Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K^trans (rate constant for plasma/interstitium contrast agent transfer), v_e (extravascular extracellular volume fraction), and v_p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T₁, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K^trans and v_p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K^trans intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K^trans) to 0.92 (for K^trans percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K^trans and k_ep (= K^trans/v_e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.     

Boosting Autofermentation Rates and Product Yields with Sodium Stress Cycling: Application to Production of Renewable Fuels by Cyanobacteria

Sodium concentration cycling was examined as a new strategy for redistributing carbon storage products and increasing autofermentative product yields following photosynthetic carbon fixation in the cyanobacterium Arthrospira (Spirulina) maxima. The salt-tolerant hypercarbonate strain CS-328 was grown in a medium containing 0.24 to 1.24 M sodium, resulting in increased biosynthesis of soluble carbohydrates to up to 50% of the dry weight at 1.24 M sodium. Hypoionic stress during dark anaerobic metabolism (autofermentation) was induced by resuspending filaments in low-sodium (bi)carbonate buffer (0.21 M), which resulted in accelerated autofermentation rates. For cells grown in 1.24 M NaCl, the fermentative yields of acetate, ethanol, and formate increase substantially to 1.56, 0.75, and 1.54 mmol/(g [dry weight] of cells·day), respectively (36-, 121-, and 6-fold increases in rates relative to cells grown in 0.24 M NaCl). Catabolism of endogenous carbohydrate increased by approximately 2-fold upon hypoionic stress. For cultures grown at all salt concentrations, hydrogen was produced, but its yield did not correlate with increased catabolism of soluble carbohydrates. Instead, ethanol excretion becomes a preferred route for fermentative NADH reoxidation, together with intracellular accumulation of reduced products of acetyl coenzyme A (acetyl-CoA) formation when cells are hypoionically stressed. In the absence of hypoionic stress, hydrogen production is a major beneficial pathway for NAD⁺ regeneration without wasting carbon intermediates such as ethanol derived from acetyl-CoA. This switch presumably improves the overall cellular economy by retaining carbon within the cell until aerobic conditions return and the acetyl unit can be used for biosynthesis or oxidized via respiration for a much greater energy return.Growth of aquatic microbial oxygenic phototrophs (AMOPs) such as cyanobacteria, algae, and diatoms as renewable feedstocks for energy production has been proposed as an advantageous alternative to growing land-based crops for biofuels (9, 11, 14). These organisms can be grown efficiently on water, sunlight, carbon dioxide, and minimal nutrients on nonarable land or at coastal marine sites. They produce easily digested biopolymers that can be more readily converted to fuels than recalcitrant cellulosic feedstocks. Nevertheless, efficient strategies for converting accumulated biomass from AMOPs into useful fuels are still needed.One strategy for converting cyanobacterial biomass to liquid and gaseous fuels is to allow these cells to rely on their own fermentative pathways—a process termed “autofermentation.” With autofermentation, cells anaerobically catabolize their internally stored carbohydrate molecules (glycogen and soluble sugars), producing CO₂, reductants, and energy as ion gradients and phosphorylation to regenerate ATP. The reducing equivalents in the form of NAD(P)H may be reused by the cell or excreted from the cell as reduced carbon products, typically organic acids and alcohols. The identities of these products are determined by the physiological conditions and by which fermentative enzymes are active. The genomes of cyanobacteria differ in terms of which genes of fermentative enzymes are present and functional, leading to a range of possible fermentative product yields and rates. A major limitation to the technological use of autofermentation for fuel production from biomass is the slow time scale of the conversion in relation to the light/dark cycle of growth.Two fermentation products useful as fuels that are excreted by some cyanobacteria are ethanol and hydrogen. Ethanol production via autofermentation occurs naturally in some cyanobacteria (22). Genetic engineering has been applied successfully to establish autofermentative ethanol production in the cyanobacterium Synechococcus sp. PCC 7942, which does not produce detectable amounts of ethanol in the wild type (10). This strain was created by insertion of the genes for pyruvate decarboxylase and alcohol dehyrogenase from Zymomonas mobilis. Genetic engineering has also been successfully applied to stimulate fermentative hydrogen production in Synechococcus sp. PCC 7002 by increasing the level of the limiting cellular reductant NADH via disruption of the lactate dehydrogenase gene (19).For biotechnological applications, the following two goals have been identified for increasing production of autofermentation products and hydrogen by AMOPs (1): (i) increase photoautotrophic accumulation of stored carbohydrates and (ii) increase the catabolic rate of carbohydrates under dark anaerobic conditions. Here we have continued our work with Arthrospira (Spirulina) maxima with efforts to achieve these two goals.Different approaches for increasing carbohydrate content for cyanobacteria exist. One is nutrient deprivation. Many cyanobacteria, including Arthrospira species, do not have nitrogenase (nif) genes and therefore require a nitrogen source (such as nitrate, ammonia, or urea) for protein synthesis. Deprivation of a nitrogen source is a well-documented strategy for increasing the glycogen content (stored as insoluble carbohydrate granules) in many nondiazotrophic cyanobacteria (3, 13, 23, 26). Sulfur deprivation has also been shown to increase the glycogen content in at least two cyanobacteria when incubated in the presence of methane (2). Recently, it was demonstrated that sulfur and nitrogen limitation, rather than complete deprivation, provides optimal autofermentative hydrogen yields in the cyanobacterium Synechocystis sp. PCC 6803 (5).A different approach, which increases soluble sugars in cyanobacteria, involves adaptation of cells to media with high concentrations of sodium salts. Many cyanobacteria accumulate organic molecules such as glucosyl-glycerol and/or trehalose to osmotically balance their cytosols with the extracellular medium (6, 17, 20). In addition to glycogen, these molecules can serve as substrates for fermentation in cyanobacteria (22). Both glucosyl-glycerol and trehalose are present in Arthrospira platensis (29) and A. maxima CS-328 (8). It was shown that a 4-fold increase in carbohydrate content can be achieved by growing A. platensis in media supplemented with additional 1 M NaCl relative to no additional NaCl (28).Acceleration of carbohydrate autofermentation in cyanobacteria by application of selective physiological stresses has been previously proposed (1). Here we report a new strategy that combines the established method for increasing carbohydrate content in Arthrospira by adapting filaments to highly saline growth media (28) with hypoionic stress to accelerate autofermentation. The entire process can be described as “sodium stress cycling,” which relies on hyperionic conditions (high salt) during growth to accumulate sugars, followed by hypoionic stress (low salt) to force catabolism during autofermentation. By resuspending filaments grown in media containing excess NaCl into buffer containing only sufficient solute to prevent lysis at the start of autofermentation, we achieve a large increase in total fermentative product yields relative to cells that were not adapted to high salt. However, this strategy did not lead to higher hydrogen yields, demonstrating that other fermentative routes (primarily ethanol production) are used for NADH recycling in A. maxima under hypoionic conditions.     

A Bayesian network approach to feature selection in mass spectrometry data

Background  

Time-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates.     

Oversynthesis of Riboflavin in the Yeast Pichia guilliermondii is Accompanied by Reduced Catalase and Superoxide Dismutases Activities

Iron deficiency causes oversynthesis of riboflavin in several yeast species, known as flavinogenic yeasts. Under iron deprivation conditions, Pichia guilliermondii cells increase production of riboflavin and malondialdehyde and the formation of protein carbonyl groups, which reflect increased intracellular content of reactive oxygen species. In this study, we found that P. guilliermondii iron deprived cells showed dramatically decreased catalase and superoxide dismutase activities. Previously reported mutations rib80, rib81, and hit1, which affect repression of riboflavin synthesis and iron accumulation by iron ions, caused similar drops in activities of the mentioned enzymes. These findings could explain the previously described development of oxidative stress in iron deprived or mutated P. guilliermondii cells that overproduce riboflavin. Similar decrease in superoxide dismutase activities was observed in iron deprived cells in the non-flavinogenic yeast Saccharomyces cerevisiae.     

Metabolic and bioprocess engineering of the yeast Candida famata for FAD production

Flavins in the form of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) play an important role in metabolism as cofactors for oxidoreductases and other enzymes. Flavin nucleotides have applications in the food industry and medicine; FAD supplements have been efficiently used for treatment of some inheritable diseases. FAD is produced biotechnologically; however, this compound is much more expensive than riboflavin. Flavinogenic yeast Candida famata synthesizes FAD from FMN and ATP in the reaction catalyzed by FAD synthetase, a product of the FAD1 gene. Expression of FAD1 from the strong constitutive promoter TEF1 resulted in 7- to 15-fold increase in FAD synthetase activity, FAD overproduction, and secretion to the culture medium. The effectiveness of FAD production under different growth conditions by one of these recombinant strains, C. famata T-FD-FM 27, was evaluated. First, the two-level Plackett–Burman design was performed to screen medium components that significantly influence FAD production. Second, central composite design was adopted to investigate the optimum value of the selected factors for achieving maximum FAD yield. FAD production varied most significantly in response to concentrations of adenine, KH₂PO₄, glycine, and (NH₄)₂SO₄. Implementation of these optimization strategies resulted in 65-fold increase in FAD production when compared to the non-optimized control conditions. Recombinant strain that has been cultivated for 40 h under optimized conditions achieved a FAD accumulation of 451 mg/l. So, for the first time yeast strains overproducing FAD were obtained, and the growth media composition for maximum production of this nucleotide was designed.     

Predicting Evolutionary Site Variability from Structure in Viral Proteins: Buriedness,Packing, Flexibility,and Design

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on nine non-homologous viral protein structures and from variation in homologous variants of those proteins, where they were available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1–0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than the more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.     

Application of single-cell sequencing technologies in pancreatic cancer

Molecular and Cellular Biochemistry - Pancreatic cancer (PC) is the third lethal disease for cancer-related mortalities globally. This is mainly because of the aggressive nature and heterogeneity...