https://research.cs.queensu.ca/home/shatkay/879papers/TargetPEmanuelsson.pdf

Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease Interpretation of these data remains, however, a major scientific challenge Here, we propose a methodology that enables us to extract and display information contained in complex networks1,2,3 Specifically, we demonstrate that we can find functional modules4,5 in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections The method thus yields a ‘cartographic representation’ of complex networks Metabolic networks6,7,8 are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability9 We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module

Functional cartography of complex metabolic networks

The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.

https://www.springer.com/cda/content/document/cda_downloaddocument/BioEnergyResearch_Second+Generation+Biofuels+High-Efficiency+Microalgae+for+Biodiesel+Production.pdf?SGWID=0-0-45-1347846-0

Second generation biofuels: high-efficiency microalgae for biodiesel production

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits distribution,andreproductioninanymedium,providedtheoriginalauthorandsourcearecredited.Thislicensedoesnot permit commercial exploitation or the creation of derivative works without specific permission.

Life on Earth depends on photosynthesis, the conversion of light energy from the Sun to chemical energy. In plants, green algae and cyanobacteria, this process is driven by the cooperation of two large protein-cofactor complexes, photosystems I and II, which are located in the thylakoid photosynthetic membranes. The crystal structure of photosystem I from the thermophilic cyanobacterium Synechococcus elongatus described here provides a picture at atomic detail of 12 protein subunits and 127 cofactors comprising 96 chlorophylls, 2 phylloquinones, 3 Fe4S4 clusters, 22 carotenoids, 4 lipids, a putative Ca2+ ion and 201 water molecules. The structural information on the proteins and cofactors and their interactions provides a basis for understanding how the high efficiency of photosystem I in light capturing and electron transfer is achieved.

Three-dimensional structure of cyanobacterial photosystem I at 2.5 Å resolution

Photosynthetic organisms are able to adjust to changing light conditions through state transitions, a process that involves the redistribution of light excitation energy between photosystem II (PSII) and photosystem I (PSI). Balancing of the light absorption capacity of these two photosystems is achieved through the reversible association of the major antenna complex (LHCII) between PSII and PSI (ref. 3). Excess stimulation of PSII relative to PSI leads to the reduction of the plastoquinone pool and the activation of a kinase; the phosphorylation of LHCII; and the displacement of LHCII from PSII to PSI (state 2). Oxidation of the plastoquinone pool by excess stimulation of PSI reverses this process (state 1). The Chlamydomonas thylakoid-associated Ser-Thr kinase Stt7, which is required for state transitions, has an orthologue named STN7 in Arabidopsis. Here we show that loss of STN7 blocks state transitions and LHCII phosphorylation. In stn7 mutant plants the plastoquinone pool is more reduced and growth is impaired under changing light conditions, indicating that STN7, and probably state transitions, have an important role in response to environmental changes.

State transitions and light adaptation require chloroplast thylakoid protein kinase STN7

Photosynthetic organisms adapt to changes in light quality by redistributing light excitation energy between two photosystems through state transition. This reorganization of antenna systems leads to an enhanced photosynthetic yield. Using a genetic approach in Chlamydomonas reinhardtii to dissect the signal transduction pathway of state transition, we identified a chloroplast thylakoid-associated serine-threonine protein kinase, Stt7, that has homologs in land plants. Stt7 is required for the phosphorylation of the major light-harvesting protein (LHCII) and for state transition.

https://science.sciencemag.org/content/sci/299/5612/1572.full.pdf

Role of Chloroplast Protein Kinase Stt7 in LHCII Phosphorylation and State Transition in Chlamydomonas

The argininosuccinate lyase (ASL) gene of Chlamydomonas reinhardtii has been cloned using four oligonucleotide probes corresponding to highly conserved regions of the ASL polypeptide sequence. The identity of the gene was confirmed by partial sequencing. It is unique, contains several introns and spans a region less than 7.8 kb that includes highly repetitive sequences. Using a particle gun, a reliable nuclear transformation system has been established by complementing three mutants deficient in ASL activity with the wild-type ASL gene. Analysis of the transformants reveals variable patterns of integration of the transforming DNA into the nuclear genome. Previous work has mapped the mutations in the mutants arg2 and arg7 to either end of the ARG7 locus 1.0 to 1.6 recombination map units apart. Our transformation results show that these two mutations are located within a region of 7.8 kb. This allows for the first correlation of the recombination map and the molecular map at the ARG7 locus and indicates a high recombination frequency in this region of the nuclear genome.

The argininosuccinate lyase gene of Chlamydomonas reinhardtii: an important tool for nuclear transformation and for correlating the genetic and molecular maps of the ARG7 locus.

Transpositions of segments of R factor (antibiotic resistance plasmids) to bacteriophage lambda have been selected and characterized Cells of Escherichia coli harboring R factors that determine kanamycin resistance were infected with phage lambda, and lambdakan transducing lines were obtained Each of the three examined is unusual when compared to lambda transducing phages containing E coli chromosomal genes: the kan insertions (a) occur at several sites, each well removed from the integration region POP', (b) are not associated with deletion of lambda phage DNA, and (c) are separable from the lambda genome during transduction or during lytic growth Two insertions from the same R factor contain 15 kilobase sequences repeated in inverted order The properties of the lambdakan phage suggest that R factors contain systems capable of mediating genetic exchange in the absence of extensive DNA homology It is suggested that such systems of exchange may have played important roles in R factor evolution

/pdf/transposition-of-r-factor-genes-to-bacteriophage-lambda-2ig9m8db12.pdf

Transposition of R factor genes to bacteriophage lambda

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Jean-David Rochaix

Papers

State transitions and light adaptation require chloroplast thylakoid protein kinase STN7

Role of Chloroplast Protein Kinase Stt7 in LHCII Phosphorylation and State Transition in Chlamydomonas

The argininosuccinate lyase gene of Chlamydomonas reinhardtii: an important tool for nuclear transformation and for correlating the genetic and molecular maps of the ARG7 locus.

Transposition of R factor genes to bacteriophage lambda

The Molecular Biology of Chloroplasts and Mitochondria in Chlamydomonas