Metagenomic tools in microbial ecology research

MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques

BACKGROUND

In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes.

RESULTS

The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest.

CONCLUSIONS

The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

The impact of land-use changes and management intensification on bacterial communities in the last decade: a review

In the last decade, advances in soil bacterial ecology have contributed to increasing agricultural production. Brazil is the world leading agriculture producer and leading soil biodiversity reservoir. Meanwhile, there is still a significant gap in the knowledge regarding the soil microscopic life and its interactions with agricultural practices, and the replacement of natural vegetation by agroecosystems is yet to be unfolded. Through high throughput DNA sequencing, scientists are now exploring the complexity of soil bacterial communities and their relationship with soil and environmental characteristics. This study aimed to investigate the progress of bacterial ecology studies in Brazil over the last 10 years, seeking to understand the effect of the conversion of natural vegetation in agricultural systems on the diversity and structure of the soil microbial communities. We conducted a systematic search for scientific publication databases. Our systematic search has matched 62 scientific articles from three different databases. Most of the studies were placed in southeastern and northern Brazil, with no records of studies about microbial ecology in 17 out of 27 Brazilian states. Out of the 26 studies that examined the effects of replacing natural vegetation with agroecosystems, most authors concluded that changes in soil pH and vegetation cover replacement were the primary drivers of shifts in microbial communities. Understanding the ecology of the bacteria inhabiting Brazilian soils in agroecosystems is paramount for developing more efficient soil management strategies and cleaner agricultural technologies.

Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems

Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.

Living in mangroves: a syntrophic scenario unveiling a resourceful microbiome

BACKGROUND

Mangroves are complex and dynamic coastal ecosystems under frequent fluctuations in physicochemical conditions related to the tidal regime. The frequent variation in organic matter concentration, nutrients, and oxygen availability, among other factors, drives the microbial community composition, favoring syntrophic populations harboring a rich and diverse, stress-driven metabolism. Mangroves are known for their carbon sequestration capability, and their complex and integrated metabolic activity is essential to global biogeochemical cycling. Here, we present a metabolic reconstruction based on the genomic functional capability and flux profile between sympatric MAGs co-assembled from a tropical restored mangrove.

RESULTS

Eleven MAGs were assigned to six Bacteria phyla, all distantly related to the available reference genomes. The metabolic reconstruction showed several potential coupling points and shortcuts between complementary routes and predicted syntrophic interactions. Two metabolic scenarios were drawn: a heterotrophic scenario with plenty of carbon sources and an autotrophic scenario with limited carbon sources or under inhibitory conditions. The sulfur cycle was dominant over methane and the major pathways identified were acetate oxidation coupled to sulfate reduction, heterotrophic acetogenesis coupled to carbohydrate catabolism, ethanol production and carbon fixation. Interestingly, several gene sets and metabolic routes similar to those described for wastewater and organic effluent treatment processes were identified.

CONCLUSION

The mangrove microbial community metabolic reconstruction reflected the flexibility required to survive in fluctuating environments as the microhabitats created by the tidal regime in mangrove sediments. The metabolic components related to wastewater and organic effluent treatment processes identified strongly suggest that mangrove microbial communities could represent a resourceful microbial model for biotechnological applications that occur naturally in the environment.

Application Progress of Culturomics in the Isolated Culture of Rhizobacteria: A Review

Comprehending the structure and function of rhizobacteria components and their regulation are crucial for sustainable agricultural management. However, obtaining comprehensive species information for most bacteria in the natural environment, particularly rhizobacteria, presents a challenge using traditional culture methods. To obtain diverse and pure cultures of rhizobacteria, this study primarily reviews the evolution of rhizobacteria culturomics and associated culture methods. Furthermore, it explores new strategies for enhancing the application of culturomics, providing valuable insights into efficiently enriching and isolate target bacterial strains/groups from the environment. The findings will help improve rhizobacteria's culturability and enrich the functional bacterial library.

EcoGenoRisk: Developing a computational ecological risk assessment tool for synthetic biology

The expanding field of synthetic biology (synbio) supports new opportunities in the design of targeted bioproducts or modified microorganisms. However, this rapid development of synbio products raises concerns surrounding the potential risks of modified microorganisms contaminating unintended environments. These potential invasion risks require new bioinformatic tools to inform the design phase. EcoGenoRisk is a newly constructed computational risk assessment tool for invasiveness that aims to predict where synbio microorganisms may establish a population by screening for habitats of genetically similar microorganisms. The first module of the tool identifies genetically similar microorganisms and potential ecological relationships such as competition, mutualism, and inhibition. In total, 520 archaeal and 32,828 bacterial complete assembly genomes were analyzed to test the specificity and accuracy of the tool as well as to characterize the enzymatic profiles of different taxonomic lineages. Additionally, ecological relationships were analyzed to determine which would result in the greatest potential overlap between shared functional profiles. Notably, competition displayed the significantly highest overlap of shared functions between compared genomes. Overall, EcoGenoRisk is a flexible software pipeline that assists environmental risk assessors to query large databases of known microorganisms and prioritize follow-up bench scale studies.

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.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

Cutting edge tools in the field of soil microbiology

The study of the whole of the genetic material contained within the microbial populations found in a certain environment is made possible by metagenomics. This technique enables a thorough knowledge of the variety, function, and interactions of microbial communities that are notoriously difficult to research. Due to the limitations of conventional techniques such as culturing and PCR-based methodologies, soil microbiology is a particularly challenging field. Metagenomics has emerged as an effective technique for overcoming these obstacles and shedding light on the dynamic nature of the microbial communities in soil. This review focuses on the principle of metagenomics techniques, their potential applications and limitations in soil microbial diversity analysis. The effectiveness of target-based metagenomics in determining the function of individual genes and microorganisms in soil ecosystems is also highlighted. Targeted metagenomics, including high-throughput sequencing and stable-isotope probing, is essential for studying microbial taxa and genes in complex ecosystems. Shotgun metagenomics may reveal the diversity of soil bacteria, composition, and function impacted by land use and soil management. Sanger, Next Generation Sequencing, Illumina, and Ion Torrent sequencing revolutionise soil microbiome research. Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)'s third and fourth generation sequencing systems revolutionise long-read technology. GeoChip, clone libraries, metagenomics, and metabarcoding help comprehend soil microbial communities. The article indicates that metagenomics may improve environmental management and agriculture despite existing limitations.Metagenomics has revolutionised soil microbiology research by revealing the complete diversity, function, and interactions of microorganisms in soil. Metagenomics is anticipated to continue defining the future of soil microbiology research despite some limitations, such as the difficulty of locating the appropriate sequencing method for specific genes.

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.

A Novel Bacillus Velezensis for Efficient Degradation of Zearalenone

Zearalenone (ZEN) is considered one of the most serious mycotoxins contaminating grains and their by-products, causing significant economic losses in the feed and food industries. Biodegradation pathways are currently considered the most efficient solution to remove ZEN contamination from foods. However, low degradation rates and vulnerability to environmental impacts limit the application of biodegradation pathways. Therefore, the main research objective of this article was to screen strains that can efficiently degrade ZEN and survive under harsh conditions. This study successfully isolated a new strain L9 which can efficiently degrade ZEN from 108 food ingredients. The results of sequence alignment showed that L9 is Bacillus velezensis. Meanwhile, we found that the L9 degradation rate reached 91.14% at 24 h and confirmed that the primary degradation mechanism of this strain is biodegradation. The strain exhibits resistance to high temperature, acid, and 0.3% bile salts. The results of whole-genome sequencing analysis showed that, it is possible that the strain encodes the key enzyme, such as chitinase, carboxylesterases, and lactone hydrolase, that work together to degrade ZEN. In addition, 227 unique genes in this strain are primarily involved in its replication, recombination, repair, and protective mechanisms. In summary, we successfully excavated a ZEN-degrading, genetically distinct strain of Bacillus velezensis that provides a solid foundation for the detoxification of feed and food contamination in the natural environment.

Extracellular Vehicles from Commensal Skin Malassezia restricta Inhibit Staphylococcus aureus Proliferation and Biofilm Formation

The colonizing microbiota on the body surface play a crucial role in barrier function. Staphylococcus aureus (S. aureus) is a significant contributor to skin infection, and the utilization of colonization resistance of skin commensal microorganisms to counteract the invasion of pathogens is a viable approach. However, most studies on colonization resistance have focused on skin bacteria, with limited research on the resistance of skin fungal communities to pathogenic bacteria. Extracellular vehicles (EVs) play an important role in the colonization of microbial niches and the interaction between distinct strains. This paper explores the impact of Malassezia restricta (M. restricta), the fungus that dominates the normal healthy skin microbiota, on the proliferation of S. aureus by examining the distribution disparities between the two microorganisms. Based on the extraction of EVs, the bacterial growth curve, and biofilm formation, it was determined that the EVs of M. restricta effectively suppressed the growth and biofilm formation of S. aureus. The presence of diverse metabolites was identified as the primary factor responsible for the growth inhibition of S. aureus, specifically in relation to glycerol phospholipid metabolism, ABC transport, and arginine synthesis. These findings offer valuable experimental evidence for understanding microbial symbiosis and interactions within healthy skin.

Genome-Resolved Metagenomics and Denitrifying Strain Isolation Reveal New Insights into Microbial Denitrification in the Deep Vadose Zone

The heavy use of nitrogen fertilizer in intensive agricultural areas often leads to nitrate accumulation in subsurface soil and nitrate contamination in groundwater, which poses a serious risk to public health. Denitrifying microorganisms in the subsoil convert nitrate to gaseous forms of nitrogen, thereby mitigating the leaching of nitrate into groundwater. Here, we investigated denitrifying microorganisms in the deep vadose zone of a typical intensive agricultural area in China through microcosm enrichment, genome-resolved metagenomic analysis, and denitrifying bacteria isolation. A total of 1000 metagenome-assembled genomes (MAGs) were reconstructed, resulting in 98 high-quality, dereplicated MAGs that contained denitrification genes. Among them, 32 MAGs could not be taxonomically classified at the genus or species level, indicating that a broader spectrum of taxonomic groups is involved in subsoil denitrification than previously recognized. A denitrifier isolate library was constructed by using a strategy combining high-throughput and conventional cultivation techniques. Assessment of the denitrification characteristics of both the MAGs and isolates demonstrated the dominance of truncated denitrification. Functional screening revealed the highest denitrification activity in two complete denitrifiers belonging to the genus Pseudomonas. These findings greatly expand the current knowledge of the composition and function of denitrifying microorganisms in subsoils. The constructed isolate library provided the first pool of subsoil-denitrifying microorganisms that could facilitate the development of microbe-based technologies for nitrate attenuation in groundwater.

Microbiome-functionality in anaerobic digesters: A critical review

Microbially driven anaerobic digestion (AD) processes are of immense interest due to their role in the biovalorization of biowastes into renewable energy resources. The function-versatile microbiome, interspecies syntrophic interactions, and trophic-level metabolic pathways are important microbial components of AD. However, the lack of a comprehensive understanding of the process hampers efforts to improve AD efficiency. This study presents a holistic review of research on the microbial and metabolic "black box" of AD processes. Recent research on microbiology, functional traits, and metabolic pathways in AD, as well as the responses of functional microbiota and metabolic capabilities to optimization strategies are reviewed. The diverse ecophysiological traits and cooperation/competition interactions of the functional guilds and the biomanipulation of microbial ecology to generate valuable products other than methane during AD are outlined. The results show that AD communities prioritize cooperation to improve functional redundancy, and the dominance of specific microbes can be explained by thermodynamics, resource allocation models, and metabolic division of labor during cross-feeding. In addition, the multi-omics approaches used to decipher the ecological principles of AD consortia are summarized in detail. Lastly, future microbial research and engineering applications of AD are proposed. This review presents an in-depth understanding of microbiome-functionality mechanisms of AD and provides critical guidance for the directional and efficient bioconversion of biowastes into methane and other valuable products.

Challenges in estimating effective population sizes from metagenome-assembled genomes

Effective population size (Ne) plays a critical role in shaping the relative efficiency between natural selection and genetic drift, thereby serving as a cornerstone for understanding microbial ecological dynamics. Direct Ne estimation relies on neutral genetic diversity within closely related genomes, which is, however, often constrained by the culturing difficulties for the vast majority of prokaryotic lineages. Metagenome-assembled genomes (MAGs) offer a high-throughput alternative for genomic data acquisition, yet their accuracy in Ne estimation has not been fully verified. This study examines the Thermococcus genus, comprising 66 isolated strains and 29 MAGs, to evaluate the reliability of MAGs in Ne estimation. Despite the even distribution across the Thermococcus phylogeny and the comparable internal average nucleotide identity (ANI) between isolate populations and MAG populations, our results reveal consistently lower Ne estimates from MAG populations. This trend of underestimation is also observed in various MAG populations across three other bacterial genera. The underrepresentation of genetic variation in MAGs, including loss of allele frequency data and variable genomic segments, likely contributes to the underestimation of Ne. Our findings underscore the necessity for caution when employing MAGs for evolutionary studies, which often depend on high-quality genome assemblies and nucleotide-level diversity.

From Metagenomics to Ecogenomics: NGS-Based Approaches for Discovery of New Circular DNA Single-Stranded Viral Species

Viruses comprise the most abundant genetic material in the biosphere; however, global viral genomic population (virome) has been largely underestimated. Recently, high-throughput sequencing (HTS) has provided a powerful tool for the detection of known viruses and the discovery of novel viral species from environmental and individual samples using metagenomics and ecogenomics approaches, respectively. Viruses with circular DNA single-stranded (ssDNA) genomes belonging to the begomovirus genera (family Geminiviridae) constitute the largest group of emerging plant viruses worldwide. The knowledge of begomoviruses viromes is mostly restricted to crop plant systems; nevertheless, it has been described that noncultivated plants specifically at the interface between wild and cultivated plants are important reservoirs leading to viral evolution and the emergence of new diseases. Here we present a protocol that allows the identification and isolation of known and novel begomoviruses species infecting cultivated and noncultivated plant species. The method consists of circular viral molecules enrichment by rolling circle amplification (RCA) from begomovirus-positive total plant DNA, followed by NGS-based metagenomic sequencing. Subsequently, metagenomic reads are processed for taxonomic classification using Viromescan software and a customized Geminiviridae family database, and begomovirus-related reads are used for contigs assembly and annotation using Spades software and Blastn algorithm, respectively. Then, the obtained begomovirus-related signatures are used as templates for specific primers design and implemented for PCR-based ecogenomic identification of individual samples harboring the corresponding viral species. Lastly, full-length begomovirus genomes are obtained by RCA-based amplification from total plant DNA of selected individual samples, cloning, and viral molecular identity corroborated by Sanger sequencing. Conclusively, the identification and isolation of a novel monopartite begomovirus species native to the New World (NW) named Gallium leaf deformation virus (GLDV) is shown.

A growing battlefield in the war against biofilm-induced antimicrobial resistance: insights from reviews on antibiotic resistance

Biofilms are a common survival strategy employed by bacteria in healthcare settings, which enhances their resistance to antimicrobial and biocidal agents making infections difficult to treat. Mechanisms of biofilm-induced antimicrobial resistance involve reduced penetration of antimicrobial agents, increased expression of efflux pumps, altered microbial physiology, and genetic changes in the bacterial population. Factors contributing to the formation of biofilms include nutrient availability, temperature, pH, surface properties, and microbial interactions. Biofilm-associated infections can have serious consequences for patient outcomes, and standard antimicrobial therapies are often ineffective against biofilm-associated bacteria, making diagnosis and treatment challenging. Novel strategies, including antibiotics combination therapies (such as daptomycin and vancomycin, colistin and azithromycin), biofilm-targeted agents (such as small molecules (LP3134, LP3145, LP4010, LP1062) target c-di-GMP), and immunomodulatory therapies (such as the anti-PcrV IgY antibodies which target Type IIIsecretion system), are being developed to combat biofilm-induced antimicrobial resistance. A multifaceted approach to diagnosis, treatment, and prevention is necessary to address this emerging problem in healthcare settings.

Benchmarking DNA isolation methods for marine metagenomics

Metagenomics is a powerful tool to study marine microbial communities. However, obtaining high-quality environmental DNA suitable for downstream sequencing applications is a challenging task. The quality and quantity of isolated DNA heavily depend on the choice of purification procedure and the type of sample. Selection of an appropriate DNA isolation method for a new type of material often entails a lengthy trial and error process. Further, each DNA purification approach introduces biases and thus affects the composition of the studied community. To account for these problems and biases, we systematically investigated efficiency of DNA purification from three types of samples (water, sea sediment, and digestive tract of a model invertebrate Magallana gigas) with eight commercially available DNA isolation kits. For each kit-sample combination we measured the quantity of purified DNA, extent of DNA fragmentation, the presence of PCR-inhibiting contaminants, admixture of eukaryotic DNA, alpha-diversity, and reproducibility of the resulting community composition based on 16S rRNA amplicons sequencing. Additionally, we determined a "kitome", e.g., a set of contaminating taxa inherent for each type of purification kit used. The resulting matrix of evaluated parameters allows one to select the best DNA purification procedure for a given type of sample.

Diversity, Taxonomic Novelty, and Encoded Functions of Salar de Ascotán Microbiota, as Revealed by Metagenome-Assembled Genomes

Salar de Ascotán is a high-altitude arsenic-rich salt flat exposed to high ultraviolet radiation in the Atacama Desert, Chile. It hosts unique endemic flora and fauna and is an essential habitat for migratory birds, making it an important site for conservation and protection. However, there is limited information on the resident microbiota's diversity, genomic features, metabolic potential, and molecular mechanisms that enable it to thrive in this extreme environment. We used long- and short-read metagenomics to investigate the microbial communities in Ascotán's water, sediment, and soil. Bacteria predominated, mainly Pseudomonadota, Acidobacteriota, and Bacteroidota, with a remarkable diversity of archaea in the soil. Following hybrid assembly, we recovered high-quality bacterial (101) and archaeal (6) metagenome-assembled genomes (MAGs), including representatives of two putative novel families of Patescibacteria and Pseudomonadota and two novel orders from the archaeal classes Halobacteriota and Thermoplasmata. We found different metabolic capabilities across distinct lineages and a widespread presence of genes related to stress response, DNA repair, and resistance to arsenic and other metals. These results highlight the remarkable diversity and taxonomic novelty of the Salar de Ascotán microbiota and its rich functional repertoire, making it able to resist different harsh conditions. The highly complete MAGs described here could serve future studies and bioprospection efforts focused on salt flat extremophiles, and contribute to enriching databases with microbial genome data from underrepresented regions of our planet.

Investigating non-fungal eukaryotic diversity in snow in the Antarctic Peninsula region using DNA metabarcoding

Snow is a unique microhabitat, despite being a harsh environment, multiple life forms have adapted to survive in it. While algae, bacteria and fungi are dominant microorganisms in Antarctic snow, little is known about other organisms that may be present in this habitat. We used metabarcoding to investigate DNA sequence diversity of non-fungal eukaryotes present in snow obtained from six different sites across the Maritime Antarctica. A total of 20 taxa were assigned to obtained sequences, representing five Kingdoms (Chromista, Protozoa, Viridiplantae and Metazoa) and four phyla (Ciliophora, Cercozoa, Chlorophyta and Cnidaria). The highest diversity indices were detected in Trinity Peninsula followed by Robert Island, Arctowski Peninsula, Deception Island, King George Island and Snow Island. The most abundant assignments were to Trebouxiophyceae, followed by Chlamydomonas nivalis and Chlamidomonadales. No taxa were detected at all sites. Three potentially new records for Antarctica were detected: two Ciliophora (Aspidisca magna and Stokesia sp.) and the green algae Trebouxia potteri. Our data suggested that similarities found between the sites may be more related with snow physicochemical properties rather than geographic proximity or latitude. This study provides new insights into the diversity and distribution of eukaryotic organisms in Antarctic snow.

The Raphidiopsis (= Cylindrospermopsis) raciborskii pangenome updated: Two new metagenome-assembled genomes from the South American clade

Two Raphidiopsis (=Cylindrospermopsis) raciborskii metagenome-assembled genomes (MAGs) were recovered from two freshwater metagenomic datasets sampled in 2011 and 2012 in Pampulha Lake, a hypereutrophic, artificial, shallow reservoir, located in the city of Belo Horizonte (MG), Brazil. Since the late 1970s, the lake has undergone increasing eutrophication pressure, due to wastewater input, leading to the occurrence of frequent cyanobacterial blooms. The major difference observed between PAMP2011 and PAMP2012 MAGs was the lack of the saxitoxin gene cluster in PAMP2012, which also presented a smaller genome, while PAMP2011 presented the complete sxt cluster and all essential proteins and clusters. The pangenome analysis was performed with all Raphidiopsis/Cylindrospermopsis genomes available at NCBI to date, with the addition of PAMP2011 and PAMP2012 MAGs (All33 subset), but also without the South American strains (noSA subset), and only among the South American strains (SA10 and SA8 subsets). We observed a substantial increase in the core genome size for the 'noSA' subset, in comparison to 'All33' subset, and since the core genome reflects the closeness among the pangenome members, the results strongly suggest that the conservation level of the essential gene repertoire seems to be affected by the geographic origin of the strains being analyzed, supporting the existence of a distinct SA clade. The Raphidiopsis pangenome comprised a total of 7943 orthologous protein clusters, and the two new MAGs increased the pangenome size by 11%. The pangenome based phylogenetic relationships among the 33 analyzed genomes showed that the SA genomes clustered together with 99% bootstrap support, reinforcing the metabolic particularity of the Raphidiopsis South American clade, related to its saxitoxin producing unique ability, while also indicating a different evolutionary history due to its geographic isolation.

Effects of Anthropogenic Disturbance and Seasonal Variation on Aerobiota in Highly Visited Show Caves in Slovenia

Aerosols in caves are natural tracers and, together with climatic parameters, provide a detailed insight into atmospheric conditions, responses to climatic changes and anthropogenic influences in caves. Microbiological air monitoring in show caves is becoming increasingly useful to understand changes in cave ecosystems and to implement and review measures for sustainable cave use and tourism development. In 2017 and 2018, air along tourist trails in caves Postojnska jama and Škocjanske jame (Slovenia) was sampled before and after tourist visits. Samples were analysed using culture-dependent methods, flow cytometry, detection of β-D-glucan and lipopolysaccharide and compared with CO2 and temperature data to measure anthropogenic influences and seasonality on aerobiota. While the presence of tourists significantly increased concentrations of airborne microorganisms (p < 0.05), β-D-glucan and CO2 did not show such a trend and were more dependent on seasonal changes. Locally, concentrations of cultivable microorganisms above 1000 CFU/m3 were detected, which could have negative effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria typically associated with humans was found in the air and identified with MALDI-TOF MS. Using MALDI-TOF MS, we achieved a 69.6% success rate in identification. Micrococcus luteus, Streptococcus mitis, Staphylococcus epidermidis and Moraxella spp. were recognized as good indicators of cave anthropisation.

Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interaction

Plant growth-promoting rhizobacteria (PGPRs) are bacteria that colonize the plant roots. These beneficial bacteria have an influence on plant development through multiple mechanisms, such as nutrient availability, alleviating biotic and abiotic stress, and secrete phytohormones. Therefore, their inoculation constitutes a powerful tool towards sustainable agriculture and crop production. To understand plant-PGPRs interaction we present the classification of PGPR using machine learning and meta-learning classifiers namely Support Vector Machine (SVM), Kernel Logistic Regression (KLR), meta-SVM and meta-KLR to predict the presence of Bacillus megaterium inoculated in tomato root tissues using publicly available transcriptomic data. The original dataset presents 36 significantly differentially expressed genes. As the meta-KLR achieved near-optimal performance considering all the relevant metrics, this meta learner was afterwards used to identify the informative genes (IGs). The outcomes showed 157 IGs, being present all significantly differentially expressed genes previously identified. Among the IGs, 113 were identified as tomato genes, 5 as Bacillus subtilis proteins, 1 as Escherichia coli protein and 6 were unidentified. Then, a functional enrichment analysis of the tomato IGs showed 175 biological processes, 22 molecular functions and 20 KEGG pathways involved in B. megaterium-tomato interaction. Furthermore, the biological networks study of their Arabidopsis thaliana orthologous genes identified the co-expression, predicted interaction, shared protein domains and co-localization networks.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03690-0.

A three-species synthetic community model whose rapid response to antagonism allows the study of higher-order dynamics and emergent properties in minutes

Microbial communities can be considered complex adaptive systems. Understanding how these systems arise from different components and how the dynamics of microbial interactions allow for species coexistence are fundamental questions in ecology. To address these questions, we built a three-species synthetic community, called BARS (Bacillota A + S + R). Each species in this community exhibits one of three ecological roles: Antagonistic, Sensitive, or Resistant, assigned in the context of a sediment community. We show that the BARS community reproduces features of complex communities and exhibits higher-order interaction (HOI) dynamics. In paired interactions, the majority of the S species (Sutcliffiella horikoshii 20a) population dies within 5 min when paired with the A species (Bacillus pumilus 145). However, an emergent property appears upon adding the third interactor, as antagonism of species A over S is not observed in the presence of the R species (Bacillus cereus 111). For the paired interaction, within the first 5 min, the surviving population of the S species acquires tolerance to species A, and species A ceases antagonism. This qualitative change reflects endogenous dynamics leading to the expression for tolerance to an antagonistic substance. The stability reached in the triple interaction exhibits a nonlinear response, highly sensitive to the density of the R species. In summary, our HOI model allows the study of the assembly dynamics of a three-species community and evaluating the immediate outcome within a 30 min frame. The BARS has features of a complex system where the paired interactions do not predict the community dynamics. The model is amenable to mechanistic dissection and to modeling how the parts integrate to achieve collective properties.

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.

Composition, structure, and functional shifts of prokaryotic communities in response to co-composting of various nitrogenous green feedstocks

BACKGROUND

Thermophilic composting is a promising method of sanitizing pathogens in manure and a source of agriculturally important thermostable enzymes and microorganisms from organic wastes. Despite the extensive studies on compost prokaryotes, shifts in microbial profiles under the influence of various green materials and composting days are still not well understood, considering the complexity of the green material sources. Here, the effect of regimens of green composting material on the diversity, abundance, and metabolic capacity of prokaryotic communities in a thermophilic compost environment was examined.

METHODS

Total community 16S rRNA was recovered from triplicate compost samples of Lantana-based, Tithonia-based, Grass-based, and mixed (Lantana + Tithonia + Grass)- based at 21, 42, 63, and 84 days of composting. The 16S rRNA was sequenced using the Illumina Miseq platform. Bioinformatics analysis was done using Divisive Amplicon Denoising Algorithm version 2 (DADA2) R version 4.1 and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2 (PICRUSt2) pipelines for community structure and metabolic profiles, respectively. In DADA2, prokaryotic classification was done using the Refseq-ribosomal database project (RDP) and SILVA version 138 databases.

RESULTS

Our results showed apparent differences in prokaryotic community structure for total diversity and abundance within the four compost regimens and composting days. The study showed that the most prevalent phyla during composting included Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, and Proteobacteria. Additionally, there were differences in the overall diversity of metabolic pathways but no significant differences among the various compost treatments on major metabolic pathways like carbohydrate biosynthesis, carbohydrate degradation, and nitrogen biosynthesis.

CONCLUSION

Various sources of green material affect the succession of compost nutrients and prokaryotic communities. The similarity of amounts of nutrients, such as total Nitrogen, at the end of the composting process, despite differences in feedstock material, indicates a significant influence of composting days on the stability of nutrients during composting.

Exploring antibiotic resistance genes, mobile gene elements, and virulence gene factors in an urban freshwater samples using metagenomic analysis

Antibiotic resistance genes (ARGs) and antimicrobial resistance elements (AMR) are novel environmental contaminants that pose a significant risk to human health globally. Freshwater contains a variety of microorganisms that might affect human health; its quality must be assessed before use. However, the dynamics of mobile genetic elements (MGEs) and ARG propagation in freshwater have rarely been studied in Singapore. Therefore, this study used metagenomics to compare diversity, virulence factor composition, and ARG and MGE co-occurrence with bacterial communities in paired (n = 8) environmental freshwater samples. KneadData, FMAP, and Kraken2 were used for bioinformatics analysis and R (v4.1.1) for statistical analysis. Sequence reads with a total of 9043 species were taxonomically classified into 66 phyla, 130 classes, 261 orders, 584 families, and 2477 genera. Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes were found the Phyla in all samples. Analysis of QIIME output by PICRUSt and ß-diversity showed unique clusters and functional microbial community structures. A total of 2961 ARGs were found that conferred resistance to multidrug, aminoglycosides, tetracyclines, elfamycins, and more. The classified ARG mechanism revealed significant distribution of virulence factors in bacterial cells. Transposes and transposon were highly correlated to ARG gene transfer. Co-occurrence network analysis showed several MGEs appear to use the same ARGs (intI and rho) and were dominant in all samples. Furthermore, ARGs are also highly correlated with bacteria like Campylobacter and Escherichia. This study enhances the understanding of antibiotic risk assessment and provides a new perspective on bacterial assembly contamination and the functional prevalence of ARGs and MGEs with antibiotic resistance bacteria. Moreover, it raises public awareness because these contaminants put people's lives at risk of acquiring bacterial infections. In addition, it can also help propose hybrid water treatment approaches.

Domain-level Identification of Single Prokaryotic Cells by Optical Photothermal Infrared Spectroscopy

Infrared spectroscopy is used for the chemical characterization of prokaryotes. However, its application has been limited to cell aggregates and lipid extracts because of the relatively low spatial resolution of diffraction. We herein report optical photothermal infrared (O-PTIR) spectroscopy of prokaryotes for a domain-level diagnosis at the single-cell level. The technique provided infrared spectra of individual bacterial as well as archaeal cells, and the resulting aliphatic CH3/CH2 intensity ratios showed domain-specific signatures, which may reflect distinctive cellular lipid compositions; however, there was interference by other cellular components. These results suggest the potential of O-PTIR for a domain-level diagnosis of single prokaryotic cells in natural environments.

Challenges and opportunities in bringing nonbiological atoms to life with synthetic metabolism

The relatively narrow spectrum of chemical elements within the microbial 'biochemical palate' limits the reach of biotechnology, because several added-value compounds can only be produced with traditional organic chemistry. Synthetic biology offers enabling tools to tackle this issue by facilitating 'biologization' of non-canonical chemical atoms. The interplay between xenobiology and synthetic metabolism multiplies routes for incorporating nonbiological atoms into engineered microbes. In this review, we survey natural assimilation routes for elements beyond the essential biology atoms [i.e., carbon (C), hydrogen (H), nitrogen (N), oxygen (O), phosphorus (P), and sulfur (S)], discussing how these mechanisms could be repurposed for biotechnology. Furthermore, we propose a computational framework to identify chemical elements amenable to biologization, ranking reactions suitable to build synthetic metabolism. When combined and deployed in robust microbial hosts, these approaches will offer sustainable alternatives for smart chemical production.

Effects of dietary crude protein levels in the concentrate supplement after grazing on rumen microbiota and metabolites by using metagenomics and metabolomics in Jersey-yak

Introduction

The crude protein level in the diet will affect the fermentation parameters, microflora, and metabolites in the rumen of ruminants. It is of great significance to study the effect of crude protein levels in supplementary diet on microbial community and metabolites for improving animal growth performance. At present, the effects of crude protein level in supplementary diet on rumen fermentation parameters, microbial community, and metabolites of Jersey-Yak (JY) are still unclear.

Methods

The purpose of this experiment was to study the appropriate crude protein level in the diet of JY. The rumen fermentation indexes (volatile fatty acids and pH) were determined by supplementary diets with crude protein levels of 15.16 and 17.90%, respectively, and the microbial community and metabolites of JYs were analyzed by non-target metabonomics and metagenome sequencing technology, and the changes of rumen fermentation parameters, microbial flora, and metabolites in the three groups and their interactions were studied.

Results and Discussion

The crude protein level in the supplementary diet had significant effects on pH, valeric acid, and the ratio of acetic acid to propionic acid (p < 0.05). The protein level had no significant effect on the dominant microflora at the phylum level (p > 0.05), and all three groups were Bacteroides and Firmicutes. The results of metabolite analysis showed that the crude protein level of supplementary diet significantly affected the metabolic pathways such as Bile secretion and styrene degradation (p < 0.05), and there were different metabolites between the LP group and HP group, and these different metabolites were related to the dominant microbial to some extent. To sum up, in this experiment, the effects of crude protein level in supplementary diet on rumen microorganisms and metabolites of JY and their relationship were studied, which provided the theoretical basis for formulating a more scientific and reasonable supplementary diet in the future.

Valorization as a biofertilizer of an agricultural residue leachate: Metagenomic characterization and growth promotion test by PGPB in the forage plant Medicago sativa (alfalfa)

The abuse of chemical fertilizers in intensive agriculture has turned out in the contamination of ground and the soil on which they are applied. Likewise, the generation, storage, and destruction of plant residues from the agri-food industry poses a threat to the environment and human health. The current situation of growing demand for food implies the urgent need to find sustainable alternatives to chemical fertilizers and the management of agricultural waste. Valorization of this plant residue to produce natural biofertilizers using microbiological treatments is presented as a sustainable alternative. The microbial activity allows the transformation into simple molecules that are easily absorbed by plants, as well as the stimulation of plant growth. This double direct and indirect action induced significant increases against the variables of germination, viability, and biomass (dry weight). To guarantee biosafety, it is necessary to use new bio-technological tools, such as metagenomics, which allow the taxonomic analysis of microbial communities, detecting the absence of pathogens. In the present paper, a physicochemical and metagenomic characterization of a fertilizer obtained from agricultural plant waste valorization is carried out. Likewise, fertigation treatments were tested to which the Plant Growth Promoting Bacteria (PGPB) Pseudomonas agronomica and Bacillus pretiosus were added, both independently and in consortium. Metagenomic analysis has identified taxa belonging to the kingdoms Bacteria and Archaea; 10 phyla, 25 families, 32 genera and 34 species, none of them previously described as pathogenic. A 1/512 dilution of the fertilizer increased the germination rate of Medicago sativa (alfalfa) by 16% at 144 h, compared to the treatment without fertilizer. Both the fertilizer and the addition of PGPB in a double direct and indirect action induced significant increases against the variables of germination, viability, and biomass (dry weight). Therefore, the use of an agricultural residue is proposed, which after the addition of two new species is transformed into a biofertilizer that significantly induces plant growth in Mendicago sativa plants.

Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.

Next Generation Sequencing Approaches to Characterize the Respiratory Tract Virome

The COVID-19 pandemic and heightened perception of the risk of emerging viral infections have boosted the efforts to better understand the virome or complete repertoire of viruses in health and disease, with a focus on infectious respiratory diseases. Next-generation sequencing (NGS) is widely used to study microorganisms, allowing the elucidation of bacteria and viruses inhabiting different body systems and identifying new pathogens. However, NGS studies suffer from a lack of standardization, in particular, due to various methodological approaches and no single format for processing the results. Here, we review the main methodological approaches and key stages for studies of the human virome, with an emphasis on virome changes during acute respiratory viral infection, with applications for clinical diagnostics and epidemiologic analyses.

Diversity in the soil virosphere: to infinity and beyond?

Viruses are key members of Earth's microbiomes, shaping microbial community composition and metabolism. Here, we describe recent advances in 'soil viromics', that is, virus-focused metagenome and metatranscriptome analyses that offer unprecedented windows into the soil virosphere. Given the emerging picture of high soil viral activity, diversity, and dynamics over short spatiotemporal scales, we then outline key eco-evolutionary processes that we hypothesize are the major diversity drivers for soil viruses. We argue that a community effort is needed to establish a 'global soil virosphere atlas' that can be used to address the roles of viruses in soil microbiomes and terrestrial biogeochemical cycles across spatiotemporal scales.

Viruses of Atlantic Bonefish (Albula vulpes) in Florida and the Caribbean show geographic patterns consistent with population declines

Atlantic Bonefish (Albula vulpes) are economically important due to their popularity with recreational anglers. In the State of Florida, USA, bonefish population numbers declined by approximately 60% between the 1990s and 2015. Habitat loss, water quality impairment, chemical inputs, and other anthropogenic factors have been implicated as causes, but the role of pathogens has been largely overlooked, especially with respect to viruses. We used a metagenomic approach to identify and quantify viruses in the blood of 103 A. vulpes sampled throughout their Western Atlantic range, including populations in Florida that have experienced population declines and populations in Belize, Mexico, Puerto Rico, and The Bahamas that have remained apparently stable. We identified four viruses, all of which are members of families known to infect marine fishes (Flaviviridae, Iflaviridae, Narnaviridae, and Nodaviridae), but all of which were previously undescribed. Bonefish from Florida and Mexico had higher viral richness (numbers of distinct viruses per individual fish) than fish sampled from other areas, and bonefish from the Upper Florida Keys had the highest prevalence of viral infection (proportion of positive fish) than fish sampled from any other location. Bonefish from Florida also had markedly higher viral loads than fish sampled from any other area, both for a novel narnavirus and for all viruses combined. Bonefish viruses may be indicators of environmentally driven physiological and immunological compromise, causes of ill health, or both.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10641-022-01306-9.

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil

Extensive use of agrochemicals is emerging as a serious environmental issue coming at the cost of the pollution of soil and water resources. Bioremediation techniques such as biostimulation are promising strategies used to remove pollutants from agricultural soils by supporting the indigenous microbial degraders. Though considered cost-effective and eco-friendly, the success rate of these strategies typically varies, and consequently, they are rarely integrated into commercial agricultural practices. In the current study, we applied metabolic-based community-modeling approaches for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Efficacy of biostimulants as enhancers of the indigenous degrader Paenarthrobacter was ranked through simulation and validated in pot experiments. A two-dimensional simulation matrix predicting the effect of different biostimulants on additional potential indigenous degraders (Pseudomonas, Clostridium, and Geobacter) was crossed with experimental observations. The overall ability of the models to predict the compounds that act as taxa-selective stimulants indicates that computational algorithms can guide the manipulation of the soil microbiome in situ and provides an additional step toward the educated design of biostimulation strategies. IMPORTANCE Providing the food requirements of a growing population comes at the cost of intensive use of agrochemicals, including pesticides. Native microbial soil communities are considered key players in the degradation of such exogenous substances. Manipulating microbial activity toward an optimized outcome in efficient biodegradation processes conveys a promise of maintaining intensive yet sustainable agriculture. Efficient strategies for harnessing the native microbiome require the development of approaches for processing big genomic data. Here, we pursued metabolic modeling for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Our genomic-based predictions point at strategies for optimizing biodegradation by the native community. Developing a systematic, data-guided understanding of metabolite-driven targeted enhancement of selected microorganisms lays the foundation for the design of ecologically sound methods for optimizing microbiome functioning.

Rhizobiome engineering: Unveiling complex rhizosphere interactions to enhance plant growth and health

Crop plants are affected by a series of inhibitory environmental and biotic factors that decrease their growth and production. To counteract these adverse effects, plants work together with the microorganisms that inhabit their rhizosphere, which is part of the soil influenced by root exudates. The rhizosphere is a microecosystem where a series of complex interactions takes place between the resident microorganisms (rhizobiome) and plant roots. Therefore, this study analyzes the dynamics of plant-rhizobiome communication, the role of exudates (diffusible and volatile) as a factor in stimulating a diverse rhizobiome, and the differences between rhizobiomes of domesticated crops and wild plants. The study also analyzes different strategies to decipher the rhizobiome through both classical cultivation techniques and the so-called "omics" sciences. In addition, the rhizosphere engineering concept and the two general strategies to manipulate the rhizobiome, i.e., top down and bottom up engineering have been revisited. In addition, recent studies on the effects on the indigenous rhizobiome of inoculating plants with foreign strains, the impact on the endobiome, and the collateral effects on plant crops are discussed. Finally, understanding of the complex rhizosphere interactions and the biological repercussions of rhizobiome engineering as essential steps for improving plant growth and health is proposed, including under adverse conditions.

Potential Use of Microbial Community Genomes in Various Dimensions of Agriculture Productivity and Its Management: A Review

Agricultural productivity is highly influenced by its associated microbial community. With advancements in omics technology, metagenomics is known to play a vital role in microbial world studies by unlocking the uncultured microbial populations present in the environment. Metagenomics is a diagnostic tool to target unique signature loci of plant and animal pathogens as well as beneficial microorganisms from samples. Here, we reviewed various aspects of metagenomics from experimental methods to techniques used for sequencing, as well as diversified computational resources, including databases and software tools. Exhaustive focus and study are conducted on the application of metagenomics in agriculture, deciphering various areas, including pathogen and plant disease identification, disease resistance breeding, plant pest control, weed management, abiotic stress management, post-harvest management, discoveries in agriculture, source of novel molecules/compounds, biosurfactants and natural product, identification of biosynthetic molecules, use in genetically modified crops, and antibiotic-resistant genes. Metagenomics-wide association studies study in agriculture on crop productivity rates, intercropping analysis, and agronomic field is analyzed. This article is the first of its comprehensive study and prospects from an agriculture perspective, focusing on a wider range of applications of metagenomics and its association studies.

Application of Nanopore Sequencing in the Detection of Foodborne Microorganisms

Foodborne pathogens have become the subject of intense interest because of their high incidence and mortality worldwide. In the past few decades, people have developed many methods to solve this challenge. At present, methods such as traditional microbial culture methods, nucleic acid or protein-based pathogen detection methods, and whole-genome analysis are widely used in the detection of pathogenic microorganisms in food. However, these methods are limited by time-consuming, cumbersome operations or high costs. The development of nanopore sequencing technology offers the possibility to address these shortcomings. Nanopore sequencing, a third-generation technology, has the advantages of simple operation, high sensitivity, real-time sequencing, and low turnaround time. It can be widely used in the rapid detection and serotyping of foodborne pathogens. This review article discusses foodborne diseases, the principle of nanopore sequencing technology, the application of nanopore sequencing technology in foodborne pathogens detection, as well as its development prospects.

Genomics and Informatics, Conjoined Tools Vital for Understanding and Protecting Plant Health

Genomics' impact on crop production continuously expands. The number of sequenced plant and microbial species and strains representing diverse populations of individual species rapidly increases thanks to the advent of next-generation sequencing technologies. Their genomic blueprints revealed candidate genes involved in various functions and processes crucial for crop health and helped in understanding how the sequenced organisms have evolved at the genome level. Functional genomics quickly translates these blueprints into a detailed mechanistic understanding of how such functions and processes work and are regulated; this understanding guides and empowers efforts to protect crops from diverse biotic and abiotic threats. Metagenome analyses help identify candidate microbes crucial for crop health and uncover how microbial communities associated with crop production respond to environmental conditions and cultural practices, presenting opportunities to enhance crop health by judiciously configuring microbial communities. Efficient conversion of disparate types of massive genomics data into actionable knowledge requires a robust informatics infrastructure supporting data preservation, analysis, and sharing. This review starts with an overview of how genomics came about and has quickly transformed life science. We illuminate how genomics and informatics can be applied to investigate various crop health-related problems using selected studies. We end the review by noting why community empowerment via crowdsourcing is crucial to harnessing genomics to protect global food and nutrition security without continuously expanding the environmental footprint of crop production.

Improved Mobilome Delineation in Fragmented Genomes

The mobilome of a microbe, i.e., its set of mobile elements, has major effects on its ecology, and is important to delineate properly in each genome. This becomes more challenging for incomplete genomes, and even more so for metagenome-assembled genomes (MAGs), where misbinning of scaffolds and other losses can occur. Genomic islands (GIs), which integrate into the host chromosome, are a major component of the mobilome. Our GI-detection software TIGER, unique in its precise mapping of GI termini, was applied to 74,561 genomes from 2,473 microbial species, each species containing at least one MAG and one isolate genome. A species-normalized deficit of ∼1.6 GIs/genome was measured for MAGs relative to isolates. To test whether this undercount was due to the higher fragmentation of MAG genomes, TIGER was updated to enable detection of split GIs whose termini are on separate scaffolds or that wrap around the origin of a circular replicon. This doubled GI yields, and the new split GIs matched the quality of single-scaffold GIs, except that highly fragmented GIs may lack central portions. Cross-scaffold search is an important upgrade to GI detection as fragmented genomes increasingly dominate public databases. TIGER2 better captures MAG microdiversity, recovering niche-defining GIs and supporting microbiome research aims such as virus-host linking and ecological assessment.

Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance

Crop production is a serious challenge to provide food for the 10 billion individuals forecasted to live across the globe in 2050. The scientists' emphasize establishing an equilibrium among diversity and quality of crops by enhancing yield to fulfill the increasing demand for food supply sustainably. The exploitation of genetic resources using genomics and metabolomics strategies can help generate resilient plants against stressors in the future. The innovation of the next-generation sequencing (NGS) strategies laid the foundation to unveil various plants' genetic potential and help us to understand the domestication process to unmask the genetic potential among wild-type plants to utilize for crop improvement. Nowadays, NGS is generating massive genomic resources using wild-type and domesticated plants grown under normal and harsh environments to explore the stress regulatory factors and determine the key metabolites. Improved food nutritional value is also the key to eradicating malnutrition problems around the globe, which could be attained by employing the knowledge gained through NGS and metabolomics to achieve suitability in crop yield. Advanced technologies can further enhance our understanding in defining the strategy to obtain a specific phenotype of a crop. Integration among bioinformatic tools and molecular techniques, such as marker-assisted, QTLs mapping, creation of reference genome, de novo genome assembly, pan- and/or super-pan-genomes, etc., will boost breeding programs. The current article provides sequential progress in NGS technologies, a broad application of NGS, enhancement of genetic manipulation resources, and understanding the crop response to stress by producing plant metabolites. The NGS and metabolomics utilization in generating stress-tolerant plants/crops without deteriorating a natural ecosystem is considered a sustainable way to improve agriculture production. This highlighted knowledge also provides useful research that explores the suitable resources for agriculture sustainability.

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.

Tapping the Role of Microbial Biosurfactants in Pesticide Remediation: An Eco-Friendly Approach for Environmental Sustainability

Pesticides are used indiscriminately all over the world to protect crops from pests and pathogens. If they are used in excess, they contaminate the soil and water bodies and negatively affect human health and the environment. However, bioremediation is the most viable option to deal with these pollutants, but it has certain limitations. Therefore, harnessing the role of microbial biosurfactants in pesticide remediation is a promising approach. Biosurfactants are the amphiphilic compounds that can help to increase the bioavailability of pesticides, and speeds up the bioremediation process. Biosurfactants lower the surface area and interfacial tension of immiscible fluids and boost the solubility and sorption of hydrophobic pesticide contaminants. They have the property of biodegradability, low toxicity, high selectivity, and broad action spectrum under extreme pH, temperature, and salinity conditions, as well as a low critical micelle concentration (CMC). All these factors can augment the process of pesticide remediation. Application of metagenomic and in-silico tools would help by rapidly characterizing pesticide degrading microorganisms at a taxonomic and functional level. A comprehensive review of the literature shows that the role of biosurfactants in the biological remediation of pesticides has received limited attention. Therefore, this article is intended to provide a detailed overview of the role of various biosurfactants in improving pesticide remediation as well as different methods used for the detection of microbial biosurfactants. Additionally, this article covers the role of advanced metagenomics tools in characterizing the biosurfactant producing pesticide degrading microbes from different environments.

Simultaneous amplicon analysis of multiple soil samples using MinION sequencing

The diversity and composition of soil microorganisms needs to be understood as they influence ecosystem processes. MinION is a relatively recent next-generation sequencer, which provides the advantage of sequencing long reads. In this study, two types of soil were prepared experimentally to investigate the possibility of simultaneously analyzing multiple environmental samples using MinION. The MinION sequencing of amplicons was adjusted by the different rounds of PCR performed. Soil fungi and bacteria were compared using ITS and 16S rRNA amplicons, respectively. For ITS, the number of reads available for MinION sequencing were simply increased by performing two PCRs and purification using Agencourt AMPure XP. However, the effect of performing PCR twice was not high for 16S rRNA. Therefore, performing PCR twice appears to be effective for analyzing ITS regions. Regarding the number of reads obtained using MinION sequencing, clustering the same sample was possible if a read of ∼2000 bases or more was obtained in 16S rRNA and ITS. Further, information on 80 samples was obtained by performing only one round of MinION sequencing. Thus, MinION sequencing can be used to analyze a large number of samples simultaneously, providing a strong tool for amplicon sequencing. • Soil microbial composition before and after treatment was compared between 16S rRNA and ITS amplicons using MinION sequencing • One PCR amplification and two PCR amplifications were also compared • Information on 80 samples was obtained by performing only one round of MinION sequencing.

Diversity of Rhizo-Bacteriome of Crocus sativus Grown at Various Geographical Locations and Cataloging of Putative PGPRs

Earlier plant growth promoting rhizo-bacteria (PGPRs) were isolated from the plants, by cultivation based techniques and the interaction was mostly thought to be bilateral. The routine bilateral study, with no information on the associated microbiome, could be one of the reasons for the limited success of PGPRs in the field conditions. Keeping in view the role of PGPRs in rhizo-bacteriome on the growth and production of plant, the present study was aimed at studying the diversity of the rhizo-bacteriome of saffron grown across three geographical locations namely Kashmir, Kishtwar and Bengaluru. Variation in the rhizo-bacteriome of saffron growing across 10 different sites from 3 geographical locations was studied using 16S rDNA amplicon metagenomic sequencing. 16 bacterial phyla, 261 genera and 73 bacterial species were cataloged from all the rhizosphere samples. Proteobacteria was a dominant phylum in all the rhizosphere samples. Rhizo-bacteriome of saffron grown in Kishtwar was found to be significantly different from the rhizo-bacteriome of saffron grown in Kashmir and Bengaluru. Interestingly, the rhizo-bacteriome of saffron grown in Bengaluru was very similar to the saffron grown in Kashmir, thereby indicating that the rhizo-bacteriome in saffron is “plant driven” as the corm sown in Bengaluru were from Kashmir. Despite variation in rhizo-bacteriome, core rhizo-bacteriome in saffron was identified that was represented by 53 genera and eight bacterial species belonging to 11 phyla irrespective of their geographical distribution. In addition, 21 PGPRs were reported for the first time from the saffron rhizosphere. The high yielding saffron field Wuyan was found to have the highest number of PGPRs; this indicates that the presence of PGPR is important for yield enhancement than diversity. The two PGPR Rhizobium leguminosarum and Luteibacter rhizovicinus were reported from all the locations except Kishtwar that had escaped isolation in our previous attempts using cultivation based techniques. It is being proposed instead of going for random isolation and screening for PGPRs from plant rhizosphere, an alternate strategy using metagenomic cataloging of the rhizo-bacteriome community and cultivation of the dominant PGPR should be undertaken. This strategy will help in the selection of dominant PGPRs, specific to the plant in question.

A critical review on the environmental application of lipopeptide micelles

The importance of lipopeptide micelles in environmental applications has been highlighted. These vessels exhibit various sizes, shapes, and surface properties under different environmental conditions. An in-depth understanding of the tunable assembling behavior of biosurfactant micelles is of great importance for their applications. However, a systematic review of such behaviors with assorted micro/nano micellar structures under given environmental conditions, particularly under low temperature and high salinity, remains untapped. Such impacts on their environmental applications have yet to be summarized. This review tried to fill the knowledge gaps by providing a comprehensive summary of the recent knowledge advancement in genetically regulated lipopeptides production, micelles associated decontamination mechanisms in low temperature and high salinity environments, and up-to-date environmental applications. This work is expected to deliver valuable insights to guide lipopeptide design and discovery. The mechanisms concluded in this study could inspire the forthcoming research efforts in the advanced environmental application of lipopeptide micelles.

Green algae (Viridiplantae) in sediments from three lakes on Vega Island, Antarctica, assessed using DNA metabarcoding

BACKGROUND

Vega Island is located off the eastern tip of the Antarctic Peninsula (Maritime Antarctica), in the Weddell Sea. In this study, we used metabarcoding to investigate green algal DNA sequence diversity present in sediments from three lakes on Vega Island (Esmeralda, Copépodo, and Pan Negro Lakes).

METHODS AND RESULTS

Total DNA was extracted and the internal transcribed spacer 2 region of the nuclear ribosomal DNA was used as a DNA barcode for molecular identification. Green algae were represented by sequences representing 78 taxa belonging to Phylum Chlorophyta, of which 32% have not previously been recorded from Antarctica. Sediment from Pan Negro Lake generated the highest number of DNA reads (11,205), followed by Esmeralda (9085) and Copépodo (1595) Lakes. Esmeralda Lake was the richest in terms of number of taxa (59), with Copépodo and Pan Negro Lakes having 30 taxa each. Bray-Curtis dissimilarity among lakes was high (~ 0.80). The Order Chlamydomonadales (Chlorophyceae) gave the highest contribution in terms of numbers of taxa and DNA reads in all lakes. The most abundant taxon was Chlorococcum microstigmatum.

CONCLUSIONS

The study confirms the utility of DNA metabarcoding in assessing potential green algal diversity in Antarctic lakes, generating new Antarctic records.