Gene expression profile of the cynobacterium synechocystis genome

The pattern of coding sequences in the chloroplast genome of Atropa belladonna and a comparative analysis with other related genomes in the nightshade family

Atropa belladonna is a valuable medicinal plant and a commercial source of tropane alkaloids, which are frequently utilized in therapeutic practice. In this study, bioinformaticmethodologies were used to examine the pattern of coding sequences and the factors thatmight influence codon usage bias in the chloroplast genome of Atropa belladonna andother nightshade genomes. The chloroplast engineering being a promising field in modernbiotechnology, the characterization of chloroplast genome is very important. The resultsrevealed that the chloroplast genomes of Nicotiana tabacum, Solanum lycopersicum, Capsicum frutescens, Datura stramonium, Lyciumbarbarum, Solanum melongena, and Solanumtuberosum exhibited comparable codon usage patterns. In these chloroplast genomes, weobserved a weak codon usage bias. According to the correspondence analysis, the genesisof the codon use bias in these chloroplast genes might be explained by natural selection,directed mutational pressure, and other factors. GC12 and GC3S were shown to have nomeaningful relationship. Further research revealed that natural selection primarily shapedthe codon usage in A. belladonna and other nightshade genomes for translational efficiency. The sequencing properties of these chloroplast genomes were also investigated by investing the occurrences of palindromes and inverted repeats, which would be useful forfuture research on medicinal plants.

Codon usage pattern and predicted gene expression in Arabidopsis thaliana

The extensive research for predicting highly expressed genes in plant genome sequences has been going on for decades. The codon usage pattern of genes in Arabidopsis thaliana genome is a classical topic for plant biologists for its significance in the understanding of molecular plant biology. Here we have used a gene expression profiling methodology based on the score of modified relative codon bias (MRCBS) to elucidate expression pattern of genes in Arabidopsis thaliana. MRCBS relies exclusively on sequence features for identifying the highly expressed genes. In this study, a critical analysis of predicted highly expressed (PHE) genes in Arabidopsis thaliana has been performed using MRCBS as a numerical estimator of gene expression level. Consistent with previous other results, our study indicates that codon composition plays an important role in the regulation of gene expression. We found a systematic strong correlation between MRCBS and CAI (codon adaptation index) or other expression-measures. Additionally, MRCBS correlates well with experimental gene expression data. Our study highlights the relationship between gene expression and compositional signature in relation to codon usage bias and sets the ground for the further investigation of the evolution of the protein-coding genes in the plant genome.

Comparative Analysis of Predicted Gene Expression among Crenarchaeal Genomes

Research into new methods for identifying highly expressed genes in anonymous genome sequences has been going on for more than 15 years. We presented here an alternative approach based on modified score of relative codon usage bias to identify highly expressed genes in crenarchaeal genomes. The proposed algorithm relies exclusively on sequence features for identifying the highly expressed genes. In this study, a comparative analysis of predicted highly expressed genes in five crenarchaeal genomes was performed using the score of Modified Relative Codon Bias Strength (MRCBS) as a numerical estimator of gene expression level. We found a systematic strong correlation between Codon Adaptation Index and MRCBS. Additionally, MRCBS correlated well with other expression measures. Our study indicates that MRCBS can consistently capture the highly expressed genes.

A predictor for predicting Escherichia coli transcriptome and the effects of gene perturbations

Background

A means to predict the effects of gene over-expression, knockouts, and environmental stimuli in silico is useful for system biologists to develop and test hypotheses. Several studies had predicted the expression of all Escherichia coli genes from sequences and reported a correlation of 0.301 between predicted and actual expression. However, these do not allow biologists to study the effects of gene perturbations on the native transcriptome.

Results

We developed a predictor to predict transcriptome-scale gene expression from a small number (n = 59) of known gene expressions using gene co-expression network, which can be used to predict the effects of over-expressions and knockdowns on E. coli transcriptome. In terms of transcriptome prediction, our results show that the correlation between predicted and actual expression value is 0.467, which is similar to the microarray intra-array variation (p-value = 0.348), suggesting that intra-array variation accounts for a substantial portion of the transcriptome prediction error. In terms of predicting the effects of gene perturbation(s), our results suggest that the expression of 83% of the genes affected by perturbation can be predicted within 40% of error and the correlation between predicted and actual expression values among the affected genes to be 0.698. With the ability to predict the effects of gene perturbations, we demonstrated that our predictor has the potential to estimate the effects of varying gene expression level on the native transcriptome.

Conclusion

We present a potential means to predict an entire transcriptome and a tool to estimate the effects of gene perturbations for E. coli, which will aid biologists in hypothesis development. This study forms the baseline for future work in using gene co-expression network for gene expression prediction.

RNA-seq based identification and mutant validation of gene targets related to ethanol resistance in cyanobacterial Synechocystis sp. PCC 6803

BACKGROUND

Fermentation production of biofuel ethanol consumes agricultural crops, which will compete directly with the food supply. As an alternative, photosynthetic cyanobacteria have been proposed as microbial factories to produce ethanol directly from solar energy and CO2. However, the ethanol productivity from photoautotrophic cyanobacteria is still very low, mostly due to the low tolerance of cyanobacterial systems to ethanol stress.

RESULTS

To build a foundation necessary to engineer robust ethanol-producing cyanobacterial hosts, in this study we applied a quantitative transcriptomics approach with a next-generation sequencing technology, combined with quantitative reverse-transcript PCR (RT-PCR) analysis, to reveal the global metabolic responses to ethanol in model cyanobacterial Synechocystis sp. PCC 6803. The results showed that ethanol exposure induced genes involved in common stress responses, transporting and cell envelope modification. In addition, the cells can also utilize enhanced polyhydroxyalkanoates (PHA) accumulation and glyoxalase detoxication pathway as means against ethanol stress. The up-regulation of photosynthesis by ethanol was also further confirmed at transcriptional level. Finally, we used gene knockout strains to validate the potential target genes related to ethanol tolerance.

CONCLUSION

RNA-Seq based global transcriptomic analysis provided a comprehensive view of cellular response to ethanol exposure. The analysis provided a list of gene targets for engineering ethanol tolerance in cyanobacterium Synechocystis.

Universal pattern and diverse strengths of successive synonymous codon bias in three domains of life, particularly among prokaryotic genomes

There has been significant progress in understanding the process of protein translation in recent years. One of the best examples is the discovery of usage bias in successive synonymous codons and its role in eukaryotic translation efficiency. We observed here a similar type of bias in the other two life domains, bacteria and archaea, although the bias strength was much smaller than in eukaryotes. Among 136 prokaryotic genomes, 98 were found to have significant bias from random use of successive synonymous codons with Z scores larger than three. Furthermore, significantly different bias strengths were found between prokaryotes grouped by various genomic or biochemical characteristics. Interestingly, the bias strength measured by a general Z score could be fitted well (R = 0.83, P < 10⁻¹⁵) by three genomic variables: genome size, G + C content, and tRNA gene number based on multiple linear regression. A different distribution of synonymous codon pairs between protein-coding genes and intergenic sequences suggests that bias is caused by translation selection. The present results indicate that protein translation is tuned by codon (pair) usage, and the intensity of the regulation is associated with genome size, tRNA gene number, and G + C content.

Directed evolution: selection of the host organism

Directed evolution has become a well-established tool for improving proteins and biological systems. A critical aspect of directed evolution is the selection of a suitable host organism for achieving functional expression of the target gene. To date, most directed evolution studies have used either Escherichia coli or Saccharomyces cerevisiae as a host; however, other bacterial and yeast species, as well as mammalian and insect cell lines, have also been successfully used. Recent advances in synthetic biology and genomics have opened the possibility of expanding the use of directed evolution to new host organisms such as microalgae. This review focuses on the different host organisms used in directed evolution and highlights some of the recent directed evolution strategies used in these organisms.