Targeting Food Allergy with Probiotics.

The dramatic increase in food allergy prevalence and severity globally is demanding effective strategies. Food allergy derives from a defect in immune tolerance mechanisms. Immune tolerance is modulated by gut microbiota composition and function, and gut microbiota dysbiosis has been associated with the development of food allergy. Selected probiotic strains could act on immune tolerance mechanisms. The mechanisms are multiple and still not completely defined. Increasing evidence is providing useful information on the choice of optimal bacterial species/strains, dosage, and timing for intervention. The increased knowledge on the crucial role played by gut microbiota-derived metabolites, such as butyrate, is also opening the way to a postbiotic approach in the stimulation of immune tolerance

G-CSF does not influence C2C12 myogenesis despite receptor expression in healthy and dystrophic skeletal muscle

Granulocyte-colony stimulating factor (G-CSF) increases recovery of rodent skeletal muscles after injury, and increases muscle function in rodent models of neuromuscular disease. However, the mechanisms by which G-CSF mediates these effects are poorly understood. G-CSF acts by binding to the membrane spanning G-CSFR and activating multiple intracellular signaling pathways. Expression of the G-CSFR within the haematopoietic system is well known, but more recently it has been demonstrated to be expressed in other tissues. However, comprehensive characterization of G-CSFR expression in healthy and diseased skeletal muscle, imperative before implementing G-CSF as a therapeutic agent for skeletal muscle conditions, has been lacking. Here we show that the G-CSFR is expressed in proliferating C2C12 myoblasts, differentiated C2C12 myotubes, human primary skeletal muscle cell cultures and in mouse and human skeletal muscle. In mdx mice, a model of human Duchenne muscular dystrophy (DMD), G-CSF mRNA and protein was down-regulated in limb and diaphragm muscle, but circulating G-CSF ligand levels were elevated. G-CSFR mRNA in the muscles of mdx mice was up-regulated however steady-state levels of the protein were down-regulated. We show that G-CSF does not influence C2C12 myoblast proliferation, differentiation or phosphorylation of Akt, STAT3, and Erk1/2. Media change alone was sufficient to elicit increases in Akt, STAT3, and Erk1/2 phosphorylation in C2C12 muscle cells and suggest previous observations showing a G-CSF increase in phosphoprotein signaling be viewed with caution. These results suggest that the actions of G-CSF may require the interaction with other cytokines and growth factors in vivo, however these data provides preliminary evidence supporting the investigation of G-CSF for the management of muscular dystrophy

Gut microbiota as target for innovative strategies against food allergy.

The dramatic increase in food allergy prevalence and severity globally requires effective strategies. Food allergy derives from a defect in immune tolerance mechanisms. Immune tolerance is modulated by gut microbiota function and structure, and microbiome alterations (dysbiosis) have a pivotal role in the development of food allergy. Environmental factors, including a low-fiber/high-fat diet, cesarean delivery, antiseptic agents, lack of breastfeeding, and drugs can induce gut microbiome dysbiosis, and have been associated with food allergy. New experimental tools and technologies have provided information regarding the role of metabolites generated from dietary nutrients and selected probiotic strains that could act on immune tolerance mechanisms. The mechanisms are multiple and still not completely defined. Increasing evidence has provided useful information on optimal bacterial species/strains, dosage, and timing for intervention. The increased knowledge of the crucial role played by nutrients and gut microbiota-derived metabolites is opening the way to a post-biotic approach in the stimulation of immune tolerance through epigenetic regulation. This review focused on the potential role of gut microbiome as the target for innovative strategies against food allergy

The stress of fire fighting - implications for long term health outcomes

Fire and rescue staff routinely endure significant psychological and environmental stress exposure on the job. While much has been done to improve understanding of the physiological effects of exposure to these conditions, little has been done to quantify the inflammatory stress response that firefighters are exposed to during wildfire suppression. Therefore the aim of the present study was to explore whether firefighters experienced a change in inflammatory markers following one day, and across two days of wildfire suppression tasks. Twelve male fire-fighters participated in two consecutive days of live-fire prescribed burn operations in Ngarkat National Park, South Australia. Typical work tasks included lighting burns, patrolling containment lines, supressing spot fires, and operating vehicles. A number of the inflammatory markers changed significantly across the course of a shift and several presented with an attenuated response across the second day. This finding implies that there was a compounding effect of repeated exposure to these stressors which could have considerable implications for managing fire-fighters health and wellbeing over a multi-day campaign. Further research is required to see which fire ground stressor, or combination of stressors is causing these changes in the inflammatory markers across consecutive work shifts

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

BACKGROUND: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. RESULTS: In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. CONCLUSION: By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach

Neurodevelopmental outcomes of very preterm infants born following early foetal growth restriction with absent end-diastolic umbilical flow

This study aims to assess the impact of time of onset and features of early foetal growth restriction (FGR) with absent end-diastolic flow (AEDF) on pregnancy outcomes and on preterm infants' clinical and neurodevelopmental outcomes up to 2 years corrected age. This is a retrospective, cohort study led at a level IV Obstetric and Neonatal Unit in Bologna, Italy. Pregnant women were eligible if having singleton pregnancies, with no major foetal anomaly detected, and diagnosed with early FGR + AEDF (defined as FGR + AEDF detected before 32 weeks gestation). Early FGR + AEDF was further classified according to time of onset and specific features into very early and persistent (VEP, FGR + AEDF first detected at 20-24 weeks gestation and persistent at the following scans), very early but transient (VET, FGR + AEDF detected at 20-24 weeks gestation and progressively improving at the following scans) and later (LA, FGR + AEDF detected between 25 and 32 weeks gestation). Pregnancy and neonatal outcomes and infant follow-up data were collected and compared among groups. Neurodevelopment was assessed using the revised Griffiths Mental Developmental Scales (GMDS-R) 0-2 years. A regression analysis was performed to identify early predictors of preterm infants' neurodevelopmental impairment. Fifty-two pregnant women with an antenatal diagnosis of early FGR + AEDF were included in the study (16 VEP, 14 VET, 22 LA). Four intrauterine foetal deaths occurred, all in the VEP group (p = 0.010). Compared to LA infants, VEP infants were born with lower gestational age and lower birth weight, had lower arterial cord blood pH and were at higher risk for intraventricular haemorrhage and periventricular leukomalacia (p < 0.05 for all comparisons). At 12 months, VEP infants had worse GMDS-R scores, both in the general quotient (mean [SD] 91.8 [12.4] vs 104.6 [8.7] in LA) and in the performance domain (mean [SD] 93.3 [15.4] vs 108.8 [8.8] in LA). This latter difference persisted at 24 months (mean [SD] 68.3 [17.0] vs 92.9 [17.7] in LA). In multivariate analysis, at 12 months corrected age, PVL was found to be an independent predictor of impaired general quotient, while the features and timing of antenatal Doppler alterations predicted worse scores in the performance domain.Conclusion: Timing of onset and features of early FGR + AEDF might impact differently on neonatal clinical and neurodevelopmental outcomes. Shared awareness of the importance of FGR + AEDF features between obstetricians and neonatologists may offer valuable tools for antenatal counselling and for tailoring pregnancy management and neonatal follow-up in light of specific antenatal and neonatal risk factors

PGC-1 alpha and PGC-1 beta increase protein synthesis via ERR alpha in C2C12 myotubes

The transcriptional coactivators peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) and PGC-1β are positive regulators of skeletal muscle mass and energy metabolism; however, whether they influence muscle growth and metabolic adaptations via increased protein synthesis is not clear. This study revealed PGC-1α or PGC-1β overexpression in C2C12 myotubes increased protein synthesis and myotube diameter under basal conditions and attenuated the loss in protein synthesis following the treatment with the catabolic agent, dexamethasone. To investigate whether PGC-1α or PGC-1β signal through the Akt/mTOR pathway to increase protein synthesis, treatment with the PI3K and mTOR inhibitors, LY294002 and rapamycin, respectively, was undertaken but found unable to block PGC-1α or PGC-1β’s promotion of protein synthesis. Furthermore, PGC-1α and PGC-1β decreased phosphorylation of Akt and the Akt/mTOR substrate, p70S6K. In contrast to Akt/mTOR inhibition, the suppression of ERRα, a major effector of PGC-1α and PGC-1β activity, attenuated the increase in protein synthesis and myotube diameter in the presence of PGC-1α or PGC-1β overexpression. To characterize further the biological processes occurring, gene set enrichment analysis of genes commonly regulated by both PGC-1α and PGC-1β was performed following a microarray screen. Genes were found enriched in metabolic and mitochondrial oxidative processes, in addition to protein translation and muscle development categories. This suggests concurrent responses involving both increased metabolism and myotube protein synthesis. Finally, based on their known function or unbiased identification through statistical selection, two sets of genes were investigated in a human exercise model of stimulated protein synthesis to characterize further the genes influenced by PGC-1α and PGC-1β during physiological adaptive changes in skeletal muscle

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

Transient Responses to NOTCH and TLX1/HOX11 Inhibition in T-Cell Acute Lymphoblastic Leukemia/Lymphoma

To improve the treatment strategies of T-cell acute lymphoblastic leukemia/lymphoma (T-ALL), further efforts are needed to identify therapeutic targets. Dysregulated expression of HOX-type transcription factors occurs in 30–40% of cases of T-ALL. TLX1/HOX11 is the prototypical HOX-type transcription factor. TLX1 may be an attractive therapeutic target because mice that are deficient in TLX1 are healthy. To test this possibility, we developed a conditional doxycycline-regulated mouse model of TLX1-initiated T-ALL. TLX1 induced T-ALL after ∼5–7 months with penetrance of 15–60%. Similar to human TLX1-type T-ALLs, the TLX1-induced tumors were arrested at the cortical stage of T-cell development and acquired activating NOTCH1 mutations. Inhibition of NOTCH signaling abrogated growth of cell lines derived from the TLX1-induced tumors. NOTCH inhibition also transiently delayed leukemia progression in vivo. Suppression of TLX1 expression slowed the growth of TLX1 tumor cell lines. Suppression of TLX1 in vivo also transiently delayed leukemia progression. We have shown that TLX1 functions as a T-cell oncogene that is active during both the induction and the maintenance phases of leukemia. However, the effect of suppressing NOTCH or TLX1 was transient. The tumors eventually “escaped” from inhibition. These data imply that the biological pathways and gene sets impacted by TLX1 and NOTCH have largely lost their importance in the fully established tumor. They have been supplanted by stronger oncogenic pathways. Although TLX1 or NOTCH inhibitors may not be effective as single agents, they may still contribute to combination therapy for TLX1-driven acute leukemia

Casual Compressive Sensing for Gene Network Inference

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered