Oral Abstract Presentation

F Valafar 1 , J Torres 1 , TC Victor 5 , TC Rodwell 2 , RS Garfein 2 , C Rodrigues 3 ,
MT Gler 4 , V Crudu 6 , T Catanzaro 2
[email protected]
Biomedical Informatics Research Center
(BMIRC)
1 Biomedical
Informatics Research Center (BMIRC)
Office: GMCS 625
San Diego State University
http://informatics.sdsu.edu/
http://informatics.sdsu.edu/
Diagnosis of isoniazid Resistance
1. MDR: Resistance to rifampicin (RMP) and isoniazid (INH)
2. INHR markers: katG: S315T, inhA: -15C-T or -17G-T
3. Frequently markers for RMP R is used as a marker for MDR
4. Two projects collaborating for complete elucidation of
mechanism of resistance to INH:
1. NIH- U01AI082229: Global Consortium for Drug-resistant TB
Diagnostics (GCDD) PI: Tony Catanzaro
2. NIH- R01AI105185: Evolutionary and Functional
Significance of Novel Mutations in MDR-XDR TB, PI:
Faramarz Valafar
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Methods: Dataset & DST
INHR INHS
GCDD Archive Set
315
31
Supplementary Set
16
4
Total
331
35
DST or Sequencing issues
32
2
Isolates with valid DST and WGS
299
33
Drug Susceptibility Testing: Upon receiving
isolates at UCSD, each isolate was subjected to
DST for INH on the Mycobacterial Growth
Indicator Tube (MGIT) 960 platform, analyzed
by EpiCenter software (BD Diagnostic Systems,
Franklin Lakes, NJ, USA), using standard
manufacturer protocol and WHO critical
concentration (CC) of 0.1mg/L for INH.
Sequencing: In this study we used three
sequencing techniques to detect and
validate polymorphisms. These are:
1. Sanger sequencing,
2. pyrosequencing, and
3. third generation whole genome
sequencing. (Pacific Biosciences RS)
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Table 1: Classification of Clinical Isolates Collected for Trademark Mutations within
the katG gene (2,447bp) and/or inhA promoter region (289bp).
Observed Mutation (s)
S315T (AGC-ACC)
-15C-T
-17G-T
Other previously characterized katG
mutations
katG/inhA promoter + Novel katG SNP
Novel katG
No Mutation
Total
INHR w/ mutation
INHS w/ Mutation
235
30
3
78.6%
10.0%
1.0%
0
1
0
0.0%
3.0%
0.0%
1
0.3%
0
0.0%
2.7%
5.0%
2.3%
0
0
32
33
0.0%
0.0%
97.0%
8
15
7
299
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Results: Detected Mutations in
299 INHR and 33 INHS Isolates
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Novel Mutations
Novel Mutations + R463L
9
Novel Mutations at Previously Reported Positions
5
Novel mutations + S315T
3
Novel Mutations + -15 C-T
5
Total
22
- Within the subset of unexplained INHR isolates, 14 novel “standalone” katG SNPs were
identified in 15 INHR isolates.
- Nine of the 14 novel katG mutations occurred alongside R463L (CGG-CTG)
polymorphism. This 463 substitution has been previously shown to have no effect on
antibiotic resistance but instead is commonly used to categorize clinical isolates into
members of genetic groups 1 or 2, by a Arg463 substitution, or genetic group 3, a Leu
463 substitution.
- Furthermore, five of the novel substitutions occur at codons that have previously been
reported as featuring a polymorphism in INHR isolates. Here, we report novel amino
acids not previously observed in these codons.
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Results: Novel Mutations
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In addition to the 14 novel katG mutations, 8 katG amino acid substitutions
were observed either in combination with katG S315T or -15C-T inhA
promoter mutation. Studies have shown that clinical isolates that hold both a 15C-T inhA promoter and a katG mutation hold higher MIC’s than either a
single S315T katG or inhA promoter mutation (19). Interestingly, the subset of
isolates which held a -15C-T inhA promoter mutation, known to convey lowlevel resistance, held no S315T katG mutations. Instead these isolates held one
of three novel katG SNPs, suggesting possibly higher levels of INH resistance.
Novel Mutations
Novel Mutations + R463L
9
Novel Mutations at Previously Reported
Positions
5
Novel mutations + S315T
3
Novel Mutations + -15 C-T
5
Total
22
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Novel Mutations
Results: Novel Mutations
Distribu
on of Novel
and Known katG
SNPs
Lastly, eleven previously
characterized
polymorphisms
were
also observed within the
collected clinical isolates. To further investigate if the novel katG mutations observed
are spatially aggregating with known INH-associated mutations. All 22 unique katG
gene mutations were plotted against the list of observed INH-associated mutations. A
subset of novel stand-alone katG polymorphism fall in close regional proximity to
known katG SNPs suggesting similar functions in INH resistance (Figure 1).
200
300
400
500
Codon
Previously characterized katG SNPs
Novel stand alone katG SNPs
Novel combina on katG SNPs
600
KatG 463
700
katG 315
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0
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1. This study is one of the first descriptive studies using whole genome sequencing on Pacific
Biosciences RS platform for characterization of INH R M. tuberculosis clinical isolates.
2. Here, we utilized PacDap (PacBio Data Analysis Protocol) pipeline to identify known and
novel katG gene polymorphisms.
3. Diagnostics: From our collection of 299 INH R and 33 INH S, 91% of INH resistance could be
explained by katG S315T, inhA promoter -15C-T, -17G-T mutations, or a combination thereof.
4. Diagnostics: Country wise observations of S315T mutation highlight regional differences and
the importance of considering geographic information when investigating cause of resistance.
Because of the geographic diversity of the dataset, the discovery of 14 “standalone” novel
katG mutations is substantial; we were able to explain 99% of INH resistance by a katG or
inhA promoter mutations.
5. With our whole genome sequencing approach we were also able to identify polymorphisms in
codon 463 allowing categorization of our clinical isolates into distinct lineage and
epidemiological genetic groups. Spatial distribution analysis suggested that a subset of our
novel katG polymorphisms lay in close proximity to other previously characterized INH R
associated mutations. 22 novel katG mutations were found as a result of this work. The
remaining unexplained 1% of INH R isolates are being evaluated for other novel resistance
conferring SNPs on a whole genome scale.
6. Diagnostics: Due to the evolving nature of resistance, and the discovery of more and more
mutations associated with resistance, point-mutation diagnostic platforms will be increasingly
at a disadvantage in comparison with those that take a broader look at an entire gene or the
whole genome.
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Conclusions
SDSU-BMIRC
•
•
•
•
•
•
•
Anu Monica Amallraja
Sarah Ramirez Busby
Donald Catanzaro
Ashu Chawla
Surabhi Choudhary
Carmela Chen
Dennis Didulo
Clinical Sites:
• Afif Elghraoui
• Amy Goodmanson
 Janice Caoili
• Min Soo Kim
 Henry Evasco
• Seema Patel
 Nazir Ismail
• Victoria Zadarozhny
 Kanchan Ajbani
UCSD
• Lynn Jackson
• Janice Kaping
Funding: This work has been funded by NIH
U01AI082229 and R01AI105185
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Acknowledgements

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