bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phyla: Towards a Foundation Model for Phylogenetic Inference
Authors: Shen Andrew , Ektefaie Yasha , Jain Lavik , Farhat Maha , Zitnik Marinka 
Published: January 2025

AbstractDeep learning has made strides in modeling protein sequences but often struggles to generalize beyond its training distribution. Current models focus on learning individual sequences through masked language modeling, but effective protein sequence analysis demands the ability to reason across sequences, a critical step in phylogenetic analysis. Training biological foundation models explicitly for intersequence reasoning could enhance their generalizability and performance for phylogenetic inference and other tasks in computational biology. Here, we report an ongoing development of PHYLA, an architecture that operates on an explicit, higher-level semantic representation of phylogenetic trees. PHYLA employs a hybrid state-space transformer architecture and a novel tree loss function to achieve state-of-the-art performance on sequence reasoning benchmarks and phylogenetic tree reconstruction. To validate PHYLA’s capabilities, we applied it to reconstruct the tree of life, where PHYLA accurately reclassified archaeal organisms, such as Lokiarchaeota, as more closely related to bacteria—aligning with recent phylogenetic insights. PHYLA represents a step toward molecular sequence reasoning, emphasizing structured reasoning over memorization and advancing protein sequence analysis and phylogenetic inference.
Novel pink-spot disease in North American kelp nurseries
Authors: Schenk Siobhan , Jackman Andrea Sarah , Clark Jennifer , Parfrey Laura Wegener 
Published: January 2025

AbstractMacroalgal cultivation is a growing industry in North America, but is well established in Asia and Africa, where macroalgal disease cause crop losses, placing a significant economic burden on farmers. As kelp cultivation intensifies in North America, kelp disease prevalence is expected to increase in tandem. With input from the kelp-growing community through an online survey, we describe the prevalence of a novel kelp disease, pink-spot disease, which has been observed in Canada and the United States onSaccharina latissima, Alaria marginata, Nereocystis luetkeana, andMacrocystis tenuifolia. Eight of the fourteen (57%) surveyed kelp growers report bright pink spots (pink-spot disease) on their kelp spools in the land-based nursery stage. Along with the grower survey, we conducted 16S rRNA amplicon sequencing in 2021 and 2022 to investigate the causative agent of pink-spot disease and associated bacterial community changes on infectedSaccharina latissima(sugar kelp) spools. Our data in both years show that a member of the genusAlgicolais enriched on visibly diseased spool regions compared to asymptomatic spool regions and may be the causative agent of pink-spot disease. As macroalgal cultivation continues to intensify, monitoring diseases is important to mitigate potential negative impacts.
Notes on the diet and size ofCassiopea
Authors: Muffett Kaden , Mammone Marta , Morejón-Arrojo Ramón D. , Toso Andrea , Fumarola Lara M. , Klovrza Anabelle Marques , Cardona Valentina , Geson Serafin Mendez , Rowe Claire E. , Bellantuono Anthony , Miglietta Maria Pia , Collin Rachel 
Published: January 2025

AbstractMedusae of the genusCassiopeaare common components of tropical and subtropical coastlines globally. Despite the broad distribution of this benthic scyphozoan, much about their ecology remains poorly described. Here, we collected over 100 adultCassiopeaindividuals from Panama, the eastern United States, Cuba, the Philippines, Italy and Australia to examine continuity and differences in their diet across space, and to investigate whether their unique lifestyle is reflected in their diet. We found the majority of prey items to be associated with the epibenthos. The recovered prey were supermajority crustaceans, mainly harpacticoid copepods, with pteropods, nematodes, and miscellaneous eggs common as secondary components. Within the gastrovascular cavity of a single medusa, we found up to 379 items. There was a limited relationship between medusa size and prey items. Location had an impact on gut content diversity and medusa size had a small impact on the number of taxa found within the gut. In some sites, prey were scarce or absent from all medusae sampled. Overall, we reaffirm the diet previously recorded for small medusae in Puerto Rico and show that similar components are common in large and small medusae from throughout the East and West Atlantic and the Philippines.
siRNA complexation with Galactose-Functionalized Dendrimer: PAMAM vs PETIM
Authors: Maity Tarun , Kumar Yogendra , Maiti Prabal K. 
Published: January 2025

AbstractsiRNA-based therapies hold promise for treating various diseases, but efficient and targeted delivery remains challenging. This study explored the potential of galactose-functionalized poly(amidoamine) (Gal-PAMAM) and poly(propyl ether amine) (Gal-PETIM) dendrimers as siRNA carriers using all-atom molecular dynamics simulations. This study revealed distinct complexation behavior for both the system in physiological (pH ∼ 7) and high (pH ∼ 10) environments. Electrostatic interactions dominated complexation, with PMF profiles revealing stronger binding affinity for Gal-PAMAM/siRNA at pH 7. siRNA displayed lower flexibility at pH 7 for both complexes, indicating enhanced stability. These findings suggest that Gal-PAMAM dendrimers at physiological pH hold promise for efficient siRNA delivery due to their superior complex stability and interaction strength. However, in polar solvents, Gal-PETIM exhibits hydrophobic behavior, enabling rapid delivery.
When neighbours play a role: a systems-level analysis of protein interactions conditioning cancer driver mutation effects
Authors: Vital Márcia FS , Miranda João AI , Carrolo Margarida , Quintela António , Pinto Francisco R 
Published: January 2025

AbstractBackgroundCancer is driven by the accumulation of somatic mutations, including driver mutations that confer a selective advantage to cancer cells. Driver proteins operate within complex interaction networks, and their activity is conditioned by neighbour proteins. Understanding the interplay between driver mutations and the expression of their neighbour proteins can provide insights into cancer biology and potential therapeutic targets.MethodsWe assessed associations between expression of neighbour proteins and driver mutation status, comparing both between and within cancer types. We further evaluated if neighbours were enriched in significant associations with multiple drivers and characterised the impact of neighbour expression on overall survival for all cancer types.ResultsWe found a significant correlation between the number of driver associations a neighbour gene has and the number of sign-coherent survival associations, particularly for neighbours enriched in positive associations, where high neighbour expression correlated with increased driver mutations and poorer survival. We identified 247 neighbours simultaneously enriched in positive driver and survival associations and 39 neighbours simultaneously enriched in negative driver and survival associations.ConclusionsOur study systematically identified neighbours associated with driver mutation status. Complementary evidence from survival analysis and the literature suggests that neighbours enriched in driver associations are promising drug target candidates.
Fibroblast Density is a Risk Factor for Drug-induced Arrhythmias
Authors: Hirose Kayo , Umezu Shinjiro , Sato Daisuke 
Published: January 2025

AbstractA recent study by Kawatouet al. has shown that the local heterogeneity of ion channel conductance is a critical substrate for focal or reentrant arrhythmias. However, the role of fibroblasts with repolarization heterogeneity in the initiation and maintenance of arrhythmias remains unknown. In this study, we investigated how diffuse fibrosis contributes to the formation of focal and reentrant arrhythmias under drug-induced heterogeneity using physiologically detailed mathematical models of the human heart. To simulate drug-induced heterogeneity, we varied the maximum conductance of transmembrane potassium and calcium currents, leading to heterogeneity in action potential duration (APD). Then, we assessed the effects of different fibrosis densities (FD) on the occurrence of premature ventricular complexes (PVCs). Fibroblasts were randomly and evenly inserted into the tissue, and various FD levels ranging from 0 to 35% were examined. We found a biphasic relationship between FD and drug-induced PVCs. Within a certain range of FD, FD positively correlated with PVC susceptibility. However, excessively high fibrosis levels were associated with reduced susceptibility to PVCs. In addition, the self-sustainability of arrhythmias exhibited a positive correlation with FD. This study demonstrates the interplay between the diffuse fibrosis and the drug-induced heterogeneity of APD in the genesis of ventricular arrhythmias.Author summarySudden cardiac death remains a leading cause of death worldwide. Understanding the mechanisms underlying arrhythmia and its precursors is critical for the development of effective therapies and drugs. Repolarization heterogeneity plays a crucial role in both the initiation and maintenance of arrhythmias. Fibroblasts constitute a vital component of cardiac structure, originating from the remodeling of ventricular wall cells or the transformation of injured myocardial cells. Fibroblasts are known to couple with and alter the electrical properties of myocardial cells. However, our understanding of the role of fibroblasts in the development of arrhythmia remains limited. In this study, we employed a physiologically detailed mathematical model of cardiac tissue to investigate the roles of drug-induced heterogeneity and diffuse fibrosis in the initiation and maintenance of arrhythmias. We used 2D and 3D computational models to simulate various levels of drug-induced heterogeneity conditions with normal to pathological levels of fibroblast density (FD). We found that within a certain range of FD, fibroblasts promote PVCs under drug-induced heterogeneity. However, if FD exceeds 30%, the occurrence of PVCs decreases (biphasic relationship). On the other hand, the self-sustainability of VF (ventricular fibrillation) consistently increases with FD. This study implies that fibroblasts in cardiac tissue may play different roles in the initiation and maintenance of arrhythmia.
A comprehensive comparison on clustering methods for multi-slide spatially resolved transcriptomics data analysis
Authors: Xiong Caiwei , Shuai Huang , Zhou Muqing , Zhang Yiyan , Wu Wenrong , Li Xihao , Yao Huaxiu , Chen Jiawen , Li Yun 
Published: January 2025

AbstractSpatial transcriptomics (ST) data, by providing spatial information, enables simultaneous analysis of gene expression distributions and their spatial patterns within tissue. Clustering or spatial domain detection represents an essential methodology for ST data, facilitating the exploration of spatial organizations with shared gene expression or histological characteristics. Traditionally, clustering algorithms for ST have focused on individual tissue sections. However, the emergence of numerous contiguous tissue sections derived from the same or similar tissue specimens within or across individuals has led to the development of multi-slide clustering methods. In this study, we assess seven single-slide and three multi-slide clustering methods on two simulated datasets and three real datasets. Additionally, we investigate the effectiveness of pre-processing techniques, including spatial coordinate alignment (for example, PASTE) and gene expression batch effect removal (for example, Harmony), on clustering performance. Our study provides a comprehensive comparison of clustering methods for multi-slide ST data, serving as a practical guide for method selection in various scenarios.
Light induces Phytochrome B SUMOylation to recruit the immune regulator NPR1 in nuclear condensates to control immunity in plants
Authors: Ghosh Srayan , Ghosh Shraboni , Mansi Mansi , Long Xian , Clark Lisa , Srivastava Anjil , Kakkunath Sumesh , Orosa Beatriz , Bailey Mark , Bhagat Prakash Kumar , Gough Catherine , Zhang Cunjin , de Lucas Miguel , Sadanandom Ari 
Published: January 2025

SUMMARYIt has long been observed that light perception by phytochromes control plant immunity, however, the underpinning molecular mechanism is less well understood. We demonstrate that light mediated SUMO conjugation to Phytochrome B (PhyB) is critical for increasing cellular salicylic-acid (SA) levels to orchestrate systemic acquired resistance (SAR) upon avirulent bacterial infection. SUMOylation is critical for PhyB nuclear condensate formation during light activated immunity. Light induced PhyB SUMOylation recruits NPR1, through its SUMO interacting motif to nuclear condensates to elevate SA levels for immune responses. In the dark during SAR, elevated SA levels substitute for light to maintain PhyB SUMOylation and immune-related photobody formation by stimulating the degradation of PhyB targeting deSUMOylase, OTS1. SUMOylated PhyB-NPR1 immune photobodies associate with TGA transcription factor associated chromatin to trigger immune gene expression. We unravel a mechanism where SUMOylation can enable light to recruit NPR1 to PhyB nuclear condensates to form immune photobodies to regulate plant immunity.HighlightsLight-dependent immunity in plants relies on the SUMO mediated interaction between the photoreceptor PhyB and the Salicylic Acid (SA) receptor NPR1.Light induced PhyB SUMOylation recruits NPR1 to nuclear condensates which we identify as immune photobodies that elevate SA levels for immune responses.In darkness, SA can replace light in enabling PhyB-NPR1 immune photobody formation by regulating SUMOylation revealing a SA mediated mechanism for controlling PhyB liquid-liquid phase separation.SUMO-modified PhyB-NPR1 immune photobodies regulate transcriptional activity of immune associated chromatin to shape defence responses in plants.
Identification of Two Elusive Human Ribonuclease MRP-Specific Protein Components
Authors: Che Rui , Mirani Bhoomi , Panah Monireh , Chen Xiaotong , Luo Hong , Alexandrov Andrei 
Published: January 2025

SUMMARYAll known protein components of one of the longest-studied human ribonucleoprotein ribozyme nuclear Ribonuclease MRP (RNase MRP), which processes pre-rRNA at ITS1 site 2, are shared with Ribonuclease P (RNase P), which cleaves pre-tRNA 5′ leader sequences. Our genome-wide forward genetic screening identified two poorly characterized human genes, which we named RPP24 and RPP64. We show that these two genes are required for pre-rRNA ITS1 site 2 processing and their protein products efficiently associate with RNA MRP. Unlike all other human RNase MRP protein components, RPP24 and RPP64 are not required for RNase P activity and do not associate with RNase P-specific RNA H1. Despite extremely limited sequence homology, RPP24 and RPP64 exhibit predicted structural similarities to two RNase MRP-specific components inS. cerevisiae, with specific differences in RPP64 regions of substrate recognition. Collectively, our functional screening and validation revealed the first two protein components unique to human nuclear RNase MRP.
To hybridise or not to hybridise? Systematic review and meta-analysis of woodpeckers
Authors: Bakai Antonii , Fuchs Jérôme , Gorman Gerard , Sajdak Dominika , Kajtoch Łukasz 
Published: January 2025

ABSTRACTHybridisation is a common phenomenon among birds in general. Woodpeckers (Picidae) are no exception, as approximately 20% of species are known to hybridise and for many others interspecific mating is suspected. However, the mechanisms and consequences (phenotypic and genetic) of hybridisation are known for only a fraction of woodpecker species. Here, we conduct a systematic review on the literature that deals with hybridisation in woodpeckers and use a meta-analytical approach to examine the available geographical and genetic data. According to available published data, the majority of woodpeckers that hybridise inhabit the Neotropics, followed by the Nearctic and the Palearctic. Hybridisation appears to be less common in the Afrotropic and Oriental regions. As expected, genetic distances are substantially lower between hybridising species pairs than between non hybridising congenerics. This implies that hybridisation is typical for “young” (sister) pairs of species, that typically have similarities in their respective reproductive biology, ecology and ethology, and thus had less time for genetic incompatibilities to build up. These similarities also explain the difficulties experienced in studies on hybrid woodpeckers, but these could be overcome by the use of modern techniques (remote sensing and/or citizen science combining with AI identification and genomic identification). As hybridisation plays a role in woodpecker evolution and ecology it should be considered when conservation policies for rare species are drafted.
Mining the endogenous peptidome for peptide binders with deep learning-driven optimization and molecular simulations
Authors: Hartman Erik , Samsudin Firdaus , Bond Peter J. , Schmidtchen Artur , Malmström Johan 
Published: January 2025

AbstractThe endogenous peptidome is the complete set of naturally occurring peptides, some of which are involved in processes like intercellular communication, and in innate immunity. The peptidome is also a potential source of uncharacterized peptide-based therapeutics, which has remained largely unexplored due to challenges in identifying bioactive peptides in vast peptidomic datasets. Here, we present a generalized computational pipeline that mines datasets for peptides with high affinity binding capacities. The approach employs protein-peptide docking, binding interface scoring and a deep ensemble model to dynamically learn the mapping between peptide embeddings and binding capacity. We demonstrate the utility of the pipeline by identifying endogenous peptide binders for the LPS-binding site of CD14 using experimentally defined wound fluid peptidomes. Promising candidates underwent conformational refinement and validation through all-atom molecular dynamics simulations. This pipeline offers a systematic way to uncover novel peptide binders within large peptidomes, paving the way for accelerated discovery of peptide-based therapeutics.
A comprehensive quality control pipeline in human microbiome research for large population studies
Authors: Li Ruolin , Verlouw Joost A.M. , Boer Cindy G. , Arp Pascal , van Meurs Joyce , Uitterlinden Andre G. , Kraaij Robert , Medina-Gomez Carolina 
Published: January 2025

AbstractThe widespread application of high-throughput Next Generation Sequencing (NGS) technologies has made microbiome research an emerging field in public health and biomedical sciences. However, there are still many challenges that need to be addressed in this field. Pipelines available to generate microbiome data across cohorts are diverse, and sources of variation to be recorded and evaluated during microbiome profiling have not been standardized. Moreover, meticulous quality control of the microbiome data processing, from collection to computational quantification is still challenging, especially in large population studies. Innovative approaches are required to handle samples and to minimize the potential bias introduced by logistic hurdles in biobanking. In this paper, we describe the methodological steps surrounding the optimization of the 16s rRNA gut microbiome profiling in two large prospective cohorts the Generation R Study (mean age 9.83 ± 0.32 years) and the Rotterdam Study (mean age 62.67 ± 5.66 years). This paper also highlights potential solutions to sample mislabeling in large-scale microbiome analysis. To summarize, our study addresses common problems in human microbiome research. It aims to improve the research quality and reliability by integrating more stringent quality control standards into microbiome research.
A self-supervised deep learning pipeline for segmentation in two-photon fluorescence microscopy
Authors: Ntiri Emmanuel Edward , Xu Tony , Rozak Matthew , Attarpour Ahmadreza , Dorr Adrienne , Stefanovic Bojana , Goubran Maged 
Published: January 2025

AbstractTwo-photon fluorescence microscopy (TPFM) allows in situ investigation of the structure and function of the brain at a cellular level, but the conventional image analyses of TPFM data are labour-intensive. Automated deep learning (DL)-based image processing pipelines used to analyze TPFM data require large labeled training datasets. Here, we developed a self supervised learning (SSL) pipeline to test whether unlabeled data can be used to boost the accuracy and generalizability of DL models for image segmentation in TPFM. We specifically developed four pretext tasks, including shuffling, rotation, axis rotation, and reconstruction, to train models without supervision using the UNet architecture. We validated our pipeline on two tasks (neuronal soma and vasculature segmentation), using large 3D microscopy datasets. We introduced a novel density-based metric, which provided more sensitive evaluation to downstream analysis tasks. We further determined the amount of labeled data required to reach performance on par with fully supervised learning (FSL) models. SSL-based models that were fine-tuned with only 50% of data were on par or superior (e.g., Dice increase of 3% for neuron segmentation and Dice score of 0.88 ± 0.09 for vessel segmentation) to FSL models. We demonstrated that segmentation maps generated by SSL models pretrained on the reconstruction and rotation tasks can be better translated to downstream tasks than can other SSL tasks. Finally, we benchmarked all models on a publicly available out-of-distribution dataset, demonstrating that SSL models outperform FSL when trained with clean data, and are more robust than FSL models when trained with noisy data.