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BackgroundBoth RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.ResultsHere we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.ConclusionThe use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.

Objective To characterise cardiac remodelling, exercise capacity and fibroinflammatory biomarkers in patients with aortic stenosis (AS) with and without diabetes, and assess the impact of diabetes on outcomes.Methods Patients with moderate or severe AS with and without diabetes underwent echocardiography, stress cardiovascular magnetic resonance (CMR), cardiopulmonary exercise testing and plasma biomarker analysis. Primary endpoint for survival analysis was a composite of cardiovascular mortality, myocardial infarction, hospitalisation with heart failure, syncope or arrhythmia. Secondary endpoint was all-cause death.Results Diabetes (n=56) and non-diabetes groups (n=198) were well matched for age, sex, ethnicity, blood pressure and severity of AS. The diabetes group had higher body mass index, lower estimated glomerular filtration rate and higher rates of hypertension, hyperlipidaemia and symptoms of AS. Biventricular volumes and systolic function were similar, but the diabetes group had higher extracellular volume fraction (25.9%±3.1% vs 24.8%±2.4%, p=0.020), lower myocardial perfusion reserve (2.02±0.75 vs 2.34±0.68, p=0.046) and lower percentage predicted peak oxygen consumption (68%±21% vs 77%±17%, p=0.002) compared with the non-diabetes group. Higher levels of renin (log10renin: 3.27±0.59 vs 2.82±0.69 pg/mL, p

Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.

We show that SARS-CoV-2 spike protein interacts with both cellular heparan sulfate and angiotensin-converting enzyme 2 (ACE2) through its receptor-binding domain (RBD). Docking studies suggest a heparin/heparan sulfate-binding site adjacent to the ACE2-binding site. Both ACE2 and heparin can bind independently to spike protein in vitro, and a ternary complex can be generated using heparin as a scaffold. Electron micrographs of spike protein suggests that heparin enhances the open conformation of the RBD that binds ACE2. On cells, spike protein binding depends on both heparan sulfate and ACE2. Unfractionated heparin, non-anticoagulant heparin, heparin lyases, and lung heparan sulfate potently block spike protein binding and/or infection by pseudotyped virus and authentic SARS-CoV-2 virus. We suggest a model in which viral attachment and infection involves heparan sulfate-dependent enhancement of binding to ACE2. Manipulation of heparan sulfate or inhibition of viral adhesion by exogenous heparin presents new therapeutic opportunities.

The SARS-CoV-2 betacoronavirus uses its highly glycosylated trimeric Spike protein to bind to the cell surface receptor angiotensin converting enzyme 2 (ACE2) glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatics analyses of natural variants and with existing 3D structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein both alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation. Taken together, these data can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.

AbstractBackgroundSegmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed and evaluated an autonomous and closed-loop control system for radial k-space sampling to increase sampling uniformity.MethodsThe closed-loop system autonomously selects radial k-space sampling trajectory during live segmented cine MRI and attempts to optimize angular sampling uniformity by selecting views in regions of k-space that were not previously well-sampled. Sampling uniformity and robustness to arrhythmias was assessed using ECG data acquired from 10 normal subjects in an MRI scanner. The approach was then implemented with a fast gradient echo sequence on a whole-body clinical MRI scanner and imaging was performed in 4 healthy volunteers. The closed-loop k-space trajectory was compared to random, uniformly distributed and golden angle view trajectories via measurement of k-space uniformity and the point spread function. Lastly, an arrhythmic dataset was used to evaluate a potential application of the approach.ResultsThe autonomous trajectory increased k-space sampling uniformity by 13±7%, main lobe point spread function (PSF) signal intensity by 14±6%, and reduced ringing relative to golden angle sampling. When implemented, the autonomous pulse sequence prescribed radial view angles faster than the scan TR (0.98 ± 0.02 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 5±12%, decreased uniformity variability by 45±14%, and increased PSF signal ratio by 5±5% relative to golden angle sampling.ConclusionThe closed-loop approach enables near-uniform radial sampling in a segmented acquisition approach which was higher than predetermined golden-angle radial sampling. This can be utilized to increase the sampling or decrease the temporal footprint of an acquisition and the closed-loop framework has the potential to be applied to patients with complex heart rhythms.

Heparin is the most widely prescribed biopharmaceutical in production globally. Its potent anticoagulant activity and safety makes it the drug of choice for preventing deep vein thrombosis and pulmonary embolism. In 2008, adulterated material was introduced into the heparin supply chain, resulting in several hundred deaths and demonstrating the need for alternate sources of heparin. Heparin is a fractionated form of heparan sulfate derived from animal sources, predominantly from connective tissue mast cells in pig mucosa. While the enzymes involved in heparin biosynthesis are identical to those for heparan sulfate, the factors regulating these enzymes are not understood. Examination of the promoter regions of all genes involved in heparin/heparan sulfate assembly uncovered a transcription factor-binding motif for ZNF263, a C2H2 zinc finger protein. CRISPR-mediated targeting and siRNA knockdown of ZNF263 in mammalian cell lines and human primary cells led to dramatically increased expression levels of HS3ST1, a key enzyme involved in imparting anticoagulant activity to heparin, and HS3ST3A1, another glucosaminyl 3-O-sulfotransferase expressed in cells. Enhanced 3-O-sulfation increased binding to antithrombin, which enhanced Factor Xa inhibition, and binding of neuropilin-1. Analysis of transcriptomics data showed distinctively low expression of ZNF263 in mast cells compared with other (non-heparin-producing) immune cells. These findings demonstrate a novel regulatory factor in heparan sulfate modification that could further advance the possibility of bioengineering anticoagulant heparin in cultured cells.

Adaptive generation of spacing intervals in learning usingresponse times improves learning relative to both adaptivesystems that do not use response times and fixed spacingschemes (Mettler, Massey & Kellman, 2016). Studies haveoften used limited presentations (e.g., 4) of each learningitem. Does adaptive practice benefit learning if items arepresented until attainment of objective mastery criteria? Doesit matter if mastered items drop out of the active learning set? We compared adaptive and non-adaptive spacing underconditions of mastery and dropout. Experiment 1 comparedrandom presentation order with no dropout to adaptivespacing and mastery using the ARTS (AdaptiveResponse-time-based Sequencing) system. Adaptive spacingproduced better retention than random presentation.Experiment 2 showed clear learning advantages for adaptivespacing compared to random schedules that also includeddropout. Adaptive spacing performs better than randomschedules of practice, including when learning proceeds tomastery and items drop out when mastered.

Spacing presentations of learning items across time improvesmemory relative to massed schedules of practice – thewell-known spacing effect. Spaced practice can be furtherenhanced by adaptively scheduling the presentation of learningitems to deliver customized spacing intervals for individualitems and learners. ARTS - Adaptive Response-time-basedSequencing (Mettler, Massey, & Kellman 2016) determinesspacing dynamically in relation to each learner’s ongoing speedand accuracy in interactive learning trials. We demonstrate theeffectiveness of ARTS when applied to chemistry nomenclaturein community college chemistry courses by comparing adaptiveschedules to fixed schedules consisting of continuouslyexpanding spacing intervals. Adaptive spacing enhanced theefficiency and durability of learning, with learning gainspersisting after a two-week delay and generalizing to astandardized assessment of chemistry knowledge after 2-3months. Two additional experiments confirmed and extendedthese results in both laboratory and community college settings.

Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code.

Abstract With a brief half-second presentation, a medical expert can determine at above chance levels whether a medical scan she sees is abnormal based on a first impression arising from an initial global image process, termed “gist.” The nature of gist processing is debated but this debate stems from results in medical experts who have years of perceptual experience. The aim of the present study was to determine if gist processing for medical images occurs in naïve (non-medically trained) participants who received a brief perceptual training and to tease apart the nature of that gist signal. We trained 20 naïve participants on a brief perceptual-adaptive training of histology images. After training, naïve observers were able to obtain abnormality detection and abnormality categorization above chance, from a brief 500 ms masked presentation of a histology image, hence showing “gist.” The global signal demonstrated in perceptually trained naïve participants demonstrated multiple dissociable components, with some of these components relating to how rapidly naïve participants learned a normal template during perceptual learning. We suggest that multiple gist signals are present when experts view medical images derived from the tens of thousands of images that they are exposed to throughout their training and careers. We also suggest that a directed learning of a normal template may produce better abnormality detection and identification in radiologists and pathologists.

Abstract Background The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. Results We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. Conclusions A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Abstract Middle Eastern immigrants constitute a growing proportion of the European population and compared to native Swedes are more insulin resistant, which can contribute to atherosclerosis. Quantitative first pass perfusion (qFPP) using cardiovascular magnetic resonance (CMR) can detect early signs of cardiovascular disease (CVD). The aim was to study if myocardial perfusion differs between healthy male Middle Eastern immigrants and native male Swedes. Eighteen Iraqi- and twelve Swedish born controls, all males, never smokers with no CVD risk factors were included. Global myocardial perfusion at rest and stress was assessed using qFPP and by phase-contrast CMR imaging of coronary sinus flow. Quantitative first pass perfusion analysis (mean ± SD) demonstrated no difference at rest between Iraqi and Swedish males (0.8 ± 0.2 vs 1.0 ± 0.4 ml/min/g, P = 0.38) but lower perfusion during adenosine in Iraqi males (2.9 ± 0.7 vs 3.5 ± 0.7 ml/min/g, P = 0.02). Myocardial perfusion assessed by coronary sinus flow demonstrated similar results with no difference in resting perfusion between groups (0.7 ± 0.2 vs 0.8 ± 0.2 ml/min/g, P = 0.21) but a lower perfusion during adenosine in the Iraqi group (3.0 ± 0.2 vs 3.7 ± 0.6 ml/min/g, P = 0.01. Myocardial perfusion during adenosine stress was lower in healthy Iraqi immigrants compared to Swedish controls suggesting impaired microvascular function and risk of underestimating CVD risk in healthy individuals of Middle Eastern origin.

Abstract Background The underlying pathophysiology of post-coronavirus disease 2019 (long-COVID-19) syndrome remains unknown, but increased cardiometabolic demand and state of mitochondrial dysfunction have emerged as candidate mechanisms. Cardiovascular magnetic resonance (CMR) provides insight into pathophysiological mechanisms underlying cardiovascular disease and 31-phosphorus CMR spectroscopy (31P-CMRS) allows non-invasive assessment of the myocardial energetic state. The main aim of the study was to assess whether long COVID-19 syndrome is associated with abnormalities of myocardial structure, function, perfusion and energy metabolism. Methods Prospective case–control study. A total of 20 patients with a clinical diagnosis of long COVID-19 syndrome (seropositive) and no prior underlying cardiovascular disease (CVD) and 10 matching healthy controls underwent 31P-CMRS and CMR at 3T at a single time point. All patients had been symptomatic with acute COVID-19, but none required hospital admission. Results Between the long COVID-19 syndrome patients and matched contemporary healthy controls there were no differences in myocardial energetics (phosphocreatine to ATP ratio), in cardiac structure (biventricular volumes), function (biventricular ejection fractions, global longitudinal strain), tissue characterization (T1 mapping and late gadolinium enhancement) or perfusion (myocardial rest and stress blood flow, myocardial perfusion reserve). One patient with long COVID-19 syndrome showed subepicardial hyperenhancement on late gadolinium enhancement imaging compatible with prior myocarditis, but no accompanying abnormality in cardiac size, function, perfusion, extracellular volume fraction, native T1, T2 or cardiac energetics. Conclusions In this prospective case–control study, the overwhelming majority of patients with a clinical long COVID-19 syndrome with no prior CVD did not exhibit any abnormalities in myocardial energetics, structure, function, blood flow or tissue characteristics.

Summary: GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference.For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with closing the core-edge integration knowledge gap being the overarching mission. New experimental validation of high-fidelity, multi-channel, non-linear gyrokinetic turbulent transport models for ITER provides strong confidence it will achieve Q ⩾ 10 operation. Experiments identify options for easing H-mode access in hydrogen, and give new insight into the isotopic dependence of transport and confinement. Analysis of 2,1 islands in unoptimized low-torque IBS demonstration discharges suggests their onset time occurs randomly in the constant β phase, most often triggered by non-linear 3-wave coupling, thus identifying an NTM seeding mechanism to avoid. Pure deuterium SPI for disruption mitigation is shown to provide favorable slow cooling, but poor core assimilation, suggesting paths for improved SPI on ITER. At the boundary, measured neutral density and ionization source fluxes are strongly poloidally asymmetric, implying a 2D treatment is needed to model pedestal fuelling. Detailed measurements of pedestal and SOL quantities and impurity charge state radiation in detached divertors has validated edge fluid modelling and new self-consistent ‘pedestal-to-divertor’ integrated modeling that can be used to optimize reactors. New feedback adaptive ELM control minimizes confinement reduction, and RMP ELM suppression with sustained high core performance was obtained for the first time with the outer strike point in a W-coated, compact and unpumped small-angle slot divertor. Advances have been made in integrated operational scenarios for ITER and power plants. Wide pedestal intrinsically ELM-free QH-modes are produced with more reactor-relevant conditions, Low torque IBS with W-equivalent radiators can exhibit predator-prey oscillations in T _e and radiation which need control. High- β _P scenarios with q _min > 2, q _95 –7.9, β _N > 4, β _T –3.3% and H _98y2 > 1.5 are sustained with high density ( $\bar n$ = 7E19 m ^−3 , f _G –1) for 6 τ _E , improving confidence in steady-state tokamak reactors. Diverted NT plasmas achieve high core performance with a non-ELMing edge, offering a possible highly attractive core-edge integration solution for reactors.