My background

• Former Pharmacist (Lille)

• 2022:  PhD degree at ENS Paris (bioinformatics)

• MSc Bioinformatics - Paris Diderot

PharmD thesis

Hematopoietic Stem Cell transplants (HSCT)

The Idea: Predict mortality or GVHD events with machine-learning

• My own project from the beginning
• Worked with a national consortium and 2 clinical experts

Preliminary results +/- encouraging

 

Still ongoing

My PhD :

PhD subject : P. tetraurelia

The difference ?

MAC  =

• Transcriptionnally active
  • Amplified
  • Freed from TEs
  • Freed from >48.000 unique "IES"

Internal Excised Sequences (IES)

We understand the recognition of ~30% of IESs (small ncRNA)

...How are the other recognized ?

Constant 6mA pattern in the MIC (ONLY !)

Our hypothesis :

But, almost nothing was known about DNA Methylation in both the MIC, and the MAC

Main idea of the project:

• Characterize WT methylation (MIC and MAC)
• Candidate methyltransferases (MTases) were identified and silenced with interferent RNAs
• See whether or not IESs are recognized correctly in silenced conditions

Problems encountered during my work

Total DNA -> 99.5% of sequencing data = waste

Reminder : 1:200 comes from the MIC

1. Remove contaminants
2. 1 molecule out of 200 comes from the MIC
3. ~ 1/6 of MIC inserts will carry an IES
4. 30% don't interest us
5. # of PacBio reads per sample : 150.000 to 300.000
6. ...

This is not much, but if had been right, 100% of them had to be methylated

Problem n°3 : PacBio SMRT-seq

PacBio SMRT sequencing

• PacBio SMRT-seq = 2  variants :
  • Long inserts (several kb)
    • Default
  • Short inserts (~350bp)
    • Very rare
    • No analysis pipeline existed for our kind of data

• No analysis pipeline existed for us
• Much retro-ingineering of proprietary software was needed

Problem n°4 : IES retention

• IESs are sometimes retained in the MAC

  • Some IES are more retained than others

  • Danger ! >> These IES come from the MAC !

 

P(R)

1 - P(R)

No methodology existed to quantify R (and therefore P(MIC|IES+)) precisely

 

Results

The analysis pipeline

After a lot of retro-engineering :

 

• A reproducible pipeline
• Benchmarked (E. coli GATC sites)
• Python
• Conda install
• Tests
• Continuous integration (CircleCI)
• Clean command-line interface (CLI)
• FDR control procedures

Where do IES+ reads come from ?

Bayesian modelling

Hamiltonian Monte-Carlo with Stan

+ Old sequencing datasets

Surprising results :

• Almost all IES+ reads actually come from the MAC (!)
  • Impossible to study the MIC with our method !
• The MAC ploidy is often said to be 800N
  • Real value  >1600N
  • >50 years old mistake spotted !!

> It turns out that all the candidate methylases we silenced were actually active in the MAC !!

Similar to Oxytricha :

Beh et al. 2019

Therefore we were actually lucky to have so much MAC data to study it

Methylated symetrically in AT sites

The real role of our MTases (2/2)

All weakly implied

All strongly implied

 

Function = Symmetry +++

• Our MTAses turn hemi-methylated sites into symetrically-methylated ones
• Total 6mA reduced by up to -50% when all MTAses are silenced

 

 

 

 

 

 

It is possible that hemi-methylation is actually preserved through DNA replication. We are still investigating this.

 

A "fun" fact about hemi-methylation

Statistical curiosity of our pipeline :

1) %6mA detected = True %6mA
2) % hemi-methylated AT sites = True % hemi-methylated AT sites

... But this is a coincidence !

 

In fact,

if Z = # of 6mA in an AT site
if D = # of detected 6mA in an AT site, then sometimes we have as low as :

 

P(Z=2 | D=2) ~ 20-50%

Even though P(D=2|Z=2) ~ 100%

 

CONCLUSIONS

• New analysis pipeline for SMRT-seq (https://github.com/EMeyerLab/SMSN)
• Retro-engineering of Pacbio proprietary software (https://github.com/EMeyerLab/ipdtools)
• Quantification of false positive hemi-methylation (https://github.com/EMeyerLab/paired_detections)
•  Long-time error spotted (MAC ploidy not 800N but >1600N)
• New method to quantify IES retention
• Demonstrated mathematically that it was impossible to answer to my problematic
• Identified the true role of our MTases
• Other secondary tools
• Eg: High-performance filtering of .bam files based on the CIGAR string
• (https://github.com/GDelevoye/cigarfilter)

 

More details :

https://drive.google.com/file/d/14Z2EEGpIRMeoXwIj4f8WhthG_x0AVF58/view

More about me

Dire Straits <3

Chess

Climbing

lolcats

who doesn't ?

Our supreme leader

Guido Van Rossum

What is he ? A man ? A God ?

We'll never know.

(when I win)

Playing music

Internet

Hiking

Thanks :)

My scientific medley

Code

Stats

 

Hematology (HSCT)

ML

Ciliates

NGS

Structural biology

Ask me at the end !

Python
C++
Bash
R

Methylase candidates in Paramecium tetraurelia

Candidates identified:

 

• NM4, NM9, NM10
• Another family: MT1a, MT1b, MT2

2) PacBio sequencing with short inserts | 6mA ++

1) Grouped silencings by sequence homology

PacBio SMRT (short inserts)

 

Inserts up to 350bp

Sequencing : unique molecule & both strands

Polymerase slowing ~ methylated adenine

The random sampling strategy

• Purify the MIC: Impossible
• Sequencing whole MIC from whole DNA : Impossible

• Solution: Random sampling
  • 1 among 200 molecules
  • Not all with cary an IES at all
  • ~20 to 100 IES only per experiment

Problem n°2

IESs are sometimes retained in the MAC

What is P(MIC|IES+) ?

Other analysis challenges

(skipped)

Details that I spare you (but don't hesitate to ask) :

• Random sampling of the MIC
• Sorting MIC and MAC
• Sorting IES specific and MIC non-IES sequences
• Filter IES retention
• Estimate Se and Sp
• Retro-ingineering PacBio's formats
• Recoding parts of the softwares for single molecules
• etc.

Detecting n6mA

Imperfect detections

No scientific method is perfectly reliable

> Expect False Positives and False Negatives

Imperfect detections (2/2)

We can use 4 extreme scenarii :

... And see what is consistent no matter the scenario we place ourselves into

Finding unbiased estimators for    and FDR

If p number of positive detections among N tests:

 

p = FP + TP

$$\pi$$

So,

Which means

 

with         fraction of 6mA

$$\pi =$$

Debiased levels of m6A in the MAC

> 95% n6mA are located in AT sites

DNA n6-mA around/in the IESs

Results

Methodological development to correct hemi-methylation detection

Debiased Hemi-methylation in MAC

Analysis showed that:

Conclusion

IGEM2014

MT and NM families :

In the MAC

Convertase activity DNMT1-like

But: Hemi-methylated AT sites kept after many mitosis

New hypothesis:

Symmetrical methylation of AT sites = Mitotic clock or maturity indicator for the cell to be allowed to go enter meiosis

A bit more about me...

I don't like

Some keywords for my PhD

1) Paramecium tetraurelia

3) DNA methylation

4) PacBio sequencing

2) Transposable elements / IESs

5) hemi-methylation of

palindromic motifs

(AT sites)

P. tetraurelia

The difference ?

MAC genome compared to MIC =

• Amplified+++
• Free from TEs
• Transcriptionnally active
• Not transmitted to progeny

Genome invaders in Paramecium tetraurelia

In the MIC:

• 45.000 unique sequences
  • Small (<27bp), Non-coding
  • Lots present in the CDS
  • Remnants of TE ?
  • 100% TA-bounded

 

--> How ?

Present in the MIC

Absent in the MAC

= Excised generation after generation

2) Transcient in the new forming MAC

1) Constant pattern in the MIC

Transcient ?

Recognition of IESs: Our hypothesis

Our hypothesis:

~2.5% of n6mA in Paramecium (MIC&MAC) : Cummings 1974

Methylase candidates

Candidates:

 

• NM4, NM9, NM10
• Another family: MT1a, MT1b, MT2

2) PacBio sequencing with short inserts | 6mA ++

1) Grouped silencings by sequence homology

PacBio SMRT principle

Reads up to 80 kbp

1) Sequencing (unique molecule)

2) High fidelity consensus

3) DNA methylation analysis

What are the results ?

2) Crosstalks in the new forming MAC

1) Constant pattern in the MIC

Transcient ?

Our hypothesis:

Prelimary analysis shows that:

~2.5% of n6mA in Paramecium (MIC&MAC) : Cummings 1974

Details invegetative MAC

• ~95% of n6mA locates in AT dinucleotides in the MAC

• 75% of the methylation in an AT dinucleotide is actually symetrically modified

Bulk of methylation:

Total = 1.2 to 1.5% of adenines

0.6% of the adenines outside AT sites:

Now a bit more details...

Imperfect detections

No scientific method is perfectly reliable

• 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

 

with         fraction of 6mA

$$\pi =$$

Debiased levels of m6A in the MAC_TA inserts

Methodological development to correct hemi-methylation detection

Debiased Hemi-methylation in MAC_TA

Conclusion

• In the MIC:
  • Probably no m6A after all
  • Hard to imagine a role in TE/IES excision
• In the MAC
  • Our 6 genes are implied in the bulk of the MAC m6A
    • Sym-methylated -> Hemi-methylated
  • MT and NM families: Functional analogs of DNMT1 ?
    • Not really: Not kept after replication
  • Lots of questions raised by the MAC methylation:
    • TSS ?
    • IES excision Junctions ?
    • Nucleosome positionning ?
• One important phenotype:
  • NM4-9-10 somehow makes the cell unable to go into autogamy

 

Thanks :)

Role of DNA-6mA in Paramecium tetraurelia

Guillaume DELEVOYE

3rd Year PhD student

Bioinformatics

P. tetraurelia: Genomic architecture

Unicellular eucaryote with 3 nuclei:

• 2xMIC nuclei (2n)
  • Germline nucleus
  • Contains: TE & IES
    • No transcription outside meiosis
• 1xMAC nucleus (up to 800n)
  • Somatic nucleus
  • Amplified and "fixed" version of the MIC
  • Free from TE and IES
  • Transcriptionnally active

Profiling the IESs

• Non-coding
• Excised after sexual processes
• Remnant of TE ?
• Most recent IES are very short (< 27bp), and are the majority of IESs

 

45.000 Unique sequences

How are IESs recognized ?

(1/2)

• 100% TA-Bounded
• Weak consensus TAYAG
  • Degenerated TC1-Mariner TE insertion site
  • Not sufficient
• Periodic size distribution

 

30% of IESs only are small-ncRNA dependant (shown by DICER-like2-3 silencing)

 

• What about the majority remaining ?

 

 

 

 

How are IES recognized ? (2/2)

The sc-RNA pathway

The DNA methylation hypothesis

6mA likely to be abundant in Paramecium:

 

• Suspected 2.5% in the MAC of P. aurelia by Cummings et Al (1975)

  • Also documented 6mA in the MIC

  •  

• Detected methylation by SMRT in the MAC in our lab (unpublished data)
   
• Detection by SMRT in the MAC by Sandra Duharcourt's lab (unpublished)
   
• Detection in Oxytrichia by L. Landweber et Al (2019)
  
   
• no 5mC, 4mC in the MAC a priori

 

2) Crosstalks in the new forming MAC

1) Constant pattern in the MIC

Transcient ?

Methylase candidates

Candidates:

 

• NM4, NM9, NM10
• Another family: MT1a, MT1b, MT2

2) PacBio sequencing with short inserts | 6mA ++

1) Grouped silencings by sequence homology

PacBio

TL;DR overview

PacBio SMRT principle

Reads up to 80 kbp

1) Sequencing (unique molecule)

2) High fidelity consensus

3) DNA methylation analysis

PacBio SMRT principle (2)

• Strategy 1: Long inserts (long reads)
  • Ideal for assembly of long repeated sequences
  • Poor resolution for DNA methylation analysis
     
• Strategy 2 : Short inserts (long reads)
  • Much higher resolution for DNA methylation analysis

 

Step 1: Consensus

99% accuracy

Max

75% accuracy

Because our inserts are circular and shorts, we can make CCS of high accuracy despite a 15% error-rate

Step 2: Sorting

Deduced origin

MIC DNA

Alignment of consensus

TA

TA

TA

Step 3 : DNA methylation analysis

• Single-molecule
• Single-nucleotide resolution
• Independant yet pairable analysis on both strands

Some more details:

the random sampling approach

Deduced origin

MIC DNA

Alignment of consensus

Only a few remaining: ~ 10 to ~200 sequences

100% should carry a methylation pattern

• 1 out of 200 comes from the MIC
   
• 1/6 of MIC inserts will carry an IES
   
• For 50% of IESs, we cannot be sure whether it come from the MIC or from the MAC
  
   
• 30% of the remnants are scanRNA dependant

Final categories

• MDS
  • = "MAC" for 99% of sequences
• MAC_IES
  • "true" MAC_IES (never seen retained)
  • other MAC_IES (sometimes retained)
• MIC
  • Other MIC specific (TE, repeated sequences)
• MAC (TA Junction)
  • Overlap a TA junction of excision
• rDNA
• mtDNA
• Other:
  • Contaminants
  • Alternative excision boundaries ("LOWID")
  • low identity consensus ("Trash")

Little bug to be corrected

30%

MDS and MAC_TA_Junction represent a vast majority

MIC specific and IES are as rare as expected

+ Applying the retention filter divides the number of "MAC_IES" approximately by 50%

Detecting m6A in details

Detecting m6A optimally

H0: ipdRatio of umethylated-Adenines

H1: ipdRatio of 6mA

S: Threshold on the pvalue

 --> Specificity

 --> Sensitivity

 

$$\alpha$$

$$1 - \beta $$

Adenine

6mA

ln(ipdRatio) ~ N(0,1)

log(ipdRatio)

Using E.coli as ground truth

E.coli:

• Feeds paramecium: contaminants+++
• Nearly 100% symetrically-methylated with m6A
  • GATC
  • EcoK
  • Few others
    • Depends a lot of the strain
• Outside of GATC and EcoK: very low levels of m6A

I investigated the PacBio's output on it's GATC & EcoK VS other sites

How log(ipdRatios) look in E.coli

Separability raises with coverage, which is expected

PacBio pvalues do have some biological meaning

PacBio pvalues do have a meaning...

• Just don't
• Obvious point n°1:
  • log(ipdRatio < 1) -> -inf

Normality assumptions under H0 are broken under high coverages

• The higher the coverage, the worse (here, >40x)
• Will cause bell-shaped pvalues under null
  • Hidden phenomena on the previous curve

Normality assumption matters

All Adenines' pvalues [E.coli] coverage > 40X

Long story short

PacBio's pvalues:

1. Are biologically relevant
   • We can build a reliable ad-hoc system with them
2. Are produced by a linear combinations of > 150 different coverages
   • Which forbids the usual statistical treatments
3. Are somehow broken on a coverage-dependant manner
   • Which forbids a simple fix for point 2
   • We can't use the classical statistical treatments directly on pvalues

Other PBio's scores for m6A

For n6mA, PacBio produces:

• A modification score --> Slowing of the polymerase
  • pvalue against H0 only (the one we presented earlier)
• An identification score --> Kinetic signature of a modification
  • loglikelihood between H0 and H1 (H1 = Other modifications or secondary peaks)
     

They are PHRED-transformed p-values of two different statistical tests, that rely on the mean of the IPDs

PHRED scores

The scores (Qv) are PHRED-transformed p-values

Typical covscore plot

Modification score / coverage

Using flat threshold on modification score = Hudge lack of power

Solution : A coverage-dependant threshold on the scores

From now on

 

"positive detection"

=

score > linear thershold

 

 

(only >25X considered)

Benchmarking

How good (or bad) is our method ?

$$Se = P(D^+|M) ~ 92\%$$

 

$$Sp = P(D^-|NM) ~ 99.8\%$$

Starting from sufficient coverages (~20X to ~30X), Se and Sp don't depend on the coverage anymore

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

Debiased levels of m6A in the MAC_TA inserts

Details in MAC HTVEG

• ~95% of the methylation locates in AT dinucleotides in the MAC

  • True in any condition
     

• 75% of the methylation in an AT dinucleotide is actually symetrically modified, independantly from being in the MAC or the MIC

Kept in MIC and MAC

All conditions

MAC_TA outside of AT sites

Detections outside AT sites are likely to be at least partly true positives

Present in all samples

Never erased

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

What it gives in Paramecium

Debiased Hemi-methylation in MAC_TA

n6mA in the MIC (preliminary)

Pure MIC sequences: Cannot yet be trusted (error in the pipeline)

Coverage >= 40X made us lose too many materials, should restart with >=20X

n6mA in the MAC_IES rarely retained ("TRUE MAC IES")

HTVEG

MT2

 

 

--> Some molecules carry all the detections, in sym-A*T

Very likely to be sequences comming from the MAC to my opinion

At first look, seems like the same in all samples

Conclusion

• Lots of sweat spent on methods : Now we start the cool things
• In the MAC
  • Quantification in the MAC is well-characterized in all the samples
  • Our 6 genes are implied in the bulk of the MAC m6A
  • MT and NM families: Functional analogs of DNMT1 ?
  • Lots of questions raised by the MAC methylation: TSS, IES Junctions, nucleosome positionning...
• In the MAC IES
  • Very preliminary analysis between TRUE mac IES and other MAC IES tends to show that all detections could come from accidental retention in the MAC, and the MIC could actually carry 0% m6A or very lower levels than 1%. Really to premature to be sure
  • HT2 and HT6 are still potentially full of surprises
• TE in the MIC: No idea yet
• It's just a question of time now ! Which we asked (prolongation LABEX memolife)

Sorry for the headaches !! Thanks for your time :)

Ciliates are great model organisms

 

1862 - Pasteur: Refutation of the spontaneous generation theory with infusoria

1937 - Sonneborn: Non-mendelian inheritance of sexual type in paramecium

Elizabeth blackburn & Carol Greider: 1985 - Telomeres and telomerases in Tetrahymena (Nobel Prize 2009)

Eric Meyer, Sandra Duharcourt

(IBENS, I. Jacques Monod 2014)

 

Sexual type in paramecium are transmitted by maternal RNAs, not by DNA

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

p values (A)

p-values (A score >20)

Out GATC score < 20

Out GATC

ipdRatio score 20

ipdRatio idv20/score20

A outAT score 20 isQv20 (812 seq)

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

ipdRatio out GATC before filtering BH vs after (qv20/idqv20)

In GATC