Allosteric modulation constitutes an interesting aspect of the molecular pharmacology of hormone receptors and enzymes with implications in basic research and drug discovery. The modulation of the angiotensin II type 1 receptor by the endogenous peptide, LVV-hemorphin 7 (LVV-H7), is an example that was recently reported using various in vitro pharmacological and biochemical approaches as well as in silico analysis. In this study, we used real-time biosensor data using BRET technology and applied sample entropy and multiscale Rényi entropy to measure the effect of LVV-H7 on receptor activity over time. LVV-H7 increased signaling complexity when used alone and stabilized receptor activity when combined with angiotensin II. These effects were different for the Gαq and β-arrestin signaling pathways. The results indicate that LVV-H7 functions to fine-tunes receptor behavior in a dynamic and pathway-specific manner. This highlights the value of entropy-based tools for tracking changes in cell signaling and further exploring hormone receptor pharmacology and signaling with potential applications in drug design and analysis. The study also suggests that endogenous peptides such as LVV-H7 could offer new ways to modulate the angiotensin II receptor for therapeutic benefit.

Metabolomic profiling enables the identification of specific biochemical alterations in various diseases, including rare monogenic diabetes and obesity syndromes such as Alström syndrome (ALMS) and Bardet–Biedl syndrome (BBS). These disorders are characterized by early-onset obesity, insulin resistance, diabetes mellitus, retinodystrophy and other symptoms, but some features may also occur in heterozygous carriers of pathogenic or likely pathogenic variants in ALMS1 and BBS genes. The aim of the study was to compare the metabolomic profiles of saliva, gingival crevicular fluid (GCF) and serum between ALMS/BBS patients and heterozygous carriers (n = 33) in relation to participants with simple obesity (n = 20) and healthy controls (n = 30) using gas chromatography coupled to mass spectrometry (GC-MS). The study showed significant differences in the levels of metabolites in saliva, GCF and serum, comparing the ALMS+BBS group with the other groups. In the analysis of statistically significant metabolites in all matrices, seven of them (valine, 3-HBA, alanine, threonine, urea, isoleucine, and phenylalanine) were consistently significant in all sample types. Levels of aromatic amino acids/branched-chain amino acids in saliva and serum were correlated with insulin resistance and the severity of obesity. In conclusion, the study identifies metabolic indicators that distinguish individuals with monogenic diabetes and obesity syndromes or genetic predisposition alone from other groups. The multi-matrix approach using available biological materials such as saliva and GCF provides a broader view of the metabolic alterations associated with variants in ALMS1 and BBS genes. This approach may support both future mechanistic and translational studies of metabolic dysfunction in these populations.

While the association between smoking and accelerated facial aging is well documented, the specific pathways underlying this association remain poorly understood. To investigate the shared genetic architecture between smoking and facial aging, we performed genetic analyses based on genome-wide association studies (GWAS) data. These analyses included linkage disequilibrium score regression (LDSC), pleiotropic analysis under composite null hypothesis (PLACO), functional mapping and annotation (FUMA), and multi-marker analysis of genomic annotation (MAGMA). To further explore the shared target genes, we utilized expression quantitative trait loci (eQTLs) and mediation Mendelian randomization (MR) analysis, with subsequent validation conducted through in vitro experiments using NIH/3T3 cells. Additionally, we carried out pan-cancer correlation analyses to assess the broader implications of the identified genes in cancer biology. Through pleiotropy and colocalization analyses, IREB2, along with CHRNA5 and AARS1, were identified as having strong evidence linking smoking and facial aging. Functional enrichment, tissue-specific analyses, and gene co-expression network were conducted to further elucidate the functions of these genes. Following eQTLs and mediation analyses, IREB2 was identified as a potential mediator connecting smoking to facial aging. Cellular experiments demonstrated that exposure to cigarette smoke particles induces cellular senescence and downregulates IREB2 expression. The pan-cancer analysis highlighted IREB2's role in shaping the tumor microenvironment and influencing immune processes. This study identifies IREB2 as a critical factor in the molecular mechanisms by which smoking accelerates facial aging, while also contributing to tumor development and immune evasion. Further functional exploration of IREB2 could uncover new therapeutic avenues to address these interconnected conditions.

Multidrug combination therapy has long been a vital approach for treating complex diseases by leveraging synergistic effects between drugs. However, drug-drug interactions (DDIs) are not uniformly beneficial. Accurate and rapid identification of DDIs is critical to mitigate drug-related side effects. Currently, many computational-based methods have been used to expedite the prediction of DDIs. However, most of these methods use a single perspective to obtain drug features, which have limited expressive capabilities and cannot fully represent the essential attributes of drugs. In this study, we propose the Multi-view Feature Embedding for drug-drug interaction prediction (MFE-DDI), which integrates SMILES information, molecular graph data and atom spatial semantic information to model drugs from multiple perspectives and encapsulate the intricate drug information crucial for predicting DDIs. Concurrently, the feature information extracted from different feature encoding channels is fused in the attention-based fusion module to fully convey the essence of drugs. Consequently, this approach enhances the efficacy of the DDI prediction task. Experimental results indicate that MFE-DDI surpasses other baseline methods on three datasets. Moreover, analysis experiments demonstrate the robustness of the model and the necessity of each component of the model. Case studies on newly approved drugs demonstrate the effectiveness of our method in real scenarios. The code and data used in MFE-DDI can be found at https://github.com/2019040445/MFE_DDI.

Viruses exhibit rapid evolutionary dynamics through random mutations and selection, driving their adaptation and cross-species transmission. To investigate these mechanisms, we designed a simulation framework with a graphical user interface (GUI), implementing random mutation and similarity-based selection. This system models the evolution of a user-supplied viral sequence toward a designated target by recursively selecting the top-N amino acid sequences with the greatest similarity in each replication cycle. Simulations tracking the evolution of SARS-CoV-2 Wuhan-Hu-1 toward the Omicron variant (BA.1) displayed plateau-like similarity trajectories, where increased substitution rates resulted in a more rapid attainment of the plateau stage. The model-generated intermediate spike sequences exhibited similarities to real-world evolutionary patterns, including B, B.1.2, B.1.160, B.1.398, B.1.1.529, and BA.1 lineages. Additionally, the approach replicated the divergent evolutionary outcomes of PEDV subjected to distinct selection regimes (with and without trypsin treatment). While the model is simplified, it provides a means to explore plausible viral evolutionary paths and may contribute to identifying potential intermediates relevant to zoonotic spillover. Integrating features such as recombination, population-level effects, and further biological constraints could substantially enhance its predictive power in future iterations.

Proteins play indispensable roles in cellular function, acting as both structural components and catalysts for essential biological processes. Their proper folding into three-dimensional structures is critical for functionality. To ensure correct folding, proteins interact with chaperones and folding catalysts such as Protein Disulfide Isomerases (PDIs), which assist in the formation and rearrangement of disulfide bonds that stabilize proteins by linking cysteine residues. PDIs are part of the thioredoxin (TRX) superfamily and are characterized by a conserved CXXC motif that contributes to their redox potential. They exhibit isomerase and oxidoreductase activities, that enable them to rearrange and form new disulfide bonds. PDI family members in sorghum (SbPDI) present a broad and largely unexplored diversity in domain order, structure, and architecture between or even within species. To shed light on this diversity, we identified and characterized PDI family members in sorghum in silico to explore their domain architecture, three-dimensional structure and functionality.

Multiple Myeloma (MM) is the second most common blood cancer, characterised by the accumulation of malignant plasma cells and the production of large amounts of a monoclonal immunoglobulin protein, in the bone marrow. The identification and progression/behaviour of molecular markers across stages remains a scientific challenge. This work aims to provide a holistic approach in the understanding of the disease progression through a computational approach, capable of characterising and distinguishing disease stages. Stage-stratified MM transcriptomic datasets were used to mine stage-specific information and calculate key entities at the gene level by means of: (a) differentially expressed genes (DEGs), (b) monotonically expressed genes (MEGs), (c) ratios of MEG-related pairs of genes (RMEGs). The performance of the RMEGs across samples has been investigated regarding their ability to characterize and discriminate the MM stages. The genes participating in the top-ranked RMEGs were further used for pathway enrichment analysis to further enlighten the functional understanding per MM stage. We show that the proposed computational methodology by means of RMEGs reveals short lists of key genes across stages, which in turn highlight significant groups of pathways associated with regulation and cell cycle. We integrate the traditional analysis of DEGs with the concepts of MEGs and RMEGs, creating a novel computational approach capable of identifying key genes and pathways that can serve as a highly-filtered pool of candidate MM stage identification markers for further experimental investigation.