Follistatin-Like-1 (FSTL1) Is a Fibroblast-Derived Growth Factor That Contributes to Progression of Chronic Kidney Disease

Differential expression of FSTL1 and its correlation with the pathological process of periodontitis

AIMS

This study aimed to elucidate the alterations in Follistatin-like protein 1 (FSTL1) and its association with the pathological process of periodontitis.

METHODS

This study included 48 patients with periodontitis and 42 healthy controls. The expression level of FSTL1 in the gingiva was determined by RT-qPCR, validated using the dataset GSE16134, and subsequently examined by western blotting. Bioinformatics analysis revealed a single-cell distribution of FSTL1, characteristic of angiogenesis and immune cell infiltration. The expression and distribution of FSTL1, vascular endothelial marker protein CD31 and myeloperoxidase (MPO), the indicator of neutrophil activity, were determined by immunohistochemistry (IHC). A series of correlation analyses was performed to determine the associations between FSTL1 and clinical parameters, including probing depth (PD) and clinical attachment loss (CAL), and their potential role in angiogenesis (CD31) and neutrophil infiltration (MPO).

RESULTS

FSTL1 was significantly upregulated in the gingiva of patients with periodontitis compared to their healthy counterparts. In addition, FSTL1 was positively correlated with the clinical parameters PD (r = .5971, p = .0005) and CAL (r = .6078, p = .0004). Bioinformatic analysis and IHC indicated that high FSTL1 expression was significantly correlated with angiogenesis and neutrophil infiltration in periodontitis. Moreover, receiver operating characteristic (ROC) analysis demonstrated that FSTL1 could serve as an independent indicator for evaluating the severity of periodontitis (area under the curve [AUC] = 0.9011, p < .0001).

CONCLUSION

This study demonstrated FSTL1 upregulation in periodontitis and its potential contribution to the disease via angiogenesis and neutrophil infiltration.

Identification and validation of immune and cuproptosis - related genes for diabetic nephropathy by WGCNA and machine learning

Background

As the leading cause of chronic kidney disease, diabetic kidney disease (DKD) is an enormous burden for all healthcare systems around the world. However, its early diagnosis has no effective methods.

Methods

First, gene expression data in GEO database were extracted, and the differential genes of diabetic tubulopathy were obtained. Immune-related genesets were generated by WGCNA and immune cell infiltration analyses. Then, differentially expressed immune-related cuproptosis genes (DEICGs) were derived by the intersection of differential genes and genes related to cuproptosis and immune. To investigate the functions of DEICGs, volcano plots and GO term enrichment analysis was performed. Machine learning and protein-protein interaction (PPI) network analysis helped to finally screen out hub genes. The diagnostic efficacy of them was evaluated by GSEA analysis, receiver operating characteristic (ROC) curve, single-cell RNA sequencing and the Nephroseq website. The expression of hub genes at the animal level by STZ -induced and db/db DKD mouse models was further verified.

Results

Finally, three hub genes, including FSTL1, CX3CR1 and AGR2 that were up-regulated in both the test set GSE30122 and the validation set GSE30529, were screened. The areas under the curve (AUCs) of ROC curves of hub genes were 0.911, 0.935 and 0.922, respectively, and 0.946 when taking as a whole. Correlation analysis showed that the expression level of three hub genes demonstrated their negative relationship with GFR, while those of FSTL1 displayed a positive correlation with the level of serum creatinine. GSEA was enriched in inflammatory and immune-related pathways. Single-nucleus RNA sequencing indicated the main distribution of FSTL1 in podocyte and mesangial cells, the high expression of CX3CR1 in leukocytes and the main localization of AGR2 in the loop of Henle. In mouse models, all three hub genes were increased in both STZ-induced and db/db DKD models.

Conclusion

Machine learning was combined with WGCNA, immune cell infiltration and PPI analyses to identify three hub genes associated with cuproptosis, immunity and diabetic nephropathy, which all have great potential as diagnostic markers for DKD and even predict disease progression.

Potential therapeutic medicines for renal fibrosis: Small-molecule compounds and natural products

Renal fibrosis is the pathological change process of chronic kidney disease deteriorating continuously. When the renal organ is stimulated by external stimuli, it will trigger the damage and phenotypic changes of some intrinsic cells in the kidney. When the body's autoimmune regulation or external treatment is not prompted enough to restore the organ, the pathological process is gradually aggravating, inducing a large amount of intracellular collagen deposition, which leads to the appearance of fibrosis and scarring. The renal parenchyma (including glomeruli and tubules) begins to harden, making it difficult to repair the kidney lesions. In the process of gradual changes in the kidney tissue, the kidney units are severely damaged and the kidney function shows a progressive decline, eventually resulting in the clinical manifestation of end-stage renal failure, namely uremia. This review provides a brief description of the diagnosis, pathogenesis, and potential therapeutic inhibitors of renal fibrosis. Since renal fibrosis has not yet had a clear therapeutic target and related drugs, some potential targets and relevant inhibitors are discussed, especially pharmacological effects and interactions with targets. Some existing natural products have potential efficacy for renal fibrosis, which is also roughly summarized, hoping that this article would have reference significance for the treatment of renal fibrosis.

Reconstructing growth and dynamic trajectories from single-cell transcriptomics data

Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented opportunities to learn dynamic processes of cellular systems. Due to the destructive nature of sequencing, it remains challenging to link the scRNA-seq snapshots sampled at different time points. Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein-Fisher-Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. Using three scRNA-seq datasets, we show the importance of growth in the temporal inference, TIGON's capability in reconstructing gene expression at unmeasured time points and its applications to temporal gene regulatory networks and cell-cell communication inference.

The use of RNA-based treatments in the field of cancer immunotherapy

Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment.

Serum Fstl1, a novel biomarker screened based on protein array technology, predict acute kidney injury and major renal adverse events after cardiac surgery: A prospective cohort study

BACKGROUND

Acute kidney injury (AKI) is a severe complication after cardiac surgery. The early prediction of AKI can facilitate timely intervention and prevent adverse outcomes. We aimed to identify unique serum biomarker that can be used to facilitate early prediction of AKI after cardiac surgery.

METHODS

A prospective cohort study was performed in cardiac surgery patients, serum samples were collected from 172 patients before surgery, 4 h and 1 day after surgery. We used protein array technology to detect the serum protein expression profile of cardiac surgery-associated AKI (CSA-AKI) patients, and verified the novel biomarker follistatin-like 1 (Fstl1) by expanding the sample size. The primary outcome was AKI, under the definition of Kidney Disease: Improving Global Outcomes (KDIGO).

RESULTS

Patients with AKI had significantly higher serum Fstl1 levels at 4 h after surgery. After multivariate adjustment, the highest quartile of postoperative serum Fstl1 level, compared with the lowest quartile, associated with 56.3-fold higher odds of AKI. Serum Fstl1 at 4 h post-surgery had a high predictive ability for AKI, severe AKI and major renal adverse events(MAKE) (AUC = 0.713, 0.869 and 0.808, respectively). Adding postoperative 4 h serum Fstl1 to the clinical model can significantly improve the predictive performance of the model.

CONCLUSIONS

Higher serum Fstl1 levels at 4 h post-surgery is associated with higher odds of AKI after cardiac surgery, and when added to clinical model and Cleveland Score, serum Fstl1 levels at 4 h after cardiac surgery enhanced early prediction of AKI, severe AKI and MAKE in patients undergoing cardiac surgery.

Roles of follistatin-like protein 3 in human non-tumor pathophysiologies and cancers

Follistatin-like protein 3 (FSTL3) is a type of FSTLs. By interacting with a disintegrin and metalloproteinase 12 (ADAM12), transforming growth factor-β ligands (activin, myostatin and growth differentiation factor (GDF) 11), FSTL3 can either activate or inhibit these molecules in human non-tumor pathophysiologies and cancers. The FSTL3 gene was initially discovered in patients with in B-cell chronic lymphocytic leukemia, and subsequent studies have shown that the FSTL3 protein is associated with reproductive development, insulin resistance, and hematopoiesis. FSTL3 reportedly contributes to the development and progression of many cancers by promoting tumor metastasis, facilitating angiogenesis, and inducing stem cell differentiation. This review summarizes the current pathophysiological roles of FSTL3, which may be a putative prognostic biomarker for various diseases and serve as a potential therapeutic target.

Follistatin-like 1 (FSTL1) interacts with Wnt ligands and Frizzled receptors to enhance Wnt/β-catenin signaling in obstructed kidneys in vivo

Follistatin (FS)-like 1 (FSTL1) is a member of the FS-SPARC (secreted protein, acidic and rich in cysteine) family of secreted and extracellular matrix proteins. The functions of FSTL1 have been studied in heart and lung injury as well as in wound healing; however, the role of FSTL1 in the kidney is largely unknown. Here, we show using single-cell RNA-Seq that Fstl1 was enriched in stromal cells in obstructed mouse kidneys. In addition, immunofluorescence demonstrated that FSTL1 expression was induced in fibroblasts during kidney fibrogenesis in mice and human patients. We demonstrate that FSTL1 overexpression increased renal fibrosis and activated the Wnt/β-catenin signaling pathway, known to promote kidney fibrosis, but not the transforming growth factor β (TGF-β), Notch, Hedgehog, or Yes-associated protein (YAP) signaling pathways in obstructed mouse kidneys, whereas inhibition of FSTL1 lowered Wnt/β-catenin signaling. Importantly, we show that FSTL1 interacted with Wnt ligands and the Frizzled (FZD) receptors but not the coreceptor lipoprotein receptor–related protein 6 (LRP6). Specifically, we found FSTL1 interacted with Wnt3a through its extracellular calcium–binding (EC) domain and von Willebrand factor type C–like (VWC) domain, and with FZD4 through its EC domain. Furthermore, we show that FSTL1 increased the association of Wnt3a with FZD4 and promoted Wnt/β-catenin signaling and fibrogenesis. The EC domain interacting with both Wnt3a and FZD4 also enhanced Wnt3a signaling. Therefore, we conclude that FSTL1 is a novel extracellular enhancer of the Wnt/β-catenin pathway.

Urinary Galectin-3 as a Novel Biomarker for the Prediction of Renal Fibrosis and Kidney Disease Progression

Plasma galectin-3 (Gal-3) is associated with organ fibrosis, but whether urinary Gal-3 is a potential biomarker of kidney disease progression has never been explored. Between 2018 and 2021, we prospectively enrolled 280 patients who underwent renal biopsy and were divided into three groups based on their urinary Gal-3 levels (<354.6, 354.6−510.7, and ≥510.8 pg/mL) to assess kidney disease progression (defined as ≥40% decline in the estimated glomerular filtration rate or end-stage renal disease) and renal histology findings. Patients in the highest urinary Gal-3 tertile had the lowest eGFRs and highest proteinuria levels. In multivariate Cox regression models, patients in the highest tertile had the highest risk of kidney disease progression (adjusted hazard ratio, 4.60; 95% confidence interval, 2.85−7.71) compared to those in the lowest tertile. Higher urinary Gal-3 levels were associated with more severe renal fibrosis. Intrarenal mRNA expression of LGALS3 (Gal-3-encoded gene) was most correlated with the renal stress biomarkers (IGFBP7 and TIMB2), renal function biomarkers (PTGDS) and fibrosis-associated genes (TGFB1). The urinary Gal-3 level may be useful for the identification of patients at high risk of kidney disease progression and renal fibrosis, and for the early initiation of treatments for these patients.