1. Dilvan Moreira (based on Prof. André Carvalho presentation)
PHYLOGENY
Dilvan Moreira (based on Prof. André Carvalho presentation)

2. Reading
Introduction to Computational Genomics: A Case Studies Approach
Chapter 7

3. Topics SARS Origin and Evolution of the Epidemic Phylogenetic Analysis
Phylogenetic Tree Construction
Neighbor-Joining Algorithm
Case Study
André de Carvalho - ICMC/USP
15/05/2018

4. Epidemic of SARS Severe Acute Respiratory Syndrome
Severe respiratory disease that hit the world in 2003
Caused by SARS coronavirus (SARS-COV)
The term corona comes from the crown that appears when the virus is observed in an electron microscope
It's not the bird flu
André de Carvalho - ICMC/USP
15/05/2018

5. Epidemic of SARS Coronavirus
Pathogens which cause a variety of diseases in animals
Any organism capable of causing infectious disease
They may exhibit frequent mutations and thus infect other species
Other coronaviruses have been identified as causing hepatitis in rats and gastroenteritis in pigs
It is the most common viruses in veterinary pathology
André de Carvalho - ICMC/USP
15/05/2018

6. Epidemic of SARS February 2003
French Hospital in Hanoi, Vietnam, called WHO to report a similar flu infection
Highly contagious
WHO expert in infectious diseases, Dr Carlo Urbani , concluded that it was a new and unusual pathogen
André de Carvalho - ICMC/USP
15/05/2018

7. Epidemic of SARS February 2003
During his stay, Dr. Urbani collected exams, examined hospital documents and organized quarantine patients
He was the first to identify and describe the new disease, SARS
Fever, dry cough, asthma, progressive worsening of the respiratory system, death by respiratory system failure
In three weeks, Dr. Urbani and five other hospital health workers have died of SARS
André de Carvalho - ICMC/USP
15/05/2018

8. Epidemic of SARS
March 2003
WHO emitted a global alert, alerting that SARS was a risk to global health
André de Carvalho - ICMC/USP
15/05/2018

9. Epidemic of SARS Hanoi’s Hospital March 2003
André de Carvalho - ICMC/USP
15/05/2018

10. Origin of SARS Epidemic
First cases occurred in 11/2002 in the province of Guangdond, China
106 people got sick in a hospital in Guangzhou city
Rest of the world did not know that
A doctor of this hospital visited Hong Kong on February 21, 2003
He stayed on the 9th floor of the city hotel
He also got sick and died, diagnosed with pneumonia
Several people who were on the 9th floor became transmitters of the disease
André de Carvalho - ICMC/USP
15/05/2018

11. André de Carvalho - ICMC/USP
15/05/2018

12. Origin of SARS Epidemic
One of the visitors of the 9th floor was an American executive
First patient treated in a French hospital in Hanoi
Infected 80 people before dying
Other visitors of the 9th floor brought the disease to Canada, Singapore and the US
In April 2003, 4,300 cases were reported with 250 deaths in 25 countries
André de Carvalho - ICMC/USP
15/05/2018

13. Origin of SARS Epidemic
March 2003
Earlier this month, WHO coordinated international research
In the end of the month, a new viruses that caused SARS was identified independently in:
Germany, Canada , USA and Hong Kong
SARS coronavirus is a viral RNA (such as HIV)
Common in humans and animals, coronaviruses cause ~25% of all upper respiratory infections
Eg: common cold
André de Carvalho - ICMC/USP
15/05/2018

14. SARS Number of Reported Cases
André de Carvalho - ICMC/USP
15/05/2018

15. SARS Statistics

16. Coronavirus SARS
Source: BBC
André de Carvalho - ICMC/USP

17. Coronavirus SARS
André de Carvalho - ICMC/USP

18. Origin of SARS Epidemic
April 2003
Canadian laboratory sequenced the RNA sequence of the SARS CoV
Phylogenetic Analysis do vírus mostrou que o coronavirus mais próximo é o da civeta]
Phylogenetic Analysis showed that the closest coronavirus is the one from civet
Popular food in Guangdong
André de Carvalho - ICMC/USP
15/05/2018

19. Origin of SARS Epidemic
May 2003
Two articles in Science presented the complete genome of the SARS CoV
Genome contains 29,751 bp
It is substantially different from all human CoVs
Also different from CoVs birds - no relation to avian flu
End of 2003
SARS has spread throughout the world
André de Carvalho - ICMC/USP
15/05/2018

20. Phylogenetic Analysis of SARS
Phylogenetic Analysis can answer questions like:
What kind of virus caused the original infection?
What is the source of the infection?
When and where the virus has crossed boundaries between species?
What are the key changes that made this intersection possible?
What is the trajectory followed to the spread of the virus?
André de Carvalho - ICMC/USP
15/05/2018

21. Phylogenetic Analysis of SARS
To respond to the previous questions, we:
Examine some key algorithms of Phylogenetic Analysis
Apply these algorithms to SARS data
Available in GenBank and on the book’s site
André de Carvalho - ICMC/USP
15/05/2018

22. Trees and Evolution
SARS advancing path can be represented by a tree
All SARS virus that appeared in the world originated from the virus found in China
New branches appear when the virus spreads
Traditionally, evolutionary history linking groups of species has been represented by a tree
Unique figure in the Darwin’s book "On the origin of species"
André de Carvalho - ICMC/USP
15/05/2018

23. Phylogenetic Trees Orangutan Gorilla Chimpanzee Human
Source: Tree of the Life Website, University of Arizona
Orangutan
Gorilla
Chimpanzee
Human
André de Carvalho - ICMC/USP
15/05/2018

24. Phylogenetic Trees Mother DNA: tctgcctc tctgcctc gatgcctc tctgcctcggg
gatgcatc
gacgcctc
gctgcctcggg
gatgaatc
gccgcctc
gctaagcctcggg
Current species
André de Carvalho - ICMC/USP
15/05/2018

25. Phylogeny
Study of the evolutionary relationship between different groups of organisms
Species
Populations
Etc.
Represented by the diagram in form of tree (phylogenetic tree)
Cladistics analysis
Usually based on morphological data
André de Carvalho - ICMC/USP
15/05/2018

26. Cladistics
André de Carvalho - ICMC/USP
15/05/2018

27. André de Carvalho - ICMC/USP
15/05/2018

28. Phylogenetic Trees
Show the evolutionary relationship between different species or individuals
Believed to have a common ancestor
Cardiogram form
Each node with descendants represents the most recent common ancestor of them
Size of the edges corresponds to the time estimates
André de Carvalho - ICMC/USP
15/05/2018

29. Phylogenetic Trees
Each node is called a taxonomic unit (taxon, plural taxa)
Internal nodes are hypothetical taxonomic units
They can not be directly observed
More complex relationships may take the form of networks
André de Carvalho - ICMC/USP
15/05/2018

30. Structure of Phylogenetic Trees
Trees have two or more taxa
Species or individual
External nodes represent existing taxa
Internal nodes represent their ancestors (usually extinct)
Trees may be:
Bifurcation
Each internal node has a maximum of 2 children
“Multifurcation”
Each internal node can have more than 2 children
Trees may or may not have root
André de Carvalho - ICMC/USP
15/05/2018

31. Rooted Phylogenetic Trees
It is defined a special internal node called root
Common ancestor to all other nodes
All evolutionary paths lead to the root
Branches are oriented from the root to the external nodes
André de Carvalho - ICMC/USP
15/05/2018

32. Rooted Phylogenetic Trees
time
Branch or edge
Internal node
External node (leaf)
Drosophila
human
Puffer fish
rat
André de Carvalho - ICMC/USP
15/05/2018

33. Rooted Phylogenetic Trees
André de Carvalho - ICMC/USP
15/05/2018

34. Non Rooted Phylogenetic Trees
Branches do not have orientation
Show topological relationship between taxa without identifying a common ancestor
There are methods to define a root of a tree without roots
Choose an edge to put a root node
Requires external biological information or at least a guess to where to insert the root
André de Carvalho - ICMC/USP
15/05/2018

35. Non Rooted Phylogenetic Trees
frog
human
Internal node
Puffer fish
rat
External node (leaf)
Branch or edge
André de Carvalho - ICMC/USP
15/05/2018

36. Non Rooted Phylogenetic Trees
The root generally is defined including one or more taxa in the data set
Which is known to be result of an older division
Most distant relationship to each taxa
This external taxon is called outgroup
A branch where the outgroup links to the other contains the root node
André de Carvalho - ICMC/USP
15/05/2018

37. Non Rooted Phylogenetic Trees
Drosophila
root
frog
human
Internal Node
Puffer fish
rat
External Node
Branch or edge
André de Carvalho - ICMC/USP
15/05/2018

38. Non Rooted Phylogenetic Trees
Drosophila
root
frog
human
Internal Node
Puffer fish
rat
External Node
Branch or edge
André de Carvalho - ICMC/USP
15/05/2018

39. Structure of Phylogenetic Trees
Rotation of the branches of an Internal Node does not alter the relationship between taxa = A B C B A C
Invariant to rotation
André de Carvalho - ICMC/USP
15/05/2018

40. Number of Possible Trees
Phylogenetic tree reconstruction from DNA sequences
Complicated because of the large number of possible trees
Possible trees without root (n  3 )
Possible trees with root (n  2 )
n: number of taxa
André de Carvalho - ICMC/USP
15/05/2018

41. Tree Representation
There are several non-graphical ways to represent a tree 9 8 7 3 6 2 1 4 5
4 5
1 2
Popular standard format:
Newick: (((1,2),3),(4,5))
André de Carvalho - ICMC/USP
15/05/2018

42. Inferring Trees
Until recently, the relationship between taxa was inferred by morphological characteristics
DNA sequences are currently used
Sequencing technology
Mutations leave a trail
Trees can be inferred from the similarity between homologous sequences
André de Carvalho - ICMC/USP
15/05/2018

43. Inferring Trees Tree branches can have different sizes,
The greater the number of mutations, bigger the branch
André de Carvalho - ICMC/USP
15/05/2018

44. Inferring Trees Given homologous sequences of a taxa group
There are several methods to reconstruct their phylogenetic relationships
Methods can be divided into two groups:
The ones that order all possible tree through some criteria to find the best one
Those who build the tree directly from the data (without setting an optimization function)
André de Carvalho - ICMC/USP
15/05/2018

45. Inferring Trees Methods that order possible trees
Criteria that (in general) seek for the tree with the fewest number of changes
Because of the huge Number of Possible Trees:
It may take much time to find the best tree
Using approaches to accelerate taxa search, one can not find the better tree
André de Carvalho - ICMC/USP
15/05/2018

46. Inferring Trees Methods that build from the data
A phylogenetic tree is constructed by methods that use algorithms and statistics
Often based on computing the distance between pairs taxa
Very popular because they are usually quick
André de Carvalho - ICMC/USP
15/05/2018

47. Methods that build from the data
Method based on the most popular distance is the Neighbor-Joining Algorithm (NJ)
Although not necessarily so well statistically supported as other methods
Robust and accurate
Guaranteed to infer the true tree if the used distances reflect the actual distance among sequences
Results not guaranteed by other methods more sophisticated (statistically)
André de Carvalho - ICMC/USP
15/05/2018

48. Methods Based in Distance
Given n taxa
Building Distance Matrix among taxa
If the branches of the tree have a specific size, distance between any two nodes can be easily computed
Total size of the only way linking them
Specifies distance among tree node leaf using additive distance
André de Carvalho - ICMC/USP
15/05/2018

49. Methods Based in Distance
Additive distance
Biologically, the additivity is an important property for a Distance Matrix
The number of substitutions separating 2 taxa from their last common ancestor forms an additive distance
Distance on the path from node i to node j
The Jukes-Cantos Model is frequently used as a Substitution Model
André de Carvalho - ICMC/USP
15/05/2018

50. Methods Based in Distance
Distance Matrix 9 8 7
L1 L2 L3 L4 L5 L L L L L 6 L1 L2 L3 L4 L5
André de Carvalho - ICMC/USP
15/05/2018

51. Neighbor-Joining Algorithm
Greedy algorithm
It begins with a phylogeny star-shaped
All taxa are directly connected to a single root node
Iteratively combines pairs of nodes
André de Carvalho - ICMC/USP
15/05/2018

52. Neighbor-Joining Algorithm
Key to the success of the algorithm:
Criteria that defines how nodes are selected to be combined each iteration
Identifies nodes that are topologically neighbors on the tree
Selected taxa are combined into a taxon
A new Distance Matrix is then calculated
Process repeated until all taxa are combined
Generated tree is rootless
André de Carvalho - ICMC/USP
15/05/2018

53. Neighbor-Joining Algorithm
Calculation of the size of the branches
Assume 3 taxa in a tree without root
3-points formula:
Lx + Ly = dAB
Lx + Lz = dAC
Ly + Lz = dBC
Lx = (dAB+dAC-dBC)/2
Ly = (dAB+dBC-dAC)/2
Lz = (dAC+dBC-dAB)/2 A B
center C Lx Ly Lz
3-point formula:
André de Carvalho - ICMC/USP
15/05/2018

54. Neighbor-Joining Algorithm
Uses 4-point condition to select neighboring nodes to be combined
Assume that 1 and 2 are neighbors
d(1,2) + d(i,j) < d(i,1) + d(2,j)
Ri = ∑j d(i,j)
M(i,j) = (n-2)d(i,j) – Ri – Rj
M(i,j) < M(i,k) para todo k  j 1 2
centro i Lx Ly Lz j Lq
Degree of vizinhancidade
Neighborhood degree
André de Carvalho - ICMC/USP
15/05/2018

55. Neighbor-Joining Algorithm
Entry: nxn array of distances D and one outgroup
Output: phylogenetic tree T with root
Update table M using D and choosing the lower value of M to select two taxa to match
Combine two taxa ti and tj in a new V node and use 3-points formula to update the Distance Matrix D' where ti e tj are replaced by V.
Calculate the size of the branches of tk to V by using the 3-point formula, T(V,1) = ti e T(V,2) = tj , TD(ti) = L(ti,V) e TD(ti) = L(ti,V)
Distance Matrixs D ' now has n-1 taxa.
if there is 2 more taxa go 1:
if there are two taxa , combines them by a size field d(ti,tj).
Set the root node as the branch connecting the outgroup to the rest of the tree 5.
André de Carvalho - ICMC/USP
15/05/2018

56. UPGMA
NJ algorithm can be reduced to a simpler method, UPGMA, when M = D
Unweighted Pair Group Method with Arithmetic Averages
One of the first in methods based in distance
Distance from the external node to the root is the same for all external nodes
Ultrametricity
Almost never valid for DNA sequences
Can lead to incorrect inference of a tree
André de Carvalho - ICMC/USP
15/05/2018

57. UPGMA A 2 2 Unweighted Pair Group Method with Arithmetic Averages
UPGMA One of the first Methods Based in Distance
Branch length assumes “molecular clock” (ultrametricidade)
Each son have equal lenght considering its father
Ultrametricity extremally simple to find the branch lenght A C B
A UPGMA Methods Based in the first Distance 2 2
Phylogenetic Trees

58. UPGMA
Example:
(Source: Mona Singh – U. Princeton)
Phylogenetic Trees

59. UPGMA Unweighted Pair Group Method with Arithmetic Averages
Phylogenetic Trees

60. UPGMA Unweighted Pair Group Method with Arithmetic Averages
Phylogenetic Trees

61. UPGMA Unweighted Pair Group Method with Arithmetic Averages
If a tree with exact values ​​exists?
Example:
Phylogenetic Trees

62. UPGMA Almost never valid for DNA sequences
Can lead to incorrect inference
Phylogenetic Trees

63. SARS: Case Study Phylogenetic Analysis of the Epidemic of SARS
SARS coronavirus genome has 6 genes
Host: civet
What do we know?
Epidemiological tree
Date of Origin
Source area
André de Carvalho - ICMC/USP
15/05/2018

64. SARS: How do we know that came from Civeta?
André de Carvalho - ICMC/USP
15/05/2018

65. SARS: Distance Matrix The Distance Matrix was obtained:
calculating the genetic distance of the global sequence alignment of the spike gene
using the Jukes-Cantor corrections

66. SARS: Epidemiological tree How does it spread? Outliner  civeta
15/05/2018

67. SARS: When does it started??
With 95 % confidence , September 16, 2002
Genetics Distance of Civet SARS X Sample Collection Date

68. SARS: How do we know that it started in Guangding?
Epidemiologic Tree shows Guangdong
Can we verify it in another way?
Lower variation outside of Guangdong Province
André de Carvalho - ICMC/USP
15/05/2018

69. Questions?

70. André de Carvalho - ICMC/USP
15/05/2018

71. Newick Format Matrix format: 1 2 3 A B C D Newick Format:
A B
C D
Newick Format:
((A, B), (C, D));
Size of the branches can be represented:
((A:1.0, B:1.0):2, (C:1, D:1):2);
Names can be assigned to internal nodes:
((A, B)2, (C, D)3)1; 1 2 3
Convention A B C D
André de Carvalho - ICMC/USP
15/05/2018