Filamentous cable bacteria are capable of centimeter-scale long-distance electron transport and play crucial roles in the biogeochemistry of aquatic sediments. However, the mechanisms underlying long-distance electron transport remain incompletely understood. This study reports dynamic contacts between separate filaments of cable bacteria, enabling them to relay electrons between sulfidic and oxic zones. Video microscopy of motile filaments in a microchamber slide setup revealed that some filaments did not fully bridge the gap between the sulfidic and oxic zone, but made transient contact with each other. Contacts were always end-to-end and often occurred repeatedly, in which filaments always followed the same trajectory back and forth. The contact frequency gradually increased over the first 20 days, and then declined afterwards. About 5.5% of cable bacterium filaments were observed to engage in contact events during a 2-hour observation window on day 20. Confocal microscopy confirmed the presence of extracellular polymer substance trails between filaments, which appear to guide consecutive end-to-end contacts. In situ Raman spectroscopy showed that connections enabled redox continuity between reduced and oxidized filaments, thus suggesting inter-filament electron transfer during physical contact. This inter-filament electron transport represents a novel type of microbial cooperation, and appears to be a strategy for establishing optimal connections between spatially separated electron donors and acceptors in a dynamic sedimentary environment.

Light-responsive proteins play an essential role in all domains of life by sensing and responding to environmental light signals. However, the de novo design of light-responsive proteins with precisely defined structures and reversible responsive behaviors is an unmet challenge. Here we describe a computational approach to design protein–protein interactions regulated by non-canonical amino acids, focusing on the light-responsive phenylalanine-4′-azobenzene (AzoF). Using this approach, we designed light-responsive cyclic homo-oligomers and heterodimers, which only assemble in AzoF’s trans configuration and disassemble when AzoF photoisomerizes to the cis configuration. Biophysical characterization confirms the light-responsive assembly and disassembly of these complexes, and the crystal structures match the design models with atomic accuracy. We demonstrate the applicability of these light-responsive proteins in constructing light-responsive hydrogels and engineering synthetic ligand receptors to optocontrol cell signalling in mammalian cells. Our approach opens avenues for designing environmentally responsive protein structures and broadens the toolkit for optogenetics and optochemistry. Light-responsive proteins hold significant potential in biotechnology but naturally occurring examples are scarce and limited in diversity. Now a totally de novo computational approach has been developed to design light-responsive proteins with diverse architectures by precisely incorporating the non-canonical amino acid phenylalanine-4′-azobenzene (AzoF), which is responsive to light.

Developing efficient and simplified tools for multiplexed genome editing remains challenging due to limitations in precursor CRISPR RNA (pre-crRNA) processing and reliance on additional RNA-based regulatory components. Cas12i.3, a small RNA-guided nuclease, reportedly lacks pre-crRNA processing ability, restricting its multiplexing capability. Here, we engineered Cas12i.3 by optimizing CRISPR RNA (crRNA) design, codon usage, and exonuclease fusion, generating initial optimized Cas12i (IOCas12i) system. Further rational design and amino acid mutations yielded the highly efficient enhanced optimized Cas12i (EOCas12i) systems, EOCas12i–Combo1 and EOCas12i–Combo2, exhibiting 2.5- to 22.8-fold and 3.0- to 60.0-fold editing efficiencies relative to wild-type Cas12i.3, comparable to Streptococcus pyogenes Cas9 (SpCas9) and Lachnospiraceae bacterium Cas12a (LbCas12a). Additionally, they exhibited high specificity and produced longer insertions and deletions (indels) that may facilitate gene knockout. Notably, both variants enabled efficient multiplexed editing of up to 30 targets using compact crRNA arrays. These advancements position EOCas12i–Combo1 and EOCas12i–Combo2 as promising platforms for multiplexed genome editing applications.

Promoters are core elements in regulating gene expression. The design and optimization of functional promoters is crucial for enhancing metabolic pathway construction and advancing gene therapy. Deep learning-based methods have shown great potential in promoter design. However, existing studies mainly focus on designing strong promoters, neglecting the practical need for promoters with varying regulatory intensities. Here, we propose a novel promoter design method, PromoDGDE, to design promoters with desirable expression levels and apply it to the promoter design of Escherichia coli and Saccharomyces cerevisiae. It uses Diffusion-GAN to learn the feature distribution of natural sequences and generate new promoters. Then, reinforcement learning and evolutionary algorithms are combined to dynamically optimize the synthetic sequences. In silico analyze results demonstrate that PromoDGDE outperforms existing methods, generating promoters that not only possess biological significance but also achieve the intended function. In vivo experiment results demonstrate that the synthetic promoters exhibit expression activity, with over 60% of the sequences showing the expected regulatory effects. These results confirm the practical effectiveness of PromoDGDE and demonstrate its ability to provide an efficient and flexible solution for complex design needs in synthetic biology.

Bacterial genome exploration and outbreak analysis rely heavily on robust whole-genome sequencing and bioinformatics analysis. Widely-used genomic methods, such as genotyping and detection of genetic markers demand high sequencing accuracy and precise genome assembly for reliable results. Methods To assess the utility of nanopore sequencing for genotyping highly pathogenic bacteria with low mutation rates, we sequenced six reference strains using Oxford Nanopore Technologies (ONT) R10.4.1 chemistry and Illumina and evaluated different assembly strategies. The publicly available RefSeq assemblies were chosen as the ground truth. Publicly available sequencing data from key foodborne and public-health-related bacterial pathogens were examined to provide a broader context for the analysis. While for Bacillus (Ba.) anthracis an almost perfect assembly was achieved, results varied for other species. For Brucella (Br.) spp., the final assemblies comprised five to 46 different nucleotides in comparison to Sanger-sequenced references. For some key foodborne and public-health-related bacterial pathogens (Klebsiella (K.) variicola, Listeria spp., Mycobacterium (M.) tuberculosis, Staphylococcus (Sta.) aureus, and Streptococcus (Str.) pyogenes) perfect genomes were obtained. Enhanced basecalling models have generally improved assembly accuracy, however, for certain species such as Br. abortus, older models have produced higher accuracy. While long-read polishing mainly improves assembly quality with only one round needed, our results indicate that this process may also degrade assembly quality. Overall, 81% of the observed errors in ONT assemblies were located within coding sequences (CDS). Furthermore, we found that methylation caused 6.5% of the errors, and the bacterial methylation-aware medaka polishing model reduced the number of errors linked to methylation. Core-genome Multilocus Sequence Typing (cgMLST) analysis revealed allele differences in Ba. anthracis, Br. abortus, and Francisella (F.) tularensis for some assemblers, although with fewer than five allele differences. In the case of Br. melitensis, some assemblies included five allele differences, whereas for Br. suis the correct cgMLST alleles were observed. Assembling nanopore data from pathogenic bacteria vary in quality across different species and methods. However, errors persist in the final assemblies, including within cgMLST loci, influencing the reliability of outbreak predictions. Nevertheless, specific combinations of existing tools can generate perfect genome assemblies from bacterial ONT sequencing data for outbreak analysis without short-read polishing.

Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with machine learning have made it possible to accurately define such molecular recognition in a biophysically interpretable way for hundreds of TFs across many structural families, providing new avenues for predicting how the sequence preference of a TF is impacted by disease-associated mutations in its DNA binding domain. We developed a method based on a reference-free tetrahedral representation of variation in base preference within a given structural family that can be used to accurately predict the effect of mutations in the protein sequence of the TF. Using the basic helix-loop-helix (bHLH) and homeodomain (HD) families as test cases, our results demonstrate the feasibility of accurately predicting the shifts (ΔΔΔG/RT) in binding free energy associated with TF mutants by leveraging high-quality DNA binding models for sets of homologous wild-type TFs.

Metatranscriptomics methods for the skin are hampered by low microbial biomass, contamination with host cells and low RNA stability. In this study, we developed a robust, clinically tractable skin metatranscriptomics workflow that provides high technical reproducibility of profiles, uniform coverage across gene bodies and strong enrichment of microbial mRNAs. Paired application of this protocol with metagenomics to five skin sites in a cohort of 27 healthy adults identifies a notable divergence between transcriptomic and genomic abundances. Specifically, Staphylococcus species and the fungi Malassezia had an outsized contribution to metatranscriptomes at most sites, despite their modest representation in metagenomes. Species-level analysis shows signatures of microbial adaptation to their niches. Gene-level analysis identifies diverse antimicrobial genes transcribed by skin commensals in situ, including several uncharacterized bacteriocins. Correlation of microbial gene expression with organismal abundances uncovers more than 20 genes that putatively mediate interactions between microbes. This work highlights how skin metatranscriptomics identifies active species and microbial functions in situ. Skin metagenomic and metatranscriptomic analysis shows divergence between microbial abundance and activity.

New reference genomes and transcriptomes are increasingly available across the tree of life, opening new avenues to tackle exciting questions. However, there are still challenges associated with annotating genomes and inferring evolutionary processes and with a lack of methodological standardization. Here, we propose a new workflow designed for evolutionary analyses to overcome these challenges, facilitating the detection of recombination suppression and its consequences in terms of rearrangements and transposable element accumulation. To do so, we assemble multiple bioinformatic steps in a single easy-to-use workflow. We combine state-of-the-art tools to detect transposable elements, annotate genomes, infer gene orthology relationships, compute divergence between sequences, infer evolutionary strata (i.e. footprints of stepwise extension of recombination suppression) and their structural rearrangements, and visualise the results. This workflow, called EASYstrata, was applied to reannotate 42 published genomes from Microbotryum fungi. We show in further case examples from a plant and an animal that we recover the same strata as previously described. While this tool was developed with the goal to infer divergence between sex or mating-type chromosomes, it can be applied to any pair of haplotypes whose pattern of divergence is of interest. This workflow will facilitate the study of non-model species for which newly sequenced phased diploid genomes are becoming available.