Location: Commodity Utilization ResearchTitle: Bioinformatics for sugar industry: metabolic potentials of microorganisms in sugarcane mill mud
|VILANOVA, BRAYAN - Cornell University
|DERITO, CHRISTOPHER - Cornell University
|HAY, ANTHONY - Cornell University
Submitted to: International Sugar Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/4/2023
Publication Date: 2/2/2023
Citation: Uchimiya, S.M., Vilanova, B., Derito, C., Hay, A.G. 2023. Bioinformatics for sugar industry: metabolic potentials of microorganisms in sugarcane mill mud. International Sugar Journal. 125 (1490):104-111.
Interpretive Summary: Marker gene (16S rRNA) sequencing is commonly used to understand microbial community in agricultural byproducts. It is much more challenging to understand the function of microbial community: what can microbes do in soil and other environments? This study employed bioinformatics to study the potential functions of micoorrganisms in byproducts from raw sugar production: sugarcane mill mud and bagasse. When compared to composts made from other types of waste materials, sugarcane mill mud had diverse functions to serve as organic fertilizer and biostimulants.
Technical Abstract: Sugarcane mill/press mud is a nutrient-dense source of organic carbon, nitrogen, and phosphorus that is pasteurized during sugar processing. Those characteristics fit the desirable product specifications for biofertilizers and biostimulants. This study reports on predicted metabolic potential of microorganisms in sugarcane mill mud and bagasse as compared with those of laboratory and industrial composts. Metabolic functions were inferred from marker gene sequence (16S rRNA gene) data to predict potential gene content of uncultured microbial community using the software PICRUSt. On average, sugar processing byproducts (mill mud and bagasse) had higher alpha diversity and higher predicted functional diversity than the two groups of compost used for comparison. When focused specifically on carbohydrate metabolism that will lead to aging/decomposition of organic fertilizer applied to soil, sugar processing byproducts had more gene functions than composts. Collectively, in silico metagenome offers a lower cost approach to predict potential metabolic functions of biofertilizer originating from raw sugar production, based on a commonly used 16S sequencing method.