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

30 Oct 2023

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.

INTRODUCTION

Understanding the therapeutic effects of traditional and natural medicine can lead to drug discoveries that reshape world welfare. For example, aspirin (acetylsalicylic acid) is extracted from willow bark, a traditional medicine practice since thousands of years ago (1). More recently, the 2015 Nobel Prize was given to the discovery of the malaria-treating artemisinin, extracted from qinghao(Artemisia annua), an herb used in traditional Chinese medicine (TCM) (2). As a famous practice of natural medicine, TCM is a personalized and holistic approach to treating diseases using natural medical products tailored to a patient’s symptoms, offering a rich pool of therapeutic candidates (3–5). However, although clinical data and studies of single herbs/prescriptions (6, 7) showed that certain TCM herbal treatments are effective, the general mechanistic principle of how TCM selects herbs to treat diseases remains unknown. Two major challenges exist in understanding the mechanistic root of TCM: (i) The lack of scientific foundation in classic TCM theory obstructs the understanding of TCM from a modern biomedical perspective; (ii) the complexity of herbs’ chemical composition and the often-unknown therapeutic protein targets of the chemicals makes conventional brute-force herb/chemical screening infeasible. Therefore, to understand and exploit the therapeutic mechanisms of TCM, it is necessary to establish a framework that can connect TCM knowledge to modern biomedical science and can handle the complexity of herb composition and target data.

An in silico strategy to understand the therapeutic effect of a natural product is to leverage the multiple protein targets of its composing chemicals via network pharmacology (8) and network medicine (9–12). Network pharmacology emphasizes the “network target, multi-components” paradigm that complements conventional research’s focus on single targets. This approach has helped researchers identify herbal chemicals with therapeutic potentials, better understand mechanisms of action, and discover drugs (13–15). However, existing TCM network pharmacology studies are limited to single herbs or single prescriptions, unable to explain the totality of TCM herb-disease relations. Moreover, many network pharmacology approaches only consider herbs/drugs that target disease genes directly, unable to account for network effects, e.g., when the impact of perturbing a target emerges further downstream and is mediated by protein interactions. Here, we propose avenues to overcome these limitations and improve our understanding of the therapeutic effects of natural products.

Network medicine leverages the human protein-protein interactome (PPI) to reveal disease and drug patterns (9). The PPI is a network consisting of nodes that are proteins that link to each other by physical (binding) interactions. Network medicine showed that disease-associated proteins tend to form locally clustered modules in the PPI, and shorter network distance between two disease modules is indicative of their comorbidity (16); moreover, drug efficacy can be predicted by leveraging the network relation between drug targets and disease modules (17, 18), leading to the development of drug-repurposing methodologies (19). These methods have been successful in identifying drug-repurposing candidates for coronavirus disease 2019 (COVID-19) and in understanding the network patterns of effective drugs (20). Furthermore, some of these tools have already affected clinical practice, like the network-based diagnostic tool available for patients with rheumatoid arthritis (21). Unlike earlier network pharmacology approaches, network medicine characterizes drug-disease relations by capturing the network effects based on protein interactions from the PPI, enabling more accurate predictions.

In this study, we develop a network medicine framework that theorizes the scientific basis of TCM as the topological relationship between symptom-associated proteins and herb targets on the protein interactome. By focusing on symptoms rather than diseases, our approach aligns with the TCM practice of diagnosing and treating patients based on their symptom phenotypes. We discover that proteins associated with a symptom tend to cluster into a local PPI module, and the network proximity between an herb’s targets and a symptom module is indicative of the herb’s effectiveness in treating the symptom. We validate our network medicine framework with empirical data and hospital patient data and highlight its potential in identifying herb discovery/repurposing opportunities. The design of our study is presented in Fig. 1.

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.

RESULTS

Symptom-associated proteins form modules in the protein interactome

Connecting TCM to the modern biomedical literature is challenging, due to the absence of the concept of “disease” in TCM. As a result, previous findings based on diseases, e.g., disease modules (16, 22, 23), are not directly applicable to TCM. To bridge this gap, we propose the use of symptom phenotypes to characterize the indications and effects of TCM and study the PPI pattern of symptoms. This approach is based on the fact that TCM clinical diagnosis and treatments are based on symptom phenotypes (24, 25), and is further supported by the availability of disease taxonomy and protein/gene association data in symptom phenotypes (26, 27).

We rely on a curated symptom-gene association dataset (28) (see Materials and Methods and data S1) to identify genes associated with each symptom, and then map these genes onto their corresponding proteins in the PPI (see Materials and Methods and data S2). We focus on 174 symptoms with at least 20 associated proteins. We find that for 108 of these 174 symptoms, their associated proteins form a connected component significantly larger than random expectation (z> 1.6; Fig. 2A and data S3). This suggests that the symptom-associated proteins agglomerate into a localized module in the PPI. In addition, we found that proteins associated with different symptoms are distant from each other (Fig. 2B), characterized by the average network separation metric (see Materials and Methods) S_ab = 0.23, larger than the random expectation of zero. This suggests that different symptoms perturb different regions of the PPI.