bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pancreas-specific miR-216a regulates proliferation and endocrine and exocrine cell functionin vivo
Authors: Erener Suheda , Ellis Cara E. , Ramzy Adam , Glavas Maria M. , O’Dwyer Shannon , Pereira Sandra , Wang Tom , Pang Janice , Bruin Jennifer E. , Riedel Michael J. , Baker Robert K. , Webber Travis D. , Kopp Janel L. , Herzig Stephan , Kieffer Timothy J. 
Published: October 2019

AbstractPancreas is a vital organ composed of exocrine and endocrine cells that aid digestion of food and regulate blood glucose levels. Perturbations in the function of pancreatic cells leads to the development of life-burdening and/or threatening diseases such as diabetes and pancreatic cancer. Thus, it is critical to understand the molecular check-points that maintain normal pancreas physiology. MicroRNAs (miRNAs) are small non-coding RNAs involved in regulating gene expression in normal and diseased tissues. Several miRNAs have tissue-specific patterns consistent with crucial functions in many biological processes. Yet, there is limited knowledge about the role of pancreas-specific miRNAs in pancreatic pathologies. Here, we report that miR-216a is a conserved, pancreas-specific miRNA that is expressed in both endocrine and exocrine cells. Deletion of miR-216a in mice leads to reduced β-cell mass and a reduction in islet size under both chow and high-fat diet feeding conditions. We show that inhibition of miR-216a increases apoptosis and decreases cell proliferation in β- and exocrine cells.Smad7is upregulated in miR-216a deficient islets and cell cycle and proliferation are among the most significantly regulated biological processes in miR-216 knockout pancreata. Re-introduction of miR-216a in the pancreatic cancer line, PANC-1, increases cell migration more than 2-fold.In vivo, deletion of miR-216a in the pancreatic cancer prone mouse lineKrasG12D;Ptf1aCreERinhibits the propensity of pancreatic cancer precursor lesions. Our study identifies miR-216a as an important pancreas-specific miRNA which may have implications for both diabetes and pancreatic cancer.
Comparative Genomics Shows Differences in the Electron Transport and Carbon Metabolic Pathways ofMycobacterium africanumrelative toMycobacterium tuberculosisand suggests an adaptation to low oxygen tension
Authors: Ofori-Anyinam Boatema , Riley Abi Janet , Jobarteh Tijan , Gitteh Ensa , Sarr Binta , Faal-Jawara Tutty Isatou , Rigouts Leen , Senghore Madikay , Kehinde Aderemi , Onyejepu Nneka , Antonio Martin , Jong Bouke C. de , Gehre Florian , Meehan Conor J. 
Published: October 2019

SummaryThe geographically restrictedMycobacterium africanumlineages (MAF) are primarily found in West Africa, where they account for a significant proportion of tuberculosis. Despite this phenomenon, little is known about the co-evolution of these ancient lineages with West Africans. MAF andM. tuberculosissensu stricto lineages (MTB) differ in their clinical, in vitro and in vivo characteristics for reasons not fully understood. Therefore, we compared genomes of 289 MAF and 205 MTB clinical isolates from the 6 main human-adaptedM. tuberculosiscomplex lineages, for mutations in their Electron Transport Chain and Central Carbon Metabolic pathway in order to explain these metabolic differences. Furthermore, we determined, in silico, whether each mutation could affect the function of genes encoding enzymes in these pathways.We found more mutations with the potential to affect enzymes in these pathways in MAF lineages compared to MTB lineages. We also found that similar mutations occurred in these pathways between MAF and some MTB lineages.Generally, our findings show further differences between MAF and MTB lineages that may have contributed to the MAF clinical and growth phenotype and indicate potential adaptation of MAF lineages to a distinct ecological niche, which we suggest includes areas characterized by low oxygen tension.
The lhfpl5 ohnologs lhfpl5a and lhfpl5b are required for mechanotransduction in distinct populations of sensory hair cells in zebrafish
Authors: Erickson Timothy , Pacentine Itallia V. , Venuto Alexandra , Clemens Rachel , Nicolson Teresa 
Published: October 2019

1AbstractHair cells sense and transmit auditory, vestibular, and hydrodynamic information by converting mechanical stimuli into electrical signals. This process of mechano-electrical transduction (MET) requires a mechanically-gated channel localized in the apical stereocilia of hair cells. In mice, lipoma HMGIC fusion partner-like 5 (LHFPL5) acts as an auxiliary subunit of the MET channel whose primary role is to correctly localize PCDH15 and TMC1 to the mechanotransduction complex. Zebrafish have two lhfpl5 genes (lhfpl5a and lhfpl5b), but their individual contributions to MET channel assembly and function have not been analyzed.Here we show that the zebrafish lhfpl5 genes are expressed in discrete populations of hair cells: lhfpl5a expression is restricted to auditory and vestibular hair cells in the inner ear, while lhfpl5b expression is specific to hair cells of the lateral line organ. Consequently, lhfpl5a mutants exhibit defects in auditory and vestibular function, while disruption of lhfpl5b affects hair cells only in the lateral line neuromasts. In contrast to previous reports in mice, localization of Tmc1 does not depend upon Lhfpl5 function in either the inner ear or lateral line organ. In both lhfpl5a and lhfpl5b mutants, GFP-tagged Tmc1 and Tmc2b proteins still localize to the stereocilia of hair cells. Using a stably integrated GFP-Lhfpl5a transgene, we show that the tip link cadherins Pcdh15a and Cdh23, along with the Myo7aa motor protein, are required for correct Lhfpl5a localization at the tips of stereocilia. Our work corroborates the evolutionarily conserved co-dependence between Lhfpl5 and Pcdh15, but also reveals novel requirements for Cdh23 and Myo7aa to correctly localize Lhfpl5a. In addition, our data suggest that targeting of Tmc1 and Tmc2b proteins to stereocilia in zebrafish hair cells occurs independently of Lhfpl5 proteins.
Sticky Situations: Bacterial Attachment Deciphered by Interferometry of Silicon Microstructures
Authors: Leonard Heidi , Holtzman Liran , Haimov Yuri , Weizman Daniel , Halachmi Sarel , Kashi Yechezkel , Segal Ester 
Published: October 2019

AbstractThe peculiarities of surface-bound bacterial cells are often overshadowed by the study of planktonic cells in clinical microbiology. Thus, we employ phase-shift reflectometric interference spectroscopic measurements to observe the interactions between bacterial cells and abiotic, microstructured material surfaces in a label-free, real-time manner. Both material characteristics (i.e., substrate surface charge and wettability) and characteristics of the bacterial cells (i.e., motility, cell charge, biofilm formation, and physiology) drive bacteria to adhere to a particular surface. We conclude that the attachment of bacterial cells to a surface is determined by the culmination of numerous factors. When specific characteristics of the bacteria are met with factors of the surface, enhanced cell attachment and biofilm formation occur. Such knowledge can be exploited to predict antibiotic efficacy, biofilm development, enhance biosensor development, as well as prevent biofouling.
Integration of Multi-level Molecular Scoring for the Interpretation of RAS-Family Genetic Variation
Authors: Tripathi Swarnendu , Dsouza Nikita R. , Urrutia Raul A. , Zimmermann Michael T. 
Published: October 2019

AbstractProtein-coding genetic variants are the first considered in most studies and Precision Medicine workflows, but their interpretation is primarily driven by DNA sequence-based analytical tools and annotations. Thus, more specific and mechanistic interpretations should be attainable by integrating DNA-based scores with scores from the protein 3D structure. However, reliable and reproducible standardization of methods that use 3D structure for genomic variation is still lacking. Further, we believe that the current paradigm of aiming to directly predict the pathogenicity of variants skips the critical step of inferring, with precision, molecular mechanisms of dysfunction. Thus, we report herein the development and evaluation of single and composite 3D structure-based scores and their integration with protein and DNA sequence-based scores to better understand not only if a genomic variant alters a protein, but how. We believe this is a critical step for understanding mechanistic changes due to genomic variants, designing functional validation tests, and for improving disease classifications. We applied this approach to the RAS gene family encoding seven distinct proteins and their 935 unique missense variants present somatically in cancer, in rare diseases (termed RASopathies), and in the currently healthy adult population. This knowledge shows that protein structure-based scores are distinct from information available from genomic annotation, that they are useful for interpreting genomic variants, and they should be taken into consideration in future guidelines for genomic data interpretation.Significance StatementGenetic information from patients is a powerful data type for understanding individual differences in disease risk and treatment, but most of the genetic variation we observe has no mechanistic interpretation. This lack of interpretation limits the use of genomics data in clinical care. Standard methods for genomics data interpretation take advantage of annotations available for the human reference genome, but they do not consider the 3D protein molecule. We believe that changes to the 3D molecule must be considered, to augment current practice and lead to more precise interpretation. In this work, we present our initial process for systematic multi-level molecular scores, including 3D, to interrogate 935 RAS-family variants that are relevant in both cancer and rare diseases.
OpenCRAVAT, an open source collaborative platform for the annotation of human genetic variation
Authors: Pagel Kymberleigh A , Kim Rick , Moad Kyle , Busby Ben , Zheng Lily , Hynes-Grace Matthew , Tokheim Collin , Ryan Michael , Karchin Rachel 
Published: October 2019

AbstractPURPOSEThe modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, andin silico variant effect prediction tools. Because variant prioritization is a multi-factorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, web dependencies that expose protected data, or limited scalability.METHODSWe present OpenCRAVAT, a new open source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing every month. Resources made available via the store are well-suited for analysis of cancer, as well as Mendelian and complex diseases.RESULTSOpenCRAVAT offers both command line utility and dynamic GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants.CONCLUSIONOpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding and non-coding variants. OpenCRAVAT is freely available athttps://opencravat.org
Structural controllability predicts functional patterns and brain stimulation benefits associated with working memory
Authors: Beynel L. , Deng L. , Crowell C.A. , Dannhauer M. , Palmer H. , Hilbig S. , Peterchev A.V. , Luber B. , Lisanby S.H. , Cabeza R. , Appelbaum L.G. , Davis S.W. 
Published: October 2019

SummaryThe brain is an inherently dynamic system, and much work has focused on the ability to modify neural activity through both local perturbations and changes in the function of global network ensembles. Network controllability is a recent concept in network science that purports to predict the influence of individual cortical sites on global network states and state changes, thereby creating a unifying account of local influences on global brain dynamics. Here, we present an integrated set of multimodal brain–behavior relationships, acquired from functional magnetic resonance imaging during a transcranial magnetic stimulation intervention, that demonstrate how network controllability influences network function, as well as behavior. This work helps to outline a clear technique for integrating structural network topology and functional activity to predict the influence of a potential stimulation target on subsequent behaviors and prescribes next steps towards predicting neuromodulatory and behavioral responses after brain stimulation.Highlights- This study tested the strength of network controllability using fMRI and rTMS- Controllability correlates with functional modulation of working memory demand load- Controllability is also correlated with the memory improvement from applied rTMS- These findings link network control theory with physiology and behavior.In briefBeynel et al. show that the benefits of functionally targeted brain stimulation on working memory performance can be predicted by network control properties at the stimulated site. Structural controllability and functional activity independently predict this cognitive benefit.Author ContributionsConceptualization & Methodology: L.B, S.W.D., B.L., R.C., L.G.A.; Investigation: L.B., L.D., S.W.D., C.A.C., M.D., H.P., S.H.; Writing—Original Draft: L.B., L.D., S.W.D.; Writing—Review & Editing: L.B., L.D., S.W.D., L.G.A., A.V.P.; Funding Acquisition: S.W.D., R.C., B.L., S.H.L., A.V.P.; Resources: L.G.A., B.L., R.C.; Supervision: L.G.A., S.W.D.
Opportunistically collected photographs can be used to estimate large-scale phenological trends
Authors: Taylor Shawn D. , Guralnick Robert P. 
Published: October 2019

ABSTRACTPremiseResearch on large-scale patterns of phenology have utilized multiple sources of data to analyze the timing of events such as flowering, fruiting, and leaf out. In-situ observations from standardized surveys are ideal, but remain spatially sparse. Herbarium records and phenology-focused citizen science programs provide a source of historic data and spatial replication, but the sample sizes for any one season are still relatively low. A novel and rapidly growing source of broad-scale phenology data are photographs from the iNaturalist platform, but methods utilizing these data must generalize to a range of different species with varying season lengths and occurring across heterogenous areas. They must also be robust to different sample sizes and potential biases toward well travelled areas such as roads and towns.Methods/ResultsWe developed a spatially explicit model, the Weibull Grid, to estimate flowering onset across large-scales, and utilized a simulation framework to test the approach using different phenology and sampling scenarios. We found that the model is ideal when the underlying phenology is non-linear across space. We then use the Weibull Grid model to estimate flowering onset of two species using iNaturalist photographs, and compare those estimates with independent observations of greenup from the Phenocam network. The Weibull Grid model estimate consistently aligned with Phenocam greenup across four years and broad latitudes.ConclusioniNaturalist observations can considerably increase the amount of phenology observations and also provide needed spatial coverage. We showed here they can accurately describe large-scale trends as long as phenological and sampling processes are considered.
Sites of Circadian Clock Neuron Plasticity Mediate Sensory Integration and Entrainment
Authors: Fernández MP , Pettibone HL , Roell CJ , Davey CE , Huynh KV , Lennox SM , Kostadinov BS , Shafer OT 
Published: October 2019

SummaryNetworks of circadian timekeeping in the brain display marked daily changes in neuronal morphology. In Drosophila melanogaster, the striking daily structural remodeling of the dorsal medial termini of the small ventral lateral neurons has long been hypothesized to mediate endogenous circadian timekeeping. To test this model, we have specifically abrogated these sites of daily neuronal remodeling through the reprogramming of neural development and assessed the effects on circadian timekeeping and clock outputs. Remarkably, the loss of these sites has no measurable effects on endogenous circadian timekeeping or on any of the major output functions of the small ventral lateral neurons. Rather, their loss reduces sites of glutamatergic sensory neurotransmission that normally encodes naturalistic time-cues from the environment. These results support an alternative model: structural plasticity in critical clock neurons is the basis for proper integration of light and temperature and gates sensory inputs into circadian clock neuron networks.
Noninvasive Sleep Scoring in Mice using Electric Field Sensors
Authors: Kloefkorn H , Aiani LM , Lakhani A , Nagesh S , Moss A , Goolsby W , Rehg JM , Pedersen NP , Hochman S 
Published: October 2019

AbstractBackgroundRodent sleep scoring in principally reliant on electroencephalogram (EEG) and electromyogram (EMG), but this approach is invasive, can be expensive, and requires expertise and specialized equipment. Affordable, simple to use, and noninvasive ways to accurately quantify rodent sleep are needed.New methodWe developed and validated a new method for sleep-wake staging in mice using cost-effective, noninvasive electric field (EF) sensors that detect respiration and other movements. We validated recordings from EF sensors attached to the exterior of specialty chambers used to continuously capture sleep with EEG/EMG, then compared this to EF sensors attached to vivarium home-cages.ResultsEF sensors quantified 3-state sleep architecture (wake, rapid eye movement – REM – sleep, and non-REM sleep) with high agreement (>93%) and comparable inter- and intra-scorer error as expert EEG/EMG scoring. Novices given an instruction document with examples were able to score sleep comparable to expert scorers (>91% agreement). Additionally, EF sensors were able to quantify 3-state sleep scoring in traditional mouse home cages.Comparison with existing methodMost noninvasive sleep assessment technology requires animal contact, altered cage environments, and/or can only discern 2 states of arousal (wake or asleep). The EF sensors are able to discriminate REM from non-REM sleep accurately and from outside the animal’s home cage.ConclusionsEF sensors provide a simple and reliable method to accurately score 3-state sleep architecture; (i) from outside the typical home cage, (ii) where noninvasive approaches are preferred, or (iii) which EEG/EMG is not possible.Graphical Abstract
Molecular Mechanism of Brassinosteroids Perception by the Plant Growth Receptor BRI1
Authors: Aldukhi Faisal , Deb Aniket , Zhao Chuankai , Moffett Alexander S. , Shukla Diwakar 
Published: October 2019

AbstractBrassinosteroids (BRs) are essential phytohormones which bind to the plant receptor, BRI1, to regulate various physiological processes. The molecular mechanism of the perception of BRs by the ectodomain of BRI1 remains not fully understood. It also remains elusive why a substantial difference in biological activity exists between the BRs. In this work, we study the binding mechanisms of the two most bioactive BRs, brassinolide (BLD) and castasterone (CAT) using molecular dynamics simulations. We report free energy landscapes of the binding processes of both ligands as well as detailed ligand binding pathways. Our results suggest that CAT has lower binding affinity compared to BLD due to its inability to form hydrogen bonding interactions with a tyrosine residue in the island domain of BRI1. We uncover a conserved non-productive binding state for both BLD and CAT, which is more stable for CAT and may further contribute to the bioactivity difference. Finally, we validate past observations about the conformational restructuring and ordering of the island domain upon BLD binding. Overall, this study provides new insights into the fundamental mechanism of the perception of two most bioactive BRs, which may create new avenues for genetic and agrochemical control of their signaling cascade.
Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data
Authors: Clivio Oscar , Lopez Romain , Regier Jeffrey , Gayoso Adam , Jordan Michael I. , Yosef Nir 
Published: October 2019

AbstractIn single-cell RNA sequencing data, biological processes or technical factors may induce an overabundance of zero measurements. Existing probabilistic approaches to interpreting these data either model all genes as zero-inflated, or none. But the overabundance of zeros might be gene-specific. Hence, we propose the AutoZI model, which, for each gene, places a spike-and-slab prior on a mixture assignment between a negative binomial (NB) component and a zero-inflated negative binomial (ZINB) component. We approximate the posterior distribution under this model using variational inference, and employ Bayesian decision theory to decide whether each gene is zero-inflated. On simulated data, AutoZI outperforms the alternatives. On negative control data, AutoZI retrieves predictions consistent to a previous study on ERCC spike-ins and recovers similar results on control RNAs. Applied to several datasets and instances of the 10x Chromium protocol, AutoZI allows both biological and technical interpretations of zero-inflation. Finally, AutoZI’s decisions on mouse embyronic stem-cells suggest that zero-inflation might be due to transcriptional bursting.
Activity labeling in vivo using CaMPARI2 reveals electrophysiological differences between neurons with high and low firing rate set points
Authors: Trojanowski Nicholas F , Turrigiano Gina G. 
Published: October 2019

AbstractIndividual excitatory neurons in visual cortex (V1) display remarkably stable mean firing rates over many days, even though these rates can differ by several orders of magnitude between neurons. When perturbed, each neuron’s firing rate is slowly regulated back to its pre-perturbation level, demonstrating that neurons maintain their mean firing rate around an individual firing rate set point (FRSP). To better understand the mechanisms that neurons within a single cell type use to maintain different FRSPs in vivo, we implemented a novel method of activity labeling that uses CaMPARI2, a fluorescent protein that undergoes Ca2+- and UV-dependent green-to-red photoconversion, to permanently label neurons in freely behaving mice based on their firing rates. We found that immediate early gene (IEG) expression was correlated with CaMPARI2 red/green ratio following an activity stimulation paradigm, and that neurons with greater photoconversion in vivo tended to have a higher firing rate ex vivo. In layer 4 (L4) pyramidal neurons in mouse monocular V1, which comprise a single transcriptional cell type, we found that high activity neurons had a left-shifted F-I curve, lower rheobase current, and decreased spike adaptation index relative to low activity neurons, demonstrating increased intrinsic excitability. Surprisingly, we found no difference in total excitatory or inhibitory synaptic current or in E/I ratio between high and low activity neurons. Thus, within a single cell type differences in intrinsic excitability and spike frequency adaptation can contribute to divergent activity set points. These results reveal that E/I ratio plays only a minor role in determining the firing rate set point of L4 pyramidal neurons, while intrinsic excitability is an important factor.