Understanding stem cell differentiation through self-organization theory

Cell‑based immunotherapy of glioblastoma multiforme (Review)

Glioblastoma multiforme (GBM) is the most aggressive and lethal primary glial brain tumor. It has an unfavorable prognosis and relatively ineffective treatment protocols, with the median survival of patients being ~15 months. Tumor resistance to treatment is associated with its cancer stem cells (CSCs). At present, there is no medication or technologies that have the ability to completely eradicate CSCs, and immunotherapy (IT) is only able to prolong the patient's life. The present review aimed to investigate systemic solutions for issues associated with immunosuppression, such as ineffective IT and the creation of optimal conditions for CSCs to fulfill their lethal potential. The present review also investigated the main methods involved in local immunosuppression treatment, and highlighted the associated disadvantages. In addition, novel treatment options and targets for the elimination and regulation of CSCs with adaptive and active IT are discussed. Antagonists of TGF-β inhibitors, immune checkpoints and other targeted medication are also summarized. The role of normal hematopoietic stem cells (HSCs) in the mechanisms underlying systemic immune suppression development in cases of GBM is analyzed, and the potential reprogramming of HSCs during their interaction with cancer cells is discussed. Moreover, the present review emphasizes the importance of the aforementioned interactions in the development of immune tolerance and the inactivation of the immune system in neoplastic processes. The possibility of solving the problem of systemic immunosuppression during transplantation of donor HSCs is discussed.

Cell-based therapy for traumatic brain injury

Traumatic brain injury is a major economic burden to hospitals in terms of emergency department visits, hospitalizations, and utilization of intensive care units. Current guidelines for the management of severe traumatic brain injuries are primarily supportive, with an emphasis on surveillance (i.e. intracranial pressure) and preventive measures to reduce morbidity and mortality. There are no direct effective therapies available. Over the last fifteen years, pre-clinical studies in regenerative medicine utilizing cell-based therapy have generated enthusiasm as a possible treatment option for traumatic brain injury. In these studies, stem cells and progenitor cells were shown to migrate into the injured brain and proliferate, exerting protective effects through possible cell replacement, gene and protein transfer, and release of anti-inflammatory and growth factors. In this work, we reviewed the pathophysiological mechanisms of traumatic brain injury, the biological rationale for using stem cells and progenitor cells, and the results of clinical trials using cell-based therapy for traumatic brain injury. Although the benefits of cell-based therapy have been clearly demonstrated in pre-clinical studies, some questions remain regarding the biological mechanisms of repair and safety, dose, route and timing of cell delivery, which ultimately will determine its optimal clinical use.

Epigenetic implication of gene-adjacent retroelements in Helicobacter pylori-infected adults

A chronic inflammatory condition of gastric mucosa can facilitate the influx of new stem cells into the stomach. Epigenetic codes, such as DNA methylation, may be responsible for the stable maintenance of epigenetic phenotypes established in the new stomach-adapted stem cells. A number of hypotheses have been made for the role of CpG-island methylation, which is common in the Helicobacter pylori-infected stomach. However, they could not explain the plausible role of CpG-island methylation in the re-establishment of epigenetic phenotypes. These islands are highly repetitive sequences densely methylated throughout the human genome, the so-called parasitic retroelements, which expand a number of cDNA copies with reverse transcriptase. The densely methylated retroelements adjacent to the host genes can form the transitional-CpG sites around gene-control regions that are barely methylated. This review focuses on the putative role of transitional CpG methylation in the adaptive differentiation of new stem cells in the H. pylori-infected stomach.

A stochastic model of epigenetic dynamics in somatic cell reprogramming

Somatic cell reprogramming has dramatically changed stem cell research in recent years. The high pace of new findings in the field and an ever increasing amount of data from new high throughput techniques make it challenging to isolate core principles of the process. In order to analyze such mechanisms, we developed an abstract mechanistic model of a subset of the known regulatory processes during cell differentiation and production of induced pluripotent stem cells. This probabilistic Boolean network describes the interplay between gene expression, chromatin modifications, and DNA methylation. The model incorporates recent findings in epigenetics and partially reproduces experimentally observed reprogramming efficiencies and changes in methylation and chromatin remodeling. It enables us to investigate, how the temporal progression of the process is regulated. It also explicitly includes the transduction of factors using viral vectors and their silencing in reprogrammed cells, since this is still a standard procedure in somatic cell reprogramming. Based on the model we calculate an epigenetic landscape for probabilities of cell states. Simulation results show good reproduction of experimental observations during reprogramming, despite the simple structure of the model. An extensive analysis and introduced variations hint toward possible optimizations of the process that could push the technique closer to clinical applications. Faster changes in DNA methylation increase the speed of reprogramming at the expense of efficiency, while accelerated chromatin modifications moderately improve efficiency.

Simulating microbial systems: addressing model uncertainty/incompleteness via multiscale and entropy methods

Most systems of interest in the natural and engineering sciences are multiscale in character. Typically available models are incomplete or uncertain. Thus, a probabilistic approach is required. We present a deductive multiscale approach to address such problems, focusing on virus and cell systems to demonstrate the ideas. There is usually an underlying physical model, all factors in which (e.g., particle masses, charges, and force constants) are known. For example, the underlying model can be cast in terms of a collection of N-atoms evolving via Newton's equations. When the number of atoms is 10(6) or more, these physical models cannot be simulated directly. However, one may only be interested in a coarse-grained description, e.g., in terms of molecular populations or overall system size, shape, position, and orientation. The premise of this chapter is that the coarse-grained equations should be derived from the underlying model so that a deductive calibration-free methodology is achieved. We consider a reduction in resolution from a description for the state of N-atoms to one in terms of coarse-grained variables. This implies a degree of uncertainty in the underlying microstates. We present a methodology for modeling microbial systems that integrates equations for coarse-grained variables with a probabilistic description of the underlying fine-scale ones. The implementation of our strategy as a general computational platform (SimEntropics™) for microbial modeling and prospects for developments and applications are discussed.

A model for genetic and epigenetic regulatory networks identifies rare pathways for transcription factor induced pluripotency

With relatively low efficiency, differentiated cells can be reprogrammed to a pluripotent state by ectopic expression of a few transcription factors. An understanding of the mechanisms that underlie data emerging from such experiments can help design optimal strategies for creating pluripotent cells for patient-specific regenerative medicine. We have developed a computational model for the architecture of the epigenetic and genetic regulatory networks which describes transformations resulting from expression of reprogramming factors. Importantly, our studies identify the rare temporal pathways that result in induced pluripotent cells. Further experimental tests of predictions emerging from our model should lead to fundamental advances in our understanding of how cellular identity is maintained and transformed.

Most cells in an organism have the same DNA. Yet, different cell types express different proteins and carry out different functions. These differences are reflected by cell epigenetics; i.e., DNA in different cell types is packaged distinctly, making it hard to express certain genes while facilitating the expression of others. During development, upon receipt of appropriate cues, pluripotent embryonic stem cells differentiate into diverse cell types that make up the organism (e.g., a human). There has long been an effort to make this process go backward— i.e., reprogram a differentiated cell (e.g., a skin cell) to pluripotent status. Recently, this has been achieved by overexpressing specific transcription factors in differentiated cells. This method does not use embryonic material and promises the development of patient-specific regenerative medicine. The mechanisms that make reprogramming rare, or even possible, are poorly understood. We have developed the first computational model of transcription factor-induced reprogramming. Results obtained from the model are consistent with diverse observations, and identify the rare pathways that allow reprogramming to occur. If validated by further experiments, our model could be further developed to design optimal strategies for reprogramming and shed light on basic questions in biology.