Cux1 transcription factor is induced in inflammatory bowel disease and protects against experimental colitis

Supplementary Hesperidin Alleviated CPT-11-Induced Diarrhea by Modulating Gut Microbiota and Inhibiting the IL-17 Signaling Pathway

Irinotecan (CPT-11) is a chemotherapy agent commonly used for the treatment of gastrointestinal tumors, with diarrhea being a frequent adverse effect. Hesperidin is a flavonoid abundant in citrus fruits and has shown potential in managing CPT-11-induced diarrhea (CID). However, the mechanisms underlying its effects remain unclear. This study established a mouse model of CID using CPT-11 administration to evaluate the effects of hesperidin on diarrhea severity, intestinal pathology, gut microbiota composition, and metabolite profiles by conducting biochemical analysis, histopathology, immunohistochemistry, 16S rRNA sequencing, and untargeted metabolomics. In addition, transcriptomic analysis, molecular docking, and molecular dynamics simulations were conducted to investigate potential mechanisms of action. Hesperidin supplementation was found to significantly alleviate CID in mice. Analysis of gut microbiota using 16S rRNA sequencing revealed that hesperidin improved microbial composition, with key taxa such as Alistipes, Limosilactobacillus, Rikenella, and Mucispirillum playing a central role in ameliorating CID. Furthermore, hesperidin enhanced intestinal barrier function by upregulating tight junction proteins, mitigating epithelial damage, and reducing the expression of IL-17A, TARF6, p38, phosphorylated-p38 (P-p38), and AP-1 proteins in the colon. These findings suggest that hesperidin supplementation mitigates CID by modulating gut microbiota and inhibiting the IL-17 signaling pathway, thereby improving intestinal barrier integrity.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.

Potential mechanism of pyrotinib-induced diarrhea was explored by gut microbiome and ileum metabolomics

Pyrotinib is a novel epidermal growth factor receptor/human epidermal growth factor receptor-2 (HER2) tyrosine kinase inhibitor that exhibited clinical efficacy in patients with HER2-positive breast cancer and HER2-mutant/amplified lung cancer. However, severe diarrhea adverse responses preclude its practical use. At present, the mechanism of pyrotinib-induced diarrhea is unknown and needs further study. First, to develop a suitable and reproducible animal model, we compared the effects of different doses of pyrotinib (20, 40, 60 and 80 mg/kg) in Wistar rats. Second, we used this model to examine the intestinal toxicity of pyrotinib. Finally, the mechanism underlying pyrotinib-induced diarrhea was fully studied using gut microbiome and host intestinal tissue metabolomics profiling. Reproducible diarrhea occurred in rats when they were given an 80 mg/kg daily dose of pyrotinib. Using the pyrotinib-induced model, we observed that Lachnospiraceae and Acidaminococcaceae decreased in the pyrotinib groups, whereas Enterobacteriaceae, Helicobacteraceae and Clostridiaceae increased at the family level by 16S rRNA gene sequence. Multiple bioinformatics methods revealed that glycocholic acid, ursodeoxycholic acid and cyclic AMP increased in the pyrotinib groups, whereas kynurenic acid decreased, which may be related to the pathogenesis of pyrotinib-induced diarrhea. Additionally, pyrotinib-induced diarrhea may be associated with a number of metabolic changes mediated by the gut microbiome, such as Primary bile acid biosynthesis. We reported the establishment of a reproducible pyrotinib-induced animal model for the first time. Furthermore, we concluded from this experiment that gut microbiome imbalance and changes in related metabolites are significant contributors to pyrotinib-induced diarrhea.

Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).

Digenic inheritance of MSH6 and MUTYH variants in familial colorectal cancer

We describe a family severely affected by colorectal cancer (CRC) where whole-exome sequencing identified the coinheritance of the germline variants encoding MSH6 p.Thr1100Met and MUTYH p.Tyr179Cys in, at least, three CRC patients diagnosed before 60 years of age. Digenic inheritance of monoallelic MSH6 variants of uncertain significance and MUTYH variants has been suggested to predispose to Lynch syndrome-associated cancers; however, cosegregation with disease in the familial setting has not yet been established. The identification of individuals carrying multiple potential cancer risk variants is expected to rise with the increased application of whole-genome sequencing and large multigene panel testing in clinical genetic counseling of familial cancer patients. Here we demonstrate the coinheritance of monoallelic variants in MSH6 and MUTYH consistent with cosegregation with CRC, further supporting a role for digenic inheritance in cancer predisposition.

Regulation of Janus Kinase 2 by an Inflammatory Bowel Disease Causal Non-coding Single Nucleotide Polymorphism

BACKGROUND AND AIMS

Among the >240 genetic loci described to date which confer susceptibility to inflammatory bowel disease, a small subset have been fine-mapped to an individual, non-coding single nucleotide polymorphism [SNP]. To illustrate a model mechanism by which a presumed-causal non-coding SNP can function, we analysed rs1887428, located in the promoter region of the Janus kinase 2 [JAK2] gene.

METHODS

We utilized comparative affinity purification-mass spectrometry, DNA-protein binding assays, CRISPR/Cas9 genome editing, transcriptome sequencing and methylome quantitative trait locus methods to characterize the role of this SNP.

RESULTS

We determined that the risk allele of rs1887428 is bound by the transcription factor [TF] RBPJ, while the protective allele is bound by the homeobox TF CUX1. While rs188748 only has a very modest influence on JAK2 expression, this effect was amplified downstream through the expression of pathway member STAT5B and epigenetic modification of the JAK2 locus.

CONCLUSION

Despite the absence of a consensus TF-binding motif or expression quantitative trait locus, we have used improved methods to characterize a putatively causal SNP to yield insight into inflammatory bowel disease mechanisms.

PODCAST

This article has an associated podcast which can be accessed at https://academic.oup.com/ecco-jcc/pages/podcast.

Supercritical Fluid Extract of Angelica sinensis and Zingiber officinale Roscoe Ameliorates TNBS-Induced Colitis in Rats

Inflammatory bowel disease (IBD) is a worldwide healthcare problem calling for the development of new therapeutic drugs. Angelica sinensis and Zingiber officinale Roscoe are two common dietetic Chinese herbs, which are traditionally used for complementary treatment of gastrointestinal disorders. As bioactive constituents, volatile and pungent substances of these two herbs could be effectively extracted together by supercritical fluid extraction. In this study, the supercritical fluid extract of Angelica sinensis and Zingiber officinale Roscoe (AZ-SFE) was obtained by an optimized extraction process and it was chemically characterized. The anti-inflammatory effect and underlying mechanism of AZ-SFE were evaluated in a lipopolysaccharide (LPS)-induced RAW264.7 cell model and a 2, 4, 6-trinitrobenzenesulfonic acid (TNBS)-induced colitis rat model. AZ-SFE notably inhibited the production of NO in LPS-stimulated macrophages, and it inhibited the proliferation of Concanavalin A (Con A)-induced splenocytes with suppression of the Th1 immune response. In vivo, the study demonstrated that AZ-SFE significantly alleviated disease activity, colonic shortening, macroscopic damage and histological injury of TNBS-treated rats with reduction of oxidative stress, suppression of inflammatory cytokines, and modulation of hepcidin and serum iron. These findings suggested that AZ-SFE may be a promising supplement for current IBD therapy.

Upregulated Expression of CUX1 Correlates with Poor Prognosis in Glioma Patients: a Bioinformatic Analysis

Cut-like homeobox-1 (CUX1) is expressed in the upper layer of the cortex and participates in DNA replication, cell cycle control, and DNA repair. It has been shown to be involved in the proliferation of various types of solid tumors. The aims of this study were to explore the relationship between CUX1 expression and the prognosis of glioma by performing a series of functional experiments and bioinformatic analyses. Firstly, we found that CUX1 expression levels differed among patients with different grades of gliomas, and they were significantly correlated with the prognosis of glioma patients according to an analysis of data from a public database. qRT-PCR, western blotting, and immunohistochemical analysis of CUX1 were performed to demonstrate that the expression of CUX1 was positively correlated with the glioma WHO grade (P < 0.05) and several malignant clinical pathological parameters, including Ki67 and P53mut. In addition, the multivariate Cox regression and Kaplan-Meier curves showed that CUX1 expression exerted predictive value for overall survival. Finally, to further investigate the functions of CUX1, we identified CUX1-associated genes and, though GO/KEGG analysis, their associated biological functions and signaling pathways; the results suggested that the activity of CUX1 might be exerted via the JAK-STAT pathway or other key regulators of the cell cycle to promote proliferation, inflammation, and chemotherapy resistance in glioma. Taken together, these results indicate that CUX1 is a potential biomarker of malignancy and prognosis and may serve as a potential therapeutic target for glioma patients.

The differential disease regulome

Background

Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information.

Results

We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.

The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.

The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available.

Conclusion

We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no.