Biology subjects

Mboning, L.

Publications and source records attributed to Mboning, L..

4 recordsLinked to original sources

bioRxiv2025

Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish

Aging is associated with progressive tissue dysfunction, leading to frailty and mortality. Characterizing aging features, such as changes in gene expression and dynamics, shared across tissues or specific to each tissue, is crucial for understanding systemic and local factors contributing to the aging process. We performed RNA-sequencing on 13 tissues at 6 different ages in the African turquoise killifish, the shortest-lived vertebrate that can be raised in captivity. This comprehensive, sex-balanced atlas dataset reveals the varying strength of sex-age interactions across killifish tissues and identifies age-altered biological pathways that are evolutionarily conserved. Demonstrating the utility of this resource, we discovered that the killifish head kidney exhibits a myeloid bias during aging, a phenomenon more pronounced in females than in males. In addition, we developed tissue-specific transcriptomic clocks and identified biomarkers predictive of chronological age. We show the importance of sex-specific clocks for selected tissues and use the tissue clocks to evaluate a dietary intervention in the killifish. Our work provides a comprehensive resource for studying aging dynamics across tissues in the killifish, a powerful vertebrate aging model.

genomics↗

bioRxiv2024

MicroBayesAge: A Maximum Likelihood Approach to Predict Epigenetic Age Using Microarray Data

Certain epigenetic modifications, such as the methylation of CpG sites, can serve as biomarkers for chronological age. Previously, we introduced our BayesAge frameworks for accurate age prediction through the use of locally weighted scatterplot smoothing (LOWESS) to capture the non-linear relationship between methylation or gene expression and age, and Maximum Likelihood Estimation (MLE) for bulk bisulfite and RNA sequencing data. Here we now introduce MicroBayesAge, a framework that enhances prediction accuracy by subdividing input data into age-specific co-horts and employing a new two-stage process for training and testing. Age prediction for younger patients was significantly improved. MicroBayesAge also exhibited minimal bias in its age predictions. Additionally, we explored the performance of our model for sex-specific age prediction which revealed slight improvements in accuracy for male patients, while no changes were observed for female patients. MicroBayesAge provides more accurate age predictions by accounting for variations in epigenetic markers of aging among different subgroups, which have been over-looked by commonly used models.

bioinformatics↗

bioRxiv2024

BayesAge 2.0: A Maximum Likelihood Algorithm To Predict Transcriptomic Age

Aging is a complex biological process influenced by various factors, including genetic and environmental influences. In this study, we present BayesAge 2.0, an improved version of our maximum likelihood algorithm designed for predicting transcriptomic age (tAge) from RNA-seq data. Building on the original BayesAge framework, which was developed for epigenetic age prediction, BayesAge 2.0 integrates a Poisson distribution to model count-based gene expression data and employs LOWESS smoothing to capture non-linear gene-age relationships. BayesAge 2.0 provides significant improvements over traditional linear models, such as Elastic Net regression. Specifically, it addresses issues of age bias in predictions, with minimal age-associated bias observed in residuals. Its computational efficiency further distinguishes it from traditional models, as reference construction and cross-validation are completed more quickly compared to Elastic Net regression, which requires extensive hyperparameter tuning. Overall, BayesAge 2.0 represents a notable advance in transcriptomic age prediction, offering a robust, accurate, and efficient tool for aging research and biomarker development.

bioinformatics↗

bioRxiv2023

BayesAge: A Maximum Likelihood Algorithm To Predict Epigenetic Age

DNA methylation is a reaction that results in the formation of 5-methylcytosine when a methyl group is added to the cytosines C5 position. As organisms age, DNA methylation patterns change in a reproducible fashion. This phenomenon has established DNA methylation as a valuable biomarker in aging studies. Epigenetic clocks based on weighted combinations of methylation sites have been developed to accurately predict the age of an individual from their methylome. However, many epigenetic clocks, particularly those that utilize penalized regression, model the changes in methylation linearly with age. Moreover, these models, which use methylation levels as features, are not robust to missing data and do not account the count-based nature of bisulfite sequence data. Additionally, the models are generally not interpretable. To overcome these challenges, we present BayesAge, an extension of the previously developed scAge approach that was developed for the analysis of single cell DNA methylation datasets. BayesAge utilizes maximum likelihood estimation (MLE) to infer ages, models count data using binomial distributions, and uses LOWESS smoothing to capture the non-linear dynamics between methylation and age. Our approach is designed for use with bulk bisulfite sequencing datasets. BayesAge outperforms scAge in several respects. Specifically, BayesAges age residuals are not age associated, thus providing a less biased representation of epigenetic age variation across populations. Moreover, BayesAge enables the estimation of error bounds on age inference and, when run on down-sampled data, its coefficient of determination between predicted and actual ages surpasses both scAge and penalized regression.

bioinformatics↗