scholarly journals Temporal gene expression in bovine corpora lutea after treatment with PGF2alpha based on serial biopsies in vivo

Reproduction ◽  
2001 ◽  
pp. 905-913 ◽  
Author(s):  
SJ Tsai ◽  
K Kot ◽  
OJ Ginther ◽  
MC Wiltbank
Keyword(s):  
Gene Expression ◽  
Corpus Luteum ◽  
Time Course ◽  
Regulatory Protein ◽  
Corpora Lutea ◽  
Multiple Time ◽  
Lh Receptor ◽  
Time Points ◽  
Multiple Biopsies ◽  
Fp Receptor

There is growing evidence to indicate that PGF(2alpha)-induced luteolysis involves altered gene expression in the corpus luteum. Concentrations of mRNA encoding nine different gene products were quantified at three time points from corpora lutea in situ. Serial luteal biopsies (2.1-5.5 mg per biopsy) were collected using an ultrasound-guided transvaginal method and mRNA concentrations were quantified with standard curve quantitative competitive RT-PCR. In the first experiment, three luteal biopsies were collected from three heifers and analysed in multiple assays to evaluate the repeatability of the methods. Concentrations of mRNA for glyceraldehyde 3-phosphate dehydrogenase (GAPDH), PGF(2alpha) receptor (FP receptor) and LH receptor were found to be highly repeatable between assays, between multiple biopsies and between animals (coefficients of variation 1.3-17.3%). In the second experiment, heifers on days 9-11 after ovulation were assigned randomly to receive saline only (n = 6), saline with biopsies taken at t = 0, 0.5 and 4.0 h after injection (n = 6), PGF(2alpha) only (n = 6) or PGF(2alpha) with biopsies taken at t = 0, 0.5 and 4.0 h after treatment (n = 7). Biopsy alone did not change corpus luteum diameter, serum progesterone concentrations or days to next ovulation within the saline- or PGF(2alpha)-treated groups. Concentrations of mRNA for steroidogenic acute regulatory protein, FP receptor, 3beta-hydroxysteroid dehydrogenase, cytosolic phospholipase A(2) and LH receptor were decreased at 4.0 h after PGF(2alpha) injection. In contrast, PGF(2alpha) increased mRNA concentrations for prostaglandin G/H synthase-2, monocyte chemoattractant protein-1 and c-fos but the time course differed for induction of these mRNAs. Concentrations of mRNA for GAPDH did not change after PGF(2alpha) treatment. In conclusion, the techniques allowed analysis of multiple, specific mRNAs in an individual corpus luteum at multiple time points without altering subsequent luteal function. Use of these techniques confirmed that luteolysis involves both up- and downregulation of specific mRNA by PGF(2alpha).

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals Time course analysis of the brain transcriptome during transitions between brood care and reproduction in the clonal raider ant

2017 ◽  
Author(s):  
Romain Libbrecht ◽  
Peter R. Oxley ◽  
Daniel J. C. Kronauer
Keyword(s):  
Gene Expression ◽  
Division Of Labor ◽  
Time Course ◽  
Resistance To Change ◽  
Delayed Response ◽  
Multiple Time ◽  
Brood Care ◽  
Insect Societies ◽  
Multiple Time Points ◽  
Care Behavior

AbstractDivision of labor between reproductive queens and non-reproductive workers that perform brood care is the hallmark of insect societies. However, the molecular basis of this fundamental dichotomy remains poorly understood, in part because the caste of an individual cannot typically be experimentally manipulated at the adult stage. Here we take advantage of the unique biology of the clonal raider ant, Ooceraea biroi, where reproduction and brood care behavior can be experimentally manipulated in adults. To study the molecular regulation of reproduction and brood care, we induced transitions between both states, and monitored brain gene expression at multiple time points. We found that introducing larvae that inhibit reproduction and induce brood care behavior caused much faster changes in adult gene expression than removing larvae. The delayed response to the removal of the larval signal prevents untimely activation of reproduction in O. biroi colonies. This resistance to change when removing a signal also prevents premature modifications in many other biological processes. Furthermore, we found that the general patterns of gene expression differ depending on whether ants transition from reproduction to brood care or vice versa, indicating that gene expression changes between phases are cyclic rather than pendular. Our analyses also identify genes with large and early expression changes in one or both transitions. These genes likely play upstream roles in regulating reproduction and behavior, and thus constitute strong candidates for future molecular studies of the evolution and regulation of reproductive division of labor in insect societies.

INHIBIN GENE EXPRESSION IN THE HUMAN CORPUS LUTEUM

1987 ◽  
Vol 115 (3) ◽  
pp. R21-R23 ◽  
Author(s):  
S.R. Davis ◽  
Z. Krozowski ◽  
R.I. McLachlan ◽  
H.G. Burger
Keyword(s):  
Gene Expression ◽  
Corpus Luteum ◽  
Luteal Phase ◽  
Corpora Lutea ◽  
Subunit Gene ◽  
Nucleic Acid Probes ◽  
Α Subunit ◽  
Normal Human ◽  
Human Menstrual Cycle ◽  
Human Corpus Luteum

ABSTRACT We report inhibin α- and βA -subunit gene expression in the human corpus luteum and placenta using human α-subunit and bovine βA -subunit nucleic acid probes. In addition, we have demonstrated the presence of immunoreactive and bioactive inhibin in human corpora lutea. Our findings suggest that this tissue is a significant source of inhibin during the luteal phase of the normal human menstrual cycle.

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 Time Course of Microbiologic Outcome and Gene Expression in Candida albicans during and following In Vitro and In Vivo Exposure to Fluconazole

2006 ◽  
Vol 50 (4) ◽  
pp. 1311-1319 ◽  
Author(s):  
A. Lepak ◽  
J. Nett ◽  
L. Lincoln ◽  
K. Marchillo ◽  
D. Andes
Keyword(s):  
Gene Expression ◽  
Candida Albicans ◽  
Time Course ◽  
Dependent Manner ◽  
Time Points ◽  
In Vivo Expression ◽  
The Impact ◽  
The Relationship

ABSTRACT Pharmacodynamics (PD) considers the relationship between drug exposure and effect. The two factors that have been used to distinguish the PD behaviors of antimicrobials are the impact of concentration on the extent of organism killing and the duration of persistent microbiologic suppression (postantibiotic effect). The goals of these studies were (i) to examine the relationship between antimicrobial PD and gene expression and (ii) to gain insight into the mechanism of fluconazole effects persisting following exposure. Microarrays were used to estimate the transcriptional response of Candida albicans to a supra-MIC F exposure over time in vitro. Fluconazole at four times the MIC was added to a log-phase C. albicans culture, and cells were collected to determine viable growth and for microarray analyses. We identified differential expression of 18% of all genes for at least one of the time points. More genes were upregulated (n = 1,053 [16%]) than downregulated (174 [3%]). Of genes with known function that were upregulated during exposure, most were related to plasma membrane/cell wall synthesis (18%), stress responses (7%), and metabolism (6%). The categories of downregulated genes during exposure included protein synthesis (15%), DNA synthesis/repair (7%), and transport (7%) genes. The majority of genes identified at the postexposure time points were from the protein (17%) and DNA (7%) synthesis categories. In subsequent studies, three genes (CDR1, CDR2, and ERG11) were examined in greater detail (more concentration and time points) following fluconazole exposure in vitro and in vivo. Expression levels from the in vitro and in vivo studies were congruent. CDR1 and CDR2 transcripts were reduced during in vitro fluconazole exposure and during supra-MIC exposure in vivo. However, in the postexposure period, the mRNA abundance of both pumps increased. ERG11 expression increased during exposure and fell in the postexposure period. The expression of the three genes responded in a dose-dependent manner. In sum, the microarray data obtained during and following fluconazole exposure identified genes both known and unknown to be affected by this drug class. The expanded in vitro and in vivo expression data set underscores the importance of considering the time course of exposure in pharmacogenomic investigations.

scholarly journals Modeling Spatio-temporal Dynamics of Chromatin Marks

2017 ◽  
Author(s):  
Petko Fiziev ◽  
Jason Ernst
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Temporal Dynamics ◽  
Spatial Dynamics ◽  
Computational Method ◽  
Time Points ◽  
Signal Density ◽  
Spatial Changes ◽  
Spatio Temporal ◽  
Over Time

ABSTRACTTo model spatial changes of chromatin mark peaks over time we developed and applied ChromTime, a computational method that predicts regions for which peaks either expand or contract significantly or hold steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provided information about gene expression changes beyond localized signal density changes. ChromTime detected asymmetric expansions and contractions, which for some marks associated with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

scholarly journals Multiplexed, sequential secretion analysis of the same single cells reveals distinct effector response dynamics dependent on the initial basal state

2019 ◽  
Author(s):  
Zhuo Chen ◽  
Yao Lu ◽  
Kerou Zhang ◽  
Yang Xiao ◽  
Jun Lu ◽  
...  
Keyword(s):  
Immune Cells ◽  
Time Course ◽  
Single Cells ◽  
Human Macrophage ◽  
Transcriptional Level ◽  
Multiple Time ◽  
Response Dynamics ◽  
Macrophage Response ◽  
Time Points ◽  
Effector Response

AbstractThe effector response of immune cells dictated by an array of secreted proteins is a highly dynamic process, requiring sequential measurement of all relevant proteins from single cells. Herein we show a microchip-based, 10-plexed, sequential secretion assay on the same single cells and at the scale of ~5000 single cells measured simultaneously over 4 time points. It was applied to investigating the time course of single human macrophage response to Toll-like receptor 4 (TLR4) ligand lipopolysaccharide and revealed four distinct activation modes for different proteins in single cells. In particular, we observed that secreted factors regulated by transcription factor NFkB (e.g., TNF and CCL2) predominantly show on-off mode over off-on mode. The dynamics of all proteins combined classified the cells into two major activation states, which were found to be dependent on the basal state of each cell. Single-cell RNA-Seq was performed on the same samples at the matched time points and further demonstrated at the transcriptional level the existence of two major activation states, which are enriched for translation vs inflammatory programs, respectively. These results showed a cell-intrinsic heterogeneous response in phenotypically homogeneous cell population. This work demonstrated the longitudinal tracking of protein secretion signature in thousands of single cells at multiple time points, providing dynamic information to better understand how individual immune cells react to pathogenic challenges over time and how they together constitute a population response.

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.

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