IN-SILICO STUDIES ILLUSTRATE THE ONCOGENIC POTENTIAL OF AN ATPASE PROTEIN NSF

IN-SILICO STUDIES ILLUSTRATE THE ONCOGENIC POTENTIAL OF AN ATPASE PROTEIN NSF

Mousumi Datta, Supratik Adhikary, Moumita Nath and Aditi Nayak *

Department of Life Science, Guru Nanak Institute of Pharmaceutical Science and Technology, 157/F, Nilgunj Rd, Sahid Colony, Panihati, Kolkata, West Bengal, India.

ABSTRACT: Cancer is a complex and life-threatening disease characterized by abnormal cell growth. Several AAA+ ATPases are involved in the oncogenic pathways, however, the role of an ATPase protein i.e. N-ethylmaleimaide-Sensitive Factor (NSF) was unclear. Therefore, we performed systematic bioinformatics analysis to predict the prospect of NSF as a cancer biomarker. NSF is found to be upregulated in some cancers whereas down regulated in others. Additionally, this expression was found to be associated with the reduced survival rate of cancer patients. We investigated NFS's mutation and copy number amplification status across the cancers since the genetic alteration is a major cause of cancer. Several hotspot mutation sites were observed at the ATPase domain of NSF indicating that these mutations might have a role in carcinogenesis. Additionally, the copy number amplification study revealed a high amplification percentage of NSF across the cancers. We further focused on the functional characteristics of NSF across cancers. Fascinatingly, we found many interacting protein partners of NSF that are key in carcinogenesis. We further focused on the probable oncogenic pathways related to NSF which indicate that NSF could be crucial for oncogenic function. Therefore, our study unravels the oncogenic potential of NSF and projects it as a potential cancer biomarker and cancer drug target.

Keywords: NSF, AAA⁺ ATPase, Carcinogenesis, Biomarker, Cancer drug target

INTRODUCTION: AAA+ ATPases are a class of mechanoenzymes, found in all biological kingdoms. They generate mechanical force by undergoing conformational changes during cycles of ATP binding and hydrolysis. This mechanical force is used to induce conformational remodeling of a wide range of substrates, including proteins and polynucleotides, thus engaging these ATPases in diverse cellular processes in a large variety of fundamental cellular pathways that govern protein homeostasis, genome stability, cell proliferation, and pathogen infection.

However, how they coordinate ATP binding/hydrolysis to drive the conformational changes needed to perform mechanical work has been a very critical question. AAA+ (ATPases Associated with various cellular Activities) family represents a molecular machine having diverse cellular functions both in prokaryotes and eukaryotes. The energy for different cellular activities is obtained by the hydrolysis of ATP.

In this way, these proteins convert the stored chemical energy into biological events such as proteolysis, protein disaggregation and refolding, Membrane fusion, and transport, DNA replication, DNA recombination and repair. These proteins are widely spread inside the cell, for example in the transmembrane region, cell organelles, and cytosol ¹. The most important and interesting thing about these proteins is they govern a common mechanism of ATP hydrolysis for divergent biological functions. Based on the number of domains AAA+ ATPases have been divided into two types, Class I (having two domains) and Class II (having one domain) ². Primarily Class I members of this family oligomerize to form hexamers and form a ring-shaped structure with a central cavity ². The ATPase domains consist of walker A (GXXXXGKT/S; X: any amino acid) and walker B (VhhhhDE; h: hydrophobic amino acid) motifs. The ATP binding pocket comprises many conserved motifs like Walker A, Walker B, Sensor 1, Sensor 2, and Arginine Fingers.

Many class I ATPases (e.g. VCP/P97, Na+/K+ ATPases, V-ATPases) ^3-5 are found to be related to cancer. N-ethylmaleimaide-Sensitive Factor (NSF) is one of this class I ATPase whose role in cancer is still elusive. This protein was discovered in the year 1984 as a vesicular transporter between the Golgi complex and the successive cisternae by Rothman and colleagues ⁶. NSF is a hexameric protein with 15nm in diameter and 12nm in height. It consists of six identical subunits with a distinct domain structure ⁷.

The major function of ATPase domain 1 (NFS-D1; 206-477 a.a.) is ATP hydrolysis and domain 2 (NFS-D2; 478-744 a.a.) is oligomerization ⁸. The role of the NFS in vesicular transport is based on the interaction with SNAP-binding receptors i.e. SNAREs ⁹, and the other one is Soluble NSF Attachment Proteins i.e. SNAPs ¹⁰. NSF may not be consistently active but can be regulated by several different mechanisms such as inactivation by S-nitrosylation and phosphorylation ^11-14.

In this study, we analyzed the oncogenic potential of NSF. For this, we undertook a multi-omics approach to investigate the molecular profile of NSF in pan-cancer. We investigated the mRNA expression of NSF, its effect on the patient’s survival rate, the causes behind the abnormal expression of NSF, the correlation between NSF and other oncogenes, and its interacting protein partners in various cancers. As per our knowledge, this is the first report detailing the pan-cancer analysis of the NSF. Our results provide novel insights concerning NSF molecular interactions and their potential role in carcinogenic mechanisms. These findings could therefore be explored for a better understanding of underlying cancer mechanisms and to identify novel biological targets for cancer treatment.

MATERIALS AND METHODS:

Sequence Similarity of NSF with other AAA+ ATPase Proteins: Multiple sequence alignments between NSF and other AAA+ ATPases were performed using the Clustal W tool (https://www.genome.jp/tools-bin/clustalw). Further, the alignments of sequences were represented for percentage similarity/identical scores using ESPIRIT 3.0 website (https://espript.ibcp.fr/ESPript/ESPript/).

NSF Transcript Expression Analyses: NSF transcript expression analyses were performed using GEPIA and UALCAN. The GEPIA (http://gepia.cancer-pku.cn/) ^{4, 15} has enormous gene expression records from TCGA (The Cancer Genome Atlas) and GTEx (the Genotype-Tissue Expression) databases. In this work, we analyzed NSF expression in human cancers. A total of 31 tumor types are available in this database. Further stage-wise transcript analyses were performed using the UALCAN (http://ualcan.path.uab.edu/) ¹⁶, a TCGA-based dataset (including normal control tissues from the GTEx database).

Prognostic Significance of NSF Transcripts on Patients’ Survival: To find out the correlation between NSF expression and patient survival, a KM plotter analysis was performed (KMP, http://kmplot.com/analysis/) ^17-20. In the overall patient survival analyses, stringency was maintained through Log Rank P value < 0.05. Here, the survival rate of patients with high and low NSF expression was investigated.

Analysis of Methylation Rate of NSF in Cancer vs Normal Samples: NSF methylation levels were investigated using the TCGA-based database ‘Wanderer’ (http://maplab.imppc.org/wanderer/) ²¹.

The Genetic Alteration Study of NSF: The cBioPortal database (http://www.cbioportal.org/) ^{22, 23} was used to analyze the genetic alterations in NSF. In our study, we analyzed the alteration frequency of NSF in different cancers based on the TCGA datasets and summarized the mutated sites in the NSF gene. Further, the mutation profiling of the NSF gene was performed using the Catalogue of Somatic Mutations in Cancer (COSMIC) database (www.sanger.ac.uk/cosmic/) ²⁴.

Protein-Protein Interaction Analyses of NSF: To identify the functional protein partners of NSF, the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) database ²⁵ was used. This tool provides critical analyses of protein-protein interaction including direct (physical) as well as indirect (function) protein associations (https://string-db.org/).

At the time of analyses presented herein, the STRING database contained data from a total of 5,090 organisms, 24,584,628 proteins, and 3,123,056,667 protein-protein interactions. The NSF protein network was constructed neighbourhood, gene fusion, co-expression, experiments, and text-mining approaches.

Canonical Pathways of NSF using DAVID Database: Pathways analysis is crucial to understand the role of an oncogene or a tumour suppressor gene in cancer. Therefore, the DAVID database (https://david.ncifcrf.gov/) ²⁶ was used to explore the probable pathways of NSF.

RESULTS:

Sequence Similarity of NSF: The gene NSF is located at chromosome 17 which is at 17q21.31 Fig. 1A. Fascinatingly, altered chromosome 17 is more frequent in numerous malignancies ²⁷. Structurally, NSF protein is 744 amino acids long (molecular weight ~ 78kDa). Being an AAA+ ATPase family protein, NSF contains two ATPase Domains (D1 and D2) and a N-terminal domain (NTD) Fig. 1B and is also proposed to have a hexameric structure Fig. 1C. The ATPase domain D1 is highly conserved Fig. 1D.

FIG. 1: STRUCTURAL OVERVIEW OF NSF. (A) NSF IS LOCATED EXACTLY AT CHROMOSOME 17Q21.31. (B) DOMAIN STRUCTURE OF HUMAN NSF PROTEIN CONSISTING OF TWO ATPASE DOMAINS AND AN NTD. THE AAA-D1 IS IN BLUE AND AAA-D2 IS IN RED AND THE NTD IS IN YELLOW. (C) THE NSF PROTEIN POSSESSES A HEXAMERIC RING STRUCTURE. HERE DOMAIN 1 (BLUE COLOR) AND DOMAIN 2 (RED COLOR) ARE REPRESENTED ON THE TOP AND BOTTOM. (D) THE REPRESENTATIVE DIAGRAM OF ATPASE DOMAIN 1 (AAA-D1) OF NSF. IT CONSISTS OF TWO CONSERVED MOTIFS LIKE WALKER A AND WALKER B. THE CLUSTAL W ANALYSIS WAS PERFORMED FOR PROTEIN SEQUENCE ALIGNMENT OF BOTH THESE MOTIFS WITH P97, ATAD2, AND NVL-LIKE AAA+ ATPASE PROTEINS. THE ALIGNMENT DATA IS PUT IN ESPRIPT 3.0 FOR REPRESENTATION AS 100% IDENTITY IN RED AND SIMILARITY IN YELLOW RESPECTIVELY. A SCORE > 0.7 IS CONSIDERED FOR THIS ANALYSIS. (E) DOMAIN STRUCTURE OF HUMAN NSF, WITH MOUSE NSF, S. CEREVISIAE CDC48P, S. CEREVISIAE SEC18, C. ELEGANS MAC-1, AND SACCHAROMYCES CEREVISIAE RIX7P. THE CLUSTAL W ANALYSIS WAS PERFORMED FOR PROTEIN SEQUENCE ALIGNMENT BETWEEN CONCERNING HUMAN NSF. THE PERCENTAGES OF IDENTITY ARE SPECIFIED FOR THE OVERALL LENGTH OF PROTEIN.

The walker A (GXXXXGKT/S) and walker B (hhhhDE) motifs of D1 share sequence similarity with most of the AAA+ ATPase family proteins Fig. 1D. Further, when the sequence of Human NSF was compared with NSF of Mouse, Cdc48p, Sec18, and RIX7P of S. cerevisiae, Mac-1 of C. elegans, and Saccharomyces cerevisiae RIX7P, it is found that all of them share conserved ATPase domain features Fig. 1E.

mRNA Expression of NSF in Cancer: To investigate NSF mRNA expression patterns, data from the TCGA database were analyzed across 31 cancer types compared to matched normal tissues Table 1, Fig. 2A.

We observed that NSF transcription was significantly higher in Cholangiocarcinoma (CHOL), Diffuse Large B Cell Lymphoma (DLBC), Pheochromocytoma and Paraganglioma (PCPG), Thymic Carcinoma (THYM), and Stomach Adenocarcinoma (STAD) (Figure 2B). NSF transcription was significantly lower in Glioblastoma Multiforme (GBM), Low-Grade Gliomas (LGG), Kidney Chromophobe (KICH), Head and Neck Squamous Cell Carcinoma (HNSC) and Adrenocortical carcinoma (ACC) (Figure C). Boxplots presented in Figure B and Figure C were generated only for those TCGA tumours which exhibited significantly high and low differential expression of NSFas compared to controls.

We further investigated NSF expression patterns concerning patients’ tumour size in 10 tumours(which showed significant differential expression of NSF i.e., CHOL, DLBC, PCPG, STAD, THYM, LGG, HNSC, GBM, ACC and KICH). Among them, hepatobiliary tumours and CHOL showed significantly increased NSF expression in all tumour stages Fig. D. However, KICH and HNSC showed significant downregulation of NSF in all stages of cancer.

TABLE 1: NSF EXPRESSION IN VARIOUS CANCERS WITH FOLD CHANGE

FIG. 2: PAN-CANCER ANALYSIS OF NSF GENE EXPRESSION. (A) THE DIFFERENTIAL EXPRESSION PATTERN OF NSFIN 31 TCGA CANCER TYPES. (B) TUMOR TYPES EXHIBITING SIGNIFICANT OVEREXPRESSION OF NSF. (C) TUMOR TYPES EXHIBITING SIGNIFICANT DOWNREGULATION OF NSF EXPRESSION. (D) STAGE-SPECIFIC EXPRESSION ANALYSIS OF NSF.

Role of NSF mRNA Levels in Prognosis/Patient Survival: Kaplan-Mayer Plotter curves were generated for pan-cancer using tumour data sets from the TCGA database to investigate the potential role of NSF mRNA levels in tumourprognosis and patient survival. Interestingly, patients with higher NSF mRNA levels showed a significantly shorter overall survival in BRCA, KIRC, and SARC Fig. 3. This result showed that NSF Expression is a critical prognostic factor in various tumours.

FIG. 3: NSF EXPRESSION AS A PROGNOSTIC FACTOR IN VARIOUS TUMOURS. PATIENTS WITH HIGH NSF EXPRESSION SHOWING LOW SURVIVAL RATE. THE RED LINE REPRESENTS TUMORS EXPRESSING HIGH LEVELS OF NSF TRANSCRIPTS WHILE THE BLACK LINES REPRESENT TUMORS WITH LOW-LEVEL NSF TRANSCRIPT EXPRESSION. THE DATA IS VERY STRINGENT SINCE THE P VALUE IS < 0.05.

Genetic Alteration Analysis of NSF Mutations in the TCGA Pan-Cancer: Next, we analyzed, the genetic alteration status of NSF in different tumour samples of the TCGA cohorts. Among all the studied cancer types, Prostate Adenocarcinoma (Pan-Cancer Atlas) was found to have the highest frequency of NSF alteration i.e. 3.04% Fig. 4A. The 3D structure of NSF is shown with mutation in green colour in Fig. 4B. Further, our study found that missense mutation of NSF is the main type of genetic alteration. Excitingly, an overall of 73 mutation sites with a missense of 54 sites, truncating of 5 sites, inframe of 1, splice of 6, and fusion of 7 sites were identified in NSF Fig. 4C. The mutation frequency and amplification frequency are shown in Table 2 and Table 3. Further analysis with the COSMIC database also indicated that the frequency of missense substitution was the highest (22.55%). There were 33.96% G > A and 19.50% C > T mutations found in NSF shown in Fig. 5.

Methylation and NSF Mutation Profile in Pan-Cancer: Since both genetic and epigenetic alterations play significant role in cancer, we focused on the role of epigenetic modifications in NSF. We analyzed the methylation rate of NSF in both cancer and normal samples Fig. 6. Interestingly, no significant changes in NSF methylation rate was observed between the cancer and normal samples.

NSF Protein Network and Co-expression Analysis: We used the STRING database to investigate potential partners of NSF protein. Proteins with the strongest NSF interaction scores included NAPA, GRIA2, SCFD1, STX5, YKT6, SEC22B, NAPB, GRIP1, NAPG, and GABARAP Fig. 7A, Table 4. Most important thing is that all of them showed positive correlation with NSF expression in most of the TCGA tumour types suggesting their potential role in carcinogenesis Fig. 7B.

FIG. 4: GENETIC ALTERATION STATUS OF NSF IN DIFFERENT TUMORS. (A) THE AMPLIFICATION FREQUENCY OF NSF IN CANCER (DISPLAYED IN RED). (B) THE MUTATION FREQUENCY OF NSF IN CANCER (DISPLAYED IN GREEN). (C) THE 3D STRUCTURE OF NSF WITH MUTATIONAL POINTS IN GREEN. (D) POTENTIAL MUTATION SITES ACROSS THE NSF PROTEIN IN VARIOUS CANCERS. THE DIFFERENT MUTATION SITES ARE REPRESENTED WITH GREEN AND BLACK DOTS RESPECTIVELY.

FIG. 5: THE COSMIC DATABASE REPRESENTS MUTATIONAL FREQUENCIES OF NSF

TABLE 2: GENETIC ALTERATION SUMMARY FOR NSF IN TOP 5 CANCERS

TABLE 3: GENETIC MUTATIONAL FREQUENCY SUMMARY FOR NSF IN TOP 5 CANCERS

FIG. 6: MEAN METHYLATION OF NSF BETWEEN CANCER AND NORMAL SAMPLES

FIG. 7: (A) PROTEIN-PROTEIN INTERACTION NETWORK OF NSF WAS IDENTIFIED USING THE STRING DATABASE. PROTEINS WITH THE STRONGEST INTERACTION SCORES AND DIRECT INTERACTION PREDICTION ARE INCLUDED IN THE FIGURE. (B) THE DIFFERENTIAL EXPRESSION OF COEXPRESSED GENES OF NSF IN CANCER VS. NORMAL SAMPLES ARE REPRESENTED IN A MATRIX PLOT FORM. THE EXPRESSIONS ARE CALCULATED IN LOG₂ (TPM/ TRANSCRIPTS PER MILLION+1). THE COLOR INTENSITY INDICATES THE LEVEL OF EXPRESSION; A MORE INTENSE COLOR MEANS A BETTER EXPRESSION OF THE RESPECTIVE GENE.

TABLE 4: TOP 10 INTERACTING PARTNERS OF NSF

Canonical Pathway Analysis of NSF: Finally, the gene NSF was analyzed for the biological pathways enrichment analysis using the DAVID tool which has highly curated canonical pathways. A cut-off of P-value <0.001 was used for the enrichment analysis. The top biological pathways are shown in Table 5. As expected, all top enriched pathways of NSF are mostly associated with cancer development and progression.

TABLE 5: SIGNALING PATHWAYS RELATED TO NSF

CONCLUSION: Cancer is a complex disease. Even if the disease originates in different organs, may share similarities at the molecular level. For example, p53 mutations drive high-grade serous ovarian, serous endometrial, and basal-like breast carcinomas; all of them share a global transcriptional signature triggering similar carcinogenic pathways ^{28, 29}. Therefore, potential molecular drivers that are expressed in different cancers demand an in-depth cross-cancer analysis. We show that mRNA expression of NSF is significantly increased in all studied cancer types and elevated NSF expression level leads to poor survival of the cancer patients. The low expression of NSF in certain cancer reflects its altered roles in different cancer types. To our knowledge, this is the first study where systematic bioinformatics analysis is applied to address the prognostic importance of NSF in multiple cancers. The analysis reported here establishes NSF as a promising prognostic marker for several cancers. Such analyses illustrate the importance of developing a comprehensive perspective across tumors. Cancer arises due to deleterious aberrations in the genome and its consequences. Therefore, fundamentally it is a genomic disease. Copy-number alteration (CNA) and point mutation are the two most important types of mutational events that impact the development and progression of the disease. In cancer, high alteration frequency is observed in NSF with73 potential mutational sites where missense mutations dominate the distribution. V628I, a mutational hotspot found in the conserved AAA+ domain. Moreover, the gene NSF makes direct interactions with many oncogenes in a dense network whose components are associated with critical cellular processes. In conclusion, our analysis predicts a high prognostic value of NSF and considers NSF as an important therapeutic target, particularly for cancer.

ACKNOWLEDGEMENT: This study was partially supported by the Guru Nanak Institute of Pharmaceutical Science and Technology in Kolkata. M.D., S.A., M.N., and A.N. acknowledge the Director and Principal, GNIPST, Kolkata for lab facilities. M.D., S.A., and M.N. received an Institutional fellowship from GNIPST, Kolkata.

Author Contributions: M.D and A.N conceived the study and designed the approach for execution. M.D and S.A. accomplished the analyses and construed the data. M.D and S.A. prepared the figures and tables. A.N. transcribed the main manuscript text with M.N. and all the authors finally reviewed this.

CONFLICTS OF INTERESTS: The author(s) state no competing interests.

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    How to cite this article:

    Datta M, Adhikary S, Nath M and Nayak A: In-silico studies illustrate the oncogenic potential of an ATpase protein NSF. Int J Pharm Sci & Res 2024; 15(2): 543-53. doi: 10.13040/IJPSR.0975-8232.15(2).543-53.

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