The Phantom Menace


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Analyzing dysregulation of intracellular signaling pathways in HTLV-1 transformed cells using Massively Parallel Signature Sequencing (MPSS)

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The Phantom Menace

  1. 1. HTLV-1 induced Adult T-cell Leukemia (ATL): “The Phantom Menace” Harshawardhan Bal, Ph.D. Department of Cancer Immunology and AIDS Dana-Farber Cancer Institute
  2. 2. Adult T-cell LeukemiaATL identified as a distinct clinical entity in 1977ATL is a fatal malignancy of mature CD4+ T-cellsClonal expansion of CD4+CD25+ T-cellsImpaired cell-mediated immunity, opportunistic infections, lymphoid organ infiltration
  3. 3. Human T-cell Lymphotropic Virus Type ICausative agent of ATL identified in 1980The first retrovirus shown to be associated with human diseaseCellular receptor is the major glucose transporter GLUT1
  4. 4. Organization of the HTLV-1 Genome ORFs involved in viral infectivity, maintenance of high viral loads, host cell activation p12, p27, p13, and p30 tax, rexRegulatory genes: transcriptional/post-transcriptional regulators of viral gene expression HTLV-1 transactivator Tax, the major viral oncoprotein
  5. 5. Pleiotropic Effects of HTLV-1 TaxTax inhibits apoptosis in infected T-cellsTax causes genomic instabilityTax affects host DNA repair mechanismsTax disregulates the cell cycle machineryTax causes neoplastic transformation
  6. 6. ATL Clinical Course (“The Dark Side”)ATL cells lose HTLV-1 gene expression Selective methylation of 5’ LTR and silencing of viral gene Txp Nonsense or missense mutation of the tax gene in fresh ATL cellsNo cell-free infectious HTLV-I particles shedATL develops after a latency period of > 30 years Chronic CTL response indicates HTLV-1 genes are expressed persistently Aggressive clinical course, MST of 3-6 monthsNo treatment currently exists for ATLInfects 15-25 million people worldwide including Southeastern United States
  7. 7. Massively Parallel Signature Sequencing (MPSS)Global transcription profiling platform Comprehensive and quantitative measurement, sequences > 1 M beads Provides 17-20 mer “signatures” of sampled transcripts Transcript abundance as “transcripts per million (TPM)” Very high dynamic range - single mRNA copy per cell 3’ UTR signatures identify homologous members of genes Not limited to a pre-determined set of genes Powerful discovery research tool
  8. 8. Massively Parallel Signature Sequencing (MPSS)
  9. 9. Massively Parallel Signature Sequencing (MPSS) Sequence
  10. 10. MPSS Data FormatSignature p-value Control TPM/SD Exp TPM/SD Class Annotation Class 1: Transcripts with poly (A) signal + poly (A) tail Class 2: Transcripts with poly (A) signal Class 3: Transcripts with poly (A) tail
  11. 11. Activated CD4+ T-cells vs Acutely HTLV-1 Transformed Cells PHA/IL-2 activated CD4+ T-cells Immortalized (IL-2 dependent) MPSS comparison Transformed (IL-2 independent)Abundantlyexpress Tax Acutely transformed (B1 and C5 cells) Activated CD4+ T-cells vs B1 Activated CD4+ T-cells vs C5
  12. 12. Mining MPSS DataSignature p-value Control TPM/SD Exp TPM/SD Class Annotation
  13. 13. Mining MPSS DataWhich signatures/transcripts are the most reliable?Which signatures/transcripts are differentially expressed?What are the molecular functions of these differentially expressed signatures?Which signatures/transcripts interact with each other?What are the common pathways that they participate in?Which pathway members are simultaneously up- or downregulated?How do these transcripts contribute to leukemogenesis?
  14. 14. Pipeline for Analysis of MPSS DataSignatures with UniGene Ids (Activated vs Transformed) Filter signatures by p-value (< 0.001) AND Class (1-3) Filter signatures by TPMUpregulated signatures Downregulated signatures Classification by molecular function/biological process Apoptosis Cell cycle Oncogenesis Anti-apoptosis DNA repair
  15. 15. Querying MPSS Data for Differential Expression
  16. 16. Viewing MPSS Data Graphically [ Upregulated ] [ Downregulated ](Exp TPM, Control TPM)
  17. 17. Analysis of Deregulation (Clustering with Spotfire)1.37E4 0 c472tpm b1tpm c5tpm Control HTLV-B1 HTLV-C5 0 500 1000 TPM
  18. 18. Data Integration, Text Mining and Analysis Perl PubMed Data integration MPSS UniGene ……… ……… ……… LocusLink ……… OMIM GOTMProtein-protein interactionsKnown disease correlationsRelation to apoptosis, cell cycle, DNA damage, …Previously reported interactions with HTLV-1 genes
  19. 19. Detailed Gene SummariesExtract gene aliases (HUGO names), RefSeq summaries and associated GO terms
  20. 20. PubMed Literature SearchExtract previously reported findings (if any) from PubMed using keywords
  21. 21. Text Mining Abstracts for Interactors & Related GenesGenes reported in PubMed literature that Genes that interact with XRCC5 and are known to interact with XRCC5 are simultaneously detected by MPSS
  22. 22. Upregulation of Anti-apoptotic Genes
  23. 23. Upregulation of NF-kB Subunits/Inducing Kinases
  24. 24. Upregulation of BCL2 Family/NF-kB: Role of Akt1NF-kB stimulates the expression of Akt1 Akt1 regulates T-cell survival via activation of NF-kBAkt1 regulates the expression of anti-apoptotic BIRC3 and BCL2Akt1 phosphorylates BAD and releases pro-apoptotic bcl-xLAkt1 induces Txp of c-FLIP which inhibits TNFR-induced apoptosis Hypothesis: Akt1 is constitutively activated in ATL
  25. 25. Downregulation of TNFR Superfamily
  26. 26. Deregulation of TNFR Superfamily: Role of GITR
  27. 27. Deregulation of TNFR Superfamily: Role of GITR A T helper cell activation marker Highly expressed on CD3+CD4+CD25+ regulatory T cells T regs profoundly suppress host immune responses and antitumor immunity GITR involved in the negative regulation of T-cell activation and T-cell apoptosisHypothesis: Inhibition of GITR-mediated signaling may abolish T-cell survival in ATL
  28. 28. Deregulation of TNFR Superfamily: Role of CD27/CD27L CD27/CD70 interactions are important for maturation/activation of B- & T-cells CD70-expressing glioma cells induce release of soluble CD27 from PBMCs CD70 mediates immune escape of human malignant gliomas Inverted profile observed in lymphoproliferative diseases such as CLL and NHLHypothesis: Overexpression of CD70 may be linked to enhanced T-cell survival in ATL
  29. 29. Deregulation of TNF Superfamily Associated Genes FADD CASP8TNFα TNFR1 TRADD RIPK1 CRADD CASP2
  30. 30. Survival Pathways in Acutely HTLV-1 Transformed Cells HTLV-1 Tax/? (PI3K) p p p bcl-xL +p +p BAD Akt? IKK NF-κB IκB NIK/COT + + + + + + bcl-xL BCL2 BIRC3 NF-κB BCL2Neoplastic c-FLIPtransformation bcl-xL CASP8 Cytochrome cGeneticalterationsDrugresistance FADD CRADD RIPK1 TRADD TNFR1/TNFR2/DR3 Apoptotic signals TNFα
  31. 31. MPSS Analysis of ATL: Insights & ConclusionsSurvival of HTLV-1 transformed T-cells due to activation of NF-kB and PI3K/Akt pathwaysand downregulation of TNF-mediated apoptotic pathwaysAkt1 and NF-kB may act in concert to form a perpetual potentiating loop and enhanceT-cell survivalDownstream effects of survival pathways likely mediated by anti-apoptotic BCL2 (BCL2,bcl-xL) and IAP family members (BIRC3)Putative Tax-independent mechanisms of NF-kB activation explained via Akt1Akt1 and BCL2/bcl-xL may explain drug resistance and genetic alterations in ATLElevated GITR may suppress T-cell immunity and antitumor responsesHigh CD70/low CD27 may help HTLV-1 evade immune response
  32. 32. Acknowledgements The Marasco Lab @ DFCI (2003-2004)Aki Murakami Jihua Cheng Aimee Tallarico Leslie MatthewsQuan Zhu Keiko Azuma Jianhua Sui Wayne Marasco Wei Wang & Tom Vasicek, Lynx Therapeutics (and George Lucas, Industrial Light & Magic)
  33. 33. Contact Harshawardhan Bal, M.Pharm., PhD Sponsored by PerlSource Informatics Visit us for online training in Perl for Bioinformatics Unix for Bioinformatics Java for BioinformaticsFoundation courses in Bioinformatics