Vitamin C Differentially Impacts the Serum Proteome Profile in Female and Male Mice.

A suboptimal blood vitamin C (ascorbate) level increases the risk of several chronic diseases. However, the detection of hypovitaminosis C is not a simple task, as ascorbate is unstable in blood samples. In this study, we examined the serum proteome of mice lacking the gulonolactone oxidase (Gulo) required for the ascorbate biosynthesis. Gulo-/- mice were supplemented with different concentrations of ascorbate in drinking water, and serum was collected to identify proteins correlating with serum ascorbate levels using an unbiased label-free liquid chromatography-tandem mass spectrometry global quantitative proteomic approach. Parallel reaction monitoring was performed to validate the correlations. We uncovered that the serum proteome profiles differ significantly between male and female mice. Also, unlike Gulo-/- males, a four-week ascorbate treatment did not entirely re-establish the serum proteome profile of ascorbate-deficient Gulo-/- females to the optimal profile exhibited by Gulo-/- females that never experienced an ascorbate deficiency. Finally, the serum proteins involved in retinoid metabolism, cholesterol, and lipid transport were similarly affected by ascorbate levels in males and females. In contrast, the proteins regulating serum peptidases and the protein of the acute phase response were different between males and females. These proteins are potential biomarkers correlating with blood ascorbate levels and require further study in standard clinical settings. The complete proteomics data set generated in this study has been deposited to the public repository ProteomeXchange with the data set identifier: PXD027019.

Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard.

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1-Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline, and Spectronaut) with or without the use of a DDA spectral library. On the basis of the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

DIA Proteomics and Machine Learning for the Fast Identification of Bacterial Species in Biological Samples.

Identification of bacterial species in biological samples is essential in many applications. However, the standard methods usually use a time-consuming bacterial culture (24-48 h) and sometimes lack in specificity. To overcome these limitations, we developed a new protocol, combining LC-MS/MS analysis in Data Independent Acquisition mode and machine learning algorithms, enabling the accurate identification of the bacterial species contaminating a sample in a few hours without bacterial culture. In this chapter, we describe the three steps of the protocol (spectral libraries generation, training step, identification step) to generate customized peptide signatures and use them for bacterial identification in biological samples through targeted proteomics analyses and prediction models.

Vitamin C modulates the levels of several proteins of the mitochondrial complex III and its activity in the mouse liver.

Ascorbate is a crucial antioxidant and essential cofactor of biosynthetic and regulatory enzymes. Unlike humans, mice can synthesize ascorbate thanks to the key enzyme gulonolactone oxidase (Gulo). In the present study, we used the Gulo-/- mouse model, which cannot synthesize their own ascorbate to determine the impact of this vitamin on the liver proteome of specific subcellular organelles. We performed label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) global quantitative proteomic profiling to identify and quantify proteins in microsomal enriched liver extracts (MEE) from Gulo-/- mice treated with 0-0.4% (w/v) ascorbate in drinking water until the age of four months. Using a principal component analysis on normalized and imputed data of the label-free protein quantifications, a sex-based difference in MEE proteome profiles was observed for all the different ascorbate treated mice. Suboptimal hepatic ascorbate concentrations affected the levels of more proteins and hence biochemical processes in females than in males. Nevertheless, Pearson correlation analyses revealed that the MS intensities of various proteins involved in complement activation inversely correlated with liver ascorbate concentrations in both Gulo-/- males and females. Moreover, the correlation analyses also indicated that several proteins in the mitochondrial complex III of the electron transport chain positively correlated with liver ascorbate concentrations in both Gulo-/- females and males. Consequently, the mitochondrial complex III activity in Gulo-/- female and male mice treated with suboptimal hepatic concentrations of ascorbate was significantly lower than Gulo-/- mice treated with optimal ascorbate concentration. Finally, the whole liver of ascorbate-deficient Gulo-/- mice exhibited lower ATP levels and increased reactive oxygen species. These findings provide new information on how ascorbate deficiency potentially induces mitochondrial dysfunction in the liver of mice.

DIA proteomics data from a UPS1-spiked E.coli protein mixture processed with six software tools.

In this article, we provide a proteomic reference dataset that has been initially generated for a benchmarking of software tools for Data-Independent Acquisition (DIA) analysis. This large dataset includes 96 DIA .raw files acquired from a complex proteomic standard composed of an E.coli protein background spiked-in with 8 different concentrations of 48 human proteins (UPS1 Sigma). These 8 samples were analyzed in triplicates on an Orbitrap mass spectrometer with 4 different DIA window schemes. We also provide the spectral libraries and FASTA file used for their analysis and the software outputs of the six tools used in this study: DIA-NN, Spectronaut, ScaffoldDIA, DIA-Umpire, Skyline and OpenSWATH. This dataset also contains post-processed quantification tables where the peptides and proteins have been validated, their intensities normalized and the missing values imputed with a noise value. All the files are available on ProteomeXchange. Altogether, these files represent the most comprehensive DIA reference dataset acquired on an Orbitrap instrument ever published. It will be a very useful resource to the proteomic scientists in order to assess the performance of DIA software tools or to test their processing pipelines, to the software developers to improve their tools or develop new ones and to the students for their training on proteomics data analysis.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers.

BACKGROUND: Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers. METHODS: Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography-tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis. RESULTS: In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064). CONCLUSION: Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

The production of preconditioned freeze-dried Oenococcus oeni primes its metabolism to withstand environmental stresses encountered upon inoculation into wine.

Oenococcus oeni is the most resistant lactic acid bacteria species to the environmental stresses encountered in wine, particularly the acidity, presence of ethanol and phenolic compounds. Indigenous strains develop spontaneously following the yeast-driven alcoholic fermentation and may perform the malolactic fermentation whereby improving taste, aroma, and the microbial stability of wine. However, spontaneous fermentation is sometimes delayed, prolonged or incomplete. In order to better control its timing and quality, O. oeni strains are selected and developed to be used as malolactic starters. They are prepared under proprietary manufacturing processes to survive direct inoculation and are predominantly provided as freeze-dried preparations. In this study, we have investigated the physiological and molecular alterations occurring in O. oeni cells prepared by an industrial process that consists of preconditioning protocols and freeze-drying, and compared them to the same strain grown in a grape juice medium. We found that compared to cultured cells, the industrial production process improved survival under extreme conditions, i. e. at low pH or high tannin concentrations. In contrast, cultured cells resumed active growth more quickly and strongly than freeze-dried preparations in standard pH wines. A proteomic analysis showed that during the industrial production most non-essential metabolic processes are shut down and components of the general and the stringent stress response are upregulated. The presence of major components of the stress response facilitates protein homeostasis and physiological changes that further ensure the integrity of cells.

T cell apoptosis characterizes severe Covid-19 disease.

Severe SARS-CoV-2 infections are characterized by lymphopenia, but the mechanisms involved are still elusive. Based on our knowledge of HIV pathophysiology, we hypothesized that SARS-CoV-2 infection-mediated lymphopenia could also be related to T cell apoptosis. By comparing intensive care unit (ICU) and non-ICU COVID-19 patients with age-matched healthy donors, we found a strong positive correlation between plasma levels of soluble FasL (sFasL) and T cell surface expression of Fas/CD95 with the propensity of T cells to die and CD4 T cell counts. Plasma levels of sFasL and T cell death are correlated with CXCL10 which is part of the signature of 4 biomarkers of disease severity (ROC, 0.98). We also found that members of the Bcl-2 family had modulated in the T cells of COVID-19 patients. More importantly, we demonstrated that the pan-caspase inhibitor, Q-VD, prevents T cell death by apoptosis and enhances Th1 transcripts. Altogether, our results are compatible with a model in which T-cell apoptosis accounts for T lymphopenia in individuals with severe COVID-19. Therefore, a strategy aimed at blocking caspase activation could be beneficial for preventing immunodeficiency in COVID-19 patients.

Proteomic Analysis of Maternal Urine for the Early Detection of Preeclampsia and Fetal Growth Restriction.

BACKGROUND: To explore the use of maternal urine proteome for the identification of preeclampsia biomarkers. METHODS: Maternal urine samples from women with and without preeclampsia were used for protein discovery followed by a validation study. The targeted proteins of interest were then measured in urine samples collected at 20-24 and 30-34 weeks among nine women who developed preeclampsia, one woman with fetal growth restriction, and 20 women with uncomplicated pregnancies from a longitudinal study. Protein identification and quantification was obtained using liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS: Among the 1108 urine proteins quantified in the discovery study, 21 were upregulated in preeclampsia and selected for validation. Nineteen (90%) proteins were confirmed as upregulated in preeclampsia cases. Among them, two proteins, ceruloplasmin and serpin A7, were upregulated at 20-24 weeks and 30-34 weeks of gestation (p < 0.05) in cases of preeclampsia, and could have served to identify 60% of women who subsequently developed preeclampsia and/or fetal growth restriction at 20-24 weeks of gestation, and 78% at 30-34 weeks, for a false-positive rate of 10%. CONCLUSIONS: Proteomic profiling of maternal urine can differentiate women with and without preeclampsia. Several proteins including ceruloplasmin and serpin A7 are upregulated in maternal urine before the diagnosis of preeclampsia and potentially fetal growth restriction.

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis.

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS. Our second objective was to identify genes that may be differentially regulated by exposure to high versus low Lp(a) levels in explanted aortic valves from patients with CAVS. We isolated Lp(a) from the blood of 21 patients with CAVS and 22 volunteers and performed untargeted label-free analysis of the Lp(a) proteome. We also investigated the transcriptomic signature of calcified aortic valves from patients who underwent aortic valve replacement with high versus low Lp(a) levels (n = 118). Proteins involved in the protein activation cascade, platelet degranulation, leukocyte migration, and response to wounding may be associated with Lp(a) depending on CAVS status. The transcriptomic analysis identified genes involved in cardiac aging, chondrocyte development, and inflammation as potentially influenced by Lp(a). Our multi-omic analyses identified biological pathways through which Lp(a) may cause CAVS, as well as key molecular events that could be triggered by Lp(a) in CAVS development.

A Comparative Analysis of the Lipoprotein(a) and Low-Density Lipoprotein Proteomic Profiles Combining Mass Spectrometry and Mendelian Randomization.

BACKGROUND: Lipoprotein(a) (Lp[a]), which consists of a low-density lipoprotein (LDL) bound to apolipoprotein(a), is one of the strongest genetic risk factors for atherosclerotic cardiovascular diseases. Few studies have performed hypothesis-free direct comparisons of the Lp(a) and the LDL proteomes. Our objectives were to compare the Lp(a) and the LDL proteomic profiles and to evaluate the effect of lifelong exposure to elevated Lp(a) or LDL cholesterol levels on the plasma proteomic profile. METHODS: We performed a label-free analysis of the Lp(a) and LDL proteomic profiles of healthy volunteers in a discovery (n = 6) and a replication (n = 9) phase. We performed inverse variance weighted Mendelian randomization to document the effect of lifelong exposure to elevated Lp(a) or LDL cholesterol levels on the plasma proteomic profile of participants of the INTERVAL study. RESULTS: We identified 15 proteins that were more abundant on Lp(a) compared with LDL (serping1, pi16, itih1, itih2, itih3, pon1, podxl, cd44, cp, ptprg, vtn, pcsk9, igfals, vcam1, and ttr). We found no proteins that were more abundant on LDL compared with Lp(a). After correction for multiple testing, lifelong exposure to elevated LDL cholesterol levels was associated with the variation of 18 plasma proteins whereas Lp(a) did not appear to influence the plasma proteome. CONCLUSIONS: Results of this study highlight marked differences in the proteome of Lp(a) and LDL as well as in the effect of lifelong exposure to elevated LDL cholesterol or Lp(a) on the plasma proteomic profile.

CONTEXTE: La lipoprotéine(a) (Lp[a]), qui est constituée d'une lipoprotéine de basse densité (LDL) liée à une apolipoprotéine(a), est l'un des plus importants facteurs de risque génétiques de survenue d'une maladie cardiovasculaire athéroscléreuse. Peu d'études comparatives directes sans hypothèse ont porté sur le protéome de la Lp(a) et celui des LDL. Nos objectifs étaient de comparer les profils protéomiques de la Lp(a) et des LDL et d'évaluer l'effet d'une exposition à vie à un taux élevé de Lp(a) ou de cholestérol LDL sur le profil protéomique plasmatique. MÉTHODOLOGIE: Nous avons réalisé une analyse sans marquage des profils protéomiques de la Lp(a) et des LDL chez des volontaires en bonne santé dans le cadre d'une phase de découverte (n = 6) et d'une phase de réplication (n = 9). Pour rendre compte de l'effet d'une exposition à vie à un taux élevé de Lp(a) ou de cholestérol des LDL sur le profil protéomique plasmatique des participants de l'étude INTERVAL, nous avons utilisé une analyse de randomisation Mendélienne avec pondération par l'inverse de la variance. RÉSULTATS: Nous avons relevé 15 protéines associées en plus grande abondance à la Lp(a) qu'aux LDL (serping1, pi16, itih1, itih2, itih3, pon1, podxl, cd44, cp, ptprg, vtn, pcsk9, igfals, vcam1 et ttr). Nous n'avons noté aucune protéine associée en plus grande abondance aux LDL qu'à la Lp(a). Après correction pour tenir compte de la multiplicité des tests, l'exposition à vie à un taux élevé de cholestérol LDL a été associée à la variation de 18 protéines plasmatiques, tandis que le taux de Lp(a) ne semblait pas influencer le protéome plasmatique. CONCLUSIONS: Les résultats de notre étude font ressortir les différences marquées entre le protéome de la Lp(a) et celui des LDL, ainsi qu'entre l'effet sur le profil protéomique plasmatique de l'exposition à vie à un taux élevé de cholestérol LDL et celui de l'exposition à vie à un taux élevé de Lp(a).

Identification of protein markers for extracellular vesicle (EV) subsets in cow's milk.

Extracellular vesicles (EVs), like exosomes, are small membrane vesicles involved in cell-to-cell communications that modulate numerous biological processes. We previously discovered a new EV subset in milk (sedimenting at 35,000 g; 35 K) that protected its cargo (RNAs and proteins) during simulated digestion and was more enriched in microRNAs than exosomes (sedimenting at 100 K). Here, we used LC-MS/MS to push further the comparison between these two pellets. Commonly used EV markers were not differentially enriched between the pellets, questioning their use with cow's milk EVs. Similarly, the majority of the quantified proteins were equally enriched between the two pellets. Nevertheless, 20 proteins were specific to 35 K, while 41 were specifically enriched in 100 K (p < 0.05), suggesting their potential use as specific markers. Loaded with these proteins, the EVs in these pellets might regulate translation, proliferation and cell survival for 35 K, and metabolism, extracellular matrix turnover and immunity for 100 K. This approach also brought new insights into milk EV-associated integrins and their possible role in specifically targeting recipient cell types. These findings may help better discriminate between milk EVs, improve our understanding of milk EV-associated protein function and their possible use as therapeutic tools for the management of immunity- and metabolism-associated disorders. WEB PAGE: http://www.crchuq.ca/en/research/researchers/4691.