bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pyramidal tract neurons drive feed-forward excitation of striatum through cholinergic interneurons
Authors: Morgenstern Nicolás A. , Isidro Ana Filipa , Israely Inbal , Costa Rui M. 
Published: December 2020

SummaryCorticostriatal connectivity is central for many cognitive and motor processes, such as reinforcement or action initiation and invigoration. The cortical input to the striatum arises from two main cortical populations: intratelencephalic (IT) and pyramidal tract (PT) neurons. We uncovered a new feedforward excitatory circuit, supported by a polysynaptic motif from PT neurons to cholinergic interneurons (ChIs) to excitatory inputs, which runs in parallel to the canonical monosynaptic corticostriatal connection. This new motif conveys a delayed second phase of excitation to striatal spiny projection neurons (SPNs), through an acetylcholine-dependent glutamate release mechanism, resulting in biphasic corticostriatal signals. These biphasic signals are a hallmark of PT, but not IT, corticostriatal inputs, due to a stronger relative input from PT neurons to ChIs. These results uncover a novel feed-forward circuit mechanism by which PT activity differentially gates excitatory inputs to the striatum, with potential implications for behavior, plasticity and learning.HighlightsPT, but not IT, corticostriatal inputs convey biphasic excitation to SPNs via a disynaptic circuit involving striatal ChIs.PT neurons recruit ChIs more efficiently than IT neurons due to a stronger relative input to ChIs.The second phase of SPN excitation is mediated by nicotinic receptors at long-range glutamatergic axons in the striatum.Suprathreshold depolarizations of SPNs by PT inputs depend on ChI→SPN excitation and result in delayed spiking.
Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR-presented peptides and epitopes
Authors: Fisch Andressa , Reynisson Birkir , Benedictus Lindert , Nicastri Annalisa , Vasoya Deepali , Morrison Ivan , Buus Søren , Ferreira Beatriz Rossetti , de Miranda Santos Isabel Kinney Ferreira , Ternette Nicola , Connelley Tim , Nielsen Morten 
Published: December 2020

AbstractMajor histocompatibility complex (MHC) peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parameters that define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein antigens to elicit functional T cell responses. Liquid chromatography-mass spectrometry (LC-MS) analysis of MHC eluted ligands (EL data) has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of antigen presentation have reached a high level of accuracy for both MHC class I and class II. Here, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte antigen class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by EL data derived from cell lines expressing a range of DRB3 alleles prevalent in Holstein-Friesian populations. The model generated (NetBoLAIIpan - available as a web-server atwww.cbs.dtu.dk/services/NetBoLAIIpan) was shown to have unprecedented predictive power to identify known BoLA-DR restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced MS peptidomics with immunoinformatics for characterization of the BoLA-DR antigen presentation system and provide a novel tool that can be utilised to assist in rational evaluation and selection of bovine CD4 T cell epitopes.
MSA: Reproducible mutational signature attribution with confidence based on simulations
Authors: Senkin Sergey 
Published: December 2020

AbstractBackgroundMutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage.De novoextraction of signatures is commonly performed using Non-Negative Matrix Factorisation (NMF) methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments.ResultsWe present MSA (Mutational Signature Attribution), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, based on Non-Negative Least Squares (NNLS) method with optimisation. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures.Availability and implementationMSA comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available fromhttps://gitlab.com/s.senkin/MSA
Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans
Authors: Korunes Katharine L , Soares-Souza Giordano Bruno , Bobrek Katherine , Tang Hua , Araújo Isabel Inês , Goldberg Amy , Beleza Sandra 
Published: December 2020

AbstractGenetic data can provide insights into population history, but first we must understand the patterns that complex histories leave in genomes. Here, we consider the admixed human population of Cabo Verde to understand the patterns of genetic variation left by social and demographic processes. First settled in the late 1400s, Cabo Verdeans are admixed descendants of Portuguese colonizers and enslaved West African people. We consider Cabo Verde’s well-studied historical record alongside genome-wide SNP data from 563 individuals from 4 regions within the archipelago. We use genetic ancestry to test for patterns of nonrandom mating and sex-specific gene flow, and we examine the consequences of these processes for common demographic inference methods and for genetic patterns. Notably, multiple population genetic tools that assume random mating underestimate the timing of admixture, but incorporating non-random mating produces estimates more consistent with historical records. We consider how admixture interrupts common summaries of genomic variation such as runs-of-homozygosity (ROH). While summaries of ROH may be difficult to interpret in admixed populations, differentiating ROH by length class shows that ROH reflect historical differences between the islands in their contributions from the source populations and post-admixture population dynamics. Finally, we find higher African ancestry on the X chromosome than on the autosomes, consistent with an excess of European males and African females contributing to the gene pool. Considering these genomic insights into population history in the context of Cabo Verde’s historical record, we can identify how assumptions in genetic models impact inference of population history more broadly.
Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences
Authors: Lanchantin Jack , Weingarten Tom , Sekhon Arshdeep , Miller Clint , Qi Yanjun 
Published: December 2020

ABSTRACTViruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins. However, experimental methods to find protein interactions are inadequate: large scale experiments are noisy, and small scale experiments are slow and expensive. Inspired by the recent successes of deep neural networks, we hypothesize that deep learning methods are well-positioned to aid and augment biological experiments, hoping to help identify more accurate virus-host protein interaction maps. Moreover, computational methods can quickly adapt to predict how virus mutations change protein interactions with the host proteins.We propose DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus–Human protein interactions for SARS-CoV-2, H1N1, and Ebola. In addition, we demonstrate how our framework can be used to predict and interpret the interactions of mutated SARS-CoV-2 Spike protein sequences.AvailabilityWe make all of our data and code available on GitHubhttps://github.com/QData/DeepVHPPI.ACM Reference FormatJack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, and Yanjun Qi. 2021. Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences. InProceedings of ACM Conference (ACM-BCB). ACM, New York, NY, USA, 10 pages. https://doi.org/??
Potent SARS-CoV-2 binding and neutralization through maturation of iconic SARS-CoV-1 antibodies
Authors: Rouet Romain , Mazigi Ohan , Walker Gregory J. , Langley David B. , Sobti Meghna , Schofield Peter , Lenthall Helen , Jackson Jennifer , Ubiparipovic Stephanie , Henry Jake Y. , Abayasingam Arunasingam , Burnett Deborah , Kelleher Anthony , Brink Robert , Bull Rowena A. , Turville Stuart , Stewart Alastair G. , Goodnow Christopher C. , Rawlinson William D. , Christ Daniel 
Published: December 2020

ABSTRACTAntibodies against coronavirus spike protein potently protect against infection and disease, however it remains unclear if such protection can be extended to variant coronaviruses. This is exemplified by a set of iconic and well-characterized monoclonal antibodies developed after the 2003 SARS outbreak including mAbs m396, CR3022, CR3014 and 80R, which potently neutralize SARS-CoV-1, but not SARS-CoV-2. Here we explore antibody maturation strategies to change and broaden their specificity, enabling potent binding and neutralization of SARS-CoV-2. Using targeted mutagenesis as well as light chain shuffling on phage, we identified variants with considerably increased affinity and neutralization potential. The most potent antibody, derived from the NIH-developed mAb m396, neutralized live SARS-CoV-2 virus with a half-maximal inhibitory concentration (IC50) of 160 ng/ml. Intriguingly, while many of the matured clones maintained specificity of the parental antibody, new specificities were also observed, which was further confirmed by X-ray crystallography and cryo-electron microscopy, indicating that a limited set of antibodies can give rise to variants targeting diverse epitopes. Our findings open up over 15 years of antibody development efforts against SARS-CoV-1 to the SARS-CoV-2 field and outline general principles for the maturation of antibody specificity against emerging viruses.
Information flow, cell types and stereotypy in a full olfactory connectome
Authors: Schlegel Philipp , Bates Alexander S. , Stürner Tomke , Jagannathan Sridhar R. , Drummond Nikolas , Hsu Joseph , Capdevila Laia Serratosa , Javier Alexandre , Marin Elizabeth C. , Barth-Maron Asa , Tamimi Imaan F. M. , Li Feng , Rubin Gerald M. , Plaza Stephen M. , Costa Marta , Jefferis Gregory S.X.E. 
Published: December 2020

AbstractThehemibrainconnectome provides large scale connectivity and morphology information for the majority of the central brain ofDrosophila melanogaster. Using this data set, we provide a complete description of theDrosophilaolfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter – versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.
A CRISPR endonuclease gene drive reveals two distinct mechanisms of inheritance bias
Authors: Verkuijl Sebald A. N. , Gonzalez Estela , Li Ming , Ang Joshua , Kandul Nikolay P. , Anderson Michelle A. E. , Akbari Omar S. , Bonsall Michael B. , Alphey Luke 
Published: December 2020

ABSTRACTRNA guided CRISPR gene drives have shown the capability of biasing transgene inheritance in multiple species. Among these, homing endonuclease drives are the most developed. In this study, we report the functioning ofsds3, bgcn, andnup50expressed Cas9 in anAedes aegyptihoming split drive system targeting thewhitegene. We report their inheritance biasing capability, propensity for maternal deposition, and zygotic/somatic expression. Additionally, by making use of the tight linkage ofwhiteto the sex-determining locus, we were able to elucidate mechanisms of inheritance bias. We find inheritance bias through homing in double heterozygous males, but find that a previous report of the same drive occurred through meiotic drive. We propose that other previously reported ‘homing’ design gene drives may in fact bias their inheritance through other mechanisms with important implications for gene drive design.
A systematic analysis of rodent models implicates adipogenesis and innate immunity in pathogenesis of fatty liver disease
Authors: Im Yu Ri , Hunter Harriet , de Gracia Hahn Dana , Duret Amedine , Cheah Qinrong , Dong Jiawen , Fairey Madison , Hjalmarsson Clarissa , Li Alice , Lim Hong Kai , McKeown Lorcán , Mitrofan Claudia-Gabriela , Rao Raunak , Utukuri Mrudula , Rowe Ian A. , Mann Jake P. 
Published: December 2020

SummaryAnimal models of human disease are a key component of translational research and yet there is often no consensus on which model is optimal for a particular disease. Here, we generated a database of 3,920 rodent models of non-alcoholic fatty liver disease (NAFLD). Study designs were highly heterogeneous therefore few models had been cited more than once. Analysis of genetic models provided evidence for the role of adipose dysfunction and perturbation of the innate immune system in the progression of NAFLD. We identified that high-fat, high-fructose diets most closely recapitulate the human phenotype of NAFLD. There was substantial variability in the nomenclature of animal models; a consensus on terminology of specialist diets is needed. More broadly, this analysis demonstrates the variability in preclinical study design, which has implications for the reproducibility ofin vivoexperiments.
Data Descriptor: Proteomic profiling across breast cancer cell lines and models
Authors: Kalocsay Marian , Berberich Matthew J. , Everley Robert A. , Nariya Maulik K. , Chung Mirra , Gaudio Ben , Victor Chiara , Bradshaw Gary A. , Hafner Marc , Sorger Peter K. , Mills Caitlin E. , Subramanian Kartik 
Published: December 2020

AbstractWe performed quantitative proteomics on 61 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.
Connecting the dots between root cross-section images and modelling tools to create a high resolution root system hydraulic maps inZea mays
Authors: Heymans Adrien , Couvreur Valentin , Lobet Guillaume 
Published: December 2020

AbstractRoot hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes.In this case study, we analysedZea mays(maize) plants of the var.B73that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kxandkr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature.The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations ofkralong the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to thekrestimation in between root type and along the root axe.Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments.One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.
Plasmodium falciparumserology: A comparison of two protein production methods for analysis of antibody responses by protein microarray
Authors: Oulton Tate , Obiero Joshua , Rodriguez Isabel , Ssewanyana Isaac , Dabbs Rebecca A. , Greenhouse Bryan , Drakeley Chris , Felgner Phil L. , Stone Will , Tetteh Kevin K.A. 
Published: December 2020

AbstractThe evaluation of protein antigens as putative serologic biomarkers of infection has increasingly shifted to high-throughput, multiplex approaches such as the protein microarray. In vitro transcription/translation (IVTT) systems – a similarly high-throughput protein expression method – are already widely utilised in the production of protein microarrays, though purified recombinant proteins derived from more traditional whole cell based expression systems also play an important role in biomarker characterisation. Here we have performed a side-by-side comparison of antigen-matched protein targets from an IVTT and purified recombinant system, on the same protein microarray. The magnitude and range of antibody responses to purified recombinants was found to be greater than that of IVTT proteins, and responses between targets from different expression systems did not clearly correlate. However, responses between amino acid sequence-matched targets from each expression system were more closely correlated. Despite the lack of a clearly defined relationship between antigen-matched targets produced in each expression system, our data indicate that protein microarrays produced using either method can be used confidently, in a context dependent manner, though care should be taken when comparing data derived from contrasting approaches.Statement of significance of the studyProtein microarray technology is increasingly being realised as a powerful tool in disease biomarker identification. Protein-based, serologic arrays are already well utilised in the characterisation of antibody responses to the malarialPlasmodium spp., drastically improving throughput and efficiency compared to more typical experimental assays. Such approaches have commonly made use of in vitro transcription/translation (IVTT) protein expression systems, though other protein expression methods are regularly employed. In this study we have directly compared antibody responses in a malaria endemic population, to matched protein antigens derived from both an IVTT and purified bacterial recombinant system in the same protein microarray. We demonstrated that the magnitude of measured antibody response to matched protein antigens tends to be greater to purified recombinants, rather than IVTT products. Further, our analysis showed highly variable levels of correlation of response between antigen-matched targets derived from each expression system, although relationships in antibody response were stronger between proteins with overlapping amino acid sequences. This study highlights the importance of considering the strengths and weaknesses of each expression system in the context of a protein microarray according to experimental hypotheses, and illustrates the need for attention when comparing data generated by these different methodologies.
Phase separation of Mer2 organizes the meiotic loop-axis structure of chromatin during meiosis I
Authors: Tsai Bin , Liu Wei , Dong Dashan , Shi Kebin , Chen Liangyi , Gao Ning 
Published: December 2020

AbstractSexually reproducing organisms acquire genetic diversity through meiotic recombination during meiosis I, which is initiated via programmed DNA double-strand breaks (DSBs) induced by Spo11-containing machinery in each meiotic cell. The combination of programmed DSB sites in each meiotic cell must be diverse, which requires a certain degree of randomness in the distribution of DSBs. The formation of programmed DSBs requires a preestablished loop-axis structure of chromatin. Here, we demonstrate that the axial element protein Mer2 undergoes liquid-liquid phase separationin vitroandin vivothrough its intrinsically disordered C-terminal domain. A DNA binding motif within its central domain is responsible for bringing DNA into Mer2 liquid droplets and Mer2-DNA complex could assemble into filamentous structures extending from the droplets. These results suggest that phase separation of Mer2 drives the formation of a droplet-loop structure of meiotic chromatin to facilitate and to diversify programmed DSB formation.