No abstract

A continuous increase in the prevalence of heart failure and the lack of adequate therapy highlight poor understanding of the underlying genetic regulatory mechanisms involved in heart failure pathogenesis. Growing evidence has demonstrated a signi cant contribution of non-coding genome regulatory elements towards transcriptomic changes in heart disease. Thus, there is a pressing need for a comprehensive resource of the human cardiac regulatory network in healthy and failing states. We applied cap analysis of gene expression sequencing to directly measure the expression of RNA associated with enhancers and promoters. Based on this data, we constructed the atlas of transcribed cardiac regulatory elements from 21 healthy and 10 failing (ischemic and non-ischemic cardiomyopathy) human hearts. In total, we have sequenced 109 samples from the left and right atria and ventricles, identifying 17,668 promoters and 14,920 enhancers associated with 14,519 genes. Leveraging this atlas, we provide insights into functional and structural regulatory changes between healthy and failing hearts. Healthy atria and ventricles had distinct pathway enrichment and transcription factor binding patterns, signi cantly remodeled by heart failure. Using the advantages of deep sequencing that allow effective analysis of cis-regulatory elements-derived RNA, we found that heart failure is associated with the expression of transcripts derived from alternative promoters and a speci c set of transcribed enhancers. Furthermore, we identi ed a high prevalence of single nucleotide polymorphisms associated with cardiovascular diseases within the regulatory regions highlighting their importance in disease pathogenesis. This open-source atlas will serve the cardiovascular community to improve understanding cardiac regulatory network and facilitate the development of novel therapeutics.

Parallel reporter assays provide rich data to decipher gene regulatory regions with deep learning. Here we introduce LegNet, a convolutional network architecture that secured the first place for our autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. To construct LegNet, we drew inspiration from EfficientNetV2 and reformulated the sequence-to-expression regression problem as a soft-classification task. Here, with published data, we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of sequence alterations, such as single-nucleotide variants.

C4 photosynthesis increases the efficiency of carbon fixation by spatially separating high concentrations of molecular oxygen from rubisco. The specialized leaf anatomy required for this separation evolved independently many times. The morphology of C4 root systems is also distinctive and adapted to support high rates of photosynthesis; however, little is known about the molecular mechanisms that have driven the evolution of C4 root system architecture (RSA). Using a mutant screen in the C4 model plant Setaria italica, we identify Siaux1-1 and Siaux1-2 and identify them as RSA mutants. Unlike in S. viridis, AUX1 is promotes lateral root development in S. italica. A cell-by-cell analysis of the Siaux1-1 root apical meristem revealed changes in the distribution of cell volumes in all cell layers and a dependence in the frequency of protophloem and protoxylem strands on SiAUX1. We explore the molecular basis of SiAUX1’s role in seedling development using an RNAseq analysis of wild type and Siaux1-1 plants and present novel targets for SiAUX1-dependent gene regulation. Using a selection sweep and haplotype analysis of SiAUX1, we show that Hap-2412TT in the promoter region of SiAUX1 is an allele which is associated with lateral root number and has been strongly selected for during Setaria domestication.

Motivation The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep learning approaches for modeling DNA regulatory grammar. Results Here we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions. By approaching the sequence-to-expression regression problem as a soft classification task, LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show how LegNet can be used in a diffusion network manner for the rational design of promoter sequences yielding the desired expression level. Availability and Implementation https://github.com/autosome-ru/LegNet. The GitHub repository includes Jupyter Notebook tutorials and Python scripts under the MIT license to reproduce the results presented in the study. Supplementary Information Online-only supplementary data are available at Bioinformatics online.