A comparison of methods used to unveil the genetic and metabolic pool in the built environment

Functional gene transcription variation in bacterial metatranscriptomes in large freshwater Lake Ecosystems: Implications for ecosystem and human health

Little is known regarding the temporal and spatial functional variation of freshwater bacterial community (BC) under non-bloom conditions, especially in winter. To address this, we used metatranscriptomics to assess bacterial gene transcription variation among three sites across three seasons. Our metatranscriptome data for freshwater BCs at three public beaches (Ontario, Canada) sampled in the winter (no ice), summer and fall (2019) showed relatively little spatial, but a strong temporal variation. Our data showed high transcriptional activity in summer and fall but surprisingly, 89% of the KEGG pathway genes and 60% of the selected candidate genes (52 genes) associated with physiological and ecological activity were still active in freezing temperatures (winter). Our data also supported the possibility of an adaptively flexible gene expression response of the freshwater BC to low temperature conditions (winter). Only 32% of the bacterial genera detected in the samples were active, indicating that the majority of detected taxa were non-active (dormant). We also identified high seasonal variation in the abundance and activity of taxa associated with health risks (i.e., Cyanobacteria and waterborne bacterial pathogens). This study provides a baseline for further characterization of freshwater BCs, health-related microbial activity/dormancy and the main drivers of their functional variation (such as rapid human-induced environmental change and climate change).

RNA-based amplicon sequencing is ineffective in measuring metabolic activity in environmental microbial communities

BACKGROUND

Characterization of microbial activity is essential to the understanding of the basic biology of microbial communities, as the function of a microbiome is defined by its biochemically active ("viable") community members. Current sequence-based technologies can rarely differentiate microbial activity, due to their inability to distinguish live and dead sourced DNA. As a result, our understanding of microbial community structures and the potential mechanisms of transmission between humans and our surrounding environments remains incomplete. As a potential solution, 16S rRNA transcript-based amplicon sequencing (16S-RNA-seq) has been proposed as a reliable methodology to characterize the active components of a microbiome, but its efficacy has not been evaluated systematically. Here, we present our work to benchmark RNA-based amplicon sequencing for activity assessment in synthetic and environmentally sourced microbial communities.

RESULTS

In synthetic mixtures of living and heat-killed Escherichia coli and Streptococcus sanguinis, 16S-RNA-seq successfully reconstructed the active compositions of the communities. However, in the realistic environmental samples, no significant compositional differences were observed in RNA ("actively transcribed - active") vs. DNA ("whole" communities) spiked with E. coli controls, suggesting that this methodology is not appropriate for activity assessment in complex communities. The results were slightly different when validated in environmental samples of similar origins (i.e., from Boston subway systems), where samples were differentiated both by environment type as well as by library type, though compositional dissimilarities between DNA and RNA samples remained low (Bray-Curtis distance median: 0.34-0.49). To improve the interpretation of 16S-RNA-seq results, we compared our results with previous studies and found that 16S-RNA-seq suggests taxon-wise viability trends (i.e., specific taxa are universally more or less likely to be viable compared to others) in samples of similar origins.

CONCLUSIONS

This study provides a comprehensive evaluation of 16S-RNA-seq for viability assessment in synthetic and complex microbial communities. The results found that while 16S-RNA-seq was able to semi-quantify microbial viability in relatively simple communities, it only suggests a taxon-dependent "relative" viability in realistic communities.  Video Abstract.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Development of sequencing-based methodologies to distinguish viable from non-viable cells in a bovine milk matrix: A pilot study

Although high-throughput DNA sequencing-based methods have been of great value for determining the composition of microbial communities in various environments, there is the potential for inaccuracies arising from the sequencing of DNA from dead microorganisms. In this pilot study, we compared different sequencing-based methods to assess their relative accuracy with respect to distinguishing between viable and non-viable cells, using a live and heat-inactivated model community spiked into bovine milk. The methods used were shotgun metagenomics with and without propidium monoazide (PMA) treatment, RNA-based 16S rRNA sequencing and metatranscriptomics. The results showed that methods were generally accurate, though significant differences were found depending on the library types and sequencing technologies. Different molecular targets were the basis for variations in the results generated using different library types, while differences in the derived composition data from Oxford Nanopore Technologies-and Illumina-based sequencing likely reflect a combination of different sequencing depths, error rates and bioinformatics pipelines. Although PMA was successfully applied in this study, further optimisation is required before it can be applied in a more universal context for complex microbiomes. Overall, these methods show promise and represent another important step towards the ultimate establishment of approaches that can be applied to accurately identify live microorganisms in milk and other food niches.

Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

Where environmental microbiome meets its host: subway and passenger microbiome relationships

Subways are urban transport systems with high capacity. Every day around the world, there are more than 150 million subway passengers. Since 2013, thousands of microbiome samples from various subways worldwide have been sequenced. Skin bacteria and environmental organisms dominate the subway microbiomes. The literature has revealed common bacterial groups in subway systems; even so, it is possible to identify cities by their microbiome. Low-frequency bacteria are responsible for specific bacterial fingerprints of each subway system. Furthermore, daily subway commuters leave their microbial clouds and interact with other passengers. Microbial exchange is quite fast; the hand microbiome changes within minutes, and after cleaning the handrails, the bacteria are re-established within minutes. To investigate new taxa and metabolic pathways of subway microbial communities, several high-quality metagenomic-assembled genomes (MAG) have been described. Subways are harsh environments unfavorable for microorganism growth. However, recent studies have observed a wide diversity of viable and metabolically active bacteria. Understanding which bacteria are living, dormant, or dead allows us to propose realistic ecological interactions. Questions regarding the relationship between humans and the subway microbiome, particularly the microbiome effects on personal and public health, remain unanswered. This review summarizes our knowledge of subway microbiomes and their relationship with passenger microbiomes.

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.

Towards reproducible metabarcoding data: Lessons from an international cross-laboratory experiment

Advances in high-throughput sequencing (HTS) are revolutionizing monitoring in marine environments by enabling rapid, accurate and holistic detection of species within complex biological samples. Research institutions worldwide increasingly employ HTS methods for biodiversity assessments. However, variance in laboratory procedures, analytical workflows and bioinformatic pipelines impede the transferability and comparability of results across research groups. An international experiment was conducted to assess the consistency of metabarcoding results derived from identical samples and primer sets using varying laboratory procedures. Homogenized biofouling samples collected from four coastal locations (Australia, Canada, New Zealand and the USA) were distributed to 12 independent laboratories. Participants were asked to follow one of two HTS library preparation workflows. While DNA extraction, primers and bioinformatic analyses were purposefully standardized to allow comparison, many other technical variables were allowed to vary among laboratories (amplification protocols, type of instrument used, etc.). Despite substantial variation observed in raw results, the primary signal in the data was consistent, with the samples grouping strongly by geographical origin for all data sets. Simple post hoc data clean-up by removing low-quality samples gave the best improvement in sample classification for nuclear 18S rRNA gene data, with an overall 92.81% correct group attribution. For mitochondrial COI gene data, the best classification result (95.58%) was achieved after correction for contamination errors. The identified critical methodological factors that introduced the greatest variability (preservation buffer, sample defrosting, template concentration, DNA polymerase, PCR enhancer) should be of great assistance in standardizing future biodiversity studies using metabarcoding.

Toward Accurate and Robust Environmental Surveillance Using Metagenomics

Environmental surveillance is a critical tool for combatting public health threats represented by the global COVID-19 pandemic and the continuous increase of antibiotic resistance in pathogens. With its power to detect entire microbial communities, metagenomics-based methods stand out in addressing the need. However, several hurdles remain to be overcome in order to generate actionable interpretations from metagenomic sequencing data for infection prevention. Conceptually and technically, we focus on viability assessment, taxonomic resolution, and quantitative metagenomics, and discuss their current advancements, necessary precautions and directions to further development. We highlight the importance of building solid conceptual frameworks and identifying rational limits to facilitate the application of techniques. We also propose the usage of internal standards as a promising approach to overcome analytical bottlenecks introduced by low biomass samples and the inherent lack of quantitation in metagenomics. Taken together, we hope this perspective will contribute to bringing accurate and consistent metagenomics-based environmental surveillance to the ground.

Profiling Airborne Microbiota in Mechanically Ventilated Buildings Across Seasons in Hong Kong Reveals Higher Metabolic Activity in Low-Abundance Bacteria

Metabolically active bacteria within built environments are poorly understood. This study aims to investigate the active airborne bacterial microbiota and compare the total and active microbiota in eight mechanically ventilated buildings over four consecutive seasons using the 16S rRNA gene (rDNA) and the 16S rRNA (rRNA), respectively. The relative abundances of the taxa of presumptive occupants and environmental origins were significantly different between the active and total microbiota. The Sloan neutral model suggested that ecological drift and random dispersal played a smaller role in the assembly of the active microbiota than the total microbiota. The seasonal nature of the active microbiota was consistent with that of the total microbiota in both indoor and outdoor environments, while only the indoor environment was significantly affected by geography. The relative abundances of the active and total taxa were positively correlated, suggesting that the high-abundance members were also the greatest contributors to the community-level metabolic activity. Based on the rRNA/rDNA ratio, the low-abundance members consistently had a higher taxon-level metabolic activity than the high-abundance members over seasons, suggesting that the low-abundance members may have the ability to survive and thrive in the indoor environment and their impact on the health of occupants cannot be overlooked.

The environmental risk assessment of cell-processing facilities for cell therapy in a Japanese academic institution

Cell therapy is a promising treatment. One of the key aspects of cell processing products is ensuring sterility of cell-processing facilities (CPFs). The objective of this study was to assess the environmental risk factors inside and outside CPFs. We monitored the temperature, humidity, particle number, colony number of microorganisms, bacteria, fungi, and harmful insects in and around our CPF monthly over one year. The temperature in the CPF was constant but the humidity fluctuated depending on the humidity outside. The particle number correlated with the number of entries to the room. Except for winter, colonies of microorganisms and harmful insects were detected depending on the cleanliness of the room. Seven bacterial and two fungal species were identified by PCR analyses. Psocoptera and Acari each accounted for 41% of the total trapped insects. These results provide useful data for taking the appropriate steps to keep entire CPFs clean.

Building upon current knowledge and techniques of indoor microbiology to construct the next era of theory into microorganisms, health, and the built environment

In the constructed habitat in which we spend up to 90% of our time, architectural design influences occupants' behavioral patterns, interactions with objects, surfaces, rituals, the outside environment, and each other. Within this built environment, human behavior and building design contribute to the accrual and dispersal of microorganisms; it is a collection of fomites that transfer microorganisms; reservoirs that collect biomass; structures that induce human or air movement patterns; and space types that encourage proximity or isolation between humans whose personal microbial clouds disperse cells into buildings. There have been recent calls to incorporate building microbiology into occupant health and exposure research and standards, yet the built environment is largely viewed as a repository for microorganisms which are to be eliminated, instead of a habitat which is inexorably linked to the microbial influences of building inhabitants. Health sectors have re-evaluated the role of microorganisms in health, incorporating microorganisms into prevention and treatment protocols, yet no paradigm shift has occurred with respect to microbiology of the built environment, despite calls to do so. Technological and logistical constraints often preclude our ability to link health outcomes to indoor microbiology, yet sufficient study exists to inform the theory and implementation of the next era of research and intervention in the built environment. This review presents built environment characteristics in relation to human health and disease, explores some of the current experimental strategies and interventions which explore health in the built environment, and discusses an emerging model for fostering indoor microbiology rather than fearing it.

RNA-based qPCR as a tool to quantify and to characterize dual-species biofilms

While considerable research has focused on studying individual-species, we now face the challenge of determining how interspecies interactions alter bacterial behaviours and pathogenesis. Pseudomonas aeruginosa and Staphylococcus aureus are often found to co-infect cystic-fibrosis patients. Curiously, their interaction is reported as competitive under laboratory conditions. Selecting appropriate methodologies is therefore critical to analyse multi-species communities. Herein, we demonstrated the major biases associated with qPCR quantification of bacterial populations and optimized a RNA-based qPCR able not only to quantify but also to characterize microbial interactions within dual-species biofilms composed by P. aeruginosa and S. aureus, as assessed by gene expression quantification. qPCR quantification was compared with flow-cytometry and culture-based quantification. Discrepancies between culture independent and culture dependent methods could be the result of the presence of viable but not-cultivable bacteria within the biofilm. Fluorescence microscopy confirmed this. A higher sensitivity to detect viable cells further highlights the potentialities of qPCR approach to quantify biofilm communities. By using bacterial RNA and an exogenous mRNA control, it was also possible to characterize bacterial transcriptomic profile, being this a major advantage of this method.

Rare Taxa Exhibit Disproportionate Cell-Level Metabolic Activity in Enriched Anaerobic Digestion Microbial Communities

Microbial communities are composed of populations with vastly different abundances and levels of metabolic and replicative activity, ranging from actively metabolizing and dividing to dormant or nonviable. The 16S rRNA/rDNA ratio is an emerging tool for evaluating cell-level metabolic activity independent of abundance. In this study, we used five long-term enriched model anaerobic digestion (AD) communities to investigate community composition, diversity, structure, and in particular activity based on the rRNA/rDNA ratio. We cross-validated the 16S amplicon-based results using two alternative operational taxonomic unit (OTU) formation methods (conventional 97% sequence similarity and 100% sequence similar zero-radius OTUs by UNOISE3) and compared these to metagenome-derived population genomes and metatranscriptomes. Significant positive correlations were observed between microbial total activity and abundance with both the amplicon- and omic-based methods. All three methods revealed disproportionately high transcription/abundance ratios for some rare taxa but lower ratios for most abundant taxa for all the communities, which was further corroborated by the high replication rate (iRep) of most low-abundance population genomes. IMPORTANCE Variation in microbial activity levels is increasingly being recognized as both an important dimension in community function and a complicating factor in sequencing-based survey methods. This study extends previous reports that rare taxa may contribute disproportionately to community activity in some natural environments, showing that this may also hold in artificially maintained model communities with well-described inputs, outputs, and biochemical functions. These results demonstrate that assessment of activity levels using the rRNA/rDNA ratio is robust across taxonomic unit formation methods and is independently corroborated by omics methods. The results also provide insight into the comparative advantages and disadvantages of different taxonomic unit formation methods in amplicon sequencing studies, showing that UNOISE3 provides comparable microbial diversity, structure, and activity information as the 97% sequence similarity method but potentially loses some phylogenetic diversity and creates more "phantom taxa" (which are present in the RNA pool but not the corresponding DNA pool).