A Review of the Evidence for Tryptophan and the Kynurenine Pathway as a Regulator of Stem Cell Niches in Health and Disease.

Stem cells are ubiquitously found in various tissues and organs in the body, and underpin the body's ability to repair itself following injury or disease initiation, though repair can sometimes be compromised. Understanding how stem cells are produced, and functional signaling systems between different niches is critical to understanding the potential use of stem cells in regenerative medicine. In this context, this review considers kynurenine pathway (KP) metabolism in multipotent adult progenitor cells, embryonic, haematopoietic, neural, cancer, cardiac and induced pluripotent stem cells, endothelial progenitor cells, and mesenchymal stromal cells. The KP is the major enzymatic pathway for sequentially catabolising the essential amino acid tryptophan (TRP), resulting in key metabolites including kynurenine, kynurenic acid, and quinolinic acid (QUIN). QUIN metabolism transitions into the adjoining de novo pathway for nicotinamide adenine dinucleotide (NAD) production, a critical cofactor in many fundamental cellular biochemical pathways. How stem cells uptake and utilise TRP varies between different species and stem cell types, because of their expression of transporters and responses to inflammatory cytokines. Several KP metabolites are physiologically active, with either beneficial or detrimental outcomes, and evidence of this is presented relating to several stem cell types, which is important as they may exert a significant impact on surrounding differentiated cells, particularly if they metabolise or secrete metabolites differently. Interferon-gamma (IFN-γ) in mesenchymal stromal cells, for instance, highly upregulates rate-limiting enzyme indoleamine-2,3-dioxygenase (IDO-1), initiating TRP depletion and production of metabolites including kynurenine/kynurenic acid, known agonists of the Aryl hydrocarbon receptor (AhR) transcription factor. AhR transcriptionally regulates an immunosuppressive phenotype, making them attractive for regenerative therapy. We also draw attention to important gaps in knowledge for future studies, which will underpin future application for stem cell-based cellular therapies or optimising drugs which can modulate the KP in innate stem cell populations, for disease treatment.

What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.

The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process.