Since their discovery, CRISPR-Cas systems have been widely applied in areas ranging from genome editing to biosensing, owing to their specific, RNA-guided target recognition. Their performance in complex biological environments has been extensively studied, particularly to optimize guide RNA (gRNA) design and minimize off-target cleavage. Here, we focus on the kinetic inhibition of the interaction between Cas12a—a Class 2, Type V effector—and its target, caused by interference from non-cognate background nucleic acids. This effect is particularly relevant for sensing applications in complex mixtures or cellular contexts, where genome- and transcriptome-derived sequences may impede CRISPR-Cas activity. Using in vitro assays under defined conditions, we systematically examine the influence of background single-stranded RNA and double-stranded DNA (dsDNA) on reaction kinetics. We find that both the purine-to-pyrimidine ratio and the GC content of the gRNA seed region significantly affect kinetic inhibition by background polynucleotides. gRNAs with low GC content and a high purine fraction in the seed region were least affected by background sequences. A gRNA with high uracil content in the seed region exhibited particularly strong inhibition in the presence of a dsDNA background. Experiments with dCas12a-based gene activation in living cells indicate that our in vitro findings may also be relevant for in vivo applications.

Engineering pyrenoid-based CO2-concentrating mechanisms (pCCMs) into crop plants is a promising strategy to boost photosynthetic performance, enhance yield potential, and improve resilience to future climates. Achieving this goal requires a deeper understanding of the molecular principles that enable pyrenoids to elevate CO2 around Rubisco. Although pyrenoids span a remarkable diversity of forms across algae and hornworts, they consistently exhibit three core features: a condensed Rubisco matrix, specialized membranes that deliver inorganic carbon, and a diffusion barrier that restricts CO2 leakage. Here, we review recent mechanistic advances from Chlamydomonas reinhardtii alongside emerging insights from other algae and hornworts that highlight both conserved strategies and lineage-specific innovations in Rubisco condensation, membrane-associated inorganic carbon channelling, and matrix encapsulation. Together, these findings refine our understanding of pCCM diversity and provide an increasingly robust blueprint for reconstructing pCCMs in vascular plant chloroplasts.

RNA structure critically governs biological function in both physiological and pathological contexts, making high-resolution structural maps essential for RNA-targeted therapeutics. Yet, despite recent advances, well-validated structural targets for drug design remain limited. To help bridge this gap, we generated the first genome-scale map of the human RNA structurome by applying ScanFold to >230 000 annotated human pre-mRNA transcripts, identifying sequences likely evolved to form highly stable and functional secondary structures. We also performed a global analysis of regions with z-scores ≤ –2 and statistically characterized their two-dimensional folding patterns. In addition, we developed the RNA-Annotator Pipeline to integrate 20 diverse biological annotations, such as tissue-specific expression and protein interactions, with the structural data. Our results reveal local folding propensities and unusually stable structures with high-confidence architectures, providing insights for prioritizing RNA targets and guiding therapeutic design, including antisense oligonucleotides and small molecules. All ScanFold results are publicly available through RNAStructuromeDB. Using the RNA-Annotator Pipeline, analysis of SMN1 and SMN2 pre-mRNAs showed that a single C-to-T transition in SMN2 induces structural rearrangements that disrupt a critical splicing enhancer. This toolkit establishes an integrated workflow that enables researchers to explore RNA structure–function relationships and accelerate advances in RNA-targeted drug discovery and RNA biology.

Targeting DNA payloads into human induced pluripotent stem cells (hiPSCs) typically requires multiple inefficient steps, slowing the testing of gene circuits and cell-fate programmes. Here we show that STRAIGHT-IN Dual enables simultaneous, allele-specific, single-copy integration of two DNA constructs efficiently within 1 week. STRAIGHT-IN Dual leverages the STRAIGHT-IN platform for near-scarless payload integration, facilitating the recycling of components for further modifications. Using STRAIGHT-IN Dual, we investigate how promoter choice and gene syntax influence transgene silencing and how these design features affect reporter expression and forward programming of hiPSCs into neurons, motor neurons and endothelial cells. We also incorporate a grazoprevir-inducible synthetic gene switch that complements tetracycline-inducible control, providing tunable and temporally controlled expression of different transcription factors within the same cell. STRAIGHT-IN Dual generates homogeneous engineered hiPSC populations, accelerating synthetic biology design–build–test cycles in stem cells and enabling controlled comparisons of circuit performances. STRAIGHT-IN Dual enables allele-specific integration of two DNA constructs into hiPSCs within 1 week. The system was used to programme hiPSCs into distinct cell types and for tunable expression of different transcription factors within the same cell.

RNAanalyzer3 (“RNA analyzer cubic” https://rnaanalyzer.bioapps.biozentrum.uni-wuerzburg.de) substitutes the frequently consulted current RNAanalyzer webserver (https://rnaanalyzer-old.bioapps.biozentrum.uni-wuerzburg.de). RNAanalyzer3 is free/open via the secure HTTPS protocol, with example data, help and tutorial, web-link to results, and rich data output. We combine a general detailed structure analysis with motif analyses. It accepts either a single plain-text nucleotide sequence or batch submission in FASTA format, which can be pasted or uploaded as a FASTA file. Our tool (i) has up-to-date software and operating systems, (ii) combines diverse RNA motif analyses with RNA structure prediction, (iii) puts found motifs into structural context, and (iv) offers dedicated tools for probing RNA–protein binding interactions. RNAanalyzer3 links motif searches to Rfam and miRNA search to miRbase. It focuses on structural features first, looks for stem-loops, hairpins, and specific enrichment regions such as stem-GG pairs, plus AU-rich regions with their locations for easier identification, while providing structural context and interactive RNA structure visualization. A tabulated overview shows all RNA features including structure details, coding potential, untranslated regions (UTRs, including Shine–Dalgarno sequences, Kozak sequences, and polyadenylation signals), transfer RNA (tRNA), microRNA (miRNA), long noncoding RNA (lncRNA), trans-splicing motifs, iron response elements (IRE), riboswitches, small nuclear ribonucleoprotein (snRNP) motifs, and spliceosomal Sm-sites.

Most proteins act through interactions with other molecules, yet predicting how single mutations perturb these interactions—defined as ‘protein codes’—remains a central challenge in computational biology. Here we introduce eSIG-Net, the edgetic mutation sequence-based interaction grammar network, a language model that integrates protein sequence embeddings with syntax-aware and evolution-aware mutation encoding and contrastive learning to predict mutation-driven interaction changes. eSIG-Net outperforms state-of-the-art sequence-based and structure-based methods, nominates causal variants and provides mechanistic insights. Together, eSIG-Net is a mutation-centric interaction language model that accurately predicts interaction-specific network rewiring from sequence information alone and generalizes across biological contexts. eSIG-Net is an interaction language model that predicts the effects of mutations on protein interaction.

Recombinant protein expression in E. coli is a key methodology for modern biomedical research. Typically, a polyhistidine-tagged (“His-tagged”) protein is purified using immobilized metal affinity chromatography (IMAC), achieving close to apparently homogenous target protein preparations. However, contaminant host proteins may nonetheless be co-purified at trace amounts. This includes bacterial catalase, which can even be found crystallized instead of an intended target protein. Here, we found that less than 0.03% of the original endogenous bacterial catalase remaining in a final recombinant protein product can easily be detected in an enzymatic H2O2 (hydrogen peroxide) scavenging assay, because of the high inherent turnover of catalase and its lack of need for additional cofactors. If present in a recombinant protein preparation, this activity may give unintended effects, especially if the target protein is a redox active enzyme, such as glutathione peroxidase, glutaredoxin, ribonucleotide reductase, thioredoxin, or peroxiredoxin. Here, we found that genetic deletion of the two katG and katE genes in a bacterial expression host could fully eliminate catalase from the recombinant protein product without any appreciable loss of final yield. We suggest that this genetic approach is to be preferred for the removal of catalase instead of using more extensive purification schemes.