Abstract
Alzheimer’s disease is a complex disorder encompassing multiple
pathological features with associated genetic and molecular culprits.
However, target-based therapeutic strategies have so far proved
ineffective. The aim of this study is to develop a methodology
harnessing the transcriptional changes associated with Alzheimer’s
disease to develop a high content quantitative disease phenotype that
can be used to repurpose existing drugs. Firstly, the Alzheimer’s
disease gene expression landscape covering severe disease stage, early
pathology progression, cognitive decline and animal models of the
disease has been defined and used to select a set of 153 drugs tending
to oppose disease-associated changes in the context of immortalised
human cancer cell lines. The selected compounds have then been assayed
in the more biologically relevant setting of iPSC-derived cortical
neuron cultures. It is shown that 51 of the drugs drive expression
changes consistently opposite to those seen in Alzheimer’s disease. It
is hoped that the iPSC profiles will serve as a useful resource for
drug repositioning within the context of neurodegenerative disease and
potentially aid in generating novel multi-targeted therapeutic
strategies.
Subject terms: Drug discovery, Neuroscience
Introduction
Global gene expression profiling can be thought of as a high content
quantitative phenotypic measure characterising tissue^[36]1, cell type
in, for example, the heterogeneous context of the brain^[37]2–[38]4 and
revealing diversity within a previously thought homogeneous
population^[39]5. Further, biological state dynamics can be modelled
through temporal patterns of expression^[40]6. In the therapeutic
context, it has been established that disease-associated expression
changes can distinguish between disease states and are consistent
across independent data sets, thus facilitating the identification of
robust biomarkers^[41]7. Disease-associated gene changes point to
modulated pathways and affected cell types, thus providing valuable
insights into mechanisms^[42]8. Interestingly, the quantitative nature
of the transcriptional phenotype has allowed for a direct mapping of
disease to potential therapeutic^[43]9–[44]12. Here the obvious
hypothesis is that drugs tending to reverse the expression changes seen
in the disease state may act to reverse the biological changes
associated with the disease itself. An important caveat here is that
some expression changes associated with Alzheimer’s disease (AD) may in
fact be compensatory and beneficial. Drug repurposing or repositioning
has resulted in successful initiatives across several
maladies^[45]13–[46]19. Further, and of more specific interest to the
present project, drugs with profiles showing significant
anti-correlation to AD gene changes have been shown to be conspicuous
for their reported neuroprotective activities^[47]12. In a recent
development, disease-associated gene expression changes have begun to
be inferred from genomic risk variant data with the Genotype-Tissue
Expression repository^[48]20 and harnessed to predict repurposing
candidates for major psychiatric conditions^[49]21. Although there is
some intriguing psychotherapeutic association of the candidate drugs in
this approach, the predicted transcriptional perturbation does not have
an overlap with that seen in diseased brain tissue [G. Williams,
unpublished observation]. In the absence of further validation of the
predicted gene changes, one must fall back on data from patient
samples.
There are no established disease-modifying drugs for the treatment of
AD, there have been no new symptomatic treatments licensed for AD for
>20 years and the pipeline of emerging therapies is very limited.
Target-based drug research in AD has led to many insights into the
disease and provided the research community with useful tool compounds.
However, the promising results seen in the laboratory have so far
failed to be carried over to the clinic and this has led to researchers
casting around for novel, non-target-based approaches^[50]22. The main
aim of transcription-based drug discovery is not target discovery, but
rather the discovery of drugs that have a disease-modulating effect
based on their global transcriptional activity. A particularly
attractive aspect of the approach is that it naturally lends itself to
repositioning existing drugs thereby bypassing the hurdles that novel
entities must overcome on the road to the clinic. AD has been
extensively studied in relation to the expression changes following
pathological and cognitive decline^[51]23–[52]26. The wealth of data
points to consistent and characteristic changes associated with the
disease and thereby makes a repositioning strategy particularly
attractive.
The application of gene expression profiling to drug repositioning is
limited at present by the fact that full drug profiles are available
only on a restricted set of immortalised human cell lines. This data is
provided by the Broad Institute connectivity map project (CMAP)^[53]11.
A more extensive drug set has been profiled on a variety of induced
pluripotent stem cell (iPSC)-derived cells, including neural stem cells
and differentiated cortical neurons. However, this data constituting
the LINCS project^[54]27 is based on profiling a set of 1000 landmark
genes and then using an optimised linear mapping to generate full
profiles. This motivated the present initiative to define the full
expression profiles of the CMAP candidate drugs in the more AD relevant
cell type of iPSC-derived cortical neurons. The new phenotypes can then
be compared to the CMAP profiles and more pertinently scored against
the disease profiles to see whether they preserve or enhance their
anti-correlation with AD. In this context, iPSC-derived cortical
neurons have now been established as a model system for the study of
neurological diseases especially the tracing of the effects of
disease-related genetic variants^[55]28–[56]31. This model provides for
an efficient moderate throughput platform to assess the transcriptional
effects of the candidate drugs in a more neurological context. It must
be remembered, however, that AD is a complex pathology also involving
multiple cell types, such as microglia and astrocytes. In this context,
assaying drug perturbations within isolated iPSC cultures facilitates
an important but limited insight into the disease.
The motivation for the work presented here is to generate a
neuronal-specific transcriptional database of compounds with a view to
drug repositioning in AD and other neurodegenerative conditions. The
initial compound set was assembled based on CMAP profiles that showed a
tendency to reverse AD-associated expression changes observed across a
variety of independent studies. The drug candidates were then profiled
for their transcriptional effects on iPSC-derived human cortical
neurons. The results indicate that at the global level there is a
degree of correspondence between the CMAP and iPSC profiles.
Furthermore, 51 of the drugs have profiles that drive transcription
changes counter to those in AD. The consistently regulated genes
correspond to those implicated in AD. It is hoped that the
transcriptional data for these drugs will be of use to the wider
community of researchers interested in neurodegenerative conditions and
facilitate further repositioning efforts.
Materials and methods
The AD-associated transcriptional landscape
The NCBI GEO database^[57]32 was queried for series containing samples
derived from postmortem AD patient brains for various stages of the
disease. Similarly, murine AD model brain samples were also collected
based on relevant query key words: 5xFAD, 3xTG, Alzheimer’s
disease+mouse. Profiles were generated based on relative levels of
non-disease and disease state sample averages, with the scaled fold
level defined as
[MATH:
f=⟨d⟩-⟨c⟩⟨d
⟩+⟨c⟩ :MATH]
, where the brackets indicate averages of the control (c) and disease
(d) samples. The statistical significance is measured by Student’s t
test and those folds falling below the 95% confidence interval were
dropped as were those with folds of <20%. The human disease versus
control AD set comprises 21 profiles derived from 13 series (NCBI GEO
accession: [58]GSE84422^[59]24, [60]GSE37263^[61]33,
[62]GSE36980^[63]34, [64]GSE39420^[65]35, [66]GSE1297^[67]23,
[68]GSE29378^[69]36, [70]GSE48350^[71]37, [72]GSE15222^[73]25,
[74]GSE26972^[75]38, [76]GSE37264^[77]39, [78]GSE28146^[79]40,
[80]GSE5281^[81]41, [82]GSE13214^[83]42) showing intra-profile
consistency based on the regression scores for significant (Student’s t
test p < 0.05) correlations, see Supplementary Table [84]1. To capture
brain region variability, the number of profiles is greater than the
number of series. In Supplementary Table [85]2, the extent of intra-
versus inter-series AD profile correlation scores are given showing
that in many cases the variability in brain region profiles is greater
than that between independent series. Cognitive decline was based on
decline in Mini-Mental State Examination (MMSE)^[86]43 represented by
two profiles from two independent series and Clinical Dementia Rating
(CDR)^[87]44 profiles from one series. Similarly, series corresponding
to murine models of AD were gathered from 5xFAD and 3xTG mice resulting
in seven profiles from three series (NCBI GEO accession:
[88]GSE50521^[89]45, [90]GSE119756^[91]46, [92]GSE101144^[93]47,
[94]GSE77574^[95]48) for the 5xFAD set and nine profiles from eight
series (NCBI GEO accession: [96]GSE31624, [97]GSE15128^[98]49,
[99]GSE36237, [100]GSE92926^[101]50, [102]GSE60460,
[103]GSE60911^[104]51, [105]GSE36981^[106]34, [107]GSE35210) for the
3xTG set. Series corresponding to BRAAK stage progression (NCBI GEO
accession: [108]GSE1297, [109]GSE84422, [110]GSE48350,
[111]GSE106241^[112]52) were generated with a linear mixed model
analysis, by fitting the gene expression level across the samples in
the series to a linear function of the BRAAK stage with categorical
calls on cell type and gender as covariates. The resulting residual
correlation Z score for gene expression against BRAAK stage constituted
the BRAAK profile. Profiles corresponding to full BRAAK progression
were not considered to be sufficiently different to the overt disease
profiles derived from the same series, where disease assignment is also
based on BRAAK staging. However, gene expression changes driving mild
BRAAK pathology should capture early disease biology invisible in the
overt profiles. In total, six profiles corresponding to mild BRAAK
pathology, level 0 to level 3, formed the mild BRAAK AD set. Similar
profiles were generated for psychiatric measures MMSE and CDR (NCBI GEO
accession: [113]GSE48350, [114]GSE1297, [115]GSE84422). In the case of
the MMSE profile, the regression signs were reversed as MMSE scores
decrease with disease progression, see Table [116]1 for an overall
comparison of the profile sets.
Table 1.
The AD sets show varying degrees of overlap
AD BRAAKmild COGI 5xFAD 3xTG
AD 11.26 ± 0.45 −1.81 ± 0.38 13.34 ± 1.08 3.38 ± 0.33 0.05 ± 0.11
BRAAKmild 4.43 ± 1.00 −1.03 ± 0.83 −0.35 ± 0.34 −0.04 ± 0.20
COGI 15.10 ± 3.47 3.09 ± 0.70 0.26 ± 0.23
5xFAD 13.23 ± 1.67 0.46 ± 0.21
3xTG −0.06 ± 0.25
[117]Open in a new tab
The overt AD profile set is highly correlated with the cognitive
decline profiles. There is a degree of overlap with the 5xFAD profiles
but poor agreement with the mild BRAAK and 3xTG animal profiles. The
3xTG profile set is conspicuous for not being internally consistent or
having significant overlap with the other AD sets. The numbers in the
table correspond to the average Z score across pairs in the sets,
excluding correlations of profiles with themselves
Representative profiles for each set were based on genes showing
consistent changes across the constituent profiles. In particular, the
sense changes (upregulation and downregulation calls) for significantly
regulated genes were summed over the profiles and only those genes
retained that had an absolute regulation fraction of >20% and with a
significant regulation statistic measured by Student’s t test of
p < 0.05. Owing to the categorical nature of the representative
profiles, correlation with the iPSC profiles was based on an enrichment
analysis. The enrichment score was generated based on a binomial
probability sum with gene probabilities scaled according to their
frequencies in SPIED^[118]53.
CMAP profiles
CMAP data were downloaded from the Broad connectivity map site
([119]www.broadinstitute.org/connectivity-map-cmap) ^[120]11. This
consisted of probe sets for each sample ranked according to expression
level relative to batch control. The data consist of 6100 samples
covering 1260 drugs and 4 cell types. The relative probe expression
ranks, defined as
[MATH:
1-2R-RminRmax-R<
/mi>min
:MATH]
, where R in the rank of a given gene’s expression change (R[max] being
the highest and R[min] being the lowest ranks), were averaged over
replicates ignoring cell type and filtered based on significance using
a one-sample Student’s t test. For genes with multiple probes, the
probe with the largest significant change was mapped to the gene. This
resulted in a unique profile for each drug in CMAP. The compound data
can be queried through SPIED^[121]53.
iPSC profiles
Following the dominant CMAP treatment protocol, cell cultures were
treated for 6 h and at compound concentrations of 10 μM. The iPSC
expression samples were generated on the Affymetrix Human Genome U133
Plus 2.0 Array platform from ThermoFisher Scientific.
Human iPSC-derived cerebral cortical neurons (HyCCNs; Ax0026) were
cultured as per the manufacturer’s guidelines
([122]www.axolbio.com/page/neural-stem-cells-cerebral-cortex). Each
drug treatment at a concentration of 10 μM for 6 h was performed on 3
independent HyCCN cultures (average density 300 K/cm^2) and RNA from
each treated well extracted by direct cell lysis and recovery using the
Absolutely RNA Microprep Kit (Agilent, as per the manufacturer’s
guidelines). Each drug-treated plate also consisted of a vehicle-only
control set of triplicate cultures. Integrity of total RNAs was
determined using an Agilent Bioanalyser as per the manufacturer’s
instructions and only samples with RNA integrity number >7 were
progressed to transcriptome analysis. Transcriptome changes driven by
exposure to the candidate drugs were determined using the Nugen Ovation
V2 labelling system ([123]https://www.nugen.com/products) followed by
Human U133 Plus 2 GeneChips as per the manufacturer’s instructions
([124]www.thermofisher.com/order/catalog/product/900466).
The NCBI GEO hosts 145,000 samples on this platform, making it the most
popular array chip. The relative expression levels of probes were
collected for the GEO data and the iPSC control data. The ranks were
scaled to lie between zero for the highest expression probe and unity
for the lowest. The relative rank of each probe was defined as
[MATH:
r0-rr0 :MATH]
for r < r[0] and
[MATH:
r0-r1-r
0 :MATH]
for r < r[0], where r and r[0] are the average probe ranks over the
iPSC samples and the set of samples deposited on GEO, respectively.
Probes were then mapped to genes and, in the case of degeneracy, the
probe with the largest relative rank mapping to the gene. The gene rank
profile was taken to be related to the relative gene expression
characterising iPSCs.
Drug treatment profiles were based on statistically filtered ratios of
drug-treated and control groups. These were generated based on a
combined set of 554 samples, which were robust multiarray averaging
normalised. The samples were distributed over 23 plates with the
corresponding dimethyl sulfoxide controls. Transcriptional profiles for
the 153 drugs were generated based on normalising to the plate control
and multiple plate drug replicates kept as separate profiles. The drug
set is enriched for CMAP based anti-AD potential (153). Rapamycin,
which has a well-defined transcriptional signature, served as a
positive control. The expression changes were either measured as scaled
folds filtered for significance with Student’s t test or as Z scores,
with significance based on the magnitude of Z. Degenerate probes were
mapped to genes based on the dominant probe responses.
Results
AD-associated expression changes
To capture as much as possible of the transcriptional landscape of AD,
different categories were defined based on overt disease versus healthy
profiles, profiles following early pathological and cognitive measures,
together with those from animal models, as described in ‘Materials and
methods’. There is a good degree of overlap between the overt AD
profiles and those following cognitive decline, see Table [125]1, but
it was reasoned that there is sufficient variability to give rise to
unique drug candidates, see section on ‘CMAP candidates’. The early
BRAAK stage profiles show little overlap with overt or cognitive
decline profiles, see Table [126]1, and thus it is anticipated that
these profiles may shed light on distinct early stage pathology and
early therapeutic intervention. The animal model data naturally
separates into those based on the 5xFAD, which is consistent with AD as
can be seen in Supplementary Table [127]3, and those based on 3xTG,
showing little overlap with AD profiles or internal consistency. A
similar analysis also including rat models of AD has been carried out
by Hargis and Blalock^[128]54. Animal model data were included in this
study because the expression changes seen in the model systems have
established causes, i.e. the inserted mutations, 5xFAD or 3xTG in our
case. Consequently, candidate drugs reversing these changes may have
more focused mechanisms of action. Furthermore, the evidence for
neuroprotection is to a large extent derived from experiments in animal
models.
CMAP candidates
In general, transcription-based repositioning results in tens of
candidates out of a total of just over a thousand drugs constituting
CMAP^[129]13–[130]19. The relatively small number of compounds that are
put forward for rigorous bio-assaying to establish firmer evidence for
a disease-modulating potential of course reflects the experimental
resource required. The basis of the present project was to select
candidates to populate a database of iPSC profiles for drugs biased
towards their predicted anti-AD and wider neuroprotective activities.
It was therefore reasoned that the thresholds for deeming a drug a
repositioning candidate had to be relaxed to allow for over a hundred
candidates to be taken forward. To this end, five AD-based profile sets
that capture distinct aspects of the disease were separately queried
against CMAP and three selection criteria were applied. In the first
instance, data were gathered on the anti-correlation rank of each
compound, with compounds showing a high rank in either of the profiles
considered as candidates, see Supplementary Table [131]4 for the
complete candidate list. A second selection was based on consistency of
the anti-correlation across profiles in each set, and finally some
compounds with conspicuously high anti-correlations with individual
profiles were added to the set. The full list of compounds is given in
Supplementary Table [132]4 and consists of 153 compounds.
Interestingly, among these drugs are established neuroprotective
entities and AD therapeutics, see below.
iPSC profiles
As a first step in establishing the phenotype of the model cell system,
the overall iPSC transcriptional profile was queried against a database
of publicly deposited gene expression profiles via
SPIED^[133]12,[134]53, see ‘Materials and methods’. The top 1000 genes
in the iPSC rank profile consists of 959 upregulated and 41
downregulated genes and this served as a query in the SPIED search. It
is perhaps worth pointing out here that the level of gene expression
unique to a given cell type will tend to be elevated relative to a
background consisting of a variety of tissue types. An analogy would be
in the context of division of labour one is characterised by what one
does not by what one does not do. The top SPIED hits show a high
correlation with human brain-derived samples, validating the cell’s
lineage, see Supplementary Table [135]5.
Comparison of iPSC and CMAP profiles
The extent to which an iPSC profile correlates with its CMAP equivalent
can be assessed by querying the CMAP database with the iPSC profile and
ranking the CMAP equivalent. The extensively studied perturbagen
rapamycin served as a positive control and eight independent profiles
were generated to assess the degree to which these profiles are
consistent with each other and with the rapamycin profile in CMAP. The
rapamycin profiles had consistently high overlaps among themselves, but
less so with the CMAP profile, with only one returning rapamycin as a
top hit, rank seven, in a CMAP query, see Supplementary Fig. [136]1. In
Supplementary Fig. [137]2, iPSC and CMAP profile pairs with the four
highest CMAP query ranks are shown. Overall, there are 30 significantly
correlating and 8 anti-correlating pairs. The overall comparison of the
iPSC and CMAP profiles can be framed in terms of an enrichment analysis
for the rank of the equivalent compound hit and the significance can be
assessed with Kolmogorov–Smirnov (KS) statistic on the maximal
deviation from the zero-enrichment diagonal line. The KS statistic
furnishes an objective measure of the robustness of the iPSC profiles
and suggest that iPSC profiles based on a Z score threshold of |Z| > 3,
see ‘Materials and methods’ for details, capture most of the
compound-associated changes. The enrichment is that of the rank of a
given iPSC compound score with itself in CMAP. The enrichment plot is
shown in Fig. [138]1. The KS statistic is highly significant with the
chance of a random compound association beating the enrichment maximum
of p = 5.1E−6.
Fig. 1. The overall comparison between the iPSC profiles and those on the
cancer cell lines can be framed as an enrichment analysis for the rank of
iPSC queries against CMAP.
Fig. 1
[139]Open in a new tab
For each drug, the correlation between iPSC and CMAP profiles are
ranked against the remainder of the CMAP data set profiles. For a good
agreement between the profiles, one would expect an enrichment in high
rank scores and this is the case for iPSC profiles. The top plot shows
the rank distributions in bins of 50 with a clear bias for high rank
scores. The bottom plot is the cumulative distribution of ranks
contrasted with the non-enriched diagonal. The significance is measured
by an MC simulation randomising rank orders and counting the number of
times peak deviation from the diagonal exceeds that in the original
enrichment
Relation of iPSC profiles to AD
Further to assessing the extent to which compounds orchestrate similar
expression changes in the cancer cell lines and differentiated cortical
neurons, it is critical to test whether the drugs also act in an
anti-AD manner in the neuronal context. To this end, the drug profiles
were scored against five representative AD reprofiles derived from the
AD sets defined above, see ‘Materials and methods’ for details. Table
[140]2 lists the compounds with at least two significant
anti-correlations with the representative AD profiles, which will be
referred to as AD hit compounds (ADC). The ADC set show a relatively
high degree of intra-profile correlation as compared to other iPSC
profile pairs, see Fig. [141]2. The average correlations in terms of
regression Z scores are: 2.43 for ADC pairs and 0.77 for all other
pairs. It is therefore of interest to see to what extent the ADC set
regulate a common set of transcripts. In Fig. [142]3, the common ADC
target genes are shown demonstrating a high degree of consistency with
a clearly defined set of upregulated and downregulated gene cohort. To
get an idea of the underlying biological networks that are being
perturbed by the ADC, a pathway enrichment analysis was performed on
each of the profiles in the ADC set. The consistently positively and
negatively regulated pathways defined by an enrichment in the
upregulated and downregulated gene sets, respectively, are given in
Supplementary Table [143]6, and these point to key processes associated
with AD that underpin the potential therapeutic action of the drugs.
The enrichment for the AD, Parkinson’s disease and mitochondrial
pathways in the positively regulated gene sets is driven by the
upregulation of cytochrome c oxidases, ubiquinone oxidoreductases and
ATP synthases. These are all key players in mitochondrial function,
which is known to be compromised in AD^[144]55,[145]56, with growing
evidence that gene variation affecting mitochondrial function may play
a role in AD^[146]57,[147]58. The downregulated set appears to be less
consistent. Nonetheless, the enrichment of immune-associated pathways
points to a possible anti-inflammatory activity of the candidate drugs.
Interestingly, the following drugs have been reported to have
neuroprotective activity: fluocinonide^[148]59, kawain^[149]60–[150]63,
allantoin^[151]64, dipyridamole^[152]65–[153]67, estriol^[154]68,
levamisole^[155]69, mycophenolic acid^[156]70, neostigmine^[157]71,
probenecid^[158]72,[159]73, chlorpromazine^[160]74, and
phenoxybenzamine^[161]75, and xamoterol has been reported to ameliorate
neuroinflammation and pathology in 5xFAD mice^[162]76 and shown to
enhance cognition in a Down syndrome mouse model^[163]77. The atypical
antipsychotic risperidone prescribed to manage psychosis in AD has
demonstrated neuroprotection in animal models of ischemia^[164]78.
Furthermore, cholinesterase inhibition is a therapeutic strategy for
AD^[165]79 and there are two such inhibitors in the candidate list with
galantamine as an established AD therapeutic^[166]80, while neostigmine
exhibits poor blood–brain barrier penetrance and is therefore not in
clinical use for AD. There does not appear to be any gene expression
signature distinguishing compounds with reported neuroprotective
activities from the other ADC compounds. This is to be expected as not
all compounds have been assayed for neuroprotection and biological
activity is not expected to be solely encoded in the transcriptome.
Table 2.
Compounds with iPSC profiles showing anti-correlation with at least two
representative AD profiles, referred to as the ADC set
[167]graphic file with name 41398_2019_555_Tab1_HTML.jpg
[168]Open in a new tab
The numbers are the correlation
[MATH:
n↑↑+n↓↓-n↑↓+n<
mi>↓↑n↑↑+n↓↓+n↑↓+n↓↑ :MATH]
and the associated binomial enrichment score is reflected in the red
intensity. The compound descriptions are given and those with reported
neuroprotective activity are highlighted in grey
Fig. 2. The ADC compounds have relatively high intra-profile correlations.
Fig. 2
[169]Open in a new tab
The correlation Z scores are shown on a heat map with the ADC component
split off to highlight the enhanced correlation. The average
correlation for intra-ADC profiles is 2.43 as opposed to 0.77 for all
other pairs
Fig. 3. The gene expression heat map for genes consistently regulated by the
ADC set.
[170]Fig. 3
[171]Open in a new tab
Genes were selected based on their having a sum sense change ratio
>33%. Specifically, the sum sense change ratio is defined as
[MATH:
1P∑i=1,
…,Psigngi :MATH]
, where g[i] is the expression change of a gene in the ith profile. The
compounds are clustered with the UPGMA algorithm and the corresponding
dendrogram shown at left
Discussion
Neurodegenerative diseases present a therapeutic challenge due to the
difficulty in establishing a clear protein or mechanistic culprit for
classic target-based intervention. Another hurdle is a consequence of
the temporal extent of disease progression and the probable need to
treat before overt symptom onset. This is a particular problem in
designing clinical trials. With this in mind, alternatives to
target-based approaches are increasingly being pursued. One recent
report compared Parkinson’s disease (PD) incidence and chronic
therapeutic use data from the Norwegian Prescription Database
([172]www.norpd.no), showing that salbutamol use reduced PD
risk^[173]81. A middle ground between target-based and epidemiological
approaches is a methodology based on the disease phenotype gleaned from
gene expression changes observed in pathological states. Underlying
this approach is the observation that disease states can effectively be
represented by characteristic expression changes, in the sense that
these changes are consistent and can function as high content
quantitative biomarkers. One avenue available to drug repositioning is
to use these transcriptional phenotypes together with the hypothesis
that an anti-correlation in phenotypes is indicative of the therapeutic
potential of the compound. Whereas the transcriptional landscape of
neurodegeneration and AD in particular has been well characterised, the
corresponding data for compounds are either limited to full profiles
defined on non-neuronal proliferating cells or partial profiles on
iPSC-derived neuronal cells. The basis of the present study is to go
some way to fill this gap in the compound-associated transcriptome with
an emphasis on drugs with an anti-AD potential.
In the context of defining the neurotherapeutic potential of candidate
drugs, a further development would be to treat wild-type or mutant AD
mice with the compounds and measure expression changes in the brain,
along the lines of the DrugMatrix project^[174]82. This approach would
have the advantage of including non-neuronal factors contributing to AD
pathology such as inflammation. However, practical considerations limit
whole-animal approaches to smaller drug sets and will therefore form
part of a subsequent endeavour based on a more limited set of drug
candidates selected based on the iPSC data.
In the present work, we have established an AD transcriptional profile
landscape and shown this to have a high degree of internal consistency.
This disease-associated transcriptional landscape served as the basis
for selecting a series of candidate drugs from the CMAP database of
cancer cell line profiles, which were then assayed for their
transcriptional effect on iPSC-derived cortical neurons. The iPSC
profiles show a degree of overlap with the corresponding CMAP profiles,
with a highly significant overall comparison in terms of the ranks
observed for iPSC queries of CMAP. Out of the 153 iPSC drug profiles,
51, termed the ADC set, showed a high degree of anti-correlation with
transcriptional changes seen in AD. A pathway enrichment analysis
performed on each of the ADC set showed that pathways related to
mitochondrial function were commonly upregulated while commonly
downregulated pathways represented immune-associated pathways.
Interestingly, these pathological features are found in multiple
neurodegenerative disorders, such as PD and Huntington’s disease, and
it would be of interest to investigate whether these compounds may have
wider therapeutic potential. Notably, 18 of the ADC drugs already have
established neuroprotective ability in published studies. Whereas we
expect that initial CMAP filtering against AD profiles has led to
increased likelihood of discovering compounds that tend to reverse
AD-associated expression changes in the context of iPSC cultures, this
can only be rigorously assessed by generating iPSC profiles for a
series of compounds randomly selected from the CMAP database, which is
outside the scope of the present study. In conclusion, approaches to
identifying a broader range of candidate therapies for AD are urgently
needed. It is therefore expected that the iPSC database will serve as a
useful platform for drug repositioning across multiple
neuropathological disorders as well as AD.
Supplementary information
[175]Supplementary Figure Legends^ (13.3KB, docx)
[176]Supplementary Table 1^ (15.1KB, xlsx)
[177]Supplementary Table 2^ (28.3KB, xlsx)
[178]Supplementary Table 3^ (14KB, xlsx)
[179]Supplementary Table 4^ (19.4KB, xlsx)
[180]Supplementary Table 5^ (13.4KB, xlsx)
[181]Supplementary Figure 1^ (4MB, tif)
[182]Supplementary Figure 2^ (9.1MB, tif)
[183]Supplementary Table 6^ (36KB, xlsx)
Acknowledgements