Temporal expression profiles indicate a primary function for microRNA during the peak of DNA replication after rat partial hepatectomy

The liver has the unique capacity to regenerate after surgical resection. However, the regulation of liver regeneration is not completely understood. Recent reports indicate an essential role for small noncoding microRNAs (miRNAs) in the regulation of hepatic development, carcinogenesis, and early regeneration. We hypothesized that miRNAs are critically involved in all phases of liver regeneration after partial hepatectomy. We performed miRNA microarray analyses after 70% partial hepatectomy in rats under isoflurane anesthesia at different time points (0 h to 5 days) and after sham laparotomy. Putative targets of differentially expressed miRNAs were determined using a bioinformatic approach. Two-dimensional (2D)-PAGE proteomic analyses and protein identification were performed on specimens at 0 and 24 h after resection. The temporal dynamics of liver regeneration were characterized by 5-bromo- 2-deoxyuridine, proliferating cell nuclear antigen, IL-6, and hepatocyte growth factor. We demonstrate that miRNA expression patterns changed during liver regeneration and that these changes were most evident during the peak of DNA replication at 24 h after resection. Expression of 13 miRNAs was significantly reduced 12-48 h after resection (>25% change), out of which downreguation was confirmed in isolated hepatocytes for 6 miRNAs at 24 h, whereas three miRNAs were significantly upregulated. Proteomic analysis revealed 65 upregulated proteins; among them, 23 represent putative targets of the differentially expressed miRNAs. We provide a temporal miRNA expression and proteomic dataset of the regenerating rat liver, which indicates a primary function for miRNA during the peak of DNA replication. These data will assist further functional studies on the role of miRNAs during liver regeneration.     

Human�Cenvironment interactions in mountain rainforests: archaeobotanical evidence from central Sulawesi, Indonesia

Palaeoecology may contribute to the debate on nature conservation and the preservation of cultural heritage. Here we present two palaeo-records from the Lore Lindu Biosphere Reserve and National Park in central Sulawesi, Indonesia. The park comprises one of the largest remaining and most biodiverse mountain forests of Sulawesi. Outstanding megalithic sites reveal long-term human impact on the area. Thus, modern vegetation composition within the park has been determined by complex site histories, natural and anthropogenic. Palaeo-records from sites between 1,000 and 1,400 m a.s.l. demonstrate that human activity as well as climate variations have to be considered as the main drivers of vegetation changes in the region for the last 2,000 years. Human impact is reflected by large amounts of charred fragments in the pollen records plus low values of arboreal pollen resulting from forest clearance. The stratigraphy of one of the pollen records shows effects of a general cooling trend during the Little Ice Age (LIA). Biomass loss due to the conversion of lower montane rainforest to grassland has reduced the carbon storage potential in the area of the Lore Lindu Park. Recommendations for conservation policy that may be made from the environmental history within the biosphere reserve thus include the consideration of human influence in easily accessible areas, particularly in face of recent settlement intensification.     

HCN1 subunits contribute to the kinetics of Ih in neonatal cortical plate neurons

The distribution of ion channels in neurons regulates neuronal activity and proper formation of neuronal networks during neuronal development. One of the channels is the hyperpolarization‐activated cyclic nucleotide‐gated (HCN) channel constituting the molecular substrate of hyperpolarization‐activated current (I_h). Our previous study implied a role for the fastest activating subunit HCN1 in the generation of I_h in rat neonatal cortical plate neurons. To better understand the impact of HCN1 in early neocortical development, we here performed biochemical analysis and whole‐cell recordings in neonatal cortical plate and juvenile layer 5 somatosensory neurons of HCN1^?/? and control HCN1^+/+ mice. Western Blot analysis revealed that HCN1 protein expression in neonatal cortical plate tissue of HCN^+/+ mice amounted to only 3% of the HCN1 in young adult cortex and suggested that in HCN1^?/? mice other isoforms (particularly HCN4) might be compensatory up‐regulated. At the first day after birth, functional ablation of the HCN1 subunit did not affect the proportion of I_h expressing pyramidal cortical plate neurons. Although the contribution of individual subunit proteins remains open, the lack of HCN1 markedly slowed the current activation and deactivation in individual I_h expressing neurons. However, it did not impair maximal amplitude/density, voltage dependence of activation, and cAMP sensitivity. In conclusion, our data imply that, although expression is relatively low, HCN1 contributes substantially to I_h properties in individual cortical plate neurons. These properties are significantly changed in HCN1^?/?, either due to the lack of HCN1 itself or due to compensatory mechanisms. © 2013 Wiley Periodicals, Inc. Develop Neurobiol 73: 785–797, 2013     

Assessment of Fetal Cell Chimerism in Transgenic Pig Lines Generated by Sleeping Beauty Transposition

Human cells migrate between mother and fetus during pregnancy and persist in the respective host for long-term after birth. Fetal microchimerism occurs also in twins sharing a common placenta or chorion. Whether microchimerism occurs in multiparous mammals such as the domestic pig, where fetuses have separate placentas and chorions, is not well understood. Here, we assessed cell chimerism in litters of wild-type sows inseminated with semen of transposon transgenic boars. Segregation of three independent monomeric transposons ensured an excess of transgenic over non-transgenic offspring in every litter. Transgenic siblings (n = 35) showed robust ubiquitous expression of the reporter transposon encoding a fluorescent protein, and provided an unique resource to assess a potential cell trafficking to non-transgenic littermates (n = 7) or mothers (n = 4). Sensitive flow cytometry, fluorescence microscopy, and real-time PCR provided no evidence for microchimerism in porcine littermates, or piglets and their mothers in both blood and solid organs. These data indicate that the epitheliochorial structure of the porcine placenta effectively prevents cellular exchange during gestation.     

An imperfect dopaminergic error signal can drive temporal-difference learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards.     

Vaccenic acid-mediated reduction in cytokine production is independent of c9,t11-CLA in human peripheral blood mononuclear cells

The ruminant trans fatty acid vaccenic acid (tVA) favorably alters markers of inflammation. However, it is not yet clear whether these effects are attributed to its endogenous partial conversion to c9,t11-CLA, which is known to possess anti-inflammatory properties. We compared the cytokine reducing potential of tVA to c9,t11-CLA in human T-helper (Th) cells as a main source of cytokine production during inflammation. Secondly, we assessed whether a bioconversion of tVA to c9,t11-CLA via stearoyl-CoA desaturase (SCD) encoded activity takes place in peripheral blood mononuclear cells (PBMC) in order to relate the outcomes of intracellular cytokine measurement to the degree of conversion. TVA reduced the percentage of both IL-2 and TNF-α expressing Th cells significantly, but to a lesser extent compared to c9,t11-CLA, as determined by flow cytometry after alloreactive stimulation of PBMC. Pre-treatment with the selective PPARγ antagonist T0070907 largely re-established the IL-2 and TNF-α positive Th cell population in both tVA and c9,t11-CLA treated cultures. Interestingly, while the portion of tVA dose-dependently increased within the cellular lipid fraction, the initially marginal amount of c9,t11-CLA remained unaltered. However, SCD mRNA although abundantly expressed in PBMC was not regulated by tVA. Conclusively, these results suggest that the cytokine reducing effect of tVA in human T cells is independent of c9,t11-CLA, since no bioconversion occurred. Moreover, the data provide evidence that tVA mechanistically acts in a manner similar to c9,t11-CLA.     

Surprised at all the entropy: hippocampal, caudate and midbrain contributions to learning from prediction errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.