Introduction Methods Results Summary GWAMAR: Genome-wide assessment of mutations associated with drug resistance in bacteria Michal Wozniak 1,2 , Limsoon Wong 2 and Jerzy Tiuryn 1 1 University of Warsaw 2 National University of Singapore 27 March, 2015 Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Methods Results Summary Introduction Mechanisms of drug action against bacteria Mechanisms of drug resistance in bacteria Methods Schema of the approach Input data Association scores Results Input datasets Comparison of different association scores Top-scoring mutations Compensatory mutations Summary Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Methods Mechanisms of drug action against bacteria Results Mechanisms of drug resistance in bacteria Summary Drug action mechanisms Rysunek : Adopted from: Platforms for antibiotic discovery; Kim Lewis; Nature Reviews; 2013 Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Methods Mechanisms of drug action against bacteria Results Mechanisms of drug resistance in bacteria Summary Timeline of antibiotics Rysunek : Timeline of the discovery and introduction of antibiotics (based on Platforms for antibiotic discovery; Kim Lewis; Nature Reviews; 2013). Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Methods Mechanisms of drug action against bacteria Results Mechanisms of drug resistance in bacteria Summary Drug resistance mechanisms I There are several known drug resistance mechanisms which can be categorized as follows (adopted from: Wright GD, Chem. Comm., 2011 ): ◮ drug target modification; ◮ drug molecule modification by specialized enzymes ◮ reduced accumulation of the drug inside a bacteria cell by decreased cell wall permeability or by pumping out the drug ◮ alternative metabolic pathways These drug resistance mechanisms can be acquired either by chromosomal mutations or horizontal gene transfer . Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Methods Mechanisms of drug action against bacteria Results Mechanisms of drug resistance in bacteria Summary Drug resistance mechanisms II Rysunek : Adopted from: Platforms for antibiotic discovery; Kim Lewis; Nature Reviews; 2013 Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Schema of the approach Methods Input data Results Association scores Summary GWAMAR: drug resistance-associated mutations Goal : identify drug resistance-associated mutations (primary and secondary) General approach implemented in GWAMAR: ◮ we use whole-genome comparative approach to identify genetic variations among multiple bacterial strains, ◮ we retrieve from literature and databases information of the drug resistance phenotypes of the strains, ◮ we associate the identified mutations with drug resistance-phenotypes based on association scores, ◮ we propose a new association score, called TGH, which implements scores phylogenetic information. Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Schema of the approach Methods Input data Results Association scores Summary Genotype and phenotype data Genotype data We consider two kinds of genetic variations (determined by eCAMBer based on gene families and their multiple alignments): ◮ gene gain/loss, ◮ amino acid point mutation. These genetic variations are represented as ’ 0 ’ - ’ 1 ’ vectors (called mutation profiles ), where ’ 0 ’ denotes the reference state and ’ 1 ’ denotes some change. Phenotype data (drug susceptibility) Phenotype data are represented as vectors, called drug resistance profiles , with possible states: ’S’ , ’R’ , ’I’ , ’?’ . Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Schema of the approach Methods Input data Results Association scores Summary Schema of the framework The pipeline of GWAMAR Preprocessing steps done by eCAMBer (this step may potentially be replaced by other tools) Download of genome sequences and annotations for a set of bacterial strains Consolidation of genome annotations for multiple bacterial strains and identification of gene families Multiple alignments of identified gene families computed using MUSCLE Reconstruction of the Identification of point phylogenetic tree employing mutations PHYLIP or PhyML Genotype data Phylogenetic tree for the set of (a set of mutations) bacterial strains Binarization of mutation The reference strain profiles into binary mutation profiles Scoring of the mutation Phenotype data collected Profiles from literature or databases (multiprocessing) (a set of drug resistance profiles) Scored list of putative associations of drug resistance with mutations Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Schema of the approach Methods Input data Results Association scores Summary Tree-aware scores We observe that subtrees of the phylogenetic tree very often correspond to geographic locations. Since drug resistance mutations are subject to e volutionary pressure caused by the drug treatment they should be independent of geographic location and therefore be more widely distributed over the tree, as opposed to mutations driven by other environmental factors which tend to rather concentrate in small subtrees. n173 HaarlemNITR202 n174 RGTB423 n175 CASNITR204 n176 n177 n178 OSDD493 OSDD105 NAA0009 n179 n180 EAIOSDD271 NAA0008 RGTB327 n181 n182 n290 n183 n187 n291 n293 n184 n185 n188 n190 PanR0304 n292 n294 n302 CDC1551 CDC1551A PanR0305 n186 SUMu001 n189 n191 n250 PanR0308 PanR0201 n295 n296 n303 n305 PanR0316 PanR0301 SUMu011 SUMu010 n192 n193 n251 n253 CPHLA K85 n297 n301 OSDD071 n304 n306 n308 n98R604INHRIFEM GM1503 n194 n199 INSSEN n252 MTB476 n254 T92 n298 EAI5 EAS054 OSDD504 OSDD518 n94M4241A n307 n309 n316 n195 n198 n200 n208 INSXDR INSMDR n255 n282 n299 n300 UM1072388579 FJ05194 CTRI4 n310 n317 n318 n196 n197 KZN4207Broad KZN4207 n201 n202 n209 n211 EAI5NITR206 n256 C n283 n4316836 PR05 T17 T46 n021987 n311 HN878 n210 n319 n324 KZNV2475 KZN1435 KZNR506 KZN605 F11 HM n203 n204 UT205 n210 n212 n217 n257 n265 PanR0315 n284 n312 n314 n320 n322 n325 n331 PanR0708 PanR0906 n205 n207 PanR1101 PanR0403 PanR0503 n213 n218 n219 GuangZ0019 n258 n266 n269 n719999 n285 X132 n313 R1746 n315 PanR0606 n321 OMV02005 n323 n326 n327 n332 n333 PanR0703 n206 PanR0704 PanR0805 n214 n215 PanR0402 PanR0203 n220 n221 n259 n264 PanR0208 n267 n270 n271 n286 n287 R1207 X28 X156 X85 BeijingNITR203 PanR0605 XDR1221 NCGM2209 R1505 R1441 R1390 n328 n329 W148 SP21 n334 n339 PanR1007 PanR0611 PanR0313 PanR0610 PanR0409 n216 PanR0602 CTRI2 n222 n223 SUMu006 n260 SUMu002 SUMu009 MTB489 n268 BTB05559 BTB05552 S96129 OSDD515 n272 PanR0907 PanR0206 n288 n289 R1842 X29 X122 n330 n335 n336 XDR1219 BT1 PanR0903 PanR0404 PanR0407 PanR0601 n224 n229 n261 n262 PanR0309 PanR0202 n273 n274 Haarlem PanR0902 Erdman PanR0801 X189 R1909 CCDC5180 BT2 n337 n338 n225 n228 n230 n234 SUMu004 SUMu005 SUMu007 n263 H37Rv SUMu012 H37RaWGS n275 CCDC5079 T85 HKBS1 WX1 WX3 n226 n227 PanR0607 PanR0609 n231 n232 n235 n239 SUMu003 SUMu008 H37Ra n276 PanR0401 PanR0205 PanR0306 PanR0209 PanR0904 PanR0803 PanR0909 n233 n236 n238 n240 n241 PanR0908 n277 PanR0207 PanR0314 PanR0707 n237 PanR0702 PanR0505 PanR0804 PanR0411 n242 n243 n278 n281 PanR0603 PanR0604 PanR0307 PanR0412 n244 n247 n279 n280 H37RvLP H37RvJO n245 n246 PanR0501 n248 H37RvHA H37RvMA H37RvAE H37RvCO H37RvBroad PanR0405 PanR0410 PanR0317 PanR0311 PanR1006 n249 PanR1005 PanR0802 Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Schema of the approach Methods Input data Results Association scores Summary Classical scores (tree-ignorant) association scores The classical scores used in genotype-phynotype association studies and co-evolution studies are tree-ignorant. ◮ odds ratio: n R 1 · n S 0 OR ( b , r ) = max ( 1 , n R 0 ) · max ( 1 , n S 1 ) ◮ mutual information: � n y n y x · n x � � � MI ( b , r ) = n · log n x · n y x ∈{ ’ 0 ’ , ’ 1 ’ } y ∈{ ’S’ , ’I’ , ’R’ } ◮ hypergeometric score n � � H ( n , n R , n 1 , i ) � H ( b , r ) = − log i = n R Michal Wozniak GWAMAR: drug resistance-associated mutations
Introduction Schema of the approach Methods Input data Results Association scores Summary Weighted support Weighted support rewards for drug-resistant strains with the mutation, penalty for drug-susceptible strains with the mutation, where weight w T ( b , r , i ) for drug resistant strains is 1 k , where k denotes the size of the largest subtree with only drug resistant strains. ✉ ✉ ✉ Drug resistance profile S R S R ? ? S R R ? R Weights -1.7 1.0 -1.7 1.0 0.0 0.0 -1.7 0.3 0.3 0.0 0.3 mutation 2 0 1 0 1 0 0 0 1 0 0 0 2.3 mutation 1 0 0 0 0 0 0 0 1 1 1 1 1.0 mutation 3 1 0 0 0 1 0 0 1 1 0 0 -1.0 ... ... ... Weighted support for mutation m is defined as follows: � WS T ( b , r ) = w T ( b , r , i )[ b ( i ) = ’ 1 ’ ] i ∈S Michal Wozniak GWAMAR: drug resistance-associated mutations
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