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High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different–omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease. [ABSTRACT FROM AUTHOR]

Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. The various-omics infection systems that include but are not limited to ferrets, mice, pigs, and even the controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infection outcomes. [ABSTRACT FROM AUTHOR]

The growing use of engineered nanoparticles (NPs) in commercial and medical applications raises the urgent need for tools that can predict NP toxicity. Global transcriptome and proteome analyses were conducted on three human cell types, exposed to two high aspect ratio NP types, to identify patterns of expression that might indicate high versus low NP toxicity. Three cell types representing the most common routes of human exposure to NPs, including macrophage-like (THP-1), small airway epithelial and intestinal (Caco-2/HT29-MTX) cells, were exposed to TiO2 nanobelts (TiO2-NB; high toxicity) and multi-walled carbon nanotubes (MWCNT; low toxicity) at low (10 µg/mL) and high (100 µg/mL) concentrations for 1 and 24 h. Unique patterns of gene and protein expressions were identified for each cell type, with no differentially expressed ( p < 0.05, 1.5-fold change) genes or proteins overlapping across all three cell types. While unique to each cell type, the early response was primarily independent of NP type, showing similar expression patterns in response to both TiO2-NB and MWCNT. The early response might, therefore, indicate a general response to insult. In contrast, the 24 h response was unique to each NP type. The most significantly ( p < 0.05) enriched biological processes in THP-1 cells indicated TiO2-NB regulation of pathways associated with inflammation, apoptosis, cell cycle arrest, DNA replication stress and genomic instability, while MWCNT-regulated pathways indicated increased cell proliferation, DNA repair and anti-apoptosis. These two distinct sets of biological pathways might, therefore, underlie cellular responses to high and low NP toxicity, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Radiation-induced genomic instability is a well-studied phenomenon that is measured as mitotically heritable genetic alterations observed in the progeny of an irradiated cell. The mechanisms that perpetuate this instability are unclear; however, a role for chronic oxidative stress has consistently been demonstrated. In the chromosomally unstable LS12 cell line, oxidative stress and genomic instability were correlated with mitochondrial dysfunction. To clarify this mitochondrial dysfunction and gain insight into the mechanisms underlying radiation-induced genomic instability we have evaluated the mitochondrial subproteome and performed quantitative mass spectrometry analysis of LS12 cells. Of 98 quantified mitochondrial proteins, 17 met criteria for fold changes and reproducibility; and 11 were statistically significant in comparison with the stable parental GM10115 cell line. Previous observations implicated defects in the electron transport chain (ETC) in the LS12 cell mitochondrial dysfunction. Proteomic analysis supports these observations, demonstrating significantly reduced levels of mitochondrial cytochrome c, the intermediary between complexes III and IV of the ETC. Results also suggest that LS12 cells compensate for ETC dysfunction and oxidative stress through increased levels of tricarboxylic acid cycle enzymes and upregulation of proteins that protect against oxidative stress and apoptosis. More than one cellular defect is likely to contribute to the genomic instability phenotype, and evaluation of gene and microRNA expression suggests that epigenetics play a role in the phenotype. These data suggest that LS12 cells have adapted mechanisms that allow survival under suboptimal conditions of oxidative stress and compromised mitochondrial function to perpetuate genomic instability. [Copyright &y& Elsevier]

Motivation: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values.Results: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs.Availability: https://www.biopilot.org/docs/Software/RMD.phpContact: bj@pnl.govSupplementary information: Supplementary material is available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract: Osteoblastic and osteocytic cells are highly responsive to the lipid growth factor lysophosphatidic acid (LPA) but the mechanisms by which LPA alters bone cell functions are largely unknown. A major effect of LPA on osteocytic cells is the stimulation of dendrite membrane outgrowth, a process that we predicted to require changes in gene expression and protein distribution. We employed DNA microarrays for global transcriptional profiling of MLO-Y4 osteocytic cells grown for 6 and 24h in the presence or absence of LPA. We identified 932 transcripts that displayed statistically significant changes in abundance of at least 1.25-fold in response to LPA treatment. Gene ontology (GO) analysis revealed that the regulated gene products were linked to diverse cellular processes, including DNA repair, response to unfolded protein, ossification, protein-RNA complex assembly, and amine biosynthesis. Gene products associated with the regulation of actin microfilament dynamics displayed the most robust expression changes, and LPA-induced dendritogenesis in vitro was blocked by the stress fiber inhibitor cytochalasin D. Mass spectrometry-based proteomic analysis of MLO-Y4 cells revealed significant LPA-induced changes in the abundance of 284 proteins at 6h and 844 proteins at 24h. GO analysis of the proteomic data linked the effects of LPA to cell processes that control of protein distribution and membrane outgrowth, including protein localization, protein complex assembly, Golgi vesicle transport, cytoskeleton-dependent transport, and membrane invagination/endocytosis. Dendrites were isolated from LPA-treated MLO-Y4 cells and subjected to proteomic analysis to quantitatively assess the subcellular distribution of proteins. Sets of 129 and 36 proteins were enriched in the dendrite fraction as compared to whole cells after 6h and 24h of LPA exposure, respectively. Protein markers indicated that membranous organelles were largely excluded from the dendrites. Highly represented among the proteins with elevated abundances in dendrites were molecules that regulate cytoskeletal function, cell motility and membrane adhesion. Our combined transcriptomic/proteomic analysis of the response of MLO-Y4 osteocytic cells to LPA indicates that dendritogenesis is a membrane- and cytoskeleton-driven process with actin dynamics playing a particularly critical role. [Copyright &y& Elsevier]

Reflecting their exceptional potential to advance a range of biomedical, aeronautic, and other industrial products, carbon nanotube (CNT) production and the potential for human exposure to aerosolized CNTs are increasing. CNTs have toxicologically significant structural and chemical similarities to asbestos (AB) and have repeatedly been shown to cause pulmonary inflammation, granuloma formation, and fibrosis after inhalation/instillation/aspiration exposure in rodents, a pattern of effects similar to those observed following exposure to AB. To determine the degree to which responses to single-walled CNTs (SWCNT) and AB are similar or different, the pulmonary response of C57BL/6 mice to repeated exposures to SWCNTs, crocidolite AB, and ultrafine carbon black (UFCB) were compared using high-throughput global high performance liquid chromatography fourier transform ion cyclotron resonance mass spectrometry (HPLC-FTICR-MS) proteomics, histopathology, and bronchoalveolar lavage cytokine analyses. Mice were exposed to material suspensions (40 micrograms per mouse) twice a week for 3 weeks by pharyngeal aspiration. Histologically, the incidence and severity of inflammatory and fibrotic responses were greatest in mice treated with SWCNTs. SWCNT treatment affected the greatest changes in abundance of identified lung tissue proteins. The trend in number of proteins affected (SWCNT [376] > AB [231] > UFCB [184]) followed the potency of these materials in three biochemical assays of inflammation (cytokines). SWCNT treatment uniquely affected the abundance of 109 proteins, but these proteins largely represent cellular processes affected by AB treatment as well, further evidence of broad similarity in the tissue-level response to AB and SWCNTs. Two high-sensitivity markers of inflammation, one (S100a9) observed in humans exposed to AB, were found and may be promising biomarkers of human response to SWCNT exposure. [ABSTRACT FROM PUBLISHER]

Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). [ABSTRACT FROM PUBLISHER]

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies. [ABSTRACT FROM AUTHOR]

Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), lowering the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions. • IMS is a widely used analytical technique providing rapid gas phase separations. • IMS coupled with MS is rapidly gaining attention for improving omic analyses. • IMS-MS is able to extract information not possible with MS alone in complex samples. • IMS-MS distinguishes isomers, isobars, molecular classes and contaminant ions. [ABSTRACT FROM AUTHOR]

Infection of type II alveolar epithelial (ATII) cells by influenza A viruses (IAV) correlates with severe respiratory disease in humans and mice. To understand pathogenic mechanisms during IAV infection of ATII cells, murine ATII cells were cultured to maintain a differentiated phenotype, infected with IAV-PR8, which causes severe lung pathology in mice, and proteomics analyses were performed using liquid chromatography–mass spectrometry. PR8 infection increased levels of proteins involved in interferon signaling, antigen presentation, and cytoskeleton regulation. Proteins involved in mitochondrial membrane permeability, energy metabolism, and chromatin formation had reduced levels in PR8-infected cells. Phenotypic markers of ATII cells in vivo were identified, confirming the differentiation status of the cultures. Surfactant protein B had decreased levels in PR8-infected cells, which was confirmed by immunoblotting and immunofluorescence assays. Analysis of ATII cell protein profiles will elucidate cellular processes in IAV pathogenesis, which may provide insight into potential therapies to modulate disease severity. [ABSTRACT FROM AUTHOR]

The goal of this study was to define pathways regulated by low dose radiation to understand how biological systems respond to subtle perturbations in their environment and prioritize pathways for human health assessment. Using an in vitro 3-D human full thickness skin model, we have examined the temporal response of dermal and epidermal layers to 10 cGy X-ray using transcriptomic, proteomic, phosphoproteomic and metabolomic platforms. Bioinformatics analysis of each dataset independently revealed potential signaling mechanisms affected by low dose radiation, and integrating data shed additional insight into the mechanisms regulating low dose responses in human tissue. We examined direct interactions among datasets (top down approach) and defined several hubs as significant regulators, including transcription factors (YY1, MYC and CREB1), kinases (CDK2, PLK1) and a protease (MMP2). These data indicate a shift in response across time — with an increase in DNA repair, tissue remodeling and repression of cell proliferation acutely (24–72 h). Pathway-based integration (bottom up approach) identified common molecular and pathway responses to low dose radiation, including oxidative stress, nitric oxide signaling and transcriptional regulation through the SP1 factor that would not have been identified by the individual data sets. Significant regulation of key downstream metabolites of nitrative stress was measured within these pathways. Among the features identified in our study, the regulation of MMP2 and SP1 was experimentally validated. Our results demonstrate the advantage of data integration to broadly define the pathways and networks that represent the mechanisms by which complex biological systems respond to perturbation. [ABSTRACT FROM AUTHOR]

Osteocytes, positioned within bone's porous structure, are subject to interstitial fluid flow upon whole bone loading. Such fluid flow is widely theorized to be a mechanical signal transduced by osteocytes, initiating a poorly understood cascade of signaling events mediating bone adaptation to mechanical load. The objective of this study was to examine the time course of flow-induced changes in osteocyte gene transcript and protein levels using high-throughput approaches. Osteocyte-like MLO-Y4 cells were subjected to 2 h of oscillating fluid flow (1 Pa peak shear stress) and analyzed following 0, 2, 8, and 24 h post-flow incubation. Transcriptomic microarray analysis, followed by gene ontology pathway analysis, demonstrated fluid flow regulation of genes consistent with both known and unknown metabolic and inflammatory responses in bone. Additionally, two of the more highly up-regulated gene products -chemokines Cxci1 and Cxcl2, supported by qPCR - have not previously been reported as responsive to fluid flow. Proteomic analysis demonstrated greatest up-regulation of the ATP-producing enzyme NDK, calcium-binding Calcyclin, and G protein-coupled receptor kinase 6. Finally, an integrative pathway analysis merging fold changes in transcript and protein levels predicted signaling nodes not directly detected at the sampled time points, including transcription factors c-Myc, c-Jun, and RelA/NF-KB. These results extend our knowledge of the osteocytic response to fluid flow, most notably up-regulation of Cxcl1 and Cxcl2 as possible paracrine agents for osteoblastic and osteoclastic recruitment. Moreover, these results demonstrate the utility of integrative, high-throughput approaches in place of a traditional candidate approach for identifying novel mechano-sensitive signaling molecules. [ABSTRACT FROM AUTHOR]