Microbiology. Genomes from metagenomics

A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry

Microbes drive the biogeochemical cycles of earth systems, yet the long-standing goal of linking emerging genomic information, microbial traits, mechanistic ecosystem models, and projections under climate change has remained elusive despite a wealth of emerging genomic information. Here we developed a general genome-to-ecosystem (G2E) framework for integrating genome-inferred microbial kinetic traits into mechanistic models of terrestrial ecosystems and applied it at a well-studied Arctic wetland by benchmarking predictions against observed greenhouse gas emissions. We found variation in genome-inferred microbial kinetic traits resulted in large differences in simulated annual methane emissions, quantitatively demonstrating that the genomically observable variations in microbial capacity are consequential for ecosystem functioning. Applying microbial community-aggregated traits via genome relative-abundance-weighting gave better methane emissions predictions (i.e., up to 54% decrease in bias) compared to ignoring the observed abundances, highlighting the value of combined trait inferences and abundances. This work provides an example of integrating microbial functional trait-based genomics, mechanistic and pragmatic trait parameterizations of diverse microbial metabolisms, and mechanistic ecosystem modeling. The generalizable G2E framework will enable the use of abundant microbial metagenomics data to improve predictions of microbial interactions in many complex systems, including oceanic microbiomes.

National antibiotic consumption is strongly related to the prevalence of antibiotic resistance across bacterial clades

The impact of societal antibiotic consumption on the prevalence of antibiotic resistance across microbial taxa in natural environments has not yet been assessed at global scales. Here, I examine the prevalence of 155 antibiotic resistance genes (ARGs) in 300,209 bacterial genomes, from non-clinical non-human-associated terrestrial environments at over 9,600 locations in 44 countries. I then compare ARG prevalences to nationwide antibiotic consumption rates, distinguishing between different ARG types. I find that depending on country and ARG type, ARG prevalences can be extremely high; for example, the probability that a given quinolone resistance gene is present in a given strain in Thailand was estimated at 42%. Further, I find strong positive correlations between nationwide antibiotic consumption rates and mean ARG prevalences for nearly all ARG types. Thus, national antibiotic consumption leaves a signal on the prevalence of ARGs across the bacterial tree, even in non-clinical environments.

The CRISPR-Cas System and Clinical Applications of CRISPR-Based Gene Editing in Hematology with a Focus on Inherited Germline Predisposition to Hematologic Malignancies

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has begun to transform the treatment landscape of genetic diseases. The history of the discovery of CRISPR/CRISPR-associated (Cas) proteins/single-guide RNA (sgRNA)-based gene editing since the first report of repetitive sequences of unknown significance in 1987 is fascinating, highly instructive, and inspiring for future advances in medicine. The recent approval of CRISPR-Cas9-based gene therapy to treat patients with severe sickle cell anemia and transfusion-dependent β-thalassemia has renewed hope for treating other hematologic diseases, including patients with a germline predisposition to hematologic malignancies, who would benefit greatly from the development of CRISPR-inspired gene therapies. The purpose of this paper is three-fold: first, a chronological description of the history of CRISPR-Cas9-sgRNA-based gene editing; second, a brief description of the current state of clinical research in hematologic diseases, including selected applications in treating hematologic diseases with CRISPR-based gene therapy, preceded by a brief description of the current tools being used in clinical genome editing; and third, a presentation of the current progress in gene therapies in inherited hematologic diseases and bone marrow failure syndromes, to hopefully stimulate efforts towards developing these therapies for patients with inherited bone marrow failure syndromes and other inherited conditions with a germline predisposition to hematologic malignancies.

The clinical importance of metagenomic next-generation sequencing in detecting disease-causing microorganisms in cases of sepsis acquired in the community or hospital setting

Objectives

Although metagenomic next-generation sequencing (mNGS) is commonly used for diagnosing infectious diseases, clinicians face limited options due to the high costs that are not covered by basic medical insurance. The goal of this research is to challenge this bias through a thorough examination and evaluation of the clinical importance of mNGS in precisely identifying pathogenic microorganisms in cases of sepsis acquired in the community or in hospitals.

Methods

A retrospective observational study took place at a tertiary teaching hospital in China from January to December 2021. Data on 308 sepsis patients were collected, and the performance of etiological examination was compared between mNGS and traditional culture method.

Results

Two hundred twenty-nine cases were observed in the community-acquired sepsis (CAS) group and 79 cases in the hospital-acquired sepsis (HAS) group. In comparison with conventional culture, mNGS showed a significantly higher rate of positivity in both the CAS group (88.21% vs. 25.76%, adj.P < 0.001) and the HAS group (87.34% vs. 44.30%, adj.P < 0.001), particularly across various infection sites and specimens, which were not influenced by factors like antibiotic exposure or the timing and frequency of mNGS technology. Sepsis pathogens detected by mNGS were broad, especially viruses, Mycobacterium tuberculosis, and atypical pathogens, with mixed pathogens being common, particularly bacterial-viral co-detection. Based on the optimization of antimicrobial therapy using mNGS, 58 patients underwent antibiotic de-escalation, two patients were switched to antiviral therapy, and 14 patients initiated treatment for tuberculosis, resulting in a reduction in antibiotic overuse but without significant impact on sepsis prognosis. The HAS group exhibited a critical condition, poor prognosis, high medical expenses, and variations in etiology, yet the mNGS results did not result in increased medical costs for either group.

Conclusions

mNGS demonstrates efficacy in identifying multiple pathogens responsible for sepsis, with mixed pathogens of bacteria and viruses being prevalent. Variability in microbiological profiles among different infection setting underscores the importance of clinical vigilance. Therefore, the adoption of mNGS for microbiological diagnosis of sepsis warrants acknowledgment and promotion.

Atherosclerosis, gut microbiome, and exercise in a meta-omics perspective: a literature review

Background

Cardiovascular diseases are the leading cause of death worldwide, significantly impacting public health. Atherosclerotic cardiovascular diseases account for the majority of these deaths, with atherosclerosis marking the initial and most critical phase of their pathophysiological progression. There is a complex relationship between atherosclerosis, the gut microbiome's composition and function, and the potential mediating role of exercise. The adaptability of the gut microbiome and the feasibility of exercise interventions present novel opportunities for therapeutic and preventative approaches.

Methodology

We conducted a comprehensive literature review using professional databases such as PubMed and Web of Science. This review focuses on the application of meta-omics techniques, particularly metagenomics and metabolomics, in studying the effects of exercise interventions on the gut microbiome and atherosclerosis.

Results

Meta-omics technologies offer unparalleled capabilities to explore the intricate connections between exercise, the microbiome, the metabolome, and cardiometabolic health. This review highlights the advancements in metagenomics and metabolomics, their applications in research, and examines how exercise influences the gut microbiome. We delve into the mechanisms connecting these elements from a metabolic perspective. Metagenomics provides insight into changes in microbial strains post-exercise, while metabolomics sheds light on the shifts in metabolites. Together, these approaches offer a comprehensive understanding of how exercise impacts atherosclerosis through specific mechanisms.

Conclusions

Exercise significantly influences atherosclerosis, with the gut microbiome serving as a critical intermediary. Meta-omics technology holds substantial promise for investigating the gut microbiome; however, its methodologies require further refinement. Additionally, there is a pressing need for more extensive cohort studies to enhance our comprehension of the connection among these element.

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Microbial methane cycling in a landfill on a decadal time scale

Landfills generate outsized environmental footprints due to microbial degradation of organic matter in municipal solid waste, which produces the potent greenhouse gas methane. With global solid waste production predicted to increase substantially in the next few decades, there is a pressing need to better understand the temporal dynamics of biogeochemical processes that control methane cycling in landfills. Here, we use metagenomic approaches to characterize microbial methane cycling in waste that was landfilled over 39 years. Our analyses indicate that newer waste supports more diverse communities with similar composition compared to older waste, which contains lower diversity and more varied communities. Older waste contains primarily autotrophic organisms with versatile redox metabolisms, whereas newer waste is dominated by anaerobic fermenters. Methane-producing microbes are more abundant, diverse, and metabolically versatile in new waste compared to old waste. Our findings indicate that predictive models for methane emission in landfills overlook methane oxidation in the absence of oxygen, as well as certain microbial lineages that can potentially contribute to methane sinks in diverse habitats.

Metagenome sequencing and recovery of 444 metagenome-assembled genomes from the biofloc aquaculture system

Biofloc technology is increasingly recognised as a sustainable aquaculture method. In this technique, bioflocs are generated as microbial aggregates that play pivotal roles in assimilating toxic nitrogenous substances, thereby ensuring high water quality. Despite the crucial roles of the floc-associated bacterial (FAB) community in pathogen control and animal health, earlier microbiota studies have primarily relied on the metataxonomic approaches. Here, we employed shotgun sequencing on eight biofloc metagenomes from a commercial aquaculture system. This resulted in the generation of 106.6 Gbp, and the reconstruction of 444 metagenome-assembled genomes (MAGs). Among the recovered MAGs, 230 were high-quality (≥90% completeness, ≤5% contamination), and 214 were medium-quality (≥50% completeness, ≤10% contamination). Phylogenetic analysis unveiled Rhodobacteraceae as dominant members of the FAB community. The reported metagenomes and MAGs are crucial for elucidating the roles of diverse microorganisms and their functional genes in key processes such as nitrification, denitrification, and remineralization. This study will contribute to scientific understanding of phylogenetic diversity and metabolic capabilities of microbial taxa in aquaculture environments.

MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenomeassembled genomes

We propose MT-MAG, a novel machine learning-based software tool for the complete or partial hierarchically-structured taxonomic classification of metagenome-assembled genomes (MAGs). MT-MAG is alignment-free, with k-mer frequencies being the only feature used to distinguish a DNA sequence from another (herein k = 7). MT-MAG is capable of classifying large and diverse metagenomic datasets: a total of 245.68 Gbp in the training sets, and 9.6 Gbp in the test sets analyzed in this study. In addition to complete classifications, MT-MAG offers a "partial classification" option, whereby a classification at a higher taxonomic level is provided for MAGs that cannot be classified to the Species level. MT-MAG outputs complete or partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks. To assess the performance of MT-MAG, we define a "weighted classification accuracy," with a weighting scheme reflecting the fact that partial classifications at different ranks are not equally informative. For the two benchmarking datasets analyzed (genomes from human gut microbiome species, and bacterial and archaeal genomes assembled from cow rumen metagenomic sequences), MT-MAG achieves an average of 87.32% in weighted classification accuracy. At the Species level, MT-MAG outperforms DeepMicrobes, the only other comparable software tool, by an average of 34.79% in weighted classification accuracy. In addition, MT-MAG is able to completely classify an average of 67.70% of the sequences at the Species level, compared with DeepMicrobes which only classifies 47.45%. Moreover, MT-MAG provides additional information for sequences that it could not classify at the Species level, resulting in the partial or complete classification of 95.13%, of the genomes in the datasets analyzed. Lastly, unlike other taxonomic assignment tools (e.g., GDTB-Tk), MT-MAG is an alignment-free and genetic marker-free tool, able to provide additional bioinformatics analysis to confirm existing or tentative taxonomic assignments.

Influence of soil nutrients on the presence and distribution of CPR bacteria in a long-term crop rotation experiment

Bacteria affiliated with the Candidate Phyla Radiation (CPR) are a hyper-diverse group of ultra-small bacteria with versatile yet sparse metabolisms. However, most insights into this group come from a surprisingly small number of environments, and recovery of CPR bacteria from soils has been hindered due to their extremely low abundance within complex microbial assemblages. In this study we enriched soil samples from 14 different soil fertility treatments for ultra-small (<0.45 μm) bacteria in order to study rare soil CPR. 42 samples were sequenced, enabling the reconstruction of 27 quality CPR metagenome-assembled genomes (MAGs) further classified as Parcubacteria/Paceibacteria, Saccharibacteria/Saccharimonadia and ABY1, in addition to representative genomes from Gemmatimonadetes, Dependentiae and Chlamydae phyla. These genomes were fully annotated and used to reconstruct the CPR community across all 14 plots. Additionally, for five of these plots, the entire microbiota was reconstructed using 16S amplification, showing that specific soil CPR may form symbiotic relationships with a varied and circumstantial range of hosts. Cullars CPR had a prevalence of enzymes predicted to degrade plant-derived carbohydrates, which suggests they have a role in plant biomass degradation. Parcubacteria appear to be more apt at microfauna necromass degradation. Cullars Saccharibacteria and a Parcubacteria group were shown to carry a possible aerotolerance mechanism coupled with potential for aerobic respiration, which appear to be a unique adaptation to the oxic soil environment. Reconstruction of CPR communities across treatment plots showed that they were not impacted by changes in nutrient levels or microbiota composition, being only impacted by extreme conditions, causing some CPR to dominate the community. These findings corroborate the understanding that soil-dwelling CPR bacteria have a very broad symbiont range and have metabolic capabilities associated to soil environments which allows them to scavenge resources and form resilient communities. The contributions of these microbial dark matter species to soil ecology and plant interactions will be of significant interest in future studies.

Trait biases in microbial reference genomes

Common culturing techniques and priorities bias our discovery towards specific traits that may not be representative of microbial diversity in nature. So far, these biases have not been systematically examined. To address this gap, here we use 116,884 publicly available metagenome-assembled genomes (MAGs, completeness ≥80%) from 203 surveys worldwide as a culture-independent sample of bacterial and archaeal diversity, and compare these MAGs to the popular RefSeq genome database, which heavily relies on cultures. We compare the distribution of 12,454 KEGG gene orthologs (used as trait proxies) in the MAGs and RefSeq genomes, while controlling for environment type (ocean, soil, lake, bioreactor, human, and other animals). Using statistical modeling, we then determine the conditional probabilities that a species is represented in RefSeq depending on its genetic repertoire. We find that the majority of examined genes are significantly biased for or against in RefSeq. Our systematic estimates of gene prevalences across bacteria and archaea in nature and gene-specific biases in reference genomes constitutes a resource for addressing these issues in the future.

Identification of PKS Gene Clusters from Metagenomic Libraries Using a Next-Generation Sequencing Approach

Microbial secondary metabolites have been an important source of bioactive compounds with diverse applications from medicine to agriculture, noticeably those encoded by polyketide synthase (PKS) clusters due to their astounding chemical diversity. While most discovered compounds originate from culturable microorganisms, yet-to-be cultured microbes represent a reservoir of previously inaccessible compounds. The advent and development of metagenomics have allowed not only the characterization of these microorganisms but also their metabolic potential, making viable the prospection of environmental PKS for natural product discovery.Study of environmental PKSs often relies on the construction of metagenomic libraries and their mining, with clones containing PKS clusters identified via amplification of conserved domains and then screened for an activity of interest. Compounds produced by clones exhibiting the desired bioactivity can be isolated and characterized. However, these approaches can be less sensitive and biased against more divergent clusters, in addition to precluding the use of bioinformatics for cluster characterization prior to expression. While direct shotgun sequencing of metagenomes has identified and profiled a great number of PKSs from different environments and yet-to-be cultured microorganisms, it does not lend itself well to heterologous expression, the cruxes of natural product discovery.Here, we describe a strategy for sequencing entire metagenomic libraries while maintaining correspondence between sequence and clone, allowing the full characterization and annotation of all clusters present in a library using bioinformatic tools and then seamlessly passing clones of interest for activity screening through heterologous expression. Once a library is sequenced, the methods herein can be adapted for the mining of any biosynthetic gene cluster of interest within a metagenomic library.

Influence of Geochemistry in the Tropical Hot Springs on Microbial Community Structure and Function

Thermophiles inhabiting high temperatures are considered primitive microorganisms on early Earth. In this regard, several works have demonstrated microbial community composition in geothermal environments. Despite that, studies on hot springs located in the Indian subcontinent viz., Surajkund in the district Hazaribag, Jharkhand; Bakreshwar in the district Birbhum, West Bengal; Tantloi in the district Dumka, and Sidpur in the district Pakur, Jharkhand are scanty. Nonetheless, the metagenomic analysis of these hot springs showed significant differences in the predominant phyla corresponding to geochemical properties. The Chloroflexi, Proteobacteria, Actinobacteria, Deinococcus-Thermus, and Firmicutes were dominant phyla in all the samples. In contrast, Meiothermus was more in comparatively low-temperature hot springs. In addition, archaeal phyla, Euryarchaeota, Candidatus Bathyarchaeota, and Crenarchaeota were predominant in all samples. The canonical correspondence analysis (CCA) showed the abundance of Deinococcus, Thermus, Pyrobaculum, Kocuria, and Geodermatophilus positively correlated with the aqueous concentration of sulfate, fluoride, and argon in relatively high-temperature (≥ 72 °C) hot springs. However, at a lower temperature (≤ 63 °C), Thermodesulfovibrio, Caldilinea, Chloroflexus, Meiothermus, and Tepidimonas are positively correlated with the concentration of zinc, iron, and dissolved oxygen. Further, hierarchical clustering exhibits variations in its functional attributes depending on the temperature gradients. Metagenome analysis predicted carbon, methane, sulfur, and nitrogen metabolism genes, indicating a wide range of bacteria and archaea habitation in these hot springs. In addition, identified several genes encode polyketide biosynthesis pathways. The present study described the microbial community composition and function in the tropical hot springs and their relationship with the environmental variables.

Constructing metagenome-assembled genomes for almost all components in a real bacterial consortium for binning benchmarking

BACKGROUND

So far, a lot of binning approaches have been intensively developed for untangling metagenome-assembled genomes (MAGs) and evaluated by two main strategies. The strategy by comparison to known genomes prevails over the other strategy by using single-copy genes. However, there is still no dataset with all known genomes for a real (not simulated) bacterial consortium yet.

RESULTS

Here, we continue investigating the real bacterial consortium F1RT enriched and sequenced by us previously, considering the high possibility to unearth all MAGs, due to its low complexity. The improved F1RT metagenome reassembled by metaSPAdes here utilizes about 98.62% of reads, and a series of analyses for the remaining reads suggests that the possibility of containing other low-abundance organisms in F1RT is greatly low, demonstrating that almost all MAGs are successfully assembled. Then, 4 isolates are obtained and individually sequenced. Based on the 4 isolate genomes and the entire metagenome, an elaborate pipeline is then in-house developed to construct all F1RT MAGs. A series of assessments extensively prove the high reliability of the herein reconstruction. Next, our findings further show that this dataset harbors several properties challenging for binning and thus is suitable to compare advanced binning tools available now or benchmark novel binners. Using this dataset, 8 advanced binning algorithms are assessed, giving useful insights for developing novel approaches. In addition, compared with our previous study, two novel MAGs termed FC8 and FC9 are discovered here, and 7 MAGs are solidly unearthed for species without any available genomes.

CONCLUSION

To our knowledge, it is the first time to construct a dataset with almost all known MAGs for a not simulated consortium. We hope that this dataset will be used as a routine toolkit to complement mock datasets for evaluating binning methods to further facilitate binning and metagenomic studies in the future.

Development of the SeqCode: A proposed nomenclatural code for uncultivated prokaryotes with DNA sequences as type

Over the last fifteen years, genomics has become fully integrated into prokaryotic systematics. The genomes of most type strains have been sequenced, genome sequence similarity is widely used for delineation of species, and phylogenomic methods are commonly used for classification of higher taxonomic ranks. Additionally, environmental genomics has revealed a vast diversity of as-yet-uncultivated taxa. In response to these developments, a new code of nomenclature, the Code of Nomenclature of Prokaryotes Described from Sequence Data (SeqCode), has been developed over the last two years to allow naming of Archaea and Bacteria using DNA sequences as the nomenclatural types. The SeqCode also allows naming of cultured organisms, including fastidious prokaryotes that cannot be deposited into culture collections. Several simplifications relative to the International Code of Nomenclature of Prokaryotes (ICNP) are implemented to make nomenclature more accessible, easier to apply and more readily communicated. By simplifying nomenclature with the goal of a unified classification, inclusive of both cultured and uncultured taxa, the SeqCode will facilitate the naming of taxa in every biome on Earth, encourage the isolation and characterization of as-yet-uncultivated taxa, and promote synergies between the ecological, environmental, physiological, biochemical, and molecular biological disciplines to more fully describe prokaryotes.

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Microbial communities of halite deposits and other hypersaline environments

Large regions of Earth's surface are underlain by salt deposits that evaporated from ancient oceans and are populated by extreme halophilic microbes. While the microbiology of ancient evaporites has been well studied, the ecology of halite deposits and more recently formed NaCl "salticle" stalactite structures (speleothems) in a Triassic halite mine are less well characterized. The microbiome of Kilroot Salt Mine was profiled using conventional and enhanced culturing techniques. From this, 89 halophilic archaeal isolates from six known genera, and 55 halophilic or halotolerant bacterial isolates from 18 genera were obtained. Culture-independent metagenomic approaches also revealed that culturing techniques were inadvertently biased toward specific taxa, and the need for optimized isolation procedures are required to enhance cultivation diversity. Speleothems formed from saturated brines are unique structures that have the potential to entomb haloarchaea cells for thousands of years within fluid inclusions. The presence of such fluid inclusions, alongside the high abundance of genes related to glycerol metabolism, biofilm formation, and persister cell formation is highly suggestive of an environmental niche that could promote longevity and survivability. Finally, previous studies reporting the discovery of novel biocatalysts from the Kilroot mine microbiome, suggests that this environment may be an untapped source of chemical diversity with high biodiscovery potential.

The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining the core microbiome is a complex task at several levels. In this review, we consider the current state of core human microbiome research. We consider the knowledge that has been gained, the factors limiting our ability to achieve a reliable description of the core human microbiome, and the fields most likely to improve that ability. DNA sequencing technologies and the methods for analyzing metagenomics and amplicon data will most likely facilitate higher accuracy and resolution in describing the microbiome. However, more effort should be invested in characterizing the microbiome's interactions with its human host, including the immune system and nutrition. Other components of this holobiontic system should also be emphasized, such as fungi, protists, lower eukaryotes, viruses, and phages. Most importantly, a collaborative effort of experts in microbiology, nutrition, immunology, medicine, systems biology, bioinformatics, and machine learning is probably required to identify the traits of the core human microbiome.

High-throughput sequencing reveals the main drivers of niche-differentiation of bacterial community in the surface sediments of the northern South China sea

Studies on marine bacterial communities have revealed endemism in local communities, yet the underlying mechanisms remained elusive. Environmental gradient settings can benefit the straightaway study of community composition changes and the mechanisms explaining them. Here, MiSeq-based 16S rRNA gene sequencing was performed on 12 surface sediment samples from the northern South China Sea (nSCS) revealing that shallow-sea samples had a higher alpha diversity than deep-sea samples, and were differentiated from them significantly based on beta diversity. Temperature, seawater depth, and salinity were the top three influential factors. Bacterial 16S rRNA gene abundance was positively correlated with temperature, and negatively correlated with salinity. Sulfate-reducing bacteria including Desulfobacteraceae, Desulfobulbaceae, and Syntrophobacteraceae were enriched in shallow-sea sediments, co-abundant with nitrite-oxidizing Nitrospira and potential sulfur-oxidizing shallow-sea specific Woeseiaceae/JTB255 clade. Meanwhile, the co-existing and co-abundant of marine anammox and n-damo bacteria were enriched in deep-sea sediments, which was firstly evidenced in this study. The global deep-sea cosmopolitans, OM1 clade, and deep-sea specific Woeseiaceae/JTB255 clade were also found enriched in deep-sea sediments of nSCS. The discovery of novel taxa which were differentially enriched in shallow-/deep-sea sediments not only shed light on enigmatic physiological properties and the natural selection mechanism, but also provided the potential ecological-functional links which invoked further genomics-based metabolic characteristics.

Functional and Taxonomic Effects of Organic Amendments on the Restoration of Semiarid Quarry Soils

The application of organic amendments to mining soils has been shown to be a successful method of restoration, improving key physicochemical soil properties. However, there is a lack of a clear understanding of the soil bacterial community taxonomic and functional changes that are brought about by these treatments. We present further metagenomic sequencing (MGS) profiling of the effects of different restoration treatments applied to degraded, arid quarry soils in southern Spain which had previously been profiled only with 16S rRNA gene (16S) and physicochemical analyses. Both taxonomic and functional MGS profiles showed clear separation of organic treatment amendments from control samples, and although taxonomic differences were quite clear, functional redundancy was higher than expected and the majority of the latter signal came from the aggregation of minor (<0.1%) community differences. Significant taxonomic differences were seen with the presumably less-biased MGS-for example, the phylum Actinobacteria and the two genera Chloracidobacterium (Acidobacteria) and Paenibacillus (Firmicutes) were determined to be major players by the MGS and this was consistent with their potential functional roles. The former phylum was much less present, and the latter two genera were either minor components or not detected in the 16S data. Mapping of reads to MetaCyc/BioCyc categories showed overall slightly higher biosynthesis and degradation capabilities in all treatments versus control soils, with sewage amendments showing highest values and vegetable-based amendments being at intermediate levels, matching higher nutrient levels, respiration rates, enzyme activities, and bacterial biomass previously observed in the treated soils. IMPORTANCE The restoration of soils impacted by human activities poses specific challenges regarding the reestablishment of functional microbial communities which will further support the reintroduction of plant species. Organic fertilizers, originating from either treated sewage or vegetable wastes, have shown promise in restoration experiments; however, we still do not have a clear understanding of the functional and taxonomic changes that occur during these treatments. We used metagenomics to profile restoration treatments applied to degraded, arid quarry soils in southern Spain. We found that the assortments of individual functions and taxa within each soil could clearly identify treatments, while at the same time they demonstrated high functional redundancy. Functions grouped into higher pathways tended to match physicochemical measurements made on the same soils. In contrast, significant taxonomic differences were seen when the treatments were previously studied with a single marker gene, highlighting the advantage of metagenomic analysis for complex soil communities.

Metagenomic sequencing-driven multidisciplinary approaches to shed light on the untapped microbial natural products

The advantage of metagenomics over the culture-based natural product (NP) discovery pipeline is the ability to access the biosynthetic potential of uncultivable microbes. Advances in DNA sequencing are revolutionizing conventional metagenomics approaches for microbial NP discovery. The genomes of (in)cultivable bugs can be resolved straightforwardly from environmental samples, enabling in situ prediction of biosynthetic gene clusters (BGCs). The predicted chemical diversities could be realized not only by heterologous expression of gene clusters originating from DNA synthesis or direct cloning, but also potentially by bioinformatic-directed organic synthesis or chemoenzymatic total synthesis. In this review, we suggest that metagenomic sequencing in tandem with multidisciplinary approaches will form a versatile platform to shed light on a plethora of microbial 'dark matter'.

Enhanced Recovery of Microbial Genes and Genomes From a Marine Water Column Using Long-Read Metagenomics

Third-generation sequencing has penetrated little in metagenomics due to the high error rate and dependence for assembly on short-read designed bioinformatics. However, second-generation sequencing metagenomics (mostly Illumina) suffers from limitations, particularly in the assembly of microbes with high microdiversity and retrieval of the flexible (adaptive) fraction of prokaryotic genomes. Here, we have used a third-generation technique to study the metagenome of a well-known marine sample from the mixed epipelagic water column of the winter Mediterranean. We have compared PacBio Sequel II with the classical approach using Illumina Nextseq short reads followed by assembly to study the metagenome. Long reads allow for efficient direct retrieval of complete genes avoiding the bias of the assembly step. Besides, the application of long reads on metagenomic assembly allows for the reconstruction of much more complete metagenome-assembled genomes (MAGs), particularly from microbes with high microdiversity such as Pelagibacterales. The flexible genome of reconstructed MAGs was much more complete containing many adaptive genes (some with biotechnological potential). PacBio Sequel II CCS appears particularly suitable for cellular metagenomics due to its low error rate. For most applications of metagenomics, from community structure analysis to ecosystem functioning, long reads should be applied whenever possible. Specifically, for in silico screening of biotechnologically useful genes, or population genomics, long-read metagenomics appears presently as a very fruitful approach and can be analyzed from raw reads before a computationally demanding (and potentially artifactual) assembly step.

Biocatalytic oxidation of fatty alcohols into aldehydes for the flavors and fragrances industry

From Egyptian mummies to the Chanel n°5 perfume, fatty aldehydes have long been used and keep impacting our senses in a wide range of foods, beverages and perfumes. Natural sources of fatty aldehydes are threatened by qualitative and quantitative variability while traditional chemical routes are insufficient to answer the society shift toward more sustainable and natural products. The production of fatty aldehydes using biotechnologies is therefore the most promising alternative for the flavors and fragrances industry. In this review, after drawing the portrait of the origin and characteristics of fragrant fatty aldehydes, we present the three main classes of enzymes that catalyze the reaction of fatty alcohols oxidation into aldehydes, namely alcohol dehydrogenases, flavin-dependent alcohol oxidases and copper radical alcohol oxidases. The constraints, challenges and opportunities to implement these oxidative enzymes in the flavors and fragrances industry are then discussed. By setting the scene on the biocatalytic production of fatty aldehydes, and providing a critical assessment of its potential, we expect this review to contribute to the development of biotechnology-based solutions in the flavors and fragrances industry.

Sequencing Ultrarare Targets with Compound Nucleic Acid Cytometry

Targeted sequencing enables sensitive and cost-effective analysis by focusing resources on molecules of interest. Existing methods, however, are limited in enrichment power and target capture length. Here, we present a novel method that uses compound nucleic acid cytometry to achieve million-fold enrichments of molecules >10 kbp in length using minimal prior target information. We demonstrate the approach by sequencing HIV proviruses in infected individuals. Our method is useful for rare target sequencing in research and clinical applications, including for identifying cancer-associated mutations or sequencing viruses infecting cells.

One cell at a time: droplet-based microbial cultivation, screening and sequencing

Microbes thrive and, in turn, influence the earth's environment, but most are poorly understood because of our limited capacity to reveal their natural diversity and function. Developing novel tools and effective strategies are critical to ease this dilemma and will help to understand their roles in ecology and human health. Recently, droplet microfluidics is emerging as a promising technology for microbial studies with value in microbial cultivating, screening, and sequencing. This review aims to provide an overview of droplet microfluidics techniques for microbial research. First, some critical points or steps in the microfluidic system are introduced, such as droplet stabilization, manipulation, and detection. We then highlight the recent progress of droplet-based methods for microbiological applications, from high-throughput single-cell cultivation, screening to the targeted or whole-genome sequencing of single cells.

Genome Recovery, Functional Profiling, and Taxonomic Classification from Metagenomes

Recovering and annotating bacterial genomes from metagenomes involves a series of complex computational tools that are often difficult to use for researches without a specialistic bioinformatic background. In this chapter we review all the steps that lead from raw reads to a collection of quality-controlled, functionally annotated bacterial genomes and propose a working protocol using state-of-the-art, open source software tools.

DRAM for distilling microbial metabolism to automate the curation of microbiome function

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

Big data-driven precision medicine: Starting the custom-made era of iatrology

Precision medicine is a new therapeutic concept and method emerging in recent years. The rapid development of precision medicine is driven by the development of omics related technology, biological information and big data science. Precision medicine is provided to implement precise and personalized treatment for diseases and specific patients. Precision medicine is commonly used in the diagnosis, treatment and prevention of various diseases. This review introduces the application of precision medicine in eight systematic diseases of the human body, and systematically presenting the current situation of precision medicine. At the same time, the shortcomings and limitations of precision medicine are pointed out. Finally, we prospect the development of precision medicine.

CSN: unsupervised approach for inferring biological networks based on the genome alone

BACKGROUND

Most organisms cannot be cultivated, as they live in unique ecological conditions that cannot be mimicked in the lab. Understanding the functionality of those organisms' genes and their interactions by performing large-scale measurements of transcription levels, protein-protein interactions or metabolism, is extremely difficult and, in some cases, impossible. Thus, efficient algorithms for deciphering genome functionality based only on the genomic sequences with no other experimental measurements are needed.

RESULTS

In this study, we describe a novel algorithm that infers gene networks that we name Common Substring Network (CSN). The algorithm enables inferring novel regulatory relations among genes based only on the genomic sequence of a given organism and partial homolog/ortholog-based functional annotation. It can specifically infer the functional annotation of genes with unknown homology. This approach is based on the assumption that related genes, not necessarily homologs, tend to share sub-sequences, which may be related to common regulatory mechanisms, similar functionality of encoded proteins, common evolutionary history, and more. We demonstrate that CSNs, which are based on S. cerevisiae and E. coli genomes, have properties similar to 'traditional' biological networks inferred from experiments. Highly expressed genes tend to have higher degree nodes in the CSN, genes with similar protein functionality tend to be closer, and the CSN graph exhibits a power-law degree distribution. Also, we show how the CSN can be used for predicting gene interactions and functions.

CONCLUSIONS

The reported results suggest that 'silent' code inside the transcript can help to predict central features of biological networks and gene function. This approach can help researchers to understand the genome of novel microorganisms, analyze metagenomic data, and can help to decipher new gene functions.

AVAILABILITY

Our MATLAB implementation of CSN is available at https://www.cs.tau.ac.il/~tamirtul/CSN-Autogen.

Microfluidic automated plasmid library enrichment for biosynthetic gene cluster discovery

Microbial biosynthetic gene clusters are a valuable source of bioactive molecules. However, because they typically represent a small fraction of genomic material in most metagenomic samples, it remains challenging to deeply sequence them. We present an approach to isolate and sequence gene clusters in metagenomic samples using microfluidic automated plasmid library enrichment. Our approach provides deep coverage of the target gene cluster, facilitating reassembly. We demonstrate the approach by isolating and sequencing type I polyketide synthase gene clusters from an Antarctic soil metagenome. Our method promotes the discovery of functional-related genes and biosynthetic pathways.

Accurate and complete genomes from metagenomes

Genomes are an integral component of the biological information about an organism; thus, the more complete the genome, the more informative it is. Historically, bacterial and archaeal genomes were reconstructed from pure (monoclonal) cultures, and the first reported sequences were manually curated to completion. However, the bottleneck imposed by the requirement for isolates precluded genomic insights for the vast majority of microbial life. Shotgun sequencing of microbial communities, referred to initially as community genomics and subsequently as genome-resolved metagenomics, can circumvent this limitation by obtaining metagenome-assembled genomes (MAGs); but gaps, local assembly errors, chimeras, and contamination by fragments from other genomes limit the value of these genomes. Here, we discuss genome curation to improve and, in some cases, achieve complete (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been generated, although notably some are from very complex systems such as soil and sediment. Through analysis of about 7000 published complete bacterial isolate genomes, we verify the value of cumulative GC skew in combination with other metrics to establish bacterial genome sequence accuracy. The analysis of cumulative GC skew identified potential misassemblies in some reference genomes of isolated bacteria and the repeat sequences that likely gave rise to them. We discuss methods that could be implemented in bioinformatic approaches for curation to ensure that metabolic and evolutionary analyses can be based on very high-quality genomes.

Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes

BACKGROUND

Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene that may not truly represent in situ functions.

RESULTS

Here, we present 2045 archaeal and bacterial metagenome-assembled genomes (MAGs) recovered from 1.35 Tb of metagenomic data generated from 114 AS samples of 23 full-scale wastewater treatment plants (WWTPs). We found that the AS MAGs have obvious plant-specific features and that few proteins are shared by different WWTPs, especially for WWTPs located in geographically distant areas. Further, we developed a novel machine learning approach that can distinguish between AS MAGs and MAGs from other environments based on the clusters of orthologous groups of proteins with an accuracy of 96%. With the aid of machine learning, we also identified some functional features (e.g., functions related to aerobic metabolism, nutrient sensing/acquisition, and biofilm formation) that are likely vital for AS bacteria to adapt themselves in wastewater treatment bioreactors.

CONCLUSIONS

Our work reveals that, although the bacterial species in different municipal WWTPs could be different, they may have similar deterministic functional features that allow them to adapt to the AS systems. Also, we provide valuable genome resources and a novel approach for future investigation and better understanding of the microbiome of AS and other ecosystems. Video Abtract.

Candidatus Mcinerneyibacterium aminivorans gen. nov., sp. nov., the first representative of the candidate phylum Mcinerneyibacteriota phyl. nov. recovered from a high temperature, high salinity tertiary oil reservoir in north central Oklahoma, USA

We report on the characterization of a novel genomic assembly (ARYD3) recovered from formation water (17.6% salinity) and crude oil enrichment amended by isolated soy proteins (0.2%), and incubated for 100 days under anaerobic conditions at 50°C. Phylogenetic and phylogenomic analysis demonstrated that the ARYD3 is unaffiliated with all currently described bacterial phyla and candidate phyla, as evident by the low AAI (34.7%), shared gene content (19.4%), and 78.9% 16S rRNA gene sequence similarity to Halothiobacillus neapolitanus, its closest cultured relative. Genomic characterization predicts a slow-growing, non-spore forming, and non-motile Gram-negative rod. Adaptation to high salinity is potentially mediated by the production of the compatible solutes cyclic 2,3-diphosphoglycerate (cDPG), α-glucosylglycerate, as well as the uptake of glycine betaine. Metabolically, the genome encodes primarily aminolytic capabilities for a wide range of amino acids and peptides. Interestingly, evidence of propionate degradation to succinate via methyl-malonyl CoA was identified, suggesting possible capability for syntrophic propionate degradation. Analysis of ARYD3 global distribution patterns identified its occurrence in a very small fraction of Earth Microbiome Project datasets examined (318/27,068), where it consistently represented an extremely rare fraction (maximum 0.28%, average 0.004%) of the overall community. We propose the Candidatus name Mcinerneyibacterium aminivorans gen. nov, sp. nov. for ARYD3^T, with the genome serving as the type material for the novel family Mcinerneyibacteriaceae fam. nov., order Mcinerneyibacteriales ord. nov., class Mcinerneyibacteria class nov., and phylum Mcinerneyibacteriota phyl. nov. The type material genome assembly is deposited in GenBank under accession number VSIX00000000.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

Genomic Characterization of Candidate Division LCP-89 Reveals an Atypical Cell Wall Structure, Microcompartment Production, and Dual Respiratory and Fermentative Capacities

Recent experimental and bioinformatic advances enable the recovery of genomes belonging to yet-uncultured microbial lineages directly from environmental samples. Here, we report on the recovery and characterization of single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) representing candidate phylum LCP-89, previously defined based on 16S rRNA gene sequences. Analysis of LCP-89 genomes recovered from Zodletone Spring, an anoxic spring in Oklahoma, predicts slow-growing, rod-shaped organisms. LCP-89 genomes contain genes for cell wall lipopolysaccharide (LPS) production but lack the entire machinery for peptidoglycan biosynthesis, suggesting an atypical cell wall structure. The genomes, however, encode S-layer homology domain-containing proteins, as well as machinery for the biosynthesis of CMP-legionaminate, inferring the possession of an S-layer glycoprotein. A nearly complete chemotaxis machinery coupled to the absence of flagellar synthesis and assembly genes argues for the utilization of alternative types of motility. A strict anaerobic lifestyle is predicted, with dual respiratory (nitrite ammonification) and fermentative capacities. Predicted substrates include a wide range of sugars and sugar alcohols and a few amino acids. The capability of rhamnose metabolism is confirmed by the identification of bacterial microcompartment genes to sequester the toxic intermediates generated. Comparative genomic analysis identified differences in oxygen sensitivities, respiratory capabilities, substrate utilization preferences, and fermentation end products between LCP-89 genomes and those belonging to its four sister phyla (Calditrichota, SM32-31, AABM5-125-24, and KSB1) within the broader FCB (Fibrobacteres-Chlorobi-Bacteroidetes) superphylum. Our results provide a detailed characterization of members of the candidate division LCP-89 and highlight the importance of reconciling 16S rRNA-based and genome-based phylogenies.IMPORTANCE Our understanding of the metabolic capacities, physiological preferences, and ecological roles of yet-uncultured microbial phyla is expanding rapidly. Two distinct approaches are currently being utilized for characterizing microbial communities in nature: amplicon-based 16S rRNA gene surveys for community characterization and metagenomics/single-cell genomics for detailed metabolic reconstruction. The occurrence of multiple yet-uncultured bacterial phyla has been documented using 16S rRNA surveys, and obtaining genome representatives of these yet-uncultured lineages is critical to our understanding of the role of yet-uncultured organisms in nature. This study provides a genomics-based analysis highlighting the structural features and metabolic capacities of a yet-uncultured bacterial phylum (LCP-89) previously identified in 16S rRNA surveys for which no prior genomes have been described. Our analysis identifies several interesting structural features for members of this phylum, e.g., lack of peptidoglycan biosynthetic machinery and the ability to form bacterial microcompartments. Predicted metabolic capabilities include degradation of a wide range of sugars, anaerobic respiratory capacity, and fermentative capacities. In addition to the detailed structural and metabolic analysis provided for candidate division LCP-89, this effort represents an additional step toward a unified scheme for microbial taxonomy by reconciling 16S rRNA gene-based and genomics-based taxonomic outlines.

virMine: automated detection of viral sequences from complex metagenomic samples

Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments.

A Novel Inorganic Sulfur Compound Metabolizing Ferroplasma-Like Population Is Suggested to Mediate Extracellular Electron Transfer

Mining and processing of metal sulfide ores produces waters containing metals and inorganic sulfur compounds such as tetrathionate and thiosulfate. If released untreated, these sulfur compounds can be oxidized to generate highly acidic wastewaters [termed ‘acid mine drainage (AMD)’] that cause severe environmental pollution. One potential method to remediate mining wastewaters is the maturing biotechnology of ‘microbial fuel cells’ that offers the sustainable removal of acid generating inorganic sulfur compounds alongside producing an electrical current. Microbial fuel cells exploit the ability of bacterial cells to transfer electrons to a mineral as the terminal electron acceptor during anaerobic respiration by replacing the mineral with a solid anode. In consequence, by substituting natural minerals with electrodes, microbial fuel cells also provide an excellent platform to understand environmental microbe–mineral interactions that are fundamental to element cycling. Previously, tetrathionate degradation coupled to the generation of an electrical current has been demonstrated and here we report a metagenomic and metatranscriptomic analysis of the microbial community. Reconstruction of inorganic sulfur compound metabolism suggested the substrate tetrathionate was metabolized by the Ferroplasma-like and Acidithiobacillus-like populations via multiple pathways. Characterized Ferroplasma species do not utilize inorganic sulfur compounds, suggesting a novel Ferroplasma-like population had been selected. Oxidation of intermediate sulfide, sulfur, thiosulfate, and adenylyl-sulfate released electrons and the extracellular electron transfer to the anode was suggested to be dominated by candidate soluble electron shuttles produced by the Ferroplasma-like population. However, as the soluble electron shuttle compounds also have alternative functions within the cell, it cannot be ruled out that acidophiles use novel, uncharacterized mechanisms to mediate extracellular electron transfer. Several populations within the community were suggested to metabolize intermediate inorganic sulfur compounds by multiple pathways, which highlights the potential for mutualistic or symbiotic relationships. This study provided the genetic base for acidophilic microbial fuel cells utilized for the remediation of inorganic sulfur compounds from AMD.

Viral Diversity and Its Relationship With Environmental Factors at the Surface and Deep Sea of Prydz Bay, Antarctica

A viral metagenomic analysis of five surface and two bottom water (878 meters below surface, mbs, and 3,357 mbs) samples from Prydz Bay, was conducted during February-March 2015. The results demonstrated that most of the DNA viruses were dsDNA viruses (79.73-94.06%, except at PBI1, 37.51%). Of these, Caudovirales (Siphoviridae, Myoviridae, and Podoviridae) phages were most abundant in surface seawater (67.67-71.99%), while nucleocytoplasmic large DNA viruses (NCLDVs) (Phycodnaviridae, Mimiviridae, and Pandoraviridae accounted for >30% of dsDNA viruses) were most abundant in the bottom water (3,357 mbs). Of the ssDNA viruses, Microviridae was the dominant family in PBI2, PBI3, PBOs, and PBI4b (57.09-87.55%), while Inoviridae (58.16%) was the dominant family in PBI1. Cellulophaga phages (phi38:1 and phi10:1) and Flavobacterium phage 11b, infecting the possible host strains affiliated with the family Flavobacteriaceae of the phylum Bacteroidetes, were abundant in surface water dsDNA viromes. The long contig (PBI2_1_C) from the viral metagenomes were most similar to the genome architectures of Cellulophaga phage phi10:1 and Flavobacterium phage 11b from the Arctic Ocean. Comparative analysis showed that the surface viral community of Prydz Bay could be clearly separated from other marine and freshwater environments. The deep sea viral community was similar to the deep sea viral metagenome at A Long-term Oligotrophic Habitat Assessment Station (ALOHA, at 22°45'N, 158°00'W). The multivariable analysis indicated that nutrients probably played an important role in shaping the local viral community structure. This study revealed the preliminary characteristics of the viral community in Prydz Bay, from both the surface and the deep sea. It provided evidence of the relationships between the virome and the environment in Prydz Bay and provided the first data from the deep sea viral community of the Southern Ocean.

Candidatus Krumholzibacterium zodletonense gen. nov., sp nov, the first representative of the candidate phylum Krumholzibacteriota phyl. nov. recovered from an anoxic sulfidic spring using genome resolved metagenomics

The accumulation of genomes of uncultured organisms has highlighted the need for devising a taxonomic and nomenclature scheme to validate names and prevent redundancies. We here report on the recovery and analysis of four phylogenetically related genomes recovered from an anoxic sulfide and sulfur-rich spring (Zodletone spring) in southwestern Oklahoma. Phylogenetic analysis based on 120 single copy markers attested to their position as a novel distinct bacterial phylum. Genomic analysis suggests Gram-negative flagellated organisms that possess type IV pili. The organisms are predicted to be rod-shaped, slow-growers, with an anoxic, heterotrophic, and fermentative lifestyle. Predicted substrate utilization pattern includes multiple amino acids, dipeptides, tripeptides, and oligpopeptides; as well as few sugars. Predicted auxotrophies include proline, vitamin B6, lipoic acid, biotin, and vitamin B12. Assessment of the putative global distribution pattern of this novel lineage suggests its preference to anoxic marine, terrestrial, hydrocarbon-impacted, and freshwater habitats. We propose the candidatus name Krumholzibacterium zodletonense gen. nov, sp. nov. for Zgenome0171^T, with the genome serving as the type material for the novel family Krumholzibacteriaceae fam. nov., order Krumholzibacteriales ord. nov., class Krumholzibacteria class nov., and phylum Krumholzibacteriota phyl. nov. The type material genome assembly is deposited in GenBank under accession number QTKG01000000.

Microbial sequence typing in the genomic era

Next-generation sequencing (NGS), also known as high-throughput sequencing, is changing the field of microbial genomics research. NGS allows for a more comprehensive analysis of the diversity, structure and composition of microbial genes and genomes compared to the traditional automated Sanger capillary sequencing at a lower cost. NGS strategies have expanded the versatility of standard and widely used typing approaches based on nucleotide variation in several hundred DNA sequences and a few gene fragments (MLST, MLVA, rMLST and cgMLST). NGS can now accommodate variation in thousands or millions of sequences from selected amplicons to full genomes (WGS, NGMLST and HiMLST). To extract signals from high-dimensional NGS data and make valid statistical inferences, novel analytic and statistical techniques are needed. In this review, we describe standard and new approaches for microbial sequence typing at gene and genome levels and guidelines for subsequent analysis, including methods and computational frameworks. We also present several applications of these approaches to some disciplines, namely genotyping, phylogenetics and molecular epidemiology.

Genetic correlation network prediction of forest soil microbial functional organization

Soil ecological functions are largely determined by the activities of soil microorganisms, which, in turn, are regulated by relevant interactions between genes and their corresponding pathways. Therefore, the genetic network can theoretically elucidate the functional organization that supports complex microbial community functions, although this has not been previously attempted. We generated a genetic correlation network based on 5421 genes derived from metagenomes of forest soils, identifying 7191 positive and 123 negative correlation relationships. This network consisted of 27 clusters enriched with sets of genes within specific functions, represented with corresponding cluster hubs. The clusters revealed a hierarchical architecture, reflecting the functional organization in the soil metagenomes. Positive correlations mapped functional associations, whereas negative correlations often mapped regulatory processes. The potential functions of uncharacterized genes were predicted based on the functions of located clusters. The global genetic correlation network highlights the functional organization in soil metagenomes and provides a resource for predicting gene functions. We anticipate that the genetic correlation network may be exploited to comprehensively decipher soil microbial community functions.

Need for Laboratory Ecosystems To Unravel the Structures and Functions of Soil Microbial Communities Mediated by Chemistry

The chemistry underpinning microbial interactions provides an integrative framework for linking the activities of individual microbes, microbial communities, plants, and their environments. Currently, we know very little about the functions of genes and metabolites within these communities because genome annotations and functions are derived from the minority of microbes that have been propagated in the laboratory. Yet the diversity, complexity, inaccessibility, and irreproducibility of native microbial consortia limit our ability to interpret chemical signaling and map metabolic networks. In this perspective, we contend that standardized laboratory ecosystems are needed to dissect the chemistry of soil microbiomes. We argue that dissemination and application of standardized laboratory ecosystems will be transformative for the field, much like how model organisms have played critical roles in advancing biochemistry and molecular and cellular biology. Community consensus on fabricated ecosystems ("EcoFABs") along with protocols and data standards will integrate efforts and enable rapid improvements in our understanding of the biochemical ecology of microbial communities.

MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle

The increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging. We describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare, and infer complex metabolic pathways in large “omic” datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use, we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways, and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H^΄), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used both to build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, receiver operator characteristic plots, and the area under the curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC = 0.985) hard-to-culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones, and metagenomic environments such as hydrothermal vents, or deep-sea sediment. Our benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and can be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa.

Effect of clinical parameters on the ocular surface microbiome in children and adults

Purpose

To perform a pilot study to characterize the effect of clinical parameters on the ocular surface microbiome (OSM) in children and adults using 16s ribosomal RNA sequencing.

Methods

Prospective, cross-sectional study using 16s sequencing to evaluate the OSM. Comparisons were made in bacterial composition by 1) age, 2) gender, 3) sampling location of the ocular and periocular surfaces, and 4) topical drop use. 16s sequencing was performed using Illumina MiSeq 250 and analyzed using Qiime.

Results

Thirty patients (15 children [mean 3.7 years], 15 adults [mean 60.4 years]) were sampled. Both principal coordinate analysis and unifrac distance analysis showed significant differences in the composition between the pediatric and adult OSMs (both p=0.001). The eyelid margin microbiota did not show any distinct clustering compared to conjunctiva within the pediatric samples but tended to show a distinction between anatomic sites in adult samples. No differences in OSM were noted by topical drop use.

Conclusion

16s sequencing is a useful tool in evaluating the OSM in patients of all ages, showing a distinct difference between pediatric and adult microbiomes.

Pseudomonas PB1-Like Phages: Whole Genomes from Metagenomes Offer Insight into an Abundant Group of Bacteriophages

Despite the abundance, ubiquity and impact of environmental viruses, their inherent genomic plasticity and extreme diversity pose significant challenges for the examination of bacteriophages on Earth. Viral metagenomic studies have offered insight into broader aspects of phage ecology and repeatedly uncover genes to which we are currently unable to assign function. A combined effort of phage isolation and metagenomic survey of Chicago&rsquo;s nearshore waters of Lake Michigan revealed the presence of Pbunaviruses, relatives of the Pseudomonas phage PB1. This prompted our expansive investigation of PB1-like phages. Genomic signatures of PB1-like phages and Pbunaviruses were identified, permitting the unambiguous distinction between the presence/absence of these phages in soils, freshwater and wastewater samples, as well as publicly available viral metagenomic datasets. This bioinformatic analysis led to the de novo assembly of nine novel PB1-like phage genomes from a metagenomic survey of samples collected from Lake Michigan. While this study finds that Pbunaviruses are abundant in various environments of Northern Illinois, genomic variation also exists to a considerable extent within individual communities.