bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shigella flexneridisruption of host cell-cell tension promotes intercellular spread
Authors: Duncan Jeffrey K. , Wiscovitch Alexandra L. , Goldberg Marcia B. , Russo Brian C. 
Published: October 2019

SummaryDuring infection, a subset of bacterial pathogens invades into the eukaryotic cytosol and spreads between cells of an epithelial layer. This intercellular spread is essential for disease and requires actin-based motility leading to the formation of plasma membrane protrusions. Protrusions are engulfed by the adjacent cell in an active process requiring both bacterial and eukaryotic proteins. Here, we demonstrate that theShigellaspp. type 3 secretion system protein IpaC promotes bacterial spread by reducing intercellular tension.S. flexneriproducing a point mutant of IpaC that cannot interact with the cell-cell adhesion protein β-catenin were unable to reduce intercellular tension, form protrusions, or spread, demonstrating that interaction of IpaC with β-catenin is required for these processes. Spread was restored by chemical reduction of intercellular tension or genetic depletion of β-catenin. This work defines a molecular mechanism by whichShigellaovercomes host cell-cell tension to mediate spread.
Reduced fronto-striatal volume in ADHD in two cohorts across the lifespan
Authors: Cupertino Renata Basso , Soheili-Nezhad Sourena , Grevet Eugenio Horacio , Bandeira Cibele Edom , Picon Felipe Almeida , Tavares Maria Eduarda , Naaijen Jilly , van Rooij Daan , Akkermans Sophie , Vitola Eduardo Schneider , Zwiers Marcel P , Hoekstra Pieter J. , Breda Vitor , Oosterlaan Jaap , Hartman Catharina A , Beckmann Christian F. , Buitelaar Jan K. , Franke Barbara , Dotto Bau Claiton Henrique , Sprooten Emma 
Published: October 2019

AbstractObjectiveNeuroimaging studies have associated Attention-Deficit/Hyperactivity Disorder (ADHD) with altered brain anatomy. However, small and heterogeneous study samples, and the use of region-of-interest and tissue-specific analyses have limited the consistency and replicability of these effects. The present study uses a fully data-driven multivariate approach to investigate alterations in both gray and white matter simultaneously, and capture neuroanatomical features associated with ADHD in two large, independent, demographically different cohorts.MethodsThe study comprised two ADHD cohorts with structural magnetic resonance imaging data: the Dutch NeuroIMAGE cohort (n=890, average age 17.2 years, discovery sample) and the Brazilian IMpACT cohort (n=180, average age 44.2 years, cross validation sample). Using independent component analysis of whole-brain morphometry images in the NeuroIMAGE cohort, 375 independent components of neuroanatomical variations were extracted and assessed their association with ADHD. Afterwards, ADHD-associated components were cross validated in the Brazilian IMpACT cohort.ResultsIn both discovery (corrected-p=0.020) and validation (p=0.033) cohorts, ADHD diagnosis was significantly associated with reduced brain volume in a component mapping to frontal lobes, striatum, and their interconnecting white-matter tracts. The most pronounced case-control differences were localized in white matter adjacent to the orbitofrontal cortex.ConclusionIndependent component analysis is a sensitive approach to uncover neuroanatomical alterations in ADHD and avoid bias attributable toa prioriregion-of-interest based methods. Current results provide further evidence for the role of the fronto-striatal circuit in ADHD. The fact that the two cohorts are from different continents and comprising different age ranges highlights the robustness of the findings.
Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis
Authors: Evrard Yvonne A. , Srivastava Anuj , Randjelovic Jelena , Arunachalam Sasi , Bult Carol J. , Chen Huiqin , Chen Lily , Davies Michael , Davies Sherri , Davis-Dusenbery Brandi , DiGiovanna Jack , Ding Li , Doroshow James H. , Fang Bingliang , Frech Christian , Govindan Ramaswamy , Ha Min Jin , Herlyn Meenhard , Jeon Ryan , Kossenkov Andrew , Lewis Michael T. , Li Shunqiang , Lloyd Michael , Meric-Bernstam Funda , Miletic Nevena , Moscow Jeffrey A. , Neuhauser Steven , Nix David , Patidar Rajesh , Rebecca Vito , Robinson Peter N. , Rosains Jacqueline , Roth Jack , Seth Isheeta , Stankovic Tamara , Stanojevic Adam , Tine Brian A. Van , Welm Alana L. , Welm Bryan E. , Wickramasinghe Jayamanna , Woo XingYi , Xiao Min , Zhao Zi-ming , Dean Dennis A. , Morris Jeffrey S. , Chuang Jeffrey H. 
Published: October 2019

AbstractPatient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.
Read coverage as an indicator of misassembly in a short-read based genome assembly
Authors: Wang Peipei , Meng Fanrui , Moore Bethany M. , Shiu Shin-Han 
Published: October 2019

ABSTRACTAvailability of genome sequences has led to significant advance in biology. With few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues. In tomato, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. We established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have lower simple sequence repeat but higher tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially mis-assembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a machine learning model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to misassembly when using short reads.
MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths
Authors: Ahmadinejad Navid , Troftgruben Shayna , Maley Carlo , Wang Junwen , Liu Li 
Published: October 2019

ABSTRACTUnderstanding intratumor heterogeneity is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). To enable accurate subclonal discovery in tumors sequenced at standard depths (30-50x), we develop a novel computational method that incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level. Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. Applications of MAGOS to whole-exome sequencing data of 331 liver cancer samples discovered a significant association between subclonal diversity and patient overall survival. MAGOS is freely available as an R package at github (https://github.com/liliulab/magos).
Genetic Algorithms for model refinement and rule discovery in a high-dimensional agent-based model of inflammation
Authors: Cockrell R Chase , An Gary 
Published: October 2019

AbstractIntroductionAgent-based modeling frequently used modeling method for multi-scale mechanistic modeling. However, the same properties that make agent-based models (ABMs) well suited to representing biological systems also present significant challenges with respect to their construction and calibration, particularly with respect to the selection of potential mechanistic rules and the large number of free parameters often present in these models. We have proposed that various machine learning approaches (such as genetic algorithms (GAs)) can be used to more effectively and efficiently deal with rule selection and parameter space characterization; the current work applies GAs to the challenge of calibrating a complex ABM to a specific data set, while preserving biological heterogeneity.MethodsThis project uses a GA to augment the rule-set for a previously validated ABM of acute systemic inflammation, the Innate Immune Response ABM (IIRABM) to clinical time series data of systemic cytokine levels from a population of burn patients. The genome for the GA is a vector generated from the IIRABM’s Model Rule Matrix (MRM), which is a matrix representation of not only the constants/parameters associated with the IIRABM’s cytokine interaction rules, but also the existence of rules themselves. Capturing heterogeneity is accomplished by a fitness function that incorporates the sample value range (“error bars”) of the clinical data.ResultsThe GA-enabled parameter space exploration resulted in a set of putative MRM rules and associated parameterizations which closely match the cytokine time course data used to design the fitness function. The number of non-zero elements in the MRM increases significantly as the model parameterizations evolve towards a fitness function minimum, transitioning from a sparse to a dense matrix. This results in a model structure that more closely resembles (at a superficial level) the structure of data generated by a standard differential gene expression experimental study.ConclusionWe present an HPC-enabled evolutionary computing approach to calibrate a complex ABM to clinical data while preserving biological heterogeneity. The integration of machine learning, HPC, and multi-scale mechanistic modeling provides a pathway forward to effectively represent the heterogeneity of clinical populations and their data.Author SummaryIn this work, we utilize genetic algorithms (GA) to operate on the internal rule set of a computational of the human immune response to injury, the Innate Immune Response Agent-Based Model (IIRABM), such that it is iteratively refined to generate cytokine time series that closely match what is seen in a clinical cohort of burn patients. At the termination of the GA, there exists an ensemble of candidate model rule-sets/parameterizations which are validated by the experimental data;
Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics
Authors: Wu Yue , Hengen Keith B. , Turrigiano Gina G. , Gjorgjieva Julijana 
Published: October 2019

AbstractHomeostasis is indispensable to counteract the destabilizing effects of Hebbian plasticity. Although it is commonly assumed that homeostasis modulates synaptic strength, membrane excitability and firing rates, its role at the neural circuit and network level is unknown. Here, we identify changes in higher-order network properties of freely behaving rodents during prolonged visual deprivation. Strikingly, our data reveal that pairwise functional correlations and their structure are subject to homeostatic regulation. Using a computational model, we demonstrate that the interplay of different plasticity and homeostatic mechanisms can capture the initial drop and delayed recovery of firing rates and correlations observed experimentally. Moreover, our model indicates that synaptic scaling is crucial for the recovery of correlations and network structure, while intrinsic plasticity is essential for the rebound of firing rates, suggesting that synaptic scaling and intrinsic plasticity can serve distinct functions in homeostatically regulating network dynamics.
Combining tRNA sequencing methods to characterize plant tRNA expression and post-transcriptional modification
Authors: Warren Jessica M. , Salinas-Giegé Thalia , Hummel Guillaume , Coots Nicole L. , Svendsen Joshua M. , Brown Kristen C. , Maréchal-Drouard Laurence , Sloan Daniel B. 
Published: October 2019

ABSTRACTDifferences in tRNA expression have been implicated in a remarkable number of biological processes. There is growing evidence that tRNA genes can play dramatically different roles depending on both expression and post-transcriptional modification, yet sequencing tRNAs to measure abundance and detect modifications remains challenging. Their secondary structure and extensive post-transcriptional modifications interfere with RNA-seq library preparation methods and have limited the utility of high-throughput sequencing technologies. Here, we combine two modifications to standard RNA-seq methods by treating with the demethylating enzyme AlkB and ligating with tRNA-specific adapters in order to sequence tRNAs from four species of flowering plants, a group that has been shown to have some of the most extensive rates of post-transcriptional tRNA modifications. This protocol has the advantage of detecting full-length tRNAs and sequence variants that can be used to infer many post-transcriptional modifications. We used the resulting data to produce a modification index of almost all unique reference tRNAs inArabidopsis thaliana, which exhibited many anciently conserved similarities with humans but also positions that appear to be “hot spots” for modifications in angiosperm tRNAs. We also found evidence based on northern blot analysis and droplet digital PCR that, even after demethylation treatment, tRNA-seq can produce highly biased estimates of absolute expression levels most likely due to biased reverse transcription. Nevertheless, the generation of full-length tRNA sequences with modification data is still promising for assessing differences in relative tRNA expression across treatments, tissues or subcellular fractions and help elucidate the functional roles of tRNA modifications.
A novel technology for objective, accurate and non-invasive early diagnosis and monitoring of Alzheimer’s disease in clinics and clinical trials
Authors: Vejdani Kaveh , Liebmann Thomas , Pannetier Nicolas , Khosravi Elham , Yousefnezhad Mohsen , Krishnamurthy Pavan , Farzan Nima , Bahar Cameron , Salehi Ahmad , Esfandyarpour Hesaam , Kamali-Zare Padideh 
Published: October 2019

AbstractWe have developed a novel medical image processing technology for objective, accurate, and non-invasive diagnosis, prognosis, and monitoring of Alzheimer’s disease using standard clinical brain MRI (magnetic resonance imaging) and basic clinical cognitive assessment. The technology is a robust, highly accurate, fully automated, high-throughput, cloud-based platform, which operates in a fully integrated and controlled environment. In diagnosis mode, our technology performs with 91% balanced accuracy on blind testing to diagnose the current neurocognitive status, and in prognosis modeor early detection at mild cognitive impairment (MCI) stage with 88% balanced accuracy on blind testing to predict progression from MCI to Alzheimer’s dementia (AD) within 5 years. Such prognostic capability is currently non-existent, even in specialty clinics and hospitals, a major factor in Alzheimer’s clinical trial failures. The algorithm’s diagnostic certainty precisely mirrors the diagnostic confidence of an expert cognitive neurologist for both MCI (Spearman’s Rho = 1) and AD (Rho = 1). In addition to widespread clinical applications, this novel technology can enable correct patient selection and therapeutic effect monitoring in clinical trials of Alzheimer’s disease, the crucial elements to finding a cure.
Paradoxical Aortic Stiffening and Subsequent Cardiac Dysfunction in Hutchinson-Gilford Progeria Syndrome
Authors: Murtada S-I. , Kawamura Y. , Caulk A.W. , Amadzadeh H. , Mikush N. , Zimmerman K. , Kavanagh D. , Weiss D. , Latorre M. , Zhang Z.W. , Shadel G.S. , Braddock D.T. , Humphrey J.D. 
Published: October 2019

SUMMARYHutchinson-Gilford Progeria Syndrome (HGPS) is an ultra-rare disorder with devastating sequelae resulting in early death, presently believed to stem primarily from heart failure secondary to central arterial stiffening. We analyze novel longitudinal cardiovascular data from a mouse model of HGPS (LmnaG609G/G609G) using allometric scaling and advanced computational modelling and show that a late-stage increase in pulse wave velocity, with associated diastolic dysfunction but preserved systolic function, emerges with a loss of aortic function, independent of sex. Specifically, there is a dramatic late-stage loss of smooth muscle function and cells and an excessive accumulation of proteoglycans along the entire aorta, which result in a loss of biomechanical function (contractility and elastic energy storage) and marked structural stiffening despite a distinctly low intrinsic material stiffness that is consistent with the lack of functional lamin A. Importantly, vascular function appears to be normal within the low stress environment of development, only to succumb progressively to pressure-related effects of the lamin A mutation and become extreme in the peri-morbid period. Because the dramatic life-threatening aortic phenotype manifests during the last quarter of life there may be a therapeutic window in maturity that could alleviate concerns with therapies administered during early periods of arterial development.DisclosuresD.T.B is an equity holder in, and receives research and consulting support from, Inozyme Pharma, Inc. for therapeutics for ENPP1 deficiency. None of the other authors declare any conflict, financial or otherwise.
Interplay between Desmoglein2 and hypoxia controls metastasis in breast cancer
Authors: Chang Po-Hao , Chen Min-Che , Tsai Ya-Ping , Tan Grace Y.T. , Hsu Pang-Hung , Jeng Yung-Ming , Tsai Yi-Fang , Yang Muh-Hwa , Hwang-Verslues Wendy W. 
Published: October 2019

AbstractMetastasis is the major cause of cancer death. An increased level of circulating tumor cells (CTCs), metastatic cancer cells that have intravasated into the circulatory system, is particularly associated with colonization of distant organs and poor prognosis. However, the key factors required for tumor cell dissemination and colonization remain elusive. We found that high expression of Desmoglein2 (DSG2), a component of desmosome-mediated intercellular adhesion complexes, promoted tumor growth, increased the prevalence of CTC clusters and facilitated distant organ colonization. The dynamic regulation of DSG2 by hypoxia was key to this process as downregulation of DSG2 in hypoxic regions of primary tumors led to elevated epithelial-mesenchymal transition (EMT) gene expression, allowing cells to detach from the primary tumor and undergo intravasation. Subsequent derepression of DSG2 after intravasation and release of hypoxic stress was associated with an increased ability to colonize distant organs. This dynamic regulation of DSG2 was mediated by Hypoxia-Induced Factor1α (HIF1α). In contrast to its more widely observed function to promote expression of hypoxia-inducible genes, HIF1α repressed DSG2 by recruitment of the Polycomb Repressive Complex 2 components, EZH2 and SUZ12, to the DSG2 promoter in hypoxic cells. Consistent with our experimental data, DSG2 expression level correlated with poor prognosis and recurrence risk in breast cancer patients. Together, these results demonstrated the importance of DSG2 expression in metastasis and revealed a new mechanism by which hypoxia drives metastasis.Significance StatementDuring metastasis, hypoxia is a major force driving primary tumor cells to disseminate into the circulatory system. The key factors that promote circulating tumor cells (CTC) dissemination and allow them to successfully colonize distal sites are still incompletely known. We found that downregulation of DSG2 in hypoxic tumor allowed single tumor cell dissemination while DSG2 expressing tumors generated more CTC clusters. Re-induction of DSG2 expression in single CTCs may contribute to CTC survival and colonization in distant organs. These findings highlight the importance of DSG2 in breast cancer progression and metastasis.
Applying Machine Learning to Investigate Long Term Insect-Plant Interactions Preserved on Digitized Herbarium Specimens
Authors: Meineke E.K. , Tomasi C. , Yuan S. , Pryer K.M. 
Published: October 2019

AbstractPremise of the studyDespite the economic importance of insect damage to plants, long-term data documenting changes in insect damage (‘herbivory’) and diversity are limited. Millions of pressed plant specimens are now available online for collecting big data on plant-insect interactions during the Anthropocene.MethodsWe initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens. We trained an insect damage detector and a damage type classifier on two distantly related plant species. We experimented with 1) classifying six types of herbivory and two control categories of undamaged leaf, and 2) detecting two of these damage categories for which several hundred annotations were available.ResultsClassification models identified the correct type of herbivory 81.5% of the time. The damage classifier was accurate for categories with at least one hundred test samples. We show anecdotally that the detector works well when asked to detect two types of damage.DiscussionThe classifier and detector together are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models to allow other researchers to extract similar data and apply them to address a variety of biological hypotheses.