ALBERT

Description: RNA molecules are attractive therapeutic targets because non-coding RNA molecules have increasingly been found to play key regulatory roles in the cell. Comparing and classifying RNA 3D structures yields unique insights into RNA evolution and function. With the rapid increase in the number of atomic-resolution RNA structures, it is crucial to have effective tools to classify RNA structures and to investigate them for structural similarities at different resolutions. We previously developed the algorithm CLICK to superimpose a pair of protein 3D structures by clique matching and 3D least squares fitting. In this study, we extend and optimize the CLICK algorithm to superimpose pairs of RNA 3D structures and RNA–protein complexes, independent of the associated topologies. Benchmarking Rclick on four different datasets showed that it is either comparable to or better than other structural alignment methods in terms of the extent of structural overlaps. Rclick also recognizes conformational changes between RNA structures and produces complementary alignments to maximize the extent of detectable similarity. Applying Rclick to study Ribonuclease III protein correctly aligned the RNA binding sites of RNAse III with its substrate. Rclick can be further extended to identify ligand-binding pockets in RNA. A web server is developed at http://mspc.bii.a-star.edu.sg/minhn/rclick.html .

Description: Despite the extensive use of  Saccharomyces cerevisiae  as a platform for synthetic biology, strain engineering remains slow and laborious. Here, we employ CRISPR/Cas9 technology to build a cloning-free toolkit that addresses commonly encountered obstacles in metabolic engineering, including chromosomal integration locus and promoter selection, as well as protein localization and solubility. The toolkit includes 23 Cas9-sgRNA plasmids, 37 promoters of various strengths and temporal expression profiles, and 10 protein-localization, degradation and solubility tags. We facilitated the use of these parts via a web-based tool, that automates the generation of DNA fragments for integration. Our system builds upon existing gene editing methods in the thoroughness with which the parts are standardized and characterized, the types and number of parts available and the ease with which our methodology can be used to perform genetic edits in yeast. We demonstrated the applicability of this toolkit by optimizing the expression of a challenging but industrially important enzyme, taxadiene synthase (TXS). This approach enabled us to diagnose an issue with TXS solubility, the resolution of which yielded a 25-fold improvement in taxadiene production.

Description: Genetic engineering projects often require control over when a protein is degraded. To this end, we use a fusion between a degron and an inactivating peptide that can be added to the N-terminus of a protein. When the corresponding protease is expressed, it cleaves the peptide and the protein is degraded. Three protease:cleavage site pairs from Potyvirus are shown to be orthogonal and active in exposing degrons, releasing inhibitory domains and cleaving polyproteins. This toolbox is applied to the design of genetic circuits as a means to control regulator activity and degradation. First, we demonstrate that a gate can be constructed by constitutively expressing an inactivated repressor and having an input promoter drive the expression of the protease. It is also shown that the proteolytic release of an inhibitory domain can improve the dynamic range of a transcriptional gate (200-fold repression). Next, we design polyproteins containing multiple repressors and show that their cleavage can be used to control multiple outputs. Finally, we demonstrate that the dynamic range of an output can be improved (8-fold to 190-fold) with the addition of a protease-cleaved degron. Thus, controllable proteolysis offers a powerful tool for modulating and expanding the function of synthetic gene circuits.

Description: The goal of pathway analysis is to identify the pathways that are significantly impacted when a biological system is perturbed, e.g. by a disease or drug. Current methods treat pathways as independent entities. However, many signals are constantly sent from one pathway to another, essentially linking all pathways into a global, system-wide complex. In this work, we propose a set of three pathway analysis methods based on the impact analysis, that performs a system-level analysis by considering all signals between pathways, as well as their overlaps. Briefly, the global system is modeled in two ways: (i) considering the inter-pathway interaction exchange for each individual pathways, and (ii) combining all individual pathways to form a global, system-wide graph. The third analysis method is a hybrid of these two models. The new methods were compared with DAVID, GSEA, GSA, PathNet, Crosstalk and SPIA on 23 GEO data sets involving 19 tissues investigated in 12 conditions. The results show that both the ranking and the P -values of the target pathways are substantially improved when the analysis considers the system-wide dependencies and interactions between pathways.

Description: Bioinformatic analysis often produces large sets of genomic ranges that can be difficult to interpret in the absence of genomic context. Goldmine annotates genomic ranges from any source with gene model and feature contexts to facilitate global descriptions and candidate loci discovery. We demonstrate the value of genomic context by using Goldmine to elucidate context dynamics in transcription factor binding and to reveal differentially methylated regions (DMRs) with context-specific functional correlations. The open source R package and documentation for Goldmine are available at http://jeffbhasin.github.io/goldmine .

Description: CCCTC-binding factor (CTCF) is a multi-functional protein that is assigned various, even contradictory roles in the genome. High-throughput sequencing-based technologies such as ChIP-seq and Hi-C provided us the opportunity to assess the multivalent functions of CTCF in the human genome. The location of CTCF-binding sites with respect to genomic features provides insights into the possible roles of this protein. Here we present the first genome-wide survey and characterization of three important functions of CTCF: enhancer insulator, chromatin barrier and enhancer linker. We developed a novel computational framework to discover the multivalent functions of CTCF based on chromatin state and three-dimensional chromatin architecture. We applied our method to five human cell lines and identified ~46 000 non-redundant CTCF sites related to the three functions. Disparate effects of these functions on gene expression were found and distinct genomic features of these CTCF sites were characterized in GM12878 cells. Finally, we investigated the cell-type specificities of CTCF sites related to these functions across five cell types. Our study provides new insights into the multivalent functions of CTCF in the human genome.

Description: Existing methods for interpreting protein variation focus on annotating mutation pathogenicity rather than detailed interpretation of variant deleteriousness and frequently use only sequence-based or structure-based information. We present VIPUR, a computational framework that seamlessly integrates sequence analysis and structural modelling (using the Rosetta protein modelling suite) to identify and interpret deleterious protein variants. To train VIPUR, we collected 9477 protein variants with known effects on protein function from multiple organisms and curated structural models for each variant from crystal structures and homology models. VIPUR can be applied to mutations in any organism's proteome with improved generalized accuracy (AUROC .83) and interpretability (AUPR .87) compared to other methods. We demonstrate that VIPUR's predictions of deleteriousness match the biological phenotypes in ClinVar and provide a clear ranking of prediction confidence. We use VIPUR to interpret known mutations associated with inflammation and diabetes, demonstrating the structural diversity of disrupted functional sites and improved interpretation of mutations associated with human diseases. Lastly, we demonstrate VIPUR's ability to highlight candidate variants associated with human diseases by applying VIPUR to de novo variants associated with autism spectrum disorders.

Description: Proteins adhere to DNA at locations and with strengths that depend on the protein conformation, the underlying DNA sequence and the ionic content of the solution. A facile technique to probe the positions and strengths of protein-DNA binding would aid in understanding these important interactions. Here, we describe a ‘DNA pulley’ for position-resolved nano-mechanical measurements of protein-DNA interactions. A molecule of DNA is tethered by one end to a glass surface, and by the other end to a magnetic bead. The DNA is stretched horizontally by a magnet, and a nanoscale knife made of silicon nitride is manipulated to contact, bend and scan along the DNA. The mechanical profile of the DNA at the contact with the knife is probed via nanometer-precision optical tracking of the magnetic bead. This system enables detection of protein bumps on the DNA and localization of their binding sites. We study theoretically the technical requirements to detect mechanical heterogeneities in the DNA itself.

Description: Synthetic biology seeks to envision living cells as a matter of engineering. However, increasing evidence suggests that the genetic load imposed by the incorporation of synthetic devices in a living organism introduces a sort of unpredictability in the design process. As a result, individual part characterization is not enough to predict the behavior of designed circuits and thus, a costly trial-error process is eventually required. In this work, we provide a new theoretical framework for the predictive treatment of the genetic load. We mathematically and experimentally demonstrate that dependences among genes follow a quantitatively predictable behavior. Our theory predicts the observed reduction of the expression of a given synthetic gene when an extra genetic load is introduced in the circuit. The theory also explains that such dependence qualitatively differs when the extra load is added either by transcriptional or translational modifications. We finally show that the limitation of the cellular resources for gene expression leads to a mathematical formulation that converges to an expression analogous to the Ohm's law for electric circuits. Similitudes and divergences with this law are outlined. Our work provides a suitable framework with predictive character for the design process of complex genetic devices in synthetic biology.

Description: Light-regulated modules offer unprecedented new ways to control cellular behavior in precise spatial and temporal resolution. The availability of such tools may dramatically accelerate the progression of synthetic biology applications. Nonetheless, current optogenetic toolbox of prokaryotes has potential issues such as lack of rapid and switchable control, less portable, low dynamic expression and limited parts. To address these shortcomings, we have engineered a novel bidirectional promoter system for Escherichia coli that can be induced or repressed rapidly and reversibly using the blue light dependent DNA-binding protein EL222. We demonstrated that by modulating the dosage of light pulses or intensity we could control the level of gene expression precisely. We show that both light-inducible and repressible system can function in parallel with high spatial precision in a single cell and can be switched stably between ON- and OFF-states by repetitive pulses of blue light. In addition, the light-inducible and repressible expression kinetics were quantitatively analysed using a mathematical model. We further apply the system, for the first time, to optogenetically synchronize two receiver cells performing different logic behaviors over time using blue light as a molecular clock signal. Overall, our modular approach layers a transformative platform for next-generation light-controllable synthetic biology systems in prokaryotes.

Description: Current DNA assembly methods for preparing highly purified linear subassemblies require complex and time-consuming in vitro manipulations that hinder their ability to construct megabase-sized DNAs (e.g. synthetic genomes). We have developed a new method designated ‘CasHRA ( Cas 9-facilitated H omologous R ecombination A ssembly)’ that directly uses large circular DNAs in a one-step in vivo assembly process. The large circular DNAs are co-introduced into Saccharomyces cerevisiae by protoplast fusion, and they are cleaved by RNA-guided Cas9 nuclease to release the linear DNA segments for subsequent assembly by the endogenous homologous recombination system. The CasHRA method allows efficient assembly of multiple large DNA segments in vivo ; thus, this approach should be useful in the last stage of genome construction. As a proof of concept, we combined CasHRA with an upstream assembly method (Gibson procedure of genome assembly) and successfully constructed a 1.03 Mb MGE-syn1.0 ( M inimal G enome of Escherichia coli ) that contained 449 essential genes and 267 important growth genes. We expect that CasHRA will be widely used in megabase-sized genome constructions.

Description: We have investigated transcriptional interference between convergent genes in E. coli and demonstrate substantial interference for inter-promoter distances of as far as 3 kb. Interference can be elicited by both strong 70 dependent and T7 promoters. In the presented design, a strong promoter driving gene expression of a ‘forward’ gene interferes with the expression of a ‘reverse’ gene by a weak promoter. This arrangement allows inversely correlated gene expression without requiring further regulatory components. Thus, modulation of the activity of the strong promoter alters expression of both the forward and the reverse gene. We used this design to develop a dual selection system for conditional operator site binding, allowing positive selection both for binding and for non-binding to DNA. This study demonstrates the utility of this novel system using the Lac repressor as a model protein for conditional DNA binding, and spectinomycin and chloramphenicol resistance genes as positive selection markers in liquid culture. Randomized LacI libraries were created and subjected to subsequent dual selection, but mispairing IPTG and selection cues in respect to the wild-type LacI response, allowing the isolation of a LacI variant with a reversed IPTG response within three rounds of library generation and dual selection.

Description: The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code ( http://www.NetDecoder.org ) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

Description: Microfluidics may revolutionize our ability to write synthetic DNA by addressing several fundamental limitations associated with generating novel genetic constructs. Here we report the first de novo synthesis and cell-free cloning of custom DNA libraries in sub-microliter reaction droplets using programmable digital microfluidics. Specifically, we developed Programmable Order Polymerization (POP), Microfluidic Combinatorial Assembly of DNA (M-CAD) and Microfluidic In-vitro Cloning (MIC) and applied them to de novo synthesis, combinatorial assembly and cell-free cloning of genes, respectively. Proof-of-concept for these methods was demonstrated by programming an autonomous microfluidic system to construct and clone libraries of yeast ribosome binding sites and bacterial Azurine, which were then retrieved in individual droplets and validated. The ability to rapidly and robustly generate designer DNA molecules in an autonomous manner should have wide application in biological research and development.

Description: Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas.

Description: It is being increasingly realized that nucleosome organization on DNA crucially regulates DNA–protein interactions and the resulting gene expression. While the spatial character of the nucleosome positioning on DNA has been experimentally and theoretically studied extensively, the temporal character is poorly understood. Accounting for ATPase activity and DNA-sequence effects on nucleosome kinetics, we develop a theoretical method to estimate the time of continuous exposure of binding sites of non-histone proteins (e.g. transcription factors and TATA binding proteins) along any genome. Applying the method to Saccharomyces cerevisiae , we show that the exposure timescales are determined by cooperative dynamics of multiple nucleosomes, and their behavior is often different from expectations based on static nucleosome occupancy. Examining exposure times in the promoters of GAL1 and PHO5, we show that our theoretical predictions are consistent with known experiments. We apply our method genome-wide and discover huge gene-to-gene variability of mean exposure times of TATA boxes and patches adjacent to TSS (+1 nucleosome region); the resulting timescale distributions have non-exponential tails.

Description: The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ~6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ~90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe . We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.

Description: Transfer RNAs (tRNAs) are essential for encoding the transcribed genetic information from DNA into proteins. Variations in the human tRNAs are involved in diverse clinical phenotypes. Interestingly, all pathogenic variations in tRNAs are located in mitochondrial tRNAs (mt-tRNAs). Therefore, it is crucial to identify pathogenic variations in mt-tRNAs for disease diagnosis and proper treatment. We collected mt-tRNA variations using a classification based on evidence from several sources and used the data to develop a multifactorial probability-based prediction method, PON-mt-tRNA, for classification of mt-tRNA single nucleotide substitutions. We integrated a machine learning-based predictor and an evidence-based likelihood ratio for pathogenicity using evidence of segregation, biochemistry and histochemistry to predict the posterior probability of pathogenicity of variants. The accuracy and Matthews correlation coefficient (MCC) of PON-mt-tRNA are 1.00 and 0.99, respectively. In the absence of evidence from segregation, biochemistry and histochemistry, PON-mt-tRNA classifies variations based on the machine learning method with an accuracy and MCC of 0.69 and 0.39, respectively. We classified all possible single nucleotide substitutions in all human mt-tRNAs using PON-mt-tRNA. The variations in the loops are more often tolerated compared to the variations in stems. The anticodon loop contains comparatively more predicted pathogenic variations than the other loops. PON-mt-tRNA is available at http://structure.bmc.lu.se/PON-mt-tRNA/ .

Description: While the cost of DNA sequencing has dropped by five orders of magnitude in the past decade, DNA synthesis remains expensive for many applications. Although DNA microarrays have decreased the cost of oligonucleotide synthesis, the use of array-synthesized oligos in practice is limited by short synthesis lengths, high synthesis error rates, low yield and the challenges of assembling long constructs from complex pools. Toward addressing these issues, we developed a protocol for multiplex pairwise assembly of oligos from array-synthesized oligonucleotide pools. To evaluate the method, we attempted to assemble up to 2271 targets ranging in length from 192–252 bases using pairs of array-synthesized oligos. Within sets of complexity ranging from 131–250 targets, we observed error-free assemblies for 90.5% of all targets. When all 2271 targets were assembled in one reaction, we observed error-free constructs for 70.6%. While the assembly method intrinsically increased accuracy to a small degree, we further increased accuracy by using a high throughput ‘Dial-Out PCR’ protocol, which combines Illumina sequencing with an in-house set of unique PCR tags to selectively amplify perfect assemblies from complex synthetic pools. This approach has broad applicability to DNA assembly and high-throughput functional screens.

Description: Sequence Logos and its variants are the most commonly used method for visualization of multiple sequence alignments (MSAs) and sequence motifs. They provide consensus-based summaries of the sequences in the alignment. Consequently, individual sequences cannot be identified in the visualization and covariant sites are not easily discernible. We recently proposed Sequence Bundles , a motif visualization technique that maintains a one-to-one relationship between sequences and their graphical representation and visualizes covariant sites. We here present Alvis, an open-source platform for the joint explorative analysis of MSAs and phylogenetic trees, employing Sequence Bundles as its main visualization method. Alvis combines the power of the visualization method with an interactive toolkit allowing detection of covariant sites, annotation of trees with synapomorphies and homoplasies, and motif detection. It also offers numerical analysis functionality, such as dimension reduction and classification. Alvis is user-friendly, highly customizable and can export results in publication-quality figures. It is available as a full-featured standalone version ( http://www.bitbucket.org/rfs/alvis ) and its Sequence Bundles visualization module is further available as a web application ( http://science-practice.com/projects/sequence-bundles ).

Description: Libraries of well-characterised components regulating gene expression levels are essential to many synthetic biology applications. While widely available for the Gram-negative model bacterium Escherichia coli , such libraries are lacking for the Gram-positive model Bacillus subtilis , a key organism for basic research and biotechnological applications. Here, we engineered a genetic toolbox comprising libraries of promoters, Ribosome Binding Sites (RBS), and protein degradation tags to precisely tune gene expression in B. subtilis . We first designed a modular Expression Operating Unit (EOU) facilitating parts assembly and modifications and providing a standard genetic context for gene circuits implementation. We then selected native, constitutive promoters of B. subtilis and efficient RBS sequences from which we engineered three promoters and three RBS sequence libraries exhibiting ~14 000-fold dynamic range in gene expression levels. We also designed a collection of SsrA proteolysis tags of variable strength. Finally, by using fluorescence fluctuation methods coupled with two-photon microscopy, we quantified the absolute concentration of GFP in a subset of strains from the library. Our complete promoters and RBS sequences library comprising over 135 constructs enables tuning of GFP concentration over five orders of magnitude, from 0.05 to 700 μM. This toolbox of regulatory components will support many research and engineering applications in B. subtilis .

Description: Functional RNA regions are often related to recurrent secondary structure patterns (or motifs), which can exert their role in several different ways, particularly in dictating the interaction with RNA-binding proteins, and acting in the regulation of a large number of cellular processes. Among the available motif-finding tools, the majority focuses on sequence patterns, sometimes including secondary structure as additional constraints to improve their performance. Nonetheless, secondary structures motifs may be concurrent to their sequence counterparts or even encode a stronger functional signal. Current methods for searching structural motifs generally require long pipelines and/or high computational efforts or previously aligned sequences. Here, we present BEAM (BEAr Motif finder), a novel method for structural motif discovery from a set of unaligned RNAs, taking advantage of a recently developed encoding for RNA secondary structure named BEAR (Brand nEw Alphabet for RNAs) and of evolutionary substitution rates of secondary structure elements. Tested in a varied set of scenarios, from small- to large-scale, BEAM is successful in retrieving structural motifs even in highly noisy data sets, such as those that can arise in CLIP-Seq or other high-throughput experiments.

Description: Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish ( Danio rerio ), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet ( www.inetbio.org/danionet ), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.

Description: Population-scale sequencing is increasingly uncovering large numbers of rare single-nucleotide variants (SNVs) in coding regions of the genome. The rarity of these variants makes it challenging to evaluate their deleteriousness with conventional phenotype–genotype associations. Protein structures provide a way of addressing this challenge. Previous efforts have focused on globally quantifying the impact of SNVs on protein stability. However, local perturbations may severely impact protein functionality without strongly disrupting global stability (e.g. in relation to catalysis or allostery). Here, we describe a workflow in which localized frustration, quantifying unfavorable local interactions, is employed as a metric to investigate such effects. Using this workflow on the Protein Databank, we find that frustration produces many immediately intuitive results: for instance, disease-related SNVs create stronger changes in localized frustration than non-disease related variants, and rare SNVs tend to disrupt local interactions to a larger extent than common variants. Less obviously, we observe that somatic SNVs associated with oncogenes and tumor suppressor genes (TSGs) induce very different changes in frustration. In particular, those associated with TSGs change the frustration more in the core than the surface (by introducing loss-of-function events), whereas those associated with oncogenes manifest the opposite pattern, creating gain-of-function events.

Description: A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Description: Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic. Availability and Implementation: A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm . Contact: peddada@niehs.nih.gov

Description: A whole-cell biosensor utilizing a transcription factor (TF) is an effective tool for sensitive and selective detection of specialty chemicals or anthropogenic molecules, but requires access to an expanded repertoire of TFs. Using homology modeling and ligand docking for binding pocket identification, assisted by conservative mutations in the pocket, we engineered a novel specificity in an Acinetobacter TF, PobR, to ‘sense’ a chemical p-nitrophenol (pNP) and measured the response via a fluorescent protein reporter expressed from a PobR promoter. Out of 10 7 variants of PobR, four were active when dosed with pNP, with two mutants showing a specificity switch from the native effector 4-hydroxybenzoate (4HB). One of the mutants, pNPmut1 was then used to create a smart microbial cell responding to pNP production from hydrolysis of an insecticide, paraoxon, in a coupled assay involving phosphotriesterase (PTE) enzyme expressed from a separate promoter. We show the fluorescence of the cells correlated with the catalytic efficiency of the PTE variant expressed in each cell. High selectivity between similar molecules (4HB versus pNP), high sensitivity for pNP detection (~2 μM) and agreement of apo- and holo-structures of PobR scaffold with predetermined computational models are other significant results presented in this work.

Description: Advances in synthetic biology and our understanding of the rules of promoter architecture have led to the development of diverse synthetic constitutive and inducible promoters in eukaryotes and prokaryotes. However, the design of promoters inducible by specific endogenous or environmental conditions is still rarely undertaken. In this study, we engineered and characterized a set of strong, synthetic promoters for budding yeast Saccharomyces cerevisiae that are inducible under acidic conditions (pH ≤ 3). Using available expression and transcription factor binding data, literature on transcriptional regulation, and known rules of promoter architecture we improved the low-pH performance of the YGP1 promoter by modifying transcription factor binding sites in its upstream activation sequence. The engineering strategy outlined for the YGP1 promoter was subsequently applied to create a response to low pH in the unrelated CCW14 promoter. We applied our best promoter variants to low-pH fermentations, enabling ten-fold increased production of lactic acid compared to titres obtained with the commonly used, native TEF1 promoter. Our findings outline and validate a general strategy to iteratively design and engineer synthetic yeast promoters inducible to environmental conditions or stresses of interest.

Description: Sensory photoreceptors have enabled non-invasive and spatiotemporal control of numerous biological processes. Photoreceptor engineering has expanded the repertoire beyond natural receptors, but to date no generally applicable strategy exists towards constructing light-regulated protein actuators of arbitrary function. We hence explored whether the homodimeric Rhodobacter sphaeroides light-oxygen-voltage (LOV) domain ( Rs LOV) that dissociates upon blue-light exposure can confer light sensitivity onto effector proteins, via a mechanism of light-induced functional site release. We chose the RNA-guided programmable DNA endonuclease Cas9 as proof-of-principle effector, and constructed a comprehensive library of Rs LOV inserted throughout the Cas9 protein. Screening with a high-throughput assay based on transcriptional repression in Escherichia coli yielded paRC9, a moderately light-activatable variant. As domain insertion can lead to protein destabilization, we also screened the library for temperature-sensitive variants and isolated tsRC9, a variant with robust activity at 29°C but negligible activity at 37°C. Biochemical assays confirmed temperature-dependent DNA cleavage and binding for tsRC9, but indicated that the light sensitivity of paRC9 is specific to the cellular setting. Using tsRC9, the first temperature-sensitive Cas9 variant, we demonstrate temperature-dependent transcriptional control over ectopic and endogenous genetic loci. Taken together, Rs LOV can confer light sensitivity onto an unrelated effector; unexpectedly, the same LOV domain can also impart strong temperature sensitivity.

Description: Sequence alignment is a long standing problem in bioinformatics. The Basic Local Alignment Search Tool (BLAST) is one of the most popular and fundamental alignment tools. The explosive growth of biological sequences calls for speedup of sequence alignment tools such as BLAST. To this end, we develop high speed BLASTN (HS-BLASTN), a parallel and fast nucleotide database search tool that accelerates MegaBLAST—the default module of NCBI-BLASTN. HS-BLASTN builds a new lookup table using the FMD-index of the database and employs an accurate and effective seeding method to find short stretches of identities (called seeds) between the query and the database. HS-BLASTN produces the same alignment results as MegaBLAST and its computational speed is much faster than MegaBLAST. Specifically, our experiments conducted on a 12-core server show that HS-BLASTN can be 22 times faster than MegaBLAST and exhibits better parallel performance than MegaBLAST. HS-BLASTN is written in C++ and the related source code is available at https://github.com/chenying2016/queries under the GPLv3 license.

Description: Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

Description: MicroRNAs (miRNAs) regulate gene expression by binding to partially complementary sequences on target mRNA transcripts, thereby causing their degradation, deadenylation, or inhibiting their translation. Genomic variants can alter miRNA regulation by modifying miRNA target sites, and multiple human disease phenotypes have been linked to such miRNA target site variants (miR-TSVs). However, systematic genome-wide identification of functional miR-TSVs is difficult due to high false positive rates; functional miRNA recognition sequences can be as short as six nucleotides, with the human genome encoding thousands of miRNAs. Furthermore, while large-scale clinical genomic data sets are becoming increasingly commonplace, existing miR-TSV prediction methods are not designed to analyze these data. Here, we present an open-source tool called SubmiRine that is designed to perform efficient miR-TSV prediction systematically on variants identified in novel clinical genomic data sets. Most importantly, SubmiRine allows for the prioritization of predicted miR-TSVs according to their relative probability of being functional. We present the results of SubmiRine using integrated clinical genomic data from a large-scale cohort study on chronic obstructive pulmonary disease (COPD), making a number of high-scoring, novel miR-TSV predictions. We also demonstrate SubmiRine's ability to predict and prioritize known miR-TSVs that have undergone experimental validation in previous studies.

Description: Transformation-associated recombination (TAR) protocol allowing the selective isolation of full-length genes complete with their distal enhancer regions and entire genomic loci with sizes up to 250 kb from complex genomes in yeast S. cerevisiae has been developed more than a decade ago. However, its wide spread usage has been impeded by a low efficiency (0.5–2%) of chromosomal region capture during yeast transformants which in turn requires a time-consuming screen of hundreds of colonies. Here, we demonstrate that pre-treatment of genomic DNA with CRISPR-Cas9 nucleases to generate double-strand breaks near the targeted genomic region results in a dramatic increase in the fraction of gene-positive colonies (up to 32%). As only a dozen or less yeast transformants need to be screened to obtain a clone with the desired chromosomal region, extensive experience with yeast is no longer required. A TAR-CRISPR protocol may help to create a bank of human genes, each represented by a genomic copy containing its native regulatory elements, that would lead to a significant advance in functional, structural and comparative genomics, in diagnostics, gene replacement, generation of animal models for human diseases and has a potential for gene therapy.

Description: Of the ~1.3 million Alu elements in the human genome, only a tiny number are estimated to be active in transcription by RNA polymerase (Pol) III. Tracing the individual loci from which Alu transcripts originate is complicated by their highly repetitive nature. By exploiting RNA-Seq data sets and unique Alu DNA sequences, we devised a bioinformatic pipeline allowing us to identify Pol III-dependent transcripts of individual Alu elements. When applied to ENCODE transcriptomes of seven human cell lines, this search strategy identified ~1300 Alu loci corresponding to detectable transcripts, with ~120 of them expressed in at least three cell lines. In vitro transcription of selected Alu s did not reflect their in vivo expression properties, and required the native 5'-flanking region in addition to internal promoter. We also identified a cluster of expressed Alu Ya5-derived transcription units, juxtaposed to snaR genes on chromosome 19, formed by a promoter-containing left monomer fused to an Alu -unrelated downstream moiety. Autonomous Pol III transcription was also revealed for Alu s nested within Pol II-transcribed genes. The ability to investigate Alu transcriptomes at single-locus resolution will facilitate both the identification of novel biologically relevant Alu RNAs and the assessment of Alu expression alteration under pathological conditions.

Description: RNA research and therapy relies primarily on synthetic RNAs. We employed recombinant RNA technology toward large-scale production of pre-miRNA agents in bacteria, but found the majority of target RNAs were not or negligibly expressed. We thus developed a novel strategy to achieve consistent high-yield biosynthesis of chimeric RNAs carrying various small RNAs (e.g. miRNAs, siRNAs and RNA aptamers), which was based upon an optimal noncoding RNA scaffold (OnRS) derived from tRNA fusion pre-miR-34a (tRNA/mir-34a). Multi-milligrams of chimeric RNAs (e.g. OnRS/miR-124, OnRS/GFP-siRNA, OnRS/Neg (scrambled RNA) and OnRS/MGA (malachite green aptamer)) were readily obtained from 1 l bacterial culture. Deep sequencing analyses revealed that mature miR-124 and target GFP-siRNA were selectively released from chimeric RNAs in human cells. Consequently, OnRS/miR-124 was active in suppressing miR-124 target gene expression and controlling cellular processes, and OnRS/GFP-siRNA was effective in knocking down GFP mRNA levels and fluorescent intensity in ES-2/GFP cells and GFP -transgenic mice. Furthermore, the OnRS/MGA sensor offered a specific strong fluorescence upon binding MG, which was utilized as label-free substrate to accurately determine serum RNase activities in pancreatic cancer patients. These results demonstrate that OnRS-based bioengineering is a common, robust and versatile strategy to assemble various types of small RNAs for broad applications.

Description: Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under physiological conditions in-vitro , are common in eukaryotes, and facilitate interactions with RNA, DNA and proteins. Current methods for prediction of IDPs and IDRs do not provide insights into their functions, except for a handful of methods that address predictions of protein-binding regions. We report first-of-its-kind computational method DisoRDPbind for high-throughput prediction of RNA, DNA and protein binding residues located in IDRs from protein sequences. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder and sequence alignment. Empirical tests demonstrate that it provides accurate predictions that are competitive with other predictors of disorder-mediated protein binding regions and complementary to the methods that predict RNA- and DNA-binding residues annotated based on crystal structures. Application in Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila melanogaster proteomes reveals that RNA- and DNA-binding proteins predicted by DisoRDPbind complement and overlap with the corresponding known binding proteins collected from several sources. Also, the number of the putative protein-binding regions predicted with DisoRDPbind correlates with the promiscuity of proteins in the corresponding protein–protein interaction networks. Webserver: http://biomine.ece.ualberta.ca/DisoRDPbind/

Description: Natural regulatory networks contain many interacting components that allow for fine-tuning of switching and memory properties. Building simple bistable switches, synthetic biologists have learned the design principles of complex natural regulatory networks. However, most switches constructed so far are so simple (e.g. comprising two regulators) that they are functional only within a limited parameter range. Here, we report the construction of robust, tunable bistable switches in Escherichia coli using three heterologous protein regulators (ExsADC) that are sequestered into an inactive complex through a partner swapping mechanism. On the basis of mathematical modeling, we accurately predict and experimentally verify that the hysteretic region can be fine-tuned by controlling the interactions of the ExsADC regulatory cascade using the third member ExsC as a tuning knob. Additionally, we confirm that a dual-positive feedback switch can markedly increase the hysteretic region, compared to its single-positive feedback counterpart. The dual-positive feedback switch displays bistability over a 10 6 -fold range of inducer concentrations, to our knowledge, the largest range reported so far. This work demonstrates the successful interlocking of sequestration-based ultrasensitivity and positive feedback, a design principle that can be applied to the construction of robust, tunable, and predictable genetic programs to achieve increasingly sophisticated biological behaviors.

Description: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Description: We describe solid-phase cloning (SPC) for high-throughput assembly of expression plasmids. Our method allows PCR products to be put directly into a liquid handler for capture and purification using paramagnetic streptavidin beads and conversion into constructs by subsequent cloning reactions. We present a robust automated protocol for restriction enzyme based SPC and its performance for the cloning of 〉60 000 unique human gene fragments into expression vectors. In addition, we report on SPC-based single-strand assembly for applications where exact control of the sequence between fragments is needed or where multiple inserts are to be assembled. In this approach, the solid support allows for head-to-tail assembly of DNA fragments based on hybridization and polymerase fill-in. The usefulness of head-to-tail SPC was demonstrated by assembly of 〉150 constructs with up to four DNA parts at an average success rate above 80%. We report on several applications for SPC and we suggest it to be particularly suitable for high-throughput efforts using laboratory workstations.

Description: Protein sequences predicted from metagenomic datasets are annotated by identifying their homologs via sequence comparisons with reference or curated proteins. However, a majority of metagenomic protein sequences are partial-length, arising as a result of identifying genes on sequencing reads or on assembled nucleotide contigs, which themselves are often very fragmented. The fragmented nature of metagenomic protein predictions adversely impacts homology detection and, therefore, the quality of the overall annotation of the dataset. Here we present a novel algorithm called GRASP that accurately identifies the homologs of a given reference protein sequence from a database consisting of partial-length metagenomic proteins. Our homology detection strategy is guided by the reference sequence, and involves the simultaneous search and assembly of overlapping database sequences. GRASP was compared to three commonly used protein sequence search programs (BLASTP, PSI-BLAST and FASTM). Our evaluations using several simulated and real datasets show that GRASP has a significantly higher sensitivity than these programs while maintaining a very high specificity. GRASP can be a very useful program for detecting and quantifying taxonomic and protein family abundances in metagenomic datasets. GRASP is implemented in GNU C++, and is freely available at http://sourceforge.net/projects/grasp-release .

Description: Genetic screens of an unprecedented scale have recently been made possible by the availability of high-complexity libraries of synthetic oligonucleotides designed to mediate either gene knockdown or gene knockout, coupled with next-generation sequencing. However, several sources of random noise and statistical biases complicate the interpretation of the resulting high-throughput data. We developed HiTSelect, a comprehensive analysis pipeline for rigorously selecting screen hits and identifying functionally relevant genes and pathways by addressing off-target effects, controlling for variance in both gene silencing efficiency and sequencing depth of coverage and integrating relevant metadata. We document the superior performance of HiTSelect using data from both genome-wide RNAi and CRISPR/Cas9 screens. HiTSelect is implemented as an open-source package, with a user-friendly interface for data visualization and pathway exploration. Binary executables are available at http://sourceforge.net/projects/hitselect/ , and the source code is available at https://github.com/diazlab/HiTSelect .

Description: Integrative analyses of epigenetic data promise a deeper understanding of the epigenome. Epidaurus is a bioinformatics tool used to effectively reveal inter-dataset relevance and differences through data aggregation, integration and visualization. In this study, we demonstrated the utility of Epidaurus in validating hypotheses and generating novel biological insights. In particular, we described the use of Epidaurus to (i) integrate epigenetic data from prostate cancer cell lines to validate the activation function of EZH2 in castration-resistant prostate cancer and to (ii) study the mechanism of androgen receptor ( AR ) binding deregulation induced by the knockdown of FOXA1 . We found that EZH2 's noncanonical activation function was reaffirmed by its association with active histone markers and the lack of association with repressive markers. More importantly, we revealed that the binding of AR was selectively reprogramed to promoter regions, leading to the up-regulation of hundreds of cancer-associated genes including EGFR . The prebuilt epigenetic dataset from commonly used cell lines (LNCaP, VCaP, LNCaP-Abl, MCF7, GM12878, K562, HeLa-S3, A549, HePG2) makes Epidaurus a useful online resource for epigenetic research. As standalone software, Epidaurus is specifically designed to process user customized datasets with both efficiency and convenience.

Description: RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ~94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ~83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred .

Description: Identifying conserved and divergent response patterns in gene networks is becoming increasingly important. A common approach is integrating expression information with gene association networks in order to find groups of connected genes that are activated or repressed. In many cases, researchers are also interested in comparisons across species (or conditions). Finding an active sub-network is a hard problem and applying it across species requires further considerations (e.g. orthology information, expression data and networks from different sources). To address these challenges we devised ModuleBlast, which uses both expression and network topology to search for highly relevant sub-networks. We have applied ModuleBlast to expression and interaction data from mouse, macaque and human to study immune response and aging. The immune response analysis identified several relevant modules, consistent with recent findings on apoptosis and NFB activation following infection. Temporal analysis of these data revealed cascades of modules that are dynamically activated within and across species. We have experimentally validated some of the novel hypotheses resulting from the analysis of the ModuleBlast results leading to new insights into the mechanisms used by a key mammalian aging protein.

Description: Here we used discriminative training methods to uncover the chromatin, transcription factor (TF) binding and sequence features of enhancers underlying gene expression in individual cardiac cells. We used machine learning with TF motifs and ChIP data for a core set of cardiogenic TFs and histone modifications to classify Drosophila cell-type-specific cardiac enhancer activity. We show that the classifier models can be used to predict cardiac cell subtype cis -regulatory activities. Associating the predicted enhancers with an expression atlas of cardiac genes further uncovered clusters of genes with transcription and function limited to individual cardiac cell subtypes. Further, the cell-specific enhancer models revealed chromatin, TF binding and sequence features that distinguish enhancer activities in distinct subsets of heart cells. Collectively, our results show that computational modeling combined with empirical testing provides a powerful platform to uncover the enhancers, TF motifs and gene expression profiles which characterize individual cardiac cell fates.

Description: A key aspect of RNA secondary structure prediction is the identification of novel functional elements. This is a challenging task because these elements typically are embedded in longer transcripts where the borders between the element and flanking regions have to be defined. The flanking sequences impact the folding of the functional elements both at the level of computational analyses and when the element is extracted as a transcript for experimental analysis. Here, we analyze how different flanking region lengths impact folding into a constrained structure by computing probabilities of folding for different sizes of flanking regions. Our method, RNAcop (RNA context optimization by probability), is tested on known and de novo predicted structures. In vitro experiments support the computational analysis and suggest that for a number of structures, choosing proper lengths of flanking regions is critical. RNAcop is available as web server and stand-alone software via http://rth.dk/resources/rnacop .

Description: Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx .

Description: Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone.

Description: Optimizing bio-production involves strain and process improvements performed as discrete steps. However, environment impacts genotype and a strain that is optimal under one set of conditions may not be under different conditions. We present a methodology to simultaneously vary genetic and process factors, so that both can be guided by design of experiments (DOE). Advances in DNA assembly and gene insulation facilitate this approach by accelerating multi-gene pathway construction and the statistical interpretation of screening data. This is applied to a 6-aminocaproic acid (6-ACA) pathway in Escherichia coli consisting of six heterologous enzymes. A 32-member fraction factorial library is designed that simultaneously perturbs expression and media composition. This is compared to a 64-member full factorial library just varying expression (0.64 Mb of DNA assembly). Statistical analysis of the screening data from these libraries leads to different predictions as to whether the expression of enzymes needs to increase or decrease. Therefore, if genotype and media were varied separately this would lead to a suboptimal combination. This is applied to the design of a strain and media composition that increases 6-ACA from 9 to 48 mg/l in a single optimization step. This work introduces a generalizable platform to co-optimize genetic and non-genetic factors.

Description: Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein–DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein–DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. The new method is verified on 67 known human regulatory SNPs, of which 47 (70%) have predicted true TFs ranked in the top 10. Importantly, the performance of BayesPI-BAR, which uses principal component analysis to integrate multiple predictions from various TF chemical potentials, is found to be better than that of existing programs, such as sTRAP and is-rSNP, when evaluated on the same SNPs. BayesPI-BAR is a publicly available tool and is able to carry out parallelized computation, which helps to investigate a large number of TFs or SNPs and to detect disease-associated regulatory sequence variations in the sea of genome-wide noncoding regions.

Description: The rapid discovery of potential driver mutations through large-scale mutational analyses of human cancers generates a need to characterize their cellular phenotypes. Among the techniques for genome editing, recombinant adeno-associated virus (rAAV)-mediated gene targeting is suited for knock-in of single nucleotide substitutions and to a lesser degree for gene knock-outs. However, the generation of gene targeting constructs and the targeting process is time-consuming and labor-intense. To facilitate rAAV-mediated gene targeting, we developed the first software and complementary automation-friendly vector tools to generate optimized targeting constructs for editing human protein encoding genes. By computational approaches, rAAV constructs for editing ~71% of bases in protein-coding exons were designed. Similarly, ~81% of genes were predicted to be targetable by rAAV-mediated knock-out. A Gateway-based cloning system for facile generation of rAAV constructs suitable for robotic automation was developed and used in successful generation of targeting constructs. Together, these tools enable automated rAAV targeting construct design, generation as well as enrichment and expansion of targeted cells with desired integrations.

Description: Degenerate codon (DC) libraries efficiently address the experimental library-size limitations of directed evolution by focusing diversity toward the positions and toward the amino acids (AAs) that are most likely to generate hits; however, manually constructing DC libraries is challenging, error prone and time consuming. This paper provides a dynamic programming solution to the task of finding the best DCs while keeping the size of the library beneath some given limit, improving on the existing integer-linear programming formulation. It then extends the algorithm to consider multiple DCs at each position, a heretofore unsolved problem, while adhering to a constraint on the number of primers needed to synthesize the library. In the two library-design problems examined here, the use of multiple DCs produces libraries that very nearly cover the set of desired AAs while still staying within the experimental size limits. Surprisingly, the algorithm is able to find near-perfect libraries where the ratio of amino-acid sequences to nucleic-acid sequences approaches 1; it effectively side-steps the degeneracy of the genetic code. Our algorithm is freely available through our web server and solves most design problems in about a second.

Description: Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo -derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/ .

Description: Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/ .

Description: Synthetic biology has significantly advanced the rational design of trigger-inducible gene switches that program cellular behavior in a reliable and predictable manner. Capitalizing on genetic componentry, including the repressor PmeR and its cognate operator O PmeR , that has evolved in Pseudomonas syringae pathovar tomato DC3000 to sense and resist plant-defence metabolites of the paraben class, we have designed a set of inducible and repressible mammalian transcription-control devices that could dose-dependently fine-tune transgene expression in mammalian cells and mice in response to paraben derivatives. With an over 60-years track record as licensed preservatives in the cosmetics industry, paraben derivatives have become a commonplace ingredient of most skin-care products including shower gels, cleansing toners and hand creams. As parabens can rapidly reach the bloodstream of mice following topical application, we used this feature to percutaneously program transgene expression of subcutaneous designer cell implants using off-the-shelf commercial paraben-containing skin-care cosmetics. The combination of non-invasive, transdermal and orthogonal trigger-inducible remote control of transgene expression may provide novel opportunities for dynamic interventions in future gene and cell-based therapies.

Description: Effector-triggered immunity (ETI) is activated when plant disease resistance (R) proteins recognize the presence of pathogen effector proteins delivered into host cells. The ETI response generally encompasses a defensive ‘hypersensitive response’ (HR) that involves programmed cell death at the site of pathogen recognition. While many R protein and effector protein pairs are known to trigger HR, other components of the ETI signaling pathway remain elusive. Effector genes regulated by inducible promoters cause background HR due to leaky protein expression, preventing the generation of relevant transgenic plant lines. By employing the HyP5SM suicide exon, we have developed a strategy to tightly regulate effector proteins such that HR is chemically inducible and non-leaky. This alternative splicing-based gene regulation system was shown to successfully control Bs2/AvrBs2-dependent and RPP1/ATR151-dependent HR in Nicotiana benthamiana and Nicotiana tabacum , respectively. It was also used to generate viable and healthy transgenic Arabidopsis thaliana plants that inducibly initiate HR. Beyond enabling studies on the ETI pathway, our regulatory strategy is generally applicable to reduce or eliminate undesired background expression of transgenes.

Description: The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens , that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey 2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2 .

Description: We have developed a method for assembling genetic pathways for expression in Saccharomyces cerevisiae . Our pathway assembly method, called VEGAS (Versatile genetic assembly system), exploits the native capacity of S. cerevisiae to perform homologous recombination and efficiently join sequences with terminal homology. In the VEGAS workflow, terminal homology between adjacent pathway genes and the assembly vector is encoded by ‘VEGAS adapter’ (VA) sequences, which are orthogonal in sequence with respect to the yeast genome. Prior to pathway assembly by VEGAS in S. cerevisiae , each gene is assigned an appropriate pair of VAs and assembled using a previously described technique called yeast Golden Gate (yGG). Here we describe the application of yGG specifically to building transcription units for VEGAS assembly as well as the VEGAS methodology. We demonstrate the assembly of four-, five- and six-gene pathways by VEGAS to generate S. cerevisiae cells synthesizing β-carotene and violacein. Moreover, we demonstrate the capacity of yGG coupled to VEGAS for combinatorial assembly.

Description: It is a routine task in metabolic engineering to introduce multicomponent pathways into a heterologous host for production of metabolites. However, this process sometimes may take weeks to months due to the lack of standardized genetic tools. Here, we present a method for the design and construction of biological parts based on the native genes and regulatory elements in Saccharomyces cerevisiae . We have developed highly efficient protocols (termed YeastFab Assembly) to synthesize these genetic elements as standardized biological parts, which can be used to assemble transcriptional units in a single-tube reaction. In addition, standardized characterization assays are developed using reporter constructs to calibrate the function of promoters. Furthermore, the assembled transcription units can be either assayed individually or applied to construct multi-gene metabolic pathways, which targets a genomic locus or a receiving plasmid effectively, through a simple in vitro reaction. Finally, using β-carotene biosynthesis pathway as an example, we demonstrate that our method allows us not only to construct and test a metabolic pathway in several days, but also to optimize the production through combinatorial assembly of a pathway using hundreds of regulatory biological parts.

Description: Targeted sequence enrichment enables better identification of genetic variation by providing increased sequencing coverage for genomic regions of interest. Here, we report the development of a new target enrichment technology that is highly differentiated from other approaches currently in use. Our method, MESA (Microfluidic droplet Enrichment for Sequence Analysis), isolates genomic DNA fragments in microfluidic droplets and performs TaqMan PCR reactions to identify droplets containing a desired target sequence. The TaqMan positive droplets are subsequently recovered via dielectrophoretic sorting, and the TaqMan amplicons are removed enzymatically prior to sequencing. We demonstrated the utility of this approach by generating an average 31.6-fold sequence enrichment across 250 kb of targeted genomic DNA from five unique genomic loci. Significantly, this enrichment enabled a more comprehensive identification of genetic polymorphisms within the targeted loci. MESA requires low amounts of input DNA, minimal prior locus sequence information and enriches the target region without PCR bias or artifacts. These features make it well suited for the study of genetic variation in a number of research and diagnostic applications.

Description: Combinatorial transcription factor (TF) binding is essential for cell-type-specific gene regulation. However, much remains to be learned about the mechanisms of TF interactions, including to what extent constrained spacing and orientation of interacting TFs are critical for regulatory element activity. To examine the relative prevalence of the ‘enhanceosome’ versus the ‘TF collective’ model of combinatorial TF binding, a comprehensive analysis of TF binding site sequences in large scale datasets is necessary. We developed a motif-pair discovery pipeline to identify motif co-occurrences with preferential distance(s) between motifs in TF-bound regions. Utilizing a compendium of 289 mouse haematopoietic TF ChIP-seq datasets, we demonstrate that haematopoietic-related motif-pairs commonly occur with highly conserved constrained spacing and orientation between motifs. Furthermore, motif clustering revealed specific associations for both heterotypic and homotypic motif-pairs with particular haematopoietic cell types. We also showed that disrupting the spacing between motif-pairs significantly affects transcriptional activity in a well-known motif-pair—E-box and GATA, and in two previously unknown motif-pairs with constrained spacing—Ets and Homeobox as well as Ets and E-box. In this study, we provide evidence for widespread sequence-specific TF pair interaction with DNA that conforms to the ‘enhanceosome’ model, and furthermore identify associations between specific haematopoietic cell-types and motif-pairs.

Description: Homologous non-coding RNAs frequently exhibit domain insertions, where a branch of secondary structure is inserted in a sequence with respect to its homologs. Dynamic programming algorithms for common secondary structure prediction of multiple RNA homologs, however, do not account for these domain insertions. This paper introduces a novel dynamic programming algorithm methodology that explicitly accounts for the possibility of inserted domains when predicting common RNA secondary structures. The algorithm is implemented as Dynalign II, an update to the Dynalign software package for predicting the common secondary structure of two RNA homologs. This update is accomplished with negligible increase in computational cost. Benchmarks on ncRNA families with domain insertions validate the method. Over base pairs occurring in inserted domains, Dynalign II improves accuracy over Dynalign, attaining 80.8% sensitivity (compared with 14.4% for Dynalign) and 91.4% positive predictive value (PPV) for tRNA; 66.5% sensitivity (compared with 38.9% for Dynalign) and 57.0% PPV for RNase P RNA; and 50.1% sensitivity (compared with 24.3% for Dynalign) and 58.5% PPV for SRP RNA. Compared with Dynalign, Dynalign II also exhibits statistically significant improvements in overall sensitivity and PPV. Dynalign II is available as a component of RNAstructure, which can be downloaded from http://rna.urmc.rochester.edu/RNAstructure.html .

Description: To reveal the full potential of human pluripotent stem cells, new methods for rapid, site-specific genomic engineering are needed. Here, we describe a system for precise genetic modification of human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). We identified a novel human locus, H11 , located in a safe, intergenic, transcriptionally active region of chromosome 22, as the recipient site, to provide robust, ubiquitous expression of inserted genes. Recipient cell lines were established by site-specific placement of a ‘landing pad’ cassette carrying attP sites for phiC31 and Bxb1 integrases at the H11 locus by spontaneous or TALEN-assisted homologous recombination. Dual integrase cassette exchange (DICE) mediated by phiC31 and Bxb1 integrases was used to insert genes of interest flanked by phiC31 and Bxb1 attB sites at the H11 locus, replacing the landing pad. This system provided complete control over content, direction and copy number of inserted genes, with a specificity of 100%. A series of genes, including mCherry and various combinations of the neural transcription factors LMX1a, FOXA2 and OTX2, were inserted in recipient cell lines derived from H9 ESC, as well as iPSC lines derived from a Parkinson’s disease patient and a normal sibling control. The DICE system offers rapid, efficient and precise gene insertion in ESC and iPSC and is particularly well suited for repeated modifications of the same locus.

Description: Recombineering, which is the use of homologous recombination for DNA engineering in Escherichia coli , usually uses antibiotic selection to identify the intended recombinant. When combined in a second step with counterselection using a small molecule toxin, seamless products can be obtained. Here, we report the advantages of a genetic strategy using CcdB as the counterselectable agent. Expression of CcdB is toxic to E. coli in the absence of the CcdA antidote so counterselection is initiated by the removal of CcdA expression. CcdB counterselection is robust and does not require titrations or experiment-to-experiment optimization. Because counterselection strategies necessarily differ according to the copy number of the target, we describe two variations. For multi-copy targets, we use two E. coli hosts so that counterselection is exerted by the transformation step that is needed to separate the recombined and unrecombined plasmids. For single copy targets, we put the ccdA gene onto the temperature-sensitive pSC101 Red expression plasmid so that counterselection is exerted by the standard temperature shift to remove the expression plasmid. To reduce unwanted intramolecular recombination, we also combined CcdB counterselection with Redα omission. These options improve the use of counterselection in recombineering with BACs, plasmids and the E. coli chromosome.

Description: Determining the taxonomic affiliation of sequences assembled from metagenomes remains a major bottleneck that affects research across the fields of environmental, clinical and evolutionary microbiology. Here, we introduce MyTaxa, a homology-based bioinformatics framework to classify metagenomic and genomic sequences with unprecedented accuracy. The distinguishing aspect of MyTaxa is that it employs all genes present in an unknown sequence as classifiers, weighting each gene based on its (predetermined) classifying power at a given taxonomic level and frequency of horizontal gene transfer. MyTaxa also implements a novel classification scheme based on the genome-aggregate average amino acid identity concept to determine the degree of novelty of sequences representing uncharacterized taxa, i.e. whether they represent novel species, genera or phyla. Application of MyTaxa on in silico generated (mock) and real metagenomes of varied read length (100–2000 bp) revealed that it correctly classified at least 5% more sequences than any other tool. The analysis also showed that ~10% of the assembled sequences from human gut metagenomes represent novel species with no sequenced representatives, several of which were highly abundant in situ such as members of the Prevotella genus. Thus, MyTaxa can find several important applications in microbial identification and diversity studies.

Description: DNA ‘assembly’ from ‘building blocks’ remains a cornerstone in synthetic biology, whether it be for gene synthesis (~1 kb), pathway engineering (~10 kb) or synthetic genomes (〉100 kb). Despite numerous advances in the techniques used for DNA assembly, verification of the assembly is still a necessity, which becomes cost-prohibitive and a logistical challenge with increasing scale. Here we describe for the first time a comprehensive, high-throughput solution for structural DNA assembly verification by restriction digest using exhaustive in silico enzyme screening, rolling circle amplification of plasmid DNA, capillary electrophoresis and automated digest pattern recognition. This low-cost and robust methodology has been successfully used to screen over 31 000 clones of DNA constructs at 〈$1 per sample.

Description: Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to ‘novel’ protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the ‘unknown’ RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw .

Description: Synthetic biology requires effective methods to assemble DNA parts into devices and to modify these devices once made. Here we demonstrate a convenient rapid procedure for DNA fragment assembly using site-specific recombination by C31 integrase. Using six orthogonal attP / attB recombination site pairs with different overlap sequences, we can assemble up to five DNA fragments in a defined order and insert them into a plasmid vector in a single recombination reaction. C31 integrase-mediated assembly is highly efficient, allowing production of large libraries suitable for combinatorial gene assembly strategies. The resultant assemblies contain arrays of DNA cassettes separated by recombination sites, which can be used to manipulate the assembly by further recombination. We illustrate the utility of these procedures to (i) assemble functional metabolic pathways containing three, four or five genes; (ii) optimize productivity of two model metabolic pathways by combinatorial assembly with randomization of gene order or ribosome binding site strength; and (iii) modify an assembled metabolic pathway by gene replacement or addition.

Description: A conditional gene expression system that is fast-acting, is tunable and achieves single-gene specificity was recently developed for yeast. A gene placed directly downstream of a modified GAL1 promoter containing six Zif268 binding sequences (with single nucleotide spacing) was shown to be selectively inducible in the presence of β-estradiol, so long as cells express the artificial transcription factor, Z 3 EV (a fusion of the Zif268 DNA binding domain, the ligand binding domain of the human estrogen receptor and viral protein 16). We show the strength of Z 3 EV-responsive promoters can be modified using straightforward design principles. By moving Zif268 binding sites toward the transcription start site, expression output can be nearly doubled. Despite the reported requirement of estrogen receptor dimerization for hormone-dependent activation, a single binding site suffices for target gene activation. Target gene expression levels correlate with promoter binding site copy number and we engineer a set of inducible promoter chassis with different input–output characteristics. Finally, the coupling between inducer identity and gene activation is flexible: the ligand specificity of Z 3 EV can be re-programmed to respond to a non-hormone small molecule with only five amino acid substitutions in the human estrogen receptor domain, which may prove useful for industrial applications.

Description: Nanotechnology and synthetic biology currently constitute one of the most innovative, interdisciplinary fields of research, poised to radically transform society in the 21st century. This paper concerns the synthetic design of ribonucleic acid molecules, using our recent algorithm, RNAiFold , which can determine all RNA sequences whose minimum free energy secondary structure is a user-specified target structure. Using RNAiFold , we design ten cis -cleaving hammerhead ribozymes, all of which are shown to be functional by a cleavage assay. We additionally use RNAiFold to design a functional cis -cleaving hammerhead as a modular unit of a synthetic larger RNA. Analysis of kinetics on this small set of hammerheads suggests that cleavage rate of computationally designed ribozymes may be correlated with positional entropy, ensemble defect, structural flexibility/rigidity and related measures. Artificial ribozymes have been designed in the past either manually or by SELEX (Systematic Evolution of Ligands by Exponential Enrichment); however, this appears to be the first purely computational design and experimental validation of novel functional ribozymes. RNAiFold is available at http://bioinformatics.bc.edu/clotelab/RNAiFold/ .

Description: The precise control of gene expression is essential in basic biological research as well as in biotechnological applications. Most regulated systems available in yeast enable only the overexpression of the target gene, excluding the possibility of intermediate or weak expression. Moreover, these systems are frequently toxic or depend on growth conditions. We constructed a heterologous transcription factor that overcomes these limitations. Our system is a fusion of the bacterial LexA DNA-binding protein, the human estrogen receptor (ER) and an activation domain (AD). The activity of this chimera, called LexA-ER-AD, is tightly regulated by the hormone β-estradiol. The selection of the AD proved to be crucial to avoid toxic effects and to define the range of activity that can be precisely tuned with β-estradiol. As our system is based on a heterologous DNA-binding domain, induction in different metabolic contexts is possible. Additionally, by controlling the number of LexA-binding sites in the target promoter, one can scale the expression levels up or down. Overall, our LexA-ER-AD system is a valuable tool to precisely control gene expression in different experimental contexts without toxic side effects.

Description: While mRNA stability has been demonstrated to control rates of translation, generating both global and local synonymous codon biases in many unicellular organisms, this explanation cannot adequately explain why codon bias strongly tracks neighboring intergene GC content; suggesting that structural dynamics of DNA might also influence codon choice. Because minor groove width is highly governed by 3-base periodicity in GC, the existence of triplet-based codons might imply a functional role for the optimization of local DNA molecular dynamics via GC content at synonymous sites (GC3). We confirm a strong association between GC3-related intrinsic DNA flexibility and codon bias across 24 different prokaryotic multiple whole-genome alignments. We develop a novel test of natural selection targeting synonymous sites and demonstrate that GC3-related DNA backbone dynamics have been subject to moderate selective pressure, perhaps contributing to our observation that many genes possess extreme DNA backbone dynamics for their given protein space. This dual function of codons may impose universal functional constraints affecting the evolution of synonymous and non-synonymous sites. We propose that synonymous sites may have evolved as an ‘accessory’ during an early expansion of a primordial genetic code, allowing for multiplexed protein coding and structural dynamic information within the same molecular context.

Description: Inspired by the developments of synthetic biology and the need for improved genetic tools to exploit cyanobacteria for the production of renewable bioproducts, we developed a versatile platform for the construction of broad-host-range vector systems. This platform includes the following features: (i) an efficient assembly strategy in which modules released from 3 to 4 donor plasmids or produced by polymerase chain reaction are assembled by isothermal assembly guided by short GC-rich overlap sequences. (ii) A growing library of molecular devices categorized in three major groups: (a) replication and chromosomal integration; (b) antibiotic resistance; (c) functional modules. These modules can be assembled in different combinations to construct a variety of autonomously replicating plasmids and suicide plasmids for gene knockout and knockin. (iii) A web service, the CYANO-VECTOR assembly portal, which was built to organize the various modules, facilitate the in silico construction of plasmids, and encourage the use of this system. This work also resulted in the construction of an improved broad-host-range replicon derived from RSF1010, which replicates in several phylogenetically distinct strains including a new experimental model strain Synechocystis sp. WHSyn, and the characterization of nine antibiotic cassettes, four reporter genes, four promoters, and a ribozyme-based insulator in several diverse cyanobacterial strains.

Description: Mammalian synthetic biology may provide novel therapeutic strategies, help decipher new paths for drug discovery and facilitate synthesis of valuable molecules. Yet, our capacity to genetically program cells is currently hampered by the lack of efficient approaches to streamline the design, construction and screening of synthetic gene networks. To address this problem, here we present a framework for modular and combinatorial assembly of functional (multi)gene expression vectors and their efficient and specific targeted integration into a well-defined chromosomal context in mammalian cells. We demonstrate the potential of this framework by assembling and integrating different functional mammalian regulatory networks including the largest gene circuit built and chromosomally integrated to date (6 transcription units, 27kb) encoding an inducible memory device. Using a library of 18 different circuits as a proof of concept, we also demonstrate that our method enables one-pot/single-flask chromosomal integration and screening of circuit libraries. This rapid and powerful prototyping platform is well suited for comparative studies of genetic regulatory elements, genes and multi-gene circuits as well as facile development of libraries of isogenic engineered cell lines.

Description: The thermophilic fungus Chaetomium thermophilum holds great promise for structural biology. To increase the efficiency of its biochemical and structural characterization and to explore its thermophilic properties beyond those of individual proteins, we obtained transcriptomics and proteomics data, and integrated them with computational annotation methods and a multitude of biochemical experiments conducted by the structural biology community. We considerably improved the genome annotation of Chaetomium thermophilum and characterized the transcripts and expression of thousands of genes. We furthermore show that the composition and structure of the expressed proteome of Chaetomium thermophilum is similar to its mesophilic relatives. Data were deposited in a publicly available repository and provide a rich source to the structural biology community.

Description: Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k -mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25 x coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.

Description: RGB marking and DNA barcoding are two cutting-edge technologies in the field of clonal cell marking. To combine the virtues of both approaches, we equipped LeGO vectors encoding red, green or blue fluorescent proteins with complex DNA barcodes carrying color-specific signatures. For these vectors, we generated highly complex plasmid libraries that were used for the production of barcoded lentiviral vector particles. In proof-of-principle experiments, we used barcoded vectors for RGB marking of cell lines and primary murine hepatocytes. We applied single-cell polymerase chain reaction to decipher barcode signatures of individual RGB-marked cells expressing defined color hues. This enabled us to prove clonal identity of cells with one and the same RGB color. Also, we made use of barcoded vectors to investigate clonal development of leukemia induced by ectopic oncogene expression in murine hematopoietic cells. In conclusion, by combining RGB marking and DNA barcoding, we have established a novel technique for the unambiguous genetic marking of individual cells in the context of normal regeneration as well as malignant outgrowth. Moreover, the introduction of color-specific signatures in barcodes will facilitate studies on the impact of different variables (e.g. vector type, transgenes, culture conditions) in the context of competitive repopulation studies.

Description: Insertional oncogene activation and aberrant splicing have proved to be major setbacks for retroviral stem cell gene therapy. Integrase-deficient human immunodeficiency virus-1-derived vectors provide a potentially safer approach, but their circular genomes are rapidly lost during cell division. Here we describe a novel lentiviral vector (LV) that incorporates human ß-interferon scaffold/matrix-associated region sequences to provide an origin of replication for long-term mitotic maintenance of the episomal LTR circles. The resulting ‘anchoring’ non-integrating lentiviral vector (aniLV) achieved initial transduction rates comparable with integrating vector followed by progressive establishment of long-term episomal expression in a subset of cells. Analysis of aniLV-transduced single cell-derived clones maintained without selective pressure for 〉100 rounds of cell division showed sustained transgene expression from episomes and provided molecular evidence for long-term episome maintenance. To evaluate aniLV performance in primary cells, we transduced lineage-depleted murine hematopoietic progenitor cells, observing GFP expression in clonogenic progenitor colonies and peripheral blood leukocyte chimerism following transplantation into conditioned hosts. In aggregate, our studies suggest that scaffold/matrix-associated region elements can serve as molecular anchors for non-integrating lentivector episomes, providing sustained gene expression through successive rounds of cell division and progenitor differentiation in vitro and in vivo .

Description: Assembly of DNA ‘parts’ to create larger constructs is an essential enabling technique for bioengineering and synthetic biology. Here we describe a simple method, PaperClip, which allows flexible assembly of multiple DNA parts from currently existing libraries cloned in any vector. No restriction enzymes, mutagenesis of internal restriction sites, or reamplification to add end homology are required. Order of assembly is directed by double stranded oligonucleotides—‘Clips’. Clips are formed by ligation of pairs of oligonucleotides corresponding to the ends of each part. PaperClip assembly can be performed by polymerase chain reaction or by cell extract-mediated recombination. Once multi-use Clips have been prepared, assembly of at least six DNA parts in any order can be accomplished with high efficiency within several hours.

Description: Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem–loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.

Description: Binding of transcription factors to their binding sites in promoter regions is the fundamental event in transcriptional gene regulation. When a transcription factor binding site is located within a nucleosome, the DNA has to partially unwrap from the nucleosome to allow transcription factor binding. This reduces the rate of transcription factor binding and is a known mechanism for regulation of gene expression via chromatin structure. Recently a second mechanism has been reported where transcription factor off-rates are dramatically increased when binding to target sites within the nucleosome. There are two possible explanations for such an increase in off-rate short of an active role of the nucleosome in pushing the transcription factor off the DNA: (i) for dimeric transcription factors the nucleosome can change the equilibrium between monomeric and dimeric binding or (ii) the nucleosome can change the equilibrium between specific and non-specific binding to the DNA. We explicitly model both scenarios and find that dimeric binding can explain a large increase in off-rate while the non-specific binding model cannot be reconciled with the large, experimentally observed increase. Our results suggest a general mechanism how nucleosomes increase transcription factor dissociation to promote exchange of transcription factors and regulate gene expression.

Description: Synthetic biology has significantly advanced the design of mammalian trigger-inducible transgene-control devices that are able to programme complex cellular behaviour. Fruit-based benzoate derivatives licensed as food additives, such as flavours (e.g. vanillate) and preservatives (e.g. benzoate), are a particularly attractive class of trigger compounds for orthogonal mammalian transgene control devices because of their innocuousness, physiological compatibility and simple oral administration. Capitalizing on the genetic componentry of the soil bacterium Comamonas testosteroni , which has evolved to catabolize a variety of aromatic compounds, we have designed different mammalian gene expression systems that could be induced and repressed by the food additives benzoate and vanillate. When implanting designer cells engineered for gene switch-driven expression of the human placental secreted alkaline phosphatase (SEAP) into mice, blood SEAP levels of treated animals directly correlated with a benzoate-enriched drinking programme. Additionally, the benzoate-/vanillate-responsive device was compatible with other transgene control systems and could be assembled into higher-order control networks providing expression dynamics reminiscent of a lap-timing stopwatch. Designer gene switches using licensed food additives as trigger compounds to achieve antagonistic dual-input expression profiles and provide novel control topologies and regulation dynamics may advance future gene- and cell-based therapies.

Description: High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A , a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor.

Description: In reverse genetics, a gene’s function is elucidated through targeted modifications in the coding region or associated DNA cis -regulatory elements. To this purpose, recently developed customizable transcription activator-like effector nucleases (TALENs) have proven an invaluable tool, allowing introduction of double-strand breaks at predetermined sites in the genome. Here we describe a practical and efficient method for the targeted genome engineering in Drosophila . We demonstrate TALEN-mediated targeted gene integration and efficient identification of mutant flies using a traceable marker phenotype. Furthermore, we developed an easy TALEN assembly (easyT) method relying on simultaneous reactions of DNA Bae I digestion and ligation, enabling construction of complete TALENs from a monomer unit library in a single day. Taken together, our strategy with easyT and TALEN-plasmid microinjection simplifies mutant generation and enables isolation of desired mutant fly lines in the F 1 generation.

Description: Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.

Description: Transcription activator-like effector nucleases (TALENs) are a powerful new approach for targeted gene disruption in various animal models, but little is known about their activities in Mus musculus, the widely used mammalian model organism. Here, we report that direct injection of in vitro transcribed messenger RNA of TALEN pairs into mouse zygotes induced somatic mutations, which were stably passed to the next generation through germ-line transmission. With one TALEN pair constructed for each of 10 target genes, mutant F0 mice for each gene were obtained with the mutation rate ranged from 13 to 67% and an average of ~40% of total healthy newborns with no significant differences between C57BL/6 and FVB/N genetic background. One TALEN pair with single mismatch to their intended target sequence in each side failed to yield any mutation. Furthermore, highly efficient germ-line transmission was obtained, as all the F0 founders tested transmitted the mutations to F1 mice. In addition, we also observed that one bi-allele mutant founder of Lepr gene, encoding Leptin receptor, had similar diabetic phenotype as db/db mouse. Together, our results suggest that TALENs are an effective genetic tool for rapid gene disruption with high efficiency and heritability in mouse with distinct genetic background.

Description: We describe a new cell-free protein synthesis (CFPS) method for site-specific incorporation of non-natural amino acids (nnAAs) into proteins in which the orthogonal tRNA (o-tRNA) and the modified protein (i.e. the protein containing the nnAA) are produced simultaneously. Using this method, 0.9–1.7 mg/ml of modified soluble super-folder green fluorescent protein (sfGFP) containing either p -azido- l -phenylalanine (pAzF) or p -propargyloxy- l -phenylalanine (pPaF) accumulated in the CFPS solutions; these yields correspond to 50–88% suppression efficiency. The o-tRNA can be transcribed either from a linearized plasmid or from a crude PCR product. Comparison of two different o-tRNAs suggests that the new platform is not limited by Ef-Tu recognition of the acylated o-tRNA at sufficiently high o-tRNA template concentrations. Analysis of nnAA incorporation across 12 different sites in sfGFP suggests that modified protein yields and suppression efficiencies (i.e. the position effect) do not correlate with any of the reported trends. Sites that were ineffectively suppressed with the original o-tRNA were better suppressed with an optimized o-tRNA (o-tRNA opt ) that was evolved to be better recognized by Ef-Tu. This new platform can also be used to screen scissile ribozymes for improved catalysis.

Description: MicroRNAs (miRNAs) constitute an important class of small regulatory RNAs that are derived from distinct hairpin precursors (pre-miRNAs). In contrast to mature miRNAs, which have been characterized in numerous genome-wide studies of different organisms, research on global profiling of pre-miRNAs is limited. Here, using massive parallel sequencing, we have performed global characterization of both mouse mature and precursor miRNAs. In total, 87 369 704 and 252 003 sequencing reads derived from 887 mature and 281 precursor miRNAs were obtained, respectively. Our analysis revealed new aspects of miRNA/pre-miRNA processing and modification, including eight Ago2-cleaved pre-miRNAs, eight new instances of miRNA editing and exclusively 5' tailed mirtrons. Furthermore, based on the sequences of both mature and precursor miRNAs, we developed a miRNA discovery pipeline, miRGrep, which does not rely on the availability of genome reference sequences. In addition to 239 known mouse pre-miRNAs, miRGrep predicted 41 novel ones with high confidence. Similar as known ones, the mature miRNAs derived from most of these novel loci showed both reduced abundance following Dicer knockdown and the binding with Argonaute2. Evaluation on data sets obtained from Caenorhabditis elegans and Caenorhabditis sp.11 demonstrated that miRGrep could be widely used for miRNA discovery in metazoans, especially in those without genome reference sequences.

Description: It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense–antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci .

Description: The two-step process of selection and counter-selection is a standard way to enable genetic modification and engineering of bacterial genomes using homologous recombination methods. The tetA and sacB genes are contained in a DNA cassette and confer a novel dual counter-selection system. Expression of tetA confers bacterial resistance to tetracycline (Tc R ) and also causes sensitivity to the lipophillic chelator fusaric acid; sacB causes sensitivity to sucrose. These two genes are introduced as a joint DNA cassette into Escherichia coli by selection for Tc R . A medium containing both fusaric acid and sucrose has been developed, in which, coexpression of tetA-sacB is orders of magnitude more sensitive as a counter-selection agent than either gene alone. In conjunction with the homologous recombination methods of recombineering and P1 transduction, this powerful system has been used to select changes in the bacterial genome that cannot be directly detected by other counter-selection systems.

Description: We developed a framework for quick and reliable construction of complex gene circuits for genetically engineering mammalian cells. Our hierarchical framework is based on a novel nucleotide addressing system for defining the position of each part in an overall circuit. With this framework, we demonstrate construction of synthetic gene circuits of up to 64 kb in size comprising 11 transcription units and 33 basic parts. We show robust gene expression control of multiple transcription units by small molecule inducers in human cells with transient transfection and stable chromosomal integration of these circuits. This framework enables development of complex gene circuits for engineering mammalian cells with unprecedented speed, reliability and scalability and should have broad applicability in a variety of areas including mammalian cell fermentation, cell fate reprogramming and cell-based assays.

Description: Synthetic biology has significantly advanced the design of synthetic control devices, gene circuits and networks that can reprogram mammalian cells in a trigger-inducible manner. Prokaryotic helix-turn-helix motifs have become the standard resource to design synthetic mammalian transcription factors that tune chimeric promoters in a small molecule-responsive manner. We have identified a family of Actinomycetes transcriptional repressor proteins showing a tandem TetR-family signature and have used a synthetic biology-inspired approach to reveal the potential control dynamics of these bi-partite regulators. Daisy-chain assembly of well-characterized prokaryotic repressor proteins such as TetR, ScbR, TtgR or VanR and fusion to either the Herpes simplex transactivation domain VP16 or the Krueppel-associated box domain (KRAB) of the human kox-1 gene resulted in synthetic bi- and even tri-partite mammalian transcription factors that could reversibly program their individual chimeric or hybrid promoters for trigger-adjustable transgene expression using tetracycline (TET), -butyrolactones, phloretin and vanillic acid. Detailed characterization of the bi-partite ScbR-TetR-VP16 (ST-TA) transcription factor revealed independent control of TET- and -butyrolactone-responsive promoters at high and double-pole double-throw (DPDT) relay switch qualities at low intracellular concentrations. Similar to electromagnetically operated mechanical DPDT relay switches that control two electric circuits by a fully isolated low-power signal, TET programs ST-TA to progressively switch from TetR-specific promoter-driven expression of transgene one to ScbR-specific promoter-driven transcription of transgene two while ST-TA flips back to exclusive transgene 1 expression in the absence of the trigger antibiotic. We suggest that natural repressors and activators with tandem TetR-family signatures may also provide independent as well as DPDT-mediated control of two sets of transgenes in bacteria, and that their synthetic transcription-factor analogs may enable the design of compact therapeutic gene circuits for gene and cell-based therapies.

Description: The coupling of chromosome conformation capture (3C) with next-generation sequencing technologies enables the high-throughput detection of long-range genomic interactions, via the generation of ligation products between DNA sequences, which are closely juxtaposed in vivo . These interactions involve promoter regions, enhancers and other regulatory and structural elements of chromosomes and can reveal key details of the regulation of gene expression. 3C-seq is a variant of the method for the detection of interactions between one chosen genomic element (viewpoint) and the rest of the genome. We present r3Cseq , an R/Bioconductor package designed to perform 3C-seq data analysis in a number of different experimental designs. The package reads a common aligned read input format, provides data normalization, allows the visualization of candidate interaction regions and detects statistically significant chromatin interactions, thus greatly facilitating hypothesis generation and the interpretation of experimental results. We further demonstrate its use on a series of real-world applications.

Description: Synthetic RNA control devices that use ribozymes as gene-regulatory components have been applied to controlling cellular behaviors in response to environmental signals. Quantitative measurement of the in vitro cleavage rate constants associated with ribozyme-based devices is essential for advancing the molecular design and optimization of this class of gene-regulatory devices. One of the key challenges encountered in ribozyme characterization is the efficient generation of full-length RNA from in vitro transcription reactions, where conditions generally lead to significant ribozyme cleavage. Current methods for generating full-length ribozyme-encoding RNA rely on a trans-blocking strategy, which requires a laborious gel separation and extraction step. Here, we develop a simple two-step gel-free process including cis-blocking and trans-activation steps to support scalable generation of functional full-length ribozyme-encoding RNA. We demonstrate our strategy on various types of natural ribozymes and synthetic ribozyme devices, and the cleavage rate constants obtained for the RNA generated from our strategy are comparable with those generated through traditional methods. We further develop a rapid, label-free ribozyme cleavage assay based on surface plasmon resonance, which allows continuous, real-time monitoring of ribozyme cleavage. The surface plasmon resonance-based characterization assay will complement the versatile cis-blocking and trans-activation strategy to broadly advance our ability to characterize and engineer ribozyme-based devices.

Description: The ability to artificially control transcription is essential both to the study of gene function and to the construction of synthetic gene networks with desired properties. Cas9 is an RNA-guided double-stranded DNA nuclease that participates in the CRISPR-Cas immune defense against prokaryotic viruses. We describe the use of a Cas9 nuclease mutant that retains DNA-binding activity and can be engineered as a programmable transcription repressor by preventing the binding of the RNA polymerase (RNAP) to promoter sequences or as a transcription terminator by blocking the running RNAP. In addition, a fusion between the omega subunit of the RNAP and a Cas9 nuclease mutant directed to bind upstream promoter regions can achieve programmable transcription activation. The simple and efficient modulation of gene expression achieved by this technology is a useful asset for the study of gene networks and for the development of synthetic biology and biotechnological applications.

Description: Antisense RNA transcription attenuators are a key component of the synthetic biology toolbox, with their ability to serve as building blocks for both signal integration logic circuits and transcriptional cascades. However, a central challenge to building more sophisticated RNA genetic circuitry is creating larger families of orthogonal attenuators that function independently of each other. Here, we overcome this challenge by developing a modular strategy to create chimeric fusions between the engineered transcriptional attenuator from plasmid pT181 and natural antisense RNA translational regulators. Using in vivo gene expression assays in Escherichia coli , we demonstrate our ability to create chimeric attenuators by fusing sequences from five different translational regulators. Mutagenesis of these functional attenuators allowed us to create a total of 11 new chimeric attenutaors. A comprehensive orthogonality test of these culminated in a 7 x 7 matrix of mutually orthogonal regulators. A comparison between all chimeras tested led to design principles that will facilitate further engineering of orthogonal RNA transcription regulators, and may help elucidate general principles of non-coding RNA regulation. We anticipate that our strategy will accelerate the development of even larger families of orthogonal RNA transcription regulators, and thus create breakthroughs in our ability to construct increasingly sophisticated RNA genetic circuitry.

Description: Studying complex biological processes such as cancer development, stem cell induction and transdifferentiation requires the modulation of multiple genes or pathways at one time in a single cell. Herein, we describe straightforward methods for rapid and efficient assembly of bacterial marker free multigene cassettes containing up to six complementary DNAs/short hairpin RNAs. We have termed this method RecWay assembly, as it makes use of both Cre recombinase and the commercially available Gateway cloning system. Further, because RecWay assembly uses truly modular components, it allows for the generation of randomly assembled multigene vector libraries. These multigene vectors are integratable, and later excisable, using the highly efficient piggyBac ( PB ) DNA transposon system. Moreover, we have dramatically improved the expression of stably integrated multigene vectors by incorporation of insulator elements to prevent promoter interference seen with multigene vectors. We demonstrate that insulated multigene PB transposons can stably integrate and faithfully express up to five fluorescent proteins and the puromycin-thymidine kinase resistance gene in vitro , with up to 70-fold higher gene expression compared with analogous uninsulated vectors . RecWay assembly of multigene transposon vectors allows for widely applicable modelling of highly complex biological processes and can be easily performed by other research laboratories.

Description: Techniques for assembly of designed DNA sequences are important for synthetic biology. So far, a few methods have been developed towards high-throughput seamless DNA assembly in vitro , including both the homologous sequences-based system and the type IIS-mediated system. Here, we describe a novel method designated ‘MASTER Ligation’, by which multiple DNA sequences can be seamlessly assembled through a simple and sequence-independent hierarchical procedure. The key restriction endonuclease used, MspJI, shares both type IIM and type IIS properties; thus, it only recognizes the methylation-specific 4-bp sites, m CNNR (R = A or G), and cuts DNA outside of the recognition sequences. This method was tested via successful assembly of either multiple polymerase chain reaction amplicons or restriction fragments of the actinorhodin biosynthetic cluster of Streptomyces coelicolor (~29 kb), which was further heterologously expressed in a fast-growing and moderately thermophilic strain, Streptomyces sp. 4F.

Description: Bacterial operons are nature’s tool for regulating and coordinating multi-gene expression in prokaryotes. They are also a gene architecture commonly used in the biosynthesis of many pharmaceutically important compounds and industrially useful chemicals. Despite being an important eukaryotic production host, Saccharomyces cerevisiae has never had such gene architecture. Here, we report the development of a system to assemble and regulate a multi-gene pathway in S. cerevisiae . Full pathways can be constructed using pre-made parts from a plasmid toolbox. Subsequently, through the use of a yeast strain containing a stably integrated gene switch, the assembled pathway can be regulated using a readily available and inexpensive compound—estradiol—with extremely high sensitivity (10 nM). To demonstrate the use of the system, we assembled the five-gene zeaxanthin biosynthetic pathway in a single step and showed the ligand-dependent coordinated expression of all five genes as well as the tightly regulated production of zeaxanthin. Compared with a previously reported constitutive zeaxanthin pathway, our inducible pathway was shown to have 50-fold higher production level.

Description: Zinc-finger nucleases (ZFNs) have been used for genome engineering in a wide variety of organisms; however, it remains challenging to design effective ZFNs for many genomic sequences using publicly available zinc-finger modules. This limitation is in part because of potential finger–finger incompatibility generated on assembly of modules into zinc-finger arrays (ZFAs). Herein, we describe the validation of a new set of two-finger modules that can be used for building ZFAs via conventional assembly methods or a new strategy—finger stitching—that increases the diversity of genomic sequences targetable by ZFNs. Instead of assembling ZFAs based on units of the zinc-finger structural domain, our finger stitching method uses units that span the finger–finger interface to ensure compatibility of neighbouring recognition helices. We tested this approach by generating and characterizing eight ZFAs, and we found their DNA-binding specificities reflected the specificities of the component modules used in their construction. Four pairs of ZFNs incorporating these ZFAs generated targeted lesions in vivo , demonstrating that stitching yields ZFAs with robust recognition properties.