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A meta-analysis of bulk RNA-
seq datasets identies potential
biomarkers and repurposable
therapeutics against Alzheimer’s
disease
Anika Bushra Lamisa1,2,4, Ishtiaque Ahammad2,4, Arittra Bhattacharjee2,4,
Mohammad Uzzal Hossain2, Ahmed Ishtiaque1, Zeshan Mahmud Chowdhury2,
Keshob Chandra Das3, Md Salimullah3 & Chaman Ara Keya1
Alzheimer’s disease (AD) poses a major challenge due to its impact on the elderly population and
the lack of eective early diagnosis and treatment options. In an eort to address this issue, a study
focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using
RNA-Seq data from AD patients and healthy individuals, 12 dierentially expressed genes (DEGs)
were identied, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX,
ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them,
TTR exhibited the lowest gene expression prole. Interestingly, functional analysis tied TTR to amyloid
ber formation and neutrophil degranulation through enrichment analysis. These ndings suggested
the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that
the FDA-approved drug Levothyroxine might be eective against the Transthyretin protein encoded by
the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin
suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro
and in vivo models are necessary before these ndings can be applied in clinical applications.
Keywords RNA-Seq, Alzheimer’s disease, Biomarker, Drug discovery
Alzheimer’s disease (AD) is a degenerative neurological condition characterized by a progressive and irreversible
decline in cognitive and functional abilities, resulting in signicant impairment in routine tasks and social
interactions1. Various environmental and genetic risk factors have been linked with its onset2,3. e pathological
mechanisms of the disease are mostly distinguished by the formation of amyloid plaques (Aβ plaques),
neurobrillary tangles, and neuronal degeneration in the brain4. Furthermore, Alzheimer’s disease (AD) is a
matter of signicant public health concern on a global scale, both in the United States and in several other
countries around the world5,6. Furthermore, it is noteworthy that this condition ranks as the h most prevalent
cause of death in the elderly population of the United States. Additionally, it is estimated that approximately
35million individuals globally are impacted by this disease, with estimations indicating that this gure will
increase to 65million by the year 20307. Additionally, the number of cases of Alzheimer’s disease in Asia, in
developed as well as developing countries, is aected by age, gender, and cultural factors8–10. In Bangladesh,
there is a lack of precise epidemiological data on the number of AD patients, and awareness of the disease is
still in the stages of development. e lack of awareness has resulted in ongoing diculties for patients and their
families. Moreover, limited funding for AD research makes it dicult for a lower-middle-income country like
Bangladesh to eectively manage the disease. erefore, it is time to consider the disease and its management
proactively and take the necessary measures11.
1Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka 1229, Bangladesh.
2Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka 1349, Ashulia, Bangladesh.
3Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka 1349, Ashulia,
Bangladesh. 4Anika Bushra Lamisa, Ishtiaque Ahammad and Arittra Bhattacharjee contributed equally to this work.
email: chaman.keya@northsouth.edu
OPEN
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Several studies have been conducted in recent times regarding the etiology of AD, with the majority of the
research indicating that the cause of AD was genetic in nature12. Despite the increasing number of genes that have
been suggested to impact vulnerability to Alzheimer’s disease (AD), the comprehension of their mechanisms
and the enhancement of disease management are still restricted by challenges in understanding the functional
implications of genetic associations13,14. Recent research has demonstrated that various mechanisms of gene
expression regulation, including interactions between mRNA and transcription factors, non-coding RNAs,
alternative splicing, and variants, may have an impact on the process of neurodegeneration and an increasing
number of developments are being observed in the concurrent analysis of transcriptome data, to investigate the
consequences of recently identied genetic risk factors on the transcriptome level15.
e area of molecular biology and genomics has grown tremendously in the previous decade, with the most
recent development content including “omics” technologies such as genomics, proteomics, transcriptomics,
and metabolomics16. e transcriptomic concept refers to the complete set of transcripts found in various cell
types, tissues, or organs. is encompasses both coding and noncoding RNA molecules that are responsible
for encoding proteins17. Several studies have utilized the transcriptomics approach to identify biomarkers
that dierentiate individuals with Alzheimer’s disease from those without Alzheimer’s. e identication of
dierentially expressed genes (DEGs) through RNA-Seq analysis is an essential part of the study of biological
pathways implicated in various neurological disorders. e purpose of conducting Dierential Expression Gene
(DEG) analysis is to identify genes that exhibit potential overexpression or underexpression in the context of a
disease state, relative to a control group that remains unaected18. Dysregulation of gene expression, whether it
be overexpression or underexpression, can lead to disruptions in various biological pathways such as metabolic
and immune pathways, which eventually result in the development of diseases19. Dierentially expressed
genes (DEGs) may exert an impact on the initiation of neurodegenerative disorders like Alzheimer’s disease.
Furthermore, it is plausible that there may exist divergences in the gene expression proles across distinct
regions of the brain20. Determining whether the transcriptional alterations may yield cumulative impacts
on established disease susceptibility factors and disease-associated pathways is of paramount signicance21.
e incidence of Alzheimer’s disease (AD) may exhibit an upward trend with advancing age, and anomalous
transcriptional alterations give rise to pathogenic mechanisms associated with the disease22. e application of
dierentially expressed genes (DEGs) in systemic biology studies can identify signicant functional components
and central hub genes that are linked to the development of various diseases. is approach may involve the
use of various tools such as Gene Ontology (GO)23 and Kyoto Encyclopedia of Genes and Genomes (KEGG)24
biological pathways.
e goal of the study was to identify dierentially expressed genes from bulk RNA-Seq data and suggest
potential biomarkers as well as nd any repurposable drug candidates, if available. For this purpose, a number
of analyses such as dierential gene expression, druggability, molecular docking and dynamics simulation were
to be carried out.
Methods
Retrieval of NGS data
e RNA-Seq datasets of 221 patients with Alzheimer’s (AD = 132) and non-Alzheimer’s (control = 89) were
obtained in FASTQ format from the public database Gene Expression Omnibus (GEO) (https://www.ncbi.
nlm.nih.gov/geo/) (Project ID: PRJNA675355, PRJNA767074, PRJNA796229, PRJNA232669, PRJNA377568,
PRJNA413568) in the National Center of Biotechnology Information (NCBI) website were collected to identify
potential biomarkers and therapeutic targets. Dataset of PRJNA683625 were used as independent dataset for
validation. e criteria used for the selection of datasets have been depicted in Fig.1. Supplementary File 1
contains information regarding the selected datasets (project ID, sample size, number of Alzheimer’s subjects,
number of healthy subjects, gender, age range, brain region, stage, reference). ese samples were subsequently
processed using various computational tools. ese steps have been presented through a owchart (Fig.2).
Processing and alignment of the reads
HISAT2 was used to align all the raw reads to the reference genome of Homo sapiens GRCh38.p13
(GCA_000001405.28) from Ensembl25. Aerwards, the mapped reads were then assigned to Ensembl genomic
features dened in GRCm38.69 (http://primerseq.sourceforge.net/gtf.html). e number of reads per gene were
quantied with the use of FeatureCounts (http://subread.sourceforge.net/).
Dierential gene expression analysis
e quantication of RNA-Seq relies on read counts that are assigned to genes in a probabilistic manner. For
the minimization of batch eect, ComBat-seq function of sva package was used for each project26. In order to
compute dierential expression, the statistical approach DESeq2 was used to predict DEGs, and false discovery
rate (FDR) was used to correct p-values and identify true DEGs. e fold change (FC) of each gene was calculated
between control and AD groups, and genes with p-adjusted value < 0.05 and |Log2FC| > 1.45 were considered
signicant DEGs27.
Functional and pathway enrichment analysis of DEGs
To understand the biological functions and pathways associated with the dierentially expressed genes (DEGs),
enrichment analyses of Gene Ontology (GO) and KEGG pathways were performed. Enrichment analysis
was carried out using Enrichr (https://maayanlab.cloud/Enrichr/), which is a web-based tool for functional
enrichment analysis. GO terms with a corrected p-value ≤ 0.05 were considered signicantly enriched. For
pathway enrichment analysis, the KEGG database was used in the Enrichr web server and pathways with a
p-value ≤ 0.05 were considered signicantly enriched.
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PPI, hub, MicroRNA, TF, and drug-gene interactions network analysis with validation on
independent dataset
Protein-protein interactions networks for the upregulated and downregulated genes in Alzheimer’s disease were
constructed and visualized using STRING and Cytoscape respectively28,29. MicroRNAs and their interacting
genes were identied using the miRTarBase database30. Similarly, the TRRUST database was used for identifying
the transcription factors and their target genes31. Subsequently, Cytoscape was used for visualizing the interactions
as networks. e hub genes within the network of upregulated and downregulated genes were detected using the
CytoNCA plug-in in Cytoscape32. e top 5 genes with the highest degree of connectivity were selected as hub
genes. e hub genes were tested for their expression levels on an independent dataset PRJNA683625 in order to
validate the ndings. In order to explore drug-gene interactions, the Drug-Gene Interaction Database (DGIdb)
was used with hub proteins as input33.
Druggability of DEG encoded proteins
e protein sequences of DEGs were retrieved from UniProtKB (https://www.uniprot.org/help/uniprotkb)
and queried in the DrugBank database (https://go.drugbank.com/) as drug targets. Proteins were considered
potential druggable targets if their sequences had a high degree of similarity (E value- <10–100, bit score > 100)
to those in the DrugBank database. ose that had no hits were regarded as novel targets34.
Molecular docking
Chemical structure of Levothyroxine was obtained from DrugBank. It was docked against the target
downregulated transthyretin protein encoded by TTR gene using Webina 1.0.3 web server (https://durrantlab.
Fig. 1. e criteria used for the selection of datasets.
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pitt.edu/webina/). Following successful molecular docking, the protein-ligand interactions were visualized using
Pymol (https://pymol.org/2/) and Poseview (https://proteins.plus/).
Molecular dynamic simulation
100 ns Molecular dynamics (MD) simulation was carried out using the GROningen MAchine for Chemical
Simulations aka GROMACS (version 2020.6) for the apo TTR and Levothyroxine-TTR complex35. e proteins
were embedded in the TIP3 water model36,37. e whole system was energetically minimized using CHARMM36m
Fig. 2. Overall workow of the study.
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force-eld38. e systems were neutralized using K+ and Cl- ions. Following energy minimization, isothermal-
isochoric (NVT) equilibration, and Isobaric (NPT) equilibration of the system were executed. Aerward, 100 ns
production MD simulation was run. Using MD simulation data, root mean square deviation (RMSD), root mean
square uctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA), and Hydrogen
bond analysis were conducted. e ggplot2 package (https://ggplot2.tidyverse.org/) in RStudio was utilized for
generating the graphs for each of these analyses. All MD simulations were performed in the high-performance
simulation stations running on Ubuntu 20.04.4 LTS operating system located at the Bioinformatics Division,
National Institute of Biotechnology, Bangladesh.
Results
Quantication of the high quality raw reads
FastQC v0.11.5 was utilized to check the quality of the raw sequencing data obtained from NCBI. All of the
raw sequences were evaluated and were found to be of high quality. Following the alignment of the reads to the
human reference genome, a total of 62,702 genes were identied which were forwarded to DEG analysis in the
quantication step.
Dierential gene expression analysis revealed signicantly upregulated and downregulated
genes
A total of 10,730 dierentially expressed genes (DEGs) were identied in AD patient samples, with 7814 genes
being upregulated and 2916 genes being downregulated. ese DEGs were visualized through volcano plots
using Rstudio. In Fig.3, the volcano plot displayed the DEGs, with the upregulated DEGs located on the right
and the downregulated DEGs on the le. e signicant DEGs (p value < 0.05) are indicated by the yellow color
while the rest was shown in red.
Signicant functional and pathway enrichment analysis of DEGs
Table1 demonstrated that out of 10,730 DEGs, a total 12 genes passed the cuto value (|Log2FC| > 1.45 ) for
signicance. Among these 12 genes, 9 DEGs were upregulated and 3 DEGs were downregulated. is gure also
depicted that in Alzheimer’s disease, PCDH11Y was the most upregulated (log2foldchange value = 1.889662998)
and TTR was the most downregulated (log2foldchange value = – 2.361971992) DEGs. e GO functional and
KEGG pathway enrichment analysis of these 12 DEGs showed that 11 GO-Biological Process (GO-BP) terms,
9 GO-Cellular Component (GO-CC) terms, 6 GO-Molecular Function (GO-MF) terms, 1 KEGG pathway and
5 Reactome pathways were associated with the 2 downregulated genes, respectively (Supplementary File 2). e
downregulated gene-associated KEGG pathway was thyroid hormone synthesis and Reactome pathways were
Amyloid ber formation, metal sequestration by antimicrobial proteins, neutrophil degranulation and innate
immune systems (Fig.4a). Moreover, 4 GO-Biological Process (GO-BP) terms, 2 GO-Cellular Component (GO-
CC) terms, 2 GO-Molecular Function (GO-MF) terms, 1 KEGG pathway were associated with the 9 upregulated
Fig. 3. Volcano plot of dierentially expressed genes. e upregulated and downregulated genes were depicted
on the right and le respectively. e genes were indicated using yellow dots.
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genes. Among them, one upregulated gene ISG15 was found to be involved in RIG-I-like receptor signaling
pathway (Fig.4b).
Network analysis revealed crucial interactions between hubs, microRNAs, TFs, drugs and
genes
Protein-protein interactions networks for the upregulated and downregulated genes in Alzheimer’s disease have
been depicted in Fig.5. e network of upregulated genes contained 4 connected components while the network
of downregulated genes contained only one connected component. For the network of upregulated genes, the
number of nodes and edges were 67 and 31 respectively whereas for the network of downregulated genes, the
number of nodes and edges were 24 and 15 respectively. MicroRNA-gene interactions analysis based on the
interactions information provided by miRTarBase database revealed that the microRNAs interact more with the
downregulated genes compared to the upregulated ones. Figure6 shows the microRNAs and their interacting
genes within the network of upregulated and downregulated genes. e complete list of microRNAs in the
upregulated and downregulated network have been provided in Supplementary File 3 and Supplementary File
4 respectively. Among the upregulated genes, DTHD1, PCDH11Y, ST8SIA2, and ISG15 were found to be the
targets of microRNAs. In the case of downregulated genes, CACNA2D1, CXCR1, CXCR4, LCN10, LTF, LYZ,
MMP8, MMP10, RGS1, RTKN2, SETD5, S100A4, TTR, VNN1, VNN2, and XIST were the targets. Transcription
factors and their target genes within the network of upregulated and downregulated genes as identied by the
TRRUST database have been presented in Fig.7. Supplementary File 5 and Supplementary File 6 contains the
list of transcription factors and their target genes within the network of upregulated and downregulated genes in
Alzheimer’s disease respectively. Transcription factors targeted upregulated genes such as GZMB, IFNG, IFNL1,
IDO1, and SELE. Several downregulated genes namely CXCR1, CXCR4, GDF3, IL1R2, LTF, LYZ, MMP10, and
S100A4 were also the targets of transcription factors. Hub gene analysis revealed the genes CXCL11, GZMB,
IFNG, IFNL1, and ISG15 as hub genes in the upregulated network. On the other hand, in the downregulated
network, the genes CXCR4, IL1R2, LTF, MMP8, and TTR were identied as hub genes (Fig.8). Drug-gene
interactions analysis presented a number of drugs that can interact with hub proteins in the networks of
upregulated and downregulated genes (Fig.9).
Levothyroxine was identied as a potential drug againstTTRgene product Transthyretin
e DrugBank webserver was used to nd potential drugs that might target the 4 downregulated genes. It revealed
that only one gene (TTR) had a corresponding FDA-approved drug called Levothyroxine. e remaining genes
did not match to any drugs in the DrugBank database, suggesting that they can be considered as potential novel
therapeutic targets.
Molecular docking analysis revealed molecular interactions between the drug and target
protein
Utilizing Webina 1.0.3 web server, a molecular docking analysis had been carried out between the FDA-
approved drug Levothyroxine and the predicted structures of the TTR encoded protein called Transthyretin.
e molecular interactions between the ligand Levothyroxine and Transthyretin indicated a signicant binding
energy value of -5.1kcal/mol. According to docking analysis, TTR gene interacted with Levothyroxine through
Arg103A, Asp99A, r119A, Ala120A, Ser100A. (Fig.10)
MD simulation analysis conrmed the stability of drug-receptor complex
Root Mean Square Deviation (RMSD) calculation was carried out in order to assess stability of the systems.
Change in RMSD value corresponds to conformational changes of the protein as a result of ligand binding. e
purple line represents the RMSD prole of apo receptor whereas the green line depicts drug-receptor complex
(Fig.11). Aer 50ns, the RMSD value of the drug-receptor complex did not increase beyond ~ 2.5nm whereas
the apo receptor RMSD value gradually increased up to ~ 4.0 nm. Root Mean Square Fluctuation (RMSF)
Gene ID Gene Name log2FoldChange p-value padj
ENSG00000187608 ISG15 1.76E + 00 2.66E–09 1.51E–06
ENSG00000197915 HRNR 1.497234321 9.75E–06 0.0003905954358
ENSG00000240409 MTATP8P1 1.46E + 00 3.01E–03 0.0188562683
ENSG00000198744 MTCO3P12 1.69E + 00 2.35E–07 3.16E–05
ENSG00000012223 LTF – 2.014635682 3.47E–11 6.81E–08
ENSG00000197057 DTHD1 1.82E + 00 7.47E–11 1.15E–07
ENSG00000077279 DCX 1.46E + 00 6.49E–10 5.82E–07
ENSG00000229807 XIST – 1.645681137 0.002128052759 0.01491435091
ENSG00000140557 ST8SIA2 1.593330969 2.06E–06 0.0001395682111
ENSG00000118271 TTR – 2.361971992 7.45E–08 1.57E–05
ENSG00000053438 NNAT 1.523563947 7.98E–13 4.30E–09
ENSG00000099715 PCDH11Y 1.889662998 8.60E–07 7.54E–05
Tab le 1. Signicant upregulated and downregulated genes.
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analysis was utilized to determine the regional exibility of the protein. e higher the RMSF, the higher is the
exibility of a given amino acid position. Figure12 demonstrates the RMSF prole of apo receptor and drug-
receptor complex. e major RMSF peaks were observed at around the 20th residue and the 85th residue. In
the peak near the 85th residue, the apo receptor showed higher mobility. e radius of gyration is a measure
to determine its degree of compactness. A relatively steady value of radius of gyration means stable folding
of a protein. Fluctuation of radius of gyration implies the unfolding of the protein. According to Fig.13, the
Levothyroxine-receptor complex went under less folding according to the Rg (nm) values. Solvent Accessible
Surface Area (SASA) is used in MD simulations to predict the hydrophobic core stability of proteins. e higher
the SASA value, the higher the chance of destabilization of the protein due to solvent accessibility. e SASA
value of apo receptor and drug-receptor was ~ 65 nm2 initially. However, aer 90 ns, the values of both proteins
overlapped (Fig.14).
Discussion
AD is a progressive and intricate condition that aects multiple brain functions. Besides the causative genes,
various risk factors can also contribute to the progression of the disease. erefore, using transcriptomics
Fig. 4. a Signicantly enriched pathway of the downregulated genes by using KEGG and Reactome database; b
Signicantly enriched pathway of the upregulated genes by using KEGG database.
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analysis is essential in comprehending the underlying mechanisms of the disease and identifying potential
targets for treatment.
In this study, 221 RNA-Seq samples were collected from the GEO database and these samples were found to
have high-quality scores. is score is an indication of the accuracy of the base call, and a higher score is generally
preferable. However, it is common for the quality of reads to decrease towards the 3’ end, and if the quality of
certain bases falls below a certain threshold, they need to be removed. For this reason, the trimming process
is necessary to eliminate poor-quality bases, trim adaptor sequences, and ensure high-quality reads. Here, the
trimming step was not required since the sample reads were already of high quality. Dierentially expressed genes
(DEGs) play a crucial role in identifying the biological pathways involved in various diseases including NDs. e
main objective of DEG analysis is to identify genes that are either upregulated or downregulated in a disease
state compared to healthy controls. Because the upregulation or downregulation of specic genes can cause
disruptions in metabolic, immune, and other pathways, potentially leading to disease development39. erefore,
identifying DEGs can help us understand the mechanisms underlying disease and develop targeted therapies
to treat them. Here, ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, PCDH11Y were
found to be most signicantly upregulated while the most signicantly downregulated genes included LTF,
XIST, and TTR. GO functional analysis revealed that the Alzheimer’s-causing genes were signicantly enriched
Fig. 6. MicroRNA interacting genes within the network of (a) upregulated and (b) downregulated genes in
Alzheimer’s disease.
Fig. 5. Protein-protein interactions network for the (a) upregulated and (b) downregulated genes in
Alzheimer’s disease.
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with positive regulation of osteoblast proliferation and development, bone morphogenesis, purine-containing
compound metabolic process, regulation of Tumor Necrosis Factor superfamily cytokine production, positive
regulation of Toll-Like Receptor 4 signaling pathway, negative regulation of ATP-dependent activity which
causes bone loss, inammation, damage to nerve cells and surrounding brain tissue which were previously
reported by other studies as well40–45. One CC term phagocytic vesicle was signicantly associated with AD.
is was observed to hinder phagocytic process and can cause reduced clearance of accumulated dystrophic
neurites46. In accordance with other studies, we have also found MF terms such as Cysteine-Type endopeptidase
inhibitor activity, Iron ion binding, protein Serine/reonine kinase activator activity, protein kinase activator
activity to be enriched in downregulated genes47–49. Finally, one upregulated gene named ISG15 was predicted to
be involved in RIG-I-like receptor signaling pathway which may lead to the inammatory response through the
activation of type I IFN production in AD50. Additionally, two downregulated genes such as TTR and LTF in AD
were predicted to be involved in thyroid hormone synthesis, amyloid ber formation, diseases associated with
visual transduction, neutrophil degranulation, and canonical retinoid cycle in rods (Twilight Vision) pathways.
e downregulation of these two genes may lead to the cognitive impairment, amyloid-beta accumulation and
retinal dysfunction and visual impairments due to the disruption in thyroid hormone signaling51–53.
e hub genes in the upregulated and downregulated network reveal important insights into AD etiology and
progression mechanisms particularly emphasizing the roles of neuroinammation, immune dysregulation, and
impaired protein homeostasis. In the upregulated network, CXCL11, GZMB, IFNG, IFNL1, and ISG15 emerge
as central players in the inammatory cascade associated with AD. CXCL11, a chemokine involved in T-cell
Fig. 8. Hub genes within the network of (a) upregulated and (b) downregulated genes in Alzheimer’s disease.
Fig. 7. Transcription factors and their target genes within the network of (a) upregulated and (b)
downregulated genes in Alzheimer’s disease.
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recruitment, is a chemokine that has been reported to play a role in the chronic neuroinammation such as those
observed in AD patients54. Higher neuronal apoptosis and extracellular matrix degradation, hinging on higher
synaptic loss and neuronal death, might be mediated through the overexpression of GZMB55. Upregulation of
IFNG reduces the clearance of amyloid-beta plaques, which in turn contributes directly to the etiology of AD56.
ere has been limited research on the signicance of IFNL1 and ISG15 in AD, but its overexpression suggests
that interferon-mediated immune responses are persistently activated, similar to the chronic immunological
activation observed in AD brains57,58. Decreased expression of hub genes- CXCR4, IL1R2, LTF, MMP8, and
TTR indicates disturbances in protein homeostasis and compromised neuroprotective mechanisms. Neuronal
survival and amyloid-beta clearance is hampered as a result of CXCR4 downregulation, which in turn aids
the progression of AD59. Decreased expression of IL1R2 and LTF promotes neuroinammatory reactions and
oxidative stress which are important elements in AD progression60,61. It has been suggested that a buildup of
pathogenic protein aggregates might occur as a result of reduced levels of MMP862. Downregulation of TTR is
known to impede the clearance of amyloid-beta aggregation resulting in plaque development in AD63.
TTR gene product Transthyretin has been reported to inhibit Amyloid-β accumulation and thus prevent
the spread of AD64,65. Hyperactivation of neutrophils is a common feature associated with the progression of
AD. Our analysis uncovered a relationship between neutrophil degranulation and the TTR gene as well66,67.
Fig. 9. Drug-gene interactions with the hub proteins in the network of (a) upregulated and (b) downregulated
genes in Alzheimer’s disease.
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Since we have also identied TTR as the most downregulated gene in AD patients, it can be predicted as a
potential biomarker for AD. Previous studies have also suggested that this gene can be targeted for treating AD68.
However, the novelty of our study lies in the fact that following whole transcriptome analysis, we looked for the
druggability of the targets and discovered that the FDA-approved drug, Levothyroxine, might be an eective
repurposable drug against the Transthyretin protein encoded by the TTR gene. In this study, Transthyretin
protein and Levothyroxine drug showed eective interaction with a high binding energy (– 5.1kcal/mol) which
indicated that Levothyroxine can potentially activate the proteins activity. e stability of a Levothyroxine-
receptor complex was investigated in this study using molecular dynamics simulations. In order to assess the
conformational changes in the protein structure upon drug binding, Root Mean Square Deviation (RMSD)
analysis was performed. e drug’s steady binding to the receptor was shown by the gradual increase in the apo
receptor’s RMSD prole around 4.0nm and the drug-receptor complex’s RMSD value being stable at 2.5nm aer
50 ns. e regional exibility of the protein was ascertained using the Root Mean Square Fluctuation (RMSF)
technique. e ~ 20th and ~ 85th residues were where the RMSF peaks were found. When compared to the
drug-receptor complex, the apo receptor had more mobility at the peak near the 85th residue. According to
this nding, the drug binding may help to stabilize certain areas of the protein structure. e degree of protein
compactness was looked at using the radius of gyration (Rg) study. Less folding was evident in the Rg (nm)
values of the Levothyroxine-receptor complex compared to the apo receptor, suggesting that the compactness
of the protein may have been impacted by the drug’s interaction. Lastly, Solvent Accessible Surface Area (SASA)
analysis was used to forecast the stability of proteins’ hydrophobic cores. Initial SASA values for the apo receptor
and drug-receptor complex were both 65 nm2, but aer 90 ns, the values converged, showing that the drug’s
interaction did not disrupt the protein’s hydrophobic core. Overall, the study’s ndings point to the stability of
the Levothyroxine-receptor complex and the possibility that the drug’s binding may have had an impact on the
protein’s stability and structure, particularly in areas with high mobility. e development of more eective and
targeted medications that target this receptor may be aected by these discoveries.
Conclusion
In this study, 7 DEGs of AD were predicted and they could be targeted as potential biomarkers for the diagnosis
of AD. ese genes could be used to monitor disease progression and treatment response, leading to more
personalized treatment options. One repurposable drug candidate, Levothyroxine was identied whose
interaction with the target Transthyretin was conrmed through molecular docking and dynamics simulation
analysis. Overall, the identication of potential biomarkers and therapeutics for AD could have signicant
implications for the diagnosis, treatment, and management of this incapacitating condition. However, in vitro
and in vivo studies are necessary for further validation of our ndings.
Fig. 10. Molecular Docking between the TTR and Levothyroxine. TTR gene interacts with Levothyroxine
through Arg103A, Asp99A, r119A, Ala120A, Ser100A.
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Fig. 11. RMSD prole of apo receptor (Purple) and Levothyroxine-receptor complex (Green).
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Fig. 12. RMSF prole of apo receptor (Purple) and Levothyroxine-receptor complex (Green).
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Fig. 13. Rg prole of apo receptor (Purple) and Levothyroxine-receptor complex (Green).
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Data availability
The datasets analysed during the current study are available in the NCBI Gene
Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) repository
under the following accession IDs: PRJNA675355, PRJNA767074, PRJNA796229,
PRJNA232669, PRJNA377568, PRJNA413568.
Received: 18 September 2023; Accepted: 4 October 2024
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Author contributions
A.B.L, I.A., and A.B. conducted the analysis. A.B.L, I.A. wrote the original dra of the manuscript. M.U.H., A.I.,
Z.M.C., and K.C.D. reviewed the manuscript. M.S. and C.A.K. supervised the research project.
Declarations
Competing interests
e authors declare no competing interests.
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