are not autonomous organisms, they are biological units which include numerous
microbial symbionts. Genomes of the microbial symbionts are also included. Our
health and well-being is determines by the microbes which are present in and on
our body. The association between the human being and their symbiotic microbes
leads to the creation of a holobiont.
Lederberg coined the term ‘microbiome’. It
denotes the ecological community of that are present in and on our body
surfaces such as commensal, symbiotic, and pathogenic microorganism1. The
mouth is one of the most common site for the presence of such microbiome.
280 bacterial species from the oral cavity have been isolated in culture and formally
named. About half of the bacterial species present in the oral cavity can be
cultivated using anaerobic microbiological methods and these amount to 500 to
700 common oral species.
oral cavity includes teeth, gingival sulcus, attached gingiva, lip, cheek, soft
palate and hard palate. Human oral microbiome is defined as all microorganisms
that are found in the oral cavity and contiguous extensions ending at the
distal esophagus. Studies have shown that distinct microbial communities
colonize the oral structures and tissues.
similarity is calculated by pairwise alignment as the percentage of sites that matches in the pairwise alignment.
The common similarity threshold which is applied is 97%. This is derived from
an empirical study that shows more 97% of 16S rRNA sequences shows similarity.
helps to unveil large part of microorganisms which constitutes a
microbiota.This usage of a culture free technology has started to spread widely
into microbial studies over the past decade4.This technology is now applied
to several domains including clinical microbiology to get signature of
microbiota which is associated with a
clinical picture5.They are also widely used in quality control processes in
industries or for studying the environmental communities in a region6,7.
refers to two distinct methods.They are shotgun metagenomics and targeted
metagenomics. In shotgun metagenomics the entire genomic content of a sample is
considered by sequencing and extracting the total DNA. So this approach offers
a complete picture of microbiota. It provides the opportunity to explore the
functional diversity and taxonomic of microbial communities8.The main disadvantage
of this technique is that this is very expensive and data analysis is
challenging, this is due to the size and the complexity of the data9.
metagenomics is also known as “metagenetics”.In targeted metagenomics a
taxonomically informative genomic marker alone is focussed. This locus is
amplified before sequencing, this greatly reduces the amount of data needed to
be sequenced and analysed. 16s rRNA gene is the target of choice for this
method. Targeted metagenomics can now be integrated into routine processes. HTS
studies can be performed at a lower cost and smaller scale due to the advent of
“Bench-top sequencing”. This has made targeted metagenomics an efficient
approach to be followed in hospitals, common laboratories10,11 and industries.
The first benchtop sequencing method was introduced by Roche it released “454
GS Junior pyrosequencer”.
Ion Torrent sequencers which use semiconductor
ion detection and the Illumina MiSeq and NextSeq sequencers which uses
fluorescent dye for detection are developed.The major difference12 between
them is the type and the abundancy of sequencing errors which impacts the
base-calling quality.After producing raw sequencing reads the next step followed
is to analyse the composition of different taxa and microbial diversity study.
different bioinformatics pipelines are available13.Pipelines are designed in
such a way that they link many steps together14.They integrate many
algorithms together to offer us different possibilities.There are different proposed
guidelines and analytica steps for each pipelines
16s rRNA gene is present in all bacteria. Since
it is highly conserved it can be easily amplified using universal primers. 16s
rRNA amplicon sequencing is often performed to analyse the environmental
microbial communities. There are nine hyper-variable regions in the 16sRNA
gene. These hyper-variable regions are used to distinguish between different
organisms. There are several pipelines available for the analysis of 16sRNA
gene sequences. The pipeline for Metagenomic analysis starts with amplifying
the hyper-variable regions in the 16sRNA gene sequences using primers. The
sequences are then clustered based upon their similarity into groups called
‘Operational Taxonomic Units.
QIIME a bioinformatics pipeline for performing
microbiome analysis.It uses raw DNA obtained from sequencing. QIIME is
specially designed to take users from raw sequencing data generated on the
Illumina or other platforms through publication quality graphics and
statistics. The common methods followed in this pipeline includes
demultiplexing and quality filtering, OTU picking, taxonomic assignment, and
phylogenetic reconstruction, and diversity analyses and visualizations.
1. Paster, B. J., S. K. Boches, J. L. Galvin, R.
E. Ericson, C. N. Lau, V. A. Levanos, A. Sahasrabudhe, and F. E. Dewhirst. 2001. Bacterial
diversity in human subgingival plaque. J. Bacteriol. 183:3770-3783
2. Aas, J. A., B. J. Paster, L. N. Stokes, I. Olsen,
and F. E. Dewhirst. 2005. Defining the normal bacterial flora of the oral
cavity. J. Clin. Microbiol. 43:5721-5732.
3. Mäntylä, P., M. Stenman, M. Paldanius, P. Saikku,
T. Sorsa, and J. H. Meurman. 2004. Chlamydia pneumoniae together with
collagenase-2 (MMP-8) in periodontal lesions. Oral Dis. 10:32-35.
Simon C, Daniel R. Metagenomic analyses:
Past and future trends. Appl Environ Microbiol2011;77:1153–61.
J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut
microbial gene catalogue established by metagenomic sequencing. Nature
JA, Jansson JK, Knight R, Gewin V, Gilbert J, Meyer F, et al. The Earth
Microbiome project: successes and aspirations.
A, Bicak M, Kottmann R, Schnetzer J, Kostadinov I, Lehmann K, et al. The ocean
sampling day consortium. Gigascience 2015
N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C. Computational
meta’omics for microbial community studies.
S, Adair KL, Gardner PP. An evaluation of the accuracy and speed of metagenome
Application Note 16S Metagenomics Studies with the MiSeq™ System.
Technologies Application Note 16S rRNA Sequencing.
S, Audebert C, Lemoine Y, Hot D. Comparison of mapping algorithms used in
high-throughput sequencing: application to Ion Torrent data. BMC Genomics
2014;15:264 doi: 10.1186/1471-2164-15-264.
IL, Gerba CP, Gentry TJ. Environmental Microbiology. Elsevier Science; 2014.
M, Lee K-H, Yoon S-W, Kim B-S, Chun J, Yi H. Analytical tools and databases for
metagenomics in the next-generation sequencing era. Genomics Inform
2013;11:102–13. doi: 10.5808/GI.2013.11.3.102.