Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

STAN, a computational framework for inferring spatially informed transcription factor activity

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.

Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex and find epigenetic signatures of variation in ATAC-Seq data in this discovery cohort. We then scale up our single-cell RNA-Seq analysis by combining our data with five recently published single nucleus RNA-Seq datasets of healthy adult hearts. We find variation such as metabolic alterations by sex and immune changes by age in differential expression tests, as well as alterations in abundance of cardiomyocytes by sex and neurons with age. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs. Our analysis represents the largest single-cell analysis of cardiac variation by age and sex to date and provides a resource for further study of healthy cardiac variation and transcriptional regulation at single-cell resolution.

O-GlcNAcylation of MITF regulates its activity and CDK4/6 inhibitor resistance in breast cancer

Cyclin-dependent kinases 4 and 6 (CDK4/6) play a pivotal role in cell cycle and cancer development. Targeting CDK4/6 has demonstrated promising effects against breast cancer. However, resistance to CDK4/6 inhibitors (CDK4/6i), such as palbociclib, remains a substantial challenge in clinical settings. Using high-throughput combinatorial drug screening and genomic sequencing, we find that the microphthalmia-associated transcription factor (MITF) is activated via O-GlcNAcylation by O-GlcNAc transferase (OGT) in palbociclib-resistant breast cancer cells and tumors. Mechanistically, O-GlcNAcylation of MITF at Serine 49 enhances its interaction with importin α/β, thus promoting its translocation to nuclei, where it suppresses palbociclib-induced senescence. Inhibition of MITF or its O-GlcNAcylation re-sensitizes resistant cells to palbociclib. Moreover, clinical studies confirm the activation of MITF in tumors from patients who are palbociclib-resistant or undergoing palbociclib treatment. Collectively, our studies shed light on the mechanism regulating palbociclib resistance and present clinical evidence for developing therapeutic approaches to treat CDK4/6i-resistant breast cancer patients.

Drug repositioning for immunotherapy in breast cancer using single-cell analysis

Immunomodulatory peptides, while exhibiting potential antimicrobial, antifungal, and/or antiviral properties, can play a role in stimulating or suppressing the immune system, especially in pathological conditions like breast cancer (BC). Thus, deregulation of these peptides may serve as an immunotherapeutic strategy to enhance the immune response. In this meta-analysis, we utilized single-cell RNA sequencing data and known therapeutic peptides to investigate the deregulation of these peptides in malignant versus normal human breast epithelial cells. We corroborated our findings at the chromatin level using ATAC-seq. Additionally, we assessed the protein levels in various BC cell lines. Moreover, our in-house drug repositioning approach was employed to identify potential drugs that could positively impact the relapse-free survival of BC patients. Considering significantly deregulated therapeutic peptides and their role in BC pathology, our approach aims to downregulate B2M and SLPI, while upregulating PIGR, DEFB1, LTF, CLU, S100A7, and SCGB2A1 in BC epithelial cells through our drug repositioning pipeline. Leveraging the LINCS L1000 database, we propose BRD-A06641369 for B2M downregulation and ST-4070043 and BRD-K97926541 for SLPI downregulation without negatively affecting the MHC complex as a significantly correlated pathway with these two genes. Furthermore, we have compiled a comprehensive list of drugs for the upregulation of other selected immunomodulatory peptides. Employing an immunotherapeutic approach by integrating our drug repositioning pipeline with single-cell analysis, we proposed potential drugs and drug targets to fortify the immune system against BC.

SMARCAL1 is a dual regulator of innate immune signaling and PD-L1 expression that promotes tumor immune evasion

Genomic instability can trigger cancer-intrinsic innate immune responses that promote tumor rejection. However, cancer cells often evade these responses by overexpressing immune checkpoint regulators, such as PD-L1. Here, we identify the SNF2-family DNA translocase SMARCAL1 as a factor that favors tumor immune evasion by a dual mechanism involving both the suppression of innate immune signaling and the induction of PD-L1-mediated immune checkpoint responses. Mechanistically, SMARCAL1 limits endogenous DNA damage, thereby suppressing cGAS-STING-dependent signaling during cancer cell growth. Simultaneously, it cooperates with the AP-1 family member JUN to maintain chromatin accessibility at a PD-L1 transcriptional regulatory element, thereby promoting PD-L1 expression in cancer cells. SMARCAL1 loss hinders the ability of tumor cells to induce PD-L1 in response to genomic instability, enhances anti-tumor immune responses and sensitizes tumors to immune checkpoint blockade in a mouse melanoma model. Collectively, these studies uncover SMARCAL1 as a promising target for cancer immunotherapy.

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

O-GlcNAcylation of MITF regulates its activity and CDK4/6 inhibitor resistance in breast cancer

Cyclin-dependent kinases 4 and 6 (CDK4/6) play a pivotal role in cell cycle and cancer development. Targeting CDK4/6 has demonstrated promising effects against breast cancer. However, resistance to CDK4/6 inhibitors (CDK4/6i), such as palbociclib, remains a substantial challenge in clinical settings. Using high-throughput combinatorial drug screening and genomic sequencing, we found that the microphthalmia-associated transcription factor (MITF) is activated via O-GlcNAcylation by O-GlcNAc transferase (OGT) in palbociclib-resistant breast cancer cells and tumors; O-GlcNAcylation of MITF at Serine 49 enhanced its interaction with importin α/β, thus promoting its translocation to nuclei, where it suppressed palbociclib-induced senescence; inhibition of MITF or its O-GlcNAcylation re-sensitized resistant cells to palbociclib. Remarkably, clinical studies confirmed the activation of MITF in tumors from patients who are palbociclib-resistant or undergoing palbociclib treatment. Collectively, our studies shed light on a novel mechanism regulating palbociclib-resistance, and present clinical evidence for developing therapeutic approaches to treat CDK4/6i-resistant breast cancer patients.

Dynamic regulatory module networks for inference of cell type-specific transcriptional networks

Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type-specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

Chromatin interaction-aware gene regulatory modeling with graph attention networks

Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements up to 2 Mb away in the genome, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than the state-of-the-art deep learning methods for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both convolutional neural networks (CNNs) and the recently proposed activity-by-contact model. Sequence-based GraphReg also accurately predicts direct transcription factor (TF) targets as validated by CRISPRi TF knockout experiments via in silico ablation of TF binding motifs. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.

CharPlant: A De Novo Open Chromatin Region Prediction Tool for Plant Genomes

Chromatin accessibility is a highly informative structural feature for understanding gene transcription regulation, because it indicates the degree to which nuclear macromolecules such as proteins and RNAs can access chromosomal DNA. Studies have shown that chromatin accessibility is highly dynamic during stress response, stimulus response, and developmental transition. Moreover, physical access to chromosomal DNA in eukaryotes is highly cell-specific. Therefore, current technologies such as DNase-seq, ATAC-seq, and FAIRE-seq reveal only a portion of the open chromatin regions (OCRs) present in a given species. Thus, the genome-wide distribution of OCRs remains unknown. In this study, we developed a bioinformatics tool called CharPlant for the de novo prediction of OCRs in plant genomes. To develop this tool, we constructed a three-layer convolutional neural network (CNN) and subsequently trained the CNN using DNase-seq and ATAC-seq datasets of four plant species. The model simultaneously learns the sequence motifs and regulatory logics, which are jointly used to determine DNA accessibility. All of these steps are integrated into CharPlant, which can be run using a simple command line. The results of data analysis using CharPlant in this study demonstrate its prediction power and computational efficiency. To our knowledge, CharPlant is the first de novo prediction tool that can identify potential OCRs in the whole genome. The source code of CharPlant and supporting files are freely available from https://github.com/Yin-Shen/CharPlant.

SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators

The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. Recently single-cell technologies have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. Selected predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states.

Expanding the phenotype of E318K (c.952G > A) MITF germline mutation carriers: case series and review of the literature

Background

The microphthalmia-associated transcription factor gene (MITF) belongs to the MYC supergene family and plays an important role in melanocytes’ homeostasis. Individuals harboring MITF germline pathogenic variants are at increased risk of developing cancer, most notably melanoma and renal cell carcinoma.

Case presentation

We describe a cohort of ten individuals who harbor the same MITF c.952G > A (p.Glu 318Lys), or p.E318K, germline pathogenic variant. Six carriers developed at least one malignancy (4 cases of breast cancer; 1 cervical cancer; 1 colon cancer; 1 melanoma; 1 ovarian/fallopian tube cancer). A significant phenotypic heterogeneity was found among these individuals and their relatives. Breast cancer was, overall, the most frequent malignancy observed in this case series, with 13 occurrences of 60 (21.67 %) total cancer cases described among the probands and their relatives.

Conclusions

Our retrospective analysis data raise the hypothesis of a possible association of the MITF p.E318K pathogenic variant with an increased risk of breast cancer.

Mechanical properties of nucleoprotein complexes determined by nanoindentation spectroscopy

The interplay between transcription factors, chromatin remodelers, 3-D organization, and mechanical properties of the chromatin fiber controls genome function in eukaryotes. Besides the canonical histones which fold the bulk of the chromatin into nucleosomes, histone variants create distinctive chromatin domains that are thought to regulate transcription, replication, DNA damage repair, and faithful chromosome segregation. Whether histone variants translate distinctive biochemical or biophysical properties to their associated chromatin structures, and whether these properties impact chromatin dynamics as the genome undergoes a multitude of transactions, is an important question in biology. Here, we describe single-molecule nanoindentation tools that we developed specifically to determine the mechanical properties of histone variant nucleosomes and their complexes. These methods join an array of cutting-edge new methods that further our quantitative understanding of the response of chromatin to intrinsic and extrinsic forces which act upon it during biological transactions in the nucleus.

Parsing multiomics landscape of activated synovial fibroblasts highlights drug targets linked to genetic risk of rheumatoid arthritis

OBJECTIVES

Synovial fibroblasts (SFs) are one of the major components of the inflamed synovium in rheumatoid arthritis (RA). We aimed to gain insight into the pathogenic mechanisms of SFs through elucidating the genetic contribution to molecular regulatory networks under inflammatory condition.

METHODS

SFs from RA and osteoarthritis (OA) patients (n=30 each) were stimulated with eight different cytokines (interferon (IFN)-α, IFN-γ, tumour necrosis factor-α, interleukin (IL)-1β, IL-6/sIL-6R, IL-17, transforming growth factor-β1, IL-18) or a combination of all 8 (8-mix). Peripheral blood mononuclear cells were fractioned into five immune cell subsets (CD4+ T cells, CD8+ T cells, B cells, natural killer (NK) cells, monocytes). Integrative analyses including mRNA expression, histone modifications (H3K27ac, H3K4me1, H3K4me3), three-dimensional (3D) genome architecture and genetic variations of single nucleotide polymorphisms (SNPs) were performed.

RESULTS

Unstimulated RASFs differed markedly from OASFs in the transcriptome and epigenome. Meanwhile, most of the responses to stimulations were shared between the diseases. Activated SFs expressed pathogenic genes, including CD40 whose induction by IFN-γ was significantly affected by an RA risk SNP (rs6074022). On chromatin remodelling in activated SFs, RA risk loci were enriched in clusters of enhancers (super-enhancers; SEs) induced by synergistic proinflammatory cytokines. An RA risk SNP (rs28411362), located in an SE under synergistically acting cytokines, formed 3D contact with the promoter of metal-regulatory transcription factor-1 (MTF1) gene, whose binding motif showed significant enrichment in stimulation specific-SEs. Consistently, inhibition of MTF1 suppressed cytokine and chemokine production from SFs and ameliorated mice model of arthritis.

CONCLUSIONS

Our findings established the dynamic landscape of activated SFs and yielded potential therapeutic targets associated with genetic risk of RA.

Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers

The ubiquitous family of dimeric transcription factors AP-1 is made up of Fos and Jun family proteins. It has long been thought to operate principally at gene promoters and how it controls transcription is still ill-understood. The Fos family protein Fra-1 is overexpressed in triple negative breast cancers (TNBCs) where it contributes to tumor aggressiveness. To address its transcriptional actions in TNBCs, we combined transcriptomics, ChIP-seqs, machine learning and NG Capture-C. Additionally, we studied its Fos family kin Fra-2 also expressed in TNBCs, albeit much less. Consistently with their pleiotropic effects, Fra-1 and Fra-2 up- and downregulate individually, together or redundantly many genes associated with a wide range of biological processes. Target gene regulation is principally due to binding of Fra-1 and Fra-2 at regulatory elements located distantly from cognate promoters where Fra-1 modulates the recruitment of the transcriptional co-regulator p300/CBP and where differences in AP-1 variant motif recognition can underlie preferential Fra-1- or Fra-2 bindings. Our work also shows no major role for Fra-1 in chromatin architecture control at target gene loci, but suggests collaboration between Fra-1-bound and -unbound enhancers within chromatin hubs sometimes including promoters for other Fra-1-regulated genes. Our work impacts our view of AP-1.

Single-cell RNA-seq dissects the intratumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high intratumoral heterogeneity. Recent studies revealed that TNBC patients might comprise cells with distinct molecular subtypes. In addition, gene regulatory networks (GRNs) constructed based on single-cell RNA sequencing (scRNA-seq) data have demonstrated the significance for decoding the key regulators. We performed a comprehensive analysis of the GRNs for the intrinsic subtypes of TNBC patients using scRNA-seq. The copy number variations (CNVs) were inferred from scRNA-seq data and identified 545 malignant cells. The subtypes of the malignant cells were assigned based on the PAM50 model. The cell-cell communication analysis revealed that the macrophage plays a dominant role in the tumor microenvironment. Next, the GRN for each subtype was constructed through integrating gene co-expression and enrichment of transcription-binding motifs. Then, we identified the critical genes based on the centrality metrics of genes. Importantly, the critical gene ETV6 was ubiquitously upregulated in all subtypes, but it exerted diverse roles in each subtype through regulating different target genes. In conclusion, the construction of GRNs based on scRNA-seq data could help us to dissect the intratumoral heterogeneity and identify the critical genes of TNBC.

Data integration for inferring context-specific gene regulatory networks

Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.

Patient-specific multi-omics models and the application in personalized combination therapy

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.