TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience

Bioactive Metabolite Survey of Actinobacteria Showing Plant Growth Promoting Traits to Develop Novel Biofertilizers

The use of chemical fertilizers and pesticides has caused harmful impacts on the environment with the increase in economic burden. Biofertilizers are biological products containing living microorganisms capable of improving plant growth through eco-friendly mechanisms. In this work, three actinobacterial strains Streptomyces violaceoruber, Streptomyces coelicolor, and Kocuria rhizophila were characterized for multiple plant growth promoting (PGP) traits such as indole acetic acid production, phosphate solubilization, N-2-fixation, and drought and salt tolerance. Then, these strains were investigated for their secreted and cellular metabolome, revealing a rich arsenal of bioactive molecules, including antibiotics and siderophores, with S. violaceoruber being the most prolific strain. Furthermore, the in vivo assays, performed on tomato (Solanum lycopersicum L.), resulted in an improved germination index and the growth of seedlings from seeds treated with PGP actinobacteria, with a particular focus on S. violaceoruber cultures. In particular, this last strain, producing volatile organic compounds having antimicrobial activity, was able to modulate volatilome and exert control on the global DNA methylation of tomato seedlings. Thus, these results, confirming the efficacy of the selected actinobacteria strains in promoting plant growth and development by producing volatile and non-volatile bioactive molecules, can promote eco-friendly alternatives in sustainable agriculture

Blob-B-Gone: a lightweight framework for removing blob artifacts from 2D/3D MINFLUX single-particle tracking data

In this study, we introduce Blob-B-Gone, a lightweight framework to computationally differentiate and eventually remove dense isotropic localization accumulations (blobs) caused by artifactually immobilized particles in MINFLUX single-particle tracking (SPT) measurements. This approach uses purely geometrical features extracted from MINFLUX-detected single-particle trajectories, which are treated as point clouds of localizations. Employing k-means++ clustering, we perform single-shot separation of the feature space to rapidly extract blobs from the dataset without the need for training. We automatically annotate the resulting sub-sets and, finally, evaluate our results by means of principal component analysis (PCA), highlighting a clear separation in the feature space. We demonstrate our approach using two- and three-dimensional simulations of freely diffusing particles and blob artifacts based on parameters extracted from hand-labeled MINFLUX tracking data of fixed 23-nm bead samples and two-dimensional diffusing quantum dots on model lipid membranes. Applying Blob-B-Gone, we achieve a clear distinction between blob-like and other trajectories, represented in F1 scores of 0.998 (2D) and 1.0 (3D) as well as 0.995 (balanced) and 0.994 (imbalanced). This framework can be straightforwardly applied to similar situations, where discerning between blob and elongated time traces is desirable. Given a number of localizations sufficient to express geometric features, the method can operate on any generic point clouds presented to it, regardless of its origin

A new MEFV gene mutation in an Iranian patient with familial Mediterranean fever.

Familial mediterranean fever (FMF) is an inherited autoinflammatory disorder characterized by recurrent episodes of fever and painful inflammation involving the intra-abdominal organs, the lungs and the joints, which is highly prevalent in specific ethnic groups including the Iranians. We report a 12-year-old boy from Iran, with a clinical history of recurrent fever. Based on the suggestive clinical data, mutational analysis revealed the presence of the novel c.1945C>T heterozygous variant in exon 10, which leads to a leucine to phenylalanine change at position 649 of the protein. The mutation was inherited from the mother. This novel mutation lies in exon 10 of the MEFV gene, which encodes for a domain called B30.2-SPRY, located in the C-terminal region of the pyrin protein and contains the most frequent mutations associated with FMF. The present report expands the spectrum of MEFV gene mutations associated with FMF. The uniqueness of this study, compared with other published case reports, consists in the new mutation found in the MEFV gene. In fact, new mutations in this gene are of high interest, in order to better understand the role of this gene in autoinflammation

Emerging glyco-based strategies to steer immune responses

Glycan structures are common posttranslational modifications of proteins, which serve multiple important structural roles (for instance in protein folding), but also are crucial participants in cell-cell communications and in the regulation of immune responses. Through the interaction with glycan-binding receptors, glycans are able to affect the activation status of antigen-presenting cells, leading either to induction of pro-inflammatory responses or to suppression of immunity and instigation of immune tolerance. This unique feature of glycans has attracted the interest and spurred collaborations of glyco-chemists and glyco-immunologists to develop glycan-based tools as potential therapeutic approaches in the fight against diseases such as cancer and autoimmune conditions. In this review, we highlight emerging advances in this field, and in particular, we discuss on how glycan-modified conjugates or glycoengineered cells can be employed as targeting devices to direct tumor antigens to lectin receptors on antigen-presenting cells, like dendritic cells. In addition, we address how glycan-based nanoparticles can act as delivery platforms to enhance immune responses. Finally, we discuss some of the latest developments in glycan-based therapies, including chimeric antigen receptor (CAR)-T cells to achieve targeting of tumor-associated glycan-specific epitopes, as well as the use of glycan moieties to suppress ongoing immune responses, especially in the context of autoimmunity

Cancer stem cells (CSCs) : metabolic strategies for their identification and eradication

Phenotypic and functional heterogeneity is one of the most relevant features of cancer cells within different tumor types and is responsible for treatment failure. Cancer stem cells (CSCs) are a population of cells with stem cell-like properties that are considered to be the root cause of tumor heterogeneity, because of their ability to generate the full rep- ertoire of cancer cell types. Moreover, CSCs have been invoked as the main drivers of metastatic dissemination and therapeutic resistance. As such, targeting CSCs may be a useful strategy to improve the effectiveness of classical anticancer therapies. Recently, metabolism has been considered as a relevant player in CSC biology, and indeed, onco- genic alterations trigger the metabolite-driven dissemination of CSCs. More interestingly, the action of metabolic pathways in CSC maintenance might not be merely a conse- quence of genomic alterations. Indeed, certain metabotypic phenotypes may play a causative role in maintaining the stem traits, acting as an orchestrator of stemness. Here, we review the current studies on the metabolic features of CSCs, focusing on the bio- chemical energy pathways involved in CSC maintenance and propagation. We provide a detailed overview of the plastic metabolic behavior of CSCs in response to microenvironment changes, genetic aberrations, and pharmacological stressors. In addition, we describe the potential of comprehensive metabolic approaches to identify and selectively eradicate CSCs, together with the possibility to ‘force’ CSCs within certain metabolic dependences, in order to effectively target such metabolic biochemical inflexibilities. Finally, we focus on targeting mitochondria to halt CSC dissemination and effectively eradicate cancer

Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens

This is the final version of the article. Available from the publisher via the DOI in this record.Background: Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses. Results: We identified a common suite of genes and conserved molecular pathways that respond to all investigated pathogens, a result that suggests a commonality in response mechanisms to diverse pathogens. We found that genes differentially expressed after infection exhibit a higher evolutionary rate than non-differentially expressed genes. Using our new bioinformatics approach, we unveiled additional pathogen-specific responses of honey bees; we found that apoptosis appeared to be an important response following microsporidian infection, while genes from the immune signalling pathways, Toll and Imd, were differentially expressed after Varroa/virus infection. Finally, we applied our bioinformatics approach and generated a gene co-expression network to identify highly connected (hub) genes that may represent important mediators and regulators of anti-pathogen responses. Conclusions: Our meta-analysis generated a comprehensive overview of the host metabolic and other biological processes that mediate interactions between insects and their pathogens. We identified key host genes and pathways that respond to phylogenetically diverse pathogens, representing an important source for future functional studies as well as offering new routes to identify or generate pathogen resilient honey bee stocks. The statistical and bioinformatics approaches that were developed for this study are broadly applicable to synthesize information across transcriptomic datasets. These approaches will likely have utility in addressing a variety of biological questions.This article is a joint effort of the working group TRANSBEE and an outcome of two workshops kindly supported by sDiv, the Synthesis Centre for Biodiversity Sciences within the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Science Foundation (FZT 118). New datasets were performed thanks to the Insect Pollinators Initiative (IPI grant BB/I000100/1 and BB/I000151/1), with participation of the UK-USA exchange funded by the BBSRC BB/I025220/1 (datasets #4, 11 and 14). The IPI is funded jointly by the Biotechnology and Biological Sciences Research Council, the Department for Environment, Food and Rural Affairs, the Natural Environment Research Council, the Scottish Government and the Wellcome Trust, under the Living with Environmental Change Partnershi

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients

The commissioning of the CUORE experiment: the mini-tower run

CUORE is a ton-scale experiment approaching the data taking phase in Gran Sasso National Laboratory. Its primary goal is to search for the neutrinoless double-beta decay in 130Te using 988 crystals of tellurim dioxide. The crystals are operated as bolometers at about 10 mK taking advantage of one of the largest dilution cryostat ever built. Concluded in March 2016, the cryostat commissioning consisted in a sequence of cool down runs each one integrating new parts of the apparatus. The last run was performed with the fully configured cryostat and the thermal load at 4 K reached the impressive mass of about 14 tons. During that run the base temperature of 6.3 mK was reached and maintained for more than 70 days. An array of 8 crystals, called mini-tower, was used to check bolometers operation, readout electronics and DAQ. Results will be presented in terms of cooling power, electronic noise, energy resolution and preliminary background measurements