Molecular Systematics

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Molecular Systematics Evolution
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Darwins letter to Thomas Huxley 1857

• The time will come I believe, though I shall not
  live to see it, when we shall have fairly true
  genealogical (phylogenetic) trees of each great
  kingdom of nature

Haeckels pedigree of man
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Aims of the course

• To introduce the theory and practice of
  phylogenetic inference from molecular data
• To introduce some of the most useful methods and
  computer programmes

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How construct a phylogeny

• What kind of data?

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Richard Owen
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Owens definition of homology

• Homologue the same organ under every variety of
  form and function (true or essential
  correspondence)
• Analogy superficial or misleading similarity
• Richard Owen 1843

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Charles Darwin
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Darwin and homology

• The natural system is based upon descent with
  modification .. the characters that naturalists
  consider as showing true affinity (i.e.
  homologies) are those which have been inherited
  from a common parent, and, in so far as all true
  classification is genealogical that community of
  descent is the common bond that naturalists have
  been seeking
• Charles Darwin, Origin of species 1859 p. 413

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Homology is...

• Homology similarity that is the result of
  inheritance from a common ancestor - the
  identification and analysis of homologies is
  central to phylogenetic systematics

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Phylogenetic systematics

• Sees homology as evidence of common ancestry
• Uses tree diagrams to portray relationships based
  upon recency of common ancestry
• Monophyletic groups (clades) - contain species
  which are more closely related to each other than
  to any outside of the group

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Cladograms and phylograms
Bacterium 1
Cladograms show branching order - branch lengths
are meaningless
Bacterium 2
Bacterium 3
Eukaryote 1
Eukaryote 2
Eukaryote 3
Eukaryote 4
Phylograms show branch order and branch lengths
Bacterium 1
Bacterium 2
Bacterium 3
Eukaryote 1
Eukaryote 2
Eukaryote 3
Eukaryote 4
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Rooting using an outgroup
archaea
archaea
Unrooted tree
archaea
Rooted by outgroup
bacteria outgroup
archaea
Monophyletic group
archaea
archaea
eukaryote
Monophyletic group
eukaryote
root
eukaryote
eukaryote
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Fossil skulls
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From left to right the primate species depicted
on the tree are a lemur (Lemur catta), an
adapid (Hoanghonius stehlini), a tarsier (Tarsius
bancanus), an omomyid (Shoshonius cooperi), a
proto-monkey (Eosimias centennicus), a South
American monkey (Saimiri sciureus), an Old World
monkey (Mandrillus sphinx), a great ape (Gorilla
gorilla), and a human (Homo sapiens)
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Microbial morphologies - some are complex but
many are simple - for example look at a drop of
lake water
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Linus Pauling
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Molecules as documents of evolutionary history

• We may ask the question where in the now living
  systems the greatest amount of information of
  their past history has survived and how it can be
  extracted
• Best fit are the different types of
  macromolecules (sequences) which carry the
  genetic information

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Exploring patterns in sequence data 1

• Which sequences should we use?
• Do the sequences contain phylogenetic signal for
  the relationships of interest? (might be too
  conserved or too variable)
• Are there features of the data which might
  mislead us about evolutionary relationships?

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The importance of 16S rRNA for those interested
in the early evolution of life lies in its slow
evolution, which allows the historical record
preserved in its gene sequences to be kept
relatively intact.
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Small subunit ribosomal RNA
18S or 16S rRNA
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An alignment involves hypotheses of positional
homology between bases or amino acids
Alignment of 16S rRNA sequences from different
bacteria
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But not all proteins have the same rate of
change.. Rates of amino acid replacement differ
in different proteins for example
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Case Study Florida Dentist case 1990

• Each Branch represents the sequence from part of
  the env gene of HIV-1
• Viral sequences were obtained from the dentist
  and seven of his former patients(A-G),also
  infected with HIV
• A,B,C,E and G have sequences very closely related
  to those of the dentist suggesting that he
  infected them.
• D and F, had other risk factors for infection,
  their viruses are separated from the dentist by
  sequences taken from controls.
• Because HIV is so variable, 2 different sequences
  are included for dentist and patient A.

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Is there a molecular clock?

• The idea of a molecular clock was initially
  suggested by Zuckerkandl and Pauling in 1962
• They noted that rates of amino acid replacements
  in animal haemoglobins were roughly proportional
  to time - as judged against the fossil record

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The molecular clock for alpha-globinEach point
represents the number of substitutions separating
each animal from humans
shark
carp
number of substitutions
platypus
chicken
cow
Time to common ancestor (millions of years)
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There is no universal molecular clock

• The initial proposal saw the clock as a Poisson
  process with a constant rate
• Now known to be more complex - differences in
  rates occur for
• different sites in a molecule
• different genes
• different regions of genomes
• different genomes in the same cell
• different taxonomic groups for the same gene
• There is no universal molecular clock

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Unequal rates in different lineages may cause
problems for phylogenetic analysis

• Felsenstein (1978) made a simple model phylogeny
  including four taxa and a mixture of short and
  long branches

TRUE TREE
WRONG TREE
p gt q

• All methods are susceptible to long branch
  problems
• Methods which do not assume that all sites change
  at the same rate are generally better at
  recovering the true tree

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Saturation in sequence data

• Saturation is due to multiple changes at the same
  site subsequent to lineage splitting
• Most data will contain some fast evolving sites
  which are potentially saturated (e.g. in proteins
  often position 3)
• In severe cases the data becomes essentially
  random and all information about relationships
  can be lost

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Multiple changes at a single site - hidden changes
Seq 1 AGCGAG Seq 2 GCGGAC
Number of changes
Seq 1

Seq 2
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Exploring patterns in sequence data 2

• Do sequences manifest biased base compositions
  (e.g thermophilic convergence) or biased codon
  usage patterns which may obscure phylogenetic
  signal

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A case study in phylogenetic analysisDeinococcus
and Thermus

• Deinococcus are radiation resistant bacteria
• Thermus are thermophilic bacteria
• BUT
• Both have the same very unusual cell wall based
  upon ornithine
• Both have the same menaquinones (Mk 9)
• Both have the same unusual polar lipids
• Congruence between these complex characters
  supports a phylogenetic relationship between
  Deinococcus and Thermus

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Congruence the agreement between estimates of
phylogeny (relationships) based on different
characters.
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Guanine Cytosine in 16S rRNA genes from
mesophiles and thermophiles
GC all sites
variable sites
Thermophiles Thermotoga maritima Thermus
thermophilus Aquifex pyrophilus Mesophiles Deino
coccus radiodurans Bacillus subtilis
62 64 65 55 55
72 72 73 52 50
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Shared nucleotide or amino acid composition
biases can also cause problems for phylogenetic
analysis
Aquifex
Thermus
Aquifex (73)
Bacillus (50)
True tree
Wrong tree
16S rRNA
Bacillus
Thermus (72)
Deinococcus
Deinococcus (52 GC)
Aquifex
The correct tree can be obtained if a model is
used which allows base/aa composition to vary
between sequences -LogDet/Paralinear
Distances Heterogeneous Maximum Likelihood
Bacillus
Thermus
Deinococcus
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Gene trees and species trees
A
a
Species tree
Gene tree
B
b
D
c
We often assume that gene trees give us species
trees
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Orthologues and paralogues
paralogous
A
C
b
orthologous
orthologous
A
c
B
C
a
b
A mixture of orthologues and paralogues sampled
Duplication to give 2 copies paralogues on the
same genome
Ancestral gene
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Two homologous genes are orthologous if their
most recent common ancestor did not undergo a
gene duplication, otherwise they are called
paralogues (or paralogous genes)
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The malic enzyme gene tree contains a mixture of
orthologues and paralogues
Gene duplication
Anas a duck!
Plant chloroplast
Plant mitochondrion
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Summary

• There may be conflicting patterns in data which
  can potentially mislead us about evolutionary
  relationships
• Our methods of analysis need to be able to deal
  with the complexities of sequence evolution and
  to recover any underlying phylogenetic signal
• Some methods may do this better than others
  depending on the properties of individual data
  sets
• All trees are simply hypotheses!

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Phylogenetic analysis requires careful thought

• Phylogenetic analysis is frequently treated as a
  black box into which data are fed (often gathered
  at considerable cost) and out of which The Tree
  springs
• (Hillis, Moritz Mable 1996, Molecular
  Systematics)