Disentangling host identity and storage time effects on gut microbiota composition in captive migratory birds using absolute and relative quantification

Chemical labeling with subsequent mass spectrometric detection represents a common approach for protein quantification. Whereas most methods make use of stable isotope labels from natural elements such as (2)D, (13)C, (15)N, or (18)O, artificially introduced metals have gained interest as alternative markers. In this work we present the application of p-hydroxymercuribenzoic acid (pHMB) as a labeling reagent for cysteine-containing proteins. As a proof of concept, insulin was chosen as the model protein, and two different workflows were developed to its absolute and relative quantification with the use of complementary MALDI-MS and ICPMS. On the basis of the synthesis of isotopically labeled [(199)Hg]pHMB, and thus on the basis of the label-specific isotope dilution concept, a differential labeling procedure can be applied either to the comparative study of two different samples (relative quantification) or to the absolute quantification of insulin. In both cases, final detection by MALDI-MS followed by isotope pattern deconvolution was applied to extract the quantitative data from the mass spectra. Good agreement with the expected values was obtained for the relative insulin quantification, and the recovery for insulin applying the absolute quantification workflow was between 90% and 110% with an RSD of better than 5% in the low picomole range.

Recent human and animal studies have found associations between gut microbiota composition and serum levels of sex hormones, indicating that they could be an important factor in shaping the microbiota. However, little is known about the effect of regular hormonal fluctuations over the menstrual cycle or CHC-related changes of hormone levels on gut microbiota structure, diversity and dynamics. The aim of this study was to investigate the effect of CHCs on human gut microbiota composition. The effect of CHC pill intake on gut microbiota composition was studied in a group of 7 healthy pre-menopausal women using the CHC pill, compared to the control group of 9 age-matched healthy women that have not used hormonal contraceptives in the six months prior the start of the study. By analyzing the gut microbiota composition in both groups during one menstrual cycle, we found that CHC usage is associated with a minor decrease in gut microbiota diversity and differences in the abundance of several bacterial taxa. These results call for further investigation of the mechanisms underlying hormonal and hormonal contraceptive-related changes of the gut microbiota and the potential implications of these changes for women's health. This article is protected by copyright. All rights reserved.

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples.

The majority of cellular processes is driven by protein complexes. These emerge as multimeric protein assemblies or are complexed with ligands such as RNA or DNA. To understand the details of the cellular process, an analysis of the protein complexes is required. This involves the identification of the protein components, the determination of the protein stoichiometries, the relative quantification of different complex states, and the study of protein-protein or protein-ligand interactions. Several mass spectrometry-based techniques have been developed to tackle these problems and greatly facilitating the high throughput analysis of protein complexes. In this study, these methods have been applied to the spliceosome, which is a protein-RNA machinery that catalyzes the excision of introns and the ligation of exons during eukaryotic pre-mRNA splicing. We used absolute and relative quantification by mass spectrometry to characterize different spliceosomal complexes or subcomplexes, in terms of their protein composition and protein stoichiometry. One spliceosomal subcomplex, the hPrp19/CDC5L complex, consists of seven individual proteins (hPrp19, Hsp70, CTNNBL1, PRL1, CDC5L, AD-002, and SPF27) and plays a crucial role in the assembly of a catalytically active spliceosome. The exact protein stoichiometries within this particular protein complex have not yet been investigated. We therefore set up a mass spectrometry-based quantification method and used the hPrp19/CDC5L complex to implement the methodology of absolute quantification (AQUA) with the help of synthetic standard peptides in combination with multiple reaction monitoring (MRM). The analyses revealed that the hPrp19/CDC5L complex consists of four copies of hPrp19, two copies of CDC5L, and one copy each of SPF27, PRL1, and CTNNBL1. In a different series of investigation, we set out to make a high throughput relative quantification of two distinct spliceosomal complexes, namely the pre-catalytic and the catalytically active spliceosomes (spliceosomal B and C complexes). For this purpose, a relative quantification approach involving iTRAQ (isobaric tags for relative and absolute quantification) labeling of in-gel digested proteins was optimized and applied to the two complexes. The results were compared to relative quantification by metabolic labeling (SILAC; stable isotope labeling with amino acids in cell culture) and semi-quantitative spectral count from proteomic analysis of the B and C complexes. We found an overall good agreement for the used quantification methods. Several proteins were found to be pre-dominantly associated with spliceosomal B or C complexes, and only few proteins were found to be present in equal amounts within the two complexes. The high dynamics of the spliceosome during its assembly pathway was thus clearly demonstrated. In a final scene of investigation, we analyzed the dynamic protein changes during the pre-mRNA splicing process. To this end, the protein compositions on different pre-mRNAs at different time points were compared by stable isotope labeling with amino acids in cell culture (SILAC). The generated timelines for the assembly of whole groups of spliceosomal proteins during spliceosomal assembly and splicing catalysis are extremely helpful in understanding the dynamic process of pre-mRNA splicing.

Migratory birds exhibit unique annual cycles that complicate their gut microbiota. However, the annual dynamics of gut microbiota in migratory birds remain unclear, hindering our understanding of their environmental adaptation. Here, we collected fecal samples from black-necked cranes (Grus nigricollis) across four seasons at their breeding grounds and used wintering ground data from databases to characterize their gut microbial compositions throughout the year. The results showed that the gut microbiota was clustered by season (Bray-Curtis: R 2 = 0.348, p < 0.001; UniFrac: R 2 = 0.352, p < 0.001). And the summer samples exhibited higher alpha (Simpson and Shannon), beta diversity (Bray-Curtis and UniFrac) and more diverse functions in gut microbiota compared to other seasons. Furthermore, in summer, the gut microbiota exhibited several balanced relative abundances at the family level, whereas Lactobacillaceae family dominated during the other seasons. Thirty-six ASVs were identified by random forest analysis to distinguish samples from distinct seasons. Despite having greater diversity, the summer gut microbiota had a simpler network structure than the other seasons (fewer edges and nodes). The dispersal limitation during random processes also significantly influenced gut microbial community assembly. Overall, the gut microbiota of the black-necked crane undergoes dynamic adjustments to adapt to seasonal environmental changes, which may be associated with the variations in diet across seasons. These results enhance our understanding of the gut microbiota of wild migratory birds and support further research on black-necked cranes.

Quantitative mass spectrometry approaches are used for absolute and relative quantification in global proteome studies. To date, relative and absolute quantification techniques are available that differ in quantification accuracy, proteome coverage, complexity and robustness. This review focuses on most common relative or absolute quantification strategies exemplified by three experimental studies. A label-free relative quantification approach was performed for the investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus. A SILAC-based relative quantification approach was successfully applied for the identification of core components and transient interactors of the peroxisomal importomer in Saccharomyces cerevisiae. Furthermore, AQUA using stable isotopes was exemplified to unraveling the prenylome influenced by novel prenyltransferase inhibitors. Characteristic enrichment and fragmentation strategies for a robust quantification of the prenylome are also summarized.

A major goal in proteomics is the comprehensive and accurate description of a proteome. This task includes not only the identification of proteins in a sample, but also the accurate quantification of their abundance. Although mass spectrometry typically provides information on peptide identity and abundance in a sample, it does not directly measure the concentration of the corresponding proteins. Specifically, most mass-spectrometry-based approaches (e.g. shotgun proteomics or selected reaction monitoring) allow one to quantify peptides using chromatographic peak intensities or spectral counting information. Ultimately, based on these measurements, one wants to infer the concentrations of the corresponding proteins. Inferring properties of the proteins based on experimental peptide evidence is often a complex problem because of the ambiguity of peptide assignments and different chemical properties of the peptides that affect the observed concentrations. We present SCAMPI, a novel generic and statistically sound framework for computing protein abundance scores based on quantified peptides. In contrast to most previous approaches, our model explicitly includes information from shared peptides to improve protein quantitation, especially in eukaryotes with many homologous sequences. The model accounts for uncertainty in the input data, leading to statistical prediction intervals for the protein scores. Furthermore, peptides with extreme abundances can be reassessed and classified as either regular data points or actual outliers. We used the proposed model with several datasets and compared its performance to that of other, previously used approaches for protein quantification in bottom-up mass spectrometry.

Study on the relationship between adolescent myopia and gut microbiota via 16S rRNA sequencing

Measurement traceability in clinical chemistry is required to standardize clinical results irrespective of the measurement procedure and laboratory. The traceability of many protein substances is maintained by reference to the first standard produced, which may no longer exist, with values assigned by consensus. Independent methods that provide traceability to the Système d'Unité International for all relevant properties of a protein standard could remove reliance on the original standard preparations. We developed a method based on the traceable quantification of tryptic peptides released from the protein by isotope dilution mass spectrometry to compare 2 standard preparations of somatropin (recombinant human growth hormone), WHO 98/574 and Ph.Eur.CRS S0947000. Relative quantification using isotope-coded affinity tagging, isobaric tagging for relative and absolute quantification, and standard additions were also performed to validate the digestion method used and to determine whether any modifications were present. The total somatropin content in both materials was determined and an uncertainty estimation undertaken [WHO 2.19 +/- 0.21) mg/vial, European Pharmacopeia 2.06 +/- 0.21 mg/vial]. Each uncertainty in this paper is a fully estimated uncertainty, with 95% CI (k = 2). Isotope coded affinity tag and standard addition results fully validated the robustness of the digestion method used. In addition, iTRAQ (isobaric tagging for relative and absolute quantification analysis) identified 2 modifications, neither of which impacted the quantification. An independent method that does not rely on a preexisting protein standard has been developed and validated for the traceable value-assignment of total somatropin. The methods reported here address the amount of substance (mass fraction) of the standard materials but address neither biological activity nor other characteristics that may be important in assessing suitability for use as a calibrator.

Transfer of Intestinal Microbiota From Lean Donors Increases Insulin Sensitivity in Individuals With Metabolic Syndrome

The change in the composition of gut microbiota has been reported in the elderly and in the frail individuals; however, studies on gut microbiota in frail elderly are limited. This study aimed to investigate the gut microbiota of the frail elderly. From September 2017 to February 2018, 27 elderly patients hospitalized in the Department of Geriatrics of our hospital were enrolled and divided into the frailty group (n = 15) and the control group (n = 12) based on the cutoff of 0.25 for the frailty index. The fecal samples were collected for 16S rRNA-amplicon sequencing to analyze the composition and richness of gut microbiota. Operational taxonomic unit (OTU) clustering was performed using Usearch software. Intra-sample diversity (alpha-diversity) analysis and inter-sample diversity (beta-diversity) analyses were performed. The community richness was compared between the two groups at family and genus levels. There were 1903 and 1880 OTUs identified in the control and frailty groups, respectively, with 1282 OTUs overlap between the two groups. The alpha diversity of microbiota community was similar between the two groups, whereas the frailty group had larger beta diversity than the control group. The top-10 taxonomy categories and abundances of gut microbiota between the two groups were similar. As for the gut microbiota composition, 4 families and 17 genera were significantly different between the two groups (p < 0.05). These results suggested that frailty can affect gut microbiota diversity and compositions in late elderly hospitalized patients.

Investigate the diversity of the gut microbiota in children with peanut allergies and assess its association with allergic reactions. Identify potential gut microbial biomarkers for the diagnosis and treatment of peanut allergies. Twenty-nine children with peanut allergy who visited the hospital from December 2020 to December 2022 were selected as the test group (PA), and twenty-seven healthy children who underwent physical examination during the same period and tested negative for peanut IgE were selected as the control group (Ctl). The differences in gut microbiota between the two groups were compared. The study enrolled 29 children with peanut allergy (PA group) and 27 healthy children (Ctl group) from December 2020 to December 2022. The PA group was defined by a positive reaction to peanut-specific IgE tests, while the Ctl group tested negative for peanut IgE and had no history of allergies. Fecal samples were collected and genomic DNA was extracted for 16S rRNA gene sequencing to assess gut microbiota composition. Alpha diversity indices, including the sob, ace, chao, shannon, and simpson indices, were calculated to assess microbial community richness and diversity. Beta diversity was analyzed using Principal Coordinate Analysis (PCoA) and Partial Least Squares Discriminant Analysis (PLS-DA) to compare microbial community structures between the PA and Ctl groups. The study identified significant differences in gut microbiota diversity between children with peanut allergy (PA group) and healthy controls (Ctl group). The PA group exhibited reduced alpha diversity, indicated by lower sob, ace, and chao indices (FDR ≤ 0.05), and a significantly lower Shannon index (FDR ≤ 0.01). Beta diversity analysis revealed distinct microbial community structures between the two groups. Notably, the PA group showed an increase in Proteobacteria and a decrease in Firmicutes and Bacteroidetes, with significant changes at the genus level, including lower relative abundance of Bacteroides and Faecalibacterium, and higher relative abundance of Bifidobacterium and Lactobacillus (FDR ≤ 0.05 or FDR ≤ 0.01). Correlation analysis highlighted a strong negative correlation between IgE levels and specific microbial groups, such as Alistipes and CAG-352 (FDR ≤ 0.001), and a positive correlation with Veillonella. Blood routine indicators were also found to be correlated with gut microbiota composition. The findings of this study provide compelling evidence that gut microbiota diversity and composition are significantly altered in children with peanut allergy. The observed shifts in microbial communities, particularly the increase in Proteobacteria and the decrease in beneficial bacteria such as Firmicutes and Bacteroidetes, underscore the potential role of the gut microbiome in the pathogenesis of peanut allergy. These results suggest that modulating the gut microbiota may be a viable therapeutic strategy for managing peanut allergy and highlight the need for further research to explore the clinical implications of these microbial changes.

Targeting the Gut Microbiota in Coronavirus Disease 2019: Hype or Hope?

The absolute and relative quantifications between the equivalence class and the target concept are the two important research endeavours in rough set theory. Double‐quantitative decision‐theoretic rough set (Dq‐DTRS) models utilise both absolute quantification and relative quantification in their upper and lower approximations to reflect the distinctive degrees of quantitative information. Herein, the authors apply the information theory to Dq‐DTRS model to characterise and measure these two types of quantitative information. The expressions of the information entropy with regard to the two quantifications and their corresponding information co‐entropy are presented in DqI‐DTRS model and DqII‐DTRSmodel, respectively. This work makes a further study of Dq‐DTRS models by discussing the information measures with respect to absolute and relative quantification.

BackgroundReference genes are commonly used as the endogenous normalisation measure for the relative quantification of target genes. The appropriate application of quantitative real-time PCR (RT-qPCR), however, requires the use of reference genes whose level of expression is not affected by the test, by general physiological conditions or by inter-individual variability. For this purpose, seven reference genes were investigated in tissues of the most important cereals (wheat, barley and oats). Titre of Barley yellow dwarf virus (BYDV) was determined in oats using relative quantification with different reference genes and absolute quantification, and the results were compared.ResultsThe expression of seven potential reference genes was evaluated in tissues of 180 healthy, physiologically stressed and virus-infected cereal plants. These genes were tested by RT-qPCR and ranked according to the stability of their expression using three different methods (two-way ANOVA, GeNorm and NormFinder tools). In most cases, the expression of all genes did not depend on abiotic stress conditions or virus infections. All the genes showed significant differences in expression among plant species. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-tubulin (TUBB) and 18S ribosomal RNA (18S rRNA) always ranked as the three most stable genes. On the other hand, elongation factor-1 alpha (EF1A), eukaryotic initiation factor 4a (EIF4A), and 28S ribosomal RNA (28S rRNA) for barley and oat samples; and alpha-tubulin (TUBA) for wheat samples were consistently ranked as the less reliable controls.The BYDV titre was determined in two oat varieties by RT-qPCR using three different quantification approaches. There were no significant differences between the absolute and relative quantifications, or between quantification using GAPDH + TUBB + TUBA +18S rRNA and EF1A + EIF4A + 28S rRNA. However, there were discrepancies between the results of individual assays.ConclusionsThe geometric average of GAPDH, 18S rRNA and TUBB is suitable for normalisation of BYDV quantification in barley tissues. For wheat and oat samples, a combination of four genes is necessary: GAPDH, 18S rRNA, TUBB and EIF4A for wheat; and GAPDH, 18S rRNA, TUBB and TUBA for oat is recommended.