Lecure 20

Question 1

Proire to genetic sequencing technologies, _____ discovery took a long time

Answer 1

Pathogen

E.g HIv was first discovered in 1981 but we know it existed before that

Question 2

How did we know HIV existed before it was discovered

Answer 2

• look at the root of the tree root
  Can calibrate the time of divergence by looking at the time the samples were taken and estimating the mutation rate - the branch of the phylogenetic trees representing divergence over time
• draw linear regression from clusters and where it crosses the X axis is when HIV had emerged into the population

Question 3

Pathogen discovery was revolutionaised when gene sequencing was introduced

Answer 3

• used to have to grow the virus in the lab to see what was causing the disease (but most viruses are not culturable - dont know the right conditions)
• can use consensusPCR to test for presence of a particular virus, consesus PCR is when you can take a conserved region of that viruses genome and test for its presence in an assay (but can’t discover new pathogens, only when u know what ur looking for)
• metagenomics allows you to look at the whole tree

This is kinda yap

Question 4

Why use genomic sequencing ?

Answer 4

• microbes can be difficult to culture
• a (sort of) unbiased way to study ALL microbes in a sample, not just those u can culture (our ability to recognise what is in there is hindered)
• allows us to understand the structure and function of microbial communities
• it is realivelt cheap

Question 5

What is the infectome

Answer 5

What we call the way of studying an entire microbial community

Question 6

What is the contents of the infectome

Answer 6

Bacteria + archaea + fungi + viruses

Question 7

What kind of sequencing can u use to target sequencing the bacteria+ archea part of the infectome

Answer 7

16s rRNA sequencing

Question 8

What kind of sequencing can u use to target sequencing the fungi part of the infectome

Answer 8

18S rRNA sequencing

Question 9

What kind of sequencing can u use to target sequencing the whole infectome

Answer 9

Metagonomic / metatrasncriptomic sequencing

Question 10

What is the difference between metagenomics and metatranscriptiomics?

Answer 10

Metagenomcis refers to DNA sequencing where as metatransctiptiomics refers to RNA sequencing

Metagenoics will tell u anything that had a DNA genome that is there
But a lot of DNA don’t transcribe DNA- only RNA so if we use metatransciptomics it will tell us anything there that is transcribing - including RNA

Question 11

What does infectome analysis using metagenomics/ metatranscriptomics tell us?

Answer 11

Who’s there
- toxonomic classification
- population analysis

What can they do?
- functional analysis

Question 12

What sequencing gives us an understanding of community structure - if its bacteria

Answer 12

16S rRNA sequencing

Question 13

What gives us a better understanding of the metabolic potential of a community?

Answer 13

Metagenomics

Question 14

Question 15

What is metatranscriptomics?

Answer 15

• total RNA sequencing (RNASeq)
• it gives us the gene expression of microbes within natural environments

Question 16

What does metatranscriptomics allow us to do?

Answer 16

Allows us to obtain whole gene expression profiling of complex microbial communities

Question 17

What is the difference between metagenomics and metatranscriptomics?

Answer 17

Metagenomics = genetic content, identifying microbes present within a community

Metatranscriptomics = diversity of the active genes within such community, quantify their expression levels and monitor how these levels change in different conditions

Question 18

What is the advantage of metatranscriptomics over metagenomics

Answer 18

It can provide information about the differences in the active functions of microbial communities which appear to be the same in terms of microbe composition

(This is important as u may have a diseases and heathy population and they may have the same community composition however if you do metatransciptomics diseased may have a much higher gene expression of a particular thing)

Question 19

Metatransciptomics can be used to study the…

Answer 19

Virosphere (but also all the parasites, bacteria, fungi = infectome) as well as the host gene expression

Question 20

Things to keep in mind - caveats

Answer 20

There is no standard way to produce metatranscripomical date
- thus if we want to compare studies of different samples we have to understand the caveats:

E.g
- sewuwncing platforms may vary
- coverage/depth may vary
- sample collection and preservation (RNA is very degradable)
- RNA/DNA extraction methods
- enrichment or depletion steps
- one sample is representive of a single time point

Question 21

Sequence analysis : assembling the metagenomics - De novo assembly…

Answer 21

putting short sequence reads together to reconstruct longer sequences (contigs)

Question 22

What are contigs?

Answer 22

Continuous sequences from shorter, overlapping reads

Question 23

Once you’ve done de novo assembly, what do you do next?

Answer 23

Annotating: once assembled, ideniftying where the contigs came form using sequence homology

OR

Map sequence reads to a reference sequence (if you know what your looking for)

Question 24

De novo assembly vs mapping

Answer 24

Question 25

Pros and cons of sequence assembly methods

Answer 25

Question 26

Challenges for assembly of microbial communities

Answer 26

• communities are more biologically complex then individual microbes
• the presence of different strains of the same species can result in fragmented reconstructions
• some sequence segments are repeated within the same organism or shared between distinct organisms e.g endogenous viral elements
• when assembling genomes, we typically assume that sequence coverage is uniform
• but the coverage of each community member depends on the abundance of its genome in partial genomes
• low abundance community members may result in partial genomes
• coverage/ depth will determine how well you can characterise low abundance community members (ie. need enough overlapping reads)

Question 27

The benifet of using metagenomics / metatranscriptomics approach is the at we dont have to know what is in the sample….

Answer 27

But this means we dont know from what organism each sequence is derived

Question 28

The benifet of using metagenomics / metatranscriptomics approach is the at we dont have to know what is in the sample….

Answer 28

But this means we dont know from what organism each sequence is derived

Question 29

Limitations of metagenomic approaches

Answer 29

• many genetic sequences are left not annotated because we don’t know what protein they encode

(The info in databases are biased towards viruses that infect humans rather than wildlife)

(This is the biased part - you can generate them unbiasdly but we we don’t know what most of them are - thus making it biased)

• available microbial genomes are biased towards model organisms, pathogens and cultivable microorganisms meaning that our discovery of novel things will likely also be biased

Question 30

The dark matter of metagenomic data

Answer 30

(Contigs we have produced and dont know what they are)

• annotating metagenomes relies on a reference database but we can only annotate sequences that share sequence homology above a certain threshold
• there are still many transcripts that do not share any sequence homology to anything known

(Grey don’t match host or anything we knows genome - probs virus as they mutate so fast)

Question 31

The dark matter of metagenomic data

Answer 31

(Contigs we have produced and dont know what they are)

• annotating metagenomes relies on a reference database but we can only annotate sequences that share sequence homology above a certain threshold
• there are still many transcripts that do not share any sequence homology to anything known

(Grey don’t match host or anything we knows genome - probs virus as they mutate so fast)

Question 32

The metagenomic identification of viruses is currently limited to those with sequence similarity to known virus the rest is identified as dark matter

Answer 32

Highly divergent viruses that comprise the “dark matter” of the virus-here remain challenging to detect
- AI related approaches - deep learning algorhytims have had a huge impact on protein structure prediction

Can we use AI to deft dark matter?

Question 33

Answer 33

Virus closely related to to human rubella virus

Question 34

The more microbes we discover, the more we understand about their potential to cause disease

Answer 34

Question 35

Answer 35

Question 36

Question 37

Question 38