Bone tissue engineering using adipose‐derived stem cells and endothelial cells: Effects of the cell ratio

The microvascular endothelial network is essential for bone formation and regeneration. In this context, endothelial cells not only support vascularization but also influence bone physiology via cell contact‐dependent mechanisms. In order to improve vascularization and osteogenesis in tissue engineering applications, several strategies have been developed. One promising approach is the coapplication of endothelial and adipose derived stem cells (ADSCs). In this study, we aimed at investigating the best ratio of human umbilical vein endothelial cells (HUVECs) and osteogenic differentiated ADSCs with regard to proliferation, apoptosis, osteogenesis and angiogenesis. For this purpose, cocultures of ADSCs and HUVECs with ratios of 25%:75%, 50%:50% and 75%:25% were performed. We were able to prove that cocultivation supports proliferation whereas apoptosis was unidirectional decreased in cocultured HUVECs mediated by a p‐BAD‐dependent mechanism. Moreover, coculturing ADSCs and HUVECs stimulated matrix mineralization and the activity of alkaline phosphatase (ALP). Increased gene expression of the proangiogenic markers eNOS, Flt, Ang2 and MMP3 as well as sprouting phenomena in matrigel assays proved the angiogenic potential of the coculture. In summary, coculturing ADSCs and HUVECs stimulates proliferation, cell survival, osteogenesis and angiogenesis particularly in the 50%:50% coculture.     

Soil and root nutrient chemistry structure root-associated fungal assemblages in temperate forests

Root-associated fungi (RAF) link nutrient fluxes between soil and roots and thus play important roles in ecosystem functioning. To enhance our understanding of the factors that control RAF, we fitted statistical models to explain variation in RAF community structure using data from 150 temperate forest sites covering a broad range of environmental conditions and chemical root traits. We found that variation in RAF communities was related to both root traits (e.g., cations, carbohydrates, NO₃⁻) and soil properties (pH, cations, moisture, C/N). The identified drivers were the combined result of distinct response patterns of fungal taxa (determined at the rank of orders) to biotic and abiotic factors. Our results support that RAF community variation is related to evolutionary adaptedness of fungal lineages and consequently, drivers of RAF communities are context-dependent.     

The effectiveness of flower strips and hedgerows on pest control,pollination services and crop yield: a quantitative synthesis

Floral plantings are promoted to foster ecological intensification of agriculture through provisioning of ecosystem services. However, a comprehensive assessment of the effectiveness of different floral plantings, their characteristics and consequences for crop yield is lacking. Here we quantified the impacts of flower strips and hedgerows on pest control (18 studies) and pollination services (17 studies) in adjacent crops in North America, Europe and New Zealand. Flower strips, but not hedgerows, enhanced pest control services in adjacent fields by 16% on average. However, effects on crop pollination and yield were more variable. Our synthesis identifies several important drivers of variability in effectiveness of plantings: pollination services declined exponentially with distance from plantings, and perennial and older flower strips with higher flowering plant diversity enhanced pollination more effectively. These findings provide promising pathways to optimise floral plantings to more effectively contribute to ecosystem service delivery and ecological intensification of agriculture in the future.     

Supply versus use designs of environmental extensions in input–output analysis: Conceptual and empirical implications for the case of energy

Input–output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), that is, the scope of physical flows and their attribution to sectors in the monetary input–output table (MIOT), remains fragmented. This article investigates the conceptual and empirical implications of applying two different but frequently used designs of EEs, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply–use dataset and apply them to the same single‐region input–output (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi‐regional input–output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product‐level results can vary by several orders of magnitude. The GMRIO‐based comparison further reveals that for a few countries the supply‐extension result can be twice the size of the use‐extension footprint (e.g., Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life‐cycle assessment and conclude that our findings are relevant for monitoring of energy efficiency and emission reduction targets and corporate footprint accounting.     

Genetic Predisposition and its Heredity in the Context of Increased Prevalence of Dermatophytoses

Mycopathologia - Dermatophytosis is a widespread disease with high prevalence and a substantial economic burden associated with costs of treatment. The pattern of this infectious disease covers a...     

Self-tuning serverless task farming using proactive elasticity control

Cluster Computing - The cloud evolved into an attractive execution environment for parallel applications, which make use of compute resources to speed up the computation of large problems in...     

Sec14 family of lipid transfer proteins in yeasts

The hydrophobicity of lipids prevents their free movement across the cytoplasm. To achieve highly heterogeneous and precisely regulated lipid distribution in different cellular membranes, lipids are transported by lipid transfer proteins (LTPs) in addition to their transport by vesicles. Sec14 family is one of the most extensively studied groups of LTPs. Here we provide an overview of Sec14 family of LTPs in the most studied yeast Saccharomyces cerevisiae as well as in other selected non-Saccharomyces yeasts—Schizosaccharomyces pombe, Kluyveromyces lactis, Candida albicans, Candida glabrata, Cryptococcus neoformans, and Yarrowia lipolytica. Discussed are specificities of Sec14-domain LTPs in various yeasts, their mode of action, subcellular localization, and physiological function. In addition, quite few Sec14 family LTPs are target of antifungal drugs, serve as modifiers of drug resistance or influence virulence of pathologic yeasts. Thus, they represent an important object of study from the perspective of human health.     

Proteomic Differences Between Hepatocellular Carcinoma and Nontumorous Liver Tissue Investigated by a Combined Gel-based and Label-free Quantitative Proteomics Study

Proteomics-based clinical studies have been shown to be promising strategies for the discovery of novel biomarkers of a particular disease. Here, we present a study of hepatocellular carcinoma (HCC) that combines complementary two-dimensional difference in gel electrophoresis (2D-DIGE) and liquid chromatography (LC-MS)-based approaches of quantitative proteomics. In our proteomic experiments, we analyzed a set of 14 samples (7 × HCC versus 7 × nontumorous liver tissue) with both techniques. Thereby we identified 573 proteins that were differentially expressed between the experimental groups. Among these, only 51 differentially expressed proteins were identified irrespective of the applied approach. Using Western blotting and immunohistochemical analysis the regulation patterns of six selected proteins from the study overlap (inorganic pyrophosphatase 1 (PPA1), tumor necrosis factor type 1 receptor-associated protein 1 (TRAP1), betaine-homocysteine S-methyltransferase 1 (BHMT)) were successfully verified within the same sample set. In addition, the up-regulations of selected proteins from the complements of both approaches (major vault protein (MVP), gelsolin (GSN), chloride intracellular channel protein 1 (CLIC1)) were also reproducible. Within a second independent verification set (n = 33) the altered protein expression levels of major vault protein and betaine-homocysteine S-methyltransferase were further confirmed by Western blots quantitatively analyzed via densitometry. For the other candidates slight but nonsignificant trends were detectable in this independent cohort. Based on these results we assume that major vault protein and betaine-homocysteine S-methyltransferase have the potential to act as diagnostic HCC biomarker candidates that are worth to be followed in further validation studies.Hepatocellular carcinoma (HCC)¹ currently is the fifth most common malignancy worldwide with an annual incidence up to 500 per 100,000 individuals depending on the geographic region investigated. Whereas 80% of new cases occur in developing countries, the incidence increases in industrialized nations including Western Europe, Japan, and the United States (1). To manage patients with HCC, tumor markers are very important tools for diagnosis, indicators of disease progression, outcome prediction, and evaluation of treatment efficacy. Several tumor markers have been reported for HCC, including α-fetoprotein (AFP) (2), Lens culinaris agglutinin-reactive fraction of AFP (AFP-L3) (3), and des-γ-carboxyl prothrombin (DCP) (4). However, none of these tumor markers show 100% sensitivity or specificity, which calls for new and better biomarkers.To identify novel biomarkers of HCC, many clinical studies using “omics”-based methods have been reported over the past decade (5–6). In particular, the proteomics-based approach has turned out to be a promising one, offering several quantification techniques to reveal differences in protein expression that are caused by a particular disease. In most studies, the well-established 2D-DIGE technique has been applied for protein quantification followed by identification via mass spectrometry (7–15). Even if the quantification is very accurate and sensitive in this gel-based approach, the relatively high amount of protein sample necessary for protein identification is the major disadvantage of this technique. Several mass-spectrometry-based quantitative studies using labeling-techniques like SILAC (stable isotope labeling by amino acids in cell culture) or iTRAQ (isobaric tags for relative and absolute quantification) have also been carried out for biomarker discovery of HCC (16–18). Here, the concomitant protein quantification and identification in a mass spectrometer allows high-throughput analyses. However, such experiments imply additional labeling reactions (in case of iTRAQ) or are limited to tissue culture systems (in case of SILAC). In the latter case, one can overcome the limitation by using the isotope-labeled proteins obtained from tissue culture as an internal standard added to a corresponding tissue sample. This approach is known as CDIT (culture-derived isotope tags) and was applied in a HCC study, very recently (19). Label-free proteomics approaches based on quantification by ion-intensities or spectral counting offer another possibility for biomarker discovery. These approaches are relatively cheap compared with the labeling approaches, because they do not require any labeling reagents and furthermore they allow for high-throughput and sensitive analyses in a mass spectrometer. A quantitative study of HCC using spectral counting has been reported (20), whereas to our knowledge an ion-intensity-based study has not been performed yet. Apart from these quantification strategies, protein alterations in HCC have been studied by MALDI imaging, as well. Here, the authors could show that based on its proteomic signature, hepatocellular carcinoma can be discriminated with high accuracy from liver metastasis samples or other cancer types (21) as well as liver cirrhosis (22). Based on these results, it could be assumed that MALDI imaging might be a promising alternative to standard histological methods in the future.Here, we report a quantitative proteomic study that combines two different techniques, namely the well-established 2D-DIGE approach and a label-free ion-intensity-based quantification via mass spectrometry and liquid chromatography. To our knowledge this is the first time such a combined study was performed with regard to hepatocellular carcinoma. By comparing the results of both studies, we aim to identify high-confident biomarker candidates of HCC, as gel- and LC-MS-based techniques are complementary. To verify the differential protein expressions detected in our proteomic studies we performed additional immunological verifications for selected proteins within two different sample sets (Fig. 1).Open in a separate windowFig. 1.Schematic representation of the applied workflow.