bioRxivGenomicsPreprint
Carbohydrate Hydrolytic Activity, Antibiotic Resistance and Stress Tolerance of Lacticaseibacillus paracasei BCRC-16100 and Lacticaseibacillus paracasei ZFM54 for Probiotics Using Genomic and Biochemical Approaches
Peter James Icalia GanniD, Jimmbeth Zenila P. FabiaiD, Althea Gay B. PagurayaniD, Ma. Joy Theresa AgcaoiliiD, Ryan James J. PascualiD, Suerte M. BarandaiD, Arc Josam J. RachoiD, Marvielyn P. OlivariD, Jayson F. CariagaiD, Alvin DomingoiD, Dionisio S. BucaoiD, Shirley C. AgrupisiD
Abstract
Probiotics are microorganisms infused in products for health benefits including acceleration of nutrient digestion, however, it is also important to ensure the safety prior to incorporation. Here, we present evidence of the ability of two probiotic isolates, Lacticaseibacillus paracasei BCRC-16100 and Lacticaseibacillus paracasei ZFM54, in the (1) enhancement of carbohydrate digestion, (2) tolerance to stress, and (3) antibiotic resistance. Approaches include whole genome sequence (WGS) analysis and bioactivity assays. WGS revealed genes suggesting the ability of the two isolates to promote carbohydrate digestion, tolerance to stress and antibiotic resistance. Carbohydrate digestive ability was confirmed through a biochemical assay where the two isolates cause glucose release from starch. The two isolates also showed versatility in a range of temperature and alcohol concentration. For antibiotic resistance particularly on vancomycin, there are three mechanisms namely transporter control, transcriptional regulation, and efflux pump. Furthermore, promoter and transposable element analysis showed that some of the active antibiotic resistant (AbR) genes can be laterally transferred. Altogether, we show the potential of two probiotic isolates to be incorporated in products for improved carbohydrate digestion and the need to address the removal of active and mobile antibiotic resistance genes that may compromise safety.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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