Ameet J. Pinto

PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets

As 16S rRNA gene targeted massively parallel sequencing has become a common tool for microbial diversity investigations, numerous advances have been made to minimize the influence of sequencing and chimeric PCR artifacts through rigorous quality control measures. However, there has been little effort towards understanding the effect of multi-template PCR biases on microbial community structure. In this study, we used three bacterial and three archaeal mock communities consisting of, respectively, 33 bacterial and 24 archaeal 16S rRNA gene sequences combined in different proportions to compare the influences of (1) sequencing depth, (2) sequencing artifacts (sequencing errors and chimeric PCR artifacts), and (3) biases in multi-template PCR, towards the interpretation of community structure in pyrosequencing datasets. We also assessed the influence of each of these three variables on α- and β-diversity metrics that rely on the number of OTUs alone (richness) and those that include both membership and the relative abundance of detected OTUs (diversity). As part of this study, we redesigned bacterial and archaeal primer sets that target the V3–V5 region of the 16S rRNA gene, along with multiplexing barcodes, to permit simultaneous sequencing of PCR products from the two domains. We conclude that the benefits of deeper sequencing efforts extend beyond greater OTU detection and result in higher precision in β-diversity analyses by reducing the variability between replicate libraries, despite the presence of more sequencing artifacts. Additionally, spurious OTUs resulting from sequencing errors have a significant impact on richness or shared-richness based α- and β-diversity metrics, whereas metrics that utilize community structure (including both richness and relative abundance of OTUs) are minimally affected by spurious OTUs. However, the greatest obstacle towards accurately evaluating community structure are the errors in estimated mean relative abundance of each detected OTU due to biases associated with multi-template PCR reactions.

Bacterial community structure in the drinking water microbiome is governed by filtration processes.

The bacterial community structure of a drinking water microbiome was characterized over three seasons using 16S rRNA gene based pyrosequencing of samples obtained from source water (a mix of a groundwater and a surface water), different points in a drinking water plant operated to treat this source water, and in the associated drinking water distribution system. Even though the source water was shown to seed the drinking water microbiome, treatment process operations limit the source waters influence on the distribution system bacterial community. Rather, in this plant, filtration by dual media rapid sand filters played a primary role in shaping the distribution system bacterial community over seasonal time scales as the filters harbored a stable bacterial community that seeded the water treatment processes past filtration. Bacterial taxa that colonized the filter and sloughed off in the filter effluent were able to persist in the distribution system despite disinfection of finished water by chloramination and filter backwashing with chloraminated backwash water. Thus, filter colonization presents a possible ecological survival strategy for bacterial communities in drinking water systems, which presents an opportunity to control the drinking water microbiome by manipulating the filter microbial community. Grouping bacterial taxa based on their association with the filter helped to elucidate relationships between the abundance of bacterial groups and water quality parameters and showed that pH was the strongest regulator of the bacterial community in the sampled drinking water system.

Differential resistance of drinking water bacterial populations to monochloramine disinfection

The impact of monochloramine disinfection on the complex bacterial community structure in drinking water systems was investigated using culture-dependent and culture-independent methods. Changes in viable bacterial diversity were monitored using culture-independent methods that distinguish between live and dead cells based on membrane integrity, providing a highly conservative measure of viability. Samples were collected from lab-scale and full-scale drinking water filters exposed to monochloramine for a range of contact times. Culture-independent detection of live cells was based on propidium monoazide (PMA) treatment to selectively remove DNA from membrane-compromised cells. Quantitative PCR (qPCR) and pyrosequencing of 16S rRNA genes was used to quantify the DNA of live bacteria and characterize the bacterial communities, respectively. The inactivation rate determined by the culture-independent PMA-qPCR method (1.5-log removal at 664 mg·min/L) was lower than the inactivation rate measured by the culture-based methods (4-log removal at 66 mg·min/L). Moreover, drastic changes in the live bacterial community structure were detected during monochloramine disinfection using PMA-pyrosequencing, while the community structure appeared to remain stable when pyrosequencing was performed on samples that were not subject to PMA treatment. Genera that increased in relative abundance during monochloramine treatment include Legionella, Escherichia, and Geobacter in the lab-scale system and Mycobacterium, Sphingomonas, and Coxiella in the full-scale system. These results demonstrate that bacterial populations in drinking water exhibit differential resistance to monochloramine, and that the disinfection process selects for resistant bacterial populations.

Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome

ABSTRACT Bacterial communities migrate continuously from the drinking water treatment plant through the drinking water distribution system and into our built environment. Understanding bacterial dynamics in the distribution system is critical to ensuring that safe drinking water is being supplied to customers. We present a 15-month survey of bacterial community dynamics in the drinking water system of Ann Arbor, MI. By sampling the water leaving the treatment plant and at nine points in the distribution system, we show that the bacterial community spatial dynamics of distance decay and dispersivity conform to the layout of the drinking water distribution system. However, the patterns in spatial dynamics were weaker than those for the temporal trends, which exhibited seasonal cycling correlating with temperature and source water use patterns and also demonstrated reproducibility on an annual time scale. The temporal trends were driven by two seasonal bacterial clusters consisting of multiple taxa with different networks of association within the larger drinking water bacterial community. Finally, we show that the Ann Arbor data set robustly conforms to previously described interspecific occupancy abundance models that link the relative abundance of a taxon to the frequency of its detection. Relying on these insights, we propose a predictive framework for microbial management in drinking water systems. Further, we recommend that long-term microbial observatories that collect high-resolution, spatially distributed, multiyear time series of community composition and environmental variables be established to enable the development and testing of the predictive framework. IMPORTANCE Safe and regulation-compliant drinking water may contain up to millions of microorganisms per liter, representing phylogenetically diverse groups of bacteria, archaea, and eukarya that affect public health, water infrastructure, and the aesthetic quality of water. The ability to predict the dynamics of the drinking water microbiome will ensure that microbial contamination risks can be better managed. Through a spatial-temporal survey of drinking water bacterial communities, we present novel insights into their spatial and temporal community dynamics and recommend steps to link these insights in a predictive framework for microbial management of drinking water systems. Such a predictive framework will not only help to eliminate microbial risks but also help to modify existing water quality monitoring efforts and make them more resource efficient. Further, a predictive framework for microbial management will be critical if we are to fully anticipate the risks and benefits of the beneficial manipulation of the drinking water microbiome. Safe and regulation-compliant drinking water may contain up to millions of microorganisms per liter, representing phylogenetically diverse groups of bacteria, archaea, and eukarya that affect public health, water infrastructure, and the aesthetic quality of water. The ability to predict the dynamics of the drinking water microbiome will ensure that microbial contamination risks can be better managed. Through a spatial-temporal survey of drinking water bacterial communities, we present novel insights into their spatial and temporal community dynamics and recommend steps to link these insights in a predictive framework for microbial management of drinking water systems. Such a predictive framework will not only help to eliminate microbial risks but also help to modify existing water quality monitoring efforts and make them more resource efficient. Further, a predictive framework for microbial management will be critical if we are to fully anticipate the risks and benefits of the beneficial manipulation of the drinking water microbiome.

Metagenomic Evidence for the Presence of Comammox Nitrospira-Like Bacteria in a Drinking Water System

Nitrification plays an important role in regulating the concentrations of inorganic nitrogen species in a range of environments, from drinking water and wastewater treatment plants to the oceans. Until recently, aerobic nitrification was considered to be a two-step process involving ammonia-oxidizing bacteria or archaea and nitrite-oxidizing bacteria. This process requires close cooperation between these two functional guilds for complete conversion of ammonia to nitrate, without the accumulation of nitrite or other intermediates, such as nitrous oxide, a potent greenhouse gas. The discovery of a single organism with the potential to oxidize both ammonia and nitrite adds a new dimension to the current understanding of aerobic nitrification, while presenting opportunities to rethink nitrogen management in engineered systems. ABSTRACT We report metagenomic evidence for the presence of a Nitrospira-like organism with the metabolic potential to perform the complete oxidation of ammonia to nitrate (i.e., it is a complete ammonia oxidizer [comammox]) in a drinking water system. This metagenome bin was discovered through shotgun DNA sequencing of samples from biologically active filters at the drinking water treatment plant in Ann Arbor, MI. Ribosomal proteins, 16S rRNA, and nxrA gene analyses confirmed that this genome is related to Nitrospira-like nitrite-oxidizing bacteria. The presence of the full suite of ammonia oxidation genes, including ammonia monooxygenase and hydroxylamine dehydrogenase, on a single ungapped scaffold within this metagenome bin suggests the presence of recently discovered comammox potential. Evaluations based on coverage and k-mer frequency distribution, use of two different genome-binning approaches, and nucleic acid and protein similarity analyses support the presence of this scaffold within the Nitrospira metagenome bin. The amoA gene found in this metagenome bin is divergent from those of canonical ammonia and methane oxidizers and clusters closely with the unusual amoA gene of comammox Nitrospira. This finding suggests that previously reported imbalances in abundances of nitrite- and ammonia-oxidizing bacteria/archaea may likely be explained by the capacity of Nitrospira-like organisms to completely oxidize ammonia. This finding might have significant implications for our understanding of microbially mediated nitrogen transformations in engineered and natural systems. IMPORTANCE Nitrification plays an important role in regulating the concentrations of inorganic nitrogen species in a range of environments, from drinking water and wastewater treatment plants to the oceans. Until recently, aerobic nitrification was considered to be a two-step process involving ammonia-oxidizing bacteria or archaea and nitrite-oxidizing bacteria. This process requires close cooperation between these two functional guilds for complete conversion of ammonia to nitrate, without the accumulation of nitrite or other intermediates, such as nitrous oxide, a potent greenhouse gas. The discovery of a single organism with the potential to oxidize both ammonia and nitrite adds a new dimension to the current understanding of aerobic nitrification, while presenting opportunities to rethink nitrogen management in engineered systems.

Emerging investigators series: microbial communities in full-scale drinking water distribution systems – a meta-analysis

In this study, we co-analyze all available 16S rRNA gene sequencing studies from bulk drinking water samples in full-scale drinking water distribution systems. Consistent with expectations, we find that Proteobacteria, particularly Alpha- and Betaproteobacteria, dominate drinking water bacterial communities irrespective of origin of study and presence/absence of or disinfectant residual type. Microbial communities in disinfectant residual free systems are more diverse than in those that maintain a disinfectant residual. Further, we find positive associations between mean relative abundance and occurrence of bacteria within a disinfectant category group. The relative abundance and occurrence of key bacterial genera (e.g. Legionella, Mycobacterium, Pseudomonas) is influenced by the presence/absence of a disinfectant residual and the type of disinfectant residual used. Similarly, we find widespread distribution of bacterial genera that are of interest from both an ecological and process perspectives (e.g. nitrification, predation). By estimating the contribution of potential contaminating genera to published drinking water datasets, we recommend that routine sequencing of negative controls be included in drinking water studies. Finally, we test the utility of predicting the metabolic potential of drinking water communities using 16S rRNA gene data and recommend against this practice. Though data heterogeneity across available datasets is a major confounding factor in our meta-analysis, we recommend that efforts to standardize sample processing protocols to address it may not be optimal for the drinking water microbial ecology field at this juncture. Rather, we recommend standardizing data and meta-data reporting, starting with making all sequencing data publicly available, and sample sharing as means of supporting future efforts for comparative analyses across drinking water systems/studies.

Design and Evaluation of Illumina MiSeq-Compatible, 18S rRNA Gene-Specific Primers for Improved Characterization of Mixed Phototrophic Communities.

ABSTRACT The use of high-throughput sequencing technologies with the 16S rRNA gene for characterization of bacterial and archaeal communities has become routine. However, the adoption of sequencing methods for eukaryotes has been slow, despite their significance to natural and engineered systems. There are large variations among the target genes used for amplicon sequencing, and for the 18S rRNA gene, there is no consensus on which hypervariable region provides the most suitable representation of diversity. Additionally, it is unclear how much PCR/sequencing bias affects the depiction of community structure using current primers. The present study amplified the V4 and V8-V9 regions from seven microalgal mock communities as well as eukaryotic communities from freshwater, coastal, and wastewater samples to examine the effect of PCR/sequencing bias on community structure and membership. We found that degeneracies on the 3′ end of the current V4-specific primers impact read length and mean relative abundance. Furthermore, the PCR/sequencing error is markedly higher for GC-rich members than for communities with balanced GC content. Importantly, the V4 region failed to reliably capture 2 of the 12 mock community members, and the V8-V9 hypervariable region more accurately represents mean relative abundance and alpha and beta diversity. Overall, the V4 and V8-V9 regions show similar community representations over freshwater, coastal, and wastewater environments, but specific samples show markedly different communities. These results indicate that multiple primer sets may be advantageous for gaining a more complete understanding of community structure and highlight the importance of including mock communities composed of species of interest. IMPORTANCE The quantification of error associated with community representation by amplicon sequencing is a critical challenge that is often ignored. When target genes are amplified using currently available primers, differential amplification efficiencies result in inaccurate estimates of community structure. The extent to which amplification bias affects community representation and the accuracy with which different gene targets represent community structure are not known. As a result, there is no consensus on which region provides the most suitable representation of diversity for eukaryotes. This study determined the accuracy with which commonly used 18S rRNA gene primer sets represent community structure and identified particular biases related to PCR amplification and Illumina MiSeq sequencing in order to more accurately study eukaryotic microbial communities.

The impact of sampling, PCR, and sequencing replication on discerning changes in drinking water bacterial community over diurnal time-scales.

High-throughput and deep DNA sequencing, particularly amplicon sequencing, is being increasingly utilized to reveal spatial and temporal dynamics of bacterial communities in drinking water systems. Whilst the sampling and methodological biases associated with PCR and sequencing have been studied in other environments, they have not been quantified for drinking water. These biases are likely to have the greatest effect on the ability to characterize subtle spatio-temporal patterns influenced by process/environmental conditions. In such cases, intra-sample variability may swamp any underlying small, systematic variation. To evaluate this, we undertook a study with replication at multiple levels including sampling sites, sample collection, PCR amplification, and high throughput sequencing of 16S rRNA amplicons. The variability inherent to the PCR amplification and sequencing steps is significant enough to mask differences between bacterial communities from replicate samples. This was largely driven by greater variability in detection of rare bacteria (relative abundance <0.01%) across PCR/sequencing replicates as compared to replicate samples. Despite this, we captured significant changes in bacterial community over diurnal time-scales and find that the extent and pattern of diurnal changes is specific to each sampling location. Further, we find diurnal changes in bacterial community arise due to differences in the presence/absence of the low abundance bacteria and changes in the relative abundance of dominant bacteria. Finally, we show that bacterial community composition is significantly different across sampling sites for time-periods during which there are typically rapid changes in water use. This suggests hydraulic changes (driven by changes in water demand) contribute to shaping the bacterial community in bulk drinking water over diurnal time-scales.

The control of disinfection byproducts and their precursors in biologically active filtration processes

While disinfection provides hygienically safe drinking water, the disinfectants react with inorganic or organic precursors, leading to the formation of harmful disinfection byproducts (DBPs). Biological filtration is a process in which an otherwise conventional granular filter is designed to remove not only fine particulates but also dissolved organic matters (e.g., DBP precursors) through microbially mediated degradation. Recently, applications of biofiltration in drinking water treatment have increased significantly. This review summarizes the effectiveness of biofiltration in removing DBPs and their precursors and identifies potential factors in biofilters that may control the removal or contribute to formation of DBP and their precursors during drinking water treatment. Biofiltration can remove a fraction of the precursors of halogenated DBPs (trihalomethanes, haloacetic acids, haloketones, haloaldehydes, haloacetonitriles, haloacetamides, and halonitromethanes), while also demonstrating capability in removing bromate and halogenated DBPs, except for trihalomethanes. However, the effectiveness of biofiltration mediated removal of nitrosamine and its precursors appears to be variable. An increase in nitrosamine precursors after biofiltration was ascribed to the biomass sloughing off from media or direct nitrosamine formation in the biofilter under certain denitrifying conditions. Operating parameters, such as pre-ozonation, media type, empty bed contact time, backwashing, temperature, and nutrient addition may be optimized to control the regulated DBPs in the biofilter effluent while minimizing the formation of unregulated emerging DBPs. While summarizing the state of knowledge of biofiltration mediated control of DBPs, this review also identifies several knowledge gaps to highlight future research topics of interest.

Probabilistic models to describe the dynamics of migrating microbial communities.

In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered the conduit and those that have become entrained into the flow due to seeding from biofilms. Examples include fluids through conduits such as drinking water pipe networks, endotracheal tubes, catheters and ventilation systems. Here we present two probabilistic models to describe changes in the composition of bulk fluid microbial communities as they are transported through a conduit whilst exposed to biofilm communities. The first (discrete) model simulates absolute numbers of individual cells, whereas the other (continuous) model simulates the relative abundance of taxa in the bulk fluid. The discrete model is founded on a birth-death process whereby the community changes one individual at a time and the numbers of cells in the system can vary. The continuous model is a stochastic differential equation derived from the discrete model and can also accommodate changes in the carrying capacity of the bulk fluid. These models provide a novel Lagrangian framework to investigate and predict the dynamics of migrating microbial communities. In this paper we compare the two models, discuss their merits, possible applications and present simulation results in the context of drinking water distribution systems. Our results provide novel insight into the effects of stochastic dynamics on the composition of non-stationary microbial communities that are exposed to biofilms and provides a new avenue for modelling microbial dynamics in systems where fluids are being transported.