Download Colorectal Disease Mechanism Case Study

Understanding disease mechanisms
through multi-omics data integration
using MetaCore pathway analysis
case study Introduction
Transcriptomic data was obtained from the MetaCore
microarray repository (Human Colorectal Cancer
vs Normal Adjacent Colon comparison) following
extraction from ArrayExpress (E-MTAB-57, Maglietta
et al. ), quality control, and filtering the DEGs fold
changes > +/- 1.5 and p value <.05.
The goal of systems biology is to understand
all elements in a biological system and their
relationships to one another. To study biological
systems, perturbations are used to monitor signaling
cascades that include omics levels such as genes,
proteins, and metabolites . Diseases are a form of
system perturbation, where normal signaling biology
is disrupted. Understanding these disruptions
mechanistically at different omics levels can help
hypothesize intervention methods to reverse the
biological disruption and disease phenotype.
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1
Results
MetaCore is a pathway analysis tool for functional
analysis of omics data. The underlying manually
curated database captures high quality experimental
evidence from peer reviewed literature. Through
curating interactions, omics-disease relationships, and
constructing canonical pathways from these omics
experiments allows one to put high dimensional data
into a systems biology context.
Classically, microarray experiments generating high
throughput transcriptomic data have been the omics
layer of choice for analysis within MetaCore. The rapid
decrease in the cost of next generation sequencing (NGS)
has led to an increase in data at the genomic level as well
as the need for tools for integrated omics level analysis.
MetaCore was designed to serve this DNA-seq need while
also incorporating filtering options to refine those most
important variants to analyze in a pathway context.
This case study showcases the MetaCore functionalities
through filtering a colorectal cancer (CRC) .vcf file,
running a pathway map enrichment with the prioritized
variants as well as incorporating CRC transcriptomic
data from the MetaCore microarray repository. Multiomics approaches have already proven a powerful
tool in uncovering disease phenotypes with limited
sample size, including n=1 . Here, we propose multiple
hypotheses around the mechanisms of CRC using a
combination of knowledge and data driven approaches
available within MetaCore.
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Materials and Methods
The genomic data was obtained from Madhavan et al ,
who produced a vcf file comparing a CRC and a normal
sample. This initial filtered file was further refined
using the following filters available in MetaCore (Figure
1): 1) Variant Classification: Type; Single, Functional
Class; Missense or Nonsense, 2) Functional Prediction:
Logistic Regression Prediction; Damaging or Absent,
LRT; Deleterious or Absent 3) Novelty: 1000 genomes;
Present or Global (gMAF)(Freq: 0.001) or Absent 4)
Disease: Colorectal Neoplasms
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Variant filtering as described in M&M resulted in 10,167
variants mapping to 1,054 network objects which were
used for one-click map folder enrichment. The most
significant map folder was colorectal neoplasms (p
value = 1.068e-39) indicating the genes these variants
are contained within would play an important role in
CRC pathological signaling. In order to understand
the relationship with colorectal cancer signaling at the
mRNA level, the filtered E-MTAB-57 transcriptomic
data was compared side by side within the map folder
ontology. One map within this folder, “Inhibition of
Calcitriol/VDR signaling in colorectal cancer”, is
shown in Figure 2. Through visualize map analysis and
the map overview content, the following hypotheses
are proposed to explain CRC specific mechanisms
stemming from the genomic and transcriptomic data:
1. CYP27A1 (3 variants post filtering) catalyzes
conversion of Colecalciferol into Calcifediol which is
converted in to Calcitrol by another enzyme . Calcitriol
signaling is downregulated in CRC and therefore, these
CYP27A1 mutations could represent loss of function
mutations that decrease catalysis of Colecalciferol and
overall decreased VDR signaling.
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2. The CRC genomic data shows a variant in ZO2
(TJP2 gene) at position 71865988 with a C>T SNP
and the transcriptomic data shows the TJP2 gene to
be upregulated 1.8 fold (p value = 0.021). This variant
could be hypothesized to lead to increased production
of TJP2 and change biological processes such as CRC
progression through cell adhesion mechanisms.
3. This map describes the cell cycle driving cell
proliferation which feeds into the CRC phenotype.
The data shows 2 variants in cyclin-dependent
kinase inhibitor 1A or p21 (positions 36651971 C>A
and 36652122 C>G) which could be involved in
dysregulation of the cell cycle leading to changes in
cell proliferation. This map also shows upregulation
of c-Myc at the mRNA level (fold change = +2.3 and
p value =0.0009) which could also contribute to
increased cell proliferation. Therefore, mechanistically
this combination of genomic and transcriptomic
changes could be hypothesized to drive CRC through
dysregulated cell proliferation.
Understanding disease mechanisms through multi-omics data integration using MetaCore pathway analysis
These 3 hypotheses represent a fraction of the hypotheses to further investigate mechanisms of colorectal cancer
progression. Through combining filtered genomic and transcriptomic data, pathway analysis allowed identification
of potential metabolomic signaling consequences as well as biological processes potentially dysregulated in CRC.
In conclusion analyzing multi omics data in a pathway analysis context provide a powerful method for generating
systems biology hypotheses.
Figure 1
A screenshot while filtering
the vcf file from Madhavan et
al. The filters described in the
M&M were applied (see selected
items) to prioritize those most
likely to contribute to CRC
related mechanisms. The filtered
list generated with MetaCore
NGS was then converted into
experiment to map the variants
and allow downstream pathway
based applications.
Figure 2
MetaCore Pathway Map:
Inhibition of Calcitriol/VDR
signaling in colorectal cancer.
The filtered CRC genomic data
and significant differentially
expressed genes as described
in the M&M are shown as
thermometers. The themometers
with stripes and #1 are the CRC
genomic data and those with #2
and red (upregulated) or blue
(downregulated) are part of the
CRC transcriptomic data.
REFERENCES
1) PMID:11701654
2) PMID:2424236
3) PMID:24312117
4) PMID:16919171
5) PMID: 9210654
6) PMID: 19667168
7) PMID: 11425375
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