1. Introduction
Multiple sclerosis (MS) is the most common chronic inflammatory, demyelinating, and neurodegenerative disease of the central nervous system, affecting young adults [ 1 , 2 ]. Significant risk factors in its pathogenesis include inflammation and genetic or environmental variables [ 3 ]. The four main types of multiple sclerosis are clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS). Depending on the extent and location of nerve fiber damage in the central nervous system, patients with MS may possess a wide range of signs and symptoms. Some individuals with severe MS may be incapable of walking independently or ambulating at all. There are also people who experience extended periods of remission without suffering any new symptoms.
Neuromyelitis optica spectrum disorder (NMOSD), formerly considered to be a subtype of multiple sclerosis (MS), is now recognized as a separate disorder that can be confused with MS [ 4 ]. In clinical practice, distinguishing between these two disorders is of the utmost importance. This is because NMOSD requires treatment long-term immunosuppressive medicine to prevent a devastating recurrence, whereas MS therapies such as interferon-β and nazumab may worsen NMOSD [ 5 , 6 ]. Currently, NMOSD and MS are mostly diagnosed using clinical parameters and imaging examinations. The new NMO criteria from 2015 and the revised McDonald criteria from 2017, the guidelines that are used to diagnose NMOSD and MS, highlight the requirements for MR imaging [ 7 , 8 ]. Researchers are actively researching the use of body fluid biomarkers for the diagnosis of these two disorders [ 9 , 10 ]. A subset of people with NMOSD display anti-myelin oligodendrocyte glycoprotein (MOG) antibodies in their blood. Seventy-five percent of patients display anti-aquaporin 4 (anti-AQP4) antibodies. Patients who test positive for anti-AQP4 NMOSD are more likely to display oligoclonal IgG protein in 20% of cases [ 3 ]. It seems that all examinations and clinical, blood, and CSF tests do not appear to permit the indefinite separation of NMOSD from MS.
With the backing of mass spectrometry and bioinformatics technologies, a high-resolution and high-accuracy proteomics technique has been established to identify new MS and NMOSD biomarkers [ 11 , 12 ] in CSF [ 13 , 14 , 15 , 16 , 17 ], sera [ 18 ], and urine [ 19 ]. These investigations demonstrate the considerable diagnostic potential of this approach in clinical diagnostics. Hence, we initially employed TMT labeling in conjunction with high-resolution LC-MS/MS analysis to identify the differential proteome p4esent in the CSF of MS and NMOSD patients for this investigation. The proposed biomarkers were further verified using ELISA in a separate cohort. This work helped us to uncover novel biomarkers of MS and NMOSD and provided a basis for further investigation into the functions of these proteins in the pathogenesis of MS and NMOSD.
2. Materials and Methods
2.1. Patients
From 2018 to 2021, a number of individuals registered at the Beijing Tiantan Hospital of Capital Medical University and 100 were enrolled in this study. The research consisted of two phases. Initially, potential biomarkers were found using discovery samples of 10 MS and 10 NMOSD patients. The identified biomarkers were subsequently validated using a validation cohort consisting of 20 cases of MS patients (7 relapsing remitting multiple sclerosis (RRMS) and 13 secondary progressive multiple sclerosis (SPMS)), 20 cases of NMOSD patients, 20 cases of non-inflammatory neurological controls (NINC), and 20 cases of healthy controls (HC).
The inclusion criteria for patients with NMOSD were as follows: (1) age ≥ 18 years; and (2) diagnosis according to the 2015 diagnostic criteria for NMOSD [ 7 ]. Inclusion criteria for MS patients were: (1) age ≥ 18 years; and (2) diagnosis according to the 2017 updated McDonald criteria [ 8 ]. Exclusion criteria were as follows: (1) age < 18 years; (2) major disorders of the heart, liver, kidney, and other critical organs or blood vessels; and (3) complications owing to a malignant tumor.
Our study was performed per the Declaration of Helsinki and authorized by the ethical committee of the Tiantan Hospital of Capital Medical University (Ethics Committee document number: KYSQ 2020-092-01). All patients provided a signed informed consent.
2.2. Quantitative CSF Proteomics Analysis Using TMT Technology
All individuals underwent a lumbar puncture under local anesthesia with lidocaine, and 3 mL of CSF was collected and stored at −80 °C in several 1.5 mL Eppendorf tubes before use. As recommended by the ProteoMiner protein enrichment kit, the protein concentration was determined with the BCA assay following the removal of abundant protein (Bio-Rad, Hercules, CA, USA).
The human CSF samples (100 μL/sample) were digested using the filter-aided sample preparation (FASP) method with minimal modifications [ 20 ]. Each sample was combined with 50 μL of lysis solution (4% SDS, 0.1 M DTT in 0.1 M Tris-HCl, pH 7.6), passed to a 10 K filter (Pall Corporation), and centrifuged for 10 min at 10,000× g and 20 °C. After adding 200 μL of urea (UA) buffer (8M urea, 0.1 M Tris-HCl, pH8.5), the sample was centrifuged at 14,000× g for further 20 min. The concentrate was then combined with 200 μL of 100 mM IAA in a UA buffer and incubated for a further 40 min at room temperature in the dark, after which the IAA was removed by centrifugation at 14,000× g for 20 min. After dilution with 200 μL of UA buffer and centrifugation twice, 200 μL of 0.5 M tetraethyl ammonium bromide (TEAB) buffer (pH 8.5) were added, followed by 20 min of centrifugation at 14,000× g . This procedure was performed twice. The samples were subsequently digested for 16 h at 37 °C, and peptides were extracted by centrifugation at 16,000× g . In order to improve peptide yield, the filter was washed twice with 500 μL of 0.5 M TEAB buffer (pH 8.5). Using a vacuum concentrator, the peptide solution was dried.
The CSF peptides were tagged using TMT 10-plexTM label reagent according to the manufacturer’s instructions (Thermo Fisher Scientific, Rockford, IL, USA). Briefly, the TMT labeling reagents were withdrawn from the freezer and brought up to room temperature prior to use (taking approximately 30 min). Then, 41 mL of acetonitrile (ACN) was added to each channel, vortexed to dissolve, centrifuged, and put aside. The reagent was applied to 100 mg of sample solution (i.e., mix 1:1). One hour was given for the solution to stand at room temperature. The reaction was terminated by incubating the solution at room temperature for 15 min after adding 8 mL of hydroxylamine. Each group’s samples from 10 channels were mixed individually and vortexed to ensure thorough mixing. Before freezing at −80 °C, the sample was cleansed of salt and other contaminants.
Using the UPLC 3000 system (Dionex, Sunnyvale, CA, USA) coupled with an XBridgeTM BEH300 C18 column, the peptides were fractionated (Waters, Milford, MA, USA). A was H2O adjusted to a pH of 10 with ammonium hydroxide, and B was acetonitrile adjusted to a pH of 10 with ammonium hydroxide. The following gradient was used to separate peptides: 8 to 18% phase B for 30 min, and 18 to 32% phase B for 22 min. A total of 48 fractions were collected, dried with a speedvac, combined to create 12 fractions, and resuspended with 0.1% formic acid.
The Easy nLC-1000 system, paired with a Q Exactive mass spectrometer, was used to isolate all tagged tryptic peptides (ThermoFisher Scientific) [ 21 ]. On a 15 cm column (i.d. 75 um) packed in-house with reverse-phase (RP) materials ReproSil-Pur C18-AQ and 3.0 um resin (Dr. Maisch GmbH, Ammerbuch-Entringen, Germany), peptides were separated. Xcalibur collected data in the data-dependent “top15” mode as follows: 15 most abundant precursor ions in each full scan (MS1 scan: 300–1500 m / z with resolution 120,000@ m / z 200, AGC target: 3E6, maximum IT: 50 ms) were selected using an isolation window of 1.0 Da. Additionally, the resolution for MS/MS spectra was set to 60,000 @ m / z 200, the target value was 1E5 (AGC control enabled, maximum IT: 50 ms), the fragmentation mode was one of higher-energy collision dissociation (HCD), there was a normalized energy of 30%, and dynamic exclusion at 20 s [ 21 ].
All MS/MS spectra were analyzed using MaxQuant (version 1.6.0.16) to identify peptides and proteins using the UniProt human FASTA protein database (dated 202104, containing 92607 protein entries) [ 21 ]. The TMT tags used to label specific amino acid residues, including lysine and peptide N termini (229.162932 Da), along with carbamidomethylation for cysteine residues (57.02146 Da), were treated as static modifications. Methionine residue oxidation was treated as a variable modification. Two missing cleavage sites were allowed. The limits for peptides and fragment ions were established at 10 ppm and 20 ppm, respectively, and strict false discovery rates for peptides and proteins were maintained (not exceeding 0.01). Using reporter ion quantitation, the abundance of each peptide was measured by extracting TMT signals using MaxQuant software. At the same time, precursor ion fraction (75%) limits were applied to minimize co-isolation interference. The relative quantities of each protein were then determined using aggregate peptide data, with normalization conducted under the premise of equal protein loading across all samples.
2.3. Enzyme-Linked Immunosorbent Assay (ELISA)
Using ELISA kits, a validation experiment was conducted (ImmunoClone, Huntington Station, NY, USA). The concentrations of insulin-like growth factor-binding protein 7 (IGFBP7) and lysosomal associated membrane protein type-2 (LAMP2) were determined using 120 CSF and plasma samples from 20 MS patients, 20 NMOSD patients, 20 non-inflammatory neurological controls (NINC), and 20 healthy controls (HC). The experiment was conducted in accordance with the manufacturer’s guidelines.
2.4. Statistical Analysis
Student’s t test and the Mann–Whitney U test were used to compare continuous variables. The test used depended on whether the data were regularly distributed. Using the Spearman rank correlation coefficient, the strength of the link between proteins in serum and CSF was assessed. The diagnostic efficiency was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC). SPSS software (version 19.0; SPSS Inc., Chicago, IL, USA) was used to analyze the data, and GraphPad Prism software (version 5.0; GraphPad Software Inc., San Diego, CA, USA) was used to make the figures. A p value of less than 0.05 was deemed to be statistically significant for all two-tailed tests.
3. Results
3.1. Comparing the Clinical Traits of Patients and Controls
Table 1 displays the clinical features of 80 participants included in the validation cohort. Age and gender did not differ substantially among MS, NMOSD, NINC, and the HC group ( p > 0.05). In addition, MRI T2 lesion counts were retrieved from the medical records. Disability was scored by a qualified neurologist using the expanded disability status scale (EDSS).
3.2. Proteomic Analysis of CSF from MS and NMOSD Patients
In the discovery cohort, 495 proteins were identified, excluding complement and immunoglobulin. Significantly differentially expressed proteins (DEPs) were selected based on fold change ≥ 1.5 or ≤0.7 and p < 0.01 ( Figure 1 ). Six proteins, including transferrin (TF), prosaposin (PSAP), insulin-like growth factor II (IGF2), insulin-like growth factor-binding protein 7 (IGFBP7), lysosome-associated membrane glycoprotein 2 (LAMP2), and EGF-containing fibulin-like extracellular matrix protein 1 (EFEMP1), were found to be differentially expressed between the MS and NMOSD groups. All of these proteins were elevated in MS patients’ CSF. The putatively harmful mechanisms of TF [ 22 , 23 ], PSAP [ 24 , 25 ], and IGF2 [ 26 , 27 ] were documented in several neurodegenerative illnesses. Few reports on EFEMP1 focused on solid tumors [ 28 ] and amyloidosis [ 29 , 30 ]. Prior investigations linked IGFBP7 and LAMP2 to demyelinating disease [ 31 ] or MS [ 32 ], while the underlying mechanism remained unknown. Hence, we selected IGFBP7 and LAMP2 as plausible candidate proteins for further experimental confirmation.
3.3. ELISA Validation of Candidate Proteins
The chosen proteins were confirmed using a commercial ELISA kit and CSF and serum samples from a separate cohort. In comparison to the NMOSD group, the MS group exhibited significantly higher levels of IGFBP7 in both serum and CSF ( Figure 2 a). This outcome is compatible with our proteomics research findings. In contrast to our proteomic findings, however, there was not a significant change in the amount of LAMP2 protein expression ( Figure 2 b). As a comparison to the NINC groups, the MS and NMOSD groups had considerably greater concentrations of serum and CSF IGFBP7 ( Figure 2 a). However, in neither the MS nor the NMOSD groups was there a discernible change in the concentration of LAMP2 in the serum or the CSF ( Figure 2 b). In addition, patients with MS and NMOSD had lower levels of IGFBP7 in their serum than in their CSF. There was not a discernible difference in LAMP2 levels between the serum and the CSF ( Figure 2 ).
3.4. Correlation between Serum and CSF Measurements of Each Protein
Figure 3 showed a substantial positive correlation between serum and CSF IGFBP7 in the MS group (r = 0.8, p < 0.0001), while there was no such correlation in the NMOSD group. For LAMP2, a correlation between serum and CSF was not found in either the MS group or the NMOSD group (MS: r = −0.3, p = 0.214; NMOSD: r = 0.4, p = 0.096, Figure 3 c,d).
3.5. Evaluating the Efficacy of IGFBP7 and LAMP2 in the Diagnosis of MS and NMOSD
We used the ROC curve analysis and the Youden index (sensitivity plus specificity minus one) to calculate the optimal diagnostic accuracy of IGFBP7 and LAMP2. For MS diagnosis, serum and CSF IGFBP7 showed excellent AUC above 0.8. The serum IGFBP7 had a diagnostic cutoff of 5.0 ng/mL with 100% sensitivity and 85% specificity, whereas CSF IGFBP7 had a diagnostic cutoff of 17.0 ng/mL with 100% sensitivity and 80% specificity. Both serum LAMP2 and CSF LAMP2 had AUCs below 0.7 and exceedingly low Youden indices. ( Figure 4 a and Table 2 ).
For NMOSD diagnosis, serum and CSF IGFBP7 also showed excellent AUC above 0.8. Serum IGFBP7 had a cutoff of 5.0 ng/mL with 100% sensitivity and 85% specificity, whereas CSF IGFBP7 had a cutoff of 16.7 ng/mL with 100% sensitivity and 80% specificity. Conversely, both serum and CSF LAMP2 exhibited extremely low AUC (less than 0.6) and sensitivity (less than 50%, Figure 4 b and Table 2 ).
3.6. Effectiveness of IGFBP7 and LAMP2 in Differentiating NMOSD from MS
In addition, the diagnostic performance of these two proteins was evaluated in terms of their ability to differentiate NMOSD from MS. The AUC values for IGFBP7 and LAMP2 were both lower than 0.6. Serum and CSF IGFBP7 showed high sensitivities of 100% but extremely low specificities of 0%. Serum LAMP2 and CSF LAMP2 displayed a poor Youden index of approximately 20% ( Table 3 and Figure 4 c).
3.7. Assessment of the Predictive Capability of IGFBP7 and LAMP2 for SPMS
We also looked into whether or not these two proteins have use as diagnostic markers for SPMS. As depicted in Table 3 and Figure 4 d, both serum and CSF IGFBP7 revealed outstanding SPMS prognostic values. CSF IGFBP7 had a cutoff of 21.5 ng/mL with a sensitivity of 76.9% and a specificity of 100%, whereas serum IGFBP7 had a cutoff of 6.0 ng/mL with a sensitivity of 92.3% and a specificity of 100%. The concern is that both serum and CSF IGFBP7 can be used to predict the course of MS, with serum IGFBP7 separating SPMS from RRMS with greater precision than CSF IGFBP7. In addition, under an optimal diagnostic performance, serum LAMP2 showed mild predictive power with a cutoff of 165.7 pg/mL, sensitivity of 69.2%, and specificity of 71.4%, while CSF LAMP2 revealed a low specificity of 14.3%.
4. Discussion
The discovery of biomarkers and their clinical implementation has proven to be particularly challenging in the cases of MS and NMOSD. This is likely due to the intricate pathology of these diseases, which includes distinct and frequently coexisting processes, such as inflammation, demyelination, and neurodegeneration [ 33 ]. MS often progresses from an early phase of RRMS to a subsequent phase of SPMS [ 34 ]. Since the transition from RRMS to SPMS is phenotypically delayed, the decision of clinicians to postpone the diagnosis of SPMS results in an evident, continuous increase in disability. The mean time for RRMS to transition to SPMS diagnostic ambiguity, according to one study, was 3.3 years [ 35 ]. Therefore, it is crucial to identify molecular biomarkers for RRMS diagnosis and to monitor the progression of the illness to confirm SPMS.
In this study, we firstly analyzed CSF from individuals with MS and NMOSD using proteomics, and identified two novel possible biomarkers (IGFBP7 and LAMP2) that might be utilized to diagnose NMOSD and MS and to differentiate NMOSD from MS. Subsequently, we examined the diagnostic performance of IGFBP7 and LAMP2 in CSF and serum using the ELISA method on a larger sample size. We demonstrated the excellent sensitivity and specificity of serum IGFBP7 and CSF IGFBP7 for the diagnosis of MS and NMOSD, as well as the outstanding quality of IGFBP7 and serum LAMP2 in forecasting the developing phenotypes of MS. To our knowledge, this is the first study to assess the diagnostic efficiency of these two proteins in both MS and NMOSD patients.
Consistent with the findings of our proteomics analysis, in the validation cohort we found that serum and CSF IGFBP7 levels in the MS group and NMOSD group were significantly higher than those in the controls, with the MS group exhibiting a greater increase than the NMOSD group. The AUC for CSF IGFBP7 in the MS group was 0.958, which was significantly higher than that seen for serum IGFBP7 (0.870), serum LAMP2 (0.558), and CSF LAMP2 (0.665). In addition, there was a highly significant positive correlation between serum IGFBP7 and CSF IGFBP7 in the MS group (r = 0.8, p < 0.0001). Hence, CSF IGFBP7 was necessary for MS diagnosis, but not for both serum IGFBP7 and CSF IGFBP7.
Excellent AUC values were also found for serum IGFBP7 (0.850) and CSF IGFBP7 (0.800) in the NMOSD group; however, the AUC values were only 0.580 for serum LAMP2 and 0.555 for CSF LAMP2. In contrast to the MS group, no significant association was detected between serum IGFBP7 and CSF IGFBP7 in the NMOSD group. Accordingly, the role of IGFBP7 in the etiology of MS and NMOSD is probably distinct and will require further study if it is to be elucidated. In general, our data demonstrated that IGFBP7 levels in serum and CSF are highly diagnostic for NMOSD.
Although the MS group had significantly greater levels of serum and CSF IGFBP7 than the NMOSD group in both the study cohort and validation cohort, IGFBP7 had an exceedingly low AUC and poor specificity (0%) when differentiating NMOSD from MS. IGFBP7 was deemed to be insufficient for discriminating between these two illnesses.
IGFBP7 is a newly identified member of the superfamily of insulin-like growth factor-binding proteins (IGFBPs), which are expressed in astrocytes, oligodendrocytes, and neurons [ 31 ]. IGFBP7 regulates the biological activity of IGFs, which is demonstrated in the development of the central nervous system and the differentiation of oligodendrocyte precursor cells [ 36 , 37 ]. Recent investigations have revealed that IGFBP7 negatively regulates oligodendrocyte differentiation via the Wnt/β-catenin signaling pathway, which is essential for myelin regeneration [ 38 ]. In the current investigation, two demyelinating diseases were found to have significantly elevated levels of serum and CSF IGFBP7. Earlier investigations also demonstrated a raised level in pathological CNS disorders, such as glioblastoma and stroke, as well as experimental autoimmune encephalomyelitis (EAE) and MS [ 21 , 31 , 39 , 40 ]. In addition, both sera, IGFBP7 and CSF IGFBP7, exhibited great sensitivity and specificity for the diagnosis of these two disorders. Therefore, IGFBP7 may have the potential to serve as a biomarker for diagnosing MS and NMOSD.
We further evaluated the ability of these two proteins to distinguish SPMS from RRMS. Serum IGFBP7 had a greater AUC (0.945) than CSF IGFBP7 (0.890), serum LAMP2 (0.720), and CSF LAMP2 (0.396), which implies the potential relevance of IGFBP7 in MS categorization; conversely, serum IGFBP7 was more appropriate for use MS phenotypes than CSF IGFBP7 in clinical practice. In general, this result highlights the potential role of IGFBP7 in MS stratification. The association between IGFBP7 expression levels and the degree of inflammatory demyelination [ 31 ] possibly explain this outcome. The astonishingly greater level of IGFBP7 in SPMS patients compared to that in RRMS patients may be a result of the disease’s progression bringing an enhanced inflammatory response burden.
In our investigation, neither serum nor CSF LAMP2 levels were considerably different across groups. The poor performance of LAMP2 in diagnosing MS (serum LAMP2: 0.558; CSF LAMP2: 0.665), diagnosing NMOSD (serum LAMP2: 0.580; CSF LAMP2: 0.555), and distinguishing NMOSD from MS (serum LAMP2: 0.533; CSF LAMP2: 0.368) was evidenced by its low AUC values. It is intriguing that serum LAMP2 had a moderate AUC (0.720) for differentiating SPMS from RRMS.
Lysosomal-associated membrane protein type-2 (LAMP2), a major component of lysosomal membrane proteins, plays a crucial role in autophagy [ 41 ]. Numerous studies have shown that autophagy directly influences the progression of MS and EAE [ 42 , 43 , 44 ]. Previous research has indicated that lysosomal membrane injury leads to the suppression of autophagy and neurodegeneration following brain trauma, as well as to increases in LAMP2 levels in the injured brain section [ 45 ]. SPMS patients in our study had greater serum LAMP2 levels than RRMS patients, which could be attributed to the more severe brain damage seen in SPMS. It was hypothesized that LAMP2 could be used as a predictor of MS progression. Nevertheless, LAMP2’s forecast ability was inferior to that of IGFBP7, and further research is required to clarify the function of LAMP2 in the progression of MS.
Limitations
Our study had some limitations, including that our study excluded patients with cancer and/or cardiac diseases, despite the fact that IGFBP7 expression was observed to be similarly e altered in these patients [ 46 , 47 ]. This factor should be considered in clinical applications. Additionally, additional research is required to determine if these two putative proteins are associated with the other two MS subtypes (primary progressive multiple sclerosis and clinical isolated syndrome). This study provides a preliminary examination of proteins with differential levels of expression in MS and NMOSD. For future elucidation of the underlying mechanisms of MS and NMOSD, it is necessary to collect additional samples to confirm these conclusions and gain a better understanding of the underlying processes of MS and NMOSD.
5. Conclusions
As far as we know, this is the first study to evaluate the clinical utility of IGFBP7 and LAMP2 for diagnosing NMOSD and MS. These findings indicated the superior performance of IGFBP7 in efficiently diagnosing MS and NMOSD. Nevertheless, for the diagnosis of MS, only CSF IGFBP7 was necessary. Serum IGFBP7, CSF IGFBP7, and serum LAMP2 had superior performance in forecasting the developing phenotypes of MS, however serum IGFBP7 is preferable. These results provided a rationale for conducting additional research on the roles of IGFBP7 and LAMP2 in the pathogenesis of MS and NMOSD.
Acknowledgments
We thank the patients and families who contribute to our research.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Author Contributions
Conceptualization, methodology, software, validation, formal analysis, resources, data curation, writing—original draft preparation, visualization, project administration, funding acquisition, T.X.; investigation, Y.S.; writing—review and editing, G.Z. (Guanghui Zheng); supervision, G.Z. (Guojun Zhang). All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Beijing Tiantan Hospital of Capital Medical University (protocol code KYSQ 2020-092-01, approved on 10 March 2020).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on reasonable request from the corresponding author. The datasets presented in this study can be found in online repositories. The names of the repositories and accession numbers can be found below: http://www.proteomexchange.org/ , IPX0002604000, PXD023027, accessed on 17 August 2021.
Conflicts of Interest
The authors declare no conflict of interest.
References
1. Filippi M. Bar-Or A. Piehl F. Preziosa P. Solari A. Vukusic S. Rocca M.A. Multiple sclerosis Nat. Rev. Dis. Prim. 2018 4 43 10.1038/s41572-018-0041-4 30410033
2. Monaco S. Nicholas R. Reynolds R. Magliozzi R. Intrathecal Inflammation in Progressive Multiple Sclerosis Int. J. Mol. Sci. 2020 21 8217 10.3390/ijms21218217 33153042 PMC7663229
3. Toczylowska B. Zieminska E. Podlecka-Pietowska A. Ruszczynska A. Chalimoniuk M. Serum metabolic profiles and metal levels of patients with multiple sclerosis and patients with neuromyelitis optica spectrum disorders—NMR spectroscopy and ICP-MS studies Mult. Scler. Relat. Disord. 2022 60 103672 10.1016/j.msard.2022.103672 35240533
4. Wingerchuk D.M. Banwell B. Bennett J.L. Cabre P. Carroll W. Chitnis T. de Seze J. Fujihara K. Greenberg B. Jacob A. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders Neurology 2015 85 177 189 10.1212/WNL.0000000000001729 26092914 PMC4515040
5. Kim S.H. Kim W. Li X.F. Jung I.J. Kim H.J. Does interferon beta treatment exacerbate neuromyelitis optica spectrum disorder? Mult. Scler. J. 2012 18 1480 1483 10.1177/1352458512439439 22354738
6. Papadopoulos M.C. Bennett J.L. Verkman A.S. Treatment of neuromyelitis optica: State-of-the-art and emerging therapies Nat. Rev. Neurol. 2014 10 493 506 10.1038/nrneurol.2014.141 25112508 PMC4229040
7. Moore R.R. Tanner M. The latest diagnostic criteria and treatment options for neuromyelitis optica JAAPA J. Am. Acad. Physician Assist. 2019 32 1 6 10.1097/01.JAA.0000558352.01234.c0 31241616
8. Petzold A. Applying the 2017 McDonald diagnostic criteria for multiple sclerosis Lancet Neurol. 2018 17 496 497 10.1016/S1474-4422(18)30159-5 29778355
9. Maciak K. Pietrasik S. Dziedzic A. Redlicka J. Saluk-Bijak J. Bijak M. Wlodarczyk T. Miller E. Th17-Related Cytokines as Potential Discriminatory Markers between Neuromyelitis Optica (Devic’s Disease) and Multiple Sclerosis—A Review Int. J. Mol. Sci. 2021 22 8946 10.3390/ijms22168946 34445668 PMC8396435
10. Teunissen C.E. Malekzadeh A. Leurs C. Bridel C. Killestein J. Body fluid biomarkers for multiple sclerosis—The long road to clinical application Nat. Rev. Neurol. 2015 11 585 596 10.1038/nrneurol.2015.173 26392381
11. Sandi D. Kokas Z. Biernacki T. Bencsik K. Klivenyi P. Vecsei L. Proteomics in Multiple Sclerosis: The Perspective of the Clinician Int. J. Mol. Sci. 2022 23 5162 10.3390/ijms23095162 35563559 PMC9100097
12. Thoman M.E. McKarns S.C. Metabolomic Profiling in Neuromyelitis Optica Spectrum Disorder Biomarker Discovery Metabolites 2020 10 374 10.3390/metabo10090374 32961928 PMC7570337
13. Kowarik M.C. Dzieciatkowska M. Wemlinger S. Ritchie A.M. Hemmer B. Owens G.P. Bennett J.L. The cerebrospinal fluid immunoglobulin transcriptome and proteome in neuromyelitis optica reveals central nervous system-specific B cell populations J. Neuroinflamm. 2015 12 19 10.1186/s12974-015-0240-9 25626447 PMC4323273
14. Bennett J.L. Owens G.P. Cerebrospinal fluid proteomics: A new window for understanding human demyelinating disorders? Ann. Neurol. 2012 71 587 588 10.1002/ana.23595 22522475
15. Komori M. Matsuyama Y. Nirasawa T. Thiele H. Becker M. Alexandrov T. Saida T. Tanaka M. Matsuo H. Tomimoto H. Proteomic pattern analysis discriminates among multiple sclerosis-related disorders Ann. Neurol. 2012 71 614 623 10.1002/ana.22633 22522477
16. Jiang S.F. Wu J. Yang Y. Liu J.R. Ding Y. Ding M.P. Proteomic analysis of the cerebrospinal fluid in multiple sclerosis and neuromyelitis optica patients Mol. Med. Rep. 2012 6 1081 1086 10.3892/mmr.2012.1025 22895575
17. Lee J. McKinney K.Q. Pavlopoulos A.J. Han M.H. Kim S.H. Kim H.J. Hwang S. Exosomal proteome analysis of cerebrospinal fluid detects biosignatures of neuromyelitis optica and multiple sclerosis Clin. Chim. Acta 2016 462 118 126 10.1016/j.cca.2016.09.001 27609124
18. Jiang S.F. Lu Q.Y. Hu S. Chen Y. Liu X.L. Yang Y. Ding M.P. Proteomics comparison of the sera from multiple sclerosis patients and neuromyelitis optica patients Genet. Mol. Res. 2014 13 9292 9299 10.4238/2014.February.14.3 24615106
19. Nielsen H.H. Beck H.C. Kristensen L.P. Burton M. Csepany T. Simo M. Dioszeghy P. Sejbaek T. Grebing M. Heegaard N.H. The Urine Proteome Profile Is Different in Neuromyelitis Optica Compared to Multiple Sclerosis: A Clinical Proteome Study PLoS ONE 2015 10 e0139659 10.1371/journal.pone.0139659 26460890 PMC4604198
20. Ludwig K.R. Schroll M.M. Hummon A.B. Comparison of In-Solution, FASP, and S-Trap Based Digestion Methods for Bottom-Up Proteomic Studies J. Proteome Res. 2018 17 2480 2490 10.1021/acs.jproteome.8b00235 29754492 PMC9319029
21. Shi Y. Ding Y. Li G. Wang L. Osman R.A. Sun J. Qian L. Zheng G. Zhang G. Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics Front. Immunol. 2021 12 700031 10.3389/fimmu.2021.700031 34489947 PMC8417809
22. Stachowska L. Koziarska D. Karakiewicz B. Kotwas A. Knyszynska A. Folwarski M. Dec K. Stachowska E. Hawrylkowicz V. Kulaszynska M. Hepcidin (rs10421768), Transferrin (rs3811647, rs1049296) and Transferrin Receptor 2 (rs7385804) Gene Polymorphism Might Be Associated with the Origin of Multiple Sclerosis Int. J. Environ. Res. Public Health 2022 19 6875 10.3390/ijerph19116875 35682458 PMC9180173
23. Li X. Chu Y. Ma R. Dou M. Li S. Song Y. Lv Y. Zhu L. Ferroptosis as a mechanism of oligodendrocyte loss and demyelination in experimental autoimmune encephalomyelitis J. Neuroimmunol. 2022 373 577995 10.1016/j.jneuroim.2022.577995 36327618
24. Akil O. Sun Y. Vijayakumar S. Zhang W. Ku T. Lee C.K. Jones S. Grabowski G.A. Lustig L.R. Spiral ganglion degeneration and hearing loss as a consequence of satellite cell death in saposin B-deficient mice J. Neurosci. 2015 35 3263 3275 10.1523/JNEUROSCI.3920-13.2015 25698761 PMC6605599
25. Meyer R.C. Giddens M.M. Coleman B.M. Hall R.A. The protective role of prosaposin and its receptors in the nervous system Brain Res. 2014 1585 1 12 10.1016/j.brainres.2014.08.022 25130661 PMC4529117
26. Bechara R. Amatya N. Majumder S. Zhou C. Li Y. Liu Q. McGeachy M.J. Gaffen S.L. The RNA-binding protein IMP2 drives a stromal-Th17 cell circuit in autoimmune neuroinflammation JCI Insight 2022 7 e152766 10.1172/jci.insight.152766 34914635 PMC8855811
27. Du L. Lin L. Li Q. Liu K. Huang Y. Wang X. Cao K. Chen X. Cao W. Li F. IGF-2 Preprograms Maturing Macrophages to Acquire Oxidative Phosphorylation-Dependent Anti-inflammatory Properties Cell Metab. 2019 29 1363 1375.e8 10.1016/j.cmet.2019.01.006 30745181
28. Zhou Y.H. Hu Y. Yu L. Ke C. Vo C. Hsu H. Li Z. Di Donato A.T. Chaturbedi A. Hwang J.W. Weaponizing human EGF-containing fibulin-like extracellular matrix protein 1 (EFEMP1) for 21(st) century cancer therapeutics Oncoscience 2016 3 208 219 10.18632/oncoscience.306 27713911 PMC5043071
29. Tasaki M. Ueda M. Hoshii Y. Mizukami M. Matsumoto S. Nakamura M. Yamashita T. Ueda A. Misumi Y. Masuda T. A novel age-related venous amyloidosis derived from EGF-containing fibulin-like extracellular matrix protein 1 J. Pathol. 2019 247 444 455 10.1002/path.5203 30565683
30. Tasaki M. Oishi T. Ueda M. EGF-containing fibulin-like extracellular matrix protein 1 (EFEMP1) amyloid deposits in the lower rectum from aged patient with bloody stools Pathol. Int. 2022 72 217 218 10.1111/pin.13199 35007371
31. Tan W. Pu Y. Shao Q. Fang X. Han D. Zhao M. Cao L. Insulin-like growth factor-binding protein 7 is up-regulated during EAE and inhibits the differentiation of oligodendrocyte precursor cells Biochem. Biophys. Res. Commun. 2015 460 639 644 10.1016/j.bbrc.2015.03.082 25819415
32. Lindberg R.L. De Groot C.J. Certa U. Ravid R. Hoffmann F. Kappos L. Leppert D. Multiple sclerosis as a generalized CNS disease—Comparative microarray analysis of normal appearing white matter and lesions in secondary progressive MS J. Neuroimmunol. 2004 152 154 167 10.1016/j.jneuroim.2004.03.011 15223248
33. Stefano N. Sormani M.P. Combining biomarkers to profile multiple sclerosis patients Nat. Rev. Neurol. 2020 16 463 464 10.1038/s41582-020-0378-7 32555516
34. Filippi M. Preziosa P. Langdon D. Lassmann H. Paul F. Rovira A. Schoonheim M.M. Solari A. Stankoff B. Rocca M.A. Identifying Progression in Multiple Sclerosis: New Perspectives Ann. Neurol. 2020 88 438 452 10.1002/ana.25808 32506714
35. Rojas J.I. Patrucco L. Alonso R. Garcea O. Deri N. Carnero Contentti E. Lopez P.A. Pettinicchi J.P. Caride A. Cristiano E. Diagnostic uncertainty during the transition to secondary progressive multiple sclerosis: Multicenter study in Argentina Mult. Scler. 2021 27 579 584 10.1177/1352458520924586 32484381
36. Carson M.J. Behringer R.R. Brinster R.L. McMorris F.A. Insulin-like growth factor I increases brain growth and central nervous system myelination in transgenic mice Neuron 1993 10 729 740 10.1016/0896-6273(93)90173-O 8386530
37. Zeger M. Popken G. Zhang J. Xuan S. Lu Q.R. Schwab M.H. Nave K.A. Rowitch D. D’Ercole A.J. Ye P. Insulin-like growth factor type 1 receptor signaling in the cells of oligodendrocyte lineage is required for normal in vivo oligodendrocyte development and myelination Glia 2007 55 400 411 10.1002/glia.20469 17186502 PMC1774584
38. Li N. Han J. Tang J. Ying Y. IGFBP-7 inhibits the differentiation of oligodendrocyte precursor cells via regulation of Wnt/beta-Catenin signaling J. Cell. Biochem. 2018 119 4742 4750 10.1002/jcb.26654 29280192
39. Jassam Y.N. Izzy S. Whalen M. McGavern D.B. El Khoury J. Neuroimmunology of Traumatic Brain Injury: Time for a Paradigm Shift Neuron 2017 95 1246 1265 10.1016/j.neuron.2017.07.010 28910616 PMC5678753
40. Salehi A. Zhang J.H. Obenaus A. Response of the cerebral vasculature following traumatic brain injury J. Cereb. Blood Flow Metab. 2017 37 2320 2339 10.1177/0271678X17701460 28378621 PMC5531360
41. Zhou C. Huang J. Li Q. Zhan C. Xu X. Zhang X. Ai D. Zhu Y. Wen Z. Wang D.W. CYP2J2-derived EETs attenuated ethanol-induced myocardial dysfunction through inducing autophagy and reducing apoptosis Free. Radic. Biol. Med. 2018 117 168 179 10.1016/j.freeradbiomed.2018.02.009 29427791
42. The Wellcome Trust Case Control Consortium Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls Nature 2007 447 661 678 10.1038/nature05911 17554300 PMC2719288
43. Zhou X.J. Lu X.L. Lv J.C. Yang H.Z. Qin L.X. Zhao M.H. Su Y. Li Z.G. Zhang H. Genetic association of PRDM1-ATG5 intergenic region and autophagy with systemic lupus erythematosus in a Chinese population Ann. Rheum. Dis. 2011 70 1330 1337 10.1136/ard.2010.140111 21622776
44. Xu H. Wu Z.Y. Fang F. Guo L. Chen D. Chen J.X. Stern D. Taylor G.A. Jiang H. Yan S.S. Genetic deficiency of Irgm1 (LRG-47) suppresses induction of experimental autoimmune encephalomyelitis by promoting apoptosis of activated CD4+ T cells FASEB J. 2010 24 1583 1592 10.1096/fj.09-137323 20056715 PMC2879948
45. Sarkar C. Jones J.W. Hegdekar N. Thayer J.A. Kumar A. Faden A.I. Kane M.A. Lipinski M.M. PLA2G4A/cPLA2-mediated lysosomal membrane damage leads to inhibition of autophagy and neurodegeneration after brain trauma Autophagy 2020 16 466 485 10.1080/15548627.2019.1628538 31238788 PMC6999646
46. Januzzi J.L. Jr. Packer M. Claggett B. Liu J. Shah A.M. Zile M.R. Pieske B. Voors A. Gandhi P.U. Prescott M.F. IGFBP7 (Insulin-Like Growth Factor-Binding Protein-7) and Neprilysin Inhibition in Patients with Heart Failure Circ. Heart Fail. 2018 11 e005133 10.1161/CIRCHEARTFAILURE.118.005133 30354399 PMC12611560
47. Jin L. Shen F. Weinfeld M. Sergi C. Insulin Growth Factor Binding Protein 7 (IGFBP7)-Related Cancer and IGFBP3 and IGFBP7 Crosstalk Front. Oncol. 2020 10 727 10.3389/fonc.2020.00727 32500027 PMC7242731