Metagenomic sequencing of an in vitro-simulated microbial community

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe, for example, different DNA extraction efficiency for Gram-positive bacteria. These processing biases impede the identification of signals that are biologically interpretable and generalizable across studies. 'Batch-correction' methods have been used to address these issues computationally with some success, but they are largely non-interpretable and often require the use of an outcome variable in a manner that risks overfitting. We present DEBIAS-M (domain adaptation with phenotype estimation and batch integration across studies of the microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using diverse benchmarks including 16S rRNA and metagenomic sequencing, classification and regression, and a variety of clinical and molecular targets, we demonstrate that using DEBIAS-M improves cross-study prediction accuracy compared with commonly used batch-correction methods. Notably, we show that the inferred bias-correction factors are stable, interpretable and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M facilitates improved modelling of microbiome data and identification of interpretable signals that generalize across studies.

De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation

INTRODUCTION

Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties.

METHODS

We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks.

RESULTS

Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions.

CONCLUSIONS

Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis. Video Abstract.

Comparative evaluation of soil DNA extraction kits for long read metagenomic sequencing

Metagenomics has been transformative in our understanding of the diversity and function of soil microbial communities. Applying long read sequencing to whole genome shotgun metagenomics has the potential to revolutionise soil microbial ecology through improved taxonomic classification, functional characterisation and metagenome assembly. However, optimisation of robust methods for long read metagenomics of environmental samples remains undeveloped. In this study, Oxford Nanopore sequencing using samples from five commercially available soil DNA extraction kits was compared across four soil types, in order to optimise read length and reproducibility for comparative long read soil metagenomics. Average extracted DNA lengths varied considerably between kits, but longer DNA fragments did not translate consistently into read lengths. Highly variable decreases in the length of resulting reads from some kits were associated with poor classification rate and low reproducibility in microbial communities identified between technical repeats. Replicate samples from other kits showed more consistent conversion of extracted DNA fragment size into read length and resulted in more congruous microbial community representation. Furthermore, extraction kits showed significant differences in the community representation and structure they identified across all soil types. Overall, the QIAGEN DNeasy PowerSoil Pro Kit displayed the best suitability for reproducible long-read WGS metagenomic sequencing, although further optimisation of DNA purification and library preparation may enable translation of higher molecular weight DNA from other kits into longer read lengths. These findings provide a novel insight into the importance of optimising DNA extraction for achieving replicable results from long read metagenomic sequencing of environmental samples.

Microbiome analyses of poultry feeds: Part I. Comparison of five different DNA extraction methods

Given extensive variability in feed composition, the absence of a dedicated DNA extraction kit for poultry feed underscores the need for an optimized extraction technique for reliable downstream sequencing analyses. This study investigates the impact of five DNA extraction techniques: Qiagen QIAamp DNA Stool Mini Kit (Qiagen), modified Qiagen with Lysing Matrix B (MQ), modified Qiagen with celite purification (MQC), polyethylene glycol (PEG), and 1-Day Direct. Genomic DNA amplification and Illumina MiSeq sequencing were conducted. QIIME2-2021.4 facilitated data analysis, revealing significant diversity and compositional differences influenced by extraction methods. Qiagen exhibited lower evenness and richness compared to other methods. 1-Day Direct and PEG enhanced bacterial diversities by employing bead beating and lysozyme. Despite similar taxonomic resolution, the Qiagen kit provides a rapid, consistent method for assessing poultry feed microbiomes. Modified techniques (MQ and MQC) improve DNA purification, reducing bias in commercial poultry feed samples. PEG and 1-Day Direct methods were effective but may require standardization. Overall, this study underscores the importance of optimized extraction techniques in poultry feed analysis, with potential implications for future standardization of effective methods.

Bioaerosol Sampling Devices and Pretreatment for Bacterial Characterization: Theoretical Differences and a Field Experience in a Wastewater Treatment Plant

Studies on bioaerosol bacterial biodiversity have relevance in both ecological and health contexts, and molecular methods, such as 16S rRNA gene-based barcoded sequencing, provide efficient tools for the analysis of airborne bacterial communities. Standardized methods for sampling and analysis of bioaerosol DNA are lacking, thus hampering the comparison of results from studies implementing different devices and procedures. Three samplers that use gelatin filtration, swirling aerosol collection, and condensation growth tubes for collecting bioaerosol at an aeration tank of a wastewater treatment plant in Trieste (Italy) were used to determine the bacterial biodiversity. Wastewater samples were collected directly from the untreated sewage to obtain a true representation of the microbiological community present in the plant. Different samplers and collection media provide an indication of the different grades of biodiversity, with condensation growth tubes and DNA/RNA shieldTM capturing the richer bacterial genera. Overall, in terms of relative abundance, the air samples have a lower number of bacterial genera (64 OTUs) than the wastewater ones (75 OTUs). Using the metabarcoding approach to aerosol samples, we provide the first preliminary step toward the understanding of a significant diversity between different air sampling systems, enabling the scientific community to orient research towards the most informative sampling strategy.

The Role of the Gut Microbiome in Hematological Cancers

Humans are in a complex symbiotic relationship with a wide range of microbial organisms, including bacteria, viruses, and fungi. The evolution and composition of the human microbiome can be an indicator of how it may affect human health and susceptibility to diseases. Microbiome alteration, termed as dysbiosis, has been linked to the pathogenesis and progression of hematological cancers. A variety of mechanisms, including epithelial barrier disruption, local chronic inflammation response trigger, antigen dis-sequestration, and molecular mimicry, have been proposed to be associated with gut microbiota. Dysbiosis may be induced or worsened by cancer therapies (such as chemotherapy and/or hematopoietic stem cell transplantation) or infection. The use of antibiotics during treatment may also promote dysbiosis, with possible long-term consequences. The aim of this review is to provide a succinct summary of the current knowledge describing the role of the microbiome in hematological cancers, as well as its influence on their therapies. Modulation of the gut microbiome, involving modifying the composition of the beneficial microorganisms in the management and treatment of hematological cancers is also discussed. Additionally discussed are the latest developments in modeling approaches and tools used for computational analyses, interpretation and better understanding of the gut microbiome data.

An Optimized Active Sampling Procedure for Aerobiological DNA Studies

The Earth's atmosphere plays a critical role in transporting and dispersing biological aerosols. Nevertheless, the amount of microbial biomass in suspension in the air is so low that it is extremely difficult to monitor the changes over time in these communities. Real-time genomic studies can provide a sensitive and rapid method for monitoring changes in the composition of bioaerosols. However, the low abundance of deoxyribose nucleic acid (DNA) and proteins in the atmosphere, which is of the order of the contamination produced by operators and instruments, poses a challenge for the sampling process and the analyte extraction. In this study, we designed an optimized, portable, closed bioaerosol sampler based on membrane filters using commercial off-the-shelf components, demonstrating its end-to-end operation. This sampler can operate autonomously outdoors for a prolonged time, capturing ambient bioaerosols and avoiding user contamination. We first performed a comparative analysis in a controlled environment to select the optimal active membrane filter based on its ability to capture and extract DNA. We have designed a bioaerosol chamber for this purpose and tested three commercial DNA extraction kits. The bioaerosol sampler was tested outdoors in a representative environment and run for 24 h at 150 L/min. Our methodology suggests that a 0.22-µm polyether sulfone (PES) membrane filter can recover up to 4 ng of DNA in this period, sufficient for genomic applications. This system, along with the robust extraction protocol, can be automated for continuous environmental monitoring to gain insights into the time evolution of microbial communities within the air.

Grinding Beads Influence Microbial DNA Extraction from Organic-Rich Sub-Seafloor Sediment

Sub-seafloor sediment is the largest microbial habitat on Earth. The study of microbes in sub-seafloor sediment is largely limited by the technical challenge of acquiring ambient microbial DNA because of sediment heterogeneity. Changes in the extraction method, even just by one step, can affect the extraction yields for complicated sediment samples. In this work, sub-seafloor sediment samples from the Baltic Sea with high organic carbon content were used to evaluate the influence of different grinding beads on DNA extraction. We found that the grinding beads can affect the DNA extraction from the organic-matter- and biosiliceous-clay-rich samples. A mixture of 0.5-mm and 0.1-mm grinding beads exhibited higher DNA yields and recovered more unique taxa than other bead combinations, such as Stenotrophomonas from Gammaproteobacteria and Leptotrichia from Fusobacteria; therefore, these beads are more suitable than the others for DNA extraction from the samples used in this study. This advantage might be magnified in samples with high biomass. On the contrary, the use of only small beads might lead to underestimation for certain Gram-positive strains. Overall, the discovery of abundant widespread deep biosphere clades in our samples indicated that our optimized DNA extraction method successfully recovered the in situ microbial community.

What Can We Learn about the Bias of Microbiome Studies from Analyzing Data from Mock Communities?

It is known that data from both 16S and shotgun metagenomics studies are subject to biases that cause the observed relative abundances of taxa to differ from their true values. Model community analyses, in which the relative abundances of all taxa in the sample are known by construction, seem to offer the hope that these biases can be measured. However, it is unclear whether the bias we measure in a mock community analysis is the same as we measure in a sample in which taxa are spiked in at known relative abundance, or if the biases we measure in spike-in samples is the same as the bias we would measure in a real (e.g., biological) sample. Here, we consider these questions in the context of 16S rRNA measurements on three sets of samples: the commercially available Zymo cells model community; the Zymo model community mixed with Swedish Snus, a smokeless tobacco product that is virtually bacteria-free; and a set of commercially available smokeless tobacco products. Each set of samples was subject to four different extraction protocols. The goal of our analysis is to determine whether the patterns of bias observed in each set of samples are the same, i.e., can we learn about the bias in the commercially available smokeless tobacco products by studying the Zymo cells model community?

Plant genetic effects on microbial hubs impact host fitness in repeated field trials

Recent demonstrations of a genetic basis for variation among hosts in the microbiome leave unresolved the question of how commonly host genetic effects influence individual microbes, and whether these effects impact host fitness. We used replicated field studies in the north and south of Sweden to map host genetic effects in microbial community networks using genome-wide association mapping. By focusing on consistent effects across sites, we found effects of genetic variation on important microbial hubs that contributed to plant fitness in a manner robust to the environment. Our results suggest that ongoing efforts to harness host genotype effects on the microbiome for agricultural purposes can be successful and highlight the value of explicitly considering abiotic variation in those efforts.

Although complex interactions between hosts and microbial associates are increasingly well documented, we still know little about how and why hosts shape microbial communities in nature. In addition, host genetic effects on microbial communities vary widely depending on the environment, obscuring conclusions about which microbes are impacted and which plant functions are important. We characterized the leaf microbiota of 200 Arabidopsis thaliana genotypes in eight field experiments and detected consistent host effects on specific, broadly distributed microbial species (operational taxonomic unit [OTUs]). Host genetic effects disproportionately influenced central ecological hubs, with heritability of particular OTUs declining with their distance from the nearest hub within the microbial network. These host effects could reflect either OTUs preferentially associating with specific genotypes or differential microbial success within them. Host genetics associated with microbial hubs explained over 10% of the variation in lifetime seed production among host genotypes across sites and years. We successfully cultured one of these microbial hubs and demonstrated its growth-promoting effects on plants in sterile conditions. Finally, genome-wide association mapping identified many putatively causal genes with small effects on the relative abundance of microbial hubs across sites and years, and these genes were enriched for those involved in the synthesis of specialized metabolites, auxins, and the immune system. Using untargeted metabolomics, we corroborate the consistent association between variation in specialized metabolites and microbial hubs across field sites. Together, our results reveal that host genetic variation impacts the microbial communities in consistent ways across environments and that these effects contribute to fitness variation among host genotypes.

Shotgun metagenomics of soil invertebrate communities reflects taxonomy, biomass, and reference genome properties

Metagenomics - shotgun sequencing of all DNA fragments from a community DNA extract - is routinely used to describe the composition, structure, and function of microorganism communities. Advances in DNA sequencing and the availability of genome databases increasingly allow the use of shotgun metagenomics on eukaryotic communities. Metagenomics offers major advances in the recovery of biomass relationships in a sample, in comparison to taxonomic marker gene-based approaches (metabarcoding). However, little is known about the factors which influence metagenomics data from eukaryotic communities, such as differences among organism groups, the properties of reference genomes, and genome assemblies.We evaluated how shotgun metagenomics records composition and biomass in artificial soil invertebrate communities at different sequencing efforts. We generated mock communities of controlled biomass ratios from 28 species from all major soil mesofauna groups: mites, springtails, nematodes, tardigrades, and potworms. We shotgun sequenced these communities and taxonomically assigned them with a database of over 270 soil invertebrate genomes.We recovered over 95% of the species, and observed relatively high false-positive detection rates. We found strong differences in reads assigned to different taxa, with some groups (e.g., springtails) consistently attracting more hits than others (e.g., enchytraeids). Original biomass could be predicted from read counts after considering these taxon-specific differences. Species with larger genomes, and with more complete assemblies, consistently attracted more reads than species with smaller genomes. The GC content of the genome assemblies had no effect on the biomass-read relationships. Results were similar among different sequencing efforts.The results show considerable differences in taxon recovery and taxon specificity of biomass recovery from metagenomic sequence data. The properties of reference genomes and genome assemblies also influence biomass recovery, and they should be considered in metagenomic studies of eukaryotes. We show that low- and high-sequencing efforts yield similar results, suggesting high cost-efficiency of metagenomics for eukaryotic communities. We provide a brief roadmap for investigating factors which influence metagenomics-based eukaryotic community reconstructions. Understanding these factors is timely as accessibility of DNA sequencing and momentum for reference genomes projects show a future where the taxonomic assignment of DNA from any community sample becomes a reality.

A health survey of the reef forming scleractinian cold-water corals Lophelia pertusa and Madrepora oculata in a remote submarine canyon on the European continental margin, NE Atlantic

Monitoring of cold-water corals (CWCs) for pathogens and diseases is limited due to the environment, protected nature of the corals and their habitat and as well as the challenging and sampling effort required. It is recognised that environmental factors such as temperature and pH can expedite the ability of pathogens to cause diseases in cold-water corals therefore the characterisation of pathogen diversity, prevalence and associated pathologies is essential. The present study combined histology and polymerase chain reaction (PCR) diagnostic techniques to screen for two significant pathogen groups (bacteria of the genus Vibrio and the protozoan Haplosporidia) in the dominant NE Atlantic deep-water framework corals Lophelia pertusa (13 colonies) and Madrepora oculata (2 colonies) at three sampling locations (canyon head, south branch and the flank) in the Porcupine Bank Canyon (PBC), NE Atlantic. One M. oculata colony and four L. pertusa colonies were collected from both the canyon flank and the south branch whilst five L. pertusa colonies were collected from the canyon head. No pathogens were detected in the M. oculata samples. Neither histology nor PCR detected Vibrio spp. in L. pertusa, although Illumina technology used in this study to profile the CWCs microbiome, detected V. shilonii (0.03%) in a single L. pertusa individual, from the canyon head, that had also been screened in this study. A macroborer was observed at a prevalence of 0.07% at the canyon head only. Rickettsiales-like organisms (RLOs) were visualised with an overall prevalence of 40% and with a low intensity of 1 to 4 (RLO) colonies per individual polyp by histology. L. pertusa from the PBC canyon head had an RLO prevalence of 13.3% with the highest detection of 26.7% recorded in the south branch corals. Similarly, unidentified cells observed in L. pertusa from the south branch (20%) were more common than those observed in L. pertusa from the canyon head (6.7%). No RLOs or unidentified cells were observed in corals from the flank. Mean particulate organic matter concentration is highest in the south branch (2,612 μg l^-1) followed by the canyon head (1,065 μg l^-1) and lowest at the canyon flank (494 μg l^-1). Although the route of pathogen entry and the impact of RLO infection on L. pertusa is unclear, particulate availability and the feeding strategies employed by the scleractinian corals may be influencing their exposure to pathogens. The absence of a pathogen in M. oculata may be attributed to the smaller number of colonies screened or the narrower diet in M. oculata compared to the unrestricted diet exhibited in L. pertusa, if ingestion is a route of entry for pathogen groups. The findings of this study also shed some light on how environmental conditions experienced by deep sea organisms and their life strategies may be limiting pathogen diversity and prevalence.

Metagenomic Analysis Using Phylogenetic Placement-A Review of the First Decade

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first 10 years. In particular, the goals of this review are 1) to motivate the usage of phylogenetic placement and illustrate some of its use cases, 2) to outline the full workflow, from raw sequences to publishable figures, including best practices, 3) to introduce the most common tools and methods and their capabilities, 4) to point out common placement pitfalls and misconceptions, 5) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.

Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

A bottleneck for microbial community experiments with many samples and/or replicates is the fast quantification of individual taxon abundances, which is commonly achieved through sequencing marker genes such as the 16S rRNA gene. Here, we propose a new approach for high-throughput and high-quality enumeration of human gut bacteria in a defined community, combining flow cytometry and supervised classification to identify and quantify species mixed in silico and in defined communities in vitro. We identified species in a 5-species in silico community with an F1 score of 71%. In addition, we demonstrate in vitro that our method performs equally well or better than 16S rRNA gene sequencing in two-species cocultures and agrees with 16S rRNA gene sequencing data on the most abundant species in a four-species community. We found that shape and size differences alone are insufficient to distinguish species, and that it is thus necessary to exploit the multivariate nature of flow cytometry data. Finally, we observed that variability of flow cytometry data across replicates differs between gut bacterial species. In conclusion, the performance of supervised classification of gut species in flow cytometry data is species-dependent, but is for some combinations accurate enough to serve as a faster alternative to 16S rRNA gene sequencing.

Comparing whole-genome shotgun sequencing and DNA metabarcoding approaches for species identification and quantification of pollen species mixtures

Molecular identification of mixed-species pollen samples has a range of applications in various fields of research. To date, such molecular identification has primarily been carried out via amplicon sequencing, but whole-genome shotgun (WGS) sequencing of pollen DNA has potential advantages, including (1) more genetic information per sample and (2) the potential for better quantitative matching. In this study, we tested the performance of WGS sequencing methodology and publicly available reference sequences in identifying species and quantifying their relative abundance in pollen mock communities. Using mock communities previously analyzed with DNA metabarcoding, we sequenced approximately 200Mbp for each sample using Illumina HiSeq and MiSeq. Taxonomic identifications were based on the Kraken k-mer identification method with reference libraries constructed from full-genome and short read archive data from the NCBI database. We found WGS to be a reliable method for taxonomic identification of pollen with near 100% identification of species in mixtures but generating higher rates of false positives (reads not identified to the correct taxon at the required taxonomic level) relative to rbcL and ITS2 amplicon sequencing. For quantification of relative species abundance, WGS data provided a stronger correlation between pollen grain proportion and sequence read proportion, but diverged more from a 1:1 relationship, likely due to the higher rate of false positives. Currently, a limitation of WGS-based pollen identification is the lack of representation of plant diversity in publicly available genome databases. As databases improve and costs drop, we expect that eventually genomics methods will become the methods of choice for species identification and quantification of mixed-species pollen samples.

The Requirement of Genetic Diagnostic Technologies for Environmental Surveillance of Antimicrobial Resistance

Antimicrobial resistance (AMR) is threatening modern medicine. While the primary cost of AMR is paid in the healthcare domain, the agricultural and environmental domains are also reservoirs of resistant microorganisms and hence perpetual sources of AMR infections in humans. Consequently, the World Health Organisation and other international agencies are calling for surveillance of AMR in all three domains to guide intervention and risk reduction strategies. Technologies for detecting AMR that have been developed for healthcare settings are not immediately transferable to environmental and agricultural settings, and limited dialogue between the domains has hampered opportunities for cross-fertilisation to develop modified or new technologies. In this feature, we discuss the limitations of currently available AMR sensing technologies used in the clinic for sensing in other environments, and what is required to overcome these limitations.

Current Methods, Common Practices, and Perspectives in Tracking and Monitoring Bioinoculants in Soil

Microorganisms promised to lead the bio-based revolution for a more sustainable agriculture. Beneficial microorganisms could be a valid alternative to the use of chemical fertilizers or pesticides. However, the increasing use of microbial inoculants is also raising several questions about their efficacy and their effects on the autochthonous soil microorganisms. There are two major issues on the application of bioinoculants to soil: (i) their detection in soil, and the analysis of their persistence and fate; (ii) the monitoring of the impact of the introduced bioinoculant on native soil microbial communities. This review explores the strategies and methods that can be applied to the detection of microbial inoculants and to soil monitoring. The discussion includes a comprehensive critical assessment of the available tools, based on morpho-phenological, molecular, and microscopic analyses. The prospects for future development of protocols for regulatory or commercial purposes are also discussed, underlining the need for a multi-method (polyphasic) approach to ensure the necessary level of discrimination required to track and monitor bioinoculants in soil.

Symbiotic Associations in Ascidians: Relevance for Functional Innovation and Bioactive Potential

Associations between different organisms have been extensively described in terrestrial and marine environments. These associations are involved in roles as diverse as nutrient exchanges, shelter or adaptation to adverse conditions. Ascidians are widely dispersed marine invertebrates associated to invasive behaviours. Studying their microbiomes has interested the scientific community, mainly due to its potential for bioactive compounds production-e.g., ET-73 (trabectedin, Yondelis), an anticancer drug. However, these symbiotic interactions embrace several environmental and biological functions with high ecological relevance, inspiring diverse biotechnological applications. We thoroughly reviewed microbiome studies (microscopic to metagenomic approaches) of around 171 hosts, worldwide dispersed, occurring at different domains of life (Archaea, Bacteria, Eukarya), to illuminate the functions and bioactive potential of associated organisms in ascidians. Associations with Bacteria are the most prevalent, namely with Cyanobacteria, Proteobacteria, Bacteroidetes, Actinobacteria and Planctomycetes phyla. The microbiomes of ascidians belonging to Aplousobranchia order have been the most studied. The integration of worldwide studies characterizing ascidians' microbiome composition revealed several functions including UV protection, bioaccumulation of heavy metals and defense against fouling or predators through production of natural products, chemical signals or competition. The critical assessment and characterization of these communities is extremely valuable to comprehend their biological/ecological role and biotechnological potential.

Identifying biases and their potential solutions in human microbiome studies

Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.

How Do the Players Play? A Post-Genomic Analysis Paradigm to Understand Aquatic Ecosystem Processes

Culture-independent and meta-omics sequencing methods have shed considerable light on the so-called “microbial dark matter” of Earth’s environmental microbiome, improving our understanding of phylogeny, the tree of life, and the vast functional diversity of microorganisms. This influx of sequence data has led to refined and reimagined hypotheses about the role and importance of microbial biomass, that paradoxically, sequencing approaches alone are unable to effectively test. Post-genomic approaches such as metabolomics are providing more sensitive and insightful data to unravel the fundamental operations and intricacies of microbial communities within aquatic systems. We assert that the implementation of integrated post-genomic approaches, specifically metabolomics and metatranscriptomics, is the new frontier of environmental microbiology and ecology, expanding conventional assessments toward a holistic systems biology understanding. Progressing beyond siloed phylogenetic assessments and cataloging of metabolites, toward integrated analysis of expression (metatranscriptomics) and activity (metabolomics) is the most effective approach to provide true insight into microbial contributions toward local and global ecosystem functions. This data in turn creates opportunity for improved regulatory guidelines, biomarker discovery and better integration of modeling frameworks. To that end, critical aquatic environmental issues related to climate change, such as ocean warming and acidification, contamination mitigation, and macro-organism health have reasonable opportunity of being addressed through such an integrative approach. Lastly, we argue that the “post-genomics” paradigm is well served to proactively address the systemic technical issues experienced throughout the genomics revolution and focus on collaborative assessment of field-wide experimental standards of sampling, bioinformatics and statistical treatments.

Diversity structure of the microbial communities in the guts of four neotropical termite species

The termite gut microbiome is dominated by lignocellulose degrading microorganisms. This study describes the intestinal microbiota of four Argentinian higher termite species with different feeding habits: Microcerotermes strunckii (hardwood), Nasutitermes corniger (softwood), Termes riograndensis (soil organic matter/grass) and Cornitermes cumulans (grass) by deep sequencing of amplified 16S rRNA and ITS genes. In addition, we have performed a taxonomic and gut community structure comparison incorporating into the analysis the previously reported microbiomes of additional termite species with varied diets. The bacterial phylum Spirochaetes was dominant in the guts of M. strunckii, N. corniger and C. cumulans, whereas Firmicutes predominated in the T. riograndensis gut microbiome. A single bacterial genus, Treponema (Spirochaetes), was dominant in all termite species, except for T. riograndensis. Both in our own sequenced samples and in the broader comparison, prokaryotic α-diversity was higher in the soil/grass feeders than in the wood feeders. Meanwhile, the β-diversity of prokaryotes and fungi was highly dissimilar among strict wood-feeders, whereas that of soil- and grass-feeders grouped more closely. Ascomycota and Basidiomycota were the only fungal phyla that could be identified in all gut samples, because of the lack of reference sequences in public databases. In summary, higher microbial diversity was recorded in termites with more versatile feeding sources, providing further evidence that diet, along with other factors (e.g., host taxonomy), influences the microbial community assembly in the termite gut.

Enriched Aerosol-to-Hydrosol Transfer for Rapid and Continuous Monitoring of Bioaerosols

Bioaerosols, including infectious diseases such as COVID-19, are a continuous threat to global public safety. Despite their importance, the development of a practical, real-time means of monitoring bioaerosols has remained elusive. Here, we present a novel, simple, and highly efficient means of obtaining enriched bioaerosol samples. Aerosols are collected into a thin and stable liquid film by the unique interaction of a superhydrophilic surface and a continuous two-phase centrifugal flow. We demonstrate that this method can provide a concentration enhancement ratio of ∼2.4 × 10⁶ with a collection efficiency of ∼99.9% and an aerosol-into-liquid transfer rate of ∼95.9% at 500 nm particle size (smaller than a single bacterium). This transfer is effective in both laboratory and external ambient environments. The system has a low limit of detection of <50 CFU/m³_air using a straightforward bioluminescence-based technique and shows significant potential for air monitoring in occupational and public-health applications.

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.

Optimization of on-bead emulsion polymerase chain reaction based on single particle analysis

Emulsion polymerase chain reaction (ePCR) enables parallel amplification of millions of different DNA molecules while avoiding bias and chimeric byproducts, essential criteria for applications including next generation sequencing, aptamer selection, and protein-DNA interaction studies. Despite these advantages, ePCR remains underused due to the lack of optimal starting conditions, straightforward methods to evaluate success, and guidelines for tuning the reaction. This knowledge has been elusive for bulk emulsion generation methods, such as stirring and vortexing, the only methods that can emulsify libraries of ≥10⁸ sequences within minutes, because these emulsions have not been characterized in ways that preserve the heterogeneity that defines successful ePCR. Our study quantifies the outcome of ePCR from conditions specified in the literature using single particle analysis, which preserves this heterogeneity. We combine ePCR with magnetic microbeads and quantify the amplification yield via qPCR and the proportion of clonal and saturated beads via flow cytometry. Our single particle level analysis of thousands of beads resolves two key criteria that define the success of ePCR: 1) whether the target fraction of 20% clonal beads predicted by the Poisson distribution is achieved, and 2) whether those beads are partially or maximally covered by amplified DNA. We found that among the two concentrations of polymerase tested, only the higher one, which is 20-fold more than the concentration recommended for conventional PCR, could yield sufficient PCR products. Dramatic increases in the concentrations of reverse primer and nucleotides recommended in literature gave no measurable change in outcome. We thus provide evidence-based starting conditions for effective and economical ePCR for real DNA libraries and a straightforward workflow for evaluating the success of tuning ePCR prior to downstream applications.

Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data

Metagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.

Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: Testing multiple eDNA workflows

The European weather loach (Misgurnus fossilis) is classified as highly endangered in several countries of Central Europe. Populations of M. fossilis are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, M. fossilis was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the threshold cycle (C t) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of M. fossilis. We tested several method combinations for long-term sample preservation, DNA capture, and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural M. fossilis population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of M. fossilis did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of M. fossilis, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of M. fossilis.

Identification of Selected Antibiotic Resistance Genes in Two Different Wastewater Treatment Plant Systems in Poland: A Preliminary Study

Antibiotic resistance is a growing problem worldwide. The emergence and rapid spread of antibiotic resistance determinants have led to an increasing concern about the potential environmental and public health endangering. Wastewater treatment plants (WWTPs) play an important role in this phenomenon since antibacterial drugs introduced into wastewater can exert a selection pressure on antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Therefore, WWTPs are perceived as the main sources of antibiotics, ARB and ARG spread in various environmental components. Furthermore, technological processes used in WWTPs and its exploitation conditions may influence the effectiveness of antibiotic resistance determinants’ elimination. The main aim of the present study was to compare the occurrence of selected tetracycline and sulfonamide resistance genes in raw influent and final effluent samples from two WWTPs different in terms of size and applied biological wastewater treatment processes (conventional activated sludge (AS)-based and combining a conventional AS-based method with constructed wetlands (CWs)). All 13 selected ARGs were detected in raw influent and final effluent samples from both WWTPs. Significant ARG enrichment, especially for tet(B, K, L, O) and sulIII genes, was observed in conventional WWTP. The obtained data did not show a clear trend in seasonal fluctuations in the abundance of selected resistance genes in wastewaters.

Tendentious effects of automated and manual metagenomic DNA purification protocols on broiler gut microbiome taxonomic profiling

Here, we developed protocols to improve sensitivity, rigor and comparability of 16S rRNA gene amplification-based next-generation sequencing (NGS) results. A thorough study was performed by evaluating extraction efficiency with respect to the yield, purity, fragmentation of the purified DNA, and sequencing metrics considering the number of quality reads, amplicon sequence variants (ASVs), community structure and biodiversity. We identified batch-effects that significantly bias broiler gastrointestinal tract (GIT) community compositions and made recommendations to improve sensitivity, consistency, and cross-study comparability. We found that the purity of the extracted nucleic acid had a strong effect on the success rate of downstream library preparations. The preparation of stool bacterial suspensions from feces showed a significant positive influence on community biodiversity by enriching Gram-negative bacteria and cataloguing low abundant taxa with greater success than direct processing of fecal material. Applications relying on the automated Roche MagNa Pure 24 magnetic-bead based method provided results with high consistency therefore it seems to be the optimal choice in large-scale studies for investigating broiler GIT microbiota.

Effectiveness of Plant Beneficial Microbes: Overview of the Methodological Approaches for the Assessment of Root Colonization and Persistence

Issues concerning the use of harmful chemical fertilizers and pesticides that have large negative impacts on environmental and human health have generated increasing interest in the use of beneficial microorganisms for the development of sustainable agri-food systems. A successful microbial inoculant has to colonize the root system, establish a positive interaction and persist in the environment in competition with native microorganisms living in the soil through rhizocompetence traits. Currently, several approaches based on culture-dependent, microscopic and molecular methods have been developed to follow bioinoculants in the soil and plant surface over time. Although culture-dependent methods are commonly used to estimate the persistence of bioinoculants, it is difficult to differentiate inoculated organisms from native populations based on morphological characteristics. Therefore, these methods should be used complementary to culture-independent approaches. Microscopy-based techniques (bright-field, electron and fluorescence microscopy) allow to obtain a picture of microbial colonization outside and inside plant tissues also at high resolution, but it is not possible to always distinguish living cells from dead cells by direct observation as well as distinguish bioinoculants from indigenous microbial populations living in soils. In addition, the development of metagenomic techniques, including the use of DNA probes, PCR-based methods, next-generation sequencing, whole-genome sequencing and pangenome methods, provides a complementary approach useful to understand plant-soil-microbe interactions. However, to ensure good results in microbiological analysis, the first fundamental prerequisite is correct soil sampling and sample preparation for the different methodological approaches that will be assayed. Here, we provide an overview of the advantages and limitations of the currently used methods and new methodological approaches that could be developed to assess the presence, plant colonization and soil persistence of bioinoculants in the rhizosphere. We further discuss the possibility of integrating multidisciplinary approaches to examine the variations in microbial communities after inoculation and to track the inoculated microbial strains.

Consistent and correctable bias in metagenomic sequencing experiments

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

Consistent and correctable bias in metagenomic sequencing experiments

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

Biological Particles in the Indoor Environment

Airborne microorganisms are very difficult to assess accurately under field conditions owing to differences in the sample collection efficiency of the selected sampler and variations in DNA extraction efficiencies. Consequently, bioaerosol abundance and biodiversity can be underestimated, making it more difficult to link specific bioaerosol components to diseases and human health risk. Owing to the low biomass in air samples, it remains a challenge to obtain a representative microbiological sample to recover sufficient DNA for downstream analyses. Improved sampling methods are particularly crucial, especially for investigating viral communities, owing to the extremely low biomass of viral particles in the air compared with other environments. Without detailed information about sampling, characterization and enumeration techniques, interpretation of exposure level is very difficult. Despite this, bioaerosol research has been enhanced by molecular tools, especially next-generation sequencing approaches that have allowed faster and more detailed characterization of air samples.

Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples

BACKGROUND

The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut.

NOVEL METHODS

Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for (a) visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, (b) clustering similar samples using a variant of the k-means method, and (c) finding phylogenetic factors using an adaptation of the Phylofactorization method. These methods enable to interpret metagenomic data in a phylogenetic context, to find patterns in the data, and to identify branches of the phylogeny that are driving these patterns.

RESULTS

To demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods.

Bioaerosol biomonitoring: Sampling optimization for molecular microbial ecology

Bioaerosols (or biogenic aerosols) have largely been overlooked by molecular ecologists. However, this is rapidly changing as bioaerosols play key roles in public health, environmental chemistry and the dispersal ecology of microbes. Due to the low environmental concentrations of bioaerosols, collecting sufficient biomass for molecular methods is challenging. Currently, no standardized methods for bioaerosol collection for molecular ecology research exist. Each study requires a process of optimization, which greatly slows the advance of bioaerosol science. Here, we evaluated air filtration and liquid impingement for bioaerosol sampling across a range of environmental conditions. We also investigated the effect of sampling matrices, sample concentration strategies and sampling duration on DNA yield. Air filtration using polycarbonate filters gave the highest recovery, but due to the faster sampling rates possible with impingement, we recommend this method for fine -scale temporal/spatial ecological studies. To prevent bias for the recovery of Gram-positive bacteria, we found that the matrix for impingement should be phosphate-buffered saline. The optimal method for bioaerosol concentration from the liquid matrix was centrifugation. However, we also present a method using syringe filters for rapid in-field recovery of bioaerosols from impingement samples, without compromising microbial diversity for high -throughput sequencing approaches. Finally, we provide a resource that enables molecular ecologists to select the most appropriate sampling strategy for their specific research question.

Genome-wide association studies on the phyllosphere microbiome: Embracing complexity in host-microbe interactions

Environmental sequencing shows that plants harbor complex communities of microbes that vary across environments. However, many approaches for mapping plant genetic variation to microbe-related traits were developed in the relatively simple context of binary host-microbe interactions under controlled conditions. Recent advances in sequencing and statistics make genome-wide association studies (GWAS) an increasingly promising approach for identifying the plant genetic variation associated with microbes in a community context. This review discusses early efforts on GWAS of the plant phyllosphere microbiome and the outlook for future studies based on human microbiome GWAS. A workflow for GWAS of the phyllosphere microbiome is then presented, with particular attention to how perspectives on the mechanisms, evolution and environmental dependence of plant-microbe interactions will influence the choice of traits to be mapped.

Comparison of Database Search Methods for the Detection of Legionella pneumophila in Water Samples Using Metagenomic Analysis

Metagenomic analysis has become a powerful tool to analyze bacterial communities in environmental samples. However, the detection of a specific bacterial species using metagenomic analysis remains difficult due to false positive detections of sequences shared between different bacterial species. In this study, 16S rRNA amplicon and shotgun metagenomic analyses were conducted on samples collected along a stream and ponds in the campus of Hokkaido University. We compared different database search methods for bacterial detection by focusing on Legionella pneumophila. In this study, we used L. pneumophila-specific nested PCR as a gold standard to evaluate the results of the metagenomic analysis. Comparison with the results from L. pneumophila-specific nested PCR indicated that a blastn search of shotgun reads against the NCBI-NT database led to false positive results and had problems with specificity. We also found that a blastn search of shotgun reads against a database of the catalase-peroxidase (katB) gene detected L. pneumophila with the highest area under the receiver operating characteristic curve among the tested search methods; indicating that a blastn search against the katB gene database had better diagnostic ability than searches against other databases. Our results suggest that sequence searches targeting long genes specifically associated with the bacterial species of interest is a prerequisite to detecting the bacterial species in environmental samples using metagenomic analyses.

Clean Low-Biomass Procedures and Their Application to Ancient Ice Core Microorganisms

Microorganisms in glacier ice provide tens to hundreds of thousands of years archive for a changing climate and microbial responses to it. Analyzing ancient ice is impeded by technical issues, including limited ice, low biomass, and contamination. While many approaches have been evaluated and advanced to remove contaminants on ice core surfaces, few studies leverage modern sequencing to establish in silico decontamination protocols for glacier ice. Here we sought to apply such “clean” sampling techniques with in silico decontamination approaches used elsewhere to investigate microorganisms archived in ice at ∼41 (D41, ∼20,000 years) and ∼49 m (D49, ∼30,000 years) depth in an ice core (GS3) from the summit of the Guliya ice cap in the northwestern Tibetan Plateau. Four “background” controls were established – a co-processed sterile water artificial ice core, two air samples collected from the ice processing laboratories, and a blank, sterile water sample – and used to assess contaminant microbial diversity and abundances. Amplicon sequencing revealed 29 microbial genera in these controls, but quantitative PCR showed that the controls contained about 50–100-times less 16S DNA than the glacial ice samples. As in prior work, we interpreted these low-abundance taxa in controls as “contaminants” and proportionally removed them in silico from the GS3 ice amplicon data. Because of the low biomass in the controls, we also compared prokaryotic 16S DNA amplicons from pre-amplified (by re-conditioning PCR) and standard amplicon sequencing, and found the resulting microbial profiles to be repeatable and nearly identical. Ecologically, the contaminant-controlled ice microbial profiles revealed significantly different microorganisms across the two depths in the GS3 ice core, which is consistent with changing climate, as reported for other glacier ice samples. Many GS3 ice core genera, including Methylobacterium, Sphingomonas, Flavobacterium, Janthinobacterium, Polaromonas, and Rhodobacter, were also abundant in previously studied ice cores, which suggests wide distribution across glacier environments. Together these findings help further establish “clean” procedures for studying low-biomass ice microbial communities and contribute to a baseline understanding of microorganisms archived in glacier ice.

Assessment of microbiota:host interactions at the vaginal mucosa interface

There is increasing appreciation of the role that vaginal microbiota play in health and disease throughout a woman's lifespan. This has been driven partly by molecular techniques that enable detailed identification and characterisation of microbial community structures. However, these methods do not enable assessment of the biochemical and immunological interactions between host and vaginal microbiota involved in pathophysiology. This review examines our current knowledge of the relationships that exist between vaginal microbiota and the host at the level of the vaginal mucosal interface. We also consider methodological approaches to microbiomic, immunologic and metabolic profiling that permit assessment of these interactions. Integration of information derived from these platforms brings the potential for biomarker discovery, disease risk stratification and improved understanding of the mechanisms regulating vaginal microbial community dynamics in health and disease.

Comparison of normalization methods for the analysis of metagenomic gene abundance data

Background

In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated.

Results

Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance.

Conclusions

This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead to incorrect or obfuscated biological interpretation.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4637-6) contains supplementary material, which is available to authorized users.

Profiling soil microbial communities with next-generation sequencing: the influence of DNA kit selection and technician technical expertise

Structure and diversity of microbial communities are an important research topic in biology, since microbes play essential roles in the ecology of various environments. Different DNA isolation protocols can lead to data bias and can affect results of next-generation sequencing. To evaluate the impact of protocols for DNA isolation from soil samples and also the influence of individual handling of samples, we compared results obtained by two researchers (R and T) using two different DNA extraction kits: (1) MO BIO PowerSoil^® DNA Isolation kit (MO_R and MO_T) and (2) NucleoSpin^® Soil kit (MN_R and MN_T). Samples were collected from six different sites on Okinawa Island, Japan. For all sites, differences in the results of microbial composition analyses (bacteria, archaea, fungi, and other eukaryotes), obtained by the two researchers using the two kits, were analyzed. For both researchers, the MN kit gave significantly higher yields of genomic DNA at all sites compared to the MO kit (ANOVA; P < 0.006). In addition, operational taxonomic units for some phyla and classes were missed in some cases: Micrarchaea were detected only in the MN_T and MO_R analyses; the bacterial phylum Armatimonadetes was detected only in MO_R and MO_T; and WIM5 of the phylum Amoebozoa of eukaryotes was found only in the MO_T analysis. Our results suggest the possibility of handling bias; therefore, it is crucial that replicated DNA extraction be performed by at least two technicians for thorough microbial analyses and to obtain accurate estimates of microbial diversity.

Bioaerosol sampling and detection methods based on molecular approaches: No pain no gain

Bioaerosols are among the less studied particles in the environment. The lack of standardization in sampling procedures, difficulties related to the effect of sampling processes on the integrity of microorganisms, and challenges associated with the application of environmental microbiology analyses and molecular and culture methods frighten many young scientists. Every microorganism has its own particularities and acts differently when aerosolized in various conditions. Because the air is an extremely biologically diluted environment, it is necessary to concentrate its content before any analysis is performed. Challenges faced when applying molecular methods to air samples reveal the need for a better standardization of approaches for cell and nucleic acid recovery, the choice of genetic markers, and interpretation of data. This paper presents a few of the limits and difficulties tackled when molecular methods are applied to bioaerosols, suggests some improvements by specifying the critical stages that should be considered when studying the microbial ecology of bioaerosols, and provides thoughtful insights on how to overcome the challenges encountered.

Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium

In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies.

Evaluation of ddRADseq for reduced representation metagenome sequencing

Background

Profiling of microbial communities via metagenomic shotgun sequencing has enabled researches to gain unprecedented insight into microbial community structure and the functional roles of community members. This study describes a method and basic analysis for a metagenomic adaptation of the double digest restriction site associated DNA sequencing (ddRADseq) protocol for reduced representation metagenome profiling.

Methods

This technique takes advantage of the sequence specificity of restriction endonucleases to construct an Illumina-compatible sequencing library containing DNA fragments that are between a pair of restriction sites located within close proximity. This results in a reduced sequencing library with coverage breadth that can be tuned by size selection. We assessed the performance of the metagenomic ddRADseq approach by applying the full method to human stool samples and generating sequence data.

Results

The ddRADseq data yields a similar estimate of community taxonomic profile as obtained from shotgun metagenome sequencing of the same human stool samples. No obvious bias with respect to genomic G + C content and the estimated relative species abundance was detected.

Discussion

Although ddRADseq does introduce some bias in taxonomic representation, the bias is likely to be small relative to DNA extraction bias. ddRADseq appears feasible and could have value as a tool for metagenome-wide association studies.

A Generalized Lattice based Probabilistic Approach for Metagenomic Clustering

Metagenomics involves the analysis of genomes of microorganisms sampled directly from their environment. Next Generation Sequencing allows a high-throughput sampling of small segments from genomes in the metagenome to generate reads. To study the properties and relationships of the microorganisms present, clustering can be performed based on the inherent composition of the sampled reads for unknown species. We propose a two-dimensional lattice based probabilistic model for clustering metagenomic datasets. The occurrence of a species in the metagenome is estimated using a lattice of probabilistic distributions over small sized genomic sequences. The two dimensions denote distributions for different sizes and groups of words respectively. The lattice structure allows for additional support for a node from its neighbors when the probabilistic support for the species using the parameters of the current node is deemed insufficient. We also show convergence for our algorithm. We test our algorithm on simulated metagenomic data containing bacterial species and observe more than 85% precision. We also evaluate our algorithm on an in vitro-simulated bacterial metagenome and on human patient data, and show a better clustering than other algorithms even for short reads and varied abundance. The software and datasets can be downloaded from https:// github.com/lattclus/lattice-metage.

Pseudomonas fluorescens Transportome Is Linked to Strain-Specific Plant Growth Promotion in Aspen Seedlings under Nutrient Stress

Diverse communities of bacteria colonize plant roots and the rhizosphere. Many of these rhizobacteria are symbionts and provide plant growth promotion (PGP) services, protecting the plant from biotic and abiotic stresses and increasing plant productivity by providing access to nutrients that would otherwise be unavailable to roots. In return, these symbiotic bacteria receive photosynthetically-derived carbon (C), in the form of sugars and organic acids, from plant root exudates. PGP activities have been characterized for a variety of forest tree species and are important in C cycling and sequestration in terrestrial ecosystems. The molecular mechanisms of these PGP activities, however, are less well-known. In a previous analysis of Pseudomonas genomes, we found that the bacterial transportome, the aggregate activity of a bacteria's transmembrane transporters, was most predictive for the ecological niche of Pseudomonads in the rhizosphere. Here, we used Populus tremuloides Michx. (trembling aspen) seedlings inoculated with one of three Pseudomonas fluorescens strains (Pf0-1, SBW25, and WH6) and one Pseudomonas protegens (Pf-5) as a laboratory model to further investigate the relationships between the predicted transportomic capacity of a bacterial strain and its observed PGP effects in laboratory cultures. Conditions of low nitrogen (N) or low phosphorus (P) availability and the corresponding replete media conditions were investigated. We measured phenotypic and biochemical parameters of P. tremuloides seedlings and correlated P. fluorescens strain-specific transportomic capacities with P. tremuloides seedling phenotype to predict the strain and nutrient environment-specific transporter functions that lead to experimentally observed, strain, and media-specific PGP activities and the capacity to protect plants against nutrient stress. These predicted transportomic functions fall in three groups: (i) transport of compounds that modulate aspen seedling root architecture, (ii) transport of compounds that help to mobilize nutrients for aspen roots, and (iii) transporters that enable bacterial acquisition of C sources from seedling root exudates. These predictions point to specific molecular mechanisms of PGP activities that can be directly tested through future, hypothesis-driven biological experiments.

Microbial diversity of a full-scale UASB reactor applied to poultry slaughterhouse wastewater treatment: integration of 16S rRNA gene amplicon and shotgun metagenomic sequencing

The 16S rRNA gene amplicon and whole-genome shotgun metagenomic (WGSM) sequencing approaches were used to investigate wide-spectrum profiles of microbial composition and metabolic diversity from a full-scale UASB reactor applied to poultry slaughterhouse wastewater treatment. The data were generated by using MiSeq 2 × 250 bp and HiSeq 2 × 150 bp Illumina sequencing platforms for 16S amplicon and WGSM sequencing, respectively. Each approach revealed a distinct microbial community profile, with Pseudomonas and Psychrobacter as predominant genus for the WGSM dataset and Clostridium and Methanosaeta for the 16S rRNA gene amplicon dataset. The virome characterization revealed the presence of two viral families with Bacteria and Archaea as host, Myoviridae, and Siphoviridae. A wide functional diversity was found with predominance of genes involved in the metabolism of acetone, butanol, and ethanol synthesis; and one-carbon metabolism (e.g., methanogenesis). Genes related to the acetotrophic methanogenesis pathways were more abundant than methylotrophic and hydrogenotrophic, corroborating the taxonomic results that showed the prevalence of the acetotrophic genus Methanosaeta. Moreover, the dataset indicated a variety of metabolic genes involved in sulfur, nitrogen, iron, and phosphorus cycles, with many genera able to act in all cycles. BLAST analysis against Antibiotic Resistance Genes Database (ARDB) revealed that microbial community contained 43 different types of antibiotic resistance genes, some of them were associated with growth chicken promotion (e.g., bacitracin, tetracycline, and polymyxin).