bluuebunny/crossref_metadata_2025 huggingface dataset:
The Immunohistochemical Expression of Programmed Death Ligand 1 (PD-L1) Is Affected by Sample Overfixation
Authors: Barberà Angels , Marginet Flinch Ruth , Martin Montserrat , Mate Jose L. , Oriol Albert , Martínez-Soler Fina , Santalucia Tomas , Fernández Pedro L.
Published: March 2020

Humanized antibodies targeting programmed death receptor 1 (PD-1) or its ligand (PD-L1) have been approved for the treatment of different cancers. Some of these antibodies show a correlation between the tissue expression of PD-L1 and response. Evaluation of PD-L1 expression presents multiple challenges, but some preanalytical issues such as tissue fixation have been scarcely evaluated. With the hypothesis that immunohistochemical staining of PD-L1 may be impacted by the time of specimen fixation, we evaluated differences in its expression in tonsil samples exposed to predefined fixation times. Random nontumoral tonsillectomy specimens were blindly evaluated in tissue microarray slides after staining with SP142 and SP263 antibodies. With fixation times ranging from 12 to 72 hours, between 2.8% and 6.1% of the samples were considered to be suboptimally stained, with no differences between the 2 antibodies within these fixation times. A significantly higher proportion of samples exposed to a fixation time of 96 hours presented suboptimal immunostaining (15.6%, P<0.0001). In addition, suboptimally stained spots were 20.8% using SP142 and 10.4% using SP263 after 96 hours of fixation (P=0.046). In conclusion, the quality of staining for PD-L1 in tonsil samples decreased with overfixation of the specimen at times >72 hours. Samples exposed to formaldehyde for longer periods presented suboptimal results for both clones, but the SP142 antibody presented a significantly lower tolerance to formalin overexposure than SP263. These results indicate the relevance of a controlled preanalytical processing of samples and particularly the length of fixation of tumor specimens.
In Vivo Aortic Magnetic Resonance Elastography in Abdominal Aortic Aneurysm
Authors: Dong Huiming , Russell Duncan S. , Litsky Alan S. , Joseph Matthew E. , Mo Xiaokui , White Richard D. , Kolipaka Arunark
Published: March 2020

Objectives

Using maximum diameter of an abdominal aortic aneurysm (AAA) alone for management can lead to delayed interventions or unnecessary urgent repairs. Abdominal aortic aneurysm stiffness plays an important role in its expansion and rupture. In vivo aortic magnetic resonance elastography (MRE) was developed to spatially measure AAA stiffness in previous pilot studies and has not been thoroughly validated and evaluated for its potential clinical value. This study aims to evaluate noninvasive in vivo aortic MRE-derived stiffness in an AAA porcine model and investigate the relationships between MRE-derived AAA stiffness and (1) histopathology, (2) uniaxial tensile test, and (3) burst testing for assessing MRE's potential in evaluating AAA rupture risk.

Materials and Methods

Abdominal aortic aneurysm was induced in 31 Yorkshire pigs (n = 226 stiffness measurements). Animals were randomly divided into 3 cohorts: 2-week, 4-week, and 4-week-burst. Aortic MRE was sequentially performed. Histopathologic analyses were performed to quantify elastin, collagen, and mineral densities. Uniaxial tensile test and burst testing were conducted to measure peak stress and burst pressure for assessing the ultimate wall strength.

Results

Magnetic resonance elastography–derived AAA stiffness was significantly higher than the normal aorta. Significant reduction in elastin and collagen densities as well as increased mineralization was observed in AAAs. Uniaxial tensile test and burst testing revealed reduced ultimate wall strength. Magnetic resonance elastography–derived aortic stiffness correlated to elastin density (ρ = −0.68; P < 0.0001; n = 60) and mineralization (ρ = 0.59; P < 0.0001; n = 60). Inverse correlations were observed between aortic stiffness and peak stress (ρ = −0.32; P = 0.0495; n = 38) as well as burst pressure (ρ = −0.55; P = 0.0116; n = 20).

Conclusions

Noninvasive in vivo aortic MRE successfully detected aortic wall stiffening, confirming the extracellular matrix remodeling observed in the histopathologic analyses. These mural changes diminished wall strength. Inverse correlation between MRE-derived aortic stiffness and aortic wall strength suggests that MRE-derived stiffness can be a potential biomarker for clinically assessing AAA wall status and rupture potential.
Clinical and High-Resolution CT Features of the COVID-19 Infection: Comparison of the Initial and Follow-up Changes
Authors: Xiong Ying , Sun Dong , Liu Yao , Fan Yanqing , Zhao Lingyun , Li Xiaoming , Zhu Wenzhen
Published: March 2020

Objectives

In late December 2019, an outbreak of coronavirus disease (COVID-19) in Wuhan, China was caused by a novel coronavirus, newly named severe acute respiratory syndrome coronavirus 2. We aimed to quantify the severity of COVID-19 infection on high-resolution chest computed tomography (CT) and to determine its relationship with clinical parameters.

Materials and Methods

From January 11, 2020, to February 5, 2020, the clinical, laboratory, and high-resolution CT features of 42 patients (26–75 years, 25 males) with COVID-19 were analyzed. The initial and follow-up CT, obtained a mean of 4.5 days and 11.6 days from the illness onset were retrospectively assessed for the severity and progression of pneumonia. Correlations among clinical parameters, initial CT features, and progression of opacifications were evaluated with Spearman correlation and linear regression analysis.

Results

Thirty-five patients (83%) exhibited a progressive process according to CT features during the early stage from onset. Follow-up CT findings showed progressive opacifications, consolidation, interstitial thickening, fibrous strips, and air bronchograms, compared with initial CT (all P < 0.05). Before regular treatments, there was a moderate correlation between the days from onset and sum score of opacifications (R = 0.68, P < 0.01). The C-reactive protein, erythrocyte sedimentation rate, and lactate dehydrogenase showed significantly positive correlation with the severity of pneumonia assessed on initial CT (R
range, 0.36–0.75; P < 0.05). The highest temperature and the severity of opacifications assessed on initial CT were significantly related to the progression of opacifications on follow-up CT (P = 0.001–0.04).

Conclusions

Patients with the COVID-19 infection usually presented with typical ground glass opacities and other CT features, which showed significant correlations with some clinical and laboratory measurements. Follow-up CT images often demonstrated progressions during the early stage from illness onset.
Wickerhamiella osmotolerans sp. nov. and Wickerhamiella tropicalis sp. nov., novel ascomycetous yeast in the family Wickerhamiellaceae
Authors: Sakpuntoon Varunya , Angchaun Jirameth , Boonmak Chanita , Chang Chin-Feng , Liu Shiu-Mei , Lee Ching-Fu , Limtong Savitree , Srisuk Nantana
Published: March 2020

Seven yeast strains, DMKU VGT1-14T, DMKU VGT1-19T, DMKU-JMGT1-28, DMKU-JMGT1-32, DMKU VGT2-06, DMKU VGT2-19 and DMKU VGT6-14, were isolated from a grease trap in Thailand and two strains, SJ-1 and SN-102 were isolated from the sea surface microlayer in Taiwan. On the basis of phenotypic characteristics and sequence analysis of the D1/D2 region of the large subunit (LSU) rRNA gene and the internal transcribed spacer (ITS) region, these strains represented two novel yeast species of the genus Wickerhamiella. In terms of pairwise sequence similarity, four strains, DMKU VGT1-14, DMKU-JMGT1-32, DMKU VGT6-14 and SN-102, were closely related to Wickerhamiella infanticola NRRL Y-17858T but differed by 13 nucleotide substitutions with one gap (2.46 %) in the D1/D2 domain of the LSU rRNA gene and 15 nucleotide substitutions with 23 gaps (4.2 %) in the ITS region. The strains DMKU VGT1-19T, DMKU-JMGT1-28, DMKU VGT2-06, DMKU VGT2-19 and SJ-1, differed from the type strain of the most closely related species, Wickerhamiella sorbophila NRRL Y-7921T, by nine nucleotide substitutions with one gap (1.66 %) in the D1/D2 domain of the LSU rRNA gene and nine nucleotide substitutions with 17 gaps (2.52%) in the ITS region. Hence, the names Wickerhamiella osmotolerans sp. nov. and Wickerhamiella tropicalis sp. nov. are proposed to accommodate these species in the genus Wickerhamiella. The holotypes are W. osmotolerans DMKU VGT1-14T (ex-type strain TBRC 11425=PYCC 8359=CGMCC 2.6179; Mycobank number 833394) and W. tropicalis DMKU VGT1-19T (ex-type strain TBRC 11426=PYCC 8360=CGMCC 2.6180; Mycobank number 833393).
ICTV Virus Taxonomy Profile: Closteroviridae
Authors: Fuchs Marc , Bar-Joseph Moshe , Candresse Thierry , Maree Hans J. , Martelli Giovanni P. , Melzer Michael J. , Menzel Wulf , Minafra Angelantonio , Sabanadzovic Sead , Report Consortium ICTV
Published: March 2020

Viruses in the family Closteroviridae have a mono-, bi- or tripartite positive-sense RNA genome of 13–19 kb, and non-enveloped, filamentous particles 650–2200 nm long and 12 nm in diameter. They infect plants, mainly dicots, many of which are fruit crops. This is a summary of the ICTV Report on the family Closteroviridae, which is available at ictv.global/report/closteroviridae.
DEN-IM: dengue virus genotyping from amplicon and shotgun metagenomic sequencing
Authors: Mendes Catarina I. , Lizarazo Erley , Machado Miguel P. , Silva Diogo N. , Tami Adriana , Ramirez Mário , Couto Natacha , Rossen John W. A. , Carriço João A.
Published: March 2020

Dengue virus (DENV) represents a public health threat and economic burden in affected countries. The availability of genomic data is key to understanding viral evolution and dynamics, supporting improved control strategies. Currently, the use of high-throughput sequencing (HTS) technologies, which can be applied both directly to patient samples (shotgun metagenomics) and to PCR-amplified viral sequences (amplicon sequencing), is potentially the most informative approach to monitor viral dissemination and genetic diversity by providing, in a single methodological step, identification and characterization of the whole viral genome at the nucleotide level. Despite many advantages, these technologies require bioinformatics expertise and appropriate infrastructure for the analysis and interpretation of the resulting data. In addition, the many software solutions available can hamper the reproducibility and comparison of results. Here we present DEN-IM, a one-stop, user-friendly, containerized and reproducible workflow for the analysis of DENV short-read sequencing data from both amplicon and shotgun metagenomics approaches. It is able to infer the DENV coding sequence (CDS), identify the serotype and genotype, and generate a phylogenetic tree. It can easily be run on any UNIX-like system, from local machines to high-performance computing clusters, performing a comprehensive analysis without the requirement for extensive bioinformatics expertise. Using DEN-IM, we successfully analysed two types of DENV datasets. The first comprised 25 shotgun metagenomic sequencing samples from patients with variable serotypes and genotypes, including an in vitro spiked sample containing the four known serotypes. The second consisted of 106 paired-end and 76 single-end amplicon sequences of DENV 3 genotype III and DENV 1 genotype I, respectively, where DEN-IM allowed detection of the intra-genotype diversity. The DEN-IM workflow, parameters and execution configuration files, and documentation are freely available at https://github.com/B-UMMI/DEN-IM).
Monitoring Disease Transmissibility of 2019 Novel Coronavirus Disease in Zhejiang, China
Authors: Chong Ka Chun , Cheng Wei , Zhao Shi , Ling Feng , Mohammad Kirran N. , Wang Maggie Haitian , Ying Zee Benny Chung , Wei Lei , Xiong Xi , Liu Hengyan , Wang Jiangxuan , Chen Enfu
Published: March 2020

We monitored the transmissibility of 2019 novel coronavirus disease in Zhejiang accounting the transmissions from imported cases. Even though Zhejiang is one of the worst-affected provinces, an interruption of disease transmission (i.e. instantaneous reproduction numbers <1) was observed in early/mid-February after an early social-distancing response to the outbreak.
A framework to identify structured behavioral patterns within rodent spatial trajectories
Authors: Donnarumma Francesco , Prevete Roberto , Maisto Domenico , Fuscone Simone , Irvine Emily M. , van der Meer Matthijs A. A. , Kemere Caleb , Pezzulo Giovanni
Published: March 2020

Animal behavior is highly structured. Yet, structured behavioral patterns – or “statistical ethograms” – are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks ormotor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.
graphsim: An R package for simulating gene expression data from graph structures of biological pathways
Authors: Kelly S. Thomas , Black Michael A.
Published: March 2020

Transcriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.
Fast oscillations >40Hz localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography
Authors: Avigdor Tamir , Abdallah Chifaou , von Ellenrieder Nicolás , Hedrich Tanguy , Rubino Annalisa , Russo Giorgio Lo , Bernhardt Boris , Nobili Lino , Grova Christophe , Frauscher Birgit
Published: March 2020

Objective

Fast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification.

Methods

We analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity.

Results

We identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively.

Conclusions

FO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches.

Significance

This study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ.

Highlights

- Fast oscillations > 40Hz are able to correctly localize the epileptogenic zone.

- HD-EEG is superior in detection and localization of fast oscillations compared to conventional EEG approaches.

- Presence of fast oscillations on the scalp might point to a superficial epileptic generator.
Genomic characterization of the adolescent idiopathic scoliosis associated transcriptome and regulome
Authors: Makki Nadja , Zhao Jingjing , Liu Zhaoyang , Eckalbar Walter L. , Ushiki Aki , Khanshour Anas M. , Wu Zhuoxi , Rios Jonathan , Gray Ryan S. , Wise Carol A. , Ahituv Nadav
Published: March 2020

Adolescent idiopathic scoliosis (AIS), a sideways curvature of the spine, is the most common pediatric musculoskeletal disorder, affecting ∼3% of the population worldwide. However, its genetic bases and tissues of origin remain largely unknown. Several genome-wide association studies (GWAS) have implicated nucleotide variants in noncoding sequences that control genes with important roles in cartilage, muscle, bone, connective tissue and intervertebral discs (IVDs) as drivers of AIS susceptibility. Here, we set out to define the expression of AIS-associated genes and active regulatory elements by performing RNA-seq and ChIP-seq against H3K27ac in these tissues in mouse and human. Our study highlights genetic pathways involving AIS-associated loci that regulate chondrogenesis, IVD development and connective tissue maintenance and homeostasis. In addition, we identify thousands of putative AIS-associated regulatory elements which may orchestrate tissue-specific expression in musculoskeletal tissues of the spine. Quantification of enhancer activity of several candidate regulatory elements from our study identifies three functional enhancers carrying AIS-associated GWAS SNPs at theADGRG6andBNC2loci. Our findings provide a novel genome-wide catalog of AIS-relevant genes and regulatory elements and aid in the identification of novel targets for AIS causality and treatment.
Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes
Authors: Nguyen Nam D. , Wang Daifeng
Published: March 2020

Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub athttps://github.com/daifengwanglab/Varmole.
Experimental evolution of nascent multicellularity: Recognizing a Darwinian transition in individuality
Authors: Rose Caroline J. , Hammerschmidt Katrin , Rainey Paul B.
Published: March 2020

Major evolutionary transitions in individuality, at any level of the biological hierarchy, occur when groups participate in Darwinian processes as units of selection in their own right. Identifying transitions in individuality can be problematic because apparent selection at one level of the biological hierarchy may be a by-product of selection occurring at another level. Here we discuss approaches to this “levels-of-selection” problem and apply them to a previously published experimental exploration of the evolutionary transition to multicellularity. In these experiments groups of the bacteriumPseudomonas fluorescenswere required to reproduce via life cycles involving soma- and germline-like phases. The rate of transition between the two cell types was a focus of selection, and might be regarded as a property of groups, cells, or even genes. By examining the experimental data under several established philosophical frameworks, we argue that in thePseudomonasexperiments, bacterial groups acquired Darwinian properties sufficient to allow the evolution of traits adaptive at the group level.
Novel Human Kidney Cell Subsets Identified by Mux-Seq
Authors: Schroeder Andrew W. , Sur Swastika , Rashmi Priyanka , Damm Izabella , Zarinsefat Arya , Kretzler Matthias , Hodgin Jeff , Hartoularos George , Sigdel Tara , Ye Jimmie Chun , Sarwal Minnie M. ,   for the Kidney Precision Medicine Project
Published: March 2020

Background

The kidney is a highly complex organ that performs multiple functions necessary to maintain systemic homeostasis, with complex interplay from different kidney sub-structures and the coordinated response of diverse cell types, few known and likely many others, as yet undiscovered. Traditional global sequencing techniques are limited in their ability to identify unique and functionally diverse cell types in complex tissues.

Methods

Herein we characterize over 45,000 cells from 10 normal human kidneys using unbiased single-cell RNA sequencing. We also apply, for the first time, an approach of multiplexing kidney samples (Mux-Seq), pooled from different individuals, to save input sample amount and cost. We applied the computational tool Demuxlet to assess differential expression across multiple individuals by pooling human kidney cells for scRNA sequencing, utilizing individual genetic variability to determine the identity of each cell.

Results

Multiplexed droplet single-cell RNA sequencing results were highly correlated with the singleplexed sample run data. One hundred distinct cell cluster populations in total were identified across the major cell types of the kidney, with varied functional states. Proximal tubular and collecting duct cells were the most heterogeneous, displaying multiple clusters with unique ontologies. Novel proximal tubular cell subsets were identified with regenerative potential. Trajectory analysis demonstrated evolution of cell states between intercalated and principal cells in the collecting duct.

Conclusions

Healthy kidney tissue has been successfully analyzed to detect all known renal cell types, inclusive of resident and infiltrating immune cells in the kidney. Mux-Seq is a unique method that allows for rapid and cost-effective single cell, in depth, transcriptional analysis of human kidney tissue.

Significance Statement

Use of renal biopsies for single cell transcriptomics is limited by small tissue availability and batch effects. In this study, we have successfully employed the use of Mux-Seq for the first time in kidney. Mux-Seq allows the use of single cell technology at a much more cost-effective manner by pooling samples from multiple individuals for a single sequencing run. This is even more relevant in the case of patient biopsies where the input of tissue is significantly limited. We show that the data from overlapping tissue samples are highly correlated between Mux-Seq and traditional Singleplexed RNA seq. Furthermore, the results from Mux-Seq of 4 pooled samples are highly correlated with singleplexed data from 10 singleplex samples despite the inherent variability among individuals.
Combining tensor decomposition and time warping models for multi-neuronal spike train analysis
Authors: Williams Alex H.
Published: March 2020

Recordings from large neural populations are becoming an increasingly popular and accessible method in experimental neuroscience. While the activity of individual neurons is often too stochastic to interrogate circuit function on a moment-by-moment basis, multi-neuronal recordings enable us to do so by pooling statistical power across many cells. For example, groups of neurons often exhibit correlated gain or amplitude modulation across trials, which can be statistically formalized in a tensor decomposition framework (Williams et al. 2018). Additionally, the time course of neural population dynamics can be shifted or stretched/compressed, which can be modeled by time warping methods (Williams et al. 2020). Here, I describe how these two modeling frameworks can be combined, and show some evidence that doing so can be highly advantageous for practical neural data analysis—for example, the presence of random time shifts hampers the performance and interpretability of tensor decomposition, while a time-shifted variant of this model corrects for these disruptions and uncovers ground truth structure in simulated data.
Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinasterAit., Pinaceae)
Authors: de Miguel Marina , Rodríguez-Quilón Isabel , Heuertz Myriam , Hurel Agathe , Grivet Delphine , Jaramillo-Correa Juan-Pablo , Vendramin Giovanni G. , Plomion Christophe , Majada Juan , Alía Ricardo , Eckert Andrew J. , González-Martínez Santiago C.
Published: March 2020

A decade of association studies in multiple organisms suggests that most complex traits are polygenic; that is, they have a genetic architecture determined by numerous loci distributed across the genome, each with small effect-size. Thus, determining the degree of polygenicity and its variation across traits, environments and years is useful to understand the genetic basis of phenotypic variation. In this study, we applied multilocus approaches to estimate the degree of polygenicity of fitness-related traits in a long-lived plant (Pinus pinasterAit., maritime pine) and to analyze how polygenicity changes across environments and years. To do so, we evaluated five categories of fitness-related traits (survival, height, phenology-related, functional, and biotic-stress response traits) in a clonal common garden network, planted in contrasted environments (over 12,500 trees). First, most of the analyzed traits showed evidence of local adaptation based onQST-FSTcomparisons. Second, we observed a remarkably stable degree of polygenicity, averaging 6% (range of 0-27%), across traits, environments and years. As previously suggested for humans, some of these traits showed also evidence of negative selection, which could explain, at least partially, the high degree of polygenicity. The observed genetic architecture of fitness-related traits in maritime pine supports the polygenic adaptation model. Because polygenic adaptation can occur rapidly, our study suggests that current predictions on the capacity of natural forest tree populations to adapt to new environments should be revised, which is of special relevance in the current context of climate change.
3D printing of Microgel-loaded Modular LEGO-like Cages as Instructive Scaffolds for Tissue Engineering
Authors: Hipfinger Christina , Subbiah Ramesh , Tahayeri Anthony , Athirasala Avathamsa , Horsophonphong Sivaporn , Thrivikraman Greeshma , França Cristiane Miranda , Cunha Diana Araujo , Mansoorifar Amin , Zahariev Albena , Jones James M. , Coelho Paulo G. , Witek Lukasz , Xie Hua , Guldberg Robert E. , Bertassoni Luiz E.
Published: March 2020

Biomaterial scaffolds have served as the foundation of tissue engineering and regenerative medicine. However, scaffold systems are often difficult to scale in size or shape in order to fit defect-specific dimensions, and thus provide only limited spatiotemporal control of therapeutic delivery and host tissue responses. Here, a lithography-based three-dimensional (3D) printing strategy is used to fabricate a novel miniaturized modular LEGO-like cage scaffold system, which can be assembled and scaled manually with ease. Scalability is based on an intuitive concept of stacking modules, like conventional LEGO blocks, allowing for literally thousands of potential geometric configurations, and without the need for specialized equipment. Moreover, the modular hollow-cage design allows each unit to be loaded with biologic cargo of different compositions, thus enabling controllable and easy patterning of therapeutics within the material in 3D. In summary, the concept of miniaturized cage designs with such straight-forward assembly and scalability, as well as controllable loading properties, is a flexible platform that can be extended to a wide range of materials for improved biological performance.

TOC

3D printed LEGO-like hollow microcagescan be easily assembled, adjoined, and stacked-up to suit the complexity of defect tissues; aid spatial loading of cells and biomolecules; instruct cells migration three-dimensionally; and facilitate cell invasion and neovascularizationin-vivo, thus accelerating the process of tissue healing and new tissue formation.
Multi-dimensional predictions of psychotic symptoms via machine learning
Authors: Taylor Jeremy A , Larsen Kit Melissa , Garrido Marta I
Published: March 2020

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.
A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial hippocampal remapping and individual grid cell field-to-field variability
Authors: Agmon Haggai , Burak Yoram
Published: March 2020

The representation of position in the brain is distributed across multiple neural populations. Grid cell modules in the medial entorhinal cortex (MEC) express activity patterns that span a low-dimensional manifold which remains stable across different environments. In contrast, the activity patterns of hippocampal place cells span distinct low-dimensional manifolds in different environments. It is unknown how these multiple representations of position are coordinated. Here we develop a theory of joint attractor dynamics in the hippocampus and the MEC. We show that the system exhibits a coordinated, joint representation of position across multiple environments, consistent with global remapping in place cells and grid cells. We then show that our model accounts for recent experimental observations that lack a mechanistic explanation: variability in the firing rate of single grid cells across firing fields, and artificial remapping of place cells under depolarization, but not under hyperpolarization, of layer II stellate cells of the MEC.
Merkel Cell Polyomavirus Small Tumor Antigen Activates Matrix Metallopeptidase-9 Gene Expression for Cell Migration and Invasion
Authors: Nwogu Nnenna , Ortiz Luz E. , Whitehouse Adrian , Kwun Hyun Jin
Published: March 2020

Merkel cell polyomavirus (MCV) small T antigen (sT) is the main oncoprotein for the development of Merkel cell carcinoma (MCC). MCC is a rare, clinically aggressive neuroendocrine tumor of the skin with a high propensity for local, regional, and distant spread. The dysregulation of matrix metalloproteinase-9 (MMP-9) has been implicated in multiple essential roles in the development of various malignant tumor cell invasion and metastasis. Previously, MCV sT was shown to induce the migratory and invasive phenotype of MCC cells through the transcriptional activation of the Sheddase molecule, ADAM 10 (A disintegrin and metalloprotease domain-containing protein 10). In this study, we show that MCV sT protein stimulates differential expression of epithelial–mesenchymal transition (EMT) associated genes, including MMP-9 and Snail. This effect is dependent on the presence of the large T stabilization domain (LSD), which is known to be responsible for cell transformation through targeting of promiscuous E3 ligases, including FBW7, a known MMP-9 and Snail regulator. Chemical treatments of MMP-9 markedly inhibited sT-induced cell migration and invasion. These results suggest that MCV sT contributes to the activation of MMP-9 as a result of FBW7 targeting, and increases the invasive potential of cells, which can be used for targeted therapeutic intervention.

IMPORTANCE

Merkel cell carcinoma (MCC) is the most aggressive cutaneous tumor without clearly defined treatment. Although MCC has a high propensity for metastasis, little is known about the underlying mechanisms that drive MCC invasion and metastatic progression. MMP-9 has shown to play a detrimental role in many metastatic human cancers, including melanoma and other non-melanoma skin cancers. Our study shows that MCV sT-mediated MMP-9 activation is driven through the LSD, a known E3 ligase targeting domain, in MCC. MMP-9 may serve as the biochemical culprit to target and develop a novel approach for the treatment of metastatic MCC.
Metabolites alleviate staphylococcal bloodstream infection in a NO-dependent manner via arginase inhibition
Authors: Pang Rui , Su Yu-bin , Zhou Hua , Chen Xinhai
Published: March 2020

Staphylococcus aureusis a notorious bacterial pathogen that often causes soft tissue and bloodstream infections and invariably garners resistance mechanisms against new antibiotics. Host innate immunity modulated by metabolites has been proved as a powerful strategy against bacterial infections. However, few studies focus on the application of this strategy againstS. aureusinfection. Here, we identified four metabolite biomarkers, L-proline, L-isoleucine, L-leucine, and L-valine (PILV), by a metabolomics study. In animal models ofS. aureusbloodstream infection, exogenous administration of each metabolite or PILV shows an anti-infective effect, while PILV treatment has higher protection than individual metabolite treatment. Each metabolite targets nitric oxide (NO) to killS. aureusvia arginase inhibition, and PILV exhibits additive inhibition of arginase activity that causes further killing. This suppression also contributes to the metabolite-mediated phagocytic killing ofS. aureusin human blood. Our finding demonstrates the metabolite-mediated innate immunity as a therapeutic intervention forS. aureusinfection.
A rapidly reversible mutation generates subclonal genetic diversity and unstable drug resistance
Authors: Dan Lufeng , Li Yuze , Chen Shuhua , Liu Jingbo , Li Fangting , Wang Yu , He Xiangwei , Carey Lucas B.
Published: March 2020

Most genetic changes have negligible reversion rates. As most mutations that confer resistance to an adversary condition (e.g., drug treatment) also confer a growth defect in its absence, it is challenging for cells to genetically adapt to transient environmental changes. Here we identify a set of rapidly reversible drug resistance mutations inS. pombethat are caused by Microhomology mediated Tandem Duplication (MTD), and reversion back to the wild-type sequence. Using 10,000x coverage whole-genome sequencing we identify near 6000 subclonal MTDs in a single clonal population, and determine using machine learning how MTD frequency is encoded in genome. We find that sequences with the highest predicted MTD rates tend to generate insertions that maintain the correct reading frame suggesting that MTD formation has shaped the evolution of coding sequences. Our study reveals a common mechanism of reversible genetic variation that is beneficial for adaptation to environmental fluctuations and facilitates evolutionary divergence.
RNA editing in inflorescences of wild grapevine unveils association to sex and development
Authors: Ramos Miguel J. N. , Faísca-Silva David , Coito João L. , Cunha Jorge , Silva Helena Gomes , Viegas Wanda , Costa M. Manuela R. , Amâncio Sara , Rocheta Margarida
Published: March 2020

RNA editing challenges the central dogma of molecular biology, by modifying the genetic information at the transcription level. Recent reports, suggesting increased levels of RNA editing in plants, raised questions on the nature and dynamics of such events during development. We here report the occurrence of distinct RNA editing patterns in wildVitisflowers during development, with twelve possible RNA editing modifications observed for the first time in plants. RNA editing events are gender and developmental stage specific, identical in subsequent years of this perennial species and with distinct nucleotide frequencies neighboring editing sites on the 5’ and 3’ flanks. The transcriptome dynamics unveils a new regulatory layer responsible for gender plasticity enhancement or underling dioecy evolution inVitis.
Tendon Cell Deletion of IKKβ/NF-κB Drives Functionally Deficient Tendon Healing and Altered Cell Survival Signaling In Vivo
Authors: Best Katherine T. , Knapp Emma , Ketonis Constantinos , Jonason Jennifer H. , Awad Hani A. , Loiselle Alayna E.
Published: March 2020

Acute tendon injuries are characterized by excessive matrix deposition that impedes regeneration and disrupts functional improvements. Inflammation is postulated to drive pathologic scar tissue formation, with nuclear factor kappa B (NF-κB) signaling emerging as a candidate pathway in this process. However, characterization of the spatial and temporal activation of canonical NF-κB signaling during tendon healingin vivo, including identification of the cell populations activating NF-κB, is currently unexplored. Therefore, we aimed to determine which cell populations activate canonical NF-κB signaling following flexor tendon repair with the goal of delineating cell-specific functions of NF-κB signaling during scar mediated tendon healing. Immunofluorescence revealed that both tendon cells and myofibroblasts exhibit prolonged activation of canonical NF-κB signaling into the remodeling phase of healing. Using cre-mediated knockout of the canonical NF-κB kinase (IKKβ), we discovered that suppression of canonical NF-κB signaling in Scleraxis-lineage cells increased myofibroblast content and scar tissue formation. Interestingly, Scleraxis-lineage specific knockout of IKKβ increased the incidence of apoptosis, suggesting that canonical NF-κB signaling may be mediating cell survival during tendon healing. These findings suggest indispensable roles for canonical NF-κB signaling during flexor tendon healing.

One Sentence Summary

Scleraxis-lineage specific knockdown of persistent canonical IKKβ/NF-κB drives scar formation and apoptotic signaling during flexor tendon healing.
Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics
Authors: Choi Kwangbom , Chen Yang , Skelly Daniel A. , Churchill Gary A.
Published: March 2020

Single-cell RNA sequencing is a powerful tool for characterizing cellular heterogeneity in gene expression. However, high variability and a large number of zero counts present challenges for analysis and interpretation. There is substantial controversy over the origins and proper treatment of zeros and no consensus on whether zero-inflated count distributions are necessary or even useful. While some studies assume the existence of zero inflation due to technical artifacts and attempt to impute the missing information, other recent studies of argue that there is no zero inflation in scRNA-Seq data. We apply a Bayesian model selection approach to unambiguously demonstrate zero inflation in multiple biologically realistic scRNA-Seq datasets. We show that the primary causes of zero inflation are not technical but rather biological in nature. We also demonstrate that parameter estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zero inflation. Despite the existence of zero inflation of scRNA-Seq counts, we recommend the generalized linear model with negative binomial count distribution (not zero-inflated) as a suitable reference model for scRNA-Seq analysis.
Predicting future drinking among young adults: using ensemble machine-learning to combine MRI with psychometrics and behaviour
Authors: Groefsema Martine M. , Luijten Maartje , Engels Rutger C.M.E. , Sescousse Guillaume , Jollans Lee
Published: March 2020

Background

While most research into predictors of problematic alcohol use has focused on adolescence, young adults are also at elevated risk, and differ from adolescents and adults in terms of exposure to alcohol and neurodevelopment. Here we examined predictors of alcohol use among young adults at a 1-year follow-up using a broad predictive modelling approach.

Methods

Data in four modalities were included from 128 men aged between 18 and 25 years; functional MRI regions-of-interest from 1) a beer-incentive delay task, and 2) a social alcohol cue-exposure task, 3) grey matter data, and 4) non-neuroimaging data (i.e. psychometric and behavioural). These modalities were combined into an ensemble model to predict follow-up Alcohol Use Disorder Identification (AUDIT) scores, and were tested separately for their contribution. To reveal specificity for the prediction of future AUDIT scores, the same analyses were carried out for current AUDIT score.

Results

The ensemble resulted in a more accurate estimation of follow-up AUDIT score than any single modality. Only removal of the social alcohol cue-exposure task and of the non-neuroimaging data significantly worsened predictions. Reporting to need a drink in the morning to start the day was the strongest unique predictor of future drinking along with anterior cingulate cortex and cerebellar activity.

Conclusions

Alcohol-related task fMRI activity is a valuable predictor for future drinking among young adults alongside non-neuroimaging variables. Multi-modal prediction models best predict future drinking among young adults and may play an important part in the move towards individualized treatment and prevention efforts.
Storing and analyzing a genome on a blockchain
Authors: Gürsoy Gamze , Brannon Charlotte M , Wagner Sarah , Gerstein Mark
Published: March 2020

The genomic characterization of individuals promises to be immensely useful for biomedical research and healthcare. However, a critical barrier to expanding personal genome sequencing is achieving secure, high-integrity storage of raw data. While cloud storage offers solutions to access such data from any place and device, the vulnerabilities of centralized storage in relation to security, data integrity, and robustness, such as single points of failure, have not yet been addressed. Blockchain is a potential alternative to these storage modes. However, storing large-scale data on blockchain can be challenging due to slow transaction speeds, the potential for chains to reach large sizes, and limitations on querying data stored on-chain. Currently, several genomic storage applications incorporate blockchain, but likely because of these challenges, many use blockchain only to facilitate and log data-access transactions, rather than to store raw genomic data on-chain. While this secures the process of data access, it does not secure the data itself, which is often stored off-chain (i.e. in a cloud or file-hosting services). Here, we developed a novel method of storing reference-aligned reads on-chain in a private blockchain network. We also developed tools for accessing and analyzing the on-chain data. We addressed the challenges of on-chain data storage by minimizing the data inserted to the chain using reference-based data compression techniques and by binning the on-chain data by genomic location to reduce retrieval times. Our tools provide open-source blockchain-based storage and access for advanced genomic analyses such as variant calling.
Multi-contact 3C data reveal that the human genome is largely unentangled
Authors: Tavares-Cadete Filipe , Norouzi Davood , Dekker Bastiaan , Liu Yu , Dekker Job
Published: March 2020

The genome is organized into chromosome territories that are themselves spatially segregated in A and B compartments. The extent to which interacting compartment domains and chromosomes are topologically entangled is not known. We show that detection of series of co-occurring chromatin interactions using multi-contact 3C (MC-3C) reveals insights into the topological entanglement of compartment domains and territories. We find that series of co-occurring interactions and their order represent interaction percolation paths through nuclear space in single cells where fragment 1 interacts with fragment 2, which in turn interacts with fragment 3 and so on. Analysis of paths that cross two chromosome territories revealed very little mixing of chromatin from the two chromosomes. Similarly, paths that cross compartment domains show that loci from interacting domains do not mix. Polymer simulations show that such paths are consistent with chromosomes and compartment domains behaving as topologically closed polymers that are not catenated with one another. Simulations show that even low levels of random strand passage, e.g. through topoisomerase II activity, would result in entanglements and mixing of loci of different chromosomes and compartment domains with concomitant changes in interaction paths inconsistent with MC-3C data. Our results show that cells maintain a largely unentangled state of chromosomes and compartment domains.