Chronic hepatitis B: dynamic change in Traditional Chinese Medicine syndrome by dynamic network biomarkers

Abstract

Background: In traditional Chinese medicine (TCM) clinical practice, TCM syndromes help to understand human homeostasis and guide individualized treatment. However, the TCM syndrome changes with disease progression, of which the scientific basis and mechanism remain unclear.

Methods: To demonstrate the underlying mechanism of dynamic changes in the TCM syndrome, we applied a dynamic network biomarker (DNB) algorithm to obtain the DNBs of changes in the TCM syndrome, based on the transcriptomic data of patients with chronic hepatitis B and typical TCM syndromes, including healthy controls and patients with liver-gallbladder dampness-heat syndrome (LGDHS), liver-depression spleen-deficiency syndrome (LDSDS), and liver-kidney yin-deficiency syndrome (LKYDS). The DNB model exploits collective fluctuations and correlations of the observed genes, then diagnoses the critical state.

Results: Our results showed that the DNBs of TCM syndromes were comprised of 52 genes and the tipping point occurred at the LDSDS stage. Meanwhile, there were numerous differentially expressed genes between LGDHS and LKYDS, which highlighted the drastic changes before and after the tipping point, implying the 52 DNBs could serve as early-warning signals of the upcoming change in the TCM syndrome. Next, we validated DNBs by cytokine profiling and isobaric tags for relative and absolute quantitation (iTRAQ). The results showed that PLG (plasminogen) and coagulation factor XII (F12) were significantly expressed during the progression of TCM syndrome from LGDHS to LKYDS.

Conclusions: This study provides a scientific understanding of changes in the TCM syndrome. During this process, the cytokine system was involved all the time. The DNBs PLG and F12 were confirmed to significantly change during TCM-syndrome progression and indicated a potential value of DNBs in auxiliary diagnosis of TCM syndrome in CHB.Trial registration Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (http://www.clinicaltrials.gov).

Keywords: Chronic hepatitis B; Disease progression; Dynamic network biomarkers; Systems biology; Traditional Chinese Medicine syndrome.

Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

© The Author(s) 2019.

Figures

Fig. 1

The progression of traditional Chinese medicine (TCM) syndrome in chronic hepatitis B (CHB) and gene expression analyses. a A schematic diagram illustrates the progression of TCM syndromes, each typical TCM syndrome has a different clinical manifestation. b Three-dimensional principal component analysis (PCA) shows clustering of 60 samples with TCM-syndrome progression. Each small spot represents the principal component score of the top three principle components for each sample. c Unsupervised hierarchical clustering of 60 samples based on 4252 differentially expressed genes (DEGs). Similar to (b), the healthy and LGDHS groups were clustered together, while the LDSDS were mingled with LKYDS. b, c Green, healthy; purple, LGDHS; blue, LDSDS; orange, LKYDS

Fig. 2

A brief model of dynamic network biomarkers (DNB) theory and DNB analysis results. a Liver-gallbladder dampness-heat syndrome (LGDHS) (excess TCM syndrome) usually happens at disease onset or TCM-syndrome change. After the tipping point, the system drastically deteriorates to weakness, for instance, liver-kidney yin-deficiency syndrome (LKYDS) (deficiency TCM syndrome). The DNB method can identify the dramatic changing state by analyzing molecular fluctuations at each stage. b These four diagrams visually show the three key criteria of DNBs over four different stages during TCM-syndrome progression. CV is the average coefficient of variance of the DNBs, PCCin is the average Pearson correlation coefficient (PCC) of the cluster of molecules in absolute values, and PCCout is the average PCC between the cluster of molecules and other molecules in absolute values, after comparing with the corresponding controls. CIs were calculated according to the DNB formula (method) to seek the system tipping point. After calculation, LDSDS was recognized as the critical stage of TCM-syndrome progression. c Series of illustrations of dynamic changes in the network structure. Node color reflects the CV of the corresponding molecule. Clearly, DNBs are strongly correlated and fluctuated at the LDSDS stage

Fig. 3

Dynamic network biomarker–differentially expressed gene (DNB–DEG) network, before and after the critical stage. a Illustrations of dynamic change in the expressions of the DNB-associated network before and after the critical stage (LDSDS). b–c Functional analyses of the flipped DEGs

Fig. 4

Functional phenotyping of dynamic network biomarkers (DNBs) and differentially expressed genes (DEGs) in a DNB-associated network. a Four dynamic expression patterns of DNBs and DNB-related DEGs were identified by the Mfuzz clustering method. b The bar graph shows related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, which were enriched according to corresponding DNBs and DNB-related DEGs in different dynamic patterns

Fig. 5

Expression of seven cytokines changed significantly from liver-gallbladder dampness-heat syndrome (LGDHS) to liver-kidney yin-deficiency syndrome (LKYDS). a–g Cytokine expression of CCL3, IL17, IL15, CCL27, LIF, CLEC11A, and CSF2. These cytokines were significantly expressed between LGHDS and LKYDS. The vertical axis represents absolute quantification of cytokines measured by enzyme-linked immunosorbent assay (ELISA). h, i Validation of DNBs plasminogen (PLG) and coagulation factor XII (F12) in the proteomic data. The vertical axis represents signal ratio compared to the healthy group. a–i The p values were measured by Mann–Whitney U tests

Fig. 6

Diagnostic ability assessment for PLG, F12, and liver function indexes. a Receiver operating characteristic (ROC) ROC curve analysis of PLG and F12. AUC of PLG = 0.795 (95% CI 0.644 to 0.938), AUC of F12 = 0.795 (95% CI 0.520 to 0.862). b PCA of 57 samples including 20 LGDHS, 20 LDSDS and 17 LKYDS in an independent cohort by PLG and F12. c PCA of samples by liver function indexes (i.e. GGT, ALB, TBIL, DBIL, GLO, AST, ALT, TP, and ALP). Purple, LGDHS; orange, LKYDS