria and fungi are key members of the microbial community, profoundly influencing the fate of nitrogen, sulfur, and carbon in terrestrial environments. Understanding the intricacies of soil ecosystems and the biological processes orchestrated by microbial communities necessitates a deep dive into their composition and metabolic activities. The advent of next-generation sequencing and 'omics' techniques, such as metagenomics and metaproteomics, has revolutionized our understanding of microbial ecology and the functional dynamics of soil microbial communities. Metagenomics enables the identification of microbial community composition in soil, while metaproteomics sheds light on the current biological functions performed by these communities. However, metaproteomics presents several challenges, both technical and computational. Factors such as the presence of humic acids and variations in extraction methods can influence protein yield, while the absence of high-resolution mass spectrometry and comprehensive protein databases limits the depth of protein identification. Notwithstanding these limitations, metaproteomics remains a potent tool for unraveling the intricate biological processes and functions of soil microbial communities. In this review, we delve into the methodologies and challenges of metaproteomics in soil research, covering aspects such as protein extraction, identification, and bioinformatics analysis. Furthermore, we explore the applications of metaproteomics in soil bioremediation, highlighting its potential in addressing environmental challenges.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Microbial central carbon metabolism in a tidal freshwater marsh and an upland mixed conifer soil under oxic and anoxic conditions

The central carbon (C) metabolic network is responsible for most of the production of energy and biosynthesis in microorganisms and is therefore key to a mechanistic understanding of microbial life in soil communities. Many upland soil communities have shown a relatively high C flux through the pentose phosphate (PP) or the Entner-Doudoroff (ED) pathway, thought to be related to oxidative damage control. We tested the hypothesis that the metabolic organization of the central C metabolic network differed between two ecosystems, an anoxic marsh soil and oxic upland soil, and would be affected by altering oxygen concentrations. We expected there to be high PP/ED pathway activity under high oxygen concentrations and in oxic soils and low PP/ED activity in reduced oxygen concentrations and in marsh soil. Although we found high PP/ED activity in the upland soil and low activity in the marsh soil, lowering the oxygen concentration for the upland soil did not reduce the relative PP/ED pathway activity as hypothesized, nor did increasing the oxygen concentration in the marsh soil increase the PP/ED pathway activity. We speculate that the high PP/ED activity in the upland soil, even when exposed to low oxygen concentrations, was related to a high demand for NADPH for biosynthesis, thus reflecting higher microbial growth rates in C-rich soils than in C-poor sediments. Further studies are needed to explain the observed metabolic diversity among soil ecosystems and determine whether it is related to microbial growth rates.IMPORTANCEWe observed that the organization of the central carbon (C) metabolic processes differed between oxic and anoxic soil. However, we also found that the pentose phosphate pathway/Entner-Doudoroff (PP/ED) pathway activity remained high after reducing the oxygen concentration for the upland soil and did not increase in response to an increase in oxygen concentration in the marsh soil. These observations contradicted the hypothesis that oxidative stress is a main driver for high PP/ED activity in soil communities. We suggest that the high PP/ED activity and NADPH production reflect higher anabolic activities and growth rates in the upland soil compared to the anaerobic marsh soil. A greater understanding of the molecular and biochemical processes in soil communities is needed to develop a mechanistic perspective on microbial activities and their relationship to soil C and nutrient cycling. Such an increased mechanistic perspective is ecologically relevant, given that the central carbon metabolic network is intimately tied to the energy metabolism of microbes, the efficiency of new microbial biomass production, and soil organic matter formation.

Bacterial tolerance and detoxification of cyanide, arsenic and heavy metals: Holistic approaches applied to bioremediation of industrial complex wastes

Cyanide is a highly toxic compound that is found in wastewaters generated from different industrial activities, such as mining or jewellery. These residues usually contain high concentrations of other toxic pollutants like arsenic and heavy metals that may form different complexes with cyanide. To develop bioremediation strategies, it is necessary to know the metabolic processes involved in the tolerance and detoxification of these pollutants, but most of the current studies are focused on the characterization of the microbial responses to each one of these environmental hazards individually, and the effect of co-contaminated wastes on microbial metabolism has been hardly addressed. This work summarizes the main strategies developed by bacteria to alleviate the effects of cyanide, arsenic and heavy metals, analysing interactions among these toxic chemicals. Additionally, it is discussed the role of systems biology and synthetic biology as tools for the development of bioremediation strategies of complex industrial wastes and co-contaminated sites, emphasizing the importance and progress derived from meta-omic studies.

Opportunities and challenges for microbiomics in ecosystem restoration

Microbiomics is the science of characterizing microbial community structure, function, and dynamics. It has great potential to advance our understanding of plant-soil-microbe processes and interaction networks which can be applied to improve ecosystem restoration. However, microbiomics may be perceived as complex and the technology is not accessible to all. The opportunities of microbiomics in restoration ecology are considerable, but so are the practical challenges. Applying microbiomics in restoration must move beyond compositional assessments to incorporate tools to study the complexity of ecosystem recovery. Advances in metaomic tools provide unprecedented possibilities to aid restoration interventions. Moreover, complementary non-omic applications, such as microbial inoculants and biopriming, have the potential to improve restoration objectives by enhancing the establishment and health of vegetation communities.

Crop microbiome: their role and advances in molecular and omic techniques for the sustenance of agriculture

This review is an effort to provide in-depth knowledge of microbe's interaction and its role in crop microbiome using combination of advanced molecular and OMICS technology to translate this information for the sustenance of agriculture. Increasing population, climate change and exhaustive agricultural practices either influenced nutrient inputs of soil or generating biological and physico-chemical deterioration of the soils and affecting the agricultural productivity and agro-ecosystems. Alarming concerns toward food security and crop production claim for renewed attention in microbe-based farming practices. Microbes are omnipresent (soil, water, and air) and their close association with plants would help to accomplish sustainable agriculture goals. In the last few decades, the search for beneficial microbes in crop production, soil fertilization, disease management, and plant growth promotion is the thirst for eco-friendly agriculture. The crop microbiome opens new paths to utilize beneficial microbes and manage pathogenic microbes through integrated advanced biotechnology. The crop microbiome helps plants acquire nutrients, growth, resilience against phytopathogens, and tolerance to abiotic stresses, such as heat, drought, and salinity. Despite the emergent functionality of the crop microbiome as a complicated constituent of the plant fitness, our understanding of how the functionality of microbiome influenced by numerous factors including genotype of host, climatic conditions, mobilization of minerals, soil composition, nutrient availability, interaction between nexus of microbes, and interactions with other external microbiomes is partially understood. However, the structure, composition, dynamics, and functional contribution of such cultured and uncultured crop microbiome are least explored. The advanced biotechnological approaches are efficient tools for acquiring the information required to investigate the microbiome and extract data to develop high yield producing and resistant variety crops. This knowledge fills the fundamental gap between the theoretical concepts and the operational use of these advanced tools in crop microbiome studies. Here, we review (1) structure and composition of crop microbiome, (2) microbiome-mediated role associated with crops fitness, (3) Molecular and -omics techniques for exploration of crop microbiome, and (4) current approaches and future prospectives of crop microbiome and its exploitation for sustainable agriculture. Recent -omic approaches are influential tool for mapping, monitoring, modeling, and management of crops microbiome. Identification of crop microbiome, using system biology and rhizho-engineering, can help to develop future bioformulations for disease management, reclamation of stressed agro-ecosystems, and improved productivity of crops. Nano-system approaches combined with triggering molecules of crop microbiome can help in designing of nano-biofertilizers and nano-biopesticides. This combination has numerous merits over the traditional bioinoculants. They stimulate various defense mechanisms in plants facing stress conditions; provide bioavailability of nutrients in the soil, helps mitigate stress conditions; and enhance chances of crops establishment.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Unique Insights into How Plants and Soil Microbiomes Interact Are at Our Fingertips

Global warming endangers our world, with probably a drastic drop in food production as one of the first vital consequences for humanity. To maintain or improve quality and sustainable yields, the burning imperative for agriculture is to rapidly integrate the essential component for plant development and soil regeneration, namely, the soil microbiome. Although enormous progress has been made in identifying the components of this microbiome, the way in which they interact with each other and with plants remains poorly understood. Lidbury I, Raguideau S, Borsetto C, Murphy A, et al. (mSystems 7:e00025-22, 2022, https://doi.org/10.1128/mSystems.00025-22) illustrate how metaproteomics helps define key interactions between plants and microorganisms at the rhizospheric interface. The many extracellular proteins identified and quantified by this methodology uniquely explain the observed phenotype. This study shows that the adoption of metaproteomics is no longer an option that microbiologists should consider but a must!

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide–spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of ‘ecosurveillance’, especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

From fungal secretomes to enzymes cocktails: The path forward to bioeconomy

Bioeconomy is seen as a way to mitigate the carbon footprint of human activities by reducing at least part of the fossil resources-based economy. In this new paradigm of sustainable development, the use of enzymes as biocatalysts will play an increasing role to provide services and goods. In industry, most of multicomponent enzyme cocktails are of fungal origin. Filamentous fungi secrete complex enzyme sets called "secretomes" that can be utilized as enzyme cocktails to valorize different types of bioresources. In this review, we highlight recent advances in the study of fungal secretomes using improved computational and experimental secretomics methods, the progress in the understanding of industrially important fungi, and the discovery of new enzymatic mechanisms and interplays to degrade renewable resources rich in polysaccharides (e.g. cellulose). We review current biotechnological applications focusing on the benefits and challenges of fungal secretomes for industrial applications with some examples of commercial cocktails of fungal origin containing carbohydrate-active enzymes (CAZymes) and we discuss future trends.

Increasing the power of interpretation for soil metaproteomics data

BACKGROUND

Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples.

RESULTS

The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases.

CONCLUSIONS

A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information. Video abstract.

Plant-Microbe Interaction: Aboveground to Belowground, from the Good to the Bad

Soil health and fertility issues are constantly addressed in the agricultural industry. Through the continuous and prolonged use of chemical heavy agricultural systems, most agricultural lands have been impacted, resulting in plateaued or reduced productivity. As such, to invigorate the agricultural industry, we would have to resort to alternative practices that will restore soil health and fertility. Therefore, in recent decades, studies have been directed towards taking a Magellan voyage of the soil rhizosphere region, to identify the diversity, density, and microbial population structure of the soil, and predict possible ways to restore soil health. Microbes that inhabit this region possess niche functions, such as the stimulation or promotion of plant growth, disease suppression, management of toxicity, and the cycling and utilization of nutrients. Therefore, studies should be conducted to identify microbes or groups of organisms that have assigned niche functions. Based on the above, this article reviews the aboveground and below-ground microbiomes, their roles in plant immunity, physiological functions, and challenges and tools available in studying these organisms. The information collected over the years may contribute toward future applications, and in designing sustainable agriculture.

Soil Metaproteomics as a Tool for Environmental Monitoring of Minelands

Opencast mining drastically alters the landscape due to complete vegetation suppression and removal of topsoil layers. Precise indicators able to address incremental changes in soil quality are necessary to monitor and evaluate mineland rehabilitation projects. For this purpose, metaproteomics may be a useful tool due to its capacity to shed light on both taxonomic and functional overviews of soil biodiversity, allowing the linkage between proteins found in soil and ecosystem functioning. We investigated bacterial proteins and peptide abundance of three different mineland rehabilitation stages and compared it with a non-rehabilitated site and a native area (evergreen dense forest) in the eastern Amazon. The total amount of identified soil proteins was significantly higher in the rehabilitating and native soils than in the non-rehabilitated site. Regarding soil bacterial composition, the intermediate and advanced sites were shown to be most similar to native soil. Cyanobacteria and Firmicutes phyla are abundant in the early stages of environmental rehabilitation, while Proteobacteria population dominates the later stages. Enzyme abundances and function in the three rehabilitation stages were more similar to those found in the native soil, and the higher accumulation of many hydrolases and oxidoreductases reflects the improvement of soil biological activity in the rehabilitating sites when compared to the non-rehabilitated areas. Moreover, critical ecological processes, such as carbon and nitrogen cycling, seem to return to the soil in short periods after the start of rehabilitation activities (i.e., 4 years). Metaproteomics revealed that the biochemical processes that occur belowground can be followed throughout rehabilitation stages, and the enzymes shown here can be used as targets for environmental monitoring of mineland rehabilitation projects.

Meta-omic evaluation of bacterial microbial community structure and activity for the environmental assessment of soils: overcoming protein extraction pitfalls

Microorganisms play unique, essential and integral roles in the biosphere. This work aims to assess the utility of soil's metaomics for environmental diagnosis. Doñana National Park (DNP) was selected as a natural lab since it contains a strictly protected core that is surrounded by numerous threats of pollution. Culture-independent high-throughput molecular tools were used to evaluate the alterations of the global structure and metabolic activities of the microbiome. 16S rRNA sequencing shows lower bacterial abundance and diversity in areas historically exposed to contamination that surround DNP. For metaproteomics, an innovative post-alkaline protein extraction protocol was developed. After NaOH treatment, successive washing with Tris-HCl buffer supplemented with glycerol was essential to eliminate interferences. Starting from soils with different physicochemical characteristics, the method renders proteins with a remarkable resolution on SDS-PAGE gels. The proteins extracted were analysed by using an in-house database constructed from the rRNA data. LC-MS/MS analysis identified 2182 non-redundant proteins with 135 showing significant differences in relative abundance in the soils around DNP. Relevant global biological processes were altered in response to the environmental changes, such as protective and antioxidant mechanisms, translation, folding and homeostasis of proteins, membrane transport and aerobic respiratory metabolism.

Improvement of Soil Microbial Diversity through Sustainable Agricultural Practices and Its Evaluation by -Omics Approaches: A Perspective for the Environment, Food Quality and Human Safety

Soil is one of the key elements for supporting life on Earth. It delivers multiple ecosystem services, which are provided by soil processes and functions performed by soil biodiversity. In particular, soil microbiome is one of the fundamental components in the sustainment of plant biomass production and plant health. Both targeted and untargeted management of soil microbial communities appear to be promising in the sustainable improvement of food crop yield, its nutritional quality and safety. –Omics approaches, which allow the assessment of microbial phylogenetic diversity and functional information, have increasingly been used in recent years to study changes in soil microbial diversity caused by agronomic practices and environmental factors. The application of these high-throughput technologies to the study of soil microbial diversity, plant health and the quality of derived raw materials will help strengthen the link between soil well-being, food quality, food safety and human health.

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.

Genomics as a potential tool to unravel the rhizosphere microbiome interactions on plant health

Intense agricultural practices to meet rising food demands have caused ecosystem perturbations. For sustainable crop production, biological agents are gaining attention, but exploring their functional potential on a multi-layered complex ecosystem like the rhizosphere is challenging. This review explains the significance of genomics as a culture-independent molecular tool to understand the diversity and functional significance of the rhizosphere microbiome for sustainable agriculture. It discusses the recent significant studies in the rhizosphere environment carried out using evolving techniques like metagenomics, metatranscriptomics, and metaproteomics, their challenges, constraints infield application, and prospective solutions. The recent advances in techniques such as nanotechnology for the development of bioformulations and visualization techniques contemplating environmental safety were also discussed. The need for development of metagenomic data sets of regionally important crops, their plant microbial interactions and agricultural practices for narrowing down significant data from huge databases have been suggested. The role of taxonomical and functional diversity of soil microbiota in understanding soil suppression and part played by the microbial metabolites in the process have been analyzed and discussed in the context of 'omics' approach. 'Omics' studies have revealed important information about microbial diversity, their responses to various biotic and abiotic stimuli, and the physiology of disease suppression. This can be translated to crop sustainability and combinational approaches with advancing visualization and analysis methodologies fix the existing knowledge gap to a huge extend. With improved data processing and standardization of the methods, details of plant-microbe interactions can be successfully decoded to develop sustainable agricultural practices.

Explorative Meta-Analysis of 417 Extant Archaeal Genomes to Predict Their Contribution to the Total Microbiome Functionality

Revealing the relationship between taxonomy and function in microbiomes is critical to discover their contribution to ecosystem functioning. However, while the relationship between taxonomic and functional diversity in bacteria and fungi is known, this is not the case for archaea. Here, we used a meta-analysis of 417 completely annotated extant and taxonomically unique archaeal genomes to predict the extent of microbiome functionality on Earth contained within archaeal genomes using accumulation curves of all known level 3 functions of KEGG Orthology. We found that intergenome redundancy as functions present in multiple genomes was inversely related to intragenome redundancy as multiple copies of a gene in one genome, implying the tradeoff between additional copies of functionally important genes or a higher number of different genes. A logarithmic model described the relationship between functional diversity and species richness better than both the unsaturated and the saturated model, which suggests a limited total number of archaeal functions in contrast to the sheer unlimited potential of bacteria and fungi. Using the global archaeal species richness estimate of 13,159, the logarithmic model predicted 4164.1 ± 2.9 KEGG level 3 functions. The non-parametric bootstrap estimate yielded a lower bound of 2994 ± 57 KEGG level 3 functions. Our approach not only highlighted similarities in functional redundancy but also the difference in functional potential of archaea compared to other domains of life.

Soil Metaproteomics for the Study of the Relationships Between Microorganisms and Plants: A Review of Extraction Protocols and Ecological Insights

Soil is a complex matrix where biotic and abiotic components establish a still unclear network involving bacteria, fungi, archaea, protists, protozoa, and roots that are in constant communication with each other. Understanding these interactions has recently focused on metagenomics, metatranscriptomics and less on metaproteomics studies. Metaproteomic allows total extraction of intracellular and extracellular proteins from soil samples, providing a complete picture of the physiological and functional state of the “soil community”. The advancement of high-performance mass spectrometry technologies was more rapid than the development of ad hoc extraction techniques for soil proteins. The protein extraction from environmental samples is biased due to interfering substances and the lower amount of proteins in comparison to cell cultures. Soil sample preparation and extraction methodology are crucial steps to obtain high-quality resolution and yields of proteins. This review focuses on the several soil protein extraction protocols to date to highlight the methodological challenges and critical issues for the application of proteomics to soil samples. This review concludes that improvements in soil protein extraction, together with the employment of ad hoc metagenome database, may enhance the identification of proteins with low abundance or from non-dominant populations and increase our capacity to predict functional changes in soil.

Indicators for assessment of soil quality: a mini-review

Soil quality is the competence of soil to perform necessary functions that are able to maintain animal and plant productivity of the soil. Soil consists of various physical, chemical, and biological parameters, and all these parameters are involved in the critical functioning of soil. There is a need for continuous assessment of soil quality as soil is a complex and dynamic constituent of Earth's biosphere that is continuously changing by natural and anthropogenic disturbances. Any perturbations in the soil cause disturbances in the physical (soil texture, bulk density, etc.), chemical (pH, salinity, organic carbon, etc.), and biological (microbes and enzymes) parameters. These physical, chemical, and biological parameters can serve as indicators for soil quality assessment. However, soil quality assessment cannot be possible by evaluating only one parameter out of physical, chemical, or biological. So, there is an emergent need to establish a minimum dataset (MDS) which shall include physical, chemical, and biological parameters to assess the quality of the given soil. This review attempts to describe various physical, chemical, and biological parameters, combinations of which can be used in the establishment of MDS.

Metaproteomics insights into traditional fermented foods and beverages

Traditional fermented foods and beverages (TFFB) are important dietary components. Multi-omics techniques have been applied to all aspects of TFFB research to clarify the composition and nutritional value of TFFB, and to reveal the microbial community, microbial interactions, fermentative kinetics, and metabolic profiles during the fermentation process of TFFB. Because of the advantages of metaproteomics in providing functional information, this technology has increasingly been used in research to assess the functional diversity of microbial communities. Metaproteomics is gradually gaining attention in the field of TFFB research because it can reveal the nature of microorganism function at the protein level. This paper reviews the common methods of metaproteomics applied in TFFB research; systematically summarizes the results of metaproteomics research on TFFB, such as sauces, wines, fermented tea, cheese, and fermented fish; and compares the differences in conclusions reached through metaproteomics versus other omics methods. Metaproteomics has great advantages in revealing the microbial functions in TFFB and the interaction between the materials and microbial community. In the future, metaproteomics should be further applied to the study of functional protein markers and protein interaction in TFFB; multi-omics technology requires further integration to reveal the molecular nature of TFFB fermentation.

Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits

The rumen microbiome constitutes a dense and complex mixture of anaerobic bacteria, archaea, protozoa, virus and fungi. Collectively, rumen microbial populations interact closely in order to degrade and ferment complex plant material into nutrients for host metabolism, a process which also produces other by-products, such as methane gas. Our understanding of the rumen microbiome and its functions are of both scientific and industrial interest, as the metabolic functions are connected to animal health and nutrition, but at the same time contribute significantly to global greenhouse gas emissions. While many of the major microbial members of the rumen microbiome are acknowledged, advances in modern culture-independent meta-omic techniques, such as metaproteomics, enable deep exploration into active microbial populations involved in essential rumen metabolic functions. Meaningful and accurate metaproteomic analyses are highly dependent on representative samples, precise protein extraction and fractionation, as well as a comprehensive and high-quality protein sequence database that enables precise protein identification and quantification. This review focuses on the application of rumen metaproteomics, and its potential towards understanding the complex rumen microbiome and its metabolic functions. We present and discuss current methods in sample handling, protein extraction and data analysis for rumen metaproteomics, and finally emphasize the potential of (meta)genome-integrated metaproteomics for accurate reconstruction of active microbial populations in the rumen.

pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies

Motivation

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment.

Results

To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally.

Availability and implementation

pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk.

Contact

dfigeys@uottawa.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

Explorative Meta-Analysis of 377 Extant Fungal Genomes Predicted a Total Mycobiome Functionality of 42.4 Million KEGG Functions

Unveiling the relationship between taxonomy and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial taxonomic diversity, our understanding of its relationship to functional diversity is still scarce. Here, we used a meta-analysis of completely annotated extant genomes of 377 taxonomically distinct fungal species to predict the total fungal microbiome functionality on Earth with accumulation curves (ACs) of all known functions from the level 3 of KEGG Orthology using both parametric and non-parametric estimates in an explorative data-mining approach. The unsaturated model extrapolating functional diversity as a function of species richness described the ACs significantly better than the saturated model that assumed a limited total number of functions, which suggested the presence of widespread and rare functions. Based on previous estimates of 3.8 million fungal species on Earth, we propagated the unsaturated model to predict a total of 42.4 ± 0.5 million KEGG level 3 functions of which only 0.06% are known today. Our approach not only highlights the presence of widespread and rare functions but points toward the necessity of novel and more sophisticated methods to unveil the entirety of functions to fully understand the involvement of the fungal microbiome in ecosystem functioning.

The Simplified Human Intestinal Microbiota (SIHUMIx) Shows High Structural and Functional Resistance against Changing Transit Times in In Vitro Bioreactors

Many functions in host-microbiota interactions are potentially influenced by intestinal transit times, but little is known about the effects of altered transition times on the composition and functionality of gut microbiota. To analyze these effects, we cultivated the model community SIHUMIx in bioreactors in order to determine the effects of varying transit times (TT) on the community structure and function. After five days of continuous cultivation, we investigated the influence of different medium TT of 12 h, 24 h, and 48 h. For profiling the microbial community, we applied flow cytometric fingerprinting and revealed changes in the community structure of SIHUMIx during the change of TT, which were not associated with changes in species abundances. For pinpointing metabolic alterations, we applied metaproteomics and metabolomics and found, along with shortening the TT, a slight decrease in glycan biosynthesis, carbohydrate, and amino acid metabolism and, furthermore, a reduction in butyrate, methyl butyrate, isobutyrate, valerate, and isovalerate concentrations. Specifically, B. thetaiotaomicron was identified to be affected in terms of butyrate metabolism. However, communities could recover to the original state afterward. This study shows that SIHUMIx showed high structural stability when TT changed-even four-fold. Resistance values remained high, which suggests that TTs did not interfere with the structure of the community to a certain degree.