Prenatal Organochlorine Exposure and Measures of Behavior in Infancy Using the Neonatal Behavioral Assessment Scale (NBAS)

Background: Previous literature suggests an association between organochlorines and behavioral measures in childhood, including inattention. Objective: This study was designed to assess whether prenatal organochlorine exposure is associated with measures of attention in early infancy. Methods: We investigated an association between cord serum polychlorinated biphenyls (PCBs) and p,p′-dichlorodiphenyl dichloroethene (DDE) levels and measures of attention from the Neonatal Behavioral Assessment Scale (NBAS) in a cohort of 788 infants born 1993–1998 to mothers residing near a PCB-contaminated harbor and Superfund site in New Bedford, Massachusetts. Results: Medians (ranges) for the sum of four prevalent PCB congeners and DDE levels were 0.19 (0.01–4.41) and 0.30 (0–10.29) ng/g serum, respectively. For the 542 subjects with an NBAS exam at 2 weeks, we observed consistent inverse associations between cord serum PCB and DDE levels and NBAS measures of alertness, quality of alert responsiveness, cost of attention, and other potential attention-associated measures including self-quieting and motor maturity. For example, the decrement in quality of alert responsiveness score was −0.51 (95% confidence interval, −0.99 to −0.03) for the highest quartile of exposure to the sum of four prevalent PCB congeners compared with the lowest quartile. We found little evidence for an association with infant orientation, habituation, and regulation of state, assessed as summary cluster measures. Conclusions: Our findings provide evidence for an association between low-level prenatal PCB and DDE exposures and poor attention in early infancy. Further analyses will focus on whether organochlorine-associated decrements in attention and attention-related skills in infancy persist in later childhood

Gene conversion in human rearranged immunoglobulin genes

Over the past 20 years, many DNA sequences have been published suggesting that all or part of the V<sub>H</sub> segment of a rearranged immunoglobulin gene may be replaced in vivo. Two different mechanisms appear to be operating. One of these is very similar to primary V(D)J recombination, involving the RAG proteins acting upon recombination signal sequences, and this has recently been proven to occur. Other sequences, many of which show partial V<sub>H</sub> replacements with no addition of untemplated nucleotides at the V<sub>H</sub>–V<sub>H</sub> joint, have been proposed to occur by an unusual RAG-mediated recombination with the formation of hybrid (coding-to-signal) joints. These appear to occur in cells already undergoing somatic hypermutation in which, some authors are convinced, RAG genes are silenced. We recently proposed that the latter type of V<sub>H</sub> replacement might occur by homologous recombination initiated by the activity of AID (activation-induced cytidine deaminase), which is essential for somatic hypermutation and gene conversion. The latter has been observed in other species, but not in human Ig genes, so far. In this paper, we present a new analysis of sequences published as examples of the second type of rearrangement. This not only shows that AID recognition motifs occur in recombination regions but also that some sequences show replacement of central sections by a sequence from another gene, similar to gene conversion in the immunoglobulin genes of other species. These observations support the proposal that this type of rearrangement is likely to be AID-mediated rather than RAG-mediated and is consistent with gene conversion

Graphs in molecular biology

Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network

Poor glycaemic control is associated with reduced exercise performance and oxygen economy during cardio-pulmonary exercise testing in people with type 1 diabetes

BackgroundTo explore the impact of glycaemic control (HbA1c) on functional capacity during cardio-pulmonary exercise testing in people with type 1 diabetes.MethodsSixty-four individuals with type 1 diabetes (age: 34 ± 8 years; 13 females, HbA1c: 7.8 ± 1% (62 ± 13 mmol/mol), duration of diabetes: 17 ± 9 years) performed a cardio-pulmonary cycle ergometer exercise test until volitional exhaustion. Stepwise linear regression was used to explore relationships between HbA1c and cardio-respiratory data with p ≤ 0.05. Furthermore, participants were divided into quartiles based on HbA1c levels and cardio-respiratory data were analysed by one-way ANOVA. Multiple regression analysis was performed to explore the relationships between changes in time to exhaustion and cardio-respiratory data. Data were adjusted for confounder.ResultsHbA1c was related to time to exhaustion and oxygen consumption at the power output elicited at the sub-maximal threshold of the heart rate turn point (r = 0.47, R2 = 0.22, p = 0.03). Significant differences were found at time to exhaustion between QI vs. QIV and at oxygen consumption at the power output elicited at the heart rate turn point between QI vs. QII and QI vs. QIV (p < 0.05). Changes in oxygen uptake, power output and in oxygen consumption at the power output elicited at the heart rate turn point and at maximum power output explained 55% of the variance in time to exhaustion (r = 0.74, R2 = 0.55, p < 0.01).ConclusionsPoor glycaemic control is related to less economical use of oxygen at sub-maximal work rates and an earlier time to exhaustion during cardio-pulmonary exercise testing. However, exercise training could have the same potential to counteract the influence of poor glycaemic control on functional capacity

A Visual Data Mining Tool that Facilitates Reconstruction of Transcription Regulatory Networks

Background: Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of ‘‘guilt by association’’, where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred. Methodology/Principal Findings: We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed metaanalysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet

Evaluation of an inter-professional workshop to develop a psychosocial assessment and child-centred communication training programme for paediatricians in training

BACKGROUND: The quality of psychosocial assessment of children in consultations varies widely. One reason for this difference is the variability in effective mental health and communication training at undergraduate and post-qualification levels. In recognition of this problem, the Royal College of Paediatrics and Child Health in the United Kingdom have developed the Child in Mind Project that aims to meet this deficit in medical training. This paper describes the evaluation of a workshop that explored the experiences and expectations of health care professionals in the development of a training programme for doctors. METHODS: The one-day inter-professional workshop was attended by 63 participants who were invited to complete evaluation forms before and immediately after the workshop. RESULTS: The results showed that the workshop was partially successful in providing an opportunity for an inter-professional group to exchange ideas and influence the development of a significant project. Exploring the content and process of the proposed training programme and the opportunity for participants to share experiences of effective practice were valued. Participants identified that the current culture within many health care settings would be an obstacle to successful implementation of a training programme. Working within existing training structures will be essential. Areas for improvement in the workshop included clearer statement of goals at the outset and a more suitable environment for the numbers of participants. CONCLUSIONS: The participants made a valuable contribution to the development of the training programme identifying specific challenges. Inter-professional collaborations are likely to result in more deliverable and relevant training programmes. Continued consultation with potential users of the programme – both trainers and trainees will be essential

Modeling the Evolution of Regulatory Elements by Simultaneous Detection and Alignment with Phylogenetic Pair HMMs

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation

Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease

Prevalence of self-reported constipation in adults from the general population

OBJECTIVE To estimate the prevalence of self-reported constipation and associated factors in the general population of a Brazilian city. METHOD Secondary analysis of an epidemiological study, population-based, cross-sectional study, about bowel habits of Brazilian population. A total of 2,162 individuals were interviewed using two instruments: sociodemographic data and the adapted and validated Brazilian version of the "Bowel Function in the Community" tool. RESULTS There was a prevalence of 25.2% for the self-reported constipation, 37.2% among women and 10.2% among men. Stroke and old age were associated with constipation in the three statistical models used. CONCLUSION The prevalence found showed to be similar to the findings in the literature, although some associated factors obtained here have never been investigated

Genome-wide expression quantitative trait loci (eQTL) analysis in maize

<p>Abstract</p> <p>Background</p> <p>Expression QTL analyses have shed light on transcriptional regulation in numerous species of plants, animals, and yeasts. These microarray-based analyses identify regulators of gene expression as either cis-acting factors that regulate proximal genes, or trans-acting factors that function through a variety of mechanisms to affect transcript abundance of unlinked genes.</p> <p>Results</p> <p>A hydroponics-based genetical genomics study in roots of a <it>Zea mays </it>IBM2 Syn10 double haploid population identified tens of thousands of cis-acting and trans-acting eQTL. Cases of false-positive eQTL, which results from the lack of complete genomic sequences from both parental genomes, were described. A candidate gene for a trans-acting regulatory factor was identified through positional cloning. The unexpected regulatory function of a class I glutamine amidotransferase controls the expression of an ABA 8'-hydroxylase pseudogene.</p> <p>Conclusions</p> <p>Identification of a candidate gene underlying a trans-eQTL demonstrated the feasibility of eQTL cloning in maize and could help to understand the mechanism of gene expression regulation. Lack of complete genome sequences from both parents could cause the identification of false-positive cis- and trans-acting eQTL.</p