Abstract
Blood-based test has been considered as a promising way to diagnose and
study Alzheimer’s disease (AD). However, the changed proportions of the
leukocytes under disease states could confound the aberrant expression
signals observed in mixed-cell blood samples. We have previously
proposed a method, Ref-REO, to detect the leukocyte specific expression
alterations from mixed-cell blood samples. In this study, by applying
Ref-REO, we detect 42 and 45 differentially expressed genes (DEGs)
between AD and normal peripheral whole blood (PWB) samples in two
datasets, respectively. These DEGs are mainly associated with
AD-associated functions such as Wnt signaling pathways and
mitochondrion dysfunctions. They are also reproducible in AD brain
tissue, and tend to interact with the reported AD-associated biomarkers
and overlap with targets of AD-associated PWB miRNAs. Moreover, they
are closely associated with aging and have severer expression
alterations in the younger adults with AD. Finally, diagnostic
signatures are constructed from these leukocyte specific alterations,
whose area under the curve (AUC) for predicting AD is higher than 0.73
in the two AD PWB datasets. In conclusion, gene expression alterations
in leukocytes could be extracted from AD PWB samples, which are closely
associated with AD progression, and used as a diagnostic signature of
AD.
Introduction
Alzheimer’s disease (AD) is the predominant form of dementia. The
pathological features of AD include the presence of amyloid plaques,
neurofibrillary tangles, synaptic loss, soluble amyloid-β (Aβ)
oligomers, neuritic dystrophy, and eventual neurodegeneration^[40]1. In
clinical practice, AD diagnosis is mainly based on PET imaging or
cerebral spinal fluid biomarkers. The major disadvantages of these
diagnostic approaches are the high cost, the low patient compliance,
and most importantly, the difficulty in diagnosing AD at an early
stage^[41]2.
The natural role of blood cells in immune response to physiologic and
pathologic changes has made blood an important source for investigation
of disease-associated molecular biomarkers^[42]3. Recent studies have
also demonstrated a significant degree of covariability in gene
expression between brain tissue and peripheral blood cells^[43]4–[44]7.
Therefore, a diagnostic blood biomarker for AD would be valuable and
convenient for the early diagnosis of patients presenting at clinics
with memory complaints. Actually, the potential use of peripheral whole
blood (PWB) or peripheral blood mononuclear cell (PBMC) gene expression
profiling in the diagnosis of brain disorders has been
described^[45]6–[46]10. These studies detected and analyzed the
significantly altered genes^[47]6,[48]9 or modules^[49]8,[50]10 by
directly comparing the expression measurements between AD and normal
blood samples. It’s noted that relative proportions of the blood cells
may shift under disease states^[51]11,[52]12, which may confound the
aberrant disease signals originated from leukocytes when directly
comparing expression values of genes between disease and normal blood
samples^[53]13,[54]14. Consequently, the changed proportions of
leukocyte subtypes could introduce some differentially expressed genes
(DEGs) between disease samples and normal controls which actually have
no expression changes in any leukocyte subtypes^[55]15. Therefore, it
is necessary to exclude alteration signals originating from leukocyte
subtype proportion changes when trying to detect AD-associated cellular
molecular changes from mixed-cell blood samples.
In recent years, researchers have developed methods based on
deconvolution^[56]16 or surrogate variable analysis
algorithms^[57]17,[58]18 to avoid the influence of relative leukocyte
subtype proportion changes on the overall signals of PWB or PBMCs.
Methods based on deconvolution algorithms aim to estimate and adjust
the proportion of each leukocyte subtype in blood samples using the
expression profiles of purified leukocyte subtypes^[59]16. However, the
absolute quantitative gene expression level measurements used in these
methods could be sensitive to systematic biases of microarray
measurements especially examined in different microarray
platforms^[60]19. Methods based on surrogate variable analysis aim to
find true disease-associated alterations by estimating and adjusting
the confounding factors that could have effects on gene expression
levels^[61]17,[62]18. However, it’s difficult for them to avoid the
influence of cell proportion changes that are indeed associated with
disease progression^[63]15. More recently, we proposed a method,
Ref-REO, to detect leukocyte-specific molecular alterations from
mixed-cell blood samples of patients through analyzing the disrupted
patterns of the pre-determined within-sample relative expression
orderings (REOs) of genes which are consistent in purified normal
leukocyte subtypes^[64]15. This method is based on the fundamental that
if the REOs of any two genes have consistent patterns (eg.
[MATH:
EA>EB :MATH]
) in all normal leukocyte subtypes, these consistent patterns could be
stable in PWB or PBMCs, no matter how the proportion of the constituent
cells changes when no expression alterations occur in leukocytes. If
inconsistent patterns are observed in disease samples, at least one of
these two genes has altered gene expression in certain leukocyte
subtypes. The Ref-REO method has been shown to have higher precision
and recall than the previous methods^[65]15. Most importantly, the REOs
of genes have been reported to be more robust than the absolute
measured levels as REOs are invariant to monotonic data transformation
(normalization) and rather resistant to batch effects^[66]19,[67]20,
indicating the disease-associated biomarkers detected by this method
could be easily validated and transferred.
Therefore, in this study, we apply the Ref-REO method to detect and
analyze the AD-associated cellular expression alterations from two
independent AD PWB datasets. The results showed that these
AD-associated molecular alterations detected from PWB by Ref-REO were
significantly enriched in AD-associated pathways. They were
reproducible in brain tissue of AD-patients, and had interactions with
reported AD biomarkers and overlaps with the targets of AD-associated
miRNAs.
Materials and Methods
Datasets
The gene expression data were downloaded from the Gene Expression
Omnibus database (GEO, [68]http://www.ncbi.nlm.nih.gov/geo/). Detailed
information for each dataset was described in Table [69]1. The PLS-47
([70]GSE28490) dataset examined 47 expression profiles for nine
leukocyte subtypes, which were isolated from healthy human blood and
assessed for cell type purity by flow cytometry^[71]21. The PLS-33
([72]GSE28491) dataset examined 33 expression profiles for seven
leukocyte subtypes, which were obtained from a separate panel of
healthy donors at the University Hospital of Geneva. These two datasets
were used to detect the gene pairs with stable REOs in each purified
leukocytes^[73]21. The PWB-AD-249 ([74]GSE63060) and PWB-AD-275
([75]GSE63061) datasets examined the PWB expression profiles for AD and
normal control samples which were obtained from the AddNeuroMed
consortium, a large cross-European AD biomarker study and a follow-on
Dementia Case Register (DCR) cohort in London^[76]22. These two
datasets were used to detect the AD-associated molecular alterations in
leukocytes. The PWB-Normal-61 dataset included the PWB expression
profiles for 61 healthy controls with age ranging from 18 to 56, which
were obtained from the GEO dataset ([77]GSE19151)^[78]23. In
[79]GSE19151, control samples of unknown age were excluded, thus only
the 61 samples with age information were collected in PWB-Normal-61,
which was used to detect aging-associated genes. The Brain-AD-161
([80]GSE5281) dataset examined 161 expression profiles of six brain
regions for AD and normal control samples^[81]24. This dataset was used
to evaluate whether the AD-associated PWB molecular alterations had
expression changes in brain tissue. For each dataset, the normalized
data were downloaded from GEO. The original platform annotation file
obtained from GEO for each dataset was used to annotate the CloneIDs to
GeneIDs. The number of genes measured in each dataset was shown in
Table [82]1. Totally, 8,708 genes commonly measured in all datasets
were analyzed in the study.
Table 1.
Datasets analyzed in this study.
Dataset^* Characteristic of Sample Age (Years) Platform (#Gene) GEO
Accession ID Ref
Purified leukocyte subtypes (PLS)
PLS-47 All: 47 Monocytes: 10, B cells:5, CD4+ T cell:5, NK cells:5,
CD8+ T cell:5, Eosinophils:4, mDCs:5; Neutrophils:3, pDCs:5 —
[83]GPL570 (11,241) [84]GSE28490 [85]21
PLC-33 All: 33 CD19+ B cells: 5, CD14+ monocytes:5, CD4+ T cells:5,
CD8+ T cells:5, Eosinophils:3, NK cells:5; Neutrophils:5 — [86]GPL570
(10,689) [87]GSE28491 [88]21
peripheral whole blood (PWB)
PWB-AD-249 ALL:249 Control: 104; AD:145 52 ~ 90 [89]GPL1122 (21067)
[90]GSE63060 [91]22
PWB-AD-275 ALL:275 Control: 135;AD:140 57 ~ 100 [92]GPL1122 (18327)
[93]GSE63061 [94]22
PWB-Normal-61 61 18 ~ 56 [95]GPL571 (12432) [96]GSE19151 [97]23
Brain tissue
Brain-AD-161 Brain region (Control: AD) Entorhinal Cortex (13:10)
Hippocampus (13:10) Middle temporal gyrus (12:16) Posterior cingulate
cortex (13:9) Superior frontal gyrus (11:23) Primary visual cortex
Control (12:19) 63 ~ 102 [98]GPL570 (20848) [99]GSE5281 [100]24
[101]Open in a new tab
^*The abbreviation of each dataset is denoted by the phenotype followed
by sample size.
Detecting disease-associated cellular alterations from AD PWB samples
The Ref-REO method was employed to detect the AD-associated cellular
alterations from AD PWB samples^[102]15. Briefly, the algorithm
detected the disease-associated cellular alterations according to the
following steps as shown in Fig. [103]1: (1) Select reference gene
pairs. Reference gene pairs are gene pairs whose REOs are stable and
consistent across different purified normal leukocytes. The REOs of
these gene pairs could be stable under normal condition or disease
state, no matter how the proportion of the constituent cells changes
when no expression alterations occur in leukocytes. (2) Filter the
reference gene pairs by the control samples in the dataset under study
to exclude the gene pairs whose REOs are easily affected by age, sex,
experimental batch effects^[104]25 and other factors. (3) Detect
reversed gene pairs. Reversed gene pairs are the gene pairs that have
inconsistent REO patterns with reference gene pairs in the disease
samples. The reversed REO patterns of these gene pairs should be caused
by expression alterations occurred in leukocyte subtypes, given that
the changed proportion of the constituent cells could not affect REOs
of the reference gene pairs when no expression alterations occur in
leukocytes. (4) Detect DEGs. Based on the filtered reference gene pairs
and reversed gene pairs, whether a gene could be observed in the
reversed gene pairs by random chance was evaluated by the
hypergeometric distribution model. The DEGs were detected as
significant if the adjusted P-value was less than 0.05^[105]15.
Figure 1.
Figure 1
[106]Open in a new tab
The flow chart of detecting AD-associated cellular alterations by the
Ref-REO method.
Detecting DEGs in various brain regions of AD patients
The student’s t-test was used to detect DEGs in various brain regions
of AD patients comparing to normal controls. P-values were adjusted for
multiple testing using the Benjamini-Hochberg procedure to control the
FDR level. If the adjusted p-value for a gene was less than 0.05, this
gene was considered as a DEG.
Detecting aging-associated genes from normal PWB samples
Because the relative proportions of the blood cells in older adults
shifted weakly as age increases^[107]12, the linear regression model
was employed to detect the aging-associated genes in the normal PWB
controls of the datasets analyzed in this study. P-values were adjusted
for multiple testing using the Benjamini-Hochberg procedure^[108]26 to
control the FDR level at 0.05. If the adjusted p-value for a gene was
less than 0.05, this gene was considered as an aging-associated gene.
Random experiments
Two random experiments were performed in this study.
The first random experiment was performed to evaluate whether the
number of reversed gene pairs detected in a dataset was significantly
more than expected by chance. Supposed there are n reversed gene pairs
observed in a study dataset. The random experiment consists of the
following steps: (1) Randomly disturb sample labels. The sample sizes
of normal controls and AD samples are kept the same in randomized data
and in the study dataset. (2) Calculate the number of reversed gene
pairs detected in the randomized data, denoted as m. (3) Repeat step 2
for 1,000 times and calculate the percentage of the random experiments
in which m is larger than n, defined as the probability of observing n
reversed gene pairs by random chance. A p-value <0.05 was considered as
significant.
The second random experiment was performed to evaluate whether the
number of observed AD-DEGs having interactions with the AD-associated
biomarkers was significantly more than expected by chance. Suppose k
out of n AD-DEGs interact with at least one of the AD-associated
biomarkers, the random experiments were done with the following steps:
(1) Randomly select n genes from the background genes as the randomized
AD-DEGs. (2) Calculate the number of the randomly defined AD-DEGs that
interact with at least one of the AD-associated biomarkers, denoted as
m. (3) Repeat step 2 for 1,000 times and calculate the percentage of
the random experiments in which m is larger than k, defined as the
probability of observing k AD-DEGs interacting with at least one of
AD-associated biomarkers by random chance. A p-value <0.05 was
considered as significant.
Enrichment analysis
The KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology
databases were used to evaluate the AD-associated cellular alterations
in PWB by the functional annotation tool DAVID
([109]https://david.ncifcrf.gov/, version 6.8)^[110]27. For a given
dataset, all of the measured genes annotated in the KEGG or Gene
Ontology database were considered as the background genes.
String, AlzGene, and miRTarBase databases
The protein-protein interactions were downloaded from STRING database
([111]https://string-db.org/, Version 10) that collected known and
predicted 82,160 protein-protein interactions involving 7,638
proteins^[112]28. The AD-associated biomarkers were download from
AlzGene database which is a collection of published AD genetic
association studies^[113]29 including 618 genes
([114]http://alsgene.org/, download at April, 2017). These two
databases were used to evaluate the interactions between AD-associated
cellular alterations in PWB and AD-associated alterations collected in
AlzGene database. MiRNA-mRNA interactions were downloaded from
miRTarBase database at April 2017
([115]http://mirtarbase.mbc.nctu.edu.tw/). In this study, only the
experimentally validated microRNA-target interactions were downloaded,
which included 322,161 miRNA-mRNA interactions involving 2,618 miRNAs
and 14,831 mRNAs^[116]30.
Results
AD-associated cellular alterations in PWB
First, gene pairs with stable REOs in different normal leukocytes were
detected in purified leukocyte expression datasets using the Ref-REO
method. Totally, 9,638,173 and 8,832,824 gene pairs were detected in
the gene expression profiles of purified leukocyte subtypes examined in
PLS-47 and PLS-33, respectively. The two lists shared 6,133,414 gene
pairs, and 99.9% of them had consistent REO patterns, which was
unlikely to happen by chance (binomial distribution test, p-value
<2.2 × 10^−16). These consistent gene pairs (totally 6,124,866) were
used as the reference for recognition of abnormal disease signals in
leukocytes.
Then, the reference gene pairs were evaluated in AD PWB expression
profiles examined in PWB-AD-249 and PWB-AD-275, respectively. For
dataset PWB-AD-249, 4,524,607 of the reference gene pairs retained the
REO patterns in at least 95% of the 104 normal PWB profiles. Among
them, 1,145 gene pairs had significantly reversed REO patterns in the
145 AD PWB profiles (Fisher exact test, adjusted p-value <0.05). For
dataset PWB-AD-275, 4,673,704 genes pairs had consistent REO patterns
with the reference in at least 95% of the 135 normal PWB profiles.
Among them, 1,249 gene pairs showed significantly reversed REO patterns
in the 140 AD PWB profiles (Fisher exact test, adjusted p-value <0.05).
Though relatively little in quantity, these reversed gene pairs may
reflect the true AD-associated information, as no reversed genes pairs
could be detected in the 1,000 random experiments by randomly
disturbing the sample labels of controls and cases (p-value <0.001, see
Materials and Methods).
Based on the reference gene pairs and reversed gene pairs, DEGs were
detected from PWB-AD-249 and PWB-AD-275 using the Ref-REO
method^[117]15. Totally, with FDR <5%, 42 and 45 DEGs were detected
respectively (Table [118]2), which shared 21 genes. All of the 21
shared DEGs had consistent expression dysregulated directions (up- or
down-regulation) in AD samples compared to normal samples in the two
datasets, which was unlikely to happen by chance (binomial distribution
test, p-value <2.2 × 10^−16). This result indicated the cellular
alterations detected from AD PWB could be reproducible. In the
following study, genes detected as significant in at least one of these
two AD datasets were analyzed, denoted as AD-DEGs, which included 66
genes.
Table 2.
DEGs detected from PWB-AD-249 and PWB-AD-275.
Gene ID Symbol Direction P-value Gene ID Symbol Direction P-value
DEGs detected from PWB-AD-249 DEGs detected from PWB-AD-275
5684 PSMA3 down <2.2 × 10^−16 2079 ERH down <2.2 × 10^−16
9360 PPIG down <2.2 × 10^−16 6160 RPL31 down <2.2 × 10^−16
51574 LARP7 down <2.2 × 10^−16 9552 SPAG7 down <2.2 × 10^−16
51611 DPH5 down <2.2 × 10^−16 51188 SS18L2 down <2.2 × 10^−16
84987 COX14 down <2.2 × 10^−16 5716 PSMD10 down <2.2 × 10^−16
7381 UQCRB down 7.00 × 10^−9 7155 TOP2B down 1.35 × 10^−11
6741 SSB down 1.98 × 10^−8 522 ATP5J down 4.74 × 10^−11
219927 MRPL21 down 3.83 × 10^−6 20 ABCA2 up 2.32 × 10^−8
57396 CLK4 down 4.04 × 10^−6 5204 PFDN5 down 5.59 × 10^−8
10600 USP16 down 1.83 × 10^−5 79746 ECHDC3 up 5.69 × 10^−8
51637 C14orf166 down 1.03 × 10^−4 10632 ATP5L down 6.84 × 10^−8
11168 PSIP1 down 3.31 × 10^−4 23500 DAAM2 up 1.85 × 10^−6
126208 ZNF787 up 5.17 × 10^−4 27089 UQCRQ down 3.35 × 10^−4
762 CA4 up 6.52 × 10^−4 8664 EIF3D down 6.21 × 10^−4
23478 SEC. 11 A down 3.00 × 10^−3 5683 PSMA2 down 1.69 × 10^−3
65056 GPBP1 down 3.32 × 10^−3 51019 CCDC53 down 1.73 × 10^−3
5685 PSMA4 down 1.20 × 10^−2 29093 MRPL22 down 1.89 × 10^−3
202018 TAPT1 down 1.51 × 10^−2 9550 ATP6V1G1 down 2.56 × 10^−3
84343 HPS3 up 1.55 × 10^−2 6256 RXRA up 4.15 × 10^−3
6038 RNASE4 up 1.49 × 10^−2 23294 ANKS1A up 4.65 × 10^−3
54556 ING3 down 4.01 × 10^−2 4082 MARCKS up 1.18 × 10^−3
85464 SSH2 up 1.66 × 10^−2
286006 LSMEM1 up 3.46 × 10^−2
22900 CARD8 up 4.26 × 10^−2
Overlapped DEGs between PWB-AD-249 and PWB-AD-275
521 ATP5I down <2.2 × 10^−16 51371 POMP down <2.2 × 10^−16
2959 GTF2B down <2.2 × 10^−16 80135 RPF1 down <2.2 × 10^−16
3301 DNAJA1 down <2.2 × 10^−16 3476 IGBP1 down 9.67 × 10^−13
4694 NDUFA1 down <2.2 × 10^−16 153527 ZMAT2 down 1.06 × 10^−11
6119 RPA3 down <2.2 × 10^−16 8813 DPM1 down 2.22 × 10^−11
6154 RPL26 down <2.2 × 10^−16 25847 ANAPC13 down 6.66 × 10^−9
6233 RPS27A down <2.2 × 10^−16 388789 LINC00493 down 1.12 × 10^−6
9553 MRPL33 down <2.2 × 10^−16 1622 DBI down 7.53 × 10^−6
51258 MRPL51 down <2.2 × 10^−16 6645 SNTB2 up 3.35 × 10^−5
51503 CWC15 down <2.2 × 10^−16 55505 NOP10 down 3.98 × 10^−3
55854 ZC3H15 down <2.2 × 10^−16
[119]Open in a new tab
For the AD-DEGs, the enrichment analysis was conducted based on KEGG
and Gene Ontology databases by DAVID^[120]27. The result showed that
these DEGs were significantly enriched in oxidative phosphorylation,
proteasome and ribosome pathways in KEGG, and Wnt signaling pathway and
translation pathway in Gene Ontology (Table [121]3). These enriched
pathways have been demonstrated to be closely associated with AD
development and progression. For example, oxidative phosphorylation has
been reported to be down-regulated in AD patients^[122]31,[123]32.
Ribosome dysfunction has been considered as an early event in AD
progression^[124]33. The inhibition of proteasome activity has been
reported to be sufficient to induce neuron degeneration in AD^[125]34.
Specially, 10 AD-DEGs were enriched in mitochondrial inner membrane,
suggesting that mitochondrial dysfunctions could play an important role
in AD development. In fact, mitochondrial dysfunctions have been found
in PWB lymphocytes of AD patients, which have been considered as a
trigger of AD pathophysiology^[126]35,[127]36.
Table 3.
Pathways and functional terms enriched for AD-DEGs.
ID Name Hit Genes P-value
KEGG
hsa00190 Oxidative phosphorylation ATP5I, ATP5J, NDUFA1, UQCRB,
ATP6V1G1, ATP5L, UQCRQ 6.63 × 10^−5
hsa03010 Ribosome RPL26, RPL31, RPS27A, MRPL21, MRPL22, MRPL33
6.62 × 10^−4
hsa03050 Proteasome PSMA2, PSMA3, PSMA4, POMP 5.15 × 10^−3
Gene ontology
molecular function
GO:0003735 structural constituent of ribosome RPL26, RPL31, RPS27A,
MRPL21, MRPL22, MRPL33, MRPL51 2.43 × 10^−4
O:0003723 RNA binding RPF1, LARP7, RPL31, C14orf166, ZMAT2, RPL26,
MRPL21, SSB, CWC15, EIF3D 1.18 × 10^−3
cellular component
GO:0005743 mitochondrial inner membrane ATP5J, ATP5I, MRPL22, UQCRB,
ATP5L, MRPL21, MRPL51, NDUFA1, UQCRQ 2.25 × 10^−4
GO:0019773 proteasome core complex, PSMA2, PSMA3, PSMA4, POMP
8.89 × 10^−4
biological process
GO:0060071 Wnt signaling pathway PSMA2, PSMA3, PSMA4, RPS27A PSMD10
9.50 × 10^−4
GO:0006412 translation RPL26, RPL31, RPS27A, MRPL21, MRPL22, MRPL33,
MRPL51 3.57 × 10^−3
[128]Open in a new tab
Expression changes of AD-associated PWB cellular alterations in various brain
regions of AD patients
The Brain-AD-161 dataset, which examined expression profiles of six
different brain regions in AD and normal controls, was used to evaluate
the expression changes of PWB AD-DEGs in brain tissue of AD patients.
By using the Student’s t-test with FDR <5%, the DEGs were detected from
various brain regions of AD patients (Table [129]4). The AD-DEGs were
found to significantly overlap with the DEGs detected from hippocampus,
middle temporal gyrus, superior frontal gyrus and primary visual cortex
of AD patients (Table [130]4). For example, among the 66 AD-DEGs, 53
DEGs were detected as DEGs in superior frontal gyrus of AD patients
(hypergeometric distribution test, p-value = 7.30 × 10^−4) and 49 out
of them had consistent alteration directions in AD patients compared to
normal controls. Totally, 60 out of the 66 AD-DEGs had expression
changes in at least one of the six brain regions between AD and normal
control samples, among which 52 genes had consistent alteration
directions in PWB and brain tissue. The results further suggested that
the cellular alterations observed in PWB could be closely associated
with AD.
Table 4.
Comparison of PWB AD-DEGs with DEGs detected from various brain regions
of AD patients.
Brain region DEGs Overlapped with AD-DEG P-value^*
Entorhinal Cortex 1334 5 (5)^# 0.98
hippocampus 2346 26 (21) 1.90 × 10^−2
Middle temporal gyrus 2253 28 (26) 2.52 × 10^−3
Posterior cingulate cortex 3273 24 (24) 0.64
Superior frontal gyrus 5344 53 (49) 7.3 × 10^−4
Primary visual cortex 373 9 (9) 1.91 × 10^−3
[131]Open in a new tab
^#The number outside the parentheses indicates the number of overlapped
genes between AD-DEGs and brain tissue DEGs and the number inside the
parentheses indicates the number of overlapped genes with consistent
expression alterations in both PWB and brain tissue of AD patients.
^*Represent the probability of observing the number of overlapped genes
by chance calculated by the hypergeometric distribution model.
AD-associated cellular alterations in PWB tend to interact with reported
AD-biomarkers
The AD-DEGs were compared with the 618 AD-associated biomarkers
collected from AlzGene database^[132]29. The result showed that only
three AD-DEGs (ABCA2, CARD8 and RXRA) overlapped with the AD-associated
biomarkers. With the protein-protein interaction data from STRING, we
found 23 AD-DEGs each interacting with at least one of the
AD-associated biomarkers (Fig. [133]2). The number was significantly
more than expected by chance (p-value = 0.027): when randomly choosing
the same number of genes as AD-DEGs, the mean number of random DEGs
having interactions with the AD-associated biomarkers was
16.37 ± 3.6878, which was estimated in the 1,000 random experiments
(see Materials and Methods). With FDR <0.05, the KEGG pathway
enrichment analysis showed the interaction partners of AD-DEGs also
tended to be enriched in the AD-associated oxidative phosphorylation
(p-value = 5.2 × 10^−4) and RRAR signaling pathways
(p-value = 1.30 × 10^−6)^[134]37.
Figure 2.
[135]Figure 2
[136]Open in a new tab
Interactions between AD-DEGs and AD-associated biomarkers.
AD-associated aberrant miRNA alterations observed in PWB were also
analyzed for the AD-DEGs. The PWB aberrant miRNAs were collected from a
study, which detected 12 diagnostic miRNAs (brain-miR-112,
brain-miR-161, hsa-let-7d-3p, hsa-miR-5010-3p, hsa-miR-26a-5p,
hsa-miR-1285-5p, hsa-miR-151a-3p, hsa-miR-103a-3p, hsa-miR-107,
hsa-miR-532-5p, hsa-miR-26b-5p and hsa-let-7f-5p) between 48 AD
patients and 22 non-AD control PWB samples^[137]38. From the miRTarBase
database^[138]30, 1620 targets of the 10 miRNAs (two miRNAs weren’t
collected by miTarbase) were downloaded. Among the 66 AD-DEGs, 19 were
included in the 1620 targets, which was unlikely to happen by chance
(hypergeometric distribution test, p-value = 0.028) (Fig. [139]3).
Figure 3.
Figure 3
[140]Open in a new tab
Interactions between AD-DEGs and PWB aberrant miRNAs.
The above results further indicated that the AD-DEGs could be closely
associated with AD development and progression.
AD-associated cellular alterations in PWB and aging-associated genes
Aging-associated genes were detected from normal control samples in
PWB-AD-249, PWB-AD-275 and PWB-Normal-61 using the linear regression
model (see Materials and Methods). With FDR <5%, 1,332 and 3,146
aging-associated genes were identified in PWB-AD-249 and PWB-Normal-61,
respectively, while no aging-associated genes were identified in
PWB-AD-275. The numbers of detected aging-associated genes differed
significantly in the three datasets, which could be explained by the
different age distribution patterns: there were 104 normal controls
with ages ranging from 52 to 87 in PWB-AD-249, while in PWB-AD-275,
there were 135 normal controls with ages ranging from 63 to 91; and the
PWB-AD-249 dataset had more samples with age ≤ 65 (9.62%) than the
PWB-AD-275 dataset (2.22%, Fisher’s exact test, p-value = 0.019). In
PWB-Normal-61, the age distribution was wider: there was 61 samples
with ages ranging from 18 to 56. Compared the aging-associated genes
identified in PWB-AD-249 and PWB-Normal-61, the results showed that
these two lists shared 515 aging-associated genes (hypergeometric
distribution test, p-value = 1.90 × 10^−2), and 479 out of them had the
same alteration directions with age in both datasets, which was
unlikely to happen by chance (binomial distribution test, p-value
<2.2 × 10^−16). Thus, only the 479 genes were analyzed in the following
study, which were denoted as aging-DEGs.
The aging-DEGs and the AD-DEGs shared 21 genes, and all of them had
consistent directions of correlations with age and expression changes
(hypergeometric distribution test, p-value = 2.02 × 10^−11). The
pathway enrichment analysis showed that these aging- and AD-associated
genes were also enriched in proteasome (p-value = 1.82 × 10^−4) and
RRAR signaling pathways (p-value = 3.70 × 10^−3). Interestingly, more
reserved gene pairs were found from AD patients with age ≤ 65 than AD
patients with age > 65. In PWB-AD-249, the average numbers of reversed
gene pairs identified from AD patients with age ≤ 65 and age > 65 were
529.64 ± 286.45 and 269.18 ± 228.31 (Wilcoxon rank sum test,
p-value = 4.5 × 10^−3), respectively. In PWB-AD-275, there were only
three AD patients with age ≤ 65. Therefore, the phenomenon that the
number of reserved gene pairs differed between the two groups was
unobvious: the average numbers of reversed gene pairs identified from
AD patients with age ≤ 65 and age > 65 were 282.52 ± 176.77 and
228.67 ± 163.95 (Wilcoxon rank sum test, p-value = 0.57), respectively.
Actually, previous studies have compared the prevalence of a number of
clinical features occurring in patients with early- and late-onset
primary degenerative dementia of the Alzheimer type. The early-onset
group demonstrated a greater prevalence of language disturbance, a
disproportionate number of left-handers and a much shorter relative
survival time than the late-onset group^[141]39.
These results showed that the occurrence of AD is closely related to
aging, further suggesting that AD could be an excessive aging disease.
Prediction performance of AD-associated cellular alterations in PWB
One task of the blood-based test is to detect biomarkers for disease
diagnostic workup. In this study, the detections of DEGs were based on
gene pairs that have reversed REO patterns in AD patients compared with
normal controls. These reversed gene pairs could be naturally used as
diagnostic biomarkers for AD. The result showed that these reversed
gene pairs had good prediction performance for classifying AD and
normal samples: the 1,145 reversed gene pairs detected in PWB-AD-249
discriminated AD from the normal samples in PWB-AD-275 with an area
under the curve (AUC) of 0.733, while the 1,249 reversed gene pairs
detected in PWB-AD-275 discriminated AD from the normal samples in
PWB-AD-249 with an AUC of 0.775 (Fig. [142]4). The prediction
performances were better than the reported prediction performance by
Nicola et.al. (AUC 0.729,^[143]10), which used a pathway based
classification method based on the same data.
Figure 4.
[144]Figure 4
[145]Open in a new tab
The prediction performance of reversed gene pairs.
Discussion
The concept of using blood such as peripheral blood cells as the source
of information to detect disease-associated molecular alterations
relies on the natural role of these cells in immune response to
physiologic and pathologic changes^[146]40. In this study, we tried to
detected AD-associated leukocyte cellular alterations from AD
peripheral whole blood samples using the Ref-REO method^[147]15, which
is based on within-sample REOs in purified leukocytes.
In the study, the number of PWB AD-DEGs identified by Ref-REO was
relatively low, suggesting that the Ref-REO method had its
disadvantages to some extent. Firstly, this method may fail to detect
some disease-associated cellular alterations, as the reference pairs
were required to have stable REO patterns in all purified leukocytes
and in more than 95% of normal PWB samples, which could miss some
AD-associated alterations. Secondly, the method of Ref-REO itself
tended to detect DEGs with larger magnitude of cellular expression
alterations, as greater expression alterations will have greater REO
changes. Thus, genes with weak changes may be missed. Lastly, the
Ref-REO method may not be able to detect those alterations occurring in
leukocyte subtypes with limited proportions, as the signal could be
easily covered by other leukocyte subtypes. However, it’s important to
note that the PWB alterations identified by Ref-REO were closely
associated with the development and progression of disease: the AD-DEGs
were significantly enriched in AD-associated pathways, reproducible in
brain tissue of AD patients and tended to interact with reported
AD-associated biomarkers. Therefore, though low in quantity, the
AD-DEGs identified by Ref-REO could be real expression alterations
occurring in PWB leukocytes^[148]15.
Interestingly, our results showed that few AD-associated cellular
alterations were enriched in PWB immune associated pathways or
functional terms. In contrast, most of them were involved in
aging-associated pathways or functional terms. For example, oxidative
phosphorylation and mitochondrial have been reported to be closely
associated with aging^[149]41. In AD blood samples, an increased
neutrophil-lymphocyte ratio has also been reported as a function of
age^[150]42. These results suggested that AD-associated cellular
alterations in PWB might mainly reflect aging-associated alterations.
Fortunately, such alterations could also be detected in the diagnostic
workup of Alzheimer’s disease (AD), as AD is an excessive aging
disease.
Acknowledgements