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    Home > Biochemistry News > Biotechnology News > How can I tell if I'm in a state of fatigue? Metabolomics helps to explore the secrets of tumors

    How can I tell if I'm in a state of fatigue? Metabolomics helps to explore the secrets of tumors

    • Last Update: 2022-09-14
    • Source: Internet
    • Author: User
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    How can I tell if I'm in a state of fatigue? Biometomics takes you to explore tumor secrets
    : Fecal microbiota and metabolic signatures
    of rectal neuroendocrine tumors
    Published in journal Theranostics
    Impact Factor: 11.
    556
    Published: 2022.
    1.
    31The
    prevalence of Rectal Neuroendocrine Tumors (RNET) has increased significantly over the past few decades, although the pathogenesis is not clear
    .
    Research on intestinal flora and metabolic disorders in various digestive tract diseases has been extensively conducted, but to date, we still know very little
    about how flora and metabolites shape the colorectal environment.
    The authors hypothesized that disturbances in the fecal microbiota and metabolism would affect the development of RNET, and the study aimed to analyze the composition and function of the fecal microbiota and metabolism of RNET individuals, and to further study the relationship
    between dysbacteriosis and the occurrence of RNET disease.
    Research Methods
    Found Cohort: 18 RNET patients, 40 controls
    Validation cohort: 15 RNET patients, 19 controls
    Multi-omics: Fecal Metagenomics + Fecal Metabolomics (LC-MS)Findings
    The taxonomic authors of the 1RNET microbiome applied metagenomic (Illumina NovaSeq 6000 platform) sequencing techniques to study the gut microbiota, generating an average of 71.
    36 million reads (11Gb of data)
    per stool sample.
    RNET patients had decreases in both richness and Shannon index compared to healthy people, but there was no significant difference (Figure 1A
    ).
    Similarly, no significant difference was observed between the two groups based on the Bray-Curtis distance (Adonis:R2=0.
    02, p=0.
    389, Figure 1B
    ).
    MetaPhlAn2 further analyzed the microbial community composition, and 58 samples annotated a total of 641 species in 217 genera, enriching 24 different species (Figure 1C
    ).
    Notably, most of the species were enriched in the healthy group, reflecting a decrease
    in the intestinal flora of the RNET group.
    Compared with the RNET group, probiotics Haemophilus parainfluenzae, Veillonella unclassified, Streptococcus salivarius, etc.
    were significantly enriched in the healthy group; In contrast, some of the flora associated with abscesses and gastrointestinal diseases, Erysipelotrichaceae bacterium_6_1_45, Varibaculum cambriense, Methanobrevibacter smithii, showed enrichment
    in the RNET group.

     
    Figure 1.
    Microbial community structure of the RNET group and the control group
     
    The authors performed co-occurrence network analysis at the subordinate and species levels to demonstrate the structure and composition of
    microbial communities.
    Compared with the healthy group, the species-level network complexity and connectivity of the RNET group were reduced (Figure 2), which may be related to the decrease in enterobacteria in the RNET group, and similar findings were also found
    in the genera-level network.

    Figure 2.
    Symbiosis network analysis
     
    2 The functional characteristics of the RNET microbiome were analyzed based on in-house and HUMAnN2, and the enriched pathways were 69 (in-house) and 38 (HUMANn2), respectively, of which 11 overlapping pathways (Figure 3A).

    。 Most of the pathways enriched based on HUMANn2 are enriched in healthy groups; Pathways associated with energy metabolism (M00157, M00164), RNA polymerase (M00183), and vitamin biosynthesis (M00125) were also mostly enriched
    in healthy groups.
    Based on the output of HUMAnN2, the authors further identified the dominant species involved in the above path (Figure 3B
    ).
    The flora Escherichia coli, Faecalibacterium prausnitzii, Bacteroides vulgatus, Haemophilus parainfluenzae, Ruminococcus torques, mainly enriched in healthy populations, are major contributors to the above pathways
    。 Also concentrated in healthy populations, the pathway Manganese/zinc/iron transportation (M00319), the dominant flora is mainly composed of microorganisms of the genus Veillonella, such as Veillonella atypica, Veillonella dispar, and Veillonella parvula
    .
    The overall findings suggest that changes in microbial community composition drive pathologic states
    by disrupting host physiological functions.

    Figure 3.
    Functional characteristics of the RNET microbiome
    3 RNET metabolome enrichment analysis uses non-targeted metabolomics techniques to detect stool samples and explore metabolic profile changes in
    patients with RNET.
    The metabolomics characteristics (based on Bray-Curtis distance) were statistically significantly different between the RNET group and the control group (adonis:R2 = 0.
    083, p=0.
    001, Figure 4A) and were not significantly affected
    by clinical factors such as sex, BMI, age, smoking and drinking history.
    A total of 545 metabolites were identified, of which 104 metabolites were significantly different
    .
    Unlike the decreased microbial flora in the RNET group, most of the differential metabolites were significantly enriched in the RNET group, while only 26 species were enriched in the control group (Figure 4B
    ).
    The main feature of the RNET metabolome is the significant upregulation of lipids and lipid molecules; In contrast, the control group showed upregulation
    of organic heterocyclic compounds, organic acids and their derivatives, and organozine compounds.
    Further KEGG analysis revealed that in patients with RNET, key pathway glycerol phospholipid metabolism was altered (Figure 4C
    ).
    Thus it is speculated that abnormal lipid metabolism is involved in the pathogenesis
    of RNET.

     
    Figure 4.
    RNET metabolome enrichment analysis
     
    Correlation analysis of the 4RNET gut microbiome to the metabolome to further explore the relationship between the microbiota and metabolites was performed using the Spearman coefficient (Figure 5
    ).
    The results of the analysis showed a strong correlation between the microbiota and metabolites, such as the concentration Ofthanobrevibacter smithii, which was enriched in the RNET group, and there was a significant positive correlation with the metabolites Cohibin B, Cohibin C, LysoPE (18:1(9Z)/0:0) also enriched in the RNET group
    .
    This positive correlation can be explained by the production of metabolites by the microflora, or the metabolites favoring the growth
    of certain flora.
    At the same time, a negative correlation
    was also found between the flora enriched in the control group and the metabolites enriched in the RNET group.
    Overall, although the direct metabolic relationship between the microbiota and metabolites has yet to be further determined, the results can still indicate that the intestinal flora and metabolites are closely related to
    the pathogenesis of RNET.

     
    Figure 5.
    Correlation analysis of RNET gut microbiome with metabolome
     
    5 RNET prediction
    based on multi-omics signals To explore potential diagnostic microbial and metabolic signatures, the authors constructed a random forest classifier (RF) model
    based on differential flora and differential metabolites in the control and RNET groups.
    A group of markers, including 3 microbiota microorganisms and 9 metabolites, were screened, and most of the filtered markers were enriched in the RENT group (Figure 6A
    ).
    Subsequently, the authors corrected for sex, age, BMI, smoking and drinking history, etc.
    , and the results showed that the screening markers were not significantly affected
    by the above clinical factors.
    The predictive model noted that in the discovery cohort, the metabolites were highly sensitive in detecting RNET, with an area under the curve (AUC) value of 1.
    0 (Figure 6B
    ).
    Compared with the single microbiota microbial prediction model (AUC=0.
    76), the composite model of microorganisms and metabolites has significantly improved in terms of classification accuracy (AUC=0.
    96).

    In addition, the authors selected a cohort (15 RNET patients, 19 controls) as an independent external validation set (Figure 6C), and the results showed that the AUC of the metabolite validation model was 0.
    83, which still had high accuracy, and the composite model AUC after the addition of the microflora was 0.
    74, which was better than the prediction effect of the microflora alone (AUC=0.
    71).

    These results suggest that random forest taxonomic models of flora and metabolites have great potential
    for development in the early diagnosis of RNET populations as a non-invasive tool.

     
    Figure 6.
    RNET prediction based on multi-omics signals
     
    Conclusions
    This study describes the imbalance of the intestinal ecological microenvironment of RNET patients, which is characterized by a decrease in microbial species and abnormal aggregation of lipids and lipid molecules, so as to infer that the disordered ecological structure may be involved in the tumorogenic process
    of such tumors.
    This work helps to explore the potential role of microbiota and metabolic disorders in the pathogenesis of RNET, providing research directions
    for microbiota-based diagnosis and treatment.

    Text/A-Fun Metabolomics
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