Proteomic Characterization of the Clostridium cellulovorans Cellulosome and Noncellulosomal Enzymes with Sorghum Bagasse

利用高粱渣对纤维素梭菌纤维素酶体和非纤维素酶进行蛋白质组学表征

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Abstract

Sorghum, the fifth major global cereal, has potential as a source crop in temperate regions. To completely use sorghum bagasse, the ideal enzyme cocktail aims to identify and select the contributed enzymatic system. This study investigated the enzymatic system of Clostridium cellulovorans cellulosome and noncellulosomal enzymes using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and liquid chromatography-tandem mass spectrometry LC-MS/MS. Enzyme solutions from treated and untreated sorghum bagasse were prepared and compared based on carboxymethyl cellulase (CMCase) activity. As a result, the enzyme solution derived from untreated sorghum bagasse had the highest activity. Protein bands from each C. cellulovorans culture showed distinct patterns on SDS-PAGE examination: three enzyme fractions, including culture supernatants, crystalline cellulose (Avicel) bound, and unbound fractions. These results suggested that untreated sorghum bagasse induced a variety of cellulosomal and uncellulosomal proteins. On the other hand, 5% or 10% sorghum supernatants could not induce Avicel-bound proteins, including the cellulosome, although even 5% sorghum juice induced three major bands: 180 kilodalton (kDa), 100 kDa, and 70 kDa, respectively. In contrast, cellobiose induced three major bands, while the total number of all isolated proteins from the cellobiose medium was the most limited among all culture media. More intriguingly, our investigation detected one cellulosomal protein, hydrophobic protein A (HbpA) and three noncellulosomal enzymes, indicating that glycosyl hydrolase family 130 (GH130) was identified as a biomass-induced enzyme in good accord with previously published proteomic studies. Therefore, the proteomic dataset generated in this study provides us a foundation for future computational approaches, including machine learning-based prediction of optimal enzyme cocktails for target biomass degradation.

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