One factor was already mentioned: the number of OTUs in the sample. In this sense, adding another OTU would affect evenness if the sample contained few OTUs. The other factor is the number of individuals in the OTU added relative to the numbers of individuals in the other OTUs in the sample. If the new OTU contains many more members than the existing ones, then its addition will decrease the evenness and therefore decrease the overall diversity, as we observed in the effect of E. coli spike-in on the low-biomass samples in the preliminary experiments. If the new OTU has fewer members than the existing ones, its addition may not affect evenness much at all, as in the case of E. coli spike-ins relative to the preliminary cultures. In either case, rarefaction to a large enough depth would help retain biological differences, in other words, differences inherent to the samples and not as consequences of mathematical manipulations. As already pointed out in literature, a major drawback of rarefaction would be losing rarer OTUs because of the lack of sampling depth, i.e. discarding rarer sequences as large samples are reduced to smaller ones. Fortunately, that was not a major concern in the preliminary experiments, as we did not design the procedure as a way to discover rare microbes or uncover whether our methods support the growth of as-of-yet uncultivated organisms. Our goals, plastic potting pots combined with the culturing methodology as well as the rarefaction threshold we chose, meant that, ultimately, we did not lose exclude crucial or even important information from the data set.It is interesting to note that streptococcal species tend to attach to a surface first and provide a suitable colonization environment for other species.
Previous studies have shown that several streptococcal species produce adhesins that preferentially bind to different substrates in human saliva and cells. For instance, S. salivarius, an OTU prevalent in our model, expresses amylase-binding proteins that interact with both salivary amylase and surface lectins that bind to extra parotid glycoproteins. S. salivarius also expresses antigen C fibrillar glycoprotein that binds to the epithelial cells on the human cheek, and fibrillar antigen B that binds to Veillonella parvula. Similarly, S. oralis expresses surface lectins and antigens that bind to salivary glycoproteins. These surface proteins facilitate streptococcal attachment to the enamel surface on one hand, and on the other help bacterial cells from other phyla immobilize in preferred environmental niches and subsequently proliferate. These proteins are doubtless a large part of the reason that streptococcal cells act as early colonizers of the dental surface. The results from the preliminary experiments indirectly support this colonization order, by the relative abundances of Streptococcus OTUs in the cultures . Incidentally, the Streptococcus OTUs were only the second most abundant in the preliminary cultures; members from the Veillonella OTUs were the most abundant. Not as much research has been done for the attachment processes of Veillonella, though members of this genus are known to be early colonizers in dental plaque as well, coaggregating with streptococcal species and using the lactic acid produced by Streptococcus oralis. Clearly, our results indicate that the methods in this phase of the project properly support some best known early colonizers in the human dental plaque bacterial community.In this phase, three volunteer hosts were used as sources of dental plaque.
Sample collection took place under protocols 3-18-0189 and 3-19-0119, approved by the UCSB Human Subjects Committee. Prior to plaque collection, hosts abstained from food, nonwater liquids, and dental hygiene for 12 hours. At the time of collection, supragingival plaque of five molar teeth was obtained from each host using a sterilized Gracey curette. Collected plaque was immediately suspended in sterile centrifuged SHI medium, gently mixed, and divided equally among wells in a sterile surface modified 24-well plate such that each well received 1.98mL of the mixture. Prior to receiving inoculated medium, wells were conditioned with an artificial pellicle formed by clarified human saliva , which was supplied as frozen fractions pooled from healthy human volunteers. Saliva was stored at -20°C until clarification, at which point we defrosted the saliva on ice. Clarification of defrosted saliva was performed on site by centrifuging at 6,000 x g for 3 minutes at 4°C, mixing with 1X PBS in a 1:1 ratio, and passing the mixture through 0.2µm filters. Unused clarified saliva was stored at 4°C for no more than 3 days before being used or discarded. The artificial pellicle in each well was formed by adding 150µL clarified saliva to the bottom of the well and air drying at 37°C for 60 minutes. The plate was then sterilized with short-wave UV light for 60 minutes. At this point, wells were considered conditioned and ready for media. For this set of experiments, cultures derived from each host received a separate 24- well plate. We divided the plates into sections to facilitate harvesting at the specific time points of 12, 24, 48, 96, and 168 hours, in order to investigate the effects of increased incubation time on culture composition.
Each plate was divided into 6 sections of 4 wells , consisting of 2 controls in the top row with the medium and pellicle and 2 cultures in the bottom row with host-plaque-inoculated medium and pellicle. After receiving sterile or inoculated medium, each well also received 20µL of 0.5% sucrose. Plates were then incubated in a sealed vessel at 37°C in an anaerobic atmosphere composed of 85% nitrogen, 10% hydrogen, and 5% carbon dioxide. At designated feeding times , all wells were supplemented with 5µL of 0.5% sucrose and specified volumes of SHI such that a constant well volume of 2.0mL was kept throughout the incubation process. If the feed time coincided with the harvest time for any particular section of wells, then feeding did not occur. At the five designated times, we harvested the designated section of wells by first aspirating the liquid, and then mixing the sedimented cells gently but well. We pipetted 900µL of properly mixed sedimented cultures into sterile microfuge tubes and added 100µL of 10% diluted E. coli cultures with OD600 values of 0.8. The resulting mixtures were pelleted, flash-frozen in liquid nitrogen, and kept at -80°C until further processing at the UC Davis Host-Microbe Systems Biology Core . To compare the culture composition with original host plaque and track the temporal development, we pelleted and froze plaque samples from all hosts at the start of incubation. In addition, we pelleted and froze 500µL aliquots of all E. coli cultures used for spike-ins at the times of the spike-ins, with the intentions of observing OTUs in pure E. coli cultures and retaining the ability to remove these OTUs from spiked cultures. In this phase of the project, we elected to keep the E. coli spike-in step such that we could continue checking the biomass of the controls as well as gain an understanding of the biomass in cultures and the potential differences in biomass across the cultures derived from different hosts.DNA extraction and sequencing were performed at the UC Davis Host Microbe Systems Biology Core , in a manner similar to the sequencing procedures from the preliminary experiments . Sequencing of properly amplified and diluted libraries was performed on the Illumina MiSeq platform using the paired-end method with a length of 253bp. Quality control of the raw sequences was first performed in QIIME by HMSB , and then formally reperformed in R with the mothur software. Briefly, 4,298,748 contigs were constructed from raw reads that were size-selected to be in the range of 240 to 275 bp. Constructed contigs were trimmed to eliminate ambiguous reads, and the resulting reads were screened for homopolymers with an upper threshold of 8 and pre-clustered. Chimeric sequences were then removed with VSearch, and non-bacterial sequences were removed based on the full-length SILVA database . The resulting 3,132,678 contigs were clustered into operational taxonomic units based on the full-length SILVA reference database at 97% level of sequence identity, approximating species-level taxa. These OTUs were constructed into a dense BIOM table for bio-informatics analysis.Bio-informatics analysis was first performed in QIIME at HMSB, and then formally reperformed in R with version 1.38.0 of the phyloseq software package. In this analysis, raspberry container growing we examined the sequencing depth, the number of OTUs, and the inverse Simpson’s index values of the samples.
As in the preliminary experiments, we considered the prevalence of organisms at the phylum level to gain an understanding of the general community structures. Then, we examined the correlation between sequencing depth and diversity using rarefaction curves and plots of read counts vs. diversity indices. To standardize sample sizes and minimize the presence of potentially spurious OTUs, we rarefied samples to a depth 30,000 and reexamined the number of OTUs and inverse Simpson’s index values. We also studied the absolute counts and relative abundances of the controls and cultures, inferring the biomass in controls and cultures relative to the E. coli spikes, to verify that there was minimal contamination throughout the culturing, extraction, amplification, and sequencing processes. After characterizing the samples as a whole and confirming minimal contamination, we focused on the culture and plaque samples. We removed the most prominent spike-in OTU reads from the rarefied read counts, converted read counts to relative abundances, and then examined the 12 most prominent OTUs in the plaque and culture samples. With the relative abundances, we performed Principal Coordinate Analysis using the Bray-Curtis dissimilarity metric, to investigate patterns in sample clustering according to incubation times and sample types. From the clustering patterns, we identified the OTUs that likely played the most important roles in sample differences. Lastly, we used the stats package for the relative abundances and the mixOmics package for the centered-log-ratio-transformed relative abundances to perform Principal Component Analysis .Sequencing of the 16S rRNA V4 region yielded a total of 4,298,748 raw contigs. Using the mothur software and release 132 of the full-length SILVA database, we trimmed and filtered out ambiguous bases, retaining 3,144,467 of the raw reads. From these, 122,617 unique sequences were found. Screening for homopolymers led to the retention of 99.6% and 98.1% of total reads and unique sequences, respectively. Pre-clustering to remove likely pyrosequencing errors with a tolerance of two mismatches resulted in 3,132,678 reads and 26,757 unique sequences, and chimera removal using VSearch led to the retention of 98.3% and 76.7% of total reads and unique sequences, respectively, from the previous step. Subsequent temoval of non-16S-rRNA sequences resulted in 20,508 unique sequences and 3,077,896 total reads. With the same full-length SILVA database, we then generated OTUs based on 97% sequence similarity and constructed the BIOM table from these OTUs.Temporal samples with spike-ins yielded high read counts across sample types and incubation times, except for one that failed to sequence well . Fewer than 200 OTUs were found in all samples regardless of rarefaction . As expected, negative controls and pure E. coli samples contained the fewest OTUs out of all sample types; the number of OTUs for negative controls and that for pure E. coli were comparable to each other. Interestingly, 4 out of 6 plaque samples contained fewer OTUs than many cultures while 2 plaque samples clearly yielded many more OTUs than all other samples. This difference deserves a much closer look, which we provide later in this section. A quick scan of the prevalence at the phylum level shows that Firmicutes and Proteobacteria were the most prevalent and the most abundant taxa, followed by Actinobacteria, Bacteriodetes, and Epsilonbacteraeota. These five taxa numbered among the most prevalent phyla in the preliminary experiments as well, indicating that the conditions we used favored members of these phyla and that the conditions were appropriate for promoting the growth of bacteria from key phyla of the oral microbiome. We then examined the rarefaction curves and plots of sequencing depth vs. diversity indices. The rarefaction plot of plaque and cultures shows that the number of OTUs discovered begins leveling around 30,000 reads for all samples. In other words, below 30,000 reads, new OTUs would still be discovered with increasing sampling depth, so rarefaction thresholds below 30,000 reads run the risk of artificially reducing true diversity in the samples. Furthermore, plots of read counts vs. diversity indices show that a somewhat weak but statistically significant negative linear correlation exists between sequencing depth and the Shannon index , and between sequencing depth and the inverse Simpson’s index , though the correlations are not immediately apparent on the plots.