Complex tsunamigenic near-trench seafloor deformation during the 2011 Tohoku-Oki earthquake.

The near-trench coseismic rupture behaviour of the 2011 Tohoku-Oki earthquake remains poorly understood due to the scarcity of near-field observations. Differential bathymetry offers a unique approach to studying offshore coseismic seafloor deformation but has a limited horizontal resolution. Here we use differential bathymetry estimates with improved horizontal resolutions to investigate near-trench coseismic slip behaviours in the 2011 Tohoku-Oki earthquake. In the main rupture region, a velocity-strengthening behaviour in the shallow fault is observed. By contrast, the seafloor uplift decreases towards the trench, but the trend inverts near the backstop interface outcrop, revealing significant off-fault deformation features. Amongst various competing off-fault effects observed, we suggest that inelastic deformation plays a predominant role in near-trench tsunami excitation. Large trench-bleaching rupture is also observed immediately north of 39°, delimiting the northern extent of the main rupture region. Overall, striking spatial heterogeneity of the shallow rupture behaviour is revealed for the region.

Multiple Mechanistic Models Reveal the Neuroprotective Effects of Diterpene Ginkgolides against Astrocyte-Mediated Demyelination via the PAF-PAFR Pathway.

Currently, therapies for ischemic stroke are limited. Ginkgolides, unique Folium Ginkgo components, have potential benefits for ischemic stroke patients, but there is little evidence that ginkgolides improve neurological function in these patients. Clinical studies have confirmed the neurological improvement efficacy of diterpene ginkgolides meglumine injection (DGMI), an extract of Ginkgo biloba containing ginkgolides A (GA), B (GB), and K (GK), in ischemic stroke patients. In the present study, we performed transcriptome analyses using RNA-seq and explored the potential mechanism of ginkgolides in seven in vitro cell models that mimic pathological stroke processes. Transcriptome analyses revealed that the ginkgolides had potential antiplatelet properties and neuroprotective activities in the nervous system. Specifically, human umbilical vein endothelial cells (HUVEC-T1 cells) showed the strongest response to DGMI and U251 human glioma cells ranked next. The results of pathway enrichment analysis via gene set enrichment analysis (GSEA) showed that the neuroprotective activities of DGMI and its monomers in the U251 cell model were related to their regulation of the sphingolipid and neurotrophin signaling pathways. We next verified these in vitro findings in an in vivo cuprizone (CPZ, bis(cyclohexanone)oxaldihydrazone)-induced model. GB and GK protected against demyelination in the corpus callosum (CC) and promoted oligodendrocyte regeneration in CPZ-fed mice. Moreover, GB and GK antagonized platelet-activating factor (PAF) receptor (PAFR) expression in astrocytes, inhibited PAF-induced inflammatory responses, and promoted brain-derived neurotrophic factor (BDNF) and ciliary neurotrophic factor (CNTF) secretion, supporting remyelination. These findings are critical for developing therapies that promote remyelination and prevent stroke progression.

Assessment of the Seafloor Topography Accuracy in the Emperor Seamount Chain by Ship-Based Water Depth Data and Satellite-Based Gravity Data.

The seafloor topography estimation is very important, while the bathymetry data and gravity data are scarce and uneven, which results in large errors in the inversion of the seafloor topography. In this paper, in order to reduce the influence of errors and improve the accuracy of seafloor inversion, the influence of different resolution data on the inversion topography in the Emperor Seamount Chain are investigated by combining ship water depth data and satellite gravity anomaly data released by SIO V29.1. Through the comparison of different resolution models, it is found that the choice of resolution affects the accuracy of the inversion terrain model. An external comparison is presented by using the international high-precision topography data and check points observations. The results show that with the increase in resolution, the fitting residuals obtained by the scale factor are optimized, and the precision of the terrain model is gradually approaching the S&S V19.1 and GEBCO-2020 models, but is better than the ETOPO1 and SRTM 30 models. By external validation using the check points, the standard deviation of the difference was reduced from 58.92 m to 47.01 m, and the correlation between the inverted terrain and the NGDC grid model was increased from 0.9545 to 0.9953. For recovering the Emperor Seamount Chain terrain, the relative error was gradually decreased with the improvement of resolution. The maximum relative error is reduced from 1.09 of 2' topography to 0.74 of 10″ topography, and the average error is reduced from 0.04 to 0.01 with an improvement by 32.11%. The terrain error between the inverted terrain model and the NGDC grid model is gradually reduced, while the error percentage is increasing by 25.51% and 21.49% in the range of -50 to 50 m and -100 to 100 m, respectively. Furthermore, the sparse area can effectively reduce the terrain standard deviation and improve the terrain correlation by increasing the resolution through the analysis of different density subsets. The error was decreased most significantly in sparse and dense homogeneous regions with increasing resolution.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning.

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Systems pharmacology reveals the multi-level synergetic mechanism of action of Ginkgo biloba L. leaves for cardiomyopathy treatment.

ETHNOPHARMACOLOGICAL RELEVANCE: Cardiomyopathy is a common cause of heart failure and may lead to increased risk of sudden cardiac death, lacking simple, safe and effective treatment strategies due to unclear pathogenesis. Ginkgo biloba L. leaves (GBLs), a traditional Chinese medicine (TCM), has been widely used in clinical medicine for improving blood circulation, and was demonstrated to be effective on cardiomyopathy in preclinical studies. However, because of the widely known holistic therapeutic philosophy via multi-target and multi-pathway effect for most TCMs, to explore its underlying molecular mechanisms of action (MoA) remains a great challenge. AIM OF STUDY: Decipher the underlying MoA of GBLs for cardiomyopathy treatment: Study design and methods: An integrated systems pharmacology framework was employed to screen potential active compounds, identify therapeutic targets, explore the action pathways and verify mechanisms of GBLs with in vitro experiments. RESULTS: We firstly confirmed the therapeutic effect of GBLs on cardiomyopathy and subsequently screened 27 active compounds from GBLs according to their pharmacokinetic properties. Then Probability Ensemble Approach was applied to identify the compound combinations that exert synergetic effect from GBLs. Network analysis and functional enrichment analysis demonstrated that these compounds exhibit synergistic therapeutic effect by acting on multiple targets and thereby regulating multiple pathways mainly involved in pro-survival, anti-apoptotic and anti-inflammatory processes. Finally, using a doxorubicin-induced myocardial injury model, therapeutic effect of ginkgolide A, ginkgolide B, isorhamnetin, as well as their synergistic effect on PI3K-AKT and NF-κB signaling pathways were validated in vitro. Importantly, we demonstrated that Ginkgo diterpene lactone meglumine injection (GDJ), an approved injection derived from GBLs, could be a promising agent for cardiomyopathy treatment. CONCLUSION: Collectively, the multi-level synergetic mechanism of GBLs on cardiomyopathy treatment was demonstrated with systems pharmacology approach, providing a paradigm for deciphering the complicated MoA of TCMs.

Systematic Evaluation of the Diagnostic and Prognostic Significance of Competitive Endogenous RNA Networks in Prostate Cancer.

Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes. Combination of expression of the 10 ceRNAs with a logistic model achieved AUC of ROC and PR curve up to ∼96 and 99% in excluding normal prostate samples, respectively. Additionally, a risk score formula constructed with the ceRNAs exhibited significant association with disease-free survival. More importantly, utilizing the expression of RNAs in the core ceRNA network as a molecular signature, the TCGA-PRAD cohort was divided into four novel clinically relevant subgroups with distinct expression patterns, highlighting a feasible way for improving patient stratification in the future. Overall, we constructed a PCa-specific core ceRNA network, which provides diagnostic and prognostic value.

Anthropogenic impacts on the decreasing sediment loads of nine major rivers in China, 1954-2015.

Over the past 60 years, because of the combined impacts of human activities and climate change, the sediment load of the nine major rivers (the Yellow, Yangtze, Pearl, Songhuajiang, Liaohe, Haihe, Huaihe, Qiantangjiang, and Minjiang rivers) in China has dropped by 85%, which had caused serious environmental problems such as reservoir siltation and estuary erosion. However, quantitatively evaluating the impact of different human activities on this decline is still an unsolved and complex problem. Based on a big new data set from 27 gauge stations and 469 meteorological stations, we established five methods to assess sediment loss of China's nine major rivers. During 1954-2015, the sediment load into the marginal seas via these nine rivers was characterized by a marked decline, from 1.95 Gt/yr (1954-1968) to 1.40 Gt/yr (1969-1985), 890 Mt/yr (1986-1998), 450 Mt/yr (1999-2003), and 310 Mt/yr (2004-2015), reflecting an 85% decrease between 1954-1968 and 2004-2015. The cumulative sediment load into the marginal seas was ~71.0 Gt, constituting ~7% of the global sediment load. The Yellow River, Yangtze River, Pearl River, and other six major rivers contributed 40.9 (58%), 22.9 (32%), 4.1 (6%), and 2.96 Gt (4%), respectively. We estimate that ~53.0 Gt of terrestrial sediment has been retained on the mainland China because of human activities, with reservoir trapping, water resource utilization, and water-soil conservation measures accounting for 45.5%, 29%, and 25.5% of the total, respectively. The contribution of climatic factors was assessed to be secondary. This drastic reduction in river sediment load could lead to a series of negative effects on deltas: decreased sediment delivery, coastal erosion, aggravated reaction to storm disasters, and most importantly, loss of new land for human use. In addition, the large amounts of sediment trapping by reservoirs over long periods will cause siltation that could reduce reservoir water storage capacity.

Systems-level analysis identifies key regulators driving epileptogenesis in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is the most prevalent and often devastating form of epilepsy. The molecular mechanism underlying the development of TLE remains largely unclear, which hinders the discovery of effective antiepileptogenic drugs. Here we adopted a systems-level approach integrating transcriptomic profiles of three epileptogenesis stages to identify key regulators underlying epilepsy progression. Associating stage-specific gene meta-signatures with brain cell-specialized modules revealed positive regulation of glial migration and adhesion, cytokine production, and neuron death, and downregulation of synaptic transmission and ion transport during epileptogenesis. We identified 265 key regulators driving these processes and 72 of them were demonstrated associating with seizure frequency and/or hippocampal sclerosis in human TLE. Importantly, the upregulation of FAM107A, LAMB2, LTBP1 and TGIF1, which are mainly involved in nervous system development, were found contributing to both conditions. Our findings present the evolution landscape of epileptogenesis and provide candidate regulators that may serve as potential antiepileptogenic targets.

Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea.

Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.

Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision.

To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method. Secondly, aiming to reduce mismatch, we improve the semi-global stereo matching method by strictly constraining the matching in the valid target area and then optimizing the basic disparity map within each super-pixel area using the least squares fitting interpolation method. Finally, based on the optimized disparity map, triangulation principle is used to calculate the three-dimensional data of the target and the 3D structure and color information of the target can be given by MeshLab. The experimental results show that for a specific size underwater target, the system can achieve higher measurement accuracy and better 3D reconstruction effect within a suitable distance.

Correction to: dengue virus causes changes of MicroRNA-genes regulatory network revealing potential targets for antiviral drugs.

After publication of the article [1], it has been brought to our attention that an author's name was spelt incorrectly in the original published article. Yonghua Wang was previously spelt "Yonghua Wan". This has now been corrected in the revised version of the article.

Dengue virus causes changes of MicroRNA-genes regulatory network revealing potential targets for antiviral drugs.

BACKGROUND: Dengue virus (DENV) is an increasing global health threat and associated with induction of both a long-lived protective immune response and immune-suppression. So far, the potency of treatment of DENV via antiviral drugs is still under investigation. Recently, increasing evidences suggest the potential role of microRNAs (miRNAs) in regulating DENV. The present study focused on the function of miRNAs in innate insusceptible reactions and organization of various types of immune cells and inflammatory responses for DENV. Three drugs were tested including antiviral herbal medicine ReDuNing (RDN), Loratadine (LRD) and Acetaminophen. RESULTS: By the microarray expression of miRNAs in 165 Patients. Results showed that 89 active miRNAs interacted with 499 potential target genes, during antiviral treatment throughout the critical stage of DENV. Interestingly, reduction of the illness threats using RDN combined with LRD treatment showed better results than Acetaminophen alone. The inhibitions of DENV was confirmed by decrease concentrations of cytokines and interleukin parameters; like TNF-α, IFN-γ, TGF-ß1, IL-4, IL-6, IL-12, and IL-17; after treatment and some coagulants factors increased. CONCLUSIONS: This study showed a preliminary support to suggest that the herbal medicine RDN combined with LRD can reduce both susceptibility and the severity of DENV.

In silico-based screen synergistic drug combinations from herb medicines: a case using Cistanche tubulosa.

Neuroinflammation is characterized by the elaborated inflammatory response repertoire of central nervous system tissue. The limitations of the current treatments for neuroinflammation are well-known side effects in the clinical trials of monotherapy. Drug combination therapies are promising strategies to overcome the compensatory mechanisms and off-target effects. However, discovery of synergistic drug combinations from herb medicines is rare. Encouraged by the successfully applied cases we move on to investigate the effective drug combinations based on system pharmacology among compounds from Cistanche tubulosa (SCHENK) R. WIGHT. Firstly, 63 potential bioactive compounds, the related 133 direct and indirect targets are screened out by Drug-likeness evaluation combined with drug targeting process. Secondly, Compound-Target network is built to acquire the data set for predicting drug combinations. We list the top 10 drug combinations which are employed by the algorithm Probability Ensemble Approach (PEA), and Compound-Target-Pathway network is then constructed by the 12 compounds of the combinations, targets, and pathways to unearth the corresponding pharmacological actions. Finally, an integrating pathway approach is developed to elucidate the therapeutic effects of the herb in different pathological features-relevant biological processes. Overall, the method may provide a productive avenue for developing drug combination therapeutics.

Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs.

Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery.

Systems pharmacology exploration of botanic drug pairs reveals the mechanism for treating different diseases.

Multi-herb therapy has been widely used in Traditional Chinese medicine and tailored to meet the specific needs of each individual. However, the potential molecular or systems mechanisms of them to treat various diseases have not been fully elucidated. To address this question, a systems pharmacology approach, integrating pharmacokinetics, pharmacology and systems biology, is used to comprehensively identify the drug-target and drug-disease networks, exemplified by three representative Radix Salviae Miltiorrhizae herb pairs for treating various diseases (coronary heart disease, dysmenorrheal and nephrotic syndrome). First, the compounds evaluation and the multiple targeting technology screen the active ingredients and identify the specific targets for each herb of three pairs. Second, the herb feature mapping reveals the differences in chemistry and pharmacological synergy between pairs. Third, the constructed compound-target-disease network explains the mechanisms of treatment for various diseases from a systematic level. Finally, experimental verification is taken to confirm our strategy. Our work provides an integrated strategy for revealing the mechanism of synergistic herb pairs, and also a rational way for developing novel drug combinations for treatments of complex diseases.

Systems Pharmacology Dissection of the Integrated Treatment for Cardiovascular and Gastrointestinal Disorders by Traditional Chinese Medicine.

Though cardiovascular diseases (CVDs) and gastrointestinal disorders (GIDs) are different diseases associated with different organs, they are highly correlated clinically. Importantly, in Traditional Chinese Medicine (TCM), similar treatment strategies have been applied in both diseases. However, the etiological mechanisms underlying them remain unclear. Here, an integrated systems pharmacology approach is presented for illustrating the molecular correlations between CVDs and GIDs. Firstly, we identified pairs of genes that are associated with CVDs and GIDs and found that these genes are functionally related. Then, the association between 115 heart meridian (HM) herbs and 163 stomach meridian (SM) herbs and their combination application in Chinese patent medicine was investigated, implying that both CVDs and GIDs can be treated by the same strategy. Exemplified by a classical formula Sanhe Decoration (SHD) treating chronic gastritis, we applied systems-based analysis to introduce a drug-target-pathway-organ network that clarifies mechanisms of different diseases being treated by the same strategy. The results indicate that SHD regulated several pathological processes involved in both CVDs and GIDs. We experimentally confirmed the predictions implied by the effect of SHD for myocardial ischemia. The systems pharmacology suggests a novel integrated strategy for rational drug development for complex associated diseases.

A novel systems pharmacology platform to dissect action mechanisms of traditional Chinese medicines for bovine viral diarrhea disease.

Due to the large direct and indirect productivity losses in the livestock industry caused by bovine viral diarrhea (BVD) and the lack of effective pharmacological therapies, developing an efficient treatment is extremely urgent. Traditional Chinese medicines (TCMs) that simultaneously address multiple targets have been proven to be effective therapies for BVD. However, the potential molecular action mechanisms of TCMs have not yet been systematically explored. In this work, take the example of a herbal remedy Huangqin Zhizi (HQZZ) for BVD treatment in China, a systems pharmacology approach combining with the pharmacokinetics and pharmacodynamics evaluation was developed to screen out the active ingredients, predict the targets and analyze the networks and pathways. Results show that 212 active compounds were identified. Utilizing these lead compounds as probes, we predicted 122 BVD related-targets. And in vitro experiments were conducted to evaluate the reliability of some vital active compounds and targets. Network and pathway analysis displayed that HQZZ was effective in the treatment of BVD by inhibiting inflammation, enhancing immune responses in hosts toward virus infection. In summary, the analysis of the complete profile of the pharmacological activities, as well as the elucidation of targets, networks and pathways can further elucidate the underlying anti-inflammatory, antiviral and immune regulation mechanisms of HQZZ against BVD.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species.

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

Large-scale Direct Targeting for Drug Repositioning and Discovery.

A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug's affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

Systems Pharmacology Dissecting Holistic Medicine for Treatment of Complex Diseases: An Example Using Cardiocerebrovascular Diseases Treated by TCM.

Holistic medicine is an interdisciplinary field of study that integrates all types of biological information (protein, small molecules, tissues, organs, external environmental signals, etc.) to lead to predictive and actionable models for health care and disease treatment. Despite the global and integrative character of this discipline, a comprehensive picture of holistic medicine for the treatment of complex diseases is still lacking. In this study, we develop a novel systems pharmacology approach to dissect holistic medicine in treating cardiocerebrovascular diseases (CCDs) by TCM (traditional Chinese medicine). Firstly, by applying the TCM active ingredients screened out by a systems-ADME process, we explored and experimentalized the signed drug-target interactions for revealing the pharmacological actions of drugs at a molecule level. Then, at a/an tissue/organ level, the drug therapeutic mechanisms were further investigated by a target-organ location method. Finally, a translational integrating pathway approach was applied to extract the diseases-therapeutic modules for understanding the complex disease and its therapy at systems level. For the first time, the feature of the drug-target-pathway-organ-cooperations for treatment of multiple organ diseases in holistic medicine was revealed, facilitating the development of novel treatment paradigm for complex diseases in the future.