Year: 2026
Type: article
Abstract: Fiber-degrading microorganisms are widely recognized for their potential to convert renewable lignocellulosic biomass into animal feed. However, translating this potential into practical application faces five critical yet underappreciated challenges. First, current screening methods, primarily including plate dilution and Congo red staining, are low-throughput, poorly reproducible and fail to capture the synergistic actions of natural microbial consortia. Second, the lack of standardized assays for quantifying cellulolytic activity compromises the reliability of cross-study comparisons. Third, safety assessments for fiber-degrading microorganisms remain superficial, with most studies neglecting mycotoxin production, antibiotic resistance gene transfer and long-term colonization risks. Fourth, fundamental differences between fungal and bacterial degradative systems, such as enzyme multiplicity, oxygen requirements and cellulosome assembly, are rarely considered in strain selection, leading to suboptimal application outcomes. Finally, the vast majority of positive in vitro degradation results fail to translate into improved animal performance in vivo, owing to poor microbial survival in the gastrointestinal tract, mismatched enzyme activity with gut pH and temperature, coupled with the absence of dose-response validation. This review critically evaluates these five bottlenecks across fiber-degrading microorganism types, screening platforms and practical livestock production applications. Overall, future progress should depend less on discovering "novel" strains and more on establishing standardized screening pipelines, rigorous safety frameworks and mechanistic understanding of in vivo efficacy, including direct head-to-head comparisons between fungal enzymes and bacterial probiotics under identical conditions.
Source: Life
DOI: 10.3390/life16061014
Language: English
Subfield: Animal Science and Zoology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7164925815