1. The microbiome in health and disease: how do we study microbial communities?
Anna Vesty
MLS, LabPlus, ADHB
PhD Candidate, The University of Auckland
As part of my PhD I study the oral microbiome and how it effects patients receiving radiotherapy treatment, which is how I have come to learn about the techniques used to study the human microbiome.
21st May 2016
Waipuna Hotel & Conference Centre
Auckland

2. A collection of microbes inhabiting environment Human microbiome:
Bacteria, fungi, viruses and protozoa
Average human body contains:
~10 trillion ‘human’ cells
~10 – 100 trillion bacterial cells
(Sender et al. Cell 2016)
The microbiome is a collection of microbes, which inhabit an environment and create an ecosystem.
Our human microbiome is made up of bacteria, fungi, viruses and protozoa that live on or within our bodies.
The trillions of bacteria alone in our microbiome are thought to outweigh our human cells by one to ten.
Not surprisingly, these microbes play many important roles in our bodies, including:
Aiding food digestion
Helping to regulate our immune system
And protection against pathogens.

3. The human microbiome was well studied in a large collaborative effort called the Human Microbiome Project. This study investigated the bacterial microbiomes at various sites of the human body in a range of healthy and diseased participants.
The results revealed that distinct microbial niches occur at different sites in the body e.g. the gut, oral cavity, skin and many more.
The authors found that gut microbiome was the most diverse site, with up to 1000 different species identified per person. The oral microbial community came second in terms of diversity, with over 700 different species identified with in the oral cavity.

4. Symbionts Commensals Pathobionts
In health, commensal and symbiotic bacteria (or “good” bacteria) help maintain a functional equilibrium, and help negate the effects of “bad” bacteria.

5. Pathogens
However, overuse of antibiotics, changes in diet or the introduction of a pathogen may upset this equilibrium – resulting in a imbalance or dysbiosis of the microbiome, potentially altering the function of this community.
Changes in diversity and dysbiosis have been correlated with a range of diseases and conditions.

6. The gut microbiome and disease correlations
Type II Diabetes
Cancer
The gut microbiome has been well studied in relation to disease.
Differences in the composition of the gut microbiome have been correlated with a range of conditions including type II diabetes and obesity, various cancers as well as autism and other mental health conditions including anxiety and depression.
Autism and
mental health
Obesity

7. The oral microbiome and disease correlations
Cardiovascular disease
Periodontal disease
The oral microbiome has also been linked to disease.
The strongest link occurs between the dysbiosis and shift to predominately Gram negative anaerobic bacilli seen in dental plaque, and the inflammatory changes associated with periodontal disease.
The oral microbiome can also have systemic effects on the body, and has been implicated in cardiovascular disease, preterm birth and Alzheimer’s disease.
Alzheimer’s disease
Preterm birth

8. Culture Genomic Analysis Functional Analysis 16S rRNA gene sequencing
Sample
Culture
Genomic Analysis
Functional Analysis
16S rRNA gene sequencing
So how do we study changes in these diverse communities?
Traditionally, culture has given us an insight into these communities, however as so many organisms are unable to be cultured, it has been estimated that we may only grow around 1% of the species present in complex communities such as the gut and oral cavity.
DNA-based approaches are culture-independent and therefore avoid this issue by targeting a region of bacterial DNA that is consistently expressed amongst all bacteria, most commonly the 16S ribosomal RNA gene. This gene is amplified in all species, regardless of their growth requirements, and is an assessment of ‘who’ is present in the microbiome.
Another molecular approach can be used to assess the function of microbial communities. So rather than just looking at ‘who’ is there, we can look at ‘what’ these organisms are doing. These methods use DNA or RNA in a shotgun sequencing approach.
However, today’s presentation will focus on the methods used to determined who is present in microbial communities.
Metagenomics
Metatranscriptomics
Metabolomics
Metaproteomics
Phylogenetic analysis

9. Culture dependent methods
PROs
Quantitative
Species identification
Antimicrobial susceptibilities
Whole genome sequencing
CONs
Many species uncultivable
Failure to grow e.g. antibiotic therapy
Requires knowledge of growth conditions
Like any method, routine laboratory culture has its advantages and disadvantages.
Culture allows us to quantitatively assess organisms. By growing each organism, we are able to identify to species-level the identity of the organism, using a range of biochemical testing or mass spectrometry. Additionally, by growing the organisms we are able to determine antimicrobial susceptibilities from a pure culture of the isolate. This isolate can also be used for whole genome sequencing, which can be important in outbreak situations and for surveillance.
However, many species are difficult to grow in a diagnostic laboratory and require certain conditions, or may fail to grow at all because the patient has begun antibiotic therapy.

10. Culture independent methods
PROs
Increased community diversity
Less labour intensive
Becoming more cost-effective
CONs
Genus-level identification
Semi-quantitative
Likewise, culture-independent methods, more specifically DNA-based approaches, have their pluses and minuses.
16S-based communities sequencing yields greater microbial diversity in samples by identify all the species present, whether they are easily cultured or not. Once established, this approach can be faster, less labour intensive and more cost-effective than culture.
However, one of the major downfalls of 16S based methods is the difficultly in reliably identifying all organisms beyond genus level.
Also, as we are not able to quantify growth as we do in culture, we gain only an insight to the relative abundance of each organism present, so the amount in relation to all the bacterial species in the community.

11. Investigating microbial communities using 16S rRNA gene sequencing
16S ribosomal RNA gene
~1,500 bp
Well-conserved function between bacterial species
Fragments <500 bp used in bacterial community sequencing
Primers target variable regions within gene
Sequence variation identifies genera/specific sequences
How and why do we use the 16S rRNA gene to study bacterial communities?
Firstly, the 16S rRNA gene is a house-keeping gene found in all bacteria. The function of this gene is well-conserved amongst bacteria as it encodes the smaller subunit of ribosomes.
The total length of the 16S gene is around 1500 base pairs, however when sequencing microbial communities, a smaller region is often targeted, usually around 500 base pairs.
Primers for PCR amplification target variable regions within the gene, as when sequenced, these regions are different enough to differentiate between species and genera.
Because we aren’t sequencing the whole 16S gene, taxonomic resolution is reduced – which is why we don’t always gain species-level identification for all members of a community.

12. Studying microbial communities using gene targeted sequencing
Targeted amplicon sequencing
Mixed DNA extracted from sample
Amplify bacterial 16S rRNA gene
So, a quick overview of how targeted gene sequencing such as 16S works.
DNA is extracted from a sample. This includes all DNA present in the sample, so this may include human, fungal DNA, not just bacterial DNA.
Primers are used to target and amplify copies of the 16S gene present in the DNA sample.
Those amplified regions are sequenced, then classified to species or genus level, and then we can count how many time that sequence occurs in a sample.
However, before we get to this stage, there are multiple bench and bioinformatic steps required, which I will discuss in more detail.
Classify and count the number of sequences belonging to each taxon

13. SAMPLE DNA EXTRACTION AMPLIFY 16S rRNA GENE SEQUENCING
Firstly, we have the bench work that prepares our samples for sequencing.
We start off with the input sample – this can be a swab, tissue, fluid – pretty much anything that is routinely received in a microbiology laboratory.
We extract all the DNA present in that sample.
Then we amplify the 16S gene using both forward and reverse primers.
The PCR amplifications are then purified to remove any remaining primers and nucleotides before the sample is submitted for sequencing.
SEQUENCING
DNA PURIFICATION

14. Taxonomic assignment against reference database
DATA PROCESSING
Sequencing returns massive data files of all the raw sequences present in the sequencing run.
The bioinformatic pipeline we use to process the data is complex, so this is an overview of how we obtain data for analysis.
Firstly, we merge all the forward and reverse reads.
We then pick out all the difference sequence variations present (at a 97% similarity threshold) and call them operational taxonomic units (OTUs) – which are similar to a species level identification. These units are then assigned a number.
The sequence data for each of the OTUs is then aligned against a reference database to assign the taxonomic information.
Sequences that don’t align with the database or fail to give adequate taxonomic resolution can be further identified using a BLAST search of the NCBI database.
Taxonomic assignment against reference database

15. Ultimately, we end up with a table that looks something like this, where the species identified are in the left hand column, followed by a column representing each of the different samples.
The numerical data here shows how many time a sequence belonging to a given species occurs in each sample.
From this stage we are able to analyse the data in multiple ways.

16. Community of microbes have a protective effect
Data Analysis
CASE STUDY:
Faecal microbiome transplantation in patients with Clostridium difficile infection
(Shahinas et al., 2012)
Community of microbes have a protective effect
To explain how data is analysed and compared, I will use data from one of the early studies that was published looking at faecal microbiome transplants in patients with Clostridium difficile infections.
So, we know that C. diff is a pathogen and when a patient is infected, a symptomatic disease stage usually follows.
The principle behind faecal microbiome transplants is that in some patients, certain communities of bacteria have an protective affect against C. diff infection – similar to a probiotic.

17. Data Analysis
Microbiome analysed from pre- and post-transplant stool specimens
Clinical cure associated with an increase in diversity
Shahinas et al. (2012) Towards an Understanding of Changes in Diversity Associated with Faecal Microbiome Transplantation Based on 16s rRNA Gene Deep Sequencing MBio 3(5): e
Alpha diversity
Beta diversity
In this study, the microbiome was analysed from stool samples take from patients with a C. diff infection before a faecal microbiome transplant and again post-transplant. The microbiome of the donor sample was also analysed.
The authors concluded that a clinical cure of the infection was associated with an increase in microbial diversity after transplantation.
How do we study diversity in these microbial communities?
There are two key measurements used in microbial ecology studies.
The first is alpha diversity, which describes the diversity or the number of species within a sample.
The second is beta diversity, which describes the microbial diversity between samples.
Diversity within a sample
Diversity between samples

18. Alpha Diversity Number of OTUs Shahinas et al. 2012
Alpha diversity is visualised on a rarefaction plot. In this plot, the number of different OTUs or species are measured on the y-axis.
The horizontal axis describes the number of sequences per sample, essentially the number of copies of the 16S gene present in the sample.
In the blue, we can see that C.diff patients have a lower diversity before transplant, so the number of species present is reduced in this group.
After transplant in red, the diversity increases and more closely resembles the diversity seen in the donor samples in green.
Shahinas et al. 2012

19. Adapted from Shahinas et al., 2012 Patient #1 Patient #2 Patient #3
Another way to visualise sample diversity is through taxa plots.
Here we select a taxonomic level to depict, for example species, genus, phylum.
This graph demonstrates the differences in phylum level diversity in four patients with C.diff pre transplant.
Next, the changes in composition are displayed after transplant. In some cases (for example patients 2 and 4) the community structure has drastically changed.
Finally, we can look at structure of the healthy donor sample for each patient, with some of the post transplant patients now more closely representing their donor microbiome.
Adapted from
Shahinas et al., 2012
Pre
Post
Donor
Pre
Post
Donor
Pre
Post
Donor
Pre
Post
Donor
Patient #1
Patient #2
Patient #3
Patient #4

20. Beta Diversity Describes diversity between samples
Unifrac is a distance metric that compares microbial communities
Considers phylogenetic similarity
Visualised in a Principle Coordinates Analysis plot (PCoA)
Next, we look at beta diversity which describes community diversity between samples.
Beta diversity is often measured using a Unifrac analysis – which is a distance metric that describes how different samples are with a numerical value.
What’s important about a Unifrac analysis is that is considers the phylogenetic relatedness of different species.
So, while one sample may contain multiple different species, they might all be very closely related, and consequently that can impact or reduce diversity.
A Unifrac analysis is visualised in a principal coordinates analysis plot.

21. Adapted from Shahinas et al., 2012
This is a principal coordinates analysis plot of patients pre and post faecal microbiome transplant and donor microbiomes.
This plot demonstrates the relatedness of the three different groups of samples.
The C.diff patients pre-transplant cluster together, as shown by the black dots, while the healthy donor samples (in green) cluster together but away from infected patients.
Patients post transplant (in grey) either cluster with their pre transplant profiles or change to reflect the profiles of their donor microbiomes.
The distances between each point on this graph can be described numerically in a distance matrix, to which we can apply statistical analyses and determine significance.
Adapted from
Shahinas et al., 2012

22. Potential for 16S rRNA bacterial community analysis in the clinical setting?
Despite limitation, technology is rapidly improving and likely to offer alternatives in the near future
Community based analysis has been used in polymicrobial and biofilm-related infections e.g.
Chronic and diabetic ulcers
Non-healing wounds
Abscesses
The C.diff study is a good way of demonstrating the value of microbiome work in a clinical setting, yet this is not what we see in a clinical laboratory from day to day.
So, is there potential for 16S based community work in a diagnostic laboratory? Despite the limitations mentioned earlier, sequencing technologies are rapidly improving and the cost of these methods drops annually.
While most of our work in a diagnostic laboratory involves identifying and reporting a pathogen from a Koch’s postulate perspective, polymicrobial infections may benefit from the increased sensitivity offered by DNA based approaches.
An example of this was demonstrated in a study of 168 swabs taken from chronic ulcers, non-healing wounds and abscesses – sites that often harbour a polymicrobial environment.

23. 168 samples using aerobic culture testing
Bacterial taxa detected from
168 samples using aerobic culture testing
No. of positive
samples using culture
samples using 16S
sequencing
Staphylococcus aureus 41 72
Enterococcus spp. 35 28
Serratia marcescens 39
Pseudomonas 24 27
Staphylococcus spp. (not S. aureus) 20 47
Streptococcus agalactiae 18 23
Proteus mirabilis 9 10
Citrobacter freundii 5 6
Escherichia coli 4 3
Klebsiella pneumoniae 8
Enterobacter aerogenes 2
Enterobacter cloacae
Morganella morganii
Streptococcus spp.
Stenotrophomonas maltophilia
Acinetobacter baumannii 1
Providencia spp.
This study, published in 2012, demonstrated the effectiveness in isolating species from these sites using 16S sequencing, compared to aerobic culture.
Interestingly, significant pathogens such as Staphylococcus aureus and Streptococcus species were identified in more samples using DNA sequencing compared to culture. This observation was consistent for all of the microbes identified from these wounds and ulcers, with the exception of Enterococcus species. It has been noted in microbiome studies that Enterococcus is often under represented in mixed communities, due to its strong cell wall that is difficult to lyse and release the cellular DNA from.
This study also found a large number of mixed anaerobes present in these samples using the molecular approach, however the swabs were not incubated in anaerobic conditions so a comparison to an anaerobic culture is not available. However, it is likely that results would closely reflect those of the aerobic culture.
Adapted from Rhoads et al., 2012

24. Summary
Rapid developments targeted amplicons sequencing have improved our understanding of complex microbial communities
Limited taxonomic resolution may hinder expansion into a diagnostic setting
Has improved our understanding of the microbiome, particularly in the context of disease
With continuous improvements and increasing cost effectiveness, community-based sequencing could potentially play a future role in diagnostic laboratories
In summary, the use of 16S targeted community sequencing has greatly expanded our knowledge of diverse microbial communities and the role they might play in health and disease.
While, highly valuable in the research setting, translation of such methods into a diagnostic setting is something to consider in the future.
Thank you.
Thank you

25. References
Sender RS, Fuchs S, Milo R (2016) Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans Cell 164(3):
Janda JM, Abbott SL (2007) 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils and Pitfalls J Clin Microbiol 45(9):
Rhoads DD, Wolcott RD, Sun Y, Dowd SE (2012) Comparison of Culture and Molecular Identification of Bacteria in Chronic Wounds Int J Mol Sci 13(3):
Shahinas D, Silverman M, et al (2012) Towards an Understanding of Changes in Diversity Associated with Faecal Microbiome Transplantation Based on 16s rRNA Gene Deep Sequencing MBio 3(5): e