This document describes a bioinformatics approach for prioritizing disease-causing human mitochondrial DNA mutations. It uses a "disease score" that averages the probabilities from six pathogenicity prediction methods to determine if a mutation is deleterious or benign. The approach was trained on 53 known disease-associated mutations and tested on 1872 observed mutations, achieving a disease score cutoff of 0.4311. Mutations meeting criteria of being rare, conserved, predicted pathogenic and occurring at low variability sites were prioritized. When tested on 21 tumor samples, 8/268 prioritized nonsynonymous mutations were tumor-specific, confirming the approach's ability to identify potentially pathogenic mutations.

1. A bioinformatics approach for the prioritization of
disease candidate human mtDNA mutations
BiP-Day 2014 Seconda Giornata della Bioinformatica Pugliese
Bari, 19 dicembre 2014
Mariangela Santorsola

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1 Mutations spreading in one or more populations and/or
haplogroup-associated events
2 Rare mutations occurring in highly conserved sites lying to
functional and selective constraints (somatic and/or germline)
Potentially affecting function
mitochondrial mutations
Mitochondrial mutations

3. MToolBox(1) functional annotation
Patho Table of all possible
non-synonymous mutations
• Nucleotide variability values (SiteVar
alghoritm (2))
• Six pathogenicity predictions from:
1 MutPred (3)
2 Polyphen-2 (4)
3 SNPs&GO (5)
1 Calabrese et al., 2014
2 Pesole and Saccone 2001.
3 Li et al., 2009
4 Adzhubei et al., 2010
5 Capriotti et al., 2013
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The discrepancy of pathogenicity predictions by different methods requires
the use of a single score able to summarize such predictions and define a
mitochondrial non-synonymous mutation as ’disease' or ’benign'.

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The weighted mean of the probabilities to be deleterious provided by
pathogenicity predictor methods for each i-th non-synonymous mutation
Disease Score
DSi =
(Pi
MP*WMP)+(Pi
PPD*WPPD)+(Pi
PPV*WPPV)+(Pi
PT*WPT)+(Pi
PS*WPS)+(Pi
SG*WSG)
WMP+WPPD+WPPV+WPT+WPS+WSG
PMP = MutPred probability WMP= MutPred weight
PPPD = Polyphen-2 HumDiv probability WPPD = Polyphen-2 HumDiv weight
PPPV = Polyphen-2 HumVar probability WPPV = Polyphen-2 HumVar weight
PPT = PANTHER probability WPT = PANTHER weight
PPS = PhD-SNP probability WPS = PhD-SNP weight
PSG = SNPs&GO probability WSG = SNPs&GO weight
Ranging between 0 and 1

5. Weight
W=(hp+rp)/2n
• hp = number of times the method
provides the higher probability
• rp = number of times the method
provides the right prediction
(“affecting function or disease”)
• n = number of training mutations
• An ideal method which provides n
times the higher probability and n
times the right prediction for n
mutations would have weight 1
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Disease Score
Training dataset
53 non-synonymous mutations
previously validated as affecting
function
• 28 disease-associated mutations,
annotated in Mitomap as
‘confirmed’ pathogenic by at
least two or more independent
laboratories (1)
• 25 cancer-associated mutations
previously validated (2)
1 http://www.mitomap.org
2 Pereira et al., 2012

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Min 0.66
Median 0.87
Max 0.92
Min 0.05
Median 0.13
Max 0.43
Disease Benign
Disease
Benign
Disease scores distribution of observed non-synonymous
mutations predicted as ‘Benign’ or ‘Disease’ by the six
pathogenicity predictors at the same time.

7. >=0.4311
Disease score cutoff
Bimodal distribution of disease scores for 1872 observed non-synonymous
mutations observed in 15385 mtDNA genomes from healthy individuals
stored in HmtDB (1) (Last update May 2014)
The disease score value for which the probability of belonging to the
second ‘disease’ component of mixture model was ten times greater than
the probability of belonging to the first ‘neutral’.
1 Rubino et al., 2012
Disease Scores
Frequency
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Disease
Benign

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Potentially affecting function mitochondrial mutations
Low nucleotide variability value
High disease score
Nucleotide variability/Disease score correlation of all possible
non-synonymous mutations

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Nucleotide variability cutoff
Nucleotide variability
Frequency
The nucleotide variability cutoff below which a mutation may be considered
potentially deleterious was determined as the third quartile of the
distribution of variability values associated to the 816 non-synonymous
events featuring disease score above the established DS-cutoff.
3rd Qu. <= 0.0026

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Polymorphic and haplogroup-associated vs
rare mutations
MToolBox Functional annotation
• Hg_MHCS (Major Haplogroup
Consensus Sequences(1) )
• rCRS (revised Cambridge
Reference Sequence(2))
• RSRS (Reconstructed Sapiens
Reference Sequence(3))
Phylogenetic relationships among virtual Major Haplogroup Consensus Sequences and
two real mitochondrial sequences (Phylotree(4)) for each haplogroup
1 Calabrese et al., 2014
2 Anderson et al., 1981
3 Behar et al., 2012
4 van Oven and Kayser 2009

11. Prioritization criteria of mtDNA non-synonymous
mutations affecting-function for future analysis
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Recognized by three reference sequences
Occurring in non-haplogroup defining sites
Featuring nucleotide variability values <= 0.0026
Featuring Disease score >= 0.4311
Heteroplasmy level (*)

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Application of MToolBox and prioritization criteria
Check of tumor-specific nature
by sequencing mtDNA from blood tissues of the same individuals
• 77.78% of prioritized variants were tumor-specific
21 ovarian tumor pre-chemio
mtDNA samples
Sample Variant Allele HF Locus AA Change Nt Var Disease score
Tumor-
specific/Germline
EOC5 3380A 0.75 MT-ND1 R25Q 0.0003 0.8764 tumor-specific
EOC40 14969C 0.50 MT-CYB Y75H 0.0003 0.8526 tumor-specific
EOC16 9837A 0.45 MT-CO3 G211S 0.0000 0.8379 tumor-specific
EOC20 15255C 0.80 MT-CYB V170A 0.0000 0.8195 tumor-specific
EOC20 10696T 0.75 MT-ND4L A76V 0.0000 0.7810 tumor-specific
EOC14 6121C 0.45 MT-CO1 I73T 0.0007 0.7054 tumor-specific
EOC5 8412C 1.00 MT-ATP8 M16T 0.0023 0.6587 germline
EOC32 14249A 1.00 MT-ND6 A142V 0.0020 0.4498 germline
List of 8/268 prioritized non-synonymous affecting function mutations
6/21 mutated samples (33%)
• All synonymous mutations, occurring in site showing variability
values below the variability cutoff, resulted to be germline

13. Acknowledgements
mar. ’15 Mariangela Santorsola
Department of Medical and Surgical Sciences
University of Bologna
Giuseppe Gasparre
Claudia Calabrese
Rosanna Clima
Giulia Girolimetti
Department of Biosciences, Biotechnologies and Biopharmaceutics,
University of Bari
Prof Marcella Attimonelli
Saverio Vicario
Domenico Simone
Maria Angela Diroma