Knowing When to Self-Eat - Fine-Tuning Autophagy Through ATG8 Iso-forms in Plants

Abstract

Autophagy is a catabolic process that takes place under both normal and adverse conditions and is important for the degradation of various organelles and proteins that are no longer needed. Thus, it can be viewed as both a constitutive recycling machinery and an adaptation mechanism. Increase in the activity of autophagy can be caused by multiple biotic and abiotic stress factors. Though intensive research in the past decade has elucidated many molecular details of plant autophagy, the mechanisms of induction and regulation of the process remain understudied. Here, we discuss the role of ATG8 proteins in autophagic signaling and regulation with an emphasis on the significance of ATG8 diversification for adapting autophagy to the changing needs of plants.

Keywords: abiotic stress; adaptation mechanism; plant autophagy; recycling; regulation target; regulator.

FIGURE 1

Autophagy sensors and regulators with or without the involvement of the TOR kinase. (A) Sugars, mineral nutrients, amino acids, and ROS affect autophagy. Glucose activates TOR while the lack of it inhibits the TOR kinase thus activating autophagy. Furthermore, SnRK1 kinase can be involved in sugar sensing upstream of TOR. Low availability of nutrients activates autophagy with C and N starvation acting through TOR inhibition. P-deficiency stimulates autophagy independently of TOR, although LST8 was shown to act as an intermediate. Lack of Fe, Zn, and Mn also activates autophagy but the participation of TOR is not clear. Changes in the levels of branched chain amino acids, with or without the involvement of TOR have been reported to modulate TOR and autophagic activity in mammals. However, the only well-studied amino acid in plants in this aspect is cysteine where the levels of the sulfur precursor affect the activity of the TOR kinase itself, while the C/N precursor levels are perceived by GCN2 kinase. ROS can activate autophagy via TOR inhibition or directly influencing multiple ATG genes/proteins downstream of the TOR kinase. (B) Phytohormones modulate the activity of TOR and/or autophagy. Auxin (NAA) stimulates TOR thereby inhibiting the autophagy process. Brassinosteroids are also able to inhibit autophagy through TOR activation while autophagy itself can affect BR signaling by degrading components of it. ABA is involved in an interplay with the TOR kinase by inhibiting its activity under stress, thereby promoting autophagy, while under favorable conditions TOR suppresses ABA signaling. A direct connection between ABA and autophagy is indicated by the involvement of two ATG8-interacting proteins ATI1 and ATI2 in ABA-mediated germination and salt tolerance. The presence of less GA in autophagy mutants indicates its possible involvement in autophagy regulation although the exact mechanisms have not been established. Ethylene stimulates autophagy by directly activating ATG8 homologs or via ROS. The exact relationships of JA and CK with TOR and autophagy have not been established. SA stimulates autophagy involved in senescence and plant immunity possibly with the involvement of ROS. NO can regulate hypoxia responses by S-nitrosylating various proteins including the master regulator of NO signaling S-nitrosoglutathione reductase thus exposing its ATG8-interacting motif and targeting it for degradation. TOR, target of rapamycin; RAPTOR, regulatory-associated protein of mTOR; LST8, lethal with sec 13 8; SnRK1, sucrose non-fermenting related kinase 1; GCN2, general control non-depressible 2; ROS, reactive oxygen species; ATG, autophagy-related; NAA, naphthaleneacetic acid; 6-BA, 6-benzyladenine; GA3, gibberellic acid; JA, jasmonic acid; ABA, abscisic acid; SA, salicylic acid; ET, ethylene; BR, brassinolide; NO, nitric oxide.

FIGURE 2

ATG8 analysis. (A) Schematic representation of cleavable and non-cleavable ATG8 iso-forms in Arabidopsis. (B) The table lists the homologs in the ATG8h-i group for a given species, the total number and the number of homologs with an exposed glycine. In some cases, the ATG8h-i group members have been secondarily lost such as in C. annuum and Z. mays. (C,D) Phylogenetic tree of ATG8h-i homologs of selected mono- and eudicot species and gymnosperms and orchids, respectively. Protein coding sequences were taken from Phytozome 12 (Goodstein et al., 2012) in (C) and from Gymno Plaza 1.0 (Proost et al., 2009) and Orchidstra 2.0 (Chao et al., 2017) for (D). (C) Unicellular algae have a single ATG8, while mosses can have one or multiple homologs both groups clustering together and completely separately from the ATG8h-i group of homologs of the higher plants. Some homologs of the ATG8h-i group have an exposed catalytic glycine (marked with an asterisk) such as all the sequenced Brassicaceae species. ATG8h-i members with and without an exposed glycine could be in the same species. The ATG8h-homologs are shown in blue and the ATG8i-homologs are depicted in green. The algae/mosses group is shown in black. In (C), all homologs with an exposed catalytic glycine are marked with an asterisk and the entire segment of the tree is colored in orange and the cleavable iso-forms are colored in blue. (D) The clades are indicated next to the tree, while the homologs with an exposed G are marked with an asterisk. The Evolutionary analysis was conducted using the Maximum Likelihood method and General Time Reversible model (Nei and Kumar, 2000) for both (C,D). The bootstrap consensus tree inferred from 1000 replicates is taken to represent the evolutionary history of the taxa analyzed (Felsenstein, 1985). Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches and the ones equal to or above 60% are indicated by a bold black line (Felsenstein, 1985). Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites [5 categories (+ G, parameter = 0.9115)]. The rate variation model allowed for some sites to be evolutionarily invariable ([ + I], 15.22% sites). This analysis involved 45 and 19 protein coding nucleotide sequences with 335 and 309 positions in the final dataset for (C) and (D) respectively. Evolutionary analyses were conducted in MEGA X (Kumar et al., 2018; Stecher et al., 2020). (E,F) Relative abundance of ATG8 transcript and protein, respectively, based on a dataset from Mergner et al. (2020) (data file 41568_2020_2094_MOESM4) containing experimental data for 30 different tissues and in both (E) and (F) non-transformed values were used for generating the graphical representation. Transcript abundance was estimated using TPM (transcript per million) and protein abundance estimation was based on iBAQ (intensity-based absolute quantification). ATG8 protein iso-forms a, d, and f can be found in every tissue examined. Values are for selected organs, tissues, and developmental stages: SD – dry seed; RT – root; CT – cotyledon; LFP – rosette leaf 7 proximal part; LFD – rosette leaf 7 distal part of the leaf; CLLF – cauline leaf 1; PT – petal; CP – carpel; P – pollen; EB – embryo; SCLF – senescent leaf.