Beyond protein expression, MOPED goes multi-omics

'Multi-omics' data integration: applications in probiotics studies

The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and the modulation of host immunity through the strengthening of the gut barrier and stimulation of antibodies. These benefits, combined with the need for improved nutraceuticals, have resulted in the extensive characterization of probiotics leading to an outburst of data generated using several 'omics' technologies. The recent development in system biology approaches to microbial science is paving the way for integrating data generated from different omics techniques for understanding the flow of molecular information from one 'omics' level to the other with clear information on regulatory features and phenotypes. The limitations and tendencies of a 'single omics' application to ignore the influence of other molecular processes justify the need for 'multi-omics' application in probiotics selections and understanding its action on the host. Different omics techniques, including genomics, transcriptomics, proteomics, metabolomics and lipidomics, used for studying probiotics and their influence on the host and the microbiome are discussed in this review. Furthermore, the rationale for 'multi-omics' and multi-omics data integration platforms supporting probiotics and microbiome analyses was also elucidated. This review showed that multi-omics application is useful in selecting probiotics and understanding their functions on the host microbiome. Hence, recommend a multi-omics approach for holistically understanding probiotics and the microbiome.

Proteomic repository data submission, dissemination, and reuse: key messages

INTRODUCTION

The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public.

AREAS COVERED

Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed.

EXPERT OPINION

The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

How Healthcare Can Refocus on Its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson

Healthcare is transforming with data-intensive omics technologies and Big Data. The "revolution" has already happened in technology, but the bottlenecks have shifted to the social domain: Who can be empowered by Big Data? Who are the users and customers? In this review and innovation field analysis, we introduce the idea of a "super-customer" versus "customer" and relate both to 21st century healthcare. A "super-customer" in healthcare is the patient, sample size of n = 1, while "customers" are the providers of healthcare (e.g., doctors and nurses). The super-customers have been patients, enabled by unprecedented social practices, such as the ability to track one's physical activities, personal genomics, patient advocacy for greater autonomy, and self-governance, to name but a few. In contrast, the originally intended customers-providers, doctors, and nurses-have relatively lagged behind. With patients as super-customers, there are valuable lessons to be learned from industry examples, such as Amazon and Uber. To offer superior quality service, healthcare organizations have to refocus on the needs, pains, and aspirations of their super-customers by enabling the customers. We propose a strategic solution to this end: the PPT-DAM (People-Process-Technology empowered by Data, Analytics, and Metrics) approach. When applied together with the classic Experiment-Execute-Evaluate iterative methodology, we suggest PPT-DAM is an extremely powerful approach to deliver quality health services to super-customers and customers. As an example, we describe the PPT-DAM implementation by the Benchmarking Improvement Program at the Seattle Children's Hospital. Finally, we forecast that cognitive systems in general and IBM Watson in particular, if properly implemented, can bring transformative and sustainable capabilities in healthcare far beyond the current ones.

Human Nup98 regulates the localization and activity of DExH/D-box helicase DHX9

Beyond their role at nuclear pore complexes, some nucleoporins function in the nucleoplasm. One such nucleoporin, Nup98, binds chromatin and regulates gene expression. To gain insight into how Nup98 contributes to this process, we focused on identifying novel binding partners and understanding the significance of these interactions. Here we report on the identification of the DExH/D-box helicase DHX9 as an intranuclear Nup98 binding partner. Various results, including in vitro assays, show that the FG/GLFG region of Nup98 binds to N- and C-terminal regions of DHX9 in an RNA facilitated manner. Importantly, binding of Nup98 stimulates the ATPase activity of DHX9, and a transcriptional reporter assay suggests Nup98 supports DHX9-stimulated transcription. Consistent with these observations, our analysis revealed that Nup98 and DHX9 bind interdependently to similar gene loci and their transcripts. Based on our results, we propose that Nup98 functions as a co-factor that regulates DHX9 and, potentially, other RNA helicases.

Genic insights from integrated human proteomics in GeneCards

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite’s next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein–RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome.

Database URL: http://www.genecards.org/

Proteomics Research in Schizophrenia

Despite intense scientific efforts, the neuropathology and pathophysiology of schizophrenia are poorly understood. Proteomic studies, by testing large numbers of proteins for associations with disease, may contribute to the understanding of the molecular mechanisms of schizophrenia. They may also indicate the types and locations of cells most likely to harbor pathological alterations. Investigations using proteomic approaches have already provided much information on quantitative and qualitative protein patterns in postmortem brain tissue, peripheral tissues and body fluids. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, SELDI-TOF, shotgun proteomics with label-based (ICAT), and label-free (MS^E) quantification have been applied to the study of schizophrenia for the past 15 years. This review summarizes the results, mostly from brain but also from other tissues and bodily fluids, of proteomics studies in schizophrenia. Emphasis is given to proteomics platforms, varying sources of material, proposed candidate biomarkers emerging from comparative proteomics studies, and the specificity of the putative markers in terms of other mental illnesses. We also compare proteins altered in schizophrenia with reports of protein or mRNA sequences that are relatively enriched in specific cell types. While proteomic studies of schizophrenia find abnormalities in the expression of many proteins that are not cell type-specific, there appears to be a disproportionate representation of proteins whose synthesis and localization are highly enriched in one or more brain cell type compared with other types of brain cells. Two of the three proteins most commonly altered in schizophrenia are aldolase C and glial fibrillary acidic protein, astrocytic proteins with entirely different functions, but the studies are approximately evenly divided with regard to the direction of the differences and the concordance or discordance between the two proteins. Alterations of common myelin-associated proteins were also frequently observed, and in four studies that identified alterations in at least two, all differences were downwards in schizophrenia, consistent with earlier studies examining RNA or targeting myelin-associated proteins.

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.

2016 update of the PRIDE database and its related tools

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data. Since the beginning of 2014, PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database. Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013. PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components. PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium. The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month). We outline some statistics on the current PRIDE Archive data contents. We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool. Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Biological databases for human research

The completion of the Human Genome Project lays a foundation for systematically studying the human genome from evolutionary history to precision medicine against diseases. With the explosive growth of biological data, there is an increasing number of biological databases that have been developed in aid of human-related research. Here we present a collection of human-related biological databases and provide a mini-review by classifying them into different categories according to their data types. As human-related databases continue to grow not only in count but also in volume, challenges are ahead in big data storage, processing, exchange and curation.