Chip-Assisted Analysis of Epithelial Transporter Proteins

Pascale Anderle, ISREC Lausanne

Overview

1. Introduction

1. Transporters in the context of the whole genome

2. Classification of transporters

3. Introduction into microarray technology

4. Overview on various microarray platforms

2. Strategies to select transporter genes and example studies

1. First example: Custom Array

1. Evaluation of transporter and channel genes in the intestine

2. Use of Hidden Markov Models

3. Summary

2. Second example: Affymetrix Platform

1. Genomic profiling of membrane transporters in the intestine

2. Gene Ontology Project

3. Importance of annotation

4. Isrec Ontologizer

5. Conclusions

3. Acknowledgment

Venter at al., Science 2001

Transporters in the Context of the Whole Genome

Membrane Transporter Proteins: Classification

Membrane Transport Proteins

Selective Channels Specific Carriers

Facilitated Diffusion Primary Active Transport Secondary Active Transport

Uniporters

GluT1-5

ATP-powered pumps

ATPases: P-type, F-type and ABC-type ATPases

(ABC transporters)

Symporters Antiporters

hPept1 SLC18A1*

Facilitated diffusionTransport of substances across the membrane by means of uniporters. Transport is from an area of higher concentration to lower concentration. Passive transport powered by the potential energy of a concentration gradient and does not require the expenditure of metabolic energy

Primary active transportEnergy derived from the hydrolysis of ATP to ADP liberating energy from high energy phosphate bond

Secondary active transport. Use of energy from another source-another secondary diffusion gradient set up across the membrane using another ion. Because this secondary diffusion gradient is initially established using an ion pump, as in primary active transport, the energy is ultimately derived from the same source-ATP hydrolysis. *Monoamine transporter, carrier of doxorubicin

http://tcdb.ucsd.edu/tcdb http://lab.digibench.net/transporter/

Introduction into Microarray Technology

Oligomers PCR products

ProbesSpotting:

Photolithography

Printing

Physical support:Glass slide, nylon membrane

Affymetrix: Short oligo chipSingle labeling

cDNA chip: Oligos or PCR products

Dual-labeling

Sample preparation and hybridization:

cRNA vs. cDNA

Single-labeling vs. dual-labeling

Fluorescence vs. radioactivity

Different Microarray Platforms

Definition of biological questions

Experimental design

Chip preparationProbe design

Probe preparationPrinting

Custom arrayPCR products

Oligomers

Commercial arrayShort oligos: Affymetrix

Long oligos: Agilent

Sample preparationcRNA/cDNA Labeling

Hybridization

Scanning

Data Acquisition and Data Analysis

Goals:

• Caco-2 cells: Differentiated cells vs. undifferentiated cells

• Small intestinal and colonic tissues vs. Caco-2 cells

Evaluation of Transporter and Channel Genes in the Intestine

Anderle et al., Pharm Res 2003

Probe Design for Custom Array

Protein seed sequence

Converged PSI-Blast

Core Protein Family

Blast humanEST db

EST nucleotide sequence

Remove vector and characterized ESTs

Assemble contigs

236 Contigs and singlets

HMM Models

Search Pfam HMM db

Run hmmsearch against GenPept db

Putative new genes

Filter genes (human only, set cut off, eliminate red. genes)

Transporters: 670Channels: 263

Keywords, seed sequences

Multiple alignment and selection of repr. genes

Run Pick70

Run Pick70Tm = 70, Palindrome Uniqueness = 15 bp

Multiple alignment and selection of repr. genes

Run Pick70

Transporters: 316Channels: 151Contigs: 156Positive Controls: 9Negative Controls: 3Controls (diff. Oligos): 9 RGS: 75FGF/RGF-like: 7ADAM family: 18

Brown et al. AAPS PharmSci. 2003 Anderle et al. Pharm Res. 2003

5 days vs. 3 weeks

A-values7 9 11 13 15

-3

-

2

-

1

0

1

2

3

Differentiation of Caco-2 cells

M-v

alu

es

- 3

-

2

-

1

0

1

2

3

A-values7 9 11 13 15

5 days vs. 5 days

M-v

alu

es

- 3

-

2

-

1

0

1

2

3

A-values7 9 11 13 15

5 days vs. 1 week 5 days vs. 2 weeks

M-v

alu

es

- 3

-

2

-

1

0

1

2

3

A-values7 9 11 13 15

Time

Summary

Differentiation of Caco-2 cells:

During differentiation: Expression pattern changes Up and down regulation usually < 2 fold Significant changes between 5 days to 1 week and 1 week to 2 weeks No significant changes between 2 weeks and 3 weeks Genes in general related to ion household No major differences between flasks and filters (except GLUT3) Typical small intestinal transporters not especially up regulated in differentiated cells

Comparison Tissue vs. Caco-2 Cells:

Changes more pronounced between tissue and cell line than between undifferentiated and differentiated

cells Tissue vs. Caco-2 cells: More ratios > 2 fold No trend observed: undiff. cells to diff. cells = colon-like to small intestinal-like cells

Genomic Profiling of Membrane Transporters in the Intestine

12 x Mu74Av2

12 x Mu74Bv2

12 x Mu74Cv2

Objective:

Identification of putative segment-specific and non-specific specific drug carriers

Study Design:

Duodenum Jejunum Ileum Colon

Triplicates → 3 Pools of 10 mice

Gene Ontology Project

GO Output

Cellular Component Molecular Function Biological processes

L3 L2 L3 GO:ZL3 L3 GO:Y L3

L4 GO:X L4 GO:Y

Ontologies are structured vocabularies in the form of directed acyclic graphs (DAGs) that represent a network in which each term may be a “child” of one or more than one ”parent”.

Two pragmatic purposes of ontology: 1. Facilitate communication between people

and organizations2. Improve interoperability between systems

ABCB1

Annotation

Affymetrix Representative Sequence

Representative sequence Consensus sequence

Comparison with UG DBBLAT against assembly

sequence from UCSC

Probes

Ensembl DB

TaggerExact mapping to UG and RefSeq DB

Exact mapping to temp cDNA DB

EnsMart DBSIB annotation 4 quality levels

NetAffxUnigene

Representative Sequence: Chosen during chip design as a sequence which is best associated with the transcribed region being interrogated

BLAT threshold: Only records whose match / Qsize >= 75% and; only records whose score >= 0.70, where score = (match - mismatch - gap# x 5 - gap_size x 2) / Qsize; If record has several mapping locations with score > 0.70, choose the highest one; if a record has several mapping locations with the same highest score, all mapping locations kept.     EnsMart Approach: cDNA sequence plus an additional length of downstream sequence immediately following the most 3' exon. The individual probe sequences are mapped, by exact matching. If more than 50 % of probes mapped, then listed as hits.

Comparison of Various Annotations

Mouse MOE A and B

Human U133 A and B

TaggerA: 20882

EnsMartA: 15421

NetAffxA: 21545

A: 2686 A: 796

A: 5085

A: 3209

A: 11269

A: 4381A: 147

B: 8473 B: 499

B: 2533

B: 904

B: 4027

B: 8610B: 77

B: 15247

B: 22014 B: 5507

TaggerA: 21675

EnsMartA: 14220

NetAffxA: 22446

A: 2384 A: 418

A: 2657

A: 1193

A: 12460

A: 6409A: 149

B: 7300 B: 355

B: 1728

B: 169

B: 1853

B: 12790B: 85

B: 16456

B: 22112 B: 2462

Quality of Probe Sets

Chip High Medium Low Undefined

HG-133A 13792 1663 1103 5657HG-133B 3795 790 519 17473Mu74v2A 5340 1283 1697 4102Mu74v2B 2587 969 1190 7665Mu74v2C 756 302 982 9828MOE-A 12683 2395 1194 6354MOE-B 2453 620 592 18846

Mapped on: RefSeqs

Chip High Medium Low Undefined

HG-133A 15703 1196 3983 1333HG-133B 10096 2026 3125 7330Mu74v2A 8015 615 2127 1665Mu74v2B 7010 1421 2306 1674Mu74v2C 2600 780 2555 5933MOE-A 18070 1222 2383 951MOE-B 11602 2376 2478 6055

Mapped on: RefSeqsmRNAsESTsHTCs

Distribution: UGs per Probe Set

1

10

100

1000

10000

100000

1 10 100

Number of UniGenes

Nu

mb

er o

f P

rob

e S

ets

EnsMart A

EnsMart B

Tagger A

Tagger B

NetAffx A

NetAffx B

Distribution: Probe Sets per UG

1

10

100

1000

10000

100000

1 10 100

Number of Probe Sets

Nu

mb

er

of

Un

iGe

ne

s

U133A

U133B

U133AB

U74Av2

U74Bv2

U74Cv2

U74ABCv2

U74ABCv3_NA

MOE430A

MOE430B

MOE430AB

Io: Isrec Ontologizer

Selection of hierarchical level

Classification of probe setsClassification of UniGenesClassification of RefSeqs

Flagging of ambiguous results

Multiple probe sets per UniGene:addressed via flagging

Multiple UniGenes per probe set:addressed via quality threshold(user defined annotation)

Io: Overview

Ontology Files Annotation Files

GO Consortium Affymetrix (Custom)

Io engine independent from data structure: Can classify anything hierarchical, provided well structured files are given to the program. (E.g.: Simple extension to spotted arrays.) Flexibility improved by a single configuration file (v0.1.2).

Quality Files

Results FileProbesets

Io: Annotation Organization

Probe sets of interest

IO classification

Loc2UG

Loc2ref

Loc2GO GO term

RefSeq ID

UniGeneTagger

Probe Set ID

UG ID

Quality Filter

RefSeqTagger

NetAffx

GO term

Probe Set ID

UG ID

RefSeq ID

Loc2UG Loc2GO

Functional classification of differentially regulated UGs along the Intestine

Function All All All AllDepth 2 Depth 3 # UG (1/F/G) # total UG % of all UG # UG filt. % of all filt. UGmolecular function 2167 (881/1096/190) 6794 31.9 1341 (1094/167/80) 4685 28.6 anticoagulant activity 0 (0/0/0) 2 0.0 0 (0/0/0) 2 0.0 antifreeze activity 0 (0/0/0) 0 0 (0/0/0) 0 antioxidant activity 9 (5/4/0) 10 90.0 5 (4/1/0) 7 71.4 apoptosis regulator activity 22 (11/11/0) 57 38.6 16 (13/3/0) 42 38.1 binding 1056 (386/586/84) 3697 28.6 610 (486/87/37) 2522 24.2 catalytic activity 24 (10/12/2) 89 27.0 11 (8/2/1) 59 18.6 cell adhesion molecule activity 24 (10/14/0) 72 33.3 13 (13/0/0) 50 26.0 chaperone activity 0 (0/0/0) 0 0 (0/0/0) 0 chaperone regulator activity 1 (0/1/0) 4 25.0 1 (0/1/0) 3 33.3 cytoskeletal regulator activity 38 (16/17/5) 79 48.1 17 (14/2/1) 47 36.2 defense/immunity protein activity 992 (421/477/94) 2642 37.5 651 (538/79/34) 1833 35.5 enzyme regulator activity 68 (34/32/2) 195 34.9 33 (27/3/3) 123 26.8 ice nucleation activity 0 (0/0/0) 0 0 (0/0/0) 0 molecular_function unknown 0 (0/0/0) 0 0 (0/0/0) 0 motor activity 16 (4/11/1) 57 28.1 10 (7/3/0) 29 34.5 nutrient reservoir activity 0 (0/0/0) 0 0 (0/0/0) 0 obsolete 109 (47/47/15) 313 34.8 71 (62/2/7) 205 34.6 protein stabilization activity 0 (0/0/0) 0 0 (0/0/0) 0 protein tagging activity 0 (0/0/0) 0 0 (0/0/0) 0 reg. of establishment of comp. for transf. activity 0 (0/0/0) 0 0 (0/0/0) 0 signal transducer activity 272 (113/137/22) 1188 22.9 160 (134/19/7) 855 18.7 structural molecule activity 126 (47/66/13) 358 35.2 83 (72/7/4) 233 35.6 surfactant activity 0 (0/0/0) 4 0.0 0 (0/0/0) 3 0.0 toxin activity 3 (3/0/0) 8 37.5 1 (1/0/0) 6 16.7 transcription regulator activity 137 (51/73/13) 539 25.4 79 (58/15/6) 371 21.3 translation regulator activity 17 (3/12/2) 58 29.3 7 (5/1/1) 33 21.2 transporter activity 233 (100/106/27) 718 32.5 149 (119/14/16) 532 28.0

amine/polyamine transporter activity 14 (9/3/2) 30 46.7 11 (9/1/1) 24 45.8 auxiliary transport protein activity 0 (0/0/0) 1 0.0 0 (0/0/0) 0 boron transporter activity 0 (0/0/0) 0 0 (0/0/0) 0 carbohydrate transporter activity 5 (3/1/1) 10 50.0 3 (1/1/1) 8 37.5 carrier activity 101 (45/44/12) 227 44.5 65 (52/6/7) 154 42.2 channel/pore class transporter activity 45 (19/23/3) 217 20.7 26 (21/4/1) 170 15.3 drug transporter activity 0 (0/0/0) 0 0 (0/0/0) 0 electron transporter activity 15 (7/7/1) 29 51.7 11 (10/0/1) 22 50.0 group translocator activity 0 (0/0/0) 0 0 (0/0/0) 0 intracellular transporter activity 1 (0/1/0) 9 11.1 1 (1/0/0) 6 16.7 ion transporter activity 42 (18/18/6) 112 37.5 30 (25/1/4) 78 38.5 lipid transporter activity 3 (0/1/2) 7 42.9 1 (1/0/0) 6 16.7 neurotransmitter transporter activity 7 (2/4/1) 15 46.7 5 (3/0/2) 12 41.7 nitric oxide transporter activity 0 (0/0/0) 0 0 (0/0/0) 0 nucleob/nucleos/nucleot./nucl.a. transp. activity 3 (2/1/0) 6 50.0 2 (2/0/0) 5 40.0 organic acid transporter activity 16 (10/4/2) 37 43.2 13 (10/2/1) 29 44.8 organic alcohol transporter activity 0 (0/0/0) 0 0 (0/0/0) 0 oxygen transporter activity 1 (0/1/0) 9 11.1 1 (1/0/0) 7 14.3 peptide transporter activity 3 (1/2/0) 5 60.0 0 (0/0/0) 2 0.0 peptidoglycan transporter activity 0 (0/0/0) 0 0 (0/0/0) 0 permease activity 0 (0/0/0) 0 0 (0/0/0) 0 protein transporter activity 38 (13/23/2) 110 34.5 25 (17/3/5) 84 29.8 toxin transporter activity 0 (0/0/0) 0 0 (0/0/0) 0 vitamin/cofactor transporter activity 0 (0/0/0) 5 0.0 0 (0/0/0) 4 0.0 water transporter activity 0 (0/0/0) 1 0.0 0 (0/0/0) 0

triplet codon-amino acid adaptor activity 0 (0/0/0) 0 0 (0/0/0) 0

Self Organizing Maps

Duodenum

pGEA = 0.01: All genes pGEA = 0.05: Transporters

Jejunum

Ileum

Colon

Duodenum

pGEA = 0.01: All genes pGEA = 0.05: Transporters

Jejunum

Ileum

Colon

Duodenum

pGEA = 0.01: All genes pGEA = 0.05: Transporters

Jejunum

Ileum

Colon

Duodenum

pGEA = 0.01: All genes pGEA = 0.05: Transporters

Jejunum

Ileum

Colon

Pair-wise Comparison: M vs A Plots

M (log2 of fold change) vs A (log2 of absolute average intensity) plots of the pair-wise comparisons of the four intestinal segments. Highlighted are genes for which a significant difference was measured between the two segments of interest and for which the annotation was of “high” or “medium” quality.• differentially regulated genes, p (GEA) ≤ 0.05; • differentially regulated transporters p (GEA) ≤ 0.05; • differentially regulated transporters p (GEA) ≤ 0.01

2 * SD according to lowess fitting3 * SD according to lowess fitting

Conclusions I

Bioinformatic aspects:

Annotation provided by NetAffx does not catch the entire complexity of Affymetrix-based microarray experiments

Heterogeneous representation of genes on GeneChips: 1 unique probe set ≠ 1 unique gene

Need of coherent and comparable annotation when comparing results of microarray experiments

Filtering of genes using an annotation quality threshold

No significant bias in general regarding the distribution of the selected probe sets into the different molecular functions for the top hierarchical levels

Possible influence regarding the distribution of the selected probe sets into the different molecular functions at lower hierarchical levels

Functional classification of gene on the UniGene level and RefSeq level yields very similar results

Flagged genes ambiguous rather due to technical issues than due to the fact that splice variants may be differentially expressed

Conclusions II

Biological aspects:

About 28 % of genes with transporter activity are differentially regulated along the intestine, thus, indicating that the majority of transporter genes are not segment specific.

Some transporters, however, or genes involved in transport activity* may be used as local specific drug targets such as:

The mRNA levels need to be quantified by quantitative RT-PCR.

The expression of SLC34A2, Xtrp3s1, CNT2, SLC10A2, SLC5A8, GLUT1, AI648912 will be measured in the villi, FAE and crypts using LDM and quantitative RT-PCR

Apoa1*, Fabp1*, Xtrp3s1, CNT2 for the small intestine GLUT1 (Slc2a1), the amino acid transporter B0+ (Slc6a14) and the multidrug-resistance

associated protein Abcc6 for the colon Fabp1* might be an interesting target for absorption of fatty acid type drugs in the proximal small

intestine The tumor suppressor gene SLC5A8 seems to be highly expressed in the more distal part of the

intestine, namely the ileum and the colon

Acknowledgments I

UCSF/OSU

Wolfgang SadéeVera RakhmanovaShoshana Brown

Joe DeRisiAdam CarrollJingchun Zhu

Xenoport

Katie WoodfordNoa Zerangue

National Cancer Institute

John WeinsteinKimberly Bussey

Acknowledgments II

ISREC

Jean-Pierre KraehenbuhlMartin Rumbo

Bioinformatics Core Facility

Mauro DelorenziThierry Sengstag

Nestlé

Gary WilliamsonMuriel FiauxRobert MansourianDavid MutchMatthew-Alan Roberts

Swiss Institute of Bioinformatics

Philipp BucherViviane PrazChristian Iseli

Statistical analysis of data

Identification of differentially regulated probe sets

Classical ANOVA or GEA ?GEA: SD a function of A

Mapping to GO terms?

GO Output

Cellular Component Molecular Function Biological processes

L3 L2 L3 GO:ZL3 L3 GO:Y L3

L4 GO:X L4 GO:Y

Functional annotation

Identification of genes with similar functions

Loess, quantile or others ?Normalization across chips

Comparability of chips

What clustering method ?Which measurement of similarity ?

Clustering of genes with similar expression profiles

Identification of similarly regulated genes

Fluorescence signal of 22 probes

1 numeric value

MAS5 or RMA ?

GO Classification Programs

Name Input GO annotation Quality threshold

Assessment of ambiguity

Statistics Selection of level

Classification on UG basis

Classification on RefSeq basis

Comments

IO PS NetAffx, LocusLink

Yes Yes No Yes Yes Yes

GenMapp/ MappFinder

GeneBank/ SwissProt, Trembl

GOA No No Yes: z-score No/Yes No No Linked to pathway maps

Onto-Express PS and others NetAffx No No No Yes No No Included in Onto-Tools: Onto-Translate etc

Affymetrix GO Mining Tool

PS NetAffx No No No No No No

GoMiner HUGO ? No No Yes: Fisher Predeterm. No No Linked to other DBs

FatiGo Depending on species: e.g. UG, SP

GOA No No Yes: Fisher, rel. Enr. factor

Yes No No

GeneSpring PS NetAffx* No No No No No No

David PS,GB,LL,RefSeq,UG

NetAffx, UM associations

No No No Prechosen No No Linked to other DBs