bluuebunny/crossref_metadata_2025 huggingface dataset:
Magia: Robust automated image processing and kinetic modeling toolbox for PET neuroinformatics
Authors: Karjalainen Tomi , Tuisku Jouni , Santavirta Severi , Kantonen Tatu , Tuominen Lauri , Hirvonen Jussi , Hietala Jarmo , Rinne Juha O. , Nummenmaa Lauri
Published: April 2019

Introduction

Modelling of the radioactivity images produced by PET scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images. The process is challenging to automatize mainly because of manual work related to input generation, thus prohibiting large-scale standardized analysis of brain PET data. To resolve this problem, we introduce the Magia pipeline that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia in the automatic brain-PET data processing with four tracers binding to different binding sites: [11C]raclopride, [11C]carfentanil, [11C]MADAM, and [11C]PiB.

Materials and methods

For each tracer, we processed 30 historical control subjects’ data with manual and automated methods. Five persons manually delineated the reference regions (cerebellar or occipital cortex depending on tracer) for each subject according to written and visual instructions. The automatic reference-region extraction was based on FreeSurfer parcellations. We first assessed inter-operator variance resulting from manual delineation of reference regions. Then we compared the differences between the manually and automatically produced reference regions and the subsequently obtained metrics.

Results

The manually delineated reference regions were remarkably different from each other. The differences translated into differences in outcome measures (binding potential or SUV-ratio), and the intra-class correlation coefficients were between 47 % and 96 % for the tracers. While the Magia-derived reference regions were topographically very different from the manually defined reference regions, Magia produced outcome measures highly consistent with average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3-5 % higher binding potentials as a result of slightly lower time-integrals of reference region time-activity curves.

Conclusion

Even if Magia produces reference regions that are anatomically different from manually drawn reference regions, the resulting outcome measures are highly similar. Based on these results and considering the high inter-operator variance of the manual method, the high level of standardization and strong scalability of Magia, we conclude that Magia can be reliably used to process brain PET data.
ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data
Authors: Smit Jochem H. , Li Yichen , Warszawik Eliza M. , Herrmann Andreas , Cordes Thorben
Published: April 2019

Single-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the requirement of various journals to make data and analysis procedures available. To address this we have developed a new data analysis package for analysis of fluorescence microscopy data of rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates of both cellular images (e.g. from transmission light or fluorescence microscopy) and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical significance of fluorescence microscopy datasets of any kind. Coli-Coords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research.

The source code and documentation can be found via the project’s GitHub page.
Selective Prespacer Processing Ensures Precise CRISPR-Cas Adaptation
Authors: Kim Sungchul , Loeff Luuk , Colombo Sabina , Brouns Stan J.J. , Joo Chirlmin
Published: April 2019

CRISPR-Cas immunity protects prokaryotes against foreign genetic elements. CRISPR-Cas uses the highly conserved Cas1-Cas2 complex to establish inheritable memory (spacers). It remains elusive how Cas1-Cas2 acquires spacers from cellular DNA fragments (prespacers) and how it integrates them into the CRISPR array in the correct orientation. By using the high spatiotemporal resolution of single-molecule fluorescence, we reveal that Cas1-Cas2 obtains prespacers in various forms including single-stranded DNA and partial duplexes by selecting them in the DNA-length and PAM-dependent manner. Furthermore, we identify DnaQ exonucleases as enzymes that can mature the Cas1-Cas2-loaded precursor prespacers into an integration-competent size. Cas1-Cas2 protects the PAM sequence from maturation, which results in the production of asymmetrically trimmed prespacers and subsequent spacer integration in the correct orientation. This kinetic coordination in prespacer selection and PAM trimming provides comprehensive understanding of the mechanisms that underlie the integration of functional spacers in the CRISPR array.
Cell-type specific epigenetic links to schizophrenia risk in brain
Authors: Mendizabal Isabel , Berto Stefano , Usui Noriyoshi , Toriumi Kazuya , Chatterjee Paramita , Douglas Connor , Huh Iksoo , Jeong Hyeonsoo , Layman Thomas , Tamminga Carol , Preuss Todd M. , Konopka Genevieve , Yi Soojin V.
Published: April 2019

The importance of cell-type specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generated cell-type specific whole-genome methylomes (N=95) and transcriptomes (N=89) from neurons and oligodendrocytes from brains of schizophrenia and matched controls. The methylomes of these two cell-types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation difference between control and schizophrenia brains is subtle compared to cell-type difference, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell-type differentially methylated sites, highlighting the significance of cell-type specific epigenetic dysregulation in a complex neuropsychiatric disorder. Our resource provides novel and comprehensive methylome and transcriptome data from distinct cell populations from schizophrenia brains, further revealing reduced cell-type epigenetic distinction in schizophrenia.
Spatiotemporally distinct neural mechanisms underlie our reactions to and comparison between value-based options
Authors: Frömer R. , Shenhav A.
Published: April 2019

Previous research suggests that people evaluate options in at least two ways: (1) appraising their overall value and (2) choosing between them. Here we test whether these processes are temporally dissociable, with appraisal-related processes tied to the time one’s options appear and choice-related processes tied to the time a decision is made. We recorded EEG while participants individually rated and subsequently made choices between consumer goods. As predicted, we found appraisal-related neural activity locked to the onset of the stimuli and choice-related activity locked to (and preceding) the response. Patterns of appraisal- and choice-related activity were further associated with distinct topographical profiles. Using a novel neural index of one’s certainty about a given item’s value, we also provide evidence that choices and choice-related activity were further modulated by this option-specific value certainty. Taken together, our results support the hypothesis that spatiotemporally distinct mechanisms underlie appraisal and choice, suggesting that commonly observed neural correlates of choice value may reflect either or both of these processes.
Biodistribution and inflammatory response to intracranial delivery of scintillating nanoparticles
Authors: Fischer Máté , Zimmerman Amber , Zhang Eric , Kolis Joseph , Dickey Ashley , Burdette Mary K. , Afaghpour-Becklund Mitra , Chander Praveen , Foulger Stephen H. , Brigman Jonathan L. , Weick Jason. P.
Published: April 2019

Nanoparticle-based delivery systems have become a popular method for targeting tumors and impermeable tissue with drugs for treatment and imaging markers for biodetection. Nanomaterials are beneficial for medical treatment because they can be modified to have increased stability and carrying capacity, and their size and surface modifications allow them to reach otherwise impenetrable tissue. Localized or systemic injection can be used for delivery of nanoparticles; however, systemic injection without appropriate surface modifications is subjected to uptake by the mononuclear phagocytic system which clears particles from the circulation rapidly limiting their accumulation at target tissue and reducing efficacy. Here we demonstrate the biodistribution of Yttrium oxyorthosilicate nanoparticles doped with Cerium after localized injection to the cerebral cortex as well as the immune response at the site of injection over time.
Identifying microbial species by single-molecule DNA optical mapping and resampling statistics
Authors: Bouwens Arno , Deen Jochem , Vitale Raffaele , D’Huys Laurens , Goyvaerts Vince , Descloux Adrien , Borrenberghs Doortje , Grussmayer Kristin , Lukes Tomas , Camacho Rafael , Su Jia , Ruckebusch Cyril , Lasser Theo , Van De Ville Dimitri , Hofkens Johan , Radenovic Aleksandra , Janssen Kris Pieter Frans
Published: April 2019

Single molecule DNA mapping has the potential to serve as a powerful complement to high-throughput sequencing in metagenomic analysis. Offering longer read lengths and forgoing the need for complex library preparation and amplification, mapping stands to provide an unbiased view into the composition of complex viromes and/or microbiomes. To fully enable mapping-based metagenomics, sensitivity and specificity of DNA map analysis and identification need to be improved. Using detailed simulations and experimental data, we first demonstrate how fluorescence imaging of surface stretched, sequence specifically labeled DNA fragments can yield highly sensitive identification of targets. Secondly, a new analysis technique is introduced to increase specificity of the analysis, allowing even closely related species to be resolved. Thirdly, we show how an increase in resolution improves sensitivity. Finally, we demonstrate that these methods are capable of identifying species with long genomes such as bacteria with high sensitivity.
The outer membrane lipoprotein NlpI nucleates hydrolases within peptidoglycan multi-enzyme complexes inEscherichia coli
Authors: Banzhaf Manuel , Yau Hamish C. L. , Verheul Jolanda , Lodge Adam , Kritikos George , Mateus André , Hov Ann Kristin , Stein Frank , Wartel Morgane , Pazos Manuel , Solovyova Alexandra S. , Savitski Mikhail M , den Blaauwen Tanneke , Typas Athanasios , Vollmer Waldemar
Published: April 2019

The peptidoglycan (PG) sacculus provides bacteria with the mechanical strength to maintain cell shape and resist osmotic stress. Enlargement of the mesh-like sacculus requires the combined activity of PG synthases and hydrolases. InEscherichia coli, the activity of the two bifunctional PG synthases is driven by lipoproteins anchored in the outer membrane. However, the regulation of PG hydrolases is less well understood, with only regulators for PG amidases having been described. Here, we identify the lipoprotein NlpI as a general adaptor protein for PG hydrolases. NlpI binds to different classes of hydrolases and can specifically form multimeric complexes with various PG endopeptidases. In addition, NlpI seems to contribute both to PG elongation and cell division biosynthetic complexes based on its localization and genetic interactions. In line with such a role, we reconstitute PG multi-enzyme complexes containing NlpI, the PG synthesis regulator LpoA, its cognate bifunctional synthase, PBP1A, and different endopeptidases. Our results indicate that PG regulators and adaptors are part of PG biosynthetic multi-enzyme complexes, regulating and potentially coordinating the spatiotemporal action of PG synthases and hydrolases.

Significance

The activity of PG hydrolases may cause lysis of the bacterial cell if left unregulated. Hence, the cell must have ways of regulating and coordinating their activities. Our current understanding of how this occurs is incomplete. In this work, we present the outer membrane (OM) anchored lipoprotein, NlpI, as a scaffold of peptidoglycan hydrolases. We propose that NlpI facilitates the formation of multi-enzyme complexes and that, along with other regulators, it coordinates a safe enlargement and separation of the PG layer inE. coli.
Predicting cognitive and mental health traits and their polygenic architecture using large-scale brain connectomics
Authors: Maglanoc Luigi A. , Kaufmann Tobias , van der Meer Dennis , Marquand Andre F. , Wolfers Thomas , Jonassen Rune , Hilland Eva , Andreassen Ole A. , Landrø Nils Inge , Westlye Lars T.
Published: April 2019

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.

Significance

Although cognitive abilities and susceptibility to mental disorders reflect individual differences in brain function, neuroimaging is yet to provide a coherent account of the neuronal underpinnings. Here, we aimed to map the brain functional connectome of (i) cognitive and mental health traits and (ii) their polygenic architecture in a large population-based sample. We discovered high prediction accuracy for age and sex, and above-chance accuracy for educational attainment and intelligence (cognitive). In contrast, accuracies for dimensional measures of depression, anxiety and neuroticism (mental health), and polygenic scores across traits, were at chance level. These findings support the link between cognitive abilities and brain connectomics and provide a reference for studies mapping the brain connectomics of mental disorders and their genetic architectures.
Balanced nutrient requirements for maize in the Northern Nigerian Savanna: Parameterization and validation of QUEFTS model
Authors: Shehu Bello M. , Lawan Bassam A. , Jibrin Jibrin M. , Kamara Alpha Y. , Mohammed Ibrahim B. , Rurinda Jairos , Zingore Shamie , Craufurd Peter , Vanlauwe Bernard , Merckx Roel
Published: April 2019

Establishing balanced nutrient requirements for maize (Zea mays L.) in the Northern Nigerian Savanna is paramount to develop site-specific fertilizer recommendations to increase maize yield, profits of farmers and avoid negative environmental impacts of fertilizer use. The model QUEFTS (QUantitative Evaluation of Fertility of Tropical Soils) was used to estimate balanced nitrogen (N), phosphorus (P) and potassium (K) requirements for maize production in the Northern Nigerian Savanna. Data from on-farm nutrient omission trials conducted in 2015 and 2016 rainy seasons in two agro-ecological zones in the Northern Nigerian Savanna (i.e. Northern Guinea Savanna “NGS” and Sudan Savanna “SS”) were used to parameterize and validate the QUEFTS model. The relations between indigenous soil N, P, and K supply and soil properties were not well described with the QUEFTS default equations and consequently new and better fitting equations were derived. The average fertilizer recovery fractions of N, P and K in the NGS were generally comparable with the QUEFTS default values, but lower recovery fractions of these nutrients were observed in the SS. The parameters of maximum accumulation (a) and dilution (d) in kg grain per kg nutrient for the QUEFTS model obtained were respectively 35 and 79 for N, 200 and 527 for P and 25 and 117 for K in the NGS zone and 32 and 79 for N, 164 and 528 for P and 24 and 136 for K in the SS zone. The model predicted a linear relationship between grain yield and above-ground nutrient uptake until yield reached about 50 to 60% of the yield potential. When the yield target reached 60% of the potential yield (i.e. 6.0 tonnes per hectare), the model showed above-ground nutrient uptake of 19.4, 3.3 and 23.0 kg N, P, and K, respectively, per one tonne of maize grain in the NGS, and 17.3, 5.3 and 26.2 kg N, P and K, respectively, per one tonne of maize grain in the SS. These results suggest an average NPK ratio in the plant dry matter of about 5.9:1:7.0 for maize in the NGS and 3.3:1:4.9 for maize in the SS. There was a close agreement between observed and parameterized QUEFTS predicted yields across the two agro-ecological zones (R2 = 0.70 for the NGS and 0.86 for the SS). We concluded that the QUEFTS model can be used for balanced nutrient requirement estimations and development of site-specific fertilizer recommendations for maize intensification in the Northern Nigerian Savanna.
Amelioration of muscular dystrophy phenotype in mdx mice by inhibition of Flt1
Authors: Verma Mayank , Shimizu-Motohashi Yuko , Asakura Yoko , Ennen James , Bosco Jennifer , Zou Zhiwei , Fong Guo-Hua , Josiah Serene , Keefe Dennis , Asakura Atsushi
Published: April 2019

Duchenne muscular dystrophy (DMD) is an X-linked recessive genetic disease in which the dystrophin coding for a membrane stabilizing protein is mutated. Recently, the vasculature has also shown to be perturbed in DMD and DMD modelmdxmice. Data-mining DMD transcriptomics revealed the defects were correlated to a vascular endothelial growth factor (VEGF) signaling pathway. To reveal the relationship between DMD and VEGF signaling,mdxmice were crossed with constitutive (CAG/CreERTM:Flt1LoxP/LoxP) and endothelial cell-specific conditional gene knockout mice (Cdh5CreERT2:Flt1LoxP/LoxP) forFlt1which is a decoy receptor for VEGF. Previous work demonstrated that heterozygous globalFlt1knockout mice increased vascular density and improved DMD phenotypes when crossed with DMD modelmdxandmdx:utrn-/-mice. Here, we showed that while constitutive deletion ofFlt1is detrimental to the skeletal muscle function, endothelial cell-specificFlt1deletion resulted in increased vascular density and improvement in the DMD-associated phenotype in themdxmice. These decreases in pathology, including improved muscle histology and function, were recapitulated inmdxmice given anti-FLT1 peptides or monoclonal antibodies, which blocked VEGF-FLT1 binding. The histological and functional improvement of dystrophic muscle by FLT1 blockade provides a novel pharmacological strategy for the potential treatment of DMD.
Effects of MCHM on yeast metabolism
Authors: Pupo Amaury , Ku Kang Mo , Gallagher Jennifer E. G.
Published: April 2019

On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM’s toxicological data were limited, an issue that have been addressed by different studies focused on understanding the immediate and long-term effects of MCHM on human health and the environment. UsingS. cerevisiaeas a model organism we study the effect of acute exposition to crude MCHM on metabolism. Yeasts were treated with MCHM 3.9 mM in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. 181 and 66 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 38 and 34 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 72 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis and decrease the levels of ergosterol, with a potential impact in the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cell’s growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression ofCYT1gene, could be the driven force behind the observed effect on yeast metabolism and growth.
Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina
Authors: Höfling Larissa , Berens Philipp , Zeck Günther
Published: April 2019

Retinal implants are used to replace lost photoreceptors in blind patients suffering from retinopathies such as retinitis pigmentosa. Patients wearing implants regain some rudimentary visual function. However, it is severely limited compared to normal vision because non-physiological stimulation strategies fail to selectively activate different retinal pathways at sufficient spatial and temporal resolution. The development of improved stimulation strategies is rendered difficult by the large space of potential stimuli. Here we systematically explore a subspace of potential stimuli by electrically stimulating healthy and blind mouse retina in epiretinal configuration using smooth Gaussian white noise delivered by a high-density CMOS-based microelectrode array. We identify linear filters of retinal ganglion cells (RGCs) by fitting a linear-nonlinear-Poisson (LNP) model. Our stimulus evokes fast, reliable, and spatially confined spiking responses in RGC which are accurately predicted by the LNP model. Furthermore, we find diverse shapes of linear filters in the linear stage of the model, suggesting diverse preferred electrical stimuli of RGCs. Our smooth electrical stimulus could provide a starting point of a model-guided search for improved stimuli for retinal prosthetics.
Mechanical constraints influence microtubule orientation in plant cells
Authors: Bachmann S , Froese R , Cytrynbaum EN
Published: April 2019

In growing plant cells, parallel ordering of microtubules (MTs) along the inner surface of the cell membrane influences the direction of cell expansion and thereby plant morphology. For correct expansion of organs that primarily grow by elongating, such as roots and stems, MTs must bend in the high-curvature direction along the cylindrically shaped cell membrane in order to form the required circumferential arrays. Previous studies, which have recapitulated the self-organization of these arrays, ignored MT mechanics and assumed MTs follow geodesics of the cell surface. Here, we show, through analysis of a derived Euler-Lagrange equation, that an elastic MT constrained to a cylindrical surface will deflect away from geodesics and toward low curvature directions to minimize bending energy. This occurs when the curvature of the cell surface is relatively high for a given anchor density. In the limit of infinite anchor density, MTs always follow geodesics. We compare our analytical predictions to measured curvatures and anchor densities and find that the regime in which cells are forming these cortical arrays straddles the region of parameter space in which arrays must form under the antagonistic influence of this mechanically induced deflection. Although this introduces a potential obstacle to forming circumferentially orientated arrays that needs to be accounted for in the models, it also raises the question of whether plants use this mechanical phenomenon to regulate array orientation. The model also constitutes an elegant generalization of the classical Euler-bucking instability along with an intrinsic unfolding of the associated pitchfork bifurcation.

SIGNIFICANCE STATEMENT

The cortical microtubule array in plant cells is a dynamic structure whose organization influences cell function, growth, and plant morphology. During cell growth, the array must be highly ordered and perpendicular to the axis of expansion. In other instances, it can be disordered or axially oriented. Experimental and modelling studies have outlined the importance of various microtubule-microtubule interactions in the ordering and orienting process. We explore the influence of cell shape on the bending of microtubules that has been largely ignored to date. We find that cell shape can induce microtubule bending that steers them away from high-curvature directions. This bending force can act as an obstacle to proposed mechanisms for array organization and also as a mechanism for regulating reorientation of the array.
Four ways to fit an ion channel model
Authors: Clerx M. , Beattie K.A. , Gavaghan D.J. , Mirams G.R.
Published: April 2019

Computational models of the cardiac action potential are increasingly being used to investigate the effects of genetic mutations, predict pro-arrhythmic risk in drug development, and to guide clinical interventions. These safety-critical applications, and indeed our understanding of the cardiac action potential, depend on accurate characterisation of the underlying ionic currents. Four different methods can be found in the literature to fit ionic current models to single-cell measurements: (Method 1) fitting model equations directly to time constant, steady-state, and I-V summary curves; (Method 2) fitting by comparing simulated versions of these summary curves to their experimental counterparts; (Method 3) fitting to the current traces themselves from a range of protocols; and (Method 4) fitting to a single current trace from an information-rich voltage clamp protocol. We compare these methods using a set of experiments in which hERG1a current from single Chinese Hamster Ovary (CHO) cells was characterised using multiple fitting protocols and an independent validation protocol. We show that Methods 3 and 4 provide the best predictions on the independent validation set, and that the short information-rich protocols of Method 4 can replace much longer conventional protocols without loss of predictive ability. While data for Method 2 is most readily available from the literature, we find it performs poorly compared to Methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.

Statement of Significance

Mathematical models have been constructed to capture and share our understanding of the kinetics of ion channel currents for almost 70 years, and hundreds of models have been developed, using a variety of techniques. We compare how well four of the main methods fit data, how reliable and efficient the process of fitting is, and how predictive the resulting models are for physiological situations. The most widely-used traditional approaches based on current-voltage and time constant-voltage curves do not produce the most predictive models. Short, optimised experimental voltage clamp protocols can be used to create models that are as predictive as ones derived from traditional protocols, opening up possibilities for measuring ion channel kinetics faster, more accurately and in single cells. As these models often form part of larger multi-scale action potential and tissue electrophysiology models, improved ion channel kinetics models could influence the findings of thousands of simulation studies.
Sleep is bi-directionally modified by amyloid beta oligomers
Authors: Özcan Güliz Gürel , Lim Sumi , Leighton Patricia L.A. , Allison W. Ted , Rihel Jason
Published: April 2019

Disrupted sleep is a major feature of Alzheimer’s Disease (AD), often arising years before symptoms of cognitive decline. Prolonged wakefulness exacerbates the production of amyloid-beta (Aβ) species, a major driver of AD progression, suggesting that sleep loss further accelerates AD through a vicious cycle. However, the mechanisms by which Aβ affects sleep are unknown. We demonstrate in zebrafish that Aβ acutely and reversibly enhances or suppresses sleep as a function of oligomer length. Genetic disruptions revealed that short Aβ oligomers induce acute wakefulness through Adrenergic receptor b2 (Adrb2) and Progesterone membrane receptor component 1 (Pgrmc1), while longer Aβ forms induce sleep through a pharmacologically tractable Prion Protein (PrP) signalling cascade. Our data indicate that Aβ can trigger a bi-directional sleep/wake switch. Alterations to the brain’s Aβ oligomeric milieu, such as during the progression of AD, may therefore disrupt sleep via changes in acute signalling events.

HIGHLIGHTS

Amyloid beta oligomers can drive either sleep or wakefulness, depending on their size

Wakefulness driven by short amyloid beta oligomers requires binding partners Adrenergic Beta Receptor 2 and Pgrmc1

Long amyloid beta oligomers drive sleep through interaction with Prion Protein

The in vivo sleep effects of amyloid beta can be pharmacologically blocked by targeting several steps of the Amyloid beta-Prion Protein signalling cascade.
A Deep Learning Approach for Rapid Mutational Screening in Melanoma
Authors: Kim Randie H. , Nomikou Sofia , Coudray Nicolas , Jour George , Dawood Zarmeena , Hong Runyu , Esteva Eduardo , Sakellaropoulos Theodore , Donnelly Douglas , Moran Una , Hatzimemos Aristides , Weber Jeffrey S. , Razavian Narges , Aifantis Ioannis , Fenyo David , Snuderl Matija , Shapiro Richard , Berman Russell S. , Osman Iman , Tsirigos Aristotelis
Published: April 2019

Image-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.
Structural Learning of Proteins Using Graph Convolutional Neural Networks
Authors: Zamora-Resendiz Rafael , Crivelli Silvia
Published: April 2019

The exponential growth of protein structure databases has motivated the development of efficient deep learning methods that perform structural analysis tasks at large scale, ranging from the classification of experimentally determined proteins to the quality assessment and ranking of computationally generated protein models in the context of protein structure prediction. Yet, the literature discussing these methods does not usually interpret what the models learned from the training or identify specific data attributes that contribute to the classification or regression task. While 3D and 2D CNNs have been widely used to deal with structural data, they have several limitations when applied to structural proteomics data. We pose that graph-based convolutional neural networks (GCNNs) are an efficient alternative while producing results that are interpretable. In this work, we demonstrate the applicability of GCNNs to protein structure classification problems. We define a novel spatial graph convolution network architecture which employs graph reduction methods to reduce the total number of trainable parameters and promote abstraction in interme-diate representations. We show that GCNNs are able to learn effectively from simplistic graph representations of protein structures while providing the ability to interpret what the network learns during the training and how it applies it to perform its task. GCNNs perform comparably to their 2D CNN counterparts in predictive performance and they are outperformed by them in training speeds. The graph-based data representation allows GCNNs to be a more efficient option over 3D CNNs when working with large-scale datasets as preprocessing costs and data storage requirements are negligible in comparison.
Growth-dependent activation of protein kinases suggests a mechanism for measuring cell growth
Authors: Jasani Akshi , Huynh Tiffany , Kellogg Douglas R.
Published: April 2019

In all cells, progression through the cell cycle occurs only when sufficient growth has occurred. Thus, cells must translate growth into a proportional signal that can be used to measure and transmit information about growth. Previous genetic studies in budding yeast suggested that related kinases called Gin4 and Hsl1 could play roles in mechanisms that measure bud growth; however, interpretation of the data was complicated by the use of gene deletions that cause complex terminal phenotypes. Here, we used the first conditional alleles of Gin4 and Hsl1 to more precisely define their functions. We show that excessive bud growth during a prolonged mitotic delay is an immediate consequence of inactivating Gin4 and Hsl1. Thus, acute loss of Gin4 and Hsl1 causes cells to behave as though they cannot detect that bud growth has occurred. We further show that Gin4 and Hsl1 undergo gradual hyperphosphorylation during bud growth that is dependent upon growth and correlated with the extent of growth. Moreover, gradual hyperphosphorylation of Gin4 during bud growth requires binding to anionic phospholipids that are delivered to the growing bud. While alternative models are possible, the data suggest that signaling lipids delivered to the growing bud generate a growth-dependent signal that could be used to measure bud growth.
FermentativeEscherichia colimakes a substantial contribution to H2production in coculture with phototrophicRhodopseudomonas palustris
Authors: Sangani Amee A. , McCully Alexandra L. , LaSarre Breah , McKinlay James B.
Published: April 2019

Individual species within microbial communities can combine their attributes to produce services that benefit society, such as the transformation of renewable resources into valuable chemicals. Under defined genetic and environmental conditions, fermentativeEscherichia coliand phototrophicRhodopseudomonas palustrisexchange essential carbon and nitrogen, respectively, to establish a mutualistic relationship. In this relationship, each species produces H2biofuel as a byproduct of their metabolism. However, the extent to which each species contributes to H2production and the factors that influence their relative contributions were previously unknown. By comparing H2yields in cocultures pairingR. palustriswith either wild-typeE. colior a formate hydrogenlyase mutant that is incapable of H2production, we determined the relative contribution of each species to total H2production. Our results indicate thatE. colicontributes between 32% and 86% of the H2produced in coculture depending on the level of ammonium excreted by theR. palustrispartner. AnR. palustrisstrain that stimulated rapidE. coligrowth through a high level of ammonium excretion resulted in earlier accumulation of formate and acidic conditions that allowedE. colito be the major contributor to H2production.
Inverse Transformed Encoding Models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding
Authors: Soch Joram , Allefeld Carsten , Haynes John-Dylan
Published: April 2019

Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF). Here, we describe inverse transformed encoding modelling (ITEM), a principled approach of accounting for those serial correlations and decoding from the resulting estimates, at low computational cost and with no loss in statistical power. We use simulated data to show that ITEM outperforms the current standard approach in terms of decoding accuracy and analyze empirical data to demonstrate that ITEM is capable of visual reconstruction from fMRI signals.
PIKfyve deficiency in myeloid cells impairs lysosomal homeostasis in macrophages and promotes systemic inflammation in mice
Authors: Min Sang Hee , Suzuki Aae , Weaver Lehn , Guzman Jessica , Zhao Liang , Gonzalez Francina , Spruce Lynn A. , Seeholzer Steven H. , Behrens Edward , Abrams Charles S.
Published: April 2019

Macrophages are professional phagocytes that are essential for host defense and tissue homeostasis. Proper membrane trafficking and degradative functions of the endolysosomal system is known to be critical for the function of these cells. We have found that PIKfyve, the kinase that synthesizes the endosomal phosphoinositide PI(3,5)P2, is an essential regulator of lysosomal biogenesis and degradative functions in macrophages. Genetically engineered mice lacking PIKfyve in their myeloid cells (PIKfyvefl/fl LysM-Cre) develop diffuse tissue infiltration of foamy macrophages, hepatosplenomegaly, and systemic inflammation. PIKfyve loss in macrophages causes enlarged endolysosomal compartments and impairs the lysosomal degradative function. Moreover, PIKfyve deficiency increases the cellular levels of lysosomal proteins. Although PIKfyve deficiency reduced the activation of mTORC1 pathway and was associated with increased cleavage of TFEB proteins, this does not translate into transcriptional activation of lysosomal genes, suggesting that PIKfyve modulates the abundance of lysosomal proteins by affecting the degradation of these proteins. Taken together, our study shows that PIKfyve modulation of lysosomal degradative activity and protein expression is essential to maintain lysosomal homeostasis in macrophages.
Protein-protein interaction network topology as a measure of bacterial plasmid-chromosome co-evolution
Authors: Tran Nghia T.H. , Decano Arun , Downing Tim , Rahm Alexander D.
Published: April 2019

In this work, we investigate the evolvability of plasmids by examining the topology of plasmid-chromosome gene interactions in Escherichia coli ST131. We discover a convergence of the ratio of non-trivial loops per protein-protein interaction, which allows us to introduce a new invariant of bacterial PPINs: the indirect connectivity value.
Accurate Prediction of Genome-wide RNA Secondary Structure Profile Based On Extreme Gradient Boosting
Authors: Ke Yaobin , Rao Jiahua , Zhao Huiying , Lu Yutong , Xiao Nong , Yang Yuedong
Published: April 2019

Motivation

Many studies have shown that RNA secondary structure plays a vital role in fundamental cellular processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. Identification of RNA secondary structure is a key step to understand the common mechanisms underlying the translation process. Recently, a few experimental methods were developed to measure genome-wide RNA secondary structure profile through high-throughput sequencing techniques, and have been successfully applied to genomes including yeast and human. However, these high-throughput methods usually have low precision and are hard to cover all nucleotides on the RNA due to limited sequencing coverage.

Results

In this study, we developed a new method for the prediction of genome-wide RNA secondary structure profile (TH-GRASP) from RNA sequence based on eXtreme Gradient Boosting (XGBoost). The method achieves an prediction with areas under the receiver operating characteristic curve (AUC) values greater than 0.9 on three different datasets, and AUC of 0.892 by an independent test on the recently released Zika virus RNA dataset. These AUCs represent a consistent increase of >6% than the recently developed method CROSS trained by a shallow neural network. A further analysis on the 1000-Genome Project data showed that our predicted unpaired probability at mutations sites are highly correlated with the minor allele frequencies (MAF) of synonymous, non-synonymous mutations, and mutations in 3’ and 5’UTR with Pearson Correlation Coefficients all above 0.8. These PCCs are consistently higher than those generated by RNAplfold method. Moreover, an investigation over all human mRNA indicated a periodic distribution of the predicted unpaired probability on codons, and a decrease of paired probability in the boundary with 5’ and 3’ untranslated regions. These results highlighted TH-GRASP is effective to remove experimental noises and to have ability to make predictions on nucleotides with low or no coverage by fitting high-throughput genomic data for RNA secondary structure profiles, and also suggested that building model on high throughput experimental data might be a future direction to substitute analytical methods.

Availability

The TH-GRASP is available for academic use athttps://github.com/sysu-yanglab/TH-GRASP.

Supplementary information

Supplementary data are available online.
The molecular aetiology of tRNA synthetase depletion: induction of aGCN4amino acid starvation response despite homeostatic maintenance of charged tRNA levels
Authors: McFarland Matthew R. , Keller Corina D. , Childers Brandon M. , Adeniyi Stephen A. , Corrigall Holly , Raguin Adélaïde , Romano M. Carmen , Stansfield Ian
Published: April 2019

During protein synthesis, charged tRNAs deliver amino acids to translating ribosomes, and are then re-charged by tRNA synthetases (aaRS). In humans, mutant aaRS cause a diversity of neurological disorders, but their molecular aetiologies are incompletely characterised. To understand system responses to aaRS depletion, the yeast glutamine aaRS gene (GLN4) was transcriptionally regulated using doxycycline bytet-offcontrol. Depletion of Gln4p inhibited growth, and induced aGCN4amino acid starvation response, indicative of uncharged tRNA accumulation and Gcn2 kinase activation. Using a global model of translation that included aaRS recharging, Gln4p depletion was simulated, confirming slowed translation. Modelling also revealed that Gln4p depletion causes negative feedback that matches translational demand for Gln-tRNAGlnto aaRS recharging capacity. This maintains normal charged tRNAGlnlevels despite Gln4p depletion, confirmed experimentally using tRNA Northern blotting. Model analysis resolves the paradox that Gln4p depletion triggers aGCN4response, despite maintenance of tRNAGlncharging levels, revealing that normally, the aaRS population can sequester free, uncharged tRNAs during aminoacylation. Gln4p depletion reduces this sequestration capacity, allowing uncharged tRNAGlnto interact with Gcn2 kinase. The study sheds new light on mutant aaRS disease aetiologies, and explains how aaRS sequestration of uncharged tRNAs can preventGCN4activation under non-starvation conditions.
YFR016c/Aip5 is part of an actin nucleation complex in budding yeast cells
Authors: Glomb Oliver , Bareis Lara , Johnsson Nils
Published: April 2019

The polarisome comprises a network of proteins that organizes polar growth in yeast and filamentous fungi. The yeastSaccharomyces cerevisiaeformin Bni1 and the actin-nucleation-promoting factor Bud6 are subunits of the polarisome that together catalyse the formation of actin filaments below the tip of budding yeast cells. We identified YFR016c (Aip5) as interaction partner of Bud6 and the polarisome scaffold Spa2. Yeast cells lacking Aip5 display a reduced number of actin cables. Aip5 binds with its N-terminal region to Spa2 and with its C-terminal region to Bud6. Both interactions collaborate to localize Aip5 at bud tip and neck, and are required to stimulate the formation of actin cables. Our experiments characterize Aip5 as a novel subunit of a complex that regulates the number of actin filaments at sites of polar growth.

Summary statement

YFR016c/Aip5 binds to the polarisome components Bud6 and Spa2 and supports the polarisome in the formation of actin filaments in yeast cells.