A computational model of feedback-mediated hematopoietic stem cell differentiation in vitro

HDAC6 deacetylates IDH1 to promote the homeostasis of hematopoietic stem and progenitor cells

Hematopoietic stem and progenitor cells (HSPCs) are cells mainly present in the bone marrow and capable of forming mature blood cells. However, the epigenetic mechanisms governing the homeostasis of HSPCs remain elusive. Here, we demonstrate an important role for histone deacetylase 6 (HDAC6) in regulating this process. Our data show that the percentage of HSPCs in Hdac6 knockout mice is lower than in wild-type mice due to decreased HSPC proliferation. HDAC6 interacts with isocitrate dehydrogenase 1 (IDH1) and deacetylates IDH1 at lysine 233. The deacetylation of IDH1 inhibits its catalytic activity and thereby decreases the 5-hydroxymethylcytosine level of ten-eleven translocation 2 (TET2) target genes, changing gene expression patterns to promote the proliferation of HSPCs. These findings uncover a role for HDAC6 and IDH1 in regulating the homeostasis of HSPCs and may have implications for the treatment of hematological diseases.

Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms

Breast cancer stem cells (BCSCs) with the ability to self-renew and differentiate have been identified in primary breast cancer tissues and cell lines. The BCSCs are often resistant to traditional radiation and/or chemotherapies. Previous studies have also shown that successful therapy must eradicate cancer stem cells. The purpose of this paper is to develop a mathematical model with self-feedback mechanism to illustrate the issues regarding the difficulties of absolutely eliminating a breast cancer. In addition, we introduce the mechanism of the epithelial-mesenchymal transition (EMT) to investigate the influence of EMT on the effects of breast cancer growth and treatment. Results indicate that the EMT mechanism facilitates the growth of breast cancer and makes breast cancer more difficult to be cured. Therefore, targeting the signals involved in EMT can halt tumor progression in breast cancer. Finally, we apply the experimental data to carry out numerical simulations and validate our theoretical conclusions.

Engineered Tissue Models to Replicate Dynamic Interactions within the Hematopoietic Stem Cell Niche

Hematopoietic stem cells are the progenitors of the blood and immune system and represent the most widely used regenerative therapy. However, their rarity and limited donor base necessitate the design of ex vivo systems that support HSC expansion without the loss of long-term stem cell activity. This review describes recent advances in biomaterials systems to replicate features of the hematopoietic niche. Inspired by the native bone marrow, these instructive biomaterials provide stimuli and cues from cocultured niche-associated cells to support HSC encapsulation and expansion. Engineered systems increasingly enable study of the dynamic nature of the matrix and biomolecular environment as well as the role of cell-cell signaling (e.g., autocrine feedback vs paracrine signaling between dissimilar cells). The inherent coupling of material properties, biotransport of cell-secreted factors, and cell-mediated remodeling motivate dynamic biomaterial systems as well as characterization and modeling tools capable of evaluating a temporally evolving tissue microenvironment. Recent advances in HSC identification and tracking, model-based experimental design, and single-cell culture platforms facilitate the study of the effect of constellations of matrix, cell, and soluble factor signals on HSC fate. While inspired by the HSC niche, these tools are amenable to the broader stem cell engineering community.

Multistage hematopoietic stem cell regulation in the mouse: A combined biological and mathematical approach

We have reconciled steady-state and stress hematopoiesis in a single mathematical model based on murine in vivo experiments and with a focus on hematopoietic stem and progenitor cells. A phenylhydrazine stress was first applied to mice. A reduced cell number in each progenitor compartment was evidenced during the next 7 days through a drastic level of differentiation without proliferation, followed by a huge proliferative response in all compartments including long-term hematopoietic stem cells, before a return to normal levels. Data analysis led to the addition to the 6-compartment model, of time-dependent regulation that depended indirectly on the compartment sizes. The resulting model was finely calibrated using a stochastic optimization algorithm and could reproduce biological data in silico when applied to different stress conditions (bleeding, chemotherapy, HSC depletion). In conclusion, our multi-step and time-dependent model of immature hematopoiesis provides new avenues to a better understanding of both normal and pathological hematopoiesis.

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We describe a new 6-compartment time-dependent regulated model of hematopoiesis

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Biological data under steady state and stress and cell dynamics were used

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Modeling is able to recapitulate effects from chemotherapy, bleeding, or HSC depletion

Effective Drug Concentration and Selectivity Depends on Fraction of Primitive Cells

Poor efficiency of chemotherapeutics in the eradication of Cancer Stem Cells (CSCs) has been driving the search for more active and specific compounds. In this work, we show how cell density-dependent stage culture profiles can be used in drug development workflows to achieve more robust drug activity (IC₅₀ and EC₅₀) results. Using flow cytometry and light microscopy, we characterized the cytological stage profiles of the HL-60-, A-549-, and HEK-293-derived sublines with a focus on their primitive cell content. We then used a range of cytotoxic substances—C-123, bortezomib, idarubicin, C-1305, doxorubicin, DMSO, and ethanol—to highlight typical density-related issues accompanying drug activity determination. We also showed that drug EC₅₀ and selectivity indices normalized to primitive cell content are more accurate activity measurements. We tested our approach by calculating the corrected selectivity index of a novel chemotherapeutic candidate, C-123. Overall, our study highlights the usefulness of accounting for primitive cell fractions in the assessment of drug efficiency.

Computational modeling of stem and progenitor cell kinetics identifies plausible hematopoietic lineage hierarchies

Classically, hematopoietic stem cell (HSC) differentiation is assumed to occur via progenitor compartments of decreasing plasticity and increasing maturity in a specific, hierarchical manner. The classical hierarchy has been challenged in the past by alternative differentiation pathways. We abstracted experimental evidence into 10 differentiation hierarchies, each comprising 7 cell type compartments. By fitting ordinary differential equation models with realistic waiting time distributions to time-resolved data of differentiating HSCs from 10 healthy human donors, we identified plausible lineage hierarchies and rejected others. We found that, for most donors, the classical model of hematopoiesis is preferred. Surprisingly, multipotent lymphoid progenitor differentiation into granulocyte-monocyte progenitors is plausible in 90% of samples. An in silico analysis confirmed that, even for strong noise, the classical model can be identified robustly. Our computational approach infers differentiation hierarchies in a personalized fashion and can be used to gain insights into kinetic alterations of diseased hematopoiesis.

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We assembled 10 lineage hierarchy models of human hematopoiesis

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Multiparameter immunophenotyping determines HSC differentiation for 10 healthy donors

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ODE fitting and model selection allows to identify plausible hierarchies

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A simulation study confirms robustness of model selection for different noise levels

Regulation of stress-induced hematopoiesis

PURPOSE OF REVIEW

The hematopoietic compartment is tasked with the establishment and maintenance of the entire blood program in steady-state and in response to stress. Key to this process are hematopoietic stem cells (HSCs), which possess the unique ability to self-renew and differentiate to replenish blood cells throughout an organism's lifetime. Though tightly regulated, the hematopoietic system is vulnerable to both intrinsic and extrinsic factors that influence hematopoietic stem and progenitor cell (HSPC) fate. Here, we review recent advances in our understanding of hematopoietic regulation under stress conditions such as inflammation, aging, mitochondrial defects, and damage to DNA or endoplasmic reticulum.

RECENT FINDINGS

Recent studies have illustrated the vast mechanisms involved in regulating stress-induced hematopoiesis, including cytokine-mediated lineage bias, gene signature changes in aged HSCs associated with chronic inflammation, the impact of clonal hematopoiesis and stress tolerance, characterization of the HSPC response to endoplasmic reticulum stress and of several epigenetic regulators that influence HSPC response to cell cycle stress.

SUMMARY

Several key recent findings have deepened our understanding of stress hematopoiesis. These studies will advance our abilities to reduce the impact of stress in disease and aging through clinical interventions to treat stress-related outcomes.

Optimal experimental design for mathematical models of haematopoiesis

The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.

The Importance of Computational Modeling in Stem Cell Research

The generation of large amounts of omics data is increasingly enabling not only the processing and analysis of large data sets but also the development of computational models in the field of stem cell research. Although computational models have been proposed in recent decades, we believe that the stem cell community is not fully aware of the potentiality of computational modeling in guiding their experimental research. In this regard, we discuss how single-cell technologies provide the right framework for computational modeling at different scales of biological organization in order to address challenges in the stem cell field and to guide experimentalists in the design of new strategies for stem cell therapies and treatment of congenital disorders.