Regulation of transferrin receptor synthesis by human cytotrophoblast cells in culture

The aim of this study was to examine the capacity of the syncytiotrophoblast to regulate transferrin receptor (TfR) synthesis in response to modulations in maternal iron supply. The model used was the primary trophoblast cell culture. Trophoblast cells isolated from term human placentas were cultured in iron-poor (Medium 199), iron-depleted (desferrioxamine (DFO)) and iron supplemented (diferric transferrin (hTf-2Fe), ferric ammonium citrate (FAG)) medium. TfR synthesis was reduced in response to hTf-2Fe supplementation. FAC did not modulate TfR synthesis. Iron deprivation by DFO resulted in clear stimulation of TfR synthesis. These results show that the differentiating trophoblast cells respond to pertubations in the (transferrin-mediated) iron supply by adjustments in the rate of TfR synthesis. Taking syncytiotrophoblast in culture as model for the maternal/fetal interface in vivo, our results would suggest that the placenta is able to make short term adjustments of the capacity for iron uptake

Mice Lacking the Circadian Modulators SHARP1 and SHARP2 Display Altered Sleep and Mixed State Endophenotypes of Psychiatric Disorders

Increasing evidence suggests that clock genes may be implicated in a spectrum of psychiatric diseases, including sleep and mood related disorders as well as schizophrenia. The bHLH transcription factors SHARP1/DEC2/BHLHE41 and SHARP2/DEC1/ BHLHE40 are modulators of the circadian system and SHARP1/DEC2/BHLHE40 has been shown to regulate homeostatic sleep drive in humans. In this study, we characterized Sharp1 and Sharp2 double mutant mice (S1/2(-/-)) using online EEG recordings in living animals, behavioral assays and global gene expression profiling. EEG recordings revealed attenuated sleep/wake amplitudes and alterations of theta oscillations. Increased sleep in the dark phase is paralleled by reduced voluntary activity and cortical gene expression signatures reveal associations with psychiatric diseases. S1/2(-/-) mice display alterations in novelty induced activity, anxiety and curiosity. Moreover, mutant mice exhibit impaired working memory and deficits in prepulse inhibition resembling symptoms of psychiatric diseases. Network modeling indicates a connection between neural plasticity and clock genes, particularly for SHARP1 and PER1. Our findings support the hypothesis that abnormal sleep and certain (endo) phenotypes of psychiatric diseases may be caused by common mechanisms involving components of the molecular clock including SHARP1 and SHARP2

SDE-driven modeling of phenotypically heterogeneous tumors: The influence of cancer cell stemness

We deduce cell population models describing the evolution of a tumor (possibly interacting with its environment of healthy cells) with the aid of differential equations. Thereby, different subpopulations of cancer cells allow accounting for the tumor heterogeneity. In our settings these include cancer stem cells known to be less sensitive to treatment and differentiated cancer cells having a higher sensitivity towards chemo- and radiotherapy. Our approach relies on stochastic differential equations in order to account for randomness in the system, arising e.g., by the therapy-induced decreasing number of clonogens, which renders a pure deterministic model arguable. The equations are deduced relying on transition probabilities characterizing innovations of the two cancer cell subpopulations, and similarly extended to also account for the evolution of normal tissue. Several therapy approaches are introduced and compared by way of tumor control probability (TCP) and uncomplicated tumor control probability (UTCP). A PDE approach allows to assess the evolution of tumor and normal tissue with respect to time and to cell population densities which can vary continuously in a given set of states. Analytical approximations of solutions to the obtained PDE system are provided as well

SDE-driven modeling of phenotypically heterogeneous tumors: The influence of cancer cell stemness

We deduce cell population models describing the evolution of a tumor (possibly interacting with its environment of healthy cells) with the aid of differential equations. Thereby, different subpopulations of cancer cells allow accounting for the tumor heterogeneity. In our settings these include cancer stem cells known to be less sensitive to treatment and differentiated cancer cells having a higher sensitivity towards chemo- and radiotherapy. Our approach relies on stochastic differential equations in order to account for randomness in the system, arising e.g., by the therapy-induced decreasing number of clonogens, which renders a pure deterministic model arguable. The equations are deduced relying on transition probabilities characterizing innovations of the two cancer cell subpopulations, and similarly extended to also account for the evolution of normal tissue. Several therapy approaches are introduced and compared by way of tumor control probability (TCP) and uncomplicated tumor control probability (UTCP). A PDE approach allows to assess the evolution of tumor and normal tissue with respect to time and to cell population densities which can vary continuously in a given set of states. Analytical approximations of solutions to the obtained PDE system are provided as well

S1/2^-/- mice show alterations of prepulse inhibition (PPI) which are resistant to Clozapine treatment.

<p>A) S1/2^-/- mice display impairment of PPI (E_genotype F_(1,43) = 7.99; p = 0.0071). Bonferroni posttest revealed significant difference in prepulse 75 und 80 dB (p_Bonf<0.01 and p_Bonf<0.05, respectively). WT: n = 24, S1/2^-/-: n = 21. B) Startle response was similar in S1/2^-/- mice and WT controls (E_genotype F_(1,88) = 0.00; p = 0.9958) and not influenced significantly by vehicle injections (E_treatment F_(1,88) = 2.18; p = 0.1434). Acute clozapine treatment (3 mg/kg) reduced startle in both genotypes to similar extend (E_treatment F_(1,82) = 11.83; p = 0.0009 and E_genotype F_(1,82) = 0.01; p = 0.9030). ‘No injections’ and ‘vehicle’ groups: WT: n = 25, S1/2^-/-: n = 21; clozapine: WT: n = 20, S1/2^-/-: n = 20. C) Acute treatment with clozapine (cloz; 3 mg/kg; n = 20) reduced PPI in WT mice when compared to vehicle (veh; n = 24) treated WT animals (E_treatment F_(1,42) = 10.33; p = 0.0025). Bonferroni posttest confirmed significant difference when prepulse 70 dB was applied (p_Bonf<0.01). D) Acute treatment with clozapine (cloz; 3 mg/kg) did not influence PPI in S1/2^-/- mice (n = 20) when compared to vehicle injected mutants (n = 21) (E_treatment F_(1,39) = 0.00; p = 0.9716). Data were analyzed with 2-way ANOVA and Bonferroni posttest (p_Bonf). ***: p<0.001; **: p<0.01; *: p<0.05. E, effect.</p

S1/2^-/- mice display novelty induced hyperactivity, decreased anxiety and exploratory behavior and working memory disturbances.

<p>A–C) Open field test performed in a novel, unfamiliar test arena. WT: n = 24, S1/2^-/-: n = 26. A) Novelty-induced hyperactivity in S1/2^-/- mice as assessed by moving distance in the open field (p_MW = 0.0006). B) Analysis in 1 min bins yielded a significant E_genotype (F_(1,48) = 16.46; p = 0.0002). Moreover, Bonferroni posttest revealed the strongest difference between the genotypes in interval 3, 5 and 10 (p_Bonf<0.01, p_Bonf<0.05 and p_Bonf<0.05, respectively). C) Mutants spent more time in the center (p_MW = 0.0004) of the test arena indicating reduced anxiety when compared to controls. D-E) Hole board test performed with a subsequent modification of the open field setup by floor insert with holes. WT: n = 24, S1/2^-/-: n = 26. D) S1/2^-/- mice displayed no alterations in the overall activity measured as total distance travelled. E) S1/2^-/- mice performed less nose pokes into holes (p_MW = 0.0014) indicating decreased curiosity-related behavior compared to WT. F-G) Y-maze test. WT: n = 23, S1/2^-/-: n = 20. F) S1/2^-/- mice showed increased activity in Y-maze test (E_genotype F_(1,41) = 10.98; p = 0.0019) most evident in interval 0-5 min (p_Bonf<0.01). G) Mutant mice performed less alterations in Y-maze than control animals (E_genotype F_(1,41) = 4.86; p = 0.0331) and p_Bonf<0.05 for interval 5–10 min. H-J) S1/2^-/- mice display impairment of working memory in the radial arm water maze (RAWM). WT: n = 29, S1/2^-/-: n = 28. H) In the visible platform task, performance was similar in both genotypes (E_genotype F_(1,55) = 0.65; p = 0.4236). I-J) S1/2^-/- mice showed increased number of working errors searching for a hidden platform on the first (I) (E_genotype F_(1,55) = 3.93; p = 0.0524; I_{genotype×time} F_(3,165) = 2.68; p = 0.0486) and the second (J) day of experiment (E_genotype F_(1,55) = 9.05; p = 0.0044) and I_{genotype×time} F_(5,275) = 2.34; p = 0.0422). Bonferroni posttest revealed significant difference during the 3^rd trial of the second day (p_Bonf<0.001). WT, black bars/circles. S1/2^-/-, white bars/circles. Data were analyzed with 2-way ANOVA with Bonferroni posttest (p_Bonf) and Mann-Whitney test (p_MW) for pairwise comparisons. ***: p<0.001; **: p<0.01; *: p<0.05. E, effect; I, interaction of factors.</p

Attenuated sleep-wake amplitude and activity profiles in S1/2^-/- mice.

<p>A) Group means (± SEM) of the total time spend in different vigilance states over 24 h LD periods. The overall time of wake, NREM and REM sleep remained unaltered between genotypes. WT: n = 7, filled bars S1/2^-/-: n = 8, empty bars. B) Group means of light-dark or amplitude differences for wake, NREM and REM sleep. S1/2^-/- animals showed a significantly reduced light-dark amplitude for all vigilance states compared to WT animals (E_genotype F_{(1, 39)} = 19.87, p<0.0001; E_{vigilance state} F_{(2, 39)} = 19.39, p<0.0001; I_{genotype×vigilance state} F_{(2, 39)} = 1.9, p = 0.16; Post hoc two-tailed T-test: **: p<0.01 *: p<0.05). WT: n = 7, filled bars S1/2^-/-: n = 8, empty bars. C) 24 h sleep-wake distribution plotted for representative individual WT (#26) and S1/2^-/- (#828) mice with black areas given as relative amount of wakefulness obtained from 5 min bins. Note the relative difference in the amount of wakefulness during the light and dark episodes in the WT and the short periods of wakefulness in the light phase. In contrast, the S1/2^-/- mouse displayed broadened periods of sleep and wakefulness during the light and dark phases. D-F) Time course of vigilance states wakefulness (D), NREM (E) and REM sleep (F). Curves connect 2 h bin mean values (± SEM) expressed as percentage of recording time (E_time: NREM: F_(11,120) = 9.74, p<0.0001; REM F_(11,120) = 9.98, p<0.0001; E_genotype: NREM n.s.; REM F_(1,120) = 7.65, p<0.01 and I_{genotype×time}: Wakefulness F_(11,120) = 2.06, p = 0.02; NREM: F_(11,120) = 1.82, p = 0.05; REM: F_(11,120) = 1.42, p =  0.17; * =  p<0.05 in two-tailed post hoc T-test. WT: n = 7, filled circles S1/2^-/-: n = 8, empty circles. G) Diurnal wheel-running profiles depicted as accumulated activities of all recordings over a 5-day period plotted as 18 min bins. S1/2^-/- mice displayed a significantly altered activity profile in LD compared to wild-type (WT) mice (I_{genotype×time} F_(86,39040) = 1.92, p<0.0001) with reduced half maximal values of nocturnal activities at ZT 17.1 for S12^-/- mice compared to WT controls with ZT 18.3. Bonferroni posttest revealed significantly reduced activities between ZT13 and 18 (p_Bonf<0.05). n = 12 each genotype. H-I) Daytime dependent gene expression analysis of the circadian marker gene <i>Per2</i> (H) and the activity-induced gene <i>Fos</i> (I) in the cortex. Daytime dependent cortical expression of the circadian marker gene <i>Per2</i> was not substantially altered in WT and S1/2^-/- mice (H). In contrast, the mRNA expression of the activity regulated marker gene <i>Fos</i> was significantly reduced in S1/2^-/- mice at ZT16 compared to WT (I). n = 3 per timepoint and genotype. Data were analyzed with 2-way ANOVA with Bonferroni posttest (p_Bonf) and Mann-Whitney test (p_MW) for pairwise comparisons. E, effect; I, interaction of factors.</p

Unbiased network modeling links cortical signaling with clock components via SHARP1, BMAL1 and PER1.

<p>A) Depicted is the network model with the highest significance computed with all genes found to be differentially regulated in the cortex at ZT4 versus ZT16 (see. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110310#pone-0110310-g002" target="_blank">Figure 2A</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110310#pone.0110310.s010" target="_blank">Tables S1</a>-S3 and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110310#pone.0110310.s009" target="_blank">Figure S9</a> for description of symbols) including SHARP1 and -2. The network connects 14 seed nodes depicted as blue circles (higher expression levels in WT are indicated by associated small red circles) extended by 13 interactors. SHARP1 and -2 are labeled by red circles; all nodes added by the algorithm are not underlined by colored circles. The structure depicts two major clusters and places the circadian regulators SHARP1 and SHARP2 as well as PER1 at central positions. The left cluster (n = 19 objects) is mainly comprised of cellular signaling components (enkephalin A, substance P both encoded by <i>Penk</i> and the GPCRs A2A and DRD2) and downstream effectors including negative regulators (DUSP1,6 and HSPs) as well as transcriptional mediators (e.g. FOS, EGR1, JUNB). The right cluster (n = 12) comprises central components of the molecular clock (e.g. the core clock transcription factors CLOCK, NPAS2 and BMAL1 as well as clock feedback regulators and modifiers including SHARP1 and -2, PER1 and -2, CRY1, DBP and NR1D1/Rev-ERBalpha). The extended network gene list was queried against the GeneGo database for enriched correlations with diseases (B) and biological processes (C). Among the ten most significant disease associations were nine mental or mood related disease classifications (B). Among the ten highest ranked biological processes were only circadian rhythm- (rank 1 and 2) and metabolism-associated (rank 3–8) processes (C). MeSH ID, unique Medical Subject Heading disease identifier; GO ID, unique identifier of the gene ontology biological process collection; p-values determined by hypergeometric tests.</p