Most serine proteases are synthesized as inactive zymogens that are activated by cleavage by another protease in a tightly regulated mechanism. The urokinase-type plasminogen activator (uPA) and plasmin cleave and activate each other, constituting a positive feedback loop. How this mutual activation cycle begins has remained a mystery. We used hydrogen deuterium exchange mass spectrometry to characterize the dynamic differences between the inactive single-chain uPA (scuPA) and its active form two-chain uPA (tcuPA). The results show that the C-terminal β-barrel and the area around the new N terminus have significantly reduced dynamics in tcuPA as compared with scuPA. We also show that the zymogen scuPA is inactive but can, upon storage, become active in the absence of external proteases. In addition to plasmin, the tcuPA can activate scuPA by cleavage at K158, a process called autoactivation. Unexpectedly, tcuPA can cleave at position 158 even when this site is mutated. TcuPA can also cleave scuPA after K135 or K136 in the disordered linker, which generates the soluble protease domain of uPA. Plasmin cleaves scuPA exclusively after K158 and at a faster rate than tcuPA. We propose a mechanism by which the uPA receptor dimerization could promote autoactivation of scuPA on cell surfaces. These results resolve long-standing controversies in the literature surrounding the mechanism of uPA activation.

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.