Omacetaxine may have a role in chronic myeloid leukaemia eradication through downregulation of Mcl-1 and induction of apoptosis in stem/progenitor cells

Chronic myeloid leukaemia (CML) is maintained by a rare population of tyrosine kinase inhibitor (TKI)-insensitive malignant stem cells. Our long-term aim is to find a BcrAbl-independent drug that can be combined with a TKI to improve overall disease response in chronic-phase CML. Omacetaxine mepesuccinate, a first in class cetaxine, has been evaluated by clinical trials in TKI-insensitive/resistant CML. Omacetaxine inhibits synthesis of anti-apoptotic proteins of the Bcl-2 family, including (myeloid cell leukaemia) Mcl-1, leading to cell death. Omacetaxine effectively induced apoptosis in primary CML stem cells (CD34<sup>+</sup>38<sup>lo</sup>) by downregulation of Mcl-1 protein. In contrast to our previous findings with TKIs, omacetaxine did not accumulate undivided cells <i>in vitro</i>. Furthermore, the functionality of surviving stem cells following omacetaxine exposure was significantly reduced in a dose-dependant manner, as determined by colony forming cell and the more stringent long-term culture initiating cell colony assays. This stem cell-directed activity was not limited to CML stem cells as both normal and non-CML CD34<sup>+</sup> cells were sensitive to inhibition. Thus, although omacetaxine is not leukaemia stem cell specific, its ability to induce apoptosis of leukaemic stem cells distinguishes it from TKIs and creates the potential for a curative strategy for persistent disease

Human activity was a major driver of the mid-Holocene vegetation change in southern Cumbria: Implications for the elm decline in the British Isles

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.The dramatic decline in elm (Ulmus) across a large swathe of north-west Europe in the mid-Holocene has been ascribed to a number of possible factors, including climate change, human activity and/or pathogens. A major limitation for identifying the underlying cause(s) has been the limited number of high-resolution records with robust geochronological frameworks. Here, we report a multiproxy study of an upland (Blea Tarn) and lowland (Urswick Tarn) landscape in southern Cumbria (British Isles) to reconstruct vegetation change across the elm decline in an area with a rich and well-dated archaeological record to disentangle different possible controls. Here we find a two-stage decline in Ulmus taking place between 6350–6150 and 6050–5850 cal a BP, with the second phase coinciding with an intensification of human activity. The scale of the decline and associated human impact is more abrupt in the upland landscape. We consider it likely that a combination of human impact and disease drove the Ulmus decline within southern Cumbria.This work was funded by a studentship for MJG from the University of Exeter and Sir John Fisher Foundation. Additional funding for 14C dating was from the Cumberland and Westmorland Antiquarian and Archaeological Society (Clare Fell Bursary to MJG), and the Australian Research Council (FL100100195)

Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis

We consider the statistical analysis of population structure using genetic data. We show how the two most widely used approaches to modeling population structure, admixture-based models and principal components analysis (PCA), can be viewed within a single unifying framework of matrix factorization. Specifically, they can both be interpreted as approximating an observed genotype matrix by a product of two lower-rank matrices, but with different constraints or prior distributions on these lower-rank matrices. This opens the door to a large range of possible approaches to analyzing population structure, by considering other constraints or priors. In this paper, we introduce one such novel approach, based on sparse factor analysis (SFA). We investigate the effects of the different types of constraint in several real and simulated data sets. We find that SFA produces similar results to admixture-based models when the samples are descended from a few well-differentiated ancestral populations and can recapitulate the results of PCA when the population structure is more “continuous,” as in isolation-by-distance models

A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression.

BACKGROUND: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process. RESULTS: We review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS). We compare on both simulated and experimental data showing that the proposed approach considerably outperforms the current state of the art. CONCLUSIONS: Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of microarray time series. The Gaussian process framework offers a natural way of handling biological replicates and missing values and provides confidence intervals along the estimated curves of gene expression. Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series

Distinct and Shared Roles of β-Arrestin-1 and β-Arrestin-2 on the Regulation of C3a Receptor Signaling in Human Mast Cells

BACKGROUND: The complement component C3a induces degranulation in human mast cells via the activation of cell surface G protein coupled receptors (GPCR; C3aR). For most GPCRs, agonist-induced receptor phosphorylation leads to the recruitment of β-arrestin-1/β-arrestin-2; resulting in receptor desensitization and internalization. Activation of GPCRs also leads to ERK1/2 phosphorylation via two temporally distinct pathways; an early response that reflects G protein activation and a delayed response that is G protein independent but requires β-arrestins. The role of β-arrestins on C3aR activation/regulation in human mast cells, however, remains unknown. METHODOLOGY/PRINCIPAL FINDINGS: We utilized lentivirus short hairpin (sh)RNA to stably knockdown the expression of β-arrestin-1 and β-arrrestin-2 in human mast cell lines, HMC-1 and LAD2 that endogenously expresses C3aR. Silencing β-arrestin-2 attenuated C3aR desensitization, blocked agonist-induced receptor internalization and rendered the cells responsive to C3a for enhanced NF-κB activity as well as chemokine generation. By contrast, silencing β-arrestin-1 had no effect on these responses but resulted in a significant decrease in C3a-induced mast cell degranulation. In shRNA control cells, C3a caused a transient ERK1/2 phosphorylation, which peaked at 5 min but disappeared by 10 min. Knockdown of β-arrestin-1, β-arrestin-2 or both enhanced the early response to C3a and rendered the cells responsive for ERK1/2 phosphorylation at later time points (10-30 min). Treatment of cells with pertussis toxin almost completely blocked both early and delayed C3a-induced ERK1/2 phosphorylation in β-arrestin1/2 knockdown cells. CONCLUSION/SIGNIFICANCE: This study demonstrates distinct roles for β-arrestins-1 and β-arrestins-2 on C3aR desensitization, internalization, degranulation, NF-κB activation and chemokine generation in human mast cells. It also shows that both β-arrestin-1 and β-arrestin-2 play a novel and shared role in inhibiting G protein-dependent ERK1/2 phosphorylation. These findings reveal a new level of complexity for C3aR regulation by β-arrestins in human mast cells

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets

Formation of Zn- and Fe-sulfides near hydrothermal vents at the Eastern Lau Spreading Center: implications for sulfide bioavailability to chemoautotrophs

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Local IL-17 Production Exerts a Protective Role in Murine Experimental Glomerulonephritis

IL-17 is a pro-inflammatory cytokine implicated in the pathogenesis of glomerulonephritis and IL-17 deficient mice are protected from nephrotoxic nephritis. However, a regulatory role for IL-17 has recently emerged. We describe a novel protective function for IL-17 in the kidney. Bone marrow chimeras were created using wild-type and IL-17 deficient mice and nephrotoxic nephritis was induced. IL-17 deficient hosts transplanted with wild-type bone marrow had worse disease by all indices compared to wild-type to wild-type bone marrow transplants (serum urea p<0.05; glomerular thrombosis p<0.05; tubular damage p<0.01), suggesting that in wild-type mice, IL-17 production by renal cells resistant to radiation is protective. IL-17 deficient mice transplanted with wild-type bone marrow also had a comparatively altered renal phenotype, with significant differences in renal cytokines (IL-10 p<0.01; IL-1β p<0.001; IL-23 p<0.01), and macrophage phenotype (expression of mannose receptor p<0.05; inducible nitric oxide synthase p<0.001). Finally we show that renal mast cells are resistant to radiation and produce IL-17, suggesting they are potential local mediators of disease protection. This is a novel role for intrinsic cells in the kidney that are radio-resistant and produce IL-17 to mediate protection in nephrotoxic nephritis. This has clinical significance as IL-17 blockade is being trialled as a therapeutic strategy in some autoimmune diseases