Identification of Ageing-related Hub Genes in Humans,Mouse and Rat Based on Bioinformatics Analysis

Abstract

Background

Ageing is a natural process observed in all living organisms. It is a complex biological process involving many genes and pathways. As such, ageing in an organism is associated with an increased likelihood of developing various neurological diseases, for example, Alzheimer’s disease (AD) and Parkinson’s disease (PD).

Purpose

Ageing is associated with many complex processes and functions that are highly interconnected. In this study, we identified pivotal nodes or hubs that significantly contribute to an ageing network, including those highly connected nodes within the network that are particularly important, using available bioinformatics tools in humans and other model organisms. Thus, mutating or altering any of these nodes in a network can result in significant changes in the overall functioning of an organism.

Methods

For this study, the ageing genes of humans and mice were retrieved from the GenAge database, while the ageing genes of rats were retrieved from the Ageing & Age related Diseases present in Rat Genome Database. STRING (version 11.5), an online tool, was used to create the network. Cytoscape (version 3.10.0), an open-source software with an integrated tool called cytoHubba, was used to identify the hubs from the STRING network in humans, mice and rats. The online tool Enrichr was used to test the functional enrichment of hub genes in humans.

Results

TP53, Trp53 and Actb were identified as important genes in the network, contributing significantly to the process of ageing in humans, mice and rats, respectively, along with others in the network.

Conclusion

Identification of these hubs from the network of ageing and ageing-associated genes deserves further investigation to advance existing knowledge and to improve our understanding of ageing in humans and other model organisms.

Keywords

Function genetics molecular structure neurology

Introduction

Ageing, as we all are aware, involves irrevocable damage that hampers movement, fitness, well-being, and intellect and cognitive abilities, ultimately leading to death. According to the available population data, the world is ageing, with every country expecting an increase in the population aged over 60. The World Health Organization (WHO) reports that one in every six people globally will be aged 60 or older by the year 2030. It is also projected that the number of individuals aged 60 and above will reach approximately 2.1 billion by the year 2050.¹ In response to this demographic shift, the United Nations General Assembly has designated the time period from 2021 to 2030 as the Decade of Healthy Ageing.

With recent advancements in computational approaches and the availability of established ageing databases of various organisms, this study identifies genes that play a significant role in the process of ageing in humans, mice and rats. Identification and interaction analysis of these genes within the hub will provide insights into the mechanisms underlying ageing and its relationship with complex diseases, leading to more targeted strategies for identifying pathways contributing to longevity.

Hubs play an important organisational role in biological networks, especially protein–protein interaction (PPI) networks.² These hubs often function as key players in the biological processes of living organisms. Studies on various organisms, including yeast, flies and worms, suggest that hub proteins in PPI networks are much more essential for life than proteins with fewer connections.³ This makes sense because when a hub protein is disrupted, it can lead to widespread network failure due to its role in connecting with other proteins,⁴ suggesting that when a network hub fails, the entire system can collapse.

Interestingly, genes responsible for Mendelian diseases (those caused by a mutation in a single gene) rarely function as hubs in PPI networks.⁵ This is because hubs are often too vital for life, and if they were altered or mutated, the organism may not survive long enough to develop a disease caused by such a mutation/alteration.⁶ Genes causing Mendelian diseases tend to exist in areas of the network that are less central or connected.⁷ This emphasises the importance of proteins within the hub in maintaining the stability and overall function of biological networks.⁸ Additionally, it is worth mentioning that the role of hubs can differ, depending on the type of network they are involved in.

One of the advantages of studying hubs is that they help identify related genes in ways that traditional methods might miss.⁹ Additionally, biological networks are influenced by both genetic and environmental factors, meaning that the identification of hubs can help uncover insights driven by these sources of variation.¹⁰ This is particularly important when studying a complex phenomenon like ageing, where environmental factors, lifestyle or exposure to toxins, etc., can interact and influence outcomes.

As such, this study mainly focuses on identifying significant hub genes in networks that can help in understanding the process of ageing in humans and other model organisms, that is, mice and rats.

Methods

The 302 human and 136 mouse ageing genes listed in the GenAge¹¹ database, and 1246 rat genes listed in the Ageing & Age related Diseases present in Rat Genome Database¹² To identify the hubs, the STRING (version 11.5)¹³ output of ageing genes of humans, mice and rats was used as an input for Cytoscape (version 3.10.0)¹⁴ at a medium confidence of 0.4. The other parameters, including the degree centrality formula, were set as default. The STRING output (accessed in June 2023) was filtered, and those having a combined score greater than 0.4 were selected for further analysis using cytoHubba, in order to identify hub genes contributing towards ageing in human, mouse and rat model organisms.

For identifying the functional enrichment of human hub genes, an online tool Enrichr15 was used (accessed in June 25).

Results

Identifying Hub in Humans

The output of the STRING, for all 302 human ageing genes present in the GenAge database, was used as an input, after selecting Homo sapiens as the organism under multiple protein sequences at medium confidence (0.4). Other parameters were set as default in order to identify the interactions among them. The STRING output and its corresponding (.tsv) file were used for further analysis as input for further exploration. The STRING output (Figure 1) shows that the network of human ageing genes is highly connected and was filtered for those that have a combined score greater than 0.4. These filtered interactions were used for further analysis in cytoHubba to identify the top 10 hub genes in humans.

Figure 1.

Network of 302 Human Ageing Genes Generated for this Study Using STRING (Version 11.5).

This string output (Figure 1) was used as an input for cytoHubba to retrieve the top 10 nodes using the degree method and the shortest path approach to identify the hubs (Figure 2).

Figure 2.

Important Nodes and Subnetworks of Human Extracted from the Larger STRING Network Using cytoHubba.

The results obtained from cytoHubba (Figure 2), suggest that TP53 has the maximum number of 244 interactions with other human ageing genes in the network including AKT1, MYC, EGFR, INS, JUN, IL6, TNF, BCL2 and ESR1, with respective interaction scores score of 191, 179, 163, 163, 160, 156, 154, 153 and 151. These are reported as the nine other highly connected genes in this network. The identical scores for EGFR and INS suggest that they have the same number of interactions with the other genes in the network.

Identifying Hub in Mouse

Similarly, the output of the STRING for the 136 mouse ageing genes listed in the GenAge database was used as an input, with Mus musculus selected as the organism under multiple protein sequences at medium confidence (0.4); other parameters were set as default. The STRING output and its respective (.tsv) file was used as an input for further exploration. This STRING output (Figure 3) was filtered, and those genes that have a combined score greater than 0.4 were used for further analysis using cytoHubba to identify the top 10 hub genes in mouse.

Figure 3.

Network of 136 Mouse Ageing Genes Generated for this Study Using STRING (Version 11.5).

cytoHubba retrieved the top 10 nodes using the degree method and the shortest path approach (Figure 4).

Figure 4.

Important Nodes and Subnetworks of Mouse Extracted from the Larger STRING Network Using cytoHubba.

The result obtained from cytoHubba (Figure 4) reveal that Trp53 has the highest number of 85 interactions with other mouse ageing genes in the network, followed by Akt1, Sirt1, Myc, Parp1, Atm, Igf1, Pten, Pparg and Mtor, with score of 70, 59, 54, 49, 48, 46, 46, 46 and 45, respectively. These scores suggest that three mouse ageing genes, namely Igf1, Pten and Pparg, have the same number of interactions with other genes in the network, implying that they are equally significant in this network.

Identifying Hub in Rat

Further, the output of the STRING for 1246 rat ageing and age-related genes was used as an input, with Rattus norvegicus selected as the organism under multiple protein sequences at medium confidence (0.4), and all other parameters were set as default. This STRING output was filtered, and those genes that have a combined score greater than 0.4 were used for further analysis through cytoHubba to identify hub genes in rats (Figure 5).

Figure 5.

Network of 1246 Rat Ageing Genes Generated for this Study Using STRING (Version 11.5).

cytoHubba retrieved the top 10 nodes using the degree method and the shortest path approach (Figure 5).

The result obtained from cytoHubba (Figure 6) reveals that Actb has the highest number of 394 interactions with other genes in the rat network, followed by Akt1, Gapdh, Il6, Tp53, Ins2, Il1b, Ins1, Casp3 and Ctnnb1, with score of 393, 393, 373, 359, 344, 336, 333, 296 and 291, respectively. These are the top 10 hub genes in this network. The identical scores for Akt1 and Gapdh suggest that they have the same number of interactions with other genes, implying that both are equally significant in the network.

Figure 6.

Important Nodes and Subnetworks of Rat Extracted from the Larger STRING Network Using cytoHubba.

Functional Enrichment of Human Hub Genes

Using the available online tool Enrichr, functional enrichments and pathways were performed for human hub genes derived from the STRING (version 11.5) output.

Bar Graph depicting the top 10 Human Hub genes for Biological process using Gene Ontology (GO) (Figure 7)

Bar Graph depicting the top 10 Human Hub genes for Cellular Component function using Gene Ontology (GO) (Figure 8)

Bar Graph depicting the top 10 Human Hub genes for Molecular Function using Gene Ontology (GO) (Figure 9)

Bar Graph depicting the Reactome Pathways of top 10 Human Hub genes (Figure 10)

Figure 7.

Bar Graph Depicting the Top 10 Human Hub Genes for Biological process Using Gene Ontology (GO).

Figure 8.

Bar Graph Depicting the Top 10 Human Hub Genes for Cellular Component Function Using Gene Ontology (GO).

Figure 9.

Bar Graph Depicting the Top 10 Human Hub Genes for Molecular Function Using Gene Ontology (GO).

Figure 10.

Bar Graph Depicting the Reactome Pathways of Top 10 Human Hub Genes.

Discussion

In the present study, using the available ageing genes of humans, mice and rats along with bioinformatics tools, the hubs were identified. Hubs are important components of biological networks and are usually associated with gene expression and regulation.

This bioinformatics analysis identified TP53, AKT1, MYC, EGFR, INS, JUN, IL6, TNF, BCL2 and ESR1 as critical genes involved in the ageing network in humans. TP53 is reported as a key gene and is associated with various signalling pathways, such as those related to Interleukin-4 and Interleukin-13,¹⁶ DNA double-strand break repair,¹⁷ and the activation of AKT signalling through PIP3.¹⁸ Its mutation plays a critical role in intracellular signalling through secondary messengers,¹⁹ RNA polymerase II transcription,²⁰ and influences the generic transcription pathway.²¹ Additionally, TP53 is essential in regulating cellular response towards stress²² and modulates SUMO E3 ligases, which SUMOylate target proteins.²³ Furthermore, it regulates metabolic genes²⁴ and orchestrates transcriptional regulation.²⁵,26 p53 is encoded by the TP53 gene.²⁷

The p53 protein level is reported to be elevated in individuals suffering from AD, leading to sustained tau hyperphosphorylation.²⁸ p53 responds to various cellular stresses, triggering downstream events that contribute to the degeneration of dopaminergic neurons. It is involved in the pathogenesis of Parkinson’s disease (PD) and is a promising target for potential therapeutic interventions.²⁹

The Trp53 gene, encoding transformation-related protein p53, is found to be the top-most hub gene in Mus musculus (house mouse). p53 regulates cellular responses to various stresses³⁰ and controls the expression of genes involved in activities related to cell cycle arrest, apoptosis, DNA repair and others.³¹ In normal cells, p53 is expressed at a minimal level, whereas in transformed or cancerous cells, its expression level is elevated, and is thought to contribute to cellular transformation and malignancy.³² As a DNA-binding protein, p53 contains domains for transcriptional activation, DNA binding and oligomerisation, and is believed to bind to specific p53-binding sites to activate genes that suppress growth and invasion, thereby acting as a tumour suppressor.³³ Mice lacking Trp53 are developmentally normal but prone to develop spontaneous tumours.³⁴ Unlike the human version, which has multiple promoters, the mouse Trp53 gene has a single promoter and produces several splice variants that encode different isoforms; however, the functional significance of all variants is yet to be fully determined.³⁵ Deficiency of p53 does not confer a notable growth advantage to adult brain cells; however, it is reported to trigger a range of oncogenic changes that collectively promote the development of gliomas.³⁶ In transgenic mouse models, high expression of p53 has been shown to contribute to the pathogenesis of AD,³⁷ and its overexpression has been linked with neuronal death.³⁸

Similarly, Actb was identified as a hub gene in R. norvegicus. It is also known as Actx and is associated with protein kinase binding activity. Actb is linked to several other biological processes, including the regulation of norepinephrine uptake, response to electrical stimuli, and cyclin-dependent protein kinase activity²⁵ as well as epileptogenesis.³⁹ It has also been studied in the context of metabolic dysfunction-associated steatotic liver disease and is recognised as a biomarker for temporal lobe epilepsy.⁴⁰

The Tp53 gene in R. norvegicus encodes for tumour protein p53, which plays a critical role in responding to different cellular stresses. It regulates genes involved in cell cycle arrest, cell death, ageing, DNA repair and metabolic changes.⁴¹ In normal cells, p53 is expressed at low levels, but its expression is elevated in many transformed cell lines, which is believed to contribute towards cellular transformation and malignancy.⁴²

Limitations and Challenges

Studying ageing-related hub genes in humans and model organisms presents several limitations and challenges, both technical and biological. Hub genes are often central in gene regulation or protein interaction networks and are therefore crucial. However, the results of the presented study have several limitations, as the analysis was performed on data retrieved from public databases, which may contain some noise. Human ageing depends on many factors, such as genetic, lifestyle, environmental, etc., therefore, the available data on hub genes activity may vary across various developmental stages. The current analysis was conducted using the online STRING (version 11.5). Since then, STRING has been updated to version 12, and the previous version (11.5) is no longer accessible online, which limits the ability to replicate the study.

Future Perspective

The hub genes identified in the network of ageing genes in humans and model organisms offer insights into their involvement in the ageing process. Hub genes are associated with multiple biological processes; therefore, understanding the underlying molecular mechanism and their interactions will help in developing targeted interventions for age-associated diseases, potentially delaying ageing. Understanding the roles of these identified hub genes in molecular pathways may aid in developing interventions that promote a healthy lifespan.

The incorporation of artificial intelligence into existing tools may enable the simulation of hub gene behaviour to identify additional interactions within the network or in other networks, ultimately helping to uncover potential target sites for delaying ageing.

Conclusion

This study aims to identify hub genes related to ageing across multiple organisms—humans, mice and rats, using bioinformatics tools. These hub genes serve as biomarkers for ageing and could act as potential therapeutic targets for slowing its effects. The essential hub genes identified in humans, mice and rats are TP53, Trp53 and Actb, respectively. These hubs play central roles in the interaction networks and, therefore, are crucial in modulating the ageing process. The pathways regulated by these hub genes can be broadly categorised into two main areas of signal transduction and gene expression. Signal transduction pathways involve a series of molecular events triggered by the binding or association of a signalling molecule with a receptor, leading to a cascade of intracellular signals. These pathways are essential for regulating various cellular processes, including growth, metabolism and responses to stress. Gene expression pathways, on the other hand, are responsible for turning specific genes on or off at appropriate times, thereby controlling the production of proteins that are critical for maintaining cellular function and integrity over time. These identified hub genes play a significant role in regulating many pathways and processes that affect these organisms at different levels. Hence, the study of these hub genes serves as a focal point for longevity research, with the ultimate goal of identifying strategies to prolong healthy lifespans. As such, these identified hub genes should be further investigated in order to comprehend the ageing process in more detail.

Footnotes

Abbreviations

AD: Alzheimer disease

PD: Parkinson disease

PPI: Protein–protein interaction

WHO: World Health Organization

Authors’ Contribution

MC: Planned and executed the work, analysed and interpreted the data, and wrote the manuscript. GP: Conceived the idea of the work, supervised, and contributed to writing the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Informed Consent

Not applicable.

Statement of Ethics

Not applicable, as this study was performed on secondary data taken from open-source, freely available databases.

ORCID iD

Mona Chaurasiya

References

World Health Organization. Decade of healthy ageing: Baseline report . World Health Organization, 2021 Jan 14.

Wimalagunasekara

, Weeraman

and Tirimanne

Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa). J Genet Eng Biotechnol 2023; 21(1): 69.

Hahn

and Kern

AD.

Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 2005; 22(4): 803–806.

Patil

, Kinoshita

and Nakamura

Hub promiscuity in protein-protein interaction networks. Int J Mol Sci 2010; 11(4): 1930–1943.

Buphamalai

, Kokotovic

, Nagy

, . Network analysis reveals rare disease signatures across multiple levels of biological organization. Nat Commun 2021; 12(1): 6306.

Lee

, Singleton

, Wallin

, . Rare genetic diseases: Nature’s experiments on human development. iScience 2020; 23(5): 101123.

Farber

and Mesner

LD.

A systems-level understanding of cardiovascular disease through networks. In: Translational cardiometabolic genomic medicine. London: Academic Press, 2016, pp.59–81.

Batada

, Hurst

and Tyers

Evolutionary and physiological importance of hub proteins. PLoS Comput Biol 2006; 2(7): e88.

Cheng

, Zheng

, Zhan

, . Identification of hub genes and pathophysiological mechanism related to acute unilateral vestibulopathy by integrated bioinformatics analysis. Front Neurol 2022; 13: 987076.

10.

Gaiteri

, Ding

, French

, . Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 2014; 13(1): 13–24.

11.

de Magalhaes

and Toussaint O. GenAge:

A genomic and proteomic network map of human ageing. FEBS Lett 2004; 571(1–3): 243–247.

12.

Twigger

, Lu

, Shimoyama

, . Rat genome database (RGD): Mapping disease onto the genome. Nucleic Acids Res 2002; 30(1): 1.

13.

Szklarczyk

, Gable

, Lyon

, . STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47(D1): D607–D613.

14.

Shannon

, Markiel

, Ozier

, . Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498–2504.

15.

Kuleshov

, Jones

, Rouillard

, . Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016; 44(W1): W90–W97.

16.

Song

, Traub

, Shi

, . Possible roles of interleukin-4 and -13 and their receptors in gastric and colon cancer. Int J Mol Sci 2021; 22(2): 727.

17.

Akyüz

, Boehden

, Süsse

, . DNA substrate dependence of p53-mediated regulation of double-strand break repair. Mol Cell Biol 2002; 22(17): 6306–6317.

18.

Chen

, Choi

, Wen

, . A p53-phosphoinositide signalosome regulates nuclear AKT activation. Nat Cell Biol 2022; 24(7): 1099–1113.

19.

Pavlakis

, Neumann

and Stiewe

Extracellular vesicles: Messengers of p53 in tumor-stroma communication and cancer metastasis. Int J Mol Sci 2020; 21(24): 9648.

20.

Ljungman

, Zhang

, Chen

, . Inhibition of RNA polymerase II as a trigger for the p53 response. Oncogene 1999; 18(3): 583–592.

21.

Woods

, Weiss

, Sokol

, . Epigenetically silenced apoptosis-associated tyrosine kinase (AATK) facilitates a decreased expression of Cyclin D1 and WEE1, phosphorylates TP53 and reduces cell proliferation in a kinase-dependent manner. Cancer Gene Ther 2022; 29(12): 1975–1987.

22.

Enserink

JM.

Sumo and the cellular stress response. Cell Div 2015; 10: 1–3.

23.

Kung

, Khaku

, Jennis

, . Identification of TRIML2, a novel p53 target that enhances p53 SUMOylation and regulates the transactivation of proapoptotic genes. Mol Cancer Res 2015; 13(2): 250–262.

24.

Maddocks

ODK

and Vousden

KH.

Metabolic regulation by p53. J Mol Med 2011; 89: 237–245.

25.

Lacroix

, Riscal

, Arena

, . Metabolic functions of the tumor suppressor p53: Implications in normal physiology, metabolic disorders, and cancer. Mol Metab 2020; 33: 2–22.

26.

Ohgaki

and Kleihues

Genetic pathways to primary and secondary glioblastoma. Am J Pathol 2007; 170(5): 1445–1453.

27.

Borrero

and El-Deiry

WS.

Tumor suppressor p53: Biology, signaling pathways, and therapeutic targeting. Biochim Biophys Acta Rev Cancer 2021; 1876(1): 188556.

28.

Jembrek

, Slade

, Hof

, . The interactions of p53 with tau and Aß as potential therapeutic targets for Alzheimer’s disease. Prog Neurobiol 2018; 168: 104–127.

29.

Luo

, Sun

, Wang

, . Association of p53 with neurodegeneration in Parkinson’s disease. Parkinsons Dis 2022; 2022(1): 6600944.

30.

Feroz

and Sheikh

AM.

Exploring the multiple roles of guardian of the genome: p53. Egypt J Med Hum Genet 2020; 21(1): 49.

31.

Chen

The cell-cycle arrest and apoptotic functions of p53 in tumor initiation and progression. Cold Spring Harb Perspect Med 2016; 6(3): a026104.

32.

Wijnhoven

, Zwart

, Speksnijder

, . Mice expressing a mammary gland-specific R270H mutation in the p53 tumor suppressor gene mimic human breast cancer development. Cancer Res 2005; 65(18): 8166–8173.

33.

Boutelle AM and Attardi LD.

p53 and tumor suppression: It takes a network. Trends Cell Biol 2021; 31(4): 298–310.

34.

Donehower

, Harvey

, Slagle

, . Mice deficient for p53 are developmentally normal but susceptible to spontaneous tumours. Nature 1992; 356(6366): 215–221.

35.

Hollstein

and Hainaut

Massively regulated genes: The example of TP53. J Pathol 2010; 220(2): 164–173.

36.

, Zhang

, Li

, . New insights into the roles of p53 in central nervous system diseases. Int J Neuropsychopharmacol 2023; 26(7): 465–473.

37.

Chen

, Lee

, Mruk

, . Fer kinase/Fer T and adherens junction dynamics in the testis: An in vitro and in vivo study. Biol Reprod 2003; 69(2): 656–672.

38.

, Hu

, Chen

, . LIMK-dependent actin polymerization in primary sensory neurons promotes the development of inflammatory heat hyperalgesia in rats. Sci Signal 2014; 7(331): ra61.

39.

Wyneken

, Smalla

, Marengo

, . Kainate-induced seizures alter protein composition and N-methyl-D-aspartate receptor function of rat forebrain postsynaptic densities. Neuroscience 2001; 102(1): 65–74.

40.

Schwarz

, Zakharova

, Kovalenko

, . Time- and region-specific selection of reference genes in the rat brain in the lithium-pilocarpine model of acquired temporal lobe epilepsy. Biomedicines 2024; 12(5): 1100.

41.

Stewart ZA and Pietenpol JA.

p53 signaling and cell cycle checkpoints. Chem Res Toxicol 2001; 14(3): 243–263.

42.

Hinds

, Finlay

, Quartin

, . Mutant p53 DNA clones from human colon carcinomas cooperate with ras in transforming primary rat cells: A comparison of the “hot spot” mutant phenotypes. Cell Growth Differ 1990; 1(12): 571–580.