An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes

Snowprint: a predictive tool for genetic biosensor discovery

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

Identification of Genomic Islands in Synechococcus sp. WH8102 Using Genomic Barcode and Whole-Genome Microarray Analysis

Background:

Synechococcus sp. WH8102 is one of the most abundant photosynthetic organisms in many ocean regions.

Objective:

The aim of this study is to identify genomic islands (GIs) in Synechococcus sp. WH8102 with integrated methods.

Methods:

We have applied genomic barcode to identify the GIs in Synechococcus sp. WH8102, which could make genomic regions of different origins visually apparent. The gene expression data of the predicted GIs was analyzed through microarray data which was collected for functional analysis of the relevant genes.

Results:

Seven GIs were identified in Synechococcus sp. WH8102. Most of them are involved in cell surface modification, photosynthesis and drug resistance. In addition, our analysis also revealed the functions of these GIs, which could be used for in-depth study on the evolution of this strain.

Conclusion:

Genomic barcodes provide us with a comprehensive and intuitive view of the target genome. We can use it to understand the intrinsic characteristics of the whole genome and identify GIs or other similar elements.

Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites

We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.

RhizoBindingSites, a Database of DNA-Binding Motifs in Nitrogen-Fixing Bacteria Inferred Using a Footprint Discovery Approach

Basic knowledge of transcriptional regulation is needed to understand the mechanisms governing biological processes, i.e., nitrogen fixation by Rhizobiales bacteria in symbiosis with leguminous plants. The RhizoBindingSites database is a computer-assisted framework providing motif-gene-associated conserved sequences potentially implicated in transcriptional regulation in nine symbiotic species. A dyad analysis algorithm was used to deduce motifs in the upstream regulatory region of orthologous genes, and only motifs also located in the gene seed promoter with a p-value of 1e-4 were accepted. A genomic scan analysis of the upstoream sequences with these motifs was performed. These predicted binding sites were categorized according to low, medium and high homology between the matrix and the upstream regulatory sequence. On average, 62.7% of the genes had a motif, accounting for 80.44% of the genes per genome, with 19613 matrices (a matrix is a representation of a motif). The RhizoBindingSites database provides motif and gene information, motif conservation in the order Rhizobiales, matrices, motif logos, regulatory networks constructed from theoretical or experimental data, a criterion for selecting motifs and a guide for users. The RhizoBindingSites database is freely available online at rhizobindingsites.ccg.unam.mx.

DOOR: a prokaryotic operon database for genome analyses and functional inference

The rapid accumulation of fully sequenced prokaryotic genomes provides unprecedented information for biological studies of bacterial and archaeal organisms in a systematic manner. Operons are the basic functional units for conducting such studies. Here, we review an operon database DOOR (the Database of prOkaryotic OpeRons) that we have previously developed and continue to update. Currently, the database contains 6 975 454 computationally predicted operons in 2072 complete genomes. In addition, the database also contains the following information: (i) transcriptional units for 24 genomes derived using publicly available transcriptomic data; (ii) orthologous gene mapping across genomes; (iii) 6408 cis-regulatory motifs for transcriptional factors of some operons for 203 genomes; (iv) 3 456 718 Rho-independent terminators for 2072 genomes; as well as (v) a suite of tools in support of applications of the predicted operons. In this review, we will explain how such data are computationally derived and demonstrate how they can be used to derive a wide range of higher-level information needed for systems biology studies to tackle complex and fundamental biology questions.

Perspectives of CRISPR/Cas-mediated cis-engineering in horticulture: unlocking the neglected potential for crop improvement

Directed breeding of horticultural crops is essential for increasing yield, nutritional content, and consumer-valued characteristics such as shape and color of the produce. However, limited genetic diversity restricts the amount of crop improvement that can be achieved through conventional breeding approaches. Natural genetic changes in cis-regulatory regions of genes play important roles in shaping phenotypic diversity by altering their expression. Utilization of CRISPR/Cas editing in crop species can accelerate crop improvement through the introduction of genetic variation in a targeted manner. The advent of CRISPR/Cas-mediated cis-regulatory region engineering (cis-engineering) provides a more refined method for modulating gene expression and creating phenotypic diversity to benefit crop improvement. Here, we focus on the current applications of CRISPR/Cas-mediated cis-engineering in horticultural crops. We describe strategies and limitations for its use in crop improvement, including de novo cis-regulatory element (CRE) discovery, precise genome editing, and transgene-free genome editing. In addition, we discuss the challenges and prospects regarding current technologies and achievements. CRISPR/Cas-mediated cis-engineering is a critical tool for generating horticultural crops that are better able to adapt to climate change and providing food for an increasing world population.

Genome-scale exploration of transcriptional regulation in the nisin Z producer Lactococcus lactis subsp. lactis IO-1

Transcription is of the most crucial steps of gene expression in bacteria, whose regulation guarantees the bacteria's ability to adapt to varying environmental conditions. Discovering the molecular basis and genomic principles of the transcriptional regulation is thus one of the most important tasks in cellular and molecular biology. Here, a comprehensive phylogenetic footprinting framework was implemented to predict maximal regulons of Lactococcus lactis subsp. lactis IO-1, a lactic acid bacterium known for its high potentials in nisin Z production as well as efficient xylose consumption which have made it a promising biotechnological strain. A total set of 321 regulons covering more than 90% of all the bacterium's operons have been elucidated and validated according to available data. Multiple novel biologically-relevant members were introduced amongst which arsC, mtlA and mtl operon for BusR, MtlR and XylR regulons can be named, respectively. Moreover, the effect of riboflavin on nisin biosynthesis was assessed in vitro and a negative correlation was observed. It is believed that understandings from such networks not only can be useful for studying transcriptional regulatory potentials of the target organism but also can be implemented in biotechnology to rationally design favorable production conditions.

Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies

Sigma factor, as a unit of RNA polymerase holoenzyme, is a critical factor in the process of gene transcriptional regulation. It recognizes the specific DNA sites and brings the core enzyme of RNA polymerase to the upstream regions of target genes. Therefore, the prediction of the promoters for a particular sigma factor is essential for interpreting functional genomic data and observation. This paper develops a new method to predict sigma-54 promoters in bacterial genomes. The new method organically integrates motif finding and machine learning strategies to capture the intrinsic features of sigma-54 promoters. The experiments on E. coli benchmark test set show that our method has good capability to distinguish sigma-54 promoters from surrounding or randomly selected DNA sequences. The applications of the other three bacterial genomes indicate the potential robustness and applicative power of our method on a large number of bacterial genomes. The source code of our method can be freely downloaded at https://github.com/maqin2001/PromotePredictor.

Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell-cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs.

RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis

Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed.

Flavivirus and Filovirus EvoPrinters: New alignment tools for the comparative analysis of viral evolution

Background

Flavivirus and Filovirus infections are serious epidemic threats to human populations. Multi-genome comparative analysis of these evolving pathogens affords a view of their essential, conserved sequence elements as well as progressive evolutionary changes. While phylogenetic analysis has yielded important insights, the growing number of available genomic sequences makes comparisons between hundreds of viral strains challenging. We report here a new approach for the comparative analysis of these hemorrhagic fever viruses that can superimpose an unlimited number of one-on-one alignments to identify important features within genomes of interest.

Methodology/Principal finding

We have adapted EvoPrinter alignment algorithms for the rapid comparative analysis of Flavivirus or Filovirus sequences including Zika and Ebola strains. The user can input a full genome or partial viral sequence and then view either individual comparisons or generate color-coded readouts that superimpose hundreds of one-on-one alignments to identify unique or shared identity SNPs that reveal ancestral relationships between strains. The user can also opt to select a database genome in order to access a library of pre-aligned genomes of either 1,094 Flaviviruses or 460 Filoviruses for rapid comparative analysis with all database entries or a select subset. Using EvoPrinter search and alignment programs, we show the following: 1) superimposing alignment data from many related strains identifies lineage identity SNPs, which enable the assessment of sublineage complexity within viral outbreaks; 2) whole-genome SNP profile screens uncover novel Dengue2 and Zika recombinant strains and their parental lineages; 3) differential SNP profiling identifies host cell A-to-I hyper-editing within Ebola and Marburg viruses, and 4) hundreds of superimposed one-on-one Ebola genome alignments highlight ultra-conserved regulatory sequences, invariant amino acid codons and evolutionarily variable protein-encoding domains within a single genome.

Conclusions/Significance

EvoPrinter allows for the assessment of lineage complexity within Flavivirus or Filovirus outbreaks, identification of recombinant strains, highlights sequences that have undergone host cell A-to-I editing, and identifies unique input and database SNPs within highly conserved sequences. EvoPrinter’s ability to superimpose alignment data from hundreds of strains onto a single genome has allowed us to identify unique Zika virus sublineages that are currently spreading in South, Central and North America, the Caribbean, and in China. This new set of integrated alignment programs should serve as a useful addition to existing tools for the comparative analysis of these viruses.

Flaviviruses, including Zika and Dengue viruses, and Filoviruses, including Ebola and Marburg viruses, are significant global public health threats. Genetic surveillance of viral isolates provides important insights into the origin of outbreaks, reveals lineage heterogeneity and diversification, and facilitates identification of novel recombinant strains and host cell modified viral genomes. We report the development of EvoPrinter, a web-accessed alignment tool for the rapid comparative analysis of viral genomes. EvoPrinter superimposes alignment data from multiple pairwise comparisons onto a single reference sequence of interest, to reveal both similarities and differences detected in hundreds of selected viral isolates. Evoprinter databases provide easy access to hundreds of non-redundant Flavivirus and Filovirus genomes. allowing the user to distinguish between sublineage identity SNPs and unique strain-specific SNPs, thus facilitating analysis of the history of viral diversification during an epidemic. EvoPrinter also proves useful in identifying recombinant strains and their parental lineages and detecting host-cell genomic editing. EvoPrinter should serve as a useful addition to existing tools for the comparative analysis of these viruses.