Introduction
Alzheimer’s disease (AD), a type of dementia, is a progressive neurodegenerative disease characterized by the presence of β-amyloid plaques and neurofibrillary tangles.1 Among all neurodegenerative disorders, AD claims unrivaled pre-eminence as the foremost prevailing affliction, which leads to severe deterioration in cognitive functions that impede everyday life activities. Apart from cognitive decline, patients with AD develop various neuropsychiatric symptoms such as depression, agitation, apathy, anxiety, and psychosis.2 As the disease progresses, individuals may experience disorientation, visuospatial abnormalities, navigation difficulties, muscular dystrophy, language disturbance, and restrictions in various daily activities. Aging, head injuries, vascular illness, infections, and environmental variables such as heavy metals, trace metals, and others are identified among the risk factors for AD.3
According to the Institute of Health Metrics and Evaluation, cases of AD have shown the quickest rates of growth and continue to be one of the significant causes of mortality. AD alone accounts for 60% to 80% of all dementia cases and affects roughly 10% of individuals aged 65 and older.4,5 It has been estimated that more than 55 million AD cases are currently present and are expected to quadruple by 2050, affecting more than 152.8 million people with AD and related dementia. Correlating to this data, the health-related burden of AD and related dementia will continue to increase, thus affecting the global economy.6 Since AD remains incurable, the World Health Organization has labeled it a “global public health priority”.7
According to anatomic pathology, AD can be recognized by two prototypical lesions: (1) Senile plaques, which are extracellular lesions of β-amyloid protein (Aβ-42), and (2) Neurofibrillary tangles that comprise phosphorylated Tau protein in neuronal cytoplasm.8 Accumulation of Aβ-42 plaques in the hippocampus, amygdala, and cerebral cortex activates microglia and astrocytes, mitochondria disturbance, synaptic loss, oxidative stress, and neurovascular dysfunction, thus ultimately triggering neuronal death.9 Aβ-42 accumulation initiates a cascade during the pathogenesis of AD which further triggers the downstream processes leading to hyper-phosphorylation of tau-forming neurofibrillary tangles, ultimately causing neuronal toxicity and degeneration of cholinergic neurons, a main characteristic found in the brains of AD patients, which is believed to cause alterations in cognitive functions, sensory information, and memory loss.3 However, recent studies also highlight the strong association of Aβ-42/40 ratio with tau and pTau181 in cerebrospinal fluid (CSF) of clinical patients compared to Aβ1-42 alone,10 favoring Aβ-42/40 ratio as a better marker for disease pathology. Moreover, a low Aβ-42/40 ratio in plasma samples of patients with mild cognitive impairment has also been shown to be negatively correlated with disease progression, indicating its role in determining early-stage dementia and its underlying AD pathology.11
Genetically, AD is broadly categorized into two types, namely Sporadic AD and Familial AD (FAD). Achieving an early clinical diagnosis and gaining insights into the subsequent occurrence of AD can significantly benefit from comprehending both Familial AD and Sporadic AD pathogenesis. Such understanding could influence the trajectory of the disease and contribute to the development of more effective treatments for this devastating neurological disorder.12 _ENREF_10Despite years of research and various clinical trials, there is still much to learn about the underlying mechanisms of pathological conditions. The development of new therapies for this appalling disease will be facilitated by knowledge of the genetic mechanisms underpinning AD pathogenesis and potentially implicated pathways.
Bioinformatics has revolutionized the biological sciences, enabling us to handle and analyze massive amounts of data. Numerous research studies emphasize the significance of analyzing extensive amounts of data and identifying new genes or pathways that could contribute to addressing the growing number of AD cases.13 To accomplish this objective, researchers are employing various tools to analyze RNA sequence data from many samples and investigate the potential involvement of multiple genes in AD progression.14 R programming is widely utilized in bioinformatics for statistical analysis of large data sets. It provides a wide range of tools and packages that enable efficient data exploration and analysis. These packages constitute an extensive collection of functions, datasets, and documentation designed to tackle particular problems. An understanding of the complex mechanisms of AD, including the role of genetic factors, protein accumulation, and neurodegenerative processes, is crucial for developing effective therapy.
Using bioinformatics and advanced data analysis tools, such as R programming, allows for exploring extensive datasets and identifying new genes or pathways implicated in AD progression.15 The datasets are derived from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/ ), which is an open international database repository of high-throughput gene expression, microarray, and other functional genomics datasets administered by the National Center for Biotechnology Information. GEO allows researchers to deposit, search, and download high-throughput gene expression datasets. This study also utilized GEO to access and extract datasets on AD.
Materials and methods
Data retrieval
The GEO repository browser contains 86,871 series or databases generated by “Expression profiling by high throughput sequencing”. After applying inclusion filters, such as “Expression profiling by high throughput sequencing” as the series type, “Homo sapiens” as the sample organism, and “Alzheimer’s disease” or “AD” as search keywords, a total of 91 series were obtained. Afterwards, exclusion criteria were applied, including “indirect tissue procurement”, “cell line-based studies”, and “studies based on certain drug treatment”. The dataset GSE203206 was finally chosen for further studies,16 which included eight healthy controls and 40 AD patients comprising early-onset AD (EOAD) and late-onset AD (LOAD). The analysis was conducted using various tools, and the pipeline is illustrated in Figure 1. The clinical delineation of patients is included in Supplementary Table 1.
Differential expression analysis
To analyze the differentially expressed genes of this dataset, the GEOquery package was used to extract the raw counts for all samples.17 It was further used to find relative expression between three groups, Control vs EOAD, Control vs LOAD, and EOAD vs LOAD, using the GDCRNATools package.18 Initially, this package employed the edgeR package for count value normalization to remove gene length-based bias and library size differences among samples to enable accurate comparisons of expression levels.19 Differentially expressed genes were obtained by further screening based on a threshold of |log2FC| > 1 and false discovery rate (FDR)-adjusted p-value < 0.05.
Gene set enrichment analysis (GSEA)
Post meticulous screening based on their ranking using log-fold changes, we employed the clusterProfiler package,20 which offers a range of tools to obtain GSEA wherein we specifically opted for Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes (KEGG) database.21 KEGG unites gene information from the genomic database and functional information using a pathway database to systematically analyze gene functions. For gene enrichment, the pre-ranked gene list, sorted based on expression change was coupled with selected gene sets. The KEGG database ran certain phenotype-based permutations to obtain functional enrichment plots and Pathway enrichment plots. The specific gene sets were then further assessed based on their enrichment scores, with statistical significance determined through permutation testing, p-value, and FDR.
Protein-protein interactions (PPI)
To analyze protein-protein interactions, the String database (StringDB) and GeneMANIA were used.22,23 In the StringDB portal, the list of dysregulated genes was uploaded separately for upregulated and downregulated sets, and PPI data were obtained, which was further refined by applying a threshold filter to obtain more reliable interactions. These interactions were further explored in GeneMANIA software by uploading the combined list of genes, which highlighted various physical and predicted interactions based on protein structure.
miRNA network formation
To analyze the mRNA-miRNA interactions, we employed miRNET,24 which is powerful bioinformatics software for extracting data from experimentally validated and computationally predicted interactions. Along with 13 dysregulated genes, the dataset also includes genes that play a direct role in the selected disease. Additionally, to obtain a more robust network, a degree filter of 2.0 and a betweenness filter of 1.0 were applied, providing 14 nodes for upregulated genes and 19 nodes for downregulated genes. The resulting network was further arranged in a “linear bipartite/tripartite” graph layout.
Statistical analysis
Data analysis was handled using R programming. Statistical analysis was done using unpaired t-test and FDR-adjusted p-value< 0.05 was considered as significant.
Results
Differential expression analysis of the genes
A total of 24,448 genes included in the dataset were evaluated to determine differential expression in control individuals (n = 8) vs AD patients (n = 39), further categorized into EOAD and LOAD patients. Initially, the comparative analysis between control and EOAD samples provided 564 dysregulated genes (Supplementary Table 2) with statistically significant differences. These genes are shown in Figure 2a using a volcano plot, representing their changes in expressions (log2-fold change) along with statistical significance (p-value). Notably, SNORA50B, DNAJC5G, PDCL3P2, and AL359955.1 are significantly downregulated, while HSPA6, STC1, TNFRSF6B, SINHCAFP3, NPM1P40, and FP236383.5 are significantly upregulated.
Additionally, the comparison of control and LOAD samples conferred a set of 405 genes (Supplementary Table 3) with distinctive expression patterns. These findings are depicted in Figure 2b, highlighting genes such as WIPF2, PDCL3P, ZPED9, LINC02068, REX01L1P, ATP5F1AP10, and CD44-AS1as downregulated, and MTCO3P12, HSPA6, STC1, ABL2, and TNFRSF6B as upregulated. The comparative analyses provided a set of 13 genes (coding and pseudogenes), to be highly dysregulated (Table 1). Notably, the relative comparison of Control-EOAD and Control-LOAD fold changes did not expose any significant change in expression among the transcripts. This indicates that a unique genetic signature is associated with the EOAD and LOAD subtypes within the AD spectrum.
Table 1List of dysregulated genes associated with EOAD and LOAD
| Genes (LOAD) | Fold change | Genes (EOAD) | Fold change |
|---|
| HSPA6 | 3.7777258 | FP671120.6 | 5.05216 |
| TNFRSF6B | 3.7232833 | FP236383.5 | 5.05216 |
| STC1 | 3.7044567 | FP236383.4 | 5.05216 |
| ABL2 | 3.6604484 | HSPA6 | 4.956657 |
| MTCO3P12 | 3.1909349 | AC010970.1 | 4.091295 |
| WIPF2 | −2.4091189 | TNFRSF6B | 3.394207 |
| | SINHCAFP3 | 3.022924 |
Functional enrichment analysis
Functional enrichment analysis of dysregulated genes was carried out with KEGG pathways using the GDCRNATools R package. This analysis helped in understanding the biological processes and pathways significantly enriched within the given gene list, shedding light on the functional implications of the input genes or proteins by identifying the most affected pathways. The phagosome pathway (hsa04145) was enriched by a set of three genes by 17-fold, while the collecting duct secretion pathway (hsa04966) was enriched by 30-fold but only by one gene (Fig. 3), highlighting their substantial role in AD. Since AD is recognized by the accumulation of Aβ-42 and τ-aggregates wherein disruptions in phagocytic clearance by microglia may lead to inefficient clearance of these aggregates, thus aiding in the progression of AD.25 Consequently, the role of three novel genes (TUBB6, TCIRG1, and MRC1) connects to the pre-existing mechanism of AD, suggesting the development of therapeutics targeting the phagocytic mechanism. Further analysis of genes involved in each pathway was conducted using data mining from the KEGG database (Table 2).
Table 2Tabular representation of genes involved in a multiple pathway, obtained through the KEGG database
| Pathways | Genes involved |
|---|
| hsa04145-Phagosome | TUBB6, TCIRG1, MRC1 |
| hsa05152-Tuberculosis | MRC1, TCIRG1 |
| hsa04966-collecting duct acid secretion | TCIRG1 |
| hsa05020-prion disease | HSPA6, TUBB6 |
| hsa04060-cytokine –cytokine receptor interaction | TNFRSF6B, LTBR |
| hsa04010- MAPK signalling pathway | HSPA6, HSPB1 |
| hsa04672-intestinal immune network for IgA production | LTBR |
| hsa05110-Vibrio cholera infection | TCIRG1 |
| hsa04340-Hedgehog signaling pathway | EVC2 |
| hsa05134-Legionellosis | HSPA6 |
| hsa04370-VEGF signaling pathway | HSPB1 |
| hsa04213-Longevity regulating pathway | HSPA6 |
| hsa04612-Antigen processing and presentation | HSPA6 |
| hsa05120-Epithelial cell signaling in Helicobacter pylori infection | TCIRG1 |
| hsa04721-Synaptic vesicle cycle | TCIRG1 |
| hsa04540-Gap junction | TUBB6 |
| hsa05323- Rheumatoid arthritis | TCIRG1 |
| hsa04061-Viral protein interaction with cytokine and cytokine receptor | LTBR |
| hsa05146-Amoebiasis | HSPB1 |
| hsa04064-NF-kappa B signaling pathway | LTBR |
Pathway enrichment analysis
The dysregulated genes were further assessed using gene set enrichment analysis. Pathway enrichment analysis using GSEA determines whether a gene set is significantly enriched in a list of differentially expressed genes. Rather than focusing on specific gene-level alterations, GSEA evaluates the overall behavior of genes within a pathway. It aids researchers in determining if particular gene sets, such as those associated with particular biological pathways or functional groups, are perpetually upregulated or downregulated in a phenotype or pathology. The analysis showed that dysregulated genes were associated with multiple pathways, highlighting significant influences on organelle organization (Fig. 4). Previous studies also highlighted the relevance of the disrupted function of lysosomes,26 mitochondria,27 and Golgi apparatus with AD.28,29
Correlation study of dysregulated genes
The 20 resultant dysregulated genes were further assessed for their correlation and affected pathways using the String database. This widely used database analyzes the PPI, predicting and creating a comprehensive network of protein interactions. The cluster with all possible PPIs (Fig. 5) and the pathways that are most affected or associated with these genes were identified. Upregulated genes affect the following pathways: (1) Circadian temperature homeostasis, (2) regulation of germinal center formation, (3) negative regulation of receptor biosynthetic processes, (4) p38MAPK cascade, and (5) type 2 immune response. Downregulated genes affect the following pathways: (1) Negative regulation of receptor biosynthetic processes, (2) regulation of germinal center formation, (3) phagosome acidification, (4) histone H4 deacetylation, and (5) regulation of T-helper 2 cell differentiation negative regulation of gene silencing by miRNA.
Since the PPIs network using STRING was unclear, we performed further interaction studies using GeneMANIA (Fig. 6). It revealed the physical interactions of ABL2 (Abelson-related gene) and HSPA6 (Heat Shock protein family A, member 6), which are further linked to Apoenzyme E and A1. ApoE (apolipoprotein E) is mainly associated with lipid transport and plays a central role in the metabolism of cholesterol and triglycerides.30 It is also involved in the clearance of Aβ-42 peptides, the aggregation of which is directly involved in AD progression.31
Correlation study of miRNA and genes
The association of dysregulated genes with miRNA was generated using miRNET – centric network analysis (Fig. 7). MiRNET software is widely used for studying and visualizing miRNA regulatory networks. This platform helps us explore miRNA-target interactions, functional enrichment analysis, and network visualization. It provides functional enrichment tools for insights into biological processes and molecular interactions using the KEGG or Gene Ontology database. It incorporates various modules to help understand miRNAs’ role in gene regulation and biological processes. The miRNA interaction plot was constructed after applying filters, i.e., “betweenness filter = 1.0”, “degree filter = 1.0”, and using the “linear bipartite/tripartite” graph layout.
Discussion
Millions of people worldwide are afflicted by dreadful neurological ailments, including AD. AD is a prevalent and debilitating neurodegeneration condition characterized by progressive cognitive decline, memory loss, and impaired daily activities.3 Despite decades of intensive research and substantial investment, the quest for successful treatments has encountered numerous setbacks. To address this serious concern, we conducted a comprehensive study utilizing the GSE203206 dataset obtained from the internationally recognized open repository, GEO. This dataset was derived from high-throughput sequencing focused on AD. Employing advanced bioinformatics tools such as GeneMania, StringDB, and R packages including GDCRNATools, edgeR, clusterProfiler, and ggplot2, we performed an in-depth analysis. In this study, we identified differentially expressed genes by comparing “control vs EOAD” and “control vs LOAD” groups, obtaining a total of 580 genes and 405 genes, respectively.
We found 13 uniquely highly dysregulated genes that might play a significant role in AD pathogenesis. These include 12 upregulated genes and one downregulated gene based on their log2-fold change (Fig. 2). We conducted a functional enrichment analysis to unravel the key pathways and biological processes associated with the dysregulated genes in AD. The hsa-04145-Phagosome pathway emerged as highly linked to the dysregulated genes involved in AD (Fig. 3). After manual filtering through the KEGG database, we identified that three dysregulated genes, i.e., HSPA6, HSPB1, and TCIRG1, are involved in multiple pathways obtained from functional enrichment analysis. Subsequently, pathway enrichment analysis revealed that organelle organization was the most significantly influenced pathway by the group of dysregulated genes associated with AD (Fig. 4).
Moreover, we conducted a PPI analysis using StringDB to gain further insights into the biological mechanisms underlying AD (Fig. 5) and GeneMANIA (Fig. 6). Additionally, we explored the association of dysregulated genes with miRNAs using the miRNET software (Fig. 7). Notably, B-cell lymphoma 6 (BCL6) is highly associated with several miRNAs among the upregulated genes, while JunB proto-oncogene (JUNB) exhibited significant associations among the downregulated genes. HSPB1 and HSPA6 were identified as the most enriched genes in the study (Fig. 3). Conclusively, according to our study, HSPA6, HSPB1, TCIRG1, and BCL6 are identified as hub genes affecting multiple targets implicating in AD.
HSPA6 (70kDa family A member 6) is a protein-coding gene primarily located on chromosome 1 (1q23.3) and encodes a protein that comprises two important structural domains, namely the N-terminal nucleotide-binding protein and the C-terminal substrate-binding domain. HSPA6 plays a crucial role in various biological processes such as cellular heat acclimation, response to unfolded protein, protein refolding, neutrophil degranulation, cellular response to unfolded protein, and chaperone cofactor-dependent protein refolding. It plays a critical role in the protein quality control system, which includes the correct folding of proteins, refolding of misfolded proteins, and governing the control of the subsequent degradation of proteins.32 Co-chaperones mediate adenosine triphosphate (ATP) binding, hydrolysis, and ATP release cycles to achieve this. According to recent studies, HSPA6 contributes to the development and advancement of tumors and triggers disorders unrelated to cancer.33 It is involved in the system that regulates the quality of the proteins, suppressing protein misfolding and aggregation, which are characteristics of neurodegeneration. Despite extensive research on HSPA6, its precise function and processes remain unknown. However, our study has highlighted its role in the progression of AD, suggesting it could be further studied and used as a prognostic marker.
HSPB1, i.e., heat shock protein family B (small) member 1, is a protein-coding gene located on chromosome 7 (7q11.23) and was first identified in 1987–1988.34 According to earlier studies, HSPB1 exhibited robust phosphorylation and the intriguing capacity to generate high molecular weight oligomers. After a long span of time, HSPB1 is identified as a molecular chaperone primarily engaged in protein folding. It plays a crucial role in capturing and storing misfolded polypeptides brought on by stress to prevent their aggregation and subsequently encourage either refolding or proteolytic destruction.34 It is abundantly expressed and can govern several cellular processes, including actin dynamics, oxidative stress regulation, and anti-apoptosis. Additionally, it serves a significant role in the regulation of cytoskeleton organization. In recent years, HSPB1 has been studied intensively due to its constitutive expression in different tissues, particularly in pathological conditions.34 HSPB1 is associated with a protein quality control system and counteracts protein misfolding and aggregation, a characteristic of neurodegeneration. Disease-causing mutations in HSPB1 cause disruption of cytoskeleton organization, apoptosis, and protein folding, and exclusively impact the peripheral nervous system, demonstrating an essential role in highly polarized motor and sensory neurons.35 Cdk5 extensively phosphorylates neuro-filaments in response to HSPB1 mutations and disturbances in axonal transport.
BCL6 (B-cell lymphoma-6) is located on chromosome 3 (3q27.3) in humans and is a chief transcription factor that governs the growth of T follicular helper cells. Three conserved domains are present in BCL6 (706 amino acids): an N-terminal BTB/POZ domain, a central region of about 400 amino acids, and a C-terminal C2H2-type zinc (Z.F.) DNA binding motifs. These domains interact with co-repressors to foster germinal center and B cell proliferation. BCL6 contributes to the pathophysiology of several human lymphomas. It binds to the ITM2B protein and represses its transcription; when BCL6 is removed, the transcription level of ITM2B demonstrates an inverse relationship between each other. Molecular modifications in the gene ITM2B were found to be associated with neurodegenerative disorders.36 Recent studies have suggested a potential involvement of BCL6 in AD pathogenesis, indicating its association with neuro-inflammation and amyloid-beta accumulation. However, additional investigations are required to fully understand the molecular mechanisms of BCL6 in AD progression and to explore its potential as a target for intervention strategies.
TCIRG1 is a protein-coding gene located on chromosome 11 (11q13.2) in humans and encodes a component (a3 subunit) of a significant protein complex, vacuolar H+ ATPase (V-ATPase). V-ATPase comprises two domains: a cytosolic V1 domain and a transmembrane V0 domain. This protein complex operates as the pump aids in passing the proton across the membrane, hence regulating the pH of the cell and surrounding environment. Protein sorting, zymogen activation, and receptor-mediated endocytosis are examples of V-ATPase-dependent organelle acidification. TCIRG1 is primarily involved in various biological pathways such as lysosome acidification, insulin receptor signaling, neutrophil acidification, proton transmembrane transport, osteoclast proliferation, apoptosis process, inflammatory pathways, and macroautophagy. TCIRG1 mutations are responsible for the rare genetic disorder called autosomal recessive osteopetrosis type-1. This genetic disorder is attributed to impaired bone resorption, resulting in an excessive build-up of thick and brittle bones.37 Although the primary manifestation of TCIRG1 mutation is related to bone disease, some indications may also play a role in neurodegeneration. However, according to several studies, TCIRG1 mutation may disrupt lysosomal function in neurons, possibly resulting in impaired protein aggregation and toxic breakdown, a neurodegeneration characteristic.38 Additionally, numerous neurodegenerative diseases, including AD, Parkinson’s disease, and amyotrophic lateral sclerosis, have been linked to lysosomal dysfunction. It is conceivable that mutations in the TCIRG1 gene could interfere with lysosomal function in neurons and contribute to the etiology of neurodegenerative studies since TCIRG1 is involved in lysosomal acidification. However, additional studies are required to elucidate the specific role of TCIRG1 and its implicated pathway.
hsa-miR-30a-5p is located on chromosome 6 (6q13). The miR-30a-5p molecule is a part of the miR-30 family, which consists of five closely related and evolutionarily conserved miRNAs. It plays a role in various cellular processes and has also been associated with the progression of different cancer type.39In vivo studies were conducted on mice, and it was found that elevated levels of miR-30a-5p resulted in neuronal damage and programmed cell death in cells associated with AD.39,40 Some studies have also indicated that miR-30a-5p may contribute to neurodegeneration by regulating key genes and pathways involved in processes such as neuronal survival, inflammation, oxidative stress, and protein aggregation. Overall, while evidence suggests the involvement of miR-30a-5p in neurodegeneration, further research is required to fully understand its specific role, target genes, and the underlying molecular mechanisms in different neurodegenerative diseases. Continued investigation into the functions of miR-30a-5p may provide valuable insights into the pathogenesis of neurodegenerative disorders and offer new therapeutic avenues for the intervention of AD.
Conclusions
The pursuit of a cure for AD has been marked by notable failures and substantial challenges. The complexity of the disease, limited understanding of underlying mechanisms, lack of early detection methods, and setbacks in clinical trial progress have impeded progress toward finding a definitive cure. The amalgamation of bioinformatics and AD research has revolutionized our understanding of the disease at a molecular level. By harnessing the power of computational analysis and data integration, bioinformatics has contributed significantly to identifying potential biomarkers, understanding disease mechanisms, and guiding the development of novel therapeutics. Our study delves into the intricate complexities of genetic mechanisms and understanding different dysregulated genes, namely, HSPA6, HSPB1, TCIRG1, BCL6, and miRNA, i.e., miR-30a-5p, and their associated pathways implicated in AD. Through an intricate dissection of molecular signatures, aberrant genetic profiles, perturbed signaling cascades have been elucidated, shedding light on the intricate molecular interplay underlying AD progression. These findings provide valuable insights into potential therapeutic targets and novel avenues for intervention to counteract this neurodegenerative disorder’s devastating consequences. Notwithstanding the elucidating findings of our study, it is imperative to underscore the necessity for subsequent investigations to validate our research outcomes and shape better treatments.
Supporting information
Supplementary material for this article is available at https://doi.org/10.14218/GE.2023.00143 .
Supplementary Table 1
Clinical data of the patients and healthy controls.
(DOCX)
Supplementary Table 2
Differentially expressed genes in the comparison group of Control and EOAD with a threshold FC = 2 and p-value < 0.05.
(DOCX)
Supplementary Table 3
Differentially expressed genes in the comparison group of Control and LOAD with a threshold FC = 2 and p-value < 0.05.
(DOCX)
Declarations
Acknowledgement
TA, RS and PJ are sincerely thankful for their fellowship from the Council of Scientific & Industrial Research.
Data sharing statement
Available on GEO, Accession: GSE203206.
Funding
The research was funded by the University Grants Commission (UGC) through start-up grant (F.30-312/2016 (BSR)), and the Science and Engineering Research Board through Early Carrier Research Grant, (ECR/2015/000240), and Core Research Grant (CRG/2020/003257).
Conflict of interest
The authors declare no conflict of interest.
Authors’ contributions
Study conceptualization, data collection and analysis (TA, HS, PJ, JP), manuscript writing (TA, JP, VP, RA), final editing (PJ, AS), and proofreading (JP, AS, HC). All authors have made a significant contribution to this study and have approved the final manuscript.