β-1,3-d-Glucan is a ubiquitous glucose polymer produced by plants, bacteria, and most fungi. It has been used as a diagnostic tool in patients with invasive mycoses via a highly-sensitive reagent consisting of the blood coagulation system of horseshoe crab. However, no method is currently available for measuring β-1,6-glucan, another primary β-glucan structure of fungal polysaccharides. Herein, we describe the development of an economical and highly-sensitive and specific assay for β-1,6-glucan using a modified recombinant endo-β-1,6-glucanase having diminished glucan hydrolase activity. The purified β-1,6-glucanase derivative bound to the β-1,6-glucan pustulan with a KD of 16.4 nm. We validated the specificity of this β-1,6-glucan probe by demonstrating its ability to detect cell wall β-1,6-glucan from both yeast and hyphal forms of the opportunistic fungal pathogen Candida albicans, without any detectable binding to glucan lacking the long β-1,6-glucan branch. We developed a sandwich ELISA-like assay with a low limit of quantification for pustulan (1.5 pg/ml), and we successfully employed this assay in the quantification of extracellular β-1,6-glucan released by >250 patient-derived strains of different Candida species (including Candida auris) in culture supernatant in vitro. We also used this assay to measure β-1,6-glucan in vivo in the serum and in several organs in a mouse model of systemic candidiasis. Our work describes a reliable method for β-1,6-glucan detection, which may prove useful for the diagnosis of invasive fungal infections.

(American Geriatrics Society) The COVID-19 pandemic has placed unprecedented pressure on societies worldwide, given the pandemic's rapid, often deadly spread. In health care, the pandemic has raised the pressing question of how society should allocate scarce resources during a crisis. This is the question experts addressed today in a new position statement published by the American Geriatrics Society (AGS) in the Journal of the American Geriatrics Society (DOI: 10.1111/jgs.16537).

Retail giants like Yoox Net-a-Porter Group and Brooks Brothers quickly pivoted to offer life-saving services.

Background: Radiopharmaceutical dosimetry depends on the localization in space and time of radioactive sources and requires the estimation of the amount of energy emitted by the sources deposited within targets. In particular, when computing resources are not accessible, this task can be carried out using precomputed tables of Specific Absorbed Fractions (SAFs) or S values based on dosimetric models. The OpenDose collaboration aims to generate and make freely available a range of dosimetric data and tools. Methods: OpenDose brings together resources and expertise from 18 international teams to produce and compare traceable dosimetric data using 6 of the most popular Monte Carlo codes in radiation transport (EGSnrc/EGS++, FLUKA, GATE, Geant4, MCNP/MCNPX and PENELOPE). SAFs are uploaded, together with their associated statistical uncertainties, in a relational database. S values are then calculated from mono-energetic SAFs, based on the radioisotope decay data presented in the International Commission on Radiological Protection (ICRP) publication 107. Results: The OpenDose collaboration produced SAFs for all source regions and targets combinations of the two ICRP 110 adult reference models. SAFs computed from the different Monte Carlo codes were in good agreement at all energies, with standard deviations below individual statistical uncertainties. Calculated S values were in good agreement with OLINDA 2 (commercial) and IDAC 2.1 (free) software. A dedicated website (www.opendose.org) has been developed to provide easy and open access to all data. Conclusion: The OpenDose website allows the display and download of SAFs and the corresponding S values for 1252 radionuclides. The OpenDose collaboration, open to new research teams, will extend data production to other dosimetric models and implement new free features, such as online dosimetric tools and patient-specific absorbed dose calculation software, together with educational resources.

In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.

We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.

Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.