Inflammatory bowel disease (IBD) has become a major health challenge worldwide. However, the precise etiological and pathophysiological factors involved in IBD remain unclear. Proteomics can be used for large-scale protein identification analysis. In the current study, using tandem mass tag- (TMT-) based shotgun proteomics, proteomic differences between intestinal tissue from health controls, patients with Crohn’s disease (CD), and patients with ulcerative colitis (UC) were compared. Proteins with fold change >2 or <0.5 and P value < 0.05 between groups were considered differentially expressed. ProteinAtlas was used to analyze the tissue specificity of differentially expressed proteins (DEPs). Reactome pathway analysis was applied to cluster functional pathways. A total of 4786 proteins were identified, with 59 proteins showing higher levels and 43 showing lower levels in patients with IBD than in controls. Seventeen proteins, including angiotensin converting enzyme 2 (ACE2) and angiotensin converting enzyme 1 (ACE), showed higher levels in CD than in UC. Several novel proteins such as CD38, chitinase 3-like 1 (CHI3L1), olfactomedin 4 (OLFM4), and intelectin 1 were screened out between patients with IBD and controls. When proteins with fold change >1.2 or <0.84 and P value < 0.05 between groups were considered differentially expressed, the expression of 10 proteins, including CD38, involved in the nicotinamide adenine dinucleotide (NAD) metabolism and signaling pathway showed significant changes in IBD. Using the NCBI GEO database, we confirmed increased CD38 mRNA expression in patients with UC and in mouse colitis models. Protein CD38 expression was higher in CD and UC than in normal controls. CD38 expression was higher in inflamed tissues than in noninflamed tissues, and CD38 was located in F4/80-positive cells. Our study may provide novel insights into the molecular pathogenesis of IBD. Further studies are required on the role of NAD metabolism and CD38 in intestinal inflammation.

Although clinical studies have shown possible links of Helicobacter pylori infection with the development of nonalcoholic fatty liver disease (NAFLD), the results remain controversial. The aim of this meta-analysis is to investigate the association between H. pylori infection and NAFLD. A comprehensive search of relevant studies was performed up to November 2018. Data on H. pylori infection in NAFLD patients and controls were extracted. Odds ratio (OR) and 95% confidence interval (CI) were calculated using a random-effects model. Twelve studies involving 27 400 NAFLD patients and 60 347 controls were included. The pooled overall OR of H. pylori infection in NAFLD patients compared with controls was 1.36 (95% CI: 1.22–1.53, I 2=89.6%, P=0.000). Meta-regression and subgroup analysis showed that the sample size and the case–control ratio may have accounted for some of the heterogeneity. When stratified by publication year, the diagnostic method used for H. pylori, and Newcastle–Ottawa Scale scores, the OR remained significant. However, possible publication bias was observed. Of the 12 studies, six had carried out multivariable analysis after adjusting for potential confounders. The pooled results from these studies still indicated a higher risk of NAFLD in patients infected with H. pylori (OR=1.17, 95% CI: 1.01–1.36, I 2=72.4%, P=0.003). There is a 36% increased risk of NAFLD in patients with H. pylori infection. Further studies are warranted to investigate whether eradication of H. pylori is useful in the prevention and treatment of NAFLD.

H. pylori infection and fatty liver have a significant positive association with gallstones. H. pylori eradication may lead to prevention of gallstones.

AimTo explore the diagnostic models of Crohn’s disease (CD), Intestinal tuberculosis (ITB) and the differential diagnostic model between CD and ITB by analyzing serum proteome profiles.MethodsSerum proteome profiles from 30 CD patients, 21 ITB patients and 30 healthy controls (HCs) were analyzed by using weak cationic magnetic beads combined with MALDI-TOF-MS technique to detect the differentially expressed proteins of serum samples. Three groups were made and compared accordingly: group of CD patients and HCs, group of ITB patients and HCs, group of CD patients and ITB patients. Wilcoxon rank sum test was used to screen the ten most differentiated protein peaks (P < 0.05). Genetic algorithm combining with support vector machine (SVM) was utilized to establish the optimal diagnostic models for CD, ITB and the optimal differential diagnostic model between CD and ITB. The predictive effects of these models were evaluated by Leave one out (LOO) cross validation method.ResultsThere were 236 protein peaks differently expressed between group of CD patients and HCs, 305 protein peaks differently expressed between group of ITB patients and HCs, 332 protein peaks differently expressed between group of CD patients and ITB patients. Ten most differentially expressed peaks were screened out between three groups respectively (P < 0.05) to establish diagnostic models and differential diagnostic model. A diagnostic model comprising of four protein peaks (M/Z 4964, 3029, 2833, 2900) can well distinguish CD patients and HCs, with a specificity and sensitivity of 96.7% and 96.7% respectively. A diagnostic model comprising four protein peaks (M/Z 3030, 2105, 2545, 4210) can well distinguish ITB patients and HCs, with a specificity and sensitivity of 93.3% and 95.2% respectively. A differential diagnostic model comprising three potential biomarkers protein peaks (M/Z 4267, 4223, 1541) can well distinguish CD patients and ITB patients, with a specificity and sensitivity of 76.2% and 80.0% respectively. Among the eleven protein peaks from the diagnostic models and differential diagnostic model, two have been successfully purified and identified, Those two peaks were M/Z 2900 from the diagnostic model between CD and HCs and M/Z 1541 from the differential diagnostic model between CD and ITB. M/Z 2900 was identified as appetite peptide, M/Z 1541 was identified as Lysyl oxidase-like 2 (LOXL-2).ConclusionThe differently expressed protein peaks analyzed by serum proteome with weak cationic magnetic beads combined MALDI-TOF-MS technique can effectively distinguish CD patients and HCs, ITB patients and HCs, CD patients and ITB patients. The diagnostic model between CD patients and HCs consisting of four protein peaks (M/Z 4964, 3029, 2833, 2900), the diagnostic model between ITB patients and HCs comprising four protein peaks (M/Z 3030, 2105, 2545, 4210) and the differential diagnostic model between CD patients and ITB patients comprising three protein peaks (M/Z 4267, 4223, 1541) had high specificity and sensitivity and can contribute to diag...