bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sexual conflict drives micro- and macroevolution of sexual dimorphism in immunity
Authors: Bagchi Basabi , Corbel Quentin , Khan Imroze , Payne Ellen , Banerji Devshuvam , Liljestrand-Rönn Johanna , Martinossi-Allibert Ivain , Baur Julian , Sayadi Ahmed , Immonen Elina , Arnqvist Göran , Söderhäll Irene , Berger David 
Published: December 2020

AbstractBackgroundSexual selection can have major effects on mating rates and sex-specific costs of mating and may thereby influence sex-differences in immunity as well as associated host-pathogen dynamics. Yet, experimental evidence linking the mating system to evolved sexual dimorphism in immunity are scarce and the direct effects of mating rate on immunity are not well established. Here, we use transcriptomic analyses, experimental evolution and phylogenetic comparative methods to study the association between the mating system and sexual dimorphism in immunity in seed beetles, where mating causes internal injuries in females.ResultsWe demonstrate that female phenoloxidase (PO) activity, involved in wound healing and defence against parasitic infections, is elevated relative to males. This difference is accompanied by concomitant sex-differences in the expression of genes in the pro-phenoloxidase activating cascade. We document substantial phenotypic plasticity in female PO activity in response to mating and show that experimental evolution under enforced monogamy (resulting in low remating rates and sexual conflict relative to natural polygamy) rapidly decreases female (but not male) PO activity. Moreover, monogamous females have evolved increased tolerance to bacterial infection unrelated to mating, implying that female responses to costly mating may trade off with other aspects of immune defence, an hypothesis which broadly accords with the documented sex differences in gene expression. Finally, female (but not male) PO activity shows correlated evolution with the perceived harmfulness of male genitalia across 12 species of seed beetles, suggesting that sexual conflict has a significant influence on sexual dimorphisms in immunity in this group of insects.ConclusionsOur study provides insights into the links between sexual conflict and sexual dimorphism in immunity at the molecular and phenotypic level and suggests that selection pressures moulded by mating interactions can lead to a sex-specific mosaic of immune responses with important implications for host-pathogen dynamics in sexually reproducing organisms.
Large variation in the association between seasonal antibiotic use and resistance across multiple bacterial species and antibiotic classes
Authors: Sun Daphne S. , Kissler Stephen M. , Kanjilal Sanjat , Olesen Scott W. , Lipsitch Marc , Grad Yonatan H. 
Published: December 2020

AbstractUnderstanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Use of all 5 classes and resistance in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter-peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.
pH and Receptor Induced Conformational Changes-Implications Towards S1 Dissociation of SARS-CoV2 Spike Glycoprotein
Authors: Castin Jesu E. , Gideon Daniel A. , Sudarsha Karthik S. , Rosita Sherlin A. 
Published: December 2020

AbstractViruses, being obligate intracellular parasites, must first attach themselves and gain entry into host cells. Viral fusion machinery is the central player in the viral attachment process in almost every viral disease. Viruses have incorporated an array of efficient fusion proteins on their surfaces to bind efficiently to host cell receptors. They make use of the host proteolytic enzymes to rearrange their surface protein(s) into the form which facilitates their binding to host-cell membrane proteins and subsequently, fusion. This stage of viral entry is very critical and has many therapeutic implications. The current global pandemic of COVID-19 has sparked severe health crisis and economic shutdowns. SARS-CoV2, the etiological agent of the disease has led to millions of deaths and brought the scientific community together in an attempt to understand the mechanisms of SARS-CoV2 pathogenesis and mortality. Like other viral fusion machinery, CoV2 spike (S) glycoprotein- ‘The Demogorgon’ poses the same questions about viral-host cell fusion. The intermediate stages of S protein-mediated viral fusion are unclear owing to the lack of structural insights and concrete biochemical evidence. The mechanism of conformational transition is still unclear. S protein binding and fusion with host cell receptors, Eg., angiotensin-converting enzyme-2 (ACE2) is accompanied by cleavage of S1/S2 subunits. To track the key events of viral-host cell fusion, we have identified (in silico) that low pH-induced conformational change and ACE-2 binding events promote S1 dissociation. Deciphering key mechanistic insights of SARS-CoV2 fusion will further our understanding of other class-I fusion proteins
The impacts of biofuel crops on local biodiversity: a global synthesis
Authors: Tudge Sophie Jane , Purvis Andy , De Palma Adriana 
Published: December 2020

AbstractConcerns about the environmental impacts of climate change have led to increased targets for biofuel in the global energy market. First-generation biofuel crops contain oil, sugar or starch and are usually also grown for food, whereas second-generation biofuel is derived from non-food sources, including lignocellulosic crops, fast-growing trees, crop residues and waste. Increasing biofuel production drives land-use change, a major cause of biodiversity loss, but there is limited knowledge of how different first- and second-generation biofuel crops affect local biodiversity. A more detailed understanding could support better decisions about the net environmental impacts of biofuels. We synthesised data from 116 sources where a potential biofuel crop was grown and estimated how two measures of local biodiversity, species richness and total abundance, responded to different crops. Local species richness and abundance were 37% and 49% lower at sites planted with first-generation biofuel crops than in sites with primary vegetation. Soybean, wheat, maize and oil palm had the worst effects; the worst affected regions were Asia and Central and South America; and plant species richness and vertebrate abundance were the worst affected biodiversity measures. Second-generation biofuels had significantly smaller effects: species richness and abundance were 19% and 25%, respectively, lower in such sites than in primary vegetation. Our models suggest that land clearance to generate biofuel results in negative impacts on local biodiversity. However, the geographic and taxonomic variation in effects, and the variation in yields among different crops, are all relevant for making the most sustainable land-use decisions.
Pastures and Climate Extremes: Impacts of cool season warming and drought on the productivity of key pasture species in a field experiment
Authors: Churchill Amber C. , Zhang Haiyang , Fuller Kathryn J. , Amiji Burhan , Anderson Ian C. , Barton Craig V. M. , Carrillo Yolima , Catunda Karen L. M. , Chandregowda Manjunatha H. , Igwenagu Chioma , Jacob Vinod , Kim Gil Won , Macdonald Catriona A. , Medlyn Belinda E. , Moore Ben D. , Pendall Elise , Plett Jonathan M. , Post Alison K. , Powell Jeff R. , Tissue David T. , Tjoelker Mark G. , Power Sally A. 
Published: December 2020

AbstractShifts in the timing, intensity and/or frequency of climate extremes, such as severe drought and heatwaves, can generate sustained shifts in ecosystem function with important ecological and economic impacts for rangelands and managed pastures. The Pastures and Climate Extremes experiment (PACE) in Southeast Australia was designed to investigate the impacts of a severe winter/spring drought (60% rainfall reduction) and, for a subset of species, a factorial combination of drought and elevated temperature (ambient +3 °C) on pasture productivity. The experiment included nine common pasture and Australian rangeland species from three plant functional groups (C3grasses, C4grasses and legumes) planted in monoculture. Winter/spring drought resulted in productivity declines of 45% on average and up to 74% for the most affected species (Digitaria eriantha) during the 6-month treatment period, with eight of the nine species exhibiting significant yield reductions. Despite considerable variation in species’ sensitivity to drought, C4grasses were more strongly affected by this treatment than C3grasses or legumes. Warming also had negative effects on cool-season productivity, associated at least partially with exceedance of optimum growth temperatures in spring and indirect effects on soil water content. The combination of winter/spring drought and year-round warming resulted in the greatest yield reductions. We identified responses that were either additive such that there was only as significant warming effect under drought (Festuca), or less-than-additive, where there was no drought effect under warming (Medicago), compared to ambient plots. Results from this study highlight the sensitivity of diverse pasture species to increases in winter and spring drought severity similar to those predicted for this region, and that anticipated benefits of cool-season warming are unlikely to be realised. Overall, the substantial negative impacts on productivity suggest that future, warmer, drier climates will result in shortfalls in cool-season forage availability, with profound implications for the livestock industry and natural grazer communities.
Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis
Authors: Ghatnatti Vikrant , Vastrad Basavaraj , Patil Swetha , Vastrad Chanabasayya , Kotturshetti Iranna 
Published: December 2020

AbstractPituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was acquired from Gene Expression Omnibus datasets, which included 5 pituitary prolactinoma samples and 4 normal pituitaries samples. We screened differentially expressed genes (DEGs) with limma and investigated their biological function by pathway and Gene Ontology (GO) enrichment analysis. A protein-protein interaction (PPI) network of the up and down DEGs were constructed and analyzed by HIPPIE and Cytoscape software. Module analyses were performed. In addition, a target gene - miRNA network and target gene - TF network of the up and down DEGs were constructed by NetworkAnalyst and Cytoscape software. The set of DEGs exhibited an intersection consisting of 989 genes (461 up-regulated and 528 down-regulated), which may be associated with pituitary prolactinoma. Pathway enrichment analysis showed that the 989 DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis also showed that sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene miRNA network and target gene - TF network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which may improve our understanding of the mechanisms of the pathogenesis and integration; genes may be therapeutic targets and prognostic markers for pituitary prolactinoma.
Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches
Authors: Onisiforou Anna , Spyrou George M. 
Published: December 2020

AbstractDuring the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to evade host immune responses, replicate and hence survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases, from cancer to neurodegenerative diseases (NDs). There are evidences suggesting that viral-mediated perturbations are a possible pathogenic aetiology in several NDs such as Amyloid Later Sclerosis, Parkinson’s disease, Alzheimer’s disease and Multiple Sclerosis (MS), as they can cause degeneration of neurons via both direct and/or indirect actions. These diseases share several common pathological mechanisms, as well as unique disease mechanisms that reflect disease phenotype. NDs are chronic degenerative diseases of the central nervous system and current therapeutic approaches provide only mild symptomatic relief rather than treating the disease at heart, therefore there is unmet need for the discovery of novel therapeutic targets and pharmacotherapies. In this paper we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards we present our integrative network-based bioinformatics approach that accounts for pathogen-genes-disease related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in MS disease. We identified 7 high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS enriched KEGG pathways network. In addition, we identified 12 KEGG pathways targeted by 67 viral proteins from 8 viral species that might exert viral-mediated pathogenic mechanisms in MS by interacting with the disease communicator nodes. Finally, our analysis highlighted the Th17 differentiation pathway, a hub-bottleneck disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.
Interpreting coronary artery disease GWAS results: A functional genomics approach assessing biological significance
Authors: Hartmann Katherine , Seweryn Michał , Sadee Wolfgang 
Published: December 2020

AbstractGenome-wide association studies (GWAS) have implicated 58 loci in coronary artery disease (CAD). However, the biological basis for these associations, the relevant genes, and causative variants often remain uncertain. Since the vast majority of GWAS loci reside outside coding regions, most exert regulatory functions. Here we explore the complexity of each of these loci, using tissue specific RNA sequencing data from GTEx to identify genes that exhibit altered expression patterns in the context of GWAS-significant loci, expanding the list of candidate genes from the 75 currently annotated by GWAS to 245, with almost half of these transcripts being non-coding. Tissue specific allelic expression imbalance data, also from GTEx, allows us to uncover GWAS variants that mark functional variation in a locus,e.g., rs7528419 residing in theSORT1locus, in liver specifically, and rs72689147 in theGUYC1A1locus, across a variety of tissues. We consider the GWAS variant rs1412444 in the LIPA locus in more detail as an example, probing tissue and transcript specific effects of genetic variation in the region. By evaluating linkage disequilibrium (LD) between tissue specific eQTLs, we reveal evidence for multiple functional variants within loci. We identify 3 variants (rs1412444, rs1051338, rs2250781) that when considered together, each improve the ability to account for LIPA gene expression, suggesting multiple interacting factors. These results refine the assignment of 58 GWAS loci to likely causative variants in a handful of cases and for the remainder help to re-prioritize associated genes and RNA isoforms, suggesting that ncRNAs maybe a relevant transcript in almost half of CAD GWAS results. Our findings support a multifactorial system where a single variant can influence multiple genes and each genes is regulated by multiple variants.
Functional Aspects of the Eustachian Tube by Means of 3D-Modeling
Authors: Robert Schuon , Schwarzensteiner Josef , Paasche Gerrit , Thomas Lenarz , John Samuel 
Published: December 2020

AbstractThe extent of dysfunction of the Eustachian tube (ET) is relevant in understanding the pathogenesis of secondary otological diseases such as acute or chronic otitis media. The underlying mechanism of ET dysfunction remains poorly understood except for an apparent genesis such as a nasopharyngeal tumor or cleft palate. To better describe the ET, its functional anatomy, and the biomechanical valve mechanism and subsequent development of diagnostic and interventional tools, a three-dimensional model based on thin-layer histology was created from an ET in this study. Blackface sheep was chosen as a donor. The 3-D model was generated by the coherent alignment of the sections. It was then compared with the cone-beam computed tomography dataset of the complete embedded specimen taken before slicing. The model shows the topographic relation of the individual components, such as the bone and cartilage, the muscles and connective tissue, as well as the lining epithelium with the lumen. It indicates a limited spiraling rotation of the cartilaginous tube over its length and relevant positional relationships of the tensor and levator veli palatine muscles.
ISSRseq: an extensible method for reduced representation sequencing
Authors: Sinn Brandon T. , Simon Sandra J. , Santee Mathilda V. , DiFazio Stephen P. , Fama Nicole M. , Barrett Craig F. 
Published: December 2020

ABSTRACTThe capability to generate densely sampled single nucleotide polymorphism (SNP) data is essential in diverse subdisciplines of biology, including crop breeding, pathology, forensics, forestry, ecology, evolution, and conservation. However, the wet-lab expertise and bioinformatics training required to conduct genome-scale variant discovery remain limiting factors for investigators with limited resources.Here we present ISSRseq, a PCR-based method for reduced representation of genomic variation using simple sequence repeats as priming sites to sequence inter simple sequence repeat (ISSR) regions. Briefly, ISSR regions are amplified with single primers, pooled, used to construct sequencing libraries with a commercially-available kit, and sequenced on the Illumina platform. We also present a flexible bioinformatic pipeline that assembles ISSR loci, calls and hard filters variants, outputs data matrices in common formats, and conducts population analyses using R.Using three angiosperm species as case studies, we demonstrate that ISSRseq is highly repeatable, necessitates only simple wet-lab skills and commonplace instrumentation, is flexible in terms of the number of single primers used, and can generate genomic-scale variant discovery on par with existing RRS methods which require more complex wet lab procedures.ISSRseq represents a straightforward approach to SNP genotyping in any organism, and we predict that this method will be particularly useful for those studying population genomics and phylogeography of non-model organisms. Furthermore, the ease of ISSRseq relative to other RRS methods should prove useful to those lacking advanced expertise in wet lab methods or bioinformatics.
Label2label: Training a neural network to selectively restore cellular structures in fluorescence microscopy
Authors: Kölln Lisa Sophie , Salem Omar , Valli Jessica , Hansen Carsten Gram , McConnell Gail 
Published: December 2020

AbstractFluorescence microscopy is an essential tool in cell biology to visualise the spatial distribution of proteins that dictates their role in cellular homeostasis, dynamic cellular processes, and dysfunction during disease. However, unspecific binding of the antibodies that are used to label a cellular target often leads to high background signals in the images, decreasing the contrast of a cellular structure of interest. Recently, convolutional neural networks (CNNs) have been successfully employed for denoising and upsampling in fluorescence microscopy, but current image restoration methods cannot correct for background signals originating from the label. Here, we report a new method to train a CNN as content filter for non-specific signals in fluorescence images that does not require a clean benchmark, using dual-labelling to generate the training data. We name this methodlabel2label(L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure of interest. We show that after L2L training a network restores images not only with reduced image noise but also label-induced unspecific fluorescence signal in images of a variety of cellular structures, resulting in images with enhanced structural contrast. By implementing a multi-scale structural similarity loss function, the performance of the CNN as a content filter is further enhanced, for example, in STED images of caveolae. We show evidence that, for this loss function, sample differences in the training data significantly decrease so-called hallucination effects in the restorations that we otherwise observe when training the CNN with images of the same label. We also assess the performance of a cycle generative adversarial network as a content filter after L2L training with unpaired image data. Lastly, we show that a CNN can be trained to separate structures in superposed fluorescence images of two different cellular targets, allowing multiplex imaging with microscopy setups where the number of excitation sources or detectors is limited.