Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.Perinteinen kiinalainen lääketiede (TCM) on ilmeinen tehokkuus taudin hoidoissa ja on arvokas lähde uuden lääkkeen löytämiseen. TCM: n farmakologisten vaikutusten taustalla oleva mekanismi pysyy kuitenkin tuntemattomassa, koska TCM on monimutkainen järjestelmä, jossa on useita yrttejä ja ainesosia, jotka tulevat yhteen reseptilääkkeeksi. Siksi on kiireellistä soveltaa Laskennallisia työkaluja TCM: lle ymmärtämään TCM-teorioiden taustalla oleva mekanismi molekyylitasolla ja käyttävät kehittyneitä verkkoalgoritmeja tutkimaan mahdollisia tehokkaita ainesosia ja havainnollistavat TCM: n periaatteita järjestelmän biologisissa näkökohdissa. Tässä opinnäytetyössä pyrimme ymmärtämään monimutkaisten TCM-järjestelmien toimintamekanismia molekyylitasolla bioinformaattilla ja laskennallisilla työkaluilla. Tutkimuksessa kehitettiin koneen oppimiskehystä yrttien ja ainesosien meridialaisista. Lopuksi saavutimme korkean tarkkuuden meridiaaneista yrtteistä ja ainesosista, mikä viittaa meridiaaneihin ja ainesosien ja yrtteihin liittyvien molekyylipiirin välillä, erityisesti koneen oppimismalleihin tärkeimmät ominaisuudet. Toiseksi ehdoimme uuden verkon lähestymistavan TCM-kaavojen tutkimiseksi kvantitoimisella vuorovaikutteisten yrttiparien vuorovaikutuksen tutkimuksessa ⅱ käyttämällä viisi verkkoetäisyyttä, mukaan lukien lähin, lyhyt, keskus, ydin sekä erottaminen. Osoitimme, että ylä-yrttiparien etäisyys on lyhyempi kuin satunnaisten yrttiparien, mikä viittaa voimakkaaseen vuorovaikutukseen ihmisellä vuorovaikutteisesti. Lisäksi Center-menetelmät ainesosan tasolla ylittivät muut menetelmät. Se vihjeitä meille, että keskeiset ainesosat ovat tärkeässä asemassa yrtteissä. Kolmanneksi tutkimme yrttien tai ainesosien välisiä yhdistyksiä ja niiden tärkeitä biologisia ominaisuuksia tutkimuksessa III, kuten ominaisuudet, meridiaanit, rakenteet tai tavoitteet klustereiden kautta moniparite-verkoston yhteisön analyysistä. Löysimme, että kasviperäiset lääkkeet samoilla klusterien keskuudessa ovat yleensä samankaltaisia ominaisuuksissa, meridiaaneissa. Samoin saman klusterin ainesosat ovat samankaltaisempia rakenteissa ja proteiinin tavoitteessa. Yhteenvetona tämä opinnäytetyö aikoo rakentaa silta TCM-järjestelmän ja nykyaikaisten lääkevalmisteiden välillä laskentatyökaluilla, mukaan lukien Meridian-teorian koneen oppimismalli, TCM-kaavojen verkkomallinnus sekä kasviperäiset lääkkeet ja niiden ainesosat Osoitimme, että uusien laskennallisten lähestymistapojen soveltaminen integroidulle korkean suorituskyvyttömiehille tarjosivat TCM: n näkemyksiä ja nopeuttaisivat romaanin huumeiden löytöä sekä toistuvat TCM: stä

Estimates of carbon storage in grassland ecosystems on the Loess Plateau

Grassland ecosystems play an important role in the carbon (C) balance of arid and semi-arid regions. These ecosystems provide C for grass growth and soil microbial activities and represent one of the main sources of atmospheric C. In this study, we estimated the C density and storage of 223 sampling sites in grassland ecosystems on the Loess Plateau using elevation, vegetation indexes, precipitation, air temperature, day and night land surface temperature (LSTd and LSTn, respectively), evapotranspiration (ET), percent tree cover and the non-vegetated area to build decision regression tree and generalized linear regression models (GLMs). The results showed that the C density decreased from south to north and ranged from 0.22 to 29.29 kg C/m(2). The average amount of C stored in the ecosystems was 1.46 Pg. The typical steppe and forest steppe stored the most C, and the steppe desert stored the least. The soil (0-1 m) stored most of the organic C, accounting for > 90%, and the belowground biomass (BGB) contained > 3 times the amount of C as the aboveground biomass (AGB). This study provides reference information for the loss of C and associated mitigation strategies on the Loess Plateau

Large-scale soil organic carbon mapping based on multivariate modelling: The case of grasslands on the Loess Plateau

The Loess Plateau is considered one of the world's regions with severe soil erosion. Grasslands are widely distributed on the Loess Plateau, accounting for approximately 40% of the total area. Soil organic carbon (SOC) plays an important role in the terrestrial carbon cycle in this region. We compiled more than 1,000 measurements of plant biomass and SOC content derived from 223 field studies of grasslands on the Loess Plateau. Combined with meteorological factors (precipitation and air temperature) and the photosynthetically active radiation factor, the topsoil SOC contents of grasslands were predicted using the random forest (RF) regression algorithm. Predicted grassland SOC content (1.70-40.34gkg(-1)) decreased from the southeast to the northwest of the Loess Plateau, with approximately 1/5 of the grassland exhibiting values lower than 4gkg(-1). Observed SOC content was positively correlated with observed plant biomass, and for predicted values, this correlation was strong in the desert steppe and the steppe desert of rocky mountains. Air temperature was the most important factor affecting SOC contents in the RF model. Moreover, the residual error of observations and predictions increased as the grazing intensity varied from none to very severe in the temperate desert steppe, and this RF model may not perform well in plains. The use of the RF model for SOC prediction in Loess Plateau grasslands provides a reference for C storage studies in arid and semi-arid regions, and aboveground biomass and temperature should receive more attention due to increasing C sequestration

Interleukin-18 enhances vascular calcification and osteogenic differentiation of vascular smooth muscle cells through TRPM7 channel activation

Objective—Vascular calcification (VC) is an important predictor of cardiovascular morbidity and mortality. Osteogenic differentiation of vascular smooth muscle cells (VSMCs) is a key mechanism of VC. Recent studies show that IL-18 (interleukin-18) favors VC while TRPM7 (transient receptor potential melastatin 7) channel upregulation inhibits VC. However, the relationship between IL-18 and TRPM7 is unclear. We questioned whether IL-18 enhances VC and osteogenic differentiation of VSMCs through TRPM7 channel activation. Approach and Results—Coronary artery calcification and serum IL-18 were measured in patients by computed tomographic scanning and enzyme-linked immunosorbent assay, respectively. Primary rat VSMCs calcification were induced by high inorganic phosphate and exposed to IL-18. VSMCs were also treated with TRPM7 antagonist 2-aminoethoxy-diphenylborate or TRPM7 small interfering RNA to block TRPM7 channel activity and expression. TRPM7 currents were recorded by patch-clamp. Human studies showed that serum IL-18 levels were positively associated with coronary artery calcium scores (r=0.91; P<0.001). In VSMCs, IL-18 significantly decreased expression of contractile markers α-smooth muscle actin, smooth muscle 22 α, and increased calcium deposition, alkaline phosphatase activity, and expression of osteogenic differentiation markers bone morphogenetic protein-2, Runx2, and osteocalcin (P<0.05). IL-18 increased TRPM7 expression through ERK1/2 signaling activation, and TRPM7 currents were augmented by IL-18 treatment. Inhibition of TRPM7 channel by 2-aminoethoxy-diphenylborate or TRPM7 small interfering RNA prevented IL-18–enhanced osteogenic differentiation and VSMCs calcification. Conclusions—These findings suggest that coronary artery calcification is associated with increased IL-18 levels. IL-18 enhances VSMCs osteogenic differentiation and subsequent VC induced by β-glycerophosphate via TRPM7 channel activation. Accordingly, IL-18 may contribute to VC in proinflammatory conditions

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.Peer reviewe

H∞ filter for flexure deformation and lever arm effect compensation in M/S INS integration

ABSTRACTOn ship, especially on large ship, the flexure deformation between Master (M)/Slave (S) Inertial Navigation System (INS) is a key factor which determines the accuracy of the integrated system of M/S INS. In engineering this flexure deformation will be increased with the added ship size. In the M/S INS integrated system, the attitude error between MINS and SINS cannot really reflect the misalignment angle change of SINS due to the flexure deformation. At the same time, the flexure deformation will bring the change of the lever arm size, which further induces the uncertainty of lever arm velocity, resulting in the velocity matching error. To solve this problem, a H∞ algorithm is proposed, in which the attitude and velocity matching error caused by deformation is considered as measurement noise with limited energy, and measurement noise will be restrained by the robustness of H∞ filter. Based on the classical “attitude plus velocity” matching method, the progress of M/S INS information fusion is simulated and compared by using three kinds of schemes, which are known and unknown flexure deformation with standard Kalman filter, and unknown flexure deformation with H∞ filter, respectively. Simulation results indicate that H∞ filter can effectively improve the accuracy of information fusion when flexure deformation is unknown but non-ignorable

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

gkab438Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.Peer reviewe