MOPED: Model Organism Protein Expression Database

Decoding Systems Biology of Inflammation Signatures in Cancer Pathogenesis: Pan-Cancer Insights from 12 Common Cancers

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

Gene-Folic Acid Interactions and Risk of Conotruncal Heart Defects: Results from the National Birth Defects Prevention Study

Conotruncal heart defects (CTDs) are heart malformations that affect the cardiac outflow tract and typically cause significant morbidity and mortality. Evidence from epidemiological studies suggests that maternal folate intake is associated with a reduced risk of heart defects, including CTD. However, it is unclear if folate-related gene variants and maternal folate intake have an interactive effect on the risk of CTDs. In this study, we performed targeted sequencing of folate-related genes on DNA from 436 case families with CTDs who are enrolled in the National Birth Defects Prevention Study and then tested for common and rare variants associated with CTD. We identified risk alleles in maternal MTHFS (OR_meta = 1.34; 95% CI 1.07 to 1.67), maternal NOS2 (OR_meta = 1.34; 95% CI 1.05 to 1.72), fetal MTHFS (OR_meta = 1.35; 95% CI 1.09 to 1.66), and fetal TCN2 (OR_meta = 1.38; 95% CI 1.12 to 1.70) that are associated with an increased risk of CTD among cases without folic acid supplementation. We detected putative de novo mutations in genes from the folate, homocysteine, and transsulfuration pathways and identified a significant association between rare variants in MGST1 and CTD risk. Results suggest that periconceptional folic acid supplementation is associated with decreased risk of CTD among individuals with susceptible genotypes.

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.

The role of 5-reduction in physiology and metabolic disease: evidence from cellular, pre-clinical and human studies

The 5-reductases (5α-reductase types 1, 2 and 3 [5αR1-3], 5β-reductase [5βR]) are steroid hormone metabolising enzymes that hold fundamental roles in human physiology and pathology. They possess broad substrate specificity converting many steroid hormones to their 5α- and 5β-reduced metabolites, as well as catalysing crucial steps in bile acid synthesis. 5αRs are fundamentally important in urogenital development by converting testosterone to the more potent androgen 5α-dihydrotestosterone (5αDHT); inactivating mutations in 5αR2 lead to disorders of sexual development. Due to the ability of the 5αRs to generate 5αDHT, they are an established drug target, and 5αR inhibitors are widely used for the treatment of androgen-dependent benign or malignant prostatic diseases. There is an emerging body of evidence to suggest that the 5-reductases can impact upon aspects of health and disease (other than urogenital development); alterations in their expression and activity have been associated with metabolic disease, polycystic ovarian syndrome, inflammation and bone metabolism. This review will outline the evidence base for the extra-urogenital role of 5-reductases from in vitro cell systems, pre-clinical models and human studies, and highlight the potential adverse effects of 5αR inhibition in human health and disease.

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.

Identification and Antimicrobial Activity of Medium-Sized and Short Peptides from Yellowfin Tuna (Thunnus albacares) Simulated Gastrointestinal Digestion

Due to the rapidly increasing resistance to conventional antibiotics, antimicrobial peptides are emerging as promising novel drug candidates. In this study, peptide fragments were obtained from yellowfin tuna muscle by simulated gastrointestinal digestion, and their antimicrobial activity towards Gram-positive and Gram-negative bacteria was investigated. In particular, the antimicrobial activity of both medium- and short-sized peptides was investigated by using two dedicated approaches. Medium-sized peptides were purified by solid phase extraction on C18, while short peptides were purified thanks to a graphitized carbon black sorbent. For medium-sized peptide characterization, a peptidomic strategy based on shotgun proteomics analysis was employed, and identification was achieved by matching protein sequence database by homology, as yellowfin tuna is a non-model organism, leading to the identification of 403 peptides. As for short peptide sequences, an untargeted suspect screening approach was carried out by means of an inclusion list presenting the exact mass to charge ratios (m/z) values for all di-, tri- and tetrapeptides. In total, 572 short sequences were identified thanks to a customized workflow dedicated to short peptide analysis implemented on Compound Discoverer software.

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.

Adding value to real-world data: the role of biomarkers

Adding biomarker information to real world datasets (e.g. biomarker data collected into disease/drug registries) can enhance mechanistic understanding of intra-patient differences in disease trajectories and differences in important clinical outcomes. Biomarkers can detect pathologies present early in disease potentially paving the way for preventative intervention strategies, which may help patients to avoid disability, poor treatment outcome, disease sequelae and premature mortality. However, adding biomarker data to real world datasets comes with a number of important challenges including sample collection and storage, study design and data analysis and interpretation. In this narrative review we will consider the benefits and challenges of adding biomarker data to real world datasets and discuss how biomarker data have added to our understanding of complex diseases, focusing on rheumatoid arthritis.

A Cetuximab-Mediated Suicide System in Chimeric Antigen Receptor-Modified Hematopoietic Stem Cells for Cancer Therapy

Using gene modification of hematopoietic stem cells (HSC) to create persistent generation of multilineage immune effectors to target cancer cells directly is proposed. Gene-modified human HSC have been used to introduce genes to correct, prevent, or treat diseases. Concerns regarding malignant transformation, abnormal hematopoiesis, and autoimmunity exist, making the co-delivery of a suicide gene a necessary safety measure. Truncated epidermal growth factor receptor (EGFRt) was tested as a suicide gene system co-delivered with anti-CD19 chimeric antigen receptor (CAR) to human HSC. Third-generation self-inactivating lentiviral vectors were used to co-deliver an anti-CD19 CAR and EGFRt. In vitro, gene-modified HSC were differentiated into myeloid cells to allow transgene expression. An antibody-dependent cell-mediated cytotoxicity (ADCC) assay was used, incubating target cells with leukocytes and monoclonal antibody cetuximab to determine the percentage of surviving cells. In vivo, gene-modified HSC were engrafted into NSG mice with subsequent treatment with intraperitoneal cetuximab. Persistence of gene-modified cells was assessed by flow cytometry, droplet digital polymerase chain reaction (ddPCR), and positron emission tomography (PET) imaging using ⁸⁹Zr-Cetuximab. Cytotoxicity was significantly increased (p = 0.01) in target cells expressing EGFRt after incubation with leukocytes and cetuximab 1 μg/mL compared to EGFRt+ cells without cetuximab and non-transduced cells with or without cetuximab, at all effector:target ratios. Mice humanized with gene-modified HSC presented significant ablation of gene-modified cells after treatment (p = 0.002). Remaining gene-modified cells were close to background on flow cytometry and within two logs of decrease of vector copy numbers by ddPCR in mouse tissues. PET imaging confirmed ablation with a decrease of an average of 82.5% after cetuximab treatment. These results give proof of principle for CAR-modified HSC regulated by a suicide gene. Further studies are needed to enable clinical translation. Cetuximab ADCC of EGFRt-modified cells caused effective killing. Different ablation approaches, such as inducible caspase 9 or co-delivery of other inert cell markers, should also be evaluated.

Synthetic analogues of 2-oxo acids discriminate metabolic contribution of the 2-oxoglutarate and 2-oxoadipate dehydrogenases in mammalian cells and tissues

The biological significance of the DHTKD1-encoded 2-oxoadipate dehydrogenase (OADH) remains obscure due to its catalytic redundancy with the ubiquitous OGDH-encoded 2-oxoglutarate dehydrogenase (OGDH). In this work, metabolic contributions of OADH and OGDH are discriminated by exposure of cells/tissues with different DHTKD1 expression to the synthesized phosphonate analogues of homologous 2-oxodicarboxylates. The saccharopine pathway intermediates and phosphorylated sugars are abundant when cellular expressions of DHTKD1 and OGDH are comparable, while nicotinate and non-phosphorylated sugars are when DHTKD1 expression is order(s) of magnitude lower than that of OGDH. Using succinyl, glutaryl and adipoyl phosphonates on the enzyme preparations from tissues with varied DHTKD1 expression reveals the contributions of OADH and OGDH to oxidation of 2-oxoadipate and 2-oxoglutarate in vitro. In the phosphonates-treated cells with the high and low DHTKD1 expression, adipate or glutarate, correspondingly, are the most affected metabolites. The marker of fatty acid β-oxidation, adipate, is mostly decreased by the shorter, OGDH-preferring, phosphonate, in agreement with the known OGDH dependence of β-oxidation. The longest, OADH-preferring, phosphonate mostly affects the glutarate level. Coupled decreases in sugars and nicotinate upon the OADH inhibition link the perturbation in glucose homeostasis, known in OADH mutants, to the nicotinate-dependent NAD metabolism.

Exploring genetic targets of psoriasis using genome wide association studies (GWAS) for drug repurposing

Psoriasis is a chronic inflammatory disease causing itching in the body and pain in the joints. Currently, no permanent cure is available at a commercial level for this disease. Genome wide association studies (GWAS) provide a deeper insight that helps in better understanding this disease and further possible cure of this disease. The major goal of the present study is to identify potent genetic targets of psoriasis disease using GWAS approach and identify drugs for repurposing. The methods used include GWAS catalogue, GeneAnalytics, canSAR protein annotation tool, VarElect, Drug bank, Proteomics database, ProTox software. By exploring GWAS catalogue, 126 psoriasis associated genes (PAG) were identified. 68 genes found to be druggable were obtained from canSAR protein annotation tool. Localization results depict that maximum genes are present in cytoplasmic cellular components. The superpathways obtained from GeneAnalytics resulted in involvement of these genes in the immune system, Jak/Stat pathway, Th17 and Wnt pathways. Two genes Interleukin 13 (IL13) and POLI are Food and Drug Administration (FDA) approved targets. Small compounds for these targets were analysed for drug-likeliness, toxicity and mutagenecity properties. The FDA approved drug pandel was found to possess desirable properties. The medications used for psoriasis causes mild to severe side effects and does not work well always. Hence we propose drug repurposing strategy to use existing drugs for new therapies. Therefore, the drug pandel could be explored further and repurposed to treat psoriasis.

Epigenetic Changes in the Pathogenesis of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects about 1% of the world’s population. The etiology of RA remains unknown. It is considered to occur in the presence of genetic and environmental factors. An increasing body of evidence pinpoints that epigenetic modifications play an important role in the regulation of RA pathogenesis. Epigenetics causes heritable phenotype changes that are not determined by changes in the DNA sequence. The major epigenetic mechanisms include DNA methylation, histone proteins modifications and changes in gene expression caused by microRNAs and other non-coding RNAs. These modifications are reversible and could be modulated by diet, drugs, and other environmental factors. Specific changes in DNA methylation, histone modifications and abnormal expression of non-coding RNAs associated with RA have already been identified. This review focuses on the role of these multiple epigenetic factors in the pathogenesis and progression of the disease, not only in synovial fibroblasts, immune cells, but also in the peripheral blood of patients with RA, which clearly shows their high diagnostic potential and promising targets for therapy in the future.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Neuronal Conditional Knockout of Collapsin Response Mediator Protein 2 Ameliorates Disease Severity in a Mouse Model of Multiple Sclerosis

We previously showed that treatment with lanthionine ketimine ethyl ester (LKE) reduced disease severity and axonal damage in an experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis and increased neuronal maturation and survival in vitro . A major target of LKE is collapsin response mediator protein 2 (CRMP2), suggesting this protein may mediate LKE actions. We now show that conditional knockout of CRMP2 from neurons using a CamK2a promoter to drive Cre recombinase expression reduces disease severity in the myelin oligodendrocyte glycoprotein (MOG)35–55 EAE model, associated with decreased spinal cord axonal damage, and less glial activation in the cerebellum, but not the spinal cord. Immunohistochemical staining and quantitative polymerase chain reaction show CRMP2 depletion from descending motor neurons in the motor cortex, but not from spinal cord neurons, suggesting that the benefits of CRMP2 depletion on EAE may stem from effects on upper motor neurons. In addition, mice in which CRMP2 S522 phosphorylation was prevented by substitution for an alanine residue also showed reduced EAE severity. These results show that modification of CRMP2 expression and phosphorylation can influence the course of EAE and suggests that treatment with CRMP2 modulators such as LKE act in part by reducing CRMP2 S522 phosphorylation.

Context-dependent prediction of protein complexes by SiComPre

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Predicting the evolution of Escherichia coli by a data-driven approach

A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence. While the mutations shared across all replicates decrease with the number of replicates, our results argue that the pairwise overlapping ratio remains the same, regardless of the number of replicates. An ensemble of predictors trained on the mutation compendium and tested in forward validation over 35 evolution replicates achieves a 49.2 ± 5.8% (mean ± std) precision and 34.5 ± 5.7% recall in predicting mutation targets. This work demonstrates how integrated datasets can be harnessed to create predictive models of evolution at a gene level and elucidate the effect of evolutionary processes in well-defined environments.

How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.

Detection of early pancreatic ductal adenocarcinoma with thrombospondin-2 and CA19-9 blood markers

Markers are needed to facilitate early detection of pancreatic ductal adenocarcinoma (PDAC), which is often diagnosed too late for effective therapy. Starting with a PDAC cell reprogramming model that recapitulated the progression of human PDAC, we identified secreted proteins and tested a subset as potential markers of PDAC. We optimized an enzyme-linked immunosorbent assay (ELISA) using plasma samples from patients with various stages of PDAC, from individuals with benign pancreatic disease, and from healthy controls. A phase 1 discovery study (n = 20), a phase 2a validation study (n = 189), and a second phase 2b validation study (n = 537) revealed that concentrations of plasma thrombospondin-2 (THBS2) discriminated among all stages of PDAC consistently. The receiver operating characteristic (ROC) c-statistic was 0.76 in the phase 1 study, 0.84 in the phase 2a study, and 0.87 in the phase 2b study. The plasma concentration of THBS2 was able to discriminate resectable stage I cancer as readily as stage III/IV PDAC tumors. THBS2 plasma concentrations combined with those for CA19-9, a previously identified PDAC marker, yielded a c-statistic of 0.96 in the phase 2a study and 0.97 in the phase 2b study. THBS2 data improved the ability of CA19-9 to distinguish PDAC from pancreatitis. With a specificity of 98%, the combination of THBS2 and CA19-9 yielded a sensitivity of 87% for PDAC in the phase 2b study. A THBS2 and CA19-9 blood marker panel measured with a conventional ELISA may improve the detection of patients at high risk for PDAC.

Toward Large-Scale Computational Prediction of Protein Complexes

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

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.

Serum Neuroinflammatory Disease-Induced Central Nervous System Proteins Predict Clinical Onset of Experimental Autoimmune Encephalomyelitis

There is an urgent need in multiple sclerosis (MS) patients to develop biomarkers and laboratory tests to improve early diagnosis, predict clinical relapses, and optimize treatment responses. In healthy individuals, the transport of proteins across the blood–brain barrier (BBB) is tightly regulated, whereas, in MS, central nervous system (CNS) inflammation results in damage to neuronal tissues, disruption of BBB integrity, and potential release of neuroinflammatory disease-induced CNS proteins (NDICPs) into CSF and serum. Therefore, changes in serum NDICP abundance could serve as biomarkers of MS. Here, we sought to determine if changes in serum NDICPs are detectable prior to clinical onset of experimental autoimmune encephalomyelitis (EAE) and, therefore, enable prediction of disease onset. Importantly, we show in longitudinal serum specimens from individual mice with EAE that pre-onset expression waves of synapsin-2, glutamine synthetase, enolase-2, and synaptotagmin-1 enable the prediction of clinical disease with high sensitivity and specificity. Moreover, we observed differences in serum NDICPs between active and passive immunization in EAE, suggesting hitherto not appreciated differences for disease induction mechanisms. Our studies provide the first evidence for enabling the prediction of clinical disease using serum NDICPs. The results provide proof-of-concept for the development of high-confidence serum NDICP expression waves and protein biomarker candidates for MS.

Bre Enhances Osteoblastic Differentiation by Promoting the Mdm2-Mediated Degradation of p53

Bre is a conserved cellular protein expressed in various tissues. Its major function includes DNA damage repair and anti-apoptosis. Recent studies indicate that Bre is potentially involved in stem cell differentiation although pathophysiological significance along with the molecular mechanisms is still unclear. Here, we report that Bre protein was substantially expressed in the bone tissue and its expression was highly upregulated during the osteogenic differentiation. To test a hypothesis that Bre plays functional roles in the process of osteogenic differentiation, we examined the expression of Bre in an osteoporosis mouse model. Compared with the normal bone tissue, Bre expression in osteoporotic bone was also significantly reduced. Moreover, knockdown of Bre in the mouse bone marrow mesenchymal cells significantly reduced the expression of osteogenic marker genes, the alkaline phosphatase activity, and the mineralization capacity, while overexpression of Bre greatly promoted the osteogenesis both in vitro and in vivo. Interestingly, we founded that knockdown of Bre led to activation of the p53 signaling pathways exhibited by increased p53, p21, and Mdm2. However, when we inhibited the p53 by siRNA silencing or pifithrin-α, the impaired osteogenesis caused by Bre knockdown was greatly restored. Finally, we found that Bre promoted the Mdm2-mediated p53 ubiquitination and degradation by physically interacting with p53. Taken together, our results revealed a novel function of Bre in osteoblast differentiation through modulating the stability of p53. Stem Cells 2017;35:1760-1772.

Dual mechanisms regulate the nucleocytoplasmic localization of human DDX6

DDX6 is a conserved DEAD-box protein (DBP) that plays central roles in cytoplasmic RNA regulation, including processing body (P-body) assembly, mRNA decapping, and translational repression. Beyond its cytoplasmic functions, DDX6 may also have nuclear functions because its orthologues are known to localize to nuclei in several biological contexts. However, it is unclear whether DDX6 is generally present in human cell nuclei, and the molecular mechanism underlying DDX6 subcellular distribution remains elusive. In this study, we showed that DDX6 is commonly present in the nuclei of human-derived cells. Our structural and molecular analyses deviate from the current model that the shuttling of DDX6 is directly mediated by the canonical nuclear localization signal (NLS) and nuclear export signal (NES), which are recognized and transported by Importin-α/β and CRM1, respectively. Instead, we show that DDX6 can be transported by 4E-T in a piggyback manner. Furthermore, we provide evidence for a novel nuclear targeting mechanism in which DDX6 enters the newly formed nuclei by "hitch-hiking" on mitotic chromosomes with its C-terminal domain during M phase progression. Together, our results indicate that the nucleocytoplasmic localization of DDX6 is regulated by these dual mechanisms.

Mathematical modeling of the effects of CK2.3 on mineralization in osteoporotic bone

Osteoporosis is caused by decreased bone mineral density (BMD) and new treatments for this disease are desperately needed. Bone morphogenetic protein 2 (BMP2) is crucial for bone formation. The mimetic peptide CK2.3 acts downstream of BMP2 and increases BMD when injected systemically into the tail vein of mice. However, the most effective dosage needed to induce BMD in humans is unknown. We developed a mathematical model for CK2.3‐dependent bone mineralization. We used a physiologically based pharmacokinetic (PBPK) model to derive the CK2.3 concentration needed to increase BMD. Based on our results, the ideal dose of CK2.3 for a healthy individual to achieve the maximum increase of mineralization was about 409 µM injected in 500 µL volume, while dosage for osteoporosis patients was about 990 µM. This model showed that CK2.3 could increase the average area of bone mineralization in patients and in healthy adults.

A quantitative model for the rate-limiting process of UGA alternative assignments to stop and selenocysteine codons

Ambiguity in genetic codes exists in cases where certain stop codons are alternatively used to encode non-canonical amino acids. In selenoprotein transcripts, the UGA codon may either represent a translation termination signal or a selenocysteine (Sec) codon. Translating UGA to Sec requires selenium and specialized Sec incorporation machinery such as the interaction between the SECIS element and SBP2 protein, but how these factors quantitatively affect alternative assignments of UGA has not been fully investigated. We developed a model simulating the UGA decoding process. Our model is based on the following assumptions: (1) charged Sec-specific tRNAs (Sec-tRNA^Sec) and release factors compete for a UGA site, (2) Sec-tRNA^Sec abundance is limited by the concentrations of selenium and Sec-specific tRNA (tRNA^Sec) precursors, and (3) all synthesis reactions follow first-order kinetics. We demonstrated that this model captured two prominent characteristics observed from experimental data. First, UGA to Sec decoding increases with elevated selenium availability, but saturates under high selenium supply. Second, the efficiency of Sec incorporation is reduced with increasing selenoprotein synthesis. We measured the expressions of four selenoprotein constructs and estimated their model parameters. Their inferred Sec incorporation efficiencies did not correlate well with their SECIS-SBP2 binding affinities, suggesting the existence of additional factors determining the hierarchy of selenoprotein synthesis under selenium deficiency. This model provides a framework to systematically study the interplay of factors affecting the dual definitions of a genetic codon.

The “code book” of protein translation maps 4³ = 64 triplets of RNA sequences (codons) into 20 canonical amino acids and the stop signal. This code book is universal in almost all organisms on earth. Selenoproteins consist of selenium-containing amino acids–selenocysteines (Sec)–that are not among the 20 canonical amino acids. The cells “borrow” a stop codon UGA to translate selenocysteines. Since UGA maps to two possible outcomes, the translation machinery can synthesize both full-length selenoproteins (when UGA encodes selenocysteine) and truncated peptide chains (when UGA encodes translational termination). Despite extensive study about selenoprotein synthesis mechanisms, a quantitative model for how cells allocate resources to synthesize each species is yet to appear. We propose a quantitative model that can explain the dependency of experimental observables such as protein stability and Sec incorporation efficiency by various factors such as selenium concentration and mRNA levels. Saturation of those quantities implies the existence of limiting factors such as mRNA transcripts and Sec-specific tRNAs. The match between model simulations and experimental data suggests that the cellular decision making of synthesizing the two species of proteins may follow simple first-order kinetics.

Effective Combination Therapies for B-cell Lymphoma Predicted by a Virtual Disease Model

The complexity of cancer signaling networks limits the efficacy of most single-agent treatments and brings about challenges in identifying effective combinatorial therapies. In this study, we used chronic active B-cell receptor (BCR) signaling in diffuse large B-cell lymphoma as a model system to establish a computational framework to optimize combinatorial therapy in silico We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex cross-talk between the NFκB, ERK, and AKT pathways and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model, we predicted viability responses of many single drug and drug combinations in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Ultimately, our work establishes a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations. Cancer Res; 77(8); 1818-30. ©2017 AACR.

MUFINS: multi-formalism interaction network simulator

Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.

Investigating mitochondrial redox state using NADH and NADPH autofluorescence

The redox states of the NAD and NADP pyridine nucleotide pools play critical roles in defining the activity of energy producing pathways, in driving oxidative stress and in maintaining antioxidant defences. Broadly speaking, NAD is primarily engaged in regulating energy-producing catabolic processes, whilst NADP may be involved in both antioxidant defence and free radical generation. Defects in the balance of these pathways are associated with numerous diseases, from diabetes and neurodegenerative disease to heart disease and cancer. As such, a method to assess the abundance and redox state of these separate pools in living tissues would provide invaluable insight into the underlying pathophysiology. Experimentally, the intrinsic fluorescence of the reduced forms of both redox cofactors, NADH and NADPH, has been used for this purpose since the mid-twentieth century. In this review, we outline the modern implementation of these techniques for studying mitochondrial redox state in complex tissue preparations. As the fluorescence spectra of NADH and NADPH are indistinguishable, interpreting the signals resulting from their combined fluorescence, often labelled NAD(P)H, can be complex. We therefore discuss recent studies using fluorescence lifetime imaging microscopy (FLIM) which offer the potential to discriminate between the two separate pools. This technique provides increased metabolic information from cellular autofluorescence in biomedical investigations, offering biochemical insights into the changes in time-resolved NAD(P)H fluorescence signals observed in diseased tissues.

Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus

BACKGROUND

Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.

METHOD

We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation.

RESULTS

Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P < 0.05 in East Asians, but none of the associations were statistically significant in African descendants. Multiple candidate functional variants are located in putative enhancer sequences. Chromatin interaction data suggested that PTHLH was the likely target gene of these enhancers. Of the six variants with the strongest evidence of potential functionality, rs11049453 was statistically significantly associated with the expression of PTHLH and its nearby gene CCDC91 at P < 0.05.

CONCLUSION

This study identified four independent association signals at 12p11 and revealed potentially functional variants, providing additional insights into the underlying biological mechanism(s) for the association observed between variants at 12p11 and breast cancer risk.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis

Objective

Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA.

Methods

An epigenome‐wide association study was performed on pretreatment whole blood DNA from patients with RA. Patients who displayed good response (n = 36) or no response (n = 36) to etanercept therapy at 3 months were selected. Differentially methylated positions were identified using linear regression. Variance of methylation at differentially methylated positions was assessed for correlation with cis‐acting single‐nucleotide polymorphisms (SNPs). A replication experiment for prioritized SNPs was performed in an independent cohort of 1,204 RA patients.

Results

Five positions that were differentially methylated between responder groups were identified, with a false discovery rate of <5%. The top 2 differentially methylated positions mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, P = 1.39 × 10⁻⁸ and cg26401028, P = 1.69 × 10⁻⁸). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top 2 differentially methylated positions (P = 2.63 × 10⁻⁷ and P = 1.05 × 10⁻⁶, respectively). Furthermore, the A allele of rs3468 was correlated with European League Against Rheumatism nonresponse in the discovery cohort (P = 0.03; n = 56) and in the independent replication cohort (P = 0.003; n = 1,204).

Conclusion

We identify DNA methylation as a potential biomarker of response to TNFi therapy, and we report the association between response and the LRPAP1 gene, which encodes a chaperone of low‐density lipoprotein receptor–related protein 1. Additional replication experiments in independent sample collections are now needed.

PP1 initiates the dephosphorylation of MASTL, triggering mitotic exit and bistability in human cells

Entry into mitosis is driven by the phosphorylation of thousands of substrates, under the master control of Cdk1. During entry into mitosis, Cdk1, in collaboration with MASTL kinase, represses the activity of the major mitotic protein phosphatases, PP1 and PP2A, thereby ensuring mitotic substrates remain phosphorylated. For cells to complete and exit mitosis, these phosphorylation events must be removed, and hence, phosphatase activity must be reactivated. This reactivation of phosphatase activity presumably requires the inhibition of MASTL; however, it is not currently understood what deactivates MASTL and how this is achieved. In this study, we identified that PP1 is associated with, and capable of partially dephosphorylating and deactivating, MASTL during mitotic exit. Using mathematical modelling, we were able to confirm that deactivation of MASTL is essential for mitotic exit. Furthermore, small decreases in Cdk1 activity during metaphase are sufficient to initiate the reactivation of PP1, which in turn partially deactivates MASTL to release inhibition of PP2A and, hence, create a feedback loop. This feedback loop drives complete deactivation of MASTL, ensuring a strong switch-like activation of phosphatase activity during mitotic exit.

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.

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.

Integrative genomic analysis of interleukin-36RN and its prognostic value in cancer

Interleukin (IL)-36RN, previously known as IL1-F5 and IL-1δ, shares a 360-kb region of chromosome 2q13 with members of IL-1 systems. IL-36RN encodes an anti-inflammatory cytokine, IL-36 receptor antagonist (IL-36Ra). In spite of IL-36Ra showing the highest homology to IL-1 receptor (IL-1R) antagonist, it differs from the latter in aspects including its binding to IL-lRrp2 but not to IL-1R1. IL-36RN is mainly expressed in epithelial cells and has important roles in inflammatory diseases. In the present study, IL-36RN was identified in the genomes of 27 species, including human, chimpanzee, mouse, horse and dolphin. Human IL-36RN was mainly expressed in the eye, head and neck, fetal heart, lung, testis, cervix and placenta; furthermore, it was highly expressed in bladder and parathyroid tumors. Furthermore, a total of 30 single nucleotide polymorphisms causing missense mutations were determined, which are considered to be the causes of various diseases, such as generalized pustular psoriasis. In addition, the link between IL-36RN and the prognosis of certain cancer types was revealed through meta-analysis. Tumor-associated transcriptional factors c-Fos, activator protein-1, c-Jun and nuclear factor κB were found to bind to the upstream region in the IL-36RN gene. This may indicate that IL-36RN is involved in tumorigenesis and tumor progression through the regulation of tumor-associated transcriptional factors. The present study identified IL-36RN in various species and investigated the associations between IL-36RN and cancer prognosis, which would determine whether IL-36RN drove the evolution of the various species with regard to tumorigenesis.

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.

CardioGenBase: A Literature Based Multi-Omics Database for Major Cardiovascular Diseases

Cardiovascular diseases (CVDs) account for high morbidity and mortality worldwide. Both, genetic and epigenetic factors are involved in the enumeration of various cardiovascular diseases. In recent years, a vast amount of multi-omics data are accumulated in the field of cardiovascular research, yet the understanding of key mechanistic aspects of CVDs remain uncovered. Hence, a comprehensive online resource tool is required to comprehend previous research findings and to draw novel methodology for understanding disease pathophysiology. Here, we have developed a literature-based database, CardioGenBase, collecting gene-disease association from Pubmed and MEDLINE. The database covers major cardiovascular diseases such as cerebrovascular disease, coronary artery disease (CAD), hypertensive heart disease, inflammatory heart disease, ischemic heart disease and rheumatic heart disease. It contains ~1,500 cardiovascular disease genes from ~2,4000 research articles. For each gene, literature evidence, ontology, pathways, single nucleotide polymorphism, protein-protein interaction network, normal gene expression, protein expressions in various body fluids and tissues are provided. In addition, tools like gene-disease association finder and gene expression finder are made available for the users with figures, tables, maps and venn diagram to fit their needs. To our knowledge, CardioGenBase is the only database to provide gene-disease association for above mentioned major cardiovascular diseases in a single portal. CardioGenBase is a vital online resource to support genome-wide analysis, genetic, epigenetic and pharmacological studies.

Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data--such as protein abundances, domain-domain interactions and functional annotations--to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.

Biomarker differences between cadaveric grafts used in human orthotopic liver transplantation as identified by coulometric electrochemical array detection (CEAD) metabolomics

Metabolomics in systems biology research unravels intracellular metabolic changes by high throughput methods, but such studies focusing on liver transplantation (LT) are limited. Microdialysate samples of liver grafts from donors after circulatory death (DCD; n=13) and brain death (DBD; n=27) during cold storage and post-reperfusion phase were analyzed through coulometric electrochemical array detection (CEAD) for identification of key metabolomics changes. Metabolite peak differences between the graft types at cold phase, post-reperfusion trends, and in failed allografts, were identified against reference chromatograms. In the cold phase, xanthine, uric acid, and kynurenine were overexpressed in DCD by 3-fold, and 3-nitrotyrosine (3-NT) and 4-hydroxy-3-methoxymandelic acid (HMMA) in DBD by 2-fold (p<0.05). In both grafts, homovanillic acid and methionine increased by 20%-30% with each 100 min increase in cold ischemia time (p<0.05). Uric acid expression was significantly different in DCD post-reperfusion. Failed allografts had overexpression of reduced glutathione and kynurenine (cold phase) and xanthine (post-reperfusion) (p<0.05). This differential expression of metabolites between graft types is a novel finding, meanwhile identification of overexpression of kynurenine in DCD grafts and in failed allografts is unique. Further studies should examine kynurenine as a potential biomarker predicting graft function, its causation, and actions on subsequent clinical outcomes.

Sharing big biomedical data

BACKGROUND

The promise of Big Biomedical Data may be offset by the enormous challenges in handling, analyzing, and sharing it. In this paper, we provide a framework for developing practical and reasonable data sharing policies that incorporate the sociological, financial, technical and scientific requirements of a sustainable Big Data dependent scientific community.

FINDINGS

Many biomedical and healthcare studies may be significantly impacted by using large, heterogeneous and incongruent datasets; however there are significant technical, social, regulatory, and institutional barriers that need to be overcome to ensure the power of Big Data overcomes these detrimental factors.

CONCLUSIONS

Pragmatic policies that demand extensive sharing of data, promotion of data fusion, provenance, interoperability and balance security and protection of personal information are critical for the long term impact of translational Big Data analytics.

Profiling of urinary proteins in Karan Fries cows reveals more than 1550 proteins

Urine is a non-invasive source of biological fluid, which reflects the physiological status of the mammals. We have profiled the cow urinary proteome and analyzed its functional significance. The urine collected from three healthy cows was concentrated by diafiltration (DF) followed by protein extraction using three methods, namely methanol, acetone, and ammonium sulphate (AS) precipitation and Proteo Spin urine concentration kit (PS). The quality of the protein was assessed by two-dimensional gel electrophoresis (2DE). In-gel digestion method revealed more proteins (1191) in comparison to in-solution digestion method (541). Collectively, 938, 606 and 444 proteins were identified in LC-MS/MS after in-gel and in-solution tryptic digestion of proteins prepared by AS, PS and DF methods, respectively resulting in identification of a total of 1564 proteins. Gene ontology (GO) using Panther7.0 grouped the majority of the proteins into cytoplasmic (location), catalytic activity (function), and metabolism (biological processes), while Cytoscape grouped proteins into complement and coagulation cascades; protease inhibitor activity and wound healing. Functional significance of few selected proteins seems to play important role in their physiology. Comparative analysis with human urine revealed 315 overlapping proteins. This study reports for the first time evidence of more than 1550 proteins in urine of healthy cow donors. This article is part of a Special Issue entitled: Proteomics in India.

iGWAS: Integrative Genome-Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis

Genome-wide association studies (GWAS) have been a standard practice in identifying single nucleotide polymorphisms (SNPs) for disease susceptibility. We propose a new approach, termed integrative GWAS (iGWAS) that exploits the information of gene expressions to investigate the mechanisms of the association of SNPs with a disease phenotype, and to incorporate the family-based design for genetic association studies. Specifically, the relations among SNPs, gene expression, and disease are modeled within the mediation analysis framework, which allows us to disentangle the genetic effect on a disease phenotype into two parts: an effect mediated through a gene expression (mediation effect, ME) and an effect through other biological mechanisms or environment-mediated mechanisms (alternative effect, AE). We develop omnibus tests for the ME and AE that are robust to underlying true disease models. Numerical studies show that the iGWAS approach is able to facilitate discovering genetic association mechanisms, and outperforms the SNP-only method for testing genetic associations. We conduct a family-based iGWAS of childhood asthma that integrates genetic and genomic data. The iGWAS approach identifies six novel susceptibility genes (MANEA, MRPL53, LYCAT, ST8SIA4, NDFIP1, and PTCH1) using the omnibus test with false discovery rate less than 1%, whereas no gene using SNP-only analyses survives with the same cut-off. The iGWAS analyses further characterize that genetic effects of these genes are mostly mediated through their gene expressions. In summary, the iGWAS approach provides a new analytic framework to investigate the mechanism of genetic etiology, and identifies novel susceptibility genes of childhood asthma that were biologically meaningful.

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.

Web resources for mass spectrometry-based proteomics

With the development of high-resolution and high-throughput mass spectrometry (MS) technology, a large quantum of proteomic data is continually being generated. Collecting and sharing these data are a challenge that requires immense and sustained human effort. In this report, we provide a classification of important web resources for MS-based proteomics and present rating of these web resources, based on whether raw data are stored, whether data submission is supported, and whether data analysis pipelines are provided. These web resources are important for biologists involved in proteomics research.

YPED: an integrated bioinformatics suite and database for mass spectrometry-based proteomics research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography-tandem mass spectrometry (LC-MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Basal and stress-inducible expression of HSPA6 in human keratinocytes is regulated by negative and positive promoter regions

Epidermal keratinocytes serve as the primary barrier between the body and environmental stressors. They are subjected to numerous stress events and are likely to respond with a repertoire of heat shock proteins (HSPs). HSPA6 (HSP70B') is described in other cell types with characteristically low to undetectable basal expression, but is highly stress induced. Despite this response in other cells, little is known about its control in keratinocytes. We examined endogenous human keratinocyte HSPA6 expression and localized some responsible transcription factor sites in a cloned HSPA6 3 kb promoter. Using promoter 5' truncations and deletions, negative and positive regulatory regions were found throughout the 3 kb promoter. A region between -346 and -217 bp was found to be crucial to HSPA6 basal expression and stress inducibility. Site-specific mutations and DNA-binding studies show that a previously uncharacterized AP1 site contributes to the basal expression and maximal stress induction of HSPA6. Additionally, a new heat shock element (HSE) within this region was defined. While this element mediates increased transcriptional response in thermally stressed HaCaT keratinocytes, it preferentially binds a stress-inducible factor other than heat shock factor (HSF)1 or HSF2. Intriguingly, this newly characterized HSPA6 HSE competes HSF1 binding a consensus HSE and binds both HSF1 and HSF2 from other epithelial cells. Taken together, our results demonstrate that the HSPA6 promoter contains essential negative and positive promoter regions and newly identified transcription factor targets, which are key to the basal and stress-inducible expression of HSPA6. Furthermore, these results suggest that an HSF-like factor may preferentially bind this newly identified HSPA6 HSE in HaCaT cells.

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.