MOPED enables discoveries through consistently processed proteomics data

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

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.

In Vitro Cell Sensitivity to Palytoxin Correlates with High Gene Expression of the Na+/K+-ATPase β2 Subunit Isoform

The marine polyether palytoxin (PLTX) is one of the most toxic natural compounds, and is involved in human poisonings after oral, inhalation, skin and/or ocular exposure. Epidemiological and molecular evidence suggest different inter-individual sensitivities to its toxic effects, possibly related to genetic-dependent differences in the expression of Na⁺/K⁺-ATPase, its molecular target. To identify Na⁺/K⁺-ATPase subunits, isoforms correlated with in vitro PLTX cytotoxic potency, sensitivity parameters (EC₅₀: PLTX concentration reducing cell viability by 50%; Emax: maximum effect induced by the highest toxin concentration; 10^-7 M) were assessed in 60 healthy donors' monocytes by the MTT (methylthiazolyl tetrazolium) assay. Sensitivity parameters, not correlated with donors' demographic variables (gender, age and blood group), demonstrated a high inter-individual variability (median EC₅₀ = 2.7 × 10^-10 M, interquartile range: 0.4-13.2 × 10^-10 M; median Emax = 92.0%, interquartile range: 87.5-94.4%). Spearman's analysis showed significant positive correlations between the β2-encoding ATP1B2 gene expression and Emax values (rho = 0.30; p = 0.025) and between Emax and the ATP1B2/ATP1B3 expression ratio (rho = 0.38; p = 0.004), as well as a significant negative correlation between Emax and the ATP1B1/ATP1B2 expression ratio (rho = -0.30; p = 0.026). This toxicogenetic study represents the first approach to define genetic risk factors that may influence the onset of adverse effects in human PLTX poisonings, suggesting that individuals with high gene expression pattern of the Na⁺/K⁺-ATPase β2 subunit (alone or as β2/β1 and/or β2/β3 ratio) could be highly sensitive to PLTX toxic effects.

Azaspiracids Increase Mitochondrial Dehydrogenases Activity in Hepatocytes: Involvement of Potassium and Chloride Ions

BACKGROUND

Azaspiracids (AZAs) are marine toxins that are produced by Azadinium and Amphidoma dinoflagellates that can contaminate edible shellfish inducing a foodborne poisoning in humans, which is characterized by gastrointestinal symptoms. Among these, AZA1, -2, and -3 are regulated in the European Union, being the most important in terms of occurrence and toxicity. In vivo studies in mice showed that, in addition to gastrointestinal effects, AZA1 induces liver alterations that are visible as a swollen organ, with the presence of hepatocellular fat droplets and vacuoles. Hence, an in vitro study was carried out to investigate the effects of AZA1, -2, and -3 on liver cells, using human non-tumor IHH hepatocytes.

RESULTS

The exposure of IHH cells to AZA1, -2, or -3 (5 × 10^-12-1 × 10^-7 M) for 24 h did not affect the cell viability and proliferation (Sulforhodamine B assay and ³H-Thymidine incorporation assay), but they induced a significant concentration-dependent increase of mitochondrial dehydrogenases activity (MTT reduction assay). This effect depends on the activity of mitochondrial electron transport chain complex I and II, being counteracted by rotenone and tenoyl trifluoroacetone, respectively. Furthermore, AZAs-increased mitochondrial dehydrogenase activity was almost totally suppressed in the K⁺-, Cl^--, and Na⁺-free media and sensitive to the specific inhibitors of K_ATP and hERG potassium channels, Na⁺/K⁺, ATPase, and cystic fibrosis transmembrane conductance regulator (CFTR) chloride channels.

CONCLUSIONS

These results suggest that AZA mitochondrial effects in hepatocytes derive from an imbalance of intracellular levels of K⁺ and, in particular, Cl^- ions, as demonstrated by the selective reduction of toxin effects by CFTR chloride channel inhibition.

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.

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.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

Proteomic Discovery and Development of a Multiplexed Targeted MRM-LC-MS/MS Assay for Urine Biomarkers of Extracellular Matrix Disruption in Mucopolysaccharidoses I, II, and VI

The mucopolysaccharidoses (MPS) are lysosomal storage disorders that result from defects in the catabolism of glycosaminoglycans. Impaired muscle, bone, and connective tissue are typical clinical features of MPS due to disruption of the extracellular matrix. Markers of MPS disease pathology are needed to determine disease severity and monitor effects of existing and emerging new treatments on disease mechanisms. Urine samples from a small cohort of MPS-I, -II, and -VI patients (n = 12) were analyzed using label-free quantative proteomics. Fifty-three proteins including many associated with extracellular matrix organization were differently expressed. A targeted multiplexed peptide MRM LC-MS/MS assay was used on a larger validation cohort of patient samples (MPS-I n = 18, MPS-II n = 12, MPS-VI n = 6, control n = 20). MPS-I and -II groups were further subdivided according to disease severity. None of the markers assessed were altered significantly in the mild disease groups compared to controls. β-galactosidase, a lysosomal protein, was elevated 3.6-5.7-fold significantly (p < 0.05) in all disease groups apart from mild MPS-I and -II. Collagen type Iα, fatty-acid-binding-protein 5, nidogen-1, cartilage oligomeric matrix protein, and insulin-like growth factor binding protein 7 concentrations were elevated in severe MPS I and II groups. Cartilage oligomeric matrix protein, insulin-like growth factor binding protein 7, and β-galactosidase were able to distinguish the severe neurological form of MPS-II from the milder non-neurological form. Protein Heg1 was significantly raised only in MPS-VI. This work describes the discovery of new biomarkers of MPS that represent disease pathology and allows the stratification of MPS-II patients according to disease severity.

Can "normal" protein expression ranges be estimated with high-throughput proteomics?

Although biological science discovery often involves comparing conditions to a normal state, in proteomics little is actually known about normal. Two Human Proteome studies featured in Nature offer new insights into protein expression and an opportunity to assess how high-throughput proteomics measures normal protein ranges. We use data from these studies to estimate technical and biological variability in protein expression and compare them to other expression data sets from normal tissue. Results show that measured protein expression across same-tissue replicates vary by ±4- to 10-fold for most proteins. Coefficients of variation (CV) for protein expression measurements range from 62% to 117% across different tissue experiments; however, adjusting for technical variation reduced this variability by as much as 50%. In addition, the CV could also be reduced by limiting comparisons to proteins with at least 3 or more unique peptide identifications as the CV was on average 33% lower than for proteins with 2 or fewer peptide identifications. We also selected 13 housekeeping proteins and genes that were expressed across all tissues with low variability to determine their utility as a reference set for normalization and comparative purposes. These results present the first step toward estimating normal protein ranges by determining the variability in expression measurements through combining publicly available data. They support an approach that combines standard protocols with replicates of normal tissues to estimate normal protein ranges for large numbers of proteins and tissues. This would be a tremendous resource for normal cellular physiology and comparisons of proteomics studies.

Deficiency of prolactin-inducible protein leads to impaired Th1 immune response and susceptibility to Leishmania major in mice

Although the strategic production of prolactin-inducible protein (PIP) at several ports of pathogen entry into the body suggests it might play a role in host defense, no study has directly implicated it in immunity against any infectious agent. Here, we show for the first time that PIP deficiency is associated with reduced numbers of CD4(+) T cells in peripheral lymphoid tissues and impaired CD4(+) Th1-cell differentiation in vitro. In vivo, CD4(+) T cells from OVA-immunized, PIP-deficient mice showed significantly impaired proliferation and IFN-γ production following in vitro restimulation. Furthermore, PIP-deficient mice were highly susceptible to Leishmani major infection and failed to control lesion progression and parasite proliferation. This susceptibility was associated with impaired NO production and leishmanicidal activity of PIP KO macrophages following IFN-γ and LPS stimulation. Collectively, our findings implicate PIP as an important regulator of CD4(+) Th1-cell-mediated immunity.

Comparison of proteomic and metabolomic profiles of mutants of the mitochondrial respiratory chain in Caenorhabditis elegans

Single-gene mutations that disrupt mitochondrial respiratory chain function in Caenorhabditis elegans change patterns of protein expression and metabolites. Our goal was to develop useful molecular fingerprints employing adaptable techniques to recognize mitochondrial defects in the electron transport chain. We analyzed mutations affecting complex I, complex II, or ubiquinone synthesis and discovered overarching patterns in the response of C. elegans to mitochondrial dysfunction across all of the mutations studied. These patterns are in KEGG pathways conserved from C. elegans to mammals, verifying that the nematode can serve as a model for mammalian disease. In addition, specific differences exist between mutants that may be useful in diagnosing specific mitochondrial diseases in patients.

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.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

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.

OMICS studies: How about metadata checklist and data publications?

Data fully utilized by the community resources promote progress rather than repetition. Effective data sharing can accelerate the transition from data to actionable knowledge, yet barriers to data sharing remain, both technological and procedural. The DELSA community has tackled the sharing barrier by creating a multi-omics metadata checklist for the life sciences. The checklist and associated data publication examples are now jointly published in Big Data and OMICS: A Journal of Integrative Biology. The checklist will enable diverse datasets to be easily harmonized and reused for richer analyses. It will facilitate data deposits, stand alone as a data publication, and grant appropriate credit to researchers. We invite the broader life sciences community to test the checklist for feedback and improvements.

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.