Biology subjects

Haug, S.

Publications and source records attributed to Haug, S..

7 recordsLinked to original sources

bioRxiv2026

Translational isoforms of mammalian heterochromatin protein 1 (HP1) tune its interactions with chromatin effectors

The heterochromatin protein 1 (HP1) protein family maintains heterochromatin by reading H3K9 methylation and recruiting diverse chromatin effectors. Multiple effectors compete for the same binding surface on HP1, requiring cells to regulate the ability of HP1 to engage the right effectors across differing conditions. Here, we show that mammalian cells produce multiple N-terminally extended isoforms of HP1{beta} from conserved, in-frame near-cognate start codons in the CBX1 5' UTR. Each of these isoforms retains the localization to H3K9me3 marks. However, the extended HP1{beta} isoforms shift its binding preference away from proteins with a conserved PxVxL motif and toward the G9a/GLP complex. Structural predictions suggest that the extension blocks the PxVxL interaction surface. Indeed, we find that an extended HP1{beta} isoform fails to bind a model PxVxL-containing substrate in vitro. Together, our data reveal an alternative HP1{beta} isoform that reshapes its effector binding preferences, providing an additional regulatory layer for heterochromatin organization.

molecular biology↗

bioRxiv2026

Sexually dimorphic effects of mutations in ccRCC driver genes in renal proximal tubule cells

Clear cell renal cell carcinoma (ccRCC) arises more frequently in men than in women, but it remains unclear why this is the case. Mouse models that mimic the truncal mutation of the two most commonly affected tumor suppressor genes in human ccRCC revealed that the transcriptional effects of Vhl or Vhl/Pbrm1 mutation in male and female proximal tubule cells only partially overlap. Cellular responses to mutations in these genes were also sex-specific. Vhl and Vhl/Pbrm1 mutation in females, but only Vhl/Pbrm1 mutation in males, induced the accumulation of PLIN2-coated lipid droplets. The accumulation of optically-clear lipid droplets, reflecting the hallmark clear-cell histological feature of ccRCC, only arose in Vhl/Pbrm1 mutant male mice. Consistent with lipotoxic tubular damage, markers of tubular repair and ongoing epithelial cell proliferation correlated with the lipid accumulation phenotypes. Vhl/Pbrm1 mutation caused stronger clonal expansion than Vhl mutation in males, but not in females. These findings suggest that there are likely to be sexual dimorphisms at the very earliest stages of ccRCC evolution that result from the interplay of intrinsic and mutation-induced transcriptional differences between male and female proximal tubule cells.

cancer biology↗

bioRxiv2026

Transcriptome-based cell type assignment for kidney cell culture models

BackgroundKidney cell lines are widely used to model kidney physiology and disease; however, their gene expression profiles may differ from primary cells due to immortalization, culture conditions, or experimental treatments. Determining whether a cell line resembles its native cell type is critical for interpreting in vitro findings. We developed a transcriptome-based approach that matches bulk RNA-seq data from kidney cell lines, primary cells, or tissues to reference cell types derived from single-cell RNA-seq (scRNA-seq) datasets. MethodsReference transcriptomic profiles were generated from two human and two murine kidney scRNA-seq datasets by pseudobulk aggregation. Bulk RNA-seq data from microdissected kidney tissue, non-kidney negative controls, and kidney cell lines were matched to these references using three statistical similarity measures (Spearman correlation, Euclidean distance, Poisson distance) and three machine learning classifiers (Random Forest, XGBoost, TabPFN). Each was assessed with global gene expression, curated kidney marker gene lists, and the most variable genes. Matching accuracy was evaluated through a three-step validation strategy: within-dataset matching, cross-reference comparison, and validation against primary kidney tissue and negative controls. ResultsGene expression rank-based Spearman correlation and TabPFN, a foundation model for tabular data, emerged as the most accurate and specific approaches, particularly with curated kidney marker gene lists. Both methods correctly identified microdissected kidney tubule segments and were robust against non-kidney negative controls. Applied to commonly used kidney cell lines, OK cells retained proximal tubule identity, particularly under shear stress, while other proximal tubule lines (HK-2, HKC-8, HKC-11) showed inconsistent matching. Collecting duct-derived mIMCD-3 maintained stable similarity across passages, culture conditions, and genetic modifications. ConclusionWe provide two complementary implementations: CellMatchR, an accessible web-based tool using Spearman correlation for routine use, and comprehensive scripts for TabPFN-based matching (link will be added after peer reviewed publication). Together, these resources enable researchers to make informed decisions about kidney cell culture model selection, interpretation, and stability. Translational StatementKidney cell lines are fundamental tools in nephrology research, yet their transcriptomic similarity to native cell types is rarely validated systematically. We demonstrate that combining bulk RNA-seq data with single-cell reference datasets enables robust assessment of cell line identity using gene expression-rank-based correlation and machine learning approaches. By providing a comprehensive evaluation of matching methods, curated kidney marker gene lists, and reference datasets, our study serves as both a practical resource and a methodological framework for the kidney research community, facilitating informed selection of cell culture models, quality control of experimental conditions, developing new experimental cell culture models, and more reliable translation of in vitro findings to kidney physiology and disease.

bioinformatics↗

bioRxiv2025

SRSF12 is a primate-specific splicing factor that induces a tissue-specific gene expression program

Alternative splicing expands proteomic diversity and is tightly regulated by splicing factors, including the serine/arginine-rich (SR) protein family. Here, we analyze the poorly characterized protein SRSF12. Although SRSF12 is conserved across vertebrates, it is poorly expressed in most mammals, and we find that SRSF12 knockout mice do not display overt physiological or transcriptomic alterations. In contrast, SRSF12 is more highly expressed in primates where it is predominantly transcribed in the testes, oocytes, and brain. SRSF12 co-localizes with other splicing components to nuclear speckles and interacts with core splicing factors in cultured human cells. Strikingly, ectopic expression of SRSF12 in human cells induces widespread transcriptional changes, activating meiosis-, testis- and brain-specific genes. SRSF12 overexpression also leads to mitotic arrest and cell death, phenotypes that require both its structured RNA recognition motif and intrinsically disordered arginine/serine-rich C-terminal domain. Together, our results suggest that SRSF12 has evolved primate-specific expression to regulate testis- and brain-specific genes.

cell biology↗

bioRxiv2025

Molecular determinants of RNase MRP specificity and function

RNase MRP and RNase P are evolutionarily related complexes that facilitate rRNA and tRNA biogenesis, respectively. The two enzymes share nearly all protein subunits and have evolutionarily related catalytic RNAs. Notably, RNase P includes a unique subunit, Rpp21, whereas no RNase MRP-specific proteins have been found in humans, limiting molecular analyses of RNase MRP function. Here, we identify the RNase MRP-specific protein, C18orf21/RMRPP1. RMRPP1 and Rpp21 display significant structural homology, but we identify specific regions that drive interactions with their respective complexes. Additionally, we reveal that RNase MRP is required for 40S, but not 60S, ribosome biogenesis uncovering an alternative pathway for ribosome assembly. Finally, we identify Nepro as an essential rRNA processing factor that associates with the RNase MRP complex. Together, our findings elucidate the molecular determinants of RNase MRP function and underscore its critical role in ribosome biogenesis.

molecular biology↗

bioRxiv2022

Multi-omic Analysis of Primary Human Kidney Tissues Identifies Medulla-Specific Gene Expression Patterns

The renal medulla is a specialized region of the kidney with important homeostatic functions. It has also been implicated in genetic and developmental disorders and ischemic and drug-induced injuries. Despite its role in kidney function and disease, the medullas baseline gene expression and epigenomic signatures have not been well described in the adult human kidney. Here we generate and analyze gene expression (RNA-seq), chromatin accessibility (ATAC-seq) and chromatin conformation (Hi-C) data from adult human kidney cortex and medulla. Using data from our carefully annotated specimens, we assign samples in the larger public GTEx database to cortex and medulla, thereby identifying several misassignments and extracting meaningful medullary gene expression signatures. Using integrated analysis of gene expression, chromatin accessibility and conformation profiles, we reveal insights into medulla development and function. Our datasets will also provide a valuable resource for researchers in the GWAS community for functional annotation of genetic variants.

genetics↗

bioRxiv2022

Designing Single-Cell RNA-Sequencing Experiments for Learning Latent Representations

To investigate the complexity arising from single-cell RNA-sequencing (scRNA-seq) data, researchers increasingly resort to deep generative models, specifically variational autoencoders (VAEs), which are trained by variational inference techniques. Similar to other dimension reduction approaches, this allows encoding the inherent biological signals of gene expression data, such as pathways or gene programs, into lower-dimensional latent representations. However, the number of cells necessary to adequately uncover such latent representations is often unknown. Therefore, we propose a single-cell variational inference approach for designing experiments (scVIDE) to determine statistical power for detecting cell group structure in a lower-dimensional representation. The approach is based on a test statistic that quantifies the contribution of every single cell to the latent representation. Using a smaller scRNA-seq data set as a starting point, we generate synthetic data sets of various sizes from a fitted VAE. Employing a permutation technique for obtaining a null distribution of the test statistic, we subsequently determine the statistical power for various numbers of cells, thus guiding experimental design. We illustrate with several data sets from various sequencing protocols how researchers can use scVIDE to determine the statistical power for cell group detection within their own scRNA-seq studies. We also consider the setting of transcriptomics studies with large numbers of cells, where scVIDE can be used to determine the statistical power for sub-clustering. For this purpose, we use data from the human KPMP Kidney Cell Atlas and evaluate the power for sub-clustering of the epithelial cells contained therein. To make our approach readily accessible, we provide a comprehensive Jupyter notebook at https://github.com/MTreppner/scVIDE.jl that researchers can use to design their own experiments based on scVIDE.

bioinformatics↗