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Biology subjects

Bell, R. D.

Publications and source records attributed to Bell, R. D..

3 recordsLinked to original sources

Sexual dimorphism in zymosan-induced arthritis is linked with a higher IFN response in myeloid cell subsets that faithfully recapitulate RA synovial cell clusters

A key feature of rheumatoid arthritis (RA) is sexual dimorphism, with a higher incidence of RA in females. Myeloid cells are key drivers of the inflammatory effector phase of RA, but little is known about their contribution to a sexually dimorphic arthritis phenotype. We wished to utilize an arthritis model that recapitulates RA pathogenic myeloid cell subsets defined by single cell transcriptome analysis to investigate sex differences in human disease-relevant cell types and related molecular pathways. We developed a computational strategy that rigorously maps scRNAseq-defined mouse arthritis myeloid cells and clusters onto previously defined RA subsets. Synovial myeloid cells in the zymosan-induced arthritis (ZIA) model closely recapitulated four pathogenic RA myeloid cell subsets, strongly supporting the disease-relevance of the ZIA model. ZIA also effectively modeled myeloid cells in immune checkpoint inhibitor arthritis (ICI-A). These RA, ICI-A and ZIA myeloid cells express genes in TNF-NF-kB, PGE2, and IFN-STAT pathways. In ZIA, an induction phase dominated by cell clusters expressing NF-kB and PGE2 pathways transitioned to peak arthritis characterized by cell subsets co-expressing NF-kB and IFN pathways, as occurs in RA and various inflammatory diseases. Female mice exhibited increased arthritis linked with early induction of interferon-stimulated genes and increased expansion of IFN signature-expressing cell subsets. Our study identifies a computational strategy and animal model that enables investigation of recently described pathogenic myeloid cell subsets, and links increased arthritis in female mice with specific myeloid cell subsets that exhibit a stronger IFN response.

immunology↗

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Quantitative description of complex anatomical structures remains challenging due to the expertise necessary for manual segmentation, labor, and interobserver variability. To overcome this, automated detection of specific landmarks can be provided by digital image analysis techniques including deep learning (DL) models. To this end, we undertook supervised automated analysis of micro-computed tomography (micro-CT) datasets of murine hindpaws and forepaws (30-33 bones). Advancing beyond previously published semi-automated (SA) marker-based watershed algorithms, we added structure enhancement, tensor voting, and output dilation to identify joint spaces. Segmentation was enhanced by the use of a DL joint space prediction model (3D U-Net architecture, ResNet-18 backbone) using wild-type (WT) hindpaw labels as ground truth. Prediction was then extended to hindpaws (52.4% in test group) and forepaws from WT and tumor necrosis factor transgenic (TNF-Tg) mice with inflammatory-erosive arthritis of both sexes across age. Segmentation accuracy improved dramatically using the DL methodology (WT male: SA 79.39{+/-}5.73% vs DL 98.16{+/-}1.47%, p<0.0001; WT female: SA 79.16{+/-}4.84% vs DL 99.19{+/-}1.63%, p<0.0001). Accuracy declined with increased disease severity and age in TNF-Tg mice (TNF-Tg male 93.54{+/-}4.73%, female 91.81{+/-}2.80%, p[&le;]0.01). Subsequent testing in forepaws also displayed progressive reduction in accuracy with increasing arthritic severity (i.e., WT 87.29{+/-}2.07%, TNF-Tg male 72.65{+/-}11.70%, p<0.0001). Overall, this supervised automated model outperforms recent SA approaches in healthy joints to enhance investigation of complex bone anatomy. Although flexible application to novel and disease-modified datasets demonstrates deprecated performance, utilization may nonetheless catalyze structure-specific segmentation model development.

pathology↗

Staphyloccocus aureus biofilm, in the absence of planktonic bacteria, produces factors that activate counterbalancing inflammatory and immune-suppressive genes in human monocytes

Staphyloccocus aureus (S. aureus) is a major bacterial pathogen in orthopedic periprosthetic joint infection (PJI). S. aureus forms biofilms that promote persistent infection by shielding bacteria from immune cells and inducing an antibiotic-resistant metabolic state. We developed an in vitro system to study S. aureus biofilm interactions with primary human monocytes in the absence of planktonic bacteria. In line with previous in vivo data, S. aureus biofilm induced expression of inflammatory genes such as TNF and IL1B, and their anti-inflammatory counter-regulator IL-10. S. aureus biofilm also activated expression of PD-1 ligands that suppress T cell function, and of IL-1RA that suppresses differentiation of protective Th17 cells. Gene induction did not require monocyte:biofilm contact and was mediated by a soluble factor(s) produced by biofilm-encased bacteria that was heat resistant and > 3 kD in size. Activation of suppressive genes by biofilm was sensitive to suppression by Jak inhibition. These results support an evolving paradigm that biofilm plays an active role in modulating immune responses, and suggest this occurs via production of a soluble vita-PAMP. Induction of T cell suppressive genes by S. aureus biofilm provides insights into mechanisms that suppress T cell immunity in PJI, and suggest that anti-PD-1 therapy that is modeled on immune checkpoint blockade for tumors may be beneficial in PJI.

immunology↗