bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deletion mapping of regulatory elements for GATA3 reveals a distal T helper 2 cell enhancer involved in allergic diseases
Authors: Chen Hsiuyi V. , Fiaux Patrick C. , Sen Arko , Luthra Ishika , Ho Aaron J. , Chen Aaron R. , Guruvayurappan Karthik , Lorenzini Michael H. , O’Connor Carolyn , McVicker Graham 
Published: May 2022

AbstractThe GATA3 gene is essential for T cell differentiation and is surrounded by risk variants for immune traits. Interpretation of these variants is challenging because the regulatory landscape of GATA3 is complex with dozens of potential enhancers spread across a large topological associating domain (TAD) and gene expression quantitative trait locus (eQTL) studies provide limited evidence for variant function. Here, we perform a tiling deletion screen in Jurkat T cells to identify 23 candidate regulatory elements. Using small deletions in primary T helper 2 (Th2) cells, we validate the function of five of these elements, two of which contain risk variants for asthma and allergic diseases. We fine-map genome-wide association study (GWAS) signals in a distal regulatory element, 1 Mb downstream, to identify 14 candidate causal variants. Small deletions spanning candidate rs725861 decrease GATA3 expression in Th2 cells suggesting a causal mechanism for this variant in allergic diseases. Our study demonstrates the power of integrating GWAS signals with deletion mapping and identifies critical regulatory sequences for GATA3.
Coral environmental history is primary driver of algal symbiont composition, despite a mass bleaching event
Authors: de Souza Mariana Rocha , Caruso Carlo , Ruiz-Jones Lupita , Drury Crawford , Gates Ruth D. , Toonen Robert J. 
Published: May 2022

AbstractCoral reefs are iconic examples of climate change impacts because climate-induced heat stress causes the breakdown of the coral-algal symbiosis leading to a spectacular loss of color, termed ‘coral bleaching’. To examine the fine-scale dynamics of this process, we re-sampled 600 individually marked Montipora capitata colonies from across Kāne‘ohe Bay, Hawai’i and compared the algal symbiont composition before and after the 2019 bleaching event. The relative proportion of the heat-tolerant symbiont Durusdinium in corals increased in most parts of the bay following the bleaching event. Despite this widespread increase in abundance of Durusdinium, the overall algal symbiont community composition was largely unchanged, and hydrodynamically defined regions of the bay retained their distinct pre-bleaching compositions. Furthermore, depth and temperature variability were the most significant drivers of Symbiodiniaceae community composition by site regardless of bleaching intensity or change in relative proportion of Durusdinium. Our results suggest that the plasticity of symbiont composition in corals may be constrained to adaptively match the long-term environmental conditions surrounding the holobiont, independent of an individual coral’s stress and bleaching response.
Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data
Authors: Geras Agnieszka , Shafighi Shadi Darvish , Domżał Kacper , Filipiuk Igor , Rączkowski Łukasz , Toosi Hosein , Kaczmarek Leszek , Koperski Łukasz , Lagergren Jens , Nowis Dominika , Szczurek Ewa 
Published: May 2022

AbstractSpatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperformed other methods on simulated data, successfully indicated known brain structures and spatially distinguished between inhibitory and excitatory neuron types based in mouse brain tissue, and dissected large heterogeneity of immune infiltrate composition in prostate gland tissue.
Improving and evaluating deep learning models of cellular organization
Authors: Sun Huangqingbo , Fu Xuecong , Abraham Serena , Shen Jin , Murphy Robert F 
Published: May 2022

AbstractMotivationCells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is not sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently-labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred.ResultsWe have developed improved GAN-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images.AvailabilityA Reproducible Research Archive containing all source code, generated images and analysis results will be made available at http://murphylab.cbd.cmu.edu/Software upon publication.Contactmurphy@cmu.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Transcriptional control of adipogenesis by PATZ1
Authors: Patel Sanil , Sparman Njeri , Sadeh Shani , Wang Jeixin , Whitelegge Julian P. , Fried Susan K. , Waki Hironori , Villanueva Claudio J. , Seldin Marcus , Sakaguchi Shinya , Ellmeier Wilfried , Tontonoz Peter , Rajbhandari Prashant 
Published: May 2022

AbstractWhite adipose tissues (WAT) play a central role in lipid storage and systemic energy, lipid, and glucose homeostasis. Understanding the intricacies of adipocyte formation could inform therapies for obesity and metabolic disorders. We have identified the POZ/BTB and AT Hook Containing Zinc Finger 1 (PATZ1) protein as an adipogenic transcription factor through an unbiased high-throughput cDNA screen for modulators of adipogenesis. PATZ1 is expressed by both human and mouse adipocyte precursor cells (APC) and adipocytes, and its expression negatively correlates with obesity. In cell models, PATZ1 expression promotes adipogenesis through a mechanism dependent on protein-protein interaction and DNA binding. Adipose-specific ablation of PATZ1 in mice leads to decreased lipid storage and fat mass and protection from diet-induced obesity. Genome-wide PATZ1 DNA binding analyses using ChIP-Seq suggest that PATZ1 facilitates adipogenesis through interactions with transcription factor machinery at the promoter regions of key adipogenic factors and histone modifiers. Global PATZ1 protein interaction studies using immunoprecipitation-mass spectrometry (IP-MS) suggest that General Transcription Factor 2I (GTF2I) binding to PATZ1 forms a repressive complex, and knockdown of GTF2I augments PATZ1 adipogenic function. Collectively these findings identify PATZ1 as a regulator of the adipocyte differentiation programs and advance our understanding of the complex transcriptional mechanisms underlying adipose tissue development and homeostasis.
Synechococcusnitrogen gene loss in iron-limited ocean regions
Authors: Sharpe Garrett , Zhao Liang , Meyer Meredith G. , Gong Weida , Burns Shannon M. , Tagliabue Allesandro , Buck Kristen N. , Santoro Alyson E. , Graff Jason R. , Marchetti Adrian , Gifford Scott 
Published: May 2022

AbstractSynechococcusare the most abundant cyanobacteria in high latitude regions and are responsible for an estimated 17% of annual marine primary productivity. Despite their biogeochemical importance,Synechococcuspopulations have been unevenly sampled across the ocean, with most studies focused on low-latitude strains. In particular, the near absence ofSynechococcusgenomes from high-latitude, High Nutrient Low Chlorophyll (HNLC) regions leaves a gap in our knowledge of picocyanobacterial adaptation to iron limitation and their influence on carbon, nitrogen, and iron cycles. We examinedSynechococcuspopulations from the subarctic North Pacific, a well-characterized HNLC region, with quantitative metagenomics. Assembly with short and long reads produced two near completeSynechococcusmetagenome-assembled genomes (MAGs). Quantitative metagenome-derived abundances of these populations matched well with flow cytometry counts, and theSynechococcusMAGs were estimated to comprise >99% of theSynechococcusat Station P. Whereas the Station PSynechococcusMAGs contained multiple genes for adaptation to iron limitation, both genomes lacked genes for uptake and assimilation of nitrate and nitrite, suggesting a dependence on ammonium, urea, and other forms of recycled nitrogen leading to reduced iron requirements. A global analysis ofSynechococcusnitrate reductase abundance in the TARA Oceans dataset found nitrate assimilation genes are also lower in other HNLC regions. We propose nitrate and nitrite assimilation gene loss inSynechococcusrepresents an adaptation to severe iron limitation in high-latitude regions where ammonium availability is higher. Our findings have implications for models that quantify the contribution of cyanobacteria to primary production and subsequent carbon export.SignificanceThe cyanobacteriumSynechococcusis a major contributor to ocean primary production and biogeochemistry. Here, we used quantitative metagenomics to assemble and enumerate twoSynechococcusgenomes from an iron-limited, High Nutrient Low Chlorophyll region. We show these genomes represent the majority ofSynechococcuscells at the site and are the first knownSynechococcusunable to assimilate either nitrate or nitrite. This gene loss is likely due to the high iron quota of these proteins and predominant availability of recycled forms of nitrogen.Synechococcus’loss of nitrate assimilation affects their role in elemental cycles (e.g., carbon, nitrogen, and iron), limits their potential for carbon export, and enhances our understanding ofSynechococcusevolution in response to nutrient limitation and competition.
How important is budding speciation for comparative studies?
Authors: Caetano Daniel S. , Quental Tiago Bosisio 
Published: May 2022

AbstractThe acknowledgment of evolutionary dependence among species has fundamentally changed how we ask biological questions. Phylogenetic models became the standard approach for studies with three or more lineages, in particular those using extant species. Most phylogenetic comparative methods (PCMs) translate relatedness into covariance, meaning that evolutionary changes before lineages split should be interpreted together whereas after the split lineages are expected to change independently. This clever realization has shaped decades of research. Here we discuss one element of the comparative method often ignored or assumed as unimportant: if nodes of a phylogeny represent the dissolution of the ancestral lineage into two new ones or if the ancestral lineage can survive speciation events (i.e., budding). Budding speciation is often reported in paleontological studies, due to the nature of the evidence for budding in the fossil record, but it is surprisingly absent in comparative methods. Here we show that many PCMs assume that divergence happens as a symmetric split, even if these methods don’t explicitly mention this assumption. We discuss the properties of trait evolution models for continuous and discrete traits and their adequacy under a scenario of budding speciation. We discuss the effects of budding speciation under a series of plausible evolutionary scenarios and show when and how these can influence our estimates. We also propose that long-lived lineages that have survived through a series of budding speciation events and given birth to multiple new lineages can produce evolutionary patterns that challenge our intuition about the most parsimonious history of trait changes in a clade. We hope our discussion can help bridge comparative approaches in paleontology and neontology as well as foster awareness about the assumptions we make when we use phylogenetic trees.
The TCR Cα domain regulates responses to self-pMHCII
Authors: Kim Caleb Y. , Parrish Heather L. , Kuhns Michael S. 
Published: May 2022

AbstractT-cells play a central role in adaptive immunity by recognizing peptide-antigens presented in MHC molecules (pMHC) via their clonotypic T-cell receptors (TCRs). αβTCRs are heterodimers, consisting of TCRα and TCRβ subunits that are composed of variable (Vα, Vβ) and constant (Cα, Cβ) domains. While the Vα, Vβ, and Cβ domains adopt typical immunoglobulin (Ig) folds in the extracellular space, the Cα domain lacks a top β sheet and instead has two loosely associated top strands (C and F strands) on its surface. Previous results suggest that this unique Ig-like fold mediates homotypic TCR interactions and influences signalingin vitro. To better understand why evolution has selected this unique structure, we asked: what is the fitness cost for development and function of CD4+T cells bearing a mutation in the Cα C-strand? In both TCR retrogenic and transgenic mice we observed increased single positive thymocytes bearing mutant TCRs compared with those expressing wild type TCRs. Furthermore, our analysis of mutant TCR transgenic mice revealed an increase in naive CD4+T cells experiencing strong tonic TCR signals, increased homeostatic survival, and increased recruitment of responders to cognate pMHCII upon immunization, compared to wild type. The mutation did not, however, overtly impact CD4+T cell proliferation or differentiation after immunization. We interpret these data as evidence that the unique Cα domain has evolved to fine-tune TCR signaling, particularly in response to weak interactions with self-pMHCII.
Short tandem repeats bind transcription factors to tune eukaryotic gene expression
Authors: Horton Connor A. , Alexandari Amr M. , Hayes Michael G. B. , Marklund Emil , Schaepe Julia M. , Aditham Arjun K. , Shah Nilay , Shrikumar Avanti , Afek Ariel , Greenleaf William J. , Gordân Raluca , Zeitlinger Julia , Kundaje Anshul , Fordyce Polly M. 
Published: May 2022

AbstractShort tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and their polymorphisms alter gene expression, yet how they regulate transcription remains unknown. We find that STRs can modulate transcription factor (TF)-DNA affinities and on rates by up to 70-fold by directly binding TF DNA-binding domains, with energetic impacts approaching or exceeding mutations to consensus sites. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density near motifs to speed target search. Confirming that STRs also impact TF binding in cells, neural networks trained only on in vivo occupancies predict identical effects to those observed in vitro. Approximately 90% of TFs preferentially bind STRs that need not resemble known motifs, providing a novel cis-regulatory mechanism to target TFs to cognate sites.
DeepPHiC: Predicting promoter-centered chromatin interactions using a novel deep learning approach
Authors: Agarwal Aman , Chen Li 
Published: May 2022

AbstractMotivationPromoter-centered chromatin interactions, which include promoter-enhancer and promoter-promoter interactions, are important to decipher gene regulation and disease mechanisms. The development of next generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues or cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions.ResultsTo overcome these challenges, we develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features including genomic sequence, epigenetic signal and anchor distance to predict tissue/cell type-specific genome-wide promoter-enhancer and promoter-promoter interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework. We demonstrate that the proposed approach outperforms state-of-the-art deep learning methods and is robust to the inclusion of anchor distance as a feature. In addition, we find that the proposed approach can achieve comparable prediction performance using biologically relevant tissues/cell types compared to using all tissues/cell types especially for predicting promoter-enhancer interactions.Availabilityhttps://github.com/lichen-lab/DeepPHiC
plASgraph - using graph neural networks to detect plasmid contigs from an assembly graph
Authors: Sielemann Janik , Sielemann Katharina , Brejová Broňa , Vinař Tomáš , Chauve Cedric 
Published: May 2022

AbstractIdentification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. In our work, we provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. Unlike previous machine-learning approaches for this problem, which classify individual contigs separately, we employ graph neural networks (GNNs) to include information from the assembly graph. Propagation of information from nearby nodes in the graph allows accurate classification of even short contigs that are difficult to classify based on sequence features or database searches alone.Our new species-agnostic software tool plASgraph outperforms recently developed PlasForest, which uses database searches to supplement sequence-based features. Since our tool does not rely on existing plasmid databases, it is more suitable for classification of contigs in novel species and discovery of previously unknown plasmid sequences. Our tool can also be trained on a specific species, and in that scenario it outperforms mlplasmids trained on the same species.On one hand, our work provides a new, accurate, and easy to use tool for plasmid classification; on the other hand, it serves as a motivation for more widespread use of GNNs in bioinformatics, such as in pangenome sequence analysis, where sequence graphs serve as a fundamental data structure.Availabilityhttps://github.com/cchauve/plASgraph
Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue
Authors: Shi Jindou , Tu Haohua , Park Jaena , Marjanovic Marina , Higham Anna M. , Luckey Natasha N. , Cradock Kimberly A. , Liu Z. George , Boppart Stephen A. 
Published: May 2022

AbstractWith the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised Deep Learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a Multiple Instance Learning-based classifier for the identification of regions of interest in coarsely labeled images, and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal to explore unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.
Rapid Loss of CD4 T Cells by Pyroptosis During Acute SIV Infection in Rhesus Macaques
Authors: He Xuan , Aid Malika , Ventura John D. , Borducchi Erica , Lifton Michelle , Liu Jinyan , Barouch Dan H. 
Published: May 2022

AbstractThe mechanisms underlying depletion of CD4 T cells during acute HIV-1 infection are not well understood. Here we show that caspase-1-induced pyroptosis, a highly inflammatory programmed cell death pathway, is the dominant mechanism responsible for the rapid depletion of CD4 T cells in gut-associated lymphatic tissue (GALT), spleen, and lymph nodes during acute simian immunodeficiency virus (SIV) infection in rhesus macaques. Upregulation of interferon-gamma inducible factor 16 (IFI16), a host DNA sensor that triggers pyroptosis, was also observed in tissue-resident CD4 T cells and correlated with viral loads and CD4 T cell loss. In contrast, caspase-3-mediated apoptosis and viral cytotoxicity only accounted for a small fraction of CD4 T cell death. Other programmed cell death mechanisms, including mitochondria-induced caspase-independent cell death, necroptosis, and autophagy, did not significantly contribute to CD4 T cell depletion. These data support a model in which caspase-1-mediated pyroptosis is the principal mechanism that results in CD4 T cell loss in the GALT and lymphoid organs and release of proinflammatory cytokines. These findings contribute to our understanding of the pathogenesis of acute SIV infection and have important implications for the development of therapeutic strategies.ImportanceDifferent mechanisms for CD4 T cell depletion during acute HIV-1 infection have been proposed. In this study, we should in SIV infected rhesus macaques that depletion of CD4 T cells is primarily due to pyroptosis. Other pathophysiologic mechanisms may also contribute in a minor way to CD4 T cell depletion.