The Nanopore and PacBio methods also tend to have high error rates, the lower limit being approximately 4.9% for insertion errors on the Nanopore platforms, and the higher limit being 11% for insertion and/or deletion on the PacBio platforms. The third platform, the Ion Torrent, outputs reads of 200bp and 400bp on two different machines at a relatively fast rate, but are prone to indel errors and homopolymers longer than 6bp. The length and historically high error rates of these three platforms make them unsuitable for the study of microbiome compositions by short genetic markers, though recent literature has shown that an optimized bioinformatics pipeline on the improved PacBio platform can achieve less than 0.01% error rate for full-length 16S rRNA sequences, though the read lengths of the PacBio platform remain suboptimal for experiments involving comparisons of large microbial communities. The fourth HTS platform comes from Illumina and has been indispensable for the study of microbiomes because of its high sequencing depth, speed of operation, cost efficiency, and low error rates. Illumina technology is based on clonal amplification on a glass surface and detection by cyclic reversible termination. Bases are detected by a coupled-charge device camera, square planter pots and the fluorescent signal incorporated into the incoming base can be easily cleaved after imaging. The most common error on Illumina platforms is substitution, and the error rate was already reduced to less than 1% morethan 10 years ago.
The MiSeq platform has been shown to be particularly suitable for studies of microbiome composition because it has low costs and short run times while still taking full advantage of the species-level differences in the variable regions of bacterial 16S rRNA component of the ribosomal 30S subunit. The species-level specificity of the conserved 16S rRNA region enables the classi- fication of organisms into operational taxonomic units , which serve as effective proxies for taxonomic levels. Great efforts have been made in determining the variable regions most suitable for distinguishing among microorganisms, and in creating the most efficient universal primers, for sequencing bacterial 16S rRNA . As universal primers and various error-correction software packages have been developed and refined, research on the human microbiome has experienced exponential growth. Previously undetectable, undifferentiable, and uncultivatable strains have been identified and classified, now aggregated into the particularly notable effort of the Human Microbiome Project, which has culminated in extensive records and repositories of the memberships, distributions, and interactions of human microbiota, including those of the oral microbiota. The exponentially growing research on the human microbiome has thrown into doubt onto the idea that a single organism or factor is responsible for the manifestation of a disease. The ability to compare community compositions between healthy and diseased hosts marked the beginning of a deepening understanding of human wellness, where the concept has emerged that some diseases may originate from the imbalance of microbial communities rather than from the actions of single organism or a group of organisms.
This idea has taken hold in therapeutic approaches, most notably in faecal transplantations to treat obesity and some forms of inflammatory bowel diseases, though treatment of some other diseases such as Crohn’s Disease by fecal transplantation resulted in mild side effects. However, to date, no oral microbiome transplantation has yet been attempted, despite the ample evidence linking disrupted oralmicrobiota to many systemic diseases. There seems to be some efforts in this direction, shown by an exceedingly recent publication detailing a protocol for developing and characterizing an oral microbiome transplant, but no results as of December 2021. The length of time between the initial studies of the oral microbiome and the potential application of oral microbiome transplantations, or between the efforts toward fecal microbiome transplantations and the current efforts toward oral microbiome transplantations, could be explained by a need for pilot studies or for observations of long-term effects. An in vitro model of the dental plaque microbiome and/or the oral microbiome that can be readily generated, easily modified, and rapidly tested would contribute significantly to the research efforts on the therapeutic potential of the oral microbiome transplantation approach.As HTS using 16S rRNA markers rose prominently as the dominant approach for identifying microorganisms and characterizing bacterial community compositions, so did the need for assessment, quality control, and optimization of the sequencing process. From the first step of the sequencing process – the amplification step – there is a need for effective assessment of the quality of universal primers. Designing universal primers that are simultaneously specific for a variable region of the bacterial 16S rRNA and are able to capture different bacterial species and strains is no small feat – it’s been shown that even a single mismatch between the primer and template can lead to thousand-fold misrepresentation of the abundance of the sequence.
These differences in primer efficiency and specificity can result in distortions in the apparent relative abundances of a community, and much effort has been devoted to optimizing primer pairs that capture the widest range of organisms and exhibit the highest specificity and reproducibility. Errors in other steps of the HTS process, such as PCR amplification and incomplete reactions during sequencing also affect the quality of the sequencing data. Raw sequences, therefore, need to be checked and filtered, and various tools have been constructed for these purposes. Some tools are built into software packages such as mothur, QIIME, and DADA2; others were created as standalone insertables that can be introduced into workflows. In addition to checking the quality of the raw reads, investigators have adopted internal sequencing standards as part of routine procedure as quality control for each sequencing run. After preprocessing raw reads to retain only high quality reads, identifying and classifying members of a community come next. A number of methods have been developed to this end, most of which fall into either phylotyping or OTU clustering. Phylotyping assigns reads into bins based on read homology and reference sequences such as those in the Human Oral Microbiome Database; OTU clustering, which frequently uses the naïve Bayesian classifier, assigns reads into bins based on distances between reads, with percent similarity cutoffs. These two approaches can be used to complement each other, as phylotyping has difficulty treating unknown organisms or incomplete sequences in the databases and OTU clustering can exhibit ambiguities that result from ill-defined percent similarities, especially when sequencing errors lead to spurious OTUs and overestimated diversity. The identification of organisms in allows for comparisons of different organisms within a sample and between samples. Members and their abundances within a sample are collectively known as the “alpha diversity” of that sample, whereas the differences between memberships and abundances across two or more samples are collectively known as “beta diversity” . Different indices have been developed to quantify these two types of diversities. Currently, there exist, for both types of diversity, indices that only account for the absence and presence of members as well as indices that account for membership and their distribution . A number of alpha diversity indices have been particularly popular in microbiome studies, including Simpson’s index, which represents the probability that two individuals randomly drawn from a sample belong to the same type; and Shannon index, which quantifies the probability of predicting the identity of the next individual drawn from a sample, based on the sample size and relative abundances. Simpson’s index is frequently used in its reciprocal form, called Inverse Simpson’s index, which represents the effective number of types. Inverse Simpson’s index is more influenced by dominant OTUs while Shannon index is more influenced by rare OTUs, so they are often used in conjunction to examine different aspects of diversity in a community. Interestingly, square pot beta-diversity indices seem to be infrequently used in microbiome research. Instead, the community has taken to using variance and distance measures in the characterization of core microbiome across different body sites and different hosts and the comparison of microbiome compositions in healthy and diseased states. A common way to assess beta diversity in microbiome research is using inter-sample distance measurements. In these types of analyses, abundance data is sorted into matrices with samples as rows and species or OTUs as columns. Distance measures representing dissimilarities between pairs of samples are computed, and the resulting triangular matrix is used for ordination approaches such as Principal Coordinate Analysis and/or Principal Component Analysis.
A number of different distance measurements have been adopted for these ordination approaches. Distance measurements, like alpha diversity indices, include those that consider membership only, those that consider membership and abundance, and those that consider phylogenetic relatedness, though the distance measurements that account for membership only are not as commonly used. Of the most frequently used indices, Bray-Curtis and weighted UniFrac, account for the abundance as well as the presence and absence of taxa, and UniFrac also considers phylogenetic relatedness. As for the implementation of these indices in ordination techniques, PCoA uses distance matrices to construct clusters of similar samples, and PCA uses distance matrices as well as ma-trix transformations to visualize sample similarities. Both techniques reduce the dimensionality represented by the large number of OTUs in microbiome datasets. In many cases, PCA and PCoA reduce the dimensionality to two or three dimensions for ease of visualization and elucidation of the major factors underlying inter-group and inter-sample similarities. The purpose of beta-diversity assessment and ordination is most often to delineate the relationships among samples, hosts, or other meta-data groupings. Of course, more rigorous statistical testing can be performed with microbiome datasets. Currently popular approaches stem from multivariate statistics, as conventional statistics are based on count data and absolute values in Euclidean space instead of relative abundance data in simplex space where abundances sum to 1 or some other constant. In simplex spaces, conventional statistical procedures such as the t-test and ANOVA can lead to high false discovery rates, in many cases because of the assumption that the underlying population distribution adopts a predefined shape . Efforts to circumvent the distributional assumption problems have led to approaches such as ANOSIM – analysis of similarities, for which no underlying population distribution is assumed but the null hypothesis of “no differences exist among samples or groupings” can still be tested; ANCOM – analysis of composition of microbiomes – an approach that also does not rely on distributions and can be implemented in linear model frameworks; and PERMANOVA, an approach based on analysis of variance that is independent of underlying distributions as well as metric distances but can still partition variances based on any distance measure. As will be evident later, the data from this project is best analyzed with PCoA, PCA, and some limited use of ANOSIM.Over the decades, there have been efforts to construct in vitro models of the human oral microbiome. Much of this effort has focused on generating laboratory conditions that most closely match those in in vivo environments. Like microorganisms in other environmental niches, human oral microorganisms form biofilms to increase their physical proximity to one another, allowing for inter-strain and inter-species cooperation for biomass accumulation and against environmental fluctuations. Hence, most oral microbiome models have been designed to promote biofilm formation. These models vary in device shape, substrate type, media composition, incubation time, and species in the inoculum. Some models pre-condition culture plates with artificial pellicle, paying little heed to the exact characteristics of the substrate surface ; others, in addition to the artificial pellicle, use substrates with surface properties similar to those of oral surfaces. Some models adopt continuous flow devices or rotating devices to mimic the salivary sheer forces in the host oral cavity; others forego this aspect of the oral cavity. Some models use host communities to inoculate the cultures; others use a number of laboratory strains to form the inoculum. Most models use media components intended for fastidious organisms – brain heart infusion, media constituted from various components that supply different amino acids, pig mucin as a major carbon source – and receive supplements such as vitamins and siderophores. Some models have complex designs that try to mimic the oral cavity while permitting non-invasive sampling and regular or continuous measurement; others seek the minimal equipment necessary for generating a model community. Despite the ample number of different models, not many longitudinal ones have been developed; those that have incubation periods of longer than 48 hours tend to use devices that supply a near-constant stream of nutrients and saliva, and little research has been done to generate a flow-device-free, fermenter-free model. Because of the lack of research in this area, we aimed to devote a considerable portion of our project to temporally extending the 24-well cultivation method with the longitudinal component to maximally simplify the procedures while retaining reproducibility.