Abstract
Unbiased phenotypic screens in patient-relevant disease models offer
the potential to detect therapeutic targets for rare diseases. In this
study, we developed a high-throughput screening assay to identify
molecules that correct aberrant protein trafficking in adapter protein
complex 4 (AP-4) deficiency, a rare but prototypical form of
childhood-onset hereditary spastic paraplegia characterized by
mislocalization of the autophagy protein ATG9A. Using high-content
microscopy and an automated image analysis pipeline, we screened a
diversity library of 28,864 small molecules and identified a lead
compound, BCH-HSP-C01, that restored ATG9A pathology in multiple
disease models, including patient-derived fibroblasts and induced
pluripotent stem cell-derived neurons. We used multiparametric
orthogonal strategies and integrated transcriptomic and proteomic
approaches to delineate potential mechanisms of action of BCH-HSP-C01.
Our results define molecular regulators of intracellular ATG9A
trafficking and characterize a lead compound for the treatment of AP-4
deficiency, providing important proof-of-concept data for future
studies.
Subject terms: High-throughput screening, Developmental disorders
__________________________________________________________________
Using an unbiased phenotypic cell-based high-throughput screen, the
authors identify and characterize a small molecule, BCH-HSP-C01, that
restores aberrant protein trafficking in neuronal models of adapter
protein complex 4 deficiency.
Introduction
Despite remarkable advances in our ability to delineate the genetic
causes of rare neurological diseases, it is estimated that specific
therapies exist for less than 5%^[61]1. Thus, there is a significant
unmet need for developing and implementing platforms for drug
discovery. Informed by disease-relevant cellular phenotypes, automated
and unbiased cell-based high-throughput small molecule screens have the
potential to uncover therapeutic targets^[62]2–[63]6.
Adapter protein complex 4 (AP-4)-related hereditary spastic paraplegia
(AP-4-HSP), which comprises AP4B1-associated SPG47 (OMIM #614066),
AP4M1-associated SPG50 (OMIM #612936), AP4E1-associated SPG51 (OMIM
#613744) and AP4S1-associated SPG52 (OMIM #614067), is a rare but
prototypical form of childhood-onset complex hereditary spastic
paraplegia (HSP) and an important genetic mimic of cerebral
palsy^[64]7,[65]8. Children with AP-4-HSP present with features of both
a neurodevelopmental disorder (e.g., early-onset global developmental
delay and seizures, microcephaly, and developmental brain
malformations) and a neurodegenerative disease (e.g., progressive
spasticity and weakness, loss of ambulation, and extrapyramidal
movement disorders)^[66]7–[67]10. AP-4-HSP is caused by bi-allelic
loss-of-function variants in any of the four AP-4 subunits (ε, β4, μ4,
σ4), leading to impaired AP-4 assembly and function^[68]11–[69]15. AP-4
is an obligate heterotetrameric protein complex^[70]16–[71]18 that
mediates transport from the trans-Golgi network (TGN) to the cell
periphery, including sites of autophagosome biogenesis^[72]19,[73]20.
Three independent groups identified the core autophagy protein and
lipid scramblase ATG9A as a major cargo of AP-4^[74]11–[75]14,[76]21,
linking loss of AP-4 function to defective autophagy^[77]22,[78]23.
AP-4 deficiency in non-neuronal^[79]11,[80]12,[81]21,[82]24,[83]25 and
neuronal cells^[84]13–[85]15 leads to an accumulation of ATG9A in the
TGN, including in iPSC-derived neurons from AP-4-HSP patients^[86]15.
From this body of work and overlapping neuronal phenotypes of
AP-4^[87]13,[88]14,[89]26,[90]27 and Atg9a^[91]28 knockout mice, the
following working model for AP-4 deficiency emerges: (1) AP-4 is
required for trafficking of ATG9A from the TGN; (2) loss-of-function
variants in AP-4 subunits lead to a loss of AP-4 function; (3) ATG9A
accumulates in the TGN leading to a reduction of axonal delivery of
ATG9A; (4) lack of ATG9A at the distal axon impairs autophagy leading
to axonal degeneration. Other AP-4 cargo proteins identified to date
include the poorly characterized transmembrane proteins SERINC1 and
SERINC3^[92]12 and the endocannabinoid-producing enzyme DAG lipase beta
(DAGLB)^[93]29.
In this study, we leverage intracellular ATG9A mislocalization as a
cellular readout for AP-4 deficiency to develop a large-scale,
automated, multiparametric, unbiased phenotypic small molecule screen
for modulators of ATG9A trafficking in patient-derived cellular models.
We employed this platform to screen a diversity library of 28,864 small
molecules in AP-4-deficient patient fibroblasts and identified 503
compounds that re-distribute ATG9A from the TGN to the cytoplasm.
Through a series of orthogonal assays in neuronal cells, including
differentiated AP4B1^KO SH-SY5Y cells and human induced pluripotent
stem cell (hiPSC)-derived neurons from AP-4-HSP patients, we defined a
series of 5 compounds that restore neuronal phenotypes of
AP-4-deficiency. In a comprehensive multiparametric analysis, a small
molecule, termed BCH-HSP-C01, emerged as a lead compound with an EC50
of ~5 μM. Target deconvolution strategies using transcriptomic and
proteomic profiling revealed that BCH-HSP-C01 modulates intracellular
vesicle trafficking and increases autophagic flux, potentially through
differential expression of several RAB (Ras-associated binding)
proteins.
Our findings demonstrate the ability of carefully designed
high-throughput screens to identify potential molecular mechanisms
involved in AP-4 deficiency and support the development of BCH-HSP-C01
as a therapeutic for AP-4-HSP.
Results
Primary screening of 28,864 compounds in fibroblasts from AP-4-HSP patients
identifies 503 active compounds
A diversity library of 28,864 small molecules was provided by Astellas
Pharma Inc. Compounds were arrayed to single wells in 384-well
microplates, and one well per compound was screened. The primary screen
was conducted in fibroblasts from a well-characterized patient with
core clinical features of SPG47^[94]8,[95]21 and bi-allelic
loss-of-function variants in AP4B1 ([96]NM_001253852.3: c.1160_1161del
(p.Thr387ArgfsTer30) / c.1345A>T (p.Arg449Ter)) (Fig. [97]1a, b).
Fibroblasts from the sex-matched parent (unaffected heterozygous
carrier) served as controls. The assay was fully automated,
miniaturized to 384-well microplates, and compounds were added for 24 h
at a single concentration of 10 µM (Fig. [98]1c).
Fig. 1. Establishment of a cell-based phenotypic small molecule screening
platform using ATG9A translocation as a surrogate for AP-4 function and
primary screening of 28,864 small molecule compounds.
[99]Fig. 1
[100]Open in a new tab
a Overview of the primary screen of 28,864 small molecules in
fibroblasts from a patient with bi-allelic LoF variants in AP4B1. b
Illustration of the automated image analysis pipeline. Representative
images of patient fibroblasts (negative control, LoF/LoF) and their
sex-matched heterozygous parent (positive control, WT/LoF) are shown.
Scale bar: 20 µm. c Overview of the high-throughput platform. Created
with BioRender.com. d–f Distribution of ATG9A fluorescence intensities
inside (d) and outside (e) the TGN, as well as ATG9A ratios (f) on a
per cell basis (n[WT/LoF] = 99,927, n[LoF/LoF] = 119,522). g Cell
counts as per well means of 1312 wells per condition from 82
independent plates. Means are shown as black dots; whiskers represent
±1.5 x IQR. h, i Replicate plots were generated by random sampling of
the 82 plates from the primary screen in two groups. Similar positions
on the assay plates were plotted against each other with respect to
ATG9A fluorescence intensities inside the TGN (h) and ATG9A ratios (i).
Replicate correlations were assessed by averaging the Pearson
correlation coefficients (r) of 100 random sampling tests. j
Discriminative power of the ATG9A ratio in separating positive and
negative controls. Statistical testing was done using the Mann-Whitney
U test. P-values are two-sided. Data points represent per well means of
1312 wells per condition from 82 independent plates. Means are shown as
black dots; whiskers represent ±1.5 x IQR. k To test the robustness of
separation of the ATG9A ratio between positive and negative controls, a
dataset containing measurement for 99,927 WT/LoF and 119,522 LoF/LoF
cells was partitioned into a training set (70% of data) and a test set
(30%). The performance of a generalized linear model is shown in (k).
The AUC is 0.96. l Impact of 28,864 compounds applied for 24 h at a
concentration of 10 µM. Z-scores for the ATG9A ratio are shown. All
data points represent per well means. The mean of the positive control
is shown as a green dotted line. The green shaded areas represent ±
1 SD. m Distribution of Z-scores of all non-toxic 27,403 compounds.
Active compounds are highlighted in blue.
The ATG9A ratio (ATG9A fluorescence intensity inside the TGN vs. in the
cytoplasm) was used as the primary assay metric, as established
previously^[101]15,[102]21. The population distributions of the
subcellular ATG9A signal inside and outside the TGN, at the level of
single cells for negative (bi-allelic loss-of-function, LoF/LoF) and
positive (heterozygous carriers, WT/LoF) controls are shown in
Fig. [103]1d, [104]e. ATG9A ratios demonstrated symmetrical and
approximately normal distributions and robust separation of both groups
(Fig. [105]1f). Cell counts were similar for positive and negative
controls, excluding cell death or changes in proliferation rates as
possible confounding factors (Fig. [106]1g). To test for
reproducibility across replicates, assay plates were randomly sampled
into two sets, and similar positions on the assay plates were plotted
against each other (Fig. [107]1h, i). Random sampling was simulated 100
times, and mean correlation coefficients were calculated. Using the
ATG9A ratio (Fig. [108]1i) as a primary readout resulted in higher
replicate correlation (mean r = 0.90 ± 0.002 SD) compared to absolute
ATG9A intensities (Fig. [109]1h) (mean r = 0.82 ± 0.0008 SD). ATG9A
ratios showed robust discriminative power between positive and negative
controls (LoF/LoF mean: 1.34 ± 0.05 SD, n = 1312 wells vs. WT/LoF mean:
1.1 ± 0.02 SD, n = 1312 wells, Mann-Whitney U test, p < 0.0001)
(Fig. [110]1j). The ATG9A ratio as the primary outcome metric was
further supported by a generalized linear model, which demonstrated
high specificity and sensitivity (Fig. [111]1k, AUC: 0.96). Source data
for assay performance are provided in Source Data file [112]1.
Throughout the screen, assay performance was monitored using
established quality control metrics for cell-based screens (Z’ robust
≥0.3, strictly standardized median difference ≥3, and an inter-assay
coefficient of variation ≤10%)^[113]30–[114]32. All assay metrics were
calculated for positive and negative controls of the same assay plate
to avoid bias by inter-plate variability. Predefined thresholds were
met by all assay plates (Supplementary Fig. [115]1a and Source Data
file [116]2). The results of the primary screen are summarized in
Fig. [117]1l, [118]m, and the complete dataset is provided in Source
Data file [119]3.
Of the 28,864 compounds, 26 were excluded due to non-quantifiable ATG9A
signal, exceptionally low cell counts or imaging artifacts. The
remaining 28,838 compounds were evaluated for changes in cell count and
the ATG9A ratio. The vast majority (n = 26,961, 93.5%) did not show any
significant reduction in the ATG9A ratio (defined as a reduction by at
least 3 SD). 1,435 (5.0%) compounds were excluded due to toxicity,
defined as a reduction in the mean cell count by at least 2 SD compared
to the negative controls. Only a small subset of 503 compounds (1.7%)
reduced the ATG9A ratio by 3 or more SD compared to negative controls
(Fig. [120]1m). Of these, 61 (0.2%) also reduced cell counts, while the
remaining 442 (1.5%) showed no toxicity.
In summary, from this high-throughput primary screen, 503 active
compounds were identified and selected for further testing.
Counter-screen in fibroblasts from AP-4-HSP patients confirms 16 compounds
that lead to a dose-dependent redistribution of ATG9A
To validate the 503 active compounds identified in the primary screen,
compounds were retested for dose-dependency using an 11-point dose
range (range: 40 nM to 40 µM) (Fig. [121]2a). Source data for the
secondary screen are provided in Source Data file [122]4. All
concentrations were screened in biological duplicates and subjected to
the same quality control metrics as in the primary screen
(Supplementary Fig. [123]1b and Source Data file [124]5). Similar to
the results from the primary screen, ATG9A ratios for negative and
positive controls showed a robust separation (LoF/LoF mean: 1.4 ± 0.07
(SD), vs. n = 269 wells vs. WT/LoF mean: 1.12 ± 0.02 (SD), n = 269
wells, Mann-Whitney U test, p < 0.0001, Fig. [125]2b). Activity in the
secondary screen was defined as the ability to reduce the ATG9A ratio
by at least 3 SD in both replicates and at least 2 different
concentrations, without exerting toxicity. 51 compounds (10.1%) met
these a priori defined criteria (Supplementary Fig. [126]2a, b). After
manually verifying image quality and validating dose-response
relationships, compounds were triaged (Fig. [127]2a and Supplementary
Fig. [128]2a, b). Seventeen compounds demonstrated a clear and
reproducible dose-response relationship without evidence of image
artifacts or autofluorescence. The EC50 for most compounds was in the
low micromolar range (median: 4.66 μM, IQR: 8.63, Fig. [129]2). 34
compounds were found to carry autofluorescence or imaging artifacts and
were thus excluded from further testing (Supplementary Fig. [130]2b).
One active compound was unavailable from the manufacturer and was
removed.
Fig. 2. Counter-screen in fibroblasts from AP-4-HSP patients confirms 16
compounds that lead to dose-dependent redistribution of ATG9A.
[131]Fig. 2
[132]Open in a new tab
a Overview of the counter-screen of the 503 active compounds identified
in the primary screen. To assess for dose-dependent effects, compounds
were screened in AP-4-HSP patient-derived fibroblasts in 384-well
microplates using 11-point titrations ranging from 40 nM to 40 µM. All
concentrations were screened in duplicates. Active compounds were a
priori defined as those reducing the ATG9A ratio by at least 3 SD
compared to negative controls in more than one concentration. Toxicity
was defined as a reduction of the cell count of at least 2 SD compared
to negative controls. b Baseline differences in the ATG9A distribution
in WT/LoF (n = 269) vs. LoF/LoF (n = 269) fibroblasts. Data points
represent per well means of 269 wells per condition from 17 independent
plates. Means are shown as black dots; whiskers represent ±1.5 x IQR.
Statistical testing was done using the Mann-Whitney U test. P-values
are two-sided. c Dose-response curves were fitted using a
four-parameter logistic regression model, and EC50 concentrations were
calculated. All concentrations were tested in biologic duplicates.
Black dots and error bars represent mean ± 1 SD. Black dashed lines
represent the a priori-defined thresholds of ± 3 SD compared to the
negative control (LoF/LoF). Red triangles represent toxic
concentrations based on the a priori-defined threshold of a reduction
of cell counts of at least 2 SD compared to the negative control. The
salmon-colored dashed line represents the mean of negative controls,
while the green-colored dashed line depicts the mean of the positive
controls (WT/LoF). Representative images of the EC50 are shown for each
active compound. Representative images show a merge of the 4 channels:
Phalloidin (gray), DAPI (blue), TGN46 (red) and ATG9A (green), as well
as the TGN46 and ATG9A channels in greyscale. For a better illustration
of differences in ATG9A signals, the fluorescence intensities of the
ATG9A channel are additionally shown using a color lookup table. Scale
bar: 20 µm. NA: not available.
In summary, a counter-screen in AP-4-deficient patient fibroblasts
confirmed and established dose-dependent effects on intracellular ATG9A
distribution for 16 compounds (Fig. [133]2c).
Orthogonal assays in neuronal models of AP-4-deficiency confirm 5 active
compounds
To validate active compounds from the secondary screen in a human cell
line with neuron-like properties, the ATG9A assay was optimized for
neuroblastoma-derived SH-SY5Y cells following a 5-day neuronal
differentiation protocol with retinoic acid^[134]33 (Fig. [135]3a).
SH-SY5Y cells with stable expression of AP4B1-targeting CRISPR/Cas9
machinery (AP4B1^KO)^[136]12 served as negative controls while
AP4B1-wildtype (AP4B1^WT) cells were used as positive controls. All 16
active compounds were tested in an 8-point dose range (50 nM to 30 µM)
with a treatment duration of 24 h. Quantification of the ATG9A ratio in
differentiated SH-SY5Y cells showed a robust separation between control
conditions (AP4B1^KO: 1.80 ± 0.06 (SD), n = 158 wells vs. AP4B1^WT:
1.17 ± 0.03 (SD), n = 160 wells, Mann-Whitney U test, p < 0.0001,
Fig. [137]3b, Source Data file [138]6). Compounds were evaluated based
on their dose-dependent reduction of the ATG9A ratio and absence of
cell toxicity. Eleven of 16 compounds were excluded due to lacking
activity (n = 7), suspicion of artifacts or autofluorescence (n = 3),
or obvious changes in cellular morphology (n = 1) (Supplementary
Fig. [139]3). Of the five remaining compounds, three restored the ATG9A
ratio to levels of wildtype controls (BCH-HSP-F01, BCH-HSP-G01 and
BCH-HSP-H01) while two compounds (BCH-HSP-B01 and BCH-HSP-C01) led to a
reduction by at least 3 SD at higher concentrations (Fig. [140]3c–h).
The chemical structures and properties of these five compounds are
summarized in Supplementary Fig. [141]4.
Fig. 3. Orthogonal assays in AP4B1^KO SH-SY5Y cells confirm 5 active
compounds.
[142]Fig. 3
[143]Open in a new tab
a Overview of the orthogonal screen of 16 active compounds in
differentiated AP4B1^KO SH-SY5Y cells. b Baseline differences in ATG9A
ratios of AP4B1^WT vs. AP4B1^KO SH-SY5Y cells were quantified from 160
AB4B1^WT and 158 AB4B1^KO wells from 5 assay plates. Means are shown as
black dots; whiskers represent ±1.5 x IQR. Statistical testing was
performed using the Mann-Whitney U test. P-values are two-sided. c–g
Dose-response curves for ATG9A ratios in AB4B1^KO cells treated with
different compounds. Data points represent per well means from 3
different assay plates. Black dots and error bars represent mean ±
1 SD. Dashed lines show mean Z-scores for positive (green) and negative
(salmon) controls. Shaded areas represent ± 1 SD. h Representative
images of the intracellular ATG9A distribution for individual
compounds. The merged image shows beta-3 tubulin (gray), DAPI (blue),
the TGN46 (red) and ATG9A (green). The TGN46 and ATG9A channels are
further separately depicted in greyscale. Scale bar: 10 µm. i Baseline
differences of DAGLB ratios in AP4B1^WT vs. AP4B1^KO cells were
quantified from 192 AB4B1^WT and 192 AB4B1^KO wells from 4 assay
plates. Means are shown as black dots; whiskers represent ±1.5 x IQR.
Statistical testing was done using the Mann-Whitney U test. P-values
are two-sided. j–n Dose-response curves for DAGLB ratios in AB4B1^KO
cells treated with different compounds. All data points represent per
well means from 4 different assay plates. Black dots and error bars
represent mean ± 1 SD. Dashed lines show mean Z-scores for positive
(green) and negative (salmon) controls. Shaded areas represent ± 1 SD.
o Representative images of the intracellular DAGLB distribution for
individual compounds. The merge shows beta-3 tubulin (gray), DAPI
(blue), the TGN46 (red) and DAGLB (green). The TGN46 and DAGLB channels
are further separately depicted in greyscale. Scale bar: 10 µm.
To assess whether these effects were specific to ATG9A or similar
effects were also present for other AP-4 cargo proteins, we turned to a
second neuronal AP-4 cargo protein, DAGLB^[144]29. Similar to ATG9A,
the DAGLB ratio (DAGLB fluorescence intensity in the TGN vs. in the
cytoplasm) showed a robust separation between AP4B1^WT and AP4B1^KO
cells (AP4B1^KO: 1.80 ± 0.1 (SD), n = 192 wells vs. AP4B1^WT:
1.36 ± 0.07 (SD), n = 192 wells, Mann-Whitney U test, p < 0.0001,
Fig. [145]3i, Source Data file [146]6). All active compounds, except
for BCH-HSP-B01, showed activity in the DAGLB assay, suggesting a
broader effect on the trafficking of at least 2 AP-4 cargo proteins
from the TGN (Fig. [147]3j–o). Again, BCH-HSP-F01, BCH-HSP-G01 and
BCH-HSP-H01 (Fig. [148]3l–n) resulted in normalization of the
intracellular DAGLB distribution, while BCH-HSP-C01 led to a moderate
reduction of DAGLB ratios at higher concentrations (Fig. [149]3k).
Since small molecules can have pleiotropic effects on cellular
functions and organellar morphology, we adopted a multiparametric
morphological profiling approach^[150]34. Eighty-five measurements of
the nucleus, cytoskeleton, global cell morphology, the TGN, and ATG9A
vesicles were automatically computed for each image, serving as a rich
and unbiased source for interrogating biological perturbations induced
by compound treatment (Source Data file [151]6). Principal component
analysis was used to reduce dimensionality and cluster images based on
their properties (Fig. [152]4a, b and Supplementary Fig. [153]5).
Positive and negative controls clustered closely together and were
separated only by the ATG9A signal (Fig. [154]4b and Supplementary
Fig. [155]5a). BCH-HSP-C01 showed properties comparable to positive and
negative controls, suggesting little off-target effects (Fig. [156]4b
and Supplementary Fig. [157]5c). BCH-HSP-B01, BCH-HSP-F01, BCH-HSP-G01
and BCH-HSP-H01, however, changed cellular morphology in a
dose-dependent manner (Fig. [158]4b and Supplementary Fig. [159]5b,
d–f), with changes mainly driven by the first principal component,
accounting for 31.1% of the observed variance (Fig. [160]4c). To
decipher the phenotypic alterations responsible for these changes, the
Pearson correlation coefficients of the first principal component with
each measurement were calculated (Fig. [161]4d). Features with a
correlation coefficient >0.75 were selected to define morphological
profiles (Fig. [162]4e). Interestingly, TGN fluorescence intensity and
morphology seemed to be the most significant drivers for the
separation, suggesting that disruption of TGN integrity potentially
biased the assessment of ATG9A ratios in cells treated with compounds
BCH-HSP-B01, BCH-HSP-F01, BCH-HSP-G01 and BCH-HSP-H01 (Fig. [163]4b and
Supplementary Fig. [164]5b, d–f).
Fig. 4. Multiparametric profiling of 5 active compounds in AP4B1^KO SH-SY5Y
cells.
[165]Fig. 4
[166]Open in a new tab
a Multiparametric profiles of images of 5373 cells were acquired using
4 fluorescent channels. Scale bar: 10 µm. A total of 90 measurements
per cell were generated for the cytoskeleton (beta-3 tubulin), the
nucleus (DAPI), the TGN (TNG46) and ATG9A vesicles (ATG9A). The
different steps of data preprocessing and phenotypic clustering using
principal component analysis (PCA) are shown. b PCA shows different
clusters of cells based on 85 phenotypic features. Experimental
conditions are color-coded. The first two principal components (PC1 and
PC2) explain 43.2% of the observed variance. c Bar plot summarizing the
variance explained by the first 10 PCs. Most of the variance is
explained by PC1 and, to a lesser degree, PC2. d Correlation analysis
of PC1 with all 85 features using the Pearson correlation coefficient.
The red dashed line represents a cutoff for correlations >0.75. e
Zoom-in on selected features of interest showing a correlation with PC1
>0.75. f Measurements of TGN intensity and descriptors of TGN shape and
network complexity for the individual hit compounds as line graphs.
Data points represent per well means of 7 independent plates. Black
dots and error bars represent mean ± 1 SD. g Information on TGN
summarized using heatmap visualization.
Following these analyses, TGN fluorescence intensity and morphological
measures such as TGN area and elongation, as well as compactness and
roughness, as indicators of the complexity of the TGN, were quantified
for cells treated with all five active compounds (Fig. [167]4f, g).
While BCH-HSP-C01 showed stable TGN signal and morphology across all
assessed measurements, the other compounds induced some degree of
change in a dose-dependent manner (Fig. [168]4f, g). Of note, these
changes to TGN morphology were not detectable by visual inspection but
only delineated through an automated analysis of ~600 images containing
~30,000 cells per group, showcasing the power of our automated,
unbiased, high-throughput platform.
BCH-HSP-C01 restores ATG9A and DAGLB trafficking in hiPSC-derived neurons
from AP-4-HSP patients
Informed by the findings in differentiated AP4B1^KO SH-SY5Y cells, we
next investigated whether these results would translate to human
neurons. hiPSCs from patients with AP-4-HSP due to bi-allelic
loss-of-function variants in AP4M1 ([169]NM_004722.4: c.916C>T
(p.Arg306Ter) / c.694dupG (p.Glu232GlyfsTer21)) and AP4B1
([170]NM_001253852.3: c.1160_1161del (p.Thr387ArgfsTer30) / c.1345A>T
(p.Arg449Ter)) were generated^[171]35,[172]36 and differentiated into
glutamatergic cortical neurons using established
protocols^[173]15,[174]37,[175]38. hiPSC-derived neurons from
sex-matched parents (unaffected heterozygous carriers) served as
controls (Fig. [176]5a and Source Data file [177]7). Baseline
quantification of ATG9A ratios in DIV (day in vitro) 14 neurons treated
with vehicle for 24 h showed robust separation between patient and
control lines, exceeding the differences observed in AP-4-deficient
fibroblasts and differentiated SH-SY5Y cells (SPG50 patient mean:
4.31 ± 0.4 (SD), n = 60 wells vs. heterozygous control: 1.56 ± 0.12
(SD), n = 60 wells, Mann-Whitney U test, p < 0.0001, Fig. [178]5b).
Neurons were treated for 24 h in 8-point dose titration experiments.
BCH-HSP-B01 and BCH-HSP-G01 lacked activity on the ATG9A ratio and were
thus excluded (Fig. [179]5d). BCH-HSP-C01, BCH-HSP-F01 and BCH-HSP-H01,
by contrast, showed a robust reduction in the ATG9A ratio
(Fig. [180]5e, f; Supplementary Fig. [181]6). A multiparametric
analysis showed that, similar to observations in AP4B1^KO SH-SY5Y
cells, only BCH-HSP-C01 preserved TGN integrity (Fig. [182]5f), while
BCH-HSP-F01 and BCH-HSP-H01 impacted TGN morphology, suggesting
off-target effects (Fig. [183]5e). Based on its favorable profile,
BCH-HSP-C01 was selected as a lead compound and was re-synthesized for
further testing.
Fig. 5. BCH-HSP-C01 restores ATG9A and DAGLB trafficking in hiPSC-derived
neurons from AP-4-HSP patients.
[184]Fig. 5
[185]Open in a new tab
a Overview of 5 active compounds in hiPSC-derived cortical neurons from
a patient with AP4M1-associated SPG50 compared to same-sex parent
(heterozygous control). b Baseline differences of ATG9A ratios using
per well means of 60 wells per condition from 5 plates. Means are shown
as black dots; whiskers represent ±1.5 x IQR. Statistical testing was
done using the Mann-Whitney U test. P-values are two-sided. c
Representative images of hiPSC neurons treated with individual
compounds at 5 µM for 24 h. Scale bar: 10 µm. d–f Dose-response curves
for ATG9A ratios in hiPSC-derived neurons from a patient with SPG50
treated with individual compounds for 24 h, along with their
morphological profiles depicted as heatmaps. All data points represent
per well means of 2 (d, e), or 4 (f) independent differentiations.
Black dots and error bars represent mean ± 1 SD. Dashed lines show mean
Z-scores for positive (green) and negative (salmon) controls. Shaded
areas represent ± 1 SD. g Time-series experiment of AP4B1^KO SH-SY5Y
cells treated with BCH-HSP-C01 with different concentrations and
treatment durations. Data points represent per well means of two
independent plates. Shapes indicate technical replicates. Dashed lines
show mean Z-scores for positive (green) and negative (salmon) controls.
Shaded areas represent ± 1 SD. h Dose-response curve for ATG9A ratios
in AB4B1^KO SH-SY5Y cells treated with BCH-HSP-C01 for 72 h. Data
points represent per well means from two independent plates. Dashed
lines show mean Z-scores for positive (green) and negative (salmon)
controls. Shaded areas represent ± 1 SD. i, j Dose-response curves for
ATG9A and DAGLB ratios in hiPSC-derived neurons from a patient with
SPG50 (i) and SPG47 (j) after prolonged treatment with BCH-HSP-C01 for
72 h, along with morphologic profiles. Data points represent per well
means of 2 independent differentiations. Dashed lines show mean
Z-scores for positive (green) and negative (salmon) controls. Shaded
areas represent ± 1 SD. k–m Quantification of ATG9A positive puncta per
neurite length in hiPSC-derived neurons from a patient with SPG50
following 24 h of BCH-HSP-C01 treatment (k, n[WT/LoF] = 837,
n[LoF/LoF] = 848, n[LoF/LoF + BCH-HSP-C01] = 72), as well as
hiPSC-derived neurons from patients with SPG50 (l, n[WT/LoF] = 843,
n[LoF/LoF] = 843, n[LoF/LoF + BCH-HSP-C01] = 70) and SPG47 (m,
n[WT/LoF] = 424, n[LoF/LoF] = 428, n[LoF/LoF + BCH-HSP-C01] = 70)
treated for 72 h with BCH-HSP-C01. All data points represent means of
single images from two independent differentiations. Statistical
testing was done using the Mann-Whitney U test. P-values are two-sided
and were adjusted for multiple testing using the Benjamini-Hochberg
procedure. Representative images are shown for all experimental
conditions. Scale bar: 10 µm.
To investigate the time- and dose-dependent effect of BCH-HSP-C01, we
used AP4B1^KO SH-SY5Y cells to conduct time-series experiments with
different concentrations of BCH-HSP-C01 (Fig. [186]5g, h, Source Data
file [187]6). All concentrations tested show a maximal effect on ATG9A
translocation after 72–96 h of treatment (Fig. [188]5g, h, Source Data
file [189]6), exceeding the effects seen after 24 h (Fig. [190]3d).
Turning back to hiPSC-derived neurons, prolonged treatment of
BCH-HSP-C01 for 72 h to test for ATG9A and DAGLB translocation
demonstrated that BCH-HSP-C01 was able to restore ratios of both AP-4
cargo proteins to levels close to controls with an EC50 of ~5 µM, while
maintaining a favorable profile (Fig. [191]5i, Source Data
file [192]7). This greater effect on ATG9A distribution, compared to
the ~50% reduction of the ATG9A ratio at 24 h treatment, again suggests
a time- and dose-dependent effect. BCH-HSP-C01 changed the ATG9A ratio
by simultaneously decreasing ATG9A intensities inside the TGN and
increasing cytoplasmic ATG9A levels, suggesting ATG9A translocation as
the most likely mechanism of action. No changes in TGN morphology or
any other cellular measurements were observed, indicating overall
preservation of cellular morphology and little off-target effects. A
similar pattern was observed with respect to DAGLB translocation
(Fig. [193]5i). These findings were confirmed in a second set of
experiments in hiPSC-derived neurons from a patient with SPG47
(Fig. [194]5j, Source Data file [195]7), demonstrating that the
findings extend to other forms of AP-4-deficiency.
Prior work in neurons isolated from AP-4-deficient mice^[196]13,[197]14
highlighted the depletion of axonal ATG9A pools. In hiPSC-derived
neurons from individuals with SPG50 and SPG47, we observed a reduction
of ATG9A puncta density in neurites. BCH-HSP-C01 treatment for both
24 h and 72 h restored neurite ATG9A puncta density to levels similar
to controls (Fig. [198]5k–m, Source Data file [199]7).
Taken together, BCH-HSP-C01 emerged as a robust modulator of ATG9A and
DAGLB trafficking in human neurons from patients with AP-4 deficiency.
Target deconvolution using transcriptomic and proteomic analyses delineates
putative mechanisms of action for BCH-HSP-C01
To explore potential mechanisms of action of BCH-HSP-C01 in an unbiased
manner, we used a multi-omics approach, combining bulk RNA sequencing
and unbiased label-free quantitative proteomics (source data are
provided in Source Data files [200]8–[201]10).
First, RNA sequencing was conducted in differentiated AP4B1^WT and
AP4B1^KO SH-SY5Y cells treated for 72 h with either vehicle or compound
BCH-HSP-C01 (5 µM, Source Data file [202]8). Analysis of differential
gene expression identified few significant transcriptional changes in
response to BCH-HSP-C01 treatment, suggesting that this compound does
not elicit major alterations in gene expression or induce many
off-target effects (Supplementary Fig. [203]7). Since changes in gene
expression caused by short-duration small molecule treatments might not
reach predefined cutoffs for standard differential expression analyses,
and because compounds might affect groups of genes in shared pathways
rather that modifying single target genes, we adapted an unbiased and
unsupervised network approach to identify groups of co-expressed genes.
Hierarchical clustering of samples showed that treatment with
BCH-HSP-C01, regardless of cell line, was the main differentiator in
our dataset (Fig. [204]6a). To identify the gene networks responsible
for these changes, weighted gene co-expression network analysis
(WGCNA)^[205]39,[206]40 was used to group the 18,506 expressed genes
into 36 co-expression modules (Fig. [207]6b). Gene expression profiles
within each module were summarized using the “module eigengene” (ME),
defined as the first principal component (PC) of a module^[208]41.
Within each module, the association of MEs with measured traits was
examined by correlation analysis (Fig. [209]6c). Eight modules that
showed an absolute correlation coefficient >0.5 were selected for
further evaluation. For these selected modules, ME-based connectivity
was determined for every gene by calculating the absolute value of the
Pearson correlation between the expression of the gene and the
respective ME, producing a quantitative measure of module membership
(MM). Similarly, the correlation of individual genes with BCH-HSP-C01
treatment was computed, defining gene significance (GS) for
BCH-HSP-C01. Using the GS and MM, an intramodular analysis was
performed, allowing identification of genes that have a high
correlation with treatment as well as high connectivity to their
modules (Fig. [210]6d). Five modules were significantly related to
BCH-HSP-C01 treatment, defined as showing an absolute correlation
coefficient between MM and GS >0.5 (Fig. [211]6e). A list of the genes
contained in each module along with their module membership is provided
in Source Data file [212]9. To summarize the biological information
contained in these modules of interest, gene ontology (GO) analysis was
performed, which demonstrated enrichment in biological pathways in
three out of the five assessed modules (Fig. [213]6f). The ‘blue
module’ showed downregulation of pathways involved in axonogenesis,
actin filament organization and proteasome-mediated pathways. The
‘light-yellow module’ contained genes involved in ER stress response,
amino acid metabolism and transcription. Finally, the ‘mediumpurple3
module’ depicted the upregulation of genes involved in vesicular
transport, particularly involving TGN and ER-associated transport, as
well as membrane and vesicle dynamics. This last module showed the
highest gene ratios (defined as the percentage of total differentially
expressed genes in the given GO term) and lowest P-values of all
differentially regulated pathways across all modules, suggesting the
upregulation of alternative vesicle-mediated transport mechanisms by
compound BCH-HSP-C01 (Fig. [214]6f).
Fig. 6. Target deconvolution using bulk RNA sequencing and weighted gene
co-expression network analysis in AP4B1^KO SH-SY5Y cells treated with
BCH-HSP-C01.
[215]Fig. 6
[216]Open in a new tab
a Hierarchical clustering of 12 samples using average linkage showed
two main clusters based on treatment with vehicle vs. BCH-HSP-C01,
irrespective of cell line. b Cluster dendrogram of 18,506 expressed
genes based on topological overlap. Clusters of co-expressed genes
(“modules”) were isolated using hierarchical clustering and adaptive
branch pruning. c Heatmap visualization of the correlation of gene
expression profiles (“module eigengene”, ME) of each module with
measured traits. Pearson correlation coefficients are shown for each
cell of the heatmap. d Intramodular analysis of module membership (MM)
and gene significance (GS) for highly correlated modules, allowing
identification of genes that have high significance with treatment as
well as high connectivity to their modules. Statistical testing was
done using the t-test. P-values are two-sided. e ME expression profiles
for the top 5 co-expressed modules. f Gene ontology enrichment analysis
showed enriched pathways in 3/5 modules. Statistical testing was done
using the hypergeometric test. P-values are one-sided. Pathways were
considered differentially expressed with an FDR <0.05.
To assess whether similar themes would emerge on the protein level, we
next used unbiased quantitative proteomics in both differentiated
SH-SY5Y cells (AP4B1^KO and AP4B1^WT) and hiPSC-derived neurons
(patient with AP4B1-associated SPG47 and control) treated for 72 h with
either vehicle or compound BCH-HSP-C01 (5 µM). After quality filtering,
8,141 unique proteins in SH-SY5Y cells and 7386 unique proteins in
hiPSC-derived neurons were quantified. Differential enrichment analyses
for both cell lines are shown in Fig. [217]7a, b, and source data are
provided in Source Data file [218]10. As expected, baseline
quantification of differentially expressed proteins in AP4B1^KO SH-SY5Y
cells showed downregulation of AP-4 subunits, AP4B1, AP4E1 and AP4M1,
and increased ATG9A and DAGLB levels, as reported in other models of
AP-4 deficiency^[219]11–[220]13,[221]29 (Supplementary Fig. [222]8a).
Moreover, additional dysregulation of proteins involved in autophagy,
Golgi dynamics and vesicular transport was identified (Supplementary
Fig. [223]8a, m, Source Data file [224]10). Of note, we observed
upregulation of ATG2A, which has recently been shown to form a complex
with ATG9A that facilitates lipid transfer from the endoplasmic
reticulum (ER) to the growing phagophore membrane^[225]42–[226]44. This
further supports that autophagosome biogenesis is dysregulated in
AP-4-deficient cells. PCA analysis of SH-SY5Y cells demonstrated 4
distinct clusters separated by BCH-HSP-C01 treatment (PC1, explaining
12.3% of variance) and genotype (PC2, explaining 8.7% of variance)
(Fig. [227]7a). Testing of vehicle vs. BCH-HSP-C01 treated cells showed
broadly similar groups of dysregulated proteins in AP4B1^WT and
AP4B1^KO SH-SY5Y cells (Supplementary Fig. [228]8b–d), suggesting a
conserved mechanism of action independent of genotype, which allowed
the pooling of cell lines to increase the power of the analysis
(Fig. [229]7a). Similar observations were made for hiPSC-derived
neurons (Fig. [230]7b and Supplementary Fig. [231]8e–h). Here, cell
lines were a stronger discriminator, likely due to heterogeneity of the
positive and negative controls, as expected in cell lines derived from
different individuals (Supplementary Fig. [232]8e, n, Source Data
file [233]10). Again, differentially enriched proteins following
BCH-HSP-C01 treatment in hiPSC neurons showed a high degree of
similarity between patient and control lines (Supplementary
Fig. [234]8f–h), allowing a combined analysis (Fig. [235]7b).
Fig. 7. Target deconvolution using unbiased quantitative proteomics in
AP4B1^KO SH-SY5Y cells and AP-4-HSP patient-derived hiPSC neurons treated
with BCH-HSP-C01.
[236]Fig. 7
[237]Open in a new tab
a–c Differential protein enrichment analysis. Statistical testing was
done using protein-wise linear models and empirical Bayes statistics
using the limma package in R (Ritchie et al.). Proteins were considered
as differentially enriched with an FDR <0.05 and a log[2] fold change
>0.3. PCA plots show the top 500 variable proteins. Differentially
enriched proteins are shown in volcano plots colored in black. Proteins
with the most consistent enrichment profiles across all experimental
conditions (see Supplementary Fig. [238]8) are colored and labeled in
red. a SH-SY5Y cells: 8141 unique proteins were analyzed. b
hiPSC-derived neurons: 7386 unique proteins were analyzed. c Integrated
analysis of SH-SY5Y cells and hiPSC-derived neurons: 5357 unique
proteins were analyzed. The dot plot summarizes dysregulated Reactome
pathways of the pooled analysis. Pathways were considered
differentially expressed with an FDR <0.05. d The RAB protein family
members RAB1B, RAB3C and RAB12 showed the most consistent profiles in
response to BCH-HSP-C01 treatment and were selected for further
analysis. LFQ intensities in SH-SY5Y cells (AP4B1^WT and AP4B1^KO
pooled; 12 independent experiments per condition; exception: RAB12 in
BCB-HSP-C01 treated SH-SY5Y cells was not detectable in 3 samples,
which is why quantification is based on 9 independent experiments), and
hiPSC-derived neurons (controls and patients pooled, 6 independent
experiments per condition) are shown. Box plots show medians (center),
upper and lower quartiles (hinges) and 1.5 x IQR (whiskers).
Statistical testing was done using pairwise t-tests. P-values are
two-sided and were adjusted for multiple testing using the
Benjamini-Hochberg procedure. e Correlation of LFQ intensities of RAB3C
and RAB12 in AP4B1^WT (n = 11 samples) and AP4B1^KO (n = 10 samples)
SH-SY5Y cells, as well as control (n = 6 samples) and patient (n = 6
samples) hiPSC-derived neurons are measured by the Pearson correlation
coefficient (r).
Despite the heterogeneity in the neuronal samples, significant overlap
was observed between the differentially enriched proteins in SH-SY5Y
cells and hiPSC-derived neurons. Data sets were thus integrated for a
combined analysis, which detected several proteins that were
dysregulated across all cell types and genotypes (Supplementary
Fig. [239]8i–l), providing strong evidence that these changes were
related to treatment with BCH-HSP-C01 (Fig. [240]7c). Consistent with
the overall changes in gene expression, pathway enrichment analysis
using the Reactome database^[241]45 highlighted engagement of
intracellular trafficking pathways as a potential mechanism of action
for BCH-HSP-C01 (Fig. [242]7c). Specifically, modulation of RAB
proteins involved in vesicle transport emerged as a consistent theme
across cell types and genotypes, with the strongest evidence for the
upregulation of RAB1B and downregulation of RAB3C and RAB12. Notably,
while BCH-HSP-C01 led to a significant change in protein levels of all
three RAB protein family members in SH-SY5Y cells, only RAB3C and RAB12
reached significance in hiPSC-derived neurons (Fig. [243]7d). This
overall pattern of RAB protein modulation was further supported by
upregulation of the RAB protein geranylgeranyltransferase components A1
(CHM) in SH-SY5Y cells and A2 (CHML) in both SH-SY5Y cells and
hiPSC-derived neurons. CHM and CHML play a vital role in tethering RAB
proteins to intracellular membranes^[244]46,[245]47. Additionally,
upregulation of transferrin receptor protein 1 (TFRC) was observed
(Fig. [246]7c), consistent with prior reports showing that reduction of
RAB12 associates with increased protein levels of TFRC^[247]48.
Collectively, these findings suggest a potential role of RAB proteins
in regulating vesicle transport in response to BCH-HSP-C01 treatment.
RAB3C and RAB12 knockout are involved in BCH-HSP-C01-mediated vesicle
trafficking and autophagy
RAB3C and RAB12 displayed the strongest and most consistent protein
expression changes in both differentiated SH-SY5Y cells and
hiPSC-derived neurons following treatment with BCH-HSP-C01
(Fig. [248]7d) and were therefore selected for further investigation.
Correlation analysis revealed a strong correlation (r = 0.93) between
the LFQ intensities of these two proteins in both cell types and across
different genotypes in response to BCH-HSP-C01 (Fig. [249]7e).
To assess whether a correlation was also present on the transcriptional
level, mRNA levels of RAB3C and RAB12 in response to BCH-HSP-C01
treatment were analyzed in AP4B1^WT and AP4B1^KO SH-SY5Y cells. While
there was a trend toward a reduction of RAB3C and elevation of RAB12
mRNA levels and correlation analysis demonstrated a moderate inverse
correlation, none of these changes reached statistical significance
(Supplementary Fig. [250]9). These findings suggest that RAB3C and
RAB12 levels are altered through a post-transcriptional mechanism
following treatment with BCH-HSP-C01.
To investigate the potential impact of RAB3C and RAB12 on ATG9A
translocation in the AP-4-deficient background, we used
CRISPR/Cas9-mediated knockouts of RAB3C and RAB12 in AP4B1^KO SH-SY5Y
cells (Fig. [251]8a, b, Supplementary Fig. [252]10 and Source Data
file [253]11). We found that knockout of RAB12 did not affect ATG9A
translocation, while knockout of RAB3C caused a moderate reduction in
the ATG9A ratio (Fig. [254]8a). Combined knockout of RAB3C and RAB12 in
AP4B1^KO SH-SY5Y cells did not show an additive effect. Interestingly,
however, the effects of BCH-HSP-C01 on ATG9A translocation were
significantly enhanced by knockout of RAB3C, but not RAB12 alone.
Combined knockout of both genes further augmented the effect of
BCH-HSP-C01. These findings suggest that both RAB3C alone or in
combination with RAB12 play a role in BCH-HSP-C01-mediated ATG9A
redistribution.
Fig. 8. RAB3C and RAB12 are involved in BCH-HSP-C01-mediated vesicle
trafficking and enhancement of autophagic flux.
[255]Fig. 8
[256]Open in a new tab
a AP4B1^KO SH-SY5Y cells transfected for 72 h with RNPs targeting
RAB3C, RAB12 or both compared to NLRP5 (non-essential control). Vehicle
vs. BCH-HSP-C01 treatment at 5 µM was administered for 24 h. Data
points represent per well means. Each experimental condition was tested
in multiple replicates (n[AP4B1KO + sgNLRP5]: 20 wells from 5
independent plates; n[AP4B1KO + sgNLRP5 + BCH-HSP-C01]: 18 wells from 5
independent plates; n[AP4B1KO + sgRAB3C]: 24 wells from 5 independent
plates; n[AP4B1KO + sgRAB3C + BCH-HSP-C01]: 22 wells from 5 independent
plates; n[AP4B1KO + sgRAB12]: 28 wells from 5 independent plates;
n[AP4B1KO + sgRAB12 + BCH-HSP-C01]: 25 wells from 5 independent plates;
n[AP4B1KO + sgRAB3C + sgRAB12]: 22 wells from 3 independent plates;
n[AP4B1KO + sgRAB3C + sgRAB12 + BCH-HSP-C01]: 22 wells from 3
independent plates). Statistical testing was done using the t-test.
P-values are two-sided. b Representative images. Scale bar: 10 µm. c
Representative western blots. Cells were treated with vehicle vs.
BCH-HSP-C01 at 5 µM for 72 h. d–f Quantification of western blots.
Experiments were performed in four biological replicates. Error bars
represent ± 1 SD. Statistical testing was done using the t-test.
P-values are two-sided and were adjusted for multiple testing using the
Benjamini-Hochberg procedure. g, h AP4B1^KO SH-SY5Y cells treated with
BCH-HSP-C01 (5 µM) were incubated with ascending non-toxic doses of
bafilomycin A1 (5 nM or 10 nM) or chloroquine (1 µM or 2 µM) for 24 h.
Each condition was tested in 16 wells from 2 independent plates.
(AP4B1^KO). i Representative images. Scale bar: 10 µm. j–l
Representative western blots and quantification of whole cell lysates
of AP4B1^KO SH-SY5Y cells transfected for 72 h with RNPs against RAB3C,
RAB12 or both, compared to NLRP5. Vehicle vs. BCH-HSP-C01 treatment was
administered for 48 h. Error bars represent ± 1 SD. Statistical testing
was done using the t-test. P-values are two-sided and were adjusted for
multiple testing using the Benjamini-Hochberg procedure. Box plots in
all experiments show medians (center), upper and lower quartiles
(hinges) and 1.5 x IQR (whiskers). Dashed lines represent a reduction
of the ATG9A ratio of −2 SD compared to negative controls.
A converging theme of ATG9A translocation and alteration of RAB protein
expression is autophagy. RAB proteins are known modulators of autophagy
with key functions in various steps of the pathway^[257]49,[258]50.
ATG9A, a core autophagy protein, acts as a lipid scramblase and
promotes autophagosome formation and
elongation^[259]43,[260]51–[261]53. To investigate whether BCH-HSP-C01
leads to changes in autophagic flux, AP4B1^WT and AP4B1^KO SH-SY5Y
cells were treated with BCH-HSP-C01 for 72 h and LC3-I to LC3-II
conversion was measured by western blotting (Fig. [262]8c–f and
Supplementary Fig. [263]10a). Levels of LC3-II were significantly
elevated in all cell lines treated with BCH-HSP-C01, suggesting
modulation of the autophagy pathway. Co-treatment with bafilomycin A1,
which blocks autophagosome-lysosome fusion, led to further LC3-II
accumulation, indicating that BCH-HSP-C01 increases autophagic flux
(Fig. [264]8c–f). Blocking the late stages of the autophagy pathway
with either bafilomycin A1 or chloroquine reversed the effect of
BCH-HSP-C01 on ATG9A translocation in a dose-dependent manner,
suggesting that this process requires intact autophagic flux
(Fig. [265]8g–i and Source Data file [266]12).
Next, since our data suggested a contribution of RAB3C and RAB12 to the
effect of BCH-HSP-C01, we investigated the impact of RAB3C and RAB12
knockout in AP4B1^KO SH-SY5Y cells with and without BCH-HSP-C01
treatment (Fig. [267]8j–l and Supplementary Fig. [268]10b–d). Neither
RAB3C nor RAB12 knockout alone led to major changes in baseline or
BCH-HSP-C01-enhanced autophagic flux (Fig. [269]8j, k). However,
combined knockout of RAB3C and RAB12, without BCH-HSP-C01 treatment,
significantly increased the ratio of LC3-II to LC3-I by approximately
36% (Fig. [270]8l). Upon treatment with bafilomycin A1, both RAB3C
knockout alone and combined knockout of RAB3C and RAB12 further
increased BCH-HSP-C01-mediated LC3-I to LC3-II conversion
(Fig. [271]8j–l). These findings suggest the possibility that RAB3C and
RAB12 modulate BCH-HSP-C01-mediated ATG9A trafficking and subsequent
autophagy induction.
Discussion
Identification of therapeutic targets for rare neurological diseases
represents a major scientific and public health
challenge^[272]1,[273]4. The increasing number of rare genetic
diseases^[274]54, the rising rate of diagnoses^[275]55, and the
significant burden for patients^[276]56,[277]57, caregivers^[278]58 and
healthcare systems^[279]59 highlight the urgent need for translational
research that moves beyond gene discovery to the identification of
disease mechanisms and therapies. Unbiased high-content small molecule
screens are a platform for drug-repurposing approaches and a starting
point for the rationale development of new compounds^[280]1–[281]6.
Disease-relevant ‘screenable’ phenotypes across cellular models,
including patient-derived cells, provide an entry point into developing
automated, high-content screening and analysis platforms.
In this study, we develop the first high-throughput cell-based
phenotypic screening platform for a prototypical form of
childhood-onset HSP caused by defective protein trafficking. Our
platform allows us to determine the subcellular localization of the
AP-4 cargo protein ATG9A in several cellular models of AP-4-deficiency.
The hypothesis that ATG9A mislocalization is a key mechanism in the
pathogenesis of AP-4-HSP is supported by the independent work of the
Robinson^[282]12, Kittler^[283]14 and
Bonifacino^[284]11,[285]13,[286]60 groups, in addition to our own
work^[287]15,[288]21,[289]24,[290]25, and by the overlapping phenotypes
of AP-4^[291]13,[292]14,[293]26 and Atg9a^[294]28 knockout mice.
ATG9A is the only conserved autophagy-related transmembrane
protein^[295]53, and in mammalian cells, cycles between the TGN and
ATG9A vesicles, which associate with endosomes^[296]61 and
autophagosome formation sites^[297]61,[298]62. ATG9A has 4
transmembrane domains and forms homotrimers that have lipid scramblase
activity^[299]51–[300]53, postulated to equilibrate lipids in the
double-membrane layer of nascent autophagosomes^[301]63,[302]64. Basal
levels of autophagy are essential for neuronal survival, and
neuron-specific ablation of the autophagy pathway leads to axonal
degeneration and cell death^[303]65–[304]67. In neurons, autophagosomes
form in the distal axon^[305]68,[306]69 and are subject to active
transport^[307]70–[308]72. Thus, efficient vesicular trafficking and
spatial distribution of ATG9A are essential for axonal function as
demonstrated in CNS-specific Atg9a knockout mice^[309]28.
Having established a robust and dynamic assay that reliably measures
intracellular ATG9A distribution, we systematically screened a large
library of 28,864 small molecules for their ability to restore ATG9A
trafficking from the TGN to the cytoplasm. Following this primary
screen, a counter-screen and a series of orthogonal experiments
identified a small molecule, termed BCH-HSP-C01, that can restore the
intracellular distribution of ATG9A and a second transmembrane AP-4
cargo protein, DAGLB, in neuronal models of AP-4 deficiency, including
hiPSC-derived neurons from two patients with AP-4-HSP.
Compound BCH-HSP-C01 has physicochemical properties that are within the
parameters that are optimal for CNS drugs^[310]73 and therefore
represents a strong candidate for an in vivo tool compound. In
addition, the low molecular weight and topological polar surface area
create opportunities for compound optimization. Since the molecular
targets of BCH-HSP-C01 are unknown, we employed a target deconvolution
strategy using transcriptomics and proteomics to define the cellular
pathways impacted by this small molecule. This approach identified two
central themes: (1) modulation of Golgi dynamics and vesicular
trafficking and (2) engagement of autophagy. At the core of the
putative pathways affected by BCH-HSP-C01, we identified the RAB
proteins RAB1B, RAB3C and RAB12, as well as the interacting RAB geranyl
transferase subunits CHM and CHML. RAB3C and RAB12 showed the strongest
and most consistent association with BCH-HSP-C01 treatment in both
SH-SY5Y cells and hiPSC-derived neurons, and our analyses suggest that
these two proteins likely contribute to BCH-HSP-C01-mediated
redistribution of ATG9A from the TGN and increase of autophagic flux.
RAB proteins comprise a large family of small guanosine triphosphate
(GTP) binding proteins that act as key regulators of intracellular
membrane trafficking in eukaryotic cells at several stages, including
cargo sorting, vesicle budding, docking, fusion and membrane
organization^[311]74,[312]75. RAB GTPases function both as soluble and
specifically localized integral-membrane proteins, the latter being
mediated by prenylation. Among the roughly 70 known RAB proteins, more
than 20 are primarily associated with the TGN, where they regulate
Golgi organization, coordinate vesicle trafficking and interact with
various steps of the autophagy pathway^[313]49,[314]50.
Following treatment with BCH-HSP-C01, the RAB protein family members
RAB3C and RAB12 were consistently downregulated in both SH-SY5Y cells
and hiPSC-derived neurons. Knockout experiments of these two proteins
revealed that their loss potentiates BCH-HSP-C01-mediated ATG9A
translocation and autophagic flux. RAB3C, which is part of the RAB3
superfamily, is primarily expressed in brain and endocrine tissues,
where it localizes to the Golgi and synaptic vesicles and is involved
in exocytosis and modulation of neurotransmitter release^[315]76. RAB12
is mainly localized to recycling endosomes, where it regulates
endosomal trafficking and lysosomal degradation and has been identified
as a modulator of autophagy^[316]77. A well-known downstream target of
RAB12 is the transferrin receptor (TfR). Knockdown of RAB12 in mouse
embryonic fibroblasts increases TfR protein levels, while
overexpression leads to its reduction^[317]48. In line with this, we
find that treatment with BCH-HSP-C01 reduced RAB12 protein levels
while, at the same time, robustly elevating transferrin receptor
protein 1 (TFRC). To the best of our knowledge, no interaction between
RAB3C and RAB12 has been described so far; however, our data support
the possibility that both proteins are involved in BCH-HSP-C01-mediated
modulation of vesicle trafficking and autophagic flux.
Our study has identified the first candidate small molecule drug
capable of restoring protein mislocalization in AP-4-deficient cells,
including human neurons from patients. We acknowledge several
limitations of our approach, some of which are inherent to
high-throughput screens and some that are specific to our assay. First,
in the primary screen, compounds were arrayed to single wells and only
one well per compound was screened. We recognize that using multiple
replicates as well as multiple concentrations and treatment durations
could have potentially decreased the rate of false negative results.
However, we prioritized efficiency and compounds that would show a
robust impact at a single low-micromolar concentration. With respect to
false positive results, these were eliminated in the counter-screen,
which was performed with biological duplicates and using dose-response
titrations covering a broad range of concentrations. Second, as ATG9A
mislocalization is a cellular phenotype of AP-4 deficiency conserved in
non-neuronal and neuronal cells both in
vitro^[318]11–[319]15,[320]25,[321]78 and in
vivo^[322]13,[323]14,[324]27, we decided to conduct the initial screen
in patient-derived fibroblasts, as a simple cellular model of AP-4
deficiency. While the use of patient fibroblasts in the primary screen
increases translational relevance, compounds that would have the
capacity to correct ATG9A trafficking exclusively in neuronal cells
could be missed at this stage. We determined that this risk was
outweighed by the benefits of a robust assay performance and the fact
that mechanisms of AP-4-mediated protein trafficking are conserved
across tissues and cell types^[325]11–[326]15,[327]35,[328]78. Third,
even though cell-based disease models can, to some extent, mimic the
complexity of therapeutic responses in biological systems, the
translation to in vivo models is often challenging, particularly for
neurodevelopmental and neurodegenerative diseases. Considerations such
as a lead compound’s ability to cross the blood-brain-barrier, target
engagement in the central nervous system, therapeutic responses in
complex neuronal networks relying on interactions with glia cells,
developmental windows amenable to therapy, as well as in vivo
off-target effects and toxicity must be considered and explored in
future studies. To mitigate some of these risks, we employed unbiased
multiparametric profiling of BCH-HSP-C01, which suggested little
off-target effects. Future studies are required to exclude pleiotropic
effects or off-target toxicity in different cell types or tissues in
vivo. Lastly, while BCH-HSP-C01 leads to a redistribution of two
well-established AP-4 cargo proteins, ATG9A and DAGLB, we are unable to
exclude the possibility that other neuron-specific cargos of AP-4 exist
and are important for the pathogenesis of AP-4-HSP. Nonetheless,
mislocalization of both proteins is proposed as the major contributor
to neuronal pathology caused by AP-4 deficiency through dysregulated
autophagy and endocannabinoid signaling,
respectively^[329]11–[330]14,[331]29. Our automated high-throughput
platform would allow for the rapid interrogation of additional AP-4
cargo proteins in the future.
In conclusion, our findings provide a solid foundation for lead
optimization of BCH-HSP-C01 and its development as a potential
therapeutic, with the next step being in vivo proof-of-concept
experiments. More broadly, our approach illustrates the development of
a small molecule screening platform for a rare neurogenetic disease,
leveraging robust cellular phenotypes. We hope this approach will
create a paradigm for other rare and more common disorders of protein
trafficking. The increase of autophagic flux through BCH-HSP-C01 offers
the intriguing possibility that this compound could be considered for
the treatment of other autophagy-associated diseases.
Methods
Clinical data from patients with AP-4-HSP
This study was approved by the Institutional Review Board at Boston
Children’s Hospital (IRB-P00033016 and IRB-P00016119). Two patients
with AP-4-HSP and their clinically-unaffected, sex-matched parents were
enrolled in the International Registry and Natural History Study for
Early-Onset Hereditary Spastic Paraplegia (ClinicalTrials.gov
Identifier: [332]NCT04712812). Both patients had a clinical and
molecular diagnosis of AP-4-HSP and presented with core clinical and
imaging features^[333]8. Patient 1 (2-year-old male) was diagnosed with
AP4B1-associated SPG47 and carries the following compound-heterozygous
variants: [334]NM_001253852.3: c.1160_1161del (p.Thr387ArgfsTer30) /
c.1345A>T (p.Arg449Ter). The sex-matched parent (38 years old) carries
the heterozygous c.1160_1161del; p.Thr387Argfs*30 variant. Patient 2
(18-month-old male) was diagnosed with AP4M1-associated SPG50 and
carries the following compound-heterozygous variants: [335]NM_004722.4:
c.916C>T (p.Arg306Ter) / c.694dupG (p.Glu232GlyfsTer21). The
sex-matched parent (40 years old) carries the heterozygous c.694dupG
(p.Glu232GlyfsTer21) variant.
Antibodies and reagents
The following reagents were used: Bovine serum albumin (AmericanBIO,
Cat# 9048-46-8), saponin (Sigma, #47036-50G-F), normal goat serum
(Sigma-Aldrich, Cat# G9023-10ML), Dulbecco’s phosphate-buffered saline
(DPBS) (Thermo Fisher Scientific, Cat# 14190-250), trypsin (Thermo
Fisher Scientific, Cat#25200056), 4% paraformaldehyde (4%) (Boston
BioProducts, Cat# BM-155), dimethyl-sulfoxide (DMSO) (American
Bioanalytical, Cat# AB03091-00100), bafilomycin A1 (Enzo Life Sciences,
Cat#BML-CM110-0100), chloroquine (MedChemExpress, Cat# HY-17589A),
Molecular Probes Hoechst 33258 (Thermo Fisher Scientific, Cat# H3569)
and Alexa Fluor 647-labeled phalloidin (Thermo Fisher Scientific,
Cat#A22287). The following primary antibodies were used: Anti-AP4E1 at
1:500 (BD Bioscience, Cat# 612019), anti-ATG9A at 1:500-1000 (Abcam,
Cat# ab108338), anti-DAGLB at 1:500 (Abcam, Cat# 191159), anti-TGN46 at
1:800 (Bio-Rad, Cat# AHP500G), anti-Golgin-97 1:500 (Abcam, Cat#
169287), anti-beta-Tubulin III 1:1000 (Synaptic Systems, Cat# 302304
and Sigma, Cat# T8660), anti-beta-Actin 1:10,000 (Sigma, Cat#
A1978-100UL), anti-SMI 312 (Biolegend, Cat # 837904), anti-pan-AKT
(Cell Signaling Technology, Cat# 4691), anti-Histon H3 (Cell Signaling
Technology, Cat # 9715), anti-RAB12 (Santa Cruz, Cat# sc-515613),
anti-RAB3C (Santa Cruz, Cat# 107 203), anti-LC3B 1:1000 (Novus,
Cat#100-2220). Fluorescently labeled secondary antibodies for
immunocytochemistry were used at 1:2000 (Thermo Fisher Scientific, Cat#
A11005, A-11008, A-11016, A-11073, A-21235, A-21245), for western
blotting at 1:5000 (LI-COR Biosciences, Cat# 926-68022, 926-68023,
926-32212, 926-32213).
Small molecule library
A diversity small molecule library containing 28,864 compounds was
provided by Astellas Pharma Inc. Compounds were arrayed in 384-well
microplates at a final concentration of 10 mM (1000-fold the screening
concentration) in DMSO. Assay plates were stored at −80 °C and thawed
30 min prior to cell plating. Active compounds from the primary screen
were re-screened in a secondary screen, using eleven-point
concentrations (range: 0.04 µM, 0.08 µM, 0.16 µM, 0.31 µM, 0.63 µM,
1.25 µM, 2.5 µM, 5 µM, 10 µM, 20 µM, 40 µM) in two biological
replicates. The chemical structures of the 5 compounds that were tested
in neuronal models were disclosed by Astellas Pharma Inc. after the
screen was completed.
Fibroblast cell culture
Fibroblast lines were collected from individuals enrolled in our
natural history study (approved at Boston Children’s Hospital,
IRB-P00033016). Probands provided written consent for routine skin
punch biopsies. Fibroblasts were derived from both patients and their
respective sex-matched heterozygous parents^[336]15. Primary human skin
fibroblasts were cultured and maintained as previously
described^[337]79. Briefly, cells were maintained in DMEM high glucose
(Gibco, #11960044) supplemented with 20% FBS (Gibco, #10082147),
penicillin 100 U/mL and streptomycin 100 µg/mL (Gibco, #15140122).
Cells were kept in culture for up to 8 passages and routinely tested
for the presence of mycoplasma contamination. For high-throughput
imaging, fibroblasts were seeded onto 384-well plates (Greiner Bio-One,
#781090) at a density of 2 × 10^3 per well using the Multidrop Combi
Reagent Dispenser (Thermo Fisher Scientific, #11388-558). Media changes
were done every 2–3 days and drugs were administered 24 h before
fixation.
SH-SY5Y cell culture
AP4B1 wildtype (AP4B1^WT) and AP4B1 knockout (AP4B1^KO) SH-SY5Y cells
were generated previously^[338]12. Undifferentiated SH-SY5Y cells were
maintained in DMEM/F12 (Gibco, Cat# 11320033) supplemented with 10%
heat-inactivated fetal bovine serum (Gibco, Cat# 10438026), 100 U/mL
penicillin and 100 μg/mL streptomycin at 37 °C under 5% CO[2]. SH-SY5Y
cells were passaged every 2–3 days and differentiated into a
neuron-like state using a 5-day differentiation protocol with
all-trans-retinoic acid (MedChemExpress, #HY-14649) as described
previously^[339]33. For assessment of ATG9A translocation,
differentiated SH-SY5Y cells were plated in 96-well plates (Greiner
Bio-One, Cat# 655090) at a density of 1 × 10^4 cells per well. Media
changes were done every 2–3 days and drugs were administered 24–72 h
before fixation.
Generation of hiPSC lines and neuronal differentiation
Fibroblasts were reprogrammed to hiPSCs using non-integrating Sendai
virus as described previously^[340]35,[341]36. Quality control
experiments, including karyotyping, embryoid body formation,
pluripotency marker expression, STR profiling and Sanger sequencing for
AP4B1 or AP4M1 variants, were reported previously^[342]35,[343]36.
hiPSC-derived neurons were generated using induced NGN2 expression
following published protocols with minor modifications^[344]37,[345]38.
hiPSCs were dissociated into single cells with accutase (Innovative
Cell Technology, Cat#AT 104–500) and seeded onto Geltrex-coated plates
(Thermo Fisher Scientific, Cat#A1413301). hiPSCs were then infected
with concentrated rtTA-and NGN2-expressing lentiviruses (FUW-M2rtTA
Addgene #20342, pTet-O-Ngn2-puro Addgene #52047) in the presence of
polybrene (8 μg/mL, Sigma-Aldrich, Cat# TR-1003-G). The next day,
hiPSCs were fed with supplemented mTeSRPlus and expanded for
cryopreservation. In parallel, a kill curve was generated to determine
the optimal puromycin concentration needed to eliminate untransduced
cells. Successful transduction was established by adding doxycycline
(2 μg/mL, Millipore, Cat#324385–1GM) to virus-treated cells for 24 h,
followed by adding the optimized puromycin concentration (Invitrogen,
1 μg/mL, Cat# ant-pr-1) for up to 48 h.
For the generation of glutamatergic neurons, NGN2 transduced hiPSCs
were dissociated into single cells using accutase and seeded onto
geltrex-coated plates. The following day, NGN2 expression was induced
using doxycycline and selected with puromycin. Growth factors BDNF
(10 ng/mL, Peprotech, Cat#450–02), NT3 (10 ng/mL, Peprotech, Cat#
450–03), and laminin (0.2 mg/L, Thermo Fisher Scientific,
Cat#23017–015) were added in N2 medium for the first 2 days. Cells were
then fed with BDNF (10 ng/mL), NT3 (10 ng/mL), laminin (0.2 mg/Lf),
doxycycline (2 μg/mL), and Ara-C (4uM, Sigma-Aldrich, Cat# C1768) in
B27 media every other day until differentiation day 6. On day 6, cells
were dissociated with papain (Worthington, Cat# [346]LK003178) and
DNaseI (Worthington, Cat# [347]LK003172) and replated on poly-D-lysine
(0.5 mg/mL; Sigma-Aldrich, Cat#P6407) and laminin (5 μg/mL; Thermo
Fisher Scientific, Cat #23017-015) coated plates either in co-culture
with hiPSC-derived astrocytes (Astro.4U, Ncardia) for
immunocytochemistry experiments, or without astrocytes for preparation
of cell lysates for proteomics experiments. For assessment of ATG9A
translocation, neurons were plated in 96-well plates at a density of
4 × 10^4 cells per well. Media changes were done every 2–3 days and
drugs were administered 24–72 h before fixation.
Immunocytochemistry
The immunocytochemistry workflow was optimized for high-throughput
using automated pipettes and reagent dispensers (Thermo Fisher
Scientific Multidrop Combi Reagent Dispenser, Integra VIAFLO 96/384
liquid handler, Integra VOYAGER pipette). Fibroblasts and SH-SY5Y cells
were fixed using 3% and 4% PFA, respectively, permeabilized with 0.1%
saponin in PBS and blocked in 1% BSA/0.01% saponin (blocking solution)
in PBS. hiPSC-derived neurons were fixed in 4% PFA and permeabilized
and blocked using 0.1% Triton X-100/2% BSA/0.05% NGS in PBS. Primary
antibody (diluted in blocking solution) was added for 1 h (fibroblasts
and SH-SY5Y cells) at room temperature or overnight (hiPSC neurons) at
4 °C. Plates were gently washed three times in blocking solution
(fibroblasts and SH-SY5Y cells) or in PBS (hiPSC neurons), followed by
addition of fluorochrome-conjugated secondary antibodies, Hoechst 33258
and phalloidin for 30 min (fibroblasts) or Hoechst 33258 for 60 min
(SH-SY5Y cells and hiPSC neurons) at room temperature. Plates were then
gently washed three times with PBS and protected from light.
High-content imaging and automated image analysis
High-throughput confocal imaging was performed on the ImageXpress Micro
Confocal Screening System (Molecular Devices) using an experimental
pipeline modified from the pipeline described in Behne et al.^[348]15.
For experiments in fibroblasts, images were acquired using a 20x S Plan
Fluor objective (NA 0.45 μM, WD 8.2–6.9 mm). Per well, 4 fields were
acquired in a 2 × 2 format (384-well plates). For experiments in
SH-SY5Y cells and hiPSC neurons, up to 36 fields were acquired in a
6 × 6 format (96-well plate) using a 40x S Plan Fluor objective (NA
0.60 µm, WB 3.6–2.8 mm). Image analysis was performed using a
customized image analysis pipeline in MetaXpress (Molecular Devices):
Briefly, cells were identified based on the presence of DAPI signal
inside a phalloidin (fibroblasts) or TUBB3 (SH-SY5Y cells and hiPSC
neurons)-positive cell body. Sequential masks were generated for (1)
the TGN by outlining the area covered by TGN marker TGN46
(TGN46-positive area, in fibroblasts and SH-SY5Y cells) or Golgin 97
(Golgin 97-positive area, in hiPSC neurons) and (2) for the cell area
outside the TGN (actin-positive area minus TGN46-positive area). ATG9A
fluorescence intensity (F.U.) was measured in both compartments in each
cell, and the ATG9A ratio was calculated by dividing the ATG9A
fluorescence intensity inside the TGN by the ATG9A fluorescence
intensity in the remaining cell body (Fig. [349]1b):
[MATH: ATG9ARatio=ATG9A
F.U.insidetheTGN
ATG9AF.U.outsidetheTGN
:MATH]
1
Additional masks for the TGN used for morphologic profiling included
TGN Roughness (shape factor in the MetaXpress software) and the
following calculated metrics:
[MATH: TGNElongation=<
mrow>TGNWith
TGNLength :MATH]
2
[MATH: TGNCompactness=TGNPerimeter24π*TGNArea
:MATH]
3
Z’-factor robust values and strictly standardized median difference
(SSMD)^[350]30 were calculated for each plate and only plates that met
the predefined quality metrics of a Z’-factor robust ≥0.3 and SSMD ≥3
were included in subsequent analyses.
Western blotting
Western blotting was done as previously described^[351]72. Briefly,
cells were lysed in RIPA lysis buffer (Thermo Fisher Scientific Cat#
89900) supplemented with cOmplete protease inhibitor (Roche Cat#
04693124001) and PhosSTOP phosphatase inhibitor (Roche Cat#
4906845001). Total protein concentration was determined using a Pierce
BCA Protein Assay Kit (Thermo Fisher Scientific, Cat# 23225). Equal
amounts of protein were solubilized in LDS sample buffer (Thermo Fisher
Scientific, Cat# NP0008) under reducing conditions, separated by gel
electrophoresis, using 4–12% (Thermo Fisher Scientific, Cat#
NW04125BOX) or 12% Bis-Tris gels (Thermo Fisher Scientific, Cat#
NP0343BOX) and MOPS or MES buffer (Thermo Fisher Scientific, #NP0001
and #NP0002) and transferred to PVDF or nitrocellulose membranes (EMD
Millipore, #SLHVR33RS). Following blocking with blocking buffer (LI-COR
Biosciences, #927-70001), membranes were incubated overnight with the
respective primary antibodies. Near-infrared fluorescent-labeled
secondary antibodies (IR800CW, IR680LT; LI-COR Biosciences) were used
and quantification was done using the Odyssey infrared imaging system
and Empiria Studio Software (LI-COR Biosciences).
Multiparametric morphological profiling
The multiparametric morphological profiling strategy employed in this
study was adapted from previously published protocols^[352]34.
Single-cell measurements of ninety distinct descriptors of shape and
intensity for the nucleus (DAPI), the cytoskeleton and global cell
morphology (anti-beta-Tubulin III), the TGN (anti-TGN46), and ATG9A
vesicles (anti-ATG9A) were acquired and automatically extracted.
Single-cell data were summarized by computing per-image medians for
each variable (Source Data file [353]6). Next, a correlation matrix was
generated using the Pearson correlation coefficient with complete
pairwise observations. Variables with zero variance and observations
with missing values were removed. Variables were then transformed to
have a mean of zero and a standard deviation of one. Principal
component analysis (PCA) was conducted to reduce dimensionality and
cluster data based on their properties. To identify the contribution of
individual features to the variance within the dataset, correlation
analysis was performed between the first principal component,
accounting for the majority of the variance within the dataset, and all
extracted features. Features displaying a correlation coefficient >0.75
were selected to define morphological profiles. Profiles were
summarized using heatmap visualization.
Sample preparation for RNA extraction
SH-SY5Y cells were differentiated with retinoic acid as described above
and subsequently treated with compounds of interest for 72 h prior to
lysis using Qiagen RTL-Buffer supplemented with 1% ß-mercaptoethanol.
RNA extraction, library preparation and sequencing were conducted at
Azenta Life Sciences (South Plainfield, NJ, USA). Total RNA was
extracted from frozen cell pellet samples using a Qiagen Rneasy mini
kit following the manufacturer’s instructions (Qiagen, Cat# 74004).
Library preparation with polyA selection and Illumina sequencing
RNA samples were quantified using a Qubit 4 Fluorometer (Life
Technologies), and RNA integrity was checked using Agilent TapeStation
4200 (Agilent Technologies). RNA sequencing libraries were prepared
using the NEBNext Ultra II RNA Library Prep Kit for Illumina using the
manufacturer’s instructions (New England Biolabs). Briefly, mRNAs were
initially enriched with Oligod(T) beads. Enriched mRNAs were fragmented
for 15 min at 94 °C. First-strand and second-strand cDNA were
subsequently synthesized. cDNA fragments were end-repaired and
adenylated at 3’ ends, and universal adapters were ligated to cDNA
fragments, followed by index addition and library enrichment by PCR
with limited cycles. The sequencing library was validated on the
Agilent TapeStation (Agilent Technologies) and quantified using Qubit 4
Fluorometer (Invitrogen) as well as by quantitative PCR (KAPA
Biosystems). The sequencing libraries were clustered on 3 lanes of a
flowcell. After clustering, the flowcell was loaded on the Illumina
instrument (HiSeq 4000 or equivalent) according to the manufacturer’s
instructions. The samples were sequenced using a 2x150bp Paired End
(PE) configuration. Image analysis and base calling were conducted by
the Control software. Raw sequence data (.bcl files) generated by the
sequencer were converted into fastq files and de-multiplexed using
Illumina’s bcl2fastq 2.17 software. One mismatch was allowed for index
sequence identification.
Downstream RNA sequencing analysis
Sequencing reads were mapped to the GRCh38 reference genome available
on ENSEMBL using the STAR aligner v.2.7.9a. Differential expression
analysis was done using the TREAT approach developed by McCarthy and
Smyth^[354]80, implemented in the edgeR package in R. Raw counts were
obtained using STAR, and low expressed genes were excluded using the
method described by Chen et al.^[355]81. Expression data were
normalized using the Trimmed Mean of M-values method implemented in the
edgeR package. Genes were considered as differentially expressed
according to default options with a false discovery rate
(Benjamini-Hochberg procedure) <0.05 and a log[2] fold change of >0.3.
Gene ontology (GO) enrichment analysis was done using
clusterProfiler^[356]82. Pathways were considered differentially
expressed with an FDR <0.05.
Network connectivity analysis
To identify transcriptional changes in co-expressed groups of genes
following compound treatment, a weighted gene co-expression network
analysis (WGCNA) was performed. Raw counts were generated, and
low-expressed genes were removed as described above. Data were
normalized using variance stabilizing transformation as described by
Anders et al.^[357]83. Batch effects were removed using the limma
package in R^[358]84. Preprocessed data were then analyzed using the
WGCNA package in R^[359]85,[360]86. In brief, pairwise Pearson
correlations were calculated between all genes, and genes with a
positive correlation were selected to form a “directed” correlation
matrix. Next, the correlations were raised to a power to approximate a
scale-free network. The adequate power was chosen based on soft
thresholding aiming for a high scale independence above 0.8 by keeping
a mean connectivity between 200 and 500. Genes were then grouped based
on topological overlap and clusters were isolated using hierarchical
clustering and adaptive branch pruning of the hierarchical cluster
dendrogram, giving rise to groups of co-expressed genes, so-called
modules. Gene expression profiles within each module were summarized
using the “module eigengene” (ME), defined as the first principal
component of a module. Within each module, the association of MEs with
measured clinical traits was examined by correlation analysis. For
these selected modules, module eigengene-based connectivity was
determined for every gene by calculating the absolute value of the
Pearson correlation between the expression of the gene and the
respective ME, producing a quantitative measure of module membership
(MM). Similarly, the correlation of individual genes with the trait of
interest was computed, defining gene significance (GS). Using the GS
and MM, an intramodular analysis was performed, allowing identification
of genes that have high significance with treatment as well as high
connectivity to their modules. The biological information contained in
modules of interest was summarized with gene ontology (GO) enrichment
analysis using clusterProfiler^[361]82. Pathways were considered
differentially expressed with a FDR <0.05.
Sample preparation for mass spectrometry
Cells were lysed for whole proteome analysis in RIPA lysis buffer
(Thermo Fisher Scientific, Cat# 89900) supplemented with cOmplete
protease inhibitor (Roche Cat# 04693124001) and PhosSTOP phosphatase
inhibitor (Roche Cat# 4906845001) and sonicated in a Bioruptor® Pico
Sonication System (one single 30 s on/off cycle at 4 °C). Protein
concentrations were determined using a Pierce BCA Protein Assay Kit
(Thermo Fisher Scientific Cat# 23225). Lysates were stored at −80 °C
until further processing. To generate peptide samples for analysis by
mass spectrometry, 30–50 µg protein was precipitated by overnight
incubation in 5 volumes of ice-cold acetone at −20 °C and pelleted by
centrifugation at 10,000×g for 5 min at 4 °C. All subsequent steps were
performed at room temperature. Precipitated protein pellets were
air-dried, resuspended for denaturation and reduction in digestion
buffer (50 mM Tris pH 8.3, 8 M Urea, 1 mM dithiothreitol (DTT)) and
incubated for 15 min. Proteins were alkylated by the addition of 5 mM
iodoacetamide for 20 min in the dark. Following reduction and
alkylation, proteins were enzymatically digested by the addition of
LysC (1 µg per 50 µg of protein; Wako, Cat# 129-02541) for overnight
incubation. Samples were then diluted four-fold with 50 mM Tris pH 8.3
before the addition of Trypsin (1 µg per 50 µg of protein;
Sigma-Aldrich, Cat# T6567) for 3 h. The digestion reaction was stopped
by the addition of 1% (v/v) trifluoroacetic acid (TFA) and samples were
incubated on ice for 5 min to precipitate contaminants, which were
pelleted by centrifugation at 10,000×g for 5 min. Acidified peptides
were transferred to new tubes before purification by solid-phase
extraction using poly(styrenedivinylbenzene) reverse-phase sulfonate
(SDB-RPS; Sigma-Aldrich, Cat# 66886-U) StageTips^[362]87. StageTips
with three SDB-RPS plugs were washed with 100% acetonitrile,
equilibrated with StageTip equilibration buffer (30% [v/v] methanol, 1%
[v/v] TFA), and washed with 0.2% (v/v) TFA. 20 μg of peptides in 1% TFA
were then loaded onto the activated StageTips, washed with 100%
isopropanol, and then 0.2% (v/v) TFA. Peptides were eluted in three
consecutive fractions by applying a step gradient of increasing
acetonitrile concentrations: 20 μL SDB-RPS-1 (100 mM ammonium formate,
40% [v/v] acetonitrile, 0.5% [v/v] formic acid), then 20 μL SDB-RPS-2
(150 mM ammonium formate, 60% [v/v] acetonitrile, 0.5% [v/v] formic
acid), then 30 μL SDB-RPS-3 (5% [v/v] NH4OH, 80% [v/v] acetonitrile).
Eluted peptides were dried in a centrifugal vacuum concentrator,
resuspended in Buffer A* (0.1% (v/v) TFA, 2% (v/v) acetonitrile), and
stored at −20 °C until analysis by mass spectrometry.
Mass spectrometry
Mass spectrometry was performed on an Exploris 480 mass spectrometer
coupled online to an EASY‐nLC 1200 via a nano-electrospray ion source
(all from Thermo Fisher Scientific). Per sample, 250 ng of peptides
were loaded on a 50 cm by 75 µm inner diameter column, packed in-house
with ReproSil-Pur C18-AQ 1.9 µm silica beads (Dr Maisch GmbH). The
column was operated at 50 °C using an in-house manufactured oven.
Peptides were separated at a constant flow rate of 300nL/min using a
linear 110 min gradient employing a binary buffer system consisting of
Buffer A (0.1% [v/v] formic acid) and Buffer B (80% acetonitrile, 0.1%
[v/v] formic acid). The gradient ran from 5 to 30% B in 84 min,
followed by an increase to 60% B in 8 min, a further increase to 95% B
in 4 min, a constant phase at 95% B for 4 min, and then a washout
decreasing to 5% B in 5 min, before re-equilibration at 5% B for 5 min.
The Exploris 480 was controlled by Xcalibur software (v.4.4, Thermo
Fisher Scientific), and data were acquired using a data-dependent
top-15 method with a full scan range of 300–1650 Th. MS1 survey scans
were acquired at 60,000 resolution, with an automatic gain control
(AGC) target of 3 × 10^6 charges and a maximum ion injection time of
25 ms. Selected precursor ions were isolated in a window of 1.4 Th and
fragmented by higher-energy collisional dissociation (HCD) with
normalized collision energies of 30. MS2 fragment scans were performed
at 15,000 resolution, with an AGC target of 1 × 10^5 charges, a maximum
injection time of 28 ms, and precursor dynamic exclusion for 30 s.
Raw mass spectrometry data analysis
Mass spectrometry raw files were processed in MaxQuant Version
2.1.4.0^[363]88,[364]89, using the human SwissProt canonical and
isoform protein database retrieved from UniProt (2022_09_26;
[365]www.uniprot.org). Label-free quantification was performed using
the MaxLFQ algorithm^[366]90. Matching between runs was enabled to
match between equivalent and adjacent peptide fractions within
replicates. LFQ minimum ratio count was set to 1 and default parameters
were used for all other settings. All downstream analyses were
performed on the ‘protein groups’ file output from MaxQuant.
Proteomic downstream data analysis
Differential enrichment analysis of proteomics data was done using the
DEP package in R. Preprocessing and quality filtering were performed
separately for SH-SY5Y cells and hiPSC-derived neurons. Proteins that
were only identified by a modification site or matched the reversed
part of the decoy database, as well as commonly occurring contaminants,
were removed. Duplicate proteins were removed based on the
corresponding gene names by keeping those with the highest total MS/MS
count across all samples. All following steps were done separately for
each cell type (SH-SY5Y cells (Fig. [367]7a and Supplementary
Fig. [368]8a–d) and hiPSC-derived neurons (Fig. [369]7b and
Supplementary Fig. [370]8e–h) and for the pooled dataset (Fig. [371]7c
and Supplementary Fig. [372]8i–l)). Low-quality entries were removed by
keeping only those proteins that had valid MS/MS counts in all
replicate samples of at least one experimental condition. Finally, only
those proteins were kept that had a maximum of one missing LFQ value in
at least one experimental condition. Filtered data were normalized
using variance stabilizing transformation, and missing values were
imputed using a manually defined left-shifted Gaussian distribution
with a width of 0.3 and a left-shift of 2.2 SD. Batch effects were
corrected using the method described by Johnson et al.^[373]91.
Statistical testing for differential protein enrichment was done using
protein-wise linear models and empirical Bayes statistics implemented
in the limma package in R. Proteins were considered as differentially
enriched with a FDR <0.05 and a log[2] fold change >0.3. The biological
information contained in differentially enriched proteins was
summarized using Reactome pathway annotation in
clusterProfiler^[374]82. Pathways were considered differentially
expressed with a FDR <0.05.
Nucleofection
sgRNAs against NLRP5, RAB3C and RAB12 were purchased as multi-guide
knockout kits (v2) from Synthego, diluted to 100 µM stock
concentrations and kept at −20 °C. Nucleofection was performed under
RNAse-free conditions on a Lonza 4D-Nucleofector (Cat# AAF-1003X,
AAF-1003B) according to the manufacturer’s protocol. Briefly, SH-SY5Y
cells were harvested, and 4 × 10^5 cells were resuspended in 5 µL
Nucleofector Solution. Then, 180 pmol sgRNAs were incubated with 20
pmol Cas9 protein in Nucleofector Solution to form ribonucleoprotein
complexes (RNPs) according to the manufacturer’s instructions. The cell
solution was then incubated with the respective RNPs and transferred
into a nucleofection strip (Cat# V4XC-2032). Strips were placed in the
4D-Nucleofector System, and nucleofection was done using the CA-137
program. Following nucleofection, pre-warmed medium was added after
10 min, and cells were plated. Compound treatment was started 48 h
after nucleofection. Knockout efficiency of sgRNAs was assessed using
the Synthego ICE Analysis online tool. Genomic DNA was extracted from
nucleofected cells using the Quick-DNA Microprep Kit (Zymo Research,
Cat# D3021) according to the manufacturer’s instructions and amplified
by PCR using the Platinum™ II Hot-Start PCR Master Mix (Thermo Fisher
Scientific, Cat# 14000012). After a hot start, a denaturation
temperature of 95 °C, an annealing temperature of 58 °C and an
extension temperature of 72 °C were chosen and repeated for 40 cycles.
For amplification the following primers were used, while for sequencing
only the forward primer was used: NLRP5 forward:
CTTGAGAATTTGCTGCAAGATCCT, NLRP5 reverse: CGATTCTTCCCTGTTCCCATGAG, RAB3C
forward: CCACTCGCCTCCTGAGTGTCTG, RAB3C reverse:
GAACAAGGCAGAAAGTTTCTCCC, RAB12 forward: CTGTGCGCATGGGAGTGTTTTC, RAB12
reverse: CTTACCCACGGTGGACTTGC.
Statistical analyses
Statistical analysis of continuous variables was performed with R
version 4.2.1 (2022-06-23) and Rstudio (version 2022.07.1; Rstudio,
Inc.) using either mean and standard deviation (SD) or median and
interquartile range (IQR), depending on the distribution of data tested
by visualization with histograms, quantile-quantile plots and normality
testing using the Shapiro-Wilk test. Sample sizes are indicated (n) for
each analysis. The t-test (for normally distributed variables) and the
Mann-Whitney U test (for non-parametric distributions) were performed
to test for statistical differences.
Reporting summary
Further information on research design is available in the [375]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[376]Supplementary Information^ (54.5MB, pdf)
[377]Peer Review File^ (3.6MB, pdf)
[378]Reporting Summary^ (91.4KB, pdf)
Source data
[379]Source Data^ (78.1MB, zip)
Acknowledgements