Biology subjects

Barton, C.

Publications and source records attributed to Barton, C..

14 recordsLinked to original sources

bioRxiv2025

microRNA-21 promotes dysregulated lipid metabolism and hepatocellular carcinoma

Backgrounds and AimsThe prevalence of hepatocellular carcinoma (HCC) is rising in parallel with increasing obesity and metabolic dysfunction-associated steatohepatitis (MASH). MicroRNAs are key post-transcriptional regulators of gene expression and are attractive targets for HCC therapy. Here we sought to identify and characterize dysregulated microRNAs in MASH-driven HCC (MASH-HCC). Approach and ResultsWe profiled microRNA expression in liver tissue from patients with MASH and/or MASH-HCC and in zebrafish HCC driven by activated {beta}-catenin (ABC), one of the most commonly mutated oncogenes in MASH-HCC. We found significant overlap between dysregulated human and zebrafish miRNAs, including miR-21, which was increasingly upregulated from normal liver to MASH to MASH-HCC. We generated transgenic zebrafish that overexpress or sponge (downregulate) miR-21. We found that miR-21 overexpression caused larval liver overgrowth and increased HCC while miR-21 sponge suppressed {beta}-catenin-driven larval liver overgrowth. By performing histologic and lipidomic analysis, we found that overexpression of miR-21, like ABC, suppressed lipid accumulation in response to a high cholesterol diet and increased accumulation of acylcarnitines. ConclusionsHere we characterize microRNA dysregulation in MASH and MASH-HCC in patients, identify miR-21 as increasingly dysregulated from MASH to MASH-HCC, and delineate the impacts of miR-21 overexpression on lipid metabolism and hepatocarcinogenesis in zebrafish {beta}-catenin-driven HCC. This study shows that miR-21, which is similarly dysregulated in human and zebrafish HCC, promotes lipid metabolic changes that may help drive hepatocarcinogenesis.

cancer biology↗

bioRxiv2023

copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling

T cell receptor (TCR) repertoire diversity enables the antigen-specific immune responses against the vast space of possible pathogens. Identifying TCR-antigen binding pairs from the large TCR repertoire and antigen space is crucial for biomedical research. Here, we introduce copepodTCR, an open-access tool to design and interpret high-throughput experimental TCR specificity assays. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, that can be used to identify the specificity of (or "deorphanize") TCRs. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased interpretation, identifies the response-eliciting peptide sequence for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up. Using in silico simulations, we demonstrate the varied experimental settings in which copepodTCR yields efficient and interpretable TCR specificity results. We validated our approach on a library of 253 overlapping peptides covering the SARS-CoV-2 spike protein, split across 12 pools. A single stimulation with combinatorial pools identified the correct epitope of two TCRs with known specificity and then deorphanized two SARS-CoV-2 associated TCRs shared among a large cohort of COVID-19 patients. We provide experimental guides to efficiently design larger screens covering thousands of peptides which will be crucial to identify antigen-specific T cells and their targets from limited clinical material.

immunology↗

bioRxiv2023

Elucidating human ageing-related phenotypic abnormalities with a novel hierarchical feature selection-based knowledge discovery framework

Ageing is a complex biological process involving multiple genes that are also related to phenotypic abnormalities. However, the micro view of the associations between ageing and human phenotypic abnormalities is still under-studied. We propose a new hierarchical feature selection method namely HIP+, which selects positive hierarchical information-preserving features according to pre-defined hierarchical ontology information. The experimental results confirm that HIP+ obtained better performance than the state-of-the-art hierarchical feature selection method in predicting human phenotypic abnormalities annotations. We further propose a HIP+-based knowledge discovery framework that also successfully highlights some important associations between biological processes and human phenotypic abnormalities.

bioinformatics↗

bioRxiv2021

The Medaka Inbred Kiyosu-Karlsruhe (MIKK) Panel

Unraveling the relationship between genetic variation and phenotypic traits remains a fundamental challenge in biology. Mapping variants underlying complex traits while controlling for confounding environmental factors is often problematic. To address this, we have established a vertebrate genetic resource specifically to allow for robust genotype-to-phenotype investigations. The teleost medaka (Oryzias latipes) is an established genetic model system with a long history of genetic research and a high tolerance to inbreeding from the wild. Here we present the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel: the first near-isogenic panel of 80 inbred lines in a vertebrate model derived from a wild founder population. Inbred lines provide fixed genomes that are a prerequisite for the replication of studies, studies which vary both the genetics and environment in a controlled manner and functional testing. The MIKK panel will therefore enable phenotype-to-genotype association studies of complex genetic traits while allowing for careful control of interacting factors, with numerous applications in genetic research, human health, and drug development and fundamental biology. Here we present a detailed characterisation of the genetic variation across the MIKK panel, which provides a rich and unique genetic resource to the community by enabling large-scale experiments for mapping complex traits.

genomics↗

bioRxiv2021

Genomic variations and epigenomic landscape of the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel

The teleost medaka (Oryzias latipes) is a well-established vertebrate model system, with a long history of genetic research, and multiple high-quality reference genomes available for several inbred strains (HdrR, HNI and HSOK). Medaka has a high tolerance to inbreeding from the wild, thus allowing one to establish inbred lines from wild founder individuals. We have exploited this feature to create an inbred panel resource: the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel. This panel of 80 near-isogenic inbred lines contains a large amount of genetic variation inherited from the original wild population. We used Oxford Nanopore Technologies (ONT) long read data to further investigate the genomic and epigenomic landscapes of a subset of the MIKK panel. Nanopore sequencing allowed us to identify a much greater variety of high-quality structural variants compared with Illumina sequencing. We also present results and methods using a pan-genome graph representation of 12 individual medaka lines from the MIKK panel. This graph-based reference MIKK panel genome revealed novel differences between the MIKK panel lines compared to standard linear reference genomes. We found additional MIKK panel-specific genomic content that would be missing from linear reference alignment approaches. We were also able to identify and quantify the presence of repeat elements in each of the lines. Finally, we investigated line-specific CpG methylation and performed differential DNA methylation analysis across the 12 lines. We thus present a detailed analysis of the MIKK panel genomes using long and short read sequence technologies, creating a MIKK panel specific pan genome reference dataset allowing for the investigation of novel variation types that would be elusive using standard approaches.

genomics↗

bioRxiv2021

Visualization of loop extrusion by DNA nanoscale tracing in single cells

The spatial organization of the genome is essential for its functions, including gene expression, DNA replication and repair, as well as chromosome segregation. Biomolecular condensates and loop extrusion have been proposed as the principal driving forces that underlie the formation of chromatin compartments and topologically associating domains, respectively. However, whether the actual 3D-fold of DNA in single cells is consistent with these mechanisms has been difficult to address in situ. Here, we present LoopTrace, a workflow for nanoscale 3D imaging of the genome sequence in structurally well-preserved nuclei in single human cells. Tracing the in situ structure of DNA in thousands of individual cells reveals that genomic DNA folds as a flexible random coil in the absence of loop extruding enzymes such as Cohesin. In the presence of Cohesin and its boundary factor CTCF, reproducibly positioned loop structures dominate the folds, while Cohesin alone leads to randomly positioned loops. The 3D structure and size variability of DNA loops we observe in a large number of single cells allow us to formulate a data-constrained computational model of genomic DNA folding that explains how sparse and dynamic loops in single cells lead to the emergence of compact topological domains in averages of cell populations.

cell biology↗

bioRxiv2020

An integrated gene catalog of the zebrafish gut microbiome reveals significant homology with mammalian microbiomes

Gut microbiome research increasingly utilizes zebrafish (Danio rerio) given their amenability to high-throughput experimental designs. However, the utility of zebrafish for discerning translationally relevant host-microbiome interactions is constrained by a paucity of knowledge about the biological functions that zebrafish gut microbiota can execute, how these functions associate with zebrafish physiology, and the degree of homology between the genes encoded by the zebrafish and human gut microbiomes. To address this knowledge gap, we generated a foundational catalog of zebrafish gut microbiome genomic diversity consisting of 1,569,102 non-redundant genes from twenty-nine individual fish. We identified hundreds of novel microbial genes as well as dozens of biosynthetic gene clusters of potential clinical interest. The genomic diversity of the zebrafish gut microbiome varied significantly across diets and this variance associated with altered expression of intestinal genes involved in inflammation and immune activation. Zebrafish, mouse, and human fecal microbiomes shared > 50% of their total genomic diversity and the vast majority of gene family abundance for each individual metagenome ([~]99%) was accounted for by genes that comprised this shared fraction. These results indicate that the zebrafish gut houses a functionally diverse microbial community that manifests extensive homology to that of humans and mice despite substantial disparities in taxonomic composition. We anticipate that the gene catalog developed here will enable future mechanistic study of host-microbiome interactions using the zebrafish model. ImportanceZebrafish have emerged as an important model system for defining host-microbiome interactions. However, the utility of this model is blunted by limited insight into the functions that are carried by zebrafish gut microbiota, their relationship with zebrafish physiology, and their consistency with the functions carried by human gut microbiota. To address these limitations, we constructed the first genomic database of zebrafish gut microbiome diversity. We use this novel resource to demonstrate that the genomic diversity of the zebrafish gut microbiome varies with diet and this variance links with altered intestinal gene expression. We also identify substantial homology between zebrafish, human, and mouse metagenomic diversity, indicating that these microbiomes may operate similarly.

microbiology↗

bioRxiv2020

Whole genome analysis of four Bangladeshi individuals

Whole-genome sequencing (WGS) is a comprehensive method for analysing entire genomes and this has been instrumental in characterizing the single nucleotide polymorphisms associated with different diseases including cancer, diabetes, cardiovascular diseases and many others. In this paper we undertake a pilot study for sequencing four Bangladeshi individuals and profiling their single nucleotide variants. Our findings shed possible light on specific biological pathways effected by such variants in this population.

genomics↗

bioRxiv2019

Celsr1a is essential for tissue homeostasis and onset of aging phenotypes in the zebrafish.

The use of experimental genetics has been invaluable in defining the complex mechanisms by which aging and longevity are regulated. Zebrafish, while a prominent model for understanding the genetic basis of vertebrate development, have not been used systematically to address questions of how and why we age. In a mutagenesis screen focusing on late developmental phenotypes, we identified a new mutant, fruehrentner, that displays typical signs of aging already at young adult stages. We find that the phenotype is due to loss-of-function in the non-classical cadherin EGF LAG seven-pass G-type receptor 1a (celsr1a). The premature aging phenotype is not associated with increased cellular senescence or decreased telomere length but is a result of a broad failure to maintain progenitor cell populations in tissues. Through the analysis of a knockin reporter line, we find that celsr1aGFP is expressed broadly in early development but becomes restricted during maturation. We show that celsr1a is essential for maintenance of stem cell progenitors and leads to shifts in cell fate determination. Although celsr1a has many signaling functions including establishment of polarity within tissues, we show that caloric restriction can ameliorate the effect of celsr1a on lifespan in part through compensatory upregulation of celsr1 paralogues. These data suggest that celsr1a function helps to mediate stem cell maintenance during maturation and homeostasis of tissues and thus regulates the onset or expressivity of aging phenotypes.

developmental biology↗

bioRxiv2018

Multicenter validation of a machine learning algorithm for 48 hour all-cause mortality prediction

PurposeThis study evaluates a machine-learning-based mortality prediction tool.\n\nMaterials and MethodsWe conducted a retrospective study with data drawn from three academic health centers. Inpatients of at least 18 years of age and with at least one observation of each vital sign were included. Predictions were made at 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated on hold-out test data from the same institution and data from the remaining institutions. Predictions were compared to those of qSOFA and MEWS using area under the receiver operating characteristic curve (AUROC).\n\nResultsFor training and testing on data from a single institution, machine learning predictions averaged AUROCs of 0.97, 0.96, and 0.95 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, the algorithm achieved AUROC up to 0.95, 0.93, and 0.91, for 12-, 24-, and 48-hour predictions, respectively. MEWS and qSOFA had average 48-hour AUROCs of 0.86 and 0.82, respectively.\n\nConclusionThis algorithm may help identify patients in need of increased levels of clinical care.

bioinformatics↗

bioRxiv2018

The contribution of mitochondrial metagenomics to largescale data mining and phylogenetic analysis of Coleoptera

A phylogenetic tree at the species level is still far off for highly diverse insect orders, including the Coleoptera, but the taxonomic breadth of public sequence databases is growing. In addition, new types of data may contribute to increasing taxon coverage, such as metagenomic shotgun sequencing for assembly of mitogenomes from bulk specimen samples. The current study explores the application of these techniques for large-scale efforts to build the tree of Coleoptera. We used shotgun data from 17 different ecological and taxonomic datasets (5 unpublished) to assemble a total of 1942 mitogenome contigs of >3000 bp. These sequences were combined into a single dataset together with all mitochondrial data available at GenBank, in addition to nuclear markers widely used in molecular phylogenetics. The resulting matrix of nearly 16000 species with two or more loci produced trees (RAxML) showing overall congruence with the Linnaean taxonomy at hierarchical levels from suborders to genera. We tested the role of full-length mitogenomes in stabilizing the tree from GenBank data, as mitogenomes might link terminals with non-overlapping gene representation. However, the mitogenome data were only partly useful in this respect, presumably because of the purely automated approach to assembly and gene delimitation, but improvements in future may be possible by using multiple assemblers and manual curation. In conclusion, the combination of data mining and metagenomic sequencing of bulk samples provided the largest phylogenetic tree of Coleoptera to date, which represents a summary of existing phylogenetic knowledge and a defensible tree of great utility, in particular for studies at the intra-familial level, despite some shortcomings for resolving basal nodes.

evolutionary biology↗

bioRxiv2018

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

ObjectivesWe validate a machine learning-based sepsis prediction algorithm (InSight) for detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings.\n\nDesignA machine learning algorithm with gradient tree boosting. Features for prediction were created from combinations of only six vital sign measurements and their changes over time.\n\nSettingA mixed-ward retrospective data set from the University of California, San Francisco (UCSF) Medical Center (San Francisco, CA) as the primary source, an intensive care unit data set from the Beth Israel Deaconess Medical Center (Boston, MA) as a transfer learning source, and four additional institutions datasets to evaluate generalizability.\n\nParticipants684,443 total encounters, with 90,353 encounters from June 2011 to March 2016 at UCSF.\n\nInterventionsnone\n\nPrimary and secondary outcome measuresArea under the receiver operating characteristic curve (AUROC) for detection and prediction of sepsis, severe sepsis, and septic shock.\n\nResultsFor detection of sepsis and severe sepsis, InSight achieves an area under the receiver operating characteristic (AUROC) curve of 0.92 (95% CI 0.90 - 0.93) and 0.87 (95% CI 0.86 - 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 -0.98), and severe sepsis with an AUROC of 0.85 (95% CI 0.79 - 0.91).\n\nConclusionsInSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions.\n\nStrengths and limitations of this studyO_LIMachine learning is applied to the detection and prediction of three separate sepsis standards in the emergency department, general ward and intensive care settings.\nC_LIO_LIOnly six commonly measured vital signs are used as input for the algorithm.\nC_LIO_LIThe algorithm is robust to randomly missing data.\nC_LIO_LITransfer learning successfully leverages large dataset information to a target dataset.\nC_LIO_LIRetrospective nature of the study does not predict clinician reaction to information.\nC_LI

bioinformatics↗

bioRxiv2017

Pediatric Severe Sepsis Prediction Using Machine Learning

Early detection of pediatric severe sepsis is necessary in order to administer effective treatment. In this study, we assessed the efficacy of a machine-learning-based prediction algorithm applied to electronic healthcare record (EHR) data for the prediction of severe sepsis onset. The resulting prediction performance was compared with the Pediatric Logistic Organ Dysfunction score (PELOD-2) and pediatric Systemic Inflammatory Response Syndrome score (SIRS) using cross-validation and pairwise t-tests. EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Patients (n = 11,127) were 2-17 years of age and 103 [0.93%] were labeled severely septic. In four-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.912 for discrimination between severely septic and control pediatric patients at onset and AUROC of 0.727 four hours before onset. Under the same measure, the prediction algorithm also significantly outperformed PELOD-2 (p < 0.05) and SIRS (p < 0.05) in the prediction of severe sepsis four hours before onset. This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction for pediatric inpatients.

bioinformatics↗

bioRxiv2017

Prediction of Acute Kidney Injury with a Machine Learning Algorithm using Electronic Health Record Data

BackgroundA major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified.\n\nMethodsWe used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data from inpatients at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. We tested the algorithms ability to detect AKI at onset, and to predict AKI 12, 24, 48, and 72 hours before onset, and compared its 3-fold cross-validation performance to the SOFA score for AKI identification in terms of Area Under the Receiver Operating Characteristic (AUROC).\n\nResultsThe prediction algorithm achieves AUROC of 0.872 (95% CI 0.867, 0.878) for AKI onset detection, superior to the SOFA score AUROC of 0.815 (P < 0.01). At 72 hours before onset, the algorithm achieves AUROC of 0.728 (95% CI 0.719, 0.737), compared to the SOFA score AUROC of 0.720 (P < 0.01).\n\nConclusionsThe results of these experiments suggest that a machine-learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.

bioinformatics↗