Impact of the Chromatin Remodeling Factor CHD1 on Gut Microbiome Composition of Drosophila melanogaster

The composition of the intestinal microbiota of Drosophila has been studied in some detail in recent years. Environmental, developmental and host-specific genetic factors influence microbiome composition in the fly. Our previous work has indicated that intestinal bacterial load can be affected by chromatin-targeted regulatory mechanisms. Here we studied a potential role of the conserved chromatin assembly and remodeling factor CHD1 in the shaping of the gut microbiome in Drosophila melanogaster. Using high-throughput sequencing of 16S rRNA gene amplicons, we found that Chd1 deletion mutant flies exhibit significantly reduced microbial diversity compared to rescued control strains. Specifically, although Acetobacteraceae dominated the microbiota of both Chd1 wild-type and mutant guts, Chd1 mutants were virtually monoassociated with this bacterial family, whereas in control flies other bacterial taxa constituted ~20% of the microbiome. We further show age-linked differences in microbial load and microbiota composition between Chd1 mutant and control flies. Finally, diet supplementation experiments with Lactobacillus plantarum revealed that, in contrast to wild-type flies, Chd1 mutant flies were unable to maintain higher L. plantarum titres over time. Collectively, these data provide evidence that loss of the chromatin remodeler CHD1 has a major impact on the gut microbiome of Drosophila melanogaster.     

Desynchronization of Theta-Phase Gamma-Amplitude Coupling during a Mental Arithmetic Task in Children with Attention Deficit/Hyperactivity Disorder

Introduction

Theta-phase gamma-amplitude coupling (TGC) measurement has recently received attention as a feasible method of assessing brain functions such as neuronal interactions. The purpose of this electroencephalographic (EEG) study is to understand the mechanisms underlying the deficits in attentional control in children with attention deficit/hyperactivity disorder (ADHD) by comparing the power spectra and TGC at rest and during a mental arithmetic task.

Methods

Nineteen-channel EEGs were recorded from 97 volunteers (including 53 subjects with ADHD) from a camp for hyperactive children under two conditions (rest and task performance). The EEG power spectra and the TGC data were analyzed. Correlation analyses between the Intermediate Visual and Auditory (IVA) continuous performance test (CPT) scores and EEG parameters were performed.

Results

No significant difference in the power spectra was detected between the groups at rest and during task performance. However, TGC was reduced during the arithmetic task in the ADHD group compared with the normal group (F = 16.70, p < 0.001). The TGC values positively correlated with the IVA CPT scores but negatively correlated with theta power.

Conclusions

Our findings suggest that desynchronization of TGC occurred during the arithmetic task in ADHD children. TGC in ADHD children is expected to serve as a promising neurophysiological marker of network deactivation during attention-demanding tasks.     

Borderline Personality and the Detection of Angry Faces

Background

Many studies have assessed emotion recognition in patients with Borderline Personality Disorder and considerable evidence has been accumulated on patients’ ability to categorize emotions. In contrast, their ability to detect emotions has been investigated sparsely. The only two studies that assessed emotion detection abilities found contradictory evidence on patients’ ability to detect angry faces.

Methods

To clarify whether patients with Borderline Personality Disorder show enhanced detection of angry faces, we conducted three experiments: a laboratory study (n = 53) with a clinical sample and two highly powered web studies that measured Borderline features (n₁ = 342, n₂ = 220). Participants in all studies completed a visual search paradigm, and the reaction times for the detection of angry vs. happy faces were measured.

Results

Consistently, data spoke against enhanced detection of angry faces in the Borderline groups, indicated by non-significant group (Borderline vs. healthy control) × target (angry vs. happy) interactions, despite highly satisfactory statistical power to detect even small effects.

Conclusions

In contrast to emotion categorization, emotion detection appears to be intact in patients with Borderline Personality Disorder and individuals high in Borderline features. The importance of distinguishing between these two processes in future studies is discussed.     

Orthotopic Hind Limb Transplantation in the Mouse

In vivo animal model systems, and in particular mouse models, have evolved into powerful and versatile scientific tools indispensable to basic and translational research in the field of transplantation medicine. A vast array of reagents is available exclusively in this setting, including mono- and polyclonal antibodies for both diagnostic and interventional applications. In addition, a vast number of genotyped, inbred, transgenic, and knock out strains allow detailed investigation of the individual contributions of humoral and cellular components to the complex interplay of an immune response and make the mouse the gold standard for immunological research. Vascularized Composite Allotransplantation (VCA) delineates a novel field of transplantation using allografts to replace "like with like" in patients suffering traumatic or congenital tissue loss. This surgical methodological protocol shows the use of a non-suture cuff technique for super-microvascular anastomosis in an orthotopic mouse hind limb transplantation model. The model specifically allows for comparison between established paradigms in solid organ transplantation with a novel form of transplants consisting of various different tissue components. Uniquely, this model allows for the transplantation of a viable vascularized bone marrow compartment and niche that have the potential to exert a beneficial effect on the balance of immune acceptance and rejection. This technique provides a tool to investigate alloantigen recognition and allograft rejection and acceptance, as well as enables the pursuit of functional nerve regeneration studies to further advance this novel field of transplantation.     

Maize rhizosphere priming: field estimates using <Superscript>13</Superscript>C natural abundance

Introduction

Root-mediated changes in soil organic matter (SOM) decomposition, termed rhizosphere priming effects (RPE), play crucial roles in the global carbon (C) cycle, but their mechanisms and field relevance remain ambiguous. We hypothesize that nitrogen (N) shortages may intensify SOM decomposition in the rhizosphere because of increase of fine roots and rhizodeposition.

Methods

RPE and their dependence on N-fertilization were studied using a C₃-to-C₄ vegetation change. N-fertilized and unfertilized soil cores, with and without maize, were incubated in the field for 50 days. Soil CO₂ efflux was measured, partitioned for SOM- and root-derived CO₂, and RPE was calculated. Plant biomass, microbial biomass C (MBC) and N (MBN), and enzyme activities (β-1,4-glucosidase; N-acetylglucosaminidase; L-leucine aminopeptidase) were analyzed.

Results

Roots enhanced SOM mineralization by 35 % and 126 % with and without N, respectively. This was accompanied by higher specific root-derived CO₂ in unfertilized soils. MBC, MBN and enzyme activities increased in planted soils, indicating microbial activation, causing positive RPE. N-fertilization had minor effects on MBC and MBN, but it reduced β-1,4-glucosidase and L-leucine aminopeptidase activities under maize through lower root-exudation. In contrast, N-acetylglucosaminidase activity increased with N-fertilization in planted and unplanted soils.

Conclusions

This study showed the field relevance of RPE and confirmed that, despite higher root biomass, N availability reduces RPE by lowering root and microbial activity.

     

phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds

The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.Seeds play a major role in keeping continuity between successive generations (Esau, 1977) and are key for the distribution and evolution (Moles et al., 2005) of higher plants. Fertile seeds carry an embryo and may contain nutrient storage tissues in cotyledons, endosperm, and/or perisperm, supporting germination and seedling development at early developmental stages. Although this is true for all seed plants, various traits of seeds, such as size, shape, weight, and chemical composition, can be very different between plant species or accessions. For example, the Arabidopsis (Arabidopsis thaliana) accession Cape Verde Islands was reported to yield on average 40% fewer seeds than Landsberg erecta, but they are almost twice as heavy (Alonso-Blanco et al., 1999). Considering today’s plant species, single-seed mass may vary over a range of 11.5 orders of magnitude (Moles et al., 2005). Seed mass is under strong genetic control, whereas the total number of seeds of a plant is largely affected by the environment (Paul-Victor and Turnbull, 2009). It has been demonstrated that the size, mass, and shape of Arabidopsis seeds may be regulated by brassinosteroid (Jiang et al., 2013), and it was shown recently that seed size in rice (Oryza sativa) can be influenced by the epiallele Epi-rav6 (Zhang et al., 2015). The ability of plants to switch between small and larger seeds may be understood as an adaptation to novel environments (Igea et al., 2016). However, it is still not fully understood whether, or to what extent, the variability of seed traits within plant species or genotypes has an impact on the development and further performance of a plant.When comparing biometric seed data of different dimensions such as length (one-dimensional), projected area (two-dimensional [2D]), or volume and mass (both three-dimensional [3D]), one can argue that mass is the most relevant parameter as a proxy for the amount of reserves a seed provides for the offspring. This might be true even when considering that the type of reserves, such as proteins, carbohydrates, or lipids (Rolletschek et al., 2015), and also different seed tissues, such as seed coat, embryo, or endosperm, may contribute differently to seed mass (Alonso-Blanco et al., 1999). While seed mass and time to germination (radicle protrusion) do not necessarily correlate (Norden et al., 2009), in particular under greenhouse conditions, higher seed mass may be advantageous for seedling establishment under adverse environmental conditions (Moles et al., 2005). For example, shade-tolerant species showed largely higher seed masses than cogeneric species growing in open habitats, indicating that seedlings under low-light conditions need more reserves than under good light (Salisbury, 1974). Seedlings of wild radish (Raphanus raphanistrum) emerged more likely from heavier seeds than from small seeds under field conditions but not in the greenhouse (Stanton, 1984), and for Arabidopsis, seed mass was reported to be higher in populations growing naturally at higher altitudes taken as a proxy for harsher conditions (Montesinos-Navarro et al., 2011).Seed mass can be measured individually (Stanton, 1984), but it is generally collected as an average value of batches of 50 to 1,000 seeds (Jako et al., 2001; Jofuku et al., 2005; Montesinos-Navarro et al., 2011; Tanabata et al., 2012). Alternatively, 2D scans are analyzed to determine parameters such as seed length, width, area, and perimeter length as a measure for seed size (Tanabata et al., 2012). This approach can be implemented in high-throughput facilities to obtain projected areas of seed grains combined with genome-wide association studies (Yang et al., 2014). Although projected seed area can easily be measured with a common office scanner (Herridge et al., 2011; Tanabata et al., 2012; Moore et al., 2013), it is not necessarily a precise or reliable measure of the true seed size because it may depend on the shape (Alonso-Blanco et al., 1999) and the orientation of a seed at scan (see “Results”). These issues also apply when using 2D projections to calculate length-to-width ratios as a simple shape factor (Tanabata et al., 2012). Projected seed area also has been used to calculate seed mass, assuming a fixed relationship between these parameters (de Jong et al., 2011; Herridge et al., 2011). This may hold with sufficient accuracy when averaging a large number of seeds but might be misleading when considering individual seeds.From a physical point of view, volume should be a much better proxy for mass than 2D traits. Although it has been stated that for 65 species analyzed seed masses can be compared easily with seed volumes (Moles et al., 2005), it is not clear how these seed volumes were determined. Volumes can be assessed using advanced methods such as x-ray computed tomography (CT) on fruits (Stuppy et al., 2003) or synchrotron radiation x-ray tomographic microscopy applied in paleobiological studies (e.g. on fruits and seed; Friis et al., 2014). Nuclear magnetic resonance (NMR) methods are used to measure water uptake in kidney beans (Phaseolus vulgaris) and adzuki beans (Vigna angularis; Kikuchi et al., 2006) or to estimate seed weight and content (Borisjuk et al., 2011; Rolletschek et al., 2015) rather than volumes. To our best knowledge, affordable methods to measure seed volumes directly are not achievable so far. For that reason, we have set up a volume-carving method for 3D seed shape reconstruction that is described briefly here and in more detail in a recent publication (Roussel et al., 2016).While traits derived from scanning procedures can easily be assigned to individual seeds (Herridge et al., 2011), further handling and processing of phenotyped single seeds is not as simple, in particular for tiny ones like those of Arabidopsis. The aim of this work was to develop an automated seed-handling system that can analyze single seeds of very different sizes or shapes, from Arabidopsis seeds up to barley (Hordeum vulgare) seeds or even bigger. The phenoSeeder system is designed to pick and place seeds, to achieve basic morphometric traits (one-dimensional and 2D data from projections, 3D reconstruction data, and mass) of each individual seed, and to store all analyzed seed traits in a database. Another goal is to use phenoSeeder for seed-to-plant tracking approaches and to analyze whether, or which, particular seed traits have an impact on plant development and performance under various environmental conditions. We describe the main features of the phenoSeeder technology and present results obtained with seeds of three accessions of Arabidopsis, rapeseed (Brassica napus), and barley, respectively. When analyzing the data, we focused particularly on correlations between projected seed area, seed volume, and seed mass, with the hypothesis that the respective seed volume may better correlate with mass than the projected area.     

Dieback of riparian alder caused by the Phytophthora alni complex: projected consequences for stream ecosystems

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