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Considerations and controls for metagenomic/microbiome projects

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Metagenomics applied to surveillance of pathogens and antimicrobial resistance

丹麦技术大学

4.7（57 个评分） | 2.7K 名学生已注册

The field of metagenomics and whole community sequencing is a promising area to unravel the content of microbial communities and their relationship to disease and antimicrobial resistance in the human population. Bioinformatic tools are extremely important for making sense out of metagenomics data, by estimating the presence of pathogens and antimicrobial resistance determinants in complex samples. Combined with relevant explanatory data, metagenomics is a powerful tool for surveillance. In this course, we teach about the potential of metagenomics for surveillance and give the learners an overview of the steps and considerations in a metagenomics study. After this course, the learners will know: - the difference between the concepts of metagenomics and other microbial genomics - the need to use controls in different steps of a metagenomics study - the advantages of metagenomics for the surveillance of antimicrobial resistance - how sampling design, sample size, sample material and sample handling influence the outcome of a metagenomics study - sample processing for bacterial and viral metagenomics - different sequencing platforms and their possibilities regarding metagenomics - the steps involved in a general metagenomics study, including quality control, mapping to different databases, and read count analysis - the principles behind various tools available for analysis of metagenomics data - how to interpret read classification results - the need for epidemiology in surveillance - the concept of global and integrated surveillance - the challenges for the use of metagenomics in surveillance - the potential of metagenomics for surveillance We look forward to welcoming you !

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4.7（57 个评分）

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从本节课中

From sampling to sequencing

In this module, you will be introduced to metagenomics, some of the considerations and controls that need to be in place in a metagenomics study, and to the topic of antimicrobial resistance. You will also learn about: 1) Sampling and sample handling - the considerations behind a sampling plan, how to perform sampling in practice, and how sample storage can affect metagenomics results; 2) DNA and RNA extraction methods - both for bacterial and viral microorganisms; 3) Sequencing - from library preparation to the basics of different sequencing technologies.

Introduction to metagenomics10:38

Considerations and controls for metagenomic/microbiome projects10:27

Introduction to antimicrobial resistance11:24

教学方

Ana Sofia Ribeiro Duarte
Assistant Professor
Tine Hald
Sünje Johanna Pamp
Associate Professor
Patrick Munk
Liese Van Gompel
PhD Candidate Epidemiology
Valeria Bortolaia
Pimlapas Leekitcharoenphon
PostDoc

脚本

Hello, and welcome to the session about

"Considerations and controls for metagenomics projects".

My name is Sünje Johanna Pamp,

and I'm a microbiologist and Associate Professor at the Technical University of Denmark.

A surveillance study, as well as many other microbiome studies,

contain a number of different steps.

In this session we will go over each of these different steps.

A project, of course, often starts with

a particular goal, or hypothesis, or question in mind.

And that is why it is very helpful to actually think through

the entire process before actually

starting the project, because otherwise some important aspects might be missed.

In the beginning, for some projects

you might have to contact authorities and get permissions from veterinarians,

medical doctors, the IRB (Institutional Review Board),

or the data protection agency, and you might also consider the Nagoya protocol.

Depending on the question,

if you are interested in diseased individuals,

you might also think about including healthy controls.

If you are interested in specific sites,

you might want to choose some sites for investigation that could serve as controls.

When collecting the sample,

you would want to make sure to collect these under aseptic conditions

to prevent contaminations entering the sample.

Also, it might be helpful to consider to include replicates,

like we usually do in experimental research.

Then also, you can include some swabs and unused tubes or

other supplies that you use for sample collection

as controls because they might actually contain some background DNA levels.

In terms of sample handling,

we sometimes end up in a situation that we are far away from

the laboratory and then it is advised to store the sample immediately on ice and,

if possible, freeze it immediately so that

the microbial composition of the samples does not change.

Also, we recommend to avoid freeze/thaw cycles.

If you would like to use the sample for different examinations,

consider to make aliquots before freezing it down.

In terms of the laboratory steps,

it is suggested to conduct these in different areas in your lab,

ideally, in different rooms - to avoid cross contamination.

The DNA or RNA extraction should really be carried out in

a very clean environment to avoid contamination of the sample.

To measure the background level,

we include blank control tubes.

These are just empty tubes without a sample,

but that are processed exactly in the same way as all the other samples.

You might also include some samples that contain

spiked organisms of known amount and ideally with a known genome sequence.

You could also include some tubes that contain a mock community with five,

ten, or even more different bacteria or

microorganisms in general, representing different phyla.

In terms of the library preparation,

we prefer PCR-free approaches and that of course requires a certain amount of DNA.

You might also consider to split up

one sample and sequence it using two different barcodes.

In that case,

you ideally should see the same microbial community composition for these aliquots.

In terms of sequencing,

which technology you choose might depend on your question because some

technologies sequence very short fragments whereas

others provide you with very long or relatively long DNA sequences.

Then dependent on your number of samples and the depth you would like to achieve,

there might be different numbers of sequencing rounds that you will be conducting.

So these are considerations that have to be made

before and potentially there could be a pilot study

sequencing a few samples fairly deep to get an idea about the complexity of the sample.

When you have the step of DNA sequence analysis,

there are different approaches to analyze the DNA read sequences,

that could be just a classification,

for example, by mapping the reads against a number

of reference databases containing whole genome sequences,

specific virulence factors, antimicrobial resistance genes, and so on.

But you could also attempt an assembly approach.

In terms of the statistical analysis,

it might really be dependent on your question,

you might be interested in diversity or might want to analyze or compare

all samples that you have analyzed using multivariate analysis or regression analysis:

if you, want to set your samples into context to other types of

metadata that you have about the sample or the environment where the sample was obtained from.

You will learn more about these approaches in other modules in detail.

In general, you would want to process all samples in the same way, because

the processing can introduce

some changes that makes it difficult to compare the samples.

We could see that, for example,

when we conducted a ring trial with

11 research groups where they all were provided with the same sample and

the same protocol, and they conducted

the DNA extraction procedure

as good as they could with the available tools they had in the lab.

What we found was,

when we sequenced all these DNA extracts that there were actually some differences,

even though the same groups of bacteria were found in the samples,

they were there to different amounts.

The same we also saw in terms of antimicrobial resistance genes.

We found the same antimicrobial resistance genes,

but in different abundances.

This can of course be a challenge if you want to compare samples from other labs.

Then it is really important to note down what were actually the differences

in the DNA extraction or sample handling used in the study.

In the sequencing step, it is also important to

randomize the samples across different sequencing runs,

not that you end up with having all the samples from, for example,

patients that have a certain disease on

one sequencing run and the healthy control samples on another sequencing run.

We also have to consider contaminations basically at

every level because they could be introduced already when the sample was acquired, and

they could be introduced during DNA extraction.

We have to look, for example,

for potential host DNA if we analyzed samples from humans or animals.

In some samples they can represent most of the DNA sequences,

over 90 percent,

in particular, if we look at body fluids from humans and animals.

When we compare our DNA sequences to reference genomes,

there could be contaminations in

these reference genomes, which we have to consider and test for.

Then, as already indicated,

we can introduce contaminations during sample handling and

DNA extraction and, which we refer to as environmental background DNA.

But the DNA, we also have to keep in mind,

cannot only come from the outer environment,

but it can also be introduced by the people handling these,

so these could be potentially skin bacteria that could enter the sample.

Whenever you have sequencing data,

we really recommend to deposit them in public repositories, for example,

EBI or NCBI, because then we can compare these samples all

together which is really important in surveillance projects.

Also, we suggest that you make your study available through bioRxiv and an

open-access journal because information really means everything

if you want to fight infectious diseases and antimicrobial resistance.

It is even better if, together with your study, you can include the code and

the analysis scripts that you have used for the analysis, to facilitate reproducibility.

A document with guidelines for metagenomic projects,

you can download from figshare,

and that is affiliated with this publication, available through bioRxiv.

Thank you for your attention.

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