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The bacterial strain of Microbulbifer sp. ALW1 has demonstrated visible ability of degrading the cell wall of Laminaria japonica, and biochemical characterization has been performed on some individual enzymes to elucidate its genetic basis. However, it still remains elusive how strain ALW1 successfully breaks down the major cell wall component alginate polysaccharide and colonizes on its marine host. In this study, a mass spectrometry-based quantitative analysis of the extracellular and intracellular proteomes was introduced to elucidate the alginate degradation pathway in ALW1 strain. Mass spectrometry and biochemical assays indicated that strain ALW1 could effectively degrade alginate polysaccharide into disaccharides and trisaccharides within 12 h. Proteome analysis identified 156 and 1,047 proteins exclusively localized in extracellular and intracellular compartments, respectively, with 1,086 protein identities of dual localization. Functional annotation of the identified proteins suggested the involvement of diverse catalytic enzymes and non-catalytic molecules for the cleavage and metabolism of alginate polysaccharide. A simplified pathway was constructed to demonstrate the extracellular digestion, active transport, and intracellular conversion of alginate polysaccharide and its fragmented oligosaccharides, casting a picture of genetic loci controlling alginate catabolism by ALW1 strain. This study aims to provide a guide for utilization and genetic manipulation of the bacterial strain ALW1 for efficient alginate oligosaccharides production by fermentation.

Nonlinear dimensionality reduction is a challenging problem encountered in a variety of high dimensional data analysis. Based on the different geometric intuitions of manifolds, maximum variance unfolding (MVU) and Laplacian eigenmaps are designed for detecting the different aspects of data set. In this paper, combining the ideas of MVU and Laplacian eigenmaps, we propose a new nonlinear dimensionality reduction method called distinguishing variance embedding (DVE), which unfolds the data manifold by maximizing the global variance subject to the proximity relation preservation constraint originated in Laplacian eigenmaps. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and rotating objects.