Corresponding Author: Azminah
E-mail: [email protected]
INTRODUCTION
Smoking is a major contributor to preventable deaths
caused by non-communicable diseases (Jafari et al., 2021). Cigarettes represent the most popular form of
tobacco product, accounting for approximately 80% of all tobacco products in
use. Worldwide, over 1 billion individuals are active smokers, making tobacco
the second most commonly used addictive substance. Tobacco comes in various
forms, including combustible products like cigarettes, cigars, pipe tobacco,
hookah, and non-combustible products such as electronic cigarettes (EC), heated
tobacco products (HTP), and various formulations used for chewing, dipping, or
snuffing (Panagiotakos et al.,
2023). The World Health Organization (WHO) reports that
tobacco use results in one in ten deaths globally, totaling 7 million deaths
annually. If the current global tobacco consumption trend persists, it is
estimated that by 2030, there will be 8 million tobacco-related deaths each
year (Jafari et al., 2021). Among 159 countries with a population of over 1
million, the prevalence of tobacco use among males aged 15 and older has
significantly increased in 20 countries over the past three decades, including
Indonesia. In Indonesia, there has been a significant surge in deaths
attributable to tobacco use, rising from 112,800 deaths in 1990 to 246,400
deaths in 2019, representing a 118% increase. Despite various efforts to reduce
smoking prevalence, significant progress has not been achieved (Dai et al., 2022).
The primary cause of tobacco addiction is nicotine
dependency (Quigley & MacCabe,
2019). According to go.drugsbank.com, nicotine is a highly
toxic alkaloid found in tobacco products. Nicotine is a tertiary amine that
features a pyrrolidine and pyridine ring selectively binding to nicotinic
cholinergic receptors at various locations in the body, including the brain,
neuromuscular junctions, adrenal medulla, and ganglia (Sandhu et al., 2023). When an individual inhales nicotine through
cigarettes, nicotine diffuses across the lungs into the circulation, ultimately
reaching the brain. This affects the centers of activity in the limbic system
and stimulates the cortex. Dopamine is released in the frontal cortex,
mesolimbic area, and corpus striatum, mediating pleasurable effects (Sandhu et al., 2023).
Recent research indicates that nicotine modulates the
MAO-A and MAO-B enzymes, which play a role in dopamine breakdown, primarily
responsible for addiction (Tiwari et al., 2020). Furthermore, the effects of nicotine can lead to
changes in nucleosome responsiveness, enhancing DNA accessibility (Brown et al., 2015). Additionally, the nicotinic acetylcholine receptor
(nAChR) is one of nicotine's biological targets in the nervous system that can
result in gene expression changes (Sherafat et al., 2021) and potentially cause issues in the nervous,
neuromuscular, cardiovascular, respiratory, immune, and gastrointestinal
systems (Lee & Fariss,
2017).
The complex effects of nicotine on various body
systems underscore the importance of a profound understanding of genetic
changes in biological responses to nicotine and the identification of
influential biological pathways due to nicotine exposure through gene
expression data analysis. Gene expression analysis is a crucial method utilized
in drug discovery, biomarker research, and pathway analysis (protein-protein
interactions). Essentially, gene expression analysis involves the detection of
differences in two or more groups, known as Differentially Expressed Genes
(DEGs). This analysis encompasses genome expression techniques such as
microarrays or RNA sequencing (RNA-seq) and more specific target gene
expression techniques like quantitative polymerase chain reaction (qPCR). NCBI
GEO and ArrayExpress serve as public providers of gene expression data. These
data repositories can contain valuable insights for new discoveries, biomarker
development, and therapies (Biocompare, 2023).
The National Center for Biotechnology Information
(NCBI) plays a crucial role as a provider of diverse biomedical data sources
necessary for nicotine exposure-related gene expression analysis. In the year
2000, NCBI launched the Gene Expression Omnibus (GEO) database as a repository
for high-throughput gene expression data. Subsequently, major journals began
mandating the storage of microarray data in public repositories in 2002, which
contributed to the growth of GEO. Moreover, high-throughput genomic experiments
have rapidly advanced since the initial use of microarrays for gene expression
analysis, prompting the GEO database to evolve to keep pace with changes in
technology and applications. Currently, GEO accepts data from various
technologies, including DNA microarrays, protein or tissue arrays,
high-throughput nucleic acid sequencing, Serial Analysis of Gene Expression
(SAGE), and reverse transcription polymerase chain reaction (RT-PCR) (Clough & Barrett,
2016).
This study aims to analyze changes in gene expression
in response to nicotine exposure. The primary focus of this investigation is
the identification of Differentially Expressed Genes (DEGs) through gene
expression data analysis. The required data will be obtained from the National
Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), a
relevant biomedical data source. The results of this search are expected to
provide crucial insights as a foundation for the development of more effective
treatment strategies for the health impacts of nicotine use based on gene
expression changes.
METHOD
This study
employs a descriptive literature review method with a primary focus on
exploring GEO DataSets through the analysis of Differentially Expressed Genes
(DEGs) in response to nicotine exposure. Samples consist of GEO DataSets of
Homo sapiens obtained from the National Center for Biotechnology Information
(NCBI), specifically from the Gene Expression Omnibus (GEO) DataSets, a curated
collection of gene expression data, including series records and original
platforms retrieved from the GEO repository. The research workflow comprises
the following steps: In the first stage, a keyword search using
"nicotine" was conducted to identify relevant gene expression studies
related to nicotine. Subsequently, the search was narrowed down to datasets pertaining
to Homo sapiens subjects. The next stage involved the filtering of gene
expression series data specifically related to the effects of nicotine on Homo
sapiens. The final step included screening Homo sapiens nicotine gene
expression series data based on predefined inclusion and exclusion criteria to
eliminate data that could not be analyzed, resulting in a refined set of data
for GEO DataSets exploration. The last step involves grouping genes in each GEO
DataSet based on functional classification.
The selection
of GEO Datasets was conducted based on predetermined inclusion and exclusion
criteria. Inclusion criteria encompassed original research GEO Datasets that
generated primary data related to gene expression in response to nicotine,
employing Homo sapiens as the study subjects and providing both complete and
incomplete textual information. Exclusion criteria were applied to eliminate
GEO Datasets that lacked specificity regarding Homo sapiens nicotine gene
expression, double GEO Datasets, and those that did not exhibit genes involved
in gene expression.
RESULTS AND DISCUSSION
The National Center for Biotechnology Information (NCBI)
was utilized to investigate the molecular impact of nicotine and aid in the
identification of potential gene targets. An initial search within the NCBI
database, encompassing nicotine gene expression data across all species,
yielded a total of 2,342 GEO DataSets. Subsequently, a focused search for
nicotine gene expression data specific to Homo sapiens resulted in 1,048 GEO
DataSets. The next stage involved the filtration of nicotine gene expression series
data for Homo sapiens, resulting in 63 GEO DataSets. Further refinement of the
Homo sapiens nicotine gene expression series data, limited to those directly
associated with nicotine, yielded 14 GEO DataSets. In the final stage, the Homo
sapiens nicotine gene expression series data underwent screening based on
predefined inclusion and exclusion criteria. This process eliminated three GEO
DataSets that were deemed non-analyzable, resulting in 11 GEO DataSets suitable
for further GEO DataSet exploration (Figure 1). Subsequently, the analysis of
these 11 GEO DataSets was performed to determine the classification of
functionally expressed genes in response to nicotine exposure (Table 1). The
findings from the analysis of these 11 GEO DataSets revealed the presence of
potential gene candidates, classified based on functional gene categories
within the body (Table 2).
Figure 1. Graph Depicting the Quantity of Homo Sapiens
Nicotine
Gene
Expression Data from NCBI GEO
Table 1. Articles Related to Gene Expression in Response
to Nicotine Exposure
|
GEO DataSet Code
|
Authors
|
Title
|
Method
|
|
GSE125217
|
Lee WH, Ong
SG, Zhou Y, Tian L et al, 2020
|
RNA-seq of
iPSC-ECs treated e-cig liquids with and without nicotine
|
Expression
profiling by high throughput sequencing
|
|
GSE148812
|
(Jiang et al., 2019)
|
An
Exome-Wide Association Study Identifies New Susceptibility Loci for the Risk
of Nicotine Dependence in European-American Populations Nicotine Dependence
in European-American Populations
|
Genome
variation profiling by genome tiling array
|
|
GSE125416
|
(Guo et al., 2019)
|
Single-Cell
RNA-Sequencing of Human Embryonic Stem Cell Differentiation Delineates
Adverse Effects of Nicotine on Embryonic Development
|
Expression
profiling by high throughput sequencing
|
|
GSE105445
|
(Lin et al., 2019)
|
Mammary
tumors of xenograft mice with the treatment of nicotine and JMY117 as well as
CHRNA9 and SLC19A7 knockdown in MDA-MB-231, A549 and Hep3B cells
|
Expression
profiling by array
|
|
GSE56398
|
(Oni et al., 2016)
|
Increased
response to nicotine in human dopaminergic neurons derived from iPSC carrying
the risk-associated SNP rs16969968
������������������������������������������������������
|
Expression
profiling by high throughput sequencing
|
|
GSE51284
|
(Bergen AW, Wacholder AC, Nishita DM, Michel M,
Krasnow R, Javitz HS, Fugman DA, Tischfield JA, Hops H, Benowitz NL, Enoch M,
2023)
|
Expression
data from monozygotic twins discordant for nicotine metabolism
|
Expression
profiling by array
|
|
GSE71795
|
(Brown et al., 2015)
|
Nucleosome
Repositioning: Nicotine- and Cocaine-induced Changes
|
Genome
binding/occupancy profiling by array
|
|
GSE56383
|
(Yoshiyama et al., 2014)
|
Vascular
smooth muscle cell: control vs. exposed to nicotine
|
Expression
profiling by array
|
|
GSE40689
|
(Pillai et al., 2015)
|
Β-arrestin1
associated genomic regions in nicotine induced Non-Small Cell Lung Carcinoma
(NSCLC) cell line A549
|
Genome
binding/occupancy profiling by high throughput sequencing
|
|
GSE11208
|
(Kuo, 2019)
|
Chronic
nicotine exposure (kuo-affy-human-232930)
|
Expression
profiling by array
|
|
GSE11142
|
(Y, 2019)
|
Nicotine
effect on CEM model T cell line (kuo-affy-human-232861)
|
Expression
profiling by array
|
Table 2. Classification of Genes Based on Their Functions
|
|
�GEO DataSet Code
|
|
Functional
Category
|
GSE125217
|
GSE148812
|
GSE125416
|
GSE105445
|
GSE56398
|
GSE51284
|
GSE71795
|
GSE56383
|
GSE40689
|
GSE11208
|
GSE11142
|
|
Metabolism
Genes
|
|
DHRS7
|
APOE
|
IFNGR1
|
|
CYP2A
|
|
|
|
|
|
|
Immunology
Genes
|
ICAM-1, TNF-α, IL-1β, IL-6, IL-10, MCSF,
MCP-1
|
|
|
CD40, APP
|
|
|
|
|
|
|
|
|
Receptor
Genes
|
|
DDR2
|
TLR4
|
nAChR,
CHRNA9
|
CHRNA5
|
|
|
nAChRs,
GPCR
|
|
nAChR
|
nAChR
|
|
Nervous
System Genes
|
|
|
nAChRs
|
NTRK2, nAChR
|
|
|
|
|
|
|
|
|
Structural
Genes
|
|
FOXN1
|
HMGB1
|
CD44,
CDH1/3, BDNF/NT-3
|
|
|
|
H-Caldesmon,
L-Caldesmon, SM22
|
VIM,
FN1,� E-cadherin,
|
|
|
|
Regulatory
Genes
|
|
|
nAChRs,
BNIP3,
|
CDK1, CHK1, ERBB2 (HER2, HER2+), EGFR (ErbB/HER2)
|
|
|
TP53, CDNK1C, LITAF, MLL3, NFKBIB, EGR1
|
|
β-arrestin-1,
E2F1, SMAD, ZEB1, ZEB2
|
|
|
|
Enzymatic
Genes
|
|
PITRM1
|
|
SLC16A7,
SLC12A9
|
|
|
|
|
|
|
|
|
Signaling
Pathway Genes
|
|
|
|
|
|
|
|
p38 MAPK, ERK1/2
|
nAChR
|
|
|
The review of the 11 GEO
Datasets involved an analysis of gene expression data at the molecular level in
response to nicotine exposure, which met the following inclusion and exclusion
criteria:
GSE125217
Research with the GEO
DataSet code GSE125217, conducted by Lee et al. (2020), aimed to investigate
the effects of flavored electronic cigarette (e-cigarette) liquids and serum
isolated from e-cigarette users on endothelial health and endothelial-dependent
macrophage activation. The GEO DataSet utilized human induced pluripotent stem cell-derived endothelial
cells (iPSC-ECs) and a high-throughput screening approach to assess endothelial
integrity following exposure to six different e-liquids with varying nicotine
concentrations and serum from e-cigarette users. The samples included 15
individuals and were divided into three groups: flavored e-liquid (RY),
Marlboro (MAR), and control (RKT). Two healthy iPSC lines (78 and 273) were
differentiated into ECs, and iPSC-ECs were treated with e-cigarette liquids RY4
(RY) or Marlboro (MAR) containing 18 mg/ml nicotine (18) and nicotine-free (0).
The review of GEO DataSet GSE125217 revealed that there
are seven genes that exhibit Differential Expressed Genes (DEG) in response to
nicotine exposure, namely TNF-α, IL-1β, IL-6, IL-10, MCSF, MCP-1, and
ICAM-1, and all of these genes are classified as "immunological
genes." TNF-α plays a role in the context of tumor necrosis factor
(TNF)-α cytokine stimulation related to the immune response and
inflammation. IL-1β and IL-6 genes are inflammatory factors produced by M1
macrophages involved in the immune response and inflammation. IL-10 gene is an
M2-related cytokine that is a component of the immune response. MCSF gene
(macrophage colony-stimulating factor) is involved in stimulating macrophage
growth and differentiation. MCP-1 gene (monocyte chemoattractant protein-1)
plays a role in attracting monocytes to the site of inflammation, and ICAM-1
gene (intracellular adhesion molecule-1) shows increased expression after
stimulation with tumor necrosis factor (TNF)-α, which is an inflammatory
cytokine.
The review of
the GEO DataSet GSE125217 provides insights into the impact of nicotine
exposure from flavored electronic cigarette (e-cigarette) liquids on
endothelial health and endothelial-dependent macrophage activation. The
analysis involved endothelial cells derived from human induced pluripotent stem
cells (iPSC-ECs) and utilized an array technology-based gene expression
profiling method to assess endothelial integrity after exposure to six
different e-cigarette liquids with varying nicotine concentrations and serum
from e-cigarette users. The findings revealed significant changes in gene
expression patterns, particularly in immunological genes such as TNF-α,
IL-1β, IL-6, IL-10, MCSF, MCP-1, and ICAM-1, due to nicotine exposure.
These results indicate the health impact of electronic cigarette use and
nicotine exposure, particularly in relation to the immune system response and endothelial integrity.
GSE148812
The research with the GEO DataSet GSE148812, conducted by Li MD
(2020), aimed to uncover the molecular mechanisms underlying various phenotypes
related to the risk of nicotine dependence in the European-American population,
with a focus on the age at smoking initiation (ASI). This GEO DataSet involved
a sample of 2,510 smokers, including individuals of African-American (AA)
descent, totaling 1,654 smokers, and European-American (EA) descent, totaling
856 smokers. The participants had smoked at least 100 cigarettes in their
lifetime and met the sample characteristics based on the Mid-South Tobacco
Case�Control (MSTCC) study. The samples were then analyzed at the single
nucleotide polymorphism (SNP) level and were separately analyzed on a
gene-based basis.
The review of GEO DataSet GSE148812 yielded insights into
the impact of nicotine exposure on gene expression, identifying four genes
classified into distinct functional categories. Firstly, the PITRM1 gene
emerged as an "enzymatic gene" with implications in enzymatic
processes and the modulation of metalloendopeptidase activity, particularly
associated with Alzheimer's disease. It encodes a 117-kDa mitochondrial matrix
enzyme involved in amyloid beta (Aβ) degradation within mitochondria, highlighting
its crucial role in Aβ digestion and mitochondrial protein transportation.
Secondly, the DDR2 gene was categorized as a "receptor gene,"
encoding a collagen-binding tyrosine kinase receptor that regulates cell
proliferation and survival, and its overexpression is associated with a poor
prognosis in diseases like lung cancer. The DDR2 receptor's role in regulating
cellular responses to external signals is underscored, impacting disease
development. Thirdly, the DHRS7 gene was classified as a "metabolism
gene" due to its contribution to xenobiotic metabolism. It encodes an
enzyme within the dehydrogenase/reductase (SDR) protein family, engaged in the
metabolism of xenobiotic compounds within the body. Lastly, the FOXN1 gene,
classified as a "structural gene," was associated with thymic
underdevelopment, affecting organ and body structure development. These
findings illuminate the molecular mechanisms and functional significance of
these genes in response to nicotine exposure.
In conclusion, this review reveals the involvement of genetic
analysis in exploring the impact of nicotine dependence at the molecular level.
The findings shed new light on the roles of PITRM1, DDR2, DHRS7, and FOXN1
genes in responding to nicotine, providing an understanding of the biological
mechanisms underlying nicotine dependence in the European-American population.
GSE125416
The study utilizing GEO DataSet GSE125416, conducted by (Guo et al., 2019), aimed to elucidate the molecular mechanisms involved in
cellular responses to nicotine exposure and their effects on gene expression
patterns. This GEO DataSet employed two distinct samples: Control_EB scRNA-seq
[D] and nicotine-exposed_EB scRNA-seq [N]. Subsequently, they underwent
analysis through single-cell RNA sequencing (scRNA-seq) involving approximately
12,500 individual cells derived from human embryoid bodies (EBs) derived from
human embryonic stem cells (hESCs). These EBs were allowed to develop over a
21-day period under culture conditions containing nicotine or in a
nicotine-free environment.
The review of GEO DataSet GSE125416 identified the
involvement of five genes in Differentially Expressed Genes (DEG) due to
nicotine exposure: APOE, HMGB1, TLR4, BNIP3, and nAChRs. These genes were then
classified into five functional categories. APOE gene was categorized as a
"metabolism gene," and its gene regulation has been associated with
brain malformations and intellectual disabilities. HMGB1 gene was classified as
a "structural gene," with increased gene expression affecting cardiac
excitation-contraction and regulating Ca2+ handling and cellular contractility
through Toll-like receptor 4 (TLR4) signaling. TLR4 gene falls into the
"receptor gene" category, playing a crucial role in the HMGB1-TLR4
signaling (Figure 2) and contributing significantly to cardiac dysfunction
pathogenesis. BNIP3 was categorized as a "regulator gene" and is
involved in regulating nicotine-induced cell death and its regulatory role in
cellular processes. Nicotinic Acetylcholine Receptors (nAChRs) are expressed in
undifferentiated and differentiated cells and play a role in various cellular
processes such as apoptosis, cell proliferation, cell differentiation,
intracellular calcium regulation, oxidative stress, inflammation, and tumor
development. Therefore, nAChRs genes can be classified into the categories of
"regulator genes" and "nervous system genes," contributing
to nicotine signaling and influencing cellular functions.
Figure 2. HMGB1-TLR4 Pathway (Guo et al., 2019)
The exploration of GSE125416 reveals that nicotine exposure can
influence the expression of specific genes associated with cellular responses,
involving genes APOE, HMGB1, TLR4, BNIP3, and nAChRs. The functional
classification of these genes provides additional insights into the biological
mechanisms underlying the impact of nicotine on gene expression patterns. This
in-depth understanding may serve as a foundation for the development of more
targeted intervention strategies related to the health effects of nicotine
exposure.
GSE105445
The study conducted using GEO DataSet GSE105445 by Lin et
al. (2019) aimed to evaluate the potential of JMY117 (Bupropion) as an
inhibitor of the metastatic effects of cancer caused by nicotine exposure. This
GEO DataSet investigated the roles of CHRNA9 and SLC16A7 in breast cancer cell
lines (MDA-MB-231), lung cancer cell lines (A549), and liver cancer cell lines
(Hep3B). The experiments involved 30 samples using a xenograft mouse model for
breast tumors, which received various treatments, including nicotine exposure
and the administration of JMY117 in two different doses. Nicotine was added to
drinking water at a concentration of 10 �g/ml. Additionally, knockdown
experiments of CHRNA9/SLC19A7 were conducted on MDA-MB-231, A549, and Hep3B
cells to understand their impact on cancer metastasis behavior.
The review of GEO DataSet GSE105445 revealed the
involvement of 15 genes that experienced Differential Expressed Genes (DEG) due
to nicotine exposure. These genes were categorized into 7 functional gene
classifications. IFNGR1 was classified as a "metabolism gene." It
plays a role in regulating cellular responses to growth factors and cytokines
that can influence cellular metabolic aspects. SLC16A7 and SLC12A9 were
classified as "enzymatic genes." These genes are involved in metabolite
transport and ion regulation through the cell membrane, involving enzymatic
reactions. Nicotinic Acetylcholine Receptors (nAChR) were classified as
"receptor genes" and "nervous system genes." These genes
are receptors involved in cellular responses to nicotine and signal
transduction in the nervous system. CHRNA9 is one of the subunits of the
nicotinic receptor. CDK1, CHK1, ERBB2 (HER2, HER2+), EGFR (ErbB/HER2) were
classified as "regulator genes." Each of these genes plays a role in
controlling the cell cycle and cell proliferation, is involved in cell cycle
regulation and responses to DNA damage, participates in cell signaling
activation regulating cell growth, and is involved in signaling pathways that
control proliferation and cell growth. CD44, CDH1/3, BDNF/NT-3 were classified
as "structural genes." Each of these genes contributes to changes in
the expression of differentiation markers, is involved in cell adhesion,
participates in the growth and differentiation of nerve cells, which can affect
the structure and function of nerve cells. NTRK2 was classified as a
"nervous system gene." This gene is involved in nerve cell responses
to growth factors. CD40 and APP were classified as "immunology
genes." Each of these genes is involved in the immune system, participates
in signal transduction mechanisms, and has roles in cellular responses.
The analysis of the GEO DataSet GSE105445 reveals that 15 genes
undergo Differential Expressed Genes (DEG) in response to nicotine exposure,
with functional classifications encompassing genes related to metabolism
(IFNGR1), enzymes (SLC16A7 and SLC12A9), receptors and the nervous system
(nAChR and CHRNA9), regulators (CDK1, CHK1, ERBB2, EGFR), structural components
(CD44, CDH1/3, BDNF/NT-3), the nervous system (NTRK2), and immunology (CD40 and
APP). These findings provide profound insights into the diverse roles of genes
in responding to nicotine, creating potential avenues for more targeted
therapies and prevention strategies related to the health impacts of nicotine
exposure.
GSE56398
The study with the GEO DataSet GSE56398, conducted by (Oni et al., 2016), aimed to understand the impact of nicotine receptor
variants in humans by distinguishing Induced Pluripotent Stem Cells (iPSC) into
dopaminergic (DA) or glutamatergic neurons and testing their functional
properties and response to nicotine. This GEO DataSet utilized iPSCs derived
from donors homozygous for the major (D398) and minor (N398) alleles of the
single nucleotide polymorphism (SNP) rs16969968 in CHRNA5. A total of 10
samples were used, consisting of three culture samples, namely, midbrain dopaminergic
(mDA) samples derived from iPSCs, totaling 3 samples. These iPSCs carried the
homozygous major allele for rs16969968 (D398). There were also 3 samples from
iPSC lines carrying the homozygous minor allele (N398). Additionally, there
were 4 control differentiation samples of iPSCs derived from CD4+ T cells and
reprogrammed using Sendai virus vectors, created from two different cell lines
in several parts.
The review of GEO DataSet GSE56398 identified the
Nicotinic Acetylcholine Receptor (nAChR) gene as a Differentially Expressed
Gene (DEG) due to nicotine exposure. This gene is classified into the
"receptor gene" category and plays a role as a receptor that responds
to nicotine in the brain and nervous system. In this context, single nucleotide
polymorphism (SNP) changes in the CHRNA5 gene that encodes the α5 subunit
of nAChR, including the SNP rs16969668, which changes the amino acid at position
398 from aspartic acid (D) to asparagine (N), affect receptor activity and can
contribute to nicotine-related addictive behavior.
The exploration of the GEO DataSet GSE56398 concludes that the
Nicotinic Acetylcholine Receptor (nAChR)
gene undergoes expression changes due to nicotine exposure, particularly at the
SNP rs16969668, influencing receptor activity. These findings contribute to
understanding the biological basis of nicotine-related addictive behavior and
illustrate the potential impact of genetic alterations on the function of
nicotine receptors in the human nervous system.
GSE51284
The review of GEO
DataSet GSE51284 identified the gene CYP2A6 as a Differentially Expressed Gene
(DEG) due to nicotine exposure. This gene is classified in the "metabolism
gene" category and plays a role in the metabolic process of nicotine, including the conversion
of nicotine into metabolites such as cotinine. The study aimed to identify
genetic factors that may influence nicotine metabolism rate (NMR) and nicotine
consumption patterns at the genetic level. The dataset included 24 samples from
8 individuals, including four pairs of monozygotic twins, each with three
replicates. Gene expression profiles were created using array-based methods.
GSE71795
The study with GEO DataSet GSE71795, conducted by (Brown et al., 2015), aimed to understand the molecular mechanisms underlying
drug-induced changes in chromatin structure. This GEO dataset utilized 16
samples derived from human neuroblastoma cells (SH-SY5Y) and conducted detailed
analyses on SH-SY5Y cells induced by nicotine. The observations were made at
10, 60, and 90 minutes with two replicates each. SH-SY5Y cells induced by
cocaine were also examined at 5, 20, 40, and 60 minutes with two replicates
each. Two SH-SY5Y cells served as controls. Subsequently, an examination of the
interaction between DNA and nucleosomes in the promoter regions of 858 genes
within nicotine- or cocaine-exposed SH-SY5Y cells was performed using a
microarray method, aiming to investigate nucleosome changes.
The review of GEO DataSet GSE71795 identified 6 genes
(NFKBIB, EGR1, TP53, CDKN1C, LITAF, and MLL3) that experienced Differential
Expressed Genes (DEG) and were classified as "regulator genes,"
meaning they play a role in controlling the expression of other genes. All of
these genes are involved in regulating gene transcription by influencing
nucleosome movement, which can modulate gene expression.
The exploration of this GSE71795 review provides insights into the
role of regulatory genes in controlling gene expression through nucleosome
changes, depicting the fundamental mechanisms involved in cellular responses to
nicotine or cocaine exposure at the molecular level. This knowledge may contribute to a deeper
understanding of the drug's impact on chromatin structure and lay the
foundation for further research in the development of targeted therapies.
GSE56383
The study with the GEO
DataSet GSE56383 conducted by (Yoshiyama et al., 2014)used human aortic smooth muscle cells (HSMC)
samples, with some cells exposed to nicotine treatment, and others serving as control, using array-based expression profiling
methods. Generally, differentiated VSMCs can exhibit a contractile phenotype,
which is a prerequisite for vasoconstriction but not for active migration. The
hypothesis behind this GEO dataset is that exposure to nicotine for 48 hours
will transform differentiated VSMCs from a contractile phenotype into a
synthetic-like phenotype, characterized by increased proliferation and
migration. It is speculated that nicotine exerts this effect through Nicotinic
Acetylcholine Receptors (nAChRs). The GEO DataSet used primary human aortic
smooth muscle cells (HuAoSMCs) treated with Transforming Growth Factor-B
(TGF-B). Initially, the dataset identified nicotine receptors and then
investigated changes in the expression levels of several differentiation
markers in the cells using DNA microarray, real-time PCR, western blot
analysis, and finally, it examined the activity of Mitogen-Activated Protein
Kinases (MAPKs).
The review of GEO DataSet GSE56383 identified 7 genes
(nAChRs, GPCR, H-Caldesmon, L-Caldesmon, SM22, p38 MAPK, and ERK1/2) that
underwent Differential Expression due to nicotine exposure. They were then
classified into 3 functional categories. nAChRs were classified as
"receptor genes," playing a role in responding to nicotine and being
involved in changes in the smooth muscle cell phenotype. GPCR genes were
associated with signaling pathways stimulated by nicotine. H-Caldesmon,
L-Caldesmon, and SM22 were classified as "structural genes" and
played a role in indicating changes in the phenotype of human aortic smooth
muscle cells (HuAoSMCs) following nicotine exposure. These genes were
influenced by nicotine exposure and played a role in describing changes in the
cells, especially concerning smooth muscle phenotype changes (see Figure 3).
p38 MAPK and ERK1/2 were classified as "signaling pathway genes." The
MAPK signaling pathway involves several protein kinases, including p38 MAPK and
ERK1/2, which are responsible for controlling cellular responses to external
stimuli.
Figure 3. Schematic Diagram of the Relationship between
Nicotine Exposure and Phenotypic Changes in HuAoSMCs (Yoshiyama et al., 2014)
The GSE56383
review successfully unveiled the molecular impact of nicotine exposure on human
aortic smooth muscle cells (HuAoSMCs). The research outcomes highlighted the
transformation of cell phenotypes following nicotine exposure, involving
changes in gene expression such as nAChRs, GPCR, H-Caldesmon, L-Caldesmon,
SM22, p38 MAPK, and ERK1/2. The classification of genes into different
functional categories aided in understanding the role of each gene in cellular
GSE11208
The study with GEO DataSet GSE11208, conducted by
Kuo Y. (2019), aims to investigate the long-term effects of chronic nicotine
exposure on the nervous system's function. GEO DataSet GSE11208 analyzed 11
samples involving gene expression profiling based on array technology as the
biological target of nicotine's action. Nicotinic Acetylcholine Receptors
(nAChR) are members of the ion channel superfamily with a crucial role in
chemical signal transduction throughout the human nervous system. GEO DataSet
GSE11208 holds significant relevance in the context of nicotine addiction,
which is a serious public health and economic issue. Its primary objective is
to identify changes in gene expression triggered by nicotine exposure in human
neuroblastoma cells SH-SY5Y, revealing at least two subtypes of nicotinic
receptors. Experiments were conducted to provide evidence of nicotine
exposure's impact on the nervous system function through alterations in gene
expression. This was achieved by exposing human neuroblastoma SH-SY5Y cells to
effective doses of nicotine or control media during two different time periods.
The review of GEO DataSet GSE11208 identified the involvement of nAChR as
experiencing Differentially Expressed Genes (DEG) due to nicotine exposure,
thus classifying them as "receptor genes." The role of nAChR in this
context focuses on the biological target of nicotine action and plays a vital
role in chemical signaling throughout the brain and body. These receptors are
responsible for regulating various nervous system functions and are involved in
nicotine addiction.
The
exploration from GSE11208 review provides a comprehensive understanding of the
long-term impact of nicotine exposure on the nervous system function. Through
gene expression analysis, with a particular focus on the role of Nicotinic
Acetylcholine Receptors (nAChR), this research successfully reveals alterations
in gene patterns resulting from nicotine exposure in human neuroblastoma cells
SH-SY5Y. The findings underscore the crucial role of nAChR in chemical signal
transduction throughout the human nervous system, particularly concerning
nicotine addiction. The experimental results also offer tangible evidence that
nicotine exposure can influence the function of the nervous system through
changes in gene expression, serving as a crucial foundation for more specific
research and therapeutic development related to the health impacts of nicotine
addiction. These findings bear significant implications in the context of
public health and further strengthen our understanding of the biological
mechanisms underlying the molecular-level
effects of nicotine.
GSE40689
The study with GEO DataSet GSE40689, conducted by (Pillai
et al., 2015), aimed to investigate the crucial role of the protein
β-arrestin-1 in signaling transduction through Nicotinic Acetylcholine
Receptors (nAChR), which are responsible for nicotine-induced proliferative
effects. GEO DataSet GSE40689 used three samples: A549 serum, A549 nicotine,
and A549 control. The analytical method involved log transformation of mRNA
expression data for four genes: β-arrestin1, vimentin (VIM), fibronectin
(FN1), and 18S rRNA to approach normal distribution. Assessment of gene-gene
correlations was conducted using Pearson rank correlation (r), while the
relationship between the mRNA expression of these genes and pathological stage
was evaluated using the appropriate normal score test. Furthermore, a
simplified monotonic regression model was used to assess the correlation
between mRNA expression of these genes and the number of years of smoking in
the population. The optimal cutoff point for disease-free survival analysis,
defined as the time between surgical resection and disease recurrence or death,
was evaluated using the maximal chi-square test. All statistical analyses were
performed using SAS software (Version 9.2; SAS Institute; Cary, NC).
The review of GEO DataSet GSE40689 identified 9 genes (nAChRs, VIM, FN1,
β-arrestin-1, E-cadherin, E2F1, SMAD, ZEB1, and ZEB2) that experienced
Differentially Expressed Genes (DEG) due to exposure to nicotine. These genes
were further classified into two functional categories. The nAChRs were
categorized as "signal pathway genes" because they are involved in
the signaling pathways that regulate nicotine's effects on the
Epithelial-Mesenchymal Transition (EMT) and metastasis processes. The genes
VIM, FN1, β-arrestin-1, E-cadherin, E2F1, SMAD, ZEB1, and ZEB2 were
classified as "regulator genes." These genes play a role in the
response to nAChR stimulation and nicotine-induced EMT. Several reasons support
this classification. The significant role of β-arrestin-1 in regulating
the expression of these genes strongly indicates that the regulation of VIM and
FN1 genes is related to the signaling pathway involving β-arrestin-1.
Transcription-level analysis results confirmed the key role of β-arrestin-1
in regulating gene expression and emphasized the regulation at the
transcription level. Furthermore, the response of VIM and FN1 genes to nicotine
indicates their involvement in the signaling pathway regulated by nAChR. In
addition, β-arrestin-1 does not play a role in gene induction mediated by
TGF-β, indicating the unique role of β-arrestin-1 in regulating the
expression of VIM and FN1 genes in response to nAChR stimulation. Lastly, the
E2F1 gene was described as having a significant role in regulating genes
involved in the Epithelial-Mesenchymal Transition (EMT), which is a crucial
process in tumor development, invasion, and metastasis regulation.
As a whole,
the exploration of the GEO DataSet GSE40689 provides a comprehensive
understanding of the role of β-arrestin-1 in signal transduction through
Nicotinic Acetylcholine Receptors (nAChR) and its impact on gene expression.
The research findings identify changes in gene expression patterns, including
nAChR, VIM, FN1, β-arrestin-1, E-cadherin, E2F1, SMAD, ZEB1, and ZEB2, in
response to nicotine exposure. By classifying these genes into "signal
pathway genes" and "regulator genes," the study highlights the
involvement of nAChR in regulating signaling pathways that influence the
Epithelial-Mesenchymal Transition (EMT) and metastasis processes. These
findings lay a crucial foundation for understanding the relationship between
nicotine exposure and cellular responses,
particularly in the context of developing more specific therapies related to
nicotine addiction and its impact on public health as a whole.
GSE11142
The research conducted by Kuo Y. (2019) with the
GEO DataSet GSE11142 aimed at a long-term investigation of the role of
Nicotinic Acetylcholine Receptors (nAChR) in the development and regulation of the immune system, particularly
in the context of nicotine exposure found in tobacco. GEO DataSet GSE11142
involved the analysis of 18 samples and utilized a gene expression profiling
method based on array technology. The analysis was designed to enable the
evaluation of the impact of nicotine exposure on gene expression in a T-cell
model.
A review of the GEO DataSet GSE11142 identified the presence of nAChR
(Nicotinic Acetylcholine Receptors) as Differentially Expressed Genes (DEG) due
to nicotine exposure. nAChR is classified as a "receptor gene,"
serving as a biological target for nicotine action. This receptor plays a role
in regulating the immune system's functions by linking nicotine exposure to
changes in gene expression within the T-cell model.
The
exploration of the GEO DataSet GSE11142 provides insights into the role of
Nicotinic Acetylcholine Receptors (nAChR) in the regulation and development of
the immune system, particularly in the context of nicotine exposure found in
tobacco. The analysis of 18 samples, utilizing gene expression profiling based
on array technology in T cells, successfully revealed significant changes in
gene expression due to nicotine exposure, with nAChR identified as a
Differentially Expressed Gene (DEG). Classified as a "receptor gene,"
nAChR proved to be a biological target in response to nicotine action,
associating the exposure with alterations in gene expression within the T cell
model. The scrutiny of the GEO DataSet provides a deeper understanding of the
molecular-level impact of nicotine on the immune system. This study contributes
to the existing knowledge of nicotine's influence on the immune system and lays
the foundation for further research in understanding the intricate interactions
at the molecular level.
The review conducted through an analysis of 11 GEO datasets revealed a
strong relationship in each GEO dataset with functional gene classifications
that indicated changes in gene expression due to nicotine exposure. The
relationships identified in the review of GEO DataSets are reflected in an
illustrative diagram, demonstrating coherence in the behavior of genes related
to their functional classifications when responding to nicotine (figure 4). The
review of GEO DataSets provides a deeper understanding of the impact of
nicotine at the molecular level and opens up opportunities for the development
of new therapies and the identification of biomarkers related to the effects of
nicotine on human health.
Figure 4. The interconnection of the 11 GEO DataSets
CONCLUSION
The review of GEO DataSets has yielded
interesting findings regarding gene expression in response to nicotine
exposure, through the analysis of 11 GEO DataSets. The review identified
Differentially Expressed Genes (DEG), which are genes that undergo changes in
expression in response to nicotine exposure. These DEGs identified in the
review can be classified into several functional categories, including receptor
genes (such as CHRNA9, nAChRs, and TLR4), regulatory genes (such as CDK1, CHK1,
ERBB2, EGFR, and E2F1), structural genes (such as H-Caldesmon, L-Caldesmon,
SM22, CDH1/3, BDNF/NT-3, and MLL3), immunological genes (including TNF-α,
IL-1β, IL-6, IL-10, MCSF, MCP-1, and ICAM-1), metabolic genes (such as
CYP2A6 and APOE), and enzymatic genes (including PITRM1, DDR2, DHRS7, and
SLC16A7). The results of the review provide a deeper understanding of the
impact of nicotine exposure at the molecular level, shedding light on the gene
expression involved in the response to nicotine. This is expected to have
potential applications in drug discovery and the search for biomarkers related
to the health effects of nicotine exposure in humans.
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