Biology subjects

Helin, K.

Publications and source records attributed to Helin, K..

5 recordsLinked to original sources

bioRxiv2023

Vitamin C activates young LINE-1 elements in mouse embryonic stem cells via H3K9me3 demethylation

Vitamin C (vitC) enhances the activity of 2-oxoglutarate-dependent dioxygenases, including TET enzymes, which catalyse DNA demethylation, and Jumonji-domain histone demethylases. The epigenetic remodelling promoted by vitC improves the efficiency of induced pluripotent stem cell derivation, and is required to attain a ground-state of pluripotency in embryonic stem cells (ESCs) that closely mimics the inner cell mass of the early blastocyst. However, genome-wide DNA and histone demethylation can lead to upregulation of transposable elements (TEs), and it is not known how vitC addition in culture media affects TE expression in pluripotent stem cells. Here we show that vitC increases the expression of evolutionarily young LINE-1 (L1) elements in mouse ESCs. We find that TET activity is dispensable for these effects, and that instead L1 upregulation occurs largely as a result of H3K9me3 loss mediated by KDM4A/C histone demethylases. Despite increased L1 levels, we did not detect increased somatic insertion rates in vitC-treated cells. Notably, treatment of human ESCs with vitC also increases L1 protein levels, which could impact the genetic and epigenetic stability of human pluripotent stem cells.

genetics↗

bioRxiv2022

An integrated approach to study chromatin and transcriptional dynamics throughout the cell cycle without pharmacological inhibition

The cellular and nuclear states are constantly changing and closely linked to DNA replication and cell division, defining cell cycle phases. Pharmacological inhibition is a widely used strategy to enrich for specific phases to study them in isolation. However, chemical-induced cell cycle synchronization may influence chromatin openness and gene expression compared to unperturbed cell cycle states. Here, we use different synchronization methods to obtain G1, S and G2/M phases from mouse embryonic stem cells (mESCs) and profile their chromatin accessibility and post- translational modifications as well as steady and nascent transcripts levels. We demonstrate that synchronization with thymidine and nocodazole leads to dramatic changes in chromatin compaction and gene expression, which only partially resemble bona fide cell cycle-dependent differences. Blocking with CDK1 inhibitor results in efficient synchronization and evokes moderate changes in chromatin accessibility, resembling G2/M phase. Correlative analysis suggests that cell cycle-specific variations in chromatin openness in unperturbed mESCs are mediated by CDK1 substrates and CTCF. This integrated strategy can be applied to most cultured cell lines under any conditions and holds the potential to further our understanding of cell cycle- dependent susceptibility to external cues and chromatin regulation.

cell biology↗

bioRxiv2021

COMPARE, an ultra-fast and robust suite for multiparametric screening, identifies phenotypic drug responses in acute myeloid leukemia

Multiparametric phenotypic screening of cells, for example assessing their responses to small molecules or knockdown/knockout of specific genes, is a powerful approach to understanding cellular systems and identifying potential new therapeutic strategies. However, automated tools for analyzing similarities and differences between a large number of tested conditions have not been readily available. Methods designed for clustering cells cannot identify differences between samples effectively. We introduce O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW for ultra-fast and robust analysis of multiparametric high-throughput screening. Applying a mass-aware gridding algorithm using hypercubes, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW performs automatic and effective similarity comparison for hundreds to thousands of tests and provides information about the treatment effect. Particularly for screening data, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW is equipped with modules to remove various sources of bias. Benchmarking tests show that O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW can circumvent batch effects and perform a similarity analysis substantially faster than conventional analysis tools. Applying O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW to high-throughput flow cytometry screening data, we were able to distinguish subtle phenotypic drug responses in a human sample and a genetically engineered mouse model with acute myeloid leukemia (AML). O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW revealed groups of drugs with similar responses even though their mechanisms are distinct from each other. In another screening, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW effectively circumvented batch effects and grouped samples from AML and myelodysplastic syndrome (MDS) patients using clinical flow cytometry data.

cancer biology↗

bioRxiv2018

Quantification of differential transcription factor activity and multiomic-based classification into activators and repressors: diffTF

Transcription factor (TF) activity is an important read-out of cellular signalling pathways and thus to assess regulatory differences across conditions. However, current technologies lack the ability to simultaneously assess activity changes for multiple TFs and in particular to determine whether a specific TF acts globally as transcriptional repressor or activator. To this end, we introduce a widely applicable genome-wide method diffTF to assess differential TF activity and to classify TFs as activator or repressor (available at https://git.embl.de/grp-zaugg/diffTF). This is done by integrating any type of genome-wide chromatin accessibility data with RNA-Seq data and in-silico predicted TF binding sites. We corroborated the classification of TFs into repressors and activators by three independent analyses based on enrichments of active/repressive chromatin states, correlation of TF activity with gene expression, and activator-and repressor-specific chromatin footprints. To show the power of diffTF, we present two case studies: First, we applied diffTF in to a large ATAC-Seq/RNA-Seq dataset comparing mutated and unmutated chronic lymphocytic leukemia samples, where we identified dozens of known (40%) and potentially novel (60%) TFs that are differentially active. We were also able to classify almost half of them as either repressor and activator. Second, we applied diffTF to a small ATAC-Seq/RNA-Seq data set comparing two cell types along the hematopoietic differentiation trajectory (multipotent progenitors - MPP - versus granulocyte-macrophage progenitors - GMP). Here we identified the known drivers of differentiation and found that the majority of the differentially active TFs are transcriptional activators. Overall, diffTF was able to recover the known TFs in both case studies, additionally identified TFs that have been less well characterized in the given condition, and provides a classification of the TFs into transcriptional activators and repressors.

bioinformatics↗

bioRxiv2018

TET2 binding to enhancers facilitates transcription factor recruitment in hematopoietic cells

The epigenetic regulator TET2 is frequently mutated in hematological diseases. Mutations have been shown to arise in hematopoietic stem cells early in disease development, lead to altered DNA methylation landscapes and to an increased risk of hematopoietic malignancy. Here, we show by genome-wide mapping of TET2 binding sites in different cell types that TET2 localizes to regions of open chromatin and cell-type specific enhancers. We find that deletion of Tet2 in native hematopoiesis as well as fully transformed Acute Myeloid Leukemia (AML) results in changes in transcription factor (TF) activity within these regions, and we demonstrate that loss of TET2 leads to enzymatic activity-dependent attenuation of chromatin binding of the hematopoietic TF CDX4. Together, these findings demonstrate that TET2 activity shapes the local chromatin environment at enhancers to facilitate TF binding and provide a compelling example of how epigenetic dysregulation can affect gene expression patterns and drive disease development.

cancer biology↗