Abstract
The COVID-19 pandemic has become a significant global issue in terms of
public health. While it is largely associated with respiratory
complications, recent reports indicate that patients also experience
neurological symptoms and other health issues. The objective of this
study is to examine the network of protein-protein interactions (PPI)
between SARS-CoV-2 proteins and human host proteins, pinpoint the
central genes within this network implicated in disease pathology, and
assess their viability as targets for drug development.
The study adopts a network-based approach to construct a network of 29
SARS-CoV-2 proteins interacting with 2896 host proteins, with 176 host
genes being identified as interacting genes with all the viral
proteins. Gene ontology and pathway analysis of these host proteins
revealed their role in biological processes such as translation, mRNA
splicing, and ribosomal pathways. We further identified EEF2, RPS3,
RPL9, RPS16, and RPL11 as the top 5 most connected hub genes in the
disease-causing network, with significant interactions among each
other. These hub genes were found to be involved in ribosomal pathways
and cytoplasmic translation. Further a disease-gene interaction was
also prepared to investigate the role of hub genes in other disorders
and to understand the condition of comorbidity in COVID-19 patients. We
also identified 13 drug molecules having interactions with all the hub
genes, and estradiol emerged as the top potential drug target for the
COVID-19 patients. Our study provides valuable insights using the
protein-protein interaction network of SARS-CoV-2 proteins with host
proteins and highlights the molecular basis of manifestation of
COVID-19 and proposes drug for repurposing.
As the pandemic continues to evolve, it is anticipated that
investigating SARS-CoV-2 proteins will remain a critical area of focus
for researchers globally, particularly in addressing potential
challenges posed by specific SARS-CoV-2 variants in the future.
Keywords: COVID-19, SARS-CoV-2 proteins, Comorbidity, Host interacting
genes, Ribosomal pathways, Drug repurposing
Highlights
* •
A network-based approach, creating a network comprising 29
SARS-CoV-2 proteins interacting with 2896 human host proteins.
* •
Network find 176 host genes interacting with all viral proteins and
involved in translation, splicing and ribosomal pathways.
* •
Top 5 most connected hub genes in the disease-associated network
were determined to be EEF2, RPS3, RPL9, RPS16, and RPL11.
* •
292 drug molecules were discovered to interact with hub genes and
13 molecules were found to interact with all hub genes.
* •
Estradiol was identified as interacting with all the hub genes in
the drug-gene interaction databases.
1. Introduction
Since its emergence in late 2019, the COVID-19 pandemic has led to
unparalleled disruptions in societies and economies worldwide [[37]1].
As of early 2023, the virus continues to pose a significant threat,
with millions of new cases and hundreds of thousands of deaths reported
each day. The SARS-CoV-2 genome exhibits a high degree of diversity,
possibly influenced by environmental pressures and regional health
indices [[38]2,[39]3]. The emergence of new variants has added further
complexity to the situation, leading to renewed concerns about the
effectiveness of existing vaccines, antivirals, and the potential for
further waves of infection [[40][4], [41][5], [42][6], [43][7],
[44][8]]. While vaccination efforts have made significant progress in
some parts of the world, the slow distribution of vaccines and the rise
of vaccine hesitancy in other areas remain major challenges.
The pandemic has sparked a surge in research endeavors focused on
comprehending the virus and devising efficacious treatments and
vaccines [[45]9,[46]10]. Studies have focused extensively on the
proteins of the virus, revealing that SARS-CoV-2, a RNA-enveloped
single-stranded virus, encodes 29 structural proteins [[47]11]. These
include structural proteins such as spike, envelope, membrane, and
nucleocapsid proteins, as well as nonstructural proteins like
RNA-dependent RNA polymerase, papain-like protease, and main protease.
These proteins are pivotal in facilitating SARS-CoV-2 replication
within the human body, leading to cell infection, illness induction,
and the onset of diverse manifestations throughout the body [[48][12],
[49][13], [50][14], [51][15]]. The systematic network theoretical
approach to studying host-pathogen protein interactions offers valuable
insights, contributing to our understanding and illumination of the
mechanisms that underlie a wide range of infectious diseases [[52][16],
[53][17], [54][18]].
Both structural and nonstructural proteins play crucial roles in the
virus lifecycle. Among them, the spike protein (S) is indispensable for
facilitating viral entry into host cells [[55]19], while the envelope
protein (E) aids in viral assembly and release [[56]20]. The membrane
protein (M) plays a role in viral assembly [[57]21], and the
nucleocapsid protein (N) binds to viral RNA and is involved in viral
replication [[58]22]. Nonstructural proteins like RdRp, PLpro, and Mpro
are crucial for viral replication and are potential targets for
antiviral drugs [[59][8], [60][9], [61][10],[62]12,[63]23]. Moreover,
recent studies have shown that the nonstructural proteins of
SARS-CoV-2, including PLpro and Mpro, can play an important role in
immune evasion strategies [[64]24].
SARS-CoV-2 proteins have been shown to interact with a large number of
host proteins, which indicates the importance of genes in the
virus-host interaction [[65]25]. In this study, we have identified 176
host genes out of 2896 total host genes that interact with all 29 viral
proteins, indicating their critical role in SARS-CoV-2 infections. The
gene ontology analysis revealed that these genes are involved in
several biological processes, including translation, mRNA splicing, and
ribosomal biogenesis. These findings suggest that targeting these genes
may provide potential therapeutic targets for the treatment of
COVID-19. Additionally, we have investigated the potential of drug
repurposing by identifying drugs that interact with the hub genes and
investigating their potential therapeutic effects.
2. Materials and methods
2.1. Generation of SARS-CoV-2 protein and host proteins interaction network
The compilation of host proteins demonstrating interaction with
SARS-CoV-2 proteins was sourced from the studies conducted by Gordan et
al. [[66]25,[67]26]. To construct the interaction network between viral
proteins and host proteins, the STRING plugin [[68]27] within the
Cytoscape tool [[69]28] was utilized. This plugin leverages various
data sources, including text-mining data, gene fusion, co-expression,
neighborhood analysis, and experimental data, to generate the
protein-protein interaction (PPI) network.
2.2. Generation of PPI network of host proteins and identification of hub
genes
The host proteins interacting with all viral proteins were compiled,
and a protein-protein interaction (PPI) network was constructed using
the Cytoscape tool. Subsequently, the network analyzer plugin within
Cytoscape was employed to compute various network parameters. Hub genes
within the network were identified based on their topological
significance. Calculated network topological properties such as Degree
of Connectivity (k), betweenness centrality, closeness centrality, and
topological coefficient values were utilized to pinpoint highly
connected nodes.The Degree of Connectivity (k) represents the number of
interactions established by nodes within a network. It is expressed as
the total number of edges connected to a particular node [Equation
[70](1)].
[MATH: Degreecentrality(k)=∑aεKbw(a,b)…… :MATH]
1
In this equation [71](1),
[MATH: Ka :MATH]
represents the node set containing all the neighbors of node a, and
w(a,b) denotes the edge weight connecting node a with node b.
Betweenness centrality (
[MATH: Cb :MATH]
) quantifies the extent to which nodes serve as intermediaries along
the shortest paths between other nodes in the network. A node with
higher betweenness centrality holds more influence over the flow of
information within the network. It is expressed as Equation [72](2):
[MATH: Cb(u)=∑k≠u≠fp(k,u,f)p(k,f)…… :MATH]
2
In Equation [73](2), p(k,u,f) represents the number of interactions
from node k to f that pass through node u, while p(k,f) denotes the
total number of shortest interactions between node k and node f.
Closeness centrality (
[MATH: Cc :MATH]
) gauges the efficiency with which information propagates from a given
node to other nodes in the network. The value of closeness centrality
ranges from 0 to 1, where isolated genes exhibit a closeness centrality
value of zero [Equation [74](3)].
[MATH: Cc(z)=1av<
mi>g(L(z,m))…… :MATH]
3
In Equation [75](3) z represents the node for which the closeness value
is being calculated, and L(z,m) denotes the length of the shortest path
between two nodes z and m. It has been observed that genes tend to have
a high degree of connectivity and also tends to have high closeness
centrality score.
The Topological coefficients (
[MATH: Tf :MATH]
) reflects the propensity of nodes in the network to share common
neighbors. Nodes with zero or one neighbor are assigned a topological
coefficient of zero. For a node n with neighbors
[MATH: kf :MATH]
the topological coefficient is calculated as [Equation [76](4)]:
[MATH: Tf=avg<
mrow>(j(f,p))kf……
:MATH]
4
In Equation [77](4), j(f,p) represents the number of shared neighbors
between node f and p, incremented by 1 if there exists an edge between
nodes f and p.
2.3. Preparation of disease-gene interaction network
Following the identification of hub genes within the network, databases
containing information on disease-gene interactions, such as
GeneORGANizer, DisGeNET, and MalaCards database [[78][29], [79][30],
[80][31]], were surveyed. This screening aimed to pinpoint the
disorders linked with the identified hub genes. These databases
facilitate the analysis of the association between genes and the organs
they affect. A comprehensive collection of over approximately 2 million
disease-gene interactions was extracted from these databases.
Subsequently, disease-gene interactions specifically relevant to the
identified hub genes were further filtered and retrieved for analysis.
2.4. Gene ontology and pathways enrichment analysis
The Database for Annotation, Visualization, and Integrated Discovery
(DAVID) [[81]32] as employed to conduct a comprehensive enrichment
analysis of RNA binding proteins. DAVID utilizes the Gene Ontology (GO)
database and the Kyoto Encyclopedia of Genes and Genomes (KEGG)
database [[82]33] for functional and pathway enrichment analysis.
Functional enrichment analysis encompasses examination at biological,
cellular, and molecular levels. Pathways and functions with a P-value
less than 0.05 were deemed significantly enriched and were further
investigated in the study.
2.5. Identification of drug compounds as regulators
For identifying the drug compound as regulators against our identified
hub-genes, databases such as PubChem [[83]34], DrugBank [[84]35], and
Comparative Toxicogenomic Databases (CTD) [[85]36] having drug-gene
interaction information were screened thoroughly. In addition to the
databases mentioned above, the Enrichr database [[86]37] was also
utilized to identify drugs interacting with the selected hub genes.
Enrichr is a web-based tool grounded on Gene Set Enrichment Analysis
(GSEA), which consolidates information concerning the function of
groups of genes. Enrichr, in its backend, scans multiple drug-gene
interaction databases alongside the GEO database and presents relevant
significant interactions.
Further, the NCBI GEO Profiling database [[87]38] was used to study the
effect of the selected drug on the expression of the identified
hub-genes.
3. Results
3.1. Protein-protein interaction network of SARS-CoV-2 proteins targeted host
proteins
The list interactions of COVID-19 viral protein-targeted human genes
were retrieved from the Gordan et al. studies. From the list, we
identified that 29 SARS-CoV-2 proteins were showing interactions with
2896 host proteins. The Protein-Protein interaction network of viral
proteins with host proteins was prepared using the Cytoscape tool
[[88]Fig. 1] [[89]Supplementary Table 1]. From the prepared network,
many host proteins were identified as having interactions with multiple
viral proteins. A total of 176 host genes were identified interacting
with all the 29 viral proteins, indicating their importance during the
COVID-19 viral attack.
Fig. 1.
[90]Fig. 1
[91]Open in a new tab
Protein-Protein interaction network of SARS-CoV-2 proteins (red nodes)
with the host proteins (blue nodes) to identify the reach of viral
protein in the hosts body.
3.2. Protein-protein interaction network of host gene and identification of
hub-genes
After identifying the 176 host proteins interacting with all the viral
proteins, we have used the STRING plugin of the cytoscape tool to
prepare a PPI network to study the interactions between the concerned
viral targeted proteins [[92]Fig. 2] [[93]Supplementary Table 2]. The
prepared network showed the high-density interactions among the
proteins suggesting high dependency in-between the proteins for their
functional role. The Gene ontology analysis of the concerned hosts
protein reveals their role in biological processes such as translation,
cytoplasmic translation, mRNA splicing via spliceosome, rRNA
processing, ribosomal small subunit biogenesis, RNA splicing, mRNA
splicing, and alternative mRNA splicing. Cellular components were
enriched in ribosome, cytosolic ribosome, cytosolic small ribosomal
subunit, and nucleoplasm. Whereas the molecular functions were enriched
in RNA binding, structural constituent of ribosome, mRNA binding,
protein binding, cadherin binding, DNA binding and ubiquitin ligase
inhibitor activity [[94]Fig. 3] [[95]Supplementary Table 3]. Pathway
analysis of the genes revealed their roles in Ribosomal pathways,
spliceosome pathways and in Parkinson disease [[96]Table 1]
[[97]Supplementary Table: 3].
Fig. 2.
[98]Fig. 2
[99]Open in a new tab
PPI network of host protein showing direct interactions with the
SARS-CoV-2 proteins.
Fig. 3.
[100]Fig. 3
[101]Open in a new tab
Gene ontology analysis of the identified 176 host proteins showing
direct interaction with viral proteins.
Table 1.
Kegg Pathways analysis of the host genes showing interaction with the
viral proteins.
Category Term Count P value
KEGG_PATHWAY hsa03010:Ribosome 47 1.40E-51
KEGG_PATHWAY hsa03040:Spliceosome 17 4.60E-11
KEGG_PATHWAY hsa05012:Parkinson disease 10 0.007
[102]Open in a new tab
Further we calculated the topological parameters of network for the
identification of the hub genes [[103]Supplementary Table 4]. Hub genes
in the network are those genes having highest direct interactions with
the other genes in the network suggesting their importance in
regulating the behavior of major part of the disease-causing network.
To reinforce our earlier assertion regarding the high-density
interaction network, we computed the eccentricity value of the network.
In a biological network, the eccentricity of a node can be understood
as its accessibility to be functionally reached by all other nodes in
the network. Nodes with a higher eccentricity value compared to the
average eccentricity value of the network can exert more influence on
other nodes in the network and, conversely, can also be more easily
influenced. Our observation revealed that more than 88 % of the nodes
in the network possessed a higher eccentricity value than the average
eccentricity value, indicating a strong interconnection among the nodes
in the network. Among the nodes, EEF2 (k = 78), RPS3 (k = 72), RPL9
(k = 71), RPS16 (k = 70), and RPL11 (k = 69) emerged as the top 5 most
connecting genes in the network, exhibiting a high degree of
connectivity (k) and betweenness centrality value. Gene ontology
analysis of the identified hub genes reveals their role in biological
processes such as translation, cytoplasmic translation and rRNA
processing. Cellular components were enriched in cytosolic ribosome,
ribosome, membrane, rRNA binding, focal adhesion, extracellular exome,
cytosol, cytoplasm, small ribosomal subunit, polysomal ribosome, rRNA
processing. Whereas the molecular functions were enriched in structural
constituent of ribosomes, RNA binding, and rRNA binding [[104]Fig. 4]
[[105]Supplementary Table:5]. Pathways analysis reveals the role of hub
genes in ribosomal pathways [[106]Table 2].
Fig. 4.
[107]Fig. 4
[108]Open in a new tab
Gene ontology analysis of the identified hubs genes from the network.
Table 2.
Kegg Pathways analysis of the identified hub genes from the host PPI
network.
Category Term Count P value Genes FDR
KEGG_PATHWAY hsa03010: Ribosome 4 3.21E-05 RPS16, RPL11, RPS3, RPL9
1.92E-04
[109]Open in a new tab
3.3. Disease & hub-genes interaction network
Following the identification of the most influential genes in the
network, a disease-gene interaction network was constructed to
establish connections between the hub genes, COVID-19, and other
disorders. This disease-gene interaction network was created to gain
insights into the comorbidities observed in COVID-19 patients. The
disease-gene interaction network reveals that out of 5 hub-genes, 4 hub
genes were having association with multiple disorders such as
respiratory difficulties, dysarthria, congestive heart failure,
melanoma, anaemia, parkinson's disease, cerebellar atrophy, truncal
ataxia and more. The network further illustrates that numerous
disorders share common genotypes. For instance, genes such as RPL11
(k = 79), EEF2 (k = 9), RPL6 (k = 3), and RPS3 (k = 2) are associated
with multiple disorders [[110]Fig. 5].
Fig. 5.
[111]Fig. 5
[112]Open in a new tab
Disease gene interaction network: The network represents the
association of the identified hub genes with other disorders creating
an association between the COVID-19 and other disorders.
3.4. Drug repurposing
The Enrichr database was used to identify the expression of the
hub-genes in COVID-19 patients, and interestingly all the five hub
genes were known to show downregulated expression in the COVID-19
patient [[113]Fig. 6A]. For identifying the potential drug targets
against the concerned hub-genes several drug-gene interaction databases
such as PubChem, DrugBank and CTD were screened. A total of 292 drug
molecules were identified interacting with the hub-genes. Out of 292
molecules, we further identified 13 molecules interacting with all the
hub-genes [[114]Fig. 6C]. Further we also screened the Enrichr database
for potential drug targets. The GSEA of the drug perturbations from GEO
database records of upregulated genes revealed Estradiol as the top
significant enriched candidates [[115]Fig. 6B]. Interestingly, while
screening the drug-gene interaction databases we also identified
estradiol interacting with all the hub genes as represented in
[116]fig-6B.
Fig. 6.
[117]Fig. 6
[118]Open in a new tab
(A) Enrichr heatmap representing the downregulation of all the
identified hub genes in COVID-19 conditions. (B) Heatmap from Enrichr
database to identify the drugs showing interactions with hub genes (C)
Drug-gene interaction network representing the drugs interacting with
the hub-genes.
Next, we scanned the GEO profile related to Estradiol against all the
concerned hub genes and interestingly identified that estradiol
increases the expression of all the hub genes (EEF2, RPS3, RPL9, RPS16,
and RPL11) [[119]Fig. 7A–E] suggesting the greater potential of
estradiol in the treatment of COVID-19 patient.
Fig. 7.
[120]Fig. 7
[121]Open in a new tab
In-silico validation of the effects of estradiol on the expression of
the concerned hub genes EEF2 (A), RPS3 (B), RPL9 (C), RPS16 (D) and
RPL11 (E).
4. Discussion
SARS-CoV-2, a single-stranded RNA virus, possesses a relatively large
size genome [[122]11]. This genome encompasses all the instructions
required for the virus to replicate and generate new viral particles.
The virus also carries several non-structural proteins that aid in
viral replication and evasion of the host immune response
[[123]12,[124]23]. There have been many thousands of studies conducted
worldwide on the SARS-CoV-2 viral proteins, in which researchers have
been focused on understanding how they function, how they interact with
host cells, and how they can be targeted by drugs or vaccines
[[125]23]. One of the most critical aspects of COVID-19 research is
identifying the interactions between viral proteins and host proteins
[[126]25]. By studying these interactions, researchers can better
understand the mechanisms by which the virus infects cells and identify
potential targets for therapeutic intervention. In this study we have
used network analysis to identify the host proteins that interact with
all 29 (structural and non-structural) SARS-CoV-2 proteins and
identified 176 critical genes that play a critical role in COVID-19
pathogenesis. Moreover, the study identified hub genes such as EEF2,
RPS3, RPL9, RPS16, and RPL11. Hub genes are those with the highest
number of direct interactions with other genes in the network. In
addition, these hub genes also show interactions with each of the 29
proteins of SARS-CoV-2 ([127]Supplementary Table 1). The extensive
connectivity of these hub genes across the viral protein repertoire
highlights their central role in orchestrating molecular responses
within the host-virus interplay.
EEF2, or eukaryotic elongation factor 2, is accountable for the
translocation of the ribosome during protein synthesis [[128]39].
Whereas the Ribosomal Protein S3 (RPS3), Ribosomal Protein L9 (RPL9),
Ribosomal Protein S16 (RPS16), and Ribosomal Protein L11(RPL11) are
ribosomal proteins that are involved in the assembly of ribosomes and
translation initiation. These proteins help to stabilize the binding of
mRNA to the ribosome for translation [[129]40]. Further we have
analysed the expression of these hub genes through Enrichr,
interestingly we found that all the five hub genes were getting
downregulated in COVID-19 patients. The downregulation of these genes
may have various effects on the body, such as the downregulation of
EEF2 in COVID-19 patients may impair protein synthesis and affect
various physiological functions [[130]41]. EEF2 has also been linked to
the regulation of autophagy, a cellular process that removes damaged
proteins and organelles [[131]42]. Downregulation of EEF2 may
consequently impact the autophagy process and potentially contribute to
the pathogenesis of COVID-19. While the RPS3, RPL9, RPS16, and RPL11
are ribosomal proteins that are essential for the formation of
ribosomes and protein synthesis [[132]43]. The downregulation of these
genes may impair the ribosome biogenesis process and affect protein
synthesis [[133]44]. This may have various effects on the body,
including a decrease in the production of immune-related proteins and
an impairment in the response to viral infections [[134]45].
Additionally, these hub genes have been associated with various
biological processes, including translation, cytoplasmic translation,
mRNA splicing, and rRNA processing. The downregulation of these genes
may thus affect these processes and contribute to the pathogenesis of
COVID-19. Overall, the downregulation of EEF2, RPS3, RPL9, RPS16, and
RPL11 genes in COVID-19 patients may have various effects on the body,
including impairments in protein synthesis, autophagy, and immune
responses [[135]46].
These hub genes are potential targets for drug repurposing, which
involves using existing drugs to treat new diseases, which is an
important approach in the search for effective COVID-19 treatments. In
this study, we identified 292 drug molecules that interact with the
EEF2, RPS3, RPL9, RPS16, and RPL11 hub genes that have been identified
as being downregulated in COVID-19 patients. Furthermore, we identified
13 potential drug molecules that interact with all the hub genes. Among
the possible therapeutic targets, estradiol was shown to interact with
all the hub genes.
Estradiol, a steroid hormone, has been identified as a common drug that
interacts with all five hub genes (EEF2, RPS3, RPL9, RPS16, and RPL11)
involved in protein synthesis and immune response in COVID-19 patients.
The significance of this study lies in the potential therapeutic value
of estradiol in the treatment of COVID-19. Research has shown that
estradiol can regulate the expression of genes involved in protein
synthesis. Estradiol has been found to increase the expression of EEF2,
a gene involved in elongation during protein synthesis, and ribosomal
proteins, genes involved in ribosome assembly and translation
initiation [[136]47,[137]48]. Furthermore, estradiol has been shown to
have immunomodulatory effects. Estradiol can regulate the production of
cytokines and chemokines, crucial components of the immune response to
infections [[138]49]. The ability of estradiol to modulate the immune
response and regulate protein synthesis makes it a promising drug
candidate for the treatment of COVID-19. Several studies have
investigated the potential therapeutic value of estradiol in COVID-19.
A study published in the Journal of Women's Health reported that
postmenopausal women taking estradiol had a reduced risk of
hospitalization and death from COVID-19 [[139]50]. Another study
published in the journal Menopause found that estradiol treatment was
associated with a lower risk of severe COVID-19 outcomes in women
[[140]51]. Indeed, it's crucial to acknowledge that not all studies
have identified a significant association between estradiol and
COVID-19 outcomes. It's worth noting that investigations into the
potential therapeutic benefits of estradiol in COVID-19 are still in
their preliminary phases, and additional research is warranted to
comprehensively comprehend its effects and potential clinical
applications. Moreover, our analysis revealed that Estradiol
upregulates the expression of all the hub genes, by interacting with
all five hub genes involved in protein synthesis and immune response in
COVID-19 patients. These findings suggest that Estradiol may have
potential therapeutic effects against COVID-19.
5. Conclusion
In conclusion, our study offers valuable insights into the gene-protein
interaction networks of SARS-CoV-2 proteins and host proteins. Our
findings suggest that targeting the identified hub genes may provide
possible targets for therapeutic intervention in the treatment of
COVID-19. Furthermore, our analysis of drug-gene interactions
identified potential drugs, specifically the Estradiol. The estradiol's
ability to interact with all five hub genes involved in protein
synthesis and immune response in COVID-19 patients makes it a promising
drug candidate for the treatment of COVID-19. The ability of estradiol
to regulate protein synthesis and modulate the immune response suggests
that it may have a significant therapeutic value in COVID-19. However,
further research is essential to fully understand the effects and
possible clinical applications of the identified drug molecules through
in-silico analysis. While this computational approach helps pinpoint
potential drugs from a large pool, it's crucial to note that the actual
effects of these candidates require validation through clinical
analysis. To address this, we are in the process of designing a
systematic pipeline for the future validation of our findings through
clinical studies. Overall, our study highlights the importance of
genes, protein-protein interaction networks, and drug repurposing in
the exploration for effective COVID-19 treatments.
Ethical statement
Present study does not require any ethical clearance.
Data availability
The data that support the findings of this study are within the
manuscript and in the supplementary file.
CRediT authorship contribution statement
Wajihul Hasan Khan: Writing – original draft, Methodology, Data
curation, Conceptualization. Razi Ahmad: Writing – original draft, Data
curation, Conceptualization. Ragib Alam: Software, Formal analysis.
Nida Khan: Software, Formal analysis, Data curation. Irfan A. Rather:
Funding acquisition, Formal analysis, Data curation. Mohmmad Younus
Wani: Writing – review & editing, Investigation, Formal analysis. R.K.
Brojen Singh: Writing – review & editing, Supervision, Resources. Aijaz
Ahmad: Writing – review & editing, Validation, Supervision, Resources,
Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgment