However, it is becoming increasingly apparent that small molecules such as amino acids, lipids and sugars play a far more important role in all aspects of disease etiology and disease treatment than we realized. This particular chapter focuses on an emerging field of bioinformatics called “chemical bioinformatics” – a discipline that has evolved to help address the blended chemical and molecular biological needs of toxicogenomics, pharmacogenomics, metabolomics and systems biology. In the following pages we will cover several topics related to chemical bioinformatics. First, a brief overview of some of the most important or useful chemical bioinformatic resources will be given. Second, a more detailed overview will be given on those particular resources that allow researchers to connect small molecules to diseases. This section will focus on describing a number of recently developed databases or knowledgebases that explicitly relate small molecules – either as the treatment, symptom or cause – to disease. Finally a short discussion will be provided on newly emerging software tools that exploit these databases as a means to discover new biomarkers or even new treatments for disease. The meaning of chemical bioinformaticsStrengths and limitations of existing chemical bioinformatic databasesUsing databases to learn about the cause and treatment of diseasesThe Small Molecule Pathway Database (SMPDB)The Human Metabolome Database (HMDB)DrugBankThe Toxin and Toxin-Target Database (T3DB)PolySearch and Metabolite Set Enrichment AnalysisPLOS Computational Biology.This article is part of the “Translational Bioinformatics” collection for For most of the past 100 years, the fields of toxicology, pharmacology and clinical biochemistry have focused on identifying the chemicals that cause (toxins), cure (drugs) or characterize (biomarkers) most human diseases. Historically, this kind of work has been reliant on the slow, careful and sometime tedious approaches of classical analytical chemistry and classical biochemistry. Nevertheless, it has led to important discoveries and enormous advances in our understanding of the actions of chemicals on genes, proteins and cells. With the recent emergence of high throughput “omics” technologies, our ability to detect, identify, and characterize small molecules along with their large molecule targets has been radically changed Chemical bioinformatics combines the sequence-centric tools of bioinformatics with the chemo-centric tools of “cheminformatics”. The term cheminformatics, which is an abbreviated form of “chemical informatics”, was first coined by Frank Brown nearly 15 years ago However, there are some distinct “cultural” differences between bioinformatics and cheminformatics. For instance, cheminformatics software is mostly designed for use by chemists, while bioinformatics software is designed for use by molecular biologists. Consequently there is often a terminology gap that makes it difficult for biologists to use cheminformatic software and chemists to use bioinformatics software. Likewise, most cheminformatic software is structure-based or picture-driven while most bioinformatic software is sequence-based or text-driven. As a result, different search and query interfaces have evolved that are quite specific to either cheminformatic or bioinformatic software. Further compounding this culture gap is the fact that most cheminformatics software and chemical compound databases were developed without the expectation that this information would ever be biologically or medically relevant. Likewise, most bioinformatics software and bioinformatic databases were developed without the intention of using this data to facilitate small molecule biomarker identification or small molecule drug discovery. Consequently most biological sequence data is not linked in any meaningful way to drug or disease information – and vice versa. However, thanks to the emergence of new fields such as pharmacogenomics, toxicogenomics, systems biology and metabolomics, there is now a growing desire to bring bioinformatics and cheminformatics closer together. This has spawned the new field of chemical bioinformatics.In this chapter we will cover several topics related to chemical bioinformatics. First, a brief overview of some of the most important chemical bioinformatic resources will be given. This will include a discussion of some of the major databases and classes of databases. Second, a more detailed overview will be given on those particular resources that allow researchers to connect small molecules to diseases. This section will focus on describing a number of recently developed databases or knowledgebases that explicitly relate small molecules – either as the treatment, symptom or cause – to disease. Finally a short discussion will be provided on newly emerging software tools that exploit these databases as a means to discover new biomarkers or even new treatments for disease.Electronic databases lie at the heart of almost any subdiscipline of bioinformatics – and chemical bioinformatics is no exception. Indeed, without databases there is essentially no foundational knowledge to the discipline, and consequently, no compelling reason to write software. Programs such as BLAST Currently there are four major classes of chemical-bioinformatic databases. These include: 1) small molecule (or metabolic) pathway databases; 2) metabolite or metabolomic databases; 3) drug databases; and 4) toxin or toxic substance databases. In an ideal world each of these database classes could/should be useful for relating small molecules to human diseases or disease treatments. For instance, metabolic pathway databases would be expected to be most useful for understanding the “big-picture” relationship between small molecules and disease – either with regard to those small molecule compounds causing disease (i.e. toxins), indicating disease (i.e. biomarkers) or being used in the treatment of disease (i.e. drugs). On the other hand, metabolite or metabolomic databases would be expected to be most useful for associating small molecule biomarkers with specific diseases, such as inborn errors of metabolism or a variety of chronic or infectious diseases characterized by metabolite imbalances. Drug databases would obviously be most relevant for identifying small molecules with disease treatments, although they could also be used to identify small molecule drugs causing adverse drug reactions. Finally toxin or toxic compound databases would be expected to be most useful for identifying the compounds causing diseases or causing symptoms associated with certain poisoning or environmental exposure incidents. This could include acute poisonings or more long-term, environmentally influenced conditions such as cancer, allergies or birth defects.However, as detailed below, not all of the available chemical-bioinformatic databases are particularly suited for these kinds of disease-associated queries. This likely reflects the relatively nascent stage of this field (it's less than five years old) and the fact that disease-related information is much more difficult to gather and codify than either chemical structure or gene sequence information. Certainly all of today's existing chemical-bioinformatic databases contain information about different classes of chemicals  and most contain some limited information about the corresponding protein and/or genetic targets. However, only a very small number of these databases actually include information on the diseases or physiological effects that may be caused, cured or characterized by these chemicals.http://themedicalbiochemistrypage.org/]. Several commercial pathway databases also exist such as TransPath (from BioBase Inc.), PathArt (from Jubilant Biosys Inc.), MetaBase (from GeneGo Inc.) and Ingenuity Pathways Analysis (Ingenuity Systems Inc.), many of which provide nicely illustrated metabolic pathway diagrams. Most of these pathway databases were designed to facilitate the exploration of metabolism and metabolites across many different species. This broad, multi-organism perspective has been critical to enhancing our basic understanding of metabolism and our appreciation of biological diversity. Metabolic pathway databases also serve as the backbone to facilitate many practical applications in biology including comparative genomics and targeted genome annotation. Among the four major classes of chemical-bioinformatic databases that are available, metabolic pathway databases are perhaps the best known and most widely used. They include a number of popular web-based resources such as the Kyoto Encyclopedia of Genes and Genomes – also known as KEGG Those metabolic pathway databases that strive for very broad organism coverage, such as KEGG and Reactome, tend to use pathway diagrams that are very generic and highly schematic, while those that are organism-specific (i.e. human-only), such as SMPDB and the Medical Biochemistry Page, tend to use diagrams that are very specific and much richer in detail, colour and content. Most pathway databases support interactive image mapping with hyperlinked information content that allows users to view chemical information (if a compound is clicked) or brief summaries of genes and/or proteins (if a protein is clicked). Almost all of the databases support some kind of limited text search and a few, such as Reactome, SMPDB and the “Cyc” databases, support the mapping of gene, protein and/or metabolite expression data onto pathway diagrams. As might be expected, the major focus of most of today's small molecule pathway databases is on basic metabolism. As a result, only one of these databases (SMPD) actually includes any pathways associated with drug action or disease.Arabadopsis metabolites, and the Golm Metabolome database The second major class of chemical-bioinformatic databases are metabolomic or metabolite databases. These databases tend to have a major focus on chemicals and chemical descriptors with a lesser (or even absent) focus on biological data. They are primarily used for metabolite identification – especially in metabolomic studies. Some databases are almost exclusively chemical in nature, containing primarily information on the chemical name(s), synonyms, InChI  identifier, structure, and molecular weight. These include Lipid Maps The third major class of chemical bioinformatic databases are the drug or pharmaceutical product databases. In particular, two types of electronic drug databases have started to emerge over the past five years: 1) clinically oriented drug databases and 2) chemically oriented drug databases. Examples of some of the better-known clinically oriented drug databases include DailyMed The final class of chemical-bioinformatic databases we will discuss are the toxic substance databases. These include the Animal Toxin Database (ATDB), SuperToxic Each of these databases addresses the needs of certain communities such as animal physiologists (ATDB), toxicogenomics or toxicology specialists (CTD and T3DB), environmental or industrial regulators (ACToR) or medicinal chemists interested in toxicity prediction (SuperToxic). However, with the exception of T3DB, most of these online toxin or toxic compound databases are relatively lightly annotated, with fewer than a dozen data fields per compound and essentially no physiological, disease or disease symptom information.Clearly not all of the chemical-bioinformatic databases we have described in this section are suitable for deriving information about small molecules and disease. Likewise, many of the databases mentioned above are not exactly suitable for translational bioinformatic questions or for applications relating to medicine, medical biochemistry or clinical research. However, there is at least one database in each of the four major chemical-bioinformatic database classes that does generally meet these criteria. In particular: 1) SMPDB is a pathway database that explicitly relates small molecules to disease and disease treatment; 2) HMDB is a metabolomic database that associates metabolites to disease biomarkers or disease diagnosis; 3) DrugBank is a drug database that links drugs and drug targets to symptoms, diseases and disease treatments and 4) T3DB is a toxic substance database that associates toxins and their biological targets with symptoms, conditions, diseases and disease treatments. A more detailed description of each of these databases is provided below.As noted earlier, SMPDB is a pathway database specifically designed to facilitate clinical “omics” studies, with a specific emphasis on clinical biochemistry and clinical pharmacology. Currently SMPD consists of more than 450 highly detailed, hand-drawn pathways describing small molecule metabolism or small molecule processes that are specific to humans. These pathways can be placed into four different categories: 1) metabolic pathways; 2) small molecule disease pathways; 3) small molecule drug pathways and 4) small molecule signaling pathways. An example of a typical SMPDB pathway (Phenylketonuria) is shown in The SMPDB interface is largely modeled after the interface used for DrugBank At the top of each pathway image is a pathway synopsis contained in a yellow box while at the bottom of each image is a list of references. On the right of each pathway image is a grey-green Highlight/Analyzer tool with a list of the key metabolites/drugs and enzymes/proteins found in the pathway. Checking on selected items when in the SMP-Highlight mode will cause the corresponding metabolite or protein in the pathway image to be highlighted with a red box. Entering concentration or relative expression values (arbitrary units) beside compound or protein names, when in the SMP-Analyzer mode, will cause the corresponding metabolites or proteins to be highlighted with differing shades of green or red to illustrate increased or decreased concentrations. As with most pathway databases, all of the chemical structures and proteins/enzymes illustrated in SMPDB's diagrams are hyperlinked to other on-line databases or tables. Specifically, all metabolites, drugs or proteins shown in the SMP-BROWSE tables or in a pathway diagram are linked to HMDB, DrugBank or UniProt The most powerful search option in SMPDB is SMP-MAP, which offers both multi-identifier searches as well as “Omic”  mapping. In contrast to SMP-BROWSE, which is used for data browsing and single entity highlighting, SMP-MAP can be used for multi-entity highlighting and mapping. In particular SMP-MAP allows users to enter lists of chemical names, gene names, protein names, UniProt IDs, GenBank IDs, Agilent IDs or Affymetrix IDs (with or without concentration data) and to have a table generated of pathways containing those components. The resulting table, like the SMP-BROWSE table, displays a thumbnail image of the matching pathways along with the list of matching components . The table is ordered by the number of matches and a significance score , with the pathway having the most matches being placed at the top. Clicking on the thumbnail image or the SMPDB pathway button brings up a full-screen image for the corresponding pathway with all the matching components  highlighted in red. Concentration data can be displayed using a red-to-yellow gradient by entering concentration data in a text box located beside the map image.SMPDB's Search menu also offers users a choice of searching the database by chemical structure (ChemQuery), text (TextQuery) or sequence (SeqSearch). The ChemQuery option allows users to draw (using MarvinSketch applet) or write (using a SMILES string) a chemical compound and to search SMPDB for drugs and metabolites similar or identical to the query compound. The TextQuery button supports a more sophisticated text search  of the text portion of SMPDB, including the accompanying pathway explanations and reference sections. The SeqSearch button allows users to conduct BLASTP (protein) sequence searches of the protein sequences contained in SMPDB. SeqSearch supports both single and multiple sequence BLAST queries.To summarize, SMPDB allows users to interactively explore, through detailed pathway diagrams, the linkage between metabolites, genes or proteins and metabolic diseases. It also allows users to investigate the connection between drugs and their protein or gene targets through comprehensive illustrations of their mechanism of action. Because of its detailed depictions of both disease and drug pathways and its extensive use of visualization and query tools, SMPDB can potentially support a variety of translational bionformatic/cheminformatic questions. For example, through SMPDB it is possible for users to: 1) identify a metabolic disease or medical condition given a list of metabolites (via SMP-MAP); 2) use experimental gene expression data to identify which diseases, conditions or pathways are most affected by a given drug, dietary or chemical treatment (via SMP-MAP); 3) use metabolomic or metabolite expression data to help understand or rationalize specific metabolic diseases, conditions or biomarkers (through SMP-MAP); 4) determine the similarity of a newly found/synthesized compound to an existing drug (via the ChemQuery search); 5) determine the possible mechanism of action or protein targets for a newly found/synthesized compound (via the ChemQuery search); 6) ascertain whether a certain protein found in bacteria, fungi or viruses could be a drug target (via the SeqSearch query); 7) ascertain whether a newly identified human protein, such as an isoform or paralogue, may be a drug target or a disease indicator (through the SeqSearch query); or 8) use the pathway visualization and mapping tools to explain or teach others about metabolic diseases, basic metabolism or drug action.1H and 13C NMR and MS/MS spectra. The entire database, including text, sequence, structure and image data occupies nearly 30 Gigabytes of data – most of which can be freely downloaded.The Human Metabolome Database (HMDB) is the by-product of the Human Metabolome Project – a 3-year (2005–2008), $7.5 million dollar project that was aimed at collating, identifying and annotating all the endogenous metabolites in the human body The HMDB is a fully searchable database with many built-in tools for viewing, sorting and extracting metabolites, biofluid concentrations, enzymes, genes, NMR or MS spectra and disease information. As with any web-enabled database, the HMDB supports standard text queries (through the text search box located near the top of each page). It also offers extensive support for higher-level database search and selection functions through a navigation bar (located at the top of each page). The navigation bar has six pull-down menu tabs . The “Browse” tab allows users to select from six browsing options including “HMDB Browse”, “Disease Browse”, “PathBrowse”, “Biofluid Browse”, “HML Browse” and “ClassBrowse”. “HMDB Browse” allows users to search through the HMDB compound by compound through a series of hyperlinked, synoptic summary tables. These metabolite tables can be rapidly browsed, sorted or reformatted in a manner similar to the way PubMed abstracts may be viewed. Clicking on the MetaboCard button found in the leftmost column of any given HMDB summary table opens a webpage describing the compound of interest in much greater detail. Each MetaboCard entry contains more than 100 data fields with half of the information being devoted to chemical or physico-chemical data and the other half devoted to biological or biomedical data. These data fields include a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations , disease associations, pathway information, enzyme data, gene sequence data, protein sequence data, SNP and mutation data as well as extensive links to images, references and other public databases such as KEGG Outside of “HMDB Browse”, there are five other browsing options that allow users to explore or navigate the database. “Disease Browse” allows users to view known metabolic disorders (as well as other diseases) and the metabolites that are typically associated with these conditions. It also allows users to enter lists of metabolites and to identify which diseases are characterized by perturbations to these metabolite levels. “PathBrowse” allows users to browse through the custom-drawn HMDB pathway images. Each pathway is named and each image is zoomable and extensively hyperlinked. Users may also search PathBrowse using lists of compounds (obtained from a metabolomic experiment) and view hyperlinked tables that display all of the pathways that are potentially affected. “Biofluid Browse” allows users to browse metabolite entries based on their concentrations and the biofluids in which they are found. Users may select entries by biofluid type and sort the table by compound name, HMDB ID, concentration, disease, age, or gender. “HML Browse” allows users to browse or search through the Human Metabolome Library (HML). The HML is a library of ∼1000 reference metabolites stored in −80°C freezers at the Human Metabolome Project Centre in Edmonton, Canada. “ClassBrowse”, is designed to allow users to view compounds according to their chemical class designation. Each displayed compound name is hyperlinked to an HMDB MetaboCard. Users may search for compounds (via a text box) or select to view certain compound classes using a pull-down menu located at the top of the ClassBrowse page.http://www.rectangular.com/kinosearch/), a chemical structure search utility based on ChemAxon's MarvinView, a relational data extraction tool, an MS spectral matching tool and an NMR spectral search tool (for identifying compounds via MS or NMR data from other metabolomic studies). These can all be accessed via the database navigation bar located at the top of every HMDB page.In addition to the data browsing and sorting features already described, the HMDB also offers a chemical structure search utility, a local BLAST search HMDB's simple text search supports text matching, text match rankings, mis-spellings (offering suggestions for incorrectly spelled words) and highlights text where the word is found. In addition to this simple text search, HMDB's TextQuery function uses the same KinoSearch engine, but also supports more sophisticated text querying functions  as well as data-field-specific queries such as finding the query word only in the “Compound Source” field.The HMDB's structure similarity search tool (ChemQuery) is the equivalent to BLAST for chemical structures. Users may sketch  or paste a SMILES string (40) of a query compound into the ChemQuery window. Submitting the query launches a structure similarity search tool that looks for common substructures from the query compound that match the HMDB's metabolite database. High scoring hits are presented in a tabular format with hyperlinks to the corresponding MetaboCards (which in turn links to the protein target). The ChemQuery tool allows users to quickly determine whether their compound of interest is a known metabolite or chemically related to a known metabolite. In addition to these structure similarity searches, the ChemQuery utility also supports compound searches on the basis of chemical formula and molecular weight ranges.HMDB's BLAST search (SeqSearch) allows users to search through the HMDB via sequence similarity as opposed to chemical similarity. A given gene or protein sequence may be searched against the HMDB's sequence database of metabolically important enzymes and transporters by pasting the FASTA formatted sequence (or sequences) into the SeqSearch query box and pressing the “submit” button. A significant hit reveals, through the associated MetaboCard hyperlink, the name(s) or chemical structure(s) of metabolites that may act on that query protein. With SeqSearch metabolite-protein interactions from model organisms  may be mapped to these organisms via the human data in the HMDB.The HMDB's data extraction utility (Data Extractor) employs a simple relational database system that allows users to select one or more data fields and to search for ranges, occurrences or partial occurrences of words or numbers. The Data Extractor uses clickable web forms so that users may intuitively construct SQL-like queries. The data extraction tool allows users to easily construct complex queries as “find all diseases where the concentration of homogentisic acid in urine is greater than 1 mM”.1H and 13C NMR spectra for 900 pure compounds (most collected in water at pH 7.0). It also contains approximately 3800 predicted 1H and 13C NMR spectra for 1900 other compounds for which authentic samples could not be acquired. The HMDB's mass spectra library contains 2400 MS/MS (Triple-Quad) spectra collected at 3 different collision energies for more than 800 pure compounds. The HMDB's spectral search utilities allow both pure compounds and mixtures of compounds to be identified from their MS or NMR spectra via peak matching algorithms. Compounds may also be identified or searched for by entering their chemical formula or their mass (either their exact mass or a mass range). The NMR and MS search utilities allow users to upload spectra (for the MS search) or peak lists (for the NMR search) and to search for matching compounds from the HMDB's collection of MS and NMR spectra. In particular, the HMDB contains more than 2000 experimentally collected To summarize the HMDB allows users to link endogenous metabolites (both their identity and their concentration) to a variety of disease conditions, including metabolic disorders, genetic diseases, chronic (age-related) disorders and a variety of infectious diseases. It also provides links between metabolites and their targets – both through descriptions of the compounds and their known biological roles and through the identification of known pathways or catalyzing enzymes. In addition, the HMDB also supports the direct identification of potential diagnostic biomarkers based on their mass, mass spectra or NMR spectra. Because of this linkage, the HMDB can potentially support a variety of translational bionformatic or cheminformatic queries. For example, through the HMDB it is possible for users to: 1) identify a novel biomarker for a given condition or disease given an NMR or GC/MS or MS/MS spectrum of the purified compound (via the MS/NMR search tools); 2) identify metabolites from a biofluid mixture that has been analyzed by NMR, GC/MS or MS/MS (via the MS/NMR search tools); 3) identify a disease or condition given a list of metabolites (via Disease Browse); 4) identify a pathway or process that has been altered/perturbed given a list of metabolites obtained from a metabolomic experiment (via PathBrowse); 5) determine normal and abnormal concentration ranges for metabolites in different biofluids (via Biofluid Browse); 6) obtain authentic standards of unique metabolites to confirm the diagnosis of a certain disease (via HML Browse); 7) determine the similarity of a newly found/synthesized compound to an existing metabolite (via the structure similarity search); 8) determine the possible mechanism of action or protein targets for a newly discovered/synthesized metabolite or metabolite analogue (via the structure similarity search); 9) diagnose or determine the cause of illnesses thought to be brought on by metabolite changes (through the text search); 10) extract detailed information on metabolites, metabolic diseases or metabolic pathways (via the data extractor); 11) extract information on common metabolite classes (via the data extractor or ClassBrowse); 12) ascertain whether a certain protein or protein homologue may also be involved in a metabolic process or pathway (via the sequence search).As previously noted, DrugBank As with the HMDB, the DrugBank website contains many built-in tools and a variety of customized features for viewing, sorting, querying and extracting drug or drug target data. These include a number of higher-level database searching functions such as a local BLAST The BLAST search (SeqSearch) is particularly useful for drug discovery applications as it can potentially allow users to quickly and simply identify drug leads from newly sequenced pathogens. Specifically, a new sequence, a group of sequences or even an entire proteome can be searched against DrugBank's database of known drug target sequences by pasting the FASTA formatted sequence (or sequences) into the SeqSearch query box and pressing the “submit” button. A significant hit can reveal the name(s) or chemical structure(s) of potential drug leads that may act on that query protein (or proteome). The structure similarity search tool (ChemQuery) can be used in a similar manner to SeqSearch. For instance, users may sketch a chemical structure or paste a SMILES string In addition to these search features, DrugBank also provides a number of general browsing tools for exploring the database as well as several specialized browsing tools such as PharmaBrowse and GenoBrowse for more specific tasks. For instance, PharmaBrowse is designed to address the needs of pharmacists, physicians and medicinal chemists who tend to think of drugs in clusters of indications or drug classes. This particular browsing tool provides navigation hyperlinks to more than 70 drug classes, which in turn list the FDA-approved drugs associated with the drugs. Each drug name is then linked to its respective DrugCard. GenoBrowse, on the other hand, is specifically designed to address the needs of geneticists or those specialists interested in specific Drug-SNP relationships. This browsing tool provides navigation hyperlinks to more than 60 different drugs, which in turn list the target genes, SNPs and the physiological effects associated with these drugs.In addition to its general utility as a general drug encyclopedia, DrugBank also contains several tables, data fields or data types that are particularly useful for pharmacogenomic or pharmacogenetic studies. These include synoptic descriptions of a given drug's Pharmacology as well as its Mechanism of Action, Contraindications, Toxicity, Phase I Metabolizing Enzymes , and associated Drug Targets . The information contained in DrugBank's Pharmacology, Mechanism of Action, Contraindications and Toxicity fields often includes details about any known adverse reactions. This may include descriptions of known phase I or phase II enzyme interactions, alternate metabolic routes or the existence of secondary drug targets. Secondary drug targets represent proteins (or other macromolecules) that are different than the primary target for which the drug was initially designed or targeted towards. Some drugs may have five or more targets, of which only one might be relevant to treating the disease. DrugBank uses a relatively liberal interpretation of drug targets in order to help identify these secondary drug targets. In particular, for DrugBank a drug target is defined as any macromolecule identified in the literature that binds, transports or transforms a drug. The binding or transformation of a drug by a secondary drug target or an “off-target” protein is one of the most common causes for unwanted side effects or adverse drug reactions (ADRs) DrugBank also provides detailed sequence and SNP data on known drug metabolizing enzymes and known drug targets. In particular DrugBank contains detailed summary tables about each of the SNPs for each of the drug targets or drug metabolizing enzymes that have been characterized by various SNP typing efforts, such as the SNP Consortium The purpose of these SNP tables is to allow one to go directly from a drug of interest to a list of potential SNPs that may contribute to the reaction or response seen in a given patient or in a given population. In particular, these SNP lists may serve as hypothesis generators that allow SNP or gene characterization studies to be somewhat more focused or targeted. By comparing the experimentally obtained SNP results to those listed in DrugBank for that drug (and its drug targets) it may be possible to ascertain which polymorphism for which drug target or drug metabolizing enzyme may be contributing to an unusual drug response. Obviously these database-derived SNP suggestions may require additional experimental validation to prove their causal association.Drugbank also includes two tables that provide much more explicit information on the relationship between drug responses/reactions and gene variant or SNP data. The two tables, which are accessible from the GenoBrowse submenu located on DrugBank's Browse menu bar, are called SNP-FX (short for SNP-associated effects) and SNP-ADR (short for SNP-associated adverse drug reactions). SNP-FX contains data on the drug, the interacting protein(s), the “causal” SNPs or genetic variants for that gene/protein, the therapeutic response or effects caused by the SNP-drug interaction  and the associated references describing these effects in more detail. SNP-ADR follows a similar format to SNP-FX but the clinical responses are restricted only to adverse drug reactions (ADR). SNP-FX contains literature-derived data on the therapeutic effects or therapeutic responses for more than 70 drug-polymorphism combinations, while SNP-ADR contains data on adverse reactions compiled from more than 50 drug-polymorphsim pairings. All of the data in these tables is hyperlinked to drug entries from DrugBank, protein data from SwissProt, SNP data from dbSNP and bibliographic data from PubMed. A screen shot of the SNP-ADR table is shown in To summarize, DrugBank allows users to link drugs to a variety of disease conditions or health indications. It also provides links between drugs and their targets – both through descriptions of the mechanism of action and through the identification of known protein (or gene) targets. Because of this kind of extensive data linkage, DrugBank can potentially support a number of translational bionformatic or cheminformatic questions. For example, through DrugBank it is possible for users to: 1) determine the similarity of a newly found/synthesized compound to an existing drug (via the structure similarity search); 2) determine the possible mechanism of action or protein targets for a newly found/synthesized compound (via the structure similarity search); 3) diagnose or determine the cause of illnesses thought to be brought on by adverse drug reactions (through the text search or SNPADR/SNPFX); 4) treat or find references to the treatment of illnesses based on symptoms or disease diagnosis (via the text search); 5) extract information on common drug targets (via the data extractor or the sequence search); 6) extract information on common drug classes or structures (via the data extractor or the structure search); 7) ascertain whether a certain protein found in bacteria, fungi or viruses could be a drug target (via the sequence search); or 8) ascertain whether a newly identified human protein, such as an isoform or paralogue, may be a drug target (through the sequence search).A toxic substance is a small molecule, peptide, or protein that is capable of causing injury, disease, genetic mutations, birth defects or death. Toxins, both natural and man-made, represent an important class of poisonous compounds that are ubiquitous in nature, in homes, and in the workplace. Common toxins include pollutants, pesticides, preservatives, drugs, venoms, food toxins, cosmetic toxins, dyes, and cleaning compounds. Because toxic compounds are essentially disease-causing agents, it has long been recognized that there is a need to associate toxic compound data with molecular toxicology and clinical symptomology. While this has been done in a variety of toxicology textbooks and medical reference manuals, it has only recently been done using electronic databases and the tools associated with bioinformatics and cheminformatics.T3DB As with HMDB and DrugBank, the T3DB is designed to be a fully searchable web resource with many built-in tools and features for viewing, sorting and extracting toxin and toxin-target annotation, including structures and gene and protein sequences. A screenshot montage illustrating the types of viewing and searching options available is shown in A ToxCard begins with various identifiers and descriptors , followed by additional structure and physico-chemical property information. The remainder of data on the toxin is devoted to providing detailed toxicity and toxicological data, including route of delivery, mechanism of action, medical information, and toxicity measurements. All of a toxin's targets are also listed within the ToxCard. Each of these targets are described by some 30 data fields that include both chemical and biological  information, as well as details on their role in the mechanism of action of the toxin. In addition to providing comprehensive numeric, sequence and textual data, each ToxCard also contains hyperlinks to other databases, abstracts, digital images and interactive applets for viewing the molecular structures of each toxic substance.A key feature that distinguishes the T3DB from other on-line toxin or toxicology resources is its extensive support for higher-level database search and selection functions. In addition to the data viewing and sorting features already mentioned, the T3DB also offers a local BLAST search that supports both single and multiple sequence queries, a boolean text search based on KinoSearch, a chemical structure search utility based on ChemAxon's MarvinView, and a relational data extraction tool similar to that found in DrugBank and the HMDB T3DB's simple text search box (located at the top of most T3DB pages) supports text matching, text match rankings, mis-spellings and highlights text where the word is found. In addition to this simple text search, T3DB's TextQuery function supports more sophisticated text querying functions including “and” and “or” queries, multi-word matching and parenthetical groupings as well as data-field-specific queries such as finding the query word only in the “Compound Source” field. Additional details and examples are provided on the T3DB's TextQuery page.T3DB's sequence searching utility (SeqSearch) allows users to search through T3DB's collection of 1450 known (human) toxin targets. This service potentially allows users to identify both orthologous and paralogous targets for known toxins or toxin targets. It also facilitates the identification of potential toxin targets from other animal species. With SeqSearch, gene or protein sequences may be searched against the T3DB's sequence database of identified toxin-target sequences by pasting the FASTA formatted sequence (or sequences) into the SeqSearch query box and pressing the “submit” button.T3DB's structure similarity search tool (ChemQuery) can be used in a similar manner as its SeqSearch tool. Users may sketch a chemical structure  or paste a SMILES string of a query compound into the ChemQuery window. Submitting the query launches a structure similarity search that looks for common substructures from the query compound that matches the T3DB's database of known toxic compounds. Users can also select the type of search (exact or Tanimoto score) to be performed. High scoring hits are presented in a tabular format with hyperlinks to the corresponding ToxCards . The ChemQuery tool allows users to quickly determine whether their compound of interest is a known toxin or chemically related to a known toxin and which target(s) it may act upon. In addition to these structure similarity searches, the ChemQuery utility also supports compound searches on the basis of SMILES strings (under the SMILES tab) and molecular weight ranges (under the Molecular Weight tab).The T3DB's data extraction utility (Data Extractor) employs a simple relational database system that allows users to select one or more data fields and to search for ranges, occurrences or partial occurrences of words, strings, or numbers. The data extractor uses clickable web forms so that users may intuitively construct SQL-like queries. Using a few mouse clicks, it is relatively simple to construct complex queries  or to build a series of highly customized tables. The output from these queries is provided in HTML format with hyperlinks to all associated ToxCards.To summarize, T3DB allows users to link toxic substances to a variety of disease conditions, including acute toxicity, long-term toxicity, birth defects, cancer, other illnesses. It also provides links between toxic substances and their targets – both through descriptions of the mechanism of action and through the identification of known protein (or gene) targets. Because of this kind of extensive data linkage, T3DB can potentially support a variety of bioinformatic or cheminformatic queries. For example, through T3DB it is possible for users to: 1) determine the similarity of a newly found/synthesized compound to an existing toxin (via the structure similarity search); 2) determine the possible mechanism of action or protein targets for a newly found/synthesized compound (via the structure similarity search); 3) diagnose or determine the cause of illnesses thought to be brought on by exposure to a given toxin (through the text search); 4) treat or find references to the treatment of illnesses brought on by exposure to a given toxin (via the text search); 5) extract information on common toxin targets (via the data extractor); 6) extract information on common toxin classes (via the data extractor); 7) ascertain whether a certain protein or protein homologue may also be a toxin target (via the sequence search); or 8) ascertain whether a newly identified peptide or protein may be a toxin (through the sequence search).With the recent emergence of chemical-bioinformatic databases having a solid translational  functionality, the way has been cleared for the development of software tools that exploit these databases. This is a natural process in both bioinformatics and cheminformatics as databases always appear before any software applications are typically developed. Given that the field of chemical bioinformatics is still quite young and the number of databases with disease and small molecule information is still relatively small, it is not surprising to find that the number of software tools developed to exploit these databases is still quite small. Here we will briefly describe two recently developed software tools – PolySearch and MSEA – that exploit the data in SMPDB, HMDB and DrugBank to perform a number of useful applications.PolySearch PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences. A central premise to PolySearch's search strategy is the assumption that the greater the frequency with which an X and Y association occurs within a collection of abstracts, the more significant the association is likely to be. For instance, if COX2 is mentioned in PubMed as being associated with colon cancer 510 times but thioredoxin is associated with colon cancer only once, then one is more likely to have more confidence in the COX2-colon cancer association. Frequency alone is not always the best way to rate a paper or a website for its relevancy. Therefore, in addition to counting the frequency of apparent associations, PolySearch employs a specially developed text-ranking scheme to score the most relevant sentences and abstracts that associate both the query and match terms with each other.In summary, PolySearch is able to exploit the name and synonym sets from a number of small-molecule and disease databases  thereby allowing users to perform a range of text mining queries on the PubMed abstract database. In particular, PolySearch allows users to find newly described or previously unknown  associations between: 1) drugs and disease; 2) metabolites and disease; 3) genes/proteins and disease; 4) drugs and drug targets; 5) metabolites and metabolizing enzymes; 6) SNPs and disease and 7) mutations and disease. In addition, through its other query fields or query options, PolySearch is able to perform a large number (>50) of other text queries that may be relevant to a variety of applications in translational bioinformatics.The Metabolite Set Enrichment Analysis (MSEA) server While the exact statistical or enrichment analysis methods used in MSEA are well beyond the scope of this chapter, suffice it to say that MSEA essentially allows one to take lists of metabolites and to identify which pathways, diseases or medical conditions are most likely to be associated with that metabolite set. It is also possible to do the same kind of operation with a list of metabolites and their absolute (or relative) concentrations. While disease/metabolite associations can be made through HMDB and SMPDB, these primitive search tools do not have the same statistical rigor that characterizes a full-fledged enrichment analysis. Furthermore, the MSEA pathway and disease data set is somewhat larger than what is found in the HMDB or SMPDB. This means that MSEA will be far more likely to find a useful  pathway or disease than what could be done with HMDB or SMPDB.Overall, MSEA is an example of an analytical software tool that exploits chemical-bioinformatic data to perform robust statistical analyses of metabolomic or clinical chemistry data. Given their close similarity, it is reasonable to expect MSEA could eventually be integrated with GSEA, thereby allowing a comprehensive analysis of both gene and metabolite expression changes on a single integrated program or website. No doubt this kind of integrated “omic” analysis tool is not far away from being developed.With today's focus on genes and proteins as the “primary” causes or biomarkers of disease, the relationship between small molecules and human disease is often overlooked. However it is important to remember that more than 95% of all diagnostic clinical assays are designed to detect small molecules . Likewise nearly 90% of all known drugs are small molecules, 50% of all drugs are derived from pre-existing metabolites and 30% of identified genetic disorders involve diseases of small molecule metabolism. Clearly, small molecules are important and given the rapid growth in metabolomics, pharmacogenomics and systems biology, it is likely that their role in disease diagnosis and disease treatment will continue to grow. Given these exciting growth prospects and given the importance of small molecules in medicine and translational research, scientists are now realizing that there is a critical need to link information about small molecules to their corresponding “big molecule” targets. This has led to the emergence of a new field of bioinformatics – called chemical bioinformatics.This chapter has covered several topics related to chemical bioinformatics and the role that chemical bioinformatics can play in identifying the chemicals that cause (toxins), cure (drugs) or characterize (biomarkers) many human diseases. The first part of the chapter gave a brief overview of some of the most important or widely used chemical bioinformatic resources along with a more detailed discussion of some of the major classes of chemical-bioinformatic databases. In particular four major database classes were described: 1) small molecule (or metabolic) pathway databases; 2) metabolite or metabolomic databases; 3) drug databases; and 4) toxin or toxic substance databases. Examples of each of these databases were given and many of their strengths and limitations were discussed. While most of these chemical-bioinformatic databases provide links between small molecules and their large molecule targets, relatively few provide linkages to clinical, physiological or disease information.The second part of this chapter focused on describing a number of recently developed databases that explicitly relate small molecules to disease. This included detailed descriptions of four databases: 1) the Small Molecule Pathway Database (SMPDB); 2) the Human Metabolome Database (HMDB); 3) DrugBank and 4) the Toxin, Toxin-Target Database (T3DB). SMPDB is a graphically oriented pathway database that contains ∼450 metabolic pathways, disease pathways and drug pathways. The HMDB is a comprehensive metabolomic database that is primarily oriented to answering questions in clinical metabolomic and clinical biochemistry. DrugBank is a comprehensive drug database containing detailed information about drugs, drug targets and clinical pharmacology. The T3DB is a toxicology database containing detailed information about toxins, toxin targets and their corresponding toxicological information. Each of these databases was described in terms of its content, general design and query/search functions. Additionally, explicit examples of various translational or disease-related applications were provided for each database. The final part of this chapter provided a short discussion of some of the newly emerging software tools that exploit these databases, including PolySearch and MSEA . PolySearch is a text-mining tool that exploits the synonym data found in these small molecule databases to allow expansive PubMed queries to be performed. MSEA is a metabolomic analysis tool that exploits the pathway and disease information found in SMPDB and HMDB to perform pathway and disease identification from raw metabolomic data.While the sub-discipline of chemical bioinformatics is still quite young, and the number of tools for translational applications is still relatively small, it should be clear that what is now out there has considerable potential for a wide range of clinical, biomedical, pharmaceutical and toxicological applications. Certainly as more tools are developed and as more databases evolve, it is likely that chemical bioinformatics will soon be able to establish itself as one of the most medically useful sub-disciplines in the entire field of bioinformatics.1) A compound with a molecular weight of 136.053 daltons has been isolated from the urine of a 3 month-old baby with unusually light coloring of the skin, eczema (an itchy skin rash), and a musty body odor. What compound is it and what disease might this baby have?2) Your natural product chemist neighbor has just isolated a compound from the Tanzanian periwinkle – a rare plant species found only in the highlands of Eastern Tanzania. Locals use the plant as a treatment for a variety of blood disorders. The structure of the compound is given by the following SMILES string: COC1 = CC = C2C( = CC1 = O)C(CCC1 = CC(OC) = C(OC)C(OC) = C21)NC(CO)What compound is this similar to, what diseases could it be used to treat and what proteins might it bind?3) A viral protein with the following sequence has been isolated from a number of dead and dying African Green Monkeys that were housed at a local zoo.PQVTLYQRPLVTIRVGGQLKEALIDTGADDTVLENMNLPGRWKPKMIGAIAGFIKVKQYDQITVEICGHKGIGTILVGPTPVNIIGRNLLTLIGCTLNFThe illness seems to be spreading to other monkey colonies in the zoo. What drugs could be used to treat the sick monkeys and to prevent the spread of the disease?4) A farmer who has just finished harvesting his barley field has come into the clinic complaining of skin irritation, burning and itching, a rash and a series of skin blisters. He also has eye pain, conjunctivitis, burning sensations about the eyes, and blurred vision. Other symptoms have included nausea, vomiting and fatigue. Suspecting that he may have been exposed to some toxin or pesticide a chemical analysis has been performed of his blood, urine and lacrimal (tear) fluid. MS analysis of all three fluids has identified an unusual compound with a molecular weight of 296.126 daltons. What compound might this be?5) What kind of drugs can be used to treat breast cancer? Describe your search strategy and your rationale for this search strategy.Answers to the Exercises can be found in Villas-Boas SG, Nielson J, Smedsgaard J, Hansen MAE, Roessner-Tunali U, editors (2007) Metabolome analysis: an introduction. New York: John Wiley & Sons.Wishart DS (2008) DrugBank and its relevance to pharmacogenomics. Pharmacogenomics 9: 1155–1162.Krawetz S, editor (2009) Bioinformatics for systems biology. Totowa: Humana Press.Wishart DS (2008) Applications of metabolomics in drug discovery and development. Drugs R D 9: 307–322.Baxevanis A (2003) Current protocols in bioinformatics. New York: John Wiley & Sons – see Chapter 14.Cheminformatics – a field of information technology that uses computers to facilitate the collection, storage, analysis and manipulation of large quantities of chemical data.DrugBank – A database containing chemical and biological data on drugs and drug targets.GSEA – Gene Set Enrichment Analysis. GSEA is a statistically based bioinformatic method designed to directly investigate the enrichment of pre-defined groups of functionally related genes (or gene sets) from gene expression data.HMDB – The Human Metabolome Database. A database containing chemical and biological data on human metabolites aimed at clinical metabolomic studies.MS – Mass Spectrometry. An analytical method that measures molecular weight of compounds based on their mass to charge ratio. Mass spectrometry is one of the standard methods to determine the molecular formula of new compounds and to confirm the identity of synthesized chemicals or natural products.Metabolome – the collection of all small molecule metabolites found in a given cell, tissue, organ or organism.Metabolomics - a branch of “omics” research that is primarily concerned with the high-throughput identification and quantification of small molecule (<1500 Da) metabolites in the metabolome.MSEA – Metabolite Set Enrichment Analysis. MSEA is a statistically based bioinformatic method designed to directly investigate the enrichment of pre-defined groups of functionally related metabolites (or metabolite sets) from metabolomic data.NMR – Nuclear Magnetic Resonance Spectroscopy. An analytical method that measures nuclear magnetism under very high magnetic fields. NMR is the standard method used by chemists today to identify and characterize small molecules.Pharmacogenomics – A newly emerging field of pharmacology that integrates genotyping and gene expression data with classical pharmacological and adverse drug reaction studies.SMPDB – The Small Molecule Pathway Database. A database containing pathway diagrams and interactive viewing tools for small molecules involved in metabolism, drug action and disease.T3DB – The Toxin, Toxin-Target Database. A database with chemical and biological data on common toxins, poisons, household chemicals, pollutants and other harmful substances.Toxicogenomics – A newly emerging field of toxicology that integrates genotyping and gene expression data with classical toxicological and toxicity studies.Text S1Answers to Exercises.(DOC)Click here for additional data file.


Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued.We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology.http://chem2bio2rdf.org/owl. The document is available at http://chem2bio2owl.wikispaces.com.Chem2Bio2OWL is available at  Recent efforts -3 in theAn ontology is a formal description of knowledge as a set of concepts within a domain, and the relationships between those concepts. Web Ontology Language (OWL) is a language for making these descriptions designed for use within Semantic Web. A variety of ontologies in the life sciences have been developed. Gene Ontology (GO)  is arguaSystems Chemical Biology   such as experimental conditions and references were separated out as individual classes, and were placed under Interaction; otherwise, they were presented as object properties. Relational Ontology (RO)  was impoData properties appeared in the original database sources were not fully covered, instead, only the important ones related to our purpose . This simplifies the ontology without losing essential knowledge. The terms including data property name, class name and relation name were manually mapped to terms in relevant ontologies in the OBO and NCBO BioPortal, and the terms in the existing ontologies are preferred if multiple terms happened. For example, for a chemical formula we chose chemicalFormula as this term is used in BioPAX. In addition, the term must conform to our name convention. If there were multiple results or no results at all, we would use the terms from primary data bases. A table was created to map data source terms to the standardized and later was applied to annotate instances. The properties of class, object and data property were further edited in protégé .http://cheminfov.informatics.indiana.edu/rest/Chem2Bio2RDF/cid_gene/5591:PPARG).Classification of chemogenomic interactions (compound-protein or drug-target) is extremely important and yet complicated . We conshttp://chem2bio2rdf.org/chem2bio2owl#compound5591). The compounds with unknown CIDs were assigned CIDs by searching PubChem using InChi, a universal structure representation. A fake CID was assigned if the compound did not exist in PubChem. Drug, protein and side effect are using DrugBank ID, UNIPROT entry name, and UMLS ID as primary IDs. Pathway name is used as pathway identifier. Diseases can be presented as MESH, OMIM ID, UMLS or free text, but no universal disease identifier has been agreed to present them. Since the Disease Ontology  .optional {?bioAssay bp:xref [c2b2r: title ?title]}}this interaction has evidence which is a bioassay; the assay has description and outcome, and has reference if exists.Following the steps in the previous query, this query is interpreted as: Find NR1I2 agonists and remove compounds with weight ≥ 500". The following SPARQL was used to retrieve 37 agonists. Their structures are quite different with 6 antagonists retrieved from another query, indicating the significance of classifying the ligands.Pregnane × receptor (NR1I2) is a transcriptional regulator of the expression of xenobiotic metabolism and transporter genes. It has multiple binding sites, accounting for different functions. Its agonists at the ligand-binding domain would trigger up-regulation of genes, increase the metabolism and excretion of therapeutic agents, and cause drug-drug interactions, but its antagonists counteract such interactions . Due to {#or ReceptorAntagonistActivity for antagonist search?interaction rdf:type c2b2r:ReceptorAgonistActivity; ro:has_participant ?chemical .?chemical rdf:type bp:SmallMolecule;c2b2r:hasPhysicalProperty [c2b2r:molecularWeight ?weight];bp:structure .FILTER(?weight < 500).}this interaction is a receptor agonist activity and has participant which is a small molecule; the molecule has physical property weight smaller than 500, as well as structure with openeye_can_smiles format.This query is interpreted as: Thiazolinediones are a class of insulin sensitizing drugs widely used to control diabetes. However, several drugs in the class have suffered from side effects resulting in drug withdrawal (Troglitazone) or restriction (Rosiglitazone). These drugs have a high degree of chemical similarity, but very different side-effects. Troglitazone is associated with an idiosyncratic reaction leading to drug-induced hepatitis or other liver toxicities  while RoChem2Bio2OWL constructs a complex network with semantic meaning of every component, in which nodes are instances and they are linked by certain semantic relations. This rich set allows the application of various graph mining techniques to discover interesting patterns , includiMany current methods and tools often convert data into RDFs directly from original data bases. They prove useful in their own right to link multiple datasets, but without a formal ontology to model the concepts and their relations, the linked data would not fully demonstrate the capability of semantics, limiting its further usage in data integration and reasoning. In addition, adding evidence and provenance to support assertions is of great importance to keep track of the record, but it has not been well implemented in the current RDFization process. Chem2Bio2OWL provides a high level structure of the entities and relations according to use case queries and instance data structures, and considers evidence and provenance. However, the case driven ontology building process has to make compromises on accuracy to many 'top down' approaches which aims to model domain knowledge oriented from philosophical perspective, nevertheless, our work serves as a local engineering solution to address the urgent needs in this area. The integrative searching of systems chemical biology data can be performed in a very intuitive and efficient way. Other than Chem2Bio2RDF, some public datasets  could be annotated using Chem2Bio2OWL.Further efforts should be made to align Chem2Bio2OWL with basic ontology  and other Bio-ontologies. For example, the major classes PhysicalEntity and Disease are continuant under BFO, and Pathway, Interaction and BioAssay are occurrent, while other data properties should also be incorporated into the basic ontology so that their usage could be maximized. For example, Utility classes and its subclassess originally serve as helper classes for data integration which are actually modeling artifacts. We did not intend to further model individual major classes, as many of them have their own domain ontology already , which can be incorporated into Chem2Bio2OWL accordingly. Since Chem2Bio2OWL initially was fully based on BioPAX which is originally designed for data integration and data exchange of the biological pathway data and has been widely used, alignment with other basic ontologies needs a collaborative work with BioPAX as well as OBO community.In summary, we have demonstrated how semantic annotation of systems chemical biology data allows scientifically meaningful, complex queries to be succinctly specified in SPARQL. We present an OWL ontology that was used to annotate our Chem2Bio2RDF set, and is also available for annotation of other integrative chemogenomics and systems chemical biology sets. This ontology was developed through a set of specific scientific use cases, which we believe has made it particularly scientifically relevant. We are currently in the process of aligning this ontology to other widely used ontologies including the Basic Formal Ontology and a variety of biological sets.The authors declare that they have no competing interests.BC, YD and DJW conceived the study, BC carried out the implementation, BC and DJW wrote the manuscript. All contributed to the intellectual evolution of this project. All authors have read and approved the final manuscript.


The quest to understand the neurobiology of schizophrenia and bipolar disorder is ongoing with multiple lines of evidence indicating abnormalities of glia, mitochondria, and glutamate in both disorders. Despite high heritability estimates of 81% for schizophrenia and 75% for bipolar disorder, compelling links between findings from neurobiological studies, and findings from large-scale genetic analyses, are only beginning to emerge.Ten publically available gene sets (pathways) related to glia, mitochondria, and glutamate were tested for association to schizophrenia and bipolar disorder using MAGENTA as the primary analysis method. To determine the robustness of associations, secondary analyses were performed with: ALIGATOR, INRICH, and Set Screen. Data from the Psychiatric Genomics Consortium (PGC) were used for all analyses. There were 1,068,286 SNP-level p-values for schizophrenia , and 2,088,878 SNP-level p-values for bipolar disorder .The Glia-Oligodendrocyte pathway was associated with schizophrenia, after correction for multiple tests, according to primary analysis  and also achieved nominal levels of significance with INRICH (p = 0.0057) and ALIGATOR (p = 0.022). For bipolar disorder, Set Screen yielded nominally and method-wide significant associations to all three glial pathways, with strongest association to the Glia-Astrocyte pathway (p = 0.002).Consistent with findings of white matter abnormalities in schizophrenia by other methods of study, the Glia-Oligodendrocyte pathway was associated with schizophrenia in our genomic study. These findings suggest that the abnormalities of myelination observed in schizophrenia are at least in part due to inherited factors, contrasted with the alternative of purely environmental causes (e.g. medication effects or lifestyle). While not the primary purpose of our study, our results also highlight the consequential nature of alternative choices regarding pathway analysis, in that results varied somewhat across methods, despite application to identical datasets and pathways. The molecular etiologies of schizophrenia and bipolar disorder are not yet understood, hindering efforts to develop novel pharmacological treatments. Accordingly, pharmaceutical companies have drastically reduced investment in psychiatric drug discovery Both schizophrenia and bipolar disorder are known to be highly heritable, with estimates of 81% for schizophrenia and 75% for bipolar disorder individually – pathway analyses are designed to determine the significance of association between phenotype and variants in a group of genes related by function. The pathway is the unit of analysis instead of each individual SNP, one by one. Regarding terminology, it is often technically more correct to say ‘gene set’ analyses because the term ‘pathway’ implies a more specific relationship among genes. However, throughout this manuscript, in keeping with common practice, we refer to ‘gene set analyses’ as ‘pathway analyses’.One analytic approach designed to provide information about the relevant biology implicated by genetic associations is known as ‘pathway analysis’. In contrast to typical GWAS – which evaluates the significance of association to phenotype for each SNP It is important to note that while these pathways contain genes that are critically important for the specified cells or processes in question, some of the same genes may be relevant in varying degrees for other cells and processes. Such sets of genes are an appropriate analysis unit for disorders such as schizophrenia and bipolar disorder that have multifactorial inheritance and are likely due to multiple, often subtle, changes in large numbers of genes. The combined effect of numerous disrupted genes impacting a specific cell type or function is exactly what pathway analysis can reveal, and the specific genes involved and their precise functions and relationships can be refined in future studies. Of course, the results may point to anomalies in other cell types or other processes that share some of the same genes, and these other processes can also be tested for disease association in further studies.Data S1).Clearly, the power of analyzing by pathways depends on accurate identification of genes that contribute to the specific pathophysiology of the illness studied. Past pathway analyses of schizophrenia have implicated synaptic, neuronal cell adhesion, and membrane scaffolding gene sets In vivo brain imaging and post-mortem studies consistently report somewhat reduced and disorganized white matter in psychosis, with these abnormalities being greater in schizophrenia, but also seen in bipolar disorder. Gene expression studies point to oligodendrocytes, the cells responsible for myelination and the maintenance of white matter, as functionally abnormal in psychotic disorders. Dysfunction in oligodendrocyte development, location, or function (i.e. ability to make myelin) might be the cause of white matter abnormalities in schizophrenia. Some evidence from brain imaging and postmortem studies also suggest abnormalities in the number or density of astrocytes, which perform crucial tasks in stabilizing glutamatergic synapses, both in bipolar disorder and schizophrenia. Of likely relevance, one of the most consistent findings in psychiatric post mortem research is a reduced density of glia in prefrontal cortex in mood disorders, including bipolar disorder. Currently it is not definitively known if the missing cells are astrocytes or oligodendrocytes.Both major subclasses of glial cells, oligodendrocytes and astrocytes, may be altered in psychotic disorders  to maintain its functions Glutamate is the major excitatory neurotransmitter in the brain. This role, its effects on cell growth and its toxic properties, have all led to proposals that abnormalities of glutamate underlie psychotic and mood disorders  and bipolar disorder . These files contain p-values for imputed SNPs and were filtered for info scores >0.8, yielding 1,068,286 SNP-level p-values for schizophrenia and 2,088,878 SNP-level p-values for bipolar disorder. Gene boundaries were defined as 35 kilobases upstream of genes and 10 kilobases downstream of genes, chosen to capture most promoter regions and cis-eQTLs Publically available Psychiatric Genomics Consortium (PGC) data were downloaded for schizophrenia 10 units) by chromosomal position (x-axis). Analytical decision points in pathway analyses (e.g. how to assign SNPs to genes) are described in the caption for http://www.broadinstitute.org/mpg/ricopili/).Data S1. A reviewer of the submitted manuscript asked whether, given the substantial overlap in genes used by different cells, any associations between pathways and illness observed in this study might generalize to other cell types. The reviewer suggested studying pathways for lymphocytes, macrophages and hepatocytes. This is an interesting question, especially as abnormalities of peripheral cells, as previously noted, have been observed in psychotic and mood disorders. While the evidence for abnormalities of CNS cells is stronger than that for peripheral cells, we created pathways for lymphocytes, macrophages and hepatocytes, using the public data sets available in Gene Ontology  Analyses of these pathways is necessarily exploratory. Per reviewer suggestion, these exploratory analyses also serve as a negative control against the possibility that genes (such as PPARG and SHH) – which are important for many cell types – might result in a non-specific association to oligodendrocytes.Based on prior evidence for the pathways/functional groupings of greatest interest, we identified 262 mitochondria related, 285 glutamate related, and 110 glia related genes sets from the Gene Ontology We used four distinct and independently developed pathway analysis methods to test hypotheses that the specified mitochondrial, glutamatergic, and glia-related gene sets are associated with schizophrenia and bipolar disorder. The primary analysis method was MAGENTA By default, MAGENTA employs two thresholds, at ‘95%’ and ‘75%’, which specify the (somewhat arbitrary) threshold above which a gene-level p-value will be deemed ‘significant’. Comparison of the observed number of ‘significant’ genes within a pathway to the expected number of significant genes provides the pathway level p-value . The latter threshold of 75%  captures weaker signals of association than the former, and may be better suited to detecting associations to highly polygenic phenotypes. These two settings were also used for ALIGATOR and INRICH. Set Screen does not have the option of specifying settings, so only one ‘setting’ is reported for Set Screen. These different analytic methods are expected to yield somewhat different results, as they do not use the same assumptions nor model the data in equivalent ways. Rather, they are complementary in seeking possible associations of pathways (gene sets) to illness. Properties of the genome and genetic data which necessitate analytical decisions in pathway analysis are explained in Table S3 provide q-values corresponding to all p-values for schizophrenia and bipolar disorder, respectively. Additional details about the application of FDR control in this report are provided in Methods S1 and Figure S1.Though three of the methods  provide corrected p-values or false discovery rates, none of the methods could provide appropriate correction for the use of two settings  or for the analysis of two different datasets (schizophrenia and bipolar disorder). We use Bonferoni correction within method , acknowledging that it is conservative given non-independence of genes within pathways and use of data across alternative settings (95% vs. 75%). There is no perfect way to correct for all 140 tests conducted in this report, but we apply FDR Table S1.Our primary analyses were performed with MAGENTA. The strongest result was an association between the Glia-Oligodendrocyte pathway and schizophrenia (p = 0.0005). This p-value is significant after conservative Bonferroni correction in our primary (MAGENTA) analysis for 40 tests (10 pathways * 2 settings * 2 disorders). Within the primary analyses, while there were trends of interest, no other pathways were method-wide or nominally significant. Gene-level p-values and other information about the 52 genes in the Glia-Oligodendrocyte pathway are provided in Interestingly, the strongest results for the MAGENTA analyses were obtained with the 75% setting, which is effectively a user-defined threshold for gene-level for significance. It is important to note that the use of the alternative (here 75% vs. 95%) threshold is not a more lenient threshold for statistical significance. Rather, adjustment of this setting allows the user to optimize the MAGENTA algorithm for the expected genetic architecture and/or expectations about the data being analyzed. As stated in the MAGENTA script, users may want to rely on the 75% setting for diseases that are known to have highly polygenic genetic architectures. This is the case for schizophrenia.true risk alleles may be relatively unimpressive, and odds ratios will not even demonstrate the expected direction of effect on a reliable basis.Another important point pertains to the data being analyzed. For example, in the case of a somewhat underpowered GWAS, p-values for Therefore, casting a wider net (via use of a 75% threshold) may allow for better capture of weak signals, which in aggregate are indicative of association. Given what is now known about schizophrenia, both extreme polygenecity and low power to detect the association of individual alleles are likely true. The latter is another reason why larger GWAS samples are needed to provide appropriate replication and clarification of these findings.Secondary analyses, to test for robustness of the primary findings and as an exploration of alternative pathways to be explored in future studies, were conducted with ALIGATOR, INRICH, and Set Screen. Consistent with the MAGENTA results, the Glia-Oligodendrocyte pathway in the schizophrenia dataset obtained nominally significant p-values using ALIGATOR and INRICH ; see Across all secondary analyses, ALIGATOR and INRICH results were similar, but not as strong, as the MAGENTA results. In contrast, eight of twenty pathways tested across the schizophrenia and bipolar disorder datasets were at least nominally significant according to Set Screen analysis. For schizophrenia, Set Screen yielded nominally significant associations for a total of five glial and mitochondrial pathways: superordinate Glia (p = 0.012), Glia-Astroctye (p = 0.007), Mitochondria-Crista (p = 0.011), Mitochondria-Fission (p = 0.035), and the larger Mitochondria-Fission_plus pathway (p = 0.031). For bipolar disorder, all three glial pathways were at least nominally significant. The Glia-Astrocyte pathway was method-wide significant (p = 0.002) whereas the Glia-Oligodendrocyte (p = 0.021) and superordinate Glia (p = 0.025) pathways were both nominally significant.Tables S2 and Table S3.In an effort to determine how many results in this report are likely to be true, we used the FDR control procedures described by Storey, Tibshirani, and Dabney Methods S1 and Table S4.As mentioned, after the completion of our primary and secondary analyses, we explored the association of pathways for peripheral cells, which share some relevant genes with brain cells, with schizophrenia and bipolar disorder. No significant associations were observed. Details are provided in the The observed Glia-Oligodendrocyte pathway association to schizophrenia is consistent with an extensive literature confirming white matter abnormalities in schizophrenia The bipolar disorder finding of association to astrocyte development and function genes (observed only with Set Screen) is also intriguing. As noted above, astrocytes may be among the reduced glial cells observed in bipolar disorder Finally, in the secondary analyses, we note that Set Screen results suggested nominally significant associations for mitochondrial fission and mitochondrial cristae in schizophrenia. It is important to note the higher likelihood of false positive results in secondary analyses. However, mitochondrial abnormalities have been reported previously in schizophrenia and bipolar disorder. Replication studies with larger samples will provide evidence about the reliability of our findings that specific mitochondrial gene sets may be related to these abnormalities.The most important consequence of all of these findings and trends may be their biological relevance, specifically their implications concerning etiology and pathophysiology. Psychotic disorders, including schizophrenia, are highly complex by cause and course. They are known to have inherited determinants, but they are also associated with infectious, toxic and traumatic exposure. In addition, life style, life events, and treatment can alter brain biology in those who develop psychosis. For this reason, in vivo brain imaging and post mortem studies are limited in determining if abnormalities observed exist prior to the onset of overt illness or are a consequence or concomitant of illness. These genomic results strongly suggest that some factors, notably those for the function of glial elements in brain, are true risk factors for illness.We realize that the concordant and discordant results obtained from the separate analytic methods is a phenomenon that is interesting in and of itself. While these differences are expected and further study of the reasons for these differences is an important area of study in the development and interpretation of pathway analysis, such comparisons are beyond the scope of this paper. They would require extensive modeling and are properly the subject of other papers. Our study used established pathway techniques to gain insight into the association of gene sets and psychotic disorders. We used a primary analysis (MAGENTA) and secondary analyses for possible confirmation or expansion of evidence of associations, not to provide a comparison of techniques.is not trivial. Indeed, we would have reached somewhat different conclusions about the association of these pathways with schizophrenia and bipolar disorder if we had applied only one method. This means that replication attempts of pathway analyses should be conducted using the same method and same settings as the original report if direct comparisons are to be made. However, these results also suggest real value in applying more than one method, so that clues to pathway associations are not missed by the limitations of any one method.That being said, we found evidence supporting the expectation that selection of pathway analysis method, and the settings used within methods, Across the four methods, which set of results is ‘right’? In one sense, they all are. Each of these methods is a valid approach to pathway analysis that creates and evaluates pathway level statistics from the relevant GWAS data. There is undoubtedly variability in the degree to which each method accurately captures genetic signals within pathways, but currently there is no ‘gold standard’. Rather, the suitability of any particular pathway analysis method will vary across pathways and phenotypes because of the (presumably) different underlying genetic architectures See . More anMethods S1.However, it is worth noting that the pattern of correlations across results from the four methods is broadly consistent with major similarities and differences among pathway analysis methods used in this report. Whereas Set-screen uses all p-values within all genes in the calculation of pathway-level p-values, ALIGATOR, INRICH, and MAGENTA use a “best-SNP per gene” approach. Further, the latter three methods may be classified as “over-representation” methods, which test the number of genes in a pathway that reach a given significance threshold, with no further differentiation among p-values above the specified threshold. These major distinctions likely explain why Set Screen results were least like results from the other three methods. For additional information about similarities and differences among these methods, please see a text section ‘Pathway analysis method comparison’ in the Our findings represent one attempt to seek associations between illness and groups of genes (pathways) chosen on the basis of independent evidence for likely mechanistic involvement in schizophrenia and/or bipolar disorder. The findings, as always, need to be subjected to a replication attempt, in an independent dataset of adequate size, and using the same methods and parameters. No dataset meeting these criteria is currently publically available. The ‘PGC2′ – Psychiatric GWAS/Genomic Consortium 2– schizophrenia dataset will be available in the future and is ideal for this application. Also regarding datasets, some common control subjects are included in the publically available PGC schizophrenia and bipolar disorder datasets. This should not pose a problem for interpretation of our results because shared controls would tend to make results for the two disorders more similar than they actually are. Of note, for the method-wide significant results, we found no evidence of overlapping associations between the two disorders. We cannot rule out the possibility that the non-significant trends observed across classes of glia are an example of such similarities introduced by the existence of some shared controls. However, these trends may also accurately reflect shared determinants of various psychotic disorders.While our manuscript was under review, an independent group reported their results showing association between glial pathways and schizophrenia Regarding the gene sets themselves, it must be noted that pathway curation relies on incomplete information from diverse sources, of variable quality. Therefore we cannot know how well gene membership within pathways accurately reflects biologic function. However, every gene listed in a pathway is in the list because there is data supporting its inclusion. Below we discuss ways in which pathways may be refined in the future. Regarding phenotype, we could not interrogate questions regarding phenotypic heterogeneity because the GWAS phenotype was binary (i.e. schizophrenia or bipolar case vs. control). Further, were not able to select the covariates used to generate the publically available GWAS data, given that it is a static resource.Lastly, we are aware that few genes and no gene sets are uniquely important to any cell type or cell function. Biology, by its nature, uses the same elements in diverse instances and interactive ways. The finding that particular gene sets, such as the oligodendrocyte pathway studied here, are associated with illness is strong evidence for a pathophysiologic role of these cells, especially as it is consistent with other evidence from independent studies performed using entirely different technologies. It does not rule out the likelihood that some of these same genes may be involved in other defects observed in schizophrenia, both in the CNS and peripheral cells.Negative results from these analyses are, unfortunately, not entirely informative because it could be the case that pathways were not well specified or that power was low. However, positive findings for the Glia-Oligodendrocyte pathway in schizophrenia and, with less certainty, the Glia-Astrocyte pathway in bipolar disorder offer confirmation of neurobiological findings about these disorders, and are important targets for replication attempts and the design of new studies. Replication attempts can occur alongside efforts to perform more refined pathway analyses based on these results. For example, one could trim the associated pathway lists to exclude genes that contributed little or detracted from observed associations, and then test these ‘short lists’ in independent GWAS datasets. It is also possible to examine subsets of the genes for associations to specific features of illness, measurable by other technologies, such as white matter integrity or glial health. Mitochondrial function can be assessed in patients or in samples derived from those patients, including the possibility of examining patient-derived glial cells via induced pluripotent stem cell (IPSC) technology Figure S1Distribution of 140 pathway-level p-values.(DOCX)Click here for additional data file.Table S1Gene-level p-values for the Glia-Oligodendrocyte pathway in schizophrenia dataset.(DOCX)Click here for additional data file.Table S2Schizophrenia pathway q-values by method and setting.(DOCX)Click here for additional data file.Table S3Bipolar disorder pathway q-values by method and setting.(DOCX)Click here for additional data file.Table S4Schizophrenia and bipolar disorder pathway p-values by method and setting.(DOCX)Click here for additional data file.Methods S1Supplemental Methods.(DOCX)Click here for additional data file.Data S1Entrez gene numbers for genes within each pathway.(DOCX)Click here for additional data file.


Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent.Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0).The developed cryptorchidism gene atlas includes 217 candidate loci  identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies.The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes. Cryptorchidism (CO) is the most frequent congenital disorder in male children  and is defined as incomplete descent of one  or both  testes and associated structures. Cryptorchidism has a potential effect on health; defects in testes descent usually cause impaired spermatogenesis, resulting in reduced fertility and increased rates of testicular neoplasia, and testicular torsion (reviewed in ). Cryptoe.g. dogs), postnatally. Beside environmental factors like endocrine disruptors, CO is at least in part determined by genetic causes (chromosome or gene mutations), and is often a common feature of different syndromes. For example, Klinefelter syndrome and mutations in INSL3 gene have already been recognized as a cause of CO in some cases ) and genomic location of their human orthologs.Click here for fileThe literature collected candidate genes involved in multiple (four or more) CO-associated pathways.Click here for fileForty-three network-predicted CO-associated candidate genes.Click here for fileProtein network information matrix for candidate genes. Protein network information matrix for candidate genes involved in at least two pathways significant for literature-collected and network-predicted candidate genes. The matrix consists of 50 rows (each row corresponds to a different gene involved in at least two pathways) and 12 columns (each column corresponds to a different pathway), where 0 indicates that the gene is not present in a pathway and 1 indicates that the gene is present in it.Click here for fileSTRING network data: list of protein network interactions present in STRING with high confidence score.Click here for file