<p>Soybean molasses is a by-product of soybean processing that contains major carbohydrates such as raffinose, stachyose, and sucrose, which can be utilized through microbial fermentation. Among various species of Lactic Acid Bacteria screened from the Dairy Industrial Culture Collection (DICC) at Northeast Agricultural University, <i>Lacticaseibacillus paracasei</i> showed superior growth and acidification performance, demonstrating higher efficiency and stronger capability in carbohydrate utilization. In this study, we evaluated the capacity of <i>L. paracasei</i> to metabolize the major carbohydrates in soybean molasses through phenotypic characterization and genomic analysis, aiming to elucidate the underlying mechanisms. Two strains, <i>L. paracasei</i> KLDS 74 and KLDS 82, which exhibited significant differences in growth and carbohydrate consumption, were selected for whole-genome sequencing. Functional annotation of genes was performed using multiple bioinformatics databases. We focused particularly on α-galactosidase, a key enzyme involved in the hydrolysis of raffinose-family oligosaccharides in soybean molasses. Both strains were found to produce α-galactosidase, but differences were observed in the number of genes encoding the enzyme and its enzymatic activity—the maximum activity in <i>L. paracasei</i> KLDS 74 was approximately twice that of KLDS 82. Further analysis via real-time PCR confirmed that variations in the expression of key α-galactosidase genes contribute significantly to the differential metabolic performance between the two strains. This study highlights the intraspecies diversity in soybean molasses metabolism within <i>L. paracasei</i> and reveals the genetic and enzymatic basis for these differences, providing a foundation for the strain-specific application in fermentation processes and suggesting directions for future optimization.</p> Graphical Abstract <p></p>

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Comparative Genomics of Lacticaseibacillus paracasei Reveal the Metabolism of Major Carbohydrates in Soybean Molasses

  • Liping Wu,
  • Linfang Zhang,
  • Sixuan He,
  • Yuanyuan Liu,
  • Shan Wu,
  • Mei Zhang,
  • Guofang Zhang,
  • Yun Zhang,
  • Ekaterina Ivanovna Reshetnik,
  • Svetlana Leonidovna Gribanova,
  • Libo Liu,
  • Song Wang

摘要

Soybean molasses is a by-product of soybean processing that contains major carbohydrates such as raffinose, stachyose, and sucrose, which can be utilized through microbial fermentation. Among various species of Lactic Acid Bacteria screened from the Dairy Industrial Culture Collection (DICC) at Northeast Agricultural University, Lacticaseibacillus paracasei showed superior growth and acidification performance, demonstrating higher efficiency and stronger capability in carbohydrate utilization. In this study, we evaluated the capacity of L. paracasei to metabolize the major carbohydrates in soybean molasses through phenotypic characterization and genomic analysis, aiming to elucidate the underlying mechanisms. Two strains, L. paracasei KLDS 74 and KLDS 82, which exhibited significant differences in growth and carbohydrate consumption, were selected for whole-genome sequencing. Functional annotation of genes was performed using multiple bioinformatics databases. We focused particularly on α-galactosidase, a key enzyme involved in the hydrolysis of raffinose-family oligosaccharides in soybean molasses. Both strains were found to produce α-galactosidase, but differences were observed in the number of genes encoding the enzyme and its enzymatic activity—the maximum activity in L. paracasei KLDS 74 was approximately twice that of KLDS 82. Further analysis via real-time PCR confirmed that variations in the expression of key α-galactosidase genes contribute significantly to the differential metabolic performance between the two strains. This study highlights the intraspecies diversity in soybean molasses metabolism within L. paracasei and reveals the genetic and enzymatic basis for these differences, providing a foundation for the strain-specific application in fermentation processes and suggesting directions for future optimization.

Graphical Abstract