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The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.

PFAS are persistent and toxic chemicals used in many commercial and industrial applications that are often added to consumer products, including those used by children and adolescents, to impart water and stain resistance. Since product labels rarely list chemical additives, including PFAS, we evaluated whether other information on product labels can be used by consumers to select products without PFAS. We selected 93 items marketed to or often used by children and adolescents across three product types (furnishings, apparel, bedding) and five labeling groups representing different combinations of water and/or stain resistance and "green" (including "nontoxic") assurances. We screened all products for total fluorine (F) and analyzed solvent extracts from a subset (

One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely “at random” or “not at random”. To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential missingness). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods.

Metaproteomics focuses on identifying and quantifying proteins in complex biological samples such as the human gut microbiota. The analysis of several hundred of samples is of interest given the growing recognition of this ecosystem as a health partner. However, the methods and protocols used so far in metaproteomics are not suitable for large-scale studies. We have therefore developed algorithms, evaluated and compared several identification approaches for peptides and proteins and proposed systematic evaluation criteria, with a particular interest in the replicability of identifications, in order to develop a pre-treatment pipeline suitable for wide-ranging studies. This work bring a methodological base so far missing in the field of the metaproteomics of the human gut microbiota. Quantification of peptides and proteins by XIC has never been performed on this type of data, we have also compared normalization methods and developed a methodology for imputing missing data to refine the abundance estimations obtained by the more classical method of SC. This thesis work has highlighted microbial biomarkers of potential interest for predicting the response to a slimming diet, or to characterize various phenotypes of IBD. We have also been able to analyse the metaproteome of more than 200 patients in the framework of the ProteoCardis ANR, which is ancillary to the European project MetaCardis, and which focuses on the potential link between gut microbiota and cardiovascular diseases. The search for proteins of interest among these data should allow us to discover protective or aggravating candidate biomarkers of cardiovascular diseases.; La métaprotéomique s’attache à identifier et quantifier les protéines d’échantillons biologiques complexes comme le microbiote intestinal humain. L’analyse de plusieurs centaines d’échantillons revêt un intérêt évident compte tenu de la reconnaissance croissante de cet écosystème en tant que partenaire santé. Cependant, les méthodes et protocoles utilisés jusqu’à ce jour en métaprotéomique ne sont pas adaptés à des études de grande ampleur. Nous avons développé des algorithmes, évalué et comparé plusieurs approches d’identification des peptides et protéines et proposé des critères d’évaluation systématiques, avec un intérêt particulier porté sur la réplicabilité des identifications, afin de développer un pipeline de prétraitement adapté à des études d’envergure. Ce travail apporte un socle méthodologique jusqu’ici manquant dans le domaine de la métaprotéomique du microbiote intestinal humain. Nous avons également comparé des méthodes de normalisation des XIC et développé une méthodologie d’imputation des données manquantes permettant d’affiner les estimations d’abondances obtenues par la méthode de SC. Ce travail de thèse a permis de mettre en évidence des biomarqueurs microbiens potentiellement d’intérêt pour prédire la réponse à un régime amaigrissant ou pour caractériser différents phénotypes de MICI. Nous avons également analysé le métaprotéome de plus de 200 patients dans le cadre de l’ANR ProteoCardis adossée au projet MetaCardis, et s’intéressant au lien possible entre microbiote intestinal et maladies cardiovasculaires. La recherche de protéines d’intérêt parmi ces données devrait permettre de découvrir des candidats biomarqueurs de maladies cardiovasculaires.

Metaproteomics focuses on identifying and quantifying proteins in complex biological samples such as the human gut microbiota. The analysis of several hundred of samples is of interest given the growing recognition of this ecosystem as a health partner. However, the methods and protocols used so far in metaproteomics are not suitable for large-scale studies. We have therefore developed algorithms, evaluated and compared several identification approaches for peptides and proteins and proposed systematic evaluation criteria, with a particular interest in the replicability of identifications, in order to develop a pre-treatment pipeline suitable for wide-ranging studies. This work bring a methodological base so far missing in the field of the metaproteomics of the human gut microbiota. Quantification of peptides and proteins by XIC has never been performed on this type of data, we have also compared normalization methods and developed a methodology for imputing missing data to refine the abundance estimations obtained by the more classical method of SC. This thesis work has highlighted microbial biomarkers of potential interest for predicting the response to a slimming diet, or to characterize various phenotypes of IBD. We have also been able to analyse the metaproteome of more than 200 patients in the framework of the ProteoCardis ANR, which is ancillary to the European project MetaCardis, and which focuses on the potential link between gut microbiota and cardiovascular diseases. The search for proteins of interest among these data should allow us to discover protective or aggravating candidate biomarkers of cardiovascular diseases., La métaprotéomique s’attache à identifier et quantifier les protéines d’échantillons biologiques complexes comme le microbiote intestinal humain. L’analyse de plusieurs centaines d’échantillons revêt un intérêt évident compte tenu de la reconnaissance croissante de cet écosystème en tant que partenaire santé. Cependant, les méthodes et protocoles utilisés jusqu’à ce jour en métaprotéomique ne sont pas adaptés à des études de grande ampleur. Nous avons développé des algorithmes, évalué et comparé plusieurs approches d’identification des peptides et protéines et proposé des critères d’évaluation systématiques, avec un intérêt particulier porté sur la réplicabilité des identifications, afin de développer un pipeline de prétraitement adapté à des études d’envergure. Ce travail apporte un socle méthodologique jusqu’ici manquant dans le domaine de la métaprotéomique du microbiote intestinal humain. Nous avons également comparé des méthodes de normalisation des XIC et développé une méthodologie d’imputation des données manquantes permettant d’affiner les estimations d’abondances obtenues par la méthode de SC. Ce travail de thèse a permis de mettre en évidence des biomarqueurs microbiens potentiellement d’intérêt pour prédire la réponse à un régime amaigrissant ou pour caractériser différents phénotypes de MICI. Nous avons également analysé le métaprotéome de plus de 200 patients dans le cadre de l’ANR ProteoCardis adossée au projet MetaCardis, et s’intéressant au lien possible entre microbiote intestinal et maladies cardiovasculaires. La recherche de protéines d’intérêt parmi ces données devrait permettre de découvrir des candidats biomarqueurs de maladies cardiovasculaires.

Background/Objective:The clinical conditions that lead to an alteration of the enamel structure are numerous. The diet high in sugars and acidifying substances, psychological stress that triggers parafunctional behaviors, the reduced intake of fiber-rich foods or alkalizing substances, together with other factors, contribute to demineralization of the tooth enamel. Dental mineralizing products on the current market are distinguished according to the dosage form, the active ingredient, the release technology, clinical indications and patient choice. Currently, it is necessary to propose to oral health professionals a guide to orient themselves in this chaotic choice, in order to prefer the most effective product for their own clinical target.Methods:Italian Society of Oral Hygiene Sciences-S.I.S.I.O. is one of the leading scientific Italian societies representing those dental hygienists working with high-quality standards and in agreement with scientific evidence: in the last year, the SISIO working group has carried out a study focused on remineralizing agents in dentistry, in order to give an authoritative point of view to indicate a guideline in the decision process of the choice of a remineralizing agent. We will report the results pointed out from the last consensus meeting in 2017.Results:We have reported the good the bad and the ugly have been discussed in a critical discussion of such topic.Conclusion:The SISIO experience has been reported in this position paper with the aim to serve as a useful aid in the daily choice of the clinical steps to perform, when dental professionals need to treat demineralized teeth.

Non-contrast CT (NCT) is commonly used to evaluate flank pain (FP). We sought to evaluate incidence of ureteral calculi on NCT in patients with FP, and to determine if clinical variables are associated with higher detection rates.Retrospective review identified 613 patients undergoing NCT for FP. Patient clinical data, NCT findings, and intervention were analyzed. Focus was placed on variables commonly associated with urolithiasis (Vstone), comprising hematuria, nausea/vomiting, and prior stone history. Statistical analysis was performed to identify risk of ureteral stones based on number and type of Vstone.No stone disease was identified on NCT in 175 patients (28.5%). NCT demonstrated 214 (35%), 72 (12%), and 152 (25%) patients with stones located in the kidney, ureter, or both, respectively. Only 33 (5%) patients had FP as their sole Vstone, with ureteral calculi identified in 6% of this cohort. The rate of ureteral calculi increased with more Vstone. Patients having all four Vstone were found to have the highest rate of ureteral stones (59%). Statistical analysis demonstrated a statistically significantly increased relative risk of stone formation given three or four Vstone when compared with FP alone.Whereas isolated FP is associated with a lower rate of ureteral calculus detection, a significant increased relative risk of ureteral calculus is seen in patients with additional clinical variables associated with stone disease. Accordingly, it may be possible to improve detection rates of ureteral stones through the use of additional clinical variables to guide NCT selection.