A sensetive zonagenetic assay for rapid in vivo assessment of estrogenic potency of xenobiotics and mycotoxins

Mounting evidence confirms that hepatic biosynthetic processes are essential for female sexual maturation in fish, which is directly controlled by estrogens. These oogenetic events (zonagenesis and vitellogenesis) are induced in both sexes by estrogens. In this paper, we report the induction of zona radiata (zr) proteins and vitellogenin in primary hepatocytes from Atlantic salmon (Salmo salar L.) exposed to xenoestrogens and mycotoxins. Cells were treated with doses of 1, 5, and 10 microM 4-nonylphenol (4-NP), o, p'-DDT, lindane ([gamma]-HCH), and bisphenol A (BPA), which all induced zr proteins and vitellogenin in an approximate dose-dependent manner. Hepatocytes were also treated with combinations of xenoestrogens at 1 or 2 microM, resulting in elevated levels of both zr proteins and vitellogenin, compared to single treatment. The estrogenic activity of the mycotoxin zearalenone (ZEA) and its metabolites [alpha]-ZEA) and ss-zearalenol (ss-ZEA)], with regard to zonagenesis and vitellogenesis, was assessed in this assay system. Mycotoxins were used at concentrations of 10, 100, or 1,000 nM. All induced zr proteins and vitellogenin, with [alpha]-ZEA being the strongest inducer. When cells were treated with xenoestrogens or mycotoxins in combination with an estrogen receptor inhibitor (ICI 182,780), the induction of both zr proteins and vitellogenin was inhibited in all cases. Thus, the reported estrogen effects are bonafide estrogen responses. Zona radiata proteins were more responsive than vitellogenin to both xenoestrogens and mycotoxins. The versatility and sensitivity of the hepatocyte assay demonstrates that biosynthesis of zr proteins provides a new supplementary method for estimating xenoestrogenicity and mycotoxin action.publishedVersio

TPH1A218C polymorphism and temperament in major depression

BACKGROUND: In major depression, one of the candidate genes possibly affecting the risk and severity of symptoms has been found to be tryptophan hydroxylase (TPH1). Variation in treatment response to antidepressive agents according to TPH1 genotype has also been found in several studies. However, the relationship between temperament and TPH1 genotype in major depression is poorly understood, as only one study has been published so far. There are no earlier studies on the interaction between temperament traits, antidepressive medication response and TPH1 genotype. This interaction was studied in 97 subjects with major depression treated for six weeks with selective serotonine reuptake inhibitors. METHODS: Temperament dimensions Harm Avoidance (HA), Novelty Seeking (NS), Reward Dependence (RD) and Persistence (P) scores at baseline (1) and endpoint (2) were rated with the Temperament and Character Inventory (TCI) and compared between TPH1 A218C genotypes. Multivariate analysis of co-variance (MANCOVA) was used to analyze the interaction between the TPH1 genotype, treatment response and the different temperament dimensions at baseline and endpoint. In the analysis model, treatment response was used as a covariate and TPH1 genotype as a factor. A post hoc analysis for an interaction between remission status and TPH1 A218C genotype at endpoint HA level was also performed. RESULTS: The number of TPH1 A-alleles was associated with increasing levels in NS1 and NS2 scores and decreasing levels in HA1 and HA2 scores between TPH1 A218C genotypes. In the MANCOVA model, TPH1 genotype and treatment response had an interactive effect on both HA1 and HA2 scores, and to a lesser degree on NS2 scores. Additionally, an interaction between remission status and TPH1 A218C genotype was found to be associated with endpoint HA score, with a more marked effect of the interaction between CC genotype and remission status compared to A-allele carriers. CONCLUSIONS: Our results suggest that in acute depression TPH1 A218C polymorphism and specifically the CC genotype together with the information on remission or treatment response differentiates between different temperament profiles and their changes

Antenatal and postnatal corticosteroid and resuscitation induced lung injury in preterm sheep

<p>Abstract</p> <p>Background</p> <p>Initiation of ventilation using high tidal volumes in preterm lambs causes lung injury and inflammation. Antenatal corticosteroids mature the lungs of preterm infants and postnatal corticosteroids are used to treat bronchopulmonary dysplasia.</p> <p>Objective</p> <p>To test if antenatal or postnatal corticosteroids would decrease resuscitation induced lung injury.</p> <p>Methods</p> <p>129 d gestational age lambs (n = 5-8/gp; term = 150 d) were operatively delivered and ventilated after exposure to either 1) no medication, 2) antenatal maternal IM Betamethasone 0.5 mg/kg 24 h prior to delivery, 3) 0.5 mg/kg Dexamethasone IV at delivery or 4) Cortisol 2 mg/kg IV at delivery. Lambs then were ventilated with no PEEP and escalating tidal volumes (<it>V</it>_T) to 15 mL/kg for 15 min and then given surfactant. The lambs were ventilated with <it>V</it>_T8 mL/kg and PEEP 5 cmH₂0 for 2 h 45 min.</p> <p>Results</p> <p>High V_Tventilation caused a deterioration of lung physiology, lung inflammation and injury. Antenatal betamethasone improved ventilation, decreased inflammatory cytokine mRNA expression and alveolar protein leak, but did not prevent neutrophil influx. Postnatal dexamethasone decreased pro-inflammatory cytokine expression, but had no beneficial effect on ventilation, and postnatal cortisol had no effect. Ventilation increased liver serum amyloid mRNA expression, which was unaffected by corticosteroids.</p> <p>Conclusions</p> <p>Antenatal betamethasone decreased lung injury without decreasing lung inflammatory cells or systemic acute phase responses. Postnatal dexamethasone or cortisol, at the doses tested, did not have important effects on lung function or injury, suggesting that corticosteroids given at birth will not decrease resuscitation mediated injury.</p

Testing a Short Nuclear Marker for Inferring Staphylinid Beetle Diversity in an African Tropical Rain Forest

The use of DNA based methods for assessing biodiversity has become increasingly common during the last years. Especially in speciose biomes as tropical rain forests and/or in hyperdiverse or understudied taxa they may efficiently complement morphological approaches. The most successful molecular approach in this field is DNA barcoding based on cytochrome c oxidase I (COI) marker, but other markers are used as well. Whereas most studies aim at identifying or describing species, there are only few attempts to use DNA markers for inventorying all animal species found in environmental samples to describe variations of biodiversity patterns.In this study, an analysis of the nuclear D3 region of the 28S rRNA gene to delimit species-like units is compared to results based on distinction of morphospecies. Data derived from both approaches are used to assess diversity and composition of staphylinid beetle communities of a Guineo-Congolian rain forest in Kenya. Beetles were collected with a standardized sampling design across six transects in primary and secondary forests using pitfall traps. Sequences could be obtained of 99% of all individuals. In total, 76 molecular operational taxonomic units (MOTUs) were found in contrast to 70 discernible morphospecies. Despite this difference both approaches revealed highly similar biodiversity patterns, with species richness being equal in primary and secondary forests, but with divergent species communities in different habitats. The D3-MOTU approach proved to be an efficient tool for biodiversity analyses.Our data illustrate that the use of MOTUs as a proxy for species can provide an alternative to morphospecies identification for the analysis of changes in community structure of hyperdiverse insect taxa. The efficient amplification of the D3-marker and the ability of the D3-MOTUs to reveal similar biodiversity patterns as analyses of morphospecies recommend its use in future molecular studies on biodiversity

Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual Attention

Visual attention is thought to be driven by the interplay between low-level visual features and task dependent information content of local image regions, as well as by spatial viewing biases. Though dependent on experimental paradigms and model assumptions, this idea has given rise to varying claims that either bottom-up or top-down mechanisms dominate visual attention. To contribute toward a resolution of this discussion, here we quantify the influence of these factors and their relative importance in a set of classification tasks. Our stimuli consist of individual image patches (bubbles). For each bubble we derive three measures: a measure of salience based on low-level stimulus features, a measure of salience based on the task dependent information content derived from our subjects' classification responses and a measure of salience based on spatial viewing biases. Furthermore, we measure the empirical salience of each bubble based on our subjects' measured eye gazes thus characterizing the overt visual attention each bubble receives. A multivariate linear model relates the three salience measures to overt visual attention. It reveals that all three salience measures contribute significantly. The effect of spatial viewing biases is highest and rather constant in different tasks. The contribution of task dependent information is a close runner-up. Specifically, in a standardized task of judging facial expressions it scores highly. The contribution of low-level features is, on average, somewhat lower. However, in a prototypical search task, without an available template, it makes a strong contribution on par with the two other measures. Finally, the contributions of the three factors are only slightly redundant, and the semi-partial correlation coefficients are only slightly lower than the coefficients for full correlations. These data provide evidence that all three measures make significant and independent contributions and that none can be neglected in a model of human overt visual attention

The poly-omics of ageing through individual-based metabolic modelling

Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells

Climate change and freshwater zooplankton: what does it boil down to?

Recently, major advances in the climate–zooplankton interface have been made some of which appeared to receive much attention in a broader audience of ecologists as well. In contrast to the marine realm, however, we still lack a more holistic summary of recent knowledge in freshwater. We discuss climate change-related variation in physical and biological attributes of lakes and running waters, high-order ecological functions, and subsequent alteration in zooplankton abundance, phenology, distribution, body size, community structure, life history parameters, and behavior by focusing on community level responses. The adequacy of large-scale climatic indices in ecology has received considerable support and provided a framework for the interpretation of community and species level responses in freshwater zooplankton. Modeling perspectives deserve particular consideration, since this promising stream of ecology is of particular applicability in climate change research owing to the inherently predictive nature of this field. In the future, ecologists should expand their research on species beyond daphnids, should address questions as to how different intrinsic and extrinsic drivers interact, should move beyond correlative approaches toward more mechanistic explanations, and last but not least, should facilitate transfer of biological data both across space and time

The Early Data Release of the Dark Energy Spectroscopic Instrument

\ua9 2024. The Author(s). Published by the American Astronomical Society. The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra