Sequence similarity is more relevant than species specificity in probabilistic backtranslation

Network reconstruction and systems analysis of cardiac myocyte hypertrophy signaling

Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression.

In silico identification of novel protective VSG antigens expressed by Trypanosoma brucei and an effort for designing a highly immunogenic DNA vaccine using IL-12 as adjuvant

African trypanosomiasis continues to be a major health problem, with more adults dying from this disease world-wide. As the sequence diversity of Trypanosoma brucei is extreme, with VSGs having 15-25% identity with most other VSGs, hence it displays a huge diversity of adaptations and host specificities. Therefore the need for an improved vaccine has become an international priority. The highly conserved and specific epitopes acting as both CD8+ and CD4+ T-cell epitopes (FLINKKPAL and FTALCTLAA) were predicted from large bunch of VSGs of T. brucei. Besides, some other potential epitopes with very high affinity for MHC I and II molecules were also determined while taking consideration on the most common HLA in the general population which accounts for major ethnicities. The vaccine candidates were found to be effective even for non-african populations as predicted by population coverage analysis. Hence the migrating travelers acting as a spread means of the infection can probably also be treated successfully after injection of such a multiepitopic vaccine. Exploiting the immunoinformatics approaches, we designed a potential vaccine by using the consensus epitopic sequence of 388 VSG proteins of T. brucei and performed in silico cloning of multiepitopic antigenic DNA sequence in pBI-CMV1 vector. Moreover, various techniques like codon adaptation, CpG optimization, removal of self recognized epitopes, use of adjuvant and co-injection with plasmids expressing immune-stimulatory molecules were implemented to enhance the immunogenicity of the proposed in silico vaccine.

You're one in a googol: optimizing genes for protein expression

A vast number of different nucleic acid sequences can all be translated by the genetic code into the same amino acid sequence. These sequences are not all equally useful however; the exact sequence chosen can have profound effects on the expression of the encoded protein. Despite the importance of protein-coding sequences, there has been little systematic study to identify parameters that affect expression. This is probably because protein expression has largely been tackled on an ad hoc basis in many independent projects: once a sequence has been obtained that yields adequate expression for that project, there is little incentive to continue work on the problem. Synthetic biology may now provide the impetus to transform protein expression folklore into design principles, so that DNA sequences may easily be designed to express any protein in any system. In this review, we offer a brief survey of the literature, outline the major challenges in interpreting existing data and constructing robust design algorithms, and propose a way to proceed towards the goal of rational sequence engineering.