Mager, M. | Search bioRxiv

bioRxiv2024

Oligonucleotide Library Assisted Sequence Mining Reveals Promoter Sequences With Distinct Temporal Expression Dynamics For Applications In Curvibacter SP. AEP1-3

AO_SCPLOWBSTRACTC_SCPLOWThe {beta}-proteobacterial species Curvibacter sp. AEP1-3 is a model organism for the study of symbiotic interactions as it is the most abundant bacterial colonizer of the basal metazoan Hydra vulgaris. Yet, genetic tools for Curvibacter are still in an infancy: few promoters have been characterized for Curvibacter. Here we employ an oligonucleotide based strategy to find potential expression systems derived from the genome of Curvibacter. Potential promoters were systematically mined from the genome in silico. The sequences were cloned as a mixed library into a mCherry reporter gene expression vector and single positive candidates were selected through Flow Cytometry based sorting to be further analyzed through bulk measurements. From 500 candidate sequences, 25 were identified as active promoters of varying expression strength levels. Bulk measurements revealed unique activity profiles for these sequences across growth phases. The expression levels of these promoters ranged over two orders of magnitudes and showed distinct temporal expression dynamics over the growth phases: while 3 sequences showed higher expression levels in the exponential phase than in the stationary phase, we found 12 sequences saturating expression during stationary phase and 10 that showed little discrimination between growth phases. From our library, promoters the genes encoding for DnaK, RpsL and an AHL synthase stood out as the most interesting candidates as their expression profiles fit a variety of applications. Examining the expression levels of successful candidates in relation to RNAseq read counts revealed only weak correlation between the two datasets. This underscores the importance of employing comprehensive high-throughput strategies when establishing expression systems for newly introduced model organisms.

synthetic biology↗

bioRxiv2022

The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain-Computer Interface Platform

Progress toward the development of brain-computer interfaces has signaled the potential to restore, replace, and augment lost or impaired neurological function in a variety of disease states. Existing approaches to developing high-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes, which limit addressable applications of the technology and the number of eligible patients. Here we describe a novel approach to constructing a neural interface, comprising conformable thin-film electrode arrays and a minimally invasive surgical delivery system that together facilitate bidirectional communication with large portions of the cortical surface (enabling both recording and stimulation). We demonstrate the feasibility and safety of delivering reversible implants containing over 2,000 microelectrodes to multiple functional regions in both hemispheres of the brain simultaneously, without requiring a craniotomy or damaging the cortical surface, at an effective insertion rate faster than 40 ms per channel. We further evaluate the performance of this system immediately following implantation for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and extents not previously possible in multiple preclinical large animal studies as well as in a five-patient pilot clinical study involving both anesthetized and awake neurosurgical patients. We characterize the spatial scales at which sensorimotor activity and speech are represented at the cortical surface, demonstrate accurate neural decoding of somatosensory, visual, and volitional walking activity, and achieve precise neuromodulation through cortical stimulation at sub-millimeter scales. The resulting system generates 90 Gb/h of electrophysiologic data, and demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces that may expand the patient population that could benefit from neural interface technology.

neuroscience↗