Routine full characterization of Mycobacterium tuberculosis is culture based, taking many weeks. Whole-genome sequencing (WGS) can generate antibiotic susceptibility profiles to inform treatment, augmented with strain information for global surveillance; such data could be transformative if provided at or near the point of care. We demonstrate a low-cost method of DNA extraction directly from patient samples for M. tuberculosis WGS. We initially evaluated the method by using the Illumina MiSeq sequencer (40 smear-positive respiratory samples obtained after routine clinical testing and 27 matched liquid cultures). M. tuberculosis was identified in all 39 samples from which DNA was successfully extracted. Sufficient data for antibiotic susceptibility prediction were obtained from 24 (62%) samples; all results were concordant with reference laboratory phenotypes. Phylogenetic placement was concordant between direct and cultured samples. With Illumina MiSeq/MiniSeq, the workflow from patient sample to results can be completed in 44/16 h at a reagent cost of £96/£198 per sample. We then employed a nonspecific PCR-based library preparation method for sequencing on an Oxford Nanopore Technologies MinION sequencer. We applied this to cultured Mycobacterium bovis strain BCG DNA and to combined culture-negative sputum DNA and BCG DNA. For flow cell version R9.4, the estimated turnaround time from patient to identification of BCG, detection of pyrazinamide resistance, and phylogenetic placement was 7.5 h, with full susceptibility results 5 h later. Antibiotic susceptibility predictions were fully concordant. A critical advantage of MinION is the ability to continue sequencing until sufficient coverage is obtained, providing a potential solution to the problem of variable amounts of M. tuberculosis DNA in direct samples.
Just like in many engineering systems, impedance-like effects, called retroactivity, arise at the interconnection of biomolecular circuits, leading to unexpected changes in a circuit's behavior. In this paper, we provide a combined experimental and theoretical study to characterize the effects of retroactivity on the temporal dynamics of a gene transcription module in vivo. The response of the module to an inducer was measured both in isolation and when the module was connected to downstream clients. The connected module, when compared to the isolated module, responded selectively to the introduction of the inducer versus its withdrawal. Specifically, a "sign-sensitive delay" appeared, in which the connected module displayed a time delay in the response to induction and anticipation in the response to de-induction. The extent of these effects can be made larger by increasing the amounts of downstream clients and/or their binding affinity to the output protein of the module. Our experimental results and mathematical formulas make it possible to predict the extent of the change in the dynamic behavior of a module after interconnection. They can be employed to both recover the predictive power of a modular approach to understand systems or as an additional design tool to shape the temporal behavior of gene transcription.
Routine full characterization of Mycobacterium tuberculosis is culture based, taking many weeks. Whole-genome sequencing (WGS) can generate antibiotic susceptibility profiles to inform treatment, augmented with strain information for global surveillance; such data could be transformative if provided at or near the point of care. We demonstrate a low-cost method of DNA extraction directly from patient samples for M. tuberculosis WGS. We initially evaluated the method by using the Illumina MiSeq sequencer (40 smear-positive respiratory samples obtained after routine clinical testing and 27 matched liquid cultures). M. tuberculosis was identified in all 39 samples from which DNA was successfully extracted. Sufficient data for antibiotic susceptibility prediction were obtained from 24 (62%) samples; all results were concordant with reference laboratory phenotypes. Phylogenetic placement was concordant between direct and cultured samples. With Illumina MiSeq/MiniSeq, the workflow from patient sample to results can be completed in 44/16 h at a reagent cost of £96/£198 per sample. We then employed a nonspecific PCR-based library preparation method for sequencing on an Oxford Nanopore Technologies MinION sequencer. We applied this to cultured Mycobacterium bovis strain BCG DNA and to combined culture-negative sputum DNA and BCG DNA. For flow cell version R9.4, the estimated turnaround time from patient to identification of BCG, detection of pyrazinamide resistance, and phylogenetic placement was 7.5 h, with full susceptibility results 5 h later. Antibiotic susceptibility predictions were fully concordant. A critical advantage of MinION is the ability to continue sequencing until sufficient coverage is obtained, providing a potential solution to the problem of variable amounts of M. tuberculosis DNA in direct samples.
Cells of Sphingomonas sp. strain BSAR-1 constitutively expressed an alkaline phosphatase, which was also secreted in the extracellular medium. A null mutant lacking this alkaline phosphatase activity was isolated by Tn5 random mutagenesis. The corresponding gene, designated phoK, was cloned and overexpressed in Escherichia coli strain BL21(DE3). The resultant E. coli strain EK4 overexpressed cellular activity 55 times higher and secreted extracellular PhoK activity 13 times higher than did BSAR-1. The recombinant strain very rapidly precipitated >90% of input uranium in less than 2 h from alkaline solutions (pH, 9 ؎ 0.2) containing 0.5 to 5 mM of uranyl carbonate, compared to BSAR-1, which precipitated uranium in >7 h. In both strains BSAR-1 and EK4, precipitated uranium remained cell bound. The EK4 cells exhibited a much higher loading capacity of 3.8 g U/g dry weight in <2 h compared to only 1.5 g U/g dry weight in >7 h in BSAR-1. The data demonstrate the potential utility of genetically engineering PhoK for the bioprecipitation of uranium from alkaline solutions.Environmental metal pollution is a serious problem, and treatment/recovery of desired metals from such wastes is a major challenge. Effective immobilization of radionuclides of metals is critical in order to prevent groundwater contamination (17). Bioremediation of the toxic metal wastes by microbes offers a relatively inexpensive and ecofriendly alternative to commonly used physical and chemical methods (7,19,26). In particular, enzymatic bioprecipitation of heavy metals as metal phosphates is very attractive, since it can recover metals from very low concentrations not amenable to chemical techniques (18). Successful bioprecipitation of metals, such as uranium and cadmium, using acid phosphatase from naturally occurring bacteria, such as Citrobacter sp. (19), has been reported. The uranium bioprecipitation potentials of Bacillus sp., Rahnella sp. (5, 20), Pseudomonas sp. (22), and Salmonella sp. (27) in an acidic-to-neutral pH range have also been explored. Genetic engineering of the radio-resistant bacterium Deinococcus radiodurans R1 by using a nonspecific acid phosphatase, PhoN, for the biorecovery of uranium from dilute acidic/neutral wastes was reported by our laboratory recently (2).Based on the process used, uranium mining and processing generate large quantities of dilute acidic and alkaline nuclear waste containing uranium, which are dumped as mill tailings. Alkaline wastes containing traces of uranium also arise from nuclear reactors and power plants using uranium as fuel. In nature, uranium (VI) forms highly soluble carbonate complexes, such as [UO 2 (CO 3 ) 2 ] Ϫ2 and [UO 2 (CO 3 ) 3 ] Ϫ4 , at alkaline pH levels (9). This leads to increase in mobility and availability of uranium to groundwater and soil from the dumped nuclear wastes, leading to health hazards. Nearly 130 million liters of alkaline nuclear wastes containing uranium carbonate awaits disposition at the Savannah River Site, Aiken, SC, alone (9). In order to extend microbial ...
While predictable design of a genetic circuit's output is a major goal of synthetic biology, it remains a significant challenge because DNA binding sites in the cell affect the concentration of available transcription factors (TF). To mitigate this problem, we propose to use a TF that results from the (reversible) phosphorylation of protein substrate as a circuit's output. We demonstrate that by comparatively increasing the amounts of substrate and phosphatase, the TF concentration becomes robust to the presence of DNA binding sites and can be kept at a desired value. The circuit's input/output gain can, in turn, be tuned by changing the relative amounts of the substrate and phosphatase, realizing an amplifying buffer circuit with tunable gain. In our experiments in E. coli, we employ phospho-NRI as the output TF, phosphorylated by the NRII kinase, and dephosphorylated by the NRII phosphatase. Amplifying buffer circuits such as ours could be used to insulate a circuit's output from the context, bringing synthetic biology one step closer to modular design.
Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. Supplementary information Supplementary data are available at Bioinformatics online.
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