IBDsite: a Galaxy-interacting, integrative database for supporting inflammatory bowel disease high throughput data analysis

Identification of Dysfunctional Gut Microbiota Through Rectal Swab in Patients with Different Severity of Acute Pancreatitis

BACKGROUND

Acute pancreatitis (AP) has a wide spectrum of severity and can be associated with considerable morbidity and mortality. Whether gut microbiota dysbiosis is associated with AP severity remains obscure.

AIMS

We aim to investigate the differences in the alterations of gut microbiota in different grades of AP severity.

METHODS

We collected clinical information and rectal swab samples from 80 individuals. The gut microbiota was tested by 16S rRNA gene sequencing, gut microbiota species composition analysis, difference analysis, random forest model prediction analysis, and gut microbiota species correlation network analysis.

RESULTS

There was a different microbiota profile in different severity grades. Bacteroides, Escherichis-Shigella, and Enterococcus were dominant species in mild, moderately severe, and severe AP, respectively. Finegoldia was the most significantly increased and Blautia the most decreased species in mild AP. Anaerococcus was the most significantly increased and Eubacterium hallii the most decreased species in moderately severe AP. Enterococcus was the most significantly increased and Eubacterium hallii the most decreased species in severe AP. Finegoldia, Eubacterium_hallii, and Lachnospiraceae were potential diagnostic biomarkers for mild AP and Eubacterium_hallii and Anaerococcus for moderately severe AP. There was a positive interaction between Firmicutes and Bacteroidetes in mild AP.

CONCLUSIONS

The disturbed gut microbiota is different among grades of AP, suggesting their potential role in the progression of disease severity. There was a different microbiota profile in different severity grades. Bacteroides, Escherichis-Shigella, and Enterococcus were dominant gut microbiota species in MAP, MSAP, and SAP, respectively. Finegoldia was the most significantly increased and Blautia the most decreased gut microbiota species in MAP. Anaerococcus was the most significantly increased and Eubacterium hallii the most decreased species in MSAP. Enterococcus was the most significantly increased and Eubacterium hallii the most decreased species in SAP. Finegoldia, Eubacterium_hallii, and Lachnospiraceae were potential diagnostic biomarkers for MAP and Eubacterium_hallii and Anaerococcus for MSAP. There was a positive interaction between Firmicutes and Bacteroidetes in MAP.

AICD: an integrated anti-inflammatory compounds database for drug discovery

Systemic or local inflammation drives the pathogenesis of various human diseases. Small compounds with anti-inflammatory properties hold great potential for clinical translation. Over recent decades, many compounds have been screened for their action against inflammation-related targets. Databases that integrate the physicochemical properties and bioassay results of these compounds are lacking. We created an “Anti-Inflammatory Compounds Database” (AICD) to deposit compounds with potential anti-inflammation activities. A total of 232 inflammation-related targets were recruited by the AICD. Gene set enrichment analysis showed that these targets were involved in various human diseases. Bioassays of these targets were collected from open-access databases and adopted to extract 79,781 small molecules with information on chemical properties, candidate targets, bioassay models and bioassay results. Principal component analysis demonstrated that these deposited compounds were closely related to US Food and Drug Administration-approved drugs with respect to chemical space and chemical properties. Finally, pathway-based screening for drug combination/multi-target drugs provided a case study for drug discovery using the AICD. The AICD focuses on inflammation-related drug targets and contains substantial candidate compounds with high chemical diversity and good drug-like properties. It could be serviced for the discovery of anti-inflammatory medicines and can be accessed freely at http://956023.ichengyun.net/AICD/index.php.

Ulcerative Colitis Database: An Integrated Database and Toolkit for Gene Function and Medication Involved in Ulcerative Colitis

BACKGROUND

Over the last decade, a massive amount of well-annotated genomic data has been accumulated on the pathogenesis and therapies for ulcerative colitis (UC). However, a comprehensive repository is not available yet.

METHODS

Ulcerative Colitis Database (UCDB) was constructed using text mining followed by manually curating on the literature to collect the reliable information of UC-related genes, drugs, and susceptibility loci. UC DNA microarray data were collected. R packages were used to implement gene expression analysis toolkit.

RESULTS

UCDB includes 4 separate but closely related components: "UC GENE," "UC DRUG," "UC LOCUS," and "UC ANALYSIS." The UC GENE contains comprehensive information for 1151 UC-related genes manually curated from 2919 publications. The UC DRUG includes information for 248 drugs manually curated from 2344 publications. "UC LOCUS" includes 110 UC susceptibility SNP loci, which were collected from 12 Genome-Wide Association Studies. A comprehensive expression quantitative trait loci browser was also implemented. The UC ANALYSIS is an expression analysis toolkit for 37 UC expression array data sets, which contains 1098 samples. The toolkit can be used to do gene expression correlation, clustering, differentially expressed, and Gene Set Enrichment Analysis (GSEA).

CONCLUSIONS

UCDB provides a comprehensive collection of well-curated UC-related genes and drugs, and straightforward interfaces for gene expression analyses. UCDB is a useful leading resource for both basic and clinical research and will benefit UC community worldwide. UCDB is freely accessible at http://seiwertlab.uchicago.edu/UCDB.

Integrating omics: the future of IBD?

The complexity of IBD is well recognized as are the putative four major components of its pathogenesis, i.e. environment, genetic makeup, gut microbiota and mucosal immune response. Each of these components is extremely complex on its own, and at present should be more appropriately defined by the terms 'exposome', 'genome', 'microbiome' and 'immunome', respectively, based on the 'ome' suffix that refers to a totality of some sort. None of these 'omes' is apparently capable of causing IBD by itself; it is instead the intricate and reciprocal interaction among them, through the so-called 'IBD interactome', that results in the emergence of IBD, or more appropriately the 'IBD integrome'. To deal with and understand such overwhelming biological complexity, new approaches and tools are needed, and these are represented by 'omics', defined as the study of related sets of biological molecules in a comprehensive fashion, such as genomics, transcriptomics, proteomics, metabolomics, and so on. Numerous bioinformatics-based tools are available to explore and take advantage of the massive amount of information that can be generated by the analysis of the various omes and their interactions, aiming at identifying the molecular interactome underlying any particular status of health and disease. These novel approaches are fully applicable to IBD and allow us to achieve the ultimate goal of developing and applying personalized medicine and far more effective therapies to individual patients with Crohn's disease and ulcerative colitis. For the practicing gastroenterologist, an omics-based delivery of healthcare may be intimidating, but it must be accepted and implemented if he or she is to provide the best possible care to IBD patients.

Systems biology approaches for inflammatory bowel disease: emphasis on gut microbial metabolism

Although the prevalence of main idiopathic forms of inflammatory bowel disease (IBD) has risen considerably over the last decades, their clinical features do not allow accurate prediction of prognosis, likelihood of disease progression, or response to specific therapy. Through a better understanding of the molecular pathways involved in IBD and the promise of more targeted therapies, the personalized approach to the management of IBD shows potential. To achieve this, there remains a significant need to better understand the disease process at cellular and molecular levels for any given individual with IBD. The complexity of biological functional networks behind the etiology of IBD highlights the need for their comprehensive analysis. In this, omics technologies can generate a systemic view of IBD pathogenesis on which to base novel, multiple pathway-integrated therapies. Omics sciences have just started to contribute here by generating gene, protein expression, metabolite data at global level and large scale, and more recently by offering new opportunities to explore gut functional ecology. In particular, there is much expectation regarding the putative role of the gut microbiome in IBD. No doubt it will provide additional insights and lead to the development of alternative, hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD. This review discusses perspectives of relevance to clinical translation with emphasis on gut microbial metabolic activities.

Effects of GWAS-associated genetic variants on lncRNAs within IBD and T1D candidate loci

Long non-coding RNAs are a new class of non-coding RNAs that are at the crosshairs in many human diseases such as cancers, cardiovascular disorders, inflammatory and autoimmune disease like Inflammatory Bowel Disease (IBD) and Type 1 Diabetes (T1D). Nearly 90% of the phenotype-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) lie outside of the protein coding regions, and map to the non-coding intervals. However, the relationship between phenotype-associated loci and the non-coding regions including the long non-coding RNAs (lncRNAs) is poorly understood. Here, we systemically identified all annotated IBD and T1D loci-associated lncRNAs, and mapped nominally significant GWAS/ImmunoChip SNPs for IBD and T1D within these lncRNAs. Additionally, we identified tissue-specific cis-eQTLs, and strong linkage disequilibrium (LD) signals associated with these SNPs. We explored sequence and structure based attributes of these lncRNAs, and also predicted the structural effects of mapped SNPs within them. We also identified lncRNAs in IBD and T1D that are under recent positive selection. Our analysis identified putative lncRNA secondary structure-disruptive SNPs within and in close proximity (+/-5 kb flanking regions) of IBD and T1D loci-associated candidate genes, suggesting that these RNA conformation-altering polymorphisms might be associated with diseased-phenotype. Disruption of lncRNA secondary structure due to presence of GWAS SNPs provides valuable information that could be potentially useful for future structure-function studies on lncRNAs.

Technical Aspects of Nominal Partitions on Accuracy of Data Mining Classification of Intestinal Microbiota - Comparison between 7 Restriction Enzymes

The application of data mining analyses (DM) is effective for the quantitative classification of human intestinal microbiota (HIM). However, there remain various technical problems that must be overcome. This paper deals with the number of nominal partitions (NP) of the target dataset, which is a major technical problem. We used here terminal restriction fragment length polymorphism data, which was obtained from the feces of 92 Japanese men. Data comprised operational taxonomic units (OTUs) and subject smoking and drinking habits, which were effectively classified by two NP (2-NP; Yes or No). Using the same OTU data, 3-NP and 5-NP were examined here and results were obtained, focusing on the accuracies of prediction, and the reliability of the selected OTUs by DM were compared to the former 2-NP. Restriction enzymes for PCR were further affected by the accuracy and were compared with 7 enzymes. There were subjects who possess HIM at the border zones of partitions, and the greater the number of partitions, the lower the obtained DM accuracy. The application of balance nodes boosted and duplicated the data, and was able to improve accuracy. More accurate and reliable DM operations are applicable to the classification of unknown subjects for identifying various characteristics, including disease.

The impact of network medicine in gastroenterology and hepatology

In the footsteps of groundbreaking achievements made by biomedical research, another scientific revolution is unfolding. Systems biology draws from the chaos and complexity theory and applies computational models to predict emerging behavior of the interactions between genes, gene products, and environmental factors. Adaptation of systems biology to translational and clinical sciences has been termed network medicine, and is likely to change the way we think about preventing, predicting, diagnosing, and treating complex human diseases. Network medicine finds gene-disease associations by analyzing the unparalleled digital information discovered and created by high-throughput technologies (dubbed as "omics" science) and links genetic variance to clinical disease phenotypes through intermediate organizational levels of life such as the epigenome, transcriptome, proteome, and metabolome. Supported by large reference databases, unprecedented data storage capacity, and innovative computational analysis, network medicine is poised to find links between conditions that were thought to be distinct, uncover shared disease mechanisms and key drivers of the pathogenesis, predict individual disease outcomes and trajectories, identify novel therapeutic applications, and help avoid off-target and undesirable drug effects. Recent advances indicate that these perspectives are increasingly within our reach for understanding and managing complex diseases of the digestive system.

Computational framework to support integration of biomolecular and clinical data within a translational approach

BACKGROUND

The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information.

RESULTS

We have implemented an extension of Chado - the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications.

CONCLUSIONS

Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different "omics" technologies with patient's clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans.