Finding protein sites where resistance has evolved
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1 Finding protein sites where resistance has evolved The amino acid (Ka) and synonymous (Ks) substitution rates Please sit in row K or forward
2 The Berlin patient: first person cured of HIV Contracted HIV in late 20s ~10 years later diagnosed with leukemia (cancer of bone marrow and blood cells) Dr. ero Hϋtter and Timothy Ray Brown
3 Leukemia and stem cell transplants Chemotherapy kills many non-cancerous blood cells A solution: stem cell transplant given after chemotherapy How could this be relevant to HIV? Donor Recipient (patient)
4 The Berlin patient: how it worked Chemotherapy Peripheral-blood stem cell transplant from CCR5 D32 donor HIV cannot spread in new immune system Why is this treatment unlikely to be very widely applied? Donor has two copies of CCR5 D32 deletion Recipient (patient)
5 Topics for today Detecting which regions of the HIV genome are evolving drug resistance: Ka/Ks Homework 4 recap Homework 5 preview
6 Multi-drug treatment and resistance: a study from Cameroon Typical multi-drug cocktail at the time: 2 NRTI, 1 NNRTI Isolate blood Sequence HIV RNA in plasma >06CM06ARC001 gi gb Q HIV-1 isolate 06CM06ARC001 from Cameroon reverse transcriptase (Pol) gene, partial cds CCAATTATCCTATTAAACTTACCATAAAACTAAACCAAATATCCCAAAATTAAACAAT CCATTACAAAAAAAAATAAACATTAAAAACATTTTACAAATAAAAAA... >06CM06ARC011 gi gb Q HIV-1 isolate 06CM06ARC011 from Cameroon reverse transcriptase (Pol) gene, partial cds CCAATTATCCTATTAAACTTACCATAAAACTAAACCAAATATCCCAAATTAAACAAT CCATTACAAAAAAAAATAAACATTAACAAAATTTTACAAATAAAAAA......
7 Can think of one sequence as being approximately ancestral to others Sequence name Collection date 06ARC LPH20SC ARC LPH17HT 2006 LPH02NJM ARC ARC ARC SE-MP
8 Resistance evolves even with a multi-drug regimen (though more slowly) Sequence Collection date Resistant? 06ARC yes 06LPH20SC 2006 yes 06ARC yes 06LPH17HT 2006 yes LPH02NJM 2007 yes 06ARC no 06ARC no 06ARC no 98SE-MP no
9 Looking for mutations associated with resistance: the ideal case All have one nucleotide at site All have one another Sequence Collection date Resistant? 06ARC yes 06LPH20SC 2006 yes 06ARC yes 06LPH17HT 2006 yes LPH02NJM 2007 yes 06ARC no 06ARC no 06ARC no 98SE-MP no
10 Looking for mutations associated with resistance 06CM06ARC001 06CM06LPH20SC 06CM06ARC036 06CM06LPH17HT 07CMLPH02NJM 06CM06ARC058 06CM06ARC075 06CM06ARC011 One part of the region coding for reverse transcriptase There is no single mutation present which can explain all cases of resistance
11 Alternate strategy: use what we know about population genetics and the genetic code to infer which regions were acted on by positive selection Second letter First letter T T C A TTT Phe F TCT Ser S TAT Tyr Y TT Cys C TTC Phe F TCC Ser S TAC Tyr Y TC Cys C TTA Leu L TCA Ser S TAA Stop TA Stop TT Leu L TC Ser S TA Stop T Trp W CTT Leu L CCT Pro P CAT His H CT Arg R C CTC Leu L CCC Pro P CAC His H CC Arg R CTA Leu L CCA Pro P CAA ln Q CA Arg R CT Leu L CC Pro P CA ln Q C Arg R ATT Ile I ACT Thr T AAT Asn N AT Ser S A ATC Ile I ACC Thr T AAC Asn N AC Ser S ATA Ile I ACA Thr T AAA Lys K AA Arg R AT Met M AC Thr T AA Lys K A Arg R TT Val V CT Ala A AT Asp D T ly TC Val V CC Ala A AC Asp D C ly TA Val V CA Ala A AA lu E A ly T Val V C Ala A A lu E ly
12 Flashback: homework 3 Case A: no selection Case B: purifying selection >>> startallele='ccc' >>> popsize=50 >>> numens=400 >>> mutprob= >>> numreps=120 >>> selecton=false >>> subl=subsim(startallele,popsize,numens, mutprob,numreps,selecton,{}) >>> printsummary(subl,".5f") ( ) ( ) ( ) >>> startallele='ccc' >>> popsize=50 >>> numens=400 >>> mutprob= >>> numreps=120 >>> selecton=true >>> subl=subsim(startallele,popsize,numens, mutprob,numreps,selecton,fitnessd1) >>> printsummary(subl,".5f") (NA-NA) (NA-NA) ( )
13 Different positions in the codon have different substitution rates due to the genetic code First letter T C A Second letter T C A TTT Phe F TCT Ser S TAT Tyr Y TT Cys C TTC Phe F TCC Ser S TAC Tyr Y TC Cys C TTA Leu L TCA Ser S TAA Stop TA Stop TT Leu L TC Ser S TA Stop T Trp W CTT Leu L CCT Pro P CAT His H CT Arg R CTC Leu L CCC Pro P CAC His H CC Arg R CTA Leu L CCA Pro P CAA ln Q CA Arg R CT Leu L CC Pro P CA ln Q C Arg R ATT Ile I ACT Thr T AAT Asn N AT Ser S ATC Ile I ACC Thr T AAC Asn N AC Ser S ATA Ile I ACA Thr T AAA Lys K AA Arg R AT Met M AC Thr T AA Lys K A Arg R TT Val V CT Ala A AT Asp D T ly TC Val V CC Ala A AC Asp D C ly TA Val V CA Ala A AA lu E A ly Case B: purifying selection >>> startallele='ccc' >>> popsize=50 >>> numens=400 >>> mutprob= >>> numreps=120 >>> selecton=true >>> subl=subsim(startallele,popsize,numens, mutprob,numreps,selecton,fitnessd1) >>> printsummary(subl,".5f") (NA-NA) (NA-NA) ( ) T Val V C Ala A A lu E ly
14 Amino acid changing vs. synonymous mutations First letter T Second letter T C A TTT Phe F TCT Ser S TAT Tyr Y TT Cys C TTC Phe F TCC Ser S TAC Tyr Y TC Cys C TTA Leu L TCA Ser S TAA Stop TA Stop Amino acid changing mutation example: CCC à ACC (Pro à Thr) TT Leu L CTT Leu L TC Ser S CCT Pro P TA Stop CAT His H T Trp W CT Arg R Synonymous mutation example: C CTC Leu L CTA Leu L CT Leu L CCC Pro P CCA Pro P CC Pro P CAC His H CAA ln Q CA ln Q CC Arg R CA Arg R C Arg R CCC à CCA (Pro à Pro) ATT Ile I ACT Thr T AAT Asn N AT Ser S A ATC Ile I ACC Thr T AAC Asn N AC Ser S ATA Ile I AT Met M TT Val V TC Val V TA Val V ACA Thr T AC Thr T CT Ala A CC Ala A CA Ala A AAA Lys K AA Lys K AT Asp D AC Asp D AA lu E AA Arg R A Arg R T ly C ly A ly Key insight: selection affects amino acid changing mutations but not synonymous ones T Val V C Ala A A lu E ly
15 Homework 3: a case with positive selection Case A: no selection Case C: positive selection >>> startallele='ccc' >>> popsize=50 >>> numens=400 >>> mutprob= >>> numreps=120 >>> selecton=false >>> subl=subsim(startallele,popsize,numens, mutprob,numreps,selecton,{}) >>> printsummary(subl,".5f") ( ) ( ) ( ) >>> startallele='ccc' >>> popsize=50 >>> numens=400 >>> mutprob= >>> numreps=120 >>> selecton=true >>> subl=subsim(startallele,popsize,numens, mutprob,numreps,selecton,fitnessd2) >>> printsummary(subl,".5f") ( ) ( ) ( )
16 An example scenario Ancestral codon known (e.g. from old samples) TA (Valine) Descendent viral sequences TTA TA CA TA TA T TT CTA We ll assume a star phylogeny
17 Attempt 1: trying to infer selection by counting substitutions CTA (aa) TTA (aa) TA First letter Second letter T C A TTT Phe F TCT Ser S TAT Tyr Y TT Cys C T TTC Phe F TCC Ser S TAC Tyr Y TC Cys C TTA Leu L TCA Ser S TAA Stop TA Stop TT Leu L TC Ser S TA Stop T Trp W CTT Leu L CCT Pro P CAT His H CT Arg R TT (syn) CA (aa) C CTC Leu L CTA Leu L CCC Pro P CCA Pro P CAC His H CAA ln Q CC Arg R CA Arg R Ancestor: TA CT Leu L ATT Ile I CC Pro P ACT Thr T CA ln Q AAT Asn N C Arg R AT Ser S A ATC Ile I ACC Thr T AAC Asn N AC Ser S T (syn) TA ATA Ile I AT Met M TT Val V ACA Thr T AC Thr T CT Ala A AAA Lys K AA Lys K AT Asp D AA Arg R A Arg R T ly TA TC Val V TA Val V CC Ala A CA Ala A AC Asp D AA lu E C ly A ly T Val V C Ala A A lu E ly
18 Attempt 1: trying to infer selection by counting substitutions CTA (aa) TTA (aa) TA Average amino acid changing subs per lineage: 3/8 Average synonymous subs per lineage: 2/8 TT (syn) Ancestor: TA CA (aa) T (syn) TA TA Does it work to compare these?
19 Attempt 2: normalizing by the number of amino acid and synonymous sites Second letter TA (Valine) First letter T C A TTT Phe F TCT Ser S TAT Tyr Y TT Cys C T TTC Phe F TCC Ser S TAC Tyr Y TC Cys C TTA Leu L TCA Ser S TAA Stop TA Stop TT Leu L TC Ser S TA Stop T Trp W CTT Leu L CCT Pro P CAT His H CT Arg R Synonymous site C CTC Leu L CTA Leu L CT Leu L CCC Pro P CCA Pro P CC Pro P CAC His H CAA ln Q CA ln Q CC Arg R CA Arg R C Arg R ATT Ile I ACT Thr T AAT Asn N AT Ser S A ATC Ile I ACC Thr T AAC Asn N AC Ser S ATA Ile I ACA Thr T AAA Lys K AA Arg R Amino acid changing sites AT Met M TT Val V AC Thr T CT Ala A AA Lys K AT Asp D A Arg R T ly TC Val V CC Ala A AC Asp D C ly TA Val V CA Ala A AA lu E A ly T Val V C Ala A A lu E ly
20 Ka: the number of amino acid changing substitutions per amino acid site 1 2 CTA (aa) 1 2 TTA (aa) TA 0 2 Average proportion of amino acid differences per amino acid changing site: 3/ TT (syn) Ancestor: TA CA (aa) 1 2! * = 3 4 ln = T (syn) TA 0 2 TA 0 2! = 3 4 ln ) Jukes cantor correction
21 Ks: the number of synonymous substitutions per synonymous site 0 1 CTA (aa) 0 1 TTA (aa) TA 0 1 Average proportion of synonymous differences per synonymous site: 2/8 1 1 TT (syn) Ancestor: TA CA (aa) 0 1! " = 3 4 ln = T (syn) TA 0 1 TA 0 1! = 3 4 ln / Jukes cantor correction
22 Ka/Ks and selection CTA (aa) TTA (aa) TA!"!# = = TT (syn) Ancestor: TA CA (aa) T (syn) TA TA Ka Ks 1 Ka Ks >1 Ka Ks <1 No selection Positive selection Purifying selection
23 Worksheet (Rip it off from the back of your packet) Name: For the ancestor and descendant sequences shown below, and assuming a star phylogeny, calculate: 1. The proportion of amino acid differences per amino acid changing site 2. The proportion of synonymous differences per synonymous site Ancestor TCC Descendent viral sequences TCC ACC TCA TCC TCC TCC TCC TCA ACC First letter T C A TTT Phe TTC Phe TTA Leu TT Leu T C A F F L L CTT Leu L CTC Leu L CTA Leu L CT Leu L ATT Ile I ATC Ile I ATA Ile I AT Met M TT Val V TC Val V TA Val V T Val V Second letter TCT Ser S TCC Ser S TCA Ser S TC Ser S CCT Pro P CCC Pro P CCA Pro P CC Pro P ACT Thr T ACC Thr T ACA Thr T AC Thr T CT Ala A CC Ala A CA Ala A C Ala A TAT Tyr Y TAC Tyr Y TAA Stop TA Stop CAT His H CAC His H CAA ln Q CA ln Q AAT Asn N AAC Asn N AAA Lys K AA Lys K AT Asp D AC Asp D AA lu E A lu E TT Cys C TC Cys C TA Stop T Trp W CT Arg R CC Arg R CA Arg R C Arg R AT Ser S AC Ser S AA Arg R A Arg R T ly C ly A ly ly
24 Worksheet (Rip it off from the back of your packet) Name: For the ancestor and descendant sequences shown below, and assuming a star phylogeny, calculate: 1. The proportion of amino acid differences per amino acid changing site 2. The proportion of synonymous differences per synonymous site Ancestor TCC Descendent viral sequences TCC ACC TCA TCC TCC TCC TCC TCA 1. TCC has two amino acid changing sites. On the 4 lineages we have 0/2, ½, 0/2, and 0/2 amino acid changes per amino acid changing sites, which averages to 1/8 2. TCC has one synonymous site. On the 4 lineages we have 0/1, 0/1, 1/1, and 0/1 synonymous changes per synonymous site. This averages ¼. First letter T C A TTT Phe TTC Phe TTA Leu TT Leu T C A F F L L CTT Leu L CTC Leu L CTA Leu L CT Leu L ATT Ile I ATC Ile I ATA Ile I AT Met M TT Val V TC Val V TA Val V T Val V Second letter TCT Ser S TCC Ser S TCA Ser S TC Ser S CCT Pro P CCC Pro P CCA Pro P CC Pro P ACT Thr T ACC Thr T ACA Thr T AC Thr T CT Ala A CC Ala A CA Ala A C Ala A The jukes cantor correction for 1/8 is 0.137, and for ¼ is.304 Ka/Ks =.137/.304 = 0.45 ACC TAT Tyr Y TAC Tyr Y TAA Stop TA Stop CAT His H CAC His H CAA ln Q CA ln Q AAT Asn N AAC Asn N AAA Lys K AA Lys K AT Asp D AC Asp D AA lu E A lu E TT Cys C TC Cys C TA Stop T Trp W CT Arg R CC Arg R CA Arg R C Arg R AT Ser S AC Ser S AA Arg R A Arg R T ly C ly A ly ly
25 Topics for today Detecting which regions of the HIV genome are evolving drug resistance: Ka/Ks Homework 4 recap Homework 5 preview
26 Homework 4 recap abon talapoin Question: If we made a tree for the HIV/SIV sequences infecting these primates, how would it compare with this host tree? Sooty mangabey Drill Chimp Human Phylogeny from Perelman et al. A molecular phylogeny of living primates 2011.
27 Conclusions based on our tree
28 Conclusions based on our tree: HIV2 Ancestor infecting Sooty mangabey Likely came from Sooty Mangabey Entered human pop at least twice independently = primate to human host switch
29 Conclusions based on our tree: HIV1 Ancestor infecting Chimpanzee Likely came from Chimpanzee Entered human pop at least twice independently = primate to human host switch
30 Other details are also inconsistent with pure co-evolution abon talapoin Sooty mangabey Drill Chimp Positions of SIV_gabonTalapoin and SIV_drill inconsistent with pure coevolution. Human
31 Conclusions based on larger data sets: HIV1 Sharp PM, Hahn BH. Origins of HIV and the AIDS Pandemic. Cold Spring Harbor Perspectives in Medicine: 2011;1(1):a doi: /cshperspect.a
32 Conclusions based on larger data sets: HIV2 Sharp PM, Hahn BH. Origins of HIV and the AIDS Pandemic. Cold Spring Harbor Perspectives in Medicine: 2011;1(1):a doi: /cshperspect.a
33 Significance of host transfer events: recently introduced viruses tend to be more virulent SIV is mild in many non-human primates Chimps: exception that proves the rule Why might recently introduced viruses be more virulent?
34 Question: why has HIV killed so many people? Defeating the immune system Attacks the immune system itself Rapid evolution Epidemiological factors HIV is a recent introduction into humans
35 Topics for today Detecting which regions of the HIV genome are evolving drug resistance: Ka/Ks Homework 4 recap Homework 5 preview
36 Homework 5 preview: estimating when HIV1 group M entered the human population A B C Cumulative branch length for D D E Cumulative branch length for a tip: the sum of all branch lengths from the root to that tip.
37 HIV sequence samples have been collected at different times Samples collected early likely to have short cumulative branch length Samples collected later likely to have long cumulative branch length We expect an association between date of collection, and cumulative branch length.
38 Tree of HIV 1 group M
39 Plotting cumulative branch length vs. collection date to estimate time of common ancestor Each data point is one tip Point where trend line crosses x-axis is estimate of date for most recent common ancestor. Cumulative branch length Trend line Collection date
40 Hand in your worksheet please! (and be sure you put your full name on it)
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