bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural determinants of calcium binding beyond the EF-hand binding site: a study of alpha parvalbumins
Authors: Immadisetty Kalyan , Sun Bin , Kekenes-Huskey Peter 
Published: September 2020

1AbstractParvalbumin (PV) is a calcium binding protein expressed in humans, fish and avian species. In these organisms, the calcium (Ca2+) affinities of specific PV isoforms can vary by orders of magnitude. Despite the availability of high resolution structural data for many PV isoforms, the structural bases for how such proteins confer widely-varying divalent Ca2+affinities and selectivities against common ions like magnesium (Mg2+) has been difficult to rationalize. We therefore conducted molecular simulations of severalα-pavalbumin (α-parvalbumin (αPV)) constructs with Ca2+affinities in the micromolar to nanomolar ranges to identify properties of conformations that contribute to their wide-ranging binding constants and selectivities against Mg2+. Specifically, we examined a D94S/G98E construct with a reported lower Ca2+affinity (≈ −18.2 kcal/mol) relative to the WT (≈ −22 kcal/mol), an S55D/E59D variant with enhanced affinity (≈ −24 kcal/mol), and a truncated variant ofαPV with weak affinity (≈ −12.6 kcal/mol). We performed molecular dynamics simulations of these constructs and assessed their Ca2+and Mg2+binding properties using scores from molecular mechanics generalized Born approximation (MM/GBSA), ion/oxygen coordination patterns and thermodynamics via mean spherical approximation (MSA) theory, as well as via metrics of protein structure and hydration. Our key findings are that although MM/GBSA and MSA scores successfully rank-ordered the variants according to their previously-published affinities and Mg2+selectivity, importantly, properties of Ca2+loops in CBPs such as coordination, and charge are alone insufficient to rationalize their binding properties. Rather, Ca2+affinity and selectivity against Mg2+are emergent properties stemming from both local effects within the proteins’ ion binding sites as well as non-local contributions from protein folding and solubility. Our findings broaden our understanding of the molecular bases governingαPV ion binding that are likely shared by many Ca2+binding proteins.
Coupled social-land dynamics and the future of sustainable consumption
Authors: Pal Saptarshi , Bauch Chris T. , Anand Madhur 
Published: September 2020

AbstractDietary patterns have long been a driver of global land use. Increasingly, they also respond to it, in part because of social forces that support adoption of sustainable diets. Here we develop a coupled social-land use dynamics model parameterised for 164 countries. We project global land use under 20 scenarios for future population, income, and agricultural yield. When future yields are low and/or population size is high, coupled social-land feedbacks can reduce the peak global land use by up to 2 billion hectares, if socio-economic barriers to adopting a sustainable diet are sufficiently low. In contrast, when population growth is low or yield is high, reductions in income elasticity can increase peak land use by 100 million hectares. The model also exhibits a regime of synergistic effects whereby simultaneous changes to multiple social and economic parameters are required to change land use projections. This research demonstrates the value of including coupled social-land feedbacks in land use projections.
Exogenous Sex Hormone Effects on Brain Microstructure in Women: A diffusion MRI Study in the UK Biobank
Authors: Nabulsi Leila , Lawrence Katherine E. , Santhalingam Vigneshwaran , Abaryan Zvart , Boyle Christina P. , Villalon-Reina Julio E. , Nir Talia M. , Gari Iyad Ba , Zhu Alyssa H. , Haddad Elizabeth , Muir Alexandra M. , Jahanshad Neda , Thompson Paul M. 
Published: September 2020

AbstractChanges in estrogen levels in women have been associated with increased risk for age-related neurodegenerative diseases, including Alzheimer’s disease, but the impact of exogenous estrogen exposure on the brain is poorly understood. Oral contraceptives (OC) and hormone therapy (HT) and are both common sources of exogenous estrogen for women in reproductive and post-menopausal years, respectively. Here we examined the association of exogenous sex hormone exposure with the brain’s white matter (WM) aging trajectories in postmenopausal women using and not using OC and HT (HT users: n=3,033, non-users n=5,093; OC users: n=6,964; non-users n=1,156), while also investigating multiple dMRI models. Cross-sectional brain dMRI data was analyzed from the UK Biobank using conventional diffusion tensor imaging (DTI), the tensor distribution function (TDF), and neurite orientation dispersion and density imaging (NODDI). Mean skeletonized diffusivity measures were extracted across the whole brain, and fractional polynomial regressions were used to characterize age-related trajectories for WM microstructural measures. Advanced dMRI model NODDI revealed a steeper WM aging trajectory in HT users relative to non-users, and for those using unopposed estrogens relative to combined estrogens treatment. By contrast, no interaction was detected between OC status and age effects on the diffusivity measures we examined. Exogenous sex hormone exposure may negatively impact WM microstructure aging in postmenopausal women. We also present normative reference curves for white matter microarchitectural parameters in women, to help identify individuals with microstructural anomalies.
Proficiency Testing for bacterial whole genome sequencing in assuring the quality of microbiology diagnostics in clinical and public health laboratories
Authors: Lau Katherine A. , da Silva Anders Gonçalves , Theis Torsten , Gray Joanna , Ballard Susan A , Rawlinson William D. 
Published: September 2020

ABSTRACTThe adoption of whole genome sequencing (WGS) data over the past decade for pathogen surveillance, and decision-making for infectious diseases has rapidly transformed the landscape of clinical microbiology and public health. However, for successful transition to routine use of these techniques, it is crucial to ensure the WGS data generated meet defined quality standards for pathogen identification, typing, antimicrobial resistance detection and surveillance. Further, the ongoing development of these standards will ensure that the bioinformatic processes are capable of accurately identifying and characterising organisms of interest, and thereby facilitate the integration of WGS into routine clinical and public health laboratory setting. A pilot proficiency testing (PT) program for WGS of infectious agents was developed to facilitate widely applicable standardisation and benchmarking standards for WGS across a range of laboratories. The PT participating laboratories were required to generate WGS data from two bacterial isolates, and submit the raw data for independent bioinformatics analysis, as well as analyse the data with their own processes and answer relevant questions about the data. Overall, laboratories used a diverse range of bioinformatics tools and could generate and analyse high-quality data, either meeting or exceeding the minimum requirements. This pilot has provided valuable insight into the current state of genomics in clinical microbiology and public health laboratories across Australia. It will provide a baseline guide for the standardisation of WGS and enable the development of a PT program that allows an ongoing performance benchmark for accreditation of WGS-based test processes.
How super-spreader cities, highways, hospital bed availability, and dengue fever influenced the COVID-19 epidemic in Brazil
Authors: Nicolelis Miguel A.L. , Raimundo Rafael L. G. , Peixoto Pedro S. , de Andreazzi Cecília S. 
Published: September 2020

ABSTRACTAlthough its international airports served as the country’s main entry points for SARS-CoV-2, the factors driving the uneven geographic spread of COVID-19 cases and deaths in Brazil remain largely unknown. Here we show that four major factors likely accounted for the entire dynamics of COVID-19 in Brazil. Mathematical modeling revealed that, initially, the “super-spreading city” of São Paulo accounted for roughly 80% of the case spread in the entire country. During the first 3 months of the epidemic, by adding only 16 other spreading cities, we accounted for 98-99% of the cases reported in Brazil at the time. Moreover, 26 of the major Brazilian federal highways accounted for about 30% of SARS-CoV-2’s case spread. As cases accumulated rapidly in the Brazilian countryside, the distribution of COVID-19 deaths began to correlate with a third parameter: the geographic distribution of the country’s hospital intensive care unit (ICU) beds, which is highly skewed towards state capitals where the epidemic began. That meant that severely ill patients living in the countryside had to be transported to state capitals to access ICU beds where they often died, creating a “boomerang effect” that contributed to the skew of the geographic distribution of COVID-19 deaths. Finally, we discovered that the geographic distribution of dengue fever, amounting to more than 3.5 million cases from January 2019 to July 2020, was highly complementary to that of COVID-19. This was confirmed by the identification of significant negative correlations between COVID-19’s incidence, infection growth rate, and mortality to the percentage of people with antibody (IgM) levels for dengue fever in each of the country’s states. No such correlations were observed when IgM data for chikungunya virus, which is transmitted by the same mosquito vector as dengue, was used. Thus, states in which a large fraction of the population had contracted dengue fever in 2019-2020 reported lower COVID-19 cases and deaths, and took longer to reach exponential community transmission, due to slower SARS-CoV-2 infection growth rates. This inverse correlation between COVID-19 and dengue fever was further observed in a sample of countries around Asia and Latin America, as well as in islands in the Pacific and Indian Oceans. This striking finding raises the intriguing possibility of an immunological cross-reactivity between DENV serotypes and SARS-CoV-2. If proven correct, this hypothesis could mean that dengue infection or immunization with an efficacious and safe dengue vaccine could produce some level of immunological protection for SARS-CoV-2, before a vaccine for SARS-CoV-2 becomes available.
Novel rRNA-depletion methods for total RNA sequencing and ribosome profiling developed for avian species
Authors: Gu Hanwen , Sun Yu H. , Li Xin Zhiguo 
Published: September 2020

ABSTRACTDeep sequencing of RNAs has greatly aided the study of the transcriptome, enabling comprehensive gene expression profiling and the identification of novel transcripts. While mRNAs are of the greatest interest in gene expression studies as they encode for proteins, mRNAs make up only 3-5% of total RNAs, with the majority comprising ribosomal RNAs (rRNAs). Therefore, applications of deep sequencing to RNA face the challenge of how to efficiently enrich mRNA species prior to library construction. Traditional methods extract mRNAs using oligo-dT primers targeting the poly-A tail on mRNAs; however, this approach is not comprehensive as it does not account for mRNAs lacking the poly-A tail or other lncRNAs that we may be interested in. Alternative methods deplete rRNAs, but such approaches require species-specific probes and the commercially available kits are costly and have only been developed for a limited number of model organisms. Here we describe a quick, cost-effective method for depleting rRNAs using custom-designed oligos. We use chickens as an example species for probe design, and the same approach also apply to mice. Using this protocol, we have not only removed the rRNAs from total RNAs for RNA-seq library construction but also depleted rRNA fragments from ribosome-protected fragments for ribosome profiling. Currently, this is the only rRNA depletion-based method for avian species; this method thus provides a valuable resource for both the scientific community and the poultry industry.
Chromogranin A regulates gut permeabilityviathe antagonistic actions of its proteolytic peptides
Authors: Muntjewerff Elke M. , Tang Kechun , Lutter Lisanne , Christoffersson Gustaf , Nicolasen Mara J.T. , Gao Hong , Katkar Gajanan D. , Das Soumita , Beest Martin ter , Ying Wei , Ghosh Pradipta , Aidy Sahar El , Oldenburg Bas , van den Bogaart Geert , Mahata Sushil K. 
Published: September 2020

AbstractAimA ‘leaky’ gut barrier has been implicated in the initiation and progression of a multitude of diseases, e.g., inflammatory bowel disease, irritable bowel syndrome, celiac disease, and colorectal cancers. Here we show how pro-hormone Chromogranin A (CgA), produced by the enteroendocrine cells, and Catestatin (CST: hCgA352-372), the most abundant CgA-derived proteolytic peptide, affect the gut barrier.MethodsColon tissues from region-specific CST-knockout (CST-KO) mice, CgA-knockout (CgA-KO) and WT mice were analyzed by immunohistochemistry, ultrastructural and flowcytometry studies. FITC-dextran assays were used to measure intestinal barrier function. Mice were supplemented with CST or CgA fragment pancreastatin (PST: CgA250-301). The microbial composition of cecum was determined. CgA and CST levels were measured in blood of IBD patients.ResultsCST-KO mice displayed (i) elongated tight, adherens junctions and desmosomes similar to IBD patients, and (ii) gut inflammation. Consistently, plasma FITC-dextran measurements showed increased intestinal paracellular permeability in the CST-knockout mice. This correlated with a higher ratio of Firmicutes to Bacteroidetes, a dysbiotic pattern commonly encountered in various diseases. Supplementation of CST-knockout mice with recombinant CST restored paracellular permeability and reversed inflammation, whereas CgA-knockout mice supplementation with CST and/or PST in CgA-KO mice showed that intestinal paracellular permeability is regulated by the antagonistic roles of these two peptides: CST reduces and PST increases permeability.ConclusionThe pro-hormone CgA regulates the intestinal paracellular permeability. CST is both necessary and sufficient to reduce permeability and primarily acts via antagonizing the effects of PST.
>Corna - An Open Source Python Tool For Natural Abundance Correction In Isotope Tracer Experiments
Authors: Raaisa Raaisa , Lathwal Shefali , Chubukov Victor , Kibbey Richard G. , Jha Abhishek K. 
Published: September 2020

AbstractBackgroundStable isotope-based approaches are used in the field of metabolomics for quantification and identification of metabolites, discovery of new pathways and measurement of intracellular fluxes. In these experiments, often performed with mass spectrometry (MS), data must be corrected for natural abundance of isotopes. Various stand-alone tools with their own separate data formats and learning curves exist for correction of data collected at different resolutions, for tandem MS, and for different number of tracer elements.ResultsWe present a Python package, Corna, that combines natural abundance correction workflows for several experimental conditions and can be used as a one-stop-shop for stable isotope labeled experiments. We validate the algorithms in Corna with published tools, where available, and include new features, such as correction of two tracer elements, that are not yet implemented in any existing software application as per our knowledge. We also present the integration of Corna with an existing open source peak integration software. The integrated workflow can reduce processing times for a typical stable isotope based workflow from days to hours for a familiar user.ConclusionsAlgorithmic advancements have been keeping up with the developments in mass spectrometry technologies and have been the focus of most existing tools for natural abundance correction. However, in this high throughput era, it is also important to recognize user experience, and integrated and reproducible workflows. Corna has been written in Python and is designed for users who have access to large amounts of data from different kinds of experiments and want to integrate a natural abundance correction tool seamlessly in their pipelines. The latest version of Corna can be accessed athttps://github.com/raaisakuk/NA_Correction.
scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data
Authors: Li Wei Vivian , Li Yanzeng 
Published: September 2020

AbstractA system-level understanding of the regulation and coordination mechanisms of gene expression is essential to understanding the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The source code used in this article is available athttps://github.com/Vivianstats/scLink.
Structure of autosynthetic models of balanced cell growth and numerical optimization of their growth rate
Authors: Sezer Deniz , Schubert Peter , Lercher Martin J. 
Published: September 2020

Genome-scale reaction network models are available for many prokaryotic organisms. Yet, to predict the proteome and metabolome of the cell from them, additional information about (i) the nonlinear enzyme kinetics and (ii) the regulation of protein expression by metabolic signals is necessary. Knowledge about the latter could be sidestepped by assuming that expression regulation has evolved to achieve the protein composition that maximizes cellular growth rate. A general mathematical framework for optimizing the growth rate of models comprising an arbitrarily complex metabolic network and a relatively simple protein-synthesis network was recently formulated independently by two research groups [de Groot et al., PLoS Comput. Biol.16, e1007559 (2020); Dourado & Lercher, Nature Commun.11, 1226 (2020)]. Here, this formalism is further developed with particular focus on carrying out the optimization numerically. To this end, we identify the concentrations of the enzymes as the independent variables of the optimization problem and propose novel multiplicative updates for the iterative calculation of the dependent metabolite concentrations. The reduced gradient method, with analytical derivatives, is employed for the numerical optimization. Additionally, the roles of the dilution of the metabolite concentrations by growth and the commonly invoked constraint on the cell dry mass density are clarified. These developments should lay the basis for the practical optimization of large-scale kinetic models, thus formally connecting the physiological “macrostate” of the cell, characterized by its growth rate, to its “microstate”, described by the cell proteome and metabolome.Author summaryAn evolving population of non-interacting, unicellular organisms in a constant environment will maximize its growth rate. By expressing the growth rate as a mathematical function of the cellular composition, it becomes possible to formulate an optimization problem whose solution yields the cell proteome and metabolome at the maximal growth rate. The formulation and solution of such an optimization problem has the potential to elucidate fundamental optimality principles in living cells and to enable the engineering of complex biological systems. Building on previous work, here we address the task of solving this optimization problem numerically. In the process, we elucidate the mathematical role of some common simplifying approximations. This allows us to organize many of the existing formulations of the optimization problem into a hierarchy, whose lower levels are reached by invoking these approximations.
Mapping the impacts of hurricanes Maria and Irma on banana production area in the Dominican Republic
Authors: Varma Varun , Thompson William , Duarte Solhanlle Bonilla , Krütli Pius , Six Johan , Bebber Daniel P 
Published: September 2020

AbstractExtreme weather events can have devastating impacts on agricultural systems, and the livelihoods that depend on them. Tools for rapid, comprehensive and cost-effective assessment of impacts, especially if carried out remotely, can be of great value in planning systematic recovery of production, as well as assessing risks from future events. Here, we use openly available remote sensing data to quantify the impacts of hurricanes Irma and Maria in 2017 on banana production area in the Dominican Republic — the world’s largest producer of organic bananas. Further, we assess the risk to current production area if a similar extreme event were to re-occur. Hurricane associated damage was mapped using a simple change detection algorithm applied to Synthetic Aperture Radar (SAR) data over the three main banana growing provinces of northern Dominican Republic, i.e. Monte Cristi, Valverde and Santiago. The map of hurricane affected area was overlaid with banana plantation distributions for 2017 and 2019 that were mapped (accuracy = 99.8%) using a random forest classifier, and a combination of SAR and multi-spectral satellite data. Our results show that 11.35% of banana plantation area was affected by hurricane damage in 2017. Between 2017 and 2019, there was a high turnover of plantation area, but with a net gain of 10.8%. However, over a quarter (26.9%) of new plantation area spatially overlapped with regions which had seen flooding or damage from hurricanes in 2017. Our results indicate that banana production systems in northern Dominican Republic saw extensive damage in the aftermath of hurricanes Irma and Maria. While production area has recovered since then, a substantial proportion of new plantations, and a greater fraction of production area in general, occur at locations at risk from future extreme events.
Genomic comparison of non-photosynthetic plants from the family Balanophoraceae with their photosynthetic relatives
Authors: Schelkunov Mikhail I. , Nuraliev Maxim S. , Logacheva Maria D. 
Published: September 2020

AbstractThe plant family Balanophoraceae consists entirely of species that have lost the ability to photosynthesize. Instead, they obtain nutrients by parasitizing other plants. Recent studies have shown that plastid genomes of Balanophoraceae have a number of interesting features, one of the most prominent of those being a tremendous AT content close to 90%. Also, the nucleotide substitution rate in the plastid genomes of Balanophoraceae is greater by an order of magnitude compared to photosynthetic relatives, without signs of relaxed selection. All these features have no definite explanations.Given these unusual features, we supposed that it would be interesting to gain insight into the characteristics ofnucleargenomes of Balanophoraceae. To do this, in the present study we analysed transcriptomes of two species from Balanophoraceae, namelyRhopalocnemis phalloidesandBalanophora fungosa, and compared them with transcriptomes of their close photosynthetic relativesDaenikerasp.,Dendropemon caribaeus, Malania oleifera.The analysis showed that the AT content of nuclear genes of Balanophoraceae does not markedly differ from that of photosynthetic relatives. The nucleotide substitution rate in genes of Balanophoraceae is for an unknown reason several times larger than in genes of photosynthetic Santalales, though the negative selection in Balanophoraceae is likely stronger.We observed an extensive loss of photosynthesis-related genes in Balanophoraceae. Also, for Balanophoraceae we did not see transcripts of several genes whose products participate in plastid genome repair. This implies their loss or very low expression, which may explain the increased nucleotide substitution rate and AT content of the plastid genomes.