Current Medicinal Chemistry - Online First

This study investigated the causes of Mitochondrial Dysfunction (MD) in Diabetic Kidney Disease (DKD) progression, and identified genes associated with DKD, especially those with significant genetic causal effects, to provide a theoretical basis for DKD treatment.

Using a large database and single-cell RNA sequencing (scRNA-seq) data, 333 MDRDEGs were discovered. MDRDEGs were linked to AGE-RAGE signaling, RNA processing, protein transport, and energy metabolism using functional enrichment analysis. Seven MDRDEGs with significant genetic causal effects in DKD were discovered using SMR and MR analyses: ACTN1, ALG11, CCNB1, HIVEP2, MANBA, TUBA1A, and WFS1. Co-localization and scRNA-seq analyses examined these genes' DKD connections. Due to the high significance of its prediction model and DKD expression, ACTN1 was studied in depth. PheWAS and molecular dynamics analysis assessed ACTN1's safety and efficacy as a therapeutic target, and its connection with other symptoms. ACTN1 protein expression in DKD tissues was confirmed by immunofluorescence.

Functional enrichment analysis revealed that MDRDEGs were mostly related to AGE-RAGE signaling, RNA processing, protein transport, and energy metabolism. Seven MDRDEGs caused DKD genetically in SMR and MR investigations. Genetic variations in ACTN1, ALG11, MANBA, and TUBA1A were linked to DKD by co-localization studies. scRNA-seq showed a dramatic increase in ACTN1 expression in DKD. Molecular dynamics analysis demonstrated that Dihydroergocristine can safely bind to ACTN1, while the PheWAS investigation found no significant relationships. DKD tissues exhibited higher ACTN1 protein levels via immunofluorescence.

This study identified MDRDEGs linked to inflammation, cytoskeletal stabilization, and glucose metabolism pathways critical in Diabetic Kidney Disease (DKD) pathogenesis, highlighting their clinical potential as therapeutic targets. Notably, ACTN1 emerged as a causally linked gene overexpressed in DKD, with the prediction of dihydroergocristine as a targeting compound, offering novel avenues for clinical intervention.

This study suggests that ACTN1 may be a therapeutic target for DKD and sheds light on its molecular pathogenesis, clinical prevention, and treatment.

Negative emotional states, such as nervousness, anxiety, depression, and tension, exert profound detrimental effects on an individual's quality of life and overall health. Although certain widely prescribed medications have been observed to modulate these emotional states, the existing body of research in this domain remains insufficient. To address this gap, Mendelian randomization (MR) methodologies, leveraging large-scale datasets, were employed to investigate the causal relationships between 21 commonly utilized medications and the manifestation of negative emotions.

The inverse variance weighting (IVW) method was employed as the primary analytical strategy to analyze causal relationships. MR-Egger, weighted mode, and weighted median approaches were utilized to enhance the robustness of the results. Sensitivity analyses were conducted to assess the stability of the data.

Agents acting on the renin-angiotensin system, β-blocking agents, antithrombotic agents, and salicylic acid and derivatives could reduce the risk of nervousness, anxiety, tension, or depression (OR = 0.61, 95% CI 0.37 to 0.99, p = 0.047; OR = 0.59, 95% CI 0.36 to 0.98, p = 0.041; OR = 0.55, 95% CI 0.34 to 0.88, p = 0.013; OR = 0.61, 95% CI 0.40 to 0.95, p = 0.030), with no heterogeneity, horizontal pleiotropy, or reverse causation (p > 0.05).

This study revealed four medications associated with reducing the risk of negative emotions, providing clinicians with a scientific basis for medication selection to better assist patients in alleviating psychological issues and improving their quality of life.

The purpose of this study is to construct and validate a neuroendocrine differentiation-related molecular model for predicting prognosis in patients with prostate cancer (PCa).

Transcriptome data for PCa were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) websites. Differentially expressed neuroendocrine differentiation related genes (NDGs) were identified. By utilizing multivariate Cox analysis, a neuroendocrine differentiation-related molecular model for predicting prognosis was constructed and validated. The study investigated the novel model’s association with the tumor immune microenvironment, clinicopathological characteristics, tumor stemness, and anticancer treatment sensitivity. Additionally, preliminary experimental verifications of Diencephalon / Mesencephalon Homeobox 1 (DMBX1) were conducted.

Finally, we identified a total of 19 differentially expressed NDGs. A neuroendocrine differentiation-related molecular model was established and successfully validated both internally and externally. The high-risk group   exhibited   significantly   poorer biochemical recurrence-free survival (BCRFS) in the training, testing, and validating cohorts. The areas under the receiver operating characteristic curves for the training, testing, and validating cohorts were 0.825, 0.719, and 0.729, respectively. The tumor immune microenvironment, clinicopathological features, tumor stemness, and anti-cancer drug sensitivity was significantly different between high and low-risk patients. Preliminary experiments revealed that higher expression of DMBX1 significantly enhanced the proliferation, migration, and neuroendocrine differentiation of PCa cells.

This research developed a unique neuroendocrine differentiation-related molecular model that is highly suitable for predicting BCRFS. High DMBX1 expression may promote the development and neuroendocrine differentiation of prostate cancer.

Anophthalmia/microphthalmia (A/M) and anterior segment dysgenesis (ASD) are severe ocular anomalies impacting eye morphology, occurring in 30 per 100,000 live births. Genetic research has identified over 30 genes linked to A/M anomalies, with their products mainly involved in eye organogenesis.

This study examined two consanguineous A/M families to identify disease-associated pathogenic mutations and predict their functional impact.

Patients were clinically examined using A-scan and ophthalmic ultrasonography. Whole exome sequencing (WES) identified candidate pathogenic variants validated through Sanger sequencing. Computational analyses assessed the impact of these mutations on protein structure and function.

The clinical diagnosis of family A revealed microphthalmia with ASD, while family B presented with an A/M phenotype. Exome analysis of family A identified a novel missense variant, NM_012293:c.A3742G [p.(Arg1248Gly)], in the peroxidasin (PXDN) gene (ClinVar ID: VCV001333267.1). At the cellular level, PXDN is involved in establishing sulfilimine bonds in collagen IV, a component of the basement membrane, suggesting that ocular defects may result from impaired integrity of the basement membrane in the developing eye. In contrast, Family B exhibited a nonsense variant NM _012186:c.720C>A (p.Cys240*) in the FOXE3 gene. This variant has been previously reported in other South Asian populations, suggesting a founder effect in subcontinent populations. Structural modeling and simulation analysis of mutant proteins revealed altered properties, thus corroborating the pathogenicity of the identified mutation.

Our findings may contribute to the elucidation of genotype-phenotype correlations, potentially facilitating the molecular diagnosis of microphthalmia and ASD.

Peripheral T-cell lymphoma (PTCL) is a rare and heterogeneous group of hematological malignancies. Treatment options are limited and often unsatisfactory, leading to a poor prognosis in most subtypes.

This study aimed to identify potential biomarker genes for PTCL and to explore the underlying mechanisms by integrating machine learning, Mendelian Randomization (MR), and experimental validation.

Microarray datasets (GSE6338, GSE14879, and GSE59307) were downloaded from the Gene Expression Omnibus database. Differential expression analysis was conducted to identify the Differentially Expressed Genes (DEGs) between patients with PTCL and controls. A machine learning algorithm was then used to further refine the selection of characteristic genes for PTCL. We integrated genome-wide association studies data with expression quantitative trait loci data to identify genes with potential causal relationships to PTCL. Functional analysis was performed to explore underlying mechanisms. Finally, the identified gene was validated in clinical samples from patients with PTCL and controls.

Based on 60 DEGs, the least absolute shrinkage and selection operator algorithm identified nine characteristic genes for PTCL. MR analysis revealed 203 genes with causal effects on PTCL, ultimately identifying one co-expressed gene: Basic Leucine Zipper ATF-like Transcription Factor 3 (BATF3). It demonstrated good predictive performance across various PTCL subtypes, with AUC values ranging from 0.7 to 1. Functional analysis suggested that BATF3 may play a role in PTCL through immune-related pathways. Experimental validation using clinical samples further suggested the potential of this biomarker gene in PTCL.

By combining machine learning, MR, and experimental validation, we identified and validated BATF3 as a promising biomarker of PTCL. These findings provide insights into the molecular mechanisms underlying PTCL and may inform the development of effective treatment strategies for this disease.

Metastasis is the leading cause of death in lung cancer patients. Pre-metastatic niche (PMN) plays an important role in pre-metastatic tumors. However, the development of clinical applications of PMN is still limited.

Expression data for lung adenocarcinoma (LUAD) patients and PMN-related genes were downloaded from the UCSC Xena website and GeneCards database, respectively. Multiple combinations based on machine learning algorithms were used to screen signature genes and construct a PMN-associated index. Spearman analysis explored the correlation between the PMN-associated index and immune cell infiltration. In addition, we analyzed the clinical value of the PMN-associated index based on drug sensitivity analysis and TIDE scores.

The enrichment analyses suggested that PMN-related genes were mainly enriched in the PI3K-Akt and HIF-1 signaling pathways. We chose random survival forest, Lasso, and multivariate Cox regression analyses to construct the PMN-associated index based on the results of multiple machine learning algorithms. Six signature genes (SNAI2, CXCR4, TNFSF11, ENG, TIMP1, and PDGFB) were screened to construct the PMN-associated index. KM analysis suggested that the survival probability was greater in the low PMN-associated index group than in the high PMN-associated index group. In addition, we confirmed that LUAD patients with a low PMN-associated index were more likely to benefit from immunotherapy.

We confirmed that the PMN-associated index is a valid predictor of prognosis, immune characteristics, and antitumor therapy efficacy in LUAD patients, which provides additional evidence for the potential clinical value of PMN development.

Sepsis remains a leading cause of global morbidity and mortality.

To identify candidate biomarkers that may be mechanistically related to the pathogenesis of sepsis.

The Gene Expression Omnibus database was leveraged to identify differentially expressed genes (DEGs) between the healthy control and septicemia groups. Genes causally related to sepsis were probed through the integration of GWAS and expression quantitative trait loci (eQTL) data in a two-sample Mendelian randomization (MR) analysis. A set of key sepsis-related genes was then selected based on the overlap between these putative causal genes and the DEGs. These genes were then subjected to enrichment analyses, testing set validation, and analyses of their expression dynamics in clinical samples.

An examination of the overlap between 228 sepsis-related DEGs identified in the training dataset and 275 candidate causal genes linked to sepsis derived from the MR analysis led to the selection of four overlapping (SLC22A15, IL5RA, HDC, and SLC46A2) that may play a key role in sepsis. Enrichment analyses indicated that these genes were involved in the regulation of histidine metabolism and immune/inflammatory responses. In immune cell infiltration analyses, these genes were positively correlated with inflammatory response activation and the suppression of adaptive immunity. Consistent findings were obtained through qPCR verification in clinical samples.

These results offer potential insight into the mechanisms that govern septicemia and thus suggest a promising series of candidates that may be amenable to targeting to prevent or treat sepsis more effectively.

The emergence of the monkeypox virus (MPXV) as a zoonotic threat has necessitated the development of effective treatments, particularly after it spread to regions outside of Central and Western Africa, such as the 2003 outbreak in the United States. Our groundbreaking study identifies CDK1 and TOP2A as key proteins in the pathogenesis of MPXV infection, utilizing network pharmacology to target these proteins for the first time. CDK1 and TOP2A, previously known for their roles in cell reprogramming, emerge as critical targets in our strategy to combat the virus.

By targeting CDK1 and TOP2A, proteins integral to cell reprogramming, with small molecules identified in our study, such as carnosic acid, rosmarinic acid, and coclaurine, we propose a novel method not only to inhibit the replication of the monkeypox virus but also to harness cellular plasticity for therapeutic purposes. The identification and targeting of these proteins with specific compounds disrupt the virus's life cycle and simultaneously enhance the efficiency of cell reprogramming.

This dual-action approach leverages the inherent plasticity of cellular reprogramming processes to combat the virus, showcasing a pioneering step in the use of regenerative medicine principles for antiviral strategies. Moreover, molecular docking and dynamic simulations strengthen our findings by demonstrating a strong binding affinity between TOP2A and CDK1, validating the synergistic effects of our identified small molecules.

Our research thus opens new avenues for addressing viral threats like monkeypox, utilizing the convergence of virology, network pharmacology, and cellular reprogramming to pave the way for innovative treatments.