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

Clustering Algorithm Based on Sparse Feature Vector without Specifying Parameter

Parameter setting is an essential factor affecting algorithm performance in data mining techniques. CABOSFV is an efficient clustering algorithm which can cluster binary data with sparse features, but it is challenging to specify the threshold parameter. To solve the difficulty of parameter decision, a clustering algorithm based on sparse feature vector without specifying parameter (CASP) is proposed in this paper. The calculation method of an upper limit of threshold is firstly defined to determine the range of threshold. Furthermore, we use the sparseness index to sort the data and conduct the clustering process based on the adjusted sparse feature vector after data sorting. An interval search strategy is adopted to find a suitable threshold within the defined threshold range, and the clustering result with the selected suitable parameter is the outcome. Experiments on 7 UCI datasets demonstrate that the clustering results of the CASP algorithm are superior to other baselines in terms of both effectiveness and efficiency. CASP not only simplifies the parameter decision process, but also obtains desirable clustering results quickly and stably, which shows the practicability of the algorithm

Database development and mechanistic study of traditional Chinese medicine by computer

Master'sMASTER OF SCIENC

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

A systems approach to sub-typing of rheumatoid arthritis

The current health care system is severely challenged by for instance rising costs, fewer new blockbuster drugs and increasing numbers of hospitalizations due to side effects. Especially in the area of chronic diseases the current disease fighting strategy is failing and a more personalized medicine approach is needed. In this thesis new sub-types of rheumatoid arthritis are characterized with metabolomics analysis and symptoms patterns. The sub-types are based on diagnostic knowledge from Chinese medicine. The two sub-types of RA patients were found to have differences in apoptosis regulation of T-cells and differences in urine acylcarnitine levels. A questionnaire was designed to distinguish the two sub-types and to evaluate symptom patterns of arthritis patients. In the future the response to treatment of these sub-types of patients can be studied and specific treatment can be targeted to these sub-types.LEI Universiteit LeidenArtrose & Reuma StichtingAnalyse en stochastie

National eHealth system – platform for preventive, predictive and personalized diabetes care

National eHealth System, covering all citizens and all healthcare levels in Republic of Macedonia, was introduced in July 2013, has been internationally recognized System for successful reduction of waiting times and instrumental in the management of national healthcare resources. For the first time, National Diabetes Committee, formed in February 2015 according to the Law on healthcare and being overall responsible for the diabetes care in the country, was able to derive exact figures on the national diabetes prevalence from the System, instead of extrapolations used before, serving as a basis for development of strategies for prediction and prevention of diabetic complications, as well as for personalized diabetes care. Number of diabetes cases identified through the National eHealth System in June 2015 was 84,568 (4.02 % of total population), 36,119 males (3.42 % of total male population) and 48,449 females (4.61% of total female population). Age stratified diabetes prevalence was as follows: less than 20 years – 549 cases (0.11 % of respective population), 20-39 years – 3,202 (0.49 %), 40-59 years – 26,561 (4.58 %), 60-79 years – 48,470 (14.57 %), 80 years or more – 5,786 (12.96 %). Addition of parameters for metabolic control and diabetic complications in the System is under way, further facilitating the modeling of diabetes treatment, metabolic control and the outcomes. Inclusion of pre-diabetes patients (IGT and IFG) is also planned, thus providing opportunity to also focus healthcare activities for prevention of progression into overt type 2 diabetes

Predictive Learning from Real-World Medical Data: Overcoming Quality Challenges

Randomized controlled trials (RCTs) are pivotal in medical research, notably as the gold standard, but face challenges, especially with specific groups like pregnant women and newborns. Real-world data (RWD), from sources like electronic medical records and insurance claims, complements RCTs in areas like disease risk prediction and diagnosis. However, RWD's retrospective nature leads to issues such as missing values and data imbalance, requiring intensive data preprocessing. To enhance RWD's quality for predictive modeling, this thesis introduces a suite of algorithms developed to automatically resolve RWD's low-quality issues for predictive modeling. In this study, the AMI-Net method is first introduced, innovatively treating samples as bags with various feature-value pairs and unifying them in an embedding space using a multi-instance neural network. It excels in handling incomplete datasets, a frequent issue in real-world scenarios, and shows resilience to noise and class imbalances. AMI-Net's capability to discern informative instances minimizes the effects of low-quality data. The enhanced version, AMI-Net+, improves instance selection, boosting performance and generalization. However, AMI-Net series initially only processes binary input features, a constraint overcome by AMI-Net3, which supports binary, nominal, ordinal, and continuous features. Despite advancements, challenges like missing values, data inconsistencies, and labeling errors persist in real-world data. The AMI-Net series also shows promise for regression and multi-task learning, potentially mitigating low-quality data issues. Tested on various hospital datasets, these methods prove effective, though risks of overfitting and bias remain, necessitating further research. Overall, while promising for clinical studies and other applications, ensuring data quality and reliability is crucial for these methods' success

Analysis of Molecular Changes in 2 Types of Non-Tumor Adjacent Breast Identified by Nucleotide Excision Repair in Early Stage Breast Cancer

NER plays an important role in remediating DNA damage. Latimer et al., have previously shown nucleotideexcision repair (NER) to be intrinsically deficient in stage I sporadic BC by function and gene expression. Previous work performed on 12 isogenically matched stage I tumors and non-tumor adjacent (NTA) samples identified 2 types of NTA tissue with regard to functional NER. One type that represented 75% ofthe samples, had lower NER capacity, similar to the tumor (Low: Low pair). The other type had high NER relative to the tumor (High: Low pair). Two isogenic NTA/tumor cell line pairs representing these 2 types ofNTA were identified by the UDS assay for downstream molecular analyses. Expression of the 20 canonical NER genes using microarray analysis was consistent with functional NER of the Low: Low and High: Low cell lines. Findings from expression microarray were validated using RNA sequencing, where 16/20 genes were significantly higher in the NTA line of the High: Low pair compared to the tumor line, but none of the 20 genes were significantly different in expression among the Low: Low pair. Protein expression was also evaluated for RPA3, XPC and RAD23B, however only RAD23B showed promising trends. We believe the mechanism of downregulation of NER genes was epigenetic based on downregulation in multiple genes and multiple patients. DNA methylation was explored as the mechanism of this phenomenon. Using MethylationEPIC array, analysis of promoter level, gene level and CpG island level methylation no correlation between methylation and gene expression in our cell line pairs. DDB1 showed differential methylation among the both High: Low and Low: Low pairs but in a direction opposite to gene expression, indicating possible inhibition of a repressor at its promoter region. RNA sequencing allowed us to explore the presence of single nucleotide variants in the 20 NER genes along with other BC genes. We discovered notable variants inERCC2, ERCC5, ERCC6 as well as in BRCA1, CHK1 and ATR, which warrant further investigation using The Cancer Genome Atlas. Finally, we were able to construct person-specific maps or our own “Vogelsteinograms” for breast carcinogenesis

Towards personalized immunotherapy : development of in vitro models for imaging natural killer cell behavior in the tumor microenvironment

Tremendous advances in the tumor immunology field have transformed immunotherapy from a promising approach to a standard clinical practice. However, a subset of cancer patients is non-responsive to immunotherapy. More research is therefore needed to understand the mechanisms underlying tumor resistance to immunotherapeutic treatments. The aim of this doctoral work was to develop new tools to study the mechanisms of cancer immunosurveillance and to test immunotherapeutic treatments in vitro. In this thesis, I describe the methods developed, and I discuss the main biological findings obtained by using these methods. The thesis is organized as follows. A short historical background of immunotherapy is provided in Chapter 1. Chapter 2 describes the principles of NK cell-mediated cancer immunosurveillance, and provides an overview on rare cancers, mainly focusing on sarcoma. The research aims are listed in Chapter 3. In Chapter 4, I describe the cell culture methods and cell analysis techniques relevant for my doctoral work. In Chapter 5, I describe the methods we developed to culture tumor spheroids in vitro using ultrasonic standing waves in microwell chips, focusing on the theory, design, and applications. Chapter 6 and Chapter 7 focus on the biological findings obtained using our platform in combination with traditional immunological methods, followed by future implementations discussed in Chapter 8. The constituent papers are provided at the end of the thesis. In Paper I, we combined the use of the microwell chip, ultrasonic standing waves and a protein-repellent polymer coating to enable the production of spheroids from multiple cell types. In absence of cell adhesion to the chip, spheroids could be collected and further analyzed by off-the-chip techniques. In Paper II, we designed a novel multichambered microwell chip to perform multiplexed fluorescence screening of two- or three-dimensional cell cultures. The platform allows the direct assessment of drug or immune cell cytotoxic efficacy, making it a promising tool for individualized cytotoxicity tests for personalized medicine. In Paper III, we investigate the function of PVR receptors in NK cells interacting with renal carcinoma spheroids, and the impact of PVR in NK cell-based cellular immunotherapy. We demonstrated that variations in PVR expression are primarily recognized by the inhibitory receptor TIGIT, while DNAM-1 strongly contributes to NK cell activation mainly through PVR-independent mechanisms. We performed NK cell-based cytotoxicity assays against renal carcinoma spheroids in the microwell chip. Anti-TIGIT treatment was effective only for TIGIThigh NK cells both when used as monotherapy or in combination with other drugs, suggesting that only a fraction of patients might respond to anti-TIGIT therapy. In Paper IV, a similar approach was used with primary sarcomas. We cultured patient-derived sarcoma spheroids and tested NK cell-based immunotherapy in the microwell chip, either alone or in combination with antibody therapy, and we identified promising treatment combinations. In Paper V, we applied the use of expansion microscopy to visualize NK cells infiltrating renal carcinoma spheroids. In conclusion, our multi-disciplinary work shows the development of new imaging-based platform and its use to study the mechanisms of NK cell-mediated tumor surveillance and for personalized therapy