Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulationsmore » in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations?« less

Natural genetic transformation is defined as the active uptake of free DNA, released in the environment through lysis or excretion, by bacterial strains that are naturally competent for transformation. It has been observed in a wide range of organisms (1). Of the three horizontal gene transfer processes — transformation, conjugation, and transduction — natural genetic transformation has the least requirements (1). For instance, there is no need for the donor cell to be alive or physically intact. Spatial and temporal separation between competent cells and the source of DNA in the environment can be overcome since nucleic acids are often found adsorbed to minerals (2), humic acids (3) or other components (1), where they are shielded from DNase attack. Consequently, DNA that survives can potentially be used for natural genetic transformation of recipient cells if those cells possess the ability to change into a state conferring competence for transformation (1). The process of transformation has been divided into the following steps (1,4,5): (i) release of DNA from cells; (ii) dispersal and (iii) persistence of the DNA in the environment; (iv) the development of competence for DNA uptake by cells in the natural habitat; (v) the interaction of cells with DNA and the uptake of DNA; and (vi) the expression of an acquired trait following DNA uptake (1).KeywordsConfocal Laser Scanning MicroscopyHorizontal Gene TransferTransformation FrequencyPeptide Nucleic AcidNucleic Acid StainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A major challenge for science in the 21st century will be to obtain an integrated understanding of living organisms. We are witnessing a fundamental change in how this goal is pursued. The reductionist approach, trying to assign function to individual genes and proteins, is now followed by systems biology with the use of high-throughput techniques to extract comprehensive datasets and integrating the data to model the organism as a functional network of interacting components. One of the essential techniques is DNA microarray technology, which allows the analysis of the entire transcriptome in a single hybridization experiment. Since the pioneering work of Schena et al. on Arabidopsis in 1995 (1), microarray hybridization technology has developed into a powerful tool for the identification of differentially-regulated genes in plants and other organisms. Inherent characteristics of the technology such as miniaturization, automation and parallelism allow the determination of transcript concentrations from thousands of genes in a single experiment with high accuracy and sensitivity. The quantitative determination of transcript concentrations with microarrays is also an auspicious way toward the elucidation of plant signaling pathways. By comparing the concentrations of individual mRNAs present in samples originating from different genotypes, developmental stages, or growth conditions, genes can be identified that are differentially expressed and, hence, may have specific metabolic or morphogenetic functions. The analysis of transcription patterns has proven to be valuable in attributing function to novel sequences. Any similarity in expression patterns observed between known genes and sequences of unknown function may indicate functional homology. The first large-scale expression profiles of light- and dark-grown Arabidopsis thaliana seedlings, for example, revealed numerous genes that were highly regulated by light, but did not have a match in the nucleotide sequence databases (2, 3). The co-regulation with well-characterized light-inducible genes may indicate as yet undefined photomorphogenetic functions for the respective proteins. Another example is provided by the work of Seki et al. (4), who used a cDNA microarray of about 1,300 full-length Arabidopsis cDNAs to identify drought- and cold-inducible genes and target genes of DREB IA, a transcription factor that controls stress-inducible gene expression. More than 60 transcripts were found to be upregulated, 50 of which were derived from novel genes that had not previously been reported as being drought-or cold-inducible. Twelve stress-inducible genes were recognized as targets of DREB1A, and six of them were novel. The assumption that two genes with similar expression patterns have similar functions or act in the same pathway is now supported by many examples demonstrating tight coupling of gene function with a specific pattern of gene expression.

Very long chain polyunsaturated fatty acids (VLC-PUFAs) are valuable commodities currently sourced either from vulnerable natural (marine) stocks or via expensive microbial fermentation. The advent of plant genetic engineering has raised the possibility of producing PUFAs in transgenic plants. This technology allows the transfer of a particular gene or trait into a non-native (heterologous) host, and has the potential to revolutionize modern agriculture. In the case of PUFAs, it is possible to envisage “designer” oilseeds in which the crop plant has been engineered to make very long chain PUFAs, even though these fatty acids do usually occur in higher plant oil seeds (1). Considering the cost-benefits of genetically-engineered oilseeds as an alternative supply to conventional sources, it is perhaps unsurprising that considerable resources have been focused on achieving this goal. We would therefore like to review the progress in this field, focusing on the underpinning molecular biology which is enabling this (potential) biotechnology revolution.

Long-term potentiation (LTP) and long-term depression (LTD) are two experimental models of synaptic plasticity that have been studied extensively in the last 25 years, as they may represent basic mechanisms to store certain types of information in neuronal networks. In several brain regions, these two forms of synaptic plasticity require dendritic depolarization, and the amplitude and duration of the depolarization-induced calcium signal are crucial parameters for the generation of either LTP or LTD. The rise in calcium concentration mediated by activation of the N-methyl-D-aspartate (NMDA) subtype of glutamate receptors has been proposed to stimulate various calcium-dependent processes that could convert the induction signal into long-lasting changes in synaptic structure and function. According to several lines of experimental evidence, alterations in synaptic function observed with LTP and LTD are thought to be the result of modifications of postsynaptic currents mediated by the a-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) subtype of glutamate receptors. The question of which type(s) of receptor changes constitutes the basis for the expression of synaptic plasticity is still very much open. Here, we review data relevant to the issue of selective modulation of AMPA receptor properties occurring after learning and memory, environmental enrichment, and synaptic plasticity. We also discuss potential cellular mechanisms whereby calcium-dependent enzymes might regulate AMPA receptor properties during LTP and LTD, focusing on protein kinases, proteases and lipases.

The number of genes implicated in the infection and disease processes of phytopathogenic fungi is increasing rapidly. Forward genetic approaches have identified mutated genes that affect pathogenicity, host range, virulence and general fitness. Likewise, candidate gene approaches have been used to identify genes of interest based on homology and recently through 'comparative genomic approaches' through analysis of large EST databases and whole genome sequences. It is becoming clear that many genes of the fungal genome will be involved in the pathogen-host interaction in its broadest sense, affecting pathogenicity and the disease process in planta. By utilizing the information obtained through these studies, plants may be bred or engineered for effective disease resistance. That is, by trying to disable pathogens by hitting them where it counts.

In eukaryotic cells the biosynthesis of membrane lipids often occurs in subcellular compartments different from their final destination. This raises a fundamental question: How are membrane lipids transported between organelles and across membranes? The intricate compartmentalization of plant lipid metabolism provides promising opportunities for the mechanistic analysis of interorganelle lipid trafficking. In plants the endoplasmic reticulum (ER) and the chloroplast envelopes participate in the biosynthesis of the lipids of photosynthetic membranes. As a consequence, trafficking of lipid precursors between these two membrane systems is substantial. As will be discussed below, Arabidopsis represents an excellent plant model to dissect this process genetically and may provide experimental access to a better mechanistic understanding of interorganelle lipid trafficking in general.

The genetics of forest tree species differs in many respects from that of agricultural crops because of biological advantages and limitations. Most tree species are essentially undomesticated, are usually outbred, have large population sizes, long generation times, long life spans, and suffer from severe inbreeding depression due to high genetic load, much like Homo sapiens. These factors have essentially precluded the development of inbred lines, near-isogenic lines, and true backcross pedigrees, which form the basis of genetic mapping studies in most crop species. In forest genetics, novel mapping strategies have had to be developed to overcome these limitations. However, the high level of diversity in tree populations, the ability to generate large progeny sets from full-sib or half-sib crosses, and, in some species, well-developed clonal propagation, have been used to advantage for genetic mapping. If an individual tree has unusual properties, it is possible to determine the genetic basis of the phenotype through genetic mapping, sometimes with no prior information from that individual or species. As a result, genetic mapping has become routine for many tree species and mapping technology is being applied to diverse problems of tree biology, quantitative genetics and tree breeding.

Compared to viruses, bacteria and nematodes, filamentous fungi are the major causes of plant disease worldwide and are responsible for significant yield losses in crops. Disease control is mainly by deployment of resistant plant cultivars and by management strategies including crop rotations, disposal of inocula such as disease-infested stubble, and use of fungicides. In contrast to appreciable knowledge of plant defense mechanisms, there is little understanding at the molecular level of how fungal pathogens cause disease. Primary recognition between plants and pathogens is often mediated by single complementary resistance genes in the host and avirulence genes in the pathogen. Avirulence genes from several fungi have been cloned and in some cases also appear to play a role in fitness of the pathogen, often acting as disease determinants (for review see ref. 1).

Cell-penetrating peptides (CPPs) are water-soluble molecules with sequences containing some cationic (basic) residues but have the ability to translocate through various cell plasma membranes with high efficiency and low lytic activity, and seemingly without any chiral receptor. Sometimes such sequences are mentioned as having membrane translocating sequences (MTS). CPP biotechnology should become an alternative to transfection methods (viral transfection, lipofection, electroporation, etc.), in particular with therapeutic applications. When covalently linked to “cargoes” (polypeptides, oligonucleotides, liposomes, nanoparticles, etc.) many times their own molecular mass, CPPs still retain their translocating ability. A CPP can contain a nuclear localization sequence (NLS), which then may guide the cargo into the nuclear compartment. With a cargo having an expected biological function, the CPP import can be established by measuring a biochemical activity. Several recent reviews have been written on the CPP topic (e.g., 1-6), and a book describing the processes and practical applications involving CPPs has been published in 2002 (7). The present review will only briefly summarize the early work and background of the CPP field, and go on to discuss some recent issues and controversies in the area.