Fungal communities in soil play important roles in decomposition processes and soil organic carbon cycling. These communities are tremendously diverse, making it challenging to assign relevant functions to individual species. Fungal communities may be differentiated at the level of functional guilds; beyond such broad classification we have little delimitation, especially in fungal taxa common in grassland and agricultural soils. To resolve the level of functional similarity in fungal communities and define traits predictive of soil carbon cycling, we characterized fungal isolates abundant in agricultural soils to test the hypotheses that (i) the majority of saprobic soil fungi have the ability to use complex carbon sources, (ii) differences in complex carbon use abilities correlate with fungal enzymatic profiles, following principles of the fungal economics spectrum and (iii) carbon use ability is a predictive trait for fungal community functions. Using specialized growth media, we isolated and characterized 105 isolates and developed a novel FungiResp approach that directly tests fungal activity on complex carbon sources. The largest amount of variance between isolates was explained by differential abilities to use cellulose and starch, with only few phylogenetically distinct fungal clades showing high respiratory activity on these biopolymers. A preference for bacterial necromass was another major distinction among taxa. These key traits correlated with soil fungal community shifts in response to carbon substrate availability. By contrast, enzymatic activity was a poor predictor of fungal carbon use ability, except for correlations in lignin use and laccase activity. The newly established functional trait of carbon use ability offers important insights into diverse fungal communities: Many taxa lack the ability to use complex carbon (on their own), while the most common enzymes analyzed in soil showed little correlation with fungal mineralization potential. The discovery of key functional traits is an important step towards predicting the significance of fungal community shifts for soil carbon cycling.

Recent advances in deep learning, particularly transformer architectures, have improved computational approaches for biological sequence analysis. Despite these advances, computational models for bacterial promoter prediction have remained limited by small datasets, species-specific training, and binary classification approaches rather than comprehensive annotation frameworks. We present PromoterAtlas, a 1.8M parameter transformer model trained on 9M regulatory sequences from 3,371 gammaproteobacterial species. The model demonstrates recognition of various regulatory elements across different species, including ribosomal binding sites, various types of bacterial promoters, transcription factor binding sites, and terminators. Using this model, we developed a whole-genome promoter annotation tool for Gammaproteobacteria, with various levels of validation that support the predictions of promoters associated with different sigma (σ) factors. Furthermore, we show that the model embeddings encode cross-species evolutionary relationships, clustering promoters by σ factor identity rather than species-specific sequence features. Finally, we show that model embeddings encode regulatory sequence information that enables effective prediction of transcription and translation levels. PromoterAtlas can contribute to our understanding of and ability to engineer bacterial regulatory sequences with potential applications in bacterial biology, synthetic biology, and comparative genomics.

Driven by consumer preferences for safety and environmental protection, the global cosmetics industry has an increasing demand for natural and sustainable ingredients. Saccharomyces cerevisiae has emerged as a powerful platform for the biosynthesis of cosmetic ingredients due to its strong metabolic capacity, genetic operability, and cost-effective production capabilities. This review focuses on the latest advances in S. cerevisiae for the production of high-value cosmetic compounds, including antioxidants, repair agents, moisturizers, and structure-maintaining ingredients. Key strategies, such as genetic and metabolic engineering, pathway modularity, and fermentation optimization, are discussed, demonstrating significant improvements in yield and efficiency. In addition, the integration of artificial intelligence and machine learning in strain design and process control is explored, providing promising solutions to overcome metabolic bottlenecks and scale up production. Despite challenges such as metabolic burden, S. cerevisiae shows great potential for sustainable and scalable biosynthesis of cosmetic ingredients, paving the way for the next generation of biobased cosmetics. This comprehensive review provides valuable insights and technical references for the development of the field of synthetic biology in the cosmetics industry.

Control over mutational library diversity is an essential consideration when engineering proteins, but is often fraught with trade-offs between diversity, specificity, and affordability. Contemporary library assembly approaches often incorporate oligonucleotide pool synthesis to achieve affordable, precise mutagenesis; however, these oligos are often reliant on complex designs to facilitate downstream PCR and/or restriction digests. Direct hybridization of oligo pools is an overlooked strategy to simplify mutagenesis, especially when paired with a type IIS restriction cloning approach. We validate this approach by designing, hybridising, and deep sequencing single and dual substitution CDR region parts derived from nanobody GA10. Assembly of these parts into a full-length nanobody CDS facilitated the phage display of variant libraries for affinity maturation against its cyclic peptide target. Variants identified through enrichment analysis were expressed in isolation and yielded improved affinities by more than 100-fold. Recent advances in machine learning have successfully inferred improved variants outside of screened library space, but require controlled, multi-mutant libraries. The library assembly approach outlined in this research is well-suited for such approaches.

Bacterial diversity can be overwhelming. There is an ever-expanding number of bacterial taxa being discovered, but many of these taxa remain uncharacterized with unknown traits and environmental preferences. This diversity makes it challenging to interpret ecological patterns in microbiomes and understand why individual taxa, or assemblages, may vary across space and time. While we can use information from the rapidly growing databases of bacterial genomes to infer traits, we still need an approach to organize what we know, or think we know, about bacterial taxa to match taxonomic and phylogenetic information to trait inferences. Inspired by the periodic table of the elements, we have constructed a 'periodic table' of bacterial taxa to organize and visualize monophyletic groups of bacteria based on the distributions of key traits predicted from genomic data. By analyzing 50,745 genomes across 31 bacterial phyla, we used the Haar-like wavelet transformation, a model-free transformation of trait data, to identify clades of bacteria which are nearly uniform with respect to six selected traits - oxygen tolerance, autotrophy, chlorophototrophy, maximum potential growth rate, GC content and genome size. The identified functionally uniform clades of bacteria are presented in a concise 'periodic table'-like format to facilitate identification and exploration of bacterial lineages in trait space. While our approach could be improved and expanded in the future, we demonstrate its utility for integrating phylogenetic information with genome-derived trait values to improve our understanding of the bacterial diversity found in environmental and host-associated microbiomes.

For Aspergillus flavus, a pathogen of considerable economic and health concern, successful gene knockout work for more than a decade has relied nearly exclusively on using nonhomologous end-joining pathway (NHEJ)-deficient recipients via forced double-crossover recombination of homologous sequences. In this study, a simple CRISPR/Cas9 genome editing system that gave extremely high (>95%) gene-targeting frequencies in A. flavus was developed. It contained a shortened Aspergillus nidulans AMA1 autonomously replicating sequence that maintained good transformation frequencies and Aspergillus oryzae ptrA as the selection marker for pyrithiamine resistance. Expression of the codon-optimized cas9 gene was driven by the A. nidulans gpdA promoter and trpC terminator. Expression of single guide RNA (sgRNA) cassettes was controlled by the A. flavus U6 promoter and terminator. The high transformation and gene-targeting frequencies of this system made generation of A. flavus gene knockouts with or without phenotypic changes effortless. Additionally, multiple-gene knockouts of A. flavus conidial pigment genes (olgA/copT/wA or olgA/yA/wA) were quickly generated by a sequential approach. Cotransforming sgRNA vectors targeting A. flavus kojA, yA, and wA gave 52%, 40%, and 8% of single-, double-, and triple-gene knockouts, respectively. The system was readily applicable to other section Flavi aspergilli (A. parasiticus, A. oryzae, A. sojae, A. nomius, A. bombycis, and A. pseudotamarii) with comparable transformation and gene-targeting efficiencies. Moreover, it gave satisfactory gene-targeting efficiencies (>90%) in A. nidulans (section Nidulantes), A. fumigatus (section Fumigati), A. terreus (section Terrei), and A. niger (section Nigri). It likely will have a broad application in aspergilli.

Unlike tandem repeats in eukaryotes, ribosomal RNA (rRNA) operons across prokaryotic genomes are widely distributed. Here, I examined the distribution of 16S ribosomal RNA gene copies using all entries from the Ribosomal RNA Operon Copy Number Database with a copy number of 2 or greater, using a metric-normalized range-that allows for comparisons between copy number. Normalized range varied across rRNA copy number, with the greatest distribution of rRNA gene copies in prokaryotic accessions with a copy number of 5. There was a significant phylogenetic signal for both bacteria and archaea (p<0.05). Archaea had higher normalized range than bacteria, even when comparing bacteria with comparable copy number (n=2-5) (p<0.05). Normalized range varied across bacterial phyla (p<0.05), with Cyanobacteria having the largest normalized range and Chlamydiae the smallest. Furthermore, normalized range was predicted by copy number, tRNA number, and pseudo-gene number, after correcting for phylum. When restricted to low bacterial copy numbers (n=2-5), normalized range was predicted by genome size, GC content, tRNA number, coding gene number, and pseudo-gene number. For archaea, normalized range was predicted by genome size, GC content, and tRNA number. Comparisons of bacteria and archaea suggest different mechanisms for rRNA copy distribution and maintenance across genomes.

Syringic acid (SA) is a key intermediate in the bacterial catabolism of syringyl lignin-derived aromatic compounds. However, bacterial SA catabolism remains largely unknown. Here, we investigated the SA catabolic system in Pseudomonas sp. NGC7, which catabolizes various lignin-derived aromatic monomers. Pathway analysis and gene disruption studies revealed that SA undergoes O demethylation by VanA1B1 to 3-O-methylgallic acid, which is subsequently catabolized through a linear pathway, involving MgaAB, MgaC, MgaD, GalD, GalB, and GalC into the TCA cycle, unlike the branching pathway found in sphingomonad strains. vanA1B1, mgaAB, mgaC, and mgaD constitute an operon whose transcription is regulated by the GntR-type transcriptional repressor VanR2. In contrast, galBCD constitutes an operon with the formaldehyde dehydrogenase gene fdhA2, and this operon is regulated by the LysR-type transcriptional activator GalR. FdhA2 is involved in the detoxification of formaldehyde, a byproduct of SA catabolism. These findings contribute to the valorization of syringyl lignin-containing biomass.

The pervasive accumulation of polyethylene terephthalate (PET) waste has emerged as a critical ecological crisis, which is mainly driven by its recalcitrance to natural degradation and widespread contamination of terrestrial and aquatic ecosystems. In response to this challenge, microbial-mediated PET biodegradation has garnered significant scientific attentions as a sustainable remediation strategy, harnessing the enzymatic cascades of specialized microorganisms to depolymerize PET into bio-assimilable monomers such as terephthalic acid (TPA) and ethylene glycol (EG). In this review, we summarize the extracellular process of PET biodegradation, including microbial attachment, colonization, and direct depolymerization, as well as the metabolic pathways of PET monomers. Strategies for developing PET-degrading chassis cells are also discussed, such as cell surface display, metabolic pathway optimization, and rational design of enzyme-PET interfaces. Microbial-enzyme consortia and molecular engineering of photosynthetic microorganisms also contribute to PET degradation. Although significant progress has been made, challenges remain in enzyme stability, metabolic bottlenecks, industrial scalability, and environmental adaptation. Overall, microbial and enzymatic strategies show great potentials in addressing PET pollution, and future interdisciplinary efforts are needed to overcome these challenges and achieve a sustainable circular plastic economy.