Engineering synthetic phosphorylation signaling networks in human cells.

Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs-reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities-are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Determining Sex-Based Differences in Inflammatory Response in an Experimental Traumatic Brain Injury Model.

Introduction: Traumatic brain injury is a leading cause of injury-related death and morbidity. Multiple clinical and pre-clinical studies have reported various results regarding sex-based differences in TBI. Our accepted rodent model of traumatic brain injury was used to identify sex-based differences in the pathological features of TBI. Methods: Male and female Sprague-Dawley rats were subjected to either controlled-cortical impact (CCI) or sham injury; brain tissue was harvested at different time intervals depending on the specific study. Blood-brain barrier (BBB) analysis was performed using infrared imaging to measure fluorescence dye extravasation. Microglia and splenocytes were characterized with traditional flow cytometry; microglia markers such as CD45, P2Y12, CD32, and CD163 were analyzed with t-distributed stochastic neighbor embedding (t-SNE). Flow cytometry was used to study tissue cytokine levels, and supplemented with ELISAs of TNF-⍺, IL-17, and IL-1ß of the ipsilateral hemisphere tissue. Results: CCI groups of both sexes recorded a higher BBB permeability at 72 hours post-injury than their respective sham groups. There was significant difference in the integrated density value of BBB permeability between the male CCI group and the female CCI group (female CCI mean = 3.08 x 108 ± 2.83 x 107, male CCI mean = 2.20 x 108 ± 4.05 x 106, p = 0.0210), but otherwise no differences were observed. Traditional flow cytometry did not distinguish any sex-based difference in regards to splenocyte cell population after CCI. t-SNE did not reveal any significant difference between the male and female injury groups in the activation of microglia. Cytokine analysis after injury by flow cytometry and ELISA was limited in differences at the time point of 6 hours post-injury. Conclusion: In our rodent model of traumatic brain injury, sex-based differences in pathology and neuroinflammation at specified time points are limited, and only noted in one specific analysis of BBB permeability.

Qualitative Analysis of Microbial Dynamics during Anaerobic Digestion of Microalgal Biomass in a UASB Reactor.

Anaerobic digestion (AD) is a microbiologically coordinated process with dynamic relationships between bacterial players. Current understanding of dynamic changes in the bacterial composition during the AD process is incomplete. The objective of this research was to assess changes in bacterial community composition that coordinates with anaerobic codigestion of microalgal biomass cultivated on municipal wastewater. An upflow anaerobic sludge blanket reactor was used to achieve high rates of microalgae decomposition and biogas production. Samples of the sludge were collected throughout AD and extracted DNA was subjected to next-generation sequencing using methanogen mcrA gene specific and universal bacterial primers. Analysis of the data revealed that samples taken at different stages of AD had varying bacterial composition. A group consisting of Bacteroidales, Pseudomonadales, and Enterobacteriales was identified to be putatively responsible for the hydrolysis of microalgal biomass. The methanogenesis phase was dominated by Methanosarcina mazei. Results of observed changes in the composition of microbial communities during AD can be used as a road map to stimulate key bacterial species identified at each phase of AD to increase yield of biogas and rate of substrate decomposition. This research demonstrates a successful exploitation of methane production from microalgae without any biomass pretreatment.