Biology subjects

Ferrer, A.

Publications and source records attributed to Ferrer, A..

3 recordsLinked to original sources

bioRxiv2026

Functional Characterization of Myeloid Neoplasm-associated DDX41 Variants Reveals Pathogenic Interaction with Acquired Hotspot Mutation

Germline variants in DDX41 are the most frequent genetic predisposition to adult hematologic malignancies. The most common variants are truncating, implicating loss of function in the pathogenesis. However, non-truncating variants account for 30-40% of cases, and their impact on essential DDX41 functions remains unknown. We utilized a genetic complementation assay to assess the functionality of 10 recurrent germline non-truncating variants of DDX41. All variants restored viability to Ddx41-deficient hematopoietic progenitor cells at exogenous expression levels. In contrast, the hotspot mutant p.R525H, which is somatically acquired at disease onset in >50% of patients, failed to restore viability. CRISPR-based modeling in cell lines and mice revealed heterogeneity: some variants were non-functional at endogenous expression levels whereas others maintained complete functionality, supporting normal cell proliferation and even lifelong hematopoiesis in a homozygous setting. Notably, co-expression of p.R525H with some variants caused impaired hematopoietic progenitor cell viability, indicating a dominant-negative effect of p.R525H. In contrast, other variants, all classified as variants of unknown significance, were unaffected by the presence of p.R525H. A screen of 100 disease-associated variants confirmed that many non-truncating germline variants are susceptible to p.R525H-mediated dominant-negative effects, whereas wild-type DDX41 is not. These findings indicate that DDX41 variant curation is complicated by variable effects on functionality and variant-specific interactions with somatically-acquired DDX41 mutations. The dominant-negative effect of p.R525H provides a mechanistic basis for the conclusion of recent patient cohort analyses that co-occurrence with a somatic hotspot mutation is a reliable indicator of DDX41-driven disease in carriers of non-truncating variants.

cancer biology↗

bioRxiv2024

Unified somatic calling and machine learning-based classification enhance the discovery of clonal hematopoiesis of indeterminate potential

Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline and artifact. In whole-exome data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes, predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had <5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies.

bioinformatics↗

bioRxiv2022

MAMBA: a model-driven, constraint-based multiomic integration method

The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).

bioinformatics↗