Scholarly Works, Chemical Engineering
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Browsing Scholarly Works, Chemical Engineering by Department "Biological Systems Engineering"
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- Characterizing glucose, illumination, and nitrogen-deprivation phenotypes of Synechocystis PCC6803 with Raman spectroscopyTanniche, 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.
- Characterizing metabolic stress-induced phenotypes of Synechocystis PCC6803 with Raman spectroscopyTanniche, 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.
- Identification of soil bacteria capable of utilizing a corn ethanol fermentation byproductPackard, Holly; Taylor, Zachary W.; Williams, Stephanie L.; Guimarães, Pedro Ivo; Toth, Jackson; Jensen, Roderick V.; Senger, Ryan S.; Kuhn, David D.; Stevens, Ann M. (PLoS, 2019-03-08)A commercial corn ethanol production byproduct (syrup) was used as a bacterial growth medium with the long-term aim to repurpose the resulting microbial biomass as a protein supplement in aquaculture feeds. Anaerobic batch reactors were used to enrich for soil bacteria metabolizing the syrup as the sole nutrient source over an eight-day period with the goal of obtaining pure cultures of facultative organisms from the reactors. Amplification of the V4 variable region of the 16S rRNA gene was performed using barcoded primers to track the succession of microbes enriched for during growth on the syrup. The resulting PCR products were sequenced using Illumina MiSeq protocols, analyzed via the program QIIME, and the alpha-diversity was calculated. Seven bacterial families were the most prevalent in the bioreactor community after eight days of enrichment: Clostridiaceae, Alicyclobacillaceae, Ruminococcaceae, Burkholderiaceae, Bacillaceae, Veillonellaceae, and Enterobacteriaceae. Pure culture isolates obtained from the reactors, and additional laboratory stock strains, capable of facultative growth, were grown aerobically in microtiter plates with the syrup substrate to monitor growth yield. Reactor isolates of interest were identified at a species level using the full 16S rRNA gene and other biomarkers. Bacillus species, commonly used as probiotics in aquaculture, showed the highest biomass yield of the monocultures examined. Binary combinations of monocultures yielded no apparent synergism between organisms, suggesting competition for nutrients instead of cooperative metabolite conversion. © 2019 Packard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Raman chemometric urinalysis (Rametrix) as a screen for bladder cancerHuttanus, Herbert M.; Vu, Tommy; Guruli, Georgi; Tracey, Andrew; Carswell, William; Said, Neveen; Du, Pang; Parkinson, Bing G.; Orlando, Giuseppe; Robertson, John L.; Senger, Ryan S. (2020-08-21)Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix (TM)) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix(TM)screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix (TM) screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine(TM). Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix(TM)screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix(TM)was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix(TM)models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix(TM)screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.
- The Rametrix (TM) PRO Toolbox v1.0 for MATLAB (R)Senger, Ryan S.; Robertson, John L. (2020-01-06)Background. Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix (TM) LITE Toolbox v1.0 for MATLAB (R) is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when "unknown" samples are introduced. Here, the Rametrix (TM) PRO Toolbox v1.0 is introduced to provide this capability. Methods. The Rametrix (TM) PRO Toolbox v1.0 was constructed for MATLAB (R) and works with the Rametrix (TM) LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix (TM) PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix (TM) PRO was used to validate Rametrix (TM) LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD. Results. The number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix (TM) PRO in evaluating chemometric models constructed with Rametrix (TM) LITE.
- Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix)Senger, Ryan S.; Kavuru, Varun; Sullivan, Meaghan; Gouldin, Austin; Lundgren, Stephanie; Merrifield, Kristen; Steen, Caitlin; Baker, Emily; Vu, Tommy; Agnor, Ben; Martinez, Gabrielle; Coogan, Hannah; Carswell, William; Karageorge, Lampros; Dev, Devasmita; Du, Pang; Sklar, Allan; Orlando, Giuseppe; Pirkle, James, Jr.; Robertson, John L. (PLOS, 2019-09-27)Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the “range of normal” for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.
- A synthetic biosensor to detect peroxisomal acetyl-CoA concentration for compartmentalized metabolic engineeringHuttanus, Herbert M.; Senger, Ryan S. (2020-09-08)Background. Sub-cellular compartmentalization is used by cells to create favorable microenvironments for various metabolic reactions. These compartments concentrate enzymes, separate competing metabolic reactions, and isolate toxic intermediates. Such advantages have been recently harnessed by metabolic engineers to improve the production of various high-value chemicals via compartmentalized metabolic engineering. However, measuring sub-cellular concentrations of key metabolites represents a grand challenge for compartmentalized metabolic engineering. Methods. To this end, we developed a synthetic biosensor to measure a key metabolite, acetyl-CoA, in a representative compartment of yeast, the peroxisome. This synthetic biosensor uses enzyme re-localization via PTS1 signal peptides to construct a metabolic pathway in the peroxisome which converts acetyl-CoA to polyhydroxybutyrate (PHB) via three enzymes. The PHB is then quantified by HPLC. Results. The biosensor demonstrated the difference in relative peroxisomal acetyl-CoA availability under various culture conditions and was also applied to screening a library of single knockout yeast mutants. The screening identified several mutants with drastically reduced peroxisomal acetyl-CoA and one with potentially increased levels. We expect our synthetic biosensors can be widely used to investigate sub-cellular metabolism and facilitate the "design-build-test" cycle of compartmentalized metabolic engineering.