Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine

TL;DR

This study introduces a network medicine framework that uncovers the systemic principles of Traditional Chinese Medicine, linking herbs and symptoms through protein interaction networks, and predicts herb effectiveness and potential new treatments.

Contribution

The paper develops a novel network-based approach to understand TCM mechanisms at a system level and predicts herb-symptom effectiveness using human protein interactome data.

Findings

Symptom-associated genes cluster into localized modules in the interactome.

Shorter network distances between symptom modules correlate with symptom co-occurrence.

Herb-target proximity to symptom modules predicts treatment effectiveness.

Abstract

Traditional Chinese medicine (TCM) relies on natural medical products to treat symptoms and diseases. While clinical data have demonstrated the effectiveness of selected TCM-based treatments, the mechanistic root of how TCM herbs treat diseases remains largely unknown. More importantly, current approaches focus on single herbs or prescriptions, missing the high-level general principles of TCM. To uncover the mechanistic nature of TCM on a system level, in this work we establish a generic network medicine framework for TCM from the human protein interactome. Applying our framework reveals a network pattern between symptoms (diseases) and herbs in TCM. We first observe that genes associated with a symptom are not distributed randomly in the interactome, but cluster into localized modules; furthermore, a short network distance between two symptom modules is indicative of the symptoms' co-occurrence and similarity. Next, we show that the network proximity of a herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment. Finally, we identified novel herb-symptom pairs in which the herb's effectiveness in treating the symptom is predicted by network and confirmed in hospital data, but previously unknown to the TCM community. These predictions highlight our framework's potential in creating herb discovery or repurposing opportunities. In conclusion, network medicine offers a powerful novel platform to understand the mechanism of traditional medicine and to predict novel herbal treatment against diseases.