Method Article

Using C. elegans to Monitor Proteostasis Imbalances

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DOI:

10.3791/64643

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August 24th, 2022

 , 
1Department of Biology, Northeastern Illinois University, 2Department of Anatomy II, Faculty of Medicine, Ludwig-Maximilians-University of Munich

In This Article

Abstract

The proteostasis network is a cellular protein quality control system that ensures proteome functionality, promoting cellular and organismal health. With age, its fidelity declines, leading to increased vulnerability of the proteome to stress and eventually to diseases such as neurodegenerative disorders and cancer. To highlight research on this evolutionarily conserved network, the Journal of Visualized Experiments (JoVE) presents a collection of methods from researchers using C. elegans to monitor proteostasis imbalances. This editorial provides a brief overview of these different experimental approaches to understand the proteostasis network at the cellular, tissue and organismal levels.

ARTICLES DISCUSSED:

Golden, N. L., Plagens, R. N., Kim Guisbert, K. S., Guisbert, E. Standardized methods for measuring induction of the heat shock response in Caenorhabditis elegans. Journal of Visualized Experiments. (161), doi:10.3791/61030 (2020).

Chauve, L. et al. High-throughput quantitative RT-PCR in single and bulk C. elegans samples using nanofluidic technology. Journal of Visualized Experiments. (159), doi:10.3791/61132 (2020).

Bar-Ziv, R. et al. Measurements of physiological stress responses in C. Elegans. Journal of Visualized Experiments. (159), doi:10.3791/61001 (2020).

Okkenhaug, H., Chauve, L., Masoudzadeh, F., Okkenhaug, L., Casanueva, O. Worm-align and Worm_CP, two open-source pipelines for straightening and quantification of fluorescence image data obtained from Caenorhabditis elegans. Journal of Visualized Experiments. (159), doi:10.3791/61136 (2020).

Lazaro-Pena, M. I., Cornwell, A. B., Samuelson, A. V. Quantifying tissue-specific proteostatic decline in Caenorhabditis elegans. Journal of Visualized Experiments. (175), doi:10.3791/61100 (2021).

Dror, S., Meidan, T. D., Karady, I., Ben-Zvi, A. Using Caenorhabditis elegans to screen for tissue-specific chaperone interactions. Journal of Visualized Experiments. (160), doi:10.3791/61140 (2020).

Miles, J., van Oosten-Hawle, P. Tissue-specific RNAi tools to identify components for systemic stress signaling. Journal of Visualized Experiments. (159), doi:10.3791/61357 (2020).

Pigazzini, M. L., Kirstein, J. In vivo quantification of protein turnover in aging C. elegans using photoconvertible dendra2. Journal of Visualized Experiments. (160), doi:10.3791/61196 (2020).

Pigazzini, M. L., Gallrein, C., Iburg, M., Kaminski Schierle, G., Kirstein, J. Characterization of amyloid structures in aging C. elegans using fluorescence lifetime imaging. Journal of Visualized Experiments. (157), doi:10.3791/61004 (2020).

Rudich, P., Snoznik, C., Puleo, N., Lamitina, T. Measuring RAN peptide toxicity in C. elegans. Journal of Visualized Experiments. (158), doi:10.3791/61024 (2020).

Arnold, M. L., Cooper, J., Grant, B. D., Driscoll, M. Quantitative approaches for scoring in vivo neuronal aggregate and organelle extrusion in large exopher vesicles in C. elegans. Journal of Visualized Experiments. (163), doi:10.3791/61368 (2020).

Editorial

Protein homeostasis, or proteostasis, is fundamental to cellular and organismal health1,2. Conserved cellular processes cooperate to maintain a properly folded proteome by influencing protein synthesis, folding, and clearance3,4. Perturbations in proteostasis have been linked to age-related neurodegenerative diseases and cancers5,6. Based on the high level of conservation, model organisms such as the nematode Caenorhabditis elegans (C. elegans) have been instrumental in understanding the role of proteostasis in human health and disease7,8. C. elegans possess many characteristics that make it an ideal experimental model, including its short life cycle, transparent cuticle, and ease of chemical and genetic manipulations9. This methods collection provides detailed protocols to monitor proteostasis imbalances at the molecular, cellular, and organismal level in C. elegans.

Cells respond to disruptions in proteostasis by activating a variety of stress responses, including the heat shock response (HSR), the unfolded protein response (UPR), and the oxidative stress response (OxSR) to readjust the cellular proteome10. To enhance reproducibility in the field, standardized practices for the robust induction of HSR are described by Golden and colleagues11. RT-qPCR is a widely used, sensitive method to analyze gene expression by quantifying mRNA, and this assay can be used to measure the cellular mRNA levels of various heat shock-inducible genes11. To extend this approach to large-scale, high-throughput analysis, Chauve et al.12 describe a novel adaptation for cDNA preparation, coupled to a nanofluidic RT-qPCR platform that is robust and highly sensitive. Precise measurements of gene expression can be performed on single worms or bulk samples12. Recommended stress response gene targets along with the primer pairs for measuring the transcriptional upregulation of stress response genes are listed in the corresponding protocols12,13.

One limitation of using RT-qPCR to assess the activation of stress responses is that it can be expensive, technically difficult, and requires the use of specialized equipment. Therefore, pathway-specific transcriptional reporter strains expressing a fluorescent protein under a stress inducible promoter (e.g., hsp-16.2p::gfp for monitoring HSR) are frequently employed to assess cellular stress responses11. Using a simple open source Fiji-based workflow named Worm-align coupled with the CellProfiler pipeline Worm_CP, Okkenhaug and colleagues14 outline steps to rapidly quantify fluorescence from individual worms. Additionally, Bar-Ziv et al.13 describe a high-throughput method using a large-particle flow cytometer that enables the capture of fluorescence intensity, size, and spatial (2D) distribution for thousands of animals. Information about transcriptional reporters for evaluating the activation of cellular stress responses along with the recommended settings for fluorescence microscopy and quantification using a worm biosorter are provided13. Importantly, these reporters can be paired with specific physiological stress survival assays that monitor the health of an animal following a specific stress regimen11,13. Although fluorescent reporters have the advantage of revealing tissue-specific differences, they can only detect induction above a certain threshold and the stability of certain fluorophores, such as GFP, precludes the detection of dynamic variations in gene expression. Furthermore, most physiological assays are time-consuming and thus have limited scalability. Despite these limitations, these methods allow for comprehensive characterization of the cellular and physiological effects of internal and external stressors.

Chronic proteotoxic perturbations are associated with the collapse of the proteostasis network (PN), the quality control machinery responsible for maintaining a functional proteome5,6. Progressive decline in proteostasis has been implicated in many age-related diseases. C. elegans is a great model system to examine the vulnerability of specific tissues to proteostasis failures. Lazaro-Pena et al.15 describe an approach to monitor body wall muscle or neuronal functionality as the worm ages. This approach can be adapted for higher-throughput genetic analysis or drug screening15. The major challenge is to understand how the interactions work between the various components of the PN in vivo. To address this shortcoming, Dror and colleagues16 have developed a system to collect specific information on the interactions of molecular chaperones, key components of the PN, in specific tissues during the course of development and aging. By coupling tissue-specific perturbations of folding equilibrium with available mutants or transgenic strains that enable tissue-specific silencing of target genes by RNAi, the chaperone network in a given tissue can be examined. Complementing this strategy with simple behavioral assays that assess the functionality of specific tissues or cell types can uncover biologically relevant interactions of the PN in vivo15,16. It is important to recognize that proteostasis imbalances in one tissue or cell type may impact the quality control system of additional tissues in a cell nonautonomous manner. Miles and van-Oosten-Hawle17 describe an approach to identify mechanisms and components of the cell-nonautonomous PN using tissue-specific RNAi approaches coupled with a range of stress reporters and proteostasis sensors17. This strategy allows for examination of the PN capacity in different tissues as a result of cell nonautonomous-activated stress response mechanisms.

C. elegans is commonly used to model age-related neurodegenerative diseases caused by polyglutamine repeat expansion mutations, such as Huntington's disease18. A proteostasis sensor that is heavily utilized is a polyglutamine (polyQ) fragment fused to a fluorescent protein that forms foci in vivo depending on polyQ length and age19. Improving reproducibility across biological trials, between individuals, and between laboratories, requires one to define what is being scored as foci. Lazaro-Pena et al.15 describe a strategy to accurately measure polyQ::YFP foci formation within either neurons or body wall muscles during aging. To monitor turnover rates of the aggregation-prone mutant huntingtin protein bearing a polyglutamine expansion, Pigazzini and Kirstein20 describe a noninvasive technique that requires the use of the photoconvertible fluorophore Dendra2. Using confocal microscopy and a C. elegans strain expressing mutant huntingtin fused to Dendra2, it is possible to monitor and quantify the degradation of this aggregation-prone protein in an aging organism20. To quantify amyloid fibrilization of aggregation-prone proteins such as mutant huntingtin, fluorescence lifetime imaging microscopy (FLIM) can be employed21. FLIM enables noninvasive analysis of the aggregation process by monitoring the quenching of fluorophores in close vicinity to amyloid structures, thereby decreasing the fluorescence lifetime21. Fluorescence lifetime negatively correlates with aggregation of the fused amyloid protein and is independent of fluorophore expression levels. This technique allows a researcher to follow the effects of aging and PN impairment on the aggregation propensity of any protein. However, if the intensity of the fluorophore is low, this may become problematic since FLIM requires the acquisition of a relatively large number of photons. A higher excitation laser power is therefore needed, leading to faster photobleaching and an unreliable decay curve. Furthermore, the technique requires sophisticated and costly equipment as an add-on to pre-existing microscopy systems. An important standardized approach is included for a different protocol investigating repeat-associated non-AUG-dependent (RAN) translation associated with RNA that contains repeat expansions. Because RAN translation does not require a start codon to determine the reading frame, the number of possible disease-associated toxic peptides increases. Rudich and colleagues22 provide details on how to quantify RAN peptide toxicity using C. elegans movement and reproduction.

Imbalances in proteostasis lead to protein misfolding and the accumulation of protein aggregates that have been linked to multiple age-related neurodegenerative diseases5,6. C. elegans strains expressing various aggregation prone proteins fused to fluorescent reporters have been utilized to monitor proteostasis imbalances across different cell and tissue types. Analysis of these models has led to a set of novel observations; Monica Driscoll and colleagues noticed that neurons release PolyQ aggregates and other cellular debris, such as damaged organelles, in “exophers”, uncovering a formerly unrecognized branch of neuronal proteostasis23. Since this is a new phenomenon, it is crucial to provide clear instructions for monitoring exopher production to ensure reproducibility within the field. Arnold et al.24 outline the physical features of C. elegans exophers, strategies for their detection, identification criteria, and optimal timing for quantification. Furthermore, this protocol highlights the critical attention needed to strictly control the growth conditions to eliminate unintended stresses that can modulate exopher levels to achieve a reproducible assessment of exopher production.

This collection provides multiple methodological strategies to monitor proteostasis imbalances at the molecular, cellular, and organismal level in C. elegans. Stressors, such as misfolded disease proteins, can be experimentally introduced into specific tissue types, while cellular and organismal response to proteotoxicity are monitored over time. This collection provides a toolkit for probing and monitoring proteostasis, evaluating quality control systems that maintain a folded proteome, and investigating the dysregulation of these systems that lead to disease.

Disclosures

The authors have nothing to disclose.

Acknowledgements

We thank our colleagues for their highly valuable contributions to this methods collection.

References

  1. Balch, W. E., Morimoto, R. I., Dillin, A., Kelly, J. W. Adapting proteostasis for disease intervention. Science. 319 (5865), 916-919 (2008).
  2. Hartl, F. U., Bracher, A., Hayer-Hartl, M. Molecular chaperones in protein folding and proteostasis. Nature. 475 (7356), 324-332 (2011).
  3. Tyedmers, J., Mogk, A., Bukau, B. Cellular strategies for controlling protein aggregation. Nature Reviews. Molecular Cell Biology. 11 (11), 777-788 (2010).
  4. Vilchez, D., Saez, I., Dillin, A. The role of protein clearance mechanisms in organismal ageing and age-related diseases. Nature Communications. 5, 5659(2014).
  5. Labbadia, J., Morimoto, R. I. The biology of proteostasis in aging and disease. Annual Review of Biochemistry. 84, 435-464 (2015).
  6. Taylor, R. C., Dillin, A. Aging as an event of proteostasis collapse. Cold Spring Harbor Perspectives in Biology. 3 (5), 004440(2011).
  7. Caldwell, K. A., Willicott, C. W., Caldwell, G. A. Modeling neurodegeneration in Caenorhabditis elegans. Disease Model Mechanism. 13 (10), (2020).
  8. Brehme, M., et al. A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Reports. 9 (3), 1135-1150 (2014).
  9. Girard, L. R., et al. WormBook: the online review of Caenorhabditis elegans biology. Nucleic Acids Research. 35, 472-475 (2007).
  10. Kourtis, N., Tavernarakis, N. Cellular stress response pathways and ageing: intricate molecular relationships. The EMBO Journal. 30 (13), 2520-2531 (2011).
  11. Golden, N. L., Plagens, R. N., Kim Guisbert, K. S., Guisbert, E. Standardized methods for measuring induction of the heat shock response in Caenorhabditis elegans. Journal of Visualized Experiments. (161), e61030(2020).
  12. Chauve, L. High-throughput quantitative RT-PCR in single and bulk C. elegans samples using nanofluidic technology. Journal of Visualized Experiments. (159), e61132(2020).
  13. Bar-Ziv, R., et al. Measurements of physiological stress responses in C. Elegans. Journal of Visualized Experiments. (159), e61001(2020).
  14. Okkenhaug, H., Chauve, L., Masoudzadeh, F., Okkenhaug, L., Casanueva, O. Worm-align and Worm_CP, two open-source pipelines for straightening and quantification of fluorescence image data obtained from Caenorhabditis elegans. Journal of Visualized Experiments. (159), e61136(2020).
  15. Lazaro-Pena, M. I., Cornwell, A. B., Samuelson, A. V. Quantifying tissue-specific proteostatic decline in Caenorhabditis elegans. Journal of Visualized Experiments. (175), e61100(2021).
  16. Dror, S., Meidan, T. D., Karady, I., Ben-Zvi, A. Using Caenorhabditis elegans to screen for tissue-specific chaperone interactions. Journal of Visualized Experiments. (160), e61140(2020).
  17. Miles, J., van Oosten-Hawle, P. Tissue-specific RNAi tools to identify components for systemic stress signaling. Journal of Visualized Experiments. (159), e61357(2020).
  18. Brignull, H. R., Morley, J. F., Garcia, S. M., Morimoto, R. I. Modeling polyglutamine pathogenesis in C. elegans. Methods Enzymology. 412, 256-282 (2006).
  19. Morley, J. F., Brignull, H. R., Weyers, J. J., Morimoto, R. I. The threshold for polyglutamine-expansion protein aggregation and cellular toxicity is dynamic and influenced by aging in Caenorhabditis elegans. Proceedings of the National Academy of Sciences of the United States of America. 99 (16), 10417-10422 (2002).
  20. Pigazzini, M. L., Kirstein, J. In vivo quantification of protein turnover in aging C. elegans using photoconvertible dendra2. Journal of Visualized Experiments. (160), e61196(2020).
  21. Pigazzini, M. L., Gallrein, C., Iburg, M., Kaminski Schierle, G., Kirstein, J. Characterization of amyloid structures in aging C. elegans using fluorescence lifetime imaging. Journal of Visualized Experiments. (157), e61004(2020).
  22. Rudich, P., Snoznik, C., Puleo, N., Lamitina, T. Measuring RAN peptide toxicity in C. elegans. Journal of Visualized Experiments. (158), e61024(2020).
  23. Melentijevic, I., et al. C. elegans neurons jettison protein aggregates and mitochondria under neurotoxic stress. Nature. 542 (7641), 367-371 (2017).
  24. Arnold, M. L., Cooper, J., Grant, B. D., Driscoll, M. Quantitative approaches for scoring in vivo neuronal aggregate and organelle extrusion in large exopher vesicles in C. elegans. Journal of Visualized Experiments. (163), e61368(2020).

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