Increasing Proteome Depth While Maintaining Quantitative Precision in Short-Gradient Data-Independent Acquisition Proteomics.

The combination of short liquid chromatography (LC) gradients and data-independent acquisition (DIA) by mass spectrometry (MS) has proven its huge potential for high-throughput proteomics. However, the optimization of isolation window schemes resulting in a certain number of data points per peak (DPPP) is understudied, although it is one of the most important parameters for the outcome of this methodology. In this study, we show that substantially reducing the number of DPPP for short-gradient DIA massively increases protein identifications while maintaining quantitative precision. This is due to a large increase in the number of precursors identified, which keeps the number of data points per protein almost constant even at long cycle times. When proteins are inferred from its precursors, quantitative precision is maintained at low DPPP while greatly increasing proteomic depth. This strategy enabled us to quantify 6018 HeLa proteins (>80 000 precursor identifications) with coefficients of variation below 20% in 30 min using a Q Exactive HF, which corresponds to a throughput of 29 samples per day. This indicates that the potential of high-throughput DIA-MS has not been fully exploited yet. Data are available via ProteomeXchange with identifier PXD036451.

Phenotypic and genotypic discrimination of Francisella tularensis ssp. holarctica clades.

Francisella tularensis is the causative agent of tularemia, a zoonotic disease with a wide host range. F. tularensis ssp. holarctica (Fth) is of clinical relevance for European countries, including Germany. Whole genome sequencing methods, including canonical Single Nucleotide Polymorphism (canSNP) typing and whole genome SNP typing, have revealed that European Fth strains belong to a few monophyletic populations. The majority of German Fth isolates belong to two basal phylogenetic clades B.6 (biovar I) and B.12 (biovar II). Strains of B.6 and B.12 seem to differ in their pathogenicity, and it has been shown that strains of biovar II are resistant against erythromycin. In this study, we present data corroborating our previous data demonstrating that basal clade B.12 can be divided into clades B.71 and B.72. By applying phylogenetic whole genome analysis as well as proteome analysis, we could verify that strains of these two clades are distinct from one another. This was confirmed by measuring the intensity of backscatter light on bacteria grown in liquid media. Strains belonging to clades B.6, B.71 or B.72 showed clade-specific backscatter growth curves. Furthermore, we present the whole genome sequence of strain A-1341, as a reference genome of clade B.71, and whole proteomes comparison of Fth strains belonging to clades B.6, B.71 and B.72. Further research is necessary to investigate phenotypes and putative differences in pathogenicity of the investigated different clades of Fth to better understand the relationship between observed phenotypes, pathogenicity and distribution of Fth strains.

Deep Time Course Proteomics of SARS-CoV- and SARS-CoV-2-Infected Human Lung Epithelial Cells (Calu-3) Reveals Strong Induction of Interferon-Stimulated Gene Expression by SARS-CoV-2 in Contrast to SARS-CoV.

Severe acute respiratory syndrome (SARS)-CoV and SARS-CoV-2 infections are characterized by remarkable differences, including infectivity and case fatality rate. The underlying mechanisms are not well understood, illustrating major knowledge gaps of coronavirus biology. In this study, protein expression of the SARS-CoV- and SARS-CoV-2-infected human lung epithelial cell line Calu-3 was analyzed using data-independent acquisition-mass spectrometry. This resulted in a comprehensive map of infection-related proteome-wide expression changes in human cells covering the quantification of 7478 proteins across four time points. Most notably, the activation of interferon type-I response was observed, which is surprisingly absent in several proteome studies. The data reveal that SARS-CoV-2 triggers interferon-stimulated gene expression much stronger than SARS-CoV, which reflects the already described differences in interferon sensitivity. Potentially, this may be caused by the enhanced abundance of the viral M protein of SARS-CoV in comparison to SARS-CoV-2, which is a known inhibitor of type I interferon expression. This study expands the knowledge on the host response to SARS-CoV-2 infections on a global scale using an infection model, which seems to be well suited to analyze the innate immunity.

Perchlorate-specific proteomic stress responses of Debaryomyces hansenii could enable microbial survival in Martian brines.

If life exists on Mars, it would face several challenges including the presence of perchlorates, which destabilize biomacromolecules by inducing chaotropic stress. However, little is known about perchlorate toxicity for microorganisms on the cellular level. Here, we present the first proteomic investigation on the perchlorate-specific stress responses of the halotolerant yeast Debaryomyces hansenii and compare these to generally known salt stress adaptations. We found that the responses to NaCl and NaClO4 -induced stresses share many common metabolic features, for example, signalling pathways, elevated energy metabolism, or osmolyte biosynthesis. Nevertheless, several new perchlorate-specific stress responses could be identified, such as protein glycosylation and cell wall remodulations, presumably in order to stabilize protein structures and the cell envelope. These stress responses would also be relevant for putative life on Mars, which-given the environmental conditions-likely developed chaotropic defence strategies such as stabilized confirmations of biomacromolecules or the formation of cell clusters.

Recombinant AcnB, NrdR and RibD of Acinetobacter baumannii and their potential interaction with DNA adenine methyltransferase AamA.

In the last decades Acinetobacter baumannii developed into an increasingly challenging nosocomial pathogen. A. baumannii ATCC 17978 harbors a DNA-(adenine N6)-methyltransferase termed AamA. Previous studies revealed a low specific activity of AamA in vitro despite proven folding, which led us to speculate about possible interaction partners assisting AamA in targeting methylation sites. Here, applying a pulldown assay with subsequent mass spectrometry we identified aconitate hydratase 2 (AcnB) as possible interaction partner. In addition, we considered the putative transcriptional regulator gene nrdR (A1S_0220) and the pyrimidine deaminase/reductase gene ribD (A1S_0221) of A. baumannii strain ATCC 17978 to encode additional potential interaction partners due to their vicinity to the aamA gene (A1S_0222). Proteins were recombinantly produced in the milligram scale, purified to near homogeneity, and interactions with AamA were studied applying blue native gel electrophoreses, electrophoretic mobility shift assay, chemical cross-linking and co-immunoprecipitation. These analyses did not provide evidence of interaction between AamA and purified proteins. Solution structures of RibD, NrdR and AcnB were studied by small-angle X-ray scattering (SAXS) alone and in combination with AamA. While in the case of RibD and AcnB no evidence of an interaction with AamA was produced, addition of AamA to NrdR resulted in dissociation of long and rod-shaped polymeric NrdR structures, implying a specific but transient interaction. Moreover, we identified a molecular crowding effect possibly impeding the DNA methyltransferase activity in vivo and a sequence-independent DNA binding activity of AamA calling for continued efforts to identify the interaction network of AamA.

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries.

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although 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 MS (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 2 mins 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.

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics Based on Acid Extraction.

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. Today's 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.

Application of spectral library prediction for parallel reaction monitoring of viral peptides.

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future.

Inactivation of Coronaviruses during Sample Preparation for Proteomics Experiments.

Mass spectrometry-based proteomics is applied in SARS-CoV-2 research and is, moreover, being discussed as a novel method for SARS-CoV-2 diagnostics. However, the safe inactivation of coronaviruses by proteomics lysis buffers has not been systematically analyzed yet. Hence, for safety reasons a heating step prior to sample preparation is often performed. This step could be omitted once the safe inactivation with the typical buffers is proven. Here we test five different proteomics lysis buffers-4% SDS, 1% SDC, TFA, 6 M GdmCl, and 8 M urea-for their inactivation capacity of coronaviruses. Two representative human coronaviruses, namely HCoV-229E and HCoV-OC43, were used as surrogate for SARS-CoV-2. Lysis was performed at room temperature and at 95 °C for 5 min. Inactivation was confirmed by the absence of a cytopathic effect in MRC-5 cells, and equivocal results were further confirmed by serial passaging and quantitative real-time PCR. While at room temperature SDS, SDC, and TFA inactivated both coronaviruses, and GdmCl and urea resulted in partially incomplete inactivation. This demonstrates that care should be taken when choosing lysis buffers for proteomics analysis of coronaviruses, because some buffers do not ensure inactivation and, hence, biosafety during the further sample preparation.

Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.

Antimicrobial resistance (AMR) poses an increasing challenge for therapy and clinical management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently from species identification. Sequencing-based approaches are possible alternatives for AMR detection, although the analysis of proteins should be superior to gene or transcript sequencing for phenotype prediction as the actual resistance to antibiotics is almost exclusively mediated by proteins. In this proof-of-concept study, we present an unbiased proteomics workflow for detecting both bacterial species and AMR-related proteins in the absence of secondary antibiotic cultivation within <4 h from a primary culture. The workflow was designed to meet the needs in clinical microbiology. It introduces a new data analysis concept for bacterial proteomics, and a software (rawDIAtect) for the prediction and reporting of AMR from peptide identifications. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms, which resulted in a sensitivity of 98% and a specificity of 100%.

Stable Isotope-Triggered Offset Fragmentation Allows Massively Multiplexed Target Profiling on Quadrupole-Orbitrap Mass Spectrometers.

Parallel-reaction monitoring (PRM) using high resolution, accurate mass (HR/AM) analysis on quadrupole-Orbitrap mass spectrometers, like the Q Exactive, is one of the most promising approaches for targeted protein analysis. However, PRM has a limited multiplexing capacity, which depends heavily on the reproducibility of peptide retention times. To overcome these limitations, we aimed to establish an easily applicable data acquisition mode that allows retention-time-independent massive multiplexing on Q Exactive mass spectrometers. The presented method is based on data-dependent acquisition and is called pseudo-PRM. In principle, high-intensity stable isotope-labeled peptides are used to trigger the repeated fragmentation of the corresponding light peptides. In this way, pseudo-PRM data can be analyzed like normal PRM data. We tested pseudo-PRM for the target detection from yeast, human cells, and serum, showing good reproducibility and sensitivities comparable to normal PRM. We demonstrated further that pseudo-PRM can be used for accurate and precise quantification of target peptides, using both precursor and fragment ion areas. Moreover, we showed multiplexing of more than 1000 targets in a single run. Finally, we applied pseudo-PRM to quantify vaccinia virus proteins during infection, verifying that pseudo-PRM presents an alternative method for multiplexed target profiling on Q Exactive mass spectrometers.

Perspective on Proteomics for Virus Detection in Clinical Samples.

One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.

TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples.

Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.

Isolation Window Optimization of Data-Independent Acquisition Using Predicted Libraries for Deep and Accurate Proteome Profiling.

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation, and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micropillar array columns, as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4% were obtained even at only 1.5 data points per peak (full width at half-maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology, and molecular biology.

Genome Mining of the Lipopeptide Biosynthesis of Paenibacillus polymyxa E681 in Combination with Mass Spectrometry: Discovery of the Lipoheptapeptide Paenilipoheptin.

Paenibacillus polymyxa strains are qualified for agro-biotechnological uses such as plant growth promotion and for biocontrol strategies against deleterious phytopathogenic competitors in the soil depending on their attractive arsenal of bioactive compounds. Moreover, they are potent producers of antibiotics for medical applications. To identify new products of such organisms, genome mining strategies in combination with mass spectrometry are the methods of choice. Herein, we performed such studies with the Paenibacillus strain E681. Bioinformatic evaluation of its genome sequence revealed four gene clusters A-D encoding nonribosomal peptide synthetases (NRPSs). Accordingly, four lipopeptide families were detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Clusters A and D codify the well known fusaricidins and polymyxins. A yet-unknown lipoheptapeptide was discovered and structurally characterized by de novo sequencing by using MALDI-LIFT-TOF/TOF MS. It was designated as paenilipoheptin. From structure predictions we infer that the production of this agent is encoded by gene cluster C. Gene cluster B encodes the synthesis of tridecaptins, a family of open-chain lipotridecapeptides. Strain E681 produces new subspecies of such compounds (tridecaptins E) showing variations both in their fatty-acid part as well as in their peptide part.

The cellular ceramide transport protein CERT promotes Chlamydia psittaci infection and controls bacterial sphingolipid uptake.

Chlamydiaceae are bacterial pathogens that cause diverse diseases in humans and animals. Despite their broad host and tissue tropism, all Chlamydia species share an obligate intracellular cycle of development and have evolved sophisticated mechanisms to interact with their eukaryotic host cells. Here, we have analysed interactions of the zoonotic pathogen Chlamydia psittaci with a human epithelial cell line. We found that C. psittaci recruits the ceramide transport protein (CERT) to its inclusion. Chemical inhibition and CRISPR/Cas9-mediated knockout of CERT showed that CERT is a crucial factor for C. psittaci infections thereby affecting different stages of the infection including inclusion growth and infectious progeny formation. Interestingly, the uptake of fluorescently labelled sphingolipids in bacteria inside the inclusion was accelerated in CERT-knockout cells indicating that C. psittaci can exploit CERT-independent sphingolipid uptake pathways. Moreover, the CERT-specific inhibitor HPA-12 strongly diminished sphingolipid transport to inclusions of infected CERT-knockout cells, suggesting that other HPA-12-sensitive factors are involved in sphingolipid trafficking to C. psittaci. Further analysis is required to decipher these interactions and to understand their contributions to bacterial development, host range, tissue tropism, and disease outcome.

Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics.

MOTIVATION: Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions. RESULTS: We introduce Pipasic (peptide intensity-weighted proteome abundance similarity correction) as a tool that corrects identification and spectral counting-based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. Pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor, as demonstrated on examples of viral diagnostics and an acid mine drainage dataset. AVAILABILITY AND IMPLEMENTATION: Pipasic source code is freely available from https://sourceforge.net/projects/pipasic/. CONTACT: RenardB@rki.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Direct translation of incoming retroviral genomes.

Viruses that carry a positive-sense, single-stranded (+ssRNA) RNA translate their genomes soon after entering the host cell to produce viral proteins, with the exception of retroviruses. A distinguishing feature of retroviruses is reverse transcription, where the +ssRNA genome serves as a template to synthesize a double-stranded DNA copy that subsequently integrates into the host genome. As retroviral RNAs are produced by the host cell transcriptional machinery and are largely indistinguishable from cellular mRNAs, we investigated the potential of incoming retroviral genomes to directly express proteins. Here we show through multiple, complementary methods that retroviral genomes are translated after entry. Our findings challenge the notion that retroviruses require reverse transcription to produce viral proteins. Synthesis of retroviral proteins in the absence of productive infection has significant implications for basic retrovirology, immune responses and gene therapy applications.

Investigation of the potential of Brevibacillus spp. for the biosynthesis of nonribosomally produced bioactive compounds by combination of genome mining with MALDI-TOF mass spectrometry.

The biosynthetic potential of 11 Brevibacillus spp. strains was investigated by combination of genome mining with mass spectrometric analysis using MALDI-TOF mass spectrometry. These endophytic, plant associated Brevibacillus strains were isolated from crop plants, such as coffee and black pepper, in Vietnam. Draft genomes of these strains were available. They were classified (a) by comparison with type strains and a collection of genome-sequenced Brevibacillus spp. deposited in the NCBI data base as well as (b) by construction of a phylogenetic tree from the core sequences of publicly available genomes of Brevibacillus strains. They were identified as Brevibacillus brevis (1 strain); parabrevis (2 strains); porteri (3 strains); and 5 novel Brevibacillus genomospecies. Our work was specifically focused on the detection and characterization of nonribosomal peptides produced by these strains. Structural characterization of these compounds was performed by LIFT-MALDI-TOF/TOF mass spectrometric sequence analysis. The highlights of our work were the demonstration of the tyrocidines, a well-known family of cyclodecapeptides of great structural variability, as the main products of all investigated strains and the identification of a novel class of pentapeptides produced by B. brevis; B. schisleri; and B. porteri which we designate as brevipentins. Our biosynthetic studies demonstrate that knowledge of their biosynthetic capacity can efficiently assist classification of Brevibacillus species.

Comparison of real-time PCR and MassTag PCR for the multiplex detection of highly pathogenic agents.

Multiplex PCR assays are a cost- as well as labour-effective way to analyse one sample for several pathogens simultaneously. Besides the mutual competition of the individual PCR reactions included in a multiplex PCR assay, their specific read-out displays a limiting factor for the total number of PCR reactions that can be multiplexed. In this study, two PCR systems with different read-out approaches are compared, using a pentaplex PCR assay for the detection of highly pathogenic agents. A pentaplex assay was used since five represents the current limit of real-time PCR multiplexing capacity due to the low resolution of fluorescence emission peaks of the current equipment. In contrast, MassTag PCR as a quite new technique offers the possibility to detect up to 20-30 target sequences from one reaction. After extensive and separate optimisation of the PCR protocol for both platforms, a comparative probit analysis showed good sensitivities for MassTag and real-time PCR detection. Nevertheless, the detection limits of MassTag PCR have been undercut by the real-time PCR for each target. We therefore conclude that MassTag PCR is a useful diagnostic technique for the sensitive screening for pathogens by highly multiplexed PCR assays, but cannot reach the sensitivity of real-time PCR for lower multiplexed PCR assays.