bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evolutionary analysis of p38 stress-activated kinases in unicellular relatives of animals suggests an ancestral function in osmotic stress
Authors: Shabardina Victoria , Charria Pedro Romero , Saborido Gonzalo Bercedo , Diaz-Mora Ester , Cuenda Ana , Ruiz-Trillo Iñaki , Sanz-Ezquerro Juan Jose 
Published: May 2022

Abstractp38 kinases are key elements of the cellular stress response in animals. They mediate the cell response to a multitude of stress stimuli, from osmotic shock to inflammation and oncogenes. However, it is unknown how such diversity of function in stress evolved in this kinase subfamily. Here, we show that the p38 kinase was already present in a common ancestor of animals and fungi. Later, in animals, it diversified into three JNK kinases and four p38 kinases. Moreover, we identified a fifth p38 paralog in fishes and amphibians. Our analysis shows that each p38 paralog has specific amino acid substitutions around the hinge point, a region between the N-terminal and C-terminal protein domains. We showed that this region can be used to distinguish between individual paralogs and predict their specific. Finally, we showed that the response to hyperosmotic stress in Capsaspora owczarzaki, a close unicellular relative of animals, follows a typical for the p38 kinases pattern of phosphorylation-dephosphorylation. At the same time, Capsaspora’s cells upregulate the expression of GPD1 protein resembling an osmotic stress response in yeasts. Overall, our results show that the ancestral p38 stress pathway originated in the root of opisthokonts, most likely as a cell’s reaction to salinity change in the environment. In animals, the pathway became more complex and incorporated more stimuli and downstream targets due to the p38 sequence evolution in the docking and substrate binding sites around the hinge region. Overall, this study improves our understanding of the p38 evolution and opens new perspectives for the p38 research.
Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence
Authors: Gisby Jack S. , Buang Norzawani B. , Papadaki Artemis , Clarke Candice L. , Malik Talat H. , Medjeral-Thomas Nicholas , Pinheiro Damiola , Mortimer Paige M. , Lewis Shanice , Sandhu Eleanor , McAdoo Stephen P. , Prendecki Maria F. , Willicombe Michelle , Pickering Matthew C. , Botto Marina , Thomas David C. , Peters James E. 
Published: May 2022

AbstractPatients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we performed longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identified transcriptomic and proteomic signatures of COVID-19 severity, and found distinct temporal molecular profiles in patients with severe disease. Supervised learning revealed that the plasma proteome was a superior indicator of clinical severity than the PBMC transcriptome. We showed that both the levels and trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, are the strongest predictors of clinical outcome. Strikingly, we observed that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.
A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability
Authors: Sarhaddi Fatemeh , Kazemi Kianoosh , Azimi Iman , Cao Rui , Niela-Vilén Hannakaisa , Axelin Anna , Liljeberg Pasi , Rahmani Amir M. 
Published: May 2022

AbstractBackgroundPhotoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and heart rate variability (HRV). The method is widely used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions.ObjectiveWe evaluate the accuracy of PPG signals – collected by the Samsung Gear Sport smartwatch in free-living conditions – in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor.MethodsWe conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods.ResultsWe found a significantly high positive correlation between the smartwatch’s and Shimmer ECG’s HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch’s and shimmer ECG’s LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances.ConclusionThe Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.
Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children
Authors: Hagenbeek Fiona A. , van Dongen Jenny , Pool René , Harms Amy C. , Roetman Peter J. , Fanos Vassilios , van Keulen Britt J. , Walker Brian R. , Karu Naama , Hulshoff Pol Hilleke E. , Rotteveel Joost , Finken Martijn J.J. , Vermeiren Robert R. J. M. , Kluft Cornelis , Bartels Meike , Hankemeier Thomas , Boomsma Dorret I. 
Published: May 2022

AbstractVariation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7 - 12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The mean monozygotic (MZ) and dizygotic (DZ) correlations for urinary metabolites were 0.51 (range: 0.25-0.75) and 0.16 (range: 0.01-0.46) for the amines, 0.52 (range: 0.33-0.64) and 0.23 (range: 0.07-0.35) for the organic acids, and 0.61 (range: 0.43-0.81) and 0.25 (range: 0.11-0.44) for the steroids. Broad-sense heritability was 0.49 (range: 0.25-0.64), 0.50 (range: 0.33-0.62), and 0.64 (range: 0.43-0.81) for 50 amines, 13 organic acids, and 6 steroids, and narrow-sense heritability was 0.50 (range: 0.37-0.68), 0.50 (0.23-0.61), and 0.47 (range: 0.32-0.70) for 6 amines, 7 organic acids, and 4 steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.
A new highly-specific Natural Killer cell-specific gene signature predicting recurrence in colorectal cancer patients
Authors: Shembrey Carolyn , Behrenbruch Corina , Loveday Benjamin PT , Heriot Alexander G , Foroutan Momeneh , Hollande Frédéric 
Published: May 2022

AbstractThe protective role of Natural Killer (NK) cell tumour immunosurveillance has long been recognised in colorectal cancer (CRC). However, as most patients show limited intra-tumoral NK cell infiltration, improving our ability to identify those with high NK cell activity might aid in dissecting the molecular features which could trigger strong response to NK cell-mediated immune killing. Here, a novel CRC-specific NK cell gene signature capable of inferring NK cell load in primary tissue samples was derived and validated in multiple patient CRC cohorts. The specificity of the signature is substantiated in tumour-infiltrating NK cells from primary CRC tumours at the single cell level, and the expression profile of each constituent gene is explored in NK cells of different maturation states, activation status and anatomical origin. Thus, in contrast with other NK cell gene signatures that have several overlapping genes across different immune cell types, our NK cell signature has been extensively refined to be specific for CRC-infiltrating NK cells and includes genes which identify a broad spectrum of NK cell subtypes. Moreover, it was shown that this novel NK cell signature accurately discriminates murine NK cells, demonstrating the potential applicability of this signature when mining datasets generated from both clinical and animal studies. Differential gene expression analysis revealed tumour-intrinsic features associated with NK cell inclusion versus exclusion in CRC patients, with those tumours with predicted high NK activity showing strong evidence of enhanced chemotactic and cytotoxic transcriptional programs. Furthermore, survival modelling indicated that NK signature expression is associated with improved survival outcomes in two large cohorts of primary CRC patients. Thus, scoring CRC samples with this refined NK cell signature might aid in identifying patients with high NK cell activity who could be prime candidates for NK cell directed immunotherapies.
ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets
Authors: Zheng Ye , Caron Daniel P. , Kim Ju Yeong , Jun Seong-Hwan , Tian Yuan , Florian Mair , Stuart Kenneth D. , Sims Peter A. , Gottardo Raphael 
Published: May 2022

AbstractCITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.
Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis
Authors: Vastrad Basavaraj , Vastrad Chanabasayya 
Published: May 2022

AbstractBipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.
Bacterial plasmid-associated and chromosomal proteins have fundamentally different properties in protein interaction networks
Authors: Downing Tim , Rahm Alexander 
Published: May 2022

AbstractPlasmids facilitate horizontal gene transfer, which enables the diversification of pathogens into new anatomical and environmental niches, implying that plasmid-encoded genes can cooperate well with chromosomal genes. We hypothesise that such mobile genes are functionally different to chromosomal ones due to this ability to encode non-essential functions like antimicrobial resistance and traverse distinct host cells. The effect of plasmid-driven gene gain on protein-protein interaction network topology is an important question in this area. Moreover, the extent to which these chromosomally- and plasmid-encoded proteins interact with proteins from their own groups compared to the levels with the other group remains unclear. Here, we examined the incidence and protein-protein interactions of all known plasmid-encoded genes across representative specimens from most bacteria using all available plasmids. We found that such plasmid-encoded genes constitute ∼0.7% of the total number of genes per bacterial sample, and that plasmid genes are preferentially associated with different species but had limited taxonomical power beyond this. Surprisingly, plasmid-encoded proteins had both more protein-protein interactions compared to chromosomal proteins, countering the hypothesis that genes with higher mobility rates should have fewer protein-level interactions. Nonetheless, topological analysis and investigation of the protein-protein interaction networks’ connectivity and change in the number of independent components demonstrated that the plasmid-encoded proteins had limited overall impact in >96% of samples. This paper assembled extensive data on plasmid-encoded proteins, their interactions and associations with diverse bacterial specimens that is available for the community to investigate in more detail.Significance statementIt is well-established that plasmids drive new traits in their bacterial hosts, but the extent to which host-plasmid co-evolution is evident at the level of protein-protein interactions remains unclear. To address this, we compiled and analysed all available valid bacterial plasmids and associated proteins to explore the compositional differences between chromosomal and plasmid-encoded proteins and their interaction levels. We found that plasmid-encoded genes were highly correlated across the bacterial samples such that they had a high association with taxonomic context. Contrasting with the complexity hypothesis, plasmid-encoded proteins had far more interactions on average than chromosomal ones, though they had minimal effects on protein-protein interaction network structure. This demonstrated that host-plasmid co-evolution is evident and detectable at the level of protein interactions.
Evaluating deep learning for predicting epigenomic profiles
Authors: Toneyan Shushan , Tang Ziqi , Koo Peter K. 
Published: May 2022

ABSTRACTDeep learning has been successful at predicting epigenomic profiles from DNA sequences. Most approaches frame this task as a binary classification relying on peak callers to define functional activity. Recently, quantitative models have emerged to directly predict the experimental coverage values as a regression. As new models continue to emerge with different architectures and training configurations, a major bottleneck is forming due to the lack of ability to fairly assess the novelty of proposed models and their utility for downstream biological discovery. Here we introduce a unified evaluation framework and use it to compare various binary and quantitative models trained to predict chromatin accessibility data. We highlight various modeling choices that affect generalization performance, including a downstream application of predicting variant effects. In addition, we introduce a robustness metric that can be used to enhance model selection and improve variant effect predictions. Our empirical study largely supports that quantitative modeling of epigenomic profiles leads to better generalizability and interpretability.
Succinate mediates inflammation-induced adrenocortical dysfunction
Authors: Mateska Ivona , Witt Anke , Hagag Eman , Sinha Anupam , Yilmaz Canelif , Thanou Evangelia , Sun Na , Kolliniati Ourania , Patschin Maria , Abdelmegeed Heba , Henneicke Holger , Kanczkowski Waldemar , Wielockx Ben , Tsatsanis Christos , Dahl Andreas , Walch Axel , Li Ka Wan , Peitzsch Mirko , Chavakis Triantafyllos , Alexaki Vasileia Ismini 
Published: May 2022

AbstractThe hypothalamus-pituitary-adrenal (HPA) axis is activated in response to inflammation leading to increased production of anti-inflammatory glucocorticoids by the adrenal cortex, thereby representing an endogenous feedback loop. However, severe inflammation reduces the responsiveness of the adrenal gland to adrenocorticotropic hormone (ACTH) although the underlying mechanisms are poorly understood. Here, we show by transcriptomic, proteomic and metabolomic analyses that LPS-induced systemic inflammation triggers profound metabolic changes in steroidogenic adrenocortical cells, including downregulation of the TCA cycle and oxidative phosphorylation. Inflammation disrupts the TCA cycle at the level of succinate dehydrogenase (SDH) leading to succinate accumulation and disturbed steroidogenesis. Mechanistically, IL-1β reduces SDHB expression through upregulation of DNA methyltransferase 1 (DNMT1) and methylation of the SDHB promoter. Consequently, increased succinate levels impair oxidative phosphorylation and ATP synthesis, leading to reduced steroidogenesis. Together, we demonstrate that the IL-1β-DNMT1-SDHB-succinate axis disrupts steroidogenesis. Our findings not only provide a mechanistic explanation for the adrenal dysfunction in severe inflammation but also a potential target for therapeutic intervention.
A pipeline for assessing the quality of images and metadata from crowd-sourced databases
Authors: Billotte Jackie 
Published: May 2022

AbstractCrowd-sourced biodiversity databases provide easy access to data and images for ecological education and research. One concern with using publicly sourced databases; however, is the quality of their images, taxonomic descriptions, and geographical metadata. The method presented in this paper attempts to address this concern using a suite of pipelines to evaluate taxonomic consistency, how well geo-tagging fits known distributions, and the image quality of crowd-sourced data acquired from iNaturalist, a crowd-sourced biodiversity database. Additionally, it provides researchers that use these datasets to report a quantifiable assessment of the taxonomic consistency. The pipeline allows users to analyze multiple images from iNaturalist and their associated metadata; to determine the level of taxonomic identification (family, genera, species) for each occurrence; whether the taxonomy label for an image matches accepted nesting of families, genera, and species; and whether geo-tags match the distribution of the taxon described using occurrence data from the Global Biodiversity Infrastructure Facility (GBIF) as a reference. Additionally, image quality is assessed using BRISQUE, an algorithm that allows for image quality evaluation without a reference photo. Entries from the order Araneae (spiders) are used as a case study. Overall, the results suggest that iNaturalist can provide large metadata and image sets for research. Given the inevitability of some low-quality observations, this pipeline provides a valuable resource for researchers and educators to evaluate the quality of iNaturalist and other crowd-sourced data.
Dopamine D1agonist effects in late-stage Parkinson’s disease
Authors: Lewis Mechelle M. , Van Scoy Lauren Jodi , Mailman Richard B. , De Jesus Sol , Hakun Jonathan , Kong Lan , Yang Yang , Snyder Bethany , Duvvuri Sridhar , Gray David L. , Huang Xuemei 
Published: May 2022

AbstractBackgroundCurrent pharmacotherapy has limited efficacy and/or intolerable side effects in late-stage Parkinson’s disease (LsPD) patients whose daily life depends primarily on caregivers and palliative care. Clinical metrics inadequately gauge efficacy in LsPD patients.ObjectiveExplore if a D1/5dopamine agonist will have efficacy in LsPD that will be detected most sensitively by caregivers in a phase I study.MethodsA double-blind controlled phase Ia/b study compared the D1/5agonist PF-06412562 to levodopa/carbidopa in six LsPD patients. Throughout the study, caregivers were with the patients. Assessments included standard quantitative scales of motor function (MDS-UPDRS-III), alertness (Glasgow Coma and Stanford Sleepiness Scales), and cognition (Severe Impairment and Frontal Assessment Batteries) at baseline (Day 1) and thrice daily during drug testing (Days 2 and 3). Clinicians and caregivers completed clinical impression of change questionnaires, and caregivers participated in a qualitative exit interview. Blinded triangulation of quantitative and qualitative data was used to integrate findings.ResultsNeither traditional scales, nor clinician impression of change, detected consistent differences between treatments in the five participants who completed the study. Conversely, the overall caregiver data strongly favored PF-06412562 over levodopa in four of five patients. The most meaningful improvements converged on motor, alertness, and engagement.ConclusionD1/5agonists may offer potential benefit for LsPD patients. Caregiver perspectives with mixed method analyses may overcome limitations in standard rater/clinician-based evaluations. Further studies are warranted and need to integrate caregiver input as an essential component of outcome evaluations.Trial Registration#ClinicalTrials.gov:NCT03665454