Biology subjects

Steelman, A.

Publications and source records attributed to Steelman, A..

3 recordsLinked to original sources

bioRxiv2025

Hybrid peptide DNA nanomaterials enable potent and broad-spectrum virus neutralization

The continued emergence of antigenic drift and drug-resistant viral strains highlights the need for antiviral strategies that deliver robust efficacy, broad subtype coverage, and minimal off-target toxicity. We demonstrate a potent and broad-spectrum strategy that employs hybrid biomaterials of Urumin (a host defense peptide) and a honeycomb (HC) DNA origami through spatially organized multivalent presentation for enhanced antiviral efficacy. Molecular dynamics simulations reveal that Urumin penetrates and destabilizes the hemagglutinin (HA) trimer core, disrupting influenza A viral (IAV) entry. Arranging Urumin in trimeric clusters on the HC enables potent multivalent binding to trimeric HAs on IAV, enhancing antiviral efficacy at nanomolar concentrations, [~]1,000-fold more effective than free Urumin. In vitro assays confirm HC-Urumin outperforms free Urumin in blocking viral entry and preserving cell viability in more IAV subtypes. In vivo studies show that compared to free Urumin, HC-Urumin treatment reduces disease severity, preserves physiological behavior, and decreases mortality in infected mice, while maintaining virus-specific adaptive immune responses without altering humoral immunity. Our study offers an advanced and effective materials platform and strategy for broad-spectrum, low-dose intervention against human and animal IAVs, which can be adapted to combat other viruses by patterning corresponding host defense peptides on custom designed DNA nanostructures.

bioengineering↗

bioRxiv2021

Automated identification of piglet brain tissue from MRI images using Region-Based Convolutional Neural Networks

Magnetic Resonance Imaging is an important tool for characterizing volumetric changes of the piglet brain during development. Typically, an early step of an imaging analysis pipeline is brain extraction, or skull stripping. Brain extractions are usually performed manually; however, this approach is time-intensive and can lead to variation between brain extractions when multiple raters are used. Automated brain extractions are important for reducing the time required for analyses and improving the uniformity of the extractions. Here we demonstrate the use of Mask R-CNN, a Region-based Convolutional Neural Network (R-CNN), for automated brain extractions of piglet brains. We validate our approach using Nested Cross-Validation on six sets of training/validation data drawn from 32 pigs. Visual inspection of the extractions shows acceptable accuracy, Dice coefficients are in the range of 0.95-0.97, and Hausdorff Distance values in the range of 4.1-8.3 voxels. These results demonstrate that R-CNNs provide a viable tool for skull stripping of piglet brains.

neuroscience↗

bioRxiv2020

Crosstalk between microglia and patient-derived glioblastoma cells inhibit invasion in a three-dimensional gelatin hydrogel model

BackgroundGlioblastoma is the most common and deadly form of primary brain cancer, accounting for more than thirteen thousand new diagnoses annually in the United States alone. Microglia are the innate immune cells within the central nervous system, acting as a front-line defense against injuries and inflammation via a process that involves transformation from a quiescent to an activated phenotype. Crosstalk between GBM cells and microglia represents an important axis to consider in the development of tissue engineering platforms to examine pathophysiological processes underlying GBM progression and therapy. MethodsThis work used a brain-mimetic hydrogel system to study patient-derived glioblastoma specimens and their interactions with microglia. Here, glioblastoma cells were either cultured alone in 3D hydrogels or in co-culture with microglia in a manner that allowed secretome-based signaling but prevented direct GBM-microglia contact. Patterns of GBM cell invasion were quantified using a three-dimensional spheroid assay. Secretome and transcriptome (via RNAseq) were used to profile the consequences of GBM-microglia interactions. ResultsMicroglia displayed an activated phenotype as a result of GBM crosstalk. Three-dimensional migration patterns of patient derived glioblastoma cells showed invasion was significantly decreased in response to microglia paracrine signaling. Potential molecular mechanisms underlying with this phenotype were identified from bioinformatic analysis of secretome and RNAseq data. ConclusionThe data demonstrate a tissue engineered hydrogel platform can be used to investigate crosstalk between immune cells of the tumor microenvironment related to GBM progression. Such multidimensional models may provide valuable insight to inform therapeutic innovations to improve GBM treatment.

cancer biology↗