The tobacco GNTI stem region harbors a strong motif for homomeric protein complex formation

IntroductionThe Golgi apparatus of plants is the central cellular organelle for glycan processing and polysaccharide biosynthesis. These essential processes are catalyzed by a large number of Golgi-resident glycosyltransferases and glycosidases whose organization within the Golgi is still poorly understood.MethodsHere, we examined the role of the stem region of the cis/medial Golgi enzyme N-acetylglucosaminyltransferase I (GNTI) in homomeric complex formation in the Golgi of Nicotiana benthamiana using biochemical approaches and confocal microscopy.ResultsTransient expression of the N-terminal cytoplasmic, transmembrane, and stem (CTS) regions of GNTI leads to a block in N-glycan processing on a co-expressed recombinant glycoprotein. Overexpression of the CTS region from Golgi α-mannosidase I, which can form in planta complexes with GNTI, results in a similar block in N-glycan processing, while GNTI with altered subcellular localization or N-glycan processing enzymes located further downstream in the Golgi did not affect complex N-glycan processing. The GNTI-CTS-dependent alteration in N-glycan processing is caused by a specific nine-amino acid sequence motif in the stem that is required for efficient GNTI-GNTI interaction.DiscussionTaken together, we have identified a conserved motif in the stem region of the key N-glycan processing enzyme GNTI. We propose that the identified sequence motif in the GNTI stem region acts as a dominant negative motif that can be used in transient glycoengineering approaches to produce recombinant glycoproteins with predominantly mannosidic N-glycans

In planta deglycosylation improves the SARS-CoV-2 neutralization activity of recombinant ACE2-Fc

SARS-CoV-2 infects human cells via binding of the viral spike glycoprotein to its main cellular receptor, angiotensin-converting enzyme 2 (ACE2). The spike protein-ACE2 receptor interaction is therefore a major target for the development of therapeutic or prophylactic drugs to combat coronavirus infections. Various engineered soluble ACE2 variants (decoys) have been designed and shown to exhibit virus neutralization capacity in cell-based assays and in vivo models. Human ACE2 is heavily glycosylated and some of its glycans impair binding to the SARS-CoV-2 spike protein. Therefore, glycan-engineered recombinant soluble ACE2 variants might display enhanced virus-neutralization potencies. Here, we transiently co-expressed the extracellular domain of ACE2 fused to human Fc (ACE2-Fc) with a bacterial endoglycosidase in Nicotiana benthamiana to produce ACE2-Fc decorated with N-glycans consisting of single GlcNAc residues. The endoglycosidase was targeted to the Golgi apparatus with the intention to avoid any interference of glycan removal with concomitant ACE2-Fc protein folding and quality control in the endoplasmic reticulum. The in vivo deglycosylated ACE2-Fc carrying single GlcNAc residues displayed increased affinity to the receptor-binding domain (RBD) of SARS-CoV-2 as well as improved virus neutralization activity and thus is a promising drug candidate to block coronavirus infection

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Detection of ctx-M gene in ESBL-producing E. coli strains isolated from urinary tract infection in Semnan, Iran

Introduction: The incidence of urinary tract infections caused by Extended-Spectrum Beta Lactamase (ESBL) producing Escherichia coli (E. coli) strains due to long term and overuse of broad-spectrum cephalosporine is on the rise. CTX beta-lactamase type, a broad-spectrum beta-lactamase, has been expanding in many countries. The ctx gene is harbored on a plasmid that is spread between Enterobacteriaceae family, especially in E. coli. The aim of this study was to determine the pattern of antimicrobial resistance and investigate the prevalent ESBL phenotype and the ctx-M gene in E. coli isolated from patients with urinary tract infections (UTI) in Semnan. Methods: A cross sectional study was performed on 109 strains of E. coli isolated from the urine culture of patient suffering from a UTI referred to Shafa hospital (Semnan, Iran) during March-July 2015. Antimicrobial susceptibility testing was applied and the prevalence of the ESBL phenotype was confirmed using combination disk. PCR methods were completed for amplification of the bla ctx gene. Data were analyzed using SPSS version 18 software. Results: One hundred ninety samples (4.16%) were identified as E. coli. Twenty one (26.6%) of E. coli were ESBL positive and 73.4% were ESBL negative. There was 100% susceptibility to imipeneme. Twenty (68.97%) out of 29 isolates were positive for the ctx-M gene, as detected by PCR. Conclusion: In urinary tract infections, antibiotic treatment was experimental and detailed information regarding the sensitivity of bacteria in the area can be useful to achieve the best treatment

Reteplase Fc-fusions produced in N. benthamiana are able to dissolve blood clots ex vivo.

Thrombolytic and fibrinolytic therapies are effective treatments to dissolve blood clots in stroke therapy. Thrombolytic drugs activate plasminogen to its cleaved form plasmin, a proteolytic enzyme that breaks the crosslinks between fibrin molecules. The FDA-approved human tissue plasminogen activator Reteplase (rPA) is a non-glycosylated protein produced in E. coli. rPA is a deletion mutant of the wild-type Alteplase that benefits from an extended plasma half-life, reduced fibrin specificity and the ability to better penetrate into blood clots. Different methods have been proposed to improve the production of rPA. Here we show for the first time the transient expression in Nicotiana benthamiana of rPA fused to the immunoglobulin fragment crystallizable (Fc) domain on an IgG1, a strategy commonly used to improve the stability of therapeutic proteins. Despite our success on the expression and purification of dimeric rPA-Fc fusions, protein instability results in high amounts of Fc-derived degradation products. We hypothesize that the "Y"- shape of dimeric Fc fusions cause steric hindrance between protein domains and leads to physical instability. Indeed, mutations of critical residues in the Fc dimerization interface allowed the expression of fully stable rPA monomeric Fc-fusions. The ability of rPA-Fc to convert plasminogen into plasmin was demonstrated by plasminogen zymography and clot lysis assay shows that rPA-Fc is able to dissolve blood clots ex vivo. Finally, we addressed concerns with the plant-specific glycosylation by modulating rPA-Fc glycosylation towards serum-like structures including α2,6-sialylated and α1,6-core fucosylated N-glycans completely devoid of plant core fucose and xylose residues

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data