NATURAL SELECTION AT THE MOLECULAR LEVEL

NATURAL SELECTION AT THE MOLECULAR LEVEL
Cynthia Couto (based on Prof. Dilvan Moreira presentation that was based on Prof. André Carvalho presentation)

Reading Introduction to Computational Genomics: A Case Studies Approach Chapter6

André de Carvalho - ICMC/USP
09/05/2018 André de Carvalho - ICMC/USP Content Motivation – A mysterious disease Evolution and Natural Selection HIV x Human Immune System Quantifying natural selection Synonymous and non-synonymous regions Case Study

9gag.com parenthesis

09/05/2018 André de Carvalho - ICMC/USP AIDS Reported for the first time in the United States in 1979 Recognized as a transmissible disease in 1981 Called Acquired Immune Deficiency Syndrome (AIDS) HIV (Human Immunodeficiency Virus) was recognized in 1983 as infectious agent 25 million people died up to now

HIV Virus

09/05/2018 André de Carvalho - ICMC/USP HIV Virus

09/05/2018 André de Carvalho - ICMC/USP HIV Virus Retrovirus Enveloped viruses with RNA genome that replicate through DNA It has the reverse transcriptase enzyme It does the reverse transcription of its genome to DNA DNA is then integrated into the host genome by the integrase enzyme

HIV emerging from infected cells
HIV Virus HIV emerging from infected cells Source:

Electronic scanner image of HIV-I emerging from a lymphocyte
09/05/2018 André de Carvalho - ICMC/USP Electronic scanner image of HIV-I emerging from a lymphocyte

09/05/2018 André de Carvalho - ICMC/USP AIDS Evolution Number of people with HIV in the world 40 Milhões 30 20 10 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Year Source: UNAIDS/OMS, 2008 1

AIDS Evolution Number of deaths (adults and children) Year 3.0 2.5 2.0
Milhões 2.5 2.0 1.5 1.0 0.5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Year Source: UNAIDS/OMS, 2008 3

09/05/2018 André de Carvalho - ICMC/USP AIDS Map in the World Estimated number of adults and children with AIDS (2007) Central Europe & Ocidental Eastern Europe & Central Asia 1.6 milhões North America 1.3 milhões Leste Asiático Middle East & North Africa Caribbean South and Southeast Asia 4.0 milhões Sub-Sahara Africa 22.5 milhões Latin America 1.6 milhões Oceania 75 000 Source: UNAIDS/OMS, 2008

09/05/2018 André de Carvalho - ICMC/USP AIDS Map in the World Estimated number of deaths caused by AIDS (2007) Central Europe & Ocidental 3 000 Eastern Europe & Central Asia 9 500 North America 21 000 Leste Asiático 7 900 Middle East & North Africa 26 000 Caribbean 11 000 South and Southeast Asia Sub-Sahara Africa 1.8 million Latin America 58 000 Oceania 1 100 Source: UNAIDS/OMS, 2008

Another parenthesis

mucous membranes

† 1990 septic shock

† 1991 bronchopneumonia

† 1992 pneumonia Anthony Perkins

† 1996 Several complications

Died a few weeks after the diagnostics
† 1994 Died a few weeks after the diagnostics Wagner Bello

09/05/2018 André de Carvalho - ICMC/USP AIDS There is no cure or effective vaccine There are drugs to keep the virus under control They are expensive (for your pocket and for your health) Mechanisms to destroy a virus : Immune system Medications How HIV escapes from attempts to destroy it?

09/05/2018 André de Carvalho - ICMC/USP AIDS Evolving (by modifying their genome) in a very short period of time Virus that kills the patient  virus that infected it Virus has: Very high mutation rates Doubling time (generation) very short Several opportunities to evolve

09/05/2018 André de Carvalho - ICMC/USP Motivation – A mysterious disease Evolution and Natural Selection HIV x Human Immune System Quantifying natural selection Synonymous and non-synonymous regions Case Study

Evolution and Natural Selection
09/05/2018 André de Carvalho - ICMC/USP Evolution and Natural Selection What is evolution? What do you understand by evolution? Evolution of a DNA sequence: Through mutations Passing to the next generation Natural selection helps to slow or accelerate the rate of evolution

09/05/2018 André de Carvalho - ICMC/USP Evolution and Natural Selection Several other viruses and bacteria are resistant to drugs and to the immune system Adaptive natural selection Charles Darwin, 1859: On the origin of species by means of natural selection

09/05/2018 André de Carvalho - ICMC/USP Evolution and Natural Selection Natural selection at the level of organisms: Best adapted individuals will survive, reproduce and generate more offspring to the next generation Natural selection at the molecular level: Remove harmful mutations Negative selection or purifying Favors the spread of advantageous mutations Positive selection

09/05/2018 André de Carvalho - ICMC/USP Evolution and Natural Selection For evolution to occur it is necessary: That individuals of the population have genetically controlled characteristics that varies a lot That these characteristics affect the survivability or reproduction of individuals

HIV x Human Immune System
09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System HIV: Protein wrap with two copies of the genome RNA strand with 9.5 Kb and only 9 genes Retroviruses: RNA -> DNA - > Virus Recognizes and infects T lymphocytes One of them is hardcore!

09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System Human immune system: When there is an infection, it finds and kills the infected cells T lymphocytes identifies the infected cells "Learn" to identify and mark each enemy

09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System HIV attacks the cells that just should identify it to be destroyed How it works: Infected cells are marked with virus protein extracts (epitopes) That are visible in the cell membrane If T lymphocytes recognize a specific epitope, the cell can be destroyed Otherwise, the virus reproduces

09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System How does it work? HIV generates new individuals with new versions of epitopes: The virus epitopes that make it "invisible" survive Very advantageous mutations Immune system "learns" to identify new epitopes It takes a few days

09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System How does it work? HIV reproduces extremely fast: 1.5 days Replication in virus is very error-prone, tending to make or cause errors: Error rate: 5 mutations per 100,000 bp each generation 1000 times greater than in humans Result: a lot of genetic variation in a few generations and shortly

09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System How does it work? Speed the immune system can recognize new epitopes < Speed HIV progresses generating ‘invisible’ individuals Result: There is no permanent immunity (as with other viruses and bacteria)

09/05/2018 André de Carvalho - ICMC/USP HIV x Human Immune System Positive natural selection acted when: Recognized epitopes reduce fitness of an individual Mutations that make the virus invisible increase that fitness Virus with such mutations survive and produce more offspring to the next generation

Natural Pause

Quantifying natural selection
09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection Mutations that arise in an individual can become fixed in the population Most fixed mutations are neutral Because they do not interfere with protein function change amino acids Detrimental to the functioning of the organism 80-90% of these mutations are harmful Very few mutations are advantageous But they are important for evolution

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection How to know which mutations are neutral, harmful or beneficial? Measuring the ability of individuals with novel mutations in the laboratory It is only possible with very short-lived organisms and in large populations Alternative: Compare the substitution rate: of mutations that can change a protein with the rate in areas that do not affect proteins

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection Most common way is to compare: Synonymous mutations: They alter the sequence of the codon, but do not change the amino acid in the translation GTT (Val) → GTA (Val) Not synonymous mutations: Alter post-translational amino acid GTT (Val) → GCT (Ala)

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection Mutations in the first position are rarely synonymous (5%) Mutations in the second position are never synonymous Mutations in the third position are, in most cases, synonymous T C A G Gly Asp Glu Ala Val Ser Arg Asn Lys Thr Ile Met His Gln Pro Leu Cys stop Trp Tyr Phe 1a base no códon 2a base no códon 3a base no códon

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection Rates of synonymous and non synonymous substitutions are proportional to the mutation rate The difference between them is due to the different acceptance rates Result of natural selection There are more non-synonymous substitutions in the coding regions, so it is used: KA: number of non-synonymous subs. / non-synonymous sites KS: number of synonymous subs. / synonymous sites

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection Motoo Kimura (1977): Comparison of non-synonymous substitutions and synonymous in a gene (KA/KS) shows the strength and shape of natural selection Reasoning: Advantageous mutations are very rare Harmful mutations do not propagate in the population Therefore: most mutations are neutral The stronger negative selection, less non-synonymous substitutions will be observed

09/05/2018 Quantifying natural selection Neutral? How? where: f0: fraction of non-synonymous neutral mutations v: mutation rate At a period of time t: KA = v f0 t KS = vt (all synonymous mutations are neutral ) KA / KS = f0 The greater the negative selection, lower the f0 Thus, KA / KS < 1 (typically between 0 and 0.3) Synonymous substitutions Usar lousa André de Carvalho - ICMC/USP

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection If KA / KS = 1 f0=1 Eg: Useless protein If non-synonymous mutations are considered advantageous: KA / KS may be greater than 1  is the proportion of beneficial mutations KA = v (f0 + )t KA / KS = f0 +  Combination of neutral and advantageous substitutions The stronger the negative selection on a gene, the fewer non-synonymous mutations will be neutral, resulting in a smaller f0;

09/05/2018 André de Carvalho - ICMC/USP Quantifying natural selection So KA / KS gives a measure of the action of natural selection on genes: KA / KS < 1: a predominance of negative selection KA / KS > 1: a predominance of positive selection KA / KS = 1: positive and negative selections cancel each other Note: rates may have different behavior in different parts of genes

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Simpler estimation methods: Account number of synonymous and non-synonymous sites in both sequences (Sc and Ac) Account number of synonymous and non-synonymous differences between the two sequences (Sd e Ad) Calculates the number of substitutions per position Applies Jukes and Cantor correction for multiple substitutions Gets KA/KS

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Synonymous and non-synonymous sites: Artificial idea Synonymous sites have the propensity to present synonymous mutations Non-synonymous sites have the tendency to present non-synonymous mutations

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Nei-Gojobori Algorithm (1986) One of the simplest Assumes: Transversions rates and equal transitions That the use of codons is not biased Input: two homologous sequences Output: KA and KS

Nei-Gojobori Algorithm
09/05/2018 André de Carvalho - ICMC/USP Nei-Gojobori Algorithm Align the two sequences Using only codons without gaps 1: calculate Ac and Sc (numbers of sites A and S) 2: calculate Ad and Sd (numbers of differences A and S) 3: calculate KA and KS Cost: linear on the length of the sequences

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Step 1 : Calculating Ac and Sc (sites) ck: k-th codon sc (ck): Number of synonyms sites on codon ck ac (ck): Number of non-synonymous sites on codon ck fi: the fraction of changes of i-th position of a codon that results in a synonymous change (i = 1 , 2, 3 )

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Example: TTA codon (leucine) T T A i 1 2 3 For i = 1 T -> A generates ATA (Ile): non-synonymous T -> C generates CTA (Leu): synonymous T -> G generates GTA (Val): non-synonymous f1 = 1/3

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Example: TTA codon (leucine) T T A i 1 2 3 For i = 2 T -> A generates TAA (stop): non-synonymous T -> C generates TCA (Ser): non-synonymous T -> G generates TGA (stop): non-synonymous f2 = 0/3 For i = 2 T -> A generates TAA ( stop) : non- synonymous T -> C generates TCA (Ser ) : non- synonymous T - > G generates TGA ( stop) : non- synonymous f2 = 0/3

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Example: TTA codon (leucine) T T A i 1 2 3 For i = 3 A -> T generates TTT (Phe): non-synonymous A -> C generates TTC (Phe): non-synonymous A -> G generates TTG (Leu): synonymous f3 = 1/3 For i = 3 A - > T generates TTT (Phe ) : non- synonymous A -> C generates TTC (Phe ) : non- synonymous A - > G generates TTG ( Leu) : synonymous f3 = 1/3

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Example: TTA codon (leucine) f1 = 1/3 f2 = 0 f3 = 1/3 T T A i 1 2 3 sc(TTA) = 1/ /3 = 2/3 ac(TTA) = 3 - 2/3 = 7/3

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS For a sequence with r codons: Number of synonyms sites Number of non-synonymous sites

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS For a sequence with r codons: To compare two string: average Sc andAc Number of synonyms sites Number of non-synonymous sites Stop codons are not used

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Step 2: calculating Ad and Sd (differences) sd (ck): number of synonymous differences in codon ck ad (ck): number of non-synonymous differences in codon ck The difference between these two sequences at a codon can be 1, 2 or 3 nucleotides

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Difference of 1 nucleotide: Immediate decision if replacement is synonymous and non-synonymous Eg: G T T - (Val) G T A - (Val) ex .: The same amino acid Synonymous substitution 1 difference sd(ck) = 1 ad (ck) = 0

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Difference of 2 nucleotides: There are 2 pathways for the observed differences Imagine 2 evolution paths leading from the first to the second codon Assume equal probability for paths Calculate sd (ck) and ad (ck) for each path Calculate the average of the two paths

09/05/2018 André de Carvalho - ICMC/USP Example T T T - (Phe) G T A - (Val)

09/05/2018 André de Carvalho - ICMC/USP Example 2 possible ways: T T T - (Phe) G T A - (Val) 2 differences

09/05/2018 André de Carvalho - ICMC/USP Example 2 possible ways: T T T (Phe) ↔ G T T (Val) ↔ G T A (Val) T T T - (Phe) G T A - (Val) sd (ck) = 1 ad (ck) = 1 non-synonymous synonymous 2 differences

09/05/2018 André de Carvalho - ICMC/USP Example 2 possible ways: T T T (Phe) ↔ G T T (Val) ↔ G T A (Val) T T T - (Phe) G T A - (Val) sd (ck) = 1 ad (ck) = 1 non-synonymous synonymous 2 differences T T T (Phe) ↔ T T A (Leu) ↔ G T A (Val) sd (ck) = 0 ad (ck) = 2 non-synonymous non-synonymous

09/05/2018 André de Carvalho - ICMC/USP Example 2 possible ways: T T T - (Phe) G T A - (Val) Path 1: sd1 (ck) = 1 ad1 (ck) = 1 Calculatin the average: sd (ck) = (1 + 0) / 2 = 0.5 ad (ck) = (1 + 2) / 2 = 1.5 2 differences Path 2: sd2 (ck) = 0 ad2 (ck) = 2

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Difference of 3 nucleotides: There are 6 possible ways, with three steps each Calculates sd (ck) and ad (ck) for each path Calculates the average of sd (ck) and ad (ck) in 6 ways

Estimating KA/KS For a sequence with r codons:
Number of synonymous differences Number of non-synonymous differences Total number of nucleotide differences between sequences

09/05/2018 André de Carvalho - ICMC/USP Estimating KA/KS Step 3: Calculating KA and KS ds: proportion of synonymous differences da : proportion of non-synonymous differences Applying the Jukes and Cantor correction to ds and da, we obtain, respectively, KA and KS

Case Study: Natural Selection and HIV
09/05/2018 André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV Genome of hundreds of sequenced individuals Many changes in regions recognized by the immune system To escape of the immune cells Positive selection Invariant regions, to maintain the virus biological functions Negative selection

09/05/2018 André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV Analyze KA/KS in various genes and different regions of the same gene It helps to understand that regions of the HIV genome are suffering adaptive evolution

09/05/2018 André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV HIV has 9 ORFs and produces15 proteins

09/05/2018 André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV Analysis: Step 1: find the ORFs Step 2: Calculate KA/KS Use 2 sequences Align the corresponding ORFs in the two sequences Calculate the ratio for each ORF

09/05/2018 André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV Natural selection in epitopes: Gene ENV Codes the glycoprotein gp160 (viruses wrap) - precursor protein: gp41 gp120: Binds to receptors on T lymphocytes (helps the virus to recognize the host) Recognized by the immune system to indicate virus infection

André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV The selection in gp120 needs: Maintaining the recognition capability of the host To avoid detection by the immune system Measuring KA/KS in the whole gene Hides both processes, giving an average value 09/05/2018

09/05/2018 André de Carvalho - ICMC/USP Case Study: Natural Selection and HIV To see what happens in each part of the gene: Use small windows to go throughout the gene and calculate KA/KS at a stretch Sliding window throughout the entire gene You can notice the different levels of selection pressure in different parts of the gene

Regions with KA/KS > 1 Positive Selection Probably regions recognized by the immune system André de Carvalho - ICMC/USP 09/05/2018

09/05/2018 Case Study: Natural Selection and HIV View of the rapid evolution of HIV using phylogenetic tree

09/05/2018 André de Carvalho - ICMC/USP Conclusion AIDS and HIV Evolution and Natural Selection HIV x Human Immune System Quantifying natural selection Synonymous and non-synonymous regions Case Study

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NATURAL SELECTION AT THE MOLECULAR LEVEL

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