Kinesins in the Arabidopsis genome: A comparative analysis among eukaryotes

Background  

Receptor protein tyrosine phosphatase rho (RPTPρ, gene symbol PTPRT) is a member of the type IIB RPTP family. These transmembrane molecules have been linked to signal transduction, cell adhesion and neurite extension. The extracellular segment contains MAM, Ig-like and fibronectin type III domains, and the intracellular segment contains two phosphatase domains. The human RPTPρ gene is located on chromosome 20q12-13.1, and the mouse gene is located on a syntenic region of chromosome 2. RPTPρ expression is restricted to the central nervous system.     

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.     

Profiling of regulatory microRNA transcriptomes in various biological processes: a review

A class of small, non-coding ribonucleic acids, termed microRNA (miRNA), has recently emerged as a new key player in the cellular control of gene expression. By either blocking translation or inducing target mRNA degradation, miRNA not only participates in regular biological processes within cells and tissues but is also involved in pathological processes. Many human malignancies have been linked to specific miRNA expression patterns, raising hopes for new approaches to therapy. While such human disease-related mechanisms have been widely discussed and frequently reviewed, miRNA’s general significance in animals has been less in editorial focus, despite its obvious role in basic physiological processes, e.g. neurosensory maturation, development of fertility, and hibernation. Using selected examples, this review highlights our current knowledge of miRNA’s potential and its promise as a new tool for gene regulation.     

Integrated pathway-based and network-based analysis of GC-MS rice metabolomics data under diazinon stress to infer affected biological pathways

Diazinon insecticide is widely applied in rice (Oryza sativa L.) fields in Iran. However, concerns are now being raised about its potential adverse impacts on rice. In this study, a time-course metabolic change in rice plants was investigated after diazinon treatment using gas chromatography-mass spectrometry (GC-MS) and subsequently three different methods, MetaboAnalyst, MetaboNetwork, and analysis of reporter reactions, as a potential multivariate method were used to find the underlying changes in metabolism with stronger evidence in order to link differentially expressed metabolites to biological pathways. Results clearly showed the similarity of acetylcholinesterase (AChE) of rice plants to that of animals in terms of its inhibitability by diazinon and emphasized that subsequent accumulation of AChE mainly affects the metabolism of osmolites and tricarboxylic acid intermediates subsequent accumulation of ACh mainly affects the metabolism of osmolites and TCA intermediates.     

Erratum to: Functional model of biological neural networks

[This corrects the article DOI: 10.1007/s11571-010-9110-4.].     

Network Analysis Tools: from biological networks to clusters and pathways

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.     

Functional identification of biological neural networks using reservoir adaptation for point processes

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.     

Using regional comparative phylogeographic data from snake lineages to infer historical processes in Middle America

Understanding how historical processes have either similarly, or differentially, shaped the evolution of lineages or biotic assemblages is important for a broad spectrum of fields. Gaining such understanding can be particularly challenging, however, especially for regions that have a complex geologic and biological history. In this study we apply a broad comparative approach to distill such regional biogeographic perspectives, by characterizing sets of divergence times for major biogeographic boundaries estimated from multiple codistributed lineages of snakes. We use a large combined (mitochondrial gene sequence) phylogeographic/phylogenetic dataset containing several clades of snakes that range across Middle America – the tropical region between Mexico and northwestern South America. This region is known for its complex tectonic history, and poorly understood historical biogeography. Based on our results, we highlight how phylogeographic transition zones between Middle and South America and the Nicaragua Depression appear to have undergone multiple episodes of diversification in different lineages. This is in contrast to other examples we find where apparently a single vicariant period is shared across multiple lineages. We specifically evaluate the distributions of divergence time estimates across multiple lineages and estimate the number of temporal periods of lineage diversification per biogeographic break. Overall, our results highlight a great deal of shared temporal divergence, and provide important hypotheses for yet unstudied lineages. These multi‐lineage comparisons across multiple spatial and temporal scales provide excellent predictive power for identifying the roles of geology, climate, ecology and natural history in shaping regional biodiversity.     

Comprehensive comparative analysis of kinesins in photosynthetic eukaryotes

Background

Kinesins, a superfamily of molecular motors, use microtubules as tracks and transport diverse cellular cargoes. All kinesins contain a highly conserved ~350 amino acid motor domain. Previous analysis of the completed genome sequence of one flowering plant (Arabidopsis) has resulted in identification of 61 kinesins. The recent completion of genome sequencing of several photosynthetic and non-photosynthetic eukaryotes that belong to divergent lineages offers a unique opportunity to conduct a comprehensive comparative analysis of kinesins in plant and non-plant systems and infer their evolutionary relationships.

Results

We used the kinesin motor domain to identify kinesins in the completed genome sequences of 19 species, including 13 newly sequenced genomes. Among the newly analyzed genomes, six represent photosynthetic eukaryotes. A total of 529 kinesins was used to perform comprehensive analysis of kinesins and to construct gene trees using the Bayesian and parsimony approaches. The previously recognized 14 families of kinesins are resolved as distinct lineages in our inferred gene tree. At least three of the 14 kinesin families are not represented in flowering plants. Chlamydomonas, a green alga that is part of the lineage that includes land plants, has at least nine of the 14 known kinesin families. Seven of ten families present in flowering plants are represented in Chlamydomonas, indicating that these families were retained in both the flowering-plant and green algae lineages.

Conclusion

The increase in the number of kinesins in flowering plants is due to vast expansion of the Kinesin-14 and Kinesin-7 families. The Kinesin-14 family, which typically contains a C-terminal motor, has many plant kinesins that have the motor domain at the N terminus, in the middle, or the C terminus. Several domains in kinesins are present exclusively either in plant or animal lineages. Addition of novel domains to kinesins in lineage-specific groups contributed to the functional diversification of kinesins. Results from our gene-tree analyses indicate that there was tremendous lineage-specific duplication and diversification of kinesins in eukaryotes. Since the functions of only a few plant kinesins are reported in the literature, this comprehensive comparative analysis will be useful in designing functional studies with photosynthetic eukaryotes.     

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.     

Biochemical analysis of genetic recombination in eukaryotes.

Recent studies concerning molecular mechanisms of genetic recombination in eukaryotes are reviewed. Since many of these studies have focused on the testable predictions arising from the hybrid DNA theory of genetic recombination, this theory is summarised. Experiments to determine the time of meiotic crossing-over and the structure of the synaptonemal complex which facilitates meiotic crossing-over are described. Investigations of DNA nicking and repair events implicated in recombination are discussed. Properties of proteins which may facilitate hybrid DNA formation, and biochemical evidence for hybrid DNA formation are presented. Finally, a nuclease which has been implicated in gene conversion is described.     

Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.     

Messenger RNA turnover in eukaryotes: pathways and enzymes

The control of mRNA degradation is an important component of the regulation of gene expression since the steady-state concentration of mRNA is determined both by the rates of synthesis and of decay. Two general pathways of mRNA decay have been described in eukaryotes. Both pathways share the exonucleolytic removal of the poly(A) tail (deadenylation) as the first step. In one pathway, deadenylation is followed by the hydrolysis of the cap and processive degradation of the mRNA body by a 5' exonuclease. In the second pathway, the mRNA body is degraded by a complex of 3' exonucleases before the remaining cap structure is hydrolyzed. This review discusses the proteins involved in the catalysis and control of both decay pathways.     

NetworkBLAST: comparative analysis of protein networks

The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Cross-species analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks. AVAILABILITY: NetworkBLAST is available as web-server at: www.cs.tau.ac.il/~roded/networkblast.htm.     

Extending pathways and processes using molecular interaction networks to analyse cancer genome data

Background  

Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.     

Shared mechanisms among neurodegenerative diseases: from genetic factors to gene networks

Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are pressing health concerns in modern societies for which effective therapies are still lacking. Recent high-throughput genomic technologies have enabled genome-scale, multidimensional investigations to facilitate a better understanding of the underlying mechanisms and the identification of novel targets. Here we review the molecular insights gained through such studies, and compare the similarities and differences between neurodegenerative diseases revealed by systems genomics and gene network modelling approaches. We focus specifically on the shared mechanisms at multiple molecular scales ranging from genetic factors to gene expression to network-level features of gene regulation, and whenever possible also point out mechanisms that distinguish one disease from another. Our review sets the stage for similar genomewide inspection in the future on shared/distinct features of neurodegenerative diseases at the levels of cellular, proteomic or epigenomic signatures, and how these features may interact to determine the progression and treatment response of different diseases afflicting the same individual.