Targeted Delivery of Probiotics: Perspectives on Research and Commercialization

Probiotics and Antimicrobial Proteins - Considering the significance of the gut microbiota on human health, there has been ever-growing research and commercial interest in various aspects of...     

Antimicrobial Activity of Lipid Extracts of Echinoderms in the Nhatrang Bay (South China Sea)

Doklady Biochemistry and Biophysics - The obtained results on the study of the antimicrobial activity of lipid extracts of tissues of starfishes Linckia laevigata and Culcita novaeguineae and sea...     

Finding the Light Emission Stimulator of Neonothopanus nambi Basidiomycete and Studying Its Properties

Doklady Biochemistry and Biophysics - A stimulator of light emission of the fungus was found in an aqueous extract from mycelium of the luminous basidiomycete Neonothopanus nambi after its...     

Contact guidance as a consequence of coupled morphological evolution and motility of adherent cells

Biomechanics and Modeling in Mechanobiology - Adherent cells seeded on substrates spread and evolve their morphology while simultaneously displaying motility. Phenomena such as contact guidance,...     

PHOTOSYSTEM II PROTEIN33, a Protein Conserved in the Plastid Lineage,Is Associated with the Chloroplast Thylakoid Membrane and Provides Stability to Photosystem II Supercomplexes in Arabidopsis

Photosystem II (PSII) is a multiprotein complex that catalyzes the light-driven water-splitting reactions of oxygenic photosynthesis. Light absorption by PSII leads to the production of excited states and reactive oxygen species that can cause damage to this complex. Here, we describe Arabidopsis (Arabidopsis thaliana) At1g71500, which encodes a previously uncharacterized protein that is a PSII auxiliary core protein and hence is named PHOTOSYSTEM II PROTEIN33 (PSB33). We present evidence that PSB33 functions in the maintenance of PSII-light-harvesting complex II (LHCII) supercomplex organization. PSB33 encodes a protein with a chloroplast transit peptide and one transmembrane segment. In silico analysis of PSB33 revealed a light-harvesting complex-binding motif within the transmembrane segment and a large surface-exposed head domain. Biochemical analysis of PSII complexes further indicates that PSB33 is an integral membrane protein located in the vicinity of LHCII and the PSII CP43 reaction center protein. Phenotypic characterization of mutants lacking PSB33 revealed reduced amounts of PSII-LHCII supercomplexes, very low state transition, and a lower capacity for nonphotochemical quenching, leading to increased photosensitivity in the mutant plants under light stress. Taken together, these results suggest a role for PSB33 in regulating and optimizing photosynthesis in response to changing light levels.PSII is a multiprotein complex in plants with 31 identified polypeptides (Wegener et al., 2011; Pagliano et al., 2013). It is associated with an extrinsic trimeric light-harvesting complex (LHC), forming the PSII-LHCII supercomplex. The PSII complex performs a remarkable biochemical reaction, the oxidation of water using light energy from the sun, which profoundly contributes to the overall biomass accumulation in the biosphere (Barber et al., 2004). Consequently, the stability and functional integrity of the PSII-LHCII supercomplex is crucially important for photosynthetic function. The energy of a photon, either absorbed directly by PSII or indirectly via energy transfer from adjacent antenna chlorophyll (Chl) molecules, excites the PSII reaction center P680. The excited state, P680*, can transfer an electron to pheophytin, producing the most powerful oxidant known in biology, P680⁺, which can remove electrons from water. Excessive input of excitation energy into PSII saturates the electron transfer system and causes either acceptor or donor site limitation in the complex. This results in increased production of reactive oxygen species (ROS): singlet oxygen at the PSII donor side and superoxide at the acceptor side (Munné-Bosch et al., 2013). Several protective mechanisms have been documented that decrease the production of singlet oxygen at the PSII donor side in photosynthetic eukaryotes. Notably, reducing energy transfer from LHC to PSII via nonphotochemical quenching (NPQ) is a key avoidance mechanism (Ruban and Murchie, 2012).Despite years of intensive study of PSII structure and function, new proteins that are associated with the PSII complex continue to be discovered, including an increasing number involved in the stability and organization of PSII-LHCII supercomplexes (García-Cerdán et al., 2011; Lu et al., 2011a; Wegener et al., 2011). Two complementary approaches (Merchant et al., 2007; Lu et al., 2008, 2011b; Ajjawi et al., 2010) that utilize phylogenomics (GreenCut) and large-scale phenotypic mutant screening (Chloroplast 2010 Project; http://www.plastid.msu.edu/) were employed by our groups to discover novel plant proteins with roles in photosynthesis. GreenCut identifies proteins found only in photosynthetic organisms, and it is likely that many of them are involved in biochemical processes associated with the structure, assembly, or function of the photosynthetic apparatus and the chloroplast that houses it (Merchant et al., 2007; Karpowicz et al., 2011). The Chloroplast 2010 Project was a large-scale reverse-genetic mutant screen in which thousands of homozygous Arabidopsis (Arabidopsis thaliana) transfer DNA (T-DNA) insertion lines were analyzed for defects in the rise and decay kinetics of Chl fluorescence (Lu et al., 2008, 2011a, 2011b; Ajjawi et al., 2010).The GreenCut and Chloroplast 2010 approaches both identified the Arabidopsis At1g71500 locus as encoding a protein of unknown function with potential relevance to photosynthesis. In this work, we demonstrate that plant lines carrying three independent mutations at this locus display severe light-induced photoinhibition due to a less stable supramolecular organization of PSII. Biochemical analyses revealed that this protein is associated with PSII complexes, and since the last described PSII protein was called PHOTOSYSTEM II PROTEIN32 (PSB32), we named the gene PSB33. The nuclear genome-encoded PSB33 is predicted to have a chloroplast transit peptide and a transmembrane domain. The biochemical analyses presented below indicate that PSB33 is required for the proper interaction and stability of PSII-LHCII supercomplexes and, in turn, in regulating photosynthesis in response to fluctuating light levels.     

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.Carbon (C) and nitrogen (N) metabolism are the basis for life on Earth. The production, balance, and tradeoffs of C and N metabolism are critical to all plant growth, yield, and local adaptation (Coruzzi and Bush, 2001; Coruzzi et al., 2007). In plants, there is a critical balance between the tissues that are producing energy (sources) and those using it (sinks), as the identities and locations of these vary through time and developmental stage (Smith et al., 2004). While a great deal of research has focused on the key genes and proteins involved in these processes (Wang et al., 1993; Kim et al., 2000; Takahashi et al., 2009), relatively little is known about the natural variation within a species that fine-tunes these processes in individual plants.In addition, a key aspect of core C metabolism involves the nature of plant photosynthesis. While the majority of plants use standard C₃ photosynthetic pathways, some, including maize (Zea mays) and many other grasses, use C₄ photosynthesis to concentrate CO₂ in bundle sheath cells to avoid wasteful photorespiration (Sage, 2004). Under some conditions (such as drought or high temperatures), C₄ photosynthesis is much more efficient than C₃ photosynthesis. Since these conditions are expected to become more prevalent in the near future due to climate change, various research groups are working to convert C₃ crop species to C₄ metabolism in order to boost crop production and food security (Sage and Zhu, 2011). Beyond this, better understanding of both C₃ and C₄ metabolic pathways will aid efforts to breed crops for superior yield, N-use efficiency, and other traits important for global food production.In the last two decades, quantitative trait locus (QTL) mapping, first with linkage analysis and later with association mapping, has been used to dissect C and N metabolism in several species, including Arabidopsis (Arabidopsis thaliana; Mitchell-Olds and Pedersen, 1998; Keurentjes et al., 2008; Lisec et al., 2008; Sulpice et al., 2009), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Hirel et al., 2001; Limami et al., 2002; Zhang et al., 2006, 2010a, 2010b). These studies identified key genetic regions underlying variation in core C and N metabolism, many of which include candidate genes known to be involved in these processes.Previous studies of genetic variation for C and N metabolism are limited by the fact that they identified trait loci only through linkage mapping in artificial families or through association mapping across populations of unrelated individuals. Linkage mapping benefits from high statistical power due to many individuals sharing the same genotype at any given location, but it suffers from low resolution due to the limited number of generations (and hence recombination events) since the initial founders. Association mapping, in turn, enjoys high resolution due to the long recombination histories of natural populations but suffers from low power, since most genotypes occur in only a few individuals. In addition, many of these studies focused on C and N in artificial settings (e.g. greenhouses or growth chambers) instead of field conditions, running the risk that important genetic loci could be missed if the conditions do not include important (and potentially unknown) natural environmental variables.To address these issues and improve our understanding of C and N metabolism in maize, we used a massive and diverse germplasm resource, the maize nested association mapping (NAM) population (Buckler et al., 2009; McMullen et al., 2009), to evaluate genetic variation underlying the accumulation of 12 targeted metabolites in maize leaf tissue under field conditions. This population was formed by mating 25 diverse maize lines to the reference line, B73, and creating a 200-member biparental family from each of these crosses. The entire 5,000-member NAM population thus combines the strengths of both linkage and association mapping (McMullen et al., 2009), and it has been used to identify QTLs for important traits such as flowering time (Buckler et al., 2009), disease resistance (Kump et al., 2011; Poland et al., 2011), and plant architecture (Tian et al., 2011; Peiffer et al., 2013). Most importantly, this combination of power and resolution frequently resolves associations down to the single-gene level, even when using field-based data.The metabolites we profiled are key indicators of photosynthesis, respiration, glycolysis, and protein and sugar metabolism in the plant (Sulpice et al., 2009). By taking advantage of a robotized metabolic phenotyping platform (Gibon et al., 2004), we performed more than 100,000 assays across 12,000 samples, with two independent samples per experimental plot. Raw data and the best linear unbiased predictors (BLUPs) of these data were included as part of a study of general functional variation in maize (Wallace et al., 2014), but, to our knowledge, this is the first in-depth analysis of these metabolic data. We find strong correlations among several of the metabolites, and we also find extensive pleiotropy among the different traits. Many of the top QTLs are also near or within candidate genes relating to C and N metabolism, thus identifying targets for future breeding and selection. These results provide a powerful resource for those working with core C and N metabolism in plants and for improving maize performance in particular.