Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine

Abstract

Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.

Fig. 1.. Study design.

To explore the mechanisms of how TCM treats disease/symptoms, we develop a generic framework that characterizes TCM mechanisms as the network-based relation between symptom-associated proteins and herb targets in the human PPI. After collecting the symptom-associated proteins and herb-target data, we designed multiple network-based metrics to unveil the network patterns connecting them, including symptom localization, symptom-symptom relation, and herb-symptom proximity. We validated these relations by showing that our network-based framework captures symptom-disease relations and herb-symptom effectiveness, leveraging online public databases and a hospital inpatient dataset. We highlight the potential application of our work in predicting herb-symptom treatments.

Fig. 2.. Symptom pattern in the human PPI.

(A) Schematic illustrating that proteins associated with a symptom form localized modules on the human protein interactome, and the inter-module network distance is indicative of symptom similarity. (B) Distribution of largest-connected-component z score formed by symptom-associated proteins, for 174 symptoms. One hundred eight out of 174 symptoms form significantly clustered local modules (z > 1.6). The blue dotted lines indicate z = ±1.6, and the red dotted line indicates z = 0. (C) Distribution of network separation (S_ab) of all symptom pairs. The average ⟨S_ab⟩ is larger than zero, the random expectation. This suggests that different symptoms perturb different/specific regions in the PPI, by forming modules distant from each other. (D) The average interactome network distance (D_ab) of a symptom pair negatively correlates with the symptoms’ co-occurrence in diseases (co-disease count), with Pearson’s correlation −0.46. Each dot represents a symptom pair. We highlight in red examples of similar and co-occurring symptoms, such as fever-diarrhea (D_ab = 1.25, co-disease count = 1278), fatigue-pain (D_ab = 1.25, co-disease count = 1163), and dizziness-headache (D_ab = 1.32, co-disease count = 917). We also highlight in green an example symptom pair with high network distance and less co-occurrence, eye pain and anorexia (D_ab = 2.91, co-disease count: 13). (E) The interactome network distance of a symptom pair negatively correlates with the biological similarity of the genes associated with the symptoms.

Fig. 3.. Herb-symptom network proximity predicts effectiveness.

(A) Schematics of the herb-symptom network proximity metric, based on shortest paths between herb-chemical targets and symptom-associated proteins in the protein interactome. (B) Workflow of the multimodal approach for eight herb-symptom proximity pipelines, with definitions of the metrics. (C) Results of the eight pipelines of network metrics for herb-symptom pairs categorized as indicated or non-indicated. Indicated herb-symptom pairs (orange bars) show lower proximity metrics (shorter network distance) than the non-indicated herb-symptom pairs (blue bars), consistently over all eight pipelines. (D) AUC (area under the receiver operating characteristic curve) performance evaluation of the eight herb-symptom proximity pipelines, using the known herb-symptom indications as positive cases. (E) Example demonstrating herb-symptom proximity: Herbs Yinchaihu and Huangbai are proximal (having highly negative network proximity z score) to the fever symptom and are used to treat fever in practice, whereas the Chuanwu herb is distant (having positive z score) to fever but proximal to abdominal pain, thus it is not used to treat fever but to treat abdominal pain.

Fig. 4.. Validation of network medicine framework with hospital inpatient data.

(A) Patient symptom data show a negative Pearson’s correlation between symptom pair relative risk (in log scale) and network distance D_ab, validating that shorter network distance between symptoms is predictive of their co-occurrence. (B) Herbs used by doctors in patient data (orange boxes) are significantly more proximal to symptoms than herbs not used in patient data (blue boxes), consistently observed over all eight pipelines, indicating that network proximity captures doctors’ knowledge. (C) The 986 effective herb-symptom pairs identified from the binary effectiveness metric (green boxes) have lower network metrics than other herb-symptom pairs (gray boxes) in all eight pipelines, i.e., network proximity metrics can predict the effective herb-symptom pairs. (D) The 86 effective herb-symptom pairs identified from propensity score matching (green boxes) have lower network metrics than other herb-symptom pairs (gray boxes) in seven of all eight pipelines, i.e., network proximity metrics can predict the significantly effective herb-symptom pairs.