Mika Keränen

Chloroplast NADPH-Thioredoxin Reductase Interacts with Photoperiodic Development in Arabidopsis

Chloroplast NADPH-thioredoxin reductase (NTRC) belongs to the thioredoxin systems that control crucial metabolic and regulatory pathways in plants. Here, by characterization of T-DNA insertion lines of NTRC gene, we uncover a novel connection between chloroplast thiol redox regulation and the control of photoperiodic growth in Arabidopsis (Arabidopsis thaliana). Transcript and metabolite profiling revealed severe developmental and metabolic defects in ntrc plants grown under a short 8-h light period. Besides reduced chlorophyll and anthocyanin contents, ntrc plants showed alterations in the levels of amino acids and auxin. Furthermore, a low carbon assimilation rate of ntrc leaves was associated with enhanced transpiration and photorespiration. All of these characteristics of ntrc were less severe when plants were grown under a long 16-h photoperiod. Transcript profiling revealed that the mutant phenotypes of ntrc were accompanied by differential expression of genes involved in stomatal development, chlorophyll biosynthesis, chloroplast biogenesis, and circadian clock-linked light perception systems in ntrc plants. We propose that NTRC regulates several key processes, including chlorophyll biosynthesis and the shikimate pathway, in chloroplasts. In the absence of NTRC, imbalanced metabolic activities presumably modulate the chloroplast retrograde signals, leading to altered expression of nuclear genes and, ultimately, to the formation of the pleiotrophic phenotypes in ntrc mutant plants.

Regulation of Root Greening by Light and Auxin/Cytokinin Signaling in Arabidopsis

Differentiation of plastids is tightly coordinated with plant development. This work shows that the development of chloroplasts in Arabidopsis roots is regulated in opposing directions by plant hormones auxin and cytokinin. Two types of transcription factors, HY5 and GLKs, are involved in this regulation; the former is a pivotal factor, and the latter is a potent activator for root greening. Tight coordination between plastid differentiation and plant development is best evidenced by the synchronized development of photosynthetic tissues and the biogenesis of chloroplasts. Here, we show that Arabidopsis thaliana roots demonstrate accelerated chlorophyll accumulation and chloroplast development when they are detached from shoots. However, this phenomenon is repressed by auxin treatment. Mutant analyses suggest that auxin transported from the shoot represses root greening via the function of INDOLE-3-ACETIC ACID14, AUXIN RESPONSE FACTOR7 (ARF7), and ARF19. Cytokinin signaling, on the contrary, is required for chlorophyll biosynthesis in roots. The regulation by auxin/cytokinin is dependent on the transcription factor LONG HYPOCOTYL5 (HY5), which is required for the expression of key chlorophyll biosynthesis genes in roots. The expression of yet another root greening transcription factor, GOLDEN2-LIKE2 (GLK2), was found to be regulated in opposing directions by auxin and cytokinin. Furthermore, both the hormone signaling and the GLK transcription factors modified the accumulation of HY5 in roots. Overexpression of GLKs in the hy5 mutant provided evidence that GLKs require HY5 to maximize their activities in root greening. We conclude that the combination of HY5 and GLKs, functioning downstream of light and auxin/cytokinin signaling pathways, is responsible for coordinated expression of the key genes in chloroplast biogenesis.

Dynamic Changes in the Proteome of Synechocystis 6803 in Response to CO2 Limitation Revealed by Quantitative Proteomics

Cyanobacteria developed efficient carbon concentrating mechanisms which significantly improve the photosynthetic performance and survival of cells under limiting CO(2) conditions. Dynamic changes of the Synechocystis proteome to CO(2) limitation were investigated using shotgun LC-MS/MS approach with isobaric tag for relative and absolute quantification (iTRAQ) technique. Synechocystis cells grown at high (3%) CO(2) were shifted to air-level CO(2) followed by protein extraction after 6, 24, and 72 h. About 19% of the cyanobacterial proteome was identified and the expression changes were quantified for 17% of theoretical ORFs. For 76 proteins, up- or down-regulation was found to be significant (more than 1.5 or less than 0.7). Major changes were observed in proteins participating in inorganic carbon uptake, CO(2) fixation, nitrogen transport and assimilation, as well as in the protection of the photosynthetic machinery from excess of light. Further, a number of hypothetical proteins with unknown functions were discovered. In general, the cells appear to acclimate to low CO(2) without a significant stress since the stress-related molecular chaperones were down-regulated and only a minor decline was detected for proteins of phycobilisomes, photosynthetic complexes, and translation machinery. The results of iTRAQ experiment were validated by the Western blot analysis for selected proteins.

The Kautsky Curve Is a Built-in Barcode

We identify objects from their visually observable morphological features. Automatic methods for identifying living objects are often needed in new technology, and these methods try to utilize shapes. When it comes to identifying plant species automatically, machine vision is difficult to implement because the shapes of different plants overlap and vary greatly because of different viewing angles in field conditions. In the present study we show that chlorophyll a fluorescence, emitted by plant leaves, carries information that can be used for the identification of plant species. Transient changes in fluorescence intensity when a light is turned on were parameterized and then subjected to a variety of pattern recognition procedures. A Self-Organizing Map constructed from the fluorescence signals was found to group the signals according to the phylogenetic origins of the plants. We then used three different methods of pattern recognition, of which the Bayesian Minimum Distance classifier is a parametric technique, whereas the Multilayer Perceptron neural network and k-Nearest Neighbor techniques are nonparametric. Of these techniques, the neural network turned out to be the most powerful one for identifying individual species or groups of species from their fluorescence transients. The excellent recognition accuracy, generally over 95%, allows us to speculate that the method can be further developed into an application in precision agriculture as a means of automatically identifying plant species in the field.

Automatic Plant Identification with Chlorophyll Fluorescence Fingerprinting

The development of precision farming needs methods for automatic identification of individual plant species. We have earlier shown that chlorophyll fluorescence induction curves can be reliably used for automatical identification of plants (Tyystjärvi et al., 1999). In the present study we show that a high accuracy of recognition can be obtained even if the teaching set for pattern recognition is collected several weeks before identifying a test batch of plants. It is also shown that very simple fluorescence traces can be used for the identification, and that dark pre-incubation of the plants can be shortened to a few seconds without seriously compromising the power of the method. The method is even more powerful if the aim is only to distinguish one crop species from weeds. The data shown here suggest that the fluorescence fingerprint can be developed to a method of practical importance for precision farming.

Nodularin uptake and induction of oxidative stress in spinach (Spinachia oleracea)

The bloom-forming cyanobacterium Nodularia spumigena produces toxic compounds, including nodularin, which is known to have adverse effects on various organisms. We monitored the primary effects of nodularin exposure on physiological parameters in Spinachia oleracea. We present the first evidence for the uptake of nodularin by a terrestrial plant, and show that the exposure of spinach to cyanobacterial crude water extract from nodularin-producing strain AV1 results in inhibition of growth and bleaching of the leaves. Despite drastic effects on phenotype and survival, nodularin did not disturb the photosynthetic performance of plants or the structure of the photosynthetic machinery in the chloroplast thylakoid membrane. Nevertheless, the nodularin-exposed plants suffered from oxidative stress, as evidenced by a high level of oxidative modifications targeted to various proteins, altered levels of enzymes involved in scavenging of reactive oxygen species (ROS), and increased levels of α-tocopherol, which is an important antioxidant. Moreover, the high level of cytochrome oxidase (COX II), a typical marker for mitochondrial respiratory protein complexes, suggests that the respiratory capacity is increased in the leaves of nodularin-exposed plants. Actively respiring plant mitochondria, in turn, may produce ROS at high rates. Although the accumulation of ROS and induction of the ROS scavenging network enable the survival of the plant upon toxin exposure, the upregulation of the enzymatic defense system is likely to increase energetic costs, reducing growth and the ultimate fitness of the plants.

Excitation-emission map as a tool in studies of photosynthetic pigment-protein complexes

Excitation-emission maps were constructed by measuring emission spectra from tobacco thylakoids and from thylakoids and intact cells of the cyanobacterium Synechocystis 6803. The measurement of such maps is greatly facilitated by the current diode-array detector technology. We show that excitation-emission maps are valuable tools for studies of the structure and energy transfer pathways in photosynthetic systems.

Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species

Proper feature analysis facilitates recognition by focusing the process to those characteristics of observed data that carry the most significant information for the given classification task. In this paper we address the problem of feature selection from a different point of view. Instead of searching for a feature subset out of a large set of predefined candidate features we consider the situation where, given the form of the features and an algorithm for extracting them from the data, the optimizer tunes the feature extraction parameters to improve class separability. This process of feature learning will be solved by the means of a genetic algorithm. The optimized feature set is subsequently used in a neural network classifier. The performance of the feature learning approach is demonstrated with the problem of automatic identification of plant species from their fluorescence induction curves. The general approach should also be useful with other pattern recognition problems where a priori unknown characteristics are extracted from a large feature space.

Thermoluminescence B and Q Bands are at the same Temperature in an Autotrophic and A Heterotrophic D1 Protein Mutant of Synechocystis sp. PCC 6803

The photosynthetic electron transfer chain of DI protein mutants of the cyanobacterium Synechocystis sp. PCC 6803 was studied with thermoluminescence (TL). The mutants have different deletions in the stromal loop between the fourth and the fifth membrane spanning helix of the D1 protein: the so called PEST-like sequence was deleted from PD (Δ(R225-F239)) and both the PEST-like sequence and the putative cleavage region of the D1 protein were deleted from PCD (A(R225-V249)). The control strain AR has antibiotic resistance cassetes interrupting psbA-1 and psbA-3 genes, so that psbA-2 is the only active D1 gene in all strains [1].

Genetic Feature Learning Algorithm for Fluorescence Fingerprinting of Plants

Feature learning aims at automatic optimization of features to be used in the classification process. We consider the situation where, given a parameterized algorithm for extracting the features from the data, the optimizer tunes the parameters so that classification accuracy is maximized. The present paper extends our previous study [4] on feature learning problem by including two important mechanisms. First, an improved genetic algorithm (GA) with variable length chromosomes controls the size of the feature set. Second, the GA operates in conjuction with a neural network classifier for maximizing the identification accuracy. The performance of the feature learning algorithm is demonstrated with a problem of automatic identification of plant species from their fluorescence induction curves. The general approach should also be useful in other types of pattern recognition applications where a priori unknown characteristics are inferred from large feature spaces.