Abstract
Background
Altered proteome profiles have been reported in both postmortem brain
tissues and body fluids of subjects with Alzheimer disease (AD), but
their broad relationships with AD pathology, amyloid pathology, and
tau-related neurodegeneration have not yet been fully explored. Using a
robust automated MS-based proteomic biomarker discovery workflow, we
measured cerebrospinal fluid (CSF) proteomes to explore their
association with well-established markers of core AD pathology.
Methods
Cross-sectional analysis was performed on CSF collected from 120 older
community-dwelling adults with normal (n = 48) or impaired cognition
(n = 72). LC-MS quantified hundreds of proteins in the CSF. CSF
concentrations of β-amyloid 1–42 (Aβ[1–42]), tau, and tau
phosphorylated at threonine 181 (P-tau181) were determined with
immunoassays. First, we explored proteins relevant to biomarker-defined
AD. Then, correlation analysis of CSF proteins with CSF markers of
amyloid pathology, neuronal injury, and tau hyperphosphorylation (i.e.,
Aβ[1–42], tau, P-tau181) was performed using Pearson’s correlation
coefficient and Bonferroni correction for multiple comparisons.
Results
We quantified 790 proteins in CSF samples with MS. Four CSF proteins
showed an association with CSF Aβ[1–42] levels (p value ≤ 0.05 with
correlation coefficient (R) ≥ 0.38). We identified 50 additional CSF
proteins associated with CSF tau and 46 proteins associated with CSF
P-tau181 (p value ≤ 0.05 with R ≥ 0.37). The majority of those proteins
that showed such associations were brain-enriched proteins. Gene
Ontology annotation revealed an enrichment for synaptic proteins and
proteins originating from reelin-producing cells and the myelin sheath.
Conclusions
We used an MS-based proteomic workflow to profile the CSF proteome in
relation to cerebral AD pathology. We report strong evidence of
previously reported CSF proteins and several novel CSF proteins
specifically associated with amyloid pathology or neuronal injury and
tau hyperphosphorylation.
Electronic supplementary material
The online version of this article (10.1186/s13195-018-0397-4) contains
supplementary material, which is available to authorized users.
Keywords: Alzheimer disease, Amyloid, Biomarker, Cerebrospinal fluid,
CSF, Mass spectrometry, Proteomics, Tau, Tandem mass tag
Background
Proteome alterations have been identified in a multitude of
pathologies, such as cancer, metabolic disorders, and brain diseases
[[47]1]. Several circulating protein markers of neurodegenerative
diseases, such as Parkinson’s disease or Alzheimer disease (AD), have
been reported [[48]2], but the ones with consistent findings or of
current clinical utility are very few [[49]3]. AD is the most common
form of dementia, and there is still an urgent need for the definition
of early detection markers as well as for a better understanding of its
pathogenesis. In the latter perspective, cerebrospinal fluid (CSF)
represents a key biofluid to decipher altered protein levels and
pathways in diseases of the central nervous system (CNS) using
large-scale proteomic technologies, such as MS-based platforms.
Because of the proximity of CSF to the brain and the presence of
proteins in CSF specific to the brain [[50]4, [51]5], the CSF proteome
can reflect the biochemical and metabolic changes in the CNS. In
particular, despite the definitive confirmation of the diagnosis of AD
being possible today only at brain autopsy, specific CSF peptides and
proteins (i.e., β-amyloid 1–42 [Aβ[1–42]], total tau, and
hyperphosphorylated tau [P-tau]) linked to the main hallmarks of AD
pathology, such as amyloid plaques and neurofibrillary tangles, can
complement clinical examination for the diagnosis of AD [[52]6, [53]7].
There is now strong evidence that suggests the development of AD
pathology begins years to decades prior to the onset of the first
clinical signs. Thus, on one hand, elderly persons with normal
cognition may already have cerebral AD pathology and be at the
preclinical stage of the disease [[54]8]; on the other hand, subjects
with cognitive deficits may present with cognitive impairment
suggesting AD but not primarily or only partially related to AD
pathology. New research criteria consider AD as a biological continuum
across the clinical spectrum from asymptomatic stage to advanced
dementia and emphasize the utility of biomarkers of AD pathology for an
accurate diagnosis, in particular at the preclinical and prodromal
disease stages [[55]8–[56]10]. In this respect, endophenotype
approaches have been proposed as innovative ways to better address AD
stages using proxy measures such as the concentrations of the
aforementioned CSF markers of core AD pathology [[57]11].
Several studies have characterized the CSF proteome with MS but mainly
using sample pools and/or a limited number of samples [[58]12–[59]14].
Because of technical constraints such as limited sample throughput
[[60]15], studies in larger clinical cohorts using MS-based proteomics
are indeed limited [[61]16–[62]21]. In recent years, our group [[63]22]
and other groups [[64]23, [65]24] have demonstrated that MS-based
proteomics enables protein biomarker discovery in large numbers of
human clinical samples, providing increased statistical power and
result robustness [[66]21, [67]22, [68]25]. Although most of these
studies were performed with plasma or serum samples [[69]26], the
analysis of the CSF proteome and its alteration using MS-based
proteomics in larger cohorts has been mostly unexplored.
Our aim in this study was to investigate the CSF proteome in relation
to the core elements of CSF-defined AD pathology in older adults
(n = 120) with normal and impaired cognition using MS-based shotgun
proteomics (Fig. [70]1). We evaluated whether the CSF proteome could
relate to AD pathology, defined as the combined presence of both
amyloid pathology and tau pathology. We then explored more deeply the
relationships of the quantified proteins in CSF with well-established
biomarkers of amyloid pathology, neuronal injury, and tau
hyperphosphorylation (i.e., Aβ[1–42], tau, and tau phosphorylated at
threonine 181 [P-tau181], respectively).
Fig. 1.
[71]Fig. 1
[72]Open in a new tab
Study design and cerebrospinal fluid (CSF) proteome profiling workflow.
CSF samples from 120 older individuals with or without cognitive
impairment were analyzed using a highly automated shotgun MS-based
proteomic workflow. The workflow consists of first removing 14 highly
abundant proteins in CSF by immunoaffinity. The rest of the workflow is
automated in a 96-well plate format and includes steps of (1)
reduction, alkylation, and enzymatic digestion; (2) isobaric labeling
and pooling; and (3) purifications. The samples are analyzed with
reversed-phase LC-MS/MS, and the data are processed with standard
bioinformatic tools
Methods
Study design
One hundred twenty community-dwelling participants were included in
this study, of whom 48 were cognitively healthy volunteers and 72 had
mild cognitive impairment (MCI) (n = 63) or mild dementia of AD type
(n = 9) [[73]27]. Diagnosis of MCI or dementia was based on
neuropsychological and clinical evaluation and made by a consensus
conference of psychiatrists and/or neurologists as well as
neuropsychologists prior to inclusion in the study. The participants
with cognitive impairment were recruited from among outpatients who
were referred to the Memory Clinics, Departments of Psychiatry, and
Department of Clinical Neurosciences, University Hospitals of Lausanne,
Switzerland. They had no major psychiatric disorders or substance abuse
or severe or unstable physical illness that might contribute to
cognitive impairment, had a Clinical Dementia Rating (CDR) [[74]28]
score > 0, and met the clinical diagnostic criteria for MCI [[75]29] or
AD mild dementia according to the recommendations of the National
Institute on Aging-Alzheimer’s Association [[76]30]. In the current
study, nine subjects met criteria for probable AD dementia. Because
there is a clinical continuum between MCI and mild dementia, and
because the participants with cognitive impairment were patients from
memory clinics recruited in the same way regardless of MCI or mild
dementia classification, these subjects were grouped and labeled as
cognitively impaired with CDR > 0 (Table [77]1). The control subjects
were recruited through journal announcements or word of mouth and had
no history, symptoms, or signs of relevant psychiatric or neurologic
disease and no cognitive impairment (CDR = 0). All participants
underwent a comprehensive clinical and neuropsychological evaluation,
structural brain imaging, and venous and lumbar punctures [[78]27].
Magnetic resonance imaging (MRI) and computed tomographic scans were
used to exclude cerebral pathologies possibly interfering with
cognitive performance.
Table 1.
Demographics and clinical characteristics
P-tau181/Aβ[1–42] ≤ 0.0779
(n = 78) P-tau181/Aβ[1–42] > 0.0779
(n = 42) CDR = 0
(n = 48) CDR > 0
(n = 72)
Age, yr, mean (SD) 68.4 (8.3) 74.1 (5.6)^a 66.0 (7.4) 73.3 (6.9)^a
Gender, n (%) of males 25 (32.05%) 18 (42.86%) 17 (35.42%) 26 (36.11%)
Education, yr, mean (SD) 12.5 (2.7) 12.1 (2.4) 13.2 (2.3) 11.8 (2.7)^a
CDR, score (% of subjects, number of subjects) 0 (60.2%, 47)
or 0.5 (37.2%, 29)
or 1 (2.6%, 2) 0 (2.4%, 1)
or 0.5 (80.9%, 34)
or 1 (16.7%, 7) 0 (100%, 48) 0.5 (87.5%, 63)
or 1 (12.5%, 9)
MMSE score, mean (SD) 27.8 (2.3) 25.2 (3.7)^a 28.5 (1.4) 25.9 (3.5)^a
APOE ε4 carriers, n (%) 13 (16.67%) 24 (57.14%)^a 11 (22.92%) 26
(36.11%)^a
CSF Aβ[1–42] (pg/ml), mean (SD) 979.9 (196.4) 601.2 (190.0)^a 957.4
(194.0) 774.0 (281.5)^a
CSF tau (pg/ml), mean (SD) 235.1 (104.2) 624.2 (322.4)^a 221.5 (82.9)
471.1 (316.6)^a
CSF P-tau181 (pg/ml), mean (SD) 46.7 (13.4) 90.3 (44.8)^a 45.9 (13.3)
72.7 (40.9)^a
CSF P-tau181/Aβ[1–42], mean (SD) 0.05 (0.01) 0.16 (0.10)^a 0.049
(0.015) 0.114 (0.097)^a
CSF albumin index^b, mean (SD) 5.9 (2.4) 6.4 (2.3) 5.3 (1.9) 6.6
(2.5)^a
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Abbreviations: Aβ[1–42] β-Amyloid 1–42, APOE Apolipoprotein E, CDR
Clinical Dementia Rating, CSF Cerebrospinal fluid, MMSE Mini Mental
State Examination, P-tau181 Tau phosphorylated at threonine 181
^aStatistically different (p ≤ 0.05) from P-tau181/Aβ[1–42] ≤ 0.0779,
and CDR = 0, respectively, using t tests for continuous variables and
binomial proportion tests for categorical variables. ^bCSF albumin
index = [CSF albumin]/[serum albumin] × 100
Neuropsychological tests were used to assess cognitive performance in
the domains of memory [[80]31], language, and visuoconstructive
functions. The Mini Mental State Examination [[81]32] was used to
assess participants’ global cognitive performance. Depression and
anxiety were assessed using the Hospital Anxiety and Depression Scale
[[82]33]. The psychosocial and functional assessments included
activities of daily living and instrumental activities of daily living,
the Neuropsychiatric Inventory Questionnaire, and the Informant
Questionnaire on Cognitive Decline in the Elderly [[83]34], and these
were completed by family members of the participants. All tests and
scales are validated and widely used in the field.
CSF sample collection
Lumbar punctures were performed between 8:30 a.m. and 9:30 a.m. after
overnight fasting. A standardized technique with a 22-gauge
“atraumatic” spinal needle and a sitting or lying position was applied
[[84]35]. A volume of 10–12 ml of CSF was collected in polypropylene
tubes. Routine cell count and protein quantification were performed.
The remaining CSF was frozen in aliquots (500 μl) no later than 1 hour
after collection and stored at − 80 °C without thawing until experiment
and assay.
MS-based proteomics
CSF samples were prepared using a highly automated shotgun proteomic
workflow as previously described [[85]36] and isobaric tags [[86]37]
for relative quantification of proteins. Reversed-phase LC-MS/MS was
performed with a hybrid linear ion trap-Orbitrap Elite and an UltiMate
3000 RSLCnano System (Thermo Scientific, Waltham, MA, USA) as recently
described [[87]38]. Protein identification was performed against the
human UniProtKB/Swiss-Prot database (08/12/2014 release). All details
are provided in Additional file [88]1: Supplementary Methods.
CSF β-amyloid 1–42, tau, tau phosphorylated at threonine 181, and APOE
genotyping
The measurements were performed using commercially available
enzyme-linked immunosorbent assay kits and TaqMan assays as described
in Additional file [89]1: Supplementary Methods.
Definition of CSF biomarker profile of Alzheimer pathology
A pathological AD CSF biomarker profile was defined as CSF
P-tau181/Aβ[1–42] ratio > 0.0779 (i.e., “high” ratio for positive CSF
profile of AD pathology), based on clinical study site data [[90]39]
and in line with previous work (i.e., 0.08) [[91]40]. The cutoff
optimized the Youden index [[92]41] of the ROC curve for the prediction
of CDR categories (CDR = 0 versus CDR > 0) as previously reported
[[93]27], where the cutoff for CSF P-tau181/Aβ[1–42] ratio was further
confirmed to be a highly significant predictor of cognitive decline.
Proteomic data management
Six CSF samples were removed because of aberrant values, leaving CSF
proteomic data available for 114 subjects (exclusion of those 6
subjects did not induce bias on the overall population characteristics)
(see Additional file [94]1: Table S1)). In total, 790 CSF proteins were
quantified.
For exploration of CSF proteins relevant to AD pathology (see below),
proteins with > 5% missingness were excluded, leaving 541 CSF proteins.
The remaining missing data (5% or less per protein) were imputed by
randomly drawing a value between the observed range of biomarker
values. Log[2] of the protein ratio fold changes were scaled to mean
zero and SD of 1 prior to statistical analyses. Calculation and
statistics were performed with the R version 3.3.2 statistical software
([95]http://www.r-project.org/).
Exploratory analysis of CSF proteins relevant to Alzheimer pathology
In a first exploratory analysis, 541 CSF proteins were tested (one by
one) in a logistic regression model as follows:
[MATH: PositiveCSFprofile ofAD~CSFprotein
biomarkers+age+gender
+years of education+presence
ofAPOEε4allele :MATH]
where positive CSF profile of AD is defined by categorizing the CSF
P-tau181/Aβ[1–42] ratio into two groups: P-tau181/Aβ[1–42] > 0.0779 for
AD CSF biomarker profile (or “high”) and P-tau181/Aβ[1–42] ≤ 0.0779 for
non-AD CSF biomarker profile (or “low”). p Values were corrected for
multiple testing using the Benjamini-Hochberg procedure. Box plots were
produced for the significant hits presenting false discovery rate
(FDR) ≤ 5%.
Selection of CSF proteins relevant to Alzheimer pathology
Least absolute shrinkage and selection operator (LASSO) logistic
regression [[96]42] selected biomarkers that best predict CSF biomarker
profile of AD pathology. A reference model was initially generated,
testing variables that are likely to be available to clinicians and
known risk factors for AD to provide a benchmark for comparison with
the model that included CSF proteins. These inputs included age,
gender, years of education, and presence of the apolipoprotein E (APOE)
ε4 allele, such as:
[MATH: PositiveCSFprofile ofAD~age+gender+years of
education+presence ofAPOEε4allele :MATH]
In addition to all variables used to make the reference models, CSF
protein measurements (i.e., 541 CSF proteins) and CSF albumin index
were then included in building so-called best models:
[MATH: PositiveCSFprofile ofAD~CSFprotein
biomarkers+CSFalbumin
index+age+gender+
years of education+presence
ofAPOEε4allele :MATH]
A tenfold cross-validation process was performed for each LASSO
analysis using the glmnet package [[97]43], which allows estimating the
confidence interval of the misclassification error for each value of
the regularization parameter λ. The LASSO analyses were repeated 100
times (1000 times for the reference models). The model that minimized
the upper limit of the cross-validated misclassification error
confidence interval across the 100 runs with less than 20 features
(when possible) was selected. The results were formally tested for
significance against the reference model using accuracy with a McNemar
test. The group differences for the CSF proteins selected in the best
models were graphically illustrated in box plots and assessed using t
test statistics. In addition, Kruskal-Wallis test statistics produced
comparable results (see Additional file [98]1: Tables S2 and S3).
Because the tests were applied only to the proteins selected with
LASSO, p values obtained from these analyses were not corrected for
multiple testing.
Statistical Pearson’s correlation and bioinformatic analysis
Correlation analysis was performed on protein fold changes of all 790
quantified proteins using Pearson’s correlation coefficient and
Bonferroni correction for multiple comparisons. In addition, Spearman’s
correlation analyses produced comparable results (see Additional file
[99]1: Tables S4–S6). Several bioinformatics tools and resources were
used for analysis and protein annotation (i.e., Database for
Annotation, Visualization and Integrated Discovery [DAVID] 6.8
[[100]44], UniProt tissue annotation database [[101]45], Gene Ontology
database [[102]46], Kyoto Encyclopedia of Genes and Genomes [KEGG]
database [[103]47], tissue atlas [[104]48], and Venny
[[105]http://bioinfogp.cnb.csic.es/tools/venny/]).
Results
Demographic and clinical characteristics of the study population
Demographics and clinical characteristics of the patient cohort are
detailed in Table [106]1. The cognitively impaired subjects (CDR > 0)
were older and less educated and had a higher prevalence of APOE ε4
genotype than the cognitively intact group (CDR = 0). In cognitive
impairment, CSF Aβ[1–42] was lower, whereas CSF tau, CSF P-tau181, and
CSF P-tau181/Aβ[1–42] were all higher. MS-based proteomic analyses were
performed in the CSF of the 120 individuals (Fig. [107]1). In total, we
measured 790 proteins in CSF. Of those, 541 proteins presented < 5%
missing values in 114 subjects (see the [108]Methods section above).
The following classification analyses of the CSF P-tau181/Aβ[1–42]
ratios were aimed at separating 39 patients with high-expression AD CSF
biomarker profiles (i.e., P-tau181/Aβ[1–42] > 0.0779) from 75
low-expression profile subjects in the complete analysis set,
regardless of the clinical diagnosis. Then, the analyses were performed
on the subset of cognitively impaired patients, where 38 and 28
subjects had high and low expression of AD CSF biomarker profiles,
respectively.
Identification of Alzheimer pathology with CSF proteins
First, we explored whether the CSF proteome presents specific
alterations in AD, endophenotypically defined a priori as a CSF
P-tau181/Aβ[1–42] ratio > 0.0779 (see the [109]Methods section above).
In the whole sample, group comparisons (i.e., “high” when
P-tau181/Aβ[1–42] > 0.0779 and “low” when P-tau181/Aβ[1–42] ≤ 0.0779)
revealed 22 CSF proteins with significant differences between AD versus
non-AD CSF biomarker profiles after correction for multiple testing
using the Benjamini-Hochberg procedure at FDR ≤ 5% (Fig. [110]2a and
Additional file [111]1: Table S7). Similarly, in the subset of
cognitively impaired subjects (see the [112]Methods section above),
group comparisons provided ten CSF proteins with significant
differences (Fig. [113]2b and Additional file [114]1: Table S8). All of
these 10 proteins were already present among the 22 proteins (Fig.
[115]2) previously identified in the whole sample.
Fig. 2.
[116]Fig. 2
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Cerebrospinal fluid (CSF) proteins relevant to Alzheimer pathology. Box
plots of CSF proteins according to CSF tau phosphorylated at threonine
181 (P-tau181)/β-amyloid 1–42 (Aβ[1–42]) ratio (i.e., “high” when
P-tau181/Aβ[1–42] > 0.0779 [blue dots] and “low” when
P-tau181/Aβ[1–42] ≤ 0.0779 [red dots]) for positive and negative CSF
profiles of AD pathology, respectively, in all subjects (a) and
restricted to subjects with cognitive impairment (b). In total, 541 CSF
proteins were tested (one by one) in a logistic regression model. P
values were corrected for multiple testing using the Benjamini-Hochberg
procedure. Box plots were produced for the significant hits presenting
false discovery rate ≤ 5%. Relative protein fold change ratios were
used (in Log2). Human proteins in the box plots are given by their
UniProtKB/Swiss-Prot entry name
As a second exploratory approach and ability assessment of the CSF
proteome to identify AD, we used LASSO logistic regression to build
mathematical models able to classify AD pathology, again defined a
priori as a CSF P-tau181/Aβ[1–42] ratio > 0.0779 (see the [118]Methods
section above). In the whole sample, the benchmark reference model for
classification of CSF P-tau181/Aβ[1–42] included age and presence of
the APOE ε4 allele. Its prediction accuracy was 78.3% (as compared with
the accuracy of a majority class prediction of 65.8%). CSF protein
biomarkers were indeed able to improve the classification of AD CSF
biomarker profile with respect to the reference model. The best model
accuracy was 100% (McNemar p value 3.35 × 10^− 7). It included 26 CSF
proteins (from the 541 provided as input) in addition of age and
presence of the APOE ε4 allele. Only seven selected CSF proteins
displayed significant group comparison differences, i.e., 14-3-3
protein ζ/δ (1433Z) (p = 1.69 × 10^− 3), SPARC-related modular
calcium-binding protein 1 (SMOC1) (p = 5.26 × 10^− 5), KICSTOR complex
protein SZT2 (SZT2) (p = 5.47 × 10^− 4), fatty acid-binding protein,
heart (FABPH) (p = 8.70 × 10^− 4), chitinase-3-like protein 1 (CH3L1)
(p = 1.23 × 10^− 3), neuromodulin (NEUM) (p = 3.40 × 10^− 3), and
keratin, type I cytoskeletal 10 (p = 0.025) (Additional file [119]1:
Figure S1a). Many of these CSF proteins were correlated with each other
(Additional file [120]1: Figure S2). Six of the seven proteins (i.e.,
1433Z, SMOC1, SZT2, FABPH, CH3L1, and NEUM) were reported in the
exploratory group comparisons (Fig. [121]2a).
In the subset of cognitively impaired subjects (see the [122]Methods
section above), the benchmark reference model to classify AD CSF
biomarker profile included age, gender, years of education, and
presence of APOE ε4 allele, with a prediction accuracy of 77.8%
(majority class prediction of 57.6%). In cognitive impairment,
inclusion of CSF protein biomarkers again improved significantly the
prediction accuracy to 100% (McNemar p value of 0.0003). In total, 18
CSF proteins (from the 541 provided as input) were included in this
best model in addition to gender and presence of the APOE ε4 allele.
Among those proteins, four displayed significant differences between
the groups: 1433Z (p = 4.04 × 10^− 5), SMOC1 (p = 5.49 × 10^− 5),
γ-synuclein (p = 1.19× 10^− 3), and macrophage colony-stimulating
factor 1 receptor (p = 0.013) (Additional file [123]1: Figure S1b).
Again, several correlations were observed between the CSF proteins
retained in the model (Additional file [124]1: Figure S3), suggesting
that models with fewer variables may still provide high classification
performance. Two of the four proteins (i.e., 1433Z and SMOC1) were
reported in the exploratory group comparisons (Fig. [125]2b). The
perfect performance to classify the participants with AD pathology
indicated that the reported models were very possibly overfitting the
data.
Associations of CSF proteins with β-amyloid 1–42, tau, and tau phosphorylated
at threonine 181
Next, we separately and more specifically studied the associations of
all 790 quantified CSF proteins (no minimal missing value criteria
applied) with CSF markers of core AD pathology (i.e., Aβ[1–42], tau,
and P-tau181). Four proteins—cannabinoid receptor 1 (CNR1, correlation
coefficient [R] = 0.3929), neuroendocrine convertase 2 (NEC2,
R = 0.3818), neuronal pentraxin-2 (NPTX2, R = 0.3868), and somatostatin
(SMS, R = 0.4188)—showed an association with CSF Aβ[1–42], which was
significant (p value ≤0.05) after Bonferroni correction for multiple
testing (Fig. [126]3a). We found 50 CSF proteins correlated with CSF
tau (Fig. [127]3b) and 46 associated with CSF P-tau181 (Fig. [128]3c)
in a significant manner after Bonferroni correction, of which 41 were
in common (Fig. [129]3d). The five strongest correlations with CSF tau
were CSF neurogranin (NEUG), sodium/potassium-transporting ATPase
subunit α-2 (AT1A2), brain acid soluble protein 1 (BASP1), 1433Z, and
NEUM. The five strongest correlations with CSF P-tau181 were CSF AT1A2,
disintegrin and metalloproteinase domain-containing protein 10 (ADA10),
N^G,N^G-dimethylarginine dimethylaminohydrolase 1 (DDAH1), NEUG, and
SMOC1. In particular, CSF NEUG and NEUM [[130]49], two synaptic
proteins, were positively correlated with CSF tau (R = 0.6721 and
0.5287, respectively) and P-tau181 (R = 0.5074 and 0.4741,
respectively) (Additional file [131]1: Figure S4). All the observed
associations are summarized in the chord diagram of Additional file
[132]1: Figure S5. With the exception of ectonucleotide
pyrophosphatase/phosphodiesterase family member 2, which negatively
correlated with tau, all reported correlations were positive.
Fig. 3.
[133]Fig. 3
[134]Open in a new tab
Correlations of cerebrospinal fluid (CSF) proteins with β-amyloid 1–42
(Aβ[1–42]), tau, and tau phosphorylated at threonine 181 (P-tau181)
concentrations in CSF. Correlation of CSF proteins with CSF Aβ[1–42]
(a), CSF tau (b), and CSF P-tau181 (c). Only significant correlations
with a p value ≤ 0.05 after Bonferroni correction for multiple testing
were retained and are displayed in the graphs. CSF proteins correlating
with CSF Aβ[1–42], tau, and P-tau181 are illustrated in a Venn diagram
(d)
Annotations of CSF proteins correlating with β-amyloid 1–42, tau, and tau
phosphorylated at threonine 181
Of the 59 proteins displaying correlations in those analyses (Fig.
[135]3d), most are expressed in the brain, in particular in the fetal
brain cortex and Cajal-Retzius cells (Fig. [136]4a). Moreover, and
based on the tissue-based map of the human proteome [[137]48], seven
proteins (i.e., SLIT and NTRK-like protein 1, NEUM, NEUG, cell adhesion
molecule 2, lymphocyte antigen 6H [LY6H], transgelin-3 [TAGL3], and
protein lifeguard) are brain-enriched (i.e., having at least fivefold
higher mRNA levels in the brain as compared with all other tissues) and
a total of 22 proteins have elevated gene expression in the brain
(i.e., in addition to the seven above, AT1A2, immunoglobulin
superfamily containing leucine-rich repeat protein 2 [ISLR2],
sodium/potassium-transporting ATPase subunit α-3 [AT1A3], BASP1, CH3L1,
CNR1, ephrin type-B receptor 6 [EPHB6], NPTX2, paralemmin-1, NEC2,
proline-rich transmembrane protein 2, SMOC1, VPS10 domain-containing
receptor SorCS1, SMS, and V-set and transmembrane domain-containing
protein 2A).
Fig. 4.
Fig. 4
[138]Open in a new tab
Annotations of cerebrospinal fluid (CSF) proteins correlating with
β-amyloid 1–42 (Aβ[1–42]), tau, and/or tau phosphorylated at threonine
181 (P-tau181) concentrations in CSF. Tissue annotation using the
UniProt tissue annotation database (a) and Gene Ontology (GO) (cellular
component category) annotation (b) obtained with DAVID software for the
59 CSF proteins correlating with CSF Aβ[1–42], tau, and/or P-tau181.
Significant enrichment (Benjamini-Hochberg procedure) is indicated with
an asterisk. The background used for the enrichment analysis was the
790 detected proteins in CSF. n.s. Nonsignificant
In Fig. [139]4b, we identified the myelin sheath as an enriched
cellular component. Of the 59 CSF proteins correlating with Aβ[1–42],
tau, and/or P-tau181, 9 proteins pertain to the myelin sheath: TAGL3,
malate dehydrogenase, cytoplasmic (MDHC), heat shock cognate 71 kDa
protein (HSP7C), AT1A2, phosphoglycerate mutase 1 (PGAM1), superoxide
dismutase [Cu-Zn] (SODC), AT1A3, pyruvate kinase PKM (KPYM), and
L-lactate dehydrogenase B chain (LDHB). Those nine proteins were
associated with tau and/or P-tau181. Pathway enrichment analysis using
the KEGG database did not yield any significant results (data not
shown).
Discussion
In the present study, we used MS-based shotgun proteomics to measure
the CSF proteomes of 120 older adults and investigate broad CSF protein
relationships with core AD pathology. Overall, human CSF proteome
coverage was composed of 790 proteins. Four CSF proteins were
associated with CSF Aβ[1–42] levels, 50 proteins with CSF tau, and 46
proteins with CSF P-tau181 levels. The CSF proteins related to Aβ[1–42]
were different from those associated with tau or P-tau181.
To explore the relevance of the CSF proteome to AD pathology, we
applied an approach that was unbiased by the clinical diagnosis and
defined endophenotypically the disease as the presence of “core” AD
pathology (i.e., the combined presence of cerebral amyloid and tau
pathology). Unbiased classification based on markers of cerebral
amyloid and tau pathology and neuronal injury has been proposed for use
across the clinical stages [[140]7]. We first used two exploratory
approaches to evaluate and select CSF proteins that were able to
stratify subjects according to levels of CSF P-tau181/Aβ[1–42]. Using
LASSO logistic regression, we observed that CSF proteins could
significantly increase the classification accuracy of non-AD versus AD
CSF biomarker profiles as compared with models based only on clinical
parameters and the presence of the APOE ε4 allele. Nonetheless, those
statistical models relying on CSF proteins might be overfitted and
should be interpreted with caution; class imbalance also affected their
strict performance. Overall, with both exploratory analyses, we
identified specific CSF proteome alterations that are related to AD
pathology and may provide novel mechanistic insights. Assessing the
whole sample and the subgroup of subjects with cognitive impairment, we
could decipher the strong contribution of some CSF proteins, such as
SMOC1 and 1433Z (Fig. [141]2 and Additional file [142]1: Figure S1). On
the basis of this performance, we specifically investigated
associations of CSF proteins with individual most validated biomarkers
of amyloid pathology, neuronal injury, and tau hyperphosphorylation
(i.e., Aβ[1–42], tau, and P-tau181, respectively) to elaborate further
on the involved mechanisms. Most of the correlations of CSF proteins
were with CSF tau and P-tau181 (Fig. [143]3d), suggesting the CSF
proteome alterations to be more representative of tau pathology than
amyloid pathology. Four CSF proteins not related to tau and P-tau181
were associated with CSF Aβ[1–42] levels, overall indicating distinct
proteome alterations related to either amyloid pathology or tau-related
neurodegeneration. The majority of these proteins were brain-enriched
proteins, including synaptic proteins, and proteins involved in
reelin-producing cells and the myelin sheath. Comparison of the
proteins found with different levels in AD versus non-AD CSF biomarker
profiles and in the models able to classify CSF-defined AD pathology
with those associated with CSF Aβ[1–42], tau, and P-tau181 in Venn
diagrams (Additional file [144]1: Figures S6 and S7, respectively)
revealed mixed overlaps. Interestingly, the 22 proteins with different
levels in AD versus non-AD CSF biomarker profiles (Fig. [145]2a) were
all associated with CSF tau; a large majority were associated with CSF
P-tau181; but none were associated with CSF Aβ[1–42] (Additional file
[146]1: Figure S6). Nevertheless, beyond those 22 proteins, 37
proteins, still representing the majority of CSF proteins associated
with CSF Aβ[1–42], tau, and P-tau181, were not evidenced as having a
relationship to AD, suggesting they might represent more general makers
of amyloid pathology, neuronal injury, and tau hyperphosphorylation.
The CSF proteins CNR1, NEC2, NPTX2, and SMS were associated with CSF
Aβ[1–42] in our study (Fig. [147]3a). CNR1 and the endocannabinoid
system were previously identified as potential targets for treatment of
neurological disorders and AD in particular [[148]50, [149]51]. In line
with our results, higher NPTX2, a proinflammatory protein involved in
synaptic plasticity, was previously associated with higher CSF Aβ[1–42]
in the Alzheimer’s Disease Neuroimaging Initiative study [[150]52].
NEC2, also known as prohormone convertase 2, is essential to the
processing of pro-islet amyloid polypeptide [[151]53]. Its role in the
processing of hormones and in particular of neuropeptide precursors in
the human cortex has been established, but the link with SMS deficiency
in AD, for instance, was not confirmed [[152]54]. Relevant to our
observations, neuropeptide SMS is known to be decreased in the CSF of
patients with AD [[153]55] and to regulate Aβ[1–42] via proteolytic
degradation [[154]56]. Together, these findings indicate
amyloid-related changes in the CSF proteome that may be particularly
relevant for early cerebral AD pathology as well as for
disease-modifying interventions targeting amyloid and starting at
preclinical disease stages.
We found that CSF Aβ[1–42], tau, and P-tau181 were mainly associated
with CSF proteins enriched in brain tissue (Fig. [155]4a), and this
despite the important proportion (about 80%) of proteins in CSF
originating from blood [[156]4]. In particular, some are expressed in
the fetal brain cortex. We observed positive correlations between CSF
tau and/or P-tau181 with 13 CSF proteins (i.e., calmodulin,
fructose-bisphosphate aldolase A [ALDOA], DDAH1, HSP7C, KPYM, LDHB,
MDHC, PGAM1, phosphatidylethanolamine-binding protein 1 [PEBP1],
stathmin, TAGL3, thioredoxin, and 1433Z) known also to be present in
reelin-producing Cajal-Retzius cells. In early AD, a massive decline of
the number of Cajal-Retzius cells was previously described [[157]57],
suggesting a link between their loss, reduction of reelin, impairment
of synaptic plasticity, amyloid plaque deposition, and neurofibrillary
tangle formation [[158]58]. Interestingly, we also revealed the
involvement of nine CSF proteins (i.e., AT1A2, AT1A3, HSP7C, KPYM,
LDHB, MDHC, PGAM1, SODC, and TAGL3), again positively correlating with
CSF tau and/or P-tau181, being specifically part of the myelin sheath.
Although amyloid plaques and neurofibrillary tangles likely induce
neuronal and synaptic loss, myelin alteration may also participate in
the development of AD dementia. Myelin content changes in the white
matter measured with MRI have been linked to CSF AD biomarkers (i.e.,
lower concentrations of Aβ[1–42] and higher concentrations of tau and
P-tau181), but mainly in association with amyloid pathology [[159]59].
Our results, including associations of AT1A2 and KPYM with both tau and
P-tau181, may suggest an underestimated connection between tau-related
neurodegeneration and (de)myelination. These specific alterations
provide new insights into the disease pathology and deserve further
exploration.
Several single relationships between CSF proteins and Aβ[1–42], tau,
and/or P-tau181 levels in our study (Fig. [160]3) have previously been
reported. A first example is the synaptic protein NEUG, which was
previously proposed as a novel candidate CSF biomarker for AD and
prodromal AD; high CSF NEUG was shown to predict future cognitive
decline and to be more specific for AD than tau [[161]60]. In addition,
CSF NEUG was reported to be increased in AD and positively correlated
with CSF tau [[162]61] and P-tau [[163]49]. In line with our
observations, positive associations were identified with NEUM for both
tau and P-tau in CSF [[164]49]. BASP1, like NEUM, is a presynaptic
membrane protein participating in axon guidance, neurodegeneration, and
synaptic plasticity [[165]62] and was found to be significantly
downregulated in AD versus control brain samples [[166]63]. Our
findings of significant association of CSF BASP1 with both CSF tau and
P-tau warrant further investigations. Mutations in the ADAM10 gene,
which encodes the major α-secretase responsible for cleaving APP, have
previously been identified in families with late-onset AD [[167]64]. In
our study, protein ADA10, which is encoded by ADAM10, was only
significantly associated with CSF P-tau181. To the best of our
knowledge, such an association between those CSF proteins has not been
observed before [[168]65].
Further and broader cross-validation of our findings can be made by
comparing them with those of a recent study investigating CSF proteins
associated with CSF AD biomarkers in 58 cognitively healthy men using
an aptamer-based technology (i.e., SOMAscan; SomaLogic, Boulder, CO,
USA) [[169]66]. Of the 59 CSF proteins associated with CSF biomarkers
of core AD pathology that we report, 28 were also measured with the
SOMAscan in that prior study; of those, 22 proteins (i.e., 78.6%
overlap) were correlated with CSF Aβ[1–42], tau, and/or P-tau
[[170]66], confirming part of our observations in an independent cohort
and using a different technology. Those proteins are ALDOA, dynein
light chain 2, cytoplasmic, polyubiquitin B, ISLR2, EPHB6, MDHC, SH3
domain-binding glutamic acid-rich-like protein, PEBP1, NPTX2,
chromogranin A, cytochrome c, SMS, 1433Z, LDHB, SMOC1, 14–3-3 protein
β/α, spondin-1, FABPH, transmembrane emp24 domain-containing protein 4,
PGAM1, cytokine-like protein 1, and HSP7C.
Altogether, our shotgun MS-based proteomic approach [[171]22] was
confirmed to provide relevant findings and to be complementary to
alternative proteomic technologies. In this perspective, the
identification of novel and strongly significant associations of CSF
proteins with CSF biomarkers of AD core pathology in our study is of
specific interest. In particular, proteins AT1A2 and KPYM implicated in
energy production, as well as 1433Z, DDAH1, and SMOC1, showing some of
the strongest associations with tau and/or P-tau181 in addition to NEUG
and NEUM, could appear relevant. Our results in a relatively large
group of subjects including both participants with cognitive impairment
and healthy volunteers are therefore encouraging. Sample fractionation
would have allowed deeper proteome coverage but with a throughput
incompatible with the analysis of 120 clinical samples in a reasonable
time frame. The proteins we have identified would deserve additional
research.
Conclusions
Using an MS-based proteomic workflow, we have quantified a number of
CSF proteins in 120 older adults with normal cognition and with
cognitive impairment. We report strong evidence of known and new CSF
proteins related to amyloid pathology, neuronal injury, and tau
hyperphosphorylation. Although we confirmed several previous findings
of CSF proteins related to AD pathology, our work reveals a large
number of additional CSF proteome alterations involving in particular
reelin-producing cells and the myelin sheath.
Additional file
[172]Additional file 1: Supplementary Methods.^ (2.5MB, docx)
Table S1. Demographics and clinical characteristics of subjects removed
from the statistical analyses. Table S2. Non-AD versus AD CSF biomarker
profile group comparison after selection in all subjects of 26 proteins
with LASSO. Table S3. Non-AD versus AD CSF biomarker profile group
comparison after selection in subjects with cognitive impairment of 18
proteins with LASSO. Table S4. Correlation of CSF proteins with CSF
Aβ1-42. Table S5. Correlation of CSF proteins with CSF tau. Table S6.
Correlation of CSF proteins with CSF P-tau181. Table S7. Group
comparisons of CSF protein measurements for AD versus non-AD CSF
biomarker profiles in all subjects. Table S8. Group comparisons of CSF
protein measurements for AD versus non-AD CSF biomarker profiles in
subjects with cognitive impairment. Figure S1. Box-plots of CSF
proteins (selected with LASSO analyses) for positive and negative CSF
profiles of AD pathology in all subjects and subjects with cognitive
impairment. Figure S2. Pairwise correlation heatmap of the 26 CSF
proteins selected with LASSO for classification of non-AD versus AD CSF
biomarker profiles for all subjects. Figure S3. Pairwise correlation
heatmap of the 18 CSF proteins selected with LASSO for classification
of non-AD versus AD CSF biomarker profiles for subjects with cognitive
impairment. Figure S4. Correlations of CSF neurogranin and neuromodulin
with CSF tau and P-tau181. Figure S5. Chord diagram of the
relationships of 59 CSF proteins with CSF tau, P-tau181, and/or Aβ1-42.
Figure S6. Venn diagrams of CSF proteins with significant group
comparison differences between AD versus non-AD CSF biomarker profiles
and those correlating with CSF Aβ1-42, tau, and P-tau181. Figure S7.
Venn diagrams of CSF proteins selected with LASSO to classify non-AD
versus AD CSF biomarker profiles and those correlating with CSF Aβ1-42,
tau, and P-tau181. (DOCX 2575 kb)
Acknowledgements