Searching Online Mendelian Inheritance in Man (OMIM) for information on genetic loci involved in human disease

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams

BACKGROUND

In this work, we aimed to demonstrate how to utilize the lab test results and other clinical information to support precision medicine research and clinical decisions on complex diseases, with the support of electronic medical record facilities. We defined "clinotypes" as clinical information that could be observed and measured objectively using biomedical instruments. From well-known 'omic' problem definitions, we defined problems using clinotype information, including stratifying patients-identifying interested sub cohorts for future studies, mining significant associations between clinotypes and specific phenotypes-diseases, and discovering potential linkages between clinotype and genomic information. We solved these problems by integrating public omic databases and applying advanced machine learning and visual analytic techniques on two-year health exam records from a large population of healthy southern Chinese individuals (size n = 91,354). When developing the solution, we carefully addressed the missing information, imbalance and non-uniformed data annotation issues.

RESULTS

We organized the techniques and solutions to address the problems and issues above into CPA framework (Clinotype Prediction and Association-finding). At the data preprocessing step, we handled the missing value issue with predicted accuracy of 0.760. We curated 12,635 clinotype-gene associations. We found 147 Associations between 147 chronic diseases-phenotype and clinotypes, which improved the disease predictive performance to AUC (average) of 0.967. We mined 182 significant clinotype-clinotype associations among 69 clinotypes.

CONCLUSIONS

Our results showed strong potential connectivity between the omics information and the clinical lab test information. The results further emphasized the needs to utilize and integrate the clinical information, especially the lab test results, in future PheWas and omic studies. Furthermore, it showed that the clinotype information could initiate an alternative research direction and serve as an independent field of data to support the well-known 'phenome' and 'genome' researches.

Action mechanism of Roman chamomile in the treatment of anxiety disorder based on network pharmacology

Anxiety disorder is a common psychiatric disease. Roman chamomile as medicine or tea has long been used as a mild tranquilizer to reduce anxiety, but the mechanism is unclear. This research is based on network pharmacology combined with database mining to find the ingredients, action pathways and key targets of Roman chamomile for the treatment of anxiety. About 126 common targets related to chamomile and anxiety were obtained, and these targets were involved in 56 KEGG pathways. GEO screened LRRK2 as a key protein, and molecular docking showed that the protein could stably bind to drug components. Roman chamomile has the characteristics of multi-target and multi-pathway in the treatment of anxiety disorder. Its possible mechanism is to intervene anxiety disorder in the process of disease development, such as neuroactive ligand-receptor interaction, serotonin synapse, and cAMP signaling pathway. LRRK2 may be an important gene for Roman chamomile in the treatment of anxiety disorder. PRACTICAL APPLICATIONS: Roman chamomile is well known for its use in medicine and tea making. It contains many nutrients, which can relieve people's anxiety, help sleep, antibacterial and anti-inflammatory. In this article, through network pharmacology combined with Gene Expression Omnibus data mining and molecular docking, the target and mechanism of Roman chamomile in the treatment of anxiety were discussed, and its efficacy was verified by model animals, which not only clarified its mechanism at the systematic level, but also proved to be effective at the biological level. It provides a reference for the further development and utilization of Roman chamomile.

Risk of epilepsy in rheumatoid arthritis: a meta-analysis of population based studies and bioinformatics analysis

Background:

An increasing number of studies support an association between rheumatoid arthritis (RA) and brain disorders. This study aims to determine the association between RA and epilepsy.

Methods:

A comprehensive search of databases in both English and Chinese was performed. Data from the selected studies were extracted and analyzed independently by two authors. Genes associated with epilepsy and RA were also collected and analyzed.

Results:

We included six nationwide population based studies (n = 7,094,113 cases in total) for the meta-analysis. The risk of epilepsy was increased in RA patients [risk ratio (RR) = 1.601; 95% confidence interval (CI): 1.089–2.354; p = 0.017; n = 3,803,535 cases] and children born to mothers with RA (RR = 1.475; 95% CI: 1.333–1.633; p < 0.001, n = 3,290,578 cases). Subgroup analysis and meta-regression showed the RR of epilepsy in RA was negatively correlated with age. Furthermore, we found that 433 identified genes in a coexpression network from the hippocampi of 129 epileptic patients were enriched in the RA and related Kyoto Encyclopedia of Genes and Genomes pathways, while 13 genes (mainly related to inflammatory cytokines and chemokines) were identified as potential key genes bridging the RA and epilepsy.

Conclusions:

Our study, utilizing meta-analysis and bioinformatical data, highlights a close association between epilepsy and RA. Further studies are still warranted to expand these findings, especially for a population that is exposed to RA during fetal and childhood periods.

TCMID 2.0: a comprehensive resource for TCM

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, Traditional Chinese Medicine (TCM) is capturing worldwide attention in life science field. Traditional Chinese Medicine Integrated Database (TCMID), which was originally launched in 2013, was a comprehensive database aiming at TCM’s modernization and standardization. It has been highly recognized among pharmacologists and scholars in TCM researches. The latest release, TCMID 2.0 (http://www.megabionet.org/tcmid/), replenished the preceding database with 18 203 herbal ingredients, 15 prescriptions, 82 related targets, 1356 drugs, 842 diseases and numerous new connections between them. Considering that chemical changes might take place in decocting process of prescriptions, which may result in new ingredients, new data containing the prescription ingredients was collected in current version. In addition, 778 herbal mass spectrometry (MS) spectra related to 170 herbs were appended to show the variation of herbal quality in different origin and distinguish genuine medicinal materials from common ones while 3895 MS spectra of 729 ingredients were added as the supplementary materials of component identification. With the significant increase of data, TCMID 2.0 will further facilitate TCM’s modernization and enhance the exploration of underlying biological processes that are response to the diverse pharmacologic actions of TCM.

DeCoST: A New Approach in Drug Repurposing From Control System Theory

In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).

Dissecting the Underlying Pharmaceutical Mechanism of Chinese Traditional Medicine Yun-Pi-Yi-Shen-Tong-Du-Tang Acting on Ankylosing Spondylitis through Systems Biology Approaches

Traditional Chinese Medicine (TCM) has been served as complementary medicine for Ankylosing Spondylitis (AS) treatment for a long time. Yun-Pi-Yi-Shen-Tong-Du-Tang (Y-Y-T) is a novel empirical formula designed by Prof. Chengping Wen. In this study, a retrospective investigation supported efficacy of Y-Y-T and then we deciphered the underlying molecular mechanism of the efficacy. Herbal ingredients and targeting proteins were collected from TCMID. PPI networks were constructed to further infer the relationship among Y-Y-T, drugs used for treating AS, differentially expressed genes of AS patients and AS disease proteins. Finally, it was suggested that TLR signaling pathway and T cell receptor signaling pathway may involve in the biological processes of AS progression and contribute to the curative effect and proteins such as JAK2, STAT3, HSP90AA1, TNF and PTEN were the key targets. Our systemic investigation to infer therapeutic mechanism of Y-Y-T for AS treatment provides a new insight in understanding TCM pharmacology.

Genetic Testing for Hereditary Nonpolyposis Colorectal Cancer (HNPCC)

Hereditary nonpolyposis colorectal cancer (HNPCC), also called Lynch syndrome, is an autosomal dominant cancer syndrome that confers an elevated risk of early-onset colorectal cancer (CRC) and increased lifetime risk for other cancers of the endometrium, stomach, small intestine, hepatobiliary system, kidney, ureter, and ovary. Lynch syndrome accounts for up to 3% of all CRC, making it the most common hereditary colorectal cancer syndrome. Germline mutations in methyl-directed mismatch repair (MMR) genes give rise to microsatellite instability (MSI) in tumor DNA. Lynch syndrome is most frequently caused by pathogrenic variants in the mismatch repair genes MLH1, MSH2, MSH6, and PMS2. Germline mutations in MLH1 and MSH2 account for approximately 90% of detected mutations in families with Lynch syndrome. Pathogenic vatiants in MSH6 have been reported in approximately 7-10% of families with Lynch syndrome. Pathogenic variants in PMS2 account for fewer than 5% of mutations in families with Lynch syndrome. This unit presents a comprehensive molecular genetic testing strategy for Lynch syndrome including MSI analysis, next generation sequencing (NGS)-based targeted sequence analysis, PCR-based Sanger sequencing and microarray-based comparative genomic hybridization (array-CGH). © 2017 by John Wiley & Sons, Inc.

Detecting APC Gene Mutations in Familial Adenomatous Polyposis (FAP)

Hereditary forms of colorectal cancer (CRC) account for up to 5% of total cases. Familial adenomatous polyposis (FAP) is an autosomal dominant condition affecting nearly 1 in 5000 people and accounts for only about 1% of all CRCs. It is characterized by the progressive development of hundreds to thousands of adenomatous colon polyps. The gene associated with FAP (APC) contains 15 coding exons. The mutation spectrum of the APC gene is broad in that 87% of causative mutations are point mutations (including other sequence variants) and around 10% to 15% are intragenic deletions and duplications. The strategy for molecular diagnostic testing for FAP involves initial full sequence analysis of APC for sequence variants followed by screening for deletion/duplications using microarray-based comparative genomic hybridization (array CGH) or Multiplex Ligation-dependent Probe Amplification (MLPA). Recently, next generation sequencing (NGS)-based targeted gene analysis has become clinically available for detection of point mutations and other sequence variants. This unit discusses detailed protocols for an NGS-based sequencing assay, PCR-based Sanger sequencing, and array CGH. © 2017 by John Wiley & Sons, Inc.

A molecular classification of human mesenchymal stromal cells

Mesenchymal stromal cells (MSC) are widely used for the study of mesenchymal tissue repair, and increasingly adopted for cell therapy, despite the lack of consensus on the identity of these cells. In part this is due to the lack of specificity of MSC markers. Distinguishing MSC from other stromal cells such as fibroblasts is particularly difficult using standard analysis of surface proteins, and there is an urgent need for improved classification approaches. Transcriptome profiling is commonly used to describe and compare different cell types; however, efforts to identify specific markers of rare cellular subsets may be confounded by the small sample sizes of most studies. Consequently, it is difficult to derive reproducible, and therefore useful markers. We addressed the question of MSC classification with a large integrative analysis of many public MSC datasets. We derived a sparse classifier (The Rohart MSC test) that accurately distinguished MSC from non-MSC samples with >97% accuracy on an internal training set of 635 samples from 41 studies derived on 10 different microarray platforms. The classifier was validated on an external test set of 1,291 samples from 65 studies derived on 15 different platforms, with >95% accuracy. The genes that contribute to the MSC classifier formed a protein-interaction network that included known MSC markers. Further evidence of the relevance of this new MSC panel came from the high number of Mendelian disorders associated with mutations in more than 65% of the network. These result in mesenchymal defects, particularly impacting on skeletal growth and function. The Rohart MSC test is a simple in silico test that accurately discriminates MSC from fibroblasts, other adult stem/progenitor cell types or differentiated stromal cells. It has been implemented in the www.stemformatics.org resource, to assist researchers wishing to benchmark their own MSC datasets or data from the public domain. The code is available from the CRAN repository and all data used to generate the MSC test is available to download via the Gene Expression Omnibus or the Stemformatics resource.

Deciphering the Potential Pharmaceutical Mechanism of Chinese Traditional Medicine (Gui-Zhi-Shao-Yao-Zhi-Mu) on Rheumatoid Arthritis

Gui-Zhi-Shao-Yao-Zhi-Mu (GSZ) decoction is a Traditional Chinese Medicine (TCM) formula commonly used for the treatment of Rheumatoid Arthritis (RA). The therapeutic effect of GSZ for RA treatment is supported by our clinical retrospective study. To uncover the potential mechanism underlying GSZ formula, we identified 1,327 targets of 673 compounds from 9 herbs that involve in Fc epsilon RI signaling pathway and regulation of immunoglobulin production. Comparison between formula targets with 79 RA drug targets and 675 RA disease genes showed that formula targets covered 31.6% RA drug targets and 19.9% RA disease genes. Formula specific targets presented expression patterns highly similar to the disease genes and drug targets based on the expression profiles of RA samples. Investigation of 10 inferred gene clusters from expression profiles with a target association network revealed that formula specific targets directly or indirectly interacted with disease genes that were essential for immune related biological processes (e.g. inflammatory responses, treatment response of rheumatoid arthritis, etc.). Our result indicated that GSZ disrupted the RA disease dysfunction modules and restored homeostasis in the human body. The systemic approach to infer therapeutic mechanisms of GSZ for RA treatment provides a new insight in the understanding of this TCM formula.

In Silico Functional Annotation of Genomic Variation

This unit describes the concepts and practical techniques for annotating genomic variants in the human genome to estimate their functional significance. With the rapid increase of available whole exome and whole genome sequencing information for human studies, annotation techniques have become progressively more important for highlighting and prioritizing nucleotide variants and their potential impact on genes and other genetic constructs. Here, we present an overview of different types of variant annotation approaches and elaborate on their foundations, assumptions, and the downstream consequences of their use. Computational approaches and tools to assign annotations and to identify variants are reviewed. Further, the general philosophy of assigning potential function to a genetic change within the biological context of a disease is discussed.

PAGER: constructing PAGs and new PAG-PAG relationships for network biology

In this article, we described a new database framework to perform integrative “gene-set, network, and pathway analysis” (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene–gene relationships and two types of 3 174 323 PAG–PAG regulatory relationships—co-membership based and regulatory relationship based. To help users assess each PAG’s biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG–PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.

Contact: jakechen@iupui.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

A Systems Biology-Based Investigation into the Pharmacological Mechanisms of Sheng-ma-bie-jia-tang Acting on Systemic Lupus Erythematosus by Multi-Level Data Integration

Sheng-ma-bie-jia-tang (SMBJT) is a Traditional Chinese Medicine (TCM) formula that is widely used for the treatment of Systemic Lupus Erythematosus (SLE) in China. However, molecular mechanism behind this formula remains unknown. Here, we systematically analyzed targets of the ingredients in SMBJT to evaluate its potential molecular mechanism. First, we collected 1,267 targets from our previously published database, the Traditional Chinese Medicine Integrated Database (TCMID). Next, we conducted gene ontology and pathway enrichment analyses for these targets and determined that they were enriched in metabolism (amino acids, fatty acids, etc.) and signaling pathways (chemokines, Toll-like receptors, adipocytokines, etc.). 96 targets, which are known SLE disease proteins, were identified as essential targets and the rest 1,171 targets were defined as common targets of this formula. The essential targets directly interacted with SLE disease proteins. Besides, some common targets also had essential connections to both key targets and SLE disease proteins in enriched signaling pathway, e.g. toll-like receptor signaling pathway. We also found distinct function of essential and common targets in immune system processes. This multi-level approach to deciphering the underlying mechanism of SMBJT treatment of SLE details a new perspective that will further our understanding of TCM formulas.

Computational prediction of disease microRNAs in domestic animals

BACKGROUND

The most important means of identifying diseases before symptoms appear is through the discovery of disease-associated biomarkers. Recently, microRNAs (miRNAs) have become highly useful biomarkers of infectious, genetic and metabolic diseases in human but they have not been well studied in domestic animals. It is probable that many of the animal homologs of human disease-associated miRNAs may be involved in domestic animal diseases. Here we describe a computational biology study in which human disease miRNAs were utilized to predict orthologous miRNAs in cow, chicken, pig, horse, and dog.

RESULTS

We identified 287 human disease-associated miRNAs which had at least one 100% identical animal homolog. The 287 miRNAs were associated with 359 human diseases referenced in 2,863 Pubmed articles. Multiple sequence analysis indicated that over 60% of known horse mature miRNAs found perfect matches in human disease-associated miRNAs, followed by dog (50%). As expected, chicken had the least number of perfect matches (5%). Phylogenetic analysis of miRNA precursors indicated that 85% of human disease pre-miRNAs were highly conserved in animals, showing less than 5% nucleotide substitution rates over evolutionary time. As an example we demonstrated conservation of human hsa-miR-143-3p which is associated with type 2 diabetes and targets AKT1 gene which is highly conserved in pig, horse and dog. Functional analysis of AKT1 gene using Gene Ontology (GO) showed that it is involved in glucose homeostasis, positive regulation of glucose import, positive regulation of glycogen biosynthetic process, glucose transport and response to food.

CONCLUSIONS

This data provides the animal and veterinary research community with a resource to assist in generating hypothesis-driven research for discovering animal disease-related miRNA from their datasets and expedite development of prophylactic and disease-treatment strategies and also influence research efforts to identify novel disease models in large animals. Integrated data is available for download at http://agbase.hpc.msstate.edu/cgi-bin/animal_mirna.cgi.

Resources for functional genomics studies in Drosophila melanogaster

Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, "meta" information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally.

Finding people who will tell you their thoughts on genomics-recruitment strategies for social sciences research

This paper offers a description of how social media, traditional media and direct invitation were used as tools for the recruitment of 6,944 research participants for a social sciences study on genomics. The remit was to gather the views of various stakeholders towards sharing incidental findings from whole genome studies. This involved recruiting members of the public, genetic health professionals, genomic researchers and non-genetic health professionals. A novel survey was designed that contained ten integrated films; this was made available online and open for completion by anyone worldwide. The recruitment methods are described together with the convenience and snowballing sampling framework. The most successful strategy involved the utilisation of social media; Facebook, Blogging, Twitter, LinkedIn and Google Ads led to the ascertainment of over 75 % of the final sample. We conclude that the strategies used were successful in recruiting in eclectic mix of appropriate participants. Design of the survey and results from the study are presented separately.

MalaCards: an integrated compendium for diseases and their annotation

Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/