bluuebunny/crossref_metadata_2025_split huggingface dataset:
A combination of mRNA features influence the efficiency of leaderless mRNA translation initiation
Authors: Bharmal Mohammed-Husain M. , Gega Alisa , Schrader Jared M. 
Published: May 2020

ABSTRACTBacterial translation is thought to initiate by base-pairing of the 16S rRNA and the Shine-Dalgarno sequence in the mRNA’s 5’UTR. However, transcriptomics has revealed that leaderless mRNAs, which completely lack any 5’UTR, are broadly distributed across bacteria and can initiate translation in the absence of the Shine-Dalgarno sequence. To investigate the mechanism of leaderless mRNA translation initiation, syntheticin vivotranslation reporters were designed that systematically tested the effects of start codon accessibility, leader length, and start codon identity on leaderless mRNA translation initiation. Using this data, a simple computational model was built based on the combinatorial relationship of these mRNA features which can accurately classify leaderless mRNAs and predict the translation initiation efficiency of leaderless mRNAs. Thus, start codon accessibility, leader length, and start codon identity combine to define leaderless mRNA translation initiation in bacteria.
Endpoint and Epitope-specific Antibody Responses as Correlates of Vaccine-mediated Protection of Mice against Ricin Toxin
Authors: Van Slyke Greta , Ehrbar Dylan J , Doering Jennifer , Yates Jennifer L. , S.Vitetta Ellen , Donini Oreola , Mantis Nicholas J 
Published: May 2020

ABSTRACTThe successful licensure of vaccines for biodefense is contingent upon the availability of well-established correlates of protection (CoP) in at least two animal species that can then be applied to humans, without the need to assess efficacy in the clinic. In this report we describe a multivariate model that combines pre-challenge serum antibody endpoint titers (EPT) and values derived from an epitope profiling immune-competition capture (EPICC) assay as a predictor in mice of vaccine-mediated immunity against ricin toxin (RT), a Category B biothreat. EPICC is a modified competition ELISA in which serum samples from vaccinated mice were assessed for their ability to inhibit the capture of soluble, biotinylated (b)-RT by a panel of immobilized monoclonal antibodies (mAbs) directed against four immunodominant toxin-neutralizing regions on the enzymatic A chain (RTA) of RT. In a test cohort of mice (n=40) vaccinated with suboptimal doses of the RTA subunit vaccine, RiVax®, we identified two mAbs, PB10 and SyH7, which had EPICC inhibition values in pre-challenge serum samples that correlated with survival following a challenge with 10 x LD50of RT administered by intraperitoneal (IP) injection. Analysis of a larger cohort of mice (n=645) revealed that a multivariate model combining endpoint titers and an epitope-profiling immune-competition capture (EPICC) assay values for PB10 and SyH7 as predictive variables had significantly higher statistical power than any one of the independent variables alone. Establishing the correlates of vaccine-mediated protection in mice represents an important steppingstone in the development of RiVax®as a medical countermeasure under the United States Food and Drug Administration’s “Two Animal Rule.”
Structural,in silico, and functional analysis of a Disabled-2-derived peptide for recognition of sulfatides
Authors: Song Wei , Gottschalk Carter J. , Tang Tuo-Xian , Biscardi Andrew , Ellena Jeffrey F. , Finkielstein Carla V. , Brown Anne M. , Capelluto Daniel G. S. 
Published: May 2020

AbstractDisabled-2 (Dab2) is an adaptor protein that regulates numerous cellular processes. Among them, Dab2 modulates the extent of platelet aggregation by two mechanisms. In the first mechanism, Dab2 intracellularly downregulates the integrin αIIbβ3receptor, converting it to a low affinity state for adhesion and aggregation processes. In the second mechanism, Dab2 is released extracellularly and interacts with both the integrin αIIbβ3receptor and sulfatides, both of which are known to be pro-aggregatory mediators, blocking their association to fibrinogen and P-selectin, respectively. Our previous research indicated that a 35-amino acid region within Dab2, which we refer to as the sulfatide-binding peptide (SBP), contains two potential sulfatide-binding motifs represented by two consecutive polybasic regions. Using a combined methodology including molecular docking, nuclear magnetic resonance, lipid-binding assays, and surface plasmon resonance, this work identifies the critical Dab2 residues within SBP that are responsible for sulfatide binding. A hydrophilic region, primarily mediated by R42, is responsible for the interaction with the sulfatide headgroup, whereas the C-terminal polybasic region contributes to interactions with the acyl chains. Furthermore, we demonstrated that, in Dab2 SBP, R42 significantly contributes to the inhibition of platelet P-selectin surface expression. The interacting Dab2 SBP residues with sulfatide resemble those described for sphingolipid-binding in other proteins, suggesting that sulfatide-binding proteins share common binding mechanisms.
Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning
Authors: Gado Japheth E. , Beckham Gregg T. , Payne Christina M. 
Published: May 2020

ABSTRACTAccurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with high Toptvalues are desired for enhanced reaction rates. Recently, a machine-learning method (TOME) for predicting Toptwas developed. TOME was trained on a normally-distributed dataset with a median Toptof 37°C and less than five percent of Toptvalues above 85°C, limiting the method’s predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on Toptvalues greater than 85°C is nearly an order of magnitude higher than the error on values between 30 and 50°C. In this study, we apply ensemble learning and resampling strategies that tackle the data imbalance to significantly decrease the error on high Toptvalues (>85°C) by 60% and increase the overall R2value from 0.527 to 0.632. The revised method, TOMER, and the resampling strategies applied in this work are freely available to other researchers as a Python package on GitHub.
Fertilizers, BT technology, and insecticides contributed to 60%, 23%, and 17%, respectively to the increase in BT cotton yield: An analysis from 2000 to 2014 in India
Authors: Parameswaran C , Cayalvizhi B 
Published: May 2020

AbstractComplementary technologies and agricultural practices capable of sustaining profitability to the farmers cultivating BtCotton in India require urgent attention. In India, approval of Btcotton, cultivation of fertilizer-intensive hybrids, higher dose of fertilizer application by farmers, usage of novel pesticides all happened simultaneously during the same period (2002-04) which makes very difficult to identify the individual effect in the yield gain of cotton. In this background, we attempted to understand the proportionate contribution of fertilizers, BT technology and novel group of pesticides in enhancing cotton yield in India. For the analysis, linear regression model and change in partial factor productivity (PFP) of cotton was considered in four different scenarios for yield estimation between 2000 and 2014, i.e. Scenario I: Cotton yield in the absence of technology and enhanced fertilizers usage, Scenario II: Cotton yield only due to enhanced fertilizer usage, Scenario III: Cotton yield with enhanced fertilizer and application of novel pesticides for the insect control, and Scenario IV: Cotton yield due to BT technology, enhanced use of fertilizer, and novel insecticides (actual yield of Cotton in India during Bt phase).Their comparison showed that the individual effect of fertilizers, BT technology and insecticides contributed to 60%, 23% and 17% of cotton yield, respectively in India. Further, 18% reduction in PFP was observed recently as compared to 2003-08. Besides, 125 Kg/ha of fertilizers was identified as optimum dose for sustaining high yield in cotton. Thus, present analysis identified the individual effect of different technologies contributing to the yield of cotton in India which can be used in decision making processes for crop improvement. Further, in our opinion, three strategies namely drip fertigation, intercrossing Bt and non-Bt hybrids for resistance management in bollworms, and IPM for sucking pests will primarily drive the research priorities and policy actions for the next 5 to 10 years in sustaining the economic benefits of the six million cotton farmers in India.
An HIV-Tat inducible mouse model system of childhood HIV-associated nephropathy
Authors: Tang Pingtao , Das Jharna R. , Li Jinliang , Yu Jing , Ray Patricio E. 
Published: May 2020

AbstractBackgroundModern antiretroviral therapies (ART) have decreased the prevalence of HIV-associated nephropathy (HIVAN). Nonetheless, we continue to see children and adolescents with HIVAN all over the world. Furthermore, once HIVAN is established in children, it is difficult to revert its long-term progression, and we need better animal models of childhood HIVAN to test new treatments.ObjectivesTo define whether the HIV-1 trans-activator (Tat) gene precipitates HIVAN in young mice, and to develop an inducible mouse model of childhood HIVAN.Design/MethodsAn HIV-Tat gene cloned from a child with HIVAN was used to generate recombinant adenoviral vectors (rAd-Tat). rAd-Tat andLacZcontrol vectors (2 × 109) were expressed in the kidney of newborn wild type and HIV-transgenic (Tg26) FVB/N mice without significant proteinuria (n = 5 - 8 per group). Mice were sacrificed 7 and 35 days later to assess their renal outcome, the expression of HIV-genes and growth factors, and markers of cell growth and differentiation by RT-qPCR, immunohistochemistry, and/or Western blots.ResultsHIV-Tat induced the expression of HIV-1 genes (env) and heparin binding growth factors in the kidney of HIV-Tg26mice, and precipitated HIVAN in the first month of life. No significant renal changes were detected in wild type mice infected with rAd-Tat vectors, suggesting that HIV-Tat alone does not induce renal disease.ConclusionThis new mouse model of childhood HIVAN highlights the critical role that HIV-Tat plays in the pathogenesis of HIVAN, and could be used to study the pathogenesis and treatment of HIVAN in children and adolescents.Summary statementWe developed a new inducible mouse model system of childhood HIV-associated nephropathy, and demonstrated that HIV-Tat plays a critical role in this renal disease acting in synergy with other HIV-1 genes and heparin binding cytokines.
Suramin inhibits SARS-CoV-2 infection in cell culture by interfering with early steps of the replication cycle
Authors: da Silva Clarisse Salgado Benvindo , Thaler Melissa , Tas Ali , Ogando Natacha S. , Bredenbeek Peter J. , Ninaber Dennis K. , Wang Ying , Hiemstra Pieter S. , Snijder Eric J. , van Hemert Martijn J. 
Published: May 2020

AbstractThe SARS-CoV-2 pandemic that originated from Wuhan, China, in December 2019 has impacted public health, society and economy and the daily lives of billions of people in an unprecedented manner. There are currently no specific registered antiviral drugs to treat or prevent SARS-CoV-2 infections. Therefore, drug repurposing would be the fastest route to provide at least a temporary solution while better, more specific drugs are being developed. Here we demonstrate that the antiparasitic drug suramin inhibits SARS-CoV-2 replication, protecting Vero E6 cells with an EC50of ∼20 µM, which is well below the maximum attainable level in human serum. Suramin also decreased the viral load by 2-3 logs when Vero E6 cells or cells of a human lung epithelial cell line (Calu-3) were treated. Time of addition and plaque reduction assays showed that suramin acts on early steps of the replication cycle, possibly preventing entry of the virus. In a primary human airway epithelial cell culture model, suramin also inhibited the progression of infection. The results of our preclinical study warrant further investigation and suggest it is worth evaluating whether suramin provides any benefit for COVID-19 patients, which obviously requires well-designed, properly controlled randomized clinical trials.
Holobiont Urbanism: sampling urban beehives reveals cities’ metagenomes
Authors: Hénaff Elizabeth , Najjar Devora , Perez Miguel , Flores Regina , Woebken Christopher , Mason Christopher E. , Slavin Kevin 
Published: May 2020

ABSTRACTOver half of the world’s population lives in urban areas with, according to the United Nations (UN), nearly 70% expected to live in cities by 2050 (United Nations, 2019). Our cities are built by and for humans, but are also complex, adaptive biological systems involving a diversity of other living species. The majority of these species are invisible and constitute the city’s microbiome. Our design decisions for the built environment shape these invisible populations, and we interact with them on a constant basis. A growing body of evidence shows us that our health and well-being are dependent on these interactions. Indeed, multicellular organisms owe meaningful aspects of their development and phenotype to interactions with the microorganisms—bacteria or fungi—with which they live in continual exchange and symbiosis. While the processing and sequencing of samples can be high-throughput, gathering samples is still very expensive, labor intensive, and can require mobilizing large numbers of volunteers to get a snapshot of the microbial landscape of a city, such as City Sampling Day (metasub.org). Here we postulate that honeybees may be effective collaborators in the sampling process, as they daily forage within a 2-mile radius of their hive. We describe the results of a pilot study conducted with 3 rooftop beehives in Brooklyn, NY, where we evaluated the potential of various hive materials (beeswax, honey, debris, pollen, propolis) to reveal information as to the surrounding metagenomic landscape, and where we conclude that the bee debris are the richest substrate. Based on these results, we profiled 4 additional cities in this manner: Sydney, Melbourne, Venice and Tokyo. While the molecular and computational methods used here were based on DNA analysis, it is possible they could be used to monitor RNA-based viruses such as Sars-Cov-2. Here we present the results of this study, and discuss them in terms of architectural implications, as well as the potential of this method for epidemic surveillance.
UropathogenicE. coliinduces DNA damage in the bladder
Authors: Chagneau Camille V. , Massip Clémence , Bossuet-Greif Nadège , Fremez Christophe , Motta Jean-Paul , Shima Ayaka , Besson Céline , Le Faouder Pauline , Cénac Nicolas , Roth Marie-Paule , Coppin Hélène , Fontanié Maxime , Martin Patricia , Nougayrède Jean-Philippe , Oswald Eric 
Published: May 2020

AbstractUrinary tract infections (UTIs) are among the most common outpatient infections, with a lifetime incidence of around 60% in women. We analysed urine samples from 223 patients with community-acquired UTIs and report the presence of a metabolite released during the synthesis of colibactin, a bacterial genotoxin, in 50 of the samples examined. UropathogenicEscherichia colistrains isolated from these patients, as well as the archetypalE. colistrain UTI89, were found to produce colibactin. In a murine model of UTI, the machinery producing colibactin was expressed during the early hours of the infection, when intracellular bacterial communities form. We observed extensive DNA damage both in umbrella and bladder progenitor cells. To the best of our knowledge this is the first report of colibactin production in UTIs in humans and its genotoxicity in bladder cells. This bacterial genotoxin, which is increasingly suspected to promote colorectal cancer, should also be scrutinised in the context of bladder cancer.
Lack of adaptation to centriolar defects leads to p53-independent microcephaly in the absence of Cep135
Authors: González-Martínez José , Cwetsch Andrzej W , Martínez-Alonso Diego , López-Sainz Luis Rodrigo , Almagro Jorge , Megías Diego , Boskovic Jasminka , Gilabert-Juan Javier , Graña-Castro Osvaldo , Pierani Alessandra , Behrens Axel , Ortega Sagrario , Malumbres Marcos 
Published: May 2020

AbstractAutosomal Recessive Primary Microcephaly (MCPH) is a rare disease associated to proteins involved in centrosomal and spindle dynamics including Cep135 (MCPH8). Although Cep135 has been associated to centriolar assembly, the mechanisms associated to the pathogenesis underlyingMCPH8mutations are unclear. By using a series of CRISPR/Cas9-edited murineCep135alleles, we report here that lack of Cep135 results in perinatal lethality accompanied by significant microcephaly in a dosis-dependent manner. Cep135 deficiency, but not that of other centrosomal microcephaly proteins such as Aspm or Cdk5rap2, induces centrosome duplication defects, and perturbed centriole structure and dynamics. Whereas other cell types are able to quickly adapt to these defects, neural progenitors display a prolonged response leading to chromosomal instability and cell death in later developmental stages. Genetic ablation ofTrp53in these mutant embryos prevents apoptotic cell death but does not rescue the microcephaly induced by Cep135 loss. These results suggest that microcephaly can arise from the lack of adaptation to centriole defects in neural progenitors of the developing neocortex in a p53-independent manner.
Machine learning with biomedical ontologies
Authors: Kulmanov Maxat , Smaili Fatima Zohra , Gao Xin , Hoehndorf Robert 
Published: May 2020

Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge, and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in biomedical ontologies, and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources athttps://github.com/bio-ontology-research-group/machine-learning-with-ontologies.Key pointsOntologies provide background knowledge that can be exploited in machine learning models.Ontology embeddings are structure-preserving maps from ontologies into vector spaces and provide an important method for utilizing ontologies in machine learning. Embeddings can preserve different structures in ontologies, including their graph structures, syntactic regularities, or their model-theoretic semantics.Axioms in ontologies, in particular those involving negation, can be used as constraints in optimization and machine learning to reduce the search space.
Enhanced super-resolution microscopy by combined Airyscan and Quantum-Dot-Triexciton Imaging
Authors: Hennig Simon , Manstein Dietmar J. 
Published: May 2020

ABSTRACTSuper-resolution fluorescence imaging provides critically improved information about the composition, organization and dynamics of sub-cellular structures. Quantum-Dot-Triexciton Imaging (QDTI) has been introduced as an easy-to-use sub-diffraction imaging method that achieves an almost 2-fold improvement in resolution when used with conventional confocal microscopes. Here we report an overall 3-fold increase in lateral and axial resolution compared to standard confocal microscopes by combining QDTI with the Airyscan approach.
Intrinsic defects in lymph node stromal cells underpin poor germinal center responses during aging
Authors: Denton Alice E , Silva-Cayetano Alyssa , Dooley James , Hill Danika L , Carr Edward J , Robert Philippe A , Meyer-Hermann Michael , Liston Adrian , Linterman Michelle A 
Published: May 2020

AbstractThe failure to generate enduring humoral immunity after vaccination is a hallmark of advancing age. This can be attributed to a reduction in the germinal center response, which generates long-lived antibody-secreting cells that provide protection against (re)infection. Despite intensive investigation into the effect of age on the lymphoid compartment, the primary cellular defect that causes impaired germinal centers in aging has not been identified. Herein we demonstrate that aging reduces the capacity of germinal center-associated stromal cells to respond to vaccination. Heterochronic parabiosis and mathematical modeling demonstrate that a poor stromal cell response limits the size of the germinal center. This study reveals that age-associated defects in stromal cells are a significant barrier to efficacious vaccine responses in older individuals.
The Impact of Graph Construction Scheme and Community Detection Algorithm on the Repeatability of Community and Hub Identification in Structural Brain Networks
Authors: Dimitriadis Stavros I. , Messaritaki Eirini , Jones Derek K. 
Published: May 2020

AbstractA critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organization of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test-retest data from the Human Connectome Project), the repeatability of 33 community detection algorithms, each paired with 7 different graph construction schemes was assessed.Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can be belong to more than one community). The highest repeatability was observed for the fast multi-scale community detection algorithm paired with a graph construction scheme that combines 9 white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on optimal pairing of community detection algorithm and graph construction scheme.