Analysing the Genetic Mosaic of Relapsing Remitting vs Clinically isolated syndrome Multiple Sclerosis: A Bioinformatics Perspective on Subtype‐Specific Variations Article Swipe
Aman Bhonsale
,
Ishita Varma
,
Atharva Subhedar
,
Shreya Khankar
,
Snehal Chandak
,
Mahesh P. Suryawanshi
,
Siddhali Mast
,
Tapesh Lalotra
,
Parag M. Phadke
,
Sarthak Pakhmode
,
S. C. Tripathi
,
Vishwajit Deshmukh
,
Ashlesh Patil
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/alz.093046
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/alz.093046
Background Multiple Sclerosis (MS) is a chronic, etiologically complex disease of the central nervous system (CNS) characterized by inflammation, demyelination, and neuronal damage. MS has seven categories based on disease course. Seventy to eighty percent of individuals with MS initially develop a clinical pattern with periodic relapses and remissions, called relapsing‐remitting MS (RRMS). Clinically isolated syndrome (CIS) refers to a single clinical inflammatory attack on the CNS. Evidence suggests that CD8+ T cells play a substantial role in MS pathophysiology. In this bioinformatic study, we aim to identify specific genes and pathways up‐regulated or down‐regulated in CD8+ T cells of RRMS and CIS subtypes. Method The GSE19285 dataset on the Gene Expression Omnibus (GEO) consists of the whole transcriptome of sorted T cells (CD8+) from treatment‐naive MS patients. Age‐matched sampling maintained uniformity. Data analysis used GEO2R with the Benjamini & Hochberg method (False discovery rate, padj < 0.05), and log fold change threshold greater than 1.5 or less than 1.5. We explored genetic profiles in immune cells, comparing RR (n = 17, age 30.76±7.05 years), CIS (n = 17, age 30.76±6.90 years), and healthy controls (n = 17, age 30.71±7.09 years). Gene pathway and protein‐protein interaction analyses of differentially expressed genes used the STRING platform. Result The study revealed upregulation of TRBV4‐2 gene in CD8+ cells of RRMS patients compared to healthy controls, while several genes, including AATK, PPP1R14C and TRIM36, were downregulated in RRMS. Two genes, DACT1 and CD4, were identified as upregulated in CD8+ cells, while several genes, including MYOM2, AATK, GPR27, LOC105370088, TRR‐ACG1‐2, KIR2DL3, and SIGLEC10, were downregulated in CIS compared to healthy controls. Additionally, CD4, DACT1, and CYBRD1 genes were up‐regulated in CIS compared to RRMS. Conclusion In RRMS, TRIM36’s role in proteostasis and neurodegeneration is highlighted, with protein‐protein interaction analysis showing dysfunctional ubiquitination. Altered antigen processing and presentation in CIS compared to healthy individuals and RRMS suggest differences in pathophysiology in this subtype. Furthermore, recent study has suggested CYBRD1 as a potential MS biomarker.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/alz.093046
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406050512
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406050512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/alz.093046Digital Object Identifier
- Title
-
Analysing the Genetic Mosaic of Relapsing Remitting vs Clinically isolated syndrome Multiple Sclerosis: A Bioinformatics Perspective on Subtype‐Specific VariationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-01Full publication date if available
- Authors
-
Aman Bhonsale, Ishita Varma, Atharva Subhedar, Shreya Khankar, Snehal Chandak, Mahesh P. Suryawanshi, Siddhali Mast, Tapesh Lalotra, Parag M. Phadke, Sarthak Pakhmode, S. C. Tripathi, Vishwajit Deshmukh, Ashlesh PatilList of authors in order
- Landing page
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https://doi.org/10.1002/alz.093046Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1002/alz.093046Direct OA link when available
- Concepts
-
Multiple sclerosis, Transcriptome, CD8, Relapsing remitting, Immune system, Gene, Clinically isolated syndrome, False discovery rate, Disease, Medicine, Inflammation, Immunology, Gene expression, Bioinformatics, Biology, Oncology, Internal medicine, GeneticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method | 142 |
| abstract_inverted_index.recent | 320 |
| abstract_inverted_index.refers | 58 |
| abstract_inverted_index.single | 61 |
| abstract_inverted_index.sorted | 121 |
| abstract_inverted_index.study, | 84 |
| abstract_inverted_index.system | 15 |
| abstract_inverted_index.(RRMS). | 53 |
| abstract_inverted_index.Altered | 299 |
| abstract_inverted_index.Omnibus | 113 |
| abstract_inverted_index.Seventy | 32 |
| abstract_inverted_index.TRIM36, | 231 |
| abstract_inverted_index.antigen | 300 |
| abstract_inverted_index.central | 13 |
| abstract_inverted_index.complex | 9 |
| abstract_inverted_index.course. | 31 |
| abstract_inverted_index.damage. | 23 |
| abstract_inverted_index.dataset | 108 |
| abstract_inverted_index.develop | 41 |
| abstract_inverted_index.disease | 10, 30 |
| abstract_inverted_index.genetic | 163 |
| abstract_inverted_index.greater | 154 |
| abstract_inverted_index.healthy | 184, 222, 266, 308 |
| abstract_inverted_index.nervous | 14 |
| abstract_inverted_index.pathway | 193 |
| abstract_inverted_index.pattern | 44 |
| abstract_inverted_index.percent | 35 |
| abstract_inverted_index.several | 225, 249 |
| abstract_inverted_index.showing | 296 |
| abstract_inverted_index.suggest | 312 |
| abstract_inverted_index.years), | 175, 182 |
| abstract_inverted_index.years). | 191 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Evidence | 68 |
| abstract_inverted_index.GSE19285 | 107 |
| abstract_inverted_index.Hochberg | 141 |
| abstract_inverted_index.KIR2DL3, | 257 |
| abstract_inverted_index.Multiple | 2 |
| abstract_inverted_index.PPP1R14C | 229 |
| abstract_inverted_index.analyses | 197 |
| abstract_inverted_index.analysis | 134, 295 |
| abstract_inverted_index.chronic, | 7 |
| abstract_inverted_index.clinical | 43, 62 |
| abstract_inverted_index.compared | 220, 264, 278, 306 |
| abstract_inverted_index.consists | 115 |
| abstract_inverted_index.controls | 185 |
| abstract_inverted_index.explored | 162 |
| abstract_inverted_index.identify | 88 |
| abstract_inverted_index.isolated | 55 |
| abstract_inverted_index.neuronal | 22 |
| abstract_inverted_index.pathways | 92 |
| abstract_inverted_index.patients | 219 |
| abstract_inverted_index.periodic | 46 |
| abstract_inverted_index.profiles | 164 |
| abstract_inverted_index.relapses | 47 |
| abstract_inverted_index.revealed | 209 |
| abstract_inverted_index.sampling | 130 |
| abstract_inverted_index.specific | 89 |
| abstract_inverted_index.subtype. | 318 |
| abstract_inverted_index.suggests | 69 |
| abstract_inverted_index.syndrome | 56 |
| abstract_inverted_index.Benjamini | 139 |
| abstract_inverted_index.SIGLEC10, | 259 |
| abstract_inverted_index.Sclerosis | 3 |
| abstract_inverted_index.TRBV4‐2 | 212 |
| abstract_inverted_index.comparing | 168 |
| abstract_inverted_index.controls, | 223 |
| abstract_inverted_index.controls. | 267 |
| abstract_inverted_index.discovery | 144 |
| abstract_inverted_index.expressed | 200 |
| abstract_inverted_index.including | 227, 251 |
| abstract_inverted_index.initially | 40 |
| abstract_inverted_index.patients. | 128 |
| abstract_inverted_index.platform. | 205 |
| abstract_inverted_index.potential | 327 |
| abstract_inverted_index.subtypes. | 104 |
| abstract_inverted_index.suggested | 323 |
| abstract_inverted_index.threshold | 153 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Clinically | 54 |
| abstract_inverted_index.Conclusion | 281 |
| abstract_inverted_index.Expression | 112 |
| abstract_inverted_index.TRIM36’s | 284 |
| abstract_inverted_index.biomarker. | 329 |
| abstract_inverted_index.categories | 27 |
| abstract_inverted_index.identified | 242 |
| abstract_inverted_index.maintained | 131 |
| abstract_inverted_index.processing | 301 |
| abstract_inverted_index.30.71±7.09 | 190 |
| abstract_inverted_index.30.76±6.90 | 181 |
| abstract_inverted_index.30.76±7.05 | 174 |
| abstract_inverted_index.differences | 313 |
| abstract_inverted_index.individuals | 37, 309 |
| abstract_inverted_index.interaction | 196, 294 |
| abstract_inverted_index.remissions, | 49 |
| abstract_inverted_index.substantial | 76 |
| abstract_inverted_index.uniformity. | 132 |
| abstract_inverted_index.upregulated | 244 |
| abstract_inverted_index.Furthermore, | 319 |
| abstract_inverted_index.highlighted, | 291 |
| abstract_inverted_index.inflammatory | 63 |
| abstract_inverted_index.presentation | 303 |
| abstract_inverted_index.proteostasis | 287 |
| abstract_inverted_index.upregulation | 210 |
| abstract_inverted_index.Additionally, | 268 |
| abstract_inverted_index.Age‐matched | 129 |
| abstract_inverted_index.LOC105370088, | 255 |
| abstract_inverted_index.bioinformatic | 83 |
| abstract_inverted_index.characterized | 17 |
| abstract_inverted_index.downregulated | 233, 261 |
| abstract_inverted_index.dysfunctional | 297 |
| abstract_inverted_index.etiologically | 8 |
| abstract_inverted_index.inflammation, | 19 |
| abstract_inverted_index.transcriptome | 119 |
| abstract_inverted_index.demyelination, | 20 |
| abstract_inverted_index.differentially | 199 |
| abstract_inverted_index.up‐regulated | 93, 275 |
| abstract_inverted_index.TRR‐ACG1‐2, | 256 |
| abstract_inverted_index.pathophysiology | 315 |
| abstract_inverted_index.ubiquitination. | 298 |
| abstract_inverted_index.down‐regulated | 95 |
| abstract_inverted_index.pathophysiology. | 80 |
| abstract_inverted_index.neurodegeneration | 289 |
| abstract_inverted_index.protein‐protein | 195, 293 |
| abstract_inverted_index.treatment‐naive | 126 |
| abstract_inverted_index.relapsing‐remitting | 51 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.26682399 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |