bluuebunny/crossref_metadata_2025_split huggingface dataset:
In-Depth Characterization and Validation in BRG1-Mutant Lung Cancers Define Novel Catalytic Inhibitors of SWI/SNF Chromatin Remodeling
Authors: Jagani Zainab , Chenail Gregg , Xiang Kay , Bushold Geoffrey , Bhang Hyo-Eun C. , Li Ailing , Elliott GiNell , Zhu Jiang , Vattay Anthony , Gilbert Tamara , Bric Anka , Kikkawa Rie , Dubost Valérie , Terranova Rémi , Cantwell John , Luu Catherine , Silver Serena , Shirley Matt , Huet Francois , Maher Rob , Reece-Hoyes John , Ruddy David , Rakiec Daniel , Korn Joshua , Russ Carsten , Ruda Vera , Dooley Julia , Costa Emily , Park Isabel , Moebitz Henrik , Nakajima Katsumasa , Adair Christopher D. , Mathieu Simon , Ntaganda Rukundo , Smith Troy , Farley David , King Daniel , Xie Xiaoling , Kulathila Raviraj , Hu Tiancen , Pan Xuewen , Ma Qicheng , Vulic Katarina , Rago Florencia , Clarkson Scott , Ge Robin , Sigoillot Frederic , Pardee Gwynn , Bagdasarian Linda , McLaughlin Margaret , Haas Kristy , Weiler Jan , Kovats Steve , Jaskelioff Mariela , Apolline-Gerard Marie , Beil Johanna , Naumann Ulrike , Fortin Pascal , Stegmeier Frank P. , Acker Michael G. , Williams Juliet , Meyer Matthew , Bradner James E. , Keen Nicholas , Sellers William R. , Hofmann Francesco , Engelman Jeffrey A. , Stuart Darrin , Papillon Julien P.N. 
Published: October 2019

AbstractMembers of the ATP-dependent SWI/SNF chromatin remodeling complexes are among the most frequently mutated genes in cancer, suggesting their dysregulation plays a critical role. The synthetic lethality between SWI/SNF catalytic subunits BRM/SMARCA2 and BRG1/SMARCA4 has instigated great interest in targeting BRM. Here we have performed a critical and in-depth investigation of novel dual inhibitors (BRM011 and BRM014) of BRM and BRG1 in order to validate their utility as chemical probes of SWI/SNF catalytic function, while obtaining insights into the therapeutic potential of SWI/SNF inhibition. In corroboration of on-target activity, we discovered compound resistant mutations through pooled screening of BRM variants in BRG1-mut cancer cells. Strikingly, genome-wide transcriptional and chromatin profiling (ATAC-Seq) provided further evidence of pharmacological perturbation of SWI/SNF chromatin remodeling as BRM011 treatment induced specific changes in chromatin accessibility and gene expression similar to genetic depletion of BRM. Finally, these compounds have the capacity to inhibit the growth of tumor-xenografts, yielding important insights into the feasibility of developing BRM/BRG1 ATPase inhibitors for the treatment of BRG1-mut lung cancers. Overall, our studies not only establish the feasibility of inhibiting SWI/SNF catalytic function, providing a framework for SWI/SNF therapeutic targeting, but have also yielded successful elucidation of small-molecule inhibitors that will be of importance in probing SWI/SNF function in various disease contexts.
Transcriptomic Profiling of Skeletal Muscle Adaptations to Exercise and Inactivity
Authors: Pillon Nicolas J. , Gabriel Brendan M. , Dollet Lucile , Smith Jonathon A. , Sardón Puig Laura , Botella Javier , Bishop David J. , Krook Anna , Zierath Juleen R. 
Published: October 2019

SummaryThe molecular mechanisms underlying the response to exercise and inactivity are not fully understood. We propose an innovative approach to profile the skeletal muscle transcriptome to exercise and inactivity using 66 published datasets. Data collected from human studies of aerobic and resistance exercise, including acute and chronic exercise training, were integrated using meta-analysis methods (www.metamex.eu). Gene ontology and pathway analyses reveal selective pathways activated by inactivity, aerobic versus resistance and acute versus chronic exercise training. We identified NR4A3 as one of the most exercise- and inactivity-responsive genes, and established a role for this nuclear receptor in mediating the metabolic responses to exercise-like stimuli in vitro. The meta-analysis (MetaMEx) also highlights the differential response to exercise in individuals with metabolic impairments. MetaMEx provides the most extensive dataset of skeletal muscle transcriptional responses to different modes of exercise and an online interface to readily interrogate the database.
Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic
Authors: Knevel Rachel , Cessie Saskia le , Terao Chikashi C. , Slowikowski Kamil , Cui Jing , Huizinga Tom W.J. , Costenbader Karen H. , Liao Katherine P. , Karlson Elizabeth W. , Raychaudhuri Soumya 
Published: October 2019

ABSTRACTSlow developing complex diseases are a clinical diagnostic challenge. Since genetic information is increasingly available prior to a patient’s first visit to a clinic, it might improve diagnostic accuracy. We aimed to devise a method to convert genetic information into simple probabilities discriminating between multiple diagnoses in patients presenting with inflammatory arthritis.We developed G-Prob, which calculates for each patient the genetic probability for each of multiple possible diseases. We tested this for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis and gout). After validating in simulated data, we tested G-Prob in biobank cohorts in which genetic data were linked to electronic medical records:-1,200 patients identified by ICD-codes within the eMERGE database (n= 52,623);-245 patients identified through ICD codes and review of medical records within the Partners Biobank (n=12,604);-243 patients selected prospectively with final diagnoses by medical record review within the Partners Biobank (n=12,604).The calibration of G-Prob with the disease status was high (with regression coefficients ranging from 0.90-1.08 (ideal would be 1.00) in all cohorts. G-Prob’s discriminative ability was high in all cohorts with pooled Area Under the Curve (AUC)=0.69 [95%CI 0.67-0.71], 0.81 [95%CI 0.76-0.84] and 0.84 [95%CI 0.81-0.86]. For all patients, at least one disease could be ruled out, and in 45% of patients a most likely diagnosis could be identified with an overall 64% positive predictive value. In 35% of instances the clinician’s initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease prediction which improved to 51% (P<0.0001) by adding G-Prob genetic data.In conclusion, by converting genotypes into an interpretable probability value for five different inflammatory arthritides, we can better discriminate and diagnose rheumatic diseases. Genotypes available prior to a clinical visit could be considered part of patients’ medical history and potentially used to improve precision and diagnostic efficiency in clinical practice.
miRViz: a novel webserver application to visualize and interpret microRNA datasets
Authors: Giroux Pierre , Bhajun Ricky , Segard Stéphane , Picquenot Claire , Charavay Céline , Desquilles Lise , Pinna Guillaume , Ginestier Christophe , Denis Josiane , Cherradi Nadia , Guyon Laurent 
Published: October 2019

AbstractMicroRNAs (miRNAs) are small non-coding RNAs that are involved in the regulation of major pathways in eukaryotic cells through repression of their target genes at the post-transcriptional level1. While high-throughput approaches are broadly used to decipher the biological relevance of miRNAs, extraction of significant information from large miRNA datasets remains challenging. For example, sequencing technologies can quantify the relative expression of up to thousands of mature miRNAs under various experimental conditions. However, in such datasets, small subsets of miRNAs can often show significant differential expression, and deciding which one(s) should be further analyzed can prove difficult. Thus, the current challenge resides in objective analysis, interpretation and visualization of these large datasets, for which specifically suited methods are lacking.
Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers
Authors: Wang Siwei , Segev Idan , Borst Alexander , Palmer Stephanie 
Published: October 2019

AbstractThe visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.Author summarySurvival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system’s processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.
A multifaceted approach to analyzing taxonomic, functional and phylogenetic β-diversity
Authors: Nakamura Gabriel , Vicentin Wagner , Súarez Yzel Rondon , Duarte Leandro 
Published: October 2019

AbstractEcological literature offers a myriad of methods for quantifying β-diversity. One such methods is determining BDtotal (BD), which, unlike other methods, can be decomposed into meaningful components that indicate how unique a community is regarding its composition (local contribution) and how unique a species is regarding its occurrence in the metacommunity (species contribution). Despite this advantage, the original formulation of the BD metric only assesses taxonomic variation and neglects other important dimensions of biodiversity. We expanded the original formulation of BD to capture variation in the functional and phylogenetic dimensions of a metacommunity by computing two new metrics — BDFun and BDPhy — as well as their respective components that represent the local and species contribution. We tested the statistical performance of these new metrics for capturing variation in functional and phylogenetic composition through simulated metacommunities and illustrated the potential use of these new metrics by analyzing β-diversity of stream fish communities. Our results demonstrated that BDPhy and BDFun have acceptable type I error and great power to detect the effect of deep evolutionary relationships and attributes mediating patterns of β-diversity. The empirical example illustrates how BDPhy and BDFun reveal complementary aspects of β-diversity relative to the original BD metric. These new metrics can be used to identify local communities that are of conservation importance because they represent unique functional, phylogenetic and taxonomic compositions. We conclude that BDPhy and BDFun are important tools for providing complementary information in the investigation of the structure of metacommunities.
Emergence of opposite neurons in a firing-rate model of multisensory integration
Authors: Chau Ho Yin , Zhang Wen-Hao , Lee Tai Sing 
Published: October 2019

ABSTRACTOpposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.
Reciprocal regulation of pancreatic ductal adenocarcinoma growth and molecular subtype by HNF4α and SIX1/4
Authors: Camolotto Soledad A. , Belova Veronika K. , Torre-Healy Luke , Vahrenkamp Jeffery M. , Berrett Kristofer C. , Conway Hannah , Stubben Chris , Moffitt Richard , Gertz Jason , Snyder Eric L. 
Published: October 2019

AbstractPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a five-year survival of less than 5%. Transcriptomic analysis has identified two clinically relevant molecular subtypes of PDAC: Classical and Basal-like. The Classical subtype is characterized by a more favorable prognosis and better response to chemotherapy than the Basal-like subtype. The Classical subtype also expresses higher levels of lineage specifiers that regulate endodermal differentiation, including the nuclear receptor HNF4α. Using in vitro and in vivo PDAC models, we show that HNF4α restrains tumor growth and drives tumor cells toward an epithelial identity. Gene expression analysis from murine models and human tumors shows that HNF4α activates expression of genes associated with the Classical subtype. Although HNF4α loss is not sufficient for complete conversion to the Basal-like subtype gene expression profile, HNF4α directly represses SIX4 and SIX1, mesodermal lineage specifiers expressed in the Basal-like subtype. Finally, HNF4α-negative PDAC cells rely on expression of SIX4 and SIX1 for proliferation in vitro and in vivo. Overall, our data show that HNF4α regulates the growth and molecular subtype of PDAC by multiple mechanisms, including activation of the Classical gene expression program and repression of SIX4 and SIX1, which may represent novel dependencies of the Basal-like subtype.
Small-scale spatial structure influences large-scale invasion rates
Authors: Plank Michael J. , Simpson Matthew J. , Binny Rachelle N. 
Published: October 2019

AbstractLocal interactions among individual members of a population can generate intricate small-scale spatial structure, which can strongly influence population dynamics. The two-way interplay between local interactions and population dynamics is well understood in the relatively simple case where the population occupies a fixed domain with a uniform average density. However, the situation where the average population density is spatially varying is less well understood. This situation includes ecologically important scenarios such as species invasions, range shifts, and moving population fronts. Here, we investigate the dynamics of the spatial stochastic logistic model in a scenario where an initially confined population subsequently invades new, previously unoccupied territory. This simple model combines density-independent proliferation with dispersal, and density-dependent mortality via competition with other members of the population. We show that, depending on the spatial scales of dispersal and competition, either a clustered or a regular spatial structure develops over time within the invading population. In the short-range dispersal case, the invasion speed is significantly lower than standard predictions of the mean-field model. We conclude that mean-field models, even when they account for non-local processes such as dispersal and competition, can give misleading predictions for the speed of a moving invasion front.
In vitro activity of beauvericin against all developmental stages of Sarcoptes scabiei
Authors: Khoury Charbel Al , Nemer Nabil , Nemer Georges , Kurban Mazen , Bernigaud Charlotte , Fischer Katja , Guillot Jacques 
Published: October 2019

AbstractBackgroundScabies is a frequent cutaneous infection caused by the mite Sarcoptes scabiei in a large number of mammals including humans. As the resistance of S. scabiei against several chemical acaricides has been previously documented, the establishment of alternative and effective control molecules is required.ObjectivesIn this study, the potential acaricidal activity of beauvericin was assessed against different life stages of S. scabiei var. suis and, in comparison with dimpylate and ivermectin, two commercially available molecules used for the treatment of S. scabiei infection in animals and/or humans.MethodsIn our in vitro model, developmental stages of S. scabiei have been placed in Petri dishes filled with Columbia agar supplemented with pig serum and different concentrations of the drugs. Moreover, the toxicity of beauvericin against cultured human fibroblast skin cells was evaluated using an MTT proliferation assayResultsBeauvericin showed higher activity against adults and eggs of S. scabiei when compared to dimpylate and ivermectin. In addition, cell sensitivity assays demonstrated low toxicity of beauvericin against primary human fibroblast skin cells.ConclusionThese results revealed that the use of beauvericin is promising and might be considered for the treatment of S. scabiei infection.
Topoisomerase IIα Prevents, But Does Not Resolve, Ultrafine Anaphase Bridges By Two Mechanisms
Authors: Gemble Simon , Buhagiar-Labarchède Géraldine , Onclercq-Delic Rosine , Lambert Sarah , Amor-Guéret Mounira 
Published: October 2019

AbstractTopoisomerase IIα (Topo IIα), a well-conserved double-stranded DNA (dsDNA)-specific decatenase, processes dsDNA catenanes resulting from DNA replication during mitosis. Topo IIα defects lead to an accumulation of ultrafine anaphase bridges (UFBs), a type of chromosome non-disjunction. Topo IIα has been reported to resolve DNA anaphase threads, possibly accounting for the increase in UFB frequency upon Topo IIα inhibition. We hypothesized that the excess UFBs might also result, at least in part, from an impairment of the prevention of UFB formation by Topo IIα. We found that Topo IIα inhibition promotes UFB formation without affecting UFB resolution during anaphase. Moreover, we showed that Topo IIα inhibition promotes the formation of two types of UFBs depending on cell-cycle phase. Topo IIα inhibition during S-phase compromises complete DNA replication, leading to the formation of UFB-containing unreplicated DNA, whereas Topo IIα inhibition during mitosis impedes DNA decatenation at metaphase-anaphase transition, leading to the formation of UFB-containing DNA catenanes. Thus, Topo IIα activity is essential to prevent UFB formation in a cell-cycle dependent manner, but dispensable for UFB resolution during anaphase.
A new method of teaching medical statistics at medical universities in Guangdong
Authors: Chen Qing-shan , Ma Xin-yue , Cheng Xin , Yang Xuesong 
Published: October 2019

AbstractAlthough statistics play a significant role in the medical profession, studying medical statistics is challenging because this topic is more difficult to comprehend than other subjects included in medical curricula. Therefore, improving the teaching of medical statistics to meet the requirements of modern medical students/physicians is an essential task. In this study, based on responses about studying medical statistics completed by medical students, we developed a novel approach to teaching medical statistics named “purpose, database, types of variable, and relationship between variables (PDTR)”, which emphasizes how to simply master statistical applications and reduces class hours for students. Also, pilot course was implemented. We discovered that the participants using PDTR performed better on examinations of medical statistics than did graduates of institutions using traditional teaching methods (86.39±3.72 vs 73.72±6.58, P < 0.001). In addition, positive feedback was received by participants (>80%). Altogether, as a completely novel pedagogical method of teaching medical statistics, PDTR overcomes the negative attitudes of students towards medical statistics, enhances enthusiasm for learning statistics, and remarkably simplifies the process of studying statistical applications. These advantages are undoubtedly conductive to improving the effective use of medical statistics in physicians’ professional work.
The influence of relationship closeness on default-mode network connectivity during social interactions
Authors: Fareri Dominic S. , Smith David V. , Delgado Mauricio R. 
Published: October 2019

AbstractReciprocated trust plays a critical role in forming and maintaining relationships, and has consistently been shown to implicate neural circuits involved in reward-related processing and social cognition. Less is known about neural network connectivity during social interactions involving trust, however, particularly as a function of closeness between an investor and a trustee. We examined network reactivity and connectivity in participants who played an economic trust game with close friends, strangers and a computer. Network reactivity analyses showed enhanced activation of the DMN to social relative to non-social outcomes. A novel network psychophysiological interaction analysis (nPPI) revealed enhanced connectivity between the DMN and the superior frontal gyrus and superior parietal lobule when experiencing reciprocated vs. violated trust from friends relative to strangers. Such connectivity tracked with differences in self-reported social closeness with these partners. Interestingly, reactivity of the executive control network (ECN), involved in decision processes, demonstrated no social vs. non-social preference, and ECN-ventral striatum (VS) connectivity did not track social closeness. Taken together, these novel findings suggest that DMN interacts with components of attention and control networks to signal the relative importance of positive experiences with close others vs. strangers.
An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests
Authors: Hecker Julian , Townes F. William , Kachroo Priyadarshini , Lasky-Su Jessica , Ziniti John , Cho Michael H. , Weiss Scott T. , Laird Nan M. , Lange Christoph 
Published: October 2019

AbstractAnalysis of rare variants in family-based studies remains a challenge. To perform a region/set-based association analysis of rare variants in family-based studies, we propose a general methodological framework that integrates higher criticism, maximum, SKATs, and burden approaches into the family-based association testing (FBAT) framework. Using the haplotype algorithm for FBATs to compute the conditional genotype distribution under the null hypothesis of Mendelian transmissions, virtually any association test statistics can be implemented in our approach and simulation-based or exact p-values can be computed without the need for asymptotic settings. Using simulations, we compare the features of the proposed test statistics in our framework with the existing region-based methodology for family-based studies under various scenarios. The tests of our framework outperform the existing approaches. We provide general guidelines for which scenarios, e.g., sparseness of the signals or local LD structure, which test statistic will have distinct power advantages over the others. We also illustrate our approach in an application to a whole-genome sequencing dataset with 897 asthmatic trios.