Can Codon Usage Bias Explain Intron Phase Distributions and Exon Symmetry?

More introns exist between codons (phase 0) than between the first and the second bases (phase 1) or between the second and the third base (phase 2) within the codon. Many explanations have been suggested for this excess of phase 0. It has, for example, been argued to reflect an ancient utility for introns in separating exons that code for separate protein modules. There may, however, be a simple, alternative explanation. Introns typically require, for correct splicing, particular nucleotides immediately 5 in exons (typically a G) and immediately 3 in the following exon (also often a G). Introns therefore tend to be found between particular nucleotide pairs (e.g., G|G pairs) in the coding sequence. If, owing to bias in usage of different codons, these pairs are especially common at phase 0, then intron phase biases may have a trivial explanation. Here we take codon usage frequencies for a variety of eukaryotes and use these to generate random sequences. We then ask about the phase of putative intron insertion sites. Importantly, in all simulated data sets intron phase distribution is biased in favor of phase 0. In many cases the bias is of the magnitude observed in real data and can be attributed to codon usage bias. It is also known that exons may carry either the same phase (symmetric) or different phases (asymmetric) at the opposite ends. We simulated a distribution of different types of exons using frequencies of introns observed in real genes assuming random combination of intron phases at the opposite sides of exons. Surprisingly the simulated pattern was quite similar to that observed. In the simulants we typically observe a prevalence of symmetric exons carrying phase 0 at both ends, which is common for eukaryotic genes. However, at least in some species, the extent of the bias in favor of symmetric (0,0) exons is not as great in simulants as in real genes. These results emphasize the need to construct a biologically relevant null model of successful intron insertion.Reviewing Editor: Dr. Manyuan Long     

Synonymous Codon Usage Bias and Overexpression of a Synthetic Gene Encoding Interferon α2b in Yeast

To achieve higher level expression of Interferon α2b(IFN-α2b)in methylotrophic yeast(Pichia pastoris),a cDNA fragment coding for the mature IFN-α2b was designed and synthesized based on the synonymous codon bias of P.pastoris and optimized G C content.The synthetic IFN-α2b was inserted into the secreted expression vector pPICZαA,and then integrated into P.pastoris GS115 genome by electroporation.Multi-copy integrants in the Mut recombinant P.pastoris strain were screened by high concentrations of Zeocin.120 hours culturing allowed expression of the IFN-α2b transformant up to 810 mg/L as detected by SDS-PAGE and quantitative methods.In addition,Western blot analysis showed that the recombinant proteins had immunogenicity.The significant antiviral activity of the recombinant IFN-α2b protein was verified by WISH/VSV system,which was 3.3×105 IU/mL.     

Correlation Between Shine–Dalgarno Sequence Conservation and Codon Usage of Bacterial Genes

In this study, we analyzed the correlation between codon usage bias and Shine–Dalgarno (SD) sequence conservation, using complete genome sequences of nine prokaryotes. For codon usage bias, we adopted the codon adaptation index (CAI), which is based on the codon usage preference of genes encoding ribosomal proteins, elongation factors, heat shock proteins, outer membrane proteins, and RNA polymerase subunit proteins. To compute SD sequence conservation, we used SD motif sequences predicted by Tompa and systematically aligned them with 5′UTR sequences. We found that there exists a clear correlation between the CAI values and SD sequence conservation in the genomes of Escherichia coli, Bacillus subtilis, Haemophilus influenzae, Archaeoglobus fulgidus, Methanobacterium thermoautotrophicum, and Methanococcus jannaschii, and no relationship is found in M. genitalium, M. pneumoniae, and Synechocystis. That is, genes with higher CAI values tend to have more conserved SD sequences than do genes with lower CAI values in these organisms. Some organisms, such as M. thermoautotrophicum, do not clearly show the correlation. The biological significance of these results is discussed in the context of the translation initiation process and translation efficiency. Received: 22 June 2000 / Accepted: 18 October 2000     

Modelling the Interior Structure of Enceladus Based on the 2014’s Cassini Gravity Data

We present a model for the internal structure of Saturn’s moon Enceladus. This model allows us to estimate the physical conditions at the bottom of the satellite’s potential subsurface water reservoir and to determine the radial distribution of pressure and gravity. This leads to a better understanding of the physical and chemical conditions at the water/rock boundary. This boundary is the most promising area on icy moons for astrobiological studies as it could serve as a potential habitat for extraterrestrial life similar to terrestrial microbes that inhabit rocky mounds on Earth’s sea floors.     

Codon Bias Patterns of E. coli’s Interacting Proteins

Synonymous codons, i.e., DNA nucleotide triplets coding for the same amino acid, are used differently across the variety of living organisms. The biological meaning of this phenomenon, known as codon usage bias, is still controversial. In order to shed light on this point, we propose a new codon bias index, CompAI, that is based on the competition between cognate and near-cognate tRNAs during translation, without being tuned to the usage bias of highly expressed genes. We perform a genome-wide evaluation of codon bias for E.coli, comparing CompAI with other widely used indices: tAI, CAI, and Nc. We show that CompAI and tAI capture similar information by being positively correlated with gene conservation, measured by the Evolutionary Retention Index (ERI), and essentiality, whereas, CAI and Nc appear to be less sensitive to evolutionary-functional parameters. Notably, the rate of variation of tAI and CompAI with ERI allows to obtain sets of genes that consistently belong to specific clusters of orthologous genes (COGs). We also investigate the correlation of codon bias at the genomic level with the network features of protein-protein interactions in E.coli. We find that the most densely connected communities of the network share a similar level of codon bias (as measured by CompAI and tAI). Conversely, a small difference in codon bias between two genes is, statistically, a prerequisite for the corresponding proteins to interact. Importantly, among all codon bias indices, CompAI turns out to have the most coherent distribution over the communities of the interactome, pointing to the significance of competition among cognate and near-cognate tRNAs for explaining codon usage adaptation. Notably, CompAI may potentially correlate with translation speed measurements, by accounting for the specific delay induced by wobble-pairing between codons and anticodons.     

Modelling the Electrical Activity of Pancreatic α-cells Based on Experimental Data from Intact Mouse Islets

Detailed experimental data from patch clamp experiments on pancreatic α-cells in intact mouse islets are used to model the electrical activity associated with glucagon secretion. Our model incorporates L- and T-type Ca²⁺ currents, delayed rectifying and A-type K⁺ currents, a voltage-gated Na⁺ current, a KATP conductance, and an unspecific leak current. Tolbutamide closes KATP channels in the α-cell, leading to a reduction of the resting conductance from 1.1 nS to 0.4 nS. This causes the α-cell to depolarise from −76 mV to 33 mV. When the basal membrane potential passes the range between −60 and −35 mV, the α-cell generates action potentials. At higher voltages, the α-cell enters a stable depolarised state and the electrical activity ceases. The effects of tolbutamide are simulated by gradually reducing the KATP conductance (g _K,ATP ) from 500 pS to 0 pS. When g _K,ATP is between 72 nS and 303 nS, the model generates action potentials in the same voltage range as the α-cell. When g _K,ATP is lower than 72 nS, the model enters a stable depolarised state, and firing of action potentials is inhibited due to voltage-dependent inactivation of the Na⁺ and T-type Ca²⁺ currents. This is in accordance with experimental results. Changing the inactivation parameters to those observed in somatostatin-secreting δ-cells abolishes the depolarised inactive state, and leads to β-cell like electrical activity with action potentials generated even after complete closure of the KATP channels.     

Membrane Domains Based on Ankyrin and Spectrin Associated with Cell–Cell Interactions

Nodes of Ranvier and axon initial segments of myelinated nerves, sites of cell–cell contact in early embryos and epithelial cells, and neuromuscular junctions of skeletal muscle all perform physiological functions that depend on clustering of functionally related but structurally diverse ion transporters and cell adhesion molecules within microdomains of the plasma membrane. These specialized cell surface domains appeared at different times in metazoan evolution, involve a variety of cell types, and are populated by distinct membrane-spanning proteins. Nevertheless, recent work has shown that these domains all share on their cytoplasmic surfaces a membrane skeleton comprised of members of the ankyrin and spectrin families. This review will summarize basic features of ankyrins and spectrins, and will discuss emerging evidence that these proteins are key players in a conserved mechanism responsible for assembly and maintenance of physiologically important domains on the surfaces of diverse cells.Spectrins are flexible rods 0.2 microns in length with actin-binding sites at each end (Shotton et al. 1979; Bennett et al. 1982) (Fig. 1A). Spectrins are assembled from α and β subunits, each comprised primarily of multiple copies of a 106-amino acid repeat (Speicher and Marchesi 1984). In addition to the canonical 106-residue repeat, β spectrins also have a carboxy-terminal pleckstrin homology domain (Zhang et al. 1995; Macias et al. 1994) and tandem amino-terminal calponin homology domains (Bañuelos et al. 1998), whereas α spectrins contain an Src homology domain 3 (SH3) site (Musacchio et al. 1992), a calmodulin-binding site (Simonovic et al. 2006), and EF hands (Travé et al. 1995) (Fig. 1A). Spectrin α and β subunits are assembled antiparallel and side-to-side into heterodimers, which in turn are associated head-to-head to form tetramers (Clarke 1971; Shotton et al. 1979; Davis and Bennett 1983) (Fig. 1A). In human erythrocytes, in which spectrin was first characterized (Marchesi and Steers 1968; Clarke 1971), actin oligomers containing 10–14 monomers are each linked to five to six spectrin tetramers by accessory proteins to form a geodesic domelike structure that has been resolved by electron microscopy (Byers and Branton 1985). The principal proteins at the spectrin–actin junction are protein 4.1, adducin, tropomyosin, tropomodulin, and dematin (Bennett and Baines 2001) (Open in a separate windowFigure 1.Domain structure and variants of spectrin and ankyrin proteins. (A) Molecular domains of spectrins: Two α spectrins and five β spectrins are shown. Spectrins are comprised of modular units called spectrin repeats (yellow). Other domains such as the ankyrin binding domain (purple), Src-homology domain 3 (SH3, blue), EF-hand domain (red), and calmodulin-binding domain (green) promote interactions with binding targets important for spectrin function. The pleckstrin homology domain (black) promotes association with the plasma membrane and the actin binding domain (grey) tethers the spectrin-based membrane skeleton to the underlying actin cytoskeleton. (B) The spectrin tetramer, the fundamental unit of the spectrin-based membrane skeleton. The spectrin repeat domains of α and β spectrin associate end-to-end to form heterodimers. Heterodimers associate laterally in an antiparallel fashion to form tetramers. The tetramers can then associate end-to-end to form extended macromolecules that link into a geodesic dome shape directly underneath the plasma membrane. (C) Molecular domains present in canonical ankyrins. The membrane binding domain of ankyrin isoforms (orange) is comprised of 24 ANK repeats. The spectrin binding domain (green-blue) allows ankyrins to coordinate integral membrane proteins to the membrane skeleton. The death domain (pink) is the most highly conserved domain. The regulatory domain (brown) is the most variable region of ankyrins. The regulatory domain interacts intramolecularly with the membrane binding domain to modulate ankyrin’s affinity for other binding partners. All ankyrins and spectrins are subject to alternative splicing, which further increases their functional diversity.

Table 1.

Binding partners of spectrin and ankyrins

Impact of Xenogeneic Silencing on Phage–Host Interactions

Phages, viruses that prey on bacteria, are the most abundant and diverse inhabitants of the Earth. Temperate bacteriophages can integrate into the host genome and, as so-called prophages, maintain a long-term association with their host. The close relationship between host and virus has significantly shaped microbial evolution and phage elements may benefit their host by providing new functions. Nevertheless, the strong activity of phage promoters and potentially toxic gene products may impose a severe fitness burden and must be tightly controlled. In this context, xenogeneic silencing (XS) proteins, which can recognize foreign DNA elements, play an important role in the acquisition of novel genetic information and facilitate the evolution of regulatory networks. Currently known XS proteins fall into four classes (H-NS, MvaT, Rok and Lsr2) and have been shown to follow a similar mode of action by binding to AT-rich DNA and forming an oligomeric nucleoprotein complex that silences gene expression. In this review, we focus on the role of XS proteins in phage–host interactions by highlighting the important function of XS proteins in maintaining the lysogenic state and by providing examples of how phages fight back by encoding inhibitory proteins that disrupt XS functions in the host. Sequence analysis of available phage genomes revealed the presence of genes encoding Lsr2-type proteins in the genomes of phages infecting Actinobacteria. These data provide an interesting perspective for future studies to elucidate the impact of phage-encoded XS homologs on the phage life cycle and phage–host interactions.     

Arrangement of the Template 3′ of the A-Site Codon on the Human 80S Ribosome

The arrangement of the template sequence 3′ of the A-site codon on the 80S ribosome was studied using mRNA analogs containing Phe codon UUU at the 5′ end and a photoreactive perfluoroarylazido group linked to C5 of U or N7 of G. The analogs were positioned on the ribosome with the use of tRNA^Phe, which directed the UUU codon to the P site, bringing a modified nucleotide to position +9 or +12 relative to the first nucleotide of the P-site codon. Upon mild UV irradiation of ribosome complexes, the analogs of both types crosslinked to the 18S rRNA and proteins of the 40S subunit. Comparisons were made with the crosslinking patterns of complexes in which an mRNA analog contained a modified nucleotide in position +7 (the crosslinking to 18S rRNA in such complexes has been studied previously). The efficiency of crosslinking to ribosomal components depended on the nature of the modified nucleotide of an mRNA analog and its position on the ribosome. The extent of crosslinking to the 18S rRNA drastically decreased as the modified nucleotide was transferred from position +7 to position +12. The 18S rRNA nucleotides involved in crosslinking were identified. A modified nucleotide in position +9 crosslinked to the invariant dinucleotide A1824/A1825 and variable A1823 in the 3′ minidomain of the 18S rRNA and to S15. The same ribosomal components have earlier been shown to crosslink to modified nucleotides in positions +4 to +7. In addition, all mRNA analogs crosslinked to invariant C1698 in the 3′ minidomain and to conserved region 605–620, which closes helix 18 in the 5′ domain.     

Synonymous Codon Usage—a Guide for Co-Translational Protein Folding in the Cell

Molecular Biology - Abstract—In the cell, protein folding begins during protein synthesis/translation and thus is a co-translational process. Co-translational protein folding is tightly...     

Efficiency of the miRNA–mRNA Interaction Prediction Programs

miRNAs play a key role in regulation of gene expression. Nowadays it is known more than 2500 human miRNAs, while a majority of miRNA–mRNA interactions remains unidentified. The recent development of a high-throughput CLASH (crosslinking, ligation and sequencing of hybrids) technique for discerning miRNA–mRNA interactions allowed an experimental analysis of the human miRNA–mRNA interactome. Therefore, it allowed us, for the first time, make an experimental analysis of the human miRNA–mRNA interactome as a whole and an evaluation of the quality of most commonly used miRNA prediction tools (TargetScan, PicTar, PITA, RNA22 and miRanda). To estimate efficiency of the miRNA–mRNA prediction tools, we used next parameters: sensitivity, positive predicted value, predictions in different mRNA regions (3' UTR, CDS, 5' UTR), predictions for different types of interactions (5 classes), predictions of “canonical” and “nocanonical” interactions, similarity with the random generated data. The analysis revealed low efficiency of all prediction programs in comparison with the CLASH data in terms of the all examined parameters.     

Setting Fish Quotas Based on Holistic Ecosystem Modelling Including Environmental Factors and Foodweb Interactions – A New Approach

We present a new approach to set fish quotas from holistic aquatic foodweb modelling (the LakeWeb-approach). This modelling includes changes in environmental conditions (nutrients, salinity, temperature, oxygen), process-based mass-balance calculations of nutrient concentrations from inflow, internal processes and outflow, calculations of how changes in nutrient concentrations affect primary production, how such changes influence secondary production and how this influence fish production and biomass. This approach gives dynamic, quantitative responses to alterations in driving variables and abiotic/biotic feedbacks. We have applied this approach for preliminary simulations of the cod biomass in the Baltic. We also show that this approach adds a new dimension in setting fish quotas, which in the future could complement, rather than compete with, the more established methods used today based on fish catch statistics and models based on other presuppositions. Our preliminary results indicate that under present environmental conditions (2003), the cod is likely to be extinct if the annual catch is between 95 and 100 kt. The present fish quota is 75 kt/yr in the Baltic, but the overfishing may be 35 kt/yr. We discuss cause–effect relationships regulating fish production, key factors influencing thresholds and points of no return connected to overfishing and changes in environmental conditions, factors regulating recovery and methods for setting optimal fish quotas using this modelling approach.     

A Generalized Michaelis–Menten Equation in Protein Synthesis: Effects of Mis-Charged Cognate tRNA and Mis-Reading of Codon

The sequence of amino acid monomers in the primary structure of a protein is decided by the corresponding sequence of codons (triplets of nucleic acid monomers) on the template messenger RNA (mRNA). The polymerization of a protein, by incorporation of the successive amino acid monomers, is carried out by a molecular machine called ribosome. We develop a stochastic kinetic model that captures the possibilities of mis-reading of mRNA codon and prior mis-charging of a tRNA. By a combination of analytical and numerical methods, we obtain the distribution of the times taken for incorporation of the successive amino acids in the growing protein in this mathematical model. The corresponding exact analytical expression for the average rate of elongation of a nascent protein is a ‘biologically motivated’ generalization of the Michaelis–Menten formula for the average rate of enzymatic reactions. This generalized Michaelis–Menten-like formula (and the exact analytical expressions for a few other quantities) that we report here display the interplay of four different branched pathways corresponding to selection of four different types of tRNA.     

Rearrangements of the Actin Cytoskeleton and E-Cadherin–Based Adherens Junctions Caused by Neoplasic Transformation Change Cell–Cell Interactions

E-cadherin–mediated cell–cell adhesion, which is essential for the maintenance of the architecture and integrity of epithelial tissues, is often lost during carcinoma progression. To better understand the nature of alterations of cell–cell interactions at the early stages of neoplastic evolution of epithelial cells, we examined the line of nontransformed IAR-2 epithelial cells and their descendants, lines of IAR-6-1 epithelial cells transformed with dimethylnitrosamine and IAR1170 cells transformed with N-RasG12D. IAR-6-1 and IAR1170 cells retained E-cadherin, displayed discoid or polygonal morphology, and formed monolayers similar to IAR-2 monolayer. Fluorescence staining, however, showed that in IAR1170 and IAR-6-1 cells the marginal actin bundle, which is typical of nontransformed IAR-2 cells, disappeared, and the continuous adhesion belt (tangential adherens junctions (AJs)) was replaced by radially oriented E-cadherin–based AJs. Time-lapse imaging of IAR-6-1 cells stably transfected with GFP-E-cadherin revealed that AJs in transformed cells are very dynamic and unstable. The regulation of AJ assembly by Rho family small GTPases was different in nontransformed and in transformed IAR epithelial cells. As our experiments with the ROCK inhibitor Y-27632 and the myosin II inhibitor blebbistatin have shown, the formation and maintenance of radial AJs critically depend on myosin II-mediated contractility. Using the RNAi technique for the depletion of mDia1 and loading cells with N17Rac, we established that mDia1 and Rac are involved in the assembly of tangential AJs in nontransformed epithelial cells but not in radial AJs in transformed cells. Neoplastic transformation changed cell–cell interactions, preventing contact paralysis after the establishment of cell–cell contact and promoting dynamic cell–cell adhesion and motile behavior of cells. It is suggested that the disappearance of the marginal actin bundle and rearrangements of AJs may change the adhesive function of E-cadherin and play an active role in migratory activity of carcinoma cells.     

Importance of Metal–Adsorbate Interactions for the Surface-enhanced Raman Scattering of Molecules Adsorbed on Plasmonic Nanoparticles

The interaction between adsorbates of different nature and plasmonic nanoparticles is reviewed here on the basis of the work done in our laboratory in the past few years. The paper is structured for analyzing the interaction of adsorbates with metal nanoparticles as function of the interacting atom (O, N, or S) and the adsorbate conformation. In the study of the adsorption of molecular species on metals, it is necessary to take into account that different interaction mechanisms are possible, leading to the existence of different molecular forms (isomers or conformers). These forms can be evidenced by changing the excitation wavelength, due to a resonant selection of these wavelengths. Charge-transfer complexes and electrostatic interactions are the usual driving forces involved in the interaction of adsorbates on metal surfaces when these metallic systems are used in wet conditions. The understanding of the metal–adsorbate interaction is crucial in the surface functionalization of metal surfaces, which has a growing importance in the development of sensing systems or optoelectronic devices. In relation to this, special attention is paid in this work to the study of the adsorption of calixarene host molecules on plasmonic nanoparticles.