Engineered live bacteria as disease detection and diagnosis tools.

Sensitive and minimally invasive medical diagnostics are essential to the early detection of diseases, monitoring their progression and response to treatment. Engineered bacteria as live sensors are being developed as a new class of biosensors for sensitive, robust, noninvasive, and in situ detection of disease onset at low cost. Akin to microrobotic systems, a combination of simple genetic rules, basic logic gates, and complex synthetic bioengineering principles are used to program bacterial vectors as living machines for detecting biomarkers of diseases, some of which cannot be detected with other sensing technologies. Bacterial whole-cell biosensors (BWCBs) can have wide-ranging functions from detection only, to detection and recording, to closed-loop detection-regulated treatment. In this review article, we first summarize the unique benefits of bacteria as living sensors. We then describe the different bacteria-based diagnosis approaches and provide examples of diagnosing various diseases and disorders. We also discuss the use of bacteria as imaging vectors for disease detection and image-guided surgery. We conclude by highlighting current challenges and opportunities for further exploration toward clinical translation of these bacteria-based systems.

A novel synthetic sRNA promoting protein overexpression in cell-free systems.

Bacterial small RNAs (sRNAs) that regulate gene expression have been engineered for uses in synthetic biology and metabolic engineering. Here, we designed a novel non-Hfq-dependent sRNA scaffold that uses a modifiable 20 nucleotide antisense binding region to target mRNAs selectively and influence protein expression. The system was developed for regulation of a fluorescent reporter in vivo using Escherichia coli, but the system was found to be more responsive and produced statistically significant results when applied to protein synthesis using in vitro cell-free systems (CFS). Antisense binding sequences were designed to target not only translation initiation regions but various secondary structures in the reporter mRNA. Targeting a high-energy stem loop structure and the 3' end of mRNA yielded protein expression knock-downs that approached 70%. Notably, targeting a low-energy stem structure near a potential RNase E binding site led to a statistically significant 65% increase in protein expression (p < 0.05). These results were not obtainable in vivo, and the underlying mechanism was translated from the reporter system to achieve better than 75% increase in recombinant diaphorase expression in a CFS. It is possible the designs developed here can be applied to improve/regulate expression of other proteins in a CFS.

Lambda-PCR for precise DNA assembly and modification.

Lambda-polymerase chain reaction (λ-PCR) is a novel and open-source method for DNA assembly and cloning projects. λ-PCR uses overlap extension to ultimately assemble linear and circular DNA fragments, but it allows the single-stranded DNA (ssDNA) primers of the PCR extension to first exist as double-stranded DNA (dsDNA). Having dsDNA at this step is advantageous for the stability of large insertion products, to avoid inhibitory secondary structures during direct synthesis, and to reduce costs. Three variations of λ-PCR were created to convert an initial dsDNA product into an ssDNA "megaprimer" to be used in overlap extension: (i) complete digestion by λ-exonuclease, (ii) asymmetric PCR, and (iii) partial digestion by λ-exonuclease. Four case studies are presented that demonstrate the use of λ-PCR in simple gene cloning, simultaneous multipart assemblies, gene cloning not achievable with commercial kits, and the use of thermodynamic simulations to guide λ-PCR assembly strategies. High DNA assembly and cloning efficiencies have been achieved with λ-PCR for a fraction of the cost and time associated with conventional methods and some commercial kits.

Characterizing metabolic stress-induced phenotypes of Synechocystis PCC6803 with Raman spectroscopy.

BACKGROUND: During their long evolution, Synechocystis sp. PCC6803 developed a remarkable capacity to acclimate to diverse environmental conditions. In this study, Raman spectroscopy and Raman chemometrics tools (RametrixTM) were employed to investigate the phenotypic changes in response to external stressors and correlate specific Raman bands with their corresponding biomolecules determined with widely used analytical methods. METHODS: Synechocystis cells were grown in the presence of (i) acetate (7.5-30 mM), (ii) NaCl (50-150 mM) and (iii) limiting levels of MgSO4 (0-62.5 mM) in BG-11 media. Principal component analysis (PCA) and discriminant analysis of PCs (DAPC) were performed with the RametrixTM LITE Toolbox for MATLABⓇ. Next, validation of these models was realized via RametrixTM PRO Toolbox where prediction of accuracy, sensitivity, and specificity for an unknown Raman spectrum was calculated. These analyses were coupled with statistical tests (ANOVA and pairwise comparison) to determine statistically significant changes in the phenotypic responses. Finally, amino acid and fatty acid levels were measured with well-established analytical methods. The obtained data were correlated with previously established Raman bands assigned to these biomolecules. RESULTS: Distinguishable clusters representative of phenotypic responses were observed based on the external stimuli (i.e., acetate, NaCl, MgSO4, and controls grown on BG-11 medium) or its concentration when analyzing separately. For all these cases, RametrixTM PRO was able to predict efficiently the corresponding concentration in the culture media for an unknown Raman spectra with accuracy, sensitivity and specificity exceeding random chance. Finally, correlations (R > 0.7) were observed for all amino acids and fatty acids between well-established analytical methods and Raman bands.

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes of Synechocystis PCC6803 with Raman spectroscopy.

BACKGROUND: Synechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix™) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods. METHODS: Synechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix™ LITE Toolbox for MATLAB® was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix™ PRO Toolbox was used to validate these models through leave-one-out routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification accuracy, sensitivity, and specificity. Raman spectra were also subjected to statistical tests (ANOVA and pairwise comparisons) to identify statistically relevant changes in Synechocystis phenotypes. Finally, experimental methods, including widely used analytical and spectroscopic assays were used to quantify the levels of glycogen, fatty acids, amino acids, and chlorophyll a for correlations with Raman data. RESULTS: PCA and DAPC models produced distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on (i) growth in the presence of 5 mM glucose, (ii) illumination (dark, light/dark [12 h/12 h], and continuous light at 20 µE), (iii) nitrogen deprivation (0-100% NaNO3 of native BG-11 medium in continuous light), and (iv) throughout a 24 h light/dark (12 h/12 h) circadian rhythm growth cycle. Rametrix™ PRO was successful in identifying glucose-induced phenotypes with 95.3% accuracy, 93.4% sensitivity, and 96.9% specificity. Prediction accuracy was above random chance values for all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results.