APDDD: animal parasitic diseases and drugs database

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APDDD: animal parasitic diseases and drugs database

https://doi.org/10.21203/rs.3.rs-3249124/v1

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Animal parasitic diseases not only have an economic impact, but also have serious social and public health impacts. Although antiparasitic drugs can treat these diseases, it seems difficult for users to comprehensively utilize the information, due to incomplete and difficult data collection. Thus, there is urgent need to establish a comprehensive database, that includes parasitic diseases and related drugs. In this paper, we develop a knowledge database dedicated to collecting and analyzing animal parasitic diseases and related drugs, named Animal Parasitic Diseases and Drugs Database (APDDD). The current version of APDDD includes animal parasitic diseases data of 8 major parasite classifications that cause common parasitic diseases and 96 subclass samples mined from many literature and authoritative books, as well as 182 antiparasitic drugs. Furthermore, we utilized APDDD data to add a knowledge graph representing the relationships between parasitic diseases, drugs, and the targeted gene of drugs acting on parasites. We hope that APDDD will become a good database for animal parasitic diseases and antiparasitic drugs research and that users can gain a more intuitive understanding of the relationships between parasitic diseases, drugs, and targeted genes through the knowledge graph.

Parasitic diseases

Drugs

Database

knowledge graph

As the demand for animal-derived food products continuing to grow(Zhang et al., 2018), the animal husbandry has become a crucial pillar of agriculture and rural economy development(Herrero et al., 2013). However, parasitic diseases in livestock and poultry pose a threat to animal health and cause significant economic losses in the animal husbandry(Nerissa Ramnath, 2009). For instance, coccidiosis in chickens, caused by members of the Eimeria genus, has been reported to be the most costly parasitic disease of poultry(Andrews, 2013). Chickens infected with coccidiosis can experience significant mortality rates(Andrews, 2013). In addition, parasitic diseases also pose a serious threat to human health(Assenov et al., 2008). Some parasitic diseases are zoonotic, that is, can be transmitted between humans and animals(Torgerson and Macpherson, 2011), such as schistosomiasis(LoVerde, 2019) and toxoplasmosis(Montoya and Liesenfeld, 2004), which pose a direct threat to human health and remain hardly to prevent(Graczyk et al., 2005). In summary, parasitic diseases bring huge economic losses to the animal husbandry(Vaumourin et al., 2015) and pose a serious threat to the health of related employees, so it is very important to find and develop effective prevention and control measures(Khemasuwan et al., 2016).

As an essential means, antiparasitic drugs can indeed be effective in controlling and preventing many parasitic diseases, thereby playing a crucial role in mitigating public health issues and minimizing economic losses(Andrews et al., 2014). Nevertheless, the varying parasitic relationship between parasites and hosts, parasites inhabit various regions within the body of the host(Mehlhorn, 2016). In addition, different types of parasites necessitate specific medications for effective treatment. Therefore, various types of parasites require distinct drug therapies. (Man et al., 2022)For example, anti-malarial drugs are ineffective against other parasites except to few apicomplexans (Flannery et al., 2013). Therefore, it is difficult for a single drug to provide comprehensive control of all parasitic diseases(Akano et al., 2018). The current drugs utilized for the treatment of these diseases have numerous limitations and outdated origins, and particularly, over time, many parasites have developed resistance to some common drugs, making treatment more difficult(Légaré and Ouellette, 2017; Sibley and Hunt, 2003). Moreover, due to the diversity of parasite species, the complex life cycles of parasites, pre-existing drug resistance, and clinical safety considerations of new drug development(Sun et al., 2014), the development of new effective drugs against animal parasitic diseases has become very challenging(Pink et al., 2005).

Currently, researchers and veterinarians solely rely on books as their primary source of information for treating parasitic diseases. Given the complexity of parasite species(Weinstein and Kuris, 2016) and the diversity of antiparasitic drugs(Pérez et al., 2009), drug development poses significant challenges. Then, the conventional approach to finding drugs for parasitic diseases may prove to be exceedingly difficult. In this scenario, most books merely offer demonstrations and raw data downloads, making it challenging for researchers and veterinarians to obtain intuitive and effective information due to the absence of unified metadata(Zong et al., 2022). Combination treatments are widely recognized for their ability to prevent the development of parasite drug resistance. However, due to the lack of comprehensive antiparasitic drugs information, researchers are currently faced with challenges in accessing the necessary information on available drugs for the treatment of parasitic diseases(Akano et al., 2018). Despite some efforts, there remains a significant shortage of animal parasitic diseases and drugs databases. Furthermore, there is a lack of a dedicated and comprehensive database specifically designed for classifying parasitic diseases and their related drugs. Consequently, there is an urgent need for a dedicated database.

In this paper, we carefully selected eight representative categories of animal parasitic diseases(Ruff, 1999), namely, sporidiosis, flagellosis, piroplasmosis, balantidiasis, trematodiasis, teniasis, nematodosis and ectoparasitic diseases, along with a dataset comprising 182 antiparasitic drugs. Here, we integrated data on common parasitic diseases and their corresponding therapeutic drugs, resulting in the development of Animal Parasitic Diseases and Drugs Database (APDDD). This comprehensive database can accommodate a vast amount of information. The database enables users to efficiently and accurately locate drugs relevant to specific parasitic diseases, thereby enhancing treatment and research outcomes. It is anticipated that this resource will have a positive impact on the field of animal parasitic diseases. Furthermore, we incorporated data from APDDD and implemented a knowledge graph. Knowledge graphs(Fan et al., 2017), which employ graphical representations, offer concise and intuitive descriptions. These graphs facilitate the organization of biological interaction information and establish connections between diseases, drugs, and genetic knowledge. Building a high-quality benchmark APDDD dataset holds significant importance for the control and prevention of parasitic diseases in this research field. Comprehensive information about animal parasitic diseases and drugs are available at http://apddd.tlds.cc/APDDD/index/.

In this study, our main contributions are summarized as follows:

1. We have integrated eight typical animal parasitic diseases and related 182 therapeutic drugs. As far as we know, this is the first time that relevant data has been systematically generalized and summarized.

2. We employed the methodology of knowledge graph to visualize certain data in the database, aiming to provide users with a more intuitive understanding of the biological interactions between diseases and drugs.

3. This database can become a publicly available gold standard benchmark database for the development and evaluation of methods for predicting parasites and drugs.

2.1 Design of database

Building and analyzing a database consists of the following steps as shown in Figure 1.

1. Collect representative animal parasitic diseases and related drugs from authoritative books and PubMed.

2. Organize the collected materials according to the design and requirements of the database.

3. Develop prediction models for knowledge graph data.

4. Build relevant database websites.

5. Complete relevant tests and online database.

2.2 Acquisition and integration of data

The primary objective of establishing a database is to compile a comprehensive collection of reliable and representative data on animal parasitic diseases and antiparasitic drugs(“dbMCS,” n.d.). Subsequently, the aim is to establish correlations between these drugs and the treatment of specific parasitic diseases. The primary sources of data on animal parasitic diseases and drugs in the database are authoritative books and the PubMed website, supplemented by a smaller portion of data derived from our prior research efforts. As depicted in Figure 1, our workflow initiates with a manual query of authoritative books on parasitic diseases and drugs. Subsequently, we select eight representative categories of parasitic diseases from these books, which includ: sporidiosis, flagellosis, piroplasmosis, balantidiasis, trematodiasis, teniasis, nematodosis and ectoparasitic diseases. Subsequently, we employed manual queries, search methods, and screening techniques to identify the subcategories of diseases within each major category of parasitic diseases. Within each subcategory of parasitic diseases, we meticulously selected 96 representative diseases as typical examples. Following this, we conducted a systematic search to identify treatment drugs associated with the parasitic diseases encompassed by these 96 subcategories. This approach allowed us to acquire valuable information regarding both parasitic diseases and the drugs employed for treating parasitic infections. Furthermore, we conducted comprehensive searches on PubMed to explore scientific literature on parasitic diseases and drugs. Using a specific set of keywords, including "anti-toxoplasmosis drugs " and "cryptosporidiosis drugs", etc. we aimed to focus on relevant scientific literature. As a result, a substantial volume of literature about diverse parasitic diseases and therapeutic drugs was identified through the search interface. During this step, we collected data on subtypes of parasitic diseases and their corresponding therapeutic drugs from the PubMed website, resulting in the acquisition of over 4,000 articles. As part of our literature screening strategy, our data analysts employ a preliminary assessment by reviewing the titles and abstracts of the required articles. Subsequently, we perform literature extraction, removing papers without full-text access and those not relevant to the APDDD. Finally, we downloaded the full-text PDF versions of the selected articles and conducted an additional manual screening of the selected literature to extract information about parasitic diseases and their corresponding drugs from the main body of the article or supplementary materials. For each disease and drug, we extracted the relevant metadata from the paper, which encompassed descriptive information about the diseases and drugs, as well as their associations. Upon aggregating this data, we have obtained a comprehensive dataset for the APDDD database system. Here, the data collection process for parasitic diseases and their corresponding drugs is now complete.

2.3 Data processing

The APDDD database website encompasses 96 distinct parasitic diseases, which are classified into eight major parasitic disease categories. Additionally, it offers comprehensive information about the drugs used in treating each specific subclass of these diseases, including their respective sources. The data processing methodology is primarily guided by the application of a tree structure(Xue et al., 2018). The gathered data is categorized into five levels: bugger-parasites-parasitic diseases-parasitic disease subtypes-corresponding therapeutic drugs. Since the primary emphasis of the APDDD database system is on providing information regarding parasitic diseases and therapeutic drugs, it does not encompass details about specific parasite species and worm genera. Consequently, this paper decides to streamline the complex five-level tree structure into a more manageable three-level structure: parasites-parasitic diseases-parasitic disease subtypes-corresponding therapeutic drugs. Once the collected data has been processed, users can access the aforementioned information on the APDDD database website using a three-level page structure.

To ensure the authenticity and accuracy of the data, data sources were carefully selected from PubMed literature and authoritative books in a manual and individualized manner during the data collection phase. As a result, no specific data cleaning step was necessary during data processing. To foster information sharing, it is crucial to conduct data standardization and annotation. Accordingly, we undertake the process of transforming the collected data into a user-accessible format. Additionally, we have included visualizations displaying the categories and corresponding percentages of parasitic diseases.

3.1 Classification and statistics of database

At present, APDDD provides eight broad categories of parasitic diseases, meorso, it also shows the parasitic diseases of their subdivisions, a total of 96 sub-categories of parasitic diseases, and on this basis, a corresponding list of 182 therapeutic drugs for parasitic diseases are provided. The statistics are presented in chart form: the eight major groups of parasitic diseases and the number of diseases in their subcategories is shown in Figure 2A, the eight major categories of parasitic diseases and the number of drugs related to the corresponding diseases is shown in Figure 2B. We have also added visualizations of the various categories of parasitic diseases and their percentages, such as: sporidiosis (15.63%), flagellosis (8.33%), piroplasmosis (7.29%), balantidiasis (1.04%), trematodiasis (13.54%), teniasis (17.71%), nematodosis (29.17%) and ectoparasitic diseases (7.29%). We welcome users to contact us via the submission panel available on the web page or via email to share new APDDD data discovered. We will validate each request and periodically collect data on parasitic diseases and related medications, and update the APDDD.

3.2 Implementation of database website

APDDD provides a user-friendly interactive website, where users can browse and search for corresponding data information. The APDDD homepage contains five panels, and a detailed website information is shown in Figure 4. The overall structure of the website is based on the Django framework(“Introduction to the Django Framework | SpringerLink,” n.d.) as the backend, and the front-end open source framework LayUI(Yeh et al., 2012) is used to construct various panels with different contents.

3.3 Home panel to search and browse information

In this interface, users can intuitively see eight categories of parasitic diseases (Figure 3A). Click on the parasitic disease you want to query, and the page will be redirected to the specific subgenus parasitic disease interface to which the parasitic disease belongs (Figure 3B). Continue to click on a subcategory parasitic disease, then the page will jump to the corresponding parasitic diseases and their therapeutic drugs (Figure 3C). In addition, we present statistics about the home page dashboard in the database to help users better understand the database information , including statistics on the number and percentage of various parasitic diseases.

3.4 About the panel to introduce the database

This panel focuses on the features and requirements of the website, as well as the purpose of the database. Users can use the modules in the panel to easily browse the APDDD database website and query the required data information (Figure 3D).

3.5 Download panel to obtain the drugs and diseases data

All data information in APDDD is open to all users. Users can click on the download button in this panel (Figure 3E) to obtain detailed information on parasitic diseases and their treatment drugs.

3.6 Submit panel to upload the new data

In recent years, due to the spread of parasitic diseases and the increase in drug resistance, researchers have been exploring parasitic diseases and corresponding drugs. Thus, as research continues, we expect that new types of parasitic diseases will emerge and new drugs will be developed to combat these diseases. In order to ensure that the database is up to date, we will continue to expand the relevant content. In addition, we encourage users to provide us with relevant data information through the submission panel (Figure 3F). After information submitting, we will review it and feed back the results to corresponding the email address that provided the data information.

3.7 Contact panel to stay in touch with us

There is our detailed email address in the contact panel (Figure 3G), and we hope that users can make corresponding comments and corrections on our database. We warmly welcome users to contribute to the usefulness/functionality of this database by actively communicating with us on relevant topics and issues.

3.8 The relationship between genes, diseases and drugs

Understanding the role of genes in animal parasitic diseases is of paramount importance, but there are often significant challenges to overcome when integrating data from disparate sources and reusing it to gain new knowledge(Choi and Lee, 2021). Knowledge graphs, based on graphical representations, provide concise and intuitive descriptions, where graphs can structure biological interaction information and connect fragmented knowledge together(Messina et al., 2018). Thus, we can obtain the associations between individual nodes through the knowledge graph. Typical knowledge graph is composed of knowledge base or triplet, representing the relationship between two entities. The composition form is head entity relationship and tail entity(Fan et al., 2017). We constructed a biological knowledge graph using disease-drug, disease-gene, and drug-gene relationships. These data were obtained from databases such as NCBI, KEGG, DRUGBANK, etc. We obtained them by inputting disease names, drug names, and targeted genes of the drugs. Then, we integrated all the files extracted from various databases to obtain the triplets. During the process of constructing the triplets, we manually collected the data to ensure its accuracy, the data were integrated and used to construct the knowledge graph. The complexity of the knowledge graph structure increases with the number of triplets. In our case, we chose only four subcategories of diseases from the database, resulting in 113 entities, 68 triplets, and 192 distinct relationships. As a result, our knowledge graph structure is relatively simple. We simply visualized some information in the database, so that users could have a more intuitive understanding of the action pathway of the drugs and the parasitic diseases treated.

To use Cytoscape for graphing(Assenov et al., 2007)(Shannon et al., 2003), we selected data on three typical subcategories of parasitic diseases from the database: coccidiosis, toxoplasmosis and cryptosporidiosis. Simultaneously, we collected data on the corresponding drugs and the targeted genes of these drugs for each of these four disease categories, as shown in Figure 4. Furthermore, we standardized the collected data and assigned unique identifiers to entities in the dataset. To clearly differentiate between diseases, drugs and genes, we marked the nodes of the same type in the same way, the red circle represents the name of parasitic diseases, while the blue hexagonal symbol denotes the gene name of the drugs target , and green rhombus refers to the name of each drug, and these solid lines connecting them represent the biological interaction(Sun and Kim, 2011) between these entities. To further illustrate the relationships between these entities, we have added a subgraph of the triplet to highlight specific examples.

For instance, toxoplasmosis/dihydrofolate reductase(Schnell et al., 2004)/pyrimethamine. Toxoplasma gondii (T. gondii) belonging to the phylum apicomplexa is an obligate intracellular protozoan parasite capable of infecting almost all nucleated cells(Belperron et al., 2004). In most prokaryotes and eukaryotes, the folate biosynthetic pathway and its key enzyme dihydrofolate reductase (DHFR) play a essential role in the synthesis of DNA precursors and some amino acids(Corral et al., 2018). DHFR is the target of several drugs used to treat various pathogens. Animals cannot synthesize folates de novo, and rely on their diets for folate intake(Hossain et al., 2004). T. gondii lacks many of the enzymes necessary for pyrimidine salvage(Reynolds and Roos, 1998) and is particularly dependent on de novo biosynthetic pathways that consume reduced folate molecules and therefore provide an important target for chemotherapy of parasite infections(Matrajt et al., 2004) . Current treatment of toxoplasmosis targets the parasite’s folate metabolism through inhibition of DHFR(Welsch et al., 2016). In T. gondii and all other protozoan parasites , a key enzymatic step in folate metabolism is the one catalyzed by the enzyme DHFR, DHFR is fused to thymidylate synthase (TS) forming a bifunctional DHFR–TS protein(Matrajt et al., 2004). The most widely used DHFR antagonist, pyrimethamine, acts by selectively inhibiting their DHFR–TS to inhibit a prior step in folate biosynthesis(Sardarian et al., 2003). Pyrimethamine, acting against parasites by this means, forms the basis of chemotherapy against T. gondii infection(Welsch et al., 2016). In conclusion, the relationship between these nodes is that pyrimethamine inhibits the growth and development of Toxoplasma by inhibiting the activity of DHFR.

Animal parasites can give rise to a wide range of complex diseases. Therefore, further research on animal parasitic diseases and antiparasitic drugs is crucial for enhancing our understanding and treatment of these diseases. Considering the multifaceted nature and inherent complexities of this matter, including challenges in collecting and mining data on parasitic diseases and drugs, progress in this research field has been hindered. This paper presents the integration of extensive existing data on animal parasitic diseases and drugs to establish APDDD, the first repository in the field. APDDD comprises eight representative categories of parasitic classifications, primarily encompassing fundamental information and supplementary annotations.

Users can utilize the information in the APDDD database to conduct targeted research, thereby enhancing their understanding of the mechanisms of existing parasitic diseases and antiparasitic drugs. As scientific technology continues to advance, we anticipate the discovery and development of more novel and efficient parasitic diseases and antiparasitic drugs. This progress will also enrich the collection of parasitic disease and antiparasitic drug data, further enhancing the content of the APDDD database.

While we have made significant achievements, we acknowledge the presence of certain shortcomings in our work. For instance, our first attempt at applying knowledge graphs to the database has resulted in the presentation of fragmented knowledge to users through visualized knowledge graphs. To ensure accuracy, we have solely included explicit associations between nodes within the knowledge graph, omitting the incorporation of potentially related predictive elements. Subsequently, we plan to enhance our work by incorporating predictive methods based on the database, collecting additional data, and expanding the number of nodes to enrich the content of the knowledge graph. Moving forward, we will persistently concentrate on the latest research advancements in the field of APDDD and continually integrate additional parasites and drugs into the APDDD database. Our aspiration is for APDDD to evolve into a distinctive platform that offers users swift access to and comprehensive understanding of parasitic diseases and drugs.

Author contributions

Yilei Zhang, Guojun Chen, Siyi Zhou, Lingru He collected the data. Yilei Zhang wrote the first version of the manuscript. Guojun Chen took part in data analysis. Ayanniyi Olalekan Opeyemi,Qianming Xu, Zhenyu Yue and Congshan Yangparticipated in manuscript revision. All authors finalized the manuscript.

Funding

This work was supported by the Natural Science Young Foundation of Anhui (2008085QC136, 2008085QF293), National Natural Science Foundation of China (62102004).

Data availability

The raw data used for this study are available from the corresponding author up on reasonable request.

Consent to participate

This is not applicable.

Consent for publication

This is not applicable.

Conflict of interest

The authors declare no competing interests.

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APDDD: animal parasitic diseases and drugs database

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