In vitro reactivation of in vivo ammonium-inactivated glutamine synthetase from Synechocystis sp. PCC 6803.

Glutamine synthetase from Synechocystis sp. strain PCC 6803 is inactivated by ammonium addition to cells growing with nitrate as the nitrogen source. The enzyme can be reactivated in vitro by different methods such as alkaline phosphatase treatment, but not phosphodiesterase, by raising the pH of the crude extract to values higher than 8, by increasing the ionic strength of the cell-free extract, or by preincubation with organic solvents, such as 2-propanol and ethanol. These results suggest that the loss of glutamine synthetase activity promoted by ammonium involves the non-covalent binding of a phosphorylated compound to the enzyme and support previous results that rule out the existence of an adenylylation/deadenylylation system functioning in the regulation of cyanobacterial glutamine synthetase.     

Protein functional features are reflected in the patterns of mRNA translation speed

Background

The degeneracy of the genetic code makes it possible for the same amino acid string to be coded by different messenger RNA (mRNA) sequences. These “synonymous mRNAs” may differ largely in a number of aspects related to their overall translational efficiency, such as secondary structure content and availability of the encoded transfer RNAs (tRNAs). Consequently, they may render different yields of the translated polypeptides. These mRNA features related to translation efficiency are also playing a role locally, resulting in a non-uniform translation speed along the mRNA, which has been previously related to some protein structural features and also used to explain some dramatic effects of “silent” single-nucleotide-polymorphisms (SNPs). In this work we perform the first large scale analysis of the relationship between three experimental proxies of mRNA local translation efficiency and the local features of the corresponding encoded proteins.

Results

We found that a number of protein functional and structural features are reflected in the patterns of ribosome occupancy, secondary structure and tRNA availability along the mRNA. One or more of these proxies of translation speed have distinctive patterns around the mRNA regions coding for certain protein local features. In some cases the three patterns follow a similar trend. We also show specific examples where these patterns of translation speed point to the protein’s important structural and functional features.

Conclusions

This support the idea that the genome not only codes the protein functional features as sequences of amino acids, but also as subtle patterns of mRNA properties which, probably through local effects on the translation speed, have some consequence on the final polypeptide. These results open the possibility of predicting a protein’s functional regions based on a single genomic sequence, and have implications for heterologous protein expression and fine-tuning protein function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1734-7) contains supplementary material, which is available to authorized users.     

Program Code Generator for Cardiac Electrophysiology Simulation with Automatic PDE Boundary Condition Handling

Clinical and experimental studies involving human hearts can have certain limitations. Methods such as computer simulations can be an important alternative or supplemental tool. Physiological simulation at the tissue or organ level typically involves the handling of partial differential equations (PDEs). Boundary conditions and distributed parameters, such as those used in pharmacokinetics simulation, add to the complexity of the PDE solution. These factors can tailor PDE solutions and their corresponding program code to specific problems. Boundary condition and parameter changes in the customized code are usually prone to errors and time-consuming. We propose a general approach for handling PDEs and boundary conditions in computational models using a replacement scheme for discretization. This study is an extension of a program generator that we introduced in a previous publication. The program generator can generate code for multi-cell simulations of cardiac electrophysiology. Improvements to the system allow it to handle simultaneous equations in the biological function model as well as implicit PDE numerical schemes. The replacement scheme involves substituting all partial differential terms with numerical solution equations. Once the model and boundary equations are discretized with the numerical solution scheme, instances of the equations are generated to undergo dependency analysis. The result of the dependency analysis is then used to generate the program code. The resulting program code are in Java or C programming language. To validate the automatic handling of boundary conditions in the program code generator, we generated simulation code using the FHN, Luo-Rudy 1, and Hund-Rudy cell models and run cell-to-cell coupling and action potential propagation simulations. One of the simulations is based on a published experiment and simulation results are compared with the experimental data. We conclude that the proposed program code generator can be used to generate code for physiological simulations and provides a tool for studying cardiac electrophysiology.     

Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug–drug complex networks

One limitation of almost all antiviral Quantitative Structure–Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug–drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug–drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg. Med. Chem. 2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg. Med. Chem. 2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg. Med. Chem. 2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug–drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity = 84.62%) and 119 of 139 non-active compounds (sensitivity = 85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug–drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.     

The Down syndrome-related protein kinase DYRK1A phosphorylates p27Kip1 and Cyclin D1 and induces cell cycle exit and neuronal differentiation

A fundamental question in neurobiology is how the balance between proliferation and differentiation of neuronal precursors is maintained to ensure that the proper number of brain neurons is generated. Substantial evidence implicates DYRK1A (dual specificity tyrosine-phosphorylation-regulated kinase 1A) as a candidate gene responsible for altered neuronal development and brain abnormalities in Down syndrome. Recent findings support the hypothesis that DYRK1A is involved in cell cycle control. Nonetheless, how DYRK1A contributes to neuronal cell cycle regulation and thereby affects neurogenesis remains poorly understood. In the present study we have investigated the mechanisms by which DYRK1A affects cell cycle regulation and neuronal differentiation in a human cell model, mouse neurons, and mouse brain. Dependent on its kinase activity and correlated with the dosage of overexpression, DYRK1A blocked proliferation of SH-SY5Y neuroblastoma cells within 24 h and arrested the cells in G₁ phase. Sustained overexpression of DYRK1A induced G₀ cell cycle exit and neuronal differentiation. Furthermore, we provide evidence that DYRK1A modulated protein stability of cell cycle-regulatory proteins. DYRK1A reduced cellular Cyclin D1 levels by phosphorylation on Thr286, which is known to induce proteasomal degradation. In addition, DYRK1A phosphorylated p27^Kip1 on Ser10, resulting in protein stabilization. Inhibition of DYRK1A kinase activity reduced p27^Kip1 Ser10 phosphorylation in cultured hippocampal neurons and in embryonic mouse brain. In aggregate, these results suggest a novel mechanism by which overexpression of DYRK1A may promote premature neuronal differentiation and contribute to altered brain development in Down syndrome.     

Bifunctional α-amylase/trypsin inhibitor activity previously ascribed to the 22 KDa TL protein, resided in a contaminant protein of 14 KDa

The previously reported inhibitory activity of a 22 KDa protease and α-amylase inhibitor extracted from maize seeds, was re-investigated in order to confirm or rectify its ability to inactivate protease and α-amylase activities. The same inhibition was detected when the 22 KDa protein was purified following the original methodology (Richardson et al. 1987). However, when a new ion-exchange chromatography step was introduced after the RP-HPLC, the apparently homogeneous 22 KDa protein was further resolved into five different fractions. Four of them corresponded to different isoforms of the 22 KDa protein, all of which lacked inhibitory activity. The other small band corresponded to a contaminant protein, which was identified as the 14 KDa α-amylase/trypsin inhibitor. This protein was responsible for the reported double inhibition (protease and α-amylase inhibition), previously assigned to the 22 KDa protein. With this result, it was then possible to settle the question concerning the ability of this 22 KDa protein to inhibit those enzymatic activities. Interestingly, the four isoforms of the 22 KDa protein fractions showed anti-fungal activity when tested in vitro. In summary, we suggest that both the PR-proteins, as well as the inhibitor's family classification, should now be corrected. Thus, the 22 KDa protein should no longer be considered as a member of either the protease or of the amylase inhibitor families. Similarly, the inhibitory activity assigned to the PR-proteins should no longer be considered.     

Cost-effectiveness of posaconazole in private and public Brazilian hospitals

Background

Posaconazole is used for the prophylaxis of invasive fungal disease (IFD). Previous studies have shown it to be cost-effective compared to fluconazole/itraconazole. However, posaconazole has never been economically evaluated in developing countries.