Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04

    • Metabotype data coupled to metagenomic measurements informat

    2018-10-30

    Metabotype data coupled to metagenomic measurements (information on gut microbiota) can enhance our knowledge about the complex interactions between the host and its gut microbiota (Kaddurah-Daouk et al., 2014) as well as their role in modulating host physiology, gut microbiome-related disorders, and metabolism of xenobiotics (Li and Jia, 2013). Even though a “core microbiome” has been proposed among different individuals and family members (Qin et al., 2010; Rajilić-Stojanović et al., 2007), the entire composition of the gut microbiota is highly variable in humans and associated with a variety of diseases (obesity, inflammatory bowel disease, diabetes, nonalcoholic fatty-liver disease, Crohn\'s disease, and colorectal cancer) (Li and Jia, 2013). Remarkably, O\'keefe et al. (2015) demonstrated that changes in the food content of fiber and fat affected profoundly the colonic microbiota as well as the metabonome of individuals from high- and low-risk cancer populations (within 2weeks) (O\'keefe et al., 2015). Additionally, metabolic networks have been analyzed to shed light on correlations between metabolites (considering even gut microbiota) and disease. Such an approach was employed for the first time during the urinary metabolomics profiling of Italian autistic pediatric patients and their healthy siblings (Noto et al., 2014). The analysis of such metabolic networks, which are indicated as nodes and edges, is crucial for the identification of the main routes connecting the entities of interest within the metabolic pathways in question. For instance, MetaMapR (http://dgrapov.github.io/MetaMapR/) generates richly connected metabolic networks via the purchase GM 6001 of enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations. Overall, such approaches are defined as “metabotype-based subtyping”. Especially in neonatology and pediatrics, there is a great potential for pharmacometabolomics studies to rationalize therapeutic use in infants and children. Drug pharmacokinetics differs substantially from those in adults and so far, a dosage approximation becomes necessary, as many drugs are not specifically approved for pediatric use. Today, very few – if any – pharmacometabolomics studies have been conducted in pediatric patients. Relevant reviews on the matter have been recently published (Mussap et al., 2013;Katsila and Patrinos, 2015). Unfortunately, both “metabotype-based pharmacokinetics/pharmacodynamics” and “metabotype-based subtyping” approaches are currently of little utility to the practicing clinician, as reproducibility issues need to be overcome (Simó et al., 2011). Furthermore, it has been shown that even small changes in physiology can have significant impact on the metabotype (Johnson and Gonzalez, 2012). Only if clinicians focus on larger stable metabolic signals, transient metabolic associations that do not represent causation could be diminished. Polypharmacy is rather challenging, too (Nicholson et al., 2012).
    Pharmacometabolomics-aided Pharmacogenomics The idea of the constructive coupling of omics technologies is not new. Pharmacogenomics and pharmacometabolomics complement each other and thus, reinforce the identification of clinically relevant associations. Instead of traditional tag SNP genotyping, genotype imputation could determine genomic variants of interest in pathways identified during pharmacometabolomics studies (Abo et al., 2012; Suhre et al., 2011). This strategy accelerates and broadens the scope of the analysis of pharmacogenomic candidate genes. Similarly, a wider survey becomes possible, reducing the need of genotyping prior to replication. In this context, Ji et al. (2011) explored citalopram/escitalopram treatment biomarkers, following a metabolomics analysis in plasma, according to which (i) glycine was reported to be negatively associated with treatment outcome leading to tag SNP genotyping for genes encoding glycine synthesis and (ii) rs10975641 (GLDC) was defined as a response biomarker in major depressive disorder patients.