Taxonomy of anaerobic digestion microbiome reveals biases associated with the applied high throughput sequencing strategies

Viral and Bacterial Community Dynamics in Food Waste and Digestate from Full-Scale Biogas Plants

Anaerobic digestion (AD) is commonly used in food waste treatment. Prokaryotic microbial communities in AD of food waste have been comprehensively studied. The role of viruses, known to affect microbial dynamics and metabolism, remains largely unexplored. This study employed metagenomic analysis and recovered 967 high-quality viral bins within food waste and digestate derived from 8 full-scale biogas plants. The diversity of viral communities was higher in digestate. In silico predictions linked 20.8% of viruses to microbial host populations, highlighting possible virus predators of key functional microbes. Lineage-specific virus-host ratio varied, indicating that viral infection dynamics might differentially affect microbial responses to the varying process parameters. Evidence for virus-mediated gene transfer was identified, emphasizing the potential role of viruses in controlling the microbiome. AD altered the specific process parameters, potentially promoting a shift in viral lifestyle from lysogenic to lytic. Viruses encoding auxiliary metabolic genes (AMGs) were involved in microbial carbon and nutrient cycling, and most AMGs were transcriptionally expressed in digestate, meaning that viruses with active functional states were likely actively involved in AD. These findings provided a comprehensive profile of viral and bacterial communities and expanded knowledge of the interactions between viruses and hosts in food waste and digestate.

Dental plaque microbiota sequence counts for microbial profiling and resistance genes detection

Shotgun metagenomics sequencing experiments are finding a wide range of applications. Nonetheless, there are still limited guidelines regarding the number of sequences needed to acquire meaningful information for taxonomic profiling and antimicrobial resistance gene (ARG) identification. In this study, we explored this issue in the context of oral microbiota by sequencing with a very high number of sequences (~ 100 million), four human plaque samples, and one microbial community standard and by evaluating the performance of microbial identification and ARGs detection through a downsampling procedure. When investigating the impact of a decreasing number of sequences on quantitative taxonomic profiling in the microbial community standard datasets, we found some discrepancies in the identified microbial species and their abundances when compared to the expected ones. Such differences were consistent throughout downsampling, suggesting their link to taxonomic profiling methods limitations. Overall, results showed that the number of sequences has a great impact on metagenomic samples at the qualitative (i.e., presence/absence) level in terms of loss of information, especially in experiments having less than 40 million reads, whereas abundance estimation was minimally affected, with only slight variations observed in low-abundance species. The presence of ARGs was also assessed: a total of 133 ARGs were identified. Notably, 23% of them inconsistently resulted as present or absent across downsampling datasets of the same sample. Moreover, over half of ARGs were lost in datasets having less than 20 million reads. This study highlights the importance of carefully considering sequencing aspects and suggests some guidelines for designing shotgun metagenomics experiments with the final goal of maximizing oral microbiome analyses. Our findings suggest varying optimized sequence numbers according to different study aims: 40 million for microbiota profiling, 50 million for low-abundance species detection, and 20 million for ARG identification. KEY POINTS: • Forty million sequences are a cost-efficient solution for microbiota profiling • Fifty million sequences allow low-abundance species detection • Twenty million sequences are recommended for ARG identification.

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.

Differences in the gut microbiome of young adults with schizophrenia spectrum disorder: using machine learning to distinguish cases from controls

While an association between the gut microbiome and schizophrenia spectrum disorders (SSD) has been suggested, the existing evidence is still inconclusive. To this end, we analyzed bacteria and bacterial genes in feces from 52 young adult SSD patients and 52 controls using fecal shotgun metagenomic sequencing. Compared to controls, young SSD patients were found to have significantly lower α-diversity and different β-diversity both regarding bacterial species (i.e., taxonomic diversity) and bacterial genes (i.e., functional diversity). Furthermore, the α-diversity measures 'Pielou's evenness' and 'Shannon' were significantly higher for both bacterial species, bacterial genes encoding enzymes and gut brain modules in young SSD patients on antipsychotic treatment (young SSD not on antipsychotics=9 patients, young SSD on antipsychotics=43 patients). We also applied machine learning classifiers to distinguish between young SSD patients and healthy controls based on their gut microbiome. Results showed that taxonomic and functional data classified young SSD individuals with an accuracy of ≥ 70% and with an area under the receiver operating characteristic curve (AUROC) of ≥ 0.75. Differential abundance analysis on the most important features in the classifier models revealed that most of the species with higher abundance in young SSD patients had their natural habitat in the oral cavity. In addition, many of the modules with higher abundance in young SSD patients were amino acid biosynthesis modules. Moreover, the abundances of gut-brain modules of butyrate synthesis and acetate degradation were lower in the SSD patients compared to controls. Collectively, our findings continue to support the presence of gut microbiome alterations in SSD and provide support for the use of machine learning algorithms to distinguish patients from controls based on gut microbiome profiles.

Microbial community dynamics and functional potentials in the conversion of oil palm wastes into biomethane

Oil palm processing generates substantial waste materials rich in organic content, posing various environmental challenges. Anaerobic digestion (AD), particularly for palm oil mill effluent (POME), offers a sustainable solution, by converting waste into valuable biomethane for thermal energy or electricity generation. The synergistic activities of the AD microbiota directly affect the biomethane production, and the microbial community involved in biomethane production in POME anaerobic digestion has been reported. The composition of bacterial and archaeal communities varies under different substrate and physicochemical conditions. This review discusses the characteristics of POME, explores the microbial members engaged in each stage of AD, and elucidates the impacts of substrate and physicochemical conditions on the microbial community dynamics, with a specific focus on POME. Finally, the review outlines current research needs and provides future perspectives on optimizing the microbial communities for enhanced biomethane production from oil palm wastes.

Development of a portable on-site applicable metagenomic data generation workflow for enhanced pathogen and antimicrobial resistance surveillance

Rapid, accurate and comprehensive diagnostics are essential for outbreak prevention and pathogen surveillance. Real-time, on-site metagenomics on miniaturized devices, such as Oxford Nanopore Technologies MinION sequencing, could provide a promising approach. However, current sample preparation protocols often require substantial equipment and dedicated laboratories, limiting their use. In this study, we developed a rapid on-site applicable DNA extraction and library preparation approach for nanopore sequencing, using portable devices. The optimized method consists of a portable mechanical lysis approach followed by magnetic bead-based DNA purification and automated sequencing library preparation, and resulted in a throughput comparable to a current optimal, laboratory-based protocol using enzymatic digestion to lyse cells. By using spike-in reference communities, we compared the on-site method with other workflows, and demonstrated reliable taxonomic profiling, despite method-specific biases. We also demonstrated the added value of long-read sequencing by recovering reads containing full-length antimicrobial resistance genes, and attributing them to a host species based on the additional genomic information they contain. Our method may provide a rapid, widely-applicable approach for microbial detection and surveillance in a variety of on-site settings.

Effect of Human Infant Gut Microbiota on Mouse Behavior, Dendritic Complexity, and Myelination

The mammalian gut microbiome influences numerous developmental processes. In human infants it has been linked with cognition, social skills, hormonal responses to stress, and brain connectivity. Yet, these associations are not necessarily causal. The present study tested whether two microbial stool communities, common in human infants, affected behavior, myelination, dendritic morphology, and spine density when used to colonize mouse models. Humanized animals were more like specific-pathogen free mice than germ-free mice for most phenotypes, although in males, both humanized groups were less social. Both humanized groups had thinner myelin sheaths in the hippocampus, than did germ-free animals. Humanized animals were similar to each other except for dendritic morphology and spine density where one group had greater dendritic length in the prefrontal cortex, greater dendritic volume in the nucleus accumbens, and greater spine density in both regions, compared to the other. Results add to a body of literature suggesting the gut microbiome impacts brain development.

Teaser

Fecal transplants from human infants with highly abundant Bifidobacterium , an important inhabitant of the intestinal tract of breastfed newborns, may promote brain connectivity in mice.

Validation of 16S rRNA gene sequencing and metagenomics for evaluating microbial immigration in a methanogenic bioreactor

To quantitatively evaluate the impact of microbial immigration from an upstream community on the microbial assembly of a downstream community, an ecological genomics (ecogenomics)-based mass balance (EGMB) model coupled with 16S rRNA gene sequencing was previously developed. In this study, a mock community was used to further validate the EGMB models and demonstrate the feasibility of using metagenome-based EGMB model to reveal both microbial activity and function. The mock community consisting of Aeromonas, Escherichia, and Pseudomonas was fed into a lab-scale methanogenic bioreactor together with dissolved organic substrate. Using qPCR, 16S rRNA gene, 16S rRNA gene copy number normalization (GCN), and metagenome, results showed highly comparable community profiles in the feed. In the bioreactor, Aeromonas and Pseudomonas exhibited negative growth rates throughout the experiment by all approaches. Escherichia's growth rate was negative by most biomarkers but was slightly positive by 16S rRNA gene. Still, all approaches showed a decreasing trend toward negative in the growth rate of Escherichia as reactor operation time increased. Uncultivated populations of phyla Desulfobacterota, Chloroflexi, Actinobacteriota, and Spirochaetota were observed to increase in abundance, suggesting their contribution in degrading the feed biomass. Based on metabolic reconstruction of metagenomes, these populations possessed functions of hydrolysis, fermentation, fatty acid degradation, or acetate oxidation. Overall results supported the application of both 16S rRNA gene- and metagenome-based EGMB models to measure the growth rate of microbes in the bioreactor, and the latter had advantage in providing insights into the microbial functions of uncultivated populations.

Metagenomics reveals novel microbial signatures of farm exposures in house dust

Indoor home dust microbial communities, important contributors to human health, are shaped by environmental factors, including farm-related exposures. Advanced metagenomic whole genome shotgun sequencing (WGS) improves detection and characterization of microbiota in the indoor built-environment dust microbiome, compared to conventional 16S rRNA amplicon sequencing (16S). We hypothesized that the improved characterization of indoor dust microbial communities by WGS will enhance detection of exposure-outcome associations. The objective of this study was to identify novel associations of environmental exposures with the dust microbiome from the homes of 781 farmers and farm spouses enrolled in the Agricultural Lung Health Study. We examined various farm-related exposures, including living on a farm, crop versus animal production, and type of animal production, as well as non-farm exposures, including home cleanliness and indoor pets. We assessed the association of the exposures on within-sample alpha diversity and between-sample beta diversity, and the differential abundance of specific microbes by exposure. Results were compared to previous findings using 16S. We found most farm exposures were significantly positively associated with both alpha and beta diversity. Many microbes exhibited differential abundance related to farm exposures, mainly in the phyla Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. The identification of novel differential taxa associated with farming at the genera level, including Rhodococcus, Bifidobacterium, Corynebacterium, and Pseudomonas, was a benefit of WGS compared to 16S. Our findings indicate that characterization of dust microbiota, an important component of the indoor environment relevant to human health, is heavily influenced by sequencing techniques. WGS is a powerful tool to survey the microbial community that provides novel insights on the impact of environmental exposures on indoor dust microbiota. These findings can inform the design of future studies in environmental health.

Inter-kingdom interactions and stability of methanogens revealed by machine-learning guided multi-omics analysis of industrial-scale biogas plants

Multi-omics analysis is a powerful tool for the detection and study of inter-kingdom interactions, such as those between bacterial and archaeal members of complex biogas-producing microbial communities. In the present study, the microbiomes of three industrial-scale biogas digesters, each fed with different substrates, were analysed using a machine-learning guided genome-centric metagenomics framework complemented with metatranscriptome data. This data permitted us to elucidate the relationship between abundant core methanogenic communities and their syntrophic bacterial partners. In total, we detected 297 high-quality, non-redundant metagenome-assembled genomes (nrMAGs). Moreover, the assembled 16 S rRNA gene profiles of these nrMAGs showed that the phylum Firmicutes possessed the highest copy number, while the representatives of the archaeal domain had the lowest. Further investigation of the three anaerobic microbial communities showed characteristic alterations over time but remained specific to each industrial-scale biogas plant. The relative abundance of various microorganisms as revealed by metagenome data was independent from corresponding metatranscriptome activity data. Archaea showed considerably higher activity than was expected from their abundance. We detected 51 nrMAGs that were present in all three biogas plant microbiomes with different abundances. The core microbiome correlated with the main chemical fermentation parameters, and no individual parameter emerged as a predominant shaper of community composition. Various interspecies H₂/electron transfer mechanisms were assigned to hydrogenotrophic methanogens in the biogas plants that ran on agricultural biomass and wastewater. Analysis of metatranscriptome data revealed that methanogenesis pathways were the most active of all main metabolic pathways.

Mitigation of N2O emissions via enhanced denitrification in a biological landfill leachate treatment using external carbon from fermented sludge

The effects of an external carbon source (C-source) on the mitigation of N₂O gas (N₂O_(g)) emissions from landfill leachate were investigated via enhanced denitrification using anaerobically fermented sewage sludge. Anaerobic fermentation of sewage sludge was conducted under thermophilic conditions with progressively increasing organic loading rates (OLR). Optimal conditions for fermentation were determined based on the efficiency of hydrolysis and the concentrations of sCOD and volatile fatty acids (VFAs) as follows: at an OLR of 40.48 ± 0.77 g COD/L·d with 1.5 days of solid retention time (SRT), 14.68 ± 0.59% of efficiency of hydrolysis, 14.42 ± 0.30 g sCOD/L and 7.85 ± 0.18 g COD/L of VFAs. Analysis of the microbial community in the anaerobic fermentation reactor revealed that degradation of sewage sludge might be potentially affected by proteolytic microorganisms producing VFAs from proteinaceous materials. Sludge-fermentate (SF) retrieved from the anaerobic fermentation reactor was used as the external C-source for denitrification testing. The specific nitrate removal rate (K_NR) of the SF-added condition was 7.54 mg NO₃^-N/g VSS·hr, which was 5.42 and 2.43 times higher than that of raw landfill leachate (LL) and a methanol-added condition, respectively. In the N₂O_(g) emission test, the liquid phase N₂O (N₂O-^N_(l)) of 20.15 mg N/L was emitted as N₂O_(g) of 19.64 ppmv under only LL-added condition. On the other hand, SF led to the specific N₂O_(l) reduction rate (K_N2O) of 6.70 mg N/g VSS hr, resulting in mitigation of 1.72 times the N₂O_(g) emission compared to under the only-LL-added condition. The present study revealed that N₂O_(g) emissions from biological landfill leachate treatment plants can be attenuated by simultaneous reduction of NO₃^-N and N₂O_(l) during enhanced denitrification via a stable supply of an external C-source retrieved from anaerobically fermented organic waste.

Metagenomics reveals novel microbial signatures of farm exposures in house dust

Indoor home dust microbial communities, important contributors to human health outcomes, are shaped by environmental factors, including farm-related exposures. Detection and characterization of microbiota are influenced by sequencing methodology; however, it is unknown if advanced metagenomic whole genome shotgun sequencing (WGS) can detect novel associations between environmental exposures and the indoor built-environment dust microbiome, compared to conventional 16S rRNA amplicon sequencing (16S). This study aimed to better depict indoor dust microbial communities using WGS to investigate novel associations with environmental risk factors from the homes of 781 farmers and farm spouses enrolled in the Agricultural Lung Health Study. We examined various farm-related exposures, including living on a farm, crop versus animal production, and type of animal production, as well as non-farm exposures, including home cleanliness and indoor pets. We assessed the association of the exposures on within-sample alpha diversity and between-sample beta diversity, and the differential abundance of specific microbes by exposure. Results were compared to previous findings using 16S. We found most farm exposures were significantly positively associated with both alpha and beta diversity. Many microbes exhibited differential abundance related to farm exposures, mainly in the phyla Actinobacteria, Bacteroidetes, Firmicutes , and Proteobacteria . The identification of novel differential taxa associated with farming at the genera level, including Rhodococcus, Bifidobacterium, Corynebacterium , and Pseudomonas , was a benefit of WGS compared to 16S. Our findings indicate that characterization of dust microbiota, an important component of the indoor environment relevant to human health, is heavily influenced by sequencing techniques. WGS is a powerful tool to survey the microbial community that provides novel insights on the impact of environmental exposures on indoor dust microbiota, and should be an important consideration in designing future studies in environmental health.

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.

Gut metagenome profile of the Nunavik Inuit youth is distinct from industrial and non-industrial counterparts

Comparative metagenomics studies have highlighted differences in microbiome community structure among human populations over diverse lifestyles and environments. With their unique environmental and historical backgrounds, Nunavik Inuit have a distinctive gut microbiome with undocumented health-related implications. Using shotgun metagenomics, we explored the taxonomic and functional structure of the gut microbiome from 275 Nunavik Inuit ranging from 16 to 30-year-old. Whole-metagenome analyses revealed that Nunavik Inuit youths have a more diverse microbiome than their non-industrialized and industrialized counterparts. A comparison of k-mer content illustrated the uniqueness of the Nunavik gut microbiome. Short-chain fatty acids producing species, and carbohydrates degradation pathways dominated Inuit metagenomes. We identified a taxonomic and functional signature unique to the Nunavik gut microbiome contrasting with other populations using a random forest classifier. Here, we show that the Nunavik Inuit gut microbiome exhibits high diversity and a distinct community structure.

Gut Microbiome Studies in Livestock: Achievements, Challenges, and Perspectives

The variety and makeup of the gut microbiome are frequently regarded as the primary determinants of health and production performances in domestic animals. High-throughput DNA/RNA sequencing techniques (NGS) have recently gained popularity and permitted previously unheard-of advancements in the study of gut microbiota, particularly for determining the taxonomic composition of such complex communities. Here, we summarize the existing body of knowledge on livestock gut microbiome, discuss the state-of-the-art in sequencing techniques, and offer predictions for next research. We found that the enormous volumes of available data are biased toward a small number of globally distributed and carefully chosen varieties, while local breeds (or populations) are frequently overlooked despite their demonstrated resistance to harsh environmental circumstances. Furthermore, the bulk of this research has mostly focused on bacteria, whereas other microbial components such as protists, fungi, and viruses have received far less attention. The majority of these data were gathered utilizing traditional metabarcoding techniques that taxonomically identify the gut microbiota by analyzing small portions of their genome (less than 1000 base pairs). However, to extend the coverage of microbial genomes for a more precise and thorough characterization of microbial communities, a variety of increasingly practical and economical shotgun techniques are currently available.

GenBank as a source to monitor and analyze Host-Microbiome data

MOTIVATION

Microbiome datasets are often constrained by sequencing limitations. GenBank is the largest collection of publicly available DNA sequences, which is maintained by the National Center of Biotechnology Information (NCBI). The metadata of GenBank records are a largely understudied resource and may be uniquely leveraged to access the sum of prior studies focused on microbiome composition. Here, we developed a computational pipeline to analyze GenBank metadata, containing data on hosts, microorganisms and their place of origin. This work provides the first opportunity to leverage the totality of GenBank to shed light on compositional data practices that shape how microbiome datasets are formed as well as examine host-microbiome relationships.

RESULTS

The collected dataset contains multiple kingdoms of microorganisms, consisting of bacteria, viruses, archaea, protozoa, fungi, and invertebrate parasites, and hosts of multiple taxonomical classes, including mammals, birds and fish. A human data subset of this dataset provides insights to gaps in current microbiome data collection, which is biased towards clinically relevant pathogens. Clustering and phylogenic analysis reveals the potential to use these data to model host taxonomy and evolution, revealing groupings formed by host diet, environment and coevolution.

AVAILABILITY AND IMPLEMENTATION

GenBank Host-Microbiome Pipeline is available at https://github.com/bcbi/genbank_holobiome. The GenBank loader is available at https://github.com/bcbi/genbank_loader.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ecosystem-specific microbiota and microbiome databases in the era of big data

The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.

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.

Influence of feedstock mix ratio on microbial dynamics during acidogenic fermentation for polyhydroxyalkanoates production

The nature of microbial populations plays an essential role in the production of volatile fatty acids (VFA) during acidogenesis, the first stage in polyhydroxyalkanoates (PHA) production using mixed cultures. However, the composition of microbial communities is generally affected by substrate alterations. This work aimed to unravel the microbial dynamics in response to a gradual change in the feedstock composition in an acidogenic reactor, with subsequent PHA production. To achieve this, co-digestion of cheese whey and brewery wastewater (BW) was carried out for the production of VFA, in which the ratio of these feedstocks was varied by gradually increasing the proportion of BW from 0 up to 50% of the organic content. Bacteria such as Megasphaera, Bifidobacterium or Caproiciproducens were the most abundant in the first stages of the co-digestion. However, when BW reached 25% of the organic load, new taxa emerged and displaced the former ones; like Selenomonas, Ethanoligenens or an undefined member of the Bacteroidales order. Accordingly, the production of butyric acid dropped from 52 down to 27%, while the production of acetic acid increased from 36 up to 52%. Furthermore, the gradual increase of the BW ratio led to a progressive drop in the degree of acidification, from 72 down to 57%. In a subsequent approach, the VFA-rich streams, obtained from the co-digestion, were used as substrates in PHA accumulation tests. All the tests yielded similar PHA contents, but with slightly different monomeric composition. The overall results confirmed that the microbiome was altered by a gradual change in the feedstock composition and, consequently, the VFA profile and the monomeric composition of the biopolymer also did.

Evaluation of Bacteria and Fungi DNA Abundance in Human Tissues

Whereas targeted and shotgun sequencing approaches are both powerful in allowing the study of tissue-associated microbiota, the human: microorganism abundance ratios in tissues of interest will ultimately determine the most suitable sequencing approach. In addition, it is possible that the knowledge of the relative abundance of bacteria and fungi during a treatment course or in pathological conditions can be relevant in many medical conditions. Here, we present a qPCR-targeted approach to determine the absolute and relative amounts of bacteria and fungi and demonstrate their relative DNA abundance in nine different human tissue types for a total of 87 samples. In these tissues, fungi genomes are more abundant in stool and skin samples but have much lower levels in other tissues. Bacteria genomes prevail in stool, skin, oral swabs, saliva, and gastric fluids. These findings were confirmed by shotgun sequencing for stool and gastric fluids. This approach may contribute to a more comprehensive view of the human microbiota in targeted studies for assessing the abundance levels of microorganisms during disease treatment/progression and to indicate the most informative methods for studying microbial composition (shotgun versus targeted sequencing) for various samples types.

Clinical Parasitology and Parasitome Maps as Old and New Tools to Improve Clinical Microbiomics

A growing body of evidence shows that dysbiotic gut microbiota may correlate with a wide range of disorders; hence, the clinical use of microbiota maps and fecal microbiota transplantation (FMT) can be exploited in the clinic of some infectious diseases. Through direct or indirect ecological and functional competition, FMT may stimulate decolonization of pathogens or opportunistic pathogens, modulating immune response and colonic inflammation, and restoring intestinal homeostasis, which reduces host damage. Herein, we discuss how diagnostic parasitology may contribute to designing clinical metagenomic pipelines and FMT programs, especially in pediatric subjects. The consequences of more specialized diagnostics in the context of gut microbiota communities may improve the clinical parasitology and extend its applications to the prevention and treatment of several communicable and even noncommunicable disorders.

Shotgun metagenomic analysis reveals the prevalence of antibiotic resistance genes and mobile genetic elements in full scale hospital wastewater treatment plants

Wastewater treatment plants are considered as hotspots of emerging antimicrobial genes and mobile genetic elements. We used a shotgun metagenomic approach to examine the wide-spectrum profiles of ARGs (antibiotic resistance genes) and MGEs (mobile genetic elements) in activated sludge samples from two different hospital trains at the wastewater treatment plants (WWTPs) in Daegu, South Korea. The influent activated sludge and effluent of two trains (six samples in total) at WWTPs receiving domestic sewage wastewater (SWW) and hospital wastewater (HWW) samples collected at multiple periods were subjected to high throughput 16S rRNA metagenome sequencing for microbial community diversity. Cloacibacterium caeni and Lewinella nigricans were predominant in SWW effluents, while Bacillus subtilis and Staphylococcus epidermidis were predominant in HWW effluents based on the Miseq platform. Totally, 20,011 reads and 28,545 metagenomic sequence reads were assigned to 25 known ARG types in the SWW2 and HWW5 samples, respectively. The higher abundance of ARGs, including multidrug resistance (>53%, MDR), macrolide-lincosamide-streptogramin (>9%, MLS), beta-lactam (>3.3%), bacitracin (>4.4%), and tetracycline (>3.4%), confirmed the use of these antibiotics in human medicine. In total, 190 subtypes belonging to 23 antibiotic classes were detected in both SWW2 and HWW5 samples. RpoB2, MacB, and multidrug (MDR) ABC transporter shared the maximum matched genes in both activated sludge samples. The high abundance of MGEs, such as a gene transfer agent (GTA) (four times higher), transposable elements (1.6 times higher), plasmid related functions (3.8 times higher), and phages (two times higher) in HWW5 than in SWW2, revealed a risk of horizontal gene transfer in HWW. Domestic wastewater from hospital patients also influenced the abundance of ARGs and MGEs in the activated sludge process.

Combined microbial transcript and metabolic analysis reveals the different roles of hydrochar and biochar in promoting anaerobic digestion of waste activated sludge

Hydrothermal pretreatment of waste activated sludge (WAS) could eliminate the rate limiting step of anaerobic digestion (AD) -hydrolysis. However, the high organic loading rate may cause acid accumulation, thus leading to an unstable system. This study compared the effect of different hydrochar (HC2-260°C and HC3-320°C) and biochar (BC5-500°C and BC7-700°C) on AD of hydrothermal pretreated WAS (HPS). Results demonstrated that hydrochar was superior to biochar in the methane yield and production rate, especially HC2. HC2 had the highest surface oxygen-containing functional groups that could facilitate direct interspecies electron transfer (DIET). The enhanced methane yield was related with the increased protein utilization, and hydrochar and biochar enriched different microbes related to protein degradation. Metabolomic analysis showed the significantly changed metabolites induced by hydrochar and biochar were involved in fatty acids and amino acids-related metabolism, indicating the rapid conversion of intermediated products, which was consistent with the microbial community structure results. Hydrochar and biochar also induced upregulation of metabolites related to microbial metabolic activity and extracellular electron transfer. Although biochar induced the same metabolic changes, the alterations of these metabolites were weaker than those of hydrochar. The results of this study offered new insights into the molecular mechanisms of enhanced AD of HPS by hydrochar and biochar.

Co-digestion of cheese whey with sewage sludge for caproic acid production: Role of microbiome and polyhydroxyalkanoates potential production

The main aim of this work was to evaluate the efficiency of producing caproic acid and other volatile fatty acids using a co-digestion between cheese whey and sewage sludge in a continuous reactor. The effect of two different feeding regimes (one and two per day) and three hydraulic retention times (HRT) (15, 10 and 6 days) on the organic acids production were studied. The optimal conditions for the process were 10 days HRT, 2 feeding cycles per day, reaching a maximum degree of acidification of 44%. Under these conditions, the most abundant organic acid was caproic acid. The analysis of the microbial community dynamics in the reactor during the HRT changes revealed a microbiome enriched in organisms involved in caproic acid production. Additionally, the production of polyhydroxyalkanoates using the organic acids stream as feeding was verified in a fed-batch experiment obtaining a copolymer formed by hydroxybutyrate, hydroxyvalerate and hydroxyhexanoate.

Gut microbiota composition associated with hepatic fibrosis in non-obese patients with non-alcoholic fatty liver disease

BACKGROUND AND AIM

Gut microbiota composition is associated with the pathogenesis of non-alcoholic fatty liver disease. However, the association between gut microbiota composition and non-alcoholic fatty liver disease in non-obese patients remains unclear. We compared clinical parameters and gut microbiota profiles of healthy controls and non-obese and obese patients with non-alcoholic fatty liver disease.

METHODS

We examined the clinical parameters and gut microbiota profiles by 16S rRNA sequences and short-chain fatty acid levels in fecal samples from 51 non-obese patients with non-alcoholic fatty liver disease (body mass index <25 kg/m² ) and 51 obese patients with non-alcoholic fatty liver disease (body mass index ≥30 kg/m² ) who underwent pathological examination and 87 controls at five hospitals in Japan.

RESULTS

Although no significant differences between the non-obese and other groups were observed in alpha diversity, a significant difference was found in beta diversity. We observed a significant decrease in serum alanine aminotransferase levels, Eubacterium population, and butyric acid levels in non-obese patients with non-alcoholic fatty liver disease compared with those in obese patients with non-alcoholic fatty liver disease. A significant negative correlation was found between the stage of hepatic fibrosis and Eubacterium abundance in non-obese patients with non-alcoholic fatty liver disease.

CONCLUSIONS

The decrease in the abundance of Eubacterium that produces butyric acid may play an important role in the development of non-alcoholic fatty liver disease in non-obese individuals. This study was registered at the University Hospital Medical Information Network clinical trial registration system (UMIN000020917).

Microbial dynamics in biogas digesters treating lipid-rich substrates via genome-centric metagenomics

Co-digestion with lipid-rich substrates is a likely strategy in biogas plants, due to their high energy content. However, the process stability is vulnerable to inhibition due to the sudden increase of fatty-acid concentration. Therefore, techniques that promote the adaptation of the microorganisms to the presence of lipids have been proposed. In this frame, the initial hypothesis of the work was that a gradual change in feedstock composition would enable us to elucidate the microbial organisation as a result of deterministic (i.e. chemical composition of influent) and stochastic (e.g. interspecies interactions) factors. This study investigates the response of the biogas microbiome to gradual increment of the Organic Loading Rate by supplementing the influent feedstock with Na-Oleate. The results showed that as a response to the feedstock shifts three clusters describing microbes behaviours were formed. The dynamics and the functional role of the formed microbial clusters were unveiled, providing explanations for their abundance and behavior. Process monitoring indicated that the reactors responded immediately to lipid supplementation and they managed to stabilize their performance in a short period of time. The dominance of Candidatus Methanoculleus thermohydrogenotrophicum in the biogas reactors fed exclusively with cattle manure indicated that the predominant methanogenic pathway was hydrogenotrophic. Additionally, the abundance of this methanogen was further enhanced upon lipid supplementation and its growth was supported by syntrophic bacteria capable to metabolize fatty acids. However, with the shift back to the original feedstock (i.e. solely cattle manure), the microbial dynamicity significantly altered with a remarkable increment in the abundance of a propionate degrader affiliated to the order of Bacteroidales, which became the predominant microorganism of the consortium.

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

The hidden layers of microbial community structure: extracting the concealed diversity dimensions from our sequencing data

Microbial metabarcoding is the standard approach to assess communities' diversity. However reports are often limited to simple OTU abundances for each phylum, giving rather one-dimensional views of microbial assemblages, overlooking other accessible aspects. The first is masked by databases incompleteness; OTU picking involves clustering at 97% (near-species) sequence identity, but different OTUs regularly end up under a same taxon name. When expressing diversity as number of obtained taxonomical names, a large portion of the real diversity lying within the data remains underestimated. Using the 16S sequencing results of an environmental transect across a gradient of 17 coastal habitats we first extracted the number of OTUs hidden under the same name. Further, we observed which was the deepest rank yielded by annotation, revealing for which microbial groups are we missing most knowledge. Data were then used to infer an evolutionary aspect: what is, in each phylum the success of the present time individuals (abundances for each OTU) in relation to their prior evolutionary success in differentiation (number of OTUs). This information reveals whether the past speciation/diversification force is matched by the present competitiveness in reproduction/persistence. The final layer explored is functional diversity, i.e. abundances of groups involved in specific environmental processes.

Multiplexed Workplace Measurements in Biogas Plants Reveal Compositional Changes in Aerosol Properties

Anaerobic digestion is an emerging technology producing energy from renewable resources or food waste. Exposure screenings, comprising hazardous substances and biological agents, at different workplaces are necessary for a comprehensive overview of potential hazards in order to assess the risk of employees in biogas plants. In order to analyse these parameters, workplace measurements were conducted in seven full-scale anaerobic digesters. Personal and stationary sampling was performed for inhalable and respirable particles, volatile organic compounds, ammonia, hydrogen sulphide, carbon monoxide, and carbon dioxide. Furthermore, concentrations of the total cell count, endotoxins, and fungi—down to species level—were determined in comparison to windward air. Sequencing of the 16S rRNA genes was utilized for the determination of the bacterial composition inside the biogas plants. Measurements of hazardous substances show hardly values reaching the specific occupational exposure limit value, except ammonia. An approximate 5-fold increase in the median of the total cell count, 15-fold in endotoxins, and 4-fold in fungi was monitored in the biogas plants compared with windward air. Specifying the comparison to selected workplaces showed the highest concentrations of these parameters for workplaces related to delivery and cleaning. Strikingly, the fungal composition drastically changed between windward air and burdened workplaces with an increase of Aspergillus species up to 250-fold and Penicillium species up to 400-fold. Sequence analyses of 16S rRNA genes revealed that many workplaces are dominated by the order of Bacillales or Lactobacillales, but many sequences were not assignable to known bacteria. Although significant changes inside the biogas plant compared with windward air were identified, that increase does not suggest stricter occupational safety measures at least when applying German policies. However, exposure to biological agents revealed wide ranges and specific workplace measurements should be conducted for risk assessment.

Evolution of the microbial community structure in biogas reactors inoculated with seeds from different origin

The aim of this work was to provide solid proofs regarding the achievement of "steady-state conditions", which means that the performance of the anaerobic digester is representative of the applied environmental conditions. For this reason, we investigated how, starting from different inoculum sources (i.e., municipal wastewater treatment, bio-waste treatment, and agricultural waste biogas plant), the microbial community adapted to the operational parameters and led to stable biogas production in thermophilic digesters treating the same influent feedstock. The results revealed that the different system achieved similar process performance and microbial community structure after a period that was equal to four hydraulic retention times, approved by a constant pH of 7.89 ± 0.08, 7.92 ± 0.05 and 7.85 ± 0.08, respectively, and stable TAN concentration of 1500 mg/L. Moreover, it was found that the microbial composition of the inocula was a key factor for the speed of achieving stable process performance; thus, a pre-adapted to the influent feedstock inoculum can shorten the stabilization process. On the contrary, after long term reactor operation, the microbial structure was shaped according to the chemical composition of the influent feedstock. The results of the study can also be used as a guide in future researches on anaerobic degradation, particularly in determining the time interval of an experiment to reflect changes in the microbial community of anaerobic digester.

Formation, characterization and modeling of emergent synthetic microbial communities

Microbial communities colonize plant tissues and contribute to host function. How these communities form and how individual members contribute to shaping the microbial community are not well understood. Synthetic microbial communities, where defined individual isolates are combined, can serve as valuable model systems for uncovering the organizational principles of communities. Using genome-defined organisms, systematic analysis by computationally-based network reconstruction can lead to mechanistic insights and the metabolic interactions between species. In this study, 10 bacterial strains isolated from the Populus deltoides rhizosphere were combined and passaged in two different media environments to form stable microbial communities. The membership and relative abundances of the strains stabilized after around 5 growth cycles and resulted in just a few dominant strains that depended on the medium. To unravel the underlying metabolic interactions, flux balance analysis was used to model microbial growth and identify potential metabolic exchanges involved in shaping the microbial communities. These analyses were complemented by growth curves of the individual isolates, pairwise interaction screens, and metaproteomics of the community. A fast growth rate is identified as one factor that can provide an advantage for maintaining presence in the community. Final community selection can also depend on selective antagonistic relationships and metabolic exchanges. Revealing the mechanisms of interaction among plant-associated microorganisms provides insights into strategies for engineering microbial communities that can potentially increase plant growth and disease resistance. Further, deciphering the membership and metabolic potentials of a bacterial community will enable the design of synthetic communities with desired biological functions.

Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota

In this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.

Metagenomics survey unravels diversity of biogas microbiomes with potential to enhance productivity in Kenya

The obstacle to optimal utilization of biogas technology is poor understanding of biogas microbiomes diversities over a wide geographical coverage. We performed random shotgun sequencing on twelve environmental samples. Randomized complete block design was utilized to assign the twelve treatments to four blocks, within eastern and central regions of Kenya. We obtained 42 million paired-end reads that were annotated against sixteen reference databases using two ENVO ontologies, prior to β-diversity studies. We identified 37 phyla, 65 classes and 132 orders. Bacteria dominated and comprised 28 phyla, 42 classes and 92 orders, conveying substrate's versatility in the treatments. Though, Fungi and Archaea comprised 5 phyla, the Fungi were richer; suggesting the importance of hydrolysis and fermentation in biogas production. High β-diversity within the taxa was largely linked to communities' metabolic capabilities. Clostridiales and Bacteroidales, the most prevalent guilds, metabolize organic macromolecules. The identified Cytophagales, Alteromonadales, Flavobacteriales, Fusobacteriales, Deferribacterales, Elusimicrobiales, Chlamydiales, Synergistales to mention but few, also catabolize macromolecules into smaller substrates to conserve energy. Furthermore, δ-Proteobacteria, Gloeobacteria and Clostridia affiliates syntrophically regulate PH2 and reduce metal to provide reducing equivalents. Methanomicrobiales and other Methanomicrobia species were the most prevalence Archaea, converting formate, CO2(g), acetate and methylated substrates into CH4(g). Thermococci, Thermoplasmata and Thermoprotei were among the sulfur and other metal reducing Archaea that contributed to redox balancing and other metabolism within treatments. Eukaryotes, mainly fungi were the least abundant guild, comprising largely Ascomycota and Basidiomycota species. Chytridiomycetes, Blastocladiomycetes and Mortierellomycetes were among the rare species, suggesting their metabolic and substrates limitations. Generally, we observed that environmental and treatment perturbations influenced communities' abundance, β-diversity and reactor performance largely through stochastic effect. Understanding diversity of biogas microbiomes over wide environmental variables and its' productivity provided insights into better management strategies that ameliorate biochemical limitations to effective biogas production.

Enhancing Phenol Conversion Rates in Saline Anaerobic Membrane Bioreactor Using Acetate and Butyrate as Additional Carbon and Energy Sources

Phenolic industrial wastewater, such as those from coal gasification, are considered a challenge for conventional anaerobic wastewater treatment systems because of its extreme characteristics such as presence of recalcitrant compounds, high toxicity, and salinity. However, anaerobic membrane bioreactors (AnMBRs) are considered of potential interest since they retain all micro-organism that are required for conversion of the complex organics. In this study, the degradation of phenol as main carbon and energy source (CES) in AnMBRs at high salinity (8.0 g Na⁺⋅L^–1) was evaluated, as well as the effect of acetate and an acetate-butyrate mixture as additional CES on the specific phenol conversion rate and microbial community structure. Three different experiments in two lab-scale (6.5 L) AnMBRs (35°C) were conducted. The first reactor (R1) was fed with phenol as the main CES, the second reactor was fed with phenol and either acetate [2 g COD⋅L^–1], or a 2:1 acetate-butyrate [2 g COD⋅L^–1] mixture as additional CES. Results showed that phenol conversion could not be sustained when phenol was the sole CES. In contrast, when the reactor was fed with acetate or an acetate-butyrate mixture, specific phenol conversion rates of 115 and 210 mgPh⋅gVSS^–1 d^–1, were found, respectively. The syntrophic phenol degrader Syntrophorhabdus sp. and the acetoclastic methanogen Methanosaeta sp. were the dominant bacteria and archaea, respectively, with corresponding relative abundances of up to 63 and 26%. The findings showed that dosage of additional CES allowed the development of a highly active phenol-degrading biomass, potentially improving the treatment of industrial and chemical wastewaters.

Intermittent aeration of landfill simulation bioreactors: Effects on emissions and microbial community

Landfill simulation experiments were run at lab-scale to compare the effects of intermittent and continuous aeration on the evolution of leachate composition and biogas production. The experiments were carried out using six reactors; two of them under continuous aeration, two under intermitted aeration and two anaerobic as a control. Different aeration regimes produced different effects on reactors. As expected, carbon discharge via biogas was higher in reactors under continuous aeration than under intermittent aeration. The evolution of leachate quality was affected by the aeration regimes; however, at test end very similar concentration were ascertained for relevant leachate parameters in all aerated reactors. A comprehensive description of the aerobic and anaerobic landfill microbiome is provided, using a metagenomic approach focused on the microbial genome reconstruction. A time course investigation evidenced the modification of the microbiome and revealed taxa and specific microbes more strictly connected to the environmental parameters of the reactors. Methanoculleus, Syntrophomonas and Parabacteroides were identified as the genera more strictly connected to biogas production, while numerous species belonging to Thiomonas, Nitrosomonas, Xanthomonadaceae, Myxococcales and Alcaligenaceae were found to be connected with NH₄⁺ oxidation.

Superior resolution characterisation of microbial diversity in anaerobic digesters using full-length 16S rRNA gene amplicon sequencing

In the past decade, the characterisation of the microbial community in anaerobic digestion was primarily done by using high-throughput short-read amplicon sequencing. However, the short-read approach has inherent primer bias and low phylogenetic resolution. Our previous study using Illumina MiSeq suggested that the heterogeneity of AD microbiome was operation-driven. To advance our knowledge towards the complexity of the AD microbiome, we performed full-length 16S rRNA gene amplicon sequencing using PacBio Sequel for a more accurate phylogenetic identification. To this end, purified DNA samples from 19 global anaerobic digesters were sequenced. Sixteen methanogenic archaea were identified at the species level. Among them, Methanosarcina horonobensis and Methanosarcina flavescens had significant presence under specific operating conditions. Methanothrix concilii presented in all digesters sequenced. Unexpectedly, over 90% of the Smithella detected were closely related to alkane-degrading Smithella strains D17 and M82, not Smithella propionica. Using LEfSe and network analysis, the interspecies relationship between the fermentative and syntrophic bacteria was addressed. Comparison of the short- and long-read sequencing results were performed and discussed. From sample preparation to data analysis, this work characterised the digester microbiomes in a superior resolution.

Detection and isolation of the typical gut indigenous bacteria from ddY mice fed a casein-beef tallow-based or egg yolk-based diet

The effects of whole egg on the cecal microbiome of ddY mice has been reported. To investigate the existence of susceptible indigenous bacteria (SIB) to egg yolks (EY), mice were fed a diet containing either 20% (w/w) milk casein and 17% beef tallow (CT) or 12% milk casein and 27% EY for 14 days, and then, the cecal microbiome was analyzed by 16S rRNA (V4) amplicon sequencing. To isolate the typical species in each diet group, culture-dependent viable bacterial counts were determined on Blood Liver (BL) and Gifu Anaerobic Medium (GAM) agar plates. The amplicon sequencing analysis revealed typical CT-SIB, such as Lachnospiraceae-like bacteria, and EY-SIB, such as Allobaculum-, Lactobacillus murinus-, and Bacteroides vulgatus-like bacteria. Two of the detected SIB species, L. murinus- and B. vulgatus-like bacteria, were successfully isolated from the BL and GAM agar plates and defined using a 16S rDNA BLAST search. PRACTICAL APPLICATIONS: The SIB defined in the CT and EY groups might have some effects on the nutritional and functional chemical compounds in the milk casein, beef tallow, and/or EY. Analysis of its functional properties of the isolates might develop the new and unique probiotic strains.

Milk microbial composition of Brazilian dairy cows entering the dry period and genomic comparison between Staphylococcus aureus strains susceptible to the bacteriophage vB_SauM-UFV_DC4

Brazil has the second-largest dairy cattle herd in the world, and bovine mastitis still can cause significant losses for dairy farmers. Despite this fact, little information is available about milk microbial composition of Brazilian dairy cows, as well as the potential use of bacteriophages in the control of S. aureus. Here, we investigated milk bacterial composition of 28 Holstein Fresian cows (109 teats), selected in the dry-off period, using 16S rRNA analysis. Furthermore, a representative S. aureus strain (UFV2030RH1) was obtained at drying-off for isolation of a bacteriophage (vB_SauM-UFV_DC4, UFV_DC4) and bacterial genomic comparison purposes. Our outcomes revealed that Staphylococcus was the third most prevalent genus and positively correlated with subclinical mastitis events. As a major finding, genomic analyses showed the presence of adhesive matrix molecules that recognize microbial surface components (MSCRAMM) in UFV2030RH1 and might indicate great biofilm formation capability. A minimum inhibitory concentration (MIC) assay showed that resistance to ampicillin was the highest among the antibiotic tested in S. aureus 3059 and UFV2030RH1, displaying values four and sixteen times greater than MIC resistance breakpoint, respectively. Together, our results suggest that Staphylococcus is highly prevalent in dairy cows at drying-off and the use of the phage UFV_DC4 as a biocontrol agent must be investigated in future studies.

Valorization of sewage sludge for volatile fatty acids production and role of microbiome on acidogenic fermentation

This work explored the production of volatile fatty acids (VFA) through the anaerobic digestion of sewage sludge (SS). The first experiment took place at batch scale to evaluate the combined effect of using a thermal pre-treatment (120 °C, 15 min) and different Substrate/Inoculum ratios (S/I) (1, 2, 4 and 6 g VS substrate/g VS inoculum) on the acidogenic potential of the SS. The results showed that the thermal pre-treatment influenced positively the degree of acidification of the SS at low S/I ratios, reaching maximum of 45%. Afterwards, a continuous lab-scale experiment, was set-up to study two ranges of organic loading rates (OLR): 1300-1600 mg COD L^-1 d^-1 and 2400-3500 mg COD L^-1 d^-1. The highest degree of acidification (22%) was achieved at the lowest OLR. Analysis of the microbial community in the reactor revealed that OTUs most abundant present genes related with amino acids and carbohydrates fermentation being crucial for VFA production.

A snapshot of microbial community structures in 20 different field-scale anaerobic bioreactors treating food waste

This study aimed to identify significant factors shaping the microbial populations in biogas plants treating food waste (FW). Twenty full-scale anaerobic acidogenic/methanogenic bioreactors, located at 11 FW treatment facilities, were compared to find patterns in their microbial community structures and potential interactions with the process parameters. Temperature, hydraulic retention time, and organic loading rate were design parameters that systematically influenced the microbial communities. The latter two clearly separated the acidogenic and methanogenic bioreactors. Lactobacillus was the dominant (69.7 ± 19.8%) bacteria in the acidogenic reactors, while hydrogen-utilizing methanogens, such as Methanoculleus (65.1 ± 33.5%), were the dominant archaea in most methanogenic digesters. Defluiviitoga was the dominant (82.7 ± 1.4%) bacteria in the thermophilic digesters, but was also the most abundant (33.1-33.6%) bacteria in dry mesophilic digesters. The two bioreactor categories had lower bacterial diversities, and also higher propionate concentrations (>5 g/L in 4 out of 5 cases), which may impose potential risks for the management of such digesters. The current 'snapshot' of the microbial communities suggests several bacterial and archaeal taxa as potential indicators of bioreactor categories and/or process variables.

Effects of various feedstocks on isotope fractionation of biogas and microbial community structure during anaerobic digestion

Feedstock type influences bacterial and methanogenic communities in anaerobic digestion. These two communities work tightly to maintain the stability of anaerobic digestion. How to quick report the changes of microbial community structure especially methanogenesis is the key issue for optimizing anaerobic digestion process. In this study, ¹³C isotope fractionations of CH₄ and CO₂ in biogas and microbial community composition were analyzed in 5 different feedstocks. Our results showed that grass silage, maize silage and swine manure fed reactors had similar δ ¹³C values and methanogenic community composition, dominated by Methanosarcinaceae. The lowest δ ¹³CH₄ values were detected in straw and chicken manure fed reactors, reflecting reduced microbial degradation of material or the presence of toxic components in these feedstocks. The straw fed bioreactor lead to low δ ¹³CH₄ values, probably reflecting relatively high levels of the syntrophic acetate oxidizing bacteria, Synergistaceae and Syntrophaceae, which might work collectively with hydrogenotrophic methanogens, resulting in the low δ ¹³CH₄ values in this bioreactor. Significantly, all core microbes in the 5 different feedstock fed bioreactors were either Clostridia species or related to the Synergistaceae (syntrophic acetate oxidizing bacteria).

Metagenomic binning reveals the functional roles of core abundant microorganisms in twelve full-scale biogas plants

The aim of this work was to elucidate the microbial ecology in twelve mesophilic and thermophilic full-scale biogas plants using a genome-centric metagenomic approach. In this study both biogas plants treating manure and those treating sludge from waste water treatment plants were considered. The identification of 132 Metagenome-Assembled Genomes (MAGs) and analysis of their abundance profile in different samples allowed the identification of the most abundant core members of the anaerobic digestion microbiome. Canonical correspondence analysis was used to determine the influence of biotic and environmental factors on MAGs abundance and to investigate the methanogenic performance of the biogas plants. Prediction of the functional properties of MAGs was obtained analyzing their KEGG pathways and their carbohydrate active domains. Network analysis allowed investigation of species-species associations and shed light on syntrophic interactions between members belonging to the anaerobic digestion dark matter (phylum Fermentibacteria). By stratifying and comparing different levels of information, it was predicted that some MAGs have a crucial role in the manure-supplemented thermophilic biogas plants and it was highlighted the importance of the glycine cleavage system in complementing the "truncated" Wood-Ljungdahl pathway.

Two-year microbial adaptation during hydrogen-mediated biogas upgrading process in a serial reactor configuration

Microbial dynamics in an upgrading biogas reactor system undergoing a more than two years-period at stable operating conditions were explored. The carbon dioxide generated during biomass degradation in the first reactor of the system was converted to methane into the secondary reactor by addition of external hydrogen. Considering the overall efficiency, the long-term operation period resulted in an improved biogas upgrading performance (99% methane content). However, a remarkable accumulation of acetate was revealed, indicating the enhancement of homoacetogenic activity. For this reason, a shift in the anaerobic digestion microbiome was expected and evaluated by 16S rRNA amplicon analysis. Results demonstrated that the most abundant archaeal species identified in the first time point, Candidatus Methanoculleus thermohydrogenotrophicum, was replaced by Methanothermobacter thermautotrophicus, becoming dominant after the community adaptation. The most interesting taxonomic units were clustered by relative abundance and six main long-term adaptation trends were found, characterizing functionally related microbes (e.g. homoacetogens).