Biology subjects

VanEpps, J. S.

Publications and source records attributed to VanEpps, J. S..

8 recordsLinked to original sources

bioRxiv2026

Dynamically actuated reconfigurable topographical surface enables active control of implant-associated infections

Implant-associated infections are driven by bacterial biofilm formation and remain difficult to eradicate using conventional antibiotic-based strategies. Here, we present a dynamically actuated reconfigurable topographical surface (DARTS) that integrates intrinsically bactericidal nanoscale surface topography with programmable mechanical actuation to achieve durable, antibiotic-free infection control. Using a scalable bottom-up nanofabrication strategy, we generate tunable wrinkled MXene topographies that exhibit contact-mediated bactericidal activity against both Gram-positive and Gram-negative bacteria without chemical leaching. Integration with a soft robotic actuator enables reversible modulation of surface geometry, which synergistically enhances bacterial removal and killing, resulting in near-complete disruption of mature biofilms. Dynamic actuation further sensitizes released bacteria to antibiotic treatment. In a mouse subcutaneous implant infection model, DARTS with actuation achieves sustained suppression of bacterial burden and markedly improves host tissue outcomes. Remote, noninvasive actuation using near-infrared laser stimulation further highlights the translational potential of this platform for implantable antibacterial applications. Significance StatementImplant infections are difficult to treat because bacteria form biofilms that protect them from antibiotics and the immune system. Current materials often rely on chemical release, which can lose effectiveness over time. Here, we present a new surface that both kills bacteria and removes them. The surface uses nanoscale features to physically damage bacterial cells, while dynamic motion clears attached bacteria and biofilms. This allows continuous, chemical-free control of infection. In a mouse implant model, the system greatly reduced bacterial burden and improved tissue healing. This work introduces a new way to control bacteria using dynamic surface design and could be applied not only to medical implants but also to environmental, food, textile, and marine systems.

bioengineering↗

bioRxiv2024

GenomicGapID: Leveraging Spatial Distribution of Conserved Genomic Sites for Broad-Spectrum Microbial Identification

Bacterial detection and identification methods can be broadly classified as either untargeted with expansive taxonomic coverage or targeted with narrow taxonomic focus. Untargeted approaches, such as culture and sequencing, are often time-consuming and/or costly, whereas targeted methods, such as PCR, can offer faster and more cost-effective results but require a priori knowledge of the likely pathogen to select the appropriate assay. GenomicGapID, a novel approach that leverages the spatial distribution of conserved genetic regions across microbial genomes, represents a significant advancement in the field of microbial identification. This technique has the potential to provide the taxonomic breadth of culture and sequencing, while maintaining the speed, simplicity, and cost-effectiveness of PCR. By leveraging the conservation and relative positioning of highly conserved coding regions across different species, GenomicGapID enables the development of universal primer sets that amplify the non-conserved gaps between these regions. This creates a unique electrophoretic signature that facilitates rapid and accurate target agnostic microbial identification. In this study, we apply the principles of GenomicGapID to the critical task of identifying clinical pathogens. We focus on expanding the coverage of a previously developed universal bacterial identification system, which initially targeted the 16s-23s internal transcribed spacer (ITS) region and was capable of discerning 45 pathogens. To enhance this system, we assembled a comprehensive database of 189 clinically relevant bacterial species. We then identified conserved primer binding sites that produce unique amplicon size signatures for each species. While we found that the use of amplicon size signatures alone would require an impractical number of universal primer sets, we demonstrate that this challenge can be effectively mitigated through concurrent melt analysis. Ultimately, we show that just three universal primer sets, guided by the GenomicGapID framework, are sufficient to cover 189 clinical bacterial pathogens, representing a majority of microbes identified in positive cultures in a clinical microbiology setting, with experimental validation of a subset of these pathogens. This study not only enhances the existing universal bacterial identification system but also establishes GenomicGapID as a versatile and powerful tool in microbial diagnostics and beyond with the potential to open new areas of investigation in genomics, with significant implications for molecular biology, clinical practice, and public health.

microbiology↗

bioRxiv2024

A rapid, inexpensive, culture-free, universal bacterial identification system using internal transcribed spacer targeting primers: a proof-of-principle study

Techniques for bacterial detection and identification can be characterized as either untargeted and taxonomically broad or targeted and taxonomically narrow. Untargeted techniques (e.g., culture and sequencing) are time-consuming and/or expensive while targeted techniques (polymerase chain reaction; PCR) can be faster and less expensive but require a strong pre-test suspicion of the potential organism to choose the correct test. We have developed a universal bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The platform utilizes a unique universal polymerase chain reaction (PCR) primer set that targets the internal transcribed spacer (ITS) regions between conserved bacterial genes, creating a distinguishable electrophoretic pattern for each bacterial species. Bioinformatic simulation demonstrates that at least 45 commonly isolated pathogenic species can be uniquely identified from a single set of PCR primers using this approach. We experimentally confirmed these predictions on seven representative human pathogens, including gram-negatives and gram-positives, aerobes and anaerobes, and spore formers. Without a priori knowledge of the organism, this system can rapidly identify the unique pattern generated by multiple species in a single reaction. Using quantitative PCR, the system can also determine the corresponding concentration of the organism in question. We also show that the primers are resilient to human DNA contamination at physiologic concentrations, eliminating the need for complex and time intensive extraction methods. Proof-of-principle testing on actual clinical specimens demonstrate that this assay can identify more than twice the species as current multiplex PCR assays (e.g., BioFire) using only one universal primer pair in a single PCR reaction, providing results in <3 hours for <$20 without bioinformatic turnaround time. SIGNIFICANCE STATEMENTThis paper describes a novel bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The assay covers more than twice the species as the leading multiplex PCR assays but uses only one universal primer pair in a single PCR reaction. It is resilient to human genomic contamination precluding the need for timely or costly methods to clear human DNA. This approach represents a major advancement in the decades-long struggle to rapidly and accurately identify bacteria.

bioengineering↗

bioRxiv2023

Stretchable, nano-crumpled MXene multilayers impart long-term antibacterial surface properties

Infections are a significant risk to patients who receive medical implants, and can often lead to implant failure, tissue necrosis, and even amputation. So far, although various surface modification approaches have been proposed for prevention and treatment of microbial biofilms on indwelling medical devices, most are too expensive/complicated to fabricate, unscalable, or limited in durability for clinical use. Here we present a new bottom-up design for fabricating scalable and durable nano-pattered coatings with dynamic topography for long-term antibacterial effects. We show that MXene layer-by-layer (LbL) self-assembled coatings -- with finely tunable crumple structures with nanometer resolution and excellent mechanical durability -- can be successfully fabricated on stretchable poly(dimethylsiloxane) (PDMS). The crumpled MXene coating with sharp-edged peaks shows potent antibacterial effects against Staphylococcus aureus and Escherichia coli. In addition, we find that on-demand dynamic deformation of the crumpled coating can remove [≥]99% of adhered bacterial cells for both species, resulting in a clean surface with restored functionality. This approach offers improved practicality, scalability, and antibacterial durability over previous methods, and its flexibility may lend itself to many types of biomaterials and implantable devices.

bioengineering↗

bioRxiv2021

A Novel Swine Model of the Acute Respiratory Distress Syndrome Using Clinically-Relevant Injury Exposures

To date, existing animal models of the acute respiratory distress syndrome (ARDS) have failed to translate preclinical discoveries into effective pharmacotherapy or diagnostic biomarkers. To address this translational gap, we developed a high-fidelity swine model of ARDS utilizing clinically-relevant lung injury exposures. Fourteen male swine were anesthetized, mechanically ventilated, and surgically instrumented for hemodynamic monitoring, blood, and tissue sampling. Animals were allocated to one of three groups: 1) Indirect lung injury only: animals were inoculated by direct injection of E. coli into the kidney parenchyma, provoking systemic inflammation and distributive shock physiology; 2) Direct lung injury only: animals received volutrauma, hyperoxia, and bronchoscope-delivered gastric particles; 3) Combined indirect and direct lung injury: animals were administered both above-described indirect and direct lung injury exposures. Animals were monitored for up to 12 hours, with serial collection of physiologic data, blood samples, and radiographic imaging. Lung tissue was acquired post-mortem for pathological examination. In contrast to indirect lung injury only and direct lung injury only groups, animals in the combined indirect and direct lung injury group exhibited all of the physiological, radiographic, and histopathologic hallmarks of human ARDS: impaired gas exchange (mean PaO2/FiO2 ratio 124.8 {+/-} 63.8), diffuse bilateral opacities on chest radiographs, and extensive pathologic evidence of diffuse alveolar damage. Our novel porcine model of ARDS, built on clinically-relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS, and fills a crucial gap in the translational study of human lung injury.

pathology↗

bioRxiv2020

Improved diagnostic prediction of the pathogenicity of bloodstream isolates of Staphylococcus epidermidis

With an estimated 440,000 active cases occurring each year, medical device associated infections pose a significant burden on the US healthcare system, costing about $9.8 billion in 2013. Staphylococcus epidermidis is the most common cause of these device-associated infections, which typically involve isolates that are multi-drug resistant and possess multiple virulence factors. S. epidermidis is also frequently a benign contaminant of otherwise sterile blood cultures. Therefore, tests that distinguish pathogenic from non-pathogenic isolates would improve the accuracy of diagnosis and prevent overuse/misuse of antibiotics. Attempts to use multi-locus sequence typing (MLST) with machine learning for this purpose had poor accuracy (~73%). In this study we sought to improve the diagnostic accuracy of predicting pathogenicity by focusing on phenotypic markers (i.e., antibiotic resistance, growth fitness in human plasma, and biofilm forming capacity) and the presence of specific virulence genes (i.e., mecA, ses1, and sdrF). Commensal isolates from healthy individuals (n=23), blood culture contaminants (n=21), and pathogenic isolates considered true bacteremia (n=54) were used. Multiple machine learning approaches were applied to characterize strains as pathogenic vs non-pathogenic. The combination of phenotypic markers and virulence genes improved the diagnostic accuracy to 82.4% (sensitivity: 84.9% and specificity: 80.9%). Oxacillin resistance was the most important variable followed by growth rate in plasma. This work shows promise for the addition of phenotypic testing in clinical diagnostic applications.

microbiology↗

bioRxiv2020

Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

BackgroundDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein-protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein-protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy. ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

bioinformatics↗

bioRxiv2019

Anti-biofilm Activity of Graphene Quantum Dots via Self-Assembly with Bacterial Amyloid Proteins

Bacterial biofilms represent an essential part of Earths ecosystem that can cause multiple ecological, technological and health problems. The environmental resilience and sophisticated organization of biofilms are enabled by the extracellular matrix that creates a protective network of biomolecules around the bacterial community. Current anti-biofilm agents can interfere with extracellular matrix production but, being based on small molecules, are degraded by bacteria and rapidly diffuse away from biofilms. Both factors severely reduce their efficacy, while their toxicity to higher organisms create additional barriers to their practicality. In this paper we report on the ability of graphene quantum dots to effectively disperse mature Staphylococcus aureus biofilms, interfering with the self-assembly of amyloid fibers - a key structural component of the extracellular matrix. Mimicking peptide-binding biomolecules, graphene quantum dots form supramolecular complexes with phenol soluble modulins, the peptide monomers of amyloid fibers. Experimental and computational results show that graphene quantum dots efficiently dock near the N-terminus of the peptide and change the secondary structure of phenol soluble modulins, which disrupts their fibrillation and represents a novel strategy for mitigation of bacterial communities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/550285v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@a94402org.highwire.dtl.DTLVardef@b009b3org.highwire.dtl.DTLVardef@14cfb70org.highwire.dtl.DTLVardef@10f8e52_HPS_FORMAT_FIGEXP M_FIG GQD mediated staphylococcal biofilm dispersal. GQDs interact with PSM peptides and frustrate the fibrillation process. The reduction in amyloid fibers prevents robust stabilization of the biofilm. In addition, there is an increase in free monomeric and oligomeric PSM peptides which trigger dispersal events. C_FIG

bioengineering↗