Genome-scale identification of transcription factors that mediate an inflammatory network during breast cellular transformation

Role of the Receptor for Advanced Glycation End Products (RAGE) and Its Ligands in Inflammatory Responses

Since its discovery in 1992, the receptor for advanced glycation end products (RAGE) has emerged as a key receptor in many pathological conditions, especially in inflammatory conditions. RAGE is expressed by most, if not all, immune cells and can be activated by many ligands. One characteristic of RAGE is that its ligands are structurally very diverse and belong to different classes of molecules, making RAGE a promiscuous receptor. Many of RAGE ligands are damaged associated molecular patterns (DAMPs) that are released by cells under inflammatory conditions. Although RAGE has been at the center of a lot of research in the past three decades, a clear understanding of the mechanisms of RAGE activation by its ligands is still missing. In this review, we summarize the current knowledge of the role of RAGE and its ligands in inflammation.

Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.

Colocalization of corneal resistance factor GWAS loci with GTEx e/sQTLs highlights plausible candidate causal genes for keratoconus postnatal corneal stroma weakening

Background: Genome-wide association studies (GWAS) for corneal resistance factor (CRF) have identified 100s of loci and proved useful to uncover genetic determinants for keratoconus, a corneal ectasia of early-adulthood onset and common indication of corneal transplantation. In the current absence of studies to probe the impact of candidate causal variants in the cornea, we aimed to fill some of this knowledge gap by leveraging tissue-shared genetic effects. Methods: 181 CRF signals were examined for evidence of colocalization with genetic signals affecting steady-state gene transcription and splicing in adult, non-eye, tissues of the Genotype-Tissue Expression (GTEx) project. Expression of candidate causal genes thus nominated was evaluated in single cell transcriptomes from adult cornea, limbus and conjunctiva. Fine-mapping and colocalization of CRF and keratoconus GWAS signals was also deployed to support their sharing causal variants. Results and discussion: 26.5% of CRF causal signals colocalized with GTEx v8 signals and nominated genes enriched in genes with high and specific expression in corneal stromal cells amongst tissues examined. Enrichment analyses carried out with nearest genes to all 181 CRF GWAS signals indicated that stromal cells of the limbus could be susceptible to signals that did not colocalize with GTEx's. These cells might not be well represented in GTEx and/or the genetic associations might have context specific effects. The causal signals shared with GTEx provide new insights into mediation of CRF genetic effects, including modulation of splicing events. Functionally relevant roles for several implicated genes' products in providing tensile strength, mechano-sensing and signaling make the corresponding genes and regulatory variants prime candidates to be validated and their roles and effects across tissues elucidated. Colocalization of CRF and keratoconus GWAS signals strengthened support for shared causal variants but also highlighted many ways into which likely true shared signals could be missed when using readily available GWAS summary statistics.

MafB, WDR77, and ß-catenin interact with each other and have similar genome association profiles

MafB (a bZIP transcription factor), ß-catenin (the ultimate target of the Wnt signal transduction pathway that acts as a transcriptional co-activator of LEF/TCF proteins), and WDR77 (a transcriptional co-activator of multiple hormone receptors) are important for breast cellular transformation. Unexpectedly, these proteins interact directly with each other, and they have similar genomic binding profiles. Furthermore, while some of these common target sites coincide with those bound by LEF/TCF, the majority are located just downstream of transcription initiation sites at a position near paused RNA polymerase (Pol II) and the +1 nucleosome. Occupancy levels of these factors at these promoter-proximal sites are strongly correlated with the level of paused Pol II and transcriptional activity.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Background

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

Results

In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.

Conclusions

Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01021-x.

Epigenetic reorganization during early embryonic lineage specification

BACKGROUND

Dynamic chromatin reorganization occurs during two waves of cell lineage specification process, blastocyst formation and gastrulation, to generate distinct cell types. Epigenetic defects have been associated with severe developmental defects and diseases. How epigenetic remodeling coordinates the two lineage specification waves is becoming uncovered, benefiting from the development and application of new technologies including low-input or single-cell epigenome analysis approached in the past few years.

OBJECTIVE

In this review, we aim to highlight the most recent findings on epigenetic remodeling in cell lineage specification during blastocyst formation and gastrulation.

METHODS

First, we introduce how DNA methylation dynamically changes in blastocyst formation and gastrulation and its function in transcriptional regulation lineage-specific genes. Then, we discuss widespread remodeling of histone modification at promoters and enhancers in orchestrating the trajectory of cell lineage specification. Finally, we review dynamics of chromatin accessibility and 3D structure regulating developmental gene expression and associating with specific transcription factor binding events at stage specific manner. We also highlight the key questions that remain to be answered to fully understand chromatin regulation and reorganization in lineage specification.

CONCLUSION

Here, we summarize the recent advances and discoveries on epigenetic reorganization and its roles in blastocyst formation and gastrulation, and how it cooperates with the lineage specification, painting from global sequencing data from mouse in vivo tissues.

YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation

The YAP and TAZ paralogs are transcriptional co-activators recruited to target sites by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB (a member of the AP-1 family) and STAT3, key transcription factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, and they stimulate transcriptional activation by AP-1 proteins. JUNB, STAT3, and TEAD co-localize at virtually all YAP/TAZ target sites, yet many target sites only contain individual AP-1, TEAD, or STAT3 motifs. This observation and differences in relative crosslinking efficiencies of JUNB, TEAD, and STAT3 at YAP/TAZ target sites suggest that YAP/TAZ is recruited by different forms of an AP-1/STAT3/TEAD complex depending on the recruiting motif. The different classes of YAP/TAZ target sites are associated with largely non-overlapping genes with distinct functions. A small minority of target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes from shared YAP/TAZ target sites. Genes containing either the AP-1 or TEAD class of YAP/TAZ sites are associated with poor survival of breast cancer patients with the triple-negative form of the disease.

Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma

Background

This study aimed to systematically investigate gene signatures for hepatoblastoma (HB) and identify potential biomarkers for its diagnosis and treatment.

Materials and Methods

GSE131329 and GSE81928 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between hepatoblastoma and normal samples were identified using the Limma package in R. Then, the similarity of network traits between two sets of genes was analyzed by weighted gene correlation network analysis (WGCNA). Cytoscape was used to visualize and select hub genes. PPI network of hub genes was construed by Cytoscape. GO enrichment and KEGG pathway analyses of hub genes were carried out using ClueGO. The random forest classifier was constructed based on the hub genes using the GSE131329 dataset as the training set, and its reliability was validated using the GSE81928 dataset. The resulting core hub genes were combined with the InnateDB database to identify the innate core genes.

Results

A total of 4244 DEGs in HB were identified. WGCNA identified four modules that were significantly correlated with the disease status. A total of 114 hub genes were obtained within the top 20 genes of each node rank. 6982 relation pairs and 3700 nodes were contained in the PPI network of 114 hub genes. GO enrichment and KEGG pathway analyses of hub genes were focused on MAPK, cell cycle, p53, and other crucial pathways involved in HB. A random forest classifier was constructed using the 114 hub genes as feature genes, resulting in a 95.5% true positive rate when classifying HB and normal samples. A total of 35 core hub genes were obtained through the mean decrease in accuracy and mean decrease Gini of the random forest model. The classification efficiency of the random forest model was 81.4%. Finally, CDK1, TOP2A, ADRA1A, FANCI, XRCC1, TPX2, CCNB2, CDK4, GLYATL1, and CFHR3 were identified by cross-comparison with the InnateDB database.

Conclusion

Our study established a random forest classifier that identified 10 core genes in HB. These findings may be beneficial for the diagnosis, prediction, and targeted therapy of HB.

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

MOTIVATION

Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which motifs are prone to impact transcriptional regulation if mutated. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects.

RESULTS

We present MAGGIE (Motif Alteration Genome-wide to Globally Investigate Elements), a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutations of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared with the state-of-the-art motif analysis approaches. We use MAGGIE to gain novel insights into the divergent functions of distinct NF-κB factors in pro-inflammatory macrophages, revealing the association of p65-p50 co-binding with transcriptional activation and the association of p50 binding lacking p65 with transcriptional repression.

AVAILABILITY AND IMPLEMENTATION

The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie. The accession number for the NF-κB ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

KRAS drives immune evasion in a genetic model of pancreatic cancer

Immune evasion is a hallmark of KRAS-driven cancers, but the underlying causes remain unresolved. Here, we use a mouse model of pancreatic ductal adenocarcinoma to inactivate KRAS by CRISPR-mediated genome editing. We demonstrate that at an advanced tumor stage, dependence on KRAS for tumor growth is reduced and is manifested in the suppression of antitumor immunity. KRAS-deficient cells retain the ability to form tumors in immunodeficient mice. However, they fail to evade the host immune system in syngeneic wild-type mice, triggering strong antitumor response. We uncover changes both in tumor cells and host immune cells attributable to oncogenic KRAS expression. We identify BRAF and MYC as key mediators of KRAS-driven tumor immune suppression and show that loss of BRAF effectively blocks tumor growth in mice. Applying our results to human PDAC we show that lowering KRAS activity is likewise associated with a more vigorous immune environment.

S100A8/S100A9 cytokine acts as a transcriptional coactivator during breast cellular transformation

Cytokines are extracellular proteins that convey messages between cells by interacting with cognate receptors at the cell surface and triggering signaling pathways that alter gene expression and other phenotypes in an autocrine or paracrine manner. Here, we show that the calcium-dependent cytokines S100A8 and S100A9 are recruited to numerous promoters and enhancers in a model of breast cellular transformation. This recruitment is associated with multiple DNA sequence motifs recognized by DNA binding transcription factors that are linked to transcriptional activation and are important for transformation. The cytokines interact with these transcription factors in nuclear extracts, and they activate transcription when artificially recruited to a target promoter. Nuclear-specific expression of S100A8/A9 promotes oncogenic transcription and leads to enhanced breast transformation phenotype. These results suggest that, in addition to its classical cytokine function, S100A8/A9 can act as a transcriptional coactivator.

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.

Pheno-RNA, a method to associate genes with a specific phenotype, identifies genes linked to cellular transformation

Cellular transformation is associated with dramatic changes in gene expression, but it is difficult to determine which regulated genes are oncogenically relevant. Here we describe Pheno-RNA, a general approach to identifying candidate genes associated with a specific phenotype. Specifically, we generate a "phenotypic series" by treating a nontransformed breast cell line with a wide variety of molecules that induce cellular transformation to various extents. By performing transcriptional profiling across this phenotypic series, the expression profile of every gene can be correlated with the strength of the transformed phenotype. We identify ∼200 genes whose expression profiles are very highly correlated with the transformation phenotype, strongly suggesting their importance in transformation. Within biological categories linked to cancer, some genes show high correlations with the transformed phenotype, but others do not. Many genes whose expression profiles are highly correlated with transformation have never been associated with cancer, suggesting the involvement of heretofore unknown genes in cancer.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.. [DOI: 10.1101/2020.10.06.328567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Upregulated proteoglycan-related signaling pathways in fluid flow shear stress-treated podocytes

The ultrafiltrate flow over the major processes and cell body generates fluid flow shear stress (FFSS) on podocytes. Hyperfiltration-associated increase in FFSS can lead to podocyte injury and detachment. Previously, we showed that FFSS-induced upregulation of the cyclooxygenase 2 (COX2)-PGE₂-prostaglandin E receptor 2 (EP2) axis in podocytes activates Akt-glycogen synthase kinase-3β-β-catenin and MAPK/ERK signaling in response to FFSS. Integrative MultiOmics Pathway Resolution (IMPRes) is a new bioinformatic tool that enables simultaneous time-series analysis of more than two groups to identify pathways and molecular connections. In the present study, we used previously characterized COX2 [prostaglandin-endoperoxide synthase 2 (Ptgs2)], EP2 (Ptger2), and β1-catenin (Ctnnb1) as "seed genes" from an array data set of four groups analyzed over a time course. The 3 seed genes shared 7 pathways and 50 genes of 14 pathways and 89 genes identified by IMPRes. A composite of signaling pathways highlighted the temporal molecular connections during mechanotransduction signaling in FFSS-treated podocytes. We investigated the "proteoglycans in cancer" and "galactose metabolism" pathways predicted by IMPRes. A custom-designed PCR array validated 60.7% of the genes predicted by IMPRes analysis, including genes for the above-named pathways. Further validation using Western blot analysis showed increased expression of phosho-Erbb2, phospho-mammalian target of rapamycin (mTOR), CD44, and hexokinase II (Hk2); decreased total Erbb2, galactose mutarotase (Galm), and β-1,4-galactosyltransferase 1 (B4galt1); and unchanged total mTOR and AKT3. These findings corroborate our previously reported results. This study demonstrates the potential of the IMPRes method to identify novel pathways. Identifying the "proteoglycans in cancer" and "galactose metabolism" pathways has generated a lead to study the significance of FFSS-induced glycocalyx remodeling and possible detachment of podocytes from the glomerular matrix.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Disruption of TFIIH activities generates a stress gene expression response and reveals possible new targets against cancer

Disruption of the enzymatic activities of the transcription factor TFIIH by the small molecules Triptolide (TPL) or THZ1 could be used against cancer. Here, we used the MCF10A-ErSrc oncogenesis model to compare the effect of TFIIH inhibitors between transformed cells and their progenitors. We report that tumour cells exhibited highly increased sensitivity to TPL or THZ1 and that the combination of both had a synergic effect. TPL affects the interaction between XPB and p52, causing a reduction in the levels of XPB, p52 and p8, but not other TFIIH subunits. RNA-Seq and RNAPII-ChIP-Seq experiments showed that although the levels of many transcripts were reduced, the levels of a significant number were increased after TPL treatment, with maintained or increased RNAPII promoter occupancy. A significant number of these genes encode for factors that have been related to tumour growth and metastasis, suggesting that transformed cells might rapidly develop resistance to TPL/THZ inhibitors. Some of these genes were also overexpressed in response to THZ1, of which depletion enhances the toxicity of TPL, and are possible new targets against cancer.

Inflammatory regulatory network mediated by the joint action of NF-kB, STAT3, and AP-1 factors is involved in many human cancers

Using an inducible, inflammatory model of breast cellular transformation, we describe the transcriptional regulatory network mediated by STAT3, NF-κB, and AP-1 factors on a genomic scale. These proinflammatory regulators form transcriptional complexes that directly regulate the expression of hundreds of genes in oncogenic pathways via a positive feedback loop. This transcriptional feedback loop and associated network functions to various extents in many types of cancer cells and patient tumors, and it is the basis for a cancer inflammation index that defines cancer types by functional criteria. We identify a network of noninflammatory genes whose expression is well correlated with the cancer inflammatory index. Conversely, the cancer inflammation index is negatively correlated with the expression of genes involved in DNA metabolism, and transformation is associated with genome instability. We identify drugs whose efficacy in cell lines is correlated with the cancer inflammation index, suggesting the possibility of using this index for personalized cancer therapy. Inflammatory tumors are preferentially associated with infiltrating immune cells that might be recruited to the site of the tumor via inflammatory molecules produced by the cancer cells.