Biology subjects

Stivers, K. B.

Publications and source records attributed to Stivers, K. B..

3 recordsLinked to original sources

bioRxiv2023

Regulating the proinflammatory response to implanted composite biomaterials comprising polylactide and hydroxyapatite by targeting immunometabolism

Composite biomaterials comprising polylactide (PLA) and hydroxyapatite (HA) are applied in bone, cartilage and dental regenerative medicine, where HA confers osteoconductive properties. However, after surgical implantation, adverse immune responses to these composites can occur, which have been attributed to size and morphology of HA particles. Approaches to effectively modulate these adverse immune responses have not been described. PLA degradation products have been shown to alter immune cell metabolism, which drives the inflammatory response. Therefore, we aimed to modulate the inflammatory response to composite biomaterials by regulating glycolytic flux with small molecule inhibitors incorporated into composites comprised of amorphous PLA (aPLA) and HA (aPLA+HA). Inhibition at specific steps in glycolysis reduced proinflammatory (CD86+CD206-) and increased pro-regenerative (CD206+) immune cell populations around implanted aPLA+HA resulting in a pro-regenerative microenvironment. Notably, neutrophil and dendritic cell (DC) numbers along with proinflammatory monocyte and macrophage populations were decreased, and Arginase 1 expression among DCs was increased. Targeting immunometabolism to control the inflammatory response to biomaterial composites, and creating a pro-regenerative microenvironment, is a significant advance in tissue engineering where immunomodulation enhances osseointegration, and angiogenesis, which will lead to improved bone regeneration.

bioengineering↗

bioRxiv2023

Immunometabolic cues recompose and reprogram the microenvironment around biomaterials

Circulating monocytes infiltrate and coordinate immune responses in various inflamed tissues, such as those surrounding implanted biomaterials, affecting therapeutic, diagnostic, tissue engineering and regenerative applications. Here, we show that immunometabolic cues in the biomaterial microenvironment govern CCR2- and CX3CR1-dependent trafficking of immune cells, including neutrophils and monocytes; ultimately, this affects the composition and activation states of macrophage and dendritic cell populations. Furthermore, immunometabolic cues around implants orchestrate the relative composition of proinflammatory, transitory and anti-inflammatory CCR2+, CX3CR1+ and CCR2+CX3CR1+ immune cell populations. Consequently, modifying immunometabolism by glycolytic inhibition drives a pro-regenerative microenvironment in part by myeloid cells around amorphous polylactide implants. In addition to, Arginase 1-expressing myeloid cells, T helper 2 cells and {gamma}{delta}+ T-cells producing IL-4 significantly contribute to shaping the metabolically reprogramed, pro-regenerative microenvironment around crystalline polylactide biomaterials. Taken together, we find that local metabolic states regulate inflammatory processes in the biomaterial microenvironment, with implications for translational medicine.

bioengineering↗

bioRxiv2022

Transfer learning of an in vivo-derived senescence signature identifies conserved and tissue-specific senescence across species and diverse pathologies

Senescent cells (SnCs) contribute to normal tissue development and repair but accumulate with aging where they are implicated in a number of pathologies and diseases. Despite their pathological role and therapeutic interest, SnC phenotype and function in vivo remains unclear due to the challenges in identifying and isolating these rare cells. Here, we developed an in vivo-derived senescence gene expression signature using a model of the foreign body response (FBR) fibrosis in a p16Ink4a-reporter mouse, a cell cycle inhibitor commonly used to identify SnCs. We identified stromal cells (CD45-CD31- CD29+) as the primary p16Ink4a expressing cell type in the FBR and collected the cells to produce a SnC transcriptomic signature with bulk RNA sequencing. To computationally identify SnCs in bulk and single-cell data sets across species and tissues, we used this signature with transfer learning to generate a SnC signature score (SenSig). We found senescent pericyte and cartilage-like fibroblasts in newly collected single cell RNAseq (scRNASeq) data sets of murine and human FBR suggesting populations associated with angiogenesis and secretion of fibrotic extracellular matrix, respectively. Application of the senescence signature to human scRNAseq data sets from idiopathic pulmonary fibrosis (IPF) and the basal cell carcinoma microenvironment identified both conserved and tissue-specific SnC phenotypes, including epithelial-derived basaloid and endothelial cells. In a wound healing model, ligand-receptor signaling prediction identified putative interactions between SnC SASP and myeloid cells that were validated by immunofluorescent staining and in vitro coculture of SnCs and macrophages. Collectively, we have found that our SenSig transfer learning strategy from an in vivo signature outperforms in vitro-derived signatures and identifies conserved and tissue-specific SnCs and their SASP, independent of p16Ink4a expression, and may be broadly applied to elucidate SnC identity and function in vivo.

bioengineering↗