bluuebunny/crossref_metadata_2025_split huggingface dataset:
Refining the cis-regulatory grammar learned by sequence-to-activity models by increasing model resolution
Authors: Chandra Nuria Alina , Hu Yan , Buenrostro Jason D. , Mostafavi Sara , Sasse Alexander 
Published: January 2025

AbstractChromatin accessibility can be measured genome-wide with ATAC-seq, enabling the discovery of regulatory regions that control gene expression and determine cell type. Deep genomic sequence-to-function (S2F) models link underlying genomic sequences to the measured chromatin state and identify motifs that regulate chromatin accessibility. Previously, we developed AI-TAC, a S2F model that predicts chromatin accessibility across 81 immune cell types and identifies sequence patterns that control their differential ATAC-seq signals. While AI-TAC provided valuable insights into the regulatory patterns that govern immune cell differentiation, later research established that ATAC-seq profiles (the distribution of Tn5 cuts) contain additional information about the exact location and strength of TF binding. To make use of this additional information, we developed bpAI-TAC, a multi-task neural network which models ATAC-seq at base-pair resolution across 90 immune cell types. We show that adding ATAC-profile information consistently improves predictions of differential chromatin accessibility. We also demonstrate that simultaneous learning of related cell types through multi-task modeling leads to better predictions than single task models. We then present a systematic framework for comparing how differences in model performance can be attributed to differences in what the model has learned. To understand what additional information bpAI-TAC gleans from ATAC-profiles, we use sequence attributions and identify motifs that have different effect sizes when trained on profiles. We conclude that modeling ATAC-seq at base-pair resolution enables the model to learn a more sensitive representation of the regulatory syntax that drives differences between immunocytes, and therefore will improve predictions of variant effects.
Star Power: Early life stages of an endangered sea star are robust to current and near-future warming
Authors: Hodin Jason , Anteau Fleur P. , Ashcraft Brook F. , Brito Michael , Curliss Fiona , Kalytiak-Davis Augustin R. , Peng James S. , Schwab Chloe J , Valdez Vannessa V. , Weertman Willem 
Published: January 2025

AbstractThe sunflower star,Pycnopodia helianthoides, was a top benthic predator throughout its former range from Alaska to northern Mexico, until its populations were devastated starting in 2013 by a disease known as seastar wasting. The subsequent absence of sunflower stars from northern California waters was coincident with a dramatic ecological phase shift from healthy bull kelp forests (Nereocystis luetkeana) to barrens formed by purple sea urchins (Strongylocentrotus purpuratus), a prey of sunflower stars. Modeling suggests that restoration and resilience of kelp forests can be enhanced by the return of sunflower stars. Towards this end we run a conservation breeding program for sunflower stars in the Salish Sea of Washington, where sunflower stars have persisted in much reduced numbers. We here report on a variety of investigations into the temperature tolerance of sunflower stars, focusing on their poorly studied early life stages from their planktonic embryos and larvae, through metamorphosis and settlement as they transition to the benthos, and then for eight months of juvenile growth. Our results indicate that the optimum temperature for early life stage sunflower stars is more than 4°C higher than ambient temperatures in the Salish Sea, and that the juveniles demonstrate enhanced performance to a simulated marine heat wave. These results suggest that Salish Sea-derived sunflower stars would be robust to current and even near-future predicted temperatures in the south of their former range.
Building Foundation Models to Characterize Cellular Interactions via Geometric Self-Supervised Learning on Spatial Genomics
Authors: You Yuning , Wang Zitong Jerry , Fleisher Kevin , Liu Rex , Thomson Matt 
Published: January 2025

AbstractCellular interactions form the fundamental/core circuits that drive development, physiology, and disease within tissues. Advances in spatial genomics (SG) and artificial intelligence (AI) offer unprecedented opportunities to computationally analyze and predict the behavior of cell intricate networks, and to identify interactions that drive disease states. However, challenges arise in bothmethodologyandscalability:(i)how to computationally characterize complicated cellular interactions of multi-scale nature where chemical genes/circuits in individual cells process information and drive interactions among large numbers of diverse cell types, and(ii)how to scale up the pipeline to accommodate the increasing volumes of SG data that map transcriptome-scale gene expression and spatial proximity across millions of cells. In this paper, we introduce theCellular Interaction Foundation Model(CI-FM), an AI foundation model functioning to analyze and simulate cellular interactions within living tissues. In the CI-FM pipeline, we explicitly capture and embed cellular interactions within microenvironments by leveraging the powerful and scalable geometric graph neural network model, and optimize the characterization of cellular interactions with a novel self-supervised learning objective – we train it to infer gene expressions of cells based upon their interacting microenvironment. As a result, we construct CI-FM with 100 million parameters by consuming SG data of 23 million cells. Our benchmarking experiments show CI-FM effectively infers gene expressions conditional on the microenvironmental contexts: we achieve a high correlation and a low mismatch error (MSE of 1.1% relative to the square median expression), with 79.4% of cells on average being annotated as the similar cell type based on their predicted and actual expressions. We demonstrate the downstream utility of CI-FM by: (i) applying CI-FM to embed tumor samples to capture cellular interactions within tumor microenvironments (ROC-AUC score of 0.76 on classifying sample conditions via linear probing on embeddings), and identifying shared signatures across samples; and (ii) using CI-FM to simulate changes in microenvironmental composition in response to T cell infiltration, which highlights how CI-FM can be leveraged to model cellular responses to tissue perturbations – an essential step toward constructing “AI virtual tissues”. Our model is open source and publicly accessible athttps://huggingface.co/ynyou/CIFM.
URAdime – a tool for analyzing primer sequences in sequencing data to identify dimers and super-amplicons
Authors: Limberis Jason D , Metcalfe John Z 
Published: January 2025

AbstractTargeted sequencing of multiplex PCR amplicons is commonplace in research laboratories and clinical diagnostics. There are numerous tools for the a priori optimization of primers and reactions, but no tools to detect specific problematic primers post hoc. We developed URAdime, a tool for analyzing primer sequences in sequencing data to identify dimers and super-amplicons. We show that it successfully detects these unwanted amplicons and accurately attributes their generation to specific primers.
Axonal pathology differentially affects human Purkinje cell subtypes in the essential tremor cerebellum
Authors: Widner James , Faust Phyllis L. , Louis Elan D. , Fujita Hirofumi 
Published: January 2025

AbstractThe cerebellar cortex is organized into discrete regions populated by molecularly distinct Purkinje cells (PCs), the sole cortical output neurons. While studies in animal models have shown that PC subtypes differ in their vulnerability to disease, our understanding of human PC subtype and vulnerability remains limited. Here, we demonstrate that human cerebellar regions specialized for motor vs cognitive functions (lobule HV vs Crus I) contain distinct PC populations characterized by specific molecular and anatomical features, which show selective vulnerability in essential tremor (ET), a cerebellar degenerative disorder. Using a known PC subtype marker, neurofilament heavy chain (NEFH), we found that motor lobule HV contains PCs with high NEFH expression, while cognitive lobule Crus I contains PCs with low NEFH expression in post-mortem samples from healthy controls. In the same cerebella, PC axons in lobule HV were 2.2-fold thicker than those in Crus I. Across lobules, axon caliber positively correlated with NEFH expression. In ET cerebella, we identified motor lobule-specific PC axon pathology with a 1.5-fold reduction in caliber and increased axon variability in lobule HV, while Crus I axons were unaffected. Tremor severity and duration in ET correlated with axon diameter variability selectively in lobule HV PCs. Given that axonal caliber is a major determinant of neural signaling capacity, our results (1) suggest that disrupted cerebellar corticonuclear signaling is occurring in ET, (2) provide evidence of region-specific PC subtypes in the human cerebellum and offer insight into how selective PC vulnerability may contribute to the pathophysiology of cerebellar degeneration.Significance StatementThe cerebellar cortex has a uniform laminar architecture but contains heterogeneous cell types. Purkinje cells (PCs), the sole output cells of the cerebellar cortex, include subtypes whose lobular distribution is thought to underlie functional segmentation and patterned degeneration of the cerebellum in animal models. However, human PC subtypes and their disease vulnerability remain unknown. Here, we establish the existence of human PC subtypes that appear conserved across mammalian species using both marker expression and axonal thickness. Consistent with its phenotype, we demonstrate differential PC vulnerability to degeneration in essential tremor cerebella, where motor function- mediating PC subtypes display significant axonal thinning, while axons of cognitive function-mediating PC subtypes are spared. These findings advance Purkinje cell type- oriented research in cerebellar disorders.
A niche-dependent redox rheostat regulates epithelial stem cell fate in the distal colon
Authors: Chen Xi , Raghunathan Krishnan , Bao Bin , Ngwa Elsy , Kwong Andrew , Wu Zhongyang , Babcock Stephen , Baek Clara , Ye George , Muppirala Anoohya , Peng Qianni , Rutlin Michael , Bhaumik Mantu , Yang Daping , Kotlarz Daniel , Jadhav Unmesh , Rao Meenakshi , Weerapana Eranthie , Zhou Xu , Ordovas-Montanes Jose , Snapper Scott B. , Thiagarajah Jay R. 
Published: January 2025

SUMMARYThe intestinal epithelium is a rapidly regenerating tissue dependent on resident stem cell self-renewal and differentiation. The reactive oxygen species (ROS) generating enzyme NADPH oxidase 1 (NOX1) is spatially restricted to the crypt base epithelium in the distal colon. Using NOX1-deletion, along with cell-specific redox reporter mice, lineage tracing, single-cell transcriptomics, cysteine proteomics, and cell metabolic studies, we investigated the role of redox balance in colonic stem cell function. We show that distal colonic stem cells uniquely require NOX1-generated ROS to maintain a relatively oxidized state that promotes stem cell self-renewal. Mechanistically, this occurs through a cellular circuit that connects hypoxia inducible factor 1 (HIF1α)-dependent signaling to ROS regulation of isocitrate dehydrogenase 1 (IDH1), to maintain efficient cell cycle entry and cell fate determination in the relatively hypoxic distal colonic niche environment. Our studies therefore provide a basis for understanding regeneration dynamics and disease propensity in the distal large intestine.GRAPHICAL ABSTRACTHIGHLIGHTSThe balance of cycling intestinal stem cells (ISCs) versus committed epithelial cells in the uniquely hypoxic niche of the distal colon is regulated by NADPH oxidase 1 (NOX1) dependent H2O2both at homeostasis and during regeneration.Physiological increases in cellular H2O2favors maintenance of glycolysis in ISCs for self-renewal through regulation of isocitrate dehydrogenase 1 activity.Maintenance of the increased cellular oxidative state stabilizes HIF1α through a re-enforcing metabolic circuit.A shift from a relatively oxidative to a reductive cell environment in distal colonic ISCs leads to decreased progression through the cell cycle and altered cell fate determination.
Spillover is the dominant non-photochemical quenching mechanism in angiosperms
Authors: Nanda Sanchali , Cainzos Maximiliano , Shutova Tatyana , Fataftah Nazeer , Fleig Verena , Lihavainen-Bag Jenna , Bag Pushan , Holzwarth Alfred R. , Jansson Stefan 
Published: January 2025

AbstractNon-photochemical quenching (NPQ) is an important photoprotective process in plants, but all molecular details of the process(es) involved are yet not understood. We have used advanced spectroscopic techniques (including simultaneous time- and spectrally-resolved room-temperature chlorophyll fluorescence analysis and spectro-kinetic deconvolution) to analyse the processes inArabidopsis, hybrid aspen and Scots pine plants. We used four well-characterizedArabidopsislines (npq1, npq2, npq4and L17) affected in NPQ, together with hybrid aspen lines with corresponding modifications that we generated. The data are described best by a model for NPQ induction with up to five fluorescence components representing distinct biochemical entities. A dominant fluorescing species at the end of NPQ induction was identified as functionally detached and quenched LHCII but most importantly we believe that one represents a “PSII-PSI (Photosystem II-Photosystem I) complex” where direct energy transfer between PSII and PSI (spillover) take place. This provides strong quenching in all three plant species. We suggest a new integrated model for NPQ in higher plants where spillover is a major element and suggest roles for PsbS and zeaxanthin. Moreover, we discuss the link between NPQ and thylakoid rearrangements as thylakoid destacking facilitates direct contact between PSII and PSI; a prerequisite for spillover.
GRNFomer: Accurate Gene Regulatory Network Inference Using Graph Transformer
Authors: Hegde Akshata , Cheng Jianlin 
Published: January 2025

AbstractMotivationGene Regulatory Networks (GRNs) are crucial for understanding cellular processes, but accurately inferring them from gene expression data remains challenging due to the complex, nonlinear interactions between genes and the high dimensionality of the data. We introduce GRNFormer, an advanced graph transformer model designed to accurately infer regulatory relationships between transcription factors and target genes from single-cell RNA-seq transcriptomics data, while supporting generalization across species and cell types.ResultsGRNFormer surpasses existing methods in both accuracy and scalability, achieving an AUROC of 90% and an AUPRC of 86% on test datasets. Our case study on human embryonic stem cells highlights its ability to identify biologically meaningful regulatory interactions and pathways. The freely accessible GRNFormer tool streamlines GRN inference, presenting significant potential to drive advancements in omics data analysis and systems biology.Availabilityhttps://github.com/BioinfoMachineLearning/GRNformer.git
Target the Heart: a new axis of Alzheimer’s disease prevention
Authors: Heller Lawrence I. , Lowe Allison S. , Del Rosario Hernández Thaís , Gore Sayali V. , Chatterjee Mallika , Creton Robbert 
Published: January 2025

ABSTRACTCyclosporine A and other calcineurin inhibitors have been identified as prospective treatments for preventing Alzheimer’s disease. Utilizing a neural network model, Z-LaP Tracker, we previously found that calcineurin inhibitors elicit a unique behavioral profile in zebrafish larvae characterized by increased activity, acoustic hyperexcitability, and reduced visually guided behaviors. Screening a large library of FDA-approved drugs using Z-LaP Tracker revealed a cluster of 65 drugs demonstrating a cyclosporine A-like behavioral profile. 14 of these drugs were heart medications, including angiotensin receptor blockers, beta-blockers, alpha-adrenergic receptor antagonists, and a statin. This suggests some heart medications may be effective in preventing or ameliorating Alzheimer’s disease pathology. Other studies have shown that many of these 14 drugs directly or indirectly inhibit the calcineurin-NFAT pathway, alike cyclosporine A. Dual administration of the heart medications with cyclosporine A in Z-LaP Tracker revealed synergistic effects: lower doses of each heart medication could be delivered in conjunction with a lower dose of cyclosporine A to evoke a similar or larger behavioral effect than higher doses of each drug independently. This indicates that co-administering a low dose of cyclosporine A with select cardiac drugs could be a potentially effective treatment strategy for Alzheimer’s diseaseandcardiovascular dysfunction, while mitigating side effects associated with higher doses of cyclosporine A. Given that heart disease precedes Alzheimer’s disease in many patients, physicians may be able to create a treatment regimen that simultaneously addresses both conditions. Our results suggest that cyclosporine A combined withsimvastatin, irbesartan, cilostazol, doxazosin,ornebivololare the most promising candidates for future exploration.
Allocentric Flocking
Authors: Salahshour Mohammad , Couzin Iain D. 
Published: January 2025

AbstractUnderstanding how group-level dynamics arise from individual interactions re- mains a core challenge in collective behavior research. Traditional models assume that animals follow simple behavioral rules, like explicitly aligning with neighbors, yet experimental support for such interactions is often lacking. Here we consider a model grounded in the neurobiological principles underlying animals’ navigational circuits, particularly the fact that animals encode their headings, and also bearings to objects (e.g., other individuals) in their environment, via a world-centered—allocentric—neural coding. We compare this to an egocentric representation, where bearings are encoded with respect to the arbitrary heading of the animal. An allocentric framework, as op- posed to an egocentric one, is shown to enable effective tracking of dynamically moving targets. Moreover, we demonstrate that when individuals themselves act as sensory inputs to each other, that sophisticated, coherent collective motion can emerge di- rectly from navigational circuits (and thus, may readily evolve in nature), without requiring explicit alignment, or additional rules of interaction.
Warming, but not acidification, increases metabolism and reduces growth of redfish (Sebastes fasciatus) in the Gulf of St. Lawrence
Authors: Guitard Joëlle J. , Chabot Denis , Senay Caroline , Robert Dominique , Deslauriers David 
Published: January 2025

AbstractUnderstanding the effects of global change, including temperature, pH, and oxygen availability, on commercially important species is crucial for anticipating consequences for these resources and their ecosystems. In the Gulf of St. Lawrence (GSL), redfish (Sebastes spp.) have been under moratorium from 1995 to 2024, with a massive recruitment observed in 2011–2013. However, little is known about their metabolic and thermal physiology, making predictions of their response to changing GSL conditions challenging. To address this, we quantified the effects of four acclimatation temperatures (2.5, 5.0, 7.5, and 10.0 ℃) and two pH levels (7.35 and 7.75) on standard and maximum metabolic rates (SMR and MMR), aerobic scope (AS), hypoxia tolerance (O2crit), food consumption, and growth in redfish. SMR, MMR, and AS increased with temperature, but growth decreased at the highest temperature, likely due to increased metabolic demand, with food consumption similar across 5.0 to 10.0 °C treatments. O2crit was lower for fish acclimated to 2.5 and 5.0 ℃, making redfish less hypoxia-tolerant at higher temperatures. Except from SMR, no significant effect of pH was observed. These results suggest that future changes in the GSL will challenge redfish, with potential long-term effects on their growth due to increased energy requirements.
MIReVTD, a Minimum Information Standard for Reporting Vector Trait Data
Authors: Ryan Sadie J. , Huxley Paul J. , Lippi Catherine A. , Pawar Samraat , Cator Lauren , Rund Samuel S.C. , Johnson Leah R. 
Published: January 2025

AbstractVector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. Here, we present MIReVTD, a Minimum Information standard for Reporting Vector Trait Data. Our reporting checklist balances completeness and labor- intensiveness with the goal of making these important experimental data easier to find and reuse, without onerous effort for scientists generating the data. To illustrate the standard, we provide an example reproducing results from anAedes aegyptimosquito study.
deepNGS Navigator: Exploring antibody NGS datasets using deep contrastive learning
Authors: MohammadiPeyhani Homa , Lee Edith , Bonneau Richard , Gligorijevic Vladimir , Lee Jae Hyeon 
Published: January 2025

AbstractHigh-throughput sequencing uncovers how B-cells adapt and evolve in response to antigens by generating B-cell receptor (BCR) sequences at an unprecedented scale. As BCR datasets grow to be millions of sequences, using efficient computational methods becomes crucial for analyzing and understanding complex patterns within the data. One important aspect of antibody sequence analysis is detecting clonal families or clusters of related sequences, whether they come from immunization, synthetic libraries or even ML-generated datasets. Such analysis helps us to understand how sequences are evolutionarily connected, and how they might have been selected or evolved. Here we introduce deepNGS Navigator, a computational tool that leverages language models and contrastive learning to transform large datasets of antibody sequences into 2D representations. The resulting 2D maps offer an intuitive visualization of overall diversity of input datasets, which can be clustered based on the sequence distances and their densities across the map. Beyond grouping related sequences, the 2D maps can also point to evolutionary trajectories and capture mutational patterns among closely related sequences. By analyzing properties like charge, hydrophobicity, number of sequence neighbors, and read counts, the maps highlight which clusters are most promising for further investigation while also detecting anomalies or noisy sequences with higher risk. We demonstrate deepNGS Navigator’s utilities on various datasets, including: 1) a synthetic library from a yeast display targeting HER2, 2) a machine learning-generated dataset with a hierarchical tree structure, 3) NGS sequences from a llama immunized against COVID RBD, 4) human naive and memory B-cell sequences, and 5) an insilico dataset simulating B-cell clonal lineages.