Browsing by Author "Tanniche, Imen"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Characterizing glucose, illumination, and nitrogen-deprivation phenotypes of Synechocystis PCC6803 with Raman spectroscopy
    Tanniche, Imen; Collakova, Eva; Denbow, Cynthia J.; Senger, Ryan S. (2020-03-30)
    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 (TM)) 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 (TM) LITE Toolbox for MATLAB (R) was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix (TM) PRO Toolbox was used to validate these models through leave-oneout 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 mE), (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 (TM) 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.
  • Thumbnail Image
    Characterizing metabolic stress-induced phenotypes of Synechocystis PCC6803 with Raman spectroscopy
    Tanniche, Imen; Collakova, Eva; Denbow, Cynthia J.; Senger, Ryan S. (2020-03-30)
    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 (Rametrix (TM)) 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 Rametrix (TM) LITE Toolbox for MATLABR (R). Next, validation of these models was realized via Rametrix (TM) 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, Rametrix (TM) 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.
  • Thumbnail Image
    Correlating antisense RNA performance with thermodynamic calculations
    Tanniche, Imen (Virginia Tech, 2013-02-08)
    Antisense RNA (asRNA) strategies are identified as an effective and specific method for gene down-regulation at the post-transcriptional level. In this study, the major purpose is to find a correlation between the expression level and minimum free energy to enable the design of specific asRNA fragments. The thermodynamics of asRNA and mRNA hybridization were computed based on the fluorescent protein reporter genes. Three different fluorescent proteins (i) green fluorescent protein (GFP), (ii) cyan fluorescent protein (CFP) and (iii) yellow fluorescent protein (YFP) were used as reporters. Each fluorescent protein was cloned into the common pUC19 vector. The asRNA fragments were randomly amplified and the resulted antisense DNA fragments were inserted into the constructed plasmid under the control of an additional inducible plac promoter and terminator. The expression levels of fluorescent reporter protein were determined in real time by plate reader. Different results have been observed according to the fluorescent protein and the antisense fragment sequence. The CFP expression level was decreased by 50 to 78% compared to the control. However, with the GFP, the down-regulation did not exceed 30% for the different constructs used. For certain constructs, the effect was the opposite of expected and the expression level was increased. In addition, the YFP showed a weak signal compared to growth media, therefore the expression level was hard to be defined. Based on these results, a thermodynamic model to describe the relationship between the particular asRNA used and the observed expression level of the fluorescent reporter was developed. The minimum free energy and binding percentage of asRNA-mRNA complex were computed by NUPACK software. The expression level was drawn as a function of the minimum free energy. The results showed a weak correlation, but linear trends were observed for low energy values and low expression levels the CFP gene. The linear aspect is not verified for higher energy values. These findings suggest that the lower the energy is, the more stable is the complex asRNA-mRNA and therefore more reduction of the expression is obtained. Meanwhile, the non-linearity involves that there are other parameters to be investigated to improve the mathematical correlation. This model is expected to offer the chance to "fine-tune" asRNA effectiveness and subsequently modulate gene expression and redirect metabolic pathways toward the desired component. In addition, the investigation of the localization of antisense binding indicates that there are some regions that favors the hybridization and promote hence the down-regulation mechanisms.
  • Thumbnail Image
    Engineered live bacteria as disease detection and diagnosis tools
    Tanniche, Imen; Behkam, Bahareh (2023-10-24)
    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.
  • Thumbnail Image
    New Methods of DNA Assembly, Gene Regulation with a Synthetic sRNA, and Cyanobacterium Phenotype Monitoring with Raman Spectroscopy
    Tanniche, Imen (Virginia Tech, 2019-06-07)
    Metabolic engineering has enabled studying microorganisms by the modification of their genetic material and analysis of their metabolism for the isolation of microbial strains capable of producing high yields of high value chemicals and biofuels. In this research, novel tools were developed to improve genetic engineering of microbial cells. In this matter, λ-PCR (lambda-PCR) was developed enabling the construction of plasmid DNA. This technique allows DNA assembly and manipulation (insertion, substitution and/or deletion) at any location of a vector. λ-PCR addresses the need for an easy, highly-efficient, rapid and inexpensive tool for genetic engineering and overcoming limitations encountered with traditional techniques. Then, novel synthetic small RNA (sRNA) regulators were designed in a cell-free-system (in vitro) in order to modulate protein expression in biosynthetic pathways. The ability of the sRNAs to regulate mRNA expression with statistical significance was demonstrated. Up to 70% decrease in protein expression level was achieved by targeting specific secondary structures of the mRNA with antisense binding regions of the sRNA. Most importantly, a sRNA was identified capable of protein overexpression by up to 65%. An understanding of its mechanism showed that its mRNA target region(s) likely lead to occlusion of RNase E binding. This mechanism was translated for expression of a diaphorase enzyme, which has relevance to synthetic biology and metabolic engineering in in vitro systems. Results were successful, showing a greater than 75% increase in diaphorase expression in a cell-free protein synthesis reaction. Next, Raman spectroscopy was employed as a near real-time method for microbial phenotyping. Here, Raman spectroscopy was used in combination with chemometric analysis methods through RametrixTM Toolboxes to study the effects of environmental conditions (i.e. illumination, glucose, nitrate deprivation, acetate, sodium chloride and magnesium sulfate) on the phenotypic response of the cyanobacterium Synechocystis sp. PCC6803. The RametrixTM LITE Toolbox for MATLAB® enabled processing of Raman spectra and application of principal component analysis (PCA) and discriminant analysis of principal components (DAPC). Two studies were performed. PCA and DAPC produces distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on the (i) presence of glucose in the growth medium, (ii) illumination, (iii) nitrate limitation, and (iv) throughout a circadian rhythm growth cycle, in the first study. The second study focused on the phenotypic response based on (i) growth in presence of acetate, (ii) presence of high concentrations of sodium chloride and (iii) magnesium sulfate starvation. RametrixTM PRO was applied for the validation of the DAPC models through leave-one-out method that allowed calculation of prediction accuracy, sensitivity and selectivity for an unkown Raman spectrum. Statistical tests (ANOVA and pairwise comparison) were performed on Raman spectra to identify statistically relevant changes in Synechocystis phenotypes. Next, comparison between Raman data and standardized analytical methods (GF-FID, UPLC, spectrometric assays) was established. Overall, good correlation were obtained (R > 0.7). Finally, genomic DNA libraries were enriched to isolate a deoxynivalenol detoxifying enzyme. To do this, library fragments from microorganisms was generated through oligonucleotide primed polymerase chain reaction (DOP-PCR) and transformed in a DON-sensitive yeast strain. Rounds of subculture were performed in the presence of DON and ferulic acid in order to isolate a strain capable of enzymatic degradation of DON.