The main challenge of bottom-up proteomic sample preparation is to extract proteomes in a manner that enables efficient protein digestion for subsequent mass spectrometric analysis. Today9s sample preparation strategies are commonly conceptualized around the removal of detergents, which are essential for extraction but strongly interfere with digestion and LC-MS. These multi-step preparations contribute to a lack of reproducibility as they are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which consists of three mandatory steps, acidification, neutralization and digestion. SPEED is a universal method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction by complete sample dissolution. The protocol is highly reproducible, virtually loss-less, enables very rapid sample processing and is superior to the detergent/chaotropic agent-based methods FASP, ISD-Urea and SP3 for quantitative proteomics. SPEED holds the potential to dramatically simplify and standardize sample preparation while improving the depth of proteome coverage especially for challenging samples. Graphical Abstract Highlights Universal and detergent-free proteomic sample preparation. Based on three simple mandatory steps (acidification, neutralization, digestion). Enhances proteome coverage especially for challenging samples. Improves quantitative reproducibility compared with ISD-Urea, FASP and SP3.

Previous controlled studies on the effect of non-pharmaceutical interventions (NPI) - namely the use of facemasks and intensified hand hygiene - in preventing household transmission of influenza have not produced definitive results. We aimed to investigate efficacy, acceptability, and tolerability of NPI in households with influenza index patients.

Influenza viral shedding studies provide fundamental information for preventive strategies and modelling exercises. We conducted a prospective household study to investigate viral shedding in seasonal and pandemic influenza between 2007 and 2011 in Berlin and Munich, Germany. Study physicians recruited index patients and their household members. Serial nasal specimens were obtained from all household members over at least eight days and tested quantitatively by qRT-PCR for the influenza virus (sub)type of the index patient. A subset of samples was also tested by viral culture. Symptoms were recorded daily. We recruited 122 index patients and 320 household contacts, of which 67 became secondary household cases. Among all 189 influenza cases, 12 were infected with seasonal/prepandemic influenza A(H1N1), 19 with A(H3N2), 60 with influenza B, and 98 with A(H1N1)pdm09. Nine (14%) of 65 non-vaccinated secondary cases were asymptomatic/subclinical (0 (0%) of 21 children, 9 (21%) of 44 adults; p = 0.03). Viral load among patients with influenza-like illness (ILI) peaked on illness days 1, 2 or 3 for all (sub)types and declined steadily until days 7–9. Clinical symptom scores roughly paralleled viral shedding dynamics. On the first day prior to symptom onset 30% (12/40) of specimens were positive. Viral load in 6 asymptomatic/subclinical patients was similar to that in ILI-patients. Duration of infectiousness as measured by viral culture lasted approximately until illness days 4–6. Viral load did not seem to be influenced by antiviral therapy, age or vaccination status. Asymptomatic/subclinical infections occur infrequently, but may be associated with substantial amounts of viral shedding. Presymptomatic shedding may arise in one third of cases, and shedding characteristics appear to be independent of (seasonal or pandemic) (sub)type, age, antiviral therapy or vaccination; however the power to find moderate differences was limited.

Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS²) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS² data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. Peptide masses are then extracted from MS¹ data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a test set of 39 LC-MS¹ peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.