Mission Critical: The Need for Proteomics in the Era of Next-Generation Sequencing and Precision Medicine.

Hum Mol Genet. 2016 Jul 20;

Authors: Cayer D, Nazor KL, Schork NJ

Abstract
Next generation sequencing (NGS) has ignited an unprecedented pace of discovery in the biomedical sciences that is fundamentally transforming the way that we understand, diagnose and treat disease, and has motivated the belief that true precision medicine - medicine that is tailored to an individual's genetic, biochemical and exposure profile - will be a reality in the near term. With minimal sample requirement, NGS can enable the concurrent genome-wide study of genetic variations, transcriptomes, and certain epigenetic modifications. However, interrogating proteins in as efficiently as DNA and RNA can be interrogated with NGS is lacking and this hampers more comprehensive views of molecular physiology and limits advances in biomedical science and precision medicine. The fact is that innovations in proteomic technologies pale in comparison to advances in NGS, with current methodologies suffering from issues related to reproducibility, sensitivity, sample requirements, and limited multiplexing capacity. The development of proteomic technologies to overcome these limitations would fill the void in systems biology research, catalyze clinical innovations, and expedite the realization of precision medicine.

PMID: 27439388 [PubMed - as supplied by publisher]

Simulation technology and its application in Systems Biology.

Nihon Yakurigaku Zasshi. 2016 Feb;147(2):101-6

Authors: Funahashi A, Hiroi N

PMID: 26860650 [PubMed - indexed for MEDLINE]

CauloBrowser: A systems biology resource for Caulobacter crescentus.

Nucleic Acids Res. 2016 Jan 4;44(D1):D640-5

Authors: Lasker K, Schrader JM, Men Y, Marshik T, Dill DL, McAdams HH, Shapiro L

Abstract
Caulobacter crescentus is a premier model organism for studying the molecular basis of cellular asymmetry. The Caulobacter community has generated a wealth of high-throughput spatiotemporal databases including data from gene expression profiling experiments (microarrays, RNA-seq, ChIP-seq, ribosome profiling, LC-ms proteomics), gene essentiality studies (Tn-seq), genome wide protein localization studies, and global chromosome methylation analyses (SMRT sequencing). A major challenge involves the integration of these diverse data sets into one comprehensive community resource. To address this need, we have generated CauloBrowser (www.caulobrowser.org), an online resource for Caulobacter studies. This site provides a user-friendly interface for quickly searching genes of interest and downloading genome-wide results. Search results about individual genes are displayed as tables, graphs of time resolved expression profiles, and schematics of protein localization throughout the cell cycle. In addition, the site provides a genome viewer that enables customizable visualization of all published high-throughput genomic data. The depth and diversity of data sets collected by the Caulobacter community makes CauloBrowser a unique and valuable systems biology resource.

PMID: 26476443 [PubMed - indexed for MEDLINE]

Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

IEEE/ACM Trans Comput Biol Bioinform. 2015 Sep-Oct;12(5):1008-20

Authors: Misra S, Pamnany K, Aluru S

Abstract
Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

PMID: 26451815 [PubMed - indexed for MEDLINE]

Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.

PLoS One. 2016;11(2):e0129122

Authors: Han C, House L, Leman SC

Abstract
Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.

PMID: 26905728 [PubMed - indexed for MEDLINE]

DESM: portal for microbial knowledge exploration systems.

Nucleic Acids Res. 2016 Jan 4;44(D1):D624-33

Authors: Salhi A, Essack M, Radovanovic A, Marchand B, Bougouffa S, Antunes A, Simoes MF, Lafi FF, Motwalli OA, Bokhari A, Malas T, Amoudi SA, Othum G, Allam I, Mineta K, Gao X, Hoehndorf R, C Archer JA, Gojobori T, Bajic VB

Abstract
Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4,538,278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.

PMID: 26546514 [PubMed - indexed for MEDLINE]

24 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/21

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/07/21

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

[HORMONALLY-GENETICALLY DEPENDENT THERAPY, USING VITAMIN K IN PATIENTS, SUFFERING THE ULCER HEMORRHAGE].

Klin Khir. 2016 Apr;(4):9-11

Authors: Duzhyi ID, Kharchenko SV

Abstract
Pathophysiological mechanisms of the vitamin K impact, including those in the gut with ulcerative affection, are studied still insufficiently. Investigations of pharmacogenomics of the vitamin K gives a new approach to therapy in patients, suffering gastro-intestinal hemorrhage. Possibilities of titration of the vitamin K3 (menadione) doses, depending on level of estrogenemia and genetic constitution, concerning genes-candidates ESR1 (rs2234693) and VKORC1 (rs9923231), were studied. There were examined 36 patients, who were treated for the ulcer hemorrhage. The blood serum concentration of estradiol was investigated in accordance to method of solid phase enzyme immunoassay, the genotyping procedure was performed in accordance to indices of polymerase chain reaction with analysis of the restrictional fragments length. The initial daily dose of menadione have constituted 20 mg. After a genotype determination made (first-second day after admittance to hospital) in patients with normoestrogenemia in genotypes CC/GG, CC/GA, CT/GG, CT/GA a vitaminotherapy was prolonged in daily dose of 20 mg, and in a conditionally-pathological variant of genotype the dose of vitamin K was enhanced up to 30 mg. Determination of hormones and the patients' genetic constitution makes possible to apply a personified approach for the vitamin K3 application in the ulcerative hemorrhage.

PMID: 27434945 [PubMed - in process]

Systems analysis of cis-regulatory motifs in C4 photosynthesis genes using maize and rice leaf transcriptomic data during a process of de-etiolation.

J Exp Bot. 2016 Jul 19;

Authors: Xu J, Bräutigam A, Li Y, Weber AP, Zhu XG

Abstract
Identification of potential cis-regulatory motifs controlling the development of C4 photosynthesis is a major focus of current research. In this study, we used time-series RNA-seq data collected from etiolated maize and rice leaf tissues sampled during a de-etiolation process to systematically characterize the expression patterns of C4-related genes and to further identify potential cis elements in five different genomic regions (i.e. promoter, 5'UTR, 3'UTR, intron, and coding sequence) of C4 orthologous genes. The results demonstrate that although most of the C4 genes show similar expression patterns, a number of them, including chloroplast dicarboxylate transporter 1, aspartate aminotransferase, and triose phosphate transporter, show shifted expression patterns compared with their C3 counterparts. A number of conserved short DNA motifs between maize C4 genes and their rice orthologous genes were identified not only in the promoter, 5'UTR, 3'UTR, and coding sequences, but also in the introns of core C4 genes. We also identified cis-regulatory motifs that exist in maize C4 genes and also in genes showing similar expression patterns as maize C4 genes but that do not exist in rice C3 orthologs, suggesting a possible recruitment of pre-existing cis-elements from genes unrelated to C4 photosynthesis into C4 photosynthesis genes during C4 evolution.

PMID: 27436282 [PubMed - as supplied by publisher]

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

J Mass Spectrom. 2016 Jul;51(7):461-75

Authors: Barnes S, Benton HP, Casazza K, Cooper SJ, Cui X, Du X, Engler J, Kabarowski JH, Li S, Pathmasiri W, Prasain JK, Renfrow MB, Tiwari HK

Abstract
The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd.

PMID: 27434804 [PubMed - in process]

Animal Models of Chemical Carcinogenesis: Driving Breakthroughs in Cancer Research for 100 Years.

Cold Spring Harb Protoc. 2015 Oct;2015(10):865-74

Authors: Kemp CJ

Abstract
The identification of carcinogens in the workplace, diet, and environment through chemical carcinogenesis studies in animals has directly contributed to a reduction of cancer burden in the human population. Reduced exposure to these carcinogens through lifestyle changes, government regulation, or change in industry practices has reduced cancer incidence in exposed populations. In addition to providing the first experimental evidence for cancer's relationship to chemical and radiation exposure, animal models of environmentally induced cancer have and will continue to provide important insight into the causes, mechanisms, and conceptual frameworks of cancer. More recently, combining chemical carcinogens with genetically engineered mouse models has emerged as an invaluable approach to study the complex interaction between genotype and environment that contributes to cancer development. In the future, animal models of environmentally induced cancer are likely to provide insight into areas such as the epigenetic basis of cancer, genetic modifiers of cancer susceptibility, the systems biology of cancer, inflammation and cancer, and cancer prevention.

PMID: 26430259 [PubMed - indexed for MEDLINE]

Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery.

Methods Inf Med. 2016 Jul 20;55(4)

Authors: Kastrin A, Rindflesch TC, Hristovski D

Abstract
OBJECTIVES: Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts.
METHODS: We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future.
RESULTS: Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76), followed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87).
CONCLUSIONS: The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.

PMID: 27435341 [PubMed - as supplied by publisher]

Comprehensive Map of Molecules Implicated in Obesity.

PLoS One. 2016;11(2):e0146759

Authors: Jagannadham J, Jaiswal HK, Agrawal S, Rawal K

Abstract
Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in "bow-tie" architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.

PMID: 26886906 [PubMed - indexed for MEDLINE]

Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat.

Am J Infect Control. 2015 Oct 1;43(10):1109-11

Authors: Lazard AJ, Scheinfeld E, Bernhardt JM, Wilcox GB, Suran M

Abstract
A diagnosis of Ebola on US soil triggered widespread panic. In response, the Centers for Disease Control and Prevention held a live Twitter chat to address public concerns. This study applied a textual analytics method to reveal insights from these tweets that can inform communication strategies. User-generated tweets were collected, sorted, and analyzed to reveal major themes. The public was concerned with symptoms and lifespan of the virus, disease transfer and contraction, safe travel, and protection of one's body.

PMID: 26138998 [PubMed - indexed for MEDLINE]

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/20

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/07/20

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Tumor deconstruction as a tool for advanced drug screening and repositioning.

Pharmacol Res. 2016 Jul 15;

Authors: Naik RR, Luo T, Kohandel M, Bapat SA

Abstract
A major focus of contemporary drug screening strategies is the identification of novel anticancer compounds, which often results in underutilization of resources. Current drug evaluation involves in vivo tumor (xenograft) regression as proof-of-principle for cytotoxicity (POC). However, this end-point lacks any assessment of drug resistance of the residual tumor and its capability to establish refractory and/or recurrent disease, which would represent more appropriate indicators of therapeutic failure. We have recently developed a flow cytometry-based approach for the analyses of intra-tumor cellular heterogeneity across stem cell hierarchies, genetic instability and differential cell cycling fractions, which can potentially be predictive of refractory disease and tumor relapse. Iterating this approach after initial POC screening in the drug discovery pipeline would have a great impact in terms of precision of drug evaluation, design of optimal drug combinations and/or drug repositioning. In this perspective, we highlight how through embracing of a comprehensive, informative and analytical assessment of the cellular content of residual tumors, the fidelity and statistical robustness of preclinical drug discovery can be greatly improved.

PMID: 27431330 [PubMed - as supplied by publisher]

Repurposing old drugs to chemoprevention: the case of metformin.

Semin Oncol. 2016 Feb;43(1):123-33

Authors: Heckman-Stoddard BM, Gandini S, Puntoni M, Dunn BK, DeCensi A, Szabo E

Abstract
Multiple epidemiologic studies have documented an association between the anti-diabetic agent metformin and reduced cancer incidence and mortality. However, this effect has not been consistently demonstrated in animal models or more recent epidemiological studies. The purpose of this paper is to examine metformin's chemopreventive potential by reviewing relevant mechanisms of action, preclinical evidence of efficacy, updated epidemiologic evidence after correction for potential biases and confounders, and recently completed and ongoing clinical trials. Although repurposing drugs with well described mechanisms of action and safety profiles is an appealing strategy for cancer prevention, there is no substitute for well executed late phase clinical trials to define efficacy and populations that are most likely to benefit from an intervention.

PMID: 26970131 [PubMed - indexed for MEDLINE]

Regulation of Inflammation by IL-17A and IL-17F Modulates Non-Alcoholic Fatty Liver Disease Pathogenesis.

PLoS One. 2016;11(2):e0149783

Authors: Giles DA, Moreno-Fernandez ME, Stankiewicz TE, Cappelletti M, Huppert SS, Iwakura Y, Dong C, Shanmukhappa SK, Divanovic S

Abstract
Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. While it is well-accepted that inflammation is central to NAFLD pathogenesis, the immune pathway(s) orchestrating disease progression are poorly defined. Notably, IL-17RA signaling, via IL-17A, plays an important role in obesity-driven NAFLD pathogenesis. However, the role of the IL-17F, another IL-17RA ligand, in NAFLD pathogenesis has not been examined. Further, the cell types expressing IL-17RA and producing IL-17RA ligands in the pathogenesis of NAFLD have not been defined. Here, IL-17RA-/-, IL-17A-/-, IL-17F-/- and wild-type (WT) mice were fed either standard chow diet or methionine and choline deficient diet (MCDD)--a diet known to induce steatosis and hepatic inflammation through beta-oxidation dysfunction--and hepatic inflammation and NAFLD progression were subsequently quantified. MCDD feeding augmented hepatic IL-17RA expression and significantly increased hepatic infiltration of macrophages and IL-17A and IL-17F producing CD4+ and CD8+ T cells in WT mice. In contrast, IL-17RA-/-, IL-17A-/-, and IL-17F-/- mice, despite increased steatosis, exhibited significant protection from hepatocellular damage compared to WT controls. Protection from hepatocellular damage correlated with decreased levels of hepatic T-cell and macrophage infiltration and decreased expression of inflammatory mediators associated with NAFLD. In sum, our results indicate that the IL-17 axis also plays a role in a MCDD-induced model of NAFLD pathogenesis. Further, we show for the first time that IL-17F, and not only IL-17A, plays an important role in NAFLD driven inflammation.

PMID: 26895034 [PubMed - indexed for MEDLINE]