Purification of the Escherichia-coli OGT gene-product to homogeneity and its rate of action on O6-methylguanine, O6-ethylguanine and O4-methylthymine in dodecadeoxyribnucleotides

The E.coli gene ogt encodes the DNA repair protein O6-alkylguanine-DNA-alkyltransferase (O6-AlkG ATase). The protein coding region of the gene was cloned into a multicopy expression vector to obtain high yields of the enzyme (˜ 0.2% of total protein) which was purified to apparent homogeneity by affinity, molecular exclusion and reverse-phase chromatography. Good correlation was found between the determined and predicted amino acid compositions. The ability of the purified protein to act on O6-methylguanine (O6-MeG), O6-ethylguanine (O6-EtG) and (O4-methylthymine (O4-MeT) in self-complementary dodecadeoxyribonucleotides was compared to that of 19 kDa fragment of the related ada-protein. With both proteins the rate order was O6-MeG > O6-EtG > O4-MeT, however, the ogt protein was found to repair O6MeG, O6-EtG and (O4-MeT, 1.1, 173 and 84 times, respectively, faster than the ada protein

Variable-temperature, variable-field magnetic circular dichroism spectroscopic study of NifEN-bound precursor and “FeMoco”

NifEN plays a key role in the biosynthesis of the iron–molybdenum cofactor (FeMoco) of nitrogenase. A scaffold protein that hosts the conversion of a FeMoco precursor to a mature cofactor, NifEN can assume three conformations during the process of FeMoco maturation. One, designated ΔnifB NifEN, contains only two permanent [Fe4S4]-like clusters. The second, designated NifENPrecursor, contains the permanent clusters and a precursor form of FeMoco. The third, designated NifEN“FeMoco”, contains the permanent [Fe4S4]-like clusters and a fully complemented, “FeMoco”-like structure. Here, we report a variable-temperature, variable-field magnetic circular dichroism spectroscopic investigation of the electronic structure of the metal clusters in the three forms of dithionite-reduced NifEN. Our data indicate that the permanent [Fe4S4]-like clusters are structurally and electronically conserved in all three NifEN species and exhibit spectral features of classic [Fe4S4]+ clusters; however, they are present in a mixed spin state with a small contribution from the S > ½ spin state. Our results also suggest that both the precursor and “FeMoco” have a conserved Fe/S electronic structure that is similar to the electronic structure of FeMoco in the MoFe protein, and that the “FeMoco” in NifEN“FeMoco” exists, predominantly, in an S = 3/2 spin state with spectral parameters identical to those of FeMoco in the MoFe protein. These observations provide strong support to the outcome of our previous EPR and X-ray absorption spectroscopy/extended X-ray absorption fine structure analysis of the three NifEN species while providing significant new insights into the unique electronic properties of the precursor and “FeMoco” in NifEN

Impact of Systemic Inflammation and Autoimmune Diseases on apoA-I and HDL Plasma Levels and Functions

The cholesterol of high-density lipoproteins (HDLs) and its major proteic component, apoA-I, have been widely investigated as potential predictors of acute cardiovascular (CV) events. In particular, HDL cholesterol levels were shown to be inversely and independently associated with the risk of acute CV diseases in different patient populations, including autoimmune and chronic inflammatory disorders. Some relevant and direct anti-inflammatory activities of HDL have been also recently identified targeting both immune and vascular cell subsets. These studies recently highlighted the improvement of HDL function (instead of circulating levels) as a promising treatment strategy to reduce inflammation and associated CV risk in several diseases, such as systemic lupus erythematosus and rheumatoid arthritis. In these diseases, anti-inflammatory treatments targeting HDL function might improve both disease activity and CV risk. In this narrative review, we will focus on the pathophysiological relevance of HDL and apoA-I levels/functions in different acute and chronic inflammatory pathophysiological conditions

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity.

Acute myeloid leukaemia is characterized by marked inter- and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics

Selecting a differential equation cell cycle model for simulating leukemia treatment

This work studies three differential equation models of the leukemia cell cycle: a population balance model (PBM) using intracellular protein expression levels as state variables representing phase progress; a delay differential equation model (DDE) with temporal phase durations as delays; and an ordinary differential equation model (ODE) of phase-to-phase progression. In each type of model, global sensitivity analysis determines the most significant parameters while parameter estimation fits experimental data. To compare models based on the output of their structural properties, an expected behavior was defined, and each model was coupled to a pharmacokinetic/pharmacodynamic model of chemotherapy delivery. Results suggest that the particular cell cycle model chosen highly affects the simulated treatment outcome, given the same steady state kinetic parameters and drug dosage/scheduling. The manuscript shows how cell cycle models should be selected according to the complexity, sensitivity, and parameter availability of the application envisioned