Dataset Information

Aphid gel saliva: sheath structure, protein composition and secretory dependence on stylet-tip milieu.

ABSTRACT: In order to separate and analyze saliva types secreted during stylet propagation and feeding, aphids were fed on artificial diets. Gel saliva was deposited as chains of droplets onto Parafilm membranes covering the diets into which watery saliva was secreted. Saliva compounds collected from the diet fluid were separated by SDS-PAGE, while non-soluble gel saliva deposits were processed in a novel manner prior to protein separation by SDS-PAGE. Soluble (watery saliva) and non-soluble (gel saliva) protein fractions were significantly different. To test the effect of the stylet milieu on saliva secretion, aphids were fed on various diets. Hardening of gel saliva is strongly oxygen-dependent, probably owing to formation of sulfide bridges by oxidation of sulphydryl groups. Surface texture of gel saliva deposits is less pronounced under low-oxygen conditions and disappears in dithiothreitol containing diet. Using diets mimicking sieve-element sap and cell-wall fluid respectively showed that the soluble protein fraction was almost exclusively secreted in sieve elements while non-soluble fraction was preferentially secreted at cell wall conditions. This indicates that aphids are able to adapt salivary secretion in dependence of the stylet milieu.

SUBMITTER: Will T

PROVIDER: S-EPMC3462764 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Aphid gel saliva: sheath structure, protein composition and secretory dependence on stylet-tip milieu.

Will Torsten T Steckbauer Kathrin K Hardt Martin M van Bel Aart J E AJ

PloS one 20121002 10

In order to separate and analyze saliva types secreted during stylet propagation and feeding, aphids were fed on artificial diets. Gel saliva was deposited as chains of droplets onto Parafilm membranes covering the diets into which watery saliva was secreted. Saliva compounds collected from the diet fluid were separated by SDS-PAGE, while non-soluble gel saliva deposits were processed in a novel manner prior to protein separation by SDS-PAGE. Soluble (watery saliva) and non-soluble (gel saliva) ...[more]

PMID: 23056521

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Project description:BackgroundCompared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins.ResultsWe present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer.ConclusionsThe main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/. We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.

Dataset Information

Aphid gel saliva: sheath structure, protein composition and secretory dependence on stylet-tip milieu.

Publications

Aphid gel saliva: sheath structure, protein composition and secretory dependence on stylet-tip milieu.

Similar Datasets

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