Model-based selection of the robust JAK-STAT activation mechanism

Stochastic dynamics of Type-I interferon responses

Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

We investigate the impact of intrinsic and extrinsic noise on the reliability of interferon signaling. Information must be transduced robustly despite existing biochemical variability and at the same time the system has to allow for cellular variability to tune it against changing environments. Getting insights into stochasticity in signaling networks is crucial to understand cellular dynamics and decision-making processes. To this end, we developed a detailed stochastic computational model based on single cell data. We are able to show that reliability is achieved despite high noise at the receptor level.

Icaritin: A Novel Natural Candidate for Hematological Malignancies Therapy

Hematological malignancies including leukemia and lymphoma can severely impact human health. With the current therapies combined with chemotherapy, stem cell transplantation, radiotherapy, and immunotherapy, the prognosis of hematologic malignancies improved significantly. However, most hematological malignancies are still incurable. Therefore, research for novel treatment options was continuing with the natural product as one source. Icaritin is a compound extracted from a traditional Chinese herb, Epimedium Genus, and demonstrated an antitumor effect in various neoplasms including hematological malignancies such as leukemia, lymphoma, and multiple myeloma. In hematological malignancies, icaritin showed multiple cytotoxic effects to induce apoptosis, arrest the cell cycle, inhibit proliferation, promote differentiation, restrict metastasis and infiltration, and suppress the oncogenic virus. The proved underlying mechanisms of the cytotoxic effects of icaritin are different in various cell types of hematological malignancies but associated with the critical cell signal pathway, including PI3K/Akt, JAK/STAT3, and MAPK/ERK/JNK. Although the primary target of icaritin is still unspecified, the existing evidence indicates that icaritin is a potential novel therapeutic agent for neoplasms as with hematological malignancies. Here, in the field of hematology, we reviewed the reported activity of icaritin in hematologic malignancies and the underlying mechanisms and recognized icaritin as a candidate for therapy of hematological malignancies.

Quantitative control of intracellular signaling activity through chimeric receptors incorporating multiple identical tyrosine motifs

Controlling activation levels and durations of native signaling molecules is important for efficiently controlling cellular fates. Previously we developed single-chain Fv (scFv)/cytokine receptor chimeras incorporating tyrosine motifs in the intracellular domain, which artificially control the activation of specific intracellular signaling proteins. In this study, to quantitatively control the activation levels of signaling molecules with an extended dynamic range, we constructed scFv/receptor chimeras incorporating multiple identical motifs at the different positions in the intracellular domain. We used retroviral transduction to express chimeric receptors with multiple STAT3 binding motifs connected with or without flexible linkers in a murine IL-3-dependent pro-B cell line, Ba/F3. Our results showed that the chimeric receptors can control the activation levels of STAT3 depending on ligand concentration and the number of motifs. The existence of linkers between the motifs also affected the signal intensity. Furthermore, the STAT3 activation levels significantly depended on the number of motifs rather than the distance from the JAK-binding region to the tyrosine motif.

Model selection in systems and synthetic biology

Developing mechanistic models has become an integral aspect of systems biology, as has the need to differentiate between alternative models. Parameterizing mathematical models has been widely perceived as a formidable challenge, which has spurred the development of statistical and optimisation routines for parameter inference. But now focus is increasingly shifting to problems that require us to choose from among a set of different models to determine which one offers the best description of a given biological system. We will here provide an overview of recent developments in the area of model selection. We will focus on approaches that are both practical as well as build on solid statistical principles and outline the conceptual foundations and the scope for application of such methods in systems biology.

Computational models of the JAK1/2-STAT1 signaling

Despite a conceptually simple mechanism of signaling, the JAK-STAT pathway exhibits considerable behavioral complexity. Computational pathway models are tools to investigate in detail signaling process. They integrate well with experimental studies, helping to explain molecular dynamics and to state new hypotheses, most often about the structure of interactions. A relatively small amount of experimental data is available for a JAK1/2-STAT1 variant of the pathway, hence, only several computational models were developed. Here we review a dominant approach of kinetic modeling of the JAK1/2-STAT1 pathway, based on ordinary differential equations. We also give a brief overview of attempts to computationally infer topology of this pathway.

Protein analysis of atrial fibrosis via label-free proteomics in chronic atrial fibrillation patients with mitral valve disease

Background

Atrial fibrosis, as a hallmark of atrial structure remodeling, plays an important role in maintenance of chronic atrial fibrillation, but interrelationship of atrial fibrosis and atrial fibrillation is uncertain. Label-free proteomics can implement high throughput screening for finding and analyzing pivotal proteins related to the disease.. Therefore, we used label-free proteomics to explore and analyze differentially proteins in chronic atrial fibrillation patients with mitral valve disease.

Methods

Left and right atrial appendages obtained from patients with mitral valve disease were both in chronic atrial fibrillation (CAF, AF≥6 months, n = 6) and in sinus rhythm (SR, n = 6). One part of the sample was used for histological analysis and fibrosis quantification; other part were analyzed by label-free proteomic combining liquid chromatography with mass spectrometry (LC-MS), we utilized bioinformatics analysis to identify differential proteins.

Results

Degree of atrial fibrosis was higher in CAF patients than that of SR patients. 223 differential proteins were detected between two groups. These proteins mainly had vital functions such as cell proliferation, stress response, focal adhesion apoptosis. We evaluated that serine/threonine protein kinase N2 (PKN2), dermatopontin(DP), S100 calcium binding protein B(S100B), protein tyrosine kinase 2(PTK2) and discoidin domain receptor tyrosine kinase 2(DDR2) played important roles in fibrotic process related to atrial fibrillation.

Conclusion

The study presented differential proteins responsible for atrial fibrosis in chronic atrial fibrillation patients through label-free proteomic analysis. We assessed some vital proteins including their characters and roles. These findings may open up new realm for mechanism research of atrial fibrillation.