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Biology subjects

Goeman, J.

Publications and source records attributed to Goeman, J..

2 recordsLinked to original sources

The Arabidopsis PAL3 is a carboxy-tyrosine ammonia lyase

Phenylalanine ammonia lyase (PAL) catalyses the deamination of L-phenylalanine, which is the first step of the plant-specific phenylpropanoid pathway. Its position at the intersection of primary and specialized metabolism, combined with its important role in plant growth and adaptive stress response, has made it a subject of extensive studies. We identified key amino acids in the catalytic pocket of several PAL enzymes, including PAL3 of Arabidopsis, that are different from the canonical PAL sites, suggesting these enzymes have a different substrate specificity and have been miscategorised for decades. By combining untargeted metabolomics with enzyme assays, we discovered that PAL3 converts 3-carboxy-tyrosine into carboxy-p-coumaric acid. Based on this specific function, we propose to denote it as a 3-carboxy-tyrosine ammonia lyase (CAL). In addition to its discovery as a novel enzyme class, paving the way for biotechnological applications, we introduce the carboxy-phenylpropanoids as a new class of specialised metabolites.

biochemistry↗

Penalized reduced rank regression for multi-outcome survival data supports a common metabolic risk score for age-related diseases

The increasing availability of multi-outcome data in health research presents new opportunities for understanding complex health processes, such as ageing. Ageing is a multifaceted process, encompassing both lifespan and healthspan, as well as the onset of age-related diseases. To model this complexity, we propose the penalized reduced rank regression model for multi-outcome survival data (penalized survRRR), which identifies shared latent factors driving multiple outcomes. The model imposes a rank constraint on the coefficient matrix to capture underlying mechanisms of ageing, while accommodating high-dimensional and correlated predictors and outcomes by introducing penalization. We discuss the statistical properties of this doubly-regularized approach and show how the optimal number of ranks can be estimated from the data. We apply a lasso-penalized reduced rank regression model to 78,553 participants of the UK Biobank, using over 200 metabolic variables as predictors and the onset of seven age-related diseases and mortality as the outcomes of interest. Our results indicate that a rank 1 model provides the best fit to the data, resulting in a single metabolite-based score of age-related disease susceptibility. This highlights the potential of the penalized survRRR model to provide new insights into the nature of the relationship between metabolomics and age-related diseases.

molecular biology↗