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Biology subjects

Keijer, J.

Publications and source records attributed to Keijer, J..

2 recordsLinked to original sources

The genome-wide mutational consequences of DNA hypomethylation

DNA methylation is important for establishing and maintaining cell identity and for genomic stability. This is achieved by regulating the accessibility of regulatory and transcriptional elements and the compaction of subtelomeric, centromeric, and other inactive genomic regions. Carcinogenesis is accompanied by a global loss in DNA methylation, which facilitates the transformation of cells. Cancer hypomethylation may also cause genomic instability, for example through interference with the protective function of telomeres and centromeres. However, understanding the role(s) of hypomethylation in tumor evolution is incomplete because the precise mutational consequences of global hypomethylation have thus far not been systematically assessed. Here we made genome-wide inventories of all possible genetic variation that accumulates in single cells upon the long-term global hypomethylation by CRISPR/CAS9-mediated conditional knockdown of DNMT1. Depletion of DNMT1 results in a genomewide reduction in DNA methylation levels. Hypomethylated cells show reduced proliferation rates, reactivation of the inactive X-chromosome and abnormal nuclear morphologies. Prolonged hypomethylation is accompanied by increased chromosomal instability. However, there is no increase in mutational burden, enrichment for certain mutational signatures or structural changes to the genome. We conclude that the primary consequence of global hypomethylation is chromosomal instability and does not necessarily lead to other small-scale or structural mutational effects.

genomics↗

OCRbayes: A Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis

BackgroundMitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. ResultsTo rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. ConclusionsWe showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

systems biology↗