Biology subjects

Hellwig, P.

Publications and source records attributed to Hellwig, P..

6 recordsLinked to original sources

bioRxiv2025

A metaproteomics-based meta study of samples from patients with inflammatory bowel disease identifies potential markers for diagnosis and therapy monitoring

Inflammatory bowel disease (IBD) is a chronic intestinal disorder involving recurring inflammation and pronounced microbial dysbiosis. Comprehensive studies with large patient cohorts are required to Identify meaningful biomarker candidates for diagnosing and monitoring IBD. In this large-scale meta-study of over 600 samples based on fecal metaproteomics, our goal was to validate known biomarkers and discover new candidates. We performed bioinformatic reanalysis using the Mascot search engine and MMUPHin for batch effect correction as well as knowledge graph-enhanced data analysis. We identified 59 protein groups that varied primarily due to disease, rather than laboratory conditions. These included Alpha-1-acid glycoprotein, which was not reported in the original studies. Of these groups, 53 were differentially abundant in at least one of the two validation datasets. Additionally, 23 of the successfully validated protein groups, primarily from human neutrophil vesicles, were found to be significantly associated with remission during treatment in an independent dataset. This finding suggests their potential for disease monitoring. Validation in other disease contexts, such as non-alcoholic steatohepatitis, diabetes, and colorectal cancer, revealed the necessity of biomarker panels, because individual biomarkers could only distinguish IBD from specific conditions. Our results demonstrate the effectiveness of metaproteomics meta-analyses in discovering and validating biomarker panels and assessing their specificity for IBD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/684320v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@18a6617org.highwire.dtl.DTLVardef@1349adaorg.highwire.dtl.DTLVardef@a27044org.highwire.dtl.DTLVardef@78aabe_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Computational study of heme b595 to heme d electron transfer in E. coli cytochrome bd-I oxidase

Cytochrome bd is a distinctive family of terminal oxidases present in the respiratory chains of many prokaryotes. Despite its biological importance, the redox chemistry of these proteins remains poorly understood, largely due to the presence of two b-type hemes and one d-type heme. Here, we report the first computational study of inter-heme electron transfer in the cytochrome bd family. We performed 10 s of molecular dynamics simulations of E. coli cytochrome bd-I embedded in realistic membranes, combined with quantum chemical calculations to estimate the thermodynamic parameters of electron transfer from heme b595 to heme d within the framework of Marcus theory. We further identify the respective contributions of the hemes, protein scaffold, lipid bilayer, water, and counterions to the driving force and reorganization energy. The inter-heme electronic coupling was calculated using the Projected Orbital Diabatization (POD) method in a hybrid Quantum Mechanics/Molecular Mechanics scheme and rationalized through electron transfer pathway analysis. This study provides fundamental insights into how electron transfer steps are orchestrated in the catalytic cycle of E. coli cytochrome bd-I. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/673948v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@14a29c8org.highwire.dtl.DTLVardef@1fd1c98org.highwire.dtl.DTLVardef@6053aaorg.highwire.dtl.DTLVardef@14ec40_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Quantitative analysis of proteomic changes in monoclonal MDCK cell lines infected with human influenza A virus

AbstractSuspension MDCK cells are a substrate for producing influenza A virus (IAV) and typically show very high virus yields compared to other animal cells. Due to the significant heterogeneity within cell populations, studying and comparing clonal cell lines with regard to specific properties, such as superior growth or higher productivity, could facilitate process optimization. In this study, we analyzed the expressed proteins of clonal cell lines to identify intrinsic characteristics of effective IAV producers. We compared proteome changes in two IAV-infected monoclonal suspension MDCK cell lines: C59, a low-yield IAV producer with fast cell growth and small cell diameter, and C113, a high-yield IAV producer with average cell growth and large cell diameter. We examined growth rate, size, metabolism and IAV production. A total of 5177 host cell proteins were detected in both cell lines using DIA-PASEF mode with a TimsTOFpro mass spectrometer. Analysis of the differentially expressed proteins revealed that fatty acid oxidation and branched-chain amino acid degradation were upregulated in highly productive cells. In contrast, steroid biosynthesis and DNA replication were more active in faster-growing cells. Following infection, 122 proteins were significantly upregulated (p<0.05, log2-fold change [≥]1) in the high-producing cell line. These proteins were associated with membrane trafficking, interactions with the IAV-NS1 protein and virus production. Additionally, 98 proteins associated with antiviral pathways such as the proto-oncogenic receptor tyrosine kinase MET and tumor necrosis factor (TNF) signaling were downregulated (p<0.05, log2-fold change [≤]1). In the cell line that produced lower IAV titers, 77 proteins were downregulated and 57 were upregulated after infection. RNA metabolism appeared to be downregulated, while the tricarboxylic acid (TCA) cycle and the stress response were both upregulated. In the high-yield C113 clone, only proteins associated with apoptosis and the target of rapamycin kinase (TOR) were expressed following infection. This may indicate a more effective release of virus particles. A comparison of intracellular IAV protein levels demonstrated that M1 and NA levels were 4-fold and 8-fold higher, respectively, for the high-yield C113 cell line. These findings again suggest better virus release.

molecular biology↗

bioRxiv2024

Creating microbiome-model harmony between metaproteomics data and the ADM1da for a two-step anaerobic digester

The effective operation, planning, and optimization of renewable energy production in anaerobic digestion (AD) plants relies on advanced process models, such as the Anaerobic Digestion Model No. 1 (ADM1). This study applies an ADM1-based model (ADM1da) to simulate a two-step digester in an industrial setting. The data demonstrate that 2.6% of the methane is lost as a result of open hydrolysis. Conversely, the incorporation of a hydrolysis fermenter enhances methane production by an average of 2.5%. Although ADM1-like models are widely recognized for accurately representing anaerobic digestion processes, mechanistic insights into the microbiome involved have been limited by the absence of tools to analyze microbial composition and functionality at the time these models were developed. To overcome this limitation, we utilized a metaproteomics approach to assess the abundance and biomass-correlated activity of microbial groups as defined by the model, aiming to bridge the gap between microbial ecology and bioprocess engineering in AD systems. We also developed and evaluated a series of rules for associating particular microbial species with functional groups of the model. Our analysis demonstrates that while the model supports the presence of a stable microbiome composition in the main fermenter, it is difficult to capture the dynamic behavior observed in the hydrolysis fermenter. Furthermore, the actual AD microbiome displays a greater versatility than the model assumes, with microorganisms performing multiple functions rather than being restricted to single roles. In conclusion, this study identifies options for improving AD models and integrating comprehensive biological knowledge to further optimize the performance of anaerobic digesters. HighlightsO_LISimulations revealed 2.6 % methane volume loss attributed to open hydrolysis C_LIO_LIImplementation of a two-step process increased methane production by 2.5% C_LIO_LIIdentification of rules to map metaproteomics data to ADM1da C_LIO_LISimulations of ADM1da depict the dynamic in the main but not in the hydrolysis fermenter C_LIO_LIMicrobial species perform multiple functions not just one as assumed in the ADM1da C_LI

bioengineering↗

bioRxiv2024

Tracing active bugs in microbial communities by BONCAT and click chemistry-based enrichment of newly synthesised proteins

A comprehensive understanding of microbial community dynamics is fundamental to the advancement of environmental microbiology, human health, and biotechnology. Metaproteomics, i.e. the analysis of all proteins in a microbial community, provides insights into these complex systems. Microbial adaptation and activity depend to an important extent on newly synthesized proteins (nP), however, the distinction between nP and bulk proteins is challenging. The application of bioorthogonal non-canonical amino acid tagging (BONCAT) with click chemistry has demonstrated efficacy in the enrichment of nP in pure cultures. However, the transfer of this technique to microbial communities has proven challenging and has therefore not been used on microbial communities before. To address this, a new workflow with efficient and specific nP enrichment was developed using a laboratory-scale mixture of labelled E. coli and unlabelled yeast. This workflow was successfully applied to an anaerobic microbial community with initially low BONCAT efficiency. A substrate shift from glucose to ethanol selectively enriched nP with minimal background. The identification of bifunctional alcohol dehydrogenase and a syntrophic interaction between an ethanol-utilizing bacterium and two methanogens (hydrogenotrophic and acetoclastic) demonstrates the potential of metaproteomics targeting nP to trace microbial activity in complex microbial communities.

microbiology↗

bioRxiv2024

Detection, isolation and characterisation of phage-host complexes using BONCAT and click chemistry

Phages are viruses that infect prokaryotes and can shape microbial communities by lysis, thus offering applications in various fields. However, challenges exist in sampling, isolation, and predicting host specificity of phages. A new workflow using biorthogonal non-canonical amino acid tagging (BONCAT) and click chemistry (CC) allows combined analysis of phages and their hosts. Replication of phage {lambda} in Escherichia coli was selected as a model for workflow development. Specific labelling of phage {lambda} proteins with the non-canonical amino acid 4-azido-L-homoalanine (AHA) during infection of E. coli was confirmed by LC-MS/MS. Subsequent tagging of AHA with fluorescent dyes via CC allowed the visualization of phages adsorbed to the cell surface by fluorescence microscopy. Flow cytometry enabled the automated detection of these fluorescent phage-host complexes. AHA-labeled phages were tagged with biotin for purification by affinity chromatography. The biotinylated phages could be purified and were infectious despite biotinylation after purification. Applying this assay approach to environmental samples would enable host screening without cultivation. A flexible and powerful workflow was established to detect and enrich phages and their hosts. In the future, fluorescence-activated cell sorting or biotin purification could be used to isolate phage-host complexes in microbial communities.

microbiology↗