Retroviruses integrate into a shared, non-palindromic motif. Paul Kirk. MASAMB 2016, Cambridge October 4, 2016
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1 Retroviruses integrate into a shared, non-palindromic motif Paul Kirk MSMB 2016, Cambridge October 4, 2016
2 Central dogma of molecular biology (Crick, 1956) eneral transfers of biological sequential information: Protein RN translation transcription DN replication MRC Medical Research Council 1 of 22
3 Central dogma of molecular biology (Crick, 1956) eneral transfers of biological sequential information: Protein RN translation transcription DN replication There are also special transfers of sequential information. MRC Medical Research Council 1 of 22
4 For example: retroviruses retrovirus: Reverse transcriptase viral RN Integrase Protease MRC Medical Research Council 2 of 22
5 For example: retroviruses retrovirus: Reverse transcriptase viral RN Integrase Protease Retroviruses are obligate parasites: they require a host cell to complete their life -cycle. MRC Medical Research Council 2 of 22
6 For example: retroviruses retrovirus: Reverse transcriptase viral RN Integrase Protease Retroviruses are obligate parasites: they require a host cell to complete their life -cycle. Examples: HIV, HTLV-1,.... MRC Medical Research Council 2 of 22
7 For example: retroviruses HOST CELL host DN MRC Medical Research Council 3 of 22
8 For example: retroviruses HOST CELL INFECTION host DN MRC Medical Research Council 3 of 22
9 For example: retroviruses HOST CELL viral RN host DN MRC Medical Research Council 3 of 22
10 For example: retroviruses HOST CELL viral RN viral DN Reverse transcriptase host DN MRC Medical Research Council 3 of 22
11 For example: retroviruses HOST CELL viral RN viral DN Reverse transcriptase host DN SNIP! Integrase host DN MRC Medical Research Council 3 of 22
12 For example: retroviruses HOST CELL viral RN Reverse transcriptase host DN viral DN Integrase host DN provirus MRC Medical Research Council 3 of 22
13 Characterising retroviral integration sites HOST DN...TCCCCTT... MRC Medical Research Council 4 of 22
14 Characterising retroviral integration sites RETROVIRUS DN INTERMEDITE HOST DN TC...CT...TCCCCTT... MRC Medical Research Council 4 of 22
15 Characterising retroviral integration sites RETROVIRUS DN INTERMEDITE HOST DN TC...CT...TCCCCTT... CUT! MRC Medical Research Council 4 of 22
16 Characterising retroviral integration sites HOST DN PSTE!...TCCC TC...CTCTT... PROVIRUS MRC Medical Research Council 4 of 22
17 Characterising retroviral integration sites HOST DN PSTE!...TCCC TC...CTCTT... PROVIRUS We would like to characterise the target integration site i.e. the regions flanking the provirus Is there a motif? MRC Medical Research Council 4 of 22
18 ligning integration sites iven a collection of integration sites, we can align them according to the position of the provirus... INTERTION SITE 1...TCCC TC...CTCTT... INTERTION SITE 2...TT TC...CTT... INTERTION SITE 3...C TC...CTCTTC... INTERTION SITE 4...TTCTCC TC...CTC... INTERTION SITE 5...CTTC TC...CTCTC... MRC Medical Research Council 5 of 22
19 ligning integration sites iven a collection of integration sites, we can align them according to the position of the provirus and then ignore/remove/mask the provirus sequence, so that we just look at the target sites: INTERTION SITE 1...TCCC CTT... INTERTION SITE 2...TT T... INTERTION SITE 3...C CTTC... INTERTION SITE 4...TTCTCC C... INTERTION SITE 5...CTTC CTC... MRC Medical Research Council 5 of 22
20 Summarising a collection of target sites Example (5 sequences) Consensus sequence Sequences...TC......TT......C......TTC......C... Complements...T......T......TT TC... Reverse complements...t......t......tt ct... Just take the most frequent letter at each position:...tc... Position probability matrix (PPM), P Estimate the probability of each letter at each position:... 3/5 1/5 1/5... T P =... 2/5 3/ C / / MRC Medical Research Council 6 of 22
21 Summarising a collection of target sites Example (5 sequences) Sequences...TC......TT......C......TTC......C... Reverse complement PPM, P (RC) Complements...T......T......TT TC... The PPM for the reverse complement sequences: /5 2/5... P (RC) T =... 1/5 1/5 3/5... C / / Reverse complements...t......t......tt ct... Note: we can get P (RC) from P (and vice versa) by swapping the rows T and C, and reversing the order of the columns. MRC Medical Research Council 7 of 22
22 Palindromic consensus sequences for HTLV-1 and HIV-1 target integration sites From 4,521 HTLV-1 target integration sites, we find the consensus: TTTCCCTT From 13,442 HIV-1 target integration sites, we find the consensus: TTTTCC MRC Medical Research Council 8 of 22
23 Palindromic consensus sequences for HTLV-1 and HIV-1 target integration sites From 4,521 HTLV-1 target integration sites, we find the consensus: TTTCCCTT From 13,442 HIV-1 target integration sites, we find the consensus: TTTTCC MRC Medical Research Council 8 of 22
24 Palindromic consensus sequences for HTLV-1 and HIV-1 target integration sites From 4,521 HTLV-1 target integration sites, we find the consensus: TTTCCCTT TTCCCTTT From 13,442 HIV-1 target integration sites, we find the consensus: TTTTCC CCTTTTT MRC Medical Research Council 8 of 22
25 Palindromic consensus sequences for HTLV-1 and HIV-1 target integration sites From 4,521 HTLV-1 target integration sites, we find the consensus: TTTCCCTT TTCCCTTT From 13,442 HIV-1 target integration sites, we find the consensus: TTTTCC CCTTTTT The target integration sites are palindromic (as already known!) MRC Medical Research Council 8 of 22
26 Palindromic PPMs for HTLV-1 and HIV-1 target integration sites For both HTLV-1 and HIV-1, we have P (RC) P Entries of reverse-complement PPM, P (RC) HTLV P (RC) = P 95% credible region Entries of reverse-complement PPM, P (RC) HIV P (RC) = P 95% credible region Entries of PPM, P Entries of PPM, P MRC Medical Research Council 9 of 22
27 Palindromic sequence logos C T T C CC T T T T T C CCC T TC C TT T C TT C CC TTT T T C C C HTLV-1: bits C TT C T T C CT C T TC T C T C T C C T CC T T C C T CT CT C T HIV-1: bits T T CT C CC TC T T C CT C T T C C T T C T C T T TCC CC MRC Medical Research Council 10 of 22
28 n attack of aibohphobia There is an almost unbelievable amount of symmetry (!) MRC Medical Research Council 11 of 22
29 n attack of aibohphobia There is an almost unbelievable amount of symmetry (!) Is this real? Do we see evidence of the symmetry within individual sequences, or just at the level of these summaries? MRC Medical Research Council 11 of 22
30 n attack of aibohphobia There is an almost unbelievable amount of symmetry (!) Is this real? Do we see evidence of the symmetry within individual sequences, or just at the level of these summaries? We introduce a palindrome index to quantify how palindromic each sequence is MRC Medical Research Council 11 of 22
31 The palindrome index TTTCCCTT MRC Medical Research Council 12 of 22
32 The palindrome index TTTCCCTT S = s -8 s -7 s -6 s -5 s -4 s -3 s -2 s -1 s 1 s 2 s 3 s 4 s 5 s 6 s 7 s 8 MRC Medical Research Council 12 of 22
33 The palindrome index Define TTTCCCTT S = s -8 s -7 s -6 s -5 s -4 s -3 s -2 s -1 s 1 s 2 s 3 s 4 s 5 s 6 s 7 s 8 ρ(s) = 1 n n I(s i = c(s i )), i=1 where 2n is the sequence length, I is the indicator function, and c(x) is the complement of x (e.g. c(t ) = ). MRC Medical Research Council 12 of 22
34 The palindrome index Define TTTCCCTT S = s -8 s -7 s -6 s -5 s -4 s -3 s -2 s -1 s 1 s 2 s 3 s 4 s 5 s 6 s 7 s 8 ρ(s) = 1 n n I(s i = c(s i )), i=1 where 2n is the sequence length, I is the indicator function, and c(x) is the complement of x (e.g. c(t ) = ). (In practice, we use an adjusted for chance version, which is maximally 1, and is 0 if S is no more palindromic than expected by chance.) MRC Medical Research Council 12 of 22
35 Observed palindrome indices 1200 HTLV HIV-1 Frequency Distribution of PI scores PI for consensus Frequency Distribution of PI scores PI for consensus djusted Palindrome Index, PI djusted Palindrome Index, PI MRC Medical Research Council 13 of 22
36 Where do the palindromes come from? The individual sequences are not palindromic MRC Medical Research Council 14 of 22
37 Where do the palindromes come from? The individual sequences are not palindromic So why do we see palindromes when we average over a large number of sequences? MRC Medical Research Council 14 of 22
38 Where do the palindromes come from? One possible explanation is that we have a mix of forward and reverse complement sequence orientations, MRC Medical Research Council 15 of 22
39 Where do the palindromes come from? One possible explanation is that we have a mix of forward and reverse complement sequence orientations, e.g. in the noiseless case Sequence 1: Sequence 2: Sequence 3: Sequence 4: Sequence 5: Sequence 6: TTTTT (Forward) TCCCTTTT (Reverse complement) TCCCTTTT (Reverse complement) TTTTT (Forward) TCCCTTTT (Forward) TTTTT (Reverse complement) T P = C = P(RC) MRC Medical Research Council 15 of 22
40 nalogy If we have a sample of many real numbers, and we take their mean and find it to be exactly zero, one possibility is that this mean is representative of the sample: 0 MRC Medical Research Council 16 of 22
41 nalogy If we have a sample of many real numbers, and we take their mean and find it to be exactly zero, one possibility is that this mean is representative of the sample: 0 nother possibility is that we have 2 symmetric components, one positive and one negative: MRC Medical Research Council 16 of 22 0
42 Mixture modelling We model the sequences as coming from two populations one with PPM P; and one with reverse complement PPM P (RC). π(s) = ωπ(s P) + (1 ω)π(s P (RC) ). MRC Medical Research Council 17 of 22
43 Mixture modelling We model the sequences as coming from two populations one with PPM P; and one with reverse complement PPM P (RC). π(s) = ωπ(s P) + (1 ω)π(s P (RC) ). Here, ω is the proportion of sequences coming from the population with PPM P. MRC Medical Research Council 17 of 22
44 Mixture modelling We model the sequences as coming from two populations one with PPM P; and one with reverse complement PPM P (RC). π(s) = ωπ(s P) + (1 ω)π(s P (RC) ). Here, ω is the proportion of sequences coming from the population with PPM P. The parameters, ω and P, can be estimated/inferred in numerous ways. I will show results from using an EM-algorithm, but identical results are obtained by: (i) maximum profile likelihood; (ii) ibbs sampling; (iii) greedy ibbs. MRC Medical Research Council 17 of 22
45 Unmixing the forward and reverse sequences T T CCC T T CT CT T T T C T CCC C C T CT TTC T C T T CT T CC C T TTCT CCC T T TC T T CC CCC TC T C TC TC T TT T CCC T C C T C T T T T CC C T T T C C T CT T T T CCCC T T TT T T CCCCC C bits Subpopulation 1 Subpopulation HTLV HTLV-1 (4521 seqs) (4521 seqs) T C C C T C C T TC C C C C TT 0.1 C CT TC 0.0 TC bits C TC T bits HIV HIV-1 (13442 seqs) (13442 seqs) T C CT C TC C T C C T C T C TC T C T TC C T CT bits T CCT C TT C C CT C T C T C T C MRC Medical Research Council 18 of 22
46 Unmixing the forward and reverse sequences T T CT C C T T C TT CCC T T C T T C C TT C T T CC C bits C C 0.0 T T CCC T T C C CC T T T C T T C T CT C T C CT T T CC bits HTLV-1 HIV-1 C T C T TC T C CT C TC T C C T CT C C C TC T T C C T CT MRC Medical Research Council 18 of 22
47 Unmixing the forward and reverse sequences T C T T CT T C T C TT CCC TT C CTTTT TC C C C C C T C C TT T C C T T C CT T C T T C C TT C T T C C C T C C T C T T C T C CT C C bits bits T T CCC T T C -13 HTLV-1 C C T C C T CT C T C T TC C T T C T C C T CT T T CC HIV-1 C T CT C T C TC T T C T CT C T C T TC T C C TC T C T C T CT bits bits MLV SLV T C T C C T CT C T C T C C T C T T T C C C T T TT CC CC TT C T C T C T CT C T C T T C C T T C T C TTT CC MRC Medical Research Council 18 of 22 C T T
48 Summary The palindrome is not observed within individual sequences. MRC Medical Research Council 19 of 22
49 Summary The palindrome is not observed within individual sequences. Hypothesis: the palindrome results from a mixture of sequences that contain a non-palindromic motif in approximately equal proportions in forward and reverse complement orientations MRC Medical Research Council 19 of 22
50 Summary The palindrome is not observed within individual sequences. Hypothesis: the palindrome results from a mixture of sequences that contain a non-palindromic motif in approximately equal proportions in forward and reverse complement orientations Modelling this hypothesis revealed a common nucleotide motif across 4 retroviruses: 5 -T(N1/2)[C(N0/1)T (W1/2)C]CW-3 MRC Medical Research Council 19 of 22
51 Summary The palindrome is not observed within individual sequences. Hypothesis: the palindrome results from a mixture of sequences that contain a non-palindromic motif in approximately equal proportions in forward and reverse complement orientations Modelling this hypothesis revealed a common nucleotide motif across 4 retroviruses: 5 -T(N1/2)[C(N0/1)T (W1/2)C]CW-3 Potential implications for understanding retroviral integration. MRC Medical Research Council 19 of 22
52 Summary The palindrome is not observed within individual sequences. Hypothesis: the palindrome results from a mixture of sequences that contain a non-palindromic motif in approximately equal proportions in forward and reverse complement orientations Modelling this hypothesis revealed a common nucleotide motif across 4 retroviruses: 5 -T(N1/2)[C(N0/1)T (W1/2)C]CW-3 Potential implications for understanding retroviral integration. True validation requires further structural information about retroviral intasomes. MRC Medical Research Council 19 of 22
53 vailability ccepted for publication in Nature Microbiology. Preprint: Kirk, Huvet, Melamed, Maertens & Bangham (2015). Retroviruses integrate into a shared, non-palindromic motif. biorxiv. Matlab code (and the HTLV-1 dataset) are available online: bioinformatics-and-statistical-genomics/ Just click on retrocode to download! MRC Medical Research Council 20 of 22
54 cknowledgements Charles Bangham Maxime Huvet nat Melamed oedele Maertens Sylvia Richardson MRC Biostatistics Unit Michael Stumpf Imperial College Theoretical Systems Biology group MRC Medical Research Council 21 of 22
55 Thanks for MRC Medical Research Council 22 of 22
56
Retroviruses integrate into a shared, non-palindromic DNA motif
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Retroviruses integrate into a shared, non-palindromic DN motif Paul D. W. Kirk 1, Maxime Huvet 2, nat Melamed
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