Environmental stresses inhibit splicing in the aquatic fungus Blastocladiella emersonii

<p>Abstract</p> <p>Background</p> <p>Exposure of cells to environmental stress conditions can lead to the interruption of several intracellular processes, in particular those performed by macromolecular complexes such as the spliceosome.</p> <p>Results</p> <p>During nucleotide sequencing of cDNA libraries constructed using RNA isolated from <it>B. emersonii </it>cells submitted to heat shock and cadmium stress, a large number of ESTs with retained introns was observed. Among the 6,350 ESTs obtained through sequencing of stress cDNA libraries, 181 ESTs presented putative introns (2.9%), while sequencing of cDNA libraries from unstressed <it>B. emersonii </it>cells revealed only 0.2% of ESTs containing introns. These data indicate an enrichment of ESTs with introns in <it>B. emersonii </it>stress cDNA libraries. Among the 85 genes corresponding to the ESTs that retained introns, 19 showed more than one intron and three showed three introns, with intron length ranging from 55 to 333 nucleotides. Canonical splicing junctions were observed in most of these introns, junction sequences being very similar to those found in introns from genes previously characterized in <it>B. emersonii</it>, suggesting that inhibition of splicing during stress is apparently a random process. Confirming our observations, analyses of <it>gpx3 </it>and <it>hsp70 </it>mRNAs by Northern blot and S1 protection assays revealed a strong inhibition of intron splicing in cells submitted to cadmium stress.</p> <p>Conclusion</p> <p>In conclusion, data indicate that environmental stresses, particularly cadmium treatment, inhibit intron processing in <it>B. emersonii</it>, revealing a new adaptive response to cellular exposure to this heavy metal.</p

Adherence to a healthy eating index from pre-school to school age and its associations with sociodemographic and early life factors

Childhood is considered an important period for the development of healthy eating behaviours. This study aimed to evaluate the influence of early life factors and sociodemographic characteristics, including early diet quality, on diet quality at 7 years. The sample includes 5013 children evaluated at the ages of 4 and 7 years from the Portuguese birth cohort Generation XXI with complete information on FFQ. A healthy eating index was developed at both ages to assess adherence to the WHO’s dietary recommendations, including eight food groups. Consumption quartiles were obtained for each group at 4 years and assigned a score between 1 and 4. A higher score represents a higher adherence to a better diet (range: 8 to 32). The associations between early life factors and sociodemographic characteristics and the score of the healthy eating index at 7 years were evaluated through linear regression models. The healthy eating index had an average score of 21⋅4 ± 3⋅53 (range: 12 to 32) at 4 years and 20⋅3 ± 3⋅36 (range: 11 to 31) at 7 years. After adjustment for confounders, a positive association was found between the healthy eating index at 4 and 7 years (β = 0⋅384, 95 % CI 0⋅356, 0⋅441). Maternal years of education (β = 0⋅094, 95 % CI 0⋅071, 0⋅116) and dietary score (β = 0⋅182, 95 % CI 0⋅155, 0⋅209) were positively associated with increasing dietary quality from 4 to 7 years. A healthier diet at preschool age, higher maternal education and a healthier diet increase the likelihood of maintaining a high healthy eating index score at school age.The authors gratefully acknowledge the families enrolled in Generation XXI for their kindness, all members of the research team for their enthusiasm and perseverance and the participating hospitals and their staff for their help and support. The authors acknowledge the support from the Epidemiology Research Unit (EPI-Unit: UID-DTP/04750/2013). Generation XXI was funded by the Health Operational Programme – Saúde XXI, Community Support Framework III and the Regional Department of Ministry of Health. It was supported by the Calouste Gulbenkian Foundation, by FEDER from the Operational Programme Factors of Competitiveness – COMPETE and through national funding from the Foundation for Science and Technology – FCT (Portuguese Ministry of Education and Science) under the project PTDC/SAU-EPI/121532/2010 (FEDER-Operational Programme Factors of Competitiveness – COMPETE – FCOMP-01-0124-FEDER-021177), and the PhD grant SFRH/BD/92389/2013 (S. V.). FCT had no role in the design, analysis or writing of this article. The authors’ contributions are as follows: M. P. C. was responsible for the analysis and interpretation of the data and wrote the first draft of the paper; C. D., C. L. and S. V. were also responsible for the analysis and interpretation of the data. All authors contributed to the concept and design of the study and paper review. There were no conflicts of interest

Revealing epilepsy type using a computational analysis of interictal EEG

This is the final version. Available from Nature Research via the DOI in this record.All materials (functional networks and code) are available upon request from the corresponding author.Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.Medical Research Council (MRC)Wellcome TrustEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome Trus

Revealing epilepsy type using a computational analysis of interictal EEG.

Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG

Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordObjective: The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. Methods: We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network’s ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. Results: The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.Medical Research CouncilEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustEngineering and Physical Sciences Research Council (EPSRC)Innovate UKEuropean Union’s Horizon 2020Alzheimer's SocietyMedical Research Counci

Elevated ictal brain network ictogenicity enables prediction of optimal seizure control

This is the final version of the article. Available from Frontiers Media via the DOI in this record.Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.ML, MG, MR, and JT gratefully acknowledge funding from the Medical Research Council via grant MR/K013998/1. MG, MR, and JT further acknowledge the financial support of the EPSRC via grant EP/N014391/1. The contribution of MG and JT was further generously supported by a Wellcome Trust Institutional Strategic Support Award (WT105618MA). MR and EA are supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust. KS gratefully acknowledges support by the Swiss National Science Foundation (SNF 32003B_155950)

Transcriptome changes in newborn goats' skeletal muscle as a result of maternal feed restriction at different stages of gestation

We investigated how feed restriction at 50% of maintenance requirements during different stages of gestation affects the transcriptome of newborn goats' skeletal muscle. Fourteen pregnant dams were randomly assigned into one of the following dietary treatments: animals fed at 50% of maintenance requirement from 8-84 d of gestation and then fed at 100% of maintenance requirement from day 85 of gestation to parturition (RM, n = 6), and animals fed at 100% of maintenance requirement from 8-84 d of gestation and then fed at 50% of maintenance requirement from day 85 of gestation to parturition (MR, n = 8). At birth, samples of offspring's Longissimus muscle were collected for total RNA extraction and sequencing. Our data showed 66 differentially expressed (DE) genes (FDR &lt; 0.05). A total of 6 genes were upregulated and 60 downregulated (FDR &lt; 0.05) in the skeletal muscle of the newborns resulting from treatment RM compared with MR. Our results suggest that the DE genes upregulated in newborn goats' skeletal muscle from the RM group compared to MR, included genes related to satellite cells, and genes that indicates impaired insulin sensitivity and changes in the composition of intramuscular fat. The DE genes upregulated in newborn goats' skeletal muscle from the MR group compared to RM, are also related to impaired insulin sensitivity, as well as a predominantly oxidative metabolism and cellular oxidative stress. However, protective mechanisms against insulin sensitivity and oxidative stress may have been augmented in the skeletal muscle of offspring from MR treatment compared to RM, in order to maintain cellular homeostasis

Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page

Energy end-use flexibility of the next generation of decision-makers in a smart grid setting: an exploratory study

Demand Response (DR) mechanisms have been developed to reshape consumption patterns in face of price signals, enabling to deal with the increasing penetration of intermittent renewable resources and balance electricity demand and supply. Although DR mechanisms have been in place for some time, it is still unclear to what extent end-users are ready, or willing, to embrace DR programs that can be complex and imply adjustments of daily routines. This work aims to understand how the next generation of Portuguese decision makers, namely young adults in higher education, are prepared to deal with energy decisions in the context of the challenges brought by the smart grids. Results demonstrate that cost savings and the contribution to environmental protection are found to be important motivating factors to enroll into DR programs, which should be further exploited in future actions for the promotion of end-user engagement. Moreover, DR solutions are well-accepted by higher education students, although with limited flexibility levels. In addition, there is room to exploit the willingness to adopt time-differentiated tariffs, yet savings should be clearer and more attractive to end-users. Also, the framing effect should be considered when promoting this type of time-differentiated tariffs.This work was partially supported by project grants UID/MULTI/00308/2013 and UID/CEC/00319/2013 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT—Portuguese Foundation for Science and Technology with in projects ESGRIDS (POCI-01-0145-FEDER-016434), Learn2Behave (02/SAICT/2016-023651), MAnAGER (POCI-01-0145-FEDER-028040), and POCI-01-0145-FEDER-007043, as well as by the Energy for Sustainability Initiative of the University of Coimbra