A Mucin-Based Bio-Ink for 3D Printing of Objects with Anti-Biofouling Properties.

With its potential to revolutionize the field of personalized medicine by producing customized medical devices and constructs for tissue engineering at low costs, 3D printing has emerged as a highly promising technology. Recent advancements have sparked increasing interest in the printing of biopolymeric hydrogels. However, owing to the limited printability of those soft materials, the lack of variability in available bio-inks remains a major challenge. In this study, a novel bio-ink is developed based on functionalized mucin-a glycoprotein that exhibits a multitude of biomedically interesting properties such as immunomodulating activity and strong anti-biofouling behavior. To achieve sufficient printability of the mucin-based ink, its rheological properties are tuned by incorporating Laponite XLG as a stabilizing agent. It is shown that cured objects generated from this novel bio-ink exhibit mechanical properties partially similar to that of soft tissue, show strong anti-biofouling properties, good biocompatibility, tunable cell adhesion, and immunomodulating behavior. The presented findings suggest that this 3D printable bio-ink has a great potential for a wide range of biomedical applications, including tissue engineering, wound healing, and soft robotics.

Multifunctional glycoprotein coatings improve the surface properties of highly oxygen permeable contact lenses.

To achieve and maintain good operability of medical devices while reducing putative side effects for the patient, a promising strategy is to tailor the surface properties of such devices as they critically dictate the tissue compatibility and the biofouling behavior. Indeed, those properties can be strongly improved by generating mucin coatings on such medical devices. However, using coatings on optical systems, e.g., contact lenses, comes with various challenges: here, the geometrical and optical characteristics of the lens may not be compromised by either the coating process or the coating itself. In this study, we show how mucin macromolecules can be attached onto the surfaces of rigid, gas permeable contact lenses while maintaining all critical lens parameters. We demonstrate that the generated coatings improve the surface wettability (contact angles are reduced from 105° to 40° and liquid film break-up times are increased from <1 s to 31 s) and prevent tribological damage to corneal tissue. Additionally, such coatings are highly transparent (transmission values above 98 % compared to an uncoated sample are reached) and efficiently reduce lipid deposition to the lens surface by 90 % but fully maintain the geometrical and mechanical properties of the lenses. Thus, such mucin coatings could also be highly beneficial for other optical systems that are used in direct contact with tissues or body fluids.

Machine learning approaches for biomolecular, biophysical, and biomaterials research.

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Machine Learning Approach to Analyze the Surface Properties of Biological Materials.

Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by time-consuming trial and error approaches and our limited understanding of how different material properties depend on each other. Here, we apply machine learning algorithms to classify complex biological materials based on their microtopography. With this approach, the surfaces of different variants of biofilms and plant leaves can not only be distinguished but also correctly classified according to their wettability. Furthermore, an importance ranking provided by one of the algorithms allows us to identify those surface features that are critical for a successful sample classification. Our study exemplifies how machine learning can contribute to the analysis and categorization of complex surfaces, a tool, which can be highly useful for other areas of materials science, such as damage assessment as well as adhesion or friction studies.

Topography quantifications allow for identifying the contribution of parental strains to physical properties of co-cultured biofilms.

Most biofilm research has so far focused on investigating biofilms generated by single bacterial strains. However, such single-species biofilms are rare in nature where bacteria typically coexist with other microorganisms. Although, from a biological view, the possible interactions occurring between different bacteria are well studied, little is known about what determines the material properties of a multi-species biofilm. Here, we ask how the co-cultivation of two B. subtilis strains affects certain important biofilm properties such as surface topography and wetting behavior. We find that, even though each daughter colony typically resembles one of the parent colonies in terms of morphology and wetting, it nevertheless exhibits a significantly different surface topography. Yet, this difference is only detectable via a quantitative metrological analysis of the biofilm surface. Furthermore, we show that this difference is due to the presence of bacteria belonging to the 'other' parent strain, which does not dominate the biofilm features. The findings presented here may pinpoint new strategies for how biofilms with hybrid properties could be generated from two different bacterial strains. In such engineered biofilms, it might be possible to combine desired properties from two strains by co-cultivation.

Highly Transparent Covalent Mucin Coatings Improve the Wettability and Tribology of Hydrophobic Contact Lenses.

A stable, good coverage of the corneal tissue by the tear film is essential for protecting the eye. Contact lenses, however, constitute a foreign body that separates the tear film into two thinner layers, which are then more vulnerable toward disruption. This effect is even more pronounced if the contact lenses possess an insufficient surface wettability, which, in addition to friction, is suggested to be linked to discomfort and damage to the ocular surface. In this study, we establish covalent surface coatings with mucin macromolecules to overcome this issue for pure silicone contact lenses. This material class, which outperforms state-of-the-art silicone hydrogels in terms of oxygen permeability, is not yet used for commercial contact lens applications, which is due to its strongly hydrophobic surface characteristics. The applied process stably attaches a transparent mucin layer onto the contact lenses and thereby establishes hydrophilic surfaces that not only prevent lipid adsorption but also interact very well with liquid environments. Most importantly, however, we show that those mucin coatings are indeed able to prevent wear formation on corneal tissue that is subjected to the tribological stress applied by a contact lens. Our results open up great possibilities for a variety of hydrophobic materials that are, to date, not suitable for a contact lens application. Furthermore, the ability of mucin coatings to reduce wear in a tissue/synthetic material contact might be also beneficial for other biomedical applications.

α-Synuclein Penetrates Mucin Hydrogels Despite Its Mucoadhesive Properties.

Recent research indicates that the progression of Parkinson's disease can start from neurons of the enteric nervous system, which are in close contact with the gastrointestinal epithelium: α-synuclein molecules can be transferred from these epithelial cells in a prion-like fashion to enteric neurons. Thin mucus layers constitute a defense line against the exposure of noninfected cells to potentially harmful α-synuclein species. We show that-despite its mucoadhesive properties-α-synuclein can translocate across mucin hydrogels, and this process is accompanied by structural rearrangements of the mucin molecules within the gel. Penetration experiments with different α-synuclein variants and synthetic peptides suggest that two binding sites on α-synuclein are required to accomplish this rearrangement of the mucin matrix. Our results support the notion that the translocation of α-synuclein across mucus barriers observed here might be a critical step in the infection of the gastrointestinal epithelium and the development of Parkinson's disease.