The entire organization of transcription units on the Bacillus subtilis genome

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

Global gene expression reveals stress-responsive genes in Aspergillus fumigatus mycelia

Background

Aspergillus fumigatus is a human fungal pathogen that causes aspergillosis in immunocompromised hosts. A. fumigatus is believed to be exposed to diverse environmental stresses in the host cells. The adaptation mechanisms are critical for infections in human bodies. Transcriptional networks in response to diverse environmental challenges remain to be elucidated. To gain insights into the adaptation to environmental stresses in A. fumigatus mycelia, we conducted time series transcriptome analyses.

Results

With the aid of RNA-seq, we explored the global gene expression profiles of mycelia in A. fumigatus upon exposure to diverse environmental changes, including heat, superoxide, and osmotic stresses. From the perspective of global transcriptomes, transient responses to superoxide and osmotic stresses were observed while responses to heat stresses were gradual. We identified the stress-responsive genes for particular stresses, and the 266 genes whose expression levels drastically fluctuated upon exposure to all tested stresses. Among these, the 77 environmental stress response genes are conserved in S. cerevisiae, suggesting that these genes might be more general prerequisites for adaptation to environmental stresses. Finally, we revealed the strong correlations among expression profiles of genes related to ‘rRNA processing’.

Conclusions

The time series transcriptome analysis revealed the stress-responsive genes underlying the adaptation mechanisms in A. fumigatus mycelia. These results will shed light on the regulatory networks underpinning the adaptation of the filamentous fungi.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4316-z) contains supplementary material, which is available to authorized users.

Transcriptome analysis reveals novel regulatory mechanisms in a genome-reduced bacterium

The avian bacterial pathogen Mycoplasma gallisepticum is a good model for systems studies due to small genome and simplicity of regulatory pathways. In this study, we used RNA-Seq and MS-based proteomics to accurately map coding sequences, transcription start sites (TSSs) and transcript 3′-ends (T3Es). We used obtained data to investigate roles of TSSs and T3Es in stress-induced transcriptional responses. We identified 1061 TSSs at a false discovery rate of 10% and showed that almost all transcription in M. gallisepticum is initiated from classic TATAAT promoters surrounded by A/T-rich sequences. Our analysis revealed the pronounced operon structure complexity: on average, each coding operon has one internal TSS and T3Es in addition to the primary ones. Our transcriptomic approach based on the intervals between the two nearest transcript ends allowed us to identify two classes of T3Es: strong, unregulated, hairpin-containing T3Es and weak, heat shock-regulated, hairpinless T3Es. Comparing gene expression levels under different conditions revealed widespread and divergent transcription regulation in M. gallisepticum. Modeling suggested that the core promoter structure plays an important role in gene expression regulation. We have shown that the heat stress activation of cryptic promoters combined with the hairpinless T3Es suppression leads to widespread, seemingly non-functional transcription.

Evidence classification of high-throughput protocols and confidence integration in RegulonDB

RegulonDB provides curated information on the transcriptional regulatory network of Escherichia coli and contains both experimental data and computationally predicted objects. To account for the heterogeneity of these data, we introduced in version 6.0, a two-tier rating system for the strength of evidence, classifying evidence as either ‘weak’ or ‘strong’ (Gama-Castro,S., Jimenez-Jacinto,V., Peralta-Gil,M. et al. RegulonDB (Version 6.0): gene regulation model of Escherichia Coli K-12 beyond transcription, active (experimental) annotated promoters and textpresso navigation. Nucleic Acids Res., 2008;36:D120–D124.). We now add to our classification scheme the classification of high-throughput evidence, including chromatin immunoprecipitation (ChIP) and RNA-seq technologies. To integrate these data into RegulonDB, we present two strategies for the evaluation of confidence, statistical validation and independent cross-validation. Statistical validation involves verification of ChIP data for transcription factor-binding sites, using tools for motif discovery and quality assessment of the discovered matrices. Independent cross-validation combines independent evidence with the intention to mutually exclude false positives. Both statistical validation and cross-validation allow to upgrade subsets of data that are supported by weak evidence to a higher confidence level. Likewise, cross-validation of strong confidence data extends our two-tier rating system to a three-tier system by introducing a third confidence score ‘confirmed’.

Database URL:http://regulondb.ccg.unam.mx/

Functional motifs in Escherichia coli NC101

Escherichia coli (E. coli) bacteria can damage DNA of the gut lining cells and may encourage the development of colon cancer according to recent reports. Genetic switches are specific sequence motifs and many of them are drug targets. It is interesting to know motifs and their location in sequences. At the present study, Gibbs sampler algorithm was used in order to predict and find functional motifs in E. coli NC101 contig 1. The whole genomic sequence of Escherichia coli NC101 contig 1 were retrieved from http://www.ncbi.nlm.nih.gov (NCBI Reference sequence: NZ_AEFA01000001.1) in order to be analyzed with DAMBE software and BLAST. The results showed that the 6-mer motif is CUGGAA in most sequences (genes1-3, 8, 9, 12, 14-18, 20-23, 25, 27, 29, 31-34), CUUGUA for gene 4 , CUGUAA for gene 5, CUGAUG for gene 6, CUGAUA for gene7, CUGAAA for genes 10, 11, 13, 26, 28, and CUGGAG for gene 19, and CUGGUA for gene30 in E. coli NC101 contig 1. It is concluded that the 6-mer motif is CUGGAA in most sequences in E. coli NC101 contig1. The present study may help experimental studies on elucidating the pharmacological and phylogenic functions of the motifs in E. coli.

Position weight matrix, gibbs sampler, and the associated significance tests in motif characterization and prediction

Position weight matrix (PWM) is not only one of the most widely used bioinformatic methods, but also a key component in more advanced computational algorithms (e.g., Gibbs sampler) for characterizing and discovering motifs in nucleotide or amino acid sequences. However, few generally applicable statistical tests are available for evaluating the significance of site patterns, PWM, and PWM scores (PWMS) of putative motifs. Statistical significance tests of the PWM output, that is, site-specific frequencies, PWM itself, and PWMS, are in disparate sources and have never been collected in a single paper, with the consequence that many implementations of PWM do not include any significance test. Here I review PWM-based methods used in motif characterization and prediction (including a detailed illustration of the Gibbs sampler for de novo motif discovery), present statistical and probabilistic rationales behind statistical significance tests relevant to PWM, and illustrate their application with real data. The multiple comparison problem associated with the test of site-specific frequencies is best handled by false discovery rate methods. The test of PWM, due to the use of pseudocounts, is best done by resampling methods. The test of individual PWMS for each sequence segment should be based on the extreme value distribution.

Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach

In the postgenomics era, integrative analysis of several "omics" data is absolutely required for understanding the cell as a system. Integrative analysis of transcriptomics and metabolomics can lead to elucidation of gene-to-metabolite networks. When integrating different time series "omics" data, it is necessary to take into consideration a time lag between those data. In the present study, we conducted an integrative analysis of time series transcriptomics and metabolomics data of Escherichia coli generated by cDNA microarray and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS), respectively. We identified a 60-min time lag between transition points of transcriptomics and metabolomics data by using a Linear Dynamical System. Furthermore, we investigated gene-to-metabolite correlations in the context of time lag, obtained the maximum number of correlated pairs at transcripts leading 60-min time lag, and finally revealed gene-to-metabolite relations in the phospholipid biosynthesis pathway. Taking into consideration the time lag between transcriptomics and metabolomics data in time series analysis could unravel novel gene-to-metabolite relations. According to gene-to-metabolite correlations, phosphatidylglycerol plays a more critical role for membrane balance than phosphatidylethanolamine in E. coli.