A nonredundant role for T cell-derived interleukin 22 in antibacterial defense of colonic crypts.

Interleukin (IL)-22 is central to immune defense at barrier sites. We examined the contributions of innate lymphoid cell (ILC) and T cell-derived IL-22 during Citrobacter rodentium (C.r) infection using mice that both report Il22 expression and allow lineage-specific deletion. ILC-derived IL-22 activated STAT3 in C.r-colonized surface intestinal epithelial cells (IECs) but only temporally restrained bacterial growth. T cell-derived IL-22 induced a more robust and extensive activation of STAT3 in IECs, including IECs lining colonic crypts, and T cell-specific deficiency of IL-22 led to pathogen invasion of the crypts and increased mortality. This reflected a requirement for T cell-derived IL-22 for the expression of a host-protective transcriptomic program that included AMPs, neutrophil-recruiting chemokines, and mucin-related molecules, and it restricted IFNγ-induced proinflammatory genes. Our findings demonstrate spatiotemporal differences in the production and action of IL-22 by ILCs and T cells during infection and reveal an indispensable role for IL-22-producing T cells in the protection of the intestinal crypts.

Photo-neuro-immuno-endocrinology: How the ultraviolet radiation regulates the body, brain, and immune system.

Ultraviolet radiation (UVR) is primarily recognized for its detrimental effects such as cancerogenesis, skin aging, eye damage, and autoimmune disorders. With exception of ultraviolet B (UVB) requirement in the production of vitamin D3, the positive role of UVR in modulation of homeostasis is underappreciated. Skin exposure to UVR triggers local responses secondary to the induction of chemical, hormonal, immune, and neural signals that are defined by the chromophores and extent of UVR penetration into skin compartments. These responses are not random and are coordinated by the cutaneous neuro-immuno-endocrine system, which counteracts the action of external stressors and accommodates local homeostasis to the changing environment. The UVR induces electrical, chemical, and biological signals to be sent to the brain, endocrine and immune systems, as well as other central organs, which in concert regulate body homeostasis. To achieve its central homeostatic goal, the UVR-induced signals are precisely computed locally with transmission through nerves or humoral signals release into the circulation to activate and/or modulate coordinating central centers or organs. Such modulatory effects will be dependent on UVA and UVB wavelengths. This leads to immunosuppression, the activation of brain and endocrine coordinating centers, and the modification of different organ functions. Therefore, it is imperative to understand the underlying mechanisms of UVR electromagnetic energy penetration deep into the body, with its impact on the brain and internal organs. Photo-neuro-immuno-endocrinology can offer novel therapeutic approaches in addiction and mood disorders; autoimmune, neurodegenerative, and chronic pain-generating disorders; or pathologies involving endocrine, cardiovascular, gastrointestinal, or reproductive systems.

PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies.

PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.

Neuroendocrine signaling in the skin with a special focus on the epidermal neuropeptides.

The skin, which is comprised of the epidermis, dermis, and subcutaneous tissue, is the largest organ in the human body and it plays a crucial role in the regulation of the body's homeostasis. These functions are regulated by local neuroendocrine and immune systems with a plethora of signaling molecules produced by resident and immune cells. In addition, neurotransmitters, endocrine factors, neuropeptides, and cytokines released from nerve endings play a central role in the skin's responses to stress. These molecules act on the corresponding receptors in an intra-, juxta-, para-, or autocrine fashion. The epidermis as the outer most component of skin forms a barrier directly protecting against environmental stressors. This protection is assured by an intrinsic keratinocyte differentiation program, pigmentary system, and local nervous, immune, endocrine, and microbiome elements. These constituents communicate cross-functionally among themselves and with corresponding systems in the dermis and hypodermis to secure the basic epidermal functions to maintain local (skin) and global (systemic) homeostasis. The neurohormonal mediators and cytokines used in these communications regulate physiological skin functions separately or in concert. Disturbances in the functions in these systems lead to cutaneous pathology that includes inflammatory (i.e., psoriasis, allergic, or atopic dermatitis, etc.) and keratinocytic hyperproliferative disorders (i.e., seborrheic and solar keratoses), dysfunction of adnexal structure (i.e., hair follicles, eccrine, and sebaceous glands), hypersensitivity reactions, pigmentary disorders (vitiligo, melasma, and hypo- or hyperpigmentary responses), premature aging, and malignancies (melanoma and nonmelanoma skin cancers). These cellular, molecular, and neural components preserve skin integrity and protect against skin pathologies and can act as "messengers of the skin" to the central organs, all to preserve organismal survival.

IL-23 Promotes a Coordinated B Cell Germinal Center Program for Class-Switch Recombination to IgG2b in BXD2 Mice.

IL-23 promotes autoimmune disease, including Th17 CD4 T cell development and autoantibody production. In this study, we show that a deficiency of the p19 component of IL-23 in the autoimmune BXD2 (BXD2-p19-/- ) mouse leads to a shift of the follicular T helper cell program from follicular T helper (Tfh)-IL-17 to Tfh-IFN-γ. Although the germinal center (GC) size and the number of GC B cells remained the same, BXD2-p19-/- mice exhibited a lower class-switch recombination (CSR) in the GC B cells, leading to lower serum levels of IgG2b. Single-cell transcriptomics analysis of GC B cells revealed that whereas Ifngr1, Il21r, and Il4r genes exhibited a synchronized expression pattern with Cxcr5 and plasma cell program genes, Il17ra exhibited a synchronized expression pattern with Cxcr4 and GC program genes. Downregulation of Ighg2b in BXD2-p19-/- GC B cells was associated with decreased expression of CSR-related novel base excision repair genes that were otherwise predominantly expressed by Il17ra + GC B cells in BXD2 mice. Together, these results suggest that although IL-23 is dispensable for GC formation, it is essential to promote a population of Tfh-IL-17 cells. IL-23 acts indirectly on Il17ra + GC B cells to facilitate CSR-related base excision repair genes during the dark zone phase of GC B cell development.

BEERE: a web server for biomedical entity expansion, ranking and explorations.

BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. 'biomedical entities', in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity-a gene or a term-within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype-phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.

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.

Decoding SARS-CoV-2 hijacking of host mitochondria in COVID-19 pathogenesis.

Because of the ongoing pandemic around the world, the mechanisms underlying the SARS-CoV-2-induced COVID-19 are subject to intense investigation. Based on available data for the SARS-CoV-1 virus, we suggest how CoV-2 localization of RNA transcripts in mitochondria hijacks the host cell's mitochondrial function to viral advantage. Besides viral RNA transcripts, RNA also localizes to mitochondria. SARS-CoV-2 may manipulate mitochondrial function indirectly, first by ACE2 regulation of mitochondrial function, and once it enters the host cell, open-reading frames (ORFs) such as ORF-9b can directly manipulate mitochondrial function to evade host cell immunity and facilitate virus replication and COVID-19 disease. Manipulations of host mitochondria by viral ORFs can release mitochondrial DNA (mtDNA) in the cytoplasm and activate mtDNA-induced inflammasome and suppress innate and adaptive immunity. We argue that a decline in ACE2 function in aged individuals, coupled with the age-associated decline in mitochondrial functions resulting in chronic metabolic disorders like diabetes or cancer, may make the host more vulnerable to infection and health complications to mortality. These observations suggest that distinct localization of viral RNA and proteins in mitochondria must play essential roles in SARS-CoV-2 pathogenesis. Understanding the mechanisms underlying virus communication with host mitochondria may provide critical insights into COVID-19 pathologies. An investigation into the SARS-CoV-2 hijacking of mitochondria should lead to novel approaches to prevent and treat COVID-19.

Correction to: Association of CMV genomic mutations with symptomatic infection and hearing loss in congenital CMV infection.

After publication of the original article [1], we were notified that Fig. 3 has "Fig. 1" posted on the top of it and Figs. 4 and 5 have "Genomic Position" in a different spot.

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology.

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.

Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information.

BACKGROUND: Group structures among genes encoded in functional relationships or biological pathways are valuable and unique features in large-scale molecular data for survival analysis. However, most of previous approaches for molecular data analysis ignore such group structures. It is desirable to develop powerful analytic methods for incorporating valuable pathway information for predicting disease survival outcomes and detecting associated genes. RESULTS: We here propose a Bayesian hierarchical Cox survival model, called the group spike-and-slab lasso Cox (gsslasso Cox), for predicting disease survival outcomes and detecting associated genes by incorporating group structures of biological pathways. Our hierarchical model employs a novel prior on the coefficients of genes, i.e., the group spike-and-slab double-exponential distribution, to integrate group structures and to adaptively shrink the effects of genes. We have developed a fast and stable deterministic algorithm to fit the proposed models. We performed extensive simulation studies to assess the model fitting properties and the prognostic performance of the proposed method, and also applied our method to analyze three cancer data sets. CONCLUSIONS: Both the theoretical and empirical studies show that the proposed method can induce weaker shrinkage on predictors in an active pathway, thereby incorporating the biological similarity of genes within a same pathway into the hierarchical modeling. Compared with several existing methods, the proposed method can more accurately estimate gene effects and can better predict survival outcomes. For the three cancer data sets, the results show that the proposed method generates more powerful models for survival prediction and detecting associated genes. The method has been implemented in a freely available R package BhGLM at https://github.com/nyiuab/BhGLM .

Regenerative Potential of Neonatal Porcine Hearts.

BACKGROUND: Rodent hearts can regenerate myocardium lost to apical resection or myocardial infarction for up to 7 days after birth, but whether a similar window for myocardial regeneration also exists in large mammals is unknown. METHODS: Acute myocardial infarction (AMI) was surgically induced in neonatal pigs on postnatal days 1, 2, 3, 7, and 14 (ie, the P1, P2, P3, P7, and P14 groups, respectively). Cardiac systolic function was evaluated before AMI and at 30 days post-AMI via transthoracic echocardiography. Cardiomyocyte cell cycle activity was assessed via immunostaining for proliferation and mitosis markers, infarct size was evaluated histologically, and telomerase activity was measured by quantitative polymerase chain reaction. RESULTS: Systolic function at day 30 post-AMI was largely restored in P1 animals and partially restored in P2 animals, but significantly impaired when AMI was induced on postnatal day 3 or later. Hearts of P1 animals showed little evidence of scar formation or wall thinning on day 30 after AMI, with increased measures of cell-cycle activity seen 6 days after AMI (ie, postnatal day 7) compared with postnatal day 7 in noninfarcted hearts. CONCLUSIONS: The neonatal porcine heart is capable of regeneration after AMI during the first 2 days of life. This phenomenon is associated with induction of cardiomyocyte proliferation and is lost when cardiomyocytes exit the cell cycle shortly after birth.

Association of CMV genomic mutations with symptomatic infection and hearing loss in congenital CMV infection.

BACKGROUND: Congenital cytomegalovirus (cCMV) infection is the most common congenital infection and a leading cause of long-term neurological and sensory sequelae, the most common being sensorineural hearing loss (SNHL). Despite extensive research, clinical or laboratory markers to identify CMV infected children with increased risk for disease have not been identified. This study utilizes viral whole-genome next generation-sequencing (NGS) of specimens from congenitally infected infants to explore viral diversity and specific viral variants that may be associated with symptomatic infection and SNHL. METHODS: CMV DNA from urine specimens of 30 infants (17 asymptomatic, 13 symptomatic) was target enriched and next generation sequenced resulting in 93% coverage of the CMV genome allowing analysis of viral diversity. RESULTS: Variant frequency distribution was compared between children with symptomatic and asymptomatic cCMV and those with (n = 13) and without (n = 17) hearing loss. The CMV genes UL48A, UL88, US19 and US22 were found to have an increase in nucleotide diversity in symptomatic children; while UL57, UL20, UL104, US14, UL115, and UL35 had an increase in diversity in children with hearing loss. An analysis of single variant differences between symptomatic and asymptomatic children found UL55 to have the highest number, while the most variants associated with SNHL were in the RL11 gene family. In asymptomatic infants with SNHL, mutations were observed more frequently in UL33 and UL20. CONCLUSION: CMV genomes from infected newborns can be mapped to 93% of the genome at a depth allowing accurate and reproducible analysis of polymorphisms for variant and gene discovery that may be linked to symptomatic and hearing loss outcomes.

Repositioning drugs by targeting network modules: a Parkinson's disease case study.

BACKGROUND: Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson's Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD. RESULTS: We built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions-the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments for PD and 6 hold potential repositioning applications. CONCLUSION: In this study, we present a systems pharmacology framework which draws on genetic data from GWAS and gene expression microarray data to reposition drugs for PD. Our innovative approach integrates gene co-expression modules with biomolecular interaction network analysis to identify network modules critical to the PD pathway and disease mechanism. We quantify the positive effects of drugs in a DESS score that is based on known drug-target activity profiles. Our results illustrate that this modular approach is promising for repositioning drugs for use in polygenic diseases such as PD, and is capable of addressing challenges of the hindered gene target in drug repositioning approaches to date.

HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions.

BACKGROUND: Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine. RESULTS: The database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI . CONCLUSIONS: While the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology.

A method for identifying discriminative isoform-specific peptides for clinical proteomics application.

BACKGROUND: Clinical proteomics application aims at solving a specific clinical problem within the context of a clinical study. It has been growing rapidly in the field of biomarker discovery, especially in the area of cancer diagnostics. Until recently, protein isoform has not been viewed as a new class of early diagnostic biomarkers for clinical proteomics. A protein isoform is one of different forms of the same protein. Different forms of a protein may be produced from single-nucleotide polymorphisms (SNPs), alternative splicing, or post-translational modifications (PTMs). Previous studies have shown that protein isoforms play critical roles in tumorigenesis, disease diagnosis, and prognosis. Identifying and characterizing protein isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer. However, there are limitations with traditional methods such as EST sequencing, Microarray profiling (exon array, Exon-exon junction array), mRNA next-generation sequencing used for protein isoform determination: 1) not in the protein level, 2) no connectivity about connection of nonadjacent exons, 3) no SNPs and PTMs, and 4) low reproducibility. Moreover, there exist the computational challenges of clinical proteomics studies: 1) low sensitivity of instruments, 2) high data noise, and 3) high variability and low repeatability, although recent advances in clinical proteomics technology, LC-MS/MS proteomics, have been used to identify candidate molecular biomarkers in diverse range of samples, including cells, tissues, serum/plasma, and other types of body fluids. RESULTS: Therefore, in the paper, we presented a peptidomics method for identifying cancer-related and isoform-specific peptide for clinical proteomics application from LC-MS/MS. First, we built a Peptidomic Database of Human Protein Isoforms, then created a peptidomics approach to perform large-scale screen of breast cancer-associated alternative splicing isoform markers in clinical proteomics, and lastly performed four kinds of validations: biological validation (explainable index), exon array, statistical validation of independent samples, and extensive pathway analysis. CONCLUSIONS: Our results showed that alternative splicing isoform makers can act as independent markers of breast cancer and that the method for identifying cancer-specific protein isoform biomarkers from clinical proteomics application is an effective one for increasing the number of identified alternative splicing isoform markers in clinical proteomics.

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/.

DMAP: a connectivity map database to enable identification of novel drug repositioning candidates.

BACKGROUND: Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. METHODS: To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. RESULTS: In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps CONCLUSIONS: Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.

A method for developing regulatory gene set networks to characterize complex biological systems.

BACKGROUND: Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. RESULTS: In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. CONCLUSIONS: R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.