Cutting Edge: Differential Regulation of PTEN by TCR, Akt, and FoxO1 Controls CD4+ T Cell Fate Decisions.

Signaling via the Akt/mammalian target of rapamycin pathway influences CD4(+) T cell differentiation; low levels favor regulatory T cell induction and high levels favor Th induction. Although the lipid phosphatase phosphatase and tensin homolog (PTEN) suppresses Akt activity, the control of PTEN activity is poorly studied in T cells. In this study, we identify multiple mechanisms that regulate PTEN expression. During Th induction, PTEN function is suppressed via lower mRNA levels, lower protein levels, and an increase in C-terminal phosphorylation. Conversely, during regulatory T cell induction, PTEN function is maintained through the stabilization of PTEN mRNA transcription and sustained protein levels. We demonstrate that differential Akt/mammalian target of rapamycin signaling regulates PTEN transcription via the FoxO1 transcription factor. A mathematical model that includes multiple modes of PTEN regulation recapitulates our experimental findings and demonstrates how several feedback loops determine differentiation outcomes. Collectively, this work provides novel mechanistic insights into how differential regulation of PTEN controls alternate CD4(+) T cell fate outcomes.

Modeling the T cell immune response: a fascinating challenge.

The immune system is designed to protect the organism from infection and to repair damaged tissue. An effective response requires recognition of the threat, the appropriate effector mechanism to clear the pathogen and a return to homeostasis with minimal damage to self-tissues. T cells play a central role in orchestrating the immune response at all stages of the response and have been the subject of intense study by both experimental immunologists and modelers. This review examines some of the more critical questions in T cell biology and describes the latest attempts to address those questions using approaches that combine mathematical modeling and experiments.

The BioRECIPE Knowledge Representation Format.

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.

Post-translational covalent assembly of CAR and synNotch receptors for programmable antigen targeting.

Chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors are engineered cell-surface receptors that sense a target antigen and respond by activating T cell receptor signaling or a customized gene program, respectively. Here, to expand the targeting capabilities of these receptors, we develop "universal" receptor systems for which receptor specificity can be directed post-translationally via covalent attachment of a co-administered antibody bearing a benzylguanine (BG) motif. A SNAPtag self-labeling enzyme is genetically fused to the receptor and reacts with BG-conjugated antibodies for covalent assembly, programming antigen recognition. We demonstrate that activation of SNAP-CAR and SNAP-synNotch receptors can be successfully targeted by clinically relevant BG-conjugated antibodies, including anti-tumor activity of SNAP-CAR T cells in vivo in a human tumor xenograft mouse model. Finally, we develop a mathematical model to better define the parameters affecting universal receptor signaling. SNAP receptors provide a powerful strategy to post-translationally reprogram the targeting specificity of engineered cells.

Guided assembly of cellular network models from knowledge in literature.

Computational modeling is crucial for understanding and analyzing complex systems. In biology, model creation is a human dependent task that requires reading hundreds of papers and conducting wet lab experiments, which would take days or months. To overcome this hurdle, we propose a novel automated method, that utilizes the knowledge published in literature to suggest model extensions by selecting most relevant and useful information in few seconds. In particular, our novel approach organizes the events extracted from the literature as a collaboration graph with additional metric that relies on the event occurrence frequency in literature. Additionally, we show that common graph centrality metrics vary in the assessment of the extracted events. We have demonstrated the reliability of the proposed method using three different selected models, namely, T cell differentiation, T cell large granular lymphocyte, and pancreatic cancer cell. Our proposed method was able to find high percent of the desired new events with an average recall of 82%.

CLARINET: efficient learning of dynamic network models from literature.

Motivation: Creating or extending computational models of complex systems, such as intra- and intercellular biological networks, is a time and labor-intensive task, often limited by the knowledge and experience of modelers. Automating this process would enable rapid, consistent, comprehensive and robust analysis and understanding of complex systems. Results: In this work, we present CLARINET (CLARIfying NETworks), a novel methodology and a tool for automatically expanding models using the information extracted from the literature by machine reading. CLARINET creates collaboration graphs from the extracted events and uses several novel metrics for evaluating these events individually, in pairs, and in groups. These metrics are based on the frequency of occurrence and co-occurrence of events in literature, and their connectivity to the baseline model. We tested how well CLARINET can reproduce manually built and curated models, when provided with varying amount of information in the baseline model and in the machine reading output. Our results show that CLARINET can recover all relevant interactions that are present in the reading output and it automatically reconstructs manually built models with average recall of 80% and average precision of 70%. CLARINET is highly scalable, its average runtime is at the order of ten seconds when processing several thousand interactions, outperforming other similar methods. Availability and implementation: The data underlying this article are available in Bitbucket at https://bitbucket.org/biodesignlab/clarinet/src/master/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

FLUTE: Fast and reliable knowledge retrieval from biomedical literature.

State-of-the-art machine reading methods extract, in hours, hundreds of thousands of events from the biomedical literature. However, many of the extracted biomolecular interactions are incorrect or not relevant for computational modeling of a system of interest. Therefore, rapid, automated methods are required to filter and select accurate and useful information. The FiLter for Understanding True Events (FLUTE) tool uses public protein interaction databases to filter interactions that have been extracted by machines from databases such as PubMed and score them for accuracy. Confidence in the interactions allows for rapid and accurate model assembly. As our results show, FLUTE can reliably determine the confidence in the biomolecular interactions extracted by fast machine readers and at the same time provide a speedup in interaction filtering by three orders of magnitude. Database URL: https://bitbucket.org/biodesignlab/flute.

Cross-talk between Colon Cells and Macrophages Increases ST6GALNAC1 and MUC1-sTn Expression in Ulcerative Colitis and Colitis-Associated Colon Cancer.

Patients with ulcerative colitis have an increased risk of developing colitis-associated colon cancer (CACC). Changes in glycosylation of the oncoprotein MUC1 commonly occur in chronic inflammation, including ulcerative colitis, and this abnormally glycosylated MUC1 promotes cancer development and progression. It is not known what causes changes in glycosylation of MUC1. Gene expression profiling of myeloid cells in inflamed and malignant colon tissues showed increased expression levels of inflammatory macrophage-associated cytokines compared with normal tissues. We analyzed the involvement of macrophage-associated cytokines in the induction of aberrant MUC1 glycoforms. A coculture system was used to examine the effects of M1 and M2 macrophages on glycosylation-related enzymes in colon cancer cells. M2-like macrophages induced the expression of the glycosyltransferase ST6GALNAC1, an enzyme that adds sialic acid to O-linked GalNAc residues, promoting the formation of tumor-associated sialyl-Tn (sTn) O-glycans. Immunostaining of ulcerative colitis and CACC tissue samples confirmed the elevated number of M2-like macrophages as well as high expression of ST6GALNAC1 and the altered MUC1-sTn glycoform on colon cells. Cytokine arrays and blocking antibody experiments indicated that the macrophage-dependent ST6GALNAC1 activation was mediated by IL13 and CCL17. We demonstrated that IL13 promoted phosphorylation of STAT6 to activate transcription of ST6GALNAC1. A computational model of signaling pathways was assembled and used to test IL13 inhibition as a possible therapy. Our findings revealed a novel cellular cross-talk between colon cells and macrophages within the inflamed and malignant colon that contributes to the pathogenesis of ulcerative colitis and CACC.See related Spotlight on p. 160.

A Faster DiSH: Hardware Implementation of a Discrete Cell Signaling Simulator.

Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has runtimes plagued by wasted CPU cycles and unnecessary processes. Hardware emulation affords substantial speedup, but prior attempts at hardware implementation of biological simulators have been limited in scope and have suffered from inaccuracies due to poor random number generation. In this work, we propose several simulation schemes utilizing novel random update index generation techniques for step-based and round-based stochastic simulations of cellular networks. Our results show improved runtimes while maintaining simulation accuracy compared to software implementations.

Automated Extension of Cell Signaling Models with Genetic Algorithm.

The number of published results in biology and medicine is growing at an exceeding rate, and thus, extracting relevant information for building useful models is becoming very laborious. Furthermore, with the newly published information, previously built models need to be extended and updated, and with the voluminous literature, it is necessary to automate the model extension process. In this work, we introduce a methodology for extending logical models of cell signaling networks using a Genetic Algorithm (GA). The proposed procedure is developed to optimally search for a subset of biological interactions that extend logical models while preserving their desired behavior. To evaluate the effectiveness of the proposed methodology, we randomly removed a subset of elements from an existing T cell differentiation model, and mixed them with randomly created interactions to mimic the output of literature reading. We then used the GA to search for the extensions that optimally reconstructed the model. The simulation results showed that the GA was able to find a set of extensions that preserved the desired behavior of the model with fewer elements than the original model. The results demonstrate that the GA is an efficient tool for model extension, and suggest that it can be used for model reduction as well.

The duration of T cell stimulation is a critical determinant of cell fate and plasticity.

Variations in T cell receptor (TCR) signal strength, as indicated by differential activation of downstream signaling pathways, determine the fate of naïve T cells after encounter with antigen. Low-strength signals favor differentiation into regulatory T (T(reg)) cells containing the transcription factor Foxp3, whereas high-strength signals favor generation of interleukin-2-producing T helper (T(H)) cells. We constructed a logic circuit model of TCR signaling pathways, a major feature of which is an incoherent feed-forward loop involving both TCR-dependent activation of Foxp3 and its inhibition by mammalian target of rapamycin (mTOR), leading to the transient appearance of Foxp3(+) cells under T(H) cell-generating conditions. Experiments confirmed this behavior and the prediction that the immunosuppressive cytokine TGF-ß (transforming growth factor-ß) could generate T(reg) cells even during continued Akt-mTOR signaling. We predicted that sustained mTOR activity could suppress FOXP3 expression upon TGF-ß removal, suggesting a possible mechanism for the experimentally observed instability of Foxp3(+) cells. Our model predicted, and experiments confirmed, that transient stimulation of cells with high-dose antigen generated T(H), T(reg), and nonactivated cells in proportions depending on the duration of TCR stimulation. Experimental analysis of cells after antigen removal identified three populations that correlated with these T cell fates. Further analysis of simulations implicated a negative feedback loop involving Foxp3, the phosphatase PTEN, and Akt-mTOR in determining fate. These results suggest that there is a critical time after TCR stimulation during which heterogeneity in the differentiating population of cells leads to increased plasticity of cell fate.

Regulatory network analysis acceleration with reconfigurable hardware.

In medical research it is of great importance to be able to quickly obtain answers to inquiries about system response to different stimuli. Modeling the dynamics of biological regulatory networks is a promising approach to achieve this goal, but existing modeling approaches suffer from complexity issues and become inefficient with large networks. In order to improve the efficiency, we propose the implementation of models of regulatory networks in hardware, which allows for highly parallel simulation of these networks. We find that our FPGA implementation of an example model of peripheral naïve T cell differentiation provides five orders of magnitude speedup when compared to software simulation.