Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks

Spatially resolved transcriptomics(SRT)is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition.To fully understand the organizational complexity and tumor immune escape mechanism,we propose stMGATF,a multiview graph attention fusion model that integrates gene expression,histological images,spatial location,and gene association.To better extract information,stMGATF exploits SimCLRv2 for visual feature exaction and employs edge feature enhanced graph attention networks for the learning potential embedding of each view.A global attention mechanism is used to adaptively integrate 3 views to obtain low-dimensional representation.Applied to diverse SRT datasets,stMGATF is robust and outperforms other methods in detecting spatial domains and denoising data even with different resolutions and platforms.In particular,stMGATF contributes to the elucidation of tissue heterogeneity and extraction of 3-dimensional expression domains.Importantly,considering the associations between genes in tumors,stMGATF can identify the spatial dark genes ignored by traditional methods,which can be used to predict tumor-driving transcription factors and reveal tumor immune escape mechanisms,providing theoretical evidence for the development of new immunotherapeutic strategies.

Oxidative stress-triggered Wnt signaling perturbation characterizes the tipping point of lung adeno-to-squamous transdifferentiation

Lkb1 deficiency confers the Kras-mutant lung cancer with strong plasticity and the potential for adeno-to-squamous transdifferentiation(AST).However,it remains largely unknown how Lkb1 deficiency dynamically regulates AST.Using the classical AST mouse model(Kras LSL-G12D/+;Lkb1flox/flox,KL),we here comprehensively analyze the temporal transcriptomic dynamics of lung tumors at different stages by dynamic network biomarker(DNB)and identify the tipping point at which the Wnt signaling is abruptly suppressed by the excessive accumulation of reactive oxygen species(ROS)through its downstream effector FOXO3A.Bidirectional genetic perturbation of the Wnt pathway using two different Ctnnb1 conditional knockout mouse strains confirms its essential role in the negative regulation of AST.Importantly,pharmacological activation of the Wnt pathway before but not after the tipping point inhibits squamous transdifferentiation,highlighting the irreversibility of AST after crossing the tipping point.Through comparative transcriptomic analyses of mouse and human tumors,we find that the lineage-specific transcription factors(TFs)of adenocarcinoma and squamous cell carcinoma form a“Yin-Yang”counteracting network.Interestingly,inactivation of the Wnt pathway preferentially suppresses the adenomatous lineage TF network and thus disrupts the“Yin-Yang”homeostasis to lean towards the squamous lineage,whereas ectopic expression of NKX2-1,an adenomatous lineage TF,significantly dampens such phenotypic transition accelerated by the Wnt pathway inactivation.The negative correlation between the Wnt pathway and AST is further observed in a large cohort of human lung adenosquamous carcinoma.Collectively,our study identifies the tipping point of AST and highlights an essential role of the ROS-Wnt axis in dynamically orchestrating the homeostasis between adeno-and squamous-specific TF networks at the AST tipping point.

Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma

Little is known about how chronic inflammation contributes to the progression of hepatoceUular carcinoma （HCC）, especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HCC and the molecular mechanisms at a network level, we analyzed the time-series proteomic data of woodchuck hepatitis virus/c.myc mice and age-matched wt-C57BL/6 mice using our dynamical network biomarker （DNB） model. DNB analysis indicated that the 5th month after birth of transgenic mice was the critical period of cancer initiation, just before the critical transition, which is consistent with clinical symptoms. Meanwhile, the DNB-associated network showed a drastic inversion of protein expression and coexpression levels before and after the critical transition. Two members of DNB, PLA2G6 and CYP2C44, along with their associated differentially expressed proteins, were found to induce dysfunction of arachidonic acid metabolism, further activate inflammatory responses through inflammatory mediator regulation of transient receptor potential channels, and finally lead to impairments of liver detoxification and malignant transition to cancer. As a c-Myc target, PLA2G6 positively correlated with c-Myc in expression, showing a trend from decreasing to increasing during carcinogenesis, with the minimal point at the critical transition or tipping point. Such trend of homologous PLA2G6 and c-Myc was also observed during human hepatocarcinogenesis, with the minimal point at high-grade dysplastic nodules （a stage just before the carcinogenesis）. Our study implies that PLA2G6 might function as an oncogene like famous c-Myc during hepatocar- cinogenesis, while downregulation of PLA2G6 and c-Myc could be a warning signal indicating imminent carcinogenesis.

Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies.It is a challenging task to deter.mine the tipping point of endocrine resistance and detect the associated molecules.Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance.We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules.The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients.The results provided the detection for the pre-resistance state or early signs of endocrine resistance.Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.

Toripalimab plus chemotherapy as second-line treatment in previously EGFR-TKI treated patients with EGFR-mutant-advanced NSCLC:a multicenter phase-II trial

This multicenter phase-II trial aimed to investigate the efficacy,safety,and predictive biomarkers of toripalimab plus chemotherapy as second-line treatment in patients with EGFR-mutant-advanced NSCLC.Patients who failed from first-line EGFR-TKIs and did not harbor T790M mutation were enrolled.Toripalimab plus carboplatin and pemetrexed were administrated every three weeks for up to six cycles,followed by the maintenance of toripalimab and pemetrexed.The primary endpoint was objective-response rate(ORR).Integrated biomarker analysis of PD-L1 expression,tumor mutational burden(TMB),CD8+tumor-infiltrating lymphocyte(TIL)density,whole-exome,and transcriptome sequencing on tumor biopsies were also conducted.Forty patients were enrolled with an overall ORR of 50.0%and disease-control rate(DCR)of 87.5%.The median progression free survival(PFS)and overall survival were 7.0 and 23.5 months,respectively.The most common treatment-related adverse effects were leukopenia,neutropenia,anemia,ALT/AST elevation,and nausea.Biomarker analysis showed that none of PD-L1 expression,TMB level,and CD8+TIL density could serve as a predictive biomarker.Integrated analysis of whole-exome and transcriptome sequencing data revealed that patients with DSPP mutation had a decreased M2 macrophage infiltration and associated with longer PFS than those of wild type.Toripalimab plus chemotherapy showed a promising anti-tumor activity with acceptable safety profiles as the second-line setting in patients with EGFR-mutant NSCLC.DSPP mutation might serve as a potential biomarker for this combination.A phase-III trial to compare toripalimab versus placebo in combination with chemotherapy in this setting is ongoing(NCT03924050).

SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT

Epithelial–mesenchymal transition(EMT) is a complex nonlinear biological process that plays essential roles in fundamental biological processes such as embryogenesis, wounding healing, tissue regeneration,and cancer metastasis. A hallmark of EMT is the switch-like behavior during state transition, which is characteristic of phase transitions. Hence, detecting the tipping point just before mesenchymal state transition is critical for understanding molecular mechanism of EMT. Through dynamic network biomarkers(DNB) model, a DNB group with 37 genes was identified which can provide the early-warning signals of EMT. Particularly, we found that two DNB genes, i.e., SMAD7 and SERPINE1 promoted EMT by switching their regulatory network which was further validated by biological experiments. Survival analyses revealed that SMAD7 and SERPINE1 as DNB genes further acted as prognostic biomarkers for lung adenocarcinoma.

Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. coexpressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.

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.

Predicting local COVID-19 outbreaks and infectious disease epidemics based on landscape network entropy

新型冠状病毒肺炎(COVID-19)的迅速传播对全人类构成了巨大的威胁.为了及时采取相应的控制措施,制定有效的策略来检测COVID-19疫情的预警信号是非常必要的.然而,与时间序列预测不同,流行病的暴发事件通常是高度非线性的,具有从缓慢变化到剧烈转变的动力学特征,因此很难预测.通过综合利用地理区域网络的高维动态信息和每日新增病例的实时数据,本研究开发了一种非线性、无模型的计算方法,即景观网络熵(LNE)方法,来探测COVID-19暴发前的预警信号及预测流感暴发. LNE方法成功应用于中国湖北省、西欧、日本关东地区、韩国、美国17个州和意大利的新冠疫情,以及日本东京流感疫情,探测到了传染病暴发前的预警信号.此外,本研究还基于LNE方法开发了一个在线分析平台,用于实时预警疫情暴发.

c-CSN:Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

t 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 datasets 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.

Big Biological Data:Challenges and Opportunities

In ＂Omics＂ era of the life sciences, data is presented in many forms, which represent the information at various levels of bio- logical systems, including data about genome, transcriptome, epigenome, proteome, metabolome, molecular imaging, molec- ular pathways, different population of people and clinical/med- ical records. The biological data is big, and its scale has already been well beyond petabyte （PB） even exabyte （EB）. Nobody doubts that the biological data will create huge amount of val- ues, if scientists can overcome many challenges, e.g., how to handle the complexity of information, how to integrate the data from very heterogeneous resources, what kind of principles or standards to be adopted when facing with the big data. Tools and techniques for analyzing big biological data enable us to translate massive amount of information into a better under- standing of the basic biomedical mechanisms, which can be fur- ther applied to translational or personalized medicine.

Detecting direct associations in a network by information theoretic approaches

Detecting direct associations or inferring networks based on the observed data is an important issue in many fields, including biology, physics, engineering and social studies. In this work, we focus on the information theoretic approaches in the network reconstruction or the direct association detection, in particular,for biological networks. We not only review the traditional approaches or measurements on the associations among the observed variables, such as correlation coefficient, mutual information and conditional mutual information(CMI), but also summarize recently developed theories and methods. The new theoretic works include:information geometry to give a unified framework in detecting causality/association, the partial independence to alleviate the singularity of CMI, and multiscale analysis of CMI to avoid the underestimation issue of CMI.The new methods include part mutual information(PMI) and partial associations(PA), which improve the old measurements in avoiding both overestimation and underestimation. All those theories and methods make important contributions as major advances in the development of network inference.

Data-driven systems biology approaches

Rapid accumulation of biological data is driving the system-level study from describing complex phe- nomena to understanding molecular mechanisms, from analyzing individual components to under-standing their networks and systems （Chen et al., 2009; Chen and Wu, 2015）. Data-driven systems biology approaches are emerging as essential tools to gain new insights into biological processes or systems. In this issue, we collect several research articles, which are all related to such data-driven methodologies or their applications, ranged from new computational tools （GWAS and signal pathway studies） to molecu- lar biology （CSRE inference） and disease analyses （detection of the disease tipping point by DNBs and key genes during glioma progression）.

Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage

Skin,as the outmost layer of human body,is frequently exposed to environmental stressors including pollutants and ultraviolet(UV),which could lead to skin disorders.Generally,skin response process to ultraviolet B(UVB)irradiation is a nonlinear dynamic process,with unknown underlying molecular mechanism of critical transition.Here,the landscape dynamic network biomarker(lDNB)analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels.The advanced l-DNB analysis approach showed that:(i)there was a tipping point before critical transition state during pigmentation process,validated by 3D skin model;(ii)13 core DNB genes were identified to detect the tipping point as a network biomarker,supported by computational assessment;(iii)core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening,validated by independent human skin data.Overall,this study provides new insights for skin response to repetitive UVB irradiation,including dynamic pathway pattern,biphasic response,and DNBs for skin lightening change,and enables us to further understand the skin resilience process after external stress.

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.

Intrinsic entropy model for feature selection of scRNA-seq data

Recent advances of single-cell RNA sequencing(scRNA-seq)technologies have led to extensive study of cellular heterogeneity and cell-to-cell variation.However,the high frequency of dropout events and noise in scRNA-seq data confounds the accuracy of the downstream analysis,i.e.clustering analysis,whose accuracy depends heavily on the selected feature genes.Here,by deriving an entropy decomposition formula,we propose a feature selection method,i.e.an intrinsic entropy(IE)model,to identify the informative genes for accurately clustering analysis.Specifically,by eliminating the‘noisy’fluctuation or extrinsic entropy(EE),we extract the IE of each gene from the total entropy(TE),i.e.TE=IE+EE.We show that the IE of each gene actually reflects the regulatory fluctuation of this gene in a cellular process,and thus high-IE genes provide rich information on celltype or state analysis.To validate the performance of the high-IE genes,we conduct computational analysis on both simulated datasets and real single-cell datasets by comparing with other representative methods.The results show that our IE model is not only broadly applicable and robust for different clustering and classification methods,but also sensitive for novel cell types.Our results also demonstrate that the intrinsic entropy/fluctuation of a gene serves as information rather than noise in contrast to its total entropy/fluctuation.

Computational systems biology for omics data analysis

Recent trend on biological data at a molecular level is omics data analysis for both bulk and single cells, in eluding genomics, proteomics, metabolomics, and epigenetics data (Wang and Zhang, 2017;Zhang et al., 2017;Zhao and Li, 2017;Cheng and Leung, 2018). Rapid accumulation of such high-dimensional biological data is driving the system-level study from describing complex phenomena to understanding molecular mechanisms (Park et al., 2018;Sun et al., 2018) and from analyzi ng in dividual components to understanding their networks and systems (Chen et al., 2009;Chen, 2017).

Data-based prediction and causality inference of nonlinear dynamics

Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.

Diagnosing phenotypes of single-sample individuals by edge biomarkers

Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However,obtaining edges orcorrelations between molecules for an individual requires measurement ofmultiple samples of that individual,which are generally unavailable in clinical practice.Thus,it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context.Here,we developed a new computational framework,EdgeBiomarker,to integrate edge and node biomarkers to diagnose phenotype of each single test sample.By applying the method to datasets of lung and breast cancer,it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages.Our method shows advantages over traditional methods:(i)edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes,suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods;(ii)edge biomarkers categorize patients into low/high survival rate in a more reliablemanner;(iii)edge biomarkers are significantly enriched in relevant biological functions or pathways,implying that the association changes ina network,rather than expression changes in individual molecules,tendtobe causally related to cancer development.The new frameworkof edgebiomarkers paves theway for diagnosing diseases and analyzing the irmolecular mechanisms by edges or networks in one-sample-for-one-individual basis.This also provides a powerful tool for precision medicine or big-data medicine.

SELF-ORGANIZING MAP OF COMPLEX NETWORKS FOR COMMUNITY DETECTION

Detecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since various networks exist in these systems. This paper proposes a new self-organizing map （SOM） based approach to community detection. By adopting a new operation and a new weightupdating scheme, a complex network can be organized into dense subgraphs according to the topological connection of each node by the SOM algorithm. Extensive numerical experiments show that the performance of the SOM algorithm is good. It can identify communities more accurately than existing methods. This method can be used to detect communities not only in undirected networks, but also in directed networks and bipartite networks.