Immune‑related gene expression in skin, inflamed and keloid tissue from patients with keloids

Multi-omics analyses reveal bacteria and catalase associated with keloid disease

BACKGROUND

The pathology of keloid and especially the roles of bacteria on it were not well understood.

METHODS

In this study, multi-omics analyses including microbiome, metaproteomics, metabolomic, single-cell transcriptome and cell-derived xenograft (CDX) mice model were used to explore the roles of bacteria on keloid disease.

FINDINGS

We found that the types of bacteria are significantly different between keloid and healthy skin. The 16S rRNA sequencing and metaproteomics showed that more catalase (CAT) negative bacteria, Clostridium and Roseburia existed in keloid compared with the adjacent healthy skin. In addition, protein mass spectrometry shows that CAT is one of the differentially expressed proteins (DEPs). Overexpression of CAT inhibited the proliferation, migration and invasion of keloid fibroblasts, and these characteristics were opposite when CAT was knocked down. Furthermore, the CDX model showed that Clostridium butyricum promote the growth of patient's keloid fibroblasts in BALB/c female nude mice, while CAT positive bacteria Bacillus subtilis inhibited it. Single-cell RNA sequencing verified that oxidative stress was up-regulated and CAT was down-regulated in mesenchymal-like fibroblasts of keloid.

INTERPRETATION

In conclusion, our findings suggest that bacteria and CAT contribute to keloid disease.

FUNDING

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Modelling Keloids Dynamics: A Brief Review and New Mathematical Perspectives

Keloids are fibroproliferative disorders described by excessive growth of fibrotic tissue, which also invades adjacent areas (beyond the original wound borders). Since these disorders are specific to humans (no other animal species naturally develop keloid-like tissue), experimental in vivo/in vitro research has not led to significant advances in this field. One possible approach could be to combine in vitro human models with calibrated in silico mathematical approaches (i.e., models and simulations) to generate new testable biological hypotheses related to biological mechanisms and improved treatments. Because these combined approaches do not really exist for keloid disorders, in this brief review we start by summarising the biology of these disorders, then present various types of mathematical and computational approaches used for related disorders (i.e., wound healing and solid tumours), followed by a discussion of the very few mathematical and computational models published so far to study various inflammatory and mechanical aspects of keloids. We conclude this review by discussing some open problems and mathematical opportunities offered in the context of keloid disorders by such combined in vitro/in silico approaches, and the need for multi-disciplinary research to enable clinical progress.

Bioinformatic analysis identifies GPR91 as a potential key gene in brain injury after deep hypothermic low flow

Purpose

Explore the transcription change of brain ischemia and reperfusion injury after deep hypothermic low flow.

Method

The data from PRJNA739516 and GSE104036 were obtained for the differentially expressed genes identification, functional enrichment analysis, gene set enrichment analysis, protein-protein interaction construction and hub gene identification. Oxygen and glucose deprivation model was set to validate the hub gene and explore the detailed brain injury mechanism.

Result

Interleukin, immunological response, NF-κB signaling pathway, G protein-coupled receptor signaling pathway and NLRP inflammatory are functional pathway were enriched in differentially expressed genes analysis. Sucnr1, Casr, Cxcr4, C5ar1, Tas2r41, Tas2r60 and Hcar2 were identified and verified in the OGD model. Knocking down GPR91 reduces the inflammatory response after OGD and GPR91 may be involved in the inflammatory pre-reaction through the synergistic activation of NF-κB, NLRP3, and IL-1β respectively.

Conclusion

Our study found that Interleukin, immunological response, NF-κB signaling pathway, G protein-coupled receptor signaling pathway and NLRP inflammatory are all associated with brain ischemia and reperfusion injury after deep hypothermic low flow and GPR91 can activate NF-κB/NLRP3 pathway and trigger the release of IL-1β in this progress.

An updated review of the immunological mechanisms of keloid scars

Keloid is a type of disfiguring pathological scarring unique to human skin. The disorder is characterized by excessive collagen deposition. Immune cell infiltration is a hallmark of both normal and pathological tissue repair. However, the immunopathological mechanisms of keloid remain unclear. Recent studies have uncovered the pivotal role of both innate and adaptive immunity in modulating the aberrant behavior of keloid fibroblasts. Several novel therapeutics attempting to restore regulation of the immune microenvironment have shown variable efficacy. We review the current understanding of keloid immunopathogenesis and highlight the potential roles of immune pathway-specific therapeutics.

The Role of CD28 and CD8+ T Cells in Keloid Development

Background: A keloid is a benign skin tumor that extends beyond the initial injury area, and its pathologic mechanism remains unclear. Method: High-throughput sequencing data were obtained from normal skin tissue of patients with keloids (Group N) and healthy controls (Group C). Important genes were mined by bioinformatics analysis and identified by RT−qPCR, Western blotting, immunohistochemistry and immunofluorescence assays. The CIBERSORT algorithm was used to convert gene expression information into immune cell information. Flow cytometry was used to verify the key immune cells. Fluorescence-activated cell sorting coculture and CCK8 experiments were used to explore the effect of CD8+ T cells on keloid-associated fibroblasts. Neural network models were used to construct associations among CD28, CD8+ T cells and the severity of keloids and to identify high-risk values. Result: The expression levels of costimulatory molecules (CD28, CD80, CD86 and CD40L) in the skin tissue of patients with keloids were higher than the levels in healthy people (p < 0.05). The number of CD8+ T cells was significantly higher in Group N than in Group C (p < 0.05). The fluorescence intensities of CD28 and CD8+ T cells in Group N were significantly higher than those in Group C (p = 0.0051). The number and viability of fibroblasts cocultured with CD8+ T cells were significantly reduced compared with those of the control (p < 0.05). The expression of CD28 and CD8+ T cells as the input layer may be predictors of the severity of keloids with mVSS as the output layer. The high-risk early warning indicator for CD28 is 10−34, and the high-risk predictive indicator for CD8+ T cells is 13−28. Conclusions: The abnormal expression of costimulatory molecules may lead to the abnormal activation of CD8+ T cells. CD8+ T cells may drive keloid-associated immunosuppression. The expression of CD28 and CD8+ T cells as an input layer may be a predictor of keloid severity. CD28 and CD8+ T cells play an important role in the development of keloids.

Single-cell RNA sequencing reveals distinct immunology profiles in human keloid

Keloids, characterized by skin fibrosis and excessive accumulation of extracellular matrix, remain a therapeutic challenge. In this study, we systematically capture the cellular composition of keloids by the single-cell RNA sequencing technique. Our results indicated that there are significant differences in most cell types present between 12 pairs of keloid and adjacent normal tissue. We found that fibroblasts, endothelial cells, mast cells, mural cells, and Schwann cells increased significantly in keloid. The proportion of mesenchymal fibroblast subpopulations in keloids was markedly higher than those in the surrounding normal skin tissue. Furthermore, we found that the immune profiles between two groups varied significantly. The proportion of macrophages in the keloid was significantly elevated compared to the surrounding normal tissue, while cDC2 cells significantly decreased. Hotspot and pseudotime trajectory analysis indicated two modules of macrophage cells (Module2: highly expresses RNASE1, C1QA, CD163, CD14, C1QC, FCGRT, MS4A7; Module10: highly expresses APOC1, CTSB, CTSL, TYROBP), which exhibited the characteristics of tumor-associated macrophages, were upregulated in more-advanced keloid cells. Subsequently, the analysis of cellular communication networks suggested that a macrophage-centered communication regulatory network may exist in keloids and that fibroblasts in keloids may facilitate the transition and proliferation of M2 macrophages, which contributes to further comprehension of the immunological features of keloids. Overall, we delineate the immunology landscape of keloids and present new insights into the mechanisms involved in its formation in this study.