Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons.

BACKGROUND: Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions. RESULTS: In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101). CONCLUSION: With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

Identification of strong promoters based on the transcriptome of Bacillus licheniformis.

OBJECTIVES: To expand the repertoire of strong promoters for high level expression of proteins based on the transcriptome of Bacillus licheniformis. RESULTS: The transcriptome of B. licheniformis ATCC14580 grown to the early stationary phase was analyzed and the top 10 highly expressed genes/operons out of the 3959 genes and 1249 operons identified were chosen for study promoter activity. Using beta-galactosidase gene as a reporter, the candidate promoter pBL9 exhibited the strongest activity which was comparable to that of the widely used strong promoter p43. Furthermore, the pro-transglutaminase from Streptomyces mobaraensis (pro-MTG) was expressed under the control of promoter pBL9 and the activity of pro-MTG reached 82 U/ml after 36 h, which is 23% higher than that of promoter p43 (66.8 U/ml). CONCLUSION: In our analyses of the transcriptome of B. licheniformis, we have identified a strong promoter pBL9, which could be adapted for high level expression of proteins in the host Bacillus subtilis.

Computational operon prediction in whole-genomes and metagenomes.

Microbial diversity in unique environmental settings enables abrupt responses catalysed by altering the gene regulation and formation of gene clusters called operons. Operons increases bacterial adaptability, which in turn increases their survival. This review article presents the emergence of computational operon prediction methods for whole microbial genomes and metagenomes, and discusses their strengths and limitations. Most of the whole-genome operon prediction methods struggle to generalize on unrelated genomes. The applicability of universal whole-genome operon prediction methods to metagenomic data is an interesting yet less investigated question. We have evaluated the potential of various operon prediction features for genomic and metagenomic data. Most of operon prediction methods with high accuracy have been compiled into databases. Despite of the high predictive performance, the data among many databases are not completely consistent for similar species. We performed a correlation analysis between the computationally predicted operon databases and experimentally validated data for Escherichia coli, Bacillus subtilis and Mycobacterium tuberculosis. Operon prediction for most of the less characterized microbes cannot be verified due to absence of experimentally validated operons. The generation of validated information for other microbes would test the authenticity of operon databases for other less annotated microbes as well. Advances in sequencing technologies and development of better analysis methods will help researchers to overcome the technological hurdles (such as long sequencing reads and improved contig size) and further improve operon predictions and better utilize operonic information.

Cautions about the reliability of pairwise gene correlations based on expression data.

BACKGROUND: Rapid growth in the availability of genome-wide transcript abundance levels through gene expression microarrays and RNAseq promises to provide deep biological insights into the complex, genome-wide transcriptional behavior of single-celled organisms. However, this promise has not yet been fully realized. RESULTS: We find that computation of pairwise gene associations (correlation; mutual information) across a set of 2782 total genome-wide expression samples from six diverse bacteria produces unexpectedly large variation in estimates of pairwise gene association-regardless of the metric used, the organism under study, or the number and source of the samples. We pinpoint the cause to sampling bias. In particular, in repositories of expression data (e.g., Gene Expression Omnibus, GEO), many individual genes show small differences in absolute gene expression levels across the set of samples. We demonstrate that these small differences are due mainly to "noise" instead of "signal" attributable to environmental or genetic perturbations. We show that downstream analysis using gene expression levels of genes with small differences yields biased estimates of pairwise association. CONCLUSIONS: We propose flagging genes with small differences in absolute, RMA-normalized, expression levels (e.g., standard deviation less than 0.5), as potentially yielding biased pairwise association metrics. This strategy has the potential to substantially improve the confidence in genome-wide conclusions about transcriptional behavior in bacterial organisms. Further work is needed to further refine strategies to identify genes with small difference in expression levels prior to computing gene-gene association metrics.

The global transcriptional landscape of Bacillus amyloliquefaciens XH7 and high-throughput screening of strong promoters based on RNA-seq data.

Bacillus amyloliquefaciens is an important industrial microbe for the production of many industrial enzymes and primary metabolites. Although the complete genome sequence of B. amyloliquefaciens has been now published, transcript structures of B. amyloliquefaciens remain poorly defined. In this study, high-throughput RNA sequencing (RNA-seq) technology was applied to dissect the transcriptome of B. amyloliquefaciens strain XH7. In total, 3936 out of a total of 4204 B. amyloliquefaciens genes (93.6%) were transcribed under the selected growth condition. Transcriptional start sites (TSS) of 1064 annotated genes and 749 operons were identified. To screen for strong promoters, a beta-galactoside reporter was fused to eight candidate promoters from 288 genes with higher expression levels (RPKM values) than the control gene P43-bgaB. The results illustrated that the candidate promoter Pr2 (promoter for the sigW gene) displayed the strongest beta-galactosidase specific activity during the post-log phase, suggesting that it could be used effectively for heterologous gene expression. The presented data will contribute to the further study of the B. amyloliquefaciens transcriptome by identifying useful promoters for industrial uses.