Biology subjects

Bouchard, L.-S.

Publications and source records attributed to Bouchard, L.-S..

7 recordsLinked to original sources

bioRxiv2026

Conformal Uncertainty Quantification for BayesAge Epigenetic Age Predictions

Epigenetic clocks predict chronological age from DNA methylation (DNAm) profiles, yet most provide point estimates without calibrated uncertainty. This limits their use when quantified error bounds are required. We apply conformal prediction to BayesAge, a maximum-likelihood clock that models nonlinear DNAm-age relationships using a small set of CpG loci and a count-based likelihood. Split conformal prediction yields distribution-free prediction intervals with finite-sample marginal coverage guarantees under exchangeability and requires only a single model fit per split. We also evaluate a locally scaled variant that produces age-dependent interval widths using a locally estimated error scale. In our targeted bisulfite sequencing cohort, split conformalized BayesAge attains near-nominal empirical coverage while preserving BayesAge point-prediction accuracy. The locally scaled variant yields wider intervals at older ages, but its coverage is less stable in small-calibration regimes, consistent with additional uncertainty from estimating the local scale. Relative to Monte Carlo intervals that propagate read-sampling variability and to higher-dimensional linear baselines (conformalized linear quantile regression and conformalized Lasso regression), conformalized BayesAge provides calibrated uncertainty using substantially fewer CpG sites and with weaker age-dependent structure in residuals. These results support conformal prediction as a practical approach for uncertainty quantification in DNAm-based age estimation.

bioinformatics↗

bioRxiv2026

MKMC enables reference-free transcriptomic analysis using k-mer representations

Traditional RNA-seq analysis depends heavily on genome alignment and gene annotation, limiting its utility in non-model organisms and introducing biases that can obscure regulatory complexity. We present MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer-based statistics to detect biological variation without requiring alignment. MKMC integrates fast k-mer counting, abundance matrix generation, normalization, dimensionality reduction, and differential analysis into a unified workflow. Across diverse datasets, MKMC recapitulates key biological signals--including sex differences in killifish liver--and matches alignment-based pipelines in differential expression analysis and transcriptomic age prediction. Notably, MKMC detects isoform-specific events missed by traditional methods, one of which we validated using in situ hybridization. These results reveal previously hidden isoform-level regulatory events that contribute to sex-and age-associated transcriptional programs. MKMC offers a robust, extensible alternative to alignment-based approaches, enabling transcriptomic discovery across both model and non-model systems. While we focus here on RNA-seq as a primary application, MKMC is broadly applicable to any k-mer-based analysis of next-generation sequencing data.

bioinformatics↗

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↗

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↗

bioRxiv2021

Global anti-tumor immunity after localized, bioengineered Treg depletion

Over 90% of deaths from cancer occur due to solid tumors, occurring at a rate of [~]1,500 deaths per day in the US, highlighting a profound and unmet need for new therapies. Solid tumors evade clearance by T cells due to a variety of immunosuppressive properties of the tumor microenvironment. However, this immunosuppression cannot be easily blocked on a global level because systemic activation of the immune system elicits a host of complications. An ideal therapy for solid tumors would act locally to activate the immune response without evoking global adverse effects. Here we present a biodegradable, macroporous scaffold that is implanted adjacent to the tumor and suppresses the main obstacle to cancer immunosurveillance: intratumoral regulatory T cells. The scaffold also promotes the recruitment and activation of T cell effectors into the tumor, resulting in clearance of otherwise aggressive and fatal tumors in mice. Unexpectedly, the local depletion of Tregs results in an "immunological abscopal effect" acting on distant tumors. We demonstrate that this versatile platform can also deliver tumor-antigen-specific T cells directly to the peri-tumoral environment, bypassing difficulties in intravenous delivery including the environmental barriers imposed by the tumors vasculature. By orchestrating multiple local immunomodulatory treatments, this scaffold offers a general approach to engineer T-cell responses to solid tumors without systemic toxicities.

immunology↗

bioRxiv2019

Augmentation of T-cell activation by oscillatory forces and engineered antigen-presenting cells

Activation of T cells by antigen presenting cells allows them to proliferate, produce cytokines, and kill infected or cancerous cells. We and others have shown that T cell receptors receive and in fact require mechanical forces from their own movements and the movements of antigen presenting cells. Emulation of T cell activation in vitro allows for the massive expansion of T cells necessary for clinical applications. In this paper, we studied the impact of augmenting novel artificial antigen presenting cells of various sizes and antigenic signal strength with mechanical, oscillatory movement. We showed that dynamic culture roughly doubles signal strength as compared to conventional, static culture. We demonstrated that tuning the strength of signal to a \"sweet spot\" allows for robust expansion of induced regulatory T cells, which is impeded by approaches that simply maximize activation.

immunology↗

bioRxiv2019

T-cell activation is modulated by the 3D mechanical microenvironment

T cells recognize mechanical forces through a variety of cellular pathways, including mechanical triggering of the T-cell receptor (TCR) and mechanical triggering of the integrin LFA-1. We show here that T cells can recognize forces arising from the rigidity of the microenvironment. We fabricated 3D hydrogels with mechanical stiffness tuned to 4 kPa and 40 kPa and specially engineered be microporous independent of stiffness. We cultured T cells and antigen presenting cells within the matrices and studied activation by flow cytometry and live imaging. We found there was an augmentation of T-cell activation in the context of mechanically stiffer 3D material as compared to the softer material. In contrast, proliferation, activation markers, and migration were all diminished in T cells cultured in the softer material. These results show that T cells can sense their mechanical environment and amplify responses in the context of mechanical stiffness.

immunology↗