bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of maternal-infant dyad inflammatory cytokines in pregnancies affected by maternal SARS-CoV-2 infection in early and late gestation
Authors: Taglauer Elizabeth S. , Dhole Yashoda , Boateng Jeffery , Snyder-Cappione Jennifer , Parker Samantha E. , Clarke Katherine , Juttukonda Lillian , Devera Jean , Hunnewell Jessica , Barnett Elizabeth , Jia Hongpeng , Yarrington Christina , Sabharwal Vishakha , Wachman Elisha M. 
Published: December 2021

ABSTRACTObjectiveSARS-CoV-2 infection induces significant inflammatory cytokine production in adults, but infant cytokine signatures in pregnancies affected by maternal SARS-CoV-2 are less well characterized. We aimed to evaluate cytokine profiles of mothers and their infants following COVID-19 in pregnancy.Study DesignSerum samples at delivery from 31 mother-infant dyads with maternal SARS-CoV-2 infection in pregnancy (COVID) were examined in comparison to 29 control dyads (Control). Samples were evaluated using a 13-plex cytokine assay.ResultsIn comparison with controls, interleukin (IL)-6 and interferon gamma-induced protein 10 (IP-10) were higher in COVID maternal and infant samples (p<0.05) and IL-8 uniquely elevated in COVID infant samples (p<0.05). Significant elevations in IL-6, IP-10 and IL-8 were found among both early (1st/2nd Trimester) and late (3rd Trimester) maternal SARS-CoV-2 infections.ConclusionsMaternal SARS-CoV-2 infections throughout gestation are associated with increased maternal and infant inflammatory cytokines at birth with potential to impact long-term infant health.
Structural basis for broad anti-phage immunity by DISARM
Authors: Bravo Jack P. K. , Aparicio-Maldonado Cristian , Nobrega Franklin L. , Brouns Stan J. J. , Taylor David W. 
Published: December 2021

AbstractIn the evolutionary arms race against phage, bacteria have assembled a diverse arsenal of antiviral immune strategies. While the recently discovered DISARM (Defense Island System Associated with Restriction-Modification) systems can provide protection against a wide range of phage, the molecular mechanisms that underpin broad antiviral targeting but avoiding autoimmunity remain enigmatic. Here, we report cryo-EM structures of the core DISARM complex, DrmAB, both alone and in complex with an unmethylated phage DNA mimetic. These structures reveal that DrmAB core complex is autoinhibited by a trigger loop (TL) within DrmA and binding to DNA substrates containing a 5’ overhang dislodges the TL, initiating a long-range structural rearrangement for DrmAB activation. Together with structure-guidedin vivostudies, our work provides insights into the mechanism of phage DNA recognition and specific activation of this widespread antiviral defense system.
Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation
Authors: Chereda Hryhorii , Leha Andreas , Beißbarth Tim 
Published: December 2021

AbstractHigh-throughput technologies are increasingly important in discovering prognostic molecular signatures and identifying novel drug targets. Molecular signatures can be obtained as a subset of features that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. However, feature selection is inherently unstable in this case. Several studies have identified gene sets that provide predictive success for patient prognosis, but these sets usually have only a few genes in common. The stability of feature selection (and reproducibility of identified gene sets) can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are techniques to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to explain GCNNs and to construct feature sets that are relevant to models by aggregating their individual explanations. We also applied more classical ML-based feature selection approaches and analyzed the stability, impact on the classification performance, and interpretability of selected feature sets.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrpContacttim.beissbarth@bioinf.med.uni-goettingen.de
Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics
Authors: Prakash Eva , Shrikumar Avanti , Kundaje Anshul 
Published: December 2021

AbstractDeep neural networks and support vector machines have been shown to accurately predict genome-wide signals of regulatory activity from raw DNA sequences. These models are appealing in part because they can learn predictive DNA sequence features without prior assumptions. Several methods such as in-silico mutagenesis, GradCAM, DeepLIFT, Integrated Gradients and Gkm-Explain have been developed to reveal these learned features. However, the behavior of these methods on regulatory genomic data remains an area of active research. Although prior work has benchmarked these methods on simulated datasets with known ground-truth motifs, these simulations employed highly simplified regulatory logic that is not representative of the genome. In this work, we propose a novel pipeline for designing simulated data that comes closer to modeling the complexity of regulatory genomic DNA. We apply the pipeline to build simulated datasets based on publicly-available chromatin accessibility experiments and use these datasets to bench-mark different interpretation methods based on their ability to identify ground-truth motifs. We find that a GradCAM-based method, which was reported to perform well on a more simplified dataset, does not do well on this dataset (particularly when using an architecture with shorter convolutional kernels in the first layer), and we theoretically show that this is expected based on the nature of regulatory genomic data. We also show that Integrated Gradients sometimes performs worse than gradient-times-input, likely owing to its linear interpolation path. We additionally explore the impact of user-defined settings on the interpretation methods, such as the choice of “reference”/”baseline”, and identify recommended settings for genomics. Our analysis suggests several promising directions for future research on these model interpretation methods. Code and links to data are available athttps://github.com/kundajelab/interpret-benchmark.
Plankton-infecting relatives of herpesviruses clarify the evolutionary trajectory of giant viruses
Authors: Gaïa Morgan , Meng Lingjie , Pelletier Eric , Forterre Patrick , Vanni Chiara , Fernandez-Guerra Antonio , Jaillon Olivier , Wincker Patrick , Ogata Hiroyuki , Krupovic Mart , Delmont Tom O. 
Published: December 2021

AbstractDNA viruses have a major influence on the ecology and evolution of cellular organisms, but their overall diversity and evolutionary trajectories remain elusive. Here, we performed a phylogeny-guided genome-resolved metagenomic survey of the sunlit oceans and discovered plankton-infecting relatives of herpesviruses that form a putative new phylum dubbed ‘Mirusviricota’. The virion morphogenesis module of this large monophyletic clade is typical of viruses from the realmDuplodnaviria, with the major capsid protein fold being a likely structural intermediate between the capsid proteins ofCaudoviricetes(tailed phages) andHerpesvirales(animal-infecting viruses). Yet, a substantial fraction of ‘Mirusviricota’genes, including hallmark transcription machinery genes missing in herpesviruses, are closely related homologs of large and giant eukaryotic DNA viruses from another viral realm. The remarkable chimeric attributes of ‘Mirusviricota’ provide missing links in the evolution of both herpesviruses and giant viruses. Furthermore, mirusviruses are widespread and transcriptionally active from pole to pole, encoding complex functional traits used during the infection of microbial eukaryotes. The ‘Mirusviricota’ prevalence, functional activity, diversification, and atypical evolutionary traits point to a lasting role of mirusviruses in the ecology of marine ecosystems that might have not only predated but also contributed to the emergence of herpesviruses and giant viruses.
Comparative analysis of two NGS platforms and different databases for analysis of AMR genes
Authors: Soni Twinkle , Pandit Ramesh , Blake Damer , Joshi Chaitanya , Joshi Madhvi 
Published: December 2021

AbstractThe use of antibiotics in human medicine and livestock production has contributed to the widespread occurrence of antimicrobial resistance (AMR). Recognizing the relevance of AMR to human and livestock health, it is important to assess the occurrence of genetic determinants of resistance in medical, veterinary, and public health settings in order to understand risks of transmission and treatment failure. Advances in Next Generation Sequencing (NGS) technologies have had a significant impact on research in microbial genetics and microbiome analyses. Now, strategies for high throughput sequencing from panels of PCR amplicons representing known AMR genes offer opportunities for targeted characterization of complex microbial populations. Aim of the present study was to compare the Illumina MiSeq and Ion Torrent S5 Plus sequencing platforms for use with the Ion AmpliSeq™ AMR Research Panel in a veterinary/public health setting. All samples were processed in parallel for the two sequencing technologies, subsequently following a common bioinformatics workflow to define the occurrence and abundance of AMR gene sequences. Regardless of sequencing platform, the results were closely comparable with minor differences. The Comprehensive Antibiotic Resistance Database (CARD), QIAGEN Microbial Insight - Antimicrobial Resistance (QMI-AR), Antimicrobial resistance database (AR), and CARD-CLC databases were compared for analysis, with the most genes identified using CARD. Drawing on these results we describe an end-to-end workflow for AMR gene analysis using NGS.
Network-guided supervised learning on gene expression using a graph convolutional neural network
Authors: Yingtaweesittikul Hatairat , Suphavilai Chayaporn 
Published: December 2021

AbstractBackgroundTranscriptomic profiles have become crucial information in understanding diseases and improving treatments. While dysregulated gene sets are identified via pathway analysis, various machine learning models have been proposed for predicting phenotypes such as disease type and drug response based on gene expression patterns. However, these models still lack interpretability, as well as the ability to integrate prior knowledge from a protein-protein interaction network.ResultsWe proposeGrandline, a graph convolutional neural network that can integrate gene expression data and structure of the protein interaction network to predict a specific phenotype. Transforming the interaction network into a spectral domain enables convolution of neighbouring genes and pinpointing high-impact subnetworks, which allow better interpretability of deep learning models. Grandline achieves high phenotype prediction accuracy (67-85% in 8 use cases), comparable to state-of-the-art machine learning models while requiring a smaller number of parameters, allowing it to learn complex but interpretable gene expression patterns from biological datasets.ConclusionTo improve the interpretability of phenotype prediction based on gene expression patterns, we developed Grandline using graph convolutional neural network technique to integrate protein interaction information. We focus on improving the ability to learn nonlinear relationships between gene expression patterns and a given phenotype and incorporation of prior knowledge, which are the main challenges of machine learning models for biological datasets. The graph convolution allows us to aggregate information from relevant genes and reduces the number of trainable parameters, facilitating model training for a small-sized biological dataset.
Improving the computation efficiency of polygenic risk score modeling: Faster in Julia
Authors: Faucon Annika , Samaroo Julian , Ge Tian , Davis Lea K. , Tao Ran , Cox Nancy J. , Shuey Megan M. 
Published: December 2021

ABSTRACTTo enable large-scale application of polygenic risk scores in a computationally efficient manner we translate a widely used polygenic risk score construction method, Polygenic Risk Score – Continuous Shrinkage (PRS-CS), to the Julia programing language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average run time by 5.5x. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes utilization of polygenic risk scores by lowering the computational burden of the PRS-CS method.
Hybrid Gaussian Process Models for continuous time series in bolus fed-batch cultures
Authors: Cruz-Bournazou M. Nicolás , Narayanan Harini , Fagnani Alessandro , Butté Alessandro 
Published: December 2021

AbstractHybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial neural network, support vector regression), while the mechanistic part is typically using differential equations to describe the dynamics of the process based on its current state. In this work we present an alternative hybrid model formulation that merges the advantages of Gaussian Process State Space Models and the numerical approximation of differential equation systems through full discretization.The use of Gaussian Process Models to describe complex bioprocesses in batch, fed-batch, has been reported in several applications. Nevertheless, handling the dynamics of the states of the system, known to have a continuous time-dependent evolution governed by implicit dynamics, has proven to be a major challenge. Discretization of the process on the sampling steps is a source of several complications, as are: 1) not being able to handle multi-rate date sets, 2) the step-size of the derivative approximation is defined by the sampling frequency, and 3) a high sensitivity to sampling and addition errors. We present a coupling of polynomial regression with Gaussian Process Models as representation of the right-hand side of the ordinary differential equation system and demonstrate the advantages in a typical fed-batch cultivation for monoclonal antibody production.
Construction and characterization of a genome-scale ordered mutant collection of Bacteroides thetaiotaomicron
Authors: Arjes Heidi A. , Sun Jiawei , Liu Hualan , Nguyen Taylor H. , Culver Rebecca N. , Celis Arianna I. , Walton Sophie Jean , Vasquez Kimberly S. , Yu Feiqiao Brian , Xue Katherine S. , Newton Daniel , Zermeno Ricardo , Weglarz Meredith , Deutschbauer Adam , Huang Kerwyn Casey , Shiver Anthony L. 
Published: December 2021

AbstractGenomic analyses have revealed how the gut microbiota impacts human health. However, knowledge about the physiology of most gut commensals is largely lacking. Here, we sorted cells from a pooled library to construct an ordered collection of transposon-insertion mutants in the model commensal Bacteroides thetaiotaomicron. We applied a pooling strategy with barcode sequencing to locate mutants and created a condensed collection with single insertions in 2,565 genes. This effort enabled the development of an accurate model for progenitor-collection assembly, which identified strain-abundance biases and multi-insertion strains as important factors that limit coverage. To demonstrate the potential for phenotypic screening, we analyzed growth dynamics and morphology of the condensed collection and identified growth defects and altered cell shape in the sphingolipid-synthesis gene BT0870 and the thiamine scavenging gene BT2397. Analyses of this collection and utilization of the platform described herein to construct future ordered libraries will increase understanding of gut commensal physiology and colonization strategies.
Encoding of odors by mammalian olfactory receptors
Authors: Vihani Aashutosh , Nagai Maira H. , Juan Conan , de March Claire A. , Hu Xiaoyang S. , Pearson John , Matsunami Hiroaki 
Published: December 2021

AbstractIdentified ligands for > 500 mouse ORsORs are specifically tuned towards individual odorants and their molecular propertiesOdor molecular properties are informative of odor responsesPredictive modeling and convergent evolution analyses suggest specific residues within a canonical location for odorant bindingOlfactory receptors (ORs) constitute the largest multi-gene family in the mammalian genome, with hundreds to thousands of loci in humans and mice respectively1. The rapid expansion of this massive family of genes has been generated by numerous duplication and diversification events throughout evolutionary history. This size, similarity, and diversity has made it challenging to define the principles by which ORs encode olfactory stimuli. Here, we performed a broad surveying of OR responses, using an in vivo strategy, against a diverse panel of odorants. We then used the resulting interaction profiles to uncover relationships between OR responses, odorants, odor molecular properties, and OR sequences. Our data and analyses revealed that ORs generally exhibited sparse tuning towards odorants and their molecular properties. Odor molecular property similarity between pairs of odorants was informative of odor response similarity. Finally, ORs sharing response to an odorant possessed amino acids at poorly conserved sites that exhibited both, predictive power towards odorant selectivity and convergent evolution. The localization of these residues occurred primarily at the interface of the upper halves of the transmembrane domains, implying that canonical positions govern odor selectivity across ORs. Altogether, our results provide a basis for translating odorants into receptor neuron responses for the unraveling of mammalian odor coding.
Chloroplast-localized translation for protein targeting in Chlamydomonas reinhardtii
Authors: Sun Yi , Bakhtiari Shiva , Valente-Paterno Melissa , Wu Yanxia , Law Christopher , Dai Daniel , Dhaliwal James , Bui Khanh Huy , Zerges William 
Published: December 2021

AbstractTranslation is localized within cells to target proteins to their proper locations. We asked whether translation occurs on the chloroplast surface in Chlamydomonas and, if so, whether it is involved in co-translational protein targeting, aligned spatially with localized translation by the bacterial-type ribosomes within this organelle, or both. Our results reveal a domain of the chloroplast envelope which is bound by translating ribosomes. Purified chloroplasts retained ribosomes and mRNAs encoding two chloroplast proteins specifically on this “translation domain”, but not a mRNA encoding a cytoplasmic protein. Ribosomes clusters were seen on this domain by electron tomography. Activity of the chloroplast-bound ribosomes is supported by results of the ribopuromycylation and puromycin-release assays. Co-translational chloroplast protein import is supported by nascent polypeptide dependency of the ribosome-chloroplast associations. This cytoplasmic translation domain aligns localized translation by organellar bacterial-type ribosomes in the chloroplast. This juxtaposition the dual translation systems facilitates the targeting and assembly of the polypeptide products.One-Sentence SummaryTranslation is localized to a domain of the chloroplast envelope for co-translational protein targeting in Chlamydomonas.
Single-cell transcriptome atlas of Drosophila gastrula 2.0
Authors: Sakaguchi Shunta , Okochi Yasushi , Tanegashima Chiharu , Nishimura Osamu , Uemura Tadashi , Kadota Mitsutaka , Naoki Honda , Kondo Takefumi 
Published: December 2021

AbstractDuring development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing both accurate spatial and lineage information is still lacking. Here, we report a new single-cell transcriptome atlas of Drosophila gastrulae, divided into 65 transcriptomically distinct clusters. We found that the expression profiles of plasma-membrane-related genes, but not those of transcription factor genes, represented each germ layer, supporting the nonequivalent contribution of each transcription factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstructed the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.
Effects of homoeologous exchange on gene expression and alternative splicing in a newly formed allotetraploid wheat
Authors: Zhang Zhibin , Xun Hongwei , Lv Ruili , Gou Xiaowan , Ma Xintong , Li Juzuo , Zhao Jing , Li Ning , Gong Lei , Liu Bao 
Published: December 2021

AbstractHomoeologous exchange (HE) is a major mechanism generating post-polyploidization genetic variation with important evolutionary consequences. However, the direct impacts of HE without entangling with additional evolutionary forces on gene expression remains to be fully understood. Here, we analyzed high-throughput RNA-seq data of young leaves from individuals of a synthetic allotetraploid wheat (AADD), which contain variable numbers of HEs. We aimed to investigate if and to which extent HE directly impacts gene expression and alternative splicing (AS). We found that HE impacts expression of genes located within HE regions primarily via cis-acting dosage effect, which led to significant changes in the total expression of homoeolog pairs, especially for homoeologs whose original expression was biased. In parallel, HE influences expression of a large amount of genes residing in non-HE regions by trans-regulation leading to convergent expression of homoeolog pairs. Intriguingly, when taking into account of the original relative homoeolog expression states, homoeolog pairs under trans-effect are more prone to showing convergent response to HE whereas those under cis-effect trended to show subgenome-specific expression. Moreover, HE induced quantitative, largely individual-specific, changes of alternative splicing (AS) events. Like homoeologs expression, homoeo-AS events which related to trans effect were more responsive to HE. HE therefore exerts multifaceted immediate effects on gene expression and, to a less extent, also transcript diversity in nascent allopolyploidy.