Advanced Methods in Reconstructing Phylogenetic Relationships 2010 Practical Course: March 8th to...
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![Page 1: Advanced Methods in Reconstructing Phylogenetic Relationships 2010 Practical Course: March 8th to 13th, 2010, Rio de Janeiro.](https://reader030.fdocuments.us/reader030/viewer/2022032704/56649d545503460f94a30da9/html5/thumbnails/1.jpg)
Advanced Methods in Reconstructing
Phylogenetic Relationships
2010 Practical Course: March 8th to 13th, 2010, Rio de Janeiro
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Darwin’s 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
Haeckel’s 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
• To encourage a critical attitude to data and its analysis
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Some definitions
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Richard Owen
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• Homologue: the same organ under every variety of form and function (true or essential correspondence)
• Analogy: superficial or misleading similarity
Richard Owen 1843
Owen’s definition of homology
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Charles Darwin
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• “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
Darwin and homology
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• Homology: similarity that is the result of inheritance from a common ancestor - the identification and analysis of homologies is central to phylogenetic systematics
Homology is...
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• 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
Phylogenetic systematics
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Bacterium 1
Bacterium 3
Bacterium 2
Eukaryote 1
Eukaryote 4
Eukaryote 3
Eukaryote 2
Bacterium 1
Bacterium 3Bacterium 2
Eukaryote 1
Eukaryote 4Eukaryote 3
Eukaryote 2
Phylograms show branch order and branch lengths
Cladograms and phylograms
Cladograms show branching order - branch lengths are meaningless
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Rooted by outgroup
Rooting using an outgroup
archaea
archaea
archaea
eukaryote
eukaryote
eukaryote
eukaryote
bacteria outgroup
root
eukaryote
eukaryote
eukaryote
eukaryote
Unrooted tree
archaea
archaea
archaea
Monophyletic group
Monophyleticgroup
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What kind of data?
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Fossil skulls
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Family tree for humans
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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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• “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”
Molecules as documents of evolutionary history
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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
<---------------(--------------------HELIX 19---------------------)<---------------(22222222-000000-111111-00000-111111-0000-22222222Thermus ruber UCCGAUGC-UAAAGA-CCGAAG=CUCAA=CUUCGG=GGGU=GCGUUGGATh. thermophilus UCCCAUGU-GAAAGA-CCACGG=CUCAA=CCGUGG=GGGA=GCGUGGGAE.coli UCAGAUGU-GAAAUC-CCCGGG=CUCAA=CCUGGG=AACU=GCAUCUGAAncyst.nidulans UCUGUUGU-CAAAGC-GUGGGG=CUCAA=CCUCAU=ACAG=GCAAUGGAB.subtilis UCUGAUGU-GAAAGC-CCCCGG=CUCAA=CCGGGG=AGGG=UCAUUGGAChl.aurantiacus UCGGCGCU-GAAAGC-GCCCCG=CUUAA=CGGGGC=GAGG=CGCGCCGAmatch ** *** * ** ** * **
Alignment of 16S rRNA sequences from different bacteria
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Automated Progressive Alignment of Sequences
• Essentially a heuristic method and as such is not guaranteed to find the ‘optimal’ alignment.
• Most successful implementation is Clustal (Des Higgins). This software is cited 3,000 times per year in the scientific literature.
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Des Higgins is very famous
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Automatic alignment programs
• There are a variety available: • Clustal W 2.0, Muscle, T-Coffee are
among the most popular• All are easy to use and relatively
quick (but this depends on how many sequences and how similar they are).
• Outputs files are produced which can be read by most phylogenetic analysis programmes.
• Can fail badly with highly divergent sequences.
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James McInerney is not here
• But he has produced a nice lecture on some background issues for multiple alignment
• This can be downloaded from the embo world 2009 directory on our lab webpage:
• http://research.ncl.ac.uk/microbial_eukaryotes/index.html
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Advice on alignments
• Treat cautiously• Can be improved by eye (usually)• Often helps to have colour-coding• Depending on the use, the user
should be able to make a judgement on those regions that are reliable or not
• For phylogeny reconstruction, only use those positions whose hypothesis of positional homology is unimpeachable (or do experiments)
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Patterns in sequence data
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• 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?
Exploring patterns in sequence data 1:
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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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Rate Heterogeneity
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Rates of amino acid replacement in different
proteins
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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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Small subunit ribosomal RNA
18S or 16S rRNA
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Failure To Accommodate Rate Heterogeneity Can Lead To
Problems When Making Trees
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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
• All methods are susceptible to “long branch” problems
• Methods which assume that all sites change at the same rate are particularly poor at recovering the true tree
A
B
C
D
TRUE TREE WRONG TREE
A B
C D
ppq
qq p > q
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Chaperonin 60 Protein Maximum Likelihood Tree (PROTML, Roger et al. 1998,
PNAS 95: 229)
Longest branches
Bootstrap values are a common way of assessing support for relationships
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High bootstrap values can be misleading - adding a single new
sequenceCucurbita sp. Cucurbita sp.Arabidopsis thaliana Arabidopsis thaliana
Plasmodium falciparum
Plasmodium falciparum
Dictyostelium discoideum
Dictyostelium discoideum
Giardia lambliaGiardia lamblia
Spironucleus barkhanus
Trichomonas vaginalis
Trichomonas vaginalis
Entamoeba histolytica Entamoeba histolytica
Drosophila melanogasterHomo sapiens
Drosophila melanogasterHomo sapiens
Saccharomyces cerevisae Saccharomyces cerevisae
Schizosaccharomyces pombe Schizosaccharomyces pombe
Trypanosoma brucei Trypanosoma brucei
Euglena gracilisEuglena gracilis
Holospora obtusa
Holospora obtusa
Ehrlichia chaffeensisEhrlichia chaffeensis
Ehrlichia sp.Ehrlichia sp.
Rickettsia tsutsugamushiRickettsia tsutsugamushi
Rhizobium melilotiRhizobium meliloti
Bartonella bacilliformis
Bartonella bacilliformisBradyrhizobium japonicum
Bradyrhizobium japonicumCaulobacter crescentus
Caulobacter crescentusRhodobacter sphaeroides
Rhodobacter sphaeroides
Escherichia coliEscherichia coliPseudomonas aeruginosa
Pseudomonas aeruginosaChromatium vinosum Chromatium vinosumNeisseria gonorrhoeae Neisseria gonorrhoeae
Chlamydia trachomatisChlamydia trachomatisTreponema pallidumTreponema pallidum
Thermus thermophilus Thermus thermophilus
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A proposal for three domains of life
(Woese, Kandler and Wheelis 1990 PNAS 87, 4576)
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archaebacteria
bacteria
eukaryotes
Concatenated LSU+SSU rRNA analyzed using a standard (GTR plus
gamma*2) model
The 3-domains tree of life
Cox et al. 2008. PNAS
eocyte archaebacteria
Two longest branches
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NDCH (GTR+g+2cv)*2Heterogeneous across tree
CAT model
bacteria
eukaryotes
0.75
0.95
Other archaebacteria
eocytes
The same RNA data analyzed using better models (Cox et al. 2008)
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• 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
Saturation in sequence data:
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Multiple changes at a single site - hidden changes
C A
C G T A1 2 3
1
Seq 1
Seq 2
Number of changes
Seq 1 AGCGAGSeq 2 GCGGAC
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Exploring patterns in sequence data
• 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 analysis:
Deinococcus 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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% Guanine + Cytosine in 16S rRNA genes from mesophiles and
thermophiles
Thermophiles:Thermotoga maritimaThermus thermophilusAquifex pyrophilus
Mesophiles:Deinococcus radioduransBacillus subtilis
626465
5555
%GCall sites
727273
5250
variable sites
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Shared nucleotide or amino acid composition biases can also cause problems for
phylogenetic analysis
True tree
Wrong tree
Aquifex Thermus
Bacillus Deinococcus
Aquifex (73%)
Thermus (72%)
Bacillus (50%)
Deinococcus(52% G+C)
16S rRNA
The correct tree can be obtained if a model is used which allows base/aa composition to vary between sequences -LogDet/Paralinear DistancesHeterogeneous Maximum Likelihood
Thermus
Deinococcus
Aquifex
Bacillus
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Gene trees and species trees
We often assume that gene trees give us species trees
a
b
c
A
B
C
Gene tree Species tree
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Orthologues and paralogues
a A*b* c BC*
Ancestral gene
Duplication to give 2 copies on the same genome = paralogues of each other
orthologousorthologous
paralogousA*C*b*
A mixture of orthologues and paralogues sampled
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The malic enzyme gene tree contains a mixture of orthologues and
paralogues
Anas = a duck!
Schizosaccharomyces
Saccharomyces
Giardia lamblia
Ascaris suum
Homo sapiens 1
Anas platyrhynchos
Homo sapiens 2
Zea mays
Flaveria trinervia
Populus trichocarpa
Lactococcus lactis
100
100
100
97100 Cyt
Mit
Ch
Trichomonas vaginalisHyd
Solanum tuberosum
Amaranthus
75 100
Cyt
Mit
ChCh
Mit
Mit
Neocallimastix
Cyt
Hyd
Gene duplication
Plant chloroplast
Plant mitochondrion
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• 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!
Summary:
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• 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)
Phylogenetic analysis requires careful thought