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

P. tetraurelia

P. tetraurelia: Nuclear dimorphism

Unicellular eucaryote (ciliate) with 3 nuclei:

• 2xMICronuclei (2n)
  • Germline nucleus
  • Contains: Transposons + 49.260 Internal Excised Sequences (IES)
  • No transcription outside of meiosis

Programmed rearrangements

After sexual processes, a new MAC is formed, 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
• Remnants of Tc1/Mariner ?
• All excised by Pgm
  • Excised  with a single-nucleotide precision
  • Life or death issue : Genes interrupted
• IES excision was exapted many times
  • e.g The mating-type !
• Size shrinks with age, most IESs are very short (26-150bp)

49.260 unique sequences

O. Arnaiz et al 2012

"Invade Bloom Abdicate Fade" model

Adapted from Glen Arthur Herrick 1997

Excision = 100% PiggyMac (Pgm)

Profiling IESs (2/2)

• 100% TA-Bounded
• Weak consensus TAYAG
  • ~ Tc1/Mariner
    
     
    
     
• Periodic size distribution ~10bp

O. Arnaiz et al. 2012

Not sufficient to distinguish IESs from the rest of the genome

IES recognition: scnRNA pathway

S. E. Allen and M. Nowacki - 2017

If not in the maternal MAC : Recognized and excised

Problematic

Problematic

Inactivation of scnRNA and iesRNA pathways:

• ~30% of IESs are retained
• Their retention is not even complete
• Oldest = More independent to small RNAs
• IES features = insufficient to explain the recognition

All IESs may be recognized through the small RNAs

... but is there a redundant system for the oldest/shortest ones ?

Problematic ~ Self VS non-self recognition

Hypothesis : Role of DNA modifications

Two possible roles of DNA methylation

DNA modifications could play a role :

• In the recognition of IESs +++
  • i.e : It is permanently present in the MIC
• In their excision
  • i.e : It is transiently present in the new forming MAC, right when the IESs are excised.
    • ~ Actor of the scnRNA pathway

Hypothesis

The fifth element

• 2.1-2.5% in the MAC and MIC of P. aurelia (Cummings et al. 1975)

• Detection by SMRT in the MAC (A. Hardy et al. 2020)
  • 0.8% and 1.6% of adenines
  • 81.5% are located in AT sites
  • Enriched downstream the Transcription Start Sites (TSS)
• In other ciliates :
  • AT sites ++ and TSS ++ :
    • Oxytricha by L. Landweber et al. (2019)
    • Tetrahymena (No 6mA in MIC)

... Could be N6-methyladenine (6mA)

?

Other:

• 4mC ?
• No 5mC in the MAC

If the pattern allows the recognition of IESs, it must allow :

1. Single-nucleotide precision
2. Distinction between a TA of an IES, from another TA elsewhere
3. Conservation through replication

A typical example : DNMT1-Like system

The DNA methylation hypothesis

Examples of possible patterns

Experimental approach

Long before I arrived in the lab !

Methylase candidates

• DAMT-1 in C. elegans (6mA) Greer et al. 2015
   
• MTA-70 domain of DAMT-1 identified in P. tetraurelia too

Sequencing strategy

Protocol :

1. Sequence vegetative and autogamous cells
2. Silence the methylase candidates by RNA interference
3.  Sequence with PacBio SMRT sequencing

Autogamy

PacBio SMRT sequencing

ipdRatio

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

(Relevant if >25 measures)

IPD

Two types of control :

• PCR
• In-silico (machine-learning)

Ideal approach

1. Purify MIC DNA
2. Make libraries of long inserts from it
   • Several kb
3. Sequence it with PacBio SMRT
4. Compare with PCR of full MIC genome

e.g

~O(20) to ~O(100) molecules

[...]

• Position n°7260 in the genome
• Strand +
•  52 molecules methylated out of 100

Problem = Purification of MIC DNA

(min.  25X)

Transcient ?

• Fish IES+ molecules in a sea of MAC molecules
• Short inserts (~350bp)
• Same molecule sequenced multiple times

Random sampling strategy

each molecule

~O(20) to ~O(100) measures

(MIC)

Workaround : work on total DNA instead

• Molécule n°49256
• Nucléotide n°7, strand + (Adénine)
• ipdRatio : 20x

But purifying the MIC is problematic

(min.  25X)

Look at methylation (only) around (some) IESs

in-silico control required

My job starts here !

NM4.bam

NM9_10.bam

MT1A_1B.bam

MT1A_1B_2.bam

MT2.bam

HTVEG.bam

MAB.bam

HT2.bam

HT6.bam

NM4_9_10.bam

Challenge = Sorting + No pipeline

The guiding thread

1. Identify MIC vs MAC molecules
2. DNA methylation detection :
   • No pipeline !
   • Implementation and test
3. 6mA MAC
4. 6mA MIC

Identify MIC vs MAC molecules

Sorting (overview)

~350bp

Alignment

MAC

MAC+IES

MIC

• Most sequences are Mac Destinated Sequences (MDS) : We cannot guess their nuclear origin
  • A vast majority of them (>99.5%) comes from the MAC

IES retention

• IESs are sometimes retained in the MAC

P(R)

1 - P(R)

Danger !

IES retention

Pitfall

IES retention

Proposed approach

IES retention

Comparison / Benchmarks

$$P(MIC|IES+) \in [9.5\% - 93.7\%], \alpha = 5\%$$

Problem : The size of confidence intervals is very big

• e.g 150 NGS reads, 0 IES+

IES retention

Workaround : Pooling samples

Rare picture of Eric, doing some archeology to find more samples to pool and gain coverage (circa 2022, colorized)

• 12 vegetative samples
• Coverage reached : 1160 - 1660X !
• Implicit assumption : R is mostly IES-dependent

Mac ploidy : 800n ?

If MAC ploidy = 800n than without retention :

$$E(IRS) = \frac{4}{800+4} \approx 0.005$$

If retention :

$$E(IRS) >> \frac{4}{800+4}$$

Something is odd !

0.002-0.003

Mac ploidy ... At least 1600n ?

Mac ploidy ... At least 1600n ?

For our calculus, we used K = 1600n

Numerical application

When using :

• Kmac = 1600n
• 12 vegetative samples to quantify R and P(MIC|IES+)
• ...

On average on all the IESs without retention :

$$R = 0 \implies P(MIC|IES^+) \in [33\% - 100\%]$$

CONCLUSION :

Numerical application

On a fake dataset (simulated retention)

Real retention R (simulated)

Summary

Can we identify the MIC sequences ?

• Very hard to have a correct estimate of P(MIC|IES+)
• When we will, it will mostly be for heavily retained IESs anyway
• With even the slightest retention :
  
   
• Only works for IESs, not other MIC
• Only works when the ploidy is well caracterized

> We should expect lots of our MIC data to be impossible to use

$$R > 0 \implies P(MIC|IES^+) \approx 0$$

The guiding thread

1. Identify MIC vs MAC molecules
   
   
    
2. DNA methylation detection :
   • Implementation and test of an analysis pipeline
3. 6mA MAC
4. 6mA MIC

(+ Found a new way to quantify IES retention)

(+ re-estimated the MAC ploidy)

DNA methylation analysis pipeline

A few months of plumbing later...

Outputs scores :

"ipdRatio": 2, 1, 4...

"ModificationQv" : 0,  30,  25...

"identificationQv": 1,  0, 50...

When do we call a nucleotide methylated ?

Pipeline output

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)

> Can be used to benchmark our pipeline's ability to detect 6mA

ipdRatio in E. coli

• The nucleotides we expect to be methylated have a high ipdRatio
• Slight changes between motifs
• Some exceptions : really not-methylated ?

Motif effect

Coverage

L. methylated

L. unmethylated

> 50

35-50

25-35

15-25

0-15

ipdRatio in E. coli

Coverage effect

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   :

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

$$Se  = 92\%$$

• Some GATC/EcoK are unmethylated
  • The real Se is actually better than 92%
•  A few amount of 6mA outside of GATC/EcoK site
  • The real Sp is actually better than 99.8%
• Se = 92% and Sp = 99.8% are worst case estimates

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

$$Sp = 99.8\%$$

Remark on hemi-methylation

We can easily have more than 50% of so-called hemi-methylated sites that are actually not hemi-methylated

Benchmark

(other modifications)

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

Qv30

• Either false positives, or a Nature paper
• "Killer experiment": See with amplified DNA
• For now, we ignored it

Analysis Pipeline

summary

Great to detect 6mA

Other ??

Potential problems when quantifying hemi-methylation

The guiding thread

1. Identify MIC vs MAC molecules
   
   
    
2. DNA methylation detection :
   • Implementation and test of an analysis pipeline
3. 6mA MAC
4. 6mA MIC

(+ Found a new way to quantify IES retention)

(+ re-estimated the MAC ploidy)

Hallmarks in HTVEG

• Between 1.25% and 1.45% of 6mA in the MAC
• Between 97.39 and 100% of them are located in
AT sites

Taking account of the uncertainty of Se and Sp :

Problem : Some results will vary greatly depending on Se and Sp !

The 4 scenarii for Se and Sp

• We don't care about Se and Sp
• Only thing that matters : Does it impact the result ?

Global level of 6mA in the MAC

 > Implied in the bulk of 6mA in the MAC

Reduction of 6mA

P. tetraurelia

Mitochondrial

Total BET

-80 to -90% NM proteins

-60 to 70% MT proteins

We expected -90%

Get only -50% ??

Less than predicted by southwestern blot

Role of our candidates

Raise of hemi-methylation, whose intensity depends importantly on how well Se and Sp are well estimated or not

Fraction of AT sites that are hemi-methylated

Role of our candidates

Role of our candidates

The capacity to make symmetrical methylation is never abolished completely

Role of our candidates

De novo methylation of unmethylated AT sites :

Unchanged NM4.

Drops everywhere else

Proposed interpretation/Hypothesis

Role of our candidates

All weakly implied except NM4 (not implied)

All strongly implied

Never abolished

Function = Symmetry +++

Outside of AT sites

Predicted FDR : 100%

But likely detection outside of AT sites too

never erased

AGAA and GAGG motif
are documented as methylated sites (6mA) in C. el-
egans too (Greer et al. 2015)

The guiding thread

1. Identify MIC vs MAC molecules
   
   
    
2. DNA methylation detection :
   • Implementation and test of an analysis pipeline
3. 6mA MAC
4. 6mA MIC

(+ Found a new way to quantify IES retention)

(+ re-estimated the MAC ploidy)

DNA modifications

In the MIC

A ruthless data shrinkage

Number of molecules with at least one exploitable adenine

- several IESs

- variable MAC regions

- extremity outliers

Is our calculus valid for several IESs ?

Remaining with computable P(MIC|IES+)

P(MIC|IES+) among the surviving molecules

We can never exclude the hypothesis that 6mA comes from the MAC

Conclusion on the MIC

• We cannot confirm of invalidate the hypothesis that there is 6mA in the MIC

• Therefore, we cannot conclude about the role of 6mA as a permanent marker of IESs in the MIC of P. tetraurelia

Summary

Methodological developments :

• Analysis pipeline
• Retention / P(MIC|IES+)
• MAC ploidy > 1600n
• [ Hemi-methylation ]

On our MAC data :

• Methylase candidates = active in the bulk of methylation in the MAC
• Symmetrical methylation of AT sites + a few de novo hemi-methylation
• AGAA / GAGG outside of AT sites

On our MIC data:

We cannot exclude the hypothesis that all 6mA comes from the MAC

Perspectives

• P(MIC|IES+) when > 1 IES ?
  • Could invalidate the hypothesis definitely
• Deeper analysis of HT2 and HT6
  • P(MIC|IES+) is irrelevant here
  • Still possible to find 6mA or other modifications around the IES very transiently
• Check 6mA / TSS
• Hemi-methylation :
  • TSS ?
  • Maintained through replication ?

On these data :

For the future :

• MIC purification +++
• Nanopore ?
• Partial purification ?
• The random sampling is doomed to fail in P. tetraurelia

MERCI !

Team Meyer

Team Genovesio

SPIBENS

Les infos et admin.

Jury: Chunlong, Sandra, Eric, Laurent

Comité de thèse: Linda, Mireille, Chunlong

La communauté paramécie

Les petites mains derrière les données

Mes collègues de pause

Toi public

Mentions spéciales contributions directes :

Eric Meyer

France Rose

Mathieu Bahin

O. Arnaiz

Leandro

MTA1 -- orthologue 4-9-10

MTA9 -- Pas catalytique chez Tetrahymena

[..] --> MT1A1B2

Pas 6mA tetrahymena MIC

MTA1 -- orthologue 4-9-10

MTA9 -- Pas catalytique chez Tetrahymena

[..] --> MT1A1B2

Pas 6mA tetrahymena MIC

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)