Biology subjects

Prelot, L.

Publications and source records attributed to Prelot, L..

3 recordsLinked to original sources

bioRxiv2025

Splicing neoepitope prediction is sensitive to methodological differences

MotivationCancer-specific neoepitopes may arise from abnormal splicing in the transcriptomic landscape (alternative splicing neoepitopes, ASNs), leading to divergent proteins with high potential immunogenicity. Predicting candidate ASNs requires a series of algorithmic and parameter choices, but no previous study has investigated the consistency and interpretability of these choices. ResultsWe apply two ASN prediction methods with 35 matched parameter sets to generate candidate ASNs for five breast and ovarian cancer samples from The Cancer Genome Atlas (TCGA). We find that: 1) junction 9-mer peptides generated by two similarly designed pipelines differed by, on average, 68.9% and 76.6% in the BRCA and OV cohorts, respectively, and putatively cancer-specific junction 9-mers from the two pipelines diverged further, by an average of 81.5 % in OV and 84.6 % in BRCA; 2) the most lenient filters in the BRCA cohort show the highest divergence, at 97 %; 3) the rate of mass spectrometry validation of ASNs protein presence in cells is dominated by the size of the input space; and 4) putatively cancer-specific ASNs found by the intersection of both pipelines can be validated when accounting for false discovery with an average of 1.74 and 284.4 candidates in the BRCA and OV cohorts, respectively. Taken together, these results highlight that ASN identification is fragile and difficult to reproduce across analysis platforms, with limited cross-pipeline overlap and strong dependence on parameter choices. Availability and ImplementationPython research code and scripts are available for download at https://github.com/ratschlab/projects2020_ohsu. Contactthompsre@ohsu.edu, raetsch@inf.ethz.ch

bioinformatics↗

bioRxiv2018

Old fibroblasts secrete inflammatory cytokines that drive variability in reprogramming efficiency and may affect wound healing between old individuals

Age-associated chronic inflammation (inflammaging) has emerged as a central hallmark of aging1-3, but its impact on specific cells is still largely unknown. Fibroblasts are present in all tissues and contribute to wound healing4-6. They are also the cell type that is mostly used for induced pluripotent stem cell (iPSC) reprogramming7 - a process that has implications for regenerative medicine and rejuvenation strategies8-17. Here we show that primary fibroblasts from old mice secrete inflammatory cytokines and that there is an increased variability in reprogramming efficiency between fibroblast cultures from old individuals. Individual-to-individual variability is emerging as a key feature of old age18-21, which could reflect distinct aging trajectories, but the underlying causes remain unknown. To identify drivers of this variability, we perform a multi-omic assessment of young and old fibroblast cultures with different reprogramming efficiency. This approach, coupled with single cell transcriptomics, reveals that old fibroblast cultures are heterogeneous and show a greater proportion of activated fibroblasts that secrete inflammatory cytokines, which correlates with reprogramming efficiency. We experimentally validate that activated fibroblasts express inflammatory cytokines in vivo and that their presence is linked to enhanced reprogramming efficiency in culture. Conditioned-media swapping experiments show that extrinsic factors secreted by activated fibroblasts are more critical than intrinsic factors for the individual-to-individual variability in reprogramming efficiency, and we identify TNF as a key inflammatory cytokine underlying this variability. Interestingly, old mice also exhibit variability in wound healing efficiency in vivo and old wounds show an increased subpopulation of activated fibroblasts with a unique TNF signature. Our study shows that a switch in fibroblast composition, and the ratio of inflammatory cytokines they secrete, drives variability in reprogramming in vitro and may influence wound healing in vivo. These findings could help identify personalized strategies to improve iPSC generation and wound healing in older individuals.

cell biology↗

bioRxiv2018

Machine Learning in Multi-Omics Data to Assess Longitudinal Predictors of Glycaemic Trait Levels

Type 2 diabetes (T2D) is a global health burden that will benefit from personalised risk prediction and targeted prevention programmes. Omics data have enabled more detailed risk prediction; however, most studies have focussed on directly on the ability of DNA variants predicting T2D onset with less attention given to epigenetic regulation and glycaemic trait variability. By applying machine learning to the longitudinal Northern Finland Birth Cohort 1966 (NFBC 1966) at 31 (T1) and 46 (T2) years old, we predicted fasting glucose (FG) and insulin (FI), glycated haemoglobin (HbA1c) and 2-hour glucose and insulin from oral glucose tolerance test (2hGlu, 2hIns) at T2 in 513 individuals from 1,001 variables at T1 and T2, including anthropometric, metabolic, metabolomic and epigenetic variables. We further tested whether the information obtained by the machine learning models in NFBC could be used to predict glycaemic traits in the independent French study with 48 matching predictors (DESIR, N=769, age range 30-65 years at recruitment, interval between data collections: 9 years). In this study, FG and FI were best predicted, with average R2 values of 0.38 and 0.53. Sex, branched-chain and aromatic amino acids, HDL-cholesterol, glycerol, ketone bodies, blood pressure at T2 and measurements of adiposity at T1, as well as multiple methylation marks at both time points were amongst the top predictors. In the validation analysis, we reached R2 values of 0.41/0.55 for FG/FI when trained and tested in NFBC1966 and 0.17/0.30 when trained in NFBC1966 and tested in DESIR. We identified clinically relevant sets of predictors from a large multi-omics dataset and highlighted the potential of methylation markers and longitudinal changes in prediction.

genomics↗