|Year : 2021 | Volume
| Issue : 4 | Page : 451-457
Identification of molecular signatures and pathways to identify novel therapeutic targets in mild cognitive impairment: Insights from a systems biomedicine perspective
Vineeta Singh, Vijaya Nath Mishra
Department of Neurology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
|Date of Submission||09-Aug-2021|
|Date of Acceptance||12-Oct-2021|
|Date of Web Publication||14-Dec-2021|
Vijaya Nath Mishra
Department of Neurology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh
Source of Support: None, Conflict of Interest: None
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. Mild cognitive impairment (MCI) is the earlier stage of AD. Aims and Objectives: This study aimed to identify the molecular signatures and target and its therapeutic intervention in MCI-AD through a detailed analysis of gene of MCI and AD. Materials and Methods: We used the disease gene set of AD and MCI (GSE4226 and GSE4229) comparing to identify common genes among them. GIn the present study we have attempted to identify gene set, protein-protein interaction and Transcription factors associated with MCI and AD. Result and Conclusion: Conclusively, the present study will provide a set of markers as biological processes, cellular components, molecular function, various pathways, and different TFs which might help in better understanding of disease mechanism progression and also might act as a target for therapeutic interventions for the treatment of MCI and AD.
Keywords: Alzheimer's disease, mild cognitive impairment, molecular signature
|How to cite this article:|
Singh V, Mishra VN. Identification of molecular signatures and pathways to identify novel therapeutic targets in mild cognitive impairment: Insights from a systems biomedicine perspective. Biomed Biotechnol Res J 2021;5:451-7
|How to cite this URL:|
Singh V, Mishra VN. Identification of molecular signatures and pathways to identify novel therapeutic targets in mild cognitive impairment: Insights from a systems biomedicine perspective. Biomed Biotechnol Res J [serial online] 2021 [cited 2023 Jun 9];5:451-7. Available from: https://www.bmbtrj.org/text.asp?2021/5/4/451/332453
| Introduction|| |
Someone in the world develops dementia every 3 s. There are over 50 million people worldwide living with dementia in 2020. This number will almost double every 20 years, reaching 82 million in 2030 and 152 million in 2050. Much of the increase will be in developing countries. Already, 60% of people with dementia live in low- and middle-income countries, but by 2050, this will rise to 71%. The fastest growth in the elderly population is taking place in China, India, and their south Asian and Western Pacific neighbors. Alzheimer's disease (AD) is a form of dementia, which is a progressive neurodegenerative disease that gives rise to dementia and severe impairment of cognitive function in affected people, and is notably associated with the formation of extracellular amyloid plaques and intracellular accumulation of neurofibrillary tangles in the brain. Out of the approximately 50 million people worldwide with dementia, between 60% and 70% are estimated to have AD. Identification of AD at an early stage will be helpful in the identification of AD and also for the development of therapeutic intervention. Mild cognitive impairment (MCI) is the earlier stage of AD or other kinds of dementia.
Cognitive impairment assessment is primarily used to identify the dementic individual. Sporadic AD is the most common form of the disease and generally affects individuals over the age of 65. Early diagnosis is rare, occurring in only a small fraction (1%–5%) of patients. Early diagnosis of AD may improve management strategies for patients. However, there are no effective treatments for the disease. Therefore, research directed toward identifying AD biomarkers is needed not just for a better understanding of the development of AD but also as an important step in discovering new treatment strategies.
AD is characterized by a gradual decline in cognitive function combined with behavioral and psychiatric symptoms. Being a multifactorial brain disorder, the exact pathophysiology of AD is not yet entirely known. However, several pathogeneses of AD have been suggested: β-amyloid oligomerization, τ-protein aggregation, cholinergic dysfunction, oxidative stress, and inflammation are implicated in the development of AD. AD is thought to be a resultant of various complex biological processes (BP). Of them, some are known and some are still to endeavor. Here, we have attempted to identify the molecular marker which could be used for the identification of AD or serve as a target to prevent or delay the onset of AD.
To identify critical genes, we used two data sets of disease which are AD and mild cognitive disorder using DisGeNet. Using DEGs identified by this approach, we performed functional enrichment analyses to identify important Gene Ontology (GO) and pathways. We apply systems biomedicine perspective using the following approaches, a protein–protein interaction (PPI) network reconstruction of the proteins encoded by common genes and identification of TFs. Thus, we have used a comprehensive systems biology pipeline to explore molecular signatures MCI-AD patients. Further, we have also identified common genes, their regulatory TFs hub proteins, and pathways that may provide putative biomarkers for early diagnosis of MCI-AD. These discoveries provide novel insights into the mechanisms underlying MCI-AD pathogenesis and act as a novel target for its delay.
| Materials and Methods|| |
Data retrieval/processing and screening of common genes
Gene expression datasets were retrieved from the specific database DisGeNET., DisGeNET is a discovery database that gathered genes and variants associated with human diseases. ClusterOne selected the common genes among MCI and AD genes.
Protein–protein interaction network construction and analysis of modules
The PPI was conducted by Search Tool for the Retrieval of Interacting Genes/Proteins database version 10.0 (STRING). The active interaction sources were based on the seven parameters including experiments, co-expression, gene fusion, co-occurrence, databases, text mining, and neighborhood. Networking visualization of output was carried out using Cytoscape 3.5 software (Cytoscape software 3.5 by Institute of Systems Biology Seattle and Cytoscape developer team/).
Functional annotation and pathway analysis of selected genes
GO analysis of common genes further performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID; https://david.ncifcrf.gov/) tool. Pathway analysis was carried out by KEGG, Biocarta, and Reactome database. To describe gene product attributes, GO provides three categories of defined terms, including BP, cellular component (CC), and molecular function (MF) categories. KEGG is an integrated database resource for the systematic analysis of gene functions, linking genomic information with higher-order functional details. DAVID is a bioinformatics data resource composed of an integrated biology knowledge base and analysis tools to extract biological meanings from large quantities of genes and protein collections through a novel agglomeration algorithm.
Regulatory transcription factor of common genes
Transcription factors (TFs) regulate the expression of genes. The TF behind these common genes of MCI and AD has been identified using BIND_PPI and UCSC_TFBS tools., These TFs might act as a molecular target for different kinds of therapeutic drugs which could be used for the intervention of MCI and AD.
| Results|| |
The present study has been started in September 2017 in the Department of Neurology, Institute of Medical Sciences, with the help of School of Biotechnology, Banaras Hindu University, Varanasi.
Common genes identification between mild cognitive impairment and Alzheimer's disease
GO study of identified genes was performed to discussing gene product attributes in three defined terms as BPs, CC, and MF. Genes responsible for MCI and AD were retrieved from DisGeNET using DisGeNET CUI: C1270972 and CUI: C002395, respectively. Around 152 genes for MCI and 3302 genes were retrieved for AD using DisGeNeT. Comparative hierarchy analysis of MCI and AD showed that 139 common genes segregated for MCI [Figure 1].
|Figure 1: Represent interaction of 132 genes of mild cognitive impairment and Alzheimer's disease|
Click here to view
STRING provides interaction of 132 genes out of 139 genes of MCI and AD. The selection of module and hub gene was performed by selection criteria which is a combined score >9.0. Present literature showed 132 PPI out of the 1102 protein interaction network with a combined score of 9.0.
Functional and pathway analysis of selected genes
GO study of identified genes was discussed in three defined terms as BPs, CC, and MF. GO analysis showed that out of 139 genes, 77 were involved in molecular pathways, 35 in CC, and 28 genes in the maintenance of CC [Table 1].
Pathway analysis of common genes
Pathway analysis of 137 genes showed the involvement of six pathways (APP, CASP3, TNF, PSEN1, APOE, GSK3B, RYR3, MAPT, PSEN2, BACE1, IL1B, MME), one from KEGG, two pathways from Biocarta, and the rest three pathways identified using Reactome pathways [Table 2].
Transcription factor of target gene
TF study of common genes showed that transcription factor Tat, calcium and integrin binding 1 (calmyrin), amyloid beta (A4) precursor protein, catenin (cadherin-associated protein), beta 1, Preselin 1, nicastrin, and genome polyprotein are involved in both MCI and AD associated gene regulation. [Table 3].
| Discussion|| |
AcquahMnsah 2017 evidenced that RORA1 regulates AD genes, Gomez-Paster showed the involvement of HSF2 in AD genes., Voljkovic showed in his research that SEF1 is also involved in the AD genes regulation. Su et al. evidenced the AP2REP involvement in the regulation of various genes. Warnatz showed BACH1 involvement in various genes. Meyer et al. showed the involvement of MEIS1 in the regulation of genes of AD. Dietrich et al. found AHRARNT involvement in regulation of various genes. Akila Parvathy Dharshini et al. showed FOXD3 involvement in regulation of various genes of AD. Pajares et al. found in his study that AD associated genes are regulated by NEF2. Galano et al. also reported found in his study that NFE2 regulates various AD genes. Katoh et al. evidenced CHX10 regulated various AD genes. Labadrof showed the involvement of BACH2 in various AD-related genes. Sparrow showed the involvement of RSRFC4 as a regulator in different AD-related genes. Zuchner et al. showed the involvement of AREB6 in various AD-related genes. Merlo found in his research that P53 protein is involved in regulation of AD genes. Tam et al. showed the involvement of GATA2 in various AD-related genes. Singh et al. found the involvement of HFH3 in regulation of different AD genes. Santiago and Potashkin showed the involvemt of HNF4 in various AD-related genes. Ow et al. showed the involvement of MAZR/PATZ1 in various AD-related genes. Kummer and Heneka evidenced that PPARA involves in the regulation of various AD-related genes. Wang et al. reported the involvement of PAX3 in various genes. Blaudin et al. reported the involvement of EN1 in various genes. Vishwamitra et al. reported the role of IK1 in various AD-related genes. Lin et al. found that CDP regulated the AD-related genes. Kong et al. found that AP1 regulated the AD-related genes. Snow and Albensi reported the involvement of NFKAPPAB65 in the regulation of various AD-related genes. Nott et al. found that HMX1 regulated the AD-related genes. Bacher et al. found that MIF1 regulated the AD-related genes. Jin et al. found that GATA regulated the AD-related genes. Jones and Kounatidis found that NFKAPPAB regulated the AD-related genes. Liang et al. found that MSX1 regulated the AD-related genes. PAX5 Izadi and Soheilifar found that PAX5 regulated the AD-related genes. Moon et al. found that CDPCR3 regulated the AD-related genes. Canchi et al. found that TGIF regulated the AD-related genes. Conti et al. found that GATA3 regulated the AD-related genes. Martínez et al. found that OLF1 regulated the AD-related genes. Mohr et al. found that MEIS1BHOXA9 regulated the AD-related genes. Du and Maniatis found that TAXCREB regulated the AD-related genes. Nativio et al. found that CEBPA regulated the AD-related genes. Busbee et al. found that AHR regulated the AD-related genes. Foster et al. found that AP2 regulated the AD-related genes. Zhang et al. found that STAT1 regulated the AD-related genes. Nowak et al. found that TCF11 regulated the AD-related genes. Wang et al. found that HNF3B regulated the AD-related genes. Gupta et al. found that NKX61regulated the AD-related genes. Ginsberg et al. found that AP4 regulated the AD-related genes. Chen et al. found that ZIC3 regulated the AD-related genes. Kong et al. found that ZIC1 regulated the AD-related genes. Pandi-Perumal et al. found that RORA2 regulated the AD-related genes. Cooney et al. found that GCNF regulated the AD-related genes. Ashton et al. also reported NRSF involvement in AD [Table 1]. The above-mentioned TF was reported to be involved in the regulation of genes responsible for the onset of AD [Figure 2].
|Figure 2: Represent genes involved in mild cognitive impairment and Alzheimer's disease|
Click here to view
| Conclusion|| |
In the present study, we have analyzed the genes profile of AD and MCI using various in silico tools and softwares to reveal molecular signatures at BPs, CC, MF, various pathways, and protein (hub proteins and TFs). A number of key hub genes were significantly enriched in pathways involved in metabolic pathways, alternative and classical complement pathways, and amyloid pathways. Thus, we have identified potential molecular signatures for AD which can be detected as a marker and these warrant clinical investigations in AD patients to evaluate their utility. The nature of these biomarkers and the pathways they participate in may reveal new aspects of MCI and AD and also validate the earlier proposed pathways responsible for MCi and AD development and progression.
The authors would like to thank Dr. Vinay Kumar Singh, School of Biotechnology and Centre for Bioinformatics, Institute of Science, BHU, Varanasi.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]