Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

Erythropoietin receptor signal is crucial for periodontal ligament stem cell-based tissue reconstruction in periodontal disease

Alveolar bone loss caused by periodontal disease eventually leads to tooth loss. Periodontal ligament stem cells (PDLSCs) are the tissue-specific cells for maintaining and repairing the periodontal ligament, cementum, and alveolar bone. Here, we investigated the role of erythropoietin receptor (EPOR), which regulates the microenvironment-modulating function of mesenchymal stem cells, in PDLSC-based periodontal therapy. We isolated PDLSCs from patients with chronic periodontal disease and healthy donors, referred to as PD-PDLSCs and Cont-PDLSCs, respectively. PD-PDLSCs exhibited reduced potency of periodontal tissue regeneration and lower expression of EPOR compared to Cont-PDLSCs. EPOR-silencing suppressed the potency of Cont-PDLSCs mimicking PD-PDLSCs, whereas EPO-mediated EPOR activation rejuvenated the reduced potency of PD-PDLSCs. Furthermore, we locally transplanted EPOR-silenced and EPOR-activated PDLSCs into the gingiva around the teeth of ligament-induced periodontitis model mice and demonstrated that EPOR in PDLSCs participated in the regeneration of the periodontal ligament, cementum, and alveolar bone in the ligated teeth. The EPOR-mediated paracrine function of PDLSCs maintains periodontal immune suppression and bone metabolic balance via osteoclasts and osteoblasts in the periodontitis model mice. Taken together, these results suggest that EPOR signaling is crucial for PDLSC-based periodontal regeneration and paves the way for the development of novel options for periodontal therapy.

Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing

BACKGROUND AND PURPOSE

Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed.

EXPERIMENTAL APPROACH

Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing.

KEY RESULTS

The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario.

CONCLUSION AND IMPLICATIONS

Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.

Molecular basis of JAK2 H608Y and H608N mutations in the pathology of acute myeloid leukemia

Risk-stratification of acute myeloid leukemia (AML) based on (cyto)genetic aberrations, including hotspot mutations, deletions and point mutations have evolved substantially in recent years. With the development of next-generation sequence technology, more and more novel mutations in the AML were identified. Thus, to unravel roles and mechanism of novel mutations would improve prognostic and predictive abilities. In this study, two novel germline JAK2 His608Tyr (H608Y) and His608Asn (H608N) mutations were identified and the molecular basis of these mutations in the leukemiagenesis of AML was elucidated. Our results indicated that JAK2 H608Y and H608N mutations disrupted the hydrogen bond between Q656 and H608 which reduced the JH2 domain's activity and abolished interactions between JH1 and JH2 domains, forced JAK2 into the active conformation, facilitated the entrance of substrates and thus caused JAK2 hyperactivation. Further studies suggested that JAK2 H608Y and H608N mutations enhanced the cell proliferation and inhibited the differentiation of Ba/F3 and MV4-11 cells via activating the JAK2-STAT5 signaling pathway. Moreover, rescue experiments demonstrated that mutations repaired the hydrogen bond between Q656 and H608 displayed opposite results. Thus, this study revealed the molecular basis of JAK2 H608Y and H608N mutations in the pathology of AML.

Efficient inference and identifiability analysis for differential equation models with random parameters

Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible; we demonstrate identifiability analysis using both a frequentist approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo. Through three case studies, we demonstrate our method by providing a didactic guide to inference and identifiability analysis of hyperparameters that relate to the statistical moments of model parameters from independent observed data. Our approach has a computational cost comparable to analysis of models that neglect heterogeneity, a significant improvement over many existing alternatives. We demonstrate how analysis of random parameter models can aid better understanding of the sources of heterogeneity from biological data.

Deciphering signal transduction networks in the liver by mechanistic mathematical modelling

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

Molecular and cellular mechanisms that regulate human erythropoiesis

To enable effective oxygen transport, ∼200 billion red blood cells (RBCs) need to be produced every day in the bone marrow through the fine-tuned process of erythropoiesis. Erythropoiesis is regulated at multiple levels to ensure that defective RBC maturation or overproduction can be avoided. Here, we provide an overview of different layers of this control, ranging from cytokine signaling mechanisms that enable extrinsic regulation of RBC production to intrinsic transcriptional pathways necessary for effective erythropoiesis. Recent studies have also elucidated the importance of posttranscriptional regulation and highlighted additional gatekeeping mechanisms necessary for effective erythropoiesis. We additionally discuss the insights gained by studying human genetic variation affecting erythropoiesis and highlight the discovery of BCL11A as a regulator of hemoglobin switching through genetic studies. Finally, we provide an outlook of how our ability to measure multiple facets of this process at single-cell resolution, while accounting for the impact of human variation, will continue to refine our knowledge of erythropoiesis and how this process is perturbed in disease. As we learn more about this intricate and important process, additional opportunities to modulate erythropoiesis for therapeutic purposes will undoubtedly emerge.