New features on the environmental regulation of metabolism revealed by modeling the cellular proteomic adaptations induced by light, carbon and inorganic nitrogen in Chlamydomonas reinhardtii

• Main Authors: Stéphanie Gérin, Pierre Leprince, Francis Sluse, Fabrice Franck, Grégory Mathy
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2016-08-01
• Series: Frontiers in Plant Science
• Subjects: environment; Bioenergetics; Design of Experiments; hierarchical clustering; metabolic network; multiple linear regression
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fpls.2016.01158/full

Microalgae are currently emerging to be very promising organisms for the production of biofuels and high-added value compounds. Understanding the influence of environmental alterations on their metabolism is a crucial issue. Light, carbon and nitrogen availability have been reported to induce important metabolic adaptations. So far, the influence of these variables has essentially been studied while varying only one or two environmental factors at the same time. The goal of the present work was to model the cellular proteomic adaptations of the green microalga Chlamydomonas reinhardtii upon the simultaneous changes of light intensity, carbon concentrations (CO2 and acetate) and inorganic nitrogen concentrations (nitrate and ammonium) in the culture medium. Statistical design of experiments (DOE) enabled to define 32 culture conditions to be tested experimentally. Relative protein abundance was quantified by two dimensional differential in-gel electrophoresis (2D-DIGE). Additional assays for respiration, photosynthesis, and lipid and pigment concentrations were also carried out. A hierarchical clustering survey enabled to partition biological variables (proteins + assays) into eight co-regulated clusters. In most cases, the biological variables partitioned in the same cluster had already been reported to participate to common biological functions (acetate assimilation, bioenergetic processes, light harvesting, Calvin cycle and protein metabolism). The environmental regulation within each cluster was further characterized by a series of multivariate methods including principal component analysis and multiple linear regressions. This metadata analysis enabled to highlight the existence of a clear regulatory pattern for every cluster and to mathematically simulate the effects of light, carbon and nitrogen. The influence of these environmental variables on cellular metabolism is described in details and thoroughly discussed. This work provides an overview of the metabolic adaptations contributing to maintain cellular homeostasis upon extensive environmental changes. Some of the results presented here could be used as starting points for more specific fundamental or applied investigations.