DNA-Bioinformatics

Researches

DNA-Bioinformatics Analysis Workflow in GeneSpring • The CEL files were imported with the following options: Analysis Type: Expression; Experiment Type: Affymetrix Expression Technology: HG-U133_Plus_2. • Summarization: MAS5 summarization was used as it also provides a measure of probeset quality in terms of flags. • Data was baselined to the median of all samples. • Treated and Untreated samples did not cluster distinctly in the PCA plot. One sample (AIAY2) clearly stood out from the rest in the Hybridization plot. Also, 2 control (BIAY1 and BIAY4) and 2 test (AIAY3 and AIAY4) samples clustered together. • We removed the three samples AIAY2, BIAY1 and BIAY4 from this study. We also opted to remove two control samples which grouped together with test samples. Retaining these samples would have skewed the results.

. The following steps were performed on the remaining samples.
. Filtered by expression on Normalized data. The default cut-off of 20-100th percentile was used resulting in 46178 entities for further analysis.
. Further, we used Filter by Flags to limit our analysis to entities flagged as Present and Marginal. 13770 entities were retained after this step.
. A t-test unpaired was used to identify the differentially expressed entities.
. Next, Fold change analysis at the cut-off of 2 was performed.
. 420 Up regulated and 165 Down regulated entities are obtained. Please note: A Gene can be represented by more than one entity.
. We have also performed Hierarchical clustering on the output.

GO ANALYSIS
. We used the Fold change output for GO analysis.
. Genes were found to be significantly enriched in 17 GO terms.

Find Significant Pathways
• We used Pathways from NCI for Find significant pathways.
• 22 significant pathways resulted that included MAPK signaling, TGFBR and C-MYC transcriptional activation.

Following the lead based on discussions with Dr. Krishna, we looked at additional genes with which Androgen receptor is found to interact. Out of couple of genes, HSP90A and TMF1 were also found to be upregulated. Literature cites interaction of TMF1 with STAT3. However, in the direct interaction network, STAT2 was found to be significant. Progressing towards the interaction of STAT2 with other genes, Direct interactions showed that IFNG plays a role in regulating it. IFNG was also found to be interacting with CASP1. A gene similar to CASP1 is found to be involved in Neurodegenrative disorder in mice. Further, IFNG was found to interact with FCGR1A and FCGR1b. FCGR1A is associated with SLE. In this interaction NR3C1 is also involved. This gene is found interact with Glucocorticoid receptors.

Image below shows the same:

We also performed MeSH pathway analysis on following terms:

1. Autoimmunity
2. Neurotransmitter : Neurotransmitter Uptake Inhibitors, Receptors and Synaptic Transmission together.
3. Cancer: Brain, Liver and Neoplastic Stem Cells seperately. We limited the network building to these terms because including all of the cancer MeSH terms lead to large network.

Following are the observations:

Significant genes from Pathways which are also 2 fold up or down regulated:

• with Autoimmunity: 18
• with Neurotransmitter: 17
• with Brain cancer: 40
• with with Liver cancer: 59
• with Neoplastic Stem cells: 18

Interestingly 8 genes were common in all the cancer lists. 3( EIF4E, BMI1, TMEF2) of these were significant in C-MYC pathway as well.

5 out of 59 significant genes from liver cancer network are also found in IL 12 mediated signaling pathways, 7 in BMP receptor signaling, 3 in IL27, 5 in TGFBR, 4 in Androgen receptor and 8 in c-myc pathway. We are sending image for C-MYC pathway with Fold change overlaid for these genes (EIF4E,EP300,BMI1,PKN2,HSP90AA1,TMEFF2,IREB2,CREB1).

We also tried GSEA analysis for the dataset wherein Kaposi_Liver_cancer_Poor_Survival_up was found to be significant.

We further included Reactome pathways and did a Find significant pathways on an entity list generated on T-Test (p-value cut-off of 0.1) and Fold change cut-off of 1.5. Many Immune response pathways are found to be significant as listed below: Pathway AndrogenReceptor
BCR
EGFR1
IL2
NOTCH
TCR
TGFBR
TNF alpha/NF-kB
C-MYC pathway
p75(NTR)-mediated signaling
Class IB PI3K non-lipid kinase events
Wnt signaling network
ATR signaling pathway
Validated targets of C-MYC transcriptional activation
CXCR4-mediated signaling events
TCR signaling in naïve CD4+ T cells
Regulation of p38-alpha and p38-beta
N-cadherin signaling events
Endogenous TLR signaling
Hypoxic and oxygen homeostasis regulation of HIF-1-alpha
Coregulation of Androgen receptor activity
Posttranslational regulation of adherens junction stability and dissassembly
Validated targets of C-MYC transcriptional repression
Androgen-mediated signaling
Cellular roles of Anthrax toxin
p38 signaling mediated by MAPKAP kinases
TCR signaling in naïve CD8+ T cells
Regulation of retinoblastoma protein
Integrins in angiogenesis
VEGFR1 specific signals
BMP receptor signaling
Regulation of nuclear beta catenin signaling and target gene transcription
IL1-mediated signaling events
BCR signaling pathway
Signaling mediated by p38-alpha and p38-beta
Regulation of Androgen receptor activity
p38 MAPK signaling pathway
HIF-1-alpha transcription factor network
Direct Interactions
Direct Interactions
proteolysis and signaling pathway of notch
overview of telomerase rna component gene hterc transcriptional regulation
corticosteroids and cardioprotection
internal ribosome entry pathway
hypoxia-inducible factor in the cardivascular system
west nile virus
ifn alpha signaling pathway
hiv-1 nef: negative effector of fas and tnf
mapkinase signaling pathway
p38 mapk signaling pathway
tumor suppressor arf inhibits ribosomal biogenesis
agrin in postsynaptic differentiation
apoptotic signaling in response to dna damage
influence of ras and rho proteins on g1 to s transition
role of erk5 in neuronal survival pathway
role of nicotinic acetylcholine receptors in the regulation of apoptosis
aspirin blocks signaling pathway involved in platelet activation
akt signaling pathway
induction of apoptosis through dr3 and dr4/5 death receptors
human cytomegalovirus and map kinase pathways
b cell survival pathway
angiotensin ii mediated activation of jnk pathway via pyk2 dependent signaling
MeSH cancer pathway
MeSH autoimmune pathway
MeSH autoimmune diseases of the nervous pathway
MeSH Brain cancer pathway
MeSH liver cancer pathway
MeSH Rheumatoid pathway
rheumatoid
Fas signaling pathway ( Fas signaling pathway )
Canonical Notch signaling pathway ( Notch signaling pathway Diagram )
Mammalian Notch signaling pathway ( Notch signaling pathway Diagram )
Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, p38 cascade) ( Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, p38 cascade) )
p38 cascade ( Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, p38 cascade) )
p38 cascade ( Toll-like receptor signaling pathway (p38 cascade) )
Signaling by PDGF
NFkB and MAP kinases activation mediated by TLR4 signaling repertoire
Cell Cycle Checkpoints
NGF signalling via TRKA from the plasma membrane
Signaling by EGFR in Cancer
Signaling by FGFR
ATM mediated response to DNA double-strand break
Destabilization of mRNA by Butyrate Response Factor 1 (BRF1)
Regulation of Glucokinase by Glucokinase Regulatory Protein
FRS2-mediated cascade
GTP hydrolysis and joining of the 60S ribosomal subunit
Toll Like Receptor 3 (TLR3) Cascade
Nonhomologous End-joining (NHEJ)
Translation
Signaling by ERBB4
Signaling by ERBB2
Cap-dependent Translation Initiation
G1/S DNA Damage Checkpoints
PERK regulated gene expression
Ribosomal scanning and start codon recognition
Synthesis and interconversion of nucleotide di- and triphosphates
p53-Dependent G1 DNA Damage Response
PI3K Cascade
Diabetes pathways
Cell Cycle
MAPK targets/ Nuclear events mediated by MAP kinases
CREB phosphorylation
Signalling to ERKs
PI-3K cascade
Activated TLR4 signalling
Metal ion SLC transporters
Toll Like Receptor 10 (TLR10) Cascade
Activation of NMDA receptor upon glutamate binding and postsynaptic events
TRIF mediated TLR3 signaling
PI3K events in ERBB2 signaling
RSK activation
Stabilization of p53
Metabolism of mRNA
MyD88:Mal cascade initiated on plasma membrane
PI3K/AKT activation
Downstream signal transduction
MAP kinase activation in TLR cascade
Prolonged ERK activation events
Activation of BAD and translocation to mitochondria
AKT phosphorylates targets in the cytosol
Toll Like Receptor TLR6:TLR2 Cascade
Toll Like Receptor TLR1:TLR2 Cascade
ATM mediated phosphorylation of repair proteins
Signaling by SCF-KIT
GAB1 signalosome
Downstream signaling of activated FGFR
Toll Like Receptor 7/8 (TLR7/8) Cascade
Destabilization of mRNA by Tristetraprolin (TTP)
TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation
Antigen processing-Cross presentation
Insulin Synthesis and Processing
Toll Like Receptor 9 (TLR9) Cascade
MyD88-independent cascade initiated on plasma membrane
IRS-mediated signalling
Toll Like Receptor 2 (TLR2) Cascade
Deadenylation-dependent mRNA decay
Post NMDA receptor activation events
Signaling by EGFR
Zinc transporters
TRAF6 Mediated Induction of proinflammatory cytokines
Metabolism of nucleotides
Frs2-mediated activation
Signalling by NGF
Deadenylation of mRNA
Glucose transport
Regulation of mRNA Stability by Proteins that Bind AU-rich Elements
Nuclear Events (kinase and transcription factor activation)
IRS-related events
Cytochrome c-mediated apoptotic response
Metabolism of RNA
PI3K events in ERBB4 signaling
Signal regulatory protein (SIRP) family interactions
Eukaryotic Translation Initiation
MyD88 dependent cascade initiated on endosome
CREB phosphorylation through the activation of Ras
PIP3 activates AKT signaling
MyD88 cascade initiated on plasma membrane
p53-Dependent G1/S DNA damage checkpoint
Direct Interactions

Limitations of the current study:
Sample size, quality and grouping logic limits the current analysis.
Future Directions:
Pathways related to Androgen Receptor, immune-response and liver cancer were highlighted as potential leads in this study. Considering the quality of samples, it is hard to confidently conclude the exact effects of the treatment. However, based on discussions with Dr. Krishna and additional background information derived from his observations, effects of the treatment of autoimmune disorders looks like a promising direction. The results of this study could be transient in nature and hence cannot fully explain the observed permanent effects of the treatment. The following processes could lead to permanent changes:
a. sequence level changes in the DNA eg: base pair changes
b. epigenetic changes eg: methylation or demethylation of DNA

Both of these need a different experimental set-up for further studies.

Based on the results of this study and due to ease of access to affected patients, it was decided to focus on and follow up on the effects of the treatment on Rheumatoid Arthritis (RA) in the next batch of studies.

The following hypotheses needs to be explored via literature survey for their role in RA and to drive the design of the next set of experiments:

1. Induction of stem cells proliferation
a. What are the specific markers that can uniquely identify stem cells
b. Prior studies that elucidate a role for stem cells in RA
2. Mitochondrial effects
a. Specific markers or genes responsible for RA
b. Reported roles of numbers, size, heteroplasmy in RA
3. Immune response