Does the Embedding Chemistry Interact with Tissue?

Resins and resin compounds interact with tissue in two ways, physico-chemically and by chemical reaction. The physico-chemical influences affect both the structure and biological activity of the tissue causing proteins to change shape or phase equilibria to be disrupted by a change in solvent or by the growth of polymer networks within existing biological polymeric structures. Chemical reactivity between tissue and resin components also reduces biological activity by changing either the structure of polymeric matter already present, e.g., crosslinking proteins, grafting hydrophobic resins onto hydrophilic protein backbones, or by modifying the hydrophobicity of specific sites, e.g., acylation of amino groups by hydrophobic anhydrides. The consequences of all these interactions can only be a fall in activity of the tissue leading to longer reaction times and the need for amplification of specific group reaction. Low crosslink densities, moderate temperatures, and partial rather than total removal of water are possible ways of reducing the effects of resin tissue interaction

Hsp90 orchestrates transcriptional regulation by Hsf1 and cell wall remodelling by MAPK signalling during thermal adaptation in a pathogenic yeast

Acknowledgments We thank Rebecca Shapiro for creating CaLC1819, CaLC1855 and CaLC1875, Gillian Milne for help with EM, Aaron Mitchell for generously providing the transposon insertion mutant library, Jesus Pla for generously providing the hog1 hst7 mutant, and Cathy Collins for technical assistance.Peer reviewedPublisher PD

Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture

"This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the Journal of Plant Nutrition 2015 March, available online at: http://www.tandfonline.com/10.1080/01904167.2014.934474."Cape gooseberry (Physalis peruviana L.) is a solanaceous plant. The growth and time-course of nutrient accumulation of the plant and its partitioning between roots, stems, leaves, and fruits were examined. The study was conducted analyzing two nutrient solutions in soilless culture under greenhouse conditions during two consecutive seasons. The macronutrient contents were analyzed. On average, the yield was 8.9 t.ha(-1). Growth of the plant until 90 d after transplanting obeys an exponential function of time and the relative growth rate for this period was determined. Nitrogen (N) was the element that showed the highest concentration, corresponding to leaves (4.67%), followed by potassium (K) in stems (4.46%). The highest accumulations of N, phosphorous (P), calcium (Ca), and magnesium (Mg) were found in leaves and of K in the stems. Potassium showed the highest nutrient accumulation (29 g.plant(-1)) and the highest specific uptake rate.Torres Rubio, JF.; Pascual Seva, N.; San Bautista Primo, A.; Pascual España, B.; López Galarza, SV.; Alagarda Pardo, J.; Maroto Borrego, JV. (2015). Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture. Journal of Plant Nutrition. 38(4):485-496. doi:10.1080/01904167.2014.934474S485496384Bellaloui, N., & Brown, P. H. (1998). Plant and Soil, 198(2), 153-158. doi:10.1023/a:1004343031242Bennett, J. P., Oshima, R. J., & Lippert, L. F. (1979). Effects of ozone on injury and dry matter partitioning in pepper plants. Environmental and Experimental Botany, 19(1), 33-39. doi:10.1016/0098-8472(79)90022-4CAUSTON, D. R. (1991). Plant Growth Analysis: The Variability of Relative Growth Rate Within a Sample. Annals of Botany, 67(2), 137-144. doi:10.1093/oxfordjournals.aob.a088112Convenio MAG-IICA (Ministerio de Agricultura y Ganadería. Institución Interamericana de Cooperación para la Agricultura). 2001. The cape gooseberry (Physalis peruvianaL.Physalis edulis). Subprograma de Cooperación Técnica, Ecuador. Available at: http://www.sica.gov.ec/agronegocios/Biblioteca/Convenio%20MAG%20IICA/productos/uvilla_mag.pdf (Accessed July 2007, in Spanish).El-Tohamy, W. A., El-Abagy, H. M., Abou-Hussein, S. D., & Gruda, N. (2009). Response of Cape gooseberry (Physalis peruviana L.) to nitrogen application under sandy soil conditions. Gesunde Pflanzen, 61(3-4), 123-127. doi:10.1007/s10343-009-0211-0Fresquet, J., Pascual, B., López-Galarza, S., Bautista, S., Baixauli, C., Gisbert, J. M., & Maroto, J. V. (2001). Nutrient uptake of pepino plants in soilless cultivation. The Journal of Horticultural Science and Biotechnology, 76(3), 338-343. doi:10.1080/14620316.2001.11511373Heuvelink, E., Bakker, M. J., Elings, A., Kaarsemaker, R. C., & Marcelis, L. F. M. (2005). EFFECT OF LEAF AREA ON TOMATO YIELD. Acta Horticulturae, (691), 43-50. doi:10.17660/actahortic.2005.691.2Leskovar, D. I., & Cantliffe, D. J. (1993). Comparison of Plant Establishment Method, Transplant, or Direct Seeding on Growth and Yield of Bell Pepper. Journal of the American Society for Horticultural Science, 118(1), 17-22. doi:10.21273/jashs.118.1.17Marcelis, L. F. M. (1993). Fruit growth and biomass allocation to the fruits in cucumber. 1. Effect of fruit load and temperature. Scientia Horticulturae, 54(2), 107-121. doi:10.1016/0304-4238(93)90059-yPuente, L. A., Pinto-Muñoz, C. A., Castro, E. S., & Cortés, M. (2011). Physalis peruviana Linnaeus, the multiple properties of a highly functional fruit: A review. Food Research International, 44(7), 1733-1740. doi:10.1016/j.foodres.2010.09.034Radford, P. J. (1967). Growth Analysis Formulae - Their Use and Abuse1. Crop Science, 7(3), 171. doi:10.2135/cropsci1967.0011183x000700030001xRamadan, M. F., & Moersel, J. T. (2007). Impact of enzymatic treatment on chemical composition, physicochemical properties and radical scavenging activity of goldenberry (Physalis peruviana L.) juice. Journal of the Science of Food and Agriculture, 87(3), 452-460. doi:10.1002/jsfa.2728Ramadan, M. F., & Moersel, J.-T. (2009). Oil extractability from enzymatically treated goldenberry (Physalis peruvianaL.) pomace: range of operational variables. International Journal of Food Science & Technology, 44(3), 435-444. doi:10.1111/j.1365-2621.2006.01511.xSalazar, M. R., Jones, J. W., Chaves, B., & Cooman, A. (2008). A model for the potential production and dry matter distribution of Cape gooseberry (Physalis peruviana L.). Scientia Horticulturae, 115(2), 142-148. doi:10.1016/j.scienta.2007.08.015Scholberg, J., McNeal, B. L., Jones, J. W., Boote, K. J., Stanley, C. D., & Obreza, T. A. (2000). Growth and Canopy Characteristics of Field-Grown Tomato. Agronomy Journal, 92(1), 152. doi:10.2134/agronj2000.921152xTrinchero, G. D., Sozzi, G. O., Cerri, A. M., Vilella, F., & Fraschina, A. A. (1999). Ripening-related changes in ethylene production, respiration rate and cell-wall enzyme activity in goldenberry (Physalis peruviana L.), a solanaceous species. Postharvest Biology and Technology, 16(2), 139-145. doi:10.1016/s0925-5214(99)00011-3Turner, A. (1994). Dry Matter Assimilation and Partitioning in Pepper Cultivars Differing in Susceptibility to Stress-induced Bud and Flower Abscission. Annals of Botany, 73(6), 617-622. doi:10.1006/anbo.1994.1077WILLIAMS, R. F. (1946). The Physiology of Plant Growth with Special Reference to the Concept of Net Assimilation Rate. Annals of Botany, 10(1), 41-72. doi:10.1093/oxfordjournals.aob.a083119Zapata, J.L., A. Saldarriaga, M. Londoño, and C. Díaz. 2002. Cape gooseberry Management in Colombia. Antioquia, Colombia: Rionegro, Programa Nacional de Transferencia de Tecnología Agropecuaria - Corpoica Regional Cuatro (in Spanish).Zerihun, A. (2000). Compensatory Roles of Nitrogen Uptake and Photosynthetic N-use Efficiency in Determining Plant Growth Response to Elevated CO2: Evaluation Using a Functional Balance Model. Annals of Botany, 86(4), 723-730. doi:10.1006/anbo.2000.123

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB

BACKGROUND: Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. RESULTS: We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion. CONCLUSION: MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML

Mining for genotype-phenotype relations in Saccharomyces using partial least squares

<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p

A Non-Death Role of the Yeast Metacaspase: Yca1p Alters Cell Cycle Dynamics

Caspase proteases are a conserved protein family predominantly known for engaging and executing apoptotic cell death. Nevertheless, in higher eukaryotes, caspases also influence a variety of cell behaviors including differentiation, proliferation and growth control. S. cerevisiae expresses a primordial caspase, yca1, and exhibits apoptosis-like death under certain stresses; however, the benefit of a dedicated death program to single cell organisms is controversial. In the absence of a clear rationale to justify the evolutionary retention of a death only pathway, we hypothesize that yca1 also influences non-apoptotic events. We report that genetic ablation and/or catalytic inactivation of Yca1p leads to a longer G1/S transition accompanied by slower growth in fermentation conditions. Downregulation of Yca1p proteolytic activity also results in failure to arrest during nocodazole treatment, indicating that Yca1p participates in the G2/M mitotic checkpoint. 20s proteasome activity and ROS staining of the Δyca1 strain is indistinguishable from its isogenic control suggesting that putative regulation of the oxidative stress response by Yca1p does not instigate the cell cycle phenotype. Our results demonstrate multiple non-death roles for yca1 in the cell cycle

The Euchromatic and Heterochromatic Landscapes Are Shaped by Antagonizing Effects of Transcription on H2A.Z Deposition

A role for variant histone H2A.Z in gene expression is now well established but little is known about the mechanisms by which it operates. Using a combination of ChIP–chip, knockdown and expression profiling experiments, we show that upon gene induction, human H2A.Z associates with gene promoters and helps in recruiting the transcriptional machinery. Surprisingly, we also found that H2A.Z is randomly incorporated in the genome at low levels and that active transcription antagonizes this incorporation in transcribed regions. After cessation of transcription, random H2A.Z quickly reappears on genes, demonstrating that this incorporation utilizes an active mechanism. Within facultative heterochromatin, we observe a hyper accumulation of the variant histone, which might be due to the lack of transcription in these regions. These results show how chromatin structure and transcription can antagonize each other, therefore shaping chromatin and controlling gene expression

Heat Shock Response in Yeast Involves Changes in Both Transcription Rates and mRNA Stabilities

We have analyzed the heat stress response in the yeast Saccharomyces cerevisiae by determining mRNA levels and transcription rates for the whole transcriptome after a shift from 25°C to 37°C. Using an established mathematical algorithm, theoretical mRNA decay rates have also been calculated from the experimental data. We have verified the mathematical predictions for selected genes by determining their mRNA decay rates at different times during heat stress response using the regulatable tetO promoter. This study indicates that the yeast response to heat shock is not only due to changes in transcription rates, but also to changes in the mRNA stabilities. mRNA stability is affected in 62% of the yeast genes and it is particularly important in shaping the mRNA profile of the genes belonging to the environmental stress response. In most cases, changes in transcription rates and mRNA stabilities are homodirectional for both parameters, although some interesting cases of antagonist behavior are found. The statistical analysis of gene targets and sequence motifs within the clusters of genes with similar behaviors shows that both transcriptional and post-transcriptional regulons apparently contribute to the general heat stress response by means of transcriptional factors and RNA binding proteins

The impact of oxygen on the transcriptome of recombinant S. cerevisiae and P. pastoris - a comparative analysis

Background: Saccharomyces cerevisiae and Pichia pastoris are two of the most relevant microbial eukaryotic platforms for the production of recombinant proteins. Their known genome sequences enabled several transcriptomic profiling studies under many different environmental conditions, thus mimicking not only perturbations and adaptations which occur in their natural surroundings, but also in industrial processes. Notably, the majority of such transcriptome analyses were performed using non-engineered strains. In this comparative study, the gene expression profiles of S. cerevisiae and P. pastoris, a Crabtree positive and Crabtree negative yeast, respectively, were analyzed for three different oxygenation conditions (normoxic, oxygen-limited and hypoxic) under recombinant protein producing conditions in chemostat cultivations. Results: The major differences in the transcriptomes of S. cerevisiae and P. pastoris were observed between hypoxic and normoxic conditions, where the availability of oxygen strongly affected ergosterol biosynthesis, central carbon metabolism and stress responses, particularly the unfolded protein response. Steady state conditions under low oxygen set-points seemed to perturb the transcriptome of S. cerevisiae to a much lesser extent than the one of P. pastoris, reflecting the major tolerance of the baker's yeast towards oxygen limitation, and a higher fermentative capacity. Further important differences were related to Fab production, which was not significantly affected by oxygen availability in S. cerevisiae, while a clear productivity increase had been previously reported for hypoxically grown P. pastoris. Conclusions: The effect of three different levels of oxygen availability on the physiology of P. pastoris and S. cerevisiae revealed a very distinct remodelling of the transcriptional program, leading to novel insights into the different adaptive responses of Crabtree negative and positive yeasts to oxygen availability. Moreover, the application of such comparative genomic studies to recombinant hosts grown in different environments might lead to the identification of key factors for efficient protein production

Systems Biology of the qa Gene Cluster in Neurospora crassa

An ensemble of genetic networks that describe how the model fungal system, Neurospora crassa, utilizes quinic acid (QA) as a sole carbon source has been identified previously. A genetic network for QA metabolism involves the genes, qa-1F and qa-1S, that encode a transcriptional activator and repressor, respectively and structural genes, qa-2, qa-3, qa-4, qa-x, and qa-y. By a series of 4 separate and independent, model-guided, microarray experiments a total of 50 genes are identified as QA-responsive and hypothesized to be under QA-1F control and/or the control of a second QA-responsive transcription factor (NCU03643) both in the fungal binuclear Zn(II)2Cys6 cluster family. QA-1F regulation is not sufficient to explain the quantitative variation in expression profiles of the 50 QA-responsive genes. QA-responsive genes include genes with products in 8 mutually connected metabolic pathways with 7 of them one step removed from the tricarboxylic (TCA) Cycle and with 7 of them one step removed from glycolysis: (1) starch and sucrose metabolism; (2) glycolysis/glucanogenesis; (3) TCA Cycle; (4) butanoate metabolism; (5) pyruvate metabolism; (6) aromatic amino acid and QA metabolism; (7) valine, leucine, and isoleucine degradation; and (8) transport of sugars and amino acids. Gene products both in aromatic amino acid and QA metabolism and transport show an immediate response to shift to QA, while genes with products in the remaining 7 metabolic modules generally show a delayed response to shift to QA. The additional QA-responsive cutinase transcription factor-1β (NCU03643) is found to have a delayed response to shift to QA. The series of microarray experiments are used to expand the previously identified genetic network describing the qa gene cluster to include all 50 QA-responsive genes including the second transcription factor (NCU03643). These studies illustrate new methodologies from systems biology to guide model-driven discoveries about a core metabolic network involving carbon and amino acid metabolism in N. crassa