Protein relative abundance patterns associated with sucrose-induced dysbiosis are conserved across taxonomically diverse oral microcosm biofilm models of dental caries
© Rudney et al. 2015
Received: 28 August 2015
Accepted: 25 November 2015
Published: 19 December 2015
The etiology of dental caries is multifactorial, but frequent consumption of free sugars, notably sucrose, appears to be a major factor driving the supragingival microbiota in the direction of dysbiosis. Recent 16S rRNA-based studies indicated that caries-associated communities were less diverse than healthy supragingival plaque but still displayed considerable taxonomic diversity between individuals. Metagenomic studies likewise have found that healthy oral sites from different people were broadly similar with respect to gene function, even though there was an extensive individual variation in their taxonomic profiles. That pattern may also extend to dysbiotic communities. In that case, shifts in community-wide protein relative abundance might provide better biomarkers of dysbiosis that can be achieved through taxonomy alone.
In this study, we used a paired oral microcosm biofilm model of dental caries to investigate differences in community composition and protein relative abundance in the presence and absence of sucrose. This approach provided large quantities of protein, which facilitated deep metaproteomic analysis. Community composition was evaluated using 16S rRNA sequencing and metaproteomic approaches. Although taxonomic diversity was reduced by sucrose pulsing, considerable inter-subject variation in community composition remained. By contrast, functional analysis using the SEED ontology found that sucrose induced changes in protein relative abundance patterns for pathways involving glycolysis, lactate production, aciduricity, and ammonia/glutamate metabolism that were conserved across taxonomically diverse dysbiotic oral microcosm biofilm communities.
Our findings support the concept of using function-based changes in protein relative abundance as indicators of dysbiosis. Our microcosm model cannot replicate all aspects of the oral environment, but the deep level of metaproteomic analysis it allows makes it suitable for discovering which proteins are most consistently abundant during dysbiosis. It then may be possible to define biomarkers that could be used to detect at-risk tooth surfaces before the development of overt carious lesions.
KeywordsMetaproteomics Dental caries Dysbiosis Sucrose Microcosm models Oral biofilm Taxonomic diversity
The lesions of enamel caries can be considered as the outcome of dysbiotic changes in the biofilm community of supragingival dental plaque [1, 2]. Demineralization occurs as the cumulative outcome of repeated shifts towards a less diverse microbiota that produces and tolerates a low pH environment in tooth sites that are sheltered from protective factors in host saliva. Although the etiology of caries is multifactorial, frequent consumption of foods rich in free sugars, notably sucrose, appears to be one of the major factors driving the microbiota in the direction of dysbiosis, particularly in the case of otherwise healthy children with normal salivary flow [3–5].
Streptococcus mutans and closely related species (such as Streptococcus sobrinus) have long been considered to play a primary etiological role in dental caries. S. mutans responds to sucrose by producing large quantities of lactic acid. It is very tolerant of low pH and produces an insoluble extracellular polysaccharide that may sequester acid at tooth surfaces . The mechanisms behind those putative virulence factors have been intensively studied in monoculture [7, 8] and recently in simple multi-species consortia . Much less is known of other species that may also contribute to or protect against dysbiosis driven by dietary carbohydrates [2, 4, 10]. Some strains of oral “non-mutans streptococci” produce and tolerate acid at levels comparable to S. mutans [11, 12], while others show increased arginolytic capabilities, which may act to raise pH within the biofilm matrix .
S. mutans tends to be a minority species even in caries-active children, and carious lesions likewise can occur in children with no detectable S. mutans [10, 14–16]. 16S rRNA-based metagenomic comparisons of caries-active and caries-free subjects have detected associations between caries and a variety of oral species, including not only non-mutans streptococci but also members of other genera, such as Scardovia and Bifidobacterium [10, 14–17]. Caries associations have not been consistent between studies. Moreover, different taxonomic clusters have been defined as subgroups within the same study . This raises an important point. Although caries-associated communities are typically less diverse than healthy supragingival plaque overall, those dysbiotic communities still display considerable taxonomic diversity between affected individuals [10, 14–19]. That in turn raises the question of whether it is desirable to define biomarkers of dysbiosis that are less dependent on taxonomy.
The Human Microbiome project generated comprehensive metagenomic data for a wide variety of body sites in healthy subjects, including supragingival plaque . Although most of that data was based on 16S rRNA sequencing, shotgun metagenomics was also used to catalog the functional potential of all microbial genes within a smaller subset of subjects. One of the key findings was that healthy sites from different people were broadly similar with respect to their functional profiles, even though there was extensive individual variation in their taxonomic profiles . It is possible that the “conservation of function” concept may also extend to dysbiotic communities. This would explain why microbial communities associated with caries still show considerable taxonomic variation. In that case, differential patterns of community-wide gene expression and/or protein relative abundance might provide a more accurate indicator of dysbiosis than can be achieved by counting caries-associated species.
Metatranscriptomic or metaproteomic approaches can be used to provide information on function. A recent metatranscriptomic comparison of subgingival plaque from healthy and periodontally diseased sites in three subjects has provided data that support the “conservation of function” concept. They observed that taxonomically diverse diseased sites shared conserved gene expression profiles . By the same token, a recent metaproteomic comparison of gut microbiotas from healthy controls to Crohn’s disease patients found that major shifts in protein relative abundance by function did not always correlate with changes in taxon relative abundance .
In this study, we used an oral microcosm biofilm model of dental caries to investigate differences in community composition and protein relative abundance in the presence and absence of sucrose. This approach provided large quantities of protein, which facilitated deep metaproteomic analysis. Community composition was evaluated using 16S rRNA sequencing and metaproteomic approaches. Although taxonomic diversity was reduced by sucrose pulsing, considerable inter-subject variation in community composition remained. By contrast, functional analysis using the SEED ontology found that sucrose induced changes in protein relative abundance patterns for pathways involving glycolysis, lactate production, aciduricity, and ammonia/glutamate metabolism that were conserved across taxonomically diverse dysbiotic oral microcosm biofilm communities. Collectively, our findings support the concept of using function-based changes in protein abundance as indicators of dysbiosis.
Results and discussion
Oral microcosm real-time pH profiles
Direct collection of supragingival plaque requires pooling to obtain adequate amounts of protein for deep shotgun metaproteomics. In our experience, pooling plaque from all sites within a single individual typically yielded only about 1 mg of total plaque by wet weight. Pooling samples from multiple subjects was not a desirable option, since it would have obscured taxonomic diversity between subjects. Accordingly, a previously validated oral microcosm biofilm model was used to scale up protein yields. Microcosms are grown using plaque from a single site as an inoculum, and 16S rRNA studies by members of our group have previously shown that samples taken from the same subjects at different times yield microcosms that are more similar within subjects than between subjects . The oral microcosm approach retains much of the taxonomic variation between individual subjects . Moreover, it simulates many aspects of the caries process, including demineralization at the interface between the tooth and dental restorations .
Taxonomic diversity within and between NS and WS pairs and original plaque inoculums
No one has yet devised a medium capable of supporting the growth of every oral species. BMM was developed as a saliva analog, with type II hog gastric mucin as the primary carbohydrate source . Additional components intended to promote the growth of oral species include hemin, menadione, urea, and arginine. 16S rRNA sequencing was used to evaluate the extent to which each NS and WS microcosm corresponded to its “parent” plaque inoculum. DNA extracts were sent to the Forsyth Dental Institute Core for Human Oral Microbiome Identification using Next Generation Sequencing (HOMINGS) analysis . The HOMINGS approach parses Illumina Mi-Seq data with a set of validated 16S rRNA sequences (defined as “probes”) to enhance and quantify species- and genus-specific identifications from oral samples.
HOMINGS taxon counts for plaque inoculums, NS microcosms, and WS microcosms
Number of taxa detecteda
Number of abundant taxab
p ≥ 1 %
NS ≥ 1 %
WS ≥ 1 %
Mean ± SD
186.6 ± 29.1
96.8 ± 14.3
84.25 + 8.0
17.1 ± 3.0
8.25 ± 2.1
7.4 ± 3.1
One limitation of the HOMINGS approach is that it will not make taxonomic assignments for reads that do not match any of the defined probe sequences. However, the average percentage of assigned reads across all 33 plaque, NS, and WS samples was 85 %, with a standard deviation of 7.6 %. That was consistent with the oral origins of those samples and suggested that HOMINGS relative abundance estimates for individual taxa were not unduly biased by the presence of unassigned reads (Additional file 1).
The heat map in Additional file 2 graphically displays HOMINGS probe counts for all HOMINGS taxa that were detected in any P, NS, or WS sample. Considerable individual variation was seen within each group. Bray-Curtis hierarchical clustering results for the samples were largely consistent with their spatial distribution within the PCoA plot. Overlaps between sample groups were seen, since 760 WS and 852 WS clustered with the NS samples, while 733 NS clustered with the WS samples. The taxon dendrogram graphically illustrated the pattern of decreasing taxonomic diversity between the P inoculums, NS microcosms, and WS microcosms but also suggested that many of the relatively abundant taxa remained consistent across all three groups. However, other relatively abundant taxa appeared to increase or decrease between the NS and WS microcosms. Those patterns likewise seemed consistent with the patterns of dispersion along coordinates 1 and 2.
Since differences in relative abundance appeared to be exerting a major influence on the PCoA and hierarchical clustering results, we used the edgeR package of the open-source R statistical software package to compare HOMINGS 16S rRNA probe counts between the NS and WS pairs and between the microcosms and their parent inoculums. The edgeR package was developed to analyze count data using generalized linear models (GLM) in conjunction with empirical Bayes estimates of gene-specific dispersions . edgeR incorporates a normalization step and corrects for multiple comparisons by estimating Benjamini-Hochberg false discovery rates (FDR). The GLM approach allows for paired samples, which made it particularly suitable for our study design.
edgeR results for HOMINGS probes abundant in ≥5 samples: NS microcosms vs. plaque
HOMINGS taxon probea
edgeR results for HOMINGS probes abundant in ≥5 samples: WS microcosms vs. Plaque
HOMINGS taxon probea
S. mutans was detected by HOMINGS in seven of the nine plaque samples and 7 of the 12 WS microcosms. Its relative abundance varied considerably but only exceeded 1 % in two plaque samples. S. mutans did not exceed 1 % relative abundance in any of the microcosms. It was undetectable in 9 of the 12 NS microcosms (S. mutans relative abundance was not significantly different between plaque and WS pairs). S. sobrinus was detectable by HOMINGS in four of nine plaque samples, 4 of 12 NS microcosms, and 5 of 12 WS microcosms but only at very low levels. Scardovia wiggsiae has recently been proposed as a novel caries-associated species in children . It was detected in five of nine plaque samples but only exceeded a relative abundance of 1 % in one plaque sample. It was present in 5 of 12 NS microcosms and 4 of 12 WS microcosms at relative abundances <1 %. Another proposed caries-associated species, Bifidobacterium dentium , was detected in only one plaque sample, at a relative abundance well below 1 % (Additional file 1).
Collectively, the HOMINGS results indicated that NS and WS microcosm biofilms retained much of the taxonomic diversity present in their parent plaque samples. Changes did occur, but mostly in taxa that were present at a relative abundance <1 % to start with. It appeared that BMM alone was less supportive of Rothia, Lautropia, Corynebacterium, and Abiotrophia, while providing more favorable conditions for the growth of Fusobacterium species. However, there is no reason to think that NS microcosms constituted a dysbiotic community with respect to caries, since their pH remained above 6.0. Indeed, some authors have proposed the clinical use of arginine or urea rinses as way of encouraging base production in plaque [13, 29], and both are components of BMM . Moreover, sucrose pulsing clearly simulated caries-like pH drops in WS microcosm biofilms, even though there were no HOMINGS taxonomic differences between NS and WS at FDR ≤ 5 %. This supported our hypothesis that taxonomy alone is not sufficient as an indicator of dysbiosis.
Taxonomic diversity of peptides within and between NS and WS pairs
A detailed description of our metaproteomic workflow is provided in the “Methods” section. Here, we note only steps that provide context for interpreting the results. Briefly, mass spectra obtained by a separate 2D MSMS analysis of each microcosm were searched against the Human Oral Microbiome Database (HOMD) genomic dataset, using our published two-step strategy [30–33]. Peptide-spectral matches at a 5 % target-decoy search local FDR threshold were used for further analysis (in proteomics, the term “local FDR” refers to the likelihood that an individual protein, peptide, or spectrum has been matched incorrectly in a target-decoy search) [31, 34]. All instances of the spectrum for a given peptide were retained, to allow for spectral counting. Those spectra were searched against the BLAST-NR database, using BLAST-P. The BLAST-P output for each microcosm was parsed using MEGAN5 software, to generate taxonomic assignments and functional analyses .
MEGAN5 uses a “Lowest-Common-Ancestor assignment algorithm” (LCA) to assign reads to taxa. Species-specific peptides are assigned at the species level. Conserved peptides with hits assigned to multiple taxa are moved up to higher taxonomic levels in the phylogenetic tree generated by MEGAN5 (e.g., genus, family, phylum, kingdom). The MEGAN5 LCA algorithm assigned 88 % of 1,126,203 total spectra into 592 different taxonomic levels across all 12 NS and WS pairs. The taxon list included 303 species, representing 11 % of normalized spectral counts. Most spectra were moved up to higher taxonomic levels (e.g., genus, family, phylum, kingdom), depending on the extent to which their parent peptides were conserved. That outcome was expected, since sequences that are critical to protein function are less likely to vary across species.
edgeR results for species-level spectra abundant in ≥5 samples: WS vs. NS microcosms
Species assigned to spectra by MEGAN5 LCA
Streptococcus sp. F0442
Fusobacterium sp. oral taxon 370
Veillonella sp. ACP1
Thirteen species that met the relative abundance criteria above did not differ between the NS and WS microcosms at FDR ≤ 5 % (Additional file 8). We noted that one of those species, Bacillus cereus, was not represented in the current list of HOMINGS probes. However, Bacillus species have been detected in the human gut , oral mucosa, periodontal pockets, and endodontic infections [37, 38]. In order to verify the presence of B. cereus 16S RNA in the original plaque inoculums, the Illumina Mi-Seq FASTQ files generated in the first step of the HOMINGS workflow were parsed with the QIIME metagenomic analysis software package . Six of the nine available plaque inoculums contained reads assigned to B. cereus at the species level, at relative abundances ranging from 0.003 to 0.79 % (mean = 0.17 %). NS and WS microcosms grown from those inoculums likewise were positive for B. cereus 16S RNA.
S. mutans spectral counts were detected in only 4 WS microcosms (at low levels), whereas HOMINGS detected it in 7 WS microcosms. S. sobrinus spectra were detected in 11 WS microcosms, while HOMINGS detected it (at low relative abundance) in only 5 WS microcosms (Additional files 1 and 8).
Collectively, the LCA results indicated that taxon-specific protein relative abundance patterns only partially corresponded with relative abundance estimates based on 16S rRNA counts. That was not surprising, since HOMINGS used only data from the V3–V4 hypervariable regions of 16S rRNA, while the LCA used data derived from a search of all genes in the BLAST-NR database. The absence of tight NS and WS clusters in the LCA PCoA plot and heat map further suggested that taxonomic information may not be the most effective way to identify microbial communities that have become dysbiotic due to frequent exposure to sucrose.
Conservation of function within and between NS and WS pairs
MEGAN5 performs functional analyses by mapping associated RefSeq IDs in BLAST-P output files to functional roles within the SEED, COGs, or KEGG ontologies. COGs is no longer being maintained by NCBI, and most KEGG pathway maps are based on the human genome. SEED is specific for prokaryotes , so it seemed the best choice for functional analysis of each metaproteome.
The SEED ontology organizes proteins into metabolic subsystems, which in turn are grouped into major functional categories (carbohydrate metabolism, amino acid metabolism, etc.). Our original intent was to compare SEED assignments for NS and WS microcosms at the subsystem level. However, many proteins were binned into more than one subsystem, an inevitable consequence of overlap between metabolic pathways. This raised the question of whether differences between NS and WS microcosms at the subsystem level might be attributable to a common set of proteins. We also observed that changes in the relative abundance of component proteins within subsystems sometimes went in opposite directions (some were higher in NS, whereas others were elevated in WS).
We decided to analyze the SEED output at the level of individual proteins, in order to facilitate interpretation of the results. Since most spectra were assigned to higher taxonomic levels by MEGAN5 (see above), the findings reported below should be interpreted as a composite picture of responses by taxonomically diverse microcosms to sucrose pulsing.
Hierarchical clustering results were fully consistent with the spatial distribution of samples in the PCoA plot. In contrast to the results for the HOMINGS and LCA taxonomy analyses, extensive differences in the protein relative abundance profiles for the two major clusters were apparent in the SEED heat map (Additional file 10).
edgeR analysis indicated that 505 proteins (26 % of the 1969 assigned) differed between the NS and WS microcosms at FDR ≤ 5 % (Additional file 11). By that criterion, functional analysis was much more successful than either the HOMINGS or LCA taxonomic analyses at identifying differences between the NS and WS microcosms. In other words, sucrose pulsing induced similar changes in protein relative abundance among microcosms that were taxonomically diverse. The following discussion describes major trends, using broadly distributed proteins (present in at least 12 samples), which differed greatly between NS and WS microcosm pairs (FDR ≤ 5 %), as representative examples (Table 4). The MetaCyc Metabolic Pathway Database was the primary reference for protein function in NS microcosms , except as noted.
The NS relative abundance pattern was consistent with mucin degradation (ABC transporter components for galactose and dipeptides), with mixed acid fermentation directed towards the generation of formate, acetyl CoA, and acetate by various pathways (pyruvate formate-lyase, acetate kinase, acetaldehyde dehydrogenase). Amino acid degradation leading to the release of ammonia was also a prominent feature (NAD-specific glutamate dehydrogenase, lysine 2,3-aminomutase, tryptophanase, histidine ammonia-lyase). All of those proteins were significantly upregulated in NS microcosms at FDR ≤ 5 %. Those mechanisms are likely to have contributed to the maintenance of NS microcosms at a stable pH between 6.0 and 7.0. The arginine deiminase pathway has been suggested to participate in ammonification of oral biofilms [13, 42]. Arginine deiminase and carbamate kinase were elevated in NS at FDR ≤ 5 %. Ornithine carbamoyltransferase showed a similar trend (FDR = 0.19).
Our LCA results indicated that S. mutans was undetectable in 8 WS microcosms and a minor component of the remaining 4 WS microcosms (see above). Nevertheless, most studies of oral microbial responses to sucrose and pH stress have focused on S. mutans, due to its status as a presumptive caries pathogen. Accordingly, we used that literature as a knowledge base for interpretation of the WS results (none of the proteins discussed could be unequivocally assigned to S. mutans at the species level).
edgeR results for proteins discussed in the text with FDR ≤ 5 % (*): WS vs. NS microcosms
Protein assigned by MEGAN5 using SEED
Pyruvate formate-lyase (EC 184.108.40.206)
Acetate kinase (EC 220.127.116.11)
NAD-specific glutamate dehydrogenase (EC 18.104.22.168)
Lysine 2,3-aminomutase (EC 22.214.171.124)
Tryptophanase (EC 126.96.36.199)
Histidine ammonia-lyase (EC 188.8.131.52)
Arginine deiminase (EC 184.108.40.206)
Carbamate kinase (EC 220.127.116.11)
Fructose-bisphosphate aldolase class II (EC 18.104.22.168)
Enolase (EC 22.214.171.124)
l-lactate dehydrogenase (EC 126.96.36.199)
Heat shock protein 60 family co-chaperone GroES
Chaperone protein DnaJ
Pyruvate oxidase (EC 188.8.131.52)
Iron-sulfur cluster assembly protein SufB
Acetolactate synthase large subunit (EC 184.108.40.206)
Ketol-acid reductoisomerase (EC 220.127.116.11)
Branched-chain amino acid aminotransferase (EC 18.104.22.168)
NADP-specific glutamate dehydrogenase (EC 22.214.171.124)
Glutamine synthetase type I (EC 126.96.36.199)
Branched-chain amino acid synthesis has been identified as an acid tolerance mechanism in S. mutans [47, 48]. Components of that system were significantly upregulated in all WS microcosms (acetolactate synthase, ketol-acid reductoisomerase, branched-chain amino acid aminotransferase), which suggests that it operates broadly across non-mutans streptococci. Another S. mutans acid response mechanism involves glutamate synthesis [49, 50]. The NADP-specific glutamate dehydrogenase was very strongly upregulated in all WS microcosms. By contrast, the NAD-specific glutamate dehydrogenase predominated in all NS microcosms. Co-factor specificity affects the direction of the reaction. The NAD-specific form degrades glutamate and generates ammonia, whereas the NADP-specific form uses ammonia to synthesize glutamate . Thus, that form has the combined potential to lower the pH via assimilating ammonia, while simultaneously upregulating other acid tolerance systems. Glutamine synthetase likewise was strongly upregulated in all WS microcosms, which further promotes ammonia assimilation. Both enzymes are part of the GlnR regulon, which appears to play a strong role in S. mutans acid tolerance . The WS microcosm results suggest that this mechanism may also be broadly conserved in other streptococci.
Our oral microcosm biofilm model of sucrose-induced dysbiosis has provided multi-omic data that support the “conservation of function” concept. The real-time pH curves confirmed that the NS and WS communities demonstrated collective pH phenotypes that were very different between groups, but very similar within groups. Two independent approaches to taxonomic analysis (HOMINGS and MEGAN5 LCA) showed that microcosm communities that retained extensive individual variation in community structure could generate similar NS and WS pH phenotypes.
By contrast, the SEED analysis showed characteristic NS and WS patterns of protein relative abundance that were highly conserved across taxonomically diverse communities. Moreover, many of the proteins that differed between each pH phenotype had functions that would act to promote maintenance of neutral pH under NS conditions or acid production and tolerance under WS conditions.
Since each NS and WS microcosm pair was grown from a single plaque inoculum, it appeared that sucrose pulsing was the main force driving the microcosms towards dysbiosis. All 12 inoculums were obtained from caries-active children, so we cannot rule out the possibility that the microbiotas from each subject were already predisposed to respond strongly to sucrose. Planned microcosm studies of plaque from caries-free children will help to address that question.
An obvious limitation of our microcosm model is that it cannot replicate all aspects of the oral environment. However, the deep level of metaproteomic analysis it allows makes it suitable as a platform for discovering which proteins are most consistently abundant during dysbiosis. Targeted proteomic approaches then can be used to determine whether those proteins are also abundant when plaque is exposed to sucrose in the mouth. In that case, it may be possible to define a set of dysbiosis biomarkers that could be used to detect at-risk tooth surfaces before the development of overt carious lesions.
Collection and processing of saliva and plaque samples
Our sample collection and processing protocol was the same as described in a previous publication . Briefly, samples were collected by a pediatric dentist from 12 children with mixed dentition (ages 6–11.5 years). Previous restorations were present in all children, and all were deemed by the clinical examiner to be at high risk for future caries. Active carious lesions were present in all subjects except 730 and 852 (Additional file 12). None of the subjects had taken antibiotics within 3 months prior to sample collection.
Children were asked to expectorate resting whole saliva into ice-cooled tubes. The dentist collected plaque from the margins of a single existing composite restoration in a primary tooth from each child (see clinical data in Additional file 12). A sterile instrument was used, and samples were immediately deposited into a vial containing pre-reduced anaerobic transfer medium. The University of Minnesota Institutional Review Board approved all procedures involving human subjects.
Each saliva sample was clarified by centrifugation, diluted twofold in a buffer simulating the ionic composition of saliva, and then filter-sterilized. Each matching plaque sample was dispersed by vortexing, and a portion was retained for DNA extraction and HOMINGS analysis (as described below).
Oral microcosm biofilm model
The remainder of each plaque suspension was incubated in paired CDC biofilm reactors, according to our published protocol . Briefly, hydroxyapatite disks were placed into sample holders for each reactor. Pellicles were formed, by coating each disk with processed saliva from a single child. Each set of coated disks then were inoculated with the plaque suspension from the corresponding child, placed into reactors containing 350 ml basal mucin medium (BMM) , and incubated at 37 °C under constant shear (125 rpm) for 24 h. BMM was then flowed through one reactor at 17 ml/min (125 rpm; 37 °C) for 48 h (NS conditions).
The second reactor additionally was sucrose-pulsed five times per day (20 v/v%, 43 ml each time) analogous to three meals and two snacks for the second and third day (the flow rate for the second reactor was set at 20 ml/min, to reduce fouling). Sucrose pulsing was discontinued at night.
Real-time pH was recorded every 15 min, throughout the 72-h incubation (NS and WS conditions). On the third day around 4:00 PM (the time when the sucrose-pulsed reactor typically reached minimum pH), biofilms from multiple disks per reactor then were pooled to create NS and WS microcosm samples. This process was repeated until paired NS and WS samples had been obtained for each of the 12 children. A portion of those samples was processed for DNA extraction, and the remainder was used for protein extraction.
DNA extraction and HOMINGS analysis
DNA was extracted using the protocol described on the HOMINGS website. The extracts then were shipped on dry ice to the HOMINGS core facility at the Forsyth Institute. Samples were processed through the HOMINGS workflow, which involves PCR amplification using universal primers for the V3–V4 regions of 16S rRNA, barcoded multiplex Illumina MiSeq sequencing, demultiplexing, and bioinformatic analysis with QIIME and ProbeSeq, a program developed at Forsyth to screen fastq files for sequences that match a validated set of probes for 638 species-level targets representing 538 oral species present in the HOMD database (plus an additional 129 genus-level probes). Additional information about HOMINGS, including validation, calibration, and reproducibility data, is available on the HOMINGS website . Submission of a methods article with a more detailed description of the HOMINGS approach is planned for November 2015 (personal communication, Dr. Bruce Paster, The Forsyth Institute).
Protein extraction from microcosm biofilms
An unanticipated challenge in developing the protein extraction protocol was that it was much more difficult to extract proteins from sucrose-pulsed biofilms. Initial yields from WS samples were much lower, and extracts also contained contaminants that interfered with mass spectrometry. It seemed likely that biofilm matrix components were not being adequately removed. A protocol combining highly denaturing conditions and pressure-cycling technology was developed to address the refractory nature of protein extraction from WS biofilms. Pressure cycling is proven to increase yields for downstream proteomics analysis . It greatly improved protein recovery from both NS and WS samples, so we used it consistently for both types of sample. Biofilms were snap-frozen and stored at −80 °C until needed. We ground the frozen biofilm with a mortar and pestle on dry ice and weighed the samples in 1.5-ml microfuge tubes. We added protein extraction buffer (7 M urea, 2 M thiourea, 0.4 M trietihylammonium bicarbonate, 20 % methanol, and 4 mM TCEP) in a ratio of 10 μl extraction buffer per milligram of biofilm. We sonicated the samples on ice at 30 % amplitude for 7 s with a Branson digital sonifier 250 (Branson Ultrasonics, Danbury, CT). We then pressure cycled the samples in a Barocycler NEP 2320 (Pressure Biosciences Inc., South Easton, MA) at 37 °C for 40 cycles of 35 kpsi for 30 s, followed by 0 kpsi for 15 s. We transferred the samples to new 1.5-ml microfuge tubes and added methyl methanethiosulfonate at an 8-mM final concentration to alkylate cysteines, and we incubated the samples for 15 min at room temperature. We centrifuged the samples at 10,000×g to spin out any insoluble material and pipetted two aliquots of each sample for Bradford assay. Once the concentration of each sample was determined, we aliquoted 150 μg of each sample for in-solution digestion. For the in-solution proteolytic digestion, we diluted the samples fourfold with ultra pure water and trypsin was added in a 1:35 ratio of trypsin—total protein. We incubated the samples at 37 °C for 16 h, and then we froze the samples and dried them in vacuo. We performed solid phase extraction on the samples with 3 cm3 Oasis MCX cartridges (Waters Corporation). The eluted peptides were dried down in vacuo.
2D liquid chromatography-mass spectrometry analysis
We processed the complex peptide mixtures by 2D LC-MS/MS. The first dimension offline HPLC system was a Shimadzu Prominence HPLC system (Shimadzu, Columbia, MD), and the HPLC column was a Phenomenex Kinetex® 5 μm EVO C18 100 Å, LC Column 150 × 2.1 mm with a Phenomenex SecurityGuard™ Gemini-NX C18 cartridge. Prior to loading, we resuspended each sample in 100 mM ammonium formate pH 10, 98 % water and 2 % acetonitrile. Buffer A was 20 mM ammonium formate, pH 10 in 98:2 water to acetonitrile, and buffer B was 20 mM ammonium formate, pH 10 in 10:90 water to acetonitrile. The flow rate was 200 μl/min with a gradient from 0–30 % buffer B over 55 min, followed by 30–60 % over 15 min. Fractions were collected every 2 min and UV absorbances were monitored at 215 and 280 nm. Peptide containing fractions was divided into two equal numbered groups, “early” and “late”. The first early fraction was concatenated with the first late fraction until all fractions were mixed together . We dried the concatenated samples in vacuo. After the first dimension peptide separation, we processed the dried peptide pellets according to the Stage Tip protocol , with the following revisions: the 3M (St Paul, MN) Empore™ solid phase extraction disks were styrenedivinylbenzene-reversed phase sulfonate (SDB-RPS), the peptides were reconstituted in aqueous 0.2 % formic acid, membranes were conditioned with acetonitrile and then ultrapure water, and wash solvent 1 was 95:5:0.2 %, water to acetonitrile to formic acid (FA). Wash solvent 2 was acetonitrile, and elution solvent was 60:35:5 %, acetonitrile to water to ammonium hydroxide. We dried the eluted peptides in a speed vacuum. We dissolved the dried peptide pellets in 98:2:0.1 %, water to acetonitrile to trifluoroacetic acid and analyzed by capillary LC-MS/MS on a Velos Orbitrap system according to the previously published LC and MS methods , with the following exceptions: lock mass was not enabled, dynamic exclusion setting list size was 200 values, duration was 30 s, and mass width was +/− 10 ppm.
The BLAST-P and Unique FASTA reads files for each sample were imported into the MEGAN5.7.1 software package  with input parameters set to Minimum BLAST Bit Score = 30, Maximum BLAST Expected Value = 3.0, Top Percent = 10. Minimum Support Percent = 0.0 (off), Minimum Support = 5, LCA Percent = 100, Minimum Complexity = −1.0 (off), and Use Minimal Coverage Heuristic = On. The BLAST-P file was parsed to extract phylogenetic and functional information for each metaproteome. Separate MEGAN5 .rma files were generated for each NS and WS samples from each subject. The MEGAN5 compare option then was used to generate a comparison file incorporating all 24 data sets. Spectral counts for each taxon and protein detected then were exported for statistical analysis in the R statistical computing environment .
Bray-Curtis distance matrices were calculated for the HOMINGS, MEGAN LCA, and MEGAN SEED datasets using functions available in the vegan package. Those were used as input for PCoA and hierarchical clustering.
The PCoA plots were generated using Kruskal’s non-metric multidimensional scaling algorithm (as implemented in the isoMDS function in the MASS package). Corresponding heat maps were generated using the heatmap.2 function from the gplots package. The linear model used with the edgeR software included within-subject effects for sucrose pulsing (NS vs. WS) and individual level. We first fitted a common dispersion parameter (using the function estimateGLMCommonDisp), then estimated how these estimates depend on the mean (using the function estimateGLMTrendedDisp), and finally smoothed the individual level dispersion parameters towards the common dispersion parameter in a data dependent fashion using an empirical Bayes approach (using the function estimateGLMTagwiseDisp). The p values from the tests at the individual protein/species level were then adjusted using the Benjamini-Hochberg procedure, controlling the FDR at 5 %.
Availability of supporting data
Multiplexed FASTQ output files for the first step in the HOMINGS analysis with barcode lists and an outline of the subsequent workflow are available at https://drive.google.com/folderview?id=0B610-sFuW0BKNUp0aHg2dHJGdlU&usp=sharing (note that samples were run in two batches). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository  with the dataset identifier PXD003151. HOMINGS probe counts, MEGAN5 LCA species-level spectral counts, SEED protein spectral counts, edgeR output files, and subject clinical metadata supporting the results of this article are included with the article as additional files.
We thank Robert S. Jones, DDS, and Ms. Patricia Lenton, CCRP at the University of Minnesota School of Dentistry, for their assistance with recruiting the plaque and saliva donors and for collecting the plaque and saliva samples, Dr. Bruce Paster and Ms. Alexis Kokaris at the Forsyth Institute HOMINGS core facility for providing the access to the raw FASTQ files, and Dr. Susan Van Riper at the University of Minnesota Center for Mass Spectrometry and Proteomics for the assistance in uploading the mass spectrometry data to PRIDE. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute and proteomics resources at the University of Minnesota Center for Mass Spectrometry and Proteomics. Funding was provided by NIH grant 5R01DE17734 (JDR), NSF grant 1147079 (TJG, PDJ, JEJ), and the University of Minnesota School of Dentistry (JDR)
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Nyvad B, Crielaard W, Mira A, Takahashi N, Beighton D. Dental caries from a molecular microbiological perspective. Caries Res. 2013;47(2):89–102. doi:https://doi.org/10.1159/000345367.View ArticlePubMedGoogle Scholar
- Simón-Soro A, Mira A. Solving the etiology of dental caries. Trends Microbiol. 2015;23(2):76–82. http://dx.doi.org/10.1016/j.tim.2014.10.010.View ArticlePubMedGoogle Scholar
- Moynihan PJ, Kelly SAM. Effect on caries of restricting sugars intake: systematic review to inform who guidelines. J Dent Res. 2014;93(1):8–18. doi:https://doi.org/10.1177/0022034513508954.PubMed CentralView ArticlePubMedGoogle Scholar
- Bradshaw DJ, Lynch RJM. Diet and the microbial aetiology of dental caries: new paradigms. Int Dent J. 2013;63:64–72. doi:https://doi.org/10.1111/idj.12082.View ArticlePubMedGoogle Scholar
- Sheiham A, James WPT. Diet and dental caries: the pivotal role of free sugars reemphasized. J Dent Res. 2015. doi:https://doi.org/10.1177/0022034515590377.
- Bowen WH, Koo H. Biology of Streptococcus mutans-derived glucosyltransferases: role in extracellular matrix formation of cariogenic biofilms. Caries Res. 2011;45(1):69–86. doi:https://doi.org/10.1159/000324598.PubMed CentralView ArticlePubMedGoogle Scholar
- Smith EG, Spatafora GA. Gene regulation in S. mutans: complex control in a complex environment. J Dent Res. 2012;91(2):133–41. doi:https://doi.org/10.1177/0022034511415415.View ArticlePubMedGoogle Scholar
- Lemos JA, Quivey RG, Koo H, Abranches J. Streptococcus mutans: a new gram-positive paradigm? Microbiol. 2013;159(Pt 3):436–45. doi:https://doi.org/10.1099/mic.0.066134-0.View ArticleGoogle Scholar
- Xiao J, Klein MI, Falsetta ML, Lu B, Delahunty CM, Yates III JR, et al. The exopolysaccharide matrix modulates the interaction between 3d architecture and virulence of a mixed-species oral biofilm. PLoS Pathog. 2012;8(4):e1002623. doi:https://doi.org/10.1371/journal.ppat.1002623.PubMed CentralView ArticlePubMedGoogle Scholar
- Gross EL, Beall CJ, Kutsch SR, Firestone ND, Leys EJ, Griffen AL. Beyond Streptococcus mutans: dental caries onset linked to multiple species by 16S rRNA community analysis. PLoS One. 2012;7(10):e47722. doi:https://doi.org/10.1371/journal.pone.0047722.PubMed CentralView ArticlePubMedGoogle Scholar
- Van Houte J, Lopman J, Kent R. The final ph of bacteria comprising the predominant flora on sound and carious human root and enamel surfaces. J Dent Res. 1996;75(4):1008–14.View ArticlePubMedGoogle Scholar
- Sansone C, Van Houte J, Joshipura K, Kent R, Margolis HC. The association of mutans streptococci and non-mutans streptococci capable of acidogenesis at a low ph with dental caries on enamel and root surfaces. J Dent Res. 1993;72(2):508–16.View ArticlePubMedGoogle Scholar
- Nascimento MM, Browngardt C, Xiaohui X, Klepac-Ceraj V, Paster BJ, Burne RA. The effect of arginine on oral biofilm communities. Mol Oral Microbiol. 2013;29(1):45–54. doi:https://doi.org/10.1111/mom.12044.View ArticlePubMedGoogle Scholar
- Kanasi E, Dewhirst FE, Chalmers NI, Kent Jr R, Moore A, Hughes CV, et al. Clonal analysis of the microbiota of severe early childhood caries. Caries Res. 2010;44(5):485–97. http://dx.doi.org/10.1159/000320158.PubMed CentralView ArticlePubMedGoogle Scholar
- Tanner ACR, Mathney JMJ, Kent RL, Chalmers NI, Hughes CV, Loo CY, et al. Cultivable anaerobic microbiota of severe early childhood caries. J Clin Microbiol. 2011;49(4):1464–74. doi:https://doi.org/10.1128/jcm.02427-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Tanner ACR, Kent RL, Holgerson PL, Hughes CV, Loo CY, Kanasi E, et al. Microbiota of severe early childhood caries before and after therapy. J Dent Res. 2011;90(11):1298–305. doi:https://doi.org/10.1177/0022034511421201.PubMed CentralView ArticlePubMedGoogle Scholar
- Gomar-Vercher S, Cabrera-Rubio R, Mira A, Montiel-Company JM, Almerich-Silla JM. Relationship of children’s salivary microbiota with their caries status: a pyrosequencing study. Clin Oral Invest. 2014:1–8. doi:https://doi.org/10.1007/s00784-014-1200-y.
- Benitez-Paez A, Belda-Ferre P, Simon-Soro A, Mira A. Microbiota diversity and gene expression dynamics in human oral biofilms. BMC Genomics. 2014;15:311. doi:https://doi.org/10.1186/1471-2164-15-311.PubMed CentralView ArticlePubMedGoogle Scholar
- Simón-Soro Á, Belda-Ferre P, Cabrera-Rubio R, Alcaraz LD, Mira A. A tissue-dependent hypothesis of dental caries. Caries Res. 2013;47(6):591–600.View ArticlePubMedGoogle Scholar
- Segata N, Haake S, Mannon P, Lemon K, Waldron L, Gevers D, et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 2012;13(6):R42.PubMed CentralView ArticlePubMedGoogle Scholar
- Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. Metatranscriptomics of the human oral microbiome during health and disease. mBio. 2014;5(2). doi:10.1128/mBio.01012-14.Google Scholar
- Juste C, Kreil DP, Beauvallet C, Guillot A, Vaca S, Carapito C, et al. Bacterial protein signals are associated with Crohn’s disease. Gut. 2014;63(10):1566–77. doi:https://doi.org/10.1136/gutjnl-2012-303786.PubMed CentralView ArticlePubMedGoogle Scholar
- Rudney JD, Chen R, Lenton P, Li J, Li Y, Jones RS, et al. A reproducible oral microcosm biofilm model for testing dental materials. J Appl Microbiol. 2012;113(6):1540–53. doi:https://doi.org/10.1111/j.1365-2672.2012.05439.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Y, Carrera C, Chen R, Li J, Lenton P, Rudney JD, et al. Degradation in the dentin-composite interface subjected to multi-species biofilm challenges. Acta Biomater. 2014;10(1):375–83. doi:https://doi.org/10.1016/j.actbio.2013.08.034.View ArticlePubMedGoogle Scholar
- Sissons CH, Anderson SA, Wong L, Coleman MJ, White DC. Microbiota of plaque microcosm biofilms: effect of three times daily sucrose pulses in different simulated oral environments. Caries Res. 2007;41(5):413–22. doi:https://doi.org/10.1159/000104801.View ArticlePubMedGoogle Scholar
- Igarashi K, Lee IK, Schachtele CF. Comparison of in vivo human dental plaque ph changes within artificial fissures and at interproximal sites. Caries Res. 1989;23(6):417–22.View ArticlePubMedGoogle Scholar
- Cotton SL, Klepac-Ceraj V, Murphy CM, Kokaras AS, Paster BJ. Species level determination of high-throughput sequencing data using homim probes. J Dent Res Spec Issue A. 2013;92:3828.Google Scholar
- McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288–97. doi:https://doi.org/10.1093/nar/gks042.PubMed CentralView ArticlePubMedGoogle Scholar
- Burne RA, Zeng L, Ahn SJ, Palmer SR, Liu Y, Lefebure T, et al. Progress dissecting the oral microbiome in caries and health. Adv Dent Res. 2012;24(2):77–80. doi:https://doi.org/10.1177/0022034512449462.PubMed CentralView ArticlePubMedGoogle Scholar
- Jagtap PD, Johnson JE, Onsongo G, Sadler FW, Murray K, Wang Y, et al. Flexible and accessible workflows for improved proteogenomic analysis using the galaxy framework. J Proteome Res. 2014;13(12):5898–908. doi:https://doi.org/10.1021/pr500812t.PubMed CentralView ArticlePubMedGoogle Scholar
- Jagtap P, Goslinga J, Kooren JA, McGowan T, Wroblewski MS, Seymour SL, et al. A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics. 2013;13(8):1352–7. doi:https://doi.org/10.1002/pmic.201200352.PubMed CentralView ArticlePubMedGoogle Scholar
- Jagtap P, McGowan T, Bandhakavi S, Tu ZJ, Seymour S, Griffin TJ, et al. Deep metaproteomic analysis of human salivary supernatant. Proteomics. 2012;12(7):992–1001. doi:https://doi.org/10.1002/pmic.201100503.PubMed CentralView ArticlePubMedGoogle Scholar
- Jagtap PD, Blakely A, Murray K, Stewart S, Kooren J, Johnson JE et al. Metaproteomic analysis using the galaxy framework. Proteomics. 2015; doi:https://doi.org/10.1002/pmic.201500074.
- Tang WH, Shilov IV, Seymour SL. Nonlinear fitting method for determining local false discovery rates from decoy database searches. J Proteome Res. 2008;7(9):3661–7. doi:https://doi.org/10.1021/pr070492f.View ArticlePubMedGoogle Scholar
- Huson DH, Weber N. Microbial community analysis using megan. Methods Enzymol. 2013;531:465–85. doi:https://doi.org/10.1016/B978-0-12-407863-5.00021-6.View ArticlePubMedGoogle Scholar
- Hong HA, Khaneja R, Tam NMK, Cazzato A, Tan S, Urdaci M, et al. Bacillus subtilis isolated from the human gastrointestinal tract. Res Microbiol. 2009;160(2):134–43. http://dx.doi.org/10.1016/j.resmic.2008.11.002.View ArticlePubMedGoogle Scholar
- Helgason E, Tourasse NJ, Meisal R, Caugant DA, Kolstø A-B. Multilocus sequence typing scheme for bacteria of the Bacillus cereus group. Appl Environ Microbiol. 2004;70(1):191–201. doi:https://doi.org/10.1128/aem.70.1.191-201.2004.PubMed CentralView ArticlePubMedGoogle Scholar
- Helgason E, Caugant DA, Olsen I, Kolstø A-B. Genetic structure of population of Bacillus cereus and B. thuringiensis isolates associated with periodontitis and other human infections. J Clin Microbiol. 2000;38(4):1615–22.PubMed CentralPubMedGoogle Scholar
- Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6. doi:https://doi.org/10.1038/nmeth.f.303.PubMed CentralView ArticlePubMedGoogle Scholar
- Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang H-Y, Cohoon M, et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 2005;33(17):5691–702. doi:https://doi.org/10.1093/nar/gki866.PubMed CentralView ArticlePubMedGoogle Scholar
- Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2014;42(D1):D459–71. doi:https://doi.org/10.1093/nar/gkt1103.PubMed CentralView ArticlePubMedGoogle Scholar
- Lemos JAC, Abranches J, Burne RA. Responses of cariogenic streptococci to environmental stresses. Curr Issues Mol Biol. 2005;7(1):95–107.PubMedGoogle Scholar
- Klein MI, Xiao J, Lu B, Delahunty CM, Yates III JR, Koo H. Streptococcus mutans protein synthesis during mixed-species biofilm development by high-throughput quantitative proteomics. PLoS One. 2012;7(9):e45795. doi:https://doi.org/10.1371/journal.pone.0045795.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng L, Itzek A, Chen Z, Kreth J. Environmental influences on competitive hydrogen peroxide production in Streptococcus gordonii. Appl Environ Microbiol. 2011;77(13):4318–28. doi:https://doi.org/10.1128/aem.00309-11.PubMed CentralView ArticlePubMedGoogle Scholar
- Kreth J, Zhang Y, Herzberg MC. Streptococcal antagonism in oral biofilms: Streptococcus sanguinis and Streptococcus gordonii interference with Streptococcus mutans. J Bacteriol. 2008;190(13):4632–40. doi:https://doi.org/10.1128/jb.00276-08.PubMed CentralView ArticlePubMedGoogle Scholar
- Ayala-Castro C, Saini A, Outten FW. Fe-S cluster assembly pathways in bacteria. Microbiol Mol Biol Rev. 2008;72(1):110–25. doi:https://doi.org/10.1128/mmbr.00034-07.PubMed CentralView ArticlePubMedGoogle Scholar
- Santiago B, MacGilvray M, Faustoferri RC, Quivey RG. The branched-chain amino acid aminotransferase encoded by ilve is involved in acid tolerance in Streptococcus mutans. J Bacteriol. 2012;194(8):2010–9. doi:https://doi.org/10.1128/jb.06737-11.PubMed CentralView ArticlePubMedGoogle Scholar
- Len ACL, Harty DWS, Jacques NA. Proteome analysis of Streptococcus mutans metabolic phenotype during acid tolerance. Microbiol. 2004;150(Pt 5):1353–66. doi:https://doi.org/10.1099/mic.0.26888-0.View ArticleGoogle Scholar
- Feehily C, Karatzas KAG. Role of glutamate metabolism in bacterial responses towards acid and other stresses. J Appl Microbiol. 2013;114(1):11–24. doi:https://doi.org/10.1111/j.1365-2672.2012.05434.x.View ArticlePubMedGoogle Scholar
- Chen P-M, Chen Y-YM YS-L, Sher S, Lai C-H, Chia J-S. Role of glnr in acid-mediated repression of genes encoding proteins involved in glutamine and glutamate metabolism in Streptococcus mutans. Appl Environ Microbiol. 2010;76(8):2478–86. doi:https://doi.org/10.1128/aem.02622-09.PubMed CentralView ArticlePubMedGoogle Scholar
- Engel P. Glutamate dehydrogenases: the why and how of coenzyme specificity. Neurochem Res. 2014;39(3):426–32. doi:https://doi.org/10.1007/s11064-013-1089-x.View ArticlePubMedGoogle Scholar
- Human oral microbiome identification using next generation sequencing (HOMINGS). The Forsyth Institute, Boston MA. 2015. http://homings.forsyth.org/index2.html.
- Freeman E, Ivanov AR. Proteomics under pressure: development of essential sample preparation techniques in proteomics using ultrahigh hydrostatic pressure. J Proteome Res. 2011;10(12):5536–46. doi:https://doi.org/10.1021/pr200805u.View ArticlePubMedGoogle Scholar
- Yang F, Shen Y, Camp DG, Smith RD. High-ph reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Rev Proteomics. 2012;9(2):129–34. doi:https://doi.org/10.1586/epr.12.15.PubMed CentralView ArticlePubMedGoogle Scholar
- Rappsilber J, Ishihama Y, Mann M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem. 2003;75(3):663–70.View ArticlePubMedGoogle Scholar
- Lin-Moshier Y, Sebastian PJ, Higgins L, Sampson ND, Hewitt JE, Marchant JS. Re-evaluation of the role of calcium homeostasis endoplasmic reticulum protein (cherp) in cellular calcium signaling. J Biol Chem. 2013;288(1):355–67. doi:https://doi.org/10.1074/jbc.M112.405761.PubMed CentralView ArticlePubMedGoogle Scholar
- Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, et al. The Paragon algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics. 2007;6(9):1638–55. doi:https://doi.org/10.1074/mcp.T600050-MCP200.View ArticlePubMedGoogle Scholar
- Bize A, Cardona L, Quéméner ED-L, Battimelli A, Badalato N, Bureau C et al. Shotgun metaproteomic profiling of biomimetic anaerobic digestion processes treating sewage sludge. Proteomics. 2015. doi:https://doi.org/10.1002/pmic.201500041.
- Huson DH. Megan5—metagenome analyzer. 2013. http://ab.inf.uni-tuebingen.de/software/megan5/.
- R: a language and environment for statistical computing [database on the Internet]. R Foundation for Statistical Computing. 2015. Available from: https://www.R-project.org.
- Vizcaíno JA, Côté RG, Csordas A, Dianes JA, Fabregat A, Foster JM, et al. The proteomics identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 2013;41(D1):D1063–9. doi:https://doi.org/10.1093/nar/gks1262.PubMed CentralView ArticlePubMedGoogle Scholar