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Detection for disease tipping points by landscape dynamic network biomarkers

National Science Review 2019年第4期6卷 775-785页

作者： Xiaoping Liu Xiao Chang Siyang Leng Hui Tang Kazuyuki Aihara Luonan Chen Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences School of Mathematics and Statistics Shandong University at Weihai Institute of Industrial Science the University of Tokyo Institute of Statistics and Applied Mathematics Anhui University of Finance & Economics School of Life Science and Technology Shanghai Tech University Center for Excellence in Animal Evolution and Genetics Research Center for Brain Science and Brain-Inspired Intelligence

A new model-free method has been developed and termed the landscape dynamic network biomarker(l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers(i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis.

关键词： single-sample network dynamic network biomarkers tipping points of complex disease

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

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Journal of Molecular Cell Biology 2021年第11期13卷 822-833页

作者： Chengming Zhang Hong Zhang Jing Ge Tingyan Mi Xiao Cui Fengjuan Tu Xuelan Gu Tao Zeng Luonan Chen State Key Laboratory of Cell Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghai 200031China University of the Chinese Academy of Sciences Chinese Academy of SciencesBeijing 100049China Unilever Research&Development Centre Shanghai Shanghai 200335China Bio-Med Big Data Center Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Guangdong Institute of Intelligence Science and Technology Zhuhai 519031China

Skin,as the outmost layer of human body,is frequently exposed to environmental stressors including pollutants and ultraviolet(UV),which could lead to skin ***,skin response process to ultraviolet B(UVB)irradiation is a nonlinear dynamic process,with unknown underlying molecular mechanism of critical ***,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 *** 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 ***,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.

关键词： single-sample network tipping point UVB irradiation living skin equivalent model time series data skin lightening

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