Modified Cytosines versus Cytosine in a DNA Polymerase: Retrieving Thermodynamic and Kinetic Constants at the Single Molecule Level

DNA methylation plays key roles in various areas, such as gene expression, regulation, epigenetics, and cancers. Since 5-methylcytosine (5mC) is commonly present in methylated DNA, characterizing the binding kinetics and...

1286. Taniborbactam Inhibits Cefepime-Hydrolyzing Variants of Pseudomonas-derived Cephalosporinase (PDC)

Background PDC is a class C β-lactamase in P. aeruginosa. PDC-88 is a variant characterized by a Thr-Pro amino acid deletion in the R2-loop (Δ289-290; Fig. 1). This deletion reduces susceptibility to cefepime (FEP), ceftazidime (CAZ), and ceftolozane-tazobactam (TOL/TZB), but the mechanism for this “gain of function” is unknown. Taniborbactam (TAN) is a novel cyclic boronate β-lactamase inhibitor (BLI) with activity against all four β-lactamase classes and is currently undergoing a phase 3 clinical trial paired with FEP. Herein, we studied the extended-spectrum AmpC (ESAC) phenotype of PDC-88 and examined the ability of TAN to inhibit this variant. Structure of PDC-1 (PDB ID: 4GZB) with PDC-88 deleted residues in red and substitutions in green. All four amino acid substitutions (T79A, V178L, V329I, and G346A) are common (occurring in 10% or more of PDC variants) and have not been associated with resistance. Image rendered using UCSF Chimera. Methods Broth microdilution minimum inhibitory concentrations (MIC) were determined in accordance with CLSI. PDC-3 and PDC-88 were purified, and steady-state enzyme kinetics were determined. Quadrupole time-of-flight mass spectrometry (Q-TOF-MS) was performed. Results In isogenic E. coli expressing PDC-3 or PDC-88, FEP MIC increased 8- or 128-fold, respectively, compared to the empty vector. Addition of TAN at 4 μg/ml restored FEP activity with MIC lowered to 0.25 μg/ml (Table 1) for both PDC-3 and PDC-88 bearing strains. PDC-88 demonstrated a 9-fold lower KM, 3.4-fold lower kcat, and 2.6-fold higher kcat/KM for FEP compared to PDC-3 (Table 2A). TAN Ki values were 4- to 10-fold lower than avibactam (AVI) and 40- to 95-fold lower than TZB. The TAN acylation constant (k2/K) was 7- to 12-fold greater than AVI and 133- to 366-fold higher than TZB (Table 2B). Q-TOF-MS revealed faster deacylation of FEP by PDC-88 compared to TOL and CAZ. TOL was acylated and deacylated by PDC-88 but not by PDC-3. CAZ was readily acylated but slowly deacylated by PDC-88 compared to PDC-3 (Fig. 2). Cefepime Minimum Inhibitory Concentration (MIC) for PDC-1 and a series of partial R2-loop deletions with and without taniborbactam, avibactam, and tazobactam. In all variants, taniborbactam and avibactam restored susceptiblity while tazobactam is less effective against PDC-88 and variants. Summary of kinetic constants. (A) Comparison of Michaelis constant (KM), turnover number (kcat), and catalytic efficiency (kcat/KM) of nitrocefin and cefepime with PDC-3 and PDC-88. (B) Comparison of inhibition constant (Ki) and acylation constant (k2/K) for avibactam, tazobactam, and taniborbactam with PDC-3 and PDC-88. Graphical summary of mass spectrometry results for substrate acyl-enzyme complex capture experiments. FEP, cefepime; CAZ, ceftazidime; TOL, ceftolozane. Primes indicate a modified substrate (loss of R2 group). TOL does not form an acyl-enzyme complex with PDC-3. Conclusion Different kinetic constants are responsible for the elevated cephalosporin MICs. We posit that PDC-88 increases FEP MIC by enhanced hydrolysis; TOL MICs by enabling acylation; and CAZ MICs by both trapping and enhanced hydrolysis. TAN inhibits both PDC-3 and PDC-88 with similar kinetic profiles. Notably, TAN appears to be a more efficient inhibitor of PDC than current BLIs targeted for use against P. aeruginosa (lower Ki, higher k2/K values). The combination of TAN and FEP may represent an important treatment option for P. aeruginosa isolates that develop ESAC phenotypes. Disclosures Focco van den Akker, PhD, Venatorx Pharmaceuticals, Inc. (Grant/Research Support) Brittany A. Miller, BS, Venatorx Pharmaceuticals, Inc. (Employee) Tsuyoshi Uehara, PhD, Venatorx Pharmaceuticals, Inc. (Employee) David A. Six, PhD, Venatorx Pharmaceuticals, Inc. (Employee) Krisztina M. Papp-Wallace, Ph.D., Merck & Co., Inc. (Grant/Research Support)Spero Therapeutics, Inc. (Grant/Research Support)Venatorx Pharmaceuticals, Inc. (Grant/Research Support)Wockhardt Ltd. (Other Financial or Material Support, Research Collaborator) Robert A. Bonomo, MD, entasis (Research Grant or Support)Merck (Grant/Research Support)NIH (Grant/Research Support)VA Merit Award (Grant/Research Support)VenatoRx (Grant/Research Support)

Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States

Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models are large. Here we show how consistent kinetic constants, metabolite concentrations, and enzyme concentrations can be determined from data if metabolic fluxes are known. The estimation method, called model balancing, can handle models with a wide range of rate laws and accounts for thermodynamic constraints between fluxes, kinetic constants, and metabolite concentrations. It can be used to estimate in-vivo kinetic constants, to complete and adjust available data, and to construct plausible metabolic states with predefined flux distributions. By omitting one term from the log posterior—a term for penalising low enzyme concentrations—we obtain a convex optimality problem with a unique local optimum. As a demonstrative case, we balance a model of E. coli central metabolism with artificial or experimental data and obtain a physically and biologically plausible parameterisation of reaction kinetics in E. coli central metabolism. The example shows what information about kinetic constants can be obtained from omics data and reveals practical limits to estimating in-vivo kinetic constants. While noise-free omics data allow for a reasonable reconstruction of in-vivo kcat and KM values, prediction from noisy omics data are worse. Hence, adjusting kinetic constants and omics data to obtain consistent metabolic models is the main application of model balancing.

Response Surface Optimization for Waste Water from Textile Industry

The findings of the study showed that at optimum conditions of the operating parameters i.e., current density = 14.17 mA/cm2 , t = 102 min, and pH = 6.25, 63.41% of COD removal, 90.93% of dye removal and 0.0035 kWh/kg of energy consumption, were observed. Kinetic studies showed that EC based treatment of STW followed first order kinetics and the kinetic constants at 30°C for each response parameter i.e., % COD removal and % dye removal were 0.0205 min-1 and 0.0097 min-1 , respectively. Similarly, at 50°C the kinetic constants for % COD removal and % dye removal were 0.037 min-1 and 0.011 min-1 , respectively. Further, it was also observed that the amount of Al in the treated STW, sludge and scum was observed to be 25.16 mg/l, 0.50778g and 0.06006 g, respectively. Keywords: Waste water, Response Surface plots and optimization