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Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-targetselection guided byCRISPR screening andnetwork), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations. Author summary: Cancer therapies require targets to function. Compared to a single target, a target combination is a better strategy for developing cancer therapies. However, predicting target combination is more complicated than predicting a single target. Current CRISPR technology enables whole-genome screening of potential targets. But most of the experiments have been conducted on a single target (gene) level. To facilitate the discovery of novel target (combinations), we developed DSCN (double-targetselection guided byCRISPR screening andnetwork) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data. DSCN showed great accuracy on different cancer types and superior performance compared to existing network-based prediction tools. We also introduced DSCNi derived from DSCN and designed specifically for predicting target combinations for single-tient patient. Our results showed synergistic target combinations predicted by DSCNi accurately reflected synergies on drug combination levels. Thus, DSCN and DSCNi have the potential to be further applied in the clinical personalized medicine practice. [ABSTRACT FROM AUTHOR]

Purpose: Abnormal expression of the NR1 subunit of the N-methyl-d-aspartate (NMDA) receptor may potentially increase the susceptibility to neuropsychiatric diseases. The purpose of this study was to investigate the functional sequence of the 3ʹUTR of the human GRIN1 gene, which encodes the GluN1 receptor to determine the effect on the expression of GluN1 receptor. Methods: We transferred seven recombinant pmirGLO recombinant vectors containing the 3ʹUTR truncated fragment of the GRIN1 gene into HEK-293, SK-N-SH, and U87 cell lines and compared the relative fluorescence intensity of adjacent length fragments. The TargetScan database was used to predict miRNAs. Then, miRNA mimics/inhibitors were co-transfected into the three cell lines with the 3ʹUTR of GRIN1 (pmirGLO - GRIN1), to investigate their influence on GRIN1 gene expression. Results: Compared with the pmirGLo-Basic vector, the relative fluorescence intensity of the complete GRIN1 gene 3ʹUTR recombinant sequence − 27 bp – +1284 bp (the next base of the stop codon is +1) was significantly decreased in all three cell lines. The relative fluorescence intensities were significantly different between − 27 bp – +294 bp and − 27 bp – +497 bp regions, and between − 27 bp – +708 bp and − 27 bp – +907 bp regions. According to the prediction of the TargetScan database and analysis, miR-212-5p, miR-324-3p and miR-326 may bind to +295 bp – +497 bp, while miR-491-5p may bind to +798 bp – +907 bp. After co-transfection of miRNA mimic/inhibitor or mimic/inhibitor NC with a recombinant vector in the 3ʹUTR region of GRIN1 gene, we found that has-miR-491-5p inhibited GRIN1 expression significantly in all three cell lines, while has-miR-326 inhibitor upregulated GRIN1 expression in HEK-293 and U87 cells. Conclusion: miR-491-5p may bind to the 3ʹUTR of the GRIN1 gene (+799 bp – +805 bp, the next base of the stop codon is +1) and down-regulate gene expression in HEK-293, SK-N-SH, and U87 cell lines, which implicates a potential role of miR-491-5p in central nervous system diseases. [ABSTRACT FROM AUTHOR]

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction. [ABSTRACT FROM AUTHOR]

Yajieshaba (YJSB), approved by the Yunnan Provincial Food and Drug Administration in 2008, are known for their anti-inflammatory, antiviral, and pro-apoptotic properties, effectively treating Hepatic fibrosis (HF). However, its mechanism of action remains unclear. The objective of this investigation is to explore how YJSB influences the TGF-β1/Smad signaling pathway as a strategy for reducing HF. The establishment of a HF model in mice involved ligation of the common bile duct, followed by administration of YJSB. Body and liver weights were measured, and the liver index calculated. Serum levels of ALT, AST, ALP, TBA, and TBIL were assessed using colorimetric methods. Additionally, liver homogenates were analyzed for PIIINP, Col-IV, LN, HA, and Hyp, as well as TGF-β1 activity, using ELISA. Histological analyses of liver sections, stained with H&E, Ag, and Masson's trichrome, were performed to examine inflammation and the accumulation of collagen and reticular fibers. These studies aimed to elucidate the pharmacodynamic effects of YJSB on HF in mice with bile duct obstruction. The target pathways of YJSB were preliminarily identified through immunofluorescence detection of TGF-β1, P-Smad2L, P-Smad2C, P-Smad3L, P-Smad3C, and Smad4 proteins. In vitro experiments included the induction of hepatic stellate cell (HSC-T6) activation by H 2 O 2. A cell injury model was established for HSC-T6, and the CCK-8 assay was used to determine the optimal YJSB concentration and treatment duration. After pirfenidone (PFD) administration, which inhibits the TGF-β1/Smad pathway, the effects of YJSB on HSC-T6 cell proliferation were observed. ELISA assays quantified Col-III, α-SMA, and Col-I in cell lysates to assess YJSB's impact on collagen synthesis in HSC-T6 cells. Western blot analysis was performed to assess the protein levels within the TGF-β1/Smad signaling cascade. In the HF mouse model, administration of YJSB notably augmented the body weight and reduced the liver index. Concurrently, there was an elevation in serum concentrations of ALP, AST, ALT, TBA, and TBIL. Similarly, in the liver homogenates of HF mice, increases were observed in the levels of HA, PIIINP, Col-IV, LN, Hyp, and TGF-β1. Histological assessments using H&E, Ag, and Masson stains indicated a substantial diminution in liver tissue damage. Through immunofluorescence analysis, it was discerned that YJSB modulated the expression of TGF-β1, P-Smad2L, P-Smad2C, and P-Smad3L downwards, while elevating P-Smad3C and Smad4 protein expressions. Additional investigations revealed a significant reduction in α-SMA, Col-I, and Col-III levels in cell culture fluids, suggesting a decrease in collagen synthesis and a protective role against cellular damage. Western blot analyses demonstrated that the TGF-β1/Smad pathway inhibitor, PFD, acted in synergy with YJSB, enhancing its regulatory effects on this pathway, decreasing levels of TGF-β1, P-Smad2L, P-Smad2C, P-Smad3L, and promoting the expression of P-Smad3C. YJSB demonstrates a pharmacodynamic effect against HF, enhancing liver functionality and effectively mitigating the damage associated with bile duct obstruction. The proposed action mechanism of YJSB involves modulation of the TGF-β1/Smad signaling pathway. Research indicates that YJSB might play a role in suppressing the movement, programmed cell death, and activation of HSC-T6, potentially decelerating the advancement of hepatic fibrosis. [Display omitted] • Revealed the pharmacodynamic effect of YJSB on HF mouse model. • YJSB can reduce collagen synthesis, and protect against HSC-T6 cell injury in HF caused by bile duct obstruction. • YJSB can regulate the TGF-β1/Smad pathway, improve liver function and enhance the protective effect on hepatocytes. [ABSTRACT FROM AUTHOR]