metabolomics

Objective: This review compiles best practices and freely accessible tools in R and Python for the statistical processing and visualization of extensive mass spectrometry-based lipidomics and metabolomics data.
Focus: The article provides a “solid core” of resources for exploratory data analysis (EDA) and visualization to help researchers identify and visualize statistically significant trends and biologically relevant differences within their complex datasets.
Implication: It guides researchers on using modern computational platforms (R/Python) and integrating metadata (e.g., clinical parameters) with their omics data to perform robust and reproducible downstream analysis.

Objective: The study systematically characterized the sources of missing values (MVs) in untargeted Mass Spectrometry (MS)-based metabolomics data and evaluated various imputation strategies.
Missing Value Types: Distinguished between systematic missingness primarily due to Limits of Detection (LOD) and random missingness due to technical issues.
Best Strategy: For the prevalent LOD-related MVs, which represent concentrations near zero, simple methods like imputation with half of the minimum observed value were found to be effective and often outperformed complex data-driven methods (e.g., PPCA).
Recommendation: Researchers should use targeted imputation strategies based on the nature of the missingness to avoid introducing bias and reducing statistical power.

Focus: This review examines how altered intracellular metabolite concentrations in cancer cells, often driven by genetic mutations, actively promote tumor initiation and progression, moving beyond the idea that metabolic changes are merely a consequence of cancer.
Oncometabolites: Specific metabolites act as effector molecules:
- 2-Hydroxyglutarate (2-HG): Produced by IDH1/2 mutations, it inhibits epigenetic regulators (like TET enzymes) leading to globally altered gene expression and oncogenesis.
- Fumarate and Succinate: Accumulate due to FH/SDH mutations, leading to the stabilization of HIF-\(1\alpha\) (pseudohypoxia), which drives proliferation and the Warburg effect.
Implication: The altered metabolome is a rich source of therapeutic targets. Strategies can focus on counteracting the effects of oncometabolites or exploiting the metabolic dependencies created by these shifts (e.g., limited aspartate for nucleotide synthesis).

Objective: This longitudinal study used metabolomics and systems epidemiology tools (Self-Organizing Map, SOM) on over 12,800 participants to dissect the complex temporal associations between Body-Mass Index (BMI), Waist-Hip Ratio (WHR), and the circulating metabolome.
Key Finding: The study revealed two dynamically different metabolic patterns for increasing obesity:
1. An increase in BMI that was not accompanied by an increase in fatty acid (FA) saturation was associated with a relatively favorable metabolic profile.
2. An increase in WHR (central adiposity) was uniformly associated with a single, highly adverse metabolic profile, characterized by worse glucose metabolism, inflammation, and high amino acid levels.
Implication: The results suggest that obesity is metabolically heterogeneous and that WHR is a more consistent marker of severe adverse metabolic risk than BMI alone, emphasizing the need to consider dynamic metabolic changes for personalized risk assessment.

Focus: This review highlights the use of networks and graph theory as powerful computational tools for analyzing and interpreting complex metabolomics data.
Network Types:
1. Analytical/Chemical Networks: Derived from Mass Spectrometry (MS) data, such as Molecular Networking (MN), which connects spectra based on chemical structural similarity to aid in compound identification.
2. Biological/Correlation Networks: Derived from quantitative metabolite abundance data. These networks use statistical correlation between metabolites to infer shared biological regulation, map metabolites onto known biochemical pathways, and facilitate multi-omics integration (connecting metabolites to genes/proteins).
Implication: Graph theory enables researchers to move beyond simple lists of metabolites to view the metabolome as a structured, interactive system, which is essential for biological interpretation and hypothesis generation.

Focus: This review highlights the use of network-based strategies as essential tools for analyzing and interpreting complex metabolomics data, especially from untargeted Mass Spectrometry (MS).
Molecular Networking (MN): Used to organize MS/MS spectral data based on chemical structural similarity, thereby facilitating the identification of unknown compounds in chemical families.
Metabolic Networks: Constructed using statistical correlation between metabolite abundance levels to infer functional and biological relationships, enabling mapping onto known biochemical pathways for biological contextualization.