Identification of Key Genes for the Ultrahigh Yield of Rice Using Dynamic Cross-tissue Network Analysis

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 candidate 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,Os SPL4,whose function was previously unknown,for gene editing experimental validation of the high yield,and confirmed that Os SPL4 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/ztpub/DCT.

Proteome-wide data analysis reveals tissue-specific network associated with SARS-CoV-2 infection

For patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the damages to multiple organs have been clinically observed.Since most of current investigations for virus-host interaction are based on cell level,there is an urgent demand to probe tissue-specific features associated with SARS-CoV-2 infection.Based on collected proteomic datasets from human lung,colon,kidney,liver,and heart,we constructed a virus-receptor network,a virus-interaction network,and a virus-perturbation network.In the tissue-specific networks associated with virus-host crosstalk,both common and different key hubs are revealed in diverse tissues.Ubiquitous hubs in multiple tissues such as BRD4 and RIPK1 would be promising drug targets to rescue multi-organ injury and deal with inflammation.Certain tissue-unique hubs such as REEP5 might mediate specific olfactory dysfunction.The present analysis implies that SARS-CoV-2 could affect multi-targets in diverse host tissues,and the treatment of COVID-19 would be a complex task.

Kinase-substrate Edge Biomarkers Provide a More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment 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,Epidemiology,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 ERpositive 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 features for both subtypes of breast cancer.Two promising kinase-substrate edge features,CSNK1A1-NFATC3 and SRC-OCLN,were identified for more accurate prognostic prediction in ERnegative breast cancer patients.

HDL quality features revealed by proteome–lipidome connectivity are associated with atherosclerotic disease

Lipoprotein,especially high-density lipoprotein(HDL),particles are composed of multiple heterogeneous subgroups containing various proteins and lipids.The molecular distribution among these subgroups is closely related to cardiovascular disease(CVD).Here,we established high-resolution proteomics and lipidomics(HiPL)methods to depict the molecular profiles across lipoprotein(Lipo-HiPL)and HDL(HDL-HiPL)subgroups by optimizing the resolution of anion-exchange chromatography and comprehensive quantification of proteins and lipids on the omics level.Furthermore,based on the Pearson correlation coefficient analysis of molecular profiles across high-resolution subgroups,we achieved the relationship of proteome–lipidome connectivity(PLC)for lipoprotein and HDL particles.By application of these methods to high-fat,high-cholesterol diet-fed rabbits and acute coronary syndrome(ACS)patients,we uncovered the delicate dynamics of the molecular profile and reconstruction of lipoprotein and HDL particles.Of note,the PLC features revealed by the HDL-HiPL method discriminated ACS from healthy individuals better than direct proteome and lipidome quantification or PLC features revealed by the Lipo-HiPL method,suggesting their potential in ACS diagnosis.Together,we established HiPL methods to trace the dynamics of the molecular profile and PLC of lipoprotein and even HDL during the development of CVD.

Pan-cancer network disorders revealed by overall and local signaling entropy

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling network.The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors.Here,we proposed signaling entropy(SR)to assess the degree of tumor network disorder.We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic data.The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell sternness,cancer type,tumor grade,and metastasis.We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation.Overall,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.