MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Soil and stone terraces offset the negative impacts of sloping cultivation on soil microbial diversity and functioning by protecting soil carbon

Cultivation of sloping land is a main cause for soil erosion. Conservation practices, such as soil and stone terraces, may reduce the impacts of erosion but their impacts on soil microbial diversity and functioning related to carbon (C) and nutrient metabolisms remain unclear. This study was conducted to evaluate the effects of slope gradients (5°, 8°, 15°, 25°) and conservation practices (cultivated, uncultivated, soil terrace, and stone terrace) on bacterial and fungal diversities, metagenomic and metabolomic functioning associated with basic soil properties. Our results showed that steep slopes at 25° significantly decreased soil pH, silt percentage, and bacterial and fungal abundances, but that soil and stone terraces increased soil organic C (SOC), silt and clay contents, and fungal abundance compared to sloping cultivated lands. In addition, soil and stone terraces increased both bacterial and fungal alpha diversities, and relative abundances of Crenarchaeota, Nitrospirota, and Latescibacterota, but reduced the proportions of Actinobacteriota and Patescibacteria, thus shifting microbial beta diversities, which were significantly associated with increased SOC and silt content. For metagenomics, soil and stone terraces greatly increased the relative abundance of functional genes related to Respiration, Virulence, disease and defense, Stress response, and nitrogen and potassium metabolisms, such as Denitrification and Potassium homeostasis. For soil metabolomics, a total of 22 soil metabolites was enriched by soil and stone terraces, such as Lipids and lipid-like molecules (Arachidonic acid, Gamma-Linolenic acid, and Pentadecanoic acid), and Organoheterocyclic compounds (Adenine, Laudanosine, Methylpyrazine, and Nicotinic acid). To sum up, soil and stone terraces could reduce some of the negative impacts of steep slope cultivation on soil microbial diversity as well as their metagenomic and metabolomic functioning related to C and nutrient metabolism useful for soil health improvement, potentially bolstering the impact of sustainable practices in erosion hotspots around the world.

Unveiling potential PET degrading eukaryotes through in silico bioprospecting of PETases

This study addresses the environmental problem of PET plastic through in silico bioprospecting for the identification and experimental validation of novel PET degrading eukaryotes through the in silico bioprospectingI of PETases, employing a methodology that combines Hidden Markov Models (HMMs), clustering techniques, molecular docking, and dynamic simulations. A total of 424 putative PETase sequences were identified from 219 eukaryotic organisms, highlighting six sequences with low affinity energies. The Aspergillus luchuensis sequence showed the lowest Gibbs free energy and exhibited stability at different temperatures in molecular dynamics assays. Experimental validation, through a plate clearance assay and HPLC, confirmed PETase activity in three wild-type fungal strains, with A. luchuensis showing the highest efficiency. The results obtained demonstrate the effectiveness of combining computational and experimental approaches as proof of concept to discover and validate eukaryotes with PET-degrading capabilities opening new perspectives for the sustainable management of this type of waste and contributing to its environmental mitigation.

Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Global soil metagenomics reveals distribution and predominance of Deltaproteobacteria in nitrogen-fixing microbiome

BACKGROUND

Biological nitrogen fixation is a fundamental process sustaining all life on earth. While distribution and diversity of N2-fixing soil microbes have been investigated by numerous PCR amplicon sequencing of nitrogenase genes, their comprehensive understanding has been hindered by lack of de facto standard protocols for amplicon surveys and possible PCR biases. Here, by fully leveraging the planetary collections of soil shotgun metagenomes along with recently expanded culture collections, we evaluated the global distribution and diversity of terrestrial diazotrophic microbiome.

RESULTS

After the extensive analysis of 1,451 soil metagenomic samples, we revealed that the Anaeromyxobacteraceae and Geobacteraceae within Deltaproteobacteria are ubiquitous groups of diazotrophic microbiome in the soils with different geographic origins and land usage types, with particular predominance in anaerobic soils (paddy soils and sediments).

CONCLUSION

Our results indicate that Deltaproteobacteria is a core bacterial taxon in the potential soil nitrogen fixation population, especially in anaerobic environments, which encourages a careful consideration on deltaproteobacterial diazotrophs in understanding terrestrial nitrogen cycling. Video Abstract.

Pre-Bleaching Coral Microbiome Is Enriched in Beneficial Taxa and Functions

Coral reef health is tightly connected to the coral holobiont, which is the association between the coral animal and a diverse microbiome functioning as a unit. The coral holobiont depends on key services such as nitrogen and sulfur cycling mediated by the associated bacteria. However, these microbial services may be impaired in response to environmental changes, such as thermal stress. A perturbed microbiome may lead to coral bleaching and disease outbreaks, which have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. The response mechanisms of the coral holobiont under high temperatures are not completely understood, but the associated microbial community is a potential source of acquired heat-tolerance. Here we investigate the effects of increased temperature on the taxonomic and functional profiles of coral surface mucous layer (SML) microbiomes in relationship to coral-algal physiology. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the SML microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat showed high similarity at the level of bacterial genera and functional genes related to nitrogen and sulfur metabolism and stress response. The coral SML microbiome responded to heat with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. Coral-algal physiology significantly explained the variation in the microbiome at taxonomic and functional levels. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance.

Biotic and abiotic factors acting on community assembly in parallel anaerobic digestion systems from a brewery wastewater treatment plant

Anaerobic digestion is a complex microbial process that mediates the transformation of organic waste into biogas. The performance and stability of anaerobic digesters relies on the structure and function of the microbial community. In this study, we asked whether the deterministic effect of wastewater composition outweighs the effect of reactor configuration on the structure and dynamics of anaerobic digester archaeal and bacterial communities. Biotic and abiotic factors acting on microbial community assembly in two parallel anaerobic digestion systems, an upflow anaerobic sludge blanket digestor (UASB) and a closed digester tank with a solid recycling system (CDSR), from a brewery WWTP were analysed utilizing 16S rDNA and mcrA amplicon sequencing and genome-centric metagenomics. This study confirmed the deterministic effect of the wastewater composition on bacterial community structure, while the archaeal community composition resulted better explained by organic loading rate (ORL) and volatile free acids (VFA). According to the functions assigned to the differentially abundant metagenome-assembled genomes (MAGs) between reactors, CDSR was enriched in genes related to methanol and methylamines methanogenesis, protein degradation, and sulphate and alcohol utilization. Conversely, the UASB reactor was enriched in genes associated with carbohydrate and lipid degradation, as well as amino acid, fatty acid, and propionate fermentation. By comparing interactions derived from the co-occurrence network with predicted metabolic interactions of the prokaryotic communities in both anaerobic digesters, we conclude that the overall community structure is mainly determined by habitat filtering.

Study of the Microbiome of the Cretan Sour Cream Staka Using Amplicon Sequencing and Shotgun Metagenomics and Isolation of Novel Strains with an Important Antimicrobial Potential

Staka is a traditional Greek sour cream made mostly from spontaneously fermented sheep milk or a mixture of sheep and goat milk. At the industrial scale, cream separators and starter cultures may also be used. Staka is sometimes cooked with flour to absorb most of the fat. In this study, we employed culture-based techniques, amplicon sequencing, and shotgun metagenomics to analyze the Staka microbiome for the first time. The samples were dominated by Lactococcus or Leuconostoc spp. Most other bacteria were lactic acid bacteria (LAB) from the Streptococcus and Enterococcus genera or Gram-negative bacteria from the Buttiauxella, Pseudomonas, Enterobacter, Escherichia-Shigella, and Hafnia genera. Debaryomyces, Kluyveromyces, or Alternaria were the most prevalent genera in the samples, followed by other yeasts and molds like Saccharomyces, Penicillium, Aspergillus, Stemphylium, Coniospotium, or Cladosporium spp. Shotgun metagenomics allowed the species-level identification of Lactococcus lactis, Lactococcus raffinolactis, Streptococcus thermophilus, Streptococcus gallolyticus, Escherichia coli, Hafnia alvei, Streptococcus parauberis, and Enterococcus durans. Binning of assembled shotgun reads followed by recruitment plot analysis of single reads could determine near-complete metagenome assembled genomes (MAGs). Culture-dependent and culture-independent analyses were in overall agreement with some distinct differences. For example, lactococci could not be isolated, presumably because they had entered a viable but not culturable (VBNC) state or because they were dead. Finally, several LAB, Hafnia paralvei, and Pseudomonas spp. isolates exhibited antimicrobial activities against oral or other pathogenic streptococci, and certain spoilage and pathogenic bacteria establishing their potential role in food bio-protection or new biomedical applications. Our study may pave the way for additional studies concerning artisanal sour creams to better understand the factors affecting their production and the quality.

A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.

The Human Gut and Dietary Salt: The Bacteroides/Prevotella Ratio as a Potential Marker of Sodium Intake and Beyond

The gut microbiota is a dynamic ecosystem that plays a pivotal role in maintaining host health. The perturbation of these microbes has been linked to several health conditions. Hence, they have emerged as promising targets for understanding and promoting good health. Despite the growing body of research on the role of sodium in health, its effects on the human gut microbiome remain under-explored. Here, using nutrition and metagenomics methods, we investigate the influence of dietary sodium intake and alterations of the human gut microbiota. We found that a high-sodium diet (HSD) altered the gut microbiota composition with a significant reduction in Bacteroides and inverse increase in Prevotella compared to a low-sodium diet (LSD). However, there is no clear distinction in the Firmicutes/Bacteroidetes (F/B) ratio between the two diet types. Metabolic pathway reconstruction revealed the presence of sodium reabsorption genes in the HSD, but not LSD. Since it is currently difficult in microbiome studies to confidently associate the F/B ratio with what is considered healthy (e.g., low sodium) or unhealthy (e.g., high sodium), we suggest that the use of a genus-based ratio such as the Bacteroides/Prevotella (B/P) ratio may be more beneficial for the application of microbiome studies in health.

Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities

Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.

Monotonic trends of soil microbiomes, metagenomic and metabolomic functioning across ecosystems along water gradients in the Altai region, northwestern China

To investigate microbial communities and their contributions to carbon and nutrient cycling along water gradients can enhance our comprehension of climate change impacts on ecosystem services. Thus, we conducted an assessment of microbial communities, metagenomic functions, and metabolomic profiles within four ecosystems, i.e., desert grassland (DG), shrub-steppe (SS), forest (FO), and marsh (MA) in the Altai region of Xinjiang, China. Our results showed that soil total carbon (TC), total nitrogen, NH4+, and NO3- increased, but pH decreased with soil water gradients. Microbial abundances and richness also increased with soil moisture except the abundances of fungi and protists being lowest in MA. A shift in microbial community composition is evident along the soil moisture gradient, with Proteobacteria, Basidiomycota, and Evosea proliferating but a decline in Actinobacteria and Cercozoa. The β-diversity of microbiomes, metagenomic, and metabolomic functioning were correlated with soil moisture gradients and have significant associations with specific soil factors of TC, NH4+, and pH. Metagenomic functions associated with carbohydrate and DNA metabolisms, as well as phages, prophages, TE, plasmids functions diminished with moisture, whereas the genes involved in nitrogen and potassium metabolism, along with certain biological interactions and environmental information processing functions, demonstrated an augmentation. Additionally, MA harbored the most abundant metabolomics dominated by lipids and lipid-like molecules and organic oxygen compounds, except certain metabolites showing decline trends along water gradients, such as N'-Hydroxymethylnorcotinine and 5-Hydroxyenterolactone. Thus, our study suggests that future ecosystem succession facilitated by changes in rainfall patterns will significantly alter soil microbial taxa, functional potential, and metabolite fractions.

ProkBERT family: genomic language models for microbiome applications

Background

In the evolving landscape of microbiology and microbiome analysis, the integration of machine learning is crucial for understanding complex microbial interactions, and predicting and recognizing novel functionalities within extensive datasets. However, the effectiveness of these methods in microbiology faces challenges due to the complex and heterogeneous nature of microbial data, further complicated by low signal-to-noise ratios, context-dependency, and a significant shortage of appropriately labeled datasets. This study introduces the ProkBERT model family, a collection of large language models, designed for genomic tasks. It provides a generalizable sequence representation for nucleotide sequences, learned from unlabeled genome data. This approach helps overcome the above-mentioned limitations in the field, thereby improving our understanding of microbial ecosystems and their impact on health and disease.

Methods

ProkBERT models are based on transfer learning and self-supervised methodologies, enabling them to use the abundant yet complex microbial data effectively. The introduction of the novel Local Context-Aware (LCA) tokenization technique marks a significant advancement, allowing ProkBERT to overcome the contextual limitations of traditional transformer models. This methodology not only retains rich local context but also demonstrates remarkable adaptability across various bioinformatics tasks.

Results

In practical applications such as promoter prediction and phage identification, the ProkBERT models show superior performance. For promoter prediction tasks, the top-performing model achieved a Matthews Correlation Coefficient (MCC) of 0.74 for E. coli and 0.62 in mixed-species contexts. In phage identification, ProkBERT models consistently outperformed established tools like VirSorter2 and DeepVirFinder, achieving an MCC of 0.85. These results underscore the models' exceptional accuracy and generalizability in both supervised and unsupervised tasks.

Conclusions

The ProkBERT model family is a compact yet powerful tool in the field of microbiology and bioinformatics. Its capacity for rapid, accurate analyses and its adaptability across a spectrum of tasks marks a significant advancement in machine learning applications in microbiology. The models are available on GitHub (https://github.com/nbrg-ppcu/prokbert) and HuggingFace (https://huggingface.co/nerualbioinfo) providing an accessible tool for the community.

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

Unraveling the functional dark matter through global metagenomics

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

Metagenomic and culture-dependent approaches unveil active microbial community and novel functional genes involved in arsenic mobilization and detoxification in groundwater

BACKGROUND

Arsenic (As) and its species are major pollutants in ecological bodied including groundwater in Bangladesh rendering serious public health concern. Bacteria with arsenotrophic genes have been found in the aquifer, converting toxic arsenite [As (III)] to less toxic arsenate [As (V)] that is easily removed using chemical and biological trappers. In this study, genomic and metagenomic approaches parallel to culture-based assay (Graphical abstract) have made it possible to decipher phylogenetic diversity of groundwater arsenotrophic microbiomes along with elucidation of their genetic determinants.

RESULTS

Seventy-two isolates were retrieved from six As-contaminated (average As concentration of 0.23 mg/L) groundwater samples from Munshiganj and Chandpur districts of Bangladesh. Twenty-three isolates harbored arsenite efflux pump (arsB) gene with high abundance, and ten isolates possessing arsenite oxidase (aioA) gene, with a wide range of minimum inhibitory concentration, MICAs (2 to 32 mM), confirming their role in arsenite metabolism. There was considerable heterogeneity in species richness and microbial community structure. Microbial taxa from Proteobacteria, Firmicutes and Acidobacteria dominated these diversities. Through these combinatorial approaches, we have identified potential candidates such as, Pseudomonas, Acinetobacter, Stenotrophomonas, Achromobacter, Paraburkholderia, Comamonas and Klebsiella and associated functional genes (arsB, acr3, arsD, arsH, arsR) that could significantly contribute to arsenite detoxification, accumulation, and immobilization.

CONCLUSIONS

Culture-dependent and -independent shotgun metagenomic investigation elucidated arsenotrophic microbiomes and their functions in As biogeochemical transformation. These findings laid a foundation for further large-scale researches on the arsenotrophic microbiomes and their concurrent functions in As biogeochemical transformation in As-contaminated areas of Bangladesh and beyond.

Microplastics drive microbial assembly, their interactions, and metagenomic functions in two soils with distinct pH and heavy metal availability

Microplastics (MPs) have emerged as widely existing global environmental concerns in terrestrial ecosystems. However, the mechanisms that how MPs are affecting soil microbes and their metagenomic functioning is currently uncertain. Herein, we investigated the response mechanisms of bacterial and fungal communities as well as the metagenomic functions to the addition of MPs in two soils with distinct pH and heavy metals. In this study, the acidic soil (Xintong) and the neutral soil (Huanshan) contaminated by heavy metals were incubated with Polyvinyl Chloride (PVC) MPs at ratios of 2.5% and 5% on 60 and 120 days. We aimed to evaluate the responding, assembly, and interactions of the metagenomic taxonomy and function. Results showed that only in the acidic soil, PVC MPs significantly increased soil pH and decreased CaCl₂-extractable heavy metals, and also reduced bacterial alpha diversity and interaction networks. The relative proportions of Proteobacteria and Bacteroidota in bacteria, and Mortierellomycota in fungi, were increased, but Chloroflexi and Acidobacteriota in bacteria, Ascomycota and Basidiomycota in fungi, were significantly decreased by PVC MPs. Metagenomic functions related to C cycling were repressed but the nutrient cycles were enriched with PVC MPs. In conclusion, our study suggests that the addition of PVC MPs could shift soil microbial community and metagenomic functioning, as well as increasing soil pH and reduced heavy metal availability.

Shotgun sequencing of sonication fluid for the diagnosis of orthopaedic implant-associated infections with Cutibacterium acnes as suspected causative agent

Orthopaedic implant-associated infections (OIAIs) due to Cutibacterium acnes can be difficult to diagnose. The aim of this pilot study was to determine if metagenomic next-generation sequencing (mNGS) can provide additional information to improve the diagnosis of C. acnes OIAIs. mNGS was performed on sonication fluid (SF) specimens derived from 24 implants. These were divided into three groups, based on culture results: group I, culture-negative (n = 4); group II, culture-positive for C. acnes (n = 10); and group III, culture-positive for other bacteria (n = 10). In group I, sequence reads from C. acnes were detected in only one SF sample, originating from a suspected case of OIAIs, which was SF and tissue culture-negative. In group II, C. acnes sequences were detected in 7/10 samples. In group III, C. acnes sequence reads were found in 5/10 samples, in addition to sequence reads that matched the bacterial species identified by culture. These samples could represent polymicrobial infections that were missed by culture. Taken together, mNGS was able to detect C. acnes DNA in more samples compared to culture and could be used to identify cases of suspected C. acnes OIAIs, in particular regarding possible polymicrobial infections, where the growth of C. acnes might be compromised due to a fast-growing bacterial species. However, since SF specimens are usually low-biomass samples, mNGS is prone to DNA contamination, possibly introduced during DNA extraction or sequencing procedures. Thus, it is advisable to set a sequence read count threshold, taking into account project- and NGS-specific criteria.

Molecular characterization of microorganisms with industrial potential for methane production in sludge from Kangemi sewage treatment plant, Nyeri county-Kenya

Microbial consortia under anaerobic conditions are involved in oxidizing organic matter in the sludge to produce methane gas. However, in developing countries like Kenya, these microbes have not been fully identified to target them for the efficient harnessing of biofuel. This study collected wet sludge from two anaerobic digestion lagoons 1 and 2 that were operational during sampling at Kangemi Sewage Treatment Plant, in Nyeri County, Kenya. DNA was extracted from samples using commercially available ZymoBIOMICS™ DNA Miniprep Kit and sequenced using Shotgun metagenomics. Samples were analyzed using MG-RAST software (Project ID: mgp100988), which allowed for identifying microorganisms directly involved in various stages of methanogenesis pathways. The study found hydrogenotrophic methanogens, such as Methanospirillum (32%), Methanobacterium (27%), Methanobrevibacter (27%), and Methanosarcina (32%), being predominant in the lagoon communities, whereas acetoclastic microorganisms such as the Methanoregula (22%) and the acetate oxidazing bacteria such as Clostridia (68%) were the key microbes for that pathway in the sewage digester sludge. Furthermore, Methanothermobacter (18%), Methanosarcina (21%), Methanosaeta (15%), and Methanospirillum (13%) carried out the methylotrophic pathway. In contrast, Methanosarcina (23%),Methanoregula (14%), methanosaeta (13%), and methnanoprevibacter (13%) seemed to play an important role in the final step of methane release. This study concluded that the sludge produced from the Nyeri-Kangemi WWTP harbors microbes with significant potential for biogas production. The study recommends a pilot study to investigate the efficiency of the identified microbes for biogas production.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Designing Knowledge-Based Bioremediation Strategies Using Metagenomics

Functional capacities for bioremediation are governed by metabolic mechanisms of inhabiting microbial communities at polluted niches. Process fluctuations lead to stress scenarios where microbes evolve continuously to adapt to sustain the harsh conditions. The biological wastewater treatment (WWT) process harbors the potential of these catabolic microbes for the degradation of organic molecules. In a typical biological WWT or soil bioremediation process, several microbial species coexist which code for enzymes that degrade complex compounds.High throughput DNA sequencing techniques for microbiome analysis in bioremediation processes have led to a powerful paradigm revealing the significance of metabolic functions and microbial diversity. The present chapter describes techniques in taxonomy and functional gene analysis for understanding bioremediation potential and novel strategies built on in silico analysis for the improvisation of existing aerobic wastewater treatment methods. Methods explaining comparative metagenomics by Metagenome Analysis server (MG-RAST) are described with successful case studies by focusing on industrial wastewaters and soil bioremediation studies.

Contamination detection and microbiome exploration with GRIMER

BACKGROUND

Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination.

RESULTS

We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles.

CONCLUSION

GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.

Recent advances in metagenomic analysis of different ecological niches for enhanced biodegradation of recalcitrant lignocellulosic biomass

Lignocellulose wastes stemming from agricultural residues can offer an excellent opportunity as alternative energy solutions in addition to fossil fuels. Besides, the unrestrained burning of agricultural residues can lead to the destruction of the soil microflora and associated soil sterilization. However, the difficulties associated with the biodegradation of lignocellulose biomasses remain as a formidable challenge for their sustainable management. In this respect, metagenomics can be used as an effective option to resolve such dilemma because of its potential as the next generation sequencing technology and bioinformatics tools to harness novel microbial consortia from diverse environments (e.g., soil, alpine forests, and hypersaline/acidic/hot sulfur springs). In light of the challenges associated with the bulk-scale biodegradation of lignocellulose-rich agricultural residues, this review is organized to help delineate the fundamental aspects of metagenomics towards the assessment of the microbial consortia and novel molecules (such as biocatalysts) which are otherwise unidentifiable by conventional laboratory culturing techniques. The discussion is extended further to highlight the recent advancements (e.g., from 2011 to 2022) in metagenomic approaches for the isolation and purification of lignocellulolytic microbes from different ecosystems along with the technical challenges and prospects associated with their wide implementation and scale-up. This review should thus be one of the first comprehensive reports on the metagenomics-based analysis of different environmental samples for the isolation and purification of lignocellulose degrading enzymes.

Mining the landfill soil metagenome for denitrifying methanotrophic taxa and validation of methane oxidation in microcosm

In the present study, the microbial community residing at different depths of the landfill was characterized to assess their roles in serving as a methane sink. Physico-chemical characterization revealed the characteristic signatures of anaerobic degradation of organic matter in the bottom soil (50-60 cm) and, active process of aerobic denitrification in the top soil (0-10 cm). This was also reflected from the higher abundance of bacterial domain in the top soil metagenome represented by dominant phyla Proteobacteria and Actinobacteria which are prime decomposers of organic matter in landfill soils. The multiple fold higher relative abundances of the two most abundant genera; Streptomyces and Intrasporangium in the top soil depicted greater denitrifying taxa in top soil than the bottom soil. Amongst the aerobic methanotrophs, the genera Methylomonas, Methylococcus, Methylocella, and Methylacidiphilum were abundantly found in the top soil metagenome that were essential for oxidizing methane generated in the landfill. On the other hand, the dominance of archaeal domain represented by Methanosarcina and Methanoculleus in the bottom soil highlighted the complete anaerobic digestion of organic components via acetoclasty, carboxydotrophy, hydrogenotrophy, methylotrophy. Functional characterization revealed a higher abundance of methane monooxygenase gene in the top soil and methyl coenzyme M reductase gene in the bottom soil that correlated with the higher relative abundance of aerobic methanotrophs in the top soil while methane generation being the active process in the highly anaerobic bottom soil in the landfill. The activity dependent abundance of endogenous microbial communities in the different zones of the landfill was further validated by microcosm studies in serum bottles which established the ability of the methanotrophic community for methane metabolism in the top soil and their potential to serve as sink for methane. The study provides a better understanding about the methanotrophs in correlation with their endogenous environment, so that these bacteria can be used in resolving the environmental issues related to methane and nitrogen management at landfill site.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Host diet shapes functionally differentiated gut microbiomes in sympatric speciation of blind mole rats in Upper Galilee, Israel

The gut microbiome is important for host nutrient metabolism and ecological adaptation. However, how the gut microbiome is affected by host phylogeny, ecology and diet during sympatric speciation remain unclear. Here, we compare and contrast the gut microbiome of two sympatric blind mole rat species and correlate them with their corresponding host phylogeny, ecology soil metagenomes, and diet to determine how these factors may influence their gut microbiome. Our results indicate that within the host microbiome there is no significant difference in community composition, but the functions between the two sympatric species populations vary significantly. No significant correlations were found between the gut microbiome differentiation and their corresponding ecological soil metagenomes and host phylogeny. Functional enrichment analysis suggests that the host diets may account for the functional divergence of the gut microbiome. Our results will help us understand how the gut microbiome changes with corresponding ecological dietary factors in sympatric speciation of blind subterranean mole rats.

Differential colonization and functioning of microbial community in response to phosphate levels

Microbes play a major role in phosphate cycling and regulate its availability in various environments. The metagenomic study highlights the microbial community divergence and interplay of phosphate metabolism functional genes in response to phosphate rich (100 mgL^-1), limiting (25 mgL^-1), and stressed (5 mgL^-1) conditions at lab-scale bioreactor. Total five core phyla were found responsive toward different phosphate (Pi) levels. However, major variations were observed in Proteobacteria and Actinobacteria with 33-81% and 5-56% relative abundance, respectively. Canonical correspondence analysis reflects the colonization of Sinorhizobium (0.8-4%), Mesorhizobium (1-4%), Rhizobium (0.5-3%) in rich condition whereas, Pseudomonas (1-2%), Rhodococcus (0.2-2%), Flavobacterium (0.2-1%) and Streptomyces (0.3-4%) colonized in limiting and stress condition. The functional profiling demonstrates that Pi limiting and stress condition subjected biomass were characterized by abundant PQQ-Glucose dehydrogenase, alkaline phosphatase, 5'-nucleotidase, and phospholipases C genes. The finding implies that the major abundant genera belonging to phosphate solubilization enriched in limiting/stressed conditions decide the functional turnover by modulating the metabolic flexibility for Pi cycling. The study gives a better insight into intrinsic ecological responsiveness mediated by microbial communities in different Pi conditions that would help to design the microbiome according to the soil phosphate condition. Furthermore, this information assists in sustainably maintaining the ecological balance by omitting excessive chemical fertilizers and eutrophication.

Microbial Dark Matter: from Discovery to Applications

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Negative impacts of sea-level rise on soil microbial involvement in carbon metabolism

Sea-level rise has been threatening the terrestrial ecosystem functioning of coastal islands, of which the most important component is carbon (C) cycling. However, metagenomic and metabolomic evidence documenting salt intrusion effects on molecular biological processes of C cycling are still lacking. Here, we investigated microbial communities, metagenomic taxonomy and function, and metabolomic profiles in the marine-terrestrial transition zone of low- and high-tide, and low- and high-land areas based on distances of 0 m, 50 m, 100 m, and 200 m, respectively, to the water-land junction of Neilingding Island. Our results showed that soil salinity (EC) was the dominant driver controlling bacterial abundance and community composition and metagenomic taxonomy and function. The metabolomic profiling at the low-tide site was significantly different from that of other sites. The low-tide site had greater abundance of Proteobacteria and Bacteroidetes (1.6-3.7 fold), especially Gammaproteobacteria, but lower abundance (62-83%) of Acidobacteria and Chloroflexi, compared with other three sites. The metagenomic functional genes related to carbohydrate metabolism decreased at the low-tide site by 15.2%, including the metabolism of aminosugars, di- and oligo-saccharides, glycoside hydrolases, and monosaccharides, leading to significant decreases in 21 soil metabolites, such as monosaccharide (l-gulose), disaccharide (sucrose and turanose), and oligosaccharides (stachyose and maltotetraose). Our study demonstrates that elevated salinity due to sea-level rise may suppress C-cycling genes and their metabolites, therefore having negative impacts on microbial metabolism of organic matter.

Coral and Seawater Metagenomes Reveal Key Microbial Functions to Coral Health and Ecosystem Functioning Shaped at Reef Scale

The coral holobiont is comprised of a highly diverse microbial community that provides key services to corals such as protection against pathogens and nutrient cycling. The coral surface mucus layer (SML) microbiome is very sensitive to external changes, as it constitutes the direct interface between the coral host and the environment. Here, we investigate whether the bacterial taxonomic and functional profiles in the coral SML are shaped by the local reef zone and explore their role in coral health and ecosystem functioning. The analysis was conducted using metagenomes and metagenome-assembled genomes (MAGs) associated with the coral Pseudodiploria strigosa and the water column from two naturally distinct reef environments in Bermuda: inner patch reefs exposed to a fluctuating thermal regime and the more stable outer reefs. The microbial community structure in the coral SML varied according to the local environment, both at taxonomic and functional levels. The coral SML microbiome from inner reefs provides more gene functions that are involved in nutrient cycling (e.g., photosynthesis, phosphorus metabolism, sulfur assimilation) and those that are related to higher levels of microbial activity, competition, and stress response. In contrast, the coral SML microbiome from outer reefs contained genes indicative of a carbohydrate-rich mucus composition found in corals exposed to less stressful temperatures and showed high proportions of microbial gene functions that play a potential role in coral disease, such as degradation of lignin-derived compounds and sulfur oxidation. The fluctuating environment in the inner patch reefs of Bermuda could be driving a more beneficial coral SML microbiome, potentially increasing holobiont resilience to environmental changes and disease.

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.

Strategies for the Identification and Assessment of Bacterial Strains with Specific Probiotic Traits

Early in the 1900s, it was proposed that health could be improved and senility delayed by manipulating gut microbiota with the host-friendly bacteria found in yogurt. Later, in 1990, the medical community reconsidered this idea and today probiotics represent a developed area of research with a billion-dollar global industry. As a result, in recent decades, increased attention has been paid to the isolation and characterization of novel probiotic bacteria from fermented foods and dairy products. Most of the identified probiotic strains belong to the lactic acid bacteria group and the genus Bifidobacterium. However, current molecular-based knowledge has allowed the identification and culture of obligatory anaerobic commensal bacteria from the human gut, such as Akkermansia spp. and Faecalibacterium spp., among other human symbionts. We are aware that the identification of new strains of these species does not guarantee their probiotic effects and that each effect must be proved through in vitro and in vivo preclinical studies before clinical trials (before even considering it as a probiotic strain). In most cases, the identification and characterization of new probiotic strain candidates may lack the appropriate set of in vitro experiments allowing the next assessment steps. Here, we address some innovative strategies reported in the literature as alternatives to classical characterization: (i) identification of alternatives using whole-metagenome shotgun sequencing, metabolomics, and multi-omics analysis; and (ii) probiotic characterization based on molecular effectors and/or traits to target specific diseases (i.e., inflammatory bowel diseases, colorectal cancer, allergies, among others).

Metagenomic profiling and transfer dynamics of antibiotic resistance determinants in a full-scale granular sludge wastewater treatment plant

In the One Health context, wastewater treatment plants (WWTPs) are central to safeguarding water resources. Nonetheless, many questions remain about their effectiveness in preventing antimicrobial resistance (AMR) dissemination. Most surveillance studies monitor the levels and removal of selected antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in intracellular DNA (iDNA) extracted from WWTP influents and effluents. The role of extracellular free DNA (exDNA) in wastewater is mostly overlooked. This study analyzed the transfer of ARGs and MGEs in a full-scale Nereda® reactor removing nutrients with aerobic granular sludge. We tracked the composition and fate of the iDNA and exDNA pools of influent, sludge, and effluent samples. Metagenomics was used to profile the microbiome, resistome, and mobilome signatures of iDNA and exDNA extracts. Selected ARGs and MGEs were analyzed by qPCR. From 2,840 ARGs identified, the genes arr-3 (2%), tetC (1.6%), sul1 (1.5%), oqxB (1.2%), and aph(3")-Ib (1.2%) were the most abundant among all sampling points and bioaggregates. Pseudomonas, Acinetobacter, Aeromonas, Acidovorax, Rhodoferax, and Streptomyces populations were the main potential hosts of ARGs in the sludge. In the effluent, 478 resistance determinants were detected, of which 89% were from exDNA potentially released by cell lysis during aeration in the reactor. MGEs and multiple ARGs were co-localized on the same extracellular genetic contigs. Total intracellular ARGs decreased 3-42% due to wastewater treatment. However, the ermB and sul1 genes increased by 2 and 1 log gene copies mL^-1, respectively, in exDNA from influent to effluent. The exDNA fractions need to be considered in AMR surveillance, risk assessment, and mitigation strategies.

Metagenomic analysis of the gut microbiota in piglets either challenged or not with enterotoxigenic Escherichia coli reveals beneficial effects of probiotics on microbiome composition, resistome, digestive function and oxidative stress responses

This study used metagenomic analysis to investigate the gut microbiota and resistome in piglets that were or were not challenged with enterotoxigenic Escherichia coli (ETEC) and had or had not received dietary supplementation with microencapsulated probiotics. The 72 piglets belonged to six groups that were either non-ETEC challenged (groups 1–3) or ETEC challenged (receiving 5ml of 10⁹ CFU/ml pathogenic ETEC strain L3.2 one week following weaning at three weeks of age: groups 4–6). On five occasions at 2, 5, 8, 11, and 14 days of piglet age, groups 2 and 5 were supplemented with 10⁹ CFU/ml of multi-strain probiotics (Lactiplantibacillus plantarum strains 22F and 25F, and Pediococcus acidilactici 72N) while group 4 received 10⁹ CFU/ml of P. acidilactici 72N. Group 3 received 300mg/kg chlortetracycline in the weaner diet to mimic commercial conditions. Rectal faecal samples were obtained for metagenomic and resistome analysis at 2 days of age, and at 12 hours and 14 days after the timing of post-weaning challenge with ETEC. The piglets were all euthanized at 42 days of age. The piglets in groups 2 and 5 were enriched with several desirable microbial families, including Lactobacillaceae, Lachnospiraceae and Ruminococcaceae, while piglets in group 3 had increases in members of the Bacteroidaceae family and exhibited an increase in tetW and tetQ genes. Group 5 had less copper and multi-biocide resistance. Mobile genetic elements IncQ1 and IncX4 were the most prevalent replicons in antibiotic-fed piglets. Only groups 6 and 3 had the integrase gene (intl) class 2 and 3 detected, respectively. The insertion sequence (IS) 1380 was prevalent in group 3. IS3 and IS30, which are connected to dietary intake, were overrepresented in group 5. Furthermore, only group 5 showed genes associated with detoxification, with enrichment of genes associated with oxidative stress, glucose metabolism, and amino acid metabolism compared to the other groups. Overall, metagenomic analysis showed that employing a multi-strain probiotic could transform the gut microbiota, reduce the resistome, and boost genes associated with food metabolism.

Soil microbial diversity and functional profiling of a tropical rainforest of a highly dissected low hill from the upper Itaya river basin revealed by analysis of shotgun metagenomics sequencing data

The tropical rainforest of a highly dissected low hill from the upper Itaya river basin belongs to the western Amazonia region. Some investigations on the biodiversity of these rainforests were more focused on animals and plants diversity. The soils of this region are composed of moderately fertile sediments deposited recently from the initiation of the Andean orogenesis in the Miocene until now. However, scientific information about the soil microbial and functional diversity is still missing. This report presents shotgun metagenomics sequencing data from soils of this rainforest type. A composite loamy soil sample was collected from a primary forest, and metagenomic DNA was purified with standardized methods. Furthermore, libraries were prepared and paired-end sequenced on the Illumina NextSeq 550 platform. Raw Illumina paired-end reads have been uploaded and analysed in the Metagenomics RAST server (MG-RAST). The raw sequence data in fastq format is available at NCBI's Sequence Read Archive (SRA) with accession number SRX12846710.

Comparison of the Microbiome of Artisanal Homemade and Industrial Feta Cheese through Amplicon Sequencing and Shotgun Metagenomics

Feta is the most renowned protected designation of origin (PDO) white brined cheese produced in Greece. The fine organoleptic characteristics and the quality of Feta rely on, among other factors, its overall microbial ecosystem. In this study, we employed 16S rDNA and internal transcribed spacer (ITS) amplicon sequencing, as well as shotgun metagenomics, to investigate the microbiome of artisanal homemade and industrial Feta cheese samples from different regions of Greece, which has very rarely been investigated. 16S rDNA data suggested the prevalence of the Lactococcus genus in the homemade samples, while Streptococcus and Lactobacillus genera prevailed in the industrial control samples. Species identification deriving from shotgun metagenomics corroborated these findings, as Lactococcus lactis dominated two homemade samples while Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus were found to be dominating one industrial sample. ITS data revealed a complex diversity of the yeast population among the samples analyzed. Debaryomyces, Kluyveromyces, Cutaneotrichosporon, Pichia, Candida, and Rhodotorula were the major genera identified, which were distributed in a rather arbitrary manner among the different samples. Furthermore, a number of potential metagenome-assembled genomes (MAGs) could be detected among assembled shotgun bins. The overall analysis of the shotgun metagenomics supported the presence of different foodborne pathogens in homemade samples (e.g., Staphylococcus aureus, Listeria monocytogenes, Enterobacter cloacae, and Streptococcus suis), but with low to very low abundances. Concluding, the combination of both amplicon sequencing and shotgun metagenomics allowed us to obtain an in-depth profile of the artisanal homemade Feta cheese microbiome.

Comprehensive 16S rRNA and metagenomic data from the gut microbiome of aging and rejuvenation mouse models

The gut microbiota is associated with the health and longevity of the host. A few methods, such as fecal microbiota transplantation and oral administration of probiotics, have been applied to alter the gut microbiome and promote healthy aging. The changes in host microbiomes still remain poorly understood. Here, we characterized both the changes in gut microbial communities and their functional potential derived from colon samples in mouse models during aging. We achieved this through four procedures including co-housing, serum injection, parabiosis, and oral administration of Akkermansia muciniphila as probiotics using bacterial 16 S rRNA sequencing and shotgun metagenomic sequencing. The dataset comprised 16 S rRNA sequencing (36,249,200 paired-end reads, 107 sequencing data) and metagenomic sequencing data (307,194,369 paired-end reads, 109 sequencing data), characterizing the taxonomy of bacterial communities and their functional potential during aging and rejuvenation. The generated data expand the resources of the gut microbiome related to aging and rejuvenation and provide a useful dataset for research on developing therapeutic strategies to achieve healthy active aging.

Microbialites show a distinct cyanobacterial phylogenetic structure and functional redundancy in Bacalar lagoon and Cenote Azul sinkhole, Yucatan peninsula, Mexico

Cyanobacterial components of microbialites from two geographically close systems, the Bacalar lagoon (BL) and the Cenote Azul sinkhole (CA) in Quintana Roo, Mexico, were characterized. BL and CA systems were studied along a longitudinal gradient (north to south) and a depth gradient (5 to 30 m), respectively. Microscopic observations, 16S rRNA amplicon sequencing, and shotgun metagenomics were used to characterize Cyanobacteria. Both systems showed similar metabolic/functional profiles but harbored completely different cyanobacterial taxa. BL was dominated by Nostocales, including a population of previously undescribed Chakia sp., while CA was dominated by an unknown taxon of Chroococcales, comprising 70% of relative abundance through all depths. Interestingly, cyanobacterial assemblages in microbialites exhibited phylogenetic overdispersion in most of the BL sites, while CA sites exhibited phylogenetic clustering, these differences were attributed to depth/light conditions and possibly different times of geological formation for BL and CA systems.

Metagenomic Characterization of Microbial Pollutants and Antibiotic- and Metal-Resistance Genes in Sediments from the Canals of Venice

The spread of fecal pollutants and antibiotic resistance in the aquatic environment represents a major public health concern and is predicted to increase in light of climate change consequences and the increasing human population pressure on the lagoon and coastal areas. The city of Venice (Italy) is affected by diverse microbial pollution sources, including domestic wastewaters that, due to the lack of modern sewage treatment infrastructure in the historical city center, are released into canals. The outflowing jets of its tidal inlets thus represent a source of contamination for the nearby beaches on the barrier island separating the lagoon from the sea. Metagenomic analyses of DNA extracted from sediment samples from six sites in the canals of the city’s historic center were undertaken to characterize the microbial community composition, the presence of fecal microbes as well as other non-enteric pathogens, and the content of genes related to antibiotic (AB) and heavy metal (HM) resistance, and virulence. The six sites hosted similar prokaryotic communities, although variations in community composition likely related to oxygen availability were observed. All sites displayed relatively high levels of fecal contamination, including the presence of Fecal Indicator Bacteria, sewage- and alternative feces-associated bacteria. Relatively high levels of other potential pathogens were also found. About 1 in 500 genes identified at these sites are related to AB and HM resistance; conversely, genes related to virulence were rare. Our data suggest the existence of widespread sediment microbial pollution in the canals of Venice, coupled with the prevalence of ARGs to antibiotics frequently used in humans as well as of HMRGs to toxic metals that still persists in the lagoon. All of this evidence raises concerns about the consequences on the water quality of the lagoon and adjacent marine areas and the potential risks for humans, deserving further studies.

Multidrug resistance from a one health perspective in Ethiopia: A systematic review and meta-analysis of literature (2015–2020)

Purpose

The emergence of antimicrobial resistance is a major global health challenge and becoming an urgent priority for policymakers. There is a paucity of scientific studies presenting the multidrug resistance pattern from one health perspective in Ethiopia. Therefore, a systematic review and meta-analysis aimed to determine the pooled prevalence of multidrug resistance in bacteria from human, animal, food, and environmental sources.

Methods

In this systematic review and meta-analysis, an electronic search was made in PubMed & Google scholar using different keywords. The studies conducted in all areas of Ethiopia, published from 2015 to 2020 in peer-reviewed journals, English full-length papers were included. The meta-analysis was done on STATA version 14. The pooled prevalence of multidrug resistance for each bacterium was analysed using the random-effects model; Cochran Q statistics and the I² statistic was used to analyse heterogeneity and considered significant at p < 0.01.

Results

81 studies were included in the systematic review and meta-analysis; 53 human studies, eight animal studies, and 16 environments/food studies. The meta-analysis included six species from gram-positive bacteria and 13 from gram-negative bacteria. S. aureus 53% (95%CI: 42–64%), Coagulase negative Staphylococci 68%(95%CI:53–82), Pseudomonas spp. 73%(95%CI:48–93%), E. coli 70% (95%CI:61–78%), Citrobacter spp. 71%(95%CI:54–87%), Klebsiella spp. 68% (54–80%), Enterobacter spp. 67% (48–83%) and Salmonella spp. 65% (95%CI:48–81%) were the common multidrug-resistant species of bacteria from two or more sources.

Conclusion

In Ethiopia, the pooled prevalence of MDR is high in most bacterial species from humans, animals, food, and environmental sources. Staphylococcus, most members of the Enterobacteriaceae and Pseudomonas, are the standard MDR bacterial population involving all sources. Therefore, integrated policy and intervention measures should be implemented to reduce the emergence and spread of MDR bacteria for better animal and human health outcomes.

Bacterial and Fungal Adaptations in Cecum and Distal Colon of Piglets Fed With Dairy-Based Milk Formula in Comparison With Human Milk

Exclusive breastfeeding is recommended to newborns during the first 6 months of life, whereas dairy-based infant formula is an alternative nutrition source offered to infants. Several studies demonstrated that breastfed infants have a different gut bacterial composition relative to formula-fed infants. In addition, animal models have shown that human milk (HM)–fed piglets had a distinct intestinal bacterial composition compared with milk formula (MF)–fed piglets. However, the gut fungal composition and the interactions with the bacterial community in breastfed compared with formula-fed infants remain to be investigated. In an attempt to evaluate such differences, we used an animal model to perform a shotgun metagenomics analysis on the cecal and distal colon contents of neonatal piglets fed with pasteurized HM or a dairy-based infant formula (MF) during the first 21 days of life. At postnatal day 21 (PND 21), a subset of piglets from each diet group (n = 11 per group) was euthanized. The remaining piglets in each group were weaned to a solid diet and euthanized at PND 51 (n = 13 per group). Large intestine contents (i.e., cecum and distal colon) were subjected to shotgun metagenomics analysis. The differential taxonomic composition of bacteria and fungi and the predicted functional gene profiling were evaluated. Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria are the most abundant bacterial phyla observed in piglets at PND 21 and PND 51. In the large intestine at PND 21 and PND 51, Proteobacteria phylum was significantly higher in MF-fed group, and species Burkholderiales bacterium of phyla was significantly higher in MF group relative to HM group. In addition, in HM group, several Lactobacillus spp. and Bacteroides spp. were higher relative to MF group in the large intestine at PND 21 and PND 51. Fungal genus Aspergillus was higher in MF, whereas Malassezia was lower relative to HM group. Persistent effects of the neonatal diets were observed at PND 51, where alpha- and beta-diversity differences were detected for bacterial and fungal species in the large intestine. Overall, our findings indicate that neonatal diet affects the large intestinal microbial community during the exclusive milk-feeding period, as well as after the introduction of the complementary food.

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

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

Effect of Pyroligneous Acid on the Microbial Community Composition and Plant Growth-Promoting Bacteria (PGPB) in Soils

Pyroligneous acid (PA) is often used in agriculture as a plant growth and yield enhancer. However, the influence of PA application on soil microorganisms is not often studied. Therefore, in this study, we investigated the effect of PA (0.01–5% w/w in soil) on the microbial diversity in two different soils. At the end of eight weeks of incubation, soil microbial community dynamics were determined by Illumina-MiSeq sequencing of 16S rRNA gene amplicons. The microbial composition differed between the lower (0.01% and 0.1%) and the higher (1% and 5%) concentration in both PA spiked soils. The lower concentration of PA resulted in higher microbial diversity and dehydrogenase activity (DHA) compared to the un-spiked control and the soil spiked with high PA concentrations. Interestingly, PA-induced plant growth-promoting bacterial (PGPB) genera include Bradyrhizobium, Azospirillum, Pseudomonas, Mesorhizobium, Rhizobium, Herbaspiriluum, Acetobacter, Beijerinckia, and Nitrosomonas at lower concentrations. Additionally, the PICRUSt functional analysis revealed the predominance of metabolism as the functional module’s primary component in both soils spiked with 0.01% and 0.1% PA. Overall, the results elucidated that PA application in soil at lower concentrations promoted soil DHA and microbial enrichment, particularly the PGPB genera, and thus have great implications for improving soil health.

Using metagenomic data to boost protein structure prediction and discovery

Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored. Here, we review how this information has been used in protein structure prediction and protein discovery. We begin by presenting some widely used metagenomic databases and analyze in detail how metagenomic data has contributed to the impressive improvement in the accuracy of structure prediction methods in recent years. We then examine how metagenomic information can be exploited to annotate protein sequences. More specifically, we focus on the role of metagenomes in the discovery of enzymes and new CRISPR-Cas systems, and in the identification of antibiotic resistance genes. With this review, we provide an overview of how metagenomic data is currently revolutionizing our understanding of protein science.

Ageing and rejuvenation models reveal changes in key microbial communities associated with healthy ageing

BACKGROUND

The gut microbiota is associated with diverse age-related disorders. Several rejuvenation methods, such as probiotic administration and faecal microbiota transplantation, have been applied to alter the gut microbiome and promote healthy ageing. Nevertheless, prolongation of the health span of aged mice by remodelling the gut microbiome remains challenging.

RESULTS

Here, we report the changes in gut microbial communities and their functions in mouse models during ageing and three rejuvenation procedures including co-housing, serum-injection and parabiosis. Our results showed that the compositional structure and gene abundance of the intestinal microbiota changed dynamically during the ageing process. Through the three rejuvenation procedures, we observed that the microbial community and intestinal immunity of aged mice were comparable to those of young mice. The results of metagenomic data analysis underscore the importance of the high abundance of Akkermansia and the butyrate biosynthesis pathway in the rejuvenated mouse group. Furthermore, oral administration of Akkermansia sufficiently ameliorated the senescence-related phenotype in the intestinal systems in aged mice and extended the health span, as evidenced by the frailty index and restoration of muscle atrophy.

CONCLUSIONS

In conclusion, the changes in key microbial communities and their functions during ageing and three rejuvenation procedures, and the increase in the healthy lifespan of aged mice by oral administration of Akkermansia. Our results provide a rationale for developing therapeutic strategies to achieve healthy active ageing. Video abstract.

Recommendations for connecting molecular sequence and biodiversity research infrastructures through ELIXIR

Threats to global biodiversity are increasingly recognised by scientists and the public as a critical challenge. Molecular sequencing technologies offer means to catalogue, explore, and monitor the richness and biogeography of life on Earth. However, exploiting their full potential requires tools that connect biodiversity infrastructures and resources. As a research infrastructure developing services and technical solutions that help integrate and coordinate life science resources across Europe, ELIXIR is a key player. To identify opportunities, highlight priorities, and aid strategic thinking, here we survey approaches by which molecular technologies help inform understanding of biodiversity. We detail example use cases to highlight how DNA sequencing is: resolving taxonomic issues; Increasing knowledge of marine biodiversity; helping understand how agriculture and biodiversity are critically linked; and playing an essential role in ecological studies. Together with examples of national biodiversity programmes, the use cases show where progress is being made but also highlight common challenges and opportunities for future enhancement of underlying technologies and services that connect molecular and wider biodiversity domains. Based on emerging themes, we propose key recommendations to guide future funding for biodiversity research: biodiversity and bioinformatic infrastructures need to collaborate closely and strategically; taxonomic efforts need to be aligned and harmonised across domains; metadata needs to be standardised and common data management approaches widely adopted; current approaches need to be scaled up dramatically to address the anticipated explosion of molecular data; bioinformatics support for biodiversity research needs to be enabled and sustained; training for end users of biodiversity research infrastructures needs to be prioritised; and community initiatives need to be proactive and focused on enabling solutions. For sequencing data to deliver their full potential they must be connected to knowledge: together, molecular sequence data collection initiatives and biodiversity research infrastructures can advance global efforts to prevent further decline of Earth's biodiversity.

Recommendations for connecting molecular sequence and biodiversity research infrastructures through ELIXIR

Threats to global biodiversity are increasingly recognised by scientists and the public as a critical challenge. Molecular sequencing technologies offer means to catalogue, explore, and monitor the richness and biogeography of life on Earth. However, exploiting their full potential requires tools that connect biodiversity infrastructures and resources. As a research infrastructure developing services and technical solutions that help integrate and coordinate life science resources across Europe, ELIXIR is a key player. To identify opportunities, highlight priorities, and aid strategic thinking, here we survey approaches by which molecular technologies help inform understanding of biodiversity. We detail example use cases to highlight how DNA sequencing is: resolving taxonomic issues; Increasing knowledge of marine biodiversity; helping understand how agriculture and biodiversity are critically linked; and playing an essential role in ecological studies. Together with examples of national biodiversity programmes, the use cases show where progress is being made but also highlight common challenges and opportunities for future enhancement of underlying technologies and services that connect molecular and wider biodiversity domains. Based on emerging themes, we propose key recommendations to guide future funding for biodiversity research: biodiversity and bioinformatic infrastructures need to collaborate closely and strategically; taxonomic efforts need to be aligned and harmonised across domains; metadata needs to be standardised and common data management approaches widely adopted; current approaches need to be scaled up dramatically to address the anticipated explosion of molecular data; bioinformatics support for biodiversity research needs to be enabled and sustained; training for end users of biodiversity research infrastructures needs to be prioritised; and community initiatives need to be proactive and focused on enabling solutions. For sequencing data to deliver their full potential they must be connected to knowledge: together, molecular sequence data collection initiatives and biodiversity research infrastructures can advance global efforts to prevent further decline of Earth's biodiversity.

Microbial communities in polychlorinated biphenyl (PCB)-contaminated wastewater lagoon sediments: PCB congener, quantitative PCR, and 16S rRNA gene amplicon sequencing datasets

The potential for aerobic and anaerobic microbial natural attenuation of PCBs in freshwater sediments is described by PCB congener, quantitative PCR, and 16S rRNA gene amplicon sequencing datasets generated, in duplicate, from 27 sediment samples collected from a PCB-contaminated freshwater lagoon (54 samples total). Sediment samples were subjected to a hexane PCB extraction protocol and the concentrations of 209 PCB congeners were determined in hexane extracts by gas chromatography with a tandem mass spectrometry detection. DNA was extracted from sediments sediment samples and used for qPCR and 16S rRNA amplicon sequencing. The abundance of 16S rRNA genes (i.e., Dehalococcoides and putative dechlorinating Chloroflexi) and functional genes (i.e., reductive dehalogenase (rdhA) and biphenyl dioxygenase (bphA)) associated with aerobic and anaerobic PCB biodegradation, along with the total 16S rRNA genes abundance, was determined by SYBR green qPCR. The microbial community composition and structure in all sediment samples was obtained by 16S rRNA gene amplicon sequencing. Primers targeting the 16S rRNA gene V4 region were used to produce 16S rRNA gene amplicons that were sequencing with the high-throughput Illumina MiSeq platform and sequencing chemistry. The 16S rRNA gene sequencing dataset along with PCB congener and qPCR datasets included as metadata, could be reused in meta-analyses that aim to determine microbial community interactions in contaminated environments, and uncover relationships between microbial community structure and environmental variable (e.g., PCB congener concentrations).