bluuebunny/crossref_metadata_2025_split huggingface dataset:
The method utilized to purify the SARS-CoV-2 N protein can affect its molecular properties
Authors: Tarczewska Aneta , Kolonko-Adamska Marta , Zarębski Mirosław , Dobrucki Jurek , Ożyhar Andrzej , Greb-Markiewicz Beata 
Published: May 2021

AbstractOne of the main structural proteins of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the nucleocapsid protein (N). The basic function of this protein is to bind genomic RNA and to form a protective nucleocapsid in the mature virion. The intrinsic ability of the N protein to interact with nucleic acids makes its purification very challenging. Therefore, typically employed purification methods appear to be insufficient for removing nucleic acid contamination. In this study, we present a novel purification protocol that enables the N protein to be prepared without any bound nucleic acids. We also performed comparative structural analysis of the N protein contaminated with nucleic acids and free of contamination and showed significant differences in the structural and phase separation properties of the protein. These results indicate that nucleic-acid contamination may severely affect molecular properties of the purified N protein. In addition, the notable ability of the N protein to form condensates whose morphology and behaviour suggest more ordered forms resembling gel-like or solid structures is described.
River phosphorus cycling constrains lake cyanobacteria blooms
Authors: King Whitney M. , Curless Susan E. , Hood James M. 
Published: May 2021

AbstractBioavailable phosphorus exports from rivers during high flow often fuel downstream harmful cyanobacterial blooms; yet whether river phosphorus cycles affect these exports is unclear. Here, we examined river phosphorus cycling during high flow events in a large agricultural watershed that drives cyanobacterial blooms in Lake Erie. We show that between 2003 and 2019 river phosphorus cycles, through phosphorus sorption, reduced bioavailable phosphorus exports by 24%, potentially constraining Lake Erie cyanobacterial blooms by 61%. Over the last 45-years, phosphorus sorption has declined with suspended sediment exports due to increases in soil-erosion-minimizing agricultural practices, likely contributing to recent cyanobacterial blooms. In this, and likely other agricultural watersheds, rivers perform an unrecognized ecosystem service during high flow creating field-river-lake linkages that need to be incorporated into phosphorus management.
Generation of SIV resistant T cells and Macrophages from Nonhuman Primate Induced Pluripotent Stem Cells with Edited CCR5 locus
Authors: D’Souza Saritha S. , Kumar Akhilesh , Weinfurter Jason , Park Mi Ae , Maufort John , Tao Lihong , Kang HyunJun , Golos Thaddeus , Thomson James A , Reynolds Matthew , Slukvin Igor 
Published: May 2021

AbstractAdoptive therapies with genetically modified somatic T cells rendered HIV resistant have shown promise for AIDS therapy. A renewable source of HIV resistant human T cells from induced pluripotent stem cells (iPSCs) would further facilitate and broaden the applicability of these therapies. Here, we report successful targeting of the CCR5 locus in iPSCs generated from peripheral blood T cells (T-iPSCs) or fibroblasts (fib-iPSCs) from Mauritian Cynomolgus macaques (MCM), using CRISPR/Cas9 technology. We found that CCR5 editing does not affect pluripotency or hematopoietic and T cell differentiation potentials of fib-iPSCs. However, deletion of CCR5 in T-iPSCs leads to selective loss of their T cell redifferentiation potential without affecting myeloid development. T cells and macrophages produced from CCR5-edited MCM- iPSCs did not support replication of the CCR5-tropic simian immunodeficiency viruses SIVmac239 (T-cell tropic) and SIVmac316 (macrophage-tropic). Overall, these studies provide a platform for further exploration of AIDS therapies based on gene-edited iPSCs in a nonhuman primate preclinical model.
Misregulation of MYB16 causes stomatal cluster formation by disrupting polarity in asymmetric cell division
Authors: Yang Shao-Li , Tran Ngan , Tsai Meng-Ying , Ho Chin-Min Kimmy 
Published: May 2021

ABSTRACTStomata and leaf cuticle regulate water evaporation from the plant body and balance the trade-off between photosynthesis and water loss. We identified MYB16, a key transcription factor controlling cutin biosynthesis, from previous stomatal lineage ground cell (SLGC)-enriched transcriptome study. The preferential localization of MYB16 in SLGCs but not meristemoids suggests a link between cutin synthesis and stomatal development. Here, we showed that downregulation of MYB16 in meristemoids was directly mediated by the stomatal master transcription factor, SPEECHLESS (SPCH). The suppression of MYB16 before asymmetric division was crucial for stomatal patterning because overexpression or ectopic expression of MYB16 in meristemoids increased impermeability and elevated stomatal density and clusters. The aberrant pattern of stomata was due to reduced and disrupted establishment of polarity during asymmetric cell division. Manipulating polarity by growing seedlings on hard agar rescued stomatal clusters and polarity defects in MYB16 ectopic lines. By expressing a cutinase in MYB16 ectopic lines, stomatal clustering was reduced, which suggests that the ectopic accumulation of cuticle affects the polarity in asymmetrically dividing cells and causes clustered stomata. Taken together, inhibiting MYB16 expression by SPCH in early stomatal lineage is required to correctly place the polarity complex for proper stomatal patterning during leaf morphogenesis.
Predicting the effects of drug combinations using probabilistic matrix factorization
Authors: Nafshi Ron , Lezon Timothy R. 
Published: May 2021

AbstractDrug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.
Circadian pacemaker neurons display co-phasic rhythms in basal calcium level and in fast calcium fluctuations
Authors: Liang Xitong , Holy Timothy E. , Taghert Paul H. 
Published: May 2021

AbstractCircadian pacemaker neurons in theDrosophilabrain display daily rhythms in the levels of intracellular calcium. These calcium rhythms are driven by molecular clocks and are required for normal circadian behavior. To study their biological basis, we employed genetic manipulations in conjunction with in vivo light-sheet microscopy to measure calcium dynamics in individual pacemaker neurons over complete 24-hour periods. We found co-phasic daily rhythms in basal calcium levels and in high frequency calcium fluctuations. Further we found that the rhythms of basal calcium levels require the activity of theIP3R, a channel that mediates calcium fluxes from internal endoplasmic reticulum (ER) calcium stores. Independently, the rhythms of fast calcium fluctuations required the T-type voltage-gated calcium channel, a conductance that mediates extracellular calcium influx. These results suggest thatDrosophilamolecular clocks regulateIP3Rand T-type channels to generate coupled rhythms in basal calcium and in fast calcium fluctuations, respectively. We propose that both internal and external calcium fluxes are essential for circadian pacemaker neurons to provide rhythmic outputs, and thereby regulate the activities of downstream brain centers.
A genetic mouse model with postnatalNf1andp53loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor
Authors: Inoue Akira , Janke Laura J. , Gudenas Brian L. , Jin Hongjian , Fan Yiping , Paré Joshua , Clay Michael R. , Northcott Paul A. , Hirbe Angela C. , Cao Xinwei 
Published: May 2021

AbstractBackgroundMalignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation ofNF1and cooperating tumor suppressors, includingCDKN2A/B, PRC2, and p53, is found in most MPNST. Inactivation of the LATS1/2 kinases of the Hippo pathway was recently shown to cause tumors resembling MPNST histologically, although Hippo pathway mutations are rarely found in MPNST. Because existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST, we aimed to establish a mouse MPNST model that recapitulated the human disease genetically, histologically, and molecularly.MethodsWe combined two genetically modified alleles, anNf1;Trp53 cis-conditional allele and an induciblePlp-CreERallele (NP-Plp), to model the somatic, possibly postnatal, mutational events in human MPNST. We also generated conditionalLats1;Lats2knockout mice. We performed histopathologic analysis of mouse MPNST models and transcriptomic comparison of mouse models and human nerve sheath tumors.ResultsPostnatalNf1;Trp53 cis-deletion resulted in GEM-MPNST that was histologically more similar to human MPNST than the widely used germlineNf1;Trp53 cis-heterozygous (NPcis) model and showed partial loss of H3K27me3. At the transcriptome level,Nf1;p53-driven GEM-MPNST were distinct fromLats-driven GEM-MPNST and resembled human MPNST more closely than doLats-driven tumors.ConclusionsThe NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly.Key PointsPostnatalNf1;p53 cis-deletion in NP-Plp mice results in tumors similar to MPNST.The transcriptomes ofNf1;p53-driven andLats-driven MPNST models are distinct.NP-Plp model resembles human MPNST genetically, histologically, and molecularly.Importance of the StudyMalignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas with a poor prognosis and limited treatment options. Existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST. To model the somatic, possibly postnatal, mutational events seen in MPNST patients, we generated a GEM-MPNST model by combining two genetically modified alleles, anNf1;Trp53 cis-conditional allele and aPlp-CreERallele. Our histologic and transcriptomic analyses showed that this NP-Plp model resembles human MPNST genetically, histologically, and molecularly—more so than the widely used NPcis model and the recently publishedLats-driven model. The NP-Plp model is genetically simple, making it easy to maintain and an ideal platform for preclinical studies. Given its tamoxifen-inducible nature, this model can be used to study the time/stage dependency of the tumorigenic potential of Schwann cells.
A combined bioinformatics and LC-MS based approach for the development and benchmarking of a comprehensive database for CNS proteins inLymnaea stagnalis
Authors: Wooller Sarah , Anagnostopoulou Aikaterini , Kuropka Benno , Crossley Michael , Benjamin Paul R. , Pearl Frances , Kemenes Ildikó , Kemenes György , Eravci Murat 
Published: May 2021

AbstractApplications of key technologies in biomedical research, such as qRT-PCR or LC-MS based proteomics, are generating large biological (-omics) data sets which are useful for the identification and quantification of biomarkers involved in molecular mechanisms of any research area of interest. Genome, transcriptome and proteome databases are already available for a number of model organisms including vertebrates and invertebrates. However, there is insufficient information available for protein sequences of certain invertebrates, such as the great pond snailLymnaea stagnalis, a model organism that has been used highly successfully in elucidating evolutionarily conserved mechanisms of learning and memory, ageing and age-related as well as amyloid-β induced memory decline. In this investigation, we used a bioinformatics approach to designing and benchmarking a comprehensive CNS proteomics database (LymCNS-PDB) for the identification of proteins from the Central Nervous System (CNS) ofLymnaea stagnalisby LC-MS based proteomics. LymCNS-PDB was created by using the Trinity TransDecoder bioinformatics tool to translate amino acid sequences from mRNA transcript assemblies obtained from an existing publishedLymnaea stagnalistranscriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to sequences for molluscan proteins (includingLymnaea stagnalisand other molluscs) available from UniProtKB. LymCNS-PDB, which contains 9,628 identified matched proteins, was then benchmarked by performing LC-MS based proteomics analysis with proteins isolated from the CNS ofLymnaea stagnalis. MS/MS analysis using the LymCNS-PDB database led to the identification of 3,810 proteins while only 982 proteins were identified by using a non-specific Molluscan database. LymCNS-PDB provides a valuable tool that will enable us to perform quantitative proteomics analysis to identify a plethora of protein interactomes involved in several CNS functions inLymnaea stagnalisincluding learning and memory, aging-related memory decline and others.
Essential Regression - a generalizable framework for inferring causal latent factors from multi-omic human datasets
Authors: Bing Xin , Lovelace Tyler , Bunea Florentina , Wegkamp Marten , Singh Harinder , Benos Panayiotis V , Das Jishnu 
Published: May 2021

AbstractHigh-dimensional cellular and molecular profiling of human samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate predictive biomarkers and prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions and their integration to infer causal relationships. Here we present Essential Regression (ER), an interpretable machine learning approach for high-dimensional multi-omic datasets, that addresses these problems by identifying latent factors and their likely cause-effect relationships with the system-wide outcome/properties of interest. ER is a novel data-distribution-free latent-factor regression model that integrates multi-omic datasets and identifies latent factors significantly associated with an outcome. ER outperforms a range of state-of-the-art methods in terms of prediction performance on simulated datasets. ER can be coupled with probabilistic graphical modeling thereby strengthening the causal inferences. ER generates novel cellular and molecular predictions, using multi-omic human systems immunology datasets, pertaining to immunosenescence and immune dysregulation.
A spatially resolved mechanistic growth law for cancer drug development predicting tumour growing fractions
Authors: Nasim Adam , Yates James , Derks Gianne , Dunlop Carina 
Published: May 2021

AbstractMathematical models used in pre-clinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumour volume, however, they typically have little connection with the underlying physiological processes. This lack of a mechanistic underpinning restricts their flexibility and inhibits their direct translation across studies including from animal to human. Here we present a mathematical model describing tumour growth for the evaluation of single agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumours. Importantly, we demonstrate the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for pre-clinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft (CDX) and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current pre-clinical modelling studies to also incorporating spatially resolved and multi-modal data without significant added complexity and creates the opportunity to improve translation and tumour response predictions.
A novel highly potent inhibitor of TMPRSS2-like proteases blocks SARS-CoV-2 variants of concern and is broadly protective against infection and mortality in mice
Authors: Shapira Tirosh , Monreal I. Abrrey , Dion Sébastien P. , Jager Mason , Désilets Antoine , Olmstead Andrea D. , Vandal Thierry , Buchholz David W. , Imbiakha Brian , Gao Guang , Chin Aaleigha , Rees William D. , Steiner Theodore , Nabi Ivan Robert , Marsault Eric , Sahler Julie , August Avery , Van de Walle Gerlinde , Whittaker Gary R. , Boudreault Pierre-Luc , Aguilar Hector C. , Leduc Richard , Jean François 
Published: May 2021

SummaryThe COVID-19 pandemic caused by the SARS-CoV-2 virus remains a global public health crisis. Although widespread vaccination campaigns are underway, their efficacy is reduced against emerging variants of concern (VOCs)1,2. Development of host-directed therapeutics and prophylactics could limit such resistance and offer urgently needed protection against VOCs3,4. Attractive pharmacological targets to impede viral entry include type-II transmembrane serine proteases (TTSPs), such as TMPRSS2, whose essential role in the virus lifecycle is responsible for the cleavage and priming of the viral spike protein5–7. Here, we identify and characterize a small-molecule compound, N-0385, as the most potent inhibitor of TMPRSS2 reported to date. N-0385 exhibited low nanomolar potency and a selectivity index of >106at inhibiting SARS-CoV-2 infection in human lung cells and in donor-derived colonoids8. Importantly, N-0385 acted as a broad-spectrum coronavirus inhibitor of two SARS-CoV-2 VOCs, B.1.1.7 and B.1.351. Strikingly, single daily intranasal administration of N-0385 early in infection significantly improved weight loss and clinical outcomes, and yielded 100% survival in the severe K18-human ACE2 transgenic mouse model of SARS-CoV-2 disease. This demonstrates that TTSP-mediated proteolytic maturation of spike is critical for SARS-CoV-2 infectionin vivoand suggests that N-0385 provides a novel effective early treatment option against COVID-19 and emerging SARS-CoV-2 VOCs.
Contingent-behavior assay to study the neurogenetics of addiction shows zebrafish preference for alcohol is biphasic
Authors: Nathan Fatima Megala , Kibat Caroline , Goel Tanisha , Stewart James , Claridge-Chang Adam , Mathuru Ajay S. 
Published: May 2021

AbstractAlcohol use disorders are complex, multifactorial phenomena with a large footprint within the global burden of diseases. Here, we report the development of an accessible, two-choice self-administration zebrafish assay (SAZA) to study the neurobiology of addiction. Using this assay, we first demonstrated that, while zebrafish avoid higher concentrations of alcohol, they are attracted to low concentrations. Pre-exposure to alcohol did not change this relative preference, but acute exposure to an alcohol deterrent approved for human use decreased alcohol self-administration. A pigment mutant used in whole-brain imaging studies displayed a similar relative alcohol preference profile, however, mutants inCCSER1, a gene associated with alcohol dependence in human genetic studies, showed a reversal in relative preference. The presence of a biphasic response (hormesis) in zebrafish validated a key aspect of vertebrate responses to alcohol. SAZA adds a new dimension for discovering novel alcohol deterrents, and studying the neurogenetics of addiction using the zebrafish.
The Automated Systematic Search Deduplicator (ASySD): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews
Authors: Hair Kaitlyn , Bahor Zsanett , Macleod Malcolm , Liao Jing , Sena Emily S. 
Published: May 2021

AbstractBackgroundResearchers who perform systematic searches across multiple databases often identify duplicate publications. Identifying such duplicates (“deduplication”) can be extremely time-consuming, but failure to remove these citations can, in the worst instance, lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews.MethodsWe evaluated ASySD’s performance on 5 unseen biomedical systematic search datasets of various sizes (1,845 – 79,880 citations), which had been deduplicated by human reviewers. We compared the performance of ASySD with Endnote’s automated deduplication option and with the Systematic Review Accelerator Deduplication Module (SRA-DM).ResultsASySD identified more duplicates than either SRA-DM or Endnote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of 0.94-0.99. The tool took less than 1 hour to deduplicate all datasets.ConclusionsFor duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.
LiPyphilic: A Python toolkit for the analysis of lipid membrane simulations
Authors: Smith Paul , Lorenz Christian D. 
Published: May 2021

AbstractMolecular dynamics simulations are now widely used to study emergent phenomena in lipid membranes with complex compositions. Here, we present LiPyphilic - a fast, fully tested, and easy to install Python package for analysing such simulations. Analysis tools in LiPyphilic include the identification of cholesterol flip-flop events, the classification of local lipid environments, and the degree of interleaflet registration. LiPyphilic is both force field and resolution agnostic, and thanks to the powerful atom selection language of MDAnalysis it can handle membranes with highly complex compositions. LiPyphilic also offers two on-the-fly trajectory transformations to i) fix membranes split across periodic boundaries and ii) perform nojump coordinate unwrapping. Our implementation of nojump unwrapping accounts for fluctuations in box volume under the NPT ensemble — an issue that most current implementations have overlooked. The full documentation of LiPyphilic, including installation instructions, is available at https://lipyphilic.readthedocs.io/en/latest.Graphical TOC Entry