Team DermGerm Freer D 1 , Jacus M 1 , Kita S 1 , Sawlani S 1 , Waters S 1 , Campbell K 2 , Dash S 2 , Huang W 2 , Padinjarekutt S 3 1 Dept. of Bioengineering, 2 Dept. of Biology, 3 Dept. of Chemistry University of Pittsburgh
Im Immunologists Bio ioengineers Derm rmatolo logists Bio iolo logists Computer Chemists Sci cientists WHO WE ARE
Develo lop Immunologists Im Bio ioengineers Derm rmatolo logists Skin in Bio iolo logists Computer Chemists Pro robio ioti tics Sci cientists WHO WE ARE
Protect against sun damage Remove pathogens Balance skin oils
PROPIONIBACTERIUM ACNES Presence of P. acnes Commensal skin bacteria associated with acne
Modeling Transformation Probiotic Functions Human Practices
Modeling Transformation Probiotic Functions Human Practices
CHALLENGES Gram negative thick cell wall Restriction modification system Growth time - 5 days Anaerobic growth $100-200 $5
P. ACNES TRANSFORMATION Transformation Protocol: Cheong et al, 2008 Erm Goal: Transform P. Acnes using electroporation protocols
Design of Experiments (DOX) • Choose trials carefully for statistical advantage • Reveals which parameters have most effect on successful transformation Parameters: - P. acnes strain - Culture temperature 16 - Amount of glycine trials - Lysozyme concentration instead - Restriction enzyme of 256 inhibitor - Amount of plasmid DNA - Electric field strength - Incubation temperature
RESULTS 1 No growth after electroporation. 2 Growth resembling P. acnes , but failed PCR validation. 3 Growth does not resemble P. acnes.
DISCUSSION • Have not yet been able to transform any P. acnes • Different strains • Parameters extreme conditions • Activity of non-type I restriction systems
FUTURE DIRECTIONS Attempt New S. Aureus Species 1. Broad Skin Location S. epidermidis 2. Transformation Protocols M. lysodeikticus Available
Modeling Transformation Probiotic Functions Human Practices
HSP60 Promoter Melanin Desaturase Cathelicidin
HSP60 PROMOTER Strong promoter from 7 1 2 3 4 5 6 Mycobacterium bovis 20 0 kb kb • Works well in other .5 .5 kb kb propionibacteria Figure 1. Gel electrophoresis showing presence of HSP60 Hsp60 mRFP1 after PCR. Lanes 2 - 4 contain hsp60 promoter. Lanes 6 - 7 contain hsp60 promoter with an RBS Hsp60 RBS mRFP1 Part:BBa_K1548000 Part:BBa_K1548001
HSP60 PROMOTER Testing in E. coli 1.78E+05 Not functional 1.76E+05 u) (au) 1.74E+05 Fluoresence (a 1.72E+05 As predicted by 1.70E+05 1.68E+05 BPROM 1.66E+05 RFP! Fl 1.64E+05 1.62E+05 mRF 1.60E+05 1.58E+05 Characterized part to be 1.56E+05 Hsp60-RBS-mRFP1 Hsp60-alo lone non-functional as expected Cons nstruct
MELANIN Goal: Insert MelA into P. acnes to produce melanin Part:BBa_K274001 – Cambridge ‘09
MELANIN 4 2 3 5 1 Vector Backbone 3,000 bp 2,000 bp Expected MelA Goal: Insert MelA into P. acnes to Figure 2. Gel electrophoresis showing melanin digestion with produce melanin XbaI and PstI Lanes 2 & 4 contain melanin and vector backbone. Part:BBa_K274001 – Cambridge ‘09
CATHELICIDIN Goal: Activate controlled cathelicidin production under blue light • Dependent on P. acnes population
CATHELICIDIN 1 2 3 4 3 kb 1.5 kb YF1 FixK2 Cath T1 Hsp60 YF1 FixJ Figure 3. Gel electrophoresis showing cathelicidin system. Lane 2 contains the Blue Promoter – Part:BBa_K1548002 Part:BBa_K1548003 mRFP1 – Cathelicidin part (~1100 bp).
DESATURASE Oleic Acid Δ 12 12-FAD Linoleic Acid Goal: Produce desaturase to restore the natural lipid composition of skin
DESATURASE Oleic Acid Δ 12 12-FAD Linoleic Acid Goal: Produce desaturase to restore the natural lipid composition of skin
DESATURASE Hsp60 RBS mRFP1 Desaturase Terminator Desaturase digestion with EcoRI & SpeI mRFP1 digestion with EcoRI & XbaI Desaturase Vector Backbone 3,000 bp Linearized 1,000 bp mRFP1 and Vector Desaturase Part Backbone
FUTURE VALIDATION MELANIN • Visual inspection
FUTURE VALIDATION MELANIN CATHELICIDIN • • Visual inspection Viability assay under blue-light
FUTURE VALIDATION MELANIN CATHELICIDIN DESATURASE • • • Visual inspection Viability assay Colorimetric assay under blue-light for oleic acid
SAFETY Safety • P. acnes naturally occurs deep in human skin • Cathelicidin is natural kill switch
SAFETY Safety • P. acnes naturally occurs deep in human skin • Cathelicidin is natural kill switch • Could add genetic fence:
Modeling Transformation Probiotic Functions Human Practices
CATHELICIDIN MODEL Cathelicidin
CATHELICIDIN MODEL Bacteriocidal Cathelicidin
CATHELICIDIN MODEL Bacteriocidal Pro-Inflammatory Cathelicidin
BOOLEAN NETWORKS Model Biological Circuits as Digital Circuits
Normal Input Network Diagram
Normal Input Output Output Network Diagram
Cathelicidin Input Topical Application Network Diagram
Cathelicidin Input Output Topical Application Output Network Diagram
Norm rmal l Topical l Node Condit itions Cath theli licidin Cathelicidin 23% 35% P. acnes 44% 37% Immune Response 21% 28% (Inflammation) Injury 52% 42% (Inflammation) Ave vera rage In Infl flammation 37% 37% 35% 35% (Immune + Injury)
Norm rmal l Topical l Node Condit itions Cath theli licidin Cathelicidin 23% 35% Lower growth P. acnes 44% 37% of P. acnes Immune Response 21% 28% Similar (Inflammation) average Injury 52% 42% inflammation (Inflammation) Ave vera rage In Infl flammation 37% 37% 35% 35% (Immune + Injury)
Injury Node Average 1.0 Normal Conditions 0.9 Topical Cathelicidin 0.8 0.7 Avg State 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1 3 5 7 9 11 13 15 17 19 21 23 25 Update Round
Modeling Transformation Probiotic Functions Human Practices
EDUCATE Ethics Research Forum Dr. Albert Kligman and Holmesburg Prison Experiments Promoted and encouraged the study of ethics in synthetic biology
EDUCATE SciencePalooza “ Dermalicious ” Mobile Science Lab “Enzyme Time”
ENGAGE
ENGAGE Wordpress Blog: http://igempitt.wordpress.com Professional consults How-to articles Team videos
COLLABORATIONS Montgomery HS Pittsburgh Meetup Virginia Survey
ACKNOWLEDGEMENTS Advi visors Gra raphic ic Art rts Jas ason Lo Lohmuelle ler – Te Team Mentor, MVP Ryan Blac ack - Butt tton Mak aker, r, Extr traordin inaire re Graham Hat atfull ll – Principal l In Inve vestigator Cat atherine Kit ita - Graphic Des esigner r Lance Dav La avid idson – Clu lub Advi visor Meli lissa Th Thompson – Graphic Desig igner Nat atasa Mis iskov-Ziv ivanov - Computer Sim imula latio ion Advi visor Anan and Mah ahali lingam – Vid ideographer Sponsors – Univ iversit ity of f Pit ittsburgh Admin inistra rativ ive Support – Univ iversity of f Pit ittsburgh San anjeev Shroff – Chair of Bio ioen engin ineering Dep epartment John Pat atzer – Bio ioen engineering Professor Pau aula la Grabowski i – Chair of f Bio iolo logy Dep epartment Alic icia Wel elsh – Bio ioen engineering Admin inistrator Dav avid Vorp rp – Ass ssociate Dea ean of f Researc rch fo for r Pat atri ricia ia Dea ean – Bio iolo logy Admin inistrator Swanson School l of f Engineerin ing Pat atri rick Mull llen – Dea ean of f Undergraduate Research fo for Amie ie DiT iTomasso – Sci cience Outreach Coordinator Die ietrich Sch chool l of f Arts an and Sci ciences Nic icki i Zev evola la – CEO & Founder, FutureDerm Sponsors – Oth ther Joseph Ayo yoob – Program Dir irector, TE TECBio REU Experiment.com te team am – Crowdfundin ing Platform rm Pau aul l Kova vach – Dire irector of f Mark rketing an and Communications Laboratory Support La Car arlos Guerrero – Graduate Student, Hat atfull Lab Lab Andre rew Fal alk – Exec ecutiv ive Dire irector r of f Corp rporate & & Chin ing-Chung Ko Ko – Graduate Stu tudent t Hat atfull ll Lab Lab Foundation Rel elations Sak Kia ia Goh – Graduate Stu tudent, Bio ioengineerin ing
pBRES36a 1 2 3 4 5 6 Lane La Reaction Re 1 1 kb Marker 2 Plasmid + XbaI 10 kb 10 kb 3 Plasmid + PstI 3 kb kb 4 Plasmid + SpeI 5 Plasmid +EcoRI Plasmid +SpeI and 6 EcoRI 1 1 kb kb
BUILDING THE MODEL Random Literature Starting Searches Conditions Network Diagram Stochastic Av Average Updates 1,0 ,000 Tria ials Boolean Rules Construction Simulation
BUILDING THE MODEL Random Literature Starting Searches Conditions Network Diagram Stochastic Av Average Updates 1,0 ,000 Tria ials Boolean Rules Construction Simulation
BUILDING THE MODEL Random Literature Starting Searches Conditions Network Diagram Stochastic Av Average Updates 1,0 ,000 Tria ials Boolean Rules Construction Simulation
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