The cyclic nucleotide phosphodiesterase gene of Dictyostelium discoideum contains three promoters specific for growth, aggregation, and late development.

The cyclic nucleotide phosphodiesterase (phosphodiesterase) plays essential roles throughout the development of Dictyostelium discoideum. It is crucial to cellular aggregation and to postaggregation morphogenesis. The phosphodiesterase gene is transcribed into three mRNAs, containing the same coding sequence connected to different 5' untranslated sequences, that accumulate at different times during the life cycle. A 1.9-kilobase (kb) mRNA is specific for growth, a 2.4-kb mRNA is specific for aggregation, and a 2.2-kb mRNA is specific for late development and is only expressed in prestalk cells. Hybridization of RNA isolated from cells at various stages of development with different upstream regions of the gene indicated separate promoters for each of the three mRNAs. The existence of specific promoters was confirmed by fusing the three putative promoter regions to the chloramphenicol acetyltransferase reporter gene, and the analysis of transformants containing these constructs. The three promoters are scattered within a 4.1-kilobase pair (kbp) region upstream of the initiation codon. The late promoter is proximal to the coding sequence, the growth-specific promoter has an initiation site that is 1.9 kbp upstream of the ATG codon, and the aggregation-specific promoter has an initiation site 3 kbp upstream.     

Functional Analysis and Randomization of the Nisin-Inducible Promoter for Tuning Gene Expression in Lactococcus lactis

Three putative promoter regions were identified preceding the nisZ gene in Lactococcus lactis HSM-22. To investigate their function in the control of nisZ biosynthesis, green fluorescence protein (GFP) was adopted as probe to determine activities of the three promoters. The results showed that PnisZ-0 containing two sets of the ?35 and ?10 regions exhibited the same maximum activity as promoter PnisZ-2 containing the putative promoter region near the start codon. However, the GFP expression level directed by PnisZ-0 was twofold higher than that found with PnisZ-2 under low-dose nisin, indicating that promoter PnisZ-1 distant from the start codon could be important in response to the inducer nisin. Then, Pnis-2 was randomized to develop functional promoters through the degenerate oligonucleotide approach in L. lactis. 35 inducible promoters and 14 constitutive promoters were obtained, covering 3–5 logs of expression levels in small increments of activity. Sequence analysis revealed that base changes in both consensus sequence and spacing sequence resulted in remarkable decrease of promoter activity, while the sequence outside ?35 and ?10 regions would influence the promoter function radically. The functional promoters were evaluated for the efficiency and stability to control β-galactosidase (Gal) expression in L. lactis. High correlation was obtained between the Gal activity and promoter strength, suggesting that promoters developed here have the potential for fine tuning gene expression in L. lactis.     

Comparison of bovine RNA polymerase III promoters for short hairpin RNA expression

RNA interference (RNAi) mediated by DNA-based expression of short hairpin RNA (shRNA) is a powerful method of sequence-specific gene knockdown. A number of vectors for expression of shRNA have been developed that feature promoters from RNA polymerase III (pol III)-transcribed genes of mouse or human origin. To advance the use of RNAi as a tool for functional genomic research and for future development of specific therapeutics in the bovine species, we have developed shRNA expression vectors that feature novel bovine RNA pol III promoters. We characterized two bovine U6 small nuclear RNA (snRNA) promoters (bU6-2 and bU6-3) and a bovine 7SK snRNA promoter (b7SK). We compared the efficiency of each of these promoters to express shRNA molecules. Promoter activity was measured in the context of RNAi by targeting and suppressing the reporter gene encoding enhanced green fluorescent protein. Results show that the b7SK promoter induced the greatest level of suppression in a range of cell lines. The comparison of these bovine promoters in shRNA expression is an important component for the future development of bovine-specific RNAi-based research.     

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI-BLAST-derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI-BLAST to BLAST-derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http://promoter.ics.uci.edu/BRNN-PRED/.     

Interpolated markov chains for eukaryotic promoter recognition.

MOTIVATION: We describe a new content-based approach for the detection of promoter regions of eukaryotic protein encoding genes. Our system is based on three interpolated Markov chains (IMCs) of different order which are trained on coding, non-coding and promoter sequences. It was recently shown that the interpolation of Markov chains leads to stable parameters and improves on the results in microbial gene finding (Salzberg et al., Nucleic Acids Res., 26, 544-548, 1998). Here, we present new methods for an automated estimation of optimal interpolation parameters and show how the IMCs can be applied to detect promoters in contiguous DNA sequences. Our interpolation approach can also be employed to obtain a reliable scoring function for human coding DNA regions, and the trained models can easily be incorporated in the general framework for gene recognition systems. RESULTS: A 5-fold cross-validation evaluation of our IMC approach on a representative sequence set yielded a mean correlation coefficient of 0.84 (promoter versus coding sequences) and 0.53 (promoter versus non-coding sequences). Applied to the task of eukaryotic promoter region identification in genomic DNA sequences, our classifier identifies 50% of the promoter regions in the sequences used in the most recent review and comparison by Fickett and Hatzigeorgiou ( Genome Res., 7, 861-878, 1997), while having a false-positive rate of 1/849 bp.     

Efficient algorithms for the discovery of gapped factors

Background

With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters.

Methods

In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot.

Results

Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity.

Conclusions

We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.     

Characterization of β-casein gene in Indian riverine buffalo

The study aimed at characterization of buffalo β-casein gene and its promoter by PCR-SSCP analysis. Complete β-casein exon VII region analysis revealed two SSCP band patterns, with pattern-I representing predominant allele B (85%) present in homozygous (genotype BB) condition and pattern-II representing a rare allele A1 present in heterozygous condition (genotype A1B). Sequencing of two patterns revealed three nucleotide substitutions at codon 68, 151 and 193 of exon VII. The cDNA sequence of buffalo β-casein gene indicated three further nucleotide substitutions between allele A1 and B at codon 10, 39, and 41. Analysis of β-casein proximal promoter region (− 350 upstream to + 32) revealed four SSCP band patterns. These SSCP patterns corresponded to nucleotide substitutions at seven locations within 382 bp 5′ UTR region of β-casein gene. Haplotype analysis suggested pattern-I of exon VII (wild type) was associated with three types of promoters and pattern-II of exon VII (rare type) corresponded to one exclusive type of promoter. The study suggested two haplotypes of exon VII and four haplotypes of promoter for buffalo β-casein.