Prediction of fine-tuned promoter activity from DNA sequence

CarD contributes to diverse gene expression outcomes throughout the genome of Mycobacterium tuberculosis

The ability to regulate gene expression through transcription initiation underlies the adaptability and survival of all bacteria. Recent work has revealed that the transcription machinery in many bacteria diverges from the paradigm that has been established in Escherichia coliMycobacterium tuberculosis (Mtb) encodes the RNA polymerase (RNAP)-binding protein CarD, which is absent in E. coli but is required to form stable RNAP-promoter open complexes (RPo) and is essential for viability in Mtb The stabilization of RPo by CarD has been proposed to result in activation of gene expression; however, CarD has only been examined on limited promoters that do not represent the typical promoter structure in Mtb In this study, we investigate the outcome of CarD activity on gene expression from Mtb promoters genome-wide by performing RNA sequencing on a panel of mutants that differentially affect CarD's ability to stabilize RPo In all CarD mutants, the majority of Mtb protein encoding transcripts were differentially expressed, demonstrating that CarD had a global effect on gene expression. Contrary to the expected role of CarD as a transcriptional activator, mutation of CarD led to both up- and down-regulation of gene expression, suggesting that CarD can also act as a transcriptional repressor. Furthermore, we present evidence that stabilization of RPo by CarD could lead to transcriptional repression by inhibiting promoter escape, and the outcome of CarD activity is dependent on the intrinsic kinetic properties of a given promoter region. Collectively, our data support CarD's genome-wide role of regulating diverse transcription outcomes.

Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning

Motivation

The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance.

Results

Here, we propose a novel algorithm based on the so-called multiple-instance learning (MIL) paradigm. MIL breaks each DNA sequence into multiple overlapping subsequences and models each subsequence separately, therefore implicitly takes into consideration binding site locations, resulting in both higher accuracy and better interpretability of the models. The result from both in vivo and in vitro TF-DNA interaction data show that our approach significantly outperform conventional single-instance learning based algorithms. Importantly, the models learned from in vitro data using our approach can predict in vivo binding with very good accuracy. In addition, the location information obtained by our method provides additional insight for motif finding results from ChIP-Seq data. Finally, our approach can be easily combined with other state-of-the-art TF-DNA interaction modeling methods.

Availability and Implementation

http://www.cs.utsa.edu/∼jruan/MIL/.

Contact

jianhua.ruan@utsa.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Development of Streptomyces sp. FR-008 as an emerging chassis

Microbial-derived natural products are important in both the pharmaceutical industry and academic research. As the metabolic potential of original producer especially Streptomyces is often limited by slow growth rate, complicated cultivation profile, and unfeasible genetic manipulation, so exploring a Streptomyces as a super industrial chassis is valuable and urgent. Streptomyces sp. FR-008 is a fast-growing microorganism and can also produce a considerable amount of macrolide candicidin via modular polyketide synthase. In this study, we evaluated Streptomyces sp. FR-008 as a potential industrial-production chassis. First, PacBio sequencing and transcriptome analyses indicated that the Streptomyces sp. FR-008 genome size is 7.26 Mb, which represents one of the smallest of currently sequenced Streptomyces genomes. In addition, we simplified the conjugation procedure without heat-shock and pre-germination treatments but with high conjugation efficiency, suggesting it is inherently capable of accepting heterologous DNA. In addition, a series of promoters selected from literatures was assessed based on GusA activity in Streptomyces sp. FR-008. Compared with the common used promoter ermE*-p, the strength of these promoters comprise a library with a constitutive range of 60-860%, thus providing the useful regulatory elements for future genetic engineering purpose. In order to minimum the genome, we also target deleted three endogenous polyketide synthase (PKS) gene clusters to generate a mutant LQ3. LQ3 is thus an "updated" version of Streptomyces sp. FR-008, producing fewer secondary metabolites profiles than Streptomyces sp. FR-008. We believe this work could facilitate further development of Streptomyces sp. FR-008 for use in biotechnological applications.

Prediction of fine-tuned promoter activity from DNA sequence

The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.