Enhanced RNA polymerase III-mediated crosstalk between DNA and RNA sensing pathways of HIV- in dendritic cells from elite controllers Enrique Martin-Gayo Ph.D
Contribution of dendritic cells to spontaneous immune control of HIV infection Which cellular mechanisms contribute to immune control of HIV- infection? -How do Dendritic cells contribute to immunological control of HIV- infection?
NK cells B cells Tfh CD4 T cells Tumors Pathogens Dendritic cells CD8 T cells Th7 Treg Th2 Th
Enhanced sensing of HIV- viral products in cdcs from elite controllers induces potent IFN responses and Ag presenting cell function 0 5 CD86 expression (MFI) 0 4 0 3 Med HIV Med HIV Med HIV Neg CP EC Increased IFN responses is associated With higher levels of maturation markersmin cdc from EC EC Neg CP Increased levels and coordination of ISG In response to HIV in cdc from EC Leads to enhanced antigen-presenting cell function upon exposure to HIV- in EC Martin-Gayo, et al, PLoS Path, 205
Accumulation of HIV- cdna in cdcs from elite controllers induces potent IFN responses and Ag presenting cell function IFN responses and function of DC from EC Depend on sensing of HIV RT products IFN responses in EC are the result of Accumulation of RT products Martin-Gayo, et al, PLoS Path, 205
Are cdcs from EC more effective responding to HIV- DNA? DC CD64 NanoGag dsdna PD-LHi CD64Hi DC HIV- Gag-dsDNA? CD83 = Gated CD64 Hi PD-L Hi Fold change in CD64 Hi PD-L Hi CD86 Hi DC Proportions of CD64Hi PDLHi cdc 2 6 4 3 2 <0.000 0.7344 TIT TITdsDNA TIT TITdsDNA Elite Controllers 0.00 Nano Gag dsdna <0.000 HIV negative - - EC 0.00 High responders 0.7344 HIVneg PD-L CD86 EC are more effective responding to HIV- dsdna due to a subgroup of high responder individuals
High Resp. CD4 Counts Low Resp. High Resp. Low Resp. High Resp. Low Resp. CD4 T cell counts VL Years from infection EC with high response to intracellular HIV- dsdna are characterized by parameters associated with more effective immune control of HIV- infection 2000 500 000 500 0 DNA good 000 40 ns 0.298 ns 0.380 DNA bad VL 800 600 400 200 0 DNA good DNA bad Years from Infection 30 20 0 0 DNA good 0.0235 DNA bad The subgroup of high responder EC are enriched In individuals expressing protective HLA-B alleles Good DNA Bad DNA High Resp. Low Resp. Protective None Both Protective High Risk None Both Total=00 Total=00 High responders to HIV- dsdna stimulation are characterized by undetectable VLs and longer duration of Control of infection.
Why are cdc from some EC more predisposed to respond to intracellular DNA stimulation????? Two strategies Differential Transcriptional Patterns in cdc from EC vs HAART RNAseq High and Low EC responders
Detection of DNA and RNA sensing pathways upregulated in cdc from EC 2 DEG (EC vs HAART) Log2 Fold change 0 RNAseq primary unstimulated cdc 9 EC versus 5 HAART DNA sensors RNA sensors IFN/TLR Signaing. Up in EC - TMEM73(STING) MB2D (cgas) DDX4 IFI6 AIM2 DAI (ZBP) TLR3 TLR8 MDA5 (IFIH) DDX58(RIG-I) MAVS DHX58 (LGP2) TRAF6 IKBKE (IKKe) TBK IKBKG (NEMO) IRF3 IRF IRF5 IRF7 NFKB NFKBIA Transcriptional signatures of EC and high responders are characterized by high activation associated with IFN, RNA and DNA sensing pathways Significant higher levels of DNA and RNA sensors and TBK than HAART patients.
Detection of DNA and RNA sensing pathways upregulated in cdc from high responder EC Transcriptional signatures of EC and high responders are characterized by high activation associated with IFN, RNA and DNA sensing pathways DEG (High vs Low Resp EC ) DNA sensors RNA sensors IFN/TLR Signaling Log2 Fold change.0 0.5 0.0-0.5 Up in High Resp EC -.0 TMEM73(STING) MB2D (cgas) DDX4 IFI6 AIM2 DAI (ZBP) TLR3 TLR8 MDA5 (IFIH) DDX58(RIG-I) MAVS DHX58 (LGP2) TRAF6 IKBKE (IKKe) TBK IKBKG (NEMO) IRF3 IRF IRF5 IRF7 NFKB NFKBIA
Could RNA sensing contribute to DNA sensing in DC? Relative cgas expression cgas.5.0 0.5 0.0 cgas RNA polymerase III DNA RNA 0.2500 0.0020 Relative AIM2 expression.5.0 0.5 0.0 AIM2 0.2500 0.000 SC-siRNA cgas-sirna SC-siRNA cgas-sirna RIG-I Proportion of cells (%).5.0 0.5 0.0 SC-siRNA RIG-I 0.2500 0.000 cgas-sirna Fold change in CD64 Hi PD-L Hi CD86 Hi DC Proportion of cells (%) 2.0.5.0 0.5 0.0 SC-TIT v 0.056 CD64 Hi PDL Hi MDDC SC-dsDNA v 0.0078 0.953 cgas-dsdna 0.0078 AIM2-dsDNA Nano - Gag-dsDNA RIG-I dsdna sirna - SC cgas AIM2 RIG-I Knock down of cgas and RIG-I significantly affects maturation of DCs in response to HIV- dsdna vns
HIV- DNA is transcribed by RNApolymerase III and allows synergy of cgas and RIG-I sensing pathways cgas RNA polymerase III DNA RNA Relative Gag RNA expression 0 0 0-0 -2 0-3 0-4 0-5 0-6 0-7 0-8 0-9 0-0 TIT Nano Gag-dsDNA - - - TITG agdna (DMSO ) TITG agdna R NAp ol III RNApol III inhib HIV DNA is transcribed by RNApol III into RNA in cdc from EC RIG-I Fold change in CD64 Proportions Hi PD-L of CD64Hi Hi CD86 PDLHi Hi DC 3 2 0 Nano Gag-dsDNA RNApol III inhib - - - EC TIT EC dsdna EC dsdnar NApol III inhib EC Neg TIT Neg dsdna - - - Neg dsdnar NApol III inhib HIVneg Effective maturation of DC from EC in response to HIV DNA is abrogated by RNApol III inhibitors
If cellular machinery is present in all patient cohorts, Why do cdcs from EC respond more efficiently to dsdna? SNP cgas, STING, TLR3, RNApol III 33 SNPs p<0e-8
DNA sensors RNA sensors GWAS TLR3 GWAS cgas GWAS STING p value 0. p value 0. p value 0. 0.0 GWAS MAVS 0.0 0.0 p value 0. GWAS AIM2 0.0 GWAS RIG-I p value 0. p value 0. 0.0 0.0
SNP for RNApol III and RIG-I present in GWAS dataset are associated with viral control and might suggest enhanced DNA-RNA sensing crosstalk in EC GWAS RNApol III p value 0. 0.0 SNP in RNApol III is enriched in cdc from good vs bad responders seqnames pos name Symbol A A2 pid response diff total_reads diff_norm 22 4527647 rs20336 POLR3H 0 0 38040 Bad 0 892035 0 22 4527647 rs20336 POLR3H 0 0 553064 Bad 0 0695682 0 22 4527647 rs20336 POLR3H 0 0 56825 Bad 0 4790236 0 22 4527647 rs20336 POLR3H 4 0 70554 Bad -4 03264-3.8753528 22 4527647 rs20336 POLR3H 3 0 64007 Good -3 448680-2.0709393 22 4527647 rs20336 POLR3H 0 0 88482 Good 0 974578 0 22 4527647 rs20336 POLR3H 3 0 595424 Good -3 9746828-3.0779244 22 4527647 rs20336 POLR3H 2 0 785360 Good -2 525495-3.8059346 22 4527647 rs20336 POLR3H 0 88703 Good - 4292-0.6997308
HIV-DNA HYPOTHETICAL MODEL HIV-DNA cgas
Martin-Gayo Lab Acknowledgements Funding HU CFAR Scholar award Tosteson and Fund ECOR developmental award -NIH R2 grant program -Atraccion de Talento tipo I. Comunidad de Madrid Instituto de Investigación Sanitaria La Princesa Francisco Sánchez Madrid Isidoro González Álvaro Ignacio de los Santos Jesús Sanz Cristina Delgado Arévalo Marta Calvet Mirabent Ildefonso Sánchez Cerrillo Instituto de Salud Carlos III José Alcami Pertejo Hospital Vall d Hebron María José Buzón Xu Yu Mathias Lichterfeld Bruce Walker Facundo Batista Ragon-IMES Alex K Shalek Kellie E Kolb