Research

We are now using TARGET (Transient Assay Reporting Genome-wide Effects of Transcription factors) procedure to get further insights into nitrate regulated transcription factors (this system is fully described in this paper here or on this dedicated webpage).

TARGET procedure helped to define different mode of regulation of the bZIP1 transcription factor. Actually, a detected binding by Chip-seq does not guaranty a regulation of the targeted gene.

3 modes of regulation are proposed: 1) stable binding and no regulation, 2) stable binding and regulation, 3) no stable binding and regulation. This latter is related to rapid regulation of genes. The model proposed for this, is a "Hit and Run" mechanism.

Check out the whole story here.

TARGET procedure also lead us to define some in planta role of one of the most Nitrate regulated gene / transcription factor in Arabidopsis, HRS1. We demonstrate that HRS1 is responsible for some aspects of the cross-talk between nitrate and phosphate signals at the Arabidopsis root tip. This work can be found here.

We also found that these TFs are involved in Nitrogen Starvation Response repression leading to important biotechnological insights (here).

2) We try to understand molecular events that entangle nutrient signaling and hormonal signaling.

As a good start on the subject you may want to check this paper out --> [here in TIPS and this more recent (2016) paper in PMB here]

Briefly, we propose that dedicated signaling pathways are linking hormonal and nutritional signals to adapt plant growth to nutrient cues and vice versa (Figure see #2 and [here]).

For instance, we demonstrated that NRT1.1/CHL1 mediates a Nitrate controlled auxin transport and that this phenomenon explains its role in lateral root development in response to nitrate [here].

We also demonstrated a role of cytokinins in root to shoot communication in Arabidopsis [here]. This system of nitrate-related long distance signaling seems to also display cytokinins independent branches [here].

We provided a resource data-set in which we studied the interaction between nitrate, ammonium, auxin, cytokinin, and abscisic acid affecting Arabidopsis root transcriptome and development [Ristova et al 2016 [here]]. This work demonstrates that plants perceive these molecules largely in a combinatorial way.

3) We apply machine learning procedures to model i) de novo Gene Regulatory Networks ii) Genetic variation effects on phenotypes (GWAS), iii) design synthetic proteins.

GRNs

Some achievements include the proposal of a predictive Gene Regulatory Network model responding to nitrate [here].

For a review paper concerning modeling GRNs in plants see here.

We developed an algorithm to simulate and learn GRNs. We demonstrated that we may, in a near future, solve the whole GRN by combining/learning-from transcriptomic datasets as well as Chip-seq, Y1e, or TARGET results. You can find our demonstration in this NPJ_Systems biol and application paper [here].

Other

We are now working on a new kind of GWAS [here]

and we trained model to design proteins witn a particular shape [here]...