Biology subjects

Latorre-Crespo, E.

Publications and source records attributed to Latorre-Crespo, E..

5 recordsLinked to original sources

bioRxiv2025

Clinical progression of clonal hematopoiesis is determined by a combination of mutation timing, fitness, and clonal structure

Clonal hematopoiesis (CH) is characterized by expanding blood cell clones carrying somatic mutations in healthy aged individuals and is associated with various age-related diseases and all-cause mortality. While CH mutations affect diverse genes associated with myeloid malignancies, their mechanisms of expansion and disease associations remain poorly understood. We investigate the relationship between clonal fitness and clinical outcomes by integrating data from three longitudinal aging cohorts (n=713, observations=2,341). We demonstrate pathway-specific fitness advantage and clonal composition influence clonal dynamics. Further, the timing of mutation acquisition is necessary to determine the extent of clonal expansion reached during the host individuals lifetime. We introduce MACS120, a metric combining mutation context, timing, and variant fitness to predict future clonal growth, outperforming traditional variant allele frequency measurements in predicting clinical outcomes. Our unified analytical framework enables standardized clonal dynamics inference across cohorts, advancing our ability to predict and potentially intervene in CH-related pathologies.

genetics↗

bioRxiv2024

High-dimensional Ageome Representations of Biological Aging across Functional Modules

The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans. Ageome is based on the premise that an organisms overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks. Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.

systems biology↗

bioRxiv2023

DNA methylation rates scale with maximum lifespan across mammals

DNA methylation rates have previously been found to broadly correlate with maximum lifespan in mammals, yet no precise relationship has been observed. We developed a statistically robust framework to compare methylation rates at conserved age-related sites across mammals. We found that methylation rates negatively scale with maximum lifespan in both blood and skin. The emergence of explicit scaling suggests that methylation rates are - or are linked to - an evolutionary constraint on maximum lifespan acting across diverse mammalian lineages.

genetics↗

bioRxiv2023

Probabilistic inference of epigenetic age acceleration from cellular dynamics

The emergence of epigenetic predictors was a pivotal moment in geroscience, propelling the measurement and concept of biological ageing into a quantitative era. However, while current epigenetic clocks have shown strong predictive power, they do not reflect the underlying biological mechanisms driving methylation changes with age. Consequently, biological interpretation of their estimates is limited. Furthermore, our findings suggest that clocks trained on chronological age are confounded by non-age-related phenomena. To address these limitations, we developed a probabilistic model that describes methylation transitions at the cellular level. Our approach reveals two measurable components, acceleration and bias, that directly relate to perturbations of the underlying cellular dynamics. Acceleration is the proportional increase in the speed of methylation transitions across CpG sites, whereas bias is the degree of global change in methylation affecting all CpG sites uniformly. Using data from 7,028 participants from the Generation Scotland study, we found the age acceleration parameter to be associated with physiological traits known to impact healthy ageing. Furthermore, a genome-wide association study of age acceleration identified four genomic loci previously linked with ageing.

cell biology↗

bioRxiv2021

Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects

The prevalence of clonal haematopoiesis of indeterminate potential (CHIP) in healthy individuals increases rapidly from age 60 onwards and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in stem cells that are also drivers of myeloid malignancies. Since mutations in stem cells often drive leukaemia, we hypothesised that stem cell fitness substantially contributes to transformation from CHIP to leukaemia. Stem cell fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. It is currently unknown whether mutations in different CHIP genes lead to distinct fitness advantages that could form the basis for patient stratification. We set out to quantify the fitness effects of CHIP drivers over a 12 year timespan in older age, using longitudinal error-corrected sequencing data. We developed a new method based on drift-induced fluctuation (DIF) filtering to extract fitness effects from longitudinal data, and thus quantify the growth potential of variants within each individual. Our approach discriminates naturally drifting populations of cells and faster growing clones, while taking into account individual mutational context. We show that gene-specific fitness differences can outweigh inter-individual variation and therefore could form the basis for personalised clinical management.

cell biology↗