Beyond protein expression, MOPED goes multi-omics.

MOPED (Multi-Omics Profiling Expression Database; http://moped.proteinspire.org) has transitioned from solely a protein expression database to a multi-omics resource for human and model organisms. Through a web-based interface, MOPED presents consistently processed data for gene, protein and pathway expression. To improve data quality, consistency and use, MOPED includes metadata detailing experimental design and analysis methods. The multi-omics data are integrated through direct links between genes and proteins and further connected to pathways and experiments. MOPED now contains over 5 million records, information for approximately 75,000 genes and 50,000 proteins from four organisms (human, mouse, worm, yeast). These records correspond to 670 unique combinations of experiment, condition, localization and tissue. MOPED includes the following new features: pathway expression, Pathway Details pages, experimental metadata checklists, experiment summary statistics and more advanced searching tools. Advanced searching enables querying for genes, proteins, experiments, pathways and keywords of interest. The system is enhanced with visualizations for comparing across different data types. In the future MOPED will expand the number of organisms, increase integration with pathways and provide connections to disease.

MOPED enables discoveries through consistently processed proteomics data.

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project's efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. MOPED supports a new omics metadata checklist to harmonize data integration, analysis, and use. MOPED's development is driven by the user community, which spans 90 countries and guides future development that will transform MOPED into a multiomics resource. MOPED encourages users to submit data in a simple format. They can use the metadata checklist to generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries.

Direct assay of delta-aminolevulinic acid dehydratase in heme biosynthesis for the detection of porphyrias by tandem mass spectrometry.

We report a new assay of human delta-aminolevulinic acid dehydratase (ALAD), an enzyme converting delta-aminolevulinic acid (ALA) into porphobilinogen. The assay is developed for use in the clinical diagnosis of delta-aminolevulinic acid dehydratase-deficient porphyria, a rare enzymatic deficiency of the heme biosynthetic pathway. The assay involves the incubation of erythrocyte lysate with the natural substrate, ALA, followed by quantitative in situ conversion of porphobilinogen to its butyramide, and liquid-liquid extraction into a mass spectrometer-friendly solvent. Quantitation of the butyrylated porphobilinogen is done by electrospray ionization tandem mass spectrometry, using a deuterium labeled internal standard. The assay stays well within the range wherein ALAD activity is linear with time. The K(m) of ALAD for ALA was measured as 333 microM, and the V(max) was 19.3 microM/h. Average enzyme activity among a random sample of 36 anonymous individuals was 277 micromol/L erythrocyte lysate/hour with a standard deviation of 90 micromol/L erythrocyte lysate/hour. The tandem mass spectrometric assay should easily detect the enzyme deficiency, which causes a reduction of activity by 95-99%. The assay shows good reproducibility and low background, requires a simple workup, and uses a commercially available substrate.

Analysis of caffeic acid derivatives in echinacea extracts by liquid chromatography particle beam mass spectrometry (LC-PB/MS) employing electron impact and glow discharge ionization sources.

A liquid chromatography-particle beam/mass spectrometry (LC-PB/MS) method with electron impact (EI) and glow discharge (GD) ionization sources is presented for the determination of caffeic acid derivatives in echinacea tinctures. In this work, two commercially available echinacea ethanolic extracts were used as the test samples for the separation, identification, and quantification of the caffeic acid derivatives (caffeic acid, chlorogenic acid, cichoric acid, and caftaric acid), which are suggested to have beneficial medicinal properties. Detailed evaluations of the two primary controlling parameters for EI (electron energy and source block temperature) and GD (discharge current and pressure) sources were performed to determine optimal instrument operation conditions. The mass spectra obtained from both ion sources provide clear and simple molecular fragmentation patterns for each of the target analytes. The absolute detection limits for the caffeic acid derivatives were determined to be at subnanogram levels for both the EI and GD sources. The separation of the caffeic acid derivatives in echinacea was accomplished by reversed-phase chromatography using a C(18) column and a gradient elution system of water containing 0.1% trifluoroacetic acid and methanol, with an analysis time of less than 40 min. A standard addition method was employed for the quantification of each of the caffeic acid derivatives in the tincture.

Integrated Proteomic and Transcriptomic-Based Approaches to Identifying Signature Biomarkers and Pathways for Elucidation of Daoy and UW228 Subtypes.

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Patient survival has remained largely the same for the past 20 years, with therapies causing significant health, cognitive, behavioral and developmental complications for those who survive the tumor. In this study, we profiled the total transcriptome and proteome of two established MB cell lines, Daoy and UW228, using high-throughput RNA sequencing (RNA-Seq) and label-free nano-LC-MS/MS-based quantitative proteomics, coupled with advanced pathway analysis. While Daoy has been suggested to belong to the sonic hedgehog (SHH) subtype, the exact UW228 subtype is not yet clearly established. Thus, a goal of this study was to identify protein markers and pathways that would help elucidate their subtype classification. A number of differentially expressed genes and proteins, including a number of adhesion, cytoskeletal and signaling molecules, were observed between the two cell lines. While several cancer-associated genes/proteins exhibited similar expression across the two cell lines, upregulation of a number of signature proteins and enrichment of key components of SHH and WNT signaling pathways were uniquely observed in Daoy and UW228, respectively. The novel information on differentially expressed genes/proteins and enriched pathways provide insights into the biology of MB, which could help elucidate their subtype classification.

Identification and Structural Characterization of Three New Metabolites of Bupropion in Humans.

Bupropion is a widely used antidepressant and the recommended CYP2B6 probe drug. However, current understanding of bupropion elimination pathways is limited. Bupropion has three active circulating metabolites, OH-bupropion, threohydrobupropion, and erythrohydrobupropion, but together with bupropion these metabolites and their conjugates in urine represent only 23% of the dose, and the majority of the elimination pathways of bupropion result in uncharacterized metabolites. The aim of this study was to determine the structures of the uncharacterized bupropion metabolites using human clinical samples and in vitro incubations. Three new metabolites, 4'-OH-bupropion, erythro-4'-OH-hydrobupropion, and threo-4'-OH-hydrobupropion, were detected in human liver microsome incubations and were isolated from human urine. The structures of the metabolites were confirmed via comparison of UV absorbance, NMR spectra, and mass spectral data to those of the synthesized standards. In total, these metabolites represented 24% of the drug related material excreted in urine.

Finding Text-Supported Gene-to-Disease Co-appearances with MOPED-Digger.

Gene/disease associations are a critical part of exploring disease causes and ultimately cures, yet the publications that might provide such information are too numerous to be manually reviewed. We present a software utility, MOPED-Digger, that enables focused human assessment of literature by applying natural language processing (NLP) to search for customized lists of genes and diseases in titles and abstracts from biomedical publications. The results are ranked lists of gene/disease co-appearances and the publications that support them. Analysis of 18,159,237 PubMed title/abstracts yielded 1,796,799 gene/disease co-appearances that can be used to focus attention on the most promising publications for a possible gene/disease association. An integrated score is provided to enable assessment of broadly presented published evidence to capture more tenuous connections. MOPED-Digger is written in Java and uses Apache Lucene 5.0 library. The utility runs as a command-line program with a variety of user-options and is freely available for download from the MOPED 3.0 website (moped.proteinspire.org).

The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders.

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

MOPED 2.5--an integrated multi-omics resource: multi-omics profiling expression database now includes transcriptomics data.

Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (MOPED, http://moped.proteinspire.org ) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of MOPED is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, MOPED 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, MOPED provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org ) metadata checklist. MOPED 2.5 has greatly increased the number of proteomics absolute and relative expression records to over 500,000, in addition to adding more than four million transcriptomics relative expression records. MOPED has an intuitive user interface with tabs for querying different types of omics expression data and new tools for data visualization. Summary information including expression data, pathway mappings, and direct connection between proteins and genes can be viewed on Protein and Gene Details pages. These connections in MOPED provide a context for multi-omics expression data exploration. Researchers are encouraged to submit omics data which will be consistently processed into expression summaries. MOPED as a multi-omics data resource is a pivotal public database, interdisciplinary knowledge resource, and platform for multi-omics understanding.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.