Biology subjects

Schüler, L.

Publications and source records attributed to Schüler, L..

2 recordsLinked to original sources

bioRxiv2026

Integrative enhancer discovery identifies functional enhancer dependencies in pediatric acute myeloid leukemia

Pediatric acute myeloid leukemia (AML) is driven by aberrant transcriptional programs sustained by poorly defined cis-regulatory mechanisms. To systematically identify functional enhancer dependencies, we developed an integrative enhancer discovery strategy that combines H3K27ac CUT&Tag profiling, enhancer-associated transcription, and CRISPR interference (CRISPRi) screening. By leveraging enhancer-associated transcription to prioritize candidate regulatory elements, we identified 321 leukemia-associated enhancers for functional interrogation. This approach uncovered the hematopoietic MYB enhancer (H-ME) within the HBS1L-MYB-AHI1 locus as a critical regulator of leukemic growth. H-ME repression reduced chromatin accessibility and active histone marks at the MYB promoter, suppressed MYB expression, and induced differentiation-associated transcriptional programs. In contrast, selective depletion of the enhancer-associated transcript had no effect on MYB expression or leukemic proliferation, demonstrating that enhancer activity resides within the underlying regulatory DNA element rather than its mature RNA product. H-ME exhibited preferential activity in megakaryocytic leukemia, and its perturbation impaired leukemic growth in primary patient-derived models in vitro and in vivo. Together, our findings establish an integrative framework for the systematic discovery of functional enhancer dependencies and identified H-ME as an RNA-independent regulator of MYB in pediatric AML.

cancer biology↗

bioRxiv2024

Simultaneous Identification of Changepoints and Model Parameters in Switching Dynamical Systems

Many complex natural systems undergo shifts in dynamics at particular points in time. Examples include phase transitions in gene expression during the cell cycle, introduced species affecting predator-prey interactions, and disease outbreaks responding to intervention measures. Such changepoints partition timeseries into different dynamical regimes characterized by distinct parameter sets, and inference on both the changepoints and regime-specific dynamical parameters is of primary interest. Conventional approaches to analyzing switching dynamical systems first estimate changepoints, and then estimate dynamical parameters assuming the changepoints are fixed and known. Such two-stage approaches are ad-hoc, can introduce biases in the analysis, and do not fully account for uncertainty. Here, we introduce a rigorous, simulation-based inference framework that simultaneously estimates changepoints and model parameters from noisy data while admitting full uncertainty. We use simulation studies of oscillatory predator-prey dynamics and stochastic gene expression to demonstrate that our method yields accurate estimates of changepoints and model parameters together with appropriate uncertainty bounds. We then apply our approach to a real-world case study of COVID-19 intervention effects, and show that our inferred changepoints aligned closely with the actual dates of intervention implementation. Taken together, these results suggest that our framework will have broad utility in diverse scientific domains.

ecology↗