Mitochondria Transplantation Mitigates Damage in an In Vitro Model of Renal Tubular Injury and in an Ex Vivo Model of DCD Renal Transplantation.

OBJECTIVES: To test whether mitochondrial transplantation (MITO) mitigates damage in 2 models of acute kidney injury (AKI). BACKGROUND: MITO is a process where exogenous isolated mitochondria are taken up by cells. As virtually any morbid clinical condition is characterized by mitochondrial distress, MITO may find a role as a treatment modality in numerous clinical scenarios including AKI. METHODS: For the in vitro experiments, human proximal tubular cells were damaged and then treated with mitochondria or placebo. For the ex vivo experiments, we developed a non-survival ex vivo porcine model mimicking the donation after cardiac death renal transplantation scenario. One kidney was treated with mitochondria, although the mate organ received placebo, before being perfused at room temperature for 24 hours. Perfusate samples were collected at different time points and analyzed with Raman spectroscopy. Biopsies taken at baseline and 24 hours were analyzed with standard pathology, immunohistochemistry, and RNA sequencing analysis. RESULTS: In vitro, cells treated with MITO showed higher proliferative capacity and adenosine 5'-triphosphate production, preservation of physiological polarization of the organelles and lower toxicity and reactive oxygen species production. Ex vivo, kidneys treated with MITO shed fewer molecular species, indicating stability. In these kidneys, pathology showed less damage whereas RNAseq analysis showed modulation of genes and pathways most consistent with mitochondrial biogenesis and energy metabolism and downregulation of genes involved in neutrophil recruitment, including IL1A, CXCL8, and PIK3R1. CONCLUSIONS: MITO mitigates AKI both in vitro and ex vivo.

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.

High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling.

The use of hydrogen (H2) as a fuel offers enhanced energy conversion efficiency and tremendous potential to decrease greenhouse gas emissions, but producing it in a distributed, carbon-neutral, low-cost manner requires new technologies. Herein we demonstrate the complete conversion of glucose and xylose from plant biomass to H2 and CO2 based on an in vitro synthetic enzymatic pathway. Glucose and xylose were simultaneously converted to H2 with a yield of two H2 per carbon, the maximum possible yield. Parameters of a nonlinear kinetic model were fitted with experimental data using a genetic algorithm, and a global sensitivity analysis was used to identify the enzymes that have the greatest impact on reaction rate and yield. After optimizing enzyme loadings using this model, volumetric H2 productivity was increased 3-fold to 32 mmol H2⋅L(-1)⋅h(-1). The productivity was further enhanced to 54 mmol H2⋅L(-1)⋅h(-1) by increasing reaction temperature, substrate, and enzyme concentrations--an increase of 67-fold compared with the initial studies using this method. The production of hydrogen from locally produced biomass is a promising means to achieve global green energy production.

Transport of magnesium by a bacterial Nramp-related gene.

Magnesium is an essential divalent metal that serves many cellular functions. While most divalent cations are maintained at relatively low intracellular concentrations, magnesium is maintained at a higher level (∼0.5-2.0 mM). Three families of transport proteins were previously identified for magnesium import: CorA, MgtE, and MgtA/MgtB P-type ATPases. In the current study, we find that expression of a bacterial protein unrelated to these transporters can fully restore growth to a bacterial mutant that lacks known magnesium transporters, suggesting it is a new importer for magnesium. We demonstrate that this transport activity is likely to be specific rather than resulting from substrate promiscuity because the proteins are incapable of manganese import. This magnesium transport protein is distantly related to the Nramp family of proteins, which have been shown to transport divalent cations but have never been shown to recognize magnesium. We also find gene expression of the new magnesium transporter to be controlled by a magnesium-sensing riboswitch. Importantly, we find additional examples of riboswitch-regulated homologues, suggesting that they are a frequent occurrence in bacteria. Therefore, our aggregate data discover a new and perhaps broadly important path for magnesium import and highlight how identification of riboswitch RNAs can help shed light on new, and sometimes unexpected, functions of their downstream genes.

Characterization of an evolved carotenoids hyper-producer of Saccharomyces cerevisiae through bioreactor parameter optimization and Raman spectroscopy.

An evolutionary engineering approach for enhancing heterologous carotenoids production in an engineered Saccharomyces cerevisiae strain was used previously to isolate several carotenoids hyper-producers from the evolved populations. ß-Carotene production was characterized in the parental and one of the evolved carotenoids hyper-producers (SM14) using bench-top bioreactors to assess the impact of pH, aeration, and media composition on ß-carotene production levels. The results show that with maintaining a low pH and increasing the carbon-to-nitrogen ratio (C:N) from 8.8 to 50 in standard YNB medium, a higher ß-carotene production level at 25.52 ± 2.15 mg ß-carotene g(-1) (dry cell weight) in the carotenoids hyper-producer was obtained. The increase in C:N ratio also significantly increased carotenoids production in the parental strain by 298 % [from 5.68 ± 1.24 to 22.58 ± 0.11 mg ß-carotene g(-1) (dcw)]. In this study, it was shown that Raman spectroscopy is capable of monitoring ß-carotene production in these cultures. Raman spectroscopy is adaptable to large-scale fermentations and can give results in near real-time. Furthermore, we found that Raman spectroscopy was also able to measure the relative lipid compositions and protein content of the parental and SM14 strains at two different C:N ratios in the bioreactor. The Raman analysis showed a higher total fatty acid content in the SM14 compared with the parental strain and that an increased C:N ratio resulted in significant increase in total fatty acid content of both strains. The data suggest a positive correlation between the yield of ß-carotene per biomass and total fatty acid content of the cell.

Near-real-time analysis of the phenotypic responses of Escherichia coli to 1-butanol exposure using Raman Spectroscopy.

Raman spectroscopy was used to study the time course of phenotypic responses of Escherichia coli (DH5α) to 1-butanol exposure (1.2% [vol/vol]). Raman spectroscopy is of interest for bacterial phenotyping because it can be performed (i) in near real time, (ii) with minimal sample preparation (label-free), and (iii) with minimal spectral interference from water. Traditional off-line analytical methodologies were applied to both 1-butanol-treated and control cells to draw correlations with Raman data. Here, distinct sets of Raman bands are presented that characterize phenotypic traits of E. coli with maximized correlation to off-line measurements. In addition, the observed time course phenotypic responses of E. coli to 1.2% (vol/vol) 1-butanol exposure included the following: (i) decreased saturated fatty acids levels, (ii) retention of unsaturated fatty acids and low levels of cyclopropane fatty acids, (iii) increased membrane fluidity following the initial response of increased rigidity, and (iv) no changes in total protein content or protein-derived amino acid composition. For most phenotypic traits, correlation coefficients between Raman spectroscopy and traditional off-line analytical approaches exceeded 0.75, and major trends were captured. The results suggest that near-real-time Raman spectroscopy is suitable for approximating metabolic and physiological phenotyping of bacterial cells subjected to toxic environmental conditions.

Cancer detection in dogs using rapid Raman molecular urinalysis.

Introduction: The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor. Methods: Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software. Results and discussion: The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.

Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies.

Background: Chronic kidney disease (CKD) poses a major public health burden. Diabetes mellitus (DM) is one of the major causes of CKD. In patients with DM, it can be difficult to differentiate diabetic kidney disease (DKD) from other causes of glomerular damage; it should not be assumed that all DM patients with decreased eGFR and/or proteinuria have DKD. Renal biopsy is the standard for definitive diagnosis, but other less invasive methods may provide clinical benefit. As previously reported, Raman spectroscopy of CKD patient urine with statistical and chemometric modeling may provide a novel, non-invasive methodology for discriminating between renal pathologies. Methods: Urine samples were collected from renal biopsied and non-biopsied patients presenting with CKD secondary to DM and non-diabetic kidney disease. Samples were analyzed by Raman spectroscopy, baselined with the ISREA algorithm, and subjected to chemometric modeling. Leave-one-out cross-validation was used to assess the predictive capabilities of the model. Results: This proof-of-concept study consisted of 263 samples, including renal biopsied, non-biopsied diabetic and non-diabetic CKD patients, healthy volunteers, and the Surine™ urinalysis control. Urine samples of DKD patients and those with immune-mediated nephropathy (IMN) were distinguished from one another with 82% sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV). Among urine samples from all biopsied CKD patients, renal neoplasia was identified in urine with 100% sensitivity, specificity, PPV, and NPV, and membranous nephropathy was identified with 66.7% sensitivity, 96.4% specificity, 80.0% PPV, and 93.1% NPV. Finally, DKD was identified among a population of 150 patient urine samples containing biopsy-confirmed DKD, other biopsy-confirmed glomerular pathologies, un-biopsied non-diabetic CKD patients (no DKD), healthy volunteers, and Surine™ with 36.4% sensitivity, 97.8% specificity, 57.1% PPV, and 95.1% NPV. The model was used to screen un-biopsied diabetic CKD patients and identified DKD in more than 8% of this population. IMN in diabetic patients was identified among a similarly sized and diverse population with 83.3% sensitivity, 97.7% specificity, 62.5% PPV, and 99.2% NPV. Finally, IMN in non-diabetic patients was identified with 50.0% sensitivity, 99.4% specificity, 75.0% PPV, and 98.3% NPV. Conclusions: Raman spectroscopy of urine with chemometric analysis may be able to differentiate between DKD, IMN, and other glomerular diseases. Future work will further characterize CKD stages and glomerular pathology, while assessing and controlling for differences in factors such as comorbidities, disease severity, and other lab parameters.

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.

Peptide-guided surface-enhanced Raman scattering probes for localized cell composition analysis.

The ability to control the localization of surface-enhanced Raman scattering (SERS) nanoparticle probes in bacterial cells is critical to the development of analytical techniques that can nondestructively determine cell composition and phenotype. Here, selective localization of SERS probes was achieved at the outer bacterial membrane by using silver nanoparticles functionalized with synthetic hydrophobic peptides.

Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine.

Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R2) of 0.91 was obtained over all hematuria levels (0-20% v/v), and an R2 of 0.92 was obtained for microhematuria (0-1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.

Disease-Associated Multimolecular Signature in the Urine of Patients with Lyme Disease Detected Using Raman Spectroscopy and Chemometrics.

A urine-based screening technique for Lyme disease (LD) was developed in this research. The screen is based on Raman spectroscopy, iterative smoothing-splines with root error adjustment (ISREA) spectral baselining, and chemometric analysis using Rametrix software. Raman spectra of urine from 30 patients with positive serologic tests (including the US Centers for Disease Control [CDC] two-tier standard) for LD were compared against subsets of our database of urine spectra from 235 healthy human volunteers, 362 end-stage kidney disease (ESKD) patients, and 17 patients with active or remissive bladder cancer (BCA). We found statistical differences (p < 0.001) between urine scans of healthy volunteers and LD-positive patients. We also found a unique LD molecular signature in urine involving 112 Raman shifts (31 major Raman shifts) with significant differences from urine of healthy individuals. We were able to distinguish the LD molecular signature as statistically different (p < 0.001) from the molecular signatures of ESKD and BCA. When comparing LD-positive patients against healthy volunteers, the Rametrix-based urine screen performed with 86.7% for overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. When considering patients with ESKD and BCA in the LD-negative group, these values were 88.7% (accuracy), 83.3% (sensitivity), 91.0% (specificity), 80.7% (PPV), and 92.4% (NPV). Additional advantages to the Raman-based urine screen include that it is rapid (minutes per analysis), is minimally invasive, requires no chemical labeling, uses a low-profile, off-the-shelf spectrometer, and is inexpensive relative to other available LD tests.

Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis.

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine 'molecular fingerprint', representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016-2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9-1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59-84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis-Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of 'Long COVID-19' symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography-Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.

Resolving complex phenotypes with Raman spectroscopy and chemometrics.

Raman spectroscopy and chemometric analyses are used to characterize phenotypes of biological samples. The approach is relevant in biotechnology to identify and monitor productive cell cultures. It can also detect the presence of pathogens in food products and screen for disease in clinical applications. Raman-based phenotyping is of interest because it is inexpensive, rapid, label-free, and is not obscured by water molecules. Here, recent applications in microbial species and tissue identification, isogenic cell/tissue phenotype changes, and characterizing biological fluids were surveyed along with the myriad spectral processing and chemometric analysis approaches. Suggestions are also given to help standardize and solidify Raman-based phenotyping as an -omics analysis method. These include offering repositories for raw spectral data and molecular assignment libraries.

A synthetic biosensor to detect peroxisomal acetyl-CoA concentration for compartmentalized metabolic engineering.

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.

The Rametrix™ PRO Toolbox v1.0 for MATLAB®.

BACKGROUND: Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix™ LITE Toolbox v1.0 for MATLAB® 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™ PRO Toolbox v1.0 is introduced to provide this capability. METHODS: The Rametrix™ PRO Toolbox v1.0 was constructed for MATLAB® and works with the Rametrix™ 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™PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix™ PRO was used to validate Rametrix™ 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™ PRO in evaluating chemometric models constructed with Rametrix™ LITE.

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.

Mitigating nutrient accumulation with microalgal growth towards enhanced nutrient removal and biomass production in an osmotic photobioreactor.

Forward osmosis (FO) has great potential for low energy consumption wastewater reuse provided there is no requirement for draw solutes (DS) regeneration. Reverse solute flux (RSF) can lead to DS build-up in the feed solution. This remains a key challenge because it can cause significant water flux reduction and lead to additional water quality problems. Herein, an osmotic photobioreactor (OsPBR) system was developed to employ fast-growing microalgae to consume the RSF nutrients. Diammonium phosphate (DAP) was used as a fertilizer DS, and algal biomass was a byproduct. The addition of microalgae into the OsPBR proved to maintain water flux while reducing the concentrations of NH4+-N, PO43--P and chemical oxygen demand (COD) in the OsPBR feed solution by 44.4%, 85.6%, and 77.5%, respectively. Due to the forward cation flux and precipitation, intermittent supplements of K+, Mg2+, Ca2+, and SO42- salts further stimulated algal growth and culture densities by 58.7%. With an optimal hydraulic retention time (HRT) of 3.33 d, the OsPBR overcame NH4+-N overloading and stabilized key nutrients NH4+-N at âˆ¼ 2.0 mg L-1, PO43--P < 0.6 mg L-1, and COD < 30 mg L-1. A moderate nitrogen reduction stress resulted in a high carbohydrate content (51.3 ± 0.1%) among microalgal cells. A solids retention time (SRT) of 17.82 d was found to increase high-density microalgae by 3-fold with a high yield of both lipids (9.07 g m-3 d-1) and carbohydrates (16.66 g m-3 d-1). This study encourages further exploration of the OsPBR technology for simultaneous recovery of high-quality water and production of algal biomass for value-added products.