Antimicrobial resistance (AMR) is a critical global threat to human, animal and environmental health, exacerbated by horizontal gene transfer (HGT) via mobile genetic elements. This poses significant challenges that have a negative impact on the sustainability of the One Health approach, hindering its long-term viability and effectiveness in addressing the interconnectedness of global health. Recent studies on livestock animals, specifically ruminants, indicate that culturable ruminal bacteria harbour AMR genes with the potential for HGT. However, these studies have focused predominantly on using the faecobiome as a proxy to the rumen microbiome or using easily isolated and culturable bacteria, overlooking the unculturable population. These unculturable microbial groups could have a profound influence on the rumen resistome and AMR dynamics within livestock ecosystems, potentially holding critical insights for advanced understanding of AMR in One Health. In order to address this gap, this review of current research on the burden of AMR in livestock was undertaken, and it is proposed that combined study of the rumen microbiome and faecobiome, termed the 'rumenofaecobiome', should be performed to enhance understanding of the risks of AMR in ruminant livestock. This review discusses the complexities of the rumen microbiome and the risks of AMR transmission in this microbiome in a One Health context. AMR transmission dynamics and methodologies for assessing the risks of AMR in livestock are summarized, and future considerations for researching the impact of AMR in the rumen microbiome and the implications within the One Health framework are discussed.

This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network and gated convolutional modules yielded a pan-cancer classification accuracy of 91.8 %. Additionally, by extracting 31 differential genes from different cancer feature maps, an interaction network diagram was drawn, identifying two key genes, ANXA5 and ACTB. These genes are potential biomarkers related to cancer progression, angiogenesis, metastasis, and treatment resistance. Survival prediction analysis on 10 cancer types, combined with feature maps and data amplification, cancer survival prediction accuracy reached from 0.75 to 0.91. This transcriptomic feature map provides a novel approach for cancer omics analysis, to facilitate personalized treatments and reflecting individual differences.

Soil microbes are among the most abundant and diverse organisms on Earth but remain poorly characterized. New technologies have made possible to sequence the DNA of uncultivated microorganisms in soil and other complex ecosystems. Genome assembly is crucial for understanding their functional potential. Nanopore sequencing technologies allow to sequence long DNA fragments, optimizing production of metagenome-assembled genomes compared to short-read technology. Extracting DNA with a very high purity and high molecular weight is key to get the most out of this long read technologies. Here we present two extraction protocols to get DNA with high purity. First protocol is optimized to reach DNA quality suiting Nanopore shotgun metagenomics. It uses a non-toxic centrifugation gradient to separate bacterial cells from soil to extract DNA directly on cells. The median length of the acquired DNA sequences (N50) was 3 to 7 times greater than previously published in the literature, achieving an N50 of ∼14 kb. The other, a modification of a commercially available MP Biomedical DNA extraction kit, yielded high-purity DNA for full-length 16S Oxford Nanopore metabarcoding, with an N50 of ∼8 kb. The MP-based protocol achieves higher yields of ultra-pure DNA compared to the Nycodenz protocol, at the expense of shorter fragment lengths.

Colorectal cancer (CRC) is a major global health challenge. Emerging research highlights the pivotal role of the gut microbiota in influencing CRC risk, progression, and treatment response. Metagenomic approaches, especially high-throughput shotgun sequencing, have provided unprecedented insights into the intricate connections between the gut microbiome and CRC. By enabling comprehensive taxonomic and functional profiling, metagenomics has revealed microbial signatures, activities, and biomarkers associated with colorectal tumorigenesis. Furthermore, metagenomics has shown a potential to guide patient stratification, predict treatment outcomes, and inform microbiome-targeted interventions. Despite remaining challenges in multi-omics data integration, taxonomic gaps, and validation across diverse cohorts, metagenomics has propelled our comprehension of the intricate gut microbiome-CRC interplay. This review underscores the clinical relevance of microbial signatures as potential diagnostic and prognostic tools in CRC. Furthermore, it discusses personalized treatment strategies guided by this omics' approach.

Metagenome-assembled genomes (MAGs) have revolutionized microbial ecology by enabling the genome-resolved study of uncultured microorganisms directly from environmental samples. By leveraging high-throughput sequencing, advanced assembly algorithms, and genome binning techniques, researchers can reconstruct microbial genomes without the need for cultivation. These methodological advances have expanded the known microbial diversity, revealing novel taxa and metabolic pathways involved in key biogeochemical cycles, including carbon, nitrogen, and sulfur transformations. MAG-based studies have identified microbial lineages form Archaea and Bacteria responsible for methane oxidation, carbon sequestration in marine sediments, ammonia oxidation, and sulfur metabolism, highlighting their critical roles in ecosystem stability. From a sustainability perspective, MAGs provide essential insights for climate change mitigation, sustainable agriculture, and bioremediation. The ability to characterize microbial communities in diverse environments, including soil, aquatic ecosystems, and extreme habitats, enhances biodiversity conservation and supports the development of microbial-based environmental management strategies. Despite these advancements, challenges such as assembly biases, incomplete metabolic reconstructions, and taxonomic uncertainties persist. Continued improvements in sequencing technologies, hybrid assembly approaches, and multi-omics integration will further refine MAG-based analyses. As methodologies advance, MAGs will remain a cornerstone for understanding microbial contributions to global biogeochemical processes and developing sustainable interventions for environmental resilience.

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Phyllosphere microbiome plays a crucial role in maintaining plant fitness. However, its response to changes in dominant tree species during forest succession still remains poorly understood.

In this study, microbial isolation and high-throughput sequencing techniques were used to analyze the community structure and diversity of phyllosphere microbes in pure Pinus massoniana forests, mixed P. massoniana and Liquidambar formosana forests, and pure L. formosana forests.

The results showed that the isolation rates of key plant fungal pathogens varied significantly in phyllosphere across forest types. In pure pine forest, Fusarium was most prevalent in pine needles, while in the mixed forest, Alternaria was dominant. For Liquidambar leaves, Phyllosticta dominated in pure forests, while Colletotrichum was prevalent in the mixed forests. Alpha diversity analysis revealed that higher microbial richness and diversity in the mixed forest compared to the pure forest. The bacterial community structure in Liquidambar leaves differed between the pure forest and the mixed forest. Co-occurrence networks confirmed more complex and stable microbial compositions and interactions in the mixed forest. Bacterial communities in pine needles exhibited higher functional capacity for methanotrophy and nitrogen fixation in the mixed forests.

The results demonstrate that the mixed forests foster greater microbial diversity, complexity, and functional potential in the phyllosphere compared to the pure forests, highlighting the importance of forest composition in shaping phyllosphere microbial communities.

The marine environment is considered one of the most important ecosystems with high biodiversity. Microorganisms in this environment are variable and coexist with other marine organisms. The microbes associated with other marine organisms produce compounds with biological activity that may help the host's defense against invading organisms. The symbiotic association of bacteria with marine invertebrates is of ecological and biotechnological importance. Biologically active metabolites isolated from bacteria associated with marine invertebrates are considered potential sources of natural antimicrobial molecules for treating infectious diseases. Many studies have been conducted to screen the antimicrobial activity of metabolites produced by bacteria associated with marine invertebrates. This work provides an overview of the advancements in antimicrobial compound research on bacteria associated with marine invertebrates.

Wood decay fungi and bacteria play a crucial role in natural ecosystems, contributing to the decomposition of lignocellulosic materials and nutrient cycling. However, their activity poses significant challenges in timber durability, impacting industries reliant on wood as a construction material. This review examines the diversity of microorganisms damaging timber used indoors and outdoors. Additionally, traditional and advanced methods for microbial identification are discussed, with a focus on DNA-based, culture-independent sequencing methods whose importance has increased massively in recent years. It also provides an overview of the various options for wood protection, starting from wood protection by design, to chemical wood preservation and wood modification methods. This should illustrate how important it is to combine an ecological understanding of the decay organisms, precise identification and innovative wood protection methods in order to achieve a long-term and thus resource-saving use of wood. KEY POINTS: • Fungi and bacteria play a crucial role in the decomposition of timber wood. • Traditional and advanced DNA-based methods for microbial identification are discussed. • An overview of the various options for wood protection is provided.

Background/Objectives: Antimicrobial resistance poses a major public health challenge. The World Health Organization has identified 15 priority pathogens that require prompt development of new antibiotics. This review systematically evaluates the antibacterial resistance of the most significant bacterial pathogens, currently available treatment options, as well as complementary approaches for the management of infections caused by the most challenging multidrug-resistant (MDR) bacteria. For carbapenem-resistant Gram-negative bacteria, treatment options include combinations of beta-lactam antibiotics and beta-lactamase inhibitors, a novel siderophore cephalosporin, known as cefiderocol, as well as older antibiotics like polymixins and tigecycline. Treatment options for Gram-positive bacteria are vancomycin, daptomycin, linezolid, etc. Although the development of new antibiotics has stagnated, various agents with antibacterial properties are currently in clinical and preclinical trials. Non-antibiotic strategies encompass antibiotic potentiators, bacteriophage therapy, antivirulence therapeutics, antimicrobial peptides, antibacterial nanomaterials, host-directed therapy, vaccines, antibodies, plant-based products, repurposed drugs, as well as their combinations, including those used alongside antibiotics. Significant challenges exist in developing new antimicrobials, particularly related to scientific and technical issues, along with policy and economic factors. Currently, most of the alternative options are not part of routine treatment protocols. Conclusions and Future Directions: There is an urgent need to expedite the development of new strategies for treating infections caused by MDR bacteria. This requires a multidisciplinary approach that involves collaboration across research, healthcare, and regulatory bodies. Suggested approaches are crucial for addressing this challenge and should be backed by rational antibiotic use, enhanced infection control practices, and improved surveillance systems for emerging pathogens.

The dysbiosis of the gut microbiota has been identified as a central factor in the pathogenesis of inflammatory bowel disease (IBD), a chronic condition characterized by frequent recurrence and various adverse effects of traditional therapies. While treatments targeting the gut microbiota show promise, their efficacy in IBD management still requires extensive evaluation. Our systematic review analyzes recent studies to elucidate the advancements and challenges in treating IBD using microbial-based therapies.

Through a comprehensive systematic review spanning key scientific databases-PubMed, Embase, Cochrane, Web of Science, Scopus, and Google Scholar-we scrutinized the impact of probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT) on individuals with IBD. Our detailed analysis covered study and participant demographics, along with seven key outcome measures: disease activity index, inflammatory markers, serum cytokines, microbiome composition, adverse effects, and the rates of remission and relapse.

From 6,080 initial search hits, we included 71 studies that assessed various interventions compared to placebo or standard medical therapy. Although there was notable variation in clinical results while assessing different outcomes, overall, probiotics, prebiotics, and synbiotics enhanced the success rates in inducing remission among IBD patients. Furthermore, we noted significant reductions in levels of pro-inflammatory markers and cytokines. Additionally, the requirement for steroids, hospitalization, and poor outcomes in endoscopic and histological scores were significantly reduced in individuals undergoing FMT.

Our investigation highlights the potential of targeting gut microbiota dysbiosis with microbial-based therapies in patients with IBD. We recommend conducting larger, placebo-controlled randomized trials with extended follow-up periods to thoroughly assess these treatments' clinical efficacy and safety before widespread recommendations for clinical application.

The Sundarbans, the world's largest tidal mangrove forest, acts as a crucial ecosystem for production, conservation, and the cycling of carbon and nitrogen. The study explored the hypothesis that microbial communities in mangrove ecosystems exhibit unique taxonomic and functional traits that play a vital part in carbon cycling and ecosystem resilience. Using metagenomic analysis to evaluate microbial communities in mangrove and non-mangrove environment, evaluating their composition, functional functions, and ecological relevance. The analysis revealed distinct microbial profiles, in mangrove and non-mangrove environments, with bacteria, proteobacteria, and viruses being the most prevalent groups, with varying abundances in each environment. Functional and taxonomical analysis identified genes involved in carbon regulation, including Triacylglycerol lipase, NarG, DsrB, DNA-binding transcriptional dual regulator CRP, Vanillate O-demethylase oxygenase, succinate-CoA ligase, Tetrahydrofolate ligase, Carboxylase, Ribulose-1,5-bisphosphate carboxylase/oxygenase, Glycine hydroxymethyltransferase, MAG: urease, Endosymbiont of Oligobrachia haakonmosbiensis, Ribulose bisphosphate carboxylase, Aconitate hydratase AcnA, and nitrous oxide reductase, suggesting the metabolic versatility of these microbial communities for carbon cycling. The findings emphasize the key role of microbial activity in preserving mangrove ecosystem health and resilience, highlighting the intricate interplay between microbial diversity, functional capabilities, and environmental factors.

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.

Antibiotics have been saving countless lives from deadly infectious diseases, which we now often take for granted. However, we are currently witnessing a significant rise in the emergence of multidrug-resistant (MDR) bacteria, making these infections increasingly difficult to treat in hospitals.

The discovery and development of new antibiotic has slowed, largely due to reduced profitability, as antibiotics often lose effectiveness quickly as pathogenic bacteria evolve into MDR strains. To address this challenge, metabolic engineering has recently become crucial in developing efficient enzymes and cell factories capable of producing both existing antibiotics and a wide range of new derivatives and analogs. In this paper, we review recent tools and strategies in metabolic engineering and synthetic biology for antibiotic discovery and the efficient production of antibiotics, their derivatives, and analogs, along with representative examples.

These metabolic engineering and synthetic biology strategies offer promising potential to revitalize the discovery and development of new antibiotics, providing renewed hope in humanity's fight against MDR pathogenic bacteria.

The 16S rRNA metabarcoding, based on Next-Generation Sequencing (NGS), is used to assess microbial biodiversity in various matrices, including food. The process involves a "dry-lab" phase where NGS data are processed through bioinformatic pipelines, which finally rely on taxonomic unit assignment against reference databases to assign them at order, genus, and species levels. Today, several public genomic reference databases are available for the taxonomic assignment of the 16S rRNA sequences. In this study, 42 insect-based food products were chosen as food models to find out how reference database choice could affect the microbiome results in food matrices. At the same time, this study aims to evaluate the most suitable reference database to assess the microbial composition of these still poorly investigated products. The V3-V4 region was sequenced by Illumina technology, and the R package "DADA2" was used for the bioinformatic analysis. After a bibliographic search, three public databases (SILVA, RDP, NCBI RefSeq) were compared based on amplicon sequence variant (ASV) assignment percentages at different taxonomic levels and diversity indices. SILVA assigned a significantly higher percentage of ASVs to the family and genus levels compared to RefSeq and RDP. However, no significant differences were noted in microbial composition between the databases according to α and β diversity results. A total of 121 genera were identified, with 56.2% detected by all three databases, though some taxa were identified only by one or two. The study highlights the importance of using updated reference databases for accurate microbiome characterization, contributing to the optimization of metabarcoding data analysis in food microbiota studies, including novel foods.

Bacterial social interactions play crucial roles in various ecological, medical, and biotechnological contexts. However, predicting these interactions from genome sequences is notoriously difficult. Here, we developed bioinformatic tools to predict whether secreted iron-scavenging siderophores stimulate or inhibit the growth of community members. Siderophores are chemically diverse and can be stimulatory or inhibitory depending on whether bacteria have or lack corresponding uptake receptors. We focused on 1928 representative Pseudomonas genomes and developed an experimentally validated coevolution algorithm to match encoded siderophore synthetases to corresponding receptor groups. We derived community-level iron interaction networks to show that siderophore-mediated interactions differ across habitats and lifestyles. Specifically, dense networks of siderophore sharing and competition were observed among environmental and nonpathogenic species, while small, fragmented networks occurred among human-associated and pathogenic species. Together, our sequence-to-ecology approach empowers the analyses of social interactions among thousands of bacterial strains and offers opportunities for targeted intervention to microbial communities.

Antibiotic-manufacturing wastewater treatment plants primarily target chemical pollutants, but their processes may select for antibiotic-resistant pathogens and antibiotic resistance genes. Leveraging the combined strengths of deep metagenomic sequencing, 16S rRNA gene sequencing, quantitative polymerase chain reaction, and bacterial culturing, we investigated bacterial communities and antibiotic resistomes across eleven treatment units in a full-scale antibiotic-manufacturing wastewater treatment plant processing wastewater from a β-lactam manufacturing facility. Both bacterial communities and antibiotic resistance gene compositions varied across the treatment units, but were associated. Certain antibiotic resistance gene persisted through treatment, either carried by identical bacterial species, or linked to mobile genetic elements in different species. Despite the satisfactory performance in chemical removal, this plant continuously enriched zoonotic antibiotic-resistant Aeromonas veronii (an emerging pathogen responsible for substantial economic losses in aquaculture and human health) from influent to effluent, probably due to prolonged β-lactam selection pressure and aquatic nature of A. veronii. This enrichment resulted in a significantly higher abundance of A. veronii than other aquatic samples worldwide. Furthermore, the closest evolutionary relative to the retrieved A. veronii was an isolate obtained from the stool of a local diarrhea patient. These findings highlighted a substantial public health risk posed by antibiotic-manufacturing wastewater treatment, underlining its potential role in enriching and disseminating zoonotic antibiotic-resistant pathogens. Beyond chemical monitoring, enhanced surveillance of antibiotic-resistant pathogens and antibiotic resistance genes is needed in effluent discharge standard for antibiotic-manufacturing wastewater treatment plants.

Esterases that can function under extreme conditions are important for industrial processing and environmental remediation. Here, we report the identification of a salt- and solvent-tolerant esterase, Dhs82, from a soil metagenomic library. Dhs82 prefers short-chain p-nitrophenyl (p-NP) esters and exhibits enzymatic activity up to 1460 ± 61 U/mg towards p-NP butyrate. Meanwhile, Dhs82 can catalyze the hydrolysis of dialkyl phthalate esters, especially the widely-used diethyl phthalate (DEP), dipropyl phthalate (DPP) and di-n-butyl phthalate (DBP). Importantly, as an acidic protein with negative charges dominating its surface, Dhs82 is highly active and extraordinarily stable at high salinity. This property is quite rare among previously reported esterases/hydrolases capable of degrading phthalate esters (PAEs). In addition, Dhs82 activity can be significantly enhanced in the presence of solvents over a concentration range of 10-30 % (v/v). Notably, Dhs82 also showed high stability towards these solvents and solvent concentrations as high as 50-60 % (v/v) are required to inactivate Dhs82. Furthermore, molecular docking revealed the key residues, including the catalytic triad (Ser156, His281, and Asp251) and the surrounding Gly84 and Gly85, involved in the interaction of Dhs82 with DBP, depicting how Dhs82 degrades PAEs as a family IV esterase. Together, these diverse properties make Dhs82 a valuable candidate for both basic research and biotechnological applications.

In aquaculture, the secretions of cultured organisms contribute to the development of aquatic antibiotic resistance. However, the antibiotic-induced changes in fish feces remain poorly understood. This study aimed to assess the short-term dynamics of fecal microbiome and antibiotic resistance in juvenile rainbow trout (Oncorhynchus mykiss) upon antibiotic treatment and withdrawal period.

Fish were orally administered diets supplemented with oxytetracycline (OTC) or sulfadiazine/trimethoprim (SDZ/TMP) for 10 consecutive days, followed by a 25-day withdrawal period. Fecal samples were collected before antibiotic treatment (day 0), and at 1, 3, 7, and 10 days post antibiotic administration (dpa), as well as 1, 3, 7, 14, and 25 days post antibiotic cessation (dpc). The fecal microbiome community was profiled using both culture-dependent and -independent methods. The relative abundance of antibiotic resistance genes (ARGs) and the class 1 integron-integrase gene (intI1) in the feces were quantified using real-time PCR.

Antibiotic treatment disrupted the fecal microbial communities, and this alteration persisted even after antibiotic cessation. Moreover, OTC treatment increased the relative abundance of tet genes, while sul and dfr genes increased in the SDZ/TMP-treated group. Notably, Flavobacterium, Pseudomonas, and Streptococcus exhibited a significant correlation with the abundance of ARGs, suggesting their potential role as carriers for ARGs.

This study demonstrates the antibiotic-induced changes in the fecal microbiome and the increase of ARGs in rainbow trout feces. These findings provide novel insights into the dynamics of microbiome recovery post-antibiotic cessation and suggest that fish feces provide a non-invasive approach to predict changes in the fish gut microbiome and resistome.

The spore-forming bacteria in the dairy industry are notable for their spores resilience and capacity to survive heating processes, allowing them to germinate and enter the vegetative stage, potentially leading to spoilage of the milk. Additionally, these spores can form biofilms, becoming a persistent source of contamination in processing environments. In this study, we collected a total of 165 raw milk from six different parts in China in spring, summer, autumn, and winter, respectively. Metagenomics sequencing method was used to explore and compare the differences in spore-forming bacterial composition and diversity in raw milk samples. Among these samples, four genera and 207 species of spore-forming bacteria were identified, with the genus Bacillus and the species Paenibacillus darwinianus dominant. Seasonal variations had a greater impact on the composition and abundance of spore-forming bacteria in raw milk than regional differences. Notable, raw milk samples collected during the spring and summer exhibited a higher number of unique spore-forming bacterial species compared to those collected in other seasons. Moreover, different regions and seasons have their own dominant bacteria. Metabolism of cofactors and vitamins, energy metabolism, carbohydrate metabolism, and amino acid metabolism were the main metabolic pathways. Hence, specific strategies need to be adopted to prevent and control spore-forming bacteria in raw milk in different regions and seasons.

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

High-throughput sequencing data lie at the heart of modern microbiome research. Effective analysis of these data requires careful preprocessing, modeling, and interpretation to detect subtle signals and avoid spurious associations. In this review, we discuss how simulation can serve as a sandbox to test candidate approaches, creating a setting that mimics real data while providing ground truth. This is particularly valuable for power analysis, methods benchmarking, and reliability analysis. We explain the probability, multivariate analysis, and regression concepts behind modern simulators and how different implementations make trade-offs between generality, faithfulness, and controllability. Recognizing that all simulators only approximate reality, we review methods to evaluate how accurately they reflect key properties. We also present case studies demonstrating the value of simulation in differential abundance testing, dimensionality reduction, network analysis, and data integration. Code for these examples is available in an online tutorial (https://go.wisc.edu/8994yz) that can be easily adapted to new problem settings.

The objective of this review is to investigate microbial dark matter (MDM) with a focus on its potential for discovering novel natural products (NPs). This first part will examine the characteristics and abundance of these previously unexplored microbial communities, as well as the challenges faced in identifying and harnessing their unique biochemical properties and novel methods in this field. MDMs are thought to hold great potential for the discovery of novel NPs, which could have significant applications in medicine, agriculture, and industry. In recent years, there has been a growing interest in exploring MDM to unlock its potential. In fact, developments in genome-sequencing technologies and sophisticated phylogenetic procedures and metagenomic techniques have contributed to drastically make important changes in our sights on the diversity of microbial life, including the very outline of the tree of life. This has led to the development of novel technologies and methodologies for studying these elusive microorganisms, such as single-cell genomics, metagenomics, and culturomics. These approaches enable researchers to isolate and analyze individual microbial cells, as well as entire communities, providing insights into their genetic and metabolic potential. By delving into the MDM, scientists hope to uncover new compounds and biotechnological advancements that could have far-reaching impacts on various fields.

In the PRIMA project ArtiSaneFood, the microbiological parameters of several artisanal cheeses produced in the Mediterranean area have been quantified. In this pilot study, we selected four of these artisanal cheese products from Italy, Portugal, Spain, and Morocco to investigate and compare their microbiomes in terms of taxonomic composition, presence of reads of foodborne pathogens, as well as virulence and antimicrobial resistance genes. Lactococcus, Streptococcus and Lactobacillus were the most represented genera in the Portuguese and Spanish cheeses, Streptococcus in the Italian cheese, and Enterococcus, Klebsiella, Escherichia, and Citrobacter in the Moroccan products. The correlation analysis indicated a negative association between the abundance of some lactic acid bacteria (i.e., Lactococcus, Lactobacillus, Streptococcus, and Leuconostoc) and foodborne pathogenic genera, like Escherichia and Salmonella. The analysis of pathogen abundance, virulence factors, and antimicrobial resistance genes showed a strong clusterization based on the cheese type, confirming that the presence of potential human health risk determinants was higher in the artisanal products derived from unpasteurized milk that underwent spontaneous fermentation.

Accurate determination and quantification of the taxonomic composition of microbial communities, especially at the species level, is one of the major issues in metagenomics. This is primarily due to the limitations of commonly used 16S rRNA reference databases, which either contain a lot of redundancy or a high percentage of sequences with missing taxonomic information. This may lead to erroneous identifications and, thus, to inaccurate conclusions regarding the ecological role and importance of those microorganisms in the ecosystem.

The current study presents MIMt, a new 16S rRNA database for archaea and bacteria's identification, encompassing 47 001 sequences, all precisely identified at species level. In addition, a MIMt2.0 version was created with only curated sequences from RefSeq Targeted loci with 32 086 sequences. MIMt aims to be updated twice a year to include all newly sequenced species. We evaluated MIMt against Greengenes, RDP, GTDB and SILVA in terms of sequence distribution and taxonomic assignments accuracy. Our results showed that MIMt contains less redundancy, and despite being 20 to 500 times smaller than existing databases, outperforms them in completeness and taxonomic accuracy, enabling more precise assignments at lower taxonomic ranks and thus, significantly improving species-level identification.

The Amuyo Ponds (APs) are a group of three brackish hydrothermal lagoons located at 3700 m above sea level in a pre-Andean setting in the Atacama Desert. Each pond shows a conspicuous green (GP), red (RP), or yellow (YP) coloration, and discharges water rich in arsenic and boron into the Caritaya River (Camarones Basin, northern Chile). Microorganisms are subjected to harsh environmental conditions in these ponds, and the microbial composition and diversity in the Amuyo Ponds' sediments are unknown. The microbial life colonizing AP sediments was explored by metagenomics analyses, showing a diverse microbial life dominated by members of the bacterial domain, with nearly 800 bacterial genome sequences, and sequences associated with Archaea, Eukarya, and viruses. The genus Pseudomonas was more abundant in GP and YP sediments, while the genera Pseudomonas, Aeromonas, and Shewanella were enriched in RP sediments. Archaeal composition was similar in all sediments, and enriched with methanogens sequences from the Archaeoglobi and Halobacteria classes. Abundant fungi sequences were detected in all sediments from the phyla Blastocladiomycota and Ascomycota. We also report putative functional capabilities related to virulence and defense genes, the biosynthesis of secondary metabolites, and tolerance to arsenic. Thirteen bacterial and fourteen viral metagenome-assembled genomes were reconstructed and informed here. This work expands our knowledge on the richness of the microorganisms in the APs and open further studies on the ecology and genomics of this striking Andean geosite.

The leather industry is embracing eco-friendly technologies for both regulatory compliance and sustainable growth. While enzymatic depilation provides a greener alternative to traditional beamhouse methods, its complexity often leads to higher costs. To address this, we examined the performance of sheepskins' native bacterial flora in acetic acid conditions with low-environmental impact.

Utilizing metagenomic techniques, we analyzed the bacterial community dynamics during the depilation process. This investigation revealed a notable increase in microbial diversity and richness in acetic acid treatments compared to water treatments. At the class level, a post-processing decrease in Gammaproteobacteria dominance was observed, while Actinomycetia numbers surged in the acetic acid group. In contrast, the water group showed an increase in Bacteroidia. Order-level analysis indicated reductions in Pseudomonadales and increases in Actinomycetales with acetic acid treatment, whereas Flavobacteriales was more prevalent in water-treated liquors. At the family level, Moraxellaceae decreased and Micrococcaceae increased in the acetic acid group, in contrast to the marked rise of Weeksellaceae in the water group. Temporal analyses further highlighted the evolving bacterial landscapes under different treatments. Moreover, acetic acid treatment fostered a stable microbial community, beneficial for sustainable leather processing. Functional pathways were predicted using PICRUSt2. It showed that significantly enriched degradation pathways in the water group were less abundant in the acetic acid group, potentially preventing substrate matrix damage during depilation.

The study underscores the transformative potential of acetic acid for the leather industry, offering a pathway to reduce pollution while maintaining economic viability. By enhancing our understanding of microbial interactions during depilation, this study opens avenues for refining these eco-friendly techniques. Our findings advocate for a shift towards greener depilation methods and contribute to the broader dialogue on sustainable manufacturing practices, emphasizing the importance of leveraging indigenous microbial communities for environmental and economic gains.

Organic pollutants can alter the physicochemical properties and microbial communities of water bodies. In water contaminated with organic pollutants, the unique extracellular electron transfer mechanisms that promote sulfamethoxazole (SMX) degradation in tri-electrode microbial electrochemical systems (TE-MES) may be impacted. To simulate biodegradable organic matter contamination, glucose (GLU) was added. Metagenomics and metabolomics were used to analyze changes in microbial community structure, metabolism, and function on the electrodes. GLU addition accelerated water quality deterioration, and enhanced SMX degradation. Microbial taxa on the electrodes experienced selective enrichment. Notably, methanogens and SMX-degrading bacteria were enriched, while denitrifying bacteria and antibiotic-resistant bacteria were suppressed. Enriched metabolites were linked to 15 metabolic pathways and other functions like microbial signaling and genetics. Non-redundant genes also clustered in metabolic pathways, aligning with metabolite enrichment results. Additional pathways involved life cycle processes and protein interactions. Enzymes related to carbon metabolism, particularly glycoside hydrolases, increased significantly, indicating a shift in carbon metabolism on microbial electrodes after GLU addition. The abundance of intracellular electron transfer enzymes rose, while outer membrane proteins decreased. This contrasts with the typical TE-MES mechanism where outer membrane proteins facilitate SMX degradation. The presence of organic pollution may shift SMX degradation from an extracellular electrochemical process to an intracellular metabolic process, possibly involving co-metabolism with simple organic compounds. This study provides mechanistic insights and theoretical guidance for using TE-MES with embedded microbial electrodes to treat antibiotic-contaminated water affected by organic pollution.

Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

This chapter explores the application of omics technologies in food mycology, emphasizing the significant impact of filamentous fungi on agriculture, medicine, biotechnology and the food industry. The chapter delves into the importance of understanding fungal secondary metabolism due to its implications for human health and industrial use. Several omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, are reviewed for their role in studying the genetic potential and metabolic capabilities of food-related fungi. The potential of CRISPR/Cas9 in fungal research is highlighted, showing its ability to unlock the full genetic potential of these organisms. The chapter also addresses the challenges posed by Big Data research in Omics and the need for advanced data processing methods. Through these discussions, the chapter highlights the future benefits and challenges of omics-based research in food mycology and its potential to revolutionize our understanding and utilization of fungi in various domains.

Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.

Microbes were the only form of life on Earth for most of its history, and they still account for the vast majority of life's diversity. They convert rocks to soil, produce much of the oxygen we breathe, remediate our sewage, and sustain agriculture. Microbes are vital to planetary health as they maintain biogeochemical cycles that produce and consume major greenhouse gases and support large food webs. Modern microbiologists analyze nucleic acids, proteins, and metabolites; leverage sophisticated genetic tools, software, and bioinformatic algorithms; and process and integrate complex and heterogeneous datasets so that microbial systems may be harnessed to address contemporary challenges in health, the environment, and basic science. Here, we consider an inevitably incomplete list of emergent themes in our discipline and highlight those that we recognize as the archetypes of its modern era that aim to address the most pressing problems of the 21st century.

Sewage metagenomics has risen to prominence in urban population surveillance of pathogens and antimicrobial resistance (AMR). Unknown species with similarity to known genomes cause database bias in reference-based metagenomics. To improve surveillance, we seek to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time. We use longitudinal sewage sampling in seven treatment plants from five major European cities to explore the utility of catch-all sequencing of these population-level samples. Using metagenomic assembly methods, we recover 2332 metagenome-assembled genomes (MAGs) from prokaryotic species, 1334 of which were previously undescribed. These genomes account for ~69% of sequenced DNA and provide insight into sewage microbial dynamics. Rotterdam (Netherlands) and Copenhagen (Denmark) show strong seasonal microbial community shifts, while Bologna, Rome, (Italy) and Budapest (Hungary) have occasional blooms of Pseudomonas-dominated communities, accounting for up to ~95% of sample DNA. Seasonal shifts and blooms present challenges for effective sewage surveillance. We find that bacteria of known shared origin, like human gut microbiota, form communities, suggesting the potential for source-attributing novel species and their ARGs through network community analysis. This could significantly improve AMR tracking in urban environments.

Have you ever deeply considered the intersections between research and education, particularly for second-level students? Traditionally, the convergence of these two realms is most often noted when considering the integration of research findings into educational practices or the involvement of, typically a small number, of students in research activities. While these practices have demonstrated efficacy, the fields of scientific research and education are evolving rapidly, necessitating a reevaluation of how we can optimize their convergence. In our discourse, we delve into these evolving trends, uncover the potential for greater integration, and, ultimately, enhance outcomes using the citizen science initiative Kefir4All as an illustrative example.

Natural acidified marine systems (ASs) are environments with relatively low pH levels due to natural causes such as volcanic activity, geochemical reactions, and biological processes. These systems act as natural laboratories for the study of the effects of ocean acidification, allowing for the observation of long-term ecological and evolutionary responses. Understanding these systems is crucial for predicting the effects of anthropogenic ocean acidification (OA) on marine ecosystems. There are 23 ASs in which scientific research has shown significant parallelisms in their results worldwide, such as the disappearance of calcareous organisms and the loss of species with key ecological functions under OA conditions. Future research should emphasize continuous collaboration among teams, as well as public access to oceanographic and biological data along with the monitoring of environmental variables at each AS. To preserve these areas, it is imperative to employ non-destructive methods and protect them as human heritage sites.

The future of agriculture is questionable under the current climate change scenario. Climate change and climate-related calamities directly influence biotic and abiotic factors that control agroecosystems, endangering the safety of the world's food supply. The intricate interactions between soil microorganisms, including plants, bacteria, and fungi, play a pivotal role in promoting sustainable agriculture and ecosystem restoration. Soil microbes play a major part in nutrient cycling, including soil organic carbon (SOC), and play a pivotal function in the emission and depletion of greenhouse gases, including CH4, CO2, and N2O, which can impact the climate. At this juncture, developing a triumphant metagenomics approach has greatly increased our knowledge of the makeup, functionality, and dynamics of the soil microbiome. Currently, the involvement of plants in climate change indicates that they can interact with the microbial communities in their environment to relieve various stresses through the innate microbiome assortment of focused strains, a phenomenon dubbed "Cry for Help." The metagenomics method has lately appeared as a new platform to adjust and encourage beneficial communications between plants and microbes and improve plant fitness. The metagenomics of soil microbes can provide a powerful tool for designing and evaluating ecosystem restoration strategies that promote sustainable agriculture under a changing climate. By identifying the specific functions and activities of soil microbes, we can develop restoration programs that support these critical components of healthy ecosystems while providing economic benefits through ecosystem services. In the current review, we highlight the innate functions of microbiomes to maintain the sustainability of agriculture and ecosystem restoration. Through this insight study of soil microbe metagenomics, we pave the way for innovative strategies to address the pressing challenges of food security and environmental conservation. The present article elucidates the mechanisms through which plants and microbes communicate to enhance plant resilience and ecosystem restoration and to leverage metagenomics to identify and promote beneficial plant-microbe interactions. Key findings reveal that soil microbes are pivotal in nutrient cycling, greenhouse gas modulation, and overall ecosystem health, offering novel insights into designing ecosystem restoration strategies that bolster sustainable agriculture. As this is a topic many are grappling with, hope these musings will provide people alike with some food for thought.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Microbial extracellular polymeric substances (EPS) are an important component in sediment ecology. However, most research is highly skewed towards the northern hemisphere and in more permanent systems. This paper investigates EPS (i.e., carbohydrates and proteins) dynamics in arid Austral zone temporary pans sediments. Colorimetric methods and sequence-based metagenomics techniques were employed in a series of small temporary pan ecosystems characterised by alternating wet and dry hydroperiods. Microbial community patterns of distribution were evaluated between seasons (hot-wet and cool-dry) and across depths (and inferred inundation period) based on estimated elevation. Carbohydrates generally occurred in relatively higher proportions than proteins; the carbohydrate:protein ratio was 2.8:1 and 1.6:1 for the dry and wet season respectively, suggesting that EPS found in these systems was largely diatom produced. The wet- hydroperiods (Carbohydrate mean 102 μg g-1; Protein mean 65 μg g-1) supported more EPS production as compared to the dry- hydroperiods (Carbohydrate mean 73 μg g-1; Protein mean 26 μg g-1). A total of 15,042 Unique Amplicon Sequence Variants (ASVs) were allocated to 51 bacterial phyla and 1127 genera. The most abundant genera had commonality in high temperature tolerance, with Firmicutes, Actinobacteria and Proteobacteria in high abundances. Microbial communities were more distinct between seasons compared to within seasons which further suggested that the observed metagenome functions could be seasonally driven. This study's findings implied that there were high levels of denitrification by mostly nitric oxide reductase and nitrite reductase enzymes. EPS production was high in the hot-wet season as compared to relatively lower rates of nitrification in the cool-dry season by ammonia monooxygenases. Both EPS quantities and metagenome functions were highly associated with availability of water, with high rates being mainly associated with wet- hydroperiods compared to dry- hydroperiods. These data suggest that extended dry periods threaten microbially mediated processes in temporary wetlands, with implications to loss of biodiversity by desiccation.

Endometrial cancer is a multifactorial disease with inflammatory, metabolic and potentially microbial cues involved in disease pathogenesis. The endometrial cancer microbiome has been poorly characterised so far and studies have often overestimated bacterial biomass due to lack of integration of appropriate contamination controls. There is also a scarcity of evidence on the functionality of microbial microenvironments in endometrial cancer. This work addresses that knowledge gap by interrogating the genuine, contamination-free microbial signatures in the female genital tract and rectum of women with endometrial cancer and the mechanistic role of microbiome on carcinogenic processes.

Here we sampled different regions of the reproductive tract (vagina, cervix, endometrium, fallopian tubes and ovaries) and rectum of 61 patients (37 endometrial cancer; 24 benign controls). We performed 16S rRNA gene sequencing of the V1-V2 hypervariable regions and qPCR of the 16S rRNA gene to qualitatively and quantitatively assess microbial communities and used 3D benign and endometrial cancer organoids to evaluate the effect of microbial products of L. crispatus, which was found depleted in endometrial cancer patients following primary analysis, on endometrial cell proliferation and inflammation. We found that the upper genital tract of a subset of women with and without endometrial cancer harbour microbiota quantitatively and compositionally distinguishable from background contaminants. Endometrial cancer was associated with reduced cervicovaginal and rectal bacterial load together with depletion of Lactobacillus species relative abundance, including L. crispatus, increased bacterial diversity and enrichment of Porphyromonas, Prevotella, Peptoniphilus and Anaerococcus in the lower genital tract and endometrium. Treatment of benign and malignant endometrial organoids with L. crispatus conditioned media exerted an anti-proliferative effect at high concentrations but had minimal impact on cytokine and chemokine profiles.

Our findings provide evidence that the upper female reproductive tract of some women contains detectable levels of bacteria, the composition of which is associated with endometrial cancer. Whether this is a cause or consequence of cancer pathophysiology and what is the functional significance of this finding remain to be elucidated to guide future screening tools and microbiome-based therapeutics. Video Abstract.

The bacterial communities related to seaweed can vary considerably across different locations, and these variations influence the seaweed's nutrition, growth, and development. To study this further, we evaluated the bacteria found on the green marine seaweed Ulva prolifera from Garorim Bay and Muan Bay, two key locations on Republic of Korea's west coast. Our analysis found notable differences in the bacterial communities between the two locations. Garorim Bay hosted a more diverse bacterial population, with the highest number of ASVs (871) compared to Muan Bay's 156 ASVs. In Muan Bay, more than 50% of the bacterial community was dominated by Pseudomonadota. On the other hand, Garorim Bay had a more balanced distribution between Bacteroidota and Pseudomonadota (37% and 35.5%, respectively). Additionally, Cyanobacteria, particularly Cyanothece aeruginosa, were found in significant numbers in Garorim Bay, making up 8% of the community. Mineral analysis indicated that Garorim Bay had higher levels of S, Na, Mg, Ca, and Fe. Function-wise, both locations exhibited bacterial enrichment in amino acid production, nucleosides, and nucleotide pathways. In conclusion, this study broadens our understanding of the bacterial communities associated with Ulva prolifera in Korean waters and provides a foundation for future research on the relationships between U. prolifera and its bacteria.