Keita Iida

Intrauterine Ischemic Reperfusion Switches the Fetal Transcriptional Pattern from HIF-1α- to P53-Dependent Regulation in the Murine Brain

Ischemic reperfusion (IR) during the perinatal period is a known causative factor of fetal brain damage. So far, both morphologic and histologic evidence has shown that fetal brain damage can be observed only several hours to days after an IR insult has occurred. Therefore, to prevent fetal brain damage under these circumstances, a more detailed understanding of the underlying molecular mechanisms involved during an acute response to IR is necessary. In the present work, pregnant mice were exposed to IR on day 18 of gestation by clipping one side of the maternal uterine horn. Simultaneous fetal electrocardiography was performed during the procedure to verify that conditions resulting in fetal brain damage were met. Fetal brain sampling within 30 minutes after IR insult revealed molecular evidence that a fetal response was indeed triggered in the form of inhibition of the Akt-mTOR-S6 synthesis pathway. Interestingly, significant changes in mRNA levels for both HIF-1α and p53 were apparent and gene regulation patterns were observed to switch from a HIF-1α-dependent to a p53-dependent process. Moreover, pre-treatment with pifithrin-α, a p53 inhibitor, inhibited protein synthesis almost completely, revealing the possibility of preventing fetal brain damage by prophylactic pifithrin-α treatment.

Vaginal LPS changed gene transcriptional regulation response to ischemic reperfusion and increased vulnerability of fetal brain hemorrhage

During pregnancy, both ischemic reperfusion and bacterial agent LPS are known risk factors for fetal brain damage. However, there is a lack of evidence to explain whether vaginal LPS affects the fetus response to ischemic reperfusion. Here we reported that there was more than 2 folds higher vulnerability of fetal brain hemorrhage response to ischemic reperfusion when mother mouse was treated with vaginal LPS. As our previously reported, ischemic reperfusion induces P53-dependent fetal brain damage was based on a molecular mechanism: the transcriptional pattern was changed from HIF-1alpha-dependent to P53-dependent immediately. In the present work, only with vaginal LPS precondition, phosphorylation of activated transcriptional factor (ATF) 2 at Thr71 appeared in response to ischemic reperfusion. Moreover, this phosphorylation was completely blocked by pre-treatment with a P53 inhibitor, pifithrin-α. We concluded that vaginal LPS precondition trigged the p53-dependent phosphorylation of ATF2 in response to ischemic reperfusion, which played an important role of increasing vulnerability to hemorrhage in fetus.

Molecular patterns of neurodevelopmental preconditioning: a study of the effects of antenatal steroid therapy in a protein-restriction mouse model.

Introduction. Prenatal programming secondary to maternal protein restriction renders an inherent susceptibility to neural compromise in neonates and any addition of glucocorticosteroids results in further damage. This is an investigation of consequent global gene activity due to effects of antenatal steroid therapy on a protein restriction mouse model. Methods. C57BL/6N pregnant mice were administered control or protein restricted diets and subjected to either 100 μg/Kg of dexamethasone sodium phosphate with normosaline or normosaline alone during late gestation (E10–E17). Nontreatment groups were also included. Brain samples were collected on embryonic day 17 and analyzed by mRNA microarray analysis. Results. Microarray analyses presented 332 significantly regulated genes. Overall, neurodevelopmental genes were overrepresented and a subset of 8 genes allowed treatment segregation through the hierarchical clustering method. The addition of stress or steroids greatly affected gene regulation through glucocorticoid receptor and stress signaling pathways. Furthermore, differences between dexamethasone-administered treatments implied a harmful effect during conditions of high stress. Microarray analysis was validated using qPCR. Conclusion. The effects of antenatal steroid therapy vary in fetuses according to maternal-fetal factors and environmental stimuli. Defining the key regulatory networks that signal either beneficial or damaging corticosteroid action would result in valuable adjustments to current treatment protocols.

Quantifying heterogeneity of stochastic gene expression

The heterogeneity of stochastic gene expression, which refers to the temporal fluctuation of the gene product and its cell-to-cell variation, has attracted considerable interest from biologists, physicists, and mathematicians. The dynamics of protein production and degradation have been modeled as random processes with transition probabilities. However, there is a gap between theory and phenomena, particularly in terms of analytical formulation and parameter estimation. In this study, we propose a theoretical framework in which we present a basic model of a gene regulatory system, derive a steady-state solution, and provide a Bayesian approach for estimating the model parameters from single-cell experimental data. The proposed framework is demonstrated to be applicable for various scales of single-cell experiments at both the mRNA and protein levels, and it is useful for comparing kinetic parameters across species, genomes, and cell strains.

Mathematical Theory to Compute Stochastic Cellular Processes

A central challenge of gene expression analysis during the last few decades has been the characterization of the expression patterns experimentally and theoretically. Modern techniques on single-cell and -molecule resolution reveal that transcriptions and translations are stochastic in time and that clonal population of cells displays heterogeneity in the abundance of a given RNA and protein per cell. Hence, to take into account a cell-to-cell variability, we consider a stochastic model of transcription and the chemical master equation. Our stochastic analysis and Monte-Carlo simulation show that the limiting distribution of mRNA copy number can be expressed by a Poisson-beta distribution. The distribution represents the four different types of expression patters, which are typically found in various experimental profiles.