bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extreme gradient boosting machine learning algorithm identifies genome-wide genetic variants in prostate cancer risk prediction
Authors: Enoma David , Osamor Victor Chukwudi , Olubanke Ogunlana 
Published: November 2023

AbstractGenome-wide association studies (GWAS) identify the variants (Single Nucleotide polymorphisms) associated with a disease phenotype within populations. These genetic differences are essential in variations in incidence and mortalities, especially for Prostate cancer in the African population. Given the complexity of cancer, it is imperative to identify the variants that contribute to the development of the disease. The standard univariate analysis employed in GWAS may not capture the non-linear additive interactions between variants, which might affect the risk of developing Prostate cancer. This is because the interactions in complex diseases such as prostate cancer are usually non-linear and would benefit from a non-linear Machine Learning gradient boosting viz XGBoost (extreme gradient boosting). We applied the XGBoost algorithm and an iterative SNP selection algorithm to find the top features (SNPs) that best predict the risk of developing prostate cancer with a Support Vector Machine (SVM). The number of subjects was 907, and input features were 1,798,727 after appropriate quality control. The algorithm involved ten trials of 5-fold cross-validation to optimize the dataset’s hyperparameters and the prediction task’s second module (utilizing SVM). The model achieved AUC-ROC cure of 0.66, 0.57 and 0.55 on the Train, Dev and Test sets, respectively. The area under the Precision-Recall Curve was 0.69, 0.60 and 0.57 on the Train, Dev and Test sets, respectively. Furthermore, the final number of predictive risk variants was 2798, associated with 847 Ensembl genes. Interaction analysis showed that Nodes were 339 and the edges were 622 in the gene interaction network. This shows evidence that the non-linear Machine learning approach offers excellent possibilities for understanding the genetic basis of complex diseases.
Deep Learning-based Optical Aberration Estimation Enables Offline Digital Adaptive Optics and Super-resolution Imaging
Authors: Qiao Chang , Chen Haoyu , Wang Run , Jiang Tao , Wang Yuwang , Li Dong 
Published: November 2023

AbstractOptical aberrations degrade the performance of fluorescence microscopy. Conventional adaptive optics (AO) leverages specific devices, such as the Shack-Hartmann wavefront sensor and deformable mirror, to measure and correct optical aberrations. However, conventional AO requires either additional hardware or a more complicated imaging procedure, resulting in higher cost or a lower acquisition speed. In this study, we proposed a novel space-frequency encoding network (SFE-Net) that can directly estimate the aberrated point spread functions (PSFs) from biological images, enabling fast optical aberration estimation with high accuracy without engaging extra optics and image acquisition. We showed that with the estimated PSFs, the optical aberration can be computationally removed by deconvolution algorithm. Furthermore, to fully exploit the benefits of SFE-Net, we incorporated the estimated PSF with neural network architecture design to devise an aberration-aware deep-learning super-resolution (DLSR) model, dubbed SFT-DFCAN. We demonstrated that the combination of SFE-Net and SFT-DFCAN enables instant digital AO and optical aberration-aware super-resolution reconstruction for live-cell imaging.
Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks
Authors: Velasco Daniel Tello , Windels Sam F. L. , Rotkevich Mikhail , Malod-Dognin Noël , Pržulj Nataša 
Published: November 2023

AbstractMotivationSpatial Analysis of Functional Enrichment (SAFE) is a popular tool for biologists to investigate the functional organisation of biological networks via highly intuitive 2D functional maps. To create these maps, SAFE uses Spring embedding to project a given network into a 2D space in which nodes connected in the network are near each other in space. However, many biological networks are scale-free, containing highly connected hub nodes. Because Spring embedding fails to separate hub nodes, it provides uninformative embeddings that resemble a “hairball”. In addition, Spring embedding only captures direct node connectivity in the network and does not consider higher-order node wiring patterns, which are best captured by graphlets, small, connected, non-isomorphic, induced subgraphs. The scale-free structure of biological networks is hypothesised to stem from an underlying low-dimensional hyperbolic geometry, which novel hyperbolic embedding methods try to uncover. These include coalescent embedding, which projects a network onto a 2D disk.ResultsTo better capture the functional organisation of scale-free biological networks, whilst also going beyond simple direct connectivity patterns, we introduce Graphlet Coalescent (GraCoal) embedding, which embeds nodes nearby on a hyperbolic disk if they tend to touch a given graphlet together. We use GraCoal embedding to extend SAFE. Through SAFE-enabled enrichment analysis, we show that GraCoal embeddings captures the functional organisation of the genetic interaction networks of fruit fly, budding yeast, fission yeast andE. colibetter than graphlet-based Spring embedding. We show that depending on the underlying graphlet, GraCoal embeddings capture different topology-function relationships. We show that triangle-based GraCoal embedding captures functional redundancy between paralogous genes.Availabilityhttps://gitlab.bsc.es/dtello/graphlet-based-SAFEContactnatasha@bsc.asSupplementary informationSupplementary data are available atBioinformaticsonline.
Frequent transitions from night-to-day activity after mass extinctions
Authors: Shafer Maxwell E.R. , Nichols Annika L.A. , Schier Alexander F. , Salzburger Walter 
Published: November 2023

ABSTRACTWhy certain species survive large-scale extinction events while others do not is poorly understood. While the fossil record can provide insights into morphological adaptations that increase survival probabilities, it provides limited information regarding behavioural traits. Here we integrate behavioural data with phylogenetic comparative models to study the evolution of day-night activity patterns (nocturnality and diurnality) in bony fishes, which have persisted through the last four mass extinction events. Our findings in fish, combined with data across all other clades of vertebrates, provide four lines of evidence that nocturnality conferred an evolutionary advantage during mass extinctions, and that frequent nocturnal-to-diurnal transitions facilitated post-extinction diversification: First, phylogenetic reconstruction indicates that the last common ancestors of all vertebrates and of clades of bony vertebrates were nocturnal. Second, in the lineage of bony fishes, which contains over half of all vertebrate species, twice as many transitions between nocturnal and diurnal activity patterns have occurred compared to tetrapods. Third, within a specific ecological niche, different species exhibit distinct temporal activity patterns, suggesting widespread temporal niche partitioning. Fourth, independent bursts in night-to-day transitions followed large-scale extinction events during the last two geological eras in all four major bony vertebrate groups. These observations suggest that ancestral nocturnality and frequent transitions to diurnality helped vertebrates survive and diversify in the face of extinction events, such as those expected during the current “6th mass extinction” event caused by anthropogenic climate change.
Mining Mass Spectra for Peptide Facts
Authors: Zumer Jeremie , Lemieux Sebastien 
Published: November 2023

AbstractThe current mainstream software for peptide-centric tandem mass spectrometry data analysis can be categorized as either database-driven, which rely on a library of mass spectra to identify the peptide associated with novel query spectra, or de novo sequencing-based, which aim to find the entire peptide sequence by relying only on the query mass spectrum. While the first paradigm currently produces state-of-the-art results in peptide identification tasks, it does not inherently make use of information present in the query mass spectrum itself to refine identifications. Meanwhile, de novo approaches attempt to solve a complex problem in one go, without any search space constraints in the general case, leading to comparatively poor results. In this paper, we decompose the de novo problem into putatively easier subproblems, and we show that peptide identification rates of database-driven methods may be improved in terms of peptide identification rate by solving one such subsproblem without requiring a solution for the complete de novo task. We demonstrate this using a de novo peptide length prediction task as the chosen subproblem. As a first prototype, we show that a deep learning-based length prediction model increases peptide identification rates in the ProteomeTools dataset as part of an Pepid-based identification pipeline. Using the predicted information to better rank the candidates, we show that combining ideas from the two paradigms produces clear benefits in this setting. We propose that the next generation of peptide-centric tandem mass spectrometry identification methods should combine elements of these paradigms by mining facts “de novo; about the peptide represented in a spectrum, while simultaneously limiting the search space with a peptide candidates database.
Insights into Molecular Diversity within the FET Family: Unraveling Phase Separation of the N-Terminal Low Complexity Domain from RNA-Binding Protein EWS
Authors: Johnson Courtney N. , Sojitra Kandarp A. , Sohn Erich J. , Moreno-Romero Alma K. , Baudin Antoine , Xu Xiaoping , Mittal Jeetain , Libich David S. 
Published: November 2023

AbstractThe FET family proteins, which includes FUS, EWS, and TAF15, are RNA chaperones instrumental in processes such as mRNA maturation, transcriptional regulation, and the DNA damage response. These proteins have clinical significance: chromosomal rearrangements in FET proteins are implicated in Ewing family tumors and related sarcomas. Furthermore, point mutations in FUS and TAF15 are associated with neurodegenerative conditions like amyotrophic lateral sclerosis and frontotemporal lobar dementia. The fusion protein EWS::FLI1, the causative mutation of Ewing sarcoma, arises from a genomic translocation that fuses the low-complexity domain (LCD) of EWS (EWSLCD) with the DNA binding domain of the ETS transcription factor FLI1. This fusion not only alters transcriptional programs but also hinders native EWS functions like splicing. However, the precise function of the intrinsically disordered EWSLCDis still a topic of active investigation. Due to its flexible nature, EWSLCDcan form transient interactions with itself and other biomolecules, leading to the formation of biomolecular condensates through phase separation – a mechanism thought to be central to the oncogenicity of EWS::FLI1.In our study, we used paramagnetic relaxation enhancement NMR, analytical ultracentrifugation, light microscopy, and all-atom molecular dynamics (MD) simulations to better understand the self-association and phase separation tendencies of EWSLCD. Our aim was to elucidate the molecular events that underpin EWSLCD-mediated biomolecular condensation. Our NMR data suggest tyrosine residues primarily drive the interactions vital for EWSLCDphase separation. Moreover, a higher density and proximity of tyrosine residues amplify the likelihood of condensate formation. Atomistic MD simulations and hydrodynamic experiments revealed that the tyrosine-rich N and C-termini tend to populate compact conformations, establishing unique contact networks, that are connected by a predominantly extended, tyrosine-depleted, linker region. MD simulations provide critical input on the relationship between contacts formed within a single molecule (intramolecular) and inside the condensed phase (intermolecular), and changes in protein conformations upon condensation. These results offer deeper insights into the condensate-forming abilities of the FET proteins and highlights unique structural and functional nuances between EWS and its counterparts, FUS and TAF15.
Cancer cell genetics shaping of the tumor microenvironment reveals myeloid cell-centric exploitable vulnerabilities in hepatocellular carcinoma
Authors: Ramirez Christel FA , Taranto Daniel , Ando-Kuri Masami , de Groot Marnix HP , Tsouri Efi , Huang Zhijie , de Groot Daniel , Kluin Roelof JC , Kloosterman Daan J , Verheij Joanne , Xu Jing , Vegna Serena , Akkari Leila 
Published: November 2023

AbstractMyeloid cells are abundant and plastic immune cell subsets in the liver, to which pro-tumorigenic, inflammatory and immunosuppressive roles have been assigned in the course of tumorigenesis. Yet several aspects underlying their dynamic alterations in hepatocellular carcinoma (HCC) progression remain elusive, including the impact of distinct genetic mutations in shaping a cancer-permissive tumor microenvironment (TME). Here, we generated somatic HCC mouse models bearing clinically-relevant oncogenic driver combinations and subsequent pathway activation that faithfully recapitulated different human HCC subclasses. We identified cancer genetics’ specific and stage-dependent alterations of the liver TME associated with distinct histopathological and malignant HCC features. These models ranged from T cell-rich, more indolent HCC to aggressive tumors exhibiting heightened myeloid cell infiltration. Interestingly, MAPK-activated,NrasG12D-driven tumors presented a mixed phenotype of prominent inflammation and immunosuppression in a T cell-excluded TME, contrasting withNrasG12VHCC, enriched in adaptive immune cells. Mechanistically, we identified aNrasG12Dcancer cell-driven, MEK-ERK1/2-SP1-dependent GM-CSF secretion enabling the accumulation of immunosuppressive and proinflammatory monocyte-derived Ly6Clowcells. GM-CSF blockade curbed the accumulation of this myeloid cell subset, reduced inflammation, induced cancer cell death and prolonged animal survival. Furthermore, the anti-tumor effect of GM-CSF neutralization synergized with the clinically-approved inhibition of the vascular endothelial growth factor (VEGF) to inhibit HCC outgrowth. These findings underscore the striking alterations of the myeloid TME consequential to MAPK pathway activation intensity and the potential of GM-CSF inhibition as a myeloid-centric therapy tailored to subsets of HCC patients.
Glioblastoma Margin as a Diffusion Barrier Revealed by Photoactivation of Plasmonic Nanovesicles
Authors: Xiong Hejian , Wilson Blake A. , Ge Xiaoqian , Gao Xiaofei , Cai Qi , Xu Xueqi , Bachoo Robert , Qin Zhenpeng 
Published: November 2023

ABSTRACTGlioblastoma (GBM) is the most complex and lethal adult primary brain cancer. Adequate drug diffusion and penetration are essential for treating GBM, but how the spatial heterogeneity in GBM impacts drug diffusion and transport is poorly understood. Herein, we report a new method, photoactivation of plasmonic nanovesicles (PANO), to measure molecular diffusion in the extracellular space of GBM. By examining three genetically engineered GBM mouse models that recapitulate key clinical features including angiogenic core and diffuse infiltration, we found that the tumor margin has the lowest diffusion coefficient (highest tortuosity) compared with the tumor core and surrounding brain tissue. Analysis of the cellular composition shows that the tortuosity in the GBM is strongly correlated with neuronal loss and astrocyte activation. Our all-optical measurement reveals the heterogeneous GBM microenvironment and highlights the tumor margin as a diffusion barrier for drug transport in the brain, with implications for therapeutic delivery.
Formation of hemiclonal reproduction and hybridogenesis inPelophylax waterfrogs studied with species-specific cytogenomic probes
Authors: Lukáš Choleva , Marie Doležálková-Kaštánková , Veronika Labajová , Alexandr Sember , Marie Altmanová , Karolína Lukšíková , Anna Chung Voleníková , Martina Dalíková , Petr Nguyen , Eleonora Pustovalova , Anna Fedorova , Dedukh Dmitrij 
Published: November 2023

ABSTRACTMeiosis is a conservative process in all sexual organisms which ensures fertility and is central for producing genetic diversity by recombination and random segregation of parental chromosomes. Yet unexplored mechanisms may disrupt it and cause ‘loss of sex’ followed by the emergence of clonal modes of reproduction. Interspecific hybridization is the primary trigger for this process, but mechanistic basis of the transition to asexuality remains still unknown for most vertebrate animals. To study these processes in water frogs, we performed reciprocal mating between two sexual species,Pelophylax ridibundusandP. lessonae, and produced vital F1 progeny (P. esculentus). The RepeatExplorer2 analysis of low-coverage genomic data of the two parental species identified theP. lessonae-specific minisatellite markerPlesSat01-48(44 bp), which hybridized to (peri)centromeric regions of two chromosome pairs inP. lessonae– the acrocentric chromosome 8 and the chromosome 10 (a carrier of nucleolar organizer region; NOR). Chromosomal mapping combining the novel hybridization probe with the previously designed marker forP. ridibundus-specific centromeric satellite DNA showed that theP. esculentusprogeny do not reproduce sexually. Instead, the F1 generation ofP. esculentusinstantly modified its gametogenesis and established asexual reproduction via hybridogenesis. Gametogenic modifications included premeiotic elimination of one of the parental genomes and clonal propagation of the remaining genome via endoreplication followed by standard meiotic division. The origin of DNA elimination and hybridogenesis in laboratory-produced hybrids supports a hypothesis thatP. esculentusarises recurrently in nature whenever parental species come into reproductive contact. Based on the observed pattern of DNA elimination in the F1 progeny we discuss the origin and evolution of population systems in water frogs and the applicability of a newly designed chromosomal probe for otherPelophylaxtaxa.
JointPRS: A Data-Adaptive Framework for Multi-Population Genetic Risk Prediction Incorporating Genetic Correlation
Authors: Xu Leqi , Zhou Geyu , Jiang Wei , Zhang Haoyu , Dong Yikai , Guan Leying , Zhao Hongyu 
Published: November 2023

AbstractGenetic prediction accuracy for non-European populations is hindered by the limited sample size of Genome-wide association studies (GWAS) data in these populations. Additionally, it is challenging to tune model parameters with a small tuning dataset for methods that require tuning data, which is often the case for non-European samples. To address these challenges, we propose JointPRS, a novel, data-adaptive framework that simultaneously models multiple populations using GWAS summary statistics. JointPRS incorporates genetic correlation structures into the prediction framework, enabling accurate performance even without individual-level tuning data. Additionally, it uniquely employs a data-adaptive approach, providing a robust solution when only a small tuning dataset is available. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in five continental populations (European (EUR); East Asian (EAS); African (AFR); South Asian (SAS); and Admixed American (AMR)) evaluated using the UK Biobank (UKBB) and All of Us (AoU), we demonstrate that JointPRS outperforms six other state-of-art methods across three different data scenarios (no tuning data, tuning and testing data from the same cohort, and tuning and testing data from different cohorts) for most traits in non-European populations, while maintaining model simplicity and computational efficiency.
A comprehensive review of South Australia’s Great Artesian Basin spring and discharge wetlands biota
Authors: Beasley-Hall P. G. , Hedges B. A. , Cooper S. J. B. , Austin A. D. , Guzik M. T. 
Published: November 2023

AbstractContextThe Great Artesian Basin (GAB) feeds thousands of springs in Australia’s arid centre, supporting relictual species not found elsewhere on Earth. Springs are considerably threatened by ongoing water abstraction by industry. Robust management plans are needed to prevent further extirpations of GAB taxa, but fundamental biodiversity knowledge is lacking.AimsWe aimed to characterise major organismal groups in South Australian GAB springs and surrounding wetlands, their conservation and taxonomic status, and potential biodiversity hotspots and connectivity of spring ecosystems.MethodsFocusing on South Australia as a case study, we conducted a comprehensive review of GAB spring biota based on the published scientific and grey literature.Key resultsAlmost 500 taxa have been recorded from GAB springs, the majority being invertebrates. Community composition is highly heterogeneous among spring clusters and the true extent of spring biodiversity is far greater than currently known.ConclusionsGAB springs have intrinsic value as refugia for both endemics and cosmopolitan taxa. GAB invertebrates are poorly conserved and largely lacking in taxonomic knowledge. We highlight several potential biodiversity hotspots that have been overlooked in the literature.ImplicationsFundamental biodiversity information on the GAB is crucial for decision-making in conservation management, for industry, and for Traditional Custodians.Lay summaryThe Great Artesian Basin (GAB) is Australia’s largest freshwater resource. Springs fed by the GAB support many species not found elsewhere on Earth, but conservation is hindered by a lack of fundamental knowledge about the plants, animals, and fungi reliant on these habitats. Using South Australia as a case study, we provide a comprehensive review of GAB biodiversity in that state.
Advancing Cell Therapies: Single-Cell Profiling, Generation, Expansion, and Gene Delivery in Rhesus Macaque Plasma B Cells
Authors: Cheng Rene Yu-Hong , Kreuser Shannon , Dahl Noelle , Honaker Yuchi , Soligalla Rupa , Lopez Christina , Rawlings David J. , James Richard G. 
Published: November 2023

AbstractEngineered long lived plasma cells have the potential to be a new area of cell therapy. A key step in developing this cell therapy is testing in a model with an intact immune system similar to humans. To that end, we have developed methods to purify, expand, and differentiate non-human primate (NHP;rhesus macaque) B cellsex vivo. We consistently achieved 10-fold expansion of NHP B cells using a readily available commercial supplement. After only seven days in culture, large percentages of cells in NHP B cell cultures were differentiated. These cells expressed surface markers found in human antibody secreting cells (CD38 and CD138) and secreted immunoglobulin G. From single cell transcriptome analysis of NHP, we verified the presence of plasma cell markers commonly shared with humans, and have unearthed less recognized markers such asCD59and CD79A. In addition, we identified unique NHP plasma cell markers that are absent in humans including the immune checkpoint moleculeCD274(PD-L1, Programmed Death-Ligand 1). Furthermore, we found that MHC class I molecules were upregulated in NHP plasma cells, in contrast to the pattern observed in humans. Lastly, we also identified the serotypes (AAVD-J) and established the conditions for efficient transduction of NHP B cells with AAV vectors, achieving an editing rate of approximately 60%. We envision that this work will accelerate proof-of-conceptin vivostudies using engineered protein-secreting B cells in the NHP model.