Investigation of tension wood formation and 2,6-dichlorbenzonitrile application in short rotation coppice willow composition and enzymatic saccharification

Background Short rotation coppice willow is a potential lignocellulosic feedstock in the United Kingdom and elsewhere; however, research on optimising willow specifically for bioethanol production has started developing only recently. We have used the feedstock Salix viminalis × Salix schwerinii cultivar 'Olof' in a three-month pot experiment with the aim of modifying cell wall composition and structure within the stem to the benefit of bioethanol production. Trees were treated for 26 or 43 days with tension wood induction and/or with an application of the cellulose synthesis inhibitor 2,6-dichlorobenzonitrile that is specific to secondary cell walls. Reaction wood (tension and opposite wood) was isolated from material that had received the 43-day tension wood induction treatment. Results Glucan content, lignin content and enzymatically released glucose were assayed. All measured parameters were altered without loss of total stem biomass yield, indicating that enzymatic saccharification yield can be enhanced by both alterations to cell wall structure and alterations to absolute contents of either glucan or lignin. Conclusions Final glucose yields can be improved by the induction of tension wood without a detrimental impact on biomass yield. The increase in glucan accessibility to cell wall degrading enzymes could help contribute to reducing the energy and environmental impacts of the lignocellulosic bioethanol production process

Insights into nitrogen allocation and recycling from nitrogen elemental analysis and 15N isotope labelling in 14 genotypes of willow

Minimizing nitrogen (N) fertilization inputs during cultivation is essential for sustainable production of bioenergy and biofuels. The biomass crop willow (Salix spp.) is considered to have low N fertilizer requirements due to efficient recycling of nutrients during the perennial cycle. To investigate how successfully different willow genotypes assimilate and allocate N during growth, and remobilize and consequently recycle N before the onset of winter dormancy, N allocation and N remobilization (to and between different organs) were examined in 14 genotypes of a genetic family using elemental analysis and N-15 as a label. Cuttings were established in pots in April and sampled in June, August and at onset of senescence in October. Biomass yield of the trees correlated well with yields recorded in the field. Genotype-specific variation was observed for all traits measured and general trends spanning these sampling points were identified when trees were grouped by biomass yield. Nitrogen reserves in the cutting fuelled the entirety of the canopy establishment, yet earlier cessation of this dependency was linked to higher biomass yields. The stem was found to be the major N reserve by autumn, which constitutes a major source of N loss at harvest, typically every 2-3 years. These data contribute to understanding N remobilization in short rotation coppice willow and to the identification of traits that could potentially be selected for in breeding programmes to further improve the sustainability of biomass production

High biomass yield increases in a primary effluent wastewater phytofiltration are associated to altered leaf morphology and stomatal size in Salix miyabeana

Municipal wastewater treatment using willow ‘phyto’-filtration has the potential for reduced environmental impact compared to conventional treatment practices. However, the physiological adaptations underpinning tolerance to high wastewater irrigation in willow are unknown. A one-hectare phytofiltration plantation established using the Salix miyabeana cultivar ‘SX67’ in Saint-Roch-de-l'Achigan, Quebec, Canada, tested the impact of unirrigated, potable water or two loads of primary effluent wastewater 19 and 30 ML ha−1 yr−1. A nitrogen load of 817 kg N ha−1 from wastewater did not increase soil pore water nitrogen concentrations beyond Quebec drinking water standards. The willow phytofiltration phenotype had increased leaf area (+106–142%) and leaf nitrogen (+94%) which were accompanied by significant increases in chlorophyll a + b content. Wastewater irrigated trees had higher stomatal sizes and a higher stomatal pore index, despite lower stomatal density, resulting in increased stomatal conductance (+42–78%). These developmental responses led to substantial increases in biomass yields of 56–207% and potable water controls revealed the nitrogen load to be necessary for the high productivity of 28–40 t ha−1 yr−1 in wastewater irrigated trees. Collectively, this study suggests phytofiltration plantations could treat primary effluent municipal wastewater at volumes of at least 19 million litres per hectare and benefit from increased yields of sustainable biomass over a two-year coppice cycle. Added-value cultivation practices, such as phytofiltration, have the potential to mitigate negative local and global environmental impact of wastewater treatment while providing valuable services and sustainable bioproducts

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p

GAIA: a gram-based interaction analysis tool – an approach for identifying interacting domains in yeast

<p>Abstract</p> <p>Background</p> <p>Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.</p> <p>Results</p> <p>In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in <it>Saccharomyces cerevisiae </it>(budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.</p> <p>Conclusion</p> <p>GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells.</p

Immunolocalization of cell wall polymers in grapevine (Vitis vinifera) internodes under nitrogen, phosphorus or sulfur deficiency

Abstract The impact on cell wall (CW) of the deficiency in nitrogen (–N), phosphorus (–P) or sulphur (–S), known to impair essential metabolic pathways, was investigated in the economically important fruit species Vitis vinifera L. Using cuttings as an experimental model a reduction in total internode number and altered xylem shape was observed. Under –N an increased internode length was also seen. CW composition, visualised after staining with calcofluor white, Toluidine blue and ruthenium red, showed decreased cellulose in all stresses and increased pectin content in recently formed internodes under –N compared to the control. Using CW-epitope specific monoclonal antibodies (mAbs), lower amounts of extensins incorporated in the wall were also observed under –N and –P conditions. Conversely, increased pectins with a low degree of methyl-esterification and richer in long linear 1,5-arabinan rhamnogalacturonan-I (RG-I) side chains were observed under –N and –P in mature internodes which, in the former condition, were able to form dimeric association through calcium ions. –N was the only condition in which 1,5-arabinan branched RG- content was not altered, as –P and –S older internodes showed, respectively, lower and higher amounts of this polymer. Higher xyloglucan content in older internodes was also observed under –N. The results suggest that impairments of specific CW components led to changes in the deposition of other polymers to promote stiffening of the CW. The unchanged extensin amount observed under –S may contribute to attenuating the effects on the CW integrity caused by this stress. Our work showed that, in organized V. vinifera tissues, modifications in a given CW component can be compensated by synthesis of different polymers and/or alternative linking between polymers. The results also pinpoint different strategies at the CW level to overcome mineral stress depending on how essential they are to cell growth and plant development

Simplified Method to Predict Mutual Interactions of Human Transcription Factors Based on Their Primary Structure

Background: Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. Methodology: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39 % on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account

Pharmacokinetics of ibuprofen in man IV: Absorption and disposition

Fifteen normal male volunters received 400, 800, and 1200 mg doses of ibuprofen as 1, 2, or 3 tablets, respectively, in crossover fashion, then 420 mg in solution form during the fourth week. Plasma concentration of ibuprofen was measured by an HPLC method. Individual subject concentration-time (C,t) data following the solution were analyzed by two different methods, and results unequivocally indicated the open two compartment model with first order absorption. However, the computer fitting of both arithmetic and geometric mean concentrations led to a different model. A method was developed to obtain absorption data (fraction of drug absorbed , F a , versus time) for a multicompartmental system from oral data alone, without intravenous data. The method assumes that V p is constant intrasubject and that absorption is complete following administration of both the solution and tablets. The method was successfully applied to the ibuprofen tablet data. It was shown also that such a method is necessary to obtain ibuprofen absorption data since intrasubject variation of the microscopic rate constants k 12 , k a21 , and k el ( as reflected by the intrasubject variation of the hybrid rate parameters λ 1 and λ 2 or Β and a) is of the same order of magnitude as intersubject variation. Absorption of ibuprofen from tablets was shown not to be simple first order as for the solution. The absorption profiles following one tablet were S- shaped, while those following 2 or 3 tablets had partial linear segments indicating zero order absorption .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45032/1/10928_2005_Article_BF01062664.pd

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN