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. 2022 Jun 10;12(7):146. doi: 10.1007/s13205-022-03207-1

Gene expression profiling and protein–protein interaction analysis reveals the dynamic role of MCM7 in Alzheimer's disorder and breast cancer

Navneeth Sriram 1,#, Sunny Mukherjee 1,#, Mahesh Kumar Sah 1,✉
PMCID: PMC9187790  PMID: 35698583

Abstract

The interrelation of cancer and Alzheimer's disorder (AD)-associated molecular mechanisms, reported last decade, paved the path for drug discoveries. In this direction, while chemotherapy is well established for breast cancer (BC), the detection and targeted therapy for AD is not advanced due to a lack of recognized peripheral biomarkers. The present study aimed to find diagnostic and prognostic molecular signature markers common to both BC and AD for possible drug targeting and repurposing. For these disorders, two corresponding microarray datasets (GSE42568, GSE33000) were used for identifying the differentially expressed genes (DEGs), resulting in recognition of CD209 and MCM7 as the two common players. While the CD209 gene was upregulated in both disorders and has been studied vastly, the MCM7 gene showed a strikingly reverse pattern of expression level, downregulated in the case of BC while upregulated in the case of AD. Thus, the MCM7 gene was further analyzed for expression, predictions, and validations of its structure and protein–protein interaction (PPI) for the possible development of new treatment methods for AD. The study concluded with indicative drug repurposing studies to check the effect of existing clinically approved drugs for BC for rectifying the expression levels of the mutated MCM7 gene in AD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03207-1.

Keywords: Breast cancer, Alzheimer's disorder, CD209, MCM7, Gene annotation, Pathway analysis

Introduction

A mass of people around the globe suffer from cancer and neurodegenerative disorder; thus deciphering molecular cues, and their role becomes pivotal for therapy. The advancement in modern technologies implemented to diagnose and treat these disorders has imparted its effectiveness and personalization. The findings based on the in silico studies have revolutionized the pace and efficacy of new treatment methods (Paryan et al. 2016; Chase et al. 2018) for such disorders. Among the cancers, breast cancer (BC) is the most common in mortality rates. WHO reported that BC surpassed lung cancer cases with 2.3 million new cases and is fifth in a row as the leading cause of cancer deaths in 2020 (Sung et al. 2021). Among different subtypes of BC, the common examples are ductal and lobular carcinoma, estrogen receptor-positive (luminal A), HER2 + (Human Epidermal Growth Factor Receptor 2), triple-negative, and basal-like (Park et al. 2016). Based on the progression, BC could be either grade 1 (well-differentiated cancer cells), grade 2 (moderately differentiated cancer cells), or grade 3 (poorly differentiated cancer cells growing faster) (Olsson et al. 2013). For the drug discovery of BC, the gene expression, pathogenesis, and prognostic markers can all be assessed using microarray and sequencing-based methods (Van De Vijver et al. 2002). The gene-expression profiling data from breast tumors can generate further molecular interaction networks, which can then be used to identify hubs (genes or proteins with a high degree of connectivity to other genes or proteins). As a result, these nodes can be used for cancer detection and optimization of therapy protocol. Metastasis events can also be predicted using these biomarkers (Hosseinalizadeh et al. 2021). Despite so much research aimed at identifying the various processes and crucial biological interactions involved in the evolution of BC, the exact mechanism remains a mystery (Kumar et al. 2021). Since the number of genes significantly exceeds the number of functional pathways, the pathway analysis can significantly improve the stability of prognostic cancer models. The functions of most of these pathways may be found in various databases. As a result, the simulated models can be better understood, and treatment methods can be developed that are more effective (Ansari et al. 2021). Alternatively, the discovery of rendezvous common nodes in molecular pathways of different disorders can guide the repurposing of drugs or therapeutic methods for the linked disorder.

AD, a progressive neurodegenerative disorder and the most common cause of dementia, has affected a large older age population in developed countries. As per the 2019 official record of the US, 121,499 deaths were reported from AD, making it the sixth-leading cause of death in the US and the fifth-leading cause of death among those aged 65 and older in the US (Wiley 2021). AD is primarily detected by the formation of amyloid plaques, NFTs (Neurofibrillary Tangles), and the neurites in the neuron cells with hyperphosphorylated tau protein (Dhanavade and Sonawane 2020; Siano et al. 2020). This further leads to neuron cell death, atrophy, and/or inflammation within the hippocampal area of the brain (Rao et al. 2022). Mutations in the APP (Amyloid Precursor Protein), PS-1 (Presenilin-1), and PS-2 (Presenilin-2) genes are among the most common causes of increased risk for Alzheimer's disease (AD) (Dhanavade and Sonawane 2020). Clusterin (CLU), complement receptor 1 (CR1), phosphatidylinositol-binding clathrin assembly protein (PICALM), and sortilin-related receptor (SORL1) have all been identified as potential risk factors for AD (Bagyinszky et al. 2014). The most recent additions are new genes that may have a role in the formation of late-onset AD, such as TREM2 (triggering receptor expressed on myeloid cells 2) and Cluster of Differentiation (CD33) (Bagyinszky et al. 2014). To better understand how AD develops and how it might be treated, finding additional genes associated with the disease is critical.

AD has common mechanisms with other human diseases and comorbidity with diabetes and other severe disorders (Surguchov 2020). On the other hand, cancer and AD are reported to have inverted dynamics at the cellular level. It has been previously established that cancer cells are in a constant state of proliferative signaling, while in AD, this signaling is disrupted (Hanahan and Weinberg 2011). The growth suppressors tend to elude cancer cells, but they significantly impact neurons affected by AD (Hanahan and Weinberg 2011). Cancer suppresses apoptosis, while AD increases it (Nudelman et al. 2019). According to Lanni and colleagues' comprehensive research, the inverse association is caused by a nucleotide replacement in the PIN1 gene's single-nucleotide polymorphisms. Two SNPs, rs2233678 and rs2287839, have been linked to an increased risk of cancer and AD. When SNP rs2233678 is overexpressed, it increases cancer risk, while SNP rs2287839 is underexpressed, it increases MCI (Mild Cognitive Impairment) and AD risk (Lanni et al. 2021). As a result of such indicative reports, anti-cancer medications are being repurposed to tackle both illnesses (Snyder et al. 2017). Presently, in vitro studies have reported ciprofloxacin (antibiotic), heliquinomycin (antibiotic), and simvastatin (anti-cholesterol drug) as MCM7 inhibitors for cancer therapy. However, these have not been evaluated for the treatment of AD (Ishimi et al., 2009; Liang et al., 2017; Simon et al. 2013; Toyokawa et al. 2011). According to the research, people who took chemotherapy had a decreased chance of AD than those who did not (Frain et al. 2017). However, the molecular cues behind this are still unsolved, and these require to be more specifically analyzed to find an improved and effective treatment method. The present in silico study aimed to decode the rendezvous pathway nodes and linked genes between BC and AD that could be further studied for drug repurposing. As the BC medications could be repurposed to treat AD by examining the genes in common pathways of these two disorders, the prognostic factors and related proteins are examined in detail to determine the regulatory effect on AD.

Experimental design

The GEO database of NCBI (https://www.ncbi.nlm.nih.gov/geo/) was used to collect the datasets for cancer and AD (Barrett et al. 2013). Based on healthy versus subjects with the disease, these datasets were compared. The data were standardized, and a log transformation was used to perform statistical analysis. Benjamini–Hochberg correction was also employed to reduce the number of false positives. Padj < 0.01 was used to download the results and the list of significant differentially expressed genes (DEGs). VENNY 2.1.0, a Venn diagram generating tool (https://bioinfogp.cnb.csic.es/tools/VENNY 2.1.0/), was used to identify the genes in common. Gene Ontology Resource (http://geneontology.org/) (The Gene Ontology Consortium 2021) and ENRICHR (Kuleshov et al. 2016) were used for the gene ontology and gene enrichment research, respectively. An investigation into protein–protein interactions and subcellular localization of the target protein was also conducted. The target protein under study was subjected to domain analysis, a structure prediction, and a confirmation of the results. Figure 1 depicts the workflow for the current methodology.

Fig. 1.

Fig. 1

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Flowsheet of methodology for the identification of common genes associated with BC and AD

Datasets and identification of DEGs from microarray datasets

The gene-expression patterns of BC patients were analyzed to identify DEGs (GSE42568) (Clarke et al. 2013). Samples from individuals with AD accounted for 467 of the total count, with the remaining 154 coming from HD (Huntington's disease) patients. There were 467 people; 135 were male AD sufferers, 123 male controls, 175 female AD sufferers, and 34 female controls. A total of 467 of these samples were subjected to examination as part of the research.

These data were analyzed using the GEO2R program, which was initially used to select the samples and assign them to a group. Benjamini–Hochberg correction was used on the analyzed data to eliminate the false discovery and then normalized using log transformation. LIMMA precision weights (Linear Models for Microarray Analysis) were also used further to evaluate the data (P < 0.01). The data were evaluated after all of these changes were made. BC control versus BC Grade 1; BC Control vs. BC Grade 2, BC Control vs. BC Grade 3, Female AD vs. Female Control, and Male AD vs. Male Control, respectively, were likewise selected as the plot presentations for these datasets to determine the DEGs for each dataset.

Gene ontology and enrichment study

The web tool Gene Ontology Resource (http://www.geneontological.org/) was utilized to undertake gene ontology research. Using VENNY 2.1.0, a program for producing Venn Diagrams, those genes were identified that were present in both sets of data and could be used to compare the two sets of data. The ENRICHR online tool was used to look for and assess enrichment in the identified common genes.

Protein–protein interaction analysis

Cytoscape v3.8.2 was used to examine the Protein–Protein Interaction (PPI) network of the target protein, MCM7, which was discovered using enrichment analysis (Otasek et al. 2019; Sriram and Sah 2021). The STRING plug-in of Cytoscape v3.8.2 was used to perform an automatic search for known interacting partners of the target protein under study using data from the STRING network database (Szklarczyk et al. 2019). Search parameters were configured as follows: confidence score (0.99), extra interactors (100), and smart delimiters (100) were used. The interactions between proteins were depicted using a network of nodes and edges. After importing the network into Cytoscape, each interacting node was visualized and exported as an image for further analysis.

In silico domain analysis and structure prediction of the target protein

The European Bioinformatics Institute (EBI) INTERPRO was used to conduct the protein domain analysis (Blum et al. 2021) after the extraction of the FASTA sequence from UniProt. The secondary structure of the protein was analyzed using PSIPRED (Buchan and Jones 2019) from the FASTA sequence to observe properties such as α-helix, β-sheet, turns, and coils (Buchan and Jones 2019), followed by the prediction of phosphorylation sites using the NetPhos 3.1 webserver (https://www.cbs.dtu.dk/services/NetPhos/). 3D homology modeling was performed using SWISS-MODEL (Adamus et al. 2020) to validate pre-existing structures from RCSB PDB (Berman et al. 2000) and to observe various reactive amino acid groups. The predicted model was validated using the MolProbity tool of SWISS-MODEL (Adamus et al. 2020).

Results and discussion

Identification of DEGs

Understanding the biological variations between healthy and diseased states requires understanding the differential gene expression. Microarray experimental data and biological literature are two significant differential gene-expression data sources. According to the microarray results, there were 4091 significant DEGs in BC Grade 1, 6548 significant DEGs in BC Grade 2, and 7081 significant DEGs in BC Grade 3, as well as only 5 significant DEGs in the AD dataset. The volcano plots of these four conditions vs. their corresponding controls were generated after the statistical analysis using NCBI's GEO2R tools (Fig. 2).

Fig. 2.

Fig. 2

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Volcano plots of DEGs between BC grade 1 (A), grade 2 (B), grade 3, and (C) normal BC tissue. Volcano plot of DEGs between AD and normal brain tissues (D). Red dots signify upregulated genes in BC tissues; blue dots signify downregulated genes in BC tissues; gray dots depict not significantly expressed genes. Adj. P < 0.01 was considered to indicate a statistically significant difference

After obtaining the list of various DEGs either upregulating or downregulating, screening out those genes specific to the diseases under study is imperative. The genes of interest were retrieved and analyzed from the volcano plot using Venny 2.1.0 to identify the common genes in all selected disease conditions (Fig. 3, Table 1). The results of Venny concluded with only two common genes (CD209 and MCM7) overlapping in both the selected diseased states. For CD209, previous reports are available for its role in AD (Walker and Lue 2015) as well as in BC (Szpor et al. 2021). However, for MCM7, there are only a few recent reports associating it with BC (Zhang and Yan 2015; Liang et al. 2017). There are no reports on MCM7 links with AD that could have provided the key for drug targeting and repurposing.

Fig. 3.

Fig. 3

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Venn diagram of overlapping DEGs from the GSE42568 (BC grade 1, grade 2, and grade 3) and GSE330000 datasets (AD)

Table 1.

Fold change and F value of common genes

Gene symbol Gene title Log2(fold change) F value
BCG1 BCG2 BCG3 AD BCG1 BCG2 BCG3 AD
MCM7 Minichromosome Maintenance Complex Component 7 − 0.176 − 0.149 − 0.207 0.399 3.471 3.908 7.155 6.407
CD209 CD209 molecule 0.917 0.862 0.844 0.366 16.766 23.925 24.647 9.523
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Gene enrichment analysis was performed to identify classes of genes over-represented in a large set of genes associated with the characteristics of diseases under study. MCM7 enrichment analysis with pathway databases (Table 2) revealed the initiation of DNA replication with both the R-HSA-176974 and R-HSA-69306 pathways associated with the expression of the MCM7 gene. Similarly, the investigation into the network pathways of S-phase transitions (R-HSA-69242), G2/M checkpoints (R-HSA-69481), and G1/S transitions (R-HSA-69206) corroborated with the previous findings mentioned above. Thus, after establishing the gene enrichment, it was necessary to analyze the gene annotation to correctly identify its structural and functional aspects before further investigating and analyzing the translated protein.

Table 2.

Gene annotation for biological process of MCM7 (Gene Ontology Resource)

Category GO ID Term P value
Biological process GO:1902299 Pre-replicative complex assembly involved in cell cycle DNA replication 0.000437
GO:0006267 Pre-replicative complex assembly involved in nuclear cell cycle DNA replication 0.000437
GO:0000727 Double-strand break repair via break-induced replication 0.000631
GO:0006268 DNA unwinding is involved in DNA replication 0.000825
GO:0006271 DNA strand elongation involved in DNA replication 0.000971
GO:0022616 DNA strand elongation 0.00121
GO:0006270 DNA replication initiation 0.00214
GO:0071364 Cellular response to epidermal growth factor stimulus 0.00223
GO:0070849 Response to epidermal growth factor 0.00243
GO:0033260 Nuclear DNA replication 0.00286
GO:0044786 Cell cycle DNA replication 0.00291
GO:0036388 Pre-replicative complex assembly 0.0032
GO:0000724 Double-strand break repair via homologous recombination 0.00495
GO:0000725 recOmbinational repair 0.00505
GO:0032508 DNA duplex unwinding 0.0052
GO:0032392 DNA geometric change 0.00554
GO:0000082 G1/S transition of mitotic cell cycle 0.00612
GO:0044843 Cell cycle G1/S-phase transition 0.00621
GO:0071466 Cellular response to xenobiotic stimulus 0.00631
GO:0009410 Response to xenobiotic stimulus 0.00665
GO:0006261 DNA-dependent DNA replication 0.00947
GO:0006302 Double-strand break repair 0.00986
GO:0006310 DNA recombination 0.0109
GO:0065004 Protein-DNA complex assembly 0.0124
GO:0006260 DNA replication 0.0132
GO:0044772 Mitotic cell cycle phase transition 0.0139
GO:0044770 Cell cycle phase transition 0.0143
GO:0071824 Protein-DNA complex subunit organization 0.0143
GO:0071103 DNA conformation change 0.0152
GO:0042493 Response to drug 0.0186
GO:0008283 Cell population proliferation 0.0232
GO:0071363 Cellular response to growth factor stimulus 0.0245
GO:0006281 DNA repair 0.0258
GO:0070848 Response to growth factor 0.0259
GO:1903047 Mitotic cell cycle process 0.0333
GO:0000278 Mitotic cell cycle 0.037
GO:0006259 DNA metabolic process 0.038
GO:0006974 Cellular response to DNA damage stimulus 0.0383
GO:0034622 Cellular protein-containing complex assembly 0.0418
Molecular function GO:1990518 Single-stranded 3'–5' DNA helicase activity 0.000243
GO:0043138 3'–5' DNA helicase activity 0.000874
GO:0017116 Single-stranded DNA helicase activity 0.000971
GO:0003688 DNA replication origin binding 0.00117
GO:0003678 DNA helicase activity 0.00369
GO:0003697 Single-stranded DNA binding 0.00578
GO:0004386 Helicase activity 0.00777
GO:0140097 Catalytic activity, acting on DNA 0.0103
GO:0016887 ATPase activity 0.0237
GO:0017111 Nucleoside-triphosphatase activity 0.042
GO:0016462 Pyrophosphatase activity 0.0447
GO:0016818 Hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides 0.0448
GO:0016817 Hydrolase activity, acting on acid anhydrides 0.0448
Cellular component GO:0071162 CMG complex 0.000534
GO:0031261 DNA replication pre-initiation complex 0.000631
GO:0042555 MCM complex 0.000631
GO:0000781 Chromosome, telomeric region 0.00728
GO:0032993 Protein-DNA complex 0.00869
GO:0000228 Nuclear chromosome 0.0111
GO:0098687 Chromosomal region 0.0163
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Gene annotation for identifying gene location and coding sections provides insight into genes' accomplishment in cellular and body functions by establishing proteins' structural and functional roles. An analysis of the annotations for MCM7 revealed its involvement in biological processes such as cell division (GO:0000278) and DNA replication (GO:0006260); molecular functions such as DNA replication initiation (GO:1990518); and cellular compartments as the protein–DNA complex (GO:0032993), DNA replication pre-initiation complex (GO: 0031261), and a component of nuclear chromosomes (GO:0000228) (Table 3). The annotation result concluded that MCM7 appears to have a ubiquitous role in maintaining DNA replication and other significant cellular processes.

Table 3.

Gene enrichment analysis of MCM7

Term Adjusted p value Associated genes
DNA Replication (hsa03030) 5.68 × 10–43 RFC5;PRIM2;FEN1;RFC3;PCNA;RFC4;MCM7;PRIM1;RPA1;RPA2;POLA1;POLA2;RPA3;POLE2;MCM3;MCM4;MCM5;MCM6;POLE;MCM2
Cell Cycle (hsa04110) 4.31 × 10–39 PCNA;MCM7;CDC7;CDC6;CCNA2;ORC5;CCNA1;ORC4;DBF4;ORC6;CDC45;ORC1;CCNE1;ORC3;CHEK1;ORC2;CDK2;MCM3;E2F1;CDK1;MCM4;MCM5;MCM6;MCM2
Nucleotide excision repair (hsa03420) 4.02 × 10–14 RFC5;RFC3;PCNA;RFC4;RPA3;POLE2;RPA1;RPA2;POLE
Mismatch repair (hsa03430) 1.19 × 10–12 RFC5;RFC3;PCNA;RFC4;RPA3;RPA1;RPA2
Cellular Senescence (hsa04218) 1.39 × 10–6 CCNA2;CCNA1;CCNE1;CHEK1;CDK2;E2F1;CDK1
Base Excision Repair (hsa03410) 1.07 × 10–5 FEN1;PCNA;POLE2;POLE
Homologous Recombination (hsa03440) 1.95 × 10–5 RPA3;RPA1;RPA2;TOPBP1
Hepatitis B (hsa05161) 1.95 × 10–5 CCNA2;CCNA1;PCNA;CCNE1;CDK2;E2F1
Viral Carcinogenesis (hsa05203) 5.78 × 10–5 CCNA2;CCNA1;CCNE1;CHEK1;CDK2;CDK1
Human T-Cell Leukemia Virus 1 infection (hsa05166) 8.45 × 10–5 CCNA2;CCNA1;CCNE1;CHEK1;CDK2;E2F1
DNA Replication R-HSA-69306 6.98 × 10–89 PRIM2;FEN1;PCNA;MCM7;MCM8;PRIM1;GMNN;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;E2F1;POLE;RFC5;GINS1;GINS2;CDT1;RFC3;RFC4;GINS3;GINS4;RPA1;RPA2;CDC7;CDC6;CCNA2;CCNA1;POLA1;POLA2;DBF4;POLE2;RPA3;CDK2;MCM3;MCM4;MCM5;MCM6;MCM2
Activation of the pre-replicative complex R-HSA-68962 7.20 × 10–77 PRIM2;MCM7;MCM8;PRIM1;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;POLE;CDT1;RPA1;RPA2;CDC7;CDC6;POLA1;POLA2;DBF4;RPA3;POLE2;CDK2;MCM3;MCM4;MCM5;MCM6;MCM2
Synthesis of DNA R-HSA-69239 6.59 × 10–76 PRIM2;FEN1;PCNA;MCM7;MCM8;PRIM1;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;POLE;RFC5;GINS1;GINS2;CDT1;RFC3;RFC4;GINS3;GINS4;RPA1;RPA2;CDC6;CCNA2;CCNA1;POLA1;POLA2;POLE2;RPA3;CDK2;MCM3;MCM4;MCM5;MCM6;MCM2
S-phase R-HSA-69242 4.84 × 10–74 PRIM2;FEN1;PCNA;MCM7;MCM8;PRIM1;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;POLE;RFC5;GINS1;GINS2;CDT1;RFC3;RFC4;GINS3;GINS4;RPA1;RPA2;CDC6;CCNA2;CCNA1;POLA1;POLA2;CCNE1;POLE2;RPA3;CDK2;MCM3;MCM4;MCM5;MCM6;MCM2
G1/S Transition R-HSA-69206 1.01 × 10–67 PRIM2;PCNA;MCM7;MCM8;PRIM1;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;E2F1;POLE;CDT1;RPA1;RPA2;CDC7;CDC6;CCNA2;CCNA1;POLA1;POLA2;DBF4;CCNE1;POLE2;RPA3;CDK2;MCM3;CDK1;MCM4;MCM5;MCM6;MCM2
Cell Cycle R-HSA-1640170 6.16 × 10–65 PRIM2;FEN1;PCNA;MCM7;MCM8;PRIM1;GMNN;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;CHEK1;ORC2;E2F1;TOPBP1;POLE;RFC5;GINS1;GINS2;CDT1;RFC3;RFC4;GINS3;GINS4;RPA1;RPA2;CDC7;CDC6;CCNA2;CCNA1;POLA1;POLA2;DBF4;CCNE1;POLE2;RPA3;CDK2;MCM3;CDK1;MCM4;MCM5;MCM6;MCM2
Mitotic G1-G1/S phases R-HSA-453579 2.03 × 10–64 PRIM2;PCNA;MCM7;MCM8;PRIM1;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;E2F1;POLE;CDT1;RPA1;RPA2;CDC7;CDC6;CCNA2;CCNA1;POLA1;POLA2;DBF4;CCNE1;POLE2;RPA3;CDK2;MCM3;CDK1;MCM4;MCM5;MCM6;MCM2
Cell Cycle, Mitotic R-HSA-69278 2.91 × 10–64 PRIM2;FEN1;PCNA;MCM7;MCM8;PRIM1;GMNN;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;E2F1;POLE;RFC5;GINS1;GINS2;CDT1;RFC3;RFC4;GINS3;GINS4;RPA1;RPA2;CDC7;CDC6;CCNA2;CCNA1;POLA1;POLA2;DBF4;CCNE1;POLE2;RPA3;CDK2;MCM3;CDK1;MCM4;MCM5;MCM6;MCM2
DNA Replication pre-initiation R-HSA-69002 1.98 × 10–62 PRIM2;MCM7;MCM8;PRIM1;GMNN;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;E2F1;POLE;CDT1;RPA1;RPA2;CDC7;CDC6;POLA1;POLA2;DBF4;POLE2;RPA3;CDK2;MCM3;MCM4;MCM5;MCM6;MCM2
M/G1 Transition R-HSA-68874 1.98 × 10–62 PRIM2;MCM7;MCM8;PRIM1;GMNN;MCM10;ORC5;ORC4;ORC6;CDC45;ORC1;ORC3;ORC2;E2F1;POLE;CDT1;RPA1;RPA2;CDC7;CDC6;POLA1;POLA2;DBF4;POLE2;RPA3;CDK2;MCM3;MCM4;MCM5;MCM6;MCM2
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Protein–protein interaction analysis

Regulating the erroneous expression at the genetic level is impossible without using advanced tools such as CRISPR. However, the regulation of the protein expression using modern small molecule inhibitors or designer peptides is possible directly (Nagamalla and Kumar 2021). Thus, it is more practical to establish a protein as a drug target rather than a gene. Hence, the present study attempted a comprehensive bioinformatics approach for establishing MCM7 as a potential drug target for AD. In biological systems, metabolic or biochemical processes are seldom regulated by a solo protein and instead are regulated by a cascade of effector molecules working in tandem with each other. The analysis of the network cascade further indicates various primary and secondary interactions of MCM7 with other effector molecules and can provide suitable therapeutic alternatives if MCM7 is undruggable. CYTOSCAPEv3.8.2 was used to create this network, which was imported from the STRING database after identifying the common genes. The network was constructed with high confidence (> 90%). Cytoscape's investigation of the MCM7 PPI network revealed 51 nodes and 363 edges. Each node represents a single protein in this network diagram, and the edges show interactions among those proteins. The lookout for theMCM7's interacting proteins in Fig. 4A settled with direct interaction with 35 other proteins and 15 proteins indirectly. Table 4 elucidates the cellular roles of the MCM7 protein's interacting partners along with disease prognosis in AD. Figure 4B visualizes the protein–protein interaction (PPI) network of MCM7 obtained using Cytoscape v3.8.2. Nodes represent the proteins, and the edges represent the interactions among them. The orange octagonal node illustrates the protein of interest selected as the central hub. Green nodes portray the closely associated proteins (undirected) of MCM7 protein. The parameters used for constructing this PPI network were the highest confidence score (0.99), maximum additional interactors (100), and smart delimiters.

Fig. 4.

Fig. 4

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A Protein–protein interaction (PPI) network of MCM7 obtained using Cytoscape v3.8.2. Purple nodes signify the proteins interacting indirectly with MCM7 B PPI network between MCM7 and its first neighbors (undirected)

Table 4.

Description of cellular functions of all the proteins associated with the PPI network of MCM7

Gene Function References
GINS complex (GINS1,GINS2, GINS3,GINS4)

Composed of four genes, GINS1-4 involved in initiation of DNA replication and progression of replication forks

CIZ1 (CDKN1A-interacting zinc-finger protein 1), a downstream target for the GINS complex during the pre-replication complex formation highly expressed in AD tissues

Kamada et al. (2007)

Dahmcke et al. (2008), Liu et al. (2016)
CDT1 (DNA replication factor Cdt1) Cooperates with CDC6 and origin recognition complexes during G1 phase to load MCMs onto DNA to give pre-replication complex Cook et al. (2004)
Replication Factor C (RFC3,RFC4, RFC5) Replication factor C (RFC), a pentameric AAA + ATPase complex made up of RFC1–5, transports PCNA to primer–template junctions Yao and O'Donnell (2012)
Replication protein A (RPA1,RPA2, RPA3)

Binds and stabilizes single-stranded DNA intermediates formed during DNA replication or stress as part of the heterotrimeric replication protein A complex (RPA/RP-A)

Prevents reannealing while also recruiting and activating various proteins and complexes involved in DNA metabolism

 (Bass et al. 2016)
Minichromosome Maintenance Complex (MCM2,MCM3,MCM4,MCM5,MCM6,MCM7,MCM8,MCM10,MCMBP)

Hexameric helicase MCM2-7 required to unwind dsDNA to generate the ssDNA template for DNA replication

MCM8 and MCM10 not part of the hexameric complex, and MCMBP is the binding protein

Phosphorylated MCM2 protein reported being associated with neurofibrillary tangles in AD

Chang et al. (2019)

Bonda et al. (2009)
Origin Recognition Complex (ORC1,ORC2,ORC3,ORC4,ORC5,ORC6)

In eukaryotes, the six-subunit origin recognition complex (ORC) binds to DNA to mark the site for replication initiation

ORC subunits take part in the regulation of cognitive functions and neuronal plasticity

Li et al. (2018)

Arendt and Brückner (2007)
Cyclin A1 and Cyclin A2 (CCNA1,CCNA2) The canonical cyclin A, known as cyclin A2, required for DNA replication and mitosis, found in almost all cell types. Cyclin A1, the second A cyclin family member, is only expressed in germline cells Radonova et al. (2020)
Cell Division Control proteins (CDC6, CDC7,CDC45) Code for the proteins required for initiation of DNA replication Carbon et al. (2021)
Cyclin dependent Kinases (CDK1, CDK2) Modulating the centrosome cycle and mitotic onset controls the eukaryotic cell cycle, promotes G2–M transition, and regulates G1 progress and G1-S transition via association with multiple interphase cyclins. Higher cells require it to enter S-phase and mitosis. CDK1 acts at the G1–S transition to promote the E2F transcriptional program and the start of DNA synthesis, and modulates G2 progression; controls the timing of mitosis/meiosis entry by controlling the subsequent activation of cyclin B/CDK1 by phosphorylation, and coordinates cyclin B/CDK1 activation at the centrosome and in the nucleus Mori et al. (2015), Jassal et al. )2020)
DNA Primase (PRIM1, PRIM2) Catalytic PRIM1 and regulatory PRIM2 subunits of DNA primase complex and also part of DNA polymerase Alpha complex. Key role in the initiation of synthesis of DNA Weiner et al. (2007)
DNA Polymerase (POLA1, POLA2, POLE, POLE2)

POLA1 and POLA2 are the catalytic and accessory subunits of DNA polymerase alpha complex, whereas POLE and POLE2 are the catalytic and accessory components of DNA polymerase epsilon. POLA1 and POLA2 involved in DNA replication; POLE and POLE2 involved in DNA repair

Deficiency in DNA polymerase leads to reduced synaptic elasticity and neuronal degeneration resulting in AD

Starokadomskyy et al. (2016), Taylor and Yeeles (2019), Dodd et al. (2020)

Sykora et al. (2015)
Protein DBF4 homolog A (DBF4) Involved in S-phase progression and regulation of DNA replication initiation Almawi et al. (2020)
Proliferating Cell Nuclear Antigen (PCNA)

Codes for the auxiliary protein of DNA polymerase delta regulate eukaryotic DNA replication by increasing the polymerase's processability during leading-strand elongation

PCNA expression levels elevate and decline sequentially in response to AD stimuli in vitro

Kulkarni et al. (2020)

Hernández-Ortega et al. (2007)
TIMELESS- interacting protein (TIPIN) Plays an essential role in regulating DNA replication and the stability of replication forks. It is critical for cell survival following DNA damage or replication stress Rageul et al. (2020)
G1/S-specific Cyclin E1(CCNE1) Involved in the control of cell cycle during G1/S transition Lepage et al. (2021)
Microtubule associated protein RP/EB family member 2(MAPRE2) Key role in the progression of mitosis and regulation of cell adhesion Thues et al. (2021)
Leucine-rich repeat and WD repeat-containing protein 1(LRWD1) Involved in G1/S transition. Recruits and stabilizes the ORC onto chromatin during G1 to establish the pre-replication complex (preRC) and to heterochromatic sites in post-replicated cells Chan and Zhang (2012)
Transcription Factor E2F1 (E2F1) Found in promoter regions of most of the genes involved in cell cycle regulation or DNA replication Manickavinayaham et al. (2019)
Geminin (GMNN) Inhibits DNA replication by not allowing the MCM complex to incorporate into the pre-replication complex Sami et al. (2021)
DNA topoisomerase 2-binding protein 1(TOPBP1) Role in the resumption of stalled replication forks and the control of checkpoints, Binds both dsDNA and ssDNA breaks and nicks Mooser et al. (2020)
Serine/threonine-protein kinase Chk1(CHEK1) Involved in checkpoint-mediated cell cycle arrest and activating DNA repair in response to DNA damage or unreplicated DNA Chao et al. (2020)
Cyclin-L1(CCNL1) Involved in splicing of pre-mRNA and acts in association with cyclin-dependent kinases Ameur et al. (2020)
Flap endonuclease 1 (FEN1) Possesses both exonuclease and endonuclease activity and is involved in DNA replication and repair Williams et al. (2021)
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In silico domain analysis and secondary structure prediction of MCM7

After successfully validating MCM7 as a genetic component with an aberrant expression profile responsible for the occurrence of cancer and Alzheimer's, it is important to evaluate its structure and physicochemical properties to propose its viability as a potential drug target. To that end, MCM7's FASTA sequence was first extracted from UniProt for an in silico proteomic study of MCM7 (The UniProt Consortium 2021). This FASTA sequence was then sent into the EBI INTERPRO as a query to look for any recognized domains, conserved sites, or motifs (Blum et al. 2021). From the data obtained through InterPro, the major domains and their positions in the protein sequence are as follows: MCM_N (MCM-N-terminal domain) (10–139), MCM_OB (oligonucleotide/oligosaccharide binding fold) (149–278), MCM_dom (322–541), MCM_2 (332–537), AAA + _ATPase (373–526) (ATPases associated with diverse cellular activities) (Abid Ali and Costa 2016), and MCM_lid (558–640) (Mistry et al. 2021). The protein's secondary structure was analyzed using PSIPRED (Buchan and Jones 2019) from the FASTA sequence to observe properties such as α-helix, β-sheet, turns, and coils (Fig. 5). Figure S1A–C of the supplemental datasheet includes results from further analysis of data collected from PSIPRED on the reactive nature of the constituent amino acids and their unique characteristics. These data when collectively taken into account help to determine protein secondary structure, detailing physicochemical nature, and guide for the next step for building a computational three-dimensional model of the MCM7.

Fig. 5.

Fig. 5

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Secondary structure prediction of MCM7 protein. The pink band shows the positioning of the helix, and the yellow band indicates the strand

Furthermore, NetPhos 3.1, a web server, predicted phosphorylation sites on MCM7 based on its amino acid sequence. Phosphorylation is known to change the conformation of the protein’s binding site, which can severely impede ligand interactions (Smith et al. 2015). It guides the researcher to avoid these sites while creating ligand-binding pockets for virtual screening. Furthermore, the mapping of phosphorylation sites is a crucial step in identifying signaling pathways and developing kinase inhibitors as anti-cancer agents (Gagic et al. 2020). There is currently no evidence for the MCM7 phosphorylation affecting disease prognosis; however, the reports on MCM2 phosphorylation (subunit of hexameric MCM complex) induce neurofibrillary tangles in AD tissues (Bonda et al. 2009) are available. The confidence score was set to 0.9 out of 1 to only obtain results with the highest probability of occurrence. The result is represented as Fig. 6. The fragments highlighted in yellow represent individual phosphorylation sites, while the peaks in the graph represent the probability of occurrence of phosphorylation sites as compared to the threshold value (0.9).

Fig. 6.

Fig. 6

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Position-dependent feature predictions mapping. A Each phosphorylation site on the MCM7 proteins and its corresponding amino acid sequence along with statistical significance. B Highlighted sections indicate the phosphorylation sites present in the target protein sequence. Note that specific phosphorylation sites overlap with one another. C The peak height of the phosphorylation features reflects the confidence of the residue prediction

Protein modeling and validation of predicted structures

After the characterization of MCM7 using the parameter of secondary structure, the next step involved the prediction of its three-dimensional model. This 3D model could be utilized to test the small molecule compounds as inhibitors or design-specific peptide sequences. Structurally similar proteins were investigated using BLASTp with a database from Protein Data Bank (PDB) as the search set for homology modeling. Only those proteins with sequence similarity over the 80% threshold as the minimum requirement for homology modeling were selected from the BLAST search results. One crystal structure present in PDB (PDB ID: 6XTX) demonstrated 100% similarity with the input FASTA sequence of MCM7 protein. The previous research on MCM7, which exists in the MCM-CMG complex, has not accounted for the missing residues in the isolated MCM7 3D structure from PDB (Ahammad et al. 2021; Alshahrani et al. 2021). Here, an attempt further to provide a more accurate model of the same with SWISS MODEL's homology protein modeling service using the crystal structure 6XTX as a template. The “degree of nativeness” of the model's structural characteristics is estimated using the QMEAN Z-score globally. It is used to evaluate whether the model's QMEAN score obtained is in support of experimental structures of similar sizes of proteins. GMQE (Global Model Quality Estimate) is a quality estimate which combines properties from the target-template alignment and the template structure. The average per-residue score has been found to correlate well with the LDDT (Local Distance Difference Test) score. This superposition-free score evaluates the local distance of all atoms in a model, including validation of stereochemical probability. The provided error estimate (Fig. 7A) is based on QMEANDisCo global scores calculated for a diverse set of similar models and represents the standard deviation from the respective LDDT scores (Benkert et al. 2008). A QMEAN Z-score of nearly zero indicates that the modeled structure outperforms the experimental structure in terms of quality. At the same time, a score of − 4.0 or below is considered a poor quality of the predicted model. For the present study, the QMEAN Z-score of the MCM7 predicted model was -2.46, suggesting that the homology model is reliable and close to fact. Figure 7A, B shows the homolog model that was expected. Figure S2A–C in the supplemental datasheet shows the results of further analyses of the protein model's characteristics. To verify the MCM7 protein 3D model obtained, the Ramachandran plot was generated with the MolProbity tool. The model was validated with the occurrence of almost all the amino acid residues within the confinement of permissible regions (Fig. 8). The MolProbity score of the MCM7 protein was 99.22% of the residues were in the permissible region, while only 0.78% of the residues lay in the outlier regions. The scores obtained satisfactorily validated the predicted model.

Fig. 7.

Fig. 7

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A Illustration of “degree of nativeness” of the model's structural characteristics using the QMEAN Z-score on a global scale. Cβ describes the confidence score for predicting beta carbons orientation in the side chains of the MCM7 protein. Solvation potential describes the burial status of the residues. Torsion describes the confidence score for predicting torsion angles in the protein structure. B, C 3D representation of predicted MCM7 protein model based on QMEAN parameter generated using SWISS-MODEL. B represents the molecular surface structure, and C represents the thread-ribbon structure. The color depiction is as follows: Orange = low confidence prediction/low QMEAN score/low sequence similarity with template structure, Purple = moderate confidence prediction/moderate QMEAN score/moderate sequence similarity with template structure, Blue = high confidence prediction/high QMEAN score/high sequence similarity with template structure

Fig. 8.

Fig. 8

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Validation of predicted structure of MCM7 protein with Ramachandran Plot using SWISS-MODEL MolProbity Tool. The colored dots confined within the bright green, light green, and pale green areas represent the presence of all residues of the predicted MCM7 model in allowed regions, and no residues present in the disallowed region are represented as blank areas

Conclusion and future prospects

The study reveals the elevated expression of MCM7 in AD which is downregulated in all the three grades of BC. The role of this gene in DNA replication and the advancement of the cell cycle is altered in diseased conditions under study. A change in the structure or folding of a subsequently translated MCM7 protein due to a mutation in its genetic code might influence DNA replication and cell cycle progression. This subsequently increases the likelihood of cellular aberrations such as the incidence of cancer and changes in protein structure or folding in AD. Upon reviewing the PDB structure, there appear to be several gaps or incomplete sites in the protein structure with unmodelled residues between them. Therefore, this present study will help bridge the gap between this structure inconsistencies and motivate future attempts to ensure complete modeling of MCM7 for aid in drug design and other therapeutic purposes. This would lead to widening of chemotherapy for AD, shortening/lessening the time, effort, and cost involved for novel drugs and toxicological research. These findings further pave the way for drug repurposing studies to check the effect of pre-existing FDA-approved drugs for BC used for rectifying the expression levels of the mutated MCM7 gene in AD.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 920 kb) (920.6KB, pdf)

Acknowledgements

The authors extend gratitude toward the MHRD (Govt. of India) for the financial support to the first and second author.

Author contributions

All authors contributed to the study conception and design. Experimental design, data collection, and analysis were performed by NS, SM, and MKS. The first draft of the manuscript was written by NS and completed with SM. All co-authors commented on the previous version of the manuscript for the revisions. Finally, all authors read and approved the manuscript for the revisions.

Funding

The authors declare that no funds, grants, or other financial support were received for the preparation of this manuscript.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

No ethical approval is required.

Footnotes

Navneeth Sriram and Sunny Mukherjee have contributed equally.

Contributor Information

Navneeth Sriram, Email: navneeths.bt.19@nitj.ac.in.

Sunny Mukherjee, Email: sunnym.bt.20@nitj.ac.in.

Mahesh Kumar Sah, Email: sahmk@nitj.ac.in.

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