Polyamine metabolic enzyme SAT1 remodels the neuronal transcriptome and rescues α-synuclein toxicity in Drosophila
Zoya R. Bangash, Hiroyoshi Matsui, Bedri Ranxhi, Sokol V. Todi, Peter A. LeWitt, Wei-Ling Tsou
TL;DR
The enzyme SAT1 reduces α-synuclein toxicity in fruit flies by altering gene activity and improving mitochondrial health.
Contribution
This study reveals SAT1's novel role in mitigating α-synuclein toxicity through transcriptomic and mitochondrial proteostasis changes.
Findings
SAT1 overexpression reduces α-synuclein levels and alters its brain distribution in Drosophila.
SAT1 modulates stress-related gene expression and mitochondrial pathways in α-synuclein models.
SAT1 improves mitochondrial proteostasis by enhancing LC3 association and reducing α-synuclein accumulation.
Abstract
Polyamine homeostasis is tightly regulated by interconversion and catabolic pathways and has been increasingly implicated in neurodegenerative disorders, including Parkinson’s disease (PD), where accumulation of α-synuclein (α-Syn) perturbs neuronal homeostasis. Spermidine/spermine N1-acetyltransferase 1 (SAT1) occupies a central position in polyamine interconversion, and alterations in SAT1 activity have been linked to α-Syn toxicity and PD-related neuropathology. To investigate how SAT1 activity influences α-Syn-associated neurodegeneration, we employed a Drosophila model of neuronal α-Syn expression. SAT1 overexpression reduced α-Syn protein levels, altered its subcellular distribution within the brain, and mitigated α-Syn-induced lifespan shortening. Transcriptomic analyses showed that SAT1 modulates stress-associated gene expression in the α-Syn background, including attenuation of…
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Taxonomy
TopicsPolyamine Metabolism and Applications · Parkinson's Disease Mechanisms and Treatments · Studies on Chitinases and Chitosanases
Introduction
Polyamines (putrescine, spermidine, and spermine) are essential polycations that maintain cellular homeostasis by regulating transcription, translation, bioenergetics, and stress responses (Agostinelli et al. 2010; Vrijsen et al. 2023). In post-mitotic neurons, these metabolites interact with nucleic acids, membranes, and proteins to influence chromatin structure, protein synthesis, membrane properties, and mitochondrial function (Polis et al. 2021; Schibalski et al. 2024). Because neurons depend on long-term proteostasis and organelle integrity, precise control of intracellular polyamine levels is particularly important (Arthur et al. 2024; Dhara et al. 2020; Rhee et al. 2007). Consistent with this vulnerability, age-associated shifts in polyamine balance have been linked to neurodegenerative disorders (Arthur et al. 2024; Malpica-Nieves et al. 2020), including Parkinson’s disease (PD) (Vrijsen et al. 2023). Metabolomic studies report altered polyamine species and acetylated derivatives in PD patient biofluids and tissues, often correlating with disease progression (LeWitt et al. 2025; LeWitt et al. 2023; Saiki et al. 2019).
Polyamine homeostasis is maintained through coordinated biosynthetic, catabolic, and transport pathways (Sagar et al. 2021), with spermidine/spermine N^1^-acetyltransferase 1 (SAT1) serving as a key regulator of polyamine catabolic flux (Fig. 1a) (Pegg 2008). SAT1 acetylates spermidine and spermine, promoting their export for further oxidative catabolism and thereby regulating the size and composition of the intracellular polyamine pool (Casero and Pegg 1993). This step is particularly important because, while polyamines can be imported into neurons through specific transport systems, direct efflux is largely restricted to putrescine (Masuko et al. 2003; Uemura et al. 2010). Higher-order polyamines such as spermidine and spermine are inefficiently exported unless first acetylated (Casero and Pegg 2009), placing SAT1 in a unique position to regulate polyamine clearance under stress conditions (Casero et al. 2018). SAT1 expression is strongly inducible by oxidative challenge, inflammation, and toxic insults (Kang et al. 2019; Sui et al. 2021), linking polyamine turnover to cellular stress adaptation. Despite this, SAT1 has been studied primarily in proliferative tissues (Holbert et al. 2022; Nakamura et al. 2021), and its roles in post-mitotic neurons remain incompletely defined.
PD is characterized by the accumulation and aggregation of α-synuclein (α-Syn) (Calabresi et al. 2023), leading to impairments in protein quality control, mitochondrial function, and neuronal survival (Mingo et al. 2025; Thorne and Tumbarello 2022). Increasing evidence links polyamine metabolism to PD-related pathology (Si et al. 2021; Vrijsen et al. 2023). Neuroimaging and postmortem analyses of human PD patients have shown that disease progression affects specific brainstem nuclei early (Lewandowski et al. 2010), with reduced expression of spermidine/spermine N^1^-acetyltransferase 1 (SAT1) in PD-vulnerable regions such as the dorsal motor nucleus of the vagus compared with relatively resistant regions, including the olivary nucleus, implicating altered polyamine regulation in region-specific vulnerability (Lewandowski et al. 2010). Consistent with these human observations, transgenic mouse models expressing human α-Syn demonstrate that pharmacologic inhibition of SAT1 with berenil (diminazene aceturate) elevates intracellular spermidine and spermine levels and exacerbates α-Syn aggregation and neuropathology (Lewandowski et al. 2010). In contrast, activation of SAT1 with the polyamine analog DENSPM reduces α-Syn accumulation and ameliorates neurodegenerative phenotypes, implicating SAT1 activity and polyamine flux in modulation of α-Syn pathology (Lewandowski et al. 2010).
At the molecular level, spermidine and spermine can bind α-Syn and accelerate fibril formation in vitro (Antony et al. 2003; Grabenauer et al. 2008; Krasnoslobodtsev et al. 2012), indicating that elevated polyamine levels can promote aggregation under certain conditions. At the same time, spermidine is a well-established inducer of autophagy (Hofer et al. 2024; Watchon et al. 2024), and increased spermidine availability enhances autophagic clearance and reduces α-Syn toxicity in multiple model systems (Buttner et al. 2014; Eisenberg et al. 2009). SAT1, by regulating polyamine interconversion and turnover, is positioned to influence this balance, shaping whether polyamines preferentially contribute to aggregation or engage in protective quality-control pathways. These observations underscore a context-dependent role for polyamines, in which their impact on α-Syn pathology depends on how polyamine flux is regulated within neurons.
In our previous work (Ranxhi et al. 2025), we established neuronal α-Syn expression in Drosophila as a robust in vivo model of PD-relevant toxicity and showed that SAT1 is a potent modifier of disease outcomes: neuronal SAT1 knockdown exacerbated α-Syn pathology, whereas SAT1 overexpression reduced α-Syn levels and conferred strong neuroprotection. The present study extends these findings by defining the molecular mechanisms underlying SAT1-mediated protection. Through transcriptomic and biochemical analyses, we identify SAT1-dependent regulation of pathways involved in protein folding, ubiquitination, mitochondrial quality control, and redox homeostasis under α-Syn stress. Together, these results position SAT1 and polyamine metabolism as central regulators of neuronal stress responses relevant to PD.
Materials and Methods
Fly lines and genetic crosses
Drosophila melanogaster stocks were maintained on standard cornmeal medium at 25°C and 40% humidity under a 12 hr light/12 hr dark cycle. The food consisted of 2% agar, 10% sucrose, 10% yeast, and standard preservatives. Pan-neuronal expression was achieved using the elav-Gal4 driver (BDSC 458), crossed to UAS-SAT1 (Ranxhi et al. 2025) and UAS-α-synuclein (wild-type human; BDSC 51374). UAS-Luciferase (BDSC 35788) or the y, w; +; attP2 landing-site line (gift from Jamie Roebuck, Duke University) were used as genetic controls. Generation and validation of the UAS-SAT1 (CG4210) overexpression line have been described previously (Ranxhi et al. 2025).
Western blotting
Fly heads (7 males + 7 females) were homogenized in hot lysis buffer (50 mM Tris, pH 6.8, 2% SDS, 10% glycerol, 100 mM DTT), sonicated, boiled for 10 min, and centrifuged at maximum speed for 10 min. Protein lysates from at least three biological replicates were resolved by SDS-PAGE using 4–20% Mini-PROTEAN TGX gels (Bio-Rad) and transferred to 0.2 μm PVDF membranes. Membranes were blocked in 5% milk in TBST and incubated overnight at 4°C with primary antibodies, followed by HRP-conjugated mouse or rabbit secondary antibodies (1:5000, Jackson ImmunoResearch) for 1 hr at room temperature. Signals were detected using EcoBright Pico or Femto HRP substrates (Innovative Solutions) and imaged on a ChemiDoc system (Bio-Rad). Membranes were stained with 0.1% Direct Blue 71 for total protein, and α-Syn signal intensity was quantified relative to total lane protein using ImageLab software (Bio-Rad). For mitochondrial fractions, Porin was used as the mitochondrial loading control. Antibodies used were mouse anti-α-Syn (1:1000, ThermoFisher MA1–90346), anti-HA (1:1000, Cell Signaling Technology 3724), anti-LC3 (1:1000, Abcam ab109364), anti-K63-linked ubiquitin (1:500, ThermoFisher 14-6077-82), and anti-Porin (1:1000, MilliporeSigma 185–197).
Adult brain dissection and immunohistochemistry
Adult male flies were briefly anesthetized with CO_2_, and brains were dissected in ice-cold PBS (pH 7.2). Following removal of the head capsule, brains were fixed in 4% paraformaldehyde for 60 min at room temperature with gentle rotation, washed in PBST, blocked for 1 hr in 2% BSA in PBST, and incubated overnight at 4°C with primary antibodies. Primary antibodies used were mouse anti-α-Syn (1:1000, Santa Cruz sc-12767), rabbit anti-HA (1:1000, Cell Signaling Technology 3724), mouse anti-ATP5A (1:4000, Abcam ab14748), and rabbit anti-LC3 (1:1000, Abcam ab109364). After washing, tissues were incubated with Alexa Fluor-conjugated secondary antibodies (goat anti-mouse Alexa 488 and goat anti-rabbit Alexa 568, both at 1:2000, ThermoFisher Scientific) for 2 hr at room temperature. Samples were washed, mounted in ProLong Glass antifade medium (ThermoFisher P36962), and imaged by confocal microscopy at 63× or 100× magnification.
Longevity assay
Approximately 20 adult flies, matched by age and separated by sex within 48 hours of eclosion as adults from their pupal cases, were collected per vial and maintained on standard cornmeal fly medium at 25°C. Flies were transferred to fresh food vials every 2 to 3 days, and mortality was monitored daily until all flies had died. Total fly numbers are indicated in each figure. Survival data were analyzed using the log-rank test in GraphPad Prism (San Diego, CA, USA).
RNA Extraction, Sequencing, and Functional Enrichment
Total RNA was extracted from 70 adult fly heads (35 males + 35 females; n = 5 biological replicates per genotype) using TRIzol reagent (Thermo Fisher Scientific). mRNA-seq libraries were prepared using the Illumina TruSeq Stranded mRNA Library Prep Kit, incorporating poly(A) selection and strand-specific reverse transcription (AZENTA). Libraries were sequenced on an Illumina NovaSeq platform, generating approximately 20 million 150-bp paired-end reads per sample. RNA-seq data analysis was performed in R (v4.3.1). Raw sequencing reads were processed using fastp (v0.23.1) for adapter trimming and quality control, aligned to the Drosophila melanogaster reference genome (BDGP6, FlyBase release FB2023_05) using STAR (v2.5.2b), and quantified at the gene level with featureCounts (Subread v1.5.2). Gene-level count files retaining FlyBase gene identifiers (FBgn) were merged into a single count matrix, and missing values introduced during merging were replaced with zeros. Genes with low expression (total counts ≤ 10 across all samples) were excluded. Raw integer counts were used for differential expression analysis, whereas log_2_-transformed counts (log_2_[count + 1]) were used for visualization and exploratory analyses. Human gene symbols were appended by merging FBgn identifiers with a curated FlyBase-to-human ortholog mapping table. Differential expression analysis was conducted using DESeq2. Sample group identities were inferred from sample names, and three biologically defined pairwise comparisons were performed: SAT1 vs Ctrl, α-Syn vs Ctrl, and α-Syn + SAT1 vs α-Syn. For each comparison, samples were subset to the relevant groups and the reference condition was explicitly set. Differential expression was assessed using DESeq2’s negative binomial generalized linear model with Wald tests and internal size-factor normalization. Genes with Benjamini-Hochberg adjusted p-values < 0.05 and absolute log_2_ fold change > 1 were considered significantly differentially expressed. Global transcriptomic relationships were examined using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) based on DESeq2 variance-stabilized expression values. Heatmaps were generated using pheatmap with gene-wise scaling and hierarchical clustering. Functional enrichment analyses of up- and downregulated gene sets were performed using the PANGEA platform for Gene Ontology and KEGG pathways. To identify genes whose α-Syn-associated expression changes were reversed by SAT1 co-expression, results from the α-Syn vs Ctrl and α-Syn + SAT1 vs α-Syn comparisons were integrated. Reversal genes were defined as those showing opposite directions of regulation between the two comparisons.
Mitochondria Isolation
Mitochondria were isolated from heads of 50 adult male Drosophila using the Mitochondria Isolation Kit for Tissue (Thermo Fisher Scientific, #89801), following the manufacturer’s protocol. Briefly, heads were homogenized on ice in isolation buffer and subjected to sequential centrifugation to fractionate cellular components. Samples were first centrifuged at 700 × g to remove insoluble debris (pellet), and the resulting supernatant was centrifuged at 12,000 × g at 4°C to separate the mitochondrial and cytosolic fractions. Mitochondrial, cytosolic, and pellet fractions were analyzed by Western blot.
Statistics
Statistical analyses for longevity assays and Western blots are described in the corresponding figure legends. For Western blot analyses, protein levels were normalized to total protein (Direct Blue 71 staining) and compared to the appropriate control groups. For mitochondrial fractions, protein signals were normalized to Porin. Data visualization and statistical analyses were performed using Prism 9 (GraphPad).
Results
SAT1 expression rescues α-Syn-induced toxicity
To assess how SAT1 modulates α-Syn-driven neurodegeneration, SAT1 and α-Syn were expressed pan-neuronally in Drosophila using the elav-Gal4 driver. Immunoblot analysis revealed that co-expression of SAT1 significantly reduced α-Syn protein abundance compared with α-Syn expression alone (Fig. 1b), suggesting that SAT1 influences α-Syn levels in vivo. While SAT1 overexpression alone caused a small reduction in lifespan, it significantly improved survival rates in α-Syn-expressing flies (Fig. 1c–d). Because α-Syn aggregation is a key pathological feature of PD, we next examined whether SAT1 expression impacts the subcellular distribution of α-Syn in the fly brain (Fig. 1e–f). In flies expressing α-Syn alone, α-Syn immunoreactivity in the optic lobe was largely confined to neuronal cell bodies, with pronounced perinuclear enrichment adjacent to DAPI-labeled nuclei. In contrast, co-expression of SAT1 resulted in a broader, more diffuse α-Syn signal and a reduction in perinuclear accumulations. These data indicate that SAT1 expression is associated with altered intracellular localization of α-Syn. Consistent with these effects, neuronal SAT1 expression was associated with lower α-Syn abundance and reduced α-Syn-associated neurotoxicity in vivo.
SAT1 and α-Syn drive distinct neuronal transcriptomic changes
To assess how SAT1 expression influences neuronal transcriptional responses in the presence or absence of α-Syn, we performed RNA sequencing on fly heads expressing: SAT1, α-Syn, α-Syn+SAT1 and control. A uniform analytical pipeline was applied to three biologically defined contrasts: SAT1 vs control (Fig. 2a–d), α-Syn vs control (Fig. 2e–h), and α-Syn+SAT1 vs α-Syn (Fig. 2i–l). Principal component analysis (PCA) demonstrated clear genotype-dependent separation across all comparisons (Fig. 2a, e, i), indicating robust global transcriptional differences. Differential expression analysis identified substantial numbers of significantly up- and downregulated genes in each contrast (adjusted p < 0.05, log_2_ fold change > 1) (Fig. 2b, f, j). Heatmaps of differentially expressed genes revealed coherent, contrast-specific expression patterns (Fig. 2c, g, k). To support biological interpretation and cross-species relevance, significantly regulated FlyBase gene identifiers were mapped to corresponding human orthologs for display in volcano plots, which illustrated the magnitude and directionality of transcriptional changes associated with SAT1 vs control, α-Syn vs control, and their combined expression vs α-Syn (Fig. 2d, h, i). Collectively, these analyses demonstrate genotype-dependent transcriptional differences and establish a basis for downstream comparative and pathway enrichment analyses.
SAT1 alters α-Syn-associated biological pathways
To characterize the functional categories underlying the transcriptional changes observed in Fig. 2, we performed enrichment analyses on significantly upregulated and downregulated genes from three contrasts: SAT1 vs control, α-Syn vs control, and α-Syn+SAT1 vs α-Syn (Fig. 3). GO and KEGG enrichment analyses identified distinct patterns across the three contrasts.
GO Biological Process analysis (Fig. 3a, b) showed that SAT1 expression alone was associated with enrichment of pathways related to DNA repair and detoxification, accompanied by reduced representation of energy homeostasis and cytoskeletal organization. In contrast, α-Syn expression preferentially enriched xenobiotic response and detoxification pathways, as well as cellular barrier maintenance processes, while suppressing pathways associated with mitochondrial ornithine transport and dormancy. Comparison of α-Syn + SAT1 versus α-Syn revealed selective enrichment of microtubule-based transport and cytoskeletal remodeling pathways, together with reduced representation of cellular stress response and energy homeostasis pathways.
GO Molecular Function analysis (Fig. 3c, d) highlighted differences in enzymatic and binding activities across conditions. SAT1 expression alone was associated with enrichment of redox- and detoxification-related enzymatic functions, along with increased representation of receptor-mediated signaling and ion transport, whereas glycoprotein glycosylation activity and structural cuticle components were reduced. In the α-Syn condition, enrichment again centered on redox-associated enzymatic activities but extended to carbohydrate metabolism and nutrient transport, accompanied by reduced representation of acetylation-related functions, proteolysis, and membrane transport. By comparison, α-Syn+SAT1 versus α-Syn showed enrichment of redox and metabolic enzyme activities and ligand-gated ion channel-mediated neuronal signaling, together with reduced representation of carbohydrate transport, metabolic support functions, and proteostasis-linked protein quality control.
KEGG pathway analysis (supplementary Fig. 1a, b) showed that SAT1 expression alone was associated with enrichment of immune-like stress response pathways, accompanied by reduced representation of protein glycosylation, cell-surface organization, and cytoskeletal architecture. In contrast, α-Syn expression preferentially enriched pathways related to one-carbon and amino acid metabolism, carbohydrate metabolism, and O-glycan biosynthesis. Addition of SAT1 in the α-Syn background was associated with selective reduction of ascorbate and aldarate metabolic pathways and components of the basal transcriptional factors.
SAT1 modifies the expression of genes involved in chaperone function, ubiquitination, mitochondrial biology, transcription, and transport
To further compare global transcriptional differences across the four genotypes (control, SAT1, α-Syn, and α-Syn+SAT1), we performed principal component analysis (PCA). PCA revealed clear separation of samples along the two major axes, with PC1 accounting for 51% of the variance and PC2 accounting for 20% (Fig. 4a). Control and SAT1-alone samples clustered closely together, indicating minimal baseline transcriptional perturbation associated with SAT1 overexpression in the absence of α-Syn. In contrast, α-Syn expression produced a pronounced shift in the transcriptome along a distinct direction. Notably, co-expression of SAT1 with α-Syn formed a separate cluster from α-Syn alone, indicating that SAT1 modifies the transcriptional response to α-Syn expression rather than simply restoring it toward baseline. Nonlinear dimensionality reduction using UMAP and t-SNE yielded similar separation patterns and confirmed high consistency among biological replicates (Fig. 4b).
We next focused on genes significantly altered by α-Syn expression and examined how their mRNA levels changed across the four genotypes (control, SAT1, α-Syn, and α-Syn+SAT1) to assess the impact of SAT1 co-expression (Fig. 4c–p). SAT1 overexpression alone had minimal effects on most genes examined, including the stress-inducible chaperone HSP70Bb (Fig. 4c) (Singh et al. 2025), the constitutive chaperone HSC70–2 (Fig. 4d) (Pemberton et al. 2011), and the ubiquitin hydrolase Uch-L5R (Fig. 4e) (Zhang et al. 2024). In contrast, α-Syn expression was associated with robust upregulation of these transcripts. Co-expression of SAT1 attenuated this induction, shifting mRNA levels toward those observed in control samples. A similar pattern was observed for ABCA3, an ATP-dependent lipid transporter (Piehler et al. 2012) elevated by α-Syn and reduced upon SAT1 co-expression (Fig. 4f). In contrast, expression of phr, a DNA photolyase involved in genomic maintenance (Ozturk 2017), was increased by both SAT1 and α-Syn, with additive effects observed upon co-expression (Fig. 4g).
Several genes associated with mitochondrial quality control also showed pronounced regulation in response to α-Syn expression, with their mRNA levels modified by SAT1 co-expression. These included CHDH, a mitochondrial enzyme (Li et al. 2023) involved in osmoprotection and metabolism (Fig. 4h); USP30 (Siwach et al. 2025), a deubiquitinase that negatively regulates mitophagy (Fig. 4i); TCAIM, a regulator of mitochondrial stress responses (Fig. 4j) (Jiahui et al. 2025); RNF185, an E3 ubiquitin ligase implicated in mitochondrial surveillance (Fig. 4k) (Tang et al. 2011); and SLC25A15, a mitochondrial ornithine carrier linked to urea cycle and amino acid metabolism (Fig. 4l) (Ersoy Tunali et al. 2014).
Together, these expression changes indicate that SAT1 co-expression is associated with altered regulation of mitochondrial-related pathways engaged by α-Syn expression.
In addition, SAT1 co-expression affected several transcriptional regulators, including TAF5L (Fig. 4m), a component of the SAGA complex (Helmlinger and Tora 2017); CTDSPL (Fig. 4n), a CTD phosphatase that negatively regulates RNA polymerase II transcriptional activity (Krasnov et al. 2019); and INTS12 (Fig. 4o), a subunit of the Integrator complex involved in transcriptional pausing and RNA processing (Chen et al. 2013). Finally, expression of SLC2A8 (Fig. 4p), a facilitative glucose and fructose transporter (Schmidt et al. 2008) strongly induced by α-Syn, was reduced upon SAT1 co-expression.
To further assess whether SAT1 counter-regulates α-Syn-associated transcriptional changes, we performed a reversal analysis integrating results from the α-Syn vs control and α-Syn+SAT1 vs α-Syn comparisons (supplementary Fig. 2). For each gene, log_2_ fold changes from the α-Syn vs control comparison were plotted on the x-axis and log_2_ fold changes from the α-Syn+SAT1 vs α-Syn comparison were plotted on the y-axis. Genes downregulated by α-Syn and upregulated upon SAT1 co-expression localized to the upper-left quadrant, whereas genes upregulated by α-Syn and suppressed by SAT1 localized to the lower-right quadrant. These reciprocal patterns indicate that SAT1 selectively counter-regulates specific α-Syn-associated transcriptional changes rather than globally restoring gene expression toward control levels.
SAT1 enhances mitochondrial LC3 association and reduces mitochondrial α-Syn accumulation
Our RNA-seq analyses indicated that genes associated with mitochondrial stress responses and quality-control pathways are prominently regulated in the context of α-Syn expression and are modified by SAT1 co-expression, suggesting mitochondria as a potential point of convergence between α-Syn toxicity and SAT1-associated protection. To directly examine mitochondrial alterations in vivo, we analyzed mitochondrial organization and autophagy-related features in fly brains expressing α-Syn alone or α-Syn together with SAT1.
Using the mitochondrial marker ATP5A and the autophagy marker LC3, α-Syn-expressing neurons exhibited reduced ATP5A signal and relatively sparse LC3 puncta (Fig. 5a). In contrast, co-expression of SAT1 was associated with increased ATP5A intensity and more abundant LC3 puncta throughout the neuropil. Merged images showed increased spatial overlap between ATP5A and LC3 in α-Syn+SAT1 brains, consistent with enhanced association of autophagy-related markers with mitochondrial compartments.
We next isolated mitochondrial fractions to further examine the subcellular distribution of SAT1, α-Syn, and autophagy-related markers (Fig. 5b). Fractionation separated cytosolic, pellet, and mitochondrial-enriched fractions. SAT1 protein was detected predominantly in the cytosolic and pellet fractions, and was not enriched in the mitochondrial fraction. In contrast, α-Syn was detected across all three fractions, and SAT1 co-expression was associated with reduced α-Syn abundance in each fraction, similar to our observations of SAT1-depedent effects on total α-Syn protein levels. Focusing on the mitochondrial fraction, α-Syn levels were lower in SAT1 + α-Syn samples compared with α-Syn alone (Fig. 5c), indicating reduced mitochondrial-associated α-Syn when SAT1 is present.
Analysis of autophagy markers in mitochondrial fractions revealed that α-Syn expression was associated with reduced total LC3 abundance, whereas SAT1 co-expression increased LC3 levels (Fig. 5c). After normalization to porin, which reflects mitochondrial content, LC3-I levels were not appreciably altered across groups (Fig. 5d). In contrast, LC3-II levels were reduced in α-Syn-expressing samples and showed partial recovery upon SAT1 co-expression (Fig. 5e). Consistent with this, the LC3-II/LC3-I ratio, which declined with α-Syn expression, was increased by SAT1 co-expression (Fig. 5f). Levels of K63-linked ubiquitin, a marker associated with selective autophagy and mitochondrial tagging, did not differ significantly across conditions (Fig. 5g).
Together, these data indicate that SAT1 co-expression is associated with increased levels of membrane-associated LC3 (LC3-II) in mitochondrial fractions and reduced mitochondrial accumulation of α-Syn, consistent with altered engagement of mitochondrial-associated quality-control processes during α-Syn-associated stress.
Discussion
Polyamine metabolism has emerged as an important determinant of cellular stress responses, with spermidine/spermine N^1^-acetyltransferase 1 (SAT1) occupying a central position in polyamine interconversion and turnover (Pegg 2008). In this study, we identify SAT1 as a robust modifier of α-Syn-related neurotoxicity and outline molecular and cellular features associated with SAT1-mediated protection in vivo. Neuronal SAT1 overexpression mitigated organismal consequences of α-Syn expression, including improved survival, and was associated with reduced α-Syn protein levels and altered α-Syn distribution within the nervous system, consistent with observations from mammalian model systems (Lewandowski et al. 2010; Zahedi et al. 2020).
Transcriptomic analysis suggests that SAT1 co-expression modulates neuronal responses to α-Syn stress, rather than simply restoring gene expression to baseline. SAT1 reduced activation of stress-responsive and energy-consuming pathways, while enhancing expression of genes involved in cytoskeletal organization and neuronal signaling. This pattern is consistent with a compensatory shift in which persistent stress is suppressed and cellular resources are redirected to support structural maintenance and synaptic function. This outcome aligns with the known roles of polyamines in translation, nucleic acid binding, and metabolic regulation (Pegg 2006).
Multiple lines of evidence point to mitochondria as a relevant downstream intersection between α-Syn toxicity and SAT1-associated protection. A subset of α-Syn-responsive genes modified by SAT1 encode proteins involved in mitochondrial metabolism and quality control, including CHDH, USP30, TCAIM, RNF185, and the mitochondrial ornithine carrier SLC25A15. While transcriptional changes alone do not establish causality, the coordinated regulation of these genes suggests that SAT1 influences neuronal responses to mitochondrial stress induced by α-Syn. Consistent with this interpretation, imaging and biochemical analyses revealed reduced mitochondrial marker signal and limited LC3 association in α-Syn-expressing brains, whereas SAT1 co-expression was associated with increased ATP5A signal, increased LC3 puncta, and greater spatial overlap between mitochondrial and autophagy markers. These observations support altered engagement of mitochondrial quality-control pathways under conditions in which SAT1 attenuates α-Syn toxicity.
Functional enrichment analyses further suggest that α-Syn expression disrupts metabolic pathways linking mitochondrial amino acid handling to cytosolic polyamine homeostasis, including reduced expression of genes involved in mitochondrial ornithine transport. Mitochondrial ornithine transport coordinates amino acid metabolism, nitrogen flux, and polyamine biosynthesis (Fiermonte et al. 2003; Monne et al. 2015); its suppression is therefore expected to uncouple mitochondrial metabolism from cellular control of polyamine balance, increasing vulnerability to metabolic and proteotoxic stress. Notably, SAT1 co-expression was associated with restoration of SLC25A15 expression in the α-Syn background, indicating that SAT1 can influence this component of mitochondrial amino acid transport under stress conditions.
Moreover, SAT1 can modulate polyamine pools directly through acetylation of spermidine and spermine (Pegg 2009), limiting accumulation of higher-order polyamines and facilitating their export or turnover (Casero and Marton 2007; Pegg 2008). Together, these effects suggest that SAT1 may act at multiple levels to stabilize polyamine homeostasis during α-Syn-induced stress, both by influencing mitochondrial amino acid transport and by adjusting intracellular polyamine composition. Although our data do not establish whether regulation of SLC25A15 is direct or secondary to broader metabolic remodeling, the coordinated transcriptional and mitochondrial-associated changes observed here support an association between SAT1 activity and improved integration of mitochondrial metabolism with polyamine homeostasis during α-Syn expression.
In conclusion, these findings place polyamine interconversion at the intersection of mitochondrial amino acid transport, cellular stress regulation, and neuronal resilience under α-Syn-induced stress. While our data define robust associations among SAT1 expression, reduced α-Syn-associated toxicity, transcriptional modulation, and mitochondrial and autophagy-linked signatures, additional studies will be required to establish causal relationships. Nonetheless, this work identifies SAT1 as a functionally relevant regulator of neuronal stress adaptation and highlights polyamine metabolism as a biologically grounded axis influencing vulnerability in synucleinopathy-related neurodegeneration.
Supplementary Material
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The reference list from the paper itself. Each links out to its DOI / PubMed record.
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