1

PROF. DR DORU PAMFILPROF. DR DORU PAMFILdpamfil@usamvcluj.rodpamfil@usamvcluj.roHEAD of ILSHEAD of ILSCalea Manastur 3-5, Calea Manastur 3-5, 400372 Cluj-Napoca400372 Cluj-Napocawww.usamvcluj.rowww.usamvcluj.ro

INSTITUTE OF LIFE SCIENCE INSTITUTE OF LIFE SCIENCE ILSILS

INSTITUTE OF LIFE SCIENCE INSTITUTE OF LIFE SCIENCE ILSILS

2

GeneralGeneral

Founded in 1869Faculty of AgricultureFaculty of Animal Science & BiotechnologyFaculty of HorticultureFaculty of Veterinary Medicine

INSTITUTE OF LIFE SCIENCE (ILS) - 2007

3

WHYWHYWHYWHY

SPINE – Structural Proteomics IN Europe

4

RationalRational 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

CompositionComposition Team: 24 center of Excellence/Expertise regionally Collective purpose: a national resource

5

TeamTeam

6

ILS Partnerships with USAMV

USAMVUSAMV

Universities at Cluj-NapocaUSAMV - 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 & ResearchInterdisciplinary Collaboration is a Hallmark of ILS Teaching & Research

7

Concept and Expansion of ILSINSTITUTE OF LIFE SCIENCE CLUJ-

NAPOCA

ILS – Innovation

ILS –Research & Co-operation

ILS – Facility

ILS – Factors of Success

ILS – Concept

8

Demand-Driven ProcessDemand-Driven Process

opportunity

implementation

results

sustainability

needs

Approximately 90% Integration into the Workforce Approximately 90% Integration into the Workforce

9

Partnerships are EssentialPartnerships are EssentialPartnerships are EssentialPartnerships are Essential

College

Workforce Development

Industry

10

VISION & MISSIONVISION & 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

PRIORITIESPRIORITIES

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 labto commercialization

Enhance high performance computing capabilities

13

om

ics

Transcriptomics

High throughput screening

Moleculebank

Genomics / proteomics

Sclerosis Multiplex, POCD, Alzheimer

Oral antidiabetics

Sa

fety p

ha

rma

colo

gy

Chemiotherapy Nanotechnology

Proteomic platform

Microbial genomics / proteomics / transcriptomics

Fermentation/Bioprotect

DNS → animal breeding

Molecular farming

Safety pharmacology

Microarray platformBio

me

dica

l m

ea

sure

me

nt

Ag

ro-b

iote

chn

olo

gy

Metabolomics

Enriched egg

Technology Transfer ManagementBiomedical Informatics

Joint technology platforms

14

ORGANISATION – ILS – CORE LABORATORIESORGANISATION – 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

Biotechnology

Medicine:

-Therapeutics-Diagnostics

-Vaccines-Early Diagnosis

Agriculture

-GMO -Plants

-Nutrition

Environment

Detection and decompositionof pollutants

Industrial Production

-Manufacturingprocesses

-Use of naturalmaterial

Use of marine organisms

-Cosmetics-Drugs

-New materials

ILS – Research & Co-operationILS – Research & Co-operation

BIOTECHNOLOGY

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-operationILS – 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-operationILS – Research & Co-operation

18

ILS wish to establishe a global network

ILS – Research & Co-operation

19

Institutional PPPFocused managementPermanent monitoring of the life cycle of the facilityOptimized risk allocationProfit oriented facility conceptMarket orientationSpecific Know How

- Real estate- Economics- Science

Interface between politics and business

ILS – Factors of SuccessILS – Factors of Success

20

R & D

Production

Human Resources

ILS

Finance & Risk Capital

Infra - structure

one stop agency

Management Services & Consulting

Political support

ILS – Factors of SuccessILS – Factors of Success

21

ILS – Factors of SuccessILS – Factors of SuccessReasons for a settlement within FIZ

0,0

1,0

2,0

3,0

4,0

5,01) Infrastructure

1a) Airport / Access to motorway network

1b) Access to public transport connection

1c) Architecture

2) Financial Center

2a) Bourse

2b) VC/PE

2c) Corporate Capital

3) Research & Developement

3a) Access to academicresearch (UNI/MPI)

3b) IP/technology transfer

3c) International cooperations

in identified areas

4) Industry

4a) Production

4b) Commercialization

4c) Clustering

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

StrengthsStrengths 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

OpportunitiesOpportunities

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

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 PetrutGroup 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

Internal collaborations

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 TopicsResearch 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

29

MICROARRAY FACILITYMICROARRAY FACILITY

30

31

Why analyze so many genes?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: tasksMicroarray facility: tasks

Microarray production

Protocol development

User support

39

User support: philosophyUser support: philosophy

CollaborationAs many groups as possibleTrain users

40

Spotted cDNA microarraysSpotted 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 arraysSpotted arrays

1 nanolitre spots90-120 um diameter

384 well source plate

chemically modified slides

steel

spotting pin

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: arraysUser support: arrays

suitable for two channel (Cy3 / Cy5) experiments; i.e. two sample comparison

44Preluat de la J Assouline, Genome-wide measurement of gene expression

45

MIAME - cADN microarrayMIAME - cADN microarray

6 STEPS of a Microarray Experiment

Array

• Array design

Hybridization protocol

• Statistical analysis • Data mining

• Image acquisition and quantification• Database building • Filtering • Normalization

HybridizationSample

(S. Sansone)

MIAME • Sample QC• Sample treatment• Metadata• mARNs Extraction protocol QC• Target preparation (labeling) - QC

QC

QC

QC

46

47

From Macgregor and Squire, Clinical Chemistry, 2002

48

49

RNA/DNA quality and quantity assesment

Spectrophotometer: quantitycDNA synthesis (>30 µg total RNA)

RNA amplification (> 1 µg total RNA)

Total RNA

Bioanalyzer: integrity of RNA

(labeled) cDNA

50

Cy3 Cy5

51

Cy-3 Cy-5 Gene D Over-expressed in normal tissue

Gene E Over-expressed in tumour

• Biomarkersof prognosis

• Genes affecting Treatment

Response

Data quality check :• QQCC: QC

NormalizedRaw 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 DesignExperimental 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”:What is a “dye switch”:What is a “dye switch”: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-processingData 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 : NormalizationData 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 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)

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”) ClusteringTwo dimensional hierarchical (“Eisen”) Clustering

From:

Dhanasekaran et al.

Nature, 421, p.822.

63

Significance Analysis of Microarrays (SAM)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: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

OvaryProstate

66

TEAM WORK in prostate cancer at the IOCN-UMF-USAMVTEAM 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:Urgent need for:

Improved detectionImproved detection

Better tumor classification

Better Better evaluation of responseevaluation of response to currently to currently used and experimental chemotherapyused and experimental chemotherapy

New therapeutic targetsNew treatments

68

Our lab’s approach: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 challengeTumor 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)Laser Capture Microdissection (LCM)

71

Microdissected PIN

(prostatic intraepithelial

neoplasia)

Before LCM

After LCM

72

Microarray data validationMicroarray data validation

cDNA microarrays: one patient - 20,000 genes

Tissue arrays: one gene -1000 patientsRT-PCRImmunohistochemistry (IHC)In situ Hybridization (ISH)

Cancer profiling arrays: one gene - 10 tumor/normal sample pairs for different tumors

73

Tissue ArraysTissue Arrays

Monni et al, Seminars in Cancer Biology, (2001), 11, p.395

74

Northern Blot, Tissue ArraysNorthern Blot, Tissue Arrays

a. Northern blot

b. Tissue array

c. 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, IHCcDNA array, Northern, ISH, IHC

Mousses et al, Cancer Research (2002), 62, p. 1256

76

Cancer Profiling Array (Clontech)Cancer Profiling Array (Clontech)

Wiechen Wiechen et alet al, American , American Journal of Pathology, (2001), Journal of Pathology, (2001), 159159, p.1635, p.1635

77

Predicting the FuturePredicting the FuturePredicting the FuturePredicting 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?

78

PROTEOMICS FACILITYPROTEOMICS FACILITY

79

80

Uncovering gene enhancer elements (& J Taipale, Biomedicum)

gene1 gene2 gene3 gene4DNA

RNA

transcription

translation

Proteins

transcription factors

enhancer module

APPLICATIONSAPPLICATIONS

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 goalsMission 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 topicsResearch 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 visionFuture 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 supportsUSAMV Rector supports

90

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

"The problem solving, systems thinking, and teamwork aspects of biotechnology can benefit all students, whether or not they ever pursue an scientific career," said Doru PAMFIL, Rector of the University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, head of INSTITUTE OF LIFE SCIENCES (LIS). 

"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." 

91

91

Helping Hands for Addressing Helping Hands for Addressing Excellence in Research, Education Excellence in Research, Education

and Training Needs for and Training Needs for BiotechnologyBiotechnology

Helping Hands for Addressing Helping Hands for Addressing Excellence in Research, Education Excellence in Research, Education

and Training Needs for and Training Needs for BiotechnologyBiotechnology

For more information on future partnerships, contact Dr. Ramona SUHAROSCHI

Email: laslora@yahoo.comTel/fax:+40 264 425575

Calea Floresti 64400509, Cluj-Napoca

LIFE SCIENCE INSTITUTELIFE SCIENCE INSTITUTECalea Manstur 3-5Calea Manstur 3-5400372, Cluj-Napoca400372, Cluj-Napoca

For more information on future partnerships, contact Dr. Ramona SUHAROSCHI

Email: laslora@yahoo.comTel/fax:+40 264 425575

Calea Floresti 64400509, Cluj-Napoca

LIFE SCIENCE INSTITUTELIFE SCIENCE INSTITUTECalea Manstur 3-5Calea Manstur 3-5400372, Cluj-Napoca400372, Cluj-Napoca

92

Thank you – I am charmed to be your guest!Thank you – I am charmed to be your guest!

93

ILS – we are looking forward to meeting You!ILS – we are looking forward to meeting You!