A supervised learning method for classifying methylation disorders.

BACKGROUND: DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal methylation levels. The standardized cutoff values used in these methods do not take into account methylation patterns which are known to differ between the sexes and with age. RESULTS: Here we profile genome-wide DNA methylation from blood samples drawn from within a cohort composed of healthy controls of different age and sex alongside patients with Prader-Willi syndrome (PWS), Beckwith-Wiedemann syndrome, Fragile-X syndrome, Angelman syndrome, and Silver-Russell syndrome. We propose a Generalized Additive Model to perform age and sex adjusted outlier analysis of around 700,000 CpG sites throughout the human genome. Utilizing z-scores among the cohort for each site, we deployed an ensemble based machine learning pipeline and achieved a combined prediction accuracy of 0.96 (Binomial 95% Confidence Interval 0.868[Formula: see text]0.995). CONCLUSION: We demonstrate a method for age and sex adjusted outlier detection of differentially methylated loci based on a large cohort of healthy individuals. We present a custom machine learning pipeline utilizing this outlier analysis to classify samples for potential methylation associated congenital disorders. These methods are able to achieve high accuracy when used with machine learning methods to classify abnormal methylation patterns.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Corrigendum: Characterising an Alternative Murine Model of Diabetic Cardiomyopathy.

[This corrects the article DOI: 10.3389/fphys.2019.01395.].

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. OBJECTIVE: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result. RESULTS: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). CONCLUSIONS: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19-positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Defining the Progression of Diabetic Cardiomyopathy in a Mouse Model of Type 1 Diabetes.

The incidence of diabetes and its association with increased cardiovascular disease risk represents a major health issue worldwide. Diabetes-induced hyperglycemia is implicated as a central driver of responses in the diabetic heart such as cardiomyocyte hypertrophy, fibrosis, and oxidative stress, termed diabetic cardiomyopathy. The onset of these responses in the setting of diabetes has not been studied to date. This study aimed to determine the time course of development of diabetic cardiomyopathy in a model of type 1 diabetes (T1D) in vivo. Diabetes was induced in 6-week-old male FVB/N mice via streptozotocin (55 mg/kg i.p. for 5 days; controls received citrate vehicle). At 2, 4, 8, 12, and 16 weeks of untreated diabetes, left ventricular (LV) function was assessed by echocardiography before post-mortem quantification of markers of LV cardiomyocyte hypertrophy, collagen deposition, DNA fragmentation, and changes in components of the hexosamine biosynthesis pathway (HBP) were assessed. Blood glucose and HbA1c levels were elevated by 2 weeks of diabetes. LV and muscle (gastrocnemius) weights were reduced from 8 weeks, whereas liver and kidney weights were increased from 2 and 4 weeks of diabetes, respectively. LV diastolic function declined with diabetes progression, demonstrated by a reduction in E/A ratio from 4 weeks of diabetes, and an increase in peak A-wave amplitude, deceleration time, and isovolumic relaxation time (IVRT) from 4-8 weeks of diabetes. Systemic and local inflammation (TNFα, IL-1ß, CD68) were increased with diabetes. The cardiomyocyte hypertrophic marker Nppa was increased from 8 weeks of diabetes while ß-myosin heavy chain was increased earlier, from 2 weeks of diabetes. LV fibrosis (picrosirius red; Ctgf and Tgf-ß gene expression) and DNA fragmentation (a marker of cardiomyocyte apoptosis) increased with diabetes progression. LV Nox2 and Cd36 expression were elevated after 16 weeks of diabetes. Markers of the LV HBP (Ogt, Oga, Gfat1/2 gene expression), and protein abundance of OGT and total O-GlcNAcylation, were increased by 16 weeks of diabetes. This is the first study to define the progression of cardiac markers contributing to the development of diabetic cardiomyopathy in a mouse model of T1D, confirming multiple pathways contribute to disease progression at various time points.

The Novel Small-molecule Annexin-A1 Mimetic, Compound 17b, Elicits Vasoprotective Actions in Streptozotocin-induced Diabetic Mice.

The formyl peptide receptor (FPR) family are a group of G-protein coupled receptors that play an important role in the regulation of inflammatory processes. It is well-established that activation of FPRs can have cardioprotective properties. Recently, more stable small-molecule FPR1/2 agonists have been described, including both Compound 17b (Cmpd17b) and Compound 43 (Cmpd43). Both agonists activate a range of signals downstream of FPR1/2 activation in human-engineered FPR-expressing cells, including ERK1/2 and Akt. Importantly, Cmpd17b (but not Cmpd43) favours bias away from intracellular Ca2+ mobilisation in this context, which has been associated with greater cardioprotection in response to Cmpd17b over Cmpd43. However, it is unknown whether these FPR agonists impact vascular physiology and/or elicit vasoprotective effects in the context of diabetes. First, we localized FPR1 and FPR2 receptors predominantly in vascular smooth muscle cells in the aortae of male C57BL/6 mice. We then analysed the vascular effects of Cmpd17b and Cmpd43 on the aorta using wire-myography. Cmpd17b but not Cmpd43 evoked a concentration-dependent relaxation of the mouse aorta. Removal of the endothelium or blockade of endothelium-derived relaxing factors using pharmacological inhibitors had no effect on Cmpd17b-evoked relaxation, demonstrating that its direct vasodilator actions were endothelium-independent. In aortae primed with elevated K+ concentration, increasing concentrations of CaCl2 evoked concentration-dependent contraction that is abolished by Cmpd17b, suggesting the involvement of the inhibition of Ca2+ mobilisation via voltage-gated calcium channels. Treatment with Cmpd17b for eight weeks reversed endothelial dysfunction in STZ-induced diabetic aorta through the upregulation of vasodilator prostanoids. Our data indicate that Cmpd17b is a direct endothelium-independent vasodilator, and a vasoprotective molecule in the context of diabetes.

Tissue-specific gene expression and protein abundance patterns are associated with fractionation bias in maize.

BACKGROUND: Maize experienced a whole-genome duplication event approximately 5 to 12 million years ago. Because this event occurred after speciation from sorghum, the pre-duplication subgenomes can be partially reconstructed by mapping syntenic regions to the sorghum chromosomes. During evolution, maize has had uneven gene loss between each ancient subgenome. Fractionation and divergence between these genomes continue today, constantly changing genetic make-up and phenotypes and influencing agronomic traits. RESULTS: Here we regenerate the subgenome reconstructions for the most recent maize reference genome assembly. Based on both expression and abundance data for homeologous gene pairs across multiple tissues, we observed functional divergence of genes across subgenomes. Although the genes in the larger maize subgenome are often expressing more highly than their homeologs in the smaller subgenome, we observed cases where homeolog expression dominance switches in different tissues. We demonstrate for the first time that protein abundances are higher in the larger subgenome, but they also show tissue-specific dominance, a pattern similar to RNA expression dominance. We also find that pollen expression is uniquely decoupled from protein abundance. CONCLUSION: Our study shows that the larger subgenome has a greater range of functional assignments and that there is a relative lack of overlap between the subgenomes in terms of gene functions than would be suggested by similar patterns of gene expression and protein abundance. Our study also revealed that some reactions are catalyzed uniquely by the larger and smaller subgenomes. The tissue-specific, nonequivalent expression-level dominance pattern observed here implies a change in regulatory control which favors differentiated selective pressure on the retained duplicates leading to eventual change in gene functions.

Annexin-A1 deficiency exacerbates pathological remodelling of the mesenteric vasculature in insulin-resistant, but not insulin-deficient, mice.

BACKGROUND AND PURPOSE: Arterial stiffness, a characteristic feature of diabetes, increases the risk of cardiovascular complications. Potential mechanisms that promote arterial stiffness in diabetes include oxidative stress, glycation and inflammation. The anti-inflammatory protein annexin-A1 has cardioprotective properties, particularly in the context of ischaemia. However, the role of endogenous annexin-A1 in the vasculature in both normal physiology and pathophysiology remains largely unknown. Hence, this study investigated the role of endogenous annexin-A1 in diabetes-induced remodelling of mouse mesenteric vasculature. EXPERIMENTAL APPROACH: Insulin-resistance was induced in male mice (AnxA1+/+ and AnxA1-/- ) with the combination of streptozotocin (55mg/kg i.p. x 3 days) with high fat diet (42% energy from fat) or citrate vehicle with normal chow diet (20-weeks). Insulin-deficiency was induced in a separate cohort of mice using a higher total streptozocin dose (55mg/kg i.p. x 5 days) on chow diet (16-weeks). At study endpoint, mesenteric artery passive mechanics were assessed by pressure myography. KEY RESULTS: Insulin-resistance induced significant outward remodelling but had no impact on passive stiffness. Interestingly, vascular stiffness was significantly increased in AnxA1-/- mice when subjected to insulin-resistance. In contrast, insulin-deficiency induced outward remodelling and increased volume compliance in mesenteric arteries, regardless of genotype. In addition, the annexin-A1 / formyl peptide receptor axis is upregulated in both insulin-resistant and insulin-deficient mice. CONCLUSION AND IMPLICATIONS: Our study provided the first evidence that endogenous AnxA1 may play an important vasoprotective role in the context of insulin-resistance. AnxA1-based therapies may provide additional benefits over traditional anti-inflammatory strategies for reducing vascular injury in diabetes.

Characterising an Alternative Murine Model of Diabetic Cardiomyopathy.

The increasing burden of heart failure globally can be partly attributed to the increased prevalence of diabetes, and the subsequent development of a distinct form of heart failure known as diabetic cardiomyopathy. Despite this, effective treatment options have remained elusive, due partly to the lack of an experimental model that adequately mimics human disease. In the current study, we combined three consecutive daily injections of low-dose streptozotocin with high-fat diet, in order to recapitulate the long-term complications of diabetes, with a specific focus on the diabetic heart. At 26 weeks of diabetes, several metabolic changes were observed including elevated blood glucose, glycated haemoglobin, plasma insulin and plasma C-peptide. Further analysis of organs commonly affected by diabetes revealed diabetic nephropathy, underlined by renal functional and structural abnormalities, as well as progressive liver damage. In addition, this protocol led to robust left ventricular diastolic dysfunction at 26 weeks with preserved systolic function, a key characteristic of patients with type 2 diabetes-induced cardiomyopathy. These observations corresponded with cardiac structural changes, namely an increase in myocardial fibrosis, as well as activation of several cardiac signalling pathways previously implicated in disease progression. It is hoped that development of an appropriate model will help to understand some the pathophysiological mechanisms underlying the accelerated progression of diabetic complications, leading ultimately to more efficacious treatment options.

Cardioprotective Actions of the Annexin-A1 N-Terminal Peptide, Ac2-26, Against Myocardial Infarction.

The anti-inflammatory, pro-resolving annexin-A1 protein acts as an endogenous brake against exaggerated cardiac necrosis, inflammation, and fibrosis following myocardial infarction (MI) in vivo. Little is known, however, regarding the cardioprotective actions of the N-terminal-derived peptide of annexin A1, Ac2-26, particularly beyond its anti-necrotic actions in the first few hours after an ischemic insult. In this study, we tested the hypothesis that exogenous Ac2-26 limits cardiac injury in vitro and in vivo. Firstly, we demonstrated that Ac2-26 limits cardiomyocyte death both in vitro and in mice subjected to ischemia-reperfusion (I-R) injury in vivo (Ac2-26, 1 mg/kg, i.v. just prior to post-ischemic reperfusion). Further, Ac2-26 (1 mg/kg i.v.) reduced cardiac inflammation (after 48 h reperfusion), as well as both cardiac fibrosis and apoptosis (after 7-days reperfusion). Lastly, we investigated whether Ac2-26 preserved cardiac function after MI. Ac2-26 (1 mg/kg/day s.c., osmotic pump) delayed early cardiac dysfunction 1 week post MI, but elicited no further improvement 4 weeks after MI. Taken together, our data demonstrate the first evidence that Ac2-26 not only preserves cardiomyocyte survival in vitro, but also offers cardioprotection beyond the first few hours after an ischemic insult in vivo. Annexin-A1 mimetics thus represent a potential new therapy to improve cardiac outcomes after MI.

MaizeGDB 2018: the maize multi-genome genetics and genomics database.

Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.

The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize.

BACKGROUND: As metabolic pathway resources become more commonly available, researchers have unprecedented access to information about their organism of interest. Despite efforts to ensure consistency between various resources, information content and quality can vary widely. Two maize metabolic pathway resources for the B73 inbred line, CornCyc 4.0 and MaizeCyc 2.2, are based on the same gene model set and were developed using Pathway Tools software. These resources differ in their initial enzymatic function assignments and in the extent of manual curation. We present an in-depth comparison between CornCyc and MaizeCyc to demonstrate the effect of initial computational enzymatic function assignments on the quality and content of metabolic pathway resources. RESULTS: These two resources are different in their content. MaizeCyc contains GO annotations for over 21,000 genes that CornCyc is missing. CornCyc contains on average 1.6 transcripts per gene, while MaizeCyc contains almost no alternate splicing. MaizeCyc also does not match CornCyc's breadth in representing the metabolic domain; MaizeCyc has fewer compounds, reactions, and pathways than CornCyc. CornCyc's computational predictions are more accurate than those in MaizeCyc when compared to experimentally determined function assignments, demonstrating the relative strength of the enzymatic function assignment pipeline used to generate CornCyc. CONCLUSIONS: Our results show that the quality of initial enzymatic function assignments primarily determines the quality of the final metabolic pathway resource. Therefore, biologists should pay close attention to the methods and information sources used to develop a metabolic pathway resource to gauge the utility of using such functional assignments to construct hypotheses for experimental studies.

A computational platform to maintain and migrate manual functional annotations for BioCyc databases.

BACKGROUND: BioCyc databases are an important resource for information on biological pathways and genomic data. Such databases represent the accumulation of biological data, some of which has been manually curated from literature. An essential feature of these databases is the continuing data integration as new knowledge is discovered. As functional annotations are improved, scalable methods are needed for curators to manage annotations without detailed knowledge of the specific design of the BioCyc database. RESULTS: We have developed CycTools, a software tool which allows curators to maintain functional annotations in a model organism database. This tool builds on existing software to improve and simplify annotation data imports of user provided data into BioCyc databases. Additionally, CycTools automatically resolves synonyms and alternate identifiers contained within the database into the appropriate internal identifiers. CONCLUSIONS: Automating steps in the manual data entry process can improve curation efforts for major biological databases. The functionality of CycTools is demonstrated by transferring GO term annotations from MaizeCyc to matching proteins in CornCyc, both maize metabolic pathway databases available at MaizeGDB, and by creating strain specific databases for metabolic engineering.

Damage-induced localized hypermutability.

Genome instability continuously presents perils of cancer, genetic disease and death of a cell or an organism. At the same time, it provides for genome plasticity that is essential for development and evolution. We address here the genome instability confined to a small fraction of DNA adjacent to free DNA ends at uncapped telomeres and double-strand breaks. We found that budding yeast cells can tolerate nearly 20 kilobase regions of subtelomeric single-strand DNA that contain multiple UV-damaged nucleotides. During restoration to the double-strand state, multiple mutations are generated by error-prone translesion synthesis. Genome-wide sequencing demonstrated that multiple regions of damage-induced localized hypermutability can be tolerated, which leads to the simultaneous appearance of multiple mutation clusters in the genomes of UV- irradiated cells. High multiplicity and density of mutations suggest that this novel form of genome instability may play significant roles in generating new alleles for evolutionary selection as well as in the incidence of cancer and genetic disease.