bluuebunny/crossref_metadata_2025_split huggingface dataset:
A sensitive assay for dNTPs based on long synthetic oligonucleotides, EvaGreen dye, and inhibitor-resistant high-fidelity DNA polymerase
Authors: Purhonen Janne , Banerjee Rishi , McDonald Allison E , Fellman Vineta , Kallijärvi Jukka 
Published: December 2019

ABSTRACTDeoxyribonucleotide triphosphates (dNTPs) are vital for the biosynthesis and repair of DNA. Their cellular concentration peaks during the S phase of the cell cycle. In non-proliferating cells dNTP concentrations are low, making their reliable quantification from tissue samples of heterogeneous cellular composition challenging. Partly because of this, the current knowledge related to regulation of and disturbances in cellular dNTP concentrations derive from cell culture experiments with little corroboration at the tissue or organismal level. Here, we fill the methodological gap by presenting a simple non-radioactive microplate assay for the quantification of dNTPs with a minimum requirement of 10 to 30 mg of biopsy material. In contrast to published assays, this assay is based on long (~200 nucleotides) synthetic single-stranded DNA templates, an inhibitor-resistant high-fidelity DNA polymerase, and the double-stranded-DNA-binding EvaGreen dye. The assay quantifies reliably as little as 100 fmol of each of the four dNTPs. Importantly, the assay allowed measurement of minute dNTP concentrations in mouse liver, heart, and skeletal muscle.
When, why and how clonal diversity predicts future tumour growth
Authors: Noble Robert , Burley John T , Sueur Cécile Le , Hochberg Michael E 
Published: December 2019

AbstractIntratumour heterogeneity holds promise as a prognostic biomarker in multiple cancer types. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate, an important predictor of clinical progression. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. Our results further suggest that prognosis can be better predicted on the basis of both clonal diversity and genomic instability than either factor alone. Nevertheless, we find that, for predicting tumour growth, clonal diversity is likely to perform worse than conventional measures of tumour stage and grade. We thus offer explanations – grounded in evolutionary theory – for empirical findings in various cancers. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.
Accurate brain age prediction with lightweight deep neural networks
Authors: Peng Han , Gong Weikang , Beckmann Christian F. , Vedaldi Andrea , Smith Stephen M. 
Published: December 2019

AbstractDeep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub.HighlightsA lightweight deep learning model, Simple Fully Convolutional Network (SFCN), is presented, achieving state-of-the-art brain age prediction and sex classification performance in UK Biobank MRI brain imaging data.Even with limited number of training subjects (e.g., 50), SFCN performs better than widely-used regression models.A semi-multimodal ensemble strategy is proposed and achieved first place in the PAC 2019 brain age prediction challenge.Linear regression can remove brain age prediction bias (even on unlabelled data) while maintaining state-of-the-art performance.
High-resolution whole-genome analysis of sister-chromatid cohesion
Authors: Espinosa Elena , Paly Evelyne , Barre François-Xavier 
Published: December 2019

AbstractSister-chromatid cohesion describes the orderly association of newly-replicated DNA molecules behind replication forks. It plays an essential role in the maintenance and the faithful transmission of genetic information. It is created by DNA topological links and proteinaceous bridges, whose formation and deposition could be potentially affected by many different DNA-binding proteins. However, a mean to analyse local variations in the duration of cohesion on a whole genome was lacking. Here, we present a High-throughput methodology to monitor Sister Chromatid Contacts (Hi-SC2), and show that it permits to analyse locus-specific variations in sister-chromatid cohesion over the whole length of chromosomes.
Microplastic vector effects: are fish at risk when exposed via the trophic chain?
Authors: Bour Agathe , Sturve Joachim , Höjesjö Johan , Carney Almroth Bethanie 
Published: December 2019

AbstractIn aquatic organisms, trophic transfer is a relevant exposure route for microplastics (MPs). Despite their relevance, effect studies on fish exposed via trophic chains are currently very scarce. MPs are known to contain many chemicals that could be transferred to organisms and induce deleterious effects. However, there is currently no consensus on whether MPs represent a significant exposure pathway to chemicals in contaminated habitats. Here, we exposed three-spined sticklebacks (Gasterosteus aculeatus) to polyethylene MPs via prey ingestion, in a one-month experiment. MPs were either pristine or spiked with chlorpyrifos (CPF), and a CPF control was included to compare vector effects of MPs and natural prey. Following exposure, we assessed AChE activity and fish behavior (feeding, locomotion, environment exploration and reaction to the introduction of a novel object). No effect was observed in fish exposed to pristine MPs. CPF accumulation was observed in fish exposed to CPF-spiked MPs (MP-CPF), confirming the vector potential of MPs. However, CPF accumulation was more important in fish exposed to CPF via prey. In fish exposed to MP-CPF, we observed significant AChE inhibition and hyperactivity, which could result in increased vulnerability to predation. CPF organ distribution differed between groups, suggesting that chemical exposure via MPs could alter organ distribution of chemicals. This can result in a change in the organs most at risk, likely increasing intestine exposure.
Antha-guided Automation of Darwin Assembly for the Construction of Bespoke Gene Libraries
Authors: Marquez P. Handal , Koch M. , Kestemont D. , Arangundy-Franklin S. , Pinheiro V. B. 
Published: December 2019

AbstractProtein engineering through directed evolution facilitates the screening and characterization of protein libraries. Efficient and effective methods for multiple site-saturation mutagenesis, such as Darwin Assembly, can accelerate the sampling of relevant sequence space and the identification of variants with desired functionalities. Here, we present the automation of the Darwin Assembly method, using a Gilson PIPETMAX™ liquid handling platform under the control of the Antha software platform, which resulted in the accelerated construction of complex, multiplexed gene libraries with minimal hands-on time and error-free, while maintaining flexibility over experimental parameters through a graphical user interface rather than requiring user-driven library-dependent programming of the liquid handling platform. We also present an approach for barcoding libraries that overcomes amplicon length limitations in next generation sequencing and enables fast reconstruction of library reads.
Limiting DNA polymerase delta alters replication dynamics and leads to a dependence on checkpoint activation and recombination-mediated DNA repair
Authors: Koussa Natasha C , Smith Duncan J. 
Published: December 2019

ABSTRACTDNA polymerase delta (Polδ) plays several essential roles in eukaryotic DNA replication and repair. At the replication fork, Polδis responsible for the synthesis and processing of the lagging-strand. At replication origins, Polδhas been proposed to initiate leading-strand synthesis by extending the first Okazaki fragment. Destabilizing mutations in human Polδsubunits cause replication stress and syndromic immunodeficiency. Analogously, reduced levels of PolδinSaccharomyces cerevisiaelead to pervasive genome instability. Here, we analyze how the depletion of Polδimpacts replication origin firing and lagging-strand synthesis during replication elongationin vivoinS. cerevisiae.By analyzing nascent lagging-strand products, we observe a genome-wide change in both the establishment and progression of replication. S-phase progression is slowed in Polδdepletion, with both globally reduced origin firing and slower replication progression. We find that no polymerase other than Polδis capable of synthesizing a substantial amount of lagging-strand DNA, even when Polδis severely limiting. We also characterize the impact of impaired lagging-strand synthesis on genome integrity and find increased ssDNA and DNA damage when Polδis limiting; these defects lead to a strict dependence on checkpoint signaling and resection-mediated repair pathways for cellular viability.SIGNIFICANCE STATEMENTDNA replication in eukaryotes is carried out by the replisome – a multi-subunit complex comprising the enzymatic activities required to generate two intact daughter DNA strands. DNA polymerase delta (Polδ) is a multi-functional replisome enzyme responsible for synthesis and processing of the lagging-strand. Mutations in Polδcause a variety of human diseases: for example, destabilizing mutations lead to immunodeficiency. We titrate the concentration of Polδin budding yeast – a simple model eukaryote with conserved DNA replication machinery. We characterize several replication defects associated with Polδscarcity. The defects we observe provide insight into how destabilizing Polδmutations lead to genome instability.
Floral vibrations by buzz-pollinating bees achieve higher frequency, velocity and acceleration than flight and defence vibrations
Authors: Pritchard David J. , Vallejo-Marín Mario 
Published: December 2019

AbstractVibrations play an important role in insect behaviour. In bees, vibrations are used in a variety of contexts including communication, as a warning signal to deter predators and during pollen foraging. However, little is known about how the biomechanical properties of bee vibrations vary across multiple behaviours within a species. In this study, we compared the properties of vibrations produced byBombus terrestris audax(Hymenoptera: Apidae) workers in three contexts: during flight, during defensive buzzing, and in floral vibrations produced during pollen foraging on two buzz-pollinated plants (Solanum, Solanaceae). Using laser vibrometry, we were able to obtain contactless measures of both the frequency and amplitude of the thoracic vibrations of bees across the three behaviours. Despite all three types of vibrations being produced by the same power flight muscles, we found clear differences in the mechanical properties of the vibrations produced in different contexts. Both floral and defensive buzzes had higher frequency and amplitude velocity, acceleration, and displacement than the vibrations produced during flight. Floral vibrations had the highest frequency, amplitude velocity and acceleration of all the behaviours studied. Vibration amplitude, and in particular acceleration, of floral vibrations has been suggested as the key property for removing pollen from buzz-pollinated anthers. By increasing frequency and amplitude velocity and acceleration of their vibrations during vibratory pollen collection, foraging bees may be able to maximise pollen removal from flowers, although their foraging decisions are likely to be influenced by the presumably high cost of producing floral vibrations.
Dendritic Morphology and Inhibitory Regulation Distinguish Dentate Semilunar Granule Cells from Granule Cells through Distinct Stages of Postnatal Development
Authors: Gupta Akshay , Proddutur Archana , Chang Yun-Juan , Raturi Vidhatri , Guevarra Jenieve , Shah Yash , Elgammal Fatima S. , Santhakumar Vijayalakshmi 
Published: December 2019

AbstractSemilunar granule cells (SGCs) have been proposed as a morpho-functionally distinct class of hippocampal dentate projection neurons contributing to feedback inhibition and memory processing in juvenile rats. However, the structural and physiological features that can reliably classify granule cells (GCs) from SGCs through postnatal development remain unresolved. Focusing on postnatal days 11-13, 28-42, and >120, corresponding with human infancy, adolescence, and adulthood, we examined the somatodendritic morphology and inhibitory regulation in SGCs and GCs to determine the cell-type specific features. Unsupervised cluster analysis confirmed that morphological features reliably distinguish SGCs from GCs irrespective of animal age. SGCs maintain higher spontaneous inhibitory postsynaptic current (sIPSC) frequency than GCs from infancy through adulthood. Although sIPSC frequency in SGCs was particularly enhanced during adolescence, sIPSC amplitude and cumulative charge transfer declined from infancy to adulthood and were not different between GCs and SGCs. Extrasynaptic GABA current amplitude peaked in adolescence in both cell types and was significantly greater in SGCs than in GCs only during adolescence. Although GC input resistance was higher than in SGCs during infancy and adolescence, input resistance decreased with developmental age in GCs while it progressively increased in SGCs. Consequently, GCs input resistance was significantly lower than SGCs in adults. The data delineate the structural features that can reliably distinguish GCs from SGCs through development. The results reveal developmental differences in passive membrane properties and steady state inhibition between GCs and SGCs which could confound their use in classifying the cell types.
Small-scale spatial structure affects predator-prey dynamics and coexistence
Authors: Surendran Anudeep , Plank Michael , Simpson Matthew 
Published: December 2019

AbstractSmall-scale spatial variability can affect community dynamics in many ecological and biological processes, such as predator-prey dynamics and immune responses. Spatial variability includes short-range neighbour-dependent interactions and small-scale spatial structure, such asclusteringwhere individuals aggregate together, andsegregationwhere individuals are spaced apart from one another. Yet, a large class of mathematical models aimed at representing these processes ignores these factors by making a classical mean-field approximation, where interactions between individuals are assumed to occur in proportion to their average density. Such mean-field approximations amount to ignoring spatial structure. In this work, we consider an individual based model of a two-species community that is composed ofconsumersandresources. The model describes migration, predation, competition and dispersal of offspring, and explicitly gives rise to varying degrees of spatial structure. We compare simulation results from the individual based model with the solution of a classical mean-field approximation, and this comparison provides insight into how spatial structure can drive the system away from mean-field dynamics. Our analysis reveals that mechanisms leading to intraspecific clustering and interspecific segregation, such as short-range predation and short-range dispersal, tend to increase the size of the resource species relative to the mean-field prediction. We show that under certain parameter regimes these mechanisms lead to the extinction of consumers whereas the classical mean-field model predicts the coexistence of both species.
Engineering combinatorial and dynamic decoders using synthetic immediate-early genes
Authors: Ravindran Pavithran T. , Wilson Maxwell Z. , Jena Siddhartha G. , Toettcher Jared E. 
Published: December 2019

AbstractFor tissues to grow and function properly, cells must coordinate actions such as proliferation, differentiation and apoptosis. This coordination is achieved in part by the activation of intracellular signaling pathways that trigger the expression of context-specific target genes. While the function of these natural circuits has been actively studied, synthetic biology provides additional powerful tools for deconstructing, repurposing, and designing novel signal-decoding circuits. Here we report the construction of synthetic immediate-early genes (synIEGs), target genes of the Erk signaling pathway that implement complex, user-defined regulation and can be monitored through the use of live-cell biosensors to track transcription and translation. We demonstrate the power and flexibility of this approach by confirming Erk duration-sensing by theFOSimmediate-early gene, elucidating how theBTG2gene is regulated by transcriptional activation and translational repression after growth-factor stimulation, and by designing a synthetic immediate-early gene that responds with AND-gate logic to the combined presence of growth factor and DNA damage stimuli. Our work paves the way to defining the molecular circuits that link signaling pathways to specific target genes, highlighting an important role for post-transcriptional regulation in signal decoding that may be masked by analyses of RNA abundance alone.
Epigenomic profiling of primate LCLs reveals the evolutionary patterns of epigenetic activities in gene regulatory architectures
Authors: García-Pérez Raquel , Esteller-Cucala Paula , Mas Glòria , Lobón Irene , Di Carlo Valerio , Riera Meritxell , Kuhlwilm Martin , Navarro Arcadi , Blancher Antoine , Di Croce Luciano , Gómez-Skarmeta José Luis , Juan David , Marquès-Bonet Tomàs 
Published: December 2019

SummaryTo gain insight into the evolution of the epigenetic regulation of gene expression in primates, we extensively profiled a new panel of human, chimpanzee, gorilla, orangutan, and macaque lymphoblastoid cell lines (LCLs), using ChIP-seq for five histone marks, ATAC-seq and RNA-seq, further complemented with WGS and WGBS. We annotated regulatory elements and integrated chromatin contact maps to define gene regulatory architectures, creating the largest catalog of regulatory elements in primates to date. We report that epigenetic conservation and its correlation with sequence conservation in primates depends on the activity state of the regulatory element. Our gene regulatory architectures reveal the coordination of different types of components and highlight the role of promoters and intragenic enhancers in the regulation of gene expression. We observed that most regulatory changes occur in weakly active intragenic enhancers. Remarkably, novel human-specific intragenic enhancers with weak activities are enriched in human-specific mutations. These elements appear in genes with signals of positive selection, tissue-specific expression and particular functional enrichments, suggesting that the regulatory evolution of these genes may have contributed to human adaptation.
Closed-loop Neuroscience of brain rhythms: optimizing real-time quantification of narrow-band signals to expedite feedback delivery
Authors: Smetanin Nikolai , Belinskaya Anastasia , Lebedev Mikhail , Ossadtchi Alexei 
Published: December 2019

AbstractClosed-loop Neuroscience is based on the experimental approach where the ongoing brain activity is recorded, processed, and passed back to the brain as sensory feedback or direct stimulation of neural circuits. The artificial closed loops constructed with this approach expand the traditional stimulus-response experimentation. As such, closed-loop Neuroscience provides insights on the function of loops existing in the brain and the ways the flow of neural information could be modified to treat neurological conditions.Neural oscillations, or brain rhythms, are a class of neural activities that have been extensively studied and also utilized in brain rhythm-contingent (BRC) paradigms that incorporate closed loops. In these implementations, instantaneous power and phase of neural oscillations form the signal that is fed back to the brain.Here we addressed the problem of feedback delay in BRC paradigms. In many BRC systems, it is critical to keep the delay short. Long delays could render the intended modification of neural activity impossible because the stimulus is delivered after the targeted neural pattern has already completed. Yet, the processing time needed to extract oscillatory components from the broad-band neural signals can significantly exceed the period of oscillations, which puts a demand for algorithms that could minimize the delay.We used EEG data collected in human subjects to systematically investigate the performance of a range of signal processing methods in the context of minimizing delay in BRC systems. We proposed a family of techniques based on the least-squares filter design – a transparent and simple approach, as it required a single parameter to adjust the accuracy versus latency trade-off. Our algorithm performed on par or better than the state-of the art techniques currently used for the estimation of rhythm envelope and phase in closed-loop EEG paradigms.