bluuebunny/crossref_metadata_2025_split huggingface dataset:
Powerful and accurate case-control analysis of spatial molecular data with deep learning-defined tissue microniches
Authors: Reshef Yakir , Sood Lakshay , Curtis Michelle , Rumker Laurie , Stein Daniel J. , Palshikar Mukta G. , Nayar Saba , Filer Andrew , Jonsson Anna Helena , Korsunsky Ilya , Raychaudhuri Soumya 
Published: February 2025

AbstractAs spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches often rely on hand-crafted features such as local abundances of manually annotated, discrete cell types, which may overlook important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA uses a variational autoencoder to extract numerical “fingerprints” from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of “microniches” – small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status. We show in simulations that VIMA is well calibrated and more powerful and accurate than other approaches. We then apply VIMA to a 140-gene spatial transcriptomics dataset in Alzheimer’s dementia, a 54-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 7-marker immunohistochemistry dataset in rheumatoid arthritis (RA), in each case recapitulating known biology and identifying novel spatial features of disease.
Large scale laboratory evolution uncovers clinically relevant collateral antibiotic sensitivity
Authors: Chowdhury Farhan R. , Banari Veronica , Lesnic Vlada , Zhanel George G. , Findlay Brandon L. 
Published: February 2025

AbstractThe increasing prevalence of antibiotic resistance is a critical challenge, necessitating the development of strategies to mitigate the evolution of resistance. Collateral sensitivity (CS)-based sequential therapies have been proposed to mitigate resistance evolution. However, the evolutionary repeatability of CS across different experimental conditions and its clinical relevance remain underexplored, hindering its potential for translation into clinical practice. Here, we evolve 20-24 lineages ofE. coliagainst tigecycline (TIG) and piperacillin (PIP), antibiotics suggested to produce CS, through three separate laboratory adaptive evolution (ALE) platforms to test for the robustness of CS interactions and the effect of the choice of ALE on CS evolution. We generate over 130 resistant mutants and 540 resistance and collateral sensitivity measurements to identify a CS relationship between TIG and polymyxin B (POL) that is highly repeatable across all the ALEs tested, suggesting that this CS interaction is preserved across different evolution microenvironments. We determine the mechanism of this novel CS by showing that cells resistant to TIG deactivate the Lon protease and overproduce negatively charged exopolysaccharides, which in turn attracts the polycationic POL and renders cells hypersensitive to the drug. We find that this CS relationship is present in a clinical dataset of over 750 uropathogenic MDRE. coliisolates, and show that the soft agar gradient evolution (SAGE) platform best predicts collateral effects (CS, neutrality or cross resistance) in this dataset. Our study provides a framework for identifying robust CS with clinical implications that can reduce the emergence of resistance to our existing antibiotics.
CommercialSaccharomyces cerevisiaebaker’s yeasts: strain redundancy, genome plasticity, and colonization of the sourdough environment and the human body
Authors: Rácz Hanna Viktória , Imre Alexandra , Németh Bálint , Antunovics Zsuzsa , Bazenova Rizagul , Bendixsen Devin , Harmath Andrea , Herman Lilla , Fülep Ádám , Lopandic Ksenija , Máthé Endre , Póliska Szilárd , Pócsi István , Stelkens Rike , Pfliegler Walter P. 
Published: February 2025

AbstractLeavening dough is one of the most widespread applications of fermentative yeast and the most common practice for the general public to come into contact with microbial cultures.Saccharomyces cerevisiaeis the typical species used for dough making, but the evolutionary origin of strains isolated from dough is mixed. Here, using 49 newly sequenced and 183 previously described isolates from the bakery environment, we show that the traditional strains used in Europe for sourdough making are more closely related to Chinese Mantou sourdough lineages than to commercially used baker yeast strains. Surprisingly, the expansion of these traditional European strains into other human-associated niches, including the human body, has been very limited. This is in stark contrast to the Mixed-origin clade commercial baking yeasts, which consists of only a few globally distributed clonal lineages that dominate the yeast market and recurrently colonize sourdoughs and human hosts. These clonal lineages consistently maintain their ploidy, unique heterozygous chromosomal rearrangements, and stable aneuploidies in addition to several diverse structural variants. In addition to the previously known diploid and tetraploid groups of commercial isolates, we describe a widely distributed stable triploid aneuploid clonal lineage. We show that company practices and global trade help the distribution of these clonal clusters and that these yeasts are characterized by their exclusively mitotic reproduction.
Temporal occupancy distributions reveal multifaceted and heterogeneous effects of climatic variation on montane butterflies
Authors: Reis Gbolahan A , Forister Matthew L , Halsch Christopher A , Dittemore Clare M , Shapiro Arthur M , Gompert Zachariah 
Published: February 2025

AbstractClimate change has substantially altered the phenology of many organisms, with profound implications for biodiversity and species interactions. However, phenological studies of insects have often focused on shifts over time, without explicitly examining how climate drives these changes. In addition, the metrics used to measure changes in phenology often capture only limited information about the flight period. In this study, we analyzed multidecadal observational and climate data using a hierarchical Bayesian framework to model the annual probability of occurrence distributions for 135 butterfly species across five montane sites along an elevational gradient. Our analysis used polynomial models that account for shifts in abundance and the timing and length of flight periods to investigate the effect of climate on butterfly phenology. We found that spring maximum and minimum temperatures, as well as winter precipitation, are important predictors of butterfly phenology. High winter precipitation delayed phenology at high-elevation sites where substantial snowfall occurs, while increased spring maximum temperatures generally advanced phenology across all elevations. Even modest increases in spring minimum temperatures caused substantial shift in phenology. We documented variability in the effect of climate on phenology across sites, among species within a site and among populations of the same species across different sites with variability among species within a site being especially pronounced. We also found that climate influences different aspects of the flight period differently (e.g., timing versus duration), underscoring the need to move beyond single metrics such as day of first flight. These findings highlight the importance of examining the entire flight period and considering the interplay of species-specific traits to improve predictions of how climate change impacts phenology. Such approaches might be essential for designing more targeted and effective conservation strategies in response to ongoing climate change.
Lactate dehydrogenase is the Achilles’ heel of Lyme disease bacteriumBorreliella burgdorferi
Authors: Sze Ching Wooen , Lynch Michael J , Zhang Kai , Neau David B. , Ealick Steven E. , Crane Brian R , Li Chunhao 
Published: February 2025

AbstractAs a zoonotic pathogen, the Lyme disease bacteriumBorreliella burgdorferihas evolved unique metabolic pathways, some of which are specific and essential for its survival and thus present as ideal targets for developing new therapeutics.B. burgdorferidispenses with the use of thiamin as a cofactor and relies on lactate dehydrogenase (BbLDH) to convert pyruvate to lactate for balancing NADH/NAD+ratios. This report first demonstrates that BbLDH is a canonical LDH with some unique biochemical and structural features. A loss-of-function study then reveals that BbLDH is essential forB. burgdorferisurvival and infectivity, highlighting its therapeutic potential. Drug screening identifies four previously unknown LDH inhibitors with minimal cytotoxicity, two of which inhibitB. burgdorferigrowth. This study provides mechanistic insights into the function of BbLDH in the pathophysiology ofB. burgdorferiand lays the groundwork for developing genus-specific metabolic inhibitors againstB. burgdorferiand potentially other tick-borne pathogens as well.
Structural basis of flagellar rod assembly on the FliPQR protein-export channel
Authors: Kinoshita Miki , Miyata Tomoko , Makino Fumiaki , Imada Katsumi , Namba Keiichi , Minamino Tohru 
Published: February 2025

AbstractThe FliPQR complex constitutes a channel for export of the bacterial flagellar proteins involved in axial structure assembly. It also serves as a template for flagellar rod assembly. A periplasmic gate, formed by the N-terminal α-helices of FliP and FliR, remains closed until FliE assembles onto FliP and FliR. The mechanism by which FliE opens the gate and assembles has remained unclear. Here, we present a cryoEM structure of the FliPQR complex in closed form at 3.0 Å resolution. A β-cap formed by the N-terminal β-strands of FliP and FliR creates a tight seal in the closed gate. Interaction of FliE with FliP and FliR induces a conformational change in FliP and FliR, with their N-terminal α-helices move up and outward. Consequently, the N-terminal β-strands of FliP and FliR start opening the periplasmic gate one after another and form a docking site for FliE to initiate rod assembly.
Real-time capture of σNtranscription initiation intermediates reveals mechanism of ATPase-driven activation by limited unfolding
Authors: Mueller Andreas U. , Molina Nina , Darst Seth A. 
Published: February 2025

AbstractBacterial σ factors bind RNA polymerase (E) to form holoenzyme (Eσ), conferring promoter specificity to E and playing a key role in transcription bubble formation. σNis unique among σ factors in its structure and functional mechanism, requiring activation by specialized AAA+ ATPases. EσNforms an inactive promoter complex where the N-terminal σNregion I (σN-RI) threads through a small DNA bubble. On the opposite side of the DNA, the ATPase engages σN-RI within the pore of its hexameric ring. Here, we perform kinetics-guided structural analysis ofde novoformed EσNinitiation complexes and engineer a biochemical assay to measure ATPase-mediated σN-RI translocation during promoter melting. We show that the ATPase exerts mechanical action to translocate about 30 residues of σN-RI through the DNA bubble, disrupting inhibitory structures of σNto allow full transcription bubble formation. A local charge switch of σN-RI from positive to negative may help facilitate disengagement of the otherwise processive ATPase, allowing subsequent σNdisentanglement from the DNA bubble.
Massively parallel assessment of gene regulatory activity at human cortical structure associated variants
Authors: Matoba Nana , McAfee Jessica C. , Krupa Oleh , Bell Jess , Le Brandon D. , Valone Jordan M. , Crawford Gregory E. , Won Hyejung , Stein Jason L. 
Published: February 2025

AbstractGenetic association studies have identified hundreds of largely non-coding loci associated with inter-individual differences in the structure of the human cortex, though the specific genetic variants that impact regulatory activity are unknown. We implemented a Massively Parallel Reporter Assay (MPRA) to measure the regulatory activity of 9,092 cortical structure associated DNA variants in human neural progenitor cells during Wnt stimulation and at baseline. We identified 918 variants with regulatory potential from 150 cortical structure associated loci (76% of loci studied), of which >50% showed allelic effects. Wnt stimulation modified regulatory activity at a subset of loci that functioned as condition-dependent enhancers. Regulatory activity in MPRA was largely induced by Alu elements that were hypothesized to contribute to cortical expansion. The regionally specific impact of genetic variants that disrupt motifs is likely mediated through the levels of transcription factor expression during development, further clarifying the molecular mechanisms altering cortical structure.
Power-law decay of force on cell membrane tethers reflects long-ranged relaxation of membrane tension
Authors: Laborie Emeline , Callan-Jones Andrew 
Published: February 2025

AbstractMembrane tension is an acknowledged regulator of a wide array of cell functions; however, whether it acts locally or globally in different contexts remains debated. Recent experiments that locally perturb tension and measure the response elsewhere on the membrane are not conclusive, and the mechanism of diffusive tension relaxation has been called into doubt. Here, we consider the tension response to a sudden extension of a membrane tether, and report a quantitative signature of dynamic tension relaxation, which up to now is missing. We present a theory based on tension diffusion leading to a prediction of power-law decay in time of the force holding the tether, with a material-independent exponent of 1/3. This prediction is confirmed to within a few percent by re-analyzing eleven sets of tether data from two cell types with distinct membrane cortical architectures. Overall, our scaling results indicate the absence of a relevant characteristic length and therefore generically reflect tension relaxation by long-range membrane lipid flows.
Multi-sample, multi-platform isoform quantification using empirical Bayes
Authors: Talukder Arghamitra , Thavarekere Shree , Knowles David A. 
Published: February 2025

AbstractAccurate quantification of RNA isoform abundance is crucial for understanding gene regulation, cellular behavior, and disease mechanisms. While short-read (SR) sequencing provides high-throughput and cost-effective transcript quantification, it suffers from read-to-transcript ambiguity. Long-read (LR) sequencing reduces this ambiguity but faces challenges such as high error rates, biases, and lower throughput. Existing methods rely on either SR or LR data and operate on single or merged samples, failing to leverage the variability across multiple samples and the complementary strengths of both technologies. As a result, they struggle to accurately quantify low-abundance and moderate-expressed isoforms and often require complex models for sample-specific bias correction. To address these limitations, we introduceJOLI, a hierarchical model that leverages multi-sample learning to enhance transcript quantification by jointly integrating SR and LR sequencing data. By incorporating multi-sample learning, JOLI captures shared transcript structures, corrects for systematic biases, and enhances statistical power, particularly for low- and moderate-abundance isoforms. Our model applies an empirical Bayes framework, learning a shared prior across samples to improve inference consistency. By jointly modeling SR and LR data, it integrates the strengths of both technologies, achieving higher accuracy and reproducibility in transcript quantification. Through benchmarking on simulated and real RNA-seq datasets, we show that JOLI consistently outperforms single-sample EM method by improving ranking consistency, proportional agreement, and estimation accuracy while enhancing reproducibility. Specifically, in simulations, JOLI multi-sample improves Spearman correlation by 9.8% for LR and 7.7% for SR data compared to single-sample method, while for real data, the improvements are 2.56% (LR) and 1.28% (SR), respectively. Multi-sample learning further improves the quantification of isoforms with low to moderate expression levels. Furthermore, JOLI performs competitively with state-of-the-art methods, highlighting its robustness in transcript quantification.
TLR4 Modulates Simvastatin’s Impact on HDL Cholesterol and Glycemic Control
Authors: Tian Xiao , Zhang Peixiang 
Published: February 2025

ABSTRACTStatins lower cardiovascular risk by inhibiting HMG-CoA reductase, with their efficacy and adverse effects linked to SREBP-regulated cholesterol metabolism. Nutritional states— particularly the alternating fasting and feeding cycles, rather than intrinsic circadian rhythms— exert tight control over hepatic lipid metabolism via SREBP-mediated regulation of cholesterol and fatty acid synthesis. We exploited the natural diurnal oscillations in food intake in mice to determine how nutritional state modulates simvastatin’s metabolic actions. When administered during fasting, simvastatin impaired glucose homeostasis through SREBP-2–driven autophagy that enhanced PPARα activity, whereas simvastatin given during feeding reduced HDL cholesterol and improved glucose metabolism. Mechanistically, a feeding-induced surge in lipopolysaccharide (LPS) suppressed hepatic oxysterol synthesis, thereby enabling simvastatin to further deplete oxysterols and inhibit activation of LXR and SREBP-1c—key regulators of PPARα signaling under both postprandial and fasting conditions. This cascade resulted in decreased HDL cholesterol levels and improved glycemic control. Moreover, TLR4 deficiency disrupted the LPS–simvastatin interplay on LXR/SREBP-1c/PPARα signaling, reversing the HDL-lowering and glucose-improving effects seen during feeding. Together, these results identify feeding-induced LPS–TLR4 signaling as a critical determinant of simvastatin’s metabolic effects and suggest strategies to optimize statin therapy based on nutritional state.
Vagus nerve stimulation modulates information representation of sustained activity in layer specific manner in the rat auditory cortex
Authors: Shiramatsu Tomoyo Isoguchi , Ibayashi Kenji , Kawai Kensuke , Takahashi Hirokazu 
Published: February 2025

AbstractThe brain of a living organism enables stable information processing in response to constantly changing external environments and internal states. As one of such cortical modulation, the present study focused on the effect of vagus nerve stimulation (VNS) therapy on information representation of the auditory cortex. By quantifying sound representation using machine learning, we investigated whether VNS alters cortical information representation in a layer-specific and frequency band-specific manner. A microelectrode array meticulously mapped the band-specific power and phase-locking value of sustained activities in every layer of the rat auditory cortex. Sparse logistic regression was used to decode the test frequency from these neural characteristics. The comparison of decoding accuracy before and after the application of VNS indicated that sound representation of the high-gamma band activity was impaired in the deeper layers, i.e., layers 5 and 6, while it was slightly improved in the superficial layers, i.e., layers 2, 3, and 4. Moreover, there was an improvement of sound representation in theta band activity in the deeper layers, demonstrating the layer-specific and frequency band-specific effect of VNS. Given that the cortical laminar structure and oscillatory activity in multiple frequency bands helps the auditory cortex to act as a hub for feed-forward and feed-back pathways in various information processing, the current findings support the possibility that VNS provide complex effects on brain function by altering the balance of cortical activity between layers and frequency bands.
Complementary MR measures of white matter and their relation to cardiovascular health and cognition
Authors: Raykov Petar P. , Correia Marta , Tsvetanov Kamen , Henriques Rafael N. , Del Cerro León Alberto , Bracher-Smith Matthew , Escott-Price Valentina , Raykov Yordan P. , Henson Richard N. 
Published: February 2025

AbstractMagnetic Resonance Imaging (MRI) offers many ways to non-invasively estimate the properties of white matter (WM) in the brain. In addition to the various metrics derived from diffusion-weighted MRI, one can estimate total WM volume from T1-weighted MRI, WM hyper-intensities from T2-weighted MRI, myelination from the T1:T2 ratio, or from the magnetisation-transfer ratio (MTR). Here we utilise the presence of all of these MR contrasts in a population based life-span cohort of 650 healthy adults [CamCAN cohort] to identify the latent factors underlying the covariance of 11 commonly-used WM metrics. Four factors were needed to explain 89% of the variance, which we interpreted in terms of 1) fibre density / myelination, 2) free-water / tissue damage, 3) fibre-crossing complexity and 4) microstructural complexity. These factors showed distinct effects of age and sex. To test the validity of these factors, we related them to measures of cardiovascular health and cognitive performance. Specifically, we ran path analyses 1) linking cardio-vascular measures to the WM factors, given the idea that WM health is related to cardiovascular health, and 2) linking the WM factors to cognitive measure, given the idea that WM health is important for cognition. Even after adjusting for age, we found that a vascular factor related to pulse pressure predicted the WM factor capturing free-water / tissue damage, and that several WM factors made unique predictions for fluid intelligence and processing speed. Our results show that there is both complementary and redundant information across common MR measures of WM, and their underlying latent factors may be useful for pinpointing the differential causes and contributions of white matter health in healthy aging.
An integrative model of AMPA receptor trafficking reveals the central contribution of local translation in subtype-specific kinetics
Authors: Wagle Surbhit , Kracht Maximilian K. , Bührke Anne , Acker-Palmer Amparo , Kraynyukova Nataliya , Hafner Anne-Sophie , Schuman Erin M. , Tchumatchenko Tatjana 
Published: February 2025

AbstractAMPA-type glutamate receptors (AMPARs) underlie most of the excitatory synaptic transmission in the brain and are crucial for implementing long-term synaptic plasticity. AMPARs are multi-protein complexes composed of two types of subunits: pore-forming subunits GluA1-4 that assemble in the endoplasmic reticulum and form the glutamate-gated ion channel, and auxiliary subunits that modulate receptor bio-physical properties and mediate their forward trafficking to the plasma membrane. Here, using a combination of theoretical and experimental approaches, we elucidate the kinetics of essential trafficking steps and the protein sources necessary to explain the experimentally observed distribution of AMPARs and the response of different AMPAR subtypes to LTP induction. Our data indicate that the mRNA coding for one of the most abundant AMPAR auxiliary subunits, CNIH-2, is abundant in dendrites. Consistent with this mRNA distribution, CNIH-2 is locally synthesized. In contrast, the pore-forming subunits GluA1 and GluA2 are mostly synthesized in the cell body. CNIH-2 synthesis increases after the (chemical) induction of long-term potentiation. Strikingly, the translation of CNIH-2 is required for the plasma membrane insertion of GluA2-containing receptors and not GluA1-homomeric AMPARs. Using the selective trafficking of GluA2-containing AMPARs by CNIH-2, our computational model can explain the distinct temporal profiles in response to plasticity induction of two major subtypes of AMPARs, the slow-response of the calciumimpermeable (GluA2-containing) and fast kinetics of the calcium-permeable (GluA2-lacking) AMPARs.