Resistance to MTB infection: Host target(s) for therapy?

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1 Resistance to MTB infection: Host target(s) for therapy? W. Henry Boom, M.D. TB Research Unit Case Western Reserve University & Univ. Hospitals Case Med. Ctr. NIAID-DMID: -AI70022

2 Resistance to MTB infection? Anecdotes: TST- long-term HCW in TB sanatoria In TB endemic settings (high FOI) +/- 10% of adults remain TST- In TB endemic settings TST = IGRA: no cutaneous homing receptor def. Absolute vs. threshold resistance Genetics of TST- in families (E. Schurr et al. JEM 09)

3 TBRU s TB Household Contact Study Kampala, Uganda Adult (> 18yo) Pulm. TB (sputum culture +) Evaluate household contacts (HIV, TST/IGRA, TB disease) F/U TST q3-6 mo. for TST- at baseline Follow for 2 yrs. Follow for TB

4 Ugandan TB Household Contacts TB Index Cases = 975 Cult+, HIV+/HIV- Contacts (HHC) = 2757 HHC >= 15 years = 1280 (13% HIV+) HHC < 15 years = 1477 (3.4% HIV+) 1114 HIV- HHC Baseline TST+ = 74.6% Baseline TST- = 23% 1429 HIV- HHC Baseline TST+ = 60.1% Baseline TST- = 38.1%

5 Outcomes for TST- HHC

6 MTB infection vs. resistance among TST-, HIV- HHC by age Age group in yrs. N=517 Converters overall N=294 Converters 3 6 mo. N=259 PTST mo. N= (N=65) 53.8% 43.1% 66.2% 2 5 (N=99) 49.5% 42.2% 50.5% 5 15 (N=187) 48.7% 41.7% 51.3% 15+ (N=166) 71.7% 66.9% 28.3%

7 Risk score for MTB exposure Mandalakas et al (2012) Int J Tuberc Lung Dis Add risk factors to obtain a score >5 = High risk exposure (0 10) Age 15+ Score PTST/R LTBI STR Similar exposure risk (P=0.072) >75% RSTR with high exposure

8 CONCLUSIONS 5-10% (7% in our study) of adult TST- TB HHC remain MTB uninfected for up to 2 yrs. No epidemiologic explanation to distinguish adult resisters from those acquiring MTB infection (CONV/LTBI) Age a factor, i.e. cumulative experience matters Absolute ( Elite RSTR?) vs. relative resistance to MTB infection?

9 Biological evidence to distinguish MTB infection from resistance Two approaches: Follow-up of genome-wide scan that identified regions linked with TB and PTST- PTST-: chr 2 and 5* TB: chr 7 and 20 (chr 20 previously identified by (C. Stein, CWRU) mrna expression profiling of MTB infected macrophages from PTST- (T. Hawn, UW) *Cobat et al. 11p14, 5p15 Cape Town, JEM 2009

10 Approach Candidate gene study Genes in TNF, TLR/NLR, and IFN /IL12 pathways Haplotype tagging SNPs (any of 3 African HapMap) 546 SNPs Fine mapping of regions on chromosomes 2 and 5 linked to PTST- Evenly spaced markers (every 10 kb) Genotyped together with 10,000 single nucleotide polymorphism (SNP) panel using Illumina iselect

11 Genetic association with PTST- (at least 2 SNPs with p < 0.05) Gene Best p- value Gene Best p- value IL SLC6A IL12RB STAT NOD TIRAP 0.01 NOD TOLLIP 0.02 Genes with 1 SNP associated: IL6, SLC11A1, TLR2, TLR4

12 Transcriptome Project: Experimental Design RSTR 13 Archived PBMC CD14+ isolation Monocytes Infect live H37Rv (MOI 10:1) 6 hrs RNA Extraction QC LTBI 22 Microarray Analysis Phenotypes A. Resistant to infection (RSTR) (persistent TST negative x 2 yrs) B. Susceptible to infection (LTBI) Illumina Bead Array HT-12 48,000+ probes covering > 25,000 genes At least two probes per gene beads per probe 100,000+ probes per bead Dynamic Range:

13 Analyses 1. Single Gene & Clustering Analysis No significant difference between RSTR and LTBI However 2. Network Analysis A. Linear Neural Network B. GSEA

14 Procedure: 1. Construct stable set of probes that show differential expression between LTBI and RSTR samples Linear Neural Network 2. Train linear neural network to differentiate between the two classes based on expression data for set of probes 3. Apply trained neural network to new data or omit data for cross-validation

15 Leave one out cross-validation RSTR LTBI 1. 80% of LTBI samples are predicted to be LTBI 2. >80% of RSTR samples predicted to be RSTR 3. Correctly classify 22/28 samples in LOOCV

16 Linear Neural Network Analysis with Leave One Out Cross Validation LTBI RSTR 7 genes distinguish RSTR from LTBI Data: Ethan Thompson

17 Gene Set Enrichment Analysis: Pathways with Possible Drug Targets TST_POS: 24 gene sets significant at FDR < 0.25 Major theme: oxidative phosphorylation and mitochondrial function (11 overlapping gene sets) Includes PPAR TST_NEG: 70 gene sets significant at FDR < 0.25 Major themes T-cell signaling (8 overlapping gene sets) Macrophage function Top hit is imatinib

18 FDR = Top Hit for RSTR Gene Name GNS glucosamine (N-acetyl)-6-sulfatase (Sanfilippo disease IIID) LAMP1 lysosomal-associated membrane protein 1 CTSB cathepsin B ENG endoglin (Osler-Rendu-Weber syndrome 1) LILRB3 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 3 ASAH1 N-acylsphingosine amidohydrolase (acid ceramidase) 1 CD163 CD163 molecule GBA glucosidase, beta; acid (includes glucosylceramidase) C5AR1 complement component 5a receptor 1 CTSD GUSB ACP5 CD68 FUCA1 cathepsin D (lysosomal aspartyl peptidase) glucuronidase, beta acid phosphatase 5, tartrate resistant CD68 molecule fucosidase, alpha-l- 1, tissue

19 Imatinib, ABL, & TB Napier & Kalman (Cell Host Microbe 2011) -Imatinib inhibits MTB entry & replication in vitro -Inhibits MTB replication in vivo Bruns & Stenger (J. Immunol 2012) -Increases expression of vacuolar ATPase -Lowers ph of phagolysosome Unc119 CRK PI3K Akt NF-kB & Cytokine Response TB ABL1 N-WASP ARP2/3 FcgR, Mannose Receptor, Complement Receptor, TLRs ABL2 Actin Polymerization Membrane Trafficking Phagosome- Lysosome Fusion Rac/CDC42

20 Summary: PTST-/RSTR vs. TST+/LTBI 1. Genetic studies suggest distinct regulation of resistance to MTB infection vs. development of TB 2. Transcriptomics of macrophages more promising 3. Linear Neural Network with LOOCV 1. Correctly classified 22/ probes identified in fixed probe analysis 4. GSEA 1. Several pathways specific to PTST- 2. Imatinib (ABL/Tyr Kinase) was a top hit (drug/pathway hypotheses) 5. Have not excluded role for innate (and even low levels of adaptive) T cell and other cellular responses

21 Studies of resistance to MTB infection may identify targets for host-directed therapy

22 Acknowledgements Makerere Univ. & Mulago Hosp. (Uganda): M Joloba, H Mayanja, E Mupere Univ. of Washington: T Hawn, C Seshadri CWRU: C Stein

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24 Leading Edge Analysis (FDR 10%) PTST-