Pharmacotranscriptomic profiles, which capture drug-induced changes in gene expression, offer vast potential for computational drug discovery and are widely used in modern medicine. However, current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect. Here, we developed a new genome-scale functional module (GSFM) transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery. GSFM employs four biologically interpretable quantifiers: GSFM_Up, GSFM_Down, GSFM_ssGSEA, and GSFM_TF to comprehensively evaluate the multi-dimension activities of each functional module (FM) at gene-level, pathway-level, and transcriptional regulatory network-level. Through a data transformation strategy, GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix, significantly outperforming other methods in terms of both robustness and accuracy. Besides, we found a positive correlation between RS_GSFM and drug efficacy, suggesting that RS_GSFM could serve as representative measure of drug efficacy. Furthermore, we identified WYE-354, perhexiline, and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma, lung adenocarcinoma, and castration-resistant prostate cancer, respectively. The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects, providing proof-of-concept for our computational approach.

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity.However,compared to bulk RNA sequencing (RNA-seq),single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage,which brings new computational difficulties.Based on statistical independence,cell-specific network (CSN) is able to quantify the overall associations between genes for each cell,yet suffering from a problem of overestimation related to indirect effects.To overcome this problem,we propose the c-CSN method,which can construct the conditional cell-specific network (CCSN) for each cell.c-CSN method can measure the direct associations between genes by eliminating the indirect associations.c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells.Intuitively,each CCSN can be viewed as the transformation from less "reliable" gene expression to more "reliable" gene-gene associations in a cell.Based on CCSN,we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell.A number of scRNA-seq data-sets were used to demonstrate the advantages of our approach.1) One direct association network is generated for one cell.2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices.3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell.c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.

Significantly increasing crop yield is a major and worldwide challenge for food supply and security. It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide. Yet, the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery. Here, we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group. We identified the top 24 candi-date high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method, i.e., dynamic cross-tissue (DCT) network analysis. We used one of the candidate genes, OsSPL4, whose function was previously unknown, for gene editing experimental validation of the high yield, and confirmed that OsSPL4 significantly affects panicle branching and increases the rice yield. This study, which included extensive field phenotyping, cross-tissue systems biology analyses, and functional validation, uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice. The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample. DCT can be downloaded from https://github.com/zt-pub/DCT.

The estrogen receptor (ER)-negative breast cancer subtype is aggressive with few treat-ment options available. To identify specific prognostic factors for ER-negative breast cancer, this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance, Epidemiol-ogy, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ER-positive breast cancer patients, we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network. Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge featuresfor both subtypes of breast cancer. Two promising kinase-substrate edge features, CSNK1A1-NFATC3 and SRC-OCLN, were identified for more accurate prognostic prediction in ER-negative breast cancer patients.

BACKGROUND:Bone marrow mesenchymal stem cel transplantation has not been thoroughly reported on its effects on apoptosis in hepatoma carcinoma cel s and inflammatory factor level.
OBJECTIVE:To investigate the effect of rat bone marrow mesenchymal stem cel s on dynamic change of inflammatory factors and cel apoptosis during hepatocarcinogenesis.
METHODS:Sixty healthy Sprague-Dawley rats were divided randomly into healthy group (n=30), control group (n=30) and transplantation group (n=30). Healthy group was given ordinary feed and normal water, while other groups were given diethylnitrosamine solution in drinking water to induce liver cancer models. Then, rats in the transplantation group were subjected to bone marrow mesenchymal stem cel transplantation via the tail vein. Two weeks after cel transplantation, CXCL5, interleukin-8 and interleukin-6 levels were tested by ELISA, mRNA level of hepatocyte nuclear factor 1αdetected by RT-PCR, expression of Bcl-2 and Bax in liver tissue measured by immunohistochemical method, and liver cancer cel apoptosis index detected by TUNEL technique.
RESULTS AND CONCLUSION:After modeling, the expressions of CXCL5, interleukin-8 and interleukin-6 in the control group were significantly higher than those in the healthy group (P<0.05), while these indexes were reduced significantly after bone marrow mesenchymal stem cel transplantation (P<0.05) and close to the normal levels (P>0.05). Bone marrow mesenchymal stem cel transplantation significantly up-regulated the mRNA level of hepatocyte nuclear factor 1αin the liver tissue that was decreased obviously after modeling (P<0.05). In addition, the expression of Bcl-2 was reduced, while the expression of Bax and the apoptosis index increased significantly in the transplantation group compared with the control group (P<0.05). These findings indicate that bone marrow mesenchymal stem cel transplantation contributes to hepatocyte differentiation and regeneration in liver cancer rats by reducing serum inflammatory factor levels and promoting apoptosis in hepatoma carcinoma cel s.

In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded.
Algorithms ; Artificial Intelligence ; Humans ; Models, Biological ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping ; Protein Interaction Maps ; Proteome ; genetics ; metabolism ; Proteomics ; Systems Biology