Combinatorial Polymer Libraries to Elucidate Dendritic Cell Phenotype-Material Property Relationships

Transcription

1 Combinatorial Polymer Libraries to Elucidate Dendritic Cell Phenotype-Material Property Relationships Kou, P.M. 1,Narayanan, P. 2, Joy, A. 2, Cunningham, B. 2, Kohn, J. 2, Babensee J.E. 1 1 Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 2 Department of Chemistry and Chemical Biology, Rutgers University, and New Jersey Center for Biomaterials, Piscataway, NJ

2 Distinct DC Phenotypes and Immunological Outcomes Key roles in: Maintaining self tolerance Immunity to pathogens Cancer Autoimmunity P.M. Kou

3 Biomaterial Immunomodulatory Effect Combination Product PLGA Adjuvant Effect biomaterial component Activated APCs Mφ, dendritic cells biological component Activated T cells Innate Immunity Adaptive Immunity (Inflammation) (Immune Response) anti-ova IgG (ng/ml) [Anti-OVA IgG] (ng/ml) Biomaterial Immunomodulatory Effect Time (weeks) M. M. Matzelle & J.E. Babensee, Biomaterials 25: (2004). time (weeks) PBS CFA 75/25 PLGA MP 75/25 PLGA SC

4 Prime Mechanism By Which Adjuvants Enhance Immune Responses Maturation Of Dendritic Cells (DCs)

5 DC Phenotype Upon Biomaterial Contact idc mdc PLGA Chitosan Supports DC Maturation Supports DC maturation Original magnification: 40. Alginate Does not support DC maturation Hyaluronic Acid Inhibits DC maturation Agarose Does not support DC maturation CD86 Fold increases over the immature DCs are shown to compare between different donors. : p < 0.05, statistically higher than immature DC (=1); : p < 0.05, statistically lower than immature DC (=1); Bracket: p < 0.05, statistically different between two biomaterial treatments; indicates or. mean±sd, n=6 donors J. Park

6 J. Park Cytokine Profiles for Biomaterial-Treated DCs (a) (b) (c) (a) (b) (c) Pro-inflammatory cytokines Chemokines Anti-inflammatory cytokines Proteins released into supernatant have been measured using Bio-Plex immunoassay (mean±sd, n=6 donors).

7 Autologous T Cell Phenotype And Polarization By Biomaterial-Treated DCs No T cell Inducer of IL-10 with or without antigens Inducer of IL-12p70 without antigens Inducer of IL-10 with antigens Inducer of IL-12p70 with antigens idc mdc PLGA Chitosan Alginate HA Agarose DCs collected Co-culture with Autologous CD3 T cells Co-culture with T cells Foxp3 Inducer of Th1 (IFN-γ) with or without antigens Inducer of Th1 (IL-12p70) & Th2 (IL-10) without antigens CD4 Inhibited with antigen treatment Inducer of Th1 (IL-12p70) & Th2 (IL-10) with antigens J. Park CD4CD25, Foxp3 Induced with antigen treatment

8 In Vivo Biomaterial Adjuvant Effect Correlates with DC Response In Vitro Anti-OVA IgG1 Concentration (ng/ml) ND PBSOVA 2 Weeks 3 Weeks 4 Weeks 8 Weeks 12 Weeks Agarose OVA SC PLGA OVA SC CFAOVA ND PBSOVA Agarose OVA SC PLGA OVA SC CFAOVA PBSOVA Agarose OVA SC PLGA OVA SC CFAOVA PBSOVA Agarose OVA SC PLGA OVA SC CFAOVA PBSOVA Agarose OVA SC PLGA OVA SC CFAOVA Treatment Group n = 6-7. Data are shown as replicates with mean represented as a line. p<0.05. ND = none detected Norton, L., Park, J., Babensee, J.E., J. Control. Rel. 146: (2010).

9 Dendritic Cells (DCs) and Biomaterial-based Immunomodulation Biomaterial Adjuvant Effect immature Hyaluronic acid Agarose mature PLGA Chitosan Biomaterials induced differential levels of DC maturation PLGA, but not agarose, enhanced humoral response to a model antigen (ovalbumin) in mice Control biomaterial systems are needed to correlate DC response to biomaterial properties Babensee, J.E. and Paranjpe A., JBMR (2005) Bennewitz, N.L. and Babensee J.E., Biomaterials (2005) M. M. Matzelle & J.E. Babensee, Biomaterials 25: (2004). Norton, L., Park, J., Babensee, J.E., J. Control. Rel. 146: (2010).

10 DC Response to Polymethacrylates (pmas) Monopolymers Copolymers Terpolymers Solvent casting of polymethacrylates Dry at 80 o C into 96-well polypropylene plates for 5 days pmas were characterized for material properties surface roughness, contact angle, and glass transition temperature All biomaterial coatings contained < 0.1 EU/mL endotoxin (FDA approved level = 0.5 EU/mL) P. M. Kou, in collaboration with J. Kohn, N. Pallassana and B. Cunningham (Rutgers U)

11 DC Responses to pmas Fix in 0.03% PF for min at RT Cytotoxicity Biomaterial Filter Cytotoxicity & cytokine profiling Incubate with anti-cd86- PE and anti-dc-sign- FITC for 1 hr at 4 o C FITC and PE fluorescence using microplate reader Mean ± SEM (n=5 donors) #: p<0.05 higher than HEMA : P<0.05 higher than all treatments!!: Signal was saturated in the assay Surface maturation marker Pro-inflammatory Anti-inflammatory Chemokine Mean ± SEM (n=6 donors) total 11 plates Pro-inflammaotry: TNF- α, IL-1β, IL-12p70, IL-15, IL-18 Anti-inflammatory: IL-16, IL-1ra, IL-10 Chemokine: IL-8, MCP-1, MIP-1α : p<0.05 different from idc #: p<0.05 different from mdc Mean ± SEM (n=6 donors) : p<0.05 different from HEMA : p<0.05 different from HEMA and hydroxypropyl brackets: p<0.05 different between treatments

12 Principal Component Analysis (PCA) Goal: reduce the number of dimensions necessary to represent the data Look for a hyperplane formed by 2 PCs that can best represent the multidimensional data Map most information from the data onto this hyperplane PCs often do not have biological meanings, but they help to let us visualize which variables are similar or different

13 Treatments for each donor 2 PCs = 58% data 3 PCs = 68% data Loadings plot PC2 22% data Principal Component Analysis (PCA) Variables: Phenotype material properties X X 11 m1 Sa IL-16 C-O C-C=O O1s X X 1n mn Theta Tg Ra SurArea IL-10 Si2p C1s C-C TNF IL-8 MIP-1a IL-1ra MCP MF PC1 36% data Treatments: All 12 pmas Phenotypematerial property variables: CD86/DC-SIGN (MF) 8 cytokines analyzed Contact angle (theta) Roughness (Ra) T g Surface area Surface chemical composition (surface C and O; C-C, C=O, O- C=O bondings) Surface carbon is associated with a mature DC phenotype Surface oxygen is associated with an immature DC phenotype Roughness, T g and surface area are not important in determining DC phenotype P12 P12 HEMA Score plot P12 P12 P2 P2 P2 P2 P2 OHpropyl P7P7P7 P7 P7 P4P4 P6P6 P6 P5P5 P1P1 P5 P3 P1P1 P3 P3 P8 P8 P8 P9P11 P10 P9P9 P11 P9 P10P10 P PC1 36% data P. M. Kou, in collaboration with M. Kemp, M. Platt (GT)

14 Combinatorial Library of Terpolymers Composition Wet Modulus (kpa) Tg ( C) Contact Angle A55T20G A40T35G A10T65G H40T35G H25T50G H10T65G A55H20G For example A55T20G25: p(55%eha-co-20%tegma-co- 25%GMA) All terpolymers have 25% GMA A40H35G A25H50G H40N35G H: HEMA (2-hydroxyethyl methacrylate) hydrophilic A: EHA (2-ethylhexyl acrylate) hydrophobic T: TEGMA (triethyleneglycolmethylether methacrylate) tailor softness N: NIPPAM (N-isopropylacrylamide) hydrophilic G: GMA (glycidyl methacrylate) allow for surface modification H25N50G H10N65G All biomaterial coatings contained < 0.3 EU/mL endotoxin (FDA approved level = 0.5 EU/mL) P. M. Kou, in collaboration with J. Kohn, N. Pallassana, A. Joy and B. Cunningham (Rutgers U)

15 Terpolymers Induced Differential DC Response Cytotoxicity Cytokines Pro-inflammatory Pleiotropic Chemokine Mean ± SEM (n = 6 donors) bracket: p<0.05 different between HEMA And H40N35G25 Surface maturation marker Mean ± SEM (n=6 donors) : p<0.05 different from idc #: p<0.05 different from mdc : p<0.05 different from all polymers EXCEPT HEMA, A40T35G25 (2) and A55T20G25 (1)

16 DC phenotype-material Property Relationships Among Terpolymers Contact Angle Glass Transition Temperature Wet Modulus IL-8 CD86/DC-SIGN R² = R² = CD86/DC-SIGN R² = R² = IL-1b CD86/DC-SIGN TNF-a R² = R² = outliers removed IL R² = MCP R² =

17 Principal Component Analysis (PCA) Treatments for each donor X X Variables: Phenotype material properties 11 m1 X X 1n mn 2 PCs = 60% data 3 PCs = 72% data Loadings plot Treatments: All terpolymers phema Phenotypematerial property variables: CD86/DC-SIGN (MF) 6 cytokines analyzed Contact angle (theta) T g Wet modulus (WM) % monomer (A, T, H, N) Tg, WM and %NIPAMM DC maturation %TEGMA (more O) DC maturation Contact angle DC maturation %EHA IL-16 production %HEMA in terpolymers DC maturation, but 100% phema is very passivating to DCs Score plot PC3 12% data PC1 32% data PC1 32% data P. M. Kou, in collaboration with M. Kemp, M. Platt (GT)

18 Summary Biomaterial adjuvant effect is observed Biomaterials can differentially affect DC maturation In vitro studies of DC phenotype upon biomaterial contact correlate with in vivo immunomodulation Clinically relevant pma library gradations in induced DC phenotype Polymers were controlled with the goal to vary only one property at a time (terpolymer library) PCA analysis identified biomaterial property DC phenotype relationships Roughness and T g are not important in determining DC phenotype (pma library) Contact angle inversely related to DC maturation (both libraries) DCs tend to be more activated on stiff and crystalline materials (terpolymer library) Goals: Immunomodulation of host response to constructs can be achieved by biomaterial selection/design Develop PLSR model to predict host response by material characteristics

19 Acknowledgements Babensee laboratory Peng Meng Kou, Melissa Matzelle, Abhijit Paranjpe, Jaehyung Park, and Dr. Lori Norton NSF CAREER grant, BES NIH Grant, 1RO1EB A1 Kohn laboratory Joachim Kohn,, Narayan Pallassana, Abraham Joy and Barry Cunningham NIH Grant, EB (RESBIO) Assistance in PCA Dr. Melissa Kemp Dr. Manu Platt