Biology subjects

Perez-Correa, J.-F.

Publications and source records attributed to Perez-Correa, J.-F..

3 recordsLinked to original sources

bioRxiv2026

Aberrant DNA methylation is co-regulated across the genome in leukemia and other types of cancer

Epigenetic dysregulation is a defining feature of cancer, but it remains poorly understood how this is coordinated across the genome. In this study we focusd on DNA methylation (DNAm) in acute myeloid leukemia (AML). Despite highly heterogeneous and largely patient-specific patterns, we identified co-regulated clusters of CpGs that could be assembled into reproducible epigenetic networks. Multilinear regression models accurately predicted the patient-specific DNAm deviations, even for CpGs located on different chromosomes. The alterations were mirrored on homologous chromosomes and there was no clear association with epigenetic driver mutations. Furthermore, we found very similar co-regulation patterns in acute lymphoblastic leukemia (ALL); with AML-derived models successfully predicting the ALL-associated DNAm changes. Notably, the top 1000 AML-associated CpGs showed also pronounced aberrations in DNAm levels across 46 other cancer types, whereas this was hardly observed across multiple non-malignant cell types. Co-regulation analysis realed very similar patterns in non-malignant blood and pan-cancer analysis, albeit the DNAm levels remained overall consistent in the controls. Collectively, our findings demonstrate that the complex, patient-specific DNAm landscapes observed in leukemia are not random. Instead, they are orchestrated within expanded epigenetic networks, which also exist in non-maligant cells, highlighting a higher-order regulatory layer in cancer epigenomics.

cancer biology↗

bioRxiv2026

The extent of myeloid skewing in blood is a biomarker of biological aging in mice and humans

Myeloid skewing is a central and therefore often cited hallmark of hematopoietic aging. Myeloid skewing refers to an elevated myeloid-to-lymphoid cell ratio in aged compared to young mice. Interestingly, whether the extent of myeloid skewing might be in itself a quantitative biological marker of aging has not been addressed yet, nor whether this parameter has also relevance for the extent of aging in humans. Aged mice with high level of myeloid skewing (>50% myeloid cells in blood) showed accelerated hematopoietic aging compared to mice with a low level of myeloid skewing (<30% of myeloid cells in blood), as well as an increased level of inflammatory cytokines and elevated levels of diseases. Hematopoietic stem cells (HSCs) from mice with high myeloid skewing showed an impaired repopulation capacity. Epigenetic clock analyses demonstrated that mice with a high level of myeloid skewing present with a biological age that is older than their chronological age. In humans, a high degree of myeloid skewing was associated with elevated levels of inflammatory markers, reduced mobility, a greater burden of comorbidities, and an increased mortality hazard ratio. The data support that, besides overall myeloid skewing being a central hallmark of aging in mice, the extent of the frequency of myeloid cells in blood might serve as a biological marker of aging and disease in both mice and humans. Key PointsThe extent of myeloid skewing in aged mice correlates to an increased hematological and epigenetic age and increased disease burden. The extent of myeloid skewing in older adults is associated with an increased hazard ratio of mortality and correlates with higher frailty and inflammatory markers.

cell biology↗

bioRxiv2024

Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age

BackgroundEpigenetic aging signatures can provide insights into the human aging process. Within the last decade many alternative epigenetic clocks have been described, which are typically based on linear regression analysis of DNA methylation at multiple CG dinucleotides (CpGs). However, this approach assumes that the epigenetic modifications follow either a continuous linear or logarithmic trajectory. In this study, we explored an alternative non-parametric approach using 2D-kernel density estimation (KDE) to determine epigenetic age. ResultsWe used Illumina BeadChip profiles of blood samples of various studies, exemplarily selected the 27 CpGs with highest linear correlation with chronological age (R2 > 0.7), and computed KDEs for each of them. The probability profiles for individual KDEs were further integrated by a genetic algorithm to assign an optimal weight to each CpG. Our weighted 2D-kernel density estimation model (WKDE) facilitated age-predictions with similar correlation and precision (R2 = 0.81, median absolute error = 4 years) as other commonly used clocks. Furthermore, our approach provided a variation score, which reflects the inherent variation of age-related epigenetic changes at different CpG sites within a given sample. An increase of the variation score by one unit reduced the mortality risk by 9.2% (95% CI (0.8387, 0.9872), P <0.0160) in the Lothian Birth Cohort 1921 after adjusting for chronological age and sex. ConclusionsWe describe a new method using weighted 2D-kernel density estimation (WKDE) for accurate epigenetic age-predictions and to calculate variation scores, which provide an additional variable to estimate biological age.

genomics↗