bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging Endogenous ADAR for Programmable Editing on RNA
Authors: Qu Liang , Yi Zongyi , Zhu Shiyou , Wang Chunhui , Cao Zhongzheng , Zhou Zhuo , Yuan Pengfei , Yu Ying , Tian Feng , Liu Zhiheng , Bao Ying , Zhao Yanxia , Wei Wensheng 
Published: April 2019

AbstractNucleic acid editing carries enormous potential for biological research and the development of therapeutics. Current tools for DNA or RNA editing rely on introducing exogenous proteins into living organisms, which is subject to potential risks or technical barriers due to possible aberrant effector activity, delivery limits and immunogenicity. Here, we report a programmable approach that employs a short RNA to leverage endogenous ADAR (Adenosine Deaminase Acting on RNA) proteins for targeted RNA editing. We engineered an RNA that is partially complementary to the target transcript to recruit native ADAR1 or ADAR2 to change adenosine to inosine at a specific site. We designated this new method as LEAPER (Leveraging Endogenous ADAR for Programmable Editing on RNA) and the ADAR-recruiting RNA as arRNA. arRNA, either expressed from plasmid or viral vector, or synthesized as an oligonucleotide, could achieve desirable editing. LEAPER has a manageable off-target rate on the targeted transcripts and rare global off-targets. We demonstrated that LEAPER could restore p53 function by repairing a specific cancer-relevant point mutation. Moreover, LEAPER could apply to a broad spectrum of cell types including multiple human primary cells, and it restored the α-L-iduronidase catalytic activity in Hurler syndrome patient-derived primary fibroblasts without evoking innate immune responses. As a single molecule system akin to RNAi, LEAPER enables precise and efficient RNA editing, offering the transformative potential for basic research and therapeutics.
Testing the predictability of morphological evolution in contrasting thermal environments
Authors: Pilakouta Natalie , Humble Joseph L. , Hill Iain D.C. , Arthur Jessica , Costa Ana P.B. , Smith Bethany A. , Kristjánsson Bjarni K. , Skúlason Skúli , Killen Shaun S. , Lindström Jan , Metcalfe Neil B. , Parsons Kevin J. 
Published: April 2019

ABSTRACTIn light of climate change, the ability to predict evolutionary responses to temperature changes is of central importance for conservation efforts. Prior work has focused on exposing model organisms to different temperatures for just one or a few generations under laboratory conditions. Using a ‘natural experiment’, we show that studying parallel evolution in wild populations from contrasting thermal environments presents a more powerful approach for understanding and predicting responses to climate change. More specifically, we used a unique study system in Iceland, where freshwater populations of threespine sticklebacks (Gasterosteus aculeatus) are found in waters warmed by geothermal activity, adjacent to populations in ambient-temperature water. We used three sympatric and three allopatric warm-cold population pairs to test for repeated patterns of morphological divergence in relation to thermal habitat. We found that thermal habitat explained over 50% of body shape variation: fish from warm habitats had a deeper mid-body, a shorter jaw, and smaller eyes. Our common garden experiment showed that most of these morphological differences between thermal habitats were heritable. Lastly, absence of gene flow seems to facilitate parallel divergence across thermal habitats: all three allopatric population pairs were on a common evolutionary trajectory, whereas sympatric pairs followed different trajectories. Our findings therefore suggest that morphological responses to rising temperatures can be predictable when there is limited gene flow. On the other hand, migration of individuals between different thermal habitats or microhabitats can exaggerate nonparallel evolution and reduce our ability to predict evolutionary responses.
Alzheimer’s patient brain myeloid cells exhibit enhanced aging and unique transcriptional activation
Authors: Srinivasan Karpagam , Friedman Brad A. , Etxeberria Ainhoa , Huntley Melanie A. , van der Brug Marcel P. , Foreman Oded , Paw Jonathan S. , Modrusan Zora , Beach Thomas G. , Serrano Geidy E. , Hansen David V. 
Published: April 2019

AbstractGene expression changes in brain microglia from mouse models of Alzheimer’s disease (AD) are highly characterized and reflect specific myeloid cell activation states that could modulate AD risk or progression. While some groups have produced valuable expression profiles for human brain cells1–4, the cellular clarity with which we now view transcriptional responses in mouse AD models has not yet been realized for human AD tissues due to limited availability of fresh tissue samples and technological hurdles of recovering transcriptomic data with cell-type resolution from frozen samples. We developed a novel method for isolating multiple cell types from frozen post-mortem specimens of superior frontal gyrus for RNA-Seq and identified 66 genes differentially expressed between AD and control subjects in the myeloid cell compartment. Myeloid cells sorted from fusiform gyrus of the same subjects showed similar changes, and whole tissue RNA analyses further corroborated our findings. The changes we observed did not resemble the “damage-associated microglia” (DAM) profile described in mouse AD models5, or other known activation states from other disease models. Instead, roughly half of the changes were consistent with an “enhanced human aging” phenotype, whereas the other half, including the AD risk gene APOE, were altered in AD myeloid cells but not differentially expressed with age. We refer to this novel profile in human Alzheimer’s microglia/myeloid cells as the HAM signature. These results, which can be browsed at research-pub.gene.com/BrainMyeloidLandscape/reviewVersion, highlight considerable differences between myeloid activation in mouse models and human disease, and provide a genome-wide picture of brain myeloid activation in human AD.
Sex and Racial Differences in Cardiovascular Disease Risk in Patients with Atrial Fibrillation
Authors: O’Neal Wesley T. , Alam Aniqa B. , Sandesara Pratik B. , Claxton J’Neka S. , MacLehose Richard F. , Chen Lin Y. , Bengtson Lindsay G. S. , Chamberlain Alanna M. , Norby Faye L. , Lutsey Pamela L. , Alonso Alvaro 
Published: April 2019

ABSTRACTBackgroundOutcomes among atrial fibrillation (AF) patients may differ according to race/ethnicity and sex due to differences in biology, the prevalence of cardiovascular risk factors, and the use and effectiveness of AF treatments. We aimed to characterize patterns of cardiovascular risk across subgroups of AF patients by sex and race/ethnicity, since doing so may provide opportunities to identify interventions. We also evaluated whether these patterns changed over time.MethodsWe utilized administrative claims data from the Optum Clinformatics® Datamart database from 2009 to 2015. Patients with AF with ≥6 months of enrollment prior to the first non-valvular AF diagnosis were included in the analysis. Final analysis utilized Cox proportional hazard models to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for cardiovascular outcomes stratified by sex and race/ethnicity. An additional analysis stratified outcomes by calendar year of AF diagnosis to evaluate changes in outcomes over time.ResultsIn a cohort of 380,636 AF patients, women had a higher risk of ischemic stroke [HR (95% CI):1.25 (1.19, 1.31)] and lower risk of heart failure and myocardial infarction [HR (95% CI): 0.91 (0.88, 0.94) and 0.81 (0.77, 0.86), respectively)] compared to men. Black patients had elevated risk across all endpoints compared to whites, while Hispanics and Asian Americans showed no significant differences in any outcome compared to white patients. These sex and race/ethnic differences did not change over time.ConclusionsWe found sex and race/ethnic disparities in risk of cardiovascular outcomes among AF patients, without evidence of improvement over time.
Single-cell analysis of a mutant library generated using CRISPR-guided deaminase
Authors: Jun Soyeong , Lim Hyeonseob , Chun Honggu , Lee Ji Hyun , Bang Duhee 
Published: April 2019

ABSTRACTCRISPR-based screening methods using single-cell RNA sequencing (scRNA-seq) technology enable comprehensive profiling of gene perturbations from knock-out mutations. However, evaluating substitution mutations using scRNA-seq is currently limited. We combined CRISPR RNA-guided deaminase and scRNA-seq technology to develop a platform for introducing mutations in multiple genes and assessing the mutation-associated signatures. Using this platform, we generated a library consisting of 420 sgRNAs, performed sgRNA tracking analysis, and assessed the effect size of the response to vemurafenib in the melanoma cell line, which has been well-studied via knockout-based drop-out screens. However, a substitution mutation library screen has not been applied and transcriptional information for mechanisms of action was not assessed. Our platform permits discrimination of several candidate mutations that function differently from other mutations by integrating sgRNA candidates and gene expression readout. We anticipate that our platform will enable high-throughput analyses of the mechanisms related to a variety of biological events.
Surveillance based dynamic empirical therapy in a health care facility: an artificial intelligence approach
Authors: Houy Nicolas , Flaig Julien 
Published: April 2019

AbstractWe present a solution method to the problem of choosing empirical treatments that minimize the cumulative infected patient-days in the long run in a health care facility. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in the health care facility, and the emergence and spread of resistance to two drugs. We assume that the parameters of the model are known. Empirical treatments are chosen at the beginning of each period based on the count of patients with each health status. The same treatment is then administered to all patients, including uninfected patients, during the period and cannot be adjusted until the next period. Our solution method is a variant of the Monte-Carlo tree search algorithm. In our simulations, it allows to reduce the average cumulative infected patient-days over two years by 47.0% compared to the best standard therapy. We explain how our algorithm can be used either to perform online optimization, or to produce data for quantitative analysis.
Bioinformatics Pipeline using JUDI: Just Do It
Authors: Pal Soumitra , Przytycka Teresa M. 
Published: April 2019

AbstractLarge-scale data analysis in Bioinformatics requires executing several software in a pipelined fashion. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g., Snakemake and Nextflow, have been developed to ensure in case of multiple invocation of the pipeline, only the bare minimum stages that are affected by the changes across invocations get executed. However, when the pipeline needs to be executed with different settings of parameters, e.g., thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution of the pipeline. We developed JUDI on top of a Python based WMS, DoIt, for a systematic handling of pipeline parameter settings based on the principles of DBMS that simplifies plug-and-play scripting. The effectiveness of JUDI is demonstrated in a pipeline for analyzing large scale HT-SELEX data for transcription factor and DNA binding where JUDI reduces scripting by a factor of five.Availabilityhttps://github.com/ncbi/JUDI
SequelTools: A Suite of Tools for Working with PacBio Sequel Raw Sequence Data
Authors: Hufnagel David E. , Hufford Matthew B. , Seetharam Arun S. 
Published: April 2019

AbstractBackgroundPacBio sequencing is an incredibly valuable third-generation DNA sequencing method due to very long read lengths, ability to detect methylated bases, and its real-time sequencing methodology. Yet, hitherto no tool was available for analyzing the quality of, subsampling, and filtering PacBio data.ResultsHere we present SequelTools, a command-line program containing three tools: Quality Control, Read Subsampling, and Read Filtering. The Quality Control tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple statistics and publication-quality plots describing the quality of the data including N50, read length and count statistics, PSR, and ZOR. The Read Subsampling tool allows the user to subsample reads by one or more of the following criteria: longest subreads per CLR or random CLR selection. The Read Filtering tool provides options for normalizing data by filtering out certain low-quality scraps reads and/or by minimum CLR length. SequelTools is implemented in bash, R, and Python using only standard libraries and packages and is platform independent.ConclusionsSequelTools is a program that provides the only free, fast, and easy-to-use quality control tool, and the only program providing this kind of read sumbsampling and read filtering for PacBio Sequel raw sequence data, and is available at https://github.com/ISUgenomics/SequelTools
Increased expression of MCPIP1 in HIV-1 controllers is correlated with overexpression of p21
Authors: de Azevedo Suwellen S. D. , Ribeiro-Alves Marcelo , Côrtes Fernanda H. , Delatorre Edson , Hoagland Brenda , Grinsztejn Beatriz , Veloso Valdilea G. , Morgado Mariza G. , Souza Thiago Moreno L. , Bello Gonzalo 
Published: April 2019

AbstractSome multifunctional cellular proteins, as the monocyte chemotactic protein-induced protein 1 (MCPIP1) and the cyclin-dependent kinase inhibitor p21, have also shown to be able to modulate the cellular susceptibility to the human immunodeficiency virus type 1 (HIV-1). Several studies described that p21 is expressed at high levelsex vivoin cells from individuals who naturally control HIV-1 replication (HIC). The expression level of MCPIP1 in HIC was never described before, but a recent study in a model of renal carcinoma cells showed that MCPIP1 overexpression was associated with an increase of both p21 transcripts and proteins levels. Here, we explored the potential associations between MCPIP1 and p21 expression, as well as with cellular activation in HIC, sustaining undetectable (elite controllers – EC) or low (viremic controllers – VC) viral loads. We found a selective upregulation of MCPIP1 and p21 mRNA levels in PBMC from HIC compared with both ART– suppressed and HIV–negative control groups (P ≤ 0.02) and a strong positive correlation (r ≥ 0.57; P ≤ 0.014) between expressions of both transcripts independently of the VL, treatment condition and HIV status. The mRNA levels of p21, but not of MCPIP1, were positively correlated with activated CD4+T cells levels in HIC and EC (r ≥ 0.53; P ≤ 0.017). In relation to the monocyte activation, the mRNA levels of both p21 (r = 0.74; P = 0.005) and MCPIP1 (r = 0.58; P = 0.040) were positively correlated with plasmatic levels of sCD14 only in EC. Multivariate analysis confirmed the association between MCPIP1 and p21 mRNA levels, and between the latter with the frequency of activated CD4+T cells. These data show for the first time the simultaneous overexpression and positive correlation of MCPIP1 and p21 transcripts in the setting of natural suppression of HIV-1 replicationin vivo. The positive correlation between MCPIP1 and p21 transcripts supports a common regulatory pathway connecting these multifunctional host factors and a possible synergistic effect on HIV-1 replication control. Pharmacological manipulation of these cellular proteins may open novel therapeutic perspectives to prevent HIV-1 replication and disease progression.
Modeling in an oral health study through two statistical methods in Uruguay
Authors: Massa Fernando , Berberian Natalia , Álvarez-Vaz Ramón 
Published: April 2019

AbstractIn epidemiological studies it is common practice to work with binary variables that reflect the presence of certain diseases, which in turn may be associated with another set of variables, that in general are assumed as risk factors of the former. In the field of epidemiological studies referred to oral health, it is common to inquire about the relationship between the presence of some pathologies and certain characteristics of the study participants through generalized linear models (GLM). However, this type of analysis is usually carried out for each variable of interest separately and at no time is a measure obtained that summarizes the status of each participant. The objective was to apply and compare two methodologies; one applying classical approach of explaining each oral disease separately from a set of explanatory variables and another using item response theory (IRT) models (specifically the Rasch model) since they allow the joint analysis of a set of variables obtaining an individual assessment as a by-product, which in this case is interpreted as “sickness proneness”. On the other hand, the analysis presented here extends the Rasch model including a linear predictor that allows to investigate about the possible effect of several factors on the propensity of the individuals to suffer the different pathologies. Our results found evidence of an effect of gender, insufficient physical activity (IPhA) and age on general proneness to oral diseases.
Haplotype-based genome wide association study using a novel SNP-set method : RAINBOW
Authors: Hamazaki Kosuke , Iwata Hiroyoshi 
Published: April 2019

AbstractBackgroundDiffculty in detecting rare variants is one of the problems in conventional genome wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set GWAS method named “RAINBOW” and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes.ResultsWe prepared 100 datasets of simulated phenotypic data and real marker genotype data ofOryza sativasubsp.indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. The results of the comparison indicated that the proposed method was able to better control false positives than the others. The proposed method was also excellent at detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset. Moreover, the proposed method showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, especially when the causal variants were located very close to each other and the directions of their effects were opposite.ConclusionThe proposed method, RAINBOW, is especially superior in controlling false positives, detecting causal variants, and detecting nearby causal variants with opposite effects. By using the SNP-set approach as the proposed method, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as the R package and is available athttps://github.com/KosukeHamazaki/RAINBOW.
Differential network analysis of human tissue interactomes highlights tissue-selective processes and genetic disorder genes
Authors: Basha Omer , Argov Chanan M. , Artzy Raviv , Zoabi Yazeed , Hekselman Idan , Alfandari Liad , Chalifa-Caspi Vered , Yeger-Lotem Esti 
Published: April 2019

ABSTRACTMotivationDifferential network analysis, designed to highlight interaction changes between conditions, is an important paradigm in network biology. However, network analysis methods have been typically designed to compare between few conditions, were rarely applied to protein interaction networks (interactomes). Moreover, large-scale benchmarks for their evaluation have been lacking.ResultsHere, we assess five network analysis methods by applying them to 34 human tissues interactomes. For this, we created a manually-curated benchmark of 6,499 tissue-specific, gene ontology biological processes, and analyzed the ability of each method to expose these tissue-process associations. The four differential network analysis methods outperformed the non-differential, expression-based method (AUCs of 0.82-0.9 versus 0.69, respectively). We then created another benchmark, of 1,527 tissue-specific disease cases, and analyzed the ability of differential network analysis methods to highlight additional disease-related genes. Compared to a non-differential subnetworks surrounding a known disease-causing gene, the extremely-differential subnetwork (top 1%) was significantly enriched for additional disease-causing genes in 18.6% of the cases (p≤10e-3). In 5/10 tissues tested, including Muscle, nerve and heart tissues (p = 2.54E-05, 2.71E-04, 3.63E-19), such enrichments were highly significant.SummaryAltogether, our study demonstrates that differential network analysis of human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. Moreover, it offers expansive manually-curated datasets of tissue-selective processes and diseases that could serve for benchmark and for analyses in many other studies.Contactestiyl@bgu.ac.il
High-fat diet enhances starvation-induced hyperactivity via sensitizing hunger-sensing neurons in Drosophila
Authors: Huang Rui , Song Tingting , Su Haifeng , Wang Liming 
Published: April 2019

ABSTRACTProper regulation of food intake can be disrupted by sustained metabolic challenges such as high-fat diet (HFD), which may result in various metabolic disorders. Previously, we showed that starvation induced sustained hyperactivity, an exploratory component of food-seeking behavior, via a specific group of octopamingeric (OA) neurons (Yu et al., 2016). In the present study, we found that HFD greatly enhanced starvation-induced hyperactivity. HFD increased the excitability of these OA neurons to a hunger hormone named adipokinetic hormone (AKH), via increasing the accumulation of AKH receptor (AKHR) in these neurons. Upon HFD, excess dietary lipids were transported by a lipoprotein LTP to enter these OA neurons via the cognate receptor LpR1, which activated AMPK-TOR signaling and suppressed autophagy-dependent degradation of AKHR. Taken together, we uncovered a mechanism that linked HFD and starvation-induced hyperactivity, providing insight in the reshaping of neural circuitry under metabolic challenges and the progression of metabolic diseases.