bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of SARS-CoV2 antibody Rapid Diagnostic Test kits (RDTs) and Real Time-Polymerase Chain Reaction (Rt-PCR) for COVID-19 Diagnosis in Kaduna, Nigeria
Authors: Ige Oluwafemi , Buru Ayuba Sunday , Lamido Tanko Zainab , Mohammed Tahir , Dogara Livingstone , Patience Ijei Ifeoma , Halima Bello-Manga , Reward Audu , Samuel Ige , Grace Nmadu
Published: November 2020

The emergence of the RNA virus SARS-CoV2, the causative agent of COVID-19 and its declaration by the World Health Organization (WHO) as a pandemic has disrupted the delicate balance in health indices globally. Its attendant immune dysregulation and pathobiology is still evolving. Currently, real time PCR is the gold standard diagnostic test, however there are several invalidated antibody-based tests available for possible community screening. With ongoing community transmission in Nigeria, neither the true burden of COVID-19 nor the performance of these kits is presently known. This study therefore, compared the performance of the SARS CoV2 antibody test and the real time Polymerase Chain Reaction (Rt-PCR) in the diagnosis of COVID-19. For the purpose of this evaluation, we used the diagnostic test kit by Innovita® Biological Technology CO., LTD China, a total of 521 venous blood samples were collected from consenting patients for the SARS COVID-19 rapid diagnostic kit and Oral and Nasopharyngeal swabs were collected and analyzed using the real time Polymerase chain reaction technique for nucleic acid detection and quantification.
Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients with COVID-19
Authors: Huntington Kelsey E. , Louie Anna D. , Lee Chun Geun , Elias Jack A. , Ross Eric A. , El-Deiry Wafik S.
Published: November 2020

Although the range of immune responses to COVID-19 infection is variable, cytokine storm is observed in many affected individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on need for hospitalization and oxygen requirement. Here we utilize a mutual information algorithm that classifies the information gain for CSS prediction provided by cytokine expression levels and clinical variables. Using this methodology, we found that a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism by which COVID-19 creates severe illness. The variables that were the most predictive of CSS included clinical variables such as age and abnormal chest x-ray as well as cytokines such as macrophage colony-stimulating factor (M-CSF), interferon-inducible protein 10 (IP-10) and Interleukin-1 Receptor Antagonist (IL-1RA). Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of Macrophage Activation Syndrome and could furthermore be used as a biomarker to predict disease severity.
Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease
Authors: Zandehshahvar Mohammadreza , van Assen Marly , Maleki Hossein , Kiarashi Yashar , De Cecco Carlo N. , Adibi Ali
Published: November 2020

We report a new approach using artificial intelligence to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with average area under curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in single patients and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in early stages. This will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.
The impact of patient body mass index on surgeon posture during simulated laparoscopy
Authors: Sers Ryan , Forrester Steph , Zecca Massimiliano , Ward Stephen , Moss Esther
Published: November 2020

Laparoscopy is a cornerstone of modern surgical care. Despite clear advantages for the patients, it has been associated with inducing upper body musculoskeletal disorders amongst surgeons due to the propensity of non-neutral postures. Furthermore, there is a perception that patients with obesity exacerbate these factors. Therefore, novice, intermediate and expert surgeon upper body posture was objectively quantified using inertial measurement units and the LUBA ergonomic framework was used to assess the subsequent postural data during laparoscopic training on patient models that simulated BMI’s of 20, 30, 40 and 50 kg/m2. In all experience groups, the posture of the upper body significantly worsened during simulated surgery on the BMI 50 kg/m2 model as compared to on the baseline BMI model of 20 kg/m2. These findings suggest that performing laparoscopic surgery on patients with severe obesity increases the prevalence of non-neutral upper body posture and may further increase the risk of musculoskeletal disorders in surgeons.
Disentangling the roles of human mobility and deprivation on the transmission dynamics of COVID-19 using a spatially explicit simulation model
Authors: Banks C.J. , Colman E. , Doherty T. , Tearne O. , Arnold M. , Atkins K. , Balaz D. , Beaunée G. , Bessell P. , Enright J. , Kleczkowski A. , Rossi G. , Ruget A.-S. , Kao R.R.
Published: November 2020

Restrictions on mobility are a key component of infectious disease controls, preventing the spread of infections to as yet unexposed areas, or to regions which have previously eliminated outbreaks. However, even under the most severe restrictions, some travel must inevitably continue, at the very minimum to retain essential services. For COVID-19, most countries imposed severe restrictions on travel at least as soon as it was clear that containment of local outbreaks would not be possible. Such restrictions are known to have had a substantial impact on the economy and other aspects of human health, and so quantifying the impact of such restrictions is an essential part of evaluating the necessity for future implementation of similar measures.

In this analysis, we built a simulation model using National statistical data to record patterns of movements to work, and implement levels of mobility recorded in real time via mobile phone apps. This model was fitted to the pattern of deaths due to COVID-19 using approximate Bayesian inference. Our model is able to recapitulate mortality considering the number of deaths and datazones (DZs, which are areas containing approximately 500-1000 residents) with deaths, as measured across 32 individual council areas (CAs) in Scotland. Our model recreates a trajectory consistent with the observed data until 1st of July. According to the model, most transmission was occurring “locally” (i.e. in the model, 80% of transmission events occurred within spatially defined “communities” of approximately 100 individuals). We show that the net effect of the various restrictions put into place in March can be captured by a reduction in transmission down to 12% of its pre-lockdown rate effective 28th March. By comparing different approaches to reducing transmission, we show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not reduce death rates significantly. As this movement of individuals from more infected areas to less infected areas has a minimal impact on transmission, this suggests that the fraction of population already immune in infected communities was not a significant factor in these early stages of the national epidemic even when local clustering of infection is taken into account.

The best fit model also shows a considerable influence of the health index of deprivation (part of the “index of multiple deprivations”) on mortality. The most likely value has the CA with the highest level of health-related deprivation to have on average, a 2.45 times greater mortality rate due to COVID-19 compared to the CA with the lowest, showing the impact of health-related deprivation even in the early stages of the COVID-19 national epidemic.
Acronym Disambiguation in Clinical Notes from Electronic Health Records
Authors: Link Nicholas B. , Huang Selena , Cai Tianrun , He Zeling , Sun Jiehuan , Dahal Kumar , Costa Lauren , Cho Kelly , Liao Katherine , Cai Tianxi , Hong Chuan ,   in collaboration with the Million Veteran Program
Published: November 2020

Objective

The use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives. Clinical notes, however, frequently contain acronyms with several potential senses (meanings) and traditional natural language processing (NLP) techniques cannot differentiate between these senses. In this study we introduce an unsupervised method for acronym disambiguation, the task of classifying the correct sense of acronyms in the clinical EHR notes.

Methods

We developed an unsupervised ensemble machine learning (CASEml) algorithm to automatically classify acronyms by leveraging semantic embeddings, visit-level text and billing information. The algorithm was validated using note data from the Veterans Affairs hospital system to classify the meaning of three acronyms: RA, MS, and MI. We compared the performance of CASEml against another standard unsupervised method and a baseline metric selecting the most frequent acronym sense. We additionally evaluated the effects of RA disambiguation on NLP-driven phenotyping of rheumatoid arthritis.

Results

CASEml achieved accuracies of 0.947, 0.911, and 0.706 for RA, MS, and MI, respectively, higher than a standard baseline metric and (on average) higher than a state-of-the-art unsupervised method. As well, we demonstrated that applying CASEml to medical notes improves the AUC of a phenotype algorithm for rheumatoid arthritis.

Conclusion

CASEml is a novel method that accurately disambiguates acronyms in clinical notes and has advantages over commonly used supervised and unsupervised machine learning approaches. In addition, CASEml improves the performance of NLP tasks that rely on ambiguous acronyms, such as phenotyping.
Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States
Authors: Burn Edward , Sena Anthony G. , Prats-Uribe Albert , Spotnitz Matthew , DuVall Scott , Lynch Kristine E. , Matheny Michael E. , Nyberg Fredrik , Ahmed Waheed-Ul-Rahman , Alser Osaid , Alghoul Heba , Alshammari Thamir , Zhang Lin , Casajust Paula , Areia Carlos , Shah Karishma , Reich Christian , Blacketer Clair , Andryc Alan , Fortin Stephen , Natarajan Karthik , Gong Mengchun , Golozar Asieh , Morales Daniel , Rijnbeek Peter , Subbian Vignesh , Roel Elena , Recalde Martina , Lane Jennifer C.E. , Vizcaya David , Posada Jose D. , Shah Nigam H. , Jonnagaddala Jitendra , Lai Lana Yin Hui , Avilés-Jurado Francesc Xavier , Hripcsak George , Suchard Marc A. , Ranzani Otavio T. , Ryan Patrick , Prieto-Alhambra Daniel , Kostka Kristin , Duarte-Salles Talita
Published: November 2020

Objective

To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO).

Design

A network cohort study.

Setting

Seven databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Hospital CDM, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP.

Patients

Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19.

Interventions

Dialysis, tracheostomy, and ECMO.

Measurements and Main Results

842,928 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 77,853 from IQVIA Hospital CDM, 533,997 from IQVIA Open Claims, 36,717 from Optum EHR, 4,336 from OPTUM SES, 156,187 from Premier, and 10,951 from VA-OMOP). Across the six databases, 35,192 (4.17% [95% CI: 4.13% to 4.22%]) patients received dialysis, 6,950 (0.82% [0.81% to 0.84%]) had a tracheostomy, and 1,568 (0.19% [95% CI: 0.18% to 0.20%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was more common among patients who were younger, male, and with fewer comorbidities. Tracheostomy was broadly used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was generally used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease.

Conclusion

Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial.
Transcranial Focused Ultrasound Alters Conflict and Emotional Processing, Physiology, and Performance I: Dorsal Anterior Cingulate Cortex Targeting
Authors: Fini Maria , Tyler William J.
Published: November 2020

The dorsal anterior cingulate cortex (dACC) operates as an integrator of bottom-up and top-down signals and is implicated in both cognitive control and emotional processing. The dACC is believed to be causally involved in switching between attention networks, and previous work has linked it to cognitive performance, concentration, relaxation, and emotional distraction. The present study was designed to evaluate the feasibility of influencing default mode network (DMN) activity and emotional attention by targeting and modulating the dACC with transcranial focused ultrasound (tFUS). Subjects were divided into two groups, one receiving MR-neuronavigated tFUS to the dACC and the other an identical, but inactive tFUS sham. Subjects performed a modified version of the Erikson flanker paradigm using fear and neutral faces as emotional background distractors. Our observations demonstrate that tFUS can be targeted to the human dACC to produce effects consistent with those expected from relaxed contention, including significantly reduced reaction time slowing due to emotional distractors, and an increase in parasympathetic markers of the HRV. These results suggest that tFUS altered emotional processing and enhanced sustained attention, perhaps by facilitating reduced attentional engagement with emotional distractors and reduced need for attention switching evidenced by significant effects on event related potentials (ERPs), reduced alpha suppression, and modulation of delta and theta EEG activity. We conclude that the dACC represents a viable neuroanatomical target for tFUS in order to modulate DMN activity, including emotional attention, conflict resolution, and cognitive control. These effects of dACC-targeted tFUS may prove useful for treating certain mental health disorders known to involve perturbed DMN activity, such as depression and anxiety.
Serum Metabolites Associated with Brain Amyloid Beta Deposition, Cognitive Dysfunction, and Alzheimer’s Disease Progression
Authors: Nho Kwangsik , Kueider-Paisley Alexandra , Arnold Matthias , MahmoudianDehkordi Siamak , Risacher Shannon L. , Louie Gregory , Blach Colette , Baillie Rebecca , Han Xianlin , Kastenmüeller Gabi , Doraiswamy P. Murali , Kaddurah-Daouk Rima , Saykin Andrew J. ,   for the Alzheimer’s Disease Neuroimaging Initiative and the Alzheimer Disease Metabolomics Consortium
Published: November 2020

RATIONALE

Metabolomics in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort provides a powerful tool for mapping biochemical changes in AD, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in AD.

OBJECTIVES

We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition, and conversion from mild cognitive impairment (MCI) to AD.

FINDINGS

Serum-based targeted metabolite levels were measured in 1,531 ADNI participants. We performed association analysis of metabolites with brain amyloid-β deposition measured from [18F] Florbetapir PET scans. We identified nine metabolites as significantly associated with amyloid-β deposition after FDR-based multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation. However, higher levels of seven phosphatidylcholines (PC) were associated with increased amyloid deposition. In addition, PC ae C44:4 was significantly associated with cognition and conversion from MCI to AD dementia.

CONCLUSION

Perturbations in PC and acylcarnitine metabolism may play a role in features intrinsic to AD including amyloid-β deposition and cognitive performance.
Finding metabolic pathways in large networks through atom-conserving substrate-product pairs
Authors: Hafner Jasmin , Hatzimanikatis Vassily
Published: November 2020

Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, especially with the integration of big data, the efficient analysis and navigation of metabolic networks remains a challenge. Here, we propose the construction of searchable graph representations of metabolic networks. Éach reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6,546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant-product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks.
Discovery of Small-Molecule Antagonists of the PWWP Domain of NSD2
Authors: de Freitas Renato Ferreira , Liu Yanli , Szewczyk Magdalena M. , Mehta Naimee , Li Fengling , McLeod David , Zepeda-Velázquez Carlos , Dilworth David , Hanley Ronan P. , Gibson Elisa , Brown Peter J. , Al-Awar Rima , James Lindsey Ingerman , Arrowsmith Cheryl H. , Barsyte-Lovejoy Dalia , Min Jinrong , Vedadi Masoud , Schapira Matthieu , Allali-Hassani Abdellah
Published: November 2020

Increased activity of the lysine methyltrans-ferase NSD2 driven by translocation and activating mutations is associated with multiple myeloma and acute lymphoblastic leukemia, but no NSD2-targeting chemical probe has been reported to date. Here, we present the first antagonists that block the protein-protein interaction between the N-terminal PWWP domain of NSD2 and H3K36me2. Using virtual screening and experimental validation, we identified the small-molecule antagonist3f, which binds to the NSD2-PWWP1 domain with a Kdof 3.4 μM and abrogates histone H3K36me2 binding in cells. This study establishes an alternative approach to targeting NSD2 and provides a small-molecule antagonist that can be further optimized into a chemical probe to better understand the cellular function of this protein.
An evolutionary algorithm for designing microbial communities via environmental modification
Authors: Pacheco Alan R. , Segrè Daniel
Published: November 2020

Despite a growing understanding of how environmental composition affects microbial community properties, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbor thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and dynamic flux balance analysis (dFBA) that selects optimal environmental compositions to produce target community phenotypes. In this framework, dFBA is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behavior of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to more closely approach this target. We apply this iterative process toin silicocommunities of varying sizes, showing how it can rapidly identify environments that yield desired phenotypes. Moreover, this novel combination of approaches produces testable predictions for thein vivoassembly of microbial communities with specific properties, and can facilitate rational environmental design processes for complex microbiomes.
ArabidopsisbZIP11 is a susceptibility factor duringPseudomonas syringaeinfection
Authors: Prior Matthew J. , Selvanayagam Jebasingh , Kim Jung-Gun , Tomar Monika , Jonikas Martin , Mudgett Mary Beth , Smeekens Sjef , Hanson Johannes , Frommer Wolf B.
Published: November 2020

Abstract

The induction of plant nutrient secretion systems is critical for successful pathogen infection. Some bacterial pathogens,e.g. Xanthomonasspecies, use TAL (transcription activator-like) effectors to induce transcription of SWEET sucrose efflux transporters.Pseudomonas syringaepathovar (pv.)tomatostrain DC3000 lacks TAL effectors, yet is able to induce multiple SWEETs inArabidopsis thalianaby unknown mechanisms. Since bacteria require other nutrients besides sugars for efficient reproduction, we hypothesized thatPseudomonasmay depend on host transcription factors involved in secretory programs to increase access to essential nutrients. Bioinformatic analyses identified theArabidopsisbasic-leucine zipper transcription factor bZIP11 as a potential regulator of nutrient transporters, including SWEETs and UmamiT amino acid transporters. Inducible downregulation ofbZIP11expression inArabidopsisresulted in reduced growth ofP. syringaepv.tomatostrain DC3000, whereas inducible overexpression ofbZIP11resulted in increased bacterial growth, supporting the hypothesis that bZIP11 regulated transcription programs are essential for maximal pathogen titer in leaves. Our data are consistent with a model in which a pathogen alters host transcription factor expression upstream of secretory transcription networks to promote nutrient efflux from host cells.
Cyber-yeast: Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control
Authors: Perrino Giansimone , Napolitano Sara , Galdi Francesca , La Regina Antonella , Fiore Davide , Giuliano Teresa , di Bernardo Mario , di Bernardo Diego
Published: November 2020

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to “synchronise” cells, these work only in the short-term. Here, we built a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a “controller” algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing a new avenue for yeast cell cycle synchronisation, our work shows that computers can automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.
Pharmacologic profiling reveals lapatinib as a novel antiviral against SARS-CoV-2 in vitro
Authors: Raymonda M. H. , Ciesla J. H. , Monaghan M. , Leach J. , Asantewaa G. , Smorodintsev-Schiller L.A. , Lutz M. M. , Schafer X. L. , Takimoto T. , Dewhurst S. , Munger J. , Harris I. S.
Published: November 2020

The emergence of SARS-CoV-2 virus has resulted in a worldwide pandemic, but an effective antiviral therapy has yet to be discovered. To improve treatment options, we conducted a high-throughput drug repurposing screen to uncover compounds that block the viral activity of SARS-CoV-2. A minimally pathogenic human betacoronavirus (OC43) was used to infect physiologically-relevant human pulmonary fibroblasts (MRC5) to facilitate rapid antiviral discovery in a preclinical model. Comprehensive profiling was conducted on more than 600 compounds, with each compound arrayed at 10 dose points (ranging from 20 μM to 1 nM). Our screening revealed several FDA-approved agents that act as novel antivirals that block both OC43 and SARS-CoV-2 viral replication, including lapatinib, doramapimod, and 17-AAG. Importantly, lapatinib inhibited SARS-CoV-2 replication by over 50,000-fold without any toxicity and at doses readily achievable in human tissues. Further, both lapatinib and doramapimod could be combined with remdesivir to dramatically improve antiviral activity in cells. These findings reveal novel treatment options for people infected with SARS-CoV-2 that can be readily implemented during the pandemic.
Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity
Authors: Busch Erica L. , Slipski Lukas , Feilong Ma , Swaroop Guntupalli J. , di Oleggio Castello Matteo Visconti , Huckins Jeremy F. , Nastase Samuel A. , Ida Gobbini M. , Wager Tor D. , Haxby James V.
Published: November 2020

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals’ brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals’ neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.
A quantitative view of strategies to engineer cell-selective ligand binding
Authors: Tan Zhixin Cyrillus , Orcutt-Jahns Brian , Meyer Aaron S.
Published: November 2020

A critical property of many therapies is their selective binding to specific target populations. Exceptional specificity can arise from high-affinity binding to unique cell surface targets. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multi-valent molecules, but these create a combinatorial explosion of design possibilities. Therefore, guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we extend a multi-valent binding model to multi-ligand and multi-receptor interactions. Using this model, we explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, and valency changes. Each of these strategies maximizes selectivity in distinct cases, leading to synergistic improvements when used in combination. Finally, we identify situations in which selectivity cannot be derived through passive binding alone to highlight areas in need of new developments. In total, this work uses a quantitative model to unify a comprehensive set of design guidelines for engineering cell-specific therapies.

Summary points

Affinity, valency, and other alterations to target cell binding provide enhanced selectivity in specific situations.

Evidence for the effectiveness and limitations of each strategy are abundant within the drug development literature.

Combining strategies can offer enhanced selectivity.

A simple, multivalent ligand-receptor binding model can help to direct therapeutic engineering.
Triple Combination Nitazoxanide, Ribavirin, and Hydroxychloroquine results in the multiplicative reduction of in vitro SARS-CoV-2 viral replication
Authors: Lian Elena , McAlister Carley , Ramirez Gabriela , Chernoff David N. , Went Gregory , Hoopes Justin , Perera Rushika
Published: November 2020

Background

An immediate unmet medical need exists to test and develop existing approved drugs against SARS-COV-2. Despite many efforts, very little progress has been made regarding finding low-cost oral medicines that can be made widely available worldwide to address the global pandemic.

Methods

We sought to examine if a triple combination of nitazoxanide (using its active metabolite tizoxanide), ribavirin, and hydroxychloroquine would lead to a multiplicative effects on viral replication of SARS-COV-2 resulting in a significant reduction of virus yield using VERO E6 cells as a model of viral replication.

Results

Virus yield measured in PFU/ml was ~ 2 logs lower with triple combination versus either drug alone, resulting in the prolongation of time to peak cytopathic effects (CPE). The time to produce 50% CPE increased from 2.8 days for viral controls versus 5.3 days for triple combination therapy. Finally, for each 1-log reduction in virus yield 24 hours post-infection, there was an additional 0.7-day delay in onset of CPE.

Conclusions

A triple combination of tizoxanide, ribavirin, and hydroxychloroquine produced a reduction in SARS-COV-2 viral replication in Vero E6 cells, warranting exploration in additional cell lines as well as human clinical trials.
Cell-Free Biosynthesis to Evaluate Lasso Peptide Formation and Enzyme-Substrate Tolerance
Authors: Si Yuanyuan , Kretsch Ashley M. , Daigh Laura M. , Burk Mark J. , Mitchell Douglas A.
Published: November 2020

Lasso peptides are ribosomally synthesized and post-translationally modified peptide (RiPP) natural products that display a unique lariat-like structure. Owing to a rigid topology, lasso peptides are unusually stable towards heat and proteolytic degradation. Some lasso peptides have been shown to bind human cell-surface receptors and exhibit anticancer properties, while others display antibacterial or antiviral activities. Known lasso peptides are produced by bacteria and genome-mining studies indicate that lasso peptides are a relatively prevalent RiPP class; however, the discovery, isolation, and characterization of lasso peptides are constrained by the lack of an efficient production system. In this study, we employ a cell-free biosynthesis (CFB) strategy to address the longstanding challenges associated with lasso peptide production. We report the successful formation of a diverse array of lasso peptides that include known examples as well as a new predicted lasso peptide from Thermobifida halotolerans. We further demonstrate the utility of CFB to rapidly generate and characterize multisite precursor peptide variants in order to evaluate the substrate tolerance of the biosynthetic pathway. We show that the lasso-forming cyclase from the fusilassin pathway can produce millions of sequence-diverse lasso peptides via CFB with an extraordinary level of sequence permissiveness within the ring region of the lasso peptide. These data lay a firm foundation for the creation of large lasso peptide libraries using CFB to identify new variants with unique properties.
An Optimised GAS-pharyngeal cell biofilm model
Authors: Vyas Heema K. N. , McArthur Jason D. , Sanderson-Smith Martina L.
Published: November 2020

Group A Streptococcus (GAS) causes 700 million infections and accounts for half a million deaths per year. Biofilm formation has been implicated in both pharyngeal and dermal GAS infections. In vitro, plate-based assays have shown that several GAS M-types form biofilms, and multiple GAS virulence factors have been linked to biofilm formation. Although the contributions of these plate-based studies have been valuable, most have failed to mimic the host environment, with many studies utilising abiotic surfaces. GAS is a human specific pathogen, and colonisation and subsequent biofilm formation is likely facilitated by distinct interactions with host tissue surfaces. As such, a host cell-GAS model has been optimised to support and grow GAS biofilms of a variety of GAS M-types. Improvements and adjustments to the crystal violet biofilm biomass assay have also been tailored to reproducibly detect delicate GAS biofilms. We propose 72 h as an optimal growth period for yielding detectable biofilm biomass. GAS biofilms formed are robust and durable, and can be reproducibly assessed via staining/washing intensive assays such as crystal violet with the aid of methanol fixation prior to staining. Lastly, SEM imaging of GAS biofilms formed by this model are resemblant of those previously found on excised tonsils of patients suffering chronic pharyngo-tonsillitis. Taken together, we outline an efficacious GAS biofilm pharyngeal cell model that can support long-term GAS biofilm formation, with biofilms formed closely resembling those seen in vivo.
The kinase specificity of protein kinase inhibitor peptide (PKI)
Authors: Chen Yao , Sabatini Bernardo L.
Published: November 2020

G-protein-coupled-receptor (GPCR) signaling is exquisitely controlled to achieve spatial and temporal specificity. The endogenous protein kinase inhibitor peptide (PKI) confines the spatial and temporal spread of the activity of protein kinase A (PKA), which integrates inputs from three major types of GPCRs. Despite its wide usage as a pharmaceutical inhibitor of PKA, it was unclear whether PKI only inhibits PKA activity. Here, the effects of PKI on 55 mouse kinases were tested inin vitroassays. We found that in addition to inhibiting PKA activity, both PKI (6-22) amide and full-length PKIα facilitated the activation of multiple isoforms of protein kinase C (PKC), albeit at much higher concentrations than necessary to inhibit PKA. Thus, our results call for appropriate interpretation of experimental results using PKI as a pharmaceutical agent. Furthermore, our study lays the foundation to explore the potential functions of PKI in regulating PKC activity and in coordinating PKC and PKA activities.
A two-state ribosome and protein model can robustly capture the chemical reaction dynamics of gene expression
Authors: Pandey Ayush , Murray Richard M.
Published: November 2020

We derive phenomenological models of gene expression from a mechanistic description of chemical reactions using an automated model reduction method. Using this method, we get analytical descriptions and computational performance guarantees to compare the reduced dynamics with the full models. We develop a new two-state model with the dynamics of the available free ribosomes in the system and the protein concentration. We show that this new two-state model captures the detailed mass-action kinetics of the chemical reaction network under various biologically plausible conditions on model parameters. On comparing the performance of this model with the commonly used mRNA transcript-protein dynamical model for gene expression, we analytically show that the free ribosome and protein model has superior error and robustness performance.
A systematic evaluation of IgM and IgG antibody assay accuracy in diagnosing acute Zika Virus infection in Brazil; lessons relevant to emerging infections
Authors: Medialdea-Carrera Raquel , Levy Flavia , Castanha Priscila , de Sequeira Patricia Carvalho , Brasil Patricia , Lewis-Ximenez Lia L , Turtle Lance , Solomon Tom , de Filippis Ana Maria Bispo , Brown David W. , Griffiths Michael J.
Published: November 2020

Accurate diagnostics underpin effective public health responses to emerging viruses. For viruses, such as Zika virus (ZIKV), where the viremia clears quickly, antibody-based (IgM or IgG) diagnostics are recommended for patients who present seven days after symptom onset. However, cross-reactive antibody responses can complicate test interpretation among populations where closely related viruses circulate.

We examined the accuracy (proportion of samples correctly categorized as Zika-positive or negative) for antibody-based diagnostics among Brazilian residents (Rio de Janeiro) during the ZIKV outbreak. Four ZIKV ELISAs (IgM and IgG Euroimmun, IgM Novagnost and CDC MAC), two dengue ELISAs (IgM and IgG Panbio), and the ZIKV plaque reduction neutralization test (PRNT) were evaluated. Positive samples were ZIKV PCR confirmed clinical cases collected in 2015-2016 (n=169); Negative samples (n=236) were collected before ZIKV was present in Brazil (≤2013).

Among serum samples collected ≥7 days from symptom onset, PRNT exhibited the highest accuracy (93.7%), followed by the Euroimmun IgG ELISA (77.9%). All IgM assays exhibited lower accuracy (<74%). IgG was detected more consistently than IgM among ZIKV cases using Euroimmun ELISAs (68% versus 22%). Anti-DENV IgM ELISA was positive in 41.1% of confirmed ZIKV samples tested.

The Euroimmun IgG assay, although misdiagnosing 22% of samples, provided the most accurate ELISA. Anti-ZIKV IgG was detected more reliably than IgM among ZIKV patients, suggesting a secondary antibody response to assay antigens following ZIKV infection. Antibody ELISAs need careful evaluation in their target population to optimise use and minimise misdiagnosis, prior to widespread deployment, particularly where related viruses co-circulate.
Sensing stress responses in potato with whole-plant redox imaging
Authors: Hipsch Matanel , Lampl Nardy , Zelinger Einat , Barda Orel , Rosenwasser Shilo
Published: November 2020

Environmental stresses are among the major factors that limit crop productivity and plant growth. Plant exposure to various abiotic stress, such as drought, cold temperatures, or high light, results in overproduction of reactive oxygen species (ROS). To avoid oxidative damage, critical mechanisms for their detoxification have evolved, consisting of ROS-scavenging enzymes and small antioxidant molecules, such as glutathione (GSH) and ascorbate. Thus, monitoring redox changes with high spatial and temporal resolution is critical for understanding oxidative stress signaling and has the potential to enable early detection of stress responses in crop plants. In this work, potato plants (‘Solanum tuberosum’) expressing a chloroplast-targeted reduction-oxidation-sensitive green fluorescent protein2 (roGFP2) were generated to report the redox potential of the glutathione (EGSH) in the chloroplast stroma. By applying whole-plant fluorescence imaging, we mapped alteration in the chloroplast EGSH under several stress conditions including, high-light, cold and drought. Extremely high increase in chloroplast EGSH was observed under the combination of high-light and low temperatures, conditions that specifically induce PSI photoinhibition. Intriguingly, whole-plant ratiometric imaging analysis noted a higher reduced state in newly developed as compared to mature leaves, suggesting a graded stress sensitivity as part of the plant strategies for coping with stress conditions. The presented observations suggest that whole-plant redox imaging can serve as a powerful tool for the basic understanding of plant stress responses as well as for applied agricultural research, such as improving phenotyping capabilities in breeding programs and early detection of stress responses in the field.
EpitopeVec: Linear Epitope Prediction Using Deep Protein Sequence Embeddings
Authors: Bahai Akash , Asgari Ehsaneddin , Mofrad Mohammad R.K. , Kloetgen Andreas , McHardy Alice C.
Published: November 2020

Motivation

B-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immunodiagnostic reagents, and antibody production, and thus generally in infectious disease prevention and diagnosis. Experimental methods used to determine BCEs are costly and time-consuming. It thus becomes essential to develop computational methods for the rapid identification of BCEs. Though several computational methods have been developed for this task, cross-testing of classifiers trained and tested on different datasets revealed their limitations, with accuracies of 51 to 53%.

Results

We describe a new method called EpitopeVec, which utilizes residue properties, modified antigenicity scales, and a Protvec representation of peptides for linear BCE prediction with machine learning techniques. Evaluating on several large and small data sets, as well as cross-testing demonstrated an improvement of the state-of-the-art performances in terms of accuracy and AUC. Predictive performance depended on the type of antigen (viral, bacterial, eukaryote, etc.). In view of that, we also trained our method on a large viral dataset to create a linear viral BCE predictor.

Availablity

The software is available at https://github.com/hzi-bifo/epitope-prediction under the GPL3.0 license.

Contact

alice.mchardy@helmholtz-hzi.de

Supplementary information

Supplementary data are available at Bioinformatics online.
The full model of the pMHC-TCR-CD3 complex: a structural and kinetics characterization
Authors: Alba Josephine , Acuto Oreste , D’Abramo Marco
Published: November 2020

The machinery involved in cytotoxic T-cell activation requires three main characters such as: the major histocompatibility complex class I (MHC I) bound to the peptide (p), the T-cell receptor (TCR), and the CD3-complex which is a multidimer interfaced with the intracellular side. The pMHC:TCR interaction has been largely studied both in experimental and computational models, giving a contribution in understanding the complexity of the TCR triggering process. Nevertheless, a detailed study of the structural and dynamical characterization of the full complex (pMHC:TCR:CD3-complex) is still missing, due to insufficient data available on the CD3-chains arrangement around the TCR. The recent determination of the TCR:CD3-complex structure by means of Cryo-EM technique has given a chance to build the entire proteins system essential in the activation of T-cell, and thus in the adaptive immune response. Here, we present the first full model of the pMHC interacting with the TCR:CD3-complex, built in a lipid environment. To describe the conformational behaviour associated with the unbound and the bound states, all atoms Molecular Dynamics simulations were performed for the TCR:CD3-complex and for two pMHC:TCR:CD3-complex systems, bound to two different peptides. Our data point out that a conformational change affecting the TCR Constant β (Cβ) region occurs after the binding to the pMHC, revealing a key role of such a region in the propagation of the signal. Moreover, we found that the TCR reduces the flexibility of the MHC I binding groove, confirming our previous results.
High mannose N-glycans on red blood cells as phagocytic ligands, mediating both sickle cell anaemia and resistance to malaria
Authors: Cao Huan , Antonopoulos Aristotelis , Henderson Sadie , Wassall Heather , Brewin John , Masson Alanna , Shepherd Jenna , Konieczny Gabriela , Patel Bhinal , Williams Maria-Louise , Davie Adam , Forrester Megan A , Hall Lindsay , Minter Beverley , Tampakis Dimitris , Moss Michael , Lennon Charlotte , Pickford Wendy , Erwig Lars , Robertson Beverley , Dell Anne , Brown Gordon D. , Wilson Heather M. , Rees David C. , Haslam Stuart M. , Rowe J. Alexandra , Barker Robert N. , Vickers Mark A.
Published: November 2020

In both sickle cell disease (SCD) and malaria, red blood cells (RBCs) are phagocytosed in the spleen, but receptor-ligand pairs mediating uptake have not been identified. Here, we report that patches of high mannose N-glycans (Man5-9GlcNAc2), expressed on diseased or oxidized RBC surfaces, bind the mannose receptor (CD206) on phagocytes to mediate clearance. Extravascular haemolysis in SCD correlates with high mannose glycan levels on RBCs. Infection of RBCs withPlasmodium falciparumexpose surface mannose N-glycans on healthy RBCs, which occurred at significantly higher levels on RBCs from subjects with sickle cell trait compared to those lacking haemoglobin S. The glycans were associated with high molecular weight complexes and protease-resistant, lower molecular weight fragments containing spectrin. Recognition of surface N-linked high mannose glycans, a novel response to cellular stress, is the first molecular mechanism common to both the pathogenesis of SCD and resistance to severe malaria in sickle cell trait.