Dataset Information

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.

ABSTRACT: The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.

SUBMITTER: Hajjo R

PROVIDER: S-EPMC9315466 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.

Hajjo Rima R Sabbah Dima A DA Al Bawab Abdel Qader AQ

Diagnostics (Basel, Switzerland) 20220719 7

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the ...[more]

PMID: 35885645

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Project description:The definition of biomarkers correlating with disease progression could have impact on the rational design of novel diagnostic approaches for bovine tuberculosis in cattle. We have used the advantages of the murine model to identify promising candidates in the host transcriptome post-infection. RNA from BALB/c splenocytes and lung cells after aerogenic Mycobacterium bovis infection were with high-density microarrays to defining biomarkers of disease progression. In antigen-stimulated splenocytes we found statically significant modulation of 1109 genes early after infection and 1134 at later time-point post-infection. 618 of these genes were modulated at both time points. In the lung 282 genes were significantly modulated post-infection. Amongst the most strongly up-regulated genes were granzyme A in spleen, and cxcl9, granzyme B, interleukin-22, interluekin-17A receptor, and ccr6 in lungs. The expression of 20 of the most up-regulated genes identified in the murine studies were evaluated using PBMC from uninfected and naturally infected cattle. We could show that the expression of cxcl9, granzyme A and interleukin-22 following in vitro stimulation with PPD was significantly increased in infected cows compared to naïve animals. Thus, we have demonstrated that murine transcriptome analysis can be used to predict responses in cattle. In addition, we have prioritised CXCL9, GnzA and IL-22 as potential additional readout systems for the ante-mortem diagnosis of bovine tuberculosis. Two groups of 5 BALB/c mice each were infecetd with approx 600 CFU M. bovis via the intranasal route. At days 3 and 14 post challenge five mice per group were euthanized and spleens and lungs harvested. One group of 5 mice were used as a control. Their splenocytes and lung cells were stimulated in vitro for 3 days with M7 protein pool. Antigen cell culture stimulations in mice were performed using an equal pool of seven secreted, immunogenic recombinant mycobacterial proteins (Rv1886c, Rv3019c, Rv3763, Rv3804c, Rv0251, Rv0287 and Rv0288) common to M. bovis and BCG, referred here as M7 protein cocktail. Each protein was used at final concentration of 2μg/ml in 3-day culture.

Project description:BackgroundCrohn's disease (CD) has an increasing incidence and prevalence worldwide. It is currently believed that both the onset and progression of the disease are closely related to immune system imbalance and the infiltration of immune cells. The aim of this study was to investigate the molecular immune mechanisms associated with CD and its fibrosis through bioinformatics analysis.MethodsThree datasets from the Gene Expression Omnibus data base (GEO) were downloaded for data analysis and validation. Single sample gene enrichment analysis (ssGSEA) was used to evaluate the infiltration of immune cells in CD samples. Immune cell types with significant differences were identified by Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Differentially expressed genes (DEGs) were screened and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional correlation analysis, as well as protein-protein interaction (PPI) network analysis. The cytoHubba program and the GSE75214 dataset were used to screen for hub genes and plot Receiver operating characteristic (ROC)curves to screen for possible biomarkers of CD based on diagnostic efficacy. The hub genes of CD were correlated with five significantly different immune cells. In addition, validation was performed by real time quantitative PCR (RT-qPCR) experiments in colonic tissue of CD intestinal fibrosis rats to further identify hub genes that are more related to CD intestinal fibrosis.ResultsThe DEGs were analyzed separately by 10 algorithms and narrowed down to 9 DEGs after taking the intersection. 4 hub genes were further screened by the GSE75214 validation set, namely COL1A1, CXCL10, MMP2 and FGF2. COL1A1 has the highest specificity and sensitivity for the diagnosis of CD and is considered to have the potential to diagnose CD. Five immune cells with significant differences were screened between CD and health controls (HC). Through the correlation analysis between five kinds of immune cells and four biomarkers, it was found that CXCL10 was positively correlated with activated dendritic cells, effector memory CD8+ T cells. MMP2 was positively correlated with activated dendritic cells, gamma delta T cells (γδ T) and mast cells. MMP2 and COL1A1 were significantly increased in colon tissue of CD fibrosis rats.ConclusionMMP2, COL1A1, CXCL10 and FGF2 can be used as hub genes for CD. Among them, COL1A1 can be used as a biomarker for the diagnosis of CD. MMP2 and CXCL10 may be involved in the development and progression of CD by regulating activated dendritic cell, effector memory CD8+ T cell, γδ T cell and mast cell. In addition, MMP2 and COL1A1 may be more closely related to CD intestinal fibrosis.

Dataset Information

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.

Publications

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.

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