In a recent Breakthrough Report, Jiarong Chen, Yanlei Feng, Yuchan Zhang, and colleagues (Chen et al. 2026) used a structure-based approach that goes beyond the traditional sequence alignment to systematically annotate previously uncharacterized plant proteins and predict their functions (Fig. 1). Analyzing protein structures from 17 flowering plant species, the team clustered >550 K predicted proteins into >170 K groups based on structural similarity. From these, they identified more than 3,000 clusters where structural alignment revealed conserved architectures that sequence-based methods missed. Further refinements narrowed this set to 1,292 high-quality protein clusters, of which 246 clusters were broadly conserved across many plant species. By manual curation, the team ultimately highlighted 120 protein clusters widely present across plants and with predicted functions that could not be inferred from sequence data alone.

Annotation of genes and transcripts is a key prerequisite for understanding the information that is encoded in newly sequenced genomes. One source of information suited for this purpose is RNA-seq data mapped to the respective genome sequence. RNA-seq-based approaches for transcript reconstruction generate transcript models from these data by combining regions of contiguous coverage (exons) and split read mappings (introns). Understanding phenotypes as a consequence of proteins encoded in a genome further requires the annotation of coding sequences within transcript models. We present GeMoSeq, a novel approach for transcript reconstruction from RNA-seq data that combines combinatorial enumeration of candidate transcripts with heuristics for splitting candidate transcripts into regions of contiguous coverage and subsequent likelihood-based quantification. Prediction of coding sequences is an integral part of the GeMoSeq algorithm. We benchmark GeMoSeq against previous approaches using a large collection of public RNA-seq data for seven species. For the majority of species, we observe an improved prediction performance of GeMoSeq, especially on the level of coding sequences and for species with dense genomes. We combine GeMoSeq with the homology-based approach GeMoMa to re-annotate two recently sequenced genomes of Nicotiana benthamiana lab strains, which illustrates the main purpose of GeMoSeq: the initial annotation of newly sequenced genomes with protein-coding genes.

Extracellular electron transfer (EET) is essential for electroactive microbes’ physiology and biotechnological applications. Many such microbes are Gram-negative bacteria, in which EET must cross two membranes and the periplasm, necessitating spatial and temporal collaborations of various EET proteins that reside at different cellular compartments, for which little is known. Using single-molecule/single-cell-level fluorescence microscopy and electrochemical manipulations, we discover that in the electroactive bacterium Shewanella oneidensis, the inner-membrane electron-transfer hub protein CymA undergoes spatial reorganization into localized regions during active EET with dispersed formation dynamics, subsequently driving the colocalization of its direct electron-transfer partners in the periplasm. Correlated single-cell-level photoelectrochemistry-fluorescence microscopy further proves the critical function of CymA reorganization in enabling EET. A multitude of evidence suggests that CymA reorganization stems from biomolecular condensate formation, likely initiated by association with menaquinone-rich inner-membrane domains. These orchestrated spatiotemporal protein dynamics extend the functional roles of biomolecular condensates to include facilitation of EET in bacteria, with broader implications for cellular processes. Extracellular electron transfer (EET) in bacteria requires the spatiotemporal coordination of many proteins. Here, authors show that the inner-membrane protein CymA reorganizes spatially into condensates and drives the colocalization of partner proteins to enable EET in Shewanella.

Genome annotation captures the essence of a genome by cataloguing its genes, transcripts, proteins and other functional elements of the DNA sequence. Accurate annotation serves as the foundation for a wide range of downstream analyses and discoveries, ranging from basic biology to an understanding of the linkage between genes and disease. Over the past two decades, advances in high-throughput sequencing techniques have enabled faster and more accurate capture of diverse genomic features, generating data at an unprecedented scale. Concurrently, computational methods for translating these data into evidence for genome annotation have steadily improved, leading to better automated genome annotation systems. As such, the growing number of sequenced genomes provides a positive feedback loop, in which database searches become more effective and shared sequence patterns emerge more clearly. These advances are promising steps towards annotating the functions of many poorly understood genes, particularly non-coding RNA genes, for which more research is needed. In this Review, Ji et al. overview how rapidly advancing experimental and computational methods are enabling improved and automated annotation of gene structure and function, providing researchers with genome annotation resources of unprecedented scale and resolution.

Non-genetic variability in gene expression is an inevitable consequence of the stochastic nature of processes driving transcription and translation. While previous studies demonstrated that gene expression noise is negatively correlated with gene body methylation, the function of this correlation remains poorly understood in multicellular systems. Here, we provide a first functional link between gene body methylation and transcription noise in plants. We investigated a mutant with partial loss of CG methylation (met1-1) and 10 epigenetic recombinant inbred lines (epiRILs) generated by a cross between Col-0 and met1-3 plants, and observed an increase in gene expression noise, but this was not the case in met1-3 with complete loss of CG methylation. Loss of CG methylation in met1-3 could be compensated by a low but significant gain of non-CG methylation that buffers the noise in gene expression. Overall, our results show that gene body methylation has a functional role in reducing variability in transcription in a large subset of housekeeping genes, which require precise expression patterns to meet metabolic requirements. Genes lacking this noise-buffering effect are mainly enriched in stress response, where variability in gene expression can be seen as highly beneficial.

Unconventional protein secretion (UcPS) enables the export of cytosolic proteins through pathways that bypass the canonical endoplasmic reticulum–Golgi secretory route. Although increasingly recognized as essential for intercellular communication, stress responses, and tissue homeostasis, UcPS remains difficult to quantify due to low secretion efficiency, high intracellular background, and the challenge of distinguishing active secretion from passive leakage. Recent methodological advances, including NanoLuc split luciferase–based reporters and the Retention Using Selective Hooks (RUSH) system for synchronized protein transport, have improved sensitivity and temporal control of trafficking. Here, we present complementary protocols integrating these tools to provide a highly sensitive, quantitative workflow centered on a split NanoLuc (HiBiT/LgBiT) complementation assay for monitoring UcPS in mammalian cells. The Basic Protocol describes a robust luminescence-based secretion assay, while the Support Protocols detail the generation of stable HiBiT reporter cell lines, approaches for probing UcPS mechanisms using siRNA-mediated gene knockdown and pharmacological perturbation, and the incorporation of the RUSH system to synchronize cargo release and identify potential trafficking intermediates. Together, these protocols provide a sensitive, scalable, high-throughput toolkit that enables analysis of UcPS mechanisms across diverse cargo proteins, cell types, and perturbations. This methodological framework allows for rigorous dissection of UcPS pathways in both physiological and disease-relevant contexts. 

Each amino acid except two is encoded by multiple synonymous codons, but at unequal frequencies. Such codon usage bias (CUB) is observable in almost all species, and commonly assumed as the result of natural selection towards an optimal CUB that matches the cellular tRNA supply. Here we hypothesize instead that the optimal CUB of a gene should slightly mismatch the tRNA supply to avoid excessive translational costs, while ensuring adequate functional payoff. By modifying the CUB of a resistance gene expressed in bacteria under antibiotic selection, we demonstrate that a small mismatch with the tRNA supply confers faster bacterial growth than those with minimized or large CUB-tRNA mismatches. Intriguingly, the optimal degree of CUB-tRNA mismatch increases as the resistance gene becomes less important in media with lower antibiotic concentrations, which is explainable by our model as a shift in the balance between the gene’s functional payoff and translational cost. Furthermore, genomic analyses in model organisms suggest that the optimal degree of CUB-tRNA mismatch is larger for endogenous genes with lower functional importance and higher mRNA abundance, respectively supporting the impact of functional payoff and translational cost. Finally, we find that mutations increasing or decreasing the CUB-tRNA mismatch of native genes are both predominantly deleterious, such that the CUB-tRNA mismatch is likely selectively maintained rather than minimized to that achievable in the presence of genetic drift and mutational bias. These results challenge the commonly assumed unidirectional selection on CUB and highlight the CUB-modulated balance between functional payoff and translational cost. Nearly all organisms exhibit codon usage bias (CUB), traditionally thought to reflect a unidirectional selection maximally matching CUB and the cellular tRNA supply. Here, the authors propose alternatively that a slight mismatch optimizes the balance between translational cost and functional payoff.