PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity

Origin of discrepancy between electrical and mechanical anomalies in lead-free (K,Na)NbO3 -based ceramics

[EN] Ferroelectric polymorphic phase coexistence, associated with either the presence of a morphotropic phase boundary or a temperature-driven polymorphic phase transition, is currently acknowledged as the key to high piezoelectric activity and is searched when new perovskite materials are developed, like lead-free alternatives to state-of-the-art Pb(Zr,Ti)O3. This requires characterization tools that allow phase coexistence and transitions to be readily identified, among which measurements of the temperature dependences of Young's modulus and mechanical losses by dynamical mechanical analysis stand out as a powerful technique to complement standard electrical characterizations. We report here the application of this technique to (K1-xNax)NbO3-based materials, which are under extensive investigation as environmentally friendly high sensitivity piezoelectrics. The elastic anomalies associated with the different phase transitions are identified and are shown to be distinctively shifted in relation to the dielectric ones. The origin of this discrepancy is discussed with the help of temperature-dependent Raman spectroscopy and is proposed to be a characteristic of diffuse phase transitions.The authors would like to thank CAPES and the Sâo Paulo Research Foundation (FAPESP), Grants No. 2012/08457-7 and No. 2013/00134-7, for the financial support. M.A. also acknowledges funding from MINECO through the MAT2014-58816-R Project.Peer Reviewe

Respiratory modulation of oscillometric cuff pressure pulses and Korotkoff sounds during clinical blood pressure measurement in healthy adults

BACKGROUND: Accurate blood pressure (BP) measurement depends on the reliability of oscillometric cuff pressure pulses (OscP) and Korotkoff sounds (KorS) for automated oscillometric and manual techniques. It has been widely accepted that respiration is one of the main factors affecting BP measurement. However, little is known about how respiration affects the signals from which BP measurement is obtained. The aim was to quantify the modulation effect of respiration on oscillometric pulses and KorS during clinical BP measurement. METHODS: Systolic and diastolic BPs were measured manually from 40 healthy subjects (from 23 to 65 years old) under normal and regular deep breathing. The following signals were digitally recorded during linear cuff deflation: chest motion from a magnetometer to obtain reference respiration, cuff pressure from an electronic pressure sensor to derive OscP, and KorS from a digital stethoscope. The effects of respiration on both OscP and KorS were determined from changes in their amplitude associated with respiration between systole and diastole. These changes were normalized to the mean signal amplitude of OscP and KorS to derive the respiratory modulation depth. Reference respiration frequency, and the frequencies derived from the amplitude modulation of OscP and KorS were also calculated and compared. RESULTS: Respiratory modulation depth was 14 and 40 % for OscP and KorS respectively under normal breathing condition, with significant increases (both p  0.05) during deep breathing, and for the oscillometric signal during normal breathing (p > 0.05). CONCLUSIONS: Our study confirmed and quantified the respiratory modulation effect on the oscillometric pulses and KorS during clinical BP measurement, with increased modulation depth under regular deeper breathing

Biasogram: visualization of confounding technical bias in gene expression data.

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results

E. coli promotes human Vγ9Vδ2 T cell transition from cytokine-producing bactericidal effectors to professional phagocytic killers in a TCR-dependent manner

γδT cells provide immune-surveillance and host defense against infection and cancer. Surprisingly, functional details of γδT cell antimicrobial immunity to infection remain largely unexplored. Limited data suggests that γδT cells can phagocytose particles and act as professional antigen-presenting cells (pAPC). These potential functions, however, remain controversial. To better understand γδT cell-bacterial interactions, an ex vivo co-culture model of human peripheral blood mononuclear cell (PBMC) responses to Escherichia coli was employed. Vγ9Vδ2 cells underwent rapid T cell receptor (TCR)-dependent proliferation and functional transition from cytotoxic, inflammatory cytokine immunity, to cell expansion with diminished cytokine but increased costimulatory molecule expression, and capacity for professional phagocytosis. Phagocytosis was augmented by IgG opsonization, and inhibited by TCR-blockade, suggesting a licensing interaction involving the TCR and FcγR. Vγ9Vδ2 cells displayed potent cytotoxicity through TCR-dependent and independent mechanisms. We conclude that γδT cells transition from early inflammatory cytotoxic killers to myeloid-like APC in response to infectious stimuli

Regulators of genetic risk of breast cancer identified by integrative network analysis.

Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.This work was funded by Cancer Research UK and the Breast Cancer Research Foundation. MAAC is funded by the National Research Council (CNPq) of Brazil. TEH held a fellowship from the US DOD Breast Cancer Research Program (W81XWH-11-1-0592) and is currently supported by an RAH Career Development Fellowship (Australia). TEH and WDT are funded by the NHMRC of Australia (NHMRC) (ID: 1008349 WDT; 1084416 WDT, TEH) and Cancer Australia/National Breast Cancer Foundation (ID 627229; WDT, TEH). BAJP is a Gibb Fellow of Cancer Research UK. We would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ng.345

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]