In silico identification of genes involved in Chronic Metabolic Acidosis
Ishfaq Ahmad Sheika,*, Adeel Malikb, Mohd Amin Bega, Sameera F. Al-Basric
a King Fahd Medical Research Center, King Abdulaziz University, Jeddah,
Kingdom of Saudi Arabia.
b Center for Bioinformatics, Perdana University, Malaysia
c Ob-Gyn and Urogynecology, KAUH, King Abdulaziz University, Jeddah,
Kingdom of Saudi Arabia
*Corresponding author: King Fahd Medical Research Center, King Abdulaziz
University, Jeddah, Kingdom of Saudi Arabia.
E-mail: [email protected]
Tel.: +966-532972254
1
ABSTRACT
Objectives: To characterize the underlying molecular mechanism of Chronic
metabolic acidosis (CMA) and identify genes associated with CMA progression
using in silico approaches.
Method: In silico approaches were adopted to characterize and identify genes
associated with CMA progression. GeneMANIA webserver was used for studying
interaction among Differentially Expressed Genes (DEGs) and other related genes in
the network. DEGs were used for our computational analysis and were downloaded
from a study on gene expression profile of duodenal epithelial cells. In this regard,
interaction network were constructed for DEGs, and hub genes as well as enriched
clusters in the network were also screened. Gene Ontology (GO) was used for
enriching functions in each cluster. DAVID functional analysis tool was used for
carrying out functional enrichment analysis.
Results: Genes associated with CMA progression were screened by identifying
DEGs with the help of bioinformatics tools. Function and pathway enrichment of
these identified genes followed by interaction and network construction was carried
out using in-silico studies. Network analysis for DEGs leads to enrichment of
neurological process like neurological system process regulation and nerve impulse
transmission. The top three hubs identified by highest node degree distribution are
genes encoding for TFF1 (Trefoil factor 1) and HTR5A (5-Hydroxytryptamine
(serotonin) receptor 5A) with a node degree of 76. The remaining two hubs are
SLC6A11 (Solute Carrier Family 6 (Neurotransmitter Transporter), Member 11) with
a node degree value of 68 and GRIN2B (Glutamate Receptor, Ionotropic, N-Methyl
D-Aspartate 2B) having node degree value of 64.
Conclusion: This study provides insight into the pathways and genes involved
into the mechanism of CMA progression. However, further investigations are
required to gain in-depth information into networks and pathways. This study
would be quite useful in better understanding of CMA and shall finally lead to
target based treatment of this disease.
Kay words: Chronic metabolic acidosis (CMA), Genes, In silico, intestinal epithelial
cells, enriched clusters
2
Chronic metabolic acidosis (CMA) is frequently observed clinical complications in
patients suffering from chronic kidney diseases (CKD). CMA is characterized by
excessive increase in plasma acidity or acidemia (pH < 7.40). CMA existing in
conjunction with CKD is assigned to reduced efficiency of kidneys to secrete
hydrogen ions and synthesize ammonia 1. Recent studies have indicated that even a
slight acidosis may act as contributing factor in loss of glomerular filtration rate 2.
Many metabolic disorders such as massive bone loss, nephrocalcinosis, hypotension,
altered blood flow to visceral organs, and multiple-organ damage have been
associated with CMA 1, 3. Other metabolic problems reported to be associated with
CMA include insulin resistance, parathyroid hormone production, elevated
inflammatory mediators, and increased production of corticosteroids 3.
One of the adverse effects associated with CMA involves hindrances of intestinal
functions. CMA hampers metabolic and barrier functions of intestines 4. In this
regard, rat intestinal epithelial cells (IEC-6 cell line) exposed to lipopolysaccharides
displayed defective epithelial barrier and was attributed to cytoplasmic acidification
resulting from extracellular acidosis 5. Cellular processes like apoptosis, proliferation,
lipid peroxidation etc which affect the survival of intestinal epithelial cells are also
sensitive to CMA 6, 7.
In association with CMA induced metabolic effects on intestines, pronounced
adaptations in the epithelial cells of intestines also occur at genomic level which
subsequently help in providing with essentially required nutrients 8, 9. Among these
CMA promoted changes and adaptations in intestinal function, some distinct features
include alterations in transepithelial transport of water, aminoacids, inorganic
phosphate, nutrients like Na, Cl, K, HCO3, Ca2 and major elements 9-13. For
example, a long term reduction in arterial pH was reported to enhance the absorption
of Na, Cl, and water in ileum and reduce the absoption of K in the jejunum 12, 14.
Moreover, in CMA, the HCO3 absorption is increased in the proximal colon while as
HCO3 secretion decreased in the distal colon 11. CMA induced due to NH4Cl has
also been reported to enhance the expression of mRNA of important genes playing a
role in Ca transport and assuage the effects of negative calcium balance and
osteopenia induced by CMA 9. Also, CMA improved glutamine absorption by
intestines which could shield mucosa from acid-induced injury 8, 15. Duodenum plays
a key role in nutrient absorption and hence has been selected for studying the profiles
of CMA altered genes 9, 16. Gene profile analysis of intestinal epithelial cells for
functional characterization was carried out using microarray and PCR techniques. In
this regard, altered expression profile of many transporter genes in response to CMA
has been reported 17.
The objective of this study is to characterize the underlying mechanism of CMA
at molecular level by identifying genes associated with CMA progression using in
silico approaches. For identification of these genes, interaction network of DEGs
was constructed, and hub genes as well as enriched clusters in the network were also
screened. Gene Ontology (GO) was used for enriching functions in each cluster.
Methods. Datasets. In this work, 88 differentially expressed genes (DEGs) were
used for our computational analysis and were downloaded from a study on gene
expression profile of duodenal epithelial cells in response to chronic metabolic
acidosis (CMA) 17. These DEGs represent the Illumina’s microarray featuring
high-performance BeadArray technology that was originally carried out on RNA
3
samples from the rat duodenal epithelial cells exposed to long-standing academia
17
. The dataset of 88 DEGs consists of 49 up-regulated and 39 down-regulated
genes and represent Table 2 and 3 of the original analysis. This data set
comprises of mRNAs whose expression levels altered by greater than 10-fold in
response to CMA.
Network Analysis. Using GeneMANIA [www.genemania.org/], functional
interaction among DEGs was determined on the basis of GO term “biological
process” and R. norvegicus (rat) as reference species. Predicted correlation
among network genes incorporates various parameters which include coexpression, biological pathways, similarity in protein domains, co-localization,
physical and genetic interactions, and predicted interactions.
Identification of hub genes. Scale free property is shown by biological
networks 18 where hubs represent nodes having multiple connections in the
network. Hubs were determined by computing node degree distribution values
using NetworkAnalyzer plugin of Cytoscape 19. In current network, top three
genes which have highest value of node degree distribution were considered as
hubs.
Community Analysis. Greedy community-structure detection algorithm via
GLay [http://brainarray.mbni.med.umich.edu/sugang/glay] 20 plugin in Cytoscape
was applied in determining modules with functional property. In each cluster
over represented biological function were identified by subjecting clusters to a
functional enrichment analysis. Only those communities were focused for
functional enrichment analysis which has at least 10 nodes. DAVID functional
analysis tool was used for carrying out functional enrichment analysis.
Results. We downloaded a list of 88 genes that were determined to be
differentially expressed by at least 10 fold for duodenal epithelial cells in
response to CMA 17. This data consists of 49 up-regulated and 39 down-regulated
genes and was further used for the network analysis.
Network construction and identification of hub genes. GeneMANIA
webserver was used for studying interaction among DEG and other related genes
in the network 21. Out of total 88 DEGs, GeneMANIA could identify only 86
genes. Network analysis using GeneMANIA for DEGs leads to enrichment of
neurological process like neurological system process regulation and nerve
impulse transmission. Other GO term “biological processes” which are overrepresented includes regulation of synaptic transmission, exocytosis,
neurotransmitter transport and axon part (Table 1).
Further analysis of interaction networks inferred from GeneMania was carried
out using Cytoscope 2.8.2 19. Initial network which consists of 180 genes and
2771 nodes was reduced to 180 genes and 2461 edges after filtering by
eliminating edges and self-loops which appear in duplicate. Network genes are
represented by circles and edges represent interactions between these genes.
Green nodes represent up-regulated genes, while as downregulated genes are
denoted by red nodes. Cyan exhibits additional related genes which have been
predicted by GeneMANIA (Figure 1).
4
Figure 1-
5
Top three hubs with highest node degree distribution represent genes encoding
for TFF1 (Trefoil factor 1) and HTR5A (5-Hydroxytryptamine (serotonin)
receptor 5A) with a node degree of 76 (Figure -2A and 2B). Other two hubs are
SLC6A11 (Solute Carrier Family 6 (Neurotransmitter Transporter), Member 11)
with a node degree value of 68 and GRIN2B (Glutamate Receptor, Ionotropic, NMethyl D-Aspartate 2B) having node degree value of 64.
Figure 2A-
6
Figure 2B-
Community analysis and functional annotation of detected modules. Fast
greedy community-structure identification algorithm led to the identification of
six biologically-related clusters (Figure 3). Out of these six clusters, cluster 1 has
69 genes and is largest (Figure 3a). Cluster 2 has 66 genes (Figure 3b) and cluster
3 has 29 genes (Fig. 3c). All the identified top hubs which have been detected in
present study (TFF1, HTR5A, SLC6A11 and GRIN2B) are present in cluster 2
(Figure 3b). In remaining clusters 4, 5 and 6 the number of genes observed was
less than 10 in each of them (Figure 3d). Hence, Communities with at least 10
nodes were chosen for enrichment studies. Therefore only 3 communities were
finally analyzed for over representation of GO terms.
7
Figure 3a-
8
Figure 3b-
9
Figure 3c-
Figure 3d-
10
In order to categorize these clusters biologically, DAVID functional analysis
tool was applied for classifying genes of each module. Enrichment of GO term
“biological process” was observed in three chosen modules. Table 2 summarizes
statistically significant top 10 enriched GO terms for DEGs in top 3 clusters for
community analysis. For Cluster 1, top three statistically significant enriched GO
terms are exocytosis, secretion by cell and secretion. Among the other
statistically significant GO terms which are included in this cluster is generation
of a signal involved in cell-cell signaling as well as regulation of system process.
For Cluster 2 higher enrichment for GO terms was observed in biological
processes associated to synaptic transmission and regulation of nerve impulse
transmission. In Cluster 3 GO terms with higher enrichment are associated with
lipid regulation, inflammatory and defense response, cholesterol and steroid
transport.
Discussion. Identification of drug targets at molecular level and targeted
therapies have proven to be important curative treatments for various diseases.
This has been of tremendous importance since last few decades due to progress
in bioinformatics. To investigate the role of DEGs that were identified from a
study on gene expression profile of duodenal epithelial cells in response to CMA
17
, and additionally related genes, an interaction network was created and node
degree for each individual gene in the network was calculated. TFF1, HTR5A,
SLC6A11 and GRIN2B were the genes with the highest degree and considered as
hubs in the network created from DEGs of duodenal epithelial cells in response
to CMA and additional related genes predicted by GeneMANIA. The statistically
significant enriched GO terms within the interaction network predicted by
GeneMANIA are neurological processes like regulation of neurological system
and nerve impulse transmission. Other GO terms which are over-represented
includes regulation of synaptic transmission, exocytosis, neurotransmitter
transport and axon part (Table 1).
11
Table 1-
S. No
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Feature
regulation of neurological system process
Exocytosis
regulation of transmission of nerve impulse
regulation of synaptic transmission
neurotransmitter transport
neurotransmitter secretion
regulation of vesicle-mediated transport
axon part
regulation of neurotransmitter levels
regulation of exocytosis
FDR
3.0272E-14
1.3713E-13
2.2024E-13
4.1962E-12
2.1815E-11
1.9134E-10
2.7835E-10
3.7407E-10
6.7995E-10
4.584E-08
TFF1 was the only gene discerned as hub from a list of DEGs, and whereas HTR5A,
SLC6A11 and GRIN2B were part of interaction network as predicted by
GeneMANIA. TFF1 (Trefoil factor 1) is a member of Trefoil factor protein family, is
expressed mainly in gastric mucosa 22 and has an important role in regulation of cell
proliferation 23. It prevents cell death in intestinal epithelia by exerting anti-apoptotic
effects 24. TFF1 may play a role in maintenance of mucosal integrity and may mediate
repair process after exposure to hazardous conditions 23-25. Another gene that was
identified as a potential hub in our study that plays an essential role in the
serotonergic synapse is Sodium- and chloride-dependent GABA transporter 3
(SLC6A11 or GABT3 or GAT3), a member of the Na+/Cl- transporter family also
involved in neurotransmitter uptake 26. Modifications in the transepithelial transport
of water, key elements, and nutrients, for example Na+, Cl-, K+, HCO3-, Ca2+,
inorganic phosphate (Pi), and amino acids, are well designated as the trademarks of
CMA-induced alterations in the intestinal functions 9-13. N-methyl D-aspartate
(NMDA) receptors such as Glutamate receptor ionotropic, NMDA 2B (GRIN2B or
NR2B) constitute the main subtype of glutamate receptors, and generally contribute in
quick excitatory synaptic transmission 27. The toxicity of NMDA receptors is reliant
on extracellular Ca2+, and reflects a great quantity of Ca2+ influx directly through the
receptor-gated ion channels 28. Additionally, several intracellular signals are able to
control NMDA receptor channel activity 29 besides inducing NMDA receptordependent cell death 28 ,30.
In order to investigate biological processes, it is essential to recognize the structure
and function of biological networks. Recently many studies have reported use of
network based studies for investigating different biological issues 31-34. In the current
study we discerned 6 functional modules or communities in the interaction network
using fast greedy algorithm implemented as GLAY 20 plugin for Cytoscape.
Moreover, with the help of functional annotation tool DAVID, only 3 modules were
explored (based on the criteria described in the methods section) for functional
enrichment. From our analysis it is clear that these modules exhibit enrichment of
12
functions related to exocytosis, secretion by cell and secretion in cluster 1. Among the
other statistically significant GO terms which are included in this cluster is generation
of a signal involved in cell-cell signaling as well as regulation of system process
(Table 2A). For cluster 2 regulation of neurological system process, regulation of
transmission of nerve impulse and cell-cell signaling are the over-represented
biological processes (Table 2B). The intestinal absorptive roles of neuropeptide Y and
its associated peptides are common in inhibitory enteric neurons or in neuroendocrine
L-cells that predominate in the colorectal mucosa 35. In Cluster 3 GO terms with
higher enrichment are associated with lipid regulation, inflammatory and defense
response, cholesterol and steroid transport as well as various metabolic processes
(Table 2C). CMA is known to cause various kinds of metabolic disturbances to
numerous organs, such as, heart, kidney, and bone. As a response to these disorders,
the body creates compensatory mechanisms to reinstate homeostasis, for instance
hyperventilation to clear CO2, renal exclusion of acid counterparts, and release of
phosphate from bone 36.
Table 2 (A,B,C)Category
Term
P-value
A)
GOTERM_BP_FAT
GO:0006887~exocytosis
2.2093E-09
GOTERM_BP_FAT
GO:0032940~secretion by cell
2.4833E-08
GOTERM_BP_FAT
GO:0046903~secretion
2.5067E-08
GOTERM_BP_FAT
8.5009E-06
GOTERM_BP_FAT
GO:0003001~generation of a signal involved in cell-cell
signaling
GO:0003001~generation of a signal involved in cell-cell
signaling
GO:0044057~regulation of system process
GOTERM_BP_FAT
GO:0044057~regulation of system process
1.2079E-05
GOTERM_BP_FAT
GO:0016192~vesicle-mediated transport
2.3768E-05
GOTERM_BP_FAT
GO:0006182~cGMP biosynthetic process
3.8979E-05
GOTERM_BP_FAT
GO:0017157~regulation of exocytosis
8.1683E-05
B)
GOTERM_BP_FAT
GO:0007268~synaptic transmission
3.9481E-10
GOTERM_BP_FAT
GO:0019226~transmission of nerve impulse
7.3096E-09
GOTERM_BP_FAT
GO:0044057~regulation of system process
2.7781E-08
GOTERM_BP_FAT
GO:0007267~cell-cell signaling
4.04E-08
GOTERM_BP_FAT
GO:0031644~regulation of neurological system process
2.9012E-06
GOTERM_BP_FAT
GO:0051969~regulation of transmission of nerve impulse
1.9756E-05
GOTERM_BP_FAT
GO:0051969~regulation of transmission of nerve impulse
1.9756E-05
GOTERM_BP_FAT
2.2752E-05
GOTERM_BP_FAT
GO:0051240~positive regulation of multicellular organismal
process
GO:0051240~positive regulation of multicellular organismal
process
GO:0007610~behavior
C)
GOTERM_BP_FAT
GO:0032368~regulation of lipid transport
1.3757E-05
GOTERM_BP_FAT
GO:0032371~regulation of sterol transport
0.00034825
GOTERM_BP_FAT
GO:0032374~regulation of cholesterol transport
0.00034825
GOTERM_BP_FAT
GO:0006954~inflammatory response
0.00038997
GOTERM_BP_FAT
GOTERM_BP_FAT
13
8.5009E-06
1.2079E-05
2.2752E-05
2.9175E-05
GOTERM_BP_FAT
GO:0006952~defense response
0.00039246
GOTERM_BP_FAT
GO:0002526~acute inflammatory response
0.00041474
GOTERM_BP_FAT
GO:0016054~organic acid catabolic process
0.00051667
GOTERM_BP_FAT
GO:0046395~carboxylic acid catabolic process
0.00051667
GOTERM_BP_FAT
GO:0009611~response to wounding
0.00051879
GOTERM_BP_FAT
GO:0019439~aromatic compound catabolic process
0.00053562
Conclusion. Genes associated with CMA were screened by identifying DEGs with
the help of bioinformatic tools. Function and pathway enrichment of these identified
genes followed by interaction and network construction was carried out using in-silico
studies. This study would help in further elucidation of mechanisms involved in
progression of CMA. However, further investigations are required to gain more
insight into networks and pathways. These studies would be quite useful in better
understanding of CMA at molecular level and shall finally lead to target based
treatment of this disease.
Disclosure of Interest. Author declare that there exists no conflict.
Acknowledgement. This work was supported by Deanship of Scientific Research
(DSR), King Abdulaziz University, Jeddah, under grant No. (141-1025-D1435), The
authors, therefore, gratefully acknowledge the DSR technical and financial support.
14
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Table and Figure Legends
Table 1: Top 10 enriched gene ontology (GO) terms for DEGs and additional
related genes as reported by GeneMANIA.
Table 2: Top 10 significantly enriched gene ontology (GO) terms detected by
DAVID functional cluster analysis in differentially expressed genes for A)
Cluster 1, B) Cluster 2, and C) Cluster 3. The clusters were detected by using
greedy algorithm (GLAY).
Figure 1: The interaction networks for DEGs and additional related genes as
predicted by GeneMANIA and visualized in Cytoscape. The red indicates downregulated genes, where as green and cyan represent up-regulated and GeneMANIA
predicted genes, respectively. The size of each node represents its node degree with
larger nodes representing higher node degree.
Figure 2: Top hubs (indicated by arrows) based on node degree as identified in
the network of DEGs. A) The hubs TFF1 and, B) HTR5A and their first neighbors.
The genes that were up-regulated are shown in green, whereas the down-regulated are
shown in red. The genes predicted by GeneMMANIA are shown as cyan. Both TFF1
and HTR5A have the node degree of 76 and are considered as the top hubs in the
network.
18
Figure 3: Communities (clusters) generated by greedy algorithm (GLAY) are
shown. A) Cluster 1- mainly involved in functions related to exocytosis, secretion and
cell-cell signaling, B) Cluster 2 - shows over-representation of functions related to
synaptic transmission, or transmission of nerve impulse, C) Cluster 3 – regulation of
lipid, cholesterol, and sterol, as well as processes related to inflammatory response are
enriched in this cluster, and D) Cluster 4-6 – no enriched process was observed in
these three clusters.
19