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  • Department of Pharmacology Faculty of Pharmacy Faculty of Biology (Experimental Biology Laboratory) Bioinformatics and Biostatistics
  • Genomics Center Real-time quantitative PCR Microarray experiments full range of genomics technologies including automatic DNA sequencing, gene expression array technologies, profiling, genotyping Bioinformatics Proteomics Center Metabolomics Center Other facilities provided for EIP purposes: HPLC and full analitical range of Mass Spectrometers Cell culture facilities Flow cytometry Confocal and deconvolution microscopy Imaging (PET, MRI, SPECT) scanning and transmission EM, atomic force microscopy Agricultural biotechnology - rural clusters Manufacturing - Biomanufacturing/ bioprocessing Informatics - life science applications linked to software/ IT clusters Research & Development – adjacent to universities with biotechnology / biomedical research focus
  • The earliest biotechnologists were probably the Sumerians who brewed their beer with yeast, thereby using living organisms. This use of biotechnology was not intentional. Today biotechnology is used in our every day lives, e.g. in the kitchen (food and dish liquid), in the bathroom (cosmetics and detergent) or whenever we are ill. In general you can say that biotechnology is the use of biological processes, organisms, or systems to manufacture products intended to improve the quality of human life. In recent years, biotechnology has expanded in sophistication, scope, and applicability. The science of biotechnology can be broken down into sub disciplines known as red, green, grey, white, and blue. Red biotechnology involves medical processes, for instance using organisms to produce new drugs, or using stem cells to regenerate damaged human tissues and perhaps re-grow entire organs. Green biotechnology applies to agriculture and involves such processes as the development of pest-resistant grains or the accelerated evolution of disease-resistant animals. Grey biotechnology is mainly focused on processes that digest existing toxic substances found in our environment. White biotechnology involves industrial processes such as the production of new chemicals or the development of new fuels for vehicles. Blue biotechnology encompasses processes in marine and aquatic environments, for example - controlling the proliferation of toxic water-borne organisms. LIS clearly focuses on red biotechnology - especially on cancer, inflammatory diseases, diseases of the central nervous system, and proteomics. This specialization is part of the overall concept and responds to the specific resources the Rhine-Main-area has to offer.
  • 1- Manufacturing of the microarray: clone collection acquisition (+ sequencing), PCR amplification and insert verification, spotting, QC. 2- Experimental design and choice of reference: what to compare to what? 3- Target preparation (labeling) and hybridization 4- Image acquisition (scanning) and quantification (signal intensity to numbers) 5- Database building, filtering and normalization 6- Statistical analysis and data mining
  • Transcript

    • 1. PROF. DR DORU PAMFIL [email_address] HEAD of ILS Calea Manastur 3-5, 400372 Cluj-Napoca INSTITUTE OF LIFE SCIENCE ILS
    • 2. General Founded in 1869 Faculty of Agriculture Faculty of Animal Science & Biotechnology Faculty of Horticulture Faculty of Veterinary Medicine INSTITUTE OF LIFE SCIENCE (ILS) - 2007
    • 3. WHY SPINE – S tructural P roteomics IN E urope
    • 4.
      • Rational
      • Capture: best practices in the development of skill standards, certification and curriculum in regionally specialized biotech training centers
      • Disseminate: make available replicable models to community colleges across Romania
      • Composition
      • Team: 24 center of Excellence/Expertise regionally
      • Collective purpose: a national resource
    • 5. Team
    • 6. ILS Partnerships with USAMV
      • USAMV
      Universities at Cluj-Napoca USAMV - ILS Grove Center University of Medicine and Phramacy “Iuliu Hatieganu” Technical University of Cluj-Napoca Faculty of Veterinary Medicine Institute of Cancer Research “dr. I. Chiricuta” Faculty of Horticulture Faculty of Animal Science & Biotechnology University of Babes Bolyai Faculty of Agriculture
      • Located in the heart of Transylvania
      • Full-time undergraduate programe, distance learning, e-learning and postgraduate program (master courses, joint doctoral)
      Interdisciplinary Collaboration is a Hallmark of ILS Teaching & Research
    • 7. Concept and Expansion of ILS INSTITUTE OF LIFE SCIENCE CLUJ-NAPOCA ILS – Innovation ILS – Research & Co-operation ILS – Facility ILS – Factors of Success ILS – Concept
    • 8. Demand-Driven Process Approximately 90% Integration into the Workforce opportunity implementation results sustainability needs
    • 9. Partnerships are Essential College Workforce Development Industry
    • 10. VISION & MISSION
      • VISION
      • Cluj-Napoca ILS is the preferred location for new business opportunities through knowledge creation in the Agriculture, Food Science, Biotechnology, Pharmaceutical, and Health Care
        • ILS the preferred Partner for Biotechnology Enterprises
      • MISSION - The Science & Technology Development Program works to strengthen Cluj universities so they can serve as foundations for biotechnology economic development by:
        • Enhancing the ability of universities to attract federal funding
        • Funding research of commercial interest
    • 11. PRIORITIES
      • Provide forum for integration
      • Promote ILS - BioTech (press, conf. etc)
      • Grow Biotechnology Research (pilot plant)
      • Educational Development (PhD, MSc., Cert., Awareness)
    • 12. Infrastructure for Universities Encourage multidisciplinary collaborative research Support faculty recruitment Provide venues for intellectual exchange Provide core facilities & equipment Move of IP from lab to commercialization Enhance high performance computing capabilities
    • 13. omics Transcriptomics High throughput screening Moleculebank Genomics / proteomics Sclerosis Multiplex, POCD, Alzheimer Oral antidiabetics Safety pharmacology Chemiotherapy Nanotechnology Proteomic platform Microbial genomics / proteomics / transcriptomics Fermentation/Bioprotect DNS -> animal breeding Molecular farming Safety pharmacology Microarray platform Biomedical measurement Agro-biotechnology Metabolomics Enriched egg Technology Transfer Management Biomedical Informatics Joint technology platforms
    • 14. ORGANISATION – ILS – CORE LABORATORIES Cell culture Transgenic animal model DNA Sequencing Post-genomic Histology, cytology, morphology Imaging Glassware & Lab maintenance Microarray laboratory Proteomics Laboratory Bioinformatics & Biostatistics High throughput analytical chemistry Translational research System biology
    • 15. ILS – Research & Co-operation BIOTECHNOLOGY Biotechnology
      • Medicine:
      • Therapeutics
      • Diagnostics
      • Vaccines
      • Early Diagnosis
      • Agriculture
      • GMO
      • Plants
      • Nutrition
      Environment Detection and decomposition of pollutants
      • Industrial
      • Production
      • Manufacturing
      • processes
      • Use of natural
      • material
      • Use of marine
      • organisms
      • Cosmetics
      • Drugs
      • New materials
    • 16.
        • Indication areas at ILS
          • Diseases of the Central Nervous System;
          • Inflammatory Diseases;
          • Cancer;
          • Proteomics
          • Probiotics, Functional Food.
        • Indication areas are based on:
          • Regional resources , know how and research co-operations within the local institutes and companies
          • Global market expectations
        •  Focus increases the efficiency of Innovation
      ILS – Research & Co-operation
    • 17.
        • Alliance of local members of the cluster by competition and supply relationships or common interests
        • examples at ILS:
      ILS – Research & Co-operation
    • 18.
        • ILS wish to establishe a global network
      ILS – Research & Co-operation
    • 19.
      • Institutional PPP
        • Focused management
        • Permanent monitoring of the life cycle of the facility
        • Optimized risk allocation
        • Profit oriented facility concept
        • Market orientation
        • Specific Know How
          • Real estate
          • Economics
          • Science
        • Interface between politics and business
      ILS – Factors of Success
    • 20. ILS – Factors of Success R & D Production Human Resources ILS Finance & Risk Capital Infra - structure one stop agency Management Services & Consulting Political support
    • 21. ILS – Factors of Success ILS
    • 22. POLICY MAKERS ACADEMIC PARTNERS ILS University of Medicine and Pharmacy University of Agricultural Sciences and Veterinary Medicine University of Babes Bolyai Technical University County Hospital of Cluj Institute of Oncology Institute of Public Health Institute of Public Health Veterinary State Direction EC NASR CNCSIS Start-up company Spin-off company PUBLIC PARTNERS
    • 23. Strengths
      • Research connected to teaching and students
      • Several internationally strong research groups
      • Two large research units
        • From Data to Knowledge - DtK
        • Institute for Information Technology - (Basic Research Unit - BRU)
      • Research infrastructure
      • Good success in the competition of research funding
    • 24. Opportunities
      • Still stronger networking with international scientific community
        • Joint European projects
        • International recruiting
      • Still stronger co-operation within Cluj campus
        • Other departments of the Faculty, University
        • UMF, UBB, UT-CN,
        • IOCN, Hospitals, University’s Cliniques
      • Still stronger co-operation with other sciences
        • Bioinformatics
        • Functional Genomics, Proteomics
      • Still stronger interaction with society
        • Industrial innovations
        • open-source source
    • 25. Internal collaborations
      • Group Ioana Neagoe
      • Al. Irimia
      • P Virag
      • I Brie
      • C Braicu
      • O Tudoran
      Group Doru Pamfil -Ioana Petricele -Iulia Francesca Pop -Cristian Sisea -CT Socol
      • Group Ramona Suharoschi
      • C Semeniuc
      • vacancy
      • S Andrei
      • V Chedea
      • R Petrut
      • Group Manuela Banciu
      • MT Chiriac
      • HL Banciu
      • LB Tudoran
      • J Endre
      • Group Liviu Marghitas
      • Dan Dezmirean
      • O Bobis
      • Laura Stan
      • Otilia Popescu
      • vacancy
      • Subgroup Tibor Krausz
      • A Gionis/SA
      • F Afrati/FDK
      • N Haiminen/FDK
      • Subgroup Maria Tofana
      • S Socaci
      • E Mudura
      • C Muresan
      • S Muste
      • R Cheleman
      • D Truta
      • D Tic
      • AM Pop
      • S Man
      • V Muresan
      • Group Laurian Vlase
      • DS Popa
      • D Muntean
      • AS Porfire
      • R Iovanov
      • SubgroupOvidiu Balacescu
      • L Balacescu
      • Subgroup Ioan Groza
      • A Gionis/SA
      • F Afrati/FDK
      • N Haiminen/FDK
      • Subgroup Raul Malutan
      • vacancy
      • Group Sorin Apostu
      • AM Rotar
      • C Lazar
      • Group Cornel Catoi
      • A Gal
      • Taulescu
      • I Rus
      • Group I Groza
      • I Moraru
      • Subgroup Carmen Socaciu
      • A Stanila
      • D Preda
      • D Vodnar
      • vacancy
    • 26. External collaborations
    • 27. Research Topics
    • 28. EU projects
      • INTERREG IVC 0340R1 - Common Land for sustainable management, COMMONS
      • 7th EU Frame Programme FP7-OC-2007-2-1705 – Molecular farming, MOLFARM
      • ANCS CG 8/0606/2008 – Investigation of ornamental germplasm exchange of breeding technology
      • 6th EU Frame Programme FP6 IRC, no. 510512 - Inovation Relay Centre
      • Leonardo, RO/03/B/NT/BB/17056 , Quality Management Network for CEECs (Central and East European Countries) - QUAMANCEEC.
      • 5th EU Frame Programme FP5-QLK3 CT-2002-02140 TRANSVIR
      • 4th EU Frame Programme FP4-INCO/COPERNICUS PL 96-6084 Study of growth regulation in plum tree – TUTOR
      • Leonardo: RO/2004/PL 91176/S
      • Leonardo: RO/2002/PL 89074
      • Mondial Bank BCUM, nr. 35/2000 - LAMARGEN
    • 30.  
    • 31. Why analyze so many genes?
      • <10% of the human genome has been studied at the level of gene function. 40,000 odd genes represent the pool of remaining drug targets
      • Patterns/clusters of expression are more predictive than looking at one or two prognostic markers
      • Increased accuracy/confidence
      • Unbiased. Empiric. Holistic. Independent of “flawed” hypotheses
    • 32.  
    • 33.  
    • 34.  
    • 35.  
    • 36.  
    • 37.  
    • 38. Microarray facility: tasks
      • Microarray production
      • Protocol development
      • User support
    • 39. User support: philosophy
      • Collaboration
      • As many groups as possible
      • Train users
    • 40. Spotted cDNA microarrays
      • Advantages
      • Lower price and flexibility
      • Simultaneous comparison of two related biological samples (tumor versus normal, treated cells versus untreated cells)
      • ESTs allow discovery of new genes
      • Disadvantages
      • Needs sequence verification
      • Measures the relative level of expression between 2 samples
    • 41. Spotted arrays 1 nanolitre spots 90-120 um diameter steel spotting pin 384 well source plate chemically modified slides
    • 42.  
    • 43.
      • spotted material:
        • gene specific oligos (70-mer) or cDNAs
        • oligos for the external controls: for various purposes (van de Peppel et al. , 2003, EMBO reports, 4, p.387-393)
      User support: arrays suitable for two channel (Cy3 / Cy5) experiments; i.e. two sample comparison
    • 44. Preluat de la J Assouline, Genome-wide measurement of gene expression
    • 45. MIAME - cADN microarray 6 STEPS of a Microarray Experiment
      • Array design
      • Statistical analysis
      • Data mining
      • Image acquisition and
      • quantification
      • Database building
      • Filtering
      • Normalization
      (S. Sansone) MIAME
      • Sample QC
      • Sample treatment
      • Metadata
      • mARNs Extraction protocol QC
      • Target preparation (labeling) - QC
      QC QC QC Array Hybridization protocol Hybridization Sample
    • 46.  
    • 47. From Macgregor and Squire, Clinical Chemistry, 2002
    • 48.  
    • 49. RNA/DNA quality and quantity assesment
      • Spectrophotometer: quantity
        • cDNA synthesis ( >30 µg total RNA)
        • RNA amplification ( > 1 µ g total RNA)
      Total RNA Bioanalyzer: integrity of RNA (labeled) cDNA
    • 50.  
    • 51. Cy-3 Cy-5 Gene D Over-expressed in normal tissue Gene E Over- expressed in tumour • Biomarkers of prognosis • Genes affecting Treatment Response
      • Data quality check :
        • QQCC: QC
      Normalized Raw data normalisation
    • 52.
      • Data storage and advanced analysis :
        • Genespring 6.0 / Genet + BASE:
    • 53. Microarray Applications (some)
      • Can identify new genes implicated in disease progression and treatment response (90% of our genes have yet to be ascribed a function)
      • Can assess side-effects or drug reaction profiles
      • Can extract prognostic information, e.g. classify tumours based on hundreds of parameters rather than 2 or 3
      • Can detect gene copy number changes in cancer (array CGH)
      • Can identify new drug targets and accelerate drug discovery and testing
      • ???
    • 54. The challenges of microarrays
      • Acquisition of high quality clinical samples , tumor and normal tissues
      • High Quality RNA
      • Experimental design : what to compare to what?
      • Data analysis -1: what to do with the data?
      • Data analysis -2: How do to it?
        • Very large number of data points
        • Size of data files
        • Choice of data analysis strategy / algorithm / software
    • 55. Experimental Design
      • Choice of reference : Common (non-biologically relevant) reference, or paired samples?
      • Number of replicates : how many are needed? (How many are affordable...?). Are the replicate results going to be averaged or treated independently?
      • Dye switches ?
      • Choice of data base : where and how to store the data?
    • 56. What is a “dye switch”:
      • One slide with experimental sample labeled with Cy5, and reference sample labeled with Cy3 (“straight”)
      • Replicate slide with experimental sample labeled with Cy3, and reference sample labeled with Cy5 (“switch”)
    • 57. Data Pre-processing
      • Filtering : background subtraction? Low intensity spots? Saturated spots? Low quality spots (ghosts spots, dust spots etc).
      • Filtering or flagging?
      • Outliers?
    • 58. Data Pre-processing : Normalization
        • Housekeeping genes / control genes
        • Intensity dependent (most commonly used): global intensity or global ratio, calculates a single normalization factor
        • Intensity independent (LOWESS – Locally Weighted Scatter plot Smoother) calculates a function
        • Global array or Sub-array
    • 59. Microarray data analysis
      • Scatter plots of intensities of tumor samples versus normal samples: quick look at the changers and overall quality of microarray
      • Supervised versus unsupervised analysis
        • Clustering : organization of genes that are similar to each other and samples that are similar to each other using clustering algorithms
        • Statistical analysis : how significant are the results?
    • 60. log/log scatter plot UP DOWN
    • 61. 2 dimensional hierarchical (“Eisen”) clustering (Eisen et al , PNAS (1998), 95 , p. 14863)
      • Unsupervised : no assumption on samples
      • The algorithm successively joins gene expression profiles to form a dendrogram based on their pair-wise similarities.
      • Two-dimensional hierarchical clustering first reorders genes and then reorders tumors based on similarities of gene expression between samples
    • 62. Two dimensional hierarchical (“Eisen”) Clustering From: Dhanasekaran et al. Nature, 421 , p.822.
    • 63. Significance Analysis of Microarrays (SAM)
      • Supervised learning software for genomic expression data mining
      • Developed at Stanford University , based on the paper of Tusher et al PNAS (2001) 98 , p. 5116.
    • 64. What SAM does:
      • SAM assigns a score to each genes on the basis of the change in gene expression relative to the standard deviation of repeated measurements.
      • For genes with scores above a certain threshold (set by the user), SAM uses permutations of the repeated measurements to estimate the % of genes identified by chance = the false discovery rate (FDR).
    • 65. Every cell in the body has the same genetic constitution. In cancer cells there is usually acquired aberrant DNA Development of “cancer genomics” to better understand the molecular basis of acquired genetic change Breast Ovary Prostate
    • 66. TEAM WORK in prostate cancer at the IOCN-UMF-USAMV
      • Clinicians / Pathologists / Basic Scientists / Computer Scientists
      • Tissue bank (tumor tissue and normal prostate tissue) of ~ 500 samples
      • New patients treated at County Clinical Hospital and IOCN for ovarian cancer (~100/ year)
      • Microarray Centre / Basic Research labs
    • 67. Urgent need for:
      • Improved detection
      • Better tumor classification
      • Better evaluation of response to currently used and experimental chemotherapy
      • New therapeutic targets
      • New treatments
    • 68. Our lab’s approach:
      • Combine the powers of protein microarrays and RNA expression (cDNA microarrays) to facilitate the identification of smaller subsets of genes pertinent to prostate cancer adenocarcinoma
    • 69. Tumor heterogeneity challenge
      • **Some cancers, such as prostate cancer, are multifocal / heterogeneous .
      • **For these tumors, “bulk” extraction of genetic material from tumor tissue will produce microarray results that are “contaminated” by normal or pre-malignant tissue
    • 70. Laser Capture Microdissection (LCM)
    • 71. Microdissected PIN (prostatic intraepithelial neoplasia) Before LCM After LCM
    • 72. Microarray data validation
      • cDNA microarrays : one patient - 20,000 genes
      • Tissue arrays : one gene -1000 patients
      • RT-PCR
      • Immunohistochemistry (IHC)
      • In situ Hybridization (ISH)
      • Cancer profiling arrays : one gene - 10 tumor/normal sample pairs for different tumors
    • 73. Tissue Arrays Monni et al , Seminars in Cancer Biology, (2001), 11 , p.395
    • 74. Northern Blot, Tissue Arrays
      • Northern blot
      • Tissue array
      • IHC, anti-hepsin antibody (1:benign-2:cancer) X 100
      • d. X 400
      Dhanasekaran et al, Nature, (2001) 421 , p.822
    • 75. cDNA array, Northern, ISH, IHC Mousses et al, Cancer Research (2002), 62 , p. 1256
    • 76. Cancer Profiling Array (Clontech) Wiechen et al , American Journal of Pathology, (2001), 159 , p.1635
    • 77. Predicting the Future
      • What is going to happen now that the human and other genomes are completed?
      • How quickly the next steps will happen?
      • What are the potential difficulties?
    • 79.  
    • 80. Uncovering gene enhancer elements (& J Taipale, Biomedicum) APPLICATIONS gene 1 gene 2 gene 3 gene 4 DNA RNA transcription translation Proteins transcription factors enhancer module
    • 81. Model of cell type specific regulation of target gene expression GLI X Y (tissue specific TFs) GLI GLI Ubiquitously expressed TF transcription transcription Common targets (e.g. Patched): Cell type specific targets (e.g. N-myc):
    • 82. Binding affinity matrices
      • Transcription factor binding sites represented by affinity matrices
      • Discovered:
        • Computationally
        • Traditional wet lab
        • Microarrays
      9 11 49 51 0 1 1 4 19 3 0 0 0 45 25 16 5 1 2 0 17 0 4 21 18 36 0 0 34 5 21 10
    • 83. Metabolic networks and systems biology (& J Rousu & VTT Biotech)
    • 84. From Data to Knowledge Research Unit - Information systems
    • 85. Mission and goals
      • Mission : provide methods for analysing and querying masses of data for useful inferred knowledge.
      • Research on data mining
        • Computational methods for data analysis
        • Theory of data mining, algorithms
        • Implementations and applications
        • Data mining in bioinformatics and language technology
      • Interaction of applications and theory
    • 86. Research topics
      • Non-redundant association rules
      • Frequent Datalog patterns
      • Fast pattern enumeration and evaluation algorithms
      • Discovery of functional dependencies
      • Text pattern induction by alignment
      • Discovery of maximal frequent sequences in text
      • Unsupervised methods for knowledge acquisition in text
      • Methods for text segmentation and its evaluation
      • Time series segmentation
      • (Efficient algorithms for) variable length Markov models
      • Bayesian model fitting using MCMC
      • Nested permutation tests
    • 87. Research projects (grouped by applications)
      • Focus on selected application topics
        • in bioinformatics and language technology
        • where we can have a significant impact
        • where we can team up with excellent application partners
      • Gene mapping
        • discover genetic patterns in case-control data
      • Haplotyping
        • find the highest probability strings (haplotypes) explaining sequences of pairs (genotypes)
      • Information extraction from epidemiological reports (ProMED-mail/Harvard Medical School)
        • extract facts (disease, location, time,…) from plain text
    • 88. Relevance and interaction with society
      • Fielded applications in industry and public sector
      • Software for human genetics (HaploRec, HPM, TreeDT), epidemiological fact base (ProMED-PLUS), technical documentation, context analysis
    • 89. Future vision
      • Continue work on important data analysis problems in bioinformatics and language technology
        • Applications, including fielded and commercialized ones
        • Theory and method development
        • Collaboration across units, disciplines, industry
      • Future emphasis on
        • Mining rich public biological databases
          • Discovery of patterns in complex irregular structures, discovery of similarities and analogies
        • Methods for semantic analysis of large text collections
          • language and domain-independence, efficiency
    • 90. USAMV Rector supports
      • Doru PAMFIL, PhD — an accomplished professor of genetics, he has expertise in the management of many national and international projects and veteran of large, public research institutions.
        • Throughout his career he has focused on expanding undergraduate research opportunities and improving the education and professional experience of graduate students, with a focus on underrepresented groups.
        • Pamfil’s academic work focuses on Biotechnology (marker assisted selection, detection of GMOs, genetic transformation, micropropagation, microbiology). The GMOs Laboratory – CERTOMG - was included in EUROPEAN NETWORK OF GMO LABORATORIES (ENGL) in 2009
      &quot;The problem solving, systems thinking, and teamwork aspects of biotechnology can benefit all students, whether or not they ever pursue an scientific career,&quot; said Doru PAMFIL, Rector of the University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, head of INSTITUTE OF LIFE SCIENCES (LIS).  &quot;An biotechnology education and science curricula that does not include at least some exposure to biotechnology is a lost opportunity for students and for the nation.&quot;  
    • 91.
      • For more information on future partnerships, contact Dr. Ramona SUHAROSCHI
      • Email: [email_address]
      • Tel/fax:+40 264 425575
      • Calea Floresti 64
      • 400509, Cluj-Napoca
      • Calea Manstur 3-5
      • 400372, Cluj-Napoca
      Helping Hands for Addressing Excellence in Research, Education and Training Needs for Biotechnology
    • 92. Thank you – I am charmed to be your guest!
    • 93. ILS – we are looking forward to meeting You!