Gene expression
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Gene Expression. Christina Azodi, Lexi Bloom, Martine Stewart & Hope Yu Middlebury College : Bioinformatics and Genomics : May 3 rd 2011. POPs Microarrays . Part I: Software Comparisons. Bioconductor BRB Array NIA Array. 1. Bioconductor. Reads Affymetrix data from CEL files

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Gene Expression

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Gene expression

Gene Expression

Christina Azodi, Lexi Bloom, Martine Stewart & Hope Yu

Middlebury College : Bioinformatics and Genomics : May 3rd 2011

POPs Microarrays


Part i software comparisons

Part I: Software Comparisons

  • Bioconductor

  • BRB Array

  • NIA Array


1 bioconductor

1. Bioconductor

  • Reads Affymetrix data from CEL files

  • Upload data and normalize the intensity values

  • Very powerful tool- R packages exist to run 100s of different types of analysis

  • Our main difficulty: beginner programmers!


2 brb array

2. BRB Array

  • Uses Excel as the front end

  • The analytic and visualization tools are developed in R, C, Fortran and Java

  • Visual Basic for Applications integrates the components and hides the complexity

  • Normalize and summarize with GC-RMA


2 brb array1

2. BRB Array


Gene expression

3. NIA Array

  • Initial Motivation

    • Trying another approach

    • No programs to download

    • Ability to analyze data stored in .txt files

      - The Hochstenbach set

    • Ability to run Principal Component Analysis


Gene expression

3. NIA Array: Basic Use

  • Obtain microarray data from NCBI GEO

    • Save in tab-delimited format

  • Upload the data matrix

  • Upload the probe matrix

  • Choose parameters

  • Fill in known information

  • Run


Gene expression

3. NIA Array: Basic Use

  • Obtain microarray data from NCBI GEO

    • Save in tab-delimited format

  • Upload the data matrix

  • Upload the probe matrix

  • Choose parameters

  • Fill in known information

  • Run


Gene expression

3. NIA Array: Basic Use

  • Obtain microarray data from NCBI GEO

    • Save in tab-delimited format

  • Upload the data matrix

  • Upload the probe matrix

  • Choose parameters

  • Fill in known information

  • Run


Gene expression

3. NIA Array: Challenges

  • Running 2-color arrays

    • Data available from NCBI GEO is already normalized to subtract out control levels

    • NIA Array requires non-normalized data (ie: hybridization values for both control and experimental) to be able to run 2-color arrays

    • Consequence: we were unable to retest the Hochstenbach gene set (which is 2-color)

  • Requires specific manipulation of input data format

    • Including specific knowledge of array type and experimental design—can be difficult to find


Gene expression

3. NIA Array: Perl to manipulate .txt

  • Goal: strip out the word “sample” from the text file (many times—tedious by hand)

  • Used

    • open (INPUT, $file) and open (OUTPUT, $fixedfile) to read the input file and write to the output file

    • $line=~ s/sample 1/ /g to replace “sample 1” with a blank space

  • This program could be manipulated to be useful for relabeling other data sets


Gene expression

3. NIA Array: Capabilities

  • Provides output of significantly up- and down-regulated genes

  • Scatter plots

  • Principal Component Analysis


Gene expression

3. NIA Array: Scatter Plots


Gene expression

3. NIA Array: Each point links to gene information


Gene expression

3. NIA Array: Principal Component Analysis

  • Concept

    • Reduce the dimensionality of the data without compromising variation

      • Makes data easier to visualize graphically

    • Reduces output to a more manageable size


Part ii a study of two pops

Part II: A Study of Two POPs

  • PCB & Dioxin

  • Disclaimers

  • Our Expression Results


Persistent organic pollutants pops

Persistent Organic Pollutants (POPs)

  • Organic chemical substances

  • Survive in the environment for long periods of time

  • Travel throughout the environment

  • Bioaccumulate

  • Toxic to humans and many ecosystems

  • Specific effects:

    • Cancer

    • Allergies

    • Damage to the nervous systems

    • Reproductive disorders

    • Damage to the immune system

http://www.arcticportal.org/features/2009/persistent-organic-pollutants-a-great-environmental-and-human-risk-in-the-arctic


Pops of interest

POPs of Interest

  • Polychlorinated biphenyls (PCBs):

  • Dioxin (TCDD):

https://sci10bestq3bm.wikispaces.com/PCB-silver+class+%2709

http://www.clu-in.org/contaminantfocus/default.focus/sec/Dioxins/cat/Overview/


Hochstenbach et al 2010

Hochstenbach et al., 2010

  • Whole-genome gene expression using microarray techniques

    • Human Peripheral Blood Mononuclear Cells (PBMCs)

  • Identified 48 genes that distinguish between immunotoxic and nonimmunotoxic chemicals.


Criteria

Criteria

Our criteria:

  • Fold change in gene expression >2 in at least two sample sets

  • Fold change in gene expression >8 in any of the sample sets

  • P<0.005 as determined by class comparison

    *venn diagrams made at http://www.pangloss.com/seidel/Protocols/venn.cgi

Hochstenbach et al.criteria:

  • Fold change in gene expression >1.5 or <0.67

  • Fold change in gene expression >1.5 or <0.67 in three of five donors

  • P<0.001 in a t-test of experimental vs. control


Tools used in brb array

Tools used in BRB Array

Scatterplots:

  • In ArrayTools, generated scatterplots with fold change limits at 2 and again at 8

  • Generated a list of both up- and down-regulated genes


Tools used in brb array1

Tools used in BRB Array

Finding significant differences in expression:

  • In ArrayTools, select class comparisons between groups of arrays

  • Generates a table of genes whose expressions are significantly different across sample groups as determined by the p-value limit (we used p<0.005)

  • Generates a table with log-transformed gene expressions for significant genes across all sample groups

  • Generates a heatmap to illustrate the differences between control and experimental conditions for all significant genes


Disclaimers

Disclaimers

  • Limited to studies available through NCBI GEO

    • Arrays were done by different labs

    • Variation in concentration and exposure time

    • Dioxin (TCDD)

    • PCBs (77, 126, 153 and Arclor)

  • Limited in our knowledge of the system


Results

Results


Results brb array

Results: BRB Array

  • FN1 was differentially regulated in 4 of our studies- two PCB & 2 dioxin

  • ATF3 was differentially expressed in Hoch, H Hep w/ PCB and in Martine's study

  • Frequently differentially expressed in our studies, but not included in Hochstenbach's top 38: CYP1A1, CYP1a2, CYP1B1, Aldh3a1, Aldh1a7, Ugt1a6

  • Lots of similarities between the female Rats treated with Arclor and TCDD


Results brb array1

Results: BRB Array

Results: BRB Array

  • Fibronectin1 (FN1)

    • High MWT glycoprotein

    • Located in plasma and at the cell surface (ECM)

  • Functions in cell adhesion and migration processes

  • 450 KDa dimer

    • Composed of repeating structural motifs: FN-I,II,III

    • FN-III10 mediates cell adhesion via an integrin-binding motif

http://www.ks.uiuc.edu/Research/fibronectin/index.old.html


Immune response role of integrins in nk cells cytotoxicity

Immune Response: Role of integrins in NK cells cytotoxicity

  • NK cells are lymphocytes that destroy infected cells

  • Integrins are adhesion receptors that function in NK cell migration and adhesion of NK to target cells

    • ICAM1, ICAM2, ICAM3, FN1, and VCAM1 are all common ligands for integrins

  • Other genes to highlight:

    • ATF3&5 were upregulated

    • SOS1&2 were upregulated

    • ICAM1&3 were upregulated

    • IL-8 was downregulated


Hypotheses

Hypotheses…

  • Transcription of FN1 may be affected by POPs and is thus downregulated so that the immune system cannot destroy infected cells

  • ICAM1 is most likely upregulated during the natural immune response in preparation to clear the body of cells infected with POPs

  • IL-8 is upregulated naturally during the inflammatory response, but found that it was downregulated in our gene set

  • ATF3 may be involved in the inhibition of some genes in the pathway


Conclusions

Conclusions

  • Microarray analysis software can be incredibly powerful, however can only get out how much you can put in

  • Limited in:

    • Programming knowledge

    • Biological system background

  • Possibilities for analysis are endless


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