Phylodynamic Inference of Bacterial Outbreak Parameters Using Nanopore Sequencing

Nanopore sequencing and phylodynamic modeling have been used to reconstruct the transmission dynamics of viral epidemics, but their application to bacterial pathogens has remained challenging. Cost-effective bacterial genome sequencing and variant calling on nanopore platforms would greatly enhance surveillance and outbreak response in communities without access to sequencing infrastructure. Here, we adapt random forest models for single nucleotide polymorphism (SNP) polishing developed by Sanderson and colleagues (2020. High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic nanopore sequencing. Genome Res. 30(9):1354–1363) to estimate divergence and effective reproduction numbers (R_e) of two methicillin-resistant Staphylococcus aureus (MRSA) outbreaks from remote communities in Far North Queensland and Papua New Guinea (PNG; n = 159). Successive barcoded panels of S. aureus isolates (2 × 12 per MinION) sequenced at low coverage (>5× to 10×) provided sufficient data to accurately infer genotypes with high recall when compared with Illumina references. Random forest models achieved high resolution on ST93 outbreak sequence types (>90% accuracy and precision) and enabled phylodynamic inference of epidemiological parameters using birth–death skyline models. Our method reproduced phylogenetic topology, origin of the outbreaks, and indications of epidemic growth (R_e > 1). Nextflow pipelines implement SNP polisher training, evaluation, and outbreak alignments, enabling reconstruction of within-lineage transmission dynamics for infection control of bacterial disease outbreaks on portable nanopore platforms. Our study shows that nanopore technology can be used for bacterial outbreak reconstruction at competitive costs, providing opportunities for infection control in hospitals and communities without access to sequencing infrastructure, such as in remote northern Australia and PNG.     

Influence of a Non-Hospital Medical Care Facility on Antimicrobial Resistance in Wastewater

The global widespread use of antimicrobials and accompanying increase in resistant bacterial strains is of major public health concern. Wastewater systems and wastewater treatment plants are considered a niche for antibiotic resistance genes (ARGs), with diverse microbial communities facilitating ARG transfer via mobile genetic element (MGE). In contrast to hospital sewage, wastewater from other health care facilities is still poorly investigated. At the instance of a nursing home located in south-west Germany, in the present study, shotgun metagenomics was used to investigate the impact on wastewater of samples collected up- and down-stream in different seasons. Microbial composition, ARGs and MGEs were analyzed using different annotation approaches with various databases, including Antibiotic Resistance Ontologies (ARO), integrons and plasmids. Our analysis identified seasonal differences in microbial communities and abundance of ARG and MGE between samples from different seasons. However, no obvious differences were detected between up- and downstream samples. The results suggest that, in contrast to hospitals, sewage from the nursing home does not have a major impact on ARG or MGE in wastewater, presumably due to much less intense antimicrobial usage. Possible limitations of metagenomic studies using high-throughput sequencing for detection of genes that seemingly confer antibiotic resistance are discussed.     

Mining the metagenome of activated biomass of an industrial wastewater treatment plant by a novel method

Metagenomic libraries herald the era of magnifying the microbial world, tapping into the vast metabolic potential of uncultivated microbes, and enhancing the rate of discovery of novel genes and pathways. In this paper, we describe a method that facilitates the extraction of metagenomic DNA from activated sludge of an industrial wastewater treatment plant and its use in mining the metagenome via library construction. The efficiency of this method was demonstrated by the large representation of the bacterial genome in the constructed metagenomic libraries and by the functional clones obtained. The BAC library represented 95.6 times the bacterial genome, while, the pUC library represented 41.7 times the bacterial genome. Twelve clones in the BAC library demonstrated lipolytic activity, while four clones demonstrated dioxygenase activity. Four clones in pUC library tested positive for cellulase activity. This method, using FTA cards, not only can be used for library construction, but can also store the metagenome at room temperature.     

Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities

Next‐generation sequencing has dramatically changed the landscape of microbial ecology, large‐scale and in‐depth diversity studies being now widely accessible. However, determining the accuracy of taxonomic and quantitative inferences and comparing results obtained with different approaches are complicated by incongruence of experimental and computational data types and also by lack of knowledge of the true ecological diversity. Here we used highly diverse bacterial and archaeal synthetic communities assembled from pure genomic DNAs to compare inferences from metagenomic and SSU rRNA amplicon sequencing. Both Illumina and 454 metagenomic data outperformed amplicon sequencing in quantifying the community composition, but the outcome was dependent on analysis parameters and platform. New approaches in processing and classifying amplicons can reconstruct the taxonomic composition of the community with high reproducibility within primer sets, but all tested primers sets lead to significant taxon‐specific biases. Controlled synthetic communities assembled to broadly mimic the phylogenetic richness in target environments can provide important validation for fine‐tuning experimental and computational parameters used to characterize natural communities.     

Amplicon-based profiling of bacteria in raw and secondary treated wastewater from treatment plants across Australia

In this study, we investigated the use of Illumina high-throughput sequencing of 16S ribosomal RNA (rRNA) amplicons to explore microbial diversity and community structure in raw and secondary treated wastewater (WW) samples from four municipal wastewater treatment plants (WWTPs A–D) across Australia. Sequence reads were analyzed to determine the abundance and diversity of bacterial communities in raw and secondary treated WW samples across the four WWTPs. In addition, sequence reads were also characterized to phenotypic features and to estimate the abundance of potential pathogenic bacterial genera and antibiotic-resistant genes in total bacterial communities. The mean coverage, Shannon diversity index, observed richness (S _obs), and abundance-based coverage estimate (ACE) of richness for raw and secondary treated WW samples did not differ significantly (P > 0.05) among the four WWTPs examined. Generally, raw and secondary treated WW samples were dominated by members of the genera Pseudomonas, Arcobacter, and Bacteroides. Evaluation of source contributions to secondary treated WW, done using SourceTracker, revealed that 8.80–61.4% of the bacterial communities in secondary treated WW samples were attributed to raw WW. Twenty-five bacterial genera were classified as containing potential bacterial pathogens. The abundance of potentially pathogenic genera in raw WW samples was higher than that found in secondary treated WW samples. Among the pathogenic genera identified, Pseudomonas and Arcobacter had the greatest percentage of the sequence reads. The abundances of antibiotic resistance genes were generally low (<0.5%), except for genes encoding ABC transporters, which accounted for approximately 3% of inferred genes. These findings provided a comprehensive profile of bacterial communities, including potential bacterial pathogens and antibiotic-resistant genes, in raw and secondary treated WW samples from four WWTPs across Australia and demonstrated that Illumina high-throughput sequencing can be an alternative approach for monitoring WW quality in order to protect environmental and human health.

     

Novel bacterial lineages associated with boreal moss species

Mosses are critical components of boreal ecosystems where they typically account for a large proportion of net primary productivity and harbour diverse bacterial communities that can be the major source of biologically‐fixed nitrogen in these ecosystems. Despite their ecological importance, we have limited understanding of how microbial communities vary across boreal moss species and the extent to which local site conditions may influence the composition of these bacterial communities. We used marker gene sequencing to analyze bacterial communities associated with seven boreal moss species collected near Fairbanks, AK, USA. We found that host identity was more important than site in determining bacterial community composition and that mosses harbour diverse lineages of potential N₂‐fixers as well as an abundance of novel taxa assigned to understudied bacterial phyla (including candidate phylum WPS‐2). We performed shotgun metagenomic sequencing to assemble genomes from the WPS‐2 candidate phylum and found that these moss‐associated bacteria are likely anoxygenic phototrophs capable of carbon fixation via RuBisCo with an ability to utilize byproducts of photorespiration from hosts via a glyoxylate shunt. These results give new insights into the metabolic capabilities of understudied bacterial lineages that associate with mosses and the importance of plant hosts in shaping their microbiomes.     

Virulence-associated and antibiotic resistance genes of microbial populations in cattle feces analyzed using a metagenomic approach

The bovine fecal microbiota impacts human food safety as well as animal health. Although the bacteria of cattle feces have been well characterized using culture-based and culture-independent methods, techniques have been lacking to correlate total community composition with community function. We used high throughput sequencing of total DNA extracted from fecal material to characterize general community composition and examine the repertoire of microbial genes present in beef cattle feces, including genes associated with antibiotic resistance and bacterial virulence. Results suggest that traditional 16S sequencing using “universal” primers to generate full-length sequence may under represent Acitinobacteria and Proteobacteria. Over eight percent (8.4%) of the sequences from our beef cattle fecal pool sample could be categorized as virulence genes, including a suite of genes associated with resistance to antibiotic and toxic compounds (RATC). This is a higher proportion of virulence genes found in Sargasso sea, chicken cecum, and cow rumen samples, but comparable to the proportion found in Antarctic marine derived lake, human fecal, and farm soil samples. The quantitative nature of metagenomic data, combined with the large number of RATC classes represented in samples from widely different habitats indicates that metagenomic data can be used to track relative amounts of antibiotic resistance genes in individual animals over time. Consequently, these data can be used to generate sample-specific and temporal antibiotic resistance gene profiles to facilitate an understanding of the ecology of the microbial communities in each habitat as well as the epidemiology of antibiotic resistant gene transport between and among habitats.     

Tales from the tomb: the microbial ecology of exposed rock surfaces

Although a broad diversity of eukaryotic and bacterial taxa reside on rock surfaces where they can influence the weathering of rocks and minerals, these communities and their contributions to mineral weathering remain poorly resolved. To build a more comprehensive understanding of the diversity, ecology and potential functional attributes of microbial communities living on rock, we sampled 149 tombstones across three continents and analysed their bacterial and eukaryotic communities via marker gene and shotgun metagenomic sequencing. We found that geographic location and climate were important factors structuring the composition of these communities. Moreover, the tombstone‐associated microbial communities varied as a function of rock type, with granite and limestone tombstones from the same cemeteries harbouring taxonomically distinct microbial communities. The granite and limestone‐associated communities also had distinct functional attributes, with granite‐associated bacteria having more genes linked to acid tolerance and chemotaxis, while bacteria on limestone were more likely to be lichen associated and have genes involved in photosynthesis and radiation resistance. Together these results indicate that rock‐dwelling microbes exhibit adaptations to survive the stresses of the rock surface, differ based on location, climate and rock type, and seem pre‐disposed to different ecological strategies (symbiotic versus free‐living lifestyles) depending on the rock type.     

DNA sequencing,genomes and genetic markers of microbes on fruits and vegetables

The development of DNA sequencing technology has provided an effective method for studying foodborne and phytopathogenic microorganisms on fruits and vegetables (F & V). DNA sequencing has successfully proceeded through three generations, including the tens of operating platforms. These advances have significantly promoted microbial whole-genome sequencing (WGS) and DNA polymorphism research. Based on genomic and regional polymorphisms, genetic markers have been widely obtained. These molecular markers are used as targets for PCR or chip analyses to detect microbes at the genetic level. Furthermore, metagenomic analyses conducted by sequencing the hypervariable regions of ribosomal DNA (rDNA) have revealed comprehensive microbial communities in various studies on F & V. This review highlights the basic principles of three generations of DNA sequencing, and summarizes the WGS studies of and available DNA markers for major bacterial foodborne pathogens and phytopathogenic fungi found on F & V. In addition, rDNA sequencing-based bacterial and fungal metagenomics are summarized under three topics. These findings deepen the understanding of DNA sequencing and its application in studies of foodborne and phytopathogenic microbes and shed light on strategies for the monitoring of F & V microbes and quality control.     

Comparative metagenomic,phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients

Terrestrial ecosystems are receiving elevated inputs of nitrogen (N) from anthropogenic sources and understanding how these increases in N availability affect soil microbial communities is critical for predicting the associated effects on belowground ecosystems. We used a suite of approaches to analyze the structure and functional characteristics of soil microbial communities from replicated plots in two long-term N fertilization experiments located in contrasting systems. Pyrosequencing-based analyses of 16S rRNA genes revealed no significant effects of N fertilization on bacterial diversity, but significant effects on community composition at both sites; copiotrophic taxa (including members of the Proteobacteria and Bacteroidetes phyla) typically increased in relative abundance in the high N plots, with oligotrophic taxa (mainly Acidobacteria) exhibiting the opposite pattern. Consistent with the phylogenetic shifts under N fertilization, shotgun metagenomic sequencing revealed increases in the relative abundances of genes associated with DNA/RNA replication, electron transport and protein metabolism, increases that could be resolved even with the shallow shotgun metagenomic sequencing conducted here (average of 75 000 reads per sample). We also observed shifts in the catabolic capabilities of the communities across the N gradients that were significantly correlated with the phylogenetic and metagenomic responses, indicating possible linkages between the structure and functioning of soil microbial communities. Overall, our results suggest that N fertilization may, directly or indirectly, induce a shift in the predominant microbial life-history strategies, favoring a more active, copiotrophic microbial community, a pattern that parallels the often observed replacement of K-selected with r-selected plant species with elevated N.     

MetaGeniE: Characterizing Human Clinical Samples Using Deep Metagenomic Sequencing

With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies “all against all” relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.     

Wastewater pollution differently affects the antibiotic resistance gene pool and biofilm bacterial communities across streambed compartments

Wastewater discharges introduce antibiotic residues and antibiotic‐resistant bacteria (ARB) into surface waters. Both inputs directly affect the streambed resistome, either by exerting a selective pressure that favour the proliferation of resistant phenotypes or by enriching the resident communities with wastewater‐associated ARB. Here, we investigated the impact of raw and treated urban wastewater discharges on epilithic (growing on rocks) and epipsammic (growing on sandy substrata) streambed biofilms. The effects were assessed by comparing control and impact sites (i) on the composition of bacterial communities; (ii) on the abundance of twelve antibiotic resistance genes (ARGs) encoding resistance to β‐lactams, fluoroquinolones, sulphonamides, tetracyclines, macrolides and vancomycin, as well as the class 1 integron‐integrase gene (intI1); (iii) on the occurrence of wastewater‐associated bacteria, including putative pathogens, and their potential linkage to target ARGs. We measured more pronounced effects of raw sewage than treated wastewater at the three studied levels. This effect was especially noticeable in epilithic biofilms, which showed a higher contribution of wastewater‐associated bacteria and ARB than in epipsammic biofilms. Comparison of correlation coefficients obtained between the relative abundance of both target ARGs and operational taxonomic units classified as either potential pathogens or nonpathogens yielded significant higher correlations between the former category and genes intI1, sul1, sul2 and ermB. Altogether, these results indicate that wastewater‐associated micro‐organisms, including potential pathogens, contribute to maintain the streambed resistome and that epilithic biofilms appear as sensitive biosensors of the effect of wastewater pollution in surface waters.     

Quantitative metagenomic analyses based on average genome size normalization

Over the past quarter-century, microbiologists have used DNA sequence information to aid in the characterization of microbial communities. During the last decade, this has expanded from single genes to microbial community genomics, or metagenomics, in which the gene content of an environment can provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data by estimating average genome sizes. This normalization can relieve comparative biases introduced by differences in community structure, number of sequencing reads, and sequencing read lengths between different metagenomes. We demonstrate the utility of this approach by comparing metagenomes from two different marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how differences in environment affect their abundances, we characterize three different types of autotrophic organisms: aerobic, photosynthetic carbon fixers (the Cyanobacteria); anaerobic, photosynthetic carbon fixers (the Chlorobi); and anaerobic, nonphotosynthetic carbon fixers (the Desulfobacteraceae). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis.     

Probabilistic Inference of Biochemical Reactions in Microbial Communities from Metagenomic Sequences

Shotgun metagenomics has been applied to the studies of the functionality of various microbial communities. As a critical analysis step in these studies, biological pathways are reconstructed based on the genes predicted from metagenomic shotgun sequences. Pathway reconstruction provides insights into the functionality of a microbial community and can be used for comparing multiple microbial communities. The utilization of pathway reconstruction, however, can be jeopardized because of imperfect functional annotation of genes, and ambiguity in the assignment of predicted enzymes to biochemical reactions (e.g., some enzymes are involved in multiple biochemical reactions). Considering that metabolic functions in a microbial community are carried out by many enzymes in a collaborative manner, we present a probabilistic sampling approach to profiling functional content in a metagenomic dataset, by sampling functions of catalytically promiscuous enzymes within the context of the entire metabolic network defined by the annotated metagenome. We test our approach on metagenomic datasets from environmental and human-associated microbial communities. The results show that our approach provides a more accurate representation of the metabolic activities encoded in a metagenome, and thus improves the comparative analysis of multiple microbial communities. In addition, our approach reports likelihood scores of putative reactions, which can be used to identify important reactions and metabolic pathways that reflect the environmental adaptation of the microbial communities. Source code for sampling metabolic networks is available online at http://omics.informatics.indiana.edu/mg/MetaNetSam/.     

Abundance and Diversity of Bacterial Nitrifiers and Denitrifiers and Their Functional Genes in Tannery Wastewater Treatment Plants Revealed by High-Throughput Sequencing

Biological nitrification/denitrification is frequently used to remove nitrogen from tannery wastewater containing high concentrations of ammonia. However, information is limited about the bacterial nitrifiers and denitrifiers and their functional genes in tannery wastewater treatment plants (WWTPs) due to the low-throughput of the previously used methods. In this study, 454 pyrosequencing and Illumina high-throughput sequencing, combined with molecular methods, were used to comprehensively characterize structures and functions of nitrification and denitrification bacterial communities in aerobic and anaerobic sludge of two full-scale tannery WWTPs. Pyrosequencing of 16S rRNA genes showed that Proteobacteria and Synergistetes dominated in the aerobic and anaerobic sludge, respectively. Ammonia-oxidizing bacteria (AOB) amoA gene cloning revealed that Nitrosomonas europaea dominated the ammonia-oxidizing community in the WWTPs. Metagenomic analysis showed that the denitrifiers mainly included the genera of Thauera, Paracoccus, Hyphomicrobium, Comamonas and Azoarcus, which may greatly contribute to the nitrogen removal in the two WWTPs. It is interesting that AOB and ammonia-oxidizing archaea had low abundance although both WWTPs demonstrated high ammonium removal efficiency. Good correlation between the qPCR and metagenomic analysis is observed for the quantification of functional genes amoA, nirK, nirS and nosZ, indicating that the metagenomic approach may be a promising method used to comprehensively investigate the abundance of functional genes of nitrifiers and denitrifiers in the environment.     

宏基因组重叠群分箱方法研究综述

宏基因组学技术可以直接从环境中提取微生物的全部遗传物质,而不需要像传统方法一样在培养基上纯培养。这种技术的出现为科学家对微生物群落的结构和功能的认识提供了重要的方法,同时对疾病的诊治、环境的治理以及生命的认识具有重大的意义。从环境中提取出微生物全部遗传物质,对其进行测序从而得到它们的reads片段,通过reads组装工具可以进一步组装成重叠群片段。对重叠群片段进行分箱,可以从宏基因组样本中重建出更多完整的基因。分箱效果的好坏直接影响到后续的生物分析,因此如何将这些含有不同微生物基因混合的重叠群序列进行有效的分箱成为了宏基因组学研究的热点和难点。机器学习方法被广泛应用于宏基因组重叠群分箱,通常分为有监督重叠群分类方法和无监督重叠群聚类方法。该综述针对宏基因组重叠群分箱方法进行了较为全面的阐述,深入剖析了重叠群分类方法与聚类方法,发现其存在分类准确率较低、分箱时间较长、难以从复杂数据集中重建更多微生物基因等问题,并对未来重叠群分箱方法的研究和发展进行了展望。作者建议可以使用半监督学习、集成学习以及深度学习方法,并采用更有效的数据特征表示等途径来提高分箱效果。     

Microbial Diversity in Cerrado Biome (Neotropical Savanna) Soils

The Cerrado, the largest savanna region in South America, is located in central Brazil. Cerrado physiognomies, which range from savanna grasslands to forest formations, combined with the highly weathered, acidic clay Cerrado soils form a unique ecoregion. In this study, high-throughput sequencing of ribosomal RNA genes was combined with shotgun metagenomic analysis to explore the taxonomic composition and potential functions of soil microbial communities in four different vegetation physiognomies during both dry and rainy seasons. Our results showed that changes in bacterial, archaeal, and fungal community structures in cerrado denso, cerrado sensu stricto, campo sujo, and gallery forest soils strongly correlated with seasonal patterns of soil water uptake. The relative abundance of AD3, WPS-2, Planctomycetes, Thermoprotei, and Glomeromycota typically decreased in the rainy season, whereas the relative abundance of Proteobacteria and Ascomycota increased. In addition, analysis of shotgun metagenomic data revealed a significant increase in the relative abundance of genes associated with iron acquisition and metabolism, dormancy, and sporulation during the dry season, and an increase in the relative abundance of genes related to respiration and DNA and protein metabolism during the rainy season. These gene functional categories are associated with adaptation to water stress. Our results further the understanding of how tropical savanna soil microbial communities may be influenced by vegetation covering and temporal variations in soil moisture.     

Bacterial Pathogens and Community Composition in Advanced Sewage Treatment Systems Revealed by Metagenomics Analysis Based on High-Throughput Sequencing

This study used 454 pyrosequencing, Illumina high-throughput sequencing and metagenomic analysis to investigate bacterial pathogens and their potential virulence in a sewage treatment plant (STP) applying both conventional and advanced treatment processes. Pyrosequencing and Illumina sequencing consistently demonstrated that Arcobacter genus occupied over 43.42% of total abundance of potential pathogens in the STP. At species level, potential pathogens Arcobacter butzleri, Aeromonas hydrophila and Klebsiella pneumonia dominated in raw sewage, which was also confirmed by quantitative real time PCR. Illumina sequencing also revealed prevalence of various types of pathogenicity islands and virulence proteins in the STP. Most of the potential pathogens and virulence factors were eliminated in the STP, and the removal efficiency mainly depended on oxidation ditch. Compared with sand filtration, magnetic resin seemed to have higher removals in most of the potential pathogens and virulence factors. However, presence of the residual A. butzleri in the final effluent still deserves more concerns. The findings indicate that sewage acts as an important source of environmental pathogens, but STPs can effectively control their spread in the environment. Joint use of the high-throughput sequencing technologies is considered a reliable method for deep and comprehensive overview of environmental bacterial virulence.     

Metagenomic Analysis of Respiratory Tract DNA Viral Communities in Cystic Fibrosis and Non-Cystic Fibrosis Individuals

The human respiratory tract is constantly exposed to a wide variety of viruses, microbes and inorganic particulates from environmental air, water and food. Physical characteristics of inhaled particles and airway mucosal immunity determine which viruses and microbes will persist in the airways. Here we present the first metagenomic study of DNA viral communities in the airways of diseased and non-diseased individuals. We obtained sequences from sputum DNA viral communities in 5 individuals with cystic fibrosis (CF) and 5 individuals without the disease. Overall, diversity of viruses in the airways was low, with an average richness of 175 distinct viral genotypes. The majority of viral diversity was uncharacterized. CF phage communities were highly similar to each other, whereas Non-CF individuals had more distinct phage communities, which may reflect organisms in inhaled air. CF eukaryotic viral communities were dominated by a few viruses, including human herpesviruses and retroviruses. Functional metagenomics showed that all Non-CF viromes were similar, and that CF viromes were enriched in aromatic amino acid metabolism. The CF metagenomes occupied two different metabolic states, probably reflecting different disease states. There was one outlying CF virome which was characterized by an over-representation of Guanosine-5′-triphosphate,3′-diphosphate pyrophosphatase, an enzyme involved in the bacterial stringent response. Unique environments like the CF airway can drive functional adaptations, leading to shifts in metabolic profiles. These results have important clinical implications for CF, indicating that therapeutic measures may be more effective if used to change the respiratory environment, as opposed to shifting the taxonomic composition of resident microbiota.