bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian inference of kinetic schemes for ion channels by Kalman filtering
Authors: Münch Jan L. , Paul Fabian , Schmauder Ralf , Benndorf Klaus 
Published: April 2020

AbstractInferring adequate kinetic schemes for ion channel gating from ensemble currents is a daunting task due to limited information in the data. We address this problem by using a parallelized Bayesian filter to specify hidden Markov models for current and fluorescence data. We demonstrate the flexibility of this algorithm by including different noise distributions. Our generalized Kalman filter outperforms both a classical Kalman filter and a rate equation approach when applied to patch-clamp data exhibiting realistic open-channel noise. The derived generalization also enables inclusion of orthogonal fluorescence data, making unidentifiable parameters identifiable and increasing the accuracy of the parameter estimates by an order of magnitude. By using Bayesian highest credibility volumes, we found that our approach, in contrast to the rate equation approach, yields a realistic quantification of uncertainty. Furthermore, the Bayesian filter delivers unbiased estimates for a wider range of data quality and identifies parameters which the rate equation approach does not identify. These results also demonstrate the power of assessing the validity of algorithms by Bayesian credibility volumes in general. Finally, we show that our Bayesian filter is more robust against errors induced by either analog filtering before analog-to-digital conversion or by limited time resolution of fluorescence data than a rate equation approach.
Learning the architectural features that predict functional similarity of neural networks
Authors: Haber Adam , Schneidman Elad 
Published: April 2020

ABSTRACTThe mapping of the wiring diagrams of neural circuits promises to allow us to link structure and function of neural networks. Current approaches to analyzingconnectomesrely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and networks, and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph theory metrics convey little information about the similarity of networks’ responses. Instead, we learn a functional metric between networks based on their synaptic differences, and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features - the sum of synaptic inputs that each neuron receives and the sum of each neuron’s synaptic outputs - predicts the functional similarity of networks of up to 100 cells, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks, which conform with experimental evidence of homeostatic mechanisms.
Context-dependent extinction learning emerging from raw sensory inputs: A reinforcement learning approach
Authors: Walther Thomas , Diekmann Nicolas , Vijayabaskaran Sandhiya , Donoso José R. , Manahan-Vaughan Denise , Wiskott Laurenz , Cheng Sen 
Published: April 2020

AbstractThe context-dependence of extinction learning has been well studied and requires the hippocampus. However, the underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement learning and deep neural networks, we developed a model that learns to navigate autonomously in biologically realistic VR environments based on raw camera inputs alone. Neither is context represented explicitly in our model, nor is context change signaled. We find that memory-intact agents learn distinct context representations, and develop ABA renewal, whereas memory-impaired agents do not. These findings reproduce the behavior of control and hippocampal animals, respectively. We therefore propose that the role of the hippocampus in the context-dependence of extinction learning might stem from its function in episodic-like memory and not in context-representation per se. We conclude that context-dependence can emerge from raw visual inputs.
The Alzheimer’s disease protective P522R variant ofPLCG2, consistently enhances stimulus-dependent PLCγ2 activation, depleting substrate and altering cell function
Authors: Maguire Emily , Menzies Georgina E. , Phillips Thomas , Sasner Michael , Williams Harriet M. , Czubala Magdalena A. , Evans Neil , Cope Emma L , Sims Rebecca , Howell Gareth R. , Lloyd-Evans Emyr , Williams Julie , Allen Nicholas D. , Taylor Philip R. 
Published: April 2020

AbstractRecent genome-wide association studies of Alzheimer’s disease (AD) have identified variants implicating immune pathways in disease development. A rare coding variant ofPLCG2, which encodes PLCγ2, shows a significant protective effect for AD (rs72824905, P522R,P=5.38×10−10, Odds Ratio = 0.68). Molecular dynamic modelling of the PLCγ2-R522 variant, situated within the auto-inhibitory domain of PLCγ2, suggests a structural change to the protein. Through CRISPR-engineering we have generated novelPLCG2-R522 harbouring human induced pluripotent cell lines (hiPSC) and a mouse knockin model, neither of which exhibits alterations in endogenousPLCG2expression. Mouse microglia and macrophages and hiPSC-derived microglia-like cells with the R522 mutation, all demonstrate a consistent non-redundant hyperfunctionality in the context of normal expression of other PLC isoforms. This signalling alteration manifests as enhanced cellular Ca2+store release (∼20-40% increase) in response to physiologically-relevant stimuli (e.g. Fc receptor ligation and Aβ oligomers). This hyperfunctionality resulted in increased PIP2depletion in the cells with the PLCγ2-R522 variant after exposure to stimuli and reduced basal detection of PIP2levelsin vivo. These PLCγ2-R522 associated abnormalities resulted in impairments to phagocytosis (fungal and bacterial particles) and enhanced endocytosis (Aβ oligomers and dextran). PLCγ2 sits downstream of disease relevant pathways, such as TREM2 and CSF1R and alterations in its activity, direct impacts cell function, which in the context of the inherent drugability of enzymes such as PLCγ2, raise the prospect of manipulation of PLCγ2 as a therapeutic target in Alzheimer’s Disease.
Expression andin vitroanticancer activity of Lp16-PSP, a member of the YjgF/YER057c/UK114 protein family from the mushroomLentinula edodesC91-3
Authors: Joseph Thomson Patrick , Zhao Qianqian , Chanda Warren , Kanwal Sadia , Fang Yukun , Zhong MinTao , Huang Min 
Published: April 2020

AbstractLatcripin-16 (Lp16-PSP) is a gene that was extracted as a result ofde novocharacterization of theLentinula edodes strainC91-3transcriptome. The aim of the present study was to clone, express and investigate the selectivein vitroanticancer potential of Lp16-PSP in human cell lines. Lp16-PSP was analyzed using bioinformatics tools, cloned in a prokaryotic expression vector pET32a (+) and transformed intoE. coliRosetta gami. It was expressed and solubilized under optimized conditions. The differential scanning fluorometry (DSF)-guided refolding method was used with modifications to identify the proper refolding conditions for the Lp16-PSP protein. In order to determine the selective anticancer potential of Lp16-PSP, a panel of human cancerous and non-cancerous cell lines was used. Lp16-PSP protein was identified as endoribonuclease L-PSP protein and a member of the highly conserved YjgF/YER057c/UK114 protein superfamily. Lp16-PSP was expressed under optimized conditions (37°C for 4 h following induction with 0.5 mM isopropyl β-D-1-thiogalactopyranoside). Solubilization was achieved with mild solubilization buffer containing 2M urea using the freeze-thaw method. The DSF guided refolding method identified the proper refolding conditions (50 mM Tris-HCl, 100 mM NaCl, 1 mM EDTA, 400 mM Arginine, 0.2 mM GSH and 2 mM GSSG; pH 8.0) for Lp16-PSP, with a melting transition of ~58°C. A final yield of ~16 mg of purified Lp16-PSP from 1 L of culture was obtained following dialysis and concentration by PEG 20,000. A Cell Counting Kit-8 assay revealed the selective cytotoxic effect of Lp16-PSP. The HL-60 cell line was demonstrated to be most sensitive to Lp16-PSP, with an IC50value of 74.4±1.07 μg/ml. The results of the present study suggest that Lp16-PSP may serve as a potential anticancer agent; however, further investigation is required to characterize this anticancer effect and to elucidate the molecular mechanism underlying the action of Lp16-PSP.
ForestSaccharomyces paradoxusare robust to seasonal biotic and abiotic changes
Authors: Boynton Primrose J. , Wloch-Salamon Dominika , Landermann Doreen , Stukenbrock Eva H. 
Published: April 2020

AbstractMicroorganisms are famous for adapting quickly to new environments. However, most evidence for rapid microbial adaptation comes from laboratory experiments or domesticated environments, and it is unclear how rates of adaptation scale from human-influenced environments to the great diversity of wild microorganisms. We examined potential monthly-scale selective pressures in the model forest yeastSaccharomyces paradoxus. Contrary to expectations of seasonal adaptation, theS. paradoxuspopulation was stable over four seasons in the face of abiotic and biotic environmental changes. While theS. paradoxuspopulation was diverse, including 41 unique genotypes among 192 sampled isolates, there was no correlation betweenS. paradoxusgenotypes and seasonal environments. Consistent with observations from otherS. paradoxuspopulations, the forest population was highly clonal and inbred. This lack of recombination, paired with population stability, implies thatS. paradoxusevolved the phenotypic plasticity needed to resist seasonal environmental fluctuations long ago, and that individualS. paradoxusare generalists with regard to seasonal environments. Similarly, while the forest population included diversity among phenotypes related to intraspecific interference competition, there was no evidence for active coevolution among these phenotypes. At least ten percent of the forestS. paradoxusindividuals produced “killer toxins”, which kill sensitiveSaccharomycescells, but the presence of a toxin-producing isolate did not predict resistance to the toxin among nearby isolates. How forest yeasts acclimate to changing environments remains an open question, and future studies should investigate the physiological responses that allow microbial cells to cope with environmental fluctuations in their native habitats.
ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data
Authors: Lähnemann David , Köster Johannes , Fischer Ute , Borkhardt Arndt , McHardy Alice C. , Schönhuth Alexander 
Published: April 2020

ABSTRACTObtaining accurate mutational profiles from single cell DNA is essential for the analysis of genomic cell-to-cell heterogeneity at the finest level of resolution. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. As a result, single cell DNA sequencing data violates the assumptions of variant callers developed for bulk sequencing, which when applied to single cells generate significant numbers of false positives and false negatives. Only dedicated models accounting for amplification bias and errors will be able to provide more accurate calls.We present ProSolo, a probabilistic model for calling single nucleotide variants from multiple displacement amplified single cell DNA sequencing data. It introduces a mechanistically motivated empirical model of amplification bias that improves the quantification of genotyping uncertainty. To account for amplification errors, it jointly models the single cell sample with a bulk sequencing sample from the same cell population—also enabling a biologically relevant imputation of missing genotypes for the single cell. Through these innovations, ProSolo achieves substantially higher performance in calling and genotyping single nucleotide variants in single cells in comparison to all state-of-the-art tools. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly; not only for single nucleotide variant calls, but also for artefacts of single cell methodology that one may wish to identify, such as allele dropout.ProSolo’s model is implemented into a flexible framework, encouraging extensions. The source code and usage instructions are available at:https://github.com/prosolo/prosolo
The hepatic compensatory response to elevated systemic sulfide promotes diabetes
Authors: Carter Roderick N. , Gibbins Matthew T.G. , Barrios-Llerena Martin E. , Wilkie Stephen E. , Freddolino Peter L. , Libiad Marouane , Vitvitsky Victor , Emerson Barry , Bihan Thierry Le , Brice Madara , Su Huizhong , Denham Scott G. , Homer Natalie Z.M. , Fadden Clare Mc , Tailleux Anne , Faresse Nourdine , Sulpice Thierry , Briand Francois , Gillingwater Tom , Ahn Kyo Han , Singha Subhankar , McMaster Claire , Hartley Richard C. , Staels Bart , Gray Gillian A. , Finch Andrew J. , Selman Colin , Banerjee Ruma , Morton Nicholas M. 
Published: April 2020

AbstractImpaired hepatic glucose and lipid metabolism are hallmarks of type–2 diabetes. Increased sulfide production from cysteine, or sulfide–donor compounds, may beneficially regulate hepatic metabolism. Disposal of sulfide through the sulfide oxidation pathway (SOP) is critical for maintaining sulfide within a safe physiological range. We show that mice lacking the liver–enriched mitochondrial SOP enzyme thiosulfate sulfur–transferase (Tst−/−mice) exhibit high circulating sulfide, increased gluconeogenesis, hypertriglyceridemia and fatty liver, despite whole–body insulin–sensitisation. Unexpectedly, hepatic sulfide levels were normal inTst−/−mice, a result of homeostatic induction of mitochondrial sulfide disposal and glutathione excretion associated with net suppression of protein persulfidation and nuclear respiratory factor–2 target proteins. Proteomic and persulfidomic profiling converged on gluconeogenesis and hepatic lipid metabolism and revealed a selective deficit in medium–chain fatty acid oxidation inTst−/−mice. We reveal a critical role for TST in hepatic metabolism that raises implications for sulfide-donor strategies in the context of liver function and metabolic disease.
Genomic characterization of a diazotrophic microbiota associated with maize aerial root mucilage
Authors: Higdon Shawn M. , Pozzo Tania , Kong Nguyet , Huang Bihua , Yang Mai Lee , Jeannotte Richard , Brown C. Titus , Bennett Alan B. , Weimer Bart C. 
Published: April 2020

AbstractA geographically isolated maize landrace cultivated on nitrogen-depleted fields without synthetic fertilizer in the Sierra Mixe region of Oaxaca, Mexico utilizes nitrogen derived from the atmosphere and develops an extensive network of mucilage-secreting aerial roots that harbors a diazotrophic microbiota. Targeting these diazotrophs, we selected nearly 600 microbes from a collection isolated from these plants and confirmed their ability to incorporate heavy nitrogen (15N2) metabolitesin vitro. Sequencing their genomes and conducting comparative bioinformatic analyses showed that these genomes had substantial phylogenetic diversity. We examined each diazotroph genome for the presence ofnifgenes essential to nitrogen fixation (nifHDKENB) and carbohydrate utilization genes relevant to the mucilage polysaccharide digestion. These analyses identified diazotrophs that possessed canonicalnifgene operons, as well as many other operon configurations with concomitant fixation and release of >700 different15N labeled metabolites. We further demonstrated that many diazotrophs possessed alternativenifgene operons and confirmed their genomic potential to derive chemical energy from mucilage polysaccharide to fuel nitrogen fixation. These results confirm that some diazotrophic bacteria associated with Sierra Mixe maize were capable of incorporating atmospheric nitrogen into their small molecule extracellular metabolites through multiplenifgene configurations while others were able to fix nitrogen without the canonical (nifHDKENB) genes.Data SummaryGenetic resources, including biological materials and nucleic acid sequences, were accessed under an Access and Benefit Sharing (ABS) Agreement between the Sierra Mixe community and the Mars Corporation, and with authorization from the Mexican government. An internationally recognized certificate of compliance has been issued by the Mexican government under the Nagoya Protocol for such activities (ABSCH-IRCC-MX-207343-3). Any party seeking access to the nucleic acid sequences underlying the analysis reported here is subject to the full terms and obligations of the ABS agreement and the authorization from the government of Mexico. Individuals wishing to access nucleic acid sequence data for scientific research activities should contact Mars Incorporated Chief Science Officer atCSO@effem.com.
Mind Wandering in Sensory Cortices
Authors: Hung Shao-Min , Hsieh Po-Jang 
Published: April 2020

AbstractMind wandering contains rich phenomenology as we experience moment by moment, however, such linkage between our subjective experiences and the underlying neural mechanism has been missing in the literature. Here we report that the sensory contents of mind wandering recruit corresponding sensory cortices, serving as the neural bases of the sensory contents in mind wandering.
An essential role for MEF2C in the cortical response to loss of sleep
Authors: Bjorness Theresa E. , Kulkarni Ashwinikumar , Rybalchenko Volodymer , Suzuki Ayako , Bridges Catherine , Harrington Adam J. , Cowan Christopher W. , Takahashi Joseph S. , Konopka Genevieve , Greene Robert W. 
Published: April 2020

AbstractNeuronal activity and gene expression in response to the loss of sleep can provide a window into the enigma of sleep function. Sleep loss is associated with brain differential gene expression, an increase in pyramidal cell mEPSC frequency and amplitude, and a characteristic rebound and resolution of slow wave sleep-slow wave activity (SWS-SWA). However, the molecular mechanism(s) mediating the sleep loss response are not well understood. We show that sleep-loss regulates MEF2C phosphorylation, a key mechanism regulating MEF2C transcriptional activity, and that MEF2C function in postnatal excitatory forebrain neurons is required for the biological events in response to sleep loss. These include altered gene expression, the increase and recovery of synaptic strength, and the rebound and resolution of SWS-SWA, which implicate MEF2C as an essential regulator of sleep function.One Sentence SummaryMEF2C is critical to the response to sleep loss.
Single-cell RNA-seq analysis of human iPSC-derived motor neurons resolves early and predictive ALS signatures
Authors: Ho Ritchie , Workman Michael J. , Mathkar Pranav , Wu Kathryn , Kim Kevin J. , O’Rourke Jacqueline G. , Kellogg Mariko , Montel Valerie , Banuelos Maria G. , Aladesuyi Olubankole , Garcia Sandra Diaz , Oheb Daniel , Huang Steven , Khrebtukova Irena , Watson Lisa , Ravits John , Taylor Kevin , Baloh Robert H. , Svendsen Clive N. 
Published: April 2020

SummaryInduced pluripotent stem cell (iPSC) derived neural cultures from amyotrophic lateral sclerosis (ALS) patients can reflect disease phenotypes targetable by treatments. However, widely used differentiation protocols produce mixtures of progenitors, neurons, glia, and other cells at various developmental stages and rostrocaudal neural tube segments. Here we present a methodology using single-cell RNA sequencing analysis to distinguish cell type expression in C9orf72 ALS, sporadic ALS, control, and genome-edited cultures across multiple subjects, experiments, and commercial platforms. Combinations of HOX and developmental gene expression with global clustering classified rostrocaudal, progenitor, and mantle zone fates. This demonstrated that iPSC-differentiated cells recapitulate fetal hindbrain and spinal cord development and resolved early, reproducible, and motor neuron-specific signatures of familial and sporadic ALS. This includes downregulated ELAVL3 expression, which persists into disease endstages. Single-cell analysis thus yielded predictive ALS markers in other human and mouse models which were otherwise undiscovered through bulk omics assays.
The partitioning of symbionts effects on host resource acquisition and developmental plasticity
Authors: Guilhot Robin , Xuéreb Anne , Fellous Simon 
Published: April 2020

AbstractMany symbionts provide nutrients to their host and/or affect its phenotypic plasticity. Such symbiont effects on host resource acquisition and allocation are often simultaneous and difficult to disentangle. Here we partitioned symbiont effects on host resource acquisition and allocation using a new framework based on the analysis of a well-established trade-off between host fitness components. This framework was used to analyze the effect of symbiotic yeast on the larval development ofDrosophilalarvae in field-realistic conditions. The screening of eighteen yeast fresh isolates showed they had similar effects on the resource acquisition inDrosophila melanogaster, D. simulansandD. suzukiibut species-specific effects on resource allocation between either larval development speed or adult size. These differences shed light on the ecology ofDrosophilaflies and illustrate why distinguishing between these qualitatively different effects of microorganisms on hosts is essential to understand and predict symbiosis evolution.
Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis
Authors: Raimundo Felix , Vallot Celine , Vert Jean Philippe 
Published: April 2020

AbstractBackgroundMany computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering or differential analysis, often relying on default parameters. Yet given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need.ResultsHere, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it.ConclusionsWe find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA and scVI can reach better performance but after parameter tuning.