Find Symposium Daniela M. Cirillo

Emerging Bacterial Pathogens Unit (EBPU), San Raffaele Scientific Institute, Milan, Italy

“Planning a future with expanded molecular DST”

• Where we come from …

– Needs for sensitivity tests

– Available tests

– Current knowledge on molecular tests

– Need for large data bases

• …and where we would like to go

– Molecular tests for key drugs with an impact on patient management

– Automated molecular prediction for response to therapy

Outline

• fewer than 5% of newly diagnosed or previously treated patients are tested for drug resistance.

• only 19% of the estimated MDR-TB burden are reported globally

• Role of DST: • early detection of DR-TB for:

• Shift of treatment • Infection control • Individualized regimens

Where we come from:

MDR-TB continues to grow globally: role of DST

Large scale implementation by

• Costly for infrastructure, equipments,maintenance ,staff and training

• MDRTB : 3-6 weeks; XDRTB : 6-9 weeks

• Reproducibility and accuracy of results are drugs dependent

Van Deun A. et al 2011. IJTLD 15(1):116-124

Correlation of sensitivity test results and clinical outcome is difficult to evaluate and we have very limited or no evidence for Pyr,E, and 2nd line drugs other than INJ and FQs on MDR cases

Phenotypic tests

1. Semi-Automated,

2. Fast detection of MDR-TB

Good infrastructure required

1. Automated,

2. User friendly,

3. Low biosafety requirements,

4. Fast detection of Rif Resistant TB

2008

2010

Molecular tests

Mechanism of

resistance

One gene/ one “hot spot”

covering the majority RIF

Few genes/single mutations:INH

One gene/ NO hot spot: PYR

One gene/ single mutation

correlation questioned: E

Few genes/ hot spot: FQ

Few genes /single mutations/

crossres unclear :INJ

Few genes single mutations

correlation to R unproven

Knowledge of DR mechanisms and molecular tests

Bactericidal antibiotic that inhibits the bacterial DNA-dependent RNA polymerase.

Target: β-subunit of the RNA polymerase (encoded by rpoB), blocking elongation of the RNA chain.

Mutations in a “hot-spot” region of 81 bp of rpoB gene (Rifampin resistance-determining region) → RIF resistance (> 95%)

Cod. 526 and 531: high level resistance to rifampicin, rifabutin e rifapentin Cod. 516 and 522: associated to rifabutin sensitivity

Mutations resulting in a sensitive DST

Absence of WT associated to failure despite a sensitive DST

“Low hanging fruit”: 1) Rifampicin testing

Mutations in KatG gene prevent INH activation (cod. 315, 60-90%) Mutations in the direct target inhA (inhA belongs to the family of short-chain dehydrogenases/reductases. It is essential in MTB)

Mutations in the promoter of inhA gene leading to drug tritration (direct target over-production)

Rattan A et al. EID 1998

KatG cod. 315 and -8 /-15 inhA promoter region are included in current diagnostic tests

targeting mycolic acid biosynthesis

2) Isoniazid

Kohanski M et al., Nature Reviews 2010

Interference with changes in DNA supercoiling by binding to topoisomerase II (DNA gyrase subunits A and B, encoded by gyrA and gyrB genes).

Ofloxacin Levofloxacin Moxifloxacin Ciprofloxacin Gatifloxacin

Full cross-resistance is commonly assumed among fluoroquinolones mutated in gyrA hot spot

Amino acid substitutions in gyrA-gyrB → resistance

However, analysis of different mutations in gyrA and gyrB has shown discordant phenotypic results among the fluoroquinolones

Fluoroquinolones

Fluoroquinolones

224 FQ-R + 297 FQ-S sequenced for gyrA and gyrB genes

FQ-R most frequent MUT:

gyrA cod. D94 41.1%; A90V, 24.2% gyrB D510D, 4.8%

Targeting most frequent MUT R detection = 70% Targeting gyrA+gyrB all gene R detection > 85%

gyrA MUT E21Q+G668D associated to FQ-R only in combination with additional mutations FQ-S only 1 case non-MDR strain harbouring S91A >mic for OFL

Candidate genes evaluated (3 genes) DNA gyrase: gyrA, gyrB

transcription factor: carD

Relevant genes: gyrA, gyrB

Fluoroquinolones

Aminoglycosides: binding to the 30S subunit of the ribosome and misincorporation of amino acids into elongating peptides (streptomycin, kanamycin, amikacin)

Mutations in the rrs gene coding for 16S rRNA → AGs resistance Mutations in the promoter region of eis → kanamycin resistance Mutations in rpsL gene (ribosomal S12 protein) → streptomycin resistance

Kohanski M et al., Nature Reviews 2010

Eis acetylates multiple amines of many AGs. Upregulation of the eis gene (mutations in the promoter region) confers resistance to Kanamycin

Polypeptides: inhibition of the translocation of peptidyl tRNA and block of initiation of protein synthesis (capreomycin, viomycin)

Mutations in the rrs gene coding for 16S rRNA → resistance Mutations in the tlyA gene coding for a 2-O-methyltransferase→ polypeptides resistance?

Multiple genes: second-line injectable drugs

Second-line injectable drugs

70% Beijing lineage

228 AG-R +192 AG-S sequenced for rrs and eis genes

AG-R most frequent MUT:

rrs a1401g, 21.9% eis g-14a, 36.8%; c-14t, 17.1%

Targeting most frequent MUT R detection = 75% Targeting rrs+eis all gene R detection > 85%

tlyA CAP-R only; marginal role gidB Further studies needed. Variations in gidB appear to be phylogenetically restricted rather than being involved in drug resistance development

(AG-S 100% WT)

Candidate genes evaluated (18 genes) rRNA: rrs (16S)

rRNA metyltransferases: ksgA, tsnR, gidB, tlyA

transcription factor: whiB7

N-acetyltransferases: eis, Rv0262c, Rv0428c, Rv0730, Rv802c, Rv0919, Rv2170, Rv2775, Rv2851c, Rv2867c, Rv3027c, Rv3225c

Relevant genes: rrs, eis, gidB, tlyA

Beijing clones resistant to Kan (eis mutation)

Role of tlyA?

eisWT eisMUT

Second-line injectables

Evaluation of Genetic Mutations Associated with Mycobacterium tuberculosis Resistance to Amikacin,Kanamycin

and Capreomycin: A Systematic Review Sophia B. Georghiou et al PlosOne 2012

Need for large data bases combining clinical, phenotypic and genotypic information

• Interferes in the biosynthesis of cell wall arabinogalactan

• Active against multiplying bacilli

• Poor performance of MGIT phenotypic test

• 50% of mutations occurs in codon 306 of embB, component of a 10 kbp operon encoding for mycobacterial arabinosyl transferase

• Compared to MIC mutation in embB Codon 306 detected by MTBDRsl has a specificity of 96.2% and sensitivity of 69.7%, and the PPV of 97.7% in clinical isolates

Plinke et al AAC 2006, Miotto et al ERJ 2012

Van Deun A. et al 2011. IJTLD 15(1):116-124

Single mutation, correlation questioned: ethambutol

1. Interspersed mutations in pncA gene (encoding the PZase enzyme) 72-98%

2. Failure of PZA uptake by resistant strains

3. Mutations in the direct target (preliminary data)

Modified from Zhang Y et al. IJTLD 2003, 7(1):6-21

3.

2.

1.

Target for molecular tests

Potential target for molecular tests

Pro-drug A. Membrane transport systems B. PZase activity C. Acid pH D. In acid conditions, POA is converted to

HPOA that kills the bacterial cell by reducing membrane potential and affecting membrane transport

E. Sept 2011 Trans-translation inhibition by direct targeting rpsA gene (30S ribosomal protein S1)

A.

B.

C.

D. E.

Active compound

One gene (?), scattered mutations, questionable DST: pyrazinamide

808 strains

696 clinical isolates

530 MDR

352 PZA-R

178 PZA-S

84%pncAWT

16%mut

166 non-MDR

60 PZA-R 106 PZA-S

112 spontaneous

mutants

112 PZA-R

77% pncA MUT 23% pncA WT

70% of mutations

35% PZAse neg

Retesting with a reduced inoculum

PZA database

Implementation of R and INH testing

Fast testing for “key” drugs: FQ,INJ

Intention to test intention to use the results for treatment readjustment or infection control purposes

Where we want to go: universal access to DST by 2015

A low density microarray may accommodate a larger number of mutations for testing simultaneously several genes with the possibility to accommodate determinants for

NEW drugs

Major bottlenecks for an expanded molecular DST

• SAMPLES Sample processing (from collection, processing, concentration to NAs extraction)

• Selective detection of metabolically viable bacteria (treatment monitoring)

• STRAINS Systematic molecular databases analysis and correlation of SNPs/ mutations/phylogenetic markers with clinical data

• Molecular testing for Rifampin has fully shown the potential (and limitation) of molecular approaches.

• The Xpert format has shown: – Sample preparation and volume are crucial factors

– Full Automation is highly “appreciated” by the lab staff

– Training of clinicians on interpretation and clinical application of results is crucial for success

• Phenotypic tests are an imperfect “gold standard” for some drugs. Mutations should be considered for patients management

• Implementation of tests for FQs and Inj should be performed in a near future (existing and/or novel testing format)

• Existing technology allows screening for multiple determinants of drug resistance in few hours (fast DST on strains)

Conclusions

• What “bacteria are” is written on their genome, coding and NON-CODING

• New generation of test based on full genome analysis may change our approach to DST by the integration of different data (SNPs, phylogenetic markers, compensatory mutations, regulatory mechanisms) providing a global approach at strain level

• Improving capacity to integrate and “interpret” genomic sequences into information able to predict the response to therapy may open a completely new scenario for individualized treatment

And the Future…

Emanuele Borroni Andrea M. Cabibbe Irene Festoso Paola Mantegani Paolo Miotto Luca Norbis Fulvio Salvo Elisa Tagliani Enrico Tortoli Ilaria Valente Diego Zallocco

Emerging Bacterial Pathogens Unit San Raffaele Scientific Institute

Acknowledgments

TB PAN-NET EU FP7 Consortium TM-REST EU FP7 Consortium TB-Child EDCTP Consortium