MMS Presentation - April 25, 2012


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  • Proviral DNA is integrated in host chromosome is approximately 9.6 kb in length, which encodes for a number of HIV proteins. Important for today’s talk is that it encodes for the HIV regulatory protein Tat. The transcription, which is driven by RNA pol, occurs through the interaction of this enzyme with the viral promoter, or long terminal repeat. Contained in the 4.4 kb segment is the vpr region, tat, envelope, nef, and the LTR.
  • Basal HIV transcription occurs through the interaction of RNA pol II enzyme with various cellular transcription, principal being NF kappa-B factors. This basal transcription results in short and long transcipts of the HIV genome. Important for the talk today is the fact that these transcripts encode for Tat and it is the build up of this protein that leads to superactivation of HIV transcription.
  • Thatsuperactivation is depicted here, where you can see Tat interacting with the TAR element, allowing for increased processivity and elongation of the RNA pol II. This leads to between a 30 and 70X increase in transcription.
  • So, my PI and I have developed a hypothesis that states that genetic variation within HIV-1 LTR and Tat co-select due to selective pressures applied to HIV genome during replication, resulting in altered function of the LTR, Tat, or both. If anyone asks: reverse transcriptase is driving the SNP changes that we see in the genome.
  • SNPs associate with CD4 T cell count and viral load. Scatter plot of p-valuesUsing the preliminary data from 350 subjects with a total of 602 visits, the results of the best fit models demonstrated both gender (5x10-5)and time (days since first visit, p=0.0021) are significantly associated with CD4 counts. Age, cocaine use, and time are all significant (0.0024, 0.0041, 1x10-7, respectively) in affecting viral load while gender is not. Cocaine users have 2.74 times more viral load than non-users. In a preliminary SNP association study, SNPs identified above were associated with CD4 T cell count and viral load. We have found 7 SNPs significantly associated with CD4 and 3 with viral load at p=0.05, after adjusting for age, gender, cocaine use, and time (Fig. 6).
  • As an initial investigation of CD4 and viral load progression, the association between the SNPs and relative changes of CD4 and viral load at following visits compared to their baseline levels using the same models were tested (Fig. 6). 8 SNPs were found significant and 6 of them are associated with greater changes of CD4 and viral load. The possible associations between these SNPs and the relative change confirm our hypothesis that SNPs may affect disease progression and prompt examination of the next hypothesis regarding progression rate and SNPs.
  • What was interesting about this patient is that the 3T/5T variation existed as a predominant quasispecies within the patient when she had a good CD4 count and low viral load.
  • Even more interesting was that these variants existed prior to this patient declining in their ability to perform well on a mini-neurologic exam. This mini-neurologic exam tests a patient for psychomotor speed, memory recall, and constructional abilities. A perfect score on this test is a 12; any score below 10 is considered neurologically impaired. As you can see on the right table, as this patient came back for subsequent visits, her neurologic score declined. In addition to the neurologic score decline, the important point to notice in that in the previous slide, her CD4 count declined. While this patient can be viewed as a proof of concept for the original 3T/5T observation, the lab was interested in understanding the functionality of the LTR and Tat from this patient and if it potentially contributed to the increase in pathogenesis.
  • Current progress to date includes growing up cultures of bacteria into which the HIV genome has already been cloned from patient 107 blood samples. I have been able to grow up clones from all patient 107 visits, and isolate the 4.4KB fragment from the genome. However, this process has not been without its problems.
  • One such portion of the genome which is amplified is called the 4.4 kb fragment. This fragment spans from the VPR to the 3 prime LTR. As you can see, within this fragment, the Tat gene is contained. Important to note is that Tat is a protein that is made through two exons which are spliced together (keep this in mind, as it will be important in describing some of the techniques that I am employing to assist this project).
  • Tat has been divided into six different functional domains. The two domains that are of importance to this presentation are 2 and 6. While I am bringing these two domains to your attention, I performed sequence analysis on all domains to look for amino acid changes that were common to patients that demonstrated neurocognitive impairment. In regards to domain 2, we found that the cysteine rich region was conserved in all patients through all samples obtained. Domain 6, however, did show some changes that correlated with neurocognitive impairment. The two important changes that were seen in patients that did demonstrate neurocognitive impairment were residue changes in both positions 74 and 100, both located in exon II domain 6 of the protein, the domain that plays a role in viral infectivity, cell surface integrin binding and NF-kappa-B transactivation of replication. Interestingly enough, both of these changes correlated with brain derived Tat.
  • When you look at position 100 specifically, you will notice that during the second visit, labeled R01, this change was detected. During subsequent visits in patients 37, 41, and 107, neurocognitive assessments scores were suboptimal, indicating neurocognitive impairment. For patient 56, the change was detected during the first visit, with neurocognitive impairment detected in all subsequent visits.
  • MMS Presentation - April 25, 2012

    1. 1. Impact of Genetic Variation within the HIV-1 LTR and Tat on Transcription David Cunningham MMS Presentation Department of Microbiology and Immunology Center for Molecular Virology and Translational Neuroscience Drexel University College of MedicineDREXEL MEDICINE
    2. 2. HIV-1 virion structure and genomic organization Adapted from MEDICINE
    3. 3. Physical structure of the HIV-1 LTR 500 400 300 200 100 +1 100 200 U3 R U5 AP-1 NFAT-1 USF NF-B TATA CTF/NFI NRRE AP-1 AP-1 SP-50 cETS-1 Sp HIP116 AP-1 AP-1 100kD 38kD C/EBP C/EBP E box YY-1 c-fos c-fos GRE URS LEF-1 LBP-1 ATF/CREB C/EBP TDP-43MODULATORY REGION ENHANCER REGION CORE REGIONDREXEL MEDICINE
    4. 4. Regulation of HIV-1 LTR basal and stimulated transcription - 405 - 245 + 20 + 165 U3 R U5 Cell activation TNF-α, IL-1β, HDAC1 IL-6, IL-7, etc… p50 p50 CTD TAR NELF TAT DSIF Ac Ac p65 Ac Nuc-0 p50 Nuc-1 TFIID TATA C/EBP US2 AP3-L C/EBP DS3 C/EBP US1 NF-B ATF/CREB AP-1 SP1 Ac Ac HAT Ac Ac Modulatory region Core region Enhancer regionDREXEL MEDICINE
    5. 5. Regulation of HIV-1 Tat-mediated LTR activation - 405 - 245 + 20 + 165 U3 R U5 PCAF cdk9 CTD cycT1 TAR TAT Ac Ac p65 Ac Nuc-0 p50 Nuc-1 TFIID TATA AP3-L C/EBP DS3 C/EBP US2 C/EBP US1 NF-B ATF/CREB AP-1 SP1 Ac Ac Ac HAT Ac Modulatory region Core region Enhancer regionDREXEL MEDICINE
    6. 6. Hypothesis• Genetic variation within HIV-1 LTR and Tat co- select due to selective pressures applied to the HIV-1 genome during replication, resulting in altered function of the LTR, Tat, or both.
    7. 7. DREXELMED HIV/AIDS Genetic Analysis Cohort in the HAART era Ficoll-Pacque Qiagen DNEasy Whole Blood Plus gradient Tissue Kit Serum & PBMC separation Isolation of PCR amplify BSL-3 Facility genomic DNA proviral DNA Serum and cell banking Separate on Clinical and virus/host genomic data management agarose gel HIV-1 Sequence Database Sequence PCR product Analysis sequencing Gel extraction pGL3 Basic pCR4-TOPO Incubate with Functional PCR amplify/ Taq to add A analysis clone proviral DNA overhangDREXEL MEDICINE
    8. 8. Summary of DREXELMED HIV/AIDS Genetic Analysis Cohort ENROLLMENT UPDATE: March 20, 2012 Definitive LTR Visit1 Number sequences2 R003 486 400 1On average, patients are seen R01 271 195 every six months R02 185 122 R03 120 72 2Definitive LTR sequences as called R04 81 31 by Brian Moldover, R05 51 15 Ph.D. R06 32 4 3R00 has 1481 R07 14 definitive LTR clones R08 5 from 110 patients R09 1 TOTALS 1246 839DREXEL MEDICINE
    9. 9. Association analysis of SNPs with regard to CD4 count and log viral load Phenotype Position TF site Ref/Mut Mutant Frequency Effect p-valueCD4 244 Pet-1 A/G 56.0% 37.630 0.0233 Effect = change in CD4 293 USF G/ACT 11.5% -54.487 0.0366 count away from the 444 Oct 1 T/C 36.9% -65.098 0.0478 averageViral Load 108 COUP/AP1 A/CGT 37.3% 153.8% 0.0263 115 unk A/GT 18.9% 61.8% 0.0418 131 unk A/CGT 12.2% 176.1% 0.0409 Effect = % change in VL 168 unk G/ACT 16.2% 56.4% 0.0156 away from the average 385 Sp III (9) G/AC 8.2% 229.6% 0.0242 605 unk C/AT 8.9% 756.8% 0.0308adjusted for sex, age, race, days since baseline visit; unk = unknownDREXEL MEDICINE
    10. 10. Association analysis of HIV-1 LTR SNPs with regard to change in CD4 count and change Phenotype Position Location Ref/Mut Mutant Frequncy Effect p-value CD4 273 unk C/A 8.3% -42.705 0.0486 304 unk T/A 13.2% -38.809 0.0336 Effect = change in 314 unk G/ACT 6.8% -60.034 0.0162 381 Sp III (5) C/T 27.9% 31.887 0.0301 CD4 count between 385 Sp III (9) G/A 8.3% -57.781 0.0215 visits 398 unk T/CGA 21.4% -42.769 0.0122 Log (Viral) 108 COUP/Ap 1 A/CGT 37.3% 0.161 0.0157 USF Effect = % change 290 C/T 6.4% 0.319 0.0191 383 Sp III (7) T/C 6.0% 0.302 0.0425 in VL between visits adjusted for sex, age, race, days since baseline visit; unk=unknownDREXEL MEDICINE
    11. 11. Electrophoretic mobility shift assay (EMSA) 3. Run native PAGE2. Incubate DNA probe with protein (N.E.) DNA (probe) antibody TF 32P TGACTCA 32P1. Label double-stranded DNA (oligonucleotide with binding element) 32P TGACTCA 32P
    12. 12. Disease severity of patient 107 increases over the four visits Li and Aiamkitsumrit et al 2011DREXEL MEDICINE
    13. 13. Patient acquires neurocognitive impairment over time Patient Neurologic Neurologic Neurologic Visit score test complications A0107- depression, bipolar, normal MSK R00 schizophrenia A0107- modified depression, bipolar, 12 R01 Hopkins schizophrenia A0107- modified depression, bipolar, 8 R02 Hopkins schizophrenia A0107- modified depression, bipolar, 9.5 R03 Hopkins schizophrenia A0107-R01 A0107-R02 A0107-R03 Score: 2/2 Score: 0/2 Score: 0/2 Li and Aiamkitsumrit et al 2011DREXEL MEDICINE
    14. 14. Status of amplification and cloning of patient 107 tat genes Isolation of Patient Growth of Round 1 Round 2 pcDNA Sequence 4.4KB Visit Culture Amplification Amplification Hygro3.1 Confirmed Fragment R00 ✔ ✔ ✔ ✔ ? R01 ✔ ✔ ✔ ✔ ? R02 ✔ ✔ ? R03 ✔ ✔ ? R04 ✔ ✔ ? R05 ✔ ✔ ? R06 ✔ ✔ ?DREXEL MEDICINE
    15. 15. Cloning vector map
    16. 16. Majority of 4.4 kb insert lost during sample preparation GAG-POL ENV tm LTR pr rt in VPR su (gp 120) (gp 41) GAG VIF tat NEF LTR rev T-7 Primer M13R Primer 4.4kbDREXEL MEDICINE
    17. 17. Domains of Tat and important amino acid motifs Basic Cysteine-rich domain/TARAcidic domain domain Core binding EXON 1 EXON 2 domain/ARM 21 22 30 37 38 48 49 57 58 72 73 101 C C C CC C C LGISYG RKKRRQRRR RGD KKK E E E I II III IV V VI Basic region 86 87 Nuclear Transactivation/ localization CycT1 binding C/EBP Co-factor binding binding CBP/p300 Sp1 binding DNA-PK binding
    18. 18. DREXELMED Cohort Tat sequences show conserved cysteine residues and position 100 variationTat AA Position 100 analysisPt ID R00 R01 R02 R03 R04 R05 R06 Time (mnths) between 9 10 8 17 6 visits37 Neuro NA NA 3.5 ND 10 11 Tat V to H Time (mnths) between 9 10 9 5 18 5 visits41 Neuro NA NA 4.5 8 5.5 ND ND Tat V to F Time (mnths) between 23 visits51 Neuro norm 9 Tat conB Time (mnths) between 19 8 12 18 visits56 Neuro sub 6 9 9 Tat V to C Time (mnths) between 14 2 11 6 3 5 visits107 Neuro norm 12 8 9.5 10 11.5 10.5 Tat V to K V to K Time (mnths) between 36 visits119 Neuro norm 5 Tat conB Time (mnths) between visits131 Neuro 4 Tat V to I Time (mnths) between visits220 Neuro 11 Tat 3 conB
    19. 19. Brian Wigdahl, Ph.D., Professor & Chair Department of Microbiology & Immunology Drexel University College of MedicineEdward Archaempong, Ph.D. Sonia Navas-Martin, Ph.D. Adriano Ferrucci Liudmila MazaleuskayaJeffrey Jacobson, M.D. Michael Nonnemacher, Ph.D. Archana Gupta Saifur RahmanPooja Jain, Ph.D. Vanessa Pirrone, Ph.D. Bryan Irish Viraj SanghviSteve Jennings, Ph.D. Laura Steel, Ph.D. Shawn Keogan Sonia ShahZafar Khan, Ph.D. Nirzari Parikh, M.S. Christina Kollias Luz Jeanette SierraSandhya Kortagere, Ph.D. Shendra Passic, M.S. Jason Lamontage Melany SimmonsFred Krebs, Ph.D. Benjamas Aiamkitsumrit Luna Li Marianne StrazzaMichele Kutzler, Ph.D. Brandon Blakely Karissa Lozenski Ken ThompsonJulio Martin-Garcia, Ph.D. Sharon Bandstra Raphael Lukov Kamiliah WilliamsBrian Moldover, Ph.D. Betty Condran Sharron Manuel Adam WojnoOlimpia Meucci, M.D., Ph.D. Satinder Dahiya Nyree Martin NINDS NIMH NCI NIDA NIAID CONRADDREXEL MEDICINE