Biology subjects

Rijnkels, M.

Publications and source records attributed to Rijnkels, M..

3 recordsLinked to original sources

bioRxiv2026

SIM2s coordinates programmed mitophagy with respiratory chain supercomplex remodeling during mammary epithelial differentiation

Lactation requires mammary epithelial cells (MECs) to rapidly expand mitochondrial function while remodeling the mitochondrial population that supports milk synthesis and secretion. Programmed mitophagy is required for MEC differentiation, yet why mitochondrial turnover is necessary during this developmental transition remains poorly understood. Using Mito-QC reporter mice, we identified developmentally regulated changes in mitolysosome burden across the transition from late pregnancy to lactation that were altered by mammary-specific gain or loss of the bhlh/PAS protein, SIM2s (single-minded 2 s). In differentiating HC11 cells, mitochondrial turnover was accompanied by increased assembly and activity of respiratory supercomplexes containing complexes I, III, and IV. Depletion of PRKN prevented acquisition of this differentiation-associated respiratory profile and impaired lactogenic differentiation. SIM2s co-migrated with higher-order respiratory assemblies, and loss of SIM2s reduced supercomplex assembly and activity in HC11 cells and mammary tissue. SIM2s also localized in close proximity to complex III in differentiated mammary epithelium, whereas loss of SIM2s reduced proximity between complexes III and IV. Together, these findings support a model in which SIM2s coordinates PRKN-dependent mitochondrial turnover with respiratory-chain remodeling during MEC differentiation. Our results suggest that programmed mitophagy does more than remove mitochondria during development; it contributes to establishment of a mitochondrial population with a respiratory-chain architecture suited to the emerging differentiated state.

cell biology↗

bioRxiv2025

An integrated multi-tissue atlas of epigenomic landscapes and regulatory elements in the bovine genome

Deciphering the regulatory syntax of the genome is essential to understand the genetic and molecular architecture of complex traits, as most trait-associated variants lie in non-coding regions. Yet, functional annotation of the bovine genome remains limited, hindering our ability to unravel the mechanisms underpinning complex traits of economic and ecological importance in cattle. Here, we present a comprehensive epigenetic atlas comprising 1,138 genome-wide epigenetic profiles, including chromatin accessibility, six histone modifications, CCCTC-binding factor (CTCF) transcription factor binding, DNA methylation, chromatin conformation, and transcriptomes across 53 adult tissues, five fetal tissues, and seven primary cell types. This atlas-level data enables us to annotate around 45% of the genome as putative regulatory elements exhibiting tissue- or cell-specific regulatory activity. Leveraging sequence-to-function deep learning models, we discovered 301 sequence motifs and predicted the functional impact of genetic variants through in silico mutagenesis, thereby facilitating the decoding of the regulatory syntax of the cattle genome and fine-mapping of GWAS loci for 22 complex traits. Cross-species analysis further revealed evolutionarily conserved features of regulatory architecture and provided evolutionary insights into complex traits and diseases in humans. Together, this atlas offers a foundational resource for advancing cattle functional genomics, sustainable breeding, and studies of regulatory evolution.

genomics↗

bioRxiv2022

Functional genomics of cattle through integration of multi-omics data

Functional annotation of the bovine genome was performed by characterizing the spectrum of RNA transcription using a multi-omics approach, combining long- and short-read transcript sequencing and orthogonal data to identify promoters and enhancers and to determine boundaries of open chromatin. A total number of 171,985 unique transcripts (50% protein-coding) representing 35,150 unique genes (64% protein-coding) were identified across tissues. Among them, 159,033 transcripts (92% of the total) were structurally validated by independent datasets such as PacBio Iso-seq, ONT-seq, de novo assembled transcripts from RNA-seq, or Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive independent data from different technologies such as WTTS-seq, RAMPAGE, ChIP-seq, and ATAC-seq. A large proportion of identified transcripts (69%) were novel, of which 87% were produced by known genes and 13% by novel genes. A median of two 5 untranslated regions was detected per gene, an increase from Ensembl and NCBI annotations (single). Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as non-coding genes in fetal tissues, but as protein coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 known gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available QTL data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network. These validated results show significant improvement over current bovine genome annotations.

bioinformatics↗