Dual-Specificity Phosphatases and Kidney Diseases

Development and evaluation of a chronic kidney disease risk prediction model using random forest

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

Revealing the Complete Bispecific Phosphatase Genes (DUSPs) across the Genome and Investigating the Expression Patterns of GH_A11G3500 Resistance against Verticillium wilt

DUSPs, a diverse group of protein phosphatases, play a pivotal role in orchestrating cellular growth and development through intricate signaling pathways. Notably, they actively participate in the MAPK pathway, which governs crucial aspects of plant physiology, including growth regulation, disease resistance, pest resistance, and stress response. DUSP is a key enzyme, and it is the enzyme that limits the rate of cell metabolism. At present, complete understanding of the DUSP gene family in cotton and its specific roles in resistance to Verticillium wilt (VW) remains elusive. To address this knowledge gap, we conducted a comprehensive identification and analysis of four key cotton species: Gossypium arboreum, Gossypium barbadense, Gossypium hirsutum, and Gossypium raimondii. The results revealed the identification of a total of 120 DUSP genes in the four cotton varieties, which were categorized into six subgroups and randomly distributed at both ends of 26 chromosomes, predominantly localized within the nucleus. Our analysis demonstrated that closely related DUSP genes exhibited similarities in terms of the conserved motif composition and gene structure. A promoter analysis performed on the GhDUSP gene promoter revealed the presence of several cis-acting elements, which are associated with abiotic and biotic stress responses, as well as hormone signaling. A tissue expression pattern analysis demonstrated significant variations in GhDUSP gene expression under different stress conditions, with roots exhibiting the highest levels, followed by stems and leaves. In terms of tissue-specific detection, petals, leaves, stems, stamens, and receptacles exhibited higher expression levels of the GhDUSP gene. The gene expression analysis results for GhDUSPs under stress suggest that DUSP genes may have a crucial role in the cotton response to stress in cotton. Through Virus-Induced Gene Silencing (VIGS) experiments, the silencing of the target gene significantly reduced the resistance efficiency of disease-resistant varieties against Verticillium wilt (VW). Consequently, we conclude that GH_A11G3500-mediated bispecific phosphorylated genes may serve as key regulators in the resistance of G. hirsutum to Verticillium wilt (VW). This study presents a comprehensive structure designed to provide an in-depth understanding of the potential biological functions of cotton, providing a strong foundation for further research into molecular breeding and resistance to plant pathogens.

Molecular Docking Analysis of Selected Urtica dioica Constituents As Human Carbonic Anhydrase II (hCA-II), Human 11 Beta-Hydroxysteroid Dehydrogenases Type 1 (h11beta-HSD1), and Human Dual Specificity Phosphatase (hCDC25B) Inhibitory Agents

Background Urtica dioica (Stinging nettle)has been reported to exhibit various pharmacological activities. In the present study, we aimed to evaluate 24 selected constituents of U. dioica as potent inhibitory agents of human carbonic anhydrase II (hCA-II), human 11 beta-hydroxysteroid dehydrogenases type 1 (h11beta-HSD1), and human dual specificity phosphatase (hCDC25B) using in silico method. Methodology The 24 selected constituents of U. dioica (Stinging nettle) were studied on the docking behavior of hCA-II, h11beta-HSD1, and hCDC25B by using the Webina docking method. In addition to docking, toxicity analysis was also performed using the pkCSM free web server, respectively. Results Toxicity analysis has shown that six ligands (25%) of U. dioica (Stinging nettle) are predicted to have hERG II (Human ether-a-go-go-related gene) inhibition activity. The docking analysis showed that afzelin, stigmastane-3, 6-diol, and astragalin of U. dioica have shown the maximum binding energy (-7.2, -9.5, and -8.5 kcal/mol) with the hCA-II, h11beta-HSD1 and hCDC25B, respectively. Conclusions Thus, the current finding provides new knowledge about the 24 selected ligands of U. dioica (Stinging nettle) as potent inhibitory agents of human carbonic anhydrase II (hCA-II), human 11 beta-hydroxysteroid dehydrogenases type 1 (h11beta-HSD1) and human dual specificity phosphatase (hCDC25B).

Minichromosome maintenance 6 protects against renal fibrogenesis by regulating DUSP6-mediated ERK/GSK-3β/Snail1 signaling

Minichromosome maintenance 6 (MCM6) has been implicated in the progression of various malignant tumors; however, its exact physiological function in kidney diseases remains unclear. Here, we demonstrated that MCM6 levels showed a significant increase in the proximal tubular cells during progressive renal fibrosis in two unrelated in vivo fibrotic models, including unilateral ureteral obstruction (UUO) and unilateral ischemia-reperfusion injury (UIRI). Depletion of MCM6 aggravated partial epithelial-mesenchymal transition, extracellular matrix accumulation, and myofibroblast activation in the kidneys of UUO or UIRI mice. Conversely, overexpression of MCM6 promoted the recovery of E-cadherin and retarded UUO- or UIRI-induced renal fibrosis. In addition, DUSP6 expression substantially decreased in fibrotic kidneys, and it might be involved in MCM6-induced renal fibrosis by regulating the activation of ERK/GSK-3β/Snail1 signaling. In conclusion, our results highlight the significance of MCM6 in renal fibrosis, providing a potential therapeutic target for patients with chronic kidney disease.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Background

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.

Methods

Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.

Results

Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.

Conclusion

Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

Enhanced Cerebral Hemodynamics and Cognitive Function Via Knockout of Dual-Specificity Protein Phosphatase 5

Alzheimer's Disease (AD) and Alzheimer's Disease-Related Dementias (ADRD) are neurodegenerative disorders. Recent studies suggest that cerebral hypoperfusion is an early symptom of AD/ADRD. Dual-specificity protein phosphatase 5 (DUSP5) has been implicated in several pathological conditions, including pulmonary hypertension and cancer, but its role in AD/ADRD remains unclear. The present study builds on our previous findings, demonstrating that inhibition of ERK and PKC leads to a dose-dependent dilation of the middle cerebral artery and penetrating arteriole, with a more pronounced effect in Dusp5 KO rats. Both ERK and PKC inhibitors resulted in a significant reduction of myogenic tone in vessels from Dusp5 KO rats. Dusp5 KO rats exhibited stronger autoregulation of the surface but not deep cortical cerebral blood flow. Inhibition of ERK and PKC significantly enhanced the contractile capacity of vascular smooth muscle cells from both strains. Finally, a significant improvement in learning and memory was observed in Dusp5 KO rats 24 hours after initial training. Our results suggest that altered vascular reactivity in Dusp5 KO rats may involve distinct mechanisms for different vascular beds, and DUSP5 deletion could be a potential therapeutic target for AD/ADRD. Further investigations are necessary to determine the effects of DUSP5 inhibition on capillary stalling, blood-brain barrier permeability, and neurodegeneration in aging and disease models.

DUSP2-mediated inhibition of tubular epithelial cell pyroptosis confers nephroprotection in acute kidney injury

Rationale: Acute kidney injury (AKI) is pathologically characterized by renal tubular epithelial cell (RTEC) death and interstitial inflammation, while their pathogenesis remains incompletely understood. Dual-specificity phosphatase 2 (DUSP2) recently emerges as a crucial regulator of cell death and inflammation in a wide range of diseases, but its roles in renal pathophysiology are largely unknown. Methods: The expression of DUSP2 in the kidney was characterized by histological analysis in renal tissues from patients and mice with AKI. The role and mechanism of DUSP2-mediated inhibition of tubular epithelial cell pyroptosis in AKI were evaluated both in vivo and in vitro, and confirmed in RTEC-specific deletion of DUSP2 mice. Results: Here, we show that DUSP2 is enriched in RTECs in the renal tissue of both human and mouse and mainly positions in the nucleus. Further, we reveal that loss-of-DUSP2 in RTECs not only is a common feature of human and murine AKI but also positively contributes to AKI pathogenesis. Especially, RTEC-specific deletion of DUSP2 sensitizes mice to AKI by promoting RTEC pyroptosis and the resultant interstitial inflammation. Mechanistic studies show that gasdermin D (GSDMD), which mediates RTEC pyroptosis, is identified as a transcriptional target of activated STAT1 during AKI, whereas DUSP2 as a nuclear phosphatase deactivates STAT1 to restrict GSDMD-mediated RTEC pyroptosis. Importantly, DUSP2 overexpression in RTECs via adeno-associated virus-mediated gene transfer significantly ameliorates AKI. Conclusion: Our findings demonstrate a hitherto unrecognized role of DUSP2-STAT1 axis in regulating RTEC pyroptosis in AKI, highlighting that DUSP2-STAT1 axis is an attractive therapeutic target for AKI.