A novel Boolean network inference strategy to model early hematopoiesis aging

Abstract

We previously analyzed 15 000 transcriptomes of mouse hematopoietic stem and progenitor cells (HSPCs) from young and aged mice and characterized the early differentiation of the hematopoietic stem cells (HSCs) according to age, thanks to cell clustering and pseudotime analysis (Herault et al, 2021). In this study, we propose an original strategy to build a Boolean gene network explaining HSC priming and homeostasis based on our previous single cell data analysis and the actual knowledge of these biological processes (graphical abstract). We first made an exhaustive analysis of the transcriptional network on selected HSPC states in the differentiation trajectory of HSCs by identifying regulons, modules formed by a transcription factor (TFs) and its targets, from the scRNA-seq data., From this global view of transcriptional regulation in early hematopoiesis, we chose to focus on 15 components, 13 selected TFs (Tal1, Fli1, Gata2, Gata1, Zfpm1, Egr1, Junb, Ikzf1, Myc, Cebpa, Bclaf1, Klf1, Spi1) and two complexes regulating the ability of HSC to cycle (CDK4/6 - Cyclines D and CIP/KIP). We then defined the relations in the differentiation dynamics we want to model ((non) reachability, attractors) between the HSPC states that are partial observations of binarized activity levels of the 15 components. Besides, we defined an influence graph of possibly involved TF interactions in the dynamic using regulon analysis on our single cell data and interactions from the literature. Next, using Answer Set Programming (ASP) and considering these inputs, we obtained a Boolean model as a final solution of a Boolean satisfiability problem. Finally, we perturbed the model according to aging differences underlined from our regulon analysis. This led us to propose new regulatory mechanisms at the origin of the differentiation bias of aged HSCs, explaining the decrease in the transcriptional priming of HSCs toward all mature cell types except megakaryocytes.

Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging

“…Then, by studying some topological properties, their analysis captured the dynamical heterogeneity, occurring with HSCs aging. Hérault et al [140] presented an original inference methodology to construct a Boolean model allowing a better understanding of the mechanisms and factors controlling the effects of aging on HSCs. They developed an inference strategy that consists in a specific combination of different methods ranging from transcriptional regulation analysis for influence graph inference to constraint programming [136] and model checking techniques for logical rule inference.…”

“…They succeeded in obtaining a Boolean model which agrees with most prior experimental observations of HSC biology. They provided expected and new predictions that could explain the myeloid bias in aged HSC differentiation, including the overactivation of Egr1 and Junb or/and the loss of Cebpa activation by Gata2 [140].…”