Deep behavioural phenotyping reveals divergent trajectories of ageing and quantifies health state in<i>C. elegans</i>

Cn, Martineau; Baskaner, Bora; Ri, Seinstra; Schafer, William R.; Brown, André E. X.; Eaa, Nollen; Laurent, Patrick

doi:10.1101/555847

Cited by 3 publications

(5 citation statements)

References 30 publications

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“…Qualitatively, the short- and long-lived wild type animals followed similar phenotypic trajectories in the subspaces we selected. Using the same methodology, we previously observed that different genotypes can follow distinct trajectories [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans

2020

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Ageing affects a wide range of phenotypes at all scales, but an objective measure of ageing remains challenging, even in simple model organisms. To measure the ageing process, we characterized the sequence of alterations of multiple phenotypes at organismal scale. Hundreds of morphological, postural, and behavioral features were extracted from high-resolution videos. Out of the 1019 features extracted, 896 are ageing biomarkers, defined as those that show a significant correlation with relative age (age divided by lifespan). We used support vector regression to predict age, remaining life and lifespan of individual C. elegans. The quality of these predictions (age R 2 = 0.79; remaining life R 2 = 0.77; lifespan R 2 = 0.72) increased with the number of features added to the model, supporting the use of multiple features to quantify ageing. We quantified the rate of ageing as how quickly animals moved through a phenotypic space; we quantified health decline as the slope of the declining predicted remaining life. In both ageing dimensions, we found that short lived-animals aged faster than long-lived animals. In our conditions, for isogenic wild-type worms, the health decline of the individuals was scaled to their lifespan without significant deviation from the average for short-or long-lived animals.

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Section: Discussionmentioning

confidence: 99%

Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans

2020

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“…Health state and longevity are affected by genetics and environment. We previously observed different phenotypic trajectories of ageing for two different genotypes (Martineau et al, 2019). However even isogenic worms ageing in a controlled environment show wide inter-individual differences in lifespan.…”

Section: Discussionmentioning

confidence: 97%

“…It is calculated as the mean difference between the maximum value (90 th percentile) and minimum value (10 th percentile) for each feature and should be an indicator of the phenotypic flexibility of the animals. This index is applicable to physiological as well as pathological ageing (Martineau et al, 2019). Our health index varies along life and appeared highly dependent on lifespan.…”

Section: Long-lived Animals Have a Better Healthmentioning

confidence: 99%

Multidimensional phenotyping predicts lifespan and quantifies health inC. elegans

Martineau

Brown²,

Laurent

2019

Preprint

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Ageing affects a wide range of phenotypes at all scales, but an objective measure of ageing remains challenging, even in simple model organisms. We assumed that a wide range of phenotypes at the organismal scale rather than a limited number of biomarkers of ageing would best describe the ageing process. Hundreds of morphological, postural and behavioural features are extracted at once from high resolutions videos. A quantitative model using this multiparametric dataset can predict the biological age and lifespan of individual C. elegans. We show that the quality of predictions on a held-out data set increases with the number of features added to the model, supporting our initial hypothesis. Despite the large diversity of ageing mechanisms, including stochastic insults, our results highlight a robust ageing trajectory, but variable ageing rates along that trajectory. We show that healthspan, which we defined as the range of abilities of the animals, is correlated to lifespan in wild-type worms.

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“…Martineau (23,32) Proteome ratios over time (numerical) Narayan (20) Gene expression over time (numerical) Hastings (21) Gene expression over time (numerical) Bgee (33) Gene effect on lifespan (categorical) GenAge (6,34) Protein Phosphorylation (numerical) Huang (24) Pathway labels (categorical) Uniprot (35) Interacting genes (categorical) Wormbase (29) Disease information (categorical) Wormbase (29) Human ortholog (categorical) Wormbase (29) Gene description (categorical) Wormbase ( 29)…”

Section: Data Collection and Formattingmentioning

confidence: 99%

“…A further benefit of working with C. elegans is the vast range of available data including time-based transcriptomic, proteomic, phosphorylation status, molecular data and detailed morphological data, allowing to describe the relationships between different Omics layers and their possible contributions to the aging process (20,21,23,24). However, prioritization of aging-related targets remains a challenge in this organism due to the many existing targets and their association with longevity and not necessarily the aging process.…”

Section: Introductionmentioning

confidence: 99%

Supervised machine learning with feature selection for prioritization of targets related to time-based cellular dysfunction in aging

Truter¹,

Rensburg²,

Oudrhiri

et al. 2022

Preprint

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Background: Global life expectancy has been increasing without a corresponding increase in health span and with greater risk for aging-associated diseases such as Alzheimer′s disease (AD). An urgent need to delay the onset of aging-associated diseases has arisen and a dramatic increase in the number of potential molecular targets has led to the challenge of prioritizing targets to promote successful aging. Here, we developed a pipeline to prioritize aging-related genes which integrates the plethora of publicly available genomic, transcriptomic, proteomic and morphological data of C. elegans by applying a supervised machine learning approach. Additionally, a unique biological post-processing analysis of the computational output was performed to better reveal the prioritized gene′s function within the context of pathways and processes involved in aging across the lifespan of C. elegans. Results: Four known aging-related genes, daf-2, involved in insulin signaling; let-363 and rsks-1, involved in mTOR signaling; age-1, involved in PI3 kinase signaling, were present in the top 10% of 4380 ranked genes related to different markers of cellular dysfunction, validating the computational output. Further, our ranked output showed that 91% of the top 438 ranked genes consisted of known genes on GenAge, while the remaining genes had thus far not yet been associated with aging-related processes. Conclusion: These ranked genes can be translated to known human orthologs potentially uncovering previously unknown information about the basic aging processes in humans. These genes (and their downstream pathways) could also serve as targets against aging-related diseases, such as AD.

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Deep behavioural phenotyping reveals divergent trajectories of ageing and quantifies health state inC. elegans

Cited by 3 publications

References 30 publications

Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans

Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans

Multidimensional phenotyping predicts lifespan and quantifies health inC. elegans

Supervised machine learning with feature selection for prioritization of targets related to time-based cellular dysfunction in aging

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