Prior metabolite extraction fully preserves RNAseq quality and enables integrative multi-'omics analysis of the liver metabolic response to viral infection

Metabolomics and proteomics in pheochromocytoma and paraganglioma: Translating biochemistry and biology to bedside

The complexity of omes - the key cellular ensembles (genome and epigenome, transcriptome, proteome, and metabolome) - is becoming increasingly understood in terms of big-data analysis, the omics. Amongst these, proteomics provides a global description of quantitative and qualitative alterations of protein expression (or protein abundance in body fluids) in response to physiologic or pathologic processes while metabolomics offers a functional portrait of the physiological state by quantifying metabolite abundances in biological samples. Here, we summarize how different techniques of proteomic and metabolic analysis can be used to define key biochemical characteristics of pheochromocytomas/paragangliomas (PPGL). The significance of omics in understanding features of PPGL biology that might translate to improved diagnosis and treatment will be highlighted.

A diverse proteome is present and enzymatically active in metabolite extracts

Metabolite extraction is the critical first-step in metabolomics experiments, where it is generally regarded to inactivate and remove proteins. Here, arising from efforts to improve extraction conditions for polar metabolomics, we discover a proteomic landscape of over 1000 proteins within metabolite extracts. This is a ubiquitous feature across several common extraction and sample types. By combining post-resuspension stable isotope addition and enzyme inhibitors, we demonstrate in-extract metabolite interconversions due to residual transaminase activity. We extend these findings with untargeted metabolomics where we observe extensive protein-mediated metabolite changes, including in-extract formation of glutamate dipeptide and depletion of total glutathione. Finally, we present a simple extraction workflow that integrates 3 kDa filtration for protein removal as a superior method for polar metabolomics. In this work, we uncover a previously unrecognized, protein-mediated source of observer effects in metabolomics experiments with broad-reaching implications across all research fields using metabolomics and molecular metabolism.

Multi-omics analyses from a single sample: prior metabolite extraction does not alter the 16S rRNA-based characterization of prokaryotic community in a diversity of sample types

Massive sequencing of the 16S rRNA gene has become a standard first step to describe and compare microbial communities from various samples. Parallel analysis of high numbers of samples makes it relevant to the statistical testing of the influence of natural or experimental factors and variables. However, these descriptions fail to document changes in community or ecosystem functioning. Nontargeted metabolomics are a suitable tool to bridge this gap, yet extraction protocols are different. In this study, prokaryotic community compositions are documented by 16S rRNA gene sequencing after direct DNA extraction or after metabolites extraction followed by DNA extraction. Results obtained using the V3-V4 region on nonaxenic cultures of cyanobacteria, lake water column, biofilm, and gut of wild and lab-reared fish indicate that prior extraction of metabolites does not influence the obtained image of prokaryotic communities. This validates sequential extraction of metabolites followed by DNA as a way to combine 16S rRNA sequencing with metabolome characterization from a single sample. This approach has the potential to complement community structure characterization with a proxy of their functioning, without the uncertainties associated with the use of separate samples.