AAV6 mediated Gsx1 expression in neural stem progenitor cells promotes neurogenesis and restores locomotor function after contusion spinal cord injury.

Genomic screened homeobox 1 (Gsx1 or Gsh1) is a neurogenic transcription factor required for the generation of excitatory and inhibitory interneurons during spinal cord development. In the adult, lentivirus (LV) mediated Gsx1 expression promotes neural regeneration and functional locomotor recovery in a mouse model of lateral hemisection spinal cord injury (SCI). The LV delivery method is clinically unsafe due to insertional mutations to the host DNA. In addition, the most common clinical case of SCI is contusion/compression. In this study, we identify that adeno-associated virus serotype 6 (AAV6) preferentially infects neural stem/progenitor cells (NSPCs) in the injured spinal cord. Using a rat model of contusion SCI, we demonstrate that AAV6 mediated Gsx1 expression promotes neurogenesis, increases the number of neuroblasts/immature neurons, restores excitatory/inhibitory neuron balance and serotonergic neuronal activity through the lesion core, and promotes locomotor functional recovery. Our findings support that AAV6 preferentially targets NSPCs for gene delivery and confirmed Gsx1 efficacy in clinically relevant rat model of contusion SCI.

A User's Guide to Machine Learning for Polymeric Biomaterials.

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

Examining polymer-protein biophysical interactions with small-angle x-ray scattering and quartz crystal microbalance with dissipation.

Polymer-protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure-function behavior. Here, we show the value of tandem small-angle x-ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer-protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer-protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration (Rg ) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer-protein hybrids.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids.

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.