Latest News

How Dietary and Pharmacological Interventions Can Slow Progression of Neurodegenerative Diseases

Protein homeostasis, or proteostasis, is essential for maintaining cellular function by ensuring proper protein folding, trafficking, and degradation. Disruption of this balance can lead to the accumulation of misfolded proteins, which contributes to a wide range of disorders including neurodegenerative diseases, cancer, and age-related cellular toxicity. Detoxification systems, such as autophagy and the ubiquitin-proteasome pathway, help maintain proteostasis by removing damaged or misfolded proteins. When these systems fail, the resulting protein aggregates can become toxic.

One major group of disorders linked to impaired proteostasis is polyglutamine (polyQ) repeat expansion diseases. Huntington’s disease (HD), the most well-known among these disorders, is caused by a CAG trinucleotide repeat expansion in the HTT gene¹. This mutation results in the production of a mutant huntingtin protein with an abnormally long polyQ tract, leading to misfolding, aggregation, and eventual neurodegeneration. Currently, pharmacological therapies for Huntington’s disease focus primarily on managing symptoms, but growing evidence points to dietary interventions² as a promising avenue for slowing disease progression.

One powerful tool in this research is the nematode Caenorhabditis elegans (C. elegans), a well-established model organism³ used to study protein aggregation in the context of aging and external influences, such as diet. C. elegans provides an excellent model for studying age-related protein aggregation because of its 83% ⁴ genetic similarity to human-disease genes. Their transparent body allows for visualization of protein aggregates⁵ in real time,  while short lifespan enables rapid large-scale studies.

vivoVerse’s Automated, Multi-Parametric Screening Platform for Quantifying PolyQ Aggregation in C. elegans

Traditional methods ⁶ to evaluate the effects of active ingredients on protein aggregation in C. elegans are a slow and costly endeavor due to manual handling of worms, individual plate preparation, and time-intensive imaging and scoring processes. This low-throughput approach significantly increases labor costs and requires extensive personnel training and time, severely limiting the number of compounds that can be tested at once. Additionally, the need to repeat experiments to ensure statistical reliability further adds to the time and financial burden. Researchers face a bottleneck in efficiently screening the vast number of compounds that could have therapeutic potential, delaying the discovery of effective interventions for Huntington’s disease.

To address this bottleneck, vivoVerse has developed a PolyQ Protein Aggregation Assay, which offers an automated and standardized method to measure how active ingredients affect protein aggregation in C. elegans. In this assay, worms that have been genetically modified to express a fluorescent reporter are exposed to a test chemical at ten different doses. Researchers then measure the ingredient’s effect on protein aggregation in aged, immobilized C. elegans using high-resolution imaging and automated analysis ⁷.

Figure 1. Schematic of an automated PolyQ Aggregate assay using C. elegans.

C. elegans strains can be genetically modified to include a green/yellow fluorescent protein (GFP/YFP) reporter, a protein that fluoresces under light when a specific gene is expressed. This allows researchers to visualize and quantify gene products in real-time. One of the strains available in vivoVerse’s assays has a YFP reporter attached to 35 glutamine repeats expressed in the body-wall muscle cells (UNC-54P::Q35::YFP). These Q35::YFP worms show a progressive transition from soluble to aggregated fluorescence signal which causes mobility loss as they age⁸. To assess the effect on protein aggregation of a test chemical, the C. elegans strains are treated with a range of doses at an early developmental stage. The treated worms are allowed to age before they are then immobilized in a vivoChip that captures ~1,000 worms from 24 unique populations. High-resolution brightfield and fluorescence images are acquired, and the number of fluorescent aggregates normalized per length of each animal is quantified using automated image analysis software.

The Influence of Food Ingredients on Protein Aggregation: The Need for Scalable and Precise Investigative Models

With the limited effectiveness of current drugs for neurodegenerative diseases and dementia, prioritizing prevention, delaying onset, and slowing progression has become crucial. Diet and nutrition play an important role in modulating protein aggregation^9,10, though researchers are still uncovering their exact mechanisms. Certain food ingredients can either exacerbate or mitigate protein misfolding. For instance, polyphenols¹¹—such as epigallocatechin gallate (EGCG), found in green tea—have demonstrated strong anti-aggregation properties in multiple neurodegenerative disorders. In C. elegans, EGCG was shown to reduce polyQ protein aggregation and improve motility, making it a particularly promising compound for further investigation. Other dietary compounds, including omega-3 fatty acids¹² and vitamin E¹³, have also shown benefits in Huntington’s disease models, improving motor function and slowing disease progression. Using C. elegans, researchers can efficiently screen novel dietary compounds to determine their effects on proteostasis and identify potential nutritional interventions.

Drug Repurposing: A Large-Scale Study Identified Pharmacological Interventions that Reduced Poly-glutamine-Induced Aggregates.

In addition to dietary screening, vivoVerse’s technology has been applied to drug repurposing efforts for neurodegenerative diseases. vivoVerse’s technology⁷ was used to screen 983 FDA-approved clinical compounds using the PolyQ protein aggregation disease model for its relevance to Huntington’s disease in humans. Of the 983 compounds tested using the PolyQ Protein Aggregation Assay, four were found to reduce the aggregation parameters significantly – a hit rate of 0.4%. This rate of confirmed hits is comparable to the hit rate of a recent cell-based screen ¹⁴ with ~900,000 small molecules that resulted in 796 primary hits (0.09%) and 263 confirmed hits (0.03%).

Figure 2. vivoVerse’s protein aggregation assay

Looking Ahead

C. elegans serves as an invaluable model for studying protein aggregation, particularly in relation to aging and diet. By leveraging vivoVerse’s high-content screening technologies, scientists can accelerate the discovery of compounds that influence protein aggregation, potentially leading to novel therapies for neurodegenerative diseases. Understanding the connection between aging, diet, and protein balance is an essential step toward preventing the harmful effects of protein misfolding and improving overall health. With vivoVerse’s continuous advancements in screening methodologies and computational analysis, the future looks bright for breakthroughs in treating protein aggregation disorders.

References:

1. 1. Caron NS, Wright GEB, Hayden MR. Huntington Disease. 1998 Oct 23 [Updated 2020 Jun 11]. In: Adam MP, Feldman J, Mirzaa GM, et al., editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2025.
   2. Ansari U, Nadora D, Alam M, Wen J, Asad S, Lui F. Influence of dietary patterns in the pathophysiology of Huntington’s Disease: A literature review. AIMS Neurosci. 2024 Apr 12;11(2):63-75. doi: 10.3934/Neuroscience.2024005. PMID: 38988882; PMCID: PMC11230857.
   3. Van Pelt KM, Truttmann MC. Caenorhabditis elegans as a model system for studying aging-associated neurodegenerative diseases. Transl Med Aging. 2020;4:60-72. doi: 10.1016/j.tma.2020.05.001. Epub 2020 Jun 10. PMID: 34327290; PMCID: PMC8317484.
   4. Lai CH, Chou CY, Ch’ang LY, Liu CS, Lin W. Identification of novel human genes evolutionarily conserved in Caenorhabditis elegans by comparative proteomics. Genome Res. 2000 May;10(5):703-13. doi: 10.1101/gr.10.5.703. PMID: 10810093; PMCID: PMC310876.
   5. Zhang S, Li F, Zhou T, Wang G, Li Z. Caenorhabditis elegans as a Useful Model for Studying Aging Mutations. Front Endocrinol (Lausanne). 2020 Oct 5;11:554994. doi: 10.3389/fendo.2020.554994. PMID: 33123086; PMCID: PMC7570440.
   6. Brenner S. The genetics of Caenorhabditis elegans. Genetics. 1974 May;77(1):71-94. doi: 10.1093/genetics/77.1.71. PMID: 4366476; PMCID: PMC1213120.
   7. Mondal S, Hegarty E, Martin C, Gökçe SK, Ghorashian N, Ben-Yakar A. Large-scale microfluidics providing high-resolution and high-throughput screening of Caenorhabditis elegans poly-glutamine aggregation model. Nat Commun. 2016 Oct 11;7:13023. doi: 10.1038/ncomms13023. PMID: 27725672; PMCID: PMC5062571.
   8. Morley JF, Brignull HR, Weyers JJ, Morimoto RI. The threshold for polyglutamine-expansion protein aggregation and cellular toxicity is dynamic and influenced by aging in Caenorhabditis elegans. Proc Natl Acad Sci U S A. 2002 Aug 6;99(16):10417-22. doi: 10.1073/pnas.152161099. Epub 2002 Jul 16. PMID: 12122205; PMCID: PMC124929.
   9. Xiang L, Wang Y, Liu S, Liu B, Jin X, Cao X. Targeting Protein Aggregates with Natural Products: An Optional Strategy for Neurodegenerative Diseases. Int J Mol Sci. 2023 Jul 10;24(14):11275. doi: 10.3390/ijms241411275. PMID: 37511037; PMCID: PMC10379780.
   10. Businaro R, Vauzour D, Sarris J, Münch G, Gyengesi E, Brogelli L and Zuzarte P (2021) Therapeutic Opportunities for Food Supplements in Neurodegenerative Disease and Depression. Front. Nutr. 8:669846. doi: 10.3389/fnut.2021.669846
   11. Freyssin A, Page G, Fauconneau B, Rioux Bilan A. Natural polyphenols effects on protein aggregates in Alzheimer’s and Parkinson’s prion-like diseases. Neural Regen Res. 2018 Jun;13(6):955-961. doi: 10.4103/1673-5374.233432. PMID: 29926816; PMCID: PMC6022479.
   12. Puri BK, Leavitt BR, Hayden MR, Ross CA, Rosenblatt A, Greenamyre JT, Hersch S, Vaddadi KS, Sword A, Horrobin DF, Manku M, Murck H. Ethyl-EPA in Huntington disease: a double-blind, randomized, placebo-controlled trial. Neurology. 2005 Jul 26;65(2):286-92. doi: 10.1212/01.wnl.0000169025.09670.6d. PMID: 16043801.
   13. Peyser CE, Folstein M, Chase GA, Starkstein S, Brandt J, Cockrell JR, Bylsma F, Coyle JT, McHugh PR, Folstein SE. Trial of d-alpha-tocopherol in Huntington’s disease. Am J Psychiatry. 1995 Dec;152(12):1771-5. doi: 10.1176/ajp.152.12.1771. PMID: 8526244.
   14. Calamini, B., Silva, M., Madoux, F. et al. Small-molecule proteostasis regulators for protein conformational diseases. Nat Chem Biol 8, 185–196 (2012).

    

    

    

    

Developmental toxicity (DevTox) occurs when a chemical or substance disrupts an organism’s normal growth and developmental processes. The mechanisms underlying developmental toxicity may include structural malformations, molecular growth pathway interference, or impaired cellular differentiation. A substance’s toxicity is typically assessed by the degree of disruption in these developmental pathways and the severity of the resulting health effects¹.

To limit public exposure to hazardous substances, proper safety testing of manufactured chemicals is critical.  Regulatory agencies like the Food and Drug Administration (FDA) and Environmental Protection Agency (EPA) in the United States and the Organisation for Economic Co-operation and Development (OECD) internationally play a crucial role in setting testing guidelines and defining toxicity parameters, including the requirement for DevTox testing for regulatory approval². These agencies monitor adverse health outcomes associated with consumer chemicals during their launch and post-marketing, and place restrictions on the intended use of chemicals as needed. Therefore, it is in the best interest of manufacturers to proactively conduct extensive toxicity testing and establish safe dosage ranges before investing significant time and resources into product development.

Conventionally, mammalian models are used to predict human responses because of shared physiology and similar biological pathways.  Testing on mammalian models—such as rats, mini pigs, dogs, and monkeys—is costly, labor-intensive, and raises ethical concerns.  In fact, the FDA recently announced an initiative to phase out animal testing in favor of New Approach Methodologies (NAMs). To ethically and cost-effectively assess the safety of the thousands of manufactured chemicals requiring evaluation, a robust and reliable alternative model for toxicity testing is needed.

C. elegans as a Model Organism for Developmental Toxicity (DevTox) Testing

Caenorhabditis elegans (C. elegans), a species of nematode, has emerged as a powerful model in predictive toxicology. Its short lifespan, high reproductive rate, and small size make it an easy and cost-effective organism to use in scientific experiments. Since its debut in laboratory research, C. elegans has been extensively characterized: its entire genome has been sequenced, its entire nervous system and connectome has been mapped, its development and cell lineage are understood down to the level of individual cells, and many of its genes and signaling pathways have been identified—approximately 60-80% of which share homology with humans. Additionally, C. elegans possesses multiple organ systems, an active metabolism, an intact reproductive system, a connectome with all the major neurotransmitters, and a transparent cuticle — enabling real-time observation of fluorescent markers and internal processes^3-5.  With widely available genetic tools (CRISPR/Cas-9 and whole-genome RNAi library) and strain resources (fluorescently labeled worms, >2000 mutants, and more than 1000 wild isolates), C. elegans is being used for a mechanistic understanding of key toxicology pathways. These features make C. elegans particularly valuable for DevTox studies.

Studies have demonstrated that C. elegans can predict mammalian developmental toxicity with approximately 89% accuracy⁶. Large-scale DevTox studies have demonstrated similar concordance with rat and rabbit data, while using a gross developmental parameter in C. elegans with a flow cytometer-based technology⁷, known to have high variability⁸. With advancements in microfluidic technology and integration with AI/ML platforms, we can now achieve high-content analysis of multiple parameters with high statistical power. By following robust, standardized protocols and maintaining proper culturing practices, vivoVerse researchers can obtain reproducible and reliable results across well-defined toxicological endpoints using C. elegans⁹.

vivoVerse Developmental Toxicity (DevTox) Assay

To support the growing needs of the industry, vivoVerse has developed the fully automated DevTox Assay using C. elegans. This assay evaluates chemical toxicity by analyzing key developmental endpoints in adult C. elegans after chronic exposure to chemicals. Age-synchronized worms are exposed to a reference chemical from their L1 developmental stage for 72 hours until adulthood. Treated worms are loaded into a vivoChip-24x, a microfluidic device that immobilizes ~1,000 worms across 24 distinct populations, and high-resolution images are taken of each worm.  The worm length, body area, and volume are then calculated and analyzed by vivoVerse’s machine-learning (ML) powered image-analysis pipeline⁹.

The microchannels in the vivoChip-24x devices are designed to capture the entire worm body in a single field of view, allowing for precise measurement. Two variants of the device support worms of different sizes, making it particularly useful in DevTox studies, where toxicity may significantly affect worm size and growth.

Figure 1: An automated developmental toxicity (DevTox) testing using C. elegans.

Scalable, Automated DevTox Data Collection

For each chemical tested, we analyze up to 1,400 C. elegans from 12 unique populations, including two assay controls (vehicle and positive controls), and three biological replicates using the vivoChip. This setup enables the collection of repeatable DevTox data across 10 concentrations and estimates effective concentrations (EC₁₀) with high statistical power. Manual analysis of such large quantities of data is incredibly time-consuming.  vivoVerse’s ML-based model segments ~1,000 individual C. elegans bodies from one vivoChip in just 10 minutes and automatically calculates three developmental endpoints: worm length, body area, and volume, analogous to key mammalian metrics. The precise ML analysis captures small variations between samples, enabling statistically powerful data with very low variability (coefficient of variance, CV <8%). The data analysis automation greatly reduces time spent analyzing data, mitigates human error and inconsistencies, and eliminates bias.

Case Study: Developmental Toxicity of a Reference Chemical

Using the vivoVerse platform, we tested several reference chemicals across various industries, including agrichemicals, industrial chemicals, pharmaceuticals, and food ingredients.  Here, we present the results of a case study from one of the reference chemicals. First, we performed the range-finding assay with the reference chemical and identified the lethal concentration (LD₅₀ = 105 µM). We then characterized three sub-lethal parameters using 10 concentrations below the lethal concentrations. All three sub-lethal DevTox parameters are shown in Figure 2. Among the three parameters, we found the volume to be most sensitive.

Figure 2: Multiple body parameters for concentration-dependent DevTox analysis with one reference chemical. C. elegans were exposed to ten concentrations of a reference chemical and 1% DMSO in the liquid culture. Concentration curves for body length (A), body area (B), and body volume (C) were collected and analyzed using the vivoVerse DevTox Assay and AI model. The data from 3 biological replicates are denoted as mean ± SEM. The vertical dotted lines represent the effective concentration (EC₁₀) values, representing a 10% change in the parameter, are determined by fitting the dataset to a 4-parameter, variable slope Hill function.

Summary:

The highly sensitive, accurate, and reproducible vivoVerse DevTox service, which includes range-finding to identify lethal and maximum sub-lethal concentrations in C. elegans, leverages the vivoChip-24x microfluidic device in conjunction with an ML model to deliver a rapid, high-throughput method for (1) screening active ingredients for toxicity, (2) prioritizing chemical leads, and (3) contributing to read-across strategies. Our services can help our customers reduce reliance on animal use while protecting the environment and promoting public health. As regulatory agencies continue to shift toward non-animal testing models, tools like the vivoVerse’s DevTox assay are poised to become a cornerstone in the future of ethical, efficient, and scalable chemical safety evaluation.

Sources

1. Rangika S. H. K. & Perera, N. Toxicity testing, developmental. Encyclopedia of toxicology, Academic Press, Editor Wexler, P. Pages 349-366 (2024). https://doi.org/10.1016/B978-0-12-824315-2.01051-4
2. Test No. 421: Reproduction/Developmental Toxicity Screening Test, OECD Guidelines for the Testing of Chemicals. (OECD, 2016).
3. Riddle D.L., et al., editors. C. elegans II. 2nd edition. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 1997. Section I, The Biological Model. Available from: https://www.ncbi.nlm.nih.gov/books/NBK20086/
4. Kaletta, T. & Hengartner, M. Finding function in novel targets: C. elegans as a model organism. Nat Rev Drug Discov 5, 387–399 (2006). https://doi.org/10.1038/nrd2031.
5. Hunt PR. The C. elegans model in toxicity testing. J Appl Toxicol. 2017 Jan;37(1):50-59. doi: 10.1002/jat.3357
6. Harlow, P., et al.The nematode Caenorhabditis elegans as a tool to predict chemical activity on mammalian development and identify mechanisms influencing toxicological outcome. Sci Rep 6, 22965 (2016). https://doi.org/10.1038/srep22965
7. Boyd W.A., et al. Developmental effects of the ToxCast™ Phase I and Phase II chemicals in Caenorhabditis elegans and corresponding responses in zebrafish, rats, and rabbits. Environ Health Perspect. May;124(5):586-93 (2016). https://doi: 10.1289/ehp.1409645. Epub 2015 Oct 23.
8. Moore B.T., Jordan J.M., & Baugh L.R. WormSizer: High-throughput analysis of nematode size and shape. PLoS ONE 8(2): e57142 (2013). https://doi.org/10.1371/journal.pone.0057142
9. DuPlissis A., et al. Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing. Sci. Rep. 2025 Jan 2;15(1):15. https://doi:0.1038/s41598-024-84842-x.