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Find video protocols related to scientific articles indexed in Pubmed.
A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury.
J. Neurosci. Res.
PUBLISHED: 06-02-2014
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The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI. © 2014 Wiley Periodicals, Inc.
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Supersymmetric model for dark matter and baryogenesis motivated by the recent CDMS result.
Phys. Rev. Lett.
PUBLISHED: 05-25-2013
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We discuss a supersymmetric model for cogenesis of dark and baryonic matter where the dark matter (DM) has mass in the 8-10 GeV range as indicated by several direct detection searches, including most recently the CDMS experiment with the desired cross section. The DM candidate is a real scalar field. Two key distinguishing features of the model are the following: (i) in contrast with the conventional weakly interacting massive particle dark matter scenarios where thermal freeze-out is responsible for the observed relic density, our model uses nonthermal production of dark matter after reheating of the Universe caused by moduli decay at temperatures below the QCD phase transition, a feature which alleviates the relic overabundance problem caused by small annihilation cross section of light DM particles and (ii) baryogenesis occurs also at similar low temperatures from the decay of TeV scale mediator particles arising from moduli decay. A possible test of this model is the existence of colored particles with TeV masses accessible at the LHC.
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Probing dark matter at the LHC using vector boson fusion processes.
Phys. Rev. Lett.
PUBLISHED: 05-15-2013
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Vector boson fusion processes at the Large Hadron Collider (LHC) provide a unique opportunity to search for new physics with electroweak couplings. A feasibility study for the search of supersymmetric dark matter in the final state of two vector boson fusion jets and large missing transverse energy is presented at 14 TeV. Prospects for determining the dark matter relic density are studied for the cases of wino and bino-Higgsino dark matter. The LHC could probe wino dark matter with mass up to approximately 600 GeV with a luminosity of 1000??fb(-1).
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Systems biology approaches for discovering biomarkers for traumatic brain injury.
J. Neurotrauma
PUBLISHED: 03-21-2013
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The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates.
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Individual vs. combinatorial effect of elevated CO2 conditions and salinity stress on Arabidopsis thaliana liquid cultures: comparing the early molecular response using time-series transcriptomic and metabolomic analyses.
BMC Syst Biol
PUBLISHED: 04-12-2010
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In this study, we investigated the individual and combinatorial effect of elevated CO2 conditions and salinity stress on the dynamics of both the transcriptional and metabolic physiology of Arabidopsis thaliana liquid hydroponic cultures over the first 30 hours of continuous treatment. Both perturbations are of particular interest in plant and agro-biotechnological applications. Moreover, within the timeframe of this experiment, they are expected to affect plant growth to opposite directions. Thus, a major objective was to investigate whether this expected "divergence" was valid for the individual perturbations and to study how it is manifested under the combined stress at two molecular levels of cellular function, using high-throughput analyses.
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Network inference and network response identification: moving genome-scale data to the next level of biological discovery.
Mol Biosyst
PUBLISHED: 12-11-2009
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The escalating amount of genome-scale data demands a pragmatic stance from the research community. How can we utilize this deluge of information to better understand biology, cure diseases, or engage cells in bioremediation or biomaterial production for various purposes? A research pipeline moving new sequence, expression and binding data towards practical end goals seems to be necessary. While most individual researchers are not motivated by such well-articulated pragmatic end goals, the scientific community has already self-organized itself to successfully convert genomic data into fundamentally new biological knowledge and practical applications. Here we review two important steps in this workflow: network inference and network response identification, applied to transcriptional regulatory networks. Among network inference methods, we concentrate on relevance networks due to their conceptual simplicity. We classify and discuss network response identification approaches as either data-centric or network-centric. Finally, we conclude with an outlook on what is still missing from these approaches and what may be ahead on the road to biological discovery.
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Time-series integrated "omic" analyses to elucidate short-term stress-induced responses in plant liquid cultures.
Biotechnol. Bioeng.
PUBLISHED: 08-07-2009
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The research that aims at furthering our understanding of plant primary metabolism has intensified during the last decade. The presented study validated a systems biology methodological framework for the analysis of stress-induced molecular interaction networks in the context of plant primary metabolism, as these are expressed during the first hours of the stress treatment. The framework involves the application of time-series integrated full-genome transcriptomic and polar metabolomic analyses on plant liquid cultures. The latter were selected as the model system for this type of analysis, because they provide a well-controlled growth environment, ensuring that the observed plant response is due only to the applied perturbation. An enhanced gas chromatography-mass spectrometry (GC-MS) metabolomic data correction strategy and a new algorithm for the significance analysis of time-series "omic" data are used to extract information about the plants transcriptional and metabolic response to the applied stress from the acquired datasets; in this article, it is the first time that these are applied for the analysis of a large biological dataset from a complex eukaryotic system. The case-study involved Arabidopsis thaliana liquid cultures subjected for 30 h to elevated (1%) CO2 stress. The advantages and validity of the methodological framework are discussed in the context of the known A. thaliana or plant, in general, physiology under the particular stress. Of note, the ability of the methodology to capture dynamic aspects of the observed molecular response allowed for 9 and 24 h of treatment to be indicated as corresponding to shifts in both the transcriptional and metabolic activity; analysis of the pathways through which these activity changes are manifested provides insight to regulatory processes.
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3-Phosphoinositide-dependent kinase 1 potentiates upstream lesions on the phosphatidylinositol 3-kinase pathway in breast carcinoma.
Cancer Res.
PUBLISHED: 07-14-2009
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Lesions of ERBB2, PTEN, and PIK3CA activate the phosphatidylinositol 3-kinase (PI3K) pathway during cancer development by increasing levels of phosphatidylinositol-3,4,5-triphosphate (PIP(3)). 3-Phosphoinositide-dependent kinase 1 (PDK1) is the first node of the PI3K signal output and is required for activation of AKT. PIP(3) recruits PDK1 and AKT to the cell membrane through interactions with their pleckstrin homology domains, allowing PDK1 to activate AKT by phosphorylating it at residue threonine-308. We show that total PDK1 protein and mRNA were overexpressed in a majority of human breast cancers and that 21% of tumors had five or more copies of the gene encoding PDK1, PDPK1. We found that increased PDPK1 copy number was associated with upstream pathway lesions (ERBB2 amplification, PTEN loss, or PIK3CA mutation), as well as patient survival. Examination of an independent set of breast cancers and tumor cell lines derived from multiple forms of human cancers also found increased PDK1 protein levels associated with such upstream pathway lesions. In human mammary cells, PDK1 enhanced the ability of upstream lesions to signal to AKT, stimulate cell growth and migration, and rendered cells more resistant to PDK1 and PI3K inhibition. After orthotopic transplantation, PDK1 overexpression was not oncogenic but dramatically enhanced the ability of ERBB2 to form tumors. Our studies argue that PDK1 overexpression and increased PDPK1 copy number are common occurrences in cancer that potentiate the oncogenic effect of upstream lesions on the PI3K pathway. Therefore, we conclude that alteration of PDK1 is a critical component of oncogenic PI3K signaling in breast cancer.
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AKT-independent signaling downstream of oncogenic PIK3CA mutations in human cancer.
Cancer Cell
PUBLISHED: 02-17-2009
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Dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway occurs frequently in human cancer. PTEN tumor suppressor or PIK3CA oncogene mutations both direct PI3K-dependent tumorigenesis largely through activation of the AKT/PKB kinase. However, here we show through phosphoprotein profiling and functional genomic studies that many PIK3CA mutant cancer cell lines and human breast tumors exhibit only minimal AKT activation and a diminished reliance on AKT for anchorage-independent growth. Instead, these cells retain robust PDK1 activation and membrane localization and exhibit dependency on the PDK1 substrate SGK3. SGK3 undergoes PI3K- and PDK1-dependent activation in PIK3CA mutant cancer cells. Thus, PI3K may promote cancer through both AKT-dependent and AKT-independent mechanisms. Knowledge of differential PI3K/PDK1 signaling could inform rational therapeutics in cancers harboring PIK3CA mutations.
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Abortive autophagy induces endoplasmic reticulum stress and cell death in cancer cells.
PLoS ONE
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Autophagic cell death or abortive autophagy has been proposed to eliminate damaged as well as cancer cells, but there remains a critical gap in our knowledge in how this process is regulated. The goal of this study was to identify modulators of the autophagic cell death pathway and elucidate their effects on cellular signaling and function. The result of our siRNA library screenings show that an intact coatomer complex I (COPI) is obligatory for productive autophagy. Depletion of COPI complex members decreased cell survival and impaired productive autophagy which preceded endoplasmic reticulum stress. Further, abortive autophagy provoked by COPI depletion significantly altered growth factor signaling in multiple cancer cell lines. Finally, we show that COPI complex members are overexpressed in an array of cancer cell lines and several types of cancer tissues as compared to normal cell lines or tissues. In cancer tissues, overexpression of COPI members is associated with poor prognosis. Our results demonstrate that the coatomer complex is essential for productive autophagy and cellular survival, and thus inhibition of COPI members may promote cell death of cancer cells when apoptosis is compromised.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.