Selection for unequal densities of sigma70 promoter-like signals in different regions of large bacterial genomes

The evolutionary processes operating in the DNA regions that participate in the regulation of gene expression are poorly understood. In Escherichia coli, we have established a sequence pattern that distinguishes regulatory from nonregulatory regions. The density of promoter-like sequences, that could be recognizable by RNA polymerase and may function as potential promoters, is high within regulatory regions, in contrast to coding regions and regions located between convergently transcribed genes. Moreover, functional promoter sites identified experimentally are often found in the subregions of highest density of promoter-like signals, even when individual sites with higher binding affinity for RNA polymerase exist elsewhere within the regulatory region. In order to see the generality of this pattern, we have analyzed 43 additional genomes belonging to most established bacterial phyla. Differential densities between regulatory and nonregulatory regions are detectable in most of the analyzed genomes, with the exception of those that have evolved toward extreme genome reduction. Thus, presence of this pattern follows that of genes and other genomic features that require weak selection to be effective in order to persist. On this basis, we suggest that the loss of differential densities in the reduced genomes of host-restricted pathogens and symbionts is an outcome of the process of genome degradation resulting from the decreased efficiency of purifying selection in highly structured small populations. This implies that the differential distribution of promoter-like signals between regulatory and nonregulatory regions detected in large bacterial genomes confers a significant, although small, fitness advantage. This study paves the way for further identification of the specific types of selective constraints that affect the organization of regulatory regions and the overall distribution of promoter-like signals through more detailed comparative analyses among closely related bacterial genomes.     

Evidence for Widespread Positive and Negative Selection in Coding and Conserved Noncoding Regions of Capsella grandiflora

The extent that both positive and negative selection vary across different portions of plant genomes remains poorly understood. Here, we sequence whole genomes of 13 Capsella grandiflora individuals and quantify the amount of selection across the genome. Using an estimate of the distribution of fitness effects, we show that selection is strong in coding regions, but weak in most noncoding regions, with the exception of 5′ and 3′ untranslated regions (UTRs). However, estimates of selection on noncoding regions conserved across the Brassicaceae family show strong signals of selection. Additionally, we see reductions in neutral diversity around functional substitutions in both coding and conserved noncoding regions, indicating recent selective sweeps at these sites. Finally, using expression data from leaf tissue we show that genes that are more highly expressed experience stronger negative selection but comparable levels of positive selection to lowly expressed genes. Overall, we observe widespread positive and negative selection in coding and regulatory regions, but our results also suggest that both positive and negative selection on plant noncoding sequence are considerably rarer than in animal genomes.     

Intergenics: A tool for extraction of intergenicregions

For the past one decade, there has been considerable explosion of interest in searching novel regulatory elements in the intergenic region between the protein coding regions. The microbial genomes are the most exploited in terms of intergenic (noncoding) regions due to its less complexity. We think, the increasing pace of genome sequencing calls for a tool which will be useful for the extraction of intergenic regions. IntergenicS (Intergenic Sequence) is a tool which can extract the intergenic regions of microbial genomes at NCBI. All the unannotated regions between annotated protein coding genes and noncoding RNA genes can be extracted. It also deals with the calculation of GC base composition of the intergenic regions. This will be a useful tool for the analysis of noncoding regions of both bacterial and archael genomes.     

Congruent evolution of different classes of non-coding DNA in prokaryotic genomes

Prokaryotic genomes are considered to be 'wall-to-wall' genomes, which consist largely of genes for proteins and structural RNAs, with only a small fraction of the genomic DNA allotted to intergenic regions, which are thought to typically contain regulatory signals. The majority of bacterial and archaeal genomes contain 6-14% non-coding DNA. Significant positive correlations were detected between the fraction of non-coding DNA and inter- and intra-operonic distances, suggesting that different classes of non-coding DNA evolve congruently. In contrast, no correlation was found between any of these characteristics of non-coding sequences and the number of genes or genome size. Thus, the non-coding regions and the gene sets in prokaryotes seem to evolve in different regimes. The evolution of non-coding regions appears to be determined primarily by the selective pressure to minimize the amount of non-functional DNA, while maintaining essential regulatory signals, because of which the content of non-coding DNA in different genomes is relatively uniform and intra- and inter-operonic non-coding regions evolve congruently. In contrast, the gene set is optimized for the particular environmental niche of the given microbe, which results in the lack of correlation between the gene number and the characteristics of non-coding regions.     

Functional annotations in bacterial genomes based on small RNA signatures

One of the key challenges in computational genomics is annotating coding genes and identification of regulatory RNAs in complete genomes. An attempt is made in this study which uses the regulatory RNA locations and their conserved flanking genes identified within the genomic backbone of template genome to search for similar RNA locations in query genomes. The search is based on recently reported coexistence of small RNAs and their conserved flanking genes in related genomes. Based on our study, 54 additional sRNA locations and functions of 96 uncharacterized genes are predicted in two draft genomes viz., Serratia marcesens Db1 and Yersinia enterocolitica 8081. Although most of the identified additional small RNA regions and their corresponding flanking genes are homologous in nature, the proposed anchoring technique could successfully identify four non-homologous small RNA regions in Y. enterocolitica genome also. The KEGG Orthology (KO) based automated functional predictions confirms the predicted functions of 65 flanking genes having defined KO numbers, out of the total 96 predictions made by this method. This coexistence based method shows more sensitivity than controlled vocabularies in locating orthologous gene pairs even in the absence of defined Orthology numbers. All functional predictions made by this study in Y. enterocolitica 8081 were confirmed by the recently published complete genome sequence and annotations. This study also reports the possible regions of gene rearrangements in these two genomes and further characterization of such RNA regions could shed more light on their possible role in genome evolution.     

发热伴血小板减少综合征布尼亚病毒微复制子的建立

发热伴血小板减少综合征布尼亚病毒(SFTSV)是我国新发现的一种布尼亚病毒,可引起人类严重发热伴血小板减少综合征。我们利用RNA聚合酶Ⅰ体系,分别构建SFTSV三个片段L、M、S微复制子,研究其非编码区调控功能。将报告基因绿色荧光蛋白(GFP)或荧光素酶(Luciferase)分别插入SFTSV三个片段5′和3′非编码区之间,所形成的嵌合cDNA反向插入含RNA聚合酶I的表达载体pHH21中,获得SFTSV微复制子重组质粒L-GFP-pHH21、M-GFP-pHH21、S-GFP-pHH21、L-Luc-pHH21、M-Luc-pHH21和S-Luc-pHH21,分别与成功表达SFTSV聚合酶蛋白(L蛋白)和结构蛋白(N蛋白)的质粒VR1012-L和VR-1012-NP共同转染293T细胞,24～48h后观察GFP表达情况或检测萤光素酶表达量。L、M、S片段GFP微复制子均可观察到特异性绿色荧光。荧光素酶定量结果显示其在不同节段非编码区中的表达量不同,提示SFTSV三个节段的非编码区启动微复制子转录和复制的强度不同。     

Constraint on di-nucleotides by codon usage bias in bacterial genomes

It has been reported earlier that the relative di-nucleotide frequency (RDF) in different parts of a genome is similar while the frequency is variable among different genomes. So RDF is termed as genome signature in bacteria. It is not known if the constancy in RDF is governed by genome wide mutational bias or by selection. Here we did comparative analysis of RDF between the inter-genic and the coding sequences in seventeen bacterial genomes, whose gene expression data was available. The constraint on di-nucleotides was found to be higher in the coding sequences than that in the inter-genic regions and the constraint at the 2nd codon position was more than that in the 3rd position within a genome. Further analysis revealed that the constraint on di-nucleotides at the 2nd codon position is greater in the high expression genes (HEG) than that in the whole genomes as well as in the low expression genes (LEG). We analyzed RDF at the 2nd and the 3rd codon positions in simulated coding sequences that were computationally generated by keeping the codon usage bias (CUB) according to genome G+C composition and the sequence of amino acids unaltered. In the simulated coding sequences, the constraint observed was significantly low and no significant difference was observed between the HEG and the LEG in terms of di-nucleotide constraint. This indicated that the greater constraint on di-nucleotides in the HEG was due to the stronger selection on CUB in these genes in comparison to the LEG within a genome. Further, we did comparative analyses of the RDF in the HEG rpoB and rpoC of 199 bacteria, which revealed a common pattern of constraints on di-nucleotides at the 2nd codon position across these bacteria. To validate the role of CUB on di-nucleotide constraint, we analyzed RDF at the 2nd and the 3rd codon positions in simulated rpoB/rpoC sequences. The analysis revealed that selection on CUB is an important attribute for the constraint on di-nucleotides at these positions in bacterial genomes. We believe that this study has come with major findings of the role of CUB on di-nucleotide constraint in bacterial genomes.