bluuebunny/crossref_metadata_2025_split huggingface dataset:
A flexible repertoire of transcription factor binding sites and diversity threshold determines enhancer activity in embryonic stem cells
Authors: Singh Gurdeep , Mullany Shanelle , Moorthy Sakthi D , Zhang Richard , Mehdi Tahmid , Tian Ruxiao , Moses Alan M , Mitchell Jennifer A 
Published: April 2020

ABSTRACTTranscriptional enhancers are critical for development, phenotype evolution and often mutated in disease contexts; however, even in well-studied cell types, the sequence code conferring enhancer activity remains unknown. We found genomic regions with conserved binding of multiple transcription factors in mouse and human embryonic stem cells (ESCs) contain on average 12.6 conserved transcription factor binding sites (TFBS). These TFBS are a diverse repertoire of 70 different sequences representing the binding sites of both known and novel ESC regulators. Remarkably, using a diverse set of TFBS from this repertoire was sufficient to construct short synthetic enhancers with activity comparable to native enhancers. Site directed mutagenesis of conserved TFBS in endogenous enhancers or TFBS deletion from synthetic sequences revealed a requirement for more than ten different TFBS. Furthermore, specific TFBS, including the OCT4:SOX2 co-motif, are dispensable, despite co-binding the OCT4, SOX2 and NANOG master regulators of pluripotency. These findings reveal a TFBS diversity threshold overrides the need for optimized regulatory grammar and individual TFBS that bind specific master regulators.
Immune-challenged vampire bats produce fewer contact calls
Authors: Stockmaier Sebastian , Bolnick Daniel. I. , Page Rachel. A. , Josic Darija , Carter Gerald G. 
Published: April 2020

AbstractInfections can affect how animals vocalize and, hence, interact with conspecifics. While this effect has been shown for mate-attraction calls, other vocalizations that facilitate social contact have received less attention. When isolated, vampire bats produce contact calls which attract highly associated groupmates. Here, we test the effect of an immune challenge on contact calling rates of individually isolated vampire bats. Sickness behavior did not appear to change call structure, but it decreased the number of contact calls produced. This effect could decrease contact with groupmates and augment other established mechanisms by which sickness reduces social encounters (e.g. mortality, lethargy, and social withdrawal or disinterest).
Structure of the TFIIIC subcomplex τA provides insights into RNA polymerase III pre-initiation complex formation
Authors: Vorländer Matthias K. , Jungblut Anna , Karius Kai , Baudin Florence , Grötsch Helga , Kosinski Jan , Müller Christoph W. 
Published: April 2020

ABSTRACTTranscription factor (TF) IIIC is a conserved eukaryotic six-subunit protein complex with dual function. It serves as a general TF for most RNA polymerase (Pol) III genes by recruiting TFIIIB, but it is also involved in chromatin organization and regulation of Pol II genes through interaction with CTCF and condensin II. Here, we report the structure of theS. cerevisiaeTFIIIC subcomplex τA, which contains the most conserved subunits of TFIIIC and is responsible for recruitment of TFIIIB and transcription start site (TSS) selection at Pol III genes. We show that τA binding to its promoter is auto-inhibited by a disordered acidic tail of subunit τ95. We further provide a negative stain reconstruction of τA bound to the TFIIIB subunits Brf1 and TBP with an unexpected location of Brf1 and TBP. This shows that a ruler element in τA achieves positioning of TFIIIB upstream of the TSS, and suggests remodeling of the complex during assembly of TFIIIB by TFIIIC.
Multimodal Influences on Learning Walks in Desert Ants (Cataglyphis fortis)
Authors: Vermehren Jose Adrian Vega , Buehlmann Cornelia , Fernandes Ana Sofia David , Graham Paul 
Published: April 2020

AbstractAnts are excellent navigators taking into account multimodal sensory information as they move through the world. To be able to accurately localise the nest at the end of a foraging journey, visual cues, wind direction and also olfactory cues need to be learnt. Learning walks are performed at the start of an ant’s foraging career or when the appearance of the nest surrounding has changed. We investigated here whether the structure of such learning walks in the desert antCataglyphis fortistakes into account wind direction in conjunction with the learning of new visual information. Ants learnt to travel back and forth between their nest and a feeder, and we then introduced a black cylinder near their nest to induce learning walks in regular foragers. By doing this across days with different prevailing wind directions, we were able to probe how ants balance the influence of different sensory modalities. We found that (i) the ants’ outwards headings are influenced by the direction of the wind with their routes deflected in such a way that they will arrive downwind of their nest when homing, (ii) a novel object along the route induces learning walks in experienced ants and (iii) the structure of learning walks is shaped by the wind direction rather than the position of the visual cue.
Learning precise spatiotemporal sequences via biophysically realistic circuits with modular structure
Authors: Cone I. , Shouval H. Z. 
Published: April 2020

AbstractThe ability to express and learn temporal sequences is an essential part of learning and memory. Learned temporal sequences are expressed in multiple brain regions and as such there may be common design in the circuits that mediate it. This work proposes a substrate for such representations, via a biophysically realistic network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a learning rule with “eligibility traces”, which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically realistic sequence learning and memory, and is in agreement with recent experimental results, which have shown sequence dependent plasticity in sensory cortex.
Terraces in Gene Tree Reconciliation-Based Species Tree Inference
Authors: Sanderson Michael J. , McMahon Michelle M. , Steel Mike 
Published: April 2020

AbstractTerraces in phylogenetic tree space are sets of trees with identical optimality scores for a given data set, arising from missing data. These were first described for multilocus phylogenetic data sets in the context of maximum parsimony inference and maximum likelihood inference under certain model assumptions. Here we show how the mathematical properties that lead to terraces extend to gene tree - species tree problems in which the gene trees are incomplete. Inference of species trees from either sets of gene family trees subject to duplication and loss, or allele trees subject to incomplete lineage sorting, can exhibit terraces in their solution space. First, we show conditions that lead to a new kind of terrace, which stems from subtree operations that appear in reconciliation problems for incomplete trees. Then we characterize when terraces of both types can occur when the optimality criterion for tree search is based on duplication, loss or deep coalescence scores. Finally, we examine the impact of assumptions about the causes of losses: whether they are due to imperfect sampling or true evolutionary deletion.
Locus coeruleus patterns differentially modulate learning and valence in ratviathe ventral tegmental area and basolateral amygdala respectively
Authors: Ghosh Abhinaba , Massaeli Faghihe , Power Kyron D. , Omoluabi Tamunotonye , Torraville Sarah E. , Pritchett Julia B. , Sepahvand Tayebeh , Strong Vanessa M. , Reinhardt Camila , Chen Xihua , Martin Gerard M. , Harley Carolyn W. , Yuan Qi 
Published: April 2020

ABSTRACTThe locus coeruleus (LC), the main source of forebrain norepinephrine, produces phasic and tonic firing patterns that are theorized to have distinct functional consequences. However, how different firing modes affect learning and valence coding of sensory information are unknown. Here bilateral optogenetic activation of rat LC neurons using 10-Hz phasic trains of either 300 msec or 10 sec accelerates acquisition of a food-rewarded similar odor discrimination, but not a dissimilar odor discrimination, consistent with LC-supported enhanced pattern separation and plasticity. Similar odor discrimination learning is impaired by noradrenergic blockade in the piriform cortex (PC). However, here 10-Hz LC phasic light-mediated learning facilitation is prevented by a dopaminergic antagonist in the PC, or by ventral tegmental area (VTA) silencing with lidocaine, suggesting an LC-VTA-PC dopamine circuitry mediates 10-Hz phasic learning facilitation. Tonic stimulation at 10 Hz did not alter odor discrimination acquisition, and was less effective in activating VTA DA neurons. For valence encoding, tonic stimulation at 25 Hz induced freezing, anxiety and conditioned odor aversion, while 10-Hz phasic stimulation produced an odor preference consistent with positive valence. Noradrenergic blockade in the basolateral amygdala (BLA) prevented conditioned odor preference and aversion induced by 10-Hz phasic and 25-Hz tonic light respectively. CTB retro-labeling showed relatively larger engagement of nucleus accumbens projecting neurons over central amygdala projecting neurons in the BLA with 10-Hz LC phasic activation, compared to 25-Hz tonic. These outcomes argue that LC pauses, as well as LC firing frequencies, differentially influence both target networks and behaviour.
What makesHemidactylusinvasions successful? A case study on the island of Curaçao
Authors: Lamb April D. , Lippi Catherine A. , Watkins-Colwell Gregory J. , Jones Andrew , Warren Dan , Iglesias Teresa L. , Brandley Matt , Neagle Connor , Dornburg Alex 
Published: April 2020

AbstractHemidactylusspp. (House geckos) rank among the most successful invasive reptile species worldwide.Hemidactylus mabouiain particular has become ubiquitous across tropical urban settings in the Western Hemisphere.H. mabouia’sability to thrive in close proximity to humans has led to the rapid displacement of native geckos in urban areas, however the mechanisms driving this displacement remain understudied. Here we combine data from nitrogen and carbon stable isotopes, stomach contents, and morphometric analyses of traits associated with feeding and locomotion to test alternate hypotheses of displacement betweenH. mabouiaand a native gecko,Phyllodactylus martini, on the island of Curaçao. Consistent with expectations of direct food resource competition, we demonstrate substantial overlap of invertebrate prey resources between the species. Additionally, we found strong evidence from both diet content and stable isotope analyses thatH. mabouiaacts as a vertebrate predator, preying uponP. martinias well as other native and non-native reptiles. Finally, we show thatH. mabouiapossesses several morphological advantages, including larger sizes in feeding-associated traits and limb proportions that could offer a propulsive locomotor advantage on vertical surfaces. Together, these findings suggest the successful establishment ofH. mabouialikely involves a combination of both exploitative interspecific competition and predation. Given the ubiquity ofH. mabouia, illuminating the role of this species as both a competitor and a predator casts new concerns on the ecological and demographic impacts of this widespread urban invader.
Transfer learning improves antibiotic resistance class prediction
Authors: Hamid Md-Nafiz , Friedberg Iddo 
Published: April 2020

AbstractMotivationAntibiotic resistance is a growing public health problem, which affects millions of people worldwide, and if left unchecked is expected to upend many aspects of healthcare as it is practiced today. Identifying the type of antibiotic resistant genes in genome and metagenomic sample is of utmost importance in the prevention, diagnosis, and treatment of infections. Today there are multiple tools available that predict antibiotic resistance class from DNA and protein sequences, yet there is a lack of benchmarks on the performances of these tools.ResultsWe have developed a dataset that is curated from 15 available databases, and annotated with their antibiotic class labels. We also developed a transfer learning approach with neural networks, TRAC, that outperforms existing antiobiotic resistance prediction tools. While TRAC provides the current state-of-the-art performance, we hope our newly developed dataset will also provide the community with a much needed standardized dataset to develop novel methods that can predict antibiotic resistance class with superior prediction performance.AvailabilityTRAC is available at github (https://github.com/nafizh/TRAC) and the datasets are available at figshare (https://doi.org/10.6084/m9.figshare.11413302).Contactmhamid@mgh.harvard.edu,idoerg@iastate.edu
Behavioral and Neural Variability of Naturalistic Arm Movements
Authors: Peterson Steven M. , Singh Satpreet H. , Wang Nancy X. R. , Rao Rajesh P. N. , Brunton Bingni W. 
Published: April 2020

AbstractMotor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed “in the wild.” Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled inter-event variability using ten behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior.
The high-energy transition state of a membrane transporter
Authors: Huysmans Gerard H. M. , Ciftci Didar , Wang Xiaoyu , Blanchard Scott C. , Boudker Olga 
Published: April 2020

AbstractMembrane transporters mediate cellular uptake of nutrients, signaling molecules and drugs. Their overall mechanisms are often well understood, but the structural features setting their rates are mostly unknown. Earlier single-molecule fluorescence imaging of a model glutamate transporter homologue suggested that the slow conformational transition from the outward- to the inward-facing state, when the bound substrate is translocated from the extracellular to the cytoplasmic side of the membrane, is rate-limiting to transport. Here, we aim to gain insight into the structure of the high-energy transition state that limits the rate of this critical isomerization reaction. Using bioinformatics, we identify gain-of-function mutants of the transporter and apply linear free energy relationship analysis to infer that the transition state structurally resembles the inward-facing conformation. Based on these analyses, we propose an approach for allosteric modulation of these transporters.
Network-principled deep generative models for designing drug combinations as graph sets
Authors: Karimi Mostafa , Hasanzadeh Arman , shen Yang 
Published: April 2020

AbstractMotivationCombination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery.ResultsWe have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed Hierarchical Variational Graph Auto-Encoders (HVGAE) trained end-to-end to jointly embed gene-gene, gene-disease, and disease-disease networks. Novel attentional pooling is introduced here for learning disease-representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, HVGAE learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems-pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space.Availabilityhttps://github.com/Shen-Lab/Drug-Combo-Generator
Topsentinol L Trisulfate, a new Marine Natural Product, that Targets Basal-like and Claudin-low Breast Cancers
Authors: El-Chaar Nader N. , Smith Thomas E. , Shrestha Gajendra , Piccolo Stephen R. , Harper Mary Kay , Van Wagoner Ryan M. , Lu Zhenyu , Venancio Ashlee R. , Ireland Chris M. , Bild Andrea H. , Moos Philip J. 
Published: April 2020

ABSTRACTBackgroundBreast cancer is a heterogeneous disease. Genomic studies have revealed five different intrinsic subtypes - Luminal A, Luminal B, HER2-enriched, Claudin-low, and Basal-like. Patients diagnosed with Basal-like or Claudin-low breast cancer (BL-CL) suffer from poor prognosis and limited treatment options. Hence, there is an urgent need to identify a new therapeutic lead that can benefit patients with BL-CL breast cancer.MethodsWe used a step-wise screening approach to screen 2778 HP20 fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancer. We also performed biochemical investigations to study the effect of the compound in the signaling pathway. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound.ResultsWe identified a previously unreported trisulfated sterol, topsentinol L trisulfate (TLT) that exhibits increased efficacy against BL-CL relative to Luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL revealed its ability to inhibit activation of AMPK and CHK1 and promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (Dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL compared to Luminal/HER2+ breast cancer. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT while glioblastoma multiforme as least sensitive.ConclusionsOur study identified TLT, a previously uncharacterized trisulfated sterol, as a potential therapeutic selective against BL-CL. Our results also showed that inhibition of AMPK and/or CHK1 might be an effective therapy in BL-CL.
Inhibition of Striatal-Enriched Protein Tyrosine Phosphatase (STEP) Activity Reverses Behavioral Deficits in a Rodent Model of Autism
Authors: Chatterjee Manavi , Singh Priya , Xu Jian , Lombroso Paul J. , Kurup Pradeep K. 
Published: April 2020

AbstractAutism spectrum disorders (ASDs) are highly prevalent childhood illnesses characterized by impairments in communication, social behavior, and repetitive behaviors. Studies have found aberrant synaptic plasticity and neuronal connectivity during the early stages of brain development and have suggested that these contribute to an increased risk for ASD. STEP is a protein tyrosine phosphatase that regulates synaptic plasticity and is implicated in several cognitive disorders. Here we test the hypothesis that STEP may contribute to some of the aberrant behaviors present in the VPA-induced mouse model of ASD.In uteroVPA exposure of pregnant dams results in autistic-like behavior in the pups, which is associated with a significant increase in the STEP expression in the prefrontal cortex. The elevated STEP protein levels are correlated with increased dephosphorylation of STEP substrates GluN2B, Pyk2 and ERK, suggesting upregulated STEP activity. Moreover, pharmacological inhibition of STEP rescues the sociability, repetitive and abnormal anxiety phenotypes commonly associated with ASD. These data suggest that STEP may play a role in the VPA model of ASD and STEP inhibition may have a potential therapeutic benefit in this model.
Image-based elastography of heterochromatin and euchromatin domains in the deforming cell nucleus
Authors: Ghosh Soham , Cuevas Victor Crespo , Seelbinder Benjamin , Neu Corey P. 
Published: April 2020

ABSTRACTChromatin of the eukaryotic cell nucleus comprises of microscopically dense heterochromatin and loosely packed euchromatin domains, each with distinct transcriptional ability and roles in cellular mechanotransduction. While recent methods have been developed to characterize the nucleus, measurement of intranuclear mechanics remains largely unknown. Here, we describe the development ofnuclear elastography, which combines microscopic imaging and computational modeling to quantify the relative elasticity of the heterochromatin and euchromatin domains. Using contracting murine embryonic cardiomyocytes, nuclear elastography reveals that the heterochromatin is almost four times stiffer than the euchromatin at peak deformation. The relative elasticity between the two domains changes rapidly during the active deformation of the cardiomyocyte in the normal physiological condition but progresses more slowly in cells cultured in a mechanically stiff environment, although the relative stiffness at peak deformation does not change. Further, we found that the disruption of the LINC complex in cardiomyocytes compromises the intranuclear elasticity distribution resulting in elastically similar heterochromatin and euchromatin. These results provide insight into the elastography dynamics of heterochromatin and euchromatin domains, and provide a non-invasive framework to further investigate the mechanobiological function of subcellular and subnuclear domains limited only by the spatiotemporal resolution of the image acquisition method.