Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Mechanistic Model of Rothia mucilaginosa Adaptation toward Persistence in the CF Lung, Based on a Genome Reconstructed from Metagenomic Data

  • Yan Wei Lim ,

    Affiliation Department of Biology, San Diego State University, San Diego, California, United States of America

  • Robert Schmieder,

    Affiliation Computational Science Research Center, San Diego State University, San Diego, California, United States of America

  • Matthew Haynes,

    Current address: DOE Joint Genome Institute, Walnut Creek, California, United States of America

    Affiliation Department of Biology, San Diego State University, San Diego, California, United States of America

  • Mike Furlan,

    Affiliation Department of Biology, San Diego State University, San Diego, California, United States of America

  • T. David Matthews,

    Affiliation Department of Biology, San Diego State University, San Diego, California, United States of America

  • Katrine Whiteson,

    Affiliation Department of Biology, San Diego State University, San Diego, California, United States of America

  • Stephen J. Poole,

    Affiliation Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, California, United States of America

  • Christopher S. Hayes,

    Affiliations Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, California, United States of America, Biomolecular Science and Engineering Program, University of California Santa Barbara, Santa Barbara, California, United States of America

  • David A. Low,

    Affiliations Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, California, United States of America, Biomolecular Science and Engineering Program, University of California Santa Barbara, Santa Barbara, California, United States of America

  • Heather Maughan,

    Affiliation Ronin Institute, Montclair, New Jersey, United States of America

  • Robert Edwards,

    Affiliations Computational Science Research Center, San Diego State University, San Diego, California, United States of America, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, United States of America

  • Douglas Conrad,

    Affiliation Department of Medicine, University of California San Diego, La Jolla, California, United States of America

  • Forest Rohwer

    Affiliation Department of Biology, San Diego State University, San Diego, California, United States of America

Mechanistic Model of Rothia mucilaginosa Adaptation toward Persistence in the CF Lung, Based on a Genome Reconstructed from Metagenomic Data

  • Yan Wei Lim, 
  • Robert Schmieder, 
  • Matthew Haynes, 
  • Mike Furlan, 
  • T. David Matthews, 
  • Katrine Whiteson, 
  • Stephen J. Poole, 
  • Christopher S. Hayes, 
  • David A. Low, 
  • Heather Maughan


The impaired mucociliary clearance in individuals with Cystic Fibrosis (CF) enables opportunistic pathogens to colonize CF lungs. Here we show that Rothia mucilaginosa is a common CF opportunist that was present in 83% of our patient cohort, almost as prevalent as Pseudomonas aeruginosa (89%). Sequencing of lung microbial metagenomes identified unique R. mucilaginosa strains in each patient, presumably due to evolution within the lung. The de novo assembly of a near-complete R. mucilaginosa (CF1E) genome illuminated a number of potential physiological adaptations to the CF lung, including antibiotic resistance, utilization of extracellular lactate, and modification of the type I restriction-modification system. Metabolic characteristics predicted from the metagenomes suggested R. mucilaginosa have adapted to live within the microaerophilic surface of the mucus layer in CF lungs. The results also highlight the remarkable evolutionary and ecological similarities of many CF pathogens; further examination of these similarities has the potential to guide patient care and treatment.


Cystic fibrosis (CF) is a genetic disease caused by mutation of the cystic fibrosis transmembrane conductance regulator (CFTR) gene [1]. In CF lungs, the defective CFTR protein affects trans-epithelial ion transport and consequently leads to the accumulation of thick and static mucus. The resultant hypoxic microenvironment encourages the colonization of opportunistic microbes, viruses, and fungi (reviewed in [2]), causing acute and chronic infection. A few of the most commonly isolated pathogens are Pseudomonas aeruginosa, Staphylococcus aureus, Haemophilus influenzae, and Burkholderia cepacia. However, an increasing number of microbial species have been detected in the CF airway using culture-independent methods such as metagenomic sequencing [3][7]. Metagenomics is a powerful approach that has been used to successfully characterize the microbial and viral communities in CF individuals [8][12]. These types of studies have illuminated the complexity of microbial and viral communities, captured the vast diversity of functions encoded by these organisms, and have been used to trace the evolution of whole genomes [13], [14].

Previous sequencing of CF metagenomes revealed the presence of Rothia mucilaginosa at relatively high abundances in most patients [12]. R. mucilaginosa was first isolated from milk in 1900 as Micrococcus mucilaginosus [15]. It was later re-isolated and further studied by Bergan et al. in 1970 [16], and renamed Stomatococcus mucilaginosus in 1982 based on its 16S rDNA and biochemical characteristics [17]. A recent study comparing S. mucilaginosus to Rothia dentocariosa and another unknown species (later known as Rothia nasimurium) led to the reclassification of S. mucilaginosus as R. mucilaginosa [18].

R. mucilaginosa is an encapsulated, Gram-positive non-motile coccus (arranged in clusters) belonging to the phylum Actinobacteria. It has variable catalase activity, reduces nitrate, and hydrolyses aesculin [17][19]. It is a facultative anaerobe commonly found in the human oral cavity and upper respiratory tract [16], [20], [21], and occasionally the gastrointestinal tract [22], small intestinal epithelial lining [23], tongue [24], [25], teeth [26], [27], colostrum [28], breast milk [29], and dental plaques [30], [31]. Although R. mucilaginosa is commonly regarded as normal flora of the oral cavity and upper respiratory tract, its association with a wide range of diseases (Table S1) highlights its potential as an opportunistic pathogen, especially in immuno-compromised patients [32].

At the genus level, Rothia has been reported by Tunney et al. [33] as an aerobic species that can be isolated from CF sputum and pediatric bronchoalveolar lavage (BAL) samples. It has also been detected under anaerobic culturing conditions and via 16S rRNA gene surveys [33]. Typically R. dentocariosa was the main species identified in these studies [34]. In addition, Bittar et al. [5] and Guss et al. [7] have characterized R. mucilaginosa as a “newly” emerging CF pathogen. Even so, R. mucilaginosa is usually treated as part of the normal oral microbiota in the clinical lab. As a result, the presence of R. mucilaginosa in CF lungs may be under-reported and the significance of infection is underestimated.

Here we confirm that R. mucilaginosa is present and metabolically active in the lungs of CF patients. Comparisons with a non-CF reference genome revealed the presence of unique R. mucilaginosa strains in each patient. A near-complete genome was reconstructed from the metagenomic reads of one patient; comparison of these sequence data with a non-CF reference genome enabled the identification of unique genomic features that may have facilitated adaptation to the lung environment.

Results and Discussion

Mutations in the CFTR locus affect proper ion transport in lung epithelial cells, impairing the clearance of mucus in the airways and encouraging microbial colonization and persistence. Until recently, most laboratory and clinical microbiology only focused on a few pathogens, particularly P. aeruginosa. This “one mutation, one pathogen” model of the CF ecosystem is being replaced by a “polyphysiology, polymicrobial” view that is expected to improve treatment until gene-therapy is able to fix the underlying genetic cause.

An important step in understanding the CF lung ecosystem, with the ultimate goal of eliminating microbes or altering their pathogenicity, is to determine which microbes are present and the ways in which their survival depends on local chemistry. An initial step in this direction is to use metagenomic and metatranscriptomic data previously generated from microbes and viruses present in CF sputum samples [12]. Metagenomic data provide information on which microbes and viruses are present, and their metabolic capabilities, while metatranscriptomic data provide information on which organisms are metabolically active [12]. Such metabolism data will enable predictions of local lung chemistry that may impose patient-specific selective pressures on the microbes. Data from eighteen microbiomes existing in six CF patients with different health statuses were analyzed here (Supporting Information S1; Table S2).

The metagenomic data showed that 15/18 (83%) sputa contained R. mucilaginosa and 16/18 (89%) sputa contained the common CF pathogen P. aeruginosa. Although P. aeruginosa was present in a greater number of samples, its abundance was lower than that of R. mucilaginosa in 11 of the 14 samples where these species co-existed. Both species abundances ranged from 1% to 62% (Figure 1). The relative percentages of these two opportunistic pathogens varied between patients and within the same patient as their health status changed. The results show no obvious pattern of synergy or competition between the two pathogens.

Figure 1. Prevalence of Rothia mucilaginosa, and the prototypical pathogen Pseudomonas aeruginosa, in eighteen microbiomes from six CF patients.

Patients were sampled at times of differing health status (Table S1; [12]). Ex: Exacerbation; Tr: On treatment; Pt: Post treatment; St: Stable; *:present in <1% of the microbiome.

A health status of ‘Ex’ (for exacerbation) indicates a stark decline in lung function that is typically treated with intravenous antibiotics. Thus, between a health status of ‘Ex’ and ‘Tr’, patients will have been given antibiotics in addition to those that are often prescribed as continued therapy. The abundance data in Figure 1 indicate that these exacerbation-associated antibiotic treatments did little to permanently exclude R. mucilaginosa from CF lung communities. Patients CF1, CF4, CF6, CF7, and CF8 all had appreciable abundances of R. mucilaginosa by the last sampling time point. Only the R. mucilaginosa population in CF5 did not recover from antibiotic treatment by the last sampling time point; however, as this patient was only followed for 21 days (compared to 17–58 for the other patients), it is possible R. mucilaginosa could still rebound from antibiotic treatment. These results indicate that R. mucilaginosa is able to survive the typical CF antibiotic treatment, as is the main CF pathogen P. aeruginosa.

R. mucilaginosa is Present in CF Lung Explants and Metabolically Active

The presence of R. mucilaginosa DNA in sputum samples (as detected by metagenome sequencing) could be explained by its abundance in the oral cavity and subsequent contamination of the sputum during collection. However, this is unlikely for several reasons. Previous studies have indicated little contamination of sampled sputa with oral inhabitants [35], [36], and the presence of Rothia has been confirmed in CF lungs [37]. Examination of lung tissue samples was the best way to definitively determine the presence of R. mucilaginosa in CF lungs from our cohort. Between 5 and 6 lung tissue sections from explanted lungs of each of four transplant patients were screened for Rothia-related microbes using 16S rDNA targeted PCR and sequencing. One out of the four patients was positive for Rothia (Supporting Information S3), indicating this bacterium is indeed present within lung airways. Unfortunately this patient was not available for the metagenome sequencing. The R. mucilaginosa population present in the oral cavity may serve as a reservoir and “stepping stone” for lower respiratory infection, as described in many respiratory chronic infections such as CF and chronic obstructive pulmonary disease [38].

The presence of R. mucilaginosa DNA in sputum or lung tissues does not necessarily indicate this bacterium is metabolically active in the lung environment. Examination of a metatranscriptome dataset indicated that mRNAs and rRNAs are being produced by Rothia species (Supporting Information S2), which suggests this bacterium is metabolically active in the CF lung.

Genetic Differences of R. mucilaginosa between Patients

Longitudinal studies of P. aeruginosa [39], Burkholderia dolosa [40], and Staphylococcus aureus [41] within and between CF patients have shown evolutionary adaption to the CF lung. Here, we define adaptation as a process where mutations that alter pathogen behavior (in this case, metabolism) become fixed in response to specific environmental pressures, e.g. the availability of nutrients, oxygen, or redox potential. The power of the metagenomic data is in its ability to uncover the genetic mutations underlying these adaptations, that occur over long periods of selection. Characterizing these mutations thus enables us to infer which selection pressures are strongest in the CF lung, whether they be the dynamic lung physiology, immune system surveillance, and/or antibiotic treatment.

We found evidence for similar evolutionary adaptation in R. mucilaginosa. The metagenomic sequences from each sample were mapped separately against the reference genome R. mucilaginosa DY-18, (GI: 283457089; originally isolated from persistent apical periodontitis lesions [42]. As shown in Figure 2, the mapped sequences reveal gaps where portions of the reference genome sequence were not covered by metagenomic reads (i.e., were absent) in the CF-derived datasets (gap patterns >5 kbp shown in Figure 2; Table S3). The out-group in Figure 2 is due to low coverage of R. mucilaginosa reads in the metagenomes of these patient samples (<1X coverage; Table S4). Most of the gaps occurred in regions of low GC content (Figure 2), which most likely represent genes acquired by DY-18 via horizontal gene transfer [43].

Figure 2. Hierarchical clustering of the sample based on gap patterns, which correspond to regions of the reference genome R. mucilaginosa DY-18 that were not represented by any metagenomic reads.

BWA mapping was used and gaps were identified in a 1 kbp stepwise window. Only gaps ≥5 kbp were plotted. Exact coordinates and annotations for the gaps are in Table S3. The clade composed of 7-D, 6-D, 6-A, and 7-C appears to be lacking the majority of the reference genome, due to the low sequence coverage of R. mucilaginosa in these metagenomes.

The gap patterns were most different between patients, indicating unique R. mucilaginosa strains exist in each patient. Within each patient, differences in gap patterns between time points were less numerous, but their existence indicates that the genome of R. mucilaginosa has been evolving independently in each patient. Combined with similar findings for P. aeruginosa [39] and S. aureus [41], this suggests that essentially every CF patient harbors a unique strain of R. mucilaginosa that evolves in the lung. If each strain also has a unique antibiotic resistance profile, then CF treatment will need to be tailored to the particular strain present in each patient.

Characteristics of CF1E Genome Scaffold

The metagenome from CF1E had over 40,000 reads mapping to the reference genome, indicating enough data may be present to reconstruct the full genome of the R. mucilaginosa strain present. All CF1E metagenomic reads were assembled de novo into 996 contigs with a N50 value (weighted median value of all contigs) of 11,178 bp. Contigs were aligned against the reference genome R. mucilaginosa DY-18 using nucmer [44], resulting in one single scaffold built from 181 contigs with an 8.8-fold average sequencing depth (Figure 3). The CF1E R. mucilaginosa genome scaffold was then annotated using the RAST server (Genome ID: 43675.9) and compared to DY-18 that had been re-annotated using the same pipeline.

Figure 3. Circular representation of R. mucilaginosa CF1E draft genome.

Genome coordinates are given in Mbp. From outside to inside, the circles represent: (i) Fragments missing in the DY-18 reference (red); CRISPR region and phage-associated genes as 1) phage lysin, 2) phage shock protein, and 3) CRISPR elements, (ii) Coverage of the genome up to the scale of 50×; *marked Region V containing a genome fragment with an average coverage of 38×(peaks at 48×) at the region of rearrangement hotspot (rhs) elements (iii) Gaps in the scaffold (red); rRNA operons (blue) (iv) Contig order and size; (v) GC skew; (vi) GC content deviation.

The CF1E genome scaffold consists of one circular chromosome of 2,278,618 bp with a GC content of 59.6%. Only large indels are reported here and SNPs were not examined. No large rearrangements were detected between CF1E and the reference genome DY-18. Phylogenetic analysis of the 16S rDNA loci indicated CF1E and the reference strain DY-18 are close relatives (Supporting Information S3), which is consistent with their average pairwise nucleotide identity of 85%. The sequence reads were relatively equally distributed across the genome, except at the multi-copy rRNA genes and in the highly conserved rhs region (Figure 3).

The High Coverage Regions – rRNA Operons and rhs Elements

The rRNA operons assembled into one single contig (contig173) and had an average coverage depth of approximately 3.5 times the average depth of coverage for the rest of the scaffold (i.e. 31X versus 8.8X). The sequence from this contig was used to fill in three gaps that were predicted to correspond to the rRNA operons, based on alignment with the reference genome (Figure 3).

The rearrangement hot spot (rhs) gene region (Figure 3: Region V marked *) also had a high average coverage of 39X. The primary structure of Rhs proteins consists of an N-terminal domain, a “core” domain, a hyperconserved domain, and a DPxGL motif followed by a C-terminus that varies between strains and species [45]. Previous studies have shown that rhs genes play a role in competition between strains or species, similar to the contact-dependent growth inhibition (CDI) system [46]. The variable C-termini of Rhs proteins have toxin activities, and the small genes that typically follow rhs genes are thought to encode proteins that provide immunity to the toxins. Kung et al. (2012) showed that the rhs-CT in P. aeruginosa delivers toxins to eukaryotic cells, activating the inflammasome [47]. The high coverage of the conserved rhs region suggests that rhs is present in high abundance in the CF microbial community. It is possible that the rhs system is widely used by CF microbes for (i) cell-to-cell interactions and communication, particularly for biofilm formation, (ii) direct antagonistic effects on the growth or viability of competitors, and/or (iii) attacking cells of the host-immune system. Additional experimental studies are needed to further assess these possibilities.

The high coverage rhs region in the CF1E genome scaffold included an rhs gene sequence related to one of the two rhs genes of DY-18 (RMDY18_19250). However, there is an apparent gap in the scaffold sequence, beginning 24 amino acids upstream of the DPxGL motif of the encoded Rhs protein. In the DY-18 reference genome, this gap corresponds to the coding sequence for the toxic C-terminal region of Rhs, and the beginning of the gene encoding the RhsI immunity protein. Assuming the presence of multiple rhs-CT/rhsI modules in the metagenome, assembling this region will be challenging.

Functional Annotation of the R. mucilaginosa Genome Scaffold

RAST predicted 1,739 gene products belonging to 248 function subsystems (Table S5). The most abundant functions included biosynthesis and degradation of amino acids and derivatives, protein metabolism, cofactor/vitamin/prosthetic group/pigment biosynthesis and metabolism, and carbohydrate metabolism (Table S6). Thirty-seven ORFs present in the DY-18 genome and absent in the CF1E scaffold are listed in Table 1 (DY-18 specific). Genes only present in CF1E are listed in Table 2 (CF1E specific). Genome regions specific to only one of the two strains ranged from multiple kbp (mostly in gene coding regions) to a few nucleotides in non-coding regions. Additional analyses were performed on several of the genomic regions unique to CF1E; regions were chosen for their potential influences on CF-lung specific evolution of niche utilization and antibiotic resistance.

Table 1. Genomic regions present in the DY-18 reference genome but missing from the CF1E draft genome.

Table 2. Predicted protein-coding sequences present in the CF1E scaffold annotated by RAST, but missing from the DY-18 reference (list excludes hypothetical proteins).

  1. L-lactate dehydrogenases (LDHs): The CF1E scaffold had a cytochrome c-dependent LDH (EC in addition to the expected NAD (P)-dependent LDH (EC The nucleotide sequence of LDH (EC was 80% identical to the LDH of R. dentocariosa ATCC 17931. Lactate is secreted by the human host, and produced by many CF-associated microbes (e.g., Staphylococcus and Streptococcus spp.) through fermentation [48]. Lactate has been detected in the CF sputum at a mean concentration of 3 mM, and higher concentrations have been correlated with lower lung function [49]. Because LDHs enable cells to use lactate as an energy source for growth and reproduction, they are considered as virulence factors. For example, utilization of lactate by Neisseria spp. (reviewed in [50]) enhances their rate of O2 metabolism [51].
    R. mucilaginosa is a facultative anaerobe. The presence of both types of LDH may allow cells to respond to micro-changes in oxygen and nutrient availability by utilizing different metabolic pathways. This would indicate that the primary niche of R. mucilaginosa is the microaerophilic environment at the epithelial surfaces in the mucus plug, which also contains lactate and oxygen from the cells and blood, respectively. A cytochrome c-dependent LDH could allow R. mucilaginosa to utilize extracellular L-lactate with cytochrome c as the terminal oxidase [52] under aerobic conditions, producing pyruvate and hydrogen peroxide (H2O2) [53]. Pyruvate could serve as a food reservoir for fermentative bacteria (e.g., R. mucilaginosa in the CF lung [54]) while also inhibiting the glucose uptake rate of competing bacteria [55]. The production of H2O2 could also serve to inhibit the growth of other organisms, or be used by microbes with catalase activity to yield water that is scarce in the dehydrated CF lung environment [56], [57]. Under anaerobic conditions, NAD-dependent LDH allows the organism to undergo fermentation through the reduction of pyruvate to lactate (reviewed in Garvie E.I. [53]).
  2. Antibiotic resistant genes: An additional copy of a gene encoding the macrolide export ATP-binding/permease protein MacB was found into the CF1E scaffold. Sequence alignments showed that the two MacB-encoding genes are only 12% identical at the nucleotide level, indicating that one MacB was acquired horizontally and did not originate by gene duplication. The protein sequence of the acquired MacB matched a hypothetical protein in R. mucilaginosa M508 and MacB from R. mucilaginosa ATCC 25296 (E-value: 0). The predicted amino acid sequences showed specific hits to the family comprising the MJ0796 ATP-binding cassette (CD03255), followed by a MacB-like periplasmic core domain (PFAM 12704) and FtsX-like permease family (PFAM02687) domain.
    In addition, modulator of drug activity B (MdaB) was present in the CF1E scaffold. Overexpression of MdaB has been shown to confer resistance against tetracycline and adriamycin in E. coli [58]. In addition to this gene, the genome of R. mucilaginosa in CF1E encoded drug resistance transporters (EmrB/QacA subfamily), multidrug resistance transporters (Bcr/CflA family), and a glycopeptide antibiotic resistance protein. These diverse strategies for antibiotic resistance may underlie R. mucilaginosa’s ability to survive antibiotic treatments (Figure 1).
  3. Type I restriction modification: The type I restriction modification (R-M) system is a mechanism to protect against foreign nucleic acids via non site specific endonucleases [59]. There are three subunits: M (Modification/Methyltransferase), S (Specificity) and R (Restriction). The M and S subunits are responsible for recognizing self and non-self, while the R subunit performs the cleavage. The S subunit contains two target recognition domains that are important for restriction specificity and modification of the complex activity. Mapping of metagenomic reads to the reference DY-18 genome (Figure 2, Table S3) showed that only the CF1E metagenome had this Type I R-M system region, whereas the other metagenomes had gaps of 7–9 kbp around this region of the DY-18 genome. The CF1E scaffold likely encodes an S subunit with different sequence specificity, as this subunit is only 37% identical (nucleotides) or 41% identical (protein) to the DY-18 copy (Table S7 and Figure S1). This is of interest because Type I R-M systems have been modified during the adaptation of P. aeruginosa and Burkholderia cenocepacia to the CF lung. For example, the Type I R-M of P. aeruginosa Liverpool epidemic strain (LES) colonizing CF patients was shown to carry a different regulatory specificity (M-subunit) in comparison to strain PA01 [60]. In addition, the expression of type I R-M was greatly increased in B. cenocepacia in the presence of sub-inhibitory concentrations of antibiotics [61]. Together these observations suggest that modification of type I R-M system could be a general mechanism for adaptation to the CF lung.
  4. Phage lysin: Phage lysins are anti-bacterial agents often used in bacterial competition, and have also been associated with the release of cellular components to the extracellular medium during biofilm formation [62], [63]. One copy of the phage lysin gene was present in CF1E, but this did not have any appreciable nucleotide similarity to any genes in phage or bacteria. However, bioinformatic analysis of the predicted amino acid sequence revealed its similarity to the N-acetylmuramoyl-L-alanine amidase of R. mucilaginosa ATCC strain 25296 (E-value: 10−150), a hypothetical protein of R. mucilaginosa M508 (E-value: 10−148), and an amidase-5 domain similar to pneumococcal bacteriophage Dp-1 (E-value: 6.88×10−42). Phage lysins are commonly found in prophages [64]. However, no prophages were detected in the CF1E genome scaffold based on PhiSpy [65]. Although it is currently unclear what, if any, advantage is offered by this phage lysin in the R. mucilaginosa genome, this lysin could provide an alternative strategy for microbial competition.
  5. Clusters of interspaced short palindromic repeats (CRISPRs): CRISPRs are characterized by stretches of short sequence repeats that flank short “spacer” sequences composed of viral or plasmid DNA. Four CRISPR elements were identified in CF1E; these were all ∼4 kbp downstream of the Cas1 CRISPR-associated gene. The length of these CRISPRs ranged from 253 bp to 1,316 bp (Table S8). All CRISPRs contained the same direct repeat sequence of 36 bp. The spacers in each CRISPR element (collectively referred to as a ‘spacer set’) ranged in copy number from 3 to 17, and their sizes ranged from 33 bp to 88 bp. Two of the spacer sets code for hypothetical proteins while the other two sets are unknown (Table S8). A total of 48 spacer sequences were extracted from the four spacer sets; these spacers were compared to the CF1E virome sequences, but no similarities were found.

Phages are an important source of genes in microbial communities. The CRISPRs found in R. mucilaginosa CF1E may correspond to previously attacking phages and plasmids that these cells were able to resist. In order to identify these phage perpetrators, spacer sequences were compared against all host-associated and environmental viromes in MyMgDB [66]. One of the spacers was identified in two human oral cavity viromes [67], whereas none of the spacers were similar to sequences from other environmental viromes (Table S9). The results suggest these bacteria may have been exposed to phages found in the oral cavity, which suggests cells may have existed in this environment prior to opportunistic infection of the CF lungs. Because these spacer sequences did not match phages in the virome sequenced from the same sample, the phages to which R. mucilaginosa is resistant are not present, or are below the detection limit, in this sample. However, if temperate phages dominate in the CF lung [67] as in the human gut virome [68], this result is expected because the virome would largely composed of free-living viruses. However it is also possible that these CRISPRs do not protect the cells against phage infection, but are involved in a CRISPR-dependent modulation of biofilm formation, as described previously in P. aeruginosa (reviewed in [69]). Biofilm formation has been shown to be important for persistent bacterial infection of CF lungs, as well as an overall decline in lung function. Therefore, the role of these CRISPRs in CF1E and other CF lung isolates’ pathogenesis should be explored further.


The metagenomic and genomic analyses presented here suggest that R. mucilaginosa is a common inhabitant of CF lungs, and that it evolves and adapts to each patient’s lung environment over the course of a persistent infection. Genomic analysis of CF1E highlighted many potential adaptations: multiple genes encoding L-lactate dehydrogenases (LDHs) that could enable utilization of lactate, many multi-drug efflux pumps for antibiotic resistance, and the modification of rhs elements and the type I restriction system. Alterations of the type I restriction system has the potential to influence horizontal transfer of genes. The CF1E genomic sequence indicates extensive phage-host interactions, including the acquisition of a phage lysin and changing CRISPR elements.

Based on these potential metabolic adaptations, we hypothesize that R. mucilaginosa lives in the microaerophilic surface of the viscous mucus layer that is characteristic of CF airways (Figure 4). Under this hypothesis, cytochrome c-dependent LDH would enable R. mucilaginosa to use extracellular lactate. However, this process would require oxygen, which is more readily available at the surface of the mucus layer (e.g., from the blood). As the oxygen level is depleted, metabolism could be supported by fermentation and anaerobic respiration with nitrate as an alternative electron acceptor, as observed in P. aeruginosa [70]. Persistence in low oxygen environments would also allow for evasion of antibiotics and ROS activity. In addition, R. mucilaginosa carries a low-pH induced ferrous ion (Fe2+) transporter along with heme and hemin uptake and utilization systems. Co-occurring CF pathogens including P. aeruginosa and S. maltophilia are known to synthesize redox active phenazines that are able to reduce Fe3+ to Fe2+ [71], [72] potentially giving R. mucilaginosa access to Fe2+ in the low pH sputum where the ferrous ion transporter is induced.

Figure 4. Hypothesized adaptions of R. mucilaginosa to the CF lung environment.

The model is based on the comparison between the reference genome DY-18 and the reconstructed genome CF1E. rhs: rearrangement hot spot; Type I R-M: Type I restriction modification; MdaB: Modular of drug activity B; ROS: reactive oxygen species; CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats.

The results presented here highlight the similar evolutionary trajectories and ecological niches of several species of bacteria that colonize the CF lung. These similarities are remarkable because each bacterial species starts with different genetic material: P. aeruginosa has a relatively large genome (>6 Mbp), whereas R. mucilaginosa has only a 2 Mbp genome (Table S10). These findings suggest that obtaining strain specific genome data can illuminate patient-specific bacterial inhabitants of CF patients. This specific information enables predictions to be made regarding the bacteria’s physiological adaptations in each patient, which would further enable physicians to optimize antibiotic treatments.

Materials and Methods

Microbial Metagenome Data

Induced sputum samples were collected from CF volunteers at the Adult CF Clinic (San Diego, CA, United States) by expectoration. All collection was approved by the University of California Institutional Review Board (HRPP 081500) and San Diego State University Institutional Review Board (SDSU IRB#2121). Written informed consent was provided by study participants and/or their legal guardians. Fresh CF sputum samples were processed as described in [12]. In brief, sputum samples were homogenized, bacterial cells were pelleted by centrifugation, and pellets were repeatedly washed and then treated with DNase to remove human DNA prior to extraction of bacterial DNA.

Sequence Read Processing

A total of 18 microbiomes were previously sequenced using Roche-454 GSFLX [12]. The data were downloaded from NCBI sequence read archive (Accession # SRP009392). Reads that were duplicates or of low quality were removed using PRINSEQ [73], and those that matched human-derived sequences were removed using DeconSeq [74]. Sequence reads with similarity to the phylum Chordata and to vector or synthetic sequences were identified by BLASTn against NCBI nucleotide database (threshold of 40% identity over at least 60% query coverage), and removed from the metagenomes. A detailed description of sample processing and preliminary analyses of these datasets has been published [12].

BWA Mapping of the Metagenomes

The processed metagenomic reads were mapped to the Rothia mucilaginosa DY-18 (GI: 283457089) reference genome using a modified version of BWA-SW 0.5.9. The coverage values based on the reference mapping are shown in Table S4.

De novo Assembly and Scaffolding

The metagenomic reads from CF1E were de novo assembled using the Newbler software version 2.6 with ≥35 bp overlap and ≥95% identity. All resultant contigs were aligned to the reference genome (R. mucilaginosa DY-18) using nucmer with its -maxmatch option (using all anchor matches regardless of their uniqueness). This option will allow repetitive or multi-copy sequences (e.g., rRNA operons) to assemble into a single contig, enabling that contig to be subsequently mapped to more than one genomic region. All alignments were examined manually. Full length contigs were ordered based on their coordinates on the reference alignment, and this ordering was used with an in-house Perl script to build the final scaffold containing 181 contigs.

Genome Annotation

The CF1E scaffold was annotated using the RAST web annotation service [75] with the latest FIGfams version 57 (Genome ID: 43675.9). In order to allow a direct comparison, the reference genomes of R. mucilaginosa, DY-18 and R. mucilaginosa M508 (downloaded from the Genome OnLine Database) were also annotated using the same pipeline. CRISPR loci were identified using CRISPRFinder [76]. The spacers between the repeats were extracted and compared to the virome sequenced from the same sample (downloaded from the NCBI (SRX090639)) [12], and other viromes in mymgdb [66].

Rothia-targeted 16S PCR of Lung Sections from Explanted Lungs

DNA was extracted from 5–6 homogenized lung tissues from explanted lungs of four transplant patients using the Macherey-Nagel Nucleospin Tissue Kit (Macherey-Nagel, Bethlehem, PA) with the Gram-positive variation that included an overnight proteinase K digestion. Extracted DNA was amplified using Actinobacteria-targeted PCR primers (Rothia_1F: 5′-GGGACATTCCACGTTTTCCG-3′, Rothia_1R: 5′-TCCTATGAGTCCCCACCATT-3′) that encompass a 322 bp region of the 16S rRNA gene including the hypervariable regions 6–7. Two of the four patients were positive for Actinobacteria; right lower and lingular (left) lobes for Lung 9, and lingular lobe for Lung 7 (Supporting Information S3). The PCR products were purified and sequenced. Sequencing of the three partial 16S gene fragments indicated Rothia was present in lungs from one of the four CF patients.

Supporting Information

Figure S1.

Dot Plot matrix view of the alignment of CF1E type I restriction modification system (subunit M, R, S) against DY-18.


Table S1.

Diseases associated with R. mucilaginosa.


Table S2.

Microbiomes used in this study. Clinical status was designated as exacerbation (prior to systemic antibiotic treatment), on treatment (during systemic antibiotic treatment), post treatment (upon completion of systemic antibiotic treatment) or stable (when clinically stable and at their clinical and physiological baseline). The samples collected during exacerbation were designated as Day 0 sample, and the times between samples are cumulatively calculated from Day 0.


Table S3.

(A) Annotation of the gaps ≥5 kbp in CF1 metagenomic reference mapping against R. mucilaginosa DY-18. Refer to Table S2 for detailed patient samples information. (B) Annotation of the gaps ≥5 kbp in CF6 metagenomic reference mapping against R. mucilaginosa DY-18. Refer to Table S2 for detailed patient samples information. (C) Annotation of the gaps ≥5 kbp in CF7 metagenomic reference mapping against R. mucilaginosa DY-18. Refer to Table S2 for detailed patient samples information. (D) Annotation of the gaps ≥5 kbp in CF8 metagenomic reference mapping against R. mucilaginosa DY-18. Refer to Table S2 for detailed patient samples information.


Table S4.

Statistics from BWA mapping of metagenomic reads against the reference genome R. mucilaginosa DY-18.


Table S5.

General features of the CF1E R. mucilaginosa scaffold, DY-18 reference genome, and M508 draft genome.


Table S6.

Subsystem feature counts of R. mucilaginosa CF1E, DY-18, and M508.


Table S7.

Sequence identities of the genes encoding the Type I restriction modification system in CF1E and DY-18, determined using BLAST. The identity value is subjected to >97% query length coverage.


Table S8.

CRISPR positions in the CF1E genome scaffold.


Table S9.

Identification of the spacer sequences in CF1E CRISPR structure from human- and environmental-viral metagenomes at 100% length coverage and ≥90% identity (≤2 mismatches).


Table S10.

A comparison of putative adaptations and predicted metabolisms of R. mucilaginosa and P. aeruginosa that are hypothesized to enable persistence in the CF lung, based on literature and genomic data.


Table S11.

Genes that are missing from the CF1E genome scaffold but present in the DY-18 reference. Genes are considered missing when the gap is within a contig.


Table S12.

Genes present in the CF1E genome scaffold but missing in the reference genome DY-18.


Table S13.

Isolation source and references of sequences extracted and used in the 16S phylogenetic analysis.


Table S14.

Protein-coding genes used for multilocus phylogenetic inference.


Table S15.

Genes missing from the CF1E genome scaffold, based on contig mapping to the reference genome DY18.


Supporting Information S2.

Rothia mucilaginosa in cystic fibrosis community metatranscriptomes.


Supporting Information S3.

Additional genome characteristic of R. mucilaginosa and phylogenetic analysis of Rothia spp. associated with cystic fibrosis.


Author Contributions

Conceived and designed the experiments: YWL MH TDM FLR. Performed the experiments: YWL MH. Analyzed the data: YWL RS MH TDM MF SP CH DL FLR. Contributed reagents/materials/analysis tools: RS MF RE DC. Wrote the paper: YWL RS HM KW.


  1. 1. Kerem B, Rommens JM, Buchanan JA, Markiewicz D, Cox TK, et al. (1989) Identification of the Cystic Fibrosis gene: Genetic analysis. Science 245: 1073–1080
  2. 2. LiPuma JJ (2010) The changing microbial epidemiology in Cystic Fibrosis. Clinical Microbiology Reviews 23: 299–323
  3. 3. Rogers GB, Carroll MP, Serisier DJ, Hockey PM, Jones G, et al. (2004) Characterization of bacterial community diversity in Cystic Fibrosis lung infections by use of 16S ribosomal DNA terminal restriction fragment length polymorphism profiling. J Clin Microbiol 42: 5176–5183
  4. 4. Harris JK, De Groote MA, Sagel SD, Zemanick ET, Kapsner R, et al. (2007) Molecular identification of bacteria in bronchoalveolar lavage fluid from children with Cystic Fibrosis. PNAS 104: 20529–20533
  5. 5. Bittar F, Richet H, Dubus J-C, Reynaud-Gaubert M, Stremler N, et al.. (2008) Molecular detection of multiple emerging pathogens in sputa from Cystic Fibrosis patients. PLoS ONE. doi:10.1371/journal.pone.0002908.
  6. 6. Cox MJ, Allgaier M, Taylor B, Baek MS, Huang YJ, et al. (2010) Airway microbiota and pathogen abundance in age-stratified Cystic Fibrosis patients. PLoS ONE 5: e11044
  7. 7. Guss AM, Roeselers G, Newton ILG, Young CR, Klepac-Ceraj V, et al. (2011) Phylogenetic and metabolic diversity of bacteria associated with cystic fibrosis. ISME J 5: 20–29.
  8. 8. Willner D, Furlan M, Haynes M, Schmieder R, Angly FE, et al. (2009) Metagenomic analysis of respiratory tract DNA viral communities in Cystic Fibrosis and non-Cystic Fibrosis individuals. PLoS ONE 4: e7370
  9. 9. Willner D, Furlan M (2010) Deciphering the role of phage in the cystic fibrosis airway. Virulence 1: 309–313
  10. 10. Willner D, Haynes MR, Furlan M, Schmieder R, Lim YW, et al. (2011) Spatial distribution of microbial communities in the cystic fibrosis lung. The ISME Journal 6: 471–474
  11. 11. Willner D, Haynes MR, Furlan M, Hanson N, Kirby B, et al. (2012) Case studies of the spatial heterogeneity of DNA viruses in the cystic fibrosis lung. Am J Respir Cell Mol Biol 46: 127–131
  12. 12. Lim YW, Schmieder R, Haynes M, Willner D, Furlan M, et al.. (2012) Metagenomics and metatranscriptomics: Windows on CF-associated viral and microbial communities. J Cyst Fibros. doi:10.1016/j.jcf.2012.07.009.
  13. 13. Narasingarao P, Podell S, Ugalde JA, Brochier-Armanet C, Emerson JB, et al. (2012) De novo metagenomic assembly reveals abundant novel major lineage of Archaea in hypersaline microbial communities. ISME J 6: 81–93
  14. 14. Iverson V, Morris RM, Frazar CD, Berthiaume CT, Morales RL, et al. (2012) Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 335: 587–590
  15. 15. Migula W (1900) System der bakterien: bd. Specielle systematik der bakterien. G. Fischer. 1164 p.
  16. 16. Bergan T, Bøvre K, Hovig B (1970) Priority of Micrococcus mucilaginosus Migula 1900 over Staphylococcus salivarius: Andrewes and Gordon 1907 with proposal of a neotype strain. International Journal of Systematic Bacteriology 20: 107–113
  17. 17. Bergan T, Kocur M (1982) NOTES: Stomatococcus mucilaginosus gen.nov., sp.nov., ep. rev., a member of the family Micrococcaceae. International Journal of Systematic Bacteriology 32: 374–377
  18. 18. Collins MD, Hutson RA, Båverud V, Falsen E (2000) Characterization of a Rothia-like organism from a mouse: description of Rothia nasimurium sp. nov. and reclassification of Stomatococcus mucilaginosus as Rothia mucilaginosa comb. nov. International Journal of Systematic and Evolutionary Microbiology 50: 1247–1251.
  19. 19. Doel JJ, Benjamin N, Hector MP, Rogers M, Allaker RP (2005) Evaluation of bacterial nitrate reduction in the human oral cavity. European Journal of Oral Sciences 113: 14–19
  20. 20. Olsen I, Preza D, Aas JA, Paster BJ (2009) Cultivated and not-yet-cultivated bacteria in oral biofilms. Microbial Ecology in Health and Disease 21: 65–71
  21. 21. Guglielmetti S, Taverniti V, Minuzzo M, Arioli S, Stuknyte M, et al. (2010) Oral bacteria as potential probiotics for the pharyngeal mucosa. Applied and Environmental Microbiology 76: 3948–3958
  22. 22. Wang M, Ahrné S, Jeppsson B, Molin G (2005) Comparison of bacterial diversity along the human intestinal tract by direct cloning and sequencing of 16S rRNA genes. FEMS Microbiology Ecology 54: 219–231
  23. 23. Ou G, Hedberg M, Horstedt P, Baranov V, Forsberg G, et al.. (2009) Proximal small intestinal microbiota and identification of rod-shaped bacteria associated with childhood celiac disease. Am J Gastroenterol. Available: Accessed 2012 Feb 20.
  24. 24. Kazor CE, Mitchell PM, Lee AM, Stokes LN, Loesche WJ, et al. (2003) Diversity of bacterial populations on the tongue dorsa of patients with halitosis and healthy patients. Journal of Clinical Microbiology 41: 558–563
  25. 25. Preza D, Olsen I, Willumsen T, Grinde B, Paster BJ (2009) Diversity and site-specificity of the oral microflora in the elderly. European Journal of Clinical Microbiology & Infectious Diseases 28: 1033–1040
  26. 26. Nyvad B, Kilian M (1987) Microbiology of the early colonization of human enamel and root surfaces in vivo. European Journal of Oral Sciences 95: 369–380
  27. 27. Philip K, Teoh WY, Muniandy S, Yaakob H (2009) Pathogenic bacteria predominate in the oral cavity of Malaysian subjects. Available:, Accessed 2012 Feb 22.
  28. 28. Jiménez E, Delgado S, Fernández L, García N, Albújar M, et al. (2008) Assessment of the bacterial diversity of human colostrum and screening of staphylococcal and enterococcal populations for potential virulence factors. Research in Microbiology 159: 595–601
  29. 29. Delgado S, Arroyo R, Martín R, Rodríguez JM (2008) PCR-DGGE assessment of the bacterial diversity of breast milk in women with lactational infectious mastitis. BMC Infectious Diseases 8: 51
  30. 30. Bowden GH (1969) The components of the cell walls and extracellular slime of four strains of Staphylococcus salivarius isolated from human dental plaque. Archives of Oral Biology 14: 685–697
  31. 31. Ready D, Lancaster H, Qureshi F, Bedi R, Mullany P, et al. (2004) Effect of amoxicillin use on oral microbiota in young children. Antimicrobial Agents and Chemotherapy 48: 2883–2887
  32. 32. Stackebrandt E (2006) The Genus Stomatococcus: Rothia mucilaginosa, basonym Stomatococcus mucilaginosus. In: Dworkin M, Falkow S, Rosenberg E, Schleifer K-H, Stackebrandt E, editors. The Prokaryotes. Springer New York, Vol. 3. 975–982. Available: Accessed 2012 Feb 19.
  33. 33. Tunney MM, Field TR, Moriarty TF, Patrick S, Doering G, et al. (2008) Detection of anaerobic bacteria in high numbers in sputum from patients with cystic fibrosis. Am J Respir Crit Care Med 177: 995–1001
  34. 34. Van der Gast CJ, Walker AW, Stressmann FA, Rogers GB, Scott P, et al. (2011) Partitioning core and satellite taxa from within Cystic Fibrosis lung bacterial communities. ISME J 5: 780–791.
  35. 35. Rogers GB, Carroll MP, Serisier DJ, Hockey PM, Jones G, et al. (2006) Use of 16S rRNA gene profiling by terminal restriction fragment length polymorphism analysis to compare bacterial communities in sputum and mouthwash samples from patients with Cystic Fibrosis. J Clin Microbiol 44: 2601–2604
  36. 36. Goddard AF, Staudinger BJ, Dowd SE, Joshi-Datar A, Wolcott RD, et al. (2012) Direct sampling of cystic fibrosis lungs indicates that DNA-based analyses of upper-airway specimens can misrepresent lung microbiota. PNAS 109: 13769–13774
  37. 37. Fodor AA, Klem ER, Gilpin DF, Elborn JS, Boucher RC, et al. (2012) The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations. PLoS ONE 7: e45001
  38. 38. Gomes-Filho IS, Passos JS, Seixas da Cruz S (2010) Respiratory disease and the role of oral bacteria. J Oral Microbiol 2. doi:10.3402/jom.v2i0.5811.
  39. 39. Oliver A, Cantón R, Campo P, Baquero F, Blázquez J (2000) High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288: 1251–1253
  40. 40. Lieberman TD, Michel J-B, Aingaran M, Potter-Bynoe G, Roux D, et al. (2011) Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nature Genetics 43: 1275–1280
  41. 41. Goerke C, Wolz C (2010) Adaptation of Staphylococcus aureus to the cystic fibrosis lung. Int J Med Microbiol 300: 520–525
  42. 42. Yamane K, Nambu T, Yamanaka T, Mashimo C, Sugimori C, et al.. (2010) Complete genome sequence of Rothia mucilaginosa DY-18: A clinical isolate with dense meshwork-like structures from a persistent apical periodontitis lesion. Sequencing 2010. doi:10.1155/2010/457236.
  43. 43. Lawrence JG, Ochman H (1998) Molecular archaeology of the Escherichia coli genome. PNAS 95: 9413–9417.
  44. 44. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, et al. (2004) Versatile and open software for comparing large genomes. Genome Biol 5: R12
  45. 45. Jackson AP, Thomas GH, Parkhill J, Thomson NR (2009) Evolutionary diversification of an ancient gene family (rhs) through C-terminal displacement. BMC Genomics 10: 584
  46. 46. Poole SJ, Diner EJ, Aoki SK, Braaten BA, T’ Kint de Roodenbeke C, et al. (2011) Identification of functional toxin/immunity genes linked to Contact-Dependent Growth Inhibition (CDI) and Rearrangement Hotspot (Rhs) systems. PLoS Genet 7: e1002217
  47. 47. Kung VL, Khare S, Stehlik C, Bacon EM, Hughes AJ, et al. (2012) An rhs gene of Pseudomonas aeruginosa encodes a virulence protein that activates the inflammasome. PNAS 109: 1275–1280
  48. 48. De Backer D, Creteur J, Zhang H, Norrenberg M, Vincent J-L (1997) Lactate production by the lungs in acute lung injury. Am J Respir Crit Care Med 156: 1099–1104.
  49. 49. Bensel T, Stotz M, Borneff-Lipp M, Wollschläger B, Wienke A, et al. (2011) Lactate in cystic fibrosis sputum. Journal of Cystic Fibrosis 10: 37–44
  50. 50. Smith H, Tang CM, Exley RM (2007) Effect of host lactate on gonococci and meningococci: New concepts on the role of metabolites in pathogenicity. Infect Immun 75: 4190–4198
  51. 51. Britigan BE, Klapper D, Svendsen T, Cohen MS (1988) Phagocyte-derived lactate stimulates oxygen consumption by Neisseria gonorrhoeae. An unrecognized aspect of the oxygen metabolism of phagocytosis. J Clin Invest 81: 318–324.
  52. 52. Lederer F (1974) On the first steps of lactate oxidation by bakers’ yeast L-(plus)-lactate dehydrogenase (Cytochrome b2). European Journal of Biochemistry 46: 393–399
  53. 53. Garvie EI (1980) Bacterial lactate dehydrogenases. Microbiol Rev 44: 106–139.
  54. 54. Price-Whelan A, Dietrich LEP, Newman DK (2007) Pyocyanin alters redox homeostasis and carbon flux through central metabolic pathways in Pseudomonas aeruginosa PA14. J Bacteriol 189: 6372–6381
  55. 55. Brown SA, Whiteley M (2007) A novel exclusion mechanism for carbon resource partitioning in Aggregatibacter actinomycetemcomitans. J Bacteriol 189: 6407–6414
  56. 56. Potter JL, Matthews LW, Spector S, Lemm J (1967) Studies on pulmonary secretions. II. Osmolality and the ionic environment of pulmonary secretions from patients with cystic fibrosis, bronchiectasis, and laryngectomy. Am Rev Respir Dis 96: 83–87.
  57. 57. Tarran R, Grubb BR, Gatzy JT, Davis CW, Boucher RC (2001) The relative roles of passive surface forces and active ion transport in the modulation of airway surface liquid volume and composition. J Gen Physiol 118: 223–236
  58. 58. Adams MA, Jia Z (2006) Modulator of drug activity B from Escherichia coli: crystal structure of a prokaryotic homologue of DT-diaphorase. J Mol Biol 359: 455–465
  59. 59. Murray NE (2000) Type I restriction systems: Sophisticated molecular machines (a legacy of Bertani and Weigle). Microbiol Mol Biol Rev 64: 412–434.
  60. 60. Smart CHM, Walshaw MJ, Hart CA, Winstanley C (2006) Use of suppression subtractive hybridization to examine the accessory genome of the Liverpool cystic fibrosis epidemic strain of Pseudomonas aeruginosa. J Med Microbiol 55: 677–688
  61. 61. Sass A, Marchbank A, Tullis E, LiPuma JJ, Mahenthiralingam E (2011) Spontaneous and evolutionary changes in the antibiotic resistance of Burkholderia cenocepacia observed by global gene expression analysis. BMC Genomics 12: 373
  62. 62. Whitchurch CB, Tolker-Nielsen T, Ragas PC, Mattick JS (2002) Extracellular DNA required for bacterial biofilm formation. Science 295: 1487
  63. 63. Carrolo M, Frias MJ, Pinto FR, Melo-Cristino J, Ramirez M (2010) Prophage spontaneous activation promotes DNA release enhancing biofilm formation in Streptococcus pneumoniae. PLoS ONE 5: e15678
  64. 64. Schmitz JE, Schuch R, Fischetti VA (2010) Identifying active phage lysins through functional viral metagenomics. Appl Environ Microbiol 76: 7181–7187
  65. 65. Akhter S, Aziz RK, Edwards RA (2012) PhiSpy: A novel algorithm for finding prophages in bacterial genomes that combines similarity-based and composition-based strategies. NAR.
  66. 66. Schmieder R, Edwards RA (n.d.) MyMGDB. Available:
  67. 67. Willner D, Furlan M, Schmieder R, Grasis JA, Pride DT, et al. (2011) Metagenomic detection of phage-encoded platelet-binding factors in the human oral cavity. Proc Natl Acad Sci U S A 108: 4547–4553
  68. 68. Reyes A, Haynes M, Hanson N, Angly FE, Heath AC, et al. (2010) Viruses in the faecal microbiota of monozygotic twins and their mothers. Nature 466: 334–338
  69. 69. Palmer KL, Whiteley M (2011) DMS3–42: the secret to CRISPR-dependent biofilm inhibition in Pseudomonas aeruginosa. J Bacteriol 193: 3431–3432
  70. 70. Hoffman LR, Richardson AR, Houston LS, Kulasekara HD, Martens-Habbena W, et al. (2010) Nutrient availability as a mechanism for selection of antibiotic tolerant Pseudomonas aeruginosa within the CF airway. PLoS Pathog 6: e1000712
  71. 71. Dietrich LEP, Teal TK, Price-Whelan A, Newman DK (2008) Redox-active antibiotics control gene expression and community behavior in divergent bacteria. Science 321: 1203–1206
  72. 72. Wang Y, Wilks JC, Danhorn T, Ramos I, Croal L, et al. (2011) Phenazine-1-carboxylic acid promotes bacterial biofilm development via ferrous iron acquisition. J Bacteriol 193: 3606–3617
  73. 73. Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27: 863–864
  74. 74. Schmieder R, Edwards R (2011) Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6: e17288
  75. 75. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, et al. (2008) The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9: 75
  76. 76. Grissa I, Vergnaud G, Pourcel C (2007) CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. NAR 35: W52–W57