bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning-based detection of insertions and deletions in the human genome
Authors: Curnin Charles , Goldfeder Rachel L. , Marwaha Shruti , Bonner Devon , Waggott Daryl ,   Undiagnosed Diseases Network, Wheeler Matthew T. , Ashley Euan A. 
Published: May 2019

AbstractInsertions and deletions (indels) make a critical contribution to human genetic variation. While indel calling has improved significantly, it lags dramatically in performance relative to single-nucleotide variant calling, something of particular concern for clinical genomics where larger scale disruption of the open reading frame can commonly cause disease. Here, we present a machine learning-based approach to the detection of indel breakpoints called Scotch. This novel approach improves sensitivity to larger variants dramatically by leveraging sequencing metrics and signatures of poor read alignment. We also introduce a meta-analytic indel caller, called Metal, that performs a “smart intersection” of Scotch and currently available tools to be maximally sensitive to large variants. We use new benchmark datasets and Sanger sequencing to compare Scotch and Metal to current gold standard indel callers, achieving unprecedented levels of precision and recall. We demonstrate the impact of these improvements by applying this tool to a cohort of patients with undiagnosed disease, generating plausible novel candidates in 21 out of 26 undiagnosed cases. We highlight the diagnosis of one patient with a 498-bp deletion in HNRNPA1 missed by traditional indel-detection tools.
The genetic architecture of helminth-specific immune responses in a wild population of Soay sheep (Ovis aries)
Authors: Sparks A. M. , Watt K. , Sinclair R. , Pilkington J. G. , Pemberton J. M. , McNeilly T. N. , Nussey D. H. , Johnston S. E. 
Published: May 2019

AbstractHost-parasite interactions are powerful drivers of evolutionary and ecological dynamics in natural populations. Variation in immune responses to infection is likely to shape the outcome of these interactions, with important consequences for the fitness of both host and parasite. However, little is known about how genetic variation contributes to variation in immune responses under natural conditions. Here, we examine the genetic architecture of variation in immune traits in the Soay sheep of St Kilda, an unmanaged population of sheep infected with strongyle gastrointestinal nematodes. We assayed IgA, IgE and IgG antibodies against the prevalent nematodeTeladorsagia circumcinctain the blood plasma of > 3,000 sheep collected over 26 years. Antibody levels were significantly heritable, ranging from 0.21 to 0.39 in lambs and from 0.23 to 0.57 in adults. IgA levels were strongly associated with a region on chromosome 24 explaining 21.1% and 24.5% of heritable variation in lambs and adults, respectively; this region was adjacent to two candidate loci, the Class II Major Histocompatibility Complex Transactivator (CIITA) and C-Type Lectin Domain Containing 16A (CLEC16A). Lamb IgA levels were also associated with the immunoglobulin heavy constant loci (IGH) complex on chromosome 18. Adult IgE levels and lamb IgG levels were associated with the major histocompatibility complex (MHC) on chromosome 20. This study provides evidence of high heritability of a complex immunological trait under natural conditions and provides the first evidence from a genome-wide study that large effect genes located outside the MHC region exist for immune traits in the wild.Author summaryHost-parasite interactions are powerful drivers of evolutionary and ecological dynamics in natural populations. Variation in immune responses to infection shapes the outcome of these interactions, with important consequences for the ability of the host and parasite to survive and reproduce. However, little is known about how much genes contribute to variation in immune responses under natural conditions. Our study investigates the genetic architecture of variation in three antibody types, IgA, IgE and IgG in a wild population of Soay sheep on the St Kilda archipelago in North-West Scotland. Using data collected over 26 years, we show that antibody levels have a heritable basis in lambs and adults and are stable over lifetime of individuals. We also identify several genomic regions with large effects on immune responses. Our study offers the first insights into the genetic control of immunity in a wild population, which is essential to understand how immune profiles vary in challenging natural conditions and how natural selection maintains genetic variation in complex immune traits.
A toxin-antidote CRISPR gene drive system for regional population modification
Authors: Champer Jackson , Lee Yoo Lim , Yang Emily , Liu Chen , Clark Andrew G. , Messer Philipp W. 
Published: May 2019

ABSTRACTEngineered gene drives have been suggested as a mechanism for rapidly spreading genetic alterations through a population. One promising type of drive is the CRISPR homing drive, which has recently been demonstrated in several organisms. However, such drives face a major obstacle in the form of resistance against the drive that typically evolves rapidly. In addition, homing-type drives are generally self-sustaining, meaning that a drive would likely spread to all individuals of a species even when introduced at low frequency in a single location. Here, we develop a new form of CRISPR gene drive, the Toxin-Antidote Recessive Embryo (TARE) drive, which successfully limits resistance by targeting a recessive lethal gene while providing a recoded sequence to rescue only drive-carrying individuals. Our computational modeling shows that such a drive will have threshold-dependent dynamics, spreading only when introduced above a frequency threshold that depends on the fitness cost of the drive. We demonstrate such a drive in Drosophila with 88-95% transmission to the progeny of female drive heterozygotes. This drive was able to spread through a large cage population in just six generations following introduction at 24% frequency without any apparent evolution of resistance. Our results suggest that TARE drives constitute promising candidates for the development of effective, regionally confined population modification drives.
Extensive intraspecies cryptic variation in an ancient embryonic gene regulatory network
Authors: Cleuren Yamila N. Torres , Ewe Chee Kiang , Chipman Kyle C. , Mears Emily , Wood Cricket G. , Al-Alami Coco A.E. , Alcorn Melissa R. , Turner Thomas L. , Joshi Pradeep M. , Snell Russell G. , Rothman Joel H. 
Published: May 2019

ABSTRACTInnovations in metazoan development arise from evolutionary modification of gene regulatory networks (GRNs). We report widespread cryptic variation in the requirement for two key regulatory inputs, SKN-1/Nrf2 and MOM-2/Wnt, into theC. elegansendoderm GRN. While some natural variants show a nearly absolute requirement for these two regulators, in others, most embryos differentiate endoderm in their absence. GWAS and analysis of recombinant inbred lines reveal multiple genetic regions underlying this broad phenotypic variation. We observe a reciprocal trend, in which genomic variants, or knockdown of endoderm regulatory genes, that result in a high SKN-1 requirement often show low MOM-2/Wnt requirement andvice-versa,suggesting that cryptic variation in the endoderm GRN may be tuned by opposing requirements for these two key regulatory inputs. These findings reveal that while the downstream components in the endoderm GRN are common across metazoan phylogeny, initiating regulatory inputs are remarkably plastic even within a single species.
Modularity and evolution of flower shape: the role of efficiency, development, and spandrels inErica
Authors: Reich Dieter , Berger Andreas , von Balthazar Maria , Chartier Marion , Sherafati Mahboubeh , Klingenberg Christian P. , Manafzadeh Sara , Staedler Yannick M. 
Published: May 2019

SummaryThree hypotheses can explain floral modularity: the attraction-reproduction, the efficiency, and the developmental hypotheses.In order to test these hypotheses and understand if pollination specialisation and pollination syndrome influence floral modularity, we focussed on the genusErica: we gathered 3D data from flowers of species with diverse pollination syndromes via Computed Tomography, and analysed their shape via geometric morphometrics. In order to provide an evolutionary framework for our results we tested the evolutionary mode of floral shape, size, and integration under pollination syndrome regimes, and - for the first time-reconstructed the high-dimensional floral shape of their most recent common ancestor.We demonstrate, for the first time, that the modularity of generalist flowers depends on development and that of specialists is linked to efficiency: in bird syndrome flower, efficiency modules were associated with pollen deposition and receipt, whereas in long-proboscid fly syndrome, they were associated with restricting the access to the floral reward. Only shape PC1 showed selection towards multiple optima, suggesting that PC1 was co-opted by evolution to adapt flowers to novel pollinators. Whole floral shape followed an OU model of evolution, and demonstrated relatively late differentiation.Flower shape modularity thus crucially depends on pollinator specialisation and class.
Multi-Scale Network Regression for Brain-Phenotype Associations
Authors: Xia Cedric Huchuan , Ma Zongming , Cui Zaixu , Bzdok Danilo , Bassett Danielle S. , Satterthwaite Theodore D. , Shinohara Russell T. , Witten Daniela M. 
Published: May 2019

AbstractComplex brain networks are increasingly characterized at different scales, including global summary statistics, community connectivity, and individual edges. While research relating brain networks to demographic and behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale, thus failing to incorporate rich information present at other scales. Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge- and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant association between functional connectivity patterns with brain development, sex differences, and motion-related artifacts. Notably, compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge- and community-level information, MSNR has the potential to yield novel insights into brain-behavior relationships.
Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants
Authors: Wang Lu , Chu Chin-Yi , McCall Matthew N. , Slaunwhite Christopher , Holden-Wiltse Jeanne , Corbett Anthony , Falsey Ann R. , Topham David J. , Caserta Mary T. , Mariani Thomas J , Walsh Edward E. , Qiu Xing 
Published: May 2019

AbstractBackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.MethodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).ResultsNGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%.ConclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.
Accessible and reproducible mass spectrometry imaging data analysis in Galaxy
Authors: Föll Melanie Christine , Moritz Lennart , Wollmann Thomas , Stillger Maren Nicole , Vockert Niklas , Werner Martin , Bronsert Peter , Rohr Karl , Grüning Björn Andreas , Schilling Oliver 
Published: May 2019

AbstractBackgroundMass spectrometry imaging is increasingly used in biological and translational research as it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired data sets are large and complex and often analyzed with proprietary software or in-house scripts, which hinder reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many MSI researchers.FindingsWe have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Further, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research.ConclusionThe Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access together with high levels of reproducibility and transparency.
Inheritance of seed and rhizosphere microbial communities through plant–soil feedback and soil memory
Authors: Kong Hyun Gi , Song Geun Cheol , Ryu Choong‐Min 
Published: May 2019

SummarySince the discovery of the role of microbes in the phytobiome, microbial communities (microbiota) have been identified and characterized based on host species, development, distribution, and condition. The microbiota in the plant rhizosphere is believed to have been established prior to seed germination and innate immune development. However, the microbiota in seeds has received little attention. Although our knowledge of the distribution of microbiota in plant seeds and rhizosphere is currently limited, the impact of these microbiota is likely to be greater than expected. This minireview suggests a new function of microbial inheritance from the seed to root and from the first generation of plants to the next. Surprisingly, recruitment and accumulation of microbiota by biotic and abiotic stresses affect plant immunity in the next generation through plant–soil feedback and soil memory. To illustrate this process, we propose a new term called ‘microbiota‐induced soil inheritance (MISI).’ A comprehensive understanding of MISI will provide novel insights into plant–microbe interactions and plant immunity inheritance.
Hydroxysafflor yellow A inhibited lipopolysaccharide‐induced non‐small cell lung cancer cell proliferation, migration, and invasion by suppressing the PI3K/AKT/mTOR and ERK/MAPK signaling pathways
Authors: Jiang Ming , Zhou Li‐Yang , Xu Nan , An Qing 
Published: May 2019

BackgroundChronic inflammation plays a significant role in the occurrence and development of non‐small cell lung cancer (NSCLC). Hydroxysafflor yellow A (HSYA), a chemical compound of the yellow color pigments extracted from the safflower, has been widely used in clinical treatment with positive antioxidation, anti‐inflammation, and antitumor effects. However, the role and underlying mechanisms of HYSA on development and progress in inflammation‐mediated NSCLC are unknown.MethodsCell counting kit‐8, colony formation, EdU, cell apoptosis, wound healing, Transwell migration and invasion, and enzyme‐linked immunosorbent assays; flow cytometry; and Western blotting were conducted using human NSCLC cell lines A549 and H1299.ResultsLipopolysaccharide (LPS) significantly promoted the proliferation and enhanced colony formation of A549 and H1299 cells, while HYSA notably reversed the effects of LPS. HYSA induced apoptosis of LPS‐mediated A549 and H1299 cells in a dose dependent manner; and remarkably suppressed migration, invasion, and epithelial–mesenchymal transition (EMT), significantly regulated production of LPS‐induced inflammation cytokines, and downregulated protein expression of PI3K/Akt/mTOR and ERK/MAPK signaling pathways in LPS‐induced A549 and H1299 cells. Furthermore, PI3K (LY294002) and ERK (SCH772984) inhibitors remarkably inhibited proliferation, migration, invasion, and EMT, and induced apoptosis in LPS‐mediated A549 and H1299 cells. These effects were even more obvious in the presence of HYSA and LY294002 or SCH772984 compared to those of either agent alone.ConclusionHYSA suppressed LPS‐mediated proliferation, migration, invasion, and EMT in A549 and H1299 cells by inhibiting the PI3K/Akt/mTOR and ERK/MAPK signaling pathways, indicating that HYSA may be a potential candidate to treat inflammation‐mediated NSCLC.
Memory and plasticity impairment after binge drinking in adolescent rat hippocampus: GluN2A/GluN2B NMDA receptor subunits imbalance through HDAC2
Authors: Drissi Ichrak , Deschamps Chloé , Fouquet Grégory , Alary Rachel , Peineau Stéphane , Gosset Philippe , Sueur Harold , Marcq Ingrid , Debuysscher Véronique , Naassila Mickael , Vilpoux Catherine , Pierrefiche Olivier 
Published: May 2019

AbstractEthanol (EtOH) induces cognitive impairment through modulation of synaptic plasticity notably in the hippocampus. The cellular mechanism(s) of these EtOH effects may range from synaptic signaling modulation to alterations of the epigenome. Previously, we reported that two binge‐like exposures to EtOH (3 g/kg, ip, 9 h apart) in adolescent rats abolished long‐term synaptic depression (LTD) in hippocampus slices, induced learning deficits, and increased N‐methyl‐d‐aspartate (NMDA) receptor signaling through its GluN2B subunit after 48 hours. Here, we tested the hypothesis of EtOH‐induced epigenetic alterations leading to modulation of GluN2B and GluN2A NMDA receptor subunits. Forty‐two days old rats were treated with EtOH or the histone deacetylase inhibitor (HDACi) sodium butyrate (NaB, 600 mg/kg, ip) injected alone or 30 minutes before EtOH. After 48 hours, learning was tested with novel object recognition while synaptic plasticity and the role of GluN2A and GluN2B subunits in NMDA‐fEPSP were measured in CA1 field of hippocampus slices. LTD and memory were impaired 48 hours after EtOH and NMDA‐fEPSP analysis unraveled changes in the GluN2A/GluN2B balance. These results were associated with an increase in histone deacetylase (HDAC) activity and HDAC2 mRNA and protein while Ac‐H4K12 labelling was decreased. EtOH increases expression of HDAC2 and mRNA level for GluN2B subunit (but not GluN2A), while HDAC2 modulates the promoter of the gene encoding GluN2B. Interestingly, NaB pretreatment prevented all the cellular and memory‐impairing effects of EtOH. In conclusion, the memory‐impairing effects of two binge‐like EtOH exposure involve NMDA receptor‐dependent LTD deficits due to a GluN2A/GluN2B imbalance resulting from changes in GluN2B expression induced by HDAC2.
Endocannabinoid contributions to alcohol habits and motivation: Relevance to treatment
Authors: Gianessi Carol A. , Groman Stephanie M. , Thompson Summer L. , Jiang Ming , van der Stelt Mario , Taylor Jane R. 
Published: May 2019

AbstractIndividuals with alcohol use disorder exhibit compulsive habitual behaviors that are thought to be, in part, a consequence of chronic and persistent use of alcohol. The endocannabinoid system plays a critical role in habit learning and in ethanol self‐administration, but the role of this neuromodulatory system in the expression of habitual alcohol seeking is unknown. Here, we investigated the role of the endocannabinoid system in established alcohol habits using contingency degradation in male C57BL/6 mice. We found that administration of the novel diacyl glycerol lipase inhibitor DO34, which decreases the biosynthesis of the endocannabinoid 2‐arachidonoyl glycerol (2‐AG), reduced habitual responding for ethanol and ethanol approach behaviors. Moreover, administration of the endocannabinoid transport inhibitor AM404 or the cannabinoid receptor type 1 antagonist AM251 produced similar reductions in habitual responding for ethanol and ethanol approach behaviors. Notably, AM404 was also able to reduce ethanol seeking and consumption in mice that were insensitive to lithium chloride‐induced devaluation of ethanol. Conversely, administration of JZL184, a monoacyl glycerol lipase inhibitor that increases levels of 2‐AG, increased motivation to respond for ethanol on a progressive ratio schedule of reinforcement. These results demonstrate an important role for endocannabinoid signaling in the motivation to seek ethanol, in ethanol‐motivated habits, and suggest that pharmacological manipulations of endocannabinoid signaling could be effective therapeutics for treating alcohol use disorder.
Understanding and using time series analyses in addiction research
Authors: Beard Emma , Marsden John , Brown Jamie , Tombor Ildiko , Stapleton John , Michie Susan , West Robert 
Published: May 2019

AbstractTime series analyses are statistical methods used to assess trends in repeated measurements taken at regular intervals and their associations with other trends or events, taking account of the temporal structure of such data. Addiction research often involves assessing associations between trends in target variables (e.g. population cigarette smoking prevalence) and predictor variables (e.g. average price of a cigarette), known as a multiple time series design, or interventions or events (e.g. introduction of an indoor smoking ban), known as an interrupted time series design. There are many analytical tools available, each with its own strengths and limitations. This paper provides addiction researchers with an overview of many of the methods available (GLM, GLMM, GLS, GAMM, ARIMA, ARIMAX, VAR, SVAR, VECM) and guidance on when and how they should be used, sample size det ermination, reporting and interpretation. The aim is to provide increased clarity for researchers proposing to undertake these analyses concerning what is likely to be acceptable for publication in journals such as Addiction. Given the large number of choices that need to be made when setting up time series models, the guidance emphasizes the importance of pre‐registering hypotheses and analysis plans before the analyses are undertaken.