IFIM: a database of integrated fitness information for microbial genes

EcoliGD: An Online Tool for Designing Escherichia coli Genome

Synthetic biology is an important interdisciplinary field that has emerged in this century, focusing on the rewriting and reprogramming of DNA through the cycles of "design-edit", and so, the cell's own operating system, its genome, is naturally coming into focus. Here, we propose EcoliGD, an online genome design tool with a visual interactive interface and the function of browsing information, as well as the ability to perform insertion, exchange, deletion, and codon replacement operations on the E. coli genome and display the results in real-time. Users can utilize EcoliGD to check various functional characteristic about E. coli genes, to help them build their genomes. Furthermore, we also collected experimentally verified large genomic segments that have been successfully deleted from the genome for users to choose from and simplify the genome. EcoliGD can help recode the entire E. coli genome, providing a novel way to explore the diversity and function of this microorganism. The EcoliGD web tool is available at http://guolab.whu.edu.cn/EcoliGD/.

Adaptation of commensal proliferating Escherichia coli to the intestinal tract of young children with cystic fibrosis

The mature human gut microbiota is established during the first years of life, and altered intestinal microbiomes have been associated with several human health disorders. Escherichia coli usually represents less than 1% of the human intestinal microbiome, whereas in cystic fibrosis (CF), greater than 50% relative abundance is common and correlates with intestinal inflammation and fecal fat malabsorption. Despite the proliferation of E. coli and other Proteobacteria in conditions involving chronic gastrointestinal tract inflammation, little is known about adaptation of specific characteristics associated with microbiota clonal expansion. We show that E. coli isolated from fecal samples of young children with CF has adapted to growth on glycerol, a major component of fecal fat. E. coli isolates from different CF patients demonstrate an increased growth rate in the presence of glycerol compared with E. coli from healthy controls, and unrelated CF E. coli strains have independently acquired this growth trait. Furthermore, CF and control E. coli isolates have differential gene expression when grown in minimal media with glycerol as the sole carbon source. While CF isolates display a growth-promoting transcriptional profile, control isolates engage stress and stationary-phase programs, which likely results in slower growth rates. Our results indicate that there is selection of unique characteristics within the microbiome of individuals with CF, which could contribute to individual disease outcomes.

Selection for energy efficiency drives strand-biased gene distribution in prokaryotes

Lagging-strand genes accumulate more deleterious mutations. Genes are thus preferably located on the leading strand, an observation known as strand-biased gene distribution (SGD). Despite of this mechanistic understanding, a satisfactory quantitative model is still lacking. Replication-transcription-collisions induce stalling of the replication machinery, expose DNA to various attacks, and are followed by error-prone repairs. We found that mutational biases in non-transcribed regions can explain ~71% of the variations in SGDs in 1,552 genomes, supporting the mutagenesis origin of SGD. Mutational biases introduce energetically cheaper nucleotides on the lagging strand, and result in more expensive protein products; consistently, the cost difference between the two strands explains ~50% of the variance in SGDs. Protein costs decrease with increasing gene expression. At similar expression levels, protein products of leading-strand genes are generally cheaper than lagging-strand genes; however, highly-expressed lagging genes are still cheaper than lowly-expressed leading genes. Selection for energy efficiency thus drives some genes to the leading strand, especially those highly expressed and essential, but certainly not all genes. Stronger mutational biases are often associated with low-GC genomes; as low-GC genes encode expensive proteins, low-GC genomes thus tend to have stronger SGDs to alleviate the stronger pressure on efficient energy usage.

Genomic Complexity Places Less Restrictions on the Evolution of Young Coexpression Networks than Protein-Protein Interactions

The differences in evolutionary patterns of young protein–protein interactions (PPIs) among distinct species have long been a puzzle. However, based on our genome-wide analysis of available integrated experimental data, we confirm that young genes preferentially integrate into ancestral PPI networks, and that this manner is consistent in all of six model organisms with widely different levels of phenotypic complexity. We demonstrate that the level of restrictions placed on the evolution of biological networks declines with a decrease of phenotypic complexity. Compared with young PPI networks, new co-expression links have less evolutionary restrictions, so a young gene with a high possibility to be coexpressed other young genes relatively frequently emerges in the four simpler genomes among the six studied. However, it is not favorable for such young–young coexpression in terms of a young gene evolving into a coexpression hub, so the coexpression pattern could gradually decline. To explain this apparent contradiction, we suggest that young genes that are initially peripheral to networks are temporarily coexpressed with other young genes, driving functional evolution because of low selective pressure. However, as the expression levels of genes increase and they gradually develop a greater effect on fitness, young genes start to be coexpressed more with members of ancestral networks and less with other young genes. Our findings provide new insights into the evolution of biological networks.

Essentiality drives the orientation bias of bacterial genes in a continuous manner

Studies had found that bacterial genes are preferentially located on the leading strands. Subsequently, the preferences of essential genes and highly expressed genes were compared by classifying all genes into four groups, which showed that the former has an exclusive influence on orientation. However, only some functional classes of essential genes have this orientation bias. Nevertheless, previous studies only performed comparative analyzes by differentiating the orientation bias extent of two types of genes. Thus, it is unclear whether the influence of essentiality on strand bias works continuously. Herein, we found a significant correlation between essentiality and orientation bias extent in 19 of 21 analyzed bacterial genomes, based on quantitative measurement of gene essentiality (or fitness). The correlation coefficient was much higher than that derived from binary essentiality measures (essential or non-essential). This suggested that genes with relatively lower essentiality, i.e., conditionally essential genes, also have some orientation bias, although it is weaker than that of absolutely essential genes. The results demonstrated the continuous influence of essentiality on orientation bias and provided details on this visible structural feature of bacterial genomes. It also proved that Geptop and IFIM could serve as useful resources of bacterial gene essentiality, particularly for quantitative analysis.

Comparative analysis of essential genes in prokaryotic genomic islands

Essential genes are thought to encode proteins that carry out the basic functions to sustain a cellular life, and genomic islands (GIs) usually contain clusters of horizontally transferred genes. It has been assumed that essential genes are not likely to be located in GIs, but systematical analysis of essential genes in GIs has not been explored before. Here, we have analyzed the essential genes in 28 prokaryotes by statistical method and reached a conclusion that essential genes in GIs are significantly fewer than those outside GIs. The function of 362 essential genes found in GIs has been explored further by BLAST against the Virulence Factor Database (VFDB) and the phage/prophage sequence database of PHAge Search Tool (PHAST). Consequently, 64 and 60 eligible essential genes are found to share the sequence similarity with the virulence factors and phage/prophages-related genes, respectively. Meanwhile, we find several toxin-related proteins and repressors encoded by these essential genes in GIs. The comparative analysis of essential genes in genomic islands will not only shed new light on the development of the prediction algorithm of essential genes, but also give a clue to detect the functionality of essential genes in genomic islands.

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions.

Three computational tools for predicting bacterial essential genes

Essential genes are those genes indispensable for the survival of any living cell. Bacterial essential genes constitute the cornerstones of synthetic biology and are often attractive targets in the development of antibiotics and vaccines. Because identification of essential genes with wet-lab ways often means expensive economic costs and tremendous labor, scientists changed to seek for alternative way of computational prediction. Aiming to help to solve this issue, our research group (CEFG: group of Computational, Comparative, Evolutionary and Functional Genomics, http://cefg.uestc.edu.cn) has constructed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. To ensure reliable predictions, the investigated species should belong to the same family (for EGP) or phylum (for CEG_Match and Geptop) with one of the reference species, respectively. As the pilot software for the issue, predicting accuracies of them have been assessed and compared with existing algorithms, and note that all of other published algorithms have not any formed online services. We hope these services at CEFG will help scientists and researchers in the field of essential genes.