Biology subjects

Zhang, J.-F.

Publications and source records attributed to Zhang, J.-F..

3 recordsLinked to original sources

bioRxiv2026

Sensitive and Selective Next-Generation FRET-based PKA Biosensors

The cyclic AMP (cAMP)/protein kinase A (PKA) signaling pathway regulates diverse cellular processes through precise spatiotemporal control across subcellular compartments. Forster resonance energy transfer (FRET)-based A-kinase activity reporters (AKARs) have enabled live-cell visualization of PKA activity, but their limited dynamic range constrains detection of subtle or compartment-specific signaling events. Here, we present a suite of sixth-generation cyan/yellow FRET-based PKA sensors (the AKAR6 series) with substantially enhanced sensitivity and improved selectivity. Systematic optimization of the FRET donor-acceptor pair and sensor backbone yields superior performance versus previous best-in-class FRET-based AKARs, enabling robust detection of subtle PKA activity changes across diverse experimental modalities, including flow cytometry, fluorescence lifetime-based FRET, and two-photon imaging in brain slices. We further leverage kinome atlas data to engineer a variant with improved selectivity for more accurate visualization of nuclear PKA activity. Using the AKAR6 toolkit, we showed that in contrast to strong GPCR-induced PKA activities across all tested compartments in PC12 cells, growth factors stimulated a significant PKA activity at the trans-Golgi network but no detectable activity at cis-Golgi, signifying highly compartmentalized PKA signaling at the sub-organelle level. Furthermore, NGF and EGF induced sustained and transient PKA activity, respectively, across various intracellular compartments, including the nucleus, suggesting that growth factor-specific temporal controls are maintained across subcellular compartments. Together, the AKAR6 toolkit provides a sensitive, selective, and versatile platform for dissecting compartmentalized PKA signaling across cells and tissues.

bioengineering↗

bioRxiv2024

Mechanical cues of extracellular matrix determines tumor innervation

Peripheral tumors can establish local autonomic and sensory nerve networks, termed as tumor innervation (TIN), to support tumorigenesis and metastasis. While nerve dependence in cancers is well-established, the mechanisms governing TIN remain unclear. Here, we report that extracellular matrix (ECM) stiffness, a major mechanical abnormality in the tumor microenvironment (TME), is an essential contributor of TIN. In preclinical models, reducing lysyl oxidase-mediated ECM crosslinking lowers tissue stiffness and TIN in pancreatic cancer, while inflammation-induced matrix stiffening boosts the hyperinnervation of the pancreatic precursor lesions. Mechanistically, {beta}1-containing integrins sense the mechanical cues exerted by ECM stiffness, and the translational co-activator YAP1 acts as an essential nuclear relay to induce the expression of neurotropic genes, particularly brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF). 3D imaging of the whole cleared pancreas reveals that blockade of mechanosensor integrin {beta}1 or pharmacological inhibition of the mechanotransducer YAP1 effectively reduces TIN. In clinical settings, tumor samples with a dense, crosslinked, and stiffened ECM exhibit significant TIN. In summary, these findings identify ECM stiffness as an important driver of TIN and suggest that targeting integrin {beta}1/YAP1-dependent mechanotransduction may counteract TIN.

neuroscience↗

bioRxiv2021

Structure and dynamics of human disease-complication network

A complication is an unanticipated disease arisen following, induced by a disease, a treatment or a procedure. We compile the Human Disease-Complication Network from the medical data and investigate the characteristics of the network. It is observed that the modules of the network are dominated by the classes of diseases. The relations between modules are unveiled in detail. Three nontrivial motifs are identified from the network. We further simulate the dynamics of motifs with the Boolean dynamic model. Each motif represents a specific dynamic behavior, which is potentially functional in the disease system, such as generating temporal progressions and governing the responses to fluctuating external stimuli. Author summaryAdvances in molecular biology lead to a new discipline of network medicine, investigating human diseases in a networked structure perspective. Recently, clinical records have been introduced to the research of complex networks of diseases. An important available medical dataset that has been overlooked so far is the complications of diseases, which are vital for human beings. We compile the Human Disease-Complication Network, representing the causality between the upstream diseases and their downstream complications. This work not only helps us to comprehend why certain groups of diseases appear collectively, but also provides a new paradigm to investigate the dynamics of disease progression. For clinical applications, the investigation of complications may yield new approaches to disease prevention, diagnosis and treatment.

bioinformatics↗