Bioengineering approaches to trained immunity: Physiologic targets and therapeutic strategies

Abstract

Trained immunity presents a unique target for modulating the immune response against infectious and non-infectious threats to human health. To address the unmet need for training-targeted therapies, we explore bioengineering methods to answer research questions and address clinical applications. Current challenges in trained immunity include self-propagating autoinflammatory disease, a lack of controllable cell and tissue specificity, and the unintentional induction of training by known drugs and diseases. The bioengineering tools discussed in this review (nanotherapeutics, biomechanical modulation, cellular engineering, and machine learning) could address these challenges by providing additional avenues to modulate and interrogate trained immunity. The preferential activation of peripheral or central training has not yet been achieved and could be accessed using nanoparticle systems. Targeted delivery of training stimuli using nanocarriers can enrich the response in various cell and organ systems, while also selectively activating peripheral training in the local tissues or central trained immunity in bone marrow progenitor cells. Beyond chemical- or pathogen-based activation of training, force-based cues, such as interaction with mechanoreceptors, can induce trained phenotypes in many cell types. Mechanotransduction influences immune cell activation, motility, and morphology and could be harnessed as a tool to modulate training states in next-generation therapies. For known genetic and epigenetic mediators of trained immunity, cellular engineering could precisely activate or deactivate programs of training. Genetic engineering could be particularly useful in generating trained cell-based therapies like chimeric antigen receptor (CAR) macrophages. Finally, machine learning models, which are rapidly transforming biomedical research, can be employed to identify signatures of trained immunity in pre-existing datasets. They can also predict protein targets for previously identified inducers of trained immunity by modeling drug-protein or protein-protein interactions in silico. By harnessing the modular techniques of bioengineering for applications in trained immunity, training-based therapies can be more efficiently translated into clinical practice.

Keywords: bioengineering; biomechanics; cellular engineering; immunology; inflammation; machine learning; nanotherapeutics; trained immunity.

Figure 1.. Proposed bioengineering approaches to understanding and applying trained immunity.

This figure was created with BioRender.com.

Figure 2.. Characteristics of central and peripheral trained immunity and target tissues for therapeutic induction of trained immunity.

(Top Right) Central training occurs in progenitor cells of the bone marrow (shown here via Bacille Calmette-Guérin, BCG training of hematopoietic stem cells, HSCs), leading to long-lived, multi-generational training in daughter cells. Central training is also directly implicated in the pathogenesis of autoinflammatory diseases, such as atherosclerosis and diabetes. Directed targeting to the bone marrow can access hematopoietic stem cells for long-lived central training. (Left and Bottom) Peripheral trained immunity can encompass tissue-resident innate immune cells and stromal cells, such as epithelial cells and fibroblasts (shown here with small molecule training in the alveoli of the lung, Kupffer cells in the liver, and Peyer’s Patches in the small intestine). Peripheral training can provide local resistance to infection, cancer, and other inflammatory insults. A combination of passive and active targeting approaches can be used to access peripheral training in the lung, gut, and liver. Respiratory delivery can be achieved with aerosols, gastrointestinal delivery can be targeted with delayed release systems, and hepatic delivery can be achieved with intravenous delivery of nanoparticles, which naturally accumulate in the liver. This figure was created with BioRender.com.

Figure 3.. Types of biomechanical modulation achieved with native and engineered in vitro, ex vivo, and in vivo systems.

Engineered scaffolds can model fluid flow, porous environments, fibrosis, and healthy extracellular matrices. Shear stress from turbulent fluid flow impacts endothelial cell susceptibility to atherosclerosis, likely due to trained immunity. Porous scaffolds can provide niches for cellular interaction, differentiation, and drug encapsulation. Fibrotic and native extracellular matrix (ECM), which exhibit differences in elasticity, stiffness, and ligand expression, can be used to measure the effects of mechanotransduction on training in healthy and diseased tissues. This figure was created with BioRender.com.

Figure 4.. Methods and targets for cellular engineering of trained immunity.

(A) As we use screening tools to elucidate the role of trained innate cells in additional autoimmune and inflammatory disorders, this concept will have increasingly more applications when designing therapeutics, including the activation and suppression of training programs. (B) Trained chimeric antigen receptor (CAR):-Macs generated ex vivo could resist immunosuppression of the tumor microenvironment to promote tumor cell death. (C) For example, trained immunity in atherosclerosis has been shown to contribute to disease pathogenesis and appears to be NLRP3 dependent (Netea et al., 2016; Moorlag et al., 2020). Knocking down NLRP3 expression in patient macrophages could decrease disease burden. This figure was created with BioRender.com.

Figure 5.. Pre-existing data sources for machine learning-based discovery in trained immunity.

Sequencing datasets, including transcriptomics, epigenomics, and translatomics can be integrated to determine the effects of intracellular regulation on trained immunity effector responses. A comparison of chemical and protein libraries with known training pathways can identify protein targets, pathways, and potential mechanisms for novel induction of trained immunity. Epidemiological and clinical datasets could yield particularly rich information, including the influence of genetic variants, microbiota, drugs, and disease states on trained immunity. This figure was created with BioRender.com.