bluuebunny/crossref_metadata_2025_split huggingface dataset:
High concordance between hippocampal transcriptome of the intraamygdala kainic acid model and human temporal lobe epilepsy
Authors: Conte Giorgia , Parras Alberto , Alves Mariana , Ollà Ivana , de Diego-Garcia Laura , Beamer Edward , Alalqam Razi , Ocampo Alejandro , Mendez Raúl , Henshall David C. , Lucas José J. , Engel Tobias 
Published: May 2020

AbstractObjectivePharmacoresistance and the lack of disease-modifying actions of current anti-seizure drugs persist as major challenges in the treatment of epilepsy. Experimental models of chemoconvulsant-induced status epilepticus remain the models of choice to discover potential anti-epileptogenic drugs but doubts remain as to the extent to which they model human pathophysiology. The aim of the present study was to compare the molecular landscape of the intraamygdala kainic acid model of status epilepticus in mice with findings in resected brain tissue from patients with drug-resistant temporal lobe epilepsy (TLE).MethodsStatus epilepticus was induced via intraamygdala microinjection of kainic acid in C57BL/6 mice and gene expression analysed via microarrays in hippocampal tissue at acute and chronic time-points. Results were compared to reference datasets in the intraperitoneal pilocarpine and intrahippocampal kainic acid model and to human resected brain tissue (hippocampus and cortex) from patients with drug-resistant TLE.ResultsIntraamygdala kainic acid injection in mice triggered extensive dysregulation of gene expression which was ∼3-fold greater shortly after status epilepticus (2729 genes) when compared to epilepsy (412). Comparison to samples of patients with TLE revealed a particular high correlation of gene dysregulation during established epilepsy. Pathway analysis found suppression of calcium signalling to be highly conserved across different models of epilepsy and patients. CREB was predicted as one of the main up-stream transcription factors regulating gene expression during acute and chronic phases and inhibition of CREB reduced seizure severity in the intraamygdala kainic acid model.SignificanceOur findings suggest the intraamygdala kainic acid model faithfully replicates key molecular features of human drug-resistant temporal lobe epilepsy and provides potential rationale target approaches for disease-modification through new insights into the unique and shared gene expression landscape in experimental epilepsy.Key point boxMore genes show expression changes shortly following intraamygdala kainic acid-induced status epilepticus when compared to established epilepsy.The intraamygdala kainic acid mouse model mimics closely the gene expression landscape in the brain of patients with temporal lobe epilepsy.Supressed calcium signalling in the brain as common feature across experimental models of epilepsy and patients with temporal lobe epilepsy.CREB is a major up-stream transcription factor during early changes following status epilepticus and once epilepsy is established.
Palmitic acid induces inflammation in placental trophoblasts and impairs their migration toward smooth muscle cells through plasminogen activator inhibitor-1
Authors: Rampersaud Amanda M. , Dunk Caroline E. , Lye Stephen J. , Renaud Stephen J. 
Published: May 2020

AbstractA critical component of early human placental development includes migration of extravillous trophoblasts (EVTs) into the decidua. EVTs migrate toward, and displace vascular smooth muscle cells (SMCs) surrounding several uterine structures, including spiral arteries. Shallow trophoblast invasion features in several pregnancy complications including preeclampsia. Maternal obesity is a risk factor for placental dysfunction, suggesting that factors within an obese environment may impair early placental development. Herein, we tested the hypothesis that palmitic acid, a saturated fatty acid circulating at high levels in obese women, induces an inflammatory response in EVTs that hinders their capacity to migrate toward SMCs. We found that SMCs and SMC-conditioned media stimulated migration and invasion of an EVT-like cell line, HTR8/SVneo. Palmitic acid impaired EVT migration and invasion toward SMCs, and induced expression of several vasoactive and inflammatory mediators in EVTs, including endothelin, interleukin (IL)-6, IL8, and PAI1. PAI1 was increased in plasma of women with early-onset preeclampsia, and PAI1-deficient EVTs were protected from the anti-migratory effects of palmitic acid. Using first trimester placental explants, palmitic acid exposure decreased EVT invasion through Matrigel. Our findings reveal that palmitic acid induces an inflammatory response in EVTs and attenuates their migration through a mechanism involving PAI1. High levels of palmitic acid in pathophysiological situations like obesity may impair early placental development and predispose to placental dysfunction.
Phenotyping Root Architecture of Soil-Grown Rice: A Robust Protocol Combining Manual Practices with Image-based Analyses
Authors: De Bauw P. , Ramarolahy J. A. , Senthilkumar K. , Rakotoson T. , Merckx R. , Smolders E. , Van Houtvinck R. , Vandamme E. 
Published: May 2020

AbstractBackgroundBreeding towards resilient rice varieties is often constrained by the limited data on root system architecture obtained from relevant agricultural environments. Knowledge on the genotypic differences and responses of root architecture to environmental factors is limited due the difficulty of analysing soil-grown rice roots. An improved method using imaging is thus needed, but the existing methods were never proven successful for rice. Here, we aimed to evaluate and improve a higher throughput method of image-based root phenotyping for rice grown under field conditions. Rice root systems from seven experiments were phenotyped based on the “shovelomics” method of root system excavation followed by manual root phenotyping and digital root analysis after root imaging. Analyzed traits were compared between manual and image-based root phenotyping systems using Spearman rank correlations to evaluate whether both methods similarly rank the phenotypes. For each trait, the relative phenotypic variation was calculated. A principal component analysis was then conducted to assess patterns in root architectural variation.ResultsSeveral manually collected and image-based root traits were identified as having a high potential of differentiating among contrasting phenotypes, while other traits are found to be inaccurate and thus unreliable for rice. The image-based traits projected area, root tip thickness, stem diameter, and root system depth successfully replace the manual determination of root characteristics, however attention should be paid to the lower accuracy of the image-based methodology, especially when working with older and larger root systems.ConclusionsThe challenges and opportunities of rice root phenotyping in field conditions are discussed for both methods. We therefore propose an integrated protocol adjusted to the complexity of the rice root structure combining image analysis in a water bath and the manual scoring of three traits (i.e. lateral density, secondary branching degree, and nodal root thickness at the root base). The proposed methodology ensures higher throughput and enhanced accuracy during root phenotyping of soil grown rice in fields or pots compared to manual scoring only, it is cheap to develop and operate, it is valid in remote environments, and it enables fast data extraction.
OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data
Authors: Haselimashhadi Hamed , Mason Jeremy C , Mallon Ann-Marie , Smedley Damian , Meehan Terrence F , Parkinson Helen 
Published: May 2020

AbstractReproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license atwww.bioconductor.org/packages/OpenStats.
Comparing orthology methods and their performance by recapitulating patterns of eukaryotic genome evolution
Authors: Deutekom Eva S. , Snel Berend , van Dam Teunis J.P. 
Published: May 2020

AbstractInsights into the evolution of ancestral complexes and pathways are generally achieved through careful and time-intensive manual analysis often using phylogenetic profiles of the constituent proteins. This manual analysis limits the possibility of including more protein-complex components, repeating the analyses for updated genome sets, or expanding the analyses to larger scales. Automated orthology inference should allow such large scale analyses, but substantial differences between orthologous groups generated by different approaches are observed.We evaluate orthology methods for their ability to recapitulate a number of observations that have been made with regards to genome evolution in eukaryotes. Specifically, we investigate phylogenetic profile similarity (co-occurrence of complexes), the Last Eukaryotic Common Ancestor’s gene content, pervasiveness of gene loss, and the overlap with manually determined orthologous groups. Moreover, we compare the inferred orthologies to each other.We find that most orthology methods reconstruct a large Last Eukaryotic Common Ancestor, with substantial gene loss, and can predict interacting proteins reasonably well when applying phylogenetic co-occurrence. At the same time derived orthologous groups show imperfect overlap with manually curated orthologous groups. There is no strong indication of which orthology method performs better than another on individual or all of these aspects. Counterintuitively, despite the orthology methods behaving similarly regarding large scale evaluation, the obtained orthologous groups differ vastly from one another.Availability and implementationThe data and code underlying this article are available in github and/or upon reasonable request to the corresponding author:https://github.com/ESDeutekom/ComparingOrthologies.SummaryWe compared multiple orthology inference methods by looking at how well they perform in recapitulating multiple observations made in eukaryotic genome evolution.Co-occurrence of proteins is predicted fairly well by most methods and all show similar behaviour when looking at loss numbers and dynamics.All the methods show imperfect overlap when compared to manually curated orthologous groups and when compared to orthologous groups of the other methods.Differences are compared between methods by looking at how the inferred orthologies represent a high-quality set of manually curated orthologous groups.We conclude that all methods behave similar when describing general patterns in eukaryotic genome evolution. However, there are large differences within the orthologies themselves, arising from how a method can differentiate between distant homology, recent duplications, or classifying orthologous groups.
Monocytes mediateSalmonellaTyphimurium-induced tumour growth inhibition
Authors: Johnson Síle A. , Ormsby Michael J. , Wessel Hannah M. , Hulme Heather E. , Bravo-Blas Alberto , McIntosh Anne , Mason Susan , Coffelt Seth B. , Tait Stephen W.G. , Mowat Allan McI. , Milling Simon W.F. , Blyth Karen , Wall Daniel M. 
Published: May 2020

AbstractThe use of bacteria as an alternative cancer therapy has been re-investigated in recent years. A number of bacterial strains for this purpose have been generated, one of which is SL7207: an auxotrophicSalmonella entericaserovar TyphimuriumaroAmutant with immune-stimulatory potential. Here we show that systemic administration of SL7207 induces melanoma tumour growth arrestin vivo, with greater survival of the SL7207-treated group compared to control PBS-treated mice. Administration of SL7207 is accompanied by a change in the immune phenotype of the tumour-infiltrating cells towards pro-inflammatory, with expression of the TH1 cytokines IFN-γ, TNF-α, and IL-12 significantly increased. Interestingly, Ly6C+MHCII+monocytes were recruited to the tumours following SL7207 treatment and were pro-inflammatory. Accordingly, the abrogation of these infiltrating monocytes using clodronate liposomes prevented SL7207-induced tumour growth inhibition. These data demonstrate a previously unappreciated role for infiltrating inflammatory monocytes underlying bacterial-mediated tumour growth inhibition. This information highlights a novel role for monocytes in controlling tumour growth, contributing to our understanding of the immune responses required for successful immunotherapy of cancer.
Sparse input neural networks to differentiate 32 primary cancer types based on somatic point mutations
Authors: Dikaios Nikolaos 
Published: May 2020

AbstractThis paper aims to differentiate cancer types from primary tumour samples based on somatic point mutations (SPM). Primary cancer site identification is necessary to perform site-specific and potentially targeted treatment. Current methods like histopathology/lab-tests cannot accurately determine cancers origin, which results in empirical patient treatment and poor survival rates. The availability of large deoxyribonucleic-acid sequencing datasets has allowed scientists to examine the ability of SPM to classify primary cancer sites. These datasets are highly sparse since most genes will not be mutated, have low signal-to-noise ratio and are imbalanced since rare cancers have less samples. To overcome these limitations a sparse-input neural network (spinn) is suggested that projects the input data in a lower dimensional space, where the more informative genes are used for learning. To train and evaluate spinn, an extensive dataset was collected from the cancer genome atlas containing 7624 samples spanning 32 cancer types. Different sampling strategies were performed to balance the dataset but have not benefited the classifiers performance except for removing Tomek-links. This is probably due to high amount of class overlapping. Spinn consistently outperformed algorithms like extreme gradient-boosting, deep neural networks and support-vector-machines, achieving an accuracy up to 73% on independent testing data.
SLE non-coding Genetic Risk Variant Determines the Epigenetic Dysfunction of an Immune Cell Specific Enhancer that Controls Disease-critical microRNA Expression
Authors: Hou Guojun , Harley Isaac T.W. , Lu Xiaoming , Zhou Tian , Xu Ning , Yao Chao , Qin Yuting , Ouyang Ye , Ma Jianyang , Zhu Xinyi , Yu Xiang , Xu Hong , Dai Dai , Ding Huihua , Tang Yuanjia , Namjou Bahram , Weirauch Matthew T. , Kottyan Leah C. , Harley John B. , Shen Nan 
Published: May 2020

AbstractThe human genome contains millions of putative regulatory elements, which regulate gene expression. We are just beginning to understand the functional consequences of genetic variation within these regulatory elements. Since the bulk of common genetic variation impacting polygenic disease phenotypes localizes to these non-coding regions of the genome, understanding the consequences will improve our understanding of the mechanisms mediating genetic risk in human disease. Here, we define the systemic lupus erythematosus (SLE) risk variant rs2431369 as likely causal for SLE and show that it is located in a functional regulatory element that modulates miR-146a expression. We use epigenomic analysis and genome-editing to show that the rs2431697-containing region is a distal enhancer that specifically regulates miR-146a expression in a cell-type dependent manner. 3D chromatin structure analysis demonstrates physical interaction between the rs2431697-containing region and the miR-146a promoter. Further, our data show that NF-kB binds the disease protective allele in a sequence-specific manner, leading to increased expression of this immunoregulatory microRNA. Our work provides a strategy for using disease-associated variants to define the functional regulatory elements of non-coding RNA molecules such as miR-146a and provides mechanistic links between autoimmune disease risk genetic variation and disease etiology.
An RNA dynamic ensemble at atomic resolution
Authors: Shi Honglue , Rangadurai Atul , Assi Hala Abou , Roy Rohit , Case David A. , Herschlag Daniel , Yesselman Joseph D. , Al-Hashimi Hashim M. 
Published: May 2020

AbstractBiomolecules do not fold into a single 3D structure but rather form dynamic ensembles of many inter-converting conformations1. Knowledge of dynamic ensembles is key for understanding how biomolecules fold and function, and for rationally manipulating their activities in drug discovery and synthetic biology2–4. However, solving dynamic ensembles of biomolecules at atomic resolution is a major challenge in structural biology because the information required to specify the position of all atoms in thousands of conformations in an ensemble far exceeds the information content of experimental measurements. Here we addressed the data gap and dramatically simplified and accelerated RNA ensemble determination by using structure prediction tools that leverage the growing database of RNA structures to generate a conformational library. Library refinement with NMR residual dipolar couplings enabled determination of an atomic-resolution ensemble for HIV-1 TAR as confirmed by quantum-mechanical calculations of NMR chemical shifts, comparison to a crystal structure of a substate, and through the successful redistribution of the ensemble by design using atomic mutagenesis. The ensemble provides an unprecedented view of how bulge residues cooperatively flip out and undergo sugar repuckering to allow the adjoining helices to stack. The generality of this approach will make determination of atomic-resolution RNA ensembles routine.
Deciphering a marine bone degrading microbiome reveals a complex community effort
Authors: Borchert Erik , García-Moyano Antonio , Sanchez-Carrillo Sergio , Dahlgren Thomas G. , Slaby Beate M. , Bjerga Gro Elin Kjæreng , Ferrer Manuel , Franzenburg Sören , Hentschel Ute 
Published: May 2020

AbstractThe marine bone biome is a complex assemblage of macro- and microorganisms, however the enzymatic repertoire to access bone-derived nutrients remains unknown. The bone matrix is a composite material made up mainly of organic collagen and inorganic hydroxyapatite. We conducted field experiments to study microbial assemblages that can use organic bone components as nutrient source. Bovine and turkey bones were deposited at 69 m depth in a Norwegian fjord (Byfjorden, Bergen). Metagenomic sequence analysis was used to assess the functional potential of microbial assemblages from bone surface and the bone eating worm Osedax mucofloris, which is a frequent colonizer of whale falls and known to degrade bone. The bone microbiome displayed a surprising taxonomic diversity revealed by the examination of 59 high-quality metagenome assembled genomes from at least 23 bacterial families. Over 700 genes encoding enzymes from twelve relevant enzymatic families pertaining to collagenases, peptidases, glycosidases putatively involved in bone degradation were identified. Metagenome assembled genomes (MAGs) of the class Bacteroidia contained the most diverse gene repertoires. We postulate that demineralization of inorganic bone components is achieved by a timely succession of a closed sulfur biogeochemical cycle between sulfur-oxidizing and sulfur-reducing bacteria, causing a drop in pH and subsequent enzymatic processing of organic components in the bone surface communities. An unusually large and novel collagen utilization gene cluster was retrieved from one genome belonging to the gammaproteobacterial genus Colwellia.ImportanceBones are an underexploited, yet potentially profitable feedstock for biotechnological advances and value chains, due to the sheer amounts of residues produced by the modern meat and poultry processing industry. In this metagenomic study we decipher the microbial pathways and enzymes that we postulate to be involved in bone degradation marine environment. We herein demonstrate the interplay between different bacterial community members, each supplying different enzymatic functions with the potential to cover an array of reactions relating to the degradation of bone matrix components. We identify and describe a novel gene cluster for collagen utilization, which is a key function in this unique environment. We propose that the interplay between the different microbial taxa is necessary to achieve the complex task of bone degradation in the marine environment.
Fear Conditioning Potentiates the Hippocampal CA1 Commissural Pathway In vivo and Increases Awake Phase Sleep
Authors: Subramaniyan Manivannan , Manivannan Sumithrra , Chelur Vikas , Tsetsenis Theodoros , Jiang Evan , Dani John A. 
Published: May 2020

AbstractThe hippocampus is essential for spatial learning and memory. To assess learning we used contextual fear conditioning (cFC), where animals learn to associate a place with aversive events like foot-shocks. Candidate memory mechanisms for cFC are long-term potentiation and long-term depression, but there is little direct evidence of them operating in the hippocampus in vivo following cFC. Also, little is known about the behavioral state changes induced by cFC. To address these issues, we recorded local field potentials in freely behaving mice by stimulating in the left dorsal CA1 region and recording in the right dorsal CA1 region. Synaptic strength in the commissural pathway was monitored by measuring field excitatory postsynaptic potentials (fEPSPs) before and after cFC. After cFC, the commissural pathway’s synaptic strength was potentiated. Although recordings occurred during the wake phase of the light/dark cycle, the mice slept more in the post-conditioning period than in the pre-conditioning period. Relative to awake periods, in non-rapid eye movement (NREM) sleep the fEPSPs were larger in both pre- and post-conditioning periods. We also found a significant negative correlation between the animal’s speed and fEPSP size. Therefore, to avoid confounds in the fEFSP potentiation estimates, we controlled for speed-related and sleep-related fEPSP changes and still found that cFC induced long-term potentiation, but no significant long-term depression. Synaptic strength changes were not found in the control group that simply explored the fear-conditioning chamber, indicating that exploration of the novel place did not produce the measurable effects caused by cFC. These results show that following cFC, the CA1 commissural pathway is potentiated, likely contributing to the functional integration of the left and right hippocampi in fear memory consolidation. In addition, the cFC paradigm produces significant changes in an animal’s behavioral state, which are observable as proximal changes in sleep patterns.