Reconstructing the lineage histories of individual cells can reveal the dynamics of developmental and disease processes. In engineered recording systems, cells stochastically edit synthetic barcode sequences as they proliferate, creating distinct, heritable edit patterns that can be used to reconstruct the lineage trees relating individual cells in a manner analogous to phylogenetic reconstruction. However, recording depth is often limited by the kinetics of the editing process: the rate of editing declines exponentially over time for an array of independently editable targets, leading to most edits occurring in early generations. Here, we introduce the hypercascade, a regenerative molecular recording system that takes advantage of the predictability of A-to-G base editing to progressively create new target sites over time. The hypercascade packs 4 editable target sites in every 20 bp of sequence, enabling high density information storage. More importantly, the hypercascade's regenerative logic leads to an approximately constant rate of mutation accumulation over time. This in turn facilitates reconstruction of deep lineage relationships. We demonstrate this by reconstructing trees spanning 23 days of editing and approximately 17 generations after a single polyclonal engineering step. Finally, simulations show that the hypercascade has the potential to record chromatin state transition dynamics across multiple genomic loci in parallel. The hypercascade thus provides a flexible and broadly useful tool for molecular recording.

Antisense oligonucleotides (ASOs) are short, synthetic nucleic acids that bind to complementary RNA sequences and alter gene expression, making them versatile therapeutic agents. Here, we identify transcription termination windows of protein-coding genes as previously unrecognized targets for ASOs. We show that ASOs can act on nascent RNA synthesised downstream of the annotated genes, leading to a pronounced decrease in the corresponding mRNA levels. These downstream-of-gene ASOs (DG-ASOs) induce RNase H1-dependent cleavage, impairing mRNA 3′ end processing, directing the unprocessed mRNAs for exosome-dependent degradation and creating early entry points in the nascent RNA for the XRN2 exonuclease termination factor. Altogether, we show that termination windows are genuine ASO targets which can be exploited to suppress gene expression. Importantly, we also reveal an underappreciated source of off-target effects which may arise from ASOs binding downstream of genes. Our findings indicate the necessity to expand ASO design and off-target assessment guidelines to include termination window sequences, thereby improving therapeutic efficacy and safety.

Many microbial communities form multispecies biofilms where cells interact through diffusible molecules. In these biofilms, multiple interactions, often with opposing effects, occur simultaneously, yet we lack quantitative frameworks to predict how they combine to shape community functions. Here, we hypothesized that complex spatial patterns can emerge when opposing interactions have distinct spatial ranges. To test this, we studied how two Pseudomonas aeruginosa exoproducts, HQNO and rhamnolipids, jointly modulate Staphylococcus aureus antibiotic tolerance by respectively increasing and decreasing it. Using microfluidics-based imaging, we quantified spatial-tolerance patterns at single-cell resolution and found that tolerance indeed shows a complex spatial pattern: S. aureus cells survived treatment only at intermediate distances from P. aeruginosa, while cells closer or farther away did not. Combining experiments and modelling, we showed that this remarkable pattern emerges because rhamnolipids have a stronger but short-ranged effect, while HQNO has a weaker but longer-ranged effect. We found that spatial arrangement affects overall tolerance by shifting the balance between the two opposing interactions. Finally, using bioprinting, we confirmed that HQNO and rhamnolipids modulate tolerance in highly mixed biofilms. In more segregated biofilms, spatial arrangement still strongly modulated tolerance, but independently of these compounds, suggesting additional interactions. Together, our results show that spatial-tolerance patterns emerge from the combined effect of opposing range-dependent interactions and cannot be predicted from either alone. By predicting how opposing interactions jointly determine community properties, our framework provides a foundation for understanding and ultimately engineering microbiome functions.

Conventional wisdom suggests that adaptive evolution proceeds more slowly in long-lived organisms than in short-lived ones due to longer generation times. As a result, long-lived organisms are often viewed as less capable of responding to rapid environmental change. However, empirical evidence challenges this view. Using mathematical models and demographic data from 322 wild animal populations, we show that long generation times slow adaptive evolution only under limited conditions, notably when selection acts on fecundity. When selection targets early survival, intermediate and long generation times can instead accelerate adaptive evolution. Remarkably, short-lived species tend to occupy demographic regimes in which fecundity is the dominant fitness component, whereas long-lived species occupy regimes in which early survival dominates. Therefore, both short- and long-lived species can potentially adapt rapidly, calling into question the widespread use of generation time as a general predictor of adaptive capacity to current environmental change.

Antimicrobial peptides (AMP) constitute key components of innate immunity across the tree of life. Canonical AMPs are typically translated as small proteins and secreted from host cells to act against microbes. However, cryptic AMP-like domains are also embedded within diverse proteins not classically associated with antimicrobial function. How such embedded AMPs first emerge and diversify remains unclear. Here we retrace the origin and evolution of the abundant mammalian protein lactoferrin and its embedded AMP, lactoferricin. By resurrecting extinct lactoferrin ancestors dating back to the earliest mammals, we identify a gradual enrichment of cationic and hydrophobic amino acids in the lactoferricin domain over time. These changes enabled ancient lactoferricin to first rupture bacterial membranes, an activity that was later enhanced in extant mammals conferring potent bactericidal activity against diverse bacteria. In addition, we find that recent natural selection within the lactoferricin domain has continued to modulate antimicrobial activity on recent evolutionary timescales. In particular, we pinpoint a single rapidly evolving site in lactoferricin among great apes that significantly enhances antimicrobial potency against major pathogenic bacteria. Together our study illustrates how novel immune protein functions can arise, evolve, and diversify to strengthen host defense against diverse microbial pathogens.

The evolutionary co-occurrence of amino acid changes between protein residues underlies key structural and functional properties of protein families. Building on these coevolution patterns, methods have been developed to identify groups of residues associated with enzyme functionalities, such as Statistical Coupling Analysis (SCA) or Specificity-Determining Position (SDP) methods. These methods and their variations differ in the metrics used to quantify coevolution, residues weighting schemes, and corrections introduced to mitigate noise and phylogenetic biases. Yet, systematic comparisons across methods are rarely performed, and the evolutionary origins of the coevolutionary patterns highlighted by each approach are seldom addressed, limiting our ability to disentangle functional from phylogenetic contributions. To address these issues, we introduce COCOA-Tree, a Python library for SCA-like dimensionality-reduction analyses. COCOA-Tree supports custom metrics and enables visualization of coevolutionary patterns on phylogenetic trees. We also provide guidance to map results onto 3D structures in PyMOL. Using COCOA-Tree, we reanalyze published datasets and uncover previously unnoticed evolutionary properties of groups of coevolving residues detected by SCA, known as sectors. In particular, in the well-studied S1A serine protease family, we show that two of the three known sectors exhibit qualitatively distinct levels of sequence conservation depending on the enzymatic functions and on the phylogenetic clades to which the proteins belong. We further show that different coevolution metrics often identify qualitatively distinct groups of coevolving residues, although they yield consistent results for mildly conserved residues. Overall, we expect COCOA-Tree to help identify residues that control protein function and thereby improve our capacity for functional engineering and our understanding of the principles governing protein evolution. COCOA-Tree website: https://tree-timc.github.io/cocoatree

Crop diseases caused by viruses can result in significant economic losses. However, the mechanisms that lead to disease are not well understood. Meanwhile, metagenomic surveys have revealed that most plants in nature are tolerant to viruses, thus maintain their fitness despite of recurrent infection. Using plants that transition from a diseased state to a tolerant state during virus infection, we demonstrate that tolerance depends on the virus-induced production of 21-nucleotide small interfering RNAs (siRNAs) from transposable elements (TEs). These findings indicate that siRNAs derived from TE sequences play a pivotal role in mitigating the detrimental effects of viral infection on plant health.

Microbial volatile organic compounds (VOCs) are integral to microbial ecological communication. Their potential as tools for sustainable crop protection is increasingly recognised, yet practical implementation remains limited. There are numerous in vitro lab-based studies focussed on screening single strains of soil or plant-associated microbes for their ability to produce VOCs and demonstrate their potential to inhibit plant pathogens or pests. Most of these, however, lack any validation in planta or in the field after petri dish experiments. This extends to a lack of understanding on whether the same VOCs are produced in vitro as in planta. How do we shift this focus and move from exciting lab-based discoveries to practical, scalable crop protection solutions for farmers? This opinion piece explores the current state of research on microbial VOCs for crop protection, translational challenges in deploying them on-farm, and highlights areas where learnings from the ecological roles of microbial VOCs can be leveraged towards field application.

Comparative genomics has traditionally relied on sequence-based markers, yet the global evolutionary landscape of protein architecture remains largely unexplored. We present the Structural History of Eukarya (SHE), a phylogeny derived from all-vs-all structural comparisons of 1,542 eukaryotic proteomes, encompassing nearly 300 trillion protein-protein alignments. This proteome-scale analysis reveals a bipartite model of eukaryotic evolution: a rigid Strict Core dominated by cytoskeletal architecture, supporting a more plastic Operational Engine centred on translational machinery. We identify lineage-specific accelerations of structural evolution in species such as birds and ants that are decoupled from proteome expansion, and show that aggregate structural topology provides a quantitative diagnostic of reference proteome quality. Finally, we demonstrate that proteome-wide structural fidelity enables a data-driven framework for model organism selection, replacing heuristic choices with quantitative matching to human biological processes. SHE is freely accessible as an interactive portal: https://she-app.serve.scilifelab.se/