Microrheology for biomaterial design

Mucus physically restricts influenza A viral particle access to the epithelium

Prior work suggests influenza A virus (IAV) crosses the airway mucus barrier in a sialic acid-dependent manner through the actions of the viral envelope proteins, hemagglutinin and neuraminidase. However, host and viral factors that influence how efficiently mucus traps IAV remain poorly defined. In this work, we assessed how the physicochemical properties of mucus influence its ability to effectively capture IAV with altered sialic acid preference using fluorescence video microscopy and multiple particle tracking. We found an airway mucus gel layer must be produced with pores on the order of size of the virus to physically constrain IAV. Sialic acid binding by IAV also improves mucus trapping efficiency, but interestingly, sialic acid preferences had little impact on the fraction of IAV particles expected to penetrate the mucus barrier. Together, this work provides new insights on mucus barrier function toward IAV with important implications on innate host defense and interspecies transmission.

Synthetic mucus biomaterials for antimicrobial peptide delivery

Despite the promise of antimicrobial peptides (AMPs) as treatments for antibiotic-resistant infections, their therapeutic efficacy is limited due to the rapid degradation and low bioavailability of AMPs. To address this, we have developed and characterized a synthetic mucus (SM) biomaterial capable of delivering LL37 AMPs and enhancing their therapeutic effect. LL37 is an AMP that exhibits a wide range of antimicrobial activity against bacteria, including Pseudomonas aeruginosa. LL37 loaded SM hydrogels demonstrated controlled release with 70%-95% of loaded LL37 over 8 h due to charge-mediated interactions between mucins and LL37 AMPs. Compared to treatment with LL37 alone where antimicrobial activity was reduced after 3 h, LL37-SM hydrogels inhibited P. aeruginosa (PAO1) growth over 12 h. LL37-SM hydrogel treatment reduced PAO1 viability over 6 h whereas a rebound in bacterial growth was observed when treated with LL37 only. These data demonstrate LL37-SM hydrogels enhance antimicrobial activity by preserving LL37 AMP activity and bioavailability. Overall, this work establishes SM biomaterials as a platform for enhanced AMP delivery for antimicrobial applications.

CO2-responsive gels

CO₂-responsive materials undergo a change in chemical or physical properties in response to the introduction or removal of CO₂. The use of CO₂ as a stimulus is advantageous as it is abundant, benign, inexpensive, and it does not accumulate in a system. Many CO₂-responsive materials have already been explored including polymers, latexes, surfactants, and catalysts. As a sub-set of CO₂-responsive polymers, the study of CO₂-responsive gels (insoluble, cross-linked polymers) is a unique discipline due to the unique set of changes in the gels brought about by CO₂ such as swelling or a transformed morphology. In the past 15 years, CO₂-responsive gels and self-assembled gels have been investigated for a variety of emerging potential applications, reported in 90 peer-reviewed publications. The two most widely exploited properties include the control of flow (fluids) via CO₂-triggered aggregation and their capacity for reversible CO₂ absorption-desorption, leading to applications in Enhanced Oil Recovery (EOR) and CO₂ sequestration, respectively. In this paper, we review the preparation, properties, and applications of these CO₂-responsive gels, broadly classified by particle size as nanogels, microgels, aerogels, and macrogels. We have included a section on CO₂-induced self-assembled gels (including poly(ionic liquid) gels).

Synthetic Mucus Biomaterials for Antimicrobial Peptide Delivery

Despite the promise of antimicrobial peptides (AMPs) as treatments for antibiotic-resistant infections, their therapeutic efficacy is limited due to the rapid degradation and low bioavailability of AMPs. To address this, we have developed and characterized a synthetic mucus (SM) biomaterial capable of delivering AMPs and enhancing their therapeutic effect. LL37 loaded SM hydrogels demonstrated controlled release of LL37 over 8 hours as a result of charge-mediated interactions between mucins and LL37 AMPs. Compared to treatment with LL37 alone where antimicrobial activity was reduced after 3 hours, LL37-SM hydrogels inhibited Pseudomonas aeruginosa PAO1 growth over 12 hours. LL37-SM hydrogel treatment reduced PAO1 viability over 6 hours whereas a rebound in bacterial growth was observed when treated with LL37 only. These data demonstrate LL37-SM hydrogels enhance antimicrobial activity by preserving LL37 AMP activity and bioavailability. Overall, this work establishes SM biomaterials as a platform for enhanced AMP delivery for antimicrobial applications.

Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment

Machine learning approaches have introduced an urgent need for large datasets of materials properties. However, for mechanical properties, current high-throughput measurement methods typically require complex robotic instrumentation, with enormous capital costs that are inaccessible to most experimentalists. A quantitative high-throughput method using only common lab equipment and consumables with simple fabrication steps is long desired. Here, we present such a technique that can measure bulk mechanical properties in soft materials with a common laboratory centrifuge, multiwell plates, and microparticles. By applying a homogeneous force on the particles embedded inside samples in the multiwell plate using centrifugation, we show that our technique measures the fracture stress of gels with similar accuracy to a rheometer. However, our method has a throughput on the order of 10³ samples per run and is generalizable to virtually all soft material systems. We hope that our method can expedite materials discovery and potentially inspire the future development of additional high-throughput characterization methods.

Particle-Based Microrheology As a Tool for Characterizing Protein-Based Materials

Microrheology based on video microscopy of embedded tracer particles has the potential to be used for high-throughput protein-based materials characterization. This potential is due to a number of characteristics of the techniques, including the suitability for measurement of low sample volumes, noninvasive and noncontact measurements, and the ability to set up a large number of samples for facile, sequential measurement. In addition to characterization of the bulk rheological properties of proteins in solution, for example, viscosity, microrheology can provide insight into the dynamics and self-assembly of protein-based materials as well as heterogeneities in the microenvironment being probed. Specifically, passive microrheology in the form of multiple particle tracking and differential dynamic microscopy holds promise for applications in high-throughput characterization because of the lack of user interaction required while making measurements. Herein, recent developments in the use of multiple particle tracking and differential dynamic microscopy are reviewed for protein characterization and their potential to be applied in a high-throughput, automatable setting.

Passive and Active Microrheology for Biomedical Systems

Microrheology encompasses a range of methods to measure the mechanical properties of soft materials. By characterizing the motion of embedded microscopic particles, microrheology extends the probing length scale and frequency range of conventional bulk rheology. Microrheology can be characterized into either passive or active methods based on the driving force exerted on probe particles. Tracer particles are driven by thermal energy in passive methods, applying minimal deformation to the assessed medium. In active techniques, particles are manipulated by an external force, most commonly produced through optical and magnetic fields. Small-scale rheology holds significant advantages over conventional bulk rheology, such as eliminating the need for large sample sizes, the ability to probe fragile materials non-destructively, and a wider probing frequency range. More importantly, some microrheological techniques can obtain spatiotemporal information of local microenvironments and accurately describe the heterogeneity of structurally complex fluids. Recently, there has been significant growth in using these minimally invasive techniques to investigate a wide range of biomedical systems both in vitro and in vivo. Here, we review the latest applications and advancements of microrheology in mammalian cells, tissues, and biofluids and discuss the current challenges and potential future advances on the horizon.

Membrane-Tethered Mucin 1 Is Stimulated by Interferon and Virus Infection in Multiple Cell Types and Inhibits Influenza A Virus Infection in Human Airway Epithelium

Influenza A virus (IAV) causes significant morbidity and mortality in the human population. Tethered mucin 1 (MUC1) is highly expressed in airway epithelium, the primary site of IAV replication, and also by other cell types that influence IAV infection, including macrophages. MUC1 has the potential to influence infection dynamics through physical interactions and/or signaling activity, yet MUC1 modulation and its impact during viral pathogenesis remain unclear. Thus, we investigated MUC1-IAV interactions in an in vitro model of human airway epithelium (HAE). Our data indicate that a recombinant IAV hemagglutinin (H3) and H3N2 virus can bind endogenous HAE MUC1. Notably, infection of HAE with H1N1 or H3N2 IAV strains does not trigger MUC1 shedding but instead stimulates an increase in cell-associated MUC1 protein. We observed a similar increase after type I or III interferon (IFN) stimulation; however, inhibition of IFN signaling during H1N1 infection only partially abrogated this increase, indicating that multiple soluble factors contribute to MUC1 upregulation during the antiviral response. In addition to HAE, primary human monocyte-derived macrophages also upregulated MUC1 protein in response to IFN treatment and conditioned media from IAV-infected HAE. Then, to determine the impact of MUC1 on IAV pathogenesis, we developed HAE genetically depleted of MUC1 and found that MUC1 knockout cultures exhibited enhanced viral growth compared to control cultures for several IAV strains. Together, our data support a model whereby MUC1 inhibits productive uptake of IAV in HAE. Infection then stimulates MUC1 expression on multiple cell types through IFN-dependent and -independent mechanisms that further impact infection dynamics.

Machine learning-informed predictions of nanoparticle mobility and fate in the mucus barrier

Nanomaterial diffusion through mucus is important to basic and applied areas of research such as drug delivery. However, it is often challenging to interpret nanoparticle dynamics within the mucus gel due to its heterogeneous microstructure and biochemistry. In this study, we measured the diffusion of polyethylene glycolylated nanoparticles (NPs) in human airway mucus ex vivo using multiple particle tracking and utilized machine learning to classify diffusive vs sub-diffusive NP movement. Using mathematic models that account for the mode of NP diffusion, we calculate the percentage of NPs that would cross the mucus barrier over time in airway mucus with varied total solids concentration. From this analysis, we predict rapidly diffusing NPs will cross the mucus barrier in a physiological timespan. Although less efficient, sub-diffusive "hopping" motion, a characteristic of a continuous time random walk, may also enable NPs to cross the mucus barrier. However, NPs exhibiting fractional Brownian sub-diffusion would be rapidly removed from the airways via mucociliary clearance. In samples with increased solids concentration (>5% w/v), we predict up to threefold reductions in the number of nanoparticles capable of crossing the mucus barrier. We also apply this approach to explore diffusion and to predict the fate of influenza A virus within human mucus. We predict only a small fraction of influenza virions will cross the mucus barrier presumably due to physical obstruction and adhesive interactions with mucin-associated glycans. These results provide new tools to evaluate the extent of synthetic and viral nanoparticle penetration through mucus in the lung and other tissues.

Influenza A virus diffusion through mucus gel networks

Mucus in the lung plays an essential role as a barrier to infection by viral pathogens such as influenza A virus (IAV). Previous work determined mucin-associated sialic acid acts as a decoy receptor for IAV hemagglutinin (HA) binding and the sialic-acid cleaving enzyme, neuraminidase (NA), facilitates virus passage through mucus. However, it has yet to be fully addressed how the physical structure of the mucus gel influences its barrier function and its ability to trap viruses via glycan mediated interactions to prevent infection. To address this, IAV and nanoparticle diffusion in human airway mucus and mucin-based hydrogels is quantified using fluorescence video microscopy. We find the mobility of IAV in mucus is significantly influenced by the mesh structure of the gel and in contrast to prior reports, these effects likely influence virus passage through mucus gels to a greater extent than HA and NA activity. In addition, an analytical approach is developed to estimate the binding affinity of IAV to the mucus meshwork, yielding dissociation constants in the mM range, indicative of weak IAV-mucus binding. Our results provide important insights on how the adhesive and physical barrier properties of mucus influence the dissemination of IAV within the lung microenvironment.

Influenza A virus movement in mucus is found to be affected by the mesh structure of the gel network and further analysis reveals weak IAV-mucus binding.

Correlation of Bulk Degradation and Molecular Release from Enzymatically Degradable Polymeric Hydrogels

In this work, we establish a quantitative correlation between molecular release and material degradation. We characterize a radical-initiated photopolymerized hydrogel and base-initiated Michael addition-polymerized hydrogel, which form gels through distinct crosslinking reactions. Both scaffolds use the same degradable peptide crosslinker, which enables them to be degraded through the same enzymatic degradation reaction. A fluorescently labeled poly(ethylene glycol) molecule is chemically conjugated into the scaffold and is released during enzymatic degradation. Real-time changes in scaffold rheological properties during degradation are measured using bulk rheology. Molecular release is measured by quantifying the change in fluorescence in the incubation liquid and the hydrogel scaffold. A complicating factor, previously described in the literature, is that shear may cause increased crosslinking, resulting in an increase in the storage modulus after initiation of degradation, which changes release profiles by limiting the initial release of molecules. Therefore, we also test the hypothesis that shear induces additional crosslinking in degrading hydrogel scaffolds. To determine whether shear changes rheological properties during scaffold degradation, enzymatic degradation is characterized using bulk rheology as materials undergo continuous or minimal shear. To determine the effect of shear on molecular release, shear is induced by shaking the material during incubation. Release is characterized from scaffolds that are incubated with continuous or without shaking. We determine that shear does not make a difference in scaffold degradation or release regardless of the gelation reaction. Instead, we determine that the type of hydrogel crosslinking reaction greatly affects both material degradation and molecular release. A hydrogel crosslinking by base-initiated Michael addition does undergo further crosslinking at the start of degradation. We correlate release with enzymatic degradation for both scaffolds. We determine that the material storage modulus is indirectly correlated with release during degradation. These results indicate that rheological characterization is a useful tool to characterize and predict the release of molecules from degrading hydrogels.