Understanding the cellular and molecular mechanisms of inflammation requires robust animal models. Sheep are commonly used in immune-related studies, yet the validity of sheep as animal models for immune and inflammatory diseases remains to be established. This cross-species comparative study analyzed the in vitro inflammatory response of ovine (oPBMCs) and human PBMCs (hPBMCs) using mass spectrometry, profiling the proteome of the secretome and whole cell lysate. Of the entire cell lysate proteome (oPBMCs: 4217, hPBMCs: 4574 proteins) 47.8% and in the secretome proteome (oPBMCs: 1913, hPBMCs: 1375 proteins) 32.8% were orthologous between species, among them 32 orthologous CD antigens, indicating the presence of six immune cell subsets. Following inflammatory stimulation, 71 proteins in oPBMCs and 176 in hPBMCs showed differential abundance, with only 7 overlapping. Network and Gene Ontology analyses identified 16 shared inflammatory-related terms and 17 canonical pathways with similar activation/inhibition patterns in both species, demonstrating significant conservation in specific immune and inflammatory responses. However, ovine PMBCs also contained a unique WC1+γδ T-cell subset, not detected in hPBMCs. Furthermore, differences in the activation/inhibition trends of seven canonical pathways and the sets of DAPs between sheep and humans, emphasize the need to consider interspecies differences in translational studies and inflammation research.

The step catalyzed by terpene synthases is a well-recognized and significant bottleneck in engineered terpenoid bioproduction. Consequently, substantial efforts have been devoted towards increasing metabolic flux catalyzed by terpene synthases, employing strategies such as gene overexpression and protein engineering. Notably, numerous studies have demonstrated remarkable titer improvements by applying translational fusion, typically by fusing the terpene synthase with a prenyl diphosphate synthase that catalyzes the preceding step in the pathway. The main appeal of the translational fusion approach lies in its simplicity and orthogonality to other metabolic engineering tools. However, there is currently limited understanding of the underlying mechanism of flux enhancement, owing to the unpredictable and often protein-specific effects of translational fusion. In this chapter, we discuss practical considerations when engineering translationally fused terpene synthases, drawing insights from our experience and existing literature. We also provide detailed experimental workflows and protocols based on our previous work in budding yeast (Saccharomyces cerevisiae). Our intention is to encourage further research into the translational fusion of terpene synthases, anticipating that this will contribute mechanistic insights not only into the activity, behavior, and regulation of terpene synthases, but also of other enzymes.

Tuberculosis (TB) is an infectious disease that remains one of the major global public health concerns. Early detection of Active Pulmonary TB is therefore of utmost importance for controlling lethality and disease spreading. Currently available TB diagnostics can be broadly categorized into microscopy, culture-based, and molecular approaches, all of which come with compromised sensitivity, limited efficacy, and high expenses. Hence, rapid, sensitive, and affordable diagnostic methods for TB is the current prerequisite for disease management. This review summarizes the proteomics investigations for host-specific biomarkers from serum, sputum, saliva, and urine samples of TB patients, along with patients having comorbidity. Thorough data mining from available literature led us to conclude that the host-specific proteins involved in immunity and defense, metabolic regulation, cellular adhesion, and motility, inflammatory responses, and tissue remodelling have shown significant deregulation upon Mycobacterium tuberculosis (Mtb) infection. Notably, the immunoregulatory protein orosomucoid (ORM) was up-regulated in active TB compared to non-TB individuals, as observed in multiple studies from diverse sample types. Mannose receptor C type 2 (MRC2) was identified as an upregulated, treatment response biomarker in two independent serum proteomics investigations. Thorough mechanistic investigation on these candidate proteins would be fascinating to dig into potential drug targets and customized therapeutics for TB patients, along with their diagnostic potentials.

Isotope tags for relative and absolute quantification (iTRAQ) are among the most widely used proteomics quantification techniques. These tags can be rapidly coupled to the primary amines of proteins/peptides through chemical reactions under mild conditions, making this technique universally applicable to any kind of sample. However, iTRAQ reagents also partially react with the hydroxyl groups of serine, threonine and tyrosine residues, particularly when these residues coexist with a histidine residue in the same peptide. This overlabeling of peptides causes systematic biases and significantly compromises protein/peptide identification rates. In this study, we report a novel iTRAQ labeling method that overcomes the detrimental overlabeling while providing high amine labeling efficiency. The impacts of reaction temperature, reactant concentrations, reaction time, buffer compositions, and pH on iTRAQ labeling performance were investigated in-depth. In a comparison experiment between our method and the standard labeling method provided by the iTRAQ manufacturer, our method reduced the number of overlabeled peptides by 55-fold while achieving comparable amine labeling efficiency. This improvement allowed our method to eliminates the systematic bias against histidyl- and hydroxyl-containing peptides, and more importantly, enabled the identification of 23.9% more peptides and 9.8% more proteins. SIGNIFICANCE: In addition to amines, the hydroxyl groups in serine, threonine, and tyrosine residues can also partially labeled by iTRAQ reagents, which leads to systematic biases and significantly compromises the analytical sensitivity. To address this issue, we developed a novel iTRAQ labeling method that overcomes the detrimental overlabeling while providing high labeling efficiency of amines. When benchmarking our method against the standard method provided by the reagent manufacturer, our method achieved comparable labeling efficiency but reduced the overlabeled species by 55-fold. This significant improvement eliminated the systematic biases, and more importantly, enabled the identification of 23.9% more peptides and 9.8% more proteins, demonstrating its superior performance and potential to enhance proteome quantification using iTRAQ labeling.

The prohibition of processed animal proteins (PAPs) has been relaxed gradually since 2007. The official control method for PAPs in feedingstuff, a combination of light microscopy (LM) followed by PCR, is no longer sufficient. Thus, a targeted LC-MS/MS method was developed, which enables a tissue-specific distinction between egg and dairy products, gelatine, and PAPs derived from blood or muscle tissue of the species ruminants, pigs, poultry, and fish. Tissue-specific proteins were analyzed after tryptic digestion to peptides with high-resolution ESI-QTOF-MS. A targeted method was developed based on untargeted proteomics approaches and the selection of specific peptides (45 unique peptides in total). Proficiency testing of blank and spiked samples revealed excellent results for trueness and selectivity. Furthermore, sensitivity was achieved at a level of 0.1% (w/w) for assessed peptides. Summing up, the developed method seems to be suitable for routine analysis after verification by ring trials.

The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.

The upper respiratory tract (URT) is home to a diverse range of microbial species. Respiratory infections disturb the microbial flora in the URT, putting people at risk of secondary infections. The potential dangers and clinical effects of bacterial and fungal coinfections with SARS-CoV-2 support the need to investigate the microbiome of the URT using clinical samples. Mass spectrometry (MS)-based metaproteomics analysis of microbial proteins is a novel approach to comprehensively assess the clinical specimens with complex microbial makeup. The coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2) is responsible for the COVID-19 pandemic resulting in a plethora of microbial coinfections impeding therapy, prognosis, and overall disease management. In this chapter, the corresponding workflows for MS-based shotgun proteomics and metaproteomic analysis are illustrated.

The oral cavity is a habitat for different microorganisms, of which bacteria are best described. Studying different bacterial taxa and their proteins is crucial to understanding their interactions with the host and other microbes. Also, for bacteria with virulence potential, identifying novel antigenic proteins is essential to finding candidates for the development of vaccines.Here, a workflow for gel-free and label-free protein analysis of oral bacterial species grown in vitro as a biofilm and a planktonic culture is described. Details on cultivation, protein extraction and digestion, peptide cleanup, LC-MS/MS run parameters, and subsequent bioinformatics analysis are included. Challenging steps in the workflow, such as growing different types of bacteria and selecting a suitable protein database, are also discussed. This protocol provides a valuable guide for metaproteomic experiments using multi-species models of oral bacteria.

Our understanding of how fungi respond and adapt to external environments can be increased by the comprehensive data sets of fungal-secreted proteins. Fungi produce a variety of secreted proteins, and environmental conditions can easily influence the fungal secretome. However, the low abundance of secreted proteins and their post-translational modifications make protein extraction more challenging. Hence, the enrichment of secreted proteins is a crucial procedure for secretome analysis. This chapter illustrates a protocol for iTRAQ-based quantitative secretome analysis describing the example of fungi exposed to different environmental conditions. The fungal-secreted proteins can be extracted by combining ultrafiltration and TCA-acetone precipitation. Subsequently, the secreted proteins can be identified and quantified by the iTRAQ-based quantitative proteomics approach.

Intestinal fungi are a fundamental component of the gut microbiome and play important roles in mammalian host biology. At the same time, the contribution of gut fungi to host health and disease remains understudied due to their low abundance. In that respect, gnotobiotic animals with defined microbial populations of reduced complexity represent a well-suited model system that highlights the effects of low abundant gut fungi on host physiology and other members of the microbial community. In this chapter, a label-free quantitative metaproteomic approach for the characterization of simplified microbial communities in gnotobiotic mice is presented. The model allows for exploring various research questions on the role of gut fungi in disease pathogenesis, microbial ecosystem maturation, or host-microbiome crosstalk.