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.

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.

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.

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.

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.

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.

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.

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.

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.

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, PO₄^3--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 SO₄^2- 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 ∼ 2.0 mg L^-1, PO₄^3--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.

Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer

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™) 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™ 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.

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.

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.

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.

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)

Raman Chemometric Urinalysis (Rametrix^TM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. Rametrix^TM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing “unknown” specimens, Rametrix^TM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. Rametrix^TM was able to identify “unknown” urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.

Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix)

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.

Identification of soil bacteria capable of utilizing a corn ethanol fermentation byproduct

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.

3D printed conformal microfluidics for isolation and profiling of biomarkers from whole organs

The ability to interface microfluidic devices with native complex biological architectures, such as whole organs, has the potential to shift the paradigm for the study and analysis of biological tissue. Here, we show 3D printing can be used to fabricate bio-inspired conformal microfluidic devices that directly interface with the surface of whole organs. Structured-light scanning techniques enabled the 3D topographical matching of microfluidic device geometry to porcine kidney anatomy. Our studies show molecular species are spontaneously transferred from the organ cortex to the conformal microfluidic device in the presence of fluid flow through the organ-conforming microchannel. Large animal studies using porcine kidneys (n = 32 organs) revealed the profile of molecular species in the organ-conforming microfluidic stream was dependent on the organ preservation conditions. Enzyme-linked immunosorbent assay (ELISA) studies revealed conformal microfluidic devices isolate clinically relevant metabolic and pathophysiological biomarkers from whole organs, including heat shock protein 70 (HSP-70) and kidney injury molecule-1 (KIM-1), which were detected in the microfluidic device as high as 409 and 12 pg mL^-1, respectively. Overall, these results show conformal microfluidic devices enable a novel minimally invasive 'microfluidic biopsy' technique for isolation and profiling of biomarkers from whole organs within a clinically relevant interval. This achievement could shift the paradigm for whole organ preservation and assessment, thereby helping to relieve the organ shortage crisis through increased availability and quality of donor organs. Ultimately, this work provides a major advance in microfluidics through the design and manufacturing of organ-conforming microfluidic devices and a novel technique for microfluidic-based analysis of whole organs.

Modification of the Mycotoxin Deoxynivalenol Using Microorganisms Isolated from Environmental Samples

The trichothecene mycotoxin deoxynivalenol (DON) is a common contaminant of wheat, barley, and maize. New strategies are needed to reduce or eliminate DON in feed and food products. Microorganisms from plant and soil samples collected in Blacksburg, VA, USA, were screened by incubation in a mineral salt media containing 100 μg/mL DON and analysis by gas chromatography mass spectrometry (GC/MS). Two mixed cultures derived from soil samples consistently decreased DON levels in assays using DON as the sole carbon source. Nuclear magnetic resonance (NMR) analysis indicated that 3-keto-4-deoxynivalenol was the major by-product of DON. Via 16S rRNA sequencing, these mixed cultures, including mostly members of the genera Acinetobacter, Leadbetterella, and Gemmata, were revealed. Incubation of one of these mixed cultures with wheat samples naturally contaminated with 7.1 μg/mL DON indicated nearly complete conversion of DON to the less toxic 3-epimer-DON (3-epi-DON). Our work extends previous studies that have demonstrated the potential for bioprospecting for microorganisms from the environment to remediate or modify mycotoxins for commercial applications, such as the reduction of mycotoxins in fuel ethanol co-products.

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. β-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 β-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 β-carotene production level at 25.52 ± 2.15 mg β-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 β-carotene g−1 (dcw)]. In this study, it was shown that Raman spectroscopy is capable of monitoring β-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 β-carotene per biomass and total fatty acid content of the cell.

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.

Magnesium ions are essential for life, and, correspondingly, all organisms must encode for proteins to transport them. Three classes of bacterial proteins (CorA, MgtE and MgtA/B) have previously been identified for transport of the ion. This current study introduces a new route of magnesium import, which, moreover, is unexpectedly provided by proteins distantly related to Natural resistance-associated macrophage proteins (Nramp). Nramp metal transporters are widespread in the three domains of life; however, most are assumed to function as transporters of transition metals such as manganese or iron. None of the previously characterized Nramps have been shown to transport magnesium. In this study, we demonstrate that certain bacterial proteins, distantly related to Nramp homologues, exhibit transport of magnesium. We also find that these new magnesium transporters are genetically controlled by a magnesium-sensing regulatory element. Importantly, we find numerous additional examples of similar genes sharing this regulatory arrangement, suggesting that these genes may be a frequent occurrence in bacteria, and may represent a class of magnesium transporters. Therefore, our aggregate data discover a new and perhaps broadly important path of magnesium import in bacteria.

Designing novel cellulase systems through agent-based modeling and global sensitivity analysis

Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement.

Resolving cell composition through simple measurements, genome-scale modeling, and a genetic algorithm

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and "easy to measure" culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined (13)C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.

Are we ready for genome-scale modeling in plants?

As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

BACKGROUND

Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico.

RESULTS

A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production.

CONCLUSIONS

FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

BACKGROUND

Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.

RESULTS

We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.

CONCLUSIONS

Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.

Microarray-based gene expression analysis as a process characterization tool to establish comparability of complex biological products: scale-up of a whole-cell immunotherapy product

Whole-cell immunotherapies and other cellular therapies have shown promising results in clinical trials. Due to the complex nature of the whole cell product and of the sometimes limited correlation of clinical potency with the proposed mechanism of action, these cellular immunotherapy products are generally not considered well characterized. Therefore, one major challenge in the product development of whole cell therapies is the ability to demonstrate comparability of product after changes in the manufacturing process. Such changes are nearly inevitable with increase in manufacturing experience leading to improved and robust processes that may have higher commercial feasibility. In order to comprehensively assess the impact of the process changes on the final product, and thus establish comparability, a matrix of characterization assays (in addition to lot release assays) assessing the various aspects of the cellular product are required. In this study, we assessed the capability of DNA-microarray-based, gene-expression analysis as a characterization tool using GVAX cancer immunotherapy cells manufactured by Cell Genesys, Inc. The GVAX immunotherapy product consists two prostate cancer cell lines (CG1940 and CG8711) engineered to secrete human GM-CSF. To demonstrate the capability of the assay, we assessed the transcriptional changes in the product when produced in the presence or absence of fetal bovine serum, and under normal and hypoxic conditions, both changes intended to stress the cell lines. We then assessed the impact of an approximately 10-fold process scale-up on the final product at the transcriptional level. These data were used to develop comparisons and statistical analyses suitable for characterizing culture reproducibility and cellular product similarity. Use of gene-expression data for process characterization proved to be a reproducible and sensitive method for detecting differences due to small or large changes in culture conditions as might be encountered in process scale-up or unanticipated bioprocess failures. Gene expression analysis demonstrated that cell products of representative lots under the same production process and at the same production scale were statistically identical. Large process changes that resulted from the artificial stress conditions used (absence of FBS and induction of hypoxia) displayed profoundly different gene expression patterns. We propose the use of simple t-test analysis in combination with the herein introduced expression ratio with mean intensity (ERMI) analysis as useful tools for process characterization by global gene expression analysis.

Genome-scale model for Clostridium acetobutylicum: Part II. Development of specific proton flux states and numerically determined sub-systems

A regulated genome-scale model for Clostridium acetobutylicum ATCC 824 was developed based on its metabolic network reconstruction. To aid model convergence and limit the number of flux-vector possible solutions (the size of the phenotypic solution space), modeling strategies were developed to impose a new type of constraint at the endo-exo-metabolome interface. This constraint is termed the specific proton flux state, and its use enabled accurate prediction of the extracellular medium pH during vegetative growth of batch cultures. The specific proton flux refers to the influx or efflux of free protons (per unit biomass) across the cell membrane. A specific proton flux state encompasses a defined range of specific proton fluxes and includes all metabolic flux distributions resulting in a specific proton flux within this range. Effective simulation of time-course batch fermentation required the use of independent flux balance solutions from an optimum set of specific proton flux states. Using a real-coded genetic algorithm to optimize temporal bounds of specific proton flux states, we show that six separate specific proton flux states are required to model vegetative-growth metabolism and accurately predict the extracellular medium pH. Further, we define the apparent proton flux stoichiometry per weak acids efflux and show that this value decreases from approximately 3.5 mol of protons secreted per mole of weak acids at the start of the culture to approximately 0 at the end of vegetative growth. Calculations revealed that when specific weak acids production is maximized in vegetative growth, the net proton exchange between the cell and environment occurs primarily through weak acids efflux (apparent proton flux stoichiometry is 1). However, proton efflux through cation channels during the early stages of acidogenesis was found to be significant. We have also developed the concept of numerically determined sub-systems of genome-scale metabolic networks here as a sub-network with a one-dimensional null space basis set. A numerically determined sub-system was constructed in the genome-scale metabolic network to study the flux magnitudes and directions of acetylornithine transaminase, alanine racemase, and D-alanine transaminase. These results were then used to establish additional constraints for the genome-scale model.

Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico.

Optimization of fed-batch parameters and harvest time of CHO cell cultures for a glycosylated product with multiple mechanisms of inactivation

Optimization of fed-batch feeding parameters was explored for a system with multiple mechanisms of product inactivation. In particular, two separate mechanisms of inactivation were identified for the recombinant tissue-type activator (r-tPA) protein. Dynamic inactivation models were written to describe particular r-tPA glycoform inactivation in the presence and absence of free-glucose. A glucose-independent inactivation mechanism was identified, and inactivation rate constants were found dependent upon the presence of glycosylation of r-tPA at N184. Inactivation rate constants of the glucose-dependent mechanism were not affected by glycosylation at N184. Fed-batch optimization was performed for r-tPA production by CHO cell culture in a stirred-tank reactor with glucose, glutamine and asparagine feed. Feeding profiles in which culture supernatant concentrations of free-glucose and amino acids (combined glutamine and asparagine) were used as control variables, were evaluated for a wide variety of set points. Simulation results for a controlled feeding strategy yielded an optimum at set points of 1.51 g L(-1) glucose and 1.18 g L(-1) of amino acids. Optimization was also performed in absence of metabolite control using fixed feed-flow rates initiate during the exponential growth phase. Fixed feed-flow results displayed a family of optimum solutions along a mass flow rate ratio of 3.15 of glucose to amino acids. Comparison of the two feeding strategies showed a slight advantage of rapid feeding at a fixed flow rate as opposed to metabolite control for a product with multiple mechanisms of inactivation.

A general framework for designing and validating oligomer-based DNA microarrays and its application to Clostridium acetobutylicum

While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.

Development of a culture sub-population induction model: signaling pathways synergy and taxanes production by Taxus canadensis

Cell cultures of Taxus canadensis were subjected to exogenously applied ethylene (ET) hormone and methyl jasmonate (MJ) elicitation in factorial design experiments. Levels of extracellular taxanes, including paclitaxel, were used with principal component analysis for fault detection and real-coded genetic algorithms for parameter optimization to construct a culture sub-population induction model. Culture sub-populations were identified by the model as (1) uninduced, (2) induced to unilateral function of the ET-signaling pathway, and (3) induced to cooperation between jasmonic acid (JA)- and ET-signaling pathways. Comprehensive model results suggested greater rates of cellular induction (resulting in exogenous taxane production) by ET gas as opposed to MJ elicitation. However, cellular induction of ET-signaling pathway genes increased the rate of induction of JA-signaling pathway genes by orders of magnitude. In addition, model results showed that induction of genes leading to extracellular production of the simple taxane 10-deacetylbaccatin III was regulated by the unilateral ET-signaling pathway. However, it was suggested that further processing of this simple taxane to complex taxane structures, such as paclitaxel, required further gene induction by the JA-signaling pathway. Thus, production rate constants of exogenous complex taxanes were predicted to be an order of magnitude lower than that for the simple taxane 10-deacetylbaccatin III. The fraction of the cell culture sub-population displaying unilateral ET-signaling pathway gene induction was found inversely proportional to levels of MJ elicitation. When coupled with simple non-growth product models, levels of all extracellular taxanes were effectively predicted using the culture sub-population induction model.

Variable Site-Occupancy Classification of N-Linked Glycosylation Using Artificial Neural Networks

A novel neural-network-based model has been developed for the prediction of N-linked glycosylation characteristics related to glycosylation site-occupancy. Intracellular oligosaccharide transfer to a polypeptide is known to be either robust or dependent upon culture conditions during pharmaceutical production. This glycan attachment is classified by the model as robust or variable and is based on an input of the polypeptide primary sequence around the site of glycosylation. The glycosylation model utilizes multiple recurrent neural networks followed by a perceptron classifier. The input length of the polypeptide chain around the site of glycosylation (glycosylation window) was optimized through multiple independent training sessions. Incorporation of five residues prior (n - 5) to the site of glycosylation (n) and four residues beyond (n + 4) the glycan attachment site led to optimal network performance. The size of the glycosylation window for site-occupancy determination is much larger than has been previously reported. This model was developed to evaluate the effects of theoretical polypeptide mutations on glycosylation site-occupancy characteristics. Following correct prediction of the model testing data set, 20 independent networks were used to predict site-occupancy characteristics of wild-type and mutants of the rabies virus glycoprotein (rgp). Simulation results strongly correlated with previously published experimental results (Kasturi, L.; Hegang, C.; Shakin-Eshleman, S. H. Regulation of N-linked core glycosylation: use of a site-directed mutagenesis approach to identify Asn-Xaa-Ser/Thr sequons that are poor oligosacchride acceptors. Biochem. J. 1997, 323, 415-419. Mellquist, J. L.; Kasturi, L.; Spitalnik, S. L.; Shakin-Eshleman, S. H. The amino acid following an Asn-X-Ser/Thr sequon is an important determinant of N-linked core glycosylation efficiency. Biochemistry 1998, 37, 6833-6837). Further simulations on purely theoretical sequences suggested that influences of charged residues were a subset of multiple mechanisms in the determination of glycosylation site-occupancy.

Neural-Network-Based Identification of Tissue-Type Plasminogen Activator Protein Production and Glycosylation in CHO Cell Culture under Shear Environment

An artificial neural network (ANN) modeling scheme has been constructed for the identification of both recombinant tissue-type plasminogen activator (r-tPA) protein production and glycosylation from Chinese hamster ovary (CHO) cell culture, cultivated in a stirred bioreactor. A series of hybrid feed-forward backpropagation neural networks were constructed to function as a software sensor. This enabled predictions of viable cell density, r-tPA content, and r-tPA glycosylation. The sensor was based on an initial input vector space consisting of simple metabolite concentrations, batch cultivation time, and a description of shear stress applied to the culture. Metabolite concentrations of the culture supernatant, included in the input vector space, were obtained from a single isocratic HPLC measurement. The shear stress component of the input space enabled accurate culture state prediction over a wide range of agitation rates. Coefficient of determination (r(2)) values between ANN predicted and experimental measurements of 0.945, 0.943, 0.956, and 0.990 were calculated to validate individual ANN prediction accuracy for total ammonia, apparent viable cell density, total r-tPA, and Type II glycoform concentrations, respectively.

Effect of shear stress on intrinsic CHO culture state and glycosylation of recombinant tissue-type plasminogen activator protein

Shear stress in suspension culture was investigated as a possible manipulative parameter for the control of glycosylation of the recombinant tissue-type plasminogen activator protein (r-tPA) produced by recombinant Chinese hamster ovary (CHO) cell culture, grown in protein-free media. Resulting fractions of partially glycosylated, Type II, and fully glycosylated, Type I, r-tPA protein were monitored as a direct function of the shear characteristics of the culture environment. The shear-induced response of CHO culture to levels of low shear stress, where exponential growth was not obtained, and to higher levels of shear stress, which resulted in extensive cell death, were examined through manipulation of the bioreactor stirring velocity. Both apparent and intrinsic cell growth, metabolite consumption, byproduct and r-tPA production, and r-tPA glycosylation, from a variable site-occupancy standpoint, were monitored throughout. Kinetic analyses revealed a shear-stress-induced alteration of cellular homeostasis resulting in a nonlinear dependency of metabolic yield coefficients and an intrinsic cell lysis kinetic constant on shear stress. Damaging levels of shear stress were used to investigate the shear dependence of cell death and lysis, as well as the effects on the intrinsic growth rate of the culture. Kinetic models were also developed on the basis of the intrinsic state of the culture and compared to traditional models. Total r-tPA production was maximized under moderate shear conditions, as was the viable CHO cell density of the culture. However, Type II r-tPA production and the fraction of Type II glycoform production ratio was maximized under damaging levels of shear stress. Analyses of biomass production yield coefficients coupled with a plug-flow reactor model of glycan addition in the endoplasmic reticulum (ER) were used to propose an overall mechanism of decreased r-tPA protein site-occupancy glycosylation with increasing shear stress. Decreased residence time of r-tPA in the ER as a result of increased protein synthesis related to shear protection mechanisms is proposed to limit contact of site Asn184 with the membrane-bound oligosaccharyltransferase enzyme in the ER.