Biology subjects

Nocedal, I.

Publications and source records attributed to Nocedal, I..

5 recordsLinked to original sources

bioRxiv2026

Compact adenine base editors to enable therapeutic rescue of Duchenne muscular dystrophy

Adenine base editors (ABEs) have emerged as a powerful gene-editing technology enabling precise and programmable adenine-to-guanine substitutions across the genome. However, their translation into in vivo therapeutics is limited by delivery challenges, as their size exceeds the packaging capacity of adeno-associated virus (AAV). Here, we report the discovery, structural characterization, and engineering of two compact, highly active ABEs built on novel deaminases and Cas9d nucleases, enabling all-in-one, single-vector AAV delivery. Applying these compact ABEs to Duchenne muscular dystrophy (DMD), we demonstrate efficient disruption of conserved splice-acceptor sites at dystrophin exons 45 and 51 in human skeletal muscle cells, enabling therapeutically relevant exon skipping. Together, these ABEs help expand the therapeutic reach of base editing towards diverse tissue types and disease targets.

bioengineering↗

bioRxiv2026

DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions

Modeling recognition between T-cell receptors (TCRs) and peptide-MHC (pMHC) complexes is a fundamental challenge in computational immunology, constrained by sparse paired interaction data relative to abundant unpaired sequences. We introduce DecoderTCR, a masked language model framework that addresses this through two contributions: (1) a compositional continual pre-training curriculum that learns component representations from marginal data before refining cross-chain dependencies from limited pairs, and (2) Iterative Entropy-Guided Refinement (IEGR), a non-autoregressive decoding algorithm that resolves high-confidence positions first to provide context for uncertain regions. On held-out benchmarks, DecoderTCR achieves 0.96 AUROC for zero-shot pMHC binding prediction and 0.76 AUROC for epitope-specific TCR recognition, approaching supervised baselines without epitope-specific training. Learned representations recover structural contacts without coordinate supervision, and generated sequences exhibit realistic recombination statistics. Experimental validation reveals a prediction-generation gap: strong discrimination does not yet yield reliable generation, highlighting an open challenge for the field.

bioinformatics↗

bioRxiv2022

Ancestral reconstruction of duplicated signaling proteins reveals the evolution of signaling specificity

Gene duplication is crucial to generating novel signaling pathways during evolution. However, it remains unclear how the redundant proteins produced by gene duplication ultimately acquire new interaction specificities to establish insulated paralogous signaling pathways. Here, we used ancestral sequence reconstruction to resurrect and characterize a bacterial two-component signaling system that duplicated in -proteobacteria. We determined the interaction specificities of the signaling proteins that existed before and immediately after this duplication event and then identified key mutations responsible for establishing specificity in the two systems. Just three mutations, in only two of the four interacting proteins, were sufficient to establish specificity of the extant systems. Some of these mutations weakened interactions between paralogous systems to limit crosstalk. However, others strengthened interactions within a system, indicating that the ancestral interaction, although functional, had the potential to be strengthened. Our work suggests that protein-protein interactions with such latent potential may be highly amenable to duplication and divergence.

evolutionary biology↗

bioRxiv2021

Global analysis of the specificities and targets of endoribonucleases from E. coli toxin-antitoxin systems

Toxin-antitoxin systems are widely distributed genetic modules typically featuring toxins that can inhibit bacterial growth and antitoxins that can reverse inhibition. Although Escherichia coli encodes 11 toxins with known or putative endoribonuclease activity, the target of most of these toxins remain poorly characterized. Using a new RNA-seq pipeline that enables the mapping and quantification of RNA cleavage with single-nucleotide resolution, we characterize the targets and specificities of 9 endoribonuclease toxins from E. coli. We find that these toxins use low-information cleavage motifs to cut a significant proportion of mRNAs in E. coli, but not tRNAs or the rRNAs from mature ribosomes. However, all the toxins, including those that are ribosome-dependent and cleave only translated RNA, inhibit ribosome biogenesis. This inhibition likely results from the cleavage of ribosomal protein transcripts, which disrupts the stoichiometry and biogenesis of new ribosomes and causes the accumulation of aberrant ribosome precursors. Collectively, our results provide a comprehensive, global analysis of endoribonuclease-based toxin-antitoxin systems in E. coli and support the conclusion that, despite their diversity, each disrupts translation and ribosome biogenesis. ImportanceToxin-antitoxin systems are widespread genetic modules found in almost all bacteria that can regulate their growth and may play prominent roles in phage defense. Escherichia coli encodes 11 TA systems in which the toxin is a known or predicted endoribonuclease. The targets and cleavage specificities of these endoribonucleases have remained largely uncharacerized, precluding an understanding of how each impacts cell growth and an assessment of whether they have distinct or overlapping targets. Using a new and broadly applicable RNA-seq pipeline, we present a global analysis of 9 endoribonulease toxins from E. coli. We find that each uses a relatively low-information cleavage motif to cut a large proportion of mRNAs in E. coli, but not tRNAs or mature rRNAs. Notably, although the precise set of targets varies, each toxin efficiently disrupts ribosome biogenesis, primarily by cleaving the mRNAs of ribosomal proteins. In sum, the analyses presented provide new, comprehensive insights into the cleavage specificities and targets of almost all endoribonuclease toxins in E. coli. Despite different specificities, our work reveals a striking commonality in function as each toxin disrupts ribosome biogenesis and translation.

microbiology↗

bioRxiv2020

Evolution of the complex transcription network controlling biofilm formation in Candida species

We examine how a complex transcription network composed of seven "master" regulators and hundreds of target genes evolved over a span of approximately 70 million years. The network controls biofilm formation in several Candida species, a group of fungi that are present in humans both as constituents of the microbiota and as opportunistic pathogens. The ability to form biofilms is crucial for microbial colonization of different host niches, particularly when an implanted medical device is present. We examined and compared the network underlying biofilm formation across four Candida species (C. albicans, C. dubliniensis, C. tropicalis, and C. parapsilosis), all of which form biofilms composed of multiple cell types. To describe the salient features of the network across different species, we employed four approaches: (1) we phenotypically characterized the biofilms formed by these species using a variety of methods; (2) we knocked out -- one by one -- the master regulators identified in C. albicans in the four species and monitored their effect on biofilm formation; (3) we identified the target genes of 18 master regulator orthologs across the four species by performing ChIP-seq experiments; and (4) we carried out transcriptional profiling across each species during biofilm formation. Additional network information was obtained by analyzing an interspecies hybrid formed between the two most closely related species, C. albicans and C. dubliniensis. We observed two major types of changes that have occurred in the biofilm circuit since the four species last shared a common ancestor. Master regulator "substitutions" occurred over relatively long evolutionary times, resulting in different species having overlapping, but different sets of master regulators of biofilm formation. Second, massive changes in the connections between the master regulators and their target genes occurred over much shorter timescales. Both types of change are crucial to account for the structures of the biofilm networks in extant species. We believe this analysis is the first detailed, empirical description of how a complex transcription network has evolved.

evolutionary biology↗