scholarly journals Placental Transfer of Maraviroc in anEx VivoHuman Cotyledon Perfusion Model and Influence of ABC Transporter Expression

2013 ◽  
Vol 57 (3) ◽  
pp. 1415-1420 ◽  
Author(s):  
C. Vinot ◽  
L. Gavard ◽  
J. M. Tréluyer ◽  
S. Manceau ◽  
E. Courbon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abc Transporter ◽  
Placental Transfer ◽  
Antiretroviral Drugs ◽  
Efflux Transporters ◽  
Expression Levels ◽  
Perfusion Model ◽  
The Mean ◽  
Transporter Expression ◽  
Gene Expression Levels

ABSTRACTNowadays, antiretroviral therapy is recommended during pregnancy to prevent mother-to-child transmission of HIV. However, for many antiretroviral drugs, including maraviroc, a CCR5 antagonist, very little data exist regarding placental transfer. Besides, various factors may modulate this transfer, including efflux transporters belonging to the ATP-binding cassette (ABC) transporter superfamily. We investigated maraviroc placental transfer and the influence of ABC transporter expression on this transfer using the human cotyledon perfusion model. Term placentas were perfusedex vivofor 90 min with maraviroc (600 ng/ml) either in the maternal-to-fetal (n= 10 placentas) or fetal-to-maternal (n= 6 placentas) direction. Plasma concentrations were determined by ultra performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Fetal transfer rates (FTR) and clearance indexes (CLI) were calculated as ratios of fetal to maternal concentrations at steady state (mean values between 30 and 90 min) and ratios of FTR of maraviroc to that of antipyrine, respectively. ABC transporter gene expression levels were determined by quantitative reverse transcription (RT)-PCR and ABCB1 protein expression by Western blotting. For the maternal-to-fetal direction, the mean FTR and CLI were 8.0% ± 3.0 and 0.26 ± 0.07, respectively, whereas the mean CLI was 0.52 ± 0.23 for the fetal-to-maternal direction. We showed a significant inverse correlation between maraviroc CLI andABCC2,ABCC10, andABCC11placental gene expression levels (P< 0.05). To conclude, we report a low maraviroc placental transfer probably involving ABC efflux transporters and thus in all likelihood associated with a limited fetal exposition. Nevertheless, these results would need to be supported byin vivodata obtained from paired maternal and cord blood samples.

Identification of Aberrantly Regulated Genes through Comparison of Genome-Wide Expression Profiles and Karyotype in Hyperdiploid >50 Chromosomes Pediatric Acute Lymphoblastic Leukemia.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 420-420
Author(s):  
Christian Flotho ◽  
Susana C. Raimondi ◽  
James R. Downing
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Expression Profiles ◽  
Chromosome 21 ◽  
Small Subset ◽  
Specific Gene ◽  
Expression Levels ◽  
Number Of Genes ◽  
The Mean ◽  
Gene Expression Levels

Abstract We have demonstrated that expression profiling of leukemic blasts can accurately identify the known prognostic subtypes of ALL, including T-ALL, E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid &gt;50 chromosomes (HD&gt;50). Interestingly, almost 70% of the genes that defined HD&gt;50 ALL localized to chromosome 21 or X. To further explore the relationship between gene expression and chromosome dosage, we compared the expression profiles obtained using the Affymetrix U133A&B microarrays of 17 HD&gt;50 ALLs to 78 diploid or pseudodiploid ALLs. Our analysis demonstrated that the average expression level for all genes on a chromosome could be used to predict chromosome copy numbers. Specifically, the copy number for each chromosome calculated by gene expression profiling predicted the numerical chromosomal abnormalities detected by standard cytogenetics. For chromosomes that were trisomic in HD&gt;50 ALL, the mean chromosome-specific gene expression level was increased approximately 1.5-fold compared to that observed in diploid or pseudodiploid ALL cases. Similarly, for chromosome 21 and X, the mean chromosome-specific gene expression levels were increased approximately 2-fold, consistent with a duplication of the active X chromosome and tetrasomy of chromosome 21, a finding verified by standard cytogenetics in &gt;90% of the HD&gt;50 cases. These finding indicate that the aberrant gene expression levels seen in HD&gt;50 ALL primarily reflect gene dosages. Importantly, we did not observe any clustering of aberrantly expressed genes across the duplicated chromosomes, making regional gain or loss of genomic material unlikely. Paradoxically, however, a more detailed analysis revealed a small but statistically significant number of genes on the trisomic/tetrasomic chromosomes whose expression levels were markedly reduced when compared to that seen in diploid or pseudodiploid leukemic samples. Using the Statistical Analysis of Microarrays (SAM) algorithm we identified 20 genes whose expression was reduced &gt;2-fold despite having an increase in copy number. Interestingly, included within this group are several known tumor suppressors, including AKAP12, which is specifically silenced by methylation in fos-transformed cells, and IGF2R and IGFBP7, negative regulators of insulin-like growth factor signaling. In addition to the silencing of a small subset of genes, we also identified 21 genes on these chromosomes whose expression levels were markedly higher (&gt;3-fold) than would be predicted solely based on copy number. Although the mechanism responsible for their increased expression remains unknown, included in this group are four genes involved in signal transduction (IL3RA, IL13RA1, SNX9, and GASP) and a novel cytokine, C17, whose expression is normally limited to CD34+ hematopoietic progenitors. Taken together, these data suggest that aberrant growth in HD&gt;50 ALL is in part driven by increased expression of a large number of genes secondary to chromosome duplications, coupled with a further enhanced expression of a limited number of growth promoting genes, and the specific silencing of a small subset of negative growth regulatory genes. Understanding the mechanisms responsible for the non-dosage related changes in gene expression should provide important insights into the pathology of HD&gt;50 ALL.

scholarly journals Influences of Serotonin Hydrochloride on Adiponectin, Ghrelin and KiSS1 Genes Expression

2020 ◽  
Vol 9 ◽  
Author(s):  
Fariba Mahmoudi ◽  
Khadijeh Haghighat Gollo
Keyword(s):  
Gene Expression ◽  
Saline Group ◽  
Polymerase Chain Reaction Method ◽  
Male Rats ◽  
Expression Levels ◽  
Serum Lh ◽  
Genes Expression ◽  
Lh Release ◽  
The Mean ◽  
Gene Expression Levels

Background: Serotonin and kisspeptin stimulates gonadotropin-releasing hormone (GnRH) and luteinizing hormone (LH) release while ghrelin and adiponectin inhibit them. In the present experimental study, the effects of central injection of serotonin were investigated on LH concentration, KiSS1, adiponectin, and ghrelin genes expression. Materials and Methods: Fifteen Wistar male rats in three groups received saline or serotonin hydrochloride via the third cerebral ventricle. Blood samples were collected via the tail vein. Serum LH concentration and relative gene expression were evaluated by radioimmunoassay and real-time polymerase chain reaction method, respectively. Results: Serotonin significantly increased the mean serum LH concentration and  KiSS1 gene expression levels compared to the saline group. Serotonin significantly decreased the mean ghrelin and adiponectin genes expression levels compared to the saline group. Conclusion: The serotonergic pathway may have stimulatory effects on hypothalamic kisspeptin synthesis, partly via inhibiting hypothalamic ghrelin and adiponectin neural activity.[GMJ.2020;9:e1767]

Bayesian functional mixed-effects models with grouped smoothness for analyzing time-course gene expression data

2020 ◽  
Vol 15 ◽  
Author(s):  
Shangyuan Ye ◽  
Ye Liang ◽  
Bo Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Penalized Splines ◽  
Expression Data ◽  
Expression Levels ◽  
The Mean ◽  
Gene Expression Levels

Objective: As a result of the development of microarray technologies, gene expression levels of thousands of genes involved in a given biological process can be measured simultaneously, and it is important to study their temporal behavior to understand their mechanisms. Since the dependence between gene expression levels over time for a given gene is often too complicated to model parametrically, sparse functional data analysis has received an increasing amount of attention for analyzing such data. Methods: We propose a new functional mixed-effects model for analyzing time-course gene expression data. Specifically, the model groups individual functions with heterogeneous smoothness. The proposed method utilizes the mixed-effects model representation of penalized splines for both the mean function and the individual functions. Given noninformative or weakly informative priors, Bayesian inference on the proposed models was developed, and Bayesian computation was implemented by using Markov chain Monte Carlo methods. Results: The performance of our new model was studied by two simulation studies and illustrated using a yeast cell cycle gene expression dataset. Simulation results suggest that our proposed methods can outperform the previously used methods in terms of the mean integrated squared error. The yeast gene expression data application suggests that the proposed model with two latent groups should be used on this dataset.

scholarly journals Exploring the immunomodulatory role of depot medroxyprogesterones acetate and endogenous progesterone levels in HIV infected and uninfected women

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Nonzwakazi Mnqonywa ◽  
Nathlee Abbai ◽  
Viswanath Ragupathy ◽  
Gita Ramjee ◽  
Indira Hewlett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chemokine Receptors ◽  
Hormonal Contraception ◽  
Fold Change ◽  
Expression Levels ◽  
Hiv Positive Women ◽  
The Mean ◽  
Gene Expression Levels ◽  
Hiv Negative

Abstract Objective The aim of this proof of concept study was to determine the effect of depot medroxyprogesterone acetate on host and viral factors in HIV infected and uninfected women. Results In this study, the gene expression levels for CCL5, CCR5 and CXCR4 was significantly higher in HIV positive women when compared to HIV negative women (p < 0.05). An upregulation of CCR5 and CXCR4 was evident in less than 20% of the HIV infected women and none of the HIV uninfected women. The mean fold change for CCL3 was much higher in HIV uninfected when compared to infected women with a borderline significance (p = 0.062). In HIV uninfected women, the mean fold change in CCL3, CCL4, and CCL5 gene expression was not statistically different between women on DMPA versus women not on hormonal contraception. The proportion of women with an upregulation of CCL4 and CCR5 was higher in HIV infected women on DMPA. There was no association between endogenous progesterone level and chemokines and the HIV-1 receptors. The gene expression levels in the chemokine receptors CCR5 and CXCR4 were significantly higher in the HIV infected women when compared to the women who remained HIV uninfected.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

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