Processive endoglucanases, which possess both endo- and exoglucanase activities, are considered highly promising catalysts in cellulose degradation. In this study, we employed multiple deep learning models, including MutCompute, DeepSequence, and ESM-1v, to guide the engineering of EG5C-1, a processive endoglucanase derived from Bacillus subtilis BS-5. This enabled a systematic exploration of the enzyme’s sequence space. Through a combination of clustering analysis and a greedy algorithm, we optimized combinations of amino acid substitutions and ultimately identified an elite variant, M8 (R23Q/E43Q/K91I/K191P/A198T/Q237D/V240P/S245A), composed entirely of substituted residues. Compared to the wild-type enzyme, M8 exhibited 10-fold and 5-fold improvements in catalytic efficiency (kcat/Km) toward soluble substrate carboxymethyl cellulose-Na (CMC) and insoluble substrate phosphoric acid-swollen cellulose (PASC), respectively, along with enhanced optimal temperature and thermostability. Molecular mechanistic analyses revealed that all distal substituted residues enhanced dynamic coupling and coordination, primarily influencing the conformation of three loops near the substrate pocket. These structural changes modulated substrate binding and product release, thereby contributing to improved catalytic efficiency (kcat/Km). This work not only suggests a feasible strategy to explore the “dark space” within sequences but also provides insights into the practical application of machine learning in experiments.

Microorganism culturing is essential in microbiological research, with the selection of suitable culture media being critical for successful microbial growth. Traditionally, this selection has relied on empirical knowledge or trial and error, often resulting in inefficiency. In this study, we analysed nutrient compositions from the MediaDive database to construct a dataset of 2369 media types. Leveraging this dataset and microbial 16S rRNA sequences, we developed 45 binary classification models using the XGBoost algorithm. These models demonstrated strong predictive performance, achieving accuracies ranging from 76% to 99.3%, with the top-performing models for J386, J50 and J66 media reaching 99.3%, 98.9% and 98.8%, respectively. The models effectively predicted growth conditions for various human gut microbes, confirming their practical utility. This research improves the efficiency of microbial cultivation and highlights the potential of machine learning to optimise culture media selection and advance microbiological studies.

The genomic signatures of microbial domestication remain poorly understood within the context of natural population variation. Here, we demonstrate that Aspergillus oryzae, the filamentous fungus used in soy sauce production, shares more recent ancestry with a predominantly northern, largely non-aflatoxigenic population of Aspergillus flavus (population C). Strikingly, A. oryzae isolates also overlap with the recently described, clinically enriched A. flavus population D, suggesting the possibility of multiple domestication events. Although A. oryzae exhibits reduced virulence compared to A. flavus, all isolates tested retained pathogenicity in a zebrafish infection model. At the transcriptomic level, Aspergillus populations are significantly differentiated, with distinct responses to population density, indicating that population-specific transcriptomes adapt differently to ecological conditions. These differences extend beyond gene content and are not always explained by phylogenetic relationships, suggesting that phenotypic diversification occurs through the rapid reorganization of transcriptomic architectures. For example, A. oryzae displays significantly elevated expression of a module enriched for carbohydrate metabolism. Population-specific variation is also evident among secondary metabolite (SM) gene clusters. While A. oryzae shows markedly reduced expression of specific SM genes, particularly those involved in aflatoxin biosynthesis, this trend does not extend across the entire secondary metabolome. Using machine-learning-based gene regulatory network inference, we identified population-specific transcriptomic differences linked to distinct transcription factors, with evidence for both cis- and trans-acting regulatory divergence, but no changes in global regulators such as laeA. Together, these findings provide new insights into the domestication of A. oryzae, its global significance, and the microevolution of fungal secondary metabolic pathways.

As the ability to synthesize complete genomes becomes increasingly accessible, the question of what should compose those sequences is becoming more prevalent. However, identifying genes essential for the survival of an organism is challenging, as gene essentiality is a nuanced concept that heavily depends on context. In this study, we identified growth medium-specific important genes by performing transposon mutagenesis in Escherichia coli BW25113 and sequencing mutant populations at multiple time points in three growth media. Our analysis revealed a core set of 412 important genes shared across all conditions, along with distinct medium-specific gene sets of varying sizes. By analyzing temporal variations in read counts for each gene, we identified additional sets of genes whose inactivation causes a lighter impact on fitness. We used this dataset to define medium-specific gene modules required to sustain robust growth under each condition. Our study underscores the context-dependent nature of gene essentiality and marks a step toward refining the concept from a universal list to a more nuanced, condition-specific framework, which will be invaluable for future genome design efforts.

This study investigated temporal dynamics in reactor performance and microbial community structure during anaerobic digestion of sewage sludge when the temperature was changed from 37°C to 55°C, followed by an increase in organic loading rate (OLR). Performance instability was observed immediately following the temperature increase and in the end of the study when the OLR was 11.1 ± 0.3 kgVS m−3d−1. The specific methane production peaked at 0.31 ± 0.06 Nm3 kg−1 volatile solids (VS) during thermophilic operation and when the OLR was 3.5 ± 0.9 kgVS m−3d−1. Using metagenomic sequencing, 304 species-representative genome bins (SGB) were assembled. Network analysis revealed that 186 SGB were associated with thermophilic conditions and several new species putatively involved in key reactor functions were identified. When reactor function initially stabilised, two hydrogenotrophic and one aceticlastic methanogen (Methanothermobacter spp. and Methanosarcina thermophila), the hydrolytic Coprothermobacter proteolyticus, and putative syntrophic propionate oxidisers (e.g., Pelotomaculaceae) had high relative abundance. During the peak in specific gas production, the community was dominated by one hydrogenotrophic Methanothermobacter species coexisting with syntrophic acetate oxidizing bacteria (Thermacetogenium phaeum and other species). Finally, when the reaction function deteriorated due to high OLR, new hydrolytic taxa emerged and the same aceticlastic methanogen as seen during the initial acclimatisation phase returned.

Honey-feeding social bees, including honey bees, bumble bees, and stingless bees, possess distinctive gut bacterial communities that provide benefits to hosts, such as defense against pathogens and parasites. Members of these communities are transmitted through social interactions within colonies. The Mexican honey wasp (Brachygastra mellifica) represents an independent origin of honey-storing within a group of social Hymenoptera. Honey wasps feed on and store honey, but, unlike bees, they prey on other insects as a protein source and do not consume pollen. We surveyed the gut bacterial communities of Mexican honey wasps across sites within Texas using 16S ribosomal RNA profiling, and we estimated bacterial titer per bee using quantitative PCR. For comparison, we also surveyed non-honey-feeding wasps from six families, collected in the same region. We found that honey wasp communities are dominated by characteristic bacterial species. In contrast, other wasps had lower absolute titers and more variable communities, dominated by environmental bacteria. Honey wasps from all sampled nests contained strains of Bifidobacterium and Bombilactobacillus that were closely related to symbionts of bumble bees and other bees, suggesting acquisition via host-switching. Some individuals also harbored a close relative of Candidatus Schmidhempelia bombi (Orbaceae), an uncultured bumble bee symbiont, again suggesting host-switching. The most prevalent species was an uncultured Lactobacillus that potentially represents an independent acquisition of environmental Lactobacillus. The transition to honey feeding, combined with a highly social life history, appears to have facilitated the establishment of a bacterial community with similarities to those of social bees.