Effects of drinking water, sanitation, handwashing and nutritional interventions on stress physiology, oxidative stress, and epigenetic programming in young children living in rural Bangladesh: A randomized clinical trial

Importance: A regulated stress response is essential for healthy trajectories, but the integrated effects of early childhood environmental and nutritional interventions on stress physiology are unknown. Objective: To assess the effects of a combined nutritional, water, sanitation, and handwashing intervention on physiological stress response, oxidative stress, and DNA methylation. Design, Setting, and Participants: In a trial in rural Bangladesh, we randomized geographical clusters of pregnant women and their in-utero children into either the combined nutritional, water, sanitation, and handwashing intervention or the control group. Physiological stress response, oxidative stress, and methylation levels of 757 children were measured at ages one and two years. Analysis was intention-to-treat. Interventions: The intervention group received combined nutritional counseling and lipid-based nutrient supplements, chlorinated drinking water, upgraded sanitation, and handwashing with soap (N+WSH). The control group did not receive interventions. Main Outcomes and Measures: We measured four isomers of urinary F2-isoprostanes [iPF(2α)-III; 2,3-dinor-iPF(2α)-III; iPF(2α)-VI; 8,12-iso-iPF(2α)-VI] at year one. At year two, we measured pre- and post-stressor concentrations of salivary alpha-amylase and cortisol, overall methylation of the glucocorticoid receptor (NR3C1) exon 1F promoter including methylation levels at the nerve growth factor-inducible protein A (NGFI-A) binding site, mean arterial pressure, and resting heart rate. Results: Children in the N+WSH group had lower levels of F2-isoprostanes compared to controls (difference -0.16 to -0.19 log ng/mg of creatinine, P<0.01). Compared to the control group, post-stressor cortisol levels were elevated (0.24 log μg/dl; 95% CI, 0.07 to 0.4; P<0.01) and the residualized gain score for cortisol was higher (0.06 μg/dl; 95% CI, 0.01 to 0.12; P=0.023) in the N+WSH group. Children in the N+WSH group exhibited decreased methylation of the NGFI-A transcription factor binding site (-0.04 logit-transformed %; 95% CI, -0.08 to 0; P=0.037). Conclusions and Relevance: A nutritional, water, sanitation, and handwashing intervention reduced oxidative stress, enhanced hypothalamic-pituitary-adrenocortical axis activity, and reduced methylation levels in a transcription factor binding site of the glucocorticoid receptor gene. A targeted environmental and nutritional intervention affected the set point, reactivity, and regulation of the physiological stress system in early childhood, which may have implications for long-term health and developmental trajectories. Trial Registration: ClinicalTrials.gov NCT01590095

Dark Matter of the Transcription Factor Binding Site Motif Universe

Background: We study the limits imposed by transcription factor specificity on the maximum size of a genetic regulatory network. Results: Most regular expressions for natural transcription factor binding site motifs are separated in sequence space by only one to three motif-discriminating positions. This mild specificity requirement puts the number of transcription factors that can coexist with minimal crosstalk on the order of ten thousand, which would fully utilize the space of DNA subsequences. An expanded alphabet with modified bases can further raise this limit by several orders of magnitude, at the expense of sequence space usage. Conclusions: Based on this analysis, thousands of transcription factor binding site motifs may await discovery.

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

DeepGRN: Prediction of transcription factor binding site across cell-types using attention-based deep neural networks

BackgroundDue to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors.ResultsIn this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.ConclusionsDeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

Bacterial transcription factor binding site inference as the basis for the study of plant-microbial interactions

Genome-wide in silico analysis of bacterial regulatory sequences reveals new regulatory interactions, is generally accessible and should become the standard in the study of plant-microbial interactions.