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Marni Falk, Convegno Mitocon 2015

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Marni Falk, Convegno Mitocon 2015

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Marni Falk, Convegno Mitocon 2015

  1. 1. New perspectives in research & therapies for mitochondrial disease in the US: Roles of networks, databases & biobanks Marni J. Falk, M.D., FACMG Assistant Professor of Pediatrics Division of Human Genetics The Children’s Hospital of Philadelphia University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania, USA & Chair, Scientific and Medical Advisory Board United Mitochondrial Disease Foundation (UMDF)
  2. 2. DISCLOSURES Marni J. Falk, M.D. is •Chair, Scientific and Medical Advisory Board & Member, Board of Trustees, United Mitochondrial Disease Foundation • Organizer, Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium • SAB Member, The Genesis Project • Consultant, Mitobridge
  3. 3. OUTLINE • Mitochondrial Disease Clinical Care & Research in US – CHOP Mito-Genetics Diagnostic Clinic experience • Crossing the line from clinical care to human research – Establishing local and/or national biospecimen repositories – Enrolling subjects in national registries and biobanks • North American Mitochondrial Disease Consortium (NAMDC) • Mitochondrial Disease Community Registry (MDCR) • Mitochondrial Disease Sequence Data Resource (MSeqDR) • Translational research with mito disease biospecimens – Etiology-based studies of genetics and metabolism – Subgroup specific pathophysiology and therapies
  4. 4. n = 152 patients referred from 2008-2011 to CHOP Mito-Genetics Diagnostic Clinic Ages 6 weeks to 81 years old. CHOP Mito-Genetics Clinic: Referral Indication
  5. 5. Genetic diagnoses made in 28% of cases referred for suspected mito disease Diagnostic Yield “PRE” Next Gen Sequencing: DEFINITE MITO DISEASE -Defined molecular etiology 14% -Biochemical findings without evident molecular etiology* 4% PROBABLE/POSSIBLE MITO DISEASE -mtDNA variant of unknown significance 5% -Normal tissue biochemistry + no clear molecular etiology 29% -UNLIKELY PRIMARY MITO DISEASE 39% -PROVEN OTHER GENETIC DISORDER 9%
  6. 6. Improved diagnostic yield from whole mtDNA genome sequencing • Pathogenic mtDNA mutations identified in 19 patients (10 kindreds): – tRNALEU 3243A>G heteroplasmy (3 patients) – tRNALEU 3288A>G heteroplasmy (6 patients) Hadjigeorgiop GM et al, 1999 – tRNALYS 8344A>G heteroplasmy (1 patient) – tRNASER(AGY) 12264C>T heteroplasmy (1 patient) Schrier SA et al, 2012 – tRNATRP 5537_5538insT heteroplasmy (2 patients) Santorelli FM et al,1997 – ND4 and ND6 11778G>A /14484T>C het/homo (3 patients) Brown MD et al, 2001 – ND4 11778G>A homoplasmy (1 patient) – ND5 13513G>A heteroplasmy (2 patients) * mtDNA common mutation panel • Potentially pathogenic mtDNA mutations (VUS) in 7 patients (5 kindreds): – tRNATYR 5836A>G homoplasmy (2 patients) – ND2 4936C>T heteroplasmy (1 patient) – ND2 and ATP8 4960C>T /8472C>T homo/homo (2 patients) – ATP6 155A>T homoplasmy (1 patient) – COXII 7962T>C homoplasmy (1 patient)
  7. 7. 15 other genetic diagnoses made • Primary mitochondrial disease (2 patients) – mtDNA deletion in muscle of 1 isolated CPEO patient – POLG-related disease in 1 kindred • Non-primary mitochondrial disease (8 patients) – Molybdenum cofactor deficiency (MOCS2) – CPT2 deficiency – WFS1-related hearing loss – Myotonia congenita – Congenital myasthenic syndrome (CHRNE) – SEPN1-related myopathy – Ullrich muscular dystrophy (COL6A1 deletion/mutation) – Gitelman syndrome • Chromosomal copy number abnormalities (5 patients) – MEF2C deletion (SNP array) – IL1RAPL2 deletion (SNP array) – 7.91 Mb deletion on chromosome 7q31.32q32.2 (SNP array) – 3-way unbalanced translocation (Karyotype/SNP array) – Isochromosome Xp (Karyotype)
  8. 8. Gene-by-gene diagnostic approach in mito disease has limited success • A dedicated Mitochondrial-Genetics Diagnostic Clinic improves the diagnosis of primary mitochondrial diseases • Recognize “classic” but complex phenotypes – mtDNA disorders > nDNA disorders • Guide optimal utilization/interpretation of metabolic screening labs, genetic testing, and tissue biopsy studies • Identify wide variety of phenotypically overlapping conditions – Time, labor, and testing intensive • Few metabolic specialists to meet growing clinical demand
  9. 9. MITO DISEASE DIAGNOSTICS IN THE ERA OF NGS • Mitochondrial disease is highly heterogeneous in causes and features ‒ Traditional single gene testing has had limited diagnostic success ‒ Newer genomics technologies enable comprehensive and efficient testing for all known genetic causes in dual genomes ‒ >200 nuclear genes ‒ All 37 mtDNA genesm ‒ Diagnose >50% of complex mitochondrial diseases in one test* ‒ Novel disease gene discovery • We have entered a computationally sophisticated molecular diagnostic age for understanding subclasses of mitochondrial disease**: **Calvo S, Mootha R, Ann Rev Genom Hum Genet, 2010; **McCormick E et al, 2012, Disc Med
  10. 10. NGS diagnostic approach comparison • Sequence ~100+ known mito disease and related disease genes • Prior unsolved cases of infantile Leigh syndrome • 23% (13/60) diagnostic rate (Calvo S et al, Nature Genetics, 2010) • Sequence ‘MitoExome’ of mitochondria-localized genes • Targeted capture of 1,381 nuclear genes + mtDNA genome • 291 OXPHOS patients (Calvo S et al, Sci Transl Med, 2011) • 24% (10/42) diagnostic rate for unsolved cases • 47% extrapolated diagnostic rate to all cases • Sequence whole nuclear exome • 38% (13/34) cases analyzed have clear genetic etiology (Baylor) – Only analyze exome of probands, not of family members – Not include analysis of mtDNA genome
  11. 11. Emerging diagnostic approach for suspected mitochondrial disease • Careful clinical evaluation/ phenotype description – History and Exam – Pedigree – Blood/urine metabolic screening laboratory studies – Tissue analyses in some • mtDNA whole genome sequence analysis + mtDNA deletions, mtDNA copy number • nDNA copy-number alterations (genome-wide SNP array) • nDNA exome capture/Next Gen sequencing analysis
  12. 12. OUTLINE • Mitochondrial Disease Clinical Care & Research in US – CHOP Mito-Genetics Diagnostic Clinic experience • Crossing the line from clinical care to human research – Establishing local and/or national biospecimen repositories – Enrolling subjects in national registries and biobanks • North American Mitochondrial Disease Consortium (NAMDC) • Mitochondrial Disease Community Registry (MDCR) • Mitochondrial Disease Sequence Data Resource (MSeqDR) • Translational research with mito disease biospecimens – Etiology-based studies of genetics and metabolism – Subgroup specific pathophysiology and therapies
  13. 13. Challenges and benefits to PI of establishing a local biorepository • Time-Intensive, Detail-Intensive, and Labor-Intensive – Genetic Counselor as common clinic & study coordinator – Local IRB establishment, approval, oversight/audits – Database establishment/maintenance/mining • Excel  REDCap – Local acquisition of tissue samples and establishment of cell lines and derived materials • High potential pay-off – High local knowledge of subject phenotypes & prior testing – Rich material for translational research at discretion of PI • Genetic etiology, Pathophysiology, and Therapeutic modeling
  14. 14. >600 mito disease + control subjects enrolled since 2008 in CHOP IRB-approved tissue research study protocol MITO DISEASE CATEGORY LCL FCL Muscle KNOWN DIAGNOSES Definite 21 18 5 mtDNA mutations (MELAS, MERRF, LHON, other tRNAs, CI subunits) nDNA mutations (POLG, MPV17, RRM2B, TIMM44, SLC25A12) Probable/Possible 14 17 1 mtDNAvariant of ? pathogenicity, RC deficiency of ? etiology, Lactic acidemia Other Disease 3 8 5 PDH or PC deficiency, SEPN1 mutation, MEF2C deletion, NMNAT1 mutation Healthy Control 14 14 10 - Blood DNA Blood RNA Muscle DNA Muscle RNA FCL DNA FCL RNA EXTRACTED SAMPLE # 33 19 47 28 26 29
  15. 15. CHOP Bioinformatics efforts to improve patient-linked registry and tissue biorepository • Sample/Data Tracking LIMS • Manage PI Level data REDCap • Clinical Data EPIC • Clinical Trial Management Oncore • Data Query + Exploration Harvest
  16. 16. Incorporate Phenotype Capture & Display Tools • REDCap – Research electronic data capture tool – Free, web-based, clickable data entry – Custom design tools to capture any desired data type • Common data elements (CDE) optimized for mitochondrial disease • Integrate with NAMDC data capture tools and fields
  17. 17. REDCap-based Mito Disease Data Capture Claire Sheldon, MD, PhD,
  18. 18. Central Issues in Multi-Investigator Biorepository • Trust – Data quality: accurate and updated information entered? – Sample quality: handling and shipment protocols? – Ongoing resource funding and sustainability? • Incentive – Why and what should local PIs contribute from own tissue repositories? – Local PI support mechanism for samples acquisition and data entry? – Ongoing, widely available source of relevant human disease subjects + tissues • Unequal burden:benefit ratio for established vs new investigators? • Data and sample governance/access – How will data be stored and accessed? – What are hurdles and costs to obtain samples out of repository? – Should/how will contributing PI be acknowledged in resulting publications? • Data transparency and systems ease-of-use
  19. 19. North American Mitochondrial Disease Consortium (NAMDC) • Established in 2011, now 17 sites in US + Canada – NICHD/NINDS R01: PI – Dr. Michio Hirano (Columbia University, NY) • Major goals: 1.Mito Disease Patient Registry (790 enrolled by June 1) A. Natural history studies (IND pre-requisite) B. Patient base for clinical trials C. Detailed phenotypic information requested from physician 1.Mito Disease Specimen and DNA Biorepository A. Central IRB protocol  biorepository at Mayo Clinic (MN) B. Any tissue type accepted
  20. 20. Mitochondrial Disease Community Registry Who? Patients, caregivers & family members Confirmed diagnosis is NOT required Why? Need patient data collected over time in order to improve diagnoses and develop treatments Key Considerations: Registrants fully control privacy settings • Allow, deny or “ask me” • Who can see anonymous data • Who can analyze anonymous data • Who can contact you about research studies and clinical trials www.umdf.org/registry
  21. 21. Mitochondrial Disease Community Registry Key Points • MDCR is sponsored by UMDF, but meant to be a community asset – UMDF pledges to steward the project and serve as guardian of collected data • MDCR seeks input from patients, caregivers and relatives of those affected by mitochondrial disease – Living or deceased / Any location – Confirmed diagnosis NOT required • MDCR is meant to collect health information over time – Surveys will be presented on a regular basis – No demands or expectations- participate when you can and want • MDCR is not meant to replace any other registry – Each registry has unique goals and capabilities • Registrants are in full control of privacy settings – Adaptable settings about who may see and do what with your information Philip Yeske, PhD UMDF Science and Alliance Officer
  22. 22. Mitochondrial Disease Community Registry Status & Future Directions Present: •~1000 accounts, ~1200 health profiles, ~120K data points (questions answered) •First survey: basic demographic info, diagnostic state, opinions on state of mitochondrial research and future MDCR directions •If already registered, please sign in again and confirm first survey complete Near Future: •Creation of FAQ based on community feedback •Selection of “Navigators”- peers willing to help peers with registry •Additional surveys presented on topics of interest In-Planning: •Uploading and sharing of genetic data (whole exome sequencing) •Importation of Electronic Health Records •International Engagement Philip Yeske, PhD UMDF Science and Alliance Officer
  23. 23. Mitochondrial Disease Sequence Data Resource (MSeqDR) MSeqDR is an international mitochondrial disease community collaborative effort to create a unified genomic data resource that facilitates diagnosis and enables improved understanding of individual mitochondrial diseases https://mseqdr.org is a central entry for clinicians, diagnostic labs, & researchers to enable genomic data sharing and analyses in suspected mitochondrial disease – Flexible, updated suite of web-based and open access software tools accessible from your office/clinic desktop to securely mine all genetic & exome data in real-time •Exploit collective information of variant allele frequencies in a large cohort of individuals with suspected mitochondrial disease (gem.app, G-browse, etcsg) •Will be linked to relevant phenotype & laboratory data •Accelerate pace and accuracy of known & novel gene discovery in mito disease – Genomic data deposition for individual and/or community mining » Deposit aggregate-level deidentified exome or variant data to share at various levels of comfort (BioDAS Server) » Patient-determined deposition & access to exome and phenotype data » Assist with data curtain & transfer to public resources (ClinVar, NCBI)
  24. 24. https://mseqdr.org Xiaowu Gai, PhD, Lishuang Shen,
  25. 25. https://mseqdr.org Falk MJ et al, Mol Gen Metab, 2015
  26. 26. MSeqDR GBrowse • Visualization of variants in both nuclear & mitochondrial (mtDNA) genomes • Hosts custom tracks for mitochondrial disease community MSeqDR LOVD • Locus specific database for all mitochondrial disease genes and all genes that encode mitochondrial proteins • Curates gene, transcript, variant, and disease data relevant to mitochondria MSeqDR-GEM.app • Web-based repository and tool to readily enable analysis of sequence data from gene panels, exomes, genomes, and mtDNA genomes • Supports analysis of data from individuals, families, or cohorts MSeqDR Tools • Centralized host and link to public and custom tools that enable users to perform dataset and variant level analyses in both nuclear & mtDNA genomes • Provides support to phenome and ontology tools for mitochondrial disease Mitochondrial Disease Sequence Data Resource: Major Domains https://mseqdr.org Falk MJ et al, Mol Gen Metab, 2015
  27. 27. • MSeqDR tools technical optimization and response to community feedback – GUID system implementation and assignment to all data types – Phenotype data integration (existing vs new data) • Integrate NINDS Mito Disease Common Data Element Terms • HPO ontology tree-like structure – Match degree of phenotype data shared to user access rights – Further integration with GEM.app (GENESIS Project) – Further integration with NIH ClinGen and NCBI (dbGAP, ClinVar) • Ethical use and oversight – Data security protections (aggregate data, cloud computing) – Develop web portal to directly deposit deidentified phenotype data • Translate access page into different languages – Develop data access and use oversight committee • Clinical diagnostic labs, researchers, physicians, family support groups, etc. MSeqDR “go-live” preparation underway
  28. 28. MSeqDR Live Hands-On Tutorials
  29. 29. MSeqDR User Analytics
  30. 30. Acknowledgements FUNDING United Mitochondrial Disease Foundation NAMDC Pilot Grant Award #NAMDC7407 (NINDS/NICHD, NIH) U01-HG006546 (NHGRI, NIH) U41-HG006834 (NHGRI, NIH) falkm@email.chop.edu MEEI/Harvard Xiaowu Gai, PhD Lishuang Shen, PhD University of Miami Stephan Zuchner, MD Michael Gonzalez, PhD NICHD, NIH Danuta Krotoski, PhD Melisa Parisi, MD, PhD UMDF Chuck Mohan, CEO Dan Wright, President Philip Yeske, PhD Janet Owens Cliff Gorski CHOP Claire Sheldon, MD, PhD Elizabeth McCormick, MS, CGC MSeqDR PROTOTYPE DEVELOPMENT PARTICIPANTS: • Doug Wallace, Michio Hirano, Doug Kerr, Curt Scharfe, Li Dong, Hakon Hakonarson, Bruce Cohen, Amy Goldstein, Richard Haas, Russell Saneto (USA) • Marcella Attimonelli, Mannis van Oven (Italy) • Holger Prokisch (Germany) • Mark Tarnopolsky, Isabella Thiffault (Canada) • Richard Rodenburg, Jan Smeitink, IFM de Coo, Bert Smeets, Fons Stassen (The Netherlands) • Virginia Brilhante (Finland) • Yasushi Okazaki (Japan) • Donna Maglott, Wendy Rubinstein (NCBI) • Heidi Rehm (ClinGen) • Clinical diagnostic laboratories: • Jeana DaRe, David Ralph (Transgenomics) • Renkui Bai, Sherri Bale (GeneDx) • Richard Boles, Christine Stanley (Courtagen)
  31. 31. OUTLINE • Mitochondrial Disease Clinical Care & Research in US – CHOP Mito-Genetics Diagnostic Clinic experience • Crossing the line from clinical care to human research – Establishing local and/or national biospecimen repositories – Enrolling subjects in national registries and biobanks • North American Mitochondrial Disease Consortium (NAMDC) • Mitochondrial Disease Community Registry (MDCR) • Mitochondrial Disease Sequence Data Resource (MSeqDR) • Translational research with mito disease biospecimens – Etiology-based studies of genetics and metabolism – Subgroup specific pathophysiology and therapies
  32. 32. VALIDATION OF GENETIC ETIOLOGY AND UNDERLYING PATHOPHYSIOLOGY
  33. 33. A lot of research is needed to validate novel mutations for suspected mito disease • Traditional Sanger sequence validation – Confirm mutation presence and segregation with disease in family • Functional analyses if novel mutation and/or disease gene – Is this clinical or research? • What does it take to make variant “medically actionable”? – Enzyme activity assay if known enzyme – Should other mito function(s) be assayed in tissue or cell lines? • RC enzyme activity • Oxidant effects • Mitochondria content • mtDNA content • Mitochondrial membrane potential – Gene rescue experiment in patient’s cells? – Transmitochondrial cybrids if novel mtDNA variant?
  34. 34. 149,953 (130,948/19,005) 20,828 (20,468/360) 10,936 Synonymous 11,179 (10,819/360) Non- Synonymous CodingTotal (SNPs/Indels) 212 genes 816 (797/19) 18 (17/1) 4 (4/0) Gene Candidates Novel Family-based whole exome sequencing: Disease diagnosis becomes a computer game Biparental compound heterozygous MitoCarta 8 genes 2 genes 1 gene 2 (2/0) Predicted pathogenic Sequence Variants Case 1: Young girl with Leigh syndrome, chronic lactic acidosis (3-5 mM), complex I/III deficiency. POLG heterozygote. Normal SNP array. Normal sequence of mtDNA genome and 18 individual nuclear genes. Only child, no family history of disease. Xiaowu Gai, PhD, Eric Pierce, MD, PhD
  35. 35. Visualization of next generation sequencing mutations Mutation #2: G>C transversion (p.N251K) 112 of 231 reads (maternally inherited) Mutation #1: G>T transversion (p.P308Q) 29 of 63 total reads (paternally inherited) Xiaowu Gai, Stephen Dingley
  36. 36. Control FCL RED: Mito Marker (anti-CcO subunit IV) GREEN: anti-FLAG (Tagged CcO, subunit Vb) Proband FCL Satish Srinivasan, PhD Case 1: Mito morphology and protein import is defective in the patient’s skin cells YELLOW: overlay
  37. 37. Human Mito Disease Subject Fibroblasts Zhang Z et al, PLOS ONE, 2013
  38. 38. Cytosolic and mitochondrial translation are differentially affected in human RC disease Zhang Z et al, PLOS ONE, 2013 Heterogeneous RC diseases, human muscle + FCLs Public transcriptome datasets (GEO)
  39. 39. Zhang and Falk, IJBCB, 2014 RC dysfunction dysregulates central nodes of the nutrient-sensing signaling network to mediate downstream cellular response METABOLIC DEMAND ENERGY SUPPLY TISSUE-SPECIFICENERGYDEFICIT MITOCHONDRIAL RESPIRATORYCHAIN DYSFUNCTION • Germline mutation • Somatic mutation • Retroviral transfection • RNA interference • Chemical inhibition • Transmitochondrial cybrid SECOND MESSENGERS - ATP - ROS - NAD+/NADH NUTRIENT-SENSING SIGNALINGNETWORK - mTORC1 - AMPK - FOXO - PPAR - SIRT NUTRIENT METABOLISM - GLUCOSE - FATTYACIDS - AMINOACIDS TRANSCRIPTION- TRANSLATION - TFBINDING - UTRSTABILITY - RIBOSOMEBIOGENESIS - PROTEASOME DYSREGULATED CELLULARPROCESSES TRANSCRIPTOME MODIFICATION F i g u r e 8 . P r i m a r y R C d y s f u n c t i o n t r a n s c r i p t i o n a l l y a n d p o s t - t r a n s c r i p t i o n a l l y d y s r e g u l a t e s t h e i n t e g r a t e d n u t r i e n t - s e n s i n g s i g n a l i n g n e t w o r k . ( A ) I n t e g r a t e d o v e r v i e w m o d e l i n g g e n e r a l i n t e r a c t i o n s b e t w e e n c e n t r a l n u t r i e n t - s e n s i n g s i g n a l i n g p a t h w a y s . A r r o w s a n d b a r s c o n v e y a c t i v a t i n g a n d i n h i b i t i n g e f f e c t s , r e s p e c t i v e l y . T F s , p h y s i o l o g i c s i g n a l s , a n d d r u g s k n o w n t o m o d u l a t e t h i s p a t h w a y a r e i n d i c a t e d i n g r e e n , b l u e , a n d p u r p l e f o n t , r e s p e c t i v e l y . ‘‘P ’’ i n d i c a t e s p a t h w a y c o m p o n e n t s w h o s e a c t i v i t y i s m o d u l a t e d b y p h o s p h o r y l a t i o n . R e d b o x e s d e t a i l p h y s i o l o g i c e f f e c t s . (B ) O l i g o m y c i n - b a s e d p h a r m a c o l o g i c R C i n h i b i t i o n i n h u m a n F C L s a l t e r s m T O R C 1 a n d A M P K p a t h w a y a c t i v i t i e s . T o c o n f i r m p r i m a r y m i t o c h o n d r i a l R C d y s f u n c t i o n w a s s u f f i c i e n t t o a l t e r m T O R C 1 s i g n a l i n g , F C L s f r o m a h e a l t h y i n d i v i d u a l w e r e t r e a t e d i n D M E M m e d i u m c o n t a i n i n g 2 0 % f e t a l b o v i n e s e r u m f o r 2 4 h o u r s a n d e i t h e r l o w (1 g / L o r 5 m M o l ) o r h i g h (4 .5 g / L o r 2 5 m M o l ) g l u c o s e , w i t h e i t h e r t h e c o m p l e x V i n h i b i t o r , O l i g o m y c i n , (‘‘O ’’, 5 u M o l ), a n A M P K a c t i v a t o r , A IC A R ( ‘‘A ’’, 2 m M o l ), o r t h e m T O R C 1 in h i b i t o r , r a p a m y c i n (‘‘R ’’, 1 0 0 n M o l ) . R e g a r d l e s s o f g l u c o s e c o n c e n t r a t i o n , e x p r e s s i o n o f a s t a n d a r d r e a d o u t o f m T O R C 1 p a t h w a y a c t i v i t y , p h o s p h o - S 6 p r o t e i n l e v e l , w a s r e d u c e d b y M i t o c h o n d r i a l D i s e a s e R e g u l a t o r y N e t w o r k
  40. 40. DEVELOPMENT OF PERSONALIZED DISEASE THERAPIES
  41. 41. WT CI CIV CI/III Primary RC disease patient fibroblasts have variable “phosphokinase” node profiles Mai Tsukikawa, MS
  42. 42. Directly inhibiting cytosolic translation rescues rotenone-induced cell death in a variety of cell types 0 25 50 75 100 1 day 2 days 3 days 4 days 5 days 6 days Control ROT ROT + CHX CellDeathPercent(%) 0 20 40 60 80 100 120 day 1 day 2 day 3 day 4 day 5 day 6 Control ROT ROT + CHX ROT + ASM ROT + ATM CellDeathofPercent(%) 0 25 50 75 100 Control 12.5nM ROT 25nM ROT 50nM ROT 100nM ROT Control + CHX 1.25nM ROT+ CHX 25nM ROT+ CHX 50nM ROT+ CHX 100nM ROT+ CHX Celldeathpercent(%) A C D B Podocytes 11 mM glc 0 25 50 75 100 1 day 2 days 3 days 4 days 5 days 6 days Control ROT ROT + CHX CellDeathPercent(%) Fibroblasts 5.6 mM glc Podocytes (48 hrs) 11 mM glc (100 nM) (1.8 uM) HeLa 5.6 mM glc (125 nM) (3.6 uM) (1.8 uM) (1.8 uM) (1.8 uM) (40 nM) (50 nM) Peng M et al, Human Molecular Genetics, In press
  43. 43. Cycloheximide maintains total cellular respiratory capacity in direct RC inhibition 0 20 40 60 80 ROUTINE LEAK ETS CONTROL 50nM ROT ROT + CHX Oxygenfluxpermass(pmol.s-1.mg-1) *** *** *** *** ** ** 0 20 40 60 80 ROUTINE LEAK ETS control 50nM AA AA + CHX Oxygenfluxpermass(pmol.s-1.mg-1) ** *** NS ** * NS 0 20 40 60 80 ROUTINE LEAK ETS control 0.25uM Oligo Oligo + CHX Oxygenfluxpermass(pmol.s-1.mg-1) *** NS *** * * NS CI CIII CV Peng M et al, Human Molecular Genetics, In press
  44. 44. 0 10,000 20,000 30,000 40,000 50,000 TMREMeanCellFluorescence (RFU) Control 25 nM 50nM 25nM 50 nM CHX ROT ROT ROT ROT + CHX + CHX 0.0E+00 4.0E+05 8.0E+05 1.2E+06 1.6E+06 2.0E+06 Control 50nM Rotenone ROT-0.9uM CHX MeanCellFluorescence (RFU) MitoTracker Green # * 1.0E+06 1.1E+06 1.2E+06 1.3E+06 1.4E+06 1.5E+06 1.6E+06 1.7E+06 1.8E+06 1.9E+06 2.0E+06 F35 Q1007 Q1007- 0.9uM CHX MitoTracker Green MeanCellFluorescence (RFU) Control FBXL4 FBXL4 + CHX * ** * 50,000 60,000 70,000 80,000 90,000 100,000 110,000 Q1007 control Q1007-0.9uM chx MeanCellFluorescence MitoSox * FBXL4 FBXL4 + CHX Peng M et al, Human Molecular Genetics, In press
  45. 45. mTORC1 S6 AMPK P P Mitochondrion Lysosome Ribosome Ribosome MITOPHAGY Proteotoxic Stress CHX RAPA Probucol LiCl, 3-MA Probucol [c] [1] [4] [d] [5] [a] [3] [b] [2] Peng M et al, Human Molecular Genetics, In press
  46. 46. Zhang Z et al, PLOS ONE, 2013 Nicotinic acid normalizes mTORC1 & AMPK activities, NADH/NAD+ levels, and total cellular respiratory capacity in ND4/ND6 human fibroblasts
  47. 47. CONCLUSION Characterizing and therapeutically targeting central alterations in the nutrient-sensing signaling network may offer a personalized means to modify global sequelae downstream of OXPHOS dysfunction and improve health outcomes in primary RC disease
  48. 48. New Model for Getting to Effective Therapies for Mitochondrial Diseases In Vitro Laboratory Drug testing in Mito Disease -Patients’ cells (Fibroblasts vs Tissue-specific) -Genetic models of RC disease -Integrated physiologic endpoints -Toxicity studies Disease Definition -Phenotype + Function -Biochemical -Organelle -Genetic etiology -Molecular Pathway Outcome Prioritization -Organ system -Pathophysiology -Function -Biomarker Treatment Options -Off-purpose FDA drugs -Medical Foods -Dietary Supplements -Vitamins -New drugs from Pharma Standard of Care Clinical Trials
  49. 49. Acknowledgements University of Pennsylvania Rui Xiao, PhD David L. Gasser, PhD Eiko Nakamaru-Ogiso, PhD Joseph Baur, PhD FUNDING R01-HD065858 (NICHD, NIH) R03-DK082521 (NIDDK, NIH) R01-DK055852 (NIDDK, NIH) IDDRC New Investigator Award, NICHD Philadelphia Foundation Tristan Mullen Fund Angelina Maio Fund Kelsey Wright Foundation Juliet’s Cure Mitochondrial Disease Research Fund Center for Mitochondrial & Epigenomic Medicine CHOP Jim (Zhe) Zhang, PhD Marc Yudkoff, MD Eric Rappaport, PhD Michael Bennett, PhD Douglas Wallace, PhD Colleen Clarke, MS, CGC Arizona State University Sid Hecht, PhD Omar Khdour, PhD FALK LAB

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