Core liver homeostatic co-expression networks are preserved but respond to perturbations in an organism- and disease-specific manner

Esmaili, Saeed; Langfelder, Peter; Belgard, T. Grant; Vitale, Daniele; Azardaryany, Mahmoud Karimi; Talesh, Ghazal Alipour; Ramezani-Moghadam, Mehdi; Ho, Vikki; Dvorkin, Daniel; Dervish, Suat; Gloss, Brian; Grønbæk, Henning; Liddle, Christopher; George, Jacob

doi:10.1016/j.cels.2021.04.004

Cited by 14 publications

(28 citation statements)

References 47 publications

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“…To date, GCNs have been used for investigating the causal mechanisms underlying NAFLD using mouse population data ( Chella Krishnan et al., 2018 ) and human population data ( Zhang et al., 2020 ) and for integrative analysis of mouse model data and patients data ( Saeed, 2021 ). However, there remains a lack of holistic studies of samples that cover a large spectrum of disease severity.…”

Section: Introductionmentioning

confidence: 99%

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

et al. 2021

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Summary Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

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Section: Introductionmentioning

confidence: 99%

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

et al. 2021

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“…Starting at 10 weeks of age, mice were assigned to one of 4 diet groups for 26 weeks: A) normal chow (NC); B) high sucrose (HS); C) NC diet supplemented with cholic acid (CA); and D) HS diet with added cholesterol and cholic acid (HS_Chol2%_CA), hereafter referred to as a cholesterol rich diet (ChR) (Figure 1A). As we have reported, a ChR diet significantly alters liver homeostasis (Esmaili et al, 2021) and was therefore used in this tumour study. The ChR diet contains sucrose and cholic acid and hence the HS and CA diets were used as additional controls.…”

Section: Resultsmentioning

confidence: 99%

“…We evaluated enrichment of significantly DE genes in the following collections of literature sets: Gene Ontology (GO)(Blake et al, 2015); Molecular Signatures Database (MSigDB) version 6.2 (Subramanian et al, 2005); NCBI BioSystems gene sets(Geer et al, 2010), including KEGG, Reactome, Lipid Pathways and BioCYC; Cell type markers for various immune and blood cells; Liver-related sets from a collection of gene sets curated from general literature; Genomic position gene sets; each set contains genes in a 5 Mb window, with two adjacent windows overlapping by 2.5 Mb; Enrichr (Chen et al, 2013) 2016 ChEA library; Enrichr 2015 ENCODE histone modification library; Enrichr 2015 ENCODE TF ChIP-seq library; Enrichr 2017 mirTarBase library (Chou et al, 2018); Sets of genes DE at FDR<0.05 in our previous study of pre-clinical dietary models of fatty liver disease (Esmaili et al, 2021); Sets of genes DE at FDR<0.05 in a study of human fatty liver disease(Suppli et al, 2019). …”

Section: Methodsmentioning

confidence: 99%

“…We carried out module preservation calculations between the current 6 network analyses and our previous study of pre-clinical dietary models of fatty liver disease (Esmaili et al, 2021), in human fatty liver data (Suppli et al, 2019) and 3 human liver cancer data sets (LICA-FR, LIHC-US and LIRI-JP) from the ICGC portal (Zhang et al, 2019). We mapped genes between mouse and human Entrez identifiers using vertebrate homology information from MGI, (http://www.informatics.jax.org/homology.shtml, downloaded March 2019).…”

Section: Methodsmentioning

confidence: 99%

“…The rationale is to exclude outliers but ensure that the number of outliers is not too large either overall or in each genotype group. This approach has previously been used in (Esmaili et al, 2021) , (Lee et al, 2018).…”

Section: Quantification and Statistical Analysis Statistical Analysis...mentioning

confidence: 99%

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Bone marrow haematopoietic stem cells influence liver homeostatic networks and cancer development

Talash

Langfelder

Vitale

et al. 2022

Preprint

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In fatty liver disease, systemic homeostasis is perturbed. While pre-clinical models are used to understand its pathogenesis, translating this knowledge to patients is difficult. However, by focusing on the most preserved homeostasis systems between species and models, novel disease dimensions can be unearthed. We interrogated core liver gene co-expression networks in a mouse model of liver cancer following dietary challenge. The behaviour of immunometabolic networks in tumours mirrored their counterparts in non-tumour liver. These modules showed temporal changes under the influence of diet duration and aging. A high immune response network was associated with a lower tumour burden in mice and humans. This module in mice was enriched for genes related to haematopoietic cell differentiation. Consistently, the bone marrow haematopoietic stem and progenitor cells response was reflective of the liver immune response. Linking haematopoiesis to hepatic homeostasis uncovers a hitherto unexplored dimension of tissue crosstalk that can inform pathogenesis.

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Exploring the role of transcriptional and post‐transcriptional processes in mRNA co‐expression

García‐Blay,

Verhagen,

Martin

et al. 2023

BioEssays

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Co‐expression of two or more genes at the single‐cell level is usually associated with functional co‐regulation. While mRNA co‐expression—measured as the correlation in mRNA levels—can be influenced by both transcriptional and post‐transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post‐transcriptional regulation of co‐expression. To enhance our understanding, we integrate four datasets spanning single‐cell gene expression data, single‐cell promoter activity data and individual transcript half‐lives. Confirming expectations, we find that positive co‐expression necessitates promoter coordination and similar mRNA half‐lives. Surprisingly, negative co‐expression is favored by differences in mRNA half‐lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half‐lives, which additional stochastic simulations suggest might give rise to the observed co‐expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.

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Core liver homeostatic co-expression networks are preserved but respond to perturbations in an organism- and disease-specific manner

Cited by 14 publications

References 47 publications

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

Bone marrow haematopoietic stem cells influence liver homeostatic networks and cancer development

Exploring the role of transcriptional and post‐transcriptional processes in mRNA co‐expression

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