Neshat, S. A. | bioRxiv Science

bioRxiv2026

Microbial community-based single-cell protein supports partial fishmeal replacement in juvenile Asian seabass diets across feeding trials and production scales

The growing demand for sustainable aquafeeds has intensified interest in alternative protein ingredients capable of reducing reliance on fishmeal without compromising fish performance. Here, we evaluated microbial community-based single-cell protein (SCP) as a fishmeal substitute in juvenile Asian seabass (Lates calcarifer) diets in two independent feeding trials of juvenile fish conducted over 49 and 56 days, respectively and compared them to a previous study that lasted 24 days. SCP was produced from nutrient-rich soybean-processing side streams by microbial communities in fermenters and incorporated into experimental diets at inclusion levels ranging from 10% to 100% fishmeal replacement. In the 24-day trial, a diet containing 50% fishmeal replacement with lab-scale produced SCP achieved 100% survival and a feed conversion ratio (FCR), specific growth rate (SGR), and weight gain comparable to the fishmeal control diet. In the 49-day trial using pilot-scale produced SCP, a 50% fishmeal replacement also maintained an FCR and feed intake comparable to the control, whereas complete replacement reduced feed intake and growth performance. In a 56-day pilot-scale trial that used 500-L fish tanks, diets containing up to 50% fishmeal replacement maintained comparable survival, weight gain, and SGR, although moderately higher FCR values were observed at higher SCP inclusion levels. Proximate composition and essential amino acid profiles of fish fed control or SCP-containing diets were comparable. Genome-resolved metagenomic analyses revealed diverse microbial taxa associated with the SCP. Collectively, these findings support microbial community-based SCP as a scalable and reproducible alternative protein platform for aquaculture feeds across independent trials and production scales.

microbiology↗

bioRxiv2025

Predicting microbial community responses to disturbance using genome-resolved trait-based life-history strategies

Understanding how microbial communities respond to disturbance remains a fundamental question in ecology, with broad implications for biodiversity, ecosystem function, and biotechnology. Trait-based approaches offer general rules to predict community responses by linking ecological strategies to measurable traits. While life-history strategy frameworks such as the competitor-ruderal-stress-tolerant (CSR) model are well established in plant and animal ecology, their application to microbial communities has been limited. Here, we experimentally tested how microbial communities shift across a gradient of disturbance frequency in replicated bioreactors treating synthetic wastewater. We applied six conditions by doubling the organic loading rate at different frequencies, from undisturbed to press disturbance, and monitored changes over 42 days using genome-resolved metagenomics, 16S rRNA gene sequencing, biomass quantification, and effluent chemistry. By integrating ordination, network analysis, and machine learning, we identified emergent community-level life-history strategies that aligned with increasing disturbance. These strategies were reflected in functional trade-offs, shifts in community composition, and genomic trait distributions. A simulation-based approach was used to generate a CSR classification of metagenome-assembled genomes, which was consistent with patterns observed in other microbial ecosystems. Our results demonstrate that life-history frameworks can capture predictable dynamics in microbial communities across disturbance regimes. This strategy provides a unifying tool for linking microbial structure, function, and traits across scales, helping to reconcile ecological theory with microbial resource management. More broadly, our findings support the integration of classical ecological theory with microbial genomics to uncover the trait-based principles that govern microbiome function and stability in both natural and engineered ecosystems.

ecology↗