bluuebunny/crossref_metadata_2025_split huggingface dataset:
Direct regulation of cell cycle regulatory gene expression by NtrX to promoteSinorhizobium meliloticell division
Authors: Xing Shenghui , An Fang , Huang Leqi , Yang Xinwei , Zeng Shuang , Li Ningning , Zhang Lanya , Zheng Wenjia , Ouenzar Khadidja , Yan Junhui , Yu Liangliang , Luo Li 
Published: November 2020

ABSTRACTCell division of the alfalfa symbiont,Sinorhizobium meliloti, is regulated by a signaling network centered on CtrA. The gene expression of regulatory proteins in the network can be regulated by nutrient signaling systems. In this study, we found that NtrX, one of the regulators of nitrogen metabolic response, can directly regulate the expression of several regulatory genes in the CtrA signaling network. Three groups ofS. meliloti ntrXmutants showed similar cell division defects, such as slow growth, abnormal morphology of some cells and delayed DNA synthesis. Quantitative RT-PCR assays indicated that in these mutants, the transcription of genes such asctrAandgcrAwas up-regulated, while the transcription of genes such asdnaAandftsZ1was down-regulated. Western blotting showed that the CtrA and GcrA proteins were apparently increased in the mutants. The 53rdaspartate conserved in NtrX homologs can be phosphorylatedin vitroandin vivo. The phosphorylated NtrX protein can bind directly to the promoter regions ofctrA, gcrA, dnaAandftsZ1by recognizing the characteristic sequence CAAN1-5TTG. Therefore, phosphorylation of NtrX is essential for cell cycle regulation ofS. meliloti. We expressed the NtrX protein carrying a phosphorylation site substitution inAgrobacterium tumefaciensand found that the expressed strains had different growth phenotypes, suggesting that NtrX also regulates cell division in this bacterial species. Our findings reveal that NtrX acts as a transcriptional regulator that positively affects bacterial cell division, associated with nitrogen metabolism.IMPORTANCESinorhizobium melilotiinfects the host legume alfalfa and induces the formation of nitrogen-fixing nodules. The proliferation of rhizobia in plant tissues and cells is strictly controlled in the early stage of the interaction between symbiotic partners. However, the control mechanism is not very clear. Cell division ofS. melilotiin the free-living state is regulated by the CtrA signaling network, but the molecular mechanisms by which the CtrA system is associated with environmental nutrient signals (e.g., ammonia nitrogen) need to be further explored. This study demonstrates that NtrX, a regulator of nitrogen metabolism, required for symbiotic nodulation and nitrogen fixation byS. meliloti1021, can act as a transcriptional regulator of the CtrA signaling system. It may link nitrogen signaling to cell cycle regulation inRhizobiumspecies.
A zebrafish model for COVID-19 recapitulates olfactory and cardiovascular pathophysiologies caused by SARS-CoV-2
Authors: Kraus Aurora , Casadei Elisa , Huertas Mar , Ye Chunyan , Bradfute Steven , Boudinot Pierre , Levraud Jean-Pierre , Salinas Irene 
Published: November 2020

SummaryThe COVID-19 pandemic has prompted the search for animal models that recapitulate the pathophysiology observed in humans infected with SARS-CoV-2 and allow rapid and high throughput testing of drugs and vaccines. Exposure of larvae to SARS-CoV-2 Spike (S) receptor binding domain (RBD) recombinant protein was sufficient to elevate larval heart rate and treatment with captopril, an ACE inhibitor, reverted this effect. Intranasal administration of SARS-CoV-2 S RBD in adult zebrafish recombinant protein caused severe olfactory and mild renal histopathology. Zebrafish intranasally treated with SARS-CoV-2 S RBD became hyposmic within minutes and completely anosmic by 1 day to a broad-spectrum of odorants including bile acids and food. Single cell RNA-Seq of the adult zebrafish olfactory organ indicated widespread loss of expression of olfactory receptors as well as inflammatory responses in sustentacular, endothelial, and myeloid cell clusters. Exposure of wildtype zebrafish larvae to SARS-CoV-2 in water did not support active viral replication but caused a sustained inhibition oface2expression, triggered type 1 cytokine responses and inhibited type 2 cytokine responses. Combined, our results establish adult and larval zebrafish as useful models to investigate pathophysiological effects of SARS-CoV-2 and perform pre-clinical drug testing and validation in an inexpensive, high throughput vertebrate model.
High-quality genome-scale metabolic model ofAurantiochytriumsp. T66
Authors: Simensen Vetle , Voigt André , Almaas Eivind 
Published: November 2020

AbstractThe long-chain,ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-qualityω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists able to synthesize and accrue large amounts of essentialω-3 PUFAs, including EPA and DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand forω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytridAurantiochytriumsp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducingin vivogrowth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66’s metabolic properties, as well as exploring metabolic engineering and process-optimization strategiesin silicofor increasedω-3 PUFA production.
Bioactivity assessment of natural compounds using machine learning models based on drug target similarity
Authors: Periwal Vinita , Bassler Stefan , Andrejev Sergej , Gabrielli Natalia , Patil Kaustubh Raosaheb , Typas Athanasios , Patil Kiran Raosaheb 
Published: November 2020

SummaryNatural compounds constitute a rich resource of potential small-molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties through a random forest model. As a training set, we used pair-wise similarity between 1410 drugs in terms of their shared protein targets. The resulting model featured high performance metrics (matthews correlation coefficient of 0.81, and balanced accuracy of 0.91) suggesting that it well-captured the structure-activity relation. The model was then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted link’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, which showed a higher similarity when considering the most weighted similarity metric but was not picked by the random forest model, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.
PyBindingCurve, simulation and curve fitting to complex binding systems at equilibrium
Authors: Shave Steven , Chen Yan-Kai , Pham Nhan T. , Auer Manfred 
Published: November 2020

AbstractUnderstanding multicomponent binding interactions in protein-ligand, protein-protein and competition systems is essential for fundamental biology and drug discovery. Hand deriving equations quickly becomes unfeasible when the number of components is increased, and direct analytical solutions only exist to a certain complexity. To address this problem and allow easy access to simulation, plotting and parameter fitting to complex systems at equilibrium, we present the Python package PyBindingCurve. We apply this software to explore homodimer and heterodimer formation culminating in the discovery that under certain conditions, homodimers are easier to break with an inhibitor than heterodimers and may also be more readily depleted. This is a potentially valuable and overlooked phenomenon of great importance to drug discovery. PyBindingCurve may be expanded to operate on any equilibrium binding system and allows definition of custom systems using a simple syntax. PyBindingCurve is available under the MIT license at:https://github.com/stevenshave/pybindingcurveas Python source code accompanied by examples and as an easily installable package within the Python Package Index.
NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data
Authors: Overbey Eliah G. , Saravia-Butler Amanda M. , Zhang Zhe , Rathi Komal S. , Fogle Homer , da Silveira Willian A. , Barker Richard J. , Bass Joseph J. , Beheshti Afshin , Berrios Daniel C. , Blaber Elizabeth A. , Cekanaviciute Egle , Costa Helio A. , Davin Laurence B. , Fisch Kathleen M. , Gebre Samrawit G. , Geniza Matthew , Gilbert Rachel , Gilroy Simon , Hardiman Gary , Herranz Raúl , Kidane Yared H. , Kruse Colin P.S. , Lee Michael D. , Liefeld Ted , Lewis Norman G. , McDonald J. Tyson , Meller Robert , Mishra Tejaswini , Perera Imara Y. , Ray Shayoni , Reinsch Sigrid S. , Rosenthal Sara Brin , Strong Michael , Szewczyk Nathaniel J , Tahimic Candice G.T. , Taylor Deanne M. , Vandenbrink Joshua P. , Villacampa Alicia , Weging Silvio , Wolverton Chris , Wyatt Sarah E. , Zea Luis , Costes Sylvain V. , Galazka Jonathan M. 
Published: November 2020

SummaryWith the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.
Characterization and Engineering ofStreptomyces griseofuscusDSM 40191 as a Potential Host for Heterologous Expression of Biosynthetic Gene Clusters
Authors: Gren Tetiana , Whitford Christopher M. , Mohite Omkar S. , Jørgensen Tue S. , Kontou Eftychia E. , Nielsen Julie B. , Lee Sang Yup , Weber Tilmann 
Published: November 2020

AbstractStreptomyces griseofuscusDSM 40191 is a fast growingStreptomycesstrain that remains largely underexplored as a heterologous host. Here, we report the genome mining ofS. griseofuscus, followed by the detailed exploration of its phenotype, including production of native secondary metabolites and ability to utilise carbon, nitrogen, sulphur and phosphorus sources. Furthermore, several routes for genetic engineering ofS. griseofuscuswere explored, including use of GusA-based vectors, CRISPR-Cas9 and CRISPR-cBEST-mediated knockouts. Using CRISPR-BEST technology, core genes of 4 biosynthetic gene clusters (BGCs) that are situated on the chromosome arms were inactivated and the outcomes of the inactivations were tested. Two out of the three native plasmids were cured using CRISPR-Cas9 technology, leading to the generation of strainS. griseofuscusDEL1. DEL1 was further modified by full deletion of a pentamycin BGC and an unknown NRPS BGC, leading to the generation of strain DEL2, lacking approx. 500 kbp of the genome, which corresponds to a 5,19% genome reduction. Sequencing confirmed that DEL2 does not bear any crucial off-target effects or rearrangements in its genome. It can be characterized by faster growth and inability to produce three main native metabolites ofS. griseofuscus: lankacidin, lankamycin, pentamycin and their derivatives. To test the ability of DEL2 to heterologously produce secondary metabolites, the actinorhodin BGC was used. We were able to confirm the production of actinorhodin by bothS. griseofuscuswild type and DEL2. We believe that this strain will serve as a good chassis for heterologous expression of BGCs.ImportanceThe rise of antibacterial resistance calls on the development of the next generation of antibiotics, majority of which are derived from natural compounds, produced by actinomycetes. The manipulation, refactoring and expression of BGCs coding for such natural products is a promising approach in secondary metabolite discovery. Thus, the development of a versatile panel of heterologous hosts for the expression of BGCs is essential. We believe that first-to-date systematic, detailed characterisation ofS. griseofuscus, a highly promising chassis strain, will not only facilitate the further development of this particular strain, but also will set a blueprint for characterisation of other potential hosts.
Universal epidemic curve for COVID-19 and its usage for forecasting
Authors: Sharma Aryan , Sapkal Srujan , Verma Mahendra K 
Published: November 2020

AbstractWe construct a universal epidemic curve for COVID-19 using the epidemic curves of eight nations that have reached saturation for the first phase, and then fit an eight-degree polynomial that passes through the universal curve. We take India’s epidemic curve up to September 22, 2020 and overlap it with the universal curve by minimizing square-root error. The constructed curve is used to forecast epidemic evolution up to January 1, 2021. The predictions of our model and those of supermodel for India are reasonably close to each other considering the uncertainties in data fitting.
A National Multicenter Evaluation of the Clinical Utility of Optical Genome Mapping for Assessment of Genomic Aberrations in Acute Myeloid Leukemia
Authors: Levy Brynn , Baughn Linda B. , Chartrand Scott , LaBarge Brandon , Claxton David , Lennon Alan , Akkari Yassmine , Cujar Claudia , Kolhe Ravindra , Kroeger Kate , Pitel Beth , Sahajpal Nikhil , Sathanoori Malini , Vlad George , Zhang Lijun , Fang Min , Kanagal-Shamanna Rashmi , Broach James 
Published: November 2020

AbstractDetection of hallmark genomic aberrations in acute myeloid leukemia (AML) is essential for prognosis and patient management. Clinical practice guidelines for identifying such structural variants (SVs), established by the World Health Organization (WHO), European Leukemia Net (ELN) and National Comprehensive Cancer Network (NCCN), rely substantially on cytogenetic/cytogenomic techniques such as karyotyping, fluorescence in situ hybridization (FISH) or chromosomal microarray analysis (CMA). However, these techniques are limited by the need for skilled personnel as well as significant time and labor, making them cost-prohibitive for some patients. Optical genome mapping (OGM) addresses these limitations and allows for the accurate identification of clinically significant SVs using a novel, high throughput, inexpensive methodology. In a single assay, OGM offers a significantly higher resolution than karyotyping with comprehensive genome-wide analysis comparable to CMA and the added unique ability to detect balanced SVs that are missed by microarray. Here, we report the performance of OGM in a cohort of 100 AML cases, which were previously characterized by karyotype alone or karyotype and FISH. CMA was performed as an additional test in some cases. OGM identified all the clinically relevant SVs and CNVs reported by these standard cytogenetic methods. Moreover, OGM identified clinically relevant SVs in 11% of cases that had been missed by the routine methods. In 24% of cases, OGM refined the underlying genomic structure reported by traditional cytogenomic testing (13%), identified additional clinically relevant variants (7%) or both (4%). Three of 48 (6.25%) cases reported with normal karyotypes were shown to have cryptic translocations involving gene fusions. Two of these cases included fusion between NSD1-NUP98. Based on the comprehensive genomic profiling of the AML patients in this multi-institutional study, we recommend that OGM be considered as a first-line test for detection and identification of clinically relevant SVs.
Differentiation independent neuroprotective role of Vcan+oligodendrocyte precursor cells in poststroke cognitive impairment recovery
Authors: Shen Shanghang , Wang Yongjie , Xia Ruixue , Zeng Fanning , Cao Jun , Liu Xiaolei , Zhang Yunbin , Wang Zhanxiang , Tao Tao 
Published: November 2020

AbstractThe cell type-specific molecular pathology of post-stroke cognitive impairment (PSCI) in the hippocampus has not been thoroughly elucidated. We analyzed 27,069 cells by using single-cell RNA sequencing, and four oligodendrocyte precursor cell (OPC) subtypes were identified, Vcan+OPCs, which were determined to be the primary cluster among them. Additionally, we examined the features of endothelial cells (ECs) and found that Lcn2+ECs might play neuroprotective roles via Vwf after stroke. These results may facilitate further studies attempting to identify new avenues of research and novel targets for PSCI treatment.
Water-immersion finger-wrinkling improves grip efficiency in handling wet objects
Authors: Davis Nick J. 
Published: November 2020

AbstractFor most people, immersing their hands in water leads to wrinkling of the skin of the fingertips. This phenomenon is very striking, yet we know little about why it occurs. It has been proposed that the wrinkles act to distribute water away from the contact surfaces of the fingertip, meaning that wet objects can be grasped more readily. This study examined the coordination between the grip force used to hold an object and the load force exerted on it, when participants used dry or wrinkly fingers, or fingers that were wet but not wrinkly. The results showed that wrinkly fingers reduce the grip force needed to grip a wet object, bringing that force in line with what is needed for handling a dry object. The results suggest that enhancing grip force efficiency in watery environments is a possible adaptive reason for the development of wrinkly fingers.
Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification
Authors: Nabi Afshan , Adebali Ogun , Tastan Oznur 
Published: November 2020

AbstractLong non-coding RNAs (lncRNAs) are the largest class of non-coding RNAs (ncRNAs). However, recent experimental evidence has shown that some lncRNAs contain small open reading frames (sORFs) that are translated into functional micropeptides. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (ribo-seq) experiments, which are expensive and cell-type dependent. In addition, while very accurate machine learning models have been trained to distinguish between coding and non-coding sequences, little attention has been paid to the increasing evidence about the incorrect ground-truth labels of some lncRNAs in the underlying training datasets. We present a framework that leverages deep learning models’ training dynamics to determine whether a given lncRNA transcript is misannotated. Our models achieve AUC scores > 91% and AUPR > 93% in classifying non-coding vs. coding sequences while allowing us to identify possible misannotated lncRNAs present in the dataset. Our results overlap significantly with a set of experimentally validated misannotated lncRNAs as well as with coding sORFs within lncRNAs found by a ribo-seq dataset. The general framework applied here offers promising potential for use in curating datasets used for training coding potential predictors and assisting experimental efforts in characterizing the hidden proteome encoded by misannotated lncRNAs. Source code is available athttps://github.com/nabiafshan/DetectingMisannotatedLncRNAs.
NBR: Network-based R-statistics for (unbalanced) longitudinal samples
Authors: Gracia-Tabuenca Zeus , Alcauter Sarael 
Published: November 2020

AbstractNetwork neuroscience models the brain as interacting elements. However, a large number of elements imply a vast number of interactions, making it difficult to assess which connections are relevant and which are spurious. Zalesky et al. (2010) proposed the Network-Based Statistics (NBS), which identifies clusters of connections and tests their likelihood via permutation tests. This framework shows a better trade-off of Type I and II errors compared to conventional multiple comparison corrections. NBS uses General Linear Hypothesis Testing (GLHT), which may underestimate the within-subject variance structure when dealing with longitudinal samples with a varying number of observations (unbalanced samples). We implemented NBR, an R-package that extends the NBS framework adding (non)linear mixed-effects (LME) models. LME models the within-subject variance in more detail, and deals with missing values more flexibly. To illustrate its advantages, we used a public dataset of 333 human participants (188/145 females/males; age range: 17.0-28.4 y.o.) with two (n=212) or three (n=121) sessions each. Sessions include a resting-state fMRI scan and psychometric data. State anxiety scores and connectivity matrices between brain lobes were extracted. We tested their relationship using GLHT and LME models for balanced and unbalanced datasets, respectively. Only the LME approach found a significant association between state anxiety and a subnetwork that includes the cingulum, frontal, parietal, occipital, and cerebellum. Given that missing data is very common in longitudinal studies, we expect that NBR will be very useful to explore unbalanced samples.Significant StatementLongitudinal studies are increasing in neuroscience, providing new insights into the brain under treatment, development, or aging. Nevertheless, missing data is highly frequent in those studies, and conventional designs may discard incomplete observations or underestimate the within-subject variance. We developed a publicly available software (R package: NBR) that implements mixed-effect models into every possible connection in a sample of networks, and it can find significant subsets of connections using non-parametric permutation tests. We demonstrate that using NBR on larger unbalanced samples has higher statistical power than when exploring the balanced subsamples. Although this method is applicable in general network analysis, we anticipate this method being potentially useful in systems neuroscience considering the increase of longitudinal samples in the field.