Neurofilament levels, disease activity and brain volume during follow-up in multiple sclerosis

Håkansson, Irene; Tisell, Anders; Cassel, Petra; Blennow, Kaj; Zetterberg, Henrik; Lundberg, Peter; Dahle, Charlotte; Vrethem, Magnus; Ernerudh, Jan

doi:10.1186/s12974-018-1249-7

Cited by 107 publications

(147 citation statements)

References 38 publications

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“…In this present study, we have shown that sNfL levels are higher in FTLD spectrum disorder patients compared to those with PPD, which is in line with a previous report on CSF samples [12] and a recent study with serum samples [6]. As previous studies have already shown a strong correlation between serum and CSF NfL levels in various neurodegenerative disorders [13][14][15], our study emphasizes the utility of sNfL levels as a potential, minimally invasive differential diagnostic marker between FTLD and PPD. Our present study revealed that a cutoff level of approximately 20 pg/mL of sNfL results in a rather high specificity (85%) in discriminating FTLD and PPD.…”

Section: Discussionsupporting

confidence: 93%

Serum neurofilament light chain is a discriminative biomarker between frontotemporal lobar degeneration and primary psychiatric disorders

et al. 2019

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Due to the significant clinical overlap between frontotemporal lobar degeneration (FTLD) spectrum disorders and lateonset primary psychiatric disorders (PPD), diagnostic biomarkers reflecting the different underlying pathophysiologies are urgently needed. Thus far, elevated cerebrospinal fluid (CSF) levels of neurofilament light chain (NfL) have been reported in various neurological conditions. Furthermore, recent advancements in ultrasensitive analytical methods (e.g., single molecule array, Simoa) have enabled sensitive and less invasive NfL detection also from blood samples. In this study, we evaluated the potential of serum NfL (sNfL) as a diagnostic tool between FTLD and PPD. We analyzed sNfL levels with Simoa from 125 participants including patients from FTLD (n = 91) and PPD (n = 34) spectra. Our results show that sNfL levels are higher in the FTLD group compared to the PPD group as well as in separate clinical subtypes of FTLD compared to different psychiatric manifestations (i.e., mood or psychotic disorders). At single-subject level, discrimination between FTLD and PPD was possible with 80% sensitivity and 85% specificity (AUC = 0.850, 95% CI 0.776-0.923), and between behavioral variant frontotemporal dementia (bvFTD) and PPD with 79% sensitivity and 85% specificity (AUC = 0.830, 95% CI 0.732-0.908). These findings highlight the potential of sNfL as a discriminating biomarker for FTLD over PPD in patients with wide-ranging behavioral, psychiatric and cognitive symptoms.

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Section: Discussionsupporting

confidence: 93%

Serum neurofilament light chain is a discriminative biomarker between frontotemporal lobar degeneration and primary psychiatric disorders

et al. 2019

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“…biomarkers [32]. Taken together, using the splice variants-to-protein model we were able to uniquely identify and validate biomarkers of MS in an independent patient cohort, while these genes could not be discovered using previous state-of-the-art test for differential gene expression.…”

Section: Applying the Model To Clinical Datasets Revealed Potential Bmentioning

confidence: 88%

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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“…Recently, the chitinase family of inflammatory proteins, particularly chitinase 3-like 1 (CHI3L1, YKL-40, or HC gp-39), chitinase 3-like 2 (CHI3L2 or YKL- 39), and pentraxin II (NPTX2 or Narp), a member of the pentraxin family, has been associated with AD pathogenesis [21,22]. Although their functions are not well understood, it is hypothesized that chitinases are involved in pro-inflammatory and proangiogenic tissue remodeling in cancer [23,24] as well as in several neurodegenerative diseases [25][26][27][28][29][30][31][32]. CHI3L1 found in the cerebral spinal fluid (CSF) obtained from preclinical and prodromal cases of AD [33][34][35] has been suggested as a biomarker for discerning cognitively normal from mild cognitive impairment (MCI) individuals [25,36,37].…”

Section: Introductionmentioning

confidence: 99%

Frontal cortex chitinase and pentraxin neuroinflammatory alterations during the progression of Alzheimer’s disease

Moreno‐Rodríguez

Perez

Nadeem

et al. 2020

J Neuroinflammation

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Background: Chitinase 3-like 1 (CHI3L1), chitinase 3-like 2 (CHI3L2), and neuronal pentraxin II (NPTX2) are inflammatory biomarkers of Alzheimer's disease (AD). Although studies have demonstrated that cerebrospinal fluid levels of these proteins are changed in AD, no studies have undertaken a detailed examination of alterations in protein levels, cellular expression, and interaction with amyloid in the brain during the progression of AD. Methods:The study evaluated levels of both CHI3L1 and CHI3L2, NPTX2, ionized calcium-binding adapter molecule 1 (Iba1), complement component 1q (C1q), glial fibrillary acidic protein (GFAP), and CD44, in the frontal cortex of people who died with an antemortem clinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI), mild/moderate AD (mAD), and severe AD (sAD) using immunoblot and immunohistochemical techniques.Results: CHI3L1-immunoreactive (-ir) astrocyte numbers were increased in the frontal cortex and white matter in sAD compared to NCI. On the other hand, increases in GFAP and Iba1-ir cell numbers were observed in MCI compared to NCI but only in white matter. Western blot analyses revealed significantly lower frontal cortex CHI3L2 levels, whereas CD44 levels were increased in sAD. No significant differences for CHI3L1, GFAP, C1q, and NPTX2 protein levels were detected between clinical groups. Strong significant correlations were found between frontal cortex CHI3L1 and Iba1-ir cell numbers in white matter and CHI3L1 and C1q protein levels in the early stages of the disease. C1q and Iba1, CD44 with CHI3L2, and GFAP protein levels were associated during disease progression. CHI3L1 and Iba1 cell numbers in white matter showed a significant associations with episodic memory and perceptual speed.Conclusions: White matter CHI3L1 inflammatory response is associated with cognitive impairment early in the onset of AD.

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Neurofilament levels, disease activity and brain volume during follow-up in multiple sclerosis

Cited by 107 publications

References 38 publications

Serum neurofilament light chain is a discriminative biomarker between frontotemporal lobar degeneration and primary psychiatric disorders

Serum neurofilament light chain is a discriminative biomarker between frontotemporal lobar degeneration and primary psychiatric disorders

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

Frontal cortex chitinase and pentraxin neuroinflammatory alterations during the progression of Alzheimer’s disease

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