BACKGROUND: Diabetic cardiomyopathy (DCM) is a common diabetes complication with limited medications. Gegen Qinlian decoction (GQD) has been used in the treatment of diabetes and its related complications in China for several decades.
OBJECTIVE: In this study, network pharmacology was employed to predict the active ingredients, key targets, and pathways involved in the treatment of DCM by GQD and to validate it by animal experiments.
METHODS: The active ingredients of GQD were retrieved from TCMSP and published literature. DCM-related gene targets were searched in Drugbank, Genecards, Disgenet, and OMIM disease databases. Protein-protein interaction networks were constructed using the STRING database and Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed using the Metascape platform. Moreover, a diabetic mouse model was established to evaluate the therapeutic effects of GQD by measuring serum biochemical markers and inflammation levels. Finally, the expression of predicted key target genes was determined using real-time quantitative PCR.
RESULTS: A total of 129 active ingredients were screened from GQD. Moreover, 146 intersecting genes related to DCM were obtained, with key targets, including AKT1, TNF, IL6, and VEGFA. Lipid and atherosclerosis, AGE-RAGE, PI3K-AKT, and MAPK pathways were identified. Blood glucose control, decreased inflammatory factors, and serum CK-MB levels were restored after GQD intervention, and the same occurred with the expressions of PPAR-γ, AKT1, APOB, and GSK3B genes.
CONCLUSIONS: Quercetin, kaempferol, wogonin, 7-methoxy-2-methyl isoflavone, and formononetin may exert major therapeutic effects by regulating key factors, such as AKT1, APOE, and GSK3B, in the inflammatory reaction, glycolipid oxidation, and glycogen synthesis related signaling pathways.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions.
OBJECTIVE: The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis.
METHODS: Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM.
RESULTS: We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance.
CONCLUSIONS: This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.

Protein-fragment complementation assays (PCAs) are powerful tools to investigate protein-protein interactions in a cellular context. These are especially useful to study unstable proteins and weak interactions that may not resist protein isolation or purification. The PCA based on the reconstitution of the Gaussia princeps luciferase (split-luc) is a sensitive approach allowing the mapping of protein-protein interactions and the semiquantitative measurement of binding affinity. Here, we describe the split-luc protocol we used to map the viral interactome of measles virus polymerase complex.

OBJECTIVE: The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain.
METHODS: Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes.
RESULTS: Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments.
CONCLUSIONS: The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.

UNASSIGNED: The aim of this study was to investigate the potential risk of drug-induced liver injury (DILI) caused by the CDK4/6 inhibitors (CDK4/6is abemaciclib, ribociclib, and palbociclib by comprehensively analyzing the FDA Adverse Event Reporting System (FAERS) database. Moreover, potential toxicological mechanisms of CDK4/6is-related liver injury were explored via drug-gene network analysis.
UNASSIGNED: In this retrospective observational study, we collected reports of DILI associated with CDK4/6i use from the FAERS dated January 2014 to March 2023. We conducted disproportionality analyses using the reporting odds ratio (ROR) with a 95% confidence interval (CI). Pathway enrichment analysis and drug-gene network analyses were subsequently performed to determine the potential mechanisms underlying CDK4/6i-induced liver injury.
UNASSIGNED: We found positive signals for DILI with ribociclib (ROR = 2.60) and abemaciclib (ROR = 2.37). DILIs associated with liver-related investigations, signs, and symptoms were confirmed in all three reports of CDK4/6is. Moreover, ascites was identified as an unlisted hepatic adverse effect of palbociclib. We isolated 189 interactive target genes linking CDK4/6 inhibitors to hepatic injury. Several key genes, such as STAT3, HSP90AA1, and EP300, were revealed via protein-protein analysis, emphasizing their central roles within the network. KEGG pathway enrichment of these genes highlighted multiple pathways.
UNASSIGNED: Our study revealed variations in hepatobiliary toxicity among the different CDK4/6 inhibitors, with ribociclib showing the highest risk of liver injury, followed by abemaciclib, while palbociclib appeared relatively safe. Our findings emphasize the need for cautious use of CDK4/6 inhibitors, and regular liver function monitoring is recommended for long-term CDK4/6 inhibitor use.

Protein-protein interactions (PPIs) are known to be involved in most cellular functions, and a detailed knowledge of such interactions is essential for studying their role in normal and pathological conditions. Significant progress is being made in the identification of PPIs through advances in computational methods. In particular, the AlphaFold2 machine learning-based model has been shown to accelerate drug discovery process by predicting the 3D structure of protein complexes. In this chapter, a straightforward protocol for predicting interprotein interactions between PAR-3 and its protein partner adapter molecule crk is provided. Such artificial intelligence-based and publicly available approaches can provide a resource for further investigation of therapeutic drug targets.

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

O6-methylguanine DNA methyl transferase (MGMT) is a significant vehicle for the cellular clearance of alkyl lesions, particularly the methyl group of the O-6 and O-4 positions of guanine and thymine, respectively. Many publications have studied the correlation between polymorphisms in MGMT and susceptibility to various cancers. In the present study, we investigated the consequence of L84F, common single-nucleotide polymorphism, K125E, site-specific mutagenesis, and L84F/K125E on conformation, stability, and behavior of MGMT in the free form and interaction with proliferating cell nuclear antigen (PCNA) and DNA as partners in the biochemical network by using molecular dynamics simulation method. Our results showed that all free variants of MGMT differed from the native form. However, among all free variants of MGMT, the L84F/K125E variant exhibited similar properties compared with the wild-type. In contrast, in complex modes, only amino acid residues of the L84F variant are involved in the interactions with PCNA and DNA somewhat differently relative to the wild-type. Furthermore, L84F SNP showed the highest binding free energy compared to other variants and native forms. These alterations in the amino acids and binding free energy of L84F relative to the native are the reasons for changing its region connection compared to the native form. Therefore, we propose conducting further investigations into the impact of inhibitors or chemotherapeutic agents to assess their effectiveness on MGMT variants compared to the wild-type, aiming to reduce the cost of cancer treatment that will depend on inhibiting native MGMT protein.Communicated by Ramaswamy H. Sarma.

The interaction between SARS-CoV-2 spike RBD and ACE2 proteins is a crucial step for host cell infection by the virus. Without it, the entire virion entrance mechanism is compromised. The aim of this study was to evaluate the capacity of various natural product classes, including flavonoids, anthraquinones, saponins, ivermectin, chloroquine, and erythromycin, to modulate this interaction. To accomplish this, we applied a recently developed a microfluidic diffusional sizing (MDS) technique that allows us to probe protein-protein interactions via measurements of the hydrodynamic radius (Rh) and dissociation constant (KD); the evolution of Rh is monitored in the presence of increasing concentrations of the partner protein (ACE2); and the KD is determined through a binding curve experimental design. In a second time, with the protein partners present in equimolar amounts, the Rh of the protein complex was measured in the presence of different natural products. Five of the nine natural products/extracts tested were found to modulate the formation of the protein complex. A methanol extract of Chenopodium quinoa Willd bitter seed husks (50 µg/mL; bisdesmoside saponins) and the flavonoid naringenin (1 µM) were particularly effective. This rapid selection of effective modulators will allow us to better understand agents that may prevent SARS-CoV-2 infection.

The non-structural protein (NSs) and nucleoprotein (NP) of the severe fever with thrombocytopenia syndrome virus (SFTSV) encoded by the S segment are crucial for viral pathogenesis. They reside in viroplasm-like structures (VLS), but their interaction and their significance in viral propagation remain unclear. Here, we investigated the significance of the association between NSs and NP during viral infection through in-silico and in-vitro analyses. Through in-silico analysis, three possible binding sites were predicted, at positions C6S (Cystein at 6th position to Serine), W61Y (Tryptophan 61st to Tyrosine), and S207T (Serine 207th to Threonine), three mutants of NSs were developed by site-directed mutagenesis and tested for NP interaction by co-immunoprecipitation. NSsW61Y failed to interact with the nucleoprotein, which was substantiated by the conformational changes observed in the structural analyses. Additionally, molecular docking analysis corroborated that the NSW61Y mutant protein does not interact well compared to wild-type NSs. Over-expression of wild-type NSs in HeLa cells increased the SFTSV replication by five folds, but NSsW61Y exhibited 1.9-folds less viral replication than wild-type. We demonstrated that the W61Y alteration was implicated in the reduction of NSs-NP interaction and viral replication. Thus, the present study identified a critical NSs site, which could be targeted for development of therapeutic regimens against SFTSV.