Fundamentals of FGF19 & FGF21 Action In Vitro and In Vivo

Fibroblast growth factors 19 (FGF19) and 21 (FGF21) have emerged as key regulators of energy metabolism. Several studies have been conducted to understand the mechanism of FGF19 and FGF21 action, however, the data presented has often been inconsistent and at times contradictory. Here in a single study we compare the mechanisms mediating FGF19/FGF21 actions, and how similarities/differences in actions at the cellular level between these two factors translate to common/divergent physiological outputs. Firstly, we show that in cell culture FGF19/FGF21 are very similar, however, key differences are still observed differentiating the two. In vitro we found that both FGF's activate FGFRs in the context of βKlotho (KLB) expression. Furthermore, both factors alter ERK phosphorylation and glucose uptake with comparable potency. Combination treatment of cells with both factors did not have additive effects and treatment with a competitive inhibitor, the FGF21 delta N17 mutant, also blocked FGF19's effects, suggestive of a shared receptor activation mechanism. The key differences between FGF21/FGF19 were noted at the receptor interaction level, specifically the unique ability of FGF19 to bind/signal directly via FGFR4. To determine if differential effects on energy homeostasis and hepatic mitogenicity exist we treated DIO and ob/ob mice with FGF19/FGF21. We find comparable efficacy of the two proteins to correct body weight and serum glucose in both DIO and ob/ob mice. Nevertheless, FGF21 and FGF19 had distinctly different effects on proliferation in the liver. Interestingly, in vivo blockade of FGF21 signaling in mice using ΔN17 caused profound changes in glycemia indicative of the critical role KLB and FGF21 play in the regulation of glucose homeostasis. Overall, our data demonstrate that while subtle differences exist in vitro the metabolic effects in vivo of FGF19/FGF21 are indistinguishable, supporting a shared mechanism of action for these two hormones in the regulation of energy balance

Runx1 Loss Minimally Impacts Long-Term Hematopoietic Stem Cells

RUNX1 encodes a DNA binding subunit of the core-binding transcription factors and is frequently mutated in acute leukemia, therapy-related leukemia, myelodysplastic syndrome, and chronic myelomonocytic leukemia. Mutations in RUNX1 are thought to confer upon hematopoietic stem cells (HSCs) a pre-leukemic state, but the fundamental properties of Runx1 deficient pre-leukemic HSCs are not well defined. Here we show that Runx1 deficiency decreases both apoptosis and proliferation, but only minimally impacts the frequency of long term repopulating HSCs (LT-HSCs). It has been variously reported that Runx1 loss increases LT-HSC numbers, decreases LT-HSC numbers, or causes age-related HSC exhaustion. We attempt to resolve these discrepancies by showing that Runx1 deficiency alters the expression of several key HSC markers, and that the number of functional LT-HSCs varies depending on the criteria used to score them. Finally, we identify genes and pathways, including the cell cycle and p53 pathways that are dysregulated in Runx1 deficient HSCs

Biomass in the manufacture of industrial products—the use of proteins and amino acids

The depletion in fossil feedstocks, increasing oil prices, and the ecological problems associated with CO2 emissions are forcing the development of alternative resources for energy, transport fuels, and chemicals: the replacement of fossil resources with CO2 neutral biomass. Allied with this, the conversion of crude oil products utilizes primary products (ethylene, etc.) and their conversion to either materials or (functional) chemicals with the aid of co-reagents such as ammonia and various process steps to introduce functionalities such as -NH2 into the simple structures of the primary products. Conversely, many products found in biomass often contain functionalities. Therefore, it is attractive to exploit this to bypass the use, and preparation of, co-reagents as well as eliminating various process steps by utilizing suitable biomass-based precursors for the production of chemicals. It is the aim of this mini-review to describe the scope of the possibilities to generate current functionalized chemical materials using amino acids from biomass instead of fossil resources, thereby taking advantage of the biomass structure in a more efficient way than solely utilizing biomass for the production of fuels or electricity

A statistical analysis of subsampling and an evaluation of the Folsom plankton splitter

Subsampling techniques are important for the determination of precise plankton density estimates. A binomial model of random subsampling, and its Poisson extension, were developed for the purpose of evaluating the performance of compartment-type plankton subsamplers. Two approaches were used to assess the performance of the Folsom plankton splitter on an extensive series of nearshore Lake Michigan crustacean zooplankton samples collected between 1974 and 1979. First, Folsom subsamples were observed to be significantly (p < 0.05) more variable than expected from the random model of subsampling. Second, a random effects ANOVA model was used to compare fractions of the total variance in density estimates that were attributable to subsampling and sampling phases of a specially designed study. Departures from randomness in subsampling were sufficiently small that an analysis of optimal allocation of effort between subsampling and sampling phases, based on the ANOVA model, indicated that only one to three subsamples needed to be examined per sample.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42920/1/10750_2004_Article_BF00016403.pd

From Shell Logs to Shell Scripts

Analysing the use of a Unix command shell is one of the classic applications in the domain of adaptive user interfaces and user modelling. Instead of trying to predict the next command from a history of commands, we automatically produce scripts that automate frequent tasks. For this we use an ILP association rule learner. We show how to speedup the learning task by dividing it into smaller tasks, and the need for a preprocessing phase to detect frequent subsequences in the data. W