Accurate annotation of coding sequences and translational features within transcript models is essential for interpreting assembled transcriptomes and their functional potential. Existing open reading frame (ORF) prediction tools typically operate on transcript FASTA files and do not reintegrate coding sequence (CDS) information back into transcript models, limiting their utility in long-read sequencing workflows where GTF/GFF annotations are the primary output. We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file. In addition, ORFannotate provides biologically informative translational context by annotating Kozak sequence strength, detecting non-overlapping upstream ORFs (uORFs) with coding probabilities, characterising 5′ and 3′ untranslated regions (UTRs), and predicting nonsense-mediated decay (NMD) susceptibility. All annotations are consolidated in a transcript-level summary to support downstream analysis. By generating GTF files with accurate CDS annotations, ORFannotate facilitates reproducible analysis of both long- and short-read transcriptomes and integrates seamlessly with visualization tools, genome browsers, and comparative transcript analysis workflows. ORFannotate is fast, scalable and provides a practical solution for transcriptome annotation beyond coding potential prediction alone.

Cas9-based genome engineering is a powerful tool for yeast strain development, but its use in the food industry is limited due to GMO concerns. As a non-GMO alternative, adaptive laboratory evolution (ALE) was applied to enhance trehalose accumulation, a stress protectant, in Saccharomyces cerevisiae. To avoid ethanol-centric metabolism and promote storage carbohydrate production, ALE was conducted under nitrogen limitation with ethanol as the sole carbon source. The evolved strain 65EV showed a 2.3-fold increase in trehalose (11.51% vs 5.25%), resulting in enhanced cell viability (77.7% vs 5.11%) and bread loaf volume (107.2 mL vs 61.5 mL) after freeze/thaw stress. Amino acid profiling revealed distinct metabolic shifts, including elevated intracellular proline and extracellular glutamate. Whole-genome sequencing and reverse engineering identified unique mutations, TSL1 (V887A) and SSA2 (F105L), associated with trehalose regulation. These findings demonstrate potential of ALE as a non-GMO strategy for improving yeast performance in food applications.

DNA-encoded library (DEL) technology enables high-throughput small-molecule discovery but is typically performed using purified proteins under in vitro conditions that do not reflect native intracellular environments. Here, we present a microfluidic agarose microdroplet platform for cellular-context DEL screening. The porous hydrogel droplets provide mechanically stable yet permeable microenvironments that protect weak protein-ligand interactions while enabling efficient buffer exchange and ligand diffusion. Importantly, mild cell permeabilization within droplets selectively retained chromatin-associated proteins, allowing screening directly in a cellular context. Using BRD4 as a model target, we validated intracellular ligand engagement by fluorescence imaging and super-resolution microscopy. Small-scale DEL screening selectively enriched JQ1 in both bead-based and cell-based formats, and large-scale DEL screening across millions of encoded compounds successfully identified hit molecules by sequencing. This agarose microdroplet based strategy expands DEL technology toward biologically relevant and chromatin-associated targets under near-native conditions.

Proteins function through hierarchical modules, yet conventional models treat sequences as linear strings of residues, overlooking the recurrent multi-residue patterns-or "Protein Words"-that govern biological architecture. We introduce a physics-aware framework that discretizes protein space into a learnable vocabulary derived from the evolutionary record. By encoding proteins as sequences of discrete "words," our model captures higher-order structural and functional signals inaccessible to residue-level models, achieving highly competitive performance against widely established baselines in remote homology and mutation effect prediction. Analysis across 54 species reveals that these words track evolutionary complexity, specifically identifying the expansion of eukaryotic disordered regions. We demonstrate the discovery potential of this semantic axis by identifying ADMAP1 as a previously uncharacterized regulator of sperm motility, validated via CRISPR-Cas9 knockout mice. Finally, this vocabulary enables programmable design, generating functional cofilin variants despite high sequence divergence. This work establishes a linguistically inspired framework for deciphering the dark proteome and engineering biological function.

Transcriptional regulation lies at the heart of cellular identity and function, hinging on the precise binding of transcription factors (TFs) and cofactors to gene regulatory elements such as promoters and enhancers. Although it is relatively routine to profile genome-wide DNA binding landscapes of proteins, identifying the specific proteins that bind to, and regulate the transcription of, a particular gene of interest (GOI) remains a persistent experimental and conceptual challenge. This gene-centric question is complicated by the multilayered regulatory environment in which each gene resides, comprising 3D chromatin structure, enhancer–promoter looping, DNA accessibility, histone modifications, and cell state–dependent protein dynamics. In this review, we dissect the strengths, limitations, and biological relevance of current approaches for studying direct protein–DNA interactions, distinguishing between protein-centric and DNA-centric methodologies. We introduce a conceptual matrix of biological relevance, integrating the origin of DNA and protein elements (cis and trans) to evaluate false-positive and false-negative risks across experimental systems. Moreover, we explore how perturbation strategies—gain and loss of function—can complement steady-state profiling to establish causality in gene regulation. By critically examining both established tools and emerging techniques such as genome editing, synthetic chromosomes, and high-resolution imaging, we provide a practical framework for investigators seeking to uncover direct regulators of specific genes. Our goal is to guide the design of experiments that balance biological relevance, sensitivity, and interpretability to ultimately answer a deceptively simple question: What TFs directly regulate the expression of my GOI?

Microbial lipids offer a promising alternative to petrochemicals, but high associated costs and low conversion efficiencies pose barriers to their commercialisation. In particular, sugar-based feedstocks are too expensive for the production of commodity chemicals, and recently attention has turned to volatile fatty acids (VFAs) as a cheaper, more widely available carbon source. Acidogenic fermentation can be used to produce high concentrations of VFAs from municipal and agricultural waste. By harnessing metabolically engineered Acinetobacter baylyi ADP1, the suitability of VFAs as sole carbon sources for wax ester (WE) production was investigated. These studies resulted in the highest WE accumulation in ADP1 achieved to date, at 37% of cell dry weight, and the first reported production of bacterial WEs from a raw, mixed waste stream, utilizing fermentate as the sole carbon source. WE titres of over 160 mg/L from VFAs were achieved, highlighting the unique benefits of mixed feedstocks typically considered problematic for bioproduction. Finally, the potential advantages of employing fermentates rich in longer chain VFAs are explored. In synthetic media, WE titres up to 190 mg/L were achieved, but translation to fermentate was challenging, emphasising the need for continued research in this area.

Lactic acid bacteria (LAB), a group of Gram-positive bacteria widely used in food fermentation, are major producers of bacteriocins─ribosomally synthesized antimicrobial peptides. These peptides have attracted considerable attention not only as natural food preservatives but also for their therapeutic potential in biomedicine. This review outlines LAB-derived bacteriocins, presenting an updated classification (Classes I–III) by structure and genetics, along with their biosynthesis and gene clusters. Furthermore, key production influencers, such as quorum sensing, two-component systems, and culture conditions, are analyzed. Besides, this review also highlights the health benefits of LAB bacteriocins against infections and cancers, achieved via mechanisms like membrane disruption. It also discusses strategies (e.g., metabolic engineering and synthetic biology, combination therapies, and novel purification methods) to enhance efficacy. Despite persistent challenges in yield optimization and clinical translation, this work provides critical insights to guide the therapeutic development and scalable production of bacteriocins.