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Developing immune-regulatory materials using immobilized monosaccharides with immune-instructive properties.

ABSTRACT: New strategies for immune modulation have shown real promise in regenerative medicine as well as the fight against autoimmune diseases, allergies, and cancer. Dendritic cells (DCs) are gatekeepers of the immune system and their ability in shaping the adaptive immune responses makes DCs ideal targets for immune modulation. Carbohydrates are abundant in different biological systems and are known to modulate DC phenotype and function. However, how simple monosaccharides instruct DC function is less well understood. In this study, we used a combinatorial array of immobilized monosaccharides to investigate how they modulate DC phenotype and function and crucially the impact of such changes on downstream adaptive immune responses. Our data show that a selection of monosaccharides significantly suppress lipopolysaccharide-induced DC activation as evidenced by a reduction in CD40 expression, IL-12 production, and indoleamine 2,3-dioxygenase activity, while inducing a significant increase in IL-10 production. These changes are indicative of the induction of an anti-inflammatory or regulatory phenotype in DCs, which was further confirmed in DC-T cell co-cultures where DCs cultured on the 'regulatory' monosaccharide-coated surfaces were shown to induce naïve T cell polarization toward regulatory phenotype. Our data also highlighted a selection of monosaccharides that are able to promote mixed Treg and Th17 cell differentiation, a T cell phenotype expected to be highly immune suppressive. These data show the potential immunomodulatory effects of immobilized monosaccharides in priming DCs and skewing T cell differentiation toward an immune-regulatory phenotype. The ability to fine-tune immune responses using these simple carbohydrate combinations (e.g. as coatings for existing materials) can be utilized as novel tools for immune modulation with potential applications in regenerative medicine, implantable medical devices, and wound healing where reduction of inflammatory responses and maintaining immune homeostasis are desirable.

SUBMITTER: Alobaid MA

PROVIDER: S-EPMC7649522 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Developing immune-regulatory materials using immobilized monosaccharides with immune-instructive properties.

Alobaid M A MA Richards S-J SJ Alexander M R MR Gibson M I MI Ghaemmaghami A M AM

Materials today. Bio 20200930

New strategies for immune modulation have shown real promise in regenerative medicine as well as the fight against autoimmune diseases, allergies, and cancer. Dendritic cells (DCs) are gatekeepers of the immune system and their ability in shaping the adaptive immune responses makes DCs ideal targets for immune modulation. Carbohydrates are abundant in different biological systems and are known to modulate DC phenotype and function. However, how simple monosaccharides instruct DC function is less ...[more]

PMID: 33205040

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Project description:There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140?000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.

Dataset Information

Developing immune-regulatory materials using immobilized monosaccharides with immune-instructive properties.

Publications

Developing immune-regulatory materials using immobilized monosaccharides with immune-instructive properties.

Similar Datasets

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