Studying DNA methylation in Paramecium with PacBio sequencing

Eric Meyer - Mathieu BAHIN

The team

Bahin Mathieu

Bioinformatics

Eric Meyer

Biology - Parameciology

Suzanne Marques (PhD student), Veronique Tanty (IE - PhD student),

Sophie Malinski (MCU)

Eric's Team:

Introduction

 

P. tetraurelia: Genomic architecture

Unicellular eucaryote with 3 nuclei:

• 2xMIC nuclei (2n)
  • Germline nuclei
  • Contains: TE & IES
    • No transcription
• 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 sequences
  • ~ 45.000
  • Inserted within genes
• Most recent IES are very short (~30bp)
• Remnant of TE ?
• ~ 100% TA-Bounded
• Weak consensus at the boundaries
  • PibbyBac transposase ?
  • How are they recognized ?

 

IES

Internal Excised Sequences

IES recognition ?

The ScanRNA pathway ?

10% of IES are scanRNA dependant.

 

30% of IES are small-RNA dependant (shown by DICER-like silencing)

 

• What about the majority remaining ?

 

 

 

 

PhD Project

One big question remains: How does the cell recognize the Scan-RNA independant IES ?

• Some motifs or specific nucleotide composition might differenciate these IES from the rest of the genome

 

• Those sequences can be permanently marked in the MIC by a DNA methylation pattern

Which epigenitic modification could we look at ?

 

n6mA is one the the 3 most frequent known methylations in DNA

6mA likely to be abundant in Paramecium:

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

• Detected methylation by SMRT in the MAC in our lab (unpublished data)
• Detection by SMRT in the MAC by Sandra Duharcourt's lab (unpublished)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)

 

Materials and methods

Identify methylase candidates

• Identification of  potential n6mA DNA methyltransferases

RNA silencing

Candidates:

• 7 "DPPW motif IV" genes
  • NM4, NM9, NM10 studied in our lab
  • others are studies by collaborators
• And another family: MT1a, MT1b, MT2

PacBio SMRT sequencing

99% accuracy

Max

85% accuracy

Methylation analysis needs local coverage> 25X

• IPD are captured and compared either with WGA or Trained model (ML - "in-sillico control")
• Allows detection of suspect downturns of polymerase (function of the -3/+8 nt context)

Reads up to 80 kbp

Analysis of methylation

Two-different approaches

Analyze every molecule independantly from the others

 

 

(Needs shorter inserts - multiple passes)

Beaulaurier et Al 2015

Strategy:

SMsn

• 1 out of 200 comes from the MIC
• 1/6 of MIC inserts will carry an IES
• 1/2 of IESs are just wrongly excised
• 30% of the remnants are scanRNA dependant

Available data at day0

PacBio sequencing

• Short insert strategy (SMSN)
• No WGA data

Wild type:

• Vegetative Cell (HTVEG)
• During autogamy  (after 2h - HT2)
• During autogamy  (after 6h - HT6)
  • Asynchronous cultures / Quite blurred state
     

Silencing of methylase candidates:

• Si/MAB (identified since then - probably a histone methylase)
   
• MT proteins:
  • Si/MT2
  • Si/MT1A-1B
  • Si/MT1A-1B-2
    
     
• NM proteins:
  • Si/NM4-9-10
  • Si/NM9-10
  • Si/NM4

Also: AggSN of PGM32

1. Sorting of the sequences

Sort the sequences

• High number of passes           accuracy  >99.9%
  • Possible to determine where it comes from:

Deduced origin

MIC DNA

Alignment of consensus

• MAC sequences clearly identified only if overlapTA of junction
• otherwise = MDS

Categories of sorting

• 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")

Output example

MDS and MAC_TA_Junction represent a vast majority

MIC specific and IES are as rare as expected

mDNA and rDNA are quite abundant too

% of total Pramecium DNA

(MIC, MAC, RDNA, mito)

Conclusion on the sorting

• We reach the expected theoritical counts
• Statisfying implementation
• Hand checking of many IES OK

 

 

2. Analysis of methylation

Can we trust our technique ?

The Bayesian ambush

Let's admit that

We measure methylation in E. coli

We have a no-so-bad test:

99% Sensitivity = P(D | M)

99% Specificity = P(ND | NM)

Our global fraction of n6mA among all adenines is around 1%

• How much n6mA will we detect ?
• What's the positive predictive value ? (PPV) that is P(M|D)

The FDR varies with the real methylation fraction

The testing dilemma

• Our test gives us the proportion of n6mA
   
• ...But we need the proportion of n6mA to estimate de proportion of n6mA !

Some priors for Paramecium

About Paramecium, preliminary data tend to show that:

• 0.5 to 2,5% n6mA
• > 90% in AT
• Vast majority of methylated AT = Symetrically-methylated

• Global level of n6mA detected: ~2%
  • (Reality: ~1%)
• True positives inside AT: 85%
  • (FDR  15%)
• True positives outside AT: 10%
  • (FDR 90%)

Approximate scenario for Paramecium

(Rough estimations,

Order of magnitude)

Assess the Se and Sp would help

• With in sillico control ? No one knows

Se, Sp : When is a n6mA modified ?

 

For n6mA, PacBio produces:

1. A modification score --> Slowing of the polymerase
2. An identification score --> Kinetic signature of a modification
   
   They are PHRED-transformed p-values of two different statistical tests, that rely on the mean of the IPDs

PHRED scores

The scores are PHRED-transformed p-values

Typical covscore plot

Modification score / coverage

Using flat threshold on modification score = Hudge lack of power

Coverage-dependant threshold

 

We implemented two thresholds that are function of the coverage:

1. A Gaussian Mixture Model (GMM) trained on MAC HTVEG
2. A very simple linear equation

 

Thresholding

How to chose ?

Choosing a threshold: E. coli contaminants

 

• Almost 100% of GATC sites are methylated
• Vast majority (>95%) of the methylation is located in the GATC sites
• 1.6% of adenines are located in a GATC site
  • ~ 1.6% of adenines should be methylated
• Most strains: ~20.000 GATC sites
  • x% symetrically methylated (?)
• Some methylation outside GATC
  • maintained by specific motif-driven enzymes

experiment
HT2           1
HTVEG       926
MAB         487
MT1A1B     1131
MT1A1B2     222
MT2         166
NM4         468
NM4910      138
NM910       357

Available data for E. coli

• HT2/6: Starved !

• 1.702% of A are in a GATC site

• Nb GATC sites: 4672

Number of consensus which mapped with >99% accuracy on a common strain reference genome

 inside GATC

covscore plots of bases above the linear separator in E. coli

outside GATC

Only adenines with coverage>25X are considered

Coverage

Score

Methylation level (E. coli)

inside VS outside GATC (E. coli)

Identification assessment

Methylation outside GATC sites

 

Modification assessment: Score20

Methylation outside GATC sites

 

Modification assessment: Linear equation

Conclusion on E. coli

• From what we expected:
  • Never 100% of GATC ! Only ~ 75% symetric
  • ~ Global methylation as expected
  • Motif-driven outside GATC as expected
  • Ratio in/out GATC ~ expected
     
• We also learnt:
  • LInear equation > GMM > Flat threshold
  • Seeking rare events requires higher specificity
  • Adding any idQv provide much higher specificity
     

Conclusion on E. coli (2)

• We can draw only quite limited conclusions:
  • Digested DNA ?
  • Unconsistent litterature
  • Specific methylation of this E. coli strain ?

In a perfect world

In an ideal world:

 

DNA n6mA

Analysis of Paramecium tetraurelia

Enfin !

Per experiment comparaison (1)

Per-experiment comparaison (2)

In the vegetative MAC

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

  • slightly lower in the MIC (5 to 5 points less)

  • 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)

(*) Linear equation / idQv20

Outside AT sites

Kept in the MAC for all experimental conditions

Impossible to tell in the MIC (not enough sequences)

In the MAC

Per genome comparaison

mDNA

42% GC

rDNA

38% GC

5. Investigation on m4C

Logo from HTVEG MAC

Identical everywhere

Conclusion

Perspectives

Short-term

• Methylation of Scan-RNA dependant VS independant IES
• Filtering the MIC on the retention level ?
• Investigate the differences between the silencings and the vegetative state

Mean-term

• FDR procedure with a priori ?

 

Later: Quit PacBio SMRT

• Content of IES (already started but stopped)
• New nanopore sequencing
• mtF-like proteins sequencing en cours

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

Thanks !

Additionnal

 

Output example

IESs (1 & 2)

MAC (1 & 2)

GMM + idQV20

 

• Raises the % located in AT
• Methylated fraction goes down to 0.9-1%
• NM4-9-10 goes down to 50% in the MAC
• Logos don't change significantly

Logo from HTVEG MAC

Identical everywhere

Laura landwebehr 2020

Oxytrichia trifallax

Modifications in AT/TA

• 95% AT
• Same MIC/MAC in AT
• No difference between experiments
• ~75% methylated symetrically

 

Modest variations in the silencing

FIndings in cytosines

Logo from HTVEG MAC

Identical everywhere

Modifications out AT

What interests us most

We didn't find no difference between experiments

Sexuated reproduction in Paramecium

The old MAC (maternal) drives everything during the formation of the new one:

 

• Active transcription
• Sexual determination (RNA)
• Excision of IES

 

= Non-mendelian / Cytoplasmic heridity

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

2.1 Retroingineering

A lazy polymerase

• Pauses
• Not related to DNA methylation
• Averaging is impossible with the pauses
• Outliers that must be removed

After rigorous checking, I stated that the capping as implemented was not a problem for our SMSN approach

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

 

2.1 Retroingineering

In-sillico control: ipdtools

• In-sillico control = Prediction of the polymerase speed (IPD)
• Black box
• Impossible to use without PacBio's default software !

Recently, I have coded ipdtools. It predicts:

• Any IPD of unmethylated sequence
• With any PacBio's chemistry (RSII, Sequel I, Sequel IIv2)

2. Understanding / Retroingineering of PacBio's softwares

Motivations

• REMINDER :
• No tool existed for our kind of analysis:
  • SMSN
  • In-sillico control
  • .bam format PacBio Sequel I

 

• We had to develop our tools
• First idea = Just customize the PacBio pipeline

Main problems encountered:

• The capping of IPDs
• Use the in-sillico model

 

3. Developping our own pipeline

Problems that we solved:

• Launch the methylation analysis program for each molecule separately
• Produce the right inputs (headers, indexinf files, etc)
• Produce outputs even in case there is no modification found
• Parallelize everything
• Optimizations on the alignment process

Develop our own pipeline with PacBio's tools

"smrtrue"

Private Github repository

AggSN or SMSN ?

Within the total DNA:

• MAC/MIC dna ratio = 200:1 --> Random sampling
• We don't want to mix MIC and MAC information
• We must use the SMSN method !