Daring to be differential: metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units

Prevalent arbuscular mycorrhizae in roots and highly variable mycobiome in leaves of epiphytic subtropical fern Ophioderma pendulum

PREMISE

Endophytic and mycorrhizal fungi are crucial in facilitating plant nutrition acquisition and stress tolerance. In epiphytic habitats, plants face nutrition and water stress, but their roots are mostly nonmycorrhizal and especially lacking in arbuscular mycorrhizal associations. Ophioderma pendulum is an epiphytic fern with a partially mycoheterotrophic lifestyle, likely heavily reliant on symbiotic fungi. To characterize fungal associations in the sporophyte of O. pendulum, we focused on leaves and roots of O. pendulum, seeking to reveal the fungal communities in these organs.

METHODS

Roots and leaves from O. pendulum in a subtropical forest were examined microscopically to observe the morphology of fungal structures and determine the percentage of various fungal structures in host tissues. Fungal composition was profiled using metabarcoding techniques that targeted ITS2 of the nuclear ribosomal DNA.

RESULTS

Roots were consistently colonized by arbuscular mycorrhizal fungi (Glomeromycota), especially Acaulospora. Unlike previous findings on epiphytic ferns, dark septate endophytes were rare in O. pendulum roots. Leaves were predominantly colonized by Ascomycota fungi, specifically the classes Dothideomycetes (46.88%), Eurotiomycetes (11.51%), Sordariomycetes (6.23%), and Leotiomycetes (6.14%). Across sampling sites, fungal community compositions were similar in the roots but differed significantly in the leaves.

CONCLUSIONS

Ophioderma pendulum maintains stable, single-taxon-dominant communities in the roots, primarily featuring arbuscular mycorrhizal fungi, whereas the leaves may harbor opportunistic fungal colonizers. Our study underlines the significance of mycorrhizal fungi in the adaptation of epiphytic ferns.

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BACKGROUND

Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.

RESULTS

In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.

CONCLUSION

This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Considerations for the inclusion of metabarcoding data in the plant protection product risk assessment of the soil microbiome

There is increasing interest in further developing the plant protection product (PPP) environmental risk assessment, particularly within the European Union, to include the assessment of soil microbial community composition, as measured by metabarcoding approaches. However, to date, there has been little discussion as to how this could be implemented in a standardized, reliable, and robust manner suitable for regulatory decision-making. Introduction of metabarcoding-based assessments of the soil microbiome into the PPP risk assessment would represent a significant increase in the degree of complexity of the data that needs to be processed and analyzed in comparison to the existing risk assessment on in-soil organisms. The bioinformatics procedures to process DNA sequences into community compositional data sets currently lack standardization, while little information exists on how these data should be used to generate regulatory endpoints and the ways in which these endpoints should be interpreted. Through a thorough and critical review, we explore these challenges. We conclude that currently, we do not have a sufficient degree of standardization or understanding of the required bioinformatics and data analysis procedures to consider their use in an environmental risk assessment context. However, we highlight critical knowledge gaps and the further research required to understand whether metabarcoding-based assessments of the soil microbiome can be utilized in a statistically and ecologically relevant manner within a PPP risk assessment. Only once these challenges are addressed can we consider if and how we should use metabarcoding as a tool for regulatory decision-making to assess and monitor ecotoxicological effects on soil microorganisms within an environmental risk assessment of PPPs. Integr Environ Assess Manag 2024;20:337-358. © 2023 SETAC.

Cutting edge tools in the field of soil microbiology

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

Biotic interactions outweigh abiotic factors as drivers of bark microbial communities in Central European forests

Bark surfaces are extensive areas within forest ecosystems, which provide an ideal habitat for microbial communities, through their longevity and seasonal stability. Here we provide a comprehensive account of the bark surface microbiome of living trees in Central European forests, and identify drivers of diversity and community composition. We examine algal, fungal, and bacterial communities and their interactions using metabarcoding on samples from over 750 trees collected in the Biodiversity Exploratories in northern, central, and southern Germany. We show that mutual biotic influence is more important than the abiotic environment with regard to community composition, whereas abiotic conditions and geography are more important for alpha diversity. Important abiotic factors are the relative humidity and light availability, which decrease the algal and bacterial alpha diversity but strongly increase fungal alpha diversity. In addition, temperature is important in shaping the microbial community, with higher temperature leading to homogeneous communities of dominant fungi, but high turnover in bacterial communities. Changes in the community dissimilarity of one organismal group occur in close relation to changes in the other two, suggesting that there are close interactions between the three major groups of the bark surface microbial communities, which may be linked to beneficial exchange. To understand the functioning of the forest microbiome as a whole, we need to further investigate the functionality of interactions within the bark surface microbiome and combine these results with findings from other forest habitats such as soil or canopy.

Community metabarcoding reveals the relative role of environmental filtering and spatial processes in metacommunity dynamics of soil microarthropods across a mosaic of montane forests

Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical data sets are required to account for taxon- and context-dependency, but relevant research in understudied areas is often compromised by the taxonomic impediment. Here we used haplotype-level community DNA metabarcoding, enabled by stringent filtering of amplicon sequence variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, operational taxonomic unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented Quercus alnifolia habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterizing metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.

Microbial community dynamics and cycling of plutonium and iron in a seasonally stratified and radiologically contaminated pond

Plutonium (Pu) cycling and mobility in the environment can be impacted by the iron cycle and microbial community dynamics. We investigated the spatial and temporal changes of the microbiome in an iron (Fe)-rich, plutonium-contaminated, monomictic reservoir (Pond B, Savannah River Site, South Carolina, USA). The microbial community composition varied with depth during seasonal thermal stratification and was strongly correlated with redox. During stratification, Fe(II) oxidizers (e.g., Ferrovum, Rhodoferax, Chlorobium) were most abundant in the hypoxic/anoxic zones, while Fe(III) reducers (e.g., Geothrix, Geobacter) dominated the deep, anoxic zone. Sulfate reducers and methanogens were present in the anoxic layer, likely contributing to iron and plutonium cycling. Multinomial regression of predicted functions/pathways identified metabolisms highly associated with stratification (within the top 5%), including iron reduction, methanogenesis, C1 compound utilization, fermentation, and aromatic compound degradation. Two sediment cores collected at the Inlet and Outlet of the pond were dominated by putative fermenters and organic matter (OM) degraders. Overall, microbiome analyses revealed the potential for three microbial impacts on the plutonium and iron biogeochemical cycles: (1) plutonium bioaccumulation throughout the water column, (2) Pu-Fe-OM-aggregate formation by Fe(II) oxidizers under microaerophilic/aerobic conditions, and (3) Pu-Fe-OM-aggregate or sediment reductive dissolution and organic matter degradation in the deep, anoxic waters.

The newest Oxford Nanopore R10.4.1 full-length 16S rRNA sequencing enables the accurate resolution of species-level microbial community profiling

The long-read amplicon provides a species-level solution for the community. With the improvement of nanopore flowcells, the accuracy of Oxford Nanopore Technologies (ONT) R10.4.1 has been substantially enhanced, with an average of approximately 99%. To evaluate its effectiveness on amplicons, three types of microbiomes were analyzed by 16S ribosomal RNA (hereinafter referred to as "16S") amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1) in the current study. We showed the error rate, recall, precision, and bias index in the mock sample. The error rate of ONT R10.4.1 was greatly reduced, with a better recall in the case of the synthetic community. Meanwhile, in different types of environmental samples, ONT R10.4.1 analysis resulted in a composition similar to Pacbio data. We found that classification tools and databases influence ONT data. Based on these results, we conclude that the ONT R10.4.1 16S amplicon can also be used for application in environmental samples. IMPORTANCE The long-read amplicon supplies the community with a species-level solution. Due to the high error rate of nanopore sequencing early on, it has not been frequently used in 16S studies. Oxford Nanopore Technologies (ONT) introduced the R10.4.1 flowcell with Q20+ reagent to achieve more than 99% accuracy as sequencing technology advanced. However, there has been no published study on the performance of commercial PromethION sequencers with R10.4.1 flowcells on 16S sequencing or on the impact of accuracy improvement on taxonomy (R9.4.1 to R10.4.1) using 16S ONT data. In this study, three types of microbiomes were investigated by 16S ribosomal RNA (rRNA) amplicon sequencing using Novaseq, Pacbio sequel II, and Nanopore PromethION platforms (R9.4.1 and R10.4.1). In the mock sample, we displayed the error rate, recall, precision, and bias index. We observed that the error rate in ONT R10.4.1 is significantly lower, especially when deletions are involved. First and foremost, R10.4.1 and Pacific Bioscience platforms reveal a similar microbiome in environmental samples. This study shows that the R10.4.1 full-length 16S rRNA sequences allow for species identification of environmental microbiota.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

Planktonic and epilithic prokaryota community compositions in a large temperate river reflect climate change related seasonal shifts

In freshwaters, microbial communities are of outstanding importance both from ecological and public health perspectives, however, they are threatened by the impact of global warming. To reveal how different prokaryotic communities in a large temperate river respond to environment conditions related to climate change, the present study provides the first detailed insight into the composition and spatial and year-round temporal variations of planktonic and epilithic prokaryotic community. Microbial diversity was studied using high-throughput next generation amplicon sequencing. Sampling was carried out monthly in the midstream and the littoral zone of the Danube, upstream and downstream from a large urban area. Result demonstrated that river habitats predominantly determine the taxonomic composition of the microbiota; diverse and well-differentiated microbial communities developed in water and epilithon, with higher variance in the latter. The composition of bacterioplankton clearly followed the prolongation of the summer resulting from climate change, while the epilithon community was less responsive. Rising water temperatures was associated with increased abundances of many taxa (such as phylum Actinobacteria, class Gammaproteobacteria and orders Synechococcales, Alteromonadales, Chitinophagales, Pseudomonadales, Rhizobiales and Xanthomonadales), and the composition of the microbiota also reflected changes of several further environmental factors (such as turbidity, TOC, electric conductivity, pH and the concentration of phosphate, sulphate, nitrate, total nitrogen and the dissolved oxygen). The results indicate that shift in microbial community responding to changing environment may be of crucial importance in the decomposition of organic compounds (including pollutants and xenobiotics), the transformation and accumulation of heavy metals and the occurrence of pathogens or antimicrobial resistant organisms.

The effect of metabarcoding 18S rRNA region choice on diversity of microeukaryotes including phytoplankton

Metabarcoding using high throughput sequencing of amplicons of the 18S rRNA gene is one of the widely used methods for assessing the diversity of microeukaryotes in various ecosystems. We investigated the effectiveness of the V4 and V8-V9 regions of the 18S rRNA gene by comparing the results of metabarcoding microeukaryotic communities using the DADA2 (ASV), USEARCH-UNOISE3 (ZOTU), and USEARCH-UPARSE (OTU with 97% similarity) algorithms. Both regions showed similar levels of genetic variability and taxa identification accuracy. Richness for DADA2 datasets of both regions was lower than for UNOISE3 and UPARSE datasets, which is due to more accurate error correction in amplicons. Microeukaryotic communities (autotrophs and heterotrophs) structure identified using both regions showed a significant relationship with phytoplankton (autotrophs) communities structure based on microscopy in a seasonal freshwater sample series. The strongest relationship was found between the phytoplankton species and V8-V9 ASVs produced by DADA2.

Patterns in soil microbial diversity across Europe

Factors driving microbial community composition and diversity are well established but the relationship with microbial functioning is poorly understood, especially at large scales. We analysed microbial biodiversity metrics and distribution of potential functional groups along a gradient of increasing land-use perturbation, detecting over 79,000 bacterial and 25,000 fungal OTUs in 715 sites across 24 European countries. We found the lowest bacterial and fungal diversity in less-disturbed environments (woodlands) compared to grasslands and highly-disturbed environments (croplands). Highly-disturbed environments contain significantly more bacterial chemoheterotrophs, harbour a higher proportion of fungal plant pathogens and saprotrophs, and have less beneficial fungal plant symbionts compared to woodlands and extensively-managed grasslands. Spatial patterns of microbial communities and predicted functions are best explained when interactions among the major determinants (vegetation cover, climate, soil properties) are considered. We propose guidelines for environmental policy actions and argue that taxonomical and functional diversity should be considered simultaneously for monitoring purposes.

Impact of ionizing radiation on the environmental microbiomes of Chornobyl wetlands

Radioactive contamination has the potential to cause damage to DNA and other biomolecules. Anthropogenic sources of radioactive contamination include accidents in nuclear power plants, such as the one in Chornobyl in 1986 which caused long-term radioactive pollution. Studies on animals within radioactive zones have provided us with a greater understanding of how wildlife can persevere despite chronic radiation exposure. However, we still know very little about the effects of radiation on the microbial communities in the environment. We examined the impact of ionizing radiation and other environmental factors on the diversity and composition of environmental microbiomes in the wetlands of Chornobyl. We combined detailed field sampling along a gradient of radiation together with 16 S rRNA high-throughput metabarcoding. While radiation did not affect the alpha diversity of the microbiomes in sediment, soil, or water, it had a significant effect on the beta diversity in all environment types, indicating that the microbial composition was affected by ionizing radiation. Specifically, we detected several microbial taxa that were more abundant in areas with high radiation levels within the Chornobyl Exclusion Zone, including bacteria and archaea known to be radioresistant. Our results reveal the existence of rich and diverse microbiomes in Chornobyl wetlands, with multiple taxonomic groups that are able to thrive despite the radioactive contamination. These results, together with additional field and laboratory-based approaches examining how microbes cope with ionizing radiation will help to forecast the functionality and re-naturalization dynamics of radiocontaminated environments.

Profiling Microbiota from Multiple Sites in the Respiratory Tract to Identify a Biomarker for PM2.5 Nitrate Exposure-Induced Pulmonary Damages

The microbiota present in the respiratory tract (RT) responds to environmental stimuli and engages in a continuous interaction with the host immune system to maintain homeostasis. A total of 40 C57BL/6 mice were divided into four groups and exposed to varying concentrations of PM_2.5 nitrate aerosol and clean air. After 10 weeks of exposure, assessments were conducted on the lung and airway microbiome, lung functions, and pulmonary inflammation. Additionally, we analyzed data from both mouse and human respiratory tract (RT) microbiomes to identify possible biomarkers for PM_2.5 exposure-induced pulmonary damages. On average, 1.5 and 13.5% inter-individual microbiome variations in the lung and airway were explained by exposure, respectively. In the airway, among the 60 bacterial OTUs (operational taxonomic units) > 0.05% proportion, 40 OTUs were significantly affected by PM_2.5 exposure (FDR ≤ 10%). Further, the airway microbiome was associated with peak expiratory flow (PEF) (p = 0.003), pulmonary neutrophil counts (p = 0.01), and alveolar 8-OHdG oxidative lesions (p = 0.0078). The Clostridiales order bacteria showed the strongest signals. For example, the o_Clostridiales;f_;g_ OTU was elevated by PM_2.5 nitrate exposure (p = 4.98 × 10^-5) and negatively correlated with PEF (r = -0.585 and p = 2.4 × 10^-4). It was also associated with the higher pulmonary neutrophil count (p = 8.47 × 10^-5) and oxidative lesion (p = 7.17 × 10^-3). In human data, we confirmed the association of airway Clostridiales order bacteria with PM_2.5 exposure and lung function. For the first time, this study characterizes the impact of PM_2.5 exposure on the microbiome of multiple sites in the respiratory tract (RT) and its relevance to airflow obstructive diseases. By analyzing data from both humans and mice, we have identified bacteria belonging to the Clostridiales order as a promising biomarker for PM_2.5 exposure-induced decline in pulmonary function and inflammation.

Soil microbiome feedback to climate change and options for mitigation

Microbes are a critical component of soil ecosystems, performing crucial functions in biogeochemical cycling, carbon sequestration, and plant health. However, it remains uncertain how their community structure, functioning, and resultant nutrient cycling, including net GHG fluxes, would respond to climate change at different scales. Here, we review global and regional climate change effects on soil microbial community structure and functioning, as well as the climate-microbe feedback and plant-microbe interactions. We also synthesize recent studies on climate change impacts on terrestrial nutrient cycles and GHG fluxes across different climate-sensitive ecosystems. It is generally assumed that climate change factors (e.g., elevated CO₂ and temperature) will have varying impacts on the microbial community structure (e.g., fungi-to-bacteria ratio) and their contribution toward nutrient turnover, with potential interactions that may either enhance or mitigate each other's effects. Such climate change responses, however, are difficult to generalize, even within an ecosystem, since they are subjected to not only a strong regional influence of current ambient environmental and edaphic conditions, historical exposure to fluctuations, and time horizon but also to methodological choices (e.g., network construction). Finally, the potential of chemical intrusions and emerging tools, such as genetically engineered plants and microbes, as mitigation strategies against global change impacts, particularly for agroecosystems, is presented. In a rapidly evolving field, this review identifies the knowledge gaps complicating assessments and predictions of microbial climate responses and hindering the development of effective mitigation strategies.

Metagenomics Provides a Deeper Assessment of the Diversity of Bacterial Communities in Polar Soils Than Metabarcoding

The diversity of soil bacteria was analyzed via metabarcoding and metagenomic approaches using DNA samples isolated from the biocrusts of 12 different Arctic and Antarctic sites. For the metabarcoding approach, the V3-4 region of the 16S rRNA was targeted. Our results showed that nearly all operational taxonomic units (OTUs = taxa) found in metabarcoding analyses were recovered in metagenomic analyses. In contrast, metagenomics identified a large number of additional OTUs absent in metabarcoding analyses. In addition, we found huge differences in the abundance of OTUs between the two methods. The reasons for these differences seem to be (1) the higher sequencing depth in metagenomics studies, which allows the detection of low-abundance community members in metagenomics, and (2) bias of primer pairs used to amplify the targeted sequence in metabarcoding, which can change the community composition dramatically even at the lower taxonomic levels. We strongly recommend using only metagenomic approaches when establishing the taxonomic profiles of whole biological communities.

PURC Provides Improved Sequence Inference for Polyploid Phylogenetics and Other Manifestations of the Multiple-Copy Problem

Inferring the true biological sequences from amplicon mixtures remains a difficult bioinformatics problem. The traditional approach is to cluster sequencing reads by similarity thresholds and treat the consensus sequence of each cluster as an "operational taxonomic unit" (OTU). Recently, this approach has been improved by model-based methods that correct PCR and sequencing errors in order to infer "amplicon sequence variants" (ASVs). To date, ASV approaches have been used primarily in metagenomics, but they are also useful for determining homeologs in polyploid organisms. To facilitate the usage of ASV methods among polyploidy researchers, we incorporated ASV inference alongside OTU clustering in PURC v2.0, a major update to PURC (Pipeline for Untangling Reticulate Complexes). In addition, PURC v2.0 features faster demultiplexing than the original version and has been updated to be compatible with Python 3. In this chapter we present results indicating that using the ASV approach is more likely to infer the correct biological sequences in comparison to the earlier OTU-based PURC and describe how to prepare sequencing data, run PURC v2.0 under several different modes, and interpret the output.

Habitat and tree species identity shape aboveground and belowground fungal communities in central European forests

Introduction

Trees interact with fungi in mutualistic, saprotrophic, and pathogenic relationships. With their extensive aboveground and belowground structures, trees provide diverse habitats for fungi. Thus, tree species identity is an important driver of fungal community composition in forests.

Methods

Here we investigate how forest habitat (bark surface vs. soil) and tree species identity (deciduous vs. coniferous) affect fungal communities in two Central European forests. We assess differences and interactions between fungal communities associated with bark surfaces and soil, in forest plots dominated either by Fagus sylvatica, Picea abies, or Pinus sylvestris in two study regions in southwestern and northeastern Germany.

Results

ITS metabarcoding yielded 3,357 fungal amplicon sequence variants (ASVs) in the northern and 6,088 in the southern region. Overall, soil communities were 4.7 times more diverse than bark communities. Habitat type explained 48-69% of the variation in alpha diversity, while tree species identity explained >1-3%. NMDS ordinations showed that habitat type and host tree species structured the fungal communities. Overall, few fungal taxa were shared between habitats, or between tree species, but the shared taxa were highly abundant. Network analyses, based on co-occurrence patterns, indicate that aboveground and belowground communities form distinct subnetworks.

Discussion

Our study suggests that habitat (bark versus soil) and tree species identity are important factors structuring fungal communities in temperate European forests. The aboveground (bark-associated) fungal community is currently poorly known, including a high proportion of reads assigned to "unknown Ascomycota" or "unknown Dothideomycetes." The role of bark as a habitat and reservoir of unique fungal diversity in forests has been underestimated.

Analysis of Bacterial Microbiota of Aerated Compost Teas and Effect on Tomato Growth

Mature composts and their water-based extracts, known as aerated compost teas (ACTs), are biofertilizers that share bioactive effects like soil restoration and plant health promotion, widely used for sustainable agriculture. Bioactive effects of compost and ACTs could be associated with their physicochemical and biological characteristics, like carbon/nitrogen (C/N) ratio and microbiota structure respectively. In our study, we elaborated ACTs using mature homemade compost, wheat bran, and grass clippings, following the C/N ratio criteria. Irrigation of tomato plantlets with ACT whose C/N ratio was close to the expected C/N ratio for mature compost evidenced plant growth promotion. Exploring the bacterial microbiota of elaborated ACTs and origin compost revealed significant structural differences, including phyla involved in N mineralization and free-living N-fixing bacteria. Therefore, ACTs harbor diverse bacterial microbiota involved in the N cycle, which would enrich plant and soil bacterial communities at the taxonomic and functional levels. Furthermore, ACTs are considered a part of agroecological and circular economy approaches.

Pesticide soil microbial toxicity: setting the scene for a new pesticide risk assessment for soil microorganisms (IUPAC Technical Report)

Pesticides constitute an integral part of modern agriculture. However, there are still concerns about their effects on non-target organisms. To address this the European Commission has imposed a stringent regulatory scheme for new pesticide compounds. Assessment of the aquatic toxicity of pesticides is based on a range of advanced tests. This does not apply to terrestrial ecosystems, where the toxicity of pesticides on soil microorganisms, is based on an outdated and crude test (N mineralization). This regulatory gap is reinforced by the recent methodological and standardization advances in soil microbial ecology. The inclusion of such standardized tools in a revised risk assessment scheme will enable the accurate estimation of the toxicity of pesticides on soil microorganisms and on associated ecosystem services. In this review we (i) summarize recent work in the assessment of the soil microbial toxicity of pesticides and point to ammonia-oxidizing microorganisms (AOM) and arbuscular mycorrhizal fungi (AMF) as most relevant bioindicator groups (ii) identify limitations in the experimental approaches used and propose mitigation solutions, (iii) identify scientific gaps and (iv) propose a new risk assessment procedure to assess the effects of pesticides on soil microorganisms.

Subsurface microbial communities as a tool for characterizing regional-scale groundwater flow

Subsurface microbial community distribution patterns are influenced by biogeochemical and groundwater fluxes and may inform hydraulic connections along groundwater-flow paths. This study examined the regional-scale microbial community of the Death Valley Regional Flow System and evaluated whether subsurface communities can be used to identify groundwater-flow paths between recharge and discharge areas. Samples were collected from 36 sites in three groundwater basins: Pahute Mesa-Oasis Valley (PMOV), Ash Meadows (AM), and Alkali Flat-Furnace Creek Ranch (AFFCR). Microbial diversity within and between communities varied by location, and communities were separated into two overall groups that affiliated with the AM and PMOV/AFFCR basins. Network analysis revealed patterns between clusters of common microbes that represented groundwaters with similar geochemical conditions and largely corroborated hydraulic connections between recharge and discharge areas. Null model analyses identified deterministic and stochastic ecological processes contributing to microbial community assemblages. Most communities were more different than expected and governed by dispersal limitation, geochemical differences, or undominating processes. However, certain communities from sites located within or near the Nevada National Security Site were more similar than expected and dominated by homogeneous dispersal or selection. Overall, the (dis)similarities between the microbial communities of DVRFS recharge and discharge areas supported previously documented hydraulic connections between: (1) Spring Mountains and Ash Meadows; (2) Frenchman and Yucca Flat and Amargosa Desert; and (3) Amargosa Desert and Death Valley. However, only a portion of the flow path between Pahute Mesa and Oasis Valley could be supported by microbial community analyses, likely due to well-associated artifacts in samples from the two Oasis Valley sites. This study demonstrates the utility of combining microbial data with hydrologic, geologic, and water-chemistry information to comprehensively characterize groundwater systems, highlighting both strengths and limitations of this approach.

Breathing can be dangerous: Opportunistic fungal pathogens and the diverse community of the small mammal lung mycobiome

Human lung mycobiome studies typically sample bronchoalveolar lavage or sputum, potentially overlooking fungi embedded in tissues. Employing ultra-frozen lung tissues from biorepositories, we obtained fungal ribosomal RNA ITS2 sequences from 199 small mammals across 39 species. We documented diverse fungi, including common environmental fungi such as Penicillium and Aspergillus, associates of the human mycobiome such as Malassezia and Candida, and others specifically adapted for lungs (Coccidioides, Blastomyces, and Pneumocystis). Pneumocystis sequences were detected in 83% of the samples and generally exhibited phylogenetic congruence with hosts. Among sequences from diverse opportunistic pathogens in the Onygenales, species of Coccidioides occurred in 12% of samples and species of Blastomyces in 85% of samples. Coccidioides sequences occurred in 14 mammalian species. The presence of neither Coccidioides nor Aspergillus fumigatus correlated with substantial shifts in the overall mycobiome, although there was some indication that fungal communities might be influenced by high levels of A. fumigatus. Although members of the Onygenales were common in lung samples (92%), they are not common in environmental surveys. Our results indicate that Pneumocystis and certain Onygenales are common commensal members of the lung mycobiome. These results provide new insights into the biology of lung-inhabiting fungi and flag small mammals as potential reservoirs for emerging fungal pathogens.

Cultivation of gastrointestinal microbiota in a new growth system revealed dysbiosis and metabolic disruptions in carcinoma-bearing rats

A challenge in the study of gastrointestinal microbiota (GITm) is the validation of the genomic data with metabolic studies of the microbial communities to understand how the microbial networks work during health and sickness. To gain insights into the metabolism of the GITm, feces from healthy and sick rats with cancer were inoculated in a defined synthetic medium directed for anaerobic prokaryote growth (INC-07 medium). Significant differences between cultures of healthy and sick individuals were found: 1) the consumption of the carbon source and the enzyme activity involved in their catabolism (e.g., sucrase, lactase, lipases, aminotransferases, and dehydrogenases); 2) higher excretion of acetic, propionic, isobutyric, butyric, valeric, and isovaleric acids; 3) methane production; 4) ability to form biofilms; and 5) up to 500 amplicon sequencing variants (ASVs) identified showed different diversity and abundance. Moreover, the bowel inflammation induced by cancer triggered oxidative stress, which correlated with deficient antioxidant machinery (e.g., NADPH-producing enzymes) determined in the GITm cultures from sick individuals in comparison with those from control individuals. Altogether, the data suggested that to preserve the microbial network between bacteria and methanogenic archaea, a complete oxidation of the carbon source may be essential for healthy microbiota. The correlation of 16S rRNA gene metabarcoding between cultures and feces, as well as metabolomic data found in cultures, suggest that INC-07 medium may be a useful tool to understand the metabolism of microbiota under gut conditions.

Life barcoded by DNA barcodes

The modern concept of DNA-based barcoding for cataloguing biodiversity was proposed in 2003 by first adopting an approximately 600 bp fragment of the mitochondrial COI gene to compare via nucleotide alignments with known sequences from specimens previously identified by taxonomists. Other standardized regions meeting barcoding criteria then are also evolving as DNA barcodes for fast, reliable and inexpensive assessment of species composition across all forms of life, including animals, plants, fungi, bacteria and other microorganisms. Consequently, global DNA barcoding campaigns have resulted in the formation of many online workbenches and databases, such as BOLD system, as barcode references, and facilitated the development of mini-barcodes and metabarcoding strategies as important extensions of barcode techniques. Here we intend to give an overview of the characteristics and features of these barcode markers and major reference libraries existing for barcoding the planet’s life, as well as to address the limitations and opportunities of DNA barcodes to an increasingly broader community of science and society.

Ecological Observations Based on Functional Gene Sequencing Are Sensitive to the Amplicon Processing Method

Until recently, the de facto method for short-read-based amplicon reconstruction was a sequence similarity threshold approach (operational taxonomic units [OTUs]). This has changed with the amplicon sequence variant (ASV) method where distributions are fitted to abundance profiles of individual genes using a noise-error model. While OTU-based approaches are still useful for 16S rRNA/18S rRNA genes, where thresholds of 97% to 99% are used, their use for functional genes is still debatable as there is no consensus on clustering thresholds. Here, we compare OTU- and ASV-based reconstruction approaches and taxonomy assignment methods, the naive Bayesian classifier (NBC) and Bayesian lowest common ancestor (BLCA) algorithm, using a functional gene data set from the microbial nitrogen-cycling community in the Brouage mudflat (France). A range of OTU similarity thresholds and ASVs were used to compare amoA (ammonia-oxidizing archaea [AOA] and ammonia-oxidizing bacteria [AOB]), nxrB, nirS, nirK, and nrfA communities between differing sedimentary structures. Significant effects of the sedimentary structure on weighted UniFrac (WUniFrac) distances were observed for AOA amoA when using ASVs, an OTU at a threshold of 97% sequence identity (OTU-97%), and OTU-85%; AOB amoA when using OTU-85%; and nirS when using ASV, OTU-90%, and OTU-85%. For AOB amoA, significant effects of the sedimentary structures on UniFrac distances were observed when using OTU-97% but not ASVs, and the inverse was found for nrfA. Interestingly, conclusions drawn for nirK and nxrB were consistent between amplicon reconstruction methods. We also show that when the sequences in the reference database are related to the environment in question, the BLCA algorithm leads to more phylogenetically relevant classifications. However, when the reference database contains sequences more dissimilar to the ones retrieved, the NBC obtains more information. IMPORTANCE Several analysis pipelines are available to microbial ecologists to process amplicon sequencing data, yet to date, there is no consensus as to the most appropriate method, and it becomes more difficult for genes that encode a specific function (functional genes). Standardized approaches need to be adopted to increase the reliability and reproducibility of environmental amplicon-sequencing-based data sets. In this paper, we argue that the recently developed ASV approach offers a better opportunity to achieve such standardization than OTUs for functional genes. We also propose a comprehensive framework for quality filtering of the sequencing reads based on protein sequence verification.

Effects of Tree Composition and Soil Depth on Structure and Functionality of Belowground Microbial Communities in Temperate European Forests

Depending on their tree species composition, forests recruit different soil microbial communities. Likewise, the vertical nutrient gradient along soil profiles impacts these communities and their activities. In forest soils, bacteria and fungi commonly compete, coexist, and interact, which is challenging for understanding the complex mechanisms behind microbial structuring. Using amplicon sequencing, we analyzed bacterial and fungal diversity in relation to forest composition and soil depth. Moreover, employing random forest models, we identified microbial indicator taxa of forest plots composed of either deciduous or evergreen trees, or their mixtures, as well as of three soil depths. We expected that forest composition and soil depth affect bacterial and fungal diversity and community structure differently. Indeed, relative abundances of microbial communities changed more across soil depths than in relation to forest composition. The microbial Shannon diversity was particularly affected by soil depth and by the proportion of evergreen trees. Our results also reflected that bacterial communities are primarily shaped by soil depth, while fungi were influenced by forest tree species composition. An increasing proportion of evergreen trees did not provoke differences in main bacterial metabolic functions, e.g., carbon fixation, degradation, or photosynthesis. However, significant responses related to specialized bacterial metabolisms were detected. Saprotrophic, arbuscular mycorrhizal, and plant pathogenic fungi were related to the proportion of evergreen trees, particularly in topsoil. Prominent microbial indicator taxa in the deciduous forests were characterized to be r-strategists, whereas K-strategists dominated evergreen plots. Considering simultaneously forest composition and soil depth to unravel differences in microbial communities, metabolic pathways and functional guilds have the potential to enlighten mechanisms that maintain forest soil functionality and provide resistance against disturbances.

Effects of Dietary Antimicrobial Growth Promoters on Performance Parameters and Abundance and Diversity of Broiler Chicken Gut Microbiome and Selection of Antibiotic Resistance Genes

Antimicrobial growth promoters (AGPs) are commonly used in broiler production. There is a huge societal concern around their use and their contribution to the proliferation of antimicrobial resistance (AMR) in food-producing animals and dissemination to humans or the environment. However, there is a paucity of comprehensive experimental data on their impact on poultry production and the AMR resistome. Here, we investigated the effect of five antimicrobial growth promoters (virginiamycin, chlortetracycline, bacitracin methyl disalicylate, lincomycin, and tylosin) used in the commercial broiler production in the Indian subcontinent and in the different parts of the world for three consecutive production cycles on performance variables and also the impact on gut bacteria, bacteriophage, and resistome profile using culture-independent approaches. There was no significant effect of AGPs on the cumulative growth or feed efficiency parameters at the end of the production cycles and cumulative mortality rates were also similar across groups. Many antibiotic resistance genes (ARGs) were ubiquitous in the chicken gut irrespective of AGP supplementation. In total, 62 ARGs from 15 antimicrobial classes were detected. Supplementation of AGPs influenced the selection of several classes of ARGs; however, this was not correlated necessarily with genes relevant to the AGP drug class; some AGPs favored the selection of ARGs related to antimicrobials not structurally related to the AGP. AGPs did not impact the gut bacterial community structure, including alpha or beta diversity significantly, with only 16-20 operational taxonomic units (OTUs) of bacteria being altered significantly. However, several AGPs significantly reduced the population density of some of the potential pathogenic genera of bacteria, such as Escherichia coli. Chlortetracycline increased the abundance of Escherichia phage, whereas other AGPs did not influence the abundance of bacteriophage significantly. Considering the evidence that AGPs used in poultry production can select for resistance to more than one class of antimicrobial resistance, and the fact that their effect on performance is not significant, their use needs to be reduced and there is a need to monitor the spread of ARGs in broiler chicken farms.

Ecological Drivers of the Soil Microbial Diversity and Composition in Primary Old-Growth Forest and Secondary Woodland in a Subtropical Evergreen Broad-Leaved Forest Biome in the Ailao Mountains, China

Replacement of primary old-growth forests by secondary woodlands in threatened subtropical biomes drives important changes at the level of the overstory, understory and forest floor, but the impact on belowground microbial biodiversity is yet poorly documented. In the present study, we surveyed by metabarcoding sequencing, the diversity and composition of soil bacteria and fungi in the old-growth forest, dominated by stone oaks (Lithocarpus spp.) and in the secondary Yunnan pine woodland of an iconic site for biodiversity research, the Ailaoshan National Nature Reserve (Ailao Mountains, Yunnan province, China). We assessed the effect of forest replacement and other environmental factors, including soil horizons, soil physicochemical characteristics and seasonality (monsoon vs. dry seasons). We showed that tree composition and variation in soil properties were major drivers for both bacterial and fungal communities, with a significant influence from seasonality. Ectomycorrhizal Operational Taxonomic Units (OTUs) dominated the functional fungal guilds. Species richness and diversity of the bacterial and fungal communities were higher in the pine woodland compared to the primary Lithocarpus forest, although prominent OTUs were different. The slightly lower complexity of the microbiome in the primary forest stands likely resulted from environmental filtering under relatively stable conditions over centuries, when compared to the secondary pine woodlands. In the old-growth forest, we found a higher number of species, but that communities were homogeneously distributed, whereas in the pine woodlands, there is a slightly lower number of species present but the communities are heterogeneously distributed. The present surveys of the bacterial and fungal diversity will serve as references in future studies aiming to assess the impact of the climate change on soil microbial diversity in both old-growth forests and secondary woodlands in Ailaoshan.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Best practices in metabarcoding of fungi: From experimental design to results

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold

Advances in the analysis of amplicon sequence datasets have introduced a methodological shift in how research teams investigate microbial biodiversity, away from sequence identity-based clustering (producing Operational Taxonomic Units, OTUs) to denoising methods (producing amplicon sequence variants, ASVs). While denoising methods have several inherent properties that make them desirable compared to clustering-based methods, questions remain as to the influence that these pipelines have on the ecological patterns being assessed, especially when compared to other methodological choices made when processing data (e.g. rarefaction) and computing diversity indices. We compared the respective influences of two widely used methods, namely DADA2 (a denoising method) vs. Mothur (a clustering method) on 16S rRNA gene amplicon datasets (hypervariable region v4), and compared such effects to the rarefaction of the community table and OTU identity threshold (97% vs. 99%) on the ecological signals detected. We used a dataset comprising freshwater invertebrate (three Unionidae species) gut and environmental (sediment, seston) communities sampled in six rivers in the southeastern USA. We ranked the respective effects of each methodological choice on alpha and beta diversity, and taxonomic composition. The choice of the pipeline significantly influenced alpha and beta diversities and changed the ecological signal detected, especially on presence/absence indices such as the richness index and unweighted Unifrac. Interestingly, the discrepancy between OTU and ASV-based diversity metrics could be attenuated by the use of rarefaction. The identification of major classes and genera also revealed significant discrepancies across pipelines. Compared to the pipeline’s effect, OTU threshold and rarefaction had a minimal impact on all measurements.

High-throughput qPCR and 16S rRNA gene amplicon sequencing as complementary methods for the investigation of the cheese microbiota

Background

Next-generation sequencing (NGS) methods and especially 16S rRNA gene amplicon sequencing have become indispensable tools in microbial ecology. While they have opened up new possibilities for studying microbial communities, they also have one drawback, namely providing only relative abundances and thus compositional data. Quantitative PCR (qPCR) has been used for years for the quantification of bacteria. However, this method requires the development of specific primers and has a low throughput. The constraint of low throughput has recently been overcome by the development of high-throughput qPCR (HT-qPCR), which allows for the simultaneous detection of the most prevalent bacteria in moderately complex systems, such as cheese and other fermented dairy foods. In the present study, the performance of the two approaches, NGS and HT-qPCR, was compared by analyzing the same DNA samples from 21 Raclette du Valais protected designation of origin (PDO) cheeses. Based on the results obtained, the differences, accuracy, and usefulness of the two approaches were studied in detail.

Results

The results obtained using NGS (non-targeted) and HT-qPCR (targeted) show considerable agreement in determining the microbial composition of the cheese DNA samples studied, albeit the fundamentally different nature of these two approaches. A few inconsistencies in species detection were observed, particularly for less abundant ones. The detailed comparison of the results for 15 bacterial species/groups measured by both methods revealed a considerable bias for certain bacterial species in the measurements of the amplicon sequencing approach. We identified as probable origin to this PCR bias due to primer mismatches, variations in the number of copies for the 16S rRNA gene, and bias introduced in the bioinformatics analysis.

Conclusion

As the normalized microbial composition results of NGS and HT-qPCR agreed for most of the 21 cheese samples analyzed, both methods can be considered as complementary and reliable for studying the microbial composition of cheese. Their combined application proved to be very helpful in identifying potential biases and overcoming methodological limitations in the quantitative analysis of the cheese microbiota.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-022-02451-y.

Patterns of Relative Bacterial Richness and Community Composition in Seawater and Marine Sediment Are Robust for Both Operational Taxonomic Units and Amplicon Sequence Variants

To understand the relative influences of operational taxonomic units (OTUs) and amplicon sequence variants (ASVs) on patterns of marine microbial diversity and community composition, we examined bacterial diversity and community composition of seawater from 12 sites in the North Atlantic Ocean and Canadian Arctic and sediment from two sites in the North Atlantic. For the seawater analyses, we included samples from three to six zones in the water column of each site. For the sediment analyses, we included over 20 sediment horizons at each of two sites. For all samples, we amplified the V4-V5 hypervariable region of the 16S ribosomal RNA (rRNA) gene. We analyzed each sample in two different ways: (i) by clustering its reads into 97%-similar OTUs and (ii) by assigning sequences to unique ASVs. OTU richness is much higher than ASV richness for every sample, but both OTUs and ASVs exhibit similar vertical patterns of relative diversity in both the water column and the sediment. Bacterial richness is highest just below the photic zone in the water column and at the seafloor in the sediment. For both OTUs and ASVs, richness estimates depend on the number of sequences analyzed. Both methods yield broadly similar community compositions for each sample at the taxonomic levels of phyla to families. While the two methods yield different richness values, broad-scale patterns of relative richness and community composition are similar with both methods.

City-Scale Meta-Analysis of Indoor Airborne Microbiota Reveals that Taxonomic and Functional Compositions Vary with Building Types

Currently, there is a lack of understanding on the variations of the indoor airborne microbiotas of different building types within a city, and how operational taxonomic unit (OTU)- and amplicon sequence variant (ASV)-based analyses of the 16S rRNA gene sequences affect interpretation of the indoor airborne microbiota results. Therefore, in this study, the indoor airborne bacterial microbiotas between commercial buildings, residences, and subways within the same city were compared using both OTU- and ASV-based analytic methods. Our findings suggested that indoor airborne bacterial microbiota compositions were significantly different between building types regardless of the bioinformatics method used. The processes of ecological drift and random dispersal consistently played significant roles in the assembly of the indoor microbiota across building types. Abundant taxa tended to be more centralized in the correlation network of each building type, highlighting their importance. Taxonomic changes between the microbiotas of different building types were also linked to changes in their inferred metabolic function capabilities. Overall, the results imply that customized strategies are necessary to manage indoor airborne bacterial microbiotas for each building type or even within each specific building.

Biochar-Enhanced Resistance to Botrytis cinerea in Strawberry Fruits (But Not Leaves) Is Associated With Changes in the Rhizosphere Microbiome

Biochar has been reported to play a positive role in disease suppression against airborne pathogens in plants. The mechanisms behind this positive trait are not well-understood. In this study, we hypothesized that the attraction of plant growth-promoting rhizobacteria (PGPR) or fungi (PGPF) underlies the mechanism of biochar in plant protection. The attraction of PGPR and PGPF may either activate the innate immune system of plants or help the plants with nutrient uptake. We studied the effect of biochar in peat substrate (PS) on the susceptibility of strawberry, both on leaves and fruits, against the airborne fungal pathogen Botrytis cinerea. Biochar had a positive impact on the resistance of strawberry fruits but not the plant leaves. On leaves, the infection was more severe compared with plants without biochar in the PS. The different effects on fruits and plant leaves may indicate a trade-off between plant parts. Future studies should focus on monitoring gene expression and metabolites of strawberry fruits to investigate this potential trade-off effect. A change in the microbial community in the rhizosphere was also observed, with increased fungal diversity and higher abundances of amplicon sequence variants classified into Granulicella, Mucilaginibacter, and Byssochlamys surrounding the plant root, where the latter two were reported as biocontrol agents. The change in the microbial community was not correlated with a change in nutrient uptake by the plant in either the leaves or the fruits. A decrease in the defense gene expression in the leaves was observed. In conclusion, the decreased infection of B. cinerea in strawberry fruits mediated by the addition of biochar in the PS is most likely regulated by the changes in the microbial community.

General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

Understanding the Shift in the Microbiome of Composts That Are Optimized for a Better Fit-for-Purpose in Growing Media

Three characteristics are considered key for optimal use of composts in growing media: maturity, pH and organic matter content. Maturation is a critical step in the processing of composts contributing to compost quality. Blending of composts with chopped heath biomass, sieving out the larger fraction of composts and acidification of composts by adding elemental sulfur may be used either to increase organic matter content or to reduce pH for a better fit in growing media. While several studies have shown the effectiveness of these treatments to improve the use of composts in growing media, the effect of these treatments on the compost microbiome has merely been assessed before. In the present study, five immature composts were allowed to mature, and were subsequently acidified, blended or sieved. Bacterial and fungal communities of the composts were characterized and quantified using 16S rRNA and ITS2 gene metabarcoding and phospholipid fatty acid analysis. Metabolic biodiversity and activity were analyzed using Biolog EcoPlates. Compost batch was shown to be more important than maturation or optimization treatments to determine the compost microbiome. Compost maturation increased microbial diversity and favored beneficial microorganisms, which may be positive for the use of composts in growing media. Blending of composts increased microbial diversity, metabolic diversity, and metabolic activity, which may have a positive effect in growing media. Blending may be used to modify the microbiome to a certain degree in order to optimize microbiological characteristics. Acidification caused a decrease in bacterial diversity and microbial activity, which may be negative for the use in growing media, although the changes are limited. Sieving had limited effect on the microbiome of composts. Because of the limited effect on the microbiome, sieving of composts may be used flexible to improve (bio)chemical characteristics. This is the first study to assess the effects of maturation and optimization treatments to either increase organic matter content or lower pH in composts on the compost microbiome.