The scaffolding function of LSD1/KDM1A reinforces a negative feedback loop to repress stem cell gene expression during primitive hematopoiesis

Mining the Dynamical Properties of Substrate and FAD Binding Pockets of LSD1: Hints for New Inhibitor Design Direction

Lysine-specific demethylase 1 (LSD1), a highly sophisticated epigenetic regulator, orchestrates a range of critical cellular processes, holding promising therapeutic potential for treating diverse diseases. However, the clinical research progress targeting LSD1 is very slow. After 20 years of research, only one small-molecule drug, BEA-17, targeting the degradation of LSD1 and CoREST has been approved by the U.S. Food and Drug Administration. The primary reason for this may be the lack of abundant structural data regarding its intricate functions. To gain a deeper understanding of its conformational dynamics and guide the drug design process, we conducted molecular dynamics simulations to explore the conformational states of LSD1 in the apo state and under the influence of cofactors of flavin adenine dinucleotide (FAD) and CoREST. Our results showed that, across all states, the substrate binding pocket exhibited high flexibility, whereas the FAD binding pocket remained more stable. These distinct dynamical properties are essential for LSD1's ability to bind various substrates while maintaining efficient demethylation activity. Both pockets can be enlarged by merging with adjacent pockets, although only the substrate binding pocket can shrink into smaller pockets. These new pocket shapes can inform inhibitor design, particularly for selectively FAD-competitive inhibitors of LSD1, given the presence of numerous FAD-dependent enzymes in the human body. More interestingly, in the absence of FAD binding, the united substrate and FAD binding pocket are partitioned by the conserved residue of Tyr761, offering valuable insights for the design of inhibitors that disrupt the crucial steric role of Tyr761 and the redox role of FAD. Additionally, we identified pockets that positively or negatively correlate with the substrate and FAD binding pockets, which can be exploited for the design of allosteric or concurrent inhibitors. Our results reveal the intricate dynamical properties of LSD1 as well as multiple novel conformational states, which deepen our understanding of its sophisticated functions and aid in the rational design of new inhibitors.

GFI1B and LSD1 repress myeloid traits during megakaryocyte differentiation

The transcription factor Growth Factor Independence 1B (GFI1B) recruits Lysine Specific Demethylase 1 A (LSD1/KDM1A) to stimulate gene programs relevant for megakaryocyte and platelet biology. Inherited pathogenic GFI1B variants result in thrombocytopenia and bleeding propensities with varying intensity. Whether these affect similar gene programs is unknow. Here we studied transcriptomic effects of four patient-derived GFI1B variants (GFI1BT174N,H181Y,R184P,Q287*) in MEG01 megakaryoblasts. Compared to normal GFI1B, each variant affected different gene programs with GFI1BQ287* uniquely failing to repress myeloid traits. In line with this, single cell RNA-sequencing of induced pluripotent stem cell (iPSC)-derived megakaryocytes revealed a 4.5-fold decrease in the megakaryocyte/myeloid cell ratio in GFI1BQ287* versus normal conditions. Inhibiting the GFI1B-LSD1 interaction with small molecule GSK-LSD1 resulted in activation of myeloid genes in normal iPSC-derived megakaryocytes similar to what was observed for GFI1BQ287* iPSC-derived megakaryocytes. Thus, GFI1B and LSD1 facilitate gene programs relevant for megakaryopoiesis while simultaneously repressing programs that induce myeloid differentiation.

TERRA-LSD1 phase separation promotes R-loop formation for telomere maintenance in ALT cancer cells

The telomere repeat-containing RNA (TERRA) forms R-loops to promote homology-directed DNA synthesis in the alternative lengthening of telomere (ALT) pathway. Here we report that TERRA contributes to ALT via interacting with the lysine-specific demethylase 1A (LSD1 or KDM1A). We show that LSD1 localizes to ALT telomeres in a TERRA dependent manner and LSD1 function in ALT is largely independent of its demethylase activity. Instead, LSD1 promotes TERRA recruitment to ALT telomeres via RNA binding. In addition, LSD1 and TERRA undergo phase separation, driven by interactions between the RNA binding properties of LSD1 and the G-quadruplex structure of TERRA. Importantly, the formation of TERRA-LSD1 condensates enriches the R-loop stimulating protein Rad51AP1 and increases TERRA-containing R-loops at telomeres. Our findings suggest that LSD1-TERRA phase separation enhances the function of R-loop regulatory molecules for ALT telomere maintenance, providing a mechanism for how the biophysical properties of histone modification enzyme-RNA interactions impact chromatin function.

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Introduction

Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation

In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement

Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology

We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results

We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion

In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.