bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preferential Release of microRNAs via Extracellular Vesicles is Associated with Ductal Carcinoma In Situ to Invasive Breast Cancer Progression
Authors: Elayapillai Sugantha Priya , Dogra Samrita , Lausen James , Bruns Matthew , Behbod Fariba , Hladik Cole , Gin Amy , Xu Chao , Zhang Roy , Ding Wei-Qun , Hannafon Bethany N. 
Published: December 2024

AbstractDuctal carcinoma in situ (DCIS) is a benign “pre-cancer” that increases the risk of invasive breast cancer (IBC). Not all DCIS progress to IBC, and the primary factors driving progression remain unclear. Small extracellular vesicles (sEVs) or exosomes are known to play a role in advanced cancers, but their involvement in DCIS is poorly understood. This study examined the role of sEVs and their RNA content in DCIS progression. Rab27A, which regulates exosome release, is elevated in DCIS and IBC tissues compared with normal breast tissues. Inhibition of sEV release via Rab27A knockdown alters pro-invasive pathways and reduces invasion in a DCIS mouse model. Using the isogenic MCF10 breast cancer progression series, we found a significant increase in microRNAs (miRNAs) in sEVs from normal to malignant states, with the highest number of differentially expressed miRNAs in IBC sEVs compared with DCIS sEVs. In vivo, DCIS invasive progression elevated circulating sEV miRNA levels, which decreased upon Rab27A knockdown. Re-expression of miR-205, preferentially loaded into IBC sEVs, reduced proliferation, invasion, and EMT marker expression in DCIS cells. Combined Rab27A knockdown and miR-205 expression repressed TGF-β signaling, activated p38, and induced cell cycle arrest and cell death. These findings illustrate that sEVs and their miRNAs promote DCIS progression, and the reintroduction of miR-205 in DCIS cells can inhibit invasive progression.
Sexual reproduction is controlled by successive transcriptomic waves inPodospora anserina
Authors: Bidard Frédérique , Grognet Pierre , Lelandais Gaëlle , Imbeaud Sandrine , Muchielli Marie-Hélène , Debuchy Robert , Berteaux-Lecellier Véronique , Malagnac Fabienne 
Published: December 2024

AbstractDespite the inherent challenge of finding suitable mating partners, most eukaryotes use sexual reproduction to produce offspring endowed with increase genetic diversity and fitness. The persistence of this mode of reproduction is a key question in evolutionary biology. Fungi offer valuable insights into this question, due to their diversity and short lifecycle. This study focuses onPodospora anserina, a pseudohomothallic ascomycete fungus that bypasses self-sterility by maintaining two compatible nuclei in one mycelium. We performed genome-wide gene expression profiling during ten stages ofP. anserinasexual reproduction and identified five major expression patterns. Our expert annotation approaches identified differentially expressed genes related to secondary metabolite production, fungal vegetative incompatibility, programmed cell death, and epigenetic regulation. In addition, master transcriptional regulators and their target networks were uncovered. This study provides a comprehensive database for future functional genomics experiments and novel pathway characterization during sexual reproduction.
Variance inC. elegansgut bacterial load suggests complex host-microbe dynamics
Authors: Boddu Satya Spandana , Martini K. Michael , Nemenman Ilya , Vega Nic M. 
Published: December 2024

AbstractVariation in bacterial composition inside a host is a result of complex dynamics of microbial community assembly, but little is known about these dynamics. To deconstruct the factors that contribute to this variation, we used a combination of experimental and modeling approaches. We found that demographic stochasticity and stationary heterogeneity in the host carrying capacity or bacterial growth rate are insufficient to explain quantitatively the variation observed in our empirical data. Instead, we found that the data can be understood if the host-bacteria system can be viewed as stochastically switching between high and low growth rates phenotypes. This suggests the dynamics significantly more complex than logistic growth used in canonical models of microbiome assembly. We develop mathematical models of this process that can explain various aspects of our data. We highlight the limitations of snapshot data in describing variation in host-associated communities and the importance of using time-series data along with mathematical models to understand microbial dynamics within a host.
Modernizing histopathological analysis: a fully automated workflow for the digital image analysis of the intestinal microcolony survival assay
Authors: Baikalov Alexander , Wang Ethan , Neill Denae , Shetty Nihar , Waldrop Trey , Liu Kevin , Delahousessaye Abagail , Aguilar Edgardo , Mims Nefetiti , Bartzsch Stefan , Schüler Emil 
Published: December 2024

AbstractBackgroundManual analysis of histopathological images is often not only time-consuming and painstaking but also prone to error from subjective evaluation criteria and human error. To address these issues, we created a fully automated workflow to enumerate jejunal crypts in a microcolony survival assay to quantify gastrointestinal damage from radiation.Methods and MaterialsAfter abdominal irradiation of mice, jejuna were obtained and prepared on histopathologic slides, and crypts were counted manually by trained individuals. The automated workflow (AW) involved obtaining images of jejunal slices from the irradiated mice, followed by cropping and normalizing the individual slice images for resolution and color; using deep learning-based semantic image segmentation to detect crypts on each slice; using a tailored algorithm to enumerate the crypts; and tabulating and saving the results. A graphical user interface (GUI) was developed to allow users to review and correct the automated results.ResultsCrypts counted manually exhibited a mean absolute percent deviation of (34 ± 26)% between individuals vs the group mean across counters, which was reduced to (11 ± 6)% across the 3 most-experienced counters. The AW processed a sample image dataset from 60 mice in a few hours and required only a few minutes of active user effort. AW counts deviated from experts’ mean counts by (10 ± 8)%. The AW thereby allowed rapid, automated evaluation of the microcolony survival assay with accuracy comparable to that of trained experts and without subjective inter-observer variation.HighlightsWe fully automated the digital image analysis of a microcolony survival assayAnalyzing 540 images takes a few hours with only minutes of active user effortThe automated workflow (AW) is just as accurate as trained expertsThe AW eliminates subjective inter-observer variation and human errorHuman review possible with built-in graphical user interfaceGraphical Abstract
Cardiac tissue perfusion at rest correlates with systemic microvascular function in those with and without atherosclerotic coronary artery disease; a cross-sectional study
Authors: Wilcox Joshua , Gosling Oliver , Casanova Francesco , He Jingzhou , Gooding Kim M , Pitt Andrew , Ball Claire , Morgan-Hughes Gareth , Bellenger Nick , Shore Angela C , Strain W David 
Published: December 2024

AbstractBackgroundCoronary microvascular disease is often defined by symptoms in the absence of epicardial coronary artery stenosis. There is, however, a growing interest in exploring the vascular physiology of patients with chest pain syndromes who have been confirmed to have unobstructed coronary arteries. As it is known that people with microvascular coronary disease have an additive poor prognosis, we aimed to determine whether this was part of a systemic microvascular dysregulation. As such, we explored the correlations between cardiovascular magnetic resonance (CMR) myocardial perfusion with cutaneous maximal hyperaemic response (MHR) and post-occlusive reactive hyperaemia (PORH), as assessed by laser Doppler fluximetry, in patients with known coronary anatomy determined via computed tomography coronary angiography (CTCA).MethodsMHR was measured in response to local heating to 42°C and PORH was measured in response to a 4-minute ischaemic stimulus in 102 participants with and without diabetes and/or coronary artery disease, defined as coronary artery calcification of >0 Agatston units. Subepicardial and subendocardial perfusion at rest and in response to adenosine stress was measured via CMR.ResultsOut of 102 participants, 47 (45.1%) had diabetes, and 59 (57.8%) had coronary artery disease, with 32 (31.4%) having both. MHR and PORH were attenuated in participants with diabetes. Resting, but not stressed, CMR perfusion in all subepicardial and subendocardial territories was proportionately impaired in those with attenuated MHR. This association was independent of conventional risk factors including age, sex, blood pressure, glycaemia, coronary artery disease and body habitus (standardised beta 0.315, p=0.012). Conversely, PORH did not correlate with CMR perfusion at rest or after stress.ConclusionsMaximal hyperaemic response is associated with resting CMR perfusion independent of conventional risk factors. This suggests that cardiac microvascular dysfunction may represent a manifestation of wider microcirculatory derangements. Further research is required to determine whether interventions that improve systemic vascular disturbances may improve cardiac microcirculation.Translational PerspectiveIt is recognised that coronary microvascular dysfunction is associated with residual symptoms in people with angina, after the correction of occlusive coronary arterial disease. As such it is a promising target for symptom control, however development of proof-of-concept trials is limited by the ability to monitor the coronary microcirculation in those trials.This manuscript identifies an appropriate surrogate endpoint that can be easily and non-invasively monitored and validates it against MRI imaging of the coronary microcirculation.
Genetic Nurture: Estimating the direct genetic effects of pediatric anthropometric traits
Authors: Ghatan Samuel , de Vries Jard , Pingault Jean-Baptiste , Jaddoe Vincent W , Cecil Charlotte , Felix Janine F , Rivadeneira Fernando , Medina-Gomez Carolina 
Published: December 2024

AbstractParental genetic variants can indirectly influence the traits of their child through the environment, a concept termed "genetic nurture", or indirect genetic effects (IGE). This study estimated the direct genetic effects (DGE), via direct allelic transmission, and IGE shaping height, body mass index (BMI), and bone mineral density (BMD) in a multi-ethnic Dutch pediatric cohort, examining children with repeated measurements at ages six, nine, and thirteen. We imputed missing parental alleles from the phased haplotypes of 1,931,478 variants (MAF >1%), utilizing snipar (single nucleotide imputation of parents). We constructed polygenic risk scores (PRSs) and jointly regressed the proband’s trait on their own PRS, while controlling for the proband’s maternal and paternal PRSs. A total of 4,488 probands, with genetic data, underwent at least one of the three specified measurements. We found statistically significant DGE estimates for the three traits across ages six, nine and thirteen. For instance, 71-77% of the BMI variance explained by the BMI-PRS can be attributed solely to the DGE. IGE estimates reached significance only for BMI measured at ages nine (Beta: 0.05, 95%CI: 0.01-0.09) and thirteen (Beta: 0.05, 95%CI: 0.01-0.09). Maternal and paternal IGE were of a similar magnitude in all our analyses. Our findings indicate that genetic nurture has limited influence on anthropometric traits during formative years. In addition, we do not observe differences between the maternal and paternal indirect contributions to these traits, opposite to the stronger maternal nurturing effect reported for other traits.
Simulate Scientific Reasoning with Multiple Large Language Models: An Application to Alzheimer’s Disease Combinatorial Therapy
Authors: Xu Qidi , Liu Xiaozhong , Jiang Xiaoqian , Kim Yejin 
Published: December 2024

AbstractMotivationThis study aims to develop an AI-driven framework that leverages large language models (LLMs) to simulate scientific reasoning and peer review to predict efficacious combinatorial therapy when data-driven prediction is infeasible.ResultsOur proposed framework achieved a significantly higher accuracy (0.74) than traditional knowledge-based prediction (0.52). An ablation study highlighted the importance of high quality few-shot examples, external knowledge integration, self-consistency, and review within the framework. The external validation with private experimental data yielded an accuracy of 0.82, further confirming the framework’s ability to generate high-quality hypotheses in biological inference tasks. Our framework offers an automated knowledge-driven hypothesis generation approach when data-driven prediction is not a viable option.Availability and implementationOur source code and data are available athttps://github.com/QidiXu96/Coated-LLM
Circulating microRNA profiles in early-stage osteoarthritis and rheumatoid arthritis
Authors: Baghel Madhu , Wilson Thomas , Ormseth Michelle , Yousif Patrick , Alkhatib Ayad , Meysami Alireza , Davis Jason , Moutzouros Vasilios , Ali Shabana Amanda 
Published: December 2024

AbstractBackgroundOsteoarthritis (OA) and rheumatoid arthritis (RA) are prevalent forms of arthritis. Early detection of OA and RA is challenging with existing methods, which can delay effective management. MicroRNAs are small molecules that have emerged as promising disease biomarkers with the potential to improve early detection and differentiation of arthritis subtypes. In this study we aimed to identify distinct circulating microRNAs in plasma from individuals with early OA and early RA, using an unbiased microRNA-sequencing approach.MethodsPlasma samples were collected from three study groups including: (a) early OA (N=20), individuals with knee OA symptoms and radiographic Kellgren-Lawrence grade 0 or 1; (b) early RA (N=12), treatment-naïve individuals with <6 months of RA symptoms in any joint; and (c) non-OA/RA (N=44), individuals with no history of arthritis. Of these, N=62 samples were subjected to microRNA-sequencing and analysis using a previously optimized pipeline. Exploratory analyses were followed by a stepwise filtering approach to shortlist both known (documented in miRBase v22.1) and novel (predicted using bioinformatics) microRNAs. Prioritized microRNAs were then validated via real-time qPCR (RT-qPCR) in N=14 independent samples.ResultsPrincipal component analyses revealed clustering of early OA versus both early RA and non-OA/RA groups, but not between early RA and non-OA/RA. In early OA, n=170 differentially expressed (DE) microRNAs were identified compared to both early RA and non-OA/RA, while no significant differences were found between early RA and non-OA/RA. Of these DE microRNAs, stepwise filtering and RT-qPCR validation identified dysregulation of six known microRNAs between early OA and early RA. Of these six microRNAs, two were upregulated in early OA, including hsa-miR-16-5p and hsa-miR-29c-3p, and four were upregulated in early RA, including hsa-miR-744-5p, hsa-miR-382-5p, hsa-miR-3074-5p, and hsa-miR-11400. Additionally, one novel microRNA sequence was found to be enriched in early OA and four in early RA.ConclusionWe identified a total of six known and five novel circulating microRNAs that differ between early OA and early RA individuals. Validation of these microRNAs in independent cohorts is warranted to establish their biomarker potential for distinguishing individuals with early OA versus early RA.
Rapid NGS Analysis on Google Cloud Platform: performance benchmark and user tutorial
Authors: Franzoso Eugenio , Santorsola Mariangela , Lescai Francesco 
Published: December 2024

AbstractNext-Generation Sequencing (NGS) is being increasingly adopted in clinical settings as a tool to increase diagnostic yield in genetically determined pathologies. However, for patients in critical conditions the time-to-results of data analysis is crucial for a rapid diagnosis and response. Sentieon DNASeq and Clara Parabricks Germline are two widely used pipelines for ultra-rapid NGS analysis, but their high computational demands often exceed the resources available in many healthcare facilities. Cloud platforms, like Google Cloud Platform (GCP), offer scalable solutions to address these limitations. Yet, setting up these pipelines in a cloud environment can be complex. This work provides a benchmark of the two solutions, and offers a comprehensive tutorial aimed at easing their implementation on GCP by healthcare bioinformaticians. Additionally, it presents a valuable cost guidance to healthcare managers who consider implementing cloud-based NGS processing. Using five publicly available exome (WES) and five genome (WGS) samples, we benchmarked both pipelines on GCP in terms of runtime, cost, and resource utilisation. Our results show that Sentieon and Parabricks perform comparably. Both pipelines are viable options for rapid, cloud-based NGS analysis, enabling healthcare providers to access advanced genomic tools without the need for extensive local infrastructure.
FaDAM3andFaDAM4are candidate genes for the regulation of seasonal dimorphism in cultivated strawberry
Authors: David Stephan , Han Jielyu , Marcelis Leo F.M. , Verdonk Julian C. 
Published: December 2024

AbstractCultivated strawberry (Fragaria ananassa) exhibits seasonal dimorphism between the summer and winter seasons. Under the influence of short day conditions, the winter morphology is induced, which includes shorter petioles, smaller and thicker leaves with higher concentrations of chlorophyll and higher density of trichomes. In peach (Prunus persica) the Dormancy-Associated MADS-box (DAM) gene family is involved in the regulation of winter dormancy, which closely resembles strawberry seasonal dimorphism in terms of timing and environmental regulation. In strawberry, six genes exist with close homology to the peachDAMgenes. We analyzed the amino acid and coding sequences of these genes, and designated themFaDAM1-4andFaSVP1-2.We found that there are three pairs of highly similar paralogs within the strawberry genome that exhibit similar expression patterns, although expression levels vary.FaDAM3andFaDAM4exhibit expression patterns that are consistent with seasonal dimorphism. We conclude thatFaDAM3andFaDAM4are candidate genes for the regulation of seasonal dimorphism in strawberry.
Identification and cryoEM Structure Determination of Escherichia Phage YDC107 Tail Found in a Bacteria-Contaminated Buffer
Authors: Jenkins Matthew C. , Dutta Tahiti , Bobe Daija , Kopylov Mykhailo 
Published: December 2024

AbstractCryo-electron microscopy data analysis can yield multiple structures from a single heterogeneous dataset. Here, we show a workflow we used for the identification of a contaminant from a cryoEM grid without prior knowledge of protein sequence. We determined the tail structure of Escherichia phage YDC107 from only several thousand particles. The workflow combines high-resolution single-particle data processing withde novomodel determination using ML-based methods. Structural analysis revealed that the central part of the phage tail has a C6 symmetry, however the overall symmetry of each segment is C3 due to dimerization of a flexible domain.Abstract Figure
Sequence motif dynamics in RNA pools
Authors: Harth-Kitzerow Johannes , Göppel Tobias , Burger Ludwig , Enßlin Torsten A. , Gerland Ulrich 
Published: December 2024

ABSTRACTIn RNA world scenarios, pools of RNA oligomers form strongly interacting, dynamic systems, which enable molecular evolution. In such pools, RNA oligomers hybridize and dehybridize, ligate and break, ultimately generating longer RNA molecules, which may fold into catalytically active ribozymes. A key process for the elongation of RNA oligomers is templated ligation, which can occur when two RNA strands are adjacently hybridized onto a template strand. Detailed simulations of the dynamics in RNA pools involve a large variety of possible sequences and reactions. Here, we develop a reduced description of these complex dynamics within the space of sequence motifs. We then explore to what extent our reduced description can capture the behavior of detailed simulations that account for the full dynamics in the space of RNA strands. Towards this end, we project the dynamics into a motif space, which accounts only for the abundance of all possible four-nucleotide motifs. A system of ordinary differential equations describes the dynamics of those motifs. Its control parameters are effective rate constants for reactions in motif space, which we obtain from the rate constants for the processes underlying the full dynamics in the space of RNA strands. We find that these reduced motif space dynamics indeed capture important aspects of the informational dynamics of RNA pools in sequence space. This approach could also provide a framework to rationalize and interpret features of the sequence dynamics observed in experimental systems.
Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning
Authors: Wozniak Spencer , Janson Giacomo , Feig Michael 
Published: December 2024

ABSTRACTMachine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pre-trained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific pKavalues, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction, and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for pKaprediction. Our GNN framework demonstrated robustness across diverse datasets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.