We share the VISION of EDUCATING ambitious young scientists and engineers to make impacts beyond individual efforts through team projects and collaborative learning in academia and industry.
Understand and control liver regeneration and chronic disease progression. We TRANSLATE technologies and knowledges into solutions for drug development, diagnostics and therapeutics.
Quantitative analysis of the dynamic process of liver regeneration and chronic liver diseases.
Investigating the formation and dynamic maintenance of inter-cellular tissue space such as bile canaliculi and sinusoids that define liver functions.
Developing novel and useful biomaterials, cell sources, and analytics for long-term maintenance of highly functional liver cells in culture.
Developing robust, scalable, low cost and predictive in vitro drug and pathogen testing platforms.
Respect : every LCTE member is important and yet consciously sensitive to other members and the collective impacts. Decisions incorporate inputs from all the stakeholders for fairness and transparency.
Professionalism: every LCTE member strives to attain ever higher quality and standard of her/his own work through mutual empowerment, critique and support to each other.
No-walls culture: solutions to real-life problems can never be confined within artificially-created boundaries (organizational, disciplinary, cultural, inter-personal, or mental inertia).
Li, H., Venkatraman, L., Narmada, B.C., White, J.K., Tucker-Kellogg, L., and Yu, H. (2017) Computational modeling of bistable TGF-β1 activation: the switch between two steady states is accompanied by a switch between positive and negative feedback.BMC Systems Biology, 21 December 2017; 11(Suppl 7): 136. DOI: 10.1186/s12918-017-0508-z
Bistable behaviors are prevalent in cell signaling and can be modeled by ordinary differential equations (ODEs) with kinetic parameters. A bistable switch has recently been found to regulate the activation of transforming growth factor-β1 (TGF-β1) in the context of liver fibrosis, and an ordinary differential equation (ODE) model was published showing that the net activation of TGF-β1 depends on the balance between two antagonistic sub-pathways. Through modeling the effects of perturbations that affect both sub-pathways, we revealed that bistability is coupled with the signs of feedback loops in the model. We extended the model to include calcium and Krüppel-like factor 2 (KLF2), both regulators of Thrombospondin-1 (TSP1) and Plasmin (PLS). Increased levels of extracellular calcium, which alters the TSP1-PLS balance, would cause high levels of TGF-β1, resembling a fibrotic state. KLF2, which suppresses production of TSP1 and plasminogen activator inhibitor-1 (PAI1), would eradicate bistability and preclude the fibrotic steady-state. Finally, the loop PLS − TGF-β1 − PAI1 had previously been reported as negative feedback, but the model suggested a stronger indirect effect of PLS down-regulating PAI1 to produce positive (double-negative) feedback in a fibrotic state. Further simulations showed that activation of KLF2 was able to restore negative feedback in the PLS − TGF-β1 − PAI1 loop. Using the TGF-β1 activation model as a case study, we showed that external factors such as calcium or KLF2 can induce or eradicate bistability, accompanied by a switch in the sign of a feedback loop (PLS − TGF-β1 − PAI1) in the model. The coupling between bistability and positive/negative feedback suggests an alternative way of characterizing a dynamical system and its biological implications.
Yan, J., Yu, Y., Kang, J.W., Tam, Z.Y., Xu, S., Fong, E.L.S., Singh, S.P., Song, Z., Tucker Kellogg, L., So, P., and Yu, H. (2017) Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy. Journal of Biophotonics, December 2017; 10(12):1703-1713. DOI: 10.1002/jbio.201600303
Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85–0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.
Song, Z., Gupta, K., Ng, I.C., Xing, J., Yang, Y.A., and Yu, H. (2017) Mechanosensing in liver regeneration. Seminar in Cells and Developmental Biology, 06 November 2017; 7: 14528. DOI: 10.1016/j.semcdb.2017.07.041
Liver is highly regenerative as it can restore its function and size even after 70% partial hepatectomy. During liver regeneration, the mechanical and chemical environment of liver is altered with accumulation of various growth factors and remodeling of extracellular environment. Cells can sense the changes in their cellular environment through various chemo and mechanosensors present on their surfaces. These changes are then transduced by initiation of multiple signaling pathways. Traditional view of liver regeneration describes the process as a cascade of chemical signaling pathways. In this review, we describe the role of mechanical forces and mechanosensing in regulating liver regeneration with focus on the role of altered shear and extracellular matrix environment following injury. These mechanosensing mechanisms either generate molecular signals that further activate downstream signaling pathways such as YAP or directly transduce mechanical signals by regulating actomyosin cytoskeleton. These signals travel to the decision center such as nucleus to switch cell fate and activate functions needed in liver regeneration, e.g. proliferation of various hepatic cell types, differentiation of hepatic stem cells, extracellular matrix remodeling and termination signals that regulate the regenerated liver size. Different mechanical and chemical signals coordinate intracellular chemical signaling pathways leading to robust liver regeneration.