theJuxtaposingAutismSpectrum genesOnNeuronsproject

Studying gene expression patterns through autism (SEPTA)

aka

CDT-DB

Jason Meyers

Jason Chan

COLGATE UNIVERSITY

JUNIATA COLLEGE

BY

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.

Project Goals:

• Understand that gene expression varies in space and time

• Compare methods for reporting gene expression

• To analyze graphical data and biological images

• Think about what types of data are useful in determining candidate genes for a disease state

• Teach students how to work with large multi-factorial databases

• Help prospect for autism candidate genes

Target audience:

• Introductory biology:o Biological examples of gene regulation varying across space

and timeo Determining which genes might work together in a network

• Upper level Neuroscience

• Upper level Developmento Relationship of particular gene functions to disease stateso Exploration of transcriptomeso Identifying new candidate disease genes

• Biotechniques labso Overview of different mRNA expression techniqueso Data, graph, and image analysis

staggerer mice as a model

Adapted 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.

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:o GeneChip

o RT-PCR

o custom arrays

o In situ hybridization (ISH)

Benefits of CDT-DB o Robust Temporal and Spatial Expression

o Gene Ontology and Neuroanatomical classification

o Gene 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, Grid2

1. Identify the expression pattern of a specific gene

- on ctdtb homepage, enter gene (Rora) in Gene Name & Keyword Search to search

Activity 1: Comparison of methods for studying gene expression

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)

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

Questions

• How 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?

Activity 2: Predicting co-regulated genes by comparison of multiple expression patterns

To 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?

Reelin (reeler) Grid2 (Lurcher) Kcnj6 (weaver) RORa (staggerer)

Questions

• How 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.

Expression of Grid2 in staggerer (RORa) mutant mice:

Data from Messer and Kang (2000)

Prospecting for Genes Related to Autism Spectrum Disorders

As 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?

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 Disorders

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?

Gene expression altered in staggerer mice (Gold et al., 2003)Probe ID Gene ANOVA F p Value Min. Fold-Change E15 and

E17F × Fold-Change

Decreased Expression

X61397_s_at Cals1 157.378 0 4.25 668.86

M21532_s_at Pcp2 116.929 0 2 233.86

M21531_s_at Calb1 73.531 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 1.25 20.44

AA408677_rc_s_at Txnrd1 14.784 0.0032 1.25 18.48

Z30940_f_at Hist2 12.012 0.0061 1.5 18.02

Msa.1076.0_at Pim1 13.469 0.0043 1.25 16.84

X03919_s_at Nmyc1 10.722 0.0084 1.5 16.08

Msa.38014.0_s_at Myh10 7.948 0.0182 1.75 13.91

D78354_at Plscr1 8.952 0.0135 1.5 13.43

V00830_f_at Krt1-10 8.721 0.0145 1.5 13.08

V00755_s_at Timp 8.657 0.0147 1.5 12.99

Msa.31660.0_s_at Cd53 8.149 0.0171 1.5 12.22

Msa.2058.0_s_at Rora 9.772 0.0108 1.25 12.21

Msa.18074.0_f_at Zfp216 6.892 0.0254 1.75 12.06

Msa.29968.0_s_at Mm.27526<comma> arginyl-tRNA synthetase

7.575 0.0204 1.5 11.36

Msa.1847.0_f_at Rpl10a 8.785 0.0142 1.25 10.98

Msa.16618.0_s_at Sfrs3 8.264 0.0165 1.25 10.33

AA450768_s_at Mm.200828<comma> ESTs

6.845 0.0258 1.5 10.27

Msa.54.0_f_at Mela 5.042 0.0486 2 10.08

U95610_s_at Nek2 7.627 0.02 1.25 9.53

Msa.19334.0_f_at Clic1 5.884 0.0357 1.5 8.83

X60304_at Pkcd 6.485 0.029 1.25 8.11

AA104750_at Trfp 5.616 0.0393 1.25 7.02

U96746_s_at Prdx4 4.99 0.0495 1.25 6.24

Increased Expression

X51468_f_at Smst 58.14 0 1.25 72.68

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.

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)

PPP2R2B 68.92 3.25E-04

DDX52 59.33 4.83E-04

CBX3 51.22 6.15E-04

UGT2B15 49.47 6.33E-04

UGT2B17 46.29 7.03E-04

LYPLA1 41.07 8.52E-04

TPD52L2 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-03

Additional potential datasets:

AutDB: http://autism.mindspec.org/autdb/Welcome.do

AutismKB:

http://autismkb.cbi.pku.edu.cn/

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

From Sarachana T and Hu VW. (2013)

Select GeneChip graph to compare all, as shown on the next slide

Top candidates from Sarachina and Hu (2013)

Which of these might be good candidates?

What additional information would you want to see?

Non-Autism related activities

The 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 interactions

To examine genes that have similar gene ontology-classified functions, and predict their interactions

1. 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?

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.

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?

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.do

2. 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.