BACKGROUND: Growing evidence suggests that distal regulatory elements are essential for cellular function and states. The sequences within these distal elements, especially motifs for transcription factor binding, provide critical information about the underlying regulatory programs. However, cooperativities between transcription factors that recognize these motifs are nonlinear and multiplexed, rendering traditional modeling methods insufficient to capture the underlying mechanisms. Recent development of attention mechanism, which exhibit superior performance in capturing dependencies across input sequences, makes them well-suited to uncover and decipher intricate dependencies between regulatory elements. RESULT: We present Transcription factors cooperativity Inference Analysis with Neural Attention (TIANA), a deep learning framework that focuses on interpretability. In this study, we demonstrated that TIANA could discover biologically relevant insights into co-occurring pairs of transcription factor motifs. Compared with existing tools, TIANA showed superior interpretability and robust performance in identifying putative transcription factor cooperativities from co-occurring motifs. CONCLUSION: Our results suggest that TIANA can be an effective tool to decipher transcription factor cooperativities from distal sequence data. TIANA can be accessed through: https://github.com/rzzli/TIANA .

MOTIVATION: Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which motifs are prone to impact transcriptional regulation if mutated. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects. RESULTS: We present MAGGIE (Motif Alteration Genome-wide to Globally Investigate Elements), a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutations of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared with the state-of-the-art motif analysis approaches. We use MAGGIE to gain novel insights into the divergent functions of distinct NF-κB factors in pro-inflammatory macrophages, revealing the association of p65-p50 co-binding with transcriptional activation and the association of p50 binding lacking p65 with transcriptional repression. AVAILABILITY AND IMPLEMENTATION: The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie. The accession number for the NF-κB ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Macrophage activation by lipopolysaccharide and adenosine triphosphate (ATP) has been studied extensively because this model system mimics the physiological context of bacterial infection and subsequent inflammatory responses. Previous studies on macrophages elucidated the biological roles of caspase-1 in post-translational activation of interleukin-1β and interleukin-18 in inflammation and apoptosis. However, the results from these studies focused only on a small number of factors. To better understand the host response, we have performed a high-throughput study of Kdo2-lipid A (KLA)-primed macrophages stimulated with ATP. RESULTS: The study suggests that treating mouse bone marrow-derived macrophages with KLA and ATP produces 'synergistic' effects that are not seen with treatment of KLA or ATP alone. The synergistic regulation of genes related to immunity, apoptosis and lipid metabolism is observed in a time-dependent manner. The synergistic effects are produced by nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) and activator protein (AP)-1 through regulation of their target cytokines. The synergistically regulated cytokines then activate signal transducer and activator of transcription (STAT) factors that result in enhanced immunity, apoptosis and lipid metabolism; STAT1 enhances immunity by promoting anti-microbial factors; and STAT3 contributes to downregulation of cell cycle and upregulation of apoptosis. STAT1 and STAT3 also regulate glycerolipid and eicosanoid metabolism, respectively. Further, western blot analysis for STAT1 and STAT3 showed that the changes in transcriptomic levels were consistent with their proteomic levels. In summary, this study shows the synergistic interaction between the toll-like receptor and purinergic receptor signaling during macrophage activation on bacterial infection. AVAILABILITY: Time-course data of transcriptomics and lipidomics can be queried or downloaded from http://www.lipidmaps.org. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We present a global clustering approach to identify putative intergenic non-coding RNAs based on the RNA polymerase II and Histone 3 lysine 4 trimethylation signatures. Both of these signatures are processed from the digital sequencing tags produced by chromatin immunoprecipitation, a high-throughput massively parallel sequencing (ChIP-Seq) technology. Our method compares favorably to the comparison method. We characterize the intergenic non-coding RNAs to have conservative promoters. We predict that these nc-RNAs are related to metabolic process without lipopolysaccharides (LPS) treatment, but shift towards developmental and immune-related functions with LPS treatment. We demonstrate that more intergenic nc-RNAs respond positively to LPS treatment, rather than negatively. Using QPCR, we experimentally validate 8 out of 11 nc-RNA regions respond to LPS treatment as predicted by the computational method.