Paul A. Jensen

TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

BackgroundSeveral methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model.ResultsWe present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGERs algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae.ConclusionThe TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease

BackgroundSystems biology holds promise as a new approach to drug target identification and drug discovery against neglected tropical diseases. Genome-scale metabolic reconstructions, assembled from annotated genomes and a vast array of bioinformatics/biochemical resources, provide a framework for the interrogation of human pathogens and serve as a platform for generation of future experimental hypotheses. In this article, with the application of selection criteria for both Leishmania major targets (e.g. in silico gene lethality) and drugs (e.g. toxicity), a method (MetDP) to rationally focus on a subset of low-toxic Food and Drug Administration (FDA)-approved drugs is introduced.ResultsThis metabolic network-driven approach identified 15 L. major genes as high-priority targets, 8 high-priority synthetic lethal targets, and 254 FDA-approved drugs. Results were compared to previous literature findings and existing high-throughput screens. Halofantrine, an antimalarial agent that was prioritized using MetDP, showed noticeable antileishmanial activity when experimentally evaluated in vitro against L. major promastigotes. Furthermore, synthetic lethality predictions also aided in the prediction of superadditive drug combinations. For proof-of-concept, double-drug combinations were evaluated in vitro against L. major and four combinations involving the drug disulfiram that showed superadditivity are presented.ConclusionsA direct metabolic network-driven method that incorporates single gene essentiality and synthetic lethality predictions is proposed that generates a set of high-priority L. major targets, which are in turn associated with a select number of FDA-approved drugs that are candidate antileishmanials. Additionally, selection of high-priority double-drug combinations might provide for an attractive and alternative avenue for drug discovery against leishmaniasis.

Miniaturized Plate Readers for Low-Cost, High-Throughput Phenotypic Screening

We present a miniaturized plate reader for measuring optical density in 96-well plates. Our standalone reader fits in most incubators, environmental chambers, or biological containment suites, allowing users to leverage their existing laboratory infrastructure. The device contains no moving parts, allowing an entire 96-well plate to be read several times per second. We demonstrate how the fast sampling rate allows our reader to detect small changes in optical density, even when the device is placed in a shaking incubator. A wireless communication module allows remote monitoring of multiple devices in real time. These features allow easy assembly of multiple readers to create a scalable, accurate solution for high-throughput phenotypic screening.

Network analysis links genome-wide phenotypic and transcriptional stress responses in a bacterial pathogen with a large pan-genome.

Background Bacteria modulate subcellular processes to handle stressful environments. Genome-wide profiling of gene expression (RNA-Seq) and fitness (Tn-Seq) allows two views of the same genetic network underlying these responses. However, it remains unclear how they combine, enabling a bacterium to overcome a perturbation. Results Here we generate RNA-Seq and Tn-Seq profiles in three strains of S. pneumoniae in response to stress defined by different levels of nutrient depletion. These profiles show that genes that change their expression and/or become phenotypically important come from a diverse set of functional categories, and genes that are phenotypically important tend to be highly expressed. Surprisingly, we find that expression and fitness changes rarely occur on the same gene, which we confirmed by over 140 validation experiments. To rationalize these unexpected results we built the first genome-scale metabolic model of S. pneumoniae showing that differential expression and phenotypic importance actually correlate between nearest neighbors, although they are distinctly partitioned into small subnetworks. Moreover, a meta-analysis of 234 S. pneumoniae gene expression studies reveals that essential genes and phenotypically important subnetworks rarely change expression, indicating that they are shielded from transcriptional fluctuations and that a clear distinction exists between transcriptional and phenotypic response networks. Conclusions We present a genome-wide computational/experimental approach that contextualizes changes that occur on transcriptomic and phenomic levels in response to stress. Importantly, this highlights the need to connect disparate response networks, for instance in antibiotic target identification, where preferred targets are phenotypically important genes that would be overlooked by transcriptomic analyses alone.

A scalable systems analysis approach for regulated metabolic networks

High-throughput data such as genome sequencing and genome expression profiling have enabled the reconstruction of cellular networks. These networks have been represented in computational frameworks that can be used to make testable predictions concerning phenotypes under a variety of experimental conditions and multiple molecular perturbations. This presentation will detail several recent advances in the analysis of these networks as well as provide an outlook of remaining challenges.

droplet-Tn-Seq combines microfluidics with Tn-Seq identifying complex single-cell phenotypes

While Tn-Seq is a powerful tool to determine genome-wide bacterial fitness in high-throughput, culturing transposon-mutant libraries in pools can mask community or other complex single-cell phenotypes. droplet-Tn-seq solves that problem by microfluidics facilitated encapsulation of individual transposon mutants into liquid-in-oil droplets, thereby enabling isolated growth, free from the influence of the population. Importantly, all advantages of Tn-Seq are conserved, while reducing costs and greatly extending its applicability.

Metabolic modeling of Streptococcus mutans reveals complex nutrient requirements of an oral pathogen

Streptococcus mutans is a Gram positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism of S. mutans on a systematic level, we manually constructed a genome-scale metabolic model of the S. mutans type strain UA159. The model, called iSMU, contains 656 reactions involving 514 metabolites and the products of 488 genes. We interrogated S. mutans’ nutrient requirements using model simulations and nutrient removal experiments in defined media. The iSMU model matched experimental results in greater than 90% of the conditions tested. We also simulated effects of single gene deletions. The model’s predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental datasets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines. We believe the iSMU model is an important resource for understanding how metabolism enables the cariogenicity of S. mutans.

Designing randomized DNA sequences free of restriction enzyme recognition sites

DNA libraries containing random “barcodes” complicate synthetic biology workflows that utilize restriction enzymes since restriction sites can appear inside some barcodes. By removing bases at particular sites in the barcodes, it is possible to create semi‐random pools of barcodes that do not contain any restriction sites. The challenge is to remove as few bases as possible to maximize the number of sequences in the pool while ensuring all sequences are free of restriction sites. The authors present CutFree, a computational approach to create pools of random DNA barcodes that lack a pre‐defined set of restriction sites. The resulting pools can be inexpensively produced en masse with standard DNA synthesis techniques. CutFree is experimentally validated by blocking digestion of pools of barcodes designed to frequently contain restriction sites. Using CutFree, a pool of 1.3 billion barcodes that are free from recognition sites for 182 commercially available restriction enzymes is designed. CutFree is available as a software package and an online tool (http://jensenlab.net/tools).

Hands-On Assembly of DNA Sequencing Reads as a Gateway to Bioinformatics †

The scale of genomic sequencing data and the complexity of bioinformatic algorithms make it difficult for students to develop a concrete understanding of assembling complete genomes from millions of short DNA sequences. We present a hands-on activity where students explore the genome assembly process using short DNA sequences printed on paper. Topics highlighted during the lesson include overlap identification, reference sequences, and the challenges arising from sequencing errors, low-frequency mutations, and repetitive regions. Sample materials provide reads and solutions for assembling clinically relevant regions of the S. gordonii penicillin binding protein and the human HTT gene. An online tool allows instructors to generate custom read sets from other DNA sequences.