Clinical data of patients with periodontitis and controls.

<p>Clinical data of patients with periodontitis and controls.</p

Main metabolites identified to discriminate periodontitis from controls according to the loadings plot analysis.

<p>Correlation between bucket intensities and discriminated group members are given with the associated p value.</p

Loadings plot of the principal component analysis (PCA) performed on clinical variables of periodontitis.

<p>Loadings are scaled so that the correlated variables correctly explained by the components are found close together and near the correlation circle. BL: bone loss; BOP: bleeding on probing; CAL: clinical attachment loss, expressed as a mean (CALMEAN) or according to the severity of the loss (CALmild, CALmoderate, and CALMAX); DMF: decay missing filled; MPPD: mean pocket depth; NRT: number of residual teeth; PCR: plaque control record; TOBACCO: smoking habits (for details see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182767#sec002" target="_blank">Materials and methods</a>).</p

Evaluation of the clinical significance of the grouped lactate-GABA-butyrate considered as one.

<p>Evaluation of the clinical significance of the grouped lactate-GABA-butyrate considered as one.</p

Multivariate logistic regression analysis.

<p>Multivariate logistic regression analysis.</p

Examples of NMR spectra in saliva and OPLS metabolomic analysis.

<p>a,b) Representative ¹H-NMR spectra obtained in (a) a control individual and (b) a case individual. c) Orthogonal projection to latent structures (OPLS model) of ¹H-NMR spectra obtained in saliva from periodontitis patients (red dots) and healthy controls (blue dots) according to the predictive (Tpred) and not predictive (Torth) components obtained from the OPLS model. d) OPLS loadings plot showing the discriminant metabolites between patients with periodontitis and controls. Variations of metabolites are represented using a line plot between 0–9 ppm. Positive signals correspond to metabolites present at increased concentrations in the patient group. Negative signals correspond to metabolites present at increased concentrations in the control group. The buckets are labelled according to metabolite assignment (1. butyrate; 2. fucose; 3. lactate; 4. acetate; 5. N-acetyl of glycoprotein; 6. GABA; 7. 3-hydroxybutyrate; 8. pyruvate; 9. methanol; 10. threonine; 11. ethanol).</p

Additional file 4: of Whole exome sequencing in three families segregating a pediatric case of sarcoidosis

Figure S1. CADD scoring of prioritized variants versus other variants in the selected genes. (PDF 71 kb

Additional file 2: of Whole exome sequencing in three families segregating a pediatric case of sarcoidosis

Table S2. Recessive variants shared by a common gene in at least two different trios. Possibly pathogenic recessive variants observed at different positions for a single gene in at least two affected children of the trios (T). Abbreviations are the same as in Tables 1, 2 and Additional file 1: Table S1. (DOCX 31 kb

Additional file 3: of Whole exome sequencing in three families segregating a pediatric case of sarcoidosis

Table S3. Composite heterozygocity observed in a common gene in at least two different trios. Possibly pathogenic compound heterozygous variants (allelic heterogeneity) observed in different positions of a common gene in at least two trios. The origin of either the paternal and maternal allele was detailed for each variant. Abbreviations are the same as in Tables 1, 2, Additional files 1 and 2: Tables S1 and S2. (DOCX 51 kb

Additional file 1: of Whole exome sequencing in three families segregating a pediatric case of sarcoidosis

Table S1. Recessive variants found in at least two affected children of different trios. Possibly pathogenic recessive variants (polymorphisms) found by whole-exome -sequencing in at least two affected children of the trios (T). Chr., chromosome; SNP, single nucleotide polymorphism; QUAL., a quality parameter measuring the probability p that the observation of the variant is due to chance (for ex: QUAL = n, p = 1/n). As detailed in the text, Alamut® Visual integrates missense variant pathogenicity prediction tools and in silico study of variants’ effect on RNA splicing, allowing the assessment of their potential impact on splice junctions and splicing regulatory sequences. Alamut® Visual helped us also to exclude well known mutations identified in recessive diseases for those genes which have been related to known genetic diseases (as shown in Table 3). (DOCX 23 kb