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The 24th C. elegans international conference will be held this year June 24-28 in Glasgow (Scotland). It is the first time the event is being organized outside of the United States. Both in-person and virtual attending options will be provided.
See the website for more details: https://genetics-gsa.org/celegans2023/
Across metazoans, lifespan varies greatly, not only among species but also within the same species. Much effort has been devoted to discovering genetic and abiotic factors associated with longevity in humans, and many lifespan-extending perturbations in model organisms like C. elegans have been discovered. Interestingly enough, homogenous genetically identical C. elegans are equally variable as the outbred human population.
In this recently published article (Kinser et al 2021) from Zachary Pincus lab, authors propose that this mode of inter-individual differences may be due to the change in the expression of key regulatory genes. To address or test this model, they used the expression of 22 miRNA promoter-driven GFP and predicted the future lifespan of animals. Intriguingly, the authors found that almost 50% of these reporters could effectively predict the lifespan of animals till they died. Moreover, 2 of these reporters (miR-47 and miR-243) that are most accurate in predicting lifespan, are involved in gene regulatory processes that do not require DAF-16/FOXO transcription factor.
Finally, they also show that three of these transgenes (miR-240-786, miR-793, and miR-47) that are expressed in different tissues, and show a differential pattern throughout life, provide redundant information about a single lifespan determinant process. This process is most probably a cell non-autonomous one that does not depend on DAF-16.
Kinser, H. E., Mosley, M. C., Plutzer, I. B. and Pincus, Z. (2021) Global, cell non-autonomous gene regulation drives individual lifespan among isogenic C. elegans. eLife; 10:e65026. DOI: https://doi.org/10.7554/eLife.65026
Recent advances in neural networks (NN) and machine learning (ML) algorithms have been beneficial in expediting labour-intensive nematode research. In this paper, the authors assessing nematode behaviour, use a high throughput screen called C. elegans Snapshot Analysis Platform (CeSnAP) which enables researchers to go beyond subjective scoring and obtain a more reliable data analysis.
The authors discovered a novel link between branched-chain amino acid transferase 1 (BCAT1) and Parkinson’s Disease (PD) as the RNAi knockdown of neuronal bcat-1 in C. elegans causes abnormal age dependant spasm-like ‘curling’ behaviour. Using CeSnAP, the authors performed high-throughput curling analysis of a total of 17,000 worms in order to identify drugs that ameliorate PD-like motor dysfunction. They found that enasidenib, ethosuximide, metformin, and nitisinone are promising candidates for PD.
This study is an example of the increasing trend towards employing neural networks and machine learning in nematode research and how beneficial it can be to effeciently collect and analyze large amounts of data.
Sohrabi, S., Mor, D. E., Kaletsky, R., Keyes, W., & Murphy, C. T. (2021). High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates. Communications biology, 4(1), 1-9.