Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data

Cell Type-Specific Whole-Genome Landscape of ΔFOSB Binding in the Nucleus Accumbens After Chronic Cocaine Exposure

BACKGROUND

The ability of neurons to respond to external stimuli involves adaptations of gene expression. Induction of the transcription factor ΔFOSB in the nucleus accumbens, a key brain reward region, is important for the development of drug addiction. However, a comprehensive map of ΔFOSB's gene targets has not yet been generated.

METHODS

We used CUT&RUN (cleavage under targets and release using nuclease) to map the genome-wide changes in ΔFOSB binding in the 2 main types of nucleus accumbens neurons-D1 or D2 medium spiny neurons-after chronic cocaine exposure. To annotate genomic regions of ΔFOSB binding sites, we also examined the distributions of several histone modifications. Resulting datasets were leveraged for multiple bioinformatic analyses.

RESULTS

The majority of ΔFOSB peaks occur outside promoter regions, including intergenic regions, and are surrounded by epigenetic marks indicative of active enhancers. BRG1, the core subunit of the SWI/SNF chromatin remodeling complex, overlaps with ΔFOSB peaks, a finding consistent with earlier studies of ΔFOSB's interacting proteins. Chronic cocaine use induces broad changes in ΔFOSB binding in both D1 and D2 nucleus accumbens medium spiny neurons of male and female mice. In addition, in silico analyses predict that ΔFOSB cooperatively regulates gene expression with homeobox and T-box transcription factors.

CONCLUSIONS

These novel findings uncover key elements of ΔFOSB's molecular mechanisms in transcriptional regulation at baseline and in response to chronic cocaine exposure. Further characterization of ΔFOSB's collaborative transcriptional and chromatin partners specifically in D1 and D2 medium spiny neurons will reveal a broader picture of the function of ΔFOSB and the molecular basis of drug addiction.

Dissecting and improving gene regulatory network inference using single-cell transcriptome data

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.

An Integrative Multiomics Framework for Identification of Therapeutic Targets in Pulmonary Fibrosis

Pulmonary fibrosis (PF) is a heterogeneous disease with a poor prognosis. Therefore, identifying additional therapeutic modalities is required to improve outcome. However, the lack of biomarkers of disease progression hampers the preclinical to clinical translational process. Here, this work assesses and identifies progressive alterations in pulmonary function, transcriptomics, and metabolomics in the mouse lung at 7, 14, 21, and 28 days after a single dose of oropharyngeal bleomycin. By integrating multi-omics data, this work identifies two central gene subnetworks associated with multiple critical pathological changes in transcriptomics and metabolomics as well as pulmonary function. This work presents a multi-omics-based framework to establish a translational link between the bleomycin-induced PF model in mice and human idiopathic pulmonary fibrosis to identify druggable targets and test therapeutic candidates. This work also indicates peripheral cannabinoid receptor 1 (CB1 R) antagonism as a rational therapeutic target for clinical translation in PF. Mouse Lung Fibrosis Atlas can be accessed freely at https://niaaa.nih.gov/mouselungfibrosisatlas.