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Citation: Gaetani, L.; Bellomo, G.; Di
Sabatino, E.; Sperandei, S.; Mancini,
A.; Blennow, K.; Zetterberg, H.;
Parnetti, L.; Di Filippo, M. The
Immune Signature of CSF in Multiple
Sclerosis with and without
Oligoclonal Bands: A Machine
Learning Approach to Proximity
Extension Assay Analysis. Int. J. Mol.
Sci. 2024,25, 139. https://doi.org/
10.3390/ijms25010139
Academic Editor: Kurt A. Jellinger
Received: 3 November 2023
Revised: 4 December 2023
Accepted: 8 December 2023
Published: 21 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Molecular Sciences
Article
The Immune Signature of CSF in Multiple Sclerosis with and
without Oligoclonal Bands: A Machine Learning Approach to
Proximity Extension Assay Analysis
Lorenzo Gaetani 1,† , Giovanni Bellomo 1,† , Elena Di Sabatino 1, Silvia Sperandei 1, Andrea Mancini 1,
Kaj Blennow 2,3, Henrik Zetterberg 2,3,4,5,6,7, Lucilla Parnetti 1and Massimiliano Di Filippo 1, *
1Section of Neurology, Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy;
lorenzo.gaetani@unipg.it (L.G.)
2Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry,
The Sahlgrenska Academy at the University of Gothenburg, 431 41 Mölndal, Sweden
3Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 41 Mölndal, Sweden
4Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square,
London WC1N 3BG, UK
5UK Dementia Research Institute at UCL, London WC1E 6BT, UK
6Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong 518172, China
7Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public
Health, University of Wisconsin-Madison, Madison, WI 53792, USA
*Correspondence: massimiliano.difilippo@unipg.it
†These authors contributed equally to this work.
Abstract: Early diagnosis of multiple sclerosis (MS) relies on clinical evaluation, magnetic resonance
imaging (MRI), and cerebrospinal fluid (CSF) analysis. Reliable biomarkers are needed to differentiate
MS from other neurological conditions and to define the underlying pathogenesis. This study aimed to
comprehensively profile immune activation biomarkers in the CSF of individuals with MS and explore
distinct signatures between MS with and without oligoclonal bands (OCB). A total of
118 subjects
,
including relapsing–remitting MS with OCB (MS OCB+) (n= 58), without OCB (MS OCB
−
)
(n= 24)
,
and controls with other neurological diseases (OND) (n= 36), were included. CSF samples were
analyzed by means of proximity extension assay (PEA) for quantifying 92 immune-related proteins.
Neurofilament light chain (NfL), a marker of axonal damage, was also measured. Machine learning
techniques were employed to identify biomarker panels differentiating MS with and without OCB
from controls. Analyses were performed by splitting the cohort into a training and a validation
set. CSF CD5 and IL-12B exhibited the highest discriminatory power in differentiating MS from
controls. CSF MIP-1-alpha, CD5, CXCL10, CCL23 and CXCL9 were positively correlated with NfL.
Multivariate models were developed to distinguish MS OCB+ and MS OCB
−
from controls. The
model for MS OCB+ included IL-12B, CD5, CX3CL1, FGF-19, CST5, MCP-1 (91% sensitivity and 94%
specificity in the training set, 81% sensitivity, and 94% specificity in the validation set). The model
for MS OCB
−
included CX3CL1, CD5, NfL, CCL4 and OPG (87% sensitivity and 80% specificity in
the training set, 56% sensitivity and 48% specificity in the validation set). Comprehensive immune
profiling of CSF biomarkers in MS revealed distinct pathophysiological signatures associated with
OCB status. The identified biomarker panels, enriched in T cell activation markers and immune
mediators, hold promise for improved diagnostic accuracy and insights into MS pathogenesis.
Keywords: multiple sclerosis; cerebrospinal fluid; biomarkers; oligoclonal bands; proximity extension
assay; machine learning
1. Introduction
Multiple sclerosis (MS) is an autoimmune inflammatory disorder that affects the
central nervous system (CNS), leading to damage in the myelin, neurons and axons [
1
].
Int. J. Mol. Sci. 2024,25, 139. https://doi.org/10.3390/ijms25010139 https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2024,25, 139 2 of 14
The diagnosis of MS in its early stages is achieved through continually updated diagnos-
tic criteria, relying on the integration of multiple investigations as no single diagnostic
test is available [
2
,
3
]. The primary diagnostic steps involve assessing the clinical presen-
tation and spatial–temporal occurrence of inflammatory lesions observed via magnetic
resonance imaging (MRI) of the CNS. Additionally, cerebrospinal fluid (CSF) examination
provides key information for differential diagnoses and offers evidence of intrathecal IgG
synthesis [2]
. The positivity of biomarkers indicating intrathecal IgG synthesis holds sig-
nificant prognostic value in MS, signaling an increased risk of disease recurrence even
after the initial clinical manifestation [
4
]. Intrathecal IgG synthesis can be detected by
using isoelectrofocusing (IEF) to assess oligoclonal bands (OCB) in paired CSF and serum
samples, with OCB presence in CSF but not serum indicating intrathecal B-cell activity [
5
].
Nevertheless, OCB lack the ability to offer a quantitative measure, and they solely reflect
intrathecal B-cell activity, thus not providing insights into the complex immunological
milieu that could characterize the CSF in a chronic inflammatory condition like MS [
6
].
Moreover, 5–10% of individuals with relapsing–remitting MS (RRMS) do not exhibit OCB,
and this percentage tends to be higher during the initial phases of the disease and in
Eastern countries [
7
]. This subset of patients presents a potential challenge in terms of
diagnosis [
8
] and prompts inquiries into whether MS cases with and without OCB might
possess distinct immune pathogeneses. A more precise biomarker-based characterization
of MS is, therefore, needed.
CSF, owing to its proximity to the CNS, offers an advantageous focus for biomarker
exploration. The identification of MS-related pathophysiological biomarkers has proven
challenging due, in part, to sensitivity issues. While multiplex analyses have been con-
ducted to characterize the immune signature of CSF in people with MS (pwMS), the results
have exhibited heterogeneity across studies, partly due to the limited scope of measured
biomarkers, primarily focused on a small subset of inflammatory proteins, and to the
challenges posed by antibody cross-reactivity and inter-assay variability [
9
]. In recent
years, targeted quantitative proteomics has emerged as a promising tool for developing
disease-specific protein signatures as biomarkers, furthering the understanding of the
molecular mechanisms underlying neurological disorders [
10
]. The application of proxim-
ity extension assay (PEA) technology, combining antibody-based binding with DNA-based
signal amplification [
11
,
12
], has shown considerable potential for quantitatively measuring
protein levels in various body fluids, including CSF, in neurological diseases [10,13,14].
In the present study, we utilized PEA technology to comprehensively measure a
panel of biomarkers reflecting immune activation in CSF samples obtained from pwMS
with and without OCB and a control group. Additionally, we quantified CSF levels of
neurofilament light chain (NfL), a well-established biomarker of axonal damage [
15
], in
order to identify the prevailing immunological profile more closely linked with axonal
injury. By applying robust machine-learning statistical models, the aim of the study was
to identify combinations of biomarkers able to differentiate pwMS with and without OCB
from controls, as well as to provide novel insights into the pathophysiology of the disease.
2. Results
2.1. Correlation Analysis of CSF PEA-Tested Proteins and NfL
Out of the 92 proteins determined through the PEA technique, 47 had less than 15%
missing values (values above the lower limit of detection) in both controls with other
neurological diseases (OND) and MS. From the correlation and cluster analyses, it emerged
that among the measured proteins, some of them strongly correlated with each other in
pwMS. The biggest cluster (cluster 1) consisted of TRAIL, IL-10RB, uPA, HGF, CX3CL1, Beta-
NGF, OPG, DNER, ADA, SCF, SIRT2, TGF-alpha, CSF-1, PD-L1, Flt3L, FGF-5, CD40, CDCP1,
TWEAK, VEGF-A and LIF-R. Another cluster of highly correlated proteins (
cluster 2)
consisted of IL-12B, CD5, TNFRSF9, CXCL9, TNFB, CXCL10 and CXCL11 (Figure 1).
Evidence of interaction is apparent for most proteins within the two clusters. However,
in cluster 1, DNER, SIRT2, LIF-R, IL-10RB and CDCP1 were previously not found to be
Int. J. Mol. Sci. 2024,25, 139 3 of 14
associated with the other proteins of cluster 1. Similarly, within cluster 2, CD5 was not
known to interact with the other proteins (Figure 2). For abbreviations of PEA-tested
proteins, see Table S1. Among the measured PEA-tested biomarkers within the RRMS
group, five of them showed a weak positive but significant correlation with CSF NfL
(Figure 3).
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 3 of 15
CX3CL1, Beta-NGF, OPG, DNER, ADA, SCF, SIRT2, TGF-alpha, CSF-1, PD-L1, Flt3L,
FGF-5, CD40, CDCP1, TWEAK, VEGF-A and LIF-R. Another cluster of highly correlated
proteins (cluster 2) consisted of IL-12B, CD5, TNFRSF9, CXCL9, TNFB, CXCL10 and
CXCL11 (Figure 1). Evidence of interaction is apparent for most proteins within the two
clusters. However, in cluster 1, DNER, SIRT2, LIF-R, IL-10RB and CDCP1 were previously
not found to be associated with the other proteins of cluster 1. Similarly, within cluster 2,
CD5 was not known to interact with the other proteins (Figure 2). For abbreviations of
PEA-tested proteins, see Table S1. Among the measured PEA-tested biomarkers within
the RRMS group, five of them showed a weak positive but significant correlation with CSF
NfL (Figure 3).
Figure 1. Correlation heatmap. Correlation coefficients were computed according to Spearman and
are displayed in absolute values. Two clusters emerge: (i) TRAIL, IL-10RB, uPA, HGF, CX3CL1, Beta-
NGF, OPG, DNER, ADA, SCF, SIRT2, TGF-alpha, CSF-1, PD-L1, Flt3L, FGF-5, CD40, CDCP1,
TWEAK, VEGF-A, LIF-R; and (ii) IL-12B, CD5, TNFRSF9, CXCL9, TNFB, CXCL10, CXCL11.
Figure 1. Correlation heatmap. Correlation coefficients were computed according to Spearman and
are displayed in absolute values. Two clusters emerge: (i) TRAIL, IL-10RB, uPA, HGF, CX3CL1,
Beta-NGF, OPG, DNER, ADA, SCF, SIRT2, TGF-alpha, CSF-1, PD-L1, Flt3L, FGF-5, CD40, CDCP1,
TWEAK, VEGF-A, LIF-R; and (ii) IL-12B, CD5, TNFRSF9, CXCL9, TNFB, CXCL10, CXCL11.
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 3 of 15
CX3CL1, Beta-NGF, OPG, DNER, ADA, SCF, SIRT2, TGF-alpha, CSF-1, PD-L1, Flt3L,
FGF-5, CD40, CDCP1, TWEAK, VEGF-A and LIF-R. Another cluster of highly correlated
proteins (cluster 2) consisted of IL-12B, CD5, TNFRSF9, CXCL9, TNFB, CXCL10 and
CXCL11 (Figure 1). Evidence of interaction is apparent for most proteins within the two
clusters. However, in cluster 1, DNER, SIRT2, LIF-R, IL-10RB and CDCP1 were previously
not found to be associated with the other proteins of cluster 1. Similarly, within cluster 2,
CD5 was not known to interact with the other proteins (Figure 2). For abbreviations of
PEA-tested proteins, see Table S1. Among the measured PEA-tested biomarkers within
the RRMS group, five of them showed a weak positive but significant correlation with CSF
NfL (Figure 3).
Figure 1. Correlation heatmap. Correlation coefficients were computed according to Spearman and
are displayed in absolute values. Two clusters emerge: (i) TRAIL, IL-10RB, uPA, HGF, CX3CL1, Beta-
NGF, OPG, DNER, ADA, SCF, SIRT2, TGF-alpha, CSF-1, PD-L1, Flt3L, FGF-5, CD40, CDCP1,
TWEAK, VEGF-A, LIF-R; and (ii) IL-12B, CD5, TNFRSF9, CXCL9, TNFB, CXCL10, CXCL11.
Figure 2. Protein–protein interaction maps of clustered proteins identified in this study. (A) First major
cluster. (B) Second minor cluster. (C) Nodes are representative of protein species, and different line
colors show the types of evidence for the association. The STRING tool (http://www.string-db.org
(accessed on 1 November 2023)) was used to construct the interaction networks. Interactive networks
are available at: (A)https://version-11-5.string-db.org/cgi/network?networkId=bI2OAa4S6dS1
(accessed on 1 November 2023) (B)https://version-11-5.string-db.org/cgi/network?networkId=b5
Rvxhh6MPHk (accessed on 1 November 2023).
Int. J. Mol. Sci. 2024,25, 139 4 of 14
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 4 of 15
Figure 2. Protein–protein interaction maps of clustered proteins identified in this study. (A) First
major cluster. (B) Second minor cluster. (C) Nodes are representative of protein species, and
different line colors show the types of evidence for the association. The STRING tool
(hp://www.string-db.org (accessed on 1 November 2023)) was used to construct the interaction
networks. Interactive networks are available at: (A) hps://version-11-5.string-
db.org/cgi/network?networkId=bI2OAa4S6dS1 (accessed on 1 November 2023) (B) hps://version-
11-5.string-db.org/cgi/network?networkId=b5Rvxhh6MPHk (accessed on 1 November 2023).
Figure 3. Significant correlations between PEA-tested proteins and CSF NfL in pwMS. (A)
Correlation coefficients depicted as heatmap (MIP-1-alpha: 0.27, p = 0.0067; CD5: 0.25, p = 0.012;
CXCL10: 0.24, p = 0.015; CCL23: 0.24, p = 0.018; CXCL9: 0.23, p = 0.025). (B) Plots showing simple
linear regression between PEA-tested proteins and CSF NfL.
2.2. Biomarkers Efficacy in Discriminating between RRMS and OND
We applied logistic regression by assuming age and sex as covariates to assess which
of the inflammatory proteins was considered more altered in the whole RRMS cohort vs.
OND comparison (Figure 4). The proteins showing a q-value (p-value adjusted for age,
sex and FDR) < 0.05 were: CD5 (AUC: 0.87, 95% CI 0.80–0.94; q = 0.002), IL-12B (AUC: 0.81,
95% CI 0.73–0.89; q = 0.007), TNFB (AUC: 0.78, 95% CI 0.68–0.86; q = 0.01), TNFSF14 (AUC:
0.70, 95% CI 0.60–0.80; q = 0.01), TNFRSF9 (AUC: 0.65, 95% CI 0.54–0.76; q = 0.04) and MIP-
1-alpha (AUC: 0.59, 95% CI 0.48–0.70; q = 0.02) (Figures 4 and S1). We then applied a
multinomial LASSO regression to differentiate between MS OCB+/− and OND, which
resulted in logistic models considering CD5, IL-12B, TNFB, MCP-1, CXCL1, CXCL9 and
NfL. Due to the poor results obtained in classifying MS OCB− (54% OCB− identified as
MS and 46% identified as OND) (Table 1), we performed separate LASSO analyses for MS
OCB+ vs. OND and MS OCB− vs. OND.
Table 1 . Confusion table showing the performance of the multinomial LASSO model in the training
set in the comparison between MS and OND (data are shown for MS OCB+ and MS OCB−
separately).
Predicted Condition
Total
118
MS OCB+
67
MS OCB−
5
OND
46
Actual
condition
MS OCB+
58 55 10 2
MS OCB−
24 1 3 1
OND
36 2 11 33
Figure 3. Significant correlations between PEA-tested proteins and CSF NfL in pwMS. (A) Correlation
coefficients depicted as heatmap (MIP-1-alpha: 0.27, p= 0.0067; CD5: 0.25, p= 0.012; CXCL10: 0.24,
p= 0.015
; CCL23: 0.24, p= 0.018; CXCL9: 0.23, p= 0.025). (B) Plots showing simple linear regression
between PEA-tested proteins and CSF NfL.
2.2. Biomarkers Efficacy in Discriminating between RRMS and OND
We applied logistic regression by assuming age and sex as covariates to assess which
of the inflammatory proteins was considered more altered in the whole RRMS cohort vs.
OND comparison (Figure 4). The proteins showing a q-value (p-value adjusted for age, sex
and FDR) < 0.05 were: CD5 (AUC: 0.87, 95% CI 0.80–0.94; q = 0.002), IL-12B (AUC: 0.81,
95% CI 0.73–0.89; q = 0.007), TNFB (AUC: 0.78, 95% CI 0.68–0.86; q = 0.01), TNFSF14 (AUC:
0.70, 95% CI 0.60–0.80; q = 0.01), TNFRSF9 (AUC: 0.65, 95% CI 0.54–0.76; q = 0.04) and
MIP-1-alpha (AUC: 0.59, 95% CI 0.48–0.70; q = 0.02) (Figures 4and S1). We then applied
a multinomial LASSO regression to differentiate between MS OCB+/
−
and OND, which
resulted in logistic models considering CD5, IL-12B, TNFB, MCP-1, CXCL1, CXCL9 and
NfL. Due to the poor results obtained in classifying MS OCB
−
(54% OCB
−
identified as
MS and 46% identified as OND) (Table 1), we performed separate LASSO analyses for MS
OCB+ vs. OND and MS OCB−vs. OND.
Table 1. Confusion table showing the performance of the multinomial LASSO model in the training
set in the comparison between MS and OND (data are shown for MS OCB+ and MS OCB
−
separately).
Predicted Condition
Total
118
MS OCB+
67
MS OCB−
5
OND
46
Actual
condition
MS OCB+
58 55 10 2
MS OCB−
24 1 3 1
OND
36 2 11 33
MS OCB+: multiple sclerosis with cerebrospinal fluid IgG oligoclonal bands. MS OCB
−
: multiple sclerosis
without cerebrospinal fluid IgG oligoclonal bands. OND: other neurological diseases.
Int. J. Mol. Sci. 2024,25, 139 5 of 14
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 5 of 15
MS OCB+: multiple sclerosis with cerebrospinal fluid IgG oligoclonal bands. MS OCB−: multiple
sclerosis without cerebrospinal fluid IgG oligoclonal bands. OND: other neurological diseases.
Figure 4. CSF proteins differentially expressed between RRMS patients and OND. (A) Logistic
regression was applied to determine, for each of the proteins considered, age-, sex- and FDR-
adjusted p-value (q-value). The coefficients resulting from logistic regressions (beta) are displayed
on the x-axis while the negative logarithm (base 10) of the q-value is reported on the y-axis. (B) Area
under the ROC curve (AUC) determined for RRMS vs. OND for each of the markers showing a q-
value < 0.05 with error bars representing the 95%CI.
2.3. Multivariate Analysis of PEA-Tested Proteins in MS OCB+ vs. OND
We created an age-matched training set composed of 18 OND and 22 MS OCB+ by
pairing as much as possible the age histograms of the two groups (p-value age = 0.73 with
t-test after matching). By cross-validation, we identified an optimal penalization
parameter λ of 0.0501 corresponding to the following coefficients for z-scored biomarkers:
intercept (0.61), IL-12B (1.14), CD5 (0.94), CX3CL1 (−0.36), FGF-19 (−0.27), CST5 (0.23),
MCP-1 (−0.16). Coefficients relative to not z-scored NPX values are reported in Table S2.
In the training set, the logistic model had a sensitivity of 91% and a specificity of 94% in
detecting MS OCB+ vs. OND. The age-unmatched OND (n = 18) and MS-OCB+ (n = 36)
groups were then used as validation sets. In this set, the model had a sensitivity of 81%
and a specificity of 94% (Table 2).
Tab l e 2. Confusion tables showing the performance of the binomial LASSO model in the training
set (A) and in the validation set (B) for the MS OCB+ vs. OND comparison.
A Predicted Condition B Predicted Condition
Total
40
MS OCB+
22
OND
18 Total
54
MS OCB+
36
OND
18
Actual condition
MS OCB+
22 20 2
Actual condition
MS OCB+
36 29 7
OND
18 2 16 OND
18 7 11
Figure 4. CSF proteins differentially expressed between RRMS patients and OND. (A) Logistic
regression was applied to determine, for each of the proteins considered, age-, sex- and FDR-adjusted
p-value (q-value). The coefficients resulting from logistic regressions (beta) are displayed on the x-axis
while the negative logarithm (base 10) of the q-value is reported on the y-axis. (B) Area under the
ROC curve (AUC) determined for RRMS vs. OND for each of the markers showing a q-value < 0.05
with error bars representing the 95% CI.
2.3. Multivariate Analysis of PEA-Tested Proteins in MS OCB+ vs. OND
We created an age-matched training set composed of 18 OND and 22 MS OCB+ by
pairing as much as possible the age histograms of the two groups (p-value age = 0.73 with
t-test after matching). By cross-validation, we identified an optimal penalization parameter
λ
of 0.0501 corresponding to the following coefficients for z-scored biomarkers: intercept
(0.61), IL-12B (1.14), CD5 (0.94), CX3CL1 (
−
0.36), FGF-19 (
−
0.27), CST5 (0.23), MCP-1
(
−
0.16). Coefficients relative to not z-scored NPX values are reported in Table S2. In the
training set, the logistic model had a sensitivity of 91% and a specificity of 94% in detecting
MS OCB+ vs. OND. The age-unmatched OND (n= 18) and MS-OCB+ (n= 36) groups were
then used as validation sets. In this set, the model had a sensitivity of 81% and a specificity
of 94% (Table 2).
Table 2. Confusion tables showing the performance of the binomial LASSO model in the training set
(A) and in the validation set (B) for the MS OCB+ vs. OND comparison.
A Predicted Condition B Predicted Condition
Total
40
MS OCB+
22
OND
18
Total
54
MS OCB+
36
OND
18
Actual
condition
MS OCB+
22 20 2
Actual
condition
MS OCB+
36 29 7
OND
18 2 16 OND
18 7 11
MS OCB+: multiple sclerosis with cerebrospinal fluid IgG oligoclonal bands. OND: other neurological diseases.
Int. J. Mol. Sci. 2024,25, 139 6 of 14
2.4. Multivariate Analysis of PEA-Tested Proteins in MS OCB−vs. OND
As for the previous comparison, we created an age-matched training set composed
of 15 OND and 15 MS OCB
−
by pairing the age histograms of the two groups as much as
possible (p-value age = 0.51, t-test). The LASSO regression identified an optimal penaliza-
tion parameter
λ
of 0.0945 and the following coefficients for z-scored biomarkers: intercept
(0.094), CX3CL1 (
−
0.58), CD5 (0.41), NfL (0.20), CCL4 (0.10) and OPG (
−
0.06). Coefficients
relative to not z-scored NPX values are reported in Table S2. In the training set, the model
had a sensitivity of 87% and a specificity of 80% in detecting MS OCB
−
vs. OND. The
age-unmatched OND (n= 21) and MS OCB+ (n= 9) were used as a small validation set. In
this set, the model had a sensitivity of 56% and a specificity of 48% (Table 3).
Table 3. Confusion tables showing the performance of the binomial LASSO model in the training set
(A) and in the validation set (B) for the MS OCB−vs. OND comparison.
A Predicted Condition B Predicted Condition
Total
30
MS OCB−
15
OND
15
Total
30
MS OCB−
9
OND
21
Actual
condition
MS OCB−
15 13 2
Actual
condition
MS OCB−
95 4
OND
15 2 13 OND
21 4 17
MS OCB
−
: multiple sclerosis without cerebrospinal fluid IgG oligoclonal bands. OND: other neurological
diseases.
3. Discussion
From the univariate analysis of PEA data, we identified a panel of six CSF immuno-
logical proteins with the highest discriminatory power in the comparison between MS and
controls. This panel was made of CD5, IL-12B, TNFB, TNFSF14, TNFRSF9 and MIP-1-alpha.
Most of these markers are involved in T cell activation. CD5 (cluster of differentiation 5) is
a T cell surface glycoprotein that may act as a receptor in regulating T cell proliferation [
16
].
IL-12B (interleukin 12 subunit beta), a component of the IL-12 cytokine, plays a critical
role in promoting the differentiation of T cells into T helper 1 (Th1) cells, and it has a clear
involvement in MS, being a genetic risk factor for the disease [
17
]. TNFRSF9 (tumor necro-
sis factor receptor superfamily member 9) is primarily expressed in antigen-presenting
cells such as B cells, macrophages and dendritic cells, and it promotes T-cell activation and
regulates proliferation and survival of T cells [
18
]. A soluble form of TNFRSF9 released by
activated lymphocytes has already been found to be significantly high in CSF and serum of
pwMS with clinically active disease [
19
], suggesting a potential of this protein as a marker
for MS. MIP-1-alpha (macrophage inflammatory protein-1 alpha) are chemokine recruiting
and activating immune cells, particularly monocytes, macrophages and T cells, to areas
of inflammation, including MS brain lesions [
20
]. TNFSF14 (tumor necrosis factor ligand
superfamily member 14) is a glycoprotein involved in dendritic cell maturation, and in its
soluble form, it may act as an inhibitor of T-cell activation [
21
]. In MS serum, an increase of
this marker has already been documented during disease activity, suggesting that soluble
TNFSF14 is protective and may act to limit inflammation [
22
]. Of interest, a polymorphism
of the gene coding TNFSF14 has been shown to increase the risk of MS in a genome-wide
association study [
23
], with allelic variants of the gene being involved in the risk of MS in
the Italian population as well [24].
Among the markers included in the panel, TNFB (tumor necrosis factor beta) stands
out as a particularly noteworthy one that has recently gained significant attention in the
context of MS. TNFB plays a crucial role in the maintenance of the immune system and
is known to be involved in cellular cytotoxicity, lymphoid neogenesis and the formation
of tertiary lymphoid-like structures (TLS) [
25
]. The presence of TLS in the subarachnoid
space has been linked, in MS, to neuronal loss in the cortical grey matter, which, in turn,
is a risk factor for faster disability progression [
26
]. Recent studies have reported higher
Int. J. Mol. Sci. 2024,25, 139 7 of 14
levels of TNFB in the CSF of individuals with RRMS exhibiting a high number of cortical
lesions compared to RRMS patients with a lower burden of cortical involvement [
27
].
Additionally, CSF TNFB levels have been found to be increased in people with progressive
MS who showed a high burden of grey matter demyelination and immune cell infiltration
in post-mortem brains. Furthermore, elevated TNFB mRNA levels have been observed
in the meninges of pwMS with secondary progressive MS, which aligns with increased
CSF TNFB levels [
27
]. Our findings reinforce the evidence of CSF TNFB as a biomarker in
MS with relevant pathophysiological meaning. Further research in this area could offer
insights into the underlying mechanisms and potential therapeutic targets of MS.
To mitigate the risk of data overfitting, a common concern when dealing with multi-
plex analyses that yield numerous outcome variables, we employed a machine learning
methodology grounded in penalized regression analysis, i.e., the LASSO. The LASSO holds
the potential to uncover the most compact set of innovative markers, ensuring both height-
ened sensitivity and specificity in distinguishing individuals with MS from the control
group. By applying multinominal LASSO, we identified a set of proteins consisting of
biomarkers of T cell activation (CD5, IL-12B) and chronic meningeal inflammation (TNFB)
already discussed, together with chemokines involved in T cell migration (CXCL1 and
CXCL9) and monocytes and memory T cell migration (MCP-1), and a biomarker of axonal
damage (NfL). Of note, it is intriguing to observe that this model exhibited contrasting
performances in pwMS based on the presence or absence of OCB.
The model demonstrated notable efficacy in classifying MS OCB+ (85% accuracy), yet
its precision was diminished in the case of MS OCB
−
. For the latter group, merely 54%
of patients were correctly identified as having MS, with the remaining 46% inaccurately
labeled as controls. For this reason, we applied binomial LASSO to classify MS OCB+
and MS OCB
−
separately from controls. In this analysis, we found that the best model to
identify MS OCB+ was made of IL-12B, CD5, CX3CL1, FGF-19, CST5 and MCP-1. Among
these, fractalkine (CX3CL1) is another protein reflecting the activation of T cells, similar to
IL-12B and CD5. CX3CL1 has been shown to increase IFN-
γ
and TNF-
α
gene expression
and IFN-
γ
secretion by CD4(+) T cells derived from RRMS patients [
28
]. Further, in people
with RRMS, CSF levels of CX3CL1 have already been demonstrated to be increased [
28
].
Cystatin-D (CST5) has shown potential as a relapse marker in an independent cohort of
pwMS [
14
]. Furthermore, it has been found to be increased in the CSF of patients with severe
traumatic brain injury [
29
]. Of note, in both these studies, CST5 was measured by means of
PEA. CST5 is an inhibitor of lysosomal and secreted cysteine proteases, originally purified
from saliva, with an undefined role [
30
]. The association between CSF CST5 relapses in
pwMS and traumatic brain injury suggests that this marker might serve as a proxy for
neuronal injury, but the interpretation of its pathophysiological meaning deserves further
investigation. Fibroblast growth factor 19 (FGF-19) belongs to the endocrine subfamily of
fibroblast growth factors (FGFs) [
31
]. FGFs are ubiquitously expressed throughout the CNS
on all cell types, and FGF signaling may regulate inflammation and myelination in MS
since an abundance of FGF members has been documented in focal inflammatory lesions
in MS [31]. However, the specific role of FGF19 in MS is yet unexplored.
As for MS OCB
−
, the most accurate diagnostic model included CX3CL1, CD5, NfL,
CCL4 and OPG. Therefore, also in this subgroup of pwMS biomarkers of T cell activation,
CX3CL1 and CD5 participate in identifying the disease, together with a biomarker of
neuronal injury (NfL), a chemokine (CCL4) and OPG. Of interest, OPG (osteoprotegerin)
has been shown to suppress the mRNA expression of CCL20, a chemokine involved in
Th17 cell recruitment with anti-inflammatory effects [
32
]. The signaling that involves OPG
has been hypothesized to act as a neuroprotectant after brain damage [
33
]. The presence of
OPG in the multivariate model distinguishing MS OCB
−
from controls and its absence in
the model for MS OCB+ is intriguing and deserves future investigation.
Notably, the model used to identify MS OCB+ demonstrates a satisfactory level of
accuracy (91% sensitivity and 94% specificity in the training set, 81% sensitivity and 94%
specificity in the validation set). The high specificity of this model is particularly relevant
Int. J. Mol. Sci. 2024,25, 139 8 of 14
for its potential application in future MS diagnosis. While the sensitivity might not be
excellent in the age-unmatched validation set, it is not a major concern, given the availability
of other sensitive diagnostic tools, especially in terms of MRI and MRI-based diagnostic
criteria. What is needed to improve the accuracy in the diagnosis of MS is the identification
of a biomarker or combination of biomarkers with high specificity for the disease. The
model we found, however, should be tested against more relevant clinical comparisons,
particularly diseases that mimic MS, to assess its robustness.
On the other hand, the model used for the identification of MS OCB
−
was found to be
less effective. This suggests that MS OCB+ and MS OCB
−
may differ from a pathophysio-
logical perspective, with MS OCB
−
showing less definite signs of immune activation in
CSF. This underscores the complexity of diagnosing MS when CSF OCB are absent. As an
example, in discriminating between MS and white matter lesions associated with migraine
and vascular lesions, the absence of CSF OCB is the most robust independent predictor of
a non-MS diagnosis [
34
]. Therefore, the investigation of biomarkers able to identify MS
OCB−is highly relevant from a clinical perspective and warrants further research.
One of the strengths of our study relies on having coupled a thorough immunological
characterization with the measurement of a well-established marker that summarizes
the overall CNS pathology occurring in MS, namely CSF NfL [
15
]. NfL has indeed been
shown to reflect both the acute neuronal injury following new focal lesion appearance, as
well as the chronic neuronal damage taking place in those phases of the disease where
neurodegeneration prevails on acute focal inflammation [35].
We found that five of the inflammation-related biomarkers in MS correlate with
CSF NfL, namely MIP-1-alpha, CD5, CXCL10, CCL23 and CXCL9. All these proteins are
implicated in T cell activation or migration [
16
,
20
,
36
], thus suggesting that CSF may provide
a robust proxy for the deleterious role of intrathecal T cell activation on neuronal survival.
Many of these proteins have already been found to be upregulated in the CSF and blood of
pwMS and to decrease after the start of a disease-modifying treatment [
14
]. Our results,
by showing an association with NfL, provide further evidence that these T cell-related
markers reflect the pathophysiology of the disease and correlate with the severity of axonal
injury in MS.
Finally, when looking at the correlations among PEA-tested proteins, we found two
clusters of inflammatory markers strongly correlated with most of these proteins already
known to interact with each other. In cluster 1, TRAIL, uPA, HGF, CX3CL1, Beta-NGF,
OPG, SCF, TGF-alpha, CSF-1, PD-L1, Flt3L, FGF-5, CD40, TWEAK and VEGF-A have
already shown evidence of interaction. As an example, most of these proteins (TRAIL,
HGF, CX3CL1, Beta-NGF, SCF, TGF-alpha, CSF-1, PD-L1, Flt3L, FGF-5, TWEAK and VEGF-
A) can influence the expression or activate uPA (urokinase-type plasminogen activator),
which plays a pivotal role in various physiological and pathological processes, including
extracellular matrix remodeling, cell migration and invasion, angiogenesis and immune
response and inflammation [37].
Interesting suggestions came from the presence in the clusters of proteins not known to
interact with the others, such as ADA, DNER, SIRT2, CDCP1, LIFR and IL-10 in cluster 1 and
CD5 in cluster 2. DNER is the delta and notch-like epidermal growth factor-related receptor;
it activates the NOTCH1 pathway, and it is known to inhibit the proliferation of neural
progenitors or induce neuronal and glial differentiation during brain development [
38
]. Its
correlation in the CSF with immune mediators suggests it may play a role in the interaction
between neuronal, glial and immune cells, especially during an inflammatory chronic
disease such as MS. DNER can be the target of immune activation within autoimmune
encephalitis with paraneoplastic etiology, such as cerebellar degeneration in Hodgkin
lymphoma [
39
]. The pathophysiological basis of the correlation between DNER and T-cell
and B-cell markers in MS must be demonstrated.
SIRT2 (sirtuin 2) is a protein deacetylase highly expressed in the mammalian CNS,
mainly found in the cytoplasm of oligodendrocytes [
40
]. It is particularly expressed
in the cortex, striatum, hippocampus and spinal cord, but its functions are still largely
Int. J. Mol. Sci. 2024,25, 139 9 of 14
unknown [
40
]. Elevated levels of SIRT2 have been found in the CSF of a neurodegenerative
disease such as Alzheimer’s disease, indicating its potential as a biomarker reflecting CNS
damage [
13
]. The correlation of CSF SIRT2 with inflammatory markers in MS suggests that
it might track neuronal damage secondary to immune dysfunction, but the plausibility of
this association deserves further investigation.
In the second minor cluster, different proteins belonging to chemokines were func-
tionally correlated, including CXCL9, CXCL10 and CXCL11, together with markers of
T cell activity, such as IL-12B. Some of these markers, particularly IL-12B, have clear in-
volvement in MS and are genetic risk factors for the disease [
17
]. Within this cluster, CD5
has not previously shown an association with the other markers. CD5 is a T cell surface
glycoprotein that may act as a receptor in regulating T cell proliferation [16]; therefore, an
association with different chemokines and with markers of T cell activation in the CSF of
MS can be expected.
In conclusion, one potential limitation of our study is its retrospective design. The
inclusion of participants was based on the availability of stored CSF samples over a three-
year period, which may not constitute a random selection. However, we believe this
does not introduce selection bias to our cohort. Our research has several strengths: (i) we
addressed the clinical challenge of diagnosing MS by focusing on pwMS with and without
OCB, a subset that poses diagnostic difficulties [
8
]. This reflects the study’s relevance
to real-world diagnostic scenarios. (ii) We employed an innovative technology, i.e. PEA
technology, which offers a promising tool for quantitatively measuring protein levels
in various body fluids, including CSF. This innovative approach enhances the precision
of biomarker exploration. (iii) To interpret the complexity of data generated by PEA,
we applied a machine learning statistical approach that enhances the robustness of our
findings [
41
], and we built training and validation sets to test the accuracy of biomarkers in
discriminating between groups.
4. Materials and Methods
4.1. CSF Sampling
We selected 118 consecutive patients for this study whose CSF samples were stored in
the Laboratory of Clinical Neurochemistry, Department of Medicine and Surgery, University
of Perugia (Perugia, Italy). CSF samples were collected over a 3-year period (January
2014–January 2017) via lumbar puncture at the Section of Neurology, Perugia University
Hospital, Perugia (Italy), using the same standard operating procedures throughout the
study, as recommended [
42
]. Specifically, lumbar puncture was performed between 8:00
and
10:00 a.m.,
and CSF was collected in sterile polypropylene tubes, centrifuged for 10 min
at 2000
×
g, divided into 0.5 mL aliquots and immediately frozen at
−
80
◦
C, together with
serum 0.5 mL aliquots, pending analysis. After lumbar puncture, patients’ demographic
and clinical data were collected in an online electronic database. The selected CSF samples
came from two groups of patients who were diagnosed at the time of CSF sampling, as
follows: (i) RRMS (n: 82), and (ii) OND (control group, n: 36). For all patients, CSF was
collected as part of their usual diagnostic work- up. The local Ethics Committee approved
the study conduction (# 1287/08 and #3933/21).
4.2. Selection of CSF Samples
For the MS group, we selected CSF samples from pwMS satisfying, at the time of
lumbar puncture, the following inclusion criteria: (i) a diagnosis of RRMS according to
the 2017 revision of the McDonald criteria [
2
], retrospectively applied; (ii) age between
18 and 60 years; (iii) no history of exposure, in the 30 days prior to CSF collection, to
immunosuppressant or immunomodulatory therapies. For the OND group, we selected
patients who underwent CSF analysis for diagnostic reasons, with the following inclusion
criteria: (i) a final diagnosis of minor non-inflammatory neurological diseases; (ii) age
between 18 and 60 years; (iii) no history of exposure to immunosuppressant therapies in
the 30 days preceding lumbar puncture. A senior neurologist with experience in the field
Int. J. Mol. Sci. 2024,25, 139 10 of 14
of MS examined all the pwMS included for this study and scored the Kurtzke’s Expanded
Disability Status Scale (EDSS) [43].
4.3. Standard CSF Analysis
OCB pattern detection was achieved by running both serum and coupled CSF samples
by means of IEF [
44
] on a semi-automated agarose electrophoresis system (Sebia Hydrasys,
Lisses, France) followed by immunofixation with a peroxidase-labeled anti-IgG (Hydragel
9 CSF Isoelectrofocusing; Sebia, Lisses, France). An aliquot of each serum sample was
appropriately diluted to adjust the IgG concentration to the same level as found in the
CSF, as specified by the manufacturer. Following OCB pattern evaluations, pwMS were
divided into two groups: (i) OCB negative (IEF patterns 1, 4 and 5) and (ii) OCB positive
(IEF patterns 2 and 3) when
≥
2 additional OCB were detected in CSF compared to serum
samples. Since, in all cases, CSF was collected during the diagnostic assessment, none of
the pwMS was on disease-modifying therapy at the time of lumbar puncture.
4.4. CSF NfL Measurement
CSF NfL was measured in the Institute of Neuroscience and Physiology, Department
of Psychiatry and Neurochemistry at the Sahlgrenska Academy, University of Gothenburg
(Mölndal, Sweden), through an in-house ELISA, as already described [
45
]. All samples
were analyzed by board-certified laboratory technicians, all blinded to clinical data, by
using one batch of reagents at a time.
4.5. CSF PEA Testing
Inflammatory protein panel testing was performed in 2017 using the multiplex PEA
technology as previously described by Olink (Uppsala, Sweden). All the samples were
run on the inflammation panel, which consists of 92 biomarkers (Table S1), with up to
96 samples
tested simultaneously on each run. The Olink panel validation data are freely
available online (https://www.olink.com/data-you-can-trust/validation/, accessed on
23 July 2021
). The resulting data for each biomarker were expressed as normalized protein
expression (NPX) values. NPX is an arbitrary unit on a log2 scale that is obtained by
normalizing the concentration values to minimize inter- and intra-assay variations. A high
NPX value corresponds to a high protein concentration and can be linearized by using the
formula 2NPX. NPX values were subsequently z-scored to allow for a better comparison in
multivariate analysis.
4.6. Characteristics of the Patients
The MS group was composed of 82 subjects (F/M 58/24) with a mean age of
37.9 ±10.6 years
. Clinical characteristics are summarized in Table 4. CSF IgG OCB were
found in 58 people with RRMS (MS OCB+: 58/82, 70.7%). The remaining 24 people
with RRMS (29.3%) did not show CSF IgG OCB (MS OCB
−
). No significant clinical
differences were found between MS OCB+ and MS OCB
−
(Table 1). The OND group
was composed of 36 individuals (F/M 18/18) with a mean age of 57.9
±
16.6 years.
This group included patients with headache (n= 16), psychiatric disorders (n= 13),
mononeuropathy (n= 4) and dysmetabolic polyneuropathy (n= 3). RRMS and OND
had significantly different mean age (p< 0.001) and sex distributions (p< 0.05). Age
and sex were, therefore, considered covariates in the subsequent analyses. CSF NfL was
found to be significantly higher in RRMS compared to OND (663 pg/mL (IQR: 748) vs.
361 pg/mL (IQR: 504), p< 0.05) (Table 4).
Int. J. Mol. Sci. 2024,25, 139 11 of 14
Table 4. Patient characteristics. Data are expressed as number (percentage), mean
±
standard
deviation or median (IQR).
OND RRMS p-Value * MS OCB+ MS OCB−p-Value **
N 36 82 - 58 24 -
Sex–F 18 (50%) 58 (70.7%) <0.05 41 (70.7%) 17 (70.8%) n.s.
Age (years) 57.9 ±16.6 37.9 ±10.6 <0.01 37 ±10.3 40.2 ±11.4 n.s.
EDSS - 1.9 ±1 - 1.8 ±0.8 2.3 ±1.5 n.s.
Disease duration
(months) - 3 (23.5) - 3 (23.6) 2 (23.5) n.s.
Recent relapse
(<30 days) - 60 (73.2%) - 43 (74.1%) 17 (70.8%) n.s.
CSF NfL (pg/mL) 361 (504) 361 (504) 361 (504) 633 (929) 637.5 (558) n.s.
* OND vs. RRMS. ** MS OCB+ vs. MS OCB
−
. CSF: cerebrospinal fluid. EDSS: expanded disability status
scale. IQR: interquartile range. MS OCB+: multiple sclerosis with cerebrospinal fluid IgG oligoclonal bands.
MS OCB−: multiple
sclerosis without cerebrospinal fluid IgG oligoclonal bands. NfL: neurofilament light chain.
n.s.: not significant. OND: other neurological diseases. RRMS: relapsing remitting multiple sclerosis.
4.7. Statistical Analysis
All the analyses reported in this manuscript were performed with R-4.3.1. Descriptive
statistics, such as mean and standard deviation, were used to summarize the characteristics
of the patients, including age, sex distribution and number of subjects in each group (RRMS
patients and OND). Fisher’s exact test was used to assess whether there were significant
differences in sex distribution between RRMS patients and OND. The Mann–Whitney
U-test was employed to compare the age of RRMS patients with that of OND subjects.
Out of the 92 tested proteins, 45 had a call rate <85% (i.e., <85% of the samples had a
valid measurement of that protein) and were therefore removed from further analysis
(Table S1). Spearman’s correlation was used to analyze the correlations between the PEA-
tested proteins. Hierarchical clustering was used for ordering proteins by using correlation
coefficients as distances, which were grouped according to Ward’s linkage criterion. Logistic
regression was applied to assess the differential abundance of inflammatory proteins in
RRMS patients compared to OND subjects and to determine the age-, sex- and false
discovery rate (FDR)-adjusted p-values (q-values) for each protein. Area under the ROC
curve (AUC) values were calculated to evaluate the diagnostic performance of the proteins
showing q-values less than 0.05 in distinguishing between RRMS patients and OND
subjects. The pROC package was used for this purpose; 95% confidence intervals (CI)
of AUC were determined by the bootstrap method (2000 replicates used). Binomial and
multinomial LASSO regressions (glmnet R package) were used to differentiate among three
groups: OND, MS OCB+ and MS OCB
−
. Cross-validation was performed to identify the
optimal penalization parameter (
λ
) for the LASSO regression models. Student’s t-test was
used to check for age matching between the training sets in the multivariate analyses for
MS OCB+ vs. OND and MS OCB−vs. OND comparisons.
5. Conclusions
In conclusion, we identified a set of six CSF immunological proteins (CD5, IL12B,
TNFB, TNFSF14, TNFRSF9 and MIP-1-alpha), each of them having discriminatory power
in distinguishing MS from controls. These markers predominantly reflect T cell activation,
chronic meningeal inflammation and chemokine-mediated immune responses. Employing
a machine learning methodology allowed us to develop diagnostic models that demon-
strated satisfactory accuracy. The model designed for MS OCB+ exhibited high specificity
and moderate sensitivity, suggesting its potential value in aiding the diagnosis of MS, espe-
cially when considered alongside other sensitive diagnostic tools. However, the model for
MS OCB
−
exhibited lower accuracy, indicating potential differences in pathophysiology be-
tween MS with and without OCB. The identification of these biomarkers holds promise for
enhancing the accuracy of MS diagnosis and offering insights into MS subgroup-specific dis-
ease mechanisms. Further validation and testing against relevant clinical comparisons are
necessary to establish the robustness and clinical utility of these biomarkers. Additionally,
Int. J. Mol. Sci. 2024,25, 139 12 of 14
the distinct profiles observed between MS patients with and without OCB raise questions
about differing pathophysiological mechanisms and warrant further investigation.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/ijms25010139/s1.
Author Contributions: Conceptualization, L.G., K.B., H.Z., L.P. and M.D.F.; methodology, L.G., K.B.,
H.Z., L.P. and M.D.F.; software, G.B.; validation, L.G. and G.B.; formal analysis, G.B.; investigation,
L.G., E.D.S., S.S., A.M. and M.D.F.; resources, K.B., H.Z., L.P. and M.D.F.; data curation, L.G. and G.B.;
writing—original draft preparation, L.G., G.B. and M.D.F.; writing—review and editing, L.G., G.B.,
E.D.S., S.S., A.M., K.B., H.Z., L.P. and M.D.F.; visualization, L.G., G.B. and M.D.F.; supervision, K.B.,
H.Z., L.P. and M.D.F.; project administration, K.B., H.Z., L.P. and M.D.F.; funding acquisition, K.B.,
H.Z., L.P. and M.D.F. All authors have read and agreed to the published version of the manuscript.
Funding: H.Z. is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2022-
01018 and #2019-02397), the European Union’s Horizon Europe research and innovation program
under grant agreement No 101053962, Swedish State Support for Clinical Research (#ALFGBG-71320),
the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund
and the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C and #ADSF-21-831377-C),
the Bluefield Project, the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen
för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2022-0270), the European Union’s Horizon 2020
research and innovation program under the Marie Skłodowska-Curie grant agreement No 860197
(MIRIADE), the European Union Joint Program—Neurodegenerative Disease Research (JPND2021-
00694), the National Institute for Health and Care Research University College London Hospitals
Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003). LP is
supported by grants from the European Union’s Horizon 2020 research and innovation program
under the Marie Skłodowska–Curie grant agreement No 860197 (MIRIADE), the European Union
Joint Program—Neurodegenerative Disease Research (JPND2021-00694).
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Ethics Committee of Regione Umbria (protocol code # 1287/08 and
#3933/21).
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 request from the
corresponding author.
Conflicts of Interest: L.G. participated on advisory boards for and received writing honoraria and
funding for traveling from Almirall, Biogen, Euroimmun, Fujirebio, Eli Lilly, Merck, Mylan, Novartis,
Roche, Sanofi, Siemens Healthineers and Teva. K.B. has served as a consultant on advisory boards
or data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu, Julius Clinical, Lilly,
MagQu, Novartis, Prothena, Roche Diagnostics and Siemens Healthineers and is a co-founder of
Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator
Program. H.Z. has served on scientific advisory boards and/or as a consultant for Abbvie, Acumen,
Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Thera-
peutics, CogRx, Denali, Eisai, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon
Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet
Therapeutics and Wave; has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon,
Fujirebio, Lilly and Roche; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS),
which is a part of the GU Ventures Incubator Program (outside submitted work). MDF participated
on advisory boards for and received speaker or writing honoraria and funding for traveling from
Bayer, Biogen, Merck, Mylan, Novartis, Roche, Sanofi, Siemens Healthineers and Teva. G.B., E.D.S.,
S.S., A.M. and L.P. report no disclosure related to this study.
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