Bioinformatics for NGS-based metagenomics and the application to biogas research

Construction of a Synthetic Microbial Community for Enzymatic Pretreatment of Wheat Straw for Biogas Production via Anaerobic Digestion

Biological pretreatment is a viable method for enhancing biogas production from straw crops, with the improvement in lignocellulose degradation efficiency being a crucial factor in this process. Herein, a metagenomic approach was used to screen core microorganisms (Bacillus subtilis, Acinetobacter johnsonii, Trichoderma viride, and Aspergillus niger) possessing lignocellulose-degrading abilities among samples from three environments: pile retting wheat straw (WS), WS returned to soil, and forest soil. Subsequently, synthetic microbial communities were constructed for fermentation-enzyme production. The crude enzyme solution obtained was used to pretreat WS and was compared with two commercial enzymes. The synthetic microbial community enzyme-producing pretreatment (SMCEP) yielded the highest enzymatic digestion efficacy for WS, yielding cellulose, hemicellulose, and lignin degradation rates of 39.85, 36.99, and 19.21%, respectively. Furthermore, pretreatment of WS with an enzyme solution, followed by anaerobic digestion achieved satisfactory results. SMCEP displayed the highest cumulative biogas production at 801.16 mL/g TS, which was 38.79% higher than that observed for WS, 22.15% higher than that of solid-state commercial enzyme pretreatment and 25.41% higher than that of liquid commercial enzyme pretreatment. These results indicate that enzyme-pretreated WS can significantly enhance biogas production. This study represents a solution to the environmental burden and energy use of crop residues.

Temperature-dependent transformation of microbial community: A systematic approach to analyzing functional microbes and biogas production

The stability and effectiveness of the anaerobic digestion (AD) system are significantly influenced by temperature. While majority research has focused on the composition of the microbial community in the AD process, the relationships between functional gene profile deduced from gene expression at different temperatures have received less attention. The current study investigates the AD process of potato peel waste and explores the association between biogas production and microbial gene expression at 15, 25, and 35 °C through metatranscriptomic analysis. The production of total biogas decreased with temperature at 15 °C (19.94 mL/g VS), however, it increased at 35 °C (269.50 mL/g VS). The relative abundance of Petrimonas, Clostridium, Aminobacterium, Methanobacterium, Methanothrix, and Methanosarcina were most dominant in the AD system at different temperatures. At the functional pathways level 3, α-diversity indices, including Evenness (Y = 5.85x + 8.85; R2 = 0.56), Simpson (Y = 2.20x + 2.09; R2 = 0.33), and Shannon index (Y = 1.11x + 4.64; R2 = 0.59), revealed a linear and negative correlation with biogas production. Based on KEGG level 3, several dominant functional pathways associated with Oxidative phosphorylation (ko00190) (25.09, 24.25, 24.04%), methane metabolism (ko00680) (30.58, 32.13, and 32.89%), and Carbon fixation pathways in prokaryotes (ko00720) (27.07, 26.47, and 26.29%), were identified at 15 °C, 25 °C and 35 °C. The regulation of biogas production by temperature possibly occurs through enhancement of central function pathways while decreasing the diversity of functional pathways. Therefore, the methanogenesis and associated processes received the majority of cellular resources and activities, thereby improving the effectiveness of substrate conversion to biogas. The findings of this study illustrated the crucial role of central function pathways in the effective functioning of these systems.

Current status and future developments of assessing microbiome composition and dynamics in anaerobic digestion systems using metagenomic approaches

The metagenomic approach stands as a powerful technique for examining the composition of microbial communities and their involvement in various anaerobic digestion (AD) systems. Understanding the structure, function, and dynamics of microbial communities becomes pivotal for optimizing the biogas process, enhancing its stability and improving overall performance. Currently, taxonomic profiling of biogas-producing communities relies mainly on high-throughput 16S rRNA sequencing, offering insights into the bacterial and archaeal structures of AD assemblages and their correlations with fed substrates and process parameters. To delve even deeper, shotgun and genome-centric metagenomic approaches are employed to recover individual genomes from the metagenome. This provides a nuanced understanding of collective functionalities, interspecies interactions, and microbial associations with abiotic factors. The application of OMICs in AD systems holds the potential to revolutionize the field, leading to more efficient and sustainable waste management practices particularly through the implementation of precision anaerobic digestion systems. As ongoing research in this area progresses, anticipations are high for further exciting developments in the future. This review serves to explore the current landscape of metagenomic analyses, with focus on advancing our comprehension and critically evaluating biases and recommendations in the analysis of microbial communities in anaerobic digesters. Its objective is to explore how contemporary metagenomic approaches can be effectively applied to enhance our understanding and contribute to the refinement of the AD process. This marks a substantial stride towards achieving a more comprehensive understanding of anaerobic digestion systems.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Yeast metagenomics: analytical challenges in the analysis of the eukaryotic microbiome

Even if their impact is often underestimated, yeasts and yeast-like fungi represent the most prevalent eukaryotic members of microbial communities on Earth. They play numerous roles in natural ecosystems and in association with their hosts. They are involved in the food industry and pharmaceutical production, but they can also cause diseases in other organisms, making the understanding of their biology mandatory. The ongoing loss of biodiversity due to overexploitation of environmental resources is a growing concern in many countries. Therefore, it becomes crucial to understand the ecology and evolutionary history of these organisms to systematically classify them. To achieve this, it is essential that our knowledge of the mycobiota reaches a level similar to that of the bacterial communities. To overcome the existing challenges in the study of fungal communities, the first step should be the establishment of standardized techniques for the correct identification of species, even from complex matrices, both in wet lab practices and in bioinformatic tools.

Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics

The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.

A Review of Basic Bioinformatic Techniques for Microbial Community Analysis in an Anaerobic Digester

Biogas production involves various types of intricate microbial populations in an anaerobic digester (AD). To understand the anaerobic digestion system better, a broad-based study must be conducted on the microbial population. Deep understanding of the complete metagenomics including microbial structure, functional gene form, similarity/differences, and relationships between metabolic pathways and product formation, could aid in optimization and enhancement of AD processes. With advancements in technologies for metagenomic sequencing, for example, next generation sequencing and high-throughput sequencing, have revolutionized the study of microbial dynamics in anaerobic digestion. This review includes a brief introduction to the basic process of metagenomics research and includes a detailed summary of the various bioinformatics approaches, viz., total investigation of data obtained from microbial communities using bioinformatics methods to expose metagenomics characterization. This includes (1) methods of DNA isolation and sequencing, (2) investigation of anaerobic microbial communities using bioinformatics techniques, (3) application of the analysis of anaerobic microbial community and biogas production, and (4) restriction and prediction of bioinformatics analysis on microbial metagenomics. The review has been concluded, giving a summarized insight into bioinformatic tools and also promoting the future prospects of integrating humungous data with artificial intelligence and neural network software.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Study of Wetland Soils of the Salar de Atacama with Different Azonal Vegetative Formations Reveals Changes in the Microbiota Associated with Hygrophile Plant Type on the Soil Surface

Salar de Atacama is located approximately 55 km south of San Pedro de Atacama in the Antofagasta region, Chile. The high UV irradiation and salt concentration and extreme drought make Salar de Atacama an ideal site to search for novel soil microorganisms with unique properties. Here, we used a metataxonomic approach (16S rRNA V3-V4) to identify and characterize the soil microbiota associated with different surface azonal vegetation formations, including strict hygrophiles (Baccharis juncea, Juncus balticus, and Schoenoplectus americanus), transitional hygrophiles (Distichlis spicata, Lycium humile, and Tessaria absinthioides), and their various combinations. We detected compositional differences among the soil surface microbiota associated with each plant formation in the sampling area. There were changes in soil microbial phylogenetic diversity from the strict to the transitional hygrophiles. Moreover, we found alterations in the abundance of bacterial phyla and genera. Halobacteriota and Actinobacteriota might have facilitated water uptake by the transitional hygrophiles. Our findings helped to elucidate the microbiota of Salar de Atacama and associate them with the strict and transitional hygrophiles indigenous to the region. These findings could be highly relevant to future research on the symbiotic relationships between microbiota and salt-tolerant plants in the face of climate change-induced desertification. IMPORTANCE The study of the composition and diversity of the wetland soil microbiota associated with hygrophilous plants in a desert ecosystem of the high Puna in northern Chile makes it an ideal approach to search for novel extremophilic microorganisms with unique properties. These microorganisms are adapted to survive in ecological niches, such as those with high UV irradiation, extreme drought, and high salt concentration; they can be applied in various fields, such as biotechnology and astrobiology, and industries, including the pharmaceutical, food, agricultural, biofuel, cosmetic, and textile industries. These microorganisms can also be used for ecological conservation and restoration. Extreme ecosystems are a unique biological resource and biodiversity hot spots that play a crucial role in maintaining environmental sustainability. The findings could be highly relevant to future research on the symbiotic relationships between microbiota and extreme-environment-tolerant plants in the face of climate change-induced desertification.

Airborne fungal and bacterial microbiome in classrooms of elementary schools during the COVID-19 pandemic period: Effects of school disinfection and other environmental factors

The aim of the study was to examine the effects of environmental factors including disinfection on airborne microbiome during the coronavirus disease 2019 pandemic, we evaluated indoor and outdoor air collected from 19 classrooms regularly disinfected. Extracted bacterial and fungal DNA samples were sequenced using the Illumina MiSeq™ platform. Using bacterial DNA copy number concentrations from qPCR analysis, multiple linear regressions including environmental factors as predictors were performed. Microbial diversity and community composition were evaluated. Classrooms disinfected with spray ≤1 week before sampling had lower bacterial DNA concentration (3116 DNA copies/m³ ) than those >1 week (5003 copies/m³ ) (p-values = 0.06). The bacterial DNA copy number concentration increased with temperature and was higher in classrooms in coastal than inland cities (p-values <0.01). Bacterial diversity in outdoor air was higher in coastal than inland cities while outdoor fungal diversity was higher in inland than coastal cities. These outdoor microbiomes affected classroom microbial diversity but bacterial community composition at the genus level in occupied classrooms were similar between coastal and inland cities. Our findings emphasize that environmental conditions including disinfection, climate, and school location are important factors in shaping classroom microbiota. Yet, further research is needed to understand the effects of modified microbiome by disinfection on occupants' health.

Next-generation -omics approaches to drive carboxylate production by acidogenic fermentation of food waste: a review

Acidogenic fermentation of food waste using mixed microbial cultures can produce carboxylates [or volatile fatty acids (VFA)] as high-valued bioproducts via a complex interplay of microorganisms during different stages of this process. However, the present fermentation systems are incapable of reaching the industrially relevant VFA production yields of ≥50 g/L primarly due to the complex process operation, competitive metabolic pathways, and limited understanding of microbial interplays. Recent reports have demonstrated the significant roles played by microbial communities from different phyla, which work together to control the process kinetics of various stages underlying acidogenic fermentation. In order to fully delineate the abundance, structure, and functionality of these microbial communities, next-generation high-throughput meta-omics technologies are required. In this article, we review the potential of metagenomics and metatranscriptomics approaches to enable microbial community engineering. Specifically, a deeper analysis of taxonomic relationships, shifts in microbial communities, and differences in the genetic expression of key pathway enzymes under varying operational and environmental parameters of acidogenic fermentation could lead to the identification of species-level functionalities for both cultivable and non-cultivable microbial fractions. Furthermore, it could also be used for successful gene sequence-guided microbial isolation and consortium development for bioaugmentation to allow VFA production with high concentrations and purity. Such highly controlled and engineered microbial systems could pave the way for tailored and high-yielding VFA synthesis, thereby creating a petrochemically competitive waste-to-value chain and promoting the circular bioeconomy.Research HighlightsMixed microbial mediated acidogenic fermentation of food waste.Metagenomics and metatranscriptomics based microbial community analysis.Omics derived function-associated microbial isolation and consortium engineering.High-valued sustainable carboxylate bio-products, i.e. volatile fatty acids.

Renaissance for Phage-Based Bacterial Control

Bacteriophages (phages) are an underutilized biological resource with vast potential for pathogen control and microbiome editing. Phage research and commercialization have increased rapidly in biomedical and agricultural industries, but adoption has been limited elsewhere. Nevertheless, converging advances in DNA sequencing, bioinformatics, microbial ecology, and synthetic biology are now poised to broaden phage applications beyond pathogen control toward the manipulation of microbial communities for defined functional improvements. Enhancements in sequencing combined with network analysis make it now feasible to identify and disrupt microbial associations to elicit desirable shifts in community structure or function, indirectly modulate species abundance, and target hub or keystone species to achieve broad functional shifts. Sequencing and bioinformatic advancements are also facilitating the use of temperate phages for safe gene delivery applications. Finally, integration of synthetic biology stands to create novel phage chassis and modular genetic components. While some fundamental, regulatory, and commercialization barriers to widespread phage use remain, many major challenges that have impeded the field now have workable solutions. Thus, a new dawn for phage-based (chemical-free) precise biocontrol and microbiome editing is on the horizon to enhance, suppress, or modulate microbial activities important for public health, food security, and more sustainable energy production and water reuse.

Current understanding and perspectives in anaerobic digestion based on genome-resolved metagenomic approaches

Anaerobic digestion (AD) is a technique that can be used to treat high concentrations of various organic wastes using a consortium of functionally diverse microorganisms under anaerobic conditions. Methane gas, a beneficial by-product of the AD process, is a renewable energy source that can replace fossil fuels following purification. However, detailed functional roles and metabolic interactions between microbial populations involved in organic waste removal and methanogenesis are yet to be known. Recent metagenomic approaches based on advanced high-throughput sequencing techniques have enabled the exploration of holistic microbial taxonomy and functionality of complex microbial populations involved in the AD process. Gene-centric and genome-centric analyses based on metagenome-assembled genomes are a platform that can be used to study the composition of microbial communities and their roles during AD. This review looks at how these up-to-date metagenomic analyses can be applied to promote our understanding and improved the development of the AD process.

Correcting the Estimation of Viral Taxa Distributions in Next-Generation Sequencing Data after Applying Artificial Neural Networks

Estimating the taxonomic composition of viral sequences in a biological samples processed by next-generation sequencing is an important step in comparative metagenomics. Mapping sequencing reads against a database of known viral reference genomes, however, fails to classify reads from novel viruses whose reference sequences are not yet available in public databases. Instead of a mapping approach, and in order to classify sequencing reads at least to a taxonomic level, the performance of artificial neural networks and other machine learning models was studied. Taxonomic and genomic data from the NCBI database were used to sample labelled sequencing reads as training data. The fitted neural network was applied to classify unlabelled reads of simulated and real-world test sets. Additional auxiliary test sets of labelled reads were used to estimate the conditional class probabilities, and to correct the prior estimation of the taxonomic distribution in the actual test set. Among the taxonomic levels, the biological order of viruses provided the most comprehensive data base to generate training data. The prediction accuracy of the artificial neural network to classify test reads to their viral order was considerably higher than that of a random classification. Posterior estimation of taxa frequencies could correct the primary classification results.

Management of microbial enzymes for biofuels and biogas production by using metagenomic and genome editing approaches

Non-renewable fossil fuels such as bitumen, coal, natural gas, oil shale, and petroleum are depleting over the world owing to unrestricted consumption. Biofuels such as biodiesel, biobutanol, bioethanol, and biogas are considered an eco-friendly and cost-effective alternatives of fossil fuels. For energy sustainability, the production of advanced biofuels is required. The advancement of genetic and metabolic engineering in microbial cells played a significant contribution to biofuels overproduction. Essential approaches such as next-generation sequencing technologies and CRISPR/Cas9-mediated genome editing of microbial cells are required for the mass manufacture of biofuels globally. Advanced "omics" approaches are used to construct effective microorganisms for biofuels manufacturing. A new investigation is required to augment the production of lignocellulosic-based biofuels with minimal use of energy. Advanced areas of metabolic engineering are introduced in the manufacture of biofuels by the use of engineered microbial strains. Genetically modified microorganisms are used for the production of biofuels in large quantities at a low-cost.

Investigation of bacterial and fungal communities in indoor and outdoor air of elementary school classrooms by 16S rRNA gene and ITS region sequencing

The advent of high-throughput sequencing methods allowed researchers to fully characterize microbial community in environmental samples, which is crucial to better understand their health effects upon exposures. In our study, we investigated bacterial and fungal community in indoor and outdoor air of nine classrooms in three elementary schools in Seoul, Korea. The extracted bacterial 16S rRNA gene and fungal ITS regions were sequenced, and their taxa were identified. Quantitative polymerase chain reaction for total bacteria DNA was also performed. The bacterial community was richer in outdoor air than classroom air, whereas fungal diversity was similar indoors and outdoors. Bacteria such as Enhydrobacter, Micrococcus, and Staphylococcus that are generally found in human skin, mucous membrane, and intestine were found in great abundance. For fungi, Cladosporium, Clitocybe, and Daedaleopsis were the most abundant genera in classroom air and mostly related to outdoor plants. Bacterial community composition in classroom air was similar among all classrooms but differed from that in outdoor air. However, indoor and outdoor fungal community compositions were similar for the same school but different among schools. Our study indicated the main source of airborne bacteria in classrooms was likely human occupants; however, classroom airborne fungi most likely originated from outdoors.

Genome-centric investigation of anaerobic digestion using sustainable second and third generation substrates

Biogas production through co-digestion of second and third generation substrates is an environmentally sustainable approach. Green willow biomass, chicken manure waste and microalgae biomass substrates were combined in the anaerobic digestion experiments. Biochemical methane potential test showed that biogas yields of co-digestions were significantly higher compared to the yield when energy willow was the sole substrate. To scale up the experiment continuous stirred-tank reactors (CSRTs) are employed, digestion parameters are monitored. Furthermore, genome-centric metagenomics approach was employed to gain functional insight into the complex anaerobic decomposing process. This revealed the importance of Firmicutes, Actinobacteria, Proteobacteria and Bacteroidetes phyla as major bacterial participants, while Methanomicrobia and Methanobacteria represented the archaeal constituents of the communities. The bacterial phyla were shown to perform the carbohydrate hydrolysis. Among the representatives of long-chain carbohydrate hydrolysing microbes Bin_61: Clostridia is newly identified metagenome assembled genome (MAG) and Bin_13: DTU010 sp900018335 is common and abundant in all CSTRs. Methanogenesis was linked to the slow-growing members of the community, where hydrogenotrophic methanogen species Methanoculleus (Bin_10) and Methanobacterium (Bin_4) predominate. A sensitive balance between H₂ producers and consumers was shown to be critical for stable biomethane production and efficient waste biodegradation.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.

An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.

Biomass-degrading glycoside hydrolases of archaeal origin

During the last decades, the impact of hyperthermophiles and their enzymes has been intensively investigated for implementation in various high-temperature biotechnological processes. Biocatalysts of hyperthermophiles have proven to show extremely high thermo-activities and thermo-stabilities and are identified as suitable candidates for numerous industrial processes with harsh conditions, including the process of an efficient plant biomass pretreatment and conversion. Already-characterized archaea-originated glycoside hydrolases (GHs) have shown highly impressive features and numerous enzyme characterizations indicated that these biocatalysts show maximum activities at a higher temperature range compared to bacterial ones. However, compared to bacterial biomass-degrading enzymes, the number of characterized archaeal ones remains low. To discover new promising archaeal GH candidates, it is necessary to study in detail the microbiology and enzymology of extremely high-temperature habitats, ranging from terrestrial to marine hydrothermal systems. State-of-the art technologies such as sequencing of genomes and metagenomes and automated binning of genomes out of metagenomes, combined with classical microbiological culture-dependent approaches, have been successfully performed to detect novel promising biomass-degrading hyperthermozymes. In this review, we will focus on the detection, characterization and similarities of archaeal GHs and their unique characteristics. The potential of hyperthermozymes and their impact on high-temperature industrial applications have not yet been exhausted.

Digitalization in microbiology - Paving the path to sustainable circular bioeconomy

The transition to a sustainable bio-based circular economy requires cutting edge technologies that ensure economic growth with environmentally responsible action. This transition will only be feasible when the opportunities of digitalization are also exploited. Digital methods and big data handling have already found their way into life sciences and generally offer huge potential in various research areas. While computational analyses of microbial metagenome data have become state of the art, the true potential of bioinformatics remains mostly untapped so far. In this article we present challenges and opportunities of digitalization including multi-omics approaches in discovering and exploiting the microbial diversity of the planet with the aim to identify robust biocatalysts for application in sustainable bioprocesses as part of the transition from a fossil-based to a bio-based circular economy. This will contribute to solving global challenges, including utilization of natural resources, food supply, health, energy and the environment.

Taxonomic and Functional Differences in Cervical Microbiome Associated with Cervical Cancer Development

The cervical microbiome is associated with cervical cancer risk, but how microbial diversity and functional profiles change in cervical cancer remains unclear. Herein, we investigated microbial-compositional and functional differences between a control group and a high-grade cervical intraepithelial neoplasia and cervical cancer (CIN2/3-CC) group. After retrospective collection of 92 cervical swab samples, we carried out 16S rRNA amplicon sequencing on 50 and 42 samples from the control and CIN2/3-CC groups, respectively. The EzBioCloud pipeline was applied to identify the genomic features associated with the groups using 16S rRNA data. A linear discriminant analysis effect size (LEfSe) was performed to assess the enrichment in the assigned taxonomic and functional profiles. We found a lower richness in the control group relative to the CIN2/3-CC group; however, the β-diversity tended to be similar between the groups. The LEfSe analysis showed that a phylum Sacchaaribacteria_TM7, 11 genera, and 21 species were more abundant in the CIN2/3-CC group and that one uncharacterized Gardnerella species was more abundant only in the control group. Further characterization of the functional pathways using EzBioCloud showed that the 4 KEGG orthologs (Phosphotransferase system [PTS] sucrose-specific IIA, IIB, IIC components and PTS cellubiose-specific IIC component) were involved in the KEGG pathway of starch and sucrose metabolism. The two pathways of folate biosynthesis and oxidative phosphorylation were more abundant in the CIN2/3-CC group. Further confirmation of these results in larger samples can help to elucidate the potential association between the cervical microbiome and cervical cancer.

Can the microbiota predict response to systemic cancer therapy, surgical outcomes, and survival? The answer is in the gut

INTRODUCTION

The gut microbiota seems to play a key role in tumorigenesis, across various hallmarks of cancer. Recent evidence suggests its potential use as a biomarker predicting drug response and adding prognostic information, generally in the context of immuno-oncology.

AREAS COVERED

In this review, we focus on the modulating effects of gut microbiota dysbiosis on various anticancer molecules used in practice, including cytotoxic and immune-modulating agents, primarily immune-checkpoint inhibitors (ICI). Pubmed/Medline-based literature search was conducted to find potential original studies that discuss gut microbiota as a prognostic and predictive biomarker for cancer therapy. We also looked at the US ClinicalTrials.gov website to find additional studies particularly ongoing human clinical trials.

EXPERT COMMENTARY

Sequencing of stool-derived materials and tissue samples from cancer patients and animal models has shown a significant enrichment of various bacteria such as Fusobacterium nucleatum and Bacteroides fragilis were associated with resistant disease and poorer outcomes. Gut microbiota was also found to be associated with surgical outcomes and seems to play a significant role in anastomotic leak (ATL) after surgery mainly by collagen breakdown. However, this research field is just at the beginning and the current findings are not yet ready to change clinical practice.

DNA sequencing, genomes and genetic markers of microbes on fruits and vegetables

The development of DNA sequencing technology has provided an effective method for studying foodborne and phytopathogenic microorganisms on fruits and vegetables (F & V). DNA sequencing has successfully proceeded through three generations, including the tens of operating platforms. These advances have significantly promoted microbial whole‐genome sequencing (WGS) and DNA polymorphism research. Based on genomic and regional polymorphisms, genetic markers have been widely obtained. These molecular markers are used as targets for PCR or chip analyses to detect microbes at the genetic level. Furthermore, metagenomic analyses conducted by sequencing the hypervariable regions of ribosomal DNA (rDNA) have revealed comprehensive microbial communities in various studies on F & V. This review highlights the basic principles of three generations of DNA sequencing, and summarizes the WGS studies of and available DNA markers for major bacterial foodborne pathogens and phytopathogenic fungi found on F & V. In addition, rDNA sequencing‐based bacterial and fungal metagenomics are summarized under three topics. These findings deepen the understanding of DNA sequencing and its application in studies of foodborne and phytopathogenic microbes and shed light on strategies for the monitoring of F & V microbes and quality control.

Impact of process temperature and organic loading rate on cellulolytic / hydrolytic biofilm microbiomes during biomethanation of ryegrass silage revealed by genome-centered metagenomics and metatranscriptomics

BACKGROUND

Anaerobic digestion (AD) of protein-rich grass silage was performed in experimental two-stage two-phase biogas reactor systems at low vs. increased organic loading rates (OLRs) under mesophilic (37 °C) and thermophilic (55 °C) temperatures. To follow the adaptive response of the biomass-attached cellulolytic/hydrolytic biofilms at increasing ammonium/ammonia contents, genome-centered metagenomics and transcriptional profiling based on metagenome assembled genomes (MAGs) were conducted.

RESULTS

In total, 78 bacterial and archaeal MAGs representing the most abundant members of the communities, and featuring defined quality criteria were selected and characterized in detail. Determination of MAG abundances under the tested conditions by mapping of the obtained metagenome sequence reads to the MAGs revealed that MAG abundance profiles were mainly shaped by the temperature but also by the OLR. However, the OLR effect was more pronounced for the mesophilic systems as compared to the thermophilic ones. In contrast, metatranscriptome mapping to MAGs subsequently normalized to MAG abundances showed that under thermophilic conditions, MAGs respond to increased OLRs by shifting their transcriptional activities mainly without adjusting their proliferation rates. This is a clear difference compared to the behavior of the microbiome under mesophilic conditions. Here, the response to increased OLRs involved adjusting of proliferation rates and corresponding transcriptional activities. The analysis led to the identification of MAGs positively responding to increased OLRs. The most outstanding MAGs in this regard, obviously well adapted to higher OLRs and/or associated conditions, were assigned to the order Clostridiales (Acetivibrio sp.) for the mesophilic biofilm and the orders Bacteroidales (Prevotella sp. and an unknown species), Lachnospirales (Herbinix sp. and Kineothrix sp.) and Clostridiales (Clostridium sp.) for the thermophilic biofilm. Genome-based metabolic reconstruction and transcriptional profiling revealed that positively responding MAGs mainly are involved in hydrolysis of grass silage, acidogenesis and / or acetogenesis.

CONCLUSIONS

An integrated -omics approach enabled the identification of new AD biofilm keystone species featuring outstanding performance under stress conditions such as increased OLRs. Genome-based knowledge on the metabolic potential and transcriptional activity of responsive microbiome members will contribute to the development of improved microbiological AD management strategies for biomethanation of renewable biomass.

Bioprospecting potential of microbial communities in solid waste landfills for novel enzymes through metagenomic approach

Landfills are repository for complex microbial diversity responsible for bio-degradation of solid waste. To elucidate this complexity, samples from three different landfill sites of North India (sample V: Bhalswa near Karnal byepass road, New Delhi, India; sample T: Chandigarh, India and sample S3: Una, H.P., India) were analyzed using metagenomic approach. Selected landfill sites had different geographical location, varied in waste composition, size of landfill and climate zone. For comparison, one sample from high altitude (sample J) having less human interference was taken in this study. The aim of this study was to explore microbial diversity of communities responsible for degradation of landfill. Samples were characterized by 16S rRNA gene sequencing. Data from three landfill sites showed abundance of phylum Proteobacteria while less contaminated sample from high altitude showed abundance of phylum Cholroflexi followed by phylum Proteobacteria. The most abundant genus was unknown suggesting that these landfills could be repository for various novel bacterial communities. Sample T was relatively more active in terms of microbial activity. It was relatively abundant in enzymes responsible for dioxin degradation, styrene degradation, steroid degradation, streptomycin biosynthesis, carbapenem biosynthesis, monobactam biosynthesis, furfural degradation pathways while sample J was predicted to be enriched in plant cell wall degrading enzymes. Co-occurrence analysis revealed presence of complex interaction networks between microbial assemblages responsible for bio-degradation of hydrocarbons. The data provides insights about synergetic interactions and functional interplay between bacterial communities in different landfill sites which could be further exploited to develop an effective bioremediation process.

Metagenomics methods for the study of plant-associated microbial communities: A review

Plant microbiota have different effects on the plant which can be beneficial or pathogenic. In this study, we concentrated on beneficial microbes associated with plants using endophytic microbes as a case study. Detailed knowledge of the microbial diversity, abundance, composition, functional genes patterns, and metabolic pathways at genome level could assist in understanding the contributions of microbial community towards plant growth and health. Recently, the study of microbial community has improved greatly with the discovery of next-generation sequencing and bioinformatics technologies. Analysis of next generation sequencing data and a proper computational method plays a key role in examining microbial metagenome. This review presents the general metagenomics and computational methods used in processing plant associated metagenomes with concentration on endophytes. This includes 1) introduction of plant-associated microbiota and the factors driving their diversity. 2) plant metagenome focusing on DNA extraction, verification and quality control. 3) metagenomics methods used in community analysis of endophytes focusing on maize plant and, 4) computational methods used in the study of endophytic microbiomes. Limitations and future prospects of metagenomics and computational methods for the analysis of plant-associated metagenome (endophytic metagenome) were also discussed with the aim of fostering its development. We conclude that there is need to adopt advanced genomic features such as k-mers of random size, which do not depend on annotation and can represent other sequence alternatives.

A microbiome reality check: limitations of in silico-based metagenomic approaches to study complex bacterial communities

In recent years, whole shotgun metagenomics (WSM) of complex microbial communities has become an established technology to perform compositional analyses of complex microbial communities, an approach that is heavily reliant on bioinformatic pipelines to process and interpret the generated raw sequencing data. However, the use of such in silico pipelines for the microbial taxonomic classification of short sequences may lead to significant errors in the compositional outputs deduced from such sequencing data. To investigate the ability of such in silico pipelines, we employed two commonly applied bioinformatic tools, i.e., MetaPhlAn2 and Kraken2 together with two metagenomic data sets originating from human and animal faecal samples. By using these bioinformatic programs that taxonomically classify WSM data based on marker genes, we observed a trend to depict a lower complexity of the microbial communities. Here, we assess the limitations of the most commonly employed bioinformatic pipelines, i.e., MetaPhlAn2 and Kraken2, and based on our findings, we propose that such analyses should ideally be combined with experimentally based microbiological validations.

Revealing the bacterial community profiles during the degradation of acetone, propionic and hexanoic acids-components of wastewater from the Fischer-Tropsch process

The Fischer-Tropsch (F-T) process for production of fuels is entrenched in several countries' approach to meeting energy demands. However, the clean water deficit associated with the down-stream processes has made it necessary to explore bioremediation methods to ameliorate the consequences of its use. In this study, a consortium of bacteria was utilized for determination of biodegradation and removal rates, based on reduction in chemical oxygen demand of a mixture of acetone, propionic acid and hexanoic acid (APH) (all components of F-T wastewater), at an organic loading of 5 and 9.53 g CODL^-1. The individual degradation efficiencies of the F-T components were determined using a gas chromatograph. Further, the bacterial consortia responsible for the degradation of the mixture of APH were determined using metagenomics data derived from next-generation sequencing. The overall chemical oxygen demand removal was found to be 88.8% and 82.3% at organic loading of 5 and 9.53 g CODL^-1, respectively. The optimal degradation efficiency of acetone, propionic acid and hexanoic acid over a period of 10 days was found to be 100%, 85% and 75.8%, respectively. The primary microbial communities presumed to be responsible for APH degradation by phyla classification across all samples were found to be Proteobacteria (55-92%), Actinobacteria (5-33%) and Firmicutes (0.08-9%). Overall, the study has demonstrated the importance of aerobic consortia interactions in the degradation of components of the F-T wastewater.

Metagenomic approaches: effective tools for monitoring the structure and functionality of microbiomes in anaerobic digestion systems

Microbial metagenome analysis has proven its usefulness to investigate the microbiomes present in technical engineered ecosystems such as anaerobic digestion systems. The analysis of the total microbial genomic DNA allows the detailed determination of both the microbial community structure and its functionality. In addition, it enables to study the response of the microbiome to alterations in technical process parameters. Strategies of functional microbial networks to face abiotic stressors, e.g., resistance, resilience, and reorganization, can be evaluated with respect to overall process optimization. The objective of this paper is to review the main metagenomic tools used for effective studies on anaerobic digestion systems in monitoring the dynamic of the microbiomes, as well as the factors that have been identified so far as limiting the metagenomic studies in this ecosystems.

Characterisation and microbial community analysis of lipid utilising microorganisms for biogas formation

In the anaerobic process, fat-oil-grease (FOG) is hydrolysed to long-chain fatty acids (LCFAs) and glycerol (GLYC), which are then used as substrates to produce biogas. The increase in FOG and LCFAs inhibits methanogenesis, and so far, most work investigating this inhibition has been carried out when FOG or LCFAs were used as co-substrates. In the current work, the inhibition of methanogenesis by FOG, LCFAs and GLYC was investigated when used as sole substrates. To gain more insight on the dynamics of this process, the change of microbial community was analysed using 16S rRNA gene amplicon sequencing. The results indicate that, as the concentrations of cooking olive oil (CO, which represents FOG) and LCFAs increase, methanogenesis is inhibited. For instance, at 0.01 g. L-1 of FOG, the rate of biogas formation was around 8 ml.L-1.day-1, and this decreased to <4 ml.L-1.day-1 at 40 g.L-1. Similar results were observed with the use of LCFAs. However, GLYC concentrations up to 100g.L-1 did not affect the rate of biogas formation. Acidic pH, temperature > = 45°C and NaCl > 3% led to a significant decrease in the rate of biogas formation. Microbial community analyses were carried out from samples from 3 different bioreactors (CO, OLEI and GLYC), on day 1, 5 and 15. In each bioreactor, microbial communities were dominated by Proteobacteria, Firmicutes and Bacteroidetes phyla. The most important families were Enterobacteriaceae, Pseudomonadaceae and Shewanellaceae (Proteobacteria phylum), Clostridiacea and Ruminococcaceae (Firmicutes) and Porphyromonadaceae and Bacteroidaceae (Bacteroidetes). In CO bioreactor, Proteobacteria bacteria decreased over time, while those of OLEI and GLYC bioreactors increased. A more pronounced increase in Bacteroidetes and Firmicutes were observed in CO bioreactor. The methanogenic archaea Methanobacteriaceae and Methanocorpusculaceae were identified. This analysis has shown that a set of microbial population is selected as a function of the substrate.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

Intermediate-Salinity Systems at High Altitudes in the Peruvian Andes Unveil a High Diversity and Abundance of Bacteria and Viruses

Intermediate-salinity environments are distributed around the world. Here, we present a snapshot characterization of two Peruvian thalassohaline environments at high altitude, Maras and Acos, which provide an excellent opportunity to increase our understanding of these ecosystems. The main goal of this study was to assess the structure and functional diversity of the communities of microorganisms in an intermediate-salinity environment, and we used a metagenomic shotgun approach for this analysis. These Andean hypersaline systems exhibited high bacterial diversity and abundance of the phyla Proteobacteria, Bacteroidetes, Balneolaeota, and Actinobacteria; in contrast, Archaea from the phyla Euryarchaeota, Thaumarchaeota, and Crenarchaeota were identified in low abundance. Acos harbored a more diverse prokaryotic community and a higher number of unique species compared with Maras. In addition, we obtained the draft genomes of two bacteria, Halomonas elongata and Idiomarina loihiensis, as well as the viral genomes of Enterobacteria lambda-like phage and Halomonas elongata-like phage and 27 partial novel viral halophilic genomes. The functional metagenome annotation showed a high abundance of sequences associated with detoxification, DNA repair, cell wall and capsule formation, and nucleotide metabolism; sequences for these functions were overexpressed mainly in bacteria and also in some archaea and viruses. Thus, their metabolic profiles afford a decrease in oxidative stress as well as the assimilation of nitrogen, a critical energy source for survival. Our work represents the first microbial characterization of a community structure in samples collected from Peruvian hypersaline systems.

A Robust and Universal Metaproteomics Workflow for Research Studies and Routine Diagnostics Within 24 h Using Phenol Extraction, FASP Digest, and the MetaProteomeAnalyzer

The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de.

Effect of Long-Term Farming Practices on Agricultural Soil Microbiome Members Represented by Metagenomically Assembled Genomes (MAGs) and Their Predicted Plant-Beneficial Genes

To follow the hypothesis that agricultural management practices affect structure and function of the soil microbiome regarding soil health and plant-beneficial traits, high-throughput (HT) metagenome analyses were performed on Chernozem soil samples from a long-term field experiment designated LTE-1 carried out at Bernburg-Strenzfeld (Saxony-Anhalt, Germany). Metagenomic DNA was extracted from soil samples representing the following treatments: (i) plough tillage with standard nitrogen fertilization and use of fungicides and growth regulators, (ii) plough tillage with reduced nitrogen fertilization (50%), (iii) cultivator tillage with standard nitrogen fertilization and use of fungicides and growth regulators, and (iv) cultivator tillage with reduced nitrogen fertilization (50%). Bulk soil (BS), as well as root-affected soil (RS), were considered for all treatments in replicates. HT-sequencing of metagenomic DNA yielded approx. 100 Giga bases (Gb) of sequence information. Taxonomic profiling of soil communities revealed the presence of 70 phyla, whereby Proteobacteria, Actinobacteria, Bacteroidetes, Planctomycetes, Acidobacteria, Thaumarchaeota, Firmicutes, Verrucomicrobia and Chloroflexi feature abundances of more than 1%. Functional microbiome profiling uncovered, i.a., numerous potential plant-beneficial, plant-growth-promoting and biocontrol traits predicted to be involved in nutrient provision, phytohormone synthesis, antagonism against pathogens and signal molecule synthesis relevant in microbe–plant interaction. Neither taxonomic nor functional microbiome profiling based on single-read analyses revealed pronounced differences regarding the farming practices applied. Soil metagenome sequences were assembled and taxonomically binned. The ten most reliable and abundant Metagenomically Assembled Genomes (MAGs) were taxonomically classified and metabolically reconstructed. Importance of the phylum Thaumarchaeota for the analyzed microbiome is corroborated by the fact that the four corresponding MAGs were predicted to oxidize ammonia (nitrification), thus contributing to the cycling of nitrogen, and in addition are most probably able to fix carbon dioxide. Moreover, Thaumarchaeota and several bacterial MAGs also possess genes with predicted functions in plant–growth–promotion. Abundances of certain MAGs (species resolution level) responded to the tillage practice, whereas the factors compartment (BS vs. RS) and nitrogen fertilization only marginally shaped MAG abundance profiles. Hence, soil management regimes promoting plant-beneficial microbiome members are very likely advantageous for the respective agrosystem, its health and carbon sequestration and accordingly may enhance plant productivity. Since Chernozem soils are highly fertile, corresponding microbiome data represent a valuable reference resource for agronomy in general.

Metaproteome analysis reveals that syntrophy, competition, and phage-host interaction shape microbial communities in biogas plants

BACKGROUND

In biogas plants, complex microbial communities produce methane and carbon dioxide by anaerobic digestion of biomass. For the characterization of the microbial functional networks, samples of 11 reactors were analyzed using a high-resolution metaproteomics pipeline.

RESULTS

Examined methanogenesis archaeal communities were either mixotrophic or strictly hydrogenotrophic in syntrophy with bacterial acetate oxidizers. Mapping of identified metaproteins with process steps described by the Anaerobic Digestion Model 1 confirmed its main assumptions and also proposed some extensions such as syntrophic acetate oxidation or fermentation of alcohols. Results indicate that the microbial communities were shaped by syntrophy as well as competition and phage-host interactions causing cell lysis. For the families Bacillaceae, Enterobacteriaceae, and Clostridiaceae, the number of phages exceeded up to 20-fold the number of host cells.

CONCLUSION

Phage-induced cell lysis might slow down the conversion of substrates to biogas, though, it could support the growth of auxotrophic microbes by cycling of nutrients.

Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants

The production of biogas by anaerobic digestion (AD) of agricultural residues, organic wastes, animal excrements, municipal sludge, and energy crops has a firm place in sustainable energy production and bio-economy strategies. Focusing on the microbial community involved in biomass conversion offers the opportunity to control and engineer the biogas process with the objective to optimize its efficiency. Taxonomic profiling of biogas producing communities by means of high-throughput 16S rRNA gene amplicon sequencing provided high-resolution insights into bacterial and archaeal structures of AD assemblages and their linkages to fed substrates and process parameters. Commonly, the bacterial phyla Firmicutes and Bacteroidetes appeared to dominate biogas communities in varying abundances depending on the apparent process conditions. Regarding the community of methanogenic Archaea, their diversity was mainly affected by the nature and composition of the substrates, availability of nutrients and ammonium/ammonia contents, but not by the temperature. It also appeared that a high proportion of 16S rRNA sequences can only be classified on higher taxonomic ranks indicating that many community members and their participation in AD within functional networks are still unknown. Although cultivation-based approaches to isolate microorganisms from biogas fermentation samples yielded hundreds of novel species and strains, this approach intrinsically is limited to the cultivable fraction of the community. To obtain genome sequence information of non-cultivable biogas community members, metagenome sequencing including assembly and binning strategies was highly valuable. Corresponding research has led to the compilation of hundreds of metagenome-assembled genomes (MAGs) frequently representing novel taxa whose metabolism and lifestyle could be reconstructed based on nucleotide sequence information. In contrast to metagenome analyses revealing the genetic potential of microbial communities, metatranscriptome sequencing provided insights into the metabolically active community. Taking advantage of genome sequence information, transcriptional activities were evaluated considering the microorganism's genetic background. Metaproteome studies uncovered enzyme profiles expressed by biogas community members. Enzymes involved in cellulose and hemicellulose decomposition and utilization of other complex biopolymers were identified. Future studies on biogas functional microbial networks will increasingly involve integrated multi-omics analyses evaluating metagenome, transcriptome, proteome, and metabolome datasets.

Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants

The production of biogas by anaerobic digestion (AD) of agricultural residues, organic wastes, animal excrements, municipal sludge, and energy crops has a firm place in sustainable energy production and bio-economy strategies. Focusing on the microbial community involved in biomass conversion offers the opportunity to control and engineer the biogas process with the objective to optimize its efficiency. Taxonomic profiling of biogas producing communities by means of high-throughput 16S rRNA gene amplicon sequencing provided high-resolution insights into bacterial and archaeal structures of AD assemblages and their linkages to fed substrates and process parameters. Commonly, the bacterial phyla Firmicutes and Bacteroidetes appeared to dominate biogas communities in varying abundances depending on the apparent process conditions. Regarding the community of methanogenic Archaea, their diversity was mainly affected by the nature and composition of the substrates, availability of nutrients and ammonium/ammonia contents, but not by the temperature. It also appeared that a high proportion of 16S rRNA sequences can only be classified on higher taxonomic ranks indicating that many community members and their participation in AD within functional networks are still unknown. Although cultivation-based approaches to isolate microorganisms from biogas fermentation samples yielded hundreds of novel species and strains, this approach intrinsically is limited to the cultivable fraction of the community. To obtain genome sequence information of non-cultivable biogas community members, metagenome sequencing including assembly and binning strategies was highly valuable. Corresponding research has led to the compilation of hundreds of metagenome-assembled genomes (MAGs) frequently representing novel taxa whose metabolism and lifestyle could be reconstructed based on nucleotide sequence information. In contrast to metagenome analyses revealing the genetic potential of microbial communities, metatranscriptome sequencing provided insights into the metabolically active community. Taking advantage of genome sequence information, transcriptional activities were evaluated considering the microorganism's genetic background. Metaproteome studies uncovered enzyme profiles expressed by biogas community members. Enzymes involved in cellulose and hemicellulose decomposition and utilization of other complex biopolymers were identified. Future studies on biogas functional microbial networks will increasingly involve integrated multi-omics analyses evaluating metagenome, transcriptome, proteome, and metabolome datasets.