Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients

One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.

Dynamic network biomarker to determine the critical point of breast cancer stage progression

BACKGROUND

The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression.

METHODS

First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network.

RESULTS

We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects.

CONCLUSION

The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.

Epileptic seizure prediction in intracranial EEG using critical nucleus based on phase transition

BACKGROUND AND OBJECTIVE

Epilepsy is the second most prevalent neurological disorder of brain activity, affecting about seventy million people, or nearly 1% of the world population. Epileptic seizures prediction is extremely important for improving the epileptic patients' life. This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG (iEEG) signals.

METHODS

This article used three key measures of fluctuation, correlation and percolation to quantify pre-ictal states of epilepsy. Based on these measures, a ritical nucleus of iEEG signals was constructed and a composite index was introduced to detect the likelihood of impending seizures. In addition, we analyzed the dynamical mechanism of seizures at the tipping point from the perspective of spatial diffusion and temporal fluctuation.

RESULTS

The empirical results supported that the seizures are self-initiated via a critical transition in pre-ictal state and showed that the proposed model can achieve a good prediction performance. The average accuracy, sensitivity, specificity and false-positive rate (FPR) attain 87.96%, 82.93%, 89.33% and 0.11/h respectively. The results also suggest that the temporal and spatial factors have strong synergistic effect on triggering seizures. For those seizures consistent with critical transition, the predictive performance was greatly improved with sensitivity up to 96.88%.

CONCLUSIONS

This article proposed a critical nucleus model combined with spatial and temporal features of iEEG signals capable of seizure prediction. The proposed model brings insight from phase transition into epileptic iEEG signals analysis and quantifies the transition of the state to predict epileptic seizures with high accuracy.

Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo

The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

Dynamic Network Biomarker of Pre-Exhausted CD8+ T Cells Contributed to T Cell Exhaustion in Colorectal Cancer

Immunotherapy has achieved positive clinical responses in various cancers. However, in advanced colorectal cancer (CRC), immunotherapy is challenging because of the deterioration of T-cell exhaustion, the mechanism of which is still unclear. In this study, we depicted CD8⁺ T-cell developmental trajectories and characterized the pre-exhausted T cells isolated from CRC patients in the scRNA-seq data set using a dynamic network biomarker (DNB). Moreover, CCT6A identified by DNB was a biomarker for pre-exhausted T-cell subpopulation in CRC. Besides, TUBA1B expression was triggered by CCT6A as DNB core genes contributing to CD8⁺ T cell exhaustion, indicating that core genes serve as biomarkers in pre-exhausted T cells. Remarkably, both TUBA1B and CCT6A expressions were significantly associated with the overall survival of COAD patients in the TCGA database (p = 0.0082 and p = 0.026, respectively). We also observed that cellular communication between terminally differentiated exhausted T cells and pre-exhausted T cells contributes to exhaustion. These findings provide new insights into the mechanism of T-cell exhaustion and provide clue for targeted immunotherapy in CRC.

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

BACKGROUND

Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems.

METHODS

By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study.

RESULTS

The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.

Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence

MOTIVATION

The evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers' attention.

METHODS

In this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).

RESULTS

This method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some "dark genes" that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

Differentiating cancer cells reveal early large-scale genome regulation by pericentric domains

Finding out how cells prepare for fate change during differentiation commitment was our task. To address whether the constitutive pericentromere-associated domains (PADs) may be involved, we used a model system with known transcriptome data, MCF-7 breast cancer cells treated with the ErbB3 ligand heregulin (HRG), which induces differentiation and is used in the therapy of cancer. PAD-repressive heterochromatin (H3K9me3), centromere-associated-protein-specific, and active euchromatin (H3K4me3) antibodies, real-time PCR, acridine orange DNA structural test (AOT), and microscopic image analysis were applied. We found a two-step DNA unfolding after 15-20 and 60 min of HRG treatment, respectively. This behavior was consistent with biphasic activation of the early response genes (c-fos - fosL1/myc) and the timing of two transcriptome avalanches reported in the literature. In control, the average number of PADs negatively correlated with their size by scale-free distribution, and centromere clustering in turn correlated with PAD size, both indicating that PADs may create and modulate a suprachromosomal network by fusing and splitting a constant proportion of the constitutive heterochromatin. By 15 min of HRG treatment, the bursting unraveling of PADs from the nucleolus boundary occurred, coinciding with the first step of H3K4me3 chromatin unfolding, confirmed by AOT. The second step after 60 min of HRG treatment was associated with transcription of long noncoding RNA from PADs and peaking of fosL1/c-myc response. We hypothesize that the bursting of PAD clusters under a critical silencing threshold pushes the first transcription avalanche, whereas the destruction of the PAD network enables genome rewiring needed for differentiation repatterning, mediated by early response bivalent genes.

Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker

Background

The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems.

Results

By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the outbreak scale. Specifically, we collected the historical information of clinic visits caused by HFMD infection between years 2009 and 2018 respectively from public records of Tokyo, Hokkaido, and Osaka, Japan. When applied to the city networks we modelled, our method successfully identified pre-outbreak signals in an average 5 weeks ahead of the HFMD outbreak. Moreover, from the performance comparisons with other methods, it is seen that the L-DNM based system performs better when given only the records of clinic visits.

Conclusions

The study on the dynamical changes of clinic visits in local district networks reveals the dynamic or landscapes of HFMD spread at the network level. Moreover, the results of this study can be used as quantitative references for disease control during the HFMD outbreak seasons.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05709-w.

Theoretical and in silico Analyses Reveal MYC as a Dynamic Network Biomarker in Colon and Rectal Cancer

In this article, we make a theoretical and in silico study for uncovering and evaluating biomarkers in colon and rectal cancer (CRC) by the dynamic network biomarker (DNB) theory. We propose a strategy to employ the theoretical concept of UICC TNM classification in CRC. To reveal the critical transition of CRC, the DNB algorithm was implemented to analyze the genome-wide dynamic network through temporal gene expression data. The relationship between gene sets and clinical features was evaluated by weighted gene co-expression network analysis. The results show that MYC was significantly associated with tumor amplification, tumor immune cells, and survival times. The candidate tumor suppressor genes were ZBTB16, MAL, LIFR, and SLIT2. Protein-protein interaction (PPI) analysis shows that these candidate tumor suppressor genes were significant in immune cells. Data from the Human Protein Atlas showed that a high expression of these candidate tumor suppressor genes was associated with favorable prognosis in TNM stages I-IV. In conclusion, this work provides significant and novel information regarding the TNM stage, cause, and consequences of elevated MYC expression in CRC. MYC expression levels had significant negative correlations with tumor suppressor genes and immune cells.

Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree

The influenza pandemic is a wide-ranging threat to people's health and property all over the world. Developing effective strategies for predicting the influenza outbreak which may prevent or at least get ready for a new influenza pandemic is now a top global public health priority. Owing to the complexity of influenza outbreaks that are usually involved with spatial and temporal characteristics of both biological and social systems, however, it is a challenging task to achieve the real-time monitoring of influenza outbreaks. In this study, by exploring the rich dynamical information of the city network during influenza outbreaks, we developed a computational method, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to identify the tipping point or critical stage prior to the influenza outbreak. With historical records of influenza outpatients between 2009 and 2018, the MST-DNM strategy has been validated by accurate predictions of the influenza outbreaks in three Japanese cities/regions, respectively, i.e., Tokyo, Osaka, and Hokkaido. These successful applications show that the early-warning signal was detected 4 weeks on average ahead of each influenza outbreak. The results show that our method is of considerable potential in the practice of public health surveillance.

Detecting the outbreak of influenza based on the shortest path of dynamic city network

The influenza pandemic causes a large number of hospitalizations and even deaths. There is an urgent need for an efficient and effective method for detecting the outbreak of influenza so that timely, appropriate interventions can be made to prevent or at least prepare for catastrophic epidemics. In this study, we proposed a computational method, the shortest-path-based dynamical network marker (SP-DNM), to detect the pre-outbreak state of influenza epidemics by monitoring the dynamical change of the shortest path in a city network. Specifically, by mapping the real-time information to a properly constructed city network, our method detects the early-warning signal prior to the influenza outbreak in both Tokyo and Hokkaido for consecutive 9 years, which demonstrate the effectiveness and robustness of the proposed method.

Identifying critical state of complex diseases by single-sample Kullback-Leibler divergence

Background

Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches.

Methods

In this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases. Specifically, given a set of reference samples which were regarded as background, a novel index called single-sample Kullback–Leibler divergence (sKLD), was proposed to explore and quantify the disturbance on the background caused by a case sample. The pre-disease state is then signaled by the significant change of sKLD.

Results

The novel algorithm was developed and applied to both numerical simulation and real datasets, including lung squamous cell carcinoma, lung adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, colon adenocarcinoma, and acute lung injury. The successful identification of pre-disease states and the corresponding dynamical network biomarkers for all six datasets validated the effectiveness and accuracy of our method.

Conclusions

The proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis.

Characterization and Validation of an "Acute Aerobic Exercise Load" as a Tool to Assess Antioxidative and Anti-inflammatory Nutrition in Healthy Subjects Using a Statistically Integrated Approach in a Comprehensive Clinical Trial

The homeostatic challenge may provide unique opportunities for quantitative assessment of the health-promoting effects of nutritional interventions in healthy individuals. Objective. The present study is aimed at characterizing and validating the use of acute aerobic exercise (AAE) on a treadmill at 60% of VO_2max for 30 min, in assessing the antioxidative and anti-inflammatory effects of a nutritional intervention. In a controlled, randomized, parallel trial of Korean black raspberry (KBR) (n = 24/group), fasting blood and urine samples collected before and following the AAE load at either baseline or 4-week follow-up were analyzed for biochemical markers, ¹H-NMR metabolomics, and transcriptomics. The AAE was characterized using the placebo data only, and either the placebo or the treatment data were used in the validation. The AAE load generated a total of 50 correlations of 44 selected markers, based on Pearson's correlation coefficient analysis of 105 differential markers. Subsequent mapping of selected markers onto the KEGG pathway dataset showed 127 pathways relevant to the AAE load. Of these, 54 pathways involving 18 key targets were annotated to be related to oxidative stress and inflammation. The biochemical responses were amplified with the AAE load as compared to those with no load, whereas, the metabolomic and transcriptomic responses were downgraded. Furthermore, target-pathway network analysis revealed that the AAE load provided more explanations on how KBR exerted antioxidant effects in healthy subjects (29 pathways involving 12 key targets with AAE vs. 12 pathways involving 2 key targets without AAE). This study provides considerable insight into the molecular changes incurred by AAE and furthers our understanding that AAE-induced homeostatic perturbation could magnify oxidative and inflammatory responses, thereby providing a unique opportunity to test functional foods for antioxidant and anti-inflammatory purposes in clinical settings with healthy subjects.

Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model

The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method.

Genome-wide dynamic network analysis reveals a critical transition state of flower development in Arabidopsis

BACKGROUND

The flowering transition which is controlled by a complex and intricate gene regulatory network plays an important role in the reproduction for offspring of plants. It is a challenge to identify the critical transition state as well as the genes that control the transition of flower development. With the emergence of massively parallel sequencing, a great number of time-course transcriptome data greatly facilitate the exploration of the developmental phase transition in plants. Although some network-based bioinformatics analyses attempted to identify the genes that control the phase transition, they generally overlooked the dynamics of regulation and resulted in unreliable results. In addition, the results of these methods cannot be self-explained.

RESULTS

In this work, to reveal a critical transition state and identify the transition-specific genes of flower development, we implemented a genome-wide dynamic network analysis on temporal gene expression data in Arabidopsis by dynamic network biomarker (DNB) method. In the analysis, DNB model which can exploit collective fluctuations and correlations of different metabolites at a network level was used to detect the imminent critical transition state or the tipping point. The genes that control the phase transition can be identified by the difference of weighted correlations between the genes interested and the other genes in the global network. To construct the gene regulatory network controlling the flowering transition, we applied NARROMI algorithm which can reduce the noisy, redundant and indirect regulations on the expression data of the transition-specific genes. In the results, the critical transition state detected during the formation of flowers corresponded to the development of flowering on the 7th to 9th day in Arabidopsis. Among of 233 genes identified to be highly fluctuated at the transition state, a high percentage of genes with maximum expression in pollen was detected, and 24 genes were validated to participate in stress reaction process, as well as other floral-related pathways. Composed of three major subnetworks, a gene regulatory network with 150 nodes and 225 edges was found to be highly correlated with flowering transition. The gene ontology (GO) annotation of pathway enrichment analysis revealed that the identified genes are enriched in the catalytic activity, metabolic process and cellular process.

CONCLUSIONS

This study provides a novel insight to identify the real causality of the phase transition with genome-wide dynamic network analysis.

Detecting early-warning signals of influenza outbreak based on dynamic network marker

The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early‐warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real‐time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high‐dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early‐warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early‐warning signal, with an average of 4‐week window lead prior to each seasonal outbreak of influenza, was provided by DNM‐based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level.

Detecting the tipping points in a three-state model of complex diseases by temporal differential networks

Background

The progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Traditional biomarkers aim to identify a disease state by exploiting the information of differential expressions for the observed molecules, but may fail to detect a pre-disease state because there are generally little significant differences between the normal and pre-disease states. Thus, it is challenging to signal the pre-disease state, which actually implies the disease prediction.

Methods

In this work, by exploiting the information of differential associations among the observed molecules between the normal and pre-disease states, we propose a temporal differential network based computational method to accurately signal the pre-disease state or predict the occurrence of severe disease. The theoretical foundation of this work is the quantification of the critical state using dynamical network biomarkers.

Results

Considering that there is one stationary Markov process before reaching the tipping point, a novel index, inconsistency score (I-score), is proposed to quantitatively measure the change of the stationary processes from the normal state so as to detect the onset of pre-disease state. In other words, a drastic increase of I-score implies the high inconsistency with the preceding stable state and thus signals the upcoming critical transition. This approach is applied to the simulated and real datasets of three diseases, which demonstrates the effectiveness of our method for predicting the deterioration into disease states. Both functional analysis and pathway enrichment also validate the computational results from the perspectives of both molecules and networks.

Conclusions

At the molecular network level, this method provides a computational way of unravelling the underlying mechanism of the dynamical progression when a biological system is near the tipping point, and thus detecting the early-warning signal of the imminent critical transition, which may help to achieve timely intervention. Moreover, the rewiring of differential networks effectively extracts discriminatively interpretable features, and systematically demonstrates the dynamical change of a biological system.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-017-1320-7) contains supplementary material, which is available to authorized users.

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

A single-cell transcriptomics analysis offers a new dynamical view of the differentiation process, involving an increase in between-cell variability prior to commitment.

The differentiation process has classically been seen as a stereotyped program leading from one progenitor toward a functional cell. This vision was based upon cell population-based analyses averaged over millions of cells. However, new methods have recently emerged that allow interrogation of the molecular content at the single-cell level, challenging this view with a new model suggesting that cell-to-cell gene expression stochasticity could play a key role in differentiation. We took advantage of a physiologically relevant avian cellular model to analyze the expression level of 92 genes in individual cells collected at several time-points during differentiation. We first observed that the process analyzed at the single-cell level is very different and much less well ordered than the population-based average view. Furthermore, we showed that cell-to-cell variability in gene expression peaks transiently before strongly decreasing. This rise in variability precedes two key events: an irreversible commitment to differentiation, followed by a significant increase in cell size variability. Altogether, our results support the idea that differentiation is not a “simple” series of well-ordered molecular events executed identically by all cells in a population but likely results from dynamical behavior of the underlying molecular network.

The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system

Background

Unlike traditional detection of a disease state in which there are clear phenomena, it is usually a challenge to identify the pre-disease state during the progression of a complex disease just before the serious deterioration, not only because of the high complexity of the biological system, but there may be few clues and apparent changes appearing until the catastrophic critical transition occurs.

Results

In this work, by exploiting the different dynamical features between the normal and pre-disease states, we present a hidden-Markov-model (HMM) based computational method to identify the pre-disease state and elucidate the essential mechanisms during the critical transition at the network level. Specifically, by considering the network variation and regarding that the pre-disease state is the end or shift-point of a stationary Markov process, a consistence score is proposed to measure the probability that a system is in consistency with the normal state. As validation, this approach is applied to detect the upcoming critical transition of complex systems based on both the dataset generated from a simulated network and the rich information provided by high-throughput microarray data. The effectiveness of our method has been demonstrated by the identification of the pre-disease states for two real datasets including HCV-induced hepatocellular carcinoma and virus-induced influenza infection.

Conclusion

From dynamical view point, the critical-transition phenomena in many biological processes are of some generic properties, which can be detected by the established method.