Developmental features of cotton fibre middle lamellae in relation to cell adhesion and cell detachment in cultivars with distinct fibre qualities.

Background: Cotton fibre quality traits such as fibre length, strength, and degree of maturation are determined by genotype and environment during the sequential phases of cotton fibre development (cell elongation, transition to secondary cell wall construction and cellulose deposition). The cotton fibre middle lamella (CFML) is crucial for both cell adhesion and detachment processes occurring during fibre development. To explore the relationship between fibre quality and the pace at which cotton fibres develop, a structural and compositional analysis of the CFML was carried out in several cultivars with different fibre properties belonging to four commercial species: Gossypium hirsutum, G. barbadense, G. herbaceum and G. arboreum. Results: Cotton fibre cell adhesion, through the cotton fibre middle lamella (CFML), is a developmentally regulated process determined by genotype. The CFML is composed of de-esterified homogalacturonan, xyloglucan and arabinan in all four fibre-producing cotton species: G. hirsutum, G. barbadense, G. herbaceum and G. arboreum. Conspicuous paired cell wall bulges are a feature of the CFML of two G. hirsutum cultivars from the onset of fibre cell wall detachment to the start of secondary cell wall deposition. Xyloglucan is abundant in the cell wall bulges and in later stages pectic arabinan is absent from these regions. Conclusions: The CFML of cotton fibres is re-structured during the transition phase. Paired cell wall bulges, rich in xyloglucan, are significantly more evident in the G. hirsutum cultivars than in other cotton species

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

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods

Pepper pectin methylesterase inhibitor protein CaPMEI1 is required for antifungal activity, basal disease resistance and abiotic stress tolerance

Pectin is one of the main components of the plant cell wall that functions as the primary barrier against pathogens. Among the extracellular pectinolytic enzymes, pectin methylesterase (PME) demethylesterifies pectin, which is secreted into the cell wall in a highly methylesterified form. Here, we isolated and functionally characterized the pepper (Capsicum annuum L.) gene CaPMEI1, which encodes a pectin methylesterase inhibitor protein (PMEI), in pepper leaves infected by Xanthomonascampestris pv. vesicatoria (Xcv). CaPMEI1 transcripts are localized in the xylem of vascular bundles in leaf tissues, and pathogens and abiotic stresses can induce differential expression of this gene. Purified recombinant CaPMEI1 protein not only inhibits PME, but also exhibits antifungal activity against some plant pathogenic fungi. Virus-induced gene silencing of CaPMEI1 in pepper confers enhanced susceptibility to Xcv, accompanied by suppressed expression of some defense-related genes. Transgenic ArabidopsisCaPMEI1-overexpression lines exhibit enhanced resistance to Pseudomonas syringae pv. tomato, mannitol and methyl viologen, but not to the biotrophic pathogen Hyaloperonospora parasitica. Together, these results suggest that CaPMEI1, an antifungal protein, may be involved in basal disease resistance, as well as in drought and oxidative stress tolerance in plants

Comparison of printed glycan array, suspension array and ELISA in the detection of human anti-glycan antibodies

Anti-glycan antibodies represent a vast and yet insufficiently investigated subpopulation of naturally occurring and adaptive antibodies in humans. Recently, a variety of glycan-based microarrays emerged, allowing high-throughput profiling of a large repertoire of antibodies. As there are no direct approaches for comparison and evaluation of multi-glycan assays we compared three glycan-based immunoassays, namely printed glycan array (PGA), fluorescent microsphere-based suspension array (SA) and ELISA for their efficacy and selectivity in profiling anti-glycan antibodies in a cohort of 48 patients with and without ovarian cancer. The ABO blood group glycan antigens were selected as well recognized ligands for sensitivity and specificity assessments. As another ligand we selected P1, a member of the P blood group system recently identified by PGA as a potential ovarian cancer biomarker. All three glyco-immunoassays reflected the known ABO blood groups with high performance. In contrast, anti-P1 antibody binding profiles displayed much lower concordance. Whilst anti-P1 antibody levels between benign controls and ovarian cancer patients were significantly discriminated using PGA (p = 0.004), we got only similar results using SA (p = 0.03) but not for ELISA. Our findings demonstrate that whilst assays were largely positively correlated, each presents unique characteristic features and should be validated by an independent patient cohort rather than another array technique. The variety between methods presumably reflects the differences in glycan presentation and the antigen/antibody ratio, assay conditions and detection technique. This indicates that the glycan-antibody interaction of interest has to guide the assay selection

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system