Evolutionary dynamics of two related malignant plasma cell lines

Cancer is the consequence of sequential acquisition of mutations within somatic cells. Mutations alter the relative reproductive fitness of cells, enabling the population to evolve in time as a consequence of selection. Cancer therapy itself can select for or against specific subclones. Given the large population of tumor cells, subclones inevitably emerge and their fate will depend on the evolutionary dynamics that define the interactions between such clones. Using a combination of in vitro studies and mathematical modeling, we describe the dynamic behavior of two cell lines isolated from the same patient at different time points of disease progression and show how the two clones relate to one another. We provide evidence that the two clones coexisted at the time of initial presentation. The dominant clone presented with biopsy-proven cardiac AL amyloidosis. Initial therapy selected for the second clone that expanded leading to a change in the diagnosis to multiple myeloma. The evolutionary dynamics relating the two cell lines are discussed and a hypothesis is generated in regard to the mechanism of one of the phenotypic characteristics that is shared by these two cell lines

In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics.

Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect, replicate, spread and kill tumor cells. The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response. Oncolytic viruses are highly efficient in killing tumor cells in vitro, especially in a 2D monolayer of tumor cells, their efficiency is significantly lower in a 3D environment, both in vitro and in vivo. This indicates that the spatial dimension may have a major influence on the dynamics of virus spread. We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models. We determine the number of nearest neighbor tumor cells in 2D (median = 6) and 3D tumor spheroids (median = 16) and how this influences virus spread and the outcome of therapy. The parameter range leading to tumor eradication is small and even harder to achieve in 3D. The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium. The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy. Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy

Distinct role of PLCβ3 in VEGF-mediated directional migration and vascular sprouting

Endothelial cell proliferation and migration is essential to angiogenesis. Typically, proliferation and chemotaxis of endothelial cells is driven by growth factors such as vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF). VEGF activates phospholipases (PLCs) – specifically PLCγ1 – that are important for tubulogenesis, differentiation and DNA synthesis. However, we show here that VEGF, specifically through VEGFR2, induces phosphorylation of two serine residues on PLCβ3, and this was confirmed in an ex vivo embryoid body model. Knockdown of PLCβ3 in HUVEC cells affects IP3 production, actin reorganization, migration and proliferation; whereas migration is inhibited, proliferation is enhanced. Our data suggest that enhanced proliferation is precipitated by an accelerated cell cycle, and decreased migration by an inability to activate CDC42. Given that PLCβ3 is typically known as an effector of heterotrimeric G-proteins, our data demonstrate a unique crosstalk between the G-protein and receptor tyrosine kinase (RTK) axes and reveal a novel molecular mechanism of VEGF signaling and, thus, angiogenesis

Choline acetyltransferase mutations cause myasthenic syndrome associated with episodic apnea in humans

Choline acetyltransferase (CHAT; EC 2.3.1.6) catalyzes the reversible synthesis of acetylcholine (ACh) from acetyl CoA and choline at cholinergic synapses. Mutations in genes encoding ChAT affecting motility exist in Caenorhabditis elegans and Drosophila, but no CHAT mutations have been observed in humans to date. Here we report that mutations in CHAT cause a congenital myasthenic syndrome associated with frequently fatal episodes of apnea (CMS-EA). Studies of the neuromuscular junction in this disease show a stimulation-dependent decrease of the amplitude of the miniature endplate potential and no deficiency of the ACh receptor. These findings point to a defect in ACh resynthesis or vesicular filling and to CHAT as one of the candidate genes. Direct sequencing of CHAT reveals 10 recessive mutations in five patients with CMS-EA. One mutation (523insCC) is a frameshifting null mutation. Three mutations (1305T, R420C, and E441K) markedly reduce ChAT expression in COS cells. Kinetic studies of nine bacterially expressed ChAT mutants demonstrate that one mutant (E441K) lacks catalytic activity, and eight mutants (L210P, P211A, 1305T, R420C, R482G, S498L, V506L, and R560H) have significantly impaired catalytic efficiencies.</p

Analysis of vancomycin loading in OPF/SMA hydrogels.

<p>(A) OPF/SMA 40% hydrogels were incubated with vancomycin at 400 μg/mL for 1 hour, 4 hours, 8 hours, 24 hours, or 72 hours. The concentrations of vancomycin in solution were then measured with HPLC coupled to UV-Vis detection at 280 nm and drug loading was determined by subtracting the initial concentration of drug from the final concentration after completion of vancomycin loading. (B) Hydrogels were incubated for 24 hours, samples were collected, and the concentration of vancomycin in solution was determined as described. Amount of drug loaded was calculated and loading efficiency was determined by dividing the mass of loaded drug by the dried hydrogel mass, such that loading efficiency is represented as μg vancomycin per mg hydrogel. (C) Maximal drug loading was determined by incubating OPF/SMA 30% hydrogel samples in increasingly concentrated solutions of vancomycin in double distilled water for 24 hours. Loading efficiency (Eff_l) was determined by measurement of the final drug concentration in the distilled water (C_f) after completion of the loading cycle, followed by comparison to concentration of initial solution (C_i) via the equation (Eff_l) = {[(C_i)—(C_f)] / (C_i)}*<b>100</b>%. (D) Diagram representing drug loading experiment. In all experiments, concentrations of vancomycin remaining were determined from a standard curve of vancomycin solution that was incubated under identical conditions to the sample of interest. Error bars represent +/- one standard deviation, N = 3 in all groups. Statistical representation: (*) indicates p<0.05.</p

Schematic depicting synthesis of OPF/SMA hydrogels.

<p>OPF/SMA hydrogels synthesized as described. Idealized OPF chain represented in blue, SMA is depicted in red, crosslinked between OPF chains. Vancomycin shown as red stars within the hydrogel.</p