Quantifying the relationship between airborne pollen and vegetation in the urban environment

The goal of this study was to quantitatively assess the relationship linking vegetation and airborne pollen. For this, we established six sampling stations in the city of Thessaloniki, Greece. Once every week for 2 years, we recorded airborne pollen in them, at breast height, by use of a portable volumetric sampler. We also made a detailed analysis of the vegetation in each station by counting all existing individuals of the woody species contributing pollen to the air, in five zones of increasing size, from 4 to 40 ha. We found the local vegetation to be the driver of the spatial variation of pollen in the air of the city. Even at very neighbouring stations, only 500 m apart, considerable differences in vegetation composition were expressed in the pollen spectrum. We modelled the pollen concentration of each pollen taxon as a function of the abundance of the woody species corresponding to that taxon by use of a Generalized Linear Model. The relationship was significant for the five most abundantly represented taxa in the pollen spectrum of the city. It is estimated that every additional individual of Cupressaceae, Pinaceae, Platanus, Ulmus and Olea increases pollen in the air by approximately 0.7, 0.2, 2, 6 and 5%, respectively. Whether the relationships detected for the above pollen taxa hold outside the domain for which we have data, as well as under different environmental conditions and/or with different assemblages of species representing them are issues to be explored in the future.     

Principal methods for isolation and identification of soil microbial communities

Soil microbial populations play crucial role in soil properties and influence below-ground ecosystem processes. Microbial composition and functioning changes the soil quality through decomposition of organic matter, recycling of nutrients, and biological control of parasites of plants. Moreover, the discovery that soil microbes may translate into benefits for biotechnology, management of agricultural, forest, and natural ecosystems, biodegradation of pollutants, and waste treatment systems maximized the need of scientists for the isolation and their characterization. Operations such as the production of antibiotics and enzymic activities from microorganisms of soil constitute objectives of industry in her effort to cope with the increase of population of earth and disturbance of environment and may ameliorate the effects of global climate change. In the past decades, new biochemical and molecular techniques have been developed in our effort to identify and classify soil bacteria. The goal of measuring the soil microbial diversity is difficult because of the limited knowledge about bacteria species and classification through families and orders. Molecular techniques extend our knowledge about microbial diversity and help the taxonomy of species. Measuring and monitoring soil microbial communities can lead us to better understanding of their composition and function in many ecosystem processes.     

A Comparison of Models to Infer the Distribution of Fitness Effects of New Mutations

Knowing the distribution of fitness effects (DFE) of new mutations is important for several topics in evolutionary genetics. Existing computational methods with which to infer the DFE based on DNA polymorphism data have frequently assumed that the DFE can be approximated by a unimodal distribution, such as a lognormal or a gamma distribution. However, if the true DFE departs substantially from the assumed distribution (e.g., if the DFE is multimodal), this could lead to misleading inferences about its properties. We conducted simulations to test the performance of parametric and nonparametric discretized distribution models to infer the properties of the DFE for cases in which the true DFE is unimodal, bimodal, or multimodal. We found that lognormal and gamma distribution models can perform poorly in recovering the properties of the distribution if the true DFE is bimodal or multimodal, whereas discretized distribution models perform better. If there is a sufficient amount of data, the discretized models can detect a multimodal DFE and can accurately infer the mean effect and the average fixation probability of a new deleterious mutation. We fitted several models for the DFE of amino acid-changing mutations using whole-genome polymorphism data from Drosophila melanogaster and the house mouse subspecies Mus musculus castaneus. A lognormal DFE best explains the data for D. melanogaster, whereas we find evidence for a bimodal DFE in M. m. castaneus.     

Temporal Dynamics of Clonal Evolution in Chronic Lymphocytic Leukemia with Stereotyped IGHV4-34/IGKV2-30 Antigen Receptors: Longitudinal Immunogenetic Evidence

Chronic lymphocytic leukemia (CLL) patients assigned to stereotyped subset 4 possess distinctive patterns of intraclonal diversification (ID) within their immunoglobulin (IG) genes. Although highly indicative of an ongoing response to antigen(s), the critical question concerning the precise timing of antigen involvement is unresolved. Hence, we conducted a large-scale longitudinal study of eight subset 4 cases totaling 511 and 398 subcloned IG heavy and kappa sequences. Importantly, we could establish a hierarchical pattern of subclonal evolution, thus revealing which somatic hypermutations were negatively or positively selected. In addition, distinct clusters of subcloned sequences with cluster-specific mutational profiles were observed initially; however, at later time points, the minor cluster had often disappeared and hence not been selected. Despite the high intensity of ID, it was remarkable that certain residues remained essentially unaltered. These novel findings strongly support a role for persistent antigen stimulation in the clonal evolution of CLL subset 4.     

Saccharomyces cerevisiae and Oenococcus oeni immobilized in different layers of a cellulose/starch gel composite for simultaneous alcoholic and malolactic wine fermentations

The production of a two-layer composite biocatalyst for immobilization of two different microorganisms for simultaneous alcoholic and malolactic fermentation (MLF) of wine in the same bioreactor is reported. The biocatalyst consisted of a tubular delignified cellulosic material (DCM) with entrapped Oenococcus oeni cells, covered with starch gel containing the alcohol resistant and cryotolerant strain Saccharomyces cerevisiae AXAZ-1. The biocatalyst was found effective for simultaneous low temperature alcoholic fermentation resulting to conversion of malic acid to lactic acid in 5 days at 10 °C. Improvement of wine quality compared with wine fermented with S. cerevisiae AXAZ-1 immobilized on DCM was attributed to MLF as well as to increased ester formation and lower higher alcohols produced at low fermentation temperatures (10 °C) as shown by GC and headspace SPME GC/MS analysis. Scanning electron microscopy showed that the preparation of a three-layer composite biocatalyst is also possible. The significance of such composite biocatalysts is the feasibility of two or three bioprocesses in the same bioreactor, thus reducing production cost in the food industry     

Sweet Cherry Cultivar Identification by High-Resolution-Melting (HRM) Analysis Using Gene-Based SNP Markers

Single nucleotide polymorphisms (SNPs) provide an important tool for cultivar identification in studies of genetic diversity, but until now, the time-consuming and costly nature of DNA sequencing has limited the identification of new markers. Herein, we describe the application of high-resolution melting (HRM), a recent enhancement to traditional DNA melting analysis, for the characterization of polymerase chain reaction products and the identification of nine gene-based SNPs for distinguishing the main Greek sweet cherry cultivars. The expected heterozygosity value of nine SNPs averaged at 0.518. The combined power of discrimination for the SNP markers was 0.999969. The ability of HRM to accurately discern nucleotide changes in a DNA sequence makes it a cost- and time-effective alternative to traditional sequencing for the detection of gene-based SNPs.     

Improving Embryonic Stem Cell Expansion through the Combination of Perfusion and Bioprocess Model Design

Background

High proliferative and differentiation capacity renders embryonic stem cells (ESCs) a promising cell source for tissue engineering and cell-based therapies. Harnessing their potential, however, requires well-designed, efficient and reproducible expansion and differentiation protocols as well as avoiding hazardous by-products, such as teratoma formation. Traditional, standard culture methodologies are fragmented and limited in their fed-batch feeding strategies that afford a sub-optimal environment for cellular metabolism. Herein, we investigate the impact of metabolic stress as a result of inefficient feeding utilizing a novel perfusion bioreactor and a mathematical model to achieve bioprocess improvement.

Methodology/Principal Findings

To characterize nutritional requirements, the expansion of undifferentiated murine ESCs (mESCs) encapsulated in hydrogels was performed in batch and perfusion cultures using bioreactors. Despite sufficient nutrient and growth factor provision, the accumulation of inhibitory metabolites resulted in the unscheduled differentiation of mESCs and a decline in their cell numbers in the batch cultures. In contrast, perfusion cultures maintained metabolite concentration below toxic levels, resulting in the robust expansion (>16-fold) of high quality ‘naïve’ mESCs within 4 days. A multi-scale mathematical model describing population segregated growth kinetics, metabolism and the expression of selected pluripotency (‘stemness’) genes was implemented to maximize information from available experimental data. A global sensitivity analysis (GSA) was employed that identified significant (6/29) model parameters and enabled model validation. Predicting the preferential propagation of undifferentiated ESCs in perfusion culture conditions demonstrates synchrony between theory and experiment.

Conclusions/Significance

The limitations of batch culture highlight the importance of cellular metabolism in maintaining pluripotency, which necessitates the design of suitable ESC bioprocesses. We propose a novel investigational framework that integrates a novel perfusion culture platform (controlled metabolic conditions) with mathematical modeling (information maximization) to enhance ESC bioprocess productivity and facilitate bioprocess optimization.     

Evaluating the Connectivity of a Protected Areas' Network under the Prism of Global Change: The Efficiency of the European Natura 2000 Network for Four Birds of Prey

Climate and land use changes are major threats to biodiversity. To preserve biodiversity, networks of protected areas have been established worldwide, like the Natura 2000 network across the European Union (EU). Currently, this reserve network consists of more than 26000 sites covering more than 17% of EU terrestrial territory. Its efficiency to mitigate the detrimental effects of land use and climate change remains an open research question. Here, we examined the potential current and future geographical ranges of four birds of prey under scenarios of both land use and climate changes. By using graph theory, we examined how the current Natura 2000 network will perform in regard to the conservation of these species. This approach determines the importance of a site in regard to the total network and its connectivity. We found that sites becoming unsuitable due to climate change are not a random sample of the network, but are less connected and contribute less to the overall connectivity than the average site and thus their loss does not disrupt the full network. Hence, the connectivity of the remaining network changed only slightly from present day conditions. Our findings highlight the need to establish species-specific management plans with flexible conservation strategies ensuring protection under potential future range expansions. Aquila pomarina is predicted to disappear from the southern part of its range and to become restricted to northeastern Europe. Gyps fulvus, Aquila chrysaetos, and Neophron percnopterus are predicted to locally lose some suitable sites; hence, some isolated small populations may become extinct. However, their geographical range and metapopulation structure will remain relatively unaffected throughout Europe. These species would benefit more from an improved habitat quality and management of the existing network of protected areas than from increased connectivity or assisted migration.     

Simulated interactions between angiotensin-converting enzyme and substrate gonadotropin-releasing hormone: novel insights into domain selectivity

Human angiotensin-I converting enzyme (ACE) is a central component of the renin-angiotensin system and a major target for cardiovascular therapies. The somatic form of the enzyme (sACE) comprises two homologous metallopeptidase domains (N and C), each bearing a zinc active site with similar but distinct substrate and inhibitor specificities. On the basis of the recently determined crystal structures of both ACE domains, we have studied their complexes with gonadotropin-releasing hormone (GnRH), which is cleaved releasing both the protected NH2- and COOH-terminal tripeptides. This is the first molecular modeling study of an ACE-peptide substrate complex that examines the structural basis of ACE's endopeptidase activity and offers novel insights into subsites that are distant from the obligatory binding site and were not identified in the crystal structures. Our data indicate that a bridging interaction between Arg500 of the N-domain and Arg8 of GnRH that involves a buried chloride ion may account for its role in the specificity of the N-domain for endoproteolytic cleavage of the substrate at the NH2-terminus in vitro. In support of this, the protected NH2-terminal dipeptide of GnRH exhibits stronger interactions than the protected COOH-terminal dipeptide with the N-domain of ACE. Further comparison of the models of ACE-substrate complexes promotes our understanding of how the two domains differ in their function and specificity and provides an extension of the pharmacophore model used for structure-based drug design up to the S7 subsite of the enzyme.