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An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.
Gutierrez DB, Gant-Branum RL, Romer CE, Farrow MA, Allen JL, Dahal N, Nei YW, Codreanu SG, Jordan AT, Palmer LD, Sherrod SD, McLean JA, Skaar EP, Norris JL, Caprioli RM
(2018) J Proteome Res 17: 3396-3408
MeSH Terms: Gene Expression Profiling, Genomics, HL-60 Cells, Humans, Metabolomics, NF-E2-Related Factor 2, NF-kappa B, Proteomics, Signal Transduction, Systems Biology, Zinc
Show Abstract · Added August 27, 2018
Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.
1 Communities
2 Members
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11 MeSH Terms
Cell-Based Systems Biology Analysis of Human AS03-Adjuvanted H5N1 Avian Influenza Vaccine Responses: A Phase I Randomized Controlled Trial.
Howard LM, Hoek KL, Goll JB, Samir P, Galassie A, Allos TM, Niu X, Gordy LE, Creech CB, Prasad N, Jensen TL, Hill H, Levy SE, Joyce S, Link AJ, Edwards KM
(2017) PLoS One 12: e0167488
MeSH Terms: Adjuvants, Immunologic, Adolescent, Adult, Antibodies, Viral, Antibody Formation, Antigen Presentation, Chemokine CXCL10, Dendritic Cells, Double-Blind Method, Female, Hemagglutination Inhibition Tests, Humans, Influenza A Virus, H5N1 Subtype, Influenza Vaccines, Influenza, Human, Interleukin-6, Killer Cells, Natural, Male, Middle Aged, Monocytes, Neutrophils, Systems Biology, Vaccination, Young Adult
Show Abstract · Added May 3, 2017
BACKGROUND - Vaccine development for influenza A/H5N1 is an important public health priority, but H5N1 vaccines are less immunogenic than seasonal influenza vaccines. Adjuvant System 03 (AS03) markedly enhances immune responses to H5N1 vaccine antigens, but the underlying molecular mechanisms are incompletely understood.
OBJECTIVE AND METHODS - We compared the safety (primary endpoint), immunogenicity (secondary), gene expression (tertiary) and cytokine responses (exploratory) between AS03-adjuvanted and unadjuvanted inactivated split-virus H5N1 influenza vaccines. In a double-blinded clinical trial, we randomized twenty adults aged 18-49 to receive two doses of either AS03-adjuvanted (n = 10) or unadjuvanted (n = 10) H5N1 vaccine 28 days apart. We used a systems biology approach to characterize and correlate changes in serum cytokines, antibody titers, and gene expression levels in six immune cell types at 1, 3, 7, and 28 days after the first vaccination.
RESULTS - Both vaccines were well-tolerated. Nine of 10 subjects in the adjuvanted group and 0/10 in the unadjuvanted group exhibited seroprotection (hemagglutination inhibition antibody titer > 1:40) at day 56. Within 24 hours of AS03-adjuvanted vaccination, increased serum levels of IL-6 and IP-10 were noted. Interferon signaling and antigen processing and presentation-related gene responses were induced in dendritic cells, monocytes, and neutrophils. Upregulation of MHC class II antigen presentation-related genes was seen in neutrophils. Three days after AS03-adjuvanted vaccine, upregulation of genes involved in cell cycle and division was detected in NK cells and correlated with serum levels of IP-10. Early upregulation of interferon signaling-related genes was also found to predict seroprotection 56 days after first vaccination.
CONCLUSIONS - Using this cell-based systems approach, novel mechanisms of action for AS03-adjuvanted pandemic influenza vaccination were observed.
TRIAL REGISTRATION - ClinicalTrials.gov NCT01573312.
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24 MeSH Terms
Integrative network analysis reveals molecular mechanisms of blood pressure regulation.
Huan T, Meng Q, Saleh MA, Norlander AE, Joehanes R, Zhu J, Chen BH, Zhang B, Johnson AD, Ying S, Courchesne P, Raghavachari N, Wang R, Liu P, International Consortium for Blood Pressure GWAS (ICBP), O'Donnell CJ, Vasan R, Munson PJ, Madhur MS, Harrison DG, Yang X, Levy D
(2015) Mol Syst Biol 11: 799
MeSH Terms: Adult, Aged, Angiotensin II, Animals, Blood Pressure, Body Mass Index, Cohort Studies, Disease Models, Animal, Female, Gene Regulatory Networks, Genetic Loci, Genome-Wide Association Study, Humans, Hypertension, Intracellular Signaling Peptides and Proteins, Linear Models, Male, Mice, Mice, Knockout, Middle Aged, Polymorphism, Single Nucleotide, Protein Interaction Domains and Motifs, RNA, Messenger, Sequence Analysis, RNA, Systems Biology, Transcriptome, Young Adult
Show Abstract · Added September 7, 2017
Genome-wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP-associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single-gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression-related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP-related genes were uncovered. As proof-of-concept, we validated SH2B3, one of the top key drivers, using Sh2b3(-/-) mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3(-/-) mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.
© 2015 The Authors. Published under the terms of the CC BY 4.0 license.
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27 MeSH Terms
A cell-based systems biology assessment of human blood to monitor immune responses after influenza vaccination.
Hoek KL, Samir P, Howard LM, Niu X, Prasad N, Galassie A, Liu Q, Allos TM, Floyd KA, Guo Y, Shyr Y, Levy SE, Joyce S, Edwards KM, Link AJ
(2015) PLoS One 10: e0118528
MeSH Terms: Blood, Humans, Influenza Vaccines, Proteome, Seasons, Systems Biology, Transcriptome
Show Abstract · Added October 2, 2015
Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.
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7 MeSH Terms
Integrated approach in systems biology.
Zheng H, Jiang R, Zhao Z
(2014) Comput Math Methods Med 2014: 656473
MeSH Terms: Animals, Computational Biology, Computer Simulation, Gene Regulatory Networks, Genome, Genomics, Humans, Signal Transduction, Species Specificity, Systems Biology
Added February 13, 2015
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10 MeSH Terms
Biology coming full circle: joining the whole and the parts.
Wikswo JP, Porter AP
(2015) Exp Biol Med (Maywood) 240: 3-7
MeSH Terms: Animals, Humans, Molecular Biology, Systems Biology
Show Abstract · Added February 2, 2015
The new cover of Experimental Biology and Medicine features the hermeneutic circle of biology, a concept we have adapted from the hermeneutic principle that one understands the whole only in terms of each part and the parts only in terms of the whole. Our hermeneutic circle summarizes the course of experimental biology through 2500 years of the achievements of reductionist research (understanding the parts), which culminates in our ability to rapidly sequence the genome. Rather than returning along the same path in a constructionist approach that simply builds upon this knowledge, but in reverse, an alternative is to close the circle with synthetic constructions that seek to integrate the full complexity of biological and physiological systems (understanding the whole), of which organs-on-chips are one example. This closing of the circle cannot be a comprehensively accurate representation of biology, but it can be a synthetic one that effectively defines particular biological subsystems. The illustration of the hermeneutic circle of biology is also intended to suggest both the multiple cycles that may be required to reach such a synthesis and the expansion of the circle in an outward spiral as knowledge increases. Our commentary explains the symbolism of the new cover in a philosophical and scientific discussion.
© The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
1 Communities
2 Members
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4 MeSH Terms
MOSBIE: a tool for comparison and analysis of rule-based biochemical models.
Wenskovitch JE, Harris LA, Tapia JJ, Faeder JR, Marai GE
(2014) BMC Bioinformatics 15: 316
MeSH Terms: Models, Biological, Signal Transduction, Software, Systems Biology
Show Abstract · Added June 8, 2016
BACKGROUND - Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics.
RESULTS - We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results.
CONCLUSIONS - We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.
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4 MeSH Terms
Systems-level view of cocaine addiction: the interconnection of the immune and nervous systems.
Marasco CC, Goodwin CR, Winder DG, Schramm-Sapyta NL, McLean JA, Wikswo JP
(2014) Exp Biol Med (Maywood) 239: 1433-42
MeSH Terms: Cocaine-Related Disorders, Humans, Immune System, Nervous System, Systems Biology
Show Abstract · Added August 21, 2014
The human body is a complex assembly of physiological systems designed to manage the multidirectional transport of both information and nutrients. An intricate interplay between the nervous, circulatory, and secretory systems is therefore necessary to sustain life, allow delivery of nutrients and therapeutic drugs, and eliminate metabolic waste products and toxins. These systems also provide vulnerable routes for modification by substances of abuse. Addictive substances are, by definition, neurologically active, but as they and their metabolites are spread throughout the body via the nervous, circulatory, respiratory and digestive systems, there is abundant opportunity for interaction with numerous cell and tissue types. Cocaine is one such substance that exerts a broad physiological effect. While a great deal of the research concerning addiction has addressed the neurological effects of cocaine use, only a few studies have been aimed at delineating the role that cocaine plays in various body systems. In this paper, we probe the current research regarding cocaine and the immune system, and map a systems-level view to outline a broader perspective of the biological response to cocaine. Specifically, our overview of the neurological and immunomodulatory effects of the drug will allow a broader perspective of the biological response to cocaine. The focus of this review is on the connection between the nervous and immune systems and the role this connection plays in the long-term complications of cocaine use. By describing the multiplicity of these connections, we hope to inspire detailed investigations into the immunological interplay in cocaine addiction.
© 2014 by the Society for Experimental Biology and Medicine.
1 Communities
3 Members
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5 MeSH Terms
Poly-omic prediction of complex traits: OmicKriging.
Wheeler HE, Aquino-Michaels K, Gamazon ER, Trubetskoy VV, Dolan ME, Huang RS, Cox NJ, Im HK
(2014) Genet Epidemiol 38: 402-15
MeSH Terms: Bayes Theorem, Case-Control Studies, Cell Growth Processes, Cholesterol, LDL, Computational Biology, Genetic Predisposition to Disease, Humans, MicroRNAs, Models, Genetic, Multifactorial Inheritance, Phenotype, RNA, Messenger, Simvastatin, Software, Systems Biology, Time Factors
Show Abstract · Added February 22, 2016
High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).
© 2014 WILEY PERIODICALS, INC.
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16 MeSH Terms
Antibody engineering and therapeutics, The Annual Meeting of the Antibody Society: December 8-12, 2013, Huntington Beach, CA.
Almagro JC, Gilliland GL, Breden F, Scott JK, Sok D, Pauthner M, Reichert JM, Helguera G, Andrabi R, Mabry R, Bléry M, Voss JE, Laurén J, Abuqayyas L, Barghorn S, Ben-Jacob E, Crowe JE, Huston JS, Johnston SA, Krauland E, Lund-Johansen F, Marasco WA, Parren PW, Xu KY
(2014) MAbs 6: 577-618
MeSH Terms: Animals, Antibodies, Directed Molecular Evolution, Drug Design, Humans, Immunotherapy, Neoplasms, Protein Engineering, Societies, Scientific, Systems Biology
Show Abstract · Added March 20, 2014
The 24th Antibody Engineering & Therapeutics meeting brought together a broad range of participants who were updated on the latest advances in antibody research and development. Organized by IBC Life Sciences, the gathering is the annual meeting of The Antibody Society, which serves as the scientific sponsor. Preconference workshops on 3D modeling and delineation of clonal lineages were featured, and the conference included sessions on a wide variety of topics relevant to researchers, including systems biology; antibody deep sequencing and repertoires; the effects of antibody gene variation and usage on antibody response; directed evolution; knowledge-based design; antibodies in a complex environment; polyreactive antibodies and polyspecificity; the interface between antibody therapy and cellular immunity in cancer; antibodies in cardiometabolic medicine; antibody pharmacokinetics, distribution and off-target toxicity; optimizing antibody formats for immunotherapy; polyclonals, oligoclonals and bispecifics; antibody discovery platforms; and antibody-drug conjugates.
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10 MeSH Terms