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

很少长期的发炎怎么贡献 hepatocellular 癌(HCC ) 的前进被知道，特别癌症的开始。揭开从长期的发炎的批评转变到在网络水平的 HCC 和分子的机制，我们用我们的动态网络 biomarker (DNB ) 分析了土拨鼠肝炎 virus/c-myc 鼠标和匹配年龄的 wt-C57BL/6 鼠标的时间系列 proteomic 数据模型。DNB 分析显示在转基因的老鼠的出生以后的第 5 月是癌症开始的批评时期，就在批评转变前，它与临床的症状一致。同时，联系 DNB 的网络在批评转变前后显示出蛋白质表示和 coexpression 层次的激烈的倒置。DNB， PLA2G6 和 CYP2C44 的二个成员，与他们的联系差别一起表示了蛋白质，被发现导致 arachidonic 酸新陈代谢的机能障碍，进一步通过短暂受体潜力隧道的煽动性的调停人规定激活煽动性的回答，并且最后导致肝 detoxification 和恶意的转变的缺陷到癌症。作为一个 c-Myc 目标， PLA2G6 断然在表示与 c-Myc 相关，显示出从减少到在 carcinogenesis 期间增加的一个趋势，与在批评转变的最小的点或付小费给的点。相应 PLA2G6 和 c-Myc 的如此的趋势也在人的 hepatocarcinogenesis 期间被观察，与在高级 dysplastic 小瘤(就在 carcinogenesis 前的一个阶段) 的最小的点。我们的学习暗示 PLA2G6 可能在 hepatocarcinogenesis 期间作为象著名 c-Myc 一样的 oncogene 工作，当 PLA2G6 和 c-Myc 的 downregulation 能是显示逼近的 carcinogenesis 的一个警告信号时。

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.

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.

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).

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.

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.

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.

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.

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.

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.

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).

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.

Visualization of Biomolecular Networks' Comparison on Cytoscape

Similarities and dissimilarities between biomolecular networks cannot be intuitively recognized even after the development of several comparison algorithms because of the lack of visualization tools. In this paper, an integrated tool kit named Biomolecular Network Match（BNMatch） is designed and developed based on Cytoscape—a popular and open-source tool for analyzing and visualizing networks. BNMatch integrates the comparison of the outputs of algorithms used for processing biomolecular networks and expresses the matching data between them by defining similar vertices and links with similar attributes. Moreover, in order to maintain consistency, their counterparts in other networks change when the nodes and edges in one of the compared networks are changed. It becomes easy for users to analyze similar networks by invoking comparison algorithms and visualizing the matching data between the networks using BNMatch.

Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes

Non-smooth or even abrupt state changes exist during many biological processes, e.g., cell differentiation processes, proliferation processes, or even disease deterioration processes. Such dynamics generally signals the emergence of critical transition phenomena, which result in drastic changes of system states or eventually qualitative changes of phenotypes. Hence, it is of great importance to detect such transitions and further reveal their molecular mechanisms at network level. Here, we review the recent advances on dynamical network biomarkers （DNBs） as well as the related theoretical foundation, which can identify not only early signals of the critical transitions but also their leading networks, which drive the whole system to initiate such transitions. In order to demonstrate the effectiveness of this novel approach, examples of complex diseases are also provided to detect pre-disease stage, for which traditional methods or biomarkers failed.

ZokorDB: tissue specific regulatory network annotation for non-coding elements of plateau zokor

Background:Plateau zokor inhabits in sealed burrows from 2,000 to 4,200 meters at Qinghai-Tibet Plateau.This extreme living env ironment makes it a great model to study animal adaptation to hypoxia,low temperature,and high carbon dioxide concentration.Methods:We provide an integrated resource,ZokorDB,for tissue specific regulatory network annotation for zokor.ZokorDB is based on a high-quality draft genome of a plateau zokor at 3,300 m and its transcriptional profiles in brain,heart,liver,kidney,and lung.The conserved non-coding elements of zokor are annotated by their nearest genes and upstream transcriptional factor motif binding sites.Results:ZokorDB provides a general draft gene regulatory network(GRN),Le?potential transcription factor(TF)binds to non-coding regulatory elements and regulates the expression of target genes(TG).Furthermore,we refined the GRN by incorporating matched RNA-seq and DNase-seq data from mouse ENCODE project and reconstructed five tissue-specific regulatory networks.Conclusions:A web-based,open-access database is developed for easily searching,visualizing,and downloading the annotation and data.The pipeline of non-coding region annotation for zokor will be useful for other non-model species.ZokorDB is free available at the website(bigd.big.ac.cn/zokordb/).