Soluble CD40 ligand contributes to blood–brain barrier breakdown and central nervous system inflammation in multiple sclerosis and neuromyelitis optica spectrum disorder

Masuda, Hiroki; Mori, Masahiro; Uchida, Toshihiro; Uzawa, Akiyuki; Ohtani, Ryohei; Kuwabara, Satoshi

doi:10.1016/j.jneuroim.2017.01.024

Cited by 41 publications

(24 citation statements)

References 32 publications

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“…Multiple population studies have demonstrated that serum sCD40L concentrations were increased in MS patients with active disease compared to healthy controls (5.65 ± 2.87 vs. 0.14 ± 0.12 ng/mL, p < 0.001) or patients with inactive MS (5.65 ± 2.87 vs. 0.64 ± 0.30 ng/mL, p < 0.001) ( 31 , 32 ). A similar increase of sCD40L was detected in the CSF of MS patients compared to patients suffering from other inflammatory or neurological diseases (38.5 vs. 4.8 pg/mL, p < 0.002; SD not mentioned in the manuscript) ( 33 ). Although serum sCD40L concentrations, but not CSF sCD40L concentrations, positively correlated (Kendall tau- b = 0.29, p = 0.044) with the CSF/serum albumin ratio (“Qalb”), an indicator of blood-brain barrier permeability ( 33 ), it is currently unknown whether sCD40L directly contributes to BBB breakdown.…”

Section: Expression Of Cd40l During Msmentioning

confidence: 60%

The CD40–CD40L Dyad in Experimental Autoimmune Encephalomyelitis and Multiple Sclerosis

et al. 2017

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The CD40–CD40L dyad is an immune checkpoint regulator that promotes both innate and adaptive immune responses and has therefore an essential role in the development of inflammatory diseases, including multiple sclerosis (MS). In MS, CD40 and CD40L are expressed on immune cells present in blood and lymphoid organs, affected resident central nervous system (CNS) cells, and inflammatory cells that have infiltrated the CNS. CD40–CD40L interactions fuel the inflammatory response underlying MS, and both genetic deficiency and antibody-mediated inhibition of the CD40–CD40L dyad reduce disease severity in experimental autoimmune encephalomyelitis (EAE). Both proteins are therefore attractive therapeutic candidates to modulate aberrant inflammatory responses in MS. Here, we discuss the genetic, experimental and clinical studies on the role of CD40 and CD40L interactions in EAE and MS and we explore novel approaches to therapeutically target this dyad to combat neuroinflammatory diseases.

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Section: Expression Of Cd40l During Msmentioning

confidence: 60%

The CD40–CD40L Dyad in Experimental Autoimmune Encephalomyelitis and Multiple Sclerosis

et al. 2017

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“…In order to further justify the relevance of the added proteins as potential biomarkers, we proceeded to study three secreted proteins that our model predicted to be differentially expressed in the MS dataset (Annexin A1, sCD40L and sCD27). Notably, these proteins have been associated with MS previously [22,23,25]. We analysed if cerebrospinal fluid (CSF) levels of these proteins related to clinical outcome and immunomodulatory treatment in two independent cohorts, namely newly diagnosed MS patients (clinically isolated syndrome (CIS) and relapsing/remitting MS, n=41) vs healthy controls (HC, n=23), and response to Natalizumab treatment in relapsing remitting MS patients (see supplementary notes, n=16).…”

Section: Applying the Model To Clinical Datasets Revealed Potential Bmentioning

confidence: 99%

A validated strategy to infer protein biomarkers from RNA-Seq by combining multiple mRNA splice variants and time-delay

Magnusson

Rundquist

Kim

et al. 2019

Preprint

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BackgroundProfiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. MethodsWe characterised time-series of primary human naïve CD4 + T cells during early T-helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We then performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time-delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-Seq datasets to evaluate biomarker discovery in five T-cell associated diseases, validating the findings for multiple sclerosis (MS) and asthma. ResultsThe new models demonstrated median correlations of mRNA-to-protein abundance of 0.79-0.94, significantly out-performing models not including the usage of multiple splice variants and time-delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was clinically validated in MS using two independent cohorts, for treatment response and prognosis. ConclusionOur splice variant and time-delay models substantially improved the prediction of protein abundance from mRNA data in three immune cell-types. The models provided valuable biomarker candidates, which were validated in clinical studies of MS and asthma. We propose that our strategy is generally applicable for biomarker discovery.MG initiated and supervised the study. RM and OR performed bioinformatics analyses, and RM performed the modelling. These analyses were led by MG, CA, JT, and DGC. OR performed experimental work on T-cell differentiation, which were supervised by CEN, MCJ, JE and MB. MJK and CHN performed the proteomics analysis, which was supervised by MSK. FP and JM recruited patients and collected clinical material, and SH performed and analysed the biomarker validation assays, which were led by IK, MCJ, and JE. All authors contributed to and approved the final draft for publication. mRNA expression levels. Mol Biosyst 2009, 5: 1512-26. 7. Vogel C, Marcotte EM: Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 2012, 13: 227-32. 8. Liu Y, Beyer A, Aebersold R: On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell 2016, 165: 535-50. 9. Zhao J, Qin B, Nikolay R, Spahn CMT, Zhang G: Translatomics: The Global View of Translation. Int J Mol Sci 2019, 20. 10. Wethmar K, Smink JJ, Leutz A: Upstream open reading frames: molecular switches in (patho)physiology. Bioessays 2010, 32: 885-93. 11. Floor SN, Doudna JA: Tunable protein synthesi...

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“…A minimum of36 independent studies have quantified the concentrations of 86 unique CSF analytes from NMOSD patients [ 134 , 137 , 142 , 164 , 170 , 172 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 ]. Several of these studies corroborate the metabolomic and proteomic observations discussed above.…”

Section: Proteomics In Nmosdmentioning

confidence: 99%

“…Regarding serum, at least 28 independent targeted immunoassay-based studies analyzed serum from NMOSD patients [ 142 , 164 , 169 , 170 , 171 , 179 , 183 , 185 , 187 , 190 , 191 , 192 , 194 , 200 , 203 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 ]. These results of these studies are summarized in Supplementary Table S3 .…”

Section: Proteomics In Nmosdmentioning

confidence: 99%

Metabolomic Profiling in Neuromyelitis Optica Spectrum Disorder Biomarker Discovery

Thoman

McKarns

2020

Metabolites

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There is no specific test for diagnosing neuromyelitis optica spectrum disorder (NMOSD), a disabling autoimmune disease of the central nervous system. Instead, diagnosis relies on ruling out other related disorders with overlapping clinical symptoms. An urgency for NMOSD biomarker discovery is underscored by adverse responses to treatment following misdiagnosis and poor prognosis following the delayed onset of treatment. Pathogenic autoantibiotics that target the water channel aquaporin-4 (AQP4) and myelin oligodendrocyte glycoprotein (MOG) contribute to NMOSD pathology. The importance of early diagnosis between AQP4-Ab+ NMOSD, MOG-Ab+ NMOSD, AQP4-Ab− MOG-Ab− NMOSD, and related disorders cannot be overemphasized. Here, we provide a comprehensive data collection and analysis of the currently known metabolomic perturbations and related proteomic outcomes of NMOSD. We highlight short chain fatty acids, lipoproteins, amino acids, and lactate as candidate diagnostic biomarkers. Although the application of metabolomic profiling to individual NMOSD patient care shows promise, more research is needed.

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Soluble CD40 ligand contributes to blood–brain barrier breakdown and central nervous system inflammation in multiple sclerosis and neuromyelitis optica spectrum disorder

Cited by 41 publications

References 32 publications

The CD40–CD40L Dyad in Experimental Autoimmune Encephalomyelitis and Multiple Sclerosis

The CD40–CD40L Dyad in Experimental Autoimmune Encephalomyelitis and Multiple Sclerosis

A validated strategy to infer protein biomarkers from RNA-Seq by combining multiple mRNA splice variants and time-delay

Metabolomic Profiling in Neuromyelitis Optica Spectrum Disorder Biomarker Discovery

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