How do elevated CO2 and O3 affect the interception and utilization of radiation by a soybean canopy?

Net productivity of vegetation is determined by the product of the efficiencies with which it intercepts light (?_i) and converts that intercepted energy into biomass (?_c). Elevated carbon dioxide (CO₂) increases photosynthesis and leaf area index (LAI) of soybeans and thus may increase ?_i and ?_c; elevated O₃ may have the opposite effect. Knowing if elevated CO₂ and O₃ differentially affect physiological more than structural components of the ecosystem may reveal how these elements of global change will ultimately alter productivity. The effects of elevated CO₂ and O₃ on an intact soybean ecosystem were examined with Soybean Free Air Concentration Enrichment (SoyFACE) technology where large field plots (20‐m diameter) were exposed to elevated CO₂ (～550 μmol mol^?1) and elevated O₃ (1.2 × ambient) in a factorial design. Aboveground biomass, LAI and light interception were measured during the growing seasons of 2002, 2003 and 2004 to calculate ?_i and ?_c. A 15% increase in yield (averaged over 3 years) under elevated CO₂ was caused primarily by a 12% stimulation in ?_c , as ?_i increased by only 3%. Though accelerated canopy senescence under elevated O₃ caused a 3% decrease in ?_i, the primary effect of O₃ on biomass was through an 11% reduction in ?_c. When CO₂ and O₃ were elevated in combination, CO₂ partially reduced the negative effects of elevated O₃. Knowing that changes in productivity in elevated CO₂ and O₃ were influenced strongly by the efficiency of conversion of light energy into energy in plant biomass will aid in optimizing soybean yields in the future. Future modeling efforts that rely on ?_c for calculating regional and global plant productivity will need to accommodate the effects of global change on this important ecosystem attribute.     

Date Palm DNA Mini-Preparation without Liquid Nitrogen

We have developed a new mini-procedure for isolation of total cellular DNA from date palm (Phoenix dactylifera L.). The procedure, which does not use liquid nitrogen, has proved useful due to temporary disruptions in supplies of liquid nitrogen that occur in countries where date palm trees are cultivated. DNA suitable for RFLP and PCR analyses is obtained.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer–Associated Stellate Cell Small Extracellular Vesicles

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography–tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1–like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.     

The Pressurized Porous Surface Model: An improved tool to study bacterial behavior under a wide range of environmentally relevant matric potentials

To study bacterial behavior under varying hydration conditions similar to surface soil, we have developed a system called the Pressurized Porous Surface Model (PPSM). Thin liquid films created by imposing a matric potential of − 0.4 MPa impact gene expression and colony development in Pseudomonas putida.     

Fluorescent analysis of alpha-keto acids in serum and urine by high-performance liquid chromatography

A selective procedure using synthetic substrates for determination of exo-1,4,-beta-glucanases in a mixture of exoglucanases , endoglucanases , and beta-glucosidases is formulated. The heterobiosides , p- nithrophenyl -beta-D- cellobioside ( pNPC ) or p-nitrophenyl-beta-D-lactoside ( pNPL ), were used as selective substrates for the measurement of exoglucanase activity. The exoglucanases (especially cellobiohydrolases , which split off cellobiose units from the nonreducing end of the cellulose chain) specifically act on the agluconic bond (between p-nitrophenyl and the disaccharide moiety) and not on the holosidic bond (between the two glucose units of cellobiose). The interfering effect of beta-glucosidase, which acts on both agluconic and holosidic bonds, is overcome by the addition of D-glucono-1,5-delta-lactone, a specific inhibitor of beta-glucosidases. The interference of endoglucanases , which also act on both agluconic and holosidic bonds, can be compensated for by prior standardization of the assay procedure with a purified endoglucanase from the studied mixture of cellulases.     

Identification of N5-methyl-N5-formyl-2,5,6-triamino-4-hydroxypyrimidine as a major adduct in rat liver DNA after treatment with the carcinogens, N,N-dimethylnitrosamine or 1,2-dimethylhydrazine

Human Tamm-Horsfall urinary glycoprotein from an individual of the blood group Sd(a+) phenotype was tritium-labelled by treatment with galactose oxidase and sodium boro[3H]hydride and was then digested with endo-beta-galactosidase. A series of dialysable, labelled fragments was released from which a pentasaccharide was isolated that strongly inhibited the agglutination of Sd(a+) red cells by human anti-Sda serum and hence contained the Sda determinant structure. Reduction, methylation analysis and sequential exo-glycosidase digestion established the structure of the pentasaccharide as: GalNAc beta(1 leads to 4)[NeuAc(2 leads to 3)]Gal beta(1 leads to 4)GlcNAc beta(1 leads to 3)Gal     

An Arabidopsis lipid map reveals differences between tissues and dynamic changes throughout development

Mass spectrometry is the predominant analytical tool used in the field of plant lipidomics. However, there are many challenges associated with the mass spectrometric detection and identification of lipids because of the highly complex nature of plant lipids. Studies into lipid biosynthetic pathways, gene functions in lipid metabolism, lipid changes during plant growth and development, and the holistic examination of the role of plant lipids in environmental stress responses are often hindered. Here, we leveraged a robust pipeline that we previously established to extract and analyze lipid profiles of different tissues and developmental stages from the model plant Arabidopsis thaliana. We analyzed seven tissues at several different developmental stages and identified more than 200 lipids from each tissue analyzed. The data were used to create a web-accessible in silico lipid map that has been integrated into an electronic Fluorescent Pictograph (eFP) browser. This in silico library of Arabidopsis lipids allows the visualization and exploration of the distribution and changes of lipid levels across selected developmental stages. Furthermore, it provides information on the characteristic fragments of lipids and adducts observed in the mass spectrometer and their retention times, which can be used for lipid identification. The Arabidopsis tissue lipid map can be accessed at http://bar.utoronto.ca/efp_arabidopsis_lipid/cgi-bin/efpWeb.cgi .     

Alpine vascular plant species richness: the importance of daily maximum temperature and pH

Species richness in the alpine zone varies dramatically when communities are compared. We explored (i) which stress and disturbance factors were highly correlated with species richness, (ii) whether the intermediate stress hypothesis (ISH) and the intermediate disturbance hypothesis (IDH) can be applied to alpine ecosystems, and (iii) whether standing crop can be used as an easily measurable surrogate for causal factors determining species richness in the alpine zone. Species numbers and standing crop were determined in 14 alpine plant communities in the Swiss Alps. To quantify the stress and disturbance factors in each community, air temperature, relative air humidity, wind speed, global radiation, UV-B radiation, length of the growing season, soil suction, pH, main soil nutrients, waterlogging, soil movement, number of avalanches, level of denudation, winter dieback, herbivory, wind damage, and days with frost were measured or observed. The present study revealed that 82% of the variance in␣vascular species richness among sites could be explained by just two abiotic factors, daily maximum temperature and soil pH. Daily maximum temperature and pH affect species richness both directly and via their effects on other environmental variables. Some stress and disturbance factors were related to species richness in a monotonic way, others in an unimodal way. Monotonic relationships suggest that the harsher the environment is, the fewer species can survive in such habitats. In cases of unimodal relationships (ISH and IDH) species richness decreases at both ends of the gradients due to the harsh environment and/or the interaction of other environmental factors. Competition and disturbance seemed only to play a secondary role in the form of fine-tuning species richness in specific communities. Thus, we concluded that neither the ISH nor the IDH can be considered useful conceptual models for the alpine zone. Furthermore, we found that standing crop can be used as an easily measurable surrogate for causal factors determining species richness in the alpine zone, even though there is no direct causality.     

An overview of mechanistic modeling of liquid chromatography

Abstract

Liquid chromatography is considered to be the bottleneck for purification of therapeutic proteins. Development and optimization of chromatography process is a cumbersome activity due to the increasing complexities in the types and content of impurities present in the high product titer cell culture harvest obtained from the upstream processing. Further, regulatory expectations are continuously rising with the recent initiatives of quality by design and process analytical technology expecting the manufacturer to have a deeper understanding of the process and the product. Mechanistic modeling is one approach to gain this deeper understanding of a process step. It involves modeling of the underlying physicochemical processes. A well calibrated model with acceptable predictability can be very effective in both process optimization and process characterization activities. In this paper we provide an overview of mechanistic modeling of liquid chromatography. We discuss the various components that such a model entails and also presents the status quo of this area.