the J - PowerPoint Presentation

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theJuxtaposingAutismSpectrum genesOnNeuronsproject

Studying gene expression patterns through autism (SEPTA) aka

CDT-DB

Jason

Meyers

Jason Chan

COLGATE UNIVERSITY

JUNIATA COLLEGE

BYSlide2

Background:The genetic changes that underlie autism are not well understood. Many studies have implicated changes in the cerebellum with autism, and many of the candidate autism genes are expressed in the cerebellum. As one example, RORa (retinoic acid receptor-related orphan

receptor alpha) is reduced in autism patients. When this gene is missing in mouse mutants, it leads to cognitive and motor defects. This problem space explores RORa

expression in the cerebellum to help make predictions about the disease, and other genes that might interact with RORA.

Dataset:

The problem space uses the The Cerebellar Development Transcriptome Database (http://www.cdtdb.neuroinf.jp) from the Neuroinformatics Japan Center and the RIKEN-Brain Science Institute in Japan.

Additional supplemental datasets from Gold et al. (2003) and Sarachana et al., (2013), AutDB, AutismKB, etc.Slide3

Project Goals:Understand that gene expression varies in space and timeCompare methods for reporting gene expressionTo analyze graphical data and biological images

Think about what types of data are useful in determining candidate genes for a disease stateTeach students how to work with large multi-factorial databasesHelp prospect for autism candidate genesSlide4

Target audience:Introductory biology:Biological examples of gene regulation varying across space and timeDetermining which genes might work together in a network

Upper level Neuroscience Upper level DevelopmentRelationship of particular gene functions to disease statesExploration of transcriptomes

Identifying new candidate disease genes

Biotechniques labsOverview of different mRNA expression techniques

Data, graph, and image analysisSlide5

staggerer mice as a modelAdapted from Sidman et al. (1962)Wild Type brain

staggerer brain

Cerebellum

Cerebellum

staggerer

mice have a very small cerebellum, and poor motor coordination, hence their characteristic staggering behavior that gives them their name. (Sidman et al., 1962). The causative mutation is in the

Rora

gene (Hamilton et al., 1996; Steinmayer et al., 1998), which is an orphan retinoic acid receptor. This

family

of nuclear receptor acts to regulate gene expression of various other genes. This exercise allows you to explore gene regulation

in silico

by analyzing large datasets of gene expression. Slide6

Tool: The Cerebellum Development Transcriptome Database (CDT-DB)

The database collects data from measuring mRNA quantity in region-specific mouse brain tissues during different stages of development. It uses four techniques:GeneChip

RT-PCR

custom arrays

In situ hybridization (ISH)Benefits of CDT-DB

Robust Temporal and Spatial ExpressionGene

Ontology and Neuroanatomical classificationGene search utility to compare many genes by adding them to personalized lists (My List)

For example, mutants with cerebellar disorders can be grouped and examined together: Reln, Rora, Kcnj6, Grid2Slide7

1. Identify the expression pattern of a specific gene- on ctdtb homepage, enter gene (Rora) in Gene Name & Keyword Search to searchActivity 1: Comparison of methods for studying gene expressionSlide8

2. Your search will lead you to a list, where you can select links to get more information about your gene of interest, including:- gene information- temporal gene expression- spatial gene expression- tissue expression- category (gene ontology)Slide9

3. Compare and contrast RORa (staggerer) expression using different techniquesSlide10

QuestionsHow do these different types of analysis of gene expression data compare?How would you describe the expression of RORa? What trends do you see?What additional information would you like to see?

How might you compare RORa to other candidate autism genes?Slide11

Activity 2: Predicting co-regulated genes by comparison of multiple expression patternsTo compare multiple genes, enter gene into Gene Search and add them individually to "My Lists." For this exercise, examine four genes that are linked with mouse cerebellar dysgenesis: RORa (staggerer), Reelin (reeler),

Kcnj6 (weaver), Grid2 (Lurcher)

Use this to generate a single graph comparing the GeneChip data for all of the genes

Examine the spatial expression domains of each of these genes

To what categories do these genes belong? What functions might they have?Slide12

Slide13

QuestionsHow would you group these genes? What criteria are important for trying to group the genes?What biological hypotheses might stem from your clustering of genes?How can you determine whether genes may be regulating one another or may be co-regulated?

Challenge Question:The Lurcher mouse is a dominant gene, but curiously, mice double mutant for both Lurcher and

staggerer do not show the Lurcher phenotype (Messer et al., 1991). Hypothesize why this may be.Slide14

Expression of Grid2 in staggerer (RORa) mutant mice:Data from Messer and Kang (2000)Slide15

Prospecting for Genes Related to Autism Spectrum DisordersAs described earlier, the molecular basis for autism is unclear. RORa, which we have seen is a selectively expressed transcription factor in a subset of neurons in the cerebellum (Purkinje Cells), is an autism candidate gene, as it shows reduced expression in autism patients (Nguyen et al., 2010; Sarachana et al., 2011). Since RORa is a transcription factor, changes in its expression likely alter expression of other genes.What would you predict should happen in autism patients for genes that RORa positively/negatively regulates?Slide16

You will now be using your skills at analyzing gene expression to look through data to find likely candidate genes that may be regulated by RORa and thus may be related to cerebellar problems and/or autism.You will have access to two datasets: 1. Genes found to be up-/down-regulated in RORa (staggerer) mutant mice 2. Genes that have a sequence upstream that RORa binds to in vitro.

Prospecting for Genes Related to Autism Spectrum DisordersSlide17

Design:Which dataset(s) will you use? What information in them is most relevant?Given the information you can obtain from the CDT database, what information might you be able to add to help determine whether a gene may be a good candidate for regulation by RORa?Slide18

Gene expression altered in staggerer mice (Gold et al., 2003)Probe IDGeneANOVA F

p ValueMin. Fold-Change E15 and E17F × Fold-ChangeDecreased Expression

X61397_s_at

Cals1

157.3780

4.25668.86M21532_s_at

Pcp2116.9290

2233.86

M21531_s_atCalb173.531

0

2.25

165.44

U44725_s_at

Kitl

48.147

0

3

144.44

D83262_at

Slc1a6 (EAAT4)

18.462

0.0015

3.25

60

AA415606_at

Baf53a

43.954

0

1.25

54.94

X17320_s_at

Pcp4

33.003

0.0002

1.25

41.25

AF026489_at

Spnb3

29.314

0.0003

1.25

36.64

AA267955_s_at

ESTs<comma> weakly similar to retinoblastoma-associated protein HEC

25.684

0.0005

1.25

32.1

Msa.1693.0_s_at

Idb2

20.554

0.0011

1.5

30.83

AA426917_s_at

Ccnb1-rs1

22.25

0.0008

1.25

27.81

X56044_s_at

Htf9c

5.674

0.0385

4.5

25.53

Msa.17592.0_s_at

pigpen protein

19.534

0.0013

1.25

24.42

Z26580_s_at

Ccna2

16.353

0.0024

1.2520.44AA408677_rc_s_atTxnrd114.7840.00321.2518.48Z30940_f_atHist212.0120.00611.518.02Msa.1076.0_atPim113.4690.00431.2516.84X03919_s_atNmyc110.7220.00841.516.08Msa.38014.0_s_atMyh107.9480.01821.7513.91D78354_atPlscr18.9520.01351.513.43V00830_f_atKrt1-108.7210.01451.513.08V00755_s_atTimp8.6570.01471.512.99Msa.31660.0_s_atCd538.1490.01711.512.22Msa.2058.0_s_atRora9.7720.01081.2512.21Msa.18074.0_f_atZfp2166.8920.02541.7512.06Msa.29968.0_s_atMm.27526<comma> arginyl-tRNA synthetase7.5750.02041.511.36Msa.1847.0_f_atRpl10a8.7850.01421.2510.98Msa.16618.0_s_atSfrs38.2640.01651.2510.33AA450768_s_atMm.200828<comma> ESTs6.8450.02581.510.27Msa.54.0_f_atMela5.0420.0486210.08U95610_s_atNek27.6270.021.259.53Msa.19334.0_f_atClic15.8840.03571.58.83X60304_atPkcd6.4850.0291.258.11AA104750_atTrfp5.6160.03931.257.02U96746_s_atPrdx44.990.04951.256.24Increased ExpressionX51468_f_atSmst58.1401.2572.68

Study compared gene expression at E15 and E17 in wild-type and

staggerer

mice. The minimum fold-change between wild-type and

staggerer

mice is shown in the fifth column.Slide19

Candidate genes regulated by RORa (Sarachana and Hu, 2013)This study used ChIP-chip to find regions of the chromosomes to which the RORa protein binds. Top candidates are shown to the left.Nearest Gene

MAT-score on T (region)p-value (region)PPP2R2B68.92

3.25E-04DDX52

59.334.83E-04

CBX351.226.15E-04

UGT2B1549.476.33E-04

UGT2B1746.297.03E-04

LYPLA141.07

8.52E-04TPD52L2

41.06

8.52E-04

CYP2R1

40.90

8.52E-04

ARPC4

39.69

8.88E-04

HSD17B10

39.13

8.88E-04

BCL11B

38.67

9.40E-04

HK1

37.16

9.93E-04

ABCC8

34.22

1.19E-03

ABHD4

33.26

1.20E-03

PTPN11

32.71

1.24E-03

LRRC7

32.41

1.26E-03

HECW1

31.52

1.36E-03

GALNTL5

31.29

1.38E-03

PIK3R1

31.17

1.39E-03Slide20

Additional potential datasets:AutDB: http://autism.mindspec.org/autdb/Welcome.doAutismKB:http://autismkb.cbi.pku.edu.cn/Slide21

Other activities- Examine the expression patterns of candidate genes involved with Autism Spectrum Disorder - students can use the datasets introduced in the previous slides or students can do their own research on genes they think might be involved with the disorder. - see list on next slide for a list of genes from the Sarachana T and Hu VW. (2013) dataset.Other quantitative analyses- Cluster analysis of groups of genes Slide22

From Sarachana T and Hu VW. (2013)Select GeneChip graph to compare all, as shown on the next slideSlide23

Top candidates from Sarachina and Hu (2013)Which of these might be good candidates?What additional information would you want to see?Slide24

Non-Autism related activitiesThe CDT-DB has more utility beyond examining the interactions between known genes of interest. It can be used to data mine for other cell and developmental biology questions.- Examine genes that have similar gene ontology-classified functions, and predict their interactions- Examine genes that have similar expression patterns (e.g. genes with peak expressions at P7), compare their spatial expression patterns, and predict their interactions

- Examine genes that are expressed in a specific cell type in the cerebellum, identify genes that have similar spatial or temporal expression patterns, and predict their interactionsSlide25

To examine genes that have similar gene ontology-classified functions, and predict their interactions1. click here (category)

2. click here for a full list of transcription factors

3. click here for a graph allowing you to examine the expression patterns of multiple transcription factors

Questions:

Which transcription factors are likely to act together, and how can you tell?Slide26

Using the Gene Expression Search menu (link on the homepage), expression data can be found by cell type, expression profile, spatial expression, brain distribution, and brain specificity. Slide27

ExampleSearching all brain dominant genes whose expression is going up during P21 of cerebellar development will give you 206 genes. Their GeneChip Graph is shown here.

From here, individual or multiple genes can be isolated and compared. Then, using the utility, spatialexpressions can be compared to predict whether they act in the same cell types, at the same time.

Questions:- Which genes match very closely, and do their spatial expressions match?

- If they do, does that mean they interact and how can you test for that?- Using other search strings, can you tell what the primary functions of a particular cell type are?Slide28

References and Resources1. Cerebellum development transcriptome database website (http://www.cdtdb.neuroinf.jp/CDT/Top.jsp)- CDT-DB user guide: http://www.cdtdb.neuroinf.jp/CDT/Download.do2. Sarachana T and Hu VW (2013) Genome-wide identification of transcriptional targets of RORA reveals direct regulation of multiple genes associated with autism spectrum disorder. Mol Autism.

May 22;4(1):14.3. Gold DA, Gent PM, Hamilton BA (2007) RORα in genetic control of cerebellum development: 50 staggering years. Brain Research. Volume 1140, 6 April 2007, Pages 19–25

4. Messer A, Eisenberg B, Plummer J (1991) The Lurcher cerebellar mutant phenotype is not expressed on a staggerer mutant background.

J Neurosci, 11: 2295-2302.

5. Messer A and Kang X (2000) Control of transcription in the RORa-staggerer mutant mouse cerebellum: glutamate receptor delta2 mRNA Int J Dev Neurosci, 18: 663-668.

6. Sarachana T, Xu M, Wu R-C, Hu VW (2011) Sex Hormones in Autism: Androgens and Estrogens Differentially and Reciprocally Regulate RORA, a Novel Candidate Gene for Autism. PLoSOne. 6:e17116.

7. Sidman, R. L., Lane, P. W., and Dickie, M. M. (1962). Staggerer, a new mutation in the mouse affecting the cerebellum. Science, 137, 610–2.