bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determining the Role of Environmental Covariates on Planktivorous Elasmobranch Population Trends within an Isolated Marine Protected Area
Authors: Saltzman Julia , White Easton R. 
Published: September 2022

AbstractSeveral studies have found predictable relationships between the behavior of planktivores and environmental conditions, suggesting that planktivores may be especially sensitive to environmental change. However, many studies to date are based on limited observations, include few of the many environmental covariates which could influence planktivores, and do not occur over long enough time periods to make inferences about the potential effects of environmental change. As such, long term datasets on planktivores are necessary to disentangle the potential impacts of oceanographic and environmental variability. In this study, to elucidate the relationship between plankivores and environmental variability, we use data obtained over the last 28 years by a small group of divemasters at Cocos Island, Costa Rica, one of the oldest marine reserves in the world. We found that, in general, for planktivorous elasmobranchs, several environmental variables, such as, chlorophyll A, lunar cycle, and salinity have clear influences on their occurrence and relative abundances. We found that in the phases of lower illuminations, there were significant increases in abundance of mobula rays. Specifically, a 0.10 mg/m3increase in Chlorophyll A correlated with 26% decrease in whale sharks. We found that increases in salinity correlated with increases in mobula abundance but did not correlate with observations of mantas or whale sharks. We also found that omission of environmental covariates can lead to overprediction and underprediction of relative abundances. Our findings highlight the need to take environmental conditions into account when evaluating the efficacy of marine protection.
Prediction and Classification of the Uptake and Disposition of Antidepressants and New CNS-Active Drugs in the Human Brain using the ANDROMEDA by Prosilico Software and Brainavailability-Matrix
Authors: Fagerholm Urban , Hellberg Sven , Alvarsson Jonathan , Spjuth Ola 
Published: September 2022

AbstractBackgroundPassive blood-brain barrier permeability (BBB Pe), fraction bound to brain tissue (fb,brain) and efflux by transport proteins MDR-1 and BCRP are essential determinants for the brain uptake and disposition of drugs.MethodsThe main objective of the study was to use the software ANDROMEDA by Prosilico to predict passive BBB Pe- and fb,brain-classes and MDR-1- and BCRP-specificities for various classes of antidepressants and for CNS-active small drugs marketed during 2020 and 2021, and then to position them according to a new 2-dimensional Brainavailability-Matrix (8 passive BBB Pex 4 fb,brainclasses, where class 11 has highest and 84 lowest values/brainavailability). Predicted estimates were used, except for cases where measured values were available.Results and ConclusionResults for 53 drugs show that adequate CNS uptake and disposition are achieved for compounds placed in the zones for low, moderate and high brainavailability, despite efflux. They also show that high brainavailability and efflux are common for CNS-active drugs and that modern CNS-active drugs generally have lower brainavailability than older antidepressive drugs. Furthermore, they demonstrate that ANDROMEDA by Prosilico and the new Brainavailability-Matrix are applicable for prediction, optimization and positioning of CNS uptake and disposition of drugs and drug candidates in man.
Multifactorial heterogeneity of the human mutation landscape related to DNA replication dynamics
Authors: Caballero Madison , Boos Dominik , Koren Amnon 
Published: September 2022

AbstractMutations do not occur uniformly across genomes but instead show biased associations with various genomic features, most notably late replication timing. However, it remains contested which mutation types in human cells relate to DNA replication dynamics and to what extents. Previous studies have been limited by the absence of cell-type-specific replication timing profiles and lack of consideration of inter-individual variation. To overcome these limitations, we performed high-resolution comparisons of mutational landscapes between and within lymphoblastoid cell lines from 1662 individuals, 151 chronic lymphocytic leukemia patients, and three colon adenocarcinoma cell lines including two with mismatch repair deficiency. Using cell type-matched replication timing profiles, we demonstrate how mutational pathways can exhibit heterogeneous replication timing associations. We further identified global mutation load as a novel, pervasive determinant of mutational landscape heterogeneity across individuals. Specifically, elevated mutation load corresponded to increased late replication timing bias as well as replicative strand asymmetries of clock-like mutations and off-target somatic hypermutation. The association of somatic hypermutation with DNA replication timing was further influenced by mutational clustering. Considering these multivariate factors, and by incorporating mutation phasing at an unprecedented scale, we identified a unique mutational landscape on the inactive X-chromosome. Overall, we report underappreciated complexity of mutational pathways and their relationship to replication timing and identify specific factors underlying differential mutation landscapes among cell types and individuals.
Predicting enzyme substrate chemical structure with protein language models
Authors: Jinich Adrian , Nazia Sakila Z. , Tellez Andrea V. , Rappoport Dmitrij , AlQuraishi Mohammed , Rhee Kyu 
Published: September 2022

AbstractThe number of unannotated or orphan enzymes vastly outnumber those for which the chemical structure of the substrates are known. While a number of enzyme function prediction algorithms exist, these often predict Enzyme Commission (EC) numbers or enzyme family, which limits their ability to generate experimentally testable hypotheses. Here, we harness protein language models, cheminformatics, and machine learning classification techniques to accelerate the annotation of orphan enzymes by predicting their substrate’s chemical structural class. We use the orphan enzymes ofMycobacterium tuberculosisas a case study, focusing on two protein families that are highly abundant in its proteome: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine (SAM)-dependent methyltransferases. Training machine learning classification models that take as input the protein sequence embeddings obtained from a pre-trained, self-supervised protein language model results in excellent accuracy for a wide variety of prediction tasks. These include redox cofactor preference for SDRs; small-molecule vs. polymer (i.e. protein, DNA or RNA) substrate preference for SAM-dependent methyltransferases; as well as more detailed chemical structural predictions for the preferred substrates of both enzyme families. We then use these trained classifiers to generate predictions for the full set of unannotated SDRs and SAM-methyltransferases in the proteomes ofM. tuberculosisand other mycobacteria, generating a set of biochemically testable hypotheses. Our approach can be extended and generalized to other enzyme families and organisms, and we envision it will help accelerate the annotation of a large number of orphan enzymes.Graphical abstract
Genetically Encoded RNA-based Bioluminescence Resonance Energy Transfer (BRET) Sensors
Authors: Mi Lan , Yu Qikun , Mudiyanselage Aruni P.K.K. Karunanayake , Wu Rigumula , Sun Zhining , Zheng Ru , Ren Kewei , You Mingxu 
Published: September 2022

AbstractRNA-based nanostructures and molecular devices have become popular for developing biosensors and genetic regulators. These programmable RNA nanodevices can be genetically encoded and modularly engineered to detect various cellular targets and then induce output signals, most often a fluorescence readout. Although powerful, the high reliance of fluorescence on the external excitation light raises concerns on its high background, photobleaching, and phototoxicity. Bioluminescence signals can be an ideal complementary readout for these genetically encoded RNA nanodevices. However, RNA-based real-time bioluminescent reporters have been rarely developed. In this study, we reported the first type of genetically encoded RNA-based bioluminescence resonance energy transfer (BRET) sensors that can be used for real-time target detection in living cells. By coupling a luciferase bioluminescence donor with a fluorogenic RNA-based acceptor, our BRET system can be modularly designed to image and detect various cellular analytes. We expect this novel RNA-based bioluminescent system can be potentially used broadly in bioanalysis and nanomedicine for engineering biosensors, characterizing cellular RNA–protein interactions, as well as high-throughput screening or in vivo imaging.
Decoding of the speech envelope from EEG using the VLAAI deep neural network
Authors: Accou Bernd , Vanthornhout Jonas , Van hamme Hugo , Francart Tom 
Published: September 2022

ABSTRACTTo investigate the processing of speech in the brain, commonly simple linear models are used to establish a relationship between brain signals and speech features. However, these linear models are ill-equipped to model a highly-dynamic, complex non-linear system like the brain, and they often require a substantial amount of subject-specific training data. This work introduces a novel speech decoder architecture: the Very Large Augmented Auditory Inference (VLAAI) network.The VLAAI network outperformed state-of-the-art subject-independent models (median Pearson correlation of 0.19, p < 0.001), yielding an increase over the well-established linear model by 52%. Using ablation techniques we identified the relative importance of each part of the VLAAI network and found that the non-linear components and output context module influenced model performance the most (10% relative performance increase). Subsequently, the VLAAI network was evaluated on a holdout dataset of 26 subjects and publicly available unseen dataset to test generalization for unseen subjects and stimuli. No significant difference was found between the holdout subjects and the default test set, and only a small difference between the default test set and the public dataset was found. Compared to the baseline models, the VLAAI network still significantly outperformed all baseline models on the public dataset. We evaluated the effect of training set size by training the VLAAI network on data from 1 up to 80 subjects and evaluated on 26 holdout subjects, revealing a logarithmic relationship between the number of subjects in the training set and the performance on unseen subjects. Finally, the subject-independent VLAAI network was fine-tuned for 26 holdout subjects to obtain subject-specific VLAAI models. With 5 minutes of data or more, a significant performance improvement was found, up to 34% (from 0.18 to 0.25 median Pearson correlation) with regards to the subject-independent VLAAI network.
30-Minute Highly Multiplexed VaxArray Immunoassay for Pneumococcal Vaccine Antigen Characterization
Authors: Hu Tianjing , Miller David F. , Taylor Amber W. , Riley Christine , McCormick Caitlin , Thomas Keely N. , Gao Rachel Y. , Rowlen Kathy L. , Byrne Emilia B. , Kumar Pardeep , Kim Soo Kyung , Dawson Erica D. 
Published: September 2022

ABSTRACTPneumonia accounts for over 20% of deaths worldwide in children aged 1 to 5 years, disproportionately affecting lower- and middle-income countries. Effective, highly multivalent pneumococcal vaccines are available to decrease disease burden, with numerous new vaccines currently under development to serve a variety of worldwide markets. However, pneumococcal conjugate vaccines are among the hardest biologics to manufacture and characterize due to their complexity and heterogeneity. Current characterization methods are often inherently singleplex, requiring separate tests for each serotype present. In addition, identity and quantity are often determined with separate methods. We have developed the VaxArray Pneumococcal Assay for applications in identity, quantity, and stability testing of pneumococcal polysaccharide and pneumococcal conjugate vaccines. The VaxArray Pneumococcal Assay has a time to result of less than 30 minutes and is an off-the-shelf multiplexed, microarray-based immunoassay kit that can identify and simultaneously quantify 23 pneumococcal polysaccharide serotypes common to many on-market and in-development vaccines. Here, we highlight the potential of the assay for identity testing by showing high reactivity and serotype-specificity to a wide variety of native polysaccharides, CRM197-conjugated polysaccharides, and drug product. The assay also has vaccine-relevant lower limits of quantification in the low to mid ng/mL range and can be used for accurate quantification even in adjuvanted vaccines. Excellent correlation to the anthrone assay is demonstrated, with VaxArray resulting in significantly improved precision over this antiquated chemical method.
Atypical B cells consist of subsets with distinct effector functions
Authors: Reyes Raphael A. , Batugedara Gayani , Dutta Paramita , Reers Ashley B. , Garza Rolando , Ssewanyana Isaac , Jagannathan Prasanna , Feeney Margaret E. , Greenhouse Bryan , Bol Sebastiaan , Ay Ferhat , Bunnik Evelien M. 
Published: September 2022

AbstractAtypical B cells are a population of activated B cells that are commonly enriched in individuals with chronic immune activation, but are also part of a normal immune response to infection or vaccination. Prior studies to determine the function of these cells have yielded conflicting results, possibly due to functional heterogeneity among this B cell population. To better define the role(s) of atypical B cells in the host adaptive immune response, we performed single-cell sequencing of transcriptomes, cell surface markers, and B cell receptors in individuals with chronicPlasmodium falciparumexposure, a condition known to lead to accumulation of circulating atypical B cells. Our studies identified three previously uncharacterized populations of atypical B cells with distinct transcriptional and functional profiles, that separate into two differentiation pathways. We identified a set of cell surface markers to distinguish these atypical B cell subsets and confirmed their presence in malaria-experienced children and adults using flow cytometry.Plasmodium falciparum-specific cells were present in equal proportions within each of these atypical B cell populations, indicating that all three subsets develop in response to antigen stimulation. However, we observed marked differences among the three subsets in their ability to produce IgG upon T-cell-dependent activation. Collectively, our findings help explain the conflicting observations in prior studies on the functions of atypical B cells and provide a better understanding of their role in the adaptive immune response in chronic inflammatory conditions.One sentence summaryAtypical B cells consist of three subsets that may play distinct roles in the host adaptive immune response.
Using Machine Learning Enabled Phenotyping To Characterize Nodulation In Three Early Vegetative Stages In Soybean
Authors: Carley Clayton N. , Zubrod Melinda , Dutta Somak , Singh Asheesh K. 
Published: September 2022

AbstractThe symbiotic relationship between soybean [Glycine max L. (Merr.)] roots and bacteria (Bradyrhizobium japonicum) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N2) is fixed into bio-available ammonia (NH3) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant-Bradyrhizobium relationship, and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.Core IdeasThe growth and development of soybean nodules on the taproot and non-taproots have unique growth and development patterns.In general, taproot nodules increase in area, while non-taproot nodules increase in count and area.Nodulation should be defined by the total nodule area as a function of both nodule count and individual nodule size.Genotypes adjust their nodulation through either increasing nodule count or nodule size to increase the total nodule area per root between each growth stage.There is a strong correlation between early growth stage taproot nodules and final seed nitrogen content.
Electrophile scanning by chemical proteomics reveals a potent pan-active DUB probe for investigation of deubiquitinase activity in live cells
Authors: Conole Daniel , Cao Fangyuan , Am Ende Christopher W. , Xue Liang , Kantesaria Sheila , Kang Dahye , Jin Jun , Owen Dafydd , Lohr Linda , Schenone Monica , Majmudar Jaimeen D. , Tate Edward W. 
Published: September 2022

ABSTRACTDeubiquitinases (DUBs) are proteases that hydrolyze isopeptide bonds linking ubiquitin to protein substrates, which can lead to reduced substrate degradation through the ubiquitin proteasome system. Deregulation of DUB activity has been implicated in many disease states, including cancer, neurodegeneration and inflammation, making them potentially attractive targets for therapeutic intervention. The >100 known DUB enzymes have been classified primarily by their conserved active sites, but we are still building our understanding of their substrate profiles, localization and regulation of DUB activity in diverse contexts. Ubiquitin-derived covalent activity-based probes (ABPs) are the premier tool for DUB activity profiling, but their large recognition element impedes cellular permeability and presents an unmet need for small molecule ABPs which account for local DUB concentration, protein interactions, complexes, and organelle compartmentalization in intact cells or organisms. Here, through comprehensive warhead profiling we identify cyanopyrrolidine (CNPy) probe IMP-2373 (12), a small molecule pan-DUB ABP to monitor DUB activity in physiologically relevant live cell systems. Through chemical proteomics and targeted assays we demonstrate that IMP-2373 quantitatively engages more than 35 DUBs in live cells across a range of non-toxic concentrations, and in diverse cell lines and disease models, and we demonstrate its application to quantification of changes in intracellular DUB activity during MYC deregulation in a model of B cell lymphoma. IMP-2373 thus offers a complementary tool to ubiquitin ABPs to monitor dynamic DUB activity in the context of disease-relevant phenotypes.SYNOPSIS TOCGraphical Abstract
Enteroviral 2C protein is an RNA-stimulated ATPase and uses a two-step mechanism for binding to RNA and ATP
Authors: Yeager Calvin , Carter Griffin , Gohara David W , Yennawar Neela H , Enemark Eric J , Arnold Jamie J , Cameron Craig E 
Published: September 2022

ABSTRACTThe enteroviral 2C protein is a therapeutic target, but the absence of a mechanistic framework for this enzyme limits our understanding of inhibitor mechanisms. Here we use poliovirus 2C and a derivative thereof to elucidate the first biochemical mechanism for this enzyme and confirm the applicability of this mechanism to other members of the enterovirus genus. Our biochemical data are consistent with a dimer forming in solution, binding to RNA, which stimulates ATPase activity by increasing the rate of hydrolysis without impacting affinity for ATP substantially. Both RNA and DNA bind to the same or overlapping site on 2C, driven by the phosphodiester backbone, but only RNA stimulates ATP hydrolysis. We propose that RNA binds to 2C driven by the backbone, with reorientation of the ribose hydroxyls occurring in a second step to form the catalytically competent state. 2C also uses a two-step mechanism for binding to ATP. Initial binding is driven by the α and ß phosphates of ATP. In the second step, the adenine base and other substituents of ATP are used to organize the active site for catalysis. These studies provide the first biochemical description of determinants driving specificity and catalytic efficiency of a picornaviral 2C ATPase.
A novel CLAVATA1 mutation causes multilocularity in Brassica rapa
Authors: Chow Hiu Tung , Kendall Timmy , Mosher Rebecca A. 
Published: September 2022

AbstractLocules are the seed-bearing structure of fruits. Multiple locules are associated with increased fruit size and seed set, and therefore control of locule number is an important agronomic trait. Locule number is controlled in part by the CLAVATA-WUSCHEL pathway. Disruption of either the CLAVATA1 receptor-like kinase or its ligand CLAVATA3 can cause larger floral meristems and an increased number of locules. In an EMS mutagenized population of Brassica rapa, we identified a mutant allele that raises the number of locules from 4 to a range of from 6 to 8. Linkage mapping and genetic analysis support that the mutant phenotype is due to a missense mutation in a CLAVATA 1 (CLV1) homolog. In addition to increased locule number, brclv1 individuals fail to terminate their floral meristems, resulting in internal gynoecia that negatively impact seed production.
Treenome Browser: co-visualization of enormous phylogenies and millions of genomes
Authors: Kramer Alexander M. , Sanderson Theo , Corbett-Detig Russell 
Published: September 2022

SummaryTreenome Browser is a web browser tool to interactively visualize millions of genomes alongside huge phylogenetic trees.Availability and ImplementationTreenome Browser for SARS-CoV-2 can be accessed at cov2tree.org, or at taxonium.org for user-provided trees. Source code and documentation are available at github.com/theosanderson/taxonium and docs.taxonium.org/en/latest/treenome.html.Contactalex.kramer@ucsc.edu, rucorbet@ucsc.edu