Role of DNA methylation in the genomic rearrangements in P. tetraurelia

PhD Defense

DELEVOYE Guillaume

09/06/2022

Supervisor : Dr MEYER Eric

Jury members : Dr DUHARCOURT Sandra, Dr CHEN Chunlong, Dr DURET Laurent

Introduction

Part 1 : Transposable Elements (TEs)

1948 : Jumping genes

Barbara McClintock discovers the As/Dc elements in maize

 Ds jumps in presence of Ac (non-autonomous)

Historical classification

(Finnegan)

"Copy paste" versus "Cut and paste"

Historical understanding

Junk/Selfish DNA

TEs were first considered as :

• "Selfish DNA"
  • They do not perform any "function" for their host
• Neutral or mildly deleterious
  • That is, evolutionary burdens

TEs are conserved not because they provide an additional fitness to their host, but despite the fact that they don't. This is the non-phenotypic selection.

Doolittle, Orgel, Crick and Sapienza (1980)

The same day in Nature journal

Modern taxonomy

Wicker et al. 2007

• 3 classes
• 9 orders
• 29 superfamilies

Based on:

• Mechanistic / Enzymatic criteria
• Structural data

The cut-paste versus copy-paste comparison turned out to less relevant over time

TEs are ubiquitous

Modern understanding

• TEs Generate selection :
  •  Purifying:           growth rate = Genome-wide invasion
  •  Adaptative: Exon shuffling, transcription regulation ++,
      exaptation (e.g Rag1 & Rag2 in mammals), etc.
• Are transferred vertically (++) and horizontally (HTT)
  • Several thousands known HTT
  • P-element invaded Drosophilia worldwide in less than 100 years !
• Are present in all cellular organisms
• Probably exist since the ~ begining of the cellular life

Regulation mechanisms

Introduction

Part 2 : P. tetraurelia

Studying ciliates

A 350 years old story

A. van Leeuwonhoek (1668)

"Animalcules"

Pasteur (1862)

Spontaneous generation

HS Jennings (~ 1900)

Paramecium as a model

T. Sonneborn (1937)

Non-mendelian inheritance

of sexual type

in Paramecium

Carol Greider &

Elizabeth Blackburn

Telomeres (1985) - Nobel prize

There is more...

• First known organisms that do not use the "universal" genetic code
  • Paramecium (Caron and Meyer 1985)
  • Tetrahymena (Preer et al. 1985)
• Histone Acetlyases (HAT)
  • Tetrahymena (Brownell et al. 1996)
• Self-splicing introns (ribozymes)
  • Tetrahymena
• Tubulin post-translational modifications

> The genome-wide programmed rearrangements <

P. tetraurelia: Nuclear dimorphism

Unicellular eucaryote with 3 nuclei:

• 2xMIC nuclei (2n)
  • Germline nucleus
  • Contains: TEs + 49.260 Internal Excised Sequences (IES)
  • No transcription outside meiosis
• 1xMAC nucleus (up to 800n)
  • Somatic nucleus
  • Amplified version of the MIC
  • Free from TEs and IESs
  • Transcriptionnally active
  • Rebuilt from a MIC after each sexual process, under the control of the maternal MAC (hence frequent non-mendelian inheritance)

Programmed rearrangements

A new MAC is formed from a MIC, with important genome re-arrangements

Results in a MAC DNA almost purely made of coding sequences

Coyne et al. 2012

Profiling IESs (1/2)

• Non-coding
• Excised after sexual processes with a single-nucleotide precision
• Relationship with TEs :
  • IESs = Degenerated remnants of Tc1/Mariner elements
  • Excised by a domesticated PiggyBac transposase (Pgm)
• Most recent IES are very short (< 27bp), and are the majority of IESs

49.260 unique sequences

O. Arnaiz et al 2012

PiggyMac (Pgm)

Profiling IESs (2/2)

• 100% TA-Bounded
• Weak consensus TAYAG
  • Degenerated TC1-Mariner TE insertion site
• Periodic size distribution

O. Arnaiz et al. 2012

This is not sufficient for the cell to distinguish IESs from the rest of the genome

IES recognition: sc-RNA pathway

E. Allen and M. Nowacki - 2017

Problematic

Problematic in a nutshell

• ~30% of IESs only are small-ncRNA dependant
  • What about the majority remaining ?
• How does the cell recognize IESs ?
  • Especially, the scnRNA independent ones
  • ~ Self VS non-self recognition
• MIC/MAC ploidy ratio = Challenge +++

The DNA methylation hypothesis

6mA abundant in Paramecium:

• 2.5% in the MAC and MIC of P. aurelia (Cummings et al. 1975)

• Detection by SMRT in the MAC (Hardy et al.)
• Detection in Oxytrichia by L. Landweber et al. (2019)
   
• no 5mC, 4mC in the MAC a priori

2) In the new forming MAC

1) Constant pattern in the MIC

Transcient ?

The DNA methylation hypothesis

And many other possibilities...

Methylase candidates

• In 2015, DAMT-1 in C. elegans (6mA) - Preer et al.
• MTA-70 domain of DAMT-1 identified in P. tetraurelia too
  • Silenced by RNA interference (Grouped by homology)
  • Sometimes, reduction of 6mA (southwestern blot)

> Sequenced with PacBio SMSN sequencing

Objectives and overview

Methodological results

Part I : Analysis pipeline

PacBio SMSN sequencing

$$ipdRatio= \frac{MeanIPD_{experience}}{unmethylated\ control}$$

~ 85% accuracy

~ 100% accuracy

Slowing around modified nucleotides (~ time x100)

Global principle

PacBio SMSN sequencing

Expected output

An analysis for each nucleotide, on each strand, of each molecule (SMSN = Single-Molecule Single Nucleotide)

Possible detection : 4mC, 5mC, 6mA, "other"

A few months of plumbing later...

Using E. coli DNA

• Nearly 100% 6mA (symmetrical):
  • GATC +++
  • EcoK
  • A few others
    • Depends a lot of the strain
       
• Nearly 0% 6mA :
  • Everything else

E.coli is used to feed paramecium (contaminants)

ipdRatio in E. coli (1/2)

• The nucleotides we expect to be methylated have a high ipdRatio
• Some exceptions : False negative or really not-methylated ?

ipdRatio in E. coli (2/2)

Separability and coverage are correlated

How to binarize the ipdRatio ?

Either a nucleotide is methylated, or it is not :

• We need to use a threshold on the ipdRatio to call modified nucleotides
• This threshold has to take account of the coverage effect
• No optimal solution anyway

Our pragmatical solution : An arbitrary linear threshold

Benchmark (6mA)

• ~92% of 6mA in EcoK and GATC
• ~99.8% of non-6mA elsewhere

If we make the simplification that all GATC/EcoK sites are methylated and that 6mA is only present there   :

$$Sensitivity = P(D|M)$$

$$Se  = 92\%$$

• Maybe some GATC/EcoK are tuly unmethylated
  • In this case, the real Se is actually better than 92%
• Maybe there is a few amount of 6mA outside of GATC/EcoK site
  • In this case, the real Sp is actually better than 99.8%
• Se = 92% and Sp = 99.8% are worst case estimates

$$Specificity = P(\overline{D}|\overline{M})$$

$$Sp = 99.8\%$$

Benchmark

(other modifications)

PacBio sequencing was already known for its propensity to generate false positives for 4mC (K. O’Brown et al. 2014)

• It is very likely that most of these detections are false positives

Objectives and overview

Methodological results

Part II : Sorting + IES retention

• We work on total DNA :
  • ~ 1 molecule out of 200 comes from the MIC
  • Sometimes the physical origin of the molecule can be guessed
  • MIC regions that are far from the IESs and the MIC-specific sequences cannot be studied

IES retention

Methodological results

Part III : The MAC ploidy

Is the MAC ploidy 800n ?

MAC ploidy

The 4 scenarii for Se and Sp

• We don't care about Se and Sp
   
• We care about the fact that eventual mis-estimations of them doesn't really change anything

Finding unbiased estimators for    and FDR

If p number of positive detections among N tests:

p = FP + TP

$$\pi$$

So,

Which means

And:

What it gives in Paramecium

Methodological development to correct hemi-methylation detection (1/2)

Let FD1 and FD2 be resp:

• Fraction of hemi-methylated AT sites
• Fraction of sym-methylated AT sites
  
  PZ0, PZ1, PZ2:  unbiased estimators of non, hémi, symetrically methylated AT sites

Then:

With

Methodological development to correct hemi-methylation detection (2/2)

We can also find the number of hemi-methylated sites being detected as such, and the proportion of sites detected as hemi-methylated that are really hemi-methylated. This is possible because we now approximately know PZ0, PZ1 and PZ2, and P(D|Z) is easy to determine:

Then, P(Z|D) can be determined through Bayes theorem using P(D|Z), P(Z) and P(D) (which are all known)

P(Z=1|D=1)

is our case of interest

What it gives in Paramecium

2.1 Retroingineering

The capping of IPDs

• modelPrediction is the predicted IPD value by the model in a given context of nucleotides at this position

• globalIPD is the mean of all the IPD values of the read.

• localIPD represents all IPDs that have been mapped at a given position in the genome, including those from other sequences

Conclusion on the capping

• Isn't coded as advertized by PacBio
• The way it's implemented for AggSN is problematic and doesn't really make sense
• Paradoxally, it should be more relevant for our approach than for the default one
• We expect no methylation to be undetected due to the capping

Laura landwebehr 2020

Oxytrichia trifallax

A outAT score 20 isQv20 (812 seq)

A outAT score20 idQv20 + Strong BH correction (176 seq)