Renal fibrosis is a common fate of chronic kidney diseases. Emerging studies suggest that unsolved inflammation will progressively transit into tissue fibrosis that finally results in an irreversible end-stage renal disease (ESRD). Renal inflammation recruits and activates immunocytes, which largely promotes tissue scarring of the diseased kidney. Importantly, studies have suggested a crucial role of innate immunity in the pathologic basis of kidney diseases. This review provides an update of both clinical and experimental information, focused on how innate immune signaling contributes to renal fibrogenesis. A better understanding of the underlying mechanisms may uncover a novel therapeutic strategy for ESRD.

Transforming growth factor-β (TGF-β) is a crucial mediator in tissue fibrosis that promotes accumulation of extracellular matrix (ECM), myofibroblasts to epithelial–mesenchymal transition (EMT), endothelial-mesenchymal transition (EndoMT), and apoptosis via canonical and noncanonical signaling pathways. In the past decades, a number of microRNAs have been reported to participate in TGF-β-mediated tissue scarring; however, the roles of long noncoding RNAs (lncRNAs) in fibrogenesis remain largely unknown. Recently, emerging evidence has shown that lncRNAs are involved in the development of different diseases, including cancer, autoimmune diseases, cardiovascular diseases, and fibrotic diseases. In this review, we summarize the current updates of lncRNAs in TGF-β1-driven tissue fibrosis and discuss their therapeutic potential for the treatment of chronic fibrotic diseases.

Transforming growth factor-β (TGF-β) is a crucial pathogenic mediator of inflammatory diseases. In tissue fibrosis, TGF-β regulates the pathogenic activity of infiltrated immunocytes and promotes extracellular matrix production via de novo myofibroblast generation and kidney cell activation. In cancer, TGF-β promotes cancer invasion and metastasis by enhancing the stemness and epithelial mesenchymal transition of cancer cells. However, TGF-β is highly pleiotropic in both tissue fibrosis and cancers, and thus, direct targeting of TGF-β may also block its protective anti-inflammatory and tumor-suppressive effects, resulting in undesirable outcomes. Increasing evidence suggests the involvement of long non-coding RNAs (lncRNAs) in TGF-β-driven tissue fibrosis and cancer progression with a high cell-type and disease specificity, serving as an ideal target for therapeutic development. In this review, the mechanism and translational potential of TGF-β-associated lncRNAs in tissue fibrosis and cancer will be discussed.

Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing kidney fibrosis severity using biochemical markers. This multi-center retrospective study included 598 patients with kidney fibrosis from four hospitals. A training cohort of 360 patients from Shanghai Tongji Hospital was used to develop a predictive nomogram and ML model, with fibrosis severity classified as mild or moderate-to-severe based on Banff scores. Logistic regression identified key predictors, which were incorporated into a nomogram and ML model. An external validation cohort of 238 patients from three additional hospitals was used for model evaluation. Serum creatinine (Scr), estimated glomerular filtration rate (eGFR), parathyroid hormone (PTH), brain natriuretic peptide (BNP), and sex were identified as independent predictors of kidney fibrosis severity. The nomogram demonstrated superior discriminative ability in the training cohort (AUC: 0.89, 95% CI: 0.85-0.92) compared to eGFR (AUC: 0.83, 95% CI: 0.78-0.87) and Scr (AUC: 0.87, 95% CI: 0.83-0.91). Among ML models, the Random Forest (RF) model achieved the highest AUC (0.98). In external validation, the nomogram and RF models maintained robust performance with AUCs of 0.86 and 0.79, respectively. This study presents a validated, noninvasive, multi-center Scr-based machine learning model for assessing kidney fibrosis severity in CKD. The integration of a clinical nomogram and ML approach offers a novel, practical alternative to biopsy for dynamic fibrosis evaluation.

Introduction: Renal fibrosis (RF), being the most important pathological change in the progression of CKD, is currently assessed by the evaluation of a biopsy. This present study aimed to apply a novel functional MRI (fMRI) protocol named amide proton transfer (APT) weighting to evaluate RF noninvasively. Methods: Male Sprague-Dawley (SD) rats were initially subjected to bilateral kidney ischemia/reperfusion injury (IRI), unilateral ureteral obstruction, and sham operation, respectively. All rats underwent APT mapping on the 7th and 14th days after operation. Besides, 26 patients underwent renal biopsy at the Nephrology Department of Shanghai Tongji Hospital between July 2022 and May 2023. Patients underwent APT and apparent diffusion coefficient (ADC) mappings within 1 week before biopsy. MRI results of both patients and rats were calculated by comparing with gold standard histology for fibrosis assessment. Results: In animal models, the cortical APT (cAPT) and medullary APT (mAPT) values were positively correlated with the degree of RF. Compared to the sham group, IRI group showed significantly increased cAPT and mAPT values on the 7th and 14th days after surgery, but no group differences were found in ADC values. Similar results were found in human patients. Cortical/medullary APT values were significantly increased in patients with moderate-to-severe fibrosis than in patients with mild fibrosis. ROC curve analysis indicated that APT value displayed a better diagnostic value for RF. Furthermore, combination of cADC and cAPT improved fibrosis detection by imaging variables alone (p < 0.1). Conclusion: APT values had better diagnostic capability at early stage of RF compared to ADC values, and the addition of APT imaging to conventional ADC will significantly improve the diagnostic performance for predicting kidney fibrosis.

Lysyl oxidase (LOX) is a copper-dependent monoamine oxidase whose primary function is the covalent cross-linking of collagen and elastin in the extracellular matrix (ECM). However, the regulation of LOX activity in renal fibrosis is not well understood. Here, our study showed that (a) LOX expression and ECM cross-linking were markedly increased in fibrotic kidneys. Reduction of copper levels in the Golgi apparatus by treatment with the copper chelator tetrathiomolybdate or by specific knockdown of copper transporter 1 (CTR1) decreased LOX activity and ameliorated renal fibrosis. (b) Overexpression of ATP7A caused an elevation of copper ions within the Golgi apparatus, resulting in increased LOX activity and enhanced ECM crosslinking, thereby promoting the progression of renal fibrosis. Knockdown of ATP7A showed the opposite result. (c) FBLN4 was essential for the ATP7A-mediated transfer of copper to LOX and formed a ternary complex of ATP7A-FBLN4-LOX. Our research revealed that high ATP7A expression induced copper overload in the Golgi apparatuses. FBLN4 then assisted ATP7A in transporting this excess copper to LOX, resulting in LOX overactivation. This, in turn, catalyzed the cross-linking of ECM components, thereby accelerating renal fibrosis. ATP7A regulates LOX activity through the induction of intra-Golgi copper accumulation, and this process is involved in FBLN4-mediated the interaction of ATP7A with LOX