Shotgun metagenomics, from sampling to analysis

Do you cov me? Effect of coverage reduction on species identification and genome reconstruction in complex biological matrices by metagenome shotgun high-throughput sequencing

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms and viruses in a single experiment, with the possibility of reconstructing de novo the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. However, we demonstrate that-for some applications-it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. We measured the variation of several summary statistics simulating a decrease in sequencing depth by randomly subsampling a number of reads. The main statistics that were compared are alpha diversity estimates, species abundance, detection threshold, and ability of reconstructing the metagenome in terms of length and completeness. Our results show that a classification of prokaryotic, eukaryotic and viral communities can be accurately performed even using very low number of reads, both in mock communities and in real complex matrices. With samples of 100,000 reads, the alpha diversity estimates were in most cases comparable to those obtained with the full sample, and the estimation of the abundance of all the present species was in excellent agreement with those obtained with the full sample. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1M reads). The length of the reconstructed assembly was smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct-even partially-the metagenome.

Do you cov me? Effect of coverage reduction on species identification and genome reconstruction in complex biological matrices by metagenome shotgun high-throughput sequencing

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms and viruses in a single experiment, with the possibility of reconstructing de novo the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. We set out to determine  if it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. We measured the variation of several summary statistics simulating a decrease in sequencing depth by randomly subsampling a number of reads. The main statistics that were compared are alpha diversity estimates, species abundance, and ability of reconstructing de novo the metagenome in terms of length and completeness. Our results show that diversity indices of complex prokaryotic, eukaryotic and viral communities can be accurately estimated with 500,000 reads or less, although particularly complex samples may require 1,000,000 reads. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1,000,000 reads). The length of the reconstructed assembly was smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct-even partially-the metagenome.

Do you cov me? Effect of coverage reduction on species identification and genome reconstruction in complex biological matrices by metagenome shotgun high-throughput sequencing

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms in a single experiment, with the possibility of de novo reconstruction of the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. However, we demonstrate that-for some applications-it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. Here we compared the results obtained on full size, real datasets with results obtained by randomly extracting a fixed number of reads. The main statistics that were compared are alpha diversity estimates, species abundance, and ability of reconstructing the metagenome in terms of length and completeness. Our results show that a classification of the communities present in a complex matrix can be accurately performed even using very low number of reads. With samples of 100,000 reads, the alpha diversity estimates were in most cases comparable to those obtained with the full sample, and the estimation of the abundance of all the present species was in excellent agreement with those obtained with the full sample. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1M reads). The length of the reconstructed assembly was sensibly smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct de novo-even partially-the metagenome.

Do you cov me? Effect of coverage reduction on metagenome shotgun sequencing studies

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms and viruses in a single experiment, with the possibility of reconstructing de novo the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. We set out to determine if it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. We used a staggered mock community to estimate the optimal threshold for species detection. We measured the variation of several summary statistics simulating a decrease in sequencing depth by randomly subsampling a number of reads. The main statistics that were compared are diversity estimates, species abundance, and ability of reconstructing de novo the metagenome in terms of length and completeness. Our results show that diversity indices of complex prokaryotic, eukaryotic and viral communities can be accurately estimated with 500,000 reads or less, although particularly complex samples may require 1,000,000 reads. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1,000,000 reads). The length of the reconstructed assembly was smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct-even partially-the metagenome.

Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature

Honey bees are considered large-scale monitoring tools due to their environmental exploration and foraging activities. Traces of these activities can be recovered in the honey that also may reflect the hive ecological micro-conditions in which it has been produced. This study applied a next generation sequencing platform (Ion Torrent) for shotgun metagenomic analysis of honey environmental DNA (eDNA). The study tested a methodological framework to interpret DNA sequence information useful to describe the complex ecosystems of the honey bee colony superorganism, its pathosphere and the heterogeneity of the agroecological environments and environmental sources that left DNA marks in the honey. Analysis of two honeys reported sequence reads from five main organism groups (kingdoms or phyla): arthropods (that mainly included reads from Apis mellifera, several other members of the Hymenotpera, in addition to members of the Diptera, Coleoptera and Lepidoptera, as well as aphids and mites), plants (that clearly confirmed the botanical origin of the two honeys, i.e. orange tree blossom and eucalyptus tree blossom honeys), fungi and bacteria (including common hive and honey bee gut microorganisms, honey bee pathogens and plant pathogens), and viruses (which accounted for the largest number of reads in both honeys, mainly assigned to Apis mellifera filamentous virus). The shotgun metagenomic approach that was used in this study can be applied in large scale experiments that might have multiple objectives according to the multi-kingdom derived eDNA that is contained in the honey.

The contribution of respiratory microbiome analysis to a treatable traits model of care

The composition of the airway microbiome in patients with chronic airway diseases, such as severe asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis and cystic fibrosis (CF), has the potential to inform a precision model of clinical care. Patients with these conditions share overlapping disease characteristics, including airway inflammation and airflow limitation. The clinical management of chronic respiratory conditions is increasingly moving away from a one-size-fits-all model based on primary diagnosis, towards care targeting individual disease traits, and is particularly useful for subgroups of patients who respond poorly to conventional therapies. Respiratory microbiome analysis is an important potential contributor to such a 'treatable traits' approach, providing insight into both microbial drivers of airways disease, and the selective characteristics of the changing lower airway environment. We explore the potential to integrate respiratory microbiome analysis into a treatable traits model of clinical care and provide a practical guide to the application and clinical interpretation of respiratory microbiome analysis.

Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes

BACKGROUND

Prokaryotes dominate the biosphere and regulate biogeochemical processes essential to all life. Yet, our knowledge about their biology is for the most part limited to the minority that has been successfully cultured. Molecular techniques now allow for obtaining genome sequences of uncultivated prokaryotic taxa, facilitating in-depth analyses that may ultimately improve our understanding of these key organisms.

RESULTS

We compared results from two culture-independent strategies for recovering bacterial genomes: single-amplified genomes and metagenome-assembled genomes. Single-amplified genomes were obtained from samples collected at an offshore station in the Baltic Sea Proper and compared to previously obtained metagenome-assembled genomes from a time series at the same station. Among 16 single-amplified genomes analyzed, seven were found to match metagenome-assembled genomes, affiliated with a diverse set of taxa. Notably, genome pairs between the two approaches were nearly identical (average 99.51% sequence identity; range 98.77-99.84%) across overlapping regions (30-80% of each genome). Within matching pairs, the single-amplified genomes were consistently smaller and less complete, whereas the genetic functional profiles were maintained. For the metagenome-assembled genomes, only on average 3.6% of the bases were estimated to be missing from the genomes due to wrongly binned contigs.

CONCLUSIONS

The strong agreement between the single-amplified and metagenome-assembled genomes emphasizes that both methods generate accurate genome information from uncultivated bacteria. Importantly, this implies that the research questions and the available resources are allowed to determine the selection of genomics approach for microbiome studies.

MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis

BACKGROUND

The study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization.

RESULTS

To address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAP's hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real data sets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization.

CONCLUSIONS

MetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/bxlab/metaWRAP .

Deletion of poly(ADP‑ribose) polymerase-1 changes the composition of the microbiome in the gut

Poly(adenosine diphosphate-ribose) polymerase (PARP)-1 is the prototypical PARP enzyme well known for its role in DNA repair and as a pro-inflammatory protein. Since PARP1 is an important co-factor of several other pro-inflammatory proteins, in the present study the possible changes in microbial flora of PARP1 knockout mice were investigated. Samples from the duodenum, cecum and feces from wild type and PARP1 knockout C57BL/6J male mice were collected and 16S ribosomal RNA genes were sequenced. Based on the sequencing results, the microbiome and compared samples throughout the lower part of the gastrointestinal system were reconstructed. The present results demonstrated that the lack of PARP1 enzyme only disturbed the microbial flora of the duodenum, where the biodiversity increased in the knockout animals on the species level but decreased on the order level. The most prominent change was the overwhelming abundance of the family Porphyromonadaceae in the duodenum of PARP1^−/− animals, which disappeared in the cecum and feces where families were spread out more evenly than in the wild type animals. The findings of the present study may improve current understanding of the role of PARP1 in chronic inflammatory diseases.

Population genomics of bacterial host adaptation

Some bacteria can transfer to new host species, and this poses a risk to human health. Indeed, an estimated 60% of all human pathogens have originated from other animal species. Similarly, human-to-animal transitions are recognized as a major threat to sustainable livestock production, and emerging pathogens impose an increasing burden on crop yield and global food security. Recent advances in high-throughput sequencing technologies have enabled comparative genomic analyses of bacterial populations from multiple hosts. Such studies are providing new insights into the evolutionary processes that underpin the establishment of bacteria in new host niches. A better understanding of the genetic and mechanistic basis for bacterial host adaptation may reveal novel targets for controlling infection or inform the design of approaches to limit the emergence of new pathogens.

Relevance of phenotypic information for the taxonomy of not-yet-cultured microorganisms

To date, far less than 1% of the estimated global species of Bacteria and Archaea have been described and their names validly published. Aside from these quantitative limitations, our understanding of phenotypic and functional diversity of prokaryotes is also highly biased as not a single species has been described for 85 of the 118 phyla that are currently recognized. Due to recent advances in sequencing technology and capacity, metagenomic datasets accumulate at an increasing speed and new bacterial and archaeal genome sequences become available at a faster rate than newly described species. The growing gap between the diversity of Bacteria and Archaea held in pure culture and that detected by molecular methods has led to the proposal to establish a formal nomenclature for not-yet-cultured taxa primarily based on sequence information. According to this proposal, the concept of Candidatus species would be extended to groups of closely related genome sequences and their names validly published following established rules of bacterial nomenclature. The corresponding sequences would be deposited in public databases as the type. The suggested alterations of the International Code of Nomenclature of Prokaryotes raise concerns regarding (1) the reliability and stability of nomenclature, (2) the technological and conceptual limitations as well as availability of reference genomes, (3) the information content of in silico functional predictions, and (4) the recognition of evolutionary units of microbial diversity. These challenges need to be overcome to arrive at a meaningful taxonomy of not-yet-cultured prokaryotes with so far poorly understood phenotypes.

Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis

The complexity of microbial communities, combined with technical biases in next-generation sequencing, pose a challenge to metagenomic analysis. Here, we develop a set of internal DNA standards, termed “sequins” (sequencing spike-ins), that together constitute a synthetic community of artificial microbial genomes. Sequins are added to environmental DNA samples prior to library preparation, and undergo concurrent sequencing with the accompanying sample. We validate the performance of sequins by comparison to mock microbial communities, and demonstrate their use in the analysis of real metagenome samples. We show how sequins can be used to measure fold change differences in the size and structure of accompanying microbial communities, and perform quantitative normalization between samples. We further illustrate how sequins can be used to benchmark and optimize new methods, including nanopore long-read sequencing technology. We provide metagenome sequins, along with associated data sets, protocols, and an accompanying software toolkit, as reference standards to aid in metagenomic studies.

Complex microbial communities pose a challenge to metagenomic analysis. Here the authors develop ‘sequins’, internal DNA standards that represent a synthetic community of artificial genomes.

Investigating Effects of Tulathromycin Metaphylaxis on the Fecal Resistome and Microbiome of Commercial Feedlot Cattle Early in the Feeding Period

The objective was to examine effects of treating commercial beef feedlot cattle with therapeutic doses of tulathromycin, a macrolide antimicrobial drug, on changes in the fecal resistome and microbiome using shotgun metagenomic sequencing. Two pens of cattle were used, with all cattle in one pen receiving metaphylaxis treatment (800 mg subcutaneous tulathromycin) at arrival to the feedlot, and all cattle in the other pen remaining unexposed to parenteral antibiotics throughout the study period. Fecal samples were collected from 15 selected cattle in each group just prior to treatment (Day 1), and again 11 days later (Day 11). Shotgun sequencing was performed on isolated metagenomic DNA, and reads were aligned to a resistance and a taxonomic database to identify alignments to antimicrobial resistance (AMR) gene accessions and microbiome content. Overall, we identified AMR genes accessions encompassing 9 classes of AMR drugs and encoding 24 unique AMR mechanisms. Statistical analysis was used to identify differences in the resistome and microbiome between the untreated and treated groups at both timepoints, as well as over time. Based on composition and ordination analyses, the resistome and microbiome were not significantly different between the two groups on Day 1 or on Day 11. However, both the resistome and microbiome changed significantly between these two sampling dates. These results indicate that the transition into the feedlot-and associated changes in diet, geography, conspecific exposure, and environment-may exert a greater influence over the fecal resistome and microbiome of feedlot cattle than common metaphylactic antimicrobial drug treatment.

Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome

The acquisition and development of the infant microbiome are key to establishing a healthy host-microbiome symbiosis. The maternal microbial reservoir is thought to play a crucial role in this process. However, the source and transmission routes of the infant pioneering microbes are poorly understood. To address this, we longitudinally sampled the microbiome of 25 mother-infant pairs across multiple body sites from birth up to 4 months postpartum. Strain-level metagenomic profiling showed a rapid influx of microbes at birth followed by strong selection during the first few days of life. Maternal skin and vaginal strains colonize only transiently, and the infant continues to acquire microbes from distinct maternal sources after birth. Maternal gut strains proved more persistent in the infant gut and ecologically better adapted than those acquired from other sources. Together, these data describe the mother-to-infant microbiome transmission routes that are integral in the development of the infant microbiome.

Regulation of lipid peroxidation and ferroptosis in diverse species

This review by Conrad et al. reviews the functions and regulation of lipid peroxidation, ferroptosis, and the antioxidant network in diverse species, including humans, other mammals and vertebrates, plants, invertebrates, yeast, bacteria, and archaea, and discusses the potential evolutionary roles of lipid peroxidation and ferroptosis.

Lipid peroxidation is the process by which oxygen combines with lipids to generate lipid hydroperoxides via intermediate formation of peroxyl radicals. Vitamin E and coenzyme Q₁₀ react with peroxyl radicals to yield peroxides, and then these oxidized lipid species can be detoxified by glutathione and glutathione peroxidase 4 (GPX4) and other components of the cellular antioxidant defense network. Ferroptosis is a form of regulated nonapoptotic cell death involving overwhelming iron-dependent lipid peroxidation. Here, we review the functions and regulation of lipid peroxidation, ferroptosis, and the antioxidant network in diverse species, including humans, other mammals and vertebrates, plants, invertebrates, yeast, bacteria, and archaea. We also discuss the potential evolutionary roles of lipid peroxidation and ferroptosis.

ASaiM: a Galaxy-based framework to analyze microbiota data

Background

New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.

Findings

We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io).

Conclusions

Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.

Thanatomicrobiome composition profiling as a tool for forensic investigation

Thanatomicrobiome, or the postmortem microbiome, has been recognized as a useful microbial marker of the time and location of host death. In this mini-review, we compare the experimental methods commonly applied to thanatomicrobiome studies to the state-of-the-art methodologies in the microbiome field. Then, we review present findings in thanatomicrobiome studies, focusing on the diversity of the thanatomicrobiome composition and prediction models that have been proposed. Finally, we discuss potential improvements and future directions of the field.

The Present and Future of Whole Genome Sequencing (WGS) and Whole Metagenome Sequencing (WMS) for Surveillance of Antimicrobial Resistant Microorganisms and Antimicrobial Resistance Genes across the Food Chain

Antimicrobial resistance (AMR) surveillance is a critical step within risk assessment schemes, as it is the basis for informing global strategies, monitoring the effectiveness of public health interventions, and detecting new trends and emerging threats linked to food. Surveillance of AMR is currently based on the isolation of indicator microorganisms and the phenotypic characterization of clinical, environmental and food strains isolated. However, this approach provides very limited information on the mechanisms driving AMR or on the presence or spread of AMR genes throughout the food chain. Whole-genome sequencing (WGS) of bacterial pathogens has shown potential for epidemiological surveillance, outbreak detection, and infection control. In addition, whole metagenome sequencing (WMS) allows for the culture-independent analysis of complex microbial communities, providing useful information on AMR genes occurrence. Both technologies can assist the tracking of AMR genes and mobile genetic elements, providing the necessary information for the implementation of quantitative risk assessments and allowing for the identification of hotspots and routes of transmission of AMR across the food chain. This review article summarizes the information currently available on the use of WGS and WMS for surveillance of AMR in foodborne pathogenic bacteria and food-related samples and discusses future needs that will have to be considered for the routine implementation of these next-generation sequencing methodologies with this aim. In particular, methodological constraints that impede the use at a global scale of these high-throughput sequencing (HTS) technologies are identified, and the standardization of methods and protocols is suggested as a measure to upgrade HTS-based AMR surveillance schemes.

Profiling microbial strains in urban environments using metagenomic sequencing data

Background

The microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample. Microbes thriving in urban environments may be crucially important for human health, but have received less attention than those of other environments. Ongoing efforts started to target urban microbiomes at a large scale, but the most recent computational methods to profile these metagenomes have never been applied in this context. It is thus currently unclear whether such methods, that have proven successful at distinguishing even closely related strains in human microbiomes, are also effective in urban settings for tasks such as cultivation-free pathogen detection and microbial surveillance. Here, we aimed at a) testing the currently available metagenomic profiling tools on urban metagenomics; b) characterizing the organisms in urban environment at the resolution of single strain and c) discussing the biological insights that can be inferred from such methods.

Results

We applied three complementary methods on the 1614 metagenomes of the CAMDA 2017 challenge. With MetaMLST we identified 121 known sequence-types from 15 species of clinical relevance. For instance, we identified several Acinetobacter strains that were close to the nosocomial opportunistic pathogen A. nosocomialis. With StrainPhlAn, a generalized version of the MetaMLST approach, we inferred the phylogenetic structure of Pseudomonas stutzeri strains and suggested that the strain-level heterogeneity in environmental samples is higher than in the human microbiome. Finally, we also probed the functional potential of the different strains with PanPhlAn. We further showed that SNV-based and pangenome-based profiling provide complementary information that can be combined to investigate the evolutionary trajectories of microbes and to identify specific genetic determinants of virulence and antibiotic resistances within closely related strains.

Conclusion

We show that strain-level methods developed primarily for the analysis of human microbiomes can be effective for city-associated microbiomes. In fact, (opportunistic) pathogens can be tracked and monitored across many hundreds of urban metagenomes. However, while more effort is needed to profile strains of currently uncharacterized species, this work poses the basis for high-resolution analyses of microbiomes sampled in city and mass transportation environments.

Reviewers

This article was reviewed by Alexandra Bettina Graf, Daniel Huson and Trevor Cickovski.

Electronic supplementary material

The online version of this article (10.1186/s13062-018-0211-z) contains supplementary material, which is available to authorized users.

Systematic and synthetic approaches to rewire regulatory networks

Microbial gene regulatory networks are composed of cis- and trans-components that in concert act to control essential and adaptive cellular functions. Regulatory components and interactions evolve to adopt new configurations through mutations and network rewiring events, resulting in novel phenotypes that may benefit the cell. Advances in high-throughput DNA synthesis and sequencing have enabled the development of new tools and approaches to better characterize and perturb various elements of regulatory networks. Here, we highlight key recent approaches to systematically dissect the sequence space of cis-regulatory elements and trans-regulators as well as their inter-connections. These efforts yield fundamental insights into the architecture, robustness, and dynamics of gene regulation and provide models and design principles for building synthetic regulatory networks for a variety of practical applications.

Gut microbiome modulates efficacy of immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) therapy is a novel strategy for cancer treatments in recent years. However, it was observed that most patients treated with ICIs could not get benefit from the therapy, which led to the limitation of clinical application. Motivated by potent and durable efficacy of ICIs, oncologists endeavor to explore the mechanisms of resistance to ICIs and increase the drug sensitivity. It is known that heterogeneity of gut microbiome in populations may result in different outcomes of therapy. In xenograft model, bacteria in gut have been proved as a crucial factor regulating immunotherapy efficacy. And the similar phenomenon was obtained in patients. In this review, we summarized relevant advancements about gut microbiome and ICIs. Furthermore, we focused on modulatory function of gut microbiome in ICIs therapy and possible antitumor mechanism of specific commensals in ICIs treatment. We propose that gut microbiome is an important predictive factor, and manipulation of gut microbiome is feasible to elevate response rate in ICIs therapy.

Species classifier choice is a key consideration when analysing low-complexity food microbiome data

BACKGROUND

The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads.

RESULTS

Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R² = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R² = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth.

CONCLUSIONS

Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.

On the Road to Strain-Resolved Comparative Metagenomics

Metagenomics has transformed microbiology, but its potential has not been fully expressed yet. From computational methods for digging deeper into metagenomes to study designs for addressing specific hypotheses, the Segata Lab is pursuing an integrative metagenomic approach to describe and model human-associated microbial communities as collections of strains.

Metagenomics has transformed microbiology, but its potential has not been fully expressed yet. From computational methods for digging deeper into metagenomes to study designs for addressing specific hypotheses, the Segata Lab is pursuing an integrative metagenomic approach to describe and model human-associated microbial communities as collections of strains. Linking strain variants to host phenotypes and performing cultivation-free population genomics require large cohorts and meta-analysis strategies to synthesize available cohorts but can revolutionize our understanding of the personalized host-microbiome interface which is at the base of human health.

Highlighting Clinical Metagenomics for Enhanced Diagnostic Decision-making: A Step Towards Wider Implementation

Clinical metagenomics (CMg) is the discipline that refers to the sequencing of all nucleic acid material present within a clinical specimen with the intent to recover clinically relevant microbial information. From a diagnostic perspective, next-generation sequencing (NGS) offers the ability to rapidly identify putative pathogens and predict their antimicrobial resistance profiles to optimize targeted treatment regimens. Since the introduction of metagenomics nearly a decade ago, numerous reports have described successful applications in an increasing variety of biological specimens, such as respiratory secretions, cerebrospinal fluid, stool, blood and tissue. Considerable advancements in sequencing and computational technologies in recent years have made CMg a promising tool in clinical microbiology laboratories. Moreover, costs per sample and turnaround time from specimen receipt to clinical management continue to decrease, making the prospect of CMg more feasible. Many difficulties, however, are associated with CMg and warrant further improvements such as the informatics infrastructure and analytical pipelines. Thus, the current review focuses on comprehensively assessing applications of CMg for diagnostic and subtyping purposes.

Draft Genome Sequences of Novel Pseudomonas, Flavobacterium, and Sediminibacterium [corrected] Strains from a Freshwater Ecosystem

Freshwater ecosystems represent 0.01% of the water on Earth, but they support 6% of global biodiversity that is still mostly uncharacterized. Here, we describe the genome sequences of three strains belonging to novel species in the Pseudomonas, Flavobacterium, and Sediminibacterium genera recovered from a water sample of Lake Garda, Italy.

Genome watch: Keeping tally in the microbiome

This month's Genome Watch highlights how the development of new approaches for quantifying the human microbiome may pave the way for a better understanding of microbial shifts in the context of human health and disease.

Human Microbiome Acquisition and Bioinformatic Challenges in Metagenomic Studies

The study of the human microbiome has become a very popular topic. Our microbial counterpart, in fact, appears to play an important role in human physiology and health maintenance. Accordingly, microbiome alterations have been reported in an increasing number of human diseases. Despite the huge amount of data produced to date, less is known on how a microbial dysbiosis effectively contributes to a specific pathology. To fill in this gap, other approaches for microbiome study, more comprehensive than 16S rRNA gene sequencing, i.e., shotgun metagenomics and metatranscriptomics, are becoming more widely used. Methods standardization and the development of specific pipelines for data analysis are required to contribute to and increase our understanding of the human microbiome relationship with health and disease status.

MetaGOmics: A Web-Based Tool for Peptide-Centric Functional and Taxonomic Analysis of Metaproteomics Data

Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed "MetaGOmics") that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis-including comparing the functional makeup between samples. MetaGOmics is freely available at https://www.yeastrc.org/metagomics/.

Airway microbial metagenomics

High-throughput untargeted metagenome sequencing provides information about the composition of the microbial communities of viruses, bacteria, archaea and unicellular eukaryotes in the habitat of interest. This review outlines the sampling, processing, sequencing and bioinformatic analysis of secretions of the respiratory tract and summarizes our current knowledge of the upper and lower human airways metagenome in health and disease.

Corrigendum: Shotgun metagenomics, from sampling to analysis

This corrects the article DOI: 10.1038/nbt.3935.