View source for 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.
<div align = "center">
{{#hsimg:1|300| |http://drive.google.com/uc?export=view&id=1B--p3me6z3Eqi4v1vMknaoQffYzqg8D2}}
{{#hsimg:1|300| |http://drive.google.com/uc?export=view&id=1aXAO7D4o5noKY0HjHwvQejxS0z03U5Rm}}
<div align = "left">
.	The following steps were performed on the remaining samples. <br>
.	Filtered by expression on Normalized data. The default cut-off of 20-100th percentile was used resulting in 46178 entities for further analysis. <br>
.	Further, we used Filter by Flags to limit our analysis to entities flagged as Present and Marginal. 13770 entities were retained after this step. <br>
.	A t-test unpaired was used to identify the differentially expressed entities. <br>
.	Next, Fold change analysis at the cut-off of 2 was performed. <br>
.	420 Up regulated and 165 Down regulated entities are obtained. Please note: A Gene can be represented by more than one entity. <br>
.	We have also performed Hierarchical clustering on the output. <br>
<div align = "center">
{{#hsimg:1|300| |http://drive.google.com/uc?export=view&id=1Lon6yJazgCq1paN13GsbDpqZokB-9wBi}}
<div align = "left">
GO ANALYSIS <br>
.	We used the Fold change output for GO analysis. <br>
.	Genes were found to be significantly enriched in 17 GO terms. <br>
<div align = "center">
{{#hsimg:1|300| |http://drive.google.com/uc?export=view&id=1TmHGQqqYwE03njELemxhGcWbpAV3fIMD}}
<div align = "left">
Find Significant Pathways <br>
•	We used Pathways from NCI for Find significant pathways. <br>
•	22 significant pathways resulted that included MAPK signaling, TGFBR and C-MYC transcriptional activation. <br>
<div align = "center">
{{#hsimg:1|300| |http://drive.google.com/uc?export=view&id=136wiLbWg6bBzcS9az3h73X4hzRupFuql}}
<div align = "left">
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:
<div align = "center">
{{#hsimg:1|300| |http://drive.google.com/uc?export=view&id=1gealRHHmcXSUSiAjrkXs1DUBP1cZXerv}}
<div algin = "left">
We also performed MeSH pathway analysis on following terms: <br>

1. Autoimmunity <br>
2. Neurotransmitter :  Neurotransmitter Uptake Inhibitors, Receptors and Synaptic Transmission together. <br>
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. <br>

Following are the observations: <br>

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

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

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

Limitations of the current study:  <br>
Sample size, quality and grouping logic limits the current analysis. <br>
Future Directions: <br>
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: <br>
a.	sequence level changes in the DNA eg: base pair changes <br>
b.	epigenetic changes eg: methylation or demethylation of DNA <br>

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

[[Category: Researches]]