Altered Genome-Wide DNA Methylation in the Duodenum of Common Variable Immunodeficiency Patients.

PURPOSE: A large proportion of Common variable immunodeficiency (CVID) patients has duodenal inflammation with increased intraepithelial lymphocytes (IEL) of unknown aetiology. The histologic similarities to celiac disease, lead to confusion regarding treatment (gluten-free diet) of these patients. We aimed to elucidate the role of epigenetic DNA methylation in the aetiology of duodenal inflammation in CVID and differentiate it from true celiac disease. METHODS: DNA was isolated from snap-frozen pieces of duodenal biopsies and analysed for differences in genome-wide epigenetic DNA methylation between CVID patients with increased IEL (CVID_IEL; n = 5) without IEL (CVID_N; n = 3), celiac disease (n = 3) and healthy controls (n = 3). RESULTS: The DNA methylation data of 5-methylcytosine in CpG sites separated CVID and celiac diseases from healthy controls. Differential methylation in promoters of genes were identified as potential novel mediators in CVID and celiac disease. There was limited overlap of methylation associated genes between CVID_IEL and Celiac disease. High frequency of differentially methylated CpG sites was detected in over 100 genes nearby transcription start site (TSS) in both CVID_IEL and celiac disease, compared to healthy controls. Differential methylation of genes involved in regulation of TNF/cytokine production were enriched in CVID_IEL, compared to healthy controls. CONCLUSION: This is the first study to reveal a role of epigenetic DNA methylation in the etiology of duodenal inflammation of CVID patients, distinguishing CVID_IEL from celiac disease. We identified potential biomarkers and therapeutic targets within gene promotors and in high-frequency differentially methylated CpG regions proximal to TSS in both CVID_IEL and celiac disease.

A novel multiscale framework for delineating cancer evolution from subclonal compositions.

OBJECTIVE: Owing to the heterogeneity in the evolution of cancer, distinguishing between diverse growth patterns and predicting long-term outcomes based on short-term measurements poses a great challenge. METHODS: A novel multiscale framework is proposed to unravel the connections between the population dynamics of cancer growth (i.e., aggressive, bounded, and indolent) and the cellular-subclonal dynamics of cancer evolution. This framework employs the non-negative lasso (NN-LASSO) algorithm to forge a link between an ordinary differential equation (ODE)-based population model and a cellular evolution model. RESULTS: The findings of our current work not only affirm the impact of subclonal composition on growth dynamics but also identify two significant subclones within heterogeneous growth patterns. Moreover, the subclonal compositions at the initial time are able to accurately discriminate diverse growth patterns through a machine learning algorithm. CONCLUSION: The proposed multiscale framework successfully delineates the intricate landscape of cancer evolution, bridging the gap between long-term growth dynamics and short-term measurements, both in simulated and real-world data. This methodology provides a novel avenue for thorough exploration into the realm of cancer evolution.

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data.

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

Mapping nucleosome and chromatin architectures: A survey of computational methods.

With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.

Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: A study of follicular lymphoma.

A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA4 and CTSO, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work combines both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.

abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis.

BACKGROUND: Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters. RESULTS: A new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach. CONCLUSION: This work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq.

Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance.

BACKGROUND: Organoids or spheroids have emerged as a physiologically relevant in vitro preclinical model to study patient-specific diseases. A recent study used spheroids of MCF10 cells to model breast cancer progression and identified targetable alterations more similar to those in vivo. Thus, it is practical and essential to explore and characterize the spheroids of the commonly used human breast cancer (BC) cells. METHODS: In this study, we conducted Hi-C analyses in three-dimensional (3D) spheroids of MCF10A, MCF7 and MCF7TR cells and compared TADs and looping genes with those in 2D monolayers. Furthermore, we performed in silico functional analysis on 3D-growth-specific looping genes and to compare patient outcomes with or without endocrinal therapy. Finally, we performed 3C/RT-qPCR validations in 3D spheroids and 3D-FISH confirmations in organoids of breast cancer patient tissues. RESULTS: We found that chromatin structures have experienced drastic changes during the 3D culture growth of BC cells although there is not much change in the quantity of chromatin domains. We also observed that the strengths of looping genes were statistically different between 2D monolayers and 3D spheroids. We further identified novel 3D growth-specific looping genes within Hippo relevant pathways, of which two genes showed potential prognostic values in measuring the outcome of the endocrine treatment. We finally confirmed a few selected genes in Hippo relevant pathways with enhanced looping in organoids of breast cancer patient tissues. CONCLUSIONS: Hence, our work has provided significant insights into our understanding of 3D-growth-specific chromatin architecture in tamoxifen-resistant breast cancer. Our analyses suggest that the strengthened looping-mediated Hippo relevant pathways may contribute to endocrine therapy resistance in breast cancer patients.

Nuclear IL-33 restrains the early conversion of fibroblasts to an extracellular matrix-secreting phenotype.

Interleukin (IL)-33 is a cytokine that appears to mediate fibrosis by signaling via its receptor ST2 (IL-33R/IL1RL1). It is also, however, a protein that after synthesis is sorted to the cell nucleus, where it appears to affect chromatin folding. Here we describe a novel role for nuclear IL-33 in regulating the fibroblast phenotype in murine kidney fibrosis driven by unilateral ureteral obstruction. Transcriptional profiling of IL-33-deficient kidneys 24 h after ligation revealed enhanced expression of fibrogenic genes and enrichment of gene sets involved in extracellular matrix formation and remodeling. These changes relied on intracellular effects of IL-33, because they were not reproduced by treatment with a neutralizing antibody to IL-33 that prevents IL-33R/ST2L receptor signaling nor were they observed in IL-33R/ST2-deficient kidneys. To further explore the intracellular function of IL-33, we established transcription profiles of human fibroblasts, observing that knockdown of IL-33 skewed the transcription profile from an inflammatory towards a myofibroblast phenotype, reflected in higher levels of COL3A1, COL5A1 and transgelin protein, as well as lower expression levels of IL6, CXCL8, CLL7 and CCL8. In conclusion, our findings suggest that nuclear IL-33 in fibroblasts dampens the initial profibrotic response until persistent stimuli, as enforced by UUO, can override this protective mechanism.

Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients.

Following a successful renal transplantation circulating markers of inflammation may remain elevated, and systemic inflammation is associated with worse clinical outcome in renal transplant recipients (RTRs). Vitamin D-receptor (VDR) activation is postulated to modulate inflammation and endothelial function. We aimed to explore if a synthetic vitamin D, paricalcitol, could influence systemic inflammation and immune activation in RTRs. Newly transplanted RTRs were included in an open-label randomized controlled trial on the effect of paricalcitol on top of standard care over the first post-transplant year. Fourteen pre-defined circulating biomarkers reflecting leukocyte activation, endothelial activation, fibrosis and general inflammatory burden were analyzed in 74 RTRs at 8 weeks (baseline) and 1 year post-engraftment. Mean changes in plasma biomarker concentrations were compared by t-test. The expression of genes coding for the same biomarkers were investigated in 1-year surveillance graft biopsies (n = 60). In patients treated with paricalcitol circulating osteoprotegerin levels increased by 0.19 ng/ml, compared with a 0.05 ng/ml increase in controls (p = 0.030). In graft tissue, a 21% higher median gene expression level of TNFRSF11B coding for osteoprotegerin was found in paricalcitol-treated patients compared with controls (p = 0.026). Paricalcitol treatment did not significantly affect the blood- or tissue levels of any other investigated inflammatory marker. In RTRs, paricalcitol treatment might increase both circulating and tissue levels of osteoprotegerin, a modulator of calcification, but potential anti-inflammatory treatment effects in RTRs are likely very modest. [NCT01694160 (2012/107D)]; [www.clinicaltrials.gov].

HMST-Seq-Analyzer: A new python tool for differential methylation and hydroxymethylation analysis in various DNA methylation sequencing data.

DNA methylation (5mC) and hydroxymethylation (5hmC) are chemical modifications of cytosine bases which play a crucial role in epigenetic gene regulation. However, cost, data complexity and unavailability of comprehensive analytical tools is one of the major challenges in exploring these epigenetic marks. Hydroxymethylation-and Methylation-Sensitive Tag sequencing (HMST-seq) is one of the most cost-effective techniques that enables simultaneous detection of 5mC and 5hmC at single base pair resolution. We present HMST-Seq-Analyzer as a comprehensive and robust method for performing simultaneous differential methylation analysis on 5mC and 5hmC data sets. HMST-Seq-Analyzer can detect Differentially Methylated Regions (DMRs), annotate them, give a visual overview of methylation status and also perform preliminary quality check on the data. In addition to HMST-Seq, our tool can be used on whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data sets as well. The tool is written in Python with capacity to process data in parallel and is available at (https://hmst-seq.github.io/hmst/).

The 3D genomic landscape of differential response to EGFR/HER2 inhibition in endocrine-resistant breast cancer cells.

BACKGROUND: Recent studies suggested that crosstalk between ERα and EGFR/HER2 pathways plays a critical role in mediating endocrine therapy resistance. Several inhibitors targeting EGFR/HER2 signaling, including FDA-approved lapatinib and gefitinib as well as a novel dual tyrosine kinase inhibitor (TKI) sapitinib, showed greater therapeutic efficacies. However, how 3D chromatin landscape responds to the inhibition of EGFR/HER2 pathway remains to be elucidated. METHODS: In this study, we conducted in situ Hi-C and RNA-seq in two ERα+ breast cancer cell systems, 1) parental MCF7 cells and its associated tamoxifen-resistant MCF7TR cells; and 2) parental T47D cells and its associated tamoxifen-resistant T47DTR cells, before and after the treatment of sapitinib. RESULTS: We identified differential responses in topologically associated domains (TADs), looping genes and expressed genes. Interestingly, we found that many differential TADs and looping genes are reversible after sapitinib treatment, indicating that EGFR/HER2 signaling may play a role in reshaping and rewiring the high order genome organization. We further examined and recapitulated the reversible looping genes in 3D spheroids of breast cancer cells, demonstrating that 3D cell culture spheroid of breast cancer cells could be a potential preclinical breast cancer model for studying 3D chromatin regulation. CONCLUSIONS: Our study has provided significant insights into our understanding of 3D genomic landscape changes in response to EGFR/HER2 Inhibition in endocrine-resistant breast cancer cells. Our data provides a rich resource for further evaluating chromatin structural responses to EGFR/HER2 targeted therapies in endocrine-resistant breast cancer cells. Our analyses suggest that these alterations of chromatin structures and transcriptional programs may provide new avenues for intervention or designing of patient selection for targeted endocrine treatment.

Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.

Current computational methods on Hi-C analysis focused on identifying Mb-size domains often failed to unveil the underlying functional and mechanistic relationship of chromatin structure and gene regulation. We developed a novel computational method HiSIF to identify genome-wide interacting loci. We illustrated HiSIF outperformed other tools for identifying chromatin loops. We applied it to Hi-C data in breast cancer cells and identified 21 genes with gained loops showing worse relapse-free survival in endocrine-treated patients, suggesting the genes with enhanced loops can be used for prognostic signatures for measuring the outcome of the endocrine treatment. HiSIF is available at https://github.com/yufanzhouonline/HiSIF .

IGAP-integrative genome analysis pipeline reveals new gene regulatory model associated with nonspecific TF-DNA binding affinity.

The human genome is regulated in a multi-dimensional way. While biophysical factors like Non-specific Transcription factor Binding Affinity (nTBA) act at DNA sequence level, other factors act above sequence levels such as histone modifications and 3-D chromosomal interactions. This multidimensionality of regulation requires many of these factors for a proper understanding of the regulatory landscape of the human genome. Here, we propose a new biophysical model for estimating nTBA. Integration of nTBA with chromatin modifications and chromosomal interactions, using a new Integrative Genome Analysis Pipeline (IGAP), reveals additive effects of nTBA to regulatory DNA sequences and identifies three types of genomic zones in the human genome (Inactive Genomic Zones, Poised Genomic Zones, and Active Genomic Zones). It also unveils a novel long distance gene regulatory model: chromosomal interactions reduce the physical distance between the high occupancy target (HOT) regions that results in high nTBA to DNA in the area, which in turn attract TFs to such regions with higher binding potential. These findings will help to elucidate the three-dimensional diffusion process that TFs use during their search for the right targets.

Author Correction: Temporal dynamic reorganization of 3D chromatin architecture in hormone-induced breast cancer and endocrine resistance.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Temporal dynamic reorganization of 3D chromatin architecture in hormone-induced breast cancer and endocrine resistance.

Recent studies have demonstrated that chromatin architecture is linked to the progression of cancers. However, the roles of 3D structure and its dynamics in hormone-dependent breast cancer and endocrine resistance are largely unknown. Here we report the dynamics of 3D chromatin structure across a time course of estradiol (E2) stimulation in human estrogen receptor α (ERα)-positive breast cancer cells. We identified subsets of temporally highly dynamic compartments predominantly associated with active open chromatin and found that these highly dynamic compartments showed higher alteration in tamoxifen-resistant breast cancer cells. Remarkably, these compartments are characterized by active chromatin states, and enhanced ERα binding but decreased transcription factor CCCTC-binding factor (CTCF) binding. We finally identified a set of ERα-bound promoter-enhancer looping genes enclosed within altered domains that are enriched with cancer invasion, aggressiveness or metabolism signaling pathways. This large-scale analysis expands our understanding of high-order temporal chromatin reorganization underlying hormone-dependent breast cancer.

BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts.

Most of somatic mutations in cancer occur outside of gene coding regions. These mutations may disrupt the gene regulation by affecting protein-DNA interaction. A study of these disruptions is important in understanding tumorigenesis. However, current computational tools process DNA sequence variants individually, when predicting the effect on protein-DNA binding. Thus, it is a daunting task to identify functional regulatory disturbances among thousands of mutations in a patient. Previously, we have reported and validated a pipeline for identifying functional non-coding somatic mutations in cancer patient cohorts, by integrating diverse information such as gene expression, spatial distribution of the mutations, and a biophysical model for estimating protein binding affinity. Here, we present a new user-friendly Python package BayesPI-BAR2 based on the proposed pipeline for integrative whole-genome sequence analysis. This may be the first prediction package that considers information from both multiple mutations and multiple patients. It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. BayesPI-BAR2 program can analyze multiple datasets of genome-wide mutations at once and generate concise, easily interpretable reports for potentially affected gene regulatory sites. The package is freely available at http://folk.uio.no/junbaiw/BayesPI-BAR2/.

Three-dimensional analysis reveals altered chromatin interaction by enhancer inhibitors harbors TCF7L2-regulated cancer gene signature.

Distal regulatory elements influence the activity of gene promoters through chromatin looping. Chromosome conformation capture (3C) methods permit identification of chromatin contacts across different regions of the genome. However, due to limitations in the resolution of these methods, the detection of functional chromatin interactions remains a challenge. In the current study, we employ an integrated approach to define and characterize the functional chromatin contacts of human pancreatic cancer cells. We applied tethered chromatin capture to define classes of chromatin domains on a genome-wide scale. We identified three types of structural domains (topologically associated, boundary, and gap) and investigated the functional relationships of these domains with respect to chromatin state and gene expression. We uncovered six distinct sub-domains associated with epigenetic states. Interestingly, specific epigenetically active domains are sensitive to treatment with histone acetyltransferase (HAT) inhibitors and decrease in H3K27 acetylation levels. To examine whether the subdomains that change upon drug treatment are functionally linked to transcription factor regulation, we compared TCF7L2 chromatin binding and gene regulation to HAT inhibition. We identified a subset of coding RNA genes that together can stratify pancreatic cancer patients into distinct survival groups. Overall, this study describes a process to evaluate the functional features of chromosome architecture and reveals the impact of epigenetic inhibitors on chromosome architecture and identifies genes that may provide insight into disease outcome.

Inhibition of Endothelial NOTCH1 Signaling Attenuates Inflammation by Reducing Cytokine-Mediated Histone Acetylation at Inflammatory Enhancers.

OBJECTIVE: Endothelial upregulation of adhesion molecules serves to recruit leukocytes to inflammatory sites and appears to be promoted by NOTCH1; however, current models based on interactions between active NOTCH1 and NF-κB components cannot explain the transcriptional selectivity exerted by NOTCH1 in this context. APPROACH AND RESULTS: Observing that Cre/Lox-induced conditional mutations of endothelial Notch modulated inflammation in murine contact hypersensitivity, we found that IL (interleukin)-1ß stimulation induced rapid recruitment of RELA (v-rel avian reticuloendotheliosis viral oncogene homolog A) to genomic sites occupied by NOTCH1-RBPJ (recombination signal-binding protein for immunoglobulin kappa J region) and that NOTCH1 knockdown reduced histone H3K27 acetylation at a subset of NF-κB-directed inflammatory enhancers. CONCLUSIONS: Our findings reveal that NOTCH1 signaling supports the expression of a subset of inflammatory genes at the enhancer level and demonstrate how key signaling pathways converge on chromatin to coordinate the transition to an infla mmatory endothelial phenotype.

Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model.

DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor-DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein-DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor-DNA interaction, as well as for predicting cancer mutation effects in the future.