Gene expression analysis at multiple time-points identifies key genes for nerve regeneration

2016 ◽  
Vol 55 (3) ◽  
pp. 373-383 ◽  
Author(s):  
Bin Pan ◽  
Yi Liu ◽  
Jia-Yin Yan ◽  
Yao Wang ◽  
Xue Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nerve Regeneration ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Multiple Time ◽  
Time Points ◽  
Key Genes ◽  
Multiple Time Points

scholarly journals PS1 - 169 Discovering the Treatment Refractory BTIC Population in Group 3 Medulloblastoma through Therapy Adapted Patient-Derived Human Mouse Xenograft (PDX) Model

2016 ◽  
Vol 43 (S4) ◽  
pp. S10-S10
Author(s):  
D. Bakhshinyan ◽  
T. Vijayakumar ◽  
B. Manoranjan ◽  
N. McFarlane ◽  
C. Venugopal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
High Rate ◽  
Multiple Time ◽  
Self Renewal ◽  
Molecular Features ◽  
Post Radiation ◽  
Group 3 ◽  
Treatment Refractory

Medulloblastoma (MB), the most common malignant pediatric brain tumor, is categorized into four molecular subgroups. Given the high rate of metastatic dissemination at diagnosis and recurrence in Group 3 MBs, these patients have the worst clinical outcome with a 5-year survivorship of approximately 50%. By adapting the existing COG (Children’s Oncology Group) Protocol for children with newly diagnosed high-risk MB, for treatment of immuno-deficient mice intracranially engrafted with human MB brain tumour initiating cells we aim to identify and characterize the treatment-refractory cell population in Group 3 MBs. Mice were sacrificed at multiple time points during the course of tumor development and therapy: (i) at engraftment; (ii) post-radiation; (iii) post-radiation and chemotherapy; and (iv) at MB recurrence. MB cell populations recovered separately from brains and spines were comprehensively profiled for gene expression analysis, stem cell and molecular features to generate a global, comparative profile of MB cells through therapy. We report a higher expression of CD133, Sox2 and Bmi1 in addition to increased self-renewal capacity following chemoradiotherapy treatment. The enrichment map constructed from global gene expression analysis showed an increase in pathways regulating self-renewal, DNA repair and chemoresistance post-therapy despite the apparent decrease in tumour size and vascularity. Additionally, from gene expression at MB recurrence, we identified a list of genes that negatively correlate with survival in patients diagnosed with Group 3 MB. A differential genomic profile of the “treatment-responsive” tumors against those that fail therapy may contribute to discovery of novel therapeutic approaches for the most aggressive subgroup of MB.

scholarly journals Retraction Note to: Key genes associated with osteoporosis revealed by genome wide gene expression analysis

2015 ◽  
Vol 42 (10) ◽  
pp. 1471-1471
Author(s):  
Jie Chen ◽  
Lei Wang ◽  
Yuhui Shen ◽  
Jian Yu ◽  
Tingjun Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Genome Wide ◽  
Key Genes ◽  
Genome Wide Gene Expression

scholarly journals Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

2017 ◽  
Author(s):  
Mariana Gómez-Schiavon ◽  
Liang-Fu Chen ◽  
Anne E. West ◽  
Nicolas E. Buchler
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Posterior Probability ◽  
Single Cells ◽  
Bayesian Posterior Probability ◽  
Model Parameters ◽  
Multiple Time ◽  
Stochastic Gene Expression ◽  
Time Points ◽  
Multiple Time Points

AbstractSingle-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution on the abundance and localization of nascent and mature transcripts in single cells. Gene expression dynamics are typically inferred by measuring mRNA abundance in small numbers of fixed cells sampled from a population at multiple time-points after induction. The sparse data that arise from the small number of cells obtained using smFISH present a challenge for inferring transcription dynamics. Here, we developed a computational pipeline (BayFish) to infer kinetic parameters of gene expression from smFISH data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons. We showed that a 2-state promoter model can recapitulate Npas4 dynamics after induction and we inferred that the transition rate from the promoter OFF state to the ON state is increased by the stimulus.Author SummaryGene expression can exhibit cell-to-cell variability due to the stochastic nature of biochemical reactions. Single cell assays (e.g. smFISH) directly quantify stochastic gene expression by measuring the number of active promoters and transcripts per cell in a population of cells. The data are distributions and their shape and time-evolution contain critical information on the underlying process of gene expression. Recent work has combined models of stochastic gene expression with maximum likelihood methods to infer kinetic parameters from smFISH distributions. However, these approaches do not provide a probability distribution or likelihood of model parameters inferred from the smFISH data. This information is useful because it indicates which parameters are loosely constrained by the data and suggests follow up experiments. We developed a suite of MATLAB programs (BayFish) that estimate the Bayesian posterior probability of model parameters from smFISH data. The user specifies an underlying model of stochastic gene expression with unknown parameters (θ) and provides smFISH data (Y). BayFish uses a Monte Carlo algorithm to estimate the Bayesian posterior probability P(θ|Y) of model parameters. BayFish is easily modified and can be applied to other models of stochastic gene expression and smFISH data sets.

74 Gene expression analysis of developmental key genes in in vitro bovine twin embryos produced by blastomeres separation and embryo bisection

2022 ◽  
Vol 34 (2) ◽  
pp. 273
Author(s):  
A. Ynsaurralde-Rivolta ◽  
V. Alberio ◽  
M. Suvá ◽  
R. Bevacqua ◽  
V. Savy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Key Genes

RETRACTED ARTICLE: Key genes associated with osteoporosis revealed by genome wide gene expression analysis

2014 ◽  
Vol 41 (9) ◽  
pp. 5971-5977 ◽  
Author(s):  
Jie Chen ◽  
Lei Wang ◽  
Yuhui Shen ◽  
Jian Yu ◽  
Tingjun Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Genome Wide ◽  
Retracted Article ◽  
Key Genes ◽  
Genome Wide Gene Expression
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