Development of Novel High-Affinity Antagonists for the Relaxin Family Peptide Receptor 1

RNA nanotherapeutics with fibrosis overexpression and retention for MASH treatment

Metabolic dysfunction-associated steatohepatitis (MASH) poses challenges for targeted delivery and retention of therapeutic proteins due to excess extracellular matrix (ECM). Here we present a new approach to treat MASH, termed "Fibrosis overexpression and retention (FORT)". In this strategy, we design (1) retinoid-derivative lipid nanoparticle (LNP) to enable enhanced mRNA overexpression in fibrotic regions, and (2) mRNA modifications which facilitate anchoring of therapeutic proteins in ECM. LNPs containing carboxyl-retinoids, rather than alcohol- or ester-retinoids, effectively deliver mRNA with over 10-fold enhancement of protein expression in fibrotic livers. The carboxyl-retinoid rearrangement on the LNP surface improves protein binding and membrane fusion. Therapeutic proteins are then engineered with an endogenous collagen-binding domain. These fusion proteins exhibit increased retention in fibrotic lesions and reduced systemic toxicity. In vivo, fibrosis-targeting LNPs encoding fusion proteins demonstrate superior therapeutic efficacy in three clinically relevant male-animal MASH models. This approach holds promise in fibrotic diseases unsuited for protein injection.

The consequences of manipulating relaxin family peptide receptor 1 (RXFP1) level in ovarian cancer cells

Deregulation of the relaxin family peptide system (RFPS) appears to increase the risk of range of cancers, including epithelial ovarian cancers (EOC). The present study examines the effect of relaxin family peptide receptor 1 (RXFP1) level on the biological properties of human epithelial ovarian adenocarcinoma cells (OVCAR4 and SKOV3). RXFP1 was downregulated (RXFP1↓) in the cells using the RXFP1 sgRNA CRISPR All-in-One Lentivirus set (pLenti-U6-sgRNA-SFFV-Cas9-2A-Puro), and upregulated (RXFP1↑) using the RXFP1 CRISPRa sgRNA Lentivector (pLenti-U6-sgRNA-PGK-Neo) kit, which activates the RXFP1 gene when paired with dCas9-SAM. The changes taking place during adhesion to extracellular matrix (ECM) proteins were assessed in multi-well plates coated with collagen, fibronectin, laminin and gelatin. Cellular viability was monitored based on mitochondrial metabolic activity (MTT Assay, Alamar Blue Assay) and adenosine triphosphate production (ATP Assay). The rate of cell proliferation was determined based on the percentage of Ki67 immunoreactive cells and the numbers of cells in particular cell-cycle phases. The mesenchymal-like (Boyden Chamber Assay) and amoeboid-like movements (Wound Healing Assay) of ovarian cancer cells were also analyzed after transfection. RXFP1 downregulation decreased the adhesion properties of ovarian cancer cells and increased the tendency for apoptosis under stressful conditions. In contrast, RXFP1 upregulation had pro-proliferative, pro-survival and promigratory effects. Our findings confirm that the relaxin-2/RXFP1 signaling pathway plays a role in the promotion of growth and progression of ovarian cancer.

Significance of plasma relaxin-2 levels in patients with primary hypertension and type 2 diabetes mellitus

BACKGROUND

This study aimed to evaluate plasma relaxin‑2 (RLN-2) levels in patients with arterial hypertension (AH) and their relationships with clinical and laboratory parameters.

METHODS

The study involved 106 hypertensive patients, including 55 with type 2 diabetes mellitus (T2DM), and 30 control subjects. Plasma RLN-2 levels were measured using an enzyme-linked immunosorbent assay kit.

RESULTS

RLN-2 levels were reduced in patients with AH compared to healthy volunteers (p < 0.001), and hypertensive patients with T2DM had lower RLN-2 levels than those without impaired glucose metabolism (p < 0.001). RLN‑2 was negatively correlated with systolic blood pressure (SBP) (p < 0.001) and anthropometric parameters such as body mass index (BMI; p = 0.027), neck (p = 0.045) and waist (p = 0.003) circumferences, and waist-to-hip ratio (p = 0.011). RLN‑2 also had inverse associations with uric acid levels (p = 0.019) and lipid profile parameters, particularly triglycerides (p < 0.001) and non-HDL-C/HDL‑C (p < 0.001), and a positive relationship with HDL‑C (p < 0.001). RLN‑2 was negatively associated with glucose (p < 0.001), insulin (p = 0.043), HbA1c (p < 0.001), and HOMA-IR index (p < 0.001). Univariate binary logistic regression identified RLN‑2 as a significant predictor of impaired glucose metabolism (p < 0.001).

CONCLUSIONS

Decreased RLN-2 levels in patients with AH and T2DM and established relationships of RLN‑2 with SBP and parameters of glucose metabolism and lipid profile suggest a diagnostic role of RLN‑2 as a biomarker for AH with T2DM.

Identification of biomarkers associated with pediatric asthma using machine learning algorithms: A review

Pediatric asthma is a complex disease with a multifactorial etiology. The identification of biomarkers associated with pediatric asthma can provide insights into the pathogenesis of the disease and aid in the development of novel diagnostic and therapeutic strategies. This study aimed to identify potential biomarkers for pediatric asthma using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms. We obtained gene expression data from publicly available databases and performed WGCNA to identify gene co-expression modules associated with pediatric asthma. We then used machine learning algorithms, including random forest, lasso regression algorithm, and support vector machine-recursive feature elimination, to classify asthma cases and controls based on the identified gene modules. We also performed functional enrichment analyses to investigate the biological functions of the identified genes.We detected 24,544 genes exhibiting differential expression between controlled and uncontrolled genes from the GSE135192 dataset. In the combined WCGNA analysis, a total of 104 co-expression genes were screened, both controlled and uncontrolled. After screening, 11 hub genes were identified. They were AK2, PDK4, PER3, GZMH, NUMBL, NRL, SCO2, CREBZF, LARP1B, RXFP1, and VDAC3P1. The areas under their receiver operating characteristic curve were above 0.78. Our study identified potential biomarkers for pediatric asthma using WGCNA and machine learning algorithms. Our findings suggest that 11 hub genes could be used as novel diagnostic markers and treatment targets for pediatric asthma. These findings provide new insights into the pathogenesis of pediatric asthma and may aid in the development of novel diagnostic and therapeutic strategies.