Dataset Information

O-SaMPle: an optimized workflow for human salivary metaproteomic analysis reveals taxonomic and functional dysbiosis in IBD patients

ABSTRACT: We developed an Optimized Salivary MetaProteomic sample analysis workflow (O-SaMPle) to enrich salivary bacteria and reduce host-derived interferences for in-depth analysis of the oral metaproteome. This workflow integrates using selective microbial enrichment buffer and differential centrifugation. In details, the selective microbial enrichment buffer can dissolves host proteins and detaches microorganisms from cells while preserving the integrity of the microbes by using a combination of low concentrations of denaturing agents and detergents, followed differential centrifugation can effectively isolate and enrich oral microbiota from human saliva or mouth rinse samples. Furthermore, we applied O-SaMPle to analyzing mouth rinse samples from IBD patients, and revealed dysregulated microbes and their corresponding dysregulated pathways under disease conditions.

ORGANISM(S): Human Oral Bacteria Ensemble

SUBMITTER: Leyuan Li

PROVIDER: PXD055269 | iProX | Mon Aug 26 00:00:00 GMT+01:00 2024

REPOSITORIES: iProX

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OSaMPle workflow for salivary metaproteomics analysis reveals dysbiosis in inflammatory bowel disease patients.

Yuan Jinhui J Sun Boyan B Li Murong M Yang Congyi C Zhang Lingqiang L Chen Ning N Chen Feng F Li Leyuan L

NPJ biofilms and microbiomes 20250423 1

The human oral microbiome has been associated with multiple inflammatory conditions including inflammatory bowel disease (IBD). Identifying functional changes in oral microbiome by metaproteomics helps understanding the factors driving dysbiosis related to intestinal diseases. However, enriching bacterial cells from oral samples (such as saliva and mouth rinse) rich in host proteins is challenging. Here, we present an Optimized Salivary MetaProteomic sample analysis workflow (OSaMPle) to enrich ...[more]

PMID: 40268913

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Project description:Background: Schizophrenia (SZ) is a chronic, severe mental disorder that presents significant challenges to diagnosis and effective treatment. Emerging evidence suggests that gut microbiota may play a role in the disease's pathogenesis. However, fewer studies have directly investigated the potential links between oral microbiota and SZ. Purpose: This study aimed to explore the relationship between salivary microbiota dysbiosis and SZ, examining microbial and metabolic alterations that may contribute to SZ pathophysiology. Methods: Salivary samples from 30 hospitalized patients diagosed with SZ and 10 healthy controls were collected. The microbial and metabolic profiles were analyzed using 16S rRNA gene sequencing and metabolomic profiling. Clinical parameters, including oral health status, were also evaluated to minimize variability in sampling. Results: Patients with SZ exhibited significantly poorer oral health compared to healthy controls, with more missing teeth and worse periodontal status. Microbiota sequencing revealed notable alterations in the overall structure and composition of the salivary microbiome in SZ patients, characterized by increased abundance of specific genera such as Neisseria and Porphyromonas. Metabolomic analysis indicated significant differences between the SZ and control groups, with upregulation of key metabolic pathways, including “β-alanine metabolism” and “vitamin digestion and absorption”. Correlations between microbial dysbiosis and elevated levels of certain metabolites, such as L-methionine sulfoxide (L-MetO) and tyramine, were observed, suggesting links to oxidative stress. Conclusion: The study highlights the presence of significant dysbiosis and metabolic dysfunction in the salivary microbiota of SZ patients, suggesting that alterations in the oral microbiome may contribute to SZ pathogenesis. These results provide new insights into potential diagnostic biomarkers and therapeutic targets for SZ. Further studies with larger sample sizes are required to validate these findings.

Project description:The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients. Thus, the systemic effects of small differences in the oral microbiota are unclear. In this study, we explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects by analyzing the hepatic gene expression and serum metabolomic profiles. The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Samples were collected five weeks after administration. Gut microbial communities were analyzed by 16S ribosomal RNA gene sequencing. Hepatic gene expression profiles were analyzed using a DNA microarray and quantitative polymerase chain reaction. Serum metabolites were analyzed by capillary electrophoresis time-of-flight mass spectrometry. The gut microbial composition at the genus level was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of Neat1, Mt1, Mt2, and Spindlin1, which are involved in lipid and glucose metabolism. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with Bifidobacterium, Atomobium, Campylobacter, and Haemophilus, which are characteristic taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice. The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.

Project description:Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora

Dataset Information

O-SaMPle: an optimized workflow for human salivary metaproteomic analysis reveals taxonomic and functional dysbiosis in IBD patients

Publications

OSaMPle workflow for salivary metaproteomics analysis reveals dysbiosis in inflammatory bowel disease patients.

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