bluuebunny/crossref_metadata_2025_split huggingface dataset:
Respiration and gas exchange under negative pressure breathing during simulated microgravity
Authors: Semenov Yury S. , Gorbunova Aleksandra A. , Popova Julia A. , Dyachenko Alexander I. 
Published: April 2024

AbstractThe research considers the effect of inspiratory negative pressure breathing (NPBin, -20 cmH2O relative to barometric pressure) on respiration and gas exchange in healthy humans under various conditions: in the sitting and supine positions as well as during simulating the physiological effects of a long stay in conditions of microgravity (dry immersion and head-down bed rest). Under NPBin, respiratory rate significantly decreased (by 7 min-1on average, in some volunteers up to two or three cycles per minute), tidal volume increased (by an average of 0.3 l), while minute ventilation decreased (on average by 1.8 l/min), and respiratory exchange ratio increased (on average by 0.09). The response of respiratory and gas exchange parameters to NPBin in all the considered conditions was almost the same (there were no significant differences in parameters changes induced by NPBin between the conditions). Despite the decrease in minute ventilation, the results indicate CO2washout from the body under NPBin.
Collateral damage of NUDT15 deficiency in cancer provides a cancer pharmacogenetic therapeutic window with thiopurines
Authors: Massey Jacob C. , Magagnoli Joseph , Sutton S. Scott , Buckhaults Phillip J. , Wyatt Michael D. 
Published: April 2024

AbstractGenome instability is a hallmark of cancer and are driven by mutations in oncogenes and tumor suppressor genes. Despite successes seen with select targeted therapeutics, this type of personalized medicine is only beneficial for a small subpopulation of cancer patients who have one of a few actionable genetic changes. Most tumors also contain hundreds of passenger mutations that offered no fitness advantage or disadvantage during tumor evolution. Mutations in known pharmacogenetic (PGx) loci for which germline variants encode variability in drug response can cause somatically acquired drug sensitivity. TheNUDT15gene is a known PGx locus that participates in the rate-limiting metabolism of thiopurines. People with two defective germline alleles of NUDT15 are hypersensitive to the toxic effects of thiopurines.NUDT15is located adjacent to the Retinoblastoma (RB1) tumor suppressor gene, which often undergoes homozygous deletion in retinoblastomas and other epithelial cancers. We observed thatRB1undergoes homozygous deletions in 9.4% of prostate adenocarcinomas and 2.5% of ovarian cancers, and in nearly all of these casesNUDT15is also lost. Moreover, 44% of prostate adenocarcinomas and over 60% of ovarian cancers have lost one allele of NUDT15, which predicts that a majority of all prostate and ovarian cancers have somatically acquired hypersensitivity to thiopurine treatment. We performed a retrospective analysis of >16,000 patients in the US Veterans Administration health care system and found concurrent xanthine oxidase inhibition (XOi) and thiopurine usage for non-cancer indications is significantly associated with reduced incidence of prostate cancer. The hazard ratio for the development of prostate cancer in patients treated with thiopurines and XOi was 0.562 (0.301-1.051) for the unmatched cohort and 0.389 (0.185-0.819) for the propensity score matched cohort. We experimentally depleted NUDT15 from ovarian and prostate cancer cell lines and observed a dramatic sensitization to thiopurine-induced and DNA damage-dependent toxicity. These results indicate that somatic loss of NUDT15 predicts therapeutic sensitivity to a low cost and well tolerated drug with a broad therapeutic window.
Spatial motifs reveal patterns in cellular architecture of complex tissues
Authors: Samadi Zainalabedin , Askary Amjad 
Published: April 2024

AbstractSpatial organization of cells is crucial to both proper physiological function of tissues and pathological conditions like cancer. Recent advances in spatial transcriptomics have enabled joint profiling of gene expression and spatial context of the cells. The outcome is an information rich map of the tissue where individual cells, or small regions, can be labeled based on their gene expression state. While spatial transcriptomics excels in its capacity to profile numerous genes within the same sample, most existing methods for analysis of spatial data only examine distribution of one or two labels at a time. These approaches overlook the potential for identifying higher-order associations between cell types – associations that can play a pivotal role in understanding development and function of complex tissues. In this context, we introduce a novel method for detecting motifs in spatial neighborhood graphs. Each motif represents a spatial arrangement of cell types that occurs in the tissue more frequently than expected by chance. To identify spatial motifs, we developed an algorithm for uniform sampling of paths from neighborhood graphs and combined it with a motif finding algorithm on graphs inspired by previous methods for finding motifs in DNA sequences. Using synthetic data with known ground truth, we show that our method can identify spatial motifs with high accuracy and sensitivity. Applied to spatial maps of mouse retinal bipolar cells and hypothalamic preoptic region, our method reveals previously unrecognized patterns in cell type arrangements. In some cases, cells within these spatial patterns differ in their gene expression from other cells of the same type, providing insights into the functional significance of the spatial motifs. These results suggest that our method can illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses.
Wisent genome assembly uncovers extended runs of homozygosity and a large deletion that inactivates the thyroid hormone responsive gene
Authors: Bortoluzzi Chiara , Mapel Xena Marie , Neuenschwander Stefan , Janett Fredi , Pausch Hubert , Leonard Alexander S. 
Published: April 2024

AbstractThe wisent (Bison bonasus) is Europe’s largest land mammal. We produced a HiFi read-based wisent assembly with a contig N50 value of 91 Mb containing 99.7% of BUSCO genes which improves contiguity a thousand-fold over an existing assembly. Extended runs of homozygosity in the wisent genome compromised the separation of the HiFi reads into parental-specific read sets, which resulted in inferior haplotype assemblies. A bovine super-pangenome built with assemblies from wisent, bison, gaur, yak, taurine and indicine cattle identified a 1,580 bp deletion removing the protein-coding sequence ofTHRSPencoding thyroid hormone-responsive protein from the wisent and bison genomes. Analysis of 725 sequenced samples across the Bovinae subfamily showed that the deletion is fixed in bothBisonspecies but absent inBosandBubalus. TheTHRSPtranscript is abundant in adipose, fat, liver, muscle, and mammary gland tissue ofBosandBubalus, but absent in bison indicating that the deletion inactivatesTHRSPpossibly contributing to low bison milk and meat fat content. We show that super-pangenomes can reveal potentially trait-associated variation across phylogenies, but also demonstrate that haplotype assemblies from species that went through population bottlenecks warrant scrutiny, as they may have accumulated long runs of homozygosity that complicate phasing.
MicrobiomePhylo: A New Tool for Metabarcoding Data Downstream Analysis - A Real-World Data Analysis Demonstration
Authors: Tafuro Camilla Veronica 
Published: April 2024

AbstractThe field of microbiome research has rapidly expanded, driven by advancements in sequencing technologies that generate vast amounts of data. To navigate this complexity, MicrobiomePhylo has been developed as a web platform tailored for the downstream analysis of microbiome amplicon sequencing data. It stands out for its ability to process QIIME2 artifact files (.qza), offering an intuitive interface, extensive analytical functions, and advanced visualization features, making it indispensable for bioinformaticians and biologists alike. MicrobiomePhylo enhances the understanding of complex datasets by facilitating detailed analysis and visualization. Its effectiveness is showcased through the analysis of the “Moving Pictures” tutorial dataset from QIIME2, a benchmark in microbiome research, featuring 16S rRNA gene sequencing data from various sample types over time.The platform supports a comprehensive workflow including data upload, pre-processing, refinement, visualization, exploration, and advanced statistical analysis. Features include data filtering, rarefaction, and diverse visualizations like barplots, boxplots, and heatmaps. Advanced analyses cover alpha and beta diversity, abundance analysis, differential abundance analysis, and correlation studies, backed by robust statistical methods.A demonstration with the “Moving Pictures” dataset highlights MicrobiomePhylo’s capability to manage real-world data. Customization options for variables, taxonomic levels, plot dimensions, and group highlighting improve result interpretability and visual appeal. Analysis at the phylum level across different body sites reveals insights into microbial composition and diversity. The generated visualizations distinguish microbial community compositions and taxa abundance between body sites, emphasizing the site-specific nature of the human microbiome.ImportanceThe significance of MicrobiomePhylo in microbiome research is profound. As sequencing technologies advance, generating vast datasets, researchers face the challenge of analyzing this data efficiently. MicrobiomePhylo, a web platform designed for microbiome amplicon sequencing data analysis, directly processes QIIME2 artifact files (.qza), offering an accessible interface for both bioinformaticians and biologists. Its comprehensive analytical and advanced visualization capabilities are crucial for understanding complex datasets, enabling deeper insights into microbial composition and diversity. The platform supports a full workflow from data upload to advanced statistical analysis, including data filtering, rarefaction, and various visualizations for taxa abundances, correlations with continuous variables, diversity and more. This facilitates a thorough investigation of microbiome data, making MicrobiomePhylo an indispensable tool in the field, enhancing our understanding of the microbiome through specialized, user-friendly analysis capabilities.
Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics
Authors: Ni Shengkun , Kong Xiangtai , Zhang Yingying , Chen Zhengyang , Wang Zhaokun , Fu Zunyun , Huo Ruifeng , Tong Xiaochu , Qu Ning , Wu Xiaolong , Wang Kun , Zhang Wei , Zhang Runze , Zhang Zimei , Shi Jiangshan , Wang Yitian , Yang Ruirui , Li Xutong , Zhang Sulin , Zheng Mingyue 
Published: April 2024

AbstractThe emergence of perturbation transcriptomics provides a new perspective and opportunity for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to improve compound-protein interaction with knowledge graph embedding of perturbation transcriptomics. PertKGE incorporates diverse regulatory elements and accounts for multi-level regulatory events within biological systems, leading to significant improvements compared to existing baselines in two critical “cold-start” settings: inferring binding targets for new compounds and conducting virtual ligand screening for new targets. We further demonstrate the pivotal role of incorporating multi- level regulatory events in alleviating dataset bias. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti- tumor immunotherapy effect of tankyrase inhibitor K-756, and the discovery of five novel hits targeting the emerging cancer therapeutic target, aldehyde dehydrogenase 1B1, with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery by elucidating mechanisms of action and identifying novel therapeutic compounds.
Building A Foundation Model for Drug Synergy Analysis Powered by Large Language Models
Authors: Liu Tianyu , Chu Tinyi , Luo Xiao , Zhao Hongyu 
Published: April 2024

AbstractDrug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel Foundation Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models for the initial representation of drugs and cell lines. After demonstrating the relevance of these embeddings, we pre-train BAITSAO with a large-scale drug synergy database under a multi-task learning framework with rigorous selections of tasks. We demonstrate the superiority of the model architecture and the pre-trained strategies of BAITSAO over other methods through comprehensive benchmark analysis. Moreover, we investigate the sensitivity of BAITSAO and illustrate its unique functions including new drug discoveries, drug combinations-gene interaction, and multi-drug synergy predictions.
Chromosomal-level genome assembly and single-nucleotide polymorphism sites of black-faced spoonbillPlatalea minor
Authors:   Hong Kong Biodiversity Genomics Consortium, Hui Jerome H.L. , Chan Ting Fung , Chan Leo L. , Cheung Siu Gin , Cheang Chi Chiu , Fang James K.H. , Gaitan-Espitia Juan Diego , Lau Stanley C.K. , Sung Yik Hei , Wong Chris K.C. , Yip Kevin Y.L. , Wei Yingying , So Wai Lok , Nong Wenyan , Law Sean T.S. , Crow Paul , Leong Aiko , Rose-Jeffreys Liz , Yip Ho Yin 
Published: April 2024

AbstractPlatalea minor, the black-faced spoonbill (Threskiornithidae) is a wading bird that is confined to coastal areas in East Asia. Due to habitat destruction, it has been classified by The International Union for Conservation of Nature (IUCN) as globally endangered species. Nevertheless, the lack of its genomic resources hinders our understanding of their biology, diversity, as well as carrying out conservation measures based on genetic information or markers. Here, we report the first chromosomal-level genome assembly ofP. minorusing a combination of PacBio SMRT and Omni-C scaffolding technologies. The assembled genome (1.24 Gb) contains 95.33% of the sequences anchored to 31 pseudomolecules. The genome assembly also has high sequence continuity with scaffold length N50 = 53 Mb. A total of 18,780 protein-coding genes were predicted, and high BUSCO score completeness (93.7% of BUSCO metazoa_odb10 genes) was also revealed. A total of 6,155,417 bi-allelic SNPs were also revealed from 13P. minorindividuals, accounting for ∼5% of the genome. The resource generated in this study offers the new opportunity for studying the black-faced spoonbill, as well as carrying out conservation measures of this ecologically important spoonbill species.
High-Dimensional Imaging of Vestibular Schwannoma Reveals Distinctive Immunological Networks Across Histomorphic Niches inNF2-related Schwannomatosis
Authors: Jones Adam P. , Haley Michael J. , Gregory Grace E. , Hannan Cathal J. , Simmons Ana K. , Bere Leoma D. , Lewis Daniel G. , Oliveira Pedro , Smith Miriam J. , King Andrew T. , Evans D Gareth R. , Paszek Pawel , Brough David , Pathmanaban Omar N. , Couper Kevin N. 
Published: April 2024

AbstractNF2-related Schwannomatosis (NF2SWN) is a rare tumour-predisposition syndrome characterised by the growth of multiple central and peripheral nervous system neoplasms. The drivers ofNF2SWN are pathogenic variants in the tumour suppressor geneNF2, encoding the protein Merlin, leading to development of bilateral vestibular schwannoma (VS) in >95% of patients. VS tumours are characterised by infiltration of myeloid cells and lymphocytes, highlighting the potential of immunotherapy for VS. However, the immunological landscape in VS and the spatial determinants within the tumour microenvironment that shape the trajectory of disease are presently unknown. In this study, to elucidate the complex immunological networks across VS, we performed imaging mass cytometry (IMC) on clinically annotated VS samples fromNF2SWN patients. We reveal the heterogeneity in neoplastic cell, myeloid cell and T cell populations that co-exist within VS, determining that the cellular composition of VS tumours is independent ofNF2-SWN genetic severity. We show that distinct myeloid cell and Schwann cell populations exist within varied spatial contextures across characteristic Antoni A and B histomorphic niches. Interestingly, we show that T-cell populations associate with tumour-associated macrophages (TAMs) in Antoni A regions, seemingly limiting their ability to interact with tumorigenic Schwann cells. This spatial landscape is altered in Antoni B regions, where T-cell populations appear to interact with PD-L1+ Schwann cells. We also demonstrate that prior bevacizumab treatment (VEGF-A antagonist) preferentially reduces alternatively-activated TAMs, whilst enhancing CD44 expression, in bevacizumab-treated tumours. Together, we describe niche-dependent modes of T-cell regulation inNF2SWN VS, indicating the potential for microenvironment-altering therapies for VS.TeaserImaging mass cytometry and spatial omic analyses illustrate spatially-distinct regions of T-cell regulation in vestibular schwannoma.
MetaSpread: A cancer growth and metastatic spread simulation program in Python
Authors: Hernández-Inostroza Alfredo , Gjini Erida 
Published: April 2024

SummaryWe develop and provide MetaSpread, an open-source simulation package and interactive program in Python for tumor growth and metastatic spread, based on a mathematical model by Franssen et al. (2019). This paper proposed a hybrid modeling and computational framework where cellular growth and metastatic spread are described and simulated in a spatially explicit manner, accounting for stochastic individual cell dynamics and deterministic dynamics of abiotic factors. This model incorporates several key processes such as the growth and movement of epithelial and mesenchymal cells, the role of the extracellular matrix, diffusion, haptotaxis, circulation and survival of cancer cells in the vasculature, and seeding and growth in secondary sites. In the software that we develop, these growth and metastatic dynamics are programmed using MESA, a Python Package for Agent-based modeling (Masad & Kazil, 2015).
Fibroblast Stromal Support Model for Predicting Human Papillomavirus-Associated Cancer Drug Responses
Authors: James Claire D. , Lewis Rachel L. , Fakunmoju Alexis L. , Witt Austin J. , Youssef Aya H. , Wang Xu , Rais Nabiha M. , Tadimari Prabhakar Apurva , Machado J. Mathew , Otoa Raymonde , Bristol Molly L. 
Published: April 2024

AbstractCurrently, there are no specific antiviral therapeutic approaches targeting Human papillomaviruses (HPVs), which cause around 5% of all human cancers. Specific antiviral reagents are particularly needed for HPV-related oropharyngeal cancers (HPV+OPCs) whose incidence is increasing and for which there are no early diagnostic tools available. We and others have demonstrated that the estrogen receptor alpha (ERα) is overexpressed in HPV+OPCs, compared to HPV-negative cancers in this region, and that these elevated levels are associated with an improved disease outcome. Utilizing this HPV+specific overexpression profile, we previously demonstrated that estrogen attenuates the growth and cell viability of HPV+keratinocytes and HPV+cancer cellsin vitro. Expansion of this workin vivofailed to replicate this sensitization. The role of stromal support from the tumor microenvironment (TME) has previously been tied to both the HPV lifecycle andin vivotherapeutic responses. Our investigations revealed thatin vitroco-culture with fibroblasts attenuated HPV+specific estrogen growth responses. Continuing to monopolize on the HPV+specific overexpression of ERα, our co-culture models then assessed the suitability of the selective estrogen receptor modulators (SERMs), raloxifene and tamoxifen, and showed growth attenuation in a variety of our models to one or both of these drugsin vitro.Utilization of these SERMsin vivoclosely resembled the sensitization predicted by our co-culture models. Therefore, thein vitrofibroblast co-culture model better predictsin vivoresponses. We propose that utilization of our co-culturein vitromodel can accelerate cancer therapeutic drug discovery.ImportanceHuman papillomavirus-related cancers (HPV+cancers) remain a significant public health concern, and specific clinical approaches are desperately needed. In translating drug response data fromin vitrotoin vivo, the fibroblasts of the adjacent stromal support network play a key role. Our study presents the utilization of a fibroblast 2D co-culture system to better predict translational drug assessments for HPV+cancers. We also suggest that this co-culture system should be considered for other translational approaches. Predicting even a portion of treatment paradigms that may failin vivowith a co-culture model will yield significant time, effort, resource, and cost efficiencies.
Bayesian Selection of Relaxed-clock Models: Distinguishing Between Independent and Autocorrelated Rates
Authors: Panchaksaram Muthukumaran , Freitas Lucas , dos Reis Mario 
Published: April 2024

AbstractIn Bayesian molecular-clock dating of species divergences, rate models are used to construct the prior on the molecular evolutionary rates for branches in the phylogeny, with independent and autocorrelated rate models being commonly used. The two classes of models, however, can result in markedly different divergence time estimates for the same dataset, and thus selecting the best rate model appears important for obtaining reliable inferences of divergence times. However, the properties of Bayesian rate model selection are not well understood, in particular when the number of sequence partitions analysed increases and when age calibrations (such as fossil calibrations) are misspecified. Furthermore, Bayesian rate model selection is computationally expensive as it requires calculation of marginal likelihoods by MCMC sampling, and therefore methods that can speed up the model selection procedure without compromising its accuracy are desirable. In this study, we use a combination of computer simulations and real data analysis to investigate the statistical behaviour of Bayesian rate model selection and we also explore approximations of the likelihood to improve computational efficiency in large phylogenomic datasets. Our simulations demonstrate that the posterior probability for the correct rate model converges to one as more molecular sequence partitions are analysed and when no calibrations are used, as expected due to asymptotic Bayesian model selection theory. Furthermore, we also show the model selection procedure is robust to slight misspecification of calibrations, and reliable inference of the correct rate model is possible in this case. However, we show that when calibrations are seriously misspecified, calculated model probabilities are completely wrong and may converge to one for the wrong rate model. Finally, we demonstrate that approximating the phylogenetic likelihood under an arcsine branch-length transform can dramatically reduce the computational cost of rate model selection without compromising accuracy. We test the approximate procedure on two large phylogenies of primates (372 species) and flowering plants (644 species), replicating results obtained on smaller datasets using exact likelihood. Our findings and methodology can assist users in selecting the optimal rate model for estimating times and rates along the Tree of Life.