bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental response in gene expression and DNA methylation reveals factors influencing the adaptive potential ofArabidopsis lyrata
Authors: Hämälä Tuomas , Ning Weixuan , Kuittinen Helmi , Aryamanesh Nader , Savolainen Outi
Published: March 2022

Understanding what factors influence plastic and genetic variation is valuable for predicting how organisms respond to changes in the selective environment. Here, using gene expression and DNA methylation as molecular phenotypes, we study environmentally induced variation amongArabidopsis lyrataplants grown at lowland and alpine field sites. Our results show that gene expression is highly plastic, as many more genes are differentially expressed between the field sites than between populations. These environmentally responsive genes evolve under strong selective constraint – the strength of purifying selection on the coding sequence is high, while the rate of adaptive evolution is low. We find, however, that positive selection oncis-regulatory variants has likely contributed to the maintenance of genetically variable environmental responses, but such variants segregate only between distantly related populations. In contrast to gene expression, DNA methylation at genic regions is largely insensitive to the environment, and plastic methylation changes are not associated with differential gene expression. Besides genes, we detect environmental effects at transposable elements (TEs): TEs at the high-altitude field site have higher expression and methylation levels, suggestive of a broad-scale TE activation. Compared to the lowland population, plants native to the alpine environment harbor an excess of recent TE insertions, and we observe that specific TE families are enriched within environmentally responsive genes. Together, our findings provide insight into selective forces shaping plastic and genetic variation. We also highlight how plastic responses at TEs can rapidly create novel heritable variation in stressful conditions.
A new method for multi-ancestry polygenic prediction improves performance across diverse populations
Authors: Zhang Haoyu , Zhan Jianan , Jin Jin , Zhang Jingning , Lu Wenxuan , Zhao Ruzhang , Ahearn Thomas U. , Yu Zhi , O’Connell Jared , Jiang Yunxuan , Chen Tony , Okuhara Dayne ,   23andMe Research Team, Garcia-Closas Montserrat , Lin Xihong , Koelsch Bertram L. , Chatterjee Nilanjan
Published: March 2022

Polygenic risk scores (PRS) increasingly predict complex traits, however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRS using ancestry-specific GWAS summary statistics from multi-ancestry training samples, integrating clumping and thresholding, empirical Bayes and super learning. We evaluate CT-SLEB and nine-alternatives methods with large-scale simulated GWAS (∼19 million common variants) and datasets from 23andMe Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across thirteen complex traits. Results demonstrate that CT-SLEB significantly improves PRS performance in non-European populations compared to simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offer insights into sample size requirements and SNP density effects on multi-ancestry risk prediction.
Multi-omic analysis of the cardiac cellulome defines a vascular contribution to cardiac diastolic dysfunction in obese female mice
Authors: Dona Malathi S. I. , Hsu Ian , Meuth Alex I. , Brown Scott M. , Bailey Chastidy , Aragonez Christian G. , Chandrasekar Bysani , Martinez-Lemus Luis A. , DeMarco Vincent G. , Grisanti Laurel A. , Jaffe Iris Z. , Pinto Alexander R. , Bender Shawn B.
Published: March 2022

Coronary microvascular dysfunction (CMD) is associated with cardiac dysfunction and predictive of cardiac mortality in obesity, especially in females. Emerging evidence suggests development of heart failure with preserved ejection fraction in females with CMD and that mineralocorticoid receptor (MR) antagonism may be more efficacious in obese female, versus male, HFpEF patients. Accordingly, we examined the hypothesis that smooth muscle cell (SMC)-specific MR deletion prevents obesity-associated coronary and cardiac diastolic dysfunction in females. Obesity was induced in female mice via western diet (WD) feeding alongside littermates fed standard diet. Initial studies revealed that global MR blockade with spironolactone prevented impaired coronary vasodilation and diastolic dysfunction in obese females. Importantly, specific deletion of SMC-MR similarly prevented obesity-associated coronary and cardiac dysfunction. Cardiac gene expression profiling suggested reduced cardiac inflammation in WD-fed mice with SMC-MR deletion independent of blood pressure, aortic stiffening, and cardiac hypertrophy. Further mechanistic studies utilizing single-cell RNA sequencing of non-cardiomyocyte cell populations revealed novel impacts of SMC-MR deletion on the cardiac cellulome in obese mice. Specifically, WD feeding induced inflammatory gene signatures in multiple non-myocyte populations (B/T cells, macrophages, and endothelium), independent of cardiac fibrosis, that was prevented by SMC-MR deletion. Further, SMC-MR deletion induced a basal reduction in cardiac mast cells and prevented WD-induced cardiac pro-inflammatory chemokine expression and leukocyte recruitment. These data reveal a central role for SMC-MR signaling in obesity-associated coronary and cardiac dysfunction thus supporting the emerging paradigm of a vascular origin of cardiac dysfunction in obesity.
TCGADEPMAP– Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas
Authors: Shi Xu , Gekas Christos , Verduzco Daniel , Petiwala Sakina , Jeffries Cynthia , Lu Charles , Murphy Erin , Anton Tifani , Vo Andy H. , Xiao Zoe , Narayanan Padmini , Barnes J. Matthew , Roy Somdutta , Ramathal Cyril , Flister Michael J. , Dezso Zoltan
Published: March 2022

The Cancer Genome Atlas (TCGA) has yielded unprecedented genetic and molecular characterization of the cancer genome, yet the functional consequences and patient-relevance of many putative cancer drivers remain undefined. TCGADEPMAPis the first hybrid map of translational tumor dependencies that was built from machine learning of gene essentiality in the Cancer Dependency Map (DEPMAP) and then translated to TCGA patients. TCGADEPMAPcaptured well-known and novel cancer lineage dependencies, oncogenes, and synthetic lethalities, demonstrating the robustness of TCGADEPMAPas a translational dependency map. Exploratory analyses of TCGADEPMAPalso unveiled novel synthetic lethalities, including the dependency ofPAPSS1driven by loss ofPAPSS2which is collaterally deleted with the tumor suppressor genePTEN. Synthetic lethality ofPAPSS1/2was validated in vitro and in vivo, including the underlying mechanism of synthetic lethality caused by the loss of protein sulfonation that requiresPAPSS1orPAPSS2. Moreover, TCGADEPMAPdemonstrated that patients with predictedPAPSS1/2synthetic lethality have worse overall survival, suggesting that these patients are in greater need of drug discovery efforts to targetPAPSS1. Other map “extensions” were built to capture unique aspects of patient-relevant tumor dependencies using the flexible analytical framework of TCGADEPMAP, including translating gene essentiality to drug response in patient-derived xenograft (PDX) models (i.e., PDXEDEPMAP) and predicting gene tolerability within normal tissues (GTEXDEPMAP). Collectively, this study demonstrates how translational dependency maps can be used to leverage the rapidly expanding catalog of patient genomic datasets to identify and prioritize novel therapeutic targets with the best therapeutic indices.
sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields
Authors: Anderson Sean C. , Ward Eric J. , English Philina A. , Barnett Lewis A. K. , Thorson James T.
Published: March 2022

Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyse these data, such as generalised linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally intensive and challenging for many users to implement. Here, we introduce the R packagesdmTMB, which extends the flexible interface familiar to users oflme4, glmmTMB, andmgcvto include spatial and spatiotemporal latent GMRFs using an SPDE-(stochastic partial differential equation) based approach. SPDE matrices are constructed withfmesherand estimation is conducted via maximum marginal likelihood withTMBor via Bayesian inference withtmbstanandrstan. We describe the model and explore case studies that illustratesdmTMB’s flexibility in implementing penalised smoothers, non-stationary processes (time-varying and spatially varying coefficients), hurdle models, cross-validation and anisotropy (directionally dependent spatial correlation). Finally, we compare the functionality, speed, and interfaces of related software, demonstrating thatsdmTMBcan be an order of magnitude faster than R-INLA. We hopesdmTMBwill help open this useful class of models to a wider field of geostatistical analysts.
Indirect decoding of behavior from brain signals via reconstruction of experimental conditions
Authors: Soch Joram , Haynes John-Dylan
Published: March 2022

The data acquired during a functional magnetic resonance imaging (fMRI) experiment usually comprise experimental conditions, brain signals and behavioral responses. This reflects the underlying causal flow where the experimental conditions evoke brain responses that in turn result in behavior. In multivariate analyses, a common approach is to focus on the second step of that chain and to decode behavioral responses from brain signals. However, a different approach would be to first reconstruct the experimental conditions from brain signals and in turn use these to predict behavior. While this indirect approach would go against the causal chain of events, it might work better under certain circumstances, especially when the experimental conditions evoke much stronger measurable brain signals than the overt motor behavior. Here we tested this question directly by assessing the various mappings between conditions, brain signals and behavior in an open dataset. We found that the path of first decoding experimental conditions works surprisingly well, even though in our example data set, it is still outperformed by directly decoding the behavior from brain responses.
AIRRscape: an interactive tool for exploring B-cell receptor repertoires and antibody responses
Authors: Waltari Eric , Nafees Saba , Wong Joan , McCutcheon Krista M. , Pak John E.
Published: March 2022

The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.

Author Summary

Technological advances in next generation sequencing have allowed for broad experimental sampling of immune repertoires, providing insight into how our immune system responds to infection, vaccination, autoimmunity, and cancer. The scale of these “big data”, however, make it difficult to bioinformatically extract the key sequence features that are shared across multiple repertoires. With AIRRscape, we enable large-scale immune repertoire visualization and analysis that requires no knowledge of the command line or advanced programming. By providing the community with an open-source, interactive, and user-friendly interface, we reduce the barriers to exploring immune repertoires at scale. We demonstrate the use of AIRRscape to characterize features of immune responses to viral infection that are shared across multiple repertoire datasets.
SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods
Authors: Serouart Mario , Madec Simon , David Etienne , Velumani Kaaviya , Lozano Raul Lopez , Weiss Marie , Baret Frédéric
Published: March 2022

The pixels segmentation of high resolution RGB images into background, green vegetation and senescent vegetation classes is a first step often required before estimating key traits of interest including the vegetation fraction, the green area index, or to characterize the sanitary state of the crop. We developed the SegVeg model for semantic segmentation of RGB images into the three classes of interest. It is based on a U-net model that separates the vegetation from the background. It was trained over a very large and diverse dataset. The vegetation pixels are then classified using a SVM shallow machine learning technique trained over pixels extracted from grids applied to images. The performances of the SegVeg model are then compared to a three classes U-net model trained using weak supervision over RGB images with predicted pixels by SegVeg as groundtruth masks.

Results show that the SegVeg model allows to segment accurately the three classes, with however some confusion mainly between the background and the senescent vegetation, particularly over the dark and bright parts of the images. The U-net model achieves similar performances, with some slight degradation observed for the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent ones. Finally, the models are used to predict the fraction of the three classes over the grids pixels or the whole images. Results show that the green fraction is very well estimated (R2=0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R2=0.70 and 0.73, respectively).

We made SegVeg publicly available as a ready-to-use script, as well as the entire dataset, rendering segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge, or at least, offering a pre-trained model to more specific use.
Genome polarisation for detecting barriers to geneflow
Authors: Baird Stuart J. E. , Petružela Jan , Jaroň Izar , Škrabánek Pavel , Martínková Natália
Published: March 2022

Semi-permeable barriers to geneflow in principle allow distantly related organisms to capture and exchange pre-adapted genes potentially speeding adaptation. However, describing barriers to geneflow on a genomic scale is non-trivial.

We extend classic diagnostic allele counting measures of geneflow across a barrier to the case of genome-scale data. Diagnostic index expectation maximisation (diem) polarises the labelling of bistate markers with respect to the sides of a barrier. An initial state of ignorance is enforced by starting with randomly generated marker polarisations. This means there is no prior on population or taxon membership of the genomes concerned. Using a deterministic data labelling, small numbers of classic diagnostic markers can be replaced by large numbers of markers, each with a diagnostic index. Individuals’ hybrid indices (genome admixture proportions) are then calculated genome wide conditioned on marker diagnosticity; within diploid, haplodiploid and/or haploid genome compartments; or indeed over any subset of markers, allowing classical cline width/barrier strength comparisons along genomes. Along-genome barrier strength hetero-geneity allows for barrier regions to be identified. Further, blocks of genetic material that have introgressed across a barrier are easily identified with high power.

diem indicates panmixis among Myotis myotis bat genomes, with a barrier separating low data quality outliers. In a Mus musculus domesticus/Mus spretus system, diem adds multiple introgressions of olfactory (and vomeronasal) gene clusters in one direction to previous demon-strations of a pesticide resistance gene introgressing in the opposite direction across a strong species barrier.

diem is a genomes analysis solution which scales over reduced representation genomics of thousands of markers to treatment of all variant sites in large genomes. While the method lends itself to visualisation, its output of markers with barrier-informative annotation will fuel research in population genetics, phylogenetics and association studies. diem can equip such downstream applications with millions of informative markers.
CellSium – Versatile Cell Simulator for Microcolony Ground Truth Generation
Authors: Sachs Christian Carsten , Ruzaeva Karina , Seiffarth Johannes , Wiechert Wolfgang , Berkels Benjamin , Nöh Katharina
Published: March 2022

To train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported.

Availability and Implementation

CellSium is free and open source software under the BSD license, implemented in Python, available at https://github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images.

Contact

k.noeh@fz-juelich.de

Supplementary information

Supplementary data are available online.
Scalable Generation of Pseudo-Unipolar Sensory Neurons from Human Pluripotent Stem Cells
Authors: Deng Tao , Tristan Carlos A. , Weber Claire , Chu Pei-Hsuan , Ryu Seungmi , Jovanovic Vukasin M. , Ormanoglu Pinar , Twumasi Prisca , Shim Jaehoon , Jayakar Selwyn , Zhang Han-Xiong Bear , Jo Sooyeon , Voss Ty C. , Simeonov Anton , Bean Bruce P. , Woolf Clifford J. , Singeç Ilyas
Published: March 2022

Development of new non-addictive analgesics requires advanced strategies to differentiate human pluripotent stem cells (hPSCs) into relevant cell types amenable for translational research. Here, we developed a highly efficient and reproducible method that differentiates hPSCs into peptidergic and non-peptidergic nociceptors. By modulating specific cell signaling pathways, hPSCs were first converted into SOX10+ neural crest cells, followed by differentiation into sensory neurons with an in vivo-like pseudo-unipolar morphology. Detailed characterization confirmed that the hPSC-derived nociceptors displayed molecular and cellular features comparable to native dorsal root ganglion (DRG) neurons, and expressed high-threshold primary sensory neuron markers, transcription factors, neuropeptides, and over 150 ion channels and receptors, including critical pain-relevant drug targets (e.g., TRPV1, TAC1, CALCA, NAV1.7, NAV1.8). Moreover, after confirming robust functional activities and differential response to noxious stimuli and specific drugs, a robotic cell culture system was employed to produce large quantities of human sensory neurons, which can be used to develop nociceptor-selective analgesics.
Microglial TNFα orchestrates brain phosphorylation during the sleep period and controls homeostatic sleep
Authors: Pinto Maria J , Cottin Léa , Dingli Florent , Laigle Victor , Ribeiro Luís F. , Triller Antoine , Henderson Fiona J , Loew Damarys , Fabre Véronique , Bessis Alain
Published: March 2022

The time we spend asleep is adjusted to previous time spent awake, and therefore believed to be under tight homeostatic control. Here, we establish microglia as a new cellular component of the sleep homeostat circuit. By using quantitative phosphoproteomics we demonstrate that microglia-derived TNFα controls thousands of phosphorylation sites during the sleep period. Substrates of microglial TNFα comprise sleep-promoting kinases and numerous synaptic proteins, including a subset whose phosphorylation status codes sleep need and determines sleep duration. As a result, lack of microglial TNFα attenuates the build-up of sleep need, as measured by slow wave activity, and prevents immediate compensation for loss of sleep. Together, we propose that microglia control sleep homeostasis by releasing TNFα that acts at the neuronal circuitry through dynamic control of phosphorylation.
ViPal: A Framework for Virulence Prediction of Influenza Viruses with Prior Viral Knowledge Using Genomic Sequences
Authors: Yin Rui , Luo Zihan , Zhuang Pei , Kwoh Chee Keong , Lin Zhuoyi
Published: March 2022

Influenza viruses pose significant threats to public health and cause enormous economic loss every year. Previous work has revealed the viral factors that influence the virulence of influenza viruses. However, taking prior viral knowledge represented by heterogeneous categorical and discrete information into account is scarce in the existing work. How to make full use of the preceding domain knowledge into virulence study is challenging but beneficial. This paper proposes a general framework named ViPal for virulence prediction that incorporates discrete prior viral mutation and reassortment information based on all eight influenza segments. The posterior regularization technique is leveraged to transform prior viral knowledge to constraint features and integrated into the machine learning models. Experimental results on influenza genomic datasets validate that our proposed framework can improve virulence prediction performance over baselines. The comparison between ViPal and other existing methods shows the computational efficiency of our framework with superior performance. Moreover, the interpretable analysis through SHAP identifies the scores of constraint features contributing to the prediction. We hope this framework could provide assistance for the accurate detection of influenza virulence and facilitate flu surveillance.
Gata2-regulated Gfi1b expression controls endothelial programming during endothelial-to-hematopoietic transition
Authors: Koyunlar Cansu , Gioacchino Emanuele , Vadgama Disha , de Looper Hans , Zink Joke , Hoogenboezem Remco , Havermans Marije , Bindels Eric , Dzierzak Elaine , Touw Ivo P , de Pater Emma
Published: March 2022

The first hematopoietic stem cells (HSCs) are formed through endothelial-to-hematopoietic transition (EHT) events during embryonic development. The transcription factor GATA2 is a crucial regulator of EHT and HSC function throughout life. Because GATA2 haploinsufficiency patients have inborn mutations, prenatal defects are likely to have an influence on disease development. In mice, Gata2 haploinsufficiency (Gata2+/-) reduces the number and the functionality of embryonic hematopoietic stem and progenitor cells (HSPCs) generated through EHT. However, the embryonic HSPC pool is heterogeneous and the mechanisms underlying this defect in Gata2+/- embryos are unclear. Here, we investigated whether Gata2 haploinsufficiency selectively affects a cellular subset undergoing EHT. We show that Gata2+/- HSPCs initiate but cannot fully activate hematopoietic programming during EHT. In addition, due to reduced activity of the endothelial repressor Gfi1b, Gata2+/- HSPCs cannot repress the endothelial identity to complete maturation. Finally, we show that hematopoietic-specific induction of gfi1b can restore HSC production in gata2b-null (gata2b-/-) zebrafish embryos. This study illustrates pivotal roles of Gata2 on the regulation of transcriptional network governing HSPC identity throughout EHT.

Highlights

Maturation of embryonic Gata2+/- HSPCs is disturbed due to aberrant endothelial gene expression and incomplete activation of hematopoietic transcriptional programming.

Gata2 activates Gfi1b to repress endothelial identity of embryonic HSPCs during maturation.

Hematopoietic-specific induction of gfi1b restores the number of embryonic HSCs in gata2b-/- zebrafish.
Germline/soma distinction in Drosophila embryos requires regulators of zygotic genome activation
Authors: Colonnetta Megan M. , Schedl Paul , Deshpande Girish
Published: March 2022

In Drosophila melanogaster embryos, somatic versus germline identity is the first cell fate decision. Zygotic genome activation (ZGA) orchestrates regionalized gene expression, imparting specific identity on somatic cells. ZGA begins with a minor wave that commences at nuclear cycle (NC) 8 under the guidance of chromatin accessibility factors (Zelda, CLAMP, GAF), followed by the major wave during NC14. By contrast, primordial germ cell (PGC) specification requires maternally deposited and posteriorly anchored germline determinants. This is accomplished by a centrosome coordinated release and sequestration of germ plasm during the precocious cellularization of PGCs in NC10. Here we report a novel requirement for Zelda and CLAMP during the establishment of the germline/soma distinction. When their activity is compromised, PGC determinants are not properly sequestered, and specification is disrupted. Conversely, the spreading of PGC determinants from the posterior pole adversely influences transcription in the neighboring somatic nuclei. These reciprocal aberrations can be correlated with defects in centrosome duplication/separation that are known to induce inappropriate transmission of the germ plasm. Interestingly, consistent with the ability of BMP signaling to influence specification of embryonic PGCs, reduction in the transcript levels of a BMP family ligand, decapentaplegic (dpp), is exacerbated at the posterior pole.
Protein synthesis inhibition and loss of homeostatic functions in astrocytes from an Alzheimer’s disease mouse model: a role for ER-mitochondria interaction
Authors: Tapella Laura , Dematteis Giulia , Moro Marianna , Pistolato Beatrice , Tonelli Elisa , Vanella Virginia Vita , Giustina Daniele , La Forgia Aleida , Restelli Elena , Barberis Elettra , Cali Tito , Brini Marisa , Villani Salvatore , Del Grosso Erika , Grilli Mariagrazia , Manfredi Marcello , Corazzari Marco , Grolla Ambra A , Genazzani Armando A , Lim Dmitry
Published: March 2022

Deregulation of protein synthesis and ER stress/unfolded protein response (ER stress/UPR) have been reported in astrocytes. However, the relationships between protein synthesis deregulation and ER stress/UPR, as well as their role in the altered homeostatic support of Alzheimer’s disease (AD) astrocytes remain poorly understood. Previously, we reported that in astrocytic cell lines from 3xTg-AD mice (3Tg-iAstro) protein synthesis was impaired and ER-mitochondria distance was reduced. Here we show that impaired protein synthesis in 3Tg-iAstro is associated with an increase of p-eIF2α and downregulation of GADD34. Although mRNA levels of ER stress/UPR markers were increased two-three-fold, we found neither activation of PERK nor downstream induction of ATF4 protein. Strikingly, the overexpression of a synthetic ER-mitochondrial linker (EML) resulted in a reduced protein synthesis and augmented p-eIF2α without any effect on ER stress/UPR marker genes.In vivo, in hippocampi of 3xTg-AD mice, reduced protein synthesis, increased p-eIF2α and downregulated GADD34 protein were found, while no increase of p-PERK or ATF4 proteins was observed, suggesting that in AD astrocytes, bothin vitroandin vivo, phosphorylation of eIF2α and impairment of protein synthesis are PERK-independent. Next, we investigated the ability of 3xTg-AD astrocytes to support metabolism and function of other cells of the central nervous system. Astrocyte conditioned medium (ACM) from 3Tg-iAstro cells significantly reduced protein synthesis rate in primary hippocampal neurons. When added as a part of pericyte/endothelial cell (EC)/astrocyte 3D co-culture, 3Tg-iAstro, but not WT-iAstro, severely impaired formation and ramification of tubules, the effect, replicated by EML overexpression in WT-iAstro cells. Finally, a chemical chaperone 4-phenylbutyric acid (4-PBA) rescued protein synthesis, p-eIF2α levels in 3Tg-iAstro cells and tubulogenesis in pericyte/EC/3Tg-iAstro co-culture. Collectively, our results suggest that a PERK-independent, p-eIF2α-associated impairment of protein synthesis compromises astrocytic homeostatic functions, and this may be caused by the altered ER-mitochondria interaction.
Proton-transporting heliorhodopsins from marine giant viruses
Authors: Hososhima Shoko , Mizutori Ritsu , Abe-Yoshizumi Rei , Rozenberg Andrey , Shigemura Shunta , Pushkarev Alina , Konno Masae , Katayama Kota , Inoue Keiichi , Tsunoda Satoshi P. , Béjà Oded , Kandori Hideki
Published: March 2022

Rhodopsins convert light into signals and energy in animals and microbes. Heliorhodopsins (HeRs), a recently discovered new rhodopsin family, are widely present in archaea, bacteria, unicellular eukaryotes, and giant viruses, but their function remains unknown. Here we report that a viral HeR from Emiliania huxleyi virus 202 (V2HeR3) is a light-gated proton channel. V2HeR3 absorbs blue-green lights, and the active intermediate contains the deprotonated retinal Schiff base. Site-directed mutagenesis study revealed that E191 in TM6 constitutes the gate together with the retinal Schiff base. E205 and E215 form a proton accepting group of the Schiff base, whose mutations converted the protein into an outward proton pump. Three environmental viral HeRs from the same group, as well as a more distantly related HeR exhibited similar proton-transport activity, indicating that HeR functions might be diverse similarly to type-1 microbial rhodopsins. Some strains of E. huxleyi contain one HeR that is related to the viral HeRs, while its viruses EhV-201 and EhV-202 contain two and three HeRs, respectively. Except for V2HeR3 from EhV-202, none of these proteins exhibit ion-transport activity. Thus, when expressed in the E. huxleyi cell membranes, only V2HeR3 has the potential to depolarize the host cells by light, possibly to overcome the host defense mechanisms or to prevent superinfection. The neuronal activity generated by V2HeR3 suggests that it can potentially be used as an optogenetic tools, like type-1 microbial rhodopsins.
LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer
Authors: Cave Donatella Delle , Buonaiuto Silvia , Mangini Maria , Sainz Bruno , Domenico Annalisa Di , Iavazzo Tea Teresa , Andolfi Gennaro , Duran Carme Cortina , Sevillano Marta , Heeschen Christopher , Colonna Vincenza , Corona Marco , Cucciardi Antonio , Guida Martina Di , Batlle Eduard , De Luca Annachiara , Lonardo Enza
Published: March 2022

Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis. Here, we report a LAMC2-positive cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation, differentiation, and driving metastasis. Mechanistically, mRNA profiling of these cells indicate a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-β signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of transforming growth factor-β (TGF-β) type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated the lung metastasis, primarily originating from LAMC2 expressing cells. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.
Long-term temperature trend in Kamchatka supports expansion of harmful algae
Authors: Samarkhanov Kanat , Mirasbekov Yersultan , Meirkhanova Ayagoz , Zhumakhanova Adina , Malashenkov Dmitry , Kovaldji Alexander , Barteneva Natasha S.
Published: March 2022

Ocean coastal ecosystems are changing, and global shifts in temperature lead to the expansion and intensification of harmful algae. In conjunction with anthropogenic effects it may result in future exacerbation of harmful algal blooms. Here we use the 2002-2020 years record of surface ocean temperature data retrieved from Sentinel-2 satellite to examine the recent temperature trend in Avacha Bay, Kamchatka Peninsula. Satellite analysis demonstrated a temperature increase trend in ocean surface water during spring and summer months and detected algal bloom in July 2020 preceding a mass death of marine benthic life in September-October 2020. Using 16S rRNA and 18S rRNA gene amplicon nanopore-based sequencing, we analyzed microbial and microalgal communities in the water samples from area of 2020 algal blooms. Our results suggest the presence of potentially toxic and bloom-forming algae from genera related to former HABs (harmful algal blooms) in the Avacha Bay region. A better understanding of the potentially toxic algae phytoplankton composition in the shifting temperature environment and time-series monitoring of HABs is of utmost importance for scientific community. We suggest that satellite analysis in combination with eDNA monitoring by nanopore-based sequencing represents promising option to detect potentially toxic algae and follow bloom development.
Polymer nanoparticles pass the plant interface
Authors: Parkinson Sam J. , Tungsirisurp Sireethorn , Sikder Amrita , Lynch Iseult , O’Reilly Rachel K. , Napier Richard M
Published: March 2022

As agriculture strives to feed an ever-increasing number of people, it must adapt to cope with climate change. It is also clear that our biosphere is suffering from an increasing burden of anthropogenic waste which includes minute plastic particles. It is not yet known whether plants will accumulate such micro- and nanoplastic materials, nor how their surface properties might influence uptake. Therefore, we prepared well-defined block copolymer nanoparticles with a range of different sizes (Dh = 20 - 100 nm) and surface chemistries by aqueous dispersion polymerisation using different functional macro chain transfer agents. A BODIPY fluorophore was then incorporated via hydrazone formation and uptake of these fluorescent nanoparticles into intact roots and protoplasts of Arabidopsis thaliana was investigated using confocal microscopy. Where uptake was seen, it was inversely proportional to nanoparticle size. Positively charged particles accumulated around root surfaces and were not taken up by roots or protoplasts, whereas negatively charged nanoparticles accumulated slowly in protoplasts and roots, becoming prominent over time in the xylem of intact roots. Neutral nanoparticles exhibited early, rapid penetration into plant roots and protoplasts, but lower xylem loads relative to the negative nanoparticles. These behaviours differ from those recorded in animal cells and our results show that, despite robust cell walls, plants are vulnerable to nanoplastic particles in the water and soil. The data form both a platform for understanding plastic waste in the farmed environment, and may also be used constructively for the design of precision delivery systems for crop protection products.

Significance Statement

Sustainable food production must keep pace with the growing global population, as well as adapt to climate change and other anthropogenic insults. It has become clear that micro-and nanoscale plastics are accumulating in all parts of the biosphere and we have set out to study how vulnerable plants are to such waste. We show that the size and surface properties of the designed plastics significantly affect both their speed of uptake and distribution within intact roots. Crucially, it is clear that rigid cell walls around plant cells are no barrier to the smallest particles and these pass into the plant’s vasculature. Our results relate to plastic waste but can also be used to develop precision vehicles for crop protection.
SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data
Authors: Kang Senbai , Borgsmüller Nico , Valecha Monica , Kuipers Jack , Alves Joao , Prado-López Sonia , Chantada Débora , Beerenwinkel Niko , Posada David , Szczurek Ewa
Published: March 2022

Single-cell DNA sequencing (scDNA-seq) has enabled the identification of single nucleotide somatic variants and the reconstruction of cell phylogenies. However, statistical phylogenetic models for cell phylogeny reconstruction from raw sequencing data are still in their infancy. Here we present SIEVE (SIngle-cell EVolution Explorer), a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from scDNA-seq reads. SIEVE leverages raw read counts for all nucleotides at candidate variant sites, and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods both in phylogenetic accuracy and variant calling accuracy. We apply SIEVE to three scDNA-seq datasets, for colorectal (CRC) and triple-negative breast cancer (TNBC), one of them generated by us. On simulated data, SIEVE reliably infers homo-and heterozygous somatic variants. The analysis of real data uncovers that double mutant genotypes are rare in CRC but unexpectedly frequent in TNBC samples.
Tracing immune cells around biomaterials with spatial anchors during large-scale wound regeneration
Authors: Yang Yang , Chu Chen-yu , Man Yi , Liu Li , Wang Chen-bing , Qu Yi-li
Published: March 2022

Inevitable skin scarring devoid of dermal appendages bring about unfavorable effects on both aesthetic and physiological functions. Here we offered an available method for large-area wound regeneration using biodegradable Aligned extracellular matrix (ECM) scaffold. Accelerating wound coverage and enhanced hair follicle (HF) neogenesis were observed with the implantation of biomaterials. Multimodal analysis during wound regeneration provides a unique lens through which we can explore the foreign body responses (FBR) around biomaterials. First, we identified cell heterogeneity in responses to ECM scaffolds using single-cell RNA sequencing. Next the spatial anchor highlighted preferential recruitment of T cells around biomaterials colocalized with hair follicle precursor cells. Reconstruction of signaling pathways suggested the pro-regenerative effect of the adaptive immune systems on HF neogenesis. Immunodeficient mice lacking mature T lymphocytes lost the ability to activate papillary fibroblast as well as stimulate HF regeneration, validating the potential therapeutic effect of the adaptive immune system activated by biomaterials. In conclusion, this study provided the further understanding of the FBR and facilitate the design of immunoregulatory biomaterials for biomedical application in the future.
Robust Estimation of 1/f Activity Improves Oscillatory Burst Detection
Authors: Seymour Robert A. , Alexander Nicholas , Maguire Eleanor A.
Published: March 2022

Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a “knee” below ∼0.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g. theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.

GRAPHICAL ABSRACT

To determine a power threshold for burst detection, the Better OSCillation framework (BOSC) estimates background 1/f activity by modelling neural power spectra. Here we introduce a modification, termed fBOSC, to more robustly estimate 1/f activity in situations with prominent oscillatory peaks and/or the presence of a non-linear “knee” in the power spectrum. This was shown to standardise burst detection across frequency bands in both simulated and empirical data.
Modeling phytoplankton-zooplankton interactions: opportunities for species richness and challenges for modern coexistence theory
Authors: Spaak Jurg W. , Adler Peter B. , Ellner Stephen P.
Published: March 2022

Many potential mechanisms can sustain biodiversity, but we know little about their relative importance. To compare multiple mechanisms, we modeled a two-trophic planktonic food-web based on mechanistic species interactions and empirically measured species traits. We simulated thousands of communities under realistic and altered trait distributions to assess the relative importance of three potential drivers of species richness: resource competition, predator-prey interactions, and trait trade-offs. Next, we computed niche and fitness differences of competing zooplankton to obtain a deeper understanding of how these mechanisms limit species richness. We found that predator-prey interactions were the most important driver of species richness and that fitness differences were a better predictor of species richness than niche differences. However, for many communities we could not apply modern coexistence theory to compute niche and fitness differences due to complications arising from trophic interactions. We therefore need to expand modern coexistence theory to investigate multi-trophic communities.
Gfastats: conversion, evaluation and manipulation of genome sequences using assembly graphs
Authors: Formenti Giulio , Abueg Linelle , Brajuka Angelo , Brajuka Nadolina , Gallardo Cristo , Giani Alice , Fedrigo Olivier , Jarvis Erich D.
Published: March 2022

Motivation

With the current pace at which reference genomes are being produced, the availability of tools that can reliably and efficiently generate genome assembly summary statistics has become critical. Additionally, with the emergence of new algorithms and data types, tools that can improve the quality of existing assemblies through automated and manual curation are required.

Results

We sought to address both these needs by developing gfastats, as part of the Vertebrate Genomes Project (VGP) effort to generate high-quality reference genomes at scale. Gfastats is a standalone tool to compute assembly summary statistics and manipulate assembly sequences in fasta, fastq, or gfa [.gz] format. Gfastats stores assembly sequences internally in a gfa-like format. This feature allows gfastats to seamlessly convert fast* to and from gfa [.gz] files. Gfastats can also build an assembly graph that can in turn be used to manipulate the underlying sequences following instructions provided by the user, while simultaneously generating key metrics for the new sequences.

Availability and implementation

Gfastats is implemented in C++. Precompiled releases (Linux, MacOS, Windows) and commented source code for gfastats are available under MIT license at https://github.com/vgl-hub/gfastats. Examples of how to run gfastats are provided in the Github. Gfastats is also available in Bioconda, in Galaxy (https://assembly.usegalaxy.eu) and as a MultiQC module [1] (https://github.com/ewels/MultiQC). An automated test workflow is available to ensure consistency of software updates.

Supplementary information

Supplementary data are available at Bioinformatics online.
A survey to assess animal methods bias in scientific publishing
Authors: Krebs Catharine E. , Lam Ann , McCarthy Janine , Constantino Helder , Sullivan Kristie
Published: March 2022

Publication of scientific findings is fundamental for research, pushing innovation and generating interventions that benefit society, but it is not without biases. Publication bias is generally recognized as journal’s preference for publishing studies based on the direction and magnitude of results. However, early evidence of a newly recognized type of publication bias has emerged in which journal policy, peer reviewers, or editors request that animal data be provided to validate studies produced using nonanimal-based approaches. We describe herein “animal methods bias” in publishing: a preference for animal-based methods where they may not be necessary or where nonanimal-based methods may be suitable, which affects the likelihood of a manuscript being accepted for publication. To gather evidence of animal methods bias, we set out to collect the experiences and perceptions of scientists and reviewers related to animal- and nonanimal-based experiments during peer review. We created a survey with 33 questions that was completed by 90 respondents working in various biological fields. Twenty-one survey respondents indicated that they have carried out animal-based experiments for the sole purpose of anticipating reviewer requests. Thirty-one survey respondents indicated that they have been asked by peer reviewers to add animal experimental data to their nonanimal study; 14 of these felt the request was sometimes justified, and 11 did not think it was justified. The data presented provide preliminary evidence of animal methods bias and indicate that status quo and conservatism biases may explain such attitudes by peer reviewers and editors.