Biology subjects

Krassowski, M.

Publications and source records attributed to Krassowski, M..

3 recordsLinked to original sources

bioRxiv2023

An integrated single-cell reference atlas of the human endometrium

The human endometrium, the inner lining of the uterus, exhibits complex, dynamic changes throughout the menstrual cycle in response to ovarian hormones. Aberrant response of endometrial cells to hormones is associated with multiple disorders, including endometriosis. Previous single-cell studies of the endometrium profiled a limited number of donors and lacked consensus in defining cell types and states. Here, we introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas, combining published and newly generated single-cell transcriptomics datasets of endometrial biopsies of women with and without endometriosis. The HECA assigned consensus cell types and states, and uncovered novel ones, which we mapped in situ using spatial transcriptomics. We quantified how coordinated interactions between cell states in space and time contribute to endometrial regeneration and differentiation. In the continuously changing functionalis layer, we identified an intricate coordination of TGF{beta} signalling between stromal and epithelial cells, likely crucial for cell differentiation. In the basalis layer, we defined signalling between fibroblasts and a new epithelial cell population expressing epithelial stem/progenitor markers, suggesting their role in endometrial regeneration. Additionally, integrating the HECA single-cell data with genome-wide association study data and comparing endometrial samples from women with and without endometriosis, we pinpointed subsets of decidualised stromal cells and macrophages as the most dysregulated cell states in endometriosis. Overall, the HECA is an invaluable resource for studying endometrial physiology, investigating endometrial disorders, and guiding the creation of endometrial microphysiological in vitro systems.

genomics↗

bioRxiv2017

Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

cancer biology↗

bioRxiv2017

ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins

Interpretation of genetic variation is required for understanding genotype-phenotype associations, mechanisms of inherited disease, and drivers of cancer. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 20% of disease-associated missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants using PTMs. We integrated >385,000 published PTM sites with [~]3.8 million missense SNVs from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and inter-individual variation from human genome sequencing projects. The database includes interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated SNVs. Users can upload mutation datasets interactively and use our application programming interface for pipelines. Integrative analysis of SNVs and PTMs helps decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of disease genes TP53, BRCA2 and VHL. The open-source database is available at https://www.ActiveDriverDB.org.

bioinformatics↗