bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges and lessons learned from the rapid operationalization of a prospective cohort to study the natural history and neurodevelopmental outcomes of postnatal Zika virus infection among infants and children in rural Guatemala
Authors: Paniagua-Avila Alejandra , Olson Daniel , Connery Amy , Calvimontes D. Mirella , Bolanos Guillermo A. , Lamb Molly M. , Bauer Desiree , Ralda Aida , Rojop Neudy , Barrios Eduardo , Chacon Andrea , Gomez Melissa , Arroyave Paola , Hernandez Sara , Martinez Maria Alejandra , Bunge-Montes Saskia , Colbert Alison , Arias Kareen , Brazeale Garret , Holliday Andrea , Tomashek Kay M. , El Sahly Hana M. , Keitel Wendy , Munoz Flor M. , Asturias Edwin J. 
Published: May 2022

AbstractDuring the course of the 2015-2017 outbreak of Zika virus (ZIKV) infection in the Americas, the emerging virus was recognized as a congenital infection that could damage the developing brain. As the Latin American ZIKV outbreak advanced, the scientific and public health community questioned if this newly recognized neurotropic flavivirus could affect the developing brain of infants and young children infected after birth. We report here the design, methods and rapid operationalization of a prospective natural history cohort study aimed at evaluating the potential neurological and neurodevelopmental effects of postnatal Zika virus infection in infants and young children, which had become epidemic in Central America. This study enrolled a cohort of 500 mothers and their infants, along with nearly 400 children 1.5-3.5 years of age who were born during the initial phase of the ZIKV epidemic in a rural area of Guatemala. Our solutions and lessons learned while tackling real-life challenges may serve as a guide to other researchers carrying out studies of emerging infectious diseases of public health priority in resource-constrained settings.
N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification
Authors: Heydari A. Ali , Davalos Oscar A. , Hoyer Katrina K. , Sindi Suzanne S. 
Published: May 2022

AbstractSingle-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods–especially deep learning models–have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as “black-box” models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural attention to detect salient genes for use in cell-types identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI.
Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects
Authors: Dey Soumen , Moqanaki Ehsan M. , Milleret Cyril , Dupont Pierre , Tourani Mahdieh , Bischof Richard 
Published: May 2022

AbstractSpatial capture-recapture (SCR) models are now widely used for estimating density from repeated individual spatial encounters. SCR accounts for the inherent spatial autocorrelation in individual detections by modelling detection probabilities as a function of distance between the detectors and individual activity centres. However, additional spatial heterogeneity in detection probability may still creep in due to environmental or sampling characteristics. if unaccounted for, such variation can lead to pronounced bias in population size estimates.Using simulations, we describe and test three Bayesian SCR models that use generalized linear mixed models (GLMM) to account for latent heterogeneity in baseline detection probability across detectors using: independent random effects (RE), spatially autocorrelated random effects (SARE), and a twogroup finite mixture model (FM).Overall, SARE provided the least biased population size estimates (median RB: -9 – 6%). When spatial autocorrelation was high, SARE also performed best at predicting the spatial pattern of heterogeneity in detection probability. At intermediate levels of autocorrelation, spatially-explicit estimates of detection probability obtained with FM where more accurate than those generated by SARE and RE. In cases where the number of detections per detector is realistically low (at most 1), all GLMMs considered here may require dimension reduction of the random effects by pooling baseline detection probability parameters across neighboring detectors (“aggregation”) to avoid over-parameterization.The added complexity and computational overhead associated with SCR-GLMMs may only be justified in extreme cases of spatial heterogeneity. However, even in less extreme cases, detecting and estimating spatially heterogeneous detection probability may assist in planning or adjusting monitoring schemes.
Proximity labelling identifies pro-migratory endocytic recycling cargo and machinery of the Rab4 and Rab11 families
Authors: Wilson Beverley , Gemperle Jakub , Lawless Craig , Flett Chloe , Hartshorn Matthew , Hinde Eleanor , Harrison Thomas , Chastney Megan , Taylor Sarah , Allen Jennifer , Caswell Patrick 
Published: May 2022

AbstractEndocytic recycling controls the return of internalised cargos to the plasma membrane to coordinate their positioning, availability and downstream signalling. The Rab4 and Rab11 small GTPase families regulate distinct recycling routes, broadly classified as fast recycling from early endosomes (Rab4) and slow recycling from perinuclear recycling endosomes (Rab11), and both routes handle a broad range of overlapping cargos to regulate cell behaviour. We adopted a proximity labelling approach, BioID, to identify and compare the protein complexes recruited by Rab4a, Rab11a and Rab25 (a Rab11 family member implicated in cancer aggressiveness), revealing statistically robust protein-protein interaction networks of both new and well characterised cargos and trafficking machinery in migratory cancer cells. Gene ontological analysis of these interconnected networks revealed that these endocytic recycling pathways are intrinsically connected to cell motility and cell adhesion. Using a knock sideways relocalisation approach we were further able to confirm novel links between Rab11/25 and the ESCPE-1 and retromer multiprotein sorting complexes and identify new endocytic recycling machinery associated with Rab4, Rab11 and Rab25 that regulates cancer cell migration in 3D-matrix.
LAWS: Local Alignment for Water Sites — tracking ordered water in simulations
Authors: Klyshko Eugene , Kim Justin Sung-Ho , Rauscher Sarah 
Published: May 2022

AbstractProtein solvation plays a crucial role in protein function; accurate modeling of protein-water interactions is important for understanding the molecular basis of protein function. In addition to protein structure, x-ray crystallography provides positions of crystallographic water sites (CWS) coordinated by the protein, which can serve as a strong benchmark for modeling accuracy. Such comparison requires special method-ological considerations that take into account the dynamic nature of proteins. However, existing methods for analyzing CWS in MD simulations rely on global alignment of the protein onto the crystal structure, which introduces substantial errors in the case of significant structural deviations. Here, we propose a method called local alignment for water sites (LAWS), which is based on multilateration — an algorithm widely used in GPS tracking. LAWS considers the contacts formed by CWS and protein atoms in the crystal structure and uses these interaction distances to track the CWS in a simulation. LAWS provides a framework to quantify how perturbations of the local protein environment affect the preservation of crystallographic waters in simulations. We applied our method to a 1 µs simulation of a protein crystal and demonstrate that 76% of CWS are preserved. Compared to existing methods, LAWS identifies more high-confidence (low B-factor) CWS, which are characterized by more prominent water density peaks and a less-perturbed protein environment.
A critical role for spike synchrony in determining steep 1/f slopes in the setting of bursting EEG patterns
Authors: Yi Justin D. , Akbari Yama 
Published: May 2022

AbstractEEG signals are commonly analyzed in the frequency-domain. Analysis of the power spectrum commonly demonstrates a reproducible fall-off of high frequencies, sometimes referred to as the “1/f slope”. The 1/f slope comes about from a linear fit of the power spectral density in log-log form and takes typical values between (minus) 0–4. This slope has been recently hypothesized to correlate with the ratio of excitatory to inhibitory synaptic weights. However, we demonstrate models of excitatory-inhibitory (E/I) balance only explain small values of 1/f slope (< 2). We seek to construct a model to explain large slopes (> 2), which have been reported under conditions of anesthesia. Using simulations of clustered spike pulses, we find that pulse widths on the order of 1-10 ms result in a transition to steep 1/f slopes between 3–4. This trend also holds when measuring the 1/f slope from a spiking recurrent network model in a synchronous regime. We conclude that steep 1/f slopes result from synchronous spiking in bulk microcircuit activity. This suggests interpretations of steep 1/f slopes should consider spike synchrony in addition to other factors like E/I balance.
Antisense pairing and SNORD13 structure guide RNA cytidine acetylation
Authors: Thalalla Gamage Supuni , Bortolin-Cavaillé Marie-Line , Link Courtney , Bryson Keri , Sas-Chen Aldema , Schwartz Schraga , Cavaillé Jérôme , Meier Jordan L. 
Published: May 2022

AbstractN4-acetylcytidine (ac4C) is an RNA nucleobase found in all domains of life. Establishment of ac4C in helix 45 (h45) of human 18S ribosomal RNA (rRNA) requires the combined activity of the acetyltransferase NAT10 and the box C/D snoRNA SNORD13. However, the molecular mechanisms governing RNA-guided nucleobase acetylation in humans remain unexplored. Here we report two assays that enable the study of SNORD13-dependent RNA acetylation in human cells. First, we demonstrate that ectopic expression of SNORD13 rescues h45 in a SNORD13 knockout cell line. Next, we show mutant snoRNAs can be used in combination with nucleotide resolution ac4C sequencing to define structure and sequence elements critical for SNORD13 function. Finally, we develop a second method that reports on the substrate specificity of endogenous NAT10-SNORD13 via mutational analysis of an ectopically-expressed pre-rRNA substrate. By combining mutational analysis of these reconstituted systems with nucleotide resolution ac4C sequencing, our studies reveal plasticity in the molecular determinants underlying RNA-guided cytidine acetylation that is distinct from deposition of other well-studied rRNA modifications (e.g. pseudouridine). Overall, our studies provide a new approach to reconstitute RNA-guided cytidine acetylation in human cells as well as nucleotide resolution insights into the mechanisms governing this process.Abstract Figure
Arabidopsis cell suspension culture that lacks circadian rhythms can be recovered by constitutive ELF3 expression
Authors: Laosuntisuk Kanjana , Desai Jigar S. , Doherty Colleen J. 
Published: May 2022

AbstractCallus and cell suspension culture techniques are valuable tools in plant biotechnology and are widely used in fundamental and applied research. For studies in callus and cell suspension cultures to be relevant, it is essential to know if the underlying biochemistry is similar to intact plants. This study examined the expression of core circadian genes in Arabidopsis callus from the cell suspension named AT2 and found that the circadian rhythms were impaired. The circadian waveforms are similar to intact plants in the light/dark cycles, but the circadian expression in the AT2 callus stopped in the free-running, constant light conditions. Temperature cycles could drive the rhythmic expression in constant conditions, but there were novel peaks at the point of temperature transitions unique to each clock gene. We found that callus freshly induced from seedlings had normal oscillations, like intact plants, suggesting that the loss of the circadian oscillation in the AT2 callus was specific to this callus. We determined that neither the media composition nor the source of the AT2 callus caused this disruption. We observed thatELF3expression was not differentially expressed between dawn and dusk in both entrained, light-dark cycles and constant light conditions. Overexpression ofELF3in the AT2 callus partially recovers the circadian oscillation in the AT2 callus. This work shows that while callus and cell suspension cultures can be valuable tools for investigating plant responses, careful evaluation of their phenotype is important. Moreover, the altered circadian rhythms under constant light and temperature cycles in the AT2 callus could be useful backgrounds to understand the connections driving circadian oscillators and light and temperature sensing at the cellular level.
Antibiotic acyldepsipeptides stimulate theStreptomycesClp-ATPase/ClpP complex for accelerated proteolysis
Authors: Reinhardt Laura , Thomy Dhana , Lakemeyer Markus , Ortega Joaquin , Sieber Stephan A. , Sass Peter , Brötz-Oesterhelt Heike 
Published: May 2022

AbstractClp proteases consist of a proteolytic, tetradecameric core ClpP and AAA+ Clp-ATPases. Streptomycetes, producers of a plethora of secondary metabolites, encode up to five different ClpP homologs and the composition of their unusually complex Clp protease machinery has remained unsolved. Here, we report on the composition of the house-keeping Clp protease inStreptomyces, consisting of a hetero-tetradecameric core built of ClpP1, ClpP2 and the cognate Clp-ATPases ClpX, ClpC1 or ClpC2, all interacting with ClpP2 only. ADEP antibiotics dysregulate the Clp protease for unregulated proteolysis. We observed that ADEP bindsStreptomycesClpP1, but not ClpP2, thereby not only triggering the degradation of non-native protein substrates but also accelerating Clp-ATPase-dependent proteolysis. The explanation is the concomitant binding of ADEP and Clp-ATPases to opposite sides of the ClpP1P2 barrel, hence revealing a third, so far unknown mechanism of ADEP action, i.e., the accelerated proteolysis of native protein substrates by the Clp protease.SignificanceClp proteases are antibiotic and anti-cancer drug targets. Composed of the proteolytic core ClpP and a regulatory Clp-ATPase, the protease machinery is important for protein homeostasis and regulatory proteolysis. The acyldepsipeptide antibiotic ADEP targets ClpP and has shown promise for treating multi-resistant and persistent bacterial infections. The molecular mechanism of ADEP is multi-layered. Here, we present a new way how ADEP can deregulate the Clp protease system. Clp-ATPases and ADEP bind to opposite sides ofStreptomycesClpP, accelerating the degradation of natural Clp protease substrates. We also demonstrate the composition of the majorStreptomycesClp protease complex, a heteromeric ClpP1P2 core with the Clp-ATPases ClpX, ClpC1 or ClpC2 exclusively bound to ClpP2, and the killing mechanism of ADEP inStreptomyces.
Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST
Authors: Schmitt Felix Johannes , Rostami Vahid , Nawrot Martin Paul 
Published: May 2022

AbstractSpiking neural networks (SNN) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments, and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large scale and realtime SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As benchmark model we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 ·109synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.
Analysis of the contribution of intrinsic disorder in shaping potyvirus genetic diversity
Authors: Lafforgue Guillaume , Michon Thierry , Charon Justine 
Published: May 2022

AbstractIntrinsically disordered regions (IDRs) are abundant in the proteome of RNA viruses. The multifunctional properties of these regions are widely documented and their structural flexibility is associated with low constraint in the amino acid positions. Therefore, from an evolutionary stand point, these regions could have a greater mutational permissiveness than highly structured regions (ORs for Ordered Regions). They could thus provide a potential adaptive reservoir. To address this hypothesis, we compared the mutational robustness of IDRs and ORs in the genome of potyviruses, a major genus of plant viruses. For this purpose, a simulation model (DOI: 10.5281/zenodo.6396239) was built and used to distinguish a possible selection phenomenon in the biological data sets from randomly generated mutations. We analyzed several short-term experimental evolution datasets. An analysis was also performed on the natural diversity of three different species of potyviruses reflecting the long-term evolution. We observed that the mutational robustness of IDRs is significantly higher than that of ORs. Moreover, the substitutions in the ORs are very constrained by the conservation of the physico-chemical properties of the amino acids. This feature is not found in the IDRs where the substitutions tend to be more random. This reflects the weak structural constraints in these regions, in which an amino acid polymorphism is naturally conserved in the course of evolution, potyvirus IDRs and ODRs follow different evolutive paths with respect to their mutational robustness. These results force to consider the hypothesis that during selection, adaptive solutions could emerge from the amino acid polymorphism carried by IDRs.
Predictive Models of Genome-wide Aryl Hydrocarbon Receptor DNA Binding Reveal Tissue Specific Binding Determinants
Authors: Filipovic David , Qi Wenjie , Kana Omar , Marri Daniel , LeCluyse Edward L. , Andersen Melvin E. , Cuddapah Suresh , Bhattacharya Sudin 
Published: May 2022

AbstractBackgroundThe Aryl Hydrocarbon Receptor (AhR) is an inducible transcription factor (TF) whose ligands include the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). TCDD-mediated toxicity occurs through activation of AhR and its subsequent binding to the Dioxin Response Element (DRE), comprising the DNA motif 5’-GCGTG-3’. However, AhR binding in human tissues is highly dynamic and tissue specific. Approximately 50% of all experimentally verified AhR binding sites do not contain a DRE. Additionally, most accessible DREs are not bound by AhR. Identification of tissue specific AhR binding determinants is crucial for understanding downstream gene regulation and potential adverse outcomes of AhR activation.ResultsWe applied XGBoost, a supervised machine learning architecture, to predict the genome wide AhR binding status of DREs in open chromatin as a function of DNA sequence flanking the DRE, chromatin accessibility, histone modifications (HM), TF binding, and proximity of the DRE to gene promoters. We trained and validated our models using 5-fold cross validation to predict the binding status of DREs in AhR-activated MCF-7 breast cancer cells, primary human hepatocytes, and lymphoblastoid GM17212 cells, as well as AhR non-activated HepG2 hepatocellular carcinoma cells. Our results demonstrate highly accurate and robust models of AhR binding; and identify patterns of transcription factor binding and histone modifications predictive of AhR binding. These patterns are consistent within tissues but highly variable across tissues, which is suggestive of tissue-specific mechanisms of AhR binding.ConclusionsAhR binding is driven by a complex interplay of tissue-agnostic DNA sequence flanking its binding motif and tissue-specific local chromatin context.
Blind spots on western blots: a meta-research study highlighting opportunities to improve figures and methods reporting
Authors: Kroon Cristina , Breuer Larissa , Jones Lydia , An Jeehye , Akan Ayça , Mohamed Ali Elkhansa Ahmed , Busch Felix , Fislage Marinus , Ghosh Biswajit , Hellrigel-Holderbaum Max , Kazezian Vartan , Koppold Alina , Moreira Restrepo Cesar Alberto , Riedel Nico , Scherschinski Lea , Urrutia Gonzalez Fernando Raúl , Weissgerber Tracey 
Published: May 2022

AbstractWestern blotting is a standard laboratory method used to detect proteins and assess their expression levels. Unfortunately, poor western blot image display practices and a lack of detailed methods reporting can limit a reader’s ability to evaluate or reproduce western blot results. While several groups have studied the prevalence of image manipulation or provided recommendations for improving western blotting, data on the prevalence of common publication practices are scarce. We systematically examined 551 articles published in the top 25% of journals in neurosciences (n=151) and cell biology (n=400) that contained western blot images, focusing on practices that may omit important information. Our data show that most published western blots are cropped and blot source data are not made available to readers in the supplement. Publishing blots with visible molecular weight markers is rare, and many blots additionally lack molecular weight labels. Western blot methods sections often lack information on the amount of protein loaded on the gel, blocking steps and antibody labeling protocol. Important antibody identifiers like source, catalog number or RRID were omitted frequently for primary antibodies, and regularly for secondary antibodies. We present detailed descriptions and visual examples to help scientists, peer reviewers and editors to publish more informative western blot figures and methods. Additional resources include a toolbox to help scientists produce more reproducible western blot data, teaching slides in English and Spanish and an antibody reporting template.