Intestinal microbial community well explain larval growth than feed types.

Black soldier fly larvae (BSFL) are considered a sustainable ingredient in livestock feed. However, addressing issues related to feed substrate and intestinal microbiota is essential to ensure optimal larval development. The aim of this study was to assess and elucidate the contribution of substrate nutrients and intestinal microbes to protein and fat synthesis in BSFL. The results showed that larvae that were fed high-quality feed (chicken feed) had high fat biomass, while larvae that were fed medium-quality feed (wheat bran) had high protein biomass. These results indicate that the original nutritional content of the feed cannot fully explain larval growth and nutrient utilization. However, the phenomenon could be explained by the functional metabolism of intestinal microbes. Chicken feed enhanced the fatty acid metabolism of middle intestine microorganisms in larvae within 0-7 days. This process facilitated larval fat synthesis. In contrast, wheat bran stimulated the amino acid metabolism in posterior intestine microorganisms in larvae within 4-7 days, leading to better protein synthesis. The findings of this study highlight the importance of the microbial functional potential in the intestine in regulating protein and lipid synthesis in BSFL, which is also influenced by the type of feed. In conclusion, our study suggests that both feed type and intestinal microbes play a crucial role in efficiently converting organic waste into high-quality insect protein and fat. Additionally, a mixed culture of chicken feed and wheat bran was found to be effective in promoting larval biomass while reducing feed costs. KEY POINTS: • Intestinal microbes explain BSFL growth better than feed substrates. • Chicken feed promotes fatty acid synthesis in the middle intestine • Wheat bran promotes amino acid synthesis in the posterior intestine.

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of specificity limits applications in which biosensors may be deployed, such as those where an unknown feed substrate must be determined. The application of genomic data and well-developed data mining technologies can overcome these limitations and advance engineering development. In the present study, 69 samples with three different substrate types (acetate, carbohydrates and wastewater) collected from various laboratory environments were evaluated to determine the ability to identify feed substrates from the resultant microbial communities. Six machine learning algorithms with four different input variables were trained and evaluated on their ability to predict feed substrate from genomic datasets. The highest accuracies of 93 ±â€¯6% and 92 ±â€¯5% were obtained using NNET trained on datasets classified at the phylum and family taxonomic level, respectively. These accuracies corresponded to kappa values of 0.87 ±â€¯0.10, 0.86 ±â€¯0.09, respectively. Four out of six of the algorithms used maintained accuracies above 80% and kappa values higher than 0.66. Different sequencing method (Roche 454 or Illumina sequencing) did not affect the accuracies of all algorithms, except SVM at the phylum level. All algorithms trained on NMDS-compressed datasets obtained accuracies over 80%, while models trained on PCoA-compressed datasets presented a 10-30% reduction in accuracy. These results suggest that incorporating microbial community data with machine learning algorithms can be used for the prediction of feed substrate and for the potential improvement of MFC-based biosensor signal specificity, providing a new use of machine learning techniques that has substantial practical applications in biotechnological fields.