Hyperactive 20S proteasome enhances proteostasis and ERAD in C. elegans via degradation of intrinsically disordered proteins

David Salcedo-Tacuma; Nadeem Asad; Md Qamrul Islam; Raymond Anderson; David M Smith

doi:10.1126/sciadv.adx3014

. 2025 Oct 10;11(41):eadx3014. doi: 10.1126/sciadv.adx3014

Hyperactive 20S proteasome enhances proteostasis and ERAD in C. elegans via degradation of intrinsically disordered proteins

David Salcedo-Tacuma ¹, Nadeem Asad ¹, Md Qamrul Islam ¹, Raymond Anderson ¹, David M Smith ^1,^2,^*

PMCID: PMC12513426 PMID: 41071871

Abstract

Age-related proteinopathies, including Alzheimer’s and Parkinson’s disease, are driven by toxic accumulation of misfolded and intrinsically disordered proteins (IDPs) that overwhelm cellular proteostasis. The proteasome clears these proteins, but its failure in disease remains unclear. We engineered a Caenorhabditis elegans model with a hyperactive 20S proteasome (α3ΔN) for selective 20S activation. α3ΔN markedly enhanced IDP and misfolded protein degradation, reduced oxidative damage, and improved endoplasmic reticulum–associated degradation (ERAD). Aggregation-prone substrates such as vitellogenins and human alpha-1 antitrypsin (ATZ) were efficiently cleared. Integrated proteomic and transcriptomic analyses reveal systemic adaptations featuring increased protein turnover and oxidative stress resistance independent of superoxide dismutases (SODs). Notably, α3ΔN extended life span and stress resistance independently of canonical unfolded protein response (UPR) signaling via xbp-1. These findings substantiate a “20S pathway” of proteostasis that directly alleviates protein aggregation and oxidative stress, offering a promising therapeutic angle for neurodegenerative diseases.

A hyperactive proteasome clears disordered proteins, boosting stress resilience and lifespan independent of canonical defenses.

INTRODUCTION

Age-related proteinopathies, such as Alzheimer’s and Parkinson’s disease, pose a challenge to public health due to their devastating impact on the aging population. These disorders are characterized by the accumulation of misfolded proteins (1–7), particularly intrinsically disordered proteins (IDPs) which lack stable three-dimensional structures and are prone to aggregation (1–7). While IDPs play key roles in cellular regulation and signaling, their structural flexibility makes them particularly vulnerable to misfolding, driving disease progression through the accumulation of toxic protein species that lead to neuronal dysfunction.

The cellular proteostasis network (PN), a complex and interconnected system, maintains protein homeostasis through the coordination of protein synthesis, folding, and degradation (1, 8–10). Molecular chaperones facilitate the folding of newly synthesized proteins, refold misfolded proteins, and prevent protein aggregation (8, 10–12). These chaperones work in concert with degradation pathways, such as autophagy, endoplasmic reticulum (ER)–associated degradation (ERAD), and the ubiquitin-proteasome system (UPS), to remove damaged or misfolded proteins and prevent the accumulation of proteotoxic species (8, 10–12). The PN also integrates stress response pathways, including the oxidative stress response, heat shock response, and unfolded protein response (UPR), which comprise stress indicators allowing the network to adapt to changing cellular conditions (13, 14). Disruptions to this finely balanced system can have far-reaching consequences, contributing to the development and progression of various diseases, particularly those associated with aging (10, 11, 15).

The UPS is central to proteostasis, targeting ubiquitinated proteins for breakdown by the 26S proteasome, which consists of a 20S core particle and 19S regulatory subunits. However, the 20S proteasome can function independently of ubiquitin and the 19S regulator, offering a specialized pathway for degrading IDPs and oxidatively damaged proteins. Notably, certain pathological oligomers, such as those derived from amyloid-β, α-synuclein, and Huntingtin, have been shown to inhibit proteasome activity by allosterically inhibiting the opening of the 20S proteasome gate (16, 17). The open 20S proteasome is highly capable of degrading IDPs since unfolded proteins can diffuse into the opened 20S. In contrast, folded proteins typically require ubiquitination and targeting to the 26S proteasome (19S to 20S), which can unfold these substrates to inject them into the 20S core (18, 19).

Consequently, the ability of the 20S proteasome to independently degrade misfolded proteins is particularly relevant in aging and neurodegenerative conditions, where proteasome pathways are often disrupted. Enhancing proteasome activity emerges as a promising strategy for combating age-related proteinopathies. For example, specific HbYX motif peptides have been shown to facilitate and enhance the 20S proteasome’s ability to degrade IDPs and reverse the inhibitory effects of toxic oligomers in vitro (17, 20, 21). In addition, pharmacological up-regulation of the 26S proteasome has been shown to improve the clearance of misfolded proteins (22). Similarly, genetic overexpression of 20S proteasome subunits in several animal models has proven beneficial, leading to enhanced aggregation-prone/misfolded protein degradation, extended life span, and increased resistance to stressors (23–29). Although overexpression-based models have demonstrated benefits, they fail to address whether intrinsic changes to 20S proteasome activity can unlock distinct degradation mechanisms and substrate preferences, potentially uncovering previously unknown aspects of proteasome function and substrate targeting.

Given the ubiquitous presence of the UPS across diverse tissues and its involvement in numerous cellular processes, elucidating the specific roles of the independent 20S proteasome is inherently challenging, since current proteasome inhibitors target the 20S and thus affect all proteasome forms including the 26S. Thus, to determine the therapeutic potential of the 20S proteasome, we selectively hyperactivated it. This targeted approach represents the most effective, perhaps only, strategy to explore 20S function in biology.

In this study, we use the nematode Caenorhabditis elegans, a powerful model organism due to its short life span, genetic flexibility, and conserved proteostasis pathways. A hyperactive 20S proteasome model was developed by using CRISPR-Cas9 to induce a truncation of the N-terminal domain of the α3 subunit (pas-3) of the 20S proteasome (α3ΔN-20S). This N-terminal truncation selectively generates a constitutively open-gate form of the 20S (28). Throughout this manuscript, we refer to this open gate mutant strain pas-3(dsm100) as α3ΔN. This mutation is considered hyperactivating since it induces a continuously activated form of the 20S that is physiologically distinct from the normal activated state of the 20S. This α3ΔN C. elegans strain differs from all other prior “proteasome enhancement” animal models available to date, which are based on proteasomal subunit overexpression methods (e.g., increased proteasome amount) or pharmacological approaches that alter signaling pathways affecting 26S regulation.

While the basic phenotypic characterization of the α3ΔN mutant demonstrated notable benefits, such as extended life span, heat shock resistance, and a specific 250-fold activation of the 20S proteasome activity relative to 26S activity (28), the underlying mechanisms linking 20S activation to these phenotypic effects and their potential therapeutic implications were not addressed. In addition, this constitutively active proteasome is expected to affect the typical degradation rates of some proteins, especially nascent proteins and IDPs, although this has not been determined. As most pathological proteins that accumulate in neurodegenerative diseases are IDPs (30–35), the disorder-dependent “20S pathway” for degradation becomes highly relevant in the context of the α3ΔN mutant’s hyperactivation.

Using combined proteomic and transcriptomic analyses, we examine how this hyperactivation of the 20S proteasome reshapes the proteome, improves protein quality control, and increases resistance to both oxidative stress and ER stress. The ERAD pathway, which clears misfolded proteins from the ER, is vital for proteostasis, and its failure is implicated in diseases like alpha-1 antitrypsin deficiency. We demonstrate that proteasome hyperactivation in C. elegans leads to the selective degradation of IDPs, which is associated with increased protein synthesis. Our hyperactive strain showed decreased oxidative damage and was even resistant to oxidative stresses in the absence of the superoxide dismutase (SOD) system the cell’s primary defense against oxidative damage. In addition, while the 26S is a known endpoint mediator of ERAD, we found that 20S proteasome hyperactivation substantially enhanced ERAD, as evidenced by the efficient clearance of aggregation-prone ER-resident proteins such as VIT-2 and ATZ. VIT-2 is an apolipoprotein B (ApoB) homolog and known endogenous ERAD substrate, while ATZ is also an ERAD substrate in humans that accumulates in the liver in alpha-1 antitrypsin deficiency (36, 37).

Our key mechanistic finding reveals that the 20S proteasome hyperactivation has a profound capacity to enhance oxidative stress resistance and ERAD function leading to increased life span independent of the UPR. Together, this study demonstrates that activation of IDP degradation in C. elegans by 20S hyperactivation is an independent mechanism for direct proteostasis management and longevity enhancement. The targeted degradation of IDPs that form aggregates highlights the potential of proteasome hyperactivation to alleviate the accumulation of toxic protein aggregates in the context of proteinopathies and age-related diseases, of which there are many. Our results reveal the 20S proteasome degradation pathways as a regulator of proteostasis and underscore its potential as a therapeutic target for diseases driven by protein misfolding and aggregation. By selectively boosting 20S activity, it may be possible to eliminate harmful proteins while preserving overall protein balance, offering a precise strategy for tackling proteinopathies and laying the groundwork for new ways to combat cellular stress and neurodegenerative diseases linked to aging.

RESULTS

Proteome reconfiguration and adaptive protein turnover following 20S proteasome hyperactivation

We assessed the overall proteomic changes in the α3ΔN mutant using tandem mass tag–mass spectrometry (TMT-MS). We focused on 8-day-old adult worms (n = 5), a postreproductive stage that allow us to compare against wild type (WT) without the confounding effects from decreased egg numbers in the α3ΔN mutant compared to WT. In addition, 5-fluorodeoxyuridine (FUdR) was used in both populations to normalize any physiological effects due to differences in fecundity, as was done in (28). Furthermore, this stage marks the onset of age-related phenotypes and detectable age-related proteomic and transcriptomic signatures in C. elegans (11, 38, 39). We identified 4267 proteins (Q value < 0.01), and unsupervised hierarchical clustering combined with principal components analysis (PCA) revealed distinct proteomic profiles between the α3ΔN mutant and WT (Fig. 1A). Using differential expression analysis of quantitative mass spectrometry data DEqMS (40) [false discovery rate (FDR) < 0.05, |log₂ fold change (FC)| > 0.59], which models peptide level variance to improve protein level significance estimates, we detected 459 differentially expressed proteins (DEPs), representing ~10% of the total proteome (table S1). Among these, 252 were down-regulated and 207 up-regulated in the α3ΔN mutant (Fig. 1B and fig. S1A).

Fig. 1. — (A) Dimensionality reduction via three-dimensional PCA was performed on TMT-MS data to depict profile differences between n = 5 WT and α3ΔN mutants. (B) Volcano plot of DEPs detected log₂FC α3ΔN/WT versus −log₁₀FDR. All points above the dotted line are statistically significant (FDR < 0.05). Blue points above the x axis dotted line represent DEPs down-regulated (FC < −1.5), and orange dots represent DEPs up-regulated (FC > 1.5). (C) GSEA of DEPs found in α3ΔN mutants, and x axis represents the normalized enriched score for each of the categories according to ClusterProfiler in R; FDR < 0.05. NADH, reduced form of nicotinamide adenine dinucleotide. (D) Rates of protein translation in live *C. elegans* determined by 6-FAM-puromycin, lysing and separating free 6-FAM-puro from labeled nascent proteins with desalting columns, followed by fluorescent quantification in a plate reader. Bars represent mean RFU ± SEM of n = 7, ***P < 0.01, ordinary one-way analysis of variance (ANOVA). RFU, relative fluorescence units.

Next, to elucidate the biological significance of the proteome profile changes, we performed gene set enrichment analysis (GSEA) (41) which tests whether predefined functional gene sets show coordinated expression shifts. The analysis identified two primary functional groups affected by proteasome hyperactivation: protein quality control and lipid metabolism processes (Fig. 1C and fig. S1B). The coordinated down-regulation of stress response and lipid transport pathways, most notably small heat shock proteins and vitellogenins, likely reflects a genuine easing of proteostatic burden, and once the hyperactive proteasome clears misfolded and aggregation-prone substrates more efficiently, cells need to deploy fewer chaperones and accumulate fewer yolk proteins which we analyzed below. On the other hand, among the up-regulated processes, we detected a notable enrichment related to folding and translational activity, including ribosome biogenesis, RNA binding proteins (RBPs), and RNA processing. In normal physiological conditions, the proteasome mediates the degradation of DNA binding proteins and RBPs, which are enriched in intrinsic disorder regions. This is essential for maintaining appropriate protein levels for vital cellular functions such as gene expression, DNA repair, and RNA processing (42–44).

The up-regulation of RBPs, DNA binding proteins and RBPs in the α3ΔN mutants suggests a selective degradation pressure on these substrates, which the organism may compensate for by up-regulating these critical proteins to high levels to preserve these vital functions. This scenario aligns with the hypothesis that enhanced degradation of IDPs, facilitated by the α3ΔN-20S, leads to a rapid turnover of certain proteins and potentially drives a higher rate of protein synthesis. To test this hypothesis, we labeled newly synthesized proteins in live worms using 6-FAM-puromycin (45). To ensure the accuracy of our measurements, we normalized the fluorescence signals to those obtained from worms treated with cycloheximide, a well-known inhibitor of protein biosynthesis. We observed a nearly twofold increase in protein synthesis in the α3ΔN mutant (Fig. 1D), validating our hypothesis. This acceleration in synthesis is likely crucial for sustaining proteome stability in the face of accelerated degradation by the α3ΔN proteasome illustrating a sophisticated mechanism by which cells adapt to ensure continued function during the increased protein turnover.

To test whether the α3ΔN mutant had enhanced protein degradation in the α3ΔN strain, we performed a cycloheximide pulse chase assay. We blocked new protein synthesis with cycloheximide and measured the decrease of total protein levels by SDS gel in the presence or absence of MG132, a well-known proteasome inhibitor which allowed us to normalize the degradation from proteasome solely. We observed that the α3ΔN mutant degraded proteins significantly more rapidly than WT (Fig. 2, A and B). The simultaneous acceleration of protein degradation and protein synthesis supports the hypothesis that enhanced protein synthesis is likely a compensatory response to balance out proteasome hyperactivation to maintain proteome stability.

Fig. 2. — (A and B) *CHX* pulse-chase assay to assess global protein degradation rates over 6 hours of CHX or CHX + MG132 treatments in α3ΔN mutants. CHX/MG132 ratios were normalized to the 0-hour time point of the respective strain. (B) Quantification of (A). Values are depicted as mean ± SEM generated from n = 3 independent experiments. Statistical significance was determined using Satterthwaite’s degrees of freedom method with ImerTest, ***P < 0.001 indicating a statistically significant difference between conditions. (C) Quantification of heat-inducible IDPs content in WT and α3ΔN mutants. IDPs from lysates were enriched by two steps of heat denaturation and precipitation of folded proteins, leaving primarily IDPs. SDS-page gels were quantified after Coomassie staining. Bars represent means ± SEM. n = 3, **P < 0.01, unpaired t test. (D and E) Proteasomal degradation of human α-synuclein. WT and α3ΔN worm lysates were incubated with purified His-tagged α-synuclein for 60 min. Residual α-syn levels were quantified by SDS-PAGE (fig. S1D) and visualized by anti-His immunoblot. (E) Values are depicted as mean ± SEM generated from n = 3 independent experiments. Statistical significance was determined using two-way ANOVA, with Tukey’s post hoc test (***P < 0.001) indicating a statistically significant difference between conditions. h, hours; IB, anti-His immunoblot.

We next investigated the impact of the hyperactive proteasome on degradation of IDPs, which can be degraded by the 20S in a ubiquitin-independent manner (30, 42, 46). To specifically assess the impact of proteasome hyperactivation on IDPs, we used a controlled heat treatment protocol, as described in prior studies (3), to selectively enrich for heat-stable proteins, e.g., pathogenic IDPs such as α-syn and tau, which are heat resistant. This approach effectively isolates a subset of thermo-resistant proteins predominantly comprising IDPs, allowing us to focus on their degradation dynamics. Using this method, our analysis (Fig. 2C) revealed a notable 50% reduction in heat-stable protein levels in the α3ΔN mutant compared to WT, demonstrating significantly accelerated degradation of this protein pool. To further support the preferential turnover of IDPs, we performed a time-course degradation assay with recombinant purified human His-α-synuclein (fig. S1C). WT and α3ΔN lysates were each incubated with His-α-syn for 0, 15, 30, and 60 min, and parallel reactions containing MG132 + epoxomicin confirmed specific proteasomal activity (fig. S1, D and E). His-α-syn degradation was further confirmed by Western blot (Fig. 2D) and quantified by Coomassie blue (Fig. 2E and fig. S1D) with normalization to the inhibitor-treated signal (fig. S1E). After 15 min, α3ΔN lysates showed a significant decrease in His-α-syn compared with WT, and by 30 min, they had degraded 95% more substrate (Fig. 2, D and E; quantification of Coomassie-stained gels). Fitting to a single-phase decay curve showed the half-life for α-syn in WT lysates was 160 min [Confidence Interval (CI) > 52] and, in α3∆N lysates, was 9 min (CI, 7.9 to 10.5 min), indicating that α3∆N lysates degraded α-syn >5.8 times faster than WT. These results confirm by three different orthogonal approaches that hyperactive proteasomes in the α3ΔN strain significantly accelerate IDP turnover and highlight how proteasome hyperactivation directly contributes to proteostasis by efficiently eliminating IDPs, which are often associated with age-related protein and cellular dysfunction.

Coordinated transcriptomic-proteomic signatures highlight broad proteostasis reprogramming in α3ΔN mutants

Given the significant alterations in protein synthesis, degradation, and translation observed in α3ΔN mutants, we hypothesized significant transcriptomic rewiring as part of a response to proteasome hyperactivation. To explore this, we conducted RNA sequencing (RNA-seq) on 8-day-old adult worms (n = 5). PCA analysis (Fig. 3A) revealed distinct transcriptomic profiles for the α3ΔN mutant and WT strains, mirroring the proteomic differences and suggesting a broad reprogramming of molecular pathways due to proteasome hyperactivity. Differential expression analysis identified 1756 significantly altered genes in α3ΔN mutants (table S2; FDR < 0.05), with 854 up-regulated and 902 down-regulated (Fig. 3B and fig. S2A). The 20S proteasomal subunit expression remained unchanged (fig. S2, B and C), confirming that enhanced proteasomal activity is attributed solely to the open-gate modification rather than altered proteasome abundance.

Fig. 3. — (A) Three Dimensional PCA of RNA-seq data to depict differences in the expression profile of WT and α3ΔN mutants (n = 5). (B) Volcano plot of differentially expressed genes (DEGs) detected log₂FC α3ΔN/WT versus −log₁₀FDR. All points above the dotted line are statistically significant (FDR < 0.05). Blue points above the x axis dotted line represent DEGs down-regulated (FC < −2), and orange dots represent DEGs up-regulated (FC > 2). (C) Integration of proteomics and RNA-seq data from α3ΔN samples using sparse projection to latent structures. Samples are color-coded and shape-coded according to specific conditions, illustrating their projection within a combined integrated space. This visualization highlights distinct clusters, demonstrating similarities and differences across conditions. (D) Barplots of loadings. The left barplot shows the loadings of RNA transcripts, and the right barplot displays the loadings of proteins in the first sparse projection to latent space variable that differentiates between the α3ΔN and WT conditions. These barplots highlight the key proteins and transcripts and their relative importance in distinguishing between these conditions. (E and F) GSEA analysis of PN components was performed on proteomic (E) and transcriptomic (F) datasets using ClusterProfiler in R. Significantly enriched components (adjusted P < 0.05) are displayed, with the x axis representing −log₁₀(P value) and the red line marking the significance threshold [−log₁₀(0.05) = 1.3]. (G and H) Enrichment analysis of the chaperone subnetwork using the hypergeometric distribution for both omic datasets with adjusted P value of <0.05 indicating significant enrichment. The x axis indicates the −log₁₀(P value), and the red line indicates a threshold of adjusted P = 0.05 [−log₁₀(0.05) =1.3].

To elucidate how changes in gene expression and protein production are interconnected in α3ΔN mutants revealing a coordinated molecular response to proteasome hyperactivity that would be overlooked by examining either dataset independently, we used data integration. We combined proteomic and transcriptomic datasets, as detailed in Materials and Methods, using canonical partial least squares (PLS) in mixOmics (47), a supervised dimension reduction approach similar to a PCA that not only finds pairs of latent components explaining maximal covariance between two or more omic datasets (unlike simple Pearson correlation) but also applies sparsity to highlight the key genes and proteins driving strain differences. The integrated analysis (Fig. 3C) revealed two distinct clusters corresponding to the α3ΔN and WT genotypes. This clear separation corroborates our previous findings, confirming that these genotypes exhibit distinct molecular profiles. Notably, the structural patterns observed in the PLS analysis are consistent with the PCA structural profiles of the individual proteomic and RNA-seq datasets (Figs. 1A and 3A), further validating these results. The data integration revealed that most genes and proteins contributed modestly to the observed separation, suggesting that the response in the α3ΔN strain is not driven by a small subset of genes or proteins (Fig. 3D). Instead, we observe a systemic shift across the entire genetic and proteomic landscape (fig. S3A). Furthermore, analysis of transcription factor binding profiles using JASPAR (see Materials and Methods) indicated the involvement of numerous transcription factors (TFs) (fig. S3B and table S3), supporting the notion of a network-wide alteration in the α3ΔN strain. Collectively, these results point to a broad, interconnected adaptation rather than isolated changes, highlighting the complex nature of the biological processes affected by the α3ΔN mutation (figs. S1B and S2D). This integrative approach allowed us to focus on system-level responses, which are crucial for a mechanistic understanding of the biological effects associated with a hyperactive proteasome.

Understanding the systemic adaptations underlying proteasome hyperactivation requires identifying the specific components driving these changes. Given the shared enrichment patterns observed across both omic datasets, we focused on identifying whether this systemic adaptation involved key elements of the PN, a critical regulator of cellular homeostasis. To do so, we used preexisting annotated families of PN components (48) and the chaperone subnetwork families from C. elegans (49) and performed enrichment analyses on both omic datasets. GSEA analysis on PN components revealed significant enrichment of UPS-related proteins and translation components across both the proteomic and trancriptomic datasets, with an additional enrichment of cytonuclear proteostasis observed specifically in the proteomic dataset (Fig. 3, E and F). This enrichment captures the broad reprogramming of proteostasis in our study, wherein increased translation (Fig. 1D) and enhanced UPS-mediated degradation (Fig. 2, A to E) together confirm the extensive compensatory response induced by 20S proteasome hyperactivation. In addition, the enrichment on the chaperone subnetwork revealed significant enrichment on small heat shock proteins (sHSP), oxidative stress defense factors, and Hsp70 family of proteins (Fig. 3, G and H; hypergeometric distribution FDR < 0.05), pathways widely recognized for their role in maintaining proteostasis, promoting longevity, and enhancing stress resilience in C. elegans. These findings highlight the selective involvement of key proteostasis components in the adaptive response, pointing to alterations in the PN due to 20S proteasome hyperactivation. Within the oxidative stress defense, SOD-2 and SOD-3 were elevated, while cytoplasmic SOD-1 and gpx-5 declined (table S1), suggesting a reduced need for cytoplasmic defenses in α3ΔN. Concurrently, several sHSPs were down-regulated (table S1), indicating that 20S gate opening diminishes reliance on conventional proteostasis machinery. We next further tested this hypothesis.

Proteasome hyperactivation enhances oxidative stress defense independently of SOD enzymes

With the observed reduction in cytosolic oxidative response factors in α3ΔN, we hypothesized that proteasome hyperactivation may bolster oxidative stress resistance. To assess whether oxidative damage is reduced in the α3ΔN strain, we analyzed posttranslational modifications (PTMs) focusing on oxidation-induced modifications in our tandem mass tag (TMT) dataset. We identified 1997 peptides exhibiting signs of oxidative modifications. Among these, 63 peptides showed a significant decrease in oxidation levels, while 22 were significantly up-regulated (Fig. 4A and table S4). The reduction in oxidative markers can be explained by increased degradation of oxidatively damaged proteins. This is consistent with the finding that the 20S proteasome is capable of degrading oxidatively damaged IDPs (42), which would be enhanced by 20S gate opening. An additional consideration is that basal oxidative stress in the α3ΔN mutants may be reduced. These results indicate a more robust and less damaged proteome, particularly in aging worms, which may confer increased resistance against oxidative stress, which we directly test below.

To confirm whether proteasome hyperactivation contributes to oxidative stress defense, we subjected α3ΔN mutants to acute oxidative stress using various compounds that induce reactive oxygen species (ROS) in the cytosol and mitochondria. Hydrogen peroxide (H₂O₂) is particularly relevant for C. elegans, as it is produced by pathogenic bacteria in their natural environment (50, 51). We also used tert-butyl hydroperoxide (tBoOH), a stable cytosolic ROS generator (52), and juglone, a mitochondrial stressor known to induce ROS through activation of the FMRFamide-like neuropeptide (FLP) neuropeptide family (53). Aldicarb, an acetylcholinesterase inhibitor used for assessing synaptic transmission (54), served as a non-ROS control stressor.

Our results revealed that α3ΔN mutants displayed markedly enhanced resistance to the oxidative stressors H₂O₂, tBoOH, and juglone (Fig. 4, B to D), while their response to the neurotoxic agent aldicarb remained unchanged (as it functions via a non-ROS mechanism) (fig. S4A), confirming that the observed resistance is specific to oxidative stress. Probit curve analysis quantified the lethal dose 50 (LD₅₀) and resistance ratios for each compound (fig. S4, B to E). The analysis indicated that a 3.43-fold higher concentration of H₂O₂ was required to affect 50% of the α3ΔN mutant population (fig. S4, B and E). Similarly, the mutants required a 4.25-fold increase in tBoOH concentration (fig. S4, C and E) and a 1.84-fold increase for juglone to reach the LD₅₀ threshold (fig. S5, D and E). These results demonstrate that α3ΔN is highly resistant to multiple forms or ROS stress, but what if we remove natural cellular ROS defenses, can the α3ΔN still protect against ROS?

SODs are well-known key effectors of oxidative stress defense, as they catalyze the dismutation of superoxide radicals into oxygen and hydrogen peroxide, thereby mitigating oxidative damage. To understand the role of SODs in the observed resistance, we crossed the α3ΔN mutants with the MQ1766 strain, which lacks all SOD genes [sod-2(ok1030) I; sod-5 (tm1146) sod-1(tm783) II; sod-4(gk101) III; sod-3(tm760); hereafter referred as sod-null] and is known to be sensitive to oxidative stressors (55). Previous studies have demonstrated that deleting even a single major sod gene, such as sod-1 or sod-2, is sufficient to markedly increase sensitivity to oxidative stress in C. elegans (55, 56), and thus the sod-null is extremely sensitive to oxidative stress. We crossed this sod-null with α3ΔN. While the mitochondrial sod-2 could not be crossed out, due to its close proximity to pas-3 (2 centiMorgans apart), all other sod genes were knocked out resulting in a quadruple sod mutant (sod-1; sod-3; sod-4; sod-5 quad-sod) in the α3ΔN background. Although the sod-null mutation diminished stress resistance overall relative to WT, the α3ΔN quad-sod mutants retained significant resistance to tBoOH and H₂O₂ stress, even into advanced age (Fig. 4E and fig. S4E). The quad-sod worms with the hyperactive proteasome showed no difference in H2O2 sensitivity at 500 μM compared to α3ΔN. The exception was juglone, which exhibited similar toxicity in sod-null worms regardless of the α3ΔN mutation, likely because juglone induces mitochondrial ROS and mitochondria lack proteasomes. Although SOD enzymes serve as primary defenses against oxidative stress by neutralizing superoxide radicals, the substantial resistance to oxidative stress observed in α3ΔN mutants, despite the absence of most SODs, demonstrates the substantial protective capacity of the 20S proteasome. In addition, the fact that the enhancement of IDP degradation is highly protective during ROS stress suggests that IDPs may be a particularly vulnerable protein species in oxidative tissues like the brain.

Our findings suggest that instead of relying on enzymatic detoxification of ROS, the hyperactive proteasome enhances the clearance of oxidatively damaged proteins, thereby reducing the accumulation of damaged macromolecules and maintaining proteome integrity. This represents a previously unidentified mechanism of oxidative damage mitigation, strengthening cellular defenses through improved protein degradation rather than traditional antioxidant pathways. The consistent resistance of α3ΔN mutants to H₂O₂ in older worms indicates that the α3ΔN mutation mitigates but, predictably, does not fully counter the organism-wide decline in proteostasis, thereby demonstrating a durable, although age-modulated, protective capacity throughout the life span of C. elegans (Fig. 4E). These findings provide mechanistic insight into how proteasome hyperactivation fortifies organisms against oxidative damage by bolstering stress response capacity, enhancing resilience to various stressors even independently of SOD enzymes.

Elevated ERAD capacity through proteasome hyperactivation mitigates vitellogenin aggregation

While our data above demonstrate that hyperactivation of the 20S proteasome could accelerate the degradation of IDPs and oxidatively damaged proteins, we did not expect the α3ΔN-20S to have impacts on ERAD since it is well known that the 26S proteasome mediates ERAD after P97/VCP extracts substrates from the ER. To determine whether ERAD was affected, we treated both WT and α3ΔN worms with tunicamycin to assess whether proteasome hyperactivation could affect ER stress response. Tunicamycin induces ER stress by inhibiting N-linked glycosylation leading to protein misfolding in the ER (57). We were surprised to find that α3ΔN mutants were significantly protected from tunicamycin compared to WT worms (Fig. 5A). These results indicate 20S hyperactivation protects against ER misfolding stress. Therefore, we anticipated that some aberrant, ER-resident aggregation-prone proteins could be selectively targeted by the hyperactive proteasome. Our proteomic analysis revealed that the entire vitellogenin family, key proteins associated with aging in C. elegans, was significantly down-regulated at the protein level (fig. S3C), while RNA-seq data showed no significant changes at the transcript level (fig. S3D). This suggests that proteasome hyperactivation promotes the degradation of vitellogenins posttranslationally, rather than affecting their transcription.

Fig. 5. — (A) Survival of WT animals and α3ΔN mutants upon tunicamycin treatment (50 μg/ml) (data from n = 3 independent experiments); P < 0.01 determined by the log-rank test. Representative images (B) and fluorescent quantification (C) of *vit-2::GFP + rol-6(su1006)* and α3ΔN;*vit-2::GFP + rol-6(su1006)* worms expressing the vitellogenin 2 protein fused to GFP VIT-S::GFP from the outcrossed RT99 strain with defective accumulation of VIT-2. Scale bar, 500 μm. Images of day 3 worms at ×4 magnification. (C) Accumulation over time. We tracked the fluorescent intensity of *vit-2::GFP* versus α3ΔN;*vit-2::GFP* worms, revealing a decrease in VIT-2::GFP accumulation in α3ΔN;*vit-2::GFP* mutants. Statistical analysis (**P < 0.01) via multiple two-tailed t tests. Data shown as mean ± SEM of three independent batches of ≥10 worms each for a total n of ≥30 worms. (D and E) VIT-2::GFP validation as proteasome substrate by quantifying GFP fluorescence after addition of proteasome inhibitor MG132 (50 μM) to the *vit-2::GFP* (D) and α3ΔN; *vit-2::GFP* (E) adult worms. Data represent mean values ± SEM (n = 6 protein lysates), *P < 0.05 by two-tailed t test with Welch correction. (F) Representative fluorescent images of postreproductive day 7 *vit-2::GFP* and α3ΔN;*vit-2::GFP* adults treated with 10 μM ML240 (cdc48 inhibitor) or control (DMSO). Scale bars, 500 μM. (G) Quantification of GFP intensity from (F). Data shown as mean ± SEM from three independent batches (≥10 worms per batch; n ≥ 30 total). Statistical significance was determined by one-way ANOVA followed by Tukey’s post hoc test (***P < 0.01). a.u., arbitrary units; ns, not significant.

In normal aging of C. elegans, dysregulation of vitellogenin expression leads to the abnormal accumulation of yolk proteins and lipoproteins, resulting in adverse effects such as senescent proteinopathy, organ degeneration, obesity, and proteotoxicity (58–61). Vitellogenins in C. elegans are homologous to human ApoB (62), which is degraded via the ERAD pathway (63); hence, we expect that the proteasome also catalyzes vitellogenin degradation. In doing so, these lipoproteins become central targets through which proteasome hyperactivation improves proteostasis by preventing their usual buildup into damaging aggregates.

We proposed that by opening the 20S proteasome gate via the α3ΔN-20S mutation could enhance degradation by the ERAD pathway. This hypothesis is mechanistically compelling because ERAD substrates are unfolded by P97/VCP to extract them from the ER and release them into the cytosol, making them potential substrates for the α3ΔN-20S proteasome. On the basis of this mechanism, we predicted that these mutants would show accelerated degradation of specific ER substrates explaining the MS results above. To validate this, we specifically monitored vitellogenin levels, by using a C. elegans strain expressing a vitellogenin–green fluorescent protein (GFP) fusion protein (vit-2::GFP (bls1)) from the RT99 strain with no rme-4 mutation (Fig. 5B). After crossing our α3ΔN mutants with our vit-2::GFP strain, we quantified GFP levels. The results showed a significant decrease in VIT-2::GFP fluorescence in the α3ΔN worms (50% reduction of VIT-2::GFP consistently across the population) (Fig. 5B), and this decline remained consistent through the aging process (Fig. 5C), corroborating the vitellogenin depletion detected in our proteomic data and supporting enhanced ERAD in the α3ΔN mutants. To confirm that the reduction in VIT-2::GFP was due to proteasome degradation, we also inhibited proteasome activity with MG132. Within 3 hours of exposure, we observed a significant increase in GFP fluorescence in WT and α3ΔN worms treated with MG132 (Fig. 4, E and F). This increase in fluorescence directly supports the hypothesis that VIT-2::GFP protein levels are regulated by proteasomal degradation, which is enhanced by proteasome hyperactivation. In addition, these data support the hypothesis that vitellogenins are key targets through which the 20S proteasome improves proteostasis in the hyperactive state.

Given that the hyperactive proteasome clears vitellogenin, we next asked whether this accelerated degradation depends on the canonical ERAD machinery. To test this, we inhibited p97/cdc48 an essential ERAD component that extracts substrates from the ER using the selective inhibitor ML240. We validated that this compound phenocopies the knockdown of two of the three C. elegans cdc-48 isoforms (fig. S5, A and B), providing a practical method to probe the pathway. Notably, exposing α3ΔN worms to ML240 triggered a significant increase in VIT-2::GFP fluorescence (Fig. 5, E and F), effectively reversing the vitellogenin depletion observed in α3ΔN. This result provides direct evidence that the α3ΔN proteasome enhances the clearance of an endogenous ER protein through a p97/cdc48-dependent mechanism.

Proteasome hyperactivation enhances the clearance of pathogenic human ATZ protein

The significant reduction of vitellogenins via enhanced ERAD in α3ΔN mutants prompted us to investigate whether proteasome hyperactivation could be effective against other misfolded ER proteins implicated in human diseases. So, in an orthogonal approach, we crossed the α3ΔN into the C. elegans strain VK2749 (vkls2749), which contains an intestinal-specific alpha-1 antitrypsin (nhx-2p::sGFP::ATZ) mutant, an ER targeted misfolded protein, along with a marker for autophagy (nhx-2p::mKate2::lgg-1). This strain models the alpha-1 antitrypsin deficiency condition marked by accumulation of ATZ in the ER while also allowing for monitoring of autophagy (64). Because of a genetic mutation of ATZ in humans, these misfolded proteins accumulate in the liver, hindering its function and elevating the risk of liver diseases (37, 65). While ATZ can be targeted by autophagy, it is directly associated with the ERAD pathway, making it heavily reliant on proteasome activity for the clearance of misfolded alpha-1 antitrypsin proteins. The α3ΔN mutants showed significantly reduced accumulation of sGFP-ATZ (Fig. 6, A and B), and this reduction persisted with aging (fig. S5C), indicating improved degradation and clearance of these aberrant proteins.

Fig. 6. — (A) Representative imaging of day 5 transgenic animals expressing ERAD substrate *nhx-2p::sGFP::ATZ* in intestinal epithelium. Scale bar, 500 μm. (B) ATZ aggregate quantification: Comparison of ATZ aggregate counts in *nhx-2p::sGFP::ATZ* (n = 35) and α3ΔN; *nhx-2p::sGFP::ATZ* (n = 42) worms on day 5. Data represent mean values ± SEM, ***P < 0.001 by two-tailed unpaired t test. The bright pharyngeal signal, indicated with an asterisk *, is a genetic marker and should not be mistaken for ATZ aggregates. (C) Representative images of *nhx-2p::sGFP::ATZ* at day 7 after treatment with the proteasome inhibitor MG132 (100 μM). Images show increased accumulation of ATZ aggregates in both *nhx-2p::sGFP::ATZ* and α3ΔN; *nhx-2p::sGFP::ATZ* worms upon proteasome inhibition. Scale bars, 500 μm. (D) Quantification of ATZ aggregates from (C). Data represent mean values ± SEM (n = ≥15 worms per group). P < 0.01 determined by one-way ANOVA followed by Tukey’s post hoc test. (E to G) Representative side-by-side fluorescent micrographs of *nhx-2p*::sGFP::ATZ animals grown on control RNAi (vector) or RNAi against *sel-11* (E), *npl-4.1 + npl-4.2* (F), or *cdc-48.1 + cdc-48.2* (G). Worms were maintained on RNAi plates until day 5 posthatching, then collected, and imaged for aggregate counting. Scale bars, 500 μm. (H to J) Quantification of sGFP::ATZ aggregates per worm in WT and α3ΔN backgrounds after RNAi of *sel-11* (H; WT n ≥ 43, α3ΔN n ≥ 35), *npl-4.1 + npl-4.2* (I; WT n ≥ 40, α3ΔN n ≥ 35), or *cdc-48.1 + cdc-48.2* (J; n = 45 each). Data are mean ± SEM from ≥3 independent cohorts. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test (***P < 0.01).

To exclude autophagy as a contributor to ATZ clearance, we monitored nhx-2p::mKate2::lgg-1 puncta in α3ΔN;nhx-2p::sGFP::ATZ worms and found no significant change in the number or size of lgg-1 positive foci (fig. S5, C and D), demonstrating that autophagosome formation is not up-regulated in this background. Therefore, it is unlikely that autophagy is responsible for the enhanced ATZ degradation in the α3∆N animals. In contrast, to determine that clearance of ATZ aggregates was due to proteasome activity, we used the proteasome inhibitor MG132 on both the ATZ-expressing worms and those crossed with the α3ΔN mutant. We observed a significant increase in the number of ATZ aggregates in both strains after proteasome inhibition (Fig. 6, C and D). This demonstrates that the efficient degradation of ATZ aggregates is reliant on proteasome function in the α3ΔN strain.

To mechanistically assess the dependency of ERAD on ATZ clearance in the α3ΔN background, we inactivated key ERAD components both genetically and pharmacologically. First, we used RNA interference (RNAi) to knock down sel-11 (the Sel1L ortholog essential for Hrd1 E3 ligase activity), npl-4 (a cofactor of cdc48), and the cdc48 adenosine triphosphatase (ATPase; cdc48.1 + cdc48.2). In parallel, we treated worms with ML240 to inhibit cdc48 function directly. Silencing of sel-11, npl-4, or cdc48 induced a significant increase in ATZ aggregate formation in α3ΔN animals (Fig. 6, E to J). Similarly, ML240 treatment significantly elevated ATZ aggregation (fig. S5, A and B). Together, these data demonstrate that efficient clearance of ATZ in the hyperactive α3ΔN proteasome background requires an intact ERAD pathway including the ERAD channel, the extraction machinery cdc-48, and at least one of its cofactor. Therefore, the enhanced clearance of ATZ in α3∆N is due to 20S hyperactivation–mediated ERAD. These results establish 20S proteasome activation as a promising therapeutic strategy for ATZ and related disorders characterized by compromised protein homeostasis.

Proteasome hyperactivation increases ER stress resistance and life span independently of the UPR

Because enhanced ERAD in α3ΔN appears to play a role in the extensive down-regulation of vit-2 in the ER (Fig. 5, D to G), which could also affect other ER-resident proteins, we sought to further determine whether 20S hyperactivation may affect the cytosolic UPR, ER’s UPR, or the mitochondrial UPR (mtUPR). To do so, we crossed the α3ΔN strain with three chaperone reporter strains. The strain CL2070, which expresses GFP (dvIs70), under the control of a small heat shock protein promotor (hsp-16.2p::gfp) widely used as a biomarker for cytosolic heat stress (66). We selected this reporter based on our enrichment analysis indicating changes in sHSP. The strain SJ4005, which expresses hsp-4::GFP (zcIs4), serves as a marker for UPR activation, as hsp-4 is the homolog to the ER chaperone Binding immunoglobulin Protein (BiP) (67). In addition, GL347, which expresses GFP [zcIs13; along with lin-15(+)] under the control of a hsp-6 promotor (hsp-6p::gfp), acts as a reporter of mtUPR induction (68). These strains were crossed with α3ΔN, and basal UPR responses were quantified. These results revealed no significant changes in mtUPR activation in α3ΔN mutants compared to WT worms through the life span, as measured by hsp-6p::GFP fluorescence (fig. S6, A and B). This demonstrates that mitochondrial stress is not affected by proteasome hyperactivation and thus should not contribute to the observed phenotypes (e.g., ER stress resistance or longevity). Notably, under normal conditions, we observed no induction of hsp-16.2, mirroring our findings for the mtUPR reporter (fig. S6, C and D). Together with the lack of hsp-16.2 induction, these results suggest that under normal conditions, neither cytosolic heat shock nor mitochondrial stress pathways are activated, emphasizing that the selective enhancement of ERAD is the primary mechanism by which 20S hyperactivation targets misfolded and aggregation-prone proteins. However, α3ΔN mutants crossed with hsp-4::GFP, the UPR reporter, exhibited increased fluorescence under normal conditions, suggesting elevated expression of hsp-4 and potential ER UPR activation.

Moreover, the reporter activity started very early in the life of our α3ΔN mutants, from the larval stage L1, and this increased hsp-4::GFP induction persisted into late life stages (Fig. 7, A and B). This sustained increase in hsp-4 levels in the α3ΔN mutants could contribute to α3ΔN’s tunicamycin resistance phenotype and increased life span (Fig. 5A). Normally, hsp-4 is induced by the UPR-ER pathway and is dependent on xbp-1, which acts as an important regulator of stress resistance and longevity in C. elegans (69). To assess whether the induction of UPR is necessary for the increase tunicamycin resistance and life-span extension in α3ΔN mutants, we introduced an xbp-1 loss of function xbp-1αΔαΔ (zc12; hereafter xbp-1 null) mutation into the hsp-4::GFP and the α3ΔN;hsp-4::GFP backgrounds. This mutation effectively disrupted the IRE-1/XBP-1 branch of the UPR in both of these xbp-1 crosses, as expected, since tunicamycin-mediated induction of hsp-4 was abolished in each (Fig. 7, C and D).

Fig. 7. — (A) Representative fluorescence images of *hsp-4*::GFP and α3ΔN;*hsp-4*::GFP during larval to adult transition (L4 imaged at 20×; adults at 4×). Scale bars, 500 μm. (B) Time-course quantification of (A). Mean ± SEM *hsp-4*::GFP intensity in *hsp-4*::GFP and α3ΔN;*hsp-4*::GFP worms over development (three independent experiments; two-way ANOVA *P <* 0.01). (C) Side-by-side images of day 1 adults treated 24 hours with DMSO or tunicamycin (50 μg/ml) at 20°C: *hsp-4*::GFP; *hsp-4*::GFP;*xbp-1*-*null*; α3ΔN;*hsp-4*::GFP, and α3ΔN;*hsp-4*::GFP;xbp-1-null. Scale bars, 500 μm. (D) Tunicamycin-induced BiP up-regulation quantification of GFP from (C). *hsp-4*::GFP shows robust induction, whereas α3ΔN;*hsp-4*::GFP;*xbp-1-null* does not (mean ± SEM; two-way ANOVA P < 0.01; ≥10 worms per batch, n ≥ 30 total). (E) Kaplan-Meier curves for *xbp-1*-*null* (black) and α3ΔN;*hsp-4*::GFP;*xbp-1 null* (red) on tunicamycin (50 μg/ml; n = 90). Median life spans: 5 and 7 days, respectively (log-rank P < 0.0001). [See fig. S6 (E to G) for full controls.] (F) Table of median survival (days), percent change versus WT, and below pairwise survival statistics. Benjamini-Hochberg–adjusted log-rank P values for all six genotype comparisons. (G) Basal life span on FUdR. Kaplan-Meier analysis of day 1 adults (n = 90) on FUdR plates. Curves *hsp-4*::GFP (light-gray △), *xbp-1 null*;*hsp-4*::GFP (dark-gray ▲), α3ΔN;*hsp-4*::GFP (light-red ○), and α3ΔN;*hsp-4*::GFP; *xbp-1-null* (red ●). Median life spans: 17, 17.5, 20, and 22 days (pairwise log-rank P < 0.0001). (H) Median survival and percent change versus WT, with pairwise log-rank P values [Benjamini-Hochberg (BH) correction; P < 0.0001].

We next assessed the effects of xbp-1–dependent UPR in α3∆N on tunicamycin sensitivity and longevity. The α3ΔN; hsp-4::GFP; xbp-1 null triple mutants exhibited substantial resistance to tunicamycin compared to its control: hsp-4::GFP; xbp-1 null alone (Fig. 7E). For comprehensive comparisons, we also determined the tunicamycin survival curve for the WT and hsp-4-GFP backgrounds with and without the α3∆N-20S mutation (Fig. 7F and fig. S6E). The hyperactive α3∆N-20S increased median survival by 29% in the WT background, 43% in the hsp-4::GFP background, and 40% in the xbp1-null;hsp-4::GFP background (Fig. 7F and fig. S6E). Thus, α3∆N-20S conferred a greater level of resistance in the absence of xbp-1 than it did in the WT background. This significant resistance, even in the absence of xbp-1 activation (and BiP induction), suggests that the hyperactive 20S proteasome in α3ΔN effectively alleviates ER stress even in the absence of this key UPR pathway.

To investigate the role of key UPR effectors in tunicamycin resistance, we used RNAi to silence hsp-4 or ire-1 in α3ΔN;hsp-4::GFP worms. As a crucial control, we first confirmed that both RNAi treatments were effective, as they each decreased survival in control animals under tunicamycin stress when compared to an empty vector (EV) control (fig. S6, F to H). Despite the effective knockdown of hsp-4, the α3ΔN;hsp-4::GFP animals showed no statistically significant reduction in tunicamycin resistance (fig. S6, F and G). Their survival remained ~20% higher than hsp-4::GFP controls and was indistinguishable from non-RNAi α3ΔN;hsp-4::GFP worms. Moreover, these worms survived 50% longer than the hsp-4::GFP control animals that were also treated with hsp-4 RNAi (median survival of 6 versus 4 days, respectively; fig. S6, F and G). Likewise, silencing ire-1 in the α3ΔN;hsp-4::GFP animals did not abolish their enhanced survival. They still lived 20% longer than the hsp-4::GFP + EV controls and had a 50% longer median survival than control worms subjected to the same ire-1 knockdown (6 versus 4 days; fig. S6, H and I). Together, these data demonstrate that the hyperactive 20S proteasome in α3ΔN worms confers robust ER stress resistance that is independent of the UPR effectors hsp-4 and ire-1.

Last, we further assessed the life span of these xbp-1 crosses to determine whether xbp-1 function was necessary for α3ΔN extended longevity. We were surprised to find that the α3∆N-20S worms, even without functional xbp-1, still retained the extended life-span phenotype compared to its control (α3ΔN;hsp-4::GFP) (Fig. 7, H and I). This result demonstrates that longevity conferred by proteasome hyperactivation is independent of UPR signaling via the XBP-1 pathway. Collectively, our findings establish that enhancing 20S proteasome activity constitutes a powerful, standalone proteostasis pathway. This mechanism confers broad resistance to diverse proteotoxic insults—from oxidative damage to aggregation-prone proteins to ER stress—and extends organismal life span, even in the absence of a functional UPR.

DISCUSSION

The accumulation of misfolded and aggregated proteins, particularly IDPs, is a hallmark of aging-related proteinopathies and neurodegenerative conditions, implicating the role of the PN and the proteasome system in maintaining cellular health (15, 67, 68). In this study, we demonstrate that constitutive activation of the 20S proteasome achieved via the α3ΔN mutation provokes a marked remodeling of both the proteome and transcriptome in C. elegans. Approximately 10% of the proteome is altered, accompanied by a network-wide shift in transcription activity. This global reprogramming is paralleled by a coordinated increase in both protein synthesis and degradation rates (Figs. 1 to 3). This adaptive coordination demonstrates the cells remarkable resilience to adapt to increased degradation rates by accelerating protein synthesis. Although our integrated omic analyses reveal a broad, network-level adaptation rather than isolated changes, future research is needed to fully map the precise mechanistic links and causal interconnections underlying this adaptation. Our pulse-chase results showed increased global protein degradation rates, and our heat treatment experiment, which enriches for IDPs, reveals a 50% reduction in heat-stable proteins in the α3ΔN strain (Fig. 1). Moreover, we found that α3ΔN lysates cleared exogenous α-synuclein, a pathological and aggregation prone IDP, greater than 5.8 times faster than WT lysates (Fig. 2 and fig. S2). Therefore, multiple orthogonal approaches definitively demonstrated that 20S gate opening in C. elegans enhances the turnover of disordered proteins, the very clients most prone to aggregation associated with pathology. Pepelnjak et al. (42) identified that many 20S substrates are RBPs and DNA binding proteins with intrinsically disordered regions. Moreover, tau degradation was also reported in an α3ΔN-20S overexpression cell model (70), consistent with these results. In addition, organismal studies using proteasome overexpression have reported increased stress resistance and extended life span, further aligning with these results (23–27). However, our approach fundamentally differs in that rather than overproducing proteasome components, we induce a structural change that constitutively opens the 20S proteasome gate. This structural modification triggers the phenotypes we observe without altering total proteasome expression levels. Because neurodegenerative diseases are driven by the accumulation of IDPs like tau, α-synuclein, TDP-43, and huntingtin, as well as many others, this 20S degradation pathway becomes highly relevant to these diseases. Therefore, our novel finding directly links the open 20S proteasome configuration to the preferential clearance of misfolded and IDP substrates critical in the pathogenesis of neurodegeneration.

In addition, our data show that hyperactivation of the 20S proteasome enhances resistance to oxidative stress, which is associated with aging and various neurodegenerative diseases. We observed reduced levels of oxidative damage markers in TMT-MS peptides. In addition, we found that α3ΔN was resistant to ROS-generating agents such as H₂O₂ and tBoOH. Unexpectedly, the open 20S was even protective in a SOD-deficient background (Fig. 4). This challenges the paradigm that ROS detoxification relies solely on enzymatic antioxidants. Instead, the 20S proteasome appears to be a strong contributor to the mitigation of oxidative damage by enhancing the clearance of oxidatively damaged proteins. This SOD-independent ROS resilience suggests that coupling proteasome activation with traditional antioxidant pathways could amplify cellular defenses in aging and disease. These results also indicate that the enhanced degradation of oxidatively damaged IDPs is a likely contributor to the longevity phenotype observed in the α3ΔN worms. However, the α3ΔN mutants also exhibits significant resistance to tunicamycin induced ER stress, likely due to enhanced ERAD that was observed. This stress resilience in the cyto/nuclear and ER compartments reveals a previously uncharacterized and integrated benefits of 20S proteasome hyperactivation combining into a robust platform for proteotoxic stress resistance, which is a critical determinant for health organismal aging.

A major previously uncharacterized finding of our work is the enhanced capacity of ERAD in the α3ΔN mutants. ERAD is essential for clearing misfolded proteins from the ER, and its failure contributes to many diseases. Vitellogenins, naturally processed by the ER and prone to aggregation, serve as endogenous substrates whose down-regulation in our model provides direct evidence of enhanced ER degradation and mirrors the proteasome-mediated clearance of ApoB in mammals, highlighting its conserved role in lipoprotein homeostasis. The robust depletion of vitellogenins, together with the observed tunicamycin resistance (Fig. 5), prompted us to evaluate additional ERAD substrates. The marked reduction in ER-resident misfolded human ATZ protein levels further emphasizes that the constitutively open 20S proteasome efficiently accelerates the degradation of even aggregation-prone proteins originating from the ER (Fig. 6). To confirm the mechanism behind this enhanced ERAD, we first verified its dependence on the canonical extraction machinery. The accelerated clearance of ER substrates in α3ΔN mutants still required the p97/Cdc48 ATPase complex (cdc-48.1/cdc-48.2), its cofactor npl-4, and the extraction channel component sel-11. Inhibiting this machinery with the p97/Cdc48 inhibitor ML240 reversed the phenotype, causing both ATZ and VIT-2 to reaccumulate in the α3ΔN background (Figs. 5 and 6 and fig. S5). These results demonstrate that constitutive 20S gate opening speeds the final proteolytic step but does not bypass the essential need for protein extraction from the ER. In a parallel control experiment, we ruled out a role for autophagy; levels of lgg-1::mKate2 puncta, a marker for autophagosome formation, remained unchanged in α3ΔN mutants, confirming that this pathway is not responsible for the accelerated clearance. Therefore, these results position 20S hyperactivation as a viable strategy for disorders marked by ERAD dysfunction. The finding that the α3ΔN-20S could accelerate ERAD function was surprising since the 26S is a known endpoint for ERAD substrates, which are typically ubiquitinated after extraction. While the α3ΔN mutation does slightly enhance the 26S-dependent degradation of a linear chain Ub4-GFP substrate, peptide degradation rates, which are gating dependent, are similar for 26S and α3ΔN-26S complexes via in-gel activity assay (28). Since P97 must unfold ER-resident proteins to extract them into the cytosol compartment, it seems more likely that the α3ΔN-20S, which is activated >100× more than the WT 20S (28), is responsible for the enhancement of ERAD that we observe in α3ΔN animals.

Mechanistically, our findings also challenge the traditional view of the 20S proteasome as merely a passive core within the 26S complex. Instead, our data indicate that an open 20S proteasome functions as a central regulator of proteostasis by directly targeting IDPs and unfolded proteins. Notably, our genetic crosses with proteostasis reporter strains revealed that mitochondrial and cytosolic stress pathways are unperturbed, although we do see an induction of the ER-specific hsp-4 reporter from early life stages, suggesting that hsp-4 elevation in α3ΔN worms represents a homeostatic fine-tuning of ER proteostasis. This selective UPR activation could explain the enhanced resistance to ER stressor tunicamycin. This observation is important because UPR activation has been shown to underlie many long-lived models in C. elegans (69, 71, 72) and is an important pathway in many proteinopathies. We fully anticipated that the hsp-4 up-regulation was driving the longevity phenotype in α3ΔN, but its cross with mutant xbp-1 null stain proved otherwise. Unexpectedly, these results showed that ER stress resistance and increased life span in the α3ΔN strain were still retained even in the absence of xbp-1–mediated UPR activation (Fig. 7). We further confirmed that knockdown of either hsp-4 or ire-1 failed to block the α3ΔN-mediated increase in tunicamycin resistance. These findings demonstrate that the α3ΔN-20S continues to confer a protective advantage to ER stress even when core UPR components are disabled, further challenging the view that UPR activation is strictly required for ER stress resilience and life-span extension in this context. These findings indicate that the enhancement of IDP degradation and ERAD function conferred by inducing 20S gate opening is the primary mechanism driving the proteotoxic stress resistance and longevity phenotypes observed in the α3ΔN strain. This suggests that the 20S proteasome is an important mediator of cellular proteostasis. This hypothesis could be further supported by approaches that impair 20S function without affecting 26S function, but major technical hurdles must be overcome and developed (if possible) to enable such capabilities.

Unexpectedly, constitutive proteasome hyperactivation in C. elegans has limited downsides. One potential trade-off is the reduction in fecundity, but this concern is irrelevant in therapeutic context and no other negative phenotypes have been observed in the α3ΔN mutants. It is also important to clarify that the eating defects found in our model, which show approximately a 10% decrease in pharyngeal pumping [(28); necessary for eating], are minimal compared to established longevity mutants like eat-2 mutants, which exhibit about a 75 to 90% reduction in feeding (73–75). Notably, unlike classic eat-2 dietary restriction models in which reduced feeding robustly induces autophagy, α3ΔN worms even with their modest ~10% pharyngeal pumping defect show no autophagy up-regulation (fig. S5), demonstrating that enhanced proteasomal activity, not starvation-induced autophagy, is the cause of their proteostasis benefit. The worms appear normal and have standard motor control, indicating no perturbations to neuronal or developmental functions. In addition, WT and α3ΔN both respond similarly to aldicarb, a neurotoxin indicating that neurons in the α3ΔN function normally and are not stressed. Therefore, therapeutic manipulation of 20S proteasome activity appears to offer distinct advantages over current strategies. 20S activators could selectively enhance degradation of toxic IDPs and oxidatively damaged proteins while sparing folded substrates. This is particularly advantageous since IDPs are the primary protein class associated with neurodegenerative disease and are particularly sensitive to oxidative damage since all of their residues are exposed to ROS, unlike folded proteins which contain many buried and protected residues. In addition, pharmacological targeting of the 20S gate is mechanistically feasible since peptides mimicking the HbYX motif or small peptide mimetics like ZYA are able to robustly induce 20S gate opening (20).

In summary, this work redefines 20S proteasome function as an important pathway in cellular health and as a tunable tool capable of mitigating age-related decline through IDP degradation, oxidative damage clearance, and ERAD enhancement. Its independent function distinct from canonical stress responses highlights its potential as a therapeutic modality for neurodegenerative diseases, alpha-1 antitrypsin deficiency, and other proteinopathies. Future studies should map structural determinants of 20S substrates (e.g., disorder and hydrophobicity) that may regulate the 20S function and explore combinatorial approaches with autophagy modulation to optimize proteostasis resilience.

MATERIALS AND METHODS

C. elegans strains and culture

The open gate mutant strain pas-3(dsm100) was used to study proteasome hyperactivation effects. Throughout this study, this strain is referred to as α3ΔN. C. elegans strains were cultured on nematode growth medium (NGM) agar plates at 20°C, with Escherichia coli strain OP50 serving as the food source, following standard protocols (74). The N2 Bristol strain was used as the WT control. Possible confounding effects due to differences in fertility were controlled for using FUDR (a standard sterilizing agent) in all the experiments unless stated otherwise. Therefore, strains were under the same fertility conditions. In addition, unless stated otherwise, all experiments were conducted on day 1 of adulthood, focusing exclusively on hermaphrodite worms. Detailed information on the strains used in this study can be found in table S5. Age synchronization was performed via alkaline bleaching. Gravid adults from two 10-cm plates were collected and washed three times with double-distilled water (ddH₂O) in 15-ml conical tubes. After reducing the volume to 3.5 ml, a 1.5-ml solution of bleach/NaOH (1 ml of 5% sodium hypochlorite and 0.5 ml of 5 N NaOH) was added. The mixture was vortexed every 2 min for 10 min until no nematode fragments remained. Sterile M9 buffer was then added to neutralize the reaction, and the tubes were centrifuged at 1,100g for 1 min to pellet the eggs. Eggs were washed with 10 ml of sterile M9 and centrifuged at 2000g for 1 min, and the supernatant was discarded. The pellet was resuspended in 7.5 ml sterile M9 in a fresh 15-ml tube and incubated overnight with gentle agitation to allow hatching. L1-arrested nematodes were harvested within 24 hours, plated on OP50-seeded plates, and incubated at 20°C until the desired stage.

TMT-MS proteomic changes in α3ΔN mutants

Worms were synchronized, and then populations were grown on plates with FUDR. Protein extracts from 8-day-old adult populations were processed using a covalent modification enrichment followed by filter-aided sample preparation (CME/FASP) Digest for trypsinization, followed by labeling with TMT10plex Isobaric Tagging for multiplex quantitative analysis. Postlabeling, samples were fractionated via high-performance liquid chromatography (HPLC), using a linear gradient from 5 to 35% acetonitrile in water over 60 min, with a flow rate of 300 μl/min, using a C18 column (20-cm length, 1.7-μm particle size). Fractions were collected every 2 min. These were then analyzed on an Orbitrap Eclipse mass spectrometer for high-resolution detection using TMT MS3; 60 min gradient per fraction. Data were then processed using MaxQuant (76) against the C. elegans protein database (76) to identify proteins and extract MS3 reporter ion intensities, with search parameters including trypsin specificity, mass tolerances of ±10 parts per million for precursors and ±0.02 Da for fragments, fixed modifications for TMT tags on lysine and peptide N termini and carbamidomethyl on cysteine, variable modifications for oxidation on methionine, peptide and protein FDR thresholds set at 1%, and quantification based on MS3 reporter ion intensities. Proteins identified as decoys and contaminants were filtered out, as well as rows with more than 80% zero values (count = 0). To handle zero values and facilitate logarithmic transformation, a constant value of +1 was added to each intensity value in the filtered dataset (77–80). The adjusted dataset was then loaded into a matrix of intensities and processed in R version 3.6.3 for log₂ ratio conversion using the median sweeping method for normalization. After normalization, PCA was plotted to visualize the expression patterns among the samples. For DEP quantification, the DEqMS package was used (40) since this method consider the specific structure of MS data and fitted best our data compare to others widely used such as Limma (81). Proteins with FCs > |1.5| and FDRs < 0.05 were defined as DEPs and captured for functional enrichment analysis. Regarding detection and quantification of PTMs, focusing on oxidation in α3ΔN mutants, we used MaxQuant which facilitated the identification of proteins and their oxidation states by matching mass spectra against the C. elegans protein database as explained above, using a site-level FDR (FDR < 00.1) to ensure accurate PTM identification (76). Quantitative data extracted from MaxQuant, including modification-specific peptides’ intensities and ratios, were then analyzed to assess the extent of oxidation in R under the Limma package (40).

RNA-seq transcriptomic changes in α3ΔN mutants

Total RNA was extracted from samples using TRIzol according to the manufacturer’s protocol. RNA purity, concentration, and integrity were assessed using a NanoDrop One spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) and an Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). All samples had a 260:280 nm ratio between 1.9 and 2.1 and RNA integrity number ≥ 9.5. Samples were sent to Novogene Corporation Inc. (Sacramento, CA, USA) for sequencing in the Illumina platform. Data quality control was performed with FastQC v.0.11. After data filtering for adapters, clean reads were mapped to the C. elegans WS245 reference genome using HISAT 2.1.0 (82). Then, featureCounts v1.22.2 (83) was used to count the number of reads for each of the identified genes. Gene count matrix was filtered for genes with constant values (either 0 or empty). The gene counts were then loaded in RStudio for differential expression analysis with DESEQ2.0 (84). Counts were normalized using the negative binomial distribution under DESEQ2.0, and genes with log₂FCs | >1| and FDRs < 0.05 were defined as differentially expressed genes (DEGs) and captured for further analysis. PCA, three-dimensional PCA volcano plots and heatmaps were performed with R v3.6.1 packages in custom in house scripts. To identify potential transcription factors involved in the observed phenotypic changes in α3ΔN mutants, we used the JASPAR (85) database. JASPAR provides a comprehensive repository of DNA binding profiles represented as position frequency matrices for transcription factors across multiple species. We specifically used the JASPAR CORE database, which contains experimentally validated binding profiles for C. elegans (https://jaspar.elixir.no). Briefly, we input our RNA-seq data and searched for transcription factors that show differential binding activity in the α3ΔN strain compared to the WT. We analyzed the enriched transcription factor binding sites using the JASPAR enrichment tool, which facilitates the identification of significantly overrepresented transcription factors in the genomic regions of interest. The results were visualized using custom scripts to plot the significance of transcription factor involvement, aiding in the mechanistic interpretation of the systemic changes observed in the omic data.

Integration of proteomics with bulk RNA-seq through projection to latent structures sparse PLS

To jointly examine patterns of variation in our TMT-MS proteome (X) and RNA-seq transcriptome (Y), we applied canonical PLS using the mixOmics v6.10.9 (47) R package. Canonical PLS finds paired latent components that maximize the covariance between X and Y without any penalization or feature selection. Both datasets were first centered and scaled to unit variance. We then invoked spls(X, Y, mode = “canonical,” ncomp = 2) to fit a two-component model and extracted gene and protein loadings via pls_canonical$loadings$X and pls_canonical$loadings$Y (Fig. 3C). The proportion of variance explained by each component was obtained from pls_canonical$explained_variance and plotted to confirm that two components captured the majority of the shared structure. To visualize global interomic relationships, we used the cim() function to generate a clustered image map of pairwise correlations between all proteins and genes, and this heatmap highlights modules of covarying features across both datasets (fig. S3A). Examination of the full loading distributions revealed that most loadings were small but nonzero, rather than dominated by a few large coefficients (Fig. 3D), indicating that the α3ΔN versus WT separation arises from subtle, distributed contributions of many molecules rather than a handful of “master regulators” (Fig. 3D; confirmed with transcription factor analysis in fig. S3B). Last, we performed hierarchical clustering on the full loading matrices {hclust[dist(...)]} and cut the resulting dendrograms into six clusters [cutree()], allowing us to group covarying genes and proteins for downstream functional enrichment.

Enrichment analysis

GSEA (41) was performed using the ClusterProfiler (86) library in-house implementation in R studio to assess enrichment signatures in the expression profiles in both TMT-MS and RNA-seq. The entire gene lists were preranked on the basis of the mean FC and significance (FDR) of each gene. The analysis included the gene set lists from the curated Gene Ontology the PN consortium (48) and the chaperome subnetwork datasets (49). The significance of enrichment was set by Benjamini-Hochberg < 0.05. Regarding chaperome subnetwork components, enriched categories were identified by hypergeometric distribution FDR < 0.05.

Toxicity assays

Toxicity assays were conducted following the guidelines of Jia and Sieburth (53) using stock solutions of juglone [50 mM in dimethyl sulfoxide (DMSO)], tBoOH (70%, 7.2 M), and H2O2 (30%, 9.8 M), freshly prepared before each assay. Synchronized day 1 adults (unless specified otherwise) C. elegans (a minimum of 40 to 100 worms) were placed in 1.5-ml Eppendorf tubes containing M9 buffer and washed three times. Oxidants were then added at final concentrations ranging from 50 to 300 μM for juglone and 100 to 2000 μM for both tBoOH and H2O2, followed by a 4-hour incubation on a rotating mixer. Postincubation, worms were washed three times with M9 and transferred to fresh NGM plates seeded with OP50 for a 16-hour recovery period in the dark at 20°C. Survival rates were determined by counting alive versus dead worms. This procedure was repeated across various concentrations in triplicate for a minimum of two separate experiments. Mortality rates were analyzed using probit analysis through the BioRssay (87) package in R, which also estimated the LD₅₀, 95% confidence limits, slope, and chi-square values. Probit regression lines of log concentration against percent mortality facilitated the determination of LD₅₀ values for each treatment, as detailed in fig. 4 (B to E).

Life-span assays in xbp-1null hsp4::GFP worms

For life-span experiments on xbp-1 null; hsp-4::GFP and α3ΔN; xbp-1 null; hsp-4::GFP strains, synchronized day 1 adults were collected and seeded in OP50 NGM plates containing 100 μM FUdR (RPI) to inhibit reproduction. Animals were scored daily as dead if they exhibited no movement upon mechanical stimulation and censored if death resulted from extraneous causes (e.g., drying out, internal hatching, or vulval protrusion). Worms were transferred to fresh plates every 2 to 3 days to prevent starvation. Statistical analyses were performed using the survminer and survival packages in R (https://github.com/kassambara/survminer/tree/master) as additional resource, and Kaplan-Meier curves were generated in GraphPad Prism version 8.0.0 (San Diego, CA, USA) (N ≥ 90). P < 0.05 was considered statistically significant. All experiments were conducted at 20°C and repeated at least three times under blinded conditions.

Toxicity assays aldicarb

For aldicarb toxicity assay, a 10 mM stock solution of aldicarb in DMSO was prepared, and synchronized day 1 adult worms were placed on NGM plates containing 1 mM aldicarb (54). Survival rates were recorded every 15 min over the course of three independent experiments, with the data presented as means ± SEM. Statistical analysis of the assays was conducted using the survminer and survival packages in R as described above.

ER stress with tunicamycin

To induce ER stress, we followed the guides of Gusarov et al. (88), and C. elegans were placed on NGM plates supplemented with tunicamycin (50 μg/ml) in a maximum of 2% DMSO and compared to control groups on plates with only 2% DMSO. Treatment started on day 1 of adulthood, with worms being transferred to fresh plates every 3 to 4 days. Survival rates were monitored daily across three independent experiments, with results expressed as means ± SEM. Data analysis was conducted using the survminer and survival packages in R.

Translation assay, 6-FAM-dC-puromycin incorporation

To assess protein synthesis, we used a fluorescent puromycin incorporation method, using 2 mM 6-FAM-dC-puromycin (Jena Bioscience), according to Wang et al. (45) with some modifications for C. elegans. Day 1 adult worms were collected in M9 buffer, then rinsed, and transferred into S-basal medium. An overnight OP50 bacterial culture was concentrated 10-fold in S-basal medium. Worms were subsequently placed into a mixture of 250 μl of S-basal medium, 200 μl of the 10-fold concentrated OP50, and 5 μl of 2 mM 6-FAM-Dc-puromycin, also in S-basal. This mixture was incubated for 4 hours on a rotating mixer. Following incubation, worms were washed at least three times in S-basal, sonicated, and centrifuged for protein extraction. After sonication, two volumes of S-basal buffer were added, and the mixtures were centrifuged using Amicon Ultra Centrifugal Filters (YM-3, Millipore) to eliminate unincorporated 6-FAM-dC-puromycin. A minimum of 10% of the reaction products was saved as input to quantify the total nascent protein chains using a plate reader. The incorporation rate was normalized against worms treated with 10 mM cycloheximide (CHX) alongside 6-FAM-dC-puromycin, to account for basal levels of protein synthesis inhibition.

Degradation assay

To evaluate the rates of time-dependent protein degradation, we used CHX. Day 1 adult worms were exposed to CHX (50 mg/ml in ethanol, Sigma-Aldrich) alone or in combination with the proteasome inhibitor MG132. We distributed and incubated groups of 800 to 1000 worms each at 20°C for time intervals of 0, 1, 3, and 6 hours. Following treatment, we harvested the worms for total protein analysis. Protein was extracted, and concentration was normalized through Bradford assay. The proteins were separated using NuPAGE 4 to 12% bis-tris gels (Invitrogen) and subjected to electrophoresis. Postelectrophoresis, gels were stained with Coomassie blue dye and then destained over a 24-hour period following the standard protocols. To quantify degradation rates, we calculated the treated-to-untreated (CHX/CHX + MG132) protein ratio for each time point. This ratio was then normalized by division with the ratio at the initial time point (0 hours). Protein band intensities were quantified using the Fiji Band/Peak Quantification Tool [National Institutes of Health (NIH) Image].

Analysis of heat-stable disordered proteins

Synchronized day 1 adult worm populations were collected in M9 buffer and then diluted with 2× lithium dodecyl sulfate (LDS) sample buffer (Invitrogen). The samples were subjected to a controlled heat treatment to induce misfolding and precipitation of folded proteins following Park et al. (3), starting with an incubation in a 70°C water bath for 15 min, with vortexing every 5 min, followed by an increase to 90°C for 5 min. This thermal stress disrupts the normal folded states of proteins causing aggregation and precipitation of well folded proteins, leading to an increased presence of IDPs, which lack folded structure and are heat resistant. After the heat treatment, the samples were centrifuged at 6000 rpm for 15 min at 4°C to remove any insoluble precipitates. The samples were normalized through Bradford assay and then loaded onto a NuPAGE 4 to 12% bis-tris gel (Invitrogen) for electrophoresis. Following electrophoresis, the gels were stained with Coomassie blue dye and destained over 24 hours according to the standard protocols. Protein bands were quantified using the Fiji Band/Peak QuantificationTool (NIH Image).

Confocal microscopy

Stress reporters hsp-4::GFP, hsp-6p::GFP, and hsp-16.2p::GFP

Transcriptional reporter strains expressing hsp-4::GFP, hsp-6p::GFP, and hsp-16.2p::GFP, crossed into the α3ΔN background, were imaged using an EVOS M7000 microscope. Synchronized worms were cultured at 20°C until day 1 of adulthood, manually picked, and immobilized on slides with 25 mM sodium azide. Images were acquired within 5 min at ×4 or ×20 magnifications. For time-course experiments, synchronized worms were imaged at each developmental stage. Imaging was performed on worm batches usually ≥10 worms side by side across three independent experiments with the following total sample sizes: n = 438 for hsp-4::GFP, n = 428 for α3ΔN;hsp-4::GFP, n = 121 for hsp-6p::GFP, n = 123 for α3ΔN;hsp-6p::GFP, and n = 30 for each strain of hsp-16.2::GFP at day 1 adulthood. GFP fluorescence intensities were quantified using Fiji (NIH, version 2.1.4). A consistent threshold was applied to all images to create a selection, which was then quantified using the “Analyze → Measure” function in the ROI. In addition, images were captured under identical exposure settings to minimize technical variability. Data were plotted using Prism 8, and statistically significant differences were determined using two-way ANOVA or paired parametric t tests.

vit-2::GFP and sGFP::ATZ

Defective mutants for accumulation of vitellogenin-2 vit-2::GFP outcrossed strain RT99 and the misfolded human protein, ATZ, involved in human α1-antitrypsin deficiency strain were crossed into α3ΔNs. Synchronized worms were imaged throughout adulthood to assess GFP accumulation. For both, vit-2::GFP and sGFP::ATZ RNAi experiments, images were captured using an EVOS M7000 microscope at ×4 or ×20 magnification; in addition, for sGFP::ATZ, imaging was also performed with a Zeiss LSM 710 multiphoton confocal microscope at ×10 magnification. Worms were mounted on a 2% agar pad, anesthetized with 25 mM sodium azide, and covered with a glass coverslip for imaging. At least 49 worms per strain were quantified for sGFP::ATZ aggregates using a consistent threshold applied to all images to create a selection, and then the “Analyze → Analyze Particles” function in Fiji was used. Three independent batches of a minimum of 10 worms per strain were assessed per day for vit-2::GFP. The bright pharyngeal signal in sGFP::ATZ images is a genetic marker and was excluded from aggregate quantification. Images were processed and quantified in Fiji as described above, and data were plotted in Prism 8. Statistical differences between ATZ aggregates were determined using an unpaired t test, whereas multiple t tests (with the Benjamini, Krieger, and Yekutieli correction, FDR < 0.01) were applied to assess significant differences in vit-2::GFP levels. Each time point was analyzed independently without assuming uniform variance across tests.

Proteasome inhibition assay in vit-2::GFP

Dependency of proteasome degradation for vit-2::GFP was assessed by treatment with MG132 which completely inhibits WT and open-gate α3ΔN proteasomes. Worms were synchronized and, similarly for toxicity assays, were collected in S-basal and exposed to 50 μm of MG132 for 1 and 3 hours. Afterward, worms were washed twice with S-basal lysate and spun down for protein extraction. At least 20% of the total protein products were used as input to estimate the amount of vit-2::GFP signal in a plate reader. Experiments were performed at least three times independently, and the percentage of increase was obtained by dividing the value of 1 and 3 hours by the starting value or time 0 of treatment. Significance was obtained with unpaired t test with Welch’s correction.

Proteasome inhibition assay in sGFP::ATZ

To test the dependence of sGFP::ATZ aggregate clearance on proteasome activity, synchronized day 1 adult worms were placed on NGM plates containing 100 μM FUdR. Beginning on day 2, cohorts were treated every 8 hours with 100 μM MG132 (dissolved in M9) by pipetting the solution evenly onto the agar surface and allowing it to absorb completely before returning plates to 20°C. Vehicle DMSO-only controls received the same volume of DMSO in M9. This regimen was maintained through day 7 to ensure continuous proteasome inhibition without mechanically disturbing the animals. On day 7, worms were gently washed twice in M9 to remove residual inhibitor and then immobilized in 25 mM sodium azide on glass slides. Fluorescence images were acquired at ×10 magnification on an EVOS M700 or Zeiss LSM 710 confocal microscope. Aggregate quantification was performed in Fiji according to the criteria described above. To confirm reproducibility, the entire assay was repeated independently at least three times, with each biological replicate consisting of a fresh synchronization and treatment series. Aggregate counts per worm were compiled for each condition, and statistical analyses were conducted by one-way analysis of variance (ANOVA) with Tukey’s post hoc test to compare across treatment groups.

RNAi experiments

RNAi clones from Horizon Discovery (Cambridge, UK) were sequence-verified, and feeding strains were prepared as described (89). Culture E. coli HT115(DE3) carrying each RNAi plasmid were grown overnight in LB with ampicillin (100 μg/ml) and 1.5 mM isopropyl-β-d-thiogalactopyranoside (IPTG), diluted 1:50 into fresh supplemented LB, and induced at 37°C with shaking to an OD₆₀₀ (optical density at 600 nm) of ~0.5. Cultures were then pelleted and resuspended in 3 ml of LB + ampicillin, and 100-μl aliquots were spread onto NGM agar plates containing ampicillin (100 μg/ml) and 1.5 mM IPTG. For ERAD knockdown, equal volume mixtures of cdc-48.1 + cdc-48.2 or npl-4.11 + npl-4.2 RNAi cultures were used, since dual depletion is required to impair ERAD function (90). Synchronized L1 larvae were placed on RNAi plates and grown to the desired stage of day 5 for sGFP::ATZ aggregate counting with transfers to fresh plates every 2 days. For tunicamycin resistance assays, tunicamycin (50 μg/ml) was included in the RNAi plates, and worms were maintained on drug-containing plates throughout the experiment, with transfers to fresh RNAi + tunicamycin plates every 2 to 3 days.

Cdc48 inhibition assay in vit-2::GFP and sGFP::ATZ

To assess the role of Cdc48 in vitellogenin and ATZ accumulation, synchronized worms were grown to day 5 postreproduction and then transferred into tubes containing 10 μM ML240 (a selective Cdc48/P97 inhibitor) (91) in M9 buffer for 4 hours. Worms were returned to standard plates overnight, and on day 7, the ML240 treatment was repeated for another 4 hours. Following treatment, animals were washed twice with M9 and mounted on 2% agar pads containing 25 mM sodium azide for immobilization. Fluorescence images of vit-2::GFP and sGFP::ATZ were acquired on an EVOS M7000 microscope at ×4 or ×20 magnification. For each strain, three independent cohorts of ≥10 worms were analyzed with a uniform intensity threshold applied to all images, selections were generated, and aggregate counts (or whole-animal GFP levels) were measured using Fiji’s Analyze → Analyze Particles function. Data were plotted in GraphPad Prism 8, and statistical significance was determined by one-way ANOVA with Tukey’s post hoc test (***P < 0.01).

α-Synuclein degradation assay

Human WT α-synuclein with an N-terminal His-tag, expressed in pET28a vector, was produced in BL21-STAR E. coli and purified using a nickel-nitrilotriacetic acid (Ni-NTA) column (QIAGEN). Pure α-synuclein monomers were isolated via size exclusion chromatography using a Superose 12 10/300 column (Cytiva) and verified by SDS–polyacrylamide gel electrophoresis (SDS-PAGE). Worm samples were washed at least three times in S-basal, sonicated, and centrifuged at 14,000g for 10 min to remove cell debris. Protein concentration in the lysate was determined using the Bradford assay. For the degradation assay, 5.2 μM α-synuclein was incubated with 12.5 μg of cleared lysate in 50 mM tris (pH 7.5) at 37°C, with measurements taken at 0, 15, 30, and 60 min. In the control experiment, the lysates were pretreated with 200 μM MG132 and 2 μM epoxomicin in 50 mM tris (pH 7.5) at room temperature for 30 min. Subsequently, α-synuclein was added and incubated at 37°C, with measurements taken at 0 and 60 min. Degradation of α-synuclein monomers was analyzed using SDS-PAGE and immunoblot (described below). Band intensities were quantified using ImageJ (NIH); for each gel, loading was normalized to a constant band in the lysate. At each time point, the remaining α-synuclein was expressed as a percentage of its t = 0 min value (set to 100%).

SDS-PAGE and immunoblotting

Proteins were separated by SDS-PAGE using mPAGE 4 to 20% bis-tris gels (MilliporeSigma) at 150 V for 40 min and visualized using GelCode Blue Safe Protein Stain (Thermo Fisher Scientific). For immunoblotting, proteins were transferred to a polyvinylidene difluoride membrane (MilliporeSigma) at 150 V for 1 hour at 4°C using Bolt transfer buffer (Invitrogen). Primary (rabbit anti–His-tag, MilliporeSigma) and secondary [goat anti-rabbit immunoglobulin G (H + L)–horseradish peroxidase (HRP) conjugate, Bio-Rad] antibodies were diluted 1:1000 and 1:3000, respectively, in Tris-buffered saline with Tween 20 (TBST) with 5% nonfat milk. The membrane was blocked in TBST + 5% nonfat milk for 1 hour at room temperature, washed briefly with TBST, incubated with primary antibody for 1 hour, washed with TBST (3 × 5 min), incubated with secondary antibody for 1 hour, and washed again with TBST (3 × 5 min). Chemiluminescence HRP substrate (SuperSignal West Pico Plus, Thermo Fisher Scientific) was applied, incubated for 1 min, and imaged using the G:BOX XX9 system (Syngene).

Acknowledgments

Our sincere appreciation extends to the Smith Lab members for insightful discussions and feedback throughout the manuscript’s development, especially D. Coleman, whose meticulous worm counts were essential to our analyses. We thank K. Courtney and R. Leonardi for careful and meticulous review of our manuscript. We also acknowledge B. Webb for providing us access to the EVOS M7000 and N. Billington and the West Virginia University Microscope Imaging Facility for support in conducting the confocal and imaging experiments.

Funding: This work was supported by the National Institutes of Health grant R01AG064188 (D.M.S.), National Institutes of Health grant R01GM107129 (D.M.S.), National Institutes of Health research grant P20RR016440 (WVU imaging facility), and National Institutes of Health research grants P30RR032138/P30GM103488 (WVU imaging facility).

Author contributions: Conceptualization: D.S.-T., N.A., R.A., and D.M.S. Methodology: D.S.-T., N.A., R.A., D.M.S., and M.Q.I. Investigation: D.S.-T., N.A., R.A., D.M.S., and M.Q.I. Visualization: D.S.-T. and N.A. Supervision: D.M.S. and N.A. Writing—original draft: D.S.-T., R.A., and D.M.S. Writing—review and editing: D.S.-T., D.M.S., and N.A.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The sequencing data have been deposited at the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) and can be accessed via GEO Series accession number GSE259419. The mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD055640 (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD055640).

Supplementary Materials

The PDF file includes:

Figs. S1 to S6

Legends for data S1 to S5

sciadv.adx3014_sm.pdf^{(4MB, pdf)}

Other Supplementary Material for this manuscript includes the following:

Data S1 to S5

sciadv.adx3014_data_s1_to_s5.zip^{(5.4MB, zip)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figs. S1 to S6

Legends for data S1 to S5

sciadv.adx3014_sm.pdf^{(4MB, pdf)}

Data S1 to S5

sciadv.adx3014_data_s1_to_s5.zip^{(5.4MB, zip)}

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PERMALINK

Hyperactive 20S proteasome enhances proteostasis and ERAD in C. elegans via degradation of intrinsically disordered proteins

David Salcedo-Tacuma

Nadeem Asad

Md Qamrul Islam

Raymond Anderson

David M Smith

Roles

Abstract

INTRODUCTION

RESULTS

Proteome reconfiguration and adaptive protein turnover following 20S proteasome hyperactivation

Fig. 1. Open gate mutant α3ΔN rewires the proteome of C. elegans increasing global synthesis.

Fig. 2. Hyperactive 20S-α3ΔN mutants accelerate global proteolysis with bias toward disordered substrates IDPs.

Coordinated transcriptomic-proteomic signatures highlight broad proteostasis reprogramming in α3ΔN mutants

Fig. 3. Open gate mutant α3ΔN affects the transcriptome of proteostasis components involved in oxidative stress defense.

Proteasome hyperactivation enhances oxidative stress defense independently of SOD enzymes

Fig. 4. Enhanced oxidative stress resistance in α3ΔN mutants is independent from SODs.

Elevated ERAD capacity through proteasome hyperactivation mitigates vitellogenin aggregation

Fig. 5. Enhanced ERAD in α3ΔN mutants reduces vitellogenin accumulation via the Cdc48/P97.

Proteasome hyperactivation enhances the clearance of pathogenic human ATZ protein

Fig. 6. Enhanced ERAD in α3ΔN mutants reduces pathogenic ATZ accumulation via Cdc48/P97 complex.

Proteasome hyperactivation increases ER stress resistance and life span independently of the UPR

Fig. 7. Enhanced ERAD and longevity in α3ΔN mutants is uncoupled from canonical UPR activation via hsp-4 (BiP) and xbp-1.

DISCUSSION

MATERIALS AND METHODS

C. elegans strains and culture

TMT-MS proteomic changes in α3ΔN mutants

RNA-seq transcriptomic changes in α3ΔN mutants

Integration of proteomics with bulk RNA-seq through projection to latent structures sparse PLS

Enrichment analysis

Toxicity assays

Life-span assays in xbp-1null hsp4::GFP worms

Toxicity assays aldicarb

ER stress with tunicamycin

Translation assay, 6-FAM-dC-puromycin incorporation

Degradation assay

Analysis of heat-stable disordered proteins

Confocal microscopy

Stress reporters hsp-4::GFP, hsp-6p::GFP, and hsp-16.2p::GFP

vit-2::GFP and sGFP::ATZ

Proteasome inhibition assay in vit-2::GFP

Proteasome inhibition assay in sGFP::ATZ

RNAi experiments

Cdc48 inhibition assay in vit-2::GFP and sGFP::ATZ

α-Synuclein degradation assay

SDS-PAGE and immunoblotting

Acknowledgments

Supplementary Materials

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Other Supplementary Material for this manuscript includes the following:

REFERENCES AND NOTES

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