Morphological, transcriptional, and metabolic analyses of osmotic-adapted mechanisms of the halophilic Aspergillus montevidensis ZYD4 under hypersaline conditions.

Halophilic fungi in hypersaline habitats require multiple cellular responses for high-salinity adaptation. However, the exact mechanisms behind these adaptation processes remain to be slightly known. The current study is aimed at elucidating the morphological, transcriptomic, and metabolomic changes of the halophilic fungus Aspergillus montevidensis ZYD4 under hypersaline conditions. Under these conditions, the fungus promoted conidia formation and suppressed cleistothecium development. Furthermore, the fungus differentially expressed genes (P < 0.0001) that controlled ion transport, amino acid transport and metabolism, soluble sugar accumulation, fatty acid ß-oxidation, saturated fatty acid synthesis, electron transfer, and oxidative stress tolerance. Additionally, the hypersalinized mycelia widely accumulated metabolites, including amino acids, soluble sugars, saturated fatty acids, and other carbon- and nitrogen-containing compounds. The addition of metabolites-such as neohesperidin, biuret, aspartic acid, alanine, proline, and ornithine-significantly promoted the growth (P ≤ 0.05) and the morphological adaptations of A. montevidensis ZYD4 grown in hypersaline environments. Our study demonstrated that morphological shifts, ion equilibrium, carbon and nitrogen metabolism for solute accumulation, and energy production are vital to halophilic fungi so that they can build tolerance to high-salinity environments.

Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins.