A reinforcement learning framework for pooled oligonucleotide design.

MOTIVATION: The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions. RESULTS: We present a general reinforcement learning (RL) framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows 'black-box' design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: (i) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); (ii) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor) and (iii) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how RL offers a general solution for complex oligo design problems. AVAILABILITY AND IMPLEMENTATION: OligoRL and all simulation codes are available as a Julia package at http://jensenlab.net/tools and archived at https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://github.com/bmdavid2/OligoRL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gapsplit: efficient random sampling for non-convex constraint-based models.

SUMMARY: Gapsplit generates random samples from convex and non-convex constraint-based models by targeting under-sampled regions of the solution space. Gapsplit provides uniform coverage of linear, mixed-integer and general non-linear models. AVAILABILITY AND IMPLEMENTATION: Python and Matlab source code are freely available at http://jensenlab.net/tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Institutional Tuberculosis Transmission. Controlled Trial of Upper Room Ultraviolet Air Disinfection: A Basis for New Dosing Guidelines.

RATIONALE: Transmission is driving the global tuberculosis epidemic, especially in congregate settings. Worldwide, natural ventilation is the most common means of air disinfection, but it is inherently unreliable and of limited use in cold climates. Upper room germicidal ultraviolet (UV) air disinfection with air mixing has been shown to be highly effective, but improved evidence-based dosing guidelines are needed. OBJECTIVES: To test the efficacy of upper room germicidal air disinfection with air mixing to reduce tuberculosis transmission under real hospital conditions, and to define the application parameters responsible as a basis for proposed new dosing guidelines. METHODS: Over an exposure period of 7 months, 90 guinea pigs breathed only untreated exhaust ward air, and another 90 guinea pigs breathed only air from the same six-bed tuberculosis ward on alternate days when upper room germicidal air disinfection was turned on throughout the ward. MEASUREMENTS AND MAIN RESULTS: The tuberculin skin test conversion rates (>6 mm) of the two chambers were compared. The hazard ratio for guinea pigs in the control chamber converting their skin test to positive was 4.9 (95% confidence interval, 2.8-8.6), with an efficacy of approximately 80%. CONCLUSIONS: Upper room germicidal UV air disinfection with air mixing was highly effective in reducing tuberculosis transmission under hospital conditions. These data support using either a total fixture output (rather than electrical or UV lamp wattage) of 15-20 mW/m(3) total room volume, or an average whole-room UV irradiance (fluence rate) of 5-7 µW/cm(2), calculated by a lighting computer-assisted design program modified for UV use.

MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data.

MOTIVATION: Metabolic reaction maps allow visualization of genome-scale models and high-throughput data in a format familiar to many biologists. However, creating a map of a large metabolic model is a difficult and time-consuming process. MetDraw fully automates the map-drawing process for metabolic models containing hundreds to thousands of reactions. MetDraw can also overlay high-throughput 'omics' data directly on the generated maps.

Surgical face masks worn by patients with multidrug-resistant tuberculosis: impact on infectivity of air on a hospital ward.

RATIONALE: Drug-resistant tuberculosis transmission in hospitals threatens staff and patient health. Surgical face masks used by patients with tuberculosis (TB) are believed to reduce transmission but have not been rigorously tested. OBJECTIVES: We sought to quantify the efficacy of surgical face masks when worn by patients with multidrug-resistant TB (MDR-TB). METHODS: Over 3 months, 17 patients with pulmonary MDR-TB occupied an MDR-TB ward in South Africa and wore face masks on alternate days. Ward air was exhausted to two identical chambers, each housing 90 pathogen-free guinea pigs that breathed ward air either when patients wore surgical face masks (intervention group) or when patients did not wear masks (control group). Efficacy was based on differences in guinea pig infections in each chamber. MEASUREMENTS AND MAIN RESULTS: Sixty-nine of 90 control guinea pigs (76.6%; 95% confidence interval [CI], 68-85%) became infected, compared with 36 of 90 intervention guinea pigs (40%; 95% CI, 31-51%), representing a 56% (95% CI, 33-70.5%) decreased risk of TB transmission when patients used masks. CONCLUSIONS: Surgical face masks on patients with MDR-TB significantly reduced transmission and offer an adjunct measure for reducing TB transmission from infectious patients.

Performance evaluation of selected n95 respirators and surgical masks when challenged with aerosolized endospores and inert particles.

The objective of this study was to assess how the relative efficiency of N95 respirators and surgical masks might vary with different challenge aerosols, utilizing a standardized manikin head form as a surrogate to human participation. A Collision nebulizer aerosolized B. anthracis Sterne strain endospores and polystyrene latex (PSL) particles to evaluate 11 models of N95 respirators and surgical masks. An automated breathing simulator, calibrated to normal tidal volume and active breathing rate, mimicked human respiration. A manikin head form with N95 respirators or surgical masks, and manikin head form without N95 respirators or surgical masks were placed in the bioaerosol chamber. An AGI-30 sampler filled with phosphate buffered water was fitted behind the mouth of each manikin head form to collect endospore bioaerosol samples. PSL aerosols concentrations were quantified by an ARTI Hand Held Particle Counter. Geometric Mean (GM) relative efficiency of N95 respirators and surgical masks challenged with endospore bioaerosol ranged from 34-65%. In PSL aerosol experiments, GM relative efficiency ranged from 35-64% for 1.3 µm particles. GM filtration efficiency of all N95 and surgical N95 respirators filter media evaluated was ≥99% when challenged with particles ≥0.1 µm. GM filtration efficiency of surgical mask filter media ranged from 70-83% with particles ≥0.1 µm and 74-92% with 1.3 µm PSL particles. Relative efficiencies of N95 respirators and surgical masks challenged with aerosolized B. anthracis endospores and PSL were similar. Relative efficiency was similar between N95 respirators and surgical masks on a manikin head form despite clear differences in filtration efficiency. This study further highlights the importance of face seal leakage in the respiratory protection provided by N95 respirators, and demonstrates it on a human surrogate.

BacterAI maps microbial metabolism without prior knowledge.

Training artificial intelligence (AI) systems to perform autonomous experiments would vastly increase the throughput of microbiology; however, few microbes have large enough datasets for training such a system. In the present study, we introduce BacterAI, an automated science platform that maps microbial metabolism but requires no prior knowledge. BacterAI learns by converting scientific questions into simple games that it plays with laboratory robots. The agent then distils its findings into logical rules that can be interpreted by human scientists. We use BacterAI to learn the amino acid requirements for two oral streptococci: Streptococcus gordonii and Streptococcus sanguinis. We then show how transfer learning can accelerate BacterAI when investigating new environments or larger media with up to 39 ingredients. Scientific gameplay and BacterAI enable the unbiased, autonomous study of organisms for which no training data exist.

Functional integration of a metabolic network model and expression data without arbitrary thresholding.

MOTIVATION: Flux balance analysis (FBA) has been used extensively to analyze genome-scale, constraint-based models of metabolism in a variety of organisms. The predictive accuracy of such models has recently been improved through the integration of high-throughput expression profiles of metabolic genes and proteins. However, extensions of FBA often require that such data be discretized a priori into sets of genes or proteins that are either 'on' or 'off'. This procedure requires selecting relatively subjective expression thresholds, often requiring several iterations and refinements to capture the expression dynamics and retain model functionality. RESULTS: We present a method for mapping expression data from a set of environmental, genetic or temporal conditions onto a metabolic network model without the need for arbitrary expression thresholds. Metabolic Adjustment by Differential Expression (MADE) uses the statistical significance of changes in gene or protein expression to create a functional metabolic model that most accurately recapitulates the expression dynamics. MADE was used to generate a series of models that reflect the metabolic adjustments seen in the transition from fermentative- to glycerol-based respiration in Saccharomyces cerevisiae. The calculated gene states match 98.7% of possible changes in expression, and the resulting models capture functional characteristics of the metabolic shift. AVAILABILITY: MADE is implemented in Matlab and requires a mixed-integer linear program solver. Source code is freely available at http://www.bme.virginia.edu/csbl/downloads/.

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 675 reactions involving 429 metabolites and the products of 493 genes. We validated iSMU by comparing simulations with growth experiments in defined medium. The model simulations matched experimental results for 17 of 18 carbon source utilization assays and 47 of 49 nutrient depletion assays. We also simulated the effects of single gene deletions. The model's predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental data sets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines and more complete than other manually curated models. We used iSMU to generate hypotheses about the S. mutans metabolic network. Subsequent genetic experiments confirmed that (i) S. mutans catabolizes sorbitol via a sorbitol-6-phosphate 2-dehydrogenase (SMU_308) and (ii) the Leloir pathway is required for growth on complex carbohydrates such as raffinose. We believe the iSMU model is an important resource for understanding the metabolism of S. mutans and guiding future experiments.IMPORTANCE Tooth decay is the most prevalent chronic disease in the United States. Decay is caused by the bacterium Streptococcus mutans, an oral pathogen that ferments sugars into tooth-destroying lactic acid. We constructed a complete metabolic model of S. mutans to systematically investigate how the bacterium grows. The model provides a valuable resource for understanding and targeting S. mutans' ability to outcompete other species in the oral microbiome.

The bare necessities: Uncovering essential and condition-critical genes with transposon sequencing.

Querying gene function in bacteria has been greatly accelerated by the advent of transposon sequencing (Tn-seq) technologies (related Tn-seq strategies are known as TraDIS, INSeq, RB-TnSeq, and HITS). Pooled populations of transposon mutants are cultured in an environment and next-generation sequencing tools are used to determine areas of the genome that are important for bacterial fitness. In this review we provide an overview of Tn-seq methodologies and discuss how Tn-seq has been applied, or could be applied, to the study of oral microbiology. These applications include studying the essential genome as a means to rationally design therapeutic agents. Tn-seq has also contributed to our understanding of well-studied biological processes in oral bacteria. Other important applications include in vivo pathogenesis studies and use of Tn-seq to probe the molecular basis of microbial interactions. We also highlight recent advancements in techniques that act in synergy with Tn-seq such as clustered regularly interspaced short palindromic repeats (CRISPR) interference and microfluidic chip platforms.

Droplet Tn-Seq combines microfluidics with Tn-Seq for 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 (dTn-Seq) solves this problem by microfluidics facilitated encapsulation of individual transposon mutants into growth medium-in-oil droplets, thereby enabling isolated growth, free from the influence of the population. Here we describe and validate microfluidic chip design, production, encapsulation, and dTn-Seq sample preparation. We determine that 1-3% of mutants in Streptococcus pneumoniae have a different fitness when grown in isolation and show how dTn-Seq can help identify leads for gene function, including those involved in hyper-competence, processing of alpha-1-acid glycoprotein, sensitivity against the human leukocyte elastase and microcolony formation. Additionally, we show dTn-Seq compatibility with microscopy, FACS and investigations of bacterial cell-to-cell and bacteria-host cell interactions. dTn-Seq reduces costs and retains the advantages of Tn-Seq, while expanding the method's original applicability.

Investigation of Mycobacterium tuberculosis transmission aboard the U.S.S. Ronald Reagan, 2006.

Pulmonary tuberculosis (TB) was diagnosed in a sailor aboard the U.S.S. Ronald Reagan; an investigation was conducted to determine a screening strategy for 1,172 civilian passengers who were aboard during a temporary guest rider program. Sailors were screened for latent TB infection (LTBI) and TB disease. A case-control study was conducted among sailors to determine factors associated with new LTBI. No secondary TB disease was identified; 13% of close contacts had new LTBI. Factors associated with new LTBI among sailors were having been born outside the United States (adjusted odds ratio = 2.80; 95% confidence interval, 1.55--5.07) and being a carrier air wing member (adjusted odds ratio = 2.89; 95% confidence interval, 1.83--4.58). Among 38 civilian passengers berthed near the patient, 1 (3%) had LTBI. The investigation results indicated that Mycobacterium tuberculosis transmission was minimal and eliminated unnecessary TB screening for 1,134 civilians which saved public health resources.

Coupling Fluxes, Enzymes, and Regulation in Genome-Scale Metabolic Models.

Genome-scale models have expanded beyond their metabolic origins. Multiple modeling frameworks are required to combine metabolism with enzymatic networks, transcription, translation, and regulation. Mathematical programming offers a powerful set of tools for tackling these "multi-modality" models, although special attention must be paid to the connections between modeling types. This chapter reviews common methods for combining metabolic and discrete logical models into a single mathematical programming framework. Best practices, caveats, and recommendations are presented for the most commonly used software packages. Methods for troubleshooting large sets of logical rules are also discussed.

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).

Complete Genome Sequences of Streptococcus sobrinus SL1 (ATCC 33478 = DSM 20742), NIDR 6715-7 (ATCC 27351), NIDR 6715-15 (ATCC 27352), and NCTC 10919 (ATCC 33402).

The bacterium Streptococcus sobrinus causes tooth decay in humans. We present complete circularized genome sequences for four strains of S. sobrinus, type strain SL1, strain NIDR 6715-7 and the related NIDR 6715-15, and strain NCTC 10919. The finished genomes will enable genomic comparisons between S. sobrinus and other cariogenic microbes.

Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria.

Bacterial genes that change in expression upon environmental disturbance have commonly been seen as those that must also phenotypically matter. However, several studies suggest that differentially expressed genes are rarely phenotypically important. We demonstrate, for Gram-positive and Gram-negative bacteria, that these seemingly uncoordinated gene sets are involved in responses that can be linked through topological network analysis. However, the level of coordination is stress dependent. While a well-coordinated response is triggered in response to nutrient stress, antibiotics trigger an uncoordinated response in which transcriptionally and phenotypically important genes are neither linked spatially nor in their magnitude. Moreover, a gene expression meta-analysis reveals that genes with large fitness changes during stress have low transcriptional variation across hundreds of other conditions, and vice versa. Our work suggests that cellular responses can be understood through network models that incorporate regulatory and genetic relationships, which could aid drug target predictions and genetic network engineering.

Guidelines for preventing the transmission of Mycobacterium tuberculosis in health-care settings, 2005.

In 1994, CDC published the Guidelines for Preventing the Transmission of Mycobacterium tuberculosis in HealthCare Facilities, 1994. The guidelines were issued in response to 1) a resurgence of tuberculosis (TB) disease that occurred in the United States in the mid-1980s and early 1990s, 2) the documentation of several high-profile health-care--associated (previously termed "nosocomial") outbreaks related to an increase in the prevalence of TB disease and human immunodeficiency virus (HIV) coinfection, 3) lapses in infection control practices, 4) delays in the diagnosis and treatment of persons with infectious TB disease, and 5) the appearance and transmission of multidrug-resistant (MDR) TB strains. The 1994 guidelines, which followed statements issued in 1982 and 1990, presented recommendations for TB infection control based on a risk assessment process that classified health-care facilities according to categories of TB risk, with a corresponding series of administrative, environmental, and respiratory protection control measures. The TB infection control measures recommended by CDC in 1994 were implemented widely in health-care facilities in the United States. The result has been a decrease in the number of TB outbreaks in health-care settings reported to CDC and a reduction in health-care-associated transmission of Mycobacterium tuberculosis to patients and health-care workers (HCWs). Concurrent with this success, mobilization of the nation's TB control programs succeeded in reversing the upsurge in reported cases of TB disease, and case rates have declined in the subsequent 10 years. Findings indicate that although the 2004 TB rate was the lowest recorded in the United States since national reporting began in 1953, the declines in rates for 2003 (2.3%) and 2004 (3.2%) were the smallest since 1993. In addition, TB infection rates greater than the U.S. average continue to be reported in certain racial/ethnic populations. The threat of MDR TB is decreasing, and the transmission of M. tuberculosis in health-care settings continues to decrease because of implementation of infection-control measures and reductions in community rates of TB. Given the changes in epidemiology and a request by the Advisory Council for the Elimination of Tuberculosis (ACET) for review and update of the 1994 TB infection control document, CDC has reassessed the TB infection control guidelines for health-care settings. This report updates TB control recommendations reflecting shifts in the epidemiology of TB, advances in scientific understanding, and changes in health-care practice that have occurred in the United States during the preceding decade. In the context of diminished risk for health-care-associated transmission of M. tuberculosis, this document places emphasis on actions to maintain momentum and expertise needed to avert another TB resurgence and to eliminate the lingering threat to HCWs, which is mainly from patients or others with unsuspected and undiagnosed infectious TB disease. CDC prepared the current guidelines in consultation with experts in TB, infection control, environmental control, respiratory protection, and occupational health. The new guidelines have been expanded to address a broader concept; health-care--associated settings go beyond the previously defined facilities. The term "health-care setting" includes many types, such as inpatient settings, outpatient settings, TB clinics, settings in correctional facilities in which health care is delivered, settings in which home-based health-care and emergency medical services are provided, and laboratories handling clinical specimens that might contain M. tuberculosis. The term "setting" has been chosen over the term "facility," used in the previous guidelines, to broaden the potential places for which these guidelines apply.

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.

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

BACKGROUND: Systems 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. RESULTS: This 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. CONCLUSIONS: A 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.