inferring antibiotic resistance from genomic data

Inferring antibiotic resistance from genomic data

Dr Sally PartridgeCentre for Infectious Diseases and Microbiology

Westmead Millennium Institute, Westmead Hospital, Sydney

[email protected]

•increasing global public health problem

•resistance currently detected by culture and (automated) phenotypic methods and

individual/multiplex PCR for specific resistance genes

•WGS data can potentially identify all resistance genes, but challenges in extracting

information

•genotype vs. resistance phenotype

•large number of genes potentially conferring resistance to each antibiotic

•problems with resistance gene nomenclature

•in Enterobacteriaceae most resistance is due to “mobile” genes on plasmids

•contributions from chromosomal mutations

•single nucleotide change can affect phenotype (also expression?)

•assembly of sequences relating to multi-resistance confounded by long repeats

multi-resistance in Gram-negatives

β-lactams & β-lactamases

*carbapenem resistance usually requires additional permeability defects in

Enterobacteriaceae

3rd-generation cephalosporins – e.g. cefotaxime, cetaxidime

“Anti-pseudomonal” β-lactam+inhibitor combinations (APP) – e.g. Timentin

carbapenems – e.g. imipenem, meropenem

β-lactamase type bla genesResistance to:

3GC APP-β CPM

ESBL TEM SHV CTX-M VEB PER yes no no

AmpC CMY DHA FOX MOX ACC yes yes (yes*)

metallo-β-lactamase

class A CPMase

IMP VIM NDM GIM SIM SPM

KPC (some GES)yes yes yes*

narrow spectrum – TEM, SHV

„classical‟ ESBL – TEM, SHV

„new‟ ESBL – e.g. CTX-M – 6 groups, >150 variants

plasmid-borne ampC – 6 groups, 200 variants

metallo-β-lactamases (MβL) - IMP (17 groups/48 variants), VIM (5/42), NDM (1/12)

class A carbapenemases – e.g. KPC (1/21)

β-lactamases (bla genes)


3GC APP-β CPM




class A CPMase




Enterobacteriaceae

>200 TEM variants

>180 SHV variants>350

ESBL

variants

narrow spectrum – TEM, SHV

„classical‟ ESBL – TEM, SHV

„new‟ ESBL – e.g. CTX-M – 6 groups, >150 variants

plasmid-borne ampC – 6 groups, 200 variants

metallo-β-lactamases (MβL) - IMP (17 groups/48 variants), VIM (5/42), NDM (1/12)

class A carbapenemases – e.g. KPC (1/21)

β-lactamases (bla genes)

>200 TEM variants

>180 SHV variants>350

ESBL

variants


3GC APP-β CPM




class A CPMase




Enterobacteriaceae

nomenclature issueshttp://www.lahey.org/Studies/

•names don‟t always indicate relationships between gene sequences

most commonly used in Australia are gentamicin, tobramycin, amikacin

resistance often conferred by aminoglycoside-modifying enzymes:

aac – acetylation

aad – adenylylation

aph – phosphorylation

each modifies only a subset

of aminoglycosides

>50 different genes

aminoglycosides



aac – acetylation



different nomenclature systems e.g. aacA4 = aac(6')-Ib


of aminoglycosides

>50 different genes

aminoglycosides



aac – acetylation




also 16S rRNA methylases – high level resistance to all aminoglycosides

armA, rmtA-H, npmA


of aminoglycosides

>50 different genes

aminoglycosides

fluoroquinolonese.g. ciprofloxacin

historically resistance conferred by chromosomal mutations, e.g. in gyrA

late 1990s - plasmid-borne genes conferring low-level resistance (PMQR):

qnrA(3/7), B (10/74), C(1), D (2),S (3/9),VC (2/6) – protect gyrase

2x aac(6')-Ib-cr variants – acetylate fluoroquinolones

qepA – efflux pump



aac – acetylation




also 16S rRNA methylases – high level resistance to all aminoglycosides

armA, rmtA-H, npmA


of aminoglycosides

>50 different genes

aminoglycosides

chromosomal mutations

low-level fluoroquinolone resistance e.g. in E. coli DNA gyrase and topoisomerase IV

Hopkins KL et al. (2005) Int J Antimicrob Agents 25 358–73

E. coli K. pneumoniae

OmpK36 na - - - +GlyAsp +ISKpn26

ertapenem 8 128 128 256 512 512

imipenem 16 64 128 128 256 512

meropenem 4 32 64 64 256 256

adapted from Nikaido (2003) Microbiol Mol Biol Rev 67:593-656

G D

ISKpn26

ompK36

carbapenem MICs for Australian isolates with blaKPC


Partridge SR et al.

Int J Antimicrob

Agents (in press)

colistin resistance

Cannatelli A et al (2013) Antimicrob Agents Chemother 57:5521-6

colistin susceptible

colistin resistant

Poirel L et al (2014) J Antimicrob Chemother

doi:10.1093/jac/dku323

•MgrB - small transmembrane protein

•-ve regulator of PhoPQ

•PhoP activates expression of pmrHFIJKLM operon

•synthesis/transfer of cationic LAra4N onto lipid A

•modification of LPS target → polymyxin resistance


tigecycline resistance


Hentschke M et al (2010) Antimicrob Agents Chemother 54:2720-3

ramR repressor of ramA

RamA - +ve regulator of AcrAB

AcrAB efflux system implicated in resistance to

tigecycline

tools for annotation of R genes

http://rast.nmpdr.org/rast.cgiAziz RK et al (2008) BMC Genomics 9:75

Overbeek R et al (2014) Nucleic Acids Res 42:D206-14

RAST

tools for annotation of R genes

•annotation of bacterial sequences, but not specifically resistance genes

•slow

•designed for use with assembled data/contigs

•annotations are often too vague

RAST

R gene RAST annotation

blaCMY-6 β-lactamase EC 3.5.2.6

aacA4* 6'-N-acetyltransferase

sul1 dihydropteroate synthase EC 2.5.1.15

rmtC hypothetical protein

aac(3)-IId aminoglycoside N(3')-acetyltransferase III EC 2.3.1.81

aphA6 aminoglycoside phosphotransferase

blaNDM-1 β-lactamase

http://cge.cbs.dtu.dk/services/ResFinder/Zankari E et al (2012) J Antimicrob Chemother 67:2640-4

ResFinder

•can use raw data or assembled sequences

•doesn‟t include chromosomal mutations

http://cge.cbs.dtu.dk/services/ResFinder/

ResFinder

ResFindersame gene,

not cr variant

wrong accession

Antibiotic Resistance Gene-ANNOTation

http://www.mediterranee-infection.com/article.php?laref=282&titer=arg-annotGupta SK et al (2014) Antimicrob Agents Chemother 58:212-20.

ARG-ANNOT

•local BLAST program in Bio-Edit, not Web interface

•quite easy to set up, but need re-download as database updated

•detects existing and putative resistance genes by homology

•output can be confusing – does provide methods to simplify

http://www.mediterranee-infection.com/article.php?laref=282&titer=arg-annot





Score E

Sequences producing significant alignments: (bits) Value

(Bla)CMY-6:AJ011293:1-1146:1146 2272 0.0 100%

Then lists another 79 blaCMY-2 variants

(AGly)Aac3-IId:EU022314:1-861:861 1699 0.0 99%

(AGly)Aac3-IIc:X54723:819-1679:861 1501 0.0 96%

(AGly)Aac3-IIa:X51534:91-951:861 1501 0.0 96%

(AGly)Aac3-IIe:EU022315:1-861:861 1485 0.0 96%

(AGly)RmtC:AB194779:6903-7748:846 1677 0.0 100%

(Sul)SulI:AF071413:6700-7539:840 1665 0.0 100%

(Bla)NDM-1:JQ080305:1-813:813 1612 0.0 100%

(Bla)NDM-6:JN967644:1-813:813 1604 0.0 99%

(Bla)NDM-4:JQ348841:1-813:813 1604 0.0 99%

(Bla)NDM-3:JQ734687:1-813:813 1604 0.0 99%

(Bla)NDM-2:JN112341:1-813:813 1604 0.0 99%

(Bla)NDM-8:AB744718:1-813:813 1596 0.0 99%

(Bla)NDM-7:JX412225:1-813:813 1596 0.0 99%

(Bla)NDM-5:JN104597:1-813:813 1596 0.0 99%

(AGly)AphA6:JF949760:695-1474:771 1443 0.0 98%

(AGly)Aac6-Ib:M21682:380-985:606 1108 0.0 99%

(AGly)AacA4:AF416297:2738-3304:567 1100 0.0

(AGly)Aac3-Ib-Aac6-Ib:AF355189:1435-2439:1005 1074 0.0

(AGly/Flqn)Aac6Ib-cr:EF636461:1124-1642:519 1013 0.0

ARG-ANNOT

Comprehensive Antibiotic Research Database

http://arpcard.mcmaster.caMcArthur et al (2013) Antimicrob Agents Chemother 57:3348-57.

CARD

•more comprehensive set of information about more than R genes, ontology

•RGI - output complex

http://arpcard.mcmaster.ca/

http://www.fibim.unisi.it/REDDB/Default.asp

RED-DB

•database – does group related genes, e.g. blaCTX-M groups

http://www.fibim.unisi.it/REDDB/Default.asp

plasmid

chromosome

R

R gene

“mobile” R genes

plasmid

chromosome

RR

R gene


plasmid

chromosome

RR

RR

R gene


R

R

transposoninsertion sequenceintegron/gene cassette

conjugative plasmidmobilisable plasmidintegrative conjugative element (ICE)

two types of mobile element

FEMS Microbiology ReviewsVolume 35, Issue 5, pages 820-855, 16 JUN 2011 DOI: 10.1111/j.1574-6976.2011.00277.xhttp://onlinelibrary.wiley.com/doi/10.1111/j.1574-6976.2011.00277.x/full#f1

http://onlinelibrary.wiley.com/doi/10.1111/fmr.2011.35.issue-5/issuetoc

http://onlinelibrary.wiley.com/doi/10.1111/j.1574-6976.2011.00277.x/full




resistance plasmids

replication

maintenance

stability

mobilisation/

conjugation

multi-resistance

region

(modular)up to ~200 kb

Partridge SR & Iredell JR AAC 2012;56:6065-7

problems with short read data

2626 Ecp126 262626

820 bp

long repeats


2626 Ecp126 262626

820 bp

long repeats

duplicated resistance genes

lacY repF blaSHV-11deoRygbJ K L M

K. pneumoniae chromosome


2626 Ecp126 262626

820 bp

long repeats


26 26


blaSHV-12 ESBL


plasmid**


2626 Ecp126 262626

820 bp

long repeats


26 26


blaSHV-12 ESBL


plasmid**

contig with blaSHV-12

https://www-is.biotoul.fr Siguier P et al (2006) Nucleic Acids Res. 34:D32-6.

http://www-genome.biotoul.fr Varani AM et al (2011) Genome Biol 12:R30

ISfinder/ISsaga

https://www-is.biotoul.fr/



http://www-genome.biotoul.fr/



Attacca/RAC

with Dr Guy Tsafnat, Centre for Health Informatics, UNSW

Attacca/RAC

•database

•annotates genes + mobile elements

•currently single sequence for

registered users (but can do whole of

GenBank, raw sequence data)

•currently only gene cassettes +

integrons publicly available, but

expanded version released soon

•shows gene positions on sequence

•draws diagrams

Summary/Conclusions

•inferring resistance from genomic data can be complicated

•resources available to detect resistance genes, but have some problems and

appropriate expertise is generally still needed to interpret results

•phenotypically important minor variations may be missed

•may have to consider both mobile R genes and chromosomal genes for full

picture

•short-read data difficult to use for epidemiological tracking of multi-resistance, as

long repeats confound assembly needed to understand links between R genes

inferring antibiotic resistance from genomic data

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