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Abstract The fetal development of organs and functions is vulnerable to perturbation by maternal inflammation which may increase susceptibility to disorders after birth. Because it is not well understood how the placenta and fetus respond to acute lung- inflammation, we characterize the response to maternal pulmonary lipopolysaccharide exposure across 24 h in maternal and fetal organs using multi-omics, imaging and integrative analyses. Unlike maternal organs, which mount strong inflammatory immune responses, the placenta upregulates immuno-modulatory genes, in particular the IL-6 signaling suppressor Socs3. Similarly, we observe no immune response in the fetal liver, which instead displays metabolic changes, including increases in lipids containing docosahexaenoic acid, crucial for fetal brain development. The maternal liver and plasma display similar metabolic alterations, potentially increasing bioavailability of docosahexaenoic acid for the mother and fetus. Thus, our integrated temporal analysis shows that systemic inflammation in the mother leads to a metabolic perturbation in the fetus.

Background and Aims: The regenerative capacity of the pancreas diminishes with age. Understanding acinar cell responses to injury and the resolution of regenerative processes is crucial for tissue homeostasis. However, knowledge about the impact of aging on these processes remains limited. Methods: To investigate the influence of aging on pancreas regeneration, we established a cohort of young (7–14 weeks) and old (18 months) C57bl/6 mice. Experimental pancreatitis was induced using caerulein, and pancreas samples were collected at various time points after induction, covering acute damage response, inflammation, peak proliferation, and inflammation resolution. Our analysis involved immunohistochemistry, quantitative imaging, and gene expression analyses. Results: Our study revealed a significant decline in the regenerative capacity of the pancreas in old mice. Despite similar morphology and transcriptional profiles between the pancreas of young and old mice under homeostasis, the aged pancreas is primed to generate an exacerbated proinflammatory reaction in response to injury. Specifically, we observed notable upregulation of Junb expression in acinar cells and aberrant myofibroblast activation in the aged pancreas. Conclusion: The response of acinar cells to injury in the pancreas of aged mice is characterized by an increased susceptibility to inflammation and stromal reactions. Our findings uncover a pre-existing proinflammatory state in aged acinar cells, offering insights into potential strategies to prevent the onset of pancreatic insufficiency and the development of inflammatory conditions. These insights hold implications for preventing conditions such as chronic pancreatitis and pancreatic ductal adenocarcinoma.

Abstract Background The lactate receptor GPR81 contributes to cancer development through unclear mechanisms. Here, we investigate the roles of GPR81 in three-dimensional (3D) and in vivo growth of breast cancer cells and study the molecular mechanisms involved. Methods GPR81 was stably knocked down (KD) in MCF-7 human breast cancer cells which were subjected to RNA-seq analysis, 3D growth, in situ- and immunofluorescence analyses, and cell viability- and motility assays, combined with KD of key GPR81-regulated genes. Key findings were additionally studied in other breast cancer cell lines and in mammary epithelial cells. Results GPR81 was upregulated in multiple human cancer types and further upregulated by extracellular lactate and 3D growth in breast cancer spheroids. GPR81 KD increased spheroid necrosis, reduced invasion and in vivo tumor growth, and altered expression of genes related to GO/KEGG terms extracellular matrix, cell adhesion, and Notch signaling. Single cell in situ analysis of MCF-7 cells revealed that several GPR81-regulated genes were upregulated in the same cell clusters. Notch signaling, particularly the Notch ligand Delta-like-4 (DLL4), was strikingly downregulated upon GPR81 KD, and DLL4 KD elicited spheroid necrosis and inhibited invasion in a manner similar to GPR81 KD. Conclusions GPR81 supports breast cancer aggressiveness, and in MCF-7 cells, this occurs at least in part via DLL4. Our findings reveal a new GPR81-driven mechanism in breast cancer and substantiate GPR81 as a promising treatment target.

ABSTRACTThe intestinal epithelium is constantly exposed to microbes residing in the lumen. Traditionally, the response to microbial interactions has been studied in cell lines derived from cancerous tissues, e.g. Caco-2. It is, however, unclear how the responses in these cancer cell lines reflect the responses of a normal epithelium and whether there might be microbial strain-specific effects. To address these questions, we derived organoids from the small intestine from a cohort of healthy individuals. Culturing intestinal epithelium on a flat laminin matrix induced their differentiation, facilitating analysis of microbial responses via the apical membrane normally exposed to the luminal content. Here, it was evident that the healthy epithelium across multiple individuals (n = 9) demonstrates robust acute both common and strain-specific responses to a range of probiotic bacterial strains (BB-12Ⓡ, LGGⓇ, DSM33361, and Bif195). Importantly, parallel experiments using the Caco-2 cell line provide no acute response. Collectively, we demonstrate that primary epithelial cells maintained as organoids represent a valuable resource for assessing interactions between the epithelium and luminal microbes across individuals, and that these models are likely to contribute to a better understanding of host microbe interactions.

The small intestine forms via crosstalk between epithelial and mesenchymal cell compartments. Here, the authors show that a gradient of Wnt signalling along the anterior-posterior axis regulates Sonic Hedgehog which is required for correct formation and regionalization of the small intestine.

Abstract Background The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: (i) full-length RNA-seq for detection of splicing patterns and (ii) high-throughput 5′ and 3′ tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts. We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings and Saccharomyces cerevisiae cells as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the most commonly used community gene models, TAIR10 and Araport11 for A.thaliana and SacCer3 for S.cerevisiae. In particular, we identify multiple transient transcripts missing from the existing annotations. Our new annotations promise to improve the quality of A.thaliana and S.cerevisiae genome research. Conclusions Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

Abstract Background 5′-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5′-end methods provide genome-wide maps of transcription start sites (TSSs) with base pair resolution. Because active enhancers often feature bidirectional TSSs, such data can also be used to predict enhancer candidates. The current availability of mature and comprehensive computational tools for the analysis of 5′-end data is limited, preventing efficient analysis of new and existing 5′-end data. Results We present CAGEfightR, a framework for analysis of CAGE and other 5′-end data implemented as an R/Bioconductor-package. CAGEfightR can import data from BigWig files and allows for fast and memory efficient prediction and analysis of TSSs and enhancers. Downstream analyses include quantification, normalization, annotation with transcript and gene models, TSS shape statistics, linking TSSs to enhancers via co-expression, identification of enhancer clusters, and genome-browser style visualization. While built to analyze CAGE data, we demonstrate the utility of CAGEfightR in analyzing nascent RNA 5′-data (PRO-Cap). CAGEfightR is implemented using standard Bioconductor classes, making it easy to learn, use and combine with other Bioconductor packages, for example popular differential expression tools such as limma, DESeq2 and edgeR. Conclusions CAGEfightR provides a single, scalable and easy-to-use framework for comprehensive downstream analysis of 5′-end data. CAGEfightR is designed to be interoperable with other Bioconductor packages, thereby unlocking hundreds of mature transcriptomic analysis tools for 5′-end data. CAGEfightR is freely available via Bioconductor: bioconductor.org/packages/CAGEfightR .

The specific effects of administering live probiotics in the human gut are not well characterized. To this end, we investigated the immediate effect of Lactobacillus rhamnosus GG (LGG) in the jejunum of 27 healthy volunteers 2 h after ingestion using a combination of global RNA sequencing of human biopsies and bacterial DNA sequencing in a multi-visit, randomized, cross-over design (ClinicalTrials.gov number NCT03140878). While LGG was detectable in jejunum after 2 h in treated subjects, the gene expression response vs. placebo was subtle if assessed across all subjects. However, clustering analysis revealed that one-third of subjects exhibited a strong and consistent LGG response involving hundreds of genes, where genes related to B cell activation were upregulated, consistent with prior results in mice. Immunohistochemistry and single cell-based deconvolution analyses showed that this B cell signature likely is due to activation and proliferation of existing B cells rather than B cell immigration to the tissue. Our results indicate that the LGG strain has an immediate effect in the human gut in a subpopulation of individuals. In extension, our data strongly suggest that studies on in vivo probiotic effects in humans require large cohorts and must take individual variation into account.

Summary: Degradation of transcripts in human nuclei is primarily facilitated by the RNA exosome. To obtain substrate specificity, the exosome is aided by adaptors; in the nucleoplasm, those adaptors are the nuclear exosome-targeting (NEXT) complex and the poly(A) (pA) exosome-targeting (PAXT) connection. How these adaptors guide exosome targeting remains enigmatic. Employing high-resolution 3′ end sequencing, we demonstrate that NEXT substrates arise from heterogenous and predominantly pA− 3′ ends often covering kilobase-wide genomic regions. In contrast, PAXT targets harbor well-defined pA+ 3′ ends defined by canonical pA site use. Irrespective of this clear division, NEXT and PAXT act redundantly in two ways: (1) regional redundancy, where the majority of exosome-targeted transcription units produce NEXT- and PAXT-sensitive RNA isoforms, and (2) isoform redundancy, where the PAXT connection ensures fail-safe decay of post-transcriptionally polyadenylated NEXT targets. In conjunction, this provides a two-layered targeting mechanism for efficient nuclear sorting of the human transcriptome. : Wu et al. demonstrate that the nuclear exosome adaptor NEXT targets poly(A)− RNAs with poorly defined 3′ ends, whereas the PAXT connection targets poly(A)+ RNAs derived from canonical poly(A) sites. NEXT substrates become polyadenylated in the absence of NEXT, causing fail-safe RNA decay via PAXT. Keywords: nuclear RNA turnover, NEXT complex, PAXT connection, 3′ end sequencing, pA+/pA− RNA

Many SNPs associated with inflammatory bowel disease are located in non-coding genomic regions. Here, the authors perform CAGE-sequencing on descending colon biopsies of Crohn’s disease and ulcerative colitis patients to map transcription start sites and enhancer activity for analysis of regulatory regions.

Crohn’s disease: Genetics impacts molecular regulation of innate immunity on miRNA, but not gene level The genetics of people with Crohn’s disease affects the molecular drivers of their dysregulated immune responses. Some individuals with Crohn’s harbor mutations in the NOD2 gene, which encodes a pathogen recognition receptor that binds to a molecule called muramyl dipeptide (MDP). To better understand how alternations in NOD2 can lead to increased susceptibility to gut inflammation, Yun Chen, Mohammad Salem and colleagues from the University of Copenhagen and Herlev Hospital, Denmark, analyzed the expression patterns of both genes and small, regulatory microRNAs in blood cells from healthy controls and from Crohn’s patients with and without NOD2 mutations. They exposed the cells to MDP, and saw that although gene acticity changed dramatically as a response, there was little difference between subjects, regardless of genetics. Conversely, microRNA expression showed genotype-specific differences that weren not impacted by MDP treatment. The findings underscore the importance of microRNAs in Crohn’s disease.

Summary: Pluripotent embryonic stem cells (ESCs) constitute an essential cellular niche sustained by epigenomic and transcriptional regulation. Any role of post-transcriptional processes remains less explored. Here, we identify a link between nuclear RNA levels, regulated by the poly(A) RNA exosome targeting (PAXT) connection, and transcriptional control by the polycomb repressive complex 2 (PRC2). Knockout of the PAXT component ZFC3H1 impairs mouse ESC differentiation. In addition to the upregulation of bona fide PAXT substrates, Zfc3h1−/− cells abnormally express developmental genes usually repressed by PRC2. Such de-repression is paralleled by decreased PRC2 binding to chromatin and low PRC2-directed H3K27 methylation. PRC2 complex stability is compromised in Zfc3h1−/− cells with elevated levels of unspecific RNA bound to PRC2 components. We propose that excess RNA hampers PRC2 function through its sequestration from DNA. Our results highlight the importance of balancing nuclear RNA levels and demonstrate the capacity of bulk RNA to regulate chromatin-associated proteins. : ZFC3H1 targets pA+ RNA for decay by the nuclear RNA exosome. Garland et al. report a disruptive relationship between excess RNA and PRC2 upon depletion of ZFC3H1 in mouse ESCs. In such conditions, RNA is bound by PRC2 components, which show reduced binding to chromatin and fellow PRC2 proteins. Keywords: nuclear RNA decay, RNA exosome, PRC2, PAXT, chromatin, H3K27 methylation, stem cell regulation

Cap Analysis of Gene Expression (CAGE) is one of the most popular 5'-end sequencing methods. In a single experiment, CAGE can be used to locate and quantify the expression of both Transcription Start Sites (TSSs) and enhancers. This is workflow is a case study on how to use the CAGEfightR package to orchestrate analysis of CAGE data within the Bioconductor project. This workflow starts from BigWig-files and covers both basic CAGE analyses such as identifying, quantifying and annotating TSSs and enhancers, advanced analysis such as finding interacting TSS-enhancer pairs and enhancer clusters, to differential expression analysis and alternative TSS usage. R-code, discussion and references are intertwined to help provide guidelines for future CAGE studies of the same kind.

The PLZF transcription factor is essential for osteogenic differentiation of hMSCs; however, its regulation and molecular function during this process is not fully understood. Here, we revealed that the ZBTB16 locus encoding PLZF, is repressed by Polycomb (PcG) and H3K27me3 in naive hMSCs. At the pre-osteoblast stage of differentiation, the locus lost PcG binding and H3K27me3, gained JMJD3 recruitment, and H3K27ac resulting in high expression of PLZF. Subsequently, PLZF was recruited to osteogenic enhancers, influencing H3K27 acetylation and expression of nearby genes important for osteogenic function. Furthermore, we identified a latent enhancer within the ZBTB16/PLZF locus itself that became active, gained PLZF, p300 and Mediator binding and looped to the promoter of the nicotinamide N-methyltransferase (NNMT) gene. The increased expression of NNMT correlated with a decline in SAM levels, which is dependent on PLZF and is required for osteogenic differentiation.

Metabolically healthy obese subjects display preserved insulin sensitivity and a beneficial white adipose tissue gene expression pattern. However, this observation stems from fasting studies when insulin levels are low. We investigated adipose gene expression by 5′Cap-mRNA sequencing in 17 healthy non-obese (NO), 21 insulin-sensitive severely obese (ISO), and 30 insulin-resistant severely obese (IRO) subjects, before and 2 hr into a hyperinsulinemic euglycemic clamp. ISO and IRO subjects displayed a clear but globally similar transcriptional response to insulin, which differed from the small effects observed in NO subjects. In the obese, 231 genes were altered; 71 were enriched in ISO subjects (e.g., phosphorylation processes), and 52 were enriched in IRO subjects (e.g., cellular stimuli). Common cardio-metabolic risk factors and gender do not influence these findings. This study demonstrates that differences in the acute transcriptional response to insulin are primarily driven by obesity per se, challenging the notion of healthy obese adipose tissue, at least in severe obesity.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.

Transcription factors have recently been shown to colocalize in hotspot regions of the genome, which are further clustered into super-enhancers. However, the detailed molecular organization of transcription factors at hotspot regions is poorly defined. Here, we have used digital genomic footprinting to precisely define factor localization at a genome-wide level during the early phase of 3T3-L1 adipocyte differentiation, which allows us to obtain detailed molecular insight into how transcription factors target hotspots. We demonstrate the formation of ATF-C/EBP heterodimers at a composite motif on chromatin, and we suggest that this may be a general mechanism for integrating external signals on chromatin. Furthermore, we find evidence of extensive recruitment of transcription factors to hotspots through alternative mechanisms not involving their known motifs and demonstrate that these alternative binding events are functionally important for hotspot formation and activity. Taken together, these findings provide a framework for understanding transcription factor cooperativity in hotspots.

Genome-wide, high-throughput methods for transcription start site (TSS) detection have shown that most promoters have an array of neighboring TSSs where some are used more than others, forming a distribution of initiation propensities. TSS distributions (TSSDs) vary widely between promoters and earlier studies have shown that the TSSDs have biological implications in both regulation and function. However, no systematic study has been made to explore how many types of TSSDs and by extension core promoters exist and to understand which biological features distinguish them. In this study, we developed a new non-parametric dissimilarity measure and clustering approach to explore the similarities and stabilities of clusters of TSSDs. Previous studies have used arbitrary thresholds to arrive at two general classes: broad and sharp. We demonstrated that in addition to the previous broad/sharp dichotomy an additional category of promoters exists. Unlike typical TATA-driven sharp TSSDs where the TSS position can vary a few nucleotides, in this category virtually all TSSs originate from the same genomic position. These promoters lack epigenetic signatures of typical mRNA promoters and a substantial subset of them are mapping upstream of ribosomal protein pseudogenes. We present evidence that these are likely mapping errors, which have confounded earlier analyses, due to the high similarity of ribosomal gene promoters in combination with known G addition bias in the CAGE libraries. Thus, previous two-class separations of promoter based on TSS distributions are motivated, but the ultra-sharp TSS distributions will confound downstream analyses if not removed.

A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to "pattern drowning". We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.

Cap Analysis of Gene Expression (CAGE) is one of the most popular 5'-end sequencing methods. In a single experiment, CAGE can be used to locate and quantify the expression of both Transcription Start Sites (TSSs) and enhancers. This is workflow is a case study on how to use the CAGEfightR package to orchestrate analysis of CAGE data within the Bioconductor project. This workflow starts from BigWig-files and covers both basic CAGE analyses such as identifying, quantifying and annotating TSSs and enhancers, advanced analysis such as finding interacting TSS-enhancer pairs and enhancer clusters, to differential expression analysis and alternative TSS usage. R-code, discussion and references are intertwined to help provide guidelines for future CAGE studies of the same kind.

Motivation: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation. Results: With the goal of automatically deciphering such regulatory structures, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk of overfitting and generally improves performance. We apply this method to identify TFBSs and to classify promoters preferentially expressed in macrophages, where it outperforms other methods due to the increased predictive power given by the grammar. Availability: The software and the datasets are available from http://modem.ucsd.edu/won/eHMM.tar.gz Contact: krogh@binf.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]