bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geny: A Genotyping Tool for Allelic Decomposition of Killer Cell Immunoglobulin-Like Receptor Genes
Authors: Zhou Qinghui , Ghezelji Mazyar , Hari Ananth , Ford Michael K.B. , Holley Connor ,   COVNET Consortium, Mirabello Lisa , Chanock Stephen , Sahinalp S. Cenk , Numanagić Ibrahim 
Published: March 2024

AbstractAccurate genotyping of Killer cell Immunoglobulin-like Receptor (KIR) genes plays a pivotal role in enhancing our understanding of innate immune responses, disease correlations, and the advancement of personalized medicine. However, due to the high variability of the KIR region and high level of sequence similarity among different KIR genes, the currently available genotyping methods are unable to accurately infer copy numbers, genotypes and haplotypes of individual KIR genes from next-generation sequencing data. Here we introduce Geny, a new computational tool for precise genotyping of KIR genes. Geny utilizes available KIR haplotype databases and proposes a novel combination of expectation-maximization filtering schemes and integer linear programming-based combinatorial optimization models to resolve ambiguous reads, provide accurate copy number estimation and estimate the haplotype of each copy for the genes within the KIR region. We evaluated Geny on a large set of simulated short-read datasets covering the known validated KIR region assemblies and a set of Illumina short-read samples sequenced from 25 validated samples from the Human Pangenome Reference Consortium collection and showed that it outperforms the existing genotyping tools in terms of accuracy, precision and recall. We envision Geny becoming a valuable resource for understanding immune system response and consequently advancing the field of patient-centric medicine.
Expression of fibroblast activation protein-α in human deep venous thrombus
Authors: Oguri Nobuyuki , Gi Toshihiro , Nakamura Eriko , Furukoji Eiji , Goto Hiroki , Maekawa Kazunari , Tsuji Atsushi B. , Nishii Ryuichi , Aman Murasaki , Moriguchi-Goto Sayaka , Sakae Tatefumi , Azuma Minako , Yamashita Atsushi 
Published: March 2024

AbstractBackgroundFibroblast activation protein-α (FAP), a type-II transmembrane serine protease, is expressed during wound healing, in cancer-associated fibroblasts, and in chronic fibrosing diseases. However, its expression in deep vein thrombus (DVT) remains unclear. Therefore, in this study, we investigated FAP expression and localization in DVT.MethodsFirst, we pathologically accessed aspirated thrombi of patients with DVT (n=14), classifying thrombotic areas as follows; fresh, cellular lysis, endothelialization, fibroblastic reaction. Endothelialization and fibroblastic reaction were defined as organizing reactions. We immunohistochemically examined FAP-expressed areas and expressed cells. Second, we analyzed FAP expression in cultured dermal fibroblasts.ResultsAll the aspirated thrombi showed a mixture of at least three of the four thrombotic areas. Specifically, 83 % of aspirated thrombi showed fresh and organizing reactions within each thrombus. Immunohistochemical expression of FAP was restricted in organizing area. Further, FAP expression in the thrombi was mainly found in vimentin-positive or α-smooth muscle actin-positive fibroblasts in double immunofluorescence. Some CD163-positive macrophages also showed FAP expression. FAP mRNA and protein levels significantly increased in cultured fibroblasts with low- proliferative activity under 0.1% fetal bovine serum (FBS) than that in fibroblasts under 10% FBS. Fibroblasts cultured in 10% FBS showed a significant decrease in FAP mRNA levels following supplementation with hemin, but not with thrombin.ConclusionsThe heterogeneous composition of the venous thrombi suggests the existence of a multistep thrombus formation process in human DVT. Further, fibroblasts or myofibroblasts may express FAP in organizing process in DVT. FAP expression may increase in fibroblasts with low-proliferative activity.
Arctic greening drives changes in the diet and gut microbiome of a resident herbivore with consequences for fitness
Authors: Kamenova Stefaniya , Albon Steve D. , Loe Leif Egil , Irvine R. Justin , Langvatn Rolf , Gusarova Galina , de Muinck Eric Jacques , Trosvik Pål 
Published: March 2024

AbstractRapid climate warming is “greening” the tundra due to higher plant productivity, particularly of deciduous shrubs and grasses, potentially changing the nutritional base for herbivores. However, the consequences for herbivores are unclear. Although the gut microbiome is an essential factor, mediating the capacity to adapt to dietary change, few studies have investigated the effect of annual changes in the diet-gut microbiome nexus on fitness traits. In a model system, the Svalbard reindeer, a species experiencing the greatest rate of climate warming on Earth, we investigate how climate-induced changes in diet and the gut microbiome impact reindeer body mass. Using high-resolution DNA metabarcoding, we quantified diet and microbiome composition in rumen samples from 97 animals culled in October from 1998 to 2004 in four different valleys. Overall diet diversity and grass consumption were significantly higher following warmer summers, while intake of the dwarf shrubSalix polarisincreased with the maximum normalised difference vegetation index (NDVI). The proportion ofSalixhad a significant positive effect on autumn body mass, which in turn increases ovulation rates and overwinter survival. There was a significant positive effect ofSalixon the microbiome diversity, while between-year variation in Bacteroidota, the most common bacteria phylum, was also significantly, positively related to maximum NDVI. However, a structural equation model revealed that the direct “path” effect of the proportion ofSalixin the diet on reindeer body mass was stronger than the indirect path effect, mediated through the gut microbiome. Our results suggest a positive impact of climate-driven Arctic greening on Svalbard reindeer fitness, operating through a change in diet composition, partially mediated by the changes in the gut microbiome. These findings advance our understanding of the mechanistic underpinnings of Arctic warming on herbivore populations and contribute to the debate about the importance of the diet−gut microbiome nexus in facilitating species resilience in the face of climate change.
Automated model discovery for human cardiac tissue: Discovering the best model and parameters
Authors: Martonová Denisa , Peirlinck Mathias , Linka Kevin , Holzapfel Gerhard A. , Leyendecker Sigrid , Kuhl Ellen 
Published: March 2024

AbstractFor more than half a century, scientists have developed mathematical models to understand the behavior of the human heart. Today, we have dozens of heart tissue models to choose from, but selecting the best model is limited to expert professionals, prone to user bias, and vulnerable to human error. Here we take the human out of the loop and automate the process of model discovery. Towards this goal, we establish a novel incompressible orthotropic constitutive neural network to simultaneously discover both, model and parameters, that best explain human cardiac tissue. Notably, our network features 32 individual terms, 8 isotropic and 24 anisotropic, and fully autonomously selects the best model, out of more than 4 billion possible combinations of terms. We demonstrate that we can successfully train the network with triaxial shear and biaxial extension tests and systematically sparsify the parameter vector withL1-regularization. Strikingly, we robustly discover a four-term model that features a quadratic term in the second invariantI2, and exponential quadratic terms in the fourth and eighth invariantsI4f,I4n, andI8fs. Importantly, our discovered model is interpretable by design and has parameters with well-defined physical units. We show that it outperforms popular existing myocardium models and generalizes well, from homogeneous laboratory tests to heterogeneous whole heart simulations. This is made possible by a new universal material subroutine that directly takes the discovered network weights as input. Automating the process of model discovery has the potential to democratize cardiac modeling, broaden participation in scientific discovery, and accelerate the development of innovative treatments for cardiovascular disease.Our source code, data, and examples are available athttps://github.com/LivingMatterLab/CANN.
ROUA Database: 300 Human Genomes from the border of Ukraine and Romania
Authors: Shchubelka Khrystyna , Wolfsberger Walter W. , Oleksyk Olga T. , Hasynets Yaroslava , Patskun Silviya , Vakerych Mykhailo , Kish Roman , Mirutenko Violeta , Mirutenko Vladislav , Cotoraci Coralia Adina , Pop Calin , Neagu Olimpia , Baltă Cornel , Herman Hildegard , Mare Paula , Dumitra Simona , Papiu Horatiu , Hermenean Anca , Oleksyk Taras K. 
Published: March 2024

AbstractWe present a multi-layered data source (ROUA Database) providing the results of Whole Genome Sequencing of two human populations in the Carpathian Mountains region, specifically Ukraine’s Transcarpathia and Romania’s Satu Mare and Baia Mare provinces, areas previously underexplored in population genomics. The database contains the raw and annotated files of the whole genome sequences from 300 individuals from these regions, including annotations of common and unique genetic variants following a sampling protocol designed to capture the genetic diversity of Ukrainians and Romanians, including minority groups like Wallachians and Roma. The data is hosted on a dedicated web resource. We provide information on how to access to results of primary and secondary analysis of the data, including comparative analysis with previously published populations from Ukraine, and populations from International Genome Sample Resource and Human Genome Diversity Project. The free research access to this database is contributing to growing understanding of human genetic diversity in Central Europe. This effort emphasizes the potential for reuse of the generated data, advocating for open access to support future research in genomics, bioinformatics, and personalized medicine.
The contribution of de novo coding mutations to meningomyelocele
Authors: Ha Yoo-Jin , Tang Isaac , Nisal Ashna , Jhamb Ishani , Wallace Cassidy , Schroeder Sarah , Lee Chanjae , Vong Keng loi , Meave Naomi , Jiwani Fiza , Barrows Chelsea , Lee Sangmoon , Jiang Nan , Patel Arzoo , Blanco Francisco A. , Yu Seyoung , Jeong Hui Su , Plutzer Isaac , Major Michael B. , Benoit Béatrice , Poüs Christian , Heffner Caleb , Kibar Zoha , Bot Gyang Markus , Northrup Hope , Au Kit Sing , Strain Madison , Ashley-Koch Allison , Finnell Richard H. , Le Joan T. , Meltzer Hal , Araujo Camila , Machado Helio R. , Stevenson Roger E. , Yurrita Anna , Mumtaz Sara , Mutchinick Osvaldo M. , Medina-Bereciartu José Ramón , Hildebrandt Friedhelm , Melikishvili Gia , Marwan Rony , Capra Valeria , Noureldeen Mahmoud M. , Salem Aida M.S. , Issa Mahmoud Y. , Zaki Maha S. , Lee Ji Eun , Alkelai Anna , Shuldiner Alan R. , Kingsmore Stephen F. , Murray Stephen A. , Gee Heon Yung , Miller W. Todd , Tolias Kimberley F. , Wallingford John B. ,   Spina Bifida Sequencing Consortium, Kim Sangwoo , Gleeson Joseph G. 
Published: March 2024

AbstractMeningomyelocele (MM) is considered a genetically complex disease resulting from failure of neural tube closure (NTD). Patients display neuromotor disability and frequent hydrocephalus requiring ventricular shunting. A few proposed genes contribute to disease susceptibility, but most risk remains unexplained1. We postulated that de novo mutations (DNMs) under purifying selection contribute to MM risk2. Here we recruited a cohort of 851 MM trios requiring shunting at birth, compared with 732 control trios, and found that de novo likely gene disrupting or damaging missense mutations occur in approximately 22.3% of subjects, 28% of which are estimated to contribute to disease risk. The 187 genes with damaging DNMs collectively define networks including actin cytoskeleton and microtubule-based processes, axon guidance, and histone modification. Gene validation demonstrates partial or complete loss of function, impaired signaling and defective neural tube closure inXenopusembryos. Our results suggest DNMs make key contributions to MM risk, and highlight critical pathways required for neural tube closure in human embryogenesis.
Glutamine addiction is targetable via altering splicing of nutrient sensors and epitranscriptome regulators
Authors: Bojko Jodie , Kollareddy Madhu , Szemes Marianna , Bellamy Jacob , Poon Evon , Moukachar Ahmad , Legge Danny , Vincent Emma E , Jones Nicholas , Malik Sally , Greenhough Alex , Paterson Alex , Park Ji Hyun , Gallacher Kelli , Chesler Louis , Malik Karim 
Published: March 2024

ABSTRACTAbout 50% of poor prognosis neuroblastoma arises due to MYCN over-expression. We previously demonstrated that MYCN and PRMT5 proteins interact and PRMT5 knockdown led to apoptosis of MYCN amplified (MNA) neuroblastoma. Here we evaluate PRMT5 inhibitors GSK3203591/GSK3326593 as targeted therapeutics for MNA neuroblastoma and show MYCN-dependent growth inhibition and apoptosis. RNAseq revealed dysregulated MYCN transcriptional programmes and altered mRNA splicing, converging on key regulatory pathways such as DNA damage response, epitranscriptomics and cellular metabolism. Metabolic tracing showed glutamine metabolism was impeded following GSK3203591 treatment, which disrupted the MLX/Mondo nutrient sensors via intron retention of MLX mRNA. Glutaminase (GLS) protein was decreased by GSK3203591 despite unchanged transcript levels, suggesting post-transcriptional regulation. We demonstrate the RNA methyltransferase METTL3 and cognate reader YTHDF3 proteins are lowered following splicing alterations; accordingly, we observed hypomethylation of GLS mRNA and decreased GLS following YTHDF3 knockdown. In vivo efficacy of GSK3326593 was confirmed by increased survival ofTh-MYCNmice together with splicing events and protein decreases consistent with in vitro data. Our study supports the spliceosome as a key vulnerability of MNA neuroblastoma and rationalises PRMT5 inhibition as a targeted therapy.GRAPHICAL ABSTRACT
Machine Learning Classification of 53BP1 Foci
Authors: Benítez-Jones María Xóchitl , Keegan Sarah , Jamshahi Sebastian , Fenyö David 
Published: March 2024

AbstractBackground53BP1 foci are reflective of DNA double-strand break formation and have been used as radiation markers. Manual focus counting, while prone to bias and time constraints, remains the most accurate mode of detecting 53BP1 foci. Several studies have pursued automated focus detection to replace manual methods. Deep learning, spatial 3D images, and segmentation techniques are main components of the highest performing automated methods. While these approaches have achieved promising results regarding accurate focus detection and cell classification, they are not compatible with time-sensitive large-scale applications due to their demand for long run times, advanced microscopy, and computational resources. Further, segmentation of overlapping foci in 2D images has the potential to represent focus morphologies inaccurately.ResultsTo overcome these limitations, we developed a novel method to classify 2D fluorescence microscopy images of 53BP1 foci. Our approach consisted of three key features: (1) general 53BP1 focus classes, (2) varied parameter space composed of properties from individual foci and their respective Fourier transform, and (3) widely-available machine learning classifiers. We identified four main focus classes, which consisted of blurred foci and three levels of overlapping foci. Our parameter space for the training focus library, composed of foci formed by fluorescently-tagged BP1-2, showed a wide correlation range between variables which was validated using a publicly-available library of immunostained 53BP1 foci. Random forest achieved one of the highest and most stable performances for binary and multiclass problems, followed by a support vector machine and k-nearest neighbors. Specific metrics impacted the classification of blurred and low overlap foci for both train and test sets.ConclusionsOur method classified 53BP1 foci across separate fluorescent markers, resolutions, and damage-inducing methods, using off-the-shelf machine learning classifiers, a diverse parameter space, and well-defined focus classes.
Karyotype Evolution in Response to Chemoradiotherapy and Upon Recurrence of Esophageal Adenocarcinomas
Authors: van der Sluis K. , van Sandick J. W. , Koemans W. J. , van den Bosch T. , Broeks A. , Peters D. , Seignette I. M. , Rausch C. R. , van Dijk E. , Snaebjornsson P. , van den Berg J. G. , van Grieken N. C. T. , Ylstra B. , Carvalho B. , Miedema D. M. , Kodach L. L. 
Published: March 2024

SummaryThe genome of esophageal adenocarcinoma (EAC) is highly unstable and might evolve over time. Here, we track karyotype evolution in EACs in response to treatment and upon recurrence through multi-region and longitudinal analysis. To this end, we introduce L-PAC, a bio-informatics technique that allows inference of absolute copy number aberrations (CNA) of low-purity samples by leveraging information of high-purity samples from the same cancer. Quantitative analysis of matched absolute CNAs reveals that the amount of karyotype evolution induced by chemoradiotherapy (CRT) is predictive for early recurrence and depends on the initial level of karyotype intra-tumor heterogeneity. We observe that CNAs acquired in response to CRT are partially reversed back to the initial state upon recurrence. CRT hence alters the fitness landscape to which tumors can adjust by adapting their karyotype. Together, our results indicate that karyotype plasticity contributes to therapy resistance of EACs.
Biotic and abiotic drivers of ecosystem functioning differ between a temperate and a tropical region
Authors: van Dijk Laura J. A. , Miraldo Andreia , Raharinjanahary Dimby , Rajoelison Eric Tsiriniaina , Fisher Brian L. , Goodsell Robert M. , Iwaszkiewicz-Eggebrecht Elzbieta , Åhlén David , Högvall Johanna , Lundberg Erika , Rova Emma , Łukasik Piotr , Ronquist Fredrik , Roslin Tomas , Tack Ayco J. M. 
Published: March 2024

AbstractAny single ecosystem will provide many ecosystem functions. Whether these functions tend to increase in concert or trade off against each other is a question of much current interest. Equally topical are the drivers behind ecosystem function rates. Yet, we lack large-scale systematic studies that investigate how abiotic factors can directly or indirectly — via effects on biodiversity — drive ecosystem functioning. In this study, we assessed the impact of climate, landscape and biotic community on ecosystem functioning and multifunctioning in the temperate and tropical zone, and investigated potential trade-offs among ecosystem functions in both zones. To achieve this, we measured a diverse set of insect-related ecosystem functions — including herbivory, seed dispersal, predation, decomposition and pollination — at 50 sites across Madagascar and 171 sites across Sweden, and characterized the insect community at each site using Malaise traps. We used structural equations models to infer causality of the effects of climate, landscape, and biodiversity on ecosystem functioning. For the temperate zone, we found that abiotic factors were more important than biotic factors in driving ecosystem functioning, while in the tropical zone, effects of biotic drivers were most pronounced. In terms of trade-offs among functions, in the temperate zone, only seed dispersal and predation were positively correlated, while all other functions were uncorrelated. By contrast, in the tropical zone, most ecosystem functions increased in concert, highlighting that tropical ecosystems can simultaneously provide a diverse set of functions. These correlated functions in Madagascar could for the most part be explained by similar responses to local climate, landscape, and biota. Our study suggests that the functioning of temperate and tropical ecosystems differs fundamentally in patterns and drivers. Without a better understanding of these differences, it will be impossible to correctly predict shifts in ecosystem functioning in response to environmental disturbances. To identify global patterns and drivers of ecosystem functioning, we will next need replicate sampling across biomes – as here achieved for two regions, thus paving the road and setting the baseline expectations.
MAssively-Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments
Authors: Liao Hsiao-Chi , Speed Terence P. , McCarthy Davis J. , Salim Agus 
Published: March 2024

AbstractMassively-Parallel Cytometry (MPC) experiments allow cost-effective quantification of more than 200 surface proteins at single-cell resolution. The Infinity Flow (Inflow) analysis protocol was developed to measure highly informative protein ‘backbone’ markers on all cells in all wells distributed across three 96-well plates, along with well-specific exploratory protein ‘infinity’ markers. Backbone markers can be used to impute the infinity markers on cells in all other wells using machine learning methods. This protocol offers unprecedented opportunities for more comprehensive classification of cell types. However, some aspects of the protocol can be improved, including methods for background correction and removal of unwanted variation. Here, we proposeMAPFXas an end-to-end toolbox that carefully pre-processes the raw data from MPC experiments, and further imputes the ‘missing’ infinity markers in the wells without those measurements. Our pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Unique features of our approach include performing background correction prior to imputation and removing unwanted variation from the data at the cell-level, while explicitly accounting for the potential association between biology and unwanted factors. We benchmark our pipeline against alternative pipelines and demonstrate that our approach is better at preserving biological signals, removing unwanted variation, and imputing unmeasured infinity markers.