Differential gene expression and developmental competence in in vitro produced bovine embryos

The embryonic developmental block occurs at the 8-cell stage in cattle and is characterized by a lengthening of the cell cycle and an increased number of embryos that stop development. The maternal-embryonic transition arises at the same stage resulting in the transcription of many genes. Gene expression studies during this stage may contribute to the understanding of the physiological mechanisms involved in the maternal-embryonic transition. Herein we identified genes differentially expressed between embryos with high or low developmental competence to reach the blastocyst stage using differential display PCR. Embryos were analysed according to developmental kinetics: fast cleavage embryos showing 8 cells at 48 h post insemination (hpi) with high potential of development (F8), and embryos with slow cleavage presenting 4 cells at 48 hpi (54) and 8 cells at 90 hpi (S8), both with reduced rates of development to blastocyst. The fluorescence DDPCR method was applied and allowed the recovery of 176 differentially expressed bands with similar proportion between high and low development potential groups (52% to F8 and 48% in S4 and S8 groups). A total of 27 isolated fragments were cloned and sequenced, confirming the expected primer sequences and allowing the identification of 27 gene transcripts. PI3KCA and ITM2B were chosen for relative quantification of mRNA using real-time PCR and showed a kinetic and a time-related pattern of expression respectively. The observed results suggest the existence of two different embryonic genome activation mechanisms: fast-developing embryos activate genes related to embryonic development, and slow-developing embryos activate genes related to cellular survival and/or death.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.