bluuebunny/crossref_metadata_2025_split huggingface dataset:
RNA-ligand interaction scoring via data perturbation and augmentation modeling
Authors: Ma Hongli , Gao Letian , Jin Yunfan , Bai Yilan , Liu Xiaofan , Bao Pengfei , Liu Ke , Xu Zhenjiang Zech , Lu Zhi John 
Published: June 2024

AbstractRNA-targeting drug discovery is undergoing an unprecedented revolution. Despite recent advances in this field, developing data-driven deep learning models remains challenging due to the limited availability of validated RNA-small molecule interactions and the scarcity of known RNA structures. In this context, we introduce RNAsmol, a novel sequence-based deep learning framework that incorporates data perturbation with augmentation, graph-based molecular feature representation and attention-based feature fusion modules to predict RNA-small molecule interactions. RNAsmol employs perturbation strategies to balance the bias between true negative and unknown interaction space thereby elucidating the intrinsic binding patterns between RNA and small molecules. The resulting model demonstrates accurate predictions of the binding between RNA and small molecules, outperforming other methods with average improvements of ∼8% (AUROC) in 10-fold cross-validation, ∼16% (AUROC) in cold evaluation (on unseen datasets), and ∼30% (ranking score) in decoy evaluation. Moreover, we use case studies to validate molecular binding hotspots in the prediction of RNAsmol, proving the model’s interpretability. In particular, we demonstrate that RNAsmol, without requiring structural input, can generate reliable predictions and be adapted to many RNA-targeting drug design scenarios.
Genetic variation modulates susceptibility to aberrant DNA hypomethylation and imprint deregulation in naïve pluripotent stem cells
Authors: Parikh C , Glenn RA , Shi Y , Chatterjee K , Swanzey EE , Singer S , Do SC , Zhan Y , Furuta Y , Tahiliani M , Apostolou E , Polyzos A , Koche R , Mezey JG , Vierbuchen T , Stadtfeld M 
Published: June 2024

SummaryNaïve pluripotent stem cells (nPSC) frequently undergo pathological and not readily reversible loss of DNA methylation marks at imprinted gene loci. This abnormality poses a hurdle for using pluripotent cell lines in biomedical applications and underscores the need to identify the causes of imprint instability in these cells. We show that nPSCs from inbred mouse strains exhibit pronounced strain-specific susceptibility to locus-specific deregulation of imprinting marks during reprogramming to pluripotency and upon culture with MAP kinase inhibitors, a common approach to maintain naïve pluripotency. Analysis of genetically highly diverse nPSCs from the Diversity Outbred (DO) stock confirms that genetic variation is a major determinant of epigenome stability in pluripotent cells. We leverage the variable DNA hypomethylation in DO lines to identify several trans-acting quantitative trait loci (QTLs) that determine epigenome stability at either specific target loci or genome-wide. Candidate factors encoded by two multi-target QTLs on chromosomes 4 and 17 suggest specific transcriptional regulators that contribute to DNA methylation maintenance in nPSCs. We propose that genetic variants represent candidate biomarkers to identify pluripotent cell lines with desirable properties and might serve as entry points for the targeted engineering of nPSCs with stable epigenomes.HighlightsNaïve pluripotent stem cells from distinct inbred mouse strains exhibit variable DNA methylation levels at imprinted gene loci.The vulnerability of pluripotent stem cells to loss of genomic imprinting caused by MAP kinase inhibition strongly differs between inbred mouse strains.Genetically diverse pluripotent stem cell lines from Diversity Outbred mouse stock allow the identification of quantitative trait loci controlling DNA methylation stability.Genetic variants may serve as biomarkers to identify naïve pluripotent stem cell lines that are epigenetically stable in specific culture conditions.
CPI203, a BET inhibitor, down-regulates a consistent set of DNA synthesis genes across a wide array of glioblastoma lines
Authors: Garrett Matthew C. , Carnwath Troy , Albano Rebecca , Zhuang Yonghua , Behrmann Catherine A. , Pemberton Merissa , Barakat Farah , Lober Robert , Hoeprich Mark , Paravati Anthony , Reed Marilyn , Spry Hailey , Woo Daniel , O’Brien Eric , VanCauwenbergh Brett , Perentesis John , Nasser Rani , Medvedovic Mario , Plas David R. 
Published: June 2024

AbstractIntroductionGlioblastomas utilize malignant gene expression pathways to drive growth. Many of these gene pathways are not directly accessible with molecularly targeted pharmacological agents. Chromatin-modifying compounds can alter gene expression and target glioblastoma growth pathways. In this study, we utilize a systematic screen of chromatin-modifying compounds on a panel of patient-derived glioblastoma lines to identify promising compounds and their associated gene targets.MethodsFive glioblastoma cell lines were subjected to a drug screen of 106 chromatin-modifying compounds representing 36 unique drug classes to determine the twelve most promising drug classes and the best candidate inhibitors in each class. These twelve drugs were then tested with a panel of twelve patient-derived gliomasphere lines to identify growth inhibition and corresponding gene expression patterns. Overlap analysis and weighted co-expression network analysis (WCGNA) were utilized to determine potential target genes and gene pathways.ResultsThe initial drug screen identified twelve candidate pharmacologic agents for further testing. Drug sensitivity testing indicated an overall high degree of variability between gliomasphere lines. However, CPI203 was the most consistently effective compound, and the BET inhibitor class was the most consistently effective class of compounds across the gliomasphere panel. Correspondingly, most of the compounds tested had highly variable effects on gene expression between gliomasphere lines. CPI203 stood out as the only compound to induce a consistent effect on gene expression across different gliomasphere lines, specifically down-regulation of DNA-synthesis genes. Amongst the twelve tested cell lines, high expression of CDKN2A and CDKN2B distinguished more drug sensitive from more drug resistant lines. WCGNA identified two oncogenic gene modules (FBXO5 and MELK) that were effectively downregulated by CPI203 (FBXO5) and ML228 (FBXO5 and MELK).ConclusionsThe bromodomain inhibitor CPI203 induced relatively consistent effects on gene expression and growth across a variety of glioblastoma lines, specifically down-regulating genes associated with DNA replication. We propose that clinically effective BET inhibitors have the potential to induce consistent beneficial effects across a spectrum of glioblastomas.
Loss ofasgr1aleads to the secretion of excess dietary cholesterol in zebrafish
Authors: Moll Tabea O.C. , Derrick Joshua T. , Sweeney Darby W. , Shin Jeffrey , Farber Steven A. 
Published: June 2024

AbstractOne of the major pathways to clear glycoproteins from circulation is via the liver-specific asialoglycoprotein receptor (ASGPR). Loss of asialoglycoprotein receptor 1 (ASGR1), the major subunit of ASGPR, was recently found to correlate with lower levels of plasma apolipoprotein B- containing lipoproteins (B-lps) and a profoundly reduced risk of cardiovascular disease in humans. We set out to identify the zebrafish ortholog ofASGR1(asgr1a) and generated two independent mutations inasgr1ausing CRISPR/Cas9. Neitherasgr1amutation displayed changes in larval, juvenile, and adult B-lp numbers or sizes. However, when challenged with a Western diet,asgr1amutant zebrafish exhibit less hepatic steatosis and lower hepatic triglyceride levels compared to control animals. Instead, the excess dietary cholesterol was excreted. While these results do not explain the cardioprotective nature of ASGR1 in humans, they indicate the importance of ASGR1 in modulating whole animal cholesterol flux.
Modeling of Rate Heterogeneity in Datasets Compiled for Use With Parsimony
Authors: Wright April M. , Wynd Brenen M. 
Published: June 2024

AbstractIn recent years, there has been an increased interest in modeling morphological traits using Bayesian methods. Much of the work associated with modeling these characters has focused on the substitution or evolutionary model employed in the analysis. However, there are many other assumptions that researchers make in the modeling process that are consequential to estimated phylogenetic trees. One of these is how among-character rate variation (ACRV) is parameterized. In molecular data, a discretized gamma distribution is often used to allow different characters to have different rates of evolution. Morphological data are collected in ways that fundamentally differ from molecular data. In this paper, we appraise the use of standard parameters for ACRV and provide recommendations to researchers who work with morphological data in a Bayesian framework.
Short-term fluctuating and long-term divergent selection on sympatric Monkeyflowers: insights from decade-spanning reciprocal transplants
Authors: Dong Caroline M. , Aponte Rolón Bolívar , Sullivan Juj K. , Tataru Diana , Deleon Max , Dennis Rachael , Dutton Spencer , Machado Perez Fidel J. , Montano Lissette , Ferris Kathleen G. 
Published: June 2024

ABSTRACTSympatric species are often locally adapted to distinct microhabitats. However, temporal variation may cause local maladaptation and species boundary breakdown, especially during extreme climatic events leading to episodic selection. Repeated reciprocal transplants can reveal the interplay between short and long-term patterns of natural selection. To examine evolutionary trajectories of sympatric Monkeyflowers adapted to different niches,Mimulus guttatusandM. laciniatus,we performed three replicated transplants and combined them with previous experiments to leverage a dataset of five transplants spanning 10 years. We performed phenotypic selection analyses on parents and hybrids in parental habitats in Yosemite NP, CA during years of drastically differing snowpack. If there is ecological isolation, then we predicted divergent phenotypic selection between habitats in line with species’ differences and local adaptation. We found interannual fluctuations in phenotypic selection, often in unpredicted directions. However, a combined-year analysis detected longer-term divergent selection on flowering time, a key temporally isolating and adaptative trait, suggesting that selection may reinforce species boundaries despite short-term fluctuations. Finally, we found temporal variation in local adaptation withM. laciniatuslocally adapted in low snowpack years, while an extremely high snowpack year contributed to average local maladaptation ofM. guttatus.
Rice novel useful semidwarf gened60on chromosome 2 causing pleiotropically gamete abortion
Authors: Tomita Motonori , Kamiya Daisuke , Okawa Keisuke , Nakayama Kohei 
Published: June 2024

AbstractRice semidwarf gened60is inherited according to a unique mode because it causes pleiotropically gamete abortion in the case of coexistence with the gamete lethal genegal. Namely, F2progenies of Koshihikari (D60D60galgal, long stem) × Hokuriku 100 (d60d60GalGal, short stem) segregated in the ratio of 1 semidwarf (1d60d60GalGal):2 tall and quarter sterile (2D60d60Galgal):6 tall (2D60d60GalGal:1D60D60GalGal:2D60D60Galgal:1D60D60galgal), which is skewed from the Mendelian 1 semidwarf:3 tall ratio. Firstly, genetic linkage was tested on the basis that the segregation ratios of the phenotypic marker genes linked tod60do not fit Mendelian ratio. F2of Koshihikari d60 line (d60d60GalGal)× lines carryingd30orgh2on chromosome 2, the segregation ratios of these alleles were deviated from 1:3 but well fitted to 5:4 whend30 and gh2were closely linked toD60. We then conducted molecular fine mapping ofd60by using DNA polymorphisms in F2of Koshihikari d60 line × line carrying the indica chromosome 2 segment in Koshihikari. As a result,d60was tightly linked with RM12970 by a recombination value 0.0 at the region 10.3 Mb away from the distal end of the short arm of chromosome 2. Whole genome sequencing of Koshihikari d60 revealed no SNPs and Indels around the 10.3 Mb region. RT-qPCR for ent-copalyl diphosphate synthase-like gene on 10.3 Mb indicated its transcription was reduced compared to that of Koshihikari.
The distribution and evolutionary dynamics of dopaminergic neurons in molluscs
Authors: Norekian Tigran P. , Moroz Leonid L. 
Published: June 2024

ABSTRACTDopamine is one of the most versatile neurotransmitters in invertebrates. It’s distribution and plethora of functions is likely coupled to feeding ecology, especially in Euthyneura (the largest clade of molluscs), which presents the broadest spectrum of environmental adaptations. Still, the analyses of dopamine-mediated signaling were dominated by studies of grazers. Here, we characterize the distribution of dopaminergic neurons in representatives of two distinct ecological groups: the sea angel - obligate predatory pelagic molluscClione limacina(Pteropoda, Gymnosomata) and its prey - the sea devilLimacina helicina(Pteropoda, Thecosomata) as well as the plankton eaterMelibe leonina(Nudipleura, Nudibranchia). By using tyrosine hydroxylase-immunoreactivity (TH-ir) as a reporter, we showed that the dopaminergic system is moderately conservative among euthyneurans. Across all studied species, small numbers of dopaminergic neurons in the central ganglia contrast to significant diversification of TH-ir neurons in the peripheral nervous system, primarily representing sensory-like cells, which predominantly concentrated in the chemotactic areas and projecting afferent axons to the central nervous system. Combined with α-tubulin immunoreactivity, this study illuminates the unprecedented complexity of peripheral neural systems in gastropod molluscs, with lineage-specific diversification of sensory and modulatory functions.
Comparison of musculoskeletal robot biomechanical properties to human participants using motion study
Authors: Sander Iain L. , Sander Aidan C. , Stebbins Julie A. , Carr Andrew J. , Mouthuy Pierre-Alexis 
Published: June 2024

AbstractAdvanced robotic systems that replicate musculoskeletal structure and function have significant potential for a wide range of applications. Although they are proposed to be better platforms for biomedical applications, little is known about how well current musculoskeletal humanoid systems mimic the motion and force profiles of humans. This is particularly relevant to the field of tendon tissue engineering, where engineered grafts require advanced bioreactor systems that accurately replicate the kinetic and kinematic profiles experienced by the humansin vivo. A motion study was conducted comparing the kinetic and kinematic profiles produced by a musculoskeletal humanoid robot shoulder to a group of human participants completing abduction/adduction tasks. Results from the study indicate that the humanoid arm can be programed to either replicate the kinematic profile or the kinetic profile of human participants during task completion, but not both simultaneously. This study supports the use of humanoid robots for applications such as tissue engineering and highlights suggestions to further enhance the physiologic relevance of musculoskeletal humanoid robotic platforms.
GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data
Authors: Zehr Simonida , Wolf Sebastian , Oellerich Thomas , Leisegang Matthias S. , Brandes Ralf P. , Schulz Marcel H. , Warwick Timothy 
Published: June 2024

AbstractExtraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulatedGeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest. The co-expression between the gene-of-interest and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resultingpvalues provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment.GeneCOCOAthereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the gene-of-interest is controlled. Despite its relative simplicity,GeneCOCOAoutperforms similar methods in the accurate recall of known gene-disease associations.GeneCOCOAis formulated as an R package for ease-of-use, available athttps://github.com/si-ze/geneCOCOA.Author summaryUnderstanding the biological functions of different genes and their respective products is a key element of modern biological research. While one can examine the relative abundance of a gene product in transcriptomics data, this alone does not provide any clue to the biological relevance of the gene. Using a type of analysis called co-expression, it is possible to identify other genes which have similar patterns of regulation to a gene-of-interest, but again, this cannot tell you what a gene does. Genes whose function has previously been studied are often assembled into groups (e.g. pathways, ontologies), which can be used to annotate gene sets of interest. However, if a gene has not yet been characterized, it will not appear in these gene set enrichment analyses. Here, we propose a new method -GeneCOCOA- which uses co-expression of a single gene with genes in functional groups to identify which functional group a gene is most similar too, resulting in a putative function for the gene, even if it has not been studied before. We testedGeneCOCOAby using it to find gene-disease links which have already been scientifically studied, and showed thatGeneCOCOAcan do this more effectively than other available methods.
Endosomal actin branching, fission and receptor recycling require FCHSD2 recruitment by MICAL-L1
Authors: Frisby Devin , Murakonda Ajay B. , Ashraf Bazella , Dhawan Kanika , Almeida-Souza Leonardo , Naslavsky Naava , Caplan Steve 
Published: June 2024

AbstractEndosome fission is required for the release of carrier vesicles and the recycling of receptors to the plasma membrane. Early events in endosome budding and fission rely on actin branching to constrict the endosomal membrane, ultimately leading to nucleotide hydrolysis and enzymatic fission. However, our current understanding of this process is limited, particularly regarding the coordination between the early and late steps of endosomal fission. Here we have identified a novel interaction between the endosomal scaffolding protein, MICAL-L1, and the human homolog of theDrosophilaNervous Wreck (Nwk) protein, FCH and double SH3 domains protein 2 (FCHSD2). We demonstrate that MICAL-L1 recruits FCHSD2 to the endosomal membrane, where it is required for ARP2/3-mediated generation of branched actin, endosome fission and receptor recycling to the plasma membrane. Since MICAL-L1 first recruits FCHSD2 to the endosomal membrane, and is subsequently responsible for recruitment of the ATPase and fission protein EHD1 to endosomes, our findings support a model in which MICAL-L1 orchestrates endosomal fission by connecting between the early actin-driven and subsequent nucleotide hydrolysis steps of the process.
Hypomorphic mutation in the large subunit of replication protein A affects mutagenesis by human APOBEC cytidine deaminases in yeast
Authors: Dennen Matthew S. , Kockler Zachary W. , Roberts Steven A. , Burkholder Adam B. , Klimczak Leszek J. , Gordenin Dmitry A. 
Published: June 2024

ABSTRACTHuman APOBEC single-strand (ss) specific DNA and RNA cytidine deaminases change cytosines to uracils and function in antiviral innate immunity, RNA editing, and can cause hypermutation in chromosomes. The resulting uracils can be directly replicated, resulting in C to T mutations, or uracil-DNA glycosylase can convert the uracils to abasic (AP) sites which are then fixed as C to T or C to G mutations by translesion DNA polymerases. We noticed that in yeast and in human cancers, contributions of C to T and C to G mutations depends on the origin of ssDNA mutagenized by APOBECs. Since ssDNA in eukaryotic genomes readily binds to replication protein A (RPA) we asked if RPA could affect APOBEC-induced mutation spectrum in yeast. For that purpose, we expressed human APOBECs in the wild-type yeast and in strains carrying a hypomorph mutationrfa1-t33in the large RPA subunit. We confirmed that therfa1-t33allele can facilitate mutagenesis by APOBECs. We also found that therfa1-t33mutation changed the ratio of APOBEC3A-induced T to C and T to G mutations in replicating yeast to resemble a ratio observed in long-persistent ssDNA in yeast and in cancers. We present the data suggesting that RPA may shield APOBEC formed uracils in ssDNA from Ung1, thereby facilitating C to T mutagenesis through the accurate copying of uracils by replicative DNA polymerases. Unexpectedly, we also found that for uracils shielded from Ung1 by wild-type RPA the mutagenic outcome is reduced in the presence of translesion DNA polymerase zeta.
AntiCPs-CompML: A Comprehensive Fast Track ML method to predict Anti-Corona Peptides
Authors: Bist Prem Singh , Bhattarai Sadik , Tayara Hilal , Chong Kil To 
Published: June 2024

AbstractThis work introduces AntiCPs-CompML, a novel Machine learning framework for the rapid identification of anti-coronavirus peptides (ACPs). ACPs, acting as viral shields, offer immense potential for COVID-19 therapeutics. However, traditional laboratory methods for ACP discovery are slow and expensive. AntiCPs-CompML addresses this challenge by utilizing three primary features for peptide sequence analysis: Amino Acid Composition (AAC), Pseudo Amino Acid Composition (PAAC), and Composition-Transition-Distribution (CTD). The framework leverages 26 different machine learning algorithms to effectively predict potential anti-coronavirus peptides. This capability allows for the analysis of vast datasets and the identification of peptides with hallmarks of effective ACPs. AntiCPs-CompML boasts unprecedented speed and cost-effectiveness, significantly accelerating the discovery process while enhancing research efficiency by filtering out less promising options. This method holds promise for developing therapeutic drugs for COVID-19 and potentially other viruses. Our model demonstrates strong performance with an F1 Score of 92.12% and a Roc AUC of 76% in the independent test dataset. Despite these promising results, we are continuously working to refine the model and explore its generalizability to unseen datasets. Future enhancements will include featurebased and oversampling augmentation strategies addressing the limitation of anti-covid peptide data for comprehensive study, along with concrete feature selection algorithms, to further refine the model’s predictive power. AntiCPs-CompML ushers in a new era of expedited anti-covid peptides discovery, accelerating the development of novel antiviral therapies.