Exploring the molecular mechanisms that prevent inflammation during caloric restriction may yield promising therapeutic targets. During fasting, activation of the nuclear receptor peroxisome proliferator-activated receptor α (PPARα) promotes the utilization of lipids as an energy source. Herein, we show that ligand activation of PPARα directly upregulates the long non-coding RNA gene Gm15441 through PPARα binding sites within its promoter. Gm15441 expression suppresses its antisense transcript, encoding thioredoxin interacting protein (TXNIP). This, in turn, decreases TXNIP-stimulated NLR family pyrin domain containing 3 (NLRP3) inflammasome activation, caspase-1 (CASP1) cleavage, and proinflammatory interleukin 1β (IL1B) maturation. Gm15441-null mice were developed and shown to be more susceptible to NLRP3 inflammasome activation and to exhibit elevated CASP1 and IL1B cleavage in response to PPARα agonism and fasting. These findings provide evidence for a mechanism by which PPARα attenuates hepatic inflammasome activation in response to metabolic stress through induction of lncRNA Gm15441.

The zonation of liver metabolic processes is well-characterized; however, little is known about the cell type-specificity and zonation of sexually dimorphic gene expression or its GH-dependent transcriptional regulators. We address these issues using single nucleus RNA-sequencing of 32,000 nuclei representing nine major liver cell types. Nuclei were extracted from livers from adult male and female mice, from males infused with GH continuously, mimicking the female plasma GH pattern, and from mice exposed to TCPOBOP, a xenobiotic agonist ligand of the nuclear receptor CAR that perturbs sex-biased gene expression. Analysis of these rich transcriptomic datasets revealed: 1) expression of sex-biased genes and their GH-dependent transcriptional regulators is primarily restricted to hepatocytes and is not a feature of liver non-parenchymal cells; 2) many sex-biased transcripts show sex-dependent zonation within the liver lobule; 3) gene expression is substantially feminized in both periportal and pericentral hepatocytes when male mice are infused with GH continuously; 4) sequencing nuclei increases the sensitivity for detecting thousands of nuclear-enriched lncRNAs and enables determination of their liver cell type-specificity, sex-bias and hepatocyte zonation profiles; 5) the periportal to pericentral hepatocyte cell ratio is significantly higher in male than female liver; and 6) TCPOBOP exposure disrupts both sex-specific gene expression and hepatocyte zonation within the liver lobule. These findings highlight the complex interconnections between hepatic sexual dimorphism and zonation at the single cell level and reveal how endogenous hormones and foreign chemical exposure can alter these interactions across the liver lobule with large effects on both protein-coding genes and lncRNAs.

Xenobiotic exposure dysregulates hundreds of protein-coding genes in mammalian liver, impacting many physiological processes and inducing diverse toxicological responses. Little is known about xenobiotic effects on long noncoding RNAs (lncRNAs), many of which have important regulatory functions. Here, we present a computational framework to discover liver-expressed, xenobiotic-responsive lncRNAs (xeno-lncs) with strong functional, gene regulatory potential and elucidate the impact of xenobiotic exposure on their gene regulatory networks. We assembled the long noncoding transcriptome of xenobiotic-exposed rat liver using RNA-seq datasets from male rats treated with 27 individual chemicals, representing 7 mechanisms of action (MOAs). Ortholog analysis was combined with coexpression data and causal inference methods to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on protein-coding gene expression and biological pathways. We discovered > 1400 liver-expressed xeno-lncs, many with human and/or mouse orthologs. Xenobiotics representing different MOAs often regulated common xeno-lnc targets: 123 xeno-lncs were dysregulated by ≥ 10 chemicals, and 5 xeno-lncs responded to ≥ 20 of the 27 chemicals investigated; 81 other xeno-lncs served as MOA-selective markers of xenobiotic exposure. Xeno-lnc—protein-coding gene coexpression regulatory network analysis identified xeno-lncs closely associated with exposure-induced perturbations of hepatic fatty acid metabolism, cell division, or immune response pathways, and with apoptosis or cirrhosis. We also identified hub and bottleneck lncRNAs, which are expected to be key regulators of gene expression. This work elucidates extensive networks of xeno-lnc—protein-coding gene interactions and provides a framework for understanding the widespread transcriptome-altering actions of foreign chemicals in a key-responsive mammalian tissue.

The advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data preprocessing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-sequencing (RNA-seq), single-cell RNA-seq, as well as digital gene expression data. This report highlights the design methodology behind Pipeliner that enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

The advent of high-throughput sequencing technologies has led to the need for flexible and userfriendly data pre-processing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), as well as Digital Gene Expression (DGE) data. This report highlights the design methodology behind Pipeliner which enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.