Additional file 3 of Normalization of the microbiota in patients after treatment for colonic lesions

Figure S2: Summary of top 10% of important OTUs for the adenoma, advanced adenoma, and carcinoma models. A) MDA of the most important variables in the adenoma model. The dark green point represents the mean and the lighter green points are the value of each of the 100 different runs. B) Summary of important variables in the advanced adenoma model. MDA of the most important variables in the SRN model. The dark yellow point represents the mean and the lighter yellow points are the value of each of the 100 different runs. C) MDA of the most important variables in the carcinoma model. The dark red point represents the mean and the lighter red points are the value of each of the 100 different runs. (PDF 87 kb

Additional file 2 of Normalization of the microbiota in patients after treatment for colonic lesions

Figure S1: ROC curves of the adenoma, advanced adenoma, and carcinoma models. A) Adenoma ROC curve: the light green shaded areas represent the range of values of a 100 different 80/20 splits of the test set data and the dark green line represents the model using 100% of the data set and what was used for subsequent classification. B) Advanced Adenoma ROC curve: the light yellow shaded areas represent the range of values of a 100 different 80/20 splits of the test set data and the dark yellow line represents the model using 100% of the data set and what was used for subsequent classification. C) Carcinoma ROC curve: the light red shaded areas represent the range of values of a 100 different 80/20 splits of the test set data and the dark red line represents the model using 100% of the data set and what was used for subsequent classification. (PDF 92 kb

Additional file 4 of Normalization of the microbiota in patients after treatment for colonic lesions

Figure S3: Pre and post-treatment relative abundance of CRC associated OTUs within the carcinoma model. (PDF 6 kb

Isobaric Protein-Level Labeling Strategy for Serum Glycoprotein Quantification Analysis by Liquid Chromatography–Tandem Mass Spectrometry

While peptide-level labeling using isobaric tag reagents has been widely applied for quantitative proteomics experiments, there are comparatively few reports of protein-level labeling. Intact protein labeling could be broadly applied to quantification experiments utilizing protein-level separations or enrichment schemes. Here, protein-level isobaric labeling was explored as an alternative strategy to peptide-level labeling for serum glycoprotein quantification. Labeling and digestion conditions were optimized by comparing different organic solvents and enzymes. Digestions with Asp-N and trypsin were found highly complementary; combining the results enabled quantification of 30% more proteins than either enzyme alone. Three commercial reagents were compared for protein-level labeling. Protein identification rates were highest with iTRAQ 4-plex when compared to TMT 6-plex and iTRAQ 8-plex using higher-energy collisional dissociation on an Orbitrap Elite mass spectrometer. The compatibility of isobaric protein-level labeling with lectin-based glycoprotein enrichment was also investigated. More than 74% of lectin-bound labeled proteins were known glycoproteins, which was similar to results from unlabeled and peptide-level labeled serum samples. Finally, protein-level and peptide-level labeling strategies were compared for serum glycoprotein quantification. Isobaric protein-level labeling gave comparable identification levels and quantitative precision to peptide-level labeling

Quantitative Analysis of Single Amino Acid Variant Peptides Associated with Pancreatic Cancer in Serum by an Isobaric Labeling Quantitative Method

Single amino acid variations are highly associated with many human diseases. The direct detection of peptides containing single amino acid variants (SAAVs) derived from nonsynonymous single nucleotide polymorphisms (SNPs) in serum can provide unique opportunities for SAAV associated biomarker discovery. In the present study, an isobaric labeling quantitative strategy was applied to identify and quantify variant peptides in serum samples of pancreatic cancer patients and other benign controls. The largest number of SAAV peptides to date in serum including 96 unique variant peptides were quantified in this quantitative analysis, of which five variant peptides showed a statistically significant difference between pancreatic cancer and other controls (<i>p</i>-value < 0.05). Significant differences in the variant peptide SDNCEDTPEAGYFA<i><u>V</u></i>AVVK from serotransferrin were detected between pancreatic cancer and controls, which was further validated by selected reaction monitoring (SRM) analysis. The novel biomarker panel obtained by combining α-1-antichymotrypsin (AACT), Thrombospondin-1 (THBS1) and this variant peptide showed an excellent diagnostic performance in discriminating pancreatic cancer from healthy controls (AUC = 0.98) and chronic pancreatitis (AUC = 0.90). These results suggest that large-scale analysis of SAAV peptides in serum may provide a new direction for biomarker discovery research

Large-Scale Identification of Core-Fucosylated Glycopeptide Sites in Pancreatic Cancer Serum Using Mass Spectrometry

Glycosylation has significant effects on protein function and cell metastasis, which are important in cancer progression. It is of great interest to identify site-specific glycosylation in search of potential cancer biomarkers. However, the abundance of glycopeptides is low compared to that of nonglycopeptides after trypsin digestion of serum samples, and the mass spectrometric signals of glycopeptides are often masked by coeluting nonglycopeptides due to low ionization efficiency. Selective enrichment of glycopeptides from complex serum samples is essential for mass spectrometry (MS)-based analysis. Herein, a strategy has been optimized using LCA enrichment to improve the identification of core-fucosylation (CF) sites in serum of pancreatic cancer patients. The optimized strategy was then applied to analyze CF glycopeptide sites in 13 sets of serum samples from pancreatic cancer, chronic pancreatitis, healthy controls, and a standard reference. In total, 630 core-fucosylation sites were identified from 322 CF proteins in pancreatic cancer patient serum using an Orbitrap Elite mass spectrometer. Further data analysis revealed that 8 CF peptides exhibited a significant difference between pancreatic cancer and other controls, which may be potential diagnostic biomarkers for pancreatic cancer

Prevalence of HPV types among women from the CARE Project.

<p><i>Note.</i> Table reports n (%). HPV  =  human papillomavirus, CARE  =  Community Awareness Resources Education.</p>a<p>High- and low-risk classification scheme is the same as a recent national study that analyzed data from the National Health and Nutrition Examination Survey (NHANES) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>.</p

HPV prevalence among women from the CARE Project and the NHANES.

<p><i>Note.</i> Table reports percents and 95% confidence intervals in parentheses. HPV  =  human papillomavirus, CARE  =  Community Awareness Resources Education, NHANES  =  National Health and Nutrition Examination Survey.</p>a<p>Estimates from the NHANES are for non-Hispanic white women <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>, since 93.9% of women from the CARE Project were non-Hispanic white.</p>b<p>Included HPV types 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 64, 66, 67, 68, 69, 70, 73, 82, and IS39 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>.</p>c<p>Included HPV types 6, 11, 40, 42, 54, 55, 61, 62, 71, 72, 81, 83, 84, and 89 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>.</p

Enjoy your food, but eat less [tipsheet] (2017)

DG TipSheet No. 18 December 2011 Revised October 2016, Reviewed 06/17/5M

Detection of any HPV type among women from the CARE Project (n = 1116).

<p><i>Note.</i> Totals may be less than stated sample size due to missing data. The multivariable model included data on 814 women due to missing data on correlates. The multivariable model did not include variables with dashes (--). HPV  =  human papillomavirus, CARE  =  Community Awareness Resources Education, OR  =  odds ratio, CI  =  confidence interval, ref.  =  referent group, STI  =  sexually transmitted infection.</p>*<p><i>p</i><0.05, **<i>p</i><0.001.</p