264 research outputs found

    Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

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    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

    Systems-based metabolomics of type 2 diabetes mellitus subtypes

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    This thesis was to combine metabolomics and Chinese medicine (CM) diagnosis to search for biomakers or metabolic profiles to subtype of type 2 diabetes (T2DM). An explorative study of 50 males with pre-diabetes was designed and two subtypes (A and B) could be identified by urine metabolomics. More metabolic disturbances were indicated in subtype B. The effects of rimonabant and a multi-component preparation (SUB885C), both with reported effects of regulating weight and the improvement on metabolic risks, were assessed by lipidomics on ApoE*3Leiden.CETP Mice. A 4-week rimonabant intervention brought a significant weight reduction, but moderate effects on lipid profile. SUB885C was able to produce multiple anti-atherogenic changes in lipids of the mice to improve metabolic parameters. A combined approach of lipidomics, biochemistry and herbal component profiling was used to evaluate the effects of the ginseng roots of 3__6 years on the regulation of dyslipidemia in diabetic Goto-Kakizaki rats. The more than 4 year ginseng proved to be valuable for drug development to regulate lipids. To conclude, the early metabolomics investigations performed in this thesis converged analytical bioscience, clinical approach and the diagnostic perspectives in other health system to provide the systems biology view on the pre-stage of T2DM.UBL - phd migration 201

    Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

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    The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

    Automated sample preparation for streamlined proteomic profiling of clinical specimens

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    The genetic information of all life is encoded within DNA molecules that are translated into functional entities, so-called proteins. They are responsible for operating and controlling a vast array of molecular mechanisms in any biological system and ubiquitous in (patho)physiology as a result. Besides, proteins are the primary target of drugs and can have a central role as biomarkers for diagnostic, prognostic, or predictive purposes. Here, many regulatory mechanisms and spatiotemporal influences prevent an accurate prediction of a proteins’ abundance and its associated functionality based on the genome information alone. Nowadays, it has become possible to measure and quantify thousands of proteins simultaneously, however, involving comprehensive sample preparation procedures. Currently, no universally standardized method enables a routine application of proteome profiling in a clinical environment. In this thesis, an automated workflow for the efficient processing of the most common and quantity-limited specimens is described. In order to demonstrate the usefulness of the end-to- end pipeline, which was termed autoSP3, it was applied to the proteome profiling of histologically defined and WHO recognized growth patterns of pulmonary adenocarcinoma (ADC) that currently have a limited clinical implication. Secondly, we investigated the proteome composition of a molecularly well-defined cohort of Ependymoma (EPN) pediatric brain tumors. Despite the availability of substantial NGS data and their ability to differentiate nine distinct subgroups, the majority of tumors remained without a functional insight. Here, the proteome profiling could provide a missing link and emphasize several subgroup-specific protein targets. In summary, this thesis describes the optimization of SP3 and its automation into a robust and cost-efficient pipeline for quantity-limited sample preparation and biological insight into the proteome composition of ADC growth patterns and EPN tumor subgroups

    Elucidating the Relationship between Chinese Medicine and Systems Biology: A Multi-Sited Ethnography

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    Ever since Chinese medicine encountered modern science in the late nineteenth century, the relationship between the two traditions has been extremely one-sided. At best, scientists perceived Chinese medicine as an archive of primitive knowledge from which potentially useful drugs could be extracted. Chinese medicine practitioners themselves, meanwhile, began a long struggle throughout the twentieth century to modernise their medicine with the help of Western theories and technology. At the turn of the twenty-first century, the involvement of systems biologists in Chinese medicine research created a new encounter, however, that, at least in the rhetoric of its actors, promised a very different kind of relationship: a match of two systems brought together by a shared interest in understanding life, health, illness and medicine as intrinsically complex and not amenable to the reductionist approaches of mainstream science. This research empirically investigates the nature of this relationship and how it emerged. It aims to contribute to the contemporary history of Chinese medicine by exploring the relationship between Chinese medicine and systems biology. This thesis argues that a heterogeneous network evolved, which is composed of human and nonhuman actors and their interactions created globally distributed research projects on Chinese medicine and systems biology. For the purpose of this research, a multi-sited ethnography was conducted over a period of eleven months and a literature survey was employed to trace the start and the development of this heterogeneous network. Ethnographic data reveals in four chapters on the rhetoric and perceptions of the actors, their involvement in Chinese medicine research, their laboratory practice, and the networks and political ties, which developed into a heterogeneous network of Chinese medicine and systems biology research. This research concludes that in the 2000s, a heterogeneous network emerged through the shared ideologies of systems thinking and holism. The shared ideologies set the groundwork for systems biologists to engage with Chinese medicine on its own terms, and created scientific practices, co-operation and funding opportunities between Europe and China

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

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    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

    Large–scale data–driven network analysis of human–plasmodium falciparum interactome: extracting essential targets and processes for malaria drug discovery

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    Background: Plasmodium falciparum malaria is an infectious disease considered to have great impact on public health due to its associated high mortality rates especially in sub Saharan Africa. Falciparum drugresistant strains, notably, to chloroquine and sulfadoxine-pyrimethamine in Africa is traced mainly to Southeast Asia where artemisinin resistance rate is increasing. Although careful surveillance to monitor the emergence and spread of artemisinin-resistant parasite strains in Africa is on-going, research into new drugs, particularly, for African populations, is critical since there is no replaceable drug for artemisinin combination therapies (ACTs) yet. Objective: The overall objective of this study is to identify potential protein targets through host–pathogen protein–protein functional interaction network analysis to understand the underlying mechanisms of drug failure and identify those essential targets that can play their role in predicting potential drug candidates specific to the African populations through a protein-based approach of both host and Plasmodium falciparum genomic analysis. Methods: We leveraged malaria-specific genome wide association study summary statistics data obtained from Gambia, Kenya and Malawi populations, Plasmodium falciparum selective pressure variants and functional datasets (protein sequences, interologs, host-pathogen intra-organism and host-pathogen inter-organism protein-protein interactions (PPIs)) from various sources (STRING, Reactome, HPID, Uniprot, IntAct and literature) to construct overlapping functional network for both host and pathogen. Developed algorithms and a large-scale data-driven computational framework were used in this study to analyze the datasets and the constructed networks to identify densely connected subnetworks or hubs essential for network stability and integrity. The host-pathogen network was analyzed to elucidate the influence of parasite candidate key proteins within the network and predict possible resistant pathways due to host-pathogen candidate key protein interactions. We performed biological and pathway enrichment analysis on critical proteins identified to elucidate their functions. In order to leverage disease-target-drug relationships to identify potential repurposable already approved drug candidates that could be used to treat malaria, pharmaceutical datasets from drug bank were explored using semantic similarity approach based of target–associated biological processes Results: About 600,000 significant SNPs (p-value< 0.05) from the summary statistics data were mapped to their associated genes, and we identified 79 human-associated malaria genes. The assembled parasite network comprised of 8 clusters containing 799 functional interactions between 155 reviewed proteins of which 5 clusters contained 43 key proteins (selective variants) and 2 clusters contained 2 candidate key proteins(key proteins characterized by high centrality measure), C6KTB7 and C6KTD2. The human network comprised of 32 clusters containing 4,133,136 interactions between 20,329 unique reviewed proteins of which 7 clusters contained 760 key proteins and 2 clusters contained 6 significant human malaria-associated candidate key proteins or genes P22301 (IL10), P05362 (ICAM1), P01375 (TNF), P30480 (HLA-B), P16284 (PECAM1), O00206 (TLR4). The generated host-pathogen network comprised of 31,512 functional interactions between 8,023 host and pathogen proteins. We also explored the association of pfk13 gene within the host-pathogen. We observed that pfk13 cluster with host kelch–like proteins and other regulatory genes but no direct association with our identified host candidate key malaria targets. We implemented semantic similarity based approach complemented by Kappa and Jaccard statistical measure to identify 115 malaria–similar diseases and 26 potential repurposable drug hits that can be 3 appropriated experimentally for malaria treatment. Conclusion: In this study, we reviewed existing antimalarial drugs and resistance–associated variants contributing to the diminished sensitivity of antimalarials, especially chloroquine, sulfadoxine-pyrimethamine and artemisinin combination therapy within the African population. We also described various computational techniques implemented in predicting drug targets and leads in drug research. In our data analysis, we showed that possible mechanisms of resistance to artemisinin in Africa may arise from the combinatorial effects of many resistant genes to chloroquine and sulfadoxine–pyrimethamine. We investigated the role of pfk13 within the host–pathogen network. We predicted key targets that have been proposed to be essential for malaria drug and vaccine development through structural and functional analysis of host and pathogen function networks. Based on our analysis, we propose these targets as essential co-targets for combinatorial malaria drug discovery

    Proteomics

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    Biomedical research has entered a new era of characterizing a disease or a protein on a global scale. In the post-genomic era, Proteomics now plays an increasingly important role in dissecting molecular functions of proteins and discovering biomarkers in human diseases. Mass spectrometry, two-dimensional gel electrophoresis, and high-density antibody and protein arrays are some of the most commonly used methods in the Proteomics field. This book covers four important and diverse areas of current proteomic research: Proteomic Discovery of Disease Biomarkers, Proteomic Analysis of Protein Functions, Proteomic Approaches to Dissecting Disease Processes, and Organelles and Secretome Proteomics. We believe that clinicians, students and laboratory researchers who are interested in Proteomics and its applications in the biomedical field will find this book useful and enlightening. The use of proteomic methods in studying proteins in various human diseases has become an essential part of biomedical research

    Role of adipose tissue in the pathogenesis and treatment of metabolic syndrome

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    © Springer International Publishing Switzerland 2014. Adipocytes are highly specialized cells that play a major role in energy homeostasis in vertebrate organisms. Excess adipocyte size or number is a hallmark of obesity, which is currently a global epidemic. Obesity is not only the primary disease of fat cells, but also a major risk factor for the development of Type 2 diabetes, cardiovascular disease, hypertension, and metabolic syndrome (MetS). Today, adipocytes and adipose tissue are no longer considered passive participants in metabolic pathways. In addition to storing lipid, adipocytes are highly insulin sensitive cells that have important endocrine functions. Altering any one of these functions of fat cells can result in a metabolic disease state and dysregulation of adipose tissue can profoundly contribute to MetS. For example, adiponectin is a fat specific hormone that has cardio-protective and anti-diabetic properties. Inhibition of adiponectin expression and secretion are associated with several risk factors for MetS. For this purpose, and several other reasons documented in this chapter, we propose that adipose tissue should be considered as a viable target for a variety of treatment approaches to combat MetS
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