Serial Endosymbiosis Hypothesis: two-step

Gram- Bacterium α -Proteobacterium

Archaebacterium Archezoan (mito-) Eukaryote (mito+)

Hydrogen Hypothesis: one step

α- proteobacterium

Archaebacterium(methanogen) Eukaryote (mito+)

Loss of genes from mitochondrial genome

Gain of new genes from nuclear genome

NEW AVENUES IN BIOLOGY THROUGH

COMPARATIVE GENOMICS

Gene transfer to the nucleus

This process achieved completion, only in the case of some Archaeozoa (eukaryotes without mitochondria)

It is still going on in plants

It is frozen in metazoa

Whatever its origin, the mitochondrial genome has followed different pathways in the evolution of eukaryotic lineages, so that we are now dealing with a great variety of mt DNA structures present in the organelles of extant organisms.

Arabidopsis

PlasmodiumHomoC. reinhardtii

S. pombe

C. eugametos

Chondrus

Phytophthora

Ochromonas

Acanthamoeba

Tetrahymena

Prototheca

Allomyces

Reclinomonas

Jakoba

Marchantia

Rickettsia

Size, Shape and Coding Capacity (in red) of Mitochondrial Genomes

Frozen features (with some exceptions)

Shape (circular) Size (about 15 kb) Gene content

Consistent between phyla Usually modest within phylum

(with some exceptions)

mtDNA in Metazoa(800-1000 million years)

Gene arrangement (mediated by tRNAs?)

Metazoan mtDNA

Introduction to Comparative Genomics - 2002

Drosophila yakuba

Apis mellifera

Meloidogyne javanica

C. elegans

Artemia franciscana

D-loopF T

P

Cyt b

E

LS

ND

4L

ND

4

R

ND

3

G

CO III

ATP 6

ATP 8

K

D

S

C O II

CO I

W AN

CY

O L

M

Q

I

ND

1L

ND

2

V

rRNA

rR NA

16S

12S

O H

H

ND

5

VertebratemtDNA

Gene order usually frozen with a few transpositions tolerated

Which are the other possible sources of variation?

Base composition

mtDNA in Vertebrata(about 350 million years)

GC Content of Metazoan mtDNA

0 10 20 30 40 50 60

Mammalia

Aves

Reptilia

Amphibia

Osteichthyes

Chondrichthyes

Agnatha

Cephalochordata

Hemichordata

Urochordata

Echinodermata

Arthropoda

Mollusca

Anellida

Nematoda

Platyhelminthes

Cnidaria

Asymmetry indexes

GC skew =G – C

G + C

AT skew =A – T

A + T

Gene order usually frozen with a few transpositions tolerated

Which are the other possible sources of variation?

Base composition

Base changes

mtDNA in Vertebrata(about 350 million years)

Gene order usually frozen with a few transpositions tolerated

Which are the other possible sources of variation?

Base composition Base changes

Regulatory region

mtDNA in Vertebrata(about 350 million years)

H S P

L S P

S RL R

E TA S d o m a in

5 'OH 3 '

L R

3 2 1

C S B d o m ain C en tra l d o m a in

E TA S s12

C S B s

R N A D N A

5 ' 3 'tR N APhe tR N A

P ro

D-loopF T

P

Cyt b

E

ND

5L

S

ND

4L

ND

4

R

ND 3

G

CO III

ATP 6

ATP8

K

D

S

CO II

CO IW A

NC

Y

OL

M

Q

I

ND

1L

ND

2

V

rRNA

rRNA16

S

12S

O H

H

VARIABILITY OF THE MITOCHONDRIAL GENOME

Taxonomic level:

Order

Species

Intraspecies

J Mol Evol (1999) 48:427–434

Nucleotide Substitution Rate of Mammalian Mitochondrial Genomes

Graziano Pesole,1 Carmela Gissi,2 Anna De Chirico,2 Cecilia Saccone2,3

Stationary Markov model of Saccone et al. (1990), also called "general time reversible"

(GTR) The GTR model has been applied to estimate the average genetic distances between the sequences of each locus within each species. The method involves the assessment of the stationarity of bases composition, an essential condition for the correct estimate of distances. At present, one assumption commonly made in molecular evolutionary studies, and especially in estimating distances, is that of stationarity, i.e., the nucleotide frequencies in sequences do not change with time (Gu and Li, 1996). If stationarity is verified, base compositions should remain similar among sequences. However, analyses of many data sets revealed that this assumption is often unrealistic and that compositional changes occur in the evolutionary process (Tourasse et al., 1999; Lockhart et al., 1994; Galtier and Gouy, 1995). For that reason, ignoring nonstationarity could mislead distances evaluation.

Nucleotide substitution rate

Central domain

0 5 10 15 20 25 30 35

Nonsyn

Syn

tRNAs

12S rRNA

16S rRNA

ETAS domain

CSB domain Subs/Site*y*10-9

Mammalian mtDNA substitution rateMammalian mtDNA substitution rate

a) High intragenomic variability in the evolutionary dynamic of mtDNA;

b) Slow- evolving regions: nonsynonymous sites (the rate of nonsynonymous sites depends on the functional constraints and therefore differs considerably between genes) the D-loop central domain, and tRNA and rRNA genes;

c) Fast-evolving regions: synonymous sites (the synonymous rate is fairly uniform over the genome), and the two peripheral D-loop region domains.

Mitochondrial vs Nuclear Rate in Mitochondrial vs Nuclear Rate in MammalsMammals

Mitochondrial vs Nuclear Rate in Mitochondrial vs Nuclear Rate in MammalsMammals

Type of sites mtDNAa nDNAa Ratio(Mt/Nuc)

Nonsynonymousb

Synonymousb

Small rRNAc

0.8 0.2

1.6 0.9

0.4 0.3

3

22

19

±

±

±

34.6tRNAsc 0.1 0.1 97±

2.6 0.4

3.9

7.7 2.4

±

±9.7 2.4±

±

Large rRNAc 4.1 0.8 4±17.2 3.8±

A Sequence divergence, expressed in % substitutions/site, has been calculated using the Stationary Markov Model (Saccone et al. 1990).

B Referred to the human-common chimpanzee pair.

C Referred the rat-mouse pair.

The commonly accepted statement that mtDNA evolves more rapidly than nuclear DNA is valid only for some regions, thus it should be referred to specific mitochondrial components. In particular:

a) Nonsynonymous sites show comparable rates in mitochondrial and nuclear genes; synonymous sites

b) Small rRNA evolve about 20 times more rapidly and tRNAs about 100 times more rapidly in mitochondria than in their nuclear counterpart.

c) A species- specific evolution is particularly evident in the Dloop region.

Mol. Biol. Evol. 17(7):1022–1031. 2000

Lineage-Specific Evolutionary Rate in Mammalian mtDNA

Carmela Gissi,* Aurelio Reyes,† Graziano Pesole,‡ and Cecilia Saccone*†

35 complete mitochondrial genomes from 14 mammalian orders have been investigated by using both first and second codon position (P12) of H-strand protein-coding genes and ribosomal RNA genes.

C.Gissi et al. (2000) Mol. Biol. Evol. 17:1022-10310

Relative Rate TestRelative Rate Test

RAB=dA/dB=(dAB+dAO-dBO)/(dAB-dAO+dBO)

ingroup A ingroup B outgroup

root

dA dB

MtDNA rate heterogeneity in MammaliaMtDNA rate heterogeneity in Mammalia

Significant rate variations have been observed among orders and among species of the same order with both markers. It was observed, however that rate variation does not exceed 1.8 between the fastest (Proboscidea and Primates) and the slowest (Perissodactyla) evolving orders. We have moreover shown that apparently the evolutionary rate differences.are not dependent on body size, generation time, and metabolic rate.

A Novel Method for Estimating Substitution Rate Variation Among Sites in a Large Dataset of Homologous DNA Sequences

Graziano Pesole* and Cecilia Saccone†

Genetics 157: 859–865 (February 2001)

It is based on the simple idea that the more closely related are the compared sequences, the higher is the probability of observing nucleotide changes at rapidly evolving sites. This analysis is crucial not only to define more realistic models of sequence evolution for phylogenetic inferences, but it may be of great practical relevance for the assessment of the implications of a given specific mutation pattern.

Site-variability mitochondrial algorithm

ij= 0 if the site is unchanged in the j-th pair

Kj= genetic distance measured with GTR method

νi =

γγii = ν = νi(norm)i(norm) / ν / νmaxmax

N=n.of multialigned sequences

νi(norm)= νi/[N(N-1)/2]

Pesole G., Saccone C. Genetics 2001 Feb;157(2):859-65

Intron distribution along mtDNA gene in Ascomycota

Santamaria Monica1, Vicario Saverio1,3 and Saccone Cecilia1,2*1Istituto di Tecnologie Biomediche, Sede di Bari, CNR, 70126 Bari, Italy2Dipartimento di Biochimica e Biologia Molecolare, Università di Bari, 70126 Bari, Italy3 Dept. Ecology and EvolutionaryBiology, Yale University, New Haven, CT, USA

Inter- versus intra- species molecular biodiversity in eukaryotic mitochondrial genome.

Saverio Vicario 1,3, Monica Santamaria1, Cecilia Lanave1, and Cecilia Saccone1,2

1Istituto di Tecnologie Biomediche, Sede di Bari, CNR, 70126 Bari, Italy2Dipartimento di Biochimica e Biologia Molecolare, Università di Bari, 70126 Bari, Italy3 Dept. Ecology and EvolutionaryBiology, Yale University, New Haven, CT, USA

See the posters…

Interspecific Genetic Divergence (GTR):five data sets with 35 individuals

Scaling divergence across species

Intra/Inter species variation

Species concept

The exact meaning of what a species marker is tracking can only be clarified by taking into account

the still hot discussion on species concept. As a matter of fact, both the discussion of what a species

is and how to define the boundaries are still open questions.

Mitochondrial gene as a good marker

a) the presence of multiple copies in each cell facilitates the retrieval even from small tissue samples;

b) the aforementioned evolutionary stability of mitochondrial coding genes makes the assessment of the homology, both at the level of loci and the level of a single site, quite simple. (Comparative studies on mitochondrial coding genes are not plagued by doubt on the orthology of the locus, as for nuclear genes although nuclear mitochondrial (NUMTs) pseudogenes could be at times problematic.

c) the evolutionary stability of those markers reduces the importance of indels as factors of molecular evolution, so that the alignment and analysis are greatly eased.

Mitochondrial gene as a good marker

Theoretical considerations from population genetics.

The mitochondrial markers have a smaller effective population size than autosomal nuclear markers, because of their matrilineal heredity. This make the markers more subject to genetic drift and, consequentially, decrease intra-specific variability by increasing the fixation rate.The lack of recombination increases the probability of selective hitchhiking, which is an event of positive selection on a single site causing fixation and cancellation of intra-specific variability on the whole mitochondrion in a given population or species. “Genetic draft. (Gillespie 2000): the net effect is an increased level of fixation rate, independent of the effective population size. (Bazin et al., 2006).

Mitochondrial variation

In the literature there is not consensus yet on what are the prevalent forces shaping the patterns of mitochondrial variation: drift, positive selection, co-adaptation to nuclear genes, parasitic meiotic drive. Clearly more data, which could give a better representation of biodiversity, need to be collected.

Data integration

A more systematic effort to integrate and access data, annotation, and taxonomy from different database sets is needed to tackle more complex problems such as the measure of co-evolution between mitochondria and nucleus and the comparison of inter-specific and intra-specific variation. In collaboration with the IBM we are implementing an integration of databases based on a federated approach so that the crossing of database queries could be implemented without the need to mirror and update the data on the local server. We think that a greater comprehension of the process directing the mitochondrial variation could be reached only through a wider integration of data.

Saverio Vicario

Cecilia Lanave

Monica Santamaria

Comparative Genomics and evolutionary analysis of gene families

Setup of protocols for production and analysis of sequence data with high throughput 454 technology

Development of methodology for molecular species assignment within the framework of “Barcode of Life”

ITB: Biodiversity Team

Domenica D’Elia

Molecular Biodiversity databases

Barcode Databases