Gold nanoparticle based double-labeling of melanoma extracellular vesicles to determine the specificity of uptake by cells and preferential accumulation in small metastatic lung tumors

Background Extracellular vesicles (EVs) have shown great potential for targeted therapy, as they have a natural ability to pass through biological barriers and, depending on their origin, can preferentially accumulate at defined sites, including tumors. Analyzing the potential of EVs to target specific cells remains challenging, considering the unspecific binding of lipophilic tracers to other proteins, the limitations of fluorescence for deep tissue imaging and the effect of external labeling strategies on their natural tropism. In this work, we determined the cell-type specific tropism of B16F10-EVs towards cancer cell and metastatic tumors by using fluorescence analysis and quantitative gold labeling measurements. Surface functionalization of plasmonic gold nanoparticles was used to promote indirect labeling of EVs without affecting size distribution, polydispersity, surface charge, protein markers, cell uptake or in vivo biodistribution. Double-labeled EVs with gold and fluorescent dyes were injected into animals developing metastatic lung nodules and analyzed by fluorescence/computer tomography imaging, quantitative neutron activation analysis and gold-enhanced optical microscopy. Results We determined that B16F10 cells preferentially take up their own EVs, when compared with colon adenocarcinoma, macrophage and kidney cell-derived EVs. In addition, we were able to detect the preferential accumulation of B16F10 EVs in small metastatic tumors located in lungs when compared with the rest of the organs, as well as their precise distribution between tumor vessels, alveolus and tumor nodules by histological analysis. Finally, we observed that tumor EVs can be used as effective vectors to increase gold nanoparticle delivery towards metastatic nodules. Conclusions Our findings provide a valuable tool to study the distribution and interaction of EVs in mice and a novel strategy to improve the targeting of gold nanoparticles to cancer cells and metastatic nodules by using the natural properties of malignant EVs.Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing

Background: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. Methods: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. Results: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. Conclusions: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.

TB and COVID-19 co-infection: rationale and aims of a global study

International audienc

Prediction of susceptibility to first-line tuberculosis drugs by DNA sequencing

BACKGROUND The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.

Tuberculosis and COVID-19 co-infection: description of the global cohort

Background Information on tuberculosis (TB) and coronavirus disease 2019 (COVID-19) is still limited. The aim of this study was to describe the features of the TB/COVID-19 co-infected individuals from a prospective, anonymised, multicountry register-based cohort with special focus on the determinants of mortality and other outcomes. Methods We enrolled all patients of any age with either active TB or previous TB and COVID-19. 172 centres from 34 countries provided individual data on 767 TB-COVID-19 co-infected patients, (>50% population-based). Results Of 767 patients, 553 (74.0%) out of 747 had TB before COVID-19 (including 234 out of 747 with previous TB), 71 (9.5%) out of 747 had COVID-19 first and 123 (16.5%) out of 747 had both diseases diagnosed within the same week (n=35 (4.6%) on the same day). 85 (11.08%) out of 767 patients died (41 (14.2%) out of 289 in Europe and 44 (9.2%) out of 478 outside Europe; p=0.03): 42 (49.4%) from COVID-19, 31 (36.5%) from COVID-19 and TB, one (1.2%) from TB and 11 from other causes. In the univariate analysis on mortality the following variables reached statistical significance: age, male gender, having more than one comorbidity, diabetes mellitus, cardiovascular disease, chronic respiratory disease, chronic renal disease, presence of key symptoms, invasive ventilation and hospitalisation due to COVID-19. The final multivariable logistic regression model included age, male gender and invasive ventilation as independent contributors to mortality. Conclusion The data suggest that TB and COVID-19 are a “cursed duet” and need immediate attention. TB should be considered a risk factor for severe COVID disease and patients with TB should be prioritised for COVID-19 preventative efforts, including vaccination