Biology subjects

Agarwal, V.

Publications and source records attributed to Agarwal, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Tertiary structure of a Nif11-like leader peptide mediates protein-protein interactions with a RiPP biosynthetic enzyme

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are produced from precursor peptides that typically contain a short, unstructured N-terminal leader region that engages RiPP biosynthetic enzymes through peptide-protein contacts. A distinct subclass of leader peptides, the nitrogen-fixing protein 11-like leader peptides (Nif11-LPs), are unusually long and conspicuously enriched in cyanobacteria. No atomic structures of Nif11-LPs have been reported. Here, we report the solution structure of MppEleader, a Nif11-LP from the marine cyanobacterium Moorena producens PAL. MppEleader adopts a bipartite architecture with a rigid N-terminal tetrahelical bundle stabilized around a hydrophobic nucleus, followed by a dynamic, largely disordered C-terminal region containing a conserved L(X)L motif. We show that the -ketoglutarate/non-heme iron enzyme MppG dihydroxylates the macrocyclized MppEcore peptide in a strictly leader-dependent manner. NMR titrations and structure-guided mutagenesis demonstrate that both the tetrahelical hydrophobic nucleus and the L(X)L motif of MppE make direct, functionally required contacts with MppG, including its RiPP recognition element (RRE) domain. An AlphaFold3-guided structural model rationalizes these contacts. Together, these findings establish a tertiary structure-dependent protein-protein interaction paradigm for RiPP core peptide modification, distinguishing Nif11-LP recognition from canonical short-leader mechanisms and expanding the understanding of substrate recognition in RiPP biosynthesis.

biochemistry↗

bioRxiv2026

taxutils: Tools for managing and operating on NCBI taxonomy and accession mappings

Research in bioinformatics demands that many workflows and operations be consistently repeated across different tools. These operations include managing a taxdump, dealing with accession mappings, and performing analysis on the taxonomic tree. We introduce taxutils to address and alleviate the strain of these common workflows. To exhibit the extent of the package's capabilities, we introduce a strategy for assessing quality of an assembly via its potential impact on and perturbation of its mapping to a lowest-common-ancestor index. We illustrate the steps required for such an analysis, and show how the tool makes data management easy and otherwise-complex workflows simple.

genomics↗

bioRxiv2026

Closed-Loop Vibrotactile Neuromodulation for Reducing Tremor-Related Propranolol Use

Pathological tremor is a neurological condition that impairs fine motor tasks, affecting 1% of the general population and 4% of the elderly. Tremors arise when muscles micro-oscillations synchronize and phase lock, typically within a 4-12 Hz frequency range. Administering beta-blockers can reduce tremor severity, but doses are hard to personalize, with heavy doses of propranolol correlating with low blood pressure, dizziness, and nausea. In this project, we aimed to model tremor and create a closed-loop control framework to suppress tremor amplitude while minimizing pharmacological dependence. Because side effects constrain the use of pharmacological suppression alone, we investigated noninvasive neuromodulation. We used vibrotactile stimulation (VTS) to disrupt pathological tremor synchronization and reduce oscillatory amplitude. We hypothesized that tremor suppression involving VTS followed a nonmonotonic relationship, tested by determining whether maximum relief requires an adaptable framework. The procedure consisted of constructing a propranolol-reduction simulation by implementing a Hill curve, where we calculated and utilized tremor reduction, heart rate (HR) drop, and blood pressure (BP) drop. We then built a device to capture tremor-related data and create vibration using two linear resonant actuator (LRA) coin motors. We connected it to a microcontroller, where we determined optimal vibration frequencies through a feedback loop. Across 50 trials, VTS alone reduced tremor amplitude by an average of 37.3%, reducing the propranolol dose needed to reach 50% total tremor reduction by 71.9%, lowering the modeled blood pressure drop from 38.1 to 18.9 mmHg. This device demonstrates proof-of-concept for a nonmonotonic tremor-vibration relationship to reduce dependency on propranolol in the treatment of pathological tremor. These propranolol dose-reduction estimates are derived from computational simulation and have not been clinically validated; they are not intended as a recommendation to alter prescribed medication.

bioengineering↗

bioRxiv2026

Phylogenomic description of three novel species of the Microbulbifer genus, phylum Pseudomonadota, isolated from marine sponges and corals

AO_SCPLOWBSTRACTC_SCPLOWUnderstudied bacterial genera present a dynamic phylogenetic landscape and opportunities for discovering new taxa as more strains are isolated and genomic data is added. Here, through phylogenomic analysis, we describe three novel species of the globally distributed cosmopolitan marine bacterial genus Microbulbifer. This genus is ubiquitous in saltwater microbiomes and is a validated source of biodegradation enzymes as well as high value small molecule natural products. Average nucleotide identity (ANI) to the closest known species, Microbulbifer variabilis ATCC 700307T, was less than 88.4% for all three novel species. Isolates of the three novel species, designated as PAAF003T (T = type strain), ZKSA006T, and SSSA003T were imaged to reveal their phormological characteristics. Based on phylogenetic data, strains PAAF003T, ZKSA006T, and SSSA003T represent three new species of the genus Microbulbifer, for which the names Microbulbifer maximicatervae sp. nov., Microbulbifer regidiadema sp. nov., and Microbulbifer mixtoriginis sp. nov. are proposed, respectively, under the SeqCode. We also reconstructed a robust phylogeny of available Microbulbifer genomes, which should faciliatate future isolation and strain description studies.

microbiology↗

bioRxiv2026

TDKC (Target Distilled K-mer Classifier): Ultrafast and Memory-Efficient Sequence Classification for Target Pathogen Diagnostics

Metagenomic sequencing can identify pathogens from clinical samples without prior knowledge of the causative agent. Yet, as sequencing workflows scale to process thousands of multiplexed samples simultaneously, classifying these samples against massive reference databases creates a significant computational bottleneck. Furthermore, large-scale applications such as screening public sequence repositories remain computationally challenging. Existing metagenomic classifiers are designed for full-taxon classification, where the goal is to identify all organisms in a sample. However, many diagnostic applications focus on detecting a specific set of clinically relevant pathogens. This constraint can be exploited to significantly lower computational costs. Here we present TDKC (Target Distilled K-mer Classifier), a method for targeted metagenomic classification. TDKC constructs a compact index by distilling target-specific k-mers from a full-taxon reference database. When classifying clinical samples, TDKC uses 16.9-33.6x less memory and is 5.1-34.7x faster than per-read full-taxon and targeted classifiers (Kraken2, Centrifuger, CLARK), while maintaining high sensitivity and low false positive rates. Against the sketch-based profiler Sylph, TDKC remains 3.8x faster and uses 8.7x less memory. TDKC also supports per-k-mer accession tracking across over 3 million source accessions for downstream subtype analysis, and domain-level detection of bacteria, archaea, and viruses. By reducing the index to only the pathogens of interest, TDKC makes targeted pathogen detection feasible at scale.

bioinformatics↗

bioRxiv2026

MetaMuse: A Multi-Agent AI System for Biomedical Metadata Curation and Harmonization

Inconsistent and unstructured metadata in public biomedical repositories, such as the Gene Expression Omnibus (GEO), severely limits data discoverability and research reproducibility. To address this, we introduce MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW, a modular, multi-agent artificial intelligence framework designed to autonomously extract, validate, and standardize unstructured biomedical metadata. Operating through a three-stage architecture utilizing large language model agents, specialized CO_SCPLOWURATORC_SCPLOWAO_SCPLOWGENTSC_SCPLOW contextually extract candidate values for specific target metadata fields. A centralized AO_SCPLOWRBITRATORC_SCPLOWAO_SCPLOWGENTC_SCPLOW enforces cross-field logical consistency to prevent contradictory annotations. Finally, a NO_SCPLOWORMALIZERC_SCPLOWAO_SCPLOWGENTC_SCPLOW leveraging a domain-specific semantic search model (SapBERT) maps these free-text candidates to formal ontological terms. We evaluated MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW on a gold-standard dataset of manually curated GEO samples, achieving over 95% curation accuracy across key target metadata fields, and demonstrated robust scalability on a broader dataset of 400 samples. Notably, MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW avoids data hallucination by defaulting to conservative false negatives when evidence is ambiguous, thereby preserving strict data integrity. By providing a fully auditable and context-aware curation pipeline, MO_SCPLOWETAC_SCPLOWMO_SCPLOWUSEC_SCPLOW offers a scalable solution for enriching public data repositories and accelerating reproducible, data-driven scientific discovery.

genomics↗

bioRxiv2026

Intuitive knowledge of object acoustics enables perceptual separation of physical variables from impact sounds

Upon hearing objects collide, humans can estimate physical attributes such as material and mass. Although the physics of sound generation is well established, the inverse problem that listeners solve - of inferring physical parameters from sound - remains poorly understood. Classical accounts posit the use of acoustic cues that correlate with physical variables, but do not explain how humans might distinguish multiple concurrent physical causes. To study this problem, we built a probabilistic generative model of impact sounds, combining theoretical acoustics with statistics of object resonances measured from hundreds of everyday objects, and used it to synthesize and manipulate experimental stimuli. Humans accurately judged object properties from collision sounds. However, when both of the colliding objects varied, performance was impaired if the distribution of object resonances deviated from those measured in real-world objects. The results suggest that listeners use internal physical models to separate the acoustic contributions of objects in the world.

neuroscience↗

bioRxiv2025

Cation-Cation Photosensitization for Protein Ligation and Intracellular Catalysis

We report here a photosensitized strategy for protein labelling in which N-substituted pyridinium salts are activated using a 2,4-diaryl-N-methyl quinolinium scaffold. Structure-reactivity relationships were performed to optimize the sensitizer structure, and ultimately generated a system that gives protein labelling in minutes at micromolar reagent concentrations. Mechanistic studies suggest a photo-induced electron transfer-based sensitization mechanism. The mildness of this system enabled us to assay sensitization both on individual biomolecules and in complex proteomes and demonstrated excellent compatibility with lysate- and the live-cell-based systems. Imaging of photolabelled HeLa cells were performed and revealed that catalysis occurs in multiple cellular compartments. Chemical proteomics performed at the lysate level resulted in the enrichment of 319 proteins with a 93% selectivity to Tryptophan residues. Live cell labelling resulted in 101 enriched proteins, primarily from the nucleus.

biochemistry↗

bioRxiv2025

GeneJEPA: A Predictive World Model of the Transcriptome

We introduce GO_SCPLOWENEC_SCPLOWJO_SCPLOWEPAC_SCPLOW, a self-supervised foundation model that learns a predictive world model of single-cell transcriptomes. Based on the Joint-Embedding Predictive Architecture, GO_SCPLOWENEC_SCPLOWJO_SCPLOWEPAC_SCPLOW predicts latent representations of masked gene sets from visible context, a shift away from reconstructing noisy expression values and toward world-model style inference over cellular state. To realize this at scale, a Perceiver encoder handles variable gene sets at fixed cost, and a tokenizer jointly represents gene identity and continuous expression using Fourier features. Trained on the Tahoe-100M atlas, GO_SCPLOWENEC_SCPLOWJO_SCPLOWEPAC_SCPLOW learns general representations that transfer across tissues and datasets. On downstream tasks, including drug response and perturbation prediction, it surpasses strong baselines and enables test-time scaling by progressively enlarging the cross-attention over the gene set, trading a small read cost for higher accuracy at inference. GO_SCPLOWENEC_SCPLOWJO_SCPLOWEPAC_SCPLOW moves toward foundation models that reason over gene-gene relations, enabling applications in annotation, prediction, and in-silico discovery.

genomics↗

bioRxiv2025

Designer Aromatic Cations for Photo-Induced Protein Ligation, Imaging, and Intracellular Labelling at Extended Wavelengths

Photo-induced protein labelling strategies have become essential tools in chemical biology, but most strategies require high energy wavelengths of light as input to drive reactivity. Recently, we reported a biocompatible method for engaging photo-induced electron transfer to drive protein labelling using biaryl pyridinium salts and, here, we report the design of a series of aromatic cation salts that trigger this process using longer wavelengths of light while maintaining a sterically minimal profile. We achieved this through the systematic study of structure-reactivity relationships of various donor-acceptor pyridinium salts possessing extended conjugation, and these studies revealed the need of a constrained trans-stilbene relationship between the probes donor and acceptor substituents in order to achieve protein labelling. Probes with chromene-based donor groups in particular showed either robust protein labelling, significant fluorescence quantum yields, or state-dependent photophysical properties; in turn enabling the same probes to be used for both photo-induced protein labelling and wash-free live-cell imaging. We also demonstrate that these enhanced probes possess robust reactivity in complex biological environments through green light-triggered intracellular labelling in live HeLa cells, resulting in the identification of 659 enriched proteins. This series of experiments not only demonstrates the ability of this latest generation of probes to engage in photo-induced labelling using lower energy light in complex proteomes, but also reveals new capabilities for photophysical state-dependent reactivity and measurements. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/681063v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1be9a0eorg.highwire.dtl.DTLVardef@741221org.highwire.dtl.DTLVardef@525596org.highwire.dtl.DTLVardef@170ed11_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

REVIVE-Flow: A Foundation Model for Blood DNAm Aging

Epigenetic clocks can predict biological age but cannot prescribe the interventions needed to reverse it. Here, we introduce REjuVenatIon Via Epigenetic Flow (Revive-Flow), a flow-matching model trained on a broad compendium of epigenetic blood studies to transport methylomes forward and backward in time. First, we learn the continuous vector field of aging as an Ordinary Differential Equation (ODE) within a stable, low-dimensional linear space. Then, the learned ODEs dynamics are integrated backward in time to define a natural, biologically-plausible rejuvenation trajectory. This path serves as a guide for a convex optimization problem that identifies the minimal, targeted CpG-level perturbation required to rejuvenate a sample. On a completely unseen test set comprising over 800 individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC-Italy) cohort, Revive achieves 0.4 years rejuvenated per commanded year (R2 {approx} 0.99), with a smooth sparsity-effect trade-off. Extensive validation confirms the effect preserves inferred cell-type composition and targets biologically plausible loci enriched in genomic regions and pathways central to aging biology.

genomics↗

bioRxiv2025

Guided multi-agent AI invents highly accurate, uncertainty-aware transcriptomic aging clocks

Scientific discovery has long relied on human creativity, with computation limited to analysis. Here we report an AI-guided system, K-Dense, that accelerates hypothesis testing and delivers robust scientific discoveries. Trained on ARCHS4 (57,584 samples, 28 tissues, 1,039 cohorts, ages 1-114), the unified ensemble clock achieved R2 = 0.854 and MAE = 4.26 years--while uniquely providing calibrated confidence intervals. This self-aware design flags predictions made at transitional or extreme ages, where biological heterogeneity peaks, suggesting clinical utility for uncertainty itself. Development revealed stage-specific markers, including CDKN2A/p16 (senescence), AMPD3 (muscle wasting), MIR29B2CHG (progeroid traits), and SEPTIN3 (neurodegeneration resistance). Sliding-window analysis across 85 overlapping ranges uncovered wave-like shifts in gene importance, showing that transcriptomic aging signatures evolve continuously, not discretely. By transforming biological age assessment from static point estimates to calibrated predictions with explicit uncertainty, this approach establishes reliable and interpretable clocks. Beyond the clock itself, K-Dense demonstrates how guided AI can compress months of exploration into weeks, pointing toward a scalable framework for accelerated scientific discovery.

systems biology↗

bioRxiv2025

The Limitations of TabPFN for High-Dimensional RNA-seq Analysis

Tabular Prior-Data Fitted Networks (TabPFN) demonstrate remarkable performance on small-to-medium tabular datasets through in-context learning, but struggle with high-dimensional genomic data such as RNA-seq with tens of thousands of features. We investigate multiple approaches to adapt TabPFN for transcriptomic analysis using two benchmark datasets: Age-ARCHS4, a regression dataset derived from the ARCHS4 dataset (57,873 samples, 10,000 genes), and an Inflammatory Bowel Disease (IBD) classification dataset encompassing Crohns Disease and Ulcerative Colitis samples (2,490 samples, 10,000 genes). Our experimental design proceeds in two phases: first evaluating existing optimization methods, then testing novel adaptations including (1) self-supervised embedding learning and (2) Bulk-Former integration. We demonstrate that when constrained to equal training conditions (500 features, 10,000 samples), TabPFN outperforms classical baselines like random forest and XGBoost. However, when classical methods utilize full feature sets while TabPFN adaptations attempt to handle higher-dimensional data, all TabPFN variants consistently underperform the naive baseline. Our findings reveal fundamental limitations in current approaches to adapting TabPFN for genomic applications, showing that architectural modifications paradoxically degrade performance, while intelligent metadata-based subgrouping emerges as the most effective strategy for deploying TabPFN on biological data.

bioinformatics↗

bioRxiv2025

Cognitive ethology of nest building in a shell-dwelling cichlid

Across animal taxa, nest-building behavior is performed using a generalizable and flexible mental representation of an action sequence - a schema. In order to accomplish its goal (a stable nest), the brain appears to compare intermediate steps in the process to stored mental images, or templates. Deviations from these templates drive progress, preferences, and corrections in the execution of this behavior. The stereotypy vs. plasticity of both schema and mental templates have rarely been defined in an experimentally accessible system. Here we investigated nest building by the cichlid Lamprologus ocellatus, a fish species that manipulates abandoned snail shells to build shelters for breeding and protection. We find that nest building is composed of a sequence of behaviors that are tied together by a series of stimulus-response loops, allowing for restarts and shortcuts as the behavioral program unfolds. The attraction to a shell object is innate, as is the final appearance of the nest. The behavior in an inexperienced animal is initially uncoordinated, but is fine-tuned by repeated building opportunities. Shells need to conform to rigid geometric criteria in order to be acceptable as a potential home. Nest building is accompanied by focused neural activity in brain regions homologous to the mammalian hippocampus and neocortex. In conclusion, we have uncovered the constraints and flexibility of cognitive template matching underlying an instinctive, goal-directed behavior.

animal behavior and cognition↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2024

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterizing shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of translation efficiency covariation (TEC), identifying coordinated translation patterns across cell types. We nominate candidate mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and uncovers gene functions that are not evident from RNA or protein co-expression. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish TEC as a conserved organizing principle of mammalian transcriptomes.

bioinformatics↗

bioRxiv2024

Predicting the translation efficiency of messenger RNA in mammalian cells

The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence1, RiboNN integrates contributions from the full-length mRNA sequence, learning that the 5' UTR, CDS, and 3' UTR respectively possess [~]67%, 31%, and 2% per-nucleotide information density in the specification of mammalian TEs. Interpretation of RiboNN revealed that the spatial positioning of low-level di- and tri-nucleotide features (i.e., including codons) largely explain model performance, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN is predictive of the translational behavior of base-modified therapeutic RNA, and can explain evolutionary selection pressures in human 5' UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability, and localization in mammalian organisms.

bioinformatics↗

bioRxiv2024

Highly synchronized inhibition from Purkinje cells entrains cerebellar output in zebrafish

Cerebellar function, known to be important for motor learning and motor coordination, is mediated by efferent neurons that project to diverse motor areas. To understand cerebellar function, it is imperative to study how these efferent neurons integrate inputs from the principal neurons of the cerebellar cortex, the inhibitory Purkinje neurons (PNs). In zebrafish, PNs are bistable and we show here that bistability influences spike synchrony among PNs. Bistability also alters spike correlation with motor bouts. We asked how PN population synchrony influences Eurydendroid cells (ECs), which are postsynaptic targets of PNs and are the cerebellar efferent cells in zebrafish. Using optogenetics, we artificially modulated population synchrony of PNs over millisecond time scales and showed that under conditions of high synchrony, EC firing is briefly suppressed and entrained by PN spiking. However, the magnitude of such modulation is relatively small and indicates a strong combined influence of other synaptic inputs on EC spiking. Key PointsO_LICerebellar Purkinje neurons (PN) in larval zebrafish alter simple spike correlations with each other based on cellular state. C_LIO_LIThey also alter simple spike correlations with motor bouts as a function of state. C_LIO_LIWe altered PN population synchrony in a graded manner using optogenetics. C_LIO_LIPN targets are cerebellar efferent neurons, which in teleosts are called eurydendroid cells. C_LIO_LIWhen PN population is firing with high synchrony, eurydendroid cells are entrained better than when the PN input is asynchronous. C_LIO_LIThis can explain how PNs use bistability to modulate their influence on cerebellar output and ultimately, motor behavior. C_LI

neuroscience↗