bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating the risk on outbreak spreading of 2019-nCoV in China using transportation data
Authors: Yuan Hsiang-Yu , Hossain M. Pear , Tsegaye Mesfin , Zhu Xiaolin , Jia Pengfei , Junus Alvin , Wen Tzai-Hung , Pfeiffer Dirk
Published: November 2020

A novel corona virus (2019-nCoV) was identified in Wuhan, China and has been causing an unprecedented outbreak in China. The spread of this novel virus can eventually become an international emergency. During the early outbreak phase in Wuhan, one of the most important public health tasks is to prevent the spread of the virus to other cities. Therefore, full-scale border control measures to prevent the spread of virus have been discussed in many nearby countries. At the same time, lockdown in Wuhan cityu (border control from leaving out) has been imposed. The challenge is that many people have traveled from Wuhan to other cities before the border control. Thus, it is difficult to forecast the number of imported cases at different cities and estimate their risk on outbreak emergence.

Here, we have developed a mathematical framework incorporating city-to-city connections to calculate the number of imported cases of the novel virus from an outbreak source, and the cumulative number of secondary cases generated by the imported cases. We used this number to estimate the arrival time of outbreak emergence using air travel frequency data from Wuhan to other cities, collected from the International Air Transport Association database. In addition, a meta-population compartmental model was built based on a classical SIR approach to simulate outbreaks at different cities.

We consider the scenarios under three basic reproductive number (R0) settings using the best knowledge of the current findings, from high (2.92), mild (1.68), to a much lower numbers (1.4). The mean arrival time of outbreak spreading has been determined. Under the high R0, the critical time is 17.9 days after December 31, 2019 for outbreak spreading. Under the low R0, the critical time is between day 26.2 to day 35 after December 31, 2019. To make an extra 30 days gain, under the low R0 (1.4), the control measures have to reduce 87% of the connections between the source and target cities. Under the higher R0 (2.92), the effect on reducing the chance of outbreak emergence is generally low until the border control measure was enhanced to reduce more than 95% of the connections.
Transfer learning applied to the forecast of mosquito-borne diseases
Authors: Coelho Flávio Codeço , de Holanda Nicolaus Linneu , Coimbra Beatriz
Published: November 2020

Here we apply the concept of transfer learning to time series forecasting models for mosquito-borne diseases. Transfer learning, in this application, allows us to use knowledge obtained from modeling one disease to predict an emerging one for which extensive data is still not available. Here we discuss the performances of two families of models for predicting Chikungunya and Zika using models trained with dengue time series, in two Brazilian cities: Rio de Janeiro and Fortaleza.
IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs
Authors: Yu Fengchao , Haynes Sarah E. , Nesvizhskii Alexey I.
Published: November 2020

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared to MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant, and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry (FAIMS) separation. Fully integrated in FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.
Strengthening the BioCompute Standard by Crowdsourcing on PrecisionFDA
Authors: Stephens Sarah H , King Charles Hadley , Watford Sean , Patel Janisha , Dean Dennis A. , Koc Soner , Xiao Nan , Donaldson Eric F. , Thompson Elaine E. , Purkayastha Anjan , Mazumder Raja , Johanson Elaine , Keeney Jonathon
Published: November 2020

Background

The field of bioinformatics has grown at such a rapid pace that a gap in standardization exists when reporting an analysis. In response, the BioCompute project was created to standardize the type and method of information communicated when describing a bioinformatic analysis. Once the project became established, its goals shifted to broadening awareness and usage of BioCompute, and soliciting feedback from a larger audience. To address these goals, the BioCompute project collaborated with precisionFDA on a crowdsourced challenge that ran from May 2019 to October 2019. This challenge had a beginner track where participants submitted BCOs based on a pipeline of their choosing, and an advanced track where participants submitted applications supporting the creation of a BCO and verification of BCO conformance to specifications.

Results

In total, there were 28 submissions to the beginner track (including submissions from a bioinformatics master’s class at George Washington University) and three submissions to the advanced track. Three top performers were selected from the beginner track, while a single top performer was selected for the advanced track. In the beginner track, top performers differentiated themselves by submitting BCOs that included more than the minimally compliant content. Advanced track submissions were very impressive. They included a complete web application, a command line tool that produced a static result, and a dockerized container that automatically created the BCO as the tool was run. The ability to harmonize the correct function, a simple user experience, and the aesthetics of the tool interface differentiated the tools.

Conclusions

Despite being new to the concept, most beginner track scores were high, indicating that most users understood the fundamental concepts of the BCO specification. Novice bioinformatics students were an ideal cohort for this Challenge because of their lack of familiarity with BioCompute, broad diversity of research interests, and motivation to submit high-quality work. This challenge was successful in introducing the BCO to a wider audience, obtaining feedback from that audience, and resulting in a tool novices may use for BCO creation and conformance. In addition, the BCO specification itself was improved based on feedback illustrating the utility of a “wisdom of the crowd” approach to standards development.
Synergistic Coding of Carbon Dioxide and a Human Sweat Odorant in the Mosquito Brain
Authors: Shankar Shruti , Tauxe Genevieve M. , Spikol Emma D. , Li Ming , Akbari Omar S. , Giraldo Diego , McMeniman Conor J.
Published: November 2020

The yellow fever mosquitoAedes aegyptiemploys olfaction to locate humans. We applied neural activity mapping to define the molecular and cellular logic of how the mosquito brain is wired to detect two human odorants that are attractive when blended together. We determined that the human breath volatile carbon dioxide (CO2) is detected by the largest unit of olfactory coding in the antennal lobe of the mosquito brain. Synergistically, CO2detection gates pre-synaptic calcium signaling in olfactory sensory neuron axon terminals that innervate unique antennal lobe regions tuned to the human sweat odorant L-(+)-lactic acid. We propose that simultaneous detection of the signature human volatiles CO2and L-(+)-lactic acid disinhibits a multimodal olfactory network for hunting humans in the mosquito brain.
Influence of nutritional status on eating habits and food choice determinants among Brazilian women during the COVID-19 pandemic
Authors: Mazzolani Bruna Caruso , Smaira Fabiana Infante , Esteves Gabriel Perri , Santo André Heloísa C. , Amarante Milla Cordeiro , Castanho Daniela , Campos Karen , Benatti Fabiana Braga , Jéssica Pinto Ana , Roschel Hamilton , Gualano Bruno , Ferreira Carolina Nicolleti
Published: November 2020

We aimed to evaluate the influence of nutritional status on eating habits and food choice determinants among Brazilian women during the COVID-19 outbreak. This cross-sectional survey was conducted between June and September, 2020, period in which social distancing measures were in place. Participants (n=1,183) were classified as normal weight (60.4%), overweight (26.2%) and obese (13.4%). Eating habits changed during quarantine irrespective of nutritional status. The number of women participating in grocery shopping was reduced by 34% during quarantine (p<0.001, OR=0.55, 0.79), whereas participation in cooking and ordering delivery service increased by 28% (p=0.004, OR=1.08, 1.51) and 146% (p<0.001, OR=2.06, 2.95), respectively. The number of participants reporting the habit of snacking (p=0.005, OR=1.07, 1.43) and eating at the table increased by 24% and 40% (p<0.001, OR=1.20, 1.64). Interestingly, the number of participants reporting the habit of dieting decreased by 41% (p<0.001, OR=0.59 [0.50, 0.70]). During the quarantine, “liking”, “need and hunger”, and “habits” were the most commonly reported determinants of food choice overall. “Health”, “natural concerns” and “need and hunger” were less important determinants for participants with overweight/obesity compared to those with normal weight. Regression models showed that(i)“health”, “natural concerns” and “affect regulation”;(ii)“health”, “pleasure”, “convenience”, and “natural concerns”; and(iii)“visual appeal” and “pleasure” were the food choice determinants more associated with eating habits among women with normal weight, overweight and obesity, respectively. In conclusion, eating habits were influenced during the pandemic despite nutritional status, whereas food choice determinants differed between overweight/obesity and normal weight women.
Coronavirus Disease 2019 (COVID-19) Candidate Chest CT Features: A Systematic Review of Extracted Imaging Features from 7571 Individuals
Authors: Zahiri Javad , Afsharinia Mohammad Hossein , Hekmati Zhaleh , Khodarahmi Mohsen , Hekmati Shahrzad , Pourghorban Ramin
Published: November 2020

Since the outbreak of Coronavirus Disease 2019 (COVID-19) causing novel coronavirus (2019-nCoV)-infected pneumonia (NCIP), over 45 million affected cases have been reported worldwide. Many patients with COVID-19 have involvement of their respiratory system. According to studies in the radiology literature, chest computed tomography (CT) is recommended in suspected cases for initial detection, evaluating the disease progression and monitoring the response to therapy. The aim of this article is to review the most frequently reported imaging features in COVID-19 patients in order to provide a reliable insight into expected CT imaging manifestations in patients with positive reverse-transcription polymerase chain reaction (RT-PCR) test results, and also for the initial detection of patients with suspicious clinical presentation whose RT-PCR test results are false negative. A total of 60 out of 173 initial COVID-19 studies, comprising 7571 individuals, were identified by searching PubMed database for articles published between the months of January and June 2020. The data of these studies were related to patients from China, Japan, Italy, USA, Iran and Singapore. Among 40 reported features, presence of ground glass opacities (GGO), consolidation, bilateral lung involvement and peripheral distribution are the most frequently observed ones, reported in 100%, 91.7%, 85%, and 83.3% of articles, respectively. In a similar way, we extracted CT imaging studies of similar pulmonary syndromes outbreaks caused by other strains of coronavirus family: Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). For MERS and SARS, 2 out of 21 and 5 out of 153 initially retrieved studies had CT findings, respectively. Herein, we have indicated the most common coronavirus family related and COVID-19 specific features. Presence of GGO, consolidation, bilateral lung involvement and peripheral distribution were the features reported in at least 83% of COVID-19 articles, while air bronchogram, multi-lobe involvement and linear opacity were the three potential COVID-19 specific CT imaging findings. This is necessary to recognize the most promising imaging features for diagnosis and follow-up of patients with COVID-19. Furthermore, we identified co-existed CT imaging features.
Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
Authors: Cao Kai , Hong Yiguang , Wan Lin
Published: November 2020

Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Although achieved state-of-the-art performance on single-cell multi-omics data integration and did not require any correspondence information, either among cells or among features, current manifold alignment based integrative methods are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure. To overcome this limitation, we present Pamona, an algorithm that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. Simulation studies and applications to four real data sets demonstrate that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in the common space. Pamona software is available athttps://github.com/caokai1073/Pamona.
Binding site localization on non-homogeneous cell surfaces using topological image averaging
Authors: Ahnlide Vibha Kumra , Ahnlide Johannes Kumra , Beech Jason P. , Nordenfelt Pontus
Published: November 2020

Antibody binding to cell surface proteins plays a crucial role in immunity and the location of an epitope can altogether determine the immunological outcome of a host-target interaction. Techniques available today for epitope identification are costly, time-consuming, and unsuited for high-throughput analysis. Fast and efficient screening of epitope location can be useful for the development of therapeutic monoclonal antibodies and vaccines. In the present work, we have developed a method for imaging-based localization of binding sites on cellular surface proteins. The cellular morphology typically varies, and antibodies often bind in a non-homogenous manner, making traditional particle-averaging strategies challenging for accurate native antibody localization. Nanometer-scale resolution is achieved through localization in one dimension, namely the distance from a bound ligand to a reference surface, by using topological image averaging. Our results show that this method is well suited for antibody binding site measurements on native cell surface morphology.
Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being
Authors: de Vries L.P. , Baselmans B.M.L. , Luykx J.J. , de Zeeuw E.L. , Minică C. , de Geus E.J.C. , Vinkers C.H. , Bartels M.
Published: November 2020

Resilience and well-being are strongly related. People with higher levels of well-being are more resilient after stressful life events or trauma and vice versa. Less is known about the underlying sources of overlap and causality between the constructs. In a sample of 11.304 twins and 2.572 siblings from the Netherlands Twin Register, we investigated the overlap and possible direction of causation between resilience (i.e. the absence of psychiatric symptoms despite negative life events) and well-being (i.e. satisfaction with life) using polygenic score (PGS) prediction, twin-sibling modelling, and the Mendelian Randomization Direction of Causality (MR-DoC) model. Longitudinal twin-sibling models showed significant phenotypic correlations between resilience and well-being (.41/.51 at time 1 and 2). Well-being PGS were predictive for both well-being and resilience, indicating that genetic factors influencing well-being also predict resilience. Twin-sibling modeling confirmed this genetic correlation (.71) and showed a strong environmental correlation (.93). In line with causality, both genetic (51%) and environmental (49%) factors contributed significantly to the covariance between resilience and well-being. Furthermore, the results of within-subject and MZ twin differences analyses were in line with bidirectional causality. Additionally, we used the MR-DoC model combining both molecular and twin data to test causality, while correcting for pleiotropy. We confirmed the causal effect from well-being to resilience, with the direct effect of well-being explaining 11% (T1) and 20% (T2) of the variance in resilience. Data limitations prevented us to test the directional effect from resilience to well-being with the MR-DoC model. To conclude, we showed a strong relation between well-being and resilience. A first attempt to quantify the direction of this relationship points towards a bidirectional causal effect. If replicated, the potential mutual effects can have implications for interventions to lower psychopathology vulnerability, as resilience and well-being are both negatively related to psychopathology.
Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO
Authors: Velten Britta , Braunger Jana M. , Arnol Damien , Argelaguet Ricard , Stegle Oliver
Published: November 2020

Factor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.
Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior
Authors: Reinl Kaitlin L. , Sterner Robert W. , Lafrancois Brenda Moraska , Brovold Sandra
Published: November 2020

Lake Superior has recently begun experiencing cyanobacterial blooms comprised ofDolichospermum lemmermanniinear the Apostle Islands and along the southern shore of the western arm. Little is known about the origin of these blooms. Experiments were conducted during the summers of 2017 and 2018 to identify sources of propagules and characteristics of sites that were potential sources. The 2017 experiments were conducting using a factorial design with three source zones (Harbor, River, and Lake), two nutrient conditions (high and low N:P), and three temperatures (15, 20, and 25 °C). At the end of the experiment, cyanobacteria were most abundant from the ‘River’ and ‘Harbor’ zones at low N:P and 20 and 25 °C, withD. lemmermanniimost abundant at 20 °C. Subsequently in 2018 we evaluated 26 specific inland locations from three waterbody types (Coastal, Lake/Pond, and River) and explored similarities among those sites that produced cyanobacteria in high abundance when samples were incubated under optimal conditions (low N:P and 25 °C). Under these growing conditions, we found high cyanobacteria abundance developed in samples from river sites with low ambient temperatures and high conductivity. Field monitoring showed that Lake Superior nearshore temperatures were higher than rivers. These observations suggest that blooms ofD. lemmermanniiin Lake Superior are initiated by fluvial seeding of propagules and highlight the importance of warmer temperatures and favorable nutrient and light conditions for subsequent extensive cyanobacterial growth. We argue that the watershed is an important source of biological loading ofD. lemmermanniito Lake Superior, and that when those cells reach the nearshore where there is warmer water temperatures and increased light, they can grow in abundance to produce blooms.
Nitrate inhibits nodule organogenesis through inhibition of cytokinin biosynthesis inLotus japonicus
Authors: Lin Jieshun , Roswanjaya Yuda Purwana , Kohlen Wouter , Stougaard Jens , Reid Dugald
Published: November 2020

Legumes balance nitrogen acquisition from soil nitrate with symbiotic nitrogen fixation. Nitrogen fixation requires establishment of a new organ, which is a cytokinin dependent developmental process in the root. We found cytokinin biosynthesis is a central integrator, balancing nitrate signalling with symbiotic acquired nitrogen. Low nitrate conditions provide a permissive state for induction of cytokinin by symbiotic signalling and thus nodule development. In contrast, high nitrate is inhibitory to cytokinin accumulation and nodule establishment in the root zone susceptible to nodule formation. This reduction of symbiotic cytokinin accumulation was further exacerbated in cytokinin biosynthesis mutants, which display hypersensitivity to nitrate inhibition of nodule development, maturation and nitrogen fixation. Consistent with this, cytokinin application can rescue nodulation and nitrogen fixation of biosynthesis mutants in a concentration dependent manner. These inhibitory impacts of nitrate on symbiosis occur in aNlp1andNlp4dependent manner and contrast with the positive influence of nitrate on cytokinin biosynthesis that occurs in non-symbiotic species. Altogether this shows that legumes, as exemplified byLotus japonicus, have evolved a different cytokinin response to nitrate compared to non-legumes.

One sentence summary

Cytokinin biosynthesis is suppressed by nitrate inLotus japonicus, providing a mechanism for nitrate inhibition of symbiotic nodule organogenesis.
Osteosarcoma tumors maintain intra-tumoral transcriptional heterogeneity during bone and lung colonization
Authors: Rajan Sanjana , Franz Emily , McAloney Camille A. , Vetter Tatyana A. , Cam Maren , Gross Amy C. , Taslim Cenny , Wang Meng , Cannon Matthew V. , Oles Alexander , Roberts Ryan D.
Published: November 2020

Background

Tumors are complex tissues containing collections of phenotypically diverse malignant and nonmalignant cells. We know little of the mechanisms that govern heterogeneity of tumor cells nor of the role heterogeneity plays in overcoming stresses, such as adaptation to different microenvironments. Osteosarcoma is an ideal model for studying these mechanisms—it exhibits widespread inter- and intra-tumoral heterogeneity, predictable patterns of metastasis, and a lack of clear targetable driver mutations. Understanding the processes that facilitate adaptation to primary and metastatic microenvironments could inform the development of therapeutic targeting strategies.

Results

We investigated single-cell RNA-sequencing profiles of 47,977 cells obtained from cell line and patient-derived xenograft models as cells adapted to growth within primary bone and metastatic lung environments. Tumor cells maintained phenotypic heterogeneity as they responded to the selective pressures imposed during bone and lung colonization. Heterogenous subsets of cells defined by distinct transcriptional profiles were maintained within bone- and lung-colonizing tumors, despite high-level selection. One prominent heterogenous feature involving glucose metabolism was clearly validated using immunofluorescence staining. Finally, using concurrent lineage tracing and single-cell transcriptomics, we found that lung colonization enriches for multiple clones with distinct transcriptional profiles that are preserved across cellular generations.

Conclusions

Response to environmental stressors occurs through complex and dynamic phenotypic adaptations. Heterogeneity is maintained, even in conditions that enforce clonal selection. These findings likely reflect the influences of developmental processes promoting diversification of tumor cell subpopulations, which are retained, even in the face of selective pressures.
Yeast synthetic minimal biosensors for evaluating protein production
Authors: Peng Kai , Koukamp Heinrich , Pretorius Isak S. , Paulsen Ian T.
Published: November 2020

The unfolded protein response (UPR) is a highly conserved cellular response in eukaryotic cells to counteract endoplasmic reticulum (ER) stress, typically triggered by unfolded protein accumulation. In addition to its relevance to human diseases like cancer cell development, the induction of the UPR has a significant impact on recombinant protein production yields in microbial cell factories, including the industrial workhorseSaccharomyces cerevisiae.Being able to accurately detect and measure this ER stress response in single cells, enables the rapid optimisation of protein production conditions and high-throughput strain selection strategies. Current methodologies to monitor the UPR inS. cerevisiaeare often temporally and spatially removed from the cultivation stage, or lack updated systematic evaluation. To this end we constructed and systematically evaluated a series of high-throughput UPR sensors by different designs, incorporating either yeast native UPR promoters or novel synthetic minimal UPR promoters. The native promoters ofDER1andERO1were identified to have suitable UPR biosensor properties and served as an expression level guide for orthogonal sensor benchmarking. Our best synthetic minimal sensor, SM1, was only 98 bp in length, had minimal homology to other native yeast sequences and displayed superior sensor characteristics. Using this synthetic minimal UPR sensor, we demonstrate its ability to accurately discriminate between cells expressing different heterologous proteins and at varying production levels. Our sensor is thus a novel high-throughput tool for determining expression/engineering strategies for optimal heterologous protein production.
Higher risk of mental health deterioration during the Covid-19 lockdown among students rather than non-students. The French Confins study
Authors: Arsandaux Julie , Montagni Ilaria , Macalli Mélissa , Texier Nathalie , Pouriel Mathilde , Germain Raphaël , Mebarki Adel , Kinouani Shérazade , Tournier Marie , Schück Stéphane , Tzourio Christophe
Published: November 2020

Background

Covid-19 pandemic and its consequences have raised fears of its psychological impact. The objective of this study was to estimate the effect of student status on mental health conditions during Covid-19 general lockdown among adults in France.

Methods

Using cross-sectional data of the Confins cohort, we estimated the effect of student status on depressive and anxiety symptoms, suicidal thoughts and perceived stress using multivariate logistic regression analyses. Stratified models for college students and non- students were performed to identify associated population-specific factors.

Results

Among the 2260 included participants, students represented 59% (n=1335 vs 925 non- students) and 78% of the total sample were female. Student status was more frequently associated with depressive symptoms (adjusted OR(aOR)=1.58; 95%CI 1.17;2.14), anxiety symptoms (aOR=1.51; 95%CI 1.10;2.07), perceived stress (n=1919, aOR=1.70, 95%CI 1.26;2.29) and frequent suicidal thoughts (n=1919, aOR=1.57, 95%CI 0.97;2.53). Lockdown conditions that could be potentially aggravating on mental health like isolation had a higher impact on students than non-students.

Limitations

Participants were volunteers, which could limit generalisation of the findings. The cross-sectional design did not allow determining if lockdown impacted directly mental health or if there is another cause. However, we adjusted analyses with the history of psychiatric disorders, and factors related to lockdown conditions were associated with mental health disturbances.

Conclusions

College student’s mental health is of great importance in the context of the general lockdown set up during the pandemic. Follow-up and interventions should be implemented especially for those at high-risk (younger people and those with history of psychiatric disorders).
The circulating SARS-CoV-2 spike variant N439K maintains fitness while evading antibody-mediated immunity
Authors: Thomson Emma C. , Rosen Laura E. , Shepherd James G. , Spreafico Roberto , da Silva Filipe Ana , Wojcechowskyj Jason A. , Davis Chris , Piccoli Luca , Pascall David J. , Dillen Josh , Lytras Spyros , Czudnochowski Nadine , Shah Rajiv , Meury Marcel , Jesudason Natasha , De Marco Anna , Li Kathy , Bassi Jessica , O’Toole Aine , Pinto Dora , Colquhoun Rachel M. , Culap Katja , Jackson Ben , Zatta Fabrizia , Rambaut Andrew , Jaconi Stefano , Sreenu Vattipally B. , Nix Jay , Jarrett Ruth F. , Beltramello Martina , Nomikou Kyriaki , Pizzuto Matteo , Tong Lily , Cameroni Elisabetta , Johnson Natasha , Wickenhagen Arthur , Ceschi Alessandro , Mair Daniel , Ferrari Paolo , Smollett Katherine , Sallusto Federica , Carmichael Stephen , Garzoni Christian , Nichols Jenna , Galli Massimo , Hughes Joseph , Riva Agostino , Ho Antonia , Semple Malcolm G. , Openshaw Peter J.M. , Baillie J. Kenneth ,   The ISARIC4C Investigators,   the COVID-19 Genomics UK (COG-UK) consortium, Rihn Suzannah J. , Lycett Samantha J. , Virgin Herbert W. , Telenti Amalio , Corti Davide , Robertson David L. , Snell Gyorgy
Published: November 2020

SARS-CoV-2 can mutate to evade immunity, with consequences for the efficacy of emerging vaccines and antibody therapeutics. Herein we demonstrate that the immunodominant SARS-CoV-2 spike (S) receptor binding motif (RBM) is the most divergent region of S, and provide epidemiological, clinical, and molecular characterization of a prevalent RBM variant, N439K. We demonstrate that N439K S protein has enhanced binding affinity to the hACE2 receptor, and that N439K virus has similar clinical outcomes and in vitro replication fitness as compared to wild- type. We observed that the N439K mutation resulted in immune escape from a panel of neutralizing monoclonal antibodies, including one in clinical trials, as well as from polyclonal sera from a sizeable fraction of persons recovered from infection. Immune evasion mutations that maintain virulence and fitness such as N439K can emerge within SARS-CoV-2 S, highlighting the need for ongoing molecular surveillance to guide development and usage of vaccines and therapeutics.
Hex1, the Major Component of Woronin Bodies, is Required for Normal Development, Pathogenicity and Stress Response in the Plant Pathogenic FungusVerticillium dahliae
Authors: Vangalis Vasileios , Papaioannou Ioannis A. , Markakis Emmanouil A. , Knop Michael , Typas Milton A.
Published: November 2020

Woronin bodies are membrane-bound organelles of filamentous ascomycetes that mediate hyphal compartmentalization by plugging septal pores upon hyphal damage. Their major component is the peroxisomal protein Hex1, which has also been implicated in additional cellular processes in fungi. Here, we analyzed the Hex1 homolog ofVerticillium dahliae, an important asexual plant pathogen, and we report its pleiotropic involvement in fungal growth, physiology, stress response and pathogenicity. Alternative splicing of theVdhex1gene can lead to the production of two Hex1 isoforms, which are structurally similar to theirNeurospora crassahomolog. We show thatVdHex1 is targeted to the septum, consistently with its demonstrated function in sealing hyphal compartments to prevent excessive cytoplasmic bleeding upon injury. Furthermore, our investigation provides direct evidence for significant contributions of Hex1 in growth and morphogenesis, as well as in asexual reproduction capacity. We discovered that Hex1 is required both for normal responses to osmotic stress and factors that affect the cell wall and plasma membrane integrity, and for normal resistance to oxidative stress and ROS homeostasis. TheVdhex1mutant exhibited diminished ability to colonize and cause disease on eggplant. Overall, we show that Hex1 has fundamentally important multifaceted roles in the biology ofV. dahliae.
Identification of genetic variants regulating the abundance of clinically relevant plasma proteins using the Diversity Outbred mouse model
Authors: Philtjens Stéphanie , Acri Dominic J. , Kim Byungwook , Kim Hyewon , Kim Jungsu
Published: November 2020

Although there have been numerous expression quantitative trait loci (eQTL) studies, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation. To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on 92 proteins using the Diversity Outbred (DO) mouse population and identified 12 significantcisand 6transpQTL. Among them, we discovered coding variants in acis-pQTL inAhrand atrans-pQTL inRfx1for the IL-17A protein. Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development.

Author Summary

Blood plasma is a body fluid that can be collected in a noninvasive way to detect diseases, such as autoimmune disease. However, it is known that plasma protein levels are affected by both the environment and genetic background. To determine the effect of genetics on plasma protein levels in human, one needs a rather large sample size. To overcome this critical issue, a mouse model, the Diversity Outbred (DO), was established that is genetically as diverse as the human population. In this study, we used N=140 DO mice and genotyped over 140,000 variants. In addition, we measured the levels of 92 proteins in plasma of these DO mice using Olink Proteomics technology. The proteins detected in this panel are known to be detectable in human plasma, making our study translatable to human. We identified 18 significant protein quantitative trait loci. Furthermore, we describe an analysis pipeline that allows for the detection of a single gene in the locus that is responsible for the differences in protein levels. We identified how variants in the Regulatory Factor X1 (Rfx1) gene regulates Interleukin-17A (IL-17A) plasma levels. Our study reports an innovative approach to identify genetic modifiers that may be targeted for drug development.
High-level expression of the monomeric SARS-CoV-2 S protein RBD 320-537 in stably transfected CHO cells by the EEF1A1-based plasmid vector
Authors: Sinegubova Maria V. , Orlova Nadezhda A. , Kovnir Sergey V. , Dayanova Lutsia K. , Vorobiev Ivan I
Published: November 2020

The spike (S) protein is one of the three proteins forming the coronaviruses’ viral envelope. The S protein of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has a spatial structure similar to the S proteins of other mammalian coronaviruses, except for a unique receptor-binding domain (RBD), which is a significant inducer of host immune response. Recombinant SARS-CoV-2 RBD is widely used as a highly specific minimal antigen for serological tests. Correct exposure of antigenic determinants has a significant impact on the accuracy of such tests – the antigen has to be correctly folded, contain no potentially antigenic non-vertebrate glycans, and, preferably, should have a glycosylation pattern similar to the native S protein. Based on the previously developed p1.1 vector, containing the regulatory sequences of the Eukaryotic translation elongation factor 1 alpha gene (EEF1A1) from Chinese hamster, we created two expression constructs encoding SARS-CoV-2 RBD with C-terminal c-myc and polyhistidine tags. RBDv1 contained a native viral signal peptide, RBDv2 – human tPA signal peptide. We transfected a CHO DG44 cell line, selected stably transfected cells, and performed a few rounds of methotrexate-driven amplification of the genetic cassette in the genome. For the RBDv2 variant, a high-yield clonal producer cell line was obtained. We developed a simple purification scheme that consistently yielded up to 30 mg of RBD protein per liter of the simple shake flask cell culture. Purified proteins were analyzed by polyacrylamide gel electrophoresis in reducing and non-reducing conditions and gel filtration; for RBDv2 protein, the monomeric form content exceeded 90% for several series. Deglycosylation with PNGase F and mass spectrometry confirmed the presence of N-glycosylation. The antigen produced by the described technique is suitable for serological tests and similar applications.
Thermodynamic evaluation of the impact of DNA mismatches in PCR-type SARS-CoV-2 primers and probes
Authors: Miranda Pâmella , Weber Gerald
Published: November 2020

Background

DNA mismatches can affect the efficiency of PCR techniques if the intended target has mismatches in primers or probes regions. The accepted rule is that mismatches are detrimental as they reduce the hybridization temperatures, yet a more quantitative assessment is rarely performed.

Methods

We calculate the hybridization temperatures of primer/probe sets after aligning to SARS-COV-2, SARS-COV-1 and non-SARS genomes, considering all possible combinations of single, double and triple consecutive mismatches. We consider the mismatched hybridization temperature within a range of 5 °C to the fully matched reference temperature.

Results

We obtained the alignments of 19 PCR primers sets that were recently reported for the detection of SARS-CoV-2 and to 21665 SARS-CoV-2 genomes as well as 323 genomes of other viruses of the coronavirus family of which 10 are SARS-CoV-1. We find that many incompletely aligned primers become fully aligned to most of the SARS-CoV-2 when mismatches are considered. However, we also found that many cross-align to SARS-CoV-1 and non-SARS genomes.

Conclusions

Some primer/probe sets only align substantially to most SARS-CoV-2 genomes if mismatches are taken into account. Unfortunately, by the same mechanism, almost 75% of these sets also align to some SARS-CoV-1 and non-SARS viruses. It is therefore recommended to consider mismatch hybridization for the design of primers whenever possible, especially to avoid undesired cross-reactivity.
Tracing production instability in a clonally-derived CHO cell line using single cell transcriptomics
Authors: Tzani Ioanna , Herrmann Nicolas , Carillo Sara , Spargo Cathy A. , Hagan Ryan , Barron Niall , Bones Jonathan , Dilmore W. Shannon , Clarke Colin
Published: November 2020

A variety of mechanisms including transcriptional silencing, gene copy loss and increased susceptibility to cellular stress have been associated with a sudden or gradual loss of monoclonal antibody (mAb) production in Chinese hamster ovary (CHO) cell lines. In this study, we utilised single cell RNA-seq (scRNA-seq) to study a clonally-derived CHO cell line that underwent production instability leading to a dramatic reduction of the levels of mAb produced. From the scRNA-seq data we identified sub clusters associated with variations in the mAb transgenes and observed that heavy chain gene expression was significantly lower than that of the light chain across the population. Using trajectory inference, the evolution of the cell line was reconstructed and was found to correlate with a reduction in heavy and light chain gene expression. Genes encoding for proteins involved in the response to oxidative stress and apoptosis were found to increase in expression as cells progressed along the trajectory. Future studies of CHO cell lines using this technology have the potential to dramatically enhance our understanding of the characteristics underpinning efficient manufacturing performance as well as product quality.

Highlights

A clonally-derived CHO cell line in our laboratory had undergone production instability – in that the amount of intact monoclonal antibody had reduced dramatically to levels at which reliable quantitation was no longer possible. We were, however, able to detect mAb heavy and light chain protein, as well as dimerised light chain species in the cell culture media.

Single cell RNA-seq was utilised to capture > 3,800 gene expression profiles from the cell line at 72hrs post seeding.

Analyses of the scRNA-seq data uncovered transcriptional heterogeneity and revealed the presence of multiple intra cell line clusters. The heavy chain transcript was detected at a significantly lower level in comparison to light chain transcripts. Light chain gene expression was not only more abundant, but also expressed more uniformly across the cell population.

Using unsupervised trajectory analysis, the emergence of heterogeneity in the cell population was traced from those cells most similar to the original isolated clone to those where transcription of the mAb heavy and light chain was undetectable.

Subsequent analysis of CHO cell gene expression patterns revealed a correlation between the progression of cells along the trajectory and the upregulation of genes involved in the cellular response to oxidative stress.
Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction
Authors: Miningou Zobon Nadiatou T. , Jędrzejewska-Szmek Joanna , Blackwell Kim T.
Published: November 2020

Long-lasting long-term potentiation (L-LTP) is a cellular mechanism of learning and memory storage. Studies have demonstrated a requirement for extracellular signal-regulated kinase (ERK) activation in L-LTP produced by a diversity of temporal stimulation patterns. Multiple signaling pathways converge to activate ERK, with different pathways being required for different stimulation patterns. To answer whether and how different temporal patterns select different signaling pathways for ERK activation, we developed a computational model of five signaling pathways (including two novel pathways) leading to ERK activation during L-LTP induction. We show that calcium and cAMP work synergistically to activate ERK and that stimuli given with large inter-trial intervals activate more ERK than shorter intervals. Furthermore, these pathways contribute to different dynamics of ERK activation. These results suggest that signaling pathways with different temporal sensitivity facilitate ERK activation to diversity of temporal patterns.
Derivation of predator functional responses using a mechanistic approach in a natural system
Authors: Beardsell Andréanne , Gravel Dominique , Berteaux Dominique , Gauthier Gilles , Clermont Jeanne , Careau Vincent , Lecomte Nicolas , Juhasz Claire-Cécile , Royer-Boutin Pascal , Bêty Joël
Published: November 2020

The functional response is central to our understanding of any predator–prey system as it establishes the link between trophic levels. Most functional responses are evaluated using phenomenological models linking predator acquisition rate and prey density. However, our ability to measure functional responses using such an approach is often limited in natural systems and the use of inaccurate functions can profoundly affect the outcomes of population and community models. Here, we develop a mechanistic model based on extensive data to assess the functional response of a generalist predator, the arctic fox (Vulpes lagopus), to various tundra prey species (lemmings and the nests of geese, passerines and sandpipers). We found that predator acquisition rates derived from the mechanistic model were consistent with field observations. Although sigmoidal functional responses were previously used to model fox-prey population dynamics, none of our simulations resulted in a saturating response in all prey species. Our results highlight the importance of predator searching components in predator-prey interactions, especially predator speed, while predator acquisition rates were not limited by handling processes. By combining theory with field observations, our study provides evidences that predator acquisition rate is not systematically limited at the highest prey densities observed in a natural system. We reinforce the idea that functional response categories, typically types I, II, and III, should be considered as particular cases along a continuum. Specific functions derived with a mechanistic approach for a range of densities observed in natural communities should improve our ability to model and understand predator-prey systems.