The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Children gut microbiota exhibits a different composition and metabolic profile after in vitro exposure to Clostridioides difficile and increases its sporulation

Clostridioides difficile (Clostridium difficile) infection (CDI) is one of the main public health concerns in adults, while children under 2 years of age are often colonized asymptomatically. In both adults and children, CDI is strongly associated with disturbances in gut microbiota. In this study, an in-vitro model of children gut microbiota was challenged with vegetative cells or a conditioned media of six different toxigenic C. difficile strains belonging to the ribotypes 027, 078, and 176. In the presence of C. difficile or conditioned medium the children gut microbiota diversity decreased and all main phyla (Bacteroidetes, Firmicutes, and Proteobacteria) were affected. The NMR metabolic spectra divided C. difficile exposed children gut microbiota into three clusters. The grouping correlated with nine metabolites (short chain fatty acids, ethanol, phenolic acids and tyramine). All strains were able to grow in the presence of children gut microbiota and showed a high sporulation rate of up to 57%. This high sporulation rate in combination with high asymptomatic carriage in children could contribute to the understanding of the reported role of children in C. difficile transmissions.

The application of artificial neural networks in metabolomics: a historical perspective

BACKGROUND

Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods.

AIM OF REVIEW

We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics.

KEY SCIENTIFIC CONCEPT OF REVIEW

Is metabolomics ready for the return of artificial neural networks?

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of 1 H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in-depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.