bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generalizable approaches for genomic prediction of metabolites in plants
Authors: Brzozowski Lauren J. , Campbell Malachy T. , Hu Haixiao , Caffe Melanie , Gutiérrez Lucía , Smith Kevin P. , Sorrells Mark E. , Gore Michael A. , Jannink Jean-Luc 
Published: November 2021

ABSTRACTPlant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of ‘similarity’ had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.
Young transposable elements rewired gene regulatory networks in human and chimpanzee hippocampal intermediate progenitors
Authors: Patoori Sruti , Barnada Samantha M. , Large Christopher , Murray John I. , Trizzino Marco 
Published: November 2021

AbstractThe hippocampus is associated with essential brain functions such as learning and memory. Human hippocampal volume is significantly greater than expected when compared to non-human apes, suggesting a recent expansion. Intermediate progenitors, which are able to undergo multiple rounds of proliferative division before a final neurogenic division, may have played a role in the evolutionary hippocampal expansion. To investigate the evolution of gene regulatory networks underpinning hippocampal neurogenesis in apes, we leveraged the differentiation of human and chimpanzee induced Pluripotent Stem Cells into TBR2-positive hippocampal intermediate progenitors (hpIPCs). We find that the gene networks active in hpIPCs are significantly different between humans and chimpanzees, with ∼2,500 genes differentially expressed. We demonstrate that species-specific transposon-derived enhancers contribute to these transcriptomic differences. Young transposons, predominantly Endogenous Retroviruses (ERVs) and SINE-Vntr-Alus (SVAs), were co-opted as enhancers in a species-specific manner. Human-specific SVAs provided substrates for thousands of novel TBR2 binding sites, and CRISPR-mediated repression of these SVAs attenuates the expression of ∼25% of the genes that are upregulated in human intermediate progenitors relative to the same cell population in the chimpanzee.Summary statementEvolution of human and chimpanzee hippocampal development was mediated by co-option of young retrotransposons into species-specific enhancers.
Hyperspectral Counting of Multiplexed Nanoparticle Emitters in Single Cells and Organelles
Authors: Jena Prakrit V. , Gravely Mitchell , Cupo Christian C. , Safaee Mohammad M. , Roxbury Daniel , Heller Daniel A. 
Published: November 2021

AbstractNanomaterials are the subject of a range of biomedical, commercial, and environmental investigations involving measurements in living cells and tissues. Accurate quantification of nanomaterials, at the tissue, cell, and organelle levels, is often difficult, however, in part due to their inhomogeneity. Here, we propose a method that uses the diverse optical properties of a nanomaterial preparation in order to improve quantification at the single-cell and organelle level. We developed ‘hyperspectral counting’, which employs diffraction-limited imagingviahyperspectral microscopy of a diverse set of nanomaterial emitters, to estimate nanomaterial counts in live cells and sub-cellular structures. A mathematical model was developed, and Monte Carlo simulations were employed, to improve the accuracy of these estimates, enabling quantification with single-cell and single-endosome resolution. We applied this nanometrology technique to identify an upper-limit of the rate of uptake into cells - approximately 3,000 particles endocytosed within 30 minutes. In contrast, conventional ROI counting results in a 230% undercount. The method identified significant heterogeneity and a broad non-Gaussian distribution of carbon nanotube uptake within cells. For example, while a particular cell contained an average of 1 nanotube per endosome, the heterogenous distribution resulted in over 7 nanotubes localizing within some endosomes, substantially changing the accounting of subcellular nanoparticle concentration distributions. This work presents a method to quantify cellular and subcellular concentrations of a heterogeneous carbon nanotube reference material, with implications for nanotoxicology, drug/gene delivery, and nanosensor fields.
NucPosDB: a database of nucleosome positioning in vivo and nucleosomics of cell-free DNA
Authors: Shtumpf Mariya , Piroeva Kristan V. , Agrawal Shivam P. , Jacob Divya R. , Teif Vladimir B. 
Published: November 2021

AbstractNucleosome positioning is involved in many gene regulatory processes happening in the cell and it may change as cells differentiate or respond to the changing microenvironment in a healthy or diseased organism. One important implication of nucleosome positioning in clinical epigenetics is its use in the “nucleosomics” analysis of cell-free DNA (cfDNA) for the purpose of patient diagnostics in liquid biopsies. The rationale for this is that the apoptotic nucleases that digest chromatin of the dying cells mostly cut DNA between nucleosomes. Thus, the short pieces of DNA in body fluids reflect the positions of nucleosomes in the cells of origin. Here we report a systematic nucleosomics database – NucPosDB, curating published nucleosome positioning datasets in vivo as well as datasets of sequenced cell-free DNA (cfDNA) that reflect nucleosome positioning in situ in the cells of origin. Users can select subsets of the database by a number of criteria and then obtain raw or processed data. NucPosDB also reports the originally determined regions with stable nucleosome occupancy across several individuals with a given condition. An additional section provides a catalogue of computational tools for the analysis of nucleosome positioning or cfDNA experiments and theoretical algorithms for the prediction of nucleosome positioning from DNA sequence. We provide an overview of the field, describe the structure of the database in this context and demonstrate data variability using examples of different medical conditions. NucPosDB is useful both for analysis of fundamental gene regulation processes and training computational models for patient diagnostics based on cfDNA. The database currently curates ∼400 publications on nucleosome positioning in cell lines and in situ as well as cfDNA from >10,000 patients and healthy volunteers. For open-access cfDNA datasets as well as key MNase-seq datasets in human cells, NucPosDB allows downloading processed mapped data in addition to the stable-nucleosome regions. NucPosDB is available at https://generegulation.org/nucposdb/.
Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth
Authors: Modabbernia Amirhossein , Whalley Heather C. , Glahn David C. , Thompson Paul M. , Kahn Rene S. , Frangou Sophia 
Published: November 2021

AbstractApplication of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related brain changes in these age-groups are dynamic. However, the comparative performance of the multiple machine learning algorithms available has not been systematically appraised. To address this gap, the present study evaluated the accuracy (Mean Absolute Error; MAE) and computational efficiency of 21 machine learning algorithms using sMRI data from 2,105 typically developing individuals aged 5 to 22 years from five cohorts. The trained models were then tested in two independent holdout datasets, comprising 4,078 pre-adolescents aged 9-10 years and another sample of 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. The best performing algorithms were Extreme Gradient Boosting (MAE of 1.49), Random Forest Regression (MAE of 1.58) and Support Vector Regression with Radial Basis Function Kernel (MAE of 1.64) which had acceptable and comparable computational efficiency. Findings of the present study could be used as a guide for optimizing methodology when quantifying age-related changes in youth.
Long-Range Electrostatic Interactions Significantly Modulate the Affinity of Dynein for Microtubules
Authors: Pabbathi Ashok , Coleman Lawrence , Godar Subash , Paul Apurba , Garlapati Aman , Spencer Matheu , Eller Jared , Alper Joshua 
Published: November 2021

AbstractThe dynein family of microtubule minus-end directed motor proteins drives diverse functions in eukaryotic cells, including cell division, intracellular transport, and flagellar beating. Motor protein processivity, which characterizes how far a motor walks before detaching from its filament, depends on the interaction between its microtubule-binding domain (MTBD) and the microtubule. Dynein’s MTBD switches between high- and low-binding affinity states as it steps. Significant structural and functional data show that specific salt bridges within the MTBD and between the MTBD and the microtubule govern these affinity state shifts. However, recent computational work suggests that non-specific, long-range electrostatic interactions between the MTBD and the microtubule may also play a significant role in the processivity of dynein. To investigate this hypothesis, we mutated negatively charged amino acids remote from the dynein MTBD-microtubule-binding interface to neutral residues and measured the binding affinity using microscale thermophoresis and optical tweezers. We found a significant increase in the binding affinity of the mutated MTBDs for microtubules. Furthermore, we found that charge screening by free ions in solution differentially affected the binding and unbinding rates of MTBDs to microtubules. Together, these results demonstrate a significant role for long-range electrostatic interactions in regulating dynein-microtubule affinity. Moreover, these results provide insight into the principles that potentially underlie the biophysical differences between molecular motors with various processivities and protein-protein interactions more generally.Statement of SignificanceThe dynein family of motor proteins drives the motility of multiple cellular functions by walking toward the minus end of microtubules. The biophysical mechanisms of dynein rely on its ability to change affinity for the microtubule as it steps. Specific short-range electrostatic interactions acting at the microtubule-binding domain (MTBD)-microtubule interface are known to govern binding affinity. This study shows that non-specific longer-range electrostatic interactions due to charged amino acids remote from the binding interface also contribute significantly to the binding affinity mechanisms. Our results suggest that subtle differences in the electrostatic charge distribution within the MTBD significantly affect the molecular biophysical motility mechanisms in the dynein family of motors.
Interaction between theta-phase and spike-timing dependent plasticity simulates theta induced memory effects
Authors: Wang Danying , Parish George , Shapiro Kimron L. , Hanslmayr Simon 
Published: November 2021

AbstractRodent studies suggest that spike timing relative to hippocampal theta activity determines whether potentiation or depression of synapses arise. Such changes also depend on spike timing between pre- and post-synaptic neurons, known as spike-timing-dependent plasticity (STDP). STDP, together with theta-phase-dependent learning, has inspired several computational models of learning and memory. However, evidence to elucidate how these mechanisms directly link to human episodic memory is lacking. In a computational model, we modulate long-term potentiation (LTP) and long-term depression (LTD) of STDP, by opposing phases of a simulated theta rhythm. We fit parameters to a hippocampal cell culture study in which LTP and LTD were observed to occur in opposing phases of a theta rhythm. Further, we modulated two inputs by cosine waves with synchronous and asynchronous phase offsets and replicate key findings in human episodic memory. Learning advantage was found for the synchronous condition, as compared to the asynchronous conditions, and was specific to theta modulated inputs. Importantly, simulations with and without each mechanism suggest that both STDP and theta-phase-dependent plasticity are necessary to replicate the findings. Together, the results indicate a role for circuit-level mechanisms, which bridges the gap between slice preparation studies and human memory.Author SummaryLong-lasting changes in synaptic connectivity between neurons have been suggested to support learning and memory processes at the cellular level in the brain. Such synaptic modifications depend on synchronous activation of neurons, which leads to generate brain oscillations. Human memory studies focus on the relationships between brain oscillations and memory processes. Direct evidence on how the cellular mechanism links to human memory behaviour is lacking. To investigate the direct link between synaptic plasticity mechanisms and human memory formation, we built a computational neural network that implements two synaptic plasticity mechanisms, which are well-established in the rodents’ hippocampus. One mechanism shows that strengthening or weakening in synaptic connectivity depends on the phases of ongoing brain oscillation at theta frequency (4 – 8 Hz), which is a dominant signal in the hippocampus. The other mechanism suggests that synaptic modification depends on the precise timing of action potentials between two neurons. Our model successfully reproduces results from rodents, as well as several human episodic memory studies which demonstrated that human associative memory performance depends on phase synchronisation in theta frequency. These findings suggest a link between specific learning mechanisms at cellular level and human memory behaviour.
Expression of mini-G proteins specifically halt cognate GPCR trafficking and intracellular signalling
Authors: Manchanda Yusman , Ramchunder Zenouska , Shchepinova Maria M , Rutter Guy A , Inoue Asuka , Tate Edward W , Jones Ben , Tomas Alejandra 
Published: November 2021

AbstractMini-G proteins are engineered thermostable variants of Gα subunits designed to specifically stabilise G protein-coupled receptors (GPCRs) in their active conformation for structural analyses. Due to their smaller size and ease of use, they have become popular tools in recent years to assess specific GPCR behaviours in cells, both as reporters of receptor coupling to each G protein subtype and for in-cell assays designed to quantify compartmentalised receptor signalling from a range of subcellular locations. Here, we describe a previously unappreciated consequence of the co-expression of mini-G proteins with their cognate GPCRs, namely a profound disruption in GPCR trafficking and intracellular signalling caused by the co-expression of the specific mini-G subtype coupled to the affected receptor. We studied the Gαs-coupled pancreatic beta cell class B GPCR glucagon-like peptide-1 receptor (GLP-1R) as a model to describe in detail the molecular consequences derived from this effect, including a complete halt in β-arrestin-2 recruitment and receptor internalisation, despite near-normal levels of receptor GRK2 recruitment and lipid nanodomain segregation, as well as the disruption of endosomal GLP-1R signalling by mini-Gs co-expression. We also extend our analysis to a range of other prototypical GPCRs covering the spectrum of Gα subtype coupling preferences, to unveil a widely conserved phenomenon of GPCR internalisation blockage by specific mini-G proteins coupled to a particular receptor. Our results have important implications for the design of methods to assess intracellular GPCR signalling. We also present an alternative adapted bystander intracellular signalling assay for the GLP-1R in which we substitute the mini-Gs by a nanobody, Nb37, with specificity for active Gαs:GPCR complexes and no deleterious effect on the capacity for GLP-1R internalisation.
The ADAR1 editome reveals drivers of editing-specificity for ADAR1-isoforms
Authors: Kleinova Renata , Leuchtenberger Alina F. , Giudice Claudio Lo , Tanzer Andrea , Derdak Sophia , Picardi Ernesto , Jantsch Michael F. 
Published: November 2021

AbstractAdenosine deaminase acting on RNA (ADAR) (also known as ADAR1) promotes A-to-I conversion in double-stranded and highly structured RNAs. ADAR1 has two isoforms transcribed from different promoters: ADAR1p150, which is mainly cytoplasmic and interferon-inducible, and constitutively expressed ADAR1p110 that is primarily localized in the nucleus.Mutations in ADAR1 cause Aicardi – Goutières syndrome (AGS), a severe autoinflammatory disease in humans associated with aberrant IFN production. In mice, deletion of ADAR1 or selective knockout of the p150 isoform alone leads to embryonic lethality driven by overexpression of interferon-stimulated genes. This phenotype can be rescued by concurrent deletion of cytoplasmic dsRNA-sensor MDA5. These findings indicate that the interferon- inducible p150 isoform is indispensable and cannot be rescued by the ADAR1p110 isoform. Nevertheless, editing sites uniquely targeted by ADAR1p150 but also mechanisms of isoform- specificity remain elusive.Here we combine RIP-seq on human cells expressing ADAR1 isoforms and combine this with analysis of isoform-specific editing patterns in genetically modified mouse cells to extensively investigate ADAR1-isoform binding- and editing characteristics.Moreover, using mutated ADAR variants, we examine the effect of two unique features of ADAR1p150 on its target specificity: 1) cytoplasmic localization and 2) Z-DNA binding domain α. Our findings indicate that ZBDα contributes only minimally to p150 editing-specificity and that isoform-specific editing is directed mainly by the cytoplasmic localization of the editase.
Spatial charting of single cell transcriptomes in tissues
Authors: Wei Runmin , He Siyuan , Bai Shanshan , Sei Emi , Hu Min , Thompson Alastair , Chen Ken , Krishnamurthy Savitri , Navin Nicholas E. 
Published: November 2021

AbstractSingle cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections, but do not have single cell genomic resolution. Here, we developed a computational approach called CellTrek that combines these two datasets to achieve single cell spatial mapping. We benchmarked CellTrek using a simulation study and two in situ datasets. We then applied CellTrek to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.
T-cell microvilli simulations show operation near packing limit and impact on antigen recognition
Authors: Morgan Jonathan , Pettmann Johannes , Dushek Omer , Lindsay Alan E. 
Published: November 2021

T-cells are immune cells that continuously scan for foreign-derived antigens on the surfaces of nearly all cells, termed antigen presenting cells (APCs). They do this by dynamically extending numerous protrusions called microvilli (MV) that contain T-cell receptors (TCRs) towards the APC surface in order to scan for antigens. The number, size, and dynamics of these MV, and the complex multi-scale topography that results, play a yet unknown role in antigen recognition. We develop an anatomically informed model of the T-cell/APC interface to elucidate the role of MV dynamics in antigen sensitivity and discrimination. We find that MV surveillance reduces antigen sensitivity compared to a completely flat interface unless MV are stabilized in an antigen-dependent manner and find that MV have only a modest impact on antigen discrimination. The model highlights that MV contacts optimise the competing demands of fast scanning speeds of the APC surface with antigen sensitivity and that T-cells operate their MV near the interface packing limit. Finally, we find that observed MV contact lifetimes can be largely influenced by conditions in the T-cell/APC interface with these lifetimes often being longer than the simulation or experimental observation period. The work highlights the role of MV in antigen recognition.Significance StatementT-cells search for foreign-derived antigens on the surface of antigen presenting cells (APC) by dynamically extending tubular protrusions called microvilli (MV) which form membrane close-contacts. Although known for decades, their role in antigen recognition remains unclear. Guided by recent experiments, we built an anatomically informed stochastic model of MV scanning and compared with a topologically flat interface. We find that MV scanning modestly impacts antigen discrimination, yet it enables T-cells to balance the competing effects of maintaining sensitivity while conducting rapid APC surveillance. The model can reconcile discrepancies in observed MV lifetimes and demonstrates that observed area coverage fractions correspond to geometric packing limits. Our work suggests that MVs promote positive signaling outcomes despite anatomical constraints to close contact formation.