TNF alpha unmasks enteric malate aspartate shuttle dysfunction bridging Parkinson disease and intestinal inflammation

Domain: neuroscience
Published: 2026-04-01
Editorial outcome: major_revision

Abstract
Gastrointestinal dysfunction often precedes motor symptoms in Parkinson’s disease (PD), suggesting the
enteric nervous system (ENS) is central to early pathogenesis. How α-synuclein contributes to ENS
dysfunction, and how inflammation modulates this, remains unclear. Here we show that Tumor Necrosis
Factor alpha enhances α-synuclein accumulation in induced pluripotent stem cell-derived enteric neurons
and glia, and impairs the malate-aspartate shuttle, a key pathway for mitochondrial energy production. This
drives a metabolic shift toward glutamine oxidation in patient cells. This metabolic impairment reduces
overall mitochondrial function, which is partially rescued by the neuroprotective compound Chicago-Sky-
Blue 6B. Furthermore, transcriptomic and histological analyses of human gut tissue from inflammatory
bowel disease patients reveal that inflammation-associated metabolic suppression and α-synuclein
upregulation occur beyond PD, representing general hallmarks of intestinal inflammation. These findings
highlight a conserved metabolic vulnerability in the ENS and establish patient-derived enteric lineages as
a robust platform to model inflammatory ENS pathology.

Introduction
Parkinson disease (PD) is the most frequent movement disorder. The motor deficits - hypokinesia, rigidity,
and tremor - are caused by dopaminergic neuronal loss in the substantia nigra1. However, non-motor
symptoms, particularly those affecting the gastrointestinal (GI) tract, are increasingly recognized as early
and clinically significant manifestations of PD2,3. Chronic constipation and abdominal bloating often precede
motor deficits by years4, suggesting a role for the enteric nervous system (ENS) in early disease
pathogenesis. The ENS, a vast network of over 500 million enteric neurons and glia within the enteric wall,
regulates motility, secretion, and blood flow while also modulating intestinal immunity and interacting with
the microbiota5. Its striking similarities to the central nervous system (CNS) in terms of neuronal diversity
and signaling suggest that ENS dysfunction could contribute to early GI symptoms in PD and influence
disease progression.

Accordingly, a leading hypothesis suggests that in the body-first subtype of PD, pathology originates in the
GI tract and spreads to the brain via autonomic nerves, including the vagus nerve6. Central to this
hypothesis is α-synuclein (α-syn), whose misfolding and aggregation defines PD pathology1. While α-syn
toxicity has been extensively studied in CNS7, its impact on the ENS remains poorly understood.
Histopathological studies confirmed α-syn accumulation in enteric neurons of PD patients8, and animal
models set the foundation for better understanding of mechanisms linking ENS dysfunction to GI
symptoms9,10. Notably, injection of α-syn epitopes in humanized mice triggered intestinal inflammation,
constipation, and enteric neuronal loss, demonstrating that α-syn can directly drive ENS dysfunction and
GI pathology11.

Neuroinflammation is increasingly recognized as a key feature of PD12,13, with cytokines playing a key role
in α-syn-driven pathology14,15. We previously demonstrated that α-syn increases the susceptibility of iPSC-
derived cortical neurons to cytokine-induced toxicity, particularly IL-17A15, suggesting that both α-syn
accumulation and inflammatory stimuli contribute to disease progression. In the CNS, proinflammatory
cytokines increase α-syn-induced mitochondrial dysfunction, oxidative stress, and synaptic
impairments1,14,15. Similarly, α-syn has been implicated in intestinal inflammation9,11,16,17, but most studies
rely on animal models, which do not fully recapitulate human pathology. While both immune activation and
mitochondrial dysfunction are recognized PD hallmarks, how cytokines and α-syn interact to drive pathology
in the human ENS remains unknown. Importantly, enteric neurons show high dependence on mitochondrial
respiration to sustain function and viability, as shown in Hirschsprung’s disease18. Disruptions in metabolic
homeostasis may therefore amplify their vulnerability to inflammation, further aggravating ENS dysfunction.
PD studies have demonstrated disease-induced alterations in mitochondrial respiration, amino acid
metabolism, and energy substrate utilization19,20, but the interplay of intestinal inflammation and
neurodegeneration has not been resolved.

Studying α-syn pathology in the human ENS faces significant challenges. Unlike classical gastrointestinal
diseases like inflammatory bowel diseases (IBD), GI specimens from PD patients are rarely available. ENS
complexity and its unique microenvironment limit insights from histopathological studies, which confirmed
α-syn accumulation8, but not its functional impact. Additionally, interspecies differences in animal models
hinder the translation of findings to human disease21. Recent advances in stem cell research offer a
promising platform for modeling α-syn pathology in both the CNS and ENS. Induced pluripotent stem cell
(iPSC)-derived neurons enable patient-specific studies22, and protocols for generating enteric neural
lineages (ENLs) from iPSCs23 have advanced the study of Hirschsprung disease24. Nevertheless, this
approach remains unexplored for PD.

To address these gaps, we utilized iPSC-ENLs from PD patients with α-syn gene triplications (SNCA 3x).
Employing multi-omics and functional analyses, we examined whether an increase in α-syn contributes to
early PD pathology in the ENS and if this process is intensified by proinflammatory cytokines. We
demonstrated that SNCA 3x ENLs exhibit enhanced Tumor Necrosis Factor alpha (TNF) susceptibility,
leading to increased mitochondrial α-syn accumulation. This triggered TNF-induced metabolic
reprogramming, impairing the malate-aspartate shuttle (MAS) and tricarboxylic acid (TCA) cycle, thereby
increasing reliance on glutamine oxidation. These metabolic shifts impaired cellular energy metabolism,
leading to increased enteric neuronal vulnerability. The effect could be rescued by targeting glutamate
metabolism with Chicago-Sky-Blue 6B (CSB6), a competitive inhibitor of vesicular glutamate transporters.
Our data highlight iPSC-derived ENLs as a powerful platform for studying human synucleinopathies and
identify cytokine signaling and metabolic dysfunction, specifically MAS impairment, as critical drivers of
enteric pathology in early PD, with broader relevance to intestinal inflammation.

Results
iPSC-ENLs as a model for studying synucleinopathies
To study the enteric nervous system in synucleinopathies, we generated ENLs from three SNCA 3x iPSC
lines and their isogenic controls (Iso)25. The differentiation protocol starts from iPSCs and involves the
induction of neural crest cells followed by enteric lineage specification to recapitulate key developmental
pathways in vitro23. Within the differentiation process towards ENLs we assessed the expression of marker
genes at three time points: iPSC at an early stage of ENC induction (day 6), a middle time point during ENL
differentiation (day 40) and full differentiation at day 70 (Figure 1A). All iPSC lines used passed the initial
quality control analysis for pluripotency markers Nanog and Lin-28A, without differences between groups
(Figure S1A). As expected, already at day 40 of ENL differentiation, Reverse Transcription Polymerase
Chain Reaction (RT-qPCR) confirmed the presence of the neuronal marker TUBB3 and enteric neuronal
markers ELAVL4, PHOX2B, and HOXB3 in both Iso and SNCA 3x lines (Figures 1B, S1B). Similarly, ENLs
showed expression of the glial marker GFAP at day 70, with no group-level differences (Figures 1B, S1B).
Immunostaining confirmed the presence of GFAP and the enteric neuron marker HuC/D in both groups,
along with a specific increase in α-syn levels in SNCA 3x ENLs at day 70 (Figure 1C).

Cellular diversity and gene expression patterns in iPSC-ENLs at day 70 of differentiation were additionally
explored using single-cell RNA sequencing (scRNAseq). Through clustering at a broad resolution and
subsequent annotation, we determined three primary cell populations: enteric neurons, glia, and
proliferating cells (Figures 1D, S1C). At this broad population level, no significant differences in abundance
were observed between Iso and SNCA 3x, as assessed by scProportion test with a log2FC threshold of 1
(Figure 1E), and the complementary neighborhood analysis using MiloR (Figure S1D, Supplementary Data
5). Each cluster expressed its classically described canonical markers23,26 (Figure 1F). We also measured
SNCA expression levels in the ENS. As expected, enteric neurons had the highest SNCA expression,
followed by glia, and proliferating cells showed the lowest expression (Figure 1G). Between the two
conditions, SNCA 3x glia exhibited higher SNCA expression compared to Iso (Figure S1E).

To highlight the broader relevance of our iPSC ENL model, we subclustered the scRNAseq enteric neurons
and glia cell populations (Figures 1D) and compared them to the previously published single-cell atlas of
human gut tissue27. We identified 14 neuronal subclusters and 13 glial subclusters (Figures 2A, 2E), with
top markers associated with these subclusters depicted in Figure S2A-B and Supplementary Data 2.
Annotation using SingleR successfully mapped several distinct neuronal and glial subtypes to the reference
human gut tissue (Figure S2 and Supplementary Data 3). This included putative excitatory motor neuron 3
(PEMN3), expressing the D2 dopamine receptor and putative inhibitory motor neuron 3 (PIMN3), marked
by adrenomedullin. The analysis also identified putative sensory neurons (PSN), which sense and respond
to chemical and mechanical stimuli and interneurons (PIN), involved in intercellular communication (Figures
2A and S2C-D). Most enteric glia subclusters matched the Glia 1 subtype, linked to GDNF receptor GFRA2
expression27 (Figures 2E and S2F-G). Following, we merged these annotations onto our UMAP plots
(Figures 2A and 2E).

Overall, these results indicate that both Iso and SNCA 3x iPSC lines differentiate effectively into ENLs,
comprising a mixed culture of enteric neurons and glial cells, which recapitulate the cellular diversity of the
human ENS. Moreover, the presence of α-syn expression in our model highlights its relevance not only for
studying synucleinopathies but also for broader ENS-related research. Crucially, the lack of significant
differences between Iso and SNCA 3x at the broad population level prompted us to investigate abundance
changes at the subcluster level, as detailed below.

SNCA 3x drives mitochondrial alterations at the transcriptional level and alters the subcluster
composition of enteric neurons and glia.
We investigated whether increased α-syn alters gene expression and cellular abundance in enteric neuron
and glial subpopulations using the scRNAseq data. Visualization of SNCA expression across enteric neuron
(Figure S2E) and glia subclusters (Figure S2H) revealed a shift in the expression distribution in the SNCA
3x line, which was highly heterogeneous when correlated to subcluster abundance. Differential expression
analyses (Supplementary Data 4) revealed mitochondrial dysfunction, a PD hallmark, as a key feature of
SNCA 3x ENLs. Accordingly, our integrated enrichment analysis highlighted several metabolic-associated
pathways as significantly changed, both in enteric neurons (Figure 2D, Supplementary Data 6) and enteric
glia (Figure 2H, Supplementary Data 6).

In enteric neurons, the subclusters 3, 4, and 9 were most strongly associated with these pathways, which
included “fatty acid transport”, “acetyl-CoA metabolic process” and “steroid metabolic process” (Figure 2D).
When plotting the genes directly linked to the enriched pathways (Supplementary Data 6) on a heatmap,
we noticed a uniform decrease across all three subclusters in the expression of key genes associated with
acetyl-CoA metabolic process (HMGCS1, MVD, ACSS2, MVK), steroid metabolic process (HMGCS1,
FDFT1, SQLE, MSMO1, LDLR, INSIG1, HMGCR, IDI1), and fatty acid transport (ACSL3) in SNCA 3x
enteric neurons (Figure 2B).

In enteric glia, subclusters 0, 1, 5, 6, and 11 exhibited strong associations with mitochondrial pathways,
including terms like “mitophagy”, “mitochondrial ATP synthesis coupled electron transport”, “release of
cytochrome c from mitochondria”, “response to oxidative stress” and “mitochondrial protein import” (Figure
2H). To further investigate the underlying stress mechanisms, we visualized the key genes driving these
enrichments on a heatmap and observed a general upregulation of the CHCHD2 gene, associated with
cellular responses to oxidative stress. Additionally, we detected increased expression of the mitophagy-
associated gene MAP1LC3A in subclusters 0, 6, and 11, and of the antioxidant gene MT3 in subclusters 0
and 6 of SNCA 3x enteric glia (Figure 2F). Moreover, subcluster 11 exhibited elevated expression of
mitochondrial protein import genes (DNAJC19, PAM16, and TIMM13) in SNCA 3x glia (Figure 2F). Finally,
a module score analysis comparing key genes regulating cholesterol metabolism and mitochondrial stress
responses (Figure S3A–B) revealed a significant negative correlation between these two modules
specifically in subcluster 12 of SNCA 3x glia, a population markedly reduced in this genotype (Figure 2G).

Compositional analysis (Figure 2C and 2G), performed using the scProportion test with a log2FC threshold of 1,
revealed several alterations in subcluster proportions driven by α-syn in both enteric neurons and glial
cells. For enteric neurons, we highlight a decrease in a population of sensory neurons (subcluster 6)
together with an increase in an interneuron subtype (subcluster 11) that is highly associated with
glutamatergic synapses (Figure 2D) in SNCA 3x lines, suggesting a functional link between α-syn
accumulation and altered gastrointestinal motility mediated by the regulation of the enteric glutamatergic
circuitry28. Concurrently, SNCA 3x enteric glia exhibited a pronounced increase in the proportion of
subcluster 0 (Glia 1), a population strongly associated with pathways of endoplasmic reticulum stress and
the release of cytochrome c from the mitochondria (Figure 2H), alongside a decline in subcluster 3 (Glia 1),
linked to response to TGF-β (Figure 2H), an immunoregulatory cytokine.

Next, we first sought to determine if the α-syn-driven differences in cell abundance were due to localized
changes within specific cell neighbourhoods using MiloR differential abundance analysis. MiloR identified
localized neighborhood-level changes specifically within the neuronal subclusters (minimal spatial
FDR=0.15; Figure S2I, Supplementary Data 5). The neuronal neighborhood changes correspond to the
same subclusters identified as decreased (e.g., subcluster 2) or increased (e.g., subcluster 13) using the
scProportion test in SNCA 3x cells. In contrast, MiloR analysis of the glial subset failed to detect localized
changes (Figure S2J, Supplementary Data 5), suggesting the glial proportional shifts are more global.

Finally, to compare SNCA 3x-associated neuronal dysfunction across distinct cellular contexts, we mapped
our iPSC-ENL scRNAseq data to published midbrain (Patikas et al.29) and cortical (Jin et al.30) SNCA 3x
organoid signatures using SingleR. Our enteric neurons primarily matched the dopaminergic neuron 2
(oDAn2)29 (Figure S3C-D) and the inhibitory neuron (INs)30 clusters (Figures S3F-G). Integrated enrichment
analysis using these cross-study neuronal signatures revealed common dysregulated pathways in SNCA
3x ENLs, predominantly related to synaptic function and cholesterol metabolism (Figures S3E, S3H).
Furthermore, leveraging the glial component in the cortical model, our enteric glia matched predominantly
the astrocyte (AS) cluster (Figures S3I-J). Enrichment analysis of the AS signature in our enteric glia
identified shared metabolic and mitochondrial dysfunction modules (e.g., acetyl-CoA metabolic process,
regulation of glucose and lipid metabolism, and cellular response to oxygen levels), consistent with the
specific phenotypes observed in our enteric glial subclusters (Figure S3K).

These data suggest that SNCA 3x disrupts mitochondrial homeostasis in enteric neurons and glia at the
transcriptional level, altering subpopulation dynamics and inducing metabolic stress-driven vulnerability.

SNCA 3x drives basal mitochondrial dysfunction and alters enteric neuron-glia communication in
iPSC-ENLs
Given the extensive transcriptional changes in mitochondrial-linked genes and the enrichment of metabolic
pathways in SNCA 3x enteric neurons and glia (Figure 2) and their relevance to PD1,19,31 we wanted to
validate the findings by analyzing different aspects of mitochondrial morphology. Using TOM20
immunostaining and automated image analysis, we assessed several mitochondrial parameters (Figures
3A, 3B). Interestingly, our data detected a significant decrease in mitochondrial count and area per cell in
SNCA 3x ENLs (Figure 3B), suggestive of mitochondrial stress and mitophagy. Additionally, the number of
branches and the branch junctions per cell were also significantly decreased in SNCA 3x ENLs (Figure 3B),
indicating shortening of the mitochondrial network, which is a classical feature of mitochondrial fission.
Next, we analyzed mitochondrial-linked gene expression along pseudotime (Figures 3C, 3D),
using Slingshot32. We selected the root of the trajectories based on the enteric neuron and glial subclusters which
showed the highest entropy levels (Figure S4A-B). Our results revealed that in SNCA 3x enteric neurons,
genes controlling oxidative phosphorylation33, ATP production33 and mitochondrial biogenesis34 were
downregulated (ATP5F1B, COX4I1, NDUFA9, PPARGC1A), while those controlling mitophagy35 (PINK1)
and fission36 (DNM1L) were upregulated (Figure 3C). Conversely, in glial cells, ATP5F1B, COX4I1, and
NDUFA9 were upregulated along with PINK1 and DNM1L, whereas PPARGC1A showed a transient peak
before downregulation (Figure 3D). Complementarily, we performed a metabolic flux analysis focusing on
mitochondrial function, which suggested an upregulation of Oxalosuccinate:NADP+ oxidoreductase in both
SNCA 3x enteric neurons (Figure 3E) and glia (Figure 3F), indicating a shift toward NADPH production and
highlighting potential compensatory metabolic adaptations to mitochondrial dysfunction.

Using the scRNAseq data, we built on the previous analyzes and explored how SNCA 3x affects cellular
communication and signaling in iPSC-ENLs using CellChat37. Globally, we observed an overall increase in
the total number and strength of inferred intercellular interactions in the SNCA 3x group (Figure S1F-G).
However, this was driven by different changes at the cell type level: enteric neurons exhibited a net
decrease in the number of inferred interactions (Figure S4C-D), while enteric glia showed a net increase in
interaction strength (Figure S4G-H). This context is crucial for interpreting subsequent specific pathway
changes. Analysis across all cell types highlighted marked alterations in VEGF-related signaling, key
regulators of cell survival38, vascularity38 and neuroinflammation39,40 (Figure S1H, Supplementary Data 7)
in SNCA 3x cells. Specifically, we observed an increased VEGFB-VEGFR1 signaling predominantly
mediating glia-glia communication, and a corresponding increase in VEGFA-VEGFR1 signaling directing
communication between enteric neurons and glia (Figure S1I, Supplementary Data 7). Focusing on enteric
neuronal communication, we detected the de novo emergence of Bone Morphogenetic Protein (BMP)
signaling, associated with neurite fasciculation and orientation within the bowel41, exclusively in the
SNCA 3x enteric neurons (Figure S4E, Supplementary Data 7), with the entire source originating from
subcluster 10 (Figure S4F, Supplementary Data 7), a population simultaneously majorly depleted (Figure
2C). Specifically, we observed a high communication probability for BMP7 (source: subcluster 10, identified
as excitatory motor neurons) signaling to BMPR1A+BMPR2 (target: subcluster 2, identified as
interneurons), with subcluster 2 also being the most significantly depleted neuronal subpopulation in
SNCA 3x neurons (Figure 2C). Finally, focused analysis of enteric glial signaling revealed a de novo
activation of Epidermal Growth Factor (EGF) signaling (Figure S4I, Supplementary Data 7), previously
linked to intestinal mucosal healing42, which was exclusively sourced by the vulnerable subcluster 8 in the
SNCA 3x line (Figure S4J, Supplementary Data 7). The EGF-EGFR signaling from subcluster 8 showed
the highest communication probability to subcluster 11 (Figure S3J, Supplementary Data 7), which is
slightly upregulated in SNCA 3x (Figure 2G) and is strongly associated with mitochondrial protein import
(Figure 2H). Subcluster 8 drove the entire EGF signal and was transcriptionally associated with glial
activation and neuroinflammatory response (Figure 2H).

Our findings demonstrate that α-syn accumulation not only induces significant mitochondrial and metabolic
stress but also fundamentally remodels the inter and intra-cellular communication architecture of the ENS,
favoring signaling pathways associated with stress, neuroinflammation, and survival. These changes set
the stage for further metabolic and functional impairments under inflammatory conditions, as explored in
the following section.

TNF uncovers genotype-specific α-syn accumulation and synaptic dysfunction in SNCA 3x ENLs
The ENS, as a vital component of the intestinal microenvironment, plays a role in coordinating local immune
responses43, besides other functions. Cytokines, as key regulators of immune signaling, mediate both
physiological and pathological processes within the ENS43.

Our scRNAseq data revealed that SNCA 3x alters biological pathways and intercellular communication.
Additional ligand-receptor pair analysis in total cells identified activated inflammatory pairs (e.g., EDN1-
EDNRB44, CRLF1-LIFR45) and repressed synapse-regulating pairs (e.g., NLGN1-NRXN346, NXPH1-
NRXN147) (Figure 4A) in SNCA 3x ENLs. Of note, most of these synapse-associated pairs were
downregulated in enteric neurons (Figure S6A) whereas the inflammation associated pair efnb3-epha348
was present both in enteric neurons and glia (Figure S6A-B). This prompted us to investigate the impact of
various cytokines (TNF, IL-10, IL-23, IL-17) – relevant both to PD and regulation of mucosal immunity1,43 -
on α-syn biology and neuronal function in iPSC-ENLs. Cytokine treatments revealed a genotype-dependent
effect (F=5.539, p=0.0289), as only TNF significantly increased total α-syn levels in SNCA 3x ENLs
compared to TNF-treated Iso controls, as measured by ELISA (Figure 4B-C). Interestingly, at a basal level,
our scRNAseq detected that SNCA 3x cells express generally higher levels of TNF-linked genes
(TNFRSF1A, NFKBIA, NFKB1, RELA, MIF) across most enteric neuronal and glial subclusters (Figure S5A-
D and S5F), indicating that both populations retain the molecular machinery required for TNF
responsiveness. Importantly, treatment with TNF did not alter cell viability for both genotypes (Figure 4D).
Dot blot analysis further confirmed higher α-syn levels in TNF-treated SNCA 3x ENLs compared to TNF-
treated Iso ENLs (Figure S6C). However, α-syn levels in TNF-treated SNCA 3x ENLs were not significantly
different from those at baseline (Figures 4C, S6C), suggesting that the SNCA 3x genotype primarily drives
α-syn elevation, while TNF further increases levels relative to controls. Given previous reports of α-syn
aggregates in the gut of PD patients8, we assessed its solubility using sarkosyl fractionation49. α-Syn signal
was detected in the pellet fractions of SNCA 3x ENLs, but no significant differences were observed between
Iso and SNCA 3x under basal conditions or following TNF stimulation (Figure S6D). We investigated further
potential changes using ELISA, which revealed a modest increase in aggregated α-syn in SNCA 3x ENLs
upon TNF stimulation (Figure 4E). Cytokine array showed no significant changes in cytokine production
between groups upon TNF treatment (Figure 4F). This suggests that SNCA 3x ENLs exhibit an intrinsic
susceptibility to TNF, with selective increase of α-syn species, occurring independently of the induction of
a broad inflammatory response.

Subsequently, to assess how TNF impacts enteric neuronal function over the time, we performed a
functional electrophysiology assay to measure neuronal activity using the Multielectrode array (MEA)
platform (Figure 4G). Interestingly, while the Iso ENLs reacted to TNF treatment by increasing the number
of spikes and active electrodes over time, SNCA 3x ENLs showed no specific response (Figures 4H-I).
There was a significant difference between Iso and SNCA 3x ENLs after 24h and 48h of TNF exposure in
number of spikes (Figure 4H) and active electrodes (Figure 4I), respectively. No differences were observed
between Iso and SNCA 3x ENLs regarding weighted mean firing rate measurements (Figure S6E). 
Accordingly, our scRNAseq data already detected that at a basal level there is reduced expression of
synaptic and neuronal identity markers such as DSCAM and GABRB3 (Figure S5E and Supplementary
Data 4) across enteric neuronal subclusters induced by α-syn triplication.

In line with these data, we utilized a previously published flow cytometry pipeline for studying the ENS50, in
which we could clearly separate the enteric neuron and glial populations based on their CD24 expression
levels (Figure S6F), also supported by our scRNAseq data (Figure S6G-H), observing that TNF stimulation
unmasks total α-syn accumulation both in enteric neurons and enteric glial cells (Fig 4J). No changes were
observed in the proportions of enteric neurons and glia between Iso and SNCA 3x, both under basal and
TNF stimulation conditions (Fig 4K), in accordance with our scRNAseq data (Fig 1E, Fig S1D).

These findings suggest that SNCA 3x ENLs exhibit an impaired capacity to modulate their neuronal activity
in response to TNF, which contrasts with the adaptive response observed in Iso ENLs. This functional
deficit, coupled with selective α-syn increase in SNCA 3x ENLs, further supports an intrinsic vulnerability of
SNCA 3x ENLs to inflammatory stress, reinforcing a link between α-syn accumulation and synaptic
dysfunction in the ENS.

TNF rewires the metabolism of SNCA 3x ENLs by disrupting the malate-aspartate shuttle and
impairing TCA cycle flux
Building on the links between α-syn and mitochondrial dysfunction identified in our scRNAseq data, we
used multiomics analyses to further examine the impact of TNF on the iPSC-ENL proteome and
metabolome (Figure 5A).

Data independent acquisition (DIA)-based shotgun proteomics coupled to label-free quantification was
used to quantify the proteomes of Iso and SNCA 3x ENLs in a basal level and upon TNF stimulation. When
these proteomes were compared (Figures 5B-C and Supplementary Data 8), differentially expressed
proteins (DEPs) were identified (p<0.05, |log2FC|≥0.8). The effect of SNCA 3x (SNCA 3x versus Iso, basal)
resulted in the most robust proteomic alteration with 116 DEPs. This indicates a strong effect of α-syn in
the proteome of iPSC-ENLs. Interestingly, only one DEP was identified when Iso cells were treated with
TNF compared to Iso basal, while 67 DEPs were identified in SNCA 3x cells upon TNF treatment compared
to SNCA 3x at a basal level and 46 DEPs were detected when comparing SNCA 3x versus Iso under TNF
treatment (Supplementary Data 8). This strongly suggests that α-syn overexpression predisposes iPSCs-
ENLs to cytokine-induced metabolic stress, establishing a specific vulnerability linked to the SNCA 3x
genotype. Gene Ontology Molecular Function analyses revealed a significant enrichment in synaptic
functions, including SNAP receptor and SNARE binding in basal SNCA 3x ENLs, suggesting
neurotransmitter release and vesicle trafficking alterations51 (Figure 5D, Supplementary Data 9). TNF
exposure shifted the profile toward oxidative stress and mitochondrial dysfunction, marked by increased
peroxidase, antioxidant, and oxygen transport functions, indicating impaired mitochondrial respiration
(Figure 5D, Supplementary Data 9). This progression from synaptic dysfunction to metabolic disruption
underscores the impact of inflammatory stress.

LC-Orbitrap-MS metabolomics highlighted 9 differentially expressed metabolites (p<0.05, |log2FC|≥0.5)
between Iso and SNCA 3x ENLs. The analysis revealed reductions in aspartate, glutamate, malate and
glutamine levels in SNCA 3x ENLs treated with TNF, suggesting malate-aspartate shuttle (MAS)
impairment, a key process for tricarboxylic acid (TCA) cycle flux and mitochondrial redox balance52 (Figure
5E, Supplementary Data 10). Accordingly, NAD+ levels were significantly reduced in these cells, with no
changes in NADH levels, suggesting altered NAD+ homeostasis, potentially due to impaired regeneration
or increased utilization in response to metabolic stress (Figure 5E, Supplementary Data 10). Metabolite-set
enrichment analysis on the differentially expressed metabolites (DEMs) (Supplementary Data 10)
confirmed MAS as a top affected pathway (Figure 5F), supporting inflammation-driven metabolic
reprogramming.

When checking for the levels of key proteins associated with MAS in our proteomics data (Supplementary
Data 8), cytosolic malate dehydrogenase (MDHC) was significantly decreased by TNF specifically in SNCA
3x ENLs, indicating a treatment effect within this genotype, but with no differences between genotypes
upon TNF exposure (Figure S7A). Mitochondrial malate dehydrogenase (MDHM) showed consistent
differences between SNCA 3x and isogenic controls under both basal and TNF conditions, suggesting a
baseline genotype effect rather than a TNF-specific response (Figure S7B). Mitochondrial aspartate
aminotransferase (AATM) did not change with TNF in either genotype (Figure S7C). By contrast, aspartate
aminotransferase (AATC) (Fig 5G) exhibited the clearest pattern of selective vulnerability: it was
significantly reduced by TNF in SNCA 3x and also differed between genotypes under TNF exposure. Taken
together, these data indicate that MAS alterations are not uniform but rather enzyme-specific, with AATC
emerging as the most compelling candidate linking TNF exposure to MAS dysfunction in the SNCA 3x
background. We then validated our findings using western-blotting for AATC, which showed a trend for
decreased levels in SNCA 3x already at a basal level (p=0.2775), with a higher tendency noted upon the
addition of TNF (p=0.1870) (Figure 5H), in line with the proteomics finding. Interestingly, when looking at
the activity of key TCA enzymes, we observed a trend for decreased citrate synthase in SNCA 3x ENLs,
especially upon TNF stimulation (Figure S7D), with no alterations in succinate dehydrogenase between
groups (Figure S7E). Finally, integrated pathway analysis linked TNF exposure to extracellular matrix
receptor interactions and glutamate metabolism (Figure 5I).

These findings demonstrate that TNF disrupts metabolic homeostasis in SNCA 3x ENLs by impairing MAS
and the TCA cycle, leading to mitochondrial dysfunction.

TNF increases α-syn-mitochondria interactions and highlights oxidative stress-associated
populations in SNCA 3x ENLs
Given that α-synuclein binds to mitochondria and that mitochondrial dysfunction can promote protein
aggregation and oxidative stress31, we next investigated whether the metabolic alterations observed at
basal conditions (Figure 3) and after TNF treatment (Figure 5) contribute to mitochondrial α-syn
accumulation and oxidative stress (Figure 6A). Proximity ligation assays (PLA) of α-syn and TOM20
confirmed increased α-syn–mitochondria interactions in SNCA 3x ENLs at baseline. TNF stimulation further
enhanced the PLA signal in both genotypes, with a greater increase in SNCA 3x ENLs compared to isogenic
controls (Figures 6B–C).

To examine mitochondrial oxidative stress at single-cell resolution, we analyzed mitoSOX fluorescence by
flow cytometry in iPSC-derived enteric neuron-like (ENL) cultures. Unsupervised t-distributed stochastic
neighbor embedding (t-SNE) of mitoSOX signals revealed a largely homogeneous distribution under basal
conditions in both control and SNCA 3x enteric neurons (Figure 6D) and glia (Figure 6E). Following TNF
exposure, a distinct mitoSOX-high subpopulation emerged predominantly in the SNCA 3x condition,
encompassing both neuronal (Figure 6D) and glial (Figure 6E) cells. This cluster was consistently detected
across biological replicates, although the overall increase in mitoSOX fluorescence per cell type did not
reach statistical significance in bulk quantification. The reproducible appearance of this discrete mitoSOX-
high cluster supports a TNF–induced oxidative stress response in a subset of SNCA 3x ENL cells,
consistent with the mitochondrial vulnerability inferred from reduced AATC abundance and impaired MAS
activity.

Collectively, these findings establish a mechanistic link between TNF–induced metabolic reprogramming
and mitochondrial pathology, in which enhanced α-syn–mitochondria interactions and increased oxidative
stress contribute to impaired bioenergetic function and neuronal vulnerability in SNCA 3x ENLs.

TNF increases the reliance of SNCA 3x ENLs on glutamine oxidation, which is rescued by CSB6
treatment
To further investigate the metabolic vulnerability of SNCA 3x ENLs and assess the potential rescue by a
glutamate metabolism modulator, CSB6, we first performed additional Seahorse Mito Stress and Mito Fuel
Flex assays (Figure 7A). These experiments revealed genotype and condition-dependent effects of TNF
and CSB6 on mitochondrial respiration (Figures 7B-F). Two-way ANOVA identified a significant genotype
versus condition interaction for both basal (F=3.3, p=0.0475) and ATP-linked oxygen consumption rates
(OCR; F=4.677, p=0.0157), indicating that treatment responses differed between genotypes. The condition
main effect was significant across multiple mitochondrial parameters, including basal, ATP-linked, maximal,
and non-mitochondrial OCR (all F>5, p<0.01), consistent with broad treatment-induced changes in
mitochondrial function.

TNF significantly reduced ATP-linked respiration in both genotypes, an effect fully rescued by CSB6
treatment (Figures 7B–C). For proton leak, CSB6 significantly counteracted TNF-induced decrease only in
SNCA 3x ENLs, indicating a genotype-selective recovery of mitochondrial reserve capacity. Post-hoc
analyses further revealed that under CSB6-only conditions, SNCA 3x ENLs displayed higher basal (p =
0.0163) and ATP-linked OCR (p = 0.0060) than isogenic controls, despite the absence of a main genotype
effect across all conditions (Figures 7B-C). This suggests that CSB6 enhances mitochondrial respiration in
both genotypes but provides a higher benefit to the metabolically compromised SNCA 3x cells. In the
mitofuel assays (Figure 7D), we again observed a significant genotype versus condition interaction for
glutamine dependency (F=4.258, p=0.0217). Specifically, TNF significantly increased glutamine
dependency in SNCA 3x ENLs, an effect fully rescued by CSB6 (Figure 7E). Analysis of fatty acid oxidation
(FAO) dependency revealed no significant differences between genotypes under basal, TNF, or CSB6-only
conditions, but showed a trend toward increased FAO dependency in SNCA 3x ENLs in the CSB6+TNF
group (Figure 7F), possibly reflecting a compensatory metabolic adaptation.

To further characterize mitochondrial functional heterogeneity, we conducted flow cytometry analyses of
Mitotracker Deep Red fluorescence, which is linked to changes in mitochondrial membrane potential, and
applied t-SNE to visualize cell populations in iPSC-ENLs under basal and TNF–stimulated conditions. In
line with Seahorse findings, SNCA 3x ENLs exhibited a trend toward reduced mitochondrial function already
under basal conditions (Figures 7G–H). Following TNF treatment, t-SNE contour maps revealed a visually
distinct loss of Mitotrackerhigh subpopulations, most evident in SNCA 3x enteric glia (Figure 7H), though this
was not statistically significant in bulk quantification.

Complementing these findings, t-SNE analysis of carnitine palmitoyltransferase IA (CPT1α ) expression
indicated that CPT1α was more prominently expressed in enteric neurons (Figure S7F) than in glia (Figure
S7G). Following TNF exposure, t-SNE contour plots showed a visually reduced CPT1α high population in
SNCA 3x neurons, accompanied by a notable appearance of CPT1α high cells in glia (Figures S7F–G).
Although these population shifts were not significant in bulk analysis, their consistent visualization across
replicates suggests a potential redistribution of FAO capacity between neurons and glia under inflammatory
conditions, in line with the TNF–induced metabolic rewiring observed in SNCA 3x ENLs.

Collectively, these results demonstrate that TNF drives a shift toward glutamine oxidation in SNCA 3×
ENLs, accompanied by altered mitochondrial integrity and a redistribution of CPT1α expression between
neurons and glia. CSB6 treatment restores mitochondrial function and normalizes substrate utilization,
underscoring its potential to counteract cytokine-induced metabolic reprogramming in α-synuclein-linked
enteric pathology.

Inflammation induces MAS suppression in the human gut
To extend our observations to human intestinal inflammation, we examined independent transcriptomic
datasets alongside tissue-level staining of ulcerative colitis (UC) patients’ biopsies.
First, we assessed the expression of GOT1 (AATC) and SNCA across three datasets of UC patients treated
with infliximab. GOT1 was consistently downregulated in patients who were non-responders (NRs).
Critically, the GSE73661 dataset showed a significant restoration of GOT1 in clinical responders (Rs)
following therapy (Figure 8B). Conversely, SNCA trended toward upregulation in NRs, and in the same
GSE73661 cohort, its expression was significantly lower in Rs compared to NRs post-treatment (Figure
8C). This crucial inverse pattern defines patients who fail to respond to anti-TNF therapy. It directly links
persistent TNF-driven inflammation to MAS suppression and -syn accumulation.

We next confirmed these findings using our in-house bulk RNA-seq dataset (IBDome53) and the integrated
scIBD database (Figure 8D). In IBDome, MAS enzymes (GOT1, MDH1, MDH2) were reduced in inflamed
versus non-inflamed tissue (Figure 8E), whereas SNCA was increased in UC inflamed (Figure 8F), also
showing a negative correlation with GOT1 which disappeared in the absence of inflammation (Figure 8G).
At the single-cell level, MAS enzyme expression was significantly decreased in neural clusters under
inflammatory conditions (Figures 8HI–I).

Finally, immunostaining of colon biopsies from UC patients and non-UC controls revealed significantly
reduced AATC and increased α-syn levels, along with an overall co-localization of α-syn and the neuronal
marker beta-III tubulin, providing evidence for transcriptional findings at the tissue level (Figures 8J-K).

In summary, our findings demonstrate that TNF reprograms metabolism in SNCA 3x ENLs by impairing
MAS, reducing TCA cycle flux, and shifting mitochondrial dependency toward glutamine oxidation as a
compensatory mechanism. This metabolic stress is linked to increased mitochondrial α-syn accumulation,
oxidative stress, and loss of mitochondrial membrane potential. CSB6 counteracts these TNF-driven
alterations by restoring mitochondrial respiration, reducing glutamine dependency and preventing
mitochondrial dysfunction (Figure 9). Importantly, by validating these signatures in human UC patients,
across bulk and single-cell transcriptomics and immunostaining, our study reveals a conserved
inflammation-induced MAS dysfunction linked to α-syn accumulation, extending the relevance of our
findings beyond PD to broader contexts of intestinal inflammation in the human gut.

Methods
Ethics statement
All protocols used in this study as well as the use of human material were approved by the Ethics Committee
of the Medical Faculty of the Friedrich-Alexander-Universität Erlangen-Nürnberg and the
Universitätsklinikum Erlangen (#22-289-Bp and #49_16B). The human cells used in this study were handled
in accordance with the principles outlined in the Declaration of Helsinki. All iPSC lines used in this study
are commercially available.

iPSC generation, characterization and culture methods
iPSC reprogramming, generation of isogenic controls
B-lymphocytes containing a triplication in the SNCA gene were obtained from the Coriell NINDS and NIGMS
Human Genetic Cell Repositories: GM1501E0 (3x-1), ND00196 (3x-2), ND00139 (3x-4, referred to as 3x-3
throughout this paper). Further phenotypic and genotypic details on these lines and subjects are available
on https://www.coriell.org under the aforementioned accession numbers. See key resources table for more
details. Reprogramming into iPSC was performed by transfection of B-lymphocytes with episomal plasmids
containing Oct3/4, L-Myc, Sox2 and Klf4, as previously described25. Isogenic controls were generated using
a dual nickase CRISPR/Cas9 strategy to disrupt exon 2 of the SNCA gene, fully characterized and validated
in a previous publication25. Copy number variation and cell karyotyping analyzes revealed absence of
chromosomal abnormalities. Iso and SNCA 3x iPSC lines were generated in the laboratory of Joseph R.
Mazzulli (The Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of
Medicine, Chicago, IL 60611, USA) and provided to this study by Friederike Zunke.

iPSC culture maintenance
iPSCs were cultured in Geltrex (Thermo Fisher)-coated 6-well plates with Essential 8 Flex medium (Thermo
Fisher), maintained at 37°C and 5% CO₂, with media change performed every two days. For thawing,
cryovials were warmed in a 37°C water bath, and cells were resuspended in fresh medium containing 10M
ROCK inhibitor (Tocris, Cat#1254). Passaging occurred at 80%-90% confluency using ReLeSR (Stemcell,
Cat#100-0483), with cells split at a 1:2 or 1:3 ratio. Cells were routinely tested for mycoplasma infection.
For freezing, cells were detached with ReLeSR and resuspended in a freezing medium containing knockout
serum replacement (KOSR, Thermo Fisher, Cat#10828028) + 10% DMSO before storage at –150°C.

iPSC characterization
iPSCs were characterized using flow cytometry staining for Nanog and Lin28A. iPSCs were initially treated
with Accutase (Gibco, USA), washed with 1× PBS, centrifuged at 300g at room temperature for 5min and
then resuspended in FC buffer (2% FCS, 0.01% sodium azide in PBS). We then counted 500,000 cells per
line and proceeded with fixation and permeabilization with 100µl BD Fixation/Permeabilization Solution (BD
Bioscience) for 10 minutes, followed by addition of 1ml BD Perm/Wash Buffer (BD Bioscience), incubation
for 5 minutes and centrifugation at 300g for 3min. For intracellular staining of iPSCs, we used a combination
of Nanog and Lin28A primary antibodies (RRID: AB_2738303 and RRID: AB_2784439, both 1:100) in BD
Perm/Wash Buffer for 30 minutes, followed by washing and resuspension in 300µl FACS buffer. Cells were
then acquired using a Cytoflex S cytometer (Beckman Coulter) and analyzed with the CytExpert 2.4
software. At least 30,000 events per sample were analyzed.

Differentiation of iPSCs into ENLs
The ENS differentiation protocol was adapted from Barber et al.23, using the novel chemically defined
method. ENS induction began on day 0 with a nearly confluent iPSC monolayer and continued for 12 days
with stepwise culture transitions. On day 0, Essential 8 medium (Thermo Fisher) was replaced with 2 ml of
Cocktail A [Essential 6 medium (Thermo Fisher), 10 µM SB431542 (Miltenyi Biotec, Cat#130-106-543), 600
nM CHIR99021 (Tocris, Cat#4423), 1 ng/ml BMP4 (Peprotech, Cat#120-05ET), 100g/mL normocin
(InvivoGen, Cat#ant-nr-2)], followed by a switch to Cocktail B [Essential 6 medium (Thermo Fisher), 10 µM
SB431542, 1.5 µM CHIR99021, 100g/mL normocin] on day 2, with media renewal on day 4. On day 6,
Cocktail B was replaced with 4 ml of Cocktail C [Essential 6 medium (Thermo Fisher), 10 µM SB431542,
1.5 µM CHIR99021, 1 µM RA (Sigma, Cat#R2625), 100g/mL normocin], with subsequent exchanges on
days 8 and 10. On day 12, ENC spheroids were formed by detaching the ENC monolayer using Accutase
for 30 min at 37°C, followed by centrifugation at 300g for 3 min, resuspension in NC-C medium [Neurobasal
medium (Thermo Fisher), 1x N2 supplement (Thermo Fisher, Cat#17502048), 1x B27 supplement w/o Vit.
A (Thermo Fisher, Cat#12587010), 1x GlutaMax (Thermo Fisher, Cat#35050038), 1x MEM NEAA (Thermo
Fisher, Cat#11140050), 10 ng/ml FGF2 (Peprotech, Cat#100-18B), 3 µM CHIR99021, 100g/mL
normocin], and transfer to ultra-low attachment (ULA) plates. Spheroids formed within two days, with media
replacement using NC-C medium. On day 15, EN induction began, involving dissociation of spheroids using
Accutase, centrifugation at 300g, and resuspension in EN-C medium [Neurobasal medium (Thermo Fisher),
1x N2 supplement, 1x B27 supplement with Vit. A (Thermo Fisher, Cat#17504044), 1x GlutaMax, 1x MEM
NEAA, 100 µM AA (Sigma, Cat#A4544), 10 ng /ml GDNF (Peprotech, Cat#450-10), 100g/mL normocin].
Cells were counted using a fluorescence cell counter and seeded onto Geltrex-coated plates at 60,000
cells/cm².

Human tissue samples
Human tissue samples were obtained from patients undergoing routine biopsy collection at the
Universitätsklinikum Erlangen, after written informed consent was obtained from the patients. Gut tissues
were used for paraffin-embedded sections. The diagnoses of UC were based on clinical, endoscopic,
radiology, and histologic findings. Data were pseudonymized. All controls used were non-UC samples.
Metadata including diagnosis, age, sex and disease duration is provided in Supplementary Data 11.

RNA extraction, cDNA synthesis and RT-qPCR
Reverse transcription-quantitative PCR (RT-qPCR) analysis was performed starting with RNA extraction
using Trizol (Invitrogen) and the RNeasy Mini Kit (Qiagen, Cat#74104). Cells were homogenized in Trizol,
followed by chloroform addition and phase separation by centrifugation at 12000g, 4°C for 15 min. The
RNA-containing aqueous phase was collected, mixed with 70% ethanol, and processed through RNeasy
columns with sequential washes using buffer RW1 and buffer RPE. RNA was eluted with RNase-free water,
its concentration measured using the NanoPhotometer NP80 (Implen), and stored at -80°C. For cDNA
synthesis, the QuantiTect Reverse Transcription Kit (Qiagen, Cat#205311) was used, with 1000ng starting
RNA concentration per reaction. After DNA removal with gDNA wipeout buffer, reverse transcription was
carried out at 42°C for 30 min, followed by inactivation at 95°C for 3 min, and storage at -20°C. RT-qPCR
was performed in triplicates for target genes (TUBB3, GFAP, PHOX2B, ELAVL4, HOXB3), with HPRT and
RPLP0 as housekeeping genes. Primer sequences used are listed in Supplementary Data 1. A primer mix
(10 µM) was prepared, and 1 µl cDNA per sample was loaded onto a 384-well plate. The qPCR master mix
was added, the plate was sealed, centrifuged, and run on the LightCycler480 equipment (Roche). Ct values
were analyzed using the delta-delta Ct method, and fold change values were calculated based on the
expression values of the Iso group at day 6 of differentiation.

Immunocytochemistry
Cells were fixed on day 70 after start of differentiation using 4% paraformaldehyde (PFA) for 30 min at room
temperature (RT) (500 µl per well in an ibidi 24-well plate), washed three times in DPBS, and stored at 4°C
until staining. Permeabilization was performed with 0.3% TritonX-100 in DPBS for 10 min at RT, followed
by blocking with 5% donkey serum in DPBS with 0.3% TritonX-100 for 30 min. Cells were then incubated
overnight at 4°C with the primary antibodies (mouse anti-a-syn 2A7 1:100, RRID: AB_1555287; mouse
anti-HuC/D 1:200, RRID: AB_221448; rabbit anti-GFAP 1:500, RRID: AB_10013382). The following day,
cells were washed, incubated for 2 h at RT with secondary antibodies (AF488 anti-mouse; AF647 anti-
rabbit, all 1:500), and counterstained with DAPI (1:5000 in DPBS) for 2 min. After final washes, samples
were mounted in Mowiol (Sigma, Cat#81381) for long-term storage at 4°C. Imaging was performed using
the Super Resolution Evident SR Spinning Disk microscope (Olympus) and the cellSens Software and
processing was performed using FIJI (ImageJ).

Paraffin-embedded tissue sections from UC patients and controls were dewaxed and rehydrated. Samples
were then fixed in a methanol solution for 20min and permeabilization was performed with 0.1% TritonX-
100 in TBS for 10 min at RT, followed by blocking with 10% Roti-block (Roth, Cat#A151.1, in a 2%BSA
TBST solution for 15 min. The sections were then incubated overnight at 4°C with the primary antibodies
(mouse anti-tubulin beta-III, 1:250, RRID: AB_2313773; rat-anti-alpha-synuclein 15G7, 1:10, RRID:
AB_11180660; rabbit-anti-aspartate aminotransferase, 1:100, RRID: AB_3598484). The following day,
cells were washed, incubated for 2 h at RT with secondary antibodies (AF488 anti-rabbit, RRID:
AB_2535792; AF546 anti-mouse, RRID: AB_11180613; AF647 anti-rat, RRID: AB_2910635, all 1:500),
and counterstained with DAPI (1:2500 in TBS) for 5 min. Finally, sections were mounted with DAKO
fluorescence mounting medium (Dako, Cat #S3023) and kept at 4°C. Immunofluorescence images were
acquired using a fluorescence microscope (Observer. Z1, Zeiss) and processing was performed using FIJI
(ImageJ). In total, we analyzed n=5941, n=3795 and n=4220 cells for controls 1, 2 and 3, respectively and
n=5797, n=5519 and n=6089 cells for UC 1, 2 and 3 respectively.

Single cell RNA sequencing
Sample preparation and sequencing
On day 70 of differentiation, we obtained single cells from iPSC-ENLs using a papain-DNAse digestion
protocol. Cells were then cryopreserved in a medium containing 90% fetal bovine serum and 10% DMSO
and sent out for library preparation and sequencing at GENEWIZ (Leipzig, Germany) with 6000 cells
targeted per sample and 50,000 reads targeted per cell. RNA samples were quantified using Qubit 4.0
Fluorometer (Life Technologies, Carlsbad, CA, USA) and RNA integrity was checked with RNA Kit on
Agilent 5300 Fragment Analyzer (Agilent Technologies, Palo Alto, CA, USA).

rRNA depletion was performed using NEBNext rRNA Depletion Kit (Human/Mouse/Rat). RNA sequencing
library preparation used NEBNext Ultra RNA Library Prep Kit for Illumina by following the manufacturer’s
recommendations (NEB, Ipswich, MA, USA). Briefly, enriched RNAs were fragmented according to
manufacturer’s instruction. First strand and second strand cDNA were subsequently synthesized. cDNA
fragments were end repaired and adenylated at 3’ends, and universal adapter was ligated to cDNA
fragments, followed by index addition and library enrichment with limited cycle PCR. Sequencing libraries
were validated using NGS Kit on the Agilent 5300 Fragment Analyzer (Agilent Technologies, Palo Alto, CA,
USA), and quantified by using Qubit 4.0 Fluorometer (Invitrogen, Carlsbad, CA).

The sequencing libraries were multiplexed and loaded on the flow cell on the Illumina NovaSeq X plus
instrument according to manufacturer’s instructions. The samples were sequenced using a 2x150 Pair-End
(PE) configuration v1.5. Image analysis and base calling were conducted by the NovaSeq Control Software
v1.7 on the NovaSeq instrument. Raw sequence data (.bcl files) generated from Illumina NovaSeq was
converted into fastq files and de-multiplexed using Illumina bcl2fastq program version 2.20. One mismatch
was allowed for index sequence identification.

Initial processing, quality control, and clustering of scRNAseq data
The fastq files were processed using CellRanger (v7.1.0) from 10X Genomics, with reads aligned to the
GRCh38 genome assembly to generate gene counts per cell. Subsequent processing, analysis, and
visualization were performed in R (v4.3.3) using Seurat (v5.1.0). To ensure quality control, outlier cells with
< 200 genes, > 7500 reads or a mitochondrial percentage > 5% were filtered out. Potential doublets were
removed using scDblfinder. Following quality control, batch effect across samples was mitigated using
Seurat’s integration workflow. Data normalization was carried out using the SCTransform algorithm.
Features to use when integrating multiple datasets were identified using SelectIntegrationFeatures.
Integration anchors computed using the FindIntegrationAnchors function were subsequently used by the
IntegrateData function to generate a batch-corrected, integrated expression matrix. Principal component
analysis (PCA) was applied for dimensionality reduction. Clustering was subsequently performed
computing the shared nearest neighbor (SNN; FindNeighbors, k = 20) and applying the graph-based Leiden
algorithm (FindClusters, resolution = 0.1) to determine the cell subtypes, followed by visualization with
Uniform Manifold Approximation and Projection (UMAP). Initial annotation of the clusters was performed
based on the expression of canonical markers23, 26.

Subclustering analysis
To further resolve cellular heterogeneity, subclustering was performed on the enteric neurons and glia cell
populations using Seurat (v5.1.0). Here, the enteric neurons and glial clusters were extracted from the
integrated Seurat object using the subset function. Data normalization was conducted using the
NormalizeData function, followed by identification of highly variable features via FindVariableFeatures with
the ‘vst’ selection method. As in the previous analysis, the data was scaled, and PCA-based dimensionality
reduction was performed. Clustering was carried out by computing the SNN (FindNeighbors, k = 20) and
applying the Leiden algorithm (FindClusters, resolution 0.5). The processed objects were saved in
H5Seurat format using the SaveH5Seurat function.

Mapping of scRNAseq dataset to a reference dataset
Mapping of the scRNAseq dataset to the reference scRNAseq dataset from Drokhylansky et al.27, Patikas
et al.29 and Jin et al.30 was performed using SingleR77 for cell-type annotation. The raw counts and the
corresponding cell-type annotations from the reference dataset were converted to a SingleCellExperiment
(SCE) object and log-normalized using logNormCounts. Query datasets of enteric neurons and glia were
similarly transformed into SCE objects and subsetted into Iso and SNCA 3x conditions. SingleR
classification was conducted separately for Iso and SNCA 3x samples to obtain cell-type predictions.
Visualization included heatmaps of classification scores (plotScoreHeatmap) and log-transformed
assignment frequencies (pheatmap::pheatmap), as well as score distribution plots (plotScoreDistribution).

Marker identification and enrichment analysis for the subclusters
Using the FindMarkers function in Seurat, markers associated with the subcluster of the enteric neuronal
and glial populations were identified. The analysis was limited to genes expressed in at least 25% of cells
(min.pct = 0.25) and exhibiting a minimum log fold-change (logFC) of 1.0 between groups. Only genes with
an adjusted p-value < 0.05 were considered significant markers. Enrichment analysis was conducted using
Gene Ontology (GO), KEGG, and Reactome pathway databases using clusterProfiler (v4.10.1).
Enrichment terms with a Benjamini-Hochberg corrected p-value< 0.05 were considered significant.
Enrichment results were visualized using ggplot2 or the sc.pl.dotplot function from scanpy (v1.11).

Differential expression between conditions
Differential expression analysis between the SNCA 3x and Iso conditions was performed using the
FindMarkers function in Seurat, with a minimum of 25% cells expressing a given gene (min.pct = 0.25) and
a minimum absolute log fold-change threshold of 1. Significant genes were selected based on an adjusted
p-value < 0.05, calculated using a Wilcoxon Rank Sum test followed by Bonferroni correction. We used
ComplexHeatmap (v2.18.0) to display significant mitochondria-related DEGs across the enteric neuron and
glia subclusters.

Compositional analysis
Compositional analysis was performed using the scProportionTest package78
(v0.0.0.9000), starting with the metadata containing the subcluster and condition information for enteric
neurons and glia. The sc_utils function was applied to process the metadata for both datasets, and
permutation testing was then conducted to evaluate whether the proportions of cells in specific clusters
differed significantly between the two conditions (Iso vs. SNCA 3x). The analysis was run with a false
discovery rate (FDR) threshold of 0.05 and a log2 fold-change threshold of 1.0.

The differential abundance analysis was performed using the MiloR79 method to identify local changes in
cell proportions. For our analysis, we used the first 30 PCA dimensions and set the neighborhood size
based on k=50 nearest neighbors with a proportional overlap of 0.1. The number of cells within each
neighborhood was then counted per biological replicate. Differential abundance was tested using a
generalized linear model that accounts for the batch effect, followed by a spatial FDR correction to the p-
values, which allowed us to identify regions on the UMAP plots where the SNCA 3x and Iso cell populations
significantly differed.

Intercellular communication analysis
Cell-cell communication analysis was performed using the CellChat package37 (v2.1.2) to investigate
signaling interactions among proliferating cells, enteric neurons and glia, as well as interactions within the
subclusters of the latter two populations. Data from both Iso and SNCA 3x conditions were processed
individually. For each condition, the dataset was imported into a CellChat object, followed by standard
preprocessing steps including normalization and the identification of overexpressed genes and receptors.
Communication probability and cellular communication network were then inferred by calculating the
probabilities of ligand–receptor interactions based on cell-specific gene expression profiles. Following
network aggregation, a merged CellChat object was generated to compare communication differences
between Iso and SNCA 3x. Additionally, number of interactions and interaction strength along with the top-
ranked signaling pathways were identified and plotted for visualization.

Pseudotime analysis of mitochondrial-linked genes
Trajectory analysis of enteric neurons and glia populations was conducted using Slingshot42 (v2.10.0).
Lineages and pseudotime trajectories were inferred with the slingshot function, specifying the root cluster
based on entropy analysis (TSCAN package v1.40.1). Mitochondrial marker genes (DNM1L, COX4I1,
ATP5F1B, NDUFA9, PPARGC1A, PINK1) were analyzed in enteric neurons and glia by assessing their
expression across pseudotime in Iso and SNCA 3x conditions. Pseudotime values were extracted using
slingPseudotime(), averaged across lineages, and validated against cell counts. Linear regression
(lm(expression ~ pseudotime * group)) was performed to assess interactions between pseudotime and
condition, followed by FDR correction of p-values to analyze the statistical differences in mitochondrial
associated genes expression dynamics between conditions.

Metabolic flux analysis
We used COMPASS80 (v0.9.10.2) to integrate scRNAseq data with flux balance analysis, inferring
metabolic states in SNCA 3x and Iso enteric neurons and glial cells to compare their metabolic differences.
Using a genome-scale metabolic model (GSMM), COMPASS allowed us to integrate transcriptomics data
with metabolic network constraints to compute "potential activity" scores for thousands of metabolic
reactions across single cells. These scores quantify the degree to which the transcriptomics profiles support
flux through specific metabolic reactions. We compared the estimated Log2Fold changes of the metabolic
reactions for specific pathways and plotted the difference in Log2Fold values to visualize the changes
between reaction scores in SNCA 3x vs. Iso cells. We computed the logarithm of the Wilcoxon adjusted p-
value to filter reaction differences. A threshold of 1.3 (corresponding to an adjusted p-value of 0.05) was
used to identify significant reactions, where values above this threshold indicated evidence of differential
activity between groups.

ELISA for total and aggregated α-syn
ELISA was used to assess the intracellular levels of total and aggregated α-syn in iPSC-ENLs. Protein was
extracted with the buffer provided by the α-syn ELISA kit (#ab260052, Abcam) and quantified using BCA
assay (Thermo) for normalization of the data. We used 50g/mL of extracted protein for the total α-syn
analysis and 300g/mL for the aggregated α-syn (#448807, Biolegend) analyzes. Reactions were
performed according to the manufacturer’s instructions, with data analyzed in triplicates.

Flow cytometry assays
Our flow cytometry data analysis was based on the protocol developed by Windster et al.50. Cells were
washed twice with PBS 1x and then incubated with Accutase for 30 min at 37°C and 5% CO₂. Subsequently
2mL of ENC Media was added to inhibit Accutase and cells were centrifuged at 300xg for 3 min, counted
and then plated in a 96 well plate. Zombie NIR viability staining dye (Biolegend Cat:423105) was added for
20min (1:2000) followed by two washing steps with FACS buffer (2% FCS, 0.01% sodium azide in PBS).
Next, antibodies and/or mitochondrial probes were incubated for 30min at 37°C and 5% CO₂ at the following
concentrations: Mitotracker Deep Red, Green and MitoSox at 1:10000 (ThermoFisher); CD56 (Biolegend,
RRID: AB_2565633, 1:100); alpha-synuclein 2A7 (Novus, RRID: AB_3198368, 1:100); CD24 (BD
Biosciences, RRID: AB_2737795, 1:100); CPT1α (BD Biosciences, RRID: AB_3684993, 1:200). Cells were
acquired in the MACSQuant Analyzer 16 Flow Cytometer (Miltenyi). Results were analyzed in FlowJo 10.1
where the dimensional reduction analysis was also employed. For this, we used unsupervised tSNE
algorithm where neuronal (CD56+CD24high) and glial (CD56+CD24low) cells were concatenated and
submitted to tSNE analysis. Plots were then generated and populations selected based on the markers of
interest.

Enzyme activity assays
We used two enzymatic assay kits to assess the activity of citrate synthase (abcam, #ab119692) and
succinate dehydrogenase (abcam, #ab228560) in iPSC-ENLs, following the manufacture’s guidelines.
Protein was extracted with the buffer provided by the kits and quantified using BCA assay (Thermo) for
normalization of the data. We used 50g/mL of protein per well for the citrate synthase assay and 300g/mL
of protein per well for the succinate dehydrogenase assay, with data analyzed in triplicates.

Dot-blotting analysis
Cell lysates were diluted in ddH₂O to achieve 8µg of total protein in a 3µL drop, which was then spotted
onto a nitrocellulose membrane and air-dried for at least 4 hours. Blocking was performed with 5% non-fat
dry milk in TBS for 1 hour at room temperature, followed by incubation with the primary antibody (mouse
anti-Syn-1, RRID: AB_398108, 1:1000) in 5% non-fat milk in 0.1% TBS-Tween20 overnight at 4°C. After
three washes with 0.1% TBS-Tween20, membranes were incubated with an HRP-conjugated secondary
anti-mouse antibody (RRID: AB_330924, 1:5000) in 5% non-fat milk in 0.1% TBS-Tween20 for 1 hour at
room temperature. The blot was then washed three times with 0.1% TBS-Tween20 before signal detection
using a chemiluminescence system (ChemiDoc MP, Biorad). Data was normalized to total protein per
membrane spot analyzed.

Fractionation protocol for the enrichment of detergent-insoluble protein aggregates
Detergent-insoluble protein aggregates were enriched using a sarkosyl-based fractionation protocol.
Samples were homogenized in ice-cold low salt (LS) buffer (50 mM HEPES pH 7.0, 250 mM sucrose, 1
mM EDTA) with 1X protease and phosphatase inhibitors on ice. Sarkosyl (1% w/v) and NaCl (0.5 M) were
added, followed by incubation on ice for 15 minutes and sonication with Diagenode Bioruptor Pica (setting:
5s ON, 30s OFF, 5 cycles, medium frequency). Protein concentrations were determined using the BCA
assay, and homogenates were ultracentrifuged at 180,000 × g for 30 minutes at 4°C. Sarkosyl-soluble
supernatants were collected, while insoluble pellets were washed with sark-buffer and solubilized in urea
buffer (50 mM Tris-HCl pH 8.5, 8M urea, 2% SDS) for 30 minutes at room temperature, followed by low-
amplitude sonication (setting: 5s ON, 30s OFF, 3 cycles, low frequency). Protein concentrations were
normalized based on BCA assay results, and samples were adjusted using sark-buffer before mixing with
5X Laemmli buffer and heating at 95°C for 5 minutes.

For Western blot analysis of the samples above, proteins were resolved via SDS-PAGE and transferred
onto a membrane using wet transfer. Membranes were blocked with 5% BSA in TBS for 1 hour at room
temperature, then incubated overnight at 4°C with primary antibodies (mouse anti-a-syn 2A7, RRID:
AB_1555287 and rabbit anti-β-actin, RRID: AB_2305186, both 1:1000). Following three washes in 0.1%
TBS-Tween20, membranes were incubated with HRP-conjugated secondary antibodies (anti-mouse,
RRID: AB_330924, and anti-rabbit, RRID: AB_2099233, both 1:5000) for 1 hour at room temperature. After
final washes, protein bands were detected via chemiluminescence (iBright system, Thermo Fisher). Data
was normalized to -actin.

Human Cytokine Array
The supernatant of iPSC-ENLs stimulated for 24h with 100ng/mL TNF was collected. We added 1mL of the
supernatant plus 500L of array buffer 5 for each membrane of the Proteome Profiler Human Cytokine
Array Kit (R&D systems, Cat#ARY005B), which detect up to 36 cytokines at the same time. Each
membrane was used for the supernatant coming from one biological sample. Analysis was performed
according to the manufacturer’s instructions.

Western blotting
Western blotting was performed according to standard protocols. 4–12% NuPAGE Bis-Tris- gels (Thermo
Fischer Scientific) were used to separate proteins by SDS-PAGE. 30µg of protein was loaded per condition.
After transfer to PVDF, the total protein was detected using (No-Stain™ Protein Labeling Reagent
(A44449)) using manufacturer’s protocol. The membrane was incubated in blocking buffer (4% milk in TBS-
T (24.8 mM Tris, pH 7.4, 137 mM NaCl, 2.7 mM KCl, 0.05% (v/v) Tween 20)) for 1 h at room temperature
and with primary antibody (anti rabbit Aspartate Aminotransferase Antibody, RRID:AB_3598484; diluted
1:1000 in blocking buffer) overnight at 4 °C. Incubation with anti-rabbit AlexaFluor 750 secondary antibody
(diluted 1:5,000 in blocking buffer) for 1 h at room temperature was followed by three washing steps with
TBS-T. Proteins were detected using Invitrogen iBright FL1500 Imaging System and analyzed by iBright
Analysis (iBA) software (Invitrogen).

Multielectrode Array recording
To measure neuronal activity, iPSC-ENLs were seeded onto a 48-well CytoView MEA plate (Axion
Biosystems) pre-coated with geltrex on day 15 of differentiation. Cells were maintained in ENC medium
until day 70 (end of differentiation). Recordings were performed for 10 minutes at 37°C in an environment
of 95% O₂ and 5% CO₂ using the Maestro MEA System and AxIS software (Axion Biosystems). Signals
were sampled at 12.5 kHz with a hardware frequency bandwidth of 200–5000 Hz and later processed using
a first-order Butterworth band-pass filter (200–5000 Hz). Spike detection was set at an adaptive threshold
of 5.5 standard deviations above background per electrode, with active electrodes defined as those
detecting ≥5 spikes per minute. Activity parameters were analyzed using the AxIS Metric Plotting Tool
(Axion Biosystems).

Proteomics analysis
Proteomics data were analyzed as previously published, with some modifications81. The cells were lysed
with lysis buffer (8M urea, 0.1M NH HCO ) and the concentration of total proteins was quantified using the
4 3
BCA kit (ThermoFischer). Samples were treated with 12 mM DTT for 30 min at room temperature followed
by alkylation with 40 mM Iodoacetamide in the dark for 45 min. After dilution of the sample to 1:5 with 0.1M
NH HCO an overnight digestion with 1 µg Trypsin (Promega) was performed. The tryptic digest was
4 3
acidified to pH<3 using TFA and desalted using C18 reversed phase spin columns (Harvard Apparatus)
according to the manufacturer’s protocol. Dried peptides were resolved in 0.1% formic acid containing 1%
acetonitrile prior to injection into the mass-spectrometer.

The peptide samples were analysed on an Orbitrap Exploris480 mass spectrometer (Thermo Scientific)
equipped with a Waters M-class UPLC system (Waters AG) operating in trap/elute mode. Peptides were
loaded and chromatographically separated using a Symmetry C18 trap column (5 µm, 180 µm X 20 mm,
Waters AG) and as separation column an HSS T3 C18 reverse-phase column (1.8 µm, 75 µm X 250 mm,
Waters AG). The columns were equilibrated with 99% solvent A (0.1% formic acid (FA) in water) and 1%
solvent B (0.1% FA in ACN). Trapping of peptides was performed at 15 µl/min for 30 sec and afterwards
the peptides were eluted using the following gradient: 1-40% B in 120 min; 40-98% B in 5 min. The flow
rate was constant 0.3 µl/min and the temperature was controlled at 50°C. High accuracy mass spectra were
acquired with a Orbitrap Exploris480 operated in data independent acquisition mode. A survey scan was
followed by up to 12 MS2 scans. The survey scan was recorded using quadrupole transmission in the mass
range of 350-1500 m/z with an AGC target of 3E6, a resolution of 120’000 at 200 m/z and a maximum
injection time of 50 ms. All fragment mass spectra were recorded with a resolution of 30’000 at 200 m/z, an
AGC target value of 1E5 and a maximum injection time of 50 ms. The normalized collision energy was set
to 28%. Dynamic exclusion was activated and set to 30 sec. For protein identification and label-free
quantification the obtained spectra were analyzed using the software FragPipe. This software was used to
automatically generate a spectral library and with this the .Raw files were uploaded as input to calculate
protein levels.

Differential expression analysis was performed using the limma package, with linear models and contrasts
applied to compare Iso and SNCA 3x groups under various treatment conditions. Differentially expressed
proteins (DEPs) were identified by filtering for p-values < 0.05 and log2 fold changes ≥ 0.8 or ≤ -0.8. Data
visualization included volcano plots to represent significance and fold change, and Venn diagrams to
identify overlapping DEPs across conditions. Gene Ontology (GO) enrichment analysis was performed on
DEPs using the clusterProfiler package, focusing on the Molecular Function category, and the results were
visualized in bar plots. Final results, including protein identifiers, fold changes, p-values, and GO
enrichment, were compiled for downstream analysis. Ingenuity pathway analysis was used to merge the
top protein-protein interaction networks found when comparing SNCA 3x vs Iso stimulated with TNF vs
basal level.

Metabolomics analysis
Untargeted metabolomics were in general performed as previously described with minor modifications82.
Cells were lysed in an 80:20 methanol-water mixture (1 mL per sample) containing recovery standards,
followed by centrifugation at 24,000 g and 4°C for 5 min. From the supernatant 350 µL each were used for
HILIC and RP chromatography and 230 µL for pooled QC samples, which were prepared the same way as
unknown samples after pooling. All supernatants were dried under nitrogen at 30°C before reconstitution
in HILIC or RP eluent. LC-MS analysis was performed using a Dionex Ultimate 3000 system coupled to a
Q Exactive Focus mass spectrometer (Thermo Fisher Scientific) with HILIC (Acquity UPLC BEH Amide,
1.7 µm) and RP (Acquity UPLC BEH C18, 1.7 µm) chromatography. The system was operated in positive
and negative ionization modes with a mass detection range from 66.7 to 1000 m/z in full-scan mode, with
the three most abundant features fragmented in ddMS2 mode. QC samples and blanks were included for
system calibration and batch correction. Data were analyzed using Compound Discoverer 3.3, with features
identified using a 5 ppm mass tolerance and 0.2 min RT tolerance. Peak areas were normalized to protein
concentration, Compound annotations were assigned via ChemSpider, mzCloud™, mzVault, and in-house
databases. Only compounds with annotation levels 1 or 2 (See Supplementary Data 10) were included.
Analysis of individual metabolites was calculated by performing a scaling of the data to consider the donor
effect, followed by Student’s unpaired two-tailed t-tests, similar to previous publications83. Metabolite-set
enrichment analysis was performed using MetaboAnalyst84, considering the DEMs (raw p-values < 0.05,
log2 fold changes ≥ 0.5 or ≤ -0.5)

Integrated pathway analysis
Integrated pathway analysis was generated using MetaboAnalyst84 by adding as an input the DEPs (raw p-
values < 0.05 and log2 fold changes ≥ 0.8 or ≤ -0.8) and DEMs (raw p-values < 0.05 and log2 fold changes
≥ 0.5 or ≤ -0.5) obtained when comparing SNCA 3x vs Iso stimulated with TNF.

Proximity ligation assay (PLA)
Imaging 24-well cell plates containing iPSC-ENLs at day 70 (basal and TNF-stimulated) were fixed with 4%
paraformaldehyde and permeabilized with 0.4% (v/v) Triton X-100. The Duolink In Situ PLA kit (Sigma
Aldrich, Cat#DUO92101) was used for PLA. Cells were blocked and incubated with mouse anti-α-syn
(RRID: AB_1555287, 1:50) or rabbit anti-TOM20 (1:250, RRID: AB_2207530) antibodies and thereafter
with the corresponding PLA probes. After ligation and amplification using the kit reagents, cells were
counterstained for α-syn and TOM20 and mounted using Duolink mounting medium with DAPI (1:2500). A
pipeline to analyze the PLA dots in the cytoplasm of the cells was created using CellProfiler (Broad Institute)
and at least 4-5 individual images per individual biological sample under each condition (basal or TNF)
were analyzed. Imaging was performed using the Super Resolution Evident SR Spinning Disk microscope
(Olympus) and the cellSens Software and processing was performed using FIJI (ImageJ).

Mitochondrial parameter analysis
Mitochondrial morphology was assessed using the TOM20 immunofluorescence stainings and analyzed in
ImageJ (Fiji) with the Mitochondria Analyzer plugin85. Images were acquired as TIFF files, and the TOM20
channel was processed for analysis. Briefly, images were converted to 8-bit grayscale, and background
was subtracted using a rolling ball algorithm (radius = 50 pixels). A global threshold (Otsu method) was
applied to segment mitochondria, followed by binary processing to refine structures. Nuclei were identified
from the DAPI channel using CellProfiler, and mitochondrial parameters were normalized to the number of
nuclei per image. A minimum of three images per biological replicate (individual Iso or SNCA 3x line)was
analyzed, and results were averaged per biological replicate. The total number of cells analyzed per cell
line and used to normalize the data was 1276 (Iso-1), 1534 (SNCA 3x-1), 810 (Iso-2), 1511 (SNCA 3x-2),
614 (Iso-3) and 1083 (SANCA 3x-3). Statistical analyses were performed in R, with group comparisons
conducted using Student’s unpaired two-tailed t-tests.

Seahorse analyzes
For Seahorse assays, we seeded 60,000 ENL precursors per cm2 on Geltrex (Thermo Fisher)-coated
Seahorse XF24 cell plates (Agilent, USA) at day 15 of the differentiation and kept them with ENC medium
until day 70, when the analyzes were performed. For mitostress assays, cells were washed two times and
incubated for 1h at 37°C in an incubator without CO , with respiratory medium consisting of DMEM XF
2
(Agilent) supplemented with 10mM glucose solution (Agilent) plus 1mM pyruvate solution (Agilent) and
2mM glutamine (Agilent). After equipment calibration, baseline respiration measurements were followed by
injection of 5μM oligomycin (Sigma), 5μM carbonyl cyanide m-chlorophenylhydrazone (CCCP) (Sigma) and
5μM rotenone (Sigma) plus 5μM antimycin A (Sigma). To perform the mitofuel assays, we used the same
medium and conditions of the mitostress assay, but the compounds were the ones provided by the
Seahorse XF Mito Fuel Flex Test Kit (Agilent), in the concentrations indicated for the metabolic dependency
analysis, which included 3M BPTES, 4M etomoxir and 2M UK5099 (final concentrations). All respiratory
modulators were previously titrated, and all plates were normalized to protein content using the BCA kit
(Thermo). Analysis was performed using the Wave software (Agilent).

Bioinformatics Analysis of Public and In-house Datasets
To investigate MAS suppression in the context of human intestinal inflammation, we analyzed three publicly
available transcriptomic datasets from ulcerative colitis (UC) patients treated with infliximab (GSE16879,
GSE12251, and GSE73661). For GSE73661, n=12 controls, n=15 UC non-responders and n=8 UC
responders. For GSE16879, n=6 controls, n=16 UC non-responders and n=8 UC responders. For
GSE12251, n=11 UC non-responders and n=12 UC responders. Processed expression matrices were
downloaded from the GEO database, and gene expression of GOT1 and SNCA was compared between
responders and non-responders. Values were normalized to a log scale.

For validation, we analyzed our in-house bulk RNA-seq dataset (IBDome53 - https://ibdome.org/),
which
includes inflamed and non-inflamed colonic biopsies from UC patients. The p-values have been calculated
using a simple Wilcoxon-Mann-Whitney test. Correlation between SNCA and GOT1 expression in inflamed
versus non-inflamed tissue was assessed using Pearson correlation.

To evaluate MAS gene expression at the single-cell level, we accessed the integrated scIBD database
(http://scibd.cn/) which compiles single-cell transcriptomic data from 12 IBD studies. We selected the
following parameters: Neural (major cluster), large intestine (tissue), Sadult (developmental stage), then
selected all studies, colon and rectum (location), the three groups of interest (healthy, UC non inflamed and
UC inflamed) and 5000 downsampled cells per subtype. Average expression of GOT1, GOT2, MDH1, and
MDH2 was compared between inflamed and non-inflamed conditions.

STATISTICS AND REPRODUCIBILITY
Unless stated otherwise, all quantitative analysis was performed using independently collected material
from three isogenic and three SNCA 3x cell lines (our biological replicates, n=3 per genotype). The number
of independent differentiations runs (technical replicates) used to generate the data for each panel is
specified in the corresponding figure legend. Each data point in the figures represents the average of these
technical replicates for each individual line. Statistical analysis was performed using Graphpad Prism 10,
by either two-tailed unpaired Student’s t test (two groups), one-way ANOVA followed by Tukey’s post-hoc
test or two-way ANOVA followed by Sidak’s, Tukey’s or Bonferroni’s post hoc test (more than two groups).
Error bars represent standard error of the mean (SEM). Statistical analysis for scRNAseq, metabolomics
and proteomics analysis were performed by different packages in R as stated. p < 0.05 was considered
significant. Details of statistics and sample sizes are described in the figure legends. No statistical method
was used to predetermine sample size. No data were excluded from the analyses. The experiments were
not randomized. The investigators were not blinded to allocation during experiments and outcome
assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Ghirotto et al aims to examine how a-synuclein (a-syn) affects the enteric nervous system (ENS) in Parkinson’s disease (PD) and the role that inflammation plays in this process. Given the focus on the ENS as a potential originating site of PD a-syn pathology this is an important question to examine. As model system they utilise three induced pluripotent stem cell (iPSC) lines from patients with SNCA triplication and their previously generated isogenic controls, which they differentiate to enteric neural linage. To my knowledge, this is the first attempt to model PD pathology in iPSC-derived enteric neural cells. The authors perform a thorough multi-omic analysis including scRNAseq, proteomics and metabolomics as well as multiple functional assays. They confirm the validity of the ENL cultures using scRNA comparison to a human gut atlas. Overall, this is exiting and well-performed work. However, some of their conclusions and interpretations of the data are not fully supported. This is mainly related to their interpretation of the effect of TNF-a exposure on SNCA 3x cultures where in several assays there is not actually a significant difference between untreated and TNF-a treated SNCA 3x ENLs and/or the isogenic control ENLs response to TNF-a is identical to the SNCA 3x ENLs. Major concerns/questions: 1. The authors show that the number of enteric neurons is significantly decreased in SNCA 3x ENL cultures at d70 based on scRNAseq whereas numbers of glia and proliferating progenitors are similar. They do not address how this difference in the number of enteric neurons arises between SNCA 3x and isogenic controls. Are fewer neurons generated or have they died during the differentiation? An important question arising from this, is how the different cellular composition of the ENLs cultures between SNCA 3x and isogenic controls affect the downstream analyses? E.g. in Fig. 3 A-B where the mitochondrial content and -morphology is analysed and found decreased in SNCA 3x. Is this a result of the difference in cell type composition/morphology as mitochondrial morphology and content can differ between neurons and glia? The difference in neuronal numbers is also important to keep in mind in the interpretation of the MEA data. 2. Of the three overall subpopulations of cells, only glia cells show significantly increased SNCA expression based on scRNAseq. This is interesting in relation to the finding of decreased numbers of neurons in SNCA 3x cultures. The authors show immunofluorescent stainings for GFAP, HuC/D and a-syn I Fig. 1C. Can they based on these stainings determine if the cell type specific SNCA expression differences translate to the protein level? 3. The authors conclude that the scRNAseq differential analysis reveal mitochondrial dysfunction in SNCA 3x ENLs, but do not refer to any figure to support this (line 161-162). The enrichment analyses in Fig. 2D+F do not include mitochondrial related terms besides from “regulation of lipid metabolic process” and “integration of energy metabolism”. Similarly, the signalling pathways identified as altered by the CellChat analysis as presented in Fig. S1H and Fig. S3E+H do not appear particularly related to mitochondria although this is mentioned in the results section? EGF and NOTCH signalling have many other more prominent functions and the majority of the other factors listed appear more related to axogenesis, neurogenesis and extracellular matrix etc. 4. The proteomics data is reported with p-values and there is no mention of correction for multiple testing. Similarly for the metabolomics data only p-values are reported. In large scale analyses such as these, correction for multiple testing should be applied and q-values/adjusted p-values reported. 5. The authors conclude from the proteomic and metabolomic analyses that MAS and TCA cycle is impaired by TNF-a in SNCA 3x. Besides from AATC are any other MAS or TCA cycle proteins changing with TNF-a exposure? In the analysis of patient data in Fig. 8 additional MAS enzyme expression levels are examined. Are any of these altered in SNCA 3x ENLs? All the metabolites shown in Fig. 5E are lower in SNCA 3x than isogenic control. Could this be a consequence of the generally lower mitochondrial content identified in Fig. 3A-B? 6. The statistical analysis of a-syn-mito co-localisation with PLA in Fig. 6C should be redone to show the average for each of the cell lines – as all other figures in the paper – instead of showing data for individual cells. The figure legends states n = 3, but the statistical analysis appears to be performed on individual cells. It is unclear whether TNF-a significantly increases a-syn-mito PLA in SNCA 3x if analysed in this way? However, it appears that TNF-a increases a-syn-mito PLA significantly for isogenic control, which is currently not mentioned/discussed? Also, could the authors please discuss the discrepancy between the results from Fig. 6C to Fig. 6E where there is no increase of a-syn in the mitochondrial fraction with TNF-a compared to untreated for either SNCA 3x or isogenic control? The western blot in figure 6D should show the starting material / un-isolated samples for comparison to demonstrate that successful isolation of mitochondria has been achieved. Why are basal conditions without TNF-a not included in Fig. 6F when the earlier data has clearly shown that baseline mitochondrial dysfunction is present in SNCA 3x ENLs? This makes it unclear whether TNF-a is having an effect or the difference is caused by SNCA 3x alone? Please include vehicle treated as well. Overall, the headline “TNF-a increases a-syn-mitochondria interactions and induces oxidatives stress in SNCA 3x ENLs” is currently not fully supported by the presented data. 7. Fig. 7B-C: the authors state that TNF-a-treated SNCA 3x ENLs showed reduced mitochondrial respiration including ATP-linked OCR. However, the effects are close to identical for the TNF-a-treated isogenic control ENLs. This should be reflected in the interpretation of the data. Overall, the effects of CSB6 appear overall similar independent of genotype. Currently, this is only really mentioned in the limitations of the study section. Only the glutamine dependency seems to really differ between SNCA 3x and isogenic control, independently of TNF-a exposure, with normalisation after CSB6 treatment for SNCA 3x. The statement that CSB6 promoted a shift toward fatty acid oxidation is confusing. FAO levels are unchanged for SNCA 3x between the basal, TNF-a- and TNF-a+CSB6-treated conditions – and significantly decreased in isogenic control after CSB6 treatment? For the Mitotracker green assay the authors note a trend towards an increase in dysfunctional mitochondria in SNCA 3x with TNF-a. However, the trend towards decreased dysfunctional mitochondria at baseline are not mentioned. CSB6 decreases the percentage dysfunctional mitochondria to close to 0 independently of genotype. However, the authors only mention the effect for SNCA ENLs as the result is apparently only significant for this group. The average for TNF-a treated isogenic control is around 20% and with CSB6 around 1 with no visible arrow bars. 8. The authors write in the abstract that they show a-syn upregulation as a general hallmark of in intestinal inflammation in human gut tissue. They mention examining SNCA expression data – together with GOT1 expression – in intestinal inflammation patient samples. However, the SNCA expression is only shown in correlation to GOT1 expression and no formal comparison of a-syn levels between inflamed and uninflamed tissue is shown. Could the authors please show the SNCA expression levels the same way the show GOT1 levels in Fig. 8B? Similarly, the SNCA expression data from inflamed and non-inflamed gut tissue from the IBDome dataset should be presented and not only correlations with GOT1. The immunofluorescent staining for GOT1/AATC and a-syn on colon biopsies should be performed on more than one patient and one control if anything is to be concluded from it. Minor concerns: 1. Fig. 2A+E Can the authors please provide legends with naming of the different subclusters of cells in the scRNAseq data? It is difficult to interpret the data when we only have subcluster numbers. 2. Is there a correlation between the subclusters of cells with the highest changes in DEGs and their SNCA expression? E.g. for neuronal subcluster 8 which is reduced in abundance in SNCA 3x ENLs the SNCA expression levels would be interesting to know. 3. If a-syn has been identified by the proteomic analysis, how are the levels in the different conditions? 4. Could the authors also please report how many proteins and metabolites were identified for the reader to understand how large a proportion of the identified proteins and metabolites differ between the different condition. 5. Can Fig. 5H please be enlarged so that it is more readable. 6. Please define in the results section (line 344) that GOT1 = AATC.", "reviewer_2": "This manuscript presents an ambitious and integrative study using human iPSC-derived enteric neural lineages (ENLs) from SNCA triplication Parkinson’s disease (PD) patients to explore the interplay between alpha-synuclein accumulation, inflammation, and metabolic dysfunction in the ENS. The authors employ a comprehensive multi-omic approach (scRNA-seq, proteomics, metabolomics), functional assays (electrophysiology, Seahorse), and validation in human UC samples to support a model in which TNF-α exposure unmasks mitochondrial and synaptic vulnerability in SNCA 3x ENLs through disruption of the malate-aspartate shuttle (MAS). The study is highly relevant, well-executed, and presents a novel mechanism with broad implications. However, several aspects of the manuscript require clarification or additional analysis to strengthen the conclusions and ensure appropriate interpretation. 1. Several results suggest that SNCA expression is not significantly different at baseline in neurons, despite conclusions about SNCA-driven effects. It would help to show quantifications and expression levels across neuronal and glial subclusters and assess SNCA⁺ cells specifically. Consider module scoring across SNCA⁺ neurons and glia, or correlation with SNCA-interacting genes. 2. Figures 3–7 present pooled ENL results despite many cell-type-specific conclusions. Where feasible, separate neuron vs glia in imaging, flow, and Seahorse analyses or explicitly qualify conclusions that cannot be deconvolved. 3. The cell types and annotations in the scRNA-seq datasets should be described in more detail. What markers were used to define each cluster? This information is critical for evaluating the biological interpretation of the single-cell results. 4. In several figures (e.g., Figure 1E), tests such as Student’s t-test are applied to proportions or counts; chi-squared or other appropriate methods should be used. Additionally, comparisons (e.g., Figure 4C) currently conflate genotype and baseline differences. Comparisons should isolate the effect of TNF within each genotype. 5. The strength of the TNF-specific effect in SNCA 3x ENLs is not always evident. some changes may be shared with ISO. Figure 5 should include the full condition matrix (ISO ± TNF, SNCA 3x ± TNF) with clearer annotations. 6. The <5% MT gene threshold may bias against metabolically stressed neurons. Suggest re-analyzing with a relaxed cutoff (e.g., 15–20%) and assessing whether stressed populations or DEGs change. 7. The manuscript suggests enteric glia compensate for neuronal mitochondrial stress, but supporting data are limited. Consider reframing as a hypothesis or provide additional evidence (e.g., selective pathway upregulation, functional protection). 8. It would be great to include CSB6-only controls in functional assays to distinguish TNF-specific rescue from general effects. 9. The proteomic/metabolomic findings should be validated by IF or flow cytometry where possible. 10. Does infliximab treatment alter SNCA-GOT1 correlation in UC datasets? 11. The quantification of GOT1 and SNCA IF in human tissue should be includes and the biopsy metadata should be clarified. Minor Comments • Figure S2 heatmaps are difficult to interpret due to inconsistent cluster ordering. Reordering or consistent labeling would help. • Clarify gene/cluster selection in Figures 2B–D, 3C–D. • In Figure 4A, define 'total cells' and clarify why neuron/glia are pooled. • Improve figure labeling for Figures 6C, 7E–F; comparisons are unclear. • In Figure 8, clarify the comparison in panels B–C; what are we meant to conclude? • Describe IBDome and scIBD datasets in more detail: sample size, condition, analysis methods.", "reviewer_3": "The manuscript investigates an important understudied area of the pathology of enteric nerves in Parkinson’s. The authors describe the iPSC derived model and the cells generated from it in a good level of detail in the isogenic and SNCA triplication lines. Overall, the data are extremely interesting and add to the field in terms of inflammation and alpha synuclein in gut neurons. Some specific comments are outlined below: In Figure 2 the authors describe changes in specific cellular subclusters which link mitochondrial stress, lipid homeostasis, cholesterol metabolism and stress responses. Whilst the authors link many of these together, it would be interesting for the authors to comment on the possible link between the changes in cholesterol metabolism and mitochondrial stress. Although a small part of the cholesterol pathway, mitochondria do play a significant role which is extremely important for bile acid synthesis, also an area of interest in enteric neurons in PD. Hence, could the authors comment on their scRNAseq data in the sub clusters from this point of view? For the data presented in Figure 3 specifically (but also to other figures throughout) could the authors be clearer in how many cells were quantified and from how many differentiations of each line? At the moment the figure legend is ambiguous with at least 3 images per line, making 3 biological repeats. To ensure the robustness of the data, it needs to be clear from how many differentiations the data was generated. In Figure 3 the authors postulate that metabolism is shifted in the SNCA triplication lines, and that this may be compensatory. Could the authors expand on why this change would be compensatory response to stress as this is not obvious from the pseudo time experiments shown in Figure 3 without further explanation for the reader. The authors data showing the changes in proteome after TNFalpha stimulation are compelling however in the manuscript the authors refer to the SNCA neurons being sensitised to inflammatory stimuli, the data from Figure 3 would suggest this is not general priming but it could be specific, could the authors comment or alter the text. Throughout the manuscript the figure legends are not detailed enough, only in the Seahorse figure legend of Figure 7 do the authors state that the mean of 3 independent experiments is shown. Could the authors please confirm this for all experiments. The data generated are compelling for the change in metabolic status after TNFalpha stimulation. The data in Figures 6 and 7 aim to show a functional effect of this change. However, many of the measurements are of mitochondrial function of OXPHOS rather than of the TCA cycle or beta oxidation. Although the fuel flex experiments show a change, in order to support the data in the previous figures and the conclusion of the his change and critical alterations in the malate shuttle, it would be beneficial if the authors were to functionally measure TCA cycle and beta oxidation activity rather than depend on secondary measurements. Furthermore, the first sets of figures suggest SNCA triplication itself causing mitochondrial abnormalities, however, the functional data in Figure 7 in particular do not support this, could the authors comment. For the CSB6 experiments do the authors show target engagement and therefore can be certain that the CSB6 treatment is having this effect via the known mechanism of action rather than off target effects, as many compounds can affect mitochondria and metabolism.", "reviewer_4": "Ghirotto and colleagues present an iPSC-derived Enteric Neuron System of the alpha-synuclein triplication as a means to investigate the pathogenesis underlying the gut-to-brain hypothesis of PD and potential other synucleinopathies. Their work identifies that soluble TNF-alpha treatment exacerbates mitochondrial dysfunction in enteric neurons. While mitochondrial dysfunction in synucleinopathies is well established, there have been no reports studying this phenomenon in the enteric nervous system. I have the following comments: 1. Given the observed cellular heterogeneity in the culture (scRNAseq), the cell types could be further refined. a) More fine-level cell typing exists within the Drokhlyansky et al. study. The authors should adequately cell type their cells to the finer levels shown in the manuscript. b) The authors should show the marker genes of the ENs in scRNAseq clusters, such as the markers that they used for their qPCRs(e.g., HOXB3 and TUBB3 are missing from Figure S2A). c) The authors should stain their cells according to the HOXB3, PHOX2B, and TUBB3 to show the percentage of cells expressing those proteins in both cell lines. 2. According to Fig. 1E, SNCA 3x cultures have less enteric neuron proportions. a) Is this something reproducible? b) Is this something technical, or do neuronal populations drop out from the SNCA 3x cultures? In any case, the authors should address how this heterogeneity drives the rest of the conclusions that rely on these bulk assays of these populations. For example, in Figure 5D, is this analysis valid if there are fewer neurons in the SNCA 3x samples? 3. Does treatment of TNF1a induce cell death in the SNCA 3x and/or Iso? 4. It would be interesting to compare the phenotypes of the SNCA 3x neuronal dysfunction with existing studies that investigate similar questions of the SNCA 3x lines but in other neuronal types: (Patikas et al. Neuroscience Research 2023; Jin et al. Science Advances 2024). Comparing the phenotypes of midbrain and cortical organoids could highlight common modules. 5. Since the whole manuscript builds upon the notion that asyn pathology spreads from the gut to the brain, the authors should consider testing asyn fibrils and tnf1a to check whether this pathology can be recapitulated in the Iso cell lines. 6. Cell-to-Cell communication. The way the data is presented, neurons and glia appear to be two separate CellChat analyses. Does this assume that the neurons do not communicate with glial cells? If so, why? 7. L465: \"iPSC-ENLs makes it challenging to separate mechanistic effects in a populational level\". What does populational level mean? 8. Where is the reported pathology owed? SNCA 3x has fewer neurons at Day 70, so does this signature come from the neurons or the glia populations? 9. L304: Does asyn interact with TOM20 in Iso cell lines? 10. L320: Fig 6C appears to have incorrect statistics; Poisson events (data should be transformed as log(counts + 1)) should be used prior to statistical testing. A similar problem might be present in the Fig. 1E. 11. L218: \"This suggests that SNCA 3x enteric glial cells compensate for neuronal stress by enhancing mitochondrial support.\". Not sure how this is supported by the data. 12. Figure S2A: HOXB5 is shown twice."}, "author_response": "We thank the reviewer for their constructive and overall positive review of our manuscript. We appreciate their helpful critique regarding the interpretation of the TNF-α exposure data in SNCA 3x ENLs. To provide stronger support for our conclusions, we have now performed several new, rigorous analyses and clarified data interpretation and statistical analyzes. In this sense, we conducted flow cytometry of the iPSC-ENLs with separation of enteric neurons and glia followed by t-SNE analysis, which clearly identified specific cell populations that are exclusively enriched in the SNCA 3x + TNF group; and we also performed additional Seahorse experiments, where the mitofuel test revealed a significant and unique metabolic impairment in the SNCA 3x+TNF group, characterized by a marked increase in glutamine dependency. These new data mechanistically reinforce our finding that the combination of SNCA triplication and TNF−α exposure unmasks a distinct cellular and metabolic phenotype. [Detailed responses to all points follow in the original text...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have fully addressed my previous concerns and the conclusions of the paper have been strengthened by the additional data and analyses.", "reviewer_2": "The authors have adequately addressed all my concerns and comments", "reviewer_3": "The authors have thoroughly addressed the reviewers comments, taking into consideration the comments, adding additional experimental work, detail, changing interpretation of results and overall enhancing the manuscript.", "reviewer_4": "In their rebuttal, the authors have performed additional analyses that substantially address most comments and have improved the quality of their manuscript, thereby strengthening the impact of their findings. A major concern remains that the scRNAseq data show significant shifts in their neuronal population, which the authors deemed non-significant, citing a log2 fold change threshold of 1. This threshold is quite stringent for population shifts and, in my opinion, arbitrary. The authors should assess the ramifications of an approximately 30% reduction in neurons (log2 fold change around -0.5, as shown in the figure for the neurons) observed in their Figure 1E in the rest of the analysis. My impression of a 30% differential abundance is substantial, and in a different research context, it would have been \"neuroprotective\". While variability between differentiated iPSC lines is a well-known problem (Jerber et al., 2021, Nature Genetics) and is well beyond the scope of this manuscript, the authors should at least address its ramifications in their data interpretation. At a minimum, authors should perform an appropriate analysis to determine which cells are dropping out, using a suitable method such as Milo (Dann et al. Nat Biotech 2022). Then, using known TNF1a pathway genes, they should determine whether these populations express them. Additionally, the limitations sections could be expanded appropriately. Furthermore, their CD24+ population does not reflect the trends observed in the data and, in my interpretation, may not be significant even at a log2 fold change> 0 threshold, which raises concerns about how much the flow cytometry experiment can be trusted. Additionally, can the authors provide a CD24 plot to assess how well this marker distinguishes glial from non-glial cells? At a more granular level, many neuronal subpopulations are differentially abundant (Figure 2C, 2G), and the authors correctly term these \"significant\" and provide additional interpretations. Still, this potential could be a differentiation artifact, but at least here it is not glossed over. While this may also be due to clonal variability, the authors should assess genes central to their question. In a similar vein to the above, they should check which of those cell types express TNF-alpha genes and could affect the differential responses observed in their bulk assays."}, "author_response": "In their rebuttal, the authors have performed additional analyses that substantially address most comments and have improved the quality of their manuscript, thereby strengthening the impact of their findings. [Detailed responses to all points follow in the original text...]"}, {"round": 3, "reviewer_comments": {"reviewer_4": "In their rebuttal, the authors have performed additional analyses and provided additional plots to support their claims about the underlying cell-type abundance in the different ENS cultures. Here are some comments I have: 1) If I understand correctly, scProportion lets the authors set a threshold. This threshold is arbitrary and is used as a null hypothesis. The authors cite a log2fold change threshold of 1 (a doubling of the population). In the second, they did a more permissive threshold of 0.58 (a 50% increase in population). The second threshold, if I understand the error bars, falls within the scProportion confidence intervals. I would argue that the most rational null is that there is no change in the population, which corresponds to log2fc=0. I understand that the authors are not concerned with this. However, this needs to be explicitly written in the main text: \"with a null hypothesis of 50%...\" 2) I don't know why the Milo plots look like this, but having lfc of 0 everywhere (Figure S1D) does not pass the smell test. The authors should double-check that the analysis was conducted properly and that the figure is displayed correctly. In the general clusters Supp Table, deposited log-fold changes range from -8.3 to 7.3. There are similar symptoms in the glia populations. 3) By looking at the Milo tables, it appears that the \"permissive\" cutoff of 0.2 was selected so that neurons pass this cutoff. If the authors, as they suggest, think their study is not properly powered to support this analysis, perhaps this analysis should be omitted from the manuscript. 4) The flow cytometry plots are helpful. The authors measure ~80% of their cells. The scRNAseq data support the existence of a CD24high/low plot. However, it is not clear how the high- and low-population groups map to the data. While I can definitely infer the CD24-high from the contour lines. However, judging from the contour lines, the CD24-low peak is not as clear as in the cited paper (Windster et al., Fig. 2a). Moreover, the authors use a negative-stain control to set a negative-low cutoff. This cutoff is very close to the peak of cells absent from the main data, as indicated by the contour lines. Could this peak (which is absent in the unstained control) correspond to CD24-low cells? Can the authors provide a justification for this? If the general abundance analysis is inconclusive, which is quite challenging in my opinion at this stage, I suggest expanding the limitation section appropriately."}, "author_response": "We thank Reviewer 4 for the additional feedback. We have addressed the final remaining points below, ensuring that our analytical choices are transparent and align with the technical specifications of the tools utilized. Changes have been highlighted in yellow throughout the manuscript."}, {"round": 4, "reviewer_comments": {"reviewer_4": "Thank you for the clarifications"}, "author_response": "We thank the reviewer for all their suggestions in our manuscript."}]

Decision letter