References of "Krishna, Abhimanyu 50002140"
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See detailEvaluation of Cell Line Suitability for Disease Specific Perturbation Experiments.
Biryukov, Maria UL; Antony, Paul UL; Krishna, Abhimanyu UL et al

in Lausen, Berthold; Krolak-Schwerdt, Sabine; Böhmer, Matthias (Eds.) Data Science, Learning by Latent Structures, and Knowledge Discovery (2015, February 20)

Cell lines are widely used in translational biomedical research to study the genetic basis of diseases. A major approach for experimental disease modeling are genetic perturbation experiments that aim to ... [more ▼]

Cell lines are widely used in translational biomedical research to study the genetic basis of diseases. A major approach for experimental disease modeling are genetic perturbation experiments that aim to trigger selected cellular disease states. In this type of experiments it is crucial to ensure that the targeted disease- related genes and pathways are intact in the used cell line. In this work we are developing a framework which integrates genetic sequence information and disease- specific network analysis for evaluating disease-specific cell line suitability. [less ▲]

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See detailSystems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson’s disease
Krishna, Abhimanyu UL; Biryukov, Maria UL; Trefois, Christophe UL et al

in BMC Genomics (2014), 15(1154),

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often ... [more ▼]

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson’s disease, the relevance of this cellular model in the context of Parkinson’s disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated. Results: We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD. Conclusions: The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson’s disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer’s disease but lower than for Huntington’s disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments. [less ▲]

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See detailDifferentiated SH-SY5Y Cells as PD Model for Mitochondrial Dysfunction: From Whole Genome Sequencing to an Educated Design of High-Throughput Experiments
Antony, Paul UL; Krishna, Abhimanyu UL; May, Patrick UL et al

Poster (2013)

Objectives: Mitochondrial dysfunction is a cellular hallmark of Parkinson's disease (PD) and energetic stress of dopaminergic neurons appears to be a physiological risk factor for mitochondrial ... [more ▼]

Objectives: Mitochondrial dysfunction is a cellular hallmark of Parkinson's disease (PD) and energetic stress of dopaminergic neurons appears to be a physiological risk factor for mitochondrial dysfunction. It is however challenging to assess the high variety of factors regulating mitochondrial physiology in living neurons in a high throughput manner. To overcome this bottleneck, we established an analysis platform, using the neuroblastoma cell line SH-SY5Y. For the first time ever we have characterized the SH-SY5Y cell line in an integrated whole genome, transcriptome, and proteome approach. In addition, we show that neuronal differentiation improves the physiological properties of this experimental model for studying mitochondrial dysfunction in PD. Methods: Whole genome sequencing, RNA-Seq, qRT-PCR, MS, FRET using Voltage sensing proteins, Immunofluorescence, cytometry, and live cell imaging. Results: The integrated molecular characterization of SH-SY5Y uncovers the level of molecular network integrity and hence the relevance of this cell line for targeted studies in selected molecular processes. Furthermore, we dissect changes in mitochondrial and energetic stress factors during the process of neuronal differentiation. Conclusions: In terms of both morphology and energetic stress response, differentiated SH-SY5Y cells are more similar to dopaminergic neurons than their undifferentiated precursors. Thanks to dividing progenitors and the short duration of differentiation, combined with the use of specific endpoints analysed with high-content microscopy, our platform paves the route for high throughput experiments on a neuronal cell culture model for PD. Our genomic characterization and expression profiling of SH-SY5Y cells furthermore helps guiding the experimental design and interpretation of such studies. [less ▲]

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See detailPredicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states
Crespo, Isaac UL; Krishna, Abhimanyu UL; Le Béchec, Antony UL et al

in Nucleic Acids Research (2013), 41(1), 8

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are ... [more ▼]

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions. [less ▲]

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