By Luis Linares-Otoya, Jaden D. Shirkey, Bhuwan Khatri Chhetri, Amira Mira, Abhishek Biswas, Samuel L. Neff, Maria V. Linares-Otoya, Ye Chen, Julio V. Campos-Florian, Mayar L. Ganoza-Yupanqui, Philip D. Jeffrey, Frederick M. Hughson & Mohamed S. Donia

Considerable advances have been made in characterizing bioactive molecules secreted by bacteria, yet the regulatory elements controlling their production remain largely understudied. Here we identify and characterize the N-acyl-cyclolysine (ACL) system—a cell-density-dependent chemical signalling system specific to and widespread in the phylum Bacteroidota (formerly Bacteroidetes)—and show that it regulates the expression of co-localized operons encoding diverse secreted molecules. Using genetic and biochemical analyses, combined with structural studies of a key biosynthetic enzyme, AclA, we elucidate the molecular structure of various ACLs and their complete biosynthetic pathway involving l-lysine acylation and ATP-dependent cyclization. Furthermore, we find that secreted ACLs are sensed by a dedicated transcription factor, AclR, resulting in the expression of associated operons and the autoinduction of ACL biosynthesis. Moreover, we show that different Bacteroidota strains produce structurally diverse ACLs and encode transcription factors with varying ligand specificities. Finally, we find that the acl circuit is widely distributed and transcribed in human gut and oral microbiome samples, with clear evidence for an active role in regulating associated operons under host colonization conditions. Understanding the function of the ACL system in different contexts has the potential to reveal details about the biology, ecology and chemistry of the Bacteroidota and how members of this phylum interact with their environments and hosts.

Read the paper: https://www.nature.com/articles/s41586-025-09418-9

By Jiayu Zhang, Elizabeth C. Chavez, Melina Winkler, Jianche Liu, Sebastian Carver, Aaron E. Lin, Abhishek Biswas, Tomokazu Tamura, Anna Tseng, Danyang Wang, Aaron Benhamou, Aoife K. O’ Connell, Mao Matsuo, Jack E. Norton, Devin Kenney, Britt Adamson, Ralph E. Kleiner, Benjamin Burwitz, Nicholas A. Crossland, Florian Douam & Alexander Ploss

The live-attenuated yellow fever 17D vaccine strain differs genetically only minimally from its virulent parent. However, it remains unclear which sequence differences lead to virulence or attenuation. Here we demonstrate, using SHAPE-MaP, that these mutations do not induce global RNA structure changes and show that protein sequence mutations are mostly responsible for the phenotypic differences between 17D and virulent YFV. Using a highly modular, combinatorial genetic approach, we identified key mutations in the envelope (E) and non-structural 2A (NS2A) proteins that increase 17D’s ability to spread and enhance host antiviral responses. Introducing these mutations into infectious clones of virulent YFV genomes results in viral attenuation in vitro and in two mouse models. Collectively, our results define the genetic basis for 17D attenuation and highlight a potentially general approach for creating live-attenuated vaccines by introducing mutations resulting in similar phenotypic changes in other pathogenic viruses.

Read the paper: https://www.nature.com/articles/s41564-025-02047-y

By Sofia A. Quinodoz, Lifei Jiang, Aya A. Abu-Alfa, Troy J. Comi, Hongbo Zhao, Qiwei Yu, Lennard W. Wiesner, Jordy F. Botello, Anita Donlic, Elizabeth Soehalim, Prashant Bhat, Christiane Zorbas, Ludivine Wacheul, Andrej Košmrlj, Denis L. J. Lafontaine, Sebastian Klinge & Clifford P. Brangwynne

Biomolecular condensates are key features of intracellular compartmentalization. As the most prominent nuclear condensate in eukaryotes, the nucleolus is a multiphase liquid-like structure in which ribosomal RNAs (rRNAs) are transcribed and processed, undergoing multiple maturation steps to form the small (SSU) and large (LSU) ribosomal subunits. However, how rRNA processing is coupled to the layered organization of the nucleolus is poorly understood owing to a lack of tools to precisely monitor and perturb nucleolar rRNA processing dynamics. Here we developed two complementary approaches to spatiotemporally map rRNA processing and engineer de novo nucleoli. Using sequencing in parallel with imaging, we found that rRNA processing steps are spatially segregated, with sequential maturation of rRNA required for its outward movement through nucleolar phases. By generating synthetic nucleoli in cells using an engineered rDNA plasmid system, we show that defects in SSU processing can alter the ordering of nucleolar phases, resulting in inside-out nucleoli and preventing rRNA outflux, while LSU precursors are necessary to build the outermost layer of the nucleolus. These findings demonstrate how rRNA is both a scaffold and substrate for the nucleolus, with rRNA acting as a programmable blueprint for the multiphase architecture that facilitates assembly of an essential molecular machine.

Read the paper: https://www.nature.com/articles/s41586-025-09207-4

By Naman Agarwal, Xinyi Chen, Evan Dogariu, Devan Shah, Hubert Strauss, Vlad Feinberg, Daniel Suo, Peter Bartlett, Elad Hazan

We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill, a general-purpose fast generation method for any sequence prediction algorithm based on convolutional operators. FutureFill reduces generation time from quadratic to quasilinear in the context length. Moreover, when generating from a prompt, it requires a prefill cache whose size grows only with the number of tokens to be generated, often much smaller than the caches required by standard convolutional or attention based models. We validate our theoretical claims with experiments on synthetic tasks and demonstrate substantial efficiency gains when generating from a deep convolutional sequence prediction model.

Read the paper: https://arxiv.org/abs/2410.03766

By Ted Zadouri, Hubert Strauss, and Tri Dao

LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequential nature of decoding limits parallelism. We analyze the interplay among arithmetic intensity, parallelization, and model quality and question whether current architectures fully exploit modern hardware. This work redesigns attention to perform more computation per byte loaded from memory to maximize hardware efficiency without trading off parallel scalability. We first propose Grouped-Tied Attention (GTA), a simple variant that combines and reuses key and value states, reducing memory transfers without compromising model quality. We then introduce Grouped Latent Attention (GLA), a parallel-friendly latent attention paired with low-level optimizations for fast decoding while maintaining high model quality. Experiments show that GTA matches Grouped-Query Attention (GQA) quality while using roughly half the KV cache and that GLA matches Multi-head Latent Attention (MLA) and is easier to shard. Our optimized GLA kernel is up to 2x faster than FlashMLA, for example, in a speculative decoding setting when the query length exceeds one. Furthermore, by fetching a smaller KV cache per device, GLA reduces end-to-end latency and increases throughput in online serving benchmarks by up to 2x.

Read the paper: https://arxiv.org/abs/2505.21487

By Lindsay A. Becker, Sofia A. Quinodoz, Troy J. Comi, Ofer Kimchi, David A. Knowles, and Clifford P. Brangwynne

Subcellular RNA organization can affect critical cellular functions. However, our understanding of RNA microenvironments, particularly biomolecular condensates, remains limited, largely due to a lack of technologies to comprehensively interrogate mesoscale RNA organization. Here, we adapt Split-Pool Recognition of Interactions by Tag Extension to map micron-scale RNA-RNA spatial proximity genome-wide across cell regions (RNA-SPRITE). Deploying RNA-SPRITE, we find extensive, conserved organization of mature mRNAs, with increased colocalization between mRNAs that share RNA-binding protein (RBP) motifs or encode functionally related proteins. Both effects are especially strong in dendrites and axons, suggesting prevalent mRNA co-regulation. Moreover, mRNAs with less compact folding, lower translation efficiency, and specific RBP motifs are more likely to be in RNA-rich condensates. However, perturbations that broadly dissolve or enhance condensation reveal that RBP motif and encoded protein-mediated colocalizations largely remain intact, independent of condensation. These results demonstrate the power of RNA-SPRITE in revealing critical aspects of RNA’s functional organization.

In Brief Unbiased, genome-wide maps of RNA-RNA mesoscale spatial proximity uncover extensive subcellular organization and its governing principles.

Highlights

• RNA-SPRITE reveals micron-scale RNA colocalization genome-wide across cell regions
• mRNA colocalization specificity is driven by shared motifs and encoded protein function
• mRNAs with less compact folding, lower translation efficiency, and distinct protein-binding motifs are more likely to be in condensates
• Neurites have a particularly high degree of sequence and function-dependent mRNA organization

Read the paper: https://www.biorxiv.org/content/10.1101/2025.04.19.649570v1

By Mohamed Abdellatif, Joel U. Bretheim, and Marina Rustow

Different writing systems have been (historically and contemporarily) used to write out the same language. This is typically done by substituting letters (or symbols, in the case of non-alphanumeric systems). However, depending on the language and the involved writing systems, the process may not be purely deterministic. Quoting Becker and Becker [2000]

even such basic acts as transliteration involve interpretation– to the extent that there is
meaning in the medium itself
.
.
In transliteration itself there is exuberance (that is, meaning is added) and deficiency
(meaning is lost).

This gives significance to the problem of Machine Translation in the intersection of Digital Humanities and Natural Language Understanding. Transformer-based models achieved success modeling human languages. However, many of them have the limitation of handling an input of maximum length of 512 tokens. To reuse a pre-trained model with this limitation for downstream tasks (e.g., Machine Transliteration) on input of sequences longer than 512 tokens, we propose a method to segment the input into interleaving (not mutually exclusive) pieces, invoke the model in a piecewise manner and construct the result. To consolidate the result, we propose a method to detect and correct potential (duplication and elimination) errors that reduces Word Error Rate from 0.0985 to 0.0.

Read the paper: https://zenodo.org/records/14982300

By Henri Schmidt, Minsi Zhang, Dimitar Chakarov, Vineet Bansal, Haralambos Mourelatos, Francisco J. Sánchez-Rivera, Scott W. Lowe, Andrea Ventura, Christina S. Leslie & Yuri Pritykin

We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly design and analysis of individual gRNAs and gRNA libraries for targeting coding and non-coding regions in custom genomes. GuideScan2 analysis identifies widespread confounding effects of low-specificity gRNAs in published CRISPR screens and enables construction of a gRNA library that reduces off-target effects in a gene essentiality screen. GuideScan2 also enables the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome. GuideScan2 will facilitate CRISPR experiments across a wide range of applications.

Read the paper: https://doi.org/10.1186/s13059-025-03488-8

By Leybova L, Biswas A, Sharan R, Trejo BM, Kim K, Soto-Muniz Y, Jones RA, Phillips BK, Devenport D.

The bending of simple cellular sheets into complex three-dimensional (3D) forms requires developmental patterning cues to specify where deformations occur, but how positional information directs morphological change is poorly understood. Here, we investigate how morphogen signaling and cell fate diversification contribute to the morphogenesis of murine hair placodes, in which collective cell movements transform radially symmetric primordia into bilaterally symmetric tubes. Through live imaging and 3D volumetric reconstructions, we demonstrate that Wnt and Shh establish radial patterns of cell fate, cell morphology, and movement within developing placodes. Cell fate diversity at different radial positions provides unique and essential contributions to placode morphogenesis. Further, we show that downstream of radial patterning, gradients of classical cadherin expression are required for efficient epithelial rearrangements. Given that the transformation of epithelial discs into 3D tubes is a common morphological motif used to shape diverse organ primordia, mechanisms of radially patterned morphogenesis are likely highly conserved across evolution.

Read the paper: https://doi.org/10.1016/j.devcel.2024.09.022

By Hayden Nunley, Binglun Shao, David Denberg, Prateek Grover, Jaspreet Singh, Maria Avdeeva, Bradley Joyce, Rebecca Kim-Yip, Abraham Kohrman, Abhishek Biswas, Aaron Watters, Zsombor Gal, Alison Kickuth, Madeleine Chalifoux, Stanislav Y. Shvartsman, Lisa M. Brown, Eszter Posfai

For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data. Here, we first report a previously unreported mouse line expressing near-infrared nuclear reporter H2B-miRFP720. We then generate a dataset (termed BlastoSPIM) of 3D images of H2B-miRFP720-expressing embryos with ground truth for nuclear instances. Using BlastoSPIM, we benchmark seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method. With our BlastoSPIM-trained Stardist-3D models, we construct a complete pipeline for nuclear instance segmentation and lineage tracking from the eight-cell stage to the end of preimplantation development (>100 nuclei). Finally, we demonstrate the usefulness of BlastoSPIM as pre-train data for related problems, both for a different imaging modality and for different model systems.

Read the paper: https://doi.org/10.1242/dev.202817