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Sulphur systems biology—making sense of omics data

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Watanabe,  M.
Amino Acid and Sulfur Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Hoefgen,  R.
Amino Acid and Sulfur Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Watanabe, M., & Hoefgen, R. (2019). Sulphur systems biology—making sense of omics data. Journal of Experimental Botany, 70(16), 4155-4170. doi:10.1093/jxb/erz260.


Cite as: http://hdl.handle.net/21.11116/0000-0004-7CF0-2
Abstract
Systems biology approaches have been applied over the last two decades to study plant sulphur metabolism. These ‘sulphur-omics’ approaches have been developed in parallel with the advancing field of systems biology, which is characterized by permanent improvements of high-throughput methods to obtain system-wide data. The aim is to obtain a holistic view of sulphur metabolism and to generate models that allow predictions of metabolic and physiological responses. Besides known sulphur-responsive genes derived from previous studies, numerous genes have been identified in transcriptomics studies. This has not only increased our knowledge of sulphur metabolism but has also revealed links between metabolic processes, thus indicating a previously unexpected complex interconnectivity. The identification of response and control networks has been supported through metabolomics and proteomics studies. Due to the complex interlacing nature of biological processes, experimental validation using targeted or systems approaches is ongoing. There is still room for improvement in integrating the findings from studies of metabolomes, proteomes, and metabolic fluxes into a single unifying concept and to generate consistent models. We therefore suggest a joint effort of the sulphur research community to standardize data acquisition. Furthermore, focusing on a few different model plant systems would help overcome the problem of fragmented data, and would allow us to provide a standard data set against which future experiments can be designed and compared.