Analytische Chemie
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Oxidative degradation processes of tetrabromobisphenol A (TBBPA), a brominated flame retardant (BFR) in wood, plastics and electronics, were investigated by electrochemistry (EC) coupled online to electrospray ionization mass spectrometry (ESI/MS). Oxidative phase I and II metabolites production was achieved using an electrochemical flow-through cell equipped with a boron doped diamond electrode. Structural elucidation and prediction of oxidative metabolism pathways of TBBPA according to type II ipso-substitution were based on retention time, m/z ratio in negative mode and fragmentation pattern. Using the data obtained through high resolution MS analysis and the identification of single electron transfer (SET) as the initial step of a two-electron oxidation provided the necessary information to propose a mechanism for the electrochemical oxidation of TBBPA. Oxidation reactions involving aromatic hydroxylation and β-scission were the main degradation observed when studying the electrochemical behavior of TBBPA. Computational chemistry experiments using density functional theory (DFT) allowed to identify mono-hydroxylated reaction intermediate and dismissed the mechanism involving two concurrent hydroxylation. TBBPA oxidation products were compared to known metabolites of its biological and environmental degradation confirming the ability of electrochemistry to simulate β-scission reactions.
Isotope Ratio Analysis
(2023)
Data file (RData) containing measurement data recorded during the production process of the Certified Reference Material BAM-A001 containing Polycyclic Aromatic Hydrocarbons (PAH) in olive oil. The data can be most conveniently openend using the Shiny-App eCerto which is accessible at https://www.bam.de/eCerto.
Probability based taxonomic profiling of viral and microbiome samples using PepGM and Unipept
(2022)
In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates
taxonomic inference in samples of unknown taxonomic origin. PepGM uses a graphical model for taxonomic profiling of viral proteomes and
metaproteomic datasets providing taxonomic confidence scores. To build the graphical model, a list of potentially present taxa needs to be
inferred. To this end, we integrate Unipept, which enables the fast querying of potentially present taxa. Together, they allow for taxonomic
inference with statistically sound confidence scores.
Introduction:
With the introduction of accurate deep learning predictors, spectral matching applications might experience a renaissance in tandem mass spectrometry (MS/MS) driven proteomics. Deep learning models, e.g., Prosit, predict complete MS/MS spectra from peptide sequences and give the unprecedented ability to accurately predict mass spectra that may arise from any given proteome. However, the amount of spectral data is enormous when querying large search spaces, e.g., metaproteomes composed of many different species.
Current spectral library search software, such as SpectraST, is not equipped to meet run time and memory constraints imposed by such large MS/MS databases, covering several millions of peptide spectrum predictions.
Methods:
Inspired by the fragment index data structure that had been introduced with MSFragger, we implement an efficient peak matching algorithm for computing spectral similarity between query and library spectra. Mistle (Metaproteomic index and spectral library search engine) uses index partitioning and SIMD (Single instruction, multiple data) intrinsics, which greatly improves speed and memory efficiency for searching large spectral libraries. Mistle is written in C++20 and highly parallelized.
Results:
We demonstrate the efficiency of Mistle on two predicted spectral libraries for the lab-assembled microbial communities 9MM and SIHUMIx. Compared to the spectral library search engine SpectraST, Mistle shows a >10-fold runtime improvement and is also faster than msSLASH, which uses locality-sensitive hashing. Although Mistle is slower than MSFragger, Mistle‘s memory footprint is an order of magnitude smaller. Furthermore, we find evidence that the spectral matching approach to predicted libraries identifies peptides with higher precision. Mistle detects peptides not found by database search via MSFragger and in turn uncovers unnoticed false discoveries among their matches.
Conclusion:
In this study, we show that predicted spectral libraries can enhance peptide identification for metaproteomics. Mistle provides the means to efficiently search large-scale spectral libraries, highlighted for the microbiota 9MM and SIHUMIx.
Metaproteomics has substantially grown over the past years and supplements other omics approaches by bringing valuable functional information, enabling genotype- phenotype linkages and connections to metabolic outputs. Currently, a wide variety of metaproteomic workflows is available, yet their impact on the results remains to be thoroughly assessed.
Here, we carried out the first community-driven, multi-lab comparison in metaproteomics: the critical assessment of metaproteome investigation (CAMPI) study. Based on well-established workflows, we evaluated the influence of sample preparation, mass spectrometry acquisition, and bioinformatic analysis using two samples: a simplified, lab-assembled human intestinal model and a human fecal sample.
Although bioinformatic pipelines contributed to variability in peptide identification, wet-lab workflows were the most important source of differences between analyses. Overall, these peptide-level differences largely disappeared at the protein group level. Differences were observed between peptide- and protein-centric approaches for the predicted community composition but similar functional profiles were found across workflows.
The CAMPI findings demonstrate the robustness of current metaproteomics research and provide a perspective for future benchmarking studies.
Driven by recent technological advances and the need for improved viral diagnostic applications, mass spectrometry-based proteomics comes into play for detecting viral pathogens accurately and efficiently. However, the lack of specific algorithms and software tools presents a major bottleneck for analyzing data from host-virus samples. For example, accurate species- and strain-level classification of a priori unidentified organisms remains a very challenging task in the setting of large search databases. Another prominent issue is that many existing solutions suffer from the protein inference issue, aggravated because many homologous proteins are present across multiple species. One of the contributing factors is that existing bioinformatic algorithms have been developed mainly for single-species proteomics applications for model organisms or human samples. In addition, a statistically sound framework was lacking to accurately assign peptide identifications to viral taxa. In this presentation, an overview is given on current bioinformatics developments that aim to overcome the above-mentioned issues using algorithmic and statistical methods. The presented methods and software tools aim to provide tailored solutions for both discovery-driven and targeted proteomics for viral diagnostics and taxonomic sample profiling. Furthermore, an outlook is provided on how the bioinformatic developments might serve as a generic toolbox, which can be transferred to other research questions, such as metaproteomics for profiling microbiomes and identifying bacterial pathogens.
The climate and energy crisis are extreme challenges. One possible solution could be hydrogen technology. Safety is a big concern. Steel used for pipelines and storage is under permanent stress from low temperatures and high pressures. The content of different alloyed metals determines the performance of the steel. Nitrogen microwave inductively coupled atmosphere pressure plasma mass spectrometry (N2-MICAP-MS) is a promising method for trace metal analysis in steel. Nitrogen is cheap and can be generated on site. It has fewer interferences than argon. Additionally, MICAP-MS is very matrix tolerant, proving the matrix-matched calibration expendable. Safety in technology and chemistry is the mission of BAM. Providing reference methods and materials can create trust in future technologies like hydrogen.
Antibodies are the most used biomolecules in analytical research. Nevertheless, the sequence and structure information of antibodies is often limited, since manufacturers keep them secret or suppliers sell them under different names. This can make it difficult to reproduce even basic experiments performed in publications as the antibodies used might not be identifiable. To overcome these problems, we developed a simple and cheap method for antibody identification by MALDI-TOF-MS fingerprinting. This technique was used to generate a library of antibody fingerprints, which enables the identification and comparison of antibodies in short time.