bluuebunny/crossref_metadata_2025_split huggingface dataset:
RRM1 promotes homologous recombination and radio/chemo-sensitivity via enhancing USP11 and E2F1-mediated RAD51AP1 transcription
Authors: Yang Shuai , Wang Ruru , Liu Lingling , Xu Feng , Zhao Xipeng , Yao Zhicheng , Zhang Jie , Cheng Xu , Xu An , Wu Lijun , Zhao Guoping 
Published: December 2024

AbstractRibonucleotide reductase M1 (RRM1), the catalytic subunit of ribonucleotide reductase, plays a pivotal role in converting ribonucleotides (NTP) into deoxyribonucleotides (dNTP), essential for DNA replication and repair. Elevated RRM1 expression is associated with various human cancers, correlating with poorer prognosis and reduced overall survival rates. Our previous study found that RRM1 will enter the nucleus to promote DNA damage repair. However, the underlying mechanism remains elusive. Here, we unveil a novel role of RRM1 in promoting homologous recombination (HR) by upregulating the expression of RAD51AP1, a critical HR factor, in an E2F1-dependent manner. We demonstrate that RRM1 interacts with USP11 in the cytoplasm, and the recruitment of RRM1 to LaminB1 induced by ionizing radiation (IR) facilitates the binding of USP11 to the nuclear pore complex (NPC), promoting USP11 entry into the nucleus. Upon nuclear translocation, USP11 binds to E2F1 and inhibits the ubiquitin-mediated degradation of E2F1, thereby enhancing the transcriptional expression of RAD51AP1. Moreover, a specific RRM1 mutant lacking amino acids 731–793, crucial for its interaction with USP11 and recruitment to LaminB1, exhibits a dominant-negative effect on RAD51AP1 expression and HR. Truncations of RRM1 fail to inhibit the ubiquitin-mediated degradation of E2F1 and cannot promote the E2F1-mediated transactivation of RAD51AP1. Lastly, the full length of RRM1, not truncations, enhances tumor cells’ sensitivity to IR, underscoring its importance in radiotherapy resistance. Collectively, our results suggest a novel function of RRM1 in promoting HR-mediated DSB repair through positive regulation of RAD51AP1 transcription by direct interaction with USP11 and promoting subsequent USP11-mediated deubiquitination of E2F1. Our findings elucidate a previously unknown mechanism whereby RRM1 promotes HR-mediated DNA repair, presenting a potential therapeutic target for cancer treatment.
An HuR mutant, HuR-V225I, identified in adult T-cell Leukemia/Lymphoma, alters the pro-apoptotic function of HuR
Authors: Colalillo Bianca , Sali Sujitha , Aldouhki Ali H. , Aubry Isabelle , Di Marco Sergio , Tremblay Michel L. , Gallouzi Imed E. 
Published: December 2024

AbstractThe RNA-binding protein HuR regulates various cellular processes, such as proliferation, differentiation, and cell fate. Moreover, recent studies have shown that HuR modulates the expression of factors important for tumor growth and progression. Despite its prominent role in tumorigenesis, until recently, there have been no reported mutations in HuR that have been associated to cancer. Here, we show that a HuR mutation, HuR-V225I, previously identified in a patient with Adult T-cell Leukemia/Lymphoma, interferes with the pro-apoptotic function of HuR. In response to apoptosis, HuR translocates to the cytoplasm and is cleaved in a caspase-dependent manner. In cervical cancer cells, neuroblastoma cells, and T-lymphocytes, we observed a decrease in cleavage of the HuR-V225I mutant under apoptotic conditions. This effect was shown to be mediated by the nuclear retention of HuR-V225I. Finally, expression of the HuR-V225I mutant decreases the cell’s response to apoptotic stimuli through the increased expression of mRNAs encoding anti-apoptotic factors, such as XIAP and BCL-2. Therefore, our data establishes that the absence of HuR cytoplasmic translocation and cleavage promotes cell viability, and that acquiring this mutation during tumorigenesis may thus reduce the efficacy of cancer therapy.
The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation
Authors: Schoeler Tabea , Pingault Jean-Baptiste , Kutalik Zoltán 
Published: December 2024

AbstractAlthough the use of short self-report measures is common practice in biobank initiatives, such a phenotyping strategy is inherently prone to reporting errors. To explore challenges related to self-report errors, we first derived a reporting error score in the UK Biobank (UKBB; n = 73,127), capturing inconsistent self-reporting in time-invariant phenotypes across multiple measurement occasions. We then performed genome-wide scans on the reporting error score, applied downstream analyses (linkage disequilibrium score regression and Mendelian randomization) and compared its properties to the UKBB participation propensity. Finally, we improved phenotype resolution for 24 measures and inspected the changes in genomic findings. We found that reporting error was present across all 33 assessed self-report measures, with repeatability levels as low as 47% (childhood body size). Reporting error was not independent from UKBB participation, evidenced by the negative genetic correlation between the two outcomes (rg = −0.77), their shared causes (for example, education) and the loss in self-report accuracy following participation bias correction. Across all analyses, the impact of reporting error ranged from reduced power (for example, for gene discovery) to biased estimates (for example, if present in the exposure variable) and attenuation of genome-wide quantities (for example, 21% relative attenuation in SNP heritability for childhood height). Our findings highlight that both self-report accuracy and selective participation are competing biases and sources of poor reproducibility for biobank-scale research.
Determining structures of RNA conformers using AFM and deep neural networks
Authors: Degenhardt Maximilia F. S. , Degenhardt Hermann F. , Bhandari Yuba R. , Lee Yun-Tzai , Ding Jienyu , Yu Ping , Heinz William F. , Stagno Jason R. , Schwieters Charles D. , Watts Norman R. , Wingfield Paul T. , Rein Alan , Zhang Jinwei , Wang Yun-Xing 
Published: December 2024

Abstract
Much of the human genome is transcribed into RNAs1, many of which contain structural elements that are important for their function. Such RNA molecules—including those that are structured and well-folded2—are conformationally heterogeneous and flexible, which is a prerequisite for function3,4, but this limits the applicability of methods such as NMR, crystallography and cryo-electron microscopy for structure elucidation. Moreover, owing to the lack of a large RNA structure database, and no clear correlation between sequence and structure, approaches such as AlphaFold5 for protein structure prediction do not apply to RNA. Therefore, determining the structures of heterogeneous RNAs remains an unmet challenge. Here we report holistic RNA structure determination method using atomic force microscopy, unsupervised machine learning and deep neural networks (HORNET), a novel method for determining three-dimensional topological structures of RNA using atomic force microscopy images of individual molecules in solution. Owing to the high signal-to-noise ratio of atomic force microscopy, this method is ideal for capturing structures of large RNA molecules in distinct conformations. In addition to six benchmark cases, we demonstrate the utility of HORNET by determining multiple heterogeneous structures of RNase P RNA and the HIV-1 Rev response element (RRE) RNA. Thus, our method addresses one of the major challenges in determining heterogeneous structures of large and flexible RNA molecules, and contributes to the fundamental understanding of RNA structural biology.
A dormant overmassive black hole in the early Universe
Authors: Juodžbalis Ignas , Maiolino Roberto , Baker William M. , Tacchella Sandro , Scholtz Jan , D’Eugenio Francesco , Witstok Joris , Schneider Raffaella , Trinca Alessandro , Valiante Rosa , DeCoursey Christa , Curti Mirko , Carniani Stefano , Chevallard Jacopo , de Graaff Anna , Arribas Santiago , Bennett Jake S. , Bourne Martin A. , Bunker Andrew J. , Charlot Stéphane , Jiang Brian , Koudmani Sophie , Perna Michele , Robertson Brant , Sijacki Debora , Übler Hannah , Williams Christina C. , Willott Chris 
Published: December 2024

AbstractRecent observations have found a large number of supermassive black holes already in place in the first few hundred million years after the Big Bang, many of which seem to be overmassive relative to their host galaxy stellar mass when compared with local relation1–9. Several different models have been proposed to explain these findings, ranging from heavy seeds to light seeds experiencing bursts of high accretion rate10–16. Yet, current datasets are unable to differentiate between these various scenarios. Here we report the detection, from the JADES survey, of broad Hα emission in a galaxy at z = 6.68, which traces a black hole with a mass of about 4 × 108M⊙ and accreting at a rate of only 0.02 times the Eddington limit. The black hole to host galaxy stellar mass ratio is about 0.4—that is, about 1,000 times above the local relation—whereas the system is closer to the local relations in terms of dynamical mass and velocity dispersion of the host galaxy. This object is most likely an indication of a much larger population of dormant black holes around the epoch of reionization. Its properties are consistent with scenarios in which short bursts of super-Eddington accretion have resulted in black hole overgrowth and massive gas expulsion from the accretion disk; in between bursts, black holes spend most of their life in a dormant state.
Timely TGFβ signalling inhibition induces notochord
Authors: Rito Tiago , Libby Ashley R. G. , Demuth Madeleine , Domart Marie-Charlotte , Cornwall-Scoones Jake , Briscoe James 
Published: December 2024

Abstract
The formation of the vertebrate body involves the coordinated production of trunk tissues from progenitors located in the posterior of the embryo. Although in vitro models using pluripotent stem cells replicate aspects of this process1–10, they lack crucial components, most notably the notochord—a defining feature of chordates that patterns surrounding tissues11. Consequently, cell types dependent on notochord signals are absent from current models of human trunk formation. Here we performed single-cell transcriptomic analysis of chick embryos to map molecularly distinct progenitor populations and their spatial organization. Guided by this map, we investigated how differentiating human pluripotent stem cells develop a stereotypical spatial organization of trunk cell types. We found that YAP inactivation in conjunction with FGF-mediated MAPK signalling facilitated WNT pathway activation and induced expression of TBXT (also known as BRA). In addition, timely inhibition of WNT-induced NODAL and BMP signalling regulated the proportions of different tissue types, including notochordal cells. This enabled us to create a three-dimensional model of human trunk development that undergoes morphogenetic movements, producing elongated structures with a notochord and ventral neural and mesodermal tissues. Our findings provide insights into the mechanisms underlying vertebrate notochord formation and establish a more comprehensive in vitro model of human trunk development. This paves the way for future studies of tissue patterning in a physiologically relevant environment.
The conformational space of RNase P RNA in solution
Authors: Lee Yun-Tzai , Degenhardt Maximilia F. S. , Skeparnias Ilias , Degenhardt Hermann F. , Bhandari Yuba R. , Yu Ping , Stagno Jason R. , Fan Lixin , Zhang Jinwei , Wang Yun-Xing 
Published: December 2024

Abstract
RNA conformational diversity has fundamental biological roles1–5, but direct visualization of its full conformational space in solution has not been possible using traditional biophysical techniques. Using solution atomic force microscopy, a deep neural network and statistical analyses, we show that the ribonuclease P (RNase P) RNA adopts heterogeneous conformations consisting of a conformationally invariant core and highly flexible peripheral structural elements that sample a broad conformational space, with amplitudes as large as 20–60 Å in a multitude of directions, with very low net energy cost. Increasing Mg2+ drives compaction and enhances enzymatic activity, probably by narrowing the conformational space. Moreover, analyses of the correlations and anticorrelations between spatial flexibility and sequence conservation suggest that the functional roles of both the structure and dynamics of key regions are embedded in the primary sequence. These findings reveal the structure–dynamics basis for the embodiment of both enzymatic precision and substrate promiscuity in the RNA component of the RNase P. Mapping the conformational space of the RNase P RNA demonstrates a new general approach to studying RNA structure and dynamics.
Hierarchical design of pseudosymmetric protein nanocages
Authors: Dowling Quinton M. , Park Young-Jun , Fries Chelsea N. , Gerstenmaier Neil C. , Ols Sebastian , Yang Erin C. , Wargacki Adam J. , Dosey Annie , Hsia Yang , Ravichandran Rashmi , Walkey Carl D. , Burrell Anika L. , Veesler David , Baker David , King Neil P. 
Published: December 2024

Abstract
Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions1,2. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry3. Here, inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540 and 960 subunits. At 49, 71 and 96 nm diameter, these nanocages are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work substantially broadens the variety of self-assembling protein architectures that are accessible through design.
A nearly telomere-to-telomere diploid genome assembly of Firmiana kwangsiensis, a threatened species in China
Authors: Wang Boqiang , Zhang Rengang , Sun Weibang , Yang Jing 
Published: December 2024

AbstractFirmiana kwangsiensis is a tree species of high ornamental value. The species is critically endangered in the wild, and is listed as a first-class national protected wild plant in China, and a Plant Species with Extremely Small Populations in need of urgent protection. We have assembled a chromosome-scale, haplotype-resolved genome for F. kwangsiensis using a combination of PacBio HiFi sequencing, ONT sequencing, and Hi-C sequencing. The final assembled genome is 2.3 G in size and comprises 2n = 40 chromosomes. All chromosomal ends contain telomeric characteristic motifs (TTTAGGG), and there are only 2 gaps within the rDNA regions, both close to a T2T genome assembly. Two complete sets of haplotypes are present, Haplotype A (1169.19 Mb) and Haplotype B (1157.87 Mb), with contig N50 lengths of 58.37 Mb and 57.27 Mb, respectively. The genome contains a total of 67,527 coding genes, with 62,351 genes functionally annotated here. This is the first report of the genome of F. kwangsiensis, and lays the foundation for future conservation genomics research into this species.
A biomathematical model of SARS-CoV-2 in Syrian hamsters
Authors: Schirm Sibylle , Nouailles Geraldine , Kirsten Holger , Trimpert Jakob , Wyler Emanuel , Teixeira Alves Luiz Gustavo , Landthaler Markus , Ahnert Peter , Suttorp Norbert , Witzenrath Martin , Scholz Markus 
Published: December 2024

AbstractWhen infected with SARS-CoV-2, Syrian hamsters (Mesocricetus auratus) develop moderate disease severity presenting key features of human COVID-19. We here develop a biomathematical model of the disease course by translating known biological mechanisms of virus-host interactions and immune responses into ordinary differential equations. We explicitly describe the dynamics of virus population, affected alveolar epithelial cells, and involved relevant immune cells comprising for example CD4+ T cells, CD8+ T cells, macrophages, natural killer cells and B cells. We also describe the humoral response dynamics of neutralising antibodies and major regulatory cytokines including CCL8 and CXCL10. The model is developed and parametrized based on experimental data collected at days 2, 3, 5, and 14 post infection. Pulmonary cell composition and their transcriptional profiles were obtained by lung single-cell RNA (scRNA) sequencing analysis. Parametrization of the model resulted in a good agreement of model and data. The model can be used to predict, for example, the time course of the virus population, immune cell dynamics, antibody production and regeneration of alveolar cells for different therapy scenarios or after multiple-infection events. We aim to translate this model to the human situation in the future.
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
Authors: Zantvoort Kirsten , Nacke Barbara , Görlich Dennis , Hornstein Silvan , Jacobi Corinna , Funk Burkhardt 
Published: December 2024

AbstractArtificial intelligence promises to revolutionize mental health care, but small dataset sizes and lack of robust methods raise concerns about result generalizability. To provide insights on minimal necessary data set sizes, we explore domain-specific learning curves for digital intervention dropout predictions based on 3654 users from a single study (ISRCTN13716228, 26/02/2016). Prediction performance is analyzed based on dataset size (N = 100–3654), feature groups (F = 2–129), and algorithm choice (from Naive Bayes to Neural Networks). The results substantiate the concern that small datasets (N ≤ 300) overestimate predictive power. For uninformative feature groups, in-sample prediction performance was negatively correlated with dataset size. Sophisticated models overfitted in small datasets but maximized holdout test results in larger datasets. While N = 500 mitigated overfitting, performance did not converge until N = 750–1500. Consequently, we propose minimum dataset sizes of N = 500–1000. As such, this study offers an empirical reference for researchers designing or interpreting AI studies on Digital Mental Health Intervention data.
SurgeryLLM: a retrieval-augmented generation large language model framework for surgical decision support and workflow enhancement
Authors: Ong Chin Siang , Obey Nicholas T. , Zheng Yanan , Cohan Arman , Schneider Eric B. 
Published: December 2024

AbstractSurgeryLLM, a large language model framework using Retrieval Augmented Generation demonstrably incorporated domain-specific knowledge from current evidence-based surgical guidelines when presented with patient-specific data. The successful incorporation of guideline-based information represents a substantial step toward enabling greater surgeon efficiency, improving patient safety, and optimizing surgical outcomes.
Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser
Authors: Ferreira de Lima Danilo Enoque , Davtyan Arman , Laksman Joakim , Gerasimova Natalia , Maltezopoulos Theophilos , Liu Jia , Schmidt Philipp , Michelat Thomas , Mazza Tommaso , Meyer Michael , Grünert Jan , Gelisio Luca 
Published: December 2024

AbstractA reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. In this paper, we present a method based on machine learning, the virtual spectrometer, that improves the resolution of non-invasive spectral diagnostics at the European XFEL by up to 40%, and significantly increases its signal-to-noise ratio. This improves the reliability of quasi-real-time monitoring, which is critical to steer the experiment, as well as the interpretation of experimental outcomes. Furthermore, the virtual spectrometer streamlines and automates the calibration of the spectral diagnostic device, which is otherwise a complex and time-consuming task, by virtue of its underlying detection principles. Additionally, the provision of robust quality metrics and uncertainties enable a transparent and reliable validation of the tool during its operation. A complete characterization of the virtual spectrometer under a diverse set of experimental and simulated conditions is provided in the manuscript, detailing advantages and limits, as well as its robustness with respect to the different test cases.
Climate change exacerbates compound flooding from recent tropical cyclones
Authors: Grimley L. E. , Hollinger Beatty K. E. , Sebastian A. , Bunya S. , Lackmann G. M. 
Published: December 2024

AbstractTropical cyclones (TCs) generate substantial damage raising concerns about how climate change may amplify their impacts. However, linking changes in TC characteristics (wind, precipitation) to shifts in flood hazards and exposure, particularly due to the interaction of multiple drivers, is challenging. In this study, we use highly resolved physics-based models to investigate how flooding from three recent TCs in North and South Carolina would change under 4 degrees Celsius of warming. Runoff processes are the largest contributor to the total flood extent in both the present and future. However, the relative contribution of compound processes increases in the future, expanding upriver and beyond the floodplain where runoff processes previously occurred in isolation. The total area exposed to compound flooding increases by 65% and flood depths in these areas increase by 0.8 m highlighting the importance of simulating compound processes in TC flood exposure assessment.
Salmonella Typhimurium effector SseI regulates host peroxisomal dynamics to acquire lysosomal cholesterol
Authors: Raj Desh , Nair Abhilash Vijay , Singh Anmol , Basu Swarnali , Sarkar Kabita , Sharma Jyotsna , Sharma Shiva , Sharma Sanmi , Rathore Manisha , Singh Shriya , Prakash Shakti , Simran  , Sahu Shikha , Kaushik Aman Chandra , Siddiqi Mohammad Imran , Ghoshal Uday C , Chandra Tulika , Bhosale Vivek , Dasgupta Arunava , Gupta Shashi Kumar , Verma Sonia , Guha Rajdeep , Chakravortty Dipshikha , Ammanathan Veena , Lahiri Amit 
Published: December 2024

Abstract

Salmonella enterica serotype Typhimurium (Salmonella) resides and multiplies intracellularly in cholesterol-rich compartments called Salmonella-containing vacuoles (SCVs) with actin-rich tubular extensions known as Salmonella-induced filaments (SIFs). SCV maturation depends on host-derived cholesterol, but the transport mechanism of low-density lipoprotein (LDL)-derived cholesterol to SCVs remains unclear. Here we find that peroxisomes are recruited to SCVs and function as pro-bacterial organelle. The Salmonella effector protein SseI is required for the interaction between peroxisomes and the SCV. SseI contains a variant of the PTS1 peroxisome-targeting sequence, GKM, localizes to the peroxisomes and activates the host Ras GTPase, ADP-ribosylation factor-1 (ARF-1). Activation of ARF-1 leads to the recruitment of phosphatidylinsolitol-5-phosphate-4 kinase and the generation of phosphatidylinsolitol-4-5-bisphosphate on peroxisomes. This enhances the interaction of peroxisomes with lysosomes and allows for the transfer of lysosomal cholesterol to SCVs using peroxisomes as a bridge. Salmonella infection of peroxisome-depleted cells leads to the depletion of cholesterol on the SCVs, resulting in reduced SIF formation and bacterial proliferation. Taken together, our work identified peroxisomes as a target of Salmonella secretory effectors, and as conveyance of host cholesterol to enhance SCV stability, SIF integrity, and intracellular bacterial growth.